Reliable Data Research Articles

Somatropin therapy in Italy during 2019-2022: prevalence and incidence rates estimated by data from the national register and regional health sources.

The availability of epidemiological data relating to the therapy with somatropin (recombinant Growth Hormone, rGH) is scarce and mostly limited to specific clinical conditions. In Italy, the National Register of rGH users (Registro nazionale degli assuntori dell'ormone della crescita - Rnaoc) has been established since 2004 as recommended by the Italian medicine agency (Aifa). However, prevalence (P) and incidence (I) estimates in the Italian population are lacking. Data from the Rnaoc and a questionnaire submitted to all the Italian regions have been used to calculate P and I rates of rGH treatment exposure in the Italian paediatric (0-17 years) and adult (≥18 years) population during 2019-2022; P and I rates of growth hormone deficiency (GHD) in paediatric patients have been estimated as well. Results from the two data sources have been compared. In 2019-2022, the estimated P rate in the paediatric population was 13,4/10.000, in adults 0,7/10.000 inhabitants; the I rate was 2,3/10.000 and 0,06/10.000 inhabitants, correspondingly. The estimated P rate of paediatric GHD was 8,9/10.000, the I rate was 1,5/10.000 inhabitants, respectively. Study results showed a better reliability of data collected via questionnaire because more consistent with Italian regional resident populations.

Pioneering diabetes screening tool: machine learning driven optical vascular signal analysis

The escalating prevalence of diabetes mellitus underscores the critical need for non-invasive screening tools capable of early disease detection. Present diagnostic techniques depend on invasive procedures, which highlights the need for advancement of non-invasive alternatives for initial disease detection. Machine learning in integration with the optical sensing technology can effectively analyze the signal patterns associated with diabetes. The objective of this research is to develop and evaluate a non-invasive optical-based method combined with machine learning algorithms for the classification of individuals into normal, prediabetic, and diabetic categories. A novel device was engineered to capture real-time optical vascular signals from participants representing the three glycemic states. The signals were then subjected to quality assessment and preprocessing to ensure data reliability. Subsequently, feature extraction was performed using time-domain analysis and wavelet scattering techniques to derive meaningful characteristics from the optical signals. The extracted features were subsequently employed to train and validate a suite of machine learning algorithms. An ensemble bagged trees classifier with wavelet scattering features and random forest classifier with time-domain features demonstrated superior performance, achieving an overall accuracy of 86.6% and 80.0% in differentiating between normal, prediabetic, and diabetic individuals based on the optical vascular signals. The proposed non-invasive optical-based approach, coupled with advanced machine learning techniques, holds promise as a potential screening tool for diabetes mellitus. The classification accuracy achieved in this study warrants further investigation and validation in larger and more diverse populations.

Design and Investigation of a High-Performance Quartz-Based SAW Temperature Sensor

In this work, a surface acoustic wave (SAW) temperature sensor based on a quartz substrate was designed and investigated. Employing the Coupling-of-Modes (COM) model, a detailed analysis was conducted on the effects of the number of interdigital transducers (IDTs), the number of reflectors, and their spacing on the performance of the SAW device. The impact of the transversal mode of quartz SAWs on the device was subsequently examined using the finite element method (FEM). The simulation results indicate that optimizing these structural parameters significantly enhances the sensor’s sensitivity and frequency stability. SAW devices with optimal structural parameters were fabricated, and their resonant frequencies were tested across a temperature range of 25–150 °C. Experimental results demonstrate that the SAW temperature sensor maintains high performance stability and data reliability throughout the entire temperature range, achieving a Bode-Q of 7700. Furthermore, the sensor exhibits excellent linearity and repeatability. An analysis of the sensor’s response under varying temperature conditions reveals a significant temperature dependency on its Temperature Coefficient of Frequency (TCF). This feature suggests that the sensor possesses potential advantages for applications in industrial process control and environmental monitoring.

Determinants Of Artificial Intelligence Adoption In Accounting Among Malaysia Small And Medium-Sized Enterprises

Malaysia Small and Medium-Sized Enterprises (MSMEs) are still in the early phases of adopting artificial intelligence (AI) to transform manual or conventional processes into ones that are supported by AI technology. Most MSMEs have yet to fully grasp the essence of digitalization as a general movement in corporate strategy and work processes to improve decision-making, boost efficiency, and discover untapped possibilities. The purpose of this research is to examine the determinants of AI adoption in accounting among MSMEs. The independent variables are perceived ease of use, management support, organizational readiness, government support, and external pressure. The dependent variable is AI adoption in accounting among MSMEs. This research applied a quantitative method by using a questionnaire consisting of 24 questions. The study used a convenience sampling method, with a sample size of 200 respondents from MSMEs. A total of 150 samples are valid. The collected data was analyzed using SPSS. The collected information was assessed using demographic descriptive analysis, data descriptive analysis, normality test, validity analysis, reliability test, correlation analysis, and regression analysis. The findings indicated that the adoption of AI in accounting among MSMEs is positively influenced by perceived ease of use, management support, organizational readiness, government support, and external pressure. The results and suggestions will be advantageous for SMEs looking to adopt AI in their accounting functions. Additionally, this research proposes a methodology and framework to assist SMEs owners and management in comprehending the determinants that drive significant AI adoption in accounting.

On the Line and Online: Higher Non‐Response to Web‐Based Surveys Over‐Represents Avid Recreational Fishers Compared With Telephone Surveys

ABSTRACTRecreational fishing surveys have an important role in providing data to inform fisheries management. The selection of a contact method is an important and often challenging consideration that influences the potential for non‐sampling errors that can result in unrepresentative data and biased estimates. Telephones are used for many off‐site recreational fishing surveys, but widespread use of digital technology suggests that online surveys could be a viable alternative. We compared phone surveys with alternative online surveys using probability sampling from a licence database. Response rates were lower for online surveys (13%–26%) than phone surveys (97%–98%). The proportion of respondents who reported marine fishing in the previous 12 months did not differ between survey methods (98% for both survey modes), but the proportion of avid fishers was higher for online surveys (41%–54%) than phone surveys (37%–40%). These differences reflect higher respondent burden for online surveys where data are self‐administered and respondents are more likely to opt‐out. Higher non‐response and over‐representation of avid fishers from online surveys may compromise data reliability. We recommend comparative studies to quantify and correct for biases when evaluating alternative survey modes for new or ongoing surveys.

Early Detection of Wildlife Disease Pathogens Using CRISPR-Cas System Methods.

Wildlife diseases are a considerable threat to human health, conservation, and the economy. Surveillance is a critical component to mitigate the impact of animal diseases in these sectors. To monitor human diseases, CRISPR-Cas (clustered regularly interspaced short palindromic repeats-CRISPR-associated protein) biosensors have proven instrumental as diagnostic tools capable of detecting unique DNA and RNA sequences related to their associated pathogens. However, despite the significant advances in the general development of CRISPR-Cas biosensors, their use to support wildlife disease management is lagging. In some cases, wildlife diseases of concern could be rapidly surveyed using these tools with minimal technical, operational, or cost requirements to end users. This review explores the potential to further leverage this technology to advance wildlife disease monitoring and highlights how concerted standardization of protocols can help to ensure data reliability.

Causal relationship between hypothyroidism and ulcerative colitis: a bidirectional Mendelian randomization study.

Ulcerative colitis (UC) and Hashimoto's thyroiditis frequently cooccur in patients with multiple autoimmune conditions, but the specific association between UC and hypothyroidism is unknown. We used Mendelian randomization (MR) methods to determine the causal relationship between UC and hypothyroidism. We obtained single nucleotide polymorphisms (SNPs) related to ulcerative colitis (UC) and hypothyroidism from genome-wide association studies (GWAS) available in the public database of the Integrated Epidemiology Unit (IEU). To assess the causal relationship between UC and hypothyroidism, we employed MR-Egger, weighted median, inverse variance weighted (IVW), simple mode, and weighted mode methods. Sensitivity analyses were performed using Cochran's Q test, the horizontal pleiotropy test, and the leave-one-out (LOO) method to assess the reliability of the MR data. The genes corresponding to instrumental variables (IVs) were subjected to Gene Ontology (GO) functional annotation, Kyoto Encyclopedia of the Genome (KEGG) pathway enrichment analysis, and protein-protein interaction (PPI) analysis to explore the mechanisms behind the causal relationships at the gene level. Forward MR analysis indicated that hypothyroidism was associated with an increased risk of UC (IVW: P = 0.02, OR = 9.71, 95% confidence interval (CI) = 1.36-69.46). In contrast, reverse MR did not demonstrate a causal relationship between UC and hypothyroidism (IVW: P = 0.53). Sensitivity analysis proved the reliability of the results. The PPI network revealed CD247, CD80, and STAT4 as central genes. GO and KEGG analyses revealed significant enrichment of the T cell, gamma interferon (IFN-γ), and programmed cell death-1 (PD-1)/programmed cell death ligand-1 (PD-L1) pathways. Hypothyroidism was a risk factor for UC. The balance of T-cell differentiation played an important role in the process of hypothyroidism-induced UC, and IL-21 might be the key to finding a cure. Enrichment of PD-1/PD-L1 might attenuate inflammation by suppressing the immune action of T cells.

Accurate and efficient AI-assisted paradigm for adding granularity to ERA5 precipitation reanalysis

Scientific inquiry has long relied on deterministic algorithms for systematic problem-solving and predictability. However, the rise of artificial intelligence (AI) has revolutionized data analysis, allowing us to uncover complex patterns in large datasets. In this study, we combine these two approaches by using AI to improve the reconstruction of past precipitation events, which is crucial for understanding climate change. Our objective is to leverage AI to map large-scale atmospheric proxies from the ERA5 climate reanalysis and multi-satellite historical precipitation data from the NASA-IMERG GPM constellation to observed precipitation, enhancing the accuracy and the resolution of climate reanalysis. Accurate climate reanalyses are essential, as they provide the most realistic representations of past atmospheric conditions, serving as benchmarks against which climate models are validated. Our AI-enhanced method offers a more accurate and computationally efficient solution compared to deterministic high-resolution precipitation downscaling methods. Additionally, it shows the capability to generalize predictions to new, previously unobserved locations, making it applicable across various regions. By integrating AI with traditional reanalysis techniques, we open up new opportunities for climate science and geosciences, with the potential to improve the accuracy and reliability of climate data, contributing to a better understanding of climate dynamics.

Machine learning techniques for enhancing security in financial technology systems

The financial technology (fintech) industry has transformed the way financial services are delivered, offering enhanced convenience, accessibility, and efficiency. However, this rapid digitization has also increased the sector’s exposure to a wide array of security risks, including cyberattacks, fraudulent activities, data breaches, and insider threats. As traditional security measures struggle to keep pace with the growing sophistication of these threats, machine learning (ML) techniques have emerged as promising tools for reinforcing security in fintech systems. Machine learning models can analyze vast amounts of data, detect anomalous behavior, predict potential risks, and respond to security threats in real-time. This paper explores the potential of machine learning to enhance security in financial technology by addressing key challenges such as anomaly detection, fraud detection, intrusion detection systems (IDS), and risk management. We provide an overview of common machine learning algorithms—such as decision trees, neural networks, support vector machines (SVM), and clustering methods—that are applied to these security tasks. Additionally, we discuss the evaluation metrics used to measure the accuracy, precision, recall, and overall effectiveness of these models. Through real-world case studies, we highlight successful implementations of machine learning in fintech security, offering insights into best practices and lessons learned. Despite the many benefits, there are also significant challenges and limitations associated with the adoption of machine learning in fintech, including concerns over data privacy, the accuracy and reliability of models, and the computational resources required for large-scale deployment. Looking ahead, this paper identifies emerging trends and potential innovations that could further enhance security in fintech, from advancements in deep learning to the integration of artificial intelligence with blockchain technology. Finally, we propose areas for future research to address unresolved issues and support the continued development of machine learning-driven security solutions in the fintech industry.

Challenges of Wireless Sensor Networks and their Solutions

Wireless Sensor Networks (WSNs) have emerged as a critical technology with uses in the fields of smart cities, industrial automation, healthcare, and environmental monitoring. This research paper provides a thorough review of the challenges inherent in WSNs, with the intention of providing light on the challenges that practitioners and researchers have while setting up and maintaining these networks. The challenges involve several aspects, comprising data management, security, scalability, energy efficiency, and dependability. Security concerns: given that these networks' communication is open and wireless, maintaining data integrity and secrecy presents substantial issues. Furthermore, it is essential to guarantee data transmission dependability in dynamic, frequently severe situations. This research analyzes these challenges, looks at the methods and solutions that are currently being offered in the literature, and also points out areas that need more research and development in the area of wireless sensor networks. Comprehending and resolving these obstacles is essential to realizing WSNs' complete potential and promoting their extensive integration in various fields.

The Influence of Consumer Behavior on Purchasing Decisions

Retail trade is a very strategic trade in Indonesia because it is able to absorb labor after the agricultural sector. There are two types of retail trade, namely traditional retail which is directly represented by small traders or grocery traders in traditional markets and in housing and modern retail which is represented by supermarkets and minimarkets such as Alfamart and Indomaret. This proves that there is quite high economic growth and creates investment, but on the other hand, this can cause lethargy for traditional kiosk traders, even killing their businesses. Yuki Mart is one of the modern shops (minimarkets) in Gunungsitoli City. This research aims to find out how much influence consumer behavior has on purchasing decisions at Yuki Mart, Gunungsitoli City. This research uses quantitative methods. Respondents were 57 consumers who shopped at Yuki Mart, Gunungsitoli City in July, where researchers collected data using a questionnaire, then carried out data validity tests, data reliability tests, classical assumption tests (normality tests), coefficient of determination, and hypothesis tests (t tests). The results of the research show that the influence of consumer behavior on purchasing decisions at Yuki Mart, Gunungsitoli City, is 16.3% and the other 83.7% is influenced by other variables not included in this research.

Sistem Otomatis Penyimpanan Data Pendeteksi Fase Pelanggan Tegangan Rendah

Inaccurate phase detection due to manual data recording often led to errors, impacting load balancing processes in electrical distribution. This research aimed to develop an automated phase detection system for low-voltage customers, utilizing a MicroSD-Card for data storage to reduce human error and enhance data reliability. The device used Power Line Carrier (PLC) communication technology integrated with customer identity (ID PEL) and employed Arduino Uno for phase data processing. The research method involved designing and testing phase detection and data storage functions across various distances to assess system accuracy. Findings indicated the system accurately detected customer phases and stored data efficiently, with minor delays only at longer distances. This automated solution effectively minimized manual errors in phase data entry. Future applications of this system could significantly improve load balancing accuracy and data management in electrical distribution networks

The Effect of Financial Education on Increasing Financial Literacy

That financial literacy is part of economic literacy that is important to develop through educational activities. Financial literacy is very important especially for individuals because this financial aspect is a determinant in finding individual life needs. although there are still individuals who have not implemented good financial literacy in their lives. As is the case with students of SMK Negeri 1 Tugala Oyo which is one of the vocational schools in Tugala Oyo District, North Nias Regency. This study aims to determine how much influence Financial Education has on improving Financial Literacy at SMK N 1 Tugala Oyo, North Nias Regency. This study uses a quantitative method, respondents are students of SMK Negeri 1 Tugala Oyo as many as 65 people, where researchers collect data using questionnaires, then conduct validity studies, and data reliability tests, correlation coefficients, classical assumption tests (normality test, multicollinearity test, heteroscedasticity test), simple linear regression, coefficient of determination, and t-test. The results of the study showed that the influence of financial education on increasing financial literacy at SMK Negeri 1 Tugala Oyo was 22.4% and 77.6% was influenced by other factors that were not influenced by this study.

Innovative approaches in complex data forensics: error rate assessment and its impact on cybersecurity

In the realm of cybersecurity, the integrity of forensic investigations is paramount, especially as the volume and complexity of data continue to escalate. This paper explores innovative approaches to complex data forensics, focusing on the methodologies used to assess error rates in data retrieval and analysis. High error rates in forensic processes can compromise the reliability of findings, leading to erroneous conclusions that may impact security measures and legal proceedings. This research examines various techniques for error rate assessment, including statistical methods and data validation protocols, which serve to quantify the accuracy of forensic analysis. Furthermore, the paper discusses the profound implications that high error rates can have on the integrity of forensic findings, emphasizing the need for meticulous attention to detail in data handling and processing. To counter these challenges, we present strategies aimed at enhancing data reliability, such as implementing rigorous quality assurance processes, leveraging machine learning algorithms for anomaly detection, and utilizing advanced encryption methods to protect data integrity throughout the forensic lifecycle. By addressing the critical role of error rate assessment in data forensics, this research contributes to the broader discourse on cybersecurity and underscores the necessity of adopting robust methodologies to ensure accurate and reliable forensic outcomes in an increasingly complex digital landscape.

Phosphorylation in the Plasmodium falciparum Proteome: A Meta-Analysis of Publicly Available Data Sets.

Malaria is a deadly disease caused by Apicomplexan parasites of the Plasmodium genus. Several species of the Plasmodium genus are known to be infectious to humans, of which P. falciparum is the most virulent. Post-translational modifications (PTMs) of proteins coordinate cell signaling and hence regulate many biological processes in P. falciparum homeostasis and host infection, of which the most highly studied is phosphorylation. Phosphosites on proteins can be identified by tandem mass spectrometry (MS) performed on enriched samples (phosphoproteomics), followed by downstream computational analyses. We have performed a large-scale meta-analysis of 11 publicly available phosphoproteomics data sets to build a comprehensive atlas of phosphosites in the P. falciparum proteome, using robust pipelines aimed at strict control of false identifications. We identified a total of 26,609 phosphorylated sites on P. falciparum proteins, split across three categories of data reliability (gold/silver/bronze). We identified significant sequence motifs, likely indicative of different groups of kinases responsible for different groups of phosphosites. Conservation analysis identified clusters of phosphoproteins that are highly conserved and others that are evolving faster within the Plasmodium genus, and implicated in different pathways. We were also able to identify over 180,000 phosphosites within Plasmodium species beyond falciparum, based on orthologue mapping. We also explored the structural context of phosphosites, identifying a strong enrichment for phosphosites on fast-evolving (low conservation) intrinsically disordered regions (IDRs) of proteins. In other species, IDRs have been shown to have an important role in modulating protein-protein interactions, particularly in signaling, and thus warranting further study for their roles in host-pathogen interactions. All data have been made available via UniProtKB, PRIDE, and PeptideAtlas, with visualization interfaces for exploring phosphosites in the context of other data on Plasmodium proteins.

Extensiveness of Teachers’ Workload in Determining the Acquisition of Kiswahili Reading Skills Among Pupils in Public Primary Schools in Tanga City Council, Tanzania

The study investigated the extensiveness of teachers’ workload in determining the acquisition of Kiswahili reading skills among public primary school pupils in Tanga City Council, Tanzania. The study employed a convergent design under mixed methods research approach to collect both quantitative and qualitative data simultaneously and compare the results to obtain a more complete understanding of the research problem. The study targeted 80 public primary schools, 80 head teachers, 264 Kiswahili subject teachers, 24 Ward Education Officer and 1 City Council Primary Education Officer. Both probability and non-probability sampling techniques were employed to obtain respondents who participated in this study. The study involved 8 public primary schools, 8 head teachers, 88 Kiswahili reading skills teachers, 8 WEO and 1 CCPEO. Data were collected using questionnaires, and interview guide. Data collection instruments were validated by Mwenge Catholic University research experts. A pilot study was conducted in two public secondary schools. The reliability of questionnaires for Likert scale items was determined using Cronbach's Alpha Coefficient where reliability coefficient r= 0.892 for Kiswahili subject teachers questionnaire was obtained. The reliability for qualitative data was established through peer debriefing and triangulation. The study adhered to research ethical principles throughout the research process. Quantitative data were analyzed using descriptive statistics particularly using frequencies, percentages and mean with the aid of SPSS Version 22. The study concludes that due to their overwhelming responsibilities, teachers find it challenging to give students timely and insightful feedback on their reading progress. Teachers find it challenging to plan and implement enjoyable reading activities that would greatly enhance their pupils Kiswahili reading skills. Teachers are unable to give each pupils the individualized support and attention they need to improve their reading skills in Kiswahili, leaving them with little time and energy. The study recommends that heads of public primary schools can support teachers in working less by making sure that teachers have enough time to plan, organize, assign reading tasks, and help pupils who are failing with learning. The administration of public primary school should make sure that the school has enough teaching and learning resources for Kiswahili reading instruction.

Development and Evaluation of a Robust UART Communication System with Enhanced Interference Resistance

Interference is a significant issue in UART communication, often causing data jitter and distortion, particularly in industrial environments. This study addresses these challenges by developing an anti-interference UART system that includes the design and implementation of receiving, processing, and sending modules. The system utilizes techniques such as multi-level sampling, synchronization, and cumulative decision-making to enhance data transmission accuracy and reliability. In embedded systems, electromagnetic interference (EMI) poses a major problem, leading to potential data loss and communication failures. Reliable communication is crucial for maintaining the performance and safety of industrial operations. The anti-interference UART system developed in this study aims to improve operational stability by mitigating the effects of EMI. The system’s performance was validated through extensive simulations, demonstrating its ability to maintain stable and accurate data transmission even under various interference conditions. The results show significant improvements in data integrity and communication reliability. This paper presents a comprehensive approach to designing, implementing, and validating a robust UART system that can effectively operate in challenging environments, ensuring reliable data communication and enhancing overall system performance.

The impact of garbage codes on the quality of death coding in Belgrade in 2010, 2015, and 2020

Abstract Background Accurate data on the causes of death (CoDs) is the foundation for analyzing the health situation and developing health policy in various countries. The percentage of garbage codes (GCs) indicates the quality of the CoDs data. Within the Western Balkan Strategic Partnership Project (Work Package 4: Use of Population Health Metrics for Improved Surveillance), we evaluated the impact of the four levels of GCs on the quality of mortality data of Belgrade. Methods In a retrospective study, we examined the underlying CoDs of Belgrade residents by age and sex and the change in GCs’ share in the CoDs in 2010, 2015, and 2020. We used anonymous individual mortality data and population age and sex annual data from the electronic databases of the City Institute of Public Health of Belgrade, as well as the list of GS levels 1-4 from the 2019 Global Burden of Disease Study. Results Over the observed years, the percentage of all GCs in underlying CoDs was approximately 20%. The GCs with profound policy implications level 1 accounted for 52% of all GCs in 2020, higher than those recorded in previous years - 46.3% in 2010 and 45.1% in 2015. The total number of GCs per 100,000 inhabitants in Belgrade was the highest in 2020, with a rate of 325.2/100,000 (males: 374.4, females: 301.3). This rate has increased by about one-third since 2010 (both sexes: 245.4, males: 269.2, females: 221.1) and by about two-fifths since 2015 (both sexes: 235.8, males: 262.7, females: 211.8). Conclusions This research has revealed a significant decline in the quality of data related to the causes of death in Belgrade. One-fifth of the recorded causes of death are inaccurate or unreliable. The study highlights the importance of maintaining strict protocols for data collection, analysis, and management to ensure the accuracy and reliability of CoDs data in developing effective public health policies. Key messages • The rate of total garbage codes per 100,000 inhabitants of Belgrade has increased by about two-fifths since 2015. • Failure in quality, accuracy, and reliability of causes of death can lead to suboptimal health policymaking.

Harnessing the power of artificial intelligence (AI): a paradigm shift in HRM practices for employee sustainable performance

Purpose This study aims to investigate the interplay between artificial intelligence (AI) integration, organizational digital culture, human resource management (HRM) practices and employee sustainable performance in luxury hotels in Malaysia. It seeks to elucidate how AI adoption influences organizational dynamics, shapes HRM practices and impacts employee sustainable performance over time. Design/methodology/approach Using a quantitative approach, survey questionnaires derived from prior research were utilized. Analysis using G*Power software determined an appropriate sample size, with psychometric evaluation validating scale development. Statistical analyses using Statistical Package for Social Sciences (SPSS) 28.0 and SmartPLS 4 confirmed data reliability and validity. Findings Out of the five hypotheses, three were supported. A positive relationship was found between AI adoption and employee sustainable performance, highlighting AI’s potential to enhance productivity and job satisfaction. However, the relationship between AI adoption and organizational digital culture was not supported. On the other hand, HRM practices positively influenced employee sustainable performance. In addition, organizational digital culture was positively associated with employee sustainable performance, underscoring the role of digital fluency in driving workforce productivity. Conversely, AI failed to moderate the relationship between HRM practices and employee sustainable performance. Research limitations/implications The study’s focus on luxury hotels in Malaysia and its reliance on cross-sectional data, suggesting the need for longitudinal designs and diverse organizational contexts in future research. Comparative studies across sectors and countries could offer insights into variations in AI adoption practices and their impact on organizational performance. Originality/value This study contributes to theoretical frameworks by empirically examining complex relationships between AI integration, HRM practices, organizational digital culture and employee performance, emphasizing the importance of considering organizational context and cultural factors in understanding the implications of AI adoption for sustainable performance enhancement.

Improving the Nonvolatile Memory Characteristics of Sol-Gel-Processed Y2O3 RRAM Devices Using Mono-Ethanolamine Additives.

In this study, Y2O3-based resistive random-access memory (RRAM) devices with a mono-ethanolamine (MEA) stabilizer fabricated using the sol-gel process on indium tin oxide/glass substrates were investigated. The effects of MEA content on the structural, optical, chemical, and electrical characteristics were determined. As the MEA content increased, film thickness and crystallite size decreased. In particular, the increase in MEA content slightly decreased the oxygen vacancy concentration. The decreased film thickness decreased the physical distance for conductive filament formation, generating a strong electric field. However, owing to the lowest oxygen vacancy concentration, a large electrical field is required. To ensure data reliability, the endurance cycles across several devices were measured and presented statistically. Additionally, endurance performance improved with the increase in MEA content. Reduced oxygen vacancy concentration can successfully suppress the excess formation of the Ag conductive filament. This simplifies the transition from the high- to the low-resistance state and vice versa, thereby improving the endurance cycles of the RRAM devices.