Search Results (17,401)

Storm Kristin affected central Portugal on 28 January 2026, producing exceptional wind gusts and widespread disruption across the natural, infrastructural and built environments. This paper presents the results of a post-disaster reconnaissance mission carried out in the municipalities of Leiria and Marinha Grande, two of the most affected areas. The methodology combined ground-based visual inspection, photographic records, unmanned aerial vehicle surveys, terrestrial laser scanning, and information provided by local authorities and infrastructure managers. The results show that the spatial distribution and severity of the damage were consistent with the occurrence of very intense localised gusts associated with a sting jet mechanism. Extensive damage was observed in vegetation, transport networks, electrical and telecommunications infrastructure, public spaces and different building typologies. Across the building stock, roofing systems were the most vulnerable components, particularly lightweight panels, ceramic tiles, ridge elements and poorly anchored envelope systems. Industrial and public buildings showed the most severe failures, including extensive roof loss, deformation of structural and envelope components, and local collapse, while residential buildings generally presented minor-to-moderate damage. Heritage buildings showed specific vulnerabilities related to cumulative degradation, exposed architectural elements and the consequences of local damage for historic fabric. The findings highlight the need for improved wind-risk preparedness, systematic post-event documentation, enhanced roof and cladding anchorage, preventive maintenance, and further investigation of the relationship between recorded wind speeds, observed damage patterns and current wind-design assumptions. Full article

Population aging is a growing problem. This process is driven not only by genetic factors but also by environmental factors, such as diet. Alzheimer’s disease (AD) is a progressive neurodegenerative disorder and the leading cause of dementia worldwide, characterized by cognitive decline, synaptic dysfunction, and neuronal loss. Despite extensive research, effective disease-modifying therapies remain limited. Increasing evidence indicates that oxidative stress plays a central role in AD pathogenesis, acting as a key link between β-amyloid accumulation, tau hyperphosphorylation, mitochondrial dysfunction, and neuroinflammation. Accordingly, dietary strategies have been proposed to mitigate these pathological processes and may represent an important component of Alzheimer’s disease prevention. Moreover, emerging evidence on the gut–brain axis highlights the critical role of gut microbiota in regulating neuroinflammation and oxidative stress. Dysbiosis has been associated with increased permeability of the intestinal barrier, systemic inflammation, and accelerated neurodegeneration. Dietary patterns such as the Mediterranean, DASH, and MIND diets may exert beneficial effects by simultaneously influencing antioxidant status and microbial composition. This review aims to provide a comprehensive overview of the role of oxidative stress in Alzheimer’s disease and evaluate the potential of dietary interventions in modulating mechanisms involved in Alzheimer’s disease pathogenesis and supporting cognitive health. Particular attention is given to the neuroprotective effects of dietary antioxidants, including vitamins, polyphenols, and polyunsaturated fatty acids, which act through the reduction in reactive oxygen species, modulation of inflammatory pathways, and support of neuronal survival. Although current findings are promising, inconsistencies in clinical data indicate the need for further well-designed studies. Future research should focus on personalized nutritional strategies integrating dietary, genetic, and microbiome-related factors. Targeting oxidative stress through diet and microbiota modulation represents a promising complementary strategy for Alzheimer’s disease prevention and supportive management, although further clinical studies are required to establish disease-modifying effects. Full article

Deep learning surrogate models trained with mean-squared-error loss produce statistically accurate but physically unconstrained flood predictions: water may flow uphill, appear spontaneously, or smooth over street-level corridors. In this work, a physics-informed training framework is developed for CNN-LSTM models that predict urban flood depths at 15 min intervals over a $128\times 128$ spatial grid. Three differentiable penalty terms are embedded directly into the loss function: (i) a gravity loss that penalizes depth increases against the water-surface-elevation gradient, (ii) a continuity loss enforcing local mass conservation with rainfall-adaptive thresholds, and (iii) a topography-aware false-alarm penalty modulated by the topographic wetness index (TWI). The framework is evaluated on the Norfolk, Virginia, flood dataset spanning two major storm events (August 2017 and September 2022) comprising 300 samples, with all variants trained on identical splits and robustness assessed over repeated random splits and leave-one-storm-out tests. A road-proximal evaluation restricted to a TWI-derived street mask quantifies street-level skill. The physics-constrained model achieves near-zero gravity violations (∼10⁻⁶) and the highest street-channel recall (0.77 ± 0.09 versus 0.44 ± 0.10 for the unconstrained baseline), the capability most relevant to downstream traffic routing, and its recall advantage more than doubles on a held-out storm, while a uniform false-alarm variant attains 16% lower mean absolute error but suppresses street recall to 0.25. The proposed TWI-modulated penalty reconciles this trade-off: it improves upon the uniform variant on every metric measured, recovering 60% higher street recall at the lowest MAE among all constrained variants and the best street-level F1 score. These results expose a fundamental tension between aggregate pixel-level error metrics and application-specific physical plausibility, and demonstrate that terrain-aware loss modulation offers a principled resolution. Full article

Convolutional neural network (CNN)-based methods have been widely adopted in hyperspectral image (HSI) classification tasks and have demonstrated superior performance due to their potent nonlinear fitting capabilities. However, as network depth increases, existing methods suffer from significant issues such as gradient vanishing and information loss, which limit further improvements in model performance. Meanwhile, current approaches primarily design entirely new network architectures to extract abstract features but fail to fully exploit the explicit gradient prior information inherent in the data, which affects model performance in complex scenarios. To address these issues, this paper proposes a Multiscale Dense HOG-driven Self-Attention Network (MDHANet) that incorporates the Histogram of Oriented Gradients (HOG) as prior knowledge. First, the model employs three densely connected branches at different scales to obtain diverse multi-level feature representations through multi-scale feature reuse. Furthermore, since HOG can explicitly encode the distribution of gradient orientations and magnitudes in local neighborhoods and accurately characterize the feature distribution, a dynamic HOG-driven self-attention module is designed. Within this module, a HOG prior-based feature rearrangement strategy together with a dual-branch self-attention mechanism enables the network to more effectively learn the spatial information of the data, enhancing the extraction of discriminative spatial–structure features and further improving classification performance. Extensive experimental results on multiple datasets demonstrate that the proposed method outperforms existing state-of-the-art approaches. Extensive experimental results on four HSI datasets (IP, LK, HH, and HC) demonstrate that the proposed method outperforms existing state-of-the-art approaches, achieving OA of 99.13%, 97.32%, 99.02%, and 99.24%, respectively, with improvements of 0.83%, 0.41%, 2.69%, and 2.47% over the best competing methods. Full article

Nature-Based Solutions (NBS) have emerged as a prominent and rapidly expanding strategic approach for addressing climate change, biodiversity loss, and socio-ecological resilience in cities, especially within European sustainability agendas where NBS are embedded in EU policy frameworks. Nevertheless, their practical implementation remains uneven across the globe, with relatively strong mainstreaming in Europe but more fragmented uptake in other regions. These disparities stem from a gap between policy-oriented NBS discourse and its translation into design and spatial practice. This study investigates how governance-related NBS research can be more effectively translated into landscape architecture and design practice. It integrates insights from narrative literature reviews, project-based experiences, and international conference sessions. We argue that effective NBS implementation requires alignment with landscape architecture (systems-multi-scale planning, design, site-specific ecological materialization and aesthetic mediation) and governance integration. Through comparative analysis of Global North and South contexts, we identify differences in institutional capacity, socio-cultural perception of nature, and knowledge systems that shape NBS implementation. We propose a multi-scale, co-creative framework connecting environmental knowledge, governance interface, and design practice. The findings demonstrate that NBS cannot succeed solely as a science and policy approach; instead, they must be spatially translated through culturally responsive and ecologically informed landscape design processes and maintenance. Full article

Building-integrated photovoltaics (BIPVs) can expand the available photovoltaic area on buildings, especially in dense urban contexts with limited roof surfaces. For transparent-envelope applications, such as photovoltaic shading devices (PVSDs) and semi-transparent photovoltaic glazing (STPV), PV-yield assessment is particularly challenging because PV elements also function as part of the building envelope. Their energy yield depends on multiple interacting factors: irradiation, orientation, shading, incidence-angle effects, operating temperature, and, for STPV, glazing thermal behavior. In early-stage building design, however, these effects must be assessed while system characteristics are still evolving, and detailed product or module data are often unavailable. To address this gap, this study develops and evaluates an EnergyPlus-based approach for PV-yield assessments for transparent-envelope BIPV applications. The approach replaces fixed PV efficiency with a time-dependent effective efficiency that accounts for incidence-angle reflection and temperature-related efficiency losses. It is evaluated through a cross-tool comparison with established PV-yield assessment tools. For PVSD, the comparison separates unshaded conditions, louver self-shading, and urban-context shading. For STPV, it includes an analysis of the PV cell-temperature estimation and the link between electricity generation and glazing heat balance. Results show that PVSD yield differences are mainly governed by shading representation, while STPV results are more sensitive to cell-temperature assessment than to thermal coupling effects. Overall, the approach provides a consistent basis for comparing PVSD and STPV yield under early-stage input constraints, while including selected AOI- and temperature-related efficiency effects. Full article

The demand for a versatile and portable point-of-use (POU) sensor platform has surged due to the pandemic, especially in countries with limited medical laboratory facilities. We recently unveiled a portable, non-planar, interdigitated, flow-through, porous electrode platform that automatically measures electrochemical impedance spectroscopy (EIS) signals from various biomarkers. However, the packed powder exhibited a loss of performance over time due to displacement, leaching, and poor stability. Herein, we modified the packing strategy by synthesizing the sensing material within the channel, thereby improving adhesion, structural integrity, and stability. Leveraging the exceptional thermal stability, mechanical strength, and chemical resistance of polyimide (PI), we developed a novel fabrication approach that combines liquid-phase inversion and breath-figure techniques to create a porous PI manifold with single-walled carbon nanotubes (SWCNTs) under varying humidity conditions. Scanning electron microscope (SEM) analysis revealed that lower relative humidity (RH) conditions yield larger but less uniformly distributed pores, leading to increased channel pressure. The manifold demonstrated exceptional stability under rigorous flow conditions, withstanding a high flow rate of 30 µL/min while maintaining consistent pressure-EIS responses. The device produced a measurable proof-of-concept impedance response following exposure to a femtomolar concentration of complementary target ssDNA in 1× PBS within 15 min. A formal limit of detection was not determined in the present study. We developed a mechanically stable sensor design with improved durability under repeated flow conditions by systematically optimizing synthesis conditions and manifold configuration. This innovative fabrication strategy demonstrates the importance of packing methodology in sensor design and paves the way for robust, scalable, and efficient diagnostic solutions in resource-limited settings. Full article

Cloud Atlas (2004) presents a process of religious genesis. This genesis is set against the backdrop of the retreat of stable institutions and recoverable origins, gradually taking shape at the far end of a broken transmission chain. Existing criticism has tended to examine this novel’s nested form separately from Sonmi~451’s posthumous sanctification. This article connects these lines of inquiry by reading the orison as a witness medium within a broken chain of narrative transmission. Along this chain, Ewing’s Pacific diary establishes a compromised model of institutional Christianity, in which faith gains public effectiveness from conversionist rhetoric. This damaged state of institutional religion constitutes a reverse baseline prior to the formation of Sonmi’s testimony as a witness medium. Sonmi’s interrogation in Nea So Copros records political speech as testimony, giving material form to a condemned voice and supporting it to survive beyond her execution at the end of her world. Zachry’s post-apocalyptic valley receives this damaged medium, whose legal grammar has vanished from local memory, but the loss of this grammar does not bring reception to an end. In this context, Meronym’s historical interpretation of Sonmi’s origin intensifies the pressure on devotional allegiance, with sacred use first persisting in prayer and ritual space, then entering the cradle-side oral narrative. The novel’s nested design allows the reader to move across separate storyworlds. Only in this cross-world position can the reader juxtapose the state-recorded testimony with its afterlife as an object of prayer. Thus, religion as a communal claim is formed under semantic loss and becomes clearly visible only from the reader’s cross-world position. Full article

This paper proposes an improved multimodal fusion framework for 3D object detection, termed BEV-Nexus, which aims to address the issues of inaccurate depth estimation and inefficient fusion paradigms in existing image-point cloud fusion methods. We introduce a Point-Cloud-Guided Depth Prediction Network (PCGD-Net), which enhances the image branch’s depth prediction capability by embedding point cloud spatial prior, ground-truth loss constraint, and projected point cloud depth filling. Additionally, we design a Dynamic Self-adaptive Feature Fusion Module (DSF-Module), which computes multimodal feature similarity using window attention and performs weighted fusion based on self-adaptive weights, resolving alignment deviations in BEV features. Finally, we propose a Dilated Attention Enhancement Block (DAEB), which expands the receptive field through dilated convolution and integrates parameter-free attention mechanism (SimAM) for feature enhancement, ensuring efficiency while improving overall feature representation. Experimental results on nuScenes validation set show that BEV-Nexus outperforms it baseline (BEVFusion) by 1.8% mAP and 1.5% NDS. On the test set, BEV-Nexus improves mAP and NDS by 1.6% and 1.4%, respectively. Furthermore, the detection FPS remains nearly unchanged, demonstrating significant lightweight advantages. Full article

To address the severe coverage blind spots and concurrent collision bottlenecks faced by LoRa networks in dense deployments and complex three-dimensional (3D) occlusion environments, this paper proposes a distributed cross-layer protocol framework for LoRa ad hoc networks supporting heterogeneous traffic. To break through the limitations of a single star architecture, this framework constructs a 3D penetration loss model at the physical layer and designs a distributed relay deployment algorithm based on hybrid simulated annealing, achieving blind-spot-free connectivity in complex spaces. At the MAC layer, a non-preemptive priority access mechanism based on symbol energy detection is introduced. Through differentiated backoff windows with time-domain isolation, it precisely guarantees the quality of service (QoS) requirements of heterogeneous traffic and significantly suppresses concurrent collisions. At the network layer, the CAM-AODV routing algorithm is proposed, which integrates hop count, link quality, MAC queue congestion, and nodal residual energy to achieve dynamic traffic diversion and network-wide energy balancing under bursty high loads. Simulation results demonstrate that this cross-layer framework effectively breaks the traditional network capacity bottlenecks. In a large-scale, high-density scenario with 300 nodes, CAM-AODV reduces the average end-to-end delay by 19.46% compared to the traditional AODV. Under high-concurrent loads, the packet delivery ratio (PDR) of the proposed framework improves by 16.32% over the traditional protocol, while the system delay is reduced by 13.66%. Furthermore, under the two aforementioned evaluation scenarios, the Energy Balancing Index (EBI) is significantly improved by 11.13% and 10.57%, respectively, compared to the traditional protocol. This study provides an efficient joint optimization scheme for building high-capacity, wide-coverage, and long-lifespan complex Internet of Things (IoT) networks. Full article

Q fever is a zoonotic disease associated with reproductive losses in ruminants. This study investigated a 2025 outbreak on a Boer goat breeding farm in South Korea and compared its molecular characteristics with those of a separate 2024 outbreak. Conventional PCR targeting the IS1111 insertion element was used to test 87 goats, three female farm dogs, and four barn-level composite environmental samples. Positive goat samples underwent multiple-locus variable-number tandem-repeat analysis and multispacer sequence typing. Coxiella burnetii was detected in 60 of 87 goats (69.0%) and in one environmental sample, whereas dogs were negative. Two multiple-locus variable-number tandem-repeat profiles were identified among four goat samples, and two goats from different barns shared a profile provisionally designated MST77-like because it clustered near, but was not identical to, the MST77 reference profile. After movement restrictions, oxytetracycline treatment, environmental management, and serial monitoring, 11 of 57 previously positive goats remained positive at the second screening, and none of these 11 goats tested positive at the final examination. Complete herd clearance and the independent effect of antimicrobial treatment could not be confirmed because previously negative goats were not retested and no untreated comparison group was available. These findings support farm-wide testing, strengthened biosecurity, and follow-up monitoring. Full article

Background/Objectives: Oral healthcare succeeds not only when treatment is biologically sound but when its outcome meets patient expectations, making expectation management a measurable component of care quality. Patient satisfaction in dental implant therapy is strongly influenced by pre-treatment expectations, yet differences between patient expectations and dentists’ perceptions of those expectations remain poorly characterized. This study compared the two using a mirror-design survey. Methods: A cross-sectional survey was administered to adult patients (n = 250) and dentists involved in implant therapy (n = 200). A 16-item questionnaire was developed in two parallel versions covering two domains, outcome expectations and surgical process apprehension, rated on a 5-point Likert scale. Internal consistency, item- and subscale-level differences, and signed and absolute between-group difference scores were analyzed. Results: Significant between-group differences were identified in 13 of the 16 items, while total scale scores did not differ between groups, producing a cancel-out effect at the aggregate level. Within the surgical process apprehension domain, patients were more optimistic than dentists perceived, with procedural pain showing the largest between-group difference (Cliff’s δ = 0.437). The opposite pattern emerged for long-term outcomes, where concern about implant loss yielded the largest difference across the entire instrument (δ = 0.645). Dentists’ clinical experience was negatively correlated with the signed total gap score (ρ = −0.342, p < 0.001), indicating reduced directional bias among more experienced clinicians. Conclusion: Patients’ expectations differed systematically from dentists’ perceptions of those expectations across both domains, but these opposing differences offset at the aggregate level. Aggregate summary scores may therefore obscure clinically relevant item-level differences, and directional item-level analyses may better capture expectation patterns in implant dentistry. Full article

Ukraine’s energy strategy under martial law necessitates decentralized local energy systems to counter electricity shortages and systemic infrastructure failures. The study develops and validates an optimization model for designing the architecture of decentralized “energy islands” based on circular bioenergy models for agricultural waste use. Empirical verification was conducted using data from the Vinnytsia region in Ukraine. The model accounts for a multi-level structure that separates micro/small generation (0.1–2.0 MW) from medium generation (1–20 MW) based on the logistical radius for raw material collection. The model incorporated the Value of Lost Load (VLL), enabling the monetization of avoided socio-economic losses from energy shortages. In addition, the coefficient of energy island sustainability (I_sred) was introduced to quantitatively assess the effectiveness of investments in terms of replacing external resources. The modeling revealed the nonlinear nature of the total cost function, enabling us to determine an optimal energy-autonomy range of 40% to 50% for communities. At this threshold, the total construction and logistics costs are minimized. The potential socio-economic losses from blackouts are effectively mitigated, as confirmed by the calculated sustainability coefficient (I_sred), which ranges from 0.78 to 0.94 across the studied communities. The resource potential assessment confirms that the region’s total potential is approaching 30 million tons of oil equivalent, driven by solid biofuels, agricultural residues, and energy crops (miscanthus, switchgrass). The classification of biomass supply chains shows that exceeding the transportation radius by more than 70 km at the meso level, or deviating from the optimal logistics lever by 20%, reduces the profitability of projects below the critical limit of 15%, which justifies strict localization within raw-material clusters. This enables local communities to eliminate natural gas consumption, reduce energy supply operating costs by 15%, and ensure the autonomous and stable operation of critical infrastructure facilities during prolonged disruptions to the national power grid. Full article

Caused by the SARS-CoV-2 virus, the COVID-19 pandemic is still considered a complex challenge, with manifestations not only of respiratory issues, but also conditions related to chronic cerebrovascular damage. Endothelial biomarkers, neuropathological and neuroimaging findings indicate endothelial dysfunction, microthrombosis, and disruption of the blood–brain barrier (BBB) are central mechanisms for acute and chronic ischemic and hemorrhagic cerebral events. Understanding these mechanisms is vital to reducing their impact on population health, whether through treatment or prevention of adverse outcomes. The objective of this study is to perform a review of the scientific literature on the post-infection effects of SARS-CoV-2 affecting the cerebral endothelium and the BBB, correlating them with potential clinical outcomes. Material and Methods: Analysis of studies extracted from the PubMed database using the following terms: Long-COVID “AND” SARS-CoV-2 “AND” blood–brain barrier. Inclusion criteria: keywords, publications related to the topic, and primary studies published after peer review. Exclusion criteria: preprint studies, publication outside of the timeframe 2020–2025, study design not compatible with this research, and full text not available. Results: An initial 121 studies were identified, of which 105 were excluded due to not meeting all inclusion criteria and 6 studies were inaccessible due to not being in the English language and full-text access limitations. Fifteen articles were included in the analysis, for topics as expression of viral receptors in the endothelium, markers of their activation, cerebral microvascular injury and coagulopathies. To clarify the pathogenic cascade, the evidence was stratified by biological model where in vitro evidence demonstrates that the Spike protein induces direct endothelial toxicity and platelet aggregation, establishing the primary molecular insult. Animal models confirm the translation of this insult into structural degradation of the BBB and pericyte loss. Clinically, infection-phase findings, characterized by multifocal microthrombosis and permeability spikes, act as the determining event that predisposes to the persistent neuroinflammatory environment. Biomarkers of BBB disruption and neuronal damage were consistently reported, with persistence of BBB dysfunction modifying risk stratification and rehabilitation efforts. Reported cases of Long-COVID demonstrated normalization of BBB markers without correlation with long-term symptoms, suggesting that other mechanisms are involved in Long-COVID. Conclusion: Cerebral endotheliopathies and BBB dysfunction in patients with COVID-19 continue to impact the health of the population. The scientific literature indicates that SARS-CoV-2 induces cerebral endothelial injury, BBB disruption, and an increased risk of vascular events related to endotheliopathy, inflammation, and hypercoagulability. Understanding the impact of COVID-19 pathology on the population and developing prospective studies is essential to quantify the prevalence and mechanisms of brain injury. The identification of molecular targets and infection pathways are promising toward defining both preventive and therapeutic strategies to improve outcomes in the Long-COVID population. Full article

Background: We aimed to investigate the prevalence of HSD17B13 polymorphisms while evaluating the correlation between HSD17B13 gene variants and NASH prevalence in the Taiwanese population. Furthermore, we tried to identify the diagnostic value of liver enzymes as non-invasive biomarkers for NASH in morbidly obese patients. Methods: Severely obese patients with non-alcoholic fatty liver disease (NAFLD) who received liver biopsy during bariatric surgery and controls of liver donors who underwent donor hepatectomy were enrolled from 2016 to 2021. Genotyping was utilized in TaqMan PCR assays. For comparison of NAFLD severity, patients were divided into no NASH (NAFLD activity score, NAS 0–2), borderline NASH (NAS 3–4), and NASH (NAS 5–8). Results: The proportion of HSD17B13 rs72613567 TA allele carriers (T/TA and TA/TA genotypes) was 96% in both groups, corresponding to an estimated TA allele frequency of approximately 50% based on a standard allele-counting method. Among morbidly obese patients, T/TA and TA/TA genotypes were associated with lower odds of NASH prevalence compared with T/T genotype. For the non-invasive predictive factors of NASH, NASH group had significantly higher level of liver enzymes compared to “no NASH” and “borderline NASH” groups. Receiver operating characteristic curve analysis revealed there were moderate predictive values for NASH in AST (AUC = 0.71, p = 0.014) and ALT (AUC = 0.73, p = 0.007). Conclusions: Our study revealed a relatively high prevalence of the loss-of-function TA variant of HSD17B13 in this southern Taiwanese cohort, irrespective of body weight. This variant may contribute to the comparatively lower prevalence of NASH in the region. Additionally, AST and ALT may have potential predictive value for NASH prevalence. Given the single-center design, further larger, population-representative studies are warranted. Full article