Search Results (12,845)

In recent years, geographic knowledge graphs (GeoKGs) have shown great promise in representing spatio-temporal and event-driven knowledge. However, existing knowledge graph embedding approaches mainly focus on structural patterns and often overlook the dynamic evolution of entities in both time and space, which limits their effectiveness in downstream reasoning tasks. To address this, we propose a spatio-temporal evolutionary knowledge embedding approach (ST-EKA) that enhances entity representations by modeling their evolution through type-aware encoding, temporal and spatial decay mechanisms, and context aggregation. ST-EKA integrates four core components, including an entity encoder constrained by relational type consistency, a temporal encoder capable of handling both time points and intervals through unified sampling and feedforward encoding, a multi-scale spatial encoder that combines geometric coordinates with semantic attributes, and an evolutionary knowledge encoder that employs attention-based spatio-temporal weighting to capture contextual dynamics. We evaluate ST-EKA on three representative GeoKG datasets—GDELT, ICEWS, and HAD. The results demonstrate that ST-EKA achieves an average improvement of 6.5774% in AUC and 5.0992% in APR on representation learning tasks. In question answering tasks, it yields a maximum average increase of 1.7907% in AUC and 0.5843% in APR. Notably, it exhibits superior performance in chain queries and complex spatio-temporal reasoning, validating its strong robustness, good interpretability, and practical application value. Full article

Obesity is a growing global health concern with widespread impacts on physical, psychological, and social well-being. Clinically, it is a major driver of type 2 diabetes (T2D), cardiovascular disease (CVD), non-alcoholic fatty liver disease (NAFLD), and cancer, reducing life expectancy by 5–20 years and imposing a staggering economic burden of USD 2 trillion annually (2.8% of global GDP). Despite its significant health and socioeconomic impact, earlier obesity medications, such as fenfluramine, sibutramine, and orlistat, fell short of expectations due to limited effectiveness, serious side effects including valvular heart disease and gastrointestinal issues, and high rates of treatment discontinuation. The advent of glucagon-like peptide-1 (GLP-1) receptor agonists (e.g., semaglutide, tirzepatide) has revolutionized obesity management. These agents demonstrate unprecedented efficacy, achieving 15–25% mean weight loss in clinical trials, alongside reducing major adverse cardiovascular events by 20% and T2D incidence by 72%. Emerging therapies, including oral GLP-1 agonists and triple-receptor agonists (e.g., retatrutide), promise enhanced tolerability and muscle preservation, potentially bridging the efficacy gap with bariatric surgery. However, challenges persist. High costs, supply shortages, and unequal access pose significant barriers to the widespread implementation of obesity treatment, particularly in low-resource settings. Gastrointestinal side effects and long-term safety concerns require close monitoring, while weight regain after medication discontinuation emphasizes the need for ongoing adherence and lifestyle support. This review highlights the transformative potential of incretin-based therapies while advocating for policy reforms to address cost barriers, equitable access, and preventive strategies. Future research must prioritize long-term cardiovascular outcome trials and mitigate emerging risks, such as sarcopenia and joint degeneration. A multidisciplinary approach combining pharmacotherapy, behavioral interventions, and systemic policy changes is critical to curbing the obesity epidemic and its downstream consequences. Full article

The paper investigates the process of degumming substandard soybean oil using an enzyme complex of phospholipases to prepare it as a feedstock for biodiesel production. Dehumidification is an important refining step aimed at reducing the phosphorus content, which exceeds the permissible limits according to ASTM, EN, and ISO standards, by re-moving phospholipids. The enzyme complex of phospholipases includes phospholipase C, which specifically targets phosphatidylinositol, and phospholipase A2, which catalyzes the hydrolysis of phospholipids into water-soluble phosphates and lysophospholipids. This process contributes to the efficient removal of phospholipids, increased neutral oil yield, and reduced residual oil in the humic phase. The use of an enzyme complex of phospholipases provides an innovative, cost-effective, and environmentally friendly method of oil purification. The results of the study demonstrate the high efficiency of using the phospholipase enzyme complex in the processing of substandard soybean oil, which allows reducing the content of total phosphorus to 0.001% by weight, turning it into a high-quality raw material for biodiesel production. The proposed approach contributes to increasing the profitability of agricultural raw materials and the introduction of environmentally friendly technologies in the field of renewable energy. Full article

Although Unmanned Aircraft Systems (UASs) offer valuable services, they also introduce certain risks—particularly to individuals on the ground—referred to as third-party risk (TPR). In general, ground-level TPR tends to rise alongside the density of people who might use these services, leading current regulations to heavily restrict UAS operations in populated regions. These operational constraints hinder the ability to gather safety insights through the conventional method of learning from real-world incidents. To address this, a promising alternative is to use dynamic simulations that model UAS collisions with humans, providing critical data to inform safer UAS design. In the automotive industry, the modelling and simulation of car crashes has been well developed. For small UAS, this dynamical modelling and simulation approach has focused on the effect of the varying weight and kinetic energy of the UAS, as well as the geometry and location of the impact on a human body. The objective of this research is to quantify the effects of UAS material and shape on-ground TPR through dynamical modelling and simulation. To accomplish this objective, five camera–drone types are selected that have similar weights, although they differ in terms of airframe structure and materials. For each of these camera–drones, a dynamical model is developed to simulate impact, with a biomechanical human body model validated for impact. The injury levels and probability of fatality (PoF) results, obtained through conducting simulations with these integrated dynamical models, are significantly different for the camera–drone types. For the uncontrolled vertical impact of a 1.2 kg UAS at 18 m/s on a model of a human head, differences in UAS designs even yield an order in magnitude difference in PoF values. Moreover, the highest PoF value is a factor of 2 lower than the parametric PoF models used in standing regulation. In the same scenario for UAS types with a weight of 0.4 kg, differences in UAS designs even considered yield an order when regarding the magnitude difference in PoF values. These findings confirm that the material and shape design of a UAS plays an important role in reducing ground TPR, and that these effects can be addressed by using dynamical modelling and simulation during UAS design. Full article

Food safety concerns related to pesticide residues in fruits and vegetables have increased globally, particularly in regions where monitoring programs are scarce or inconsistent. This study provides the first multi-year evaluation of pesticide contamination and associated dietary risks in Cape Verde, an African island nation increasingly reliant on imported produce. A total of 570 samples of fruits and vegetables—both locally produced and imported—were collected from major markets across the country between 2017 and 2020 and analyzed using validated multiresidue methods based on gas chromatography coupled to Ion Trap mass spectrometry (GC-IT-MS/MS), and both gas and liquid chromatography coupled to triple quadrupole tandem mass spectrometry (GC-QqQ-MS/MS and LC-QqQ-MS/MS). Residues were detected in 63.9% of fruits and 13.2% of vegetables, with imported fruits showing the highest contamination levels and diversity of compounds. Although only one sample exceeded the maximum residue limits (MRLs) set by the European Union, 80 different active substances were quantified—many of them not authorized under the current EU pesticide residue legislation. Dietary exposure was estimated using median residue levels and real consumption data from the national nutrition survey (ENCAVE 2019), enabling a refined risk assessment based on actual consumption patterns. The cumulative hazard index for the adult population was 0.416, below the toxicological threshold of concern. However, when adjusted for children aged 6–11 years—taking into account body weight and relative consumption—the cumulative index approached 1.0, suggesting a potential health risk for this vulnerable group. A limited number of compounds, including omethoate, oxamyl, imazalil, and dithiocarbamates, accounted for most of the risk. Many are banned or heavily restricted in the EU, highlighting regulatory asymmetries in global food trade. These findings underscore the urgent need for strengthened residue monitoring in Cape Verde, particularly for imported products, and support the adoption of risk-based food safety policies that consider population-specific vulnerabilities and mixture effects. The methodological framework used here can serve as a model for other low-resource countries seeking to integrate analytical data with dietary exposure in a One Health context. Full article

This paper explores the impact of applying a powder additive in the form of halloysite and mullite on the thermal protection properties of a composite. The authors used CES R70 epoxy resin with CES H72 hardener, modified by varying the amount of powder additive. The composite samples were exposed to a mixture of combustible gases at a temperature of approximately 1000 °C. The primary parameters analyzed during this study were the temperature on the rear surface of the sample and the ablative mass loss of the tested material. The temperature increase on the rear surface of the sample, which was exposed to the hot stream of flammable gases, was measured for 120 s. Another key parameter considered in the data analysis was the ablative mass loss. The charred layer of the sample played a crucial role in this process, as it helped block oxygen diffusion from the boundary layer of the original material. This charred layer absorbed thermal energy until it reached a temperature at which it either oxidized or was mechanically removed due to the erosive effects of the heating factor. The incorporation of mullite reduced the rear surface temperature from 58.9 °C to 49.2 °C, and for halloysite, it was reduced the rear surface temperature to 49.8 °C. The ablative weight loss dropped from 57% to 18.9% for mullite and to 39.9% for halloysite. The speed of mass ablation was reduced from 77.9 mg/s to 25.2 mg/s (mullite) and 52.4 mg/s (halloysite), while the layer thickness loss decreased from 7.4 mm to 2.8 mm (mullite) and 4.4 mm (halloysite). This research is innovative in its use of halloysite and mullite as functional additives to enhance the ablative resistance of polymer composites under extreme thermal conditions. This novel approach not only contributes to a deeper understanding of composite behavior at high temperatures but also opens up new avenues for the development of advanced thermal protection systems. Potential applications of these materials include aerospace structures, fire-resistant components, and protective coatings in environments exposed to intense heat and flame. Full article

Background/Objectives: The major limitations of self-nanoemulsifying systems include complex processing and expensive instrumentation required for solidification approaches. In this study, smart poloxamer-based solidification strategies were used to develop and optimize febuxostat-loaded formulations. Methods: A self-nanoemulsifying drug delivery system (SNEDDS) component was selected based on solubility and emulsification tests. The influence of poloxamer molecular weight (low or high) and its concentration (2–10% w/w) on formulation performance was assessed through the design of experiments. Finally, in-vitro melting assessment and a comparative dissolution test were performed on the optimized SNEDDS formulation. Results: Imwitor 988 and Tween 20 were selected to prepare the formulations. Increasing the molecular weight and concentration of the poloxamer significantly increased the temperature and time required for the melting of the SNEDDS formulation. The optimized SNEDDS formulation comprised 3.98% w/w poloxamer 188, which melts at 36 °C within 111 s. In-vitro melting showed that the formulation completely converted to a liquid state upon exposure to body temperature. Finally, the optimized SNEDDS formulation exhibited superior dissolution efficiency (96.66 ± 0.28%) compared to raw febuxostat (72.09 ± 4.33%) and marketed tablets (82.23 ± 3.10%). Conclusions: The poloxamer-based approach successfully addressed the limitations associated with conventional solidification while maintaining superior dissolution performance. Therefore, it emerges as a promising alternative approach for enhancing the bioavailability of poorly water-soluble drugs. Full article

Background: Spinal muscular atrophy type 1 (SMA1) is a severe neuromuscular disorder characterized by progressive muscle weakness and atrophy, including the muscles of the oral cavity and esophagus. Eosinophilic esophagitis (EoE), a chronic, allergic disease, presents with eosinophilic infiltration of the esophagus, leading to esophageal dysmotility. Feeding difficulties may occur in both conditions. So far, the coexistence of EoE and SMA1 has not been described; we present the first such case. Case presentation: The patient was a girl with SMA1 diagnosed shortly after birth, treated with nusinersen and onasemnogene abeparvovec, and fed a standard industrial diet through a gastrostomy. In her second year of life, she developed increasing symptoms: distress during feeding, regurgitation, vomiting, and weight loss. She was treated with proton pump inhibitors without clinical improvement. Gastroscopy was performed, revealing superficial epithelial damage with bleeding in the proximal esophagus. Histopathology showed chronic inflammation with up to 150 eosinophils per high-power field, microabscesses, spongiosis, and basal layer hypertrophy. The girl was diagnosed with EoE. Her diet was switched from a standard industrial formula to an amino acid-based formula, which led to marked clinical improvement, the resolution of symptoms, and appropriate weight gain. Conclusions: This case report highlights the challenges of diagnosing EoE in SMA1 patients and emphasizes the need for multidisciplinary approaches and further investigation of allergic manifestations in SMA1 patients. Full article

The process of geothermal energy development may cause induced seismic activities, posing a potential threat to the sustainable utilization and safety of geothermal energy. To effectively evaluate the danger of induced seismic activities, this paper establishes an artificial neural network model and selects nine influencing factors as the input parameters of the neurons. Based on the results of induced seismic activity under different parameter conditions, a sensitivity analysis is conducted for each parameter, and the influence degree of each parameter on the magnitude of induced seismic activity is ranked from largest to smallest as follows: in situ stress state, fault presence or absence, depth, degree of fracture aggregation, maximum in situ stress, distance to fault, injection volume, fracture dip angle, angle between fracture, and fault. Then, the weights of each parameter in the model are modified to improve the accuracy of the model. Finally, through data collection and the literature review, the Pohang EGS project in South Korea is analyzed, and the induced seismic activity influencing factors of the Pohang EGS site are analyzed and evaluated using the induced seismic activity evaluation model. The results show that the induced seismicity are all located below 3.7 km (drilling depth). As the depth increases, the seismicity magnitude also shows a gradually increasing trend. An increase in injection volume and a shortening of the distance from faults will also lead to an increase in the seismicity magnitude. When the injection volume approaches 10,000 cubic meters, the intensity of the seismic activity sharply increases, and the maximum magnitude reaches 5.34, which is consistent with the actual situation. This model can be used for the induced seismic evaluation of future EGS projects and provide a reference for project site selection and induced seismic risk warning. Full article

This paper presents a hybrid indoor localization framework combining time difference of arrival (TDoA) measurements with a swarm intelligence optimization technique. To address the nonlinear optimization challenges in three-dimensional (3D) indoor localization via TDoA measurements, we systematically evaluate the artificial bee colony (ABC) algorithm and chimpanzee optimization algorithm (ChOA). Through comprehensive Monte Carlo simulations in a cubic 3D environment with eight beacons, our comparative analysis reveals that the ChOA achieves superior localization accuracy while maintaining computational efficiency. Building upon the ChOA framework, we introduce a multi-beacon fusion strategy incorporating a local outlier factor-based linear weighting mechanism to enhance robustness against measurement noise and improve localization accuracy. This approach integrates spatial density estimation with geometrically consistent weighting of distributed beacons, effectively filtering measurement outliers through adaptive sensor fusion. The experimental results show that the proposed algorithm exhibits excellent convergence performance under the condition of a low population size. Its anti-interference capability against Gaussian white noise is significantly improved compared with the baseline algorithms, and its anti-interference performance against multipath noise is consistent with that of the baseline algorithms. However, in terms of dealing with UWB device failures, the performance of the algorithm is slightly inferior. Meanwhile, the algorithm has relatively good time-lag performance and target-tracking performance. The study provides theoretical insights and practical guidelines for deploying reliable localization systems in complex indoor environments. Full article

Human T-cell lymphotropic virus (HTLV), a retrovirus associated with severe conditions such as leukemia/lymphoma and myelopathy, exhibits variable global prevalence, with higher rates observed in regions such as northeastern Brazil and sub-Saharan Africa. While intrauterine transmission can occur via viral expression in placental tissue and contact with umbilical cord blood, the predominant route is vertical transmission through breastfeeding. Diagnostic testing, particularly serological screening with ELISA and confirmatory methods such as Western blot and PCR, is essential for early detection during pregnancy. The implementation of prenatal screening programs, as seen in Japan and Brazil, has proven effective in reducing vertical transmission by guiding interventions such as breastfeeding cessation in infected mothers. Beyond clinical implications, the psychosocial impact on affected pregnant women highlights the need for an interdisciplinary approach. Although the association between HTLV infection and adverse obstetric outcomes remains controversial, studies suggest increased risks of preterm birth, low birth weight, and other neonatal complications. Given the importance of early diagnosis and prevention, universal prenatal screening protocols represent a critical strategy to reduce viral transmission and its long-term consequences. Full article

Collaborative business ecosystems (CBEs) are increasingly exposed to disruptive events (e.g., pandemics, supply chain breakdowns, cyberattacks) that challenge organizational adaptability and value creation. Traditional approaches to resilience and robustness often fail to capture the full range of systemic responses. This study introduces a unified mathematical framework to evaluate four crisis response modes—plasticity, resilience, transformative resilience, and antifragility—within complex adaptive networks. Grounded in complex systems and collaborative network theory, our model formalizes both internal organizational capabilities (e.g., adaptability, learning, innovation, structural flexibility) and strategic interventions (e.g., optionality, buffering, information sharing, fault-injection protocols), linking them to pre- and post-crisis performance via dynamic adjustment functions. A composite performance score is defined across four dimensions (Innovation, Contribution, Prestige, and Responsiveness to Business Opportunities), using capability–strategy interaction matrices, weighted performance change functions, and structural transformation modifiers. The sensitivity analysis and scenario simulations enable a comparative evaluation of organizational configurations, strategy impacts, and phase-transition thresholds under crisis. This indicator-based formulation provides a quantitative bridge between resilience theory and practice, facilitating evidence-based crisis management in networked business environments. Full article

Multi-source fusion techniques have emerged as cutting-edge approaches for spatial precipitation estimation, yet they face persistent accuracy limitations, particularly under extreme conditions. Machine learning offers new opportunities to improve the precision of these estimates. To bridge this gap, we propose a hybrid artificial neural network–geographically weighted regression (ANN–GWR) model that synergizes event recognition and quantitative estimation. The ANN module dynamically identifies precipitation events through nonlinear pattern learning, while the GWR module captures location-specific relationships between multi-source data for calibrated rainfall quantification. Validated against 60-year historical data (1960–2020) from China’s Yongding River Basin, the model demonstrates superior performance through multi-criteria evaluation. Key results reveal the following: (1) the ANN-driven event detection achieves 10% higher accuracy than GWR, with a 15% enhancement for heavy precipitation events (>50 mm/day) during summer monsoons; (2) the integrated framework improves overall fusion accuracy by more than 10% compared to conventional GWR. This study advances precipitation estimation by introducing an artificial neural network into the event recognition period. Full article

Background/Objectives: Banhasasim-tang (BHSST) is a traditional herbal formula commonly used to treat gastrointestinal (GI) disorders and has been considered a potential therapeutic option for irritable bowel syndrome (IBS). This study aimed to explore the molecular targets and underlying mechanisms of BHSST in IBS using a combination of network pharmacology, molecular docking, molecular dynamics simulations, and in vivo validation. Methods: Active compounds in BHSST were screened based on drug-likeness and oral bioavailability. Potential targets were predicted using ChEMBL, and IBS-related targets were obtained from GeneCards and DisGeNET. A compound–target–disease network was constructed and analyzed via Gene Ontology and KEGG pathway enrichment. Compound–target interactions were further assessed using molecular docking and molecular dynamics simulations. The in vivo effects of eudesm-4(14)-en-11-ol, elemol, and BHSST were evaluated in a zymosan-induced IBS mouse model. Results: Twelve BHSST-related targets were associated with IBS, with enrichment analysis identifying TNF signaling and apoptosis as key pathways. In silico simulations suggested stable binding of eudesm-4(14)-en-11-ol to TNF-α and kanzonol T to PIK3CD, whereas elemol showed weak interaction with PRKCD. In vivo, eudesm-4(14)-en-11-ol improved colon length, weight, stool consistency, TNF-α levels, and pain-related behaviors—effects comparable to those of BHSST. Elemol, however, showed no therapeutic benefit. Conclusions: These findings provide preliminary mechanistic insight into the anti-inflammatory potential of BHSST in IBS. The integrated in silico and in vivo approaches support the contribution of specific components, such as eudesm-4(14)-en-11-ol, to its observed effects, warranting further investigation. Full article

Background: Short-stem hip replacements are designed to provide improved load distribution and to mimic natural biomechanics. The interplay between implant design, positioning, and resulting bone biomechanics in individual patients remains underexplored, and the relationship between radiographically assessed bone remodeling around short stems and biomechanical predictions has not been previously reported. Methods: This study evaluated three short-stem hip implant designs: Proxima, Collo-MIS, and Minima. Postoperative bone remodeling patterns were analyzed, categorizing remodeling as bone gain, bone loss, or no observable activity, with changes tracked over time. Patient-specific biomechanical models were generated from 6-week postoperative radiographs. Finite element simulations incorporated body weight and gluteal muscle forces to estimate stress and strain distributions within the proximal femur. Strain energy was then applied to a mechanostat-based remodeling algorithm to predict bone remodeling patterns. These biomechanical predictions were compared to observed radiographic remodeling at 2 years post-surgery. A validated biomechanical model was further used to simulate different postoperative positions of the three types of stems. Results: No differences in bone remodeling patterns were observed among the three short-stem designs. Computational modeling demonstrated a statistically significant correlation between predicted remodeling and radiographic measurements at 2 years (p < 0.001). Proxima stems showed a tendency towards increased cortical bone loading under pronounced varus or valgus position in comparison to other two stems, although this observation requires further validation. Conclusions: This exploratory study demonstrates the feasibility of using biomechanical modeling to estimate bone remodeling around short-stem hip implants based on early postoperative radiographs. While the results are promising, they should be interpreted with caution due to the limited cohort size. The proposed modeling approach may offer clinical value in evaluating implant behavior and informing patient-specific treatment strategies. However, further research with larger populations is necessary to refine and validate these predictive tools. Full article