Effective Management Policies Research Articles

Analysis Of Financial Performance Of Muhammadiyah Aisyiyah Hospital In Jakarta Before And After The Covid-19 Pandemic

This study aims to analyze differences in the financial performance of the Muhammadiyah-Aisyiyah Hospital (RSMA) in Jakarta before and after the COVID-19 pandemic. Data was obtained from financial reports (2017Q1-2022Q4) which were analyzed using the ANOVA difference test. Based on the analysis and discussion, it was found that there was a significant difference in financial performance in the financial ratios of TATO and PBT, while the difference in DAR was not significant before and after the COVID-19 pandemic in the RSIJ Group. Apart from that, it was found that financial performance was better after the COVID-19 pandemic.This research provides practical insights for hospital managers and stakeholders in designing more effective policies for financial management during and after the pandemic. The increase in the Debt to Assets Ratio in several hospitals shows the need for better debt management strategies, while the decrease in the Total Assets Turnover Ratio emphasizes the importance of increasing the efficiency of assets use. The increase in Profit Before Tax in several hospitals indicates opportunities to increase profitability through appropriate management despite existing challenges. To improve the financial performance of Muhammadiyah private hospitals in DKI Jakarta, it is recommended to improve asset management to make it more effective, diversify income sources to reduce financial risks, improve revenue and operational expense management strategies, and invest in advanced health technology.

Both meteorological droughts and human activities modulated groundwater variations in the northern Yellow River Basin

ABSTRACT Groundwater level declines are largely associated with natural processes and human activities. In particular, the drivers of groundwater change can be more complex during meteorological drought owing to human activities. However, disentangling their specific contribution remains poorly understood. By focusing on semiarid ecosystems in the northern Yellow River Basin – the Ordos – here we elucidate the impact of human activities on the propagation of meteorological droughts to groundwater systems. To comprehensively analyze groundwater variations, we employ the K-means, categorizing them into four distinct patterns. Based on the Pearson correlation coefficient analysis between standardized precipitation index (SPI) and groundwater depth (GWD), we found that the majority of lag time for GWD response to SPI is less than 3 months, and the drivers influencing GWD are classified into three categories: SPI, human activities related to SPI, and human activities unrelated to SPI. Our results reveal that both meteorological droughts and human activities jointly influence GWD across the entire region. Notably, human activities unrelated to SPI have the greatest impact in the irrigation district of Ordos, followed by the western part of Ordos and the Mu Us sandy land in central Ordos. Our findings can guide to formulate effective drought management policies and practices in semiarid regions.

Impact of social isolation on intersex individuals: Implications for inclusive education policy

The intersex person’s social isolation condition is the leading concern in inclusive educational practices. It provides for the relevance of intersex communities with the influence of social isolation on their education and well-being. Given the underlying problem, this paper stresses the isolation-free condition of the intersex community by facilitating inclusive education. The Atkinson and Shiffrin Model and Behaviorism-Based Intersex Theory supports inclusive education by extending the desire to significantly manage stereotypes, quality teaching, parental beliefs, expressions, physique, and intersex attribution. The qualitative research method analyses the reducing role of social isolation for inclusive education. The semi-structured interview research instrument is used for the data collection from the Ministry of Human Rights, Educational Institutions, and inter-sex Representatives. The results show that managing directors and heads of educational institutions frame policy management for the free social isolation of intersex persons, which is relevant through inclusive education. This paper aims to provide a better social condition for intersex persons and promote inclusive education through effective policy management.

Innovative foresight for water utilities asset management using PRISM software

Our analysis aims to better understand water losses by developing a specific methodology for estimating water leak flow rates and durations. Additionally, it aims to improve asset management practices for drinking water utilities by forecasting the costs and benefits of investment and operational decisions on the network. Current utility practices are compared to a "do nothing" policy and to the potential effective asset management policies proposed by an artificial neural network (ANN) based software, PRISM. Applied to four utilities in France, our methodology provides valuable insights for enhancing water loss estimates and finding trade-off asset management policies.

A Future Beyond the Impasse? Exploring Post-Conflict Religious Heritage

ABSTRACT With the alarming rise in intercommunal conflicts worldwide, knowledge regarding post-conflict cultural heritage issues must advance to help formulate effective management systems and policies. Through an Actor-Network Theory-inspired approach, this paper explores the actors’ role in protecting religious heritage sites abandoned by displaced communities. Discourse analysis of relevant literature and empirical data from exhaustive fieldwork documenting and categorising the use-status and preservation state of church sites from Northern Cyprus has revealed multiple networks of human and non-human actors and actants. Through ANT concepts of fact-building and translation, a flat platform has been developed showing the positions and interests of the conflicting parties with a focus on the neglected or underestimated dimensions of heritage protection. This platform allows heritage conservation to be viewed from a broader perspective and, in a sense, helps dilute the conflicting parties’ positions and make their stakes less critical. It also emphasises the importance of compromise solutions, including pragmatically negotiating potential alternative uses and actively integrating the local community with the stakeholder group to protect the heritage of the displaced communities.

Peningkatan Kapasitas Bisnis Sampah Rumah Tangga Pada Kelompok Bank Sampah Tunas Sejahtera Kota Blitar

Since the establishment of the Sustainable Development Goals (SDGs) in 2015, sustainability issues in the economic and social sectors have gained significant attention. The Indonesian government is committed to restoring the national economy to a better state than before the pandemic by transitioning towards a green or circular economy. The circular economy aims to maximize the utility and added value of raw materials, components, and products to reduce waste. According to the 2022 data from the National Waste Management Information System (SIPSN), 34.29% (7.2 million tons) of the 21.1 million tons of national waste remain unmanaged. Collaborative efforts between central and regional governments continue to implement effective waste management policies based on circular economy principles. The methods employed in this program include training and mentoring in processing organic and inorganic waste into marketable products, information management training, business management enhancement, human resources training, and collaboration with other waste banks. The results indicate an increase in the production capacity of the Tunas Sejahtera Waste Bank from 300 kg to 1 ton per month. Training in management and marketing has also improved operational efficiency and expanded market reach, thereby increasing revenue and enhancing the circular economy's impact

Forecasting energy consumption with a novel ensemble deep learning framework

Energy baseline development is a central and significant component of an energy management system. In commercial buildings where a wide variety of internal and external variables might impact the electricity usage, having a robust forecasting method is elemental to evaluate and enhance the energy performance over time. Due to extensive progress in data analytics, a number of single prediction methods have been developed recently. Nevertheless, either development of the theoretical study or application of ensemble learning methods to enhance prediction performance and stability find lacking in energy management of commercial sectors. This research proposes a novel heterogeneous ensemble model framework for short-term electricity consumption prediction under the hourly and daily granularity to bridge this research gap. The framework is composed of two levels. The first uses five stand-alone deep learning models including Recurrent Neural Network (RNN), Long Short-term Memory (LSTM), Bidirectional-LSTM (Bi-LSTM), Gated Recurrent Unit (GRU) and Convolutional Neural Network (CNN) as base models. In contrast, the second level applies the predicted outputs of the base models as feature inputs of k nearest neighbors (KNN), which is a strong model. The Grid Search is also used to search for the key optimal hyper parameters of the base models and the strong model. To demonstrate the developed model's effectiveness, four different space types of real-world educational buildings are used for verification. The extensive database has been setup for this study to collect data from Jan. 2018 to Dec. 2022. The quantitative analysis of the model was conducted in contrast with the base models and prevalent machine learning methods, including artificial neural network (ANN), KNN, support vector regression (SVR), AdaBoost, and linear regression (LR) during the testing phase. The prediction accuracy has improved substantially, with roughly a 95% reduction in CV-RMSE compared to that of AdaBoost and SVR. Moreover, the results signify the introduced model outperforms the base models and the ML methods in all studied buildings, thereby providing a foundation for developing effective management policies.

The association of road, traffic, and environmental factors with wrong-way riding behavior on shared e-bikes

Wrong-way riding (WWR) behavior of electric bikes (e-bikes) is a hazardous practice that frequently threatens traffic safety. It is imperative to accurately detect instances of WWR and analyze the various factors contributing to this behavior to develop and implement effective management policies. The emergence of shared e-bikes and advancements in machine learning have opened up new avenues for accurately examining the determinants of WWR behavior. Therefore, this study utilizes a large-scale dataset of shared e-bike trajectory data to establish a framework for detecting WWR behavior. Subsequently, association rule mining investigates the correlation among road, traffic, environmental factors, and the likelihood of higher and lower WWR behavior. The results indicate that: (1) In the association rules for higher WWR frequency, the absence of central separation facilities and bike lane separation facilities on the tertiary road, as well as lower land use density and the lack of public transportation facilities, enhanced the likelihood of WWR occurrence; (2) In the association rules for lower WWR frequency, longer crossing facility distance, physical bike lane separation, along with higher land use mixing and the presence of public transportation facilities, reduce the probability of WWR occurrence; (3) Time conditions display spatial heterogeneity, and different land use and road condition modulate the impact of time factors on rider’ intention for WWR. Finally, based on the study findings, adequate policy recommendations are proposed for urban management agencies to reduce WWR behavior and enhance the safety of the e-bike traffic system.

Spatiotemporal dynamics, bioaccumulation, and critical influencing factors of antibiotics in tilapia aquaculture: A study on source identification and environmental fate within typical farming systems

This study investigated the spatiotemporal dynamics, bioaccumulation, and critical influencing factors of antibiotics in tilapia aquaculture, focusing on source identification and environmental fate within typical farming systems. The results revealed a progressive increase in antibiotic concentrations in pond water and sediments over the cultivation period, with suspended solids and chemical oxygen demand identified as significant environmental factors influencing the distribution and dissemination of antibiotics. The aquaculture water source was the primary contributor of antibiotics in the farming system. Furthermore, the bioaccumulation factor (BAF) calculations indicated varying degrees of antibiotic enrichment in tilapia tissues, with sulfadimethoxine exhibiting the highest BAFs. Correlation analyses, redundancy analysis, and multivariate linear regression analysis provided insights into the relationship between environmental factors and antibiotics, identifying key antibiotics and influencing factors. The study highlighted the importance of managing and treating water sources to reduce the inflow of antibiotics into aquaculture systems and emphasized the need for non-antibiotic aquaculture practices to minimize the impact on the environment and public health. In conclusion, this research contributes valuable information for the development of effective management strategies and policies aimed at curbing antibiotic pollution in aquaculture environments, ensuring the sustainability of the aquaculture industry, and protecting ecosystem and consumer health.

Calculation of the Optimal Scale of Urban Green Space for Alleviating Surface Urban Heat Islands: A Case Study of Xi’an, China

Research has demonstrated that urban green spaces play a crucial role in mitigating the severe urban heat island (UHI) effect. However, existing studies often suffer from limitations such as the neglect of the cooling effect of water bodies within the green spaces and incomplete considerations of the overall cooling effect. These limitations may lead to inaccuracies in the research findings. Therefore, the present study takes the city of Xi’an as a case study to explore the optimal green space size for achieving efficient cooling. The results indicate that (i) urban green spaces exhibit robust cooling effects, with variations observed among the various types; (ii) for community parks without water, and for street gardens, the optimal areas of these green spaces are 3.44 and 0.83 hectares, respectively; (iii) for community parks with water, the area of internal water bodies should ideally be maintained at around 29.43% of the total green space area in order to achieve an optimal cooling efficiency. In conclusion, this study introduces a new perspective and new optimization methods for urban green space planning, thereby offering scientific guidance to urban planners in formulating effective development and management policies and urban planning schemes.

Effects of exogenous copper on microbial metabolic function and carbon use efficiency of Panax notoginseng planting soil.

Soil copper (Cu) pollution is a serious environmental risk in the Panax notoginseng planting area. However, the effect of Cu on soil microbial metabolism and nutrient cycling in this area remains unknown. Therefore, Biolog ECO-plate and enzyme stoichiometry methods were utilized in this study to investigate the impact of exogenous Cu (control: 0 mg·kg-1; Cu100: 100 mg·kg-1; Cu400: 400 mg·kg-1; and Cu600: 600 mg·kg-1) on the metabolic function of soil microbial and nutrient limitation in the P. notoginseng soil. The results indicated that Cu100 significantly increased soil organic carbon (SOC), total phosphorus (TP), soil C:N, microbial biomass carbon (MBC), and microbial biomass nitrogen (MBN) 9.89%, 15.65%, 17.91%, 61.87%, and 90.56% higher than the control, respectively. Moreover, the carbon source utilization ratio of carbohydrates, amino acids, and amphiphilic compounds of Cu100 also increased by 7.16%, 25.47%, and 84.68%, respectively, compared with the control. The activities of β-1,4-glucosidase, cellobiohyrolase, leucine amino peptidase, β-1,4-N-acetylglucosaminidase, and phosphatase significantly decreased with increasing Cu concentration. Soil enzyme stoichiometry showed that all treatments were limited by nitrogen (vector angle < 45°; 19.045-22.081). Cu600 led to the lowest carbon limitation (1.798) and highest carbon use efficiency (CUE:0.267). The PLS-SEM model also showed that MBC, MBN, MBP, and microbial diversity positively affected carbon and nitrogen limitation (0.654 and 0.424). Soil carbon, nitrogen, phosphorus, stoichiometric ratio, MBC, MBN, and MBP positively affected CUE (0.527 and 0.589). The microbial diversity index significantly negatively affected CUE (-1.490). Multiple linear stepwise regression analyses showed that CUE was mainly influenced by MBC, AP, C:P, and LAP. Thus, P. notoginseng soil can benefit soil microbial carbon and nitrogen limitations at low Cu concentrations. Clarifying the metabolic activity and nutritional status of microorganisms under Cu stress can provide some theoretical basis for realizing China's comprehensive and effective management and control policies for environmental risks from metals by 2035.

A geospatial approach-based assessment of soil erosion impacts on the dams silting in the semi-arid region

Soil erosion significantly impacts dam functionality by leading to reservoir siltation, reducing capacity, and heightening flood risks. This study aims to map soil erosion within a Geographic Information Systems (GIS) framework to estimate the siltation of the K'sob dam and compare these estimates with bathymetric observations. Focused on one of the Hodna basin’s sub-basins, the K'sob watershed (1477 km2), the assessment utilizes the Revised Universal Soil Loss Equation (RUSLE) integrated with GIS and remote sensing data to predict the spatial distribution of soil erosion. Remote sensing data were pivotal in updating land cover parameters critical for RUSLE, enhancing the precision of our erosion predictions. Our results indicate an average annual soil erosion rate of 7.83 t/ha, with variations ranging from 0 to 224 t/ha/year. With a typical relative error of about 13% in predictions, these figures confirm the robustness of our methodology. These insights are crucial for crafting mitigation strategies in areas facing high to extreme soil loss and will assist governmental agencies in prioritizing actions and formulating effective soil erosion management policies. Future studies should explore the integration of real-time data and advanced modeling techniques to further refine these predictions and expand their applicability in similar environmental assessments.

Quantitative assessment of runoff change and its drivers in a multi-scale framework

ABSTRACT Identifying runoff changes and quantifying the impacts of climate change and human activities are important for water resources planning and management in river basins. The impacts of climate change and human activities on runoff can be more accurately assessed through scientific hydrological modeling and data analysis methods. In this study, an integrated assessment framework was established to quantitatively separate the driving mechanisms of runoff at different time scales. The results show that the runoff of Wu River has shown a decreasing trend since 2004, with a change degree of 56%, and the monthly average flow indexes of August and September have changed significantly, both are over 90%. The NSE coefficient of the SWAT simulation effect is above 0.8, we validate the simulation results based on the LSTM model. It was found that climate change was the main factor affecting the runoff of Wu River, with the contribution rates reaching 60 and 57%, respectively. Pearson correlation analysis found that rainfall was the most important factor affecting the runoff. The results of this study are helpful to formulate effective water resources management policies and measures to ensure the sustainable utilization and management of water resources in the Wu River Basin.

SNSMiner_VAC: Analyzing vaccination based on social network service data for safety surveillance

COVID-19 has created a major issue around the world, posing a serious threat to socioeconomic and human health, and there is still fear of new infectious diseases like this. In Korea, in response to COVID-19, the COVID-19 vaccine approved by the Ministry of Food and Drug Safety was sequentially administered to each group, raising national interest in the safety, side effects, and effectiveness of the vaccine, and securing the basis for related policies. Many attempts have been made for this. We are interested in a methodology to establish a scientific basis for how to respond to issues regarding vaccine stability due to the emergence of new infectious diseases. In this study, we use comments posted on Naver, the largest social platform in Korea, to obtain consumer sentiment information about vaccines and understand the change in Korean public sentiment in the first half of 2021 for each vaccine based on the analysis pipeline (SNSMiner_VAC). We investigated the Korean public’s response to the COVID-19 vaccination on social media from November 30, 2021, to January 31, 2022. We collected comments related to the COVID-19 vaccination using the Korean words for “COVID-19 vaccination” as keywords. Of the 329,559 comments, 240,322 were analyzed after preprocessing. We developed three lexicons (stopwords, sentiment words, and custom words related to COVID-19) for Korean natural language processing. Furthermore, we extracted five clusters — including symptoms, side effects, vaccine hesitancy, pro-vaccination, and infectious disease control policy — through network analysis. The sentiment analysis revealed that negative public opinion was prominent even after the commencement of the phased recovery of daily life. From these results, we were able to automatically collect data-based information on patients’ symptoms and side effects for the new vaccine and prepare and analyze the basis for collecting public opinions about vaccination. We hope that the pipeline proposed in this study can be used as a tool to create a data source for effective management policies for vaccines being developed against the emergence of new infectious diseases.

Influence Of Good Governance on Employee Satisfaction in Faith-Based Higher Educational Institutions: The Mediating Role of Policy Management

The ever-increasing role of employee satisfaction in the survival and success of higher educational institutions points towards a strong emphasis on good governance. This study examined the mediating role of policy management on the relationship between good governance and employee satisfaction. Two hundred and sixty-eight respondents participated in the study, representing the population of Faith-Based Higher Educational Institutions in the (AHEI's) Philippines. The descriptive, correlational, regression, t-test, and structural equation modeling techniques were used to analyze the data. The results exhibit strong implementation of good governance, high levels of employee satisfaction, and effective policy management as perceived by the employees. Moreover, the study found a positive correlation between good governance, employee satisfaction, and policy management. It was also discovered that the sex, marital status, educational attainment, and salary of the respondents do not moderate the influence of good governance on employee satisfaction. Finally, the findings also revealed that policy management partially mediates the relationship between good governance and employee satisfaction.

Multi-isotopes (δD, δ18Owater, 87Sr/86Sr, δ34S and δ18Osulfate) as indicators for groundwater salinization genesis and evolution of a large agricultural drainage lake basin in Inner Mongolia, Northwest China

Groundwater salinization, a major eco-environmental problem in arid and semi-arid areas, can accelerate soil salinization, reducing crop productivity and imbalances in ecosystem diversity. This study classified water samples collected from the Ulansuhai Lake basin into five clusters using self-organizing maps (SOM). On this basis, multiple isotopes (δ18Owater, δD, 87Sr/86Sr, δ18Osulfate and δ34S) and isotopic models (Rayleigh fractionation and Bayesian isotope mixing models) were used to identify and quantify the genesis and evolution of groundwater salinization. The results showed that the samples were brackish or saline water, and the hydrochemical types were dominated by Na + K-Cl (SO4). It has been proved that the processes associated with groundwater salinization in the Ulansuhai Lake basin were dominated by water-rock interaction and human inputs. Among them, evaporite dissolution contributed substantially to groundwater salinity. Furthermore, salt inputs from human activities cannot be negligible. Based on the model calculations, evaporite dissolution accounted for the most significant proportion of all sources, with a mean value of 53 %. In addition, human inputs from regular agricultural activities (28 % from sewage and manure and 8 % from fertilizers) constituted another vital source of groundwater salinization associated with extensive agricultural activities in the study area. This study's results can deepen our understanding of the genesis of groundwater salinization and the evolution of the agricultural drainage lake basin. This knowledge will assist the Environmental Protection Department in developing effective policies for groundwater management in the Yellow River Basin.

Water quality assessment in a large plateau lake in China from 2014 to 2021 with machine learning models: Implications for future water quality management

Amid the global surge of eutrophication in lakes, investigating and analyzing water quality and trends of lakes becomes imperative for formulating effective lake management policies. Water quality index (WQI) is one of the most used tools to assess water quality by integrating data from multiple water quality parameters. In this study, we analyzed the spatio-temporal variations of 11 water quality parameters in one of the largest plateau lakes, Erhai Lake, based on surveys from January 2014 to December 2021. Leveraging machine learning models, we gauged the relative importance of different water quality parameters to the WQI and further utilized stepwise multiple linear regression to derive an optimal minimal water quality index (WQImin) that required the minimal number of water quality parameters without compromising the performance. Our results indicated that the water quality of Erhai Lake typically showed a trend towards improvement, as indicated by the positive Mann-Kendall test for WQI performance (Z = 2.89, p < 0.01). Among the five machine learning models, XGBoost emerged as the best performer (coefficient of determination R2 = 0.822, mean squared error = 3.430, and mean absolute error = 1.460). Among the 11 water quality parameters, only four (i.e., dissolved oxygen, ammonia nitrogen, total phosphorus, and total nitrogen) were needed for the optimal WQImin. The establishment of the WQImin helps reduce cost in future water quality monitoring in Erhai Lake, which may also serve as a valuable framework for efficient water quality monitoring in similar waters. In addition, the elucidation of spatio-temporal patterns and trends of Erhai Lake's water quality serves as a compass for authorities, offering insights to bolster lake management strategies in the future.

Talent management of library and information science professionals: A review of research and future directions

Background This study aims to review the extant literature on talent management with the objective of influencing library and information management by addressing the key facets of talent management, such as talent management strategies, importance of career development, evaluation of talented employees, and organizational resilience. Methodology Literature on the development of talent and career management was retrieved from various scholarly papers indexed in Scopus and Web of Science to have a meticulous literature review serving as the platform of the present study. In light of the authors’ observations, two models were developed. The extant literature provides precise information that talent management plays a decisive role in promoting organizational excellence invariably in all kinds of organizations in general and libraries in particular. Results This study provides constructive recommendations for the implementation of effective talent management and retention policies for library and information professionals. Moreover, this study adds immense value to the corpus of existing literature to set a platform for the augmentation of library management in futuristic vision. Conclusion This study provides constructive recommendations to policy makers and library administrators to foster talented employees for excelling library and information services for the next several decades.

Implementasi Manajemen Sumber Daya Manusia dalam Meningkatkan Motivasi Kerja

Work motivation is an important factor in improving employee performance and achieving organizational goals. Human Resource Management (HRM) plays a crucial role in motivating employees through various strategies and policies. The method used in this study is a type of qualitative research. The data sources obtained in this study are from the results of library research from various sources. The main source of data for this research is the results of research published on google scholar. Data analysis was carried out qualitatively with the stages of data reduction, data presentation, and conclusion drawn. The results of this study are obtained how the role of human resource management in increasing work motivation, effective human resource management strategies and policies to increase work motivation, as well as challenges and solutions in the implementation of human resource management to increase work motivation

Impact of Monetary Policy on Exchange Rate Stability in Nigeria

This study investigates the impact of interest rates, money supply, and Central Bank reserves on exchange rate stability in Nigeria. Utilizing quarterly economic data from 1980 to 2023, sourced from the Central Bank of Nigeria (CBN) Statistical Bulletin, the research employs a longitudinal survey design to assess the relationships between these monetary variables and exchange rate volatility. The findings indicate that interest rates, money supply, and Central Bank reserves significantly influence exchange rate fluctuations, with Central Bank reserves having the most substantial impact. Specifically, interest rates exhibit a moderate effect on exchange rate volatility (t = 3.513, p = .001, Beta = .342), while money supply also significantly affects exchange rate volatility (t = 2.713, p = .010, Beta = .305). Central Bank reserves, however, have the most pronounced impact (t = 4.141, p = .000, Beta = .467). These results highlight the critical role of monetary policy in maintaining exchange rate stability. The significant positive relationship between interest rates and exchange rate volatility suggests that monetary authorities should carefully consider the ramifications of interest rate adjustments. Similarly, the influence of money supply underscores the need for meticulous management to prevent destabilization. Robust Central Bank reserves emerge as a crucial buffer against exchange rate fluctuations, emphasizing the importance of effective reserve management policies. The findings provide actionable insights for policymakers aiming to enhance exchange rate stability through strategic interest rate policies, careful money supply management, and maintaining adequate Central Bank reserves.