Budget Constraints Research Articles

Exploring educational trends and challenges in the MENA region amidst a global crisis: An empirical analysis of the pandemic’s impact on SDG4

The paper presents a cross-country analysis of seven countries in the MENA region and explores the effects of COVID-19 on educational policymaking. The paper uses a quasi-thematic literature review to highlight the emerging themes related to the quality of education during the pandemic. The paper investigates the educational trends in seven MENA region countries and provides descriptive analysis to understand if there were changes from 2016 to 2020. In order to investigate these trends, the study uses the conceptualization of Sustainable Development Goals (SDG 4) “Quality of education.” The analysis is based on the data from the World Bank Development indicators. The paper’s evidence identifies a difference in the level of government expenditure on education within the MENA region. Pre and during COVID-19 provides crucial insights such as all countries increased their government expenditure on education except Egypt and Bahrain. The paper further addresses the contributing factors to the educational gaps in the region, such as digital exclusion, inequality (income and gender), and the government’s budget constraints during the pandemic. This study presents recommendations to bridge these identified gaps in the post-pandemic era.

Cross-sectoral role and community empowerment in controlling schistosomiasis in Indonesia

The role of cross-sectors in synergy with community empowerment in controlling schistosomiasis is a very effective model. Until mid-2017, the incidence rate of disease in humans in 28 endemic villages still ranged 0.00-2.15%. This research aims to evaluate schistosomiasis control activities that have been carried out across sectors based on a road map for schistosomiasis eradication and community empowerment in controlling schistosomiasis. The study was mixed method. Data were obtained by in-depth interview with key informants and evaluation of the results of cross-sectoral and community activities. The research was carried out in an endemic area for schistosomiasis on the Bada Plateau, Indonesia, from January to November 2019. In conclusion, the potential of cross-sector collaboration in controlling schistosomiasis has not been fully realized due to budget constraints. Community empowerment is carried out to eliminate snail foci, increase the scope of feces collection and change community behavior for the better in order to avoid infection and always try to clean up snail foci.

Performance analysis of dual material control gate cavity on source electrically doped TFET biosensor for biomedical applications

The fabrication complexity, low sensitivity, detection speed, and costs associated with nanoscale devices have been significant concerns in the development of label-free biosensors. To address these issues, we report a novel dual material control gate cavity on source electrically doped tunnel field-effect transistor (DM-CG-CS-ED-TFET) for label-free biosensors. In this regard, the n+ drain and p+ source regions within the proposed device are induced by applying polarity gate (PG) bias voltages of PG-1 = +1.2 V and PG-2 = −1.2 V, respectively, across the respective polarity gate electrodes. This approach not only overcomes doping control issues but also avoids thermal budget constraints and minimizes fabrication complexity when compared to conventional TFET. For biomolecule sensing in the device, a nanogap cavity is created within the gate dielectric by selectively etching a portion of the polarity gate dielectric layer towards the source side. The performance of the proposed biosensor device is evaluated based on the variations in carrier concentration profile, energy band diagram, electric field, transfer (IDS - VGS) characteristics, drain current (IDS) sensitivity, ON-state current (ION) sensitivity, switching ratio (ION/IOFF) and subthreshold swing (SS) sensitivity. The sensitivity of the device is also investigated based on nano-cavity dimensions, practical challenges such as various filling factors, and the step profile generated from the steric hindrance. Moreover, the effect of temperature on sensitivity has also been investigated. For this, various biomolecules including Streptavidin (k = 2.1), APTES (k = 3.57), ferrocytochrome c (k = 4.7), keratin (k = 8) and Gelatin (k = 12), have been investigated for their performance using Silvaco ATLAS device simulator. The simulation results demonstrate that the proposed biosensor is a viable option for biosensing applications in biomedical engineering.

Increasing Challenges and Shrinking Roles of Environmental Journalists in Nepal

ABSTRACT This study investigates the perceived challenges of environmental journalists in Nepal, examining individual and organizational barriers to effectively covering environmental issues. Based on in-depth interviews with 10 journalists, it highlights the impact of global trends, such as reliance on international news, limited editorial autonomy, and unique challenges faced within the Nepali context. The study reveals how shifts in the media industry and budget constraints impede journalists’ ability to conduct thorough investigations, with implications for the quality and depth of environmental news coverage. Utilizing the hierarchy of influence model, the article critically analyzes these challenges, including the influence of NGOs and INGOs on news content and agendas, which can constrain journalistic independence. The findings suggest a decline in environmental news coverage attributed to time constraints and the transition to online media, further exacerbated by the diminishing role of environmental journalists due to the downsizing of the news media. This research sheds light on the evolving landscape of environmental journalism in Nepal and calls for further exploration into these challenges.

Generalized Riskiness Index in Vehicle Routing Under Uncertain Travel Times: Formulations, Properties, and Exact Solution Framework

We consider a vehicle routing problem with time windows under uncertain travel times where the goal is to determine routes for a fleet of homogeneous vehicles to arrive at the locations of customers within their stipulated time windows to the maximum extent while ensuring that the total travel cost does not exceed a prescribed budget. Specifically, a novel performance measure that accounts for the riskiness associated with late arrivals at the customers, called the generalized riskiness index (GRI), is optimized. The GRI covers several existing riskiness indices as special cases and generates new ones. We demonstrate its salient managerial and computational properties to motivate it better. We propose alternative set partitioning-based models of the problem. To obtain the optimal solution, we develop an exact solution framework combining route enumeration and branch-price-and-cut algorithms, in which the GRI is dealt with in route enumeration and column generation subproblems. We mainly reduce the solution space by exploiting the GRI and budget constraints’ properties without losing optimality. The proposed method is tested on a collection of instances derived from the literature. The results show that a new instance of the GRI outperforms several existing riskiness indices in mitigating lateness. The exact method can solve instances with up to 100 nodes to optimality. It can consistently solve instances involving up to 50 nodes, outperforming state-of-the-art methods by more than doubling the manageable instance size. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72101187, 72371204, 72021002, and 71901180], the Qatar National Research Fund [Grant ARG01-0430-230029], Natural Science Foundation of Sichuan Province [24NSFSC6232], and Guanghua Talent Project of the Southwestern University of Finance and Economics. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0345 .

The public debt sustainability and its determinants in Sub-Sahara African countries

PurposeThis paper aims to answer the following important questions: Is public debt in Sub-Saharan African (SSA) countries sustainable? What are the determinants of public debt sustainability in these countries, and do these determinants exhibit heterogeneity due to regional, natural resource, and income differences among SSA countries?Design/methodology/approachThis study analyzes the public debt sustainability in SSA countries using the theoretical model known as the Present Value Budget Constraint (PVBC) model developed by Hamilton and Flavin (1986), and adopts the econometric testing method proposed by Trehan and Walsh (1991). Moreover, to empirically investigate the determinants of public debt sustainability in SSA countries, the System-Generalized Method of Moments (System-GMM) method is applied. Furthermore, this study conducts heterogeneity analysis by categorizing the sample based on different regions, natural resource endowments, and income levels. The data of this study are sourced from the IMF and World Bank databases for 45 SSA countries from 2005 to 2021.FindingsFindings reveal that public debt in SSA countries is not sustainable in the long run, with factors such as the previous government debt, long-term debt ratio, debt repayment capacity, economic growth rate, inflation rate, export to GDP, and government fiscal deficit rate influencing sustainability. Additionally, the factors exhibit heterogeneity attributed to regional, natural resource, and income variations among SSA countries.Practical implicationsThe findings of our study will serve as a catalyst for policymakers in the SSA countries to embrace and sustain robust fiscal consolidation and debt stabilization measures. Moreover, countries with distinct characteristics should implement tailored approaches. Additionally, policymakers in SSA countries should implement economic measures to address public debt issues. These measures include improving the macroeconomic structure, promoting economic transformation and diversification of industries, fostering sustainable economic growth, ensuring price stability, and strengthening resilience against external shocks and debt risks. Specifically, countries endowed with indigenous species, resources, and tourism potential should adopt a well-coordinated strategy that utilizes agriculture, tourism, ecotourism, and the hospitality industry as instruments for sustainable local community and rural development.Originality/valueFirstly, it assesses the sustainability of public debt and its determinants for countries in SSA, which distinguishes it from previous studies that only focus on either debt sustainability or determinants of debt separately. Secondly, by including multiple SSA countries in the analysis, this study stands out from prior research that predominantly concentrates on specific nations. Thirdly, the utilization of the System-GMM method for analyzing determinants adds a novel dimension to this study, departing from earlier literature primarily focused on debt thresholds. Lastly, the heterogeneity analysis conducted in this study provides an empirical foundation for tailoring policies to different countries, addressing a facet often overlooked in earlier literature.

Automated Coupon Generator for Subsidy (ACG)

The proposed system is Automated Coupon Generator also known as ACG which introduces a dynamic coupon generation mechanism empowered by machine learning algorithms. This system is there to enhance people's engagement and optimize the coupon distribution process to befair. The coupon distribution process will be in such a way that those farmers who are less privileged will be prioritized. The project involves several key steps. This project involves key stages including data gathering, preprocessing, feature enhancement, and autonomous decision-making logic development, taking into account diverse factors such as farmer segments and budget constraints. The system will interact with HTML, PHP, CSS, and JavaScript, with Python supporting the implementation. It aims to continuously adapt the model to meet evolving customer behaviors and preferences. This technological approach is essential for optimizing coupons effectively and staying responsive to real-time farmer interactions and preferences

Refining Travel Suggestions using Python

In the era of personalized travel experiences, the need for intelligent and adaptive planning tools is paramount. This is developed using Python, harnesses the power of machine learning to analyse traveller preferences and patterns, offering customized destination suggestions. The core of this study is to demonstrate how machine learning can transform traditional travel planning into a more efficient, personalized experience. By integrating user data and preferences, the system proposes itineraries that not only align with individual interests but also enhance the overall travel experience. This details the development process, the machine learning techniques employed, and the efficacy of Python in creating a dynamic and responsive travel planning website. The goal is to enhance the user experience in planning trips by providing optimizing destination suggestions, considering factors like historical preferences, budget constraints, and time availability.

Analyzing groundwater storage anomalies in data‐scarce areas of Ethiopia's Rift Valley Basin using artificial neural network

AbstractGroundwater storage anomalies (GWSA) study requires the use of hydro‐climatic models coupled with Gravity Recovery and Climate Experiment climate (GRACE) and GRACE Follow‐On (GRACE‐FO) Terrestrial Water Storage experiments. Understanding groundwater storage anomalies in the research area is difficult due to the restricted hydro‐meteorological data availability, especially, groundwater levels, changes in groundwater storage, and budgetary and technical constraints. No such investigation of GWS anomalies prior to this study in this Basin was conducted. The research focuses on GWSA investigations in the basin employing Global Land Data Assimilation Systems and Artificial Neural Networks‐Back Propagation Algorithm techniques are required. The ultimate objective of this study was to reduce the GRACE GWS from 1° to 0.25° and close the discontinuity between GRACE/GRACE‐FO observations. The results illustrated that GRACE/GRACE‐FO GWS throughout the basin were overall declining. The GWS anomaly significantly decreased between 2011 and 2013, with the highest monthly depletion in March 2011 (−158 mm). In contrast, from mid‐June to mid‐October, the maximum positive value varied from 20.80 mm in the southernmost basin to 39.49 mm in the central‐southeastern basin. The basin's eastern, central‐southeastern, northern, and southern areas displayed positive monthly GWS trends. The entire dry season has been influenced by negative anomalies. Hence, the identification of GWS variations and a comprehensive understanding of their effects provided valuable scientific insights. Identifying and analyzing anomalies in groundwater storage (GWS) can provide valuable information. This data can be efficiently used to develop appropriate regulatory frameworks, which may contribute to the long‐term viability of the availability of groundwater.

A cost-effective seed selection model for multi-constraint influence maximization in social networks

The Influence Maximization (IM) problem aims to maximize the diffusion of information or adoption of products among users within a social network by identifying and activating a set of initial users. It is not unrealistic to have a higher activation cost for more influential users in real-life applications. However, existing works on IM focus solely on finding the most influential users as the seed set, without considering either the activation costs of individual nodes and the total budget or the size of the seed set. This oversight may lead to infeasible solutions, particularly from financial and managerial perspectives, respectively. To address these issues, we propose a more realistic and generalized formulation called Multi-Constraint Influence Maximization (MCIM) to achieve a cost-effective solution under both budgetary and cardinality constraints. The MCIM model allows for variable-length solutions, necessitating the exclusion of seed nodes from the influence spread estimation. Consequently, unlike existing IM formulations, the spread function under the MCIM model is no longer monotonic but submodular. As it has also been proven to be an NP-hard problem, we propose a Simple Additive Weighting (SAW)-assisted Differential Evolution (DE) algorithm for solving large-size real-world IM problems. Experimental results on four datasets demonstrate the effectiveness of the proposed formulation and algorithm in finding optimal and cost-effective solutions.

Evaluating proficiency testing implementation and identifying challenges that government comprehensive specialized hospital laboratories in northwest Ethiopia faced as they participated in the external quality assessment scheme: A document-based, interview-driven evaluation.

To assess the implementation of proficiency testing in the northwest Ethiopian government comprehensive specialized hospital laboratories, with a focus on identifying and understanding the challenges encountered during their participation in the external quality assessment scheme. A cross-sectional study was conducted among 3 comprehensive specializedhospitals in northwest Ethiopia, analyzing 41 documented laboratory test parameters from 2020 to 2022. In addition, face-to-face, in-depth interviews were carried out to identify the major challenges the participating institutions faced. The study covered a total of 41 tests across 9 cycles. Overall, proper implementation of proficiency testing was observed in 59.3% of the tests, with 61.8% maintaining consistent implementation status over 3 consecutive years. In addition, the overall performance of the laboratory was 54.3%, with a 68.7% participation rate. The predominantly identified challenges included the lack of participation, insufficient reagents and supplies, inadequacy of suitable proficiency testing materials, equipment malfunction and downtime, lack of management support, insufficient budget, and inadequate training and awareness. The results of this study highlight the ineffective implementation of proficiency testing. Contributing factors include personnel issues, equipment and supplies challenges, managerial shortcomings, difficulties with proficiency testing providers, budgetary constraints, and a lack of training and motivation.

A cost-benefit analysis of mass prostate cancer screening

BackgroundProstate cancer (PCa) causes a substantial health and financial burden worldwide, underscoring the need for efficient mass screening approaches. This study attempts to evaluate the Net Cost-Benefit Index (NCBI) of PCa screening in Iran to offer insights for informed decision-making and resource allocation.MethodThe Net Cost-Benefit Index (NCBI) was calculated for four age groups (40 years and above) using a decision-analysis model. Two screening strategies, prostate-specific antigen (PSA) solely and PSA with Digital Rectal Examination (DRE), were evaluated from the health system perspective. A retrospective assessment of 1402 prostate cancer (PCa) patients’ profiles were conducted, and direct medical and non-medical costs were calculated based on the 2021 official tariff rates, patient records, and interviews. The monetary value of mass screening was determined through Willingness to Pay (WTP) assessments, which served as a measure for the benefit aspect.ResultThe combined PSA and DRE strategy of screening is cost-effective, yields up to $3 saving in costs per case and emerges as the dominant strategy over PSA alone. Screening for men aged 70 and above does not meet economic justification, indicated by a negative Net Cost-Benefit Index (NCBI). The 40–49 age group exhibits the highest net benefit, $13.81 based on basic information and $13.54 based on comprehensive information. Sensitivity analysis strongly supports the cost-effectiveness of the combined screening approach.ConclusionThis study advocates prostate cancer screening with PSA and DRE, is economically justified for men aged 40–69. The results of the study recommend that policymakers prioritize resource allocation for PCa screening programs based on age and budget constraints. Men’s willingness to pay, especially for the 40–49 age group which had the highest net benefit, leverages their financial participation in screening services. Additionally, screening services for other age groups, such as 50–54 or 55–59, can be provided either for free or at a reduced cost.

Minimum cost with consensus constraint to support multi-attribute product design

Classical discrete multi-attribute product design (DMAPD) problem aims to design the product with the maximum number of approval individuals. However, the consensus constraint, which measures the uniformity of the opinions among different individuals, is rarely considered in the existing DMAPD studies. To fill this gap, we propose a novel minimum cost discrete multi-attribute product design (MCDMAPD) model with consensus constraint. Firstly, we provide a method to determine the multi-attribute satisfaction degrees and the satisfaction threshold. Then, under the constraint of the limited design budget, the DMAPD model maximizing the number of approval individuals is developed. Next, we define a consensus measure on the designed product, and provide the method for determining the unit adjustment cost of individuals. Furthermore, by considering the strategic proof and the envy-free constraints, the MCDMAPD model with consensus constraint is developed to reach the acceptable consensus. Finally, we conduct the simulation analysis and comparison analysis to justify the MCDMAPD model.

Governance of Higher Education Institutions through Faculty Perspectives

The present study investigated the factors influencing faculty satisfaction within higher education institutions. A quantitative survey with 30 items was administered online to faculty members (n=72) from diverse disciplines and institutional roles. Specifically, the study examined the effects of top-down leadership, hierarchical organizational culture, and the current challenges faced by academic institutions. The results showed that faculty satisfaction was significantly influenced by the leadership style at the institutional level while institutions with rigid hierarchical cultures tended to have lower faculty satisfaction. The study also identified several challenges faced by academic institutions, including budget constraints, increased workload, and shifting educational paradigms. These challenges also directly impacted faculty satisfaction in a negative way. In conclusion, to enhance faculty satisfaction, institutions should foster transparent leadership, promote a participatory organizational culture, and proactively address the challenges posed by the evolving higher education landscape.

Optimization of bulk microdefect performance in epitaxial silicon wafer

Bulk microdefects (BMDs) in epitaxial silicon wafers are pivotal for advanced node integrated circuits, offering enhanced mechanical strength and metal gettering capabilities. This study introduces two heat treatment procedures to optimize BMD density and radial uniformity in lightly doped wafers, addressing the challenges associated with large-diameter crystal growth and the thermal budget constraints of advanced node processes. We demonstrate that Rapid Thermal Annealing (RTA) at temperatures exceeding 1175 °C significantly improves BMD uniformity. A post-RTA stabilization step, when performed prior to epitaxial growth, enhances BMD density by retaining nuclei generated by RTA. Conversely, when the RTA with stabilization step follows epitaxial growth, a higher BMD density and larger BMD size are achieved. Light Scattering Tomography (LST) analysis confirms the optimal conditions for these treatments. The findings contribute to the semiconductor industry by optimizing wafer properties for high-performance ICs.

Review of Optimized Feeder Vehicle Systems in Developing Countries

With the population growth and changes in travel patterns, transportation demand in urban cities continue to increase rapidly. Since it is a cause for higher traffic densities, poor parking facilities, air pollution and road accidents, transport planners have to provide a system, which can ensure safe and comfortable mobility to all users as well as suppliers’ perspectives. Therefore, developing an affordable and efficient public transportation network is a critical goal for developing countries seeking to address urban mobility challenges. Feeder vehicle systems, which provide the first and last mile connectivity to the passengers, offers an opportunity for creating a system capable of meeting multiple needs of users and operators. It is an effective public transportation system that already operates in developed countries. It offers immense potential for improving accessibility and reducing congestion. However, the implementation of effective feeder system in developing countries is fraught with challenges due to uncoordinated transit networks, infrastructure limitations, and budget constraints. This paper presents a critical review of recent planning methodologies and selected decision support systems for optimizing feeder transport services.

Assessing grid hardening strategies to improve power system performance during storms using a hybrid mechanistic-machine learning outage prediction model

Improvements of power system performance during severe weather events are targeted through grid hardening actions, such as strengthening aging infrastructure or performing vegetation management. However, due to the complexity of modeling power system interactions with the environment and the impacts of proposed reinforcements, the benefits of such hardening actions are often unclear. To support hardening assessments, comprehensive models capturing these intricate relationships are necessary. To this end, this study presents a novel application of a hybrid mechanistic-data-driven outage prediction model (OPM) designed to incorporate changes in infrastructure or environmental/vegetation parameters in the prediction of storm outages. The model incorporates physics-based structural fragility curves of the overhead pole-wire system within a machine learning OPM trained on meteorological, topographic, infrastructure, and historic outage data to quantify the benefits of adaptive change in reducing storm-related power outages. A prioritization scheme is formulated to inform improved strategies to strengthen grid resilience under budgetary constraints. As a case study of the distribution system in Connecticut, the model was trained on historical outage data from storms between 2005 and 2020, and a counterfactual analysis is conducted. The results indicate tree removal and pole strengthening are the most cost-effective strategies; for instance, under a $600 million budget, tree removal could have reduced up to 15,000 distribution grid outages over the events analyzed. The developed framework and models can be useful for utility companies, regulatory agencies, and governments to highlight areas of need, inform cost-risk-benefit analyses, and aid in the optimal allocation of funds and resources toward grid hardening.

Should School Buses Be Green? A Sustainability Decision-Making Case Study

ABSTRACT The Director of Transportation for Grenze Independent School District wants to transform the bus fleet to clean energy, but budget constraints are a constant barrier. The health and safety of pupils, particularly those with asthma, is a compelling feature of this case to engage students and explore the benefits and challenges of electric vehicles. The purpose of this case study is to encourage students to consider all stakeholders when analyzing data for business decision-making, thus utilizing the triple bottom line. Learning objectives for Module 1 are to (1) analyze financial data for optimal decision-making, (2) identify and describe environmental and social concerns, and (3) evaluate and support recommendations considering financial, environmental, and social factors. Module 2 learning objectives focus on governmental accounting concepts to (4) analyze the impact of bond issuance on taxpayers, (5) prepare related journal entries, and (6) explain the reporting of transactions on fund-based and government-wide statements.

Enhancing reclaimed water distribution network resilience with cost-effective meshing

Water Distribution Networks (WDNs) are critical infrastructures that ensure a continuous supply of safe water to homes. In the face of challenges, like water scarcity, establishing resilient networks is imperative, especially in regions vulnerable to water crises. This study evaluates the resilience of network designs through graph theory, including its hydraulic feasibility using EPANET software, an aspect often overlooked. Novel mathematical algorithms, including Resilience by Design (RbD) and Resilience-strengthening (RS) algorithms, provide cost-effective and resilient network designs, even with budget constraints. A novel metric, Water Availability (WA), is introduced to offer a comprehensive measure of network resilience, thereby addressing ongoing discrepancies in resilience evaluation methods. Practical benefits are illustrated through a case study in which a resilient-by-design reclaimed water network is created, and an existing equivalent non-resilient network is improved. The resilient-by-design network demonstrates remarkably better results compared to the equivalent non-resilient design, including up to a 36 % reduction in the probability of service disruptions and a nearly 65 % decrease in the annual average unserved water due to service disruptions. These findings underscore the enormous advantages of a resilience-focused network design approach. When compared to the equivalent non-resilient design, the resilient-by-design network generated effectively safeguards up to a significant 91,700m3 of water from the impacts of water disruption events over a 50-year operational period. In addition, the resilient-by-design WDN solution incurs a subtle decrease in overall costs compared to consuming tap water from the drinking WDN baseline over a 50-year operational period. These findings highlight the cost-effectiveness of the approach, even offering financial benefits. This paper builds on our previous research by expanding its scope to include resilience considerations, providing algorithms that can be easily adapted from reclaimed to drinking WDNs. Ultimately, we contribute to the enhancement of water resource management and infrastructure planning in ever-evolving urban environments.

Community, Public, and Population Health Content in Nursing Curricula: National Survey Results.

Policy, societal, and system changes are prompting nursing programs to expand population health content in curricula. This study examined the current state of community, public, and population health (CPPH) education in nursing curricula throughout the United States. This descriptive study examined CPPH education in nursing programs nationally. A survey was developed and distributed to nursing programs from January to May 2021. CPPH content integration occurred across all program levels, and the majority of the participants were involved in the development of CPPH-specific curriculum. Programs experienced reductions in CPPH curriculum due to coronavirus disease 2019 (COVID-19), lack of experienced faculty, budget constraints, and an emphasis on acute care. The continuation of CPPH education in current nursing curricula is critical. National and academic nursing organizations must continue to monitor CPPH content in nursing curricula to assure a competent CPPH nursing workforce. [J Nurs Educ. 2024;63(5):282-291.].