Static analysis tools (SATs) are widely adopted in both academia and industry for improving software quality, yet their practical use is often hindered by high false positive rates, especially in large-scale enterprise systems. These false alarms demand substantial manual inspection, creating severe inefficiencies in industrial code review. While recent work has demonstrated the potential of large language models (LLMs) for false alarm reduction on open-source benchmarks, their effectiveness in real-world enterprise settings remains unclear. To bridge this gap, we conduct the first comprehensive empirical study of diverse LLM-based false alarm reduction techniques in an industrial context at Tencent, one of the largest IT companies in China. Using data from Tencent's enterprise-customized SAT on its large-scale Advertising and Marketing Services software, we construct a dataset of 433 alarms (328 false positives, 105 true positives) covering three common bug types. Through interviewing developers and analyzing the data, our results highlight the prevalence of false positives, which wastes substantial manual effort (e.g., 10-20 minutes of manual inspection per alarm). Meanwhile, our results show the huge potential of LLMs for reducing false alarms in industrial settings (e.g., hybrid techniques of LLM and static analysis eliminate 94-98% of false positives with high recall). Furthermore, LLM-based techniques are cost-effective, with per-alarm costs as low as 2.1-109.5 seconds and $0.0011-$0.12, representing orders-of-magnitude savings compared to manual review. Finally, our case analysis further identifies key limitations of LLM-based false alarm reduction in industrial settings.
Function-Correcting Codes (FCCs) are a novel class of codes designed to protect function evaluations of messages against errors while minimizing redundancy. A theoretical framework for systematic FCCs to channels matched to the Lee metric has been studied recently, which introduced function-correcting Lee codes (FCLCs) and also derived upper and lower bounds on their optimal redundancy. In this paper, we first propose a Plotkin-like bound for irregular Lee-distance codes. We then construct explicit FCLCs for specific classes of functions, including the Lee weight, Lee weight distribution, modular sum, and locally bounded function. For these functions, lower bounds on redundancy are obtained, and our constructions are shown to be optimal in certain cases. Finally, a comparative analysis with classical Lee error-correcting codes and codes correcting errors in function values, demonstrates that FCLCs can significantly reduce redundancy while preserving function correctness.
Mingyang Xu, Ryan Zheng He Liu, Mark Stoodley
et al.
Modern software systems require various capabilities to meet architectural and operational demands, such as the ability to scale automatically and recover from sudden failures. Self-adaptive software systems have emerged as a critical focus in software design and operation due to their capacity to autonomously adapt to changing environments. However, educating students on this topic is scarce in academia, and a survey among practitioners identified that the lack of knowledgeable individuals has hindered its adoption in the industry. In this paper, we present our experience teaching a course on self-adaptive software systems that integrates theoretical knowledge and hands-on learning with industry-relevant technologies. To close the gap between academic education and industry practices, we incorporated guest lectures from experts and showcases featuring industry professionals as judges, improving technical and communication skills for our students. Feedback based on surveys from 21 students indicates significant improvements in their understanding of self-adaptive systems. The empirical analysis of the developed course demonstrates the effectiveness of the proposed course syllabus and teaching methodology. In addition, we provide a summary of the educational challenges of running this unique course, including balancing theory and practice, addressing the diverse backgrounds and motivations of students, and integrating the industry-relevant technologies. We believe these insights can provide valuable guidance for educating students in other emerging topics within software engineering.
The Advanced Dynamic Security Learning (DSL) Process Model is an Industry 4.0 cybersecurity incident response architecture proposed in this paper. This model addresses proactive and reflective cybersecurity governance across complex cyber-physical systems by combining Argyris and Schön's double-loop learning theory with Crossan's 4I organizational learning framework. Given that 65% of industrial companies suffer ransomware attacks annually and many of them lack cybersecurity awareness, this reveals the gravity of cyber threats. Feedforward and feedback learning loops in this paradigm help promote strategic transformation and ongoing growth. The DSL model helps Industry 4.0 organizations adapt to growing challenges posed by the projected 18.8 billion IoT devices by bridging operational obstacles and promoting systemic resilience. This research presents a scalable, methodical cybersecurity maturity approach based on a comprehensive analysis of the literature and a qualitative study.
Marcos Kalinowski, Lucas Romao, Ariane Rodrigues
et al.
Lean R&D has been used at PUC-Rio to foster industry-academia collaboration in innovation projects across multiple sectors. This industrial experience paper describes recent experiences and evaluation results from applying Lean R&D in partnership with Petrobras in the oil and gas sector and Americanas in retail. The findings highlight Lean R&D's effectiveness in transforming ideas into meaningful business outcomes. Based on responses from 57 participants - including team members, managers, and sponsors - the assessment indicates that stakeholders find the structured phases of Lean R&D well-suited to innovation projects and endorse the approach. Although acknowledging that successful collaboration relies on various factors, this industrial experience positions Lean R&D as a promising framework for industry-academia projects focused on achieving rapid, impactful results for industry partners.
Despina Tomkou, George Fatouros, Andreas Andreou
et al.
This paper introduces a novel integration of Retrieval-Augmented Generation (RAG) enhanced Large Language Models (LLMs) with Extended Reality (XR) technologies to address knowledge transfer challenges in industrial environments. The proposed system embeds domain-specific industrial knowledge into XR environments through a natural language interface, enabling hands-free, context-aware expert guidance for workers. We present the architecture of the proposed system consisting of an LLM Chat Engine with dynamic tool orchestration and an XR application featuring voice-driven interaction. Performance evaluation of various chunking strategies, embedding models, and vector databases reveals that semantic chunking, balanced embedding models, and efficient vector stores deliver optimal performance for industrial knowledge retrieval. The system's potential is demonstrated through early implementation in multiple industrial use cases, including robotic assembly, smart infrastructure maintenance, and aerospace component servicing. Results indicate potential for enhancing training efficiency, remote assistance capabilities, and operational guidance in alignment with Industry 5.0's human-centric and resilient approach to industrial development.
Advanced water electrolysis powered by renewable energy is the most ideal and environmentally friendly approach for hydrogen production, serving as a technological foundation for large-scale hydrogen energy applications. This process can significantly reduce environmental pollution from energy consumption and support China’s carbon neutrality goals. However, the high energy demands and costs of noble metals pose challenges to scaling up hydrogen production from water electrolysis. To enhance efficiency, developing low-cost yet highly efficient noble metal-free electrocatalysts for the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) is crucial. Understanding the mechanisms behind HER and OER helps identify factors affecting electrocatalyst efficiency and design strategies to improve performance. Moreover, replacing the energy-intensive OER with more energy-efficient reactions offers another promising way to promote hydrogen production. This review summarizes recent advancements in nonprecious transition metal-based electrocatalysts for water electrolysis. Compared to noble metal-based electrocatalysts, nonprecious transition metal-based electrocatalysts like Fe, Co, and Ni-based oxides, (oxy) hydroxides, chalcogenides, and their derivates offer abundant reserves, lower costs, and adjustable catalytic properties, making them viable alternatives for large-scale water splitting. Understanding how these materials catalyze HER and the OER in different electrolytes is key to designing strategies, such as element doping, hetero-structuring, lattice defect construction, carbon composite coupling, and surface reconstruction, to reduce energy costs of electrochemical water splitting. The mechanisms behind these strategies for enhancing water electrolysis are explained through the thermodynamics of absorbed intermediates and the reaction kinetics. Beyond reducing overpotentials, another strategy involves replacing OER with the anodic oxidation reaction of organic molecules, effectively lowering the overall voltage. This review highlights recent progress and strategies for designing efficient electrocatalysts for the anodic oxidation of diverse organics, including urea, amine, hydrazine, alcohol, aldehyde, and sulfates, in substitution of water molecules. This review also addresses the gap between lab-scale research and industry-scale application of hydrogen production. It considers research on water splitting mechanisms, catalyst development, and OER-substituting electrooxidation reactions alongside electrolyzer design, synthesis costs, working conditions, and evaluation criteria. It also compares recent advancements in state-of-the-art water electrolysis technologies and summarizes their application prospects in hydrogen production. The review aims to provide theoretical guidance for designing and synthesizing advanced transition-metal-based electrocatalysts for HER, OER, and substitution anodic reactions for energy-efficient hydrogen production while also shedding light on opportunities for energy-efficient hybrid water-splitting applications.
The construction industry, being labor-intensive, prioritizes productivity to boost project performance, yet struggles to achieve expected levels despite increased focus by scholars and practitioners. This lagging causes significant losses in time, cost, and quality performance of construction projects but also broader implications for resource efficiency and environmental impacts. As a remedy to the multifaceted issue, this study aims to identify and evaluate life cycle risks of productivity management in construction projects in Türkiye. A comprehensive literature review identified risk factors affecting labor productivity, followed by a discussion session to finalize the decision framework, including life cycle phases of productivity management and risk factors in each phase. Then, the fuzzy analytical hierarchy (AHP) process revealed the most critical risk factors in each phase, followed by semi-structured interviews to reveal measures for addressing the most significant risks. The findings show that productivity management in construction projects contains nine phases. In addition, the most important factors were chiefly related to collaboration, information sharing, lack of supervision, work interruptions, and changes. Findings from semi-structured interviews emphasize regular employee training and open communication to enhance project outcomes, optimize workflows, and promote sustainability. The study’s key contribution is introducing a life cycle approach to construction productivity management, a previously unexplored perspective. This provides an effective framework that can be implemented in construction projects to manage and improve labor productivity as a whole-life cycle approach.
Huiqiong Huang, Kangning Xiong, Jiawang Yan
et al.
It is crucial to clarify the relationship between ecological industry development and ecological civilization construction, as well as their driving forces, to promote high-quality local development. The ecological environment of the karst region is fragile, and it faces a contradiction between ecological preservation and economic advancement. Coordinating the relationship between economic development and ecological protection is crucial for achieving sustainable development in rural karst regions. This study identified karst characteristics in Guizhou province, China, by constructing an index system for ecological industry development and civilization construction. It employed the entropy weight method to calculate a comprehensive score and utilized a coupling coordination model to analyze interactions and symbiotic coordination. Finally, a linear regression analysis model was employed to analyze the impact of ecological industrial development on the construction of ecological civilization. The results indicate the following: (1) The ecological industry and ecological civilization construction levels exhibited a relatively stable growth trajectory across three research areas from 2011 to 2021, with the ecological civilization construction index outperforming the ecological industry development index. (2) The correlation analysis indicated a relationship between the two indices in the research areas, and the divergence trend among the three research areas rose in a uniform direction, indicating a strong positive correlation between the two indices. From the perspective of the coupling degree (C), the degree of coupling between ecological industry and ecological civilization construction in the three research areas exceeded 0.9, indicating a high level of coordination. This suggests that ecological civilization construction and ecological industry in these research areas are effectively coordinated and exist in a state of harmonious co-promotion. There were differences from the coupling coordination degree (D) perspective, but they increased in the three research areas. (3) The regression analysis results indicate that the per capita agricultural output value, per capita forestry output value, per capita forage industry output value, industrial solid waste utilization rate, energy consumption per unit of GDP, tourism income, rocky desertification level, and proportion of the labor force population with a high school education or higher significantly contribute to the development of ecological civilization. The per capita forestry output value greatly advances ecological civilization, significantly enhancing ecological culture and security. The coefficients are 0.0354 and 0.0393, respectively, indicating that a 1% rise in the per capita forestry output value results in increases of 0.0354% and 0.0393% in the ecological culture and security indices.
This paper presents a comprehensive sensitivity analysis of the pioneering real-world deployment of computer vision-enabled construction waste sorting in Finland, implemented by a leading provider of robotic recycling solutions. Building upon and extending the findings of prior field research, the study analyzes an industry flagship case to examine the financial feasibility of computer vision-enabled robotic sorting compared to conventional sorting. The sensitivity analysis covers cost parameters related to labor, wages, personnel training, machinery (including AI software, hardware, and associated components), and maintenance operations, as well as capital expenses. We further expand the existing cost model by integrating the net present value (NPV) of investments. The results indicate that the computer vision-enabled automated system (CVAS) achieves cost competitiveness over conventional sorting (CS) under conditions of higher labor-related costs, such as increased headcount, wages, and training expenses. For instance, when annual wages exceed EUR 20,980, CVAS becomes more cost-effective. Conversely, CS retains cost advantages in scenarios dominated by higher machinery and maintenance costs or extremely elevated discount rates. For example, when the average machinery cost surpasses EUR 512,000 per unit, CS demonstrates greater economic viability. The novelty of this work arises from the use of a pioneering real-world case study and the improvements offered to a comprehensive comparative cost model for CVAS and CS, and furthermore from clarification of the impact of key cost variables on solution (CVAS or CS) selection.
Workplace accidents continue to pose significant risks for human safety, particularly in industries such as construction and manufacturing, and the necessity for effective Personal Protective Equipment (PPE) compliance has become increasingly paramount. Our research focuses on the development of non-invasive techniques based on the Object Detection (OD) and Convolutional Neural Network (CNN) to detect and verify the proper use of various types of PPE such as helmets, safety glasses, masks, and protective clothing. This study proposes the SH17 Dataset, consisting of 8,099 annotated images containing 75,994 instances of 17 classes collected from diverse industrial environments, to train and validate the OD models. We have trained state-of-the-art OD models for benchmarking, and initial results demonstrate promising accuracy levels with You Only Look Once (YOLO)v9-e model variant exceeding 70.9% in PPE detection. The performance of the model validation on cross-domain datasets suggests that integrating these technologies can significantly improve safety management systems, providing a scalable and efficient solution for industries striving to meet human safety regulations and protect their workforce. The dataset is available at https://github.com/ahmadmughees/sh17dataset.
Recently, generative AI (GAI), with their emerging capabilities, have presented unique opportunities for augmenting and revolutionizing industrial recommender systems (Recsys). Despite growing research efforts at the intersection of these fields, the integration of GAI into industrial Recsys remains in its infancy, largely due to the intricate nature of modern industrial Recsys infrastructure, operations, and product sophistication. Drawing upon our experiences in successfully integrating GAI into several major social and e-commerce platforms, this survey aims to comprehensively examine the underlying system and AI foundations, solution frameworks, connections to key research advancements, as well as summarize the practical insights and challenges encountered in the endeavor to integrate GAI into industrial Recsys. As pioneering work in this domain, we hope outline the representative developments of relevant fields, shed lights on practical GAI adoptions in the industry, and motivate future research.
Daniel Barros, Paula Fraga-Lamas, Tiago M. Fernandez-Carames
et al.
The Industry 5.0 paradigm focuses on industrial operator well-being and sustainable manufacturing practices, where humans play a central role, not only during the repetitive and collaborative tasks of the manufacturing process, but also in the management of the factory floor assets. Human factors, such as ergonomics, safety, and well-being, push the human-centric smart factory to efficiently adopt novel technologies while minimizing environmental and social impact. As operations at the factory floor increasingly rely on collaborative robots (CoBots) and flexible manufacturing systems, there is a growing demand for redundant safety mechanisms (i.e., automatic human detection in the proximity of machinery that is under operation). Fostering enhanced process safety for human proximity detection allows for the protection against possible incidents or accidents with the deployed industrial devices and machinery. This paper introduces the design and implementation of a cost-effective thermal imaging Safety Sensor that can be used in the scope of Industry 5.0 to trigger distinct safe mode states in manufacturing processes that rely on collaborative robotics. The proposed Safety Sensor uses a hybrid detection approach and has been evaluated under controlled environmental conditions. The obtained results show a 97% accuracy at low computational cost when using the developed hybrid method to detect the presence of humans in thermal images.
Under the background of the rapid development of new media technology, the cultural development between cities has become increasingly important. At present, the development of digital media has spawned a new form different from the traditional fan culture. With the widespread use of social media platforms, fan culture and live broadcasting industry play a crucial role in shaping urban brands and enhancing urban competitiveness. Based on this background, this paper will conduct a comprehensive analysis of urban culture through literature research and summary, conduct quantitative research and platform content analysis, and study the influence of fan culture on the construction of urban brands through social media.
Victor Andre Ariza Flores, Fernanda Oliveira de Sousa, Sandra Oda
This study examines the integration of epistemological principles into road infrastructure risk management, emphasizing the need for adaptive strategies in the face of inherent climate uncertainties, particularly flash floods. A systematic review of peer-reviewed articles, industry reports, and case studies from the past two decades was conducted, focusing on the application of epistemological approaches within the infrastructure sector. The research employs a mixed methods approach. Quantitatively, the risk of pavement failure is measured by analyzing the relationship between pavement serviceability rates and Intensity–Duration–Frequency (IDF) data in areas frequently affected by flash floods. For example, rainfall intensities during flood events on the BR-324 highway in Brazil were significantly higher than monthly averages, with maximum values reaching 235.73 mm for a 5 min duration over a 50-year return period. These intensities showed an increase of approximately 15% over 5 to 10 years and 8% over 50 to 75 years. Qualitatively, traditional risk management methods are combined with epistemological concepts. This integrated approach fosters reflective practice, encourages the use of both quantitative and qualitative data, promotes a dynamic management environment, and supports sustainable development goals by aligning risk management with environmental and social sustainability. This study finds that incorporating epistemological insights can lead to more fluid and continuously improving risk management practices in construction, design, and maintenance. It concludes with a call for future research to explore the integration of emerging technologies such as artificial intelligence to further refine these approaches and more effectively manage complexity and uncertainty.
Brain of computility network is the intelligent center of computility network, and it is the key system to realize multi-element orchestration and scheduling of computility network. Intelligence is the core of realizing and improving the flexible, agile and efficient orchestration, management and operation capability of the brain of computility network. Many companies in the communication industry have focused on the introduction of intelligent technology of the brain of computility network on the basis of the early realization of the products, and put forward high expectations for the intelligent evolution of the brain of computility network in the future. However, at present, there is no systematic evaluation method for the intelligence level of the brain of computility network, which cannot well evaluate the intelligence level, upgrading stages and key indicators to be optimized. Based on the existing work and demand analysis in the industry, the goal and stage capability of the intelligent evolution of the brain of computility network were studied, and a classification method of the intelligent level of the brain of computility network was put forward, which included detailed steps such as the selection of the evaluation object, the decomposition of the evaluation process, the consideration of the constraint indicators, and a complete set of rules for the evaluation of the intelligent level of the brain of computility network. Finally a concrete application example was given. The proposed method can effectively quantify the specific level of the brain of computility network system, clarify the direction and index of its evolution to the next level, and provide reference for industry intelligent evaluation and system construction.
Abstract In recent years, China’s economic development is dominated by an eco-friendly and low-carbon transition, making the low-carbon advancement of the construction sector urgent. Local governments play a crucial role in this process. This paper, utilizing provincial panel data from 2007 to 2021, empirically analyzes the impact of government environmental governance (EG) on the carbon intensity of the construction industry (CCEI) through the panel regression, spatial econometric and dynamic threshold models. The findings indicate that (1) EG has a significant inverted U-shaped effect on CCEI, with initial increases in carbon intensity followed by reductions once EG intensity surpasses a certain level. (2) Significant spatial spillover effects reveal that increased EG in one region exerts a similar inverted U-shaped impact on both local and neighboring CCEI. (3) Under China’s fiscal decentralization framework, two dimensions—vertical decentralization and horizontal competition—serve as forms of fiscal decentralization, each with dynamic threshold effects: EG’s influence on CCEI turns negative under high vertical decentralization, and positive under intense horizontal competition. These results could offer insights from China’s emission reduction experiences in the energy-intensive sector, serving as a valuable reference for environmental decision-makers worldwide.
Hosseinikavkani Seyed Milad, Sedaghati Reza, Ghaedi Amir
The production and consumption of non-renewable energy resources have
disrupted the environment's biodiversity cycle. Global climate change,
including worldwide warming, has made human life both now and in the future.
The construction industry in the world has a significant share in the demand
for energy consumption in these challenges. Therefore, the primary purpose
of this paper is to implement standards to save and prevent energy loss to
control and limit the demand for energy requested from the power network.
Constructing a building with self-sufficient energy production that meets
its energy needs by producing clean energy becomes more important. It also
sells the excess energy to the grid, known as zero energy buildings. In the
present paper, the issue is a constrained optimization problem that aims to
minimize the total annual cost, including the initial investment cost for PV
and batteries and their maintenance costs, as well as the cost of network
exchanges. Among the limitations, the proposed model can mention the
restrictions governing the battery, such as the limitations of the battery
state of charge (SoC). The problem under optimization is a mixed integers
nonlinear programming (MINLP) that will be solved by a particle swarm
optimization (PSO) algorithm considering the total cost minimization.
Menna Salah, Mohamed Elmasry, Ibrahim M. Mashhour
et al.
Construction projects can have adverse possible environmental consequences. The construction sector contributes to around 25% of air pollution, 40% of water pollution, and 50% of landfill wastes. The United Nations Sustainable Development Goals (SDGs) focus on sustainable development, where the construction industry can assist in achieving many of these goals. This research presents a framework for assessing the sustainability of construction projects. The research identifies the factors that are linked to sustainability in construction. To determines the relative weights of these factors, multicriteria decision-making techniques, namely, Shannon entropy and the CRiteria Importance Through Intercriteria Correlation (CRITIC) methods, were used. To assess the sustainability of construction projects Hierarchical evidential reasoning was used to aggregate the different factors found in different projects based on weights and magnitude of each factor. To examine the plausibility of the proposed framework, three case studies for new development projects were used. The results from the proposed framework were verified using face verification. The results showed that the difference in relative weights of the different indicators were statistically insignificant indicating that the accuracy of the calculated weights. Additionally, the experts sought to verify the applicability of the framework indicated that it can be widely used in different projects to assess how sustainable they are. The proposed framework can be used in various construction projects by stakeholders, such as contractors and developers, to assess the sustainability of new construction projects and capitalize on their corporate social responsibility, ultimately enhancing the construction industry quality and standards.
Renewable energy sources, Environmental engineering