Hasil untuk "Engineering"

W. Smith

T. F. Gilbert

K. Chawla

J. Nelson, Debora J. Miller

J. Ridgway, L. Presta, P. Carter

C. Laurencin, A. M. Ambrosio, M. Borden et al.

J. Hubbell

M. Y. Rafiq, G. Bugmann, D. Easterbrook

Alan J. Dutson, R. H. Todd, S. Magleby et al.

G. Stephanopoulos, A. Aristidou, J. Nielsen

R. Felder, D. R. Woods, J. Stice et al.

G. Hazelrigg

Eleftherios Fyntikakis, Spyridon Plakias, Themistoklis Tsatalas et al.

Background: Strength training (ST) is essential for enhancing athletic performance and reducing injury risk, yet traditional monitoring relies heavily on subjective assessment, limiting objective and individualized evaluation. Objective: This scoping review critically synthesizes current applications of artificial intelligence (AI) and wearable technologies (WT) in ST, with emphasis on methodological approaches, data characteristics, explainability, and practical readiness. Methods: Searches of PubMed and Scopus identified 13 peer-reviewed studies (2015–2025). Evidence was charted and synthesized to compare AI models, wearable sensor configurations, validation strategies, and translational potential. Results: Studies employed classical machine learning, deep learning, and hybrid approaches alongside inertial, force, strain, and physiological sensors to support exercise classification, load estimation, fatigue detection, and performance monitoring. Deep learning models dominated movement recognition tasks, whereas simpler models often aligned better with small datasets and interpretability requirements. However, most studies relied on limited, homogeneous samples and internal validation, restricting generalizability and real-world applicability. Explainability was inconsistently addressed, particularly in higher-risk applications such as injury prediction. Conclusions: AI-enhanced wearables provide objective and individualized ST monitoring, but current evidence remains largely experimental. To ensure a practical application is implemented, standardized datasets, robust external validation, and greater integration of explainable AI are required to support and deliver trustworthy decision-making.

T. Lincoln

A classic example of how human intervention in natural processes can have severe unforeseen side-effects comes from the Hawaiian island of Oahu. Coastal armouring structures, designed to prevent shoreline erosion, do indeed do that. But erosional forces instead become concentrated on the beaches in front of them, and the beaches no longer have the continual renewal of sandy substrate from sources locked into the shoreline. About 27 per cent of Oahu's 115 kilometres of beaches have been lost or severely affected in this way over the past 27 years.

D. Buede

Jayanaka L. Dantanarayana, Savini Kashmira, Thakee Nathees et al.

AI-Integrated programming is emerging as a foundational paradigm for building intelligent systems with large language models (LLMs). Recent approaches such as Meaning Typed Programming (MTP) automate prompt generation by leveraging the semantics already present in code. However, many real-world applications depend on contextual cues, developer intent, and domain-specific reasoning that extend beyond what static code semantics alone can express. To address this limitation, we introduce Semantic Engineering, a lightweight method for enriching program semantics so that LLM-based systems can more accurately reflect developer intent without requiring full manual prompt design. We present Semantic Context Annotations (SemTexts), a language-level mechanism that allows developers to embed natural-language context directly into program constructs. Integrated into the Jac programming language, Semantic Engineering extends MTP to incorporate these enriched semantics during prompt generation. We further introduce a benchmark suite designed to reflect realistic AI-Integrated application scenarios. Our evaluation shows that Semantic Engineering substantially improves prompt fidelity, achieving performance comparable to Prompt Engineering while requiring significantly less developer effort.

Bertrand Meyer

Vibe coding, the much-touted use of AI techniques for programming, faces two overwhelming obstacles: the difficulty of specifying goals ("prompt engineering" is a form of requirements engineering, one of the toughest disciplines of software engineering); and the hallucination phenomenon. Programs are only useful if they are correct or very close to correct. The solution? Combine the creativity of artificial intelligence with the rigor of formal specification methods and the power of formal program verification, supported by modern proof tools.

Yutao Feng, Weibin Li

Self-centering structures have emerged as a promising seismic design solution, offering advantages in structural safety, rapid post-earthquake functionality recovery, and life-cycle economy. This paper introduces a self-centering beam–column joint that integrates superelastic shape memory alloys (SMAs) and prestressed steel tendons as restoring components. A numerical model was developed in OpenSees and validated against experimental results, with discrepancies in residual deformation within 10%. The validated model was used for parametric studies on strand area, prestress, and SMA configuration. The results show that the proposed joint sustains a maximum drift of 6% while maintaining nearly zero residual drift (less than 0.2%), and its hysteresis curve exhibits a stable flag-shaped pattern. The equivalent viscous damping ratio exceeds 0.1, confirming excellent deformation and energy dissipation capacities. These findings highlight the joint’s potential for application in seismic-resilient steel frames.

Youngseob Lee, Kiwan Kim, Yunseo Kim et al.

With the rising demand for data centers, the need for an efficient thermal management approach becomes increasingly critical. This study examines the enhancement in pool boiling heat transfer on a customized multichip module, designed to mimic artificial intelligence chip layouts for high‐performance computing. Experiments are conducted on smooth surfaces and hierarchical structures integrating micropillars and porous copper, specifically copper inverse opal (CuIO) and copper nanowire (NW). The results demonstrate significant enhancements in critical heat flux (CHF) and heat transfer coefficient (HTC) through these hierarchical structures. Notably, the NW‐CuIO‐integrated hierarchical structure exhibits the highest CHF (234 W cm−2), achieving a 166% enhancement over smooth silicon. The HTC enhancement is more pronounced for the CuIO‐integrated hierarchical structure; this structure achieves an HTC of 70.3 kW m−2 K−1, which represents a 166% improvement. The heater layout, engineered surfaces, and their synergistic effects are analyzed through visualization. The observed boiling inversion phenomena further underscore the importance of sequential activation of nucleation sites in improving boiling performance. This study provides valuable insights into the mechanisms governing the enhancement of boiling heat transfer and offers practical guidance for developing efficient thermal management solutions for data centers.

Boniphace Kutela, Frank Ngeni, Cuthbert Ruseruka et al.

Over-speeding is a pivotal factor in fatal traffic crashes globally, necessitating robust speed management strategies to augment road safety. In 2021, the National Highway Traffic Safety Administration reported over 12 000 speed-related fatalities in the United States alone. Previous studies aggregated over-speeding tendencies; however, the extent of over-speeding has a significant implication on the crash outcome. This study delves into the prevalence and magnitude of over-speeding in various scenarios, utilizing data from traffic cameras in Edmonton, Canada, and employing a negative binomial statistical model for analysis. The model elucidates the significance and likelihood of over-speeding tendencies by incorporating temporal and built environment variables, i.e., year, month, number of lanes, dwelling unit types, school-related, and open green space. Study results indicated that the aggregation of the over-speeding data tends to underestimate the influence of various factors. Notably, the estimated impact of the posted speed limit for the disaggregated models is up to over two times that for the aggregated model. Further, the summer months exhibit a roughly 25% uptick in speed limit violations for aggregated models while about a 40% uptick in the speed limit violations for disaggregated approaches. Conversely, a discernible decline in over-speeding tendencies is observed with camera enforcement, showcasing a 25% reduction over four years. Built environment variables presented mixed results, with one-unit dwellings associated with a 12% increase in over-speeding, while proximity to schools indicated a 10% decrease. These pivotal findings provide policymakers and practitioners with valuable insights to formulate targeted interventions and countermeasures to curtail speed limit violations and bolster overall road safety conditions.