Analysis Platform Research Articles

Impact of early onset colorectal cancer on mental and physical health: A comparative analysis of self-reported mental and physical health outcomes using the PROMIS framework in the NIH All of Us Research Program.

241 Background: Patients with early-onset colorectal cancer (EOCRC) have complex psycho-social needs that can impact their overall health and well-being. With the increasing incidence of EOCRC, it is essential to investigate the effects of this diagnosis on patients' mental and physical health. This study uses the Patient-Reported Outcomes Measurement Information System (PROMIS) framework to compare patients' self-reported mental and physical health perceptions with EOCRC and those with late-onset colorectal cancer (LOCRC). Methods: We utilized the National Institutes of Health's (NIH) All of Us database Registered Tier Dataset v7, which includes 266,520 participants who agreed to share their EHR information, for analysis. Participants completed PROMIS surveys for physical health (PROMIS-PH) and mental health (PROMIS-MH). We categorized patients with colorectal cancer into EOCRC (age at diagnosis <50) and LOCRC (age at diagnosis >=50) using ICD 9/10 codes. We calculated the raw summed scores for PROMIS-PH and PROMIS-MH for each group. We transformed the raw summed score into a T-score. The study compared scores in EOCRC and LOCRC using the Mann-Whitney U test with a significance level of P<0.05. We used the R programming language in the web-based Jupyter Notebook platform for data analysis. Results: Our study revealed 1764 colorectal cancer patients, with 408 cases of EOCRC (mean age of onset: 42.13 years, SD: 7.0) and 1356 cases of LOCRC (mean age: 63.72 years, SD: 8.35). Analysis of PROMIS Global scores showed that EOCRC patients had a mean PROMIS-MH score of 13.90 (SD: 3.42, T-score: 45.8, 95% CI: 38.74-52.86) and mean PROMIS-PH score of 13.95 (SD: 3.38, T-score: 42.3, 95% CI: 34.07-50.53). In contrast, LOCRC patients had mean PROMIS-MH and PROMIS-PH scores of 14.47 (SD: 3.37, T-score: 48.3, 95% CI: 41.05-55.55) and 14.24 (SD: 3.35, T-score: 44.9, 95% CI: 36.47-53.33), respectively. The PROMIS-MH scores were significantly higher in LOCRC than in EOCRC (p-value <0.001). However, there was no significant difference between the PROMIS-PH scores in both groups (p-value= 0.191). Conclusions: The study suggests that patients with EOCRC have a lower level of perceived mental health compared to LOCRC. Also, the EOCRC had a lower level of perceived physical health, although not statistically significant. These findings emphasize the significance of age-specific approaches in cancer research and underscore the importance of focused interventions to meet the unique mental and physical health needs of patients with EOCRC.

Snowmotion A Wearable Sensor-based Mobile Platform For Alpine Skiing Motion Capture And Analysis

Snowmotion A Wearable Sensor-based Mobile Platform For Alpine Skiing Motion Capture And Analysis

SNX4 Is Correlated With Immune Infiltration and Prognosis in Clear Cell Renal Cell Carcinoma.

Clear cell renal cell carcinoma (ccRCC) is known as the most common and malignant histologic subtype of renal carcinoma. Sorting nexin 4 (SNX4) plays a regulatory role in recycling from endosomes to the plasma membrane and promotes autophagosome assembly and transport, which may exert the cancerous growth and progression. This study aimed to assess the biological role of SNX4 in ccRCC and their clinical association via public biological data platforms combined with experimental verification. In our study, we analyzed the mRNA and protein expression of SNX4 in ccRCC under different clinicopathological characteristics through The Cancer Genome Atlas (TCGA), Human Protein Atlas (HPA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC) databases. We used the Gene Expression Profiling Interactive Analysis (GEPIA) platform to conduct the survival analysis and figure out the immune cell infiltration level under different expression levels of SNX4 combined with Tumor Immune Estimation Resource (TIMER) database. Furthermore, we predicted competing endogenous RNA (ceRNA) regulatory network using TargetScan, miRDB, starBase and miRTarBase online databases. We totally collected six paired ccRCC tissues and adjacent tissues and applied quantitative real-time polymerase chain reaction (qRT-PCR) and western blot (WB) to detect the expression of SNX4 in the collected clinical specimens. The mRNA and protein expression level of SNX4 was significantly lower in ccRCC than those in normal tissues. The results proposed that lower SNX4 was expressed in patients with higher histologic grade and in male patients. Kaplan-Meier analysis demonstrated that lower mRNA expression level of SNX4 was correlated with poorer prognosis. SNX4 had positive correlation with immune cell infiltrating levels and programmed cell death-ligand 1 (PD-L1) expression. Furthermore, we constructed the SNX4/miR-221-3p/miR-222-3p/DHRS4-AS1 axis, which may be the underlying ceRNA interaction network. Finally, we verified the reduced expression of SNX4 in ccRCC by qRT-PCR and WB. The expression of SNX4 in ccRCC was lower than adjacent tissues and its downregulated expression was associated with poor prognosis of ccRCC patients. SNX4 may exert critical roles in the tumorigenesis, development and migration of ccRCC via various mechanisms.

PENGEMBANGAN SISTEM IOT UNTUK PEMANTAUAN KESEHATAN DOMBA DENGAN ALGORITMA C4.5 BERBASIS THINGSPEAK

Effective livestock health monitoring is one of the primary challenges in modern farming, especially in detecting early health disorders in sheep. This research aims to develop a sheep health monitoring system based on the Internet of Things (IoT) using the C4.5 algorithm and the ThingSpeak platform. The system collects vital sheep data such as body temperature, heart rate, sound, and physical activity in real time through sensors and microphones connected to IoT devices. The data is then transmitted to the ThingSpeak platform for analysis and storage. The C4.5 algorithm is used to build a decision model capable of classifying the health conditions of sheep based on collected parameters such as temperature, heart rate, and respiration. The processed data results are displayed in the form of graphs and warning notifications on the ThingSpeak platform, allowing farmers to monitor livestock health easily and responsively. The accuracy test yielded a 90% accuracy rate using a confusion matrix with a data sampling split of 80% for training data and 20% for testing data. This indicates that the system has a high level of accuracy in detecting sheep health conditions. Consequently, the system has the potential to assist farmers in improving the efficiency of livestock health monitoring automatically and in real time. Moreover, the application of IoT technology and the C4.5 algorithm in the livestock sector is expected to provide innovative solutions to support productivity and animal welfare.

Data-Driven Audits: Audit Analytic Platforms and General Ledger Analytic Tools

SUMMARY The future of auditing is a data-driven audit, which extracts and analyzes digital data stored in client accounting systems. To help conduct data-driven audits, audit firms are developing audit analytic platforms (AAPs). This paper focuses on one key application tool of AAPs that analyzes the entire population of general ledger transactions. These general ledger analytics tools (GLATs) use assessment routines to assign a risk score to each transaction, helping guide the auditor’s planning, risk assessment, and selection of high-risk transactions for substantive procedures. GLATs help support audit quality and provide valuable new insights about client operations. We discuss the analytics routines used by GLATs, the forces driving their use, and some potential limitations of this technology. Data Availability: Data are available from the authors. JEL Classifications: M4.

Quantitative 13C-IS pyrolysis-GC-MS lignin analysis: Overcoming matrix effects in animal feed and faeces

Recently, a pyrolysis-GC-MS methodology for specific lignin quantification and structural characterisation was developed, relying on the use of uniformly 13C-labeled polymeric lignin isolate as internal standard (IS). The 13C-IS py-GC-MS method has been validated for grasses, woods, and applied in various showcases. To study the fate of lignin in animals, this method still requires careful validation in animal feeds and, especially complex faecal samples, hence the aim of this work. Hereto, faecal material was collected from pigs fed with wheat straw as lignin source and subjected to the py-GC-MS analytical platform for thorough examination of IS pyrolysis behaviour in terms of response and structural features. Next, 13C-ISpy-lignin contents and corrected Klason lignin contents were compared. Most importantly, we revealed that pyrolysis behaviour of 13C-IS lignin and 12C-sample lignin was differently affected in the faecal matrix, resulting in the ultimate underestimation of 13C-ISpy-lignin contents. In-depth examination and evaluation of matrix constituents showed that predominantly matrix ash was responsible for the effects observed. We further demonstrated that said matrix effects can be overcome by water extraction of the samples prior to analysis. Our validation and approach extend the use of the specific 13C-IS py-GC-MS methodology for accurate quantitative lignin analysis to biomass samples with complex matrices like pig faeces, and now call for application in future digestibility studies.

Characterization, biological activity, and anticancer effect of green-synthesized gold nanoparticles using Nasturtium officinale L.

BackgroundNanostructured materials used have unique properties and many uses in nanotechnology. The most striking of these is using herbal compounds for the green synthesis of nanoparticles. Among the nanoparticle types used for green synthesis, gold nanoparticles (AuNPs) are used for cancer therapy due to their stable structure and non-cytotoxic. Lung cancer is the most common and most dangerous cancer worldwide in terms of survival and prognosis. In this study, Nasturtium officinale (L.) extract (NO), which contains biomolecules with antioxidant and anticancer effects, was used to biosynthesize AuNPs, and after their characterization, the effect of the green-synthesized AuNPs against lung cancer was evaluated in vitro.MethodsUltraviolet‒visible (UV‒Vis) spectrophotometry, scanning electron microscopy (SEM), transmission electron microscopy (TEM), energy dispersive X-ray spectroscopy (EDS), multiple analysis platform (MAP), and Fourier transform infrared (FT-IR) spectroscopy analyses were performed to characterize the AuNPs prepared from the N. officinale plant extract. Moreover, the antioxidant activity, total phenolic and flavonoid contents and DNA interactions were examined. Additionally, A549 lung cancer cells were treated with 2–48 µg/mL Nasturtium officinale gold nanoparticles (NOAuNPs) for 24 and 48 h to determine the effects on cell viability. The toxicity of the synthesized NOAuNPs to lung cancer cells was determined by the 3-(4,5-dimethylthiazol-2-il)-2,5-diphenyltetrazolium bromide (MTT) assay, and the anticancer effect of the NOAuNPs was evaluated by apoptosis and cell cycle analyses using flow cytometry.ResultsThe average size of the NPs was 56.4 nm. The intensities of the Au peaks from EDS analysis indicated that the AuNPs were synthesized successfully. Moreover, the in vitro antioxidant activities of the NO and NOAuNPs were evaluated; these materials gave values of 31.78 ± 1.71% and 31.62 ± 0.46%, respectively, in the 1,1-diphenyl-2-picrylhydrazyl (DPPH) assay at 200 g/mL and values of 25.89 ± 1.90% and 33.81 ± 0.62%, respectively, in the 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) assay. The NO and NOAuNPs gave values of 0.389 ± 0.027 and 0.308 ± 0.005, respectively, in the ferrous ion reducing antioxidant capacity (FRAP) assay and values of 0.078 ± 0.009 and 0.172 ± 0.027, respectively, in the copper ion reducing antioxidant capacity (CUPRAC) assay. When the DNA cleavage activities of NO and the NOAuNPs were evaluated via hydrolysis, both samples cleaved DNA starting at a concentration of 25 g/mL in the cell culture analysis, while the nanoformulation of the NO components gave greater therapeutic and anticancer effects. We determined that the Au nanoparticles were not toxic to A549 cells. Moreover, after treatment with the half-maximal inhibitory concentration (IC50), determined by the MTT assay with A549 cells, we found that at 24 and 48 h, while the necrosis rates were high in cells treated with NO, the rates of apoptosis were greater in cells treated with NOAuNPs. Notably, for anticancer treatment, activating apoptotic pathways that do not cause inflammation is preferred. We believe that these results will pave the way for the use of NOAuNPs in in vitro studies of other types of cancer.ConclusionIn this study, AuNPs were successfully synthesized from N. officinale extract. The biosynthesized AuNPs exhibited toxicity to and apoptotic effects on A549 lung cancer cells. Based on these findings, we suggest that green-synthesized AuNPs are promising new therapeutic agents for lung cancer treatment. However, since this was an in vitro study, further research should be performed in in vivo lung cancer models to support our findings and to explain the mechanism of action at the molecular level.

Energy Transition Analysis and Climate Action Strategy in the Power Sector: A Case Study of the Java-Bali System

Abstract The transformation of societies over time has led to a marked shift in energy consumption patterns. In the face of rapid industrial growth and a burgeoning population, Indonesia confronts the multifaceted challenge of achieving a balance among energy trilemma: energy security, economic affordability, and environmental sustainability. This study assesses the feasibility of low-carbon energy systems in Indonesia, evaluating current policies and proposing a roadmap for net-zero emissions. Utilizing the Low Emissions Analysis Platform (LEAP) and the Next Energy Modeling System for Optimization (NEMO), the study explores multiple scenarios—namely, Business-as-Usual (BAU), Carbon Capture and Storage Mitigation (CCSM), and New and Renewable Energy Mitigation (NREM). Of these, the NREM scenario, emphasizing the integration of innovative and renewable energy sources, achieves the most substantial reduction in greenhouse gas emissions while maintaining economic viability. This strategy advocates for the comprehensive adoption of new and renewable energy, aiming to approximate zero emissions by 2060. The peak direct cost of production for this scenario is estimated at $270.8 billion. However, each scenario presents its own set of technological, economic, and policy-related challenges, necessitating strategic solutions for a successful transition to sustainable energy systems.

Sonochemically Synthesised Nano‐Stone Structured Vanadium Tungstate for Electrochemical Monitoring of Antibiotic Drug Nitrofurantoin in Real Samples

AbstractThe development of simple, accurate, responsive, and selective antimicrobial drug analysis platforms is extremely important for biomedical research. Developing a novel electrode material that possesses low detection limit, high sensitivity, and stability continues to be a difficult undertaking. Here, vanadium tungstate nanostones (VWNs) were synthesized by utilizing facile sonochemical method with the aid of urea and ethylene glycol for application in electrochemical sensing of antibiotic drug nitrofurantoin (NFT). Using a distinct analytical methods, the structural and morphological characteristics of the as‐prepared transducer material were described. Further, electrochemical behaviour of modified electrode were analysed using cyclic voltammetry and liner sweep voltammetry. Under the optimal conditions VWNs/GCE electrode exhibited superior electrocatalytic activity towards NFT with linear range (20–160 μM), limit of detection (0.019 μM) with better sensitivity (0.69 μM−1 cm−2). The modified electrode demonstrated remarkable repeatability, reproducibility, and excellent stability, indicating its potential as an electrocatalyst. Ultimately, the suggested electrode was used to measure FLT in real samples like tap water and NFT capsule.

Current trends and hotspots of etiology of auditory neuropathy in the past 30 years: A bibliometric analysis

ObjectiveTo make an overview of global research trends in the etiology of auditory neuropathy (AN) over the past 30 years using a bibliometric approach. MethodsBibliometric analyses were conducted by GraphPad Prism 9.0, Citespace 6.2.R2, and an online analysis platform to analyze and visualize publications related to etiology of AN from the Web of Science Core Collection database (WoSCC) database from 1996 to 2022. Additionally, genetic factors in human AN were analyzed. ResultsIn total, 604 original articles and reviews related to the etiology of AN from the WoSCC were included for bibliometric analysis. The results showed that annual publications and trend on etiology of AN increased linearly from 2000. Among them, the United States and China published nearly 400 records (40.32%). From the 604 records, a total of 752 keywords and 10 clustered network maps were extracted by Citespace, and ‘mutations’ was among the top 10 keywords. Analysis of genetic factors found that more than 30 genes were related to AN, and the latest burst occurred in 2022. ConclusionThe bibliometric analysis mapped the global research trends and analyzed hotspots for future. The results indicated that the annual publications increased linearly from 2000. Notably, there was a burst in genetic factors in 2022, which identified that genetic factor would remain a focus of future research.

N-glycan core tri-fucosylation requires golgi α-mannosidase III activity that impacts nematode growth and behavior

N-glycans with complex core chitobiose modifications (CCMs) are observed in various free-living and parasitic nematodes but are absent in mammals. Using Caenorhabditis elegans as a model, we demonstrated that the core N-acetylglucosamine (GlcNAc) residues are modified by three fucosyltransferases, namely FUT-1, FUT-6 and FUT-8. Interestingly, FUT-6 can only fucosylate N-glycans lacking the α1,6-mannose upper arm, indicating that a specific α-mannosidase is required to generate substrates for subsequent FUT-6 activity. By analysing the N-glycomes of aman-3 knockouts using offline HPLC-MALDI-TOF MS/MS, we observed that the absence of aman-3 abolishes α1,3-fucosylation of the distal GlcNAc of N-glycans, which suggests that AMAN-3 is the relevant mannosidase on whose action FUT-6 depends. Enzymatic characterisation of recombinant AMAN-3 and confocal microscopy studies using a knock-in strain (aman-3::eGFP) demonstrated a Golgi localisation. In contrast to the classical Golgi α-mannosidase II (AMAN-2), AMAN-3 displayed a cobalt-dependent α1,6-mannosidase activity towards N-glycans. Using AMAN-3 and other C. elegans glycoenzymes, we were able to mimic nematode N-glycan biosynthesis in vitro by remodelling a fluorescein conjugated-glycan and generate a tri-fucosylated structure. In addition, using a high-content computer-assisted C. elegans analysis platform, we observed that aman-3 deficient worms display significant developmental delays, morphological and behavioural alterations in comparison to the wild type. Our data demonstrated that AMAN-3 is a Golgi α-mannosidase required for core fucosylation of the distal GlcNAc of N-glycans. This enzyme is essential for the formation of the unusual tri-fucosylated CCMs in nematodes, which may play important roles in nematode development and behaviour.

Prevention of respiratory syncytial virus from 1991 to 2024: a systematic review and bibliometrics analysis.

Respiratory syncytial virus (RSV) puts children and elderly individuals worldwide at risk for severe health issues and financial difficulties. Prevention is the main treatment for RSV infection, as there is currently no particular therapy. By using bibliometrics analysis, this study attempted to map the increasing tendency in the prevention of RSV infection from January 1991 to August 2024 and to examine the frontiers and hotspots of related research. We extracted pertinent articles through the Web of Science Core Collection (WoSCC) on August 26, 2024, covering the period between January 1991 and August 2024. Then, an online bibliometrix interface (https://bibliometrics.com), R software (version 4.3.2), CiteSpace V6.1R6 (64-bit) software, and the Online Analysis Platform of Literature Metrology were used to analyze the data. A total of 709 eligible data points pertaining to the prevention of RSV were included. The United States, England, and the Netherlands were the three major contributors to this field. The most productive journal was Vaccine. Centers for Disease Control and Prevention ranked first, with 22 publications in this field. The fusion (F) protein, nonstructural (NS) protein and glycoprotein (G) protein are the target proteins of RSV prevention drugs. In the past 30 years, the research on RSV prevention has entered a stage of rapid development, and many vaccines and monoclonal antibodies have entered the clinical research stage, and some have been marketed.

Role and molecular mechanisms of HuangQiSiJunZi decoction for treating triple-negative breast cancer as explored via network pharmacology and bioinformatics analyses

ObjectiveIn this study, we evaluated the molecular mechanisms of HuangQiSiJunZi Decoction (HQSJZD) for treating triple-negative breast cancer (TNBC) using network pharmacology and bioinformatics analyses.MethodsEffective chemical components together with action targets of HQSJZD were selected based on the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). Meanwhile, differentially expressed genes (DEGs) were extracted from TNBC sample data in The Cancer Genome Atlas (TCGA) database. Additionally, we built a protein-protein interaction (PPI) network and acquired hub genes. Gene Expression Omnibus(GEO) datasets were utilized to verify the accuracy of hub gene expression. Additionally, enrichment analyses were conducted on key genes. Furthermore, TNBC severity-related high-risk factors were screened through univariate together with multivariate Cox regressions; next, the logistic regression prediction model was built. Moreover, differential levels of 22 immune cell types in TNBC tissues compared with normal tissues were analyzed. The hub gene levels within pan-cancer and the human body were subsequently visualized and analyzed. Finally, quantitative PCR (RT-qPCR) was used to validate the correlation of the hub genes in TNBC cells.ResultsThe study predicted 256 targets of active ingredients and 1791 DEGs in TNBC, and obtained 16 hub genes against TNBC. The prognostic signature based on FOS, MMP9, and PGR was independent in predicting survival. A total of seven types of immune cells, such as CD4 + memory T cells, showed a significant difference in infiltration (p < 0.05), and immune cells were related to the hub genes. The HPA database was adopted for hub gene analyses, and as determined, FOS was highly expressed in most human organs. The results of RT-qPCR validation for the FOS hub gene were consistent with those of bioinformatic analyses.ConclusionHQSJZD might regulate the interleukin-17 and aging pathways via FOS genes to increase immune cell infiltration in TNBC tissues, and thus, may treat TNBC and improve the prognosis. The FOS genes are likely to be a new marker for TNBC.

IMPLEMENTATION OF SIMOYU METHOD FOR MODELING OF TRANSIENTS OF CONTROL OBJECT

In this paper transients in the control system are investigated on the basis of experimental data. The construction of the transfer function of the control object using Simoyu method is realized by means of Python language. The model of the control system of the object, selection of the regulator and its settings are implemented using SimInTech modeling environment. Within the framework of the conducted research, methodological approaches to the formation of transfer functions of control objects represented in the form of polynomial expressions of various degrees of complexity, starting with polynomials of the first degree in the numerator and the second degree in the denominator, and ending with polynomials of the second degree in the denominator against the third degree in the numerator, have been developed and tested. A procedure for reading data in CSV format was used to build the Python program interface, which helped to simplify the integration of experimental results with analytical tools, providing a powerful platform for subsequent analysis, visualization, and interpretation of the resulting transfer functions. The procedures of debugging and optimization of the technique of visualization of results and estimation of calculation errors have been carried out, which allowed to provide a visual representation of data and high accuracy of the obtained transfer functions. In contrast to the known analytical studies in the field of differential equations describing transient processes, the use of numerical methods implemented by means of Python libraries and programming environments, in particular SimInTech, allows to simplify the analysis of transient processes of control objects.

Design of a Technology Verification Platform for Space Electromagnetic Interference Signal Testing and Analysis

This paper designs a space electromagnetic interference signal test and analysis technology verification platform. The article firstly introduces the general scheme of the technical verification platform and then describes each component unit of the hardware and the overall structure of the software in detail. The platform can achieve a 10 MHz ~ 50 GHz working frequency band, 1.2 GHz acquisition and real-time recording bandwidth, 6 GB/s recording rate, and 12 TB recording capacity.

Social media and health care: a cross-sectional analysis of X (Twitter) posts using the NodeXL platform

Introduction. The landscape of healthcare and medical education is rapidly evolving, driven by the popularity of social media platforms like Facebook, WeChat, and X (Twitter). These platforms have transformed healthcare communication, enabling trainees, medical students, and practitioners to enhance learning and clinical practice. Educators leverage social media to simplify complex medical concepts, fostering continuous learning and professional development. Despite their benefits, social media, notably X (Twitter), boasting over 186 million active users, are susceptible to misinformation, underscoring the importance of verifying data accuracy and identifying influential medical sources. This study aims to identify the most influential sources of health information through X (Twitter) analysis using the NodeXL tool. Methods. Employing a cross-sectional design, this study utilized the NodeXL platform for social media analysis. NodeXL facilitated the identification of prevalent topics in public health discussions on X (Twitter). Influencers, users, and prominent healthcare providers were categorized to assess the credibility of their information. Data collection extended up to March 1, 2024. Results. Analysis revealed that the #hospital hashtag garnered the highest number of tweets among popular healthcare hashtags (6,438 tweets, with 84% being unique). Conversely, #medicalcare and #medicine had the lowest citation counts (58 and 116 respectively). @MahaHealthIE emerged as the most famous account using the #healthservices hashtag. Notable influencers with the #hospital hashtag included YouTube, Narendra Modi, and Al Jazeera English. @GreenSwelfares led in using the #medicalcare hashtag, while YouTube and the World Health Organization (@WHO) were prominent among accounts using #medicine. Conclusion. This study underscores the significance of monitoring social media and tracking key influencers periodically. However, the study's limitation lies in its snapshot nature, failing to capture the dynamic shifts in influencer popularity trends over time.

ECONOMIC ASSESSMENT OF THE ACTIVITY OF RETAIL INVESTORS ON THE STOCK MARKET

The article examines the activity of a retail investor on the stock market. Issues of economic assessment of their activity and problems of increasing its effectiveness are studied. The dynamics of the share of retail investors in the total volume of trading in the stock market is analyzed. The key factors of growth of retail investors' activity in the stock market are studied. It was determined that the share of retail investors in the volume of trades on the stock market has almost doubled. The factors that caused such a powerful dynamic were analyzed. Two factors such as available investment opportunities and empowerment through information are discussed in detail. An economic assessment of the effectiveness of the activities of retail investors was carried out. The reasons for the negative results of their work on the stock market have been revealed. The importance of developing specific investment goals before starting investment activities on the stock market and adapting investment strategies to them is determined. It is suggested to use paper trading simulators that reproduce the real trading environment. It has been found to allow investors to make virtual trades and track their performance in real time. It is recommended to engage more comprehensive analytical and research platforms that provide tools for charting and price research. This has been proven to allow investors to more effectively analyze market trends, identify potential economic opportunities, and develop effective trading strategies. Possibilities of minimizing transaction costs through the use of ETFs-shares of investment funds, which are often traded on the stock exchange, are considered. As a result of the conducted research, the possibilities of further scientific research on this topic in the direction of using the latest financial programs based on artificial intelligence and comprehensive assessment of the interaction of various sectors of the stock market with their help were considered.

IoT Smart Gardening on Herbal Plant and Analytic Virtualization Platform System

The Internet of Things (IoT) is an emerging technology developing recently. The technology of IoT allows users to communicate with controllers and sensors, which are widely used in the agricultural sector, such as farming and weather forecasts. Recently, it has been identified that traditional herbal home gardens are difficult to monitor where herbal plants are sensitive and need a proper monitor. This research has developed an IoT smart gardening for herbal plant systems that helps to analyze four herbal plant types: Aloe Barbadensis miller, Salvia Rosmarimus, Clinacanthus nutans, and Curcuma. The system was designed using a NodeMCU ESP32 controller, soil moisture sensor, and DHT11 temperature and humidity sensor. The collected data was monitored in real-time on the Blynk platform via mobile phones and web browsers on sunny and rainy days. The result shows that the highest temperature on a sunny day was captured at 30°C, and the lowest was 26°C. However, during a rainy day, the highest temperature captured is 28°C and the lowest was 24°C. The highest humidity percentage on a sunny day was 80%, and the lowest was 40%, while the highest humidity on a rainy day was 98%, and the lowest was 68%. The soil moisture data that captured the water content of the soil of each herbal plant on a sunny day has been lost due to high temperatures. However, each herbal plant barely lost its water content due to low temperature and high humidity percentage conditions. The comparison showed that temperature, humidity, and weather could affect the water content in the soil. This research has significantly improved the agriculture sector in monitoring herbal plants and moving from traditional planting with an IoT system concept.

IoT Enabled Motor Drive Vehicle for the Early Fault Detection in New EnergyConservation

An IoT-enabled motor drive vehicle integrates Internet of Things (IoT) technology with traditional vehicle systems to enhance control, monitoring, and automation. Sensors and connected devices within the vehicle collect real-time data on parameters such as speed, battery status, motor performance, and environmental conditions. This data is transmitted to cloud-based platforms for analysis, enabling remote diagnostics, predictive maintenance, and optimization of vehicle performance. IoT integration also facilitates features like vehicle tracking, smart navigation, and user-specific adjustments, improving overall efficiency, safety, and user experience. This paper investigates the utilization of IoT enabled Programmable Logic Controller (PLC) technology to enhance fault detection in new energy vehicle (NEV) motor drive systems. With the increasing adoption of electric vehicles, ensuring the safety and reliability of motor drive systems becomes paramount. The study begins with a comprehensive review of existing literature, examining various fault detection methodologies and technologies. Subsequently, simulation analyses are conducted to evaluate the performance of IOT enabled PLC-based fault detection algorithms under different operating conditions. This paper presents the numerical results of fault detection in new energy vehicle (NEV) motor drive systems using Programmable Logic Controller (IOT enabled PLC) technology. Through comprehensive simulations and experimental validations, the IOT enabled PLC-based fault detection algorithms achieved an average detection accuracy of 93% across various fault scenarios. The response time of the fault detection system.

Smartphone-assisted paper-based colorimetric analytical platform for simultaneous determination of barium and strontium ions

Smartphone-assisted paper-based colorimetric analytical platform for simultaneous determination of barium and strontium ions