Search Results (27,964)

The intensification and continuous evolution of dairy sheep and goat farming have played an essential role in the development and implementation of milking equipment. The increasing demand for time-efficient milking procedures, reduced labour costs, sustained milk production, and optimal mammary health have driven the widespread adoption and optimisation of machine milking technologies. The objectives of this article are (i) the review of milking systems and relevant technological developments in milking equipment and (ii) the evaluation and description of their impact on udder health, as applied on dairy small ruminant farms. Milking systems used on farms depend on the available space and number of animals on the farms. Appropriate settings in milking systems are important for ensuring good milk quality; among them, vacuum level, pulsation rate and ratio are important characteristics that must be monitored regularly. Further, use of appropriate teatcups specific to the animal species to be milked is significant. An important aspect of proper maintenance of the milking system is the cleaning procedure after completion of milking. Points for consideration are quality and temperature of the water used for cleaning, use of detergents and disinfectants, and maintenance schedule and teatcup replacement. Some technological features that are part of milking systems include automatic vacuum shut off, electronic milk recording, electronic identification of animals, automatic flushing of milking clusters and automatic pre-stimulators. Farms will benefit from applying precision technologies, which will use data from tools related to animal genetic background, animal behavioural indicators, environmental conditions and disease-related functions for more holistic and cost-effective farm management. In this context, integration of sensor-based technologies in milking systems will be able to provide real-time information regarding quality of milk produced at individual and farm levels. Moreover, the introduction of automatic system flushing in-between animals during the milking procedure can contribute to breaking chains of potential bacterial transfer and reducing animal infections during milking. Overall, although machine milking has certainly contributed to improved efficiency, milk quality and labour conditions, flaws in system function may adversely affect mammary health. Full article

Optoelectronic devices are emerging as a cornerstone of advanced renewable energy technologies, offering innovative routes for energy harvesting, conversion, and management with high efficiency and versatility. This review summarizes recent advances in the semiconductor materials engineering field, device configurations, and light–matter interaction mechanisms that underpin advanced optoelectronic systems for solar energy harvesting, solar-driven chemical conversion, and smart grid integration, among others. Emphasis is placed on the breakthroughs achieved in the perovskite and hybrid photovoltaics, photoelectrochemical energy conversion, and nanostructured optoelectronic platforms that enable much-increased light absorption, reduced recombination losses, and scalable large-scale fabrications. Moreover, the challenges closely linked with long-term stability, environmental durability and benevolence, and worldwide deployment are critically addressed, together with the emerging opportunities in AI design, tandem device technological solutions, integrated energy systems, and machine learning approaches for optimizing device performance, thermal management, and energy storage capabilities. Finally, the present review concludes by outlining the future research directions that could accelerate the transition toward high-performance, cost-effective, and sustainable optoelectronic solutions responsive to global renewable energy requirements. Full article

The relationship between corporate digitalization and green management practices has received increasing scholarly attention, but existing empirical findings remain inconsistent. To clarify this relationship, this research conducts a meta-analysis based on 94 effect sizes from 82 empirical studies, adopting a multivariable research framework to integrate existing findings and explore the factors that contribute to the generation of heterogeneity. The findings indicate that corporate digitalization facilitates green management practices, a conclusion robust across three key dimensions: environmental performance, green innovation, and green supply chain management. Furthermore, the findings show that digitalization exerts a stronger positive effect in non-manufacturing firms, non-heavy-polluting firms, and high-tech firms, while measurement approaches emerge as a critical factor influencing empirical outcomes. These findings provide integrated evidence on the digitalization–green management relationship, clarify its key boundary conditions, and offer practical implications for firms seeking to advance low-carbon transformation through digital technologies. Full article

Municipal solid waste (MSW) management is a critical challenge to advancing recycling and circular economy approaches. This review provides a comprehensive overview of MSW management, encompassing sourcing, policy frameworks, characterization techniques, separation technologies, preprocessing strategies, and utilization pathways. First, generation patterns and sourcing mechanisms are discussed in both U.S. and global contexts, with emphasis on the influence of policy frameworks on waste reduction and diversion. Second, characterization techniques are evaluated, focusing on physical and chemical analysis for material recyclability. Third, sorting technologies are critically reviewed, covering conventional methods and emerging sensor-based approaches. Preprocessing techniques are then evaluated for their role in improving downstream conversion efficiency. Finally, valorization pathways such as waste-to-syngas, waste-to-biochar, and waste-to-sustainable aviation fuel (SAF) are assessed in terms of their role in climate mitigation and the circular economy. It is anticipated that this review will provide a foundational reference for researchers, policymakers, and industry stakeholders aiming to strengthen the recyclability infrastructure and maximize the efficiency of MSW management systems in the framework of the circular economy. Full article

The digital transformation of agrifood systems demands an integrated infrastructure to ensure traceability, trust, and intelligent decision-making across complex and heterogeneous value chains. METROFOOD-IT, a large-scale national research infrastructure in food metrology aligned with the ESFRI METROFOOD-RI, addresses these challenges by combining advanced experimental facilities with a comprehensive digital ecosystem. This paper focuses on the IT kernel of METROFOOD-IT and presents an integrated architectural model that brings together four key technological paradigms: data acquisition through Internet of Things (IoT) and laboratory infrastructures, an Open Data Platform for interoperability and sharing, blockchain-based notarization for integrity and provenance, and Artificial Intelligence (AI) for knowledge extraction and decision support. Rather than describing these components in isolation, the paper abstracts from their implementation within the Italian National Recovery and Resilience Plan (NRRP) project METROFOOD-IT to distill a coherent and reusable architectural pattern in which data management, trust enforcement, and intelligent analytics are tightly coupled. Five explicit design principles are identified and articulated: federated data with centralized metadata, selective on-chain anchoring, user-unobtrusive trust infrastructure, explainability as a first-class architectural concern, and machine learning as the backbone of decision-making. Two empirical case studies—one centered on explainable AI for hyperspectral crop nitrogen assessment and the other on IoT-driven sustainable agriculture monitoring secured by distributed ledger technology—serve a dual role: they motivate and shape the architectural pattern, and they exemplify the operational regimes the resulting design supports. A reference deployment on the Ethereum Sepolia public test network, grounded on an IBM Power E1050 and IBM Storage Scale enterprise substrate, provides quantitative evidence for the proposed hybrid on-chain/off-chain pattern with streaming hash-only notarization. The architecture illustrates how research infrastructures can evolve into integrated digital platforms that enable transparent, verifiable, and scalable agrifood systems, and offers a foundation for generalizable design principles in data-intensive and trust-sensitive settings. Full article

Nowadays, the literature on Generative AI (GenAI) in Information Technology (IT) project management is fragmented, focusing mainly on isolated tools, specific process groups, or practitioners’ perspectives, without offering a comprehensive synthesis. Therefore, there is a lack of systematic reviews to guide researchers in effectively and responsibly leveraging GenAI, including emerging innovations such as AI agents. This paper aims to synthesize current knowledge on GenAI in IT project management, combining a PRISMA-compliant systematic review of the peer-reviewed literature, a complementary analysis of commercial and open-source platforms, and a forward-looking research agenda featuring our vision on agentic AI architectures for IT project management. For the systematic review based on academic sources we have used the Web of Science (WoS) database in our study. Studies were eligible if published between 2021 and 2026 in English, as journal articles or conference proceedings, across major publishers (IEEE, Springer, Elsevier, MDPI, ACM, and others), and indexed under computer science, engineering, or AI categories in WoS. For industry-driven analysis, sources included vendor documentation, official product pages, and publicly accessible repository specifications, selected for relevance through manual search. The review reveals that while academic research remains largely focused on prompt-based applications of foundation models such as GPT, commercial and open-source platforms have progressed toward embedding GenAI as an operational capability within project workflows. Therefore, we consider that agentic architecture represents a promising future direction for enabling autonomous task execution, collaborative decision-making, and human–AI orchestration and integration across the project lifecycle. Full article

While the operational benefits of Artificial Intelligence (AI) are well-documented, the mechanisms through which firms leverage AI for strategic exploration and radical innovation remain under-theorized. This study addresses the “black box” of AI value creation by integrating the Technology–Organization–Environment (TOE) framework with the Dynamic Capabilities View (DCV). We propose that AI adoption is not a direct antecedent to performance but a multi-stage process wherein technological, organizational, and environmental factors enable the development of sensing capability, which in turn fosters a novel capability we term “AI-Enabled Exploration.” Analyzing survey data from 245 senior executives in Saudi Arabia, a high-growth economy undergoing state-led digital transformation, we employed Partial Least Squares Structural Equation Modeling (PLS-SEM) to test the model. The results confirm a serial mediation chain: organizational readiness and technology compatibility drive sensing capability, which subsequently powers AI-enabled exploration to enhance innovation performance. Contrary to expectations, government support was not a significant predictor of sensing capability, suggesting that in resource-rich environments, external incentives are necessary but insufficient for capability building. Furthermore, competitive pressure was found to positively moderate the relationship between organizational readiness and exploration, acting as a critical catalyst that converts latent resources into active experimentation. These findings offer a theoretical roadmap for firms attempting to transition from AI-driven efficiency to AI-driven ambidexterity. Full article

This study investigates the determinants of driver acceptance of “M-Flow”, Thailand’s first Advanced Traffic Management solution utilizing Multi-Lane Free Flow (MLFF) technology. While designed to eliminate toll plaza bottlenecks through AI-driven automated billing, the system’s operational efficiency is hindered by a “trust gap” caused by a stringent ten-fold penalty for late payment compliance. By integrating the Technology Readiness Index (TRI 2.0) with the Technology Acceptance Model (TAM), this research explores how psychological readiness dictates the success of smart traffic infrastructures. Data from 485 drivers were analyzed using Structural Equation Modeling (SEM). The results reveal that while technological optimism and innovativeness act as motivators, Insecurity (β = −0.723) emerges as the dominant psychological barrier, directly suppressing the perceived ease of use and triggering behavioral resistance. The findings demonstrate that technical efficiency and diverse payment options alone are insufficient to ensure mass adoption if the regulatory climate fosters financial anxiety. To maximize system throughput, this study recommends that policymakers shift from punitive enforcement to “trust engineering.” By enhancing financial transparency, simplifying the registration-to-payment workflow, and mitigating the “penalty trap” perception, authorities can achieve the psychological seamlessness that is a strict prerequisite for a fully trusted smart transportation infrastructure in Thailand. Full article

Introduction: The discharge of treated wastewater into coastal and marine environments represents a continuous source of pollutants, including pharmaceuticals and plastic additives with endocrine-disrupting (ED) potential. These compounds are of increasing concern for the European Union due to their capacity to interfere with hormonal systems and their inclusion in current environmental monitoring priorities. ED compounds may induce sublethal effects in aquatic organisms, particularly in vertebrates, where endocrine pathways are highly conserved. In this context, the use of Cyprinodon variegatus, a euryhaline fish species, provides a suitable model to assess potential risks in marine ecosystems. Despite advances in wastewater treatment technologies, the persistence of biologically active substances in treated effluents remains a concern. Objective: This study aims to evaluate whether treated effluent water still contains compounds with endocrine-disrupting activity and to assess their potential effects on marine organisms. Methodology: Larvae of C. variegatus from a laboratory stock maintained at ECIMAT (University of Vigo), one of the few available stocks of this species in Europe, were exposed for 48 h to environmentally relevant dilutions (1:10, 1:30, and 1:100) of wastewater treatment plant effluent collected after UV disinfection as the final treatment step. Pools of 10 larvae were used for each condition. Sublethal effects were assessed through gene expression analysis using quantitative PCR (qPCR), targeting biomarkers involved in endocrine regulation. Two housekeeping genes (tbp and hprt) were used for normalization. Estrogenic responses were evaluated through vtgab and zp2, while androgenic responses were assessed using 17hsd and 11hsd. Results: Preliminary results indicate significant alterations in estrogen-related gene expression, particularly in vitellogenin (vtgab) and zona pellucida (zp2), highlighting the activation of estrogenic pathways and supporting the presence of endocrine-disrupting activity in treated effluent water. Conclusions: This study highlights the relevance of assessing endocrine disrupting activity in treated effluents and supports the use of molecular biomarkers as sensitive tools for evaluating their potential impact on marine ecosystems, contributing to the improvement of wastewater monitoring and management strategies. Full article

Developing-country construction sectors continue to record disproportionately high occupational accident rates, partly attributable to the slow adoption of digital safety technologies, including Building Information Modeling (BIM) and Internet of Things (IoT) systems. While prior empirical research has established the population-level factors that explain stakeholder adoption intention through survey-based frameworks, the ability to classify individual stakeholder readiness for targeted, pre-deployment intervention remains methodologically unaddressed. This study fills that gap by applying three supervised machine learning classifiers (Random Forest [RF], XGBoost (XGB), and Support Vector Machine (SVM)) to a dataset of 107 construction professionals purposively sampled from large-scale infrastructure projects in Pakistan, including China−Pakistan Economic Corridor (CPEC) packages and the Barakahu Bypass project. Five construct-level features derived from an integrated Technology Acceptance Model and Technology−Organization−Environment (TAM-TOE) survey instrument were used to classify stakeholders into High, Moderate, and Low readiness tiers. XGBoost achieved the best classification performance (accuracy = 93%, macro F1 = 0.93), followed by RF (91%, F1 = 0.91) and SVM (87%, F1 = 0.87). The convergent performance across three structurally different algorithm families indicates that the readiness signal reflects a consistent attitudinal pattern rather than an artifact of any single modeling assumption. Feature importance analysis consistently identified Perceived Benefits (32%) and Technology Awareness (25%) as the dominant predictive features, followed by Organizational Readiness (20%), Perceived Barriers (15%), and Respondent Profile (8%). Attitudinal readiness mapping classified 62% of stakeholders as High readiness, 28% as Moderate, and 10% as Low, providing an exploratory attitudinal segmentation framework to assist construction managers in prioritizing capacity-building investments, subject to longitudinal behavioral validation. The study also finds that awareness of digital technology consistently outpaces Organizational Readiness for implementation, a pattern consistent with findings from analogous developing-country construction contexts. Full article

Educational information systems have evolved into highly interconnected digital landscapes that support learning management platforms, student information systems, institutional repositories, and online assessment environments. As these systems increasingly operate across cloud infrastructures and mobile devices, ensuring the confidentiality, integrity, and availability (CIA Triad) of educational data is critical for safeguarding institutional operations and maintaining trust in digital education services. This paper investigates how next-generation security protocols, such as adaptive multi-factor authentication and advanced access control and data protection mechanisms, can reinforce ISO/IEC 27001:2022 requirements within contemporary educational information systems. The analysis maps emerging protocol capabilities to relevant new ISO/IEC 27001:2022 control domains, illustrating how they mitigate threats associated with unauthorized access, data manipulation, and service disruption. The proposed framework is further supported by an implementation-oriented mapping and an illustrative operational architecture that demonstrates the feasibility of translating prioritized security determinants into practical mechanisms. The FAHP analysis identifies access control mechanisms, backup and recovery, and data validation as the three highest-weighted determinants, with aggregate weights of 0.061, 0.059, and 0.057, respectively. These determinants are translated into a determinant-driven Security Operationalization Matrix that connects ISO/IEC 27001:2022 control domains, CIA dimensions, and technology recommendations, and is complemented by implementation feasibility considerations tailored to the budgetary, infrastructural, and resource constraints characteristic of educational institutions. Based on the prioritization results and conceptual operationalization, the proposed integrative approach provides a structured and progressively adoptable foundation for CIA-oriented security governance in digital educational environments. Full article

Smart labels are emerging as a key enabling technology for product traceability, environmental monitoring, and user interaction within Internet of Things (IoT) ecosystems. This work presents the design and experimental validation of a low-power smart label platform integrating Bluetooth Low Energy (BLE) communication, temperature sensing, and dynamic e-paper visualization based on the HY0020 System-on-Chip (SoC). This platform was implemented on a custom Printed Circuit Board (PCB) designed around a 1.02-inch monochrome e-paper display and incorporates a TXS0108E interface to support reliable display communication. The developed prototype enables wireless user interaction, dynamic QR code rendering, and ambient temperature monitoring while maintaining low average power consumption. Experimental evaluation included BLE communication testing, display operation validation, temperature monitoring assessment using the integrated HY0020 sensor, and energy consumption characterization. Experimental results confirmed reliable BLE connectivity, stable temperature monitoring performance under normal environmental conditions, and an estimated battery lifetime of approximately 54 days under the evaluated operating profile. The presented platform demonstrates the feasibility of integrating sensing, wireless communication, and electrophoretic display technology within a compact battery-powered smart label device. The proposed architecture provides a practical proof-of-concept foundation for future applications involving product traceability, digital information management, and Digital Product Passport (DPP)-oriented services. Full article

The worldwide shift to electric mobility has intensified in recent years owing to heightened apprehensions over greenhouse gas emissions, energy security, and the necessity for sustainable transportation systems. Electric vehicles (EVs) are acknowledged as a viable alternative for diminishing reliance on fossil fuels and enhancing energy efficiency in the transportation sector. While affluent nations have achieved considerable advancements in electric vehicle adoption and charging infrastructure, numerous developing countries still encounter significant technical and infrastructural obstacles that hinder extensive EV integration. In South Africa, these difficulties are exacerbated by ongoing electrical supply limitations, deteriorating transmission and distribution facilities, and recurrent load shedding, which heighten worries about the dependability and stability of the national power grid. The rising adoption of electric vehicles adds extra electrical demands to power systems, especially at the distribution network level, where most of the charging takes place. Disorganized EV charging can substantially modify current load patterns, leading to heightened peak demand, voltage variations, transformer overload, and network congestion. The technical consequences are especially significant in South Africa, where the power grid functions with constricted generation capacity and minimal reserve margins. Various mitigating measures have been suggested to tackle these difficulties, including intelligent charging, demand-side management, time-of-use pricing, and vehicle-to-grid technologies. This paper establishes a basic theoretical framework through an extensive literature review to investigate the technological problems related to electric vehicle adoption in South Africa, while assessing the environmental and economic ramifications for sustainable urban transportation systems. Full article

Smart Mobility plays a key role in Smart Cities, given its ability to support the rollout of intelligent transport systems, allowing for more sustainable urban transportation and greater interoperability across diverse mobility modes. Furthermore, Smart Mobility is essential to maximize the quality of life for the community while advancing principles of sustainability, economic development, technological innovation, and collaborative governance. Real-Time Traffic Management (RTTM) emerges as a vital technology for optimizing traffic management in Smart Mobility. Using the PRISMA framework, the proposed systematic literature review examines 165 peer-reviewed publications related to RTTM research work published between 2019 and 2025. This review identified eleven application domains, with Urban Traffic Management Systems (36.97%) and Artificial Intelligence (AI) and Predictive Analytics (12.73%) representing the most prominent areas. A retrospective analysis of the literature on control architecture used in closed-loop feedback systems indicates that most studies (89%) have adopted a more dynamic control model, while 7.8% adopted a Digital Twin (DT)-based approach. However, several implementation barriers persist, including limited integration of online optimization and learning loops into RTTM systems, gaps in performance comparisons between simulation and reality, scalability issues due to heterogeneous environments, inconsistent data quality caused by various sensor types, and difficulties integrating sensors into a control system. In addition, this paper proposes a taxonomy of RTTM applications and control architectures, while outlining key practical barriers to implementation and charting future research directions for advancing Smart Mobility through robust RTTM. Full article

Porous ceramics have shown great application potential in aerospace, electronics, and lithium-ion battery thermal management due to their low density, high specific strength, and excellent thermal insulation. Material Jetting (MJ), a high-precision 3D printing technology, enables the fabrication of porous ceramics with tailored pore structures, but the synergistic effects of pore structure parameters (configuration, porosity, and number of periods) on their thermal insulation performance remain insufficiently explored. This study systematically investigates the thermal insulation behavior of zirconia porous ceramics fabricated by MJ through experimental tests and numerical simulations. Three typical lattice configurations (Octet, Schwarz, and Gyroid) were selected, and samples with varying porosities (40%, 50%, 60%) and numbers of periods (1, 2, 3) were prepared. The results indicate that the Octet configuration (60% porosity, 3 periods) exhibits the optimal thermal insulation performance, with a minimum cold-end temperature of 58.5 °C (experiment) and 59.21 °C (simulation), attributed to its strut-based structure that forms a more tortuous heat conduction path. For the Gyroid configuration, thermal insulation performance improves with increasing porosity (reducing solid conduction dominance under non-forced convection) and decreases with decreasing number of periods (due to inhomogeneous pore distribution extending heat transfer paths). Notably, the trend of porosity affecting thermal insulation is opposite to that of compressive performance. Numerical simulation results are consistent with experimental data in both values and trends, verifying the reliability of the model. This work clarifies the key factors regulating the thermal insulation of MJ-fabricated porous ceramics and provides practical structural design guidelines for applications such as lithium-ion battery thermal runaway management. Full article