Search Results (294)

The German industrial and energy sectors accounted for over 52% of national greenhouse gas emissions in 2024. This is influenced both by an ongoing demand for fossil fuels and the usage of emission-intensive raw and processed materials. With the current European directive on corporate sustainability reporting, a push is being made for companies to publish annual emission reports. However, as per a study conducted by the authors, small and medium-sized companies have difficulties accurately calculating emissions across their supply chain without relying on external service providers. As a scientific institute with a real production facility for metal machining, the ETA (Energy Technologies and Applications) Factory bridges the gap between academia and manufacturing enterprises. The authors have used this disposition to calculate scope 1–3 emissions for the factory as per the Greenhouse Gas Protocol across three years, while progressively attempting to automate data collection for all scopes. CO₂e emissions for the years 2022–2024 were 86.3 tCO₂e, 146.9 tCO₂e, and 86.1 tCO₂e, respectively. Emission categories were assessed in terms of relevance to the institute and subsequently used to analyze the emission activities of the factory. The highest contributor to emissions was electricity purchasing for 2022 and 2024, along with business travel for 2023. Within scope 3, the emissions produced by business travel showed the highest impact across all years, followed by either energy-related activities or purchased goods. The sensitivity of CO₂e factors was also investigated, showing discrepancies between 25% and 130% for the utilized CO₂e factor for steel. Automation of data collection benefits largely from implemented manufacturing systems, such as manufacturing execution systems or enterprise resource planning systems. Full article

The demands for equalization accuracy in traveling wave tube power amplifiers (TWTAs) are increasingly stringent, and traditional analog equalizers are no longer sufficient. Furthermore, the low level of digitization in TWTAs makes the direct application of digital equalization techniques difficult. This study designs a digital equalizer system for Ka-band TWTAs that controls high-precision digital step attenuators (DSAs). By processing the RF link, the dynamic analog power signal was converted into a digital square wave, and digital equalization control was achieved using an STM32F103 microcontroller (STMicroelectronics, Geneva, Switzerland; Origin: Taiwan, China). Simulation and experimental results show that the system operates stably within the input dynamic power range of −20 to 0 dBm, with an overall control delay of approximately 2 ms, a frequency measurement error of less than 0.02%, and an equalization accuracy better than 0.25 dB. This work addresses the critical interface bottleneck between high-frequency analog TWT chains and digital control circuits, offering a reusable engineering solution for the digital upgrade of TWTA products. Full article

Background: The COVID-19 pandemic caused disruptions in HPV vaccination and may have severely undermined global cervical cancer prevention, posing long-term risks to controlling cervical cancer and other HPV-related diseases. Objective: We conducted a scoping review to map and synthesize available evidence on how the COVID-19 pandemic has affected human papillomavirus (HPV) vaccination programs in low- and middle-income countries (LMICs) focusing on changes in vaccine delivery and coverage, determinants of uptake, economic and programmatic consequences and vaccine hesitancy. Methods: Inclusion criteria were limited to studies published in the English language between January 2020 to May 2025, and followed JBI and Arksey & O’Malley’s scoping review guidelines. The review proceeded through three stages: database searches, gray literature and citation tracking and used a PRISMA-ScR checklist to guide narrative and tabular synthesis. Results: A total of 1063 records, 57 studies were included in the final analysis, and these were spread out across 37 low- and middle-income countries (LMICs) mainly in Africa, Asia, and Latin America. Our analysis revealed that HPV vaccination coverage declined substantially during the COVID-19 pandemic, with reductions of up to 90% reported across the included studies, in the context of school closures, workforce redeployment, and supply-chain disruptions. Recovery efforts also faced major barriers including vaccine hesitancy, misinformation about COVID-19 vaccines, and travel restrictions. Strategies like digital tools, mobile clinics, and community health workers showed promise alongside integrated school- and facility-based approaches, although there is limited evidence on cost-effectiveness and long-term sustainability of these strategies. Conclusions: HPV vaccination in LMICs was significantly disrupted by the COVID-19 pandemic due to unreliable vaccine supply chains, health-worker shortages, and challenges tied to school-based vaccine delivery. Although recovery methods show potential, longer observation periods are needed to determine their full effectiveness. Full article

High-performance athletes are increasingly exposed to frequent trans-meridian travel, leading to profound circadian desynchronization and gastrointestinal distress. This review examines the complex interplay between the host’s central circadian system and the gut microbiota (GM), both of which exhibit synchronised daily oscillations essential for homeostasis. Rapid time-zone transitions, such as those anticipated for the 2026 FIFA World Cup, induce a state of “gut jet lag,” characterised by the loss of rhythmic microbial functions and impaired intestinal barrier integrity. Circadian misalignment is associated with increased systemic inflammation and disrupted metabolic regulation, which may contribute to impairments in cognitive performance, sleep quality, and muscle recovery. Critically, travel-induced dysbiosis may reduce the production of microbial metabolites, specifically short-chain fatty acids (SCFAs) like acetate, propionate, and butyrate. These SCFAs serve as energy substrates that may enhance glucose uptake, lipid oxidation, and glycogen storage in skeletal muscle. Evidence suggests that travel-related stressors—including dehydration, psychological stress, and shifts toward highly processed diets—further exacerbate the loss of beneficial taxa. To mitigate these effects, this article proposes evidence-informed strategies: timed light exposure to reset the master clock, chronobiotic meal timing to entrain peripheral tissues, and targeted symbiotic supplementation to restore SCFA-producing populations. Integrating these personalised, evidence-informed protocols may support the optimisation of physiological resilience and performance. Full article

We analyse pebble RFID tracing observations to investigate sediment transport dynamics in gravel-bed rivers using statistical modelling. This study examines a dataset of nearly 3500 tracer displacement measurements collected during 27 sediment-mobilizing events in a pre-Alpine reach in Italy. Our analysis follows three main steps, addressing tracer mobility patterns, event-scale transport dynamics, and reach-scale bedload inference. First, using Markov Chain analysis of state transitions on typical and high-magnitude transport events, we demonstrate that pebbles tend to maintain their mobility state between events, characterizing the between-event intermittency of bedload transport. A subsequent analysis of flow characteristics reveals that consecutive floods of similar magnitude exhibit increasing movement probability while maintaining similar virtual velocities. Finally, we train Gradient Boosting regression models to estimate distributions of pebble displacements and virtual velocities (defined, following common usage, as the ratio between the distance a tracer travels during a mobilising event and the duration of that event). Together with Monte Carlo propagation, these models are used to derive reach-scale volume estimates. The models identify flow rate and event duration as primary controls, while grain size has minimal influence within the sampled range of tracer dimensions. To strengthen our approach, we implement an extensive multi-stage validation process aimed at both single-tracer predictions and overall basin-scale movement estimates. The results indicate that high-magnitude transport events (12% of observations) contribute similar bedload volumes as typical events (88% of observations), highlighting the significant role of extreme events in total sediment transport. Model predictions yield bedload volume estimates that align well with independent measurements from a downstream sediment retention basin. Full article

Widespread, sometimes careless use of antibiotics has accelerated the rise and spread of antibiotic-resistant pathogens. These resistant bacteria are now often found in animal-based foods like meat, milk, and eggs, as well as in plant-based foods such as fruits and vegetables. Contaminated food is a key way these bacteria travel through the food chain and eventually reach people. This review brings together global trends in antibiotic contamination, explains the molecular mechanisms underlying antimicrobial resistance, and examines current approaches to addressing this problem. It also highlights new technologies that could work alongside or improve on traditional antibiotics. Some promising options are antimicrobial peptides, natural bioactive compounds, nanomaterials, and monoclonal antibody-based therapies. Tackling antimicrobial resistance requires teamwork across fields such as microbiology, food science, pharmacology, environmental science, and public health. Future research should strengthen global surveillance, standardize resistance-assessment methods, expand studies on non-bacterial pathogens, and ensure rigorous evaluation of novel therapies for pharmacokinetics, toxicity, scalability, and regulatory compliance. Ongoing global cooperation and new scientific ideas are crucial to slow the spread of resistant microbes and protect food safety and human health. Full article

Background: Combinatorial optimization problems (COPs) are central to Logistics and Supply Chain decision making, yet their NP-hardness prevents exact optimal solutions in reasonable time. Methods: This work addresses that limitation by developing a novel ternary network flow linear programming (LP) model of the assignment problem (AP) polytope. The model is very large scale (with $\mathsf{\Theta }\left(m^9\right)$ variables and $\mathsf{\Theta }\left(m^8\right)$ constraints, where m is the number of assignments). Although not intended to compete with conventional two-dimensional formulations of the AP with respect to solution procedures, it enables hard COPs to be solved exactly as “strict” (integrality requirements-free) LPs through simple transformations of their cost functions. Illustrations are given for the quadratic assignment problem (QAP) and the traveling salesman problem (TSP). Results: Because the proposed LP model is polynomial-sized and there exist polynomial-time algorithms for solving LPs, it affirms “$P=NP$.” A separable substructure of the model shows promise for practical-scale instances due to its suitability for large-scale optimization techniques such as Dantzig–Wolfe Decomposition, Column Generation, and Lagrangian Relaxation. The formulation also has greater robustness relative to standard network flow models. Conclusions: Overall, the approach provides a systematic, modeling-barrier-free framework for representing NP-complete problems as polynomial-sized LPs, with clear theoretical interest and practical potential for medium to large-scale Logistics and other COP-intensive applications. Full article

The rapid expansion of telecommunication infrastructure in developing countries has increased the demand for sustainable strategies to deploy field engineers in tower maintenance operations. Traditional approaches often neglect spatial factors, resulting in inefficient workforce allocation, excessive travel, and higher carbon emissions. This study develops an applied geospatial deployment framework that integrates spatial analysis with sustainable supply chain management (SSCM) principles to support operational decision-making in resource-constrained telecommunication maintenance environments. Using publicly available tools, tower and homebase coordinates were mapped and analyzed through Haversine-based geodesic distance calculations, with a comparative assessment against Euclidean approximation, while incorporating operational constraints such as service time per tower, available personnel, and work-hour limitations. The results indicate that the existing two-homebase deployment strategy leads to unbalanced workloads and unnecessary travel distances. By introducing a cluster-based restructuring using k-means to identify four sub-homebases, the proposed approach reduces total round-trip travel distance from 9120 km to 5913 km per maintenance cycle, representing a 35.2% reduction. This distance reduction corresponds to an estimated saving of approximately 593 kg of CO₂ emissions per maintenance cycle, representing an operational-scale reduction in travel-related emissions based on distance-derived fuel consumption modeling and assuming typical fuel efficiency for service vehicles. In addition, the optimized spatial configuration enables a more equitable distribution of engineers and reduces travel-related fatigue. These findings demonstrate the value of integrating geospatial optimization with sustainable supply chain management by aligning operational efficiency with quantifiable environmental and social sustainability outcomes. The proposed framework offers a replicable, low-cost, and data-driven solution for telecommunication infrastructure providers seeking to enhance the sustainability of field service operations in resource-constrained environments. Full article

Background: The constant growth in demand for sustainable energy products and the development of the circular economy have created a critical need for an efficient supply chain for biomass. However, the inherent challenges of biomass make its harvesting, collection, storage, and transport difficult, impacting logistical efficiency and the viability of bioenergy and bioproduct production. This study analyzes how combining artificial intelligence (AI) with multimodal transport can optimize and improve efficiency, as well as reduce costs, in biomass logistics. Methods: The study uses a tiered research framework that encompasses the physical domain (biomass limitations), the structural domain (mathematical modeling for multimodal transport), the intelligence domain (AI-based decision making), and the strategic approach. Results: The outcomes indicate that while truck transport is ideal for short distances, integrating rail and water transport through AI-driven optimization reduces costs and greenhouse gas emissions for long-distance travel. AI technologies, such as digital twins and machine learning, improve demand forecasting, real-time routing, and cargo consolidation, leading to enhanced prediction accuracy for transport costs. Conclusions: The integration of AI and multimodal networks builds resilient and sustainable biomass supply chains. However, full implementation requires addressing data fragmentation and investing in digital infrastructure to enable seamless coordination between supply chain stakeholders. Full article

This paper investigates travellers’ intentions, with regard to preferences for a green and/or ethical hotel, boycotting hotels accused of extreme environmental damages or over-exploitation of workers, and sharing relevant information on social media. Questioning the claim that intentions to prefer a green hotel are based mainly or even solely on practical criteria, this study focuses on examining the influencing power of values. The Values-Beliefs-Norms model was employed and modified as the New Environmental Paradigm was replaced by climate change risk perception. Personal interviews were conducted with consumers in the urban area of Thessaloniki, Greece, using a structured questionnaire for data collection. Area sampling, in combination with quota sampling, in terms of gender and age, was used. Results provided that egoistic and altruistic values were excluded from the final structural model, and just biospheric values indicated a statistically significant positive relationship with Risk Perception. The other hypothesised consecutive relationships between Biospheric Values (BV), Risk Perception (RP), Awareness of Consequences (AC), Ascription of Responsibility (AR), Personal Norms (PN) and Intentions (Int) were found to be statistically significant and positive. Overall, 80.9% of the variance in Intentions was explained, while Personal Norms indicated the stronger impact on Intentions among all other relationships in the chain. Eventually, theoretical and practical implications, as well as future research directions, are suggested. Full article

High-quality, well-structured trip chain data are essential for analyzing the daily activity patterns, travel behaviors, and logistical decisions of commercial vehicles, as well as for supporting sustainability-oriented freight management and low-carbon urban logistics. This study introduces a novel methodology for analyzing truck travel patterns using extensive GPS data, focusing on identifying freight trip chains and enhancing urban freight systems. A road-constrained clustering approach was developed to accurately identify vehicle stops and truck stop locations, addressing limitations in previous studies that struggled with misclassification. A trip chain reconstruction methodology was formulated, key characteristics were extracted and clustering techniques were applied to categorize trucks based on their travel behavior. A case study in Chongqing demonstrates that the proposed method outperforms traditional clustering algorithms, reducing misclassification rates in stop location identification. The findings reveal consistent trip chain patterns and distinct travel behaviors within truck groups. This research presents a data-driven framework that provides a foundation for optimizing logistics, fleet management, and low-carbon freight system planning. By enhancing the accuracy of trip chain analysis, this methodology contributes to the design of energy-efficient and sustainable urban freight systems, helping reduce emissions and foster eco-friendly logistics solutions. Full article

Background: The vehicle routing problem (VRP) is of great importance in the Industry 4.0 era because enabling technologies such as the internet of things (IoT), artificial intelligence (AI), big data, and geographic information systems (GISs) allows for real-time solutions to versions of the problem, adapting to changing conditions such as traffic or fluctuating demand. Methods: In this paper, we model and optimize a classic multi-link distribution network topology, including randomness in travel times, vehicle availability times, and product demands, using a hybrid approach of nested linear stochastic programming and Monte Carlo simulation under a time-window scheme. The proposed solution is compared with cutting-edge metaheuristics such as Ant Colony Optimization (ACO), Tabu Search (TS), and Simulated Annealing (SA). Results: The results suggest that the proposed method is computationally efficient and scalable to large models, although convergence and accuracy are strongly influenced by the probability distributions used. Conclusions: The developed proposal constitutes a viable alternative for solving real-world, large-scale modeling cases for transportation management in the supply chain. Full article

The automatic emergency braking (AEB) system plays a crucial role in reducing rear-end collisions and is mandatory on certain heavy-duty vehicles, with future regulations extending to passenger cars. However, most current AEB systems are designed based on onboard sensors such as cameras and radar, which may fail to prevent collisions in scenarios where the lead vehicle is already in a collision. To address this issue, this study proposes an enhanced AEB control method based on Vehicle-to-Vehicle (V2V) communication and onboard sensors. The method utilizes V2V communication and onboard sensors to predict obstacles ahead, applying effective braking when necessary. Simulation results in Matlab/Simulink R2022a show that the proposed V2V-based AEB control method reduces the risk of chain collisions, ensuring that the ego vehicle can avoid rear-end collisions even when the lead vehicle is involved in a crash. Three simulation scenarios were designed, where both the subject vehicle and the lead vehicle travel at 120 km/h. The following three distances between the subject vehicle and the lead vehicle were considered: 45 m, 70 m, and 30 m. When the lead vehicle detects an obstacle 30 m ahead and suddenly applies emergency braking, the lead vehicle fails to avoid a collision. In this case, the subject vehicle, equipped only with onboard sensors, is also unable to successfully avoid the crash. However, when the subject vehicle is equipped with both onboard sensors and vehicle-to-vehicle communication, it can prevent a rear-end collision with the lead vehicle, maintaining a vehicle-to-vehicle distance of 1 m, 6.8 m, and 3.1 m, respectively, during the stopping process. This control method contributes to advancing the active safety technologies of autonomous vehicles. Full article

Measles, also known as rubeola, is a highly contagious and potentially life-threatening disease caused by the measles virus. It classically presents with fever, cough, coryza, conjunctivitis, and a maculopapular rash. Despite the availability of an effective vaccine for decades, measles outbreaks continue to occur globally, largely driven by declining vaccination coverage and increased international travel. With no specific antiviral therapy available, rapid and accurate diagnosis remains essential for timely clinical management and effective outbreak control. Diagnostic methods have evolved from traditional virus isolation in cell culture to serologic assays and, more recently, to molecular techniques such as real-time reverse transcriptase polymerase chain reaction (rRT-PCR). Each diagnostic method has unique advantages and limitations influenced by specimen type, timing of collection, and laboratory capacity. This minireview summarizes the progress of measles virus diagnostics, outlines current laboratory detection strategies, and discusses emerging technologies and ongoing challenges amid global measles resurgence and increasing public health demands. Full article

Growing EV adoption is reshaping how Distributed Energy Resources (DERs) interact with the grid, playing a pivotal role in global decarbonization efforts and the transition towards a sustainable energy future. This study built a Virtual Power Plant (VPP) operation framework centered on EV behavioral dynamics, connecting individual driving and charging behaviors with the physical and economic layers of energy management. The EV behavioral dynamic model quantifies the stochastic travel, parking, and charging behaviors of individual EVs through an Agent-Based Trip and Charging Chain (AB-TCC) simulation, producing a Behavioral Flexibility Trace (BFT) that represents time-resolved EV availability and flexibility. The Forecasting Model employs a Bi-directional Long Short-Term Memory (Bi-LSTM) network trained on historical meteorological data to predict short-term renewable generation and represent physical variability. The two-stage optimization model integrates behavioral and physical information with market price signals to coordinate day-ahead scheduling and real-time operation, minimizing procurement costs and mitigating imbalance penalties. Simulation results indicate that the proposed framework yielded an approximately 15% increase in revenue over 7 days through EV-based flexibility utilization. These findings demonstrate that the proposed approach effectively leverages EV flexibility to manage renewable generation variability, thereby enhancing both the profitability and operational reliability of VPPs in local distribution systems. This facilitates greater penetration of intermittent renewable energy sources, accelerating the transition to a low-carbon energy system. Full article