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Cultural visitation routes represent an important opportunity for the public display of the famous historical and cultural city of Beijing, and its pedestrian environment, as a linear spatial carrier linking various historical and cultural attractions, is of great significance for the preservation of the famous historical and cultural city of Beijing through analysis of its spatial quality. At present, Beijing’s cultural visitation routes are in the stage of exploration and improvement, and scholars mainly focus on the selection, integration, and construction of cultural visitation routes in their research on cultural visitation routes, while the amount of research on the quality of the pedestrian environment for visitors is relatively small; in particular, the evaluation methods and indicator systems are still in the exploration stage. In this study, from the perspective of environmental behaviour theory, we took the Forbidden City–The Red House of Peking University–Wangfujing cultural visitation route as the research object and constructed a structural equation model to determine the perception of the pedestrian environment quality of the cultural visitation route in Beijing, starting from the visitors’ feelings of the pedestrian environment of the cultural visiting route. This study found that there is a positive correlation between the quality of spatial behaviours, the quality of the pedestrian environment, and the quality of the facilities on the emotional response of the visitors, with the quality of the facilities having the greatest impact on the emotional response of the visitors; among the observational variables, cultural and scientific facilities, spatial landscapes, street furniture, and safety play a more obvious role, and they are the main factors affecting the emotional response of the visitors. Based on the results of this study, an optimisation strategy for enhancing the pedestrian environments of Beijing’s cultural visitation routes is proposed to provide a reference for their design and optimisation. Full article

An Ultraviolet-Visible Hyperspectral Sensors (UVS) instrument is an ultraviolet-visible imaging spectrograph equipped with two-dimensional charge-coupled device detectors. It records both the spectrum and the swath perpendicular to the flight direction, offering a wide 112° swath. This configuration enables global daily ground coverage with high spatial resolution. The absolute values of in-orbit solar irradiance can be evaluated using the bidirectional reflectance distribution function (BRDF), with the measurement accuracy directly affecting the accuracy of constituent inversion. This paper outlines the calibration process for the BRDF of the UVS, detailing the calibration methods and equipment used. It also proposes a BRDF model and discusses key coefficients. The accuracy levels of the UVS in the UV1, UV2, and VIS channels were 2.162%, 2.162%, and 2.173%, respectively. Full article

Extracellular vesicles (EVs) play a pivotal role in a variety of physiologically relevant processes, including lung inflammation. Recent attention has been directed toward EV-derived microRNAs (miRNAs), such as miR-191-5p, particularly in the context of inflammation. Here, we investigated the impact of miR-191-5p-enriched EVs on the activation of NF-κB and the expression of molecules associated with inflammation such as interleukin-8 (IL-8). To this aim, cells of bronchial epithelial origin, 16HBE, were transfected with miR-191-5p mimic and inhibitor and subsequently subjected to stimulations to generate EVs. Then, bronchial epithelial cells were exposed to the obtained EVs to evaluate the activation of NF-κB and IL-8 levels. Additionally, we conducted a preliminary investigation to analyze the expression profiles of miR-191-5p in EVs isolated from the plasma of patients diagnosed with chronic obstructive pulmonary disease (COPD). Our initial findings revealed two significant observations. First, the exposure of bronchial epithelial cells to miR-191-5p-enriched EVs activated the NF-kB signaling and increased the synthesis of IL-8. Second, we discovered the presence of miR-191-5p in peripheral blood-derived EVs from COPD patients and noted a correlation between miR-191-5p levels and inflammatory and functional parameters. Collectively, these data corroborate and further expand the proinflammatory role of EVs, with a specific emphasis on miR-191-5p as a key cargo involved in this process. Consequently, we propose a model in which miR-191-5p, carried by EVs, plays a role in airway inflammation and may contribute to the pathogenesis of COPD. Full article

To establish battery electric vehicles as an attractive alternative to internal combustion vehicles, charging times of 15 min or less are increasingly demanded. This is especially challenging for lower battery temperatures, as this exacerbates the risk of accelerated battery degradation due to lithium plating. Therefore, active battery heating is utilized in state-of-the-art electric vehicles. To evaluate the impact of such heating strategies at vehicle level, we deployed an electrochemical battery model coupled with a longitudinal vehicle dynamics model. Using anode potential control to prevent lithium plating, we assess the time-saving potential versus the energy cost of different preconditioning and fast-charging strategies. The results reveal substantial energy saving and charge speed increase potential through optimal charge-stop planning, preconditioning timing, cost-adjusted thermal management thresholds, and considering driving behavior. This emphasizes the need for advanced operation strategies, taking into account both battery-level electrical and thermal restrictions, as well as vehicle integration and route planning. Full article

The use of model-based systems engineering (MBSE) has been increasingly explored recently in industries that require multi-discipline engineering coordination. In the European space industry, applying MBSE for the engineering of space systems has been an ongoing undertaking on many missions. In the following paper, the MBSE activities in CAMEO conducted during the A/B1 phases of a typical Earth observation satellite engineered by Airbus are discussed in detail in the form of a case study. The analyses shown are based around the modeling of the spacecraft electrical interfaces in CAMEO. This model was used to automate electrical interface control documents (EICDs) and enable the control of electrical interface development. These methodologies were further put in the context of Airbus’ satellite design processes to assess the benefits of an MBSE approach to the current electrical interface engineering procedure and the potential for the reuse of CAMEO models between satellite projects. The reduction in system engineering effort through the reuse of models to modularly create similar satellite systems for efficient concept evaluation and comparison is a clear benefit. Full article

Many individuals aspire to start their own businesses and achieve financial success. Before launching a business, they must decide on a location and the type of service to offer. This decision requires collecting and analyzing various characteristics of potential locations and services, such as average revenues and foot traffic. However, this process is challenging because it demands expert knowledge in data collection and analysis. To address this issue, we propose Neural Tensor Factorization (NeuralTF) and Explainable Neural Tensor Factorization (XNeuralTF). These methods automatically analyze these characteristics and predict revenues. NeuralTF integrates Tensor Factorization (TF) with Multi-Layer Perceptron (MLP). This integration allows it to handle multi-dimensional tensors effectively. It also learns both explicit and implicit higher-order feature interactions, leading to superior predictive performance. XNeuralTF extends NeuralTF by providing explainable recommendations for three-dimensional tensors. Additionally, we introduce two novel metrics to evaluate the explainability of recommendation models. We conducted extensive experiments to assess both predictive performance and explainability. Our results show that XNeuralTF achieves comparable or superior performance to state-of-the-art methods, while also offering the highest level of explainability. Full article

Background: Hyperhomocysteinemia can increase the risk of cardiovascular disease (CVD), cancer, and neurological disorders; however, hypohomocysteinemia is generally not considered harmful. This study aimed to evaluate the relationship between all levels of homocysteine, both low and high homocysteine levels, and the risk of all-cause and cause-specific mortality in adult Korean men. Methods: Adult Korean men (n = 221,356) were categorized into quintiles based on their homocysteine levels. The primary endpoints were all-cause, CVD, cancer, and dementia mortality. Hazard ratios were calculated using Cox proportional hazards models, and the dose–response relationship between homocysteine levels and mortality risk was further explored using restricted cubic spline models. Results: Compared with the reference category (Q2, 8.8–9.9 µmol/L), there was a significant increase in all-cause mortality associated with both low and high levels after multivariable adjustment (P_interaction = 0.002). Additionally, in spline regression, a U-shaped association between homocysteine levels and all-cause and CVD mortality was observed (inflection point = 9.1 µmol/L). This association was not observed in the vitamin supplementation subgroup. Conclusion: Among Korean adult men, both low and high homocysteine levels increased the risk of all-cause and CVD mortality, indicating a U-shaped relationship. However, this relationship was not statistically significant with vitamin supplementation, suggesting a potential protective role for vitamins. Full article

Background: Satisfaction with prosthesis plays a key role in regaining mobility and is important for optimizing prosthetic usage, mobility, and increasing compliance with medical regimen. Despite unchangeable factors like age and comorbidities, other factors, like pain, received rehabilitation, satisfaction with assistive devices, service, and information, can be changed and might contribute to a better usage and acceptance of the prosthesis and amputees’ mobility. Objectives: The aim of the study was to analyze mobility, pain, supply of assistive devices, and additional therapies received after major limb amputations. Furthermore, a correlation of those parameters was evaluated. Methods: Retrospective identification of patients with major limb amputation (operation and procedures classification system (OPS)) and relevant related demographics within the clinical documentation system during a four-year observation time. In addition, we undertook prospective assessment of mobility (K-level), pain qualities, additional therapies, self-rated overall quality of life (QoL) and degree of adaptation to the life after amputation, dependency from caregivers, and satisfaction with the provided assistive devices (QUEST). Results: A total of 164 patients (mean age 68, age range: 19 to 97 years) underwent major limb amputation. A total of 27 questionnaires were returned and analyzed. All those traumatic and nontraumatic amputees received assistive devices. Although mobility and QoL decreased significantly after amputation, a high satisfaction with provided prosthetic and assistive devices and care was found. Conclusions: Amputation registries are becoming elementary to allow for nationwide comparisons of clinics, to identify the requirements of amputees, and to design an interdisciplinary care model for a successful comprehensive approach. Full article

Dental diseases are among the most prevalent diseases globally, and accurate segmentation of dental calculus images plays a crucial role in periodontal disease diagnosis and treatment planning. However, the current methods are not stable and reliable enough due to the variable morphology of dental calculus and the blurring of the boundaries between the dental edges and the surrounding tissues; therefore, our hope is to propose an accurate and reliable calculus segmentation algorithm to improve the efficiency of clinical detection. We propose a multi-scale attention pyramid pooling network (MAPPNet) to enhance the performance of dental calculus segmentation. The network incorporates a multi-scale fusion strategy in both the encoder and decoder, forming a model with a dual-ended multi-scale structure. This design, in contrast to employing a multi-scale fusion scheme at a single end, enables more effective capturing of features from diverse scales. Furthermore, the attention pyramid pooling module (APPM) reconstructs the features on this map by leveraging a spatial-first and channel-second attention mechanism. APPM enables the network to adaptively adjust the weights of different locations and channels in the feature map, thereby enhancing the perception of important regions and key features. Experimental evaluation of our collected dental calculus segmentation dataset demonstrates the superior performance of MAPPNet, which achieves an intersection-over-union of 81.46% and an accuracy rate of 98.35%. Additionally, on two publicly available datasets, ISIC2018 (skin lesion dataset) and Kvasir-SEG (gastrointestinal polyp segmentation dataset), MAPPNet achieved an intersection-over-union of 76.48% and 91.38%, respectively. These results validate the effectiveness of our proposed network in accurately segmenting lesion regions and achieving high accuracy rates, surpassing many existing segmentation methods. Full article

Manure distribution in soil creates a ground environment that is conducive to crop cultivation. However, the lumping and concentration of manure in the field can occur, hindering the fertilization of the soil for plant growth, and the randomization of nutrients under different soil depths accelerates it. To overcome the challenges associated with agricultural testing, such as high cost, inclement weather, and other constraints, computational analysis is often used. In this study, rotary operations are performed using the discrete element method (DEM) to ensure the uniform distribution of manure and four soil layers. DEM analysis was conducted with three experimental factors, and simulation sets were designed using the Box-Behnken central combination method. The DEM results were evaluated using the uniformity index (UI), and the field test of the rotary operation was performed with the set showing the most uniform distribution among the results. Due to undistinguishable particles in reality, the uniformity was validated by a comparison of the chemical characteristics of the L₁ and L₅ in terms of before and after the rotary operation. The DEM parameter of the soil was determined by performing field measurements at different soil depths (0–20 cm), and this parameter was calibrated by conducting a penetration test. The Box–Behnken central combination method was implemented using the following factors: tillage depth (X₁), PTO revolution speed (X₂), and forward machine velocity (X₃). These factors were obtained using the UI regression model and the response surface method. In the results, it was indicated that the UI was affected by the factors in the following order: X₁ > X₂ > X₃. The optimized factor values were X₁ = 25 cm, X₂ = 800 RPM, and X₃ = 1.8 km/h, leading to a UI of 6.07, which was consistent with the analysis results. The operating parameters were maintained throughout the field test, and the acquired data were input into the measurement system. The lowest UI value of 6.07 had the strongest effect on decreasing the disparity between L₁ and L₅, especially in terms of pH, organic matter, P, Ca, and Mg. In summary, the results indicated that soil distribution can be controlled by adjusting mechanical parameters to ensure uniform chemical characteristics across various soil depths. Full article

The main source of electricity worldwide stems from fossil fuels, contributing to air pollution, global warming, and associated adverse effects. This study explores wind energy as a potential alternative. Nevertheless, the variable nature of wind introduces uncertainty in its reliability. Thus, it is necessary to identify an appropriate machine learning model capable of reliably forecasting wind speed under various environmental conditions. This research compares the effectiveness of Dynamic Architecture for Artificial Neural Networks (DAN2), convolutional neural networks (CNN), random forest and XGBOOST in predicting wind speed across three locations in South Africa, characterised by different weather patterns. The forecasts from the four models were then combined using quantile regression averaging models, generalised additive quantile regression (GAQR) and quantile regression neural networks (QRNN). Empirical results show that CNN outperforms DAN2 in accurately forecasting wind speed under different weather conditions. This superiority is likely due to the inherent architectural attributes of CNNs, including feature extraction capabilities, spatial hierarchy learning, and resilience to spatial variability. The results from the combined forecasts were comparable with those from the QRNN, which was slightly better than those from the GAQR model. However, the combined forecasts were more accurate than the individual models. These results could be useful to decision-makers in the energy sector. Full article

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remains a public health concern due to infections with new SARS-CoV-2 variants. Therefore, finding effective preventive and therapeutic treatments against all SARS-CoV-2 variants is of great interest. In this study, we examined the capacity of eucalyptus essential oil (EEO) and eucalyptol (EOL) to prevent SARS-CoV-2 infection, using as a model SARS-CoV-2 Spike pseudotyped lentivirus (SARS-CoV-2 pseudovirus) and 293T cells transfected with human angiotensin-converting enzyme 2 (hACE2-293T cells). First, we determined the cytotoxicity of EEO and EOL using the MTT colorimetric assay, selecting non-cytotoxic concentrations ≤ 0.1% (v/v) for further analysis. Subsequently, we evaluated the capacity of EEO and EOL in cell cultures to preclude infection of hACE2-293T cells by SARS-CoV-2 pseudovirus, using a luciferase-based assay. We found that EEO and EOL significantly reduced SARS-CoV-2 pseudovirus infection, obtaining IC₅₀ values of 0.00895% and 0.0042% (v/v), respectively. Likewise, EEO and EOL also reduced infection by vesicular stomatitis virus (VSV) pseudovirus, although higher concentrations were required. Hence, EEO and EOL may be able to inhibit SARS-CoV-2 infection, at least partially, through a Spike-independent pathway, supporting the implementation of aromatherapy with these agents as a cost-effective antiviral measure. Full article

COVID-19 has generated a global impact due to its contagiousness and high lethality rates, with a large number of deaths occurring in intensive care units (ICUs). This study aimed to verify the occurrence of and understand the factors related to mortality in adult patients with COVID-19 admitted to the ICU in a tertiary hospital. This is a retrospective cohort study, which included COVID-19 patients admitted between March 2020 and December 2021. A total of 588 patients were included, of whom the majority (55.27%) did not survive. Invasive mechanical ventilation was the strongest predictor of the risk of death in the ICU with OR = 97.85 (95% CI = 39.10–244.86; p < 0.001), along with age and Simplified Acute Physiology Score 3 (SAPS3). The length of the ICU stay was protective. Evaluating patients on invasive mechanical ventilation in isolation, using an adjusted model, we found the following risk factors: use of vasopressin, renal replacement therapy, red cell distribution width > 15, use of hydrocortisone, and age in years. Protective factors included the days of mechanical ventilation use, being admitted from another service, and being of female sex. Identifying early predictors of mortality in patients with COVID-19 who require hospitalization is essential in the search for actions to prevent and manage complications, which can increase the survival of these patients and reduce the impact on health services. Full article

The HZ 26-B buried hill reservoir is located in the eastern part of the South China Sea. This reservoir is characterized by the development of natural fractures, a high density, and a complex geological structure, featuring an upper condensate gas layer and a lower volatile oil layer. These characteristics present significant challenges for oilfield exploration. To address these challenges, this study employed advanced embedded discrete fracture methods to conduct comprehensive numerical simulations of the fractured buried hill reservoirs. By meticulously characterizing the flow mechanisms within these reservoirs, the study not only reveals their unique characteristics but also establishes an embedded discrete fracture numerical model at the oilfield scale. Furthermore, a combination of single-factor sensitivity analysis and the Pearson correlation coefficient method was used to identify the primary controlling factors affecting the development of complex condensate reservoirs in ancient buried hills. The results indicate that the main factors influencing the production capacity are the matrix permeability, geomechanical effects, and natural fracture length. In contrast, the impact of the threshold pressure gradient and bottomhole flow pressure is relatively weak. This study’s findings provide a scientific basis for the efficient development of the HZ 26-B oilfield and offer valuable references and insights for the exploration and development of similar fractured buried hill reservoirs. Full article

With the development of satellite technology, the importance of multi-temporal remote sensing (RS) image change detection (CD) in urban planning, environmental monitoring, and other fields is increasingly prominent. Deep learning techniques enable a profound exploration of the intrinsic features within hyperspectral (HS) data, leading to substantial enhancements in CD accuracy while addressing several challenges posed by traditional methodologies. However, existing convolutional neural network (CNN)-based CD approaches frequently encounter issues during the feature extraction process, such as the loss of detailed information due to downsampling, which hampers a model’s ability to accurately capture complex spectral features. Additionally, these methods often neglect the integration of multi-scale information, resulting in suboptimal local feature extraction and, consequently, diminished model performance. To address these limitations, we propose a multi-scale fusion network (MsFNet) which leverages dynamic spectral features for effective multi-temporal HS-CD. Our approach incorporates a dynamic convolution module with spectral attention, which adaptively modulates the receptive field size according to the spectral characteristics of different bands. This flexibility enhances the model’s capacity to focus on critical bands, thereby improving its ability to identify and differentiate changes across spectral dimensions. Furthermore, we develop a multi-scale feature fusion module which extracts and integrates features from deep feature maps, enriching local information and augmenting the model’s sensitivity to local variations. Experimental evaluations conducted on three real-world HS-CD datasets demonstrate that the proposed MsFNet significantly outperforms contemporary advanced CD methods in terms of both efficacy and performance. Full article