Search Results (357)

The logistics industry faces challenges from evolving consumer expectations, technological advances, sustainability demands, and market disruptions. Logistics collaboration is in theory perceived as one of the most promising solutions to solve these issues, but here are still a lot of challenges that needs to be better understood and addressed. While vertical collaboration among supply chain actors is well advanced, horizontal collaboration among competing service providers remains under-explored. This study developed a novel methodology based on the COM-B behaviour-change framework to better understand the main challenges, opportunities, capabilities and drivers that would motivate competing companies to exploit the potential of horizontal logistics collaboration. A survey was designed and conducted among 71 logistics service providers in Slovenia, chosen for its fragmented market and low willingness to collaborate. Statistical analysis reveals cost reduction (M = 4.21/5) and improved vehicle utilization (M = 4.29/5) as the primary motivators. On the other hand, maintaining company reputation (M = 4.64/5), fair resource sharing (M = 4.20/5), and transparency of logistics processes (M = 4.17/5) all persist as key enabling conditions. These findings underscore the pivotal role of behavioural drivers and suggest strategies that combine economic incentives with targeted trust-building measures. Future research should employ experimental designs in diverse national contexts and integrate vertical–horizontal approaches to validate causal pathways and advance theory. Full article

As the climate problem is getting more and more serious and the “low-carbon revolution” of globalization is emerging, the logistics industry, as a high-end service industry, must also take the road of low-carbon development. Improving logistics carbon emission efficiency (LCEE) is gradually becoming an inevitable choice to maintain sustainable social development. The study uses the Super-SBM (Super-Slack-Based Measure) model to evaluate the urban LCEE from 2013 to 2022, explores the contribution of efficiency changes and technological progress to LCEE through the decomposition of the GML (Global Malmquist–Luenberger) index, and reveals the influence of digital transformation and energy consumption structure on LCEE by using the Spatial Durbin Model, concluding as follows: (1) LCEE declines from east to west, with large regional differences. (2) LCEE has steadily increased over the past decade, with slower growth from east to west. It fell in 2020 due to COVID-19 but has since recovered. (3) LCEE shows a catching-up effect among the three major regions, with technological progress being a key driver of improvement. (4) LCEE has significant spatial dependence. Energy consumption structure has a short-term negative spillover effect, while digital transformation has a positive spillover effect. Full article

Hand hygiene (HH) is essential for preventing disease transmission, particularly in schools where children are in close contact with other children. This study evaluated a school-based intervention on observed HH practices and hand cleanliness in six primary schools in Guatemala. Hand cleanliness was measured using the Quantitative Personal Hygiene Assessment Tool. The intervention included (1) HH behavior change promotion through Handwashing Festivals, and (2) increased access to HH materials at HH stations. Handwashing Festivals were day-long events featuring creative student presentations on HH topics. Schools were provided with soap and alcohol-based hand rub throughout the project to support HH practices. Appropriate HH practices declined from 51.2% pre-intervention to 33.1% post-intervention, despite an improvement in median Quantitative Personal Hygiene Assessment Tool scores from 6 to 8, indicating cleaner hands. Logistic regression showed higher odds of proper HH when an assistant was present. The decline in HH adherence was likely influenced by fewer assistants and changes in COVID-19 policies, while improvements in hand cleanliness may reflect observational bias. These findings emphasize the importance of sustained behavior change strategies, reliable HH material access, and targeted interventions to address gaps in HH practices, guiding school health policy and resource allocation. Full article

Terahertz (THz) communications and simultaneous wireless information and power transfer (SWIPT) hold the potential to energize battery-less Internet-of-Things (IoT) devices while enabling multi-gigabit data transmission. However, severe path loss, blockages, and rectifier nonlinearity significantly hinder both throughput and harvested energy. Additionally, high-power THz beams pose safety concerns by potentially exceeding specific absorption rate (SAR) limits. We propose a sensing-adaptive power-focusing (APF) framework in which a reconfigurable intelligent surface (RIS) embeds low-rate THz sensors. Real-time backscatter measurements construct a spatial map used for the joint optimisation of (i) RIS phase configurations, (ii) multi-tone SWIPT waveforms, and (iii) nonlinear power-splitting ratios. A weighted MMSE inner loop maximizes the data rate, while an outer alternating optimisation applies semidefinite relaxation to enforce passive-element constraints and SAR compliance. Full-stack simulations at 0.3 THz with 20 GHz bandwidth and up to 256 RIS elements show that APF (i) improves the rate–energy Pareto frontier by 30–75% over recent adaptive baselines; (ii) achieves a 150% gain in harvested energy and a 440 Mbps peak per-user rate; (iii) reduces energy-efficiency variance by half while maintaining a Jain fairness index of 0.999;; and (iv) caps SAR at 1.6 W/kg, which is 20% below the IEEE C95.1 safety threshold. The algorithm converges in seven iterations and executes within <3 ms on a Cortex-A78 processor, ensuring compliance with real-time 6G control budgets. The proposed architecture supports sustainable THz-powered networks for smart factories, digital-twin logistics, wire-free extended reality (XR), and low-maintenance structural health monitors, combining high-capacity communication, safe wireless power transfer, and carbon-aware operation for future 6G cyber–physical systems. Full article

The aim of the article is to highlight the increasing environmental burden on aquatic ecosystems in Slovakia due to continuous pollution from municipal, industrial and agricultural sources. Laboratory analyses have shown alarming exceedance of the limit values of contaminants, with nitrate nitrogen (NO₃⁻) reaching 5.8 mg/L compared to the set limit of 2.5 mg/L and phosphorus concentrations exceeding the permissible values by a factor of five, thereby escalating the risk of eutrophication and loss of ecological stability of the aquatic ecosystem. The accumulation of heavy metals is also a problem—lead (Pb) concentrations reach up to 9.7 μg/L, which exceeds the safe limit by a factor of ten. Despite the measures implemented, such as scum barriers, there is continuous contamination of the aquatic environment, with illegal waste dumps and uncontrolled runoff of agrochemicals playing a significant role. The research results underline the critical need for a more effective environmental policy and more rigorous monitoring of toxic substances in real time. These findings highlight not only the urgency of more effective environmental policy and stricter real-time monitoring of toxic substances, but also the necessity of integrating environmental logistics into the design of sustainable solutions. Logistical approaches including the optimization of waste collection, coordination of stakeholders and creation of infrastructural conditions can significantly contribute to reducing environmental burdens and ensure the continuity of environmental management in ecologically sensitive areas. Full article

As global climate change intensifies and the transition to clean energy accelerates, lithium-ion batteries—critical components of electric vehicles—are becoming increasingly vital in international trade networks. This study investigates the structural evolution and determinants of the electric vehicle lithium-ion battery trade network among European Union (EU) member states from 2012 to 2023, employing social network analysis and the multiple regression quadratic assignment procedure method. The findings demonstrate the transformation of the network from a centralized and loosely connected structure, with Germany as the dominant hub, to a more interconnected and decentralized system in which Poland and Hungary emerge as the leading players. Key network metrics, such as the density, clustering coefficients, and average path lengths, reveal increased regional trade connectivity and enhanced supply chain efficiency. The analysis identifies geographic and economic proximity, logistics performance, labor cost differentials, energy resource availability, and venture capital investment as significant drivers of trade flows, highlighting the interaction among spatial, economic, and infrastructural factors in shaping the network. Based on these findings, this study underscores the need for targeted policy measures to support Central and Eastern European countries, including investment in logistics infrastructure, technological innovation, and regional cooperation initiatives, to strengthen their integration into the supply chain and bolster their export capacity. Furthermore, fostering balanced inter-regional collaborations is essential in building a resilient trade network. Continued investment in transportation infrastructure and innovation is recommended to sustain the EU’s competitive advantage in the global electric vehicle lithium-ion battery supply chain. Full article

In the low-carbon era, there is a serious challenge of climate change, which urgently needs to promote low-carbon consumption behavior in order to build sustainable low-carbon consumption patterns. The establishment of this model not only requires in-depth theoretical research as support, but also requires tripartite cooperation between the government, enterprises and the public to jointly promote the popularization and practice of the low-carbon consumption concept. Therefore, by constructing a tripartite evolutionary game model and simulation analysis, this study deeply discusses the mechanism of government policy on the strategy choice of logistics enterprises. The stability strategy and satisfying conditions are deeply analyzed by constructing a tripartite evolutionary game model of the logistics industry, government, and consumers. With the help of MATLAB R2023b simulation analysis, the following key conclusions are drawn: (1) The strategic choice of logistics enterprises is affected by various government policies, including research and development intensity, construction intensity, and punishment intensity. These government policies and measures guide logistics enterprises toward low-carbon development. (2) The government’s research, development, and punishment intensity are vital in determining whether logistics enterprises adopt low-carbon strategies. R&D efforts incentivize logistics companies to adopt low-carbon technologies by driving technological innovation and reducing costs. The penalties include economic sanctions to restrain companies that do not comply with low-carbon standards. In contrast, construction intensity mainly affects the consumption behavior of consumers and then indirectly affects the strategic choice of logistics enterprises through market demand. (3) Although the government’s active supervision is a necessary guarantee for logistics enterprises to implement low-carbon strategies, more is needed. This means that in addition to the government’s policy support, it also needs the active efforts of the logistics enterprises themselves and the improvement of the market mechanism to promote the low-carbon development of the logistics industry jointly. This study quantifies the impact of different factors on the system’s evolution, providing a precise decision-making basis for policymakers and helping promote the logistics industry’s and consumers’ low-carbon transition. It also provides theoretical support for the logistics industry’s low-carbon development and green low-carbon consumption and essential guidance for sustainable development. Full article

With the current acceleration of climate change, there is a global demand for sustainable development and carbon emission reduction. As a major link in the global supply chain, the logistics industry’s green and low-carbon transformation has become a critical breakthrough in achieving the objective of reducing carbon emissions. This study develops a multidimensional assessment index method for the green logistics level. The study selects 51 major economies worldwide from 2000 to 2022 as research subjects. The cloud model–entropy value–TOPSIS method is applied to measure the green logistics level. The results of the green logistics level are analyzed from the perspectives of developed and developing countries, and their spatiotemporal evolution characteristics are explored. The study shows that (1) the green logistics level in developed countries is relatively high, mainly due to policy-driven, core technology advantages. However, they continue to encounter issues, such as regional imbalance and excessive green costs. (2) The green logistics level in developing countries is in the middle to lower level, limited by technological dependence, outdated infrastructure, and so on. They are generally caught in a “high-carbon lock-in” situation. (3) From the perspective of time, the global level of green logistics shows a rising trend year by year. The peak of the kernel density curve of the green logistics level is characterized by an “I” shape. There is a significant disparity in each country’s green logistics level, although it is narrowing every year. (4) From the spatial perspective, the green logistics level in each country shows a rising trend year by year vertically, while the horizontal disparity between countries is enormous. The development of the green logistics level between continents is unbalanced. The study presents several recommendations, including boosting technology transfer, giving financial support, strengthening international cooperation, and developing green infrastructure, to promote the global logistics industry’s green and low-carbon transformation to accomplish sustainable development goals. Full article

Healthcare cost acceleration and resource allocation issues have worsened across European health systems, where a small group of patients drives excessive healthcare spending. The prediction of high-cost utilization patterns is important for the sustainable management of healthcare and focused intervention measures. The aim of our study was to derive and validate machine learning algorithms for high-cost healthcare utilization prediction based on detailed administrative data and by comparing three algorithmic methods for the best risk stratification performance. The research analyzed extensive insurance beneficiary records which compile data from health group collective funds operated by non-life insurers across EU countries, across multiple service classes. The definition of high utilization was equivalent to the upper quintile of overall health expenditure using a moderate cost threshold. The research applied three machine learning algorithms, namely logistic regression using elastic net regularization, the random forest, and support vector machines. The models used a comprehensive set of predictor variables including demographics, policy profiles, and patterns of service utilization across multiple domains of healthcare. The performance of the models was evaluated using the standard train–test methodology and rigorous cross-validation procedures. All three models demonstrated outstanding discriminative ability by achieving area under the curve values at near-perfect levels. The random forest achieved the best test performance with exceptional metrics, closely followed by logistic regression with comparable exceptional performance. Service diversity proved to be the strongest predictor across all models, while dentistry services produced an extraordinarily high odds ratio with robust confidence intervals. The group of high utilizers comprised approximately one-fifth of the sample but demonstrated significantly higher utilization across all service classes. Machine learning algorithms are capable of classifying patients eligible for the high utilization of healthcare services with nearly perfect discriminative ability. The findings justify the application of predictive analytics for proactive case management, resource planning, and focused intervention measures across private group health insurance providers in EU countries. Full article

This research explores how artificial intelligence (AI) can be leveraged to optimize packaging design, reduce operational costs, and enhance sustainability in e-commerce. As packaging waste and shipping inefficiencies grow alongside global online retail demand, traditional methods for determining box size, material use, and logistics planning have become economically and environmentally inadequate. Using a three-phase framework, this study integrates data-driven diagnostics, AI modeling, and real-world validation. In the first phase, a systematic analysis of current packaging inefficiencies was conducted through secondary data, benchmarking, and cost modeling. Findings revealed significant waste caused by over-packaging, suboptimal box-sizing, and poor alignment between product characteristics and logistics strategy. In the second phase, a random forest (RF) machine learning model was developed to predict optimal packaging configurations using key product features: weight, volume, and fragility. This model was supported by AI simulation tools that enabled virtual testing of material performance, space efficiency, and damage risk. Results demonstrated measurable improvements in packaging optimization, cost reduction, and emission mitigation. The third phase validated the AI framework using practical case studies—ranging from a college textbook to a fragile kitchen dish set and a high-volume children’s bicycle. The model successfully recommended right-sized packaging for each product, resulting in reduced material usage, improved shipping density, and enhanced protection. Simulated cost-saving scenarios further confirmed that smart packaging and AI-generated configurations can drive efficiency. The research concludes that AI-based packaging systems offer substantial strategic value, including cost savings, environmental benefits, and alignment with regulatory and consumer expectations—providing scalable, data-driven solutions for e-commerce enterprises such as Amazon and others. Full article

Water insecurity, intensified by climate change, presents a significant challenge globally, especially in arid and semi-arid regions of Africa. In northern Ghana, where agriculture heavily depends on seasonal rainfall, prolonged dry seasons exacerbate water and food insecurity. Despite efforts to improve water access, there is limited understanding of how climate change preparedness affects water insecurity risk in rural contexts. This study investigates the relationship between climate preparedness and water insecurity in semi-arid northwestern Ghana. Grounded in the Sustainable Livelihoods Framework, data was collected through a cross-sectional survey of 517 smallholder households. Nested ordered logistic regression was used to analyze how preparedness measures and related socio-environmental factors influence severe water insecurity. The findings reveal that higher levels of climate change preparedness significantly reduce water insecurity risk at individual [odds ratio (OR) = 0.35, p < 0.001], household (OR = 0.037, p < 0.001), and community (OR = 0.103, p < 0.01) levels. In contrast, longer round-trip water-fetching times (OR = 1.036, p < 0.001), water-fetching injuries (OR = 1.054, p < 0.01), reliance on water borrowing (OR = 1.310, p < 0.01), untreated water use (OR = 2.919, p < 0.001), and exposure to climatic stressors like droughts (OR = 1.086, p < 0.001) and floods (OR = 1.196, p < 0.01) significantly increase insecurity. Community interventions, such as early warning systems (OR = 0.218, p < 0.001) and access to climate knowledge (OR = 0.228, p < 0.001), and long-term residency further reduce water insecurity risk. These results underscore the importance of integrating climate preparedness into rural water management strategies to enhance resilience in climate-vulnerable regions. Full article

Background: Antimicrobial resistance (AMR) poses a critical global health challenge, particularly in resource-limited settings. A hub-and-spoke model, decentralising expertise and distributing resources to peripheral facilities, has been proposed as a strategy to enhance the antimicrobial stewardship (AMS) capacity in low- and middle-income countries. Aim: This study sought to understand healthcare professionals’ experiences of a hub-and-spoke AMS model in the Volta Region of Ghana and its influence on clinical practice, leadership, and collaborative endeavours to address AMR. Methods: A qualitative descriptive design was adopted. In-depth interviews were conducted with 11 healthcare professionals who participated in the AMS program. Thematic analysis was used to identify key themes related to the knowledge and skills that were gained, clinical and leadership practice changes, capacity building, and challenges. Results: Participants reported an increased awareness of AMR, particularly regarding the scale and clinical implications of antimicrobial misuse. The clinical practice improvements included more judicious prescribing and enhanced adherence to infection prevention and control measures. Many respondents highlighted stronger leadership skills and a commitment to capacity building through AMS committees, multidisciplinary collaboration, and cross-organisational knowledge exchange. Despite resource constraints and logistical hurdles, participants expressed optimism, citing data-driven approaches such as point prevalence surveys to track progress and inform policy. Engagement with hospital management and public outreach were viewed as essential to sustaining AMS efforts and curbing over-the-counter antibiotic misuse. Conclusions: The hub-and-spoke model caused observable improvements in AMS knowledge, clinical practice, and leadership capacity among healthcare professionals in Ghana. While challenges remain, particularly in securing sustainable resources and shifting community behaviours, these findings underscore the potential of network-based programs to catalyse systemic changes in tackling AMR. Future research should explore long-term outcomes and strategies for embedding AMS practices more deeply within healthcare systems and communities. Full article

Objectives: This study aims to assess the clinical efficacy of micro-fragmented adipose tissue (MFAT) therapy over three years in patients with KOA and to determine whether short-term improvements at three months can forecast long-term outcomes. Methods: A retrospective, observational study was conducted on 335 patients diagnosed with KOA who received a single MFAT injection. The patients were followed up at 3 months, 6 months, 1 year, 2 years, and 3 years, with assessments using the Visual Analog Scale (VAS), Oxford Knee Score (OKS), Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), and Knee Injury and Osteoarthritis Outcome Score (KOOS). Statistical analysis was performed to assess the differences in preoperative and postoperative scores (VAS, OKS, WOMAC, KOOS) to evaluate the predictive role of 3-month score changes on long-term clinical outcomes. Results: All measured scores (VAS, OKS, WOMAC, KOOS) showed significant improvement at 3 months, with sustained improvements through 3 years (p < 0.001). Early score changes at 3 months were significantly associated with improved clinical outcomes at 1, 2, and 3 years (p < 0.05). Logistic regression confirmed early post-treatment improvements as independent predictors of long-term benefit, except for the VAS score at 3 years (p = 0.098). A comparative analysis between completers and dropouts showed no baseline differences; however, significant outcome differences emerged at later follow-up points. Due to insufficient data at the 3-year mark among dropouts, statistical comparisons were not possible for that time point. Conclusions: MFAT treatment was associated with consistent symptomatic improvement in patients with KOA, and early clinical response at 3 months served as a reliable predictor of long-term pain and function outcomes. While this study focused on patient-reported symptom relief and not structural regeneration, the results support MFAT as a minimally invasive option for symptom management. Early post-treatment response may serve as a useful tool for clinicians to predict long-term therapeutic success and personalize treatment strategies for KOA patients. Full article

The rapid expansion of shrimp aquaculture has led to the generation of nutrient-rich effluents, which contribute to environmental degradation if inadequately managed. This study evaluated the potential of Dunaliella salina for the reuse of shrimp aquaculture wastewater (SAW) in biofloc production systems under varying dilution levels (0%, 25%, and 50%) and the simultaneous production of high-value biomass. Growth kinetics were modeled using a four-parameter logistic model, and nutrient removal, biochemical composition, and fatty acid profile were assessed. D. salina exhibited substantial growth in undiluted SAW, achieving over 80% removal of total nitrogen and reducing the organic load, as measured by a chemical oxygen demand reduction of more than 79%. In SAW treatments, the protein content ranged from 24.7% to 26.3%, while the lipid content reached up to 67.1% in a 25% SAW dilution. Chlorophyll a and total carotenoids were measured at 5.3–7 µg/mL and 4.1–5.7 µg/mL, respectively, in SAW treatments. The polyunsaturated fatty acid content in undiluted SAW was 34.5%, with α-linolenic acid (C18:3n3) and linoleic acid (C18:2n6) comprising 12% and 7.5%, respectively. This study demonstrates the ability of D. salina to valorize shrimp aquaculture wastewater in biofloc systems into lipid-rich, bioactive biomass, supporting its use in integrated aquaculture biotechnology systems for sustainable wastewater management and bioproduct generation. Full article

Sustainability can be achieved through the widespread adoption of electrification across multiple sectors of activity, which would thereby enable increased operational efficiency and reduce the environmental impact. The attainment of this purpose relies on electrical circuits that convert electrical energy from renewable power plants into forms that are compatible with the specific requirements of the load. Failure of the aforementioned circuits, denominated as power converters, can lead to financial losses resulting from unexpected shutdowns and, in critical systems, can pose significant risks to human life. This article focuses on the topic of fault diagnosis in power converters. Some of the most vulnerable components of these converters are the capacitors used in the DC-link, whose failure evolves gradually. When the capacitor internal resistance (ESR) or the capacitor capacitance (C) exceeds a certain threshold value, it is advisable to propose a system shutdown, as soon as possible, to replace the capacitor. The solution presented in this article combines signal processing techniques (SPTs) with a machine learning (ML) algorithm to determine the optimal time for capacitor replacement. The ML algorithm employed herein was a logistic regression (LR) algorithm which classified the capacitor into one of two states: normal operation (0) or failure (1). To train and evaluate the LR model, two different datasets were created using various electrical quantities that can be measured non-invasively. The model demonstrated excellent performance, achieving an accuracy, precision, recall, and F1 score above 0.99. Full article