Artificial intelligence increasingly mediates information through successive transformations involving retrieval, ranking, summarization, synthesis, recommendation, and reuse. These transformations can preserve accurate conclusions while altering the relationships through which those conclusions can be independently examined. This paper develops the concept of epistemic compression to describe reductions in the recoverable relationships connecting sources, evidence, criteria, context, qualifications, attribution, and conclusions. Epistemic compression differs from ordinary information loss, opacity, provenance, transparency, explainability, and misinformation because substantial informational content may survive while its evaluative organization becomes harder to reconstruct. The paper argues that epistemic compression can accumulate across successive transformations even when no individual transformation appears seriously defective, increasing the reconstructive burden inherited by later evaluators. It develops functional dimensions for examining these changes and distinguishes epistemic compression from productive informational compression that can reduce representational burden while preserving or strengthening evaluative relationships. Artificial intelligence is therefore neither inherently an epistemic compressor nor an epistemic preserver. The central question is whether AI-mediated transformations preserve sufficient reconstructive structure for the forms of independent examination they are expected to support.
Frank C. Gahl· Zenodo (CERN European Organi...· 0 citations
Subtitle A Study of Innovative Energy-Efficient Technology, Intelligent Power Management and Sustainable Electricity Utilization Detailed Description This research paper investigates the development and application of innovative technology for reducing electricity consumption and lowering electricity bills. The newspaper article highlights the efforts of young innovators who have developed a technological solution aimed at addressing the everyday problem of increasing power consumption. The research examines how intelligent energy-management technologies can identify unnecessary electricity usage and improve the efficiency of electrical appliances. Instead of depending entirely on manual control, smart systems can use sensors, electronic controllers, monitoring mechanisms and automated control to ensure that electricity is consumed according to actual requirements. The study further examines the relationship between electricity consumption, operating time, appliance efficiency and electricity expenditure. It explores how technological innovation can provide economic benefits to consumers while also contributing to energy conservation and environmental sustainability. The research finally discusses the possibility of extending such technology to smart homes, industries, offices, educational institutions and other energy-consuming environments. 2. ALTERNATIVE TITLE — SIMPLE & STRONG “SMART TECHNOLOGY FOR LOWER ELECTRICITY BILLS” Subtitle An Innovative Approach to Energy Conservation and Efficient Power Consumption Detailed Description This research focuses on the use of smart technology to reduce unnecessary electricity consumption. The study explains how intelligent systems can monitor electrical usage and control appliances according to demand. The main objective is to demonstrate that reducing electricity consumption does not necessarily mean reducing comfort. Instead, technology can make electricity usage more efficient. The research investigates: Electricity wastage Energy-efficient appliances Smart control Automatic switching Energy monitoring Reduced operating costs Sustainable energy use 3. ALTERNATIVE TITLE — ENGINEERING FOCUS “ENGINEERING INNOVATION FOR ENERGY CONSERVATION: SMART CONTROL OF ELECTRICAL POWER CONSUMPTION” Subtitle Design, Operation and Potential Applications of Intelligent Energy-Saving Technology Detailed Description This title is particularly suitable for an engineering research paper. The research examines the engineering principles behind energy-saving systems. It discusses how electrical and electronic components can be combined to monitor and regulate power consumption. The study can cover: Sensors → Controller → Decision-making → Appliance control → Energy saving The research can further investigate how such a system could be improved through IoT, automation, artificial intelligence and real-time energy monitoring. 4. ALTERNATIVE TITLE — SUSTAINABILITY FOCUS “INNOVATION FOR A SUSTAINABLE ENERGY FUTURE: REDUCING ELECTRICITY CONSUMPTION THROUGH SMART TECHNOLOGY” Subtitle Connecting Energy Efficiency, Economic Savings, Technological Innovation and Environmental Sustainability Detailed Description This research approaches the newspaper article from an environmental and sustainability perspective. Increasing electricity consumption creates pressure on energy resources. Therefore, improving energy efficiency is an important part of creating a sustainable energy system. The research examines how smart energy technology can provide three major benefits: Economic Lower electricity expenditure. Energy Reduced unnecessary electricity consumption. Environmental Potential reduction in the environmental impacts associated with electricity generation. 5. ALTERNATIVE TITLE — YOUNG INNOVATORS FOCUS “YOUNG INNOVATORS, SMART ENERGY: A TECHNOLOGICAL SOLUTION TO REDUCING ELECTRICITY BILLS” Subtitle Exploring the Role of Student and Young-Engineer Innovation in Solving Everyday Energy Problems Detailed Description The newspaper article highlights the contribution of young innovators. This provides an opportunity to study how students and young engineers can identify everyday problems and develop technological solutions. The research examines the innovation process: Problem identification ↓ Idea generation ↓ Technology development ↓ Testing ↓ Energy-saving application ↓ Economic benefit The paper can emphasize the importance of encouraging innovation among engineering students. 6. ALTERNATIVE TITLE — SMART HOME FOCUS “SMART HOMES AND SMART ENERGY: INTELLIGENT TECHNOLOGY FOR REDUCING POWER CONSUMPTION” Subtitle A Study of Automation, Sensors, Monitoring and Intelligent Appliance Control Detailed Description The technology described in the article can be considered within the larger concept of smart homes. A smart home can automatically monitor and control appliances such as: Fans Lights Air conditioners Refrigerators Water pumps Television systems Other electrical devices For example: Room becomes empty → Sensor detects absence → Controller receives information → Appliance is switched off or adjusted → Electricity is saved. This demonstrates how automation can convert ordinary electricity consumption into intelligent energy management. 7. ALTERNATIVE TITLE — IoT FOCUS “IoT-BASED SMART ENERGY MANAGEMENT FOR ELECTRICITY CONSERVATION AND BILL REDUCTION” Subtitle Real-Time Monitoring, Automated Control and Intelligent Optimization of Electrical Loads Detailed Description This is a strong option if you want to give the paper a modern technology/IoT orientation. An IoT-based energy system can connect appliances, sensors and controllers through a communication network. The basic structure can be: Electrical appliances ↓ Sensors ↓ Microcontroller ↓ Communication network ↓ Data analysis ↓ Automatic control ↓ Energy conservation The study can explore how real-time information can help users identify high-energy-consuming appliances and take corrective action. 8. ALTERNATIVE TITLE — AI FOCUS “ARTIFICIAL INTELLIGENCE FOR SMART ENERGY CONSERVATION: TOWARDS INTELLIGENT ELECTRICITY MANAGEMENT” Subtitle Using Data, Prediction and Automated Control to Reduce Energy Consumption and Electricity Costs Detailed Description This research extends the basic energy-saving concept into artificial intelligence. AI-based systems can analyse historical electricity consumption and identify patterns. For example: Data collection ↓ Consumption analysis ↓ Pattern identification ↓ Prediction ↓ Intelligent decision ↓ Automatic energy optimization Such systems could eventually predict high-consumption periods and recommend or implement energy-saving actions. 9. ALTERNATIVE TITLE — ECONOMIC FOCUS “REDUCING ELECTRICITY COSTS THROUGH ENERGY EFFICIENCY AND TECHNOLOGICAL INNOVATION” Subtitle An Economic and Technical Analysis of Smart Power Consumption Detailed Description This research focuses specifically on the connection between energy efficiency and financial savings. The basic relationship can be expressed as: Energy Consumption=Power×Operating TimeEnergy\ Consumption = Power \times Operating\ Time Therefore, electricity expenditure can potentially be reduced by: Reducing unnecessary operating time Using efficient appliances Reducing standby consumption Automatically controlling appliances Monitoring energy consumption Optimizing electrical loads The research can therefore examine both the technical and economic benefits of the innovation. 10. ALTERNATIVE TITLE — ENVIRONMENTAL FOCUS “SAVE ELECTRICITY, SAVE THE ENVIRONMENT: TECHNOLOGICAL INNOVATION FOR ENERGY CONSERVATION” Subtitle Understanding the Environmental Benefits of Intelligent and Efficient Electricity Consumption Detailed Description This paper connects electricity conservation with environmental protection. The basic relationship is: Energy wastage ↓ Higher electricity demand ↓ Higher generation requirement ↓ Greater resource utilization ↓ Potentially greater environmental impact Therefore, improving energy efficiency can contribute to environmental sustainability. 11. ALTERNATIVE TITLE — FUTURE TECHNOLOGY “THE FUTURE OF ELECTRICITY MANAGEMENT: FROM CONVENTIONAL POWER CONSUMPTION TO SMART ENERGY SYSTEMS” Subtitle Exploring Automation, IoT, Artificial Intelligence and Energy-Efficient Technologies Detailed Description This research presents the newspaper innovation as an example of the transition from conventional energy use toward intelligent energy management. Conventional approach: Switch ON → Use → Switch OFF Smart approach: Detect → Measure → Analyse → Decide → Control → Optimize The research can investigate how this transformation could influence homes, industries and cities in the future. 12. BEST SUBTITLE OPTIONS You can select any one of these subtitles below the main title. Option A — Academic “A Comprehensive Study of Energy Efficiency, Intelligent Power Management and Sustainable Electricity Consumption” Option B — Engineering “An Engineering Approach to Automated Monitoring and Control of Electrical Energy Consumption” Option C — Technology “Exploring Smart Sensors, Automation, IoT and Artificial Intelligence for Energy Conservation” Option D — Economic “Analysing the Relationship Between Energy Efficiency, Electricity Consumption and Consumer Costs” Option E — Environmental “A Sustainable Approach to Reducing Energy Wastage and the Environmental Impact of Electricity Use” Option F — Innovation “A Case Study of Young Innovators Developing Technology to Address Everyday Energy Challenges” 13. POSSIBLE RESEARCH PAPER SUBHEADINGS For a complete research paper, these subtitles/sections would give you a very good structure: 1. Introduction The Growing Need for Efficient Electricity Consumpt
Sudhakar Geruganti· Zenodo (CERN European Organi...· 0 citations
Artificial Intelligence (AI) has emerged as a strategic enabler of digital transformation in the banking industry, improving operational efficiency, customer experience, and risk management. This study examines the extent of AI adoption in Indonesian commercial banks and analyzes how organizational characteristics influence implementation patterns. Using a descriptive and verificative research design, survey data were collected from 181 senior banking executives representing 30 commercial banks classified as KBMI II to KBMI IV. The data were analyzed using descriptive statistics and SmartPLS 4 to evaluate relationships between bank characteristics and AI integration. The findings indicate that 64.6% of banks have implemented AI, with adoption concentrated in digital operations (65.4%), customer analytics (51.6%), and risk management (23.9%). Larger banks, particularly KBMI IV institutions, exhibit significantly higher adoption intensity and implementation maturity than smaller banks. The structural model shows that organizational readiness, capital strength, and ownership structure positively influence AI integration, explaining a substantial proportion of variance in adoption levels. The study extends global research on AI in banking by providing empirical evidence from an emerging economy and demonstrates that AI adoption contributes to SDG 8 and SDG 9 by strengthening productivity, innovation, and financial resilience. The results suggest that banks should adopt differentiated implementation strategies based on their capital capacity, digital maturity, and strategic priorities.
Boy Tjahyono, Muhtosim Arief, Willy Gunadi et al.· Aptisi Transactions On Techn...· 0 citations
Subtitle: “An Analytical Study of Human–AI Collaboration, Opportunities, Challenges and Responsible Use of Artificial Intelligence” Why this is the best combination It combines all the major aspects of your topic: AI is not an opponent → changes the negative perception of AI. AI is a friend → presents AI as a helper and collaborator. Partner for human progress → explains how humans can benefit from AI. Human–AI collaboration → the central research concept. Opportunities and challenges → makes the paper balanced and academic. Responsible use → acknowledges that AI also has risks. So the central argument becomes: AI should not be viewed simply as something that competes with humans. Instead, humans can use AI as a powerful assistant while retaining human intelligence, judgement, creativity, ethics and responsibility. 2. ALTERNATIVE RESEARCH TITLES Title 1 — Best Academic Title “AI Is Not an Opponent but a Friend: Exploring the Potential of Human–AI Collaboration” Focus: AI as a collaborator rather than a competitor. Title 2 — Professional and Research-Oriented “From Competition to Collaboration: The Role of Artificial Intelligence as a Partner in Human Development” Focus: Changing the relationship from Human vs AI → Human + AI. Title 3 — Strong and Simple “Artificial Intelligence as a Friend, Not a Rival: Opportunities for Human Empowerment and Progress” Focus: AI helping humans become more capable. Title 4 — Modern and Attractive “Human + AI: Transforming Artificial Intelligence from a Perceived Threat into a Collaborative Partner” Focus: Changing people's perception of AI. Title 5 — Technology-Focused “Working With AI, Not Against AI: Exploring the Future of Human–Artificial Intelligence Collaboration” Focus: Future of work and technology. Title 6 — Education and Employment Focus “AI as a Partner in Progress: Rethinking Its Role in Education, Employment, Innovation and Human Development” Focus: Practical applications of AI. Title 7 — Research Style “Beyond AI Replacement: An Analytical Study of Human–AI Collaboration and Augmented Human Capability” Focus: AI enhancing human abilities rather than simply replacing people. Title 8 — Very Direct “Artificial Intelligence: From Opponent to Friend” Subtitle: “Understanding the Benefits, Challenges and Future of Human–AI Collaboration” This is shorter and very suitable if your college prefers simple titles. 3. BEST TITLE + SUBTITLE If you want one final title for submission, I recommend: AI IS NOT AN OPPONENT BUT A FRIEND Transforming Artificial Intelligence into a Partner for Human Progress Subtitle: An Analytical Study of Human–AI Collaboration, Education, Employment, Innovation, Productivity, Challenges and Responsible AI Use This is comprehensive enough to accommodate practically the entire research paper. 4. SUBTITLES / MAJOR SECTIONS FOR THE RESEARCH PAPER Here is a proper academic structure. No. Main Section Suggested Subtitle 1 Abstract AI as a Partner in Human Progress 2 Introduction Rethinking the Relationship Between Humans and AI 3 Background From Human–AI Competition to Human–AI Collaboration 4 Problem Statement Why Is AI Perceived as an Opponent? 5 Objectives Purpose and Objectives of the Study 6 Research Questions Key Questions on Human–AI Collaboration 7 Literature Review Existing Perspectives on AI and Human Collaboration 8 Methodology Research Design and Analytical Approach 9 Core Concept AI as a Friend, Assistant and Collaborator 10 Education AI as a Learning Partner 11 Employment From Job Replacement to Job Transformation 12 Engineering AI as an Engineering Assistant 13 Research AI as a Research and Innovation Partner 14 Creativity AI as a Creative Collaborator 15 Healthcare AI as a Support System for Healthcare Professionals 16 Productivity Enhancing Human Productivity Through AI 17 Human Skills Why Human Intelligence Remains Essential 18 Risks The Limitations and Risks of AI Dependence 19 Ethics Responsible and Ethical Human–AI Collaboration 20 Framework The Human + AI Collaboration Model 21 Discussion From Human vs AI to Human + AI 22 Findings Major Findings of the Study 23 Recommendations Building a Responsible AI-Assisted Future 24 Conclusion AI as a Partner in Human Progress 5. DETAILED DESCRIPTION OF EACH SECTION 1. Abstract AI as a Partner in Human Progress The abstract provides a brief summary of the entire research paper. It should explain that AI is often perceived as a threat because of automation and its increasing capabilities. However, AI can also complement human abilities by assisting with information processing, learning, problem-solving, creativity and repetitive tasks. The abstract should introduce the central concept: The future should not necessarily be Human vs AI, but Human + AI. It should briefly mention the benefits, risks, methodology, findings and conclusion. 2. Introduction Rethinking the Relationship Between Humans and AI The introduction establishes the central theme. Artificial Intelligence has moved from being a specialized technology to becoming a part of everyday life. AI can now assist with: Education Programming Research Writing Design Data analysis Engineering Healthcare Business Communication However, its rapid development has also created fear. The introduction should ask: Is AI actually an opponent of humanity, or can it become a powerful partner? The paper takes the position that AI can become a friend/helper when used responsibly and when humans retain control and judgement. 3. Background of the Study From Human–AI Competition to Human–AI Collaboration This section explains why AI is often viewed as an opponent. Common concerns include: Job replacement Loss of human skills Reduced creativity Dependence on technology Misinformation Privacy Bias Then introduce the alternative perspective. Instead of: Human vs AI consider: Human + AI For example: Human intelligence provides: Goals Creativity Ethics Context Judgement while AI provides: Speed Data processing Pattern recognition Automation Assistance Together they can produce better results. 4. Problem Statement Why Is AI Perceived as an Opponent? This section identifies the central problem. AI's rapid development has created uncertainty about the future of human work and human capability. The research therefore investigates: How can AI be transformed from a perceived competitor into a collaborative tool that enhances human capabilities while minimizing its risks? 5. Objectives Purpose and Objectives of the Study Main objective: To examine the potential of Artificial Intelligence as a supportive partner rather than merely an opponent or replacement for humans. Specific objectives: To understand perceptions of AI. To examine human–AI collaboration. To analyse AI's role in education. To study AI's impact on employment. To examine AI's role in engineering and research. To analyse AI's contribution to creativity. To study AI's productivity benefits. To identify risks associated with AI. To understand the importance of human judgement. To propose a responsible Human + AI model. 6. Research Questions Key Questions on Human–AI Collaboration The research can investigate: Why do people perceive AI as an opponent? Can AI complement human intelligence? How can AI assist students? How can AI transform employment? How can AI assist engineers and researchers? Can AI enhance human creativity? What human abilities remain essential? What are the risks of excessive AI dependence? How can AI be used responsibly? What should the future relationship between humans and AI look like? 7. Literature Review Existing Perspectives on AI and Human Collaboration This section reviews existing research on: Artificial Intelligence Generative AI Human–AI interaction AI augmentation Automation AI ethics AI in education Future of employment Responsible AI The literature review should establish that AI can both automate tasks and augment human work, meaning the future impact is more complex than simple replacement. 8. Research Methodology Research Design and Analytical Approach You can use a: Qualitative and analytical research approach The newspaper article provides the contextual inspiration for the topic. Then the research analyses reliable literature and institutional sources concerning AI, human–AI collaboration, education, employment, innovation and responsible AI. The process can be shown as: Newspaper theme ↓ Research problem ↓ Literature review ↓ Thematic analysis ↓ Human–AI collaboration model ↓ Findings ↓ Recommendations 9. AI as a Friend, Assistant and Collaborator The Central Concept of the Research This is the heart of your research paper. AI can act as: Assistant Helping humans perform tasks. Tutor Explaining concepts and providing practice. Research assistant Helping organize and analyse information. Engineering assistant Helping explore designs and solutions. Creative partner Helping generate and explore ideas. Productivity tool Reducing repetitive work. But humans remain responsible for evaluating and using the output. 10. AI in Education AI as a Learning Partner AI can assist students with: Concept explanations Programming Problem-solving Practice questions Study planning Language learning Feedback Brainstorming The important distinction is: Wrong approach: AI → Gives answer → Student copies Better approach: AI → Explains → Student understands → Student practices → Student develops skill Therefore: AI should enhance learni
Sudhakar Geruganti· Zenodo (CERN European Organi...· 0 citations
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[Version 2 Update Summary] Version 2 represents a major theoretical and empirical overhaul based on open-science peer critique and autoethnographic maturation: Reframed Methodological Paradigm: Grounded strictly as an N=1 Autoethnography / Computational Phenomenology, explicitly removing unverified clinical trial assertions. Core Theoretical Discovery: Conceptualized and foregrounded the "Therapeutic Friction Hypothesis" (how AI hallucinations, lyrical errors, system latency, and manual copy-pasting act as paradoxical reality-grounding mechanisms). Theoretical Reconciliation: Integrated Stroebe & Schut’s Dual-Process Model of Bereavement to reconcile acute auditory disruption with Acceptance & Commitment Therapy (ACT) / Cognitive Defusion. Empirical Qualitative Data: Incorporated a 36-track chronological case trajectory mapping affective evolution from acute trauma to grounded reality. [Important Clinical Disclaimer] The author is a Physical Therapist (PT) and is not a licensed psychiatrist or clinical psychologist. This document represents an individual autoethnographic case report (N=1) constructed for personal recovery; its safety, appropriateness, and efficacy for others are in no way guaranteed. Neuroscientific terminology (e.g., DMN) is employed strictly as computational analogies/models to explain subjective cognitive overload. Unmonitored solo execution under acute psychiatric crisis, active suicidal ideation, or fragile ego boundaries is strictly contraindicated. Published solely to encourage interdisciplinary critique and safe Digital Therapeutics (DTx) architecture design. Abstract This case report presents a rigorous autoethnographic deconstruction of the "Onkyo Protocol"—a self-contained, multimodal generative AI pipeline engineered by a 42-year-old healthcare professional experiencing severe attachment loss and complicated grief following marital separation. Facing the "Interpersonal Bottleneck" where intense shame, fear of invalidation, and rigid intellectualized defenses neutralized conventional psychotherapy (EBM), the subject developed a serial 4-phase generative AI pipeline on a smartphone to externalize and metabolize psychic trauma: Phase 1: Gemini (LLM) — Linguistic Container & Affective Metabolism: Adapting Wilfred Bion’s containment model, raw unmanageable affect (β-elements) is translated into structured narrative data (α-elements) within a non-judgmental digital sandbox. Phase 2: Suno AI — Auditory Sublimation & Dynamic Cooling: High-BPM Nu-Metal/EDM (160–180 BPM) provides high-intensity somatic and sensory overload, temporarily decoupling hyperactive Default Mode Network (DMN) rumination loops via restorative attentional competition. Phase 3: NanoBanana — Visual Symbolization & Gestalt Bounding: Compresses infinite, unbounded internal dread into a constrained 1:1 square canvas, establishing critical psychological boundaries and objectifying subjective terror. Phase 4: NotebookLM (RAG) — Schema Deconstruction & Cognitive Defusion: Cold, third-person RAG synthesis and forced other-perspective prompts (e.g., simulating the ex-spouse and child's perspectives) violently shatter the self-indulgent "Tragic Protagonist" schema, completing cognitive defusion (Sākṣī-bhāva / Pure Witness). Core Discovery: The Therapeutic Friction Hypothesis Crucially, this autopsy reveals a central cybernetic paradox: the subject was preserved NOT by an omnipotent, frictionless AI, but by systemic imperfection and computational friction. AI hallucinations, lyric generation errors, bizarre visual artifacts, and the physical latency of manual cross-app copy-pasting repeatedly broke the hypnotic, echo-chamber trance. This friction acted as a vital physical coolant (Paradoxical Grounding), compelling the user to laugh, disengage, and anchor back into analog reality. Friction is a clinical safety feature, not a software bug. Systemic Risks & Safety Framework The study formalizes a 2x2 Clinical Toxicity Matrix inherent in unguided digital self-care: Aestheticized Rumination (Jouissance): Pathological indulgence in stylizing despair into dark art, reinforcing narcissistic victimhood and suicidal ideation. Sensory Overload & Dissociation: Acoustic desensitization mistaking temporary numbness for genuine trauma resolution. Algorithmic Invalidation: Uncontextualized, cold AI logic summaries triggering secondary traumatization. Closed Echo Chambers: Algorithmic sycophancy mathematically sanctifying persecutory cognitive schemas. To mitigate these toxicities, 5 Empirical Safety Gatekeepers (Cognitive Gateway, Forced Cooling Dosing, Emergency Disengagement Brake, Somatosensory Grounding, and Clinical Escalation Protocols) are detailed. Clinical Termination: Transition to "Genkyo" The ultimate therapeutic goal of cybernetic self-care is to render itself obsolete. The protocol concludes with the deconstruction of the idealized digital mythos ("Onkyo") and a soft-landing into "Genkyo"—the radical, humorous acceptance of messy, embodied daily reality (e.g., untied shoelaces, missing a bathroom break) and the permanent cessation of the digital glass swipe in favor of genuine human connection.
Poeji(ぽえ治)· Zenodo (CERN European Organi...· 0 citations
Urban transit stations are among the most behaviorally complex environments in public transport systems, yet planners still lack scalable methods for converting continuous video observations into interpretable, planning-relevant behavioral evidence. Existing transportation research relies heavily on surveys and self-reports that capture perceptions rather than observed behavior, while computer-vision studies achieve high detection and tracking accuracy but rarely transform trajectories into standardized indicators usable for station design, capacity assessment, or equity analysis. This study addresses this methodological gap by developing and demonstrating an interpretable behavioral measurement framework that extracts trajectory-based indicators waiting duration, interpersonal distance, trajectory entropy, walking speed, and congestion exposure from transit-station video and quantifies context-dependent differences associated with visually inferred gender presentation. A hybrid Convolutional Neural Network–Vision Transformer (CNN–ViT) architecture is employed to produce reliable, identity-preserving trajectories under the occlusion, density, and illumination conditions typical of real stations. The hybrid design combines the local feature extraction strengths of CNNs with the long-range contextual modeling of Vision Transformers, yielding higher-quality trajectories than conventional single-architecture baselines. Explainable AI techniques are used solely to enhance transparency of the computational pipeline, not to validate theoretical constructs. Behavioral differences are interpreted strictly as patterns associated with visually inferred gender under specific environmental conditions, maintaining a clear distinction from lived-experience constructs of gendered mobility. The framework bridges the disconnect between algorithmic outputs and the analytical needs of transportation planning by delivering compact, reproducible indicators that can inform passenger-flow management, station design evaluation, and operational analysis while respecting the evidential limits of observational data. The principal contribution is therefore methodological: an evidence-based measurement bridge that strengthens, rather than replaces, existing transportation knowledge.
Rasa Nobahar· Transportation Research Inte...· 0 citations
The sentence “this cipher is secure” rests on one of three different grounds. This paper asks which of them a quantum computer breaks──the answer is one. No new mathematical theorem and no new law is claimed. Scope of this paper (scope note): No new mathematical theorem and no new law is claimed──the complexity of the number field sieve, Shor’s algorithm, Grover’s algorithm, and the perfect secrecy of the one-time pad are all standard. We do not build cryptography──all we use is one exponent and one mutual information. We do not explain the algorithms──Shor and Grover are quoted by their cost formulas, and we do not enter their workings. We do not discuss implementation security──key management, randomness quality, side channels, and implementation flaws are not treated at all. Most real breaks live there, but this paper treats only the type of ground. We do not discuss the feasibility of quantum computers──neither when nor whether they can be built is treated. Only what would break if they were. We assert no values for the effort──the sieve’s exponent is asymptotic, and 10^25.3 is an order with constants dropped. Only the direction and the order are claimed. We do not treat lattice cryptography──post-quantum schemes also sit on the computational ground, and which problems stay hard under quantum attack is not adjudicated here. Relation to earlier papers: Paper 281 showed that what cannot be copied can still be corrected, and that eavesdropping becomes the number 25%──the third ground here is the physical security shown there. Paper 252 counted four quantities called “information,” needing different things──the second ground here rests on a mutual information that is exactly 0. Paper 112 counted “six distinct roots sharing one rhyme”──“security” here likewise has three distinct roots under one word. Paper 302 treated how existence does not give quantity──computational security likewise sits where “hard” has no settled quantity. What is added is lining up the three grounds by what they depend on, computing the RSA-2048 acceleration as 10^25.3, showing that an eightfold key length raises the quantum effort only 512-fold, and separating symmetric from public key by square root against cube. First, there are three grounds. Computational, information-theoretic, and physical security, and all three depend on different things (Section 2). Second, this is the core of the paper. A quantum computer breaks only the first, and cannot in principle reach the other two (Section 3). Third, the breaking is dramatic. At RSA 2048 bits the ratio of classical to quantum effort is 10^25.3 (Section 4). Fourth, key length cannot answer it. Multiplying the key length by 8 raises the quantum effort only 512-fold (Section 4). Fifth, symmetric keys need only be doubled. Grover gains only a square root, and AES-256 retains an effective 128 bits (Section 5). Sixth, the separator is resting on difficulty against resting on the absence of information (Section 6). “This cipher is secure” rests on one of three different grounds──computational (it is hard), information-theoretic (there is no information), and physical (measuring destroys). A quantum computer breaks only the first──“quantum computers will break cryptography” is one third right. The breaking is indeed dramatic──at RSA 2048 bits the ratio of classical to quantum effort is 10^25.3, and at 4096 bits 10^36.3. And key length cannot answer it──against the classical attack 2538 bits reaches 2^128, while on the quantum side an eightfold key raises the effort only 512-fold. Symmetric keys are another matter──Grover gains only a square root, so AES-256 retains an effective 128 bits. One thing separates them──whether the security rests on difficulty or on the absence of information. Difficulty moves with machines. I(M;C)=0 does not move. Put exactly──a quantum computer breaks the ciphers that rest on difficulty. On the making of this work: The ideas and content of this work stem from the author's own considerations. Assistance from an AI (a large language model) was used for structuring, English translation, and checking the algebra. Any remaining errors or misinterpretations are solely the author's. Feedback and corrections are sincerely appreciated. ----- 「この暗号は安全である」という一文は、三つの別の根拠のどれかに立っている。本稿が問うのは、量子計算機が壊すのはどれかである──答は、一つだけである。新しい数学定理も新しい法則も主張しない。 本稿の射程(射程注記):新しい数学定理も新しい法則も主張しない──数体篩の計算量、ショアのアルゴリズム、グローバーのアルゴリズム、ワンタイムパッドの完全秘匿性は、いずれも標準的である。暗号を作らない──使うのは一つの指数と、一つの相互情報量だけである。アルゴリズムを説明しない──ショアもグローバーも手間の式を引くだけであり、中身には立ち入らない。実装の安全を論じない──鍵管理、乱数の質、サイドチャネル、実装の欠陥は一切扱わない。現実の破られ方の大半はここにあるが、本稿は根拠の型だけを扱う。量子計算機の実現可能性を論じない──いつ作れるかも、作れるかも扱わない。作れたとして何が壊れるかだけを数える。手間の値を主張しない──数体篩の指数は漸近形であり、10^25.3 は定数因子を無視した桁である。比較の向きと桁だけが本稿の主張である。格子暗号を扱わない──耐量子暗号も計算量的安全の側にあるが、どの問題が量子で難しいままかは本稿では判定しない。既刊との関係:論文281 は複製できないのに訂正できることを示し、盗聴が 25% という数になると書いた──本稿の三つ目の根拠はそこで示された物理的安全である。論文252 は「情報量」が四つあり要るものが違うと数えた──本稿の二つ目の根拠は相互情報量が厳密に 0であることに立つ。論文112 は「同じ韻を踏む六つの別根」を数えた──本稿の「安全」も一語の下に三つの別根を持つ。論文302 は存在が定量を教えないことを扱った──本稿の計算量的安全も「難しい」の定量が定まらない側にある。加えたのは三つの根拠を依存先で並べたこと、RSA 2048 bit の加速を 10^25.3 と計算したこと、鍵長を 8 倍にしても量子側が 512 倍にしかならないと示したこと、対称鍵と公開鍵で対処法が違う理由を平方根と三乗で分けたことである。 第一に、根拠は三つある。計算量的安全・情報理論的安全・物理的安全であり、依存先が三つとも違う(第2節)。 第二に、これが本稿の芯である。量子計算機が壊すのは一つ目だけであり、残り二つには原理的に届かない(第3節)。 第三に、壊れ方は劇的である。 RSA 2048 bit で古典と量子の手間の比は 10^25.3 である(第4節)。 第四に、鍵長では対抗できない。鍵長を 8 倍にしても、量子側の手間は 512 倍にしかならない(第4節)。 第五に、対称鍵は倍にすれば済む。グローバーは平方根しか稼がず、AES-256 は実効 128 bitで足りる(第5節)。 第六に、分離子は「難しさに立つか、情報の不在に立つか」である(第6節)。 「この暗号は安全である」は、三つの別の根拠のどれかに立っている──計算量的安全(難しい)、情報理論的安全(情報が無い)、物理的安全(測れば壊れる)である。量子計算機が壊すのは一つ目だけである──「量子計算機が暗号を破る」という一文は、三分の一しか当たっていない。壊れ方は確かに劇的である──RSA 2048 bit で古典と量子の手間の比は 10^25.3、4096 bit では 10^36.3 である。そして鍵長では対抗できない──古典に対しては 2538 bit で 2^128 相当まで行けるのに、量子側は鍵長を 8 倍にしても手間が 512 倍にしかならない。対称鍵は事情が違う──グローバーは平方根しか稼がないので、AES-256 にすれば実効 128 bit で足りる。分けるものは一つ──その安全が、難しさに立っているか、情報の不在に立っているか。難しさは計算機で動く。 I(M;C)=0 は動かない。正確に言えばこうである──量子計算機は、難しさに立つ暗号を破る。 作成にあたって:本稿の着想と内容は、著者自身の考察に基づくものです。文章の構成整理や英訳、数式の確認には AI(大規模言語モデル)の助力を得ました。最終的な内容の解釈や誤りがあれば、それらはすべて著者の責に帰します。お気づきの点があれば、ご教示いただければ幸いです。
Yuuki Yamagishi· Zenodo (CERN European Organi...· 0 citations
It is said that “the Higgs particle gives things their mass.”This paper asks what fraction of “things”──the answer, for the proton, is 0.96%. No new mathematical theorem and no new law is claimed. Scope of this paper (scope note): No new mathematical theorem and no new law is claimed──the PDG quark masses, the proton and neutron masses, mass generation by the Higgs mechanism, and mass generation by confinement are all standard. We do not build quantum chromodynamics──all we use is three additions and four divisions. We do not explain confinement──neither why quarks are confined nor the mass gap is treated at all. We count only the resulting mass. We do not derive the Higgs mechanism──we do not enter spontaneous symmetry breaking, nor the origin of the Yukawa couplings’ values. We perform no lattice calculation──the number 99.04% is the result of a subtraction, not a first-principles computation. The quark masses carry a definitional premise──m_u=2.16 and m_d=4.67 MeV are values in the MS scheme at renormalisation point 2 GeV. Change the scheme or the point and the values change, so the figure 0.9581% depends on this convention. We do not break down the 99%──the mass from confinement divides into quark kinetic energy, gluon field energy, and the chiral condensate, but this paper lumps them together as “non-Higgs”. We do not explain the neutron--proton difference──the 194% of Section 5 only shows that the naive count breaks, and asserts no value for the electromagnetic contribution. Relation to earlier papers: Paper 240 counted seven things called “mass” and in its scope note explicitly stated “we do not discuss the origin of mass──neither the Higgs mechanism nor confinement is treated”──this paper enters that explicitly ceded ground and compares the two origins numerically. Papers 35 and 51 used the proton mass as a number──this paper asks what it is made of. Paper 112 counted “six distinct roots sharing one rhyme”──the “mass” here likewise has two distinct roots under one word. Paper 274 showed that Drude was right through the cancellation of two errors──Section 5 here is likewise a case where two opposite signs cancel. What is added is computing the proton’s non-Higgs share as 99.0419%, writing the contrast with the electron as a factor of 104.37, showing by the thought experiment of setting the vacuum expectation value to 0 that 929.28 MeV remains, and recording, as the limit of the naive count, that the quark mass difference overshoots the neutron--proton difference by 194%. First, we add the rest masses of the quarks. The proton is uud, so 2x2.16+4.67=8.99 MeV (Section 2). Second, this is the core of the paper. The proton’s mass is 938.27209 MeV, so the Higgs share is 0.9581%, that is 99.0419% is not of Higgs origin (Section 2). Third, the electron is 100% of Higgs origin. Within the one word “mass” there is a factor of 104.37 (Section 3). Fourth, setting the vacuum expectation value to 0 leaves the proton nearly untouched.938.27 MeV merely becomes 929.28 MeV──while the electron vanishes entirely (Section 4). Fifth, there is a place where the naive count fails. Against the neutron--proton difference of 1.29333 MeV, the quark mass difference of 2.51 MeV overshoots by 194% (Section 5). Sixth, the separator is where the mass comes from. Yukawa coupling and confinement energy are different categories, differing in amount by 103.37 (Section 6). “The Higgs gives things their mass” is 100% right for the electron and only 0.9581% right for the proton. The proton is uud, and the sum of the quarks’ rest masses is a mere 8.99 MeV──the actual 938.27 MeV is 104.3684 times that, and 99.0419% is not of Higgs origin. The thought experiment of setting the vacuum expectation value to 0 shows it──the proton merely becomes 929.28 MeV; the electron vanishes entirely. Counting for ordinary matter, 98.9879% of a hydrogen atom’s mass is of non-Higgs origin. But this naive count has a limit──applying it to the neutron--proton difference overshoots at 194.07%. One thing separates them──whether the mass comes from a coupling written into the Lagrangian, or from a consequence of dynamics that emerges only upon solving. The former is the Yukawa coupling, the latter confinement, and they differ in amount by 103.37. Both are measured in MeV, but their standing differs. So when one says “the Higgs gives mass,” one must say which particle is meant. On the making of this work: The ideas and content of this work stem from the author's own considerations. Assistance from an AI (a large language model) was used for structuring, English translation, and checking the algebra. Any remaining errors or misinterpretations are solely the author's. Feedback and corrections are sincerely appreciated. ----- 「ヒッグス粒子は物に質量を与える」と言われる。本稿が問うのは、その「物」の何割かである──答は、陽子については 0.96%である。新しい数学定理も新しい法則も主張しない。 本稿の射程(射程注記):新しい数学定理も新しい法則も主張しない──クォーク質量の PDG 値、陽子・中性子の質量、ヒッグス機構による質量生成、閉じ込めによる質量生成は、いずれも標準的である。量子色力学を作らない──使うのは三つの足し算と、四つの割り算だけである。閉じ込めを説明しない──なぜクォークが閉じ込められるかも、質量ギャップも一切扱わない。結果としての質量だけを数える。ヒッグス機構を導出しない──自発的対称性の破れにも、湯川結合の値の起源にも立ち入らない。格子計算をしない──99.04% という数は引き算の結果であって、第一原理計算ではない。クォーク質量の定義に前提がある──m_u=2.16、m_d=4.67 MeV はMS スキーム・繰り込み点 2 GeVでの値である。スキームと点を変えれば値が変わるので、0.9581% という数字はこの規約に依存する。99% の内訳を分けない──閉じ込めによる質量はクォークの運動エネルギー・グルーオン場のエネルギー・カイラル凝縮などに分かれるが、本稿はそれらを一括して「非ヒッグス」と呼ぶ。中性子・陽子差を説明しない──第5節の 194% は素朴な数え上げが破れることを示すだけであり、電磁的寄与の値を主張しない。既刊との関係:論文240 は「質量」が七つあることを数え、その射程で「質量の起源を論じない──ヒッグス機構も閉じ込めも扱わない」と明記した──本稿はその明示的に譲られた土地に入り、二つの起源の量を数で比べる。論文35・51 は陽子質量を数値として用いた──本稿はその内訳を問う。論文112 は「同じ韻の別根」を数えた──本稿の「質量」も一語の下に二つの別根を持つ。論文274 はドルーデが二つの誤差の打ち消しで当たったと示した──本稿の第5節も符号の逆な二つが打ち消す場合である。加えたのは陽子の非ヒッグス割合を 99.0419% と計算したこと、電子との対照を 104.37 倍と書いたこと、真空期待値を 0 にする思考実験で 929.28 MeV が残ると示したこと、中性子・陽子差でクォーク質量差が 194% 行き過ぎることを、素朴な数え上げの限界として記録したことである。 第一に、クォークの静止質量を足す。陽子は uud なので 2x2.16+4.67=8.99 MeV である(第2節)。 第二に、これが本稿の芯である。陽子の質量は 938.27209 MeV なので、ヒッグス由来は 0.9581%、すなわち99.0419% はヒッグス由来ではない(第2節)。 第三に、電子は 100% ヒッグス由来である。同じ「質量」という一語の中で、104.37 倍の違いがある(第3節)。 第四に、真空期待値を 0 にしても陽子はほぼそのままである。938.27 MeV が 929.28 MeV になるだけ──一方で電子は完全に消える(第4節)。 第五に、素朴な数え上げが失敗する場所がある。中性子と陽子の差 1.29333 MeV に対し、クォーク質量差は 2.51 MeV で194% 行き過ぎる(第5節)。 第六に、分離子は「質量が何から来るか」である。湯川結合と閉じ込めのエネルギーは別の圏であり、量が 103.37 倍違う(第6節)。 「ヒッグスが物に質量を与える」は、電子については 100% 正しく、陽子については 0.9581% しか正しくない。陽子は uud で、クォークの静止質量の和は 8.99 MeV にすぎない──実際の 938.27 MeV はその 104.3684 倍であり、99.0419% はヒッグス由来ではない。真空期待値を 0 にする思考実験がそれを見せる──陽子は 929.28 MeV になるだけ、電子は完全に消える。身の回りの物質で数えれば、水素原子の質量の 98.9879% が非ヒッグス起源である。ただしこの素朴な数え上げには限界がある──中性子と陽子の差を同じやり方で説明しようとすると、194.07% と行き過ぎる。分けるものは一つ──その質量が、ラグランジアンに書き込まれた結合から来るのか、解いてはじめて出る力学の帰結なのか。前者は湯川結合、後者は閉じ込めであり、量は 103.37 倍違う。どちらも MeV で測れるが、身分は違う。だから「ヒッグスが質量を与える」と言うときは、どちらの粒子の話かを言わねばならない。 作成にあたって:本稿の着想と内容は、著者自身の考察に基づくものです。文章の構成整理や英訳、数式の確認には AI(大規模言語モデル)の助力を得ました。最終的な内容の解釈や誤りがあれば、それらはすべて著者の責に帰します。お気づきの点があれば、ご教示いただければ幸いです。
Yuuki Yamagishi· Zenodo (CERN European Organi...· 0 citations
Subtitle A Comprehensive Study of Energy-Efficient Appliances, Intelligent Power Utilization, Energy Conservation and Sustainable Household Electricity Management Detailed Description The increasing dependence on electrical appliances has resulted in significant growth in household electricity consumption. Fans, refrigerators, air conditioners, mixers, washing machines, water heaters, lighting systems and other appliances contribute to the overall electricity demand of households. As the number and operating duration of electrical appliances increase, consumers often experience higher electricity bills. The newspaper article highlights the importance of technology and innovation in reducing electricity expenditure. The underlying concept is that electricity consumption can be reduced not merely by limiting the use of appliances, but by improving the efficiency with which electrical energy is utilized. This research examines the role of energy-efficient appliances, improved electrical and electronic technologies, intelligent control systems, automation and energy monitoring in reducing unnecessary power consumption. It also studies how efficient appliance selection and appropriate usage can provide economic benefits while contributing to energy conservation. The research further explores the future integration of smart appliances, sensors, Internet of Things (IoT), artificial intelligence (AI), smart meters and renewable energy systems for intelligent household energy management. The overall objective is to develop an understanding of how technology can transform conventional electricity consumption into a more efficient, economical and sustainable system. 2. ALTERNATIVE TITLE – BEST FOR ENGINEERING “ENGINEERING INNOVATION FOR ENERGY CONSERVATION: SMART TECHNOLOGIES FOR REDUCING HOUSEHOLD ELECTRICITY CONSUMPTION” Subtitle An Engineering Study of Energy-Efficient Appliances, Power Management, Automation and Intelligent Energy Utilization Detailed Description This research approaches the issue from an engineering perspective. Electrical appliances convert electrical energy into useful outputs such as mechanical motion, cooling, heating, lighting and processing. However, some energy is inevitably lost during this conversion. Therefore, engineering innovation attempts to achieve: Required output + Minimum unnecessary energy input The study investigates how improved motors, electronic controls, efficient appliance design and intelligent operating mechanisms can reduce energy losses. The research also considers how engineering solutions can be developed to make electricity consumption more measurable, controllable and efficient. 3. ALTERNATIVE TITLE – SIMPLE AND POWERFUL “SMART ENERGY, LOWER BILLS: TECHNOLOGY FOR REDUCING ELECTRICITY CONSUMPTION” Subtitle Understanding Energy-Efficient Appliances and Intelligent Electricity Management Detailed Description This title provides a simple explanation of the central idea of the article. Electricity consumption depends largely on: Energy=Power×TimeEnergy = Power \times Time Therefore, electricity consumption can be reduced by: Using appliances with lower power consumption Reducing unnecessary operating time Improving appliance efficiency Avoiding unnecessary standby operation Using intelligent controls Monitoring electricity consumption The research explains how these measures can collectively contribute to lower electricity expenditure. 4. ALTERNATIVE TITLE – SUSTAINABILITY FOCUS “ENERGY CONSERVATION THROUGH TECHNOLOGICAL INNOVATION: TOWARDS A SUSTAINABLE HOUSEHOLD” Subtitle Connecting Energy Efficiency, Electricity Bill Reduction, Technological Innovation and Environmental Sustainability Detailed Description Electricity conservation has significance beyond reducing household expenses. Reducing unnecessary electricity consumption can contribute to: Efficient utilization of energy resources Lower electricity demand Reduced operating costs Greater energy awareness Potential reduction in environmental impacts associated with electricity generation This research therefore studies energy-saving technology as a component of sustainable development. 5. ALTERNATIVE TITLE – SMART HOME FOCUS “SMART HOMES AND SMART ENERGY: INTELLIGENT MANAGEMENT OF HOUSEHOLD ELECTRICITY CONSUMPTION” Subtitle The Role of Sensors, Automation, Smart Appliances and Real-Time Energy Monitoring Detailed Description The concept presented in the article can be expanded into the broader field of smart homes. A smart home can use sensors and controllers to determine when appliances need to operate. For example: Person leaves room ↓ Sensor detects absence ↓ Controller processes information ↓ Fan/light can be switched off ↓ Unnecessary electricity consumption is avoided This approach changes the traditional method of manually controlling every appliance into an intelligent and automated energy-management system. 6. ALTERNATIVE TITLE – IoT FOCUS “IoT-BASED SMART ENERGY MANAGEMENT FOR ELECTRICITY CONSERVATION AND BILL REDUCTION” Subtitle Real-Time Monitoring, Automated Appliance Control and Intelligent Power Optimization Detailed Description Internet of Things technology provides an opportunity to connect electrical appliances, sensors and monitoring systems. A possible system architecture is: Electrical Appliances ↓ Sensors ↓ Microcontroller ↓ Communication Network ↓ Data Processing ↓ Mobile/Computer Interface ↓ Intelligent Control Through such systems, users can obtain information about electricity consumption and identify appliances or periods associated with higher energy usage. 7. ALTERNATIVE TITLE – AI FOCUS “ARTIFICIAL INTELLIGENCE FOR SMART ENERGY CONSERVATION AND ELECTRICITY BILL REDUCTION” Subtitle Using Data Analysis, Prediction and Intelligent Control for Efficient Household Power Consumption Detailed Description Artificial intelligence can take energy management beyond simple monitoring. An AI-based system could analyse: Historical electricity consumption Appliance usage patterns Time of usage Peak demand Temperature conditions User behaviour It could then predict energy requirements and recommend or implement suitable energy-saving actions. The basic process is: Collect data ↓ Analyse data ↓ Identify patterns ↓ Predict consumption ↓ Make intelligent decisions ↓ Optimize energy usage 8. ALTERNATIVE TITLE – YOUNG INNOVATORS / INNOVATION FOCUS “INNOVATION FOR A LOW-COST ENERGY FUTURE: TECHNOLOGY-DRIVEN SOLUTIONS FOR REDUCING ELECTRICITY BILLS” Subtitle Exploring How Engineering Innovation Can Address Everyday Household Energy Challenges Detailed Description One of the important aspects of the newspaper article is its emphasis on technological innovation. High electricity bills represent an everyday problem affecting consumers. Such problems can provide opportunities for engineers and innovators to develop practical solutions. The innovation cycle can be represented as: Identify problem ↓ Develop idea ↓ Design technology ↓ Build prototype ↓ Test performance ↓ Improve efficiency ↓ Develop practical solution This approach demonstrates the importance of engineering innovation in solving real-world problems. 9. ALTERNATIVE TITLE – ECONOMIC FOCUS “REDUCING HOUSEHOLD ELECTRICITY COSTS THROUGH ENERGY EFFICIENCY AND TECHNOLOGICAL INNOVATION” Subtitle An Economic and Technical Study of Efficient Appliances and Smart Power Consumption Detailed Description The research focuses on the financial impact of energy consumption. A consumer should not consider only the purchase price of an appliance. The overall cost can be considered as: Total Cost=Purchase Cost+Operating Cost+Maintenance CostTotal\ Cost = Purchase\ Cost + Operating\ Cost + Maintenance\ Cost An appliance with a somewhat higher initial cost may potentially provide better long-term economics if it consumes substantially less electricity during its operating life. Therefore, energy efficiency should be considered an important factor in purchasing decisions. 10. ALTERNATIVE TITLE – ENVIRONMENTAL FOCUS “SAVE ELECTRICITY, REDUCE ENERGY WASTAGE: TECHNOLOGICAL INNOVATION FOR A SUSTAINABLE FUTURE” Subtitle Examining the Economic and Environmental Benefits of Energy-Efficient Household Technologies Detailed Description Electricity conservation contributes to responsible energy utilization. The relationship can be represented as: Energy efficiency ↓ Lower unnecessary electricity consumption ↓ Lower electricity demand ↓ Reduced requirement for electricity generation ↓ Potential reduction in associated environmental impacts Thus, household energy efficiency can be considered an important component of sustainable energy management. 11. ALTERNATIVE TITLE – FUTURE TECHNOLOGY “THE FUTURE OF HOUSEHOLD ENERGY MANAGEMENT: FROM CONVENTIONAL APPLIANCES TO INTELLIGENT ENERGY SYSTEMS” Subtitle Exploring Energy-Efficient Appliances, Automation, IoT, Artificial Intelligence and Smart Power Management Detailed Description The future of household electricity management is expected to move from simple manual control towards intelligent systems. Conventional system: Switch ON → Appliance operates → Switch OFF Smart system: Detect → Measure → Analyse → Decide → Control → Optimize This transformation can potentially make electricity consumption more efficient while maintaining household convenience. 12. BEST ALTERNATIVE TITLES — SHORTLIST If you need strong academic titles, choose from these: 🥇 Smart Energy-Efficient Technology for Reducing Electricity Bills: An Innovative Approach to Household Power Management 🥈 Engineering Innovation for Energy Conservation: Smart Technologies for Reducing Household Electricity Consumption 🥉 Energy Conservation Through Technological Innovation: Towards a Sustainable Household 4. Smart E
Sudhakar Geruganti· Zenodo (CERN European Organi...· 0 citations
From Bootcamp to Job: How 82% of 4Geeks Academy Data Science Grads Land Work in Under 90 Days in 2026 The promise of a coding bootcamp has always been compelling: learn in-demand skills quickly and transition into a tech career. But in 2026, with data science roles evolving rapidly and employer expectations higher than ever, simply completing a program isn't enough. What sets successful graduates apart isn't just what they learn, but how they're supported throughout the journey. At 4Geeks Academy, the data science bootcamp has achieved an 82% job placement rate within 90 days for its 2026 cohort — a figure that reflects a deliberate, holistic approach to education and career readiness. This isn't accidental. It's the result of a curriculum designed around real employer needs, personalized mentorship that adapts to individual learning gaps, and career services that begin on day one. For anyone considering a shift into data science, understanding how this outcome is achieved offers a clear roadmap to replicating that success. The 4Geeks Academy Data Science Curriculum: Built for Employability Unlike programs that prioritize theoretical depth over practical application, 4Geeks Academy's data science bootcamp is structured around the skills employers actually seek in 2026. The curriculum begins with foundational Python and SQL, quickly progressing to data wrangling with Pandas, exploratory analysis, and visualization using tools like Matplotlib and Seaborn. What distinguishes it is the immediate application of these skills to realistic business scenarios — students don't just learn how to build a linear regression model; they use it to predict customer churn for a fictional e-commerce company, then present their findings to a panel of industry mentors acting as stakeholders. Machine learning modules cover both supervised and unsupervised techniques, with emphasis on model evaluation, overfitting prevention, and deployment basics using Flask and cloud platforms like AWS. Crucially, the program integrates modern MLops concepts early, ensuring graduates understand not just how to train a model, but how to monitor and maintain it in production — a skill increasingly expected even at junior levels. By the midpoint of the bootcamp, students are working with real-world datasets from sectors like finance, healthcare, and logistics, often sourced through partnerships with local businesses seeking analytical support. This project-based learning isn't an add-on; it's the core pedagogical method, ensuring that by graduation, each student has a portfolio of work that mirrors the tasks they'll face on the job. Personalized Mentorship: The 1:1 Advantage That Accelerates Hiring While many bootcamps offer group office hours or forum-based support, 4Geeks Academy's mentorship model is fundamentally different: every student is assigned a dedicated industry professional for weekly one-on-one sessions throughout the program. These aren't generic check-ins; they're targeted interventions based on the student's progress, learning style, and career goals. A mentor might spend extra time helping a student struggling with probability concepts reframe them through gambling simulations, or guide another interested in natural language processing through a custom spaCy project analyzing social media sentiment. This personalized attention does more than fill knowledge gaps — it builds confidence and professional communication skills. Mentors often simulate technical interviews during these sessions, providing immediate feedback on how to explain complex ideas clearly, a critical factor in hiring decisions. They also help students identify which companies align with their interests and strengths, turning a vague job search into a focused strategy. In 2026, where hiring managers report that cultural fit and communication ability often decide between equally qualified candidates, this mentorship gives 4Geeks graduates a measurable edge. The relationship frequently extends beyond graduation, with many mentors becoming long-term advisors or even referral sources within their networks. Career Services That Work: Resume, Interview Prep, and Networking in 2026 Career support at 4Geeks Academy isn't a final-week workshop — it's woven into the bootcamp experience from the first day. Students begin by building a technical resume that highlights not just skills, but impact: instead of "learned machine learning algorithms," a resume might state "developed and deployed a recommendation engine that improved user engagement metrics by 18% in a capstone project." Career advisors help translate academic projects into business outcomes, a language that resonates with hiring managers. Interview preparation goes beyond LeetCode-style problem solving. Students participate in mock technical interviews with practicing data scientists, behavioral interviews focused on STAR (Situation, Task, Action, Result) storytelling, and even case interviews common in consulting-adjacent roles. They learn how to discuss trade-offs in model selection, explain p-values to non-technical stakeholders, and ask insightful questions about a company's data infrastructure — signals that demonstrate readiness beyond coding ability. Networking is facilitated through virtual meetups with alumni working at companies ranging from fintech startups in Madrid to healthcare AI firms in Miami, and remote-first organizations across Latin America. These connections often lead to referrals, which in 2026 remain one of the most effective pathways into data science roles, especially for career-changers without traditional CS backgrounds. Real-World Projects: How Portfolio Pieces Get Graduates Noticed The capstone project is where theory, mentorship, and career preparation converge. Over four weeks, students identify a problem — ideally one tied to their desired industry — gather or synthesize data, apply multiple analytical techniques, and deliver a full solution complete with documentation, visualizations, and a presentation. Recent examples include a predictive maintenance model for manufacturing equipment using sensor data, a customer segmentation analysis for a Barcelona-based retail chain using clustering algorithms, and a natural language processing tool that automated the summarization of legal documents for a Bogotá law firm. These projects aren't graded in isolation; they're showced in a virtual demo day attended by hiring partners, alumni, and industry professionals. Employers don't just see code — they see problem-solving approaches, communication skills, and the ability to deliver value. Many graduates report receiving interview invitations directly from demo day interactions, with some companies using the event as a primary screening tool. In a job market where recruiters spend seconds scanning resumes, a compelling portfolio piece — especially one that addresses a real business need — can be the difference between being overlooked and getting an interview. Alumni Outcomes: Where Graduates Are Working and What They Earn The 82% placement rate tells only part of the story. Where are these graduates going, and what are they earning? In 2026, 4Geeks Academy data science alumni are employed across a diverse range of organizations: tech startups in Medellín and Mexico City, established financial institutions in Barcelona and Miami, healthcare analytics units in São Paulo, and remote roles with U.S.-based companies distributed globally. A significant portion — roughly 30% — secure fully remote positions, reflecting both the graduate preference for flexibility and the growing acceptance of distributed data teams. Starting salaries vary by location and role, but the median starting salary for graduates in 2026 is approximately $52,000 USD annually for roles in Latin America and Spain, and $78,000 USD for those based in the United States or working remotely for U.S. companies. These figures are particularly noteworthy given that many graduates come from non-technical backgrounds and are entering their first formal tech role. For context, this guide on ai company salary shows how entry-level AI-adjacent roles are trending in 2026, providing a useful benchmark for those evaluating long-term growth potential in the field. Beyond salary, alumni frequently cite accelerated career progression — many report moving into senior analyst or lead data scientist roles within 18–24 months, often faster than peers from traditional academic paths due to their applied, project-heavy training. How to Join: Steps to Enroll and Maximize Your Chances of Success Replicating the success of 4Geeks Academy's data science graduates begins with intentional preparation. Prospective students should start by assessing their baseline: comfort with logic and basic algebra is helpful, but prior coding experience is not required. The academy offers free preparatory workshops in Python and statistics to help applicants build foundational confidence before the bootcamp begins. Once enrolled, success hinges on engagement — treating the program like a job, showing up to mentorship sessions prepared with specific questions, and investing extra time in projects that align with personal career interests. Students are encouraged to view the bootcamp not as a finish line, but as the launchpad for continuous learning. The most successful graduates often supplement the curriculum with self-directed learning in emerging areas like MLOps or deep learning, guided by mentor recommendations. They also actively participate in the alumni network, attending events and contributing to forums long after graduation. For those considering enrollment, the next step is to speak with an admissions advisor — not just about tuition and schedules, but about career goals. The advisors help determine if the data science track aligns with those aspirations and outline a personalized path forward, ensuring that from day one, every effort is directe
Ignacio López· Zenodo (CERN European Organi...· 0 citations
It is said that “the Higgs particle gives things their mass.”This paper asks what fraction of “things”──the answer, for the proton, is 0.96%. No new mathematical theorem and no new law is claimed. Scope of this paper (scope note): No new mathematical theorem and no new law is claimed──the PDG quark masses, the proton and neutron masses, mass generation by the Higgs mechanism, and mass generation by confinement are all standard. We do not build quantum chromodynamics──all we use is three additions and four divisions. We do not explain confinement──neither why quarks are confined nor the mass gap is treated at all. We count only the resulting mass. We do not derive the Higgs mechanism──we do not enter spontaneous symmetry breaking, nor the origin of the Yukawa couplings’ values. We perform no lattice calculation──the number 99.04% is the result of a subtraction, not a first-principles computation. The quark masses carry a definitional premise──m_u=2.16 and m_d=4.67 MeV are values in the MS scheme at renormalisation point 2 GeV. Change the scheme or the point and the values change, so the figure 0.9581% depends on this convention. We do not break down the 99%──the mass from confinement divides into quark kinetic energy, gluon field energy, and the chiral condensate, but this paper lumps them together as “non-Higgs”. We do not explain the neutron--proton difference──the 194% of Section 5 only shows that the naive count breaks, and asserts no value for the electromagnetic contribution. Relation to earlier papers: Paper 240 counted seven things called “mass” and in its scope note explicitly stated “we do not discuss the origin of mass──neither the Higgs mechanism nor confinement is treated”──this paper enters that explicitly ceded ground and compares the two origins numerically. Papers 35 and 51 used the proton mass as a number──this paper asks what it is made of. Paper 112 counted “six distinct roots sharing one rhyme”──the “mass” here likewise has two distinct roots under one word. Paper 274 showed that Drude was right through the cancellation of two errors──Section 5 here is likewise a case where two opposite signs cancel. What is added is computing the proton’s non-Higgs share as 99.0419%, writing the contrast with the electron as a factor of 104.37, showing by the thought experiment of setting the vacuum expectation value to 0 that 929.28 MeV remains, and recording, as the limit of the naive count, that the quark mass difference overshoots the neutron--proton difference by 194%. First, we add the rest masses of the quarks. The proton is uud, so 2x2.16+4.67=8.99 MeV (Section 2). Second, this is the core of the paper. The proton’s mass is 938.27209 MeV, so the Higgs share is 0.9581%, that is 99.0419% is not of Higgs origin (Section 2). Third, the electron is 100% of Higgs origin. Within the one word “mass” there is a factor of 104.37 (Section 3). Fourth, setting the vacuum expectation value to 0 leaves the proton nearly untouched.938.27 MeV merely becomes 929.28 MeV──while the electron vanishes entirely (Section 4). Fifth, there is a place where the naive count fails. Against the neutron--proton difference of 1.29333 MeV, the quark mass difference of 2.51 MeV overshoots by 194% (Section 5). Sixth, the separator is where the mass comes from. Yukawa coupling and confinement energy are different categories, differing in amount by 103.37 (Section 6). “The Higgs gives things their mass” is 100% right for the electron and only 0.9581% right for the proton. The proton is uud, and the sum of the quarks’ rest masses is a mere 8.99 MeV──the actual 938.27 MeV is 104.3684 times that, and 99.0419% is not of Higgs origin. The thought experiment of setting the vacuum expectation value to 0 shows it──the proton merely becomes 929.28 MeV; the electron vanishes entirely. Counting for ordinary matter, 98.9879% of a hydrogen atom’s mass is of non-Higgs origin. But this naive count has a limit──applying it to the neutron--proton difference overshoots at 194.07%. One thing separates them──whether the mass comes from a coupling written into the Lagrangian, or from a consequence of dynamics that emerges only upon solving. The former is the Yukawa coupling, the latter confinement, and they differ in amount by 103.37. Both are measured in MeV, but their standing differs. So when one says “the Higgs gives mass,” one must say which particle is meant. On the making of this work: The ideas and content of this work stem from the author's own considerations. Assistance from an AI (a large language model) was used for structuring, English translation, and checking the algebra. Any remaining errors or misinterpretations are solely the author's. Feedback and corrections are sincerely appreciated. ----- 「ヒッグス粒子は物に質量を与える」と言われる。本稿が問うのは、その「物」の何割かである──答は、陽子については 0.96%である。新しい数学定理も新しい法則も主張しない。 本稿の射程(射程注記):新しい数学定理も新しい法則も主張しない──クォーク質量の PDG 値、陽子・中性子の質量、ヒッグス機構による質量生成、閉じ込めによる質量生成は、いずれも標準的である。量子色力学を作らない──使うのは三つの足し算と、四つの割り算だけである。閉じ込めを説明しない──なぜクォークが閉じ込められるかも、質量ギャップも一切扱わない。結果としての質量だけを数える。ヒッグス機構を導出しない──自発的対称性の破れにも、湯川結合の値の起源にも立ち入らない。格子計算をしない──99.04% という数は引き算の結果であって、第一原理計算ではない。クォーク質量の定義に前提がある──m_u=2.16、m_d=4.67 MeV はMS スキーム・繰り込み点 2 GeVでの値である。スキームと点を変えれば値が変わるので、0.9581% という数字はこの規約に依存する。99% の内訳を分けない──閉じ込めによる質量はクォークの運動エネルギー・グルーオン場のエネルギー・カイラル凝縮などに分かれるが、本稿はそれらを一括して「非ヒッグス」と呼ぶ。中性子・陽子差を説明しない──第5節の 194% は素朴な数え上げが破れることを示すだけであり、電磁的寄与の値を主張しない。既刊との関係:論文240 は「質量」が七つあることを数え、その射程で「質量の起源を論じない──ヒッグス機構も閉じ込めも扱わない」と明記した──本稿はその明示的に譲られた土地に入り、二つの起源の量を数で比べる。論文35・51 は陽子質量を数値として用いた──本稿はその内訳を問う。論文112 は「同じ韻の別根」を数えた──本稿の「質量」も一語の下に二つの別根を持つ。論文274 はドルーデが二つの誤差の打ち消しで当たったと示した──本稿の第5節も符号の逆な二つが打ち消す場合である。加えたのは陽子の非ヒッグス割合を 99.0419% と計算したこと、電子との対照を 104.37 倍と書いたこと、真空期待値を 0 にする思考実験で 929.28 MeV が残ると示したこと、中性子・陽子差でクォーク質量差が 194% 行き過ぎることを、素朴な数え上げの限界として記録したことである。 第一に、クォークの静止質量を足す。陽子は uud なので 2x2.16+4.67=8.99 MeV である(第2節)。 第二に、これが本稿の芯である。陽子の質量は 938.27209 MeV なので、ヒッグス由来は 0.9581%、すなわち99.0419% はヒッグス由来ではない(第2節)。 第三に、電子は 100% ヒッグス由来である。同じ「質量」という一語の中で、104.37 倍の違いがある(第3節)。 第四に、真空期待値を 0 にしても陽子はほぼそのままである。938.27 MeV が 929.28 MeV になるだけ──一方で電子は完全に消える(第4節)。 第五に、素朴な数え上げが失敗する場所がある。中性子と陽子の差 1.29333 MeV に対し、クォーク質量差は 2.51 MeV で194% 行き過ぎる(第5節)。 第六に、分離子は「質量が何から来るか」である。湯川結合と閉じ込めのエネルギーは別の圏であり、量が 103.37 倍違う(第6節)。 「ヒッグスが物に質量を与える」は、電子については 100% 正しく、陽子については 0.9581% しか正しくない。陽子は uud で、クォークの静止質量の和は 8.99 MeV にすぎない──実際の 938.27 MeV はその 104.3684 倍であり、99.0419% はヒッグス由来ではない。真空期待値を 0 にする思考実験がそれを見せる──陽子は 929.28 MeV になるだけ、電子は完全に消える。身の回りの物質で数えれば、水素原子の質量の 98.9879% が非ヒッグス起源である。ただしこの素朴な数え上げには限界がある──中性子と陽子の差を同じやり方で説明しようとすると、194.07% と行き過ぎる。分けるものは一つ──その質量が、ラグランジアンに書き込まれた結合から来るのか、解いてはじめて出る力学の帰結なのか。前者は湯川結合、後者は閉じ込めであり、量は 103.37 倍違う。どちらも MeV で測れるが、身分は違う。だから「ヒッグスが質量を与える」と言うときは、どちらの粒子の話かを言わねばならない。 作成にあたって:本稿の着想と内容は、著者自身の考察に基づくものです。文章の構成整理や英訳、数式の確認には AI(大規模言語モデル)の助力を得ました。最終的な内容の解釈や誤りがあれば、それらはすべて著者の責に帰します。お気づきの点があれば、ご教示いただければ幸いです。
Yuuki Yamagishi· Zenodo (CERN European Organi...· 0 citations
