Search Results (755)

Software-intensive organizations increasingly face the challenge of integrating ethical considerations into routine work practices while maintaining process quality and organizational performance. This study develops the Quality Value Driver (QVD) framework, a managerial approach for embedding ethical reflection into everyday organizational processes. The framework links ethical values, organizational drivers, and observable quality indicators, providing a structured mechanism through which ethical values can be operationalized and evaluated. Following constructive research methodology, the study introduces the framework and illustrates its application through a proof-of-concept case involving an Agile team developing an AI-based facial palsy classification system. In the demonstrated application, collaboration was selected as the target value, Balint Groups served as the organizational driver, and process-quality indicators were used to assess development outcomes. The findings do not establish causal effectiveness but illustrate the feasibility of operationalizing ethical values through routine organizational practices within the specific context examined. The study contributes a software-engineering perspective that conceptualizes ethical values as potential process-quality drivers and advances the Ethics by Design approach by integrating ethical reflection into everyday software-development decision-making. Full article

Background: Polypharmacy remains a significant challenge, often leading to adverse drug events and increased healthcare costs. One effective strategy to mitigate these risks is deprescribing, the systematic and intentional discontinuation or reduction of medications. Despite growing recognition of deprescribing’s importance, limited research has examined the impact of interprofessional education interventions on developing deprescribing competencies among healthcare professional students. This study aims to examine the perspectives of healthcare students, including student pharmacists, medical students, and nurse practitioner students, regarding their roles and responsibilities in the deprescribing process. Methods: Following completion of a Deprescribing Interprofessional Education Simulation activity (DIPE-SA), participants were invited to join focus groups. The study included students from the University of Tennessee Health Science Center (UTHSC) Colleges of Medicine, Nursing, and Pharmacy. Constructivist Grounded Theory (CGT) underpinned the qualitative research approach, informing the development of the study framework. Each focus group was facilitated by two researchers and recorded. Recruitment continued until saturation was achieved. Data were transcribed verbatim and analyzed for themes using Dedoose, qualitative analysis software. Results: A total of 19 participants attended four focus groups. Three major themes emerged and guided the development of the Roles–Collaboration–Communication (RCC) interprofessional framework. The first theme, roles, focuses on the unique responsibilities and functions healthcare professionals assume within the team and throughout the deprescribing process. The second theme, collaboration, emphasizes coordinated efforts and shared decision-making among members of the interprofessional healthcare team in deprescribing. Finally, the third theme, communication, highlights the importance of effective information and intention exchange, serving as both a mediator and connector, linking roles and collaboration, and ultimately enabling successful deprescribing. Conclusions: This study’s findings, together with the RCC interprofessional framework, demonstrate the interplay of roles, collaboration, and communication in medication management. The RCC framework highlights the value of dynamic team processes for achieving safe and effective deprescribing, which in turn improves patient care and health outcomes. Full article

Cyber–physical systems (CPSs) increasingly rely on complex software components whose vulnerabilities may affect both digital services and physical processes. Fuzzing is a practical technique for discovering such vulnerabilities in CPS-facing parsers, protocol handlers, and edge services. Distributed fuzzing improves throughput, but outsourcing fuzzing tasks to multiple untrusted nodes introduces privacy risks: valuable seeds, especially crash-triggering samples, may reveal vulnerability information before affected users are protected. In this paper, we propose PrivFuzz, a privacy-preserving collaborative fuzzing framework. PrivFuzz allows organizations and individuals to collaborate and receive rewards while keeping fuzzing seeds confidential and enabling controlled encrypted seed reuse among untrusted fuzzing nodes. The key idea is to combine trusted execution environments (TEEs) with blockchain-based smart contracts to support confidentiality and fair reward settlement. We give game-based definitions and reduction-style arguments for seed confidentiality, worker soundness, outsourcer atomicity, and duplicate-claim resistance under an attested execution model. We implement a PrivFuzz prototype and evaluate it on four open-source parsing targets. Separately, native AFL++ sanity checks suggest that CPS-facing industrial protocol parsers such as Modbus and OPC UA fall within the same fuzzable target domain. Demonstrating end-to-end PrivFuzz on CPS control programs is left as future work. Using PrivFuzz, we discovered nine bugs and reported them to the developers. Full article

Against the backdrop of rapid development in marine engineering, the construction industry faces practical challenges, such as water scarcity, limited availability of natural river sand, and high raw material transportation costs. This has led to an increasing demand for resource-efficient concrete production technologies and improved construction economic efficiency. Seawater–sea-sand concrete (SWSSC) offers a locally sourced solution that effectively reduces the construction sector’s overreliance on freshwater and river sand, lowers material transportation costs for coastal infrastructure projects, and supports marine engineering and infrastructure development along the Belt and Road Initiative. However, existing research lacks systematic organization and visualized quantitative analysis. Utilizing the CiteSpace 7.0.R0 knowledge graph analysis software, this study selects 982 relevant papers published in the Web of Science (WOS) Core Collection between 2016 and 2025 as the sample. By employing analytical methods—including annual publication volume statistics, collaboration networks among researchers, keyword co-occurrence patterns, and temporal evolution charts—we systematically delineate the overall research landscape, distribution of key research institutions, trends in research hotspots, and future frontier directions in this field. The analysis results indicate that: (1) The total number of publications in the global seawater–sand concrete field has been increasing year by year. From 2016 to 2018, it was the basic exploration period, with an average annual publication volume of less than 10. From 2019 to 2021, it was the deepening and expansion period, with research expanding from the performance of a single material to material modification and structural application. From 2022 to 2025, it was the rapid prosperity period, with the publication volume reaching its peak in 2024–2025 (208 articles and 203 articles), and the publication volume continued to rise. (2) China ranks first globally with 798 publications, but its centrality in international cooperation networks is only 0.24, reflecting low overall collaboration density and loose partnerships between institutions and authors, without the formation of cross-institutional core research teams with global leadership. (3) Research hotspots in this field primarily focus on material properties, durability characteristics, and mechanical strength, among which FRP reinforcement systems serve as a bridge for interdisciplinary research bridging material fundamentals and engineering applications, representing a key research branch. (4) From the perspective of evolutionary trends, the field exhibits three major developmental shifts from macroscopic mechanical performance characterization to in-depth investigation of microscopic damage mechanisms, from single-material studies to composite structural systems, and from short-term laboratory accelerated testing to full life-cycle performance evaluation, with the low-carbon potential of seawater–sand concrete increasingly becoming a prominent research focus. Therefore, this paper advocates strengthening international and inter-institutional academic collaboration, fostering multidisciplinary innovation, and prioritizing breakthroughs in key areas, such as large-scale intelligent performance prediction, long-term performance database development, and digital-twin-based operation and maintenance management, to facilitate the transition of seawater–sand concrete technology toward efficient, low-carbon, safe, and intelligent engineering applications. Full article

The rapid adoption of small drones for applications such as surveillance, disaster response, and infrastructure inspection has increased demand for coordinated multi-drone operations, in which information exchange is essential. However, effective collaboration across different drones remains challenging due to differences in information exchange and communication protocols. This paper proposes a Networked Drone Information Exchange Model (NDIEM), an XML-based model that enables interoperability of the structural information. NDIEM defines five essential element categories: Identification, Telemetry, Command and Control, Sensor, and Mission. NDIEM can be used with protocol-specific adapters for exchanging information in multi-drone operations. The proposed model has been implemented and evaluated on three different drones (an ArduPilot-based Hexacopter, a Crazyflie 2.1, and a Tello EDU) using real sensor telemetry captured under controlled conditions, in which each platform’s onboard sensors were manipulated to generate representative telemetry variation. Experimental results demonstrate information interoperability with 0.013–0.019 ms processing overhead, 0.049–0.073 ms transformation latency per message, and XSD schema validation compliance, achieving 69.7% transformation completeness for telemetry data. These findings show that NDIEM can provide a practical and scalable foundation for software development for drone collaboration and interoperability. Full article

Transformation in the hospitality industry increasingly requires graduates who possess not only technical knowledge and operational skills, but also behavioural and interpersonal competencies for sustainability in the industry. This demand from higher education students focuses on employability skills, knowledge, and abilities relevant to the hospitality industry post-COVID-19. This study explored industry practitioners’ perceptions of the transversal competencies required for graduate employability in the South African hospitality industry. An interpretivist research philosophy was adopted to support a subjective viewpoint through a qualitative research study, implemented through interviews with 17 purposively selected industry practitioners in Gauteng, South Africa. This includes managers and supervisors responsible for overseeing student internships during their work-integrated learning programmes. Data were analysed using interpretive thematic analysis supported by Atlas.ti v25 software program. The findings reveal that, in addition to technical knowledge and practical skills, employers highly value behavioural competencies such as adaptability, positive attitude, passion, work ethics, and the ability to think critically under pressure. The study further highlights the need for stronger collaboration between industry and higher education institutions to improve curriculum relevance, internship structures, and competency development. The findings contribute to hospitality employability literature by identifying industry-informed transversal competencies necessary to enhance graduate preparedness and employability within the post-pandemic hospitality sector. Full article

Integrated project delivery (IPD), as the practical manifestation of lean construction principles, integrates people, systems, and business structures into a collaborative process that harnesses the collective skills of all participants. This approach enhances sustainability by reducing waste and advancing alignment with the United Nations Sustainable Development Goals (SDGs). The purpose of this study is to develop a practical framework that elucidates the interacting factors influencing IPD implementation within New Zealand’s (NZ) construction industry, offering transferable insights for other countries with comparable demographics. A systematic literature review of 66 articles identified 127 factors affecting IPD adoption. Deductive reasoning informed the development of open-ended questions for semi-structured interviews with 18 experts, yielding 142 factor interactions. These findings were triangulated with 137 survey responses for validation. Statistical analysis using t-tests confirmed 88 significant interactions (based on p-values), which were ranked via the Relative Importance Index (RII), visualised using Vensim software, and analysed through centrality techniques to construct a comprehensive IPD interaction framework. This study represents the first comprehensive investigation of IPD factor interactions in the NZ construction sector. The resulting framework provides researchers and practitioners with deeper insights into these dynamics to overcome barriers and promote wider IPD adoption for sustainable construction outcomes. Full article

Artificial intelligence (AI) is reshaping the architecture, engineering and construction (AEC) sector. However, AEC education remains rooted within traditional disciplinary boundaries and a technology-centric training model, creating a widening mismatch between graduates’ capabilities and the cognitive, collaborative, and interdisciplinary demands of AI-enabled practice. Situated within the Chinese higher education context, this study bridges this gap through a multi-stakeholder survey (n = 352) noting perspectives from academia, industry and research. One-way ANOVA and Tukey’s HSD test were used to examine differences across stakeholder groups and disciplines, while a Bayesian Network was developed to model competency pathways, simulate intervention scenarios and identify key leverage points for curriculum reform. The analysis reveals that meaningful AI integration requires a reconstruction of competency, rather than the mere addition of standalone technical or software courses; it calls for fundamental changes in professional formation, curricula and pedagogy. Four core competencies emerged from the data: professional expertise, systems thinking, interdisciplinary collaboration and AI-enabled problem-solving. Bayesian Network simulations further indicated that curriculum expansion alone improved AI knowledge acquisition by 39.6%, but yielded only modest gains in practical skills (13.9%) and application competencies (4.5%). By contrast, integrated interventions that combined teacher development, university–industry collaboration and project-based practice produced substantial improvements in AI application competencies (37.9%), employment adaptability (29.3%) and industry satisfaction (14.1%). These divergent findings highlight the necessity of coordinated educational interventions to reconcile stakeholder expectations and foster AI-oriented competency development. Based on this evidence, the study proposes a competency-oriented framework and a phased curriculum transformation pathway, providing an empirical foundation for AI-driven curriculum reform and competency reconstruction in AEC education. Full article

Mitigating population exposure to fine particulate matter (PM_2.5) is a core prerequisite for advancing Sustainable Development Goal 11.6.2 and global urban sustainability. This study integrated 0.1° × 0.1° gridded PM_2.5 reanalysis data, WorldPop high-resolution population datasets, and official SDG 11.6.2 scoring records spanning 2000–2019, and adopted multi-scale spatial statistics and population-weighted exposure models to systematically explore the spatiotemporal differentiation of global PM_2.5 concentrations and associated population exposure risks, as well as their coupling relationship with SDG 11.6.2 implementation progress. This study employs ArcGIS 10.6 software to harmonize the spatial scales of multi-source heterogeneous data through spatial statistics and resampling methods, and applies the SDSN (Sustainable Development Solutions Network) standardized scoring framework to quantify long-term progress across regions in meeting urban air quality targets. The results reveal significant latitudinal spatial heterogeneity in global PM_2.5 concentrations, with values ranging from 0.95 to 262.15 μg/m³; severe pollution hotspots exceeding 35 μg/m³ were agglomerated across Asia, Africa and South America, while Canada, Greenland, the Tibetan Plateau and eastern Russia maintained ultra-low PM_2.5 levels below 5 μg/m³. Global SDG 11.6.2 standardized scores rose steadily from 71.05 in 2000 to 76.64 in 2019, with Asia and Africa achieving the most remarkable score growth despite low initial baselines. Regional gaps in target realization remained stark: North America, Oceania and Northern Europe basically met the PM_2.5 sustainability standards, whereas densely populated regions of Asia and Africa faced critical governance challenges. Among nine typical countries, only China and India recorded population-weighted PM_2.5 concentrations above 25 μg/m³ in 2019; China’s air pollution control policies delivered continuous emission reductions after 2013, while India sustained extremely high exposure risks. From 2010 to 2019, the global range of urban population-weighted PM_2.5 concentrations narrowed, yet Afghanistan, Tajikistan and North Korea remained the most severely exposed nations. This study verifies the severe cross-regional inequity of PM_2.5 exposure risks under the SDG framework, and provides multi-scale empirical evidence for differentiated air quality governance and international collaborative interventions to accelerate the delivery of the 2030 sustainable development agenda. Full article

Software vulnerability mining is important for improving software reliability and security. Compared with sequence-based models, graph neural networks (GNNs) can explicitly model program structures such as syntax, control flow, data flow, call relations, and dependency paths, and have therefore been widely studied for vulnerability detection, localization, explanation, and repair. This paper presents a PRISMA-informed structured topical review of 87 studies and addresses five research questions concerning program graph representation, homogeneous and heterogeneous GNN architectures, Graph–LLM integration, evaluation reliability, and future research directions. The reviewed evidence shows that graph-based methods are most effective when vulnerability mechanisms can be faithfully represented through explicit structural relations. However, their reported performance remains strongly affected by duplicated samples, random function-level splits, noisy labels, incomplete repository context, graph-construction errors, and weak explanation protocols. Homogeneous GNNs provide efficient structural message passing but may mix different semantic relations, whereas heterogeneous GNNs preserve relation types more explicitly at the cost of greater graph-quality and computational requirements. Graph–LLM systems can improve semantic reasoning, repository-level analysis, explanation generation, and repair support, but their benefits should be evaluated together with memory consumption, inference latency, deployment complexity, and verification cost. This review further proposes minimum requirements for reliable vulnerability benchmarks and verifiable explanations, and develops a strategic agenda covering leakage-resistant datasets, uncertainty-aware graph construction, repository-level evaluation, cost-effective Graph–LLM collaboration, and graph-guided autonomous vulnerability repair. Full article

The transition to a globalized, fabless semiconductor manufacturing model has integrated third-party foundries and external intellectual property (IP) vendors into the integrated circuit (IC) design cycle. While this collaborative system promotes innovation, it also exposes layouts to security threats. Protecting these designs is paramount; however, applying traditional encryption methodologies fundamentally alters the syntactic hierarchy of the industry-standard GDSII stream format, causing electronic design automation (EDA) tools to crash. Furthermore, a full encryption hinders modern system-on-chip (SoC) development, where different teams require access to specific modules of the design, without exposing the entire IP. To resolve this issue between collaborative layout sharing and zero-trust security, this paper presents a software implementing an encryption engine. By applying the NIST-standardized FF1 Format-Preserving Encryption (FPE) algorithm directly to the geometric data, the proposed software obfuscates sensitive spatial coordinates and structural nomenclature while maintaining the native GDSII format. The engine embeds multi-tiered cryptographic access control directly into the layout, utilizing native metadata properties. This framework allows proprietary logic to be securely compartmentalized, ensuring that interacting parties only view the specific structures they possess the clearance to decrypt. Full article

Existing BIM-based workflows for complex spatial steel bridges often support 3D visualization and documentation, but they still lack a formal requirement-to-function traceability mechanism and a reliable automated link from special-shaped surface modeling to fabrication-oriented data. To address this gap, this study develops and validates a Model-Based Systems Engineering (MBSE)-oriented design–fabrication integration workflow for special-shaped steel bridges. The workflow combines requirement decomposition, functional architecture modeling, ENOVIA-based collaborative data management, skeleton-driven parametric modeling, User-Defined Feature (UDF) templates, Engineering Knowledge Language (EKL) batch instantiation, an IFC-based manufacturing information extension, ProNest nesting, and model-driven NC-code generation. The method was implemented for the Q7 North Pedestrian Bridge, a spatially twisted special-shaped steel landscape bridge. In the case study, the proposed workflow reduced typical repetitive component modeling time by 70.8%, shortened drawing generation time by 80.0%, increased nesting material utilization from 84.6% to 91.8%, and controlled the maximum coordinate-transformation deviation of formwork points within 1.42 mm. Field validation showed a mean fabrication deviation of 1.6 mm and a maximum site assembly closure deviation of 4.5 mm. The results indicate that the proposed MBSE-oriented digital thread improves design consistency, reduces manual data re-entry, and strengthens traceability from requirements to manufacturing and assembly. The study provides a reproducible case-study framework for model-driven steel bridge design–fabrication integration and identifies the limitations of UDF-library construction cost, software-specific learning requirements, and single-project validation. Full article

Large language models (LLMs) are changing how students approach problem solving, code interpretation, and software development. Successful task completion with AI assistance, however, does not necessarily indicate conceptual understanding of underlying programming principles, making it difficult to determine how programming skills develop over time. This study examines whether students’ critical engagement, collaborative learning practices, and exploratory use of LLMs are associated with self-reported programming competence, coding practices, and longer-term knowledge retention. Survey data from 189 students with varying levels of LLM use in educational and coding-related contexts were analyzed using partial least squares structural equation modeling (PLS-SEM). The findings suggest that students perceive LLMs as more educationally valuable when they actively question, reinterpret, and adapt AI-generated responses rather than accept them as final answers. Students who interacted more reflectively with AI outputs reported stronger perceived competence and more deliberate attention to code organization, readability, and maintainability. Collaborative use corresponded to broader development of programming abilities, whereas creative experimentation was more closely related to stylistic refinement and perceived benefits for longer-term retention. Active engagement with AI-generated material may therefore promote analytical reasoning and deeper conceptual involvement instead of merely accelerating code production. By moving beyond technology adoption and productivity-oriented perspectives, the study highlights the role of learner agency in shaping LLM-supported programming education. Full article

Shared bikes are frequently parked in disorder, resulting in low efficiency of manual collection and transfer and heavy workload for maintenance staff. Random parking across various areas forces shared bikes to occupy sidewalks and fire exits, damaging urban landscapes and disrupting traffic order. To tackle these industrial pain points, this paper develops an integrated intelligent robot system equipped with functions of multi-pose grasping, automatic transfer and fixed-point delivery of shared bikes, which can effectively address the drawbacks of low efficiency and high labor costs in traditional manual maintenance. This paper focuses on the completion of the robot’s overall mechanical structure design, stiffness–precision collaborative optimization model construction, finite-element static simulation verification, 1:7 scaled prototype development and performance testing. Firstly, the overall layout design of the multi-posture adaptive floating clamping mechanism, transfer-bearing frame, and Mecanum wheel omnidirectional mobile chassis is completed, and the structural parameters and assembly benchmarks of the core components are clarified. Secondly, a stiffness–precision coupling optimization model is established, and the static analysis under extreme load conditions is carried out through Abaqus finite-element software, which verifies the rationality of 45# carbon steel material selection and the safety of structural strength. Subsequently, a 1:7 scaled principle prototype is developed, and repetitive grabbing and transfer tests are carried out to verify the system operation feasibility, stability and grabbing accuracy. Finally, the statistical analysis of the test data and the horizontal comparison of similar schemes are completed. The test and simulation results show that the maximum stress of the system under extreme working conditions is 131.21 MPa, which is far lower than the allowable stress of 355 MPa of 45# steel, and the safety factor reaches 2.71. The maximum total deformation is 4.0552 mm, which is concentrated at the end of the front-end clamping mechanism, and is within the allowable stiffness deviation range of the transfer system. The average value of the single clamping positioning error of the scaled prototype is 0.476 mm, with a 95% confidence interval of 0.457–0.495 mm, which is converted to a positioning error of ≤3.4 mm for the full-scale prototype, which is far better than similar industry solutions. The average time of a single complete grabbing and transfer operation is 12.38 s, which is more than 45% higher than the traditional manual mode. The structural design, grabbing accuracy and operation stability of the robot designed in this paper all meet the requirements of actual working conditions of urban sidewalks, which can effectively reduce the intensity of manual labor and improve the operation and maintenance efficiency of shared bicycles. It has strong engineering application value and can provide reference for the design and manufacturing of intelligent collection and transfer systems for shared two-wheelers. Full article

The use of industrial and collaborative robots in tasks requiring high precision places increasing demands on the evaluation of their performance. In practice, parameters such as positioning accuracy, repeatability and stability are typically assessed according to ISO 9283, based on repeated measurements and comparison of commanded and measured positions. This paper presents a measurement and analysis system developed for this purpose. The system combines selected measurement hardware with a software solution that covers the full workflow from data acquisition to result evaluation. A Python-based backend is used to handle communication with measuring devices, data processing and storage, while a web-based interface provides access to system control, real-time monitoring, and visualization of results. The separation of these components allows the system to remain stable even if the user interface is interrupted. Measured data are evaluated using statistical methods based on repeated measurements, with results presented in both numerical and graphical form. This approach simplifies interpretation and reduces the need for additional external tools. The proposed solution provides a practical and extendable framework for evaluating robot performance in laboratory as well as industrial conditions. Based on the obtained data, the robot’s performance can be evaluated in terms of pose accuracy and pose repeatability. In addition, robot parameters can be monitored and evaluated over an extended period, which allows the proposed solution to be used in predictive maintenance. This article primarily focuses on verifying pose accuracy, since these data were required by the robot user, who specified a minimum of 30 measurement repetitions. The maximum allowable deviation was ±0.01 mm. In the case of pose repeatability and drift of pose characteristics, the calculated value obtained from the measured data must not exceed ±0.02 mm, which is the value declared by the robot manufacturer. Full article