Search Results (9,391)

Small-scale thermal treatment systems can provide environmentally improved sewage sludge treatment due to processing sludge locally, which lowers transport costs and emissions. However, the combined effect of confined volume and complex sludge properties makes achieving strict regulations on flue gas emissions and end-ash composition challenging. System development thus requires the use of advanced, 3D CFD simulation supported studies. An important step forward regarding these is the application of combustion models which introduce tailored surrogate fuels and apply detailed chemical kinetics to achieve a high-fidelity combustion description in confined volumes. In relation to this, the paper presents an innovative computationally efficient sewage sludge surrogate-based combustion model capable of defining surrogates, tailored to sewage sludge, and capable of providing detailed insight into reaction zone evolution in small-scale sludge incineration systems. The validity of the proposed model and surrogates is confirmed via simulated temperatures differing from measurements in the small-scale system for less than 30 K. The validated model of a small-scale system is used in the parametric analysis of variable air–fuel ratios, higher fuel moisture presence, varying bed temperature, and varying thermal power to enable unprecedentedly accurate and efficient definition of design features of small-scale systems and to provide key guidelines for operation optimization. Full article

In the context of digital school development, the leadership practices of school leadership teams play a significant role. If leadership teams want to enact leadership practices effectively, they require strong connections to the entire teaching staff as well as close contact with other key actors in the digital process. Since little is known about these connection patterns of school leadership teams, this study aims to uncover them. The aim is to provide practical advice to school administrators and schools regarding digital school development, and to derive concrete recommendations for action concerning their relationships and management. To this end, we examined the social networks of the teaching staff of 13 German secondary schools (N = 817 teachers) by asking all the teachers to complete a questionnaire about their contacts in relation to digital school development. We conducted a social network analysis and extracted various network metrics pertaining to the school leadership teams of these institutions, considering not only their integration within the overall network but also their connections with a pivotal stakeholder: the digital coordinator. To contextualize our findings, we compared the network metrics of the two different professional target groups using t-tests. The results reveal significant variability in the connection patterns of school leadership teams across different schools. Furthermore, our analysis indicates that digital coordinators consistently exhibit higher levels of connectedness within the realm of digital school development than the members of the school leadership teams. These findings highlight the importance of close collaboration between school leadership teams and the digital coordinator in order to advance digital school development. It is also suggested that school leadership teams should consider delegating more responsibilities to the digital coordinator, particularly those necessitating close collaboration with the teaching staff. Full article

Quality-of-life (QoL) needs among gynecologic cancer survivors are multifaceted and culturally mediated, yet limited research has examined how survivors in the Middle East prioritize key domains such as sexual function, emotional well-being, and relational quality. This study aimed to identify subgroups of survivors based on the perceived importance of these domains and to explore demographic and clinical predictors of subgroups within the Saudi Arabian context. We conducted a cross-sectional, survey-based study among 129 women with a history of breast or cervical cancer attending a tertiary oncology center in Jeddah, Saudi Arabia. Participants rated the importance of sexual, emotional, and relational QoL domains using a 4-point Likert scale. Latent class analysis (LCA) was used to segment survivors based on their perceived domain importance. Differences in demographic and clinical characteristics across classes were assessed using chi-square tests. A decision tree classifier was employed. Three latent classes emerged: Class 0 (48.8%) prioritized all domains highly; Class 1 (17.8%) reported low importance across domains; and Class 2 (33.3%) emphasized emotional and relational domains while downplaying sexual function. Class group was significantly associated with age (p = 0.001), education (p = 0.04), nationality (p = 0.03), and number of children (p < 0.001). Decision tree analysis identified number of children, age, and marital status as the strongest predictors of high-importance class group. Gynecologic cancer survivors in Saudi Arabia hold diverse priorities regarding QoL domains, primarily shaped by sociocultural context than clinical variables. Tailored survivorship interventions that reflect survivors’ lived values, particularly in relation to age, family structure, and cultural norms, are critical for person-centered oncology care in the region. Full article

Accurate prediction of earthquake—induced building damage is essential for timely disaster response and effective risk mitigation. This study explores a machine learning (ML)-based classification approach using data from the 2015 Gorkha, Nepal earthquake, with a specific focus on reinforced concrete (RC) structures. The original dataset from the 2015 Nepal earthquake contained 762,094 building entries across 127 variables describing structural, functional, and contextual characteristics. Three ensemble ML modelsGradient Boosting Machine (GBM), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM) were trained and tested on both the full dataset and a filtered RC-only subset. Two target variables were considered: a three-class variable (damage_class) and the original five-level damage grade (damage_grade). To address class imbalance, oversampling and undersampling techniques were applied, and model performance was evaluated using accuracy and F1 scores. The results showed that LightGBM consistently outperformed the other models, especially when oversampling was applied. For the RC dataset, LightGBM achieved up to 98% accuracy for damage_class and 93% accuracy for damage_grade, along with high F1 scores ranging between 0.84 and 1.00 across all classes. Feature importance analysis revealed that structural characteristics such as building area, age, and height were the most influential predictors of damage. These findings highlight the value of building-type-specific modeling combined with class balancing techniques to improve the reliability and generalizability of ML-based earthquake damage prediction. Full article

The Mycobacterium tuberculosis complex (MTBC) represents one of the most significant bacterial pathogen groups affecting both animals and humans worldwide. This review provides a comprehensive analysis of MTBC species distribution across different animal hosts and evaluates current laboratory diagnostic methodologies for pathogen detection and identification. The complex comprises seven primary species: Mycobacterium bovis, M. caprae, M. tuberculosis, M. microti, M. canettii, M. africanum, and M. pinnipedii, each exhibiting distinct host preferences, geographical distributions, and pathogenic characteristics. Despite sharing >99% genetic homology, these species demonstrate variable biochemical properties, morphological features, and pathogenicity profiles across mammalian species. Current diagnostic approaches encompass both traditional culture-based methods and advanced molecular techniques, including whole genome sequencing. This review emphasises the critical importance of rapid, accurate detection methods for effective tuberculosis surveillance and control programmes in veterinary and public health contexts. Full article

Strawberry (Fragaria × ananassa Duch.) is a widely cultivated and economically important fruit crop with increasing consumer demand worldwide. Nowadays, in Uzbekistan, strawberry cultivation surpasses that of many other fruits and vegetables in terms of production volume. However, most genetic studies have focused on a limited set of cultivars, leaving a substantial portion of varietal diversity unexplored. This study aimed to evaluate the genetic variability and heritability among selected strawberry cultivars, as well as correlations between certain valuable agronomic traits, using molecular and statistical approaches. Polymorphism analysis was performed, using 67 gene-specific SSR markers, through PCR, and allele variations were observed in 46.3% of the markers analyzed. Among them, 31 markers displayed polymorphic bands, identifying fifty alleles, with one to four alleles per marker. Phylogenetic analysis was performed using MEGA 11 software, while statistical evaluations included AMOVA (GenAIEx), correlation (OriginPro), and descriptive statistics based on standard agronomic methods. Additionally, the degree of cross-compatibility and pollen viability among the cultivars were studied, and their significance for cultivar hybridization was analyzed. The highest fruit weight was observed in the Cinderella cultivar (26.2 g), and a moderate negative correlation (r = −0.688) was found between fruit number and fruit weight. These findings demonstrate the potential of molecular tools for assessing genetic diversity and provide valuable insights for breeding programs aimed at developing improved strawberry cultivars with desirable agronomic traits. Full article

Species distribution models were built for three short-palped crane fly species of the genus Dicranomyia: Dicranomyia affinis, Dicranomyia chorea, and Dicranomyia mitis. The main objective of this study was to assess potential habitat suitability in undersampled regions and explore differences in environmental space. Dicranomyia affinis was historically considered a variety of Dicranomyia mitis due to their morphological similarity. In contrast, Dicranomyia chorea is a widespread species. The biology and ecology of these species remain poorly understood. Models were developed using Maxent, a widely used tool. Our results indicate that Dicranomyia affinis and Dicranomyia chorea share highly similar predicted habitat suitability, with high suitability across the Mediterranean, Central, and Northern Europe, moderate suitability in Eastern Europe, and low suitability in Central Asia. In contrast, Dicranomyia mitis is predicted to have greater habitat suitability in Eastern Europe and Scandinavia, with lower suitability in Mediterranean regions. Analysis of variable importance revealed possible ecological differences between the species. While climatic factors primarily influenced the models for Dicranomyia affinis and Dicranomyia chorea, Dicranomyia mitis was more strongly influenced by the variable pH. These findings may provide insights into potential distributions in undersampled areas and improve our understanding of the species’ ecology. Full article

Breast cancer remains a leading cause of global cancer burden, with marked differences in incidence across countries. While biological risk factors are well established, understanding the broader structural and sociocultural influences has been less comprehensive. In this study, we analyzed harmonized data from 183 countries (2017–2023), encompassing 33 variables and 7 subvariables related to demographics, nutrition, environment, health, and healthcare access, drawn from open-access international databases. Spearman correlation analysis identified strong positive associations between breast cancer incidence and discontinued breastfeeding, high LDL cholesterol, out-of-pocket healthcare expenditure, and educational attainment. Conversely, poor sanitation, lack of handwashing facilities, unsafe water, and certain nutritional deficiencies exhibited robust negative correlations, likely reflecting under detection and reporting limitations in lower-resource settings rather than true protective effects. These findings were further explored using multiple linear regression, which explained approximately 73% of the variance in global breast cancer incidence. The final model highlighted discontinued breastfeeding, prevalence of cocaine use, unsafe sanitation, high out-of-pocket healthcare expenditure, limited handwashing access, and high processed meat consumption as the most influential independent predictors. Receiver operating characteristic (ROC) analysis confirmed strong predictive value for discontinued breastfeeding and out-of-pocket expenditure, with sanitation and hygiene variables showing paradoxical inverse associations. Our results emphasize that breast cancer risk is shaped not only by individual behaviors and genetics, but also by larger-scale structural, socioeconomic, and environmental factors. These patterns suggest that targeted interventions addressing both lifestyle behaviors and systemic inequities—such as promoting breastfeeding, reducing financial barriers to healthcare, and strengthening public health infrastructure—could meaningfully reduce the global burden of breast cancer. In conclusion, this study underscores the importance of multisectoral, equity-focused prevention strategies. It also highlights the value of country-level ecological analyses in uncovering upstream determinants of cancer incidence and calls for further research to disentangle individual and contextual effects in cancer epidemiology. Full article

Evaporation Duct Height (EDH) is a crucial parameter in evaporation duct modeling, as it directly influences the strength of the waveguide trapping effect and significantly impacts the over-the-horizon detection performance of maritime radars. To address the limitations of low prediction accuracy and limited interpretability in existing deep learning models under complex marine meteorological conditions, this study proposes a surrogate model, BLA-EDH, designed to emulate the output of the Naval Postgraduate School (NPS) model for real-time EDH estimation. Experimental results demonstrate that BLA-EDH can effectively replace the traditional NPS model for real-time EDH prediction, achieving higher accuracy than Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) models. Random Forest analysis identifies relative humidity (0.2966), wind speed (0.2786), and 2-m air temperature (0.2409) as the most influential environmental variables, with importance scores exceeding those of other factors. Validation using the parabolic equation shows that BLA-EDH attains excellent fitting performance, with coefficients of determination reaching 0.9999 and 0.9997 in the vertical and horizontal dimensions, respectively. This research provides a robust foundation for modeling radio wave propagation in the Yellow Sea and Bohai Sea regions and offers valuable insights for the development of marine communication and radar detection systems. Full article

The rapid accumulation of public debt raises global concern over its implications for financial markets. This study examines the effect of domestic public debt on financial development in Middle East and North Africa (MENA) countries, a region marked by sharp heterogeneity in institutions, debt dynamics, and oil dependence, using annual panel data for 16 countries over the period (2000–2020). Our analysis employs a fractional response model (FRM), which accounts for the bounded nature of the dependent variable, corrects for heteroskedasticity, and incorporates country fixed effects. The findings reveal a significant negative effect of domestic public debt on financial development, consistent with the lazy banks and crowding-out hypotheses. This adverse relationship persists across different income groups and debt percentiles, with modest attenuation at higher debt levels. Oil rents are also found to exert a robust negative effect, highlighting the structural vulnerabilities associated with oil dependence. These results emphasize the importance of debt management, fiscal frameworks that account for commodity cycles, and policies to reduce the sovereign–bank nexus in fostering sustainable financial development in the region. Full article

The success and impact of biological invasions depend on adaptations to novel abiotic and biotic selective pressures. However, the genetic mechanisms underlying adaptations in invasive species are inadequately understood. Taenioides sp. is an invasive worm goby, originally endemic to brackish waters in the estuaries of Southeastern China, and now colonizes multiple inland freshwaters of North China within decades as a byproduct of the East Route of South-to-North Water Transfer (ESNT) project. However, the molecular mechanisms underlying their adaptations to the climate of North China, especially the temperature regime, are unknown. Here, we performed genomic resequencing analysis to assess genetic diversity and population genetic structure, and further investigated the genomic signatures of local adaptation in the invasive population of Taenioides sp. during their northward invasion. We revealed that all invasive populations exhibited no genetic differentiation but low gene flow and an obvious signal of population bottleneck. Yangtze River estuary may serve as the source population, while Gaoyou Lake serves as a potential bridgehead of the invasion. Selective sweep analyses revealed 117 genomic regions, containing 673 candidate genes, under positive selection in populations at the invasive front. Redundancy analysis suggested that local temperature variables, particularly the monthly minimum temperature, represent critical evolutionary forces in driving adaptive divergence. Functional enrichment analyses revealed that multiple biological processes, including metabolism and energy production, substance transmembrane transport, and neural development and synaptic transmission, may play important roles in adaptation to regional temperature conditions. Our findings revealed a scenario of adaptive evolution in teleost species that underpins their regional climate adaptation and successful establishment of invasive populations in a human-facilitated invasion context. Proper management strategies should be established to manage Taenioides sp invasion as soon as possible. Full article

This paper presents a digital approach for the identification of apple varieties bred in Kazakhstan using deep learning methods and transfer learning. The main objective of this study is to develop and evaluate an algorithm for automatic varietal classification of apples based on color images obtained under controlled conditions. Five representative cultivars were selected as research objects: Aport Alexander, Ainur, Sinap Almaty, Nursat, and Kazakhskij Yubilejnyj. The fruit samples were collected in the pomological garden of the Kazakh Research Institute of Fruit and Vegetable Growing, ensuring representativeness and taking into account the natural variability of the cultivars. Two convolutional neural network (CNN) architectures—GoogLeNet and SqueezeNet—were fine-tuned using transfer learning with different optimization settings. The data processing pipeline included preprocessing, training and validation set formation, and augmentation techniques to improve model generalization. Network performance was assessed using standard evaluation metrics such as accuracy, precision, and recall, complemented by confusion matrix analysis to reveal potential misclassifications. The results demonstrated high recognition efficiency: the classification accuracy exceeded 95% for most cultivars, while the Ainur variety achieved 100% recognition when tested with GoogLeNet. Interestingly, the Nursat variety achieved the best results with SqueezeNet, which highlights the importance of model selection for specific apple types. These findings confirm the applicability of CNN-based deep learning for varietal recognition of Kazakhstan apple cultivars. The novelty of this study lies in applying neural network models to local Kazakhstan apple varieties for the first time, which is of both scientific and practical importance. The practical contribution of the research is the potential integration of the developed method into industrial fruit-sorting systems, thereby increasing productivity, objectivity, and precision in post-harvest processing. The main limitation of this study is the relatively small dataset and the use of controlled laboratory image acquisition conditions. Future research will focus on expanding the dataset, testing the models under real production environments, and exploring more advanced deep learning architectures to further improve recognition performance. Full article

Motion capture (MoCap) data derived from wearable Inertial Measurement Units is essential to applications in sports science and healthcare robotics. However, a significant amount of the potential of this data is limited due to missing data derived from sensor limitations, network issues, and environmental interference. Such limitations can introduce bias, prevent the fusion of critical data streams, and ultimately compromise the integrity of human activity analysis. Despite the plethora of data imputation techniques available, there have been few systematic performance evaluations of these techniques explicitly for the time series data of IMU-derived MoCap data. We address this by evaluating the imputation performance across three distinct contexts: univariate time series, multivariate across players, and multivariate across kinematic angles. To address this limitation, we propose a systematic comparative analysis of imputation techniques, including statistical, machine learning, and deep learning techniques, in this paper. We also introduce the first publicly available MoCap dataset specifically for the purpose of benchmarking missing value imputation, with three missingness mechanisms: missing completely at random, block missingness, and a simulated value-dependent missingness pattern simulated at the signal transition points. Using data from 53 karate practitioners performing standardized movements, we artificially generated missing values to create controlled experimental conditions. We performed experiments across the 53 subjects with 39 kinematic variables, which showed that discriminating between univariate and multivariate imputation frameworks demonstrates that multivariate imputation frameworks surpassunivariate approaches when working with more complex missingness mechanisms. Specifically, multivariate approaches achieved up to a 50% error reduction (with the MAE improving from 10.8 ± 6.9 to 5.8 ± 5.5) compared to univariate methods for transition point missingness. Specialized time series deep learning models (i.e., SAITS, BRITS, GRU-D) demonstrated a superior performance with MAE values consistently below 8.0 for univariate contexts and below 3.2 for multivariate contexts across all missing data percentages, significantly surpassing traditional machine learning and statistical methods. Notable traditional methods such as Generative Adversarial Imputation Networks and Iterative Imputers exhibited a competitive performance but remained less stable than the specialized temporal models. This work offers an important baseline for future studies, in addition to recommendations for researchers looking to increase the accuracy and robustness of MoCap data analysis, as well as integrity and trustworthiness. Full article

This study presents a comparative analysis of electrocardiogram (ECG) and accelerometer (ACC) data in the context of unsupervised human activity recognition and subject identification. Recordings were obtained from 30 participants performing activities of daily living such as walking, sitting, lying, cleaning the floor, and climbing stairs. Distance-based signal comparison methods and clustering techniques were employed to evaluate the feasibility of each modality, both individually and in combination, to discriminate between individuals and activities. Results indicate that ACC signals provide superior performance in activity recognition (NMI = 0.728, accuracy = 0.817), while ECG signals show higher discriminative power for subject identification (NMI = 0.641, accuracy = 0.500). In contrast, combining ACC and ECG signals yielded lower scores in both tasks, suggesting that multimodal fusion introduced additional variability. These findings highlight the importance of selecting the most appropriate modality depending on the recognition objective and emphasize the challenges associated with multimodal approaches in unsupervised scenarios. Full article

African swine fever virus (ASFV) is an important transboundary animal pathogen with significant impacts on the global swine industry. Overwhelming proinflammatory responses are a major virulence mechanism for ASFV, but the dynamics of these changes during clinical disease are not completely understood. We constructed a detailed portrait of the innate immune responses during acute African swine fever (ASF) at the cellular, transcriptomic, and cytokine levels. Samples serially obtained from infected piglets show that progression of acute ASF is characterized by rapid increases in plasma type I interferons, TNF-α, IL-12p40, and IL-10, which coincide with the manifestation of clinical disease and viral DNAemia. Lymphocytes and natural killer (NK) cells progressively declined, with fluctuations in B cell, CD8+ T cell, and CD4+/CD8+ T cell populations. Blood monocytes and macrophages were highly variable throughout infection, with an abrupt spike in CD203+ mature macrophages immediately prior to death. Transcriptomic analysis of blood showed downregulation of cellular translation as early as 1 day post-challenge (DPC) and significant upregulation of antiviral immune processes at 5 DPC and 7 DPC, which overlapped with the onset of clinical disease. Together, these results present a detailed delineation of fatal ASF which involves an initial infection and damage of susceptible myeloid cells prior to symptomatic disease characterized by pro-inflammatory immune responses, lymphoid depletion, and clinical deterioration. Full article