Hasil untuk "Bridge engineering"

V. Ashokkumar, R. Venkatkarthick, S. Jayashree et al.

Lignocellulosic biomass is a highly renewable, economical, and carbon-neutral feedstock containing sugar-rich moieties that can be processed to produce second-generation biofuels and bio-sourced compounds. However, due to their heterogeneous multi-scale structure, the lignocellulosic materials have major limitations to valorization and exhibit recalcitrance to saccharification or hydrolysis by enzymes. In this context, this review focuses on the latest methods available and state-of-the-art technologies in the pretreatment of lignocellulosic biomass, which aids the disintegration of the complex materials into monomeric units. In addition, this study also deals with the genetic engineering techniques to develop advanced strategies for fermentation processes or microbial cell factories to generate desired products in native or modified hosts. Further, this study also intends to bridge the gap in developing various economically feasible lignocellulosic products and chemicals using biorefining technologies.

Chi Chen, Juliet F. Khosrowabadi Kotyk, Stafford W. Sheehan

Summary Climate change is one of the greatest challenges facing humanity, and our continued sustainable development requires a portfolio of solutions to ultimately reduce the use of fossil fuels and decrease the concentration of carbon dioxide in our atmosphere. Chemistry is central to tackling this issue, and of the pathways to transform carbon dioxide into value-added compounds, single-step electrically driven chemical methods have attracted substantial interest in the last decade. This review places emphasis on the barriers that chemists must overcome to realize this technology and enable commercial use of electrochemical carbon dioxide reduction. We outline design strategies for gas-diffusion electrodes and electrolyzers that follow fundamental principles of catalysis to bridge the gap between catalyst discovery and integrated system engineering. These should address both technical (thermodynamic and kinetic) and practical (infrastructural) hurdles to implementation. We conclude by discussing how these approaches can be improved to help achieve a carbon-neutral economy.

Chao Ma, Yansong Peng, Hongtong Li et al.

The pharmaceutical industry has been desperately searching for efficient drug discovery methods. Organ-on-a-Chip, a cutting-edge technology that can emulate the physiological environment and functionality of human organs on a chip for disease modeling and drug testing, shows great potential for revolutionizing the drug development pipeline. However, successful translation of this novel engineering platform into routine pharmacological and medical scenarios remains to be realized. In this review, we discuss how the Organ-on-a-Chip technology can have critical roles in different preclinical stages of drug development and highlight the current challenges in translation and commercialization of this technology for the pharmacological and medical end-users. Moreover, this review sheds light on the future developmental trends and need for a next-generation Organ-on-a-Chip platform to bridge the gap between animal studies and clinical trials for the pharmaceutical industry.

S. Nižetić, N. Djilali, A. Papadopoulos et al.

Abstract The role of smart technologies can become very important and useful to solve the main population issues nowadays and provide foundations for a sustainable future. A smart approach is an opportunity for knowledge integration, necessary to solve crucial problems of contemporary societies. Today, the main challenge is to reduce the effects of global warming and ensure a balanced economic development of society. The close collaboration of all involved engineering professions is mandatory to achieve interdisciplinary synergies and can bridge challenging engineering tasks. Intense research efforts should be directed towards balanced resource utilization, efficient energy conversion technologies, integration of renewable energy systems, effective approaches to enable circular economy framework, effective process integration as well as other issues important to the population. This review editorial is primarily focused on the contributions presented at the 3rd International Conference on Smart and Sustainable Technologies held in Split, Croatia, in 2018 (SpliTech2018). The SpliTech2018 conference was a multidisciplinary event with research topics related to the main conference tracks, i.e. Smart City/Environment, Energy, Engineering Modelling and e-Health. The strategic focus of the conference was to help solve crucial issues of our times, mainly related to the sustainability and smart utilisation of limited and valuable resources. This contribution brings new ideas and discusses present issues as well as challenges that should lead towards a sustainable future based on the application of the smart technologies. The herein addressed papers bring together latest research progress into four main topic areas: (i) Green Buildings, Energy Use and Consumption, (ii) Solar Energy Utilisation, (iii) Efficiency and Waste Elimination, (iv) Smart Cities and Internet of Things. The main results of this introduction review article include a discussion of different concepts and technologies that bring further development on a broad range of topics focused on efficiency improvement, smart and sustainable resource management, cleaner production concepts and on the discussion of the various actions which would lead towards a sustainable future.

Y. Wan, Jian-gang Li, Yong Liu et al.

B. Shneiderman

This article attempts to bridge the gap between widely discussed ethical principles of Human-centered AI (HCAI) and practical steps for effective governance. Since HCAI systems are developed and implemented in multiple organizational structures, I propose 15 recommendations at three levels of governance: team, organization, and industry. The recommendations are intended to increase the reliability, safety, and trustworthiness of HCAI systems: (1) reliable systems based on sound software engineering practices, (2) safety culture through business management strategies, and (3) trustworthy certification by independent oversight. Software engineering practices within teams include audit trails to enable analysis of failures, software engineering workflows, verification and validation testing, bias testing to enhance fairness, and explainable user interfaces. The safety culture within organizations comes from management strategies that include leadership commitment to safety, hiring and training oriented to safety, extensive reporting of failures and near misses, internal review boards for problems and future plans, and alignment with industry standard practices. The trustworthiness certification comes from industry-wide efforts that include government interventions and regulation, accounting firms conducting external audits, insurance companies compensating for failures, non-governmental and civil society organizations advancing design principles, and professional organizations and research institutes developing standards, policies, and novel ideas. The larger goal of effective governance is to limit the dangers and increase the benefits of HCAI to individuals, organizations, and society.

Peggy A. Ertmer, T. Newby

Zhihang Deng, Minshui Huang, Neng Wan et al.

The health monitoring system of a bridge is an important guarantee for the safe operation of the bridge and has always been a research hotspot in the field of civil engineering. This paper reviews the latest progressions in bridge health monitoring over the past five years. This paper is organized according to the various links of the bridge health monitoring system. Firstly, the literature on monitoring technology is divided into two categories, sensor technology and computer vision technology, for review. Secondly, based on the obtained monitoring data, the data processing methods including preprocessing, noise reduction, and reconstruction are summarized. Then, the technical literature on abnormal data early warning systems is summarized. The recent advances in vibration-based and non-destructive testing-based damage identification methods are reviewed in the next section. Finally, the advantages and disadvantages of the existing research and the future research directions are summarized. This review aims to provide a clear framework and some reliable methods for future research.

Qibin Lin, P. Cao, Guanping Wen et al.

Abstract Layered rock masses with joints are widely found in nature. Their mechanical behavior plays a key role in rock engineering applications. However, previous studies have concentrated on the single lithologic layer, and few studies have reported the crack coalescence mechanism in layered rock masses with joints. In this study, uniaxial compression tests were performed on jointed rock-like specimens with two dissimilar layers. The acoustic emission (AE) and the digital image correlation (DIC) techniques are employed to investigate the crack coalescence process of specimens with two dissimilar layers. The influence of the joint angle and rock bridge angle on the mechanical behavior and failure processes in layered rock masses is investigated. The results show that the peak strength is associated with the joint angle and the rock bridge angle. Seven types of crack coalescence have been identified in the specimens, which are not only related to the joint angle and the rock bridge angle but also influenced by the rock layers. Cracks easily coalesce when the joint angle and the rock bridge angle are small. However, when the joint angle and the rock bridge angle are larger, it is difficult for cracks to initiate and propagate in the layer with higher strength. The failure mechanism of the specimens is primarily caused by crack propagation in the layer with lower strength.

Zhengquan Li, Bin Yan, Qingzhen Meng et al.

Strain is an important monitoring item in bridge structural health monitoring, providing a crucial basis for fatigue and safety assessments of structures. Under operational conditions, temperature and random traffic loads pose challenges for bridge strain prediction. To address this issue, this paper proposes a strain prediction framework over future forecasting horizons that explicitly considers both temperature and traffic loads. Historical traffic loads and temperatures are used as exogenous variables, and the TimeXer network is employed to predict the characteristics of temperature-related and traffic-induced strain in bridges, enabling the prediction of hourly strain characteristics over future horizons of 24, 48, and 96 h. Based on a year-long monitoring dataset from a large-span steel arch bridge, a strain dataset for typical locations was generated to validate the proposed method. The results demonstrate that TimeXer can accurately predict temperature-related strain and also effectively capture the trends of traffic-induced strain. Compared with traditional long short-term memory or other Transformer-based models, TimeXer, by incorporating exogenous variables, significantly improves prediction accuracy, achieving the smallest average error across all datasets. Based on the data from six strain measurement points on the in-service bridge, the proposed prediction method demonstrated the best overall performance. The findings demonstrate that incorporating physically relevant exogenous variables significantly enhances strain prediction accuracy and provides reliable support for bridge condition assessment and early warning applications.

Massimiliano Di Penta, Kelly Blincoe, Marsha Chechik et al.

As software engineering conferences grow in size, rising costs and outdated formats are creating barriers to participation for many researchers. These barriers threaten the inclusivity and global diversity that have contributed to the success of the SE community. Based on survey data, we identify concrete actions the ACM Special Interest Group on Software Engineering (SIGSOFT) can take to address these challenges, including improving transparency around conference funding, experimenting with hybrid poster presentations, and expanding outreach to underrepresented regions. By implementing these changes, SIGSOFT can help ensure the software engineering community remains accessible and welcoming.

Ana B. M. Bett, Thais S. Nepomuceno, Edson OliveiraJr et al.

Context: The empirical software engineering (ESE) community has contributed to improving experimentation over the years. However, there is still a lack of rigor in describing controlled experiments, hindering reproducibility and transparency. Registered Reports (RR) have been discussed in the ESE community to address these issues. A RR registers a study's hypotheses, methods, and/or analyses before execution, involving peer review and potential acceptance before data collection. This helps mitigate problematic practices such as p-hacking, publication bias, and inappropriate post hoc analysis. Objective: This paper presents initial results toward establishing an RR template for Software Engineering controlled experiments using the Open Science Framework (OSF). Method: We analyzed templates of selected OSF RR types in light of documentation guidelines for controlled experiments. Results: The observed lack of rigor motivated our investigation of OSF-based RR types. Our analysis showed that, although one of the RR types aligned with many of the documentation suggestions contained in the guidelines, none of them covered the guidelines comprehensively. The study also highlights limitations in OSF RR template customization. Conclusion: Despite progress in ESE, planning and documenting experiments still lack rigor, compromising reproducibility. Adopting OSF-based RRs is proposed. However, no currently available RR type fully satisfies the guidelines. Establishing RR-specific guidelines for SE is deemed essential.

Mohammed Alsharqawi

Civil infrastructure has increasingly adopted Artificial Intelligence (AI), reshaping bridge engineering and management practices, particularly during the construction phase. This review provides a comprehensive assessment of AI applications, specifically machine learning (ML), deep learning (DL), and computer vision (CV), employed in bridge construction planning, scheduling, monitoring, quality assurance, and safety management. Given the growing complexity of bridge projects and the persistent demands for enhanced safety, efficiency, and cost-effectiveness, integrating intelligent, data-driven methodologies has become essential. Utilizing a mixed-methodology approach, this study combines scientometric and systematic literature reviews to critically analyze peer-reviewed publications spanning the years 2000–2025. The findings indicate substantial advancements in AI techniques, demonstrating notable improvements in resource optimization, risk prediction accuracy, and proactive safety management. However, the implementation of AI in bridge construction also faces challenges, such as high computational resource requirements, data quality issues, model scalability concerns, and integration complexities. By identifying key research trends, technological benefits, and existing limitations, this paper contributes valuable insights and proposes future research directions to enhance the practical integration of AI, ultimately aiming to improve the resilience, reliability, and longevity of bridge infrastructure.

Michał Krzysztof

Abstract: This article presents the world's first miniature MEMS scanning electron microscope. The device, thanks to its small size, low power consumption, and durable construction, can be used in previously inaccessible places, including space missions for imaging samples of cosmic dust, lunar, or Martian soil. Keywords: Miniature scanning electron microscope; Electron source; MEMS; Imaging

Malak Elbatarny, Yu Tong Lu, Mostin Hu et al.

Summary: Background: Cyanotic congenital heart disease (CCHD) affects over 3 million individuals globally and can progress to heart failure. Mitochondrial dysfunction is well established in adult heart failure and is also a central feature of CCHD. CCHD cyanosis itself contributes to further mitochondrial dysfunction. Systems biology methods detail the epigenomic, transcriptomic, and metabolomic profile of biological samples. This systematic review highlights CCHD systems biology literature related to mitochondrial dysfunction. Methods: OVID/Medline was searched between January 2010 and June 2025. Studies implementing untargeted systems biology methods in CCHD tissue or plasma were included. Genes with differential expression between CCHD and unaffected controls were pooled and analysed using GO term functional enrichment for pathway analysis, transcription factor and kinase enrichment, and metabolic pathways. Findings: From 31 included studies (genomic: n = 5, epigenomic: n = 3, transcriptomic: n = 23, proteomic: n = 2, metabolomic: n = 3, lipidomic: n = 1), we identified 8 pathogenic/likely pathogenic single nucleotide polymorphisms, 73 differentially methylated genes, 4170 differentially expressed genes, 173 differentially expressed proteins between CCHD versus unaffected controls. Several genes involved in mitochondrial respiratory chain (NDUFV1, NDUFV2, NDUFA5, NDUFS3, COX5A, COQ7) were identified. Interpretation: CCHD pathogenesis and progression are associated with mitochondrial dysfunction through changes in metabolism, fission, and fusion. Funding: Vanier CIHR Scholarship, UHN Research Studentship, and Ontario Graduate Scholarship. Translational Biology and Engineering Program seed operating funds and research funding from the Heart and Stroke Foundation of Canada.

WU Ling, LIU Jianbei, ZHANG Zhiwei et al.

To identify the traffic blockage state on highways to Xizang in extreme environments, comprehensive evaluation indicators for traffic blockage state based on the entropy-weighted TOPSIS method were proposed by using the traffic blockage event parameters observed on four major highways to Xizang: Sichuan‒Xizang Highway, Yunnan‒Xizang Highway, Qinghai‒Xizang Highway, and Xinjiang‒Xizang Highway. The K-Medoids clustering algorithm was used to categorize the traffic blockage state into different levels. By considering factors such as hazard types, road types, traffic volume, and vehicle type ratios, a classification model for the traffic blockage state on highways to Xizang was constructed based on machine learning algorithms. The results show that the Qinghai‒Xizang Highway has the highest average blockage duration, length, and severity. The Sichuan‒Xizang Highway, while having a slightly lower duration compared to the Qinghai‒Xizang Highway, has a shorter average blockage length, resulting in a lower overall severity of blockage events. Compared with the Yunnan‒Xizang Highway, the Xinjiang‒Xizang Highway has a higher average blockage duration, yet both have shorter traffic blockage length, leading to lower blockage severity. The identification model based on entropy-weighted TOPSIS and K-Medoids clustering can effectively distinguish between different levels of the traffic blockage state on highways to Xizang. The LightGBM algorithm achieves the highest accuracy on the test set, with an accuracy rate of 96.5%. The results indicate that due to differences in geological terrain, climate, traffic volume, and primary functions of each route, there are differences in the traffic blockage characteristics. The model proposed effectively classifies and predicts the traffic blockage state on highways to Xizang with promising accuracy.

Jamal A.H. Doski

The deformation of Earth’s surface caused by earthquakes stands as a critical geological hazard in regions characterized by active tectonic structures. This study investigates the impact of a low-to-moderate magnitude earthquake (Mw 4.9) that occurred on January 26, 2021, in the Sinjar – Hasakah area (N Iraq and NE Syria). This seismic event marks the most significant occurrence in the study area over the past 48 years. The earthquake’s moment tensor solution suggests the presence of a right-lateral (dextral) strike-slip fault. 4 Sentinel-1A SAR images were processed by the DInSAR technique to analyze the surface deformation and identify the seismogenic fault of the 26 January 2021 earthquake. The most significant deformation observed along these active faults ranged from – 7.56 cm (subsidence) to + 3.75 cm (uplift) in the ascending orbit, and from – 4.56 cm (subsidence) to + 4.61 cm (uplift) in the descending orbit along the Line of Sight (LOS). It is inferred that the Hasakah seismogenic fault is responsible for the 26 January 2021 earthquake. This fault is a NW-trending, steeply dipping seismically active dextral strike-slip basement fault that formed during the Late Pliocene structural inversion. It extends over 120 km from the vicinity of Hasakah city in the northwest into the epicentral area in the southeast, traversing the boundary between the Sinjar and Abd El Aziz uplifts. Moreover, this seismogenic fault intersects with an active E-trending, S-dipping thrust basement fault that cuts through the northern limbs of both the Abd El Aziz and Sinjar anticlines.

Arvind W Kiwelekar

Quantum mechanics, the fundamental theory that governs the behaviour of matter and energy at microscopic scales, forms the foundation of quantum computing and quantum information science. As quantum technologies progress, software engineers must develop a conceptual understanding of quantum mechanics to grasp its implications for computing. This article focuses on fundamental quantum mechanics principles for software engineers, including wave-particle duality, superposition, entanglement, quantum states, and quantum measurement. Unlike traditional physics-oriented discussions, this article focuses on computational perspectives, assisting software professionals in bridging the gap between classical computing and emerging quantum paradigms.

Martin Obaidi, Marc Herrmann, Jil Klünder et al.

Software development relies heavily on text-based communication, making sentiment analysis a valuable tool for understanding team dynamics and supporting trustworthy AI-driven analytics in requirements engineering. However, existing sentiment analysis tools often perform inconsistently across datasets from different platforms, due to variations in communication style and content. In this study, we analyze linguistic and statistical features of 10 developer communication datasets from five platforms and evaluate the performance of 14 sentiment analysis tools. Based on these results, we propose a mapping approach and questionnaire that recommends suitable sentiment analysis tools for new datasets, using their characteristic features as input. Our results show that dataset characteristics can be leveraged to improve tool selection, as platforms differ substantially in both linguistic and statistical properties. While transformer-based models such as SetFit and RoBERTa consistently achieve strong results, tool effectiveness remains context-dependent. Our approach supports researchers and practitioners in selecting trustworthy tools for sentiment analysis in software engineering, while highlighting the need for ongoing evaluation as communication contexts evolve.

Dehai Zhao, Zhenchang Xing, Qinghua Lu et al.

UI automation is a useful technique for UI testing, bug reproduction, and robotic process automation. Recording user actions with an application assists rapid development of UI automation scripts, but existing recording techniques are intrusive, rely on OS or GUI framework accessibility support, or assume specific app implementations. Reverse engineering user actions from screencasts is non-intrusive, but a key reverse-engineering step is currently missing - recognizing human-understandable structured user actions ([command] [widget] [location]) from action screencasts. To fill the gap, we propose a deep learning-based computer vision model that can recognize 11 commands and 11 widgets, and generate location phrases from action screencasts, through joint learning and multi-task learning. We label a large dataset with 7260 video-action pairs, which record user interactions with Word, Zoom, Firefox, Photoshop, and Windows 10 Settings. Through extensive experiments, we confirm the effectiveness and generality of our model, and demonstrate the usefulness of a screencast-to-action-script tool built upon our model for bug reproduction.