Resource-constrained Environments Research Articles

Efficient and accountable anti-leakage attribute-based encryption scheme for cloud storage

To ensure secure and flexible data sharing in cloud storage, attribute-based encryption (ABE) is introduced to meet the requirements of fine-grained access control and secure one-to-many data sharing. However, the computational burden imposed by attribute encryption renders it unsuitable for resource-constrained environments such as the Internet of Things (IoT) and edge computing. Furthermore, the issue of accountability for illegal keys is crucial, as authorized users may actively disclose or sell authorization keys for personal gain, and keys may also passively leak due to management negligence or hacking incidents. Additionally, since all authorization keys are generated by the attribute authorization center, there is a potential risk of unauthorized key forgery. In response to these challenges, this paper proposes an efficient and accountable leakage-resistant scheme based on attribute encryption. The scheme adopts more secure online/offline encryption mechanisms and cloud server-assisted decryption to alleviate the computational burden on resource-constrained devices. For illegal keys, the scheme supports accountability for both users and the authorization center, allowing the revocation of decryption privileges for malicious users. In the case of passively leaked keys, timely key updates and revocation of decryption capabilities for leaked keys are implemented. Finally, the paper provides selective security and accountability proofs for the scheme under standard models. Efficiency analysis and experimental results demonstrate that the proposed scheme enhances encryption/decryption efficiency, and the storage overhead for accountability is also extremely low.

Impact of a Nyakaza Move-for-Health Intervention Programme among Adolescents in a Resource-Constrained South African Community

Adolescents in resource-constrained environments face increasing sedentary lifestyles and obesity rates, necessitating effective interventions for promoting physical activity and combating non-communicable diseases. This study evaluates the impact of a 12 week Nyakaza Move-for-Health intervention on physical activity, anthropometry, cardiorespiratory fitness, and behaviour change among adolescents in KwaZulu-Natal, South Africa. One hundred twenty-eight adolescents participated, with assessments including self-reported physical activity, anthropometric indices, and cardiorespiratory fitness measures. The intervention significantly increased physical activity levels. The treatment group’s mean score was 2.2 (0.4) at baseline and 2.6 (0.4) (F (14, 79) = 4.884, p = <0.001, η2 = 0.156) at the endline. The body mass index decreased (mean = 22.4 (4.6) at baseline and 21.9 (4.2) at endline; p = 0.025, partial eta squared = 0.025, η2 = 0.004). The intervention programme significantly affected the WHR (mean = 0.80 (0.10) at baseline and 0.76 (0.9) at endline; p < 0.001, partial eta squared = 0.327, η2 = 0.100) and the predicted maximal oxygen uptake (VO2 max) for the treatment group (mean = 42.4 (8.7) at baseline and mean = 43.6 (8.7) at endline; p < 0.711, partial eta squared = 0.017, η2 = 0.033). Focus group discussions indicated shifts in knowledge, attitudes, and motivation towards physical activity. Power analysis revealed strong observed power (PA: 0.983, BMI: 0.098, WHR: 0.887), indicating the robustness of the intervention’s effects. These findings underscore the effectiveness of the intervention in improving physical health outcomes. It is recommended that longitudinal studies be conducted to assess the long-term sustainability and impact of such interventions on adolescents’ health outcomes, thereby informing the development of comprehensive public health policies and programmes to promote physical activity and combat non-communicable diseases in similar settings.

Building Geriatric Trauma Programs in Resource-Constrained Environments: Trauma Quality Improvement Program Guideline Implementation at Two Safety Net Hospitals in Southern California.

Background: To improve care of geriatric trauma patients, the American College of Surgeons (ACS) Trauma Quality Improvement Program (TQIP) updated guidelines in 2021. Amid geriatrician shortages in Southern California, 2 Los Angeles County safety net hospitals were tasked with creating a strategy to meet geriatric trauma guidelines despite constrained resources. Methods: All trauma patients ≥ 60years admitted to a safety net hospital in Southern California were enrolled without exclusions (August 2022-April 2023). Primary outcome was frailty screening with documentation to identify older trauma patients at a high risk for adverse outcomes. Results: Needs assessment discovered no standardized process to identify high-risk geriatric patients, no geriatric care guidelines, and no inpatient geriatric consultation service. An action plan composed of a resident-led frailty screen resulted in identification of high-risk patients. Overall, 217 patients met criteria. Ninety-six patients (44%) successfully underwent frailty screening. Frailty screening compliance increased over the study, beginning at 37% capture in the first month and increasing to 81% in the final study month. After achieving nearly uniform frailty screening, a form was developed for the EMR for ease of documentation, data capture/tracking, and compliance monitoring. Discussion: In this study, creativity, collaboration, and resourcefulness allowed TQIP guideline implementation at 2 county hospitals. A systematic process is now in place to identify and triage high-risk geriatric trauma patients based on frailty screen to receive inpatient medicine consultation for medical comorbidity optimization. Continued interdisciplinary and interfacility collaboration will be crucial for continued delivery of the optimal care to older injured patients.

Evaluating Cardiovascular Risks: The Platelet Lymphocyte Ratio and the Neutrophil Lymphocyte Ratio As High-Risk Heart Score Predictors in Non-ST Elevation Myocardial Infarction (NSTEMI) and Unstable Angina Patients.

Introduction Acute coronary syndromes (ACS), encompassing non-ST elevation myocardial infarction (NSTEMI) and unstable angina (UA), present significant challenges in risk assessment and management, particularly in resource-constrained environments like India. The burden of cardiovascular diseases in such regions necessitates cost-effective and readily accessible tools for risk stratification. Previous research has emphasized the role of inflammatory markers in coronary artery disease (CAD), prompting investigations into simple and affordable biomarkers for risk assessment. Platelet lymphocyte ratio (PLR) and neutrophil lymphocyte ratio (NLR) have emerged as potential biomarkers for thrombotic activity in cardiac illnesses, offering simplicity, accessibility, and cost-effectiveness in risk assessmentmaking them particularly valuable in resource-poor settings like India, where advanced diagnostic tools may be limited. Objective This study aims to evaluate the effectiveness of PLR and NLR as predictors of high-risk HEART(history, ECG, age, risk factors, and troponin) scores in patients with NSTEMI and UA. Methods A prospective cross-sectional study was conducted at the Saveetha Medical College and Hospitals in Chennai, India, from March 2021 to September 2022. The study included 288 adults diagnosed with NSTEMIor UA, aged 18 years and above. The inclusion criteria comprised patients with confirmed diagnoses of NSTEMI or UA based on clinical symptoms, electrocardiographic findings, and cardiac biomarker elevation. The exclusion criteria encompassed patients with active infections, acute traumatic injuries, end-stage renal disease, malignant neoplasms, and ST-elevation myocardial infarction (STEMI). In addition to the HEART score, PLR, and NLR were computed to assess the prognosis of patients admitted to the Saveetha Medical College and Hospitals. Results The statistical analysis revealed significant correlations between PLR, NLR, and HEART score risk categories. The Pearson's correlation coefficient indicated strong associations between PLR/NLR values and HEART score risk groups, suggesting their potential as predictive markers for adverse clinical outcomes. Additionally, analysis of variance (ANOVA) demonstrated significant differences in PLR/NLR values across different HEART score risk categories, further highlighting their relevance in risk stratification. The effect sizes for these correlations were moderate to large, indicating clinically meaningful associations between PLR/NLR and cardiovascular risk. Conclusion In cases of NSTEMI and UA, PLR and NLR show potential as simple and inexpensive indicators of high-risk patients. By leveraging these inexpensive biomarkers, healthcare providers can enhance risk assessment and prognostication in patients presenting with ACS, facilitating timely interventions and tailored management strategies.

Using Brownian model to study the effect of temperature on diffusion coefficient

This study employs molecular simulation techniques, particularly the Brownian motion model, to investigate the influence of temperature on diffusion coefficients. Leveraging Einsteins relationship for calculating diffusion coefficients and a one-dimensional random walk model, we systematically explore the impact of simulation direction, particle quantity, and simulation duration on diffusion behavior. The research extends its scope to three-dimensional Brownian motion simulations, offering insights into the stochastic motion of particles in a fluid. The primary objective is to analyze the relationship between temperature and diffusion coefficients. Through rigorous linear regression analysis, a significant and strong linear association is identified, demonstrating that an increase in temperature correlates with an increase in the diffusion coefficient. The research not only contributes to the fundamental understanding of molecular motion but also provides practical recommendations for simulation parameters, especially in resource-constrained computational environments. The study acknowledges certain limitations in the current Brownian motion algorithm and proposes avenues for future research to enhance computational efficiency and precision. This abstract encapsulates the key methodologies, findings, and implications of the research, laying the foundation for a comprehensive exploration of temperature-dependent diffusion coefficients in molecular systems.

Automated Monitoring of Human-Computer Interaction for Assessing Teachers' Digital Competence Based on LMS Data Extraction.

The fast-paced evolution of technology has compelled the digitalization of education, requiring educators to interact with computers and develop digital competencies relevant to the teaching-learning process. This need has prompted various organizations to define frameworks for assessing digital competency emphasizing teachers' interaction with computer technologies in education. Different authors have presented assessment methods for teachers' digital competence based on the video analysis of recorded classes using sensors such as cameras, microphones, or electroencephalograms. The main limitation of these solutions is the large number of resources they require, making it difficult to assess large numbers of teachers in resource-constrained environments. This article proposes the automation of teachers' digital competence evaluation process based on monitoring metrics obtained from teachers' interaction with a Learning Management System (LMS). Based on the Digital Competence Framework for Educators (DigCompEdu), indicators were defined and extracted that allow automatic measurement of a teacher's competency level. A tool was designed and implemented to conduct a successful proof of concept capable of automating the evaluation process of all university faculty, including 987 lecturers from different fields of knowledge. Results obtained allow for drawing conclusions on technological adoption according to the teacher's profile and planning educational actions to improve these competencies.

Lightweight and Error-Tolerant Stereo Matching with a Stochastic Computing Processor

Stereo matching, utilized in diverse fields, poses a challenge to systems in resource-constrained environments due to the significant growth of computational load with image resolution. The challenge is crucial for the systems because fields utilizing stereo matching require short operational time for real-time applications and low power architecture. Stochastic computing (SC) is able to be a valuable approach to address the challenge by reducing the computational load by representing binary numbers with stochastic sequences, which are encoded as a probability value, and by leveraging the concept of mathematical probability. Also, it is possible for a system to be error-tolerant by utilizing the characteristics of stochastic computing. Therefore, in this paper, we propose an approach for lightweight and error-tolerant stereo matching with a hardware-implemented stochastic computing processor. To verify the feasibility and error tolerance of the proposed system, we implemented the proposed system and conducted experiments comparing depth maps with or without stochastic computing by calculating similarities. According to the experimental results, the proposed system indicated no significant differences in output depth maps and achieved an improvement in the depth maps from error-injected input images by an average of 58.95%. Therefore, we demonstrated that stereo matching with stochastic computing is feasible and error-tolerant.

ELIDS: Ensemble Feature Selection for Lightweight IDS against DDoS Attacks in Resource-Constrained IoT Environment

Distributed Denial of Service (DDoS) attacks are increasingly prevalent in today’s digital era, particularly due to the growth of unprotected Internet of Things (IoT) devices. This escalation in attacks necessitates the development of adaptive Intrusion Detection Systems (IDS) that are both efficient in detection and less resource intensive, suitable for resource-constraint IoT devices. Feature Selection (FS) techniques are most popular for developing efficient and lightweight IDS, but their reliance on a single method can be risky due to inherent dataset biasness, necessitating diverse and adaptable FS approaches. This paper proposes a new Ensemble FS approach, termed as Ensemble Feature Selection for Lightweight IDS (ELIDS), by leveraging the strength of seven different filter-based methods. ELIDS not only reduces features but also converges the selection towards the most significant features out of those identified by the individual FS methods. Considering several learning algorithms, robust classification models are built out of ELIDS that are comprehensively evaluated for performance and resumption through both in-domain and cross-domain testing. During in-domain testing, results indicate that classification models built out of ELIDS generally exhibit similar accuracy to those built out of the existing individual FS techniques, where peak accuracy rates of 99.8% are achieved. However, cross-domain testing reveals that classifiers built using individual FS methods fall noticeably in their accuracy whereas the proposed ELIDS show robustness by outperforming existing techniques by large margins. In-fact, ELIDS exhibits a higher accuracy rate by at-least 23.7% over existing solutions while considering Random Forest (RF) as the learning algorithm.

Optimizing network lifetime: ERBS-REE for resilient object detection and tracking in resource-constrained WSN environments

Optimizing network lifetime: ERBS-REE for resilient object detection and tracking in resource-constrained WSN environments

The community of inquiry as a tool for measuring student engagement in blended massive open online courses (MOOCs): a case study of university students in a developing country

While massive open online courses (MOOCs) promise to democratise access to education, the literature reveals a nuanced understanding of engagement in these settings, especially in resource-constrained environments. Blended MOOCs combine MOOCs and physical classroom settings of contents and instructions. This study extends this discourse by focusing on blended MOOCs, which remain under-explored in the context of developing countries. The blended MOOC at the University of Cape Coast (UCC), Ghana, deals with third-party MOOCs as open educational resources (OERs) integrated with campus-based courses. UCC students have been using such blended MOOCs since 2016, when all level 100 students were mandated to enrol in a course entitled Information Technology Skills (ITS101). ITS101 is aligned to courses in a MOOC platform called Alison as an OER. Students' engagement is key to their continued use and satisfaction with online learning, such as MOOCs. However, among all the e-learning modes, students' engagement is the lowest in MOOCs, leading to high dropout rates. Blended MOOCs are one of the techniques recommended to reverse the undesirability of MOOCs, including engagement. However, few studies have been conducted on students' engagement in blended MOOCs, especially among university students in sub-Saharan Africa using MOOCs as OERs. Thus, this paper aims to measure student engagement in blended MOOCs using the revised Community of Inquiry for university students in a developing country. The rationale is to determine whether factors affect engagement positively or negatively. A two-stage cluster sampling technique was used to determine the participants for this study. A list of blended MOOC classes offered at UCC was obtained from the staff's mailing list. In the first stage, academic levels (100, 200, 300 and 800) were randomly selected from the strata using a lottery sampling technique. In the second stage, another simple random selection of blended MOOC courses or classes was used within each selected academic level. All students in the selected classes were then included in the study. Partial Least Squares Structural Equation Modelling was used to validate the model on the predictive relationships existing among the four presences (cognitive, learning, social and teaching) and engagement. Results from the structural model analysis proved a statistically significant predictive relationship among the constructs within the model. Learning presence had the most significant effect on student engagement. Thus, it should be included as one of the presences in the community of inquiry.

Collaborative Localization Strategy Based on Node Selection and Power Allocation in Resource-Constrained Environments

Collaborative Localization Strategy Based on Node Selection and Power Allocation in Resource-Constrained Environments

Stoma-related complications and quality-of-life assessment: A cross-sectional study with patients from Ethiopia and Sweden.

The situation for patients with ostomy can be challenging, probably more in a resource-constrained environment. Our objective was to evaluate quality of life (QoL) (using EQ5D-5L) and stoma-specific QoL (using Stoma QoL) in a high- and low-income setting. In this cross-sectional study from the Tikur Anbessa Specialized Hospital (TASH), Addis Ababa, and South General Hospital (SGH), Stockholm, patients with a permanent or temporary ostomy at TASH (EthioPerm), (EthioTemp), and patients with ostomy at SGH (SweSto) were included in October 2022-January 2023. Patients N=66 were included in groups: EthioPerm N=28, EthioTemp N=17, and SweSto N=21. In EthioTemp, 88% used homemade stoma bags. Although morbidity related to the nipple itself was similar in the groups, the overall score from Stoma QoL was significantly lower in EthioPerm, 48/100 than in SweSto, 74/100. Scores were significantly lower for pouch-related problems and social interactions in Ethiopian patients. In EthioPerm, 71% of the patients worried that they were a burden to the people close to them compared to 14% in SweSto (p<0.001). Leakage was over four times more common in EthioPerm than in SweSto. Mean overall EQ5D-5L score was 18 percentage points lower than the national mean score in EthioPerm and 2 percentage points lower in SweSto. QoL was more affected in the Ethiopian study participants than in the Swedish, even when commercial stoma bags were available. The largest problems were leakage, embarrassment with social interactions, and pouch-related problems. NCT05970458 Clinicaltrials.gov, https://clinicaltrials.gov/study/NCT05970458?locStr=Ethiopia&country=Ethiopia&distance=50&cond=Stoma%20Ileostomy&rank=1.

STAM-LSGRU: a spatiotemporal radar echo extrapolation algorithm with edge computing for short-term forecasting

With the advent of Mobile Edge Computing (MEC), shifting data processing from cloud centers to the network edge presents an advanced computational paradigm for addressing latency-sensitive applications. Specifically, in radar systems, the real-time processing and prediction of radar echo data pose significant challenges in dynamic and resource-constrained environments. MEC, by processing data near its source, not only significantly reduces communication latency and enhances bandwidth utilization but also diminishes the necessity of transmitting large volumes of data to the cloud, which is crucial for improving the timeliness and efficiency of radar data processing. To meet this demand, this paper proposes a model that integrates a spatiotemporal Attention Module (STAM) with a Long Short-Term Memory Gated Recurrent Unit (ST-ConvLSGRU) to enhance the accuracy of radar echo prediction while leveraging the advantages of MEC. STAM, by extending the spatiotemporal receptive field of the prediction units, effectively captures key inter-frame motion information, while optimizations to the convolutional structure and loss function further boost the model’s predictive performance. Experimental results demonstrate that our approach significantly improves the accuracy of short-term weather forecasting in a mobile edge computing environment, showcasing an efficient and practical solution for processing radar echo data under dynamic, resource-limited conditions.

Three-way interaction effect of entrepreneurial orientation, CEO power, and organizational slack on Indonesian firms’ performance

This study aims to explore the complex interplay between entrepreneurial orientation, CEO power, and organizational slack, and their collective impact on firm performance within Indonesia’s manufacturing sector, providing actionable insights to optimize operations in a dynamic, resource-constrained environment. The paper employs a longitudinal approach, utilizing dynamic panel data from 127 publicly-traded manufacturing companies listed on the Indonesia Stock Exchange from 2014 to 2022, focusing on this sector due to its significant role in Indonesia’s economy and the unique challenges these firms face in adapting to market changes and competitive pressures. The study initially found that entrepreneurial orientation did not significantly influence firm performance. However, when CEO power was introduced into the analysis, it significantly, albeit negatively, moderated the effect of entrepreneurial orientation, suggesting that higher CEO power may actually diminish beneficial impacts of entrepreneurial orientation on performance (β = –48.041, p &amp;amp;lt; 0.05). Importantly, the analysis revealed that organizational slack can positively interact with both entrepreneurial orientation and CEO power, mitigating this negative influence and enhancing firm performance (β = 15.261, p &amp;amp;lt; 0.05). These findings illuminate the complex interdependencies within strategic management, underscoring the necessity of aligning upper echelon power dynamics with organizational resources. This alignment is crucial for leveraging the full potential of entrepreneurial orientation to enhance firm performance. AcknowledgmentsThis study received sponsorship from Beasiswa Pendidikan Indonesia and Lembaga Pengelola Dana Pendidikan (Indonesia Endowment Fund for Education). The support from these organizations has been invaluable in the completion of this work, enabling comprehensive data collection and analysis crucial to complete the study.

Lightweight Convolutional Network with Integrated Attention Mechanism for Missing Bolt Detection in Railways

Railway infrastructure safety is a paramount concern, with bolt integrity being a critical component. In the realm of railway maintenance, the detection of missing bolts is a vital task that ensures the stability and safety of tracks. Traditionally, this task has been approached through manual inspections or conventional automated methods, which are often time-consuming, costly, and prone to human error. Addressing these challenges, this paper presents a state-of-the-art solution with the development of a lightweight convolutional neural network (CNN) featuring an integrated attention mechanism. This novel model is engineered to be computationally efficient while maintaining high accuracy, making it particularly suitable for real-time analysis in resource-constrained environments commonly found in railway inspections. The proposed CNN utilises a distinctive architecture that synergises the speed of lightweight networks with the precision of attention-based mechanisms. By integrating an attention mechanism, the network selectively concentrates on regions of interest within the image, effectively enhancing the model’s capability to identify missing bolts with remarkable accuracy. Comprehensive testing showcases a remarkable 96.43% accuracy and an impressive 96 F1-score, substantially outperforming existing deep learning frameworks in the context of missing bolt detection. Key contributions of this research include the model’s innovative attention-integrated approach, which significantly reduces the model complexity without compromising detection performance. Additionally, the model offers scalability and adaptability to various railway settings, proving its efficacy not just in controlled environments but also in diverse real-world scenarios. Extensive experiments, rigorous evaluations, and real-time deployment results collectively underscore the transformative potential of the presented CNN model in advancing the domain of railway safety maintenance.

Age-of-Information based Multi-UAV Trajectories Using Deep Reinforcement Learning

The use of unmanned aerial vehicles (UAVs) as a unified platform for sensing and communication is especially relevant in environments with inadequate infrastructure. In this paper, a multi-UAV system is constructed for dynamic data collection in a resource-constrained environment. The devised approach involves the implementation of an access platform referred to as the Access UAV (A_UAV). This A_UAV orchestrates the data collection process from Inspection-UAVs (I_UAVs), each equipped with a visual sensor, facilitating the relay of collected data to the cloud. Our approach jointly considers the trajectory scheduling of A_UAV and I_UAV to collect data from specific points in a decentralized manner. Specifically, a Deep Reinforcement Learning framework utilizing an actor-critic network is formulated for A_UAV, aiming to generate an equitable access schedule for I_UAVs. Moreover, trajectory scheduling of A_UAV ensures dynamic data collection while minimizing total system energy and the Age of Information (AoI) of data arriving from I_UAVs. The simulation results validate the performance of our proposed approach against several baselines under different parameter settings.

A mobile edge computing-focused transferable sensitive data identification method based on product quantization

Sensitive data identification represents the initial and crucial step in safeguarding sensitive information. With the ongoing evolution of the industrial internet, including its interconnectivity across various sectors like the electric power industry, the potential for sensitive data to traverse different domains increases, thereby altering the composition of sensitive data. Consequently, traditional approaches reliant on sensitive vocabularies struggle to adequately address the challenges posed by identifying sensitive data in the era of information abundance. Drawing inspiration from advancements in natural language processing within the realm of deep learning, we propose a transferable Sensitive Data Identification method based on Product Quantization, named PQ-SDI. This innovative approach harnesses both the composition and contextual cues within textual data to accurately pinpoint sensitive information within the context of Mobile Edge Computing (MEC). Notably, PQ-SDI exhibits proficiency not only within a singular domain but also demonstrates adaptability to new domains following training on heterogeneous datasets. Moreover, the method autonomously identifies sensitive data throughout the entire process, eliminating the necessity for human upkeep of sensitive vocabularies. Extensive experimentation with the PQ-SDI model across four real-world datasets, resulting in performance improvements ranging from 2% to 5% over the baseline model and achieves an accuracy of up to 94.41%. In cross-domain trials, PQ-SDI achieved comparable accuracy to training and identification within the same domain. Furthermore, our experiments showcased the product quantization technique significantly reduces the parameter size by tens of times for the subsequent sensitive data identification phase, particularly beneficial for resource-constrained environments characteristic of MEC scenarios. This inherent advantage not only bolsters sensitive data protection but also mitigates the risk of data leakage during transmission, thus enhancing overall security measures in MEC environments.

Budgetary fund management in resource-constrained environments: financing strategies

In the process of European integration, it is necessary to implement economic reforms to ensure effective management of state finances. Changes in budgetary relations and the allocation of budget monetary resources are observed in connection with the process of decentralisation and transformation processes in the budgetary system. The aim of this article is to establish the significance of financing budget managers, even under resource constraints. The study focuses on the process of financing budget managers. The research methods employed include analysis, systemic-structural, comparative-legal, and dialectical methods. The results indicate that the current state and societal development necessitate the establishment of a strong and effective state economy. The financing of budget managers under resource constraints is a pertinent issue. Recent events in the country highlight that local budgets encounter various problems and inconsistencies, mainly due to limited financial resources, unclear expenditure powers between different levels of the budgetary system, inefficient spending models, and low investment capacity. Planning and executing local budgets' expenditure is a crucial aspect of budget policy that determines the effectiveness of local self-government bodies in fulfilling their functions and powers. Financial constraints of the state are especially significant in relation to the competence level of participants in the budget process, the ability to allocate budget funds rationally and in line with the priorities of budget policy, and the need to achieve maximum effect with minimal costs for socio-economic development. Proper use of local budgets requires evaluating financial procedures, monitoring the fulfilment of budgetary powers, assessing expected results, and accurately defining needs, among other factors. To address financial constraints, it is necessary to review and adjust budget expenditures and regulate the powers of local authorities regarding expenditure. Increasing spending by local budgets to complete their own responsibilities will enhance the investment aspect of local self-government budgets.

Task Scheduling in a Cloud Environment: A comparative Study

In this study, we explore the application of Monarch Butterfly Optimization (MBO) algorithms for task scheduling in cloud computing, comparing its performance against widely used optimization techniques, namely Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO).Task scheduling in the cloud is a critical aspect influencing resource utilization, turnaround time, and overall system efficiency. MBO, known for its effective exploration- exploitation balance, is examined for its suitability in addressing the complexities of cloud computing environments. The study investigates MBO's advantages, such as enhanced adaptability to dynamic conditions, effective handling of multi-objective optimization, and its consideration of bandwidth as a critical resource. Comparative analyses with ACO and PSO highlight MBO's superior performance in achieving near-optimal task schedules, emphasizing its potential to offer innovative solutions to the challenges posed by task scheduling in dynamic and resource-constrained cloud environments. This research contributes valuable insights into the strengths of MBO, paving the way for advancements in optimization methodologies tailored for cloud computing systems.

Developing classroom ICT teaching techniques, principles and practice for teachers in rural Ghana without access to computers or internet: a framework based on literature review

PurposeThis paper aims to propose a conceptual framework for integrating information and communication technologies (ICT) into teaching practices for educators in rural Ghana lacking computer and internet access. Bridging the rural–urban digital divide is critical for equitable quality education. This research identifies distinct infrastructure, financial, human, and socio-cultural challenges of ICT adoption in underserved Ghanaian schools through an exhaustive literature review. The framework provides tailored strategies to empower teachers to leverage ICT for innovative pedagogy despite limitations. Ultimately, the goal is to offer pragmatic, evidence-based solutions to advance ICT-enabled instruction and digital equity in marginalized settings.Design/methodology/approachThis study utilizes a qualitative literature review methodology to develop a conceptual framework for ICT teaching techniques tailored to the rural Ghanaian context. Published empirical studies, theoretical papers, and reports focused on ICT education in developing countries, particularly sub-Saharan Africa, were systematically reviewed across multidisciplinary databases. Relevant theories and models, like TPACK and SAMR, were analyzed to determine appropriate modifications for the framework design. The proposed strategies emphasize affordable technologies, teacher training, localized content, and culturally relevant pedagogies selected based on evidence from literature to address the unique barriers and needs of the rural setting.FindingsThe literature review revealed profound infrastructure, financial, human, and socio-cultural barriers to ICT adoption in rural Ghanaian schools. These include lack of electricity, internet, hardware, and software alongside inadequate teacher training and support. The proposed conceptual framework offers solutions including leveraging low-cost technologies like mobile devices, prioritizing teacher professional development, developing localized digitally-enabled resources, and fostering community participation. Culturally relevant pedagogies are also emphasized. This tailored, evidence-based approach holds promise for enhancing ICT-enabled instruction and bridging digital divides to promote equitable quality education. Further findings will emerge through framework implementation and evaluation.Research limitations/implicationsThis conceptual framework requires implementation and evaluation to truly determine its impact and effectiveness in the intended rural Ghanaian context. As a literature review, primary data collection was not undertaken. Practical application of the framework in schools can yield direct insights through surveys, interviews, observations, and analysis of key educational outcomes pre and post-implementation. Comparisons with control groups may further illuminate its efficacy. Monitoring and adaptation over time is also essential. Ultimately, this research sets the foundation for praxis-oriented work transforming ICT integration in marginalized communities to advance digital equity and inclusion.Practical implicationsThis research provides an evidence-based, context-aware framework with pragmatic solutions to enhance ICT-enabled teaching and learning in underserved rural Ghanaian schools. It offers guidance to teachers, administrators, and policymakers on integrating technology despite infrastructure and resource constraints. The emphasis on cost-effective innovations, educator training, localized content, and community participation can inform planning and investments in ICT education. Broader applications include adapting the framework for other developing country contexts facing comparable challenges. Overall, this work promotes digital equity and quality education access, catalyzing social development and aligning with the UN Sustainable Development Goals.Social implicationsThis research highlights the urgent need to address digital divides exacerbating educational inequalities between rural and urban communities in Ghana. Lack of technology access and utilization in rural schools threatens to widen gaps in skills, capabilities, and opportunities for students. Strategic integration of ICT can promote social inclusion, cultivate digital literacy, and equip youth to participate in the global digital economy, thereby enhancing prospects. More broadly, advancing digital equity and ICT-enabled education empowers marginalized groups, fosters community empowerment, and drives progressive social change. This work underscores that localized, context-appropriate solutions are pivotal for promoting development and social justice.Originality/valueThis research fills a crucial gap by proposing a conceptual framework tailored specifically for integrating ICT in teaching practices in rural Ghanaian schools lacking digital access, responding to context-specific barriers and needs. While extensive literature examines ICT education models globally, few studies offer localized strategies for resource-constrained environments. This framework adapts established models based on a systematic literature review, emphasizing affordability, capacity building, and cultural relevance. By synthesizing evidence-based, pragmatic solutions to advance ICT-enabled instruction in marginalized settings, this work provides a valuable foundation for praxis transforming rural education. The localization approach could inform frameworks for other developing regions.