Hasil untuk "Industrial engineering. Management engineering"

H. B. McMahan, Gary Holt, D. Sculley et al.

Jie Deng, Chulheung Bae, A. Denlinger et al.

Jie Deng is a research engineer in the Department of Electrification Subsystems and Power Supply at Ford Motor Company. He has extensive experience in computer-aided engineering analysis (structural, fluid, and thermal), battery simulations, and material characterization. He got his PhD in Mechanical Engineering from Florida State University and has published over 30 papers. His current research mainly focuses on battery array design and multi-physics modeling and testing of battery behaviors under various abuse conditions. Chulheung Bae is a high-voltage battery systems group supervisor at Ford Motor Company, where his research activities focus on lithium ion battery system development and validation for automotive applications. Dr. Bae has over 22 years of experience in advanced battery materials and various energy storage devices, including Lithium Ion, NiZn, Lead-Acid and redox flow batteries, and ultra-Capacitors. Dr. Bae has a Doctorate in Chemical Engineering from University of Manchester in the UK. Adam Denlinger is manager of high-voltage systems research and development at Ford Motor Company. Adam’s team is responsible for delivering high-voltage battery system innovations—including packaging, durability, thermal, management and controls, and EMC—as well as human-centered technologies targeting an enhanced electrified vehicle ownership experience. The team also leads multiple collaborations in this field with industry, university, and national lab partners. Adam has worked with Ford for 22 years, with experience delivering powertrain technologies, including Ford’s first Ecoboost engine application, industry-first hydrogen internal combustion engine vehicle fleet, and multiple high-voltage battery systems for battery electric (BEV) and plug-in electric (PHEV) vehicles. Ted Miller is manager of electrification subsystems and power supply research. His team is responsible for Ford global electrification subsystem and power supply research, delivering battery system design innovations in advanced cell technology, packaging, thermal, EDS, EMC, charging, power conversion, and energy management and modeling. They provide subject matter expertise from raw materials to end-of-life recycling. The team also leads collaboration with university, industrial, and National Lab partners. Mr. Miller is chairman of the United States Advanced Battery Consortium and a member of the Idaho National Laboratory Strategic Advisory Committee and the University of Michigan Energy Institute External Advisory Board.

D. Mcgregor

G. Foulger, M. Wilson, J. Gluyas et al.

The Human-induced Earthquake Database, HiQuake, is a comprehensive record of earthquake sequences postulated to be induced by anthropogenic activity. It contains over 700 cases spanning the period 1868–2016. Activities that have been proposed to induce earthquakes include the impoundment of water reservoirs, erecting tall buildings, coastal engineering, quarrying, extraction of groundwater, coal, minerals, gas, oil and geothermal fluids, excavation of tunnels, and adding material to the subsurface by allowing abandoned mines to flood and injecting fluid for waste disposal, enhanced oil recovery, hydrofracturing, gas storage and carbon sequestration. Nuclear explosions induce earthquakes but evidence for chemical explosions doing so is weak. Because it is currently impossible to determine with 100% certainty which earthquakes are induced and which not, HiQuake includes all earthquake sequences proposed on scientific grounds to have been human-induced regardless of credibility. Challenges to constructing HiQuake include under-reporting which is ~ 30% of M ~ 4 events, ~ 60% of M ~ 3 events and ~ 90% of M ~ 2 events. The amount of stress released in an induced earthquake is not necessarily the same as the anthropogenic stress added because pre-existing tectonic stress may also be released. Thus earthquakes disproportionately large compared with the associated industrial activity may be induced. Knowledge of the magnitude of the largest earthquake that might be induced by a project, MMAX, is important for hazard reduction. Observed MMAX correlates positively with the scale of associated industrial projects, fluid injection pressure and rate, and the yield of nuclear devices. It correlates negatively with calculated inducing stress change, likely because the latter correlates inversely with project scale. The largest earthquake reported to date to be induced by fluid injection is the 2016 M 5.8 Pawnee, Oklahoma earthquake, by water-reservoir impoundment the 2008 M ~ 8 Wenchuan, People's Republic of China, earthquake, and by mass removal the 1976 M 7.3 Gazli, Uzbekistan earthquake. The minimum amount of anthropogenic stress needed to induce an earthquake is an unsound concept since earthquakes occur in the absence of industrial activity. The minimum amount of stress observed to modulate earthquake activity is a few hundredths of a megapascal and possibly as little as a few thousandths, equivalent to a few tens of centimeters of water-table depth. Faults near to failure are pervasive in the continental crust and induced earthquakes may thus occur essentially anywhere. In intraplate regions neither infrastructure nor populations may be prepared for earthquakes. Human-induced earthquakes that cause nuisance are rare, but in some cases may be a significant problem, e.g., in the hydrocarbon-producing areas of Oklahoma, USA. As the size of projects and density of populations increase, the potential nuisance of induced earthquakes is also increasing and effective management strategies are needed.

J. Gardan

S. Sepasgozar

Construction projects and cities account for over 50% of carbon emissions and energy consumption. Industry 4.0 and digital transformation may increase productivity and reduce energy consumption. A digital twin (DT) is a key enabler in implementing Industry 4.0 in the areas of construction and smart cities. It is an emerging technology that connects different objects by utilising the advanced Internet of Things (IoT). As a technology, it is in high demand in various industries, and its literature is growing exponentially. Previous digital modeling practices, the use of data acquisition tools, human–computer–machine interfaces, programmable cities, and infrastructure, as well as Building Information Modeling (BIM), have provided digital data for construction, monitoring, or controlling physical objects. However, a DT is supposed to offer much more than digital representation. Characteristics such as bi-directional data exchange and real-time self-management (e.g., self-awareness or self-optimisation) distinguish a DT from other information modeling systems. The need to develop and implement DT is rising because it could be a core technology in many industrial sectors post-COVID-19. This paper aims to clarify the DT concept and differentiate it from other advanced 3D modeling technologies, digital shadows, and information systems. It also intends to review the state of play in DT development and offer research directions for future investigation. It recommends the development of DT applications that offer rapid and accurate data analysis platforms for real-time decisions, self-operation, and remote supervision requirements post-COVID-19. The discussion in this paper mainly focuses on the Smart City, Engineering and Construction (SCEC) sectors.

Weihao Zhang, Yitong Zhou, Huanyu Qu et al.

As LLM-based multi-agent systems (MAS) become more autonomous, their free-form interactions increasingly dominate system behavior. However, scaling the number of agents often amplifies context pressure, coordination errors, and system drift. It is well known that building robust MAS requires more than prompt tuning or increased model intelligence. It necessitates engineering discipline focused on architecture to manage complexity under uncertainty. We characterize agentic software by a core property: \emph{runtime generation and evolution under uncertainty}. Drawing upon and extending software engineering experience, especially object-oriented programming, this paper introduces \emph{Loosely-Structured Software (LSS)}, a new class of software systems that shifts the engineering focus from constructing deterministic logic to managing the runtime entropy generated by View-constructed programming, semantic-driven self-organization, and endogenous evolution. To make this entropy governable, we introduce design principles under a three-layer engineering framework: \emph{View/Context Engineering} to manage the execution environment and maintain task-relevant Views, \emph{Structure Engineering} to organize dynamic binding over artifacts and agents, and \emph{Evolution Engineering} to govern the lifecycle of self-rewriting artifacts. Building on this framework, we develop LSS design patterns as semantic control blocks that stabilize fluid, inference-mediated interactions while preserving agent adaptability. Together, these abstractions improve the \emph{designability}, \emph{scalability}, and \emph{evolvability} of agentic infrastructure. We provide basic experimental validation of key mechanisms, demonstrating the effectiveness of LSS.

Kevin Frederick Yapiter, Alfin, Yoga Hasim et al.

The resume screening process is a critical stage in recruitment, yet conventional methods and traditional applicant tracking systems (ATS) often rely on manual review or keyword matching, resulting in slow, biased, and less objective evaluations. This study proposes an integrated automated screening system that combines RoBERTa for contextual feature extraction, Random Forest for candidate classification, and SHAP-based Explainable AI for interpretable decisions, enhancing transparency, efficiency, and fairness beyond traditional ATS. The dataset consists of real resumes and synthetically generated ones designed to mimic the distribution of real data, with K-means clustering used to establish labeling thresholds. Experimental results show that RoBERTa achieved an F1 Score of 81.08% in feature extraction, while Random Forest reached 96% accuracy in suitability classification. SHAP-based explanations provide insights into feature contributions for each prediction, offering an actionable understanding for recruiters. This integrated framework not only improves the efficiency and fairness of resume screening but also demonstrates a practical application of explainable AI in recruitment.

Isaac Segovia Ramirez, Alba Muñoz del Río, Fausto Pedro García Márquez

Sofía García-Manglano, Julien Maheut, Julio J. García-Sabater et al.

Nayaab Azim, Sadath Ullah Khan Mohammed, Evan Phaup et al.

In recent years, the field of software engineering has experienced a considerable increase in demand for competent experts, resulting in an increased demand for platforms that connect software engineers and facilitate collaboration. In response to this necessity, in this paper we present a project to solve the lack of a proper one-stop connection platform for software engineers and promoting collaborative learning and upskilling. The idea of the project is to develop a web-based application (NEXAS) that would facilitate connecting and collaborating between software engineers. The application would perform algorithmic matching to suggest user connections based on their technical profiles and interests. The users can filter profiles, discover open projects, and form collaboration groups. Using this application will enable users to connect with peers having similar interests, thereby creating a community network tailored exclusively for software engineers.

Agrawal Naman, Ridwan Shariffdeen, Guanlin Wang et al.

Large Language Models (LLMs) are becoming increasingly competent across various domains, educators are showing a growing interest in integrating these LLMs into the learning process. Especially in software engineering, LLMs have demonstrated qualitatively better capabilities in code summarization, code generation, and debugging. Despite various research on LLMs for software engineering tasks in practice, limited research captures the benefits of LLMs for pedagogical advancements and their impact on the student learning process. To this extent, we analyze 126 undergraduate students' interaction with an AI assistant during a 13-week semester to understand the benefits of AI for software engineering learning. We analyze the conversations, code generated, code utilized, and the human intervention levels to integrate the code into the code base. Our findings suggest that students prefer ChatGPT over CoPilot. Our analysis also finds that ChatGPT generates responses with lower computational complexity compared to CoPilot. Furthermore, conversational-based interaction helps improve the quality of the code generated compared to auto-generated code. Early adoption of LLMs in software engineering is crucial to remain competitive in the rapidly developing landscape. Hence, the next generation of software engineers must acquire the necessary skills to interact with AI to improve productivity.

R. Isermann

İdris Cesur, Beytullah Eren

In recent years, increasing concerns about vehicle emissions' environmental and public health impacts have led to the desire to use eco-friendly fuels as alternatives to traditional fossil fuels. Biofuels, hydrogen, and electric power offer lower greenhouse gas emissions and improved air quality, resulting in their development and adoption globally. Predicting vehicle emissions using these fuels is crucial for assessing their environmental benefits. This study proposes using artificial neural networks (ANN), a machine learning technique, to accurately predict vehicle emissions associated with eco-friendly fuels across different compositions and engine speeds. The ANN model has a strong correlation between predicted and observed emissions values, indicating the effectiveness of its model. The research underscores the importance of adopting innovative approaches to address environmental challenges and promote sustainable transportation solutions. This study contributes to reducing the adverse effects of vehicle emissions on air quality and public health by assisting policymakers, car manufacturers, and city planners in making effective decisions. It promotes environmental sustainability by providing valuable insights into vehicle emissions prediction and guiding the development of eco-friendly fuels for a more efficient transportation system.

Brad W. Brazeau, John A. Cunningham, David C. Hodgins

Background: Self-paced internet interventions for gambling problems offer cost-effective, accessible, and private alternatives to traditional psychotherapy for a population that rarely seeks help. However, these interventions have been relatively slow to develop, evaluate, and deploy at scale relative to those for other addictive behaviors. Moreover, user engagement remains low despite the high interest. Motivational interviews have improved the effectiveness gambling bibliotherapy but have not been augmented with an analogous web-based self-guided program. Objectives: This trial aimed to replicate and extend prior work by translating a paperback workbook to the internet and pairing it with a single motivational interview. It was hypothesized that the motivational interview would enhance program engagement and gambling outcomes. Methods: A two-arm randomised controlled trial was conducted. Treatment-seeking Canadian adults recruited solely via social media received one year of access to a web-based self-guided program, either alone (N = 158) or in combination with a virtual motivational interview completed upon enrolment (N = 155). The program was based on principles of cognitive-behavioral therapy and motivational interviewing. Gambling severity, expenditures, frequency, and duration were assessed via online questionnaires at baseline and 3-, 6-, and 12-months post-baseline, along with secondary outcomes (i.e., depression, anxiety, nonspecific psychological distress, alcohol consumption). Results: Baseline characteristics were indicative of severe gambling problems and concurrent mental health problems but not problematic alcohol consumption in this sample. Both treatment groups demonstrated roughly equal improvements across all gambling outcomes and most secondary outcomes over time, except alcohol consumption, which did not meaningfully change. Changes were most prominent by 3 months, followed by more gradual change by 6 and 12 months. Only 57 % of gamblers who were assigned to receive a motivational interview completed that interview. About 40 % of users did not complete any program modules and 11 % completed all four. No group differences in program engagement were observed, although the number of modules completed was associated with greater reductions in gambling behaviors in both groups. Discussion: The problem of user engagement with web-based self-help programs remains. There is a dose-response relationship between engagement and outcomes when engagement is measured in terms of therapeutic content completed. Conclusions: The addition of a motivational interview to a web-based self-help program for gambling problems was unsuccessful in improving engagement or outcomes. Future work should aim to make self-guided programs more engaging rather than solely making users more engaged. Trial registration: Registered on 7 July 2020 (ISRCTN13009468).

Mitra Sofiyati, Fandi Azam Wiranata, Wervyan Shalannanda et al.

In Indonesia, many people with visual impairments are drawing public attention to their rights as fellow humans. One of the limitations that individuals with low vision face is their ability to recognize objects and navigate their surroundings due to difficulties in visual perception. In this modern era, deep learning technologies, especially in image classification, can help people with low vision overcome these challenges. In this paper, we discuss a deep learning system that optimizes image classification on users' smartphones to enhance visual support for individuals with low vision. We present an Android-based app, LoVi, designed to assist users with low vision. Powered by core systems within Sherpa models (TrotoarNet, IndoorNet, and CurrencyNet), LoVi has three modes: outdoor, indoor, and currency. The LoVi application provides over 80% accuracy for navigation on sidewalks, indoor object recognition, and currency identification. TrotoarNet aids in sidewalk navigation, IndoorNet assists with indoor object identification, and CurrencyNet recognizes Rupiah banknotes. Additionally, low-vision users can receive voice feedback for further accessibility.

Wang Zou, Wubo Zhang, Zhuofeng Tian et al.

Abstract The aspect-based sentiment analysis (ABSA) consists of two subtasks: aspect term extraction (AE) and aspect term sentiment classification (ASC). Previous research on the AE task has not adequately leveraged syntactic information and has overlooked the issue of multi-word aspect terms in text. Current researchers tend to focus on one of the two subtasks, neglecting the connection between the AE and ASC tasks. Moreover, the problem of error propagation easily occurs between two independent subtasks when performing the complete ABSA task. To address these issues, we present a unified ABSA model based on syntactic features and interactive learning. The proposed model is called syntactic interactive learning based aspect term sentiment classification model (SIASC). To overcome the problem of extracting multi-word aspect terms, the model utilizes part-of-speech features, words features, and dependency features as textual information. Meanwhile, we designs a unified ABSA structure based on the end-to-end framework, reducing the impact of error propagation issues. Interaction learning in the model can establish a connection between the AE task and the ASC task. The information from interactive learning contributes to improving the model’s performance on the ASC task. We conducted an extensive array of experiments on the Laptop14, Restaurant14, and Twitter datasets. The experimental results show that the SIASC model achieved average accuracy of 84.11%, 86.65%, and 78.42% on the AE task, respectively. Acquiring average accuracy of 81.35%, 86.71% and 76.56% on the ASC task, respectively. The SIASC model demonstrates superior performance compared to the baseline model.

Muhammad Husni Wahid, Erik Iman Heri Ujianto

This research aims to apply pattern recognition technology, specifically through the Convolutional Neural Network (CNN) approach, in identifying and translating Sundanese script accurately. This research is focused on recognizing rarangken script patterns based on ngalagena script in Indonesian cultural heritage. This study uses the MobileNetV2 based CNN model, utilizing transfer learning and trained for 50 epochs using the Adam optimizer with a learning rate of 0.0001, to achieve a training accuracy of 98.75% and test accuracy of 96.95% in 1 hour and 23 minutes, respectively. The results of the study show that the simpler CNN architecture without augmentation achieved the highest accuracy of 99.26%, and the augmented CNN model achieved 94.42% accuracy in 2 hours and 22 minutes. These results enable practical applications in both education and cultural preservation, demonstrating how modern technology can effectively contribute to maintaining traditional cultural elements in the digital era.

Tamara Zhukabayeva, Nurdaulet Karabayev, Asel Nurusheva et al.

The article proposes an approach to information security vulnerability analysis and threat modeling in wireless Internet of Things networks for Smart City infrastructures. Currently, such infrastructures are becoming increasingly widespread in a variety of Smart City application areas, including industrial life support systems, pipelines, communication networks, and transportation systems. The wide coverage of end users, the critical nature of such infrastructures and the value of their inherent assets determine the increasing importance of solving problems of determining the security level of such infrastructures and the timely application of protective measures. The ultimate goal of the proposed approach is to assess the security of the infrastructure. This article analyses articles at the intersection of the subject area of vulnerability and attack analysis in information systems and networks and the area of Smart City infrastructure issues. The proposed approach includes the use of an analytical model of an intruder which, together with the analysis of the specification of a specific Smart City infrastructure, allows us to determine the current types of attacks. In order to obtain infrastructure security assessments, the CAPEC database of wireless network vulnerabilities and attack patterns is analysed. In this case, the main attributes of the attacks are identified, unified and transformed into a single format using the numerical values of the considered attributes. The feasibility of the proposed approach is also analysed and its main advantages and disadvantages are considered. In addition, the main areas of further activity and tasks related to testing and improving the proposed approach in practice are identified.

Kuo-Chien Liao, Jian-Liang Liou, Muhamad Hidayat et al.

Pre-flight inspection and maintenance are essential prerequisites for aviation safety. This study focused on developing a real-time monitoring system designed to assess the condition of composite material structures on the exterior of aircraft. Implementing such a system can reduce operational costs, enhance flight safety, and increase aircraft availability. This study aims to detect defects in aircraft fuselages manufactured from composite materials by applying image visual recognition technology. This study integrated a drone and an infrared camera for real-time image transmission to ground stations. MATLAB image analysis software (MATLAB 2020b) was used to analyze infrared (IR) images and detect structural defects in the aircraft’s appearance. This methodology was based on the inspection of damaged engine cowlings. The developed approach compares composite material conditions with known defects before and after repair, considering mechanical performance, defect size, and strength. Simultaneously, tests were conducted on various composite material panels with unknown defects, yielding favorable results. This study underscores an integrated system offering rapid detection, real-time feedback, and analysis, effectively reducing time, and potential hazards associated with high-altitude operations. Furthermore, it addresses blind spots in aircraft inspections, contributing to effective flight safety maintenance.