Search Results (2,672)

The date palm (Phoenix dactylifera L.) is a significant perennial crop in arid and semi-arid regions. Understanding the evolution of research on this crop is vital for identifying major research trends, current challenges, and emerging areas for future agricultural innovation and sustainable crop management strategies. This study conducts a comprehensive scientometric analysis of 9062 scientific publications indexed in the Scopus database between 1837 and 2025, spanning nearly two centuries of research on date palm. Using bibliometric tools such as Bibliometrix and ScientoPy, the study examines patterns of scientific production, collaboration networks, institutional participation, thematic evolution, and emerging research trends. The results indicate a marked increase in scientific publications, especially after 2007, with Saudi Arabia, Egypt, and Iran among the most productive countries. The thematic structure of the literature shows a shift from early studies on diseases and oasis cultivation to recent research focusing on biomass valorization, activated carbon production, antioxidant properties, pest management with special emphasis on the red palm weevil (Rhynchophorus ferrugineus), mechanical properties of date palm fibers, and plant biotechnology on methods like micropropagation and somatic embryogenesis. Geographically, research activity is concentrated in the Middle East and North Africa, the primary palm-producing region, with Saudi Arabia leading in institutions, researchers, funding, and international collaborations in date palm research. Emerging trends indicate a rising interest in digital tools, particularly artificial intelligence and advanced analytical tools, which are increasingly being explored to improve crop management. Overall, these findings provide a structured overview of the historical development of date palm research and contribute to a deeper understanding of the evolution and organization of scientific knowledge in this field. Additionally, the identification of key research pathways and emerging trends offers valuable insights for guiding future agronomic innovation, supporting evidence-based crop management strategies, and promoting the sustainable development of date palm production systems. Full article

In highly digitalized and networked modern trading systems, large volumes of heterogeneous data are continuously generated from multiple sources during market operations. However, due to the complexity of data structures, significant differences in temporal scales, and constraints imposed by data privacy protection, traditional single-source modeling approaches are unable to fully exploit multisource information. To address this issue, a federated multimodal prediction framework for complex market systems, termed Federated Market-Sensor Transformer (FMST), is proposed. In this framework, data originating from different information sources are uniformly modeled as multimodal time series. A multimodal market-sensor representation module is constructed to perform unified feature encoding, and a cross-modal Transformer fusion architecture is employed to characterize dynamic interaction relationships among different information sources. Meanwhile, a federated collaborative learning mechanism is introduced during the training phase, enabling multiple data nodes to perform collaborative model optimization without sharing raw data. In this manner, data privacy can be preserved while improving the cross-region generalization capability of the model. Systematic experimental evaluation is conducted on the constructed multimodal market-sensor dataset. The experimental results demonstrate that the proposed method consistently outperforms traditional statistical models and deep learning approaches across multiple evaluation metrics. In the main prediction experiment, FMST achieves a root mean square error (RMSE) of $0.1136$, a mean absolute error (MAE) of $0.0832$, and a coefficient of determination $R^2$ of $0.8517$, while the direction prediction accuracy reaches $74.56\%$, clearly outperforming baseline models including ARIMA, LSTM, Temporal CNN, Transformer, and FedAvg-LSTM. In the cross-region generalization experiment, FMST maintains strong performance, achieving an RMSE of $0.1242$, an MAE of $0.0908$, an $R^2$ value of $0.8261$, and a direction prediction accuracy of $72.48\%$. The ablation study further indicates that the three core components—multimodal market-sensor representation, cross-modal Transformer fusion, and federated collaborative learning—each make important contributions to the overall model performance. These experimental findings demonstrate that the proposed method can effectively integrate multisource market information and significantly enhance the prediction capability for complex market dynamics, providing a new technical pathway for the application of artificial intelligence-driven multimodal sensing systems in economic data analysis. Full article

The growing reliance on virgin resources in construction, alongside accelerated urban development and the significant volumes of waste generated at the end-of-life phase of buildings, has intensified environmental impacts across the built environment. These challenges highlight the urgent need to transition towards a circular economy (CE) in the construction sector. At the same time, the sector’s ongoing digital transformation presents opportunities to enhance stakeholder collaboration and improve construction and demolition waste management (CDWM) practices. This paper aims to develop a conceptual framework for a Digital Information Management System (DIMS) to support CE implementation in construction through improved CDWM. Following the Design Science Research methodology, this paper addresses the first two stages: problem identification and solution proposition. A questionnaire survey with industry experts was conducted to validate the problem areas identified in the literature and assess the applicability of the proposed conceptual framework. The findings confirm critical gaps in CDWM, including limited stakeholder collaboration, fragmented processes, and the absence of lifecycle-spanning information systems, and validate the proposed conceptual framework solution, particularly the integration of BIM and IoT to support material and product flow tracking throughout the project lifecycle, supported by clearly defined stakeholder roles and engagements. However, respondents expressed reservations regarding Blockchain due to concerns about energy consumption and long-term data storage. Overall, the validated conceptual framework for DIMS provides a robust foundation for future studies, to focus on co-creating and developing a detailed conceptual model for DIMS for future real-world implementation. Full article

Autonomous supply chains (ASC) are the next generation of digitally empowered logistics and operations systems that can make adaptive, data-driven, and intelligent decisions. Innovations in artificial intelligence (AI), digital twins (DT), and predictive analytics (PA) are transforming traditional supply chains into integrated and interactive networks to detect disruptions, simulate the future, and automatically modify operational decisions. This paper reviews the ASC mechanism and summarizes the increasing literature on the technologies and analytical capabilities available to support intelligent supply chain decision systems. A structured literature review was conducted using Scopus, Web of Science, and Google Scholar, resulting in 52 relevant studies after screening and eligibility assessment. The paper discusses the recent advances in AI-based forecasting, simulation environments using digital twins, data integration using the Internet of Things (IoT), and predictive analytics. These technologies can help an organization gain real-time visibility of the supply chain networks. They improve the precision of demand forecasting, optimize inventory and production planning, and dynamically coordinate logistics operations. Digital twins allow the development of virtual models of supply chain ecosystems, which could be used to test scenarios, analyze risks, and plan strategies. These capabilities combined can be used to create predictive and self-adaptive supply networks capable of being responsive to uncertainty and market volatility. Besides examining the technological foundations, the paper also tracks key challenges related to the move towards autonomous supply chains, such as data governance, system interoperability, cybersecurity risks, algorithm transparency, and the necessity of successful human-AI collaboration in decision-making. The synthesis leads to a multi-layered framework that integrates data acquisition, analytics, simulation, and execution for autonomous decision-making in supply chains. Future research directions in relation to resilient supply networks, intelligent automation, and adaptive supply chain ecosystems are also provided in the study. Through integrating existing information on the new forms of intelligent technology and how it can be incorporated into the supply chain systems, this review contributes to the literature on next-generation supply chains. It will also offer information to both researchers and practitioners aiming at designing autonomous as well as data-driven supply networks. Full article

The adoption of disruptive technologies has become increasingly critical for organizations, particularly following the global shifts prompted by the COVID-19 pandemic. Despite the potential benefits, many organizations, including those in the Fast-Moving Consumer Goods (FMCG) industry, face significant hurdles in this transition. Consequently, this study aims to understand the primary challenges and enabling factors influencing the adoption of disruptive technologies for sustainable digital transformation within the South African FMCG sector. A quantitative methodology was employed, utilizing a questionnaire for data collection. Data from 102 respondents were analyzed using SPSS version 28, involving descriptive statistics (mean item score) to rank factors and exploratory factor analysis (EFA) to identify underlying constructs, and a reliability test was carried out with a score of 0.7. Key challenges identified include high initial costs and poor collaboration. Prominent enabling factors include top management commitment and operational cost reduction. The EFA revealed significant underlying challenge dimensions such as “Infrastructural and Resources Constraints” and “Human Factors Constraints,” and enabling dimensions including “Organizational Commitment and Strategy” and “Leadership.” The study concludes with key implications for promoting successful adoption. The adoption of disruptive technologies has become a strategic imperative for sustainable digital transformation (SDT), particularly in emerging markets such as South Africa’s FMCG sector. This study investigates the key challenges and enabling factors shaping technology adoption within this context. A quantitative methodology was employed, using a structured questionnaire distributed to 102 professionals across FMCG organizations in Gauteng. Exploratory factor analysis (EFA) revealed latent dimensions within both challenges and enablers, which were then interpreted through the lens of Rogers’ Diffusion of Innovation (DOI) theory. To enhance analytical clarity, a matrix model was developed linking factor dimensions to DOI attributes such as relative advantage, complexity, compatibility, trialability, and observability. The study found that high initial costs, poor collaboration, and human capability gaps significantly impede adoption, while strong leadership, strategic alignment, and operational cost savings facilitate it. The findings underscore the need for systemic interventions that address not only technical readiness but also leadership, organizational culture, and structural alignment. Practical implications are outlined for both policy and management, particularly in leveraging DOI attributes to accelerate digital transformation, as well optimize innovation diffusion within resource-constrained environments. For the future, the study proposed a hybrid methodology incorporating qualitative interviews to enhance depth and suggests longitudinal tracking to capture temporal shifts in transformation maturity. Full article

In the European Union, mandatory labeling of food and feed products is required when authorized genetically modified organisms (GMOs) exceed 0.9% per ingredient, necessitating reliable analytical methods for official control laboratories. Event-specific PCR assays validated according to ISO/IEC 17025 are the reference approach for GMO detection, identification, and quantification. The growing use of digital PCR (dPCR) has encouraged the adaptation of real-time PCR methods to dPCR-based strategies, as dPCR enables absolute quantification without calibration standards, shows reduced sensitivity to inhibitors, and allows for the design of a multiplex assay. In this study, an in-house validation of a duplex dPCR assay targeting the maize GM event NK603 and the HMG reference gene was performed on three platforms: Bio-Rad QX200™ (Pleasanton, CA, USA), Qiagen QIAcuity (Venlo, The Netherlands), and Thermo Fisher QuantStudio Absolute Q (Waltham, MA, USA). All validation parameters met the Joint Research Centre (JRC) acceptance criteria. In particular, this assay demonstrated high specificity, sensitivity (limit of quantification or LOQ < 35 copies per reaction), precision, and trueness (RSDr and bias <25%). The data indicate that the duplex dPCR assay can be used for routine GMO analysis and future collaborative validation studies. Full article

The rapid growth of digital communication platforms has generated vast volumes of user-generated textual data and digital footprints, creating growing demand for scalable artificial intelligence systems capable of supporting evidence-based decision-making. This study proposes and evaluates a human–AI collaborative analytical pipeline for multi-class sentiment and aggression analysis of large-scale social media data (N = 15,064 messages) related to an urban infrastructure project. The proposed framework integrates standard NLP preprocessing, machine learning-based classifiers, temporal aggregation, and controlled large language model (LLM)-assisted classification within a structured analytical workflow that incorporates expert validation and oversight. A stratified manual validation procedure (n = 301) demonstrated substantial inter-annotator agreement (κ = 0.70) and stable multi-class classification accuracy (80%). The results indicate that combining sentiment polarity and aggression detection as complementary linguistic indicators improves sensitivity to shifts in discourse dynamics and enables early identification of emerging social tension. The study demonstrates the potential of human–AI collaborative analytical frameworks for transparent, interpretable, and predictive large-scale social media analysis in decision-support contexts. Full article

Precast concrete (PC) component production scheduling is essential to the efficiency and reliability of industrialized construction. Although intelligent algorithms have been widely applied in this field, the relationships among research evolution, collaboration patterns, and industrial applicability remain insufficiently understood. To address this issue, this study presents a bibliometric review of 1272 publications indexed in the Web of Science Core Collection from 1990 to 2025. CiteSpace was employed to analyze publication trends, collaboration networks, co-citation structures, keyword co-occurrence, and burst terms. On this basis, a technology adaptability evaluation framework was developed to assess the alignment between algorithmic advances and industrial implementation in terms of dynamic adaptability, verification completeness, and technological generation gap. The results indicate that the field has evolved through four broad stages, from early static optimization to multi-objective coordination, digital twin-enabled dynamic scheduling, and emerging human-centric intelligent autonomous systems. The analysis also shows an increasing convergence of operations research, computer science, and civil engineering. However, a gap remains between academic output and industrial application. Specifically, 32% of the retrieved studies focused on genetic algorithms, whereas only 6% reported full-process industrial validation. In addition, Gen 4.0-related studies showed a technological generation gap of 82.5%, indicating that many frontier technologies have not yet reached broad industrial implementation. The collaboration network further reveals a “high-output, low-synergy” pattern, in which major publishing countries contribute substantially to the literature but exhibit limited cross-institutional integration. This study provides a structured overview of the development of PC component production scheduling research and highlights future directions for digital twin integration, human–robot collaboration, and cross-sector validation platforms. Full article

Background: In-person conferences (IPCs) in family medicine remain central for cross-border collaboration and early-career development. With the rise of digital formats, the motivations of young general practitioners (GPs) to attend or organise IPCs require closer investigation. Methods: Using a modified two-round Delphi design, we surveyed 107 participants and 23 organisers of the 2024 and 2025 EYFDM (European Young Family Doctors’ Movement) Forums. Round one included open and closed questions; round two involved prioritisation tasks. Quantitative data were analysed with non-parametric statistics; qualitative responses were thematically coded. Results: Participants primarily attended in-person conferences for networking (56.1%), workshops, and inspiration, while formal content played a secondary role. Organisers emphasised personal development, citing project management and teamwork as key benefits, though 34.8% reported workload and lack of recognition as major barriers. A strong preference for in-person formats (94.4%) reflected the perceived importance of interpersonal interaction, which online formats could not replicate. Conclusions: The findings highlight IPC as key environments for identity formation, motivation, and sustainable European collaboration in family medicine. Organising offers learning opportunities but demands better structural support. Future conference planning must prioritise in-person interaction, while using hybrid formats as complementary tools. IPCs remain essential for fostering authentic networks and collaboration among young GPs. Full article

During offshore oilfield development, traditional injection–production processes commonly suffer from delayed regulation, low operational efficiency, and heavy reliance on manual intervention. Achieving real-time diagnosis of injection–production anomalies and dynamic optimization under complex geological conditions and harsh marine environments represents a core scientific challenge. This study presents the development and field deployment of an intelligent cable-controlled injection–production integrated management system. The work is positioned as an application- and system-oriented study, focusing on addressing practical challenges in offshore oilfield operations through the integration of established machine learning techniques into a cohesive operational platform. The system employs a cloud-native microservice architecture and integrates nine functional modules, enabling closed-loop management from data acquisition to intelligent decision making. Key methodological contributions include: (1) a weighted ensemble model combining Random Forest and SVM for blockage diagnosis, balancing global feature learning with boundary sample discrimination to achieve 92% diagnostic accuracy; (2) a Bayesian fusion framework that integrates static geological priors with dynamic sensitivity analysis for probabilistic quantification of injector–producer connectivity, achieving 85% identification accuracy with rigorous uncertainty propagation; and (3) a three-stage human–machine collaborative mechanism that substantially reduces anomaly response latency while ensuring field safety. Field application in Bohai oilfields demonstrates that the system shortens the injection–production response cycle by approximately 42%, reduces anomaly response time from over 72 h to less than 2 h (a 97% reduction), decreases water consumption per ton of oil by 27.6%, and increases injection–production uptime by 11.3 percentage points. This study provides an interpretable, extensible, and closed-loop technical solution for intelligent offshore oilfield development, with future directions including digital twin predictive simulation and reinforcement learning for real-time optimization. Full article

With the advent of the Fourth Industrial Revolution and the rise of new forms of productive forces, the ways humans interact with space, objects, and information are being profoundly reshaped, bringing unprecedented possibilities for upgrading interactive spaces—human settlements that integrate physical and digital environments. Against this background, using the literature on interactive space research from the Web of Science (WoS) Core Collection between 1990 and 2025 as the data source, this study employs CiteSpace software to generate scientific knowledge maps, analyzing the historic development, hotspots, and trends in the research of interactive space, providing both theoretical and data support. In terms of results, a total of 458 papers were collected, demonstrating a consistent year-on-year increase. The research spans multiple fields, including computer science, architecture, ecology, physics, design, and behavioristics. Specifically, results indicate that research hotspots in interactive spaces include collaborative governance, social coexistence, and sustainable renewal, all of which are highly relevant to activating human settlements. The vitality of interactive spaces can be constructed across multiple dimensions, (for instance, enhancement based on ecology, environment, culture, and other factors of the space). However, research on interactive spaces still suffers from a lack of interdisciplinary collaboration and multi-domain integration; therefore, it is essential to strengthen cooperation among relevant fields. Current research lacks interdisciplinary integration and dynamic response mechanisms. Based on these findings, this study, through visual analysis, reveals the research hotspots and evolutionary trajectory of interactive spaces and proposes a “technology–humanism–governance” trinity framework. This system should be based on technology as the means, humanism as the guiding principle, and effective governance as the goal. It aims to explore how to leverage the service-oriented and convenient nature of technology in interactive spaces to deepen human-centric design and thereby drive the optimization of systems. Based on these findings, future research on interactive spaces should shift its design philosophy to be more human-centric, establish a multidisciplinary research system, utilize local empirical cases, and develop scalable, applicable theories to construct harmonious, open spaces, enhance human–environment relationships, and provide other countries undergoing urbanization with practical solutions. Full article

Based on the panel data of Chinese A-share listed companies from 2012 to 2023, this paper takes the pilot policy of Pilot Zones for Innovative Application of Artificial Intelligence as an exogenous shock, and adopts a multi-period difference-in-differences (DID) model to systematically examine the causal effect of this policy on the quality and efficiency of enterprise innovation and its mechanism of action. It is found that the Pilot Zones for Innovative Application of Artificial Intelligence significantly improve enterprises’ innovation quality and efficiency. Mechanism tests show that the pilot policy enhances enterprise innovation quality and efficiency by driving digital transformation, eliminating information barriers, and upgrading supply chain collaboration. Heterogeneity analysis confirms that the policy dividends are more fully released in non-state-owned enterprises, high-tech enterprises, labor-intensive and technology-intensive enterprises, as well as enterprises located in cities with a higher degree of marketization. In addition, the life-cycle heterogeneity analysis shows that the pilot policy exerts the strongest and most comprehensive innovation-promoting effect on maturity-stage firms, mainly improves innovation efficiency for decline-stage firms, and does not produce significant effects for growth-stage firms. The findings offer practical insights for policymakers and local governments in refining AI-related innovation policies and pilot-zone implementation, and for enterprise managers in strategically adopting AI to strengthen innovation capability and long-term sustainable development. Full article

Post-blast operations in tunnel construction represent a critical bottleneck due to mandatory downtime and hazardous environmental conditions. This study addresses these challenges by developing and validating an integrated cyber–physical architecture that coordinates an autonomous Unmanned Aerial Vehicle (UAV) and an Autonomous Wheel Loader (AWL) under the supervision of a Digital Twin acting as central operational digital interface. Specifically, this technology was designed to access the tunnel, evaluate post-blasting conditions, and initiate operations during mandatory exclusion periods for personnel. The system was validated in a realistic, Global Navigation Satellite System (GNSS)-denied tunnel environment emulating post-detonation visibility constraints. The results demonstrate that the aerial agent successfully navigated and mapped the excavation front in less than 8 min, establishing a shared coordinate system for the ground machinery. Through this collaborative workflow, the autonomous deployment enabled operations to commence 50% to 80% earlier than conventional manual procedures. Furthermore, the system reduced daily operational time by approximately 8%, with an estimated return on financial investment between one and seven months. Overall, the proposed framework eliminates human exposure during high-risk inspections and transforms the fragmented excavation cycle into a continuous, data-driven process. Full article

Background/Objectives: Chronic diseases pose a major challenge for healthcare systems, requiring integrated, patient-centered approaches that combine clinical management, prevention, and self-care. Lifestyle Medicine (LM) and lifestyle in general offers complementary frameworks to address these needs. However, the potential integration of LM within community nursing—particularly through the role of Family and Community Nurse (FCN)—has not been comprehensively synthesized. This narrative review aimed to synthesize international evidence on the role of community nursing—particularly FCN—in integrating chronic care management and LM view. Methods: For quality assessment, a narrative review was conducted in accordance with the SANRA criteria to enable the integration of heterogeneous evidence and a comprehensive synthesis of this complex topic. Literature searches were performed in the PubMed–Medline database, and the final screening of references from included studies was used to identify relevant manuscripts. Primary studies published in English over the past ten years were screened and analyzed using the PICOS framework. Sixteen eligible studies were included in the final synthesis. Results: The included studies indicated that nurse-led community interventions in LM view were associated with improvements in self-management, treatment adherence, and selected clinical outcomes, such as blood pressure, glycated hemoglobin, and physical activity levels. Empowerment-based approaches and the use of digital or telehealth tools supported patient engagement and health literacy. At the organizational level, multidisciplinary collaboration, shared protocols, and professional leadership emerged as key factors in sustaining continuity and quality of care, while organizational fragmentation and limited training in behavioral counseling were commonly reported barriers. Conclusions: Community nursing, particularly through FCNs, plays a relevant role in integrating chronic care management and LM approaches, contributing to improved self-management, treatment adherence, and selected clinical outcomes. The evidence highlights the importance of empowerment-based interventions, digital support tools, and multidisciplinary collaboration in enhancing care continuity and patient engagement. Addressing organizational barriers and strengthening behavioral counseling training remain essential to support effective implementation in community settings. Full article

Recent advancements in artificial intelligence (AI) have strengthened the interaction between sports and digital technologies. However, unlike widely studied sports such as football and basketball, handball has received limited attention from the scientific community, despite its fast-paced nature and strategic importance. This study focuses on object detection in handball and targets key entities, such as players, referees, goalkeepers, and the ball. A comprehensive dataset was created through a collaborative annotation process, consisting of annotated images extracted from real handball games. The YOLOv8 model was then trained and evaluated on this dataset to assess its effectiveness in entity recognition. The proposed approach achieved an object detection accuracy of 86.8% on a relatively small held-out test set, providing an indicative first benchmark for the application of state-of-the-art machine learning models to handball. To the best of our knowledge, the dataset generated in this study is the first comprehensive collection of annotated handball images, providing a valuable resource for further research. By bridging sports analytics and computer vision, this study contributes to the advancement of performance assessment in handball. These exploratory results suggest potential directions for future real-time systems and practical applications, such as improved understanding of player performance, team dynamics, and strategic decision-making. Full article