Management Factors Research Articles

CPM and turnover intentions: dubai study of bankers explores mediating effects of employee motivation and job autonomy

PurposeThis paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies.Design/methodology/approachThis briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context.FindingsA study from of 373 employees in Dubai’s banking industry concluded that the key predictors of employee motivation, job autonomy and turnover were continuous performance management (CPM) factors. The research also identified the mediation effects of employee motivation and job autonomy between CPM and turnover intention.Originality/valueThe briefing saves busy executives, strategists and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

Assessing Transition Cow Management in Dairy Cows for Improved Health, Milk Production, Pregnancy and Culling Outcomes

Understanding of the social environment has the potential to benefit dairy cow welfare and production. Our aim was to evaluate the associations of stocking density, calving density, days spent in a pre-partum group before calving (days spent in close-up, DCU) and the number of days from a pen filling event (addition of new cows to the pre-partum pen) on early-lactation health, production, pregnancy and culling outcomes in dairy cows. Data were gathered from 2780 cows in 2 herds. Herd management and reproductive records were analyzed for cows receiving treatment in the first 30 d of lactation (days in milk, DIM) for clinical mastitis, reproductive tract disease, ketosis, milk fever and displaced abomasum. Principal component analysis was used to account for the relationship between gestation length (GL) and DCU and their association with early lactation disease, milk production, pregnancy and culling outcomes. The effect of stocking density, the number of days from a pen filling event to calving and the calving density experienced by a cow in her week of calving was also evaluated. Causal inference was used to correct for confounding bias due to farm identity. The odds of disease in the first 30 DIM increased with stocking density before calving. A quadratic association was found between the first principal component (PC1), representing the combined effect of GL and DCU, and the odds of disease in multiparous cows only. Early lactation milk production and 305 d milk production in multiparous cows increased with PC1 score. Quadratic relationships were found between stocking density at d 8 to 2 before calving with both early lactation and 305 d milk production in multiparous cows but no associations were found in primiparous cows. Calving density showed a quadratic association with 305 d milk production in primiparous cows. The number of days from the last pen filling event to calving was not associated with changes in milk production. Disease occurrence was negatively associated with both early lactation and 305 d milk yield in multiparous cows but only with early lactation milk production in primiparous cows. The occurrence of disease was associated with a delayed time to pregnancy only in primiparous cows while both disease and being in lactation group ≥ 3 were negatively associated with time to pregnancy in multiparous cows. Week 4 milk (W4MK) was positively associated with reduced time to pregnancy in multiparous cows. For primiparous cows, increasing age at calving was associated with increased culling risk, while being in lactation group ≥ 3 was associated with increased culling risk in multiparous cows. Culling risk decreased with increasing W4MK in all cows. These results suggest that gestation length, time spent in close-up and stocking density are important factors influencing disease incidence in early lactation and subsequent lactation performance but had differing effects on primiparous versus multiparous cows. A better understanding of how pre-partum management factors influence postpartum health and milk production can help farms to plan facilities and organize the day-to-day management of cows and will assist in improving cow welfare and productivity.

Seroprevalence of swine hepatitis E virus and the farmers' potential risk of infection in the Province of Bali, Indonesia.

Hepatitis E virus (HEV) infection formerly and predominantly occurred in rural areas. However, it has recently been spread to urban and peri-urban areas. This study aimed to estimate the seroprevalence of HEV in pigs collected from urban and rural areas in Bali. The potential of the pig farmers' risk level for being exposed to HEV and the virus transmitted to them in association with their pig-rearing practices was also assessed. A total of 183 pigs from 68 herds were sampled in this study, with 91 pigs collected from Denpasar as the representative samples of urban areas and 92 pigs from Karangasem Regency as the representative samples from rural areas. Sera from the sampled pigs were collected and immunoglobulin G antibodies against HEV were detected using a commercial enzyme-linked immunosorbent assay. A questionnaire was prepared for interviewing the farmers. Bivariable and multivariable logistic regression analyses were performed to identify the putative factors associated with seropositivity. Meanwhile, the potential risk-incurring practices of the farmers for HEV being transmitted to them from their pig-rearing practices were assessed by scoring their responses from the interview. Overall, 23.5% (43/183) (95% confidence interval [CI]: 17.6-30.3) pig sera tested were detected to have the antibodies against HEV. Among 68 pig herds, 36.8% (25) (95% CI: 25.4-49.3) of them had antibodies in at least one pig sampled from each herd. Pigs sampled from Karangasem were 5 times (Odds ratio [OR] 5.34, 95% CI: 2.27-13.54, p < 0.001) more likely to be seropositive than pigs collected from Denpasar. However, no difference was found in the seropositivity to HEV in pig herds between Denpasar and Karangasem (p = 0.05). In assessing the pig rearing management factors, pig farmers from Denpasar were 3 times (OR 3.0, 95% CI: 1.07-8.52, p = 0.05) more likely to rear pigs for economic investment compared to the farmers from Karangasem. Regarding anticipating pig diseases that can be transmitted to humans, farmers from Denpasar were 6 times (OR 5.72, 95% CI: 1.48-26.7, p = 0.0074) more likely to anticipate zoonotic diseases compared to the farmers from Karangasem. Similarly, pig farmers from Denpasar were 3 times (OR 3.29, 95% CI: 1.08-10.23, p = 0.035) more likely to anticipate pig diseases that could be transmitted to humans than the farmers from Karangasem. Pig farmers from Denpasar had 4 times the odds (OR 4.49, 95% CI: 1.11-18.19, p = 0.03) of washing their hands after going to the pigpens compared to the farmers from Karangasem. All the participants were categorized as being at high risk of HEV exposure and transmission. IgG antibodies against HEV were detected among pigs reared in rural areas of Karangasem and those reared in urban areas of Denpasar. This suggests that the risk of HEV exposure and transmission in these areas is not negligible. To minimize the risk, public education on zoonotic diseases, including HEV infection, transmission, and prevention, needs to be implemented and particularly targeted to local pig farmers.

Predictors of complications and mortality among patients undergoing pacemaker implantation in resource-limited settings: a 10-year retrospective follow-up study

IntroductionPacemakers (PMs) are used to treat patients with severe bradycardia symptoms. They do, however, pose several complications. Even with these risks, there are only a few studies assessing PM implantation outcomes in resource-limited settings like Ethiopia and other sub-Saharan countries in general. Therefore, this study aims to assess the mid-term outcome of PM implantation in patients who have undergone PM implantation in the Cardiac Center of Ethiopia by identifying the rate and predictors of complications and death.MethodologyThis retrospective study was conducted at the Cardiac Center of Ethiopia from October 2023 to January 2024 on patients who had PM implantation from September 2012 to August 2023 to assess the midterm outcome of the patients. Complication rate and all-cause mortality rate were the outcomes of our study. Multivariable logistic regression was used to identify factors associated with complications and death. To analyze survival times, a Kaplan–Meier analysis was performed.ResultsThis retrospective follow-up study included 182 patients who underwent PM implantation between September 2012 and August 2023 and were at least 18 years old. The patients' median follow-up duration was 72 months (Interquartile range (IQR): 36–96 months). At the end of the study, 26.4% of patients experienced complications. The three most frequent complications were lead dislodgement, which affected 6.6% of patients, PM-induced tachycardia, which affected 5.5% of patients, and early battery depletion, which affected 5.5% of patients. Older age (Adjusted Odds Ratio (AOR) 1.1, 95% CI 1.04–1.1, p value < 0.001), being female (AOR 4.5, 95%CI 2–9.9, p value < 0.001), having dual chamber PM (AOR 2.95, 95%CI 1.14–7.6, p value = 0.006) were predictors of complications. Thirty-one (17%) patients died during the follow-up period. The survival rates of our patients at 3, 5, and 10 years were 94.4%, 92.1%, and 65.5% respectively with a median survival time of 11 years. Patients with a higher Charlson comorbidity index before PM implantation (AOR 1.2, 95% CI 1.1–1.8, p = 0.04), presence of complications (AOR 3.5, 95% CI 1.2–10.6, p < 0.03), and New York Heart Association (NYHA) class III or IV (AOR 3.3, 95% CI 1.05–10.1, p = 0.04) were associated with mortality.ConclusionMany complications were experienced by patients who had PMs implanted, and several factors affected their prognosis. Thus, it is essential to identify predictors of both complications and mortality to prioritize and address the manageable factors associated with both mortality and complications.

Factors driving tree community structure in traditional home gardens in the Mayan forest

Introduction'Peasant populations in tropical areas use Traditional Home Gardens (THG) as a key resource for their homes. THG supports significant plant biodiversity, most directly related to asset production, including locally rare species related to food, fuelwood, cultural, ornamental, shade, and wind barriers. Previous studies have considered factors driving tree diversity in THG; however, few have evaluated the biological, environmental, and management factors that shape tree diversity. Our objective in the present study was to identify the drivers of the tree community in THG across a precipitation gradient, evaluating biological, environmental and management factors. MethodsWe visited forty-eight THG distributed across the precipitation gradient spanning around 300 km The tropical Mayan Forest in the Yucatan Peninsula, Mexico., where tree inventory and identification were obtained from specimens with a diameter at breast height > 5 cm. Interviews were conducted with owners about the history of THG. Metadata were captured and analyzed for 19 putative determinants of tree population structure. Differences in tree diversity by individual THG and region were determined statistically. Modeled interaction networks were determined based on a co-occurrence matrix. ResultsTHG tree abundances are mainly explained by the relative abundance of not deliberately established individuals, the organic carbon content in the soil, and surrounding forest integrity. Management history of THG by peasants, including factors such as irrigation, TGH area, and fertilization, showed smaller but significant differences. Tree distribution is at a lower degree influenced by the precipitation gradient in the Yucatan Peninsula, which shows different abundances even for species established by THG owners. ConclusionsOur results suggest that biological and environmental factors may shape tree abundance and distribution over anthropic activities in THG. We identified rare, region-specific, and indicator species by region and modeled interaction networks that show coincidences with the empirical knowledge of THG owners.

Predicting Plant Growth Stages Using Random Forest Classifier: A Machine Learning Approach

The optimization of plant growth through predictive modelling is a crucial aspect of modern agricultural practices. This study investigates the application of a Random Forest Classifier to predict plant growth stages based on various environmental and management factors. The dataset, sourced from Kaggle, includes variables such as soil type, sunlight hours, water frequency, fertilizer type, temperature, and humidity. The research involves extensive data pre-processing, including encoding categorical variables, scaling data, and splitting it into training (80%) and testing (20%) sets. The Random Forest Classifier is implemented with 5-fold cross-validation, and its performance is evaluated using accuracy, precision, recall, and F1-score metrics. The model exhibits robust performance with an average accuracy of 84.27%, precision of 85.59%, recall of 84.27%, and F1-score of 83.98%. Visualization techniques such as correlation heatmaps, PCA plots, t-SNE plots, and violin plots are used to provide insights into the data structure and feature relationships. The results confirm the hypothesis that machine learning can effectively predict plant growth stages, offering significant implications for precision agriculture. By accurately identifying growth stages, farmers and greenhouse managers can optimize resource allocation and management practices, leading to enhanced crop yields and sustainability. The study's limitations include the specificity of the dataset and the sole use of the Random Forest Classifier. Future research should explore additional machine learning models and incorporate more diverse datasets to improve generalizability. The findings contribute to the growing body of knowledge on the application of machine learning in agriculture and suggest practical applications for improving agricultural productivity

User acceptance as key factor for development and risk management of an AI-based robot arm control

User acceptance as key factor for development and risk management of an AI-based robot arm control

Optimal mesh size of traps for marbled flounder (Pseudopleuronectes yokohamae) based on biological and economic factors

AbstractGear selectivity is crucial in fisheries management because it influences the size and species of fish caught, with traditional methods often basing optimal mesh size on the mature length of a target species. However, to ensure sustainable fisheries, both biological aspects of resource protection and fishing operation efficiency must be considered. This study aimed to determine the ideal mesh size for trap fishing of marbled flounder (Pseudopleuronectes yokohamae) in Gyeongsangnam‐do, South Korea, by using biological and economic factors for sustainable fisheries management. We sampled fish caught in traps of varying mesh sizes (35, 50, 65, 80, and 100 mm) and developed two models based on fish maturity, bycatch ratio, and CPUE. Model 1 used a maturity criterion of 17.8 cm, and Model 2 used the legal minimum length limit of 20 cm. Net selectivity curves indicated that the optimal mesh size ranged 50–65 mm based only on a minimum mature body length of 17.8 cm. In contrast, the optimal mesh size based on both biological and economic factors ranged 57.5–90 mm. Our findings could guide policymakers in enhancing marble flounder population management strategies through policy adjustments in mesh size and gear restrictions.

Impact of a Health Coach-Led, Text-Based Digital Behavior Change Intervention on Weight Loss and Psychological Well-Being in Patients Receiving a Procedureless Intragastric Balloon Program: Prospective Single-Arm Study.

Digital health interventions show promise for weight management. However, few text-based behavior change interventions have been designed to support patients receiving intragastric balloons, and none have simultaneously evaluated weight loss, psychological well-being, and behavior change despite the crucial interplay of these factors in weight management. This study aims to assess whether a health coach-led, asynchronous, text-based digital behavior change coaching intervention (DBCCI) delivered to participants receiving an intragastric balloon and its aftercare program was feasible and acceptable to participants and supported improved outcomes, including weight loss, psychological well-being, and lifestyle behavior change conducive to weight loss maintenance. This 12-month, single-arm prospective study enrolled adults aged 21 to 65 years with BMI ≥27kg/m2 receiving a procedureless intragastric balloon (PIGB) at 5 bariatric clinics in the United Kingdom and the Netherlands. Participants received the DBCCI and the clinic-led PIGB aftercare program (remotely delivered) for 6 months after PIGB placement and then no intervention for an additional 6 months. The DBCCI was an evidence-based, personalized intervention wherein health coaches supported participants via exchanged asynchronous in-app text-based messages. Over the 12-month study, we assessed percentage of total body weight loss and psychological well-being via self-administered validated questionnaires (Warwick-Edinburgh Mental Wellbeing Scale, Generalized Anxiety Disorder Scale, Impact of Weight on Quality of Life-Lite-Clinical Trials Version, Loss of Control Over Eating Scale-Brief, Weight Efficacy Lifestyle Questionnaire-Short Form, and Barriers to Being Active Quiz). Participant engagement with and acceptability of the intervention were assessed via self-reported surveys. Overall, 107 participants (n=96, 89.7% female; mean baseline BMI 35.4, SD 5.4kg/m2) were included in the analysis. Mean total body weight loss was 13.5% (SEM 2.3%) at the end of the DBCCI and 11.22% (SEM 2.3%) at the 12-month follow-up (P<.001). Improvements were observed for all psychological well-being measures throughout the 12 months except for the Generalized Anxiety Disorder Scale (improvement at month1) and Barriers to Being Active Quiz (improvements at months3 and 6). Surveys showed high levels of engagement with and acceptability of the DBCCI. This study provides evidence that the health coach-led, asynchronous, text-based DBCCI was engaging and acceptable to participants with overweight and obesity. The DBCCI, delivered alongside the PIGB and its aftercare program, supported improved weight loss outcomes and psychological well-being versus baseline and was associated with lifestyle behavior changes known to help achieve and maintain long-term weight loss and improved health outcomes. Follow-up findings suggest a potential need for longer-term, more intense coaching to focus on weight loss maintenance and support ongoing self-coaching. This could be achieved by leveraging generative artificial intelligence to provide ongoing automated behavior change coaching support to augment human-led care. ClinicalTrials.gov NCT05884606; https://clinicaltrials.gov/study/NCT05884606.

Psychosocial factors in chronic disease management: Implications for health psychology

Chronic diseases present significant challenges to individual well-being, necessitating comprehensive management strategies that extend beyond medical treatments. This paper explores the crucial role of psychosocial factors in chronic disease management, highlighting their impact on treatment adherence, quality of life, and overall health outcomes. It delves into various psychosocial elements such as social support, coping mechanisms, health beliefs, and mental health comorbidities, discussing their implications for health psychology interventions. Through the synthesis of current research and illustrative case studies, this paper aims to deepen the understanding of these factors and advocate for holistic approaches in chronic disease management. By integrating psychosocial and medical interventions, healthcare providers can enhance patient well-being and optimize long-term health outcomes, ultimately fostering resilience and empowering individuals to effectively manage their chronic conditions.

Evaluation analysis of electronic governance in the Malang City communication and information service

An innovative public service system can certainly produce more conduciveconditions and foster and develop creativity in the management of serviceunits and government institutions. This article identifies problems or obstaclesthat may occur in electronic governance services at the Malang City Diskominfo,especially on the Sambat Online website. By evaluating the quality and effectiveness of existing services, corrective and enhancing steps can be taken toensure that these electronic services truly provide benefits to the people ofMalang City. This article focuses on the evaluation analysis of Electronic Governance services carried out at the Malang City Communication and Information Service (Diskominfo) using descriptive qualitative research methods. Then the data collection technique was carried out by observation and distributingquestionnaires via Google Forms. The result of this article is that the SambatOnline website service mechanism itself makes it easier for people to makecomplaints online. The service in responding to complaints from the people ofMalang City is in web form (sambat.malangkota.go.id) via SMS, website andAndroid. The supporting factors for managing online sambat in responding tocomplaints from the people of Malang City are the available budget, supporting facilities and infrastructure. Meanwhile, the inhibiting factors for onlinesambat management in responding to complaints from the people of MalangCity are the lack of super admin in the Malang City communications andinformation services department, budgets that have not been allocated to eachregional apparatus and frequent rollovers in the distribution of employeeswithin the Malang City Government.

The phenotypic characteristics and morphology of Jabres and Pasundan cattle differences

Abstract. Suryaka Y, Sutopo S, Setiaji A. 2024. The phenotypic characteristics and morphology of Jabres and Pasundan cattle differences. Biodiversitas 25: 3239-3246. Pasundan and Jabres cattle, prominent Indonesian breeds, exhibit unique phenotypic characteristics crucial for sustainable breeding and conservation. This study, aimed at delineating phenotypic differences between Pasundan and Jabres cattle, two prominent Indonesian breeds, is a hope for the country's sustainable breeding and conservation efforts. Data were collected from 131 adult cattle (71 Pasundan, 60 Jabres) from Kuningan and Brebes Districts, spanning July 2022 to June 2023. Morphological and morphometric analyses highlighted key distinctions: Pasundan cattle predominantly feature a solid reddish-brown coat, while Jabres cattle exhibit more diverse colors, including black. Jabres cattle showed significantly greater body length and chest girth than Pasundan cattle. Canonical Discriminant Analysis (CDA) effectively distinguished the breeds, especially in females, with 83.33% accuracy for Pasundan and 79.31% for Jabres. Critical discriminant traits included chest width and rump length. The findings suggest that Pasundan and Jabres cattle possess unique phenotypic traits driven by genetic, environmental, and management factors. These insights are crucial for developing targeted breeding strategies to enhance breed-specific characteristics, laying the groundwork for a promising future for Indonesia's cattle industry.

Identification of climatic and management factors influencing wheat’s yield variability using AgMERRA dataset and DSSAT model across a temperate region

One of the main challenges of today’s agriculture to ensure food security is developing strategies to deal with potential negative impacts of adaptation to climate variability. This study was conducted to determine climatic and management factors influencing wheat yield variability throughout a temperate region in Northeastern Iran in the period of 1980–2010. The growth stages and yield of wheat crop were simulated via DSSAT model, using AgMERRA gridded weather dataset. Also, the effect of climatic variables on yield was identified using Panel Data Regression (PDA). According to the results, 63% of the changes in irrigated wheat yield are explained by climatic factors (temperature and precipitation) and 37% by management factors. PDA revealed that among the climatic variables, the number of days with temperatures above 30 °C during the growing season, mean temperature, as well as amount and frequency of precipitation have a significant effect on irrigated wheat yield (p ≤ 0.05). The management practices, including provision of inputs such as chemical fertilizers, modified seeds, tillage machinery and equipment, information transfer and the penetration of knowledge in the field, would increase yields by 5 kg on average per year in study region. In general, employing effective management methods, in particular selecting the appropriate planting date that could result in better adaptation of the phenological stages of wheat to climatic conditions, thus improving the wheat yield. The results of this research suggest that use of valid AgMERRA meteorological dataset as input for DSSAT crop model could produce reliable simulations which in turn could be employed by food policy and decision makers, farmers, and managers in a temperate region.

Understanding Rural – Urban Migration in Uganda’s Refugee Camps Authors

With continued conflicts all over the world, more refugees will continue migrating to countries they consider safe. According to Konrad-Adenauer-Stiftung and Young Leaders Think Tank (2017), refugees prefer dwelling in urban areas, which can be attributed to the global urbanisation phenomenon. This paper examines the factors influencing rural-urban migration to inform refugee management in urban settings. According to the Office of the Prime Minister, Uganda Refugee Statistics of September 2023, Uganda was host to over 1.5 million refugees, mainly from, South Sudan, the Democratic Republic of Congo, Somalia, Burundi, Eritrea and Rwanda. These refugees are registered in the rural settlements (refugee camps) as provided for by Uganda’s legal framework on the management of refugees, which framework also only recognises refugees in the capital Kampala as urban refugees. However, the landscape has evolved as some refugees have self-settled in various urban centres across Uganda, rapidly increasing the urban population and stressing urban services. Our study findings in Koboko Municipality show various personal, push and pull factors responsible for the refugees’ decision for rural-urban migration. These factors relate to demographic age, education level, gender, better social services, payable employment, economic activities, and utilisation of skills among others. The findings signal the need to recognise and plan for urban refugees in urban areas other than Kampala City. Urbanisation is a factor in refugee management in Uganda. Keywords: Urban refugee, Rural-urban migration, Koboko municipality, Uganda

Regional gray matter changes in steatotic liver disease provide a neurobiological link to depression: A cross-sectional UK Biobank cohort study

BackgroundSteatotic liver disease (SLD) is characterized by excessive accumulation of lipids in the liver. It is associated with elevated risk of hepatic and cardiometabolic diseases, as well as mental disorders such as depression. Previous studies revealed global gray matter reduction in SLD. To investigate a possible shared neurobiology with depression, we examined liver fat-related regional gray matter alterations in SLD and its most significant clinical subgroup metabolic dysfunction-associated steatotic liver disease (MASLD). MethodsWe analyzed regional cortical thickness and area obtained from brain MRI in 29,051 participants in UK Biobank. Liver fat amount was computed as proton density fat fraction (PDFF) from liver MRI scans. We examined the relationship between brain structure and PDFF, adjusting for sociodemographic, physical, lifestyle, and environmental factors, as well as alcohol intake and a spectrum of cardiometabolic covariates. Finally, we compared patterns of brain alterations in SLD/MASLD and major depressive disorder (MDD) using previously published results. ResultsPDFF-related gray matter alterations were region-specific, involving both increases and decreases in cortical thickness, and increased cortical area. In several regions, PDFF effects on gray matter could also be attributed to cardiometabolic covariates. However, PDFF was consistently associated with lower cortical thickness in middle and superior temporal regions and higher cortical thickness in pericalcarine and right frontal pole regions. PDFF-related alterations for the SLD and the MASLD group correlated with those observed in MDD (Pearson r = 0.45–0.54, p < 0.01). ConclusionThese findings suggest the presence of shared biological mechanisms linking MDD to SLD and MASLD. They might explain the well-known elevated risk of depression in these groups and support early lifestyle interventions and treatment of metabolic risk factors for the successful management of the interconnected diseases depression and SLD/MASLD.

Evaluating financial risk management in corporation financial security systems

Systemic risks are relevant to most segments of financial and economic activity, which creates the threat of financial instability of enterprises. Financial security, in this case, implies the state of financial soundness; it is expressed through various parameters including the level of solvency, financial stability, business activity, and efficiency of management. These questions are specific to each indicator, and this paper aims to understand their relations and how changes in them can result in risks. It elaborates the risk analysis and assessment procedure that uses a set of common financial analysis tools and determines key financial coefficients. Liquidity ratios, solvency ratios, profitability ratios, and the risk exposure metrics are the key areas that the research is based on. Correlating the financial indicators from a rich dataset from Yahoo’s Stock market data, regression analysis is used to determine these relationships between these indicators and risk management factors. A positive relationship between liquidity ratios and profitability is detected points to the fact that, firms with high liquidity levels are more capable of attaining good financial health and returns. It establishes the need for the development of a credible risk management structure that would lower threats to fiscal and economic activities. In light of this, the research shows that the liquidity and solvency management lowers financial risk and improves return for companies. The regression analysis was conducted to ascertain the degree of predictability of each independent variable to the dependent variable and the findings revealed that approximately 94 percent of each independent variable is predictable of the dependent variable that able to establish as much as 5% of the total variability in profitability, with the PE ratios being an indication of this aspect, was attributed to stock prices, as a result, the firm must be keen on its financials. This paper explores the employability of the proposed methods and models to assess the risks with high accuracy and supports optimum decision making in regard with the needed security of the enterprises. By adopting these methodologies, the businesses can observe and estimate risks which exist in the future which would help them to avoid certain issues or dangers and also pave the way for the improvements of the financial stability of the businesses. On this note, the study establishes the fact that incorporating the notion of total risk management into the framework of analyzing business and making financial decisions is inevitable in maintaining corporate financial stability and realizing perpetual economic solidity. This research enriches the literature on financial risk management, as it outlines viable strategies for the protection of enterprise financial sustainability.

A data-driven Bayesian network of management and organizational factors for human reliability analysis in the process industry

According to historical statistical data, management and organizational factors (MOFs) contribute more to process accidents than technique factors. Under the umbrella of socio-tech system theory, human reliability analysis (HRA) has become a critical part of systemic probability risk analysis. In many HRA techniques, MOFs are among the performance shaping factors (PSFs). However, the interactions and causality of MOFs to human errors are still difficult to quantify and lack validation. To fill these gaps, a framework is proposed, considering data source selection, CBN construction algorithm comparison, and results validation. The case study employed the open access eMARS database as a data source. The optimized hybrid structure learning algorithm and Bayesian criteria parameter learning algorithm are employed to build a Causal Bayesian Network (CBN) of (MOFs) that lead to human error. The proposed kernel CBN is validated through prediction accuracy and sensitivity analysis. For theoretical contribution, the validated kernel BN could generally serve as the heart part of more specific CBNs as a basis for future works. For practical applications, an application shows the model's ability to quantify the contribution of MOFs to system reliability. The results show that human-machine interacting system reliability is most sensitive to organizational factors such as adequate training and procedures.

Penilaian Tingkat Kekeritisan Lahan Pertambangan dan Beberapa Pengguanaan Lahan Lainnya di Kalimantan Selatan

Critical land is land that is no longer productive from an agricultural perspective, because management and use do not consider land management requirements and soil conservation principles. This research aims to assess the level of criticality of land based on the level of erosion that occurs at various coal mining land locations in several districts in South Kalimantan. This research is non-experimental research using exploratory descriptive methods. The data collected is secondary data obtained through official publications from the Environmental Service and related agencies from several environmental documents of coal mining companies that have received approval from the authorized Agency. The secondary data used is the data needed to calculate the amount of erosion which is used as a basis for evaluating land criticality. These data include: soil texture, permeability, bulk density, rainfall, slope and slope length, management factors, land cover vegetation factors and soil organic C. Erosion estimation uses the Universal Soil Loss Equation (USLE) method. The results of erosion calculations using USLE show that in open mine land in Banjar Regency it is 233.9 tons ha-1 year-1, while in Tanah Laut Regency is 173.1 tons ha-1 year-1 and in Kotabaru Regency is 275.1 tons ha-1 year-1. The level of criticality of land in the mining area is at a critical and very critical level compared to natural land and cultivated land due to differences in erosion values ​​and also the thickness of the soil solum studied.

Spatial Analysis of Health System Factors in Infectious Disease Management: Lessons Learned from the COVID-19 Pandemic in Korea.

Infectious disease outbreaks present ongoing and substantial challenges to health systems at local, national, and global levels, testing their preparedness, response capabilities, and resilience. This study aimed to identify and analyze critical health system-level factors that influence infection outbreaks, focusing on the experience of the COVID-19 pandemic in Korea. Conducted as a secondary data analysis, this study utilized national datasets from Korea. Given the inherent spatial dependencies in the spread of infectious diseases, we employed a spatial lag model to analyze data. While city-specific characteristics did not emerge as significant factors, health system variables, particularly the number of community health centers and health budgets, showed significant influence on the course of the COVID-19 outbreak, along with spatial autocorrelation coefficients. Our findings underscore the importance of enhancing public healthcare infrastructure, considering regional specificities, and promoting collaboration among local governments to bolster preparedness for future outbreaks. These insights are crucial for policymakers and healthcare professionals in formulating effective strategies to prevent, manage, and mitigate the impact of infectious disease outbreaks.

Knowledge Management Factors and Its Impact on Green Innovation: A Systematic Literature Review

Environmental problems are becoming more severe due to firms' operational activities, such as industrial production and manufacturing, which damage the environment. Consequently, Green Innovation (GI) is necessary to mitigate these negative effects. Although studies indicate that Knowledge Management (KM) impacts companies with GI, the specific impacts and mediating roles remain unclear. This study aimed to identify factors affecting KM in companies with GI and to understand how KM influences these companies. Using Kitchenham's systematic literature review of 20 articles from the past five years, this study found that green entrepreneurial orientation, green transformational leadership, knowledge-oriented leadership, embeddedness, stakeholder pressure, big data analytics capabilities, and training influence KM in companies with GI. Additionally, KM affects companies with GI directly and indirectly through human capital, sustainable development practices, environmental awareness, and green creativity. A conceptual model was proposed to illustrate these relationships, providing insights and recommendations for optimizing KM implementation in GI settings.