Future Technology

Rural cultural landscape evaluation system based on analytic hierarchy process: a case study of Hong Fanchi Spring

Yangrui Wu, Raziah Ahmad, Amalina Mohd Fauzi, Chen Ma — Sun, 25 May 2025 00:00:00 +0000

Based on a combined approach of Analytic Hierarchy Process and fuzzy comprehensive evaluation, this study constructs a rural landscape evaluation system for the Hong Fanchi Spring area in Jinan, China. The research aims to systematically evaluate the cultural landscape quality, identify key factors affecting landscape value, and propose targeted protection strategies. Through literature review, expert interviews (n=18), and the Delphi method, a four-level hierarchical evaluation framework was established with 4 criterion-layer and 14 sub-criterion-layer indicators. Data collected from 389 valid questionnaires across six towns were analyzed using SPSS 27.0. Results revealed an overall cultural landscape evaluation score of 2.77 (on a 5-point scale), indicating below-average quality and considerable room for improvement. Among the four landscape types evaluated, Village Cultural Landscape ranked highest (2.80), followed by Planting Cultural Landscape (2.79), Religious Cultural Landscape (2.77), and Spring Water Cultural Landscape (2.75). The study identified cultural value (weight: 30.61%) and historical value (28.28%) as the most influential indicators, while public recognition (C9) demonstrated the greatest variation across landscape types. Based on these findings, six targeted recommendations are proposed, including classified protection priorities, enhanced community participation, improved legal frameworks, cultural-economic integration, strengthened environmental management, and promotion of sustainable development practices. This evaluation framework provides a reference model for other rural cultural landscape assessments and management strategies.

Optimized PID control for automated blood pressure management in post-operative care

Tue, 27 May 2025 00:00:00 +0000

Maintaining optimal Blood Pressure (BP) is vital, as abnormal BP levels pose substantial challenges to patient recovery in post-operative care. The manual administration of Sodium Nitroprusside (SNP) is a common approach to lower BP by relaxing peripheral vascular smooth muscles. Nevertheless, because of the inconsistency in drug sensitivity between patients, manual dosing is inaccurate and labour-intensive as it necessitates continuous expert monitoring. Therefore, this research adapts a control method to regulate BP in post-operative patients with hypertension. The Prairie Dog Optimization-based Proportional-Integral-Derivative (PDO-PID) controller adapts in real-time to the particular physiological responses of the patients, assuring precise and individualized SNP dosing. According to simulation results, the controller effectively controls BP levels over an extended time, generating an execution time of 63.613s and a reduced settling time of 1.05s. Corresponding SNP infusion levels are also effectively regulated, which is significantly smaller than the previous control approaches.

Enhanced cardiac arrhythmia classification through integration of ensemble empirical mode decomposition and heart rate variability analysis

T.Raghavendra Gupta, D Umanandhini — Wed, 28 May 2025 00:00:00 +0000

Cardiac arrhythmias are critical conditions requiring accurate classification for effective diagnosis as well as treatment. In this investigation, we provide a novel approach for cardiac arrhythmia classification that integrates two advanced techniques for feature extraction from ECG signals: “Ensemble Empirical Mode Decomposition” (EEMD) and “Heart Rate Variability” (HRV) analysis. The proposed approach employs EEMD to decompose ECG signals into intrinsic mode functions, capturing signal features, while HRV analysis provides additional physiological insights into heart rate fluctuations. Combining two strategies, our approach leverages a comprehensive set of features to improve the accuracy and resilience of arrhythmia classification. The system's effectiveness is explained via simulated tests utilizing the MIT-BIH arrhythmia database, with performance evaluated based on recall, accuracy, and precision metrics. Our results indicate that integrating EEMD and HRV features provides a more reliable and detailed classification of cardiac arrhythmias, offering a holistic perspective on heart rhythm dynamics.

Energy-aware power and rate control in MANETs using adaptive game theory and grey wolf optimization

Chandrashekhar Goswami, Chin-Shiuh Shieh , Prasun Chakrabarti — Thu, 29 May 2025 00:00:00 +0000

Inherent resource constraints within Mobile Ad Hoc Networks (MANETs) necessitate resource optimization, specifically power and rate control, as a critical focus for enhancing network performance in terms of energy, throughput, and delay. Although traditional power and rate control mechanisms have successfully improved throughput or energy efficiency, they fail to address the complex trade-offs between delay, energy consumption, and network stability, particularly in highly dynamic or unpredictable networks. Motivated by this, this study introduces a new Dynamic Power-Rate Optimization Grey Wolf Algorithm (DPRO-GWA) mechanism derived from a game-theoretic framework that balances outage probability and residual energy demands to achieve energy efficiency and quality of service (QoS) in mobile ad hoc networks (MANETs). The proposed approach formulates power and rate allocation as a super-modular game, which ensures both the existence and uniqueness of a Nash Equilibrium (NE) as the optimal solution for distributed non-cooperative nodes. We subsequently introduce an Adaptive Grey Wolf Optimizer (AGWO), which enhances the Grey Wolf Optimizer (GWO) by increasing convergence speed through adaptive tuning of the exploration-exploitation trade-off. Extensive simulation results demonstrate that DPRO-GWA significantly outperforms existing algorithms, including the Dynamic Rate and Power Allocation Algorithm (DRPAA), Energy Conserving Power and Rate Control (ECPRC), and Rate-Effective Network Utility Maximization (RENUM) in terms of energy consumption, throughput, and delay. Additionally, the proposed method maximizes the energy-delay trade-off, leading to considerable improvements in the network lifetime and performance, particularly in time-variant and fading channel environments. Thus, this study creates a promising avenue for refining power and rate control protocols for next-generation MANETs.

Research on the impact mechanism of AI-driven supply chain creditworthiness assessment on commercial banks' credit policies for SMEs

Feng Wu, Nusanee Meekaewkunchom, Chaiyawit Muangmee, Tatchapong Sattabut — Fri, 30 May 2025 00:00:00 +0000

This study investigates how AI-driven supply chain creditworthiness assessment transforms commercial banks' credit policies for small and medium-sized enterprises (SMEs), addressing the persistent SME financing gap through technological innovation. Using structural equation modeling, we analyzed data from 360 commercial banking professionals across China to test five hypotheses grounded in information asymmetry theory, relationship lending theory, group lending theory, and supply chain finance theory. SME credit status and core enterprise influence significantly impact bank credit policies (β = 0.285 and β = 0.317, p < 0.001), with AI-enhanced bank cognition serving as a partial mediator (indirect effects: β = 0.167 and β = 0.193, p < 0.001). Critically, AI assessment accuracy moderates these relationships, with higher-accuracy systems demonstrating stronger policy effects (β = 0.124 and β = 0.138, p < 0.001). AI fundamentally transforms SME credit evaluation by enhancing risk assessment accuracy, effectively leveraging supply chain relationships, and augmenting banks' cognitive capabilities. The moderating role of AI precision emphasizes the importance of technological sophistication for maximizing benefits. This research provides empirical evidence that AI-powered supply chain finance offers a viable solution to global SME financing constraints while maintaining robust risk management standards.

Breaking data silos in multi-tier suppliers and designing intelligent collaborative trust

Qiuya Ma, Danqing Wu — Fri, 30 May 2025 00:00:00 +0000

Data silos across multi-tier supply chains create significant barriers to operational efficiency and resilience, where information fragmentation undermines collaborative intelligence and increases disruption vulnerability. This research investigates data silo formation mechanisms and develops an intelligent collaborative trust framework leveraging artificial intelligence to address integration challenges. The study employs mixed-methods analysis across 47 manufacturing organizations selected through stratified purposive sampling from China's industrial regions. A hybrid architecture combining blockchain with federated learning enables secure cross-organizational information exchange while preserving competitive advantages through reputation-based smart contracts and algorithmic trust mechanisms. Network analysis identifies six primary data silo types, with technological barriers most prevalent at 31.4 percent and organizational barriers at 23.8 percent. Randomized controlled trials demonstrate significant performance improvements over conventional approaches. Supply chain visibility increases by 39%, while coordination costs decrease by 28%. The neural network ensemble achieves a 7.3-day average disruption prediction lead time improvement, with pharmaceutical manufacturers experiencing 9.8 days of early warning enhancement. Mean absolute prediction error reduces by 42 percent, and inventory optimization shows 156 percent cost efficiency improvement. This research contributes to supply chain digitalization theory by reconceptualizing trust as an algorithmically-mediated construct, establishing selective transparency frameworks that enable distributed intelligence architectures to achieve.

Hybrid contrast-limited adaptive histogram equalization and Deep Learning techniques for improving liver tumor detection

Priyah R R, S. Kamalakkannan — Sat, 31 May 2025 00:00:00 +0000

Deep Learning and advanced image processing can enhance the detection and prognosis of liver cancer using medical imaging, such as Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scans. Liver cancer detection is a challenging task due to factors such as poor contrast, noise in imaging techniques, limited annotated datasets, and the complex characteristics of tumors. This study proposes a hybrid technique that combines Contrast-Limited Adaptive Histogram Equalization (CLAHE), Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), and Transfer Learning (TL) to improve the precision and accuracy of liver tumor detection. A conventional technique for image enhancement, CLAHE increases the contrast of medical images, making malignant tumors more apparent. CLAHE, however, does not provide a thorough tumor characterization; instead, it focuses on enhancing image quality. CNN is used to extract features, find and learn important patterns, such as edges, textures, and shapes that are pertinent to the diagnosis of tumors. Finally, TL utilizes pre-trained models (Inception V3) for classification, enabling the effective learning of tumor features and achieving high diagnostic precision with fewer computational resources. A hybrid approach combining CNN, GAN, and TL may give an integrated and effective solution for identifying and diagnosing liver tumors. The hybrid technique performed significantly better than independent DL approaches, achieving an accuracy of 93.3%, a sensitivity of 92.2%, a specificity of 94.5%, and an F1-score of 92.8%.

Enhanced toxic comment detection model through Deep Learning models using Word embeddings and transformer architectures

Sushma S, Sasmita Kumari Nayak, M. Vamsi Krishna — Sat, 31 May 2025 00:00:00 +0000

The proliferation of harmful and toxic comments on social media platforms necessitates the development of robust methods for automatically detecting and classifying such content. This paper investigates the application of natural language processing (NLP) and ML techniques for toxic comment classification using the Jigsaw Toxic Comment Dataset. Several deep learning models, including recurrent neural networks (RNN, LSTM, and GRU), are evaluated in combination with feature extraction methods such as TF-IDF, Word2Vec, and BERT embeddings. The text data is pre-processed using both Word2Vec and TF-IDF techniques for feature extraction. Rather than implementing a combined ensemble output, the study conducts a comparative evaluation of model-embedding combinations to determine the most effective pairings. Results indicate that integrating BERT with traditional models (RNN+BERT, LSTM+BERT, GRU+BERT) leads to significant improvements in classification accuracy, precision, recall, and F1-score, demonstrating the effectiveness of BERT embeddings in capturing nuanced text features. Among all configurations, LSTM combined with Word2Vec and LSTM with BERT yielded the highest performance. This comparative approach highlights the potential of combining classical recurrent models with transformer-based embeddings as a promising direction for detecting toxic comments. The findings of this work provide valuable insights into leveraging deep learning techniques for toxic comment detection, suggesting future directions for refining such models in real-world applications.

Artificial intelligence prediction model for the relationship between obstructive sleep Apnea severity and maxillofacial developmental disorders in children: a prospective cohort study

Hao Dong, Rasheed Abdulsalam Abdullah — Sun, 01 Jun 2025 00:00:00 +0000

This study aimed to develop an artificial intelligence-based prediction model for evaluating the relationship between obstructive sleep apnea (OSA) severity and maxillofacial developmental disorders in children. A prospective cohort design was employed, monitoring 50 children (mean age 8.4±2.3 years, 58% male) with varying degrees of maxillofacial abnormalities over a 12-month period. Participants were stratified into four groups: maxillary constriction (n=15), mandibular retrognathia (n=15), mixed phenotype (n=10), and control (n=10). Comprehensive assessments included cephalometric measurements, intraoral scans, and polysomnography performed at baseline, 6-month, and 12-month intervals. A hybrid artificial intelligence architecture integrating gradient boosting algorithms and deep neural networks was developed using multimodal data. Results demonstrated significant correlations between specific maxillofacial parameters and OSA severity, with SNB angle (r=-0.68, p<0.001) and maxillary width (r=-0.61, p<0.001) showing the strongest associations. Multiple regression analysis identified SNB angle (β=-0.46, p<0.001), maxillary width (β=-0.39, p<0.001), and BMI (β=0.28, p=0.012) as significant independent predictors of AHI, collectively explaining 72% of OSA severity variance. The AI model achieved an overall accuracy of 89.6% in classifying OSA severity, with differential performance across phenotype groups (mandibular retrognathia: 93.1%, maxillary constriction: 88.5%, mixed phenotype: 85.2%). Longitudinal follow-up revealed significant correlations between improvements in maxillofacial parameters and reductions in AHI, with stronger associations in younger children (5-8 years) compared to older children (9-12 years). This research provides an effective tool for assessing the relationship between OSA severity and maxillofacial developmental abnormalities in children, offering valuable insights for early risk stratification and personalized treatment strategies in pediatric sleep medicine.

AI-assisted customer behavior analysis and hotel loyalty strategy optimization

Danqing Wu, Qiuya Ma — Sun, 01 Jun 2025 00:00:00 +0000

This research explores the application of artificial intelligence (AI) technologies in transforming the analysis of customer behavior and refining customer loyalty strategies in the hospitality sector. Most traditional loyalty programs are characterized by static segmentation and standardized reward frameworks, often disregarding evolving customer priorities and shifting market dynamics. Using an AI-powered system based on deep learning, natural language processing, and predictive analytics, we analyzed 3.2 million transactions from 846,000 customers across five international hotel chains globally. The system identifies behavioral patterns that are overlooked by traditional analysis methods through the continuous processing of heterogeneous data streams such as booking, service usage, social media sentiment analysis, and feedback loops. Results indicate that customer retention increased by 27.3% while AI-driven strategies heightened engagement with loyalty programs by 42.1%, yielding 18.5% additional revenue per loyal customer when juxtaposed with traditional methods. The framework's dynamic loyalty incentive modification and proactive journey mapping surpass conventional segmentation techniques through hyper-personalized recommendations. This work advances the hospitality management body of knowledge by formulating a robust architectural design to formulate loyalty strategy design and provide implementation frameworks for hoteliers seeking the integration of advanced technologies in customer relationship management. Futuristic lines of inquiry are the ethical considerations of algorithmic and automated decision-making in the customer relationship management domain and the effectiveness of AI-powered loyalty programs in different cultures.

Mechanisms of short video selection behavior in elderly hypertensives under health information overload: a cognitive load theory

Ruina Guo, Arina Anis Azlan, Emma Mohamad — Thu, 05 Jun 2025 00:00:00 +0000

The proliferation of digital health information through short video platforms creates cognitive overload challenges for elderly hypertensive patients managing chronic conditions, compromising effective health information processing and decision-making capabilities. This research investigates the mechanisms of short video selection behavior among elderly hypertensive patients under health information overload, employing cognitive load theory integrated with artificial intelligence analytics to optimize content delivery strategies. A mixed-methods design involving 128 elderly participants (mean age, 71.3 years) from Jiangsu Province utilized behavioral tracking, physiological monitoring, and AI-powered content analysis over a two-week period. The study employed ensemble machine learning algorithms, integrated cognitive load assessment, and structural equation modeling to examine selection pathways and predictive mechanisms. Results demonstrate that cognitive load substantially impacts information processing efficiency, with performance declining from 89.4% accuracy under low cognitive load to 41.2% under high load scenarios. The artificial intelligence framework achieved exceptional predictive performance with 94.2% training accuracy, 92.8% validation accuracy, and 91.5% test accuracy. Feature importance analysis reveals that cognitive variables dominate prediction mechanisms, accounting for 63% of the total importance distribution, compared to behavioral features (23%) and demographic factors (14%). Working memory emerges as the most influential predictor (importance score: 0.847, contributing 18.3% to prediction accuracy), followed by processing speed (16.8%) and attention allocation (15.2%). The research establishes evidence-based guidelines for cognitive-centered health communication design, enabling personalized digital health interventions that optimize content complexity, delivery timing, and presentation modalities based on individual cognitive capacities, ultimately advancing therapeutic outcomes for vulnerable elderly populations through intelligent, adaptive content delivery systems.

Deep Learning models for cultural pattern recognition: preserving intangible heritage of Li ethnic subgroups through intelligent documentation systems

Jing Sun, Kartini Aboo Talib Khalid, Chan Suet Kay — Thu, 05 Jun 2025 00:00:00 +0000

This study develops an advanced intelligent documentation system using deep learning models to preserve intangible cultural heritage for the Li ethnic minorities. Traditional heritage documentation models face significant obstacles in systematically capturing oral traditions and inter-group cultural differences. The proposed comprehensive multimodal fusion framework integrates visual pattern analysis through convolutional neural networks, temporal cultural depiction via bidirectional LSTM networks, and semantic comprehension using transformer-based models. Collaborative fieldwork across five Li subgroups (Ha, Qi, Run, Sai, and Meifu) in Hainan Province documented 4,450 cultural samples, including traditional textiles, music, oral traditions, artifacts, and architectural heritage. The five-layer distributed system architecture employs pattern recognition, semantic indexing, and recommendation algorithms for scalable cultural preservation. Experimental results demonstrate remarkable 94.8% accuracy across Li subgroups, significantly outperforming traditional single-modality systems (CNN: 85.3%, RNN: 87.6%, Transformer: 89.4%). System implementation yielded unprecedented improvements in cultural transmission effectiveness: 73% increase in knowledge retention, 121% in skill transfer, and 280% in digital archiving abilities. Community participation increased exponentially, with 340% growth in active users and a 665% increase in monthly contributions. The system achieves robust operational performance with sub-200ms response times and 99.7% stability. User satisfaction and expert evaluation scores of 4.4 and 4.6, respectively, confirm reliable cultural preservation functionality. This framework establishes advanced benchmarks for computational heritage preservation methods, demonstrating the effective integration of technological innovation with ethnographic sensitivity for the sustainable documentation and transmission of minority cultures.

Digital marketing integration and educational product innovation: the mediating effect of organizational innovation climate

Xinrui Liang, Wan Mohd Hirwani Wan Hussain, Rabiah Abdul Kadir — Sat, 07 Jun 2025 00:00:00 +0000

This study investigates the relationships between digital marketing strategies (social media marketing, video marketing, and artificial intelligence marketing), organizational innovation climate, and product innovation performance in Malaysian educational institutions, focusing on the mediating effect of organizational innovation climate. A quantitative cross-sectional survey design is employed, collecting data from 169 employees working in Malaysian educational institutions, including administrative staff, marketing personnel, academic leaders, and innovation team members from both public and private institutions. The research model is tested using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings reveal that all three dimensions of digital marketing positively impact organizational innovation climate, with artificial intelligence marketing demonstrating the strongest effect (β = 0.323), followed by video marketing (β = 0.289) and social media marketing (β = 0.247). Organizational innovation climate significantly influences product innovation performance (β = 0.683). While social media marketing and video marketing exhibit both direct and indirect effects on innovation performance, artificial intelligence marketing operates entirely through organizational innovation climate, indicating full mediation. The results suggest that educational institutions should implement advanced digital marketing tools alongside nurturing organizational structures that support innovation, with artificial intelligence marketing investments requiring simultaneous development of innovation-friendly climates. Strategic digital marketing significantly impacts educational product innovation through organizational innovation climate, enabling institutions to adapt to emerging insights and design innovative educational products tailored to student demands.

From robotic arms to AI-assisted: the evolution and interdisciplinary integration of robotic surgery technology based on bibliometron

Yuchi Liu, Mohd Wira Mohd Shafiei — Sat, 07 Jun 2025 00:00:00 +0000

With medical technology innovation, robotic surgery has evolved from mechanical arm operations to AI-assisted decision-making, promoting deep integration of surgical medicine with engineering and computer science. This study employed CiteSpace software to conduct a bibliometric analysis of robotic surgical technology evolution literature from the Web of Science (2014-2024). Analysis of 520 publications revealed explosive growth from <5 annual papers (2014-2017) to 177 papers in 2024, representing a 3,540% increase. The dataset encompassed 2,968 authors, 1,957 institutions, and 266 journals across 77 countries/regions. The United States dominated with 191 publications (36.73%), followed by China (88, 16.92%) and the United Kingdom (71, 13.65%). The University of London emerged as the most productive institution (28 publications). Keyword burst analysis identified "artificial intelligence" (2019-2024) and "deep learning methods" (2022-2024) as dominant emerging themes. Computer science categories comprised >10% of publications, demonstrating strong interdisciplinary integration centered on surgery (31.54%) and biomedical engineering (12.31%). The field demonstrated clear evolution from basic instrument innovation to AI-driven, multi-disciplinary collaborative intelligent surgical systems, with Italy (centrality 0.18) and France (0.16) serving as critical knowledge brokers despite moderate publication volumes.

Generative AI-enabled intelligent auditing: an organizational adaptation mechanism study based on dynamic capability theory

Deng Wei, Obed Rashdi Syed, Xiaoli Xu, Hongli Sang, Jiang Wang — Thu, 12 Jun 2025 00:00:00 +0000

This study investigates how audit organizations leverage generative artificial intelligence technologies to enhance auditing capabilities through organizational adaptation mechanisms, examining the role of dynamic capabilities in facilitating successful AI adoption and performance improvements. A quantitative cross-sectional survey collected data from 312 audit professionals across diverse organizational contexts. Structural equation modeling examined relationships between dynamic capabilities, generative AI adoption, organizational adaptation mechanisms, and auditing performance with comprehensive measurement validation. Dynamic capabilities significantly influence generative AI adoption (β = 0.453, p < 0.001), which drives organizational adaptation mechanisms (β = 0.312, p < 0.001) that enhance auditing performance (β = 0.378, p < 0.001). Organizational adaptation mechanisms mediate 41.4% of the capability-performance relationship. The model explains 28.3% variance in AI adoption, 35.7% in adaptation mechanisms, and 31.2% in auditing performance. Audit organizations should prioritize developing sensing, seizing, and reconfiguring capabilities before AI investments, requiring comprehensive change management addressing structural, processual, and cultural dimensions simultaneously. AI-driven competitive advantages emerge through organizational transformation processes, with dynamic capabilities as antecedents and adaptation mechanisms as mediating processes.

An effective power quality enhancement system for integrated photovoltaic cells utilizing cascaded ANFIS in a unified power quality conditioner

Sun, 15 Jun 2025 00:00:00 +0000

The arrival of power electronic devices for the control of loads has an effect on the Power Quality (PQ) at the utility grid’s distribution side. Meanwhile, PQ problems cause malfunctioning equipment, lost production time, loss of money for industry, inconvenience, and possible damage to household electrical appliances. Thus, the requirement for increased system efficiency is essential. Hence, this study proposes the control of a Unified Power Quality Conditioner (UPQC) in conjunction with a Photovoltaic (PV) system. Shunt and series converters attached back-to-back via a shared DC-link make up the PV-UPQC system. Subsequently, the Artificial Neural Network (ANN) controller reduces PQ problems and simplifies the control complexity. A Coupled quadratic Single Ended Primary Inductor Converter (SEPIC) connects the PV system to UPQC, and the Cascaded Adaptive Neuro Fuzzy Inference System- Maximum Power Point Tracking (ANFIS-MPPT) technique enables the optimization of power extraction from PV sources. The developed approach is implemented using the MATLAB/Simulink platform, and its performance is evaluated for Total Harmonic Distortion (THD), sag, and swell. The results show that the control maintains THD within the B-phase THD of 3.97% and R and Y phase THDs of 4.82% and 4.86%, and also obtained a voltage gain ratio of 1:15; the output levels increase substantially with reduced voltage stresses on the switching devices.

A study on AI-enhanced green policy support system for training centers in Liujiang Vernacular area: a dual perspective of eco-infrastructure and sustainable talent development

Yanan Ma, Chutima Maneewattana (Ajarn Chuti） — Sun, 15 Jun 2025 00:00:00 +0000

This research analyzes policy integration for Zhuang folk song training centers in the Liujiang area through a mixed-methods examination of 60 centers over a 28-month period. Centers implementing green infrastructure achieved 45% greater resource utilization efficiency, 52% energy reduction, and 30% lower operational costs while maintaining cultural authenticity. Teachers receiving ecological-cultural training demonstrated 38% higher teaching effectiveness, with students showing 40% improved comprehension of natural imagery in folk songs. Environmental performance revealed 45-ton average annual carbon reductions per center and 37-42% lifecycle carbon savings through adaptive reuse strategies. Focus groups (n=12) achieved strong stakeholder consensus for multifunctional teaching classrooms (92% approval), ecological-cultural mentorship programs (92% approval), and digital technology integration (85% approval). Statistical analysis revealed significant positive correlations between environmental sustainability and cultural transmission outcomes (r=0.68-0.71, p<0.01). Machine learning algorithms with digital twin technology demonstrated 12% additional energy efficiency improvements while maintaining cultural preservation quality. The study proposes establishing a "Cultural-Ecological Integration Fund" and an "ECO-Cultural Mentor" certification system. This framework addresses the gap between technological advancement and cultural preservation, providing a comprehensive approach for indigenous cultural transmission in sustainable contexts.

Machine Learning-based integration of multi-omics data for identification of tubular epithelial cell-specific biomarkers in diabetic nephropathy

Wenning Li, Suriyakala Perumal Chandran — Mon, 16 Jun 2025 00:00:00 +0000

Diabetic nephropathy is a leading cause of end-stage renal disease. Current diagnostic methods, which utilize conventional biomarkers, fail to adequately capture early-stage tubular epithelial cell dysfunction, a condition that likely occurs prior to glomerular damage. This study developed a comprehensive machine learning framework integrating multi-omics data to identify tubular epithelial cell-specific biomarkers for diabetic nephropathy. We systematically collected omics data from established public databases, analyzing 245 transcriptomic samples (18,632 features), 198 proteomic samples (4,521 features), and 167 metabolomic samples (812 features), resulting in an integrated dataset of 156 samples with 23,965 molecular features. Following stringent quality control, batch effect removal, and normalization, we implemented an ensemble learning approach combining Random Forest, Support Vector Machine, and XGBoost algorithms. The ensemble model achieved superior performance with 91.4% accuracy, 89.6% sensitivity, 92.8% specificity, and an AUC of 0.947, representing significant improvement over conventional clinical markers. We identified ten tubular epithelial cell-specific candidate biomarkers, with KIM-1 showing the highest importance score (0.092), followed by NGAL (0.087) and L-FABP (0.084). These markers demonstrated progressive upregulation throughout disease stages with 1.5-fold to 3.2-fold increases in advanced states. Analysis revealed perturbations in inflammatory response pathways, oxidative stress processes, and epithelial-to-mesenchymal transition. Independent cohort validation across three geographically distinct populations confirmed the robustness and generalizability of identified biomarkers. The findings demonstrate the potential of machine learning-based multi-omics integration for enhanced diabetic nephropathy detection and provide novel insights into tubular pathophysiology that could facilitate earlier intervention and personalized treatment strategies.

Harnessing wind and solar power for electric vehicle charging: a feasibility study at Ikas supermarket, Lefkosa, Northern Cyprus

Youssef Kassem, Hüseyin Çamur, Ahmad Hussein — Thu, 19 Jun 2025 00:00:00 +0000

Electric vehicles (EVs) have replaced conventional bio-fuel cars over the past ten years. Electric vehicles, or EVs, have become popular for both financial and environmental reasons. One of the most significant challenges facing humanity today is environmental degradation. From both an economic and ecological perspective, it would be highly beneficial if electric automobiles could be charged using renewable energy. The use of EVs in Northern Cyprus remains in its early stages. Thus, the viability of charging from renewable sources is investigated. In addition to comparing fuel-based and electric vehicles and determining the economic viability of charging using renewable sources, the study explains ways to charge electric vehicles using hybrid wind and solar power systems. The costs of the required components have been obtained from manufacturers, and the average cost is then taken into account. The results demonstrated that the developed system achieved a maximum monthly energy output of 13,500 kWh in March and ensured stable production throughout the seasons by utilizing solar and wind resources in combination. Additionally, it has the capacity to support 58 EV chargers per day, which can charge approximately 1,700 EVs per month, including the GÜNSEL B9 model. Economically, the system was extremely viable with a payback time of just 3.34 years when electricity was sold at $0.31/kWh. Moreover, the proposed system offered a significant 96% reduction in carbon emissions compared to conventional grid electricity. These results demonstrate the hybrid system's success in facilitating sustainable, high-capacity EV charging, yielding significant environmental and economic benefits. Additionally, compared to fuel vehicles, EVs are almost twice as advantageous and environmentally friendly.

Optimization of small submersible pressure hull based on MOGA

Xiaoyu Liu, Aldrin D Calderon, Xiangyao Wu — Thu, 19 Jun 2025 00:00:00 +0000

This study presents the structural optimization of a small-scale Autonomous Underwater Vehicle (AUV) designed for shallow-water marine aquaculture applications, such as monitoring water quality and the living conditions of farmed species. A cylindrical pressure hull model was developed using ANSYS Workbench and analyzed under a constant pressure of 0.5 MPa. Latin Hypercube Sampling (LHS) and Multi-Objective Genetic Algorithm (MOGA) were employed to optimize three key design variables: shell thickness, inner radius, and length. The final optimized design resulted in a 54.78% reduction in hull mass, a 25.25% decrease in maximum deformation, and maintained stress levels well below the allowable limit of 328 MPa. The optimization process significantly enhanced the AUV's structural efficiency, safety, and agility, offering valuable insights for the design of lightweight submersible structures in practical environments.

Media framing and public risk communication: Deep Learning-based crisis narrative analysis and optimization

Yue Zhang — Thu, 19 Jun 2025 00:00:00 +0000

This research aims to develop a comprehensive framework for analyzing and optimizing media framing in crisis communication through advanced deep learning techniques, addressing the critical gap in understanding how narrative structures influence public risk perception and response. By analyzing crisis narratives across multiple media platforms, we identify predominant framing patterns and their temporal evolution during crisis events. Our novel deep learning model demonstrates superior accuracy of 91.2% in recognizing subtle framing mechanisms that influence public risk perception, representing a 14.7 percentage point improvement over traditional machine learning baselines. Analysis of 15,873 media items reveals six major frame types, with attribution frames being most prevalent (28.7%), followed by human impact (22.3%) and conflict frames (19.5%). The study establishes an optimization framework for crisis communication that balances narrative structure, emotional factors, and information transparency, identifying critical transparency-trust thresholds at 62% and 87% disclosure levels where trust gains show non-linear patterns. Findings suggest that adaptive framing strategies significantly enhance public understanding and appropriate response to risk situations, with problem-solution narratives achieving effectiveness scores of 0.87 for technological crises and empathy-focused communication reaching 0.90 for natural disasters. This research contributes to both the theoretical understanding of crisis communication and the practical applications for media organizations, risk managers, and policymakers.

AI-enhanced spatial value reassessment in digital transformation: impacts of smart eco-city management paradigms on housing price formation mechanisms

Ping Li, Cheok Mui Yee, Wanyi He, Lijuan Lu, Kaizhou Qin — Fri, 20 Jun 2025 00:00:00 +0000

This study examines the transformative impact of artificial intelligence-enhanced smart eco-city management paradigms on spatial value assessment and housing price formation mechanisms. Through sophisticated mixed-methods analysis of 320 neighborhoods across five urban areas, employing advanced machine learning algorithms for pattern recognition, the research identifies significant synergistic relationships between digital infrastructure and environmental quality that profoundly influence housing valuations. Empirical evidence demonstrates that neighborhoods exhibiting high levels of both digital connectivity and environmental amenities command substantial price premiums of 60-100% above baseline areas, markedly exceeding the combined individual effects of digital (25-45%) and environmental (15-40%) factors alone. The strength of this synergistic relationship manifests in robust correlations between combined Digital-Environmental indices and housing prices (r = 0.83), with AI-driven predictive models achieving exceptional accuracy in forecasting spatial value shifts (R²=0.861). The study contributes a multidimensional analytical framework linking technological innovation, artificial intelligence applications, environmental governance, and housing market dynamics. Policy implications suggest the necessity for integrated governance approaches spanning digital and environmental planning spheres, with particular attention to algorithmic equity considerations given the widening price gaps between digitally-enabled and analog neighborhoods. Effective development of smart eco-cities necessitates the implementation of comprehensive strategies that not only create value through AI optimization but also ensure its equitable distribution across diverse urban communities.

The impact of job substitution and job intensity on job performance in the process of enterprise digital transformation

Kang Li, Daranee Pimchangthong — Sat, 21 Jun 2025 00:00:00 +0000

This study explores the effects of job substitution and job intensity on employee performance in the context of digital transformation, focusing on the mediating role of job insecurity (unemployment insecurity and job mobility insecurity). Using confirmatory research methods, we analyzed 1,002 valid samples from seven Chinese furniture manufacturers. A structural equation model (SEM) developed via AMOS 27.0 revealed: (1) Job substitution (standardized coefficient = -0.254, p < 0.001) and job intensity (standardized coefficient = -0.264, p < 0.001) significantly negatively impact job performance; (2) Unemployment insecurity (mediating effect = -0.087 for job substitution; -0.10 for job intensity) and job mobility insecurity (mediating effect = -0.083 for job substitution; -0.113 for job intensity) fully mediate these relationships. This research validates relevant theories, clarifies variable relationships, and enriches digital transformation and human resource management theories. Practically, it provides HR management advice for enterprises, facilitating performance improvement and sustainable development. Methodologically, it constructs a comprehensive framework considering multiple variables, offering a new perspective to analyze the impact of transformation on employees.

Dynamic reward systems and customer loyalty: reinforcement learning-optimized personalized service strategies

Xiaojing Nie, Fauziah Sh. Ahmad — Sun, 22 Jun 2025 00:00:00 +0000

Traditional customer loyalty programs employing static reward structures demonstrate fundamental limitations in adapting to evolving customer preferences and behaviors within digital commerce environments. This research addresses the critical gap in personalization capabilities by developing a reinforcement learning (RL)-based dynamic reward system that optimizes customer engagement through real-time adaptive reward allocation mechanisms. The investigation centers on designing and validating an intelligent system capable of automatically adjusting reward types, values, and timing parameters based on continuous analysis of individual customer interactions and feedback patterns. The proposed methodology implements a multi-armed bandit framework utilizing Thompson Sampling algorithms integrated with contextual learning mechanisms, thereby achieving an optimal balance between exploration and exploitation in reward optimization processes. Comprehensive experimental simulations compare the RL-based approach against traditional rule-based systems and random allocation strategies across five distinct customer segments, enabling robust performance evaluation under diverse operational conditions. Empirical results demonstrate that the RL-based system achieves 145% of baseline customer lifetime value (CLV), representing a 45% improvement over traditional methods, accompanied by corresponding enhancements in retention rate (32%) and engagement frequency (28%). The system maintains robust performance under budget constraints, sustaining 118% of baseline CLV despite a 30% budget reduction, with statistical analysis confirming significant improvements across all metrics (p < 0.001, Cohen's d > 1.7). These findings provide organizations with a scalable framework for implementing adaptive loyalty programs that respond dynamically to customer preferences while optimizing resource allocation efficiency. The research contributes to the expanding literature on AI-driven customer relationship management by demonstrating the practical effectiveness of reinforcement learning in personalization contexts.

Technology exploring the impact of digital transformation on sustainable performance in the retail industry: the moderating role of market turbulence and innovative culture

Mon, 23 Jun 2025 00:00:00 +0000

Digital transformation has emerged as a strategic imperative for organizations seeking competitive advantage in rapidly evolving markets. However, its impact on sustainable performance remains theoretically and empirically contested. This study examines the relationship between digital transformation and sustainable performance, investigating the moderating roles of market turbulence and innovative culture within China’s retail sector. Grounded in Dynamic Capability Theory, we employed a quantitative approach using survey data from 353 Chinese retail managers. Structural equation modeling via SmartPLS was utilized to test the proposed hypotheses and validate measurement scales. Results demonstrate a significant positive relationship between digital transformation and sustainable performance (β = 0.453, P < 0.001). The model explained 24.4% of the variance in sustainable performance (R² = 0.244). Innovative culture significantly enhances this relationship through positive moderation (β = 0.216, P = 0.004), indicating that organizations with strong innovation-oriented cultures better leverage digital investments for sustainability outcomes. Market turbulence showed no significant moderating effect (β = 0.099, P = 0.051) but exhibited a direct negative impact on sustainable performance. Contrary to expectations, market turbulence does not moderate the digital transformation-sustainability relationship but exerts a direct negative effect on sustainable performance. These findings provide critical insights for retail managers pursuing digital-enabled sustainability strategies and offer practical guidance for enterprises entering emerging markets characterized by digital disruption.