Abstract: This study focuses on the factors influencing the performance of agribusiness MSME clusters in Central Java Province, an area that has not been previously studied. The novelty of this study lies in the use of cluster performance variables and indicators that differ from those in previous studies. This study uses a descriptive analytical method. The research locations were determined purposively, specifically in Pati Regency, Rembang, Demak, Purbalingga, Pekalongan, Sukoharjo, and Magelang City, which have a larger number of agribusiness MSME clusters compared to other regions. The research sample consisted of 251 agribusiness MSMEs, selected proportionally across seven areas. Data analysis used the SEM method with PLS tools. The results of the study indicate that social capital is the factor influencing the performance of agribusiness MSME clusters. Social capital drives the performance of agribusiness MSME clusters. While collective efficiency, social and economic benefits, and MSME performance do not affect the performance of agribusiness MSME clusters. Collective efficiency influences social capital, meaning that collective efficiency drives the development of social capital. Additionally, collective efficiency also influences socioeconomic benefits, indicating that collective efficiency drives socioeconomic benefits. However, collective efficiency does not affect MSME performance. Social capital influences socioeconomic benefits, meaning that it drives these benefits; however, social capital does not directly affect MSME performance. Socioeconomic benefits influence the performance of MSMEs, which means that socioeconomic benefits drive their performance.

Abstract: Smart Energy Power systems – encompassing oil, gas, nuclear, mining, and electricity – are undergoing rapid transformation driven by digitalization, decarbonization, and geopolitical uncertainty. Ensuring stability in energy communities within this complex, multi-sector landscape requires analytical frameworks that integrate both “hard” and “soft” dimensions of energy systems. This study proposes an integrated approach combining hard analyses, such as capacity and technical capability of energy infrastructures, as well as the security of supply of energy raw materials., with soft analyses, including international relations and energy diplomacy, in the context of stability in energy communities. By bridging technical and social perspectives, the framework captures interdependencies that are often overlooked when sectors or methodologies are treated in isolation. The paper conceptualizes energy communities as adaptive socio-technical systems in which technological performance and social acceptance co-evolve. Through comparative analysis across fossil fuel, nuclear, mining, and electricity domains, the study demonstrates how misalignment between hard and soft factors can amplify instability, while strategic integration enhances resilience and long-term sustainability. The findings highlight the necessity of interdisciplinary planning tools, data-driven decision support, and inclusive governance mechanisms to manage transition risks and operational uncertainties. This integrated model contributes to energy policy and systems engineering by offering a holistic lens for designing stable, smart energy power systems capable of supporting secure, equitable, and resilient energy communities in a rapidly changing global context.

Abstract: Objective: Nocturia, defined as waking from sleep to void, is a frequent lower urinary tract symptom associated with impaired sleep quality and reduced quality of life. This study aimed to evaluate the prevalence of nocturia episodes and their impact on sleep disturbance and health-related quality of life. Methods: A questionnaire-based cross-sectional study was conducted at the Urology Outpatient Clinic of the General Hospital of Eastern Achaia between November 2023 and May 2024. Participants reporting nocturia were assessed using the Nocturia Quality of Life (N-QOL) questionnaire, the Athens Insomnia Scale (AIS), and the EQ-5D questionnaire. Demographic data and comorbid conditions were also collected. Univariate analyses and multiple linear regression were applied to identify factors associated with nocturia-related outcomes. Results: A total of 89 participants (78 men and 11 women; mean age 68.9 years) were included. Most participants reported 2–3 nocturnal voids per night. The N-QOL score was significantly associated with the frequency of nocturia episodes (r = −0.55, p < 0.0001), and regression analysis confirmed this relationship (coefficient: −6.7; 95% CI: −10.4 to −3.1). Individuals scoring ≥ 8 on the OAB-V8 scale demonstrated significantly lower N-QOL performance. Conclusions: Increasing nocturia frequency is associated with impaired sleep, reduced vitality, and diminished quality of life, particularly among older adults. Nocturia should be recognized as a clinically relevant symptom requiring targeted evaluation and personalized management strategies.

Abstract: The paper presents a rapid predictive analysis for aircraft sensors monitoring data using Regression Polynomials with Process Similarity Criteria Fit (RPPSCF). Two necessary conditions of the method are formulated. It provides a comparison of machine learning methods for predicting aircraft health status. The Long Short-term Memory (LSTM) model is identified as the most efficient for aircraft engine health prediction, according to the analysis of the research papers on aircraft prediction. It is shown that machine learning models that have high prediction efficiency are suitable for long-term aircraft health prognostics and are not applicable for predicting parameters with rapidly changing characteristics. The paper introduces information about rapid machine learning algorithms and their limitations due to low efficiency. Due to the low computational complexity of the RPSCF, the method can be used for the maintenance and supply chain prognostics of the high volume of monitoring data. The method implementation is not limited to aircraft monitoring parameters abnormality detection. The results of the prediction with minimum least square approximation, LSTM, and RPPSCF are illustrated in the charts.The mathematical expression for method implementation for the arbitrary polynomial power is provided. The solution of the non-linear operator equations for defining polynomial coefficients is obtained using the hypernumber theory. Further directions are identified for automated cognition of the similarity criteria and combining the hypernumber method of solving operator equations with Kantorovich's modified Newton method of solving non-linear equations. The second direction would allow us to increase the computational speed.

Abstract: Deep learning-based Human Activity Recognition (HAR) systems using multimodal wearable sensors are increasingly deployed in safety-critical applications including healthcare monitoring, elderly care, and security authentication. However, the vulnerability of these systems to adversarial attacks remains insufficiently understood, particularly for attacks that must evade detection while manipulating multiple sensor modalities simultaneously. This paper presents STAR-RL (Stealth-aware Targeted Adversarial attack via Reinforcement Learning), a novel framework that generates effective and stealthy adversarial examples against multimodal sensor-based HAR systems. STAR-RL introduces three key innovations: (1) a multi-strategy attack engine that adaptively selects among diverse perturbation algorithms based on real-time attack progress, (2) a sensor-aware stealth mechanism that concentrates perturbations on naturally noisy sensors to minimize detection likelihood, and (3) a reinforcement learning-based meta-controller that learns optimal attack policies through interaction with the target classifier. Comprehensive experiments on the MHEALTH dataset demonstrate that STAR-RL achieves 95.20% attack success rate, substantially outperforming baseline methods including FGSM (6.00%), PGD (88.60%), and C&W (69.00%). The stealth analysis confirms that 51.35% of perturbation energy is successfully directed to weak sensors (gyroscopes and magnetometers), validating the effectiveness of the sensor-aware allocation strategy. Our findings reveal critical security vulnerabilities in production HAR systems and provide insights for developing robust defense mechanisms against adaptive adversarial threats.

Abstract: (1) Background: Since its introduction to China, PWD has caused severe damage to coniferous forests in affected areas. Currently, the disease continues to expand towards the northwest regions, posing a serious threat to the ecological security of Xinjiang. (2) Methods: This study utilized MaxEnt model to predict the potential transmission areas of PWD and the potential suitable habitats of Monochamus saltuarius. After coupling the results of both, the potential occurrence areas of PWD in Xinjiang were ultimately determined. (3) Results: Human factors are the main driving forces behind the spread of PWD, with activities in scenic areas and human impact factors playing a key role in transmission. Altitude and Isothermality are the primary limiting factors for vector insects. Xinjiang has potential occurrence areas of PWD, covering 88% of the total coniferous forest area in Xinjiang. (4) Conclusions: Urumqi City, Ili Kazakh Autonomous Prefecture, and the Altay Prefecture are high-risk areas for PWD. This study clarifies the potential transmission routes of PWD and analyzes its high-risk areas, providing a scientific basis for forestry and relevant departments to implement prevention and control measures.

Abstract: Background: Family planning remains a cornerstone of reproductive health strategies to reduce maternal and child mortality by preventing unintended and high-risk pregnancies. Despite the implementation of the FP2020 initiative, empirical evidence on its population-level impact in the Democratic Republic of the Congo (DRC) remains scarce. This study aimed to evaluate the effect of modern contraceptive use on high-risk pregnancies and under-five mortality using nationally representative data. Methods: A quasi-experimental Difference-in-Differences (DiD) design was applied using Demographic and Health Survey (DHS) data from 2013 (pre-intervention) and 2023 (post-intervention). Women aged 15–49 years with at least one live birth were included for maternal outcomes, while all live-born children within five years preceding each survey were analyzed for child outcomes. Weighted analyses employed Linear Probability Models (LPM), adjusting by potential confounders variables. Results :The prevalence of high-risk pregnancies among modern contraceptive users declined from 58.6% in 2013 to 54.5% in 2023, while under-five mortality decreased from 10.4% to 5.9% over the same period. DiD estimates revealed a significant reduction in high-risk pregnancies among users in urban areas (β = -0.067) (95% CI: -0.133 to -0.003), and a substantial decline in under-five mortality in rural areas (β = -0.031) (95% CI: -0.059 to -0.002). Results remained robust across model specifications; and parallel trends test confirmed model validity (p &gt; 0.5). Conclusions: Findings demonstrate that the FP2020 initiative and increased modern contraceptive use contributed to measurable reductions in maternal and child health risks in the DRC. Expanding access to family planning within universal health coverage (UHC) frameworks could further reduce health inequalities and accelerate progress toward the Sustainable Development Goals (SDGs).

Abstract: This paper does not present new experimental data, but it offers unique insights into the replication mechanism and DNA topology based on ambidextrous double helix model. Analysis of positively and negatively supercoiled plasmids revealed that the differences between them could be reasonably explained by the ambidextrous double helix model, but not by the classical double helix model. The superhelical structure of DNA has been understood and explained in an unprecedented way, which may help us better unravel the mysteries of nature. Acquiring knowledge is important, but finding the right way of thinking that breaks with tradition and inspires new ideas is even more crucial.

Abstract: Crop diseases and pests pose significant threats to global food security, demanding precise and efficient management solutions. While Multimodal Large Language Models (M-LLMs) offer promising avenues for intelligent agricultural diagnosis, general-purpose models often falter due to a lack of specialized visual feature extraction, inadequate understanding of agricultural terminology, and insufficient precision in prevention advice. To address these challenges, this paper introduces AgriM-LLM, a novel agriculture-specific multimodal large language model designed for enhanced crop disease and pest identification and prevention. AgriM-LLM integrates several key innovations: an Enhanced Vision Encoder featuring a Multi-Scale Feature Fusion module for capturing subtle visual symptoms; an Agriculture-Knowledge-Enhanced Q-Former that injects structured agricultural knowledge to guide cross-modal alignment; and a Domain-Adaptive Language Model employing a multi-stage progressive fine-tuning strategy for expert-level advice generation. Furthermore, an efficient LoRA-based fine-tuning strategy ensures practical computational resource utilization. Evaluated on a comprehensive Chinese agricultural multimodal dataset, AgriM-LLM consistently outperforms existing general-purpose and domain-specific baselines. Our ablation studies confirm the critical contribution of each proposed component, and detailed analyses demonstrate superior visual encoding, knowledge integration, and linguistic specialization. AgriM-LLM represents a significant step towards providing timely, accurate, and actionable intelligent decision support for farmers, thereby fostering sustainable agricultural development.

Abstract: Motion artifacts corruptwearable ECG signals and generate false alarms of arrhythmias, limiting the clinical adoption of continuous cardiacmonitoring. We present a dual-streamdeep learning framework formotionrobust binary arrhythmia classification throughmulti-modal sensor fusion andmulti-SNR training. ResNet-18 processes ECG spectrograms,while CNN-BiLSTMencodes accelerometermotion patterns; attention-gated fusion with gate diversity regularization adaptively weightsmodalities based on signal reliability. Training in MIT-BIHdata augmented at three noise levels (24, 12, 6 dB) enables noise-invariant learningwith successful generalization to unseen conditions. The framework achieves 99.5%accuracy under clean signals, gracefully degrading to 88.2%at extreme noise (-6 dB SNR)—a 46%improvement over trainingwith single-SNR. The high gate diversity (σ > 0.37) confirms adaptive context-dependent fusion. With 0.09% false positive rate and real-time processing (238 beats/second), the systemprovides practical continuous arrhythmia screening, establishing the foundation for hierarchical monitoring systems where binary screening activates detailed multi-class diagnosis.

Abstract: Africa has the youngest population worldwide, with many young people engaged in informal or temporary employment. Long-term financial resilience in this demographic requires that they develop strong digital financial literacy (DFL) skills, including saving, investing, and managing risk through digital platforms. This study investigates digital financial literacy (DFL) among 300 Rwandan young adults aged 18–32 years and explores an AI-enabled intervention, aligning with Sustainable Development Goals (SDGs) 1 (No Poverty), 5 (Gender Equality), 8 (DecentWork and Economic Growth), 9 (Industry, Innovation and Infrastructure), and 10 (Reduced Inequalities). Findings reveal average financial knowledge, moderate digital literacy, and engagement in budgeting and saving behaviors, but persistent gaps in access to formal financial services and cybersecurity practices. Significant gender disparities were identified, with men demonstrating higher financial knowledge and participation in savings and investments, and higher educational attainment was positively associated with DFL.The low-fidelity chatbot intervention for loan literacy, delivered via a mobile money platform—designed based on survey insights—showed limited usability and acceptability due to participants’ low awareness of personal finances and prolonged task times. These results highlight the need for inclusive, context-sensitive digital financial education solutions and responsible AI integration within digital financial ecosystems to advance sustainable financial inclusion and economic empowerment in low-resource settings.

Abstract: This paper proposes a new non-perturbative quantum gravity framework based on quantum topological structures. By introducing "quantum vortices" to characterize the topological order of the statistical average of microscopic particles and embedding them into AdS/CFT holographic duality, the formation of "black hole singularities" is prevented (similar to singularity resolution) without the need for renormalization. Theoretical derivations show that the gravitational potential generated by the quantum vortex field forms a repulsive barrier within the critical radius (

Abstract: The presence of heavy metal in the sediments of tropical rivers is a significant hazard to the ecological quality and human health; however, the relationships of the biomagnification of metals have not been sufficiently investigated in urban West African environments. The current study determined the bioavailability and ecological risk of eight heavy metals (As, Cd, Cr, Fe, Mn, Ni, Pb, Se) in Nworie and Otamiri Rivers sediments, Nigeria. The BCR sequential extraction procedure was used to determine concentrations and distribution among four fractions (F1 -F4 ). The Risk Characterization used the Risk Ranking Index (RAC), the Geo-accumulation Index (Igeo), and the Potential Ecological Risk Index (PERI) and principal component analysis (PCA) supported the process of source apportionment. Findings showed that there was a strong spatial and temporal heterogeneity. The potentially bioavailable factions (F1 + F2 + F3) had maximums downstream at sites affected by urban activity cadmium at 1.95 mg/kg (81 %, SS5), lead at 5.81 mg/kg (72% SS7), and nickel at 5.37 mg/kg (100% SS7). The RAC more than 30% on cadmium showed increased mobility, and enhanced PERI (maximum of 285) on SS5 induced mainly by cadmium (E:241). The PCA revealed that 78% of the variance was explained, PC1 (54 %) linked cadmium and lead and nickel with anthropogenic urban runoff, and PC2 (24%) related geogenic iron and manganese with the remaining fraction F4. The Nworie and Otamiri river systems has a significantly high ecological risk. More studies focusing on the organization of fraction F3 and the following bioaccumulation mechanisms should be suggested to optimize the risk management approaches in this urban-tropical nexus.

Abstract: Results showed the presence of 425 species of macro fungi and 96 families in natural forest, Ascomycetes had 19 families and 41 species, 4 species are edible and 1 medicinal; the Basidiomycetes had 78 families and 384 species, and 50 species are edible, 6 medicinal, 65 toxic, 4 hallucinogenic and 3 bioluminescent. Regarding life forms Ascomycetes had 24 species saprotroph, 13 parasites and 1 mycorrhizal. Basidiomycetes had 229 saprotroph species, 119 mycorrhizal and 34 parasitic. Pure culture growth of 110 species was measured, saprotroph species grew 3.5 cm, mycorrhizal 0.7 cm and parasitic species 0.4 cm at 7 days from incubation. The Kruskall-Wallis analysis showed significant differences in the average growth of the species groups (p< 0.05). A pairwise analysis, after the Kruskall-Wallis, showed that growth of saprotrophs was significantly greater than mycorrhizal and parasitic species; the last two groups were not significantly different. Native edible strains of Pleurotus dejamour and Hericium erinaceus were grown and had statistically significant differences (P<0.05) for fruiting bodies production.

Abstract: This research answers the knowledge gap regarding the explanation of the quantum jump of the electron. This scientific paper aims to complete Einstein’s research regarding general relativity and attempt to link general relativity to quantum laws.

Abstract:

This study evaluated effects of straw particle size (short or long) and corn physical form (ground or whole) in diets on growth performance, rumen fermentation and fecal score in calves. Sixty female newborn calves were randomly assigned to one of the four treatments: 90% pelleted starter and 10% short straw (PSS); 70% pelleted starter, 20% whole corn and 10% short straw (PWCSS); 90% pelleted starter and 10% long straw (PLS); 70% pelleted starter, 20% whole corn and 10% long straw (PWCLS). In PSS and PLS treatments, all amount of corn was within pelleted starter. Calves were weaned at 68 days of age. Body weight (BW), wither height and heart girth were measured at 3 and 68 days of age. Feed intakes and fecal scores were measured daily. Rumen fluid and blood samples were collected for rumen pH, rumen volatile fatty acid (VFA) and blood β-hydroxy butyrate (BHB) measurements at 68 days of age. Weaning BW, average daily weight gain (ADG) and weaning wither height were significantly lower in PLS compared to other treatments. Weaning heart girth was significantly lower in PSS and PLS than PWCSS and PWCLS. Feed intake was significantly higher for PWCSS than PWCLS. PWCLS had a significantly lower feed efficiency (starter feed intake/ADG) than PLS. No significant differences were observed for ruminal pH, ruminal acetate and blood BHB among the treatments. In the diets including short straw, ruminal propionate, butyrate and total VFA concentrations were significantly higher for PWCSS than PSS. In the diets including long straw, ruminal propionate level was significantly greater for PLS than PWCLS and ruminal butyrate and total VFA concentrations were not different for PLS and PWCLS. This study indicated that the effect of corn physical form (ground or whole) on ruminal propionate, butyrate and total VFA concentrations could vary depending on straw particle size. Fecal score was significantly lower in PSS compared to other treatments. In conclusion, long straw combined with pelleted concentrate reduced growth performance in pre-weaning calves. Whole corn inclusion in the diets with long straw increased ADG and weaning BW and improved feed efficiency.

Abstract: Accurate forecasting of central bank policy rates is critical for guiding monetary policy, shaping market expectations, and maintaining macroeconomic stability. In emerging economies such as Mongolia, conventional econometric approaches, including the Taylor Rule, ARIMA, and SVAR, often struggle to capture nonlinear dynamics, temporal dependencies, and structural breaks. This study addresses these limitations by developing and evaluating modern forecasting methods that combine machine learning and deep learning models within hybrid frameworks. The analysis employs a comprehensive monthly dataset of 26 macroeconomic indicators spanning January 2008 to December 2024. Seven models are constructed and assessed using RMSE, MAE, and R² metrics. The empirical results show that hybrid approaches, particularly XGBoost combined with Gradient Boosting and LSTM integrated with XGBoost, deliver the highest predictive accuracy, with the leading model reaching an R² of 0.9355. These hybrid methods consistently outperform both traditional econometric and standalone ML or DL models in capturing complex macroeconomic patterns and structural changes. The findings provide a robust data-driven framework to support evidence-based monetary policy in Mongolia and offer a transferable methodology for other emerging markets facing similar economic challenges.

Abstract: Subject-driven text-to-image generation presents a significant challenge: faithfully reproducing a specific subject's identity within novel, text-described scenes. Existing solutions typically involve computationally expensive model fine-tuning or less performant training-free methods. This paper introduces Content-Adaptive Grafting (CAG), a novel, efficient, and entirely training-free framework designed to achieve high subject fidelity and strong text alignment. CAG operates without modifying the underlying generative model's weights, instead leveraging intelligent noise initialization and adaptive feature fusion during inference. Our framework comprises Initial Structure Guidance (ISG), which prepares a structurally consistent starting point via an inverted collage image, and Dynamic Content Fusion (DCF), which adaptively infuses multi-scale reference features using a gated attention mechanism and a time-dependent decay strategy. Extensive experiments demonstrate that CAG significantly outperforms state-of-the-art training-free baselines in subject fidelity and text alignment, while maintaining competitive efficiency. Ablation studies and human evaluations further validate the critical contributions of ISG and DCF, affirming CAG's leading position in providing a high-quality, practical solution for subject-driven text-to-image generation.

Abstract: To enhance the accuracy and efficiency of mining grassroots network data and to better support practical applications, this study proposes a personalized mining algorithm for grassroots network data based on deep learning. A multi-module neural network architecture is designed to process, filter, and transform raw grassroots network data. Through data preprocessing and hierarchical refinement, the algorithm generates high-precision, structured datasets suitable for personalized mining tasks. A five-layer neural network-comprising an input layer, convolutional input layer, hidden layer, convolutional output layer, and prediction output layer-is constructed to support an integrated training and testing workflow. A redundancy-elimination rule is introduced to prune unnecessary neural network parameters, followed by a maximum weight extraction rule to guide the personalized mining of grassroots network data. Experimental results demonstrate that the proposed algorithm achieves high convergence speed during training and offers superior performance in testing accuracy, enabling precise and reliable mining of high-dimensional and heavily interconnected data. This work lays a technical foundation for the effective utilization and intelligent analysis of grassroots network data.

Abstract: Anti-allergic and anti-inflammatory activities of the extracts of rosebuds newly-crossbred in Korea were investigated in vitro and in vivo. Twenty-four candidate rosebuds were extracted with 80% ethanol, and analyzed for polyphenols, flavonoids, tannins, proanthocyanidins, and pyrogallol (1,2,3-benzenetriol). The extracts’ in vitro anti-allergic and anti-inflammatory activities were analyzed through inhibitory effects on the β-hexosaminidase release from Compound 48/80-stimulated RBL-2H3 cells and nitric oxide production from lipopolysacchrade-activated RAW 264.7 macrophages, respectively. The in vivo activity was assessed via protection against lethality and itching (scratching) symptoms in mice challenged with Compound 48/80. Among candidates, Lover Shy, Pretty Velvet, Ice Wing, Red Perfume, Onnuri, Jaemina Red, and Hanggina were found to possess high concentrations of antioxidative components. By comparison, Pretty Velvet, Red Perfume, Jaemina Red, Hanggina, Onnuri, and Ice Wing were highly effective in anti-allergic and anti-inflammatory activities in vitro, in parallel with their concentrations of pyrogallol. Their anti-allergic effects were confirmed in mice: The 6 extracts protected against Compound 48/80-induced mortality and scratching behaviors in a dose-dependent manner. The allergen-induced increases in serum IgE and histamine as well as inflammatory cytokines, tumor-necrosis factor-α and interleukin-1β, were remarkably attenuated following treatment with the rosebud extracts. Therefore, it is suggested that the extracts and active ingredients from cross-bred Korean rosebuds exert anti-allergic and anti-inflammatory activities through their high levels of antioxidants and pyrogallol, and that could be promising candidates to overcome allergic responses such as atopic dermatitis.