Varying Sample Sizes Research Articles

Can Chatbots Alleviate Depression? Results of a Systematic Review

Purpose: This article systematically reviews the impact of chatbots on the treatment of depression, evaluating studies published between January 2019 and April 2024. Depression, exacerbated by factors such as the COVID-19 pandemic, requires accessible and effective treatments. Chatbots, using artificial intelligence and natural language processing, emerge as accessible alternatives, offering interventions based on Cognitive-Behavioral Therapy (CBT). Methods: Using databases such as Cochrane Library, PubMed, Scopus, and ScienceDirect, 321 articles were identified, of which 12 met the inclusion criteria. These studies evaluated changes in depression symptoms using validated instruments in individuals who interacted with chatbots. Results: The results indicate that chatbots can significantly reduce depression symptoms, although their effectiveness varies depending on the design and implementation of the intervention. A lack of gender balance and variations in sample sizes and intervention durations, ranging from one to 16 weeks, were found. Methodological limitations include selection bias and lack of clear information on random allocation and blinding. Conclusions: The bibliometric analysis highlights the interrelation of key terms such as ”chatbot,” ”depression,” and ”cognitive-behavioral therapy,” underscoring the importance of advanced technologies in developing these tools. Despite their potential, chatbots should not be considered a substitute for professional treatment in severe cases. This study suggests that chatbots are promising for psychological support, but more research is needed to optimize their clinical effectiveness and user acceptance.

A Hybrid Gain Analog Offline EnKF for Paleoclimate Data Assimilation

AbstractFor Paleoclimate data assimilation (PDA), a hybrid gain analog offline ensemble Kalman filter (HGAOEnKF) is proposed. It keeps the benefits of the analog offline ensemble Kalman filter (AOEnKF) that constructs analog ensembles from existing climate simulations with joint information of the proxies. The analog ensembles can provide more accurate prior ensemble mean and “flow‐dependent” error covariances than randomly sampled ensembles. HGAOEnKF further incorporates the benefits of static prior error covariances that better capture large‐scale error correlations and mitigate sampling errors than the sample prior error covariances, through a hybrid gain approach within an ensemble framework. Observing system simulation experiments are conducted for various data assimilation methods, using ensemble simulations from the Community Earth System Model‐Last Millennium Ensemble Project. Results show that using the static prior error covariances estimated from a sufficiently large sample set is beneficial for the traditional offline ensemble Kalman filter (OEnKF) and AOEnKF. HGAOEnKF method is superior to the OEnKF and AOEnKF with and without static prior error covariances, especially for the reconstruction of extreme events. The advantages of HGAOEnKF over OEnKF and AOEnKF with and without static prior error covariances are persistent with varying sample sizes and presence of model errors.

WORK-LIFE BALANCE AND PSYCHOLOGICAL CAPITAL AMONGST WOMEN: GEN Z AND MILLENIALS

With an emphasis on Gen Z and Millennials, the study explores the connection between psychological capital (PsyCap), wellbeing, and work-life balance (WLB) among Indian women. Positive connections between PsyCap, WLB, and well-being were discovered using a quantitative method with 240 individuals; well-being acted as a mediator in the relationship between PsyCap and WLB. PsyCap, WLB, and well-being were all higher among millennials than among Generation Z. A small sample size and reliance on self-reported data are among the limitations. Larger, more varied sample sizes and longitudinal approaches should be investigated in future studies. It is essential to support PsyCap in order to improve WLB and overall wellbeing in female employees. Policymakers must to take into account generational disparities in the workplace. The intricate relationships between PsyCap, WLB, and wellbeing are clarified by this study, which highlights their significance for working women's well-being across generations in India.

Likelihood Ratio Based Voice Comparison Using Cepstral Coefficients and GAN

In order to identify suspects in digital forensics, Forensic Voice Comparison (FVC) entails comparing trace voices with known suspect voice samples. The FVC uses semi-automatic approaches to identify speakers using acoustic features. Two Australian datasets such as forensic evaluation dataset 1 & English dataset 2 were used. The current research on FVC samples is noise-reduced using a stationary noise reduction algorithm within a likelihood ratio-based framework. To demonstrate the power of knowledge representation, acoustic features like Mel Frequency Cepstral Coefficients(MFCC) and Linear Predictive Cepstral Coefficients (LPCC) were extracted. The suspect is then identified by classifying the model using the Generative Adversarial Network(GAN) and Artificial Neural Network (ANN). The accuracy is 80% & 90% for MFCC for dataset 1 & 2. Similarly, accuracy is 20% & 40% for LPCC for dataset 1 & 2. The overall comparison of acoustic features with two different datasets the MFCC performs better accurate results for Australian English dataset 2 with varying sample sizes.

Reliability analysis at usual operating settings for Weibull Constant-stress model with improved adaptive Type-Ⅱ progressively censored samples

<abstract><p>An improved adaptive Type-Ⅱ progressive censoring scheme was recently introduced to ensure that the examination duration will not surpass a specified threshold span. Employing this plan, this paper aimed to investigate statistical inference using Weibull constant-stress accelerated life tests. Two classical setups, namely maximum likelihood and maximum product of spacings, were explored to estimate the scale, shape, and reliability index under normal use conditions as well as their asymptotic confidence intervals. Through the same suggested classical setups, the Bayesian estimation methodology via the Markov chain Monte Carlo technique based on the squared error loss was considered to acquire the point and credible estimates. To compare the efficiency of the various offered approaches, a simulation study was carried out with varied sample sizes and censoring designs. The simulation findings show that the Bayesian approach via the likelihood function provides better estimates when compared with other methods. Finally, the utility of the proposed techniques was illustrated by analyzing two real data sets indicating the failure times of a white organic light-emitting diode and a pump motor.</p></abstract>

An EWMA sign chart for monitoring processes with fixed and variable sample sizes

AbstractThis study addresses limitations in the nonparametric EWMA sign chart with fixed control limits (FCLs), particularly when facing time‐varying sample sizes. The FCLs‐based EWMA sign chart has a variable conditional false alarm rate (CFAR), especially at the startup of a process or after recovering from an out‐of‐control signal. To overcome these limitations, we propose a nonparametric EWMA sign chart based on dynamic probability control limits. This chart is capable of monitoring the process target with fixed, as well as time‐varying sample sizes. Monte Carlo simulations are used to estimate the CFARs, zero‐state (ZS) and steady‐state (SS) average run‐length profiles of the EWMA sign charts. It turns out that the proposed chart outperforms the existing chart, particularly in detecting shifts during the process startup, while maintaining the desired CFAR levels in both ZS and SS scenarios. A real data example is given to demonstrate the implementation of the EWMA sign charts.

Causal associations of immune cell phenotypes with migraine: A mendelian randomization study.

The interaction between the immune system and the brain, crucial for blood-brain barrier integrity, is a potential factor in migraine development. Although there's evidence of a connection between immune dysregulation and migraine, a clear causal link has been lacking. To bridge this knowledge gap, we performed a two-sample Mendelian randomization (MR) analysis of 731 immune cell phenotypes to determine their causality with migraine, of which parameters included fluorescence, cell abundance, count, and morphology. Sensitivity and pleiotropy checks validated our findings. After applying a false discovery rate correction, our MR study identified 35 of 731 immune phenotypes with a significant causal link to migraine (p < 0.05). Of these, 24 showed a protective effect (inverse variance weighting : p< 0.05, odds ratio <1), and 11 were risk factors (inverse variance weighting : p< 0.05, odds ratio >1). Although limited by population sample size and potential population-specific genetic variations, our study uncovers a significant genetic link between certain immune cell markers and migraine, providing new insights into the disorder's pathophysiology. These discoveries are crucial for developing targeted biomarkers and personalized treatments. The research enhances our understanding of immune cells' role in migraine and may substantially improve patient outcomes and lessen its socio-economic impact.

Investigating privacy-preserving machine learning for healthcare data sharing through federated learning

Privacy-Preserving Machine Learning (PPML) is a pivotal paradigm in healthcare research, offering innovative solutions to the challenges of data sharing and privacy preservation. In the context of Federated Learning, this paper investigates the implementation of PPML for healthcare data sharing, focusing on the dynamic nature of data collection, sample sizes, data modalities, patient demographics, and comorbidity indices. The results reveal substantial variations in sample sizes across substudies, underscoring the need to align data collection with research objectives and available resources. The distribution of measures demonstrates a balanced approach to healthcare data modalities, ensuring data fairness and equity. The interplay between average age and sample size highlights the significance of tailored privacy-preserving strategies. The comorbidity index distribution provides insights into the health status of the studied population and aids in personalized healthcare. Additionally, the fluctuation of sample sizes over substudies emphasizes the adaptability of privacy-preserving machine learning models in diverse healthcare research scenarios. Overall, this investigation contributes to the evolving landscape of healthcare data sharing by addressing the challenges of data heterogeneity, regulatory compliance, and collaborative model development. The findings empower researchers and healthcare professionals to strike a balance between data utility and privacy preservation, ultimately advancing the field of privacy-preserving machine learning in healthcare research.

Trends Stability of Reliability Coefficient Based on Sample Size and Ability of Test-takers

One aspect that needs to be considered in the assessment of learning outcomes is the quality of the test by a stable reliability coefficient. This study aims to determine the trend of the stability of the reliability coefficient of the mathematics formative test based on the sample size and the ability of the test takers. The study was experimental in the form of a simulation, using a population of scores based on the answers of 403 test takers. The research sample was taken from the population of scores with 19 variations in sample sizes. Each sample size was repeated 31 times with the return technique; the reliability coefficient was calculated for each repetition and was used as the unit of analysis. In addition to the differences in sample sizes, the differences in the abilities of the test takers were also seen in two categories of high and low. Data were analyzed using exploratory-descriptive statistics and analysis of variance. Results showed as follows: first, the formative test of mathematics that was developed by the teacher at school has a reliability coefficient in the inadequate category; second, the reliability coefficient of the test tends to be more stable with increasing sample sizes; third, the difference in the ability of the test takers does not make a significant difference to the reliability coefficient; fourth, there is no interaction between sample sizes and abilities of the test takers on the reliability coefficient of the test.

THEORETICAL AND METHODOLOGICAL ASPECTS OF CLOUD WATER INTERCEPTION

Cloud water interception (CWI) occurs when water contained in fog and wind-driven rain collides with vegetation, merges into larger droplets, and precipitates to the ground. CWI has an important function as an additional source of water and its relationships with tropical cloud forests have often been emphasized. Despite its importance, there is no standardization of measurement methods, nor of the terms that designate the process in Portuguese. Therefore, a systematic analysis of research on CWI is necessary. To this end, the present study carried out a review of the theoretical and methodological aspects of CWI through description and analysis of terminology; history and chronology of studies on the topic; survey of the environmental conditions necessary for the CWI process to occur; analysis of methodological aspects relating to the measurement of CWI; and synthesis and discussion of magnitudes described in scientific literature. As a result, of the 31 publications reviewed, 14 different words were found, the most common being “Cloud Water Interception” (19.4%) and “Fog Drip” (16.1%). In general, CWI is more common in places such as continental edges and islands that are constantly subject to sea breezes. In most cases, the below-canopy measurement approach can be considered more accurate than those obtained by fog collectors. CWI is on average responsible for 42% of effective precipitation (n:41). The values listed show a large variation, between 0.5% and 462%, probably due to the different environmental characteristics of the sampled locations as well as variations in sample sizes.

The Long Shadows of Brexit: Implications for African Countries

Abstract The exit of the United Kingdom from the European Union (EU) single market and customs union has adversely affected trade prospects of many developing economies that depended on the UK market for their exports. This paper investigates the impact of Brexit on African countries' exports to the UK. The comparison is based upon trade between the set of African countries which export most to the UK and the EU. It provides a quantitative assessment of the trade effect through the use of descriptive analysis and empirical estimations by employing the difference-in-difference (DID) estimation approach. The descriptive analysis finds that the share of African exports sent to the UK has declined since the Brexit announcement in 2016. The empirical estimations using the DID approach also demonstrate a drop of 20–30% in African countries exports to the UK relative to the EU-27 in this period. These results hold to a battery of robustness checks, including the use of an alternative estimation approach, varying sample size, and the use of alternative counterfactuals. We further show that the trade flows started to drop immediately after the announcement of the Brexit referendum in 2015 but the main drop came after the Brexit referendum results became evident. These findings imply the need for policy intervention and support for African countries to revitalize their trade flows and alleviate the unintended effects of this trade shock.

A variable sample size side-sensitive synthetic coefficient of variation chart

A major challenge for control charts monitoring the coefficient of variation is to quickly detect shifts in this parameter, so that assignable cause(s) can be quickly removed and the process can operate in an in-control state with a stable coefficient of variation. This is especially so when there are constraints in the sample size. One proposed strategy is to vary the sample size according to the most recent information. However, a side-sensitive synthetic chart monitoring the coefficient of variation with variable sample size is not available. This paper contributes to the literature by developing a variable sample size side-sensitive synthetic chart for the coefficient of variation. The main contributions are in terms of illustrating the operations of the chart, deriving the formulae to evaluate its performance and developing the algorithms to optimize its performance. Comparisons with current charts show that the proposed chart outperforms all existing synthetic-type charts monitoring the coefficient of variation. The proposed chart also outperforms the variable sample size coefficient of variation chart for all shift sizes. In addition, it outperforms the variable sample size run sum and variable sample size Exponentially Weighted Moving Average charts monitoring the coefficient of variation for moderate and large shift sizes.

Variable Sample Size Control Charts for Monitoring the Multivariate Coefficient of Variation Based on Median Run Length and Expected Median Run Length

The monitoring of a well-functioning process system has always held significant importance. In recent times, there has been notable attention towards employing control charts to oversee both univariate and multivariate coefficients of variation (MCV). This shift is in response to the concern of erroneous outcomes that can arise when traditional control charts are applied under the condition of dependent mean and standard deviation, as highlighted by prior research. To address this, the remedy lies in adopting the coefficient of variation. Furthermore, this study underscores the application of MCV in scenarios where multiple quality attributes are simultaneously under surveillance within an industrial process. This aspect has demonstrated considerable enhancement in chart performance, especially when incorporating the variable sample size (VSS) feature into the MCV chart. Adaptive VSS, evaluated through metrics like median run length (MRL) and expected median run length (EMRL), is also integrated for MCV monitoring. In contrast to earlier studies that predominantly focused on average run length (ARL), this research acknowledges the potential inaccuracies in ARL measurement. In this study, two optimal designs for VSS MCV charts are formulated by minimizing two criteria: firstly, MRL; and secondly, EMRL, both accounting for deterministic and unknown shift sizes. Additionally, to assess the distribution's variability in run lengths, the study provides the 5th and 95th percentiles. The research delves into two VSS schemes: one with a defined small sample size (nS), and another with a predetermined large sample size (nL) for the initial subgroup (n(1)). The approach taken involves the development of a Markov chain method for designing and deriving performance measures of the proposed chart. These measures include MRL and EMRL. Moreover, a comparative analysis between the proposed chart's performance and the standard MCV chart (STD) is presented in terms of MRL and EMRL criteria. The outcomes illustrate the superiority of the proposed chart over the STD MCV chart for all shift sizes, whether they are upward or downward, and when n(1) equals nS or nL.

Chemical Elements in Hair and Their Association with Autism Spectrum Disorder: A Comprehensive Systematic Review

Background: Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition with increasing prevalence. This review aims to systematically investigate the relationship between the levels of toxic and trace elements in hair and the occurrence of ASD. Methods: Original articles reporting trace element levels in hair were included. A comprehensive search was conducted in databases such as Medline (via PubMed), Scopus, Web of Science, and Google Scholar, covering publications from 1 January 2000, to 2 January 2023. Keywords including “hair element analysis”, “trace elements”, and “autism”, were utilized in the search. Results: A direct comparison of the data was not possible due to the variety of methodologies observed in the available studies, including variations in sample sizes and analytical procedures. Conclusions: Exposure to toxic elements, notably Mercury (Hg) and Lead (Pb), which act as neurotoxicants, was found to be associated with the pathogenesis of ASD. Furthermore, a significant correlation was identified between the Zinc (Zn) to Copper (Cu) ratio and ASD.

Prevalence of Hepatitis B and C in Pakistan from 2001 to 2022: A Systematic Review

Background: Hepatitis B and C represent a significant public health concern globally, with a substantial burden in developing countries, particularly Pakistan. The country's diverse demographic and socio-economic landscape presents challenges in the surveillance and management of these infectious diseases. Objective: This systematic review aims to consolidate and analyse the prevalence data of Hepatitis B and C infections reported across Pakistan from 2001 to 2022 to understand the epidemiological trends and to inform public health interventions. Methods: A thorough search of various research databases was conducted to identify studies that reported the prevalence of Hepatitis B surface antigen (HBsAg) and anti-Hepatitis C virus (anti-HCV) antibodies. Studies were selected based on inclusion criteria focused on the population of Pakistan between 2001 and 2022. Data extraction was systematic, although a comprehensive quality assessment of each study was not completed. Results: The review included 54 studies with varying sample sizes and methodologies, including ELISA, ICT, PCR, and other serological assays. Prevalence rates for HBsAg and anti-HCV varied significantly, with HBsAg ranging from 0.9% to 44.4% and anti-HCV from 0.3% to 62.6%. These findings highlight a considerable regional variability and suggest an upward trend in HCV prevalence over the past two decades. Conclusion: The elevated prevalence rates of Hepatitis B and C across Pakistan underscore the need for robust public health policies, standardized surveillance, and targeted interventions. Despite the systematic approach to data extraction, the review is limited by the absence of a comprehensive quality assessment, which is essential for future research to build upon.

EEG-Based Emotion Recognition Using Trainable Adjacency Relation Driven Graph Convolutional Network

In recent years, there has been a growing research interest in using deep learning to resolve the issue of Electroencephalogram (EEG)-based emotion recognition. Current research emphasizes exploiting the useful information from each single EEG channel or each individual set of multi-channel EEG, but overlooks the correlation information among different multi-channel EEG sets. To explore such discriminative correlation information, we propose a novel and effective method“, Trainable Adjacency Relation Driven Graph Convolutional Network (TARDGCN)", which contains two complementary modules, TAR and GCN. TAR optimizes the local pair-wise positions of multi-channel EEG sets, which helps form an improved graphic representation for GCN to learn the global correlation among these sets for classification. The proposed method is capable of dealing with the problem of small sample size but large data variation in this issue. Our experimental results conducted on the databases DREAMER and DEAP in the subject-dependent and subject-independent modes show that TARDGCN outperforms the state-of-the-art approaches in classifying all of valence, arousal, and dominance.

Resolving inconsistencies in environmental impact assessments of organic farming: a comparison of two meta-analyses.

Abstract CONTEXT: In the midst of the growing popularity of organic farming, persistent questions surround its environmental effects. A 2017 meta-analysis suggested elevated acidification and eutrophication potentials per land area for organic farming relative to conventional farming, influencing policy and individual choices. However, a recent 2023 meta-analysis contradicted these findings, revealing reduced or comparable impacts across various environmental indicators, including acidification and eutrophication potentials. OBJECTIVE: These discrepant findings from these studies highlight the need for a comprehensive understanding of the underlying causes and implications. This study seeks to unravel the source of these disparities. METHODS: Initially, we scrutinized whether the differences in findings could be attributed to variations in the agricultural products selected for analysis. Subsequently, we explored the possibility that disparities were influenced by distinct averaging methods employed in the two studies. To address the impact of sample size, our investigation considered whether differences in findings could be attributed to varying sample sizes. RESULTS AND CONCLUSIONS: This study reveals that the disparities between these meta-analyses stem from differences in averaging methods and sample sizes. The 2023 meta-analysis, employing a more robust averaging method and a larger sample size, emerges as a more reliable source of information. Researchers are encouraged to incorporate the 2023 findings into their investigations, potentially necessitating a reconsideration of agricultural policies and dietary choices to align with updated environmental impact assessments. SIGNIFICANCE: By resolving inconsistencies in environmental impact assessments of organic farming, this study facilitates more effective societal and individual decision-making for greater sustainability.

Impact of Listening to Indian Classical Music, or Rāgas, on the Electroencephalogram: A Meta-Analysis.

Ancient Indian classical music (ICM) has long been lauded and recognized for influencing emotional responses by influencing the human body's resonance. A meta-analysis of prospective case studies published in the last ten years on the effect of ancient Indian music rāgas on brain waves is investigated. This meta-analysis aimed to analyze published prospective studies investigating the effect of ancient Indian rāgas on EEG in healthy subjects. The present study included prospective studies published since 2012. Studies were obtained by searching four databases, such as PsychINFO, PubMed, Google Scholar, and JSTOR, and searching related journals. Eligibility criteria included studies assessing the impact of listening to Indian classical music on the EEG. Primary outcomes were changes in the brain waves, frequency, and power and their relationship to activity-related arousal, attention, and mental tasks. The studies were analyzed according to the PRISMA guidelines. There were a total of five included studies with 71 participants in the age range of 19-30, and the conditions for the test groups were generally similar except for varying types of rāgas used and time of day. Analysis of the data collected from 71 participants revealed that music interventions had statistically significant effects on increasing alpha activity and attention scores. Fractal analysis was sensitive enough to detect EEG brainwave changes while and after listening to the rāga musical intervention. Rāgas stimulate arousal in different areas of the brain, depending on the emotions they are designed to evoke. However, the synchronized studies together could not highlight a significant relationship between rāgas and EEG fractal dimension values. Although the meta-analysis failed to reproduce the same results from the individual studies, potentially due to the small sample size and study variation, the meta-analysis opens doors to the potential of rāgas to elicit distinct emotions and serve as robust predictors of emotional response. Future studies can explore the therapeutic potential of various rāgas in the clinical setting, such as in the management of cognitive disorders and stress or in modulating heart rate variability and cognitive performance.

Prognostic value of lymphocyte-to-monocyte ratio in gastric cancer patients treated with immune checkpoint inhibitors: a systematic review and meta-analysis.

Emerging evidence suggests a correlation between the lymphocyte-monocyte ratio (LMR) and the prognosis in patients with gastric cancer (GC) undergoing immune checkpoint inhibitor (ICI) therapy. Nevertheless, the existing findings remain contentious. A comprehensive search of literature was conducted in databases including PubMed, Embase, Web of Science, and the Cochrane Library, spanning from the inception of each database to August 30, 2023 to collect studies exploring the interplay between LMR and clinical outcomes. Eligible studies were selected following predefined inclusion and exclusion criteria. Primary outcomes encompassed progression-free survival (PFS) and overall survival (OS), which were estimated using hazard ratios (HR) and corresponding 95% confidence intervals (CI). Our analysis incorporated eight cohort studies, involving 815 patients. Aggregate data revealed associations between an elevated LMR at baseline and prolonged PFS (HR=0.58; 95% CI: 0.47-0.71, p<0.00001) and improved OS (HR=0.51, 95% CI: 0.33-0.79; p=0.003). Furthermore, LMR exhibited a favorable association with PFS after treatment (HR=0.48; 95% CI: 0.29-0.79; p= 0.004), while such a correlation was not evident in the OS analysis. Importantly, a high level of LMR was associated with prolonged PFS across varying sample sizes, follow-up duration, treatment combinations, line of therapy, and cut-off values. A high pre-treatment LMR is associated with improved OS and PFS in GC patients treated with ICIs. LMR emerges as a potent biomarker for prognostic assessment in these patients, offering valuable insights for informed treatment decisions within the domain of GC immunotherapy. PROSPERO, identifier CRD42021228512.

Impact of Strategic Leadership, Entrepreneurial Orientation, and Organizational Innovation on Organizational Performance: Harnessing Data to Understand the Mediating Role of Knowledge Management: A Case Study of SMEs in Pakistan,

In the current era of competitiveness and digitalization SME’s can play an important role in economic development of a country like Pakistan. The aim of this study is to investigate the impact of strategic leadership, Entrepreneurial and orientation organizational innovation on SME’s performance in Pakistan with the mediation role of knowledge management. A self-administered survey questionnaire is used to collect the data from SME’s Managers and owners. PLS is used for data analysis. The findings of the study reveal that there is a significant relationship between strategic leadership, Entrepreneurial orientation and organizational innovation and organizational performance. Moreover, Knowledge management has significant relationship with organizational performance. Further, statistical values show that knowledge management fully mediate the relationship. It is a unique study to test this combination of variables in the context of SME’s of Pakistan. As there are very limited studies available to discuss about SME’s performance specifically in the context of Pakistan. Current study will help the policy makers to develop the effective policies for SME’s sector development. Moreover, use of knowledge management as mediator indicate the novelty of current study. Conclusively, current study is limited to Pakistan in future researchers may use this framework to other developing countries with the variation of sample size and industrial sector.