Statistical Measures Research Articles

Detection of Sleeping Pattern Using EEG Signals with Deep Learning

Background: Recorded brain activity analysis is a major tool used in modern medicine and sleep-based research. However, the volume and complexity of the data make manual investigation difficult. Furthermore, high inter-subject variability presents a challenge to deep learning techniques, especially in the detection of sleep-related EEG patterns and artifacts. Methods: Sleep Pattern Identifier (SPI), a hybrid classifier, was used in this study to address the aforementioned issues. SPI tests the efficacy of artifact detection while examining strategies and tactics used in real-world sleep research. A focus was on inter-subject variability, especially in data gathered from participants suffering from sleep disorders. For comparison and visual inspection of the data, formal statistical measures like accuracy, model loss, precision, recall, and ROC were employed. Results: With an accuracy of 94.85%, the suggested model outperformed current techniques and demonstrated higher accuracy. Further, the Sleep-EDF dataset was used in this investigation. Conclusion: The conclusion highlights the effectiveness of the SPI hybrid classifier in identifying EEG patterns, especially in situations where sleep research is conducted in the real world. Research methodologies based on sleep have made significant progress in handling artifacts and adapting to inter-subject variability.

Suspended Load Estimation in Data Scarce Rivers

AbstractSediment rating curves (SRCs) are tools of satisfactory reliability in the attempt to describe the sediment regime in catchments with limited or poor-quality records. The study valorised the most suitable SRC development method for the estimation of the coarse suspended sediment load at the outlet of nine Mediterranean sub-watersheds. Four established grouping techniques were assessed, to minimize the uncertainty of the results, namely simple rating curve, different ratings for the dry and wet season of the year, hydrographic classification, and broken line interpolation, at three major Greek rivers (Aliakmon, Acheloos – upper route, Arachthos). The methods’ performance was benchmarked against sediment discharge field records, utilizing statistical measures and graphical analyses. The necessary observations were conducted by the Greek Public Power Corporation. The results were site/station dependent, and no methodology emerged as universally accepted. The analysis designated that the simple rating curve performs best at the cross-sections Moni Ilarion, Moni Prodromou, and Arta bridge, the different ratings for the dry and wet season of the year at Grevena bridge and Gogo bridge, the hydrographic classification at Velventos and Plaka bridge, and the broken line interpolation at Avlaki dam and Tsimovo bridge. In this regard, the study advocates the use of multiple SRC methods. Despite its limitations, the method merits a rather simple and cost-effective generation of a (continuous, detailed, sufficiently accurate) synthetic suspended sediment discharge timeseries, with high interpolating, extrapolating and reproducibility potential. The success of the application could benefit, among others, water quality restoration and dam management operations.

Thai Baht and Chinese Yuan Exchange Rate Forecasting Models: ARIMA and SMA-ARIMA Comparison

This study evaluates the effectiveness of classical Autoregressive Integrated Moving Average (ARIMA) models and kth Simple Moving Average - ARIMA (kth SMA-ARIMA) models in forecasting the exchange rate between the Thai Baht (THB) and the Chinese Yuan (CNY). The analysis uses a dataset of historical monthly exchange rates from January 2011 to November 2022, covering 143 months. The dataset is divided into two segments: the initial 127 months are used as the training dataset for model development, while the subsequent 16 months serve as the testing dataset to evaluate forecast accuracy. The Akaike Information Criterion (AIC) is the decision criterion for model selection during the development phase. The forecasting models' effectiveness is subsequently assessed on the testing dataset using two statistical measures: the Mean Absolute Percentage Error (MAPE) and the Root Mean Square Error (RMSE). The findings indicate that the classical ARIMA (0,1,1) model outperforms the kth SMA-ARIMA models in this study, exhibiting the lowest RMSE and MAPE of 0.1702 and 2.6644, respectively. Additionally, a focused comparison of the kth SMA-ARIMA models for k = 2, 3, and 4 reveals that the 2nd SMA-ARIMA (0,1,2) model demonstrates superior performance compared to the 3rd and 4th SMA-ARIMA models. This superiority is reflected in their respective RMSE values of 0.3202, 0.5146, and 0.6339, and corresponding MAPE values of 5.3533, 8.7531, and 10.4949. These results provide valuable insights for decision-makers in the financial sector, enhancing investment strategy formulation based on anticipated currency movements.

MEASURING CAREER EXPECTATIONS OF GENERATION Z: KARABUK UNIVERSITY EXAMPLE

This study aims to ascertain the career expectations of Generation Z. To achieve this objective, it analyzes the concepts and characteristics of Traditionalists, Baby Boomers, Generation X, Generation Y, and Generation Z, with the underlying reasons for their distinctions. The research comprises 551 male and 253 female students currently enrolled at Karabük University in Türkiye. This study utilizes a descriptive research model, employing online survey forms as the primary data collection methodology. The gathered data analysis was done using t-tests and ANOVA statistical measurements. After a thorough evaluation, the findings revealed significant differences in the responses between participants from Generation Z and Generation X. While the majority of the respondents expressed confidence in possessing adequate knowledge about their future job prospects and expectations, most respondents expressed the need for assistance with career services. Therefore, the findings recommend that employers should be prepared to address the unique needs and work styles of Gen Z.

The benefits of implementing Robotic Process Automation in Accounting

Purpose: The purpose of the paper is to identify and evaluate the main benefits associated with implementing process automation in accountingMethodology/approach: The empirical part of the article includes the results of quantitative research, which was conducted using the survey method, employing purposive sampling of respondents. The study was conducted in the first quarter of 2024 on a sample of 109 enterprises based on a research questionnaire. Descriptive statistics measures were utilized in data analysis, taking into account the nature of the questions and scales included in the research tool.Findings: The research demonstrates the following key benefits of implementing ac-counting process automation in the surveyed enterprises: process transparency, risk minimization, increased enterprise productivity, and improved employee satisfaction.Research limitations/implications: The empirical research was exploratory and referred to a limited group of enterprises. However, they can constitute the basis for further research in assessing the use of process automation in accounting.Originality/value: The paper discusses the relatively unexplored area of modern accounting and presents empirical research findings on the benefits of implementing process automation in accounting. This study partially fills the research gap and contributes to the discussion on the use of modern technologies in accounting. The paper is one of the first studies to present the results of research conducted among business entities operating in Poland.

Intelligent computing applications to study the tri-hybrid nanofluid past over the stretched surface

In this study, a recurrent neural network with the Levenberg-Marquardt backpropagation algorithm is used to examine the effects of various parameters on cooling mechanisms involving tri-hybrid nanofluid consisting of Al2O3, Cu, TiO2 and magnetohydrodynamic stagnation point flow. This research focus on nanofluid in the existence of an asymmetrically stretched disc in water, which plays an imprortant role for understanding heat transfer in addition to the dynamics of momentum and thermal boundary surface. Transform the complex partial differential system through suitable similarity transformations into the ordinary differential equations for numerical computing. These equations have been solved numerically by using the Adams method in Mathematica to generate datasets for various parameters.The current study demonstrates that the velocity profile rises with higher magnetic, velocity slip, and mass suction parameters. The fluid thermal level tends to decrease with an increase in mass suction parameters, whereas an increase in the magnetic parameter and Eckert number raises the fluid’s thermal level. Furthermore, it is noticed that the flow resistance and internal friction of the fluid are proportional to the viscosity parameter. The proposed scheme has shown its effectiveness in a wide range of scenarios and cases. This was achieved by comparing the mean square error metric for the squeezing flow tri-hybrid nanofluid problem with the obtained results. In addition, various statistical measures like state transitions, fitness assessments, error histograms, and regression analyses, provide further evidence of the solver’s robustness and reliability. The excellent agreement between the reference datasets and refined numerical solutions through the scheme showcases the method’s effectiveness, achieving remarkable accuracy levels ranging from 10-6 to 10-12.

Self-care, Emotional Intelligence, and Self-assessed Wisdom: A Structural Equation Model on the Quality of Life among Elementary Public School Teachers

This quantitative research study intended to identify the best-fit model based on self-care, emotional intelligence, and self-assessed wisdom for quality of life among primary public school teachers in Region XI. This research employed a structural equation model (SEM) with 400 respondents who comprised the sample through stratified random technique. The data of this study were gathered using an electronic survey. The employed questionnaires underwent modifications and reliability and validity tests. This descriptive and causal investigation employed statistical measures such as mean, standard deviation, Pearson product-moment correlation, and structural equation model (SEM). The study found that both the levels of self-care and emotional intelligence were high, and the levels of self-assessed wisdom and quality of life were very high. On top of that, a significant relationship was discovered between emotional intelligence and quality of life, self-assessed wisdom and quality of life, and self-care and quality of life. Each of its exogenous variables significantly predicted the quality of life. Only Model 5 met all the requirements to be the best-fit model for quality of life as determined by its exogenous variables out of the five (5) generated models. As a result, it is regarded as the most relevant model for identifying and addressing the various aspects of quality of life that improve the general well-being of elementary public school teachers.

Performance analysis of machine learning models for AQI prediction in Gorakhpur City: a critical study.

Air pollution and climate change are two complementary forces that directly or indirectly affect the environment's physical, chemical, and biological processes. The air quality index is a parameter defined to cope with this effect of air pollution. This study delves deeper into predicting this AQI parameter using multiple machine learning-based models. The AQI pollutants considered for this study are particulate matter (PM10, PM2.5), SO2, and NO2. It also tries to develop a comparative analysis of two different machine learning (ML) models viz. a viz. XGBoost and Lasso regression. An ever-changing emission concentration of pollutants is displayed by this study conducted in the urban city of Gorakhpur Uttar Pradesh, India. The validation of prediction accuracies of models was done over several statistical metrics. The value of the R2 metric for XGBoost (0.9985) is comparatively more than the R2 value for Lasso regression (0.9218) indicating lesser variance and higher accuracy of XGBoost in predicting AQI. Various statistical measures are taken into consideration in this study, including mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), T-test and p-values, and confidence intervals (CI). An increased degree of model accuracy is suggested as XGBoost's MAE, MSE, and RMSE values are significantly lower than Lasso's. Statistically significant performance differences between the XGBoost and Lasso regression models are demonstrated by T-statistics and p-values for MAE, MSE, RMSE, and R2.

Analyzing symmetric distributions by utilizing extropy measures based on order statistics

Quantification of the uncertainty of distribution functions, by using entropy and extropy, is important in many statistical analyses. Inspired by this, our study uses extropy and several related measures (including the cumulative residual extropy, cumulative past extropy, and extropy-inaccuracy measure) of order statistics (OSs) to offer multiple characterizations of symmetric continuous distributions. We demonstrate that a defining feature of symmetric distributions is the equality of these measures of upper and lower OSs. Using concomitants of OSs based on the bivariate distributions belonging to the Farlie-Gumbel-Morgenstern (FGM) family, the same characteristic is demonstrated for these measures. Finally, a real data set is used to illustrate the applicability of the suggested test.

Data-driven evolutionary programming for evaluating the mechanical properties of concrete containing plastic waste

Plastic waste (PW) has emerged as a global environmental concern due to its detrimental impact on ecosystems and human health. Traditional concrete heavily relies on natural aggregates like sand, gravel, and crushed stone, whose extraction leads to environmental degradation, including habitat destruction and resource depletion. Recently, the use of PW in concrete has gained attention as a sustainable alternative to these conventional aggregates. By incorporating PW as a partial replacement for natural aggregates, the construction industry can reduce its reliance on finite resources while also addressing the issue of PW. However, despite its potential environmental benefits, the incorporation of PW into concrete has primarily been explored through experimental studies, which are often time-consuming and resource-intensive. Therefore, this study aims to optimize the utilization of waste plastic in concrete through machine learning (ML) techniques, specifically Multi-Expression Programming (MEP) and Gene Expression Programming (GEP). A comprehensive literature review was conducted to compile a database for evaluating the compressive strength (CS) and tensile strength (TS) of PW concrete. The most influential parameters, such as plastic (P), gravel (G), water (W), cement (C), sand (S), and age (A), were considered as inputs in the models' development. The models developed were thoroughly evaluated using multiple statistical measures. Additionally, sensitivity analysis was conducted to discern and highlight influential factors that have a significant impact on the predicted outcomes. The findings indicate that both MEP (CS_R2 = 0.88, and TS_R2 = 0.89) and GEP (CS_R2 = 0.87, and TS_R2 = 0.88) models performed well, with MEP demonstrating slightly superior performance. Sensitivity analysis highlights the significant influence of cement (25.63 % and 24.53 %) and plastic (22.4 % and 23.44 %) on concrete strength properties. Furthermore, the equations provided by GEP and MEP models are simple to use from a practical perspective. Overall, this study contributes to sustainability efforts by promoting the incorporation of waste materials in concrete mixtures, thereby reducing reliance on cement.

Model-Informed Radiopharmaceutical Therapy Optimization: A Study on the Impact of PBPK Model Parameters on Physical, Biological, and Statistical Measures in 177Lu-PSMA Therapy.

Purpose: To investigate the impact of physiologically based pharmacokinetic (PBPK) parameters on physical, biological, and statistical measures in lutetium-177-labeled radiopharmaceutical therapies (RPTs) targeting the prostate-specific membrane antigen (PSMA). Methods: Using a clinically validated PBPK model, realistic time-activity curves (TACs) for tumors, salivary glands, and kidneys were generated based on various model parameters. These TACs were used to calculate the area-under-the-TAC (AUC), dose, biologically effective dose (BED), and figure-of-merit BED (fBED). The effects of these parameters on radiobiological, pharmacokinetic, time, and statistical features were assessed. Results: Manipulating PBPK parameters significantly influenced AUC, dose, BED, and fBED outcomes across four different BED models. Higher association rates increased AUC, dose, and BED values for tumors, with minimal impact on non-target organs. Increased internalization rates reduced AUC and dose for tumors and kidneys. Higher serum protein-binding rates decreased AUC and dose for all tissues. Elevated tumor receptor density and ligand amounts enhanced uptake and effectiveness in tumors. Larger tumor volumes required dosimetry adjustments to maintain efficacy. Setting the tumor release rate to zero intensified the impact of association and internalization rates, enhancing tumor targeting while minimizing the effects on salivary glands and kidneys. Conclusions: Optimizing PBPK parameters can enhance the efficacy of lutetium-177-labeled RPTs targeting PSMA, providing insights for personalized and effective treatment regimens to minimize toxicity and improve therapeutic outcomes.

Detection of diffusion anisotropy from an individual short particle trajectory

In parallel with advances in microscale imaging techniques, the fields of biology and materials science have focused on precisely extracting particle properties based on their diffusion behavior. Although the majority of real-world particles exhibit anisotropy, their behavior has been studied less than that of isotropic particles. In this study, we introduce a method for estimating the diffusion coefficients of individual anisotropic particles using short-trajectory data on the basis of a maximum likelihood framework. Traditional estimation techniques often use mean-squared displacement (MSD) values or other statistical measures that inherently remove angular information. Instead, we treated the angle as a latent variable and used belief propagation to estimate it while maximizing the likelihood using the expectation-maximization algorithm. Compared to conventional methods, this approach facilitates better estimation of shorter trajectories and faster rotations, as confirmed by numerical simulations and experimental data involving bacteria and quantum rods. Additionally, we performed an analytical investigation of the limits of detectability of anisotropy and provided guidelines for the experimental design. In addition to serving as a powerful tool for analyzing complex systems, the proposed method will pave the way for applying maximum likelihood methods to more complex diffusion phenomena. Published by the American Physical Society 2024

An Unsupervised Approach for Treatment Effectiveness Monitoring Using Curvature Learning

Contrast-enhanced ultrasound could assess whether cancer chemotherapeutic agents work in days, rather than waiting 2–3 months, as is typical using the Response Evaluation Criteria in Solid Tumors (RECIST), therefore avoiding toxic side effects and expensive, ineffective therapy. A total of 40 mice were implanted with human colon cancer cells: treatment-sensitive mice in control (n = 10, receiving saline) and treated (n = 10, receiving bevacizumab) groups and treatment-resistant mice in control (n = 10) and treated (n = 10) groups. Each mouse was imaged using 3D dynamic contrast-enhanced ultrasound with Definity microbubbles. Curvature learning, an unsupervised learning approach, quantized pixels into three classes—blue, yellow, and red—representing normal, intermediate, and high cancer probability, both at baseline and after treatment. Next, a curvature learning score was calculated for each mouse using statistical measures representing variations in these three color classes across each frame from cine ultrasound images obtained during contrast administration on a given day (intra-day variability) and between pre- and post-treatment days (inter-day variability). A Wilcoxon rank-sum test compared score distributions between treated, treatment-sensitive mice and all others. There was a statistically significant difference in tumor score between the treated, treatment-sensitive group (n = 10) and all others (n = 30) (p = 0.0051). Curvature learning successfully identified treatment response, detecting changes in tumor perfusion before changes in tumor size. A similar technique could be developed for humans.

Orthophoto-Based Vegetation Patch Analyses—A New Approach to Assess Segmentation Quality

The following paper focuses on evaluating the quality of image prediction in the context of searching for plants of a single species, using the example of Heracleum sosnowskyi Manden, in a given area. This process involves a simplified classification that ends with a segmentation step. Because of the particular characteristics of environmental data, such as large areas of plant occurrence, significant partitioning of the population, or characteristics of a single individual, the use of standard statistical measures such as Accuracy, the Jaccard Index, or Dice Coefficient does not produce reliable results, as shown later in this study. This issue demonstrates the need for a new method for assessing the betted prediction quality adapted to the unique characteristics of vegetation patch detection. The main aim of this study is to provide such a metric and demonstrate its usefulness in the cases discussed. Our proposed metric introduces two new coefficients, M+ and M−, which, respectively, reward true positive regions and penalise false positive regions, thus providing a more nuanced assessment of segmentation quality. The effectiveness of this metric has been demonstrated in different scenarios focusing on variations in spatial distribution and fragmentation of theoretical vegetation patches, comparing the proposed new method with traditional metrics. The results indicate that our metric offers a more flexible and accurate assessment of segmentation quality, especially in cases involving complex environmental data. This study aims to demonstrate the usefulness and applicability of the metric in real-world vegetation patch detection tasks.

Determining the minimum data set of geriatric assessment at the Iran primary health care referral system: shifting from fragmentation to integration care for older people

BackgroundGeriatric assessment (GA) is a multidimensional process that disrupts the primary health care (PHC) referral system. Accessing consistent data is central to the provision of integrated geriatric care across multiple healthcare settings. However, due to poor-quality data and documentation of GA, developing an agreed minimum data set (MDS) is required. Therefore, this study aimed to develop a GA-MDS in the PHC referral system to improve data quality, data exchange, and continuum of care to address the multifaceted necessities of older people.MethodsIn our study, the items to be included within GA-MDS were determined in a three-stepwise process. First, an exploratory literature search was done to determine the related items. Then, we used a two-round Delphi survey to obtain an agreement view on items to be contained within GA-MDS. Finally, the validity of the GA-MDS content was evaluated.ResultsSixty specialists from different health geriatric care disciplines scored data items. After, the Delphi phase from the 230 selected items, 35 items were removed by calculating the content validity index (CVI), content validity ratio (CVR), and other statistical measures. Finally, GA-MDS was prepared with 195 items and four sections including administrative data, clinical, physiological, and psychological assessments.ConclusionsThe development of GA-MDS can serve as a platform to inform the geriatric referral system, standardize the GA process, and streamline their referral to specialized levels of care. We hope GA-MDS supports clinicians, researchers, and policymakers by providing aggregated data to inform medical practice and enhance patient-centered outcomes.

Directionality theory and mortality patterns across the primate lineage

Empirical studies of aging in primates show that local selective forces rather than phylogenetic history determine the exceptional nature of human longevity (Bronikowski et al., Science 331:1325–1328, 2011). This article proposes an evolutionary rationale for this pattern of primate mortality by invoking the parameter, Life-Table Entropy, a measure of the uncertainty in the life span of a randomly chosen newborn. Life-table entropy is positively correlated with maximal life span, that is, the mean life span of a species living under favourable conditions.The logic which underlies the exceptional nature of human longevity derives from the terrestrial life-history of humans – a singularity within the primate lineage; and the concomitant ecological constraints—the hunter-gatherer, agricultural, and industrial modes of subsistence, that have defined human evolutionary history. The effect of these ecological constraints on the evolution of life span is encoded in the Entropic Principle of Longevity: life-table entropy increases in equilibrium species, populations evolving in environments with stable, renewable resources; and decreases in opportunistic species, populations subject to fluctuating resource endowments.The Entropic Principle of Longevity is a derivative of Directionality Theory, an analytic study of the evolutionary process of variation and selection based on Evolutionary Entropy, a statistical measure of the uncertainty in the age of the mother of a randomly chosen newborn. Evolutionary entropy is the organizing concept of The Entropic Principle of Evolution: Evolutionary Entropy increases in equilibrium species and decreases in opportunistic species.

Increased coherence predicts medical refractoriness in patients with temporal lobe epilepsy on monotherapy

Among patients with epilepsy, 30–40% experience recurrent seizures even after adequate antiseizure medications therapies, making them refractory. The early identification of refractory epilepsy is important to provide timely surgical treatment for these patients. In this study, we analyze interictal electroencephalography (EEG) data to predict drug refractoriness in patients with temporal lobe epilepsy (TLE) who were treated with monotherapy at the time of the first EEG acquisition. Various EEG features were extracted, including statistical measurements and interchannel coherence. Feature selection was performed to identify the optimal features, and classification was conducted using different classifiers. Functional connectivity and graph theory measurements were calculated to identify characteristics of refractory TLE. Among the 48 participants, 34 (70.8%) were responsive, while 14 (29.2%) were refractory over a mean follow-up duration of 38.5 months. Coherence feature within the gamma frequency band exhibited the most favorable performance. The light gradient boosting model, employing the mutual information filter-based feature selection method, demonstrated the highest performance (AUROC = 0.821). Compared to the responsive group, interchannel coherence displayed higher values in the refractory group. Interestingly, graph theory measurements using EEG coherence exhibited higher values in the refractory group than in the responsive group. Our study has demonstrated a promising method for the early identification of refractory TLE utilizing machine learning algorithms.

On autoregressive processes with Lindley-distributed innovations: modeling and simulation

In this paper, we develop an autoregressive process of order one, assuming that the innovation random variable has a Lindley distribution. The key properties of the process are investigated. Five distinct estimation techniques are used to estimate the parameters and simulation studies are conducted. The stationarity of the process is tested using a unit root test. The application of the proposed process to the analysis of time series data is demonstrated using real data sets. Based on some important statistical measures, the analysis of the data sets reveals that the proposed model fits well, and the errors are independent and Lindley-distributed.

Understanding Peer Sexual Norms, Attitudes, and Pressure: Correlates of Risky Sexual Behaviours among Secondary School Adolescents in Anambra State, Nigeria

Aims: Sexual behaviour initiation often takes place throughout adolescence and is typically linked to engaging in unprotected sexual practices and other high-risk sexual behaviours. This research investigated the correlation between peer sexual behaviour, sexual attitudes, pressure to engage in sexual activity, and sexual risk behaviours among secondary school adolescents in Anambra State. Methodology: The study was carried out with a correlational research approach. The research included 1,200 individuals from a population of 19,048 SS2 teenagers in public secondary schools. Cronbach's Alpha statistics were used to evaluate the internal consistency of three scales: the Brief Sexual Attitudes Scale (BSAS), the Pressure to Have Sex Questionnaire (PTHSQ), and the modified Sexual Risk Behaviour Scale (SRBS). The reliability coefficients for the Adolescent Sexual Behaviour Index (ASBI) were 0.86, 0.96, and 0.80, respectively. The reliability index of the ASBI was determined using Kuder Richardson statistics, resulting in a coefficient of 0.86. The data was obtained by directly administering the instruments and processed using the Statistical Package for Social Sciences (SPSS). Pearson Product Moment Correlation is a statistical measure that quantifies the strength and direction of the linear relationship between two variables. Coefficients were used to address the research enquiries, while the null hypotheses were examined using a t-test to determine significant association. Results: The results indicated a weak positive association between the sexual conduct of peers, sexual attitudes, and pressure to engage in sexual activity, and sexual risk behaviours among secondary school teenagers in Anambra State. Furthermore, the findings demonstrated a notable association between these variables and the sexual risk behaviours of secondary school adolescents in Anambra State. Conclusion: Based on these data, it was determined that there is a weak positive link between peer sexual behaviour, sexual attitude, peer pressure to engage in sexual activity, and unsafe sexual behaviours among secondary school teenagers in Anambra State. Given this information, it is advisable for school administrators to establish health initiatives aimed at assisting young people in adopting enduring sexual habits that promote their overall health and well-being. These practices should also aim to minimise their susceptibility to HIV and other sexually transmitted diseases.

Sustainability Strategies and Performance of International Not-For-Profit Organizations in Kenya: A Case of World Food Programme

Purpose: The study aimed at identifying sustainability strategies adopted by international not-for-profit organizations in Kenya and how the strategies influenced the organizations’ performance. This research specifically focused on the World Food Programme (WFP) as a case study. The research aimed to examine the impact of fund generation, talent development, managerial capacities and collaboration on the success of international not-for-profit organizations in Kenya. Methodology: The research was informed by Resource Dependency Theory, Dynamic Capabilities Theory, Contingency Theory of Leadership, and Stakeholder Theory. This study used a descriptive research approach. The research focused on a specific group of individuals, consisting of 319 senior and middle management personnel that work at World Food Programme headquarters located in Nairobi County, Kenya. The research used Fishers' et al. methodology to get the sample size of 174 respondents. The research used a questionnaire to gather primary data. The obtained data was encoded and exported into the computer software program known as Statistical Package of Social Sciences (SPSS) for analysis, which produced conclusions related to the topic of the research. Descriptive statistics were used to combine quantitative analytic methodologies. The main data was summarized, organized, interpreted, and presented using frequencies, percentages, mean scores, and standard deviations. The research findings are shown using statistical graphs, data tabulation, and measurements of central tendency. The principles of impartiality, secrecy, anonymity, and voluntary involvement were scrupulously followed. Findings: The results showed that implementing fund development as a sustainability strategy has a notable and beneficial impact on the performance of international not-for-profit organizations in Kenya (β = 0.263; P = 0.000<0.05). The study revealed that talent development has a noteworthy impact on the success of international not-for-profit organizations in Kenya (β = 0.334; P = 0.000<0.05). The study revealed that management qualities had a strong and beneficial impact on the success of international not-for-profit organizations in Kenya (β = 0.308; P = 0.000<0.05). The results also indicated that cooperation and collaboration had a crucial role in influencing the success of foreign non-profit organizations in Kenya (β = 0.206; P = 0.007<0.05). The study found that implementing sustainability strategies significantly improved the performance of international not-for-profit organizations in Kenya. Unique Contribution to Theory, Practice and Policy: Therefore, the study recommends that leaders of these organizations prioritize the adoption of these strategies to further enhance performance.