Search Results (2,268)

Volatile anesthetics have steadily become more popular in intensive care units for sedation for reasons related to their beneficial pharmacokinetic and pharmacodynamic properties. Common anesthetics such as isoflurane and sevoflurane rapidly reach sedative levels in the body, but they are also rapidly eliminated, allowing for quick recovery. These agents have minimal impact on the liver and kidneys, which makes them attractive options when compared to other agents including opioids, benzodiazepines, ketamine, and propofol. Use of delivery systems like AnaConDa^® (Anaesthetic Conserving Device; Sedana Medical AB, Danderyd, Sweden) has enabled providers to easily use these agents in the Intensive Care Unit (ICU). In this regard, they have recently provided additional beneficial consideration during intravenous drug shortages seen during the COVID-19 pandemic and at other times. These agents have shown organ-protective effects in the kidneys and lungs, which may even reduce the total time spent in the ICU. Pharmacodynamically, these anesthetics mediate their effects through central nervous system ion channels to exert analgesic and anxiolytic actions, thereby minimizing effects in the kidneys and lungs. These agents are primarily eliminated via exhalation, which makes them potential options for those with liver or kidney failure. This narrative review examines current efficacy and risks of using volatile anesthetics for sedation in the ICU setting and clinical roles for the future. Full article

The Air-front Smart City (ASC) concept is proposed to address the stagnation of industries in developed countries and stimulate economic growth in developing countries while maintaining a higher quality of life for people and contributing to decarbonization and overall United Nations SDGs in an existing study. However, no studies have been conducted to assess ASC policies. Therefore, this study integrates the integrated accessibility index into the quality of life (QOL) and quality of business (QOB) evaluation models to assess the startup ecosystem in Aichi, Singapore, and Munich within the ASC concept. The study uses survey data conducted in Aichi to estimate monetary values of QOL and QOB component indicators, calculates the integrated accessibility indices, and estimates QOL and QOB. Furthermore, the study sets scenarios to assess the impacts of living and business urban policies in Aichi. Additionally, the study using Aichi parameters compares the startup ecosystem in Singapore and Munich. The result shows that the key drivers of startup attraction are corporate tax rate, economic growth, and safety; enhancing these indicators directly increases startups’ QOB, business partners, and residents’ QOL. It was found that QOB in Singapore is comparatively higher, whereas QOL is higher in Aichi. Full article

Quinoa attracts growing interest thanks to its nutritional value, biomass potential, and tolerance to cold, salinity, and drought, making it suitable for Mediterranean environments. Harvesting can be carried out with conventional wheat combine harvesters, although specific adjustments are required to ensure efficient seed–biomass separation and minimize losses. This study examined technical and environmental aspects of mechanized quinoa harvesting in southern Italy to identify the most effective threshing drum (TD) speed that limits losses while ensuring adequate seed separation. Field trials conducted in Puglia in 2022 and 2024, using modified combine harvesters and TD speeds between 600 and 900 rpm, showed wide variability in seed losses across settings. The 700-rpm setting yielded minimal losses in 2022 (Threshing Index, TI 6%), but proved inadequate in 2024 (TI 93%), as uneven ripening and lower yields compromised threshing efficiency. Conversely, 900 rpm produced the highest losses in 2022 (TI 67%) and the lowest cleaning efficiency with the highest residue percentage in 2024, confirming excessive mechanical aggressiveness. In 2024, 650 rpm showed relatively low losses (53%), but these were affected by reduced yield and incomplete detachment (TI 50%). In both years, 750 rpm provided the most stable performance, offering a balanced compromise between efficient seed detachment (TI 23% in 2022; 55% in 2024) and moderate seed losses (25% and 63%, respectively). Adaptive harvesting strategies, focused on appropriate machinery calibration and optimized agronomic practices, could promote the sustainable integration of quinoa into Mediterranean crop diversification systems. Full article

Sustainable human resource management (HRM) has attracted growing attention as a new paradigm for enhancing organizational resilience. However, prior studies mainly examined the effects of individual practices, offering a limited explanation of how organizational resilience emerges as an integrated mechanism. To address this theoretical gap, we conceptualize sustainable HRM as an integral talent management process in which multiple practices operate interdependently and investigate the configurational mechanisms through which organizational resilience is generated in Japanese firms and discuss these from the perspective of socioformation. Based on six analytical dimensions derived from a tertiary literature review, we conducted a crisp-set qualitative comparative analysis (csQCA) using securities report data from 36 listed Japanese companies. The results revealed that organizational resilience is not achieved through a single best practice, but rather points to a new form of integrated human resource management aimed at sustainable value creation. From a socioformation perspective, employees are viewed not merely as productive inputs but as agents capable of continuous development through sustained investment in human potential. From this perspective, sustainable social development cannot be reduced to well-being or inclusion indicators alone but also encompasses ethical, collaborative, territorial, and interdisciplinary dimensions of transformation. The findings clarify the theoretical role of integral talent management in sustainable value creation and provide practical implications for human-centred management. Full article

Multimodal Emotion Recognition in Conversation (ERC) has attracted attention as a key technology in human–computer interaction, mental healthcare, and intelligent services. However, deploying ERC in real-world settings remains challenging due to reliability gaps across modalities, instability in visual representations, and the high computational cost of large pretrained models. In particular, on resource-constrained edge devices, it is difficult to reduce model size and inference latency while preserving accuracy. To address these challenges, we jointly propose a knowledge-distillation-based multimodal ERC model, called DDVLM, with an edge-optimized Weight-Only Quantization (WOQ) pipeline for efficient edge deployment. DDVLM assigns the textual modality as the teacher and the visual modality as the student, transferring emotion-distribution knowledge to improve non-verbal representations and stabilize multimodal learning. In addition, Exponential Moving Average (EMA)-based self-distillation enhances the consistency and generalization capability of text features. Meanwhile, the proposed WOQ pipeline quantizes linear-layer weights to INT8 while preserving precision-sensitive operations in mixed precision, thereby minimizing accuracy loss and reducing model size, memory usage, and inference latency. Experiments on the MELD dataset demonstrated that the proposed approach achieves state-of-the-art performance while also enabling real-time inference on edge devices such as NVIDIA Jetson. Overall, this work presents a practical ERC framework that jointly considers accuracy and deployability. Full article

This paper introduces the New Bipolar Intuitionistic Fuzzy Metric Space (NBIFM-space)—a mathematical framework that extends intuitionistic and previously proposed bipolar intuitionistic structures by providing a complete three-component formulation based on positive similarity, negative similarity, and indeterminacy. Unlike earlier bipolar intuitionistic models, the NBIFM-space employs normalized metric components and coordinated triangular norms denoted by t-norm/t-conorm interactions, yielding a fully consistent topological and analytic setting. We have developed the basic properties of this structure and have demonstrated its effectiveness in image processing, where the explicit separation of attraction, repulsion, and uncertainty leads to robust edge-preserving filtering. Furthermore, a Banach-type fixed point theorem is established in the full NBIFM framework. Full article

Electroencephalography (EEG) has attracted growing interest as a biometric modality because it reflects ongoing brain activity and is inherently difficult to counterfeit. At the same time, EEG signals are influenced by internal conditions such as emotions, which may affect identification stability, particularly when recordings are obtained using portable consumer-grade systems. This study examines how emotional states influence EEG-based biometric performance and evaluates deep learning architectures to determine an effective modeling approach for cross-emotion robustness. EEG data were collected from 65 participants using a 14-channel Emotiv EPOC X headset, with 54 subjects retained after self-reported emotional validation. Recordings were acquired under neutral, positive, and negative visual stimuli. To address variability associated with portable acquisition, preprocessing made use of the device’s internal signal quality metrics to select reliable segments, compensate for degraded regions, and reduce noise. Among the evaluated models, a Bidirectional Long Short-Term Memory (BiLSTM) network enhanced with Convolutional Block Attention Module (CBAM) and Multi-Head Self-Attention (MHSA) achieved highest performance in our experiments. The model was trained on neutral-state data and subsequently evaluated under emotional conditions. It reached 95.91% accuracy in the neutral condition and maintained high performance under positive (94.31%) and negative (92.99%) states. Despite a modest decline under negative stimuli, identification performance remained stable. These findings support the feasibility of robust EEG-based biometric authentication using consumer-grade devices in realistic settings. Full article

Background/Objectives: The integration of deep learning and computer vision into healthcare has improved medical diagnosis and image analysis. Among object detection algorithms, the YOLO family has attracted substantial attention due to its ability to analyze images in real time with reported improvements in detection performance across multiple studies. This systematic review examines the evolution of YOLO algorithms for diagnostic applications in healthcare from YOLOv1 to YOLOv12. Methods: Peer-reviewed scientific articles published up to 1 January 2026 were retrieved from major scientific databases in accordance with PRISMA 2020 guidelines. The included studies applied YOLO models to medical imaging tasks, including disease and lesion detection and support for clinical procedures. Performance was synthesized using reported metrics such as average precision, accuracy, inference time, and computational efficiency. Results: The reviewed literature suggests progressive architectural refinements associated with reported improvements in diagnostic performance. YOLOv5 and YOLOv8 are the most frequently used architectures in diagnostic settings, reflecting a favorable trade-off between accuracy and computational complexity. YOLO-based methods have demonstrated strong performance across radiological, pathological, ophthalmological, and endoscopic applications. Conclusions: YOLO models have matured into robust and optimized solutions for medical image analysis; however, challenges remain in interpretability, cross-institution generalization, and deployment on edge devices. Future work on explainable YOLO-based diagnostics and energy-efficient model design will be particularly valuable. Full article

The coprime array, proposed in recent years as a special type of sparse array, combines the advantages of sparse sensing with the unique properties of prime numbers, enabling a larger array aperture and higher degrees of freedom with the same number of physical sensors. In underwater array signal processing, the high-resolution potential of coprime arrays has attracted significant attention. However, in complex ocean environments, leveraging the advantages of coprime arrays to achieve high-resolution and robust target detection still faces challenges posed by sensor failures. Element failures can disrupt the physical structure of the coprime array, leading to significantly increased energy in grating lobes and side lobes of the beam pattern, thereby raising the probability of false target azimuth identification. To address this issue, this paper analyzes the virtual array set mapped from the physical coprime array and proposes a multiplepath matching pursuit method for underwater vector coprime array target azimuth detection based on random virtual array set construction and verification techniques. Cases of continuous and non-continuous virtual arrays are analyzed, and corresponding solutions are proposed. Through simulations and analyses of sea trial data, it is demonstrated that the proposed method can achieve high-resolution target azimuth detection as well as robust target detection in the presence of physical sensor failures. Full article

Photosynthetic algae–microbial fuel cells (PAMFCs) are attractive for energy-positive wastewater treatment and carbon mitigation. However, PAMFC performance under continuous flow is often constrained by limited cathodic electron-acceptor supply and unstable photosynthetic biofilms, while the extent to which cathode interfacial engineering can stabilize diurnal power output and assimilative NH₄⁺–N removal remains unclear. In this study, the sponge-like and petal-like ZnO_0.2-NiO@rGO-modified carbon fibers (ZnO_0.2-NiO@rGO-pCFs and ZnO_0.2-NiO@rGO-pCFp) and pre-fabricated carbon felt (pCF) were used as cathode materials to construct three sets of PAMFC systems. Under light–dark cycling, the engineered cathodes reached steady operation within about 6.5 d and increased the steady-state voltage to approximately 0.35 V, compared with approximately 0.08 V for pCF. Under continuous-flow conditions, cathodic NH₄⁺–N removal exhibited a stable diurnal rhythm, with higher removal during illumination at about 43–51% than in the dark at about 29–30%, consistent with algal assimilation as the primary nitrogen sink, while cathode modification mainly improved the cathodic microenvironment and response stability. Compared with pCF, the ZnO_0.2–NiO@rGO cathode enriched a more even, Chlorophyta-dominated algal biofilm with an approximate relative abundance of 80%, indicating that its selective interfacial environment favors biofilm stabilization and sustains in situ oxygen production and cathodic electron-acceptor supply. Consequently, the composite cathode enhanced voltage output and stabilized light-enhanced, assimilative NH₄⁺–N removal under aeration-free operation, while establishing an interpretable link between electrochemical performance and 18S rDNA-derived community assembly features, thereby providing a low-cost cathode design basis for nitrogen removal in wastewater treatment. Full article

Oropouche fever (OF), caused by Oropouche virus (OROV), has expanded beyond its Amazonian range into Minas Gerais (MG), Brazil, raising concern about transmission in extra-Amazonian Atlantic Forest landscapes. Critical gaps persist regarding Culicoides vector communities, anthropophily, and climate-sensitive transmission risk in these newly affected regions. We conducted targeted entomological surveys outbreak-driven by human OF cases, standardized across five MG communities using CDC light traps and Protected Human Attraction (PHA) to characterize Culicoides composition. Females of Culicoides underwent RT-qPCR for OROV (n = 819) and physiological assessment (n = 312). We developed an entomological alert framework that integrates blood-fed abundance, minimum infection rate (MIR) upper confidence bounds, and environmental drivers (i.e., mean temperature, relative humidity and precipitation) via generalized additive mixed models, which explained 68% of the variability in Culicoides abundance and the alert index across communities. We collected 1171 Culicoides individuals representing five species (C. leopoldoi, C. paraensis, C. pusillus, C. foxi, and C. limai). C. leopoldoi (79.1%) and C. paraensis (20.3%) were the predominant species; notably, C. paraensis is recognized as the primary vector of OROV in the Americas. C. paraensis was documented for the first time in all five outbreak areas and dominated PHA captures (90%), suggesting anthropophily. Although no specimens tested OROV-positive (consistent with expected field infection rates of 0.01–1%), MIR upper bounds reached 132/1000 in low-sample settings and humidity and temperature strongly modulated abundance. This operational baseline and alert index transform virologically negative, sparse surveillance data into prioritized targets for intensified sampling and vector control during early, low-prevalence phases, when containment of OROV’s extra-Amazonian spread is still achievable. Full article

Wearable systems based on inertial measurement units (IMUs) have attracted considerable interest in recent years in the field of gait analysis. However, most gait studies using such devices have been conducted in laboratory rather than clinical settings. This study evaluated a commercially available IMU-based insole system in two cohorts: a clinical group (59 ± 18, years) recruited from podiatry clinics and a non-clinical group (28 ± 7, years) recruited from a university with no reported complaints. Participants wore the IMU-based device and performed treadmill walking (clinical group) and overground walking (non-clinical group). Spatiotemporal parameters were compared between groups using statistical analyses included the Shapiro–Wilk test, Mann–Whitney test, and Welch’s t-tests for non-bilateral data, and a two-factor linear mixed-effects model estimated by restricted maximum likelihood (REML) for bilateral spatiotemporal parameters to evaluate group, foot-side, and interaction effects. Ten of the twenty-two spatiotemporal parameters showed significant group differences, with statistical significance observed in at least one foot for parameters measured bilaterally. The observed differences may reflect a combination of clinical characteristics, age-related effects, and walking environment influences. Findings are discussed in relation to potential biomechanical mechanisms, factors influencing results and the clinical utility of IMU systems. Future research should investigate specific foot conditions under standardized walking conditions with age-matched cohorts. Full article

Background: At the end of each year, stakeholders of the Essential Immunization Programme (EPI) in the DR Congo meet to review progress made and lessons learned from the implementation of the Annual Operational Plan (AOP) and to set priorities for the following year. This paper presents a conference report that summarizes the main outcomes of the 2025 annual review meeting, which took place from 15 to 20 December 2025, and attracted 76 participants. Conference takeaways: While the 2024 WUENIC data show that the DR Congo is off-track for the 2030 Immunization agenda targets for all antigens, the administrative coverages were reported as optimal in 2025. EPI activities are planned based on administrative coverages, likely overestimated. In 2025, 47% of health zones in North-Kivu, South-Kivu and Ituri (49 out of 104) were fully or partially controlled by armed groups, leading to partial disruptions of immunization service delivery. In 2025, the DR Congo successfully launched the measles–rubella vaccine introduction preceded by a catch-up vaccination campaign in children aged from 6 months to 14 years old and continued to roll out malaria vaccines using a phased approach. Conclusions: Learning from the implementation of the 2025 AOP, the EPI stakeholders adopted a set of priority actions for the immunization programme in 2026. Full article

Urban business-to-business distribution in Casablanca relies heavily on light commercial vehicles (LCVs) operating in a constrained street environment where loading/unloading access, intersection capacity, and recurring bottlenecks jointly shape performance and environmental impacts. However, high-resolution freight origin–destination (OD) observations and junction calibration data are limited, which complicates direct estimations of congestion and externalities attributable to commercial activity. This study develops a reproducible, large-scale modeling workflow that couples tour-based freight demand generation in order units with simulation-based traffic assignment (SBA) on a metropolitan network and translates network performance into emissions and monetary losses. Warehouses are modeled as primary producers and commercial activity zones as attractors via sector-tagged production and attraction functions; the resulting order distribution is converted to OD vehicle trips using the tour-based trip generation procedure with the mean targets-per-tour fixed to one to ensure numerical stability, yielding a direct-shipment approximation appropriate for stress–response analysis. Junction impedance is represented through turn-type volume–delay relationships and node-level impedance procedures, and congestion is evaluated using vehicle kilometers traveled/vehicle hours traveled (VKT/VHT)-based indicators, delay-intensity measures, and link/node bottleneck rankings. Across demand-scaling scenarios, VKT increases from 302,159 to 1,017,686 veh·km/day, while network delay rises nonlinearly from 392.5 to 2738.4 veh·h/day, indicating saturation-driven amplification of time losses. The Handbook of Emission Factors for Road Transport (HBEFA)-compatible emission estimates scale with activity: total carbon dioxide (CO₂) increases from 154.1 to 519.5 t/day, and nitrogen oxides (NOx) and particulate matter (PM_2.5) totals rise proportionally under fixed fleet assumptions. Monetizing delay with a purchasing-power-adjusted value-of-time range yields a congestion cost per trip that increases from approximately 0.20 to 0.41 Moroccan dirham, MAD/trip (at 60 MAD/veh·h), consistent with rising delay intensity. Bottleneck extraction shows welfare losses to be structurally concentrated on a small persistent corridor set, led by ‘Boulevard de la Résistance’, with recurrent hotspots including ‘Rue d’Arcachon’ and ‘Rue d’Ifni’. The framework supports policy-relevant reporting of congestion, emissions, and welfare impacts under data scarcity, with explicit sensitivity bounds. Full article

Chrysomya megacephala is a synanthropic fly with a high potential to act as a mechanical vector of pathogenic bacteria, surpassing Musca domestica in both bacterial load and diversity. Native to Asia and Africa, it has become a cosmopolitan species, successfully adapting to a wide range of environments, including natural ecosystems. In Colombia, studies on its role as a vector are limited and have largely relied on traditional culturing methods. This study aimed to characterize the pathogenic bacterial microbiota associated with C. megacephala using 16S rRNA gene sequencing in urban, rural, and forest settings of a coastal tourist city. Flies were collected using Van Someren Rydon traps with attractants and sterile materials. Bacterial identification was performed through Oxford Nanopore MinION sequencing (Manufactured by Oxford Nanopore Technologies, Oxford, UK). A total of 49 bacterial species were identified, with urban environments showing the highest taxonomic richness. The forest environment was characterized by a highly dominant community structure, led by Vagococcus carniphilus. Notably, 20 bacterial species of public health relevance were detected, including Clostridium botulinum, Clostridium perfringens, Ignatzschineria ureiclastica, Escherichia coli, and Streptococcus agalactiae. These findings indicate that bacterial community composition varies by environment and underscore the potential role of C. megacephala as a mechanical vector, highlighting the importance of surveillance for its public health implications. Full article