bluuebunny/crossref_metadata_2025 huggingface dataset:
Integrative data analysis to identify persistent post-concussion deficits and subsequent musculoskeletal injury risk: project structure and methods
Authors: Anderson Melissa , Claros Claudio Cesar , Qian Wei , Brockmeier Austin , Buckley Thomas A
Published: January 2024

Concussions are a serious public health problem, with significant healthcare costs and risks. One of the most serious complications of concussions is an increased risk of subsequent musculoskeletal injuries (MSKI). However, there is currently no reliable way to identify which individuals are at highest risk for post-concussion MSKIs. This study proposes a novel data analysis strategy for developing a clinically feasible risk score for post-concussion MSKIs in student-athletes. The data set consists of one-time tests (eg, mental health questionnaires), relevant information on demographics, health history (including details regarding the concussion such as day of the year and time lost) and athletic participation (current sport and contact level) that were collected at a single time point as well as multiple time points (baseline and follow-up time points after the concussion) of the clinical assessments (ie, cognitive, postural stability, reaction time and vestibular and ocular motor testing). The follow-up time point measurements were treated as individual variables and as differences from the baseline. Our approach used a weight-of-evidence (WoE) transformation to handle missing data and variable heterogeneity and machine learning methods for variable selection and model fitting. We applied a training-testing sample splitting scheme and performed variable preprocessing with the WoE transformation. Then, machine learning methods were applied to predict the MSKI indicator prediction, thereby constructing a composite risk score for the training-testing sample. This methodology demonstrates the potential of using machine learning methods to improve the accuracy and interpretability of risk scores for MSKI.
Pregnancy outcomes in women with rheumatoid arthritis: an 11-year French nationwide study
Authors: Pina Vegas Laura , Drouin Jérôme , Weill Alain , Dray-Spira Rosemary
Published: January 2024

Background

Rheumatoid arthritis (RA) can affect women of childbearing age. The management of patients with RA during pregnancy has evolved over the past decades, especially with the availability of new therapeutic molecules.

Objectives

To describe pregnancy in women with RA, to compare pregnancy outcomes with those of women in the general population and to compare pregnancy outcomes in women with active and inactive RA.

Methods

Using the French National Health Data System, we identified all pregnancies ending between 2010 and 2020 in patients with and without RA. Characteristics were described. Active RA was defined by conventional synthetic/biological/targeted synthetic disease-modifying antirheumatic drug initiation, systemic or intra-articular corticosteroid administration and/or RA-related hospitalisation. Pregnancy outcomes were compared computing multivariable logistic marginal regression model using generalised estimating equation (GEE).

Results

We included 11 792 RA and 10 413 681 non-RA pregnancies. Among RA pregnancies, 74.5% ended in live births and 0.4% in stillbirths. RA pregnancies resulted more frequently in preterm births (adjusted OR (ORa) 1.84; 95% CI 1.69 to 2.00) and very preterm births (ORa1.43; 95% CI 1.20 to 1.71), low birth weight (ORa1.65; 95% CI: 1.52 to 1.90), caesarean section (ORa1.46; 95% CI 1.38 to 1.55) and pregnancy-related hospitalisation (ORa1.30; 95% CI 1.22 to 1.39). Disease activity decreased during pregnancy. Active RA had higher rates of prematurity (ORa2.02; 95% CI 1.71 to 2.38), small for gestational age (ORa1.53; 95% CI 1.28 to 1.83) and caesarean section (ORa1.25; 95% CI 1.11 to 1.40) than non-active RA.

Conclusion

Pregnancies in women with RA were associated with more adverse outcomes, especially if the disease was active. These findings should encourage physicians to closely monitor RA during this crucial period.
Propagation dynamics of diffusive disease models with infectious stages
Authors: Yang Xiying , Lin Guo
Published: January 2024

This paper studies the spatial propagation dynamics in diffusive disease transmission models with infectious stages, which may be nonmonotonic and partially degenerate. Using the solutions of linear heat equations, we transform the system into a new system that contains nonlocal delays and two unknown functions. From this new system, it is easy to obtain an important threshold and the numerical estimation for this threshold is easy. When the initial infection satisfies proper decaying behavior, we find that this threshold is the spreading speed for all infectious subgroups. Then the minimal wave speed of traveling wave solutions is proved to be the threshold, which connects the disease-free state to the endemic case. Moreover, some numerical examples are presented to explore the possible extension.
Record Fusion via Inference and Data Augmentation
Authors: Heidari Alireza , Michalopoulos George , Ilyas Ihab F. , Rekatsinas Theodoros
Published: January 2024

We introduce a learning framework for the problem of unifying conflicting data in multiple records referring to the same entity—we call this problem “record fusion.” Record fusion generalizes two known problems: “data fusion” and “golden record.” Our approach expresses record fusion as a learning problem over probabilistic models. In contrast to prior approaches, our method achieves high performance with or without the records source information and outperforms state-of-the-art baselines. Furthermore, we show how our learned fusion model can solve the problem of scarcity of training data. On multiple datasets, we show that our framework fuses records with an average precision of ∼98% when source information is available and ∼94% without source information across a diverse array of datasets. We compare our approach to a comprehensive collection of data fusion and entity consolidation methods, ranging from source information–related methods to approaches that do not need any source information. We show that our approach can achieve an average improvement of ∼20/∼45 precision points with/without source information. Our data augmentation method improves previous approaches an average of ∼10 precision points.
Data Management for ML-Based Analytics and Beyond
Authors: Kang Daniel , Guibas John , Bailis Peter , Hashimoto Tatsunori , Sun Yi , Zaharia Matei
Published: January 2024

The increasing capabilities of machine learning (ML) has enabled the deployment of ML methods in a variety of applications, ranging from unstructured data analytics to autonomous vehicles. Due to the volumes of data over which ML is deployed, it is infeasible for humans to monitor deployments: the Tesla fleet of vehicles produces exabytes of data and millions of hours of video per day. As a result, ML deployments can fail in unexpected and catastrophic ways.

In this work, we highlight three important but underlooked aspects of ML deployment pipelines: (1) managing high-quality training data, (2) monitoring ML errors at deployment time, and (3) connecting end use to deployment algorithms. We first demonstrate that training labels are often erroneous, contrary to standard practice, even when labeled by leading vendors. We then demonstrate that standard methods of deploying ML methods can lead to downstream errors. As a first step toward addressing these issues, we review and contextualize two abstractions for finding errors in training data and deployments. We further describe how to improve algorithms for analytics queries as a case study for optimizing ML pipelines end to end.
A Numerical Simulation Approach for Superheated Steam Flow during Multipoint Steam Injection in Horizontal Well
Authors: Du Qiuying , Li Mingzhong , Liu Chenwei , Bai Zhifeng , Zhou Chenru , Wang Xiangyu
Published: January 2024

Superheated steam flow during multipoint steam injection technology has a good effect on improving the steam absorption profile of heavy oil thermal recovery wells, enhancing the production degree of horizontal section of thermal recovery wells, and enhancing oil recovery. Based on the structure of multipoint steam injection horizontal string, considering the characteristics of variable mass flow, pressure drop of steam-liquid two-phase flow, and throttling pressure difference of steam injection valve in the process of steam injection, this paper establishes the calculation model of various parameters of multipoint steam injection horizontal wellbore and calculates the distribution of steam injection rate, temperature, pressure gradient, and dryness along the section of multipoint steam injection in horizontal wellbore. The results show that the temperature and pressure decrease gradually from heel to toe, and the steam dryness decreases gradually. Considering the influence of throttle pressure difference of steam injection valve and pressure drop of gas-liquid two-phase flow in the wellbore, the traditional calculation model of steam injection thermodynamic parameters is optimized, and the optimization of wellbore structure and steam injection parameters is an effective method to achieve uniform steam injection in horizontal wells. The steam injection uniformity of horizontal wells can be effectively improved by adjusting the steam injection valve spacing and steam injection parameters. When the steam injection volume is 200 m3/d and the steam injection valve spacing is 20 m, a more stable steam injection effect can be obtained. The findings of this study can help for better understanding of improving the uniformity of steam injection and enhancing the recovery factor.
A Probabilistic Approach for Missing Data Imputation
Authors: Arefin Muhammed Nazmul , Masum Abdul Kadar Muhammad
Published: January 2024

In the context of data analysis, missing data imputation is a vital issue due to the typically large scale and complexity of the datasets. It often results in a higher incidence of missing data. So, addressing missing data through the imputation technique is essential to ensure the integrity and completeness of the data. It will ultimately improve the accuracy and validity of the data analysis. The prime objective of this study is to propose an imputation model. This paper presents a method for imputing missing employee data through a combination of features and probability calculations. The study utilized employee datasets that were collected from the Kaggle along with primary data collected from RMG factories located in Chittagong. The suggested algorithm demonstrated a notable level of accuracy on the datasets, and the average accuracy for each identified technique was also quite satisfactory. This study contributes to the existing body of research on missing data imputation in big data analysis and offers practical implications for handling missing data in different datasets. Usage of this technique will enhance the accuracy of data analysis and decision-making in organizations.
Effects of Compaction Water Content on Water Retention and Deformation Behavior of Compacted Loess
Authors: Di S. J. , Zhang Y. , Zhou H. , Xie W. L. , He X. L. , Li Y. T. , Mu Q. Y.
Published: January 2024

Compacted loess is widely used as a construction material in engineering practices. The compaction dry density and compaction water content have significant effects on the hydromechanical behavior of compacted loess, thereby influencing the serviceability and safety of engineering structures. The former was widely studied in previous studies, while the latter is rarely known. In this study, the water retention, compression, and collapse behavior of compacted loess at different compaction water contents are investigated through pressure plate and oedometer tests. Microstructure analysis was carried out for insight analysis of the test results. The air entry value and yield stress of compacted loess decreased by 64% and 50%, respectively, with the compaction water content increasing from 12.4% to 19.0%. This is due to the fact that the number of clods increases with increasing the compaction water content, leading to many large‐sized pores (i.e., diameter > 1,000 μm) and weaker soil skeletons. The influence of compaction water content on the water retention and compression behavior of loess is more pronounced at lower compaction dry densities and lower testing water contents, respectively. In addition, the specimen shows smaller collapse indexes at higher compaction water contents, mainly because of the larger deformation induced by the initial compression.
Glucose Fluctuation Inhibits Nrf2 Signaling Pathway in Hippocampal Tissues and Exacerbates Cognitive Impairment in Streptozotocin‐Induced Diabetic Rats
Authors: Chi Haiyan , Sun Yujing , Lin Peng , Zhou Junyu , Zhang Jinbiao , Yang Yachao , Qiao Yun , Liu Deshan
Published: January 2024

Background. This research investigated whether glucose fluctuation (GF) can exacerbate cognitive impairment in streptozotocin‐induced diabetic rats and explored the related mechanism. Methods. After 4 weeks of feeding with diets containing high fats plus sugar, the rat model of diabetes mellitus (DM) was established by intraperitoneal injection of streptozotocin (STZ). Then, GF was triggered by means of alternating satiety and starvation for 24 h. The weight, blood glucose level, and water intake of the rats were recorded. The Morris water maze (MWM) test was carried out to appraise the cognitive function at the end of week 12. Moreover, the morphological structure of hippocampal neurons was viewed through HE and Nissl staining, and transmission electron microscopy (TEM) was performed for ultrastructure observation. The protein expression levels of Nrf2, HO‐1, NQO‐1, Bax, Bcl‐2, and Caspase‐3 in the hippocampal tissues of rats were measured via Western blotting, and the mRNA expressions of Nrf2, HO‐1, and NQO‐1 were examined using qRT‐PCR. Finally, Western blotting and immunohistochemistry were conducted to detect BDNF levels. Results. It was manifested that GF not only aggravated the impairment of spatial memory in rats with STZ‐induced type 2 DM but also stimulated the loss, shrinkage, and apoptosis of hippocampal neurons. Regarding the expressions in murine hippocampal tissues, GF depressed Nrf2, HO‐1, NQO‐1, Bcl‐2, and BDNF but boosted Caspase‐3 and Bax. Conclusions. GF aggravates cognitive impairment by inhibiting the Nrf2 signaling pathway and inducing oxidative stress and apoptosis in the hippocampal tissues.
Exercise-Induced Functional Changes in People with Parkinson’s Disease following External Cueing and Task-Based Intervention
Authors: Clarkin Christine M. , Ward-Ritacco Christie L. , Mahler Leslie
Published: January 2024

Introduction. The purpose of this study was to evaluate change in motor function, gait speed, dynamic balance, balance confidence, and quality of life (QoL) in nine participants with Parkinson’s disease (PwPD) completing Lee Silverman Voice Treatment BIG (LSVT-BIG), an external cueing and task-based intervention. Although supported as an efficacious treatment in PwPD, there is limited research examining clinically meaningful change in outcome measures related to external cueing and task-based interventions. Materials and Methods. This was a case series of nine PwPD (age range 64-76 years, 55% male) who completed the LSVT-BIG protocol. Disease duration ranged from 1 to 17 years and was classified as moderate in all participants (Hoehn and Yahr=2 or 3). Outcome measures included motor function (MDS-UPDRS Part III Motor), gait speed, dynamic balance (MiniBEST), Activities-specific Balance Confidence (ABC), and Summary Index for PD Quality of Life 39 (PDQ-SI). Assessments were completed at baseline (BASE), end of treatment (EOT), and 4 weeks after EOT (EOT+4). Results. Minimal detectable change (MDC) or minimal clinical important difference (MCID) was observed in one or more outcome measures in 8 of 9 participants at EOT and EOT+4 across domains of motor function (67%, 78%), gait speed (78%, 67%), balance confidence (44%, 33%), quality of life (44%, 78%), and dynamic balance (22%, 22%). Discussion. In this case series, 8 of 9 participants showed MDC or MCID changes across multiple functional domains. Improvements were observed immediately post (EOT) and 4-week post-treatment (EOT+4) suggesting a temporal component of the LSVT-BIG impact on functional change. Future research should include clinical trials to examine additional external cueing and task-based intervention efficacy with consideration of intensity, frequency, and mode of delivery across disease severity.
Experimental Study and Seismic Response Evaluation of Chlorobutyl Rubber‐Based Viscoelastic Dampers
Authors: Roshan-Tabari Farnoosh , Toopchi-Nezhad Hamid , Hashemi-Motlagh Ghodratollah
Published: January 2024

Conventional viscoelastic devices often use high‐damping elastomeric pads, typically made of patented formulations, that are bonded to steel plates. The response properties of these pads under cyclic shear deformations directly influence the load‐deformation hysteretic response of the device. Chlorobutyl (CIIR) is a high‐damping rubber commonly used in industrial applications. However, this study found that the damping properties of a typical CIIR rubber compound are insufficient for effective structural seismic mitigation at ambient temperatures above 0°C. The goal of this study was to develop a new composite of CIIR, referred to as modified CIIR, with improved damping properties and to compare its performance with that of the reference CIIR rubber. In the first phase of the experimental studies, the viscoelastic characteristics of the reference and modified CIIR rubber materials were evaluated using dynamic mechanical thermal analysis (DMTA) in tension mode. Prototype viscoelastic damper devices were then fabricated from both the reference and modified CIIR rubber materials and subjected to cyclic shear tests at room temperature and various loading frequencies. The results showed that the modified CIIR rubber exhibited significantly improved effective damping compared to the reference CIIR. The final component of this study involved investigating the seismic response of a 2D frame structure equipped with prototype dampers made from both reference and modified CIIR materials, using nonlinear time‐history analyses. The analysis results indicated that the modified CIIR rubber can be effectively utilized in the seismic response mitigation of structures.
Geochemical Characterization of Sedimentary Materials (Limestone, Gypsum, Coal, and Iron Ore) along the Nile River Basin, South Wollo, Ethiopia
Authors: Endalew Sisay Awoke , Ejigu Assamen Ayalew , Ketemu Desalegn Gezahegn , Assen Wudu Yimer
Published: January 2024

Sedimentary rocks are produced by the weathering of preexisting rocks and the subsequent transportation and deposition of the weathering products. Among the sedimentary rocks, especially limestone is a crucial raw material for cement production. The purpose of this study was to characterize the valuable industrial raw materials, limestone, gypsum, clay, coal, and iron ore, along with the Nile River basin. For sample collection, a random sampling method was applied. Different analytical methods were carried out for complete oxide analysis such as LiBO2 fusion, HF attack, and gravimetric, calorimetric, and atomic absorption spectroscopy (AAS). The percentages of oxides detected in this study are in the range of acceptable values (high content of CaO ranging from 47.3 to 50.4% and less content of SiO2 ranging from 8.72 to 11.24%) for good proposal as a potential raw material for cement production. The most dominant and wide-range coverage of limestone along with the Nile basin, particularly near Arsema Monastery, was found as matured limestone. The petrographic analysis of gypsum, sandstone, and clay samples indicated that all the samples taken from Wegidi revealed that the high percentage of gypsum ranges from 90 to 95%. Sandstone is dominantly preset in Kelala to Jamma road along with Beto River with high content of SiO2 ranging from 61 to 95%. The results of this study indicate that the treated coal samples are relative to high calorific value, fixed carbon, and low ash content. Coal and iron ore from Jamma revealed that high calorific value is 4929.24 and hematite content is 52.2, respectively. The result of this study revealed that a huge amount of limestone reservoir is detected in Borena Wereda, Amhara, Ethiopia.
N6-Methyladenosine Reader YTHDF1 Promotes Stemness and Therapeutic Resistance in Hepatocellular Carcinoma by Enhancing NOTCH1 Expression
Authors: Zhang Xinyue , Su Tianhong , Wu Yifan , Cai Yuhong , Wang Lina , Liang Cong , Zhou Lei , Wang Shiyan , Li Xiao-Xing , Peng Sui , Kuang Ming , Yu Jun , Xu Lixia
Published: January 2024

N6-methyladenosine (m6A) RNA modification is the most common and conserved epigenetic modification in mRNA and has been shown to play important roles in cancer biology. As the m6A reader YTHDF1 has been reported to promote progression of hepatocellular carcinoma (HCC), it represents a potential therapeutic target. In this study, we evaluated the clinical significance of YTHDF1 using human HCC samples and found that YTHDF1 was significantly upregulated in HCCs with high stemness scores and was positively associated with recurrence and poor prognosis. Analysis of HCC spheroids revealed that YTHDF1 was highly expressed in liver cancer stem cells (CSC). Stem cell–specific conditional Ythdf1 knockin (CKI) mice treated with diethylnitrosamine showed elevated tumor burden as compared with wild-type mice. YTHDF1 promoted CSCs renewal and resistance to the multiple tyrosine kinase inhibitors lenvatinib and sorafenib in patient-derived organoids and HCC cell lines, which could be abolished by catalytically inactive mutant YTHDF1. Multiomic analysis, including RNA immunoprecipitation sequencing, m6A methylated RNA immunoprecipitation sequencing, ribosome profiling, and RNA sequencing identified NOTCH1 as a direct downstream of YTHDF1. YTHDF1 bound to m6A modified NOTCH1 mRNA to enhance its stability and translation, which led to increased NOTCH1 target genes expression. NOTCH1 overexpression rescued HCC stemness in YTHDF1-deficient cells in vitro and in vivo. Lipid nanoparticles targeting YTHDF1 significantly enhanced the efficacy of lenvatinib and sorafenib in HCC in vivo. Taken together, YTHDF1 drives HCC stemness and drug resistance through an YTHDF1–m6A–NOTCH1 epitranscriptomic axis, and YTHDF1 is a potential therapeutic target for treating HCC.

Significance:

Inhibition of YTHDF1 expression suppresses stemness of hepatocellular carcinoma cells and enhances sensitivity to targeted therapies, indicating that targeting YTHDF1 may be a promising therapeutic strategy for liver cancer.
Nutrient Sensing in Macrophages Linked to Reorganized Tumor Vasculature
Authors: Chan Hilda L. , Zhang Xiang H.-F.
Published: January 2024

Macrophages are plastic immune cells that have varying functions dependent on stimulation from their environment. In a recent issue of Immunity, Do and colleagues demonstrated that activating mechanistic target of rapamycin complex 1 signaling in tumor macrophages alters their metabolism, localization, and function. Specifically, these tumor macrophages promote vascular remodeling that develops a hypoxic environment toxic to cancer cells. This culminates in a tangible reduction in tumor burden in a murine model of breast cancer. Their findings reveal a unique strategy to promote vascular remodeling through macrophage polarization and thereby highlight the intimate connections between macrophage metabolism and function. Additionally, their model highlights parallels between tumor progression and wound healing contexts while emphasizing the amplified effect of small perturbations to a tumor ecosystem.
T-cell States, Repertoire, and Function in Classical Hodgkin Lymphoma Revealed through Single-Cell Analyses
Authors: Chen Xiufen , Yu Jovian , Venkataraman Girish , Smith Sonali M. , Chen Mengjie , Cooper Alan , Tumuluru Sravya , Brody Joshua D. , Godfrey James , Kline Justin
Published: January 2024

The classical Hodgkin lymphoma (cHL) environment is comprised of a dense and complex immune cell infiltrate interspersed with rare malignant Hodgkin-Reed-Sternberg (HRS) cells. HRS cells are actively surveilled by endogenous T cells, but data linking phenotypic and functional T-cell states with clonality at the single-cell level in cHL is lacking. To address this knowledge gap, we performed paired single-cell RNA and T-cell receptor sequencing on 14 cHL and 5 reactive lymphoid tissue specimens. Conventional CD4+ T cells dominated the cHL landscape. However, recurrent clonal expansion within effector and exhausted CD8+ T-cell and regulatory T-cell clusters was uniquely observed in cHL specimens. Multiplex flow cytometric analysis revealed that most lymphoma-resident T cells produced effector cytokines upon ex vivo restimulation, arguing against a profound dysfunctional T-cell state in cHL. Our results raise new questions about the nature of T cells that mediate the antilymphoma response following programmed cell death protein 1 (PD-1) blockade therapy in cHL.
Trichobezoar Found Accidently while Diagnosing Resistance to Thyroid Hormone
Authors: Szopińska Kinga , Tracz Karolina , Malczyk Żaneta , Jarzumbek Anna , Grabowski Andrzej , Bąk-Drabik Katarzyna
Published: January 2024

Introduction: Bezoars are masses of indigestible foreign material in the gastrointestinal tract, usually in the stomach. These materials could be indigestible fruits, hair, milk products, or tablets. In children, the most common type of bezoar is trichobezoar (formed from hair). Case Presentation: We describe a female patient who has been complaining about deterioration of mood, collapse without losing consciousness, scotomas, and cardiac arrhythmia for 2 years. Based on the results of thyroid hormone, resistance to thyroid hormone (RTH) was suspected. Physical examination during hospitalization revealed a palpable upper abdominal mass. Several diagnostic examinations were performed. The abdominal ultrasound showed acoustic shadowing caused by a pathological structure in the upper abdomen. Therefore, the contrast X-ray of the digestive tract revealed a deficit of contrast with an irregular shape in the stomach body and the pylorus region. Due to these results, a gastroscopy was performed, which revealed a large trichobezoar of the stomach. The trichobezoar was surgically removed without complications. Conclusion: The case presented shows that these nonspecific symptoms and laboratory test suggesting RTH require multi-path diagnostics and the cooperation of many specialists, ultimately giving a surprising diagnosis. It is crucial to interpret diagnostic examinations with regard to the patient’s physical condition. Diagnosis of trichobezoar requires a detailed search of causes to avoid another incident.
TLR10: An intriguing Toll-like receptor with many unanswered questions
Authors: Rodrigues Carolina Rego , Balachandran Yadu , Aulakh Gurpreet Kaur , Singh Baljit
Published: January 2024

Background: Toll-like receptors (TLRs) are one of the first pattern recognition receptors (PRRs) found in the innate immune system. The TLR family has 12 members (TLR1-TLR9, TLR11-TLR13) in mice and 10 members (TLR1-TLR10) in humans, with TLR10 being the latest identified. Summary: Considerable research has been performed on TLRs, however, TLR10 is known as an orphan receptor for the lack of information on its signalling, role, and ligands. Even though there are recent studies pointing towards the potential TLR10 ligands, its function and signalling pathway are yet to be determined. Key messages: This review gives an insight into recent findings on TLR10 pro- and anti-inflammatory properties, with the goal of outlining existing results and indicating future research topics on this receptor.
Immunogenicity and Safety of the Three-Dose COVID-19 Vaccine Regimen in Patients Receiving Renal Replacement Therapy: A Systematic Review and Meta-Analysis
Authors: Ma Becky Mingyao , Tam Anthony Raymond , Chan Kam Wa , Hung Ivan Fan Ngai , Tang Sydney Chi Wai , Chan Tak Mao , Yap Desmond Yat-Hin
Published: January 2024

Background: A three-dose regimen is the current standard for COVID-19 vaccination, but systematic data on immunogenicity and safety in chronic kidney disease patients remains limited. Objectives: We conducted a meta-analysis on the immunogenicity and safety of three-dose COVID-19 vaccination in patients on renal replacement therapy (RRT). Methods: Systematic literature search in four electronic databases yielded twenty eligible studies (2,117 patients, 94% of whom received mRNA vaccines) for meta-analysis. Results: The overall seropositivity rate of anti-SARS-CoV-2 was 74.2% (95% CI: 65.0–83.4%) after three-dose COVID-19 vaccination. The seropositivity rate of anti-SARS-CoV-2 in kidney transplant recipients (KTRs) was 64.6% (95% CI: 58.7–70.5%), and 43.5% (95% CI: 38.5–48.6%) of non-responders after second dose became seropositive after third dose. The seropositivity rate of anti-SARS-CoV-2 was 92.9% (95% CI: 89.5–96.2%) in dialysis patients, and 64.6% (95% CI: 46.8–82.3%) of non-responders after second dose became seropositive after third dose. In KTRs, each year increase in transplant vintage was associated with 35.6% increase in anti-SARS-CoV-2 seropositivity (95% CI: 15.9–55.4%, p = 0.01). There were no serious adverse events attributed to vaccination in KTRs, and the commonest local and systemic adverse events were injection site pain and fatigue, respectively. Conclusion: Three-dose COVID-19 vaccination regimen in patients on RRT is associated with reduced immunogenicity, especially in KTRs. There are no adverse events associated with third-dose COVID-19 vaccine in KTRs.
Fetal electrocardiogram prediction using machine learning: a random forest-based approach
Authors: Moutaib Mohammed , Fattah Mohammed , Farhaoui Yousef , Aghoutane Badraddine , El Bekkali Moulhime
Published: January 2024

Monitoring fetal health during pregnancy ensures safe delivery and the newborn’s well-being. The fetal electrocardiogram (fetal ECG) is a valuable tool for assessing fetal cardiac health, but interpretation of ECG data can be challenging due to its complexity and variability. In this work, we explore the application of machine learning, particularly random forest, to predict and analyze fetal ECGs. With its ability to manage large datasets and provide precise insights, random forest is a promising solution for this challenge. By comparing our random forest-based approach with other standard machine learning techniques such as artificial neural network (ANN), support vector machines (SVM), and recurrent neural networks (RNN), we observed that our solution outperformed these methods in accuracy, robustness, and reliability. This article details the methodology used, the implementation of the algorithm, as well as the comparative results obtained. Emphasis is placed on the benefits of random forest in this specific medical context, highlighting its potential as a future tool for fetal ECG prediction. Ultimately, our research suggests a shift toward random forest-based solutions for more efficient and accurate analysis of fetal ECGs, with direct implications for clinical practice and fetal well-being.
Fast region based convolutional neural network ResNet-50 model for on tree Mango fruit yield estimation
Authors: Managali Vasanth Neethi , PPandian Raviraj
Published: January 2024

The foundation of the Indian economy is agriculture, the amount of land available for agricultural activities has decreased due to numerous factors. To fulfill the demands of the expanding population, the maximum yield must be produced on the least amount of land that is accessible. To overcome the challenges of agriculture, many researches have been carried out to adopt technology into agriculture. As India is one of the world's top producers of Mangoes and has a vast market, and has encouraged extensive Mango farm development. Automatic yield estimation of Mangoes in the early stage is important to improve the quality and quantity of production which improves both domestic and export markets. The work proposes a fast region (FR) based convolutional neural network (CNN) residual network (ResNet)-50 model for efficient deep learning-based Mango crop yield estimation system to count the Mango fruit from the images of individual trees. A temporal Mango fruit database is used to estimate the yield of on tree Mango fruits, and a framework is provided to estimate Mango fruit yield in red, green, and blue (RGB) image. This experiment shows that the suggested FRCNN ResNet-50 model attained a better accuracy of 98.20% on the proposed dataset.
Automatic question generation using extended dependency parsing
Authors: Tewabe Sewunetie Walelign , Kovacs Laszlo
Published: January 2024

The importance of automatic question generation (AQG) systems in education is recognized for automating tasks and providing adaptive assessments. Recent research focuses on improving quality with advanced neural networks and machine learning techniques. However, selecting the appropriate target sentences and concepts remains challenging in AQG systems. To address this problem, the authors created a novel system that combined sentence structure analysis, dependency parsing approach, and named entity recognition techniques to select the relevant target words from the given sentence. The main goal of this paper is to develop an AQG system using syntactic and semantic sentence structure analysis. Evaluation using manual and automatic metrics shows good performance on simple and short sentences, with an overall score of 3.67 out of 5.0. As the field of AQG continues to evolve rapidly, future research should focus on developing more advanced models that can generate a wider range of questions, especially for complex sentence structures.
Single nucleotide polymorphism based on hypertension potential risk prediction using LSTM with Adam optimizer
Authors: Muflikhah Lailil , Cholissodin Imam , Widodo Nashi , Eko Herman Feri , Liliana Wargasetia Teresa , Ratnawati Hana , Sarno Riyanarto
Published: January 2024

Recent healthcare research has focused a great deal of interest on using genetic data analysis to predict the risk of hypertension. This paper presents a unique method for accurately predicting the vulnerability to hypertension by utilizing single nucleotide polymorphism (SNP) data. We present a novel neural network design utilizing the adaptive moment (Adam) optimizer to describe the intricate temporal correlations in SNPs. The study used a dataset with carefully preprocessed SNP data from a broad cohort for model input. The long short-term memory (LSTM) network was methodically built and trained with hyper-parameter and fine-tuning using the Adam optimizer to converge on ideal weights. Our findings indicate encouraging predictive performance, highlighting the suggested methodology’s usefulness in determining hypertension risk factors. The result showed that the proposed method achieved stability in the performance of 89% accuracy, 96% precision, 88% recall, and 92% F1-score. Due to its higher accuracy and greater predictive power, our SNP-based LSTM methodology is superior to the conventional machine learning method. By providing a novel framework that uses genetic data to predict the risk of hypertension, this research makes substantial contribution to the field of predictive healthcare. This framework helps with early intervention and customized preventative efforts.
Proposed algorithm base optimisation plan for feature selection-based intrusion detection in cloud computing
Authors: Laassar Imane , Youssef Hadi Moulay , Arifullah Arifullah , Remmach Hassnae , Salam Khan Fawad
Published: January 2024

A crucial element in detecting unusual network system behavior is the network intrusion detection system (NIDS), which also helps to stop network attacks from happening. Despite the fact that a great deal of machine learning techniques has been used in intrusion detection, current solutions still struggle to provide accurate classification results. Furthermore, when dealing with imbalanced multi-category traffic data, a single classifier may not be able to produce a superior. Particularly, internet of things (IoT) gadgets is now a commonplace aspect of life. On the other hand, some problems are becoming worse and lack clear remedies. Convergence, communication speed, and security between various IoT devices are among the primary concerns. In order to achieve this goal, an enhanced artificial bee colony technique utilizing binary search equations and neural networks—known as the (BABCN) algorithm for intrusion detection in terms of convergence and communication speed—is presented in this study. The artificial bee is improved by the depth-first search framework and binary search equations upon which the BABCN method is based. The suggested approach has a good ability to detect intrusions in the network and enhances categorization, according to the findings obtained by using the NSL-KDD dataset.
Spark-MLlib intrusion detection mechanism using machine learning models
Authors: Sarwath Asra , Gulmeher Raafiya , Sultana Zeenath
Published: January 2024

Typically, a single method is employed in machine learning (ML) based intrusion detection to identify intrusion information. However, this approach lacks flexibility, has a low detection rate, and struggles to handle high-dimensional data. Consequently, it is not efficient in addressing these challenges. This study proposes a new intrusion detection architecture that utilizes Spark and ensures resilient data dissemination across the platform to improve its effectiveness. It consists of preprocessing module, a label encoder module, a feature extraction module, a classification module and a database module. The preprocessing module compresses information by utilizing the module for label encoding. This generates a lower-dimensional reconstruction and classification characteristic. The database module has the capability to store the compressed characteristics of all traffic. This enables the classifier to be tested and then returns these features back into the original traffic, facilitating retraining. In order to evaluate the efficacy of the framework, simulations were conducted using the CICIDS 2017 dataset to accurately replicate the network traffic. Based on the test findings, the accuracy of both multiclass and binary classification surpasses that of earlier studies. High precision was achieved for the traffic that was restored. The possible application of the proposed architecture for edge/fog networks is discussed in the conclusion.
Integrating random forest model and internet of things-based sensor for smart poultry farm monitoring system
Authors: Fahrurrozi Imam , Wahyono Wahyono , Sari Yunita , Kartika Sari Anny , Usuman Ilona , Ariyadi Bambang
Published: January 2024

The global poultry industry has encountered growing concerns related to foodborne illnesses, misuse of antibiotics, and environmental impacts. To tackle these issues, this study aims to develop an intelligent poultry farm with real-time environmental monitoring and predictive models. The primary objective is to combine a machine learning-based prediction model with internet of things (IoT) devices to gather and analyze environmental data, such as temperature, humidity, and ammonia levels, to forecast the conditions within poultry houses. These sensor data and additional information, such as feed consumption, water consumption, poultry weight, capacity, and poultry house dimensions will serve as inputs for supervised machine learning models. Among these models, the proposed random forest (RF) model, when augmented with timestamp features, achieves the highest accuracy rate of 96.665%, surpassing other models such as logistic regression (LR), k-nearest neighbor (KNN), decision tree (DT), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), support vector machine (SVM), and multi-layer perceptron (MLP) in identifying poultry house conditions. Additionally, this study demonstrates how the trained model can be effectively applied in a web-based monitoring system, delivering real-time data to farmers for well-informed decision-making and ultimately enhancing productivity in smart poultry farming.
Dynamic line rating for grid transfer capability optimization in Malaysia
Authors: Husniyah Abas Nurul , Zainal Abidin Ab Kadir Mohd , Azis Norhafiz , Jasni Jasronita , Fadilah Ab Aziz Nur
Published: January 2024

This paper details a case study on the implementation of dynamic line rating (DLR) to enhance the ampacity rating of Malaysia’s grid. Utilizing heat balance equations endorsed by the Institute of Electrical and Electronics Engineering (IEEE 738) and the International Council on Large Electric Systems (CIGRE technical brochure 601), the ampacity rating of a Zebra-type aluminum cable steel reinforced (ACSR) conductor on a 275 kV transmission line has been assessed. Real-time weather conditions and conductor temperatures, measured hourly by the DLR sensor over the course of a year, were incorporated into the ampacity calculation to determine the available margin. The weather parameters were analyzed based on the monsoon seasons. A comparative analysis between various methods outlined in the standards and the estimated ampacity rating derived from both standards is presented. According to both standards, the findings indicate that DLR surpasses static line rating (SLR), highlighting the presence of untapped ampacity for grid optimization. Remarkably, CIGRE TB 601 exhibits a higher ampacity rating margin than the IEEE 738 standard, with a percentage difference of 16.20%. The study concludes that the conductor is underutilized and proposes optimization through the integration of real-time weather conditions data into the heat balance equations.
Design of battery state of charge monitoring and control system using coulomb counting method based
Authors: Syafii Syafii , El Fakhri Irfan , Kurnia Agung Thoriq , Azizah Farah
Published: January 2024

Lead-acid batteries are commonly used in photovoltaic systems to store solar energy for continuous use. However, lead-acid batteries have a relatively short lifespan due to frequent over-charging and over-discharging. A battery management system (BMS) is essential for accurately predicting the battery state of charge (SoC) value in order to extend the battery lifespan. In this research, a BMS is developed using the coulomb counting method to estimate the SoC value of a lead-acid battery. The coulomb counting algorithm provides a reliable estimation of the battery’s SoC value by calculating the incoming and outgoing currents. The BMS also uses two normally closed relays to prevent overcharging and over-discharging. The first relay turns on when the SoC reaches 100% full charge and turns off when the SoC decreases to 70%. The second relay turns on when the SoC reaches 20%. The BMS was tested using Blynk, a cloud-based internet of things (IoT) platform. The results showed that the BMS successfully provided monitoring and reliable control of the lead-acid battery, with a low margin of error. This demonstrates that the developed BMS can be practically implemented in photovoltaic (PV)-battery systems to extend the battery lifespan and improve the overall performance of the system.
A novel 7-level reduced-switch MLI topology fed PMSM drive for electric vehicle system
Authors: Anil Kumar Chinta , Jothinathan Kandasamy , Shanmukha Rao Lingineni
Published: January 2024

The compact and efficient design motivates the renewable energy-powered permanent magnet synchronous motor drive for electric vehicle applications. The available renewable energy is interfaced with a power drive by employing an electronic commutator such as a conventional 3-level inverter. But, the multilevel inverter produces favorable merits by producing staircase output voltage from several input direct current (DC) sources. The cascaded H-bridge multilevel inverter plays a significant role in many applications, but it was developed only for limited voltage levels. The major problem in cascaded h-bridge multilevel inverter (CHBMLI) requires more switching devices for higher voltage levels, which increases the size, cost and space of the electric vehicle (EV). To overcome above-mentioned problems, a new objective has been developed by employing the novel Reduced-switch multilevel inverter topology for higher voltage levels. This improves the voltage quality and reducing the harmonic level, and common-mode voltage issues. The main contribution of this work is, developing the novel 5-level, 7-level reduced-switch multilevel inverter (RSMLI) topologies with reduced switching devices with favourable merits over CHBMLI topology. Finally, the performance of proposed novel 5-level and 7-level RSMLI topologies fed PMSM drive for EV system has been verified, by using MATLAB/Simulink computing tool, and simulation results are presented with comparisons.
A low-cost dual bandpass planar filter for WiMAX and mobile communications
Authors: Kadiri Amal , Tajmouati Abdelali , Zbitou Jamal , Lakhssassi Ahmed
Published: January 2024

This study proposes a new design of low-cost dual-bandpass filter for worldwide interoperability and microwave access (WiMAX) band at
 3.50 GHz and mobile communications band at 1.19 GHz. A high pass filter and a stopband filter make up the new dual-bandpass filter structure. Different theoretical studies were carried out for the design of the proposed filter. This filter’s base is a RO5880 substrate with a dielectric permittivity constant of 2.2, loss tangent of 0.0009 and 1.6 mm thickness. High mashing density was used to validate the various simulated structures while accounting for two numerical methods: the moment technique and the finite integration method. The final circuit's overall dimensions are 60×178,3 mm2.
Alzheimer image registration using hybrid random forest and deep regression network algorithm
Authors: Siddabathuni Ramakoteswararao , Palanivel Sivagurunathan , Lakshmi Narasimha Murthy Godavarthi
Published: January 2024

Image registration involves superimposing images (two or more) of similar background obtained at various periods of time, at different angles, and/or with various detectors. Geometrical alignment of two scans, reference image as well as capture image. The current dissimilarity between images is because of distinct image conditions. Image registration is difficult step in image analysis works on change detection, image fusion as well as
 multi-channel images recovery to obtain concluded data from integration of different sources. In this analysis image registration using hybrid random forest (RF) and deep regression network algorithm for magnetic resonance imaging (MRI) applications is implemented. The Alzheimer’s disease neuroimaging initiative (ADNI) database provided by the dataset utilised in this implementation. From results it can observe that compared with individual random of forest, Hybrid RF and deep regression network algorithm improves the accuracy, precision and F1-score in effective way.