Management Procedures Research Articles

Drivers of domestic revenue mobilization in Somalia: Exploring the role of external grants and public expenditures

The purpose of this paper is to examine the drivers of domestic revenue mobilization in Somalia by applying the autoregressive distributed lag model, where monthly data are used. The study assessed whether external grants reduce the incentive of the Somali fiscal authorities to mobilize domestic revenue and whether the operating expenditures significantly explain the domestic revenue mobilization effort more than other expenditures, including the expenditures on the social assistance benefits. The study found that external grants reduce the government’s incentive to enhance domestic revenue in the short and long run, and the operational expenditures are more significant for the domestic revenue mobilization effort than other expenditures, including the social assistance benefits. The study also found that COVID-19 has a significant negative impact on domestic revenue, as the pandemic hit Somalia, domestic revenue dropped by 8% in 2020, falling to approximately USD 211.2 million from USD 230.3 million in 2019. This study does not recommend the reduction or termination of the external grants since it is a critical support for Somalia’s state-building efforts. However, it recommends improvement in the quality of grant management procedures and urgent reconsideration of expenditure priorities by giving greater importance to development expenditures over recurrent expenditures.

Water, sanitation, and hygiene in selected health facilities in Ethiopia: risks for healthcare acquired antimicrobial-resistant infections

BackgroundInadequate water, sanitation and hygiene (WASH) in health facilities, and the low adherence to infection control protocols can increase the risk of hospital-acquired (nosocomial) infections (HAIs). The risk for HAIs can increase morbidity, and mortality, health care cost, but also contribute to increased microbial resistance.ObjectivesThe study aimed to assess WASH facilities and practices, and levels of nosocomial pathogens in selected health facilities in Oromia Region and Southern, Nations and Nationalities and Peoples (SNNPs) Region.Materials and methodsAn observational cross-sectional study design was employed to assess the WASH facilities in health care in SNNPs (Bulle and Doyogena) and Oromia (Bidre) regions through interviews and direct observations (n = 26 facilities). Water and surface samples were collected from major hospitals and health centers. A total of 90 surface swabs and 14 water samples were collected identified, characterized and tested for antimicrobial susceptibility. Epi-info was used for data entry and the data was subsequently exported to Stata version 17 for data cleaning and analysis.ResultsWater supply, toilet facilities, and waste management procedures were suboptimal (below the minimum standards of WHO). Only 11/26 of the health facilities had access to water at the time of the survey. The lowest hand-hygiene compliance was for Bidre (4%), followed by Doyogena (14%), and Bulle (36%). Over 70% of the identified bacteria were from four categories: Staphylococcus spp., Bacillus spp., E. coli, and Klebsiella spp. These bacteria also found in high-risk locations including neonatal intensive care units, delivery and surgical rooms. Antimicrobial susceptibility detected in ≥50% of the isolates for penicillin, cefazolin, ampicillin, oxacillin, and cotrimoxazole, and ≥ 50% of the isolates displayed multi-drug resistance.ConclusionInvesting in WASH infrastructures, promotion of handwashing practices, implementing infection prevention and control (IPC) measures and antibiotic stewardship is critical to ensure quality care in these settings. We recommend careful use of higher generation cephalosporins and fluoroquinolones.

A new reliability-based procedure for life-cycle management of new and existing constructions

A new reliability-based procedure for life-cycle management of new and existing constructions

Efficient management of pulmonary embolism diagnosis using a two-step interconnected machine learning model based on electronic health records data.

Pulmonary Embolism (PE) is a life-threatening clinical disease with no specific clinical symptoms and Computed Tomography Angiography (CTA) is used for diagnosis. Clinical decision support scoring systems like Wells and rGeneva based on PE risk factors have been developed to estimate the pre-test probability but are underused, leading to continuous overuse of CTA imaging. This diagnostic study aimed to propose a novel approach for efficient management of PE diagnosis using a two-step interconnected machine learning framework directly by analyzing patients' Electronic Health Records data. First, we performed feature importance analysis according to the result of LightGBM superiority for PE prediction, then four state-of-the-art machine learning methods were applied for PE prediction based on the feature importance results, enabling swift and accurate pre-test diagnosis. Throughout the study patients' data from different departments were collected from Sina educational hospital, affiliated with the Tehran University of medical sciences in Iran. Generally, the Ridge classification method obtained the best performance with an F1 score of 0.96. Extensive experimental findings showed the effectiveness and simplicity of this diagnostic process of PE in comparison with the existing scoring systems. The main strength of this approach centered on PE disease management procedures, which would reduce avoidable invasive CTA imaging and be applied as a primary prognosis of PE, hence assisting the healthcare system, clinicians, and patients by reducing costs and promoting treatment quality and patient satisfaction.

Biodegradable Gelatin–Carrageenan Sponges: High-Potential Functional Nasal Packs for Efficient Secretome Delivery

Nasal packing is a critical procedure in postoperative care and trauma management aimed at controlling bleeding, providing structural support, and promoting tissue healing. However, conventional nasal packs often lead to discomfort, infection risks, and secondary tissue damage. To address these challenges, this study explores the potential use of biodegradable and biocompatible gelatin–carrageenan composite scaffolds as an alternative nasal packing material. Five compositions of gelatin–carrageenan scaffolds (ratios 10:0, 7:3, 5:5, 3:7, and 0:10) were fabricated and evaluated for physicochemical properties, hemocompatibility, and cytocompatibility. Results suggest that balanced ratios, such as 7:3 and 5:5, may provide a combination of structural integrity, improved biocompatibility, and controlled degradation, making them a potential candidate for nasal packing applications. The scaffolds exhibited low cytotoxicity and reasonable blood compatibility, which could reduce the risks associated with conventional materials. While these findings are promising, further in vivo studies are necessary to validate the efficacy and safety of these scaffolds in clinical settings. If proven effective, gelatin–carrageenan scaffolds may help address some of the limitations of conventional nasal packing materials and improve postoperative care outcomes.

A retrospective study of WeChat app-based health management for patients with gestational diabetes mellitus

Background This study aimed to explore and evaluate the effectiveness of WeChat app-based health management in patients with gestational diabetes mellitus (GDM). Methods This study retrospectively analysed a cohort of 100 patients with GDM. Since the application we used went live in April 2023, we enrolled patients from April 2022 to June 2022 into the control group (n = 50) and patients from April 2023 to June 2023 into the observation group (n = 50) for contemporaneous comparisons. The control group received routine health management procedures, whereas the observation group received app-based health management. Fasting plasma glucose, 2-hour postprandial blood glucose, and haemoglobin A1c levels were compared between the two groups before and four and eight weeks after app management; the incidence of adverse pregnancy outcomes and weight gain before and after pregnancy was also compared between the two groups. Results Fasting plasma glucose and 2-hour postprandial blood glucose levels were lower in the observation group than in the control group four and eight weeks after intervention (p < 0.05). In addition, the incidence of adverse pregnancy outcomes and weight gain was lower in the observation group than in the control group (p < 0.05). Conclusions Implementing WeChat app-based health management can effectively improve blood glucose levels, reduce adverse pregnancy events in patients with GDM, and decrease weight gain during pregnancy.

A comprehensive model for ensuring data compliance in cloud computing environment

As organizations increasingly adopt cloud computing, ensuring data compliance has become a critical priority. Cloud environments pose unique challenges in meeting regulatory requirements due to their distributed and often multi-tenant nature. This review presents a comprehensive model for achieving data compliance in cloud computing, addressing essential aspects such as data privacy, security, sovereignty, and governance. The model emphasizes a structured, layered approach to compliance, integrating risk assessment, data governance, continuous monitoring, and incident management to ensure compliance throughout the data lifecycle. Key components of the proposed model include robust data privacy and protection mechanisms aligned with global regulations such as GDPR and CCPA, data security protocols for safeguarding confidentiality and access control, and data sovereignty policies to handle jurisdictional requirements and cross-border data flows. Additionally, continuous monitoring and automated auditing tools enhance real-time compliance management, while incident management procedures prepare organizations to respond promptly to breaches or policy violations. The framework also incorporates advanced technological solutions, including compliance automation, data loss prevention, and blockchain for traceability, to streamline compliance tasks and improve transparency. By addressing both the technical and governance aspects of data compliance, this model supports organizations in navigating complex regulatory landscapes across multi-cloud and hybrid environments. The review highlights best practices for ensuring compliance, such as regular policy updates, employee training, and third-party compliance management, to sustain long-term adherence. As the regulatory landscape evolves and new privacy-preserving technologies emerge, this model offers a scalable and adaptive approach, positioning organizations to manage data compliance effectively within dynamic cloud infrastructures. This comprehensive approach ensures secure, compliant data operations, fostering trust and accountability in cloud computing environments.

Strategic Management Practices on the Performance of Non-Government Organizations in Kisii County, Kenya

Running Non-Governmental organizations still has its fair share of challenges. Managerial efficiency is still wanting, its performance is very low with most of them witnessing increased corruption cases. Thus, the purpose of this study was to assess the influence of strategic management practices on the performance of NGOs in Kisii County, Kenya. The target population was 906 respondents comprising 16 Presidents, 16 Treasurers and 804 members of the board. The sample size for the study was 112 respondents comprised of 16 presidents and 16 treasurers who were sampled purposively while 80 board members were sampled randomly, 5 of them per NGO. Questionnaires were used to collect data from the board members whereas interviews were used to collect data from presidents and treasurers. Piloting of research instruments was conducted amongst 10.0% of the sample size of the respondents in the neighbouring Kisii County to establish validity, reliability, credibility, and dependability. Validity was ascertained through expert judgment. The reliability of the instruments was determined using the test-retest technique. The correlation coefficient was obtained using Pearson’s Product Moment Correlation Method. If a coefficient of r≥0.7 is obtained, then it indicates the high internal reliability of the instruments. Credibility was established by data triangulation through multiple analysts whereas dependability was established through detailed reporting. Data analysis began by identifying common themes from the respondents’ descriptions of their experiences. Frequency counts of the responses were then obtained to generate information about the respondents and to illustrate the general trend of findings on the various variables that were under investigation. Qualitative data was analysed thematically along the objectives and presented in narrative forms whereas quantitative data was analysed descriptively using frequencies, percentages, mean and standard deviation and inferentially using correlation and linear regression with the help of Statistical Packages for Social Science (SPSS Version 23). The study had a response rate of 87% which was considered to be very good, and 32 respondents were interviewed. The findings of the study were presented using tables and charts. After the analysis was conducted, it was evident that the majority of the NGOs did not engage in the expected procedures of strategic management, specifically planning, implementation and evaluation. Though there was some level of agreement in understanding the external environment in a firm operated, the majority of the respondents were not aware of how dynamic their work environment can change thus affecting their firm’s objectives. Ensuring that firms conducted an internal analysis to identify their strengths and weaknesses, ensuring firms had crisis management plans, well-defined objectives that would enable the achievement of long-term goals, and practical milestones that could be used to gauge the accomplishment of the set goals were among the significant predictors are detailed in the inferential statistics sections.

Relationship between nurses' motivation and compliance with case reflection discussions on standard operating procedures for pain management in the Darmawan Pavilion Care Unit at RSPAD Gatot Soebroto

Motivation is a psychological characteristic of humans that contributes to their level of commitment. This study aims to analyze the relationship between nurses' motivation and compliance with case reflection discussions on the standard operating procedures for pain management in the Darmawan Pavilion Care Unit at RSPAD Gatot Soebroto. The independent variables are nurses' motivation and compliance, while the dependent variable is case reflection discussions on the standard operating procedures for pain management. The research design uses a cross-sectional quantitative approach with a sample of 57 respondents selected through purposive sampling, comprising implementing nurses assigned to case reflection discussions. Bivariate analysis was conducted using the chi-square test. The results indicate a relationship between nurses' motivation and compliance with case reflection discussions on the standard operating procedures for pain management in the Darmawan Pavilion Care Unit at RSPAD Gatot Soebroto. The bivariate test results show that nurses' compliance is the most dominant variable, with a p-value = 0.028 (? &lt; 0.05), indicating that nurses' compliance has the most significant influence on the dependent variable.

Experiment of laparoscopic sleeve gastrectomy in Al-Imamein Al-Kadhimein Medical City

Background: Obesity is a multifactorial metabolic disorder characterized by the accumulation of excess energy in adipose tissue. The risk of this disorder lies in genetic problems, intake problems, psychological disorders, lack of exercise, eating habits, and medical conditions. Weight loss attempts involving dietary changes, behavioral modifications, and exercise have been of limited benefit to most affected individuals. Weight loss operations have obviously evolved in recent years. The most commonly used and best-studied procedure is laparoscopic sleeve gastrectomy (LSG). Aim: The aim of this study was to assess certain points of LSG (benefits and complications for patients with morbid obesity) performed in Al-Imamein Al-Kadhimein Medical City. Patients and Methods: Data were collected in a prospective cohort study in Al-Imamein Al-Kadhimein Medical City between January 2021 and December 2022. LSG was performed for 40 patients for the treatment of obesity and included the following demographic data: body mass index (BMI, weight in kilograms divided by height in square meters (kg/m2)), hip circumference, waist circumference, total cholesterol, triglyceride, low-density lipoprotein, high-density lipoprotein, HbA1c%, and fasting blood sugar, presence of comorbidity (e.g. diabetes and hypertension), and complications following surgery. All patients were followed 3 months and 9 months postoperatively. Results: A total of 40 obese patients underwent bariatric surgery (laparoscopic sleeve gastrectomy), there were 65% female and 35% male, their mean age was 34.30 ± 5.31 years (range:23-45 years), the mean weight of patients was 146.70 ± 21.92 kg (115- 200 kg), the mean BMI was 51.45 ± 5.65 kg/m2 (37.5-63.8 kg/m2). Excess weight loss was 69% after 9 months. The improvement in comorbid diseases was significant. Conclusion: LSG is a useful procedure for the surgical management of morbid obesity. Most people who underwent these operations experienced a reduction in complications of obesity, such as hypertension and diabetes mellitus.

A sectoral perspective on corporate cash holdings

The main purpose of this research is to investigate the cash holdings behaviour on sectoral level for South African firms listed on the Johannesburg Stock Exchange (JSE). The accounting cash ratio is used to identify abnormal (excess) cash holdings for the firms listed on the JSE. This informed the panel regression analysis to identify cash holdings determinants on a sectoral level. The sample data included 255 firms of which 102 represent Financial Firms and 153 represent Non-Financial Firms for 2005 to 2019. The findings show the significant internal and external determinants of cash holdings. Comparing coefficient sizes, this research finds that financial and non-financial sectors with abnormal (excess) cash holdings exhibit higher coefficient sizes as opposed to sectors without. As a result, the higher coefficient size shows that the internal and external determinants of cash holdings have a greater effect on the cash holding levels of these sectors. The implications of the findings of this study are that each sector operates differently and that each firm within each sector has differing cash management policies and procedures. Therefore, analyzing cash holdings behaviour on an aggregated level and assuming that all sectors and firms within the collective operate the same is an erroneous assumption, as shown by this study. This research firstly contributed by introducing the use of the accounting cash ratio to indicate the presence of abnormal (excess) cash holdings. Most research focus on cash holdings of Non-Financial Firms. Therefore, the second contribution of this research is that both Non-Financial and Financial Firms with and without abnormal (excess) cash holdings were included to identify determinants of cash holdings, this was also done on a sectoral level.

A GIS Plugin for the Assessment of Deformations in Existing Bridge Portfolios via MTInSAR Data

The paper presents a GIS plugin, named Bridge Assessment System via MTInSAR (BAS-MTInSAR), aimed at assessing deformations in existing simply supported concrete girder bridges through Multi-Temporal Interferometry Synthetic Aperture Radar (MTInSAR). Existing bridges require continuous maintenance to ensure functionality toward external effects undermining the safety of these structures, such as aging, material degradation, and environmental factors. Although effective and standardized methodologies exist (e.g., structural monitoring, periodic onsite inspections), new emerging technologies could be employed to provide time- and cost-effective information on the current state of structures and to drive prompt interventions to mitigate risk. One example is represented by MTInSAR data, which can provide near-continuous information about structural displacements over time. To easily manage these data, the paper presents BAS-MTInSAR. The tool allows users to insert information of the focused bridge (displacement time series, structural information, temperature data) and, through a user-friendly GUI, observe the occurrence of abnormal deformations. In addition, the tool implements a procedure of multisource data management and defines proper thresholds to assess bridge behavior against current code prescriptions. BAS-MTInSAR is fully described throughout the text and was tested on a real case study, showing the main potentialities of the tool in managing bridge portfolios.

Prevalence and risk factors of gunshot wound infection in the eastern Democratic Republic of the Congo: A hospital‐based cross‐sectional study

AbstractBackgroundSurvivors of gunshot injuries are at high risk of developing infections at the injury site. However, data on gunshot wound infections and their associated burdens are limited. This study aims to determine the prevalence of gunshot wound infections and identify the risk factors associated with them in a tertiary hospital located in the eastern part of the Democratic Republic of the Congo.Materials and MethodsThis retrospective observational study was conducted at the Provincial General Reference Hospital of Bukavu in the Democratic Republic of the Congo, reviewing data from patients hospitalised between January 1, 2019, and December 31, 2020.ResultsA total of 549 patients were included in the study, with wound infections occurring in 26.6% of cases. Multivariate logistic regression analysis identified fractures (adjusted odds ratio [aOR] = 1.81; 95% confidence interval [CI] 1.12–2.92; P = .0146), nerve damage (aOR = 2.65; 95% CI 1.70–4.14; P &lt; .0001), trunk injuries (aOR = 5.64; 95% CI 2.61–12.16; P &lt; .0001), lower extremity injuries (aOR = 2.71; 95% CI 1.39–5.28; P = .0034), and multiple injuries (&gt;2; aOR = 2.35; 95% CI 1.05–5.25; P = .0378) as independent predictors of gunshot wound infection.ConclusionThe main factors predisposing gunshot wounds to infection, identified in this study, require thorough evaluation and urgent management. In cases of abdominal gunshot trauma, fractures, and vascular or nerve lesions, management procedures are aimed at reducing the infection burden.

Research on Urban Traffic Flows Prediction Model Based on Deep Learning

Abstract. Vehicle accumulation in urban regions is a significant complication, giving rise to monetary and sustainability-related concerns. The most important procedure for effective traffic management is the precise forecasting of traffic flow. This study leverages Long Short-Term Memory (LSTM) networks to predict traffic volume on Interstate-94 (I-94) in the US, using hourly traffic and weather data from 2012 to 2018, which was normalized using Min-Max scaling, and the LSTM model was trained on 80% of the data, with the remaining 20% used for testing. Evaluation of the model was conducted using performance indicators such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). Showing its strong forecasting ability, evidenced by its low MAE and RMSE, which highlight the model's high accuracy, and forecast the traffic flow under varying conditions. This study highlights the effectiveness of LSTM networks for traffic prediction, offering a significant tool for the management of the metropolitan areas. Upcoming studies might prioritize on real-time implementation and integrating additional data sources to further enhance prediction accuracy.

Research on Urban Traffic Flows Prediction Model Based on Deep Learning

Abstract. Vehicle accumulation in urban regions is a significant complication, giving rise to monetary and sustainability-related concerns. The most important procedure for effective traffic management is the precise forecasting of traffic flow. This study leverages Long Short-Term Memory (LSTM) networks to predict traffic volume on Interstate-94 (I-94) in the US, using hourly traffic and weather data from 2012 to 2018, which was normalized using Min-Max scaling, and the LSTM model was trained on 80% of the data, with the remaining 20% used for testing. Evaluation of the model was conducted using performance indicators such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). Showing its strong forecasting ability, evidenced by its low MAE and RMSE, which highlight the model's high accuracy, and forecast the traffic flow under varying conditions. This study highlights the effectiveness of LSTM networks for traffic prediction, offering a significant tool for the management of the metropolitan areas. Upcoming studies might prioritize on real-time implementation and integrating additional data sources to further enhance prediction accuracy.

Climate-readiness of fishery management procedures with application to the southeast US Atlantic

Abstract Global climate change threatens the assumption of stationarity inherent in many fisheries management decisions. This heightens the importance of developing management strategies that are robust to future uncertainty. Management strategy evaluation (MSE) is a framework in which management procedures (MPs) can be developed and tested using closed-loop simulation. We explored the performance of various model-based and empirical MPs with nonstationary future projections for three commercially and recreationally important fish stocks in the southeast US Atlantic. Using openMSE, we tested candidate MP performance across projections designed to emulate plausible future conditions, including regime shifts, nonstationarity, and observation error shifts in the survey index. Candidate MP performance was primarily measured based on its ability to maintain a healthy stock biomass. Results of this MSE demonstrate that several empirical MPs may be better able to adapt to regime shifts and nonstationary dynamics compared to traditional model-based MPs that employ full age-structured stock assessments, though empirical MPs struggle to maintain stock biomass when facing artificial index observation error shifts. Relative performance of model-based versus empirical MPs varied by stock and climate-change scenario. These findings highlight the value that adaptive MPs may hold for climate-ready fisheries management.

Common sports-related nerve injuries seen by the electrodiagnostic medical consultant.

The high physiologic demands of sports create dynamic stress on joints, soft tissues, and nerves which may lead to injuries in the athlete. Electrodiagnostic (EDx) assessment is essential to identify the correct diagnosis, localization, and prognosis, to guide management of sports-related neuropathies. A comprehensive review was performed to provide the EDx medical consultant with a practical approach to the common peripheral nerve disorders seen in athletes. Sports-related neuropathies reviewed include transient traumatic irritation of the brachial plexus and/or cervical nerve roots ("Burners and stingers,") suprascapular and axillary neuropathies, ulnar neuropathy at the elbow (UNE) in throwers, ulnar neuropathy at the hand/wrist in cyclists, multi-ligamentous knee injury, and foot/ankle neuropathies including tarsal tunnel syndrome. A thorough understanding of peripheral anatomy, possible entrapment sites, mechanisms of injury, and key physical examination findings is essential for correct diagnosis. EDx assessments beyond routine studies are generally required for sports-related neuropathy, which may not necessarily follow typical entrapment patterns. Adjunct diagnostic imaging, such as point-of-care ultrasound and magnetic resonance imaging, are helpful tools to identify associated musculoskeletal pathology such as compressive cysts or nerve entrapment, which may be amenable to interventional or surgical treatment. When no clear reversible structural pathology exists, management of sports-related neuropathy is athlete-specific and generally multi-modal, involving a combination of physical rehabilitation techniques to address muscle imbalances, load management, protective equipment, and interventional pain procedures.

An Algerian Soil-Living Streptomyces alboflavus Strain as Source of Antifungal Compounds for the Management of the Pea Pathogen Fusarium oxysporum f. sp. pisi.

Fusarium wilt caused by Fusarium oxysporum f. sp. pisi (Fop) poses significant threats to pea cultivation worldwide. Controlling this disease is mainly achieved through the integration of various disease management procedures, among which biological control has proven to be a safe and effective approach. This study aims to extract and identify antifungal secondary metabolites from Streptomyces alboflavus KRO3 strain and assess their effectiveness in inhibiting the in vitro growth of Fop. This bacterial strain exerts in vitro antagonistic activity against Fop, achieving highly significant inhibition over one week. The ethyl acetate extract, obtained from its ISP2 agar medium culture, also exhibited strong antifungal activity, maintaining an inhibition rate of approximately 90% at concentrations up to 250 µg/plug compared to the control. Thus, the organic extract has been fractionated using chromatographic techniques and its bioguided purification allowed us to isolate the main bioactive compound. This latter was identified as metacycloprodigiosin using nuclear magnetic resonance (NMR) spectroscopy, electrospray ionization mass spectrometry (ESI-MS), and specific optical rotation data. Metacycloprodigiosin demonstrates dose-dependent inhibitory activity against the phytopathogen with an effective concentration of 125 µg/plug. The other secondary metabolites present in the ethyl acetate extract were also identified by gas chromatography-mass spectrometry (GC-MS) and nuclear magnetic resonance (NMR). This study highlighted the potential of S. alboflavus KRO3 strain and its antimicrobial compounds for the management of the pea pathogen Fusarium oxysporum f. sp. pisi.

Liquidity Management Practices and Financial Performance of Microfinance Banks in Nairobi City County, Kenya

Microfinance banks are financial institutions that provide loans, insurance, saving platforms and other financial products to various individuals. Thus, these institutions require sufficient liquid assets to enable them to meet their routine financial obligations. However, in Kenya, microfinance banks have been recording a declining trend regarding their financial performance over time, leading to grave concerns. Thus, this study aimed to assess cash management practices and their effect on Nairobi City County’s MFB's financial performance. The analysis and elucidation of the literature was facilitated by the examination of the Keynesian Liquidity theory. A descriptive research design with a regression analysis model was embraced in determining the predictor variable’s impact on the explained variable. Data review guide was then utilized to gather secondary data, which was obtained from the CBK and covered the years 2017 to 2022. According to the study, operational cash flow management and cash management significantly and favorably impacted the financial performance of Kenyan microfinance institutions. Nonetheless, the financial performance of Kenya's microfinance institutions was significantly impacted negatively by operational efficiency management. As per the study's findings, microfinance banks in Kenya carry out cash budget preparation, bank reconciliation, cash inflow and outflow recording, cash expense payment, and cash surplus investment. As a result, they manage their funds carefully. Furthermore, the stability and functioning of the financial system depend heavily on efficient cash flow management. It entails keeping an eye on money coming in from investments, sales, and outside funding sources as well as tracking money going out to pay for bills, obligations, and loan repayments. Microfinance banks are able to balance inflows and outflows, lower the risk of liquidity crises, and take advantage of growth possibilities by managing cash flow well. This allows microfinance institutions to make well-informed choices regarding debt management, expansion strategies, and investments—all of which are essential for overall financial success. To maximize the beneficial effects on financial performance, the study recommends adhering to the statutory minimum liquidity ratio. Since excessive liquidity and illiquidity reduce the institutions' profitability, microfinance banks should implement effective cash management procedures.

The impact of artificial intelligence in the diagnosis and management of acoustic neuroma: A systematic review.

Schwann cell sheaths are the source of benign, slowly expanding tumours known as acoustic neuromas (AN). The diagnostic and treatment approaches for AN must be patient-centered, taking into account unique factors and preferences. The purpose of this study is to investigate how machine learning and artificial intelligence (AI) can revolutionise AN management and diagnostic procedures. A thorough systematic review that included peer-reviewed material from public databases was carried out. Publications on AN, AI, and deep learning up until December 2023 were included in the review's purview. Based on our analysis, AI models for volume estimation, segmentation, tumour type differentiation, and separation from healthy tissues have been developed successfully. Developments in computational biology imply that AI can be used effectively in a variety of fields, including quality of life evaluations, monitoring, robotic-assisted surgery, feature extraction, radiomics, image analysis, clinical decision support systems, and treatment planning. For better AN diagnosis and treatment, a variety of imaging modalities require the development of strong, flexible AI models that can handle heterogeneous imaging data. Subsequent investigations ought to concentrate on reproducing findings in order to standardise AI approaches, which could transform their use in medical environments.