Limited Capabilities Research Articles

Passive wireless RFID strain sensor for directional-independent structural deformation monitoring

The advancement of Radio-frequency identification (RFID) sensor technology presents a novel approach to monitoring the health of structures. However, the traditional RFID antenna sensor encounters issues with limited sensitivity and strain measurement capabilities in a single direction. In this investigation, we propose, model, analyze, and test an enhanced wireless and passive RFID strain sensor that offers improved sensitivity to strains and quantifiable measurements in orthogonal directions. Theoretical analysis based on multiphysics elastodynamics and electromagnetics illustrating the resonant response and distribution of current field density between two sets of orthogonally installed sensors under applied stress. Our findings reveal that the RFID strain sensor exhibits characteristics to electromagnetically induced transparency like (EIT-like) effect, enabling independent and intensified measurement of strains both horizontally and vertically. Experimental data demonstrates a linear correlation between the shift in resonant frequency of the sensor antenna and transverse strains as well as longitudinal strains, with sensitivity coefficients measuring −1.76 kHz με−1 for transverse strain and 2.17 kHz με−1 for longitudinal strain respectively. Consequently, by accurately analyzing variations in resonant frequency it becomes possible to deduce both magnitude and direction of strains on the antenna effectively laying groundwork for future engineering applications.

Strengthening Policies for Education, Innovation, and Digitization Through Teacher Training: Evaluating ProFuturo’s Open Model in Ecuador

Teacher training and a commitment to innovation in teaching are determining factors in the success of technology adoption processes. This article presents a study on the opportunities produced through the collaboration of the Ecuadorian Ministry of Education and the ProFuturo program, which arose during the COVID-19 pandemic. This collaboration resulted in the improvement of digital competency among teachers and pupils and in transference to educational practise. It also strengthened the existing limited capabilities for developing mass training programs for teachers in the country. The research was conducted through an online survey, with a cross-sectional, quantitative, and non-experimental focus from two data sources. A total of 3,565 teachers answered the digital survey for teachers trained using the Open Model in Ecuador between 2020 and 2022. On the other hand, 7,257 teachers answered the ProFuturo Self-Assessment of Digital Skills of Teachers (https://competencyassessment.profuturo.education/?lang=en). The results show an improvement in the competency of teachers following their participation in the program and confirm that they considered digital transformation in the classroom to be of great utility. Teacher training remains a cornerstone of high-quality education and research as this contribution proves a positive impact on learning experiences, where there was a significant transference, driven by an improvement in digital skills applied to the teaching process.

Efficient and Compressed Deep Learning Model for Brain Tumour Classification With Explainable AI for Smart Healthcare and Information Communication Systems

ABSTRACTThe detection of brain tumours presents a significant challenge in the medical domain, where prompt and precise diagnosis is crucial as patient outcomes depend on it. Conventional deep neural networks perform well in carrying out various imaging tasks within the healthcare sector; however, their effectiveness often falls short of expectations in practical applications due to the substantial computational resources required and issues with reliability. In this research, an optimised and effective deep learning model founded on the DenseNet‐169 architecture is introduced for the classification of magnetic resonance imaging brain tumours, which is particularly advantageous for smart healthcare systems and information and communication technology (ICT) settings with limited computational capabilities. The model compression methodologies, including pruning and quantization, have been employed to significantly diminish the dimensions and intricacy of the model while achieving a classification accuracy of 97.07%. Furthermore, this endeavour necessitates the enhancement of the model's interpretability through the utilisation of explainable artificial intelligence methodologies such as Gradient‐weighted Class Activation Mapping (Grad‐CAM) and SHapley Additive exPlanations (SHAP), which will aid clinicians in highlighting crucial areas of the images and validating feature importance concerning the decisions rendered by the model. A comparative performance evaluation is conducted against DenseNet‐169, ResNet‐50 and various other models to delineate the superior efficacy of our model, rendering it exceptionally adept for knowledge‐driven, real‐time brain tumour diagnosis within smart healthcare and ICT systems where resources are constrained.

Eco-evolutionary Guided Pathomic Analysis to Predict DCIS Upstaging.

Cancers evolve in a dynamic ecosystem. Thus, characterizing cancer's ecological dynamics is crucial to understanding cancer evolution and can lead to discovering novel biomarkers to predict disease progression. Ductal carcinoma in situ (DCIS) is an early-stage breast cancer characterized by abnormal epithelial cell growth confined within the milk ducts. In this study, we show that ecological habitat analysis of hypoxia and acidosis biomarkers can significantly improve prediction of DCIS upstaging. First, we developed a novel eco-evolutionary designed approach to define habitats in the tumor intraductal microenvironment based on oxygen diffusion distance. Then, we identify cancer cells with metabolic phenotypes attributed to their habitat conditions, such as the expression of CA9 indicating hypoxia responding phenotype, and LAMP2b indicating the acid adaptation. Traditionally these markers have shown limited predictive capabilities for DCIS upstaging, if any. However, when analyzed from an ecological perspective, their power to differentiate between pure DCIS and upstaged DCIS increased significantly. Second, using eco-evolutionary guided computational and digital pathology techniques, we discovered distinct niches with spatial patterns of these biomarkers and used the distribution of such niches to predict patient upstaging. The niches patterns were characterized by pattern analysis of both cellular and spatial features. With a 5-fold validation on the biopsy cohort, we trained a random forest classifier to achieve the area under curve (AUC) of 0.74. Our results affirm the importance of using eco-evolutionary-designed approaches in biomarkers discovery studies in the era of digital pathology by demonstrating the role of tumor ecological habitats and niches.

Promoting Sustainability: Collaborative Governance Pathways for Virtual Water Interactions and Environmental Emissions

This study explores the water consumption and greenhouse gas (GHG) emissions in the Yarkand River Basin, focusing on their dynamic interactions across industrial sectors. Utilizing environmental input–output analysis (IOA), the CROPWAT model, and life cycle assessment (LCA), we quantified the historical evolution of physical and virtual water cycles in relation to the water–carbon nexus. Our findings reveal that the planting industry, particularly the production of export-oriented, water-intensive crops like cotton, significantly contributes to both blue and green water consumption, exacerbating regional water scarcity. The persistent external market demand drives this over-extraction, further strained by the basin’s limited water retention capabilities. Although advancements have been made in reducing the per-unit water footprint of crops, total water consumption continues to rise due to agricultural expansion, intensifying pressure on blue water resources. Additionally, agricultural GHG emissions have surged, driven by increased electricity consumption, heavy fertilizer use, and escalating soil N2O emissions. In light of these challenges, our research underscores the critical need for integrated resource management strategies that align with sustainable development goals. By promoting efficient water allocation within the agricultural sector and diversifying crop structures downstream, we can enhance ecosystem resilience and reduce environmental degradation. Furthermore, the advancement of value-added agricultural processing and the implementation of innovative water conservation technologies are essential for fostering economic sustainability. These strategies not only mitigate the environmental impacts associated with agricultural practices but also strengthen the region’s adaptive capacity in the face of climate change and fluctuating market demands. Our findings contribute to the broader discourse on sustainable agricultural practices, emphasizing the interconnectedness of water management, climate resilience, and economic viability in arid regions.

Solvent-adaptive hydrogels with lamellar confinement cellular structure for programmable multimodal locomotion

Biological organisms can perform flexible and controllable multimodal motion under external stimuli owing to the hierarchical assembly of anisotropic structures across multiple length scales. However, artificial soft actuators exhibit the limited response speed, deformation programmability and movement capability especially in harsh environments because of insufficient anisotropic hierarchy and precision in structural design. Here, we report a programmed assembly directed confinement polymerization method for the fabrication of environmentally tolerant and fast responsive hydrogels with lamellar assembly-confined cellular structure interpenetrated with highly aligned nanopillars by the directional freezing-assisted polymerization in the predesigned anisotropic laminar scaffold. The obtained hydrogel exhibits ultrafast responsiveness and anisotropic deformation exposed to temperature/light/solvent stimulation, maintaining highly consistent responsive deformation capability in all-polarity solvents over 100 days of soaking. Moreover, the hydrogels implement photoactive programmable multi-gait locomotion whose amplitude and directionality are precisely regulated by the intrinsic structure, including controlled crawling and rotation in water and non-polar solvents, and 3D self-propulsion floating and swimming in polar solvents. Thus, this hydrogel with hierarchically ordered structure and dexterous locomotion may be suitable for flexible intelligent actuators serving in harsh solvent environments.

Emergence of information aggregation to rational expectations equilibria in markets populated by biased heuristic traders

Information aggregation is a key economic function of markets. We report results of a computational experiment with markets populated by simple algorithmic traders who follow two heuristics usually thought of as leading to biased information processing in behavioral economics literature (anchor-and-adjust, and representativeness). Outcomes of these markets either tend to cluster around (or fail to do so) rational expectations equilibria under specific conditions, consistent with markets populated by profit-motivated human traders. Algorithmic trader convergence is slower and noisier than that of human traders. Our results illustrate the emergence of rational expectations equilibria through complex interactions among actions of biased heuristic traders with limited information processing capabilities.

Application of SPNGO-VMD-SVM in rolling bearing fault diagnosis

Abstract Traditional bearing fault feature extraction and fault classification methods have low recognition accuracy and limited recognition capability in noisy environments. To address this problem, this paper proposes an improved Northern Goshawk Algorithm to optimize the variational modal decomposition (VMD) and support vector machine (SVM) to achieve bearing fault diagnosis. Firstly, to overcome the disadvantages of the Northern Goshawk Algorithm, such as easy fall into local optimal solutions and slow convergence speed, the Sine Cosine Strategy (SCA) and Position Optimisation Search Algorithm (POS) are introduced to optimize the Northern Goshawk Algorithm. The improved algorithm is called SPNGO for short. The superiority of the SPNGO algorithm is proved by comparing different algorithms. Then, SPNGO-VMD is used to adaptively decompose the vibration signals of faulty bearings and generate multiple modal components IMF. The effective IMF components are screened based on the craggy principle to reconstruct the signals. Finally, the reconstructed feature signals are input into SPNGO-SVM for fault classification and compared with other fault diagnosis models. The research results show that the proposed SPNGO-VMD-SVM fault diagnosis model is compared with the data set of Case Western Reserve University and the data set of Xi’an Jiaotong University. The diagnostic accuracy of the two groups of experiments can reach 96.67% and 98.89% respectively, and the intelligent diagnosis of different fault states of rolling bearings can be realized simultaneously.

Cluster consensus of finite-field networks with stochastic communication topology

Most of the multi-agent systems (MASs) in real life are with limited storage and communication capabilities; studies on such systems, including consensus problem, however have been mainly based on real number field models. In this article, the cluster consensus problem of MASs with stochastic communication topology over finite-field known as the cluster consensus of finite-field networks (FFNs), is considered. Using the semi-tensor product of matrices, the dynamics of FFNs with stochastic communication topology are equivalently converted into a logical algebraic form, which facilitates further studies. Then a permutation system is introduced. The relationship between cluster consensus of FFNs with stochastic communication topology and the set stability with probability one (SSPO) of the permutation system is revealed. In such an approach, some verifiable criteria can be derived for the cluster consensus of FFNs with stochastic communication topology, without requesting the connectivity of the whole network. Finally, an example is presented to validate the main results.

Request Deadline Split and Interference‐Aware Request Migration in Edge Cloud

ABSTRACTEdge computing extends computing resources from the data center to the edge of the network to better handle latency‐sensitive tasks. However, with the rise of the Internet of Things, edge devices with limited processing capabilities face difficulties in executing requests with fluctuating request peaks. In order to meet the deadline constraints of latency‐sensitive tasks, a feasible solution is to offload some latency‐sensitive tasks to other nearby edge devices. This article studies the problem of request migration in edge computing systems and minimizes the request deadline violation rate based on actual online arrival patterns, performance interference phenomena, and deadline constraints. Since a request contains multiple services and request migration will lead to changes in server resource competition pressure, we split the problem into three sub‐problems, dividing the request deadline to determine the maximum response time of the service, determining the performance of the service under different resource pressures and the request migration strategies. To this end, we propose two deadline splitting methods, a performance interference model under multi‐resource pressure, and two heuristic request migration strategies. Since this article considers online edge scenarios, the number and type of requests are black boxes. We conduct simulation experiments and find that our method has only one‐third the number of request violations of other methods.

“It's not that we Feel Incompetent, We Simply Lack the Institutional Solutions” Applying a Collective Capabilities Perspective to Voluntary Organisations in the Homelessness Field

ABSTRACT In today’s changing care and welfare landscape, voluntary organisations play an important role in providing care to homeless people. The limited literature on such initiatives is often sceptical about their effectiveness, although they do point to their positive contributions. In this paper, we use a Collective Capabilities lens to analyse the role, value, and limitations of volunteer initiatives addressing homelessness. We apply this framework to four case studies of volunteers-only initiatives providing care to homeless people in Brussels, Belgium. Data were collected through participant observation, interviews and document analysis. Our results highlight the trade-offs that voluntary organisations make between organisational capacities and agency. While formalisation and cooperation may contribute to an organisation's mission, these processes are sometimes avoided to preserve the unique contributions these organisations believe they make, such as providing care for people who fall between the cracks of the welfare state. Our results suggest that while voluntary organisations working with homeless people are aware of their limited capabilities regarding structural solutions, they do respond to unmet needs precisely because of their agency. However, there is a risk that fledgling organisations may take reckless and unsustainable actions.

A comprehensive review of climate information systems in the arid and semi-arid lands (ASALs) of Kenya for enhanced decision-making process

The reality of climate change has had far-reaching effects on climate and weather patterns globally. High carbon emissions have resulted in wildfires causing migration and death from natural disasters such as droughts, floods, and strong winds. Climate change has also led to reduced crop and livestock farming yields, and worsening incidences of hunger and famine. The use of climate information systems (CIS) in the Arid and Semi-Arid regions of Kenya has not been extensively reviewed. This study reviews the use of CIS in Kenya's Arid and Semi-Arid Lands (ASALs) to improve decision-making and adapt to climate change. We reviewed past and present research on Kenya's ASAL counties’ smallholder farmers’ CIS use. National and international attempts to develop early warning systems through CIS to help stakeholders and end users make farm-level decisions were reviewed. Kenya leads Eastern African countries in emphasizing CIS to reduce climate change's effects on sustainable agriculture. In ASAL countries, the gap between CIS information and its applicability for smallholder farmers’ farm-level decisions, low finance, weak infrastructure, and limited capability are major issues. Limited indigenous knowledge integration and the absence of regular updates and end-user co-development were also noted. Despite these obstacles, CIS can improve decision-making, catastrophe risk management, and socioeconomic growth in these places. Tailored CIS solutions are essential to address the specific needs of smallholder farmers in ASAL regions. Improved coordination and leveraging newer technologies are crucial for effective CIS implementation.

Fault diagnosis of transformer based on XGBoost

Abstract. With the continuous development of technology, the fault diagnosis of transformer is also continuously advancing. Traditional fault diagnosis methods such as the three-ratio method and the characteristic gas method have the disadvantages of missing coding and limited identification capabilities. Data-driven fault diagnosis methods can integrate various information for data analysis to accurately diagnose the type of fault. This paper proposes a fault diagnosis method for transformers based on the XGBoost model. First, the input data is selected for the ratio feature engineering, and the obtained ratios are also used as input data, and then the input data is normalized for data preprocessing. Secondly, the XGBoost model is introduced, which minimizes the prediction errors in each round of gradient boosting, thereby improving the accuracy and recall rate. Finally, through case analysis, comparisons are made with other methods in various performance indicators, and the accuracy is improved. The results show that the XGBoost is more accurate in the fault diagnosis of transformers, proving that the research has certain value and significance.

A preliminary biomechanical assessment and user perceptions of a motorized lifter for patient transfer

ABSTRACT A “no-lift” policy in healthcare discourages manual patient lifting to reduce worker injuries. This study explores modifications to a floor lift (NEAR-1) to align with this policy. NEAR-1 features a shorter lower lifting arm and a redesigned sling, enabling patients to (a) transfer in a sitting position, unlike a floor lift’s semi-reclining position, and (b) feel more in control by removing the sling’s rotational degree of freedom and letting the patient’s feet rest on the lifter base. The device also reduced patient weight-bearing, compared to sit-to-stand lifts. Biomechanical analysis was done to map the risk to the nurses and capture the patient’s experience with the modified lift (NEAR-1). Thirteen subjects carried out patient transfer tasks using both the NEAR-1 and the manual transfer approach, and this comparison will provide the baseline data for future systematic reviews. From the results, NEAR-1 significantly reduced the L5-S1 compression, the mediolateral shear forces, and the anteroposterior shear forces, with the exception of the muscle activities in the lower back, the shoulder, and the biceps. Moreover, patients felt safer and more comfortable with the device. Overall, the NEAR-1 lifter presents a biomechanically beneficial and user-friendly option for patients with limited weight-bearing capability.

Analysis of the organization and conduct of sports events in the Armed Forces of Ukraine and the armed forces of the leading states – NATO member states with military personnel with limited functional capabilities

During the full-scale invasion of the Russian Federation into Ukraine, servicemen of the Armed Forces of Ukraine, defending their country from the aggressor, received injuries of various degrees of severity, including amputation of limbs. After receiving injuries and wounds, servicemen go through significant psychological and physical disorders, which are difficult for them to deal with alone. Therefore, with the specified category of servicemen, it is necessary to carry out medical, psychological, and physical rehabilitation, as well as to develop programs for their adaptation to civilian life with the mandatory involvement of them in sports events. The Russian-Ukrainian war leads to an increase in the number of servicemen with limited functional capabilities. This indicates the search for optimal ways of their adaptation to the normal conditions of existence and functioning in the social environment and among the collective of their units and parts. Unfortunately, historical analysis shows that our servicemen with limited functional capabilities are in urgent need of social adaptation, especially with the start of hostilities on the territory of Ukraine since 2014, in connection with Russia's armed aggression against Ukraine. That is why various sports federations, public organizations and active public figures in Ukraine hold competitions involving servicemen with limited functional capabilities, namely; "National Invincible Games", "Strong of Ukraine", "Kyiv Unbreakable", "Hero Games", "Veteran Ten", "Invincible Cup" football, "Strong of Ukraine", "I am your hand" and others. According to the results of the performances, the best athletes with disabilities represent our country at international level competitions, such as the Invictus Games.

Exploring the relationship between strategic attention breadth and performance: The role of corporate social responsibility

Abstract The attention-based view contends that executives possess limited attentional capabilities that must be carefully allocated across different strategic issues. Although many scholars contend that narrow strategic attention breadth leads to better performance, others argue that broad strategic attention breadth may be more beneficial due to better opportunity scanning. We posit that the relationship between strategic attention breadth and performance will be inverted U-shaped, where strategic attention breadth is positively related to firm performance up to an optimal point, after which firms will see declining benefits due to executive cognitive overload. Furthermore, we propose that executives’ assessment of strategic opportunities will be influenced by the firm’s corporate social responsibility perspective, as the firm’s environmental and ethical commitment may mitigate executive blind spots and enhance opportunity selection. We support our hypotheses with multiple measures of firm performance and a content analysis of annual reports corresponding to a 5-year longitudinal sample of 2,245 S&P 500 firms.

The impact of government support and organizational culture on sustainable performance: Unveiling the mediating role of circular economy and eco-innovation

Attaining a sustainable competitive edge and exceptional performance stands as the foremost objective for business organizations. Manufacturing industries frequently encounter challenges that hinder their success, primarily due to well recognized factors such as insufficient resources, limited financial capabilities, and a deficiency in managerial skills. Consequently, they frequently seek out sources that are less risky and more convenient to maintain their competitive edge in the market, ultimately leading to limited growth and innovation. Numerous factors have been examined in relation to improving sustainable performance (SP); however, the influence of government support (GS) and organizational culture (OC) in this context has garnered limited focus. This study explores the impact of GS and OC on the SP of manufacturing industries, with the mediating roles of eco-innovation (EI) and the circular economy (CE). Hypotheses were examined through structural equation modeling utilizing Smart PLS4 software, applied to a dataset comprising 200 manufacturing industries in Pakistan. The findings suggest that while GS does not exert a substantial direct effect on SP, its impact is mediated by EI and CE practices. The OC plays a crucial role in improving SP, impacting it in both direct and indirect ways. It is recommended that governmental entities extend both financial and non-financial assistance to the manufacturing sector, which can subsequently enhance economic growth and sustainability. It is recommended that the government implement mechanisms to evaluate adherence to policies and regulations, ensuring that incentives are effectively promoting sustainable practices to enhance organizational sustainable performance. To enhance sustainability results, organizations ought to prioritize developing a culture that encourages innovation and circular practices.

Tumor detection in breast cancer pathology patches using a Multi-scale Multi-head Self-attention Ensemble Network on Whole Slide Images

Breast cancer (BC) is the most common type of cancer among women globally and is one of the leading causes of cancer-related deaths among women. In the diagnosis of BC, histopathological assessment is the gold standard, where automated tumor detection technologies play a pivotal role. Utilizing Convolutional Neural Networks (CNNs) for automated analysis of image patches from Whole Slide Images (WSIs) enhances detection accuracy and alleviates the workload of pathologists. However, CNNs often face limitations in handling pathological patches due to a lack of sufficient contextual information and limited feature generation capabilities. To address this, we propose a novel Multi-scale Multi-head Self-attention Ensemble Network (MMSEN), which integrates a multi-scale feature generation module, a convolutional self-attention module, and an adaptive feature integration with an output module, effectively optimizing the performance of classical CNNs. The design of MMSEN optimizes the capture of key information and the comprehensive integration of features in WSIs pathological patches, significantly enhancing the precision of tumor detection. Validation results from a five-fold cross-validation experiment on the PatchCamelyon (PCam) dataset demonstrate that MMSEN achieves a ROC-AUC of 99.01% ± 0.02%, an F1-score of 98.00% ± 0.08%, a Balanced Accuracy (B-Acc) of 98.00% ± 0.08%, and a Matthews Correlation Coefficient (MCC) of 96.00% ± 0.16% (p<0.05). These results demonstrate the effectiveness and potential of MMSEN in detecting tumors from pathological patches in WSIs for BC.

Optimal planning of SOP in distribution network considering 5G BS collaboration

AbstractThe flexibility of soft open point (SOP) in spatial power regulation enhances the distribution network's (DN) integration of large‐scale renewable energy sources. However, the high cost of SOP and its limited capability for temporal power regulation impede its widespread adoption. Given the rapid expansion of 5G base stations (BSs), utilizing their energy storage to participate in DN planning and operation optimization provides a promising solution. Therefore, this paper proposes an optimal planning method of SOP in DN, considering collaborations with 5G BSs. The objective is to enhance DN’s power regulation in both temporal and spatial dimensions, while minimizing the investment cost of SOP and fully utilizing the unused capacity in base station energy storage (BSES). Firstly, the flexible regulation models of SOP and 5G BS are established, with the real‐time dispatchability of BSES formulated. Then, a bi‐level optimization model is proposed, where the planning layer aims to minimize the total cost, while the operational layer aims to decrease the average voltage deviation. Additionally, an improved Shapley value method based on interactive power is developed for benefit allocation, which enhances the engagement of 5G BSs to participate in DN regulation. The effectiveness of proposed method is validated by simulation results.

FTDZOA: An Efficient and Robust FS Method with Multi-Strategy Assistance.

Feature selection (FS) is a pivotal technique in big data analytics, aimed at mitigating redundant information within datasets and optimizing computational resource utilization. This study introduces an enhanced zebra optimization algorithm (ZOA), termed FTDZOA, for superior feature dimensionality reduction. To address the challenges of ZOA, such as susceptibility to local optimal feature subsets, limited global search capabilities, and sluggish convergence when tackling FS problems, three strategies are integrated into the original ZOA to bolster its FS performance. Firstly, a fractional order search strategy is incorporated to preserve information from the preceding generations, thereby enhancing ZOA's exploitation capabilities. Secondly, a triple mean point guidance strategy is introduced, amalgamating information from the global optimal point, a random point, and the current point to effectively augment ZOA's exploration prowess. Lastly, the exploration capacity of ZOA is further elevated through the introduction of a differential strategy, which integrates information disparities among different individuals. Subsequently, the FTDZOA-based FS method was applied to solve 23 FS problems spanning low, medium, and high dimensions. A comparative analysis with nine advanced FS methods revealed that FTDZOA achieved higher classification accuracy on over 90% of the datasets and secured a winning rate exceeding 83% in terms of execution time. These findings confirm that FTDZOA is a reliable, high-performance, practical, and robust FS method.