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
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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
Official Codebase Release (v1.0.1) Accompanying reproducible code, neural architectures, data pipelines, and decision-support tools for the Springer Nature Monograph: "AI-Driven Geospatial Hydrology: Machine Learning and Decision Support for Surface Water Dynamics" (2026) by Burak Can. Key Capabilities & Modules Earth Observation Pipelines: Cloud-native STAC metadata querying, COG windowed streaming, and 6S radiative transfer normalization (Chapters 1–2). Deep Vision for Water Extraction: Boundary-Aware ResUNet, Hierarchical SegFormer (MiT-B3), and multi-modal SAR-optical FCLS unmixing (Chapters 3–5). Hydrodynamic Sequence Forecasting: Differentiable Kling-Gupta Efficiency (KGE) optimization, Temporal Fusion Transformers, and physics-informed mass conservation loss ($\mathcal{L}_{\text{mass}}$) (Chapter 6). Explainable AI (XAI): Path-integral Integrated Gradients and SHAP feature attribution decoupling climatic stress from anthropogenic water extraction (Chapter 7). Cloud Microservices & Agentic Tools: Dynamic TiTiler tile streaming, PostGIS schemas, and Model Context Protocol (FastMCP) server implementations (Chapters 8–10). Citation @book{can2026geospatial, title = {AI-Driven Geospatial Hydrology: Machine Learning and Decision Support for Surface Water Dynamics}, author = {Can, Burak}, year = {2026}, publisher = {Springer Nature} }
Burak Can· Zenodo (CERN European Organi...· 0 citations
Economic Convergence Under Technological and Climate Transformation presents a comprehensive macro‑structural framework explaining how emerging markets can achieve sustained catch‑up growth amid two simultaneous global transitions: the Technological Wave—AI, robotics, digital automation—and the Climate Wave—decarbonization, physical climate shocks, and tightening planetary boundaries. As the monograph states, “the classical development escalator has fundamentally broken down” and EMDEs must now navigate a dual transformation to avoid “premature de‑industrialization” and climate‑induced debt traps. The work demonstrates that modern convergence is driven not by cheap labor or fossil resources, but by low-cost clean electricity, Digital Public Infrastructure (DPI), STEM human capital, smart grid flexibility, and reduced cost of capital. It introduces the concept of Green Comparative Advantage, showing how renewable endowments and critical minerals can anchor high-value industrial ecosystems. Empirical models reveal that synchronizing digital intelligence with clean physical systems yields TFP gains of 19–34%, more than double standalone adoption. Through regional case studies—Vietnam, Malaysia, India, Kenya, Chile, Morocco—the monograph illustrates how nations can integrate clean power corridors, sovereign cloud clusters, geothermal baseload, digital payment rails, and green data centers to build globally competitive industrial bases. It further outlines the emerging financial architecture required to overcome the Cost of Capital Divide, including MDB capital adequacy reforms, TCX currency hedging, and Climate‑Resilient Debt Clauses. The strategic roadmap (2026–2050) provides a phased implementation architecture for policymakers, emphasizing smart grids, universal DPI, green compute mandates, digital product passports, regional clean power pools, and autonomous net‑zero industrial systems. Ultimately, the monograph argues that the dual transformation is “the most powerful catalyst for sustainable human progress ever conceived,” enabling EMDEs to achieve rapid, resilient, and equitable convergence.
Hunter Hughes· Zenodo (CERN European Organi...· 0 citations
Why this is a strong title This title combines the main ideas of the research paper: Beyond Medical Treatment → healthcare is more than diagnosis and medicines. Role of Healthcare Professionals → doctors, nurses and other professionals. Patient-Centred → importance of patient needs and participation. Safe → patient safety and quality of care. Supportive Healthcare → communication, counselling, empathy and follow-up. This would be my first choice for a college research paper. 2. Alternative Research Paper Titles Title 1 — Broad and Academic “The Evolving Role of Healthcare Professionals in Modern Patient-Centred Healthcare” Focus: How the responsibilities of healthcare professionals have changed from simply treating diseases to providing comprehensive patient care. Title 2 — Strong and Research-Oriented “Beyond Diagnosis and Treatment: Exploring the Role of Healthcare Professionals in Comprehensive Patient Care” Focus: Diagnosis and treatment are only part of healthcare; communication, education, emotional support and follow-up are also important. Title 3 — Healthcare + Human Support “Healthcare Professionals as Partners in Patient Care: Integrating Medical Expertise with Human Support” Focus: Combining scientific/medical knowledge with empathy, communication and patient support. Title 4 — Patient-Centred Approach “Patient-Centred Healthcare: The Importance of Communication, Safety and Professional Support in Modern Medicine” Focus: Patient-centred healthcare, communication and safety. Title 5 — Modern Healthcare “Transforming Healthcare Delivery: The Role of Medical Professionals, Technology and Patient Participation” Focus: The interaction between healthcare professionals, technology and patients. Title 6 — Quality of Healthcare “Improving Quality of Healthcare Through Professional Expertise, Patient Support and Effective Communication” Focus: How healthcare quality can be improved through professional and interpersonal factors. Title 7 — More Suitable for a Social/Healthcare Research Paper “From Treatment to Partnership: Understanding the Changing Relationship Between Healthcare Professionals and Patients” Focus: The changing doctor-patient relationship. Title 8 — Simple but Effective “The Role of Healthcare Professionals in Providing Quality, Safe and Patient-Centred Care” Focus: Directly covers quality, safety and patient-centred care. 3. Recommended Subtitle You can place a subtitle below the main title to make the research paper look more professional. Main Title: Beyond Medical Treatment: The Role of Healthcare Professionals in Patient-Centred, Safe and Supportive Healthcare Subtitle: “A Qualitative Study of Communication, Patient Participation, Healthcare Technology, Safety and Multidisciplinary Care” So the complete title page heading becomes: BEYOND MEDICAL TREATMENT: THE ROLE OF HEALTHCARE PROFESSIONALS IN PATIENT-CENTRED, SAFE AND SUPPORTIVE HEALTHCAREA Qualitative Study of Communication, Patient Participation, Healthcare Technology, Safety and Multidisciplinary Care 4. Suggested Research Paper Subtitles / Section Headings Here is a proper structure you can use for the research paper. No. Main Section Suggested Subtitle What It Covers 1 Introduction Understanding Modern Healthcare Beyond Treatment Introduces the changing nature of healthcare 2 Background From Disease-Centred to Patient-Centred Healthcare Explains the shift in healthcare approach 3 Problem Statement Challenges in Contemporary Healthcare Delivery Identifies current healthcare problems 4 Objectives Purpose and Objectives of the Study Explains what the research aims to investigate 5 Methodology Research Design and Methodological Approach Explains how the study was conducted 6 Healthcare Professionals The Expanding Role of Healthcare Professionals Discusses doctors, nurses and other professionals 7 Patient Support Healthcare Professionals as Partners in Patient Care Explains support beyond treatment 8 Communication Communication as a Foundation of Effective Healthcare Discusses doctor-patient communication 9 Patient-Centred Care Putting the Patient at the Centre of Healthcare Explains patient-centred care 10 Safety Patient Safety and Quality of Healthcare Discusses medical errors and safety 11 Technology Technology and the Transformation of Healthcare Discusses digital healthcare and AI 12 Teamwork The Importance of Multidisciplinary Healthcare Teams Discusses cooperation between professionals 13 Patient Role Patient and Family Participation in Healthcare Explains active patient involvement 14 Discussion Integrating Medical Expertise with Human Care Brings the major ideas together 15 Findings Major Findings of the Study Presents key observations 16 Recommendations Strategies for Improving Patient-Centred Healthcare Gives practical recommendations 17 Conclusion Towards a More Human-Centred Healthcare System Concludes the research 5. Detailed Description of Each Subtitle 1. Understanding Modern Healthcare Beyond Treatment Description: This section introduces the central idea of the research paper. Healthcare should not be viewed merely as diagnosing diseases and prescribing medicines. Modern healthcare involves diagnosis, treatment, communication, education, prevention, rehabilitation, emotional support and follow-up. The section establishes the argument that successful healthcare requires both medical expertise and human-centred care. 2. From Disease-Centred to Patient-Centred Healthcare Description: Traditional healthcare primarily focused on the disease and its treatment. Modern healthcare increasingly considers the patient as a whole person. The section can explain the difference: Traditional approach: Disease → Diagnosis → Treatment Modern approach: Patient → Diagnosis → Treatment → Communication → Support → Follow-up → Prevention The patient's preferences, concerns, values and individual circumstances are considered along with the clinical condition. 3. Challenges in Contemporary Healthcare Delivery Description: This section identifies the major problems that can prevent patients from receiving effective healthcare. These include: Poor communication Lack of awareness Delayed diagnosis Limited accessibility Patient anxiety Medication errors Lack of coordination High healthcare costs Inadequate follow-up Overdependence on technology The section establishes why improving healthcare delivery is necessary. 4. Purpose and Objectives of the Study Description: This section clearly states what the research is trying to discover. The main objective can be: To examine the changing role of healthcare professionals and their contribution to safe, supportive and patient-centred healthcare. Other objectives can include studying: Communication Patient participation Healthcare safety Technology Teamwork Patient education Follow-up care 5. Research Design and Methodological Approach Description: This section explains how the research was developed. For your paper, you can describe it as a qualitative descriptive study based on analysis of the newspaper article/theme and supporting healthcare literature. The methodology can include: Source identification → Literature review → Thematic analysis → Interpretation → Findings → Recommendations This makes the paper look more academically structured. 6. The Expanding Role of Healthcare Professionals Description: This is one of the most important sections. Explain that healthcare professionals have responsibilities beyond simply diagnosing and treating diseases. Their responsibilities include: Diagnosis Treatment Patient education Counselling Communication Monitoring Prevention Rehabilitation Follow-up Patient safety The section can also discuss the roles of: Doctors Nurses Pharmacists Laboratory professionals Physiotherapists Counsellors Other specialists 7. Healthcare Professionals as Partners in Patient Care Description: This section develops the central argument of your paper. A healthcare professional should not be considered merely a service provider. Instead, the professional can act as a partner and guide throughout the patient's healthcare journey. For example: Diagnosis → Explanation → Treatment → Emotional Support → Monitoring → Follow-up This section can discuss trust, empathy, reassurance and continuous support. 8. Communication as a Foundation of Effective Healthcare Description: Communication is one of the most important components of healthcare. A healthcare professional needs to explain: What the condition is Why tests are required Available treatment options Benefits and risks Medication instructions Follow-up requirements Preventive measures Medical terminology should be explained in language that the patient can understand. Key idea: Good medical knowledge becomes more effective when it is communicated clearly to the patient. 9. Putting the Patient at the Centre of Healthcare Description: This section focuses specifically on patient-centred care. Patient-centred healthcare means considering: Patient needs Patient preferences Patient values Patient concerns Patient understanding Patient participation Instead of: Doctor decides → Patient follows the approach becomes: Doctor explains → Patient understands → Options are discussed → Appropriate decision is made This can be connected to shared decision-making. 10. Patient Safety and Quality of Healthcare Description: This section explains why healthcare must not only be effective but also safe. Potential risks include: Medication errors Incorrect patient identification Communication failures
Sudhakar Geruganti· Zenodo (CERN European Organi...· 0 citations
Official Codebase Release (v1.0.1) Accompanying reproducible code, neural architectures, data pipelines, and decision-support tools for the Springer Nature Monograph: "AI-Driven Geospatial Hydrology: Machine Learning and Decision Support for Surface Water Dynamics" (2026) by Burak Can. Key Capabilities & Modules Earth Observation Pipelines: Cloud-native STAC metadata querying, COG windowed streaming, and 6S radiative transfer normalization (Chapters 1–2). Deep Vision for Water Extraction: Boundary-Aware ResUNet, Hierarchical SegFormer (MiT-B3), and multi-modal SAR-optical FCLS unmixing (Chapters 3–5). Hydrodynamic Sequence Forecasting: Differentiable Kling-Gupta Efficiency (KGE) optimization, Temporal Fusion Transformers, and physics-informed mass conservation loss ($\mathcal{L}_{\text{mass}}$) (Chapter 6). Explainable AI (XAI): Path-integral Integrated Gradients and SHAP feature attribution decoupling climatic stress from anthropogenic water extraction (Chapter 7). Cloud Microservices & Agentic Tools: Dynamic TiTiler tile streaming, PostGIS schemas, and Model Context Protocol (FastMCP) server implementations (Chapters 8–10). Citation @book{can2026geospatial, title = {AI-Driven Geospatial Hydrology: Machine Learning and Decision Support for Surface Water Dynamics}, author = {Can, Burak}, year = {2026}, publisher = {Springer Nature} }
Burak Can· Zenodo (CERN European Organi...· 0 citations
Praktisch nutzbare Künstliche Intelligenz entstand aus dem Bündnis kluger Köpfe mit den rechentechnischen Fähigkeiten Boolescher Automaten – natürliche Intelligenz dagegen aus Selbstorganisation und Evolution. Dieser zweite Weg rückt mit dem neuromorphen Rechnen zunehmend wieder in das Blickfeld von Wissenschaft und Praxis: Spikende neuronale Netze (SNN) stellen dem wachsenden Energiehunger moderner KI-Systeme eine reale Alternative entgegen und erweitern das Methodenrepertoire der KI um Elemente der Selbstorganisation. Das vorliegende Werk erläutert dieses Spannungsfeld allgemeinverständlich und zugleich mathematisch nachvollziehbar. Am Beispiel des Texterzeugungsmoduls von ChatGPT werden Konzept, Eleganz und Aufwand der modernen Transformer-basierten KI Schritt für Schritt sichtbar gemacht und mit den Kodierungs-, Lern- und Netzgestaltungsmethoden spikender neuronaler Netze verglichen. Den Hintergrund bilden ausgewählte Wirkungsmechanismen des menschlichen Nervensystems – die kybernetischen Vorbilder des neuromorphen Rechnens. Behandelt werden u. a. Spike-Timing-Dependent Plasticity, Populations- und Zeitkodierung, das Neural Engineering Framework, neuromorphe Hardware von Loihi 2 bis zu kommerziellen Edge-Prozessoren sowie die Perspektiven der wechselseitigen Befruchtung klassischer und neuromorpher KI – bis hin zum optischen Rechnen. (ca. 90 Seiten, 20 Abbildungen) English abstract: Practically usable artificial intelligence arose from the alliance of ingenious minds with the computational power of Boolean automata – natural intelligence, by contrast, from self-organization and evolution. With neuromorphic computing, this second path is moving back into the focus of science and engineering: spiking neural networks (SNN) offer a real alternative to the growing energy appetite of modern AI systems and fundamentally extend the methodological repertoire of AI by elements of self-organization. This monograph (in German) explains this field of tension in a generally accessible yet mathematically traceable way. Using the text-generation module of ChatGPT as a representative example, the concept, elegance and computational cost of modern transformer-based AI are made visible step by step and compared with the coding, learning and network-design methods of spiking neural networks. The background is provided by selected mechanisms of the human nervous system – the cybernetic archetypes of neuromorphic computing. Topics include spike-timing-dependent plasticity, population and temporal coding, the Neural Engineering Framework, neuromorphic hardware from Loihi 2 to commercial edge processors, and the prospects of mutual enrichment of classical and neuromorphic AI – up to optical computing. (approx. 90 pages, 20 figures)
Volker Kempe· Zenodo (CERN European Organi...· 0 citations
FieldMaizeLeaf-8 consists of 3,975 early vegetative maize leaf images collected as a research resource for studies in maize stress classification, computer vision, deep learning, and explainable artificial intelligence. The images were collected from real field conditions in Daudkandi, Cumilla, Bangladesh, and represent approximately 1–2 month-old maize plants, capturing the natural variations encountered during field-based maize leaf imaging. The dataset contains images representing eight maize leaf classes: Fall Armyworm Damage, Healthy Leaf, Leaf Spot, Magnesium Deficiency, Nitrogen Deficiency, Northern Leaf Blight, Phosphorus Deficiency, and Potassium Deficiency. These classes cover both biotic stresses, including diseases and insect damage, and abiotic stresses associated with nutrient deficiencies, along with healthy leaves. The images were collected from natural field environments and selected based on their suitability for analysis. As a real-field dataset, it contains natural variations in illumination, background, leaf orientation, image quality, viewing angle, symptom severity, and leaf appearance. These variations make the dataset suitable for developing and evaluating robust deep learning models under realistic agricultural conditions. FieldMaizeLeaf-8 is intended for research in areas including maize stress classification, plant disease recognition, nutrient deficiency detection, computer vision, deep learning, transfer learning, image-based agricultural diagnosis, and explainable artificial intelligence (XAI). The dataset can also support the development of practical AI-based tools for early maize stress identification and precision agriculture applications.
Laboni Akter, Sakibul Hasan Chowdhury, Md. Shohel Arman· Zenodo (CERN European Organi...· 0 citations
