Search Results (16,465)

Background/Objectives: This single-cohort follow-up study describes the median overall survival (OS) in patients with unresectable head and neck squamous cell carcinoma (HNSCC) due to invasion of vital structures, which is under-represented in the current literature. Secondarily, subgroups were evaluated according to the type of presentation, in order to identify clinical characteristics and contribute to developing an appropriate treatment plan and managing patient’s expectations. Methods: This single-cohort observational study analysed the OS of 39 patients from the Otolaryngology Department with advanced-stage head and neck cancer with invasion of vital anatomical structures considered ineligible for surgical treatment. Secondarily, subgroups were evaluated according to type of presentation and various clinical characteristics. Results: A total of 39 patients radiologically classified as having unresectable HNSCC (i.e., unsuitable for surgical resection), with a mean age of 66.87 years, were included during a 24-month follow-up. By the end of the study, 56.4% of the patients had died. The median OS was 16.09 months. Statistically significant differences were observed when comparing human papilloma virus (HPV)-positive and -negative status and when comparing initial and recurrent tumours. Conclusions: The invasion of anatomical structures such as the skull base, internal carotid artery, and prevertebral space was associated with a marked decrease in survival, with an OS time of 16 months. This study provides valuable evidence in patients with unresectable HNSCC, highlighting tumour recurrence and HPV-negative status as important indicators of poor prognosis. Full article

Acromegaly is caused by an excessive secretion of growth hormone and the secondary elevation of IGF-1 levels, leading to progressive changes in multiple body systems, including the craniofacial region and oral cavity. Dental manifestations such as mandibular overgrowth, macroglossia, malocclusion, periodontal disease, and prosthetic difficulties represent not only a clinical component of the disease but also a significant therapeutic and diagnostic challenge. The aim of this review is to present the current state of knowledge on the relationship between acromegaly and oral health and to analyze the role of interdisciplinary collaboration between endocrinologists and dentists in patient care. For this narrative review, a literature search was conducted in the PubMed, Scopus, and Web of Science databases covering the period from 2000 to 2025. Sixty-two peer-reviewed publications meeting the methodological and thematic criteria were included in the analysis, including original studies, meta-analyses, systematic reviews, and case reports. The results indicate significant correlations between disease activity and the severity of periodontal and microbiological changes, while effective endocrine treatment only results in the partial regression of morphological changes. Particular attention was given to the role of the dentist in recognizing the early symptoms of the disease, planning prosthetic and surgical treatment, and monitoring therapy-related complications. Interdisciplinary collaboration models, including integrated clinics and co-managed care, were also described as optimal systemic solutions for improving treatment quality. The conclusion drawn from the analysis are as follows: there is a need for the permanent integration of dentistry into the standard of interdisciplinary care for patients with acromegaly, in both diagnostic and therapeutic dimensions. Increasing awareness among dentists and developing integrated collaboration models may reduce the time to diagnosis, improve patients’ quality of life, and enable the more effective management of craniofacial complications in the course of this rare disease. Full article

In China’s Loess Plateau, sustainable agricultural end products are affected by an insufficiency of water resources. Rising crop water use efficiency (WUE) through field management pattern improvement is a crucial plan of action to address this issue. However, there is no agreement among researchers on the most appropriate field management practices regarding WUE, which requires further integrated quantitative analysis. We conducted a meta-analysis by quantifying the effect of agricultural practices surrounding nitrogen (N) fertilizer management. The two experimental cultivars were Yunhan–20410 and Yunhan–618. The subplots included nitrogen 0 kg·ha⁻¹ (N0), 90 kg·ha⁻¹ (N90), 180 kg·ha⁻¹ (N180), 210 kg·ha⁻¹ (N210), and 240 kg·ha⁻¹ (N240). Our results show that higher N rates (up to N210) enhanced water consumption during the node-flowering and flowering-maturity time periods. YH–618 showed higher water use during the sowing–greening and node-flowering periods but decreased use during the greening-node and flowering-maturity periods compared to YH–20410. The N210 treatment under YH–618 maximized water use efficiency (WUE). Increased N rates (N180–N210) decreased covering temperatures (T_max, T_min, T_aver) during flowering, increasing the level of grain filling. Spike numbers rose with N application, with an off-peak at N210 for strong-gluten wheat. The 1000-grain weight was at first enhanced but decreased at the far end of N180–N210. YH–618 with N210 achieved a harvest index (HI) similar to that of YH–20410 with N180, while excessive N (N240) or water reduced the HI. Dry matter accumulation increased up to N210, resulting in earlier stabilization. Soil water consumption from wintering to jointing was strongly correlated with pre-flowering dry matter biological process and yield, while jointing–flowering water use was linked to post-flowering dry matter and spike numbers. Post-flowering dry matter accumulation was critical for yield, whereas spike numbers positively impacted yield but negatively affected 1000-grain weight. In conclusion, our results provide evidence for determining suitable integrated agricultural establishment strategies to ensure efficient water use and sustainable production in the Loess Plateau region. Full article

The Tibetan-Himalayan region, recognized as a global biodiversity hotspot, is increasingly threatened by the dual pressures of climate change and human activities. Understanding the vulnerability of plant species to these forces is crucial for effective ecological conservation in this region. This study employed an improved Climate Niche Factor Analysis (CNFA) framework to assess the vulnerability of six representative alpine endemic herbaceous plants in this ecologically sensitive region under future climate changes. Our results show distinct spatial vulnerability patterns for the six species, with higher vulnerability in the western regions of the Tibetan-Himalayan region and lower vulnerability in the eastern areas. Particularly under high-emission scenarios (SSP5-8.5), climate change is projected to substantially intensify threats to these plant species, reinforcing the imperative for targeted conservation strategies. Additionally, we found that the current coverage of protected areas (PAs) within the species’ habitats was severely insufficient, with less than 25% coverage overall, and it was even lower (<7%) in highly vulnerable regions. Human activity hotspots, such as the regions around Lhasa and Chengdu, further exacerbate species vulnerability. Notably, some species currently classified as least concern (e.g., Stipa purpurea (S. purpurea)) according to the IUCN Red List exhibit higher vulnerability than species listed as near threatened (e.g., Cyananthus microphyllus (C. microphylla)) under future climate change. These findings suggest that existing biodiversity assessments, such as the IUCN Red List, may not adequately account for future climate risks, highlighting the importance of incorporating climate change projections into conservation planning. Our study calls for expanding and optimizing PAs, improving management, and enhancing climate resilience to mitigate biodiversity loss in the face of climate change and human pressures. Full article

The rapid growth of electric vehicles (EVs) and new energy systems has put lithium-ion batteries at the center of the clean energy change. Nevertheless, to achieve the best battery performance, safety, and sustainability in many changing circumstances, major innovations are needed in Battery Management Systems (BMS). This review paper explores how artificial intelligence (AI) and digital twin (DT) technologies can be integrated to enable the intelligent BMS of the future. It investigates how powerful data approaches such as deep learning, ensembles, and models that rely on physics improve the accuracy of predicting state of charge (SOC), state of health (SOH), and remaining useful life (RUL). Additionally, the paper reviews progress in AI features for cooling, fast charging, fault detection, and intelligible AI models. Working together, cloud and edge computing technology with DTs means better diagnostics, predictive support, and improved management for any use of EVs, stored energy, and recycling. The review underlines recent successes in AI-driven material research, renewable battery production, and plans for used systems, along with new problems in cybersecurity, combining data and mass rollout. We spotlight important research themes, existing problems, and future drawbacks following careful analysis of different up-to-date approaches and systems. Uniting physical modeling with AI-based analytics on cloud-edge-DT platforms supports the development of tough, intelligent, and ecologically responsible batteries that line up with future mobility and wider use of renewable energy. Full article

Urban flooding has significantly impacted the livelihoods of households and communities worldwide. It highlights the urgency of focusing on both flood preparedness and adaptation strategies to understand the community’s perception and adaptive capacity. This study investigates the levels of risk perception, flood preparedness, and adaptive capacity, while also exploring the inter-relationships among these factors within the context of urban flooding in Malaysia. A quantitative approach was employed, involving a structured questionnaire administered to residents in flood-prone urban areas across Greater Kuala Lumpur, Malaysia. A total of 212 responses were analysed using descriptive statistics, categorical index classification, and Spearman correlation analysis. The findings indicate that residents generally reported high levels of risk perception and preparedness, although adaptive capacity exhibited greater variability, with a mean score of 3.97 (SD = 0.64). Positive associations were found among risk perception, flood preparedness, and adaptive capacity. This study contributes to the existing knowledge by providing evidence on community resilience and highlighting key factors that can guide flood management policies and encourage adaptive planning at the community level. Full article

Background and Clinical Significance: Brugada syndrome (BrS) is a rare inherited cardiac channelopathy, often associated with SCN5A loss-of-function mutations. Clinical presentations range from asymptomatic to malignant arrhythmias and sudden cardiac death. Physiological and pharmacological stressors affecting sodium channel function—such as pyrexia, certain medications, and possibly pregnancy—may unmask or exacerbate arrhythmic risk. However, there is limited information regarding pregnancy and obstetric outcomes. Obstetric management remains largely informed by isolated case reports and small case series. A literature review was conducted using OVID Medline and Embase, identifying case reports, case series, and one retrospective cohort study reporting clinical presentation, obstetric management, and outcomes in maternal BrS. A case is presented detailing coordinated multidisciplinary input, antenatal surveillance, and intrapartum and postpartum care to contribute to the growing evidence base guiding obstetric care in this complex setting. Case Presentation: A 30-year-old G2P0 woman with asymptomatic BrS (SCN5A-positive) was referred at 31 + 5 weeks’ gestation for multidisciplinary antenatal care. Regular review and collaborative planning involving cardiology, anaesthetics, maternal–fetal medicine, and obstetrics guided a plan for vaginal delivery with continuous cardiac and fetal monitoring. At 38 + 0 weeks, the woman presented with spontaneous rupture of membranes and underwent induction of labour. A normal vaginal delivery was achieved without arrhythmic events. Epidural block with ropivacaine and local anaesthesia with lignocaine were well tolerated, and 24 h postpartum monitoring revealed no abnormalities. Conclusions: This case adds to the limited but growing literature suggesting that with individualised planning and multidisciplinary care, pregnancies in women with BrS can proceed safely and without complication. Ongoing case reporting is essential to inform future guidelines and optimise maternal and fetal outcomes. Full article

We studied Galápagos barn owls on Santa Cruz Island in the Galápagos Archipelago. We collected and analyzed pellets to determine diet composition. Barn-owl diet consisted—in terms of biomass—of ~89% rodents and ~10% insects. Bird remains occurred in 1% of the pellets. Foraging was studied with data loggers, a method not previously applied to the study of Galápagos barn owls. Owls rested during the day in natural and human-built roosts such as lava holes, trees, or huts. Night-time foraging was characterized by periods during which the bird moved and periods during which the bird stayed within one place, with the latter amounting to ~56% of the time away from the day roost. Birds began foraging shortly after sunset and returned to their day roost before sunrise. The duration of foraging was approximately 11 h per night. Foraging areas were small (median value: 0.28 km²). Although our data demonstrate a continued presence of the subspecies, we regard the situation for this subspecies as labile, as multiple threats, such as road kills, poisoning, and intentional killing by farmers, have increased recently, and suggest the development of a management plan to improve its conservation. Full article

Wildfires have become increasingly frequent and destructive environmental hazards, especially in boreal ecosystems facing prolonged droughts and temperature extremes. This study presents an integrated spatio-temporal framework that combines Spatio-Temporal Density-Based Spatial Clustering of Applications with Noise (ST-DBSCAN), Fire Radiative Power (FRP), and the differenced Normalized Burn Ratio (ΔNBR) to characterize the dynamics and ecological impacts of large-scale wildfires, using the extreme 2023 Quebec fire season as a case study. The analysis of 80,228 VIIRS fire detections resulted in 19 distinct clusters across four fire zones. Validation against the National Burned Area Composite (NBAC) showed high spatial agreement in densely burned areas, with Intersection over Union (IoU) scores reaching 62.6%. Gaussian Process Regression (GPR) revealed significant non-linear relationships between FRP and key fire behavior metrics. Higher mean FRP was associated with both longer durations and greater burn severity. While FRP was also linked to faster spread rates, this relationship varied by zone. Notably, Fire Zone 2 exhibited the most severe ecological impact, with 83.8% of the area classified as high-severity burn. These findings demonstrate the value of integrating spatial clustering, radiative intensity, and post-fire vegetation damage into a unified analytical framework. Unlike traditional methods, this approach enables scalable, hypothesis-driven assessment of fire behavior, supporting improved fire management, ecosystem recovery planning, and climate resilience efforts in fire-prone regions. Full article

Background: this research presents an integrated approach to enhancing the sustainability of plastic bag supply chains in Indonesia by addressing critical issues related to ineffective post-consumer waste management and low recycling rates. The objective of this study is to develop a combined framework of material flow analysis (MFA) and sustainable supply chain planning to improve demand forecasting and inflow management across the plastic bag lifecycle. Method: the research adopts a quantitative method using the XGBoost algorithm for forecasting and is supported by a polymer-based MFA framework that maps material flows from production to end-of-life stages. Result: the findings indicate that while production processes achieve high efficiency with a yield of 89%, more than 60% of plastic bag waste remains unmanaged after use. Moreover, scenario analysis demonstrates that single interventions are insufficient to achieve circularity targets, whereas integrated strategies (e.g., reducing export volumes, enhancing waste collection, and improving recycling performance) are more effective in increasing recycling rates beyond 35%. Additionally, the study reveals that increasing domestic recycling capacity and minimizing dependency on exports can significantly reduce environmental leakage and strengthen local waste management systems. Conclusions: the study’s novelty lies in demonstrating how machine learning and material flow data can be synergized to inform circular supply chain decisions and regulatory planning. Full article

Electricity distribution systems face increasing challenges due to demand growth, regulatory requirements, and the integration of distributed generation. In this context, distribution companies must make strategic and well-supported investment decisions, particularly in asset reinforcement actions such as reconductoring. This paper presents a multi-stage methodology to optimize reconductoring investments under load forecasting uncertainties. The approach combines a decomposition strategy with Monte Carlo simulation to capture demand variability. By discretizing a lognormal probability density function and selecting the largest loads in the network, the methodology balances computational feasibility with modeling accuracy. The optimization model employs exhaustive search techniques independently for each network branch, ensuring precise and consistent investment decisions. Tests conducted on the IEEE 123-bus feeder consider both operational and regulatory constraints from the Brazilian context. Results show that uncertainty-aware planning leads to a narrow investment range—between USD 55,108 and USD 66,504—highlighting the necessity of reconductoring regardless of demand scenarios. A comparative analysis of representative cases reveals consistent interventions, changes in conductor selection, and schedule adjustments based on load conditions. The proposed methodology enables flexible, cost-effective, and regulation-compliant investment planning, offering valuable insights for utilities seeking to enhance network reliability and performance while managing demand uncertainties. Full article

While horizontal roughing filters (HRFs) remain widely acclaimed for their exceptional efficiency in water treatment, especially in developing countries, they are inherently susceptible to clogging, which necessitates timely maintenance interventions. Conventional methods for managing clogging in HRFs typically involve evaluating filter head loss coefficients against established water quality standards. This study utilizes artificial neural network (ANN) for the prediction of clogging duration and effluent turbidity in HRF equipment. The ANN was configured with two outputs, the clogging duration and effluent turbidity, which were predicted concurrently. Effluent turbidity was modeled to enhance the network’s learning process and improve the accuracy of clogging prediction. The network steps of the iterative training process of ANN used different types of input parameters, such as influent turbidity, filtration rate, pH, conductivity, and effluent turbidity. The training, in addition, optimized network parameters such as learning rate, momentum, and calibration of neurons in the hidden layer. The quantities of the dataset accounted for up to 70% for training and 30% for testing and validation. The optimized structure of ANN configured in a 4-8-2 topology and trained using the Levenberg–Marquardt (LM) algorithm achieved a mean square error (MSE) of less than 0.001 and R-coefficients exceeding 0.999 across training, validation, testing, and the entire dataset. This ANN surpassed models of scaled conjugate gradient (SCG) and obtained a percentage of average absolute deviation (%AAD) of 9.5. This optimal structure of ANN proved to be a robust tool for tracking the filter clogging duration in HRF equipment. This approach supports proactive maintenance and operational planning in HRFs, including data-driven scheduling of backwashing based on predicted clogging trends. Full article

With the accelerating global transition toward sustainable energy systems, power grids with a high share of renewable energy face increasing challenges due to volatility and uncertainty, necessitating advanced flexibility resource planning and stability optimization strategies. This paper presents a comprehensive distribution network planning framework that coordinates and integrates multiple types of flexibility resources through joint optimization and network reconfiguration to enhance system adaptability and operational resilience. A novel virtual network coupling modeling approach is proposed to address topological constraints during network reconfiguration, ensuring radial operation while allowing rapid topology adjustments to isolate faults and restore power supply. Furthermore, to mitigate the uncertainty and fault risks associated with extreme weather events, a CVaR-based risk quantification framework is incorporated into a bi-level optimization model, effectively balancing investment costs and operational risks under uncertainty. In this model, the upper-level planning stage optimizes the siting and sizing of flexibility resources, while the lower-level operational stage coordinates real-time dispatch strategies through demand response, energy storage operation, and dynamic network reconfiguration. Finally, a hybrid SA-PSO algorithm combined with conic programming is employed to enhance computational efficiency while ensuring high solution quality for practical system scales. Case study analyses demonstrate that, compared to single-resource configurations, the proposed coordinated planning of multiple flexibility resources can significantly reduce the total system cost and markedly improve system resilience under fault conditions. Full article

Objective: This study aimed to improve the understanding of the lives of patients with chronic neuropathic pain planned for invasive motor cortex stimulation (iMCS) and assess their expectations towards this intervention and its impact. Methods: Semi-structured face-to-face interviews were conducted until saturation of data was reached. Patients were recruited from one university medical center in the Netherlands. All interviews were audio-recorded, transcribed verbatim, and subjected to thematic analysis using iterative and inductive coding by two researchers independently. Results: Fifteen patients were included (11 females; mean age 63 ± 9.4 yrs). Analysis of the coded interviews revealed seven themes: (1) the consequences of living with chronic neuropathic pain; (2) loss of autonomy and performing usual activities; (3) balancing energy and mood; (4) intimacy; (5) feeling understood and accepted; (6) meaning of life; and (7) the expectations of iMCS treatment. Conclusions: This is the first qualitative study that describes the suffering of patients with chronic neuropathic pain, and their expectations prior to invasive brain stimulation. Significant themes in the lives of patients with chronic pain have been brought to light. The findings strengthen communication between physicians, caregivers, and patients. Practice Implications: The insights gathered from the interviews create a structured framework for comprehending the values and expectations of patients living with central pain and reveal the impact of symptoms due to the central pain. This knowledge improves the communication between physicians and caregivers on one side and the patient on the other side. Furthermore, the framework enhances the capacity for shared decision-making, particularly in managing expectations related to iMCS. Full article

This study presents the scientific and methodological foundation of Spain’s first national framework for the ethical management of community cat populations: the Action Plan for the Management of Community Cat Colonies (PACF), launched in 2025 under the mandate of Law 7/2023. This pioneering legislation introduces a standardized, nationwide obligation for trap–neuter–return (TNR)-based management of free-roaming cats, defined as animals living freely, territorially attached, and with limited socialization toward humans. The PACF aims to support municipalities in implementing this mandate through evidence-based strategies that integrate animal welfare, biodiversity protection, and public health objectives. Using standardized data submitted by 1128 municipalities (13.9% of Spain’s total), we estimated a baseline population of 1.81 million community cats distributed across 125,000 colonies. These data were stratified by municipal population size and applied to national census figures to generate a model-ready demographic structure. We then implemented a stochastic simulation using Vortex software to project long-term population dynamics over a 25-year horizon. The model integrated eight demographic–environmental scenarios defined by a combination of urban–rural classification and ecological reproductive potential based on photoperiod and winter temperature. Parameters included reproductive output, mortality, sterilization coverage, abandonment and adoption rates, stochastic catastrophic events, and territorial carrying capacity. Under current sterilization rates (~20%), our projections indicate that Spain’s community cat population could surpass 5 million individuals by 2050, saturating ecological and social thresholds within a decade. In contrast, a differentiated sterilization strategy aligned with territorial reproductive intensity (50% in most areas, 60–70% in high-pressure zones) achieves population stabilization by 2030 at approximately 1.5 million cats, followed by a gradual long-term decline. This scenario prioritizes feasibility while substantially reducing reproductive output, particularly in rural and high-intensity contexts. The PACF combines stratified demographic modeling with spatial sensitivity, offering a flexible framework adaptable to local conditions. It incorporates One Health principles and introduces tools for adaptive management, including digital monitoring platforms and standardized welfare protocols. While ecological impacts were not directly assessed, the proposed demographic stabilization is designed to mitigate population-driven risks to biodiversity and public health without relying on lethal control. By integrating legal mandates, stratified modeling, and realistic intervention goals, this study outlines a replicable and scalable framework for coordinated action across administrative levels. It exemplifies how national policy can be operationalized through data-driven, territorially sensitive planning tools. The findings support the strategic deployment of TNR-based programs across diverse municipal contexts, providing a model for other countries seeking to align animal welfare policy with ecological planning under a multi-level governance perspective. Full article