Budget Constraints Research Articles

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.

Assessment of veterinary pharmaceutical warehouse management practices and its associated challenges in four selected zones and Bahir Dar city of Amhara regional state, Ethiopia

A pharmaceutical warehouse is part of the pharmaceutical supply chain and is essential to maintaining the quality and efficacy of veterinary pharmaceuticals for successful animal health service delivery. However, poor storage conditions, improper handling, and inappropriate use and disposal constitute challenges for veterinary supplies in animal health services. Therefore, this study aimed to assess the existing practices and challenges in warehouse management in government veterinary clinics and private veterinary drug wholesalers in Ethiopia. A cross-sectional study was conducted on 37 veterinary health facilities in four selected zones (south Gondar, west Gondar, central Gondar, and west Gojam zones) and Bahir Dar administrative city. Zones were selected using a simple random sampling technique. Data was collected using a structured questionnaire, pre-defined and tested observational checklists, and semi-structured interview guides. Descriptive statistics were used to analyze the quantitative data, while qualitative data was analyzed using a thematic approach. The study revealed the presence of poor stock management practices, such as the absence of standard operating procedures for warehouse activities in ~59.5% of facilities surveyed. In none of the surveyed facilities, bin cards and system software utilization were satisfactory. The absence of disposal guidelines was detected in 83.8% of the facilities, and the practice of timely disposal of expired drugs was not satisfactory. Compared to the government veterinary clinics, private veterinary drug wholesalers had better storage practices (86.25%) following theoretical recommendations. The storage conditions in government clinics were rated poor at 48.3% (>80%, which is the limit to the acceptable rate for good storage conditions). The challenges of inadequate infrastructure, a lack of qualified staff, problems with the availability and affordability of pharmaceutical products, insufficient regulatory practice, and budget constraints were identified. A holistic approach involving related stakeholders should be followed to improve the existing challenges and the sector's efficiency.

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.

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.

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.

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.].

Resource allocation of cognitive relay networks under continuous transmission mechanism

This paper considers the joint power allocation, path selection and subcarrier pairing design of cognitive radio cooperative networks, where the transmission power of secondary users is limited by its own power budget and the tolerable interference threshold of primary users. In particular, a continuous transmission mechanism rather than intermittent emission, leading to severe signal interference between primary and secondary users, as well as mutual interference between secondary users, is taken into account. This cooperative cognitive radio network is seen as an improvement in the sense that the source transmitter can continuously transmit symbols without silence. Although traditional cooperative network technology has long been applied to the cognitive radio field and it has been proposed to allow continuous transmission signals by source transmitter, the deployment of such improved cooperative behavior in cognitive radio networks has not been reported yet. This improvement assumption may render serious mutual interference between the relay station and the destination receiver, and the signal interference borne by the primary user will also exceed the threshold value, if there is no elaborate signal design and resource scheduling. In order to make full use of the available resources, this paper presents the closed-form solution of the power allocation, the decision criteria of the path selection and the subcarrier pairing technique for the problem of maximization of the sum rate of secondary users in such networks under the dual constraints of the power budget and the interference threshold. Simulation results show that the continuous transmission behavior at the source significantly outperforms the traditional switching back and forth between on and off.

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

Joint In-Orbit Computation and Communication for Minimizing Download Time From LEO Satellites

Downloading a large amount of data from a low Earth orbit satellite to a ground station can be challenging due to the limited contact window, dynamic channel quality, solar energy supply, and thermal management without an atmosphere. Considering such dynamics, this paper proposes a joint design of in-orbit computation and communication for download time minimization. We combine the non-convex thermal constraints and energy constraints into unified energy budget constraints with upper bound approximation, and computational efficiency is achieved by decomposing the resulting large-scale problem into a non-convex communication sub-problem, a convex computation sub-problem solvable with interior point method and a master problem that optimizes the energy budget allocation between computation and communication. The communication sub-problem is solved with a generalized-benders-decomposition-based algorithm that decouples downlink scheduling and power allocation based on a closed-form solution of optimal dual variables in the power allocation primal problem. And the master problem is solved with ternary search by proving the minimal download time is quasi-convex with respect to the energy budget allocation between computation and communication. Simulation results demonstrate that the proposed solution effectively reduces the download time, especially under strict energy constraints and severe channel variations.

Moderated Mediation Dynamics: Influence of Strategic Linkages and Policy Implementation on Corporate Leadership and Kenya Agricultural Research Organization's Performance

Global demand for safe, affordable, and accessible nutritious food currently exceeds governmental efforts, notwithstanding huge investments in enhancing capacity of organizations mandated to reverse such trends. With the projected 27% increase in global population by 2050, the need for corporate leadership and policy implementation efforts is urgent. The study explored the moderated mediation effect of strategic linkages and policy implementation on the relationship between corporate leadership and performance of Kenya Agricultural and Livestock Research Organization (KALRO). Rooted in organizational excellence, upper echelons, legitimacy, institutional, and stakeholder theories, the study employed a pragmatic philosophy, and mixed methods design to mitigate bias. The target population included 75 management organs, with 60 directors and 188 top leadership units as units of observation. Quantitative data was analyzed using SPSS, and qualitative data using NVivo. Cronbach’s alpha (α ≥ .700) confirmed internal consistency. Both confirmatory factor and principal component analyses aided data reduction. Moderated mediation analysis, using Hayes' PROCESS Macro, showed significant interaction (p = .004) between policy implementation (mediator) and strategic linkages (moderator), indicating that the effect of policy implementation on KALRO’s performance varies with levels of strategic linkages (set at -.229). Index below -0.229 shows significant relationships, and vice versa. Recommendations encompass gender diversity, inclusive leadership development, regular performance appraisals, an enhanced monitoring and evaluation system, and improved linkages. The findings provide valuable guidance for KALRO's leadership amid competing priorities and budgetary constraints. Keywords: Moderated mediation, policy implementation, strategic linkages, corporate leadership, KALRO

Developing accounting students as responsible leaders: A workshop on social innovation

Purpose: As accountants are often business leaders, it is crucial that accounting students are equipped to become responsible leaders. Thus, accounting students should be taught to value citizenship, for example, through exposure to social innovation as an expression of personal citizenship. The research aim of this study was to determine whether students perceived a social innovation workshop with subsequent reflection as effective in developing citizenship as a responsible leadership value during the accounting curriculum.Design/methodology/approach: Students who attended a social innovation workshop and carried out a subsequent reflection on citizenship were asked to complete a purposively designed online questionnaire, which gathered their perceptions on the workshop, social innovation and personal citizenship.Findings/results: Students perceived a workshop on social innovation with subsequent reflection as helpful in developing citizenship as a value. Students suggested that citizenship should be incorporated into the technical curriculum and that opportunities for service learning should be provided.Practical implications: Higher education institutions may draw on the findings in developing their citizenship curriculums amid the challenges of large class sizes and budget constraints. It is suggested that students enrolled in accounting programmes be provided with specific guidance to develop their reflective abilities.Originality/value: As student voice on responsible leadership and citizenship values in management education was mostly absent from literature (especially in emerging economies), this study obtained the perceptions of South African accounting students on this matter. Moreover, the study identified knowledge regarding social innovation as possible activator for developing students’ personal citizenship and enabling responsible leadership.

Money is power: Carpooling stimulus with evidence from an interactive long-term laboratory experiment

Carpooling is one of the travel demand management strategies to mitigate road congestion. Incentive-Based Travel Demand Management (IBTDM) strategies are pivotal for carpool promotion by providing incentives to address inconveniences and privacy apprehensions, yet their efficacy lacks validation. Considering the constraints of incentive budget as well as the penetration rate, it is important to assess the long-term impact of incentives on carpooling, improve the programs in term of information feedback and identify the individuals who are most inclined to carpool. To achieve the goals, we conducted a laboratory experiment through WeChat applet platform focused on carpooling and departure time choices. 194 participants successfully completed the entire experiment. The experiment emphasizes several crucial elements, including long term (participants making daily travel choices in one month), the interactivity and information feedback mechanisms, instruments enhancing participant engagement (redeeming incentives at the end of the experiment, etc.), exit questionnaire to collect travelers' behavioral strategies. The findings elucidate several key insights: 1) Incentives have a significant long-term influence on encouraging travelers to carpool, and compensatory mechanisms for unsuccessful carpool matches further amplify travelers' impetus. 2) IBTDM administrations can promote carpooling by improving feedback information according to travelers' learning effect in each round, such as introducing real-time displays of the numbers of carpool drivers and passengers. 3) Drawing from the analysis of travelers’ participation, behavioral compliance and psychological strategies of carpooling, it can be inferred that individuals with higher incomes, fewer commuting days who have a solid grasp of the experimental mechanism, constitute prospective candidates for carpooling programs. This work contributes to helping IBTDM administrations clarify the target participants and develop a more reasonable carpooling program. The behavioral data collection approach based on our laboratory experiment is portable for future studies.

A Joint Communication and Computation Design for Probabilistic Semantic Communications

In this paper, the problem of joint transmission and computation resource allocation for a multi-user probabilistic semantic communication (PSC) network is investigated. In the considered model, users employ semantic information extraction techniques to compress their large-sized data before transmitting them to a multi-antenna base station (BS). Our model represents large-sized data through substantial knowledge graphs, utilizing shared probability graphs between the users and the BS for efficient semantic compression. The resource allocation problem is formulated as an optimization problem with the objective of maximizing the sum of the equivalent rate of all users, considering the total power budget and semantic resource limit constraints. The computation load considered in the PSC network is formulated as a non-smooth piecewise function with respect to the semantic compression ratio. To tackle this non-convex non-smooth optimization challenge, a three-stage algorithm is proposed, where the solutions for the received beamforming matrix of the BS, the transmit power of each user, and the semantic compression ratio of each user are obtained stage by stage. The numerical results validate the effectiveness of our proposed scheme.