Variable Usage Research Articles

Trend and determinants of tobacco use among Indian males over a 22-year period (1998-2021) using nationally representative data.

Tobacco consumption is associated with an increased risk of morbidity and mortality. India is one of the largest consumers of tobacco worldwide. We assessed the trend of tobacco use among Indian males over a period of 20 years using data obtained from four rounds of the National Family Health Survey (NFHS). Data on tobacco usage and relevant socioeconomic variables obtained from NFHS rounds 2 to 5 over the period 1998-2021, was used for analysis. Specifically, data were available for 138,951 males from NFHS-2, 74,369 males from NFHS-3, 112,222 males from NFHS-4, and 101,839 males from NFHS-5. Significance of association between various socio-economic factors and tobacco usage was ascertained using a multicategory logistic regression model. Among all the forms of tobacco, smokeless tobacco was predominantly used by Indian males. The proportion of smokers and those using both smoke and smokeless forms peaked during NFHS-3, followed by a consistent dip; however, the use of smokeless tobacco plateaued from NFHS-4 to NFHS-5. NFHS-5 shows that 19.2% of Indian males smoked, 27.0% used smokeless tobacco, and 6.3% used both. Tobacco use has declined significantly over the last two decades. Tobacco usage is noticeably higher among the elderly, Muslims, and those from the backward classes, while it was considerably lower for individuals belonging to the educated and wealthier segment. There has been a steady decline in tobacco use in India over the past 22 years, specifically in the smoke-form category. However, smokeless tobacco use remains nearly unaffected. The outcome of this study might aid policymakers in devising targeted tobacco control policies and improving existing ones.

Risk Factors for Musculoskeletal Disorders in Korean Farmers: Survey on Occupational Diseases in 2020 and 2022.

This study investigated factors influencing the prevalence of musculoskeletal disorders (MSDs) resulting from agricultural work, utilizing the 2020 and 2022 occupational disease survey data collected by the Rural Development Administration. The combined data from these years indicated a 6.02% prevalence of MSDs, reflecting a significant class imbalance in the binary response variables. This imbalance could lead to classifiers overlooking rare events, potentially inflating accuracy assessments. We evaluated five distinct models to compare their performance using both original and synthetic data and assessing the models' performance based on synthetic data generation. In the multivariate logistic model, we focused on the main effects of the covariates as there were no statistically significant second-order interactions. Focusing on the random over-sampling examples (ROSE) method, gender, age, and pesticide use were particularly impactful. The odds of experiencing MSDs were 1.29 times higher for females than males. The odds increased with age: 2.66 times higher for those aged 50-59, 4.60 times higher for those aged 60-69, and 7.16 times higher for those aged 70 or older, compared to those under 50. Pesticide use was associated with 1.26 times higher odds of developing MSDs. Among body part usage variables, all except wrists and knees were significant. Farmers who frequently used their necks, arms, and waist showed 1.27, 1.11, and 1.23 times higher odds of developing MSDs, respectively. The accuracy of the raw method was high, but the ROSE method outperformed it for precision and F1 score, and both methods showed similar AUC.

Impact of Social Media Usage on Students’ Mental Health and Overall Well-Being of University Undergraduate Students

The function of social media was substantial and its advancement had impacted different spheres of people’s lives, the leading change being in terms of cognitive processes and corresponding daily routines, interpersonal communication, knowledge acquisition, self-identification, and ways of perceiving environment. This study investigated the link between social media usage and the mental health and well-being of university students. Study included variables of social media usage, stress level, constant thinking, self- esteem, sleep schedule, emotional well-being, and daily routine. The main motive of the study was to examine the effect of digital media on student's mental health and well-being of undergraduate’s university students and to explore the demographic differences like gender on the influence of social media usage on the mental health and well-being of university undergraduate students through survey research method. The research methodology was used in this study quantitative and descriptive type nature research. The data were collected from 250 students from the undergraduates’ students of University of Sargodha through Convenient sampling technique. The research instrument for this study was self-developed by the researcher. In this present study independent sample T-Test was used to check the demographic difference and Pearson Correlation was used for checking the link between social media and mental health. Study revealed a significant positive relationship between social media usage and mental well-being indicating that expanded social media usage was associated with improved mental well-being. In this study no notable gender gap were discovered in the relationship between social media usage and mental health outcomes. Findings suggested that social media usage may had a beneficial impact on mental well-being among university students.

Existence of quantum states for Klein–Gordon particles based on exact and approximate scenarios

In the present study, Kummer’s eigenvalue spectra from a charged spinless particle located at spherical pseudo-dot of the form r 2 + 1/r 2 is reported. Here, it is shown how confluent hypergeometric functions have principal quantum numbers for considered spatial confinement. To study systematically both constant rest-mass, m 0 c 2 and spatial-varying mass of the radial distribution m 0 c 2 + S(r), the Klein–Gordon equation is solved under exact case and approximate scenario for a constant mass and variable usage, respectively. The findings related to the relativistic eigenvalues of the Klein–Gordon particle moving in the spherical space show the dependence of mass distribution, so it has been obtained that the energy spectra has bigger eigenvalues than m 0 c 2 = 1 fm−1 in exact scenario. Following analysis also shows eigenvalues satisfy the range of E < m 0 c 2 through approximate scenario.

Leak identification and quantification in gas network using operational data and deep learning framework

In this study, we introduce an innovative deep learning framework designed to achieve precise detection, localization, and rate estimation of gas distribution pipeline system leakages. Our method surpasses conventional statistical approaches, particularly those based on Bayesian inference, by accommodating the system’s intricate behaviors, including variable usage and production from both sources and sinks. Notably, our approach demonstrates remarkable accuracy in localizing leakages even amidst multiple occurrences within the system. Specifically, achieving over 98% accuracy in single-leakage scenarios underscores its effectiveness. Furthermore, through data augmentation involving the introduction of noise into the training dataset, we significantly enhance the model’s performance, particularly when tested against real-world-like noisy data. This study not only showcases the efficacy of our proposed deep learning framework but also underscores its adaptability and robustness in addressing complex challenges in gas pipeline systems.

Informing targeted Demand-Side Management: Leveraging appliance usage patterns to model residential energy demand heterogeneity

The transition towards decentralised and decarbonised electricity systems requires a detailed understanding of residential energy demand to inform effective Demand-Side Management (DSM) strategies. Current modelling approaches predominantly rely on socio-demographic factors to capture occupant behaviour heterogeneity, potentially overlooking critical household energy usage variability essential for low-voltage grid analysis.Alternatively, we propose a methodology centred on temporal and intensity patterns of appliance usage. Specifically, we develop an activity-based energy demand model that establishes pattern-dependent links between occupants' activity schedules and appliance switch-on events. Furthermore, we evaluate our approach's ability in estimating the distribution of DSM potential within the population, comparing it with conventional approaches based on socio-demographic classes.By examining laundry and dishwashing as case studies, the results demonstrate that our approach provides a more comprehensive characterisation of energy demand heterogeneity compared to conventional socio-demographic-based approaches, offering three key advantages: (i) enabling the identification and targeting of population segments with higher flexibility potential, (ii) determining the optimal contribution of different population segments to various flexibility services, and (iii) informing the design of actionable DSM interventions. Lastly, we propose strategies for operationalizing a pattern-oriented approach in real-world contexts and emphasise the need to integrate different data sources to advance DSM research.

Impact of the chemical modification of tRNAs anticodon loop on the variability and evolution of codon usage in proteobacteria.

Despite the highly conserved nature of the genetic code, the frequency of usage of each codon can vary significantly. The evolution of codon usage is shaped by two main evolutionary forces: mutational bias and selection pressures. These pressures can be driven by environmental factors, but also by the need for efficient translation, which depends heavily on the concentration of transfer RNAs (tRNAs) within the cell. The data presented here supports the proposal that tRNA modifications play a key role in shaping the overall preference of codon usage in proteobacteria. Interestingly, some codons, such as CGA and AGG (encoding arginine), exhibit a surprisingly low level of variation in their frequency of usage, even across genomes with differing GC content. These findings suggest that the evolution of GC content in proteobacterial genomes might be primarily driven by changes in the usage of a specific subset of codons, whose usage is itself influenced by tRNA modifications.

PENGARUH PENERAPAN WEBSITE E-PERSURATAN TERHADAP KINERJA KANTOR KESYAHBANDARAN DAN OTORITAS PELABUHAN KELAS I SAMARINDA

The aim of this research is to find out how the E-Persorat system is implemented and its influence on the Harbor Master's Office and Class I Samarinda Port Authority. This research uses a quantitative research design. The object of this research is the users of the E-Persuratan Website, namely all employees. The data analysis technique used was carried out with the help of statistics and the data analysis used was descriptive and multiple linear regression. The division of testing includes instrument requirements using validity and reliability tests, data analysis requirements testing using linearity tests and data analysis testing using multiple linear regression tests and hypotheses (T test). Based on the analysis test results, it was found that the implementation of the E-Persuratan website had an effect on the performance of the Harbor Master's office and the Samarinda Class I Port Authority. Meanwhile, the implementation of the governance system has no effect on office performance. The phenomenon of these results can be seen from the data validity test of the two variables which are considered valid, the reliability of the two variables is considered valid, the linearity of the two variables is calculated linearly, the multiple linear regression analysis shows that there is a unidirectional relationship between the two variables, while the hypothesis test shows that there is an influence on the variables Website Use and The system usage variable has no influence on office performance

Evaluation of Sleep Behaviors in Male Football Players Participating in Youth Football Leagues

This article addresses a research study aimed at evaluating the sleep behaviors of young football players in Turkey. The research was conducted using a correlational survey design. The population consisted of male football players participating in the Turkey Football U 19 Elite A, U 17 Elite A, U 17 Elite B, U 19 Regional Development, U 17 Regional Development, and U 16 Development leagues, with a sample size of 203 players. Statistical methods such as normality analysis, Mann-Whitney U test, and Kruskal-Wallis H test were used for analysis. The study found no statistically significant differences in the variables of football year, position status, training day, regular medication use status, education level, and sleep duration among young football players. The data revealed that young players who stayed with friends at home or alone had higher sleep quality compared to those staying with their families at home. When analyzed according to the computer and phone usage variable, except for the sleep disorder factor, all sub-dimensions favored those using computers and phones. Regarding the playing status in development leagues, significant results were found in the sports factor sub-dimension for U 19 Elite A and U 19 Regional Development league players in relation to sleep quality factors. According to ASBQ Total Score averages, the values of U 19 Elite A league players were more significant compared to U 17 Elite A league players and U 16 Development league players. Regarding the sleep problem status variable, players indicating sleep problems had statistically higher values in sleep quality factors, Habitual sleep efficiency factors, and ASBQ Total Scores. In conclusion, it can be stated that there is a direct relationship between sleep and performance among U 19 Elite A young football players, and they act with this awareness.

Gender Differences in Electronic Health Record Usage Among Surgeons.

Understanding gender differences in electronic health record (EHR) use among surgeons is crucial for addressing potential disparities in workload, compensation, and physician well-being. To investigate gender differences in EHR usage patterns. This cross-sectional study examined data from an EHR system (Epic Signal) at a single academic hospital from January to December 2022. Participants included 224 attending surgeons with patient encounters in the outpatient setting. Statistical analysis was performed from May 2023 to April 2024. Surgeon's gender. The primary outcome variables were progress note length, documentation length, time spent in medical records, and time spent documenting patient encounters. Continuous variables were summarized with median and IQR and assessed via the Kruskal-Wallis test. Categorical variables were summarized using proportion and frequency and compared using the χ2 test. Multivariate linear regression was used with primary EHR usage variables as dependent variables and surgeon characteristics as independent variables. This study included 222 529 patient encounters by 224 attending surgeons, of whom 68 (30%) were female and 156 (70%) were male. The median (IQR) time in practice was 14.0 (7.8-24.3) years. Male surgeons had more median (IQR) appointments per month (78.3 [39.2-130.6] vs 57.8 [25.7-89.8]; P = .005) and completed more medical records per month compared with female surgeons (43.0 [21.8-103.9] vs 29.1 [15.9-48.1]; P = .006). While there was no difference in median (IQR) time spent in the EHR system per month (664.1 [301.0-1299.1] vs 635.0 [315.6-1192.0] minutes; P = .89), female surgeons spent more time logged into the system both outside of 7am to 7pm (36.4 [7.8-67.6] vs 14.1 [5.4-52.2] min/mo; P = .05) and outside of scheduled clinic hours (134.8 [58.9-310.1] vs 105.2 [40.8-214.3] min/mo; P = .05). Female surgeons spent more median (IQR) time per note (4.8 [2.6-7.1] vs 2.5 [0.9-4.2] minutes; P < .001) compared with male surgeons. Male surgeons had a higher number of median (IQR) days logged in per month (17.7 [13.8-21.3] vs 15.7 [10.7-19.7] days; P = .03). Female surgeons wrote longer median (IQR) inpatient progress notes (6025.1 [3692.1-7786.7] vs 4307.7 [2808.9-5868.4] characters/note; P = .001) and had increased outpatient document length (6321.1 [4079.9-7825.0] vs 4445.3 [2934.7-6176.7] characters/note; P < .001). Additionally, female surgeons wrote a higher fraction of the notes manually (17% vs 12%; P = .006). After using multivariable linear regression models, male gender was associated with reduced character length for both documentations (regression coefficient, -1106.9 [95% CI, -1981.5 to -232.3]; P = .01) and progress notes (regression coefficient, -1119.0 [95% CI, -1974.1 to -263.9]; P = .01). Male gender was positively associated with total hospital medical records completed (regression coefficient, 47.3 [95% CI, 28.3-66.3]; P < .001). There was no difference associated with gender for time spent in each note, time spent outside of 7 am to 7 pm, or time spent outside scheduled clinic hours. This cross-sectional study of EHR data found that female surgeons spent more time documenting patient encounters, wrote longer notes, and spent more time in the EHR system compared with male surgeons. These findings have important implications for understanding the differential burdens faced by female surgeons, including potential contributions to burnout and payment disparities.

The Influence Of E-Wallet Usage, Financial Literacy And Lifestyle On Financial Management Of Female Civil Servants In Mempawah District

The purpose of this study was to determine the effect of e-wallet usage, financial literacy and lifestyle on financial management of Female Civil Servants in Mempawah Regency. The method used in this research is quantitative method. The population in this study were female civil servants in Mempawah Regency with a total sample of 156 people. The data analysis technique used is multiple linear regression analysis, multiple correlation coefficient (R), coefficient of determination (R2), simultaneous test (F test) and partial test (t test). The results showed a multiple linear regression equation, namely: Y = 4.296 + 0.563 X1 + 0.249 X2 + 0.367 X3. The results of the multiple correlation coefficient show an R value of 0.784, meaning that the variables of e-wallet usage, financial literacy and lifestyle on financial management have a strong relationship. The results of the coefficient of determination R2 show that the contribution of the variables of e-wallet usage, financial literacy and lifestyle in influencing the ups and downs of financial management of Female Civil Servants in Mempawah Regency is 74.7% while the remaining 25.3% is explained by other variables outside the regression model. The results of the simultaneous test (F test) in this study showed an Fcount value of 7.183&gt; 2.66 Ftable. Thus it can be concluded that the use of e-wallets, financial literacy and lifestyle on financial management simultaneously affect the dependent variable, namely financial management. The results of the partial test (t test) on the e-wallet usage variable show a tcount value of 3.811&gt; 1.975 t table, meaning that there is a partially significant influence between the relationship with the use of e-wallets on financial management, while the financial literacy variable shows a tcount value of 2.581&gt; 1.975 t table, meaning that there is a partially significant influence between the relationship with financial literacy on financial management and finally the lifestyle variable shows a tcount value of 3.466&gt; 1.975 t table, meaning that there is a partially significant influence between the relationship with lifestyle on financial management.

Examining customer intentions to purchase intelligent robotic products and services in Taiwan using the theory of planned behaviour

BackgroundThe literature for assessing online and offline shopping behaviours that are linked to intelligent robotic goods and services is inadequate. In this study, we applied the Theory of Planned Behaviour model for guidance regarding how consumer behaviour affects their purchase intentions for intelligent robotic goods and services.MethodsData from 408 respondents were gathered through an online questionnaire binned into Online and Overall Shoppers, and analysed using SPSS, AMOS, and Covariance-Based Structural Equation Modelling software to evaluate the appropriateness of the measurements and to confirm data reliability, convergence, divergence, and validity. These tools were also used to track and test hypothesized relationships between the variables and model constructs used in this study.Results and conclusionsThe overarching outcomes from the data analyses indicated the Ease of Usage, Brand Perception, and Product Pricing variables causally impacted the TPB model constructs, namely Attitude, Subjective Norms, and Perceived Behaviour Control for the two populations tested with respect to their intention to purchase intelligent robotic goods and services. The reliability measurements for Ease of Usage, Brand Perception, and Product Pricing are discussed. The results are important for companies and future investors because opportunities to study the complex relationships that ultimately drive consumer behaviour and their intention to purchase intelligent robotic goods and services are provided.

Pengaruh penggunaan learning management system dan motivasi berprestasi terhadap hasil belajar siswa program international baccalaureate di Sekolah Pelita Harapan Lippo Cikarang

This study aimed 1) to determine the effect of using Learning Management System (LMS) on student learning outcomes, 2) to determine the effect of achievement motivation on student learning outcomes, 3) to determine the effect of using LMS and achievement motivation simultaneously on student learning outcomes in the International Baccalaureate (IB) program at Sekolah Pelita Harapan Lippo Cikarang. The research method used is quantitative research method. The research sample amounted to 32 people who were the entire population of students in the IB program (saturation sampling). The results showed that 1) The use of LMS (X1) has a positive and significant effect on student learning outcomes (Y) at Sekolah Pelita Harapan (SPH) Lippo Cikarang. This is evidenced by the results of statistical tests where the use of LMS has a significance value of 0.000 &lt;0.05 and the value of t count&gt; t table (4.771&gt; 2.045); 2) Achievement motivation (X2) does not have a positive and significant effect on student learning outcomes at Sekolah Pelita Harapan Lippo. Statistical tests show that the achievement motivation variable has a significance value of 0.309&gt; 0.05 and the t value &lt; t table (-1.037&gt; 2.045); 3) The use of LMS (X1) and Achievement motivation (X2) simultaneously has a positive and significant effect on student learning outcomes (Y) at Sekolah Pelita Harapan Lippo Cikarang. This is evidenced by the statistical test results where the value of Sig. 0.000 &lt;0.05 and the calculated F value is 12.687&gt; 3.32. The test results of the Coefficient of Determination resulted in 0.467 or 46.7%. This means that the variables of LMS usage (X1) and Achievement motivation (X2) have an influence of 46.7% on student learning outcomes (Y), the remaining 53.3% is influenced by other factors.

Enumerating Valid Non-Alpha-Equivalent Programs for Interpreter Testing

Skeletal program enumeration (SPE) can generate a great number of test programs for validating the correctness of compilers or interpreters. The classic SPE generates programs by exhaustively enumerating all possible variable usage patterns into a given syntactic structure. Even though it is capable of producing many test programs, the exhaustive enumeration strategy generates a large number of invalid programs, which may waste plenty of testing time and resources. To address the problem, this article proposes a tree-based SPE technique. Compared to the state-of-the-art, the key merit of the tree-based approach is that it allows us to take the dependency information into consideration when producing test programs and, thus, make it possible to (1) directly generate non-equivalent programs and (2) apply dominance relations to eliminate invalid test programs that have undefined variables. Hence, our approach significantly saves the cost of the naïve SPE approach. We have implemented our approach into an automated testing tool, IFuzzer , and applied it to test eight different implementations of Python interpreters, including CPython, PyPy, IronPython, Jython, RustPython, GPython, Pyston, and Codon. In three months of fuzzing, IFuzzer detected 142 bugs, of which 87 have been confirmed to be previously unknown bugs, of which 34 have been fixed. Compared to the state-of-the-art SPE techniques, IFuzzer takes only 61.0% of the time cost given the same number of testing seeds and improves 5.3% source code function coverage in the same time budget of testing.

Is Workload Associated With Oblique Injuries in Professional Baseball Players?

Oblique strains have become a common injury among professional baseball players. The influence of player workload on oblique strains remains unknown. To determine whether workload is a risk factor for oblique strains in professional baseball players. We hypothesized that fewer days of rest, more innings pitched/fielded per game, and more batters faced/plate appearances per game would significantly increase a player's risk of sustaining an oblique strain. Case-control study; Level of evidence, 3. All professional baseball players who sustained an oblique strain between 2011 and 2017 were identified using the Major League Baseball Health and Injury Tracking System. A separate dataset of player usage-days of rest per game, innings pitched or fielded per game, and batters faced or plate appearances per game-was used to determine the workload. We compared these usage variables between player games ≤2, ≤6, ≤12, and >12 weeks before a documented oblique strain with player games from a control group of players with no oblique strains. In a paired analysis, we compared acute (player games ≤2, ≤6, and ≤12 weeks preinjury) versus chronic (player games >12 weeks preinjury) workloads. There were 311 oblique strains in pitchers and 392 oblique strains in position players during the study period. In pitchers, more innings pitched and more batters faced were associated with a subsequent oblique strain (P < .001 for all). In position players, fewer days of rest, more innings fielded, and more plate appearances were associated with a subsequent oblique strain (P < .001 for all). Pitchers who pitched ≥7 innings per game had a 2.4-fold (95% CI, 1.4-4.9) increased risk of subsequent oblique strain compared with those who pitched 1 inning per game. The percentage of position players with a subsequent oblique strain increased by 2.1-fold (95% CI, 1.3-3.5) with >4 plate appearances compared with 1 plate appearance per game. Our analysis demonstrated that workload was associated with an increased risk of sustaining an oblique injury in professional baseball players. High workload over time was more predictive of oblique strains compared to acute increases over chronic baseline workload.

Adaptive fault detection for lithium-ion battery combining physical model-based observer and BiLSTMNN learning approach

Lithium-ion batteries possess one of the best energy-weight ratios, while safety and reliability are critical issues for its continued operation. Due to extreme fast-charging, huge application sizes and variable usage environment, the risk of soft battery faults, including soft internal short circuit (SISC), is increased and may evolve into severe accidents. Therefore, accurate early detection of lithium-ion battery fault is imperative to guarantee the battery performance. Motivated by this fact, we proposed a real time fault detection framework for battery soft faults. Based on the Equivalent Circuit Model (ECM) and coupling thermal model, Extended Kalman Filter (EKF) observer is used for reliable monitoring of battery voltage and surface temperature. Inspired by uncertainty interference during the observation process, we explore an observed-based learning approach that combine physical model-based observer and Bidirectional Long Short-Term Memory neural networks (BiLSTMNN) to empower the observer with the ability to learn the uncertainties, which is efficient in distinguishing soft faults information and uncertainties from residual. Furthermore, the memory characteristics of BiLSTMNN are considered to improve the robustness of the detection system, including comprehensive training data and optimized input features. Finally, through the comparative analysis and the robustness evaluation experiments including various driving cycles and internal short circuits (ISC) faults test verity the good performance of the proposed method.

An online intervention for carers to manage behavioral symptoms in motor neuron disease (MiNDToolkit): a randomized parallel multi-center feasibility trial

Background Evidence on management of behavioral symptoms in motor neuron disease (MND) is lacking. The MiNDToolkit, an online psychoeducational platform, supports carers dealing with behavioral symptoms (BehSymp). The study objectives were to ascertain recruitment and retention rates, carer and healthcare professional (HCP) use of the platform, and completion of online assessments, to inform a full-scale trial. Design: Randomized, parallel, multi-center, feasibility trial. Setting England and Wales, across diverse MND services; recruitment from July/21 to November/22; last participant follow-up in March/23. Participants Carers of people with motor neuron disease (PwMND) with BehSymp, recruited through MND services. After confirming eligibility, participants completed screening and baseline assessments online via the MiNDToolkit platform and were randomized centrally in a 1:1 ratio to MiNDToolkit or control. Intervention MiNDToolkit offered tailored modules to carers for the 3-month study period. Carers in the intervention group could receive additional support from MiNDToolkit trained HCPs. The control group was offered access to the intervention at the end of the study. Data were collected on platform usage and psychosocial variables. Main outcomes One hundred and fifty-one carers from 11 sites were invited to join the study (letter, face-to-face); 30 were screened; 29 were randomized. Fifteen people were allocated to the control arm; 14 to intervention. Carers were mostly female; median age for was 62.5 (IQR: 58, 68; intervention) and 57 (IQR: 56, 70; controls). Study retention was high (24/29 = 82.76%); carers engaged with the platform on average 14 times (median (IQR):14.0 (10.0, 18.5)) during the study period. Conclusion The MiNDToolkit study was feasible and well accepted by carers and trained HCPs. A definitive trial is warranted.

Tele-Trafficking of Virtual Data Storage Obtained from Smart Grid by Replicated Gluster in Syntose Environment

One of the most important developments in the energy industry is the evolution of smart grids, which record minute details of voltage levels, energy usage, and other critical electrical variables through General Packet Radio Service (GPRS)-enabled meters. This phenomenon creates an extensive dataset for the optimization of the grid system. However, the minute-by-minute energy details recorded by GPRS meters are challenging to store and manage in physical storage resources (old techniques lead to a memory shortage). This study investigates using the distributed file system, replicated Gluster, as a reliable storage option for handling and protecting the enormous volumes of data produced by smart grid components. This study performs two essential tasks. (1) The storage of virtual data received from GPRS meters and load flow analysis of SynerGee Electric 4.0 software from the smart grid (we have extracted electrical data from 16 outgoing feeders, distributed lines, in this manuscript). (2) Tele-trafficking is performed to check the performance of replicated Gluster (RG) for virtual data (electrical data received from the smart grid) storage in terms of User Datagram Protocol (UDP), Transmission Control Protocol (TCP), data flow, and jitter delays. This storage technique provides more opportuni11ty to analyze and perform smart techniques efficiently for future requirement, analysis, and load estimation in smart grids compared to traditional storage methods.

Enhanced mechanical endurance and power conversion efficiency in flexible Cu2ZnSnS4 solar cells using ZnO/Ag-Ag2O/ZnO film

Cu2ZnSnS4 (CZTS) solar cells occupy an important direction in the field of clean energy research due to their environmental friendliness, low cost, and industrial technology compatibility. The complex and variable usage environment also accelerates the development of flexible CZTS solar cells. However, the inferior flexibility of ITO films limited their development. ZnO/Ag/ZnO (ZAZ) films are potential choices for transparent conductive layers of CZTS solar cells due to their high transmittance, conductivity, and flexibility. To further enhance the photovoltaic performance and mechanical durability of ZAZ films and solar cells, Ag2O was introduced into ZAZ film by magnetron sputtering. An ultra-high average transmittance of 96.6% in 400–800 nm was achieved for ZnO/Ag-Ag2O/ZnO (ZAAZ) films at Ag2O sputtering power of 40 W, which was attributed to the fact that Ag2O effectively inhibited the growth of 3D islands of Ag film, and formed a more uniform Ag film. ZAAZ films offered a superior figure of merit value of 0.1041 Ω−1. The power conversion efficiency (PCE) of ZAAZ solar cells was 6.94%, higher than that of ZAZ solar cells (6.69%). Remarkably, the PCE of ZAAZ solar cells was 7.9% higher than that of ZAZ solar cells after 1000 bending cycles. Therefore, Therefore, this approach may provide a promising way to improve the photovoltaic performance and mechanical durability of flexible solar cells.

OVERVIEW OF FINANCIAL TECHNOLOGY IN BANKING SECTOR: A BIBLIOMETRIC STUDY

This study aims to provide empirical evidence regarding the growth and trend of fintech-related publications in the banking sector and examine what variables are often associated with fintech. Utilizing bibliometric analysis via the VOSviewer application, this study analyzes 816 articles published on Scopus from 2013 to 2023. The results of this study indicate that research publications on fintech in the banking sector have been widely carried out in various countries. Several variables related to fintech and banking topics include technology, industry, covid, future, and blockchain. Some of the authors that are widely found are M.K. Hassan, H. Banna, and M.R. Rabbani which can be considered as references. The study's limitation lies in its inability to provide an overview of variable usage trends in 2023. This research is expected to provide implications for the development of future research, especially related to fintech and banking.