Search Results (83)

Poor rural households still face vulnerability of the sustainable livelihood capacity caused by multiple risk disturbances even after they are lifted out of poverty, and become vulnerable poverty-eradicated households. However, quantifying the spatiotemporal heterogeneity of the impact of rural household livelihood vulnerability on resilience from a multi-risk perspective remains a challenge. This study integrates the theoretical connotations of livelihood vulnerability and resilience to develop a systematic analysis framework of sustainable livelihood-vulnerability-resilience for rural households from the perspective of multi-risk disturbance, and reveals the dynamic interaction process and mechanism of the three. On this basis, the VEP model for forward-looking and multi-risk perspectives, which embeds multiple risk factors as feature vectors, and the cloud-based fuzzy integrated evaluation method are employed to measure rural households’ livelihood vulnerability and resilience, respectively. Subsequently, based on clarifying the correlation between the two, we use the quantile regression method and factor contribution model to reveal the spatiotemporal impact mechanism of multi-level and multi-risk dominated vulnerability of rural households on resilience. These methods collectively enable us to quantify the spatiotemporal heterogeneity of vulnerability and resilience impacts from a risk perspective, taking a step forward and broadening the analytical perspective in the field of sustainable livelihoods research. The case study in Fugong County of China shows that, both rural households’ livelihood vulnerability and resilience exhibit spatiotemporal heterogeneity, and the negative correlation between the two gradually increases over time; as the level of livelihood vulnerability increases, the internal main contributing factors of livelihood resilience and their degree of contribution change accordingly; as the types of risks that dominate vulnerability change, the impact of vulnerability on the overall livelihood resilience and its internal dimensions also varies, where the change in resilience is greatest when the vulnerability is dominated by social risks, while the least change occurred when vulnerability is dominated by labor and income risks. This study provides a feasible methodological reference and a technical foundation for decision-making aimed at guiding rural households out of poverty sustainably and achieving sustainable livelihood. It can effectively enhance the predictive and post-event coping capacity of vulnerable rural households when subjected to multi-risk disturbances. Full article

The education sector in Tanzania faces significant challenges, especially in public primary schools. Unmanageably large classes and critical teacher–pupil ratios hinder the provision of tailored tutoring, impeding pupils’ educational growth. However, artificial intelligence (AI) could provide a way forward. Advances in generative AI can be leveraged to create interactive and effective intelligent tutoring systems, which have recently been built into embodied systems such as social robots. Motivated by the pivotal influence of teachers’ attitudes on the adoption of educational technologies, this study undertakes a qualitative investigation of Tanzanian primary school mathematics teachers’ perceptions of contextualised intelligent social robots. Thirteen teachers from six schools in both rural and urban settings observed pupils learning with a social robot. They reported their views during qualitative interviews. The results, analysed thematically, reveal a generally positive attitude towards using social robots in schools. While commended for their effective teaching and suitability for one-to-one tutoring, concerns were raised about incorrect and inconsistent feedback, language code-switching, response latency, and the lack of support infrastructure. We suggest actionable steps towards adopting tutoring systems and social robots in schools in Tanzania and similar low-resource countries, paving the way for their adoption to redress teachers’ workloads and improve educational outcomes. Full article

As cyber systems increasingly converge with physical infrastructure and social processes, they give rise to Complex Cyber–Physical–Social Systems (C-CPSS), whose emergent behaviors pose unique risks to security and mission assurance. Traditional cyber–physical system models often fail to address the unpredictability arising from human and organizational dynamics, leaving critical gaps in how cyber risks are assessed and managed across interconnected domains. The challenge lies in building resilient systems that not only resist disruption, but also absorb, recover, and adapt—especially in the face of complex, nonlinear, and often unintentionally emergent threats. This paper introduces the concept of ‘responsible resilience’, defined as the capacity of systems to adapt to cyber risks using trustworthy, transparent agent-based models that operate within socio-technical contexts. We identify a fundamental research gap in the treatment of social complexity and emergence in existing the cyber–physical system literature. To address this, we propose the E3R modeling paradigm—a novel framework for conceptualizing Emergent, Risk-Relevant Resilience in C-CPSS. This paradigm synthesizes human-in-the-loop diagrams, agent-based Artificial Intelligence simulations, and ontology-driven representations to model the interdependencies and feedback loops driving unpredictable cyber risk propagation more effectively. Compared to conventional cyber–physical system models, E3R accounts for adaptive risks across social, cyber, and physical layers, enabling a more accurate and ethically grounded foundation for cyber defence and mission assurance. Our analysis of the literature review reveals the underrepresentation of socio-emergent risk modeling in the literature, and our results indicate that existing models—especially those in industrial and healthcare applications of cyber–physical systems—lack the generalizability and robustness necessary for complex, cross-domain environments. The E3R framework thus marks a significant step forward in understanding and mitigating emergent threats in future digital ecosystems. Full article

Wind is one of the most important resources in the renewable energy basket. However, there are questions regarding wind as a sustainable solution, especially concerning its upfront costs, visual impact, noise pollution, and bird collisions. These challenges arise in commercial windmills, whereas for domestic small-scale windmills, these challenges are limited. On the other hand, accurate wind speed prediction (WSP) is crucial for optimizing power management in renewable energy systems. Existing research focuses on proposing model architectures and optimizing hyperparameters to improve model performance. This approach often results in larger models, which are hosted on cloud servers. Such models face challenges, including bandwidth utilization leading to data delays, increased costs, security risks, concerns about data privacy, and the necessity of continuous internet connectivity. Such resources are not available for domestic windmills. To overcome these obstacles, this work proposes a transformer model integrated with Long Short-Term Memory (LSTM) units, optimized for memory-constrained devices (MCDs). A contribution of this research is the development of a novel cost function that balances the reduction of mean squared error with the constraints of model size. This approach enables model deployment on low-power devices, avoiding the challenges of cloud-based deployment. The model, with its tuned hyperparameters, outperforms recent methodologies in terms of mean squared error, mean absolute error, model size, and R-squared scores across three different datasets. This advancement paves the way for more dynamic and secure on-device wind speed prediction (WSP) applications, representing a step forward in renewable energy management. Full article

Complex systems often face significant challenges in both efficiency and performance when making long-term degradation predictions. To address these issues, this paper proposes a predictive architecture based on multi-output sparse probabilistic model regression. An adaptive health index (HI) extraction method was also introduced, which leverages unsupervised deep learning and variational mode decomposition to effectively extract health indicators from multiple measurements of complex systems. The effectiveness of the proposed method was validated using both the C-MAPSS and FLEA datasets. The case study results demonstrate that the proposed prognostic method delivered an outstanding performance. Specifically, the feature extraction method effectively reduced the measurement noise and produced robust HIs, while the multi-output sparse probabilistic model achieved lower prediction errors and a higher accuracy. Compared to traditional single-step forward-prediction methods, the proposed approach significantly reduced the time required for long-term predictions in complex systems, thus improving support for online status monitoring. Full article

The agricultural sector faces growing environmental and societal pressures to balance natural resource use with food security, particularly within the Water-Energy-Food-Ecosystems Nexus (WEFE). Increasing water demand, competition, and challenges like droughts and desertification are driving the need for innovative irrigation practices. Pressurized irrigation systems, such as sprinkler and micro-irrigation, are gaining prominence due to their automation, labor savings, and increased water application efficiency. To support farmers in designing and managing these systems, the R&D project AGIR developed a computational tool that simulates water application efficiency under site-specific conditions. The tool integrates key parameters, including system design, scheduling, soil properties, topography, meteorological data, and vegetation cover, providing a robust methodological framework with classification criteria for evaluating irrigation options. Validated using data from six case studies, the tool achieved simulated irrigation efficiencies of 73% to 90%, which are consistent with field observations. By simplifying complex irrigation requirement calculations, the model offers a user-friendly alternative while maintaining accuracy at the farm level. This innovative tool enables stakeholders to optimize irrigation systems, reduce water losses, and establish standardized recommendations for design, management, performance, and socio-economic considerations. It represents a significant step forward in supporting sustainable water management and advancing the goals of Agriculture 4.0. Full article

Perovskite solar cells have garnered significant attention due to their outstanding optoelectronic properties, ease of fabrication, and cost-effectiveness, making them a promising candidate for next-generation photovoltaic technologies. However, CsPbIBr₂-based perovskites currently face critical challenges regarding their limited efficiency and relatively poor long-term stability, hindering their broader commercial applications. In this study, we systematically investigated the morphological effects induced by different solvents, including dimethylformamide (DMF), N-methyl-2-pyrrolidone (NMP), and dimethyl sulfoxide (DMSO), on the formation and characteristics of lead bromide (PbBr₂) complexes. Further optimization was achieved through the innovative incorporation of trimesoyl chloride (TMC) doping into the perovskite precursor solution. The optimized precursor solution was subsequently processed using a spin-coating and annealing method, resulting in high-quality CsPbIBr₂ perovskite thin films with improved morphological and optoelectronic properties. The experimental results demonstrated a remarkable enhancement in power conversion efficiency (PCE), with an increase from an initial value of 6.2% up to 10.2%. Furthermore, the optimized CsPbIBr₂ solar cells exhibited excellent stability, maintaining over 80% of their initial efficiency after continuous aging for 250 h in ambient air conditions. This study presents an effective strategy for the controlled morphological and compositional engineering of wide-bandgap perovskite materials, providing a significant step forward in the advancement of perovskite photovoltaic technology. Full article

The approach for the diagnosis and treatment of interstitial lung diseases (ILDs) has changed in recent years, mainly for the identification of new entities, such as interstitial lung abnormalities (ILAs) and progressive pulmonary fibrosis (PPF). Clinicians and radiologists are facing new challenges for the screening, diagnosis, prognosis, and follow-up of ILDs. The detection and classification of ILAs or the identification of fibrosis progression at high-resolution computed tomography (HRCT) is difficult, with high inter-reader variability, particularly for non-expert radiologists. In the last few years, various software has been developed for ILD evaluation at HRCT, with excellent results, equal to or more reliable than humans. AI tools can classify ILDs, quantify the extent, analyze the features hidden from the human eye, predict prognosis, and evaluate the progression of the disease. More advanced tools can incorporate clinical and radiological data to obtain personalized prognosis, with the potential ability to steer treatment decisions. To step forward and implement in daily practice such tools, more collaboration is required to collect more homogeneous clinical and radiological data; furthermore, more robust, prospective trials, with the new AI-derived biomarkers compared with each other, are needed to demonstrate the real reliability of the computer-aided evaluation of ILDs. Full article

Solving the Euler equations often requires expensive computations of complex, high-order time derivatives. Although Taylor Discontinuous Galerkin (TDG) schemes are renowned for their accuracy and stability, directly evaluating third-order tensor derivatives can significantly reduce computational efficiency, particularly for large-scale, intricate flow problems. To overcome this difficulty, this paper presents an optimized numerical procedure that combines Taylor series time integration with the Discontinuous Galerkin (DG) approach. By replacing cumbersome tensor derivatives with simpler time derivatives of the Jacobian matrix and finite difference method inside the element to calculate the high-order time derivative terms, the proposed method substantially decreases the computational cost while maintaining accuracy and stability. After verifying its fundamental feasibility in one-dimensional tests, the optimized TDG method is applied to a two-dimensional forward-facing step problem. In all numerical tests, the optimized TDG method clearly exhibits a computational efficiency advantage over the conventional TDG method, therefore saving a great amount of time, nearly 70%. This concept can be naturally extended to higher-dimensional scenarios, offering a promising and efficient tool for large-scale computational fluid dynamics simulations. Full article

The current trend in machining with robotic arms involves leveraging Industry 4.0 technologies to propose solutions that reduce path deviation errors. This approach presents significant challenges alongside promising advancements, as well as a substantial increase in the cost of future industrial robotic cells, which is not always amortizable. As an alternative or complementary approach to this trend, methods encouraging the occasional use of Industry 4.0 devices for characterizing the behavior of the actual physical cell, calibration, or adjustment are proposed. One such method, called FlePFaM, predicts flatness errors in face milling operations using robotic arms. This is achieved by estimating tool path deviation errors through the integration of a simple model of the robot arm’s mechanics with the cutting forces vector of the process, thereby optimizing machining conditions. These conditions are determined through prior empirical estimations of mass, stiffness, and damping. The conducted tests enabled the selection of the most favorable combination of variables, such as the robot wrist configuration, the position and orientation of the workpiece, and the predominant milling orientation. This led to the identification of the configuration with the lowest absolute flatness error according to the model’s predictions. The results demonstrated a high degree of similarity—between 97% for the closest case and 57% for the farthest case—between simulated and experimental flatness error values. FlePFaM represents a significant step forward in adopting innovative robotic arm solutions for reliable and efficient production. FlePFaM includes dimensional flatness indicators that provide practical support for decision making. Full article

There is a need to address challenges faced in detecting and segmenting defects in micro-vibration motor armatures, which are crucial components used in digital devices. Due to their complex structure and tiny size, quality control during assembly is difficult. In this paper, an adaptive segmentation quantization (ASQ) system based on YOLO 11 and SAM is proposed to address the issue above. The system consists of a target detection (TD) unit, shape segmentation (SS) unit, and quantitative assessment (AS) unit, and introduces a practical combination of YOLO11 for defect detection and SAM for segmentation, integrating this with a novel quantitative assessment framework to measure defect severity and occurrence. This approach is efficient and cost-effective, supporting real-time industrial applications by allowing for automated, rapid analysis and improvement identification. Finally, a quantitative evaluation standard with more than 90% accuracy was achieved. Additionally, a hardware system was developed to implement this framework in industrial settings. The proposed framework adopts a strategy of intelligent morphological feature extraction and computation, focusing on pixel-level segmentation and quantitative assessment. This research makes a significant step forward in automating quality control processes for micro-scale components, providing a robust and adaptive solution for the enhancement of manufacturing efficiency and product quality. Full article

The Indonesian maritime territory harbors a rich diversity of marine resources, making up approximately 37% of global fish species diversity. Tuna, particularly in Maluku Province, stands out as a vital economic asset with growing production and export numbers. Current practices for processing and evaluating tuna meat, however, face significant limitations due to basic infrastructure and reliance on manual inspection methods, leading to potential contamination risks and treatment identification errors. This research addresses these challenges by implementing an advanced deep learning solution based on convolutional neural networks (CNNs) to automatically identify three distinct treatment categories for tuna loin: No-Treatment, CO-Treatment, and CS-Treatment. Trained on a comprehensive image dataset, the model demonstrated exceptional performance with 95% accuracy. While field testing confirmed the model’s strong performance in correctly identifying treatment categories, occasional classification errors highlighted areas for improvement in data preprocessing. This study provides a significant step forward in automated fish processing assessment technology, offering a promising solution to longstanding challenges in the marine processing industry. Full article

A numerical simulation of the circular cylinder as an obstacle in a double forward-facing (DFFS) step was performed. The size and position of the upstream cylinder (c₁) and downstream cylinder (c₂) were varied to explore their role in heat transfer in both laminar and turbulent conditions. Comparative results of the upper and lower half of the downstream cylinder were plotted as results to understand the heat transfer and flow characteristics around the downstream cylinder due to the effect of the upstream cylinder’s dimension and position. For Re = 800, when the c₁ is placed near the bottom of the wall, it results in a pair of rear-side symmetrical vortices, and the c₂ cylinder vortices become larger when the c₁ is shifted towards the top wall. Additional flow separation happens adjacent to the steps when c₁ is greater than c₂. These vortices strongly influence the convection heat transfer from the step. However, when Reynolds number (Re) is increased from 800 to 80,000, these vortices’ size is decreased. When c₁ moves from 0.375H to 0.75H, the average Nusselt number is increased significantly. Moreover, a hike in Re results in a higher average Nusselt number irrespective of the position of obstacles. The upstream cylinder significantly enhances the Nusselt number when it is placed near the top wall rather than the bottom wall. Full article

Breast cancer remains a leading cause of cancer-related deaths among women, emphasizing the critical need for early detection and monitoring techniques. Conventional imaging modalities such as mammography, MRI, and ultrasound have face sensitivity, specificity, cost, and patient comfort limitations. This study introduces a handheld Near-Infrared Diffuse Optical Tomography (NIR DOT) probe for breast cancer imaging. The NIRscan probe utilizes multi-wavelength light-emitting diodes (LEDs) and a linear charge-coupled device (CCD) sensor to acquire real-time optical data, reconstructing cross-sectional images of breast tissue based on scattering and absorption coefficients. With wavelengths optimized for the differential optical properties of tissue components, the probe enables functional imaging, distinguishing between healthy and malignant tissues. Clinical evaluations have demonstrated its potential for precise tumor localization and monitoring therapeutic responses, achieving a sensitivity of 94.7% and specificity of 84.2%. By incorporating machine learning algorithms and a modified diffusion equation (MDE), the system enhances the accuracy and speed of image reconstruction, supporting rapid, non-invasive diagnostics. This development represents a significant step forward in portable, cost-effective solutions for breast cancer detection, with potential applications in low-resource settings and diverse clinical environments. Full article

Cancer is a multifaceted disease characterised by uncontrolled cellular proliferation and metastasis, resulting in significant global mortality. Current therapeutic strategies, including surgery, chemotherapy, and radiation therapy, face challenges such as systemic toxicity and tumour resistance. Recent advancements have shifted towards targeted therapies that act selectively on molecular structures within cancer cells, reducing off-target effects. Mitochondria have emerged as pivotal targets in this approach, given their roles in metabolic reprogramming, retrograde signalling, and oxidative stress, all of which drive the malignant phenotype. Targeting mitochondria offers a promising strategy to address these mechanisms at their origin. Synthetic derivatives of natural compounds hold particular promise in mitochondrial-targeted therapies. Innovations in drug design, including the use of conjugates and nanotechnology, focus on optimizing these compounds for mitochondrial specificity. Such advancements enhance therapeutic efficacy while minimizing systemic toxicity, presenting a significant step forward in modern anticancer strategies. Full article