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The Internet of Things is used in many application areas in our daily lives. Ensuring the security of valuable data transmitted over the Internet is a crucial challenge. Hash functions are used in cryptographic applications such as integrity, authentication and digital signatures. Existing lightweight hash functions leverage task parallelism but provide limited scalability. There is a need for lightweight algorithms that can efficiently utilize multi-core platforms or distributed computing environments with high degrees of parallelization. For this purpose, a data-parallel approach is applied to a lightweight hash function to achieve massively parallel software. A novel structure suitable for data-parallel architectures, inspired by basic tree construction, is designed. Furthermore, the proposed hash function is based on a lightweight block cipher and seamlessly integrated into the designed framework. The proposed hash function satisfies security requirements, exhibits high efficiency and achieves significant parallelism. Experimental results indicate that the proposed hash function performs comparably to the BLAKE implementation, with slightly slower execution for large message sizes but marginally better performance for smaller ones. Notably, it surpasses all other evaluated algorithms by at least 20%, maintaining a consistent 20% advantage over Grostl across all data sizes. Regarding parallelism, the proposed PLWHF achieves a speedup of approximately 40% when scaling from one to two threads and 55% when increasing to three threads. Raspberry Pi 4-based tests for IoT applications have also been conducted, demonstrating the hash function’s effectiveness in memory-constrained IoT environments. Statistical tests demonstrate a precision of ±0.004, validate the hypothesis in distribution tests and indicate a deviation of ±0.05 in collision tests, confirming the robustness of the proposed design. Full article

Building Information Modeling (BIM) has become an indispensable tool for risk management and construction oversight, especially in the case of complex and irregularly shaped buildings. BIM’s ability to reduce construction errors has been proven through advanced features like clash detection, schedule forecasting, and cost estimation. As the adoption of BIM grows, software providers such as Autodesk, Bentley, Trimble, and Nemetschek have developed advanced tools that incorporate Project Lifecycle Management (PLM). However, these tools are not easily transferable to Asian countries, where construction management often uses unit pricing rather than the more intricate systems common in Europe and the US. Legacy data also play a crucial role in Asian construction management, impacting risk profiling and cost predictions for similar projects. This study explores the integration of 4D BIM data within a Work Breakdown Structure (WBS) framework in a real-world setting. The first step was the creation of an in-house BIM platform, CEV (Civil Easy View), built on the Autodesk Forge viewer. CEV is designed as a BIM viewer tailored for field staff and supervisors. This 4D BIM application showed strong connectivity through standardized WBS codes, allowing for automatic synchronization between object and schedule data. Full article

Divergent thinking tests are popular instruments to measure a person’s creativity. They often involve scoring fluency, which refers to the count of ideas generated in response to a prompt. The two-parameter Poisson counts model (2PPCM), a generalization of the Rasch Poisson counts model (RPCM) that includes discrimination parameters, has been proposed as a useful approach to analyze fluency scores in creativity tasks, but its estimation was presented in the context of generalized structural equation modeling (GSEM) commercial software (e.g., Mplus). Here, we show how the 2PPCM (and RPCM) can be estimated in a Bayesian multilevel regression framework and interpreted using the R package brms, which provides an interface for the Stan programming language. We illustrate this using an example dataset, which contains fluency scores for three tasks and 202 participants. We discuss model specification, estimation, convergence, fit and comparisons. Furthermore, we provide instructions on plotting item response functions, comparing models, calculating overdispersion and reliability, as well as extracting factor scores. Full article

The global installation capacity of solar photovoltaic (PV) systems is exponentially increasing. However, the accumulation of soil and debris on solar panels significantly reduces their efficiency, necessitating frequent cleaning to maintain optimal energy output. This study presents a deep learning-based approach for the recognition and classification of soiled PV images, aimed at enhancing the capabilities of solar cleaning robots through the HALCON software framework. Using EANN and CNN architecture along with advanced image processing techniques, the proposed system achieves precise detection and classification of soiling patterns. The HALCON framework facilitates image acquisition, preprocessing, segmentation, and deployment of trained models for robotic control. The trained models demonstrate exceptional accuracy, with the EANN and CNN achieving classification precision of 99.87% and 99.91%, respectively. Experimental results highlight the system’s potential to improve automation of cleaning strategies, reduce unnecessary cleaning cycles, and enhance the overall performance of solar panels. This research underscores the transformative role of intelligent visual analysis in optimizing maintenance practices for renewable energy applications. Full article

Secondary systems in electrical engineering often rely on traditional CAD software (AutoCAD v2024.1.6) or non-structured, paper-based diagrams for fieldwork, posing challenges for digital transformation. Electrical diagram recognition technology bridges this gap by converting traditional diagram operations into a “digital” model, playing a critical role in power system scheduling, operation, and maintenance. However, conventional recognition methods, which primarily rely on partition detection, face significant limitations such as poor adaptability to diverse diagram styles, interference among recognition objects, and reduced accuracy in handling complex and varied electrical diagrams. This paper introduces a novel layered framework for electrical diagram recognition that sequentially extracts the element layer, text layer, and connection relationship layer to address these challenges. First, an improved YOLOv7 model, combined with a multi-scale sliding window strategy, is employed to accurately segment large and small diagram objects. Next, PaddleOCR, trained with electrical-specific terminology, and PaddleClas, using multi-angle classification, are utilized for robust text recognition, effectively mitigating interference from diagram elements. Finally, clustering and adaptive FcF-inpainting algorithms are applied to repair the connection relationship layer, resolving local occlusion issues and enhancing the overall coupling of the diagram. Experimental results demonstrate that the proposed method outperforms existing approaches in robustness and universality, particularly for complex diagrams, providing technical support for intelligent power grid construction and operation. Full article

The automobile is evolving from a mechanically dominated to a cyber-physical, software-defined system. Future complex functionalities, such as autonomous driving, require multidisciplinary, interconnected systems. Hence, interdisciplinary architecture development based on Systems Engineering principles becomes essential, leading to a methodology for vehicle system architecture with the RFLP-Framework. For its application in the automotive industry, building upon the established mechanical and electronical platform approaches, the methodology incorporates the concept of a Reference System Architecture. This is defined as a basic architecture that sets out the common architectural specifications to support the efficient and systemic interdisciplinary architecture development across multiple projects. Its corresponding characteristics and quality criteria are defined and the understanding of the functional and logical reference architecture view, based on the RFLP-Framework, is described. Based on this understanding, an exemplary functional and logical Reference System Architecture for passenger vehicles is proposed. Its methodical user-oriented and knowledge-based development within the scientific circumstances is discussed and concluded. Full article

In the context of economic globalization, waterborne transportation plays an important role in international trade and logistics. However, waterborne traffic accidents pose a severe threat to life, property safety, and the environment. To gain a deeper understanding of the causal mechanisms behind waterborne traffic accidents, we conducted a data-driven analysis of the causal chain of waterborne traffic accidents. By constructing a hybrid framework integrating an improved HFACS (Human Factors Analysis and Classification System) with a Bayesian network model, we conducted a multi-dimensional analysis of accident causes. The constructed model was quantitatively analyzed by applying genie software to the accident samples collected from the China MSA. The results indicate that there are 12, 3, 6, 2, 4, and 7 causal chains leading to collisions, contact, fires/explosions, windstorm accidents, sinking, and other types of accidents, respectively. These research results can serve as a reference for the enhancement of the safety of waterborne transportation. Full article

Background: Investigating the way in which individuals are genetically related has been a long-standing application of forensic DNA typing. Whilst capillary electrophoresis (CE)-based STR analysis is likely to provide sufficient data to resolve regularly encountered paternity cases, its power to adequately resolve more distant or complex relationships can be limited. Massively parallel sequencing (MPS) has become a popular alternative method to CE for analysing genetic markers for forensic applications, including kinship analysis. Data workflows used in kinship testing are well-characterised for CE-based methodologies but are much less established for MPS. When incorporating this technology into routine relationship casework, modifications to existing procedures will be required to ensure that the full power of MPS can be utilised whilst maintaining the authenticity of results. Methods: Empirical data generated with MPS for forensically relevant STRs and SNPs and real-world case experience have been used to determine the necessary workflow adaptations. Results: The four considerations highlighted in this work revolve around the distinctive properties of sequence-based data and the need to adapt CE-based data analysis workflows to ensure compatibility with existing kinship software. These considerations can be summarised as the need for a suitable sequence-based allele nomenclature; methods to account for mutational events; appropriate population databases; and procedures for dealing with rare allele frequencies. Additionally, a practical outline of the statistical adjustments required to account for genetic linkage between loci, within the expanded marker sets associated with MPS, has been presented. Conclusions: This article provides a framework for laboratories wishing to implement MPS into routine kinship analysis, with guidance on aspects of the data analysis and statistical interpretation processes. Full article

This paper presents a strategic planning model aimed at optimizing the economic and environmental impacts of palm oil mill effluent (POME) treatment systems. The model determines the optimal selection of POME treatment systems to minimize the environmental impact, specifically focusing on three systems: an anaerobic digester tank system (ADT), a covered lagoon system (CL) with biogas capture, and an open pond system (OP). The model incorporates constraints related to fresh fruit bunch (FFB) production, POME generation, the biological oxygen demand (BOD), the chemical oxygen demand (COD), and carbon dioxide (CO₂) emissions. The optimization framework, formulated as a mixed-integer linear programming (MILP) model, is solved using the GAMS 40.1.0 software. Integer decision variables are used to represent the choice of POME treatment system that minimizes the environmental impact. The study specifically considers the ADT, CL, and OP systems, with the results indicating that the ADT system is the most effective in reducing the BOD, COD, and CO₂-equivalent emissions, thereby highlighting its environmental benefits. The model selects the ADT treatment system, which exhibits the lowest COD, BOD, and CO₂e emissions. Specifically, the COD registered an 85% reduction, from 84,830 mg/L to 12,725 mg/L. The BOD level was reduced by 88%, resulting in a BOD level of 41,208 mg/L to 4945 mg/L. The minimum CO₂e emissions that could be achieved was about 3173 t CO₂e per annum. This model provides a valuable tool for governmental agencies and policymakers to guide the private sector in developing environmentally sustainable POME treatment strategies. Full article

Gamification has become a motivational technique for enhancing engagement and productivity, extending into agile software development. However, integrating gamification into agile frameworks such as the Scrum framework has led to the emergence of gamification risks, which may have adverse impacts on agile roles and tasks. These risks include an increase in the number of unassigned tasks affecting sprint velocity, novelty-seeking and quick boredom, clustering group, and intimidation, thus showing the need for a structured approach toward their management, their impacts on team dynamics and project outcomes. This study proposes the Agile Gamification Risk Management (AGRM) process, focused on identifying, assessing, and mitigating gamification risks in agile software enterprises. AGRM introduces artifacts such as the Gamification Risk Reporting Form, Personalized Risk Profiles, Task Impact Matrix, and Gamification Risk Register, enabling real-time proactive risk management. By leveraging a gamification risk taxonomy, AGRM categorizes and prioritizes risks, aligning mitigation efforts effectively. This paper details a two-phased empirical study to evaluate our proposed AGRM process. The proposed process identified 17 and mitigated 9 gamification risks for two agile teams in two software development enterprises. Unlike ad hoc practices, AGRM provides a structured approach, empowering teams to manage risks during sprint events. By incorporating artifacts like the Gamification Risk Register (GRR) and Personalized Risk Profiles (PRPs), teams can assess risks in context, enhancing productivity, collaboration, and project outcomes. The results demonstrate AGRM’s ability to boost team morale and confidence in addressing gamification risks effectively. Full article

The aviation industry relies on continuous inspections to ensure infrastructure safety, particularly in confined spaces like aircraft fuel tanks, where human inspections are labor-intensive, risky, and expose workers to hazardous exposures. Robotic systems present a promising alternative to these manual processes but face significant technical and operational challenges, including technological limitations, retraining requirements, and economic constraints. Additionally, existing prototypes often lack open-source documentation, which restricts researchers and developers from replicating setups and building on existing work. This study addresses some of these challenges by proposing a modular, open-source framework for robotic inspection systems that prioritizes simplicity and scalability. The design incorporates a robotic arm and an end-effector equipped with three RGB-D cameras to enhance the inspection process. The primary contribution lies in the development of decentralized software modules that facilitate integration and future advancements, including interfaces for teleoperation and motion planning. Preliminary results indicate that the system offers an intuitive user experience, while also enabling effective 3D reconstruction for visualization. However, improvements in incremental obstacle avoidance and path planning inside the tank interior are still necessary. Nonetheless, the proposed robotic system promises to streamline development efforts, potentially reducing both time and resources for future robotic inspection systems. Full article

This paper explores the potential of Digital Twin (DT) technology for Permanent Magnet Synchronous Motors (PMSMs) and establishes a foundation for its modeling and applications. While DTs have been widely applied in complex systems and simulation software, their use in electric motors, especially PMSMs, remains limited. This study examines physics-based, data-driven, and hybrid modeling approaches and evaluates their feasibility for real-time simulation, fault detection, and predictive maintenance. It also identifies key challenges such as computational demands, data integration, and the lack of standardized frameworks. By assessing current developments and outlining future directions, this work provides insights into how DTs can be implemented for PMSMs and drive advancements in industrial applications. Full article

Introduction: Obesity is a chronic disease associated with increased risk for several cancers, including colorectal cancer (CRC), a leading cause of cancer-related mortality. The majority of CRC cases are associated with modifiable risk factors. Metabolic and bariatric surgery (MBS) is a proven, durable, and successful intervention for obesity. This study aimed to evaluate the impact of MBS on CRC risk through measures of association, such as relative risk (RR) and odds ratio (OR). Methods: A systematic search of PubMed, Scopus, Web of Science, ScienceDirect, and Embase was conducted to identify systematic reviews (SR) and meta-analyses examining the relationship between obesity treated with MBS and CRC incidence. The PICO framework guided inclusion criteria, and three independent reviewers screened articles using Rayyan software. Quality assessment was performed using AMSTAR2. Results: Of 1336 screened articles, 10 SR met inclusion criteria, encompassing 53,452,658 patients. Meta-analyses consistently showed a significant reduction in CRC risk following MBS in patients with severe obesity. Risk reductions were reported by Liu et al. (RR: 0.46, 95% CI: 0.32–0.67, p < 0.01), Chierici et al. (RR: 0.46, 95% CI: 0.28–0.75, p = 0.018), Wilson et al. (RR: 0.69, 95% CI: 0.53–0.88, p = 0.003), and Pararas et al. (RR: 0.56, 95% CI: 0.40–0.80, p < 0.001). Sensitivity analyses supported these findings. For colon cancer, Liu and Chierici both reported an RR of 0.75 (95% CI: 0.46–1.21, p = 0.2444) with significant heterogeneity (I² = 89%). A trend towards reduced rectal cancer risk (RR: 0.74, 95% CI: 0.40–1.39, p = 0.3523) was noted but limited by fewer studies. Sex-specific analyses revealed protective effects in both sexes, with a more pronounced impact in females (RR: 0.54, 95% CI: 0.37–0.79, p = 0.0014). Conclusions: This umbrella review synthesizes current evidence on the impact of MBS on CRC risk, highlighting a consistent protective association. The findings also indicate a potential risk reduction for both colon and rectal cancer, with a more pronounced effect observed among females compared to males. Given the profound implications of MBS on cancer incidence, morbidity, and mortality, further high-quality, long-term studies are essential to deepen our understanding and optimize its role in cancer prevention and patient care. Full article

The adoption rate of battery electric vehicles (EVs) is rapidly increasing. Electric vehicles differ significantly from conventional internal combustion engine vehicles and vary widely across different manufacturers. Emergency responders (ERs) and recovery personnel may have less experience with EVs and lack timely access to critical information such as the extent of the stranded energy present, high-voltage safety hazards, and post-crash handling procedures in a user-friendly manner. This paper presents a software/hardware interactive tool named Electric Vehicle Information for Incident Response Solutions (EVIRS) to aid in the quick access to emergency response and recovery information. The current prototype of EVIRS identifies EVs using the VIN or Make, Model, and Year, and offers several useful features for ERs and recovery personnel. These features include integration and easy access to emergency response procedures tailored to an identified EV, vehicle structural schematics, the quick identification of battery pack specifications, and more. For EVs that are not severely damaged, EVIRS can perform calculations to estimate stranded energy in the EV’s battery and discharge time for various power loads using either EV dashboard information or operational data accessed through the CAN interface. Knowledge of this information may be helpful in the post-crash handling, management, and storage of an EV. The functionality and accuracy of EVIRS were demonstrated through laboratory tests using a 2021 Ford Mach-E and associated data acquisition system. The results indicated that when the remaining driving range was used as an input, EVIRS was able to estimate the pack voltage with an error of less than 3 V. Conversely, when pack voltage was used as an input, the estimated state of charge (SOC) error was less than 5% within the range of 30–90% SOC. Additionally, other features, such as retrieving emergency response guides for identified EVs and accessing lessons learned from archived incidents, have been successfully demonstrated through EVIRS for quick access. EVIRS can be a valuable tool for emergency responders and recovery personnel, both in action and during offline training, by providing crucial information related to assessing EV/battery safety risks, appropriate handling, de-energizing, transport, and storage in an integrated and user-friendly manner. Full article

The role of General Practice Pharmacists (GPPs) has expanded in response to increasing demands on primary care services, particularly in managing chronic conditions. While GPPs are recognised for their contributions to medication optimisation and patient care, challenges such as role clarity, workload pressures, and confidence in clinical decision-making remain underexplored. This study aims to investigate the tasks, professional identity, confidence levels, and challenges faced by GPPs in Northern Ireland. A mixed-methods design was employed, incorporating a questionnaire distributed to GPPs across Northern Ireland. The questionnaire comprised 20 multiple-choice questions and 5 open-ended questions, focusing on demographics, tasks, confidence levels, role clarity, and professional challenges. Quantitative data were analysed using descriptive and inferential statistics, while qualitative responses underwent thematic analysis using NVIVO software. A total of 44 GPPs participated, with a majority being female and aged 34–39 years. Most participants had 4–6 years of experience as GPPs. Quantitative findings revealed significant correlations between clinical confidence and factors such as years of experience, age, and employment type. Qualitative analysis revealed key themes: clinical confidence was enhanced by training and experience, but workload pressures often limited time for patient care. Variability in role integration and the lack of public awareness were highlighted as barriers to maximising the GPP role. This study highlights the key challenges of workload distribution and role ambiguity in the GPP role. Delegating administrative tasks and developing clear frameworks for role integration could address these barriers. Additionally, targeted training programs and public education campaigns are essential to enhance the impact of GPPs in primary care. Full article