Search Results (2,626)

Antimicrobial resistance (AMR) in livestock production systems has emerged as a major global health concern, threatening not only animal welfare and agricultural productivity but also food safety and public health. The widespread, and often poorly regulated, use of antimicrobials for growth promotion, prophylaxis, and metaphylaxis has accelerated the emergence and dissemination of resistant bacteria and resistance genes. These elements circulate across interconnected animal, environmental, and human ecosystems, driven by mobile genetic elements and amplified through the food production chain. It is estimated that more than two-thirds of medically important antimicrobials are used in animals, and AMR could cause millions of human deaths annually by mid-century if unchecked. In some livestock systems, multidrug-resistant E. coli prevalence already exceeds half of isolates, particularly in poultry and swine in low- and middle-income countries (LMICs). This narrative review provides a comprehensive overview of the molecular epidemiology, ecological drivers, and One Health implications of AMR in food-producing animals. We highlight key zoonotic and foodborne bacterial pathogens—including Escherichia coli, Salmonella enterica, and Staphylococcus aureus—as well as underappreciated reservoirs in commensal microbiota and livestock environments. Diagnostic platforms spanning phenotypic assays, PCR, MALDI-TOF MS, whole-genome sequencing, and CRISPR-based tools are examined for their roles in AMR detection, surveillance, and resistance gene characterization. We also evaluate current antimicrobial stewardship practices, global and regional surveillance initiatives, and policy frameworks, identifying critical implementation gaps, especially in low- and middle-income countries. Emerging sectors such as aquaculture and insect farming are considered for their potential role as future AMR hotspots. Finally, we outline future directions including real-time genomic surveillance, AI-assisted resistance prediction, and integrated One Health data platforms as essential innovations to combat AMR. Mitigating the threat of AMR in animal agriculture will require coordinated scientific, regulatory, and cross-sectoral responses to ensure the long-term efficacy of antimicrobial agents for both human and veterinary medicine. Full article

This work presents a proof of concept for a short-range high spectral resolution lidar (SR-HSRL) optimized for aerosol characterization in the first kilometer of the atmosphere. The system is based on a compact, high-repetition-rate diode-based fiber laser with a 300 MHz linewidth and 5 ns pulse duration, coupled with an iodine absorption cell. A central challenge in the instrument’s development was identifying a laser source that offered both sufficient spectral resolution for HSRL retrievals and nanosecond pulse durations for high spatiotemporal resolution, while also being compact, tunable, and cost-effective. To address this, we developed a methodology for complete spectral and temporal laser characterization. A two-day field campaign conducted in July 2024 in Tsukuba, Japan, validated the system’s performance. Despite the relatively broad laser linewidth, we successfully retrieved aerosol backscatter coefficient profiles from 50 to 1000 m, with a spatial resolution of 7.5 m and a temporal resolution of 6 s. The results demonstrate the feasibility of using SR-HSRL for detailed studies of aerosol layers, cloud interfaces, and aerosol–cloud interactions. Future developments will focus on extending the technique to ultra-short-range applications (<100 m) from ground-based and mobile platforms, to retrieve aerosol extinction coefficients and lidar ratios to improve the characterization of near-source aerosol properties and their radiative impacts. Full article

Global climate change mitigation has prompted the construction sector to pursue decarbonization strategies, with timber structures offering significant carbon reduction potential. Wood serves as a sustainable material that sequesters carbon during growth while reducing emissions across the entire construction supply chain. Robotic construction of timber structures is increasingly promoted as a low-carbon, intelligent alternative for small- and medium-scale projects, yet the energy consumption and environmental impacts of robotic automated assembly using self-tapping screws remain understudied. This study presents a construction-phase life-cycle assessment (LCA) of an innovative vertically mobile robotic construction system for automated timber structure. The system integrates a KUKA KR 6 R900 (KUKA Robotics Corporation, Augsburg, Germany) six-axis robot with an electrically actuated lifting platform and specialized end-effector, enabling fully autonomous assembly of a Layered Interlaced Timber Arch-Shell (LITAS) structure using Hinoki cypress timber and self-tapping screws. This research provides the first comprehensive LCA dataset for robotic screw-fastened timber construction and establishes a replicable framework for sustainable automated building practices, with methodology scalability enabling application to diverse timber construction scenarios and advancing intelligent and decarbonized transformation in the construction industry. Full article

This study presents a high-fidelity digital twin (DT) framework designed to evaluate and improve vertiport operations for Advanced Air Mobility (AAM). By integrating Unreal Engine, AirSim, and Cesium, the framework enables real-time simulation of Unmanned Aerial Vehicles (UAVs), including unmanned electric vertical take-off and landing (eVTOL) operations under nominal and disrupted conditions, such as adverse weather and engine failures. The DT supports interactive visualisation and risk-free analysis of decision-making protocols, vertiport layouts, and UAV handling strategies across multi-scenarios. To validate system realism, mixed-reality experiments involving physical UAVs, acting as surrogates for eVTOL platforms, demonstrate consistency between simulations and real-world flight behaviours. These UAV-based tests confirm the applicability of the DT environment to AAM. Intelligent algorithms detect Final Approach and Take-Off (FATO) areas and adjust flight paths for seamless take-off and landing. Live environmental data are incorporated for dynamic risk assessment and operational adjustment. A structured capacity evaluation method is proposed, modelling constraints including turnaround time, infrastructure limits, charging requirements, and emergency delays. Mitigation strategies, such as ultra-fast charging and reconfiguring the layout, are introduced to restore throughput. This DT provides a scalable, drone-integrated, and data-driven foundation for vertiport optimisation and regulatory planning, supporting safe and resilient integration into the AAM ecosystem. Full article

With the rise of e-commerce, mobile devices, and social media, consumers’ online social and shopping behaviors have become increasingly integrated, making social commerce a major force in the digital marketplace. In this context, consumer behaviors on social media can exert a profound influence on purchase decisions. This research investigates the impact of social media self-presentation, a key social behavior on social media, on consumers’ preference for experiential consumption. Drawing on one survey study and one experimental study, the findings reveal that social media self-presentation significantly predicts a stronger preference for experiential consumption (e.g., travel) over material consumption (e.g., tangible goods), with this effect being particularly salient among female participants. Furthermore, self-concept clarity mediates this relationship: both positive and authentic self-presentation enhance individuals’ clarity of self-concept, which in turn promotes a greater inclination toward experiential purchases. These findings highlight the key role of social media behavior in shaping consumer behaviors. The results offer important theoretical and practical insights into consumer decision-making in digital contexts and guide platform design and personalized recommendation systems. Full article

This paper presents a comprehensive study of an ESP32 microcontroller-based self-balancing mobile robot system designed in conjunction with an Android app for Bluetooth control. The robot employs an MPU6050 accelerometer/gyroscope to execute dynamic equilibrium control for robotic balance. This study explores the design of a system composed of an ESP32-based dual-platform architecture. The firmware for the ESP32 executes real-time motor control and sensor processing, while the Android application provides the user interface, data visualization, and command transmission. The system achieves stable operation with tilt angle variations of ±2.5° ($\sigma =0.8°$, n = 50 trials) during normal operation with a PID controller tuned to KP = 6.0, KI = 0.1, and KD = 1.5. In experimental tests, control latency was measured at 38–72 ms (mean = 55 ms, $\sigma =12$ ms) over distances of 1–10 m with a robust Bluetooth connection. Extended operational tests indicated the reliability of both autonomous obstacle avoidance mode and manual control exceeding 95%. Key contributions include gyro drift compensation using a progressive calibration scheme, intelligent battery management for operational efficiency, and a dual-mode control interface to facilitate seamless transition between manual and autonomous operation. Processing of real-time telemetry on the Android application allows visualization of important parameters like tilt angle, motor speeds, and sensor readings. This work contributes to a cost-effective mobile robotics platform (total cost: USD 127) through the provision of detailed design specifications, implementation strategies, and performance characteristics. Full article

Urban growth and rising vehicle usage have intensified congestion, accidents, and environmental impact, exposing the limitations of traditional traffic management systems. This study introduces a dual-incident simulation framework to investigate the potential of Vehicle-to-Everything (V2X) technologies in enhancing urban mobility. Using the Eclipse MOSAIC platform integrated with SUMO, a realistic network in Iași, Romania, was modeled under single- and dual-incident scenarios with three V2X penetration levels: 0%, 50%, and 100%. Unlike prior works that focus on single-incident cases or assume full penetration, our approach evaluates cascading disruptions under partial adoption, providing a more realistic transition path for mid-sized European cities. Key performance indicators, i.e., average speed, vehicle density, time loss, and waiting time, were calculated using mathematically defined formulas and validated across multiple simulation runs. Results demonstrate that full V2X deployment reduces average time loss by 18% and peak density by more than 70% compared to baseline conditions, while partial adoption delivers measurable yet limited benefits. The dual-incident scenario shows that V2X-enabled rerouting significantly mitigates cascading congestion effects. These contributions advance the state of the art by bridging microscopic vehicle dynamics with network-level communication modeling, offering quantitative insights for phased V2X implementation and the design of resilient, sustainable intelligent transportation systems. Full article

Electric vehicle (EV) charging has become a key factor in grid integration, impact analysis, and the development of intelligent charging strategies. However, the rapid rise in EV adoption poses challenges for charging infrastructure and grid stability due to the inherently variable and uncertain charging behavior. Limited access to high-resolution, location-specific data further hinders accurate modeling, emphasizing the need for reliable, privacy-preserving tools to forecast EV-related grid impacts. This study introduces a comprehensive methodology to emulate real-world EV charging behavior using a real-time simulation environment. A physics-based EV charger model was developed on the Typhoon HIL platform, incorporating detailed electrical dynamics and control logic representative of commercial chargers. Simulation outputs, including active power consumption and state-of-charge evolution, were validated against field data captured via phasor measurement units, showing strong alignment across all charging phases, including SOC-dependent current transitions. Quantitative validation yielded an MAE of 0.14 and an RMSE of 0.36, confirming the model’s high accuracy. The study also reflects practical BMS strategies, such as early charging termination near 97% SOC to preserve battery health. Overall, the proposed real-time framework provides a high-fidelity platform for analyzing grid-integrated EV behavior, testing smart charging controls, and enabling digital twin development for next-generation electric mobility. Full article

Identifying the botanical origin of honey is essential for ensuring its quality, preventing adulteration, and protecting consumers. Traditional techniques, such as melissopalynology, physicochemical analysis, and PCR, are often labor-intensive, time-consuming, or limited to the detection of only known species, while advanced DNA sequencing remains prohibitively costly. In this study, we aim to develop a deep learning-based approach for identifying pollen grains extracted from honey and captured through microscopic imaging. To achieve this, we first constructed a dataset named VNUA-Pollen52, which consists of microscopic images of pollen grains collected from flowers of plant species cultivated in the surveyed area in Hanoi, Vietnam. Second, we evaluated the classification performance of advanced deep learning models, including MobileNet, YOLOv11, and Vision Transformer, on pollen grain images. To improve performances of these model, we proposed data augmentation and hybrid fusion strategies to improve the identification accuracy of pollen grains extracted from honey. Third, we developed an online platform to support experts in identifying these pollen grains and to gather expert consensus, ensuring accurate determination of the plant species and providing a basis for evaluating the proposed identification strategy. Experimental results on 93 images of pollen grains extracted from honey samples demonstrated the effectiveness of the proposed hybrid fusion strategy, achieving 70.21% accuracy at rank 1 and 92.47% at rank 5. This study demonstrates the capability of recent advances in computer vision to identify pollen grains using their microscopic images, thereby opening up opportunities for the development of automated systems that support plant traceability and quality control of honey. Full article

Nanopore-based DNA sequencing has emerged as a transformative biosensing technology, enabling real-time molecular diagnostics in compact and mobile form factors. However, the computational complexity of the basecalling process—the step that translates raw nanopore signals into nucleotide sequences—poses a critical energy challenge for mobile deployment. While deep learning (DL) models currently dominate this task due to their high accuracy, they demand substantial power budgets and computing resources, making them unsuitable for portable or field-scale biosensor platforms. In this work, we propose an embedded hardware–software framework for DNA sequence detection that leverages a Viterbi-based Hidden Markov Model (HMM) implemented on a custom 64-bit RISC-V core. The proposed HMM detector is realized on an off-the-shelf Virtex-7 FPGA and evaluated against state-of-the-art DL-based basecallers in terms of energy efficiency and inference accuracy. From one side, the experimental results show that our system achieves an energy efficiency improvement of 6.5×, 5.5×, and 4.6×, respectively, compared to similar HMM-based detectors implemented on a commodity x86 processor, Cortex-A9 ARM embedded system, and a previously published Rocket-based system. From another side, the proposed detector demonstrates 15× and 2.4× energy efficiency superiority over state-of-the-art DL-based detectors, with competitive accuracy and sufficient throughput for field-based genomic surveillance applications and point-of-care diagnostics. This study highlights the practical advantages of classical probabilistic algorithms when tightly integrated with lightweight embedded processors for biosensing applications constrained by energy, size, and latency. Full article

Climate change affects all human and ecological systems. The rapid climate impacts are increasingly evident on all economic activities, including tourism. Regarding the fact that “the window is closing”, climate resilience is urgently needed to protect tourism resources and maintain the quality of tourism offerings. Since the recent climate and tourism scientific literature emphasizes the necessity to mobilize existing knowledge, standardize practices, and explore appropriate tools related to tourism adaptation, we provided desk research and discussed the latest achievements of the Intergovernmental Panel on Climate Change’s (IPCC) and related knowledge platforms. According to the results of this review, it seems that the vast majority of the authors use vulnerability assessment (VA) to provide a solid basis for climate change adaptation (CCA) options applicable to tourism. Also, there is a lack of application of the latest IPCC recommendations founded in climate risk assessment (CRA). In the context of CRA, vulnerability was often assessed in a static way, with limited consideration of future hazards, probabilistic estimates, and the interactions between climatic and non-climatic drivers. Moreover, the methodologies applied to assess climate-related issues in tourism have been highly heterogeneous, hindering comparability and aggregation of results. Since risk is a useful conceptual framework for understanding tourism’s climate issues and modalities to reach its climate resilience, we discussed the significance of shifting the vulnerability concept towards a risk-centered framework. This review paper also provides a basis for a common understanding of CRA, a step-by-step approach to its assessment, and the explanation of CCA options to strengthen the tourism community, since a decisive decade of climate action is upon us. Full article

Autonomous driving represents a transformative advancement with the potential to significantly impact daily mobility, including enabling independent vehicle operation for individuals with visual disabilities. The commercialization of autonomous driving requires guaranteed safety and accuracy, underscoring the need for robust localization and environmental perception algorithms. In cost-sensitive platforms such as delivery robots and electric vehicles, cameras are increasingly favored for their ability to provide rich visual information at low cost. Despite recent progress, existing visual–inertial odometry systems still suffer from degraded accuracy in challenging conditions, which limits their reliability in real-world autonomous navigation scenarios. Estimating 3D positional changes using only 2D image sequences remains a fundamental challenge primarily due to inherent scale ambiguity and the presence of dynamic scene elements. In this paper, we present a visual–inertial odometry framework incorporating a depth estimation model trained without ground-truth depth supervision. Our approach leverages a self-supervised learning pipeline enhanced with knowledge distillation via foundation models, including both self-distillation and geometry-aware distillation. The proposed method improves depth estimation performance and consequently enhances odometry estimation without modifying the network architecture or increasing the number of parameters. The effectiveness of the proposed method is demonstrated through comparative evaluations on both the public KITTI dataset and a custom campus driving dataset, showing performance improvements over existing approaches. Full article

Infrared video imaging is an cornerstone technology for environmental perception, particularly in drone-based remote sensing applications such as disaster assessment and infrastructure inspection. Conventional systems, however, rely on bulky optical architectures that limit deployment on lightweight aerial platforms. Computational imaging offers a promising alternative by integrating optical encoding with algorithmic reconstruction, enabling compact hardware while maintaining imaging performance comparable to sophisticated multi-lens systems. Nonetheless, achieving real-time video-rate computational image restoration on resource-constrained unmanned aerial vehicles (UAVs) remains a critical challenge. To address this, we propose Mobile Wavelet Restoration-Net (MWR-Net), a lightweight deep learning framework tailored for real-time infrared image restoration. Built on a MobileNetV4 backbone, MWR-Net leverages depthwise separable convolutions and an optimized downsampling scheme to minimize parameters and computational overhead. A novel wavelet-domain loss enhances high-frequency detail recovery, while the modulation transfer function (MTF) is adopted as an optics-aware evaluation metric. With only 666.37 K parameters and 6.17 G MACs, MWR-Net achieves a PSNR of 37.10 dB and an SSIM of 0.964 on a custom dataset, outperforming a pruned U-Net baseline. Deployed on an RK3588 chip, it runs at 42 FPS. These results demonstrate MWR-Net’s potential as an efficient and practical solution for UAV-based infrared sensing applications. Full article

Rapid urbanization has intensified the systemic inequities of resources and infrastructure distribution in informal settlements, particularly in the Global South. Digital Twin Modeling (DTM), as an effective data-driven representation, enables real-time analysis, scenario simulation, and design optimization, making it a promising tool to support urban resilience. This study introduces the Integrated Modification Methodology (IMM), developed by Politecnico di Milano (Italy), to explore how DTM can be systematically structured and transformed into an active instrument, linking theories with practical application. Focusing on Paranoá (Brasília), a case study developed under the NBSouth project in collaboration with the Politecnico di Milano and the University of Brasília, this research integrates advanced spatial mapping with comprehensive key performance indicators (KPIs) analysis to address developmental and environmental challenges during the regeneration process. Key metrics—Green Space Diversity, Ecosystem Service Proximity, and Green Space Continuity—were analyzed by a Geographic Information System (GIS) platform on 30 m by 30 m sampling grids. Additional KPIs across urban structural, environmental, and mobility layers were calculated to support the decision-making process for strategic mapping. This study contributes to theoretical advancements in DTM and broader discourse on urban regeneration under climate stress, offering a systemic and practical approach for multi-dimensional digitalization of urban structure and performance, supporting a more adaptive, data-based, and transferable planning process in the Global South. Full article

Unanticipated interplay among digital access, institutional prestige, and support systems shapes academic outcomes in higher education. Survey responses from 387 undergraduates in Taiwan and Vietnam—two markets that experienced 80–130 percent growth in mobile broadband penetration between 2015 and 2023—reveal that greater university resource intensity is associated with higher course grades, whereas Reputation Capital and National Context factors unexpectedly correlate with lower performance. Moreover, while individual motivation robustly predicts achievement, a strong future orientation (long-term mindset) is linked to modest declines in grades, perhaps reflecting difficulties in balancing forward-looking goals with the demands of fast-paced, digitally mediated coursework. These counter-intuitive findings underscore the intricate dynamics of student success in technology-saturated learning environments and suggest that effective use of institutional resources and digital platforms requires targeted interventions—such as training in digital self-regulation and curricular designs that mitigate the downsides of prestige and pervasive connectivity—to optimize academic performance. Full article