Search Results (1,044)

This paper introduces a novel numerical approach for solving fractional stochastic differential equations (FSDEs) using bilinear time-series models, driven by the Caputo–Katugampola (C-K) fractional derivative. The C-K operator generalizes classical fractional derivatives by incorporating an additional parameter, enabling the enhanced modeling of memory effects and hereditary properties in stochastic systems. The primary contribution of this work is the development of an efficient numerical framework that combines bilinear time-series discretization with the C-K derivative to approximate solutions for FSDEs, which are otherwise analytically intractable due to their nonlinear and memory-dependent nature. We rigorously analyze the impact of fractional-order dynamics on system behavior. The bilinear time-series framework provides a computationally efficient alternative to traditional methods, leveraging multiplicative interactions between past observations and stochastic innovations to model complex dependencies. A key advantage of our approach is its flexibility in handling both stochasticity and fractional-order effects, making it suitable for applications in a famous nuclear physics model. To validate the method, we conduct a comparative analysis between exact solutions and numerical approximations, evaluating convergence properties under varying fractional orders and discretization steps. Our results demonstrate robust convergence, with simulations highlighting the superior accuracy of the C-K operator over classical fractional derivatives in preserving system dynamics. Additionally, we provide theoretical insights into the stability and error bounds of the discretization scheme. Using the changes in the number of simulations and the operator parameters of Caputo–Katugampola, we can extract some properties of the stochastic fractional differential model, and also note the influence of Brownian motion and its formulation on the model, the main idea posed in our contribution based on constructing the fractional solution of a proposed fractional model using known bilinear time series illustrated by application in nuclear physics models. Full article

Artificial intelligence (AI) and assistive robotics can transform older-person care by offering new, personalised solutions for an ageing population. This paper outlines recent advances in AI-driven applications and robotic assistance in silver care, emphasising their role in improved healthcare services, quality of life and ageing-in-place and alleviating pressure on healthcare systems. Advances in machine learning, natural language processing and computer vision have enabled more accurate early diagnosis, targeted treatment plans and robust remote monitoring for elderly patients. These innovations support continuous health tracking and timely interventions to improve patient outcomes and extend home-based care. In addition, AI-powered assistive robots with advanced motion control and adaptive response mechanisms are studied to support physical and cognitive health. Among these, companion robots, often enhanced with emotional AI, have shown potential in reducing loneliness and increasing connectedness. The combined goal of these technologies is to offer holistic patient-centred care, which preserves the autonomy and dignity of our seniors. This paper also touches on the technical and ethical challenges of integrating AI/robotics into eldercare, like privacy and accessibility, and alludes to future directions on optimising AI-human interaction, expanding preventive healthcare applications and creating an effective, ethical framework for eldercare in the digital age. Full article

Digital technologies are reshaping how firms create, deliver, and capture value, prompting growing interest in digital business model innovation (DBMI). Despite increasing scholarly attention, the existing research remains fragmented and often assumes stable environments, limiting its applicability in today’s complex and dynamic contexts. To address this gap, this study conducts a systematic literature review (SLR) to gather and critically synthesize the fragmented and evolving body of knowledge on DBMI. The review identifies key research perspectives, highlights their underlying assumptions, and reveals the limitations in addressing environmental and knowledge complexity. In response, the paper introduces the knowledge system perspective (KSP) as a novel lens that views DBMI as a knowledge-driven, adaptive process. This perspective advances the DBMI literature by integrating knowledge dynamics and contextual complexity, offering a more robust understanding of how firms navigate digital transformation. The study concludes by outlining future research opportunities and providing practical implications for managing DBMI in turbulent environments. Full article

In the process of social development, there is a contradiction between economic development and the ecological environment. Western countries were the first to experience the inverted U-shaped development model of “destruction first and compensation later”, and China is also facing similar problems. To reveal the formation mechanism and dynamic evolution of the inflection point of ecological environment changes in China, this paper combines the entropy weight method, the analytic hierarchy process, and quadratic curve fitting to construct the “Ecological Pressure Index—GDPP Model” and studies the inflection point of ecological pressure during China’s economic development from 2000 to 2022. The study shows that the key inflection point of China’s ecological environment pressure is between 2016 and 2017, which is mainly affected by multiple factors such as the economy, domestic and international situations, and policy adjustments. For example, the implementation of the “Supply-side Structural Reform” and the environmental protection supervision system has significantly reduced the pollution pressure. At the same time, the “inflection point” is applied to dynamically adjust the PSR model, revealing the stage transition of China’s environmental governance focus. For instance, from 2000 to 2016, end-of-pipe pollution treatment was dominant (for example, the weights of pollution emission indicators X₅–X₈ were relatively high), while after 2016, the focus of governance shifted to the restoration of ecological space (for example, the weight of nature reserves X₂₂ was 2.759%). The theoretical contribution of this paper lies in proposing the concept of “Policy-driven EKC”, emphasizing the core role of policy intervention in the formation of the inflection point of the ecological environment. In addition, the dynamic adjustment of the PSR model using the “inflection point” better interprets China’s self-transformation in the development process and provides other developing countries with a Chinese solution of “institutional innovation first” and the “Policy-driven EKC—Chinese PSR Model” for reference in balancing economic growth and ecological protection. Full article

Biofilms, structured microbial consortia embedded in self-produced extracellular matrices, pose significant challenges across the medical, industrial, and environmental sectors due to their resistance to antimicrobial therapies and ability to evade the immune system. Their resilience is driven by multifaceted mechanisms, including matrix-mediated drug sequestration, metabolic dormancy, and quorum sensing (QS)-regulated virulence, which collectively sustain persistent infections and contribute to the amplification of antimicrobial resistance (AMR). This review critically examines the potential of plant-derived essential oils (EOs) as innovative agents for biofilm control. EOs exhibit broad-spectrum antibiofilm activity through multi-target mechanisms, including disrupting initial microbial adhesion, degrading extracellular polymeric substances (EPSs), suppressing QS pathways, and compromising membrane integrity. Their ability to act synergistically with conventional antimicrobials at sub-inhibitory concentrations enhances therapeutic efficacy while reducing the selection pressure for resistance. Despite their potential, EO applications face technical challenges, such as compositional variability due to botanical sources, formulation stability issues, and difficulties in standardization for large-scale production. Clinical translation is further complicated by biofilm stage- and strain-dependent efficacy, insufficient in vivo validation of therapeutic outcomes, and potential cytotoxicity at higher doses. These limitations underscore the need for optimized delivery systems, such as nanoencapsulation, to enhance bioavailability and mitigate adverse effects. Future strategies should include combinatorial approaches with antibiotics or EPS-degrading enzymes, advanced formulation technologies, and standardized protocols to bridge laboratory findings to clinical practice. By addressing these challenges, EOs hold transformative potential to mitigate biofilm-associated AMR, offering sustainable, multi-target alternatives for infection management and biofilm prevention in diverse contexts. Full article

Global estimates suggest that over a billion people worldwide—more than 15% of the global population—live with some form of mobility disability, underscoring the pressing need for innovative technological solutions. Recent advancements in artificial vision systems, driven by deep learning and image processing techniques, offer promising avenues for detecting mobility aids and monitoring gait or posture anomalies. This paper presents a systematic review conducted in accordance with ProKnow-C guidelines, examining key methodologies, datasets, and ethical considerations in mobility impairment detection from 2015 to 2025. Our analysis reveals that convolutional neural network (CNN) approaches, such as YOLO and Faster R-CNN, frequently outperform traditional computer vision methods in accuracy and real-time efficiency, though their success depends on the availability of large, high-quality datasets that capture real-world variability. While synthetic data generation helps mitigate dataset limitations, models trained predominantly on simulated images often exhibit reduced performance in uncontrolled environments due to the domain gap. Moreover, ethical and privacy concerns related to the handling of sensitive visual data remain insufficiently addressed, highlighting the need for robust privacy safeguards, transparent data governance, and effective bias mitigation protocols. Overall, this review emphasizes the potential of artificial vision systems to transform assistive technologies for mobility impairments and calls for multidisciplinary efforts to ensure these systems are technically robust, ethically sound, and widely adoptable. Full article

The increasing digitalization of the world underscores the critical importance of both social and technical aspects in software engineering practice. While prior research links socio-technical congruence (STC) to positive workstream outcomes, the current convergence of digital products, technologies, and social systems introduces novel and often unpredictable results, driven by the complex interplay of leadership, organizational culture, and software engineering practices operating as a complex adaptive system (CAS). This paper proposes a novel model for adopting socio-cultural practices to bridge the social and technical divide through the lens of STC. The innovation of the model lies in its socio-technical evolutionary journey, built upon dual systems: (1) an analytical System-I focused on enhancing robustness via compliance with Lean and Agile socio-cultural practices, and (2) a holistic System-II emphasizing resilience through an acceptance of interdependence of system actors that requires sense-making techniques. A methodology based on this model was piloted across six case studies: three in an Enterprise IT organization and three in two business units undergoing transformations on Lean and Agile plus DevOps adoption. System-I’s robustness was evaluated through surveys and structured STC maturity assessments (self and guided ones). System-II employed sense-making techniques to foster resilience within the system of work (SoW), laying the groundwork for their evolutionary journeys. The findings reveal a significant need for greater alignment between management (as transformation agents) and software engineering practices. However, the study suggests actionable guidelines, grounded in new principles and mental models for operating within a CAS, to cultivate enhanced resilience and robustness in a VUCA world. Full article

Integrated photonic biosensors are revolutionizing lab-on-a-chip technologies by providing highly sensitive, miniaturized, and label-free detection solutions for a wide range of biological and chemical targets. This review explores the foundational principles behind their operation, including the use of resonant photonic structures such as microring and whispering gallery mode resonators, as well as interferometric and photonic crystal-based designs. Special focus is given to the design strategies that optimize light–matter interaction, enhance sensitivity, and enable multiplexed detection. We detail state-of-the-art fabrication approaches compatible with complementary metal-oxide-semiconductor processes, including the use of silicon, silicon nitride, and hybrid material platforms, which facilitate scalable production and seamless integration with microfluidic systems. Recent advancements are highlighted, including the implementation of optofluidic photonic crystal cavities, cascaded microring arrays with subwavelength gratings, and on-chip detector arrays capable of parallel biosensing. These innovations have achieved exceptional performance, with detection limits reaching the parts-per-billion level and real-time operation across various applications such as clinical diagnostics, environmental surveillance, and food quality assessment. Although challenges persist in handling complex biological samples and achieving consistent large-scale fabrication, the emergence of novel materials, advanced nanofabrication methods, and artificial intelligence-driven data analysis is accelerating the development of next-generation photonic biosensing platforms. These technologies are poised to deliver powerful, accessible, and cost-effective diagnostic tools for practical deployment across diverse settings. Full article

Cardiac tumors, though rare, present significant diagnostic and therapeutic challenges due to their heterogeneous nature and anatomical complexity. This narrative review synthesizes current evidence on prevalence, diagnostic modalities, and management strategies for primary and metastatic cardiac tumors. Echocardiography, cardiac MRI, and CT remain cornerstone imaging tools for differentiating tumors from non-neoplastic masses, while advances in PET/CT and tissue characterization techniques refine staging and treatment planning. Surgical resection with clear margins (R0) is critical for resectable tumors, particularly benign myxomas, though malignant tumors like sarcomas require multimodal approaches combining surgery, radiotherapy, and systemic therapies. Emerging strategies such as heart autotransplantation and staged resections offer promise for complex cases, while oligometastatic disease management highlights the role of stereotactic radiotherapy and immunotherapy. Key challenges include standardizing resection margins, optimizing neoadjuvant therapies, and addressing high recurrence rates in malignancies. Future directions emphasize integrating AI-driven imaging analysis, molecular biomarkers, and genomic profiling to personalize therapies, alongside global registries to enhance data on rare tumors. Equitable access to advanced diagnostics and multidisciplinary collaboration are essential to improve outcomes. This review underscores the need for standardized guidelines, technological innovation, and patient-centered research to address gaps in cardiac oncology. Full article

Social media addiction has become a global public health challenge, and understanding its mechanism’s complexity requires the integration of the transitional characteristics of addiction development stages and breaking through the traditional single-reinforcement-path explanatory framework. This study is based on the dual pathway of positive and negative emotional reinforcement, integrating multidisciplinary evidence from neuroscience, psychology, and computational behavioral science to propose an independent and dynamic interaction mechanism of positive reinforcement (driven by social rewards) and negative reinforcement (driven by emotional avoidance) in social media addiction. Through a review, it was found that early addiction is mediated by the midbrain limbic dopamine system due to immediate pleasurable experiences (such as liking), while late addiction is maintained by negative emotional cycles due to the dysfunction of the prefrontal limbic circuit. The transition from early addiction to late addiction is characterized by independence and interactivity. Based on this, a phased intervention strategy is proposed, which uses reward competition strategies (such as cognitive behavioral therapy and alternative rewards) to weaken dopamine sensitization in the positive reinforcement stage, enhances self-control by blocking emotional escape (such as through mindfulness training and algorithm innovation) in the negative reinforcement stage, and uses cross-pathway joint intervention in the interaction stage. This study provides a theoretical integration framework for interdisciplinary research on social media addiction from a dynamic perspective for the first time. It is recommended that emotional reinforcement variables are included in addiction diagnosis, opening up new paths for precise intervention in different stages of social media addiction development. Full article

This study investigates the institutional innovation pathways for integrating comprehensive transportation networks into China’s territorial spatial planning system, with a focus on resolving the conflicts between ecological conservation and infrastructure development. By proposing a ‘constraint-coupling-innovation’ framework, this research addresses the gaps in existing spatial governance mechanisms, particularly the insufficient alignment between transportation planning and the ‘three zones and three lines’ (ecological conservation, agricultural production, and urban development zones with binding redline) system. The study employs mixed-method approaches, including geospatial conflict analysis (GIS), AI-driven policy coordination tools, and case studies from the Yangtze River Economic Belt. It demonstrates that rigid ecological constraints (e.g., ecological sensitivity veto power) can reduce planning conflicts effectively, while adaptive governance models enhance land use efficiency and stakeholder collaboration. Key findings reveal a significant negative correlation (R² = 0.75) between ecological protection redline (EPR) coverage and transportation network density, underscoring the necessity for differentiated governance strategies in high-conflict regions. A comparative analysis with the EU’s Natura 2000 sites and TEN-T networks further highlights China’s unique hierarchical governance model, which integrates top-down ecological mandates with localized technological innovations, such as digital twins and polycentric decision making. This study contributes to global debates on sustainable spatial planning by offering actionable pathways for balancing infrastructure expansion with ecological resilience, while also proposing institutional reforms, such as a National Transportation Spatial Governance Index (NTSGI), to standardize ecological compliance. These insights provide both theoretical advancements in spatial institutionalism and practical tools for policymakers navigating the dual challenges of urbanization and climate resilience. Full article

The accurate estimation of double-season rice yield is critical for ensuring national food security. To address the limitations of traditional crop models in spatial resolution and accuracy, this study innovatively developed the HASM-APSIM coupled model by integrating High-Accuracy Surface Modeling (HASM) with the Agricultural Production Systems sIMulator (APSIM) to simulate the historical yield of double-season rice in Jiangxi Province from 2000 to 2018. The methodological advancements included the following: the localized parameter optimization of APSIM using the Nelder–Mead simplex algorithm and NSGA-II multi-objective genetic algorithm to adapt to regional rice varieties, enhancing model robustness; coarse-resolution yield simulations (10 km grids) driven by meteorological, soil, and management data; and high-resolution refinement (1 km grids) via HASM, which fused APSIM outputs with station-observed yields as optimization constraints, resolving the trade-off between accuracy and spatial granularity. The results showed that the following: (1) Compared to the APSIM model, the HASM-APSIM model demonstrated higher accuracy and reliability in simulating historical yields of double-season rice. For early rice, the R-value increased by 14.67% (0.75→0.86), RMSE decreased by 34.02% (838.50→553.21 kg/hm²), MAE decreased by 31.43% (670.92→460.03 kg/hm²), and MAPE dropped from 11.03% to 7.65%. For late rice, the R-value improved by 27.42% (0.62→0.79), RMSE decreased by 36.75% (959.0→606.58 kg/hm²), MAE reduced by 26.37% (718.05→528.72 kg/hm²), and MAPE declined from 11.05% to 8.08%. (2) Significant spatiotemporal variations in double-season rice yields were observed in Jiangxi Province. Temporally, the simulated yields of early and late rice aligned with statistical yields in terms of numerical distribution and interannual trends, but simulated yields exhibited greater fluctuations. Spatially, high-yield zones for early rice were concentrated in the eastern and central regions, while late rice high-yield areas were predominantly distributed around Poyang Lake. The 1 km resolution outputs enabled the precise identification of yield heterogeneity, supporting targeted agricultural interventions. (3) The growth rate of double-season rice yield is slowing down. To safeguard food security, the study area needs to boost the development of high-yield and high-quality crop varieties and adopt region-specific strategies. The model proposed in this study offers a novel approach for simulating crop yield at the regional scale. The findings provide a scientific basis for agricultural production planning and decision-making in Jiangxi Province and help promote the sustainable development of the double-season rice industry. Full article

As populations age globally, governments face mounting challenges in reconfiguring healthcare and housing systems to support aging-in-place. This study offers a comparative analysis of South Korea and the United Kingdom, examining how each country integrates digital technologies, such as Artificial Intelligence (AI), telecare, and smart housing systems, into their aging strategies. South Korea employs a centralized, technology-driven approach that prioritizes the national rollout of AI-enabled smart homes and digital health infrastructure. In contrast, the UK advances a decentralized, community-based model emphasizing social housing, localized care delivery, and telecare integration. Despite these differing trajectories, both nations face shared limitations, including high implementation costs, digital literacy barriers, and concerns about data privacy. Critically, the study finds that the success of aging-in-place efforts is shaped not only by technological capacity but also by governance dynamics, political continuity, and institutional coordination. In response, the paper proposes policy recommendations alongside an ethical framework grounded in transparency, autonomy, informed consent, and equity. Sustainable aging-in-place strategies require not only innovative technologies, but also inclusive governance and ethically robust design to ensure accessibility, trust, and long-term impact. Full article

This study investigates the demand for high-precision trajectory similarity assessment in intelligent maritime navigation. This is done by analyzing discrepancies between GPS-derived trajectories and actual vessel paths, while identifying critical limitations in existing evaluation methods. To address these challenges, we propose a robust framework that integrates three core innovations: firstly, a linear feature accuracy-constrained resampling method to ensure computational precision under diverse complexity conditions, validated through experimental verification; secondly, a shape feature extraction and transformation protocol designed to maintain consistency across multi-scale and heterogeneous operational scenarios; thirdly, a quantitative similarity evaluation criterion based on extracted shape characteristics, enabling systematic alignment between localized trajectory segments and historical navigation patterns. The experimental results confirm the method’s enhanced robustness and its capability to bridge local and global trajectory comparisons, demonstrating that shape-driven quantification significantly refines similarity analysis. This approach advances intelligent maritime systems by providing a technically rigorous solution for real-time decision support and actionable insights into next-generation navigation applications. Full article

Hybrid Energy Systems (HES) are pivotal in modern power systems. They incorporate conventional and renewable energy sources, energy storage, and main grids to deliver reliable and sustainable power. To ensure the smooth functioning of such systems, stability analysis is essential, particularly in dynamic and unpredictable situations. Despite tremendous progress, the stability analysis of HES is still complex due to challenges such as nonlinearity, system complexity, and uncertainty in renewable energy generation. A thorough understanding of stability analysis for HES is crucial to ensure the reliable and efficient design of these complex power systems. Particularly in the current data-intensive era, vast volumes of data are being collected through advanced sensors and communication technologies. However, no thorough and organised discussion of every facet of HES stability analysis is available in the literature. This paper aims to review various types and techniques for analysing frequency, transient, small-signal, and converter-driven stability, and to assess the importance and challenges of such analyses for HES. By emphasising the need for innovative approaches for stability enhancement, the paper also discusses the importance of continued research in optimising the operation and reliability of hybrid energy systems. Full article