Distinct Problems Research Articles

Computing the Set of RBF-FD Weights Within the Integrals of a Kernel-Based Function and Its Applications

This paper offers an approach to computing Radial Basis Function–Finite Difference (RBF-FD) weights by integrating a kernel-based function. We derive new weight sets that effectively approximate both the first and second differentiations of a function, demonstrating their utility in interpolation and the resolution of Partial Differential Equations (PDEs). Particularly, the paper evaluates the theoretical weights in interpolation tasks, highlighting the observed numerical orders, and further applies these weights to solve two distinct time-dependent PDE problems.

Assessment of the efficiency of distinct surface treatments to mitigate ASR-induced development

Over the years, various coating materials have been used to mitigate/rehabilitate concrete once alkali-silica reaction (ASR) takes place. Although promising results are demonstrated, their efficiency is compromised by the progression of ASR and crack formation. Recently, new coatings with higher penetrability and enhanced self-healing properties have shown good performance against distinct durability problems in concrete, yet their behaviour under ASR development is unknown. This research appraises the ability of hydrophilic self-healing coating (CA) mixtures to mitigate concrete deterioration caused by ASR in its initial, moderate and advanced phases. Their efficiency is multi-level assessed, and comparisons with other systems (e.g., silane/siloxane, rigid-coating and lithium-based) are also performed. Results indicate that the surface treatments with CA changed ASR kinetics while not altering the ASR mechanism of deterioration. Finally, qualitative charts are provided to help select different types of surface treatments and the most appropriate “timing” for their application.

Towards Unified Robustness Against Both Backdoor and Adversarial Attacks.

Deep Neural Networks (DNNs) are known to be vulnerable to both backdoor and adversarial attacks. In the literature, these two types of attacks are commonly treated as distinct robustness problems and solved separately, since they belong to training-time and inference-time attacks respectively. However, this paper revealed that there is an intriguing connection between them: (1) planting a backdoor into a model will significantly affect the model's adversarial examples and (2) for an infected model, its adversarial examples have similar features as the triggered images. Based on these observations, a novel Progressive Unified Defense (PUD) algorithm is proposed to defend against backdoor and adversarial attacks simultaneously. Specifically, our PUD has a progressive model purification scheme to jointly erase backdoors and enhance the model's adversarial robustness. At the early stage, the adversarial examples of infected models are utilized to erase backdoors. With the backdoor gradually erased, our model purification can naturally turn into a stage to boost the model's robustness against adversarial attacks. Besides, our PUD algorithm can effectively identify poisoned images, which allows the initial extra dataset not to be completely clean. Extensive experimental results show that, our discovered connection between backdoor and adversarial attacks is ubiquitous, no matter what type of backdoor attack. The proposed PUD outperforms the state-of-the-art backdoor defense, including the model repairing-based and data filtering-based methods. Besides, it also has the ability to compete with the most advanced adversarial defense methods. The code is available here.

The role of entrepreneurial leadership in influencing the performance of SMMEs in the Vhembe district Municipality

SMMEs in the Vhembe district encounter distinct problems, such as restricted access to financing, inadequate infrastructure, and insufficient support services. This study examines the impact of entrepreneurial leadership traits, including innovation, risk-taking, and proactive decision-making, on corporate outcomes such as growth, competitiveness, and sustainability. The study examined the correlation between leadership types and business success to provide actionable recommendations for governments and business leaders in promoting entrepreneurial leadership. The research utilised a qualitative methodology to investigate the influence of entrepreneurial leadership on small, medium, and micro enterprises (SMMEs) in the Vhembe district. Data were collected via semi-structured interviews with 16 leaders of small, medium, and micro enterprises (SMMEs), offering a varied perspective across multiple industries and business scales. The findings indicate that entrepreneurial leadership substantially influences SMMEs in the Vhembe District, with transformational and collaborative approaches improving financial and non-financial performance, innovation, and customer satisfaction. The study indicates that implementing transformational and collaborative leadership styles can markedly enhance financial performance, innovation, and customer happiness for SMMEs in the Vhembe District. This research provides novel perspectives on entrepreneurial leadership by examining the distinct setting of the Vhembe District. It contests prevailing ideas by emphasising the necessity for a leadership strategy adapted to local dynamics and market conditions. The study's novelty and significance stem from its distinctive emphasis on the Vhembe District, providing a novel viewpoint on entrepreneurial leadership.

Digital transformation strategies in the financial services sector

The main objective of this research is to conduct a thorough analysis of the digital transformation tactics employed in the specific subsector of the financial services industry that has been selected for study. The objective of this study is to provide a comprehensive analysis of the distinct problems, opportunities, and repercussions that are linked to the process of digital transformation within this particular subsector. Through this endeavor, this research provides a valuable contribution to the existing body of knowledge in this particular domain, thereby enabling industry leaders, policymakers, and other relevant stakeholders to make more well-informed decisions., a mixed-methods research design was used in this study. This technique integrates qualitative and quantitative methodologies to offer a holistic comprehension of digital transformation initiatives within a specific subsector of the financial services industry. The results of this research underscore the significance of digital transformation initiatives in improving consumer experiences, meeting regulatory requirements, and capitalizing on technical progress. The obstacles that have been identified, such as the need to comply with regulations and address cybersecurity issues, highlight the intricate environment that financial institutions must navigate. Furthermore, the acknowledgment of customer-centric approaches and the significance of data analytics and personalization validate the industry's ability to adjust to client preferences and competitive dynamics.

Democracy and Climate Change for Realists: An Integrated Empirical and Normative Assessment

Abstract When scholars consider the relation between domestic political regimes and global climate change, they typically rely on assumptions about liberal democracy's core competencies with respect to environmental protection in general. As a result, an ostensibly normative debate pitting democracy against “eco-authoritarianism” depends for its resolution on causal theory and empirical analysis. I offer an integrated assessment of both empirical (causal) and normative (ethical) theories on democracy, autocracy, and climate change, concluding that democracy's superiority is less compellingly supported than regime-neutrality. Theoretically, the causal logic of environmentally protective democracy cannot readily be extended to climate change as a distinctive political problem. Empirically, my review of global statistical analyses of regime type and climate change reveals that the evidence is more consistent with regime-neutrality than democratic advantage. Regime-neutrality, in turn, revitalizes difficult normative arguments about trade-offs between democratic freedom and climatic stability. Something resembling the “win-win” situation of climate-fixing democracy may still be salvaged, theoretically. But political science's search for this outcome should take a realist turn: abandoning the liberal-authoritarian dichotomy in favor of a new one, pitting post-liberal forms of democracy against climate-fixing hybrid regimes.

A novel binomial-based fuzzy type-2 approach for topology and size optimization of skeletal structures

The current work introduces a new probability-based fuzzy type-2 decision mechanism to adjust the optimization process during the simultaneous size and topology optimization of the Skeletal structural systems. For the probabilistic part a binomial module is developed that feeds the fuzzy mechanism by forecasting success probability for future topological actions. The proposed fuzzy decision mechanism permanently monitors the optimization process and attends to dynamically tune the balance between size and topology actions. The presented strategy, by reducing the number of ineffective iterations, significantly enhances the efficiency of the optimization process. Since the proposed decision mechanism is designed as an auxiliary separate module it can be integrated with different optimization methods. Accordingly, in this study, it is integrated with four different optimization algorithms and applied to solve distinct size and topology problems. To comprehensively evaluate the effect of the proposed strategy a new performance index is defined and employed. The acquired results demonstrate that the proposed decision mechanism considerably enhances the search performance of the algorithms on handling the structural size and topology optimization problems.

An enhanced slime mould algorithm with triple strategy for engineering design optimization

Abstract This paper introduces an enhanced slime mould algorithm (EESMA) designed to address critical challenges in engineering design optimization. The EESMA integrates three novel strategies: the Laplace logistic sine map technique, the adaptive t-distribution elite mutation mechanism, and the ranking-based dynamic learning strategy. These enhancements collectively improve the algorithm’s search efficiency, mitigate convergence to local optima, and bolster robustness in complex optimization tasks. The proposed EESMA demonstrates significant advantages over many conventional optimization algorithms and performs on par with, or even surpasses, several advanced algorithms in benchmark tests. Its superior performance is validated through extensive evaluations on diverse test sets, including IEEE CEC2014, IEEE CEC2020, and IEEE CEC2022, and its successful application in six distinct engineering problems. Notably, EESMA excels in solving economic load dispatch problems, highlighting its capability to tackle challenging optimization scenarios. The results affirm that EESMA is a competitive and effective tool for addressing complex optimization issues, showcasing its potential for widespread application in engineering and beyond.

Mathematical skills classification through primary education

By the end of primary school, children are expected to acquire a range of mathematical skills that progressively develop. This study aimed to gain insight into how a large number of numerical and geometrical measures are grouped and whether the structures shift or remain invariant along child’s development based on the data obtained from a sample of 1346 s to sixth grade children. On the basis of correlation analyses and exploratory factor analyses, we came up with an invariant four-factor structure for each grade. The four factors obtained were the following: (a) early and retrieval skills (subitizing, enumeration, number facts) (b) transcoding and ordinality skills, (c) numerical advanced skills and (d) visual-spatial advanced skills. Confirmatory factor analyses showed that the four-factor model fits well to the data (RMSEA < 0.06, CFI > 0.95, GFI > 0.93, SRMR < 0.05) at all grades. The associations between the mathematical measures were captured in each factor and the revealed invariant structure is discussed and compared with previous classification models of mathematical skills. Given that the difficulties in learning mathematics is currently being viewed as a continuum of academic abilities instead of a distinct problem, the underlying invariant four-factor structure can facilitate experts and educators to better understand how a broad area of mathematical skills are related across the primary education, in order to carry our comprehensive assessment of both the mathematical strengths and weaknesses of their students and to apply the appropriate customized teaching strategies.

A Multi-Feature Fusion Approach for Dialect Identification using 1D CNN

The phonological variety of Kurdish, a language with several dialects, poses a distinct problem in automatically identifying dialects. This study examines and evaluates several sound criteria for identifying Kurdish dialects: Badini, Hawrami, and Sorani. We deployed a dataset including 6,000 samples and utilized a mix of 1D convolutional neural networks (CNN) and fully connected layers to conduct the identification job. Our study aimed to assess the efficacy of different sound characteristics in accurately identifying dialects. We employed the Mel-frequency Cepstral Coefficients (MFCC) and other features such as the Mel spectrogram, spectral contrast, and polynomial features to extract the sound characteristics. We conducted training and testing of our models utilizing both individual characteristics and a composite of all features. Our analysis revealed that the identification task achieved excellent accuracy rates, suggesting a promising potential for success. We achieved 95.75% accuracy using MFCC combined with a Mel spectrogram. The accuracy improved by including contrast in the MFCC feature extraction process to 91.42%. Similarly, using poly_features resulted in an accuracy of 90.83%. Remarkably, accuracy reached a maximum of 96.5% when all the attributes were combined.

Spurious Accelerations in Finite Element Analysis of Geotechnical Problems: Cause and Remedy

Present study investigates the response of the two distinct geotechnical problems under earthquake loading. These two problems are: 1) nailed soil slope and 2) 1-D site response analysis. The aim of the study is to understand the cause of spurious accelerations and suggest a remedy. Two distinct interface conditions were considered in modelling the nailed soil slope. In first, the soil nail interface was considered to be perfectly bonded. In second case, the sliding at the interface was allowed. The analysis resulted into the physically acceptable deformations. However, the dumbbell shaped spurious acceleration pattern was observed in perfect bonding case. Further, the maximum acceleration was found to span from 4 g to 13 g. Such extremely high accelerations are unphysical and hence, unacceptable. In case of 1-D site response analysis, misleading acceleration spike of magnitude twice that of true acceleration was noted. The introduction of the numerical damping successfully removed the spurious accelerations and resulted in physically acceptable response for both problems.

ICT Awareness Among the Higher Secondary Students, A Study

In the ongoing advancing digital era, emergence of Information and Communication Technology (ICT) has instinctively refashioned the strategy of all demeanors of pursuits within business, society, governance and in education beyond expectance. Panoramic advancement depends heavily on qualified personnel which is doable through christian upbringing. It has progressively revamped education from traditional to noble and fervent teaching methodology, learning processes, scientific research and archiving information. This empirical study has made an attempt to assess ICT awareness among the selected higher secondary level students in the district of Purulia of West Bengal state, a region portrayed by its distinct societal problems and more, to explore the eventuality for implicit growth. By employing a structured survey methodology, data have been obtained from a representative sample of students over varied schools in the district. Findings are expected to provide crucial visions into the current state of ICT literacy among these students and underscore the need for tailored approach to traverse digital gap. This study will further the genesis of strategies quested after boosting ICT education, safeguarding equal opportunities to digital resources, and nurturing a more in-depth learning surround in Purulia.

Interplay Between Nucleation and Kinetics in Dynamic Twinning

Abstract In this work, we apply a phase-field modeling framework to elucidate the interplay between nucleation and kinetics in the dynamic evolution of twinning interfaces. The key feature of this phase-field approach is the ability to transparently and explicitly specify nucleation and kinetic behavior in the model, in contrast to other regularized interface models. We use this to study two distinct problems where it is essential to explicitly specify the kinetic and nucleation behavior governing twin evolution. First, we study twinning interfaces in 2D. When these interfaces are driven to move, we find that significant levels of twin nucleation occur ahead of the moving interface. Essentially, the finite interface velocity and the relaxation time of the stresses ahead of the interface allow for nucleation to occur before the interface is able to propagate to that point. Second, we study the growth of needle twins in antiplane elasticity. We show that both nucleation and anisotropic kinetics are essential to obtain predictions of needle twins. While standard regularized interface approaches do not permit the transparent specification of anisotropic kinetics, this is readily possible with the phase-field approach that we have used here.

Comparative study of optimization methods for building energy consumption and daylighting performance

When striving for sustainable development, designers encounter the formidable task of integrating the advantages of daylighting into energy-efficient designs. The process of investigating a multitude of design variables can be arduous and time-intensive. However, computational simulations provide indispensable insights into forecasting performance. A considerable number of these design challenges do not have explicit mathematical solutions; thus, black-box optimization techniques are favored for the purpose of determining the most effective design solutions. Popular meta-heuristic optimization algorithms, including the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), Sine Cosine Algorithm (SCA), Grey Wolf Optimizer (GWO), Ant Lion Optimizer (ALO), and Multi-Verse Optimizer (MVO), are described and thoroughly analyzed in this article. In addition, the present study run empirical studies to compare these optimization algorithms by applying them to tackle five distinct daylighting and energy optimization problems associated with enhancing building performance. In the test cases, the present study results demonstrate that the PSO and MVO algorithms exhibit better performance compared to the GA and WOA algorithms. The selection of the most suitable algorithm is contingent upon the distinctive attributes of the problem, and the intricacy of the optimization landscape.

THE ROLE OF AI IN HEALTHCARE INDUSTRY

This article analyses the present condition of technological applications based on artificial intelligence (AI) and their influence on the healthcare sector. This work conducted a comprehensive literature research and examined certain real-world instances of AI implementations in the healthcare sector. Undoubtedly, the fast progress of artificial intelligence (AI) and associated technologies will enable healthcare providers to provide fresh value for their patients and enhance the effectiveness of their internal operations. However, successful deployment of AI will always pose distinct problems and the adoption of specific approaches to revolutionize the whole care service and operations in order to fully utilize the advantages of future technology. The analysis is derived on an examination of several academic sources, encompassing research from ScienceDirect, MDPI, Elsevier, and the Journal of Consortium. These sources address studies and data published from recent years till 2024.

Agile Project Planning and Execution in Large-Scale IT Projects

Agile methods have changed project management, particularly for major IT projects. This abstract examines Agile project planning and execution in complex IT settings, where size and scope bring distinct problems. Traditional project management methods struggle to handle major IT projects' fast changes and complexity. Agile's focus on flexibility, collaboration, and customer-centricity makes it ideal for these situations due to its adaptability. Agile planning breaks complicated IT project needs into manageable components or iterations in large-scale initiatives. Iterative development keeps projects manageable and adaptable. Agile stresses frequent feedback loops, where each iteration is examined, to help teams change and realign their objectives. Large IT projects need ongoing feedback since mistakes may be costly and team alignment is essential. Cross-functional teams are crucial to Agile in big projects. Agile fosters cross-disciplinary cooperation, which is crucial in big IT environments where development, operations, security, and business teams must collaborate. Teams meet regularly, called'scrums,' to review progress, identify barriers, and plan future actions. With its focus on openness and responsibility, the scrum structure keeps team members aligned and the project on pace. Resource management is another important part of Agile project planning for big IT projects. Agile resource allocation is dynamic, unlike set schedules. Large IT projects, where resource demands vary fast, need this flexibility. Thus, agile techniques reduce waste and ensure teams have what they need at each project stage by optimizing resource utilization. Large Agile project execution involves risk management. Due to Agile's iterative structure, teams may regularly analyze risk and address problems before they become significant. Risks may be exacerbated in big IT projects, thus proactive risk management is crucial. Agile teams see risks as opportunities to improve, promoting continual learning and adaptability.

CMA-ES-based topology optimization accelerated by spectral level-set-boundary modeling

Topology optimization commonly encounters several challenges, such as ill-posedness, grayscale issues, interdependencies among design variables, multimodality, and the curse of dimensionality. Furthermore, addressing the latter two concurrently presents considerable difficulty. In this study, we introduce a framework aimed at mitigating all the above obstacles simultaneously. The objective is to achieve optimal configurations in a notably reduced timeframe eliminating the need for the initial trial-and-error iterations. The topology optimization approach we propose is implemented via precise structural boundary modeling utilizing a body-fitted mesh generated using a Fourier series expanded level-set method. This methodology expedites the exploration of optimal solutions. We employ the covariance matrix adaptation-evolution strategy to address multimodality, thereby enhancing the optimization process. The implementation of the Fourier-series-expanded level-set method reduces the number of design variables while maintaining accuracy in finite-element analyses by replacing design variables from discretized level-set functions with the coefficients of the Fourier series expansion. To facilitate the exploration of optimal solutions, a method is also introduced for handling box constraints through an adaptive penalty function. To demonstrate the effectiveness of the proposed scheme, we address three distinct problems: mean compliance minimization, heat flux manipulation, and the control of electromagnetic wave scattering. Despite each system being governed by different equations, topology optimization method consistently yields notable acceleration in computational efficiency across all scenarios, and remarkably without requiring initial guesses.

Supermarket-chain grocery delivery optimization through crowdshipping

Acknowledging the rising significance of online sales, the grocery business has embraced the challenge of fulfilling the consequently growing consumer expectations for the last-mile delivery efficiency. This paper investigates the grocery delivery optimisation for the supermarket chain based on the crowdshipping mechanism, which can be one of the viable strategies for establishing prompt and affordable delivery service for customers. Considering the deterministic optimisation setting, this study presents a characteristic routing model with crowdsourced couriers named supermarket-chain grocery delivery crowdshipping problem (SCGDCP), which is a variant of the pickup-and-delivery problem, and develops a corresponding mixed integer linear programming (MILP) model. The SCGDCP involves distinctive problem features including individual depots for couriers, multi-trip open routing, and dual time windows of courier operating and order arrival, which pose the computational challenge in problem solving. A bespoke solution procedure based on adaptive variable neighbourhood search (AVNS) strategy is thus designed for tackling the practical-size SCGDCP. The conducted numerical experiments demonstrate the computational efficiency of the proposed MILP model for the small-size instances with no more than 30 grocery orders and the superiority of the developed AVNS procedure for the Grubhub sampling test instances with up to 200 orders.

Analyzing antimicrobial resistance as a series of collective action problems

AbstractAntimicrobial resistance (AMR) causes over 1.27 million deaths annually, making it one of today's most urgent health threats. Given its urgency, there are often calls for large‐scale global initiatives to address AMR. However, theories of collective action have yet to be applied to the problem in a systematic and holistic manner. Fuller engagement with collective action theory is necessary to avoid three risks, namely: mischaracterizing the kinds of challenges that AMR presents; over‐simplifying the problem by reducing it to a single type of collective action problem while ignoring others; and overstating the ability of collective action theory to formulate effective solutions. This article relies on the work of Elinor Ostrom to develop an analytical framework for collective action problems around public and common goods. When analyzed through this framework, we find that AMR poses at least nine distinct collective action problems. This more granular framing of AMR provides, in our view, a better basis to develop policy solutions to address this multifaceted challenge. We conclude with proposals for future research.

Assessing Opportunities and Barriers to Improving the Secondary Use of Health Care Data at the National Level: Multicase Study in the Kingdom of Saudi Arabia and Estonia.

Digitization shall improve the secondary use of health care data. The Government of the Kingdom of Saudi Arabia ordered a project to compile the National Master Plan for Health Data Analytics, while the Government of Estonia ordered a project to compile the Person-Centered Integrated Hospital Master Plan. This study aims to map these 2 distinct projects' problems, approaches, and outcomes to find the matching elements for reuse in similar cases. We assessed both health care systems' abilities for secondary use of health data by exploratory case studies with purposive sampling and data collection via semistructured interviews and documentation review. The collected content was analyzed qualitatively and coded according to a predefined framework. The analytical framework consisted of data purpose, flow, and sharing. The Estonian project used the Health Information Sharing Maturity Model from the Mitre Corporation as an additional analytical framework. The data collection and analysis in the Kingdom of Saudi Arabia took place in 2019 and covered health care facilities, public health institutions, and health care policy. The project in Estonia collected its inputs in 2020 and covered health care facilities, patient engagement, public health institutions, health care financing, health care policy, and health technology innovations. In both cases, the assessments resulted in a set of recommendations focusing on the governance of health care data. In the Kingdom of Saudi Arabia, the health care system consists of multiple isolated sectors, and there is a need for an overarching body coordinating data sets, indicators, and reports at the national level. The National Master Plan of Health Data Analytics proposed a set of organizational agreements for proper stewardship. Despite Estonia's national Digital Health Platform, the requirements remain uncoordinated between various data consumers. We recommended reconfiguring the stewardship of the national health data to include multipurpose data use into the scope of interoperability standardization. Proper data governance is the key to improving the secondary use of health data at the national level. The data flows from data providers to data consumers shall be coordinated by overarching stewardship structures and supported by interoperable data custodians.