Allocation Management Research Articles

The Influence of Regional Own-Source Revenue, General Allocation Funds, Special Allocation Funds, and Capital Expenditure on Regional Financial Independence Through Economic Growth in North Sulawesi Province

In the context of fiscal decentralization in Indonesia, regional financial independence represents a significant challenge, given the considerable dependence on central fund transfers, including the General Allocation Fund (DAU) and the Special Allocation Fund (DAK). This issue is further compounded by suboptimal management of local own-source revenues (PAD) and ineffective capital expenditure allocations, which have a detrimental impact on infrastructure development and economic growth. Fiscal imbalances between regions also contribute to the widening of economic disparities, impeding efforts to achieve sustainable financial independence. This study aims to analyze the influence of Regional Original Revenue (PAD), General Allocation Fund (DAU), Special Allocation Fund (DAK), and Capital Expenditure on the level of regional financial independence through economic growth in North Sulawesi Province. Using secondary data from the 2011-2023 period, the path analysis method was applied to evaluate the direct and indirect relationships between variables. The results of the study show that PAD has a significant positive effect on economic growth and regional financial independence. DAU and DAK, although contributing to fiscal needs, show varying influences on economic growth and financial independence. These findings emphasize the importance of optimizing PAD and capital expenditure allocation to support economic development and reduce dependence on central fund transfers.

Implementation of the Village Fund Allocation (ADD) Management Policy in Kon Tumere Village, Kabawo Sub-district, Muna Regency

Village, Kabawo District, Muna Regency and to find out the factors that influence the Implementation of the Village Fund Allocation Policy (ADD). In this research, data collection was carried out in the following way, collecting data or information through: observation, semi-structured interviews (open interviews), documentation. Data analysis methods include data reduction, data presentation, and drawing conclusions. When viewed from the perspective of achieving objectives, the results of this research can be concluded that the implementation of the Village Fund Allocation Policy in Kontumere Village, Kabawo District, Muna District has not been achieved comprehensively or optimally. This is influenced by several inhibiting factors, namely (1) communication, the inhibiting factor in this communication is socialization to the public regarding the non-existent ADD policy (weakness). This results in the difficulty of inviting community participation in implementing ADD and in monitoring activities (treatment/threats). (2) resources, lack of ability of implementers to operate computers (Weakness), and lack of adequate village income support (Treatment/Threat), (3) Attitude of Implementers, The inhibiting factor in the attitude of implementers is the lack of responsiveness of ADD implementers ( Weakness/Weakness) which considers the ADD policy to be a mere routine policy (Treatment/Threat). (4) Bureaucratic structure, namely the lack of coordination between policy implementers.

Explainable predictions of a machine learning model to forecast the postoperative length of stay for severe patients: machine learning model development and evaluation.

Predicting the length of stay in advance will not only benefit the hospitals both clinically and financially but enable healthcare providers to better decision-making for improved quality of care. More importantly, understanding the length of stay of severe patients who require general anesthesia is key to enhancing health outcomes. Here, we aim to discover how machine learning can support resource allocation management and decision-making resulting from the length of stay prediction. A retrospective cohort study was conducted from January 2018 to October 2020. A total cohort of 240,000 patients' medical records was collected. The data were collected exclusively for preoperative variables to accurately analyze the predictive factors impacting the postoperative length of stay. The main outcome of this study is an analysis of the length of stay (in days) after surgery until discharge. The prediction was performed with ridge regression, random forest, XGBoost, and multi-layer perceptron neural network models. The XGBoost resulted in the best performance with an average error within 3days. Moreover, we explain each feature's contribution over the XGBoost model and further display distinct predictors affecting the overall prediction outcome at the patient level. The risk factors that most importantly contributed to the stay after surgery were as follows: a direct bilirubin laboratory test, department change, calcium chloride medication, gender, and diagnosis with the removal of other organs. Our results suggest that healthcare providers take into account the risk factors such as the laboratory blood test, distributing patients, and the medication prescribed prior to the surgery. We successfully predicted the length of stay after surgery and provide explainable models with supporting analyses. In summary, we demonstrate the interpretation with the XGBoost model presenting insights on preoperative features and defining higher risk predictors to the length of stay outcome. Our development in explainable models supports the current in-depth knowledge for the future length of stay prediction on electronic medical records that aids the decision-making and facilitation of the operation department.

Honey bee inspired resource allocation scheme for IoT-driven smart healthcare applications in fog-cloud paradigm.

The Internet of Things (IoT) paradigm is a foundational and integral factor for the development of smart applications in different sectors. These applications are comprised over set of interconnected modules that exchange data and realize the distributed data flow (DDF) model. The execution of these modules on distant cloud data-center is prone to quality of service (QoS) degradation. This is where fog computing philosophy comes in to bridge this gap and bring the computation closer to the IoT devices. However, resource management in fog and optimal allocation of fog devices to application modules is critical for better resource utilization and achieve QoS. Significant challenge in this regard is to manage the fog network dynamically to determine cost effective placement of application modules on resources. In this study, we propose the optimal placement strategy for smart health-care application modules on fog resources. The objective of this strategy is to ensure optimal execution in terms of latency, bandwidth and earliest completion time as compared to few baseline techniques. A honey bee inspired strategy has been proposed for allocation and utilization of the resource for application module processing. In order to model the application and measure the effectiveness of our strategy, iFogSim Java-based simulation classes have been extended and conduct the experiments that demonstrate the satisfactory results.

The Impact of Capital Expenditure on Ethiopian Economic Growth

This study investigates the effect of disaggregated capital expenditure on economic growth in Ethiopia over the period from 1981 to 2021. An autoregressive distributed lag (ARDL) model, combined with a Granger causality test, is used for the econometric analysis. The empirical results reveal that capital expenditure on economic development (CAEE), recurrent expenditure (RCE), and the inflation rate (CPI) have significant positive impacts on economic growth in both the long run and short run. However, capital expenditure on administrative and general development (CEAG) has a significant negative effect on economic growth in the long run. The Granger causality test further indicates unidirectional causality from economic growth (GNI) to capital expenditure on economic development (CAEE). Based on these findings, the study recommends that the Ministry of Finance should increase its budget allocations for capital expenditures aimed at economic development in order to further stimulate economic growth. Additionally, the government should implement stricter follow-up and monitoring mechanisms to ensure the proper management of budget allocations, particularly regarding capital expenditures on social development and administrative and general development. These measures are crucial for sustaining long-term economic growth in Ethiopia.

Cardiopulmonary exercise testing predicts risk of progressive heart failure and long term mortality in ambulatory patients awaiting isolated heart transplant

Abstract Introduction Cardiopulmonary exercise testing (CPET) parameters such as peak VO2 ≤ 14ml/kg/min, percentage predicted peak VO2 (ppVO2) ≤ 50% and ventilation equivalent of carbon dioxide (Ve/VCO2) >35 l/min/l/min are recommended thresholds to list for isolated heart transplant. There is minimal data on clinical events after listing to guide clinical management or priority in organ allocation. Purpose To assess the effect of peak VO2, ppVO2, Ve/VCO2 on clinical events after listing for isolated heart transplant. Methods In a single centre retrospective study, we identified patients who were status 6 or status 2 prior to 2018 at time of listing for isolated heart transplant between 2007 and 2021. Clinical data were collected by chart review. Primary outcome of progressive heart failure was defined as death or upgrade in transplant priority status (new inotropes or mechanical circulatory support). Comparison was made between peak VO2 strata (> 14 vs ≤ 14 ml/kg/min), ppVO2 strata (> 50% vs 40-50% vs <40%) and Ve/VCO2 strata (<35 vs >35 l/min/l/min). Secondary outcome was time dependent survival after listing for heart transplant. Results We identified 141 patients of which 133 underwent CPET and follow up was available for 130 who were included in the analysis. Median age was 53 years (interquartile range 41-59 years), 29.1% of patients were female. Median peak VO2 was 14.0 ml/kg/min (11.2-15.7), ppVO2 was 44% (36-52), Ve/VCO2 slope was 34.0 (31.0-37.0) and RER was 1.1 (1.1-1.2). 87 patients had progressive heart failure consisting of 34 deaths and 83 patients having upgrade in priority status. There was a significant difference in risk of progressive heart failure comparing peak VO2 strata (Figure 1) (freedom from event at one year 71.4% with peak VO2 > 14 vs 46.5% with peak VO2 ≤ 14, p=0.00061) and ppVO2 strata (Figure 2) (freedom from event at one year 75.0% with ppVO2 >50% vs 61.2% with ppVO2 40-50% vs 42.6% with ppVO2 <40%, p=0.0015). There was no significant difference with Ve/VCO2 strata (freedom from event at one year 59.8% with Ve/VCO2 <34 vs 54.9% with Ve/VCO2 >34, p=0.52). There were 34 deaths before transplant and 23 deaths after transplant. 87 patients underwent transplant. In a time dependent survival analysis, age, gender and ppVO2<50% (HR 1.65 (0.87-3.12)) were not significant predictors of mortality. Both early transplant (defined as < 1 year after listing, HR 0.14 (0.06-0.34) and late transplant (defined as > 1 year after listing, HR 0.16 (0.08-0.32)) were significant predictors of lower mortality whilst Ve/VCO2>35 (HR 2.09 (1.19-3.68)) was a significant predictor of higher mortality. Conclusions Peak VO2 and ppVO2 stratify risk of progressive heart failure in ambulatory patients awaiting isolated heart transplant. However, Ve/VCO2 is a predictor of significantly higher mortality. These findings should affect clinical management and transplant priority status; and influence revision of future guidelines.Freedom from event by peak VO2 strataFreedom from event by ppVO2 strata

Multi-function vortex array radar

In the realm of automation systems, multi-function radars serve as essential sensory components for self-driving vehicles and airbornes. Effective resource allocation management is crucial, requiring a high level of versatility to accomplish multiple tasks, especially, for increasingly miniaturized hardware. Here, we advance a balanced protocol for detecting, positioning, and tracking moving targets in real-time. Our protocol integrates efficient data processing methods with robust hardware. Specifically, detection signals are modulated by optical vortices for imaging, and real time processing of the image field facilitates target positioning and tracking. Moreover, the protocol extends its utility to serve as a topographic laser profiling system for natural landscapes, highlighting its adaptability. This adaptability and versatility well position the proposed protocol to support a wide range of applications, spanning self-driving vehicles and aerial systems, underscoring its potential significance across multiple platforms.

Analysis of Accountability and Transparency of Village Fund Allocation Management in Sebani Village, Pandaan District, Pasuruan Regency

The village as the smallest government system demands renewal to support improved village development and the standard of living of village communities that are far from poverty. The various problems in the village are very complex, making it a reason for the village to develop. The development progress in each village is no less important. This research aims to analyze accountability and transparency of village fund allocation management in Sebani Village, Pandaan District, Pasuruan Regency. The method in this study is a descriptive qualitative method by means of direct interviews with informants. The location of this accountability and transparency research is in Sebani Village, Pandaan District, Pasuruan Regency. This research was carried out for a period of 6 months, starting from January 2024 to June 2024. The data is processed using data analysis techniques with stages, namely data reduction, data presentation, and drawing conclusions. The results of the study based on the Regulation of the Minister of Home Affairs Number 20 of 2018 show that the management of village fund allocations in Sebani Village is accountable and transparent in the stages of administration, reporting, and accountability.

Analysis of Accountability and Transparency of Village Fund Allocation Management in Sebani Village, Pandaan District, Pasuruan Regency

Analysis of Accountability and Transparency of Village Fund Allocation Management in Sebani Village, Pandaan District, Pasuruan Regency

End-to-End Resource Allocation Management Model in Next-Generation Network: Survey

Network communication has grown rapidly with massive demands of services. Moreover, resource allocation in networking is a fundamental and crucial issue that cannot ensure the network's stability and efficiency with the myriad requirements of different services. Various vertical businesses may seek varied network services, particularly in the Fifth-Generation networks and Beyond (5G+). The pros of Fifth-Generation communication networks are to outperform 4G in performance by having higher bandwidth, minimum latency, more capacity, and QoS (Quality of Service). Software-Defined Network (SDN) and Network Function Virtualization (NFV) are two technologies that are combined in the next generation cellular network to provide improved network management. The primary idea behind resource allocation (RA) in the next generation network is the concept of network slicing where the network resources are virtually partitioning into many separate networks. Each separated network must satisfy the unique needs of the required service to achieve the required QoS. In this survey, we focus on resource management issues related to network slicing and tackling the biggest obstacles in this field while offering a thorough and up-to-date overview of this field. Thus, thorough analysis of the allocation of resources on the access side and core side of the network communication was sought. Also, demonstrates how revolutionary techniques that are used to support the management of sliced networks which are based on Machine Learning (ML) and Artificial Intelligence (AI). Importantly, use appropriate ML techniques such as deep learning for predicting the network condition and Reinforcement learning to learn optimal allocation policy without depending on prior knowledge and other techniques such as classification and clustering to aggregate the similar needs of users into separate slices. This could help to enhance resource utilization by allocating a sufficient amount of resource as needed based on ML algorithms and optimal utilization of resources and reducing operational costs by real-time adjustment of it based on user demands and network conditions

Study of the Effectiveness of Management of Allocation of Village Funds and Village Funds in Sirenja Donggala District – Indonesia

Study of the Effectiveness of Management of Allocation of Village Funds and Village Funds in Sirenja Donggala District – Indonesia

Analysis of the application of behavioral accounting aspects in the management of village fund allocation in Pineleng Satu Village of Pineleng District in Minahasa Regency

This study aims to analyze: (1) the application of behavioral accounting aspects in the management of village fund allocations in Pineleng Satu Village of Pineleng District in Minahasa Regency, and (2) factors that cause delays in completing the accountability report. This type of research is qualitative with a case study approach. Determination of research subjects using purposive sampling technique. This research uses data collection techniques through interviews and documentation. The data analysis techniques are data reduction, data presentation, conclusion drawing, and data verification. Data validity tests include credibility tests with triangulation of sources and techniques, and dependability tests. The results showed: (1) the application of behavioral accounting aspects in the management of village fund allocations in Pineleng Satu Village has not been fully implemented properly. This can be seen from the aspect of reporting requirements where the village government is slow in completing the accountability report, resulting in the accountability report not being reported according to the specified time. (2) The first factor that causes delays in completing the accountability report is related to human resources and the second factor is the behavior of implementers in collecting complementary documents for the accountability report.

Performance Assessment of Public Hospitals with the Entropy-Weighted TOPSIS Method: The Case of Turkey

Background: Hospitals make up the main cost factor of health systems and also face increasing pressure to improve efficiency. Improving the performance of hospitals, which are vital for public health, is very important for developing countries such as Turkey. Aims: We conducted this study to assess the performance of 544 secondary-level public hospitals in Turkey by geographical region. Methods: In the study, we used the entropy weighting method to obtain the objective weights of indicators with the evaluation criteria, and then we utilized the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method to rank the performance of hospitals by geographical region. Results: The comparison of hospital performances by geographical region indicated that the highest and the lowest mean scores belonged to the Mediterranean Region and the Eastern Anatolia Region, respectively. Conclusions: The socio-economic differences between geographical regions in Turkey were in parallel with the findings of the study. Although there is a need for professional management in resource allocation and use to improve the performance of public hospitals, it is necessary to focus on the origin of the problems first.

A joint optimization of resource allocation management and multi-task offloading in high-mobility vehicular multi-access edge computing networks

Vehicular communications have advanced data exchange and real-time services in intelligent transportation systems by exploiting advanced communication between vehicles and infrastructure. The emergence of Multi-access Edge Computing (MEC) has further elevated this field by utilizing distributed edge resources near vehicles for low-latency data processing and high-reliability communication. In this dynamic environment, adequate resource allocation and task offloading are pivotal to ensure superior performance, lower latency, and efficient network resource utilization, enhancing Quality of Service (QoS) and overall driving experience and safety. This paper presents a developed vehicular network and offloading mechanism, introducing a resource management model with real-time allocation and load balancing. The proposed method integrates task prioritization, multi-agent collaboration, context-aware decision-making, and distributed learning to optimize network performance. The introduced optimized algorithm initializes Q-networks and target networks, sets up an experience replay buffer, and configures agents with local state representations. Agents use an ε-greedy policy for action selection, update Q-values through experience replay, and prioritize tasks based on urgency while sharing state information for collaborative decision-making. Evaluations through simulation demonstrate optimized performance, enhancing efficiency in vehicular MEC networks compared to baseline and the other well-known algorithms.

Analysis of Financial Performance of Village Fund Allocation Managers Based on Effectiveness Ratio and Growth Ratio at the Sipahutar III Village Office, Sipahutar District, North Tapanuli District

This research aims to determine the effectiveness of financial performance growth in managing Village Fund Allocations (ADD) at the Sipahutar III Village Office, Sipahutar District, North Tapanuli Regency. The data used comes from the Village Fund Allocation Financial Report from 2020 to 2022. Data collection methods used include documentation, literature study and observation. The data analysis technique applied is descriptive statistics using effectiveness ratios and growth ratios. The research results show that the financial performance of Village Fund Allocation (ADD) at the Sipahutar III Village Office is categorized as effective or good in terms of effectiveness ratio, with an average effectiveness of 100% during the 2020-2022 period. However, in terms of income and expenditure growth, there was a decline with an average negative growth of -3%. This research provides an illustration that although the effectiveness of ADD management in Sipahutar III Village is good, the challenge lies in the growth of income and expenditure which shows a decline. The implications of this research can be used as a basis for improving financial management strategies in order to support better growth in the future

Accountability Analysis of Village Fund Allocation Management (ADD) Based on Minister of Home Affairs Regulation No. 20 of 2018 in Kalasey One Village, Mandolang District

The purpose of this study is to evaluate the level of accountability in managing the village fund allocation in Kalasey Satu Village, Mandolang District, starting from the stages of planning, implementation, administration, reporting, and accountability. This study in Kalasey Satu Village employed descriptive qualitative research methods. The data utilized in this study is derived from the Village Revenue and Expenditure Budget. The findings indicate that the management of the village fund allocation in Kalasey Satu Village, Mandolang District demonstrates a high level of accountability across the stages of planning, implementation, administration, reporting, and accountability

The Effects of Transparency, Accountability, Village Development, and Political Interest on Village Fund Allocation Management

This research evaluates the influence of transparency, accountability, village development, and political interest on Village Fund Allocations (ADD) management by village governments, addressing community concerns about fund usage. Using a quantitative approach with multiple linear regression, data from 100 villagers in four Ponorogo Regency villages, East Java, were analyzed. Results indicate that transparency, accountability, and village development positively impact ADD management, while political interest has a negative effect, highlighting potential challenges associated with political influences on fund management. However, the study has limitations, such as a small sample size and a focus on a specific regency, limiting generalizability. Recommendations include conducting future research with larger, more diverse samples for broader insights. Practical implications suggest implementing policies and practices to enhance transparency, accountability, and village development for effective fund utilization. Additionally, awareness of the negative impact of political interest underscores the need for measures to mitigate undue political involvement in fund management.

Analisis Pengelolaan Alokasi Dana Desa dalam Pembangunan Infrastruktur di Desa Pangkoh Sari, Kecamatan Pandih Batu Kabupaten Pulang Pisau

This study aims to determine, analyze, and describe the extent to which the management of Village Fund Allocation (ADD) in Pangkoh Sari Village is conducted properly and in accordance with the applicable regulations, while also ensuring that the villagers understand its allocation and use. The research employs a descriptive qualitative approach, with data collected through observation, interviews, and documentation in Pangkoh Sari Village. Data analysis includes procedures for data reduction, data presentation, and drawing conclusions or verification. The findings indicate that the ADD in Pangkoh Sari Village has not been fully utilized for infrastructure development, instead focusing on improving social welfare, community services, and local income. However, one of the main obstacles identified is the lack of community awareness and participation in bureaucratic socialization efforts, leading to uneven development across the village. The study emphasizes the importance of communication and collaboration between the village government and the community to ensure more transparent and targeted use of the ADD. Improving socialization and community participation could help address these obstacles.

EVALUATING RESOURCE ALLOCATION MANAGEMENT TO HOSPITALS IN BAUCHI STATE: A CASE STUDY OF FEDERAL MEDICAL CENTRE AZARE AND PRIMARY HEALTHCARE CENTRE AZARE

Background: This study evaluates resource allocation management in hospitals in Bauchi State, focusing on Federal Medical Centre (FMC) Azare and Primary Healthcare Centre (PHC) Azare. The research addresses significant disparities in resource allocation that affect healthcare service delivery and patient outcomes. Methods: Using a mixed-method approach, the study combines quantitative data analysis of financial records, resource allocation reports, and healthcare service delivery metrics with qualitative insights from interviews and focus group discussions. Data Envelopment Analysis (DEA) was utilized to measure the relative efficiency of the hospitals in converting inputs such as financial resources, staff, and infrastructure into healthcare services and outputs. Additionally, descriptive analysis was conducted using IBM SPSS version 27 to analyze 100 questionnaire responses and demographic data. Results: The findings reveal significant disparities between FMC Azare and PHC Azare, with FMC Azare benefiting from more adequate financial resources, human resources, medical supplies, and infrastructure. These disparities lead to differences in patient satisfaction, service delivery efficiency, patient outcomes, and waiting times. Conclusions: The study underscores the need for more equitable resource allocation to improve healthcare delivery and patient outcomes at under-resourced centers like PHC Azare. Recommendations for policy interventions include addressing resource allocation inequities, staffing solutions, supply chain improvements, and infrastructure development. These recommendations aim to enhance resource allocation management, thereby improving healthcare services in Bauchi State and providing insights applicable to similar contexts in Nigeria, Africa, and globally. KEYWORDS: Resource Allocation Management, Healthcare Delivery, Data Envelopment Analysis (DEA), Federal Medical Centre, Primary Healthcare Centre.

POLICIAMENTO PREDITIVO E INTELIGÊNCIA ARTIFICIAL: ANÁLISE DE DESEMPENHO DO ALGORITMO DE APRENDIZADO DE MÁQUINA SUPERVISIONADO RANDOM FOREST NA PREDIÇÃO DE OCORRÊNCIAS POLICIAIS DE ROUBO NAS ZONAS DA REGIÃO METROPOLITANA DE SÃO LUÍS

Predictive policing is a concept based on the premise that it is possible to predict when and where crimes will occur again in the future by using sophisticated computer analysis based on data from previously committed crimes (UCHIDA, as cited in NORTON, 2013, pp. 32–33). The aim of this article is to analyze the spatial prediction of crimes, highlighting the importance of innovative approaches in the field of public safety management. Specifically, the article explores practical applications of using Artificial Intelligence (AI) through supervised machine learning algorithms for classification, using the emergency call records database (190) of robbery incidents in the metropolitan region of São Luís as input, and comparing results through validation techniques for the Random Forest algorithm. Through detailed analysis of these data, the potential of predictive policing is illustrated not only to anticipate criminal events but also to develop a computational tool capable of assisting and supporting strategic decision-making, significantly contributing to troop allocation management and, consequently, to the enhancement of preventive urban patrol services.