Search Results (211)

Products with complex structures are structurally intricate and involve multiple professional fields and engineering construction elements, making it difficult for a single contractor to independently develop and manufacture such complex structural products. Therefore, during the research, development, and production of complex products, collaboration between manufacturers and suppliers is essential to ensure the smooth completion of projects. In this process, a complex supply chain network is often formed to achieve collaborative cooperation among all project participants. Within such a complex supply chain network, issues such as delayed delivery, poor product quality, or low resource utilization by any participant may trigger the bullwhip effect. This, in turn, can negatively impact the delivery cycle, product cost, and quality of the entire complex product, causing it to lose favorable competitive positions such as quality advantages and delivery advantages in fierce market competition. Therefore, this paper firstly explores the mechanism of complex product manufacturing and the supply network of complex product manufacturing, in order to grasp the inherent structure of complex product manufacturing with a focus on identifying symmetrical properties among supply chain nodes. Secondly, a complex product supply chain network model is constructed with the Graphical Evaluation and Review Technique (GERT), incorporating symmetry constraints to reflect balanced resource allocation and mutual dependencies among symmetrical nodes. Then, from the perspective of supply chain, we focus on identifying the shortcomings of supply chain suppliers and optimizing the management cost of the whole supply chain in order to improve the quality of complex products, delivery level, and cost saving level. This study constructs a Restricted Grey GERT (RG-GERT) network model with constrained outputs, integrates moment-generating functions and Mason’s Formula to derive transfer functions, and employs a hybrid algorithm (genetic algorithm combined with non-linear programming) to solve the multi-objective optimization problem (MOOP) for joint optimization of delivery time, quality, and cost. Empirical analysis is conducted using simulated data from Y Company’s aerospace equipment supply chain, covering interval parameters such as delivery time [5–30 days], cost [40,000–640,000 CNY], and quality [0.85–1.0], validated with industry-specific constraints. Empirical analysis using Y Company’s aerospace supply chain data shows that the model achieves a maximum customer satisfaction of 0.96, with resource utilization efficiency of inefficient suppliers improved by 15–20% (p < 0.05) after secondary optimization. Key contributions include (1) integrating symmetry analysis to simplify network modeling; (2) extending GERT with grey parameters for non-probabilistic uncertainty; (3) developing a two-stage optimization framework linking customer satisfaction and resource efficiency. Full article

Having in view the current global disruptive social and economic landscape, sustainability becomes more important than ever. As producers become more concerned about adopting more sustainable practices, customer awareness towards sustainable behavior must be the focus of all stakeholders. Within this context, the SHIFT framework (proposed in 2019) highlights the manner in which consumers’ traits and attitudes influence their propensity towards sustainable consumption. It consists of five factors considered to be relevant to consumer behavior: Social influence, Habit formation, Individual self, Feelings and cognition, and Tangibility. Different from previous studies, this research focuses on applying the SHIFT framework to the automotive industry, taking into consideration the contribution of digital technologies to fostering sustainable consumer behavior throughout the entire product lifecycle. Using a qualitative research approach, the most relevant digital technologies in the automotive industry were identified and mapped in relation to the three phases of consumption (choice, usage, and disposal). The research aimed to develop and test an original conceptual framework, starting from the SHIFT. The results of the study highlight the fact that the digital technologies, in their diversity, are integrated in different ways into each of the three phases, facilitating the adoption of sustainable consumption. To achieve sustainability, the two key stakeholders, consumers and producers, should share a common ground on capitalizing the opportunities offered by digital technologies. Full article

Unmanned aerial vehicles (UAVs) are increasingly used for road infrastructure inspection and monitoring. However, challenges such as scale variation, complex background interference, and the scarcity of annotated UAV datasets limit the performance of traditional segmentation models. To address these challenges, this study proposes UAV-YOLOv12, a multi-scale segmentation model specifically designed for UAV-based road imagery analysis. The proposed model builds on the YOLOv12 architecture by adding two key modules. It uses a Selective Kernel Network (SKNet) to adjust receptive fields dynamically and a Partial Convolution (PConv) module to improve spatial focus and robustness in occluded regions. These enhancements help the model better detect small and irregular road features in complex aerial scenes. Experimental results on a custom UAV dataset collected from national highways in Wuxi, China, show that UAV-YOLOv12 achieves F1-scores of 0.902 for highways (road-H) and 0.825 for paths (road-P), outperforming the original YOLOv12 by 5% and 3.2%, respectively. Inference speed is maintained at 11.1 ms per image, supporting near real-time performance. Moreover, comparative evaluations with U-Net show that UAV-YOLOv12 improves by 7.1% and 9.5%. The model also exhibits strong generalization ability, achieving F1-scores above 0.87 on public datasets such as VHR-10 and the Drone Vehicle dataset. These results demonstrate that the proposed UAV-YOLOv12 can achieve high accuracy and robustness in diverse road environments and object scales. Full article

Despite navigation advancements in enhanced sensor utilization and increased focus on maritime training and education, most marine accidents still involve collisions with high human involvement. Furthermore, navigators’ knowledge and application of the most often misunderstood Rule 10 Traffic Separation Schemes (TSS) according to the Convention on the International Regulations for Preventing Collisions at Sea (COLREG) represents the first focus in this study. To provide insight into the level of understanding and knowledge regarding COLREG Rule 10, a customized, worldwide survey has been created and disseminated among marine industry professionals. The survey results reveal a notable knowledge gap in Rule 10, where we initially assumed that more than half of the respondents know COLREG regulations well. According to the probability calculation and chi-square test results, all three categories (OOW, Master, and others) have significant rule misunderstanding. In response to the COLREG misunderstanding, together with the increasing density of maritime traffic, the implementation of Decision Support Systems (DSS) in navigation has become crucial for ensuring compliance with regulatory frameworks and enhancing navigational safety in general. This study presents a structural approach to vessel prioritization and decision-making within a DSS framework, focusing on the classification and response of the own vessel (OV) to bow-crossing scenarios within the TSS. Through the real-time integration of AIS navigational status data, the proposed DSS Architecture offers a structured, rule-compliant architecture to enhance navigational safety and the decision-making process within the TSS. Furthermore, implementing a Fall-Back Strategy (FBS) represents the key innovation factor, which ensures system resilience by directing operator response if opposing vessels disobey COLREG rules. Based on the vessel’s dynamic context and COLREG hierarchy, the proposed DSS Architecture identifies and informs the navigator regarding stand-on or give-way obligations among vessels. Full article

We live in an era where digitalization and omnichannel strategies significantly transform retail landscapes, and accurate spatial analytics from Geographic Information Systems (GIS) can deliver substantial competitive benefits. Nonetheless, despite evident practical advantages for specific targeting strategies and operational efficiency, the degree of GIS integration into academic marketing literature remains ambiguous. Clarifying this uncertainty is beneficial for advancing theoretical understanding and ensuring retail strategies fully leverage robust, data-driven spatial intelligence. To examine the intellectual development of the field, co-occurrence analysis, topic mapping, and citation structure visualization were performed on 4952 peer-reviewed articles using the Bibliometrix R package (version 4.3.3) within R software (version 4.4.1). The results demonstrate that although GIS-based methods have been effectively incorporated into fields like site selection and spatial segmentation, traditional marketing research has not yet entirely adopted them. One of the study’s key findings is the distinction between “author keywords” and “keywords plus,” where researchers concentrate on novel topics like omnichannel retail, artificial intelligence, and logistics. However, “Keywords plus” still refers to more traditional terms such as pricing, customer satisfaction, and consumer behavior. This discrepancy presents a misalignment between current research trends and indexed classification practices. Although the mainstream retail research lacks terminology connected to geomarketing, a theme evolution analysis reveals a growing focus on technology-driven and sustainability-related concepts associated with the Retail 4.0 and 5.0 paradigms. These findings underscore a conceptual and structural deficiency in the literature and indicate the necessity for enhanced integration of GIS and spatial decision support systems (SDSS) in retail marketing. Full article

This study explores the use of Large Language Models (LLMs), specifically GPT, for different safety management applications in the construction industry. Many studies have explored the integration of GPT in construction safety for various applications; their primary focus has been on the feasibility of such integration, often using GPT models for specific applications rather than a thorough evaluation of GPT’s limitations and capabilities. In contrast, this study aims to provide a comprehensive assessment of GPT’s performance based on established key criteria. Using structured use cases, this study explores GPT’s strength and weaknesses in four construction safety areas: (1) delivering personalized safety training and educational content tailored to individual learner needs; (2) automatically analyzing post-accident reports to identify root causes and suggest preventive measures; (3) generating customized safety guidelines and checklists to support site compliance; and (4) providing real-time assistance for managing daily safety tasks and decision-making on construction sites. LLMs and NLP have already been employed in each of these four areas for improvement, making them suitable areas for further investigation. GPT demonstrated acceptable performance in delivering evidence-based, regulation-aligned responses, making it valuable for scaling personalized training, automating accident analyses, and developing safety protocols. Additionally, it provided real-time safety support through interactive dialogues. However, the model showed limitations in deeper critical analysis, extrapolating information, and adapting to dynamic environments. The study concludes that while GPT holds significant promise for enhancing construction safety, further refinement is necessary. This includes fine-tuning for more relevant safety-specific outcomes, integrating real-time data for contextual awareness, and developing a nuanced understanding of safety risks. These improvements, coupled with human oversight, could make GPT a robust tool for safety management. Full article

As sustainability becomes increasingly central to organizational strategy, social economy organizations (SEOs) are rethinking their business models. This study employs stakeholder analysis using the value mapping (VM) tool developed by Short, Rana, Bocken, and Evans for the development of the VOLTO JÁ project. The objective of the VOLTO JÁ project is to operationalize a senior exchange programme between SEOs. The VM approach extends beyond conventional customer value propositions to prioritize sustainability for all stakeholders and identify key drivers of sustainable business model (SBM) innovation. The multi-stakeholder methodology comprises the following elements: (1) sequential focus groups aimed at enhancing sustainable business thinking; (2) semi-structured interviews; and (3) workshop to facilitate qualitative analysis and co-create the VM. The findings are then categorized into four value dimensions: (1) value captured—improved participant well-being, enhanced reputational capital, mitigation of social asymmetries, and affordable service experiences; (2) value lost—underused community assets; (3) value destroyed—institutional and systemic barriers to innovation; and (4) new value opportunities—knowledge sharing, service diversification, and open innovation to foster collaborative networks. The study demonstrates that the application of VM in SEOs supports SBM development by generating strategic insights, enhancing resource efficiency, and fostering the delivery of socially impactful services. Full article

South Africa’s bioeconomy strategy identifies bio-innovation as a key driver of economic growth and social development, with plant breeding playing a central role in improving food security through the development of high-yielding, resilient, and high-quality crops. However, consumer perceptions of recent advances, particularly new breeding techniques (NBTs), remain underexplored. This study examines South African consumer attitudes towards plant breeding innovations, using a mixed-methods approach. The initial focus group interviews informed the development of a structured quantitative survey examining familiarity, perceptions, and acceptance of plant breeding technologies. Consumer awareness of plant breeding principles was found to be limited, with 67–68% of respondents unfamiliar with both conventional and modern plant breeding procedures. Despite this information gap, consumers expressed conditional support for modern breeding techniques, especially when associated with actual benefits like increased nutritional value, environmental sustainability, and crop resilience. When favourable effects were outlined, support for general investment in modern breeding practices climbed from 45% to 74%. Consumer purchase decisions emphasised price, product quality, and convenience over manufacturing techniques, with sustainability ranked last among the assessed factors. Trust in the sources of food safety information varied greatly, with medical experts and scientists being ranked highly, while government sources were viewed more sceptically. The results further suggest that targeted education could improve customer confidence, as there is a significant positive association (R² = 0.938) between familiarity and acceptance. These findings emphasise the significance of open communication strategies and focused consumer education in increasing the adoption of plant breeding breakthroughs. The study offers useful insights for policymakers, researchers, and industry stakeholders working on engagement strategies to facilitate the ethical growth and application of agricultural biotechnology in support of food security and quality in South Africa. This study contributes to a better understanding of South African consumers’ perceptions of plant breeding innovations and food safety. The research findings offer valuable insights for policymakers, researchers, and industry stakeholders in developing effective engagement and communication strategies that address consumer concerns and promote the adoption of products derived from diverse plant breeding technologies. Full article

Parkinson’s disease (PD) is a progressive neurodegenerative disorder leading to movement impairment, cognitive decline, and psychiatric symptoms. Key manifestations of PD include bradykinesia (the slowness of movement), changes in voice or speech, and gait disturbances. The quantification of neurological disorders through voice analysis has emerged as a rapidly expanding research domain, offering the potential for non-invasive and large-scale monitoring. This review explores existing research on the application of machine learning (ML) in speech, voice, and language processing for the diagnosis of PD. It comprehensively analyzes current methodologies, highlights key findings and their associated limitations, and proposes strategies to address existing challenges. A systematic review was conducted following PRISMA guidelines. We searched four databases: PubMed, Web of Science, Scopus, and IEEE Xplore. The primary focus was on the diagnosis, detection, or identification of PD through voice, speech, and language characteristics. We included 34 studies that used ML techniques to detect or classify PD based on vocal features. The most used approaches involved free speech and reading-speech tasks. In addition to widely used feature extraction toolkits, several studies implemented custom-built feature sets. Although nearly all studies reported high classification performance, significant limitations were identified, including challenges in comparability and incomplete integration with clinical applications. Emerging trends in this field include the collection of real-world, everyday speech data to facilitate longitudinal tracking and capture participants’ natural behaviors. Another promising direction involves the incorporation of additional modalities alongside voice analysis, which may enhance both analytical performance and clinical applicability. Further research is required to determine optimal methodologies for leveraging speech and voice changes as early biomarkers of PD, thereby enhancing early detection and informing clinical intervention strategies. Full article

Crowdsourcing has become increasingly relevant in academic research due to its role in the evolving digital landscape, where information is a key driver of organizational performance. In a context dominated by emerging technologies and digital platforms, organizations are turning to external sources for data and idea generation. This paper offers a comprehensive review of the literature on crowdsourcing and digital information, using bibliometric techniques and qualitative analysis to identify major trends. The findings reveal several shifts in focus: from conceptual frameworks to practical applications; from customer participation to broader stakeholder involvement; and from general technological and managerial approaches to specific technologies and emerging perspectives in entrepreneurship and finance. The primary contributing disciplines are Computer Science, Engineering, and Information Science. Recent research (post 2023) emphasizes constructs such as “crowdfunding”, “digital platforms”, and “machine learning”, moving beyond earlier focuses like “citizen science” and “social media.” This review also reveals growing interest in managerial, medical, and cultural heritage applications, alongside a decline in research related to geography and crisis management over the past two years. This study enhances our understanding of current research directions and practical implications in crowdsourcing and digital information, offering valuable insights for both academics and practitioners. Full article

The Fourth Industrial Revolution and artificial intelligence (AI) technology are driving the transformation of the meat industry from mechanization and automation to intelligence and digitization. This paper provides a systematic review of key technological innovations in this field, including physical technologies (such as smart cutting precision improved to the millimeter level, pulse electric field sterilization efficiency exceeding 90%, ultrasonic-assisted marinating time reduced by 12 h, and ultra-high-pressure processing extending shelf life) and digital technologies (IoT real-time monitoring, blockchain-enhanced traceability transparency, and AI-optimized production decision-making). Additionally, it explores the potential of alternative meat production technologies (cell-cultured meat and 3D bioprinting) to disrupt traditional models. In application scenarios such as central kitchen efficiency improvements (e.g., food companies leveraging the “S2B2C” model to apply AI agents, supply chain management, and intelligent control systems, resulting in a 26.98% increase in overall profits), end-to-end temperature control in cold chain logistics (e.g., using multi-array sensors for real-time monitoring of meat spoilage), intelligent freshness recognition of products (based on deep learning or sensors), and personalized customization (e.g., 3D-printed customized nutritional meat products), these technologies have significantly improved production efficiency, product quality, and safety. However, large-scale application still faces key challenges, including high costs (such as the high investment in cell-cultured meat bioreactors), lack of standardization (such as the absence of unified standards for non-thermal technology parameters), and consumer acceptance (surveys indicate that approximately 41% of consumers are concerned about contracting illnesses from consuming cultured meat, and only 25% are willing to try it). These challenges constrain the economic viability and market promotion of the aforementioned technologies. Future efforts should focus on collaborative innovation to establish a truly intelligent and sustainable meat production system. Full article

Polyimine-based composites have emerged as a promising class of dynamic covalent thermosets, combining high mechanical strength, thermal stability, self-healing, recyclability, and reprocessability. This review systematically summarizes recent advances in polyimine synthesis, highlighting dynamic covalent chemistry (DCC) strategies such as imine exchange and reversible Schiff base reactions. Structural customization can be achieved by incorporating reinforcing phases such as carbon nanotubes, graphene, and bio-based fibers. Advanced fabrication methods—including solution casting, hot pressing, and interfacial polymerization—enable precise integration of these components while preserving structural integrity and adaptability. Mechanical performance analysis emphasizes the interplay between dynamic bonds, interfacial engineering, and multiscale design strategies. Polyimine composites exhibit outstanding performance characteristics, including a self-healing efficiency exceeding 90%, a tensile strength reaching 96.2 MPa, and remarkable chemical recyclability. Emerging engineering applications encompass sustainable green materials, flexible electronics, energy storage devices, and flame-retardant systems. Key challenges include balancing multifunctionality, enhancing large-scale processability, and developing low-energy recycling strategies. Future efforts should focus on interfacial optimization and network adaptivity to accelerate the industrial translation of polyimine composites, advancing next-generation sustainable materials. Full article

Background: Finger orthoses are essential for treating injuries, deformities, and disorders of the upper limbs by supporting, immobilizing, or correcting deformities. Recent advances in three-dimensional (3D) printing have significantly enhanced precision and customization compared to traditional fabrication methods such as thermoplastic molding, plaster or fiberglass casting, and the use of prefabricated splints. Methods: The present review was conducted using PubMed, Scopus, and other databases with keywords such as “hand therapy”, “additive manufacturing”, “finger and thumb”, and “orthosis”. Only English-language publications were considered, with a primary focus on articles published between 2010 and 2025. Key themes were identified and categorized into conditions necessitating finger orthoses, types and classifications, ergonomic design considerations, and advancements in additive manufacturing. Results: Finger orthoses address musculoskeletal injuries, inflammatory diseases, and neuromuscular disorders. Three-dimensional printing provides enhanced customization, reduced material waste, rapid prototyping, and the ability to create complex geometries, improving patient comfort and functionality. Conclusions: Finger orthoses effectively treat various conditions by supporting and stabilizing fingers. A thorough understanding of anatomy, biomechanics, and fabrication methods is crucial for achieving functional and comfortable designs. Three-dimensional printing offers a transformative approach to producing lightweight, customizable, and cost-effective orthoses, enabling innovative and personalized solutions. By bridging clinical needs and design strategies, this review may guide future innovations in patient-specific orthotic development. Full article

To address key challenges in clothing trademark quality inspection—namely, insufficient defect samples, unstable performance in complex industrial environments, and low detection efficiency—this paper proposes DLF-YOLO, an enhanced YOLOv11-based model optimized for industrial deployment. To mitigate the problem of limited annotated data, an unsupervised generative network, CycleGAN, is employed to synthesize rare defect patterns and simulate diverse environmental conditions (e.g., rotation, noise, and contrast variations), thereby improving data diversity and model generalization. To reduce the impact of industrial noise, a novel multi-scale dynamic synergy attention (MDSA) attention mechanism is introduced, which utilizes dual attention in both channel and spatial dimensions to focus more accurately on key regions of the trademark, effectively suppressing false detections caused by lighting variations and fabric textures. Furthermore, the high-level selective feature pyramid network (HS-FPN) module is adopted to make the neck structure more lightweight, where the feature selection sub-module enhances the perception of fine edge defects, while the feature fusion sub-module achieves a balance between model lightweighting and detection accuracy through the aggregation of hierarchical multi-scale context information. In the backbone, DWConv replaces standard convolutions before the C3k2 module to reduce computational complexity, and HetConv is integrated into the C3k2 module to simultaneously reduce computational cost and enhance feature extraction capabilities, achieving the goal of maintaining model accuracy. Experimental results on a custom-built dataset demonstrate that DLF-YOLO achieves an mAP@0.5:0.95 of 80.2%, with a 49.6% reduction in parameters and a 25.6% reduction in computational load compared to the original YOLOv11. These results highlight the potential of DLF-YOLO as a scalable and efficient solution for lightweight, industrial-grade defect detection in clothing trademarks. Full article

Ports play a crucial role in Indonesia’s economy, yet many, particularly smaller ports, lack standardized port performance indicators (PPIs) to assess and improve operational efficiency. Existing studies primarily focus on financial and operational performance, often employing either the balanced scorecard (BSC) or PESTLE analysis in isolation, with limited integration of sustainability concepts, such as smart port and green port frameworks. This study bridges this gap, aiming to develop and validate a comprehensive PPI framework that combines BSC, PESTLE, and circular economy smart and green port principles to create holistic performance assessment tools for ports. The research used a three-round Delphi method, incorporating expert evaluations and consensus from academics, consultants, port authorities, and customers to validate key performance indicators. A total of 127 PPIs were initially identified through a literature review and expert consultations, using strict selection criteria—standard deviation ≤ 1.5, interquartile range (Q3–Q1) ≤ 2.5, and ≥51% expert agreement (ratings 8–10). The final validated framework includes 114 indicators covering financial, operational, environmental, and strategic dimensions. This study provides valuable insights for port authorities to optimize performance and align with global best practices by integrating internal and external factors into a comprehensive model. Full article