Search Results (270)

This review examines how the integration of circular bioeconomy principles with digital technologies can drive climate change mitigation, improve resource efficiency, and facilitate sustainable biorefinery development. This highlights the urgent need to transition away from fossil fuels and introduces the bio-circular economy as a regenerative model focused on biomass valorization, reuse, recycling, and biodegradability. This study compares linear, circular, and bio-circular approaches and analyzes key policy frameworks in Europe, Latin America, and Asia linked to several UN Sustainable Development Goals. A central focus is the role of digitalization, particularly artificial intelligence (AI), the Internet of Things (IoT), and blockchain. Examples include AI-based biomass yield prediction and biorefinery optimization, IoT-enabled real-time monitoring of material and energy flows, and blockchain technology for supply chain traceability and transparency. Applications in agricultural waste valorization, bioplastics, bioenergy, and nutraceutical extraction are also discussed in this review. Sustainability tools, such as automated life-cycle assessment (LCA) and Industry 4.0 integration, are outlined. Finally, future perspectives emphasize autonomous smart biorefineries, biotechnology–nanotechnology convergence, and international collaboration supported by open data platforms. Full article

Accurate and efficient tomato ripeness estimation is crucial for robotic harvesting and supply chain grading in smart agriculture. However, manual visual inspection is subjective, slow and difficult to scale, while existing vision models often struggle with cluttered field backgrounds, small targets and limited throughput. To overcome these limitations, we introduce TSE-YOLO, an improved real-time detector tailored for tomato ripeness estimation with joint detection and segmentation. In the TSE-YOLO model, three key enhancements are introduced. The C2PSA module is improved with ConvGLU, adapted from TransNeXt, to strengthen feature extraction within tomato regions. A novel segmentation head is designed to accelerate ripeness-aware segmentation and improve recall. Additionally, the C3k2 module is augmented with partial and frequency-dynamic convolutions, enhancing feature representation under complex planting conditions. These components enable precise instance-level localization and pixel-wise segmentation of tomatoes at three ripeness stages: verde, semi-ripe (semi-maduro), and ripe. Experiments on a self-constructed tomato ripeness dataset demonstrate that TSE-YOLO achieves 92.5% mAP@0.5 for detection and 92.2% mAP@0.5 for segmentation with only 9.8 GFLOPs. Deployed on Android via Ncnn Convolutional Neural Network (NCNN), the model runs at 30 fps on Dimensity 9300, offering a practical solution for automated tomato harvesting and grading that accelerates smart agriculture’s industrial adoption. Full article

Lithium has emerged as a pivotal material for the global energy transition, yet its supply security is challenged by limited geographical availability and growing demand. These constraints highlight the need for analytical methods that are not only accurate but also sustainable and deployable across the entire lithium value chain. In this context, Laser-Induced Breakdown Spectroscopy (LIBS) offers distinctive advantages, including minimal sample preparation, real-time and in situ analysis and the potential for portable and automated implementation. Such features make LIBS a valuable tool for monitoring and optimizing lithium extraction, refining and recycling processes. This review critically examines the recent progress in the use of LIBS for lithium detection and quantification in geological, industrial, biological and extraterrestrial matrices. It also discusses emerging applications in closed-loop recycling and highlights future prospects related to the integration of LIBS with artificial intelligence and machine learning to enhance analytical accuracy and material classification. Full article

Building a resilient and efficient supply chain system is critical for sustaining firm operations in an increasingly uncertain global environment. This study examines whether the firm-level exposure to industry-wide robot penetration mitigates firm-level supply chain risks. By adopting Bartik’s instrumental variable approach to decompose industry-level robot data to the firm level (from the International Federation of Robotics, IFR), and using a novel text-mining-based supply chain risk index, constructed via a tailored “supply chain risk” dictionary, to quantify sentences containing both keywords from firms’ annual report MD&A sections, we apply a fixed effects model, and find that robot adoption significantly reduces supply chain risk by enhancing firms’ discourse power and improving supply chain coordination. The effect is more pronounced in firms with higher capital intensity, greater international exposure, stronger regulatory oversight, and better ESG (Environmental, Social, and Governance) performance. By integrating automation adoption with supply chain risk management, this study extends the literature on production economics and supply chain resilience. Our findings reveal that industrial robots, beyond enhancing productivity, function as a risk-mitigating technology that strengthens supply chain stability and operational continuity in volatile global production networks. Full article

Small- and Medium-sized Enterprises (SMEs) play a crucial role in the global economy, accounting for approximately two-thirds of global employment and contributing significantly to the GDP of developed countries. Despite the availability of various cybersecurity standards and frameworks, SMEs remain highly vulnerable to cyber threats. Limited resources and a lack of expertise in cybersecurity make them frequent targets for cyberattacks. It is essential to identify the challenges faced by SMEs and explore effective defensive strategies to enhance the implementation of cybersecurity measures. The study aims to bridge the gap and help these organizations in implementing cost-effective and practical cybersecurity approaches through a systematic mapping study (SMS) conducted, where 73 articles were thoroughly reviewed. This research will shed light on the current cybersecurity approaches (practices) posture for different SMEs, along with the threats they are facing, which have stopped them from deciding, planning, and implementing cybersecurity measures. The study identified a wide range of cybersecurity threats, including phishing, social engineering, insider threats, ransomware, malware, denial of services attacks, and weak password practices, which are the most prevalent for SMEs. This study identified defensive practices, such as cybersecurity awareness and training, endpoint protection tools, incident response planning, network segmentation, access control, multi-factor authentication (MFA), access controls, privilege management, email authentication and encryption, enforcing strong password policies, cloud security, secure backup solutions, supply chain visibility, and automated patch management tools, as key measures. The study provides valuable insights into the specific gaps and challenges faced by SMEs, as well as their preferred methods of seeking and consuming cybersecurity assistance. The findings can guide the development of targeted defensive practices and policies to enhance the cybersecurity posture of SMEs for successful software development. This SMS will also provide a foundation for future research and practical guidelines for SMEs to improve the process of secure software development. Full article

Embodied carbon (EC) of buildings has been gaining attention among researchers and the industry to achieve the carbon targets by 2050. With this interest, the development of technology-based EC tracking and monitoring systems for buildings has increased. The existing literature lacks a comprehensive review of technology-based EC tracking and monitoring systems, their benefits, limitations, and adoption challenges related to buildings. Thus, this study conducted a systematic literature review, with studies published between 1996 and 2025. The results revealed 16 systems, most of which are integrated with the Internet of Things (IoT) and Building Information Modelling (BIM). The results identified 6 benefits, 7 key limitations, 17 adoption challenges, and future research directions. By integrating these findings, a conceptual framework was developed that highlights the strategic roles of key stakeholders in the effective implementation of these systems. Findings revealed that the key limitations are included in lack of a feasible EC emission reduction target, lack of an early-stage EC emissions reduction decision-making process, difficulty in tracing the responsible stakeholders to reduce the EC throughout the whole supply chain of buildings, limited automated third-party verification process and transparency issues, uncertainty of the use data, limited system boundary and the scope of works and lack of industry-level applications to test the developed systems. The challenges include data quality, scalability and cost, technology, organisational, and external challenges. The findings can serve as a benchmark for academics, researchers and practitioners to guide future developments in effectively tracking and monitoring the EC in buildings. Full article

Accurate vibration measurement is crucial for maintaining the performance, reliability, and safety of automated manufacturing environments. Abnormal vibrations caused by faults in gears or bearings can degrade positional accuracy, reduce productivity, and, over time, significantly impair production efficiency and product quality. Such vibrations may also disrupt supply chains, cause financial losses, and pose safety risks to workers through collisions, falling objects, or other operational hazards. Conventional vibration measurement techniques, such as wired accelerometers and strain gauges, are typically limited to a few discrete measurement points. Achieving multi-point measurements requires numerous sensors, which increases installation complexity, wiring constraints, and setup time, making the process both time-consuming and costly. The integration of high-frame-rate (HFR) cameras with Digital Image Correlation (DIC) enables non-contact, multi-point, full-field vibration measurement of robot manipulators, effectively addressing these limitations. In this study, HFR cameras were employed to perform non-contact, full-field vibration measurements of industrial robots. The HFR camera recorded the robot’s vibrations at 1000 frames per second (fps), and the resulting video was decomposed into individual frames according to the frame rate. Each frame, with a resolution of 1920 × 1080 pixels, was divided into 128 × 128 pixel blocks with a 64-pixel stride, yielding 435 sub-images. This setup effectively simulates the operation of 435 virtual vibration sensors. By applying mask processing to these sub-images, eight key points representing critical robot components were selected for multi-point DIC displacement measurements, enabling effective assessment of vibration distribution and real-time vibration visualization across the entire manipulator. This approach allows simultaneous capture of displacements across all robot components without the need for physical sensors. The transfer function is defined in the frequency domain as the ratio between the output displacement of each robot component and the input excitation applied by the shaker mounted on the end-effector. The frequency–domain transfer functions were computed for multiple robot components, enabling accurate and full-field vibration analysis during operation. Full article

This paper presents a ubiquitous, blockchain-based system designed to improve transparency, traceability and trust in supply chains involving autonomous vehicles (AVs). The framework integrates Internet of Things (IoT) sensors, radio-frequency identification (RFID) and QR identifiers, global positioning system (GPS) tracking, and mobile communications with smart contracts implemented on the Ethereum 2.0 blockchain. The main contributions are as follows: (1) an architecture enabling real-time monitoring and automated verification of logistics transactions; (2) a proof of concept integrating blockchain, the IoT and Android-based OBUs; and (3) a quantitative analysis of gas and smart contract execution costs. Experimental tests show gas consumption ranging from 21,000 to 5,000,000 units and transaction costs ranging from 0.0001 to 0.0033 ETH, confirming the system’s technical feasibility and cost-efficiency. As well as cost and efficiency, the process improved transparency, real-time traceability and decentralized verification, confirming the system’s efficacy for supply chains involving autonomous vehicles. Full article

A Virtual Organization (VO) unites companies or independent individuals to achieve a shared, short-term objective by leveraging information technologies for communication and coordination in personalized product creation. Despite extensive research, existing VO management architectures lack alignment with Industry 4.0 standards, do not incorporate intelligent requirement-gathering mechanisms, and are not based on the RAMI 4.0 framework. These limitations hinder support for Autonomous Virtual Organizations (AVOs) in evaluation, risk management, and continuity, often excluding small and medium-sized enterprises (SMEs) during the partner selection process. This study proposes a comprehensive architecture for AVO management, grounded in ACODAT (Autonomous Cycle of Data Analysis Tasks) and RAMI 4.0 principles. The methodology includes a literature review, an architectural design, and a detailed specification of the ACODAT for the digital supply chain design. A prototype was developed and applied in a case study involving a virtual organization within an editorial consortium. Evaluation addressed core service performance, scalability of the batch selection algorithm, resource-use efficiency, and accessibility/SEO compliance. Benchmarking demonstrated that the prototype met or exceeded thresholds for scalability, efficiency, and accessibility, with minor performance deviations attributed to the testing environment. The results highlight significant time savings and improved automation in requirement identification, partner selection, and supply chain configuration, underscoring the architecture’s effectiveness and inclusivity. Full article

With the increasing globalization of supply chains, ensuring food safety has become more complex, necessitating advanced approaches for risk assessment. This study aims to review the transformative role of machine learning (ML) and deep learning (DL) in enabling intelligent food safety management by efficiently analyzing high-quality and nonlinear data. We systematically summarize recent advances in the application of ML and DL, focusing on key areas such as biotoxin detection, heavy metal contamination, analysis of pesticide and veterinary drug residues, and microbial risk prediction. While traditional algorithms including support vector machines and random forests demonstrate strong performance in classification and risk evaluation, unsupervised methods such as K-means and hierarchical cluster analysis facilitate pattern recognition in unlabeled datasets. Furthermore, novel DL architectures, such as convolutional neural networks, recurrent neural networks, and transformers, enable automated feature extraction and multimodal data integration, substantially improving detection accuracy and efficiency. In conclusion, we recommend future work to emphasize model interpretability, multi-modal data fusion, and integration into HACCP systems, thereby supporting intelligent, interpretable, and real-time food safety management. Full article

In the context of growing data availability and increasing complexity of demand patterns in retail fuel distribution, selecting effective forecasting models for large collections of time series is becoming a key operational challenge. This study investigates the effectiveness of a hybrid forecasting approach combining ARIMA models with dynamically updated Markov Chains. Unlike many existing studies that focus on isolated or small-scale experiments, this research evaluates the hybrid model across a full set of approximately 150 time series collected from multiple petrol stations, without pre-clustering or manual selection. A comprehensive set of statistical and structural features is extracted from each time series to analyze their relation to forecast performance. The results show that the hybrid ARIMA–Markov approach significantly outperforms both individual statistical models and commonly applied machine learning methods in many cases, particularly for non-stationary or regime-shifting series. In 100% of cases, the hybrid model reduced the error compared to both baseline models—the median RMSE improvement over ARIMA was 13.03%, and 15.64% over the Markov model, with statistical significance confirmed by the Wilcoxon signed-rank test. The analysis also highlights specific time series features—such as entropy, regime shift frequency, and autocorrelation structure—as strong indicators of whether hybrid modeling yields performance gains. Feature-conditioning analyses (e.g., lag-1 autocorrelation, volatility, entropy) explain when hybridization helps, enabling a feature-aware workflow that selectively deploys model components and narrows parameter searches. The greatest benefits of applying the hybrid model were observed for time series characterized by high variability, moderate entropy of differences, and a well-defined temporal dependency structure—the correlation values between these features and the improvement in hybrid performance relative to ARIMA and Markov models reached 0.55–0.58, ensuring adequate statistical significance. Such approaches are particularly valuable in enterprise environments dealing with thousands of time series, where automated model configuration becomes essential. The findings position interpretable, adaptive hybrids as a practical default for short-horizon demand forecasting in fuel supply chains and, more broadly, in energy-use applications characterized by heterogeneous profiles and evolving regimes. Full article

In contemporary e-commerce, enterprises coordinate transactions, supply chains, and customer interactions within platform-based, data-intensive ecosystems. Integrated office application (IOA) serves as the operational backbone of these ecosystems by unifying communication, content management, workflow automation, and analysis across procurement, fulfillment, and after-sales service processes. As e-commerce processes become fully digitized, employees’ daily interactions with IOA directly impact service quality, operational efficiency, and sustainability outcomes. However, the micro-mechanisms by which IOA attributes translate into sustainable work practices are under-explored in the e-commerce literature. This study aims to explore how system quality, information quality, and collaboration quality influence user perceptions (perceived ease of use and usefulness), social influences (subjective norms), and satisfaction, thus jointly driving user intention and IOA-enabled sustainable behaviors. By integrating the Technology Acceptance Model, the Theory of Planned Behavior, and the IS Success Model, this research elaborates in a human-centered way on how an e-commerce enterprise’s system support can promote corporate and individual sustainability through employees’ adoption and continuous effective use. Full article

Global strategies for ensuring access to clean and safe drinking water are increasingly shifting toward a preventive approach based on risk assessment and risk management of the entire water supply and production chain. However, many developing countries, including South Africa, still lag in adopting advanced real-time water monitoring technologies aligned with Water 4.0 principles. To transition to these innovative technologies, it is essential to understand current gaps in water monitoring and the challenges to adopting these systems. This systemic review aims to assess current monitoring practices, identify implementation challenges, and explore strategic pathways for adopting smart water infrastructure in eThekwini Municipality, South Africa. This review identifies critical gaps in eThekwini’s water quality monitoring, including limited real-time surveillance, fragmented data systems, budgetary constraints, cybersecurity vulnerabilities, uneven rural–urban access, slow commercialization of academic innovations, policy misalignment, and insufficient technical capacity. It emphasizes the potential of real-time monitoring systems, automation, and artificial intelligence (AI) to address existing water quality monitoring challenges. Additionally, special focus is given to the role of electronic sensors in measuring physicochemical parameters like turbidity, pH, and dissolved oxygen as cost-effective indicators for detecting microbial contaminants. Implementing Water 4.0 strategies provides eThekwini and similar municipalities an opportunity to develop a more proactive, resilient, and sustainable approach to water quality management. Full article

Port congestion, particularly excessive truck turn time (TTT), disrupts supply chains, increases costs, and contributes to environmental impacts. This study evaluates the potential of integrating autonomous vehicles (AVs) into port operations to reduce TTT, using the Port of Montreal’s Viau Terminal as a case study. A discrete event simulation (DES) with agent-based logic was developed to model landside processes, including gate, yard, and staging operations, while differentiating between human-driven vehicles (HDVs) and AVs. Four scenarios were tested: Baseline indicating current operations, Truck Appointment System (TAS), partial AV integration (35% AVs) with shared resources, and AVs with dedicated staging areas and cranes. Model inputs were informed by port publicly available data and validated against observed TTT metrics. Results show that TAS reduced average TTT from 88.2 to 78.37 min; partial AV integration lowered it further to 55.91 min, with AVs averaging 45.33 min; dedicated AV infrastructure yielded the lowest AV TTT (32.86 min) but slightly increased overall TTT due to HDV delays. Findings suggest that combining AV adoption with demand management and targeted infrastructure investments can substantially improve efficiency. The study offers quantitative evidence and strategic recommendations to support port authorities in planning for automation while ensuring balanced resource allocation. Full article

Shellfish aquaculture is considered a major pillar of the seafood industry for its high market value, which increases the value for global food security and sustainability, often constrained in terms of conventional, labor-intensive practices. This review outlines the importance of automation and its advances in the shellfish value chain, starting from the hatchery operations to harvesting, processing, traceability, and logistics. Emerging technologies such as imaging, computer vision, artificial intelligence, robotics, IoT, blockchain, and RFID provide a major impact in transforming the shellfish sector by improving the efficiency, reducing the labor costs and environmental impacts, enhancing the food safety, and providing transparency throughout the supply chain. The studies involving the bivalves and crustaceans on their automated feeding, harvesting, grading, depuration, non-destructive quality assessments, and smart monitoring in transportation are highlighted in this review to address concerns involved with conventional practices. The review puts forth the need for integrating automated technologies into farm management and post-harvest operations to scale shellfish aquaculture sustainably, meeting the rising global demand while aligning with the Sustainability Development Goals (SDGs). Full article