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Aims: Exploring key genes and potential molecular pathways of ferroptosis in immunoglobulin A nephropathy (IgAN). Methods: The IgAN datasets and ferroptosis‐related genes (FRGs) were obtained in the Gene Expression Omnibus (GEO) and FerrDb database. Differentially expressed genes (DEGs) were identified using R software and intersected with FRGs to obtain differentially expressed FRGs (DE‐FRGs). After that, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis (PEA) and Gene Ontology (GO) functional annotation were performed on DE‐FRGs. In the Search Tool for the Retrieval of Interacting Genes (STRING) website, we construct a protein–protein interaction (PPI) network. The PPI network was further investigated with screening hub genes with Cytoscape software. The core genes were then subjected to gene set enrichment analysis (GSEA). Finally, the samples were analyzed for immune infiltration in R, and the correlation between hub genes and immune cells was analyzed. Results: A total of 347 DEGs were identified. CD44, CDO1, CYBB, IL1B, RRM2, AKR1C1, activated transcription factor‐3 (ATF3), CDKN1A, GDF15, JUN, MGST1, MIOX, MT1G, NR4A1, PDK4, TNFAIP3, and ZFP36 were determined as DE‐FRGs. JUN, IL1B, and ATF3 were then screened as hub genes. GSEA and immune infiltration analysis revealed that the hub genes were closely associated with immune inflammatory responses such as NOD‐like receptor signaling, IL‐17 signaling, and TNF signaling. Conclusions: Our results show that JUN and ATF3 are possibly critical genes in the process of IgAN ferroptosis and may be related with immune cell infiltration. [ABSTRACT FROM AUTHOR]

Very low earth orbit (VLEO) spacecraft have become an attractive proposition with obvious advantages in various missions, including communication and ground observation. Higher requirements for precise orbit decay prediction (PODP) technology are requested, which provides accurate state references for orbit maintenance and necessary database for space situational awareness. The effectiveness of the traditional orbital prediction method for PODP is limited by inaccurate estimation of the spacecraft's physical parameters and space environments. Generalization performance of machine learning techniques (MLTs) is blocked by the universal challenge known as catastrophic forgetting, resulting in limited improvement on PODP. In this study, a method of expandable orbit decay propagator (EODP) for spacecraft PODP in VLEO, based on model‐agnostic MLTs, is proposed. The plasticity of the proposed model is discussed, which originates from the uncertainty of neural network (NN) parameters. The proposed method overcomes the negative effects of uncertain physical parameters and complex space environments. The model achieves at least a 70% improvement in accuracy compared to the high‐precision orbital propagator (HPOP) and presents a novel approach for the future implementation of machine learning–based methods in the field of orbit prediction. [ABSTRACT FROM AUTHOR]

The precise relationship between service innovation and financial outcomes in this context remains ambiguous. This study examines the extent to which service innovation contributes to financial outcomes, analyzing data from 512 manufacturing companies across China from 1993 to 2017, sourced from the CSMAR database. Utilizing OLS regression and fixed effects model method, we identify a positive nonlinear correlation between service innovation and financial performance, highlighting a "service dilemma" that varies between state‐owned and private enterprises, which follows a "saddle‐type" pattern. In addition, the impact of service innovation shows considerable variation across different industries. This research provides critical insights into optimizing financial performance through strategic service innovation across various types of enterprises and industries. It underscores the importance of tailored industrial strategies and equitable competition in enhancing service innovation. The study's extensive sample size and its focus on the heterogeneity among enterprise types and industries mark its key contributions to understand service innovation's role in financial performance. [ABSTRACT FROM AUTHOR]

Global warming and energy‐saving efforts make it necessary to look for energy‐free technology to reduce buildings' temperature. This work aims to develop a system for monitoring green panels' physical variables (atmospheric temperature, humidity, soil humidity, amount of light, and vibration) to determine their efficiency. A model of a green panel was outfitted with detectors that recorded atmospheric variable modifications within and outside the green panel under evaluation. Open‐source code and hardware were used to record all variables and store the data. Once the monitoring system is completed, a green panel model is developed as concept proof for monitoring the physical variables; a database is created, studied, and tested using the formulated hypothesis. The variables were analyzed using current techniques of statistical description, and the results obtained support the idea underpinning the feasibility of implementing green panels in city houses and buildings. A substantial difference in temperature up to 12°C was found when using green walls and ceiling systems at the hottest times of the day. The humidity in the air is up to 88%, generating an atmosphere of comfort in buildings where these systems are installed. [ABSTRACT FROM AUTHOR]

In this paper, we present a new approach of document information extraction by studying the document anatomy where we investigated the possible variants and forms it could have for each document component. To overcome the lack of publicly available document datasets, we used a generated invoice database where we conceived 9 different templates and generated 100 samples for each one where the documents were annotated automatically during the generation process. We analysed the following invoice components: dates block (invoice date and invoice due date), address block, amounts block (tax‐free amount, tax amount, and total amount), and lines block (lines table) by investigating the impact of training our model on various block variants. We conducted several experiments where we compared the results obtained when we tested on templates that included variants not encountered during the training phase versus when we introduced them to the training dataset. This allowed us to analyse the improvement in results after adding these previously unseen variants. The obtained results have shown that the model generalises better when trained on a large variety of cases and achieves remarkable performance. We conducted experiments on various models to highlight the model‐agnostic character of our proposed approach. This methodology allows to have great performance, even with models that have significantly fewer parameters, especially in comparison to recently published models with millions of parameters. [ABSTRACT FROM AUTHOR]

Boza, a cereal‐based beverage popular in southeast Europe, is fortified with probiotics and is believed to positively impact the composition of the gut microflora. This investigation focused on fermented cereal‐based beverage boza to identify strains of probiotic Lactobacillus spp. capable of inhibiting carbohydrate‐hydrolysing enzymes α‐glucosidase (AG) and α‐amylase (AA). The isolated bacterial strains underwent a comprehensive assessment, including biochemical, molecular, and probiotic trait analyses such as tolerance survivability, adhesion, safety, and health‐promoting attributes. We evaluated the inhibitory potential of the supernatant, cell lysate, and intact cells of Lactobacillus spp. Molecular analysis has revealed that isolates RAMULAB30 and RAMULAB29 exhibit a significant genetic similarity (>97%) to Lacticaseibacillus paracasei and Limosilactobacillus fermentum, respectively. These findings are documented in the NCBI database. They exhibited significant resistance to gastrointestinal and intestinal fluids, also indicating their potential for adhesion. Additionally, the isolates showed a significant antibacterial activity, particularly against Micrococcus luteus. They showed resistance to vancomycin and methicillin antibiotics but were more susceptible to streptomycin and ampicillin. Furthermore, the strains demonstrated antioxidant properties. To ensure their safety, a haemolytic assay was conducted despite their general recognition as safe (GRAS) status. The study primarily aimed to evaluate the inhibitory effects of the extract on enzymes AG and AA. Bacterial isolates demonstrated a significant inhibitory activity against both enzyme AG (32%–67% inhibition) and enzyme AA (18%–46% inhibition) in different forms, including supernatant (CS), lysed extract (CE), and intact cell (IC). These findings underscore the potential of bacterial isolates to inhibit the enzyme activity effectively. Furthermore, the L. fermentum RAMULAB29 and L. paracasei RAMULAB30 strains exhibit remarkable antidiabetic potential. Food products incorporating these strains have promising prospects as nutraceuticals, providing improved health benefits. [ABSTRACT FROM AUTHOR]

The paper introduces a pioneering taxonomy for 5G jamming attacks, meticulously crafted through diverse research methodologies, including literature review, case studies, incident database analysis, and network architecture assessment. This taxonomy is based on six dimensions: attack classification, attack techniques, jammer characteristics, impact and target analysis, countermeasure strategies, and security layers. It gives a full picture of what is needed to protect 5G networks from new security threats, especially jamming attacks. Real‐world case studies from regions like Ukraine, the South China Sea, and Cuba validate the taxonomy's efficacy in analyzing varied jamming scenarios and offer practical insights into cyber threats and mitigation strategies. By looking closely at attack details, network effects, countermeasure strategies, and security layers, this research makes a big difference in improving the security of 5G networks. It gives everyone involved, the tools and knowledge they need to protect themselves from jamming attacks and make networks more resilient in a world where technology and connectivity are changing quickly. The key findings of this research include identifying key 5G network vulnerabilities to jamming attacks and presenting a detailed six‐dimensional taxonomy: attack classification, techniques, jammer characteristics, impact analysis, countermeasures, and security layers. It emphasizes the need for strong security strategies to improve 5G network resilience against evolving threats, which case studies from Cuba, the South China Sea, and Ukraine have validated. [ABSTRACT FROM AUTHOR]

Introduction. Surgical procedures have undergone a paradigm shift in the last 3 decades, with minimally invasive surgery becoming standard of care for a number of surgeries, including the treatment of benign gallbladder diseases. By providing a thorough and impartial summary of the earlier published systematic reviews, the current systematic review is the first to present comparison results. This review illustrates the data of intraoperative and postoperative results of each laparoscopic cholecystectomy technique. Materials and Methods. The Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) guideline was meticulously followed to conduct the present systematic review. MEDLINE (via PubMed), Cochrane Database of Systematic Reviews, and Web of Science were searched for eligible publications, and a total of 14 systematic reviews were included. A newly developed extraction table was utilized to obtain the predefined parameters from eligible systematic reviews, including operative time, conversion rate, estimated blood loss, bile leak, length of hospital stay, postoperative pain, and cosmetic results. All statistical analyses were conducted using Statistical Package for the Social Sciences (SPSS) software, version 26.0. The analysis of dichotomous results was summarized using relative risks and 95% confidence intervals (95% CI), and continuous results were summarized using mean differences and 95% CIs. The proportions were compared using a single proportion z‐test. Results. The analysis of our primary and secondary outcomes revealed a statistically significant improvement in aesthetic results after single‐incision laparoscopic cholecystectomy (SILC) in comparison to the multiport approach of laparoscopic cholecystectomy. This, however, is accompanied by extended operative timing and subsequently, prolonged exposure to anesthesia. Conclusion. Patients should be carefully selected for SILC to minimize technical difficulties and prevent complications both intraoperatively and shortly after the procedure. This trial is registered with CRD42023392037. [ABSTRACT FROM AUTHOR]

Image segmentation is one important process in image analysis and computer vision and is a valuable tool that can be applied in fields of image processing, health care, remote sensing, and traffic image detection. Given the lack of prior knowledge of the ground truth, unsupervised learning techniques like clustering have been largely adopted. Fuzzy clustering has been widely studied and successfully applied in image segmentation. In situations such as limited spatial resolution, poor contrast, overlapping intensities, and noise and intensity inhomogeneities, fuzzy clustering can retain much more information than the hard clustering technique. Most fuzzy clustering algorithms have originated from fuzzy c‐means (FCM) and have been successfully applied in image segmentation. However, the cluster prototype of the FCM method is hyperspherical or hyperellipsoidal. FCM may not provide the accurate partition in situations where data consists of arbitrary shapes. Therefore, a Fuzzy C‐Regression Model (FCRM) using spatial information has been proposed whose prototype is hyperplaned and can be either linear or nonlinear allowing for better cluster partitioning. Thus, this paper implements FCRM and applies the algorithm to color segmentation using Berkeley's segmentation database. The results show that FCRM obtains more accurate results compared to other fuzzy clustering algorithms. [ABSTRACT FROM AUTHOR]

Purpose. Breast cancer is considered a huge health problem among women all over the world due to its increased mortality rate. Breast cancer deaths are decreased by 28–34% due to improvement in treatment and early detection. In order to detect breast cancer early and provide the best possible therapy, knowledge and awareness are essential. So, this study aimed to assess risk of breast cancer among nurses, the knowledge and practices of breast self-examination (BSE) and determine its barriers, and implement an educational program for them. Design. A quasiexperimental research design was used. Methods. This study was conducted at two university hospitals (main university hospital and woman's health hospital). A purposive sample of 280 nurses with age more than thirty-five years were included in the study using three tools, namely, structure interview questionnaire, breast cancer risk assessment tool, and a structured breast cancer knowledge and practice questionnaire. Nurses' knowledge and practices regarding breast cancer and BSE were first assessed and then they were provided with two educational courses covering theoretical and practical information. One month after the program ended, the nurses' knowledge and practices were assessed again. Results. About 3.2% of the participants have five years high risk of developing breast cancer according the risk assessment tool, the main barriers of do not do BSE did not find any symptoms and scared from finding any abnormality. There was a significant increase in knowledge and practice of breast self-examination posteducation compared to pre-education (73.2 and 98.2%, respectively). Also, there was a statistically significant difference between pre- and postprogram with a p value of 0.0001. Conclusion. This study concluded that 3.2% of the nurses with high risk of developing breast cancer needed a follow-up; more than half of them do not perform breast self-examination because they think that do not have any symptoms in the breast and the educational program with a significant effect on knowledge and practices of nurses. So, the researchers recommend the nurses need for an empowerment program and support by providing assistance in places of work as done free clinical examination and mammography by female specialist for privacy. [ABSTRACT FROM AUTHOR]

Background. As people live longer, there is an increasing need for hard tissue regeneration and whole-tooth regeneration. Despite the advancements in the field of medicine, the field of regenerative dentistry is still challenging due to the complexity of dental hard tissues. Cross-disciplinary collaboration among material scientists, cellular biologists, and odontologists aimed at developing strategies and uncovering solutions related to dental tissue regeneration. Methodology. A search of the literature was done for pertinent research. Consistent with the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) 2020 Statement, the electronic databases looked at were PubMed, Science Direct, Scopus, and Google Scholar, with the keyword search "hard dental tissue regeneration." Results. Database analysis yielded a total of 476 articles. 222 duplicate articles have been removed in total. Articles that have no connection to the directed regeneration of hard dental tissue were disregarded. The review concluded with the inclusion of four studies that were relevant to our research objective. Conclusion. Current molecular signaling network investigations and novel viewpoints on cellular heterogeneity have made advancements in understanding of the kinetics of dental hard tissue regeneration possible. Here, we outline the fundamentals of stem hard dental tissue maintenance, regeneration, and repair, as well as recent advancements in the field of hard tissue regeneration. These intriguing findings help establish a framework that will eventually enable basic research findings to be utilized towards oral health-improving medicines. [ABSTRACT FROM AUTHOR]

The literature concerning resident involvement in shoulder surgery is limited. The purpose of this study was to examine whether resident involvement across all orthopedic shoulder surgeries is associated with adverse 30-day outcomes. Utilizing the American College of Surgeons National Surgical Quality Improvement Program database, patients who underwent shoulder surgery with or without a resident present were analyzed. Independent t-test and chi-square or Fischer's exact test were used appropriately. A logistic regression model was used to calculate adjusted odds ratios. This study examined 5,648 patients: 3,455 patients in the "Attending alone" group and 2,193 in the "Attending and resident in the operating room" group. Resident presence in the operating room was not associated with increased complications, except for bleeding transfusions (OR 1.71, CI 1.32–2.21, P ≤ 0.001). This study demonstrates that resident involvement in orthopedic shoulder surgery does not present an increased risk for 30-day complications when compared to surgeries performed with the attending surgeon alone. [ABSTRACT FROM AUTHOR]

Background. Vitiligo is a dermatological disorder characterized by the depletion of melanocytes. The key to its treatment lies in the promotion of melanin regeneration. The Wnt/β-catenin signalling pathway assumes a pivotal role in this regenerative process. Fire needle therapy (FNT), a traditional Chinese medical technique, has emerged as a promising intervention. Methods. We analyzed gene expression associated with the Wnt/β-catenin signalling pathway in both normal skin and the lesions of patients with vitiligo in the Gene Expression Omnibus database (GEO database). Furthermore, we evaluated the gene expression of this pathway and assessed the effects of FNT on pigmentation and its influence on the Wnt/β-catenin signalling pathway in a vitiligo mouse model. Results. Compared to that of the normal skin, the mRNA levels of WNT10A, WNT10B, WNT2, WNT2B, WNT3, WNT3A, WNT4, WNT5B, WNT7B, and LEF1 displayed significant decreases in the lesions of patients with vitiligo. Moreover, vitiligo mouse skin lesions improved following FNT, and the number of melanocytes and melanin levels increased. In addition, posttreatment, genes associated with the Wnt/β-catenin signalling pathway were revealed to be upregulated. Conclusion. FNT may play an important role in promoting pigmentation in vitiligo through modulation of the Wnt/β-catenin signalling pathway. This adds to the growing body of literature supporting the use of FNT to treat vitiligo. This, in turn, may inform clinical practice and improve patient outcomes. [ABSTRACT FROM AUTHOR]

In this work, a machine learning (ML) metamodel is developed for the time-series forecasting of a typical nuclear power plant response undergoing a loss of coolant accident (LOCA). The plant model of choice is based on the APR1400 nuclear reactor. The key systems and components of APR1400 relevant to the investigated scenario are modelled using the thermal-hydraulic code, RELAP5/MOD3.4, following the description published in the design control document. The model is tested under a spectrum of initial and boundary conditions via propagation of key uncertain parameters (UPs) which are derived from the phenomena identification and ranking table (PIRT). This is achieved by loosely coupling RELAP5/MOD3.4 with the statistical tool, Dakota. The most probable nuclear power plant (NPP) response was calculated using the best estimate plus uncertainty (BEPU) approach. Next, the database generated from the NPP system response was used as an input for the ML model. The NPP system response was represented by peak cladding temperature (PCT), safety injection system (SIT), mass flow rate, reactor power, and primary system pressure. In this research, two regression models were tested with reasonably good performance, namely, the gated recurrent unit (GRU) and the long short-term memory (LSTM). [ABSTRACT FROM AUTHOR]

Measuring blood oxygen saturation (SpO2) is crucial in a triage process for identifying patients with respiratory distress or shock, since low SpO2 levels indicate compromised hemostability and the need for priority treatment. This paper explores the use of wearable mechanical deflection sensors based on laser-induced graphene (LIG) for SpO2 estimation. The LIG sensors are attached to a subject's chest for real-time monitoring of respiratory signals. We have developed a novel database of the respiratory signals, with corresponding SpO2 values ranging from 86% to 100%. The database is used to develop an artificial neural network model for SpO2 estimation. The neural network performance is promising, with regression metrics mean squared error = 0.184, mean absolute error = 0.301, root mean squared error = 0.429, and R-squared = 0.804. The use of mechanical respiration sensors in combination with neural networks in biosensing opens new possibilities for noninvasive SpO2 monitoring and other innovative applications. [ABSTRACT FROM AUTHOR]

This study aims to survey the bibliometric properties of distributed decisions, decentralized decisions, and fuzzy articles published between 1995 and 2023 in the Web of Science (WoS) database. During the analysis process, the keywords "distributed decision, decentralized decision, and fuzzy" were scanned in all languages, both in the titles and the content of all publication types. As a result of the analysis, 79 articles in all fields comprised the dataset. The most used keywords in the articles were related to the distributed decision, decentralized decision, and fuzzy logic, and the most frequently cited publications were examined using the social network analysis method, which uses VOSviewer (version 1.6.19) to visualize the relationships. The study's goal on "active researchers, active journals, journal metrics, title document type, active countries, and active institutions" was to look at the words most frequently used in articles on distributed, decentralized, and fuzzy logic. The social network analysis represented the relationships between these keywords and the most frequently cited publications. The findings demonstrated a significant correlation between using these keywords in academic literature and their contribution to this field's research. These results can assist researchers in finding potential partners and keeping up with current research trends. Overall, this study offers important new perspectives on the state of research on fuzzy logic, distributed decision making, and decentralized decision making. [ABSTRACT FROM AUTHOR]

Introduction. The use of antidepressant and anxiolytic drugs has changed in Scandinavian countries over recent decades, with notable national variations. Objective. To describe and compare antidepressant and anxiolytic drug use in Norway, Sweden, and Denmark. Methods. Data included each country's prescription registers from 2006 to 2021. The measures were period (1-year) prevalence (users per 1000 inhabitants) and therapeutic intensity (TI; daily defined dose (DDD) per 1000 inhabitants per day), overall, by drug classes and age groups. Results. The prevalence of antidepressant use increased from 2006 to 2021 and was highest in Sweden (78 to 107 users per 1000 inhabitants) and lowest in Norway (61 to 69 users per 1000 inhabitants). The prevalence of anxiolytic use decreased, most steeply in Denmark (50 to 18 users per 1000 inhabitants). The TI of antidepressants increased consistently in Norway and Sweden, but more variably in Denmark. Sweden had the highest increase in TI of antidepressants (56%). The TI of anxiolytics declined most markedly in Denmark (by 75%). The prevalence of antidepressant and anxiolytic use was highest among adults ≥65 years. The prevalence of antidepressant use increased across age groups in Sweden and young people (5-19 years) in Norway, but not in Denmark. Conclusions. The use of antidepressants increased in Scandinavia in 2006-2021, but decreased for anxiolytics, with country variations in the number of users and the amount used. Future research should target factors underlying high antidepressant and anxiolytic use in older adults across countries and increasing antidepressant use in Sweden and among young Norwegians. [ABSTRACT FROM AUTHOR]

The research group utilized the estimation model of energy consumption capacity for reinforced concrete components without axial force to assess the energy consumption capacity of 92-reinforced concrete components from the PEER database, which were subjected to axial force and bending. The study also examined the impact of design parameters, including longitudinal reinforcement ratio, transverse reinforcement ratio, axial compression ratio, and shear-span ratio, on the estimation results. The research findings revealed that when applying the estimation model of energy consumption capacity for reinforced concrete components without axial force to calculate the energy consumption capacity of reinforced concrete components with axial force, there was a significant deviation rate in the estimation of cumulative energy consumption. The relationship between the deviation rate of cumulative energy consumption and longitudinal reinforcement ratio, axial compression ratio, and shear-span ratio remained unclear. However, a more apparent linear relationship was observed with the transverse reinforcement ratio. By conducting a quantitative analysis of the transverse reinforcement ratio, the researchers proposed an modified estimation model of energy consumption capacity for reinforced concrete components with axial force. Nonetheless, the accuracy of the modified estimation model was found to be high within the range of 0–250,000 kN mm of cumulative energy consumption. For cumulative energy consumption exceeding 250,000 kN mm, further experimental and theoretical research is still required to enhance the reliability of the modified estimation model. [ABSTRACT FROM AUTHOR]

Background. Currently, the substance basis and function of Polygonatum sibiricum leaf remain unknown. Objective. This study aims to investigate the antitumor mechanism of P. sibiricum leaf tea through network pharmacology. Methods. Ultraperformance liquid chromatography quadrupole exactive mass spectrometry (UPLC-Q-Exactive-MS) was employed to analyze the chemical components in the extract of P. sibiricum leaf tea. Compounds were screened using the PubChem database and SwissADME platform. The compound targets were identified using the Swiss Target Prediction database, while the disease targets were obtained from the GeneCards database. Subsequently, a compound-protein interaction network was constructed, and KEGG pathway enrichment analysis was performed to elucidate the antitumor activity pathway of P. sibiricum leaf tea. Molecular docking verification was carried out as well. Results. From the extract of P. sibiricum leaf tea, a total of fifty-six components were screened, including 27 active components. Seventy-two corresponding targets closely associated with the antitumor activity of P. sibiricum leaf tea were identified. By constructing a compound-target-pathway network diagram, three key compounds (pubescenol, avenanthramide E, and 13-cis-acitretin) and eleven key targets (AKT1, EGFR, CASP3, CCND1, MTOR, MMP9, ERBB2, BCL2L1, MAPK1, PPARG, and PIK3CA) were determined. Additionally, KEGG pathway analysis identified 30 antitumor related pathways. The results of molecular docking between the three key compounds and the top three targets (AKT1, EGFR, and CASP3) were consistent with the findings of network pharmacology. Conclusion. This study highlights the distinctive features of P. sibiricum leaf tea extract, which possesses multiple components, targets, and pathways. It successfully identifies the active components of P. sibiricum leaf tea that exhibit antitumor properties, along with their potential mechanisms of action. These findings offer valuable insights and inspiration for further research on novel mechanisms of action. [ABSTRACT FROM AUTHOR]

Introduction. Research shows the correlation between angiotensin-converting enzyme (ACE) deletion and insertion (D/I) polymorphism and COVID-19 risk; yet, conclusive evidence is still lacking. Thus, a meta-analysis of relevant articles was performed to more accurately estimate the relationship of ACE I/D polymorphism with the risk of COVID-19. Material and Methods. Relevant literature from the PubMed database was systematically reviewed, and odds ratios (ORs) and associated 95% confidence intervals (CIs) were measured. Additionally, the metapackage from Stata version 15.0 was used for statistical analysis. Results. The meta-analysis eventually contained 8 studies, including 1362 COVID-19 cases and 4312 controls. Based on the data, the ACE I/D polymorphism did not show an association with COVID-19 risk (D vs. I: OR = 1.25 , 95% CI = 0.96 – 1.64 ; DD vs. II: OR = 1.89 , 95% CI = 0.95 – 3.74 ; DI vs. II: OR = 1.75 , 95% CI = 0.92 – 3.31 ; dominant model: OR = 1.88 , 95% CI = 0.99 – 3.53 ; and recessive model: OR = 1.24 , 95% CI = 0.81 – 1.90). Further, subgroup analyses stratified based on case proved that the ACE D allele demonstrated an association with increasing risk of COVID-19 severity (D vs. I: OR = 1.64 , 95% CI = 1.01 – 2.66 ; DD vs. II: OR = 4.62 , 95% CI = 2.57 – 8.30 ; DI vs. II: OR = 3.07 , 95% CI = 1.75 – 5.38 ; dominant model: OR = 3.74 , 95% CI = 2.15 – 6.50 ; and recessive model: OR = 1.28 , 95% CI = 0.46 – 3.51). Conclusions. The ACE D allele was clearly related to an enhanced risk of COVID-19 severity. Hence, it is imperative to take into account the influence of genetic factors during the development of future vaccines. [ABSTRACT FROM AUTHOR]

The problems of operating range and costs are the two most critical bottlenecks restricting the extensive application of electric vehicles (EVs) in China and some other countries. There are also some prominent problems in China's EVs, which lead to poor competitiveness of EVs compared with traditional internal combustion engine vehicles. This paper analyzes the key mathematical factors restricting the development and popularization of EVs in China from the aspects of strategic policy, sales situation, and self-problems. For the best-selling family cars in China, select the representative models with the same body structure, and make statistics on various parameters most concerned by consumers, establish a database through data statistics and analysis software. Through summarizing France's standardization development strategy and relevant issues reflected by the development data of its EVs, this paper puts forward the hypothesis leading the development of EVs through standardization to enhance their competitiveness, gives the specific suggestions, and briefly analyzes the feasibility from the aspects of product situation. The research content of this paper provides a certain basis and ideas for the future research work. [ABSTRACT FROM AUTHOR]

Objectives. To investigate adverse events (AEs) associated with upadacitinib in the real world using data mining from the FDA Adverse Event Reporting System (FAERS). Methods. Disproportionality analysis, including the reporting odds ratio (ROR), the proportional reporting ratio (PRR), the Bayesian confidence propagation neural network (BCPNN), and the multiitem gamma Poisson shrinker (MGPS) algorithms, was used to quantify the signals of upadacitinib-associated AEs. Results. The study found 23683 reports of AEs associated with upadacitinib. A total of 149 substantial disproportionality preferred terms (PTs) that complied with all algorithms were identified. The infections discovered matched those mentioned in the specification and clinical trials, including pneumonia, upper respiratory tract infection, herpes zoster, and acne. Malignant and thrombotic AEs were also noted. Diverticulitis, myocardial infarction, transient ischaemic attack, and dysstasia were among the new AEs found. Upadacitinib-related AEs had a median onset time of 237 days and an interquartile range (IQR) of 78–509 days. Conclusions. The findings of our study were in line with clinical observations, and we also identified potential novel and unexpected AEs signals for upadacitinib, indicating the necessity for prospective clinical trials to corroborate these findings and demonstrate their link. Our results offered significant support for additional upadacitinib safety research. [ABSTRACT FROM AUTHOR]

This paper investigates the effectiveness of machine learning (ML) models available in MATLAB Regression Learner app and MATLAB App Designer, both low-code applications, for accurately predicting punching shear strength (PSS) in reinforced concrete (RC) slabs. A database of 379 RC slab samples without transverse reinforcement was compiled from renowned publications. RandomSearch and Bayesian optimisation were employed for tuning hyperparameters. The performance of these models was compared with six empirical models, which included three current design codes, three equations from other researchers, and 227 finite-element simulations conducted by the authors. The ML models and finite-element method (FEM) demonstrated superior performance compared with the literature and practical codes. Furthermore, the results emphasised the exceptional performance of the Gaussian process regression (GPR) with optimised hyperparameters, exhibiting the best performance in validation, training, and testing datasets with R2 values of 0.95, 0.99, and 0.98, respectively. A user-friendly standalone application was developed, providing real-time predictions of the PSS using the two best-developed ML models, GPR and support vector machine (SVM), as well as six empirical models from the literature. This tool offers users flexibility in choosing the most appropriate model for their specific needs, delivering reliable, and accurate results for estimating the PSS of RC slabs. [ABSTRACT FROM AUTHOR]

Objective. Elevated red cell distribution (RDW) has been reported to be associated with mortality in patients with acute pancreatitis and cholecystitis admitted to the intensive care unit (ICU). However, evidence for the relationship between RDW and paralytic intestinal obstruction is lacking. Therefore, the article aims to investigate the relationship between RDW and 28-day mortality of the patients with paralytic intestinal obstruction. Patients and Methods. This is a single-center retrospective study. Based on a particular screening criterion, 773 patients with paralytic intestinal obstruction were selected from the Medical Information Mart for Intensive Care III (MIMIC-III) database. Indicators of the first 24 h into the ICU were used to analyze the relationship between RDW and 28-day death from paralytic intestinal obstruction by Kaplan−Meier (K-M) analysis, logistic regression analysis, and stratification analysis. Results. The curve fitting exhibited a nonlinear relationship. The K-M curve showed that groups with higher RDW values had lower survival rates. The logistic regression analysis revealed that RDW increased with 28-day mortality in patients with paralytic intestinal obstruction in the fully adjusted model. In the fully adjusted model, OR value and 95% CI from the second to the third quantiles compared to the first quartile (reference group) were 1.89 (1.04, 3.44) and 3.29 (1.82, 5.93), respectively. The results of stratified analysis of each layer had the same trend as those of regression analysis, and the interaction results were not significant. Conclusion. Elevated RDW was associated with increased 28-day mortality from paralytic intestinal obstruction in the ICU. This study can help to further explore the relationship between RDW and death in patients with paralytic intestinal obstruction. [ABSTRACT FROM AUTHOR]

The process of identifying a person using their facial traits is referred to as face recognition, and it is a form of biometric identification. The use of facial recognition might range from that of an entertainment tool to one of a security tool. Even while other forms of biometric identification, such as fingerprints and iris scans, are reliable, they require the active participation of an individual. As a result, criminals cannot rely on them as the most reliable means of verification. When a criminal database, which stores the individual details of a criminal, and facial recognition technology are brought together, it can identify a criminal who is depicted in an image or seen in a video feed. Not only does a criminal recognition system needs to have a high level of accuracy, but it also needs to be able to adapt to significant changes in lighting, occlusion, aging, expressions, and other factors. In this study, they were analyzed and compared with the many methods of face detection and face recognition, such as HAAR cascades, local binary patterns histogram, support vector machines, convolutional neural networks, and ResNet-34. These methods include a variety of different approaches to recognizing faces. An analysis of these strategies is also conducted and then put into practice to those that seem to be the most effective for the designed criminal recognition system. In addition to that, a variety of uses of this criminal recognition in the real world are also discussed. [ABSTRACT FROM AUTHOR]

Two-wheeled (TW) vehicle accidents are one of the major types of urban traffic accidents. TW cyclists who lack safety protection usually suffer more serious injuries and deaths in collisions. Developments in automotive active safety technologies are expected to reduce cyclist injuries and deaths, such as automatic emergency braking (AEB). To facilitate the development and testing of AEB technology, typical TW vehicle scenarios need to be constructed. Based on 400 cases of car-to-TW vehicle accident data from the National Automobile Accident In-Depth Investigation System (NAIS) database, we investigated the scenario elements that influence AEB robustness, such as weather, accident time, and road wetness. We obtained seven static scenarios using an improved clustering method, and we obtained specific speed and distance combinations in each scenario using a deduction method. Further, we compared the present findings to those of other scholars and the China New Car Assessment Program (C-NCAP). The kinematic states of the two were similar to that of C-NCAP, but the speed distribution was significantly different. The TW vehicle speed in the C-NCAP is set to 15 km/h or 20 km/h concerning the European test scenarios, but the TW vehicle speed in the present study was 10–60 km/h. Thus, the present findings recommended that subsequent C-NCAP test scenarios increase the category of motorcycles and the speed range of cars covering 20–70 km/h and consider the test conditions of bad weather and wet roads, to test the robustness of AEB. [ABSTRACT FROM AUTHOR]

Augmented reality can provide an interactive learning environment. This leads to an exploration of new possibilities in the educational domain. In this work, a systematic literature review of AR applications developed for preschool and primary education is presented. To conduct this review, the Google Scholar database was used. The articles were screened using a set of five inclusion-exclusion criteria for suitability. In total, seventy-one articles published between 2012 to 2021 were selected. Of those, forty-five are conference papers, one is a book chapter, and the remaining twenty-five are journals. These works were analyzed by nine research questions proposed by us. We identified enjoyment, engagement, and learning gains as the key achievements of AR-based applications. Besides this, improvement in motivation, learning interest, participation, understanding, and self-learning are also found important in our review. Although, there are challenges—usability issues, technical problems, acceptance, cognitive overload, and health issues; however, due to their numerous benefits, AR-based applications may be another mode of teaching-learning method in the near future. [ABSTRACT FROM AUTHOR]

Prescription doses of levodopa in patients with advanced Parkinson's disease (PD) are generally lower in Japan than in the United States or Europe, although Japanese guidelines for the management of PD recommend increasing the dosage as the disease progresses. However, data regarding levodopa prescription practices in patients with advanced PD in the clinical setting are limited. This retrospective observational study analyzed patterns of drug use for patients with advanced PD in Japan using claims data from hospitalized patients in the Medical Data Vision Co. database. Eligible patients had at least two PD-associated claims in two different quarters between April 1, 2008, and November 30, 2018, and a 10-item activities of daily living score <60 upon hospital discharge (as a proxy for advanced PD). The primary endpoint was the prescribed dosage of levodopa at the index hospitalization. Dosages of other PD drugs (medications with an on-label indication for PD) and non-PD drugs were also assessed. Overall, 4029 patients met the inclusion criteria (mean age, 76.9 years; 83.3% aged ≥70 years). At the index date, 74.0% were receiving levodopa. Patients received a median of one PD drug in addition to levodopa, and 27.4% and 20.2% received one or two concomitant PD drugs, respectively. Patients received a median of two non-PD drugs. The median levodopa dosage and total levodopa equivalent dosage (LED) at the index hospitalization were 418.2 and 634.8 mg/day (adjusted for body weight, 9.0 and 13.7 mg/kg/day), respectively. The median levodopa and total LED dosage in each 6-month increment during the 5 years before and after the index date ranged between 263.9 and 330.2 mg/day (5.0 and 6.5 mg/kg/day) and 402.0 and 504.9 mg/day (8.3 and 10.1 mg/kg/day), respectively. This study suggests that many Japanese patients with advanced PD could receive more intensive treatment with higher doses of levodopa. [ABSTRACT FROM AUTHOR]

Blockchain technology can be used to record real data at various nodes in the entire supply chain and can achieve timely data sharing and full traceability of the whole lifecycle of products in the supply chain. This study conducts a systematic literature review based on the WOS database, focusing on the research topic of the application of blockchain technology in supply chain management. We propose clear search and screening criteria based on 4 research questions and finally obtain 591 target studies. We conduct a detailed study on the distribution of the target literature in this research area, country/region, institution, and journal. Different countries/regions cooperate with each other and, thus, are clustered into five significant countries regions' cooperation networks. There is a strong coupling relationship between research institutions, forming four major institutional cooperation networks. According to the analysis of those papers with a high number of citations, most of such papers were published in 2019, while the cross-citation phenomenon between papers occurred more frequently in 2021 and 2022. We use VOSviewer for visual coupling analysis of all keywords, which are automatically clustered into three research hotspots. Using CiteSpace to perform timeline-based keyword cooccurrence analysis, we find that scholars have gone through a "discovery-acceptance-question-improvement" process for the application of blockchain technology in supply chain management. According to the intensity and duration of burst detection words in different years, we draw three key points for future research. (i) Blockchain technology should be used to restructure and optimize the high-end food and medical cold chain supply chains. (ii) Blockchain technology should be used to enhance the cooperative relationship between supply chain members and the overall competitiveness of the supply chain. (iii) Innovate blockchain encryption technology that should be used to reduce the risk of information and privacy leakage in the supply chain. [ABSTRACT FROM AUTHOR]

The lateral–torsional buckling (LTB) performance assessment of laminated glass (LG) beams is a remarkably critical issue because—among others—it can involve major consequences in terms of structural safety. Knowledge of LTB load-bearing capacity (in terms of critical buckling load Fcr and corresponding lateral displacement dLT), in this regard, is, thus, a primary step for more elaborated design considerations. The present study examines how machine learning (ML) techniques can be used to predict the response of laterally unrestrained LG beams in LTB. The potential and accuracy of artificial neural networks (ANN), based on ML methods, are addressed based on validation toward literature data. In particular, to detect the best-performing data-driven ML model, the load-bearing capacity of LG beams (i.e., Fcr and dLT) is set as output response, while geometric properties (length, width, thickness) and material features (for glass and interlayers) are used as input variables. A major advantage is taken from a literature database of 540 experiments and simulations carried out on two-ply LG beams in LTB setup. To determine the best-performing ANN model, different strategies are considered and compared. Additionally, the Bayesian regularization backpropagation (trainbr) algorithm is used to optimize the input–output relationship accuracy. The suitability of present modeling strategy for LG beams in LTB is quantitatively discussed based on error and performance trends. [ABSTRACT FROM AUTHOR]

Objectives. To evaluate the relationship between the plasma miR-223 expression level and clopidogrel resistance in acute coronary syndrome (ACS) patients. Methods. We performed a search for publications using online databases including PubMed, EMBASE, Cochrane Library, and Chinese Databases (CNKI database, Weipu database, and Wanfang database) from the inception of the databases to June 18, 2023, to identify studies reporting the relationship between the plasma miR-223 level and clopidogrel resistance in ACS patients. Two researchers independently searched and screened to ensure the consistency of the results and assess the quality of the included studies according to the Newcastle-Ottawa scale. A fixed-effects model was used for pooling data with STATA 14.0. Results. Four articles including 399 Chinese ACS patients were eligible for the meta-analysis. Low plasma miR-223 levels were independently correlated with clopidogrel resistance in Chinese ACS patients (OR 0.58, 95% CI: 0.33–1.04). Conclusion. Lower plasma miR-223 levels are associated with clopidogrel resistance in Chinese ACS patients, suggesting that miR-223 may be a potential diagnostic biomarker of clopidogrel resistance. [ABSTRACT FROM AUTHOR]

The rapid expansion of metro networks, e.g., in many cities of China, continuously introduces the operation of new stations every year. Due to the lack of historical data and complicate variations of short-term passenger flow in the early stage of operation, it is difficult to accurately predict inbound and outbound passenger flows of new metro stations in the short term, which would be the database for train scheduling for new stations before operation, dynamic capacity optimization for new stations under operation, short-term prediction of cycle sharing demands near new stations, and so on. Traditional methods usually failed to exactly reflect the complicate rules or were unusable without the new station's historical data. In order to solve the above problems, this paper proposes a short-term inbound and outbound passenger flow prediction model for new metro stations at the early stage of operation by combining the K-means clustering algorithm, an improved spatiotemporal long short-term memory model (Sp-LSTM), and a real-time feedback error model (mean absolute error, MAE), where passenger flows' spatial-temporal characteristics and land-use relevance are considered. The application in Guangzhou Metro, China, where Line 21 is regarded as a new line, shows that the proposed K-Sp-LSTM model has the best prediction accuracy compared with traditional methods. [ABSTRACT FROM AUTHOR]

Multi-keyword conjunctive query is the most common searchable encryption (SE) scheme and gives practical search capability. This paper focuses on constructing a conjunctive query scheme with high privacy and accurate query result. For efficiency, this paper introduces a novel counter vector (CV) data structure instead of inverted index and builds the CV index database with three-tuple keywords. We use two kinds of cryptography primitives to encrypt the CV index database, respectively. One is symmetric encryption for efficiency, which gives two schemes, CVX and ICV. Experiments show that both CVX and ICV have much higher efficiency than the existing schemes. The other is BGN homomorphic encryption for privacy and gives the scheme HCV. HCV reduces much RP information leakage and even achieves "ideal" leakage for 3-keyword query. Experiments show that the HCV scheme achieves high privacy while compromising not so much storage. [ABSTRACT FROM AUTHOR]

Paris polyphylla Smith (family: Melanthiaceae), a high-value medicinal herb endemic to the Himalayan region, has drawn much attention recently due to its immense use in the traditional healthcare system since ancient times. In the present review, an extensive database on P. polyphylla was systematically searched from databases such as Medline/PubMed, Scopus, the Web of Science, and the online service E-library.ru and SCImago (https://www.scimagojr.com/). Information on species, ecology, distribution, trade, ethnopharmacology, pharmacology, biotechnology, and molecular biology was gathered from 1979 to 2023 using 116 research publications. Major steroidal saponins such as Paris saponin I, V, VI, VII, and H have been found substantially effective in anticancer activity, abnormal uterine bleeding, dysfunctional uterine bleeding, and menorrhagia. Traditional breeding and propagation techniques cannot keep up with the world's growing demand for herbal drugs. Therefore, it is critically necessary to take conservation measures and develop novel techniques for growing and cultivating this economically significant and highly valuable therapeutic herb. The advanced biotechnological approaches like micropropagation and genetic analysis introduced long back are either rare or lacking in the case of P. polyphylla. It contains a wealth of information that will serve as a baseline data source for various stakeholders, researchers working on various research aspects, and policymakers to define appropriate utilization and conservation plans for a high-value commercial medicinal plant called P. polyphylla. The review provides an updated overview and critical assessment of secondary data regarding the past and recent applications and interventions of P. polyphylla. [ABSTRACT FROM AUTHOR]

Background. PTGES3 is upregulated in multiple cancer types and promotes tumorigenesis and progression. However, the clinical outcome and immune regulation of PTGES3 in lung adenocarcinoma (LUAD) are not fully understood. This study aimed to explore the expression level and prognostic value of PTGES3 and its correlation with potential immunotherapy in LUAD. Methods. All data were obtained from several databases, including the Cancer Genome Atlas database. Firstly, gene and protein expression of PTGES3 were analyzed using Tumor Immune Estimation Resource (TIMER), R software, Clinical Proteomic Tumor Analysis Consortium (CPTAC), and Human Protein Atlas (HPA). Thereafter, survival analysis was conducted using the R software, Gene Expression Profiling Interactive Analysis 2 (GEPIA2), and Kaplan–Meier Plotter. In addition, gene alteration and mutation analyses were conducted using the cBio Cancer Genomics Portal (cBioPortal) and Catalog of Somatic Mutations in Cancer (COSMIC) databases. The molecular mechanisms associated with PTGES3 were assessed via Search Tool for the Retrieval of Interacting Genes/Proteins (STRING), GeneMANIA, GEPIA2, and R software. Lastly, the role of PTGES3 in immune regulation in LUAD was investigated using TIMER, Tumor-Immune System Interaction Database (TISIDB), and SangerBox. Results. The gene and protein expression of PTGES3 were elevated in LUAD tissues and compared to the normal tissues, and the high expression of PTGES3 was correlated with cancer stage and tumor grade. Survival analysis revealed that overexpression of PTGES3 was associated with poor prognosis of LUAD patients. Moreover, gene alteration and mutation analysis revealed the occurrence of several types of PTGES3 gene alterations in LUAD. Moreover, co-expression analysis and cross-analysis revealed that three genes, including CACYBP, HNRNPC, and TCP1, were correlated and interacted with PTGES3. Functional analysis of these genes revealed that PTGES3 was primarily enriched in oocyte meiosis, progesterone-mediated oocyte maturation, and arachidonic acid metabolism pathways. Furthermore, we found that PTGES3 participated in a complex immune regulation network in LUAD. Conclusion. The current study indicated the crucial role of PTGES3 in LUAD prognosis and immune regulation. Altogether, our results suggested that PTGES3 could serve as a promising therapeutic and prognosis biomarker for the LUAD. [ABSTRACT FROM AUTHOR]

With the exponential enhancement of manufacturing effectivity and the structural transformation of the software of manufacturing elements induced by using the new spherical of scientific and technological revolution, the sample of comparative gain of manufacturing elements in the worldwide manufacturing enterprise has passed through profound changes, progressively forming a transformation from the industrial chain to the price chain, therefore promoting the transformation of manufacturing mode and aid utilization mode. Based on the applicable theories of wise manufacturing, this paper proposes an clever manufacturing functionality assessment and prediction mannequin primarily based on utilized mathematical modelling, which integrates the contrast facts of agencies into a database, establishes the corresponding time sequence relationship of the comparison data, and establishes a relationship with the corresponding organization attributes, fashions the assessment data, and combines them into a total prediction mannequin through built-in learning. By functionality of migration learning, the prediction model applicable to large statistics is migrated to small information to recognize the distinction of the equal type alternatively special entities. Input the comparison facts of the organization to be evaluated into the prediction mannequin to reap the shrewd manufacturing functionality maturity fee of the enterprise. It realizes the automated comparison of shrewd manufacturing capability, saves human cost, and improves the accuracy of evaluation. It solves the troubles that manufacturing companies have no longer deep hold close and understanding of smart manufacturing, inaccurate identification of their very personal smart manufacturing enhancement stage, and unscientific self-evaluation and diagnosis. [ABSTRACT FROM AUTHOR]

The genus Holosticha s. l. is a typical "melting pot" group with an intricate history, and so far, it has been divided into eleven genera. Both newly obtained taxonomic and molecular data provide the opportunity to gain more insights to outline the taxa in it and to understand their systematic and evolutionary relationship. Here, we describe Caudikeronopsis monilata sp. nov. from intertidal sediment on the China coast of the Yellow Sea and analyze the phylogenetic relationships of Holosticha s. l. by obtaining a total of 16 new sequences of seven isolates. The results demonstrate that (1) the morphological features of Holosticha s. str. are outlined very well, but its systematic relationship with Uncinata is still puzzling; (2) based on both morphological and molecular databases, the genera Adumbratosticha, Arcuseries, Caudikeronopsis, Extraholosticha, and "Holosticha + Uncinata" complex are separated clearly from each other in the phylogenetic analyses; and (3) the Anteholosticha isolates are dispersed among the urostylids in the phylogenetic analyses, even though its generic diagnostic features are described very clearly. In the present work, however, the secondary structure predictions do not provide better resolutions for understanding the systematic and evolutionary relationships among the holostichids. And the genus Anteholosticha becomes a new "melting pot" taxon. [ABSTRACT FROM AUTHOR]

Background. Post-renal transplant patients have a high likelihood of developing renal cancer. However, the underlying biological mechanisms behind the development of renal cancer in post-kidney transplant patients remain to be elucidated. Therefore, this study aimed to investigate the underlying biological mechanism behind the development of renal cell carcinoma in post-renal transplant patients. Methods. Next-generation sequencing data and corresponding clinical information of patients with clear cell renal cell carcinoma (ccRCC) were obtained from The Cancer Genome Atlas Program (TCGA) database. The microarray data of kidney transplant patients with or without rejection response was obtained from the Gene Expression Omnibus (GEO) database. In addition, statistical analysis was conducted in R software. Results. We identified 55 upregulated genes in the transplant patients with rejection from the GEO datasets (GSE48581, GSE36059, and GSE98320). Furthermore, we conducted bioinformatics analyses, which showed that all of these genes were upregulated in ccRCC tissue. Moreover, a prognosis model was constructed based on four rejection-related genes, including PLAC8, CSTA, AIM2, and LYZ. The prognosis model showed excellent performance in prognosis prediction in a ccRCC cohort. In addition, the machine learning algorithms identified 19 rejection-related genes, including PLAC8, involved in ccRCC occurrence. Finally, the PLAC8 was selected for further research, including its clinical and biological role. Conclusion. In all, our study provides novel insight into the transition from the rejection of renal transplant to renal cancer. Meanwhile, PLAC8 could be a potential biomarker for ccRCC diagnosis and prognosis in post-kidney transplant patients. [ABSTRACT FROM AUTHOR]

The rapid development of network technology makes the world enter a new era, but it also brings a variety of severe information security problems, which makes the network security more and more important. Based on the existing literature and information, this paper expounds the current situation of database information system security. In view of the security risks of the media database information, this paper proposes to use the remote control system of the network robot, through sorting out the organizational structure characteristics and technology of multimedia data, establish the security defense system, and try to manage the multimedia data security. This system takes the mobile robot as the control object and adds the vision and ultrasonic transducer for the robot, so that it can carry on the semi-autonomous movement under the control of human. In addition, the host PC is used as the main control terminal to send instructions to the remote robot and receive the feedback data information. The simulation results show that the robot remote control system has good security, can ensure the establishment of database security defense technology system, and has the characteristics of convenient operation, easy to expand, and strong mobility. From the research point of view of this paper, the combination of robotics and multimedia information security is booming and will become a new application prospect. [ABSTRACT FROM AUTHOR]

We assessed earthquake source parameters compiled from previous studies and international databases. In addition, moment tensor inversion is made from the broadband seismic data of two earthquakes that occurred in the region in 2017 and 2018 with magnitudes Mw 5.0 and 5.1, respectively. As a result, the two events' reliable source parameters are retrieved. We found that earthquakes are distributed in the rift floor, at margins and adjacent plateaus. Because the majority of earthquakes occur on the rift floor, deformation is most likely caused by strain accumulation transferred from border faults to magmatic segments along the rift floor. Predominantly normal faulting is observed, but some strike-slip events are also observed. Normal faulting mechanisms are consistent with major plate divergence, whereas the strike-slip components observed in the region might be associated with the counterclockwise rotation of the Danakil microplate, and the mechanism would indicate an oblique-slip deformation between the Nubian plate and the Danakil microplate. However, the focal mechanism obtained from the moment tensor inversion for the Mw 5.1 event indicates dominant normal faulting accompanied by a minor strike-slip component at the western margin of Afar, whereas the Mw 5.0 event has a significant strike-slip component at the central part of MER. The majority of focal depths of earthquakes are found within the upper crust, including the 2017 (Mw 5.0) event with a focal depth of 9.7 km that was computed using moment tensor inversion. A significant number of earthquakes are also found within the lower crust, including the 2018 (Mw 5.1) event with a focal depth of 20.2 km. However, earthquakes with focal depths within the upper mantle are also found in the compiled international database, which may not be consistent with the previously published works in the region. The observed focal depth may suggest a widespread deformation throughout the upper and lower crusts, implying that magmatic intrusions and faulting play a central role in facilitating the seismicity of the main Ethiopian rift (MER). The current investigation will provide further information on the earthquake source parameters and seismogenic depth of earthquake occurrence in the MER. [ABSTRACT FROM AUTHOR]

Background. The incidence of diabetes mellitus (both pregestational and gestational) is increasing worldwide, and hyperglycemia during pregnancy is associated with adverse pregnancy outcomes. Evidence on the safety and efficacy of metformin during pregnancy has accumulated resulting in an increase in its prescription in many reports. Aims. We aimed to determine the prevalence of antidiabetic drug use (insulins and blood glucose-lowering drugs) before and during pregnancy in Switzerland and the changes therein during pregnancy and over time. Methods. We conducted a descriptive study using Swiss health insurance claims (2012-2019). We established the MAMA cohort by identifying deliveries and estimating the last menstrual period. We identified claims for any antidiabetic medication (ADM), insulins, blood glucose-lowering drugs, and individual substances within each class. We defined three groups of pattern use based on timing of dispensation: (1) dispensation of at least one ADM in the prepregnancy period and in or after trimester 2 (T2) (pregestational diabetes); (2) dispensation for the first time in or after T2 (GDM); and (3) dispensation in the prepregnancy period and no dispensation in or after T2 (discontinuers). Within the pregestational diabetes group, we further defined continuers (dispensation for the same group of ADM) and switchers (different ADM group dispensed in the prepregnancy period and in or after T2). Results. MAMA included 104,098 deliveries with a mean maternal age at delivery of 31.7. Antidiabetic dispensations among pregnancies with pregestational and gestational diabetes increased over time. Insulin was the most dispensed medication for both diseases. Between 2017 and 2019, less than 10% of pregnancies treated for pregestational diabetes continued metformin rather than switching to insulin. Metformin was offered to less than 2% of pregnancies to treat gestational diabetes (2017-2019). Conclusion. Despite its position in the guidelines and the attractive alternative that metformin represents to patients who may encounter barriers with insulin therapy, there was reluctance to prescribe it. [ABSTRACT FROM AUTHOR]

A safe introduction of automated driving systems on urban roads requires a thorough understanding of the traffic conflicts and accidents. This understanding is paramount to constructively safeguard these systems, i.e., to design a system that exhibits an adequate performance even in critical situations. In this work, we present an approach to gather knowledge by analyzing the German In-Depth Accident Study (GIDAS) database, which is representative of all German traffic accidents, along with the influencing factors that are hypothesized to be associated with increased criticality in relation to automated driving. In order to gain an insight into the risk associated with these factors in real-world accidents, we determine their presence in the database's accident cases within a selected operational domain, enabled by translation from a natural language description to the database scheme employed by GIDAS. This initial catalog as well as the subsequent statistical considerations is motivated by analyzing the criticality for automated driving systems in urban areas. Based on this catalog, our work delineates a method for quantification of risk associated with such influencing factors in a given operational domain based on real-world accident data. This quantification can subsequently be used in decompositional, scenario-based risk assessment before system design and for the embedding safety argumentation. This paper, therefore, provides a blueprint of how the matured field of traffic accident research studies and its results, in particular accident databases, can be leveraged for risk assessment of the operational domain of automated driving systems. [ABSTRACT FROM AUTHOR]

Aiming at the network security problem of power system cable trench control industrial Internet system, we studied an intrusion detection method applied to the embedded industrial Internet of Things gateway. This method extracts rules from the DBN-DNN deep neural network to obtain intrusion detection models that are conducive to integration into embedded systems. We first use the DBN network to reduce the dimensionality of the data, then use the DNN to train the classification model, and extract the rules from the DNN's neurons to form a rule tree for intrusion detection. The KDD CUP99 training database is used to verify the feasibility of the method, and the test is carried out in the embedded gateway. The results show that the detection method based on rule extraction used in this paper can ensure detection efficiency and accuracy compared to the traditional detection methods. At the same time, it saves more computing resources and is more conducive to integration in embedded gateway systems. [ABSTRACT FROM AUTHOR]

Background. Idiopathic pulmonary fibrosis (IPF) is a disease with significant morbidity, progressive deterioration of lung function till death, and lack of effective treatment options. This study aims to explore the global research trends in IPF and immune response to predict the research hotspot in the future. Materials and methods. All related publications on IPF and immune response since the establishment of diagnostic criteria for IPF were retrieved using the Web of Science (WOS) database. VOSviewer, GraphPad Prism 6, CiteSpace version 5.6. R5 64-bit, and a bibliometrics online platform were used to extract and analyze the trends in relevant fields. Results. From March 1, 2000, to September 30, 2022, a total of 658 articles with 25,126 citations met the inclusion criteria. The United States ranked first in number of publications (n = 217), number of citations (n = 14,745), and H-index (62). China ranked second in publications (n = 124) and seventh and fifth for citation frequency and H-index, respectively. The American Journal of Respiratory and Critical Care Medicine (impact factor = 30.528) published the most articles in the field. The author Kaminski N. from the United States was the most influential author with 26 publications and an H-index of 24. Among the 52 keywords that co-occurred at least 20 times, the main keywords were concentrated in "Inflammation related" and "Biomarker related" clusters. "biomarker" (AAY 2018.64, 25 times) was a newly emerged keyword. Conclusions. The United States has an unequivocal advantage in IPF and immunization, but China shows a faster developing trend. The American Journal of Respiratory and Critical Care Medicine should be prioritized for leading articles. This study indicates that exploration of ideal immune-related biomarkers to provide evidence for the clinical work of IPF might be a hotspot in the near future. [ABSTRACT FROM AUTHOR]

This article proposes the use of Wi-Fi ToF and a deep learning approach to build a cheap, practical, and highly-accurate IPS. To complement that, rather than using the classic geometrical approach (such as multilateration), it uses a more data-driven approach, i.e., the location fingerprinting technique. The fingerprint of a location, in this case, is a set of Wi-Fi ToFs between the target device and an access point (AP). Therefore, the number of APs in the area dictates the set size. The location fingerprinting technique requires a collection of fingerprints of various locations in the area to build a reference database or map. This database or map contains the information used to carry out the main task of the location fingerprinting technique, namely, estimating the position of a device based on its location fingerprint. For that task, we propose using a fully connected deep neural network (FCDNN) model to act as a positioning engine. The model is given a location fingerprint as its input to produce the estimated location coordinates as its output. We conduct an experiment to analyze the impact of the available AP pair in the dataset, from 1 unique AP pair, 2 AP pairs, and more, using WKNN and FCDNN to compare their performance. Our experimental results show that our IPS, DeepIndoor, can achieve an average positioning error or mean square error of 0.1749 m, and root mean square error of 0.5740 m in scenario 3, where 1–10 AP pairs or the raw dataset is used. [ABSTRACT FROM AUTHOR]

This study investigates the feasibility of a mobile robot navigating and discovering its location in unknown environments, followed by the creation of maps of these navigated environments for future use. First, a real mobile robot named TurtleBot3 Burger was used to achieve the simultaneous localization and mapping (SLAM) technique for a complex environment with 12 obstacles of different sizes based on the Rviz library, which is built on the robot operating system (ROS) booted in Linux. It is possible to control the robot and perform this process remotely by using an Amazon Elastic Compute Cloud (Amazon EC2) instance service. Then, the map to the Amazon Simple Storage Service (Amazon S3) cloud was uploaded. This provides a database to display maps and use them at any time for navigation without the need to redraw the map. This map can be accessed by using an authentication process (username and password) supervised by the cloud server administrator. After that, using the serverless image handler (SIH), with the aid of this solution, you can change the size of images, change the color of the background, format them, or add watermarks. Experiment results demonstrated the ability to build a map of an unknown location in a complex environment and use it for navigation tasks on a real mobile robot via remote control. It also showed the success of the process of storing the map for future use and the process of modifying the map using SIH. [ABSTRACT FROM AUTHOR]

Computation of correlation coefficient among attributes of ordinary database is important especially in the classification and analysis of data. Due to the hesitations in the process of data classification, the idea of intuitionistic fuzzy data (IFD) is appropriate for a reliable classification. To achieve a dependable correlation, the construct of partial correlation coefficient based on IFD has been considered. The construct of partial correlation coefficient of intuitionistic fuzzy sets (PCCIFSs) is reasonable since correlation coefficients of intuitionistic fuzzy sets (CCIFSs) are limited in the sense that it only expressed linear association and direction of such relation between IFD without minding the effect of other IFD. On the contrary, partial correlation coefficient finds the exact association between any two IFD by muting the effect of other IFD which could sway the result of the correlation coefficient. In previous works, the idea of PCCIFSs was introduced based on the multivariate correlation model using empirical logit transform. Besides the fact that the outputs of multivariate correlation model are not always easy to interpret, the approach also never considered the three parameters of IFSs and does not use the values of CCIFSs for the computational process. With these setbacks, we are motivated to propose a novel approach of finding PCCIFSs by incorporating the three parameters of IFD based on a modified CCIFSs approach. A comparative analysis of the robust PCCIFSs approach and the existing approach is considered to justify the novel approach. An application of the new approach of PCCIFSs is considered in the case of pattern recognition where the patterns are represented as intuitionistic fuzzy data. [ABSTRACT FROM AUTHOR]

The incidence of pediatric diabetic ketoacidosis (DKA) increased during the peak of the COVID-19 pandemic. The objective of this study was to investigate whether rates of hyperosmolar therapy administration for suspected clinically apparent brain injury (CABI) complicating DKA also increased during this period as compared to the three years immediately preceding the pandemic and to compare the characteristics of patients with suspected CABI before the pandemic, patients with suspected CABI during the peak of the pandemic, and those with DKA but without suspected CABI during the pandemic. Patients aged ≤18 years presenting with DKA before (March 11, 2017–March 10, 2020) and during the peak of the pandemic (March 11, 2020–March 10, 2021) were identified through a rigorous search of two databases. Predefined criteria were used to diagnose suspected CABI. Biochemical, clinical, and sociodemographic data were collected from a comprehensive review of the electronic medical record. The proportion of patients with DKA who received hyperosmolar therapy was significantly higher (P = 0.014) during the pandemic compared to the prepandemic period; however, this was only significant among patients with newly diagnosed diabetes. Both groups with suspected CABI had more severe acidosis, lower Glasgow Coma Scale scores, and longer hospital admissions (P < 0.001 for all) than cases without suspected CABI. During the pandemic, the blood urea nitrogen concentration was significantly higher in patients with suspected CABI than those without suspected CABI, suggesting they were more severely dehydrated. The clinical, biochemical, and sociodemographic characteristics of patients with suspected CABI were indistinguishable before and during the pandemic. In conclusion, administration of hyperosmolar therapy for suspected CABI was more common during the peak of the COVID-19 pandemic, possibly a result of delayed presentation, highlighting the need for increased awareness and early recognition of the signs and symptoms of diabetes and DKA, especially during future surges of highly transmissible infections. [ABSTRACT FROM AUTHOR]

As a highly malignant tumor, the morbidity and mortality of cutaneous melanoma (CM) are increasing year by year. A novel type of cell death connected to mitochondrial metabolism is called cuproptosis. Cuproptosis regulates tumor biological behavior. Thus, genes controlling cuproptosis could be a promising candidate bioindicator for cancer therapy. Datasets of CM patients were obtained from the public database that includes clinical information and RNA-seq data. We divided CM patients into three different subgroups by unsupervised clustering method and explored the differences in functional pathways among the three subgroups by GSVA to prove the possible potential mechanism of copper death-related genes in the formation and development of CM. Secondly, we used differential analysis and Cox regression analysis to find the differential genes related to prognosis, constructed the CRG score, found the critical score for dividing high and low CRG score groups, and then analyzed the prognosis and immune infiltration of high and low CRG score groups. The results show a great correlation between OS and CRG scores. Compared with patients with high CRG scores, patients with low CRG scores have a significantly higher survival rate. In a word, copper sagging plays a certain role in the progress of CM. [ABSTRACT FROM AUTHOR]

Background. No pan-cancer study has been conducted till date to explore the comprehensive oncogenic roles of pyruvate kinase M2 (PKM2). Methods. TCGA, TIMER, GEPIA, UALCAN, STRING, and other databases were used to analyze the expression, prognostic roles, epigenetic variants, and possible oncogenic mechanisms of PKM2. Proteomic sequencing data and PRM were applied to validate. Results. PKM2 showed higher expression in majority of cancers, the expression being significantly correlated with the clinical stage. Higher expression of PKM2 was associated with lower OS and DFS in several cancers, such as MESO and PAAD. In addition, the epigenetic variation of PKM2, including gene alteration, mutation type and sites, DNA methylation, and phosphorylation, showed diversity in different cancers. All four methods indicated that PKM2 is positively associated with the immune infiltration of tumor-associated fibroblasts, such as in THCA, GBM, and SARC. Further mechanistic exploration suggested that the ribosome pathway might play an essential role in the regulation of PKM2, and interestingly, four out of ten hub genes were found to be highly related to OS in several cancers. Finally, in thyroid cancer specimen, we validated the expression and potential mechanisms by proteomic sequencing and PRM validation. Conclusion. In the majority of cancers, the higher expression of PKM2 was highly associated with poor prognosis. Further molecular mechanism exploration implied that PKM2 might serve as a potential target for cancer survival and immunotherapy by regulating the ribosome pathway. [ABSTRACT FROM AUTHOR]