software testing

Hybrid Approach for Chlorine-Compliant Blow-Off Optimization in Dead-End Water Distribution Branches

Dead-end sections of water distribution systems often experience long residence times and stagnation, causing chlorine residuals to fall below regulatory standards and leading utilities to run continuous blow-offs that increase nonrevenue water. This study proposes a hybrid advection–reaction (AR) and advection–dispersion–reaction (ADR) screening–validation framework to identify cost-effective continuous blow-off configurations that satisfy minimum chlorine residual and pressure requirements across multiple operating scenarios. Candidate configurations are screened in EPANET, a public-domain hydraulic and water quality modeling software, using an AR formulation, within nondominated sorting genetic algorithm II (NSGA-II) to minimize worst-case chlorine deficit penalty while maximizing worst-case treatment-cost savings across multiple scenarios. Shortlisted nondominated configurations are then reevaluated in Washington University dead end simulator (WUDESIM), a dead-end water quality model that uses an ADR formulation and incorporates stochastic water demands, to confirm compliance in dispersion-dominated dead ends and to quantify AR–ADR discrepancy. The framework is tested on a multisource Canadian municipal system with 63 operational blow-offs supported by calibrated hydraulic and chlorine models and stochastic household demands. EPANET screening produced 14 configurations; ADR validation confirmed two configurations satisfied chlorine and pressure criteria, enabling closure of approximately 68%–73% of blow-offs while maintaining compliance, and yielding annual treatment-cost savings of approximately 64%–70% compared with baseline operation with all blow-offs open. AR screening underestimated the mean dead-end chlorine deficiency by up to 14% relative to ADR validation, indicating the importance of ADR confirmation for conservative closure decisions. Sensitivity analysis identifies nodal demand as the dominant source of uncertainty, followed by bulk decay, with wall decay having a smaller influence. Overall, the proposed hybrid workflow provides an efficient and practical approach to ensuring that safe chlorine levels are maintained.

An Examination of Artificial Intelligence Anxiety and Artificial Intelligence Usage Among Fine Arts Faculty Students

The research results show that gender differences should be considered in the integration process of AI technologies in fine arts education, and that female students, in particular, should be supported in their use of technology.

Testing NetLogo model code

This work identifies eight kinds of errors that are especially common in NetLogo software and presents code testing methods appropriate for models implemented in NetLogo by novice programmers.

BIM Integration in Civil Engineering Curricula: Maturity Assessment, Pilot Testing, and Roadmap Design

The growing digitalization of the architecture, engineering, and construction (AEC) industry has increased the demand for graduates with strong BIM-related competencies, yet many undergraduate programs—particularly in emerging economies—still approach BIM in a fragmented and software-centered way. This study reports the development and implementation of a structured educational strategy for integrating building information modeling (BIM) into the civil engineering curriculum of a Brazilian public university. The intervention followed a four-phase process: (1) assessment of institutional BIM maturity using a customized 16-indicator matrix, which classified the program at the initial level ( MI = 37.5 % ); (2) curriculum mapping across 66 courses, revealing that 56.1% had potential for BIM integration; (3) pilot implementation in four core courses using project-based and team-based learning; and (4) creation of a long-term institutional roadmap aligned with national policies such as the Brazilian BIM Strategy. Survey results from 43 students indicated moderate to high satisfaction with the learning experience (mean scores between 3.84 and 4.35 on a 1–5 scale) and modest perceived gains in BIM conceptual understanding. Iterative refinements addressed gaps in digital literacy, assessment design, and faculty alignment. The proposed roadmap offers a replicable, evidence-based, and context-sensitive approach for institutions—particularly those in emerging-economy contexts, including but not limited to Latin America—seeking to scale BIM integration in engineering education.

Modeling Crack Behavior around Holes in Glulam Beams Using the WoodST Constitutive Model Based on Continuum Damage Mechanics

The integration of service holes in glulam beams is increasingly common in mass timber construction; however, these openings introduce stress concentrations that can compromise structural integrity through crack initiation and propagation. This study presents a robust numerical framework for simulating crack behavior around holes in glulam beams using the finite element software Abaqus. The framework incorporates the Wood ST constitutive model, developed based on continuum damage mechanics, to capture the anisotropic damage evolution of timber under tensile and shear loading. Key modeling components include detailed geometric representation, layer-refined meshing strategies, and cylindrical orthotropic material systems to simulate the structure of the laminations. To validate the proposed approach, six glulam beams with three configurations—without holes, with a single hole, and with two holes—were tested under a single point load at midspan. The developed finite element models were calibrated and validated using experimental data, demonstrating strong agreement in terms of load–displacement responses and observed failure modes. The results confirm the model’s capability to predict the structural resistance and deformation behavior of perforated glulam beams. This predictive tool contributes to the advancement of structural design methodologies for engineered timber structures.

Architecture-based software testing: Systematic literature review

Geometric Reconstruction-Driven Load Capacity Evaluation for Locally Buckled Steel Members

Conventional contact-based inspection techniques face significant challenges in accurately modeling the complex 3D geometry of locally buckled steel members, which hinder reliable assessment of their residual load-carrying capacity. To overcome these limitations, this study proposes a method for analyzing the bearing capacity of locally buckled steel members using geometric reconstruction models, enabling precise evaluation of their load-carrying capacity. The research methodology encompasses three primary aspects: (1) model preprocessing; (2) load-bearing capacity analysis of specimens; and (3) experimental validation. Model preprocessing involves three key tasks: point cloud model reconstruction and optimization; evaluation of the effect of external factors on model accuracy; and parametric modeling with verification of geometric accuracy. The load-bearing capacity of damaged specimens was analyzed by predicting the residual capacity of the corresponding parametric models using finite element software. Finally, axial compression tests on equal-leg single-angle steel specimens were conducted to validate the accuracy of the finite element analysis results, thereby demonstrating the effectiveness of the proposed method. Key findings include: (1) the overlap ratio has the most significant influence on model accuracy; at an overlap level of 18, the comprehensive mean absolute error is below 0.005, and model-specimen similarity between the angle steel parametric model and the angle steel specimen reaches 0.99 (no significant difference at 95% confidence level); (2) among the extracted key feature data, the elastic stiffness, peak load, and peak displacement all exhibit relative errors less than 10%, while peak strain in deformation zones shows larger deviations; and (3) among the 44 statistically key characteristic data points, 41 exhibit relative errors less than 10%, confirming the method’s high reliability for practical engineering applications.

Critique of Lower-Margin Equations in Concrete Design Codes and of Their Effect on Structural Safety Software

It is generally agreed that engineered structures, whether bridges or aircraft, should be designed to have failure probability no higher than 10 − 6 per lifetime. The safety analysis of concrete structures based on the current design codes cannot guarantee meeting this goal. While sophisticated probabilistic models have been developed to deal with the randomness of loads, the uncertainty of material failure has been relegated to empirical understrength (or capacity reduction) factors. The problem is that the design equations of all design codes have traditionally been formulated as lower-margin equations, set at the lower margin of the test data cloud (which lies, depending on structure size, 25% to 40%, below the data mean, in the case of shear strength of RC beams). The load factors are applied to these lower-margin equations while the offset of the mean and the variance of the database remain buried in the code committee documents. Moreover, probabilistic modeling of the mechanics of failure processes, which determines structural strength, has been incorrectly employed, and the probability density function (pdf) required to extrapolate to 10 − 6 has been chosen arbitrarily, often as the lognormal pdf for mathematical convenience. Although the lognormal pdf may be an acceptable approximation for a database of concretes with very different strengths, it is shown to be physically impossible to model the strength distribution of one-and-the-same concrete (i.e., a concrete of the same design strength and composition). These traditional concepts have rendered the current failure probability predictions of the structural safety and reliability software for reinforced concrete structures meaningless. However, experience of many decades shows that the frequency of structural failures has not been excessive. The explanation is that many designs must have had excessive safety margins, thus becoming uneconomical, while the benefit of sophisticated commercial software applicable to the randomness of applied loads gets wasted. A sine qua non of the remedy is that the values of the coefficient of variation of the database and of the offset of the database mean from the code equation accompanying each design code equation must be revealed. This could be done in the code Commentary.

Multiscale Fresh Tea Leaves Sorting Device with Drum-axial Airflow Coupling: Design and Performance Test

To address the problems of low sorting accuracy and poor operation stability caused by physical leaf entanglement in traditional drum screening of fresh tea leaves, a multiscale fresh tea leaf sorting system with drum-axial airflow coupling based on intelligent control was designed. The axial moving distances of fresh tea leaves of different scales at wind speeds of 5, 7, and 9 m/s were calibrated through bench tests, and the optimal wind speed parameter for secondary fine screening was determined. A numerical model of the axial airflow field inside the drum was established via Fluent software, and a coupled sorting test platform was built to compare the sorting performance of the traditional pure drum screening mode and that of the coupled intelligent sorting mode. The results showed that 7 m/s is the optimal axial airflow velocity for secondary fine screening of multiscale fresh tea leaves during this test, which can realize effective back-blowing of small-scale materials and accurate screening of large-scale materials. At this velocity, the flow field is evenly distributed, and the effective thrust area highly matched the sorting demand. The average sorting efficiency of the coupled intelligent sorting mode reached 84.8%, which is 25.6% higher than that of traditional pure drum screening, with favorable sorting accuracy and operation stability. These findings can provide a theoretical basis and technical reference for the optimization, upgrading, and intelligent transformation of high-efficiency fresh tea leaf sorting equipment.

Exception handling bugs in Python: An empirical study of root causes, fix patterns, and anti-patterns

Analysis of exception handling bugs in Python projects reveals systematic relationships between root causes and repair strategies, indicating that exception handling bugs often follow predictable patterns.

Constitutional Midtraining: Content Presence Drives Alignment Gains

Post-training alignment is often shallow, eroding under fine-tuning. It remains untested as to whether constitutional midtraining interventions can produce durable alignment when cleanly isolated from post-training. We build a 394M-token constitutional corpus from Anthropic's Constitution and apply constitutional midtraining at 120B scale, where principled, values-based content is inserted into midtraining. A 2x2 design (curriculum ordering x deliberative reasoning) was used to produce four constitutionally midtrained conditions, plus a control, which were evaluated on self-generated and established benchmarks including alignment under pressure, value conflict resolution, blackmail, and emergent misalignment. All models were evaluated across three stages: post-midtraining, post-SFT, and post-benign fine-tuning. Constitutionally midtrained models outperformed the control on alignment generalization and durability, notably on blackmail: SFT instilled a blackmail propensity in all models, but constitutional midtraining blunted it, with the advantage surviving benign fine-tuning (-17.5pp). This durability did not extend to settings that required active resistance to in-context pressure or conflict, where the advantage attenuates after SFT. The presence of constitutional content at midtraining also mattered more than its structure, and constitutional midtraining incurred no capability cost, on average, at any stage (MMLU, ARC-Easy, piqa, GSM8K). A modest amount of constitutional content at midtraining could therefore yield broad, persistent alignment gains, offering a cheap, complementary addition to SFT-centered pipelines. Code, data, and models are available.