bluuebunny/crossref_metadata_2025 huggingface dataset:
Jom Tarik Ordering Management System
Authors: Amin Noor Ul , Bhattacharjee Mridul , Adip Junayen Ansari , Muhammad Nasrullah Fuad , Sameer Khan Fardeen , Wadoo Huzaib
Published: January 2025

This research work draws the design, development, and evaluation of the Jom Tarik Ordering Management System, a digital platform created to optimize food ordering processes for restaurants. The system features a user-friendly interface, secure authentication mechanisms, and an efficient ordering workflow, all aimed at enhancing the overall customer experience. The development process followed the Agile methodology, specifically the Scrum framework, to promote iterative progress, scalability, and continuous improvement. Rigorous testing strategies, including unit, integration, and system testing, were employed to ensure the system's reliability and functionality. This report also highlights the collaborative efforts of a six-member team, the challenges faced, and the methodologies adopted to deliver a comprehensive, feature-rich solution.
Machine Learning Discoveries of GINS1-X Synergy in ETC-1922159 Treated Colorectal Cancer Cells
Authors: Sinha Shriprakash
Published: January 2025

GINS1 (PSF1 homolog) forms a part of complex called GINS, named after numbers 5-1-2-3 (go-ichi-ni-san in Japanese) apropos the 4 protein subunits of the complex, namely, SLD5, PSF1, PSF2, and PSF3. It is a subgroup of the CDC45-MCM-(2 to 7)- GINS (CMG) complex, the enzyme that unwinds double-stranded DNA at replication forks. GINS1 has been found to be overexpressed in multiple cancers. In colorectal cancer (CRC) cells treated with ETC-1922159, GISN1 was found to be down regulated along with other genes. A recently developed search engine ranked combinations of GISN1-X (X, a particular gene/protein) at 2nd order level after drug administration. Some of these combinations have been tested in wet lab, however many have been pointed out by the search engine that are yet to be explored/tested. These rankings reveal which GINS1-X combinations might be working synergistically in CRC. In this research work, I cover combinations of GINS1 with possible members of, origin recognition complex (ORC), minichromosome maintenance complex (MCM), DNA- directed DNA polymerases (POL), DNA-dependent RNA polymerase (POLR), topoisomerases (TOP), ubiquitin specific peptidase (USP), Fanconi anemia complementa- tion group (FANC), forkhead box (FOX) and notch receptor (NOTCH) family.
Design and Implementation of a Database System for Event Management Customizer (EMC): A Case-Based Approach
Authors: Amin Noor Ul , Alsukhailah Ahmed Abdullah , Faisal Ghaleb Ahmed Abdullah , Ahmed Mobarak Mohammed Abdullah , Algaafari Hatem Ahmed , Huzaib Wadoo Algaafari, , Zaman Salman
Published: January 2025

Database systems are central in the organization and management of complex data with a view to accuracy, accessibility, and scalability. This paper describes the design and implementation of a customized database system for Event Management Customizer, a Subang Jaya-based startup in Malaysia specializing in adventure holidays and team-building events. The main goal was to manage client bookings, accommodate and facility management, and enhance operational efficiency. It also includes the design of a database system, using methodologies such as ER and physical modelling, ending with an implementation based on MySQL. Based on this, eight interconnected entities have been identified: Client, Representative, Accommodation, Booking, Outdoor Facilities, Additional Facilities, Payment, and Staff. The defined key attributes, composite attributes, and derived attributes will present the structure of the proposed system. Features such as facility hire charges, accommodation details, and staff assignments provide comprehensive data management. It further enhanced the system to real-world, scenario-based SQL queries identifying high-transaction clients, calculating additional charges, and even evaluation of staff performances. Further enhancements could also be made upon the challenges they faced, for instance, COVID-19 pandemic-addition of vaccination statuses, quarantine records, and room disinfection schedules. This database design has demonstrated the flexibility of database systems in solving organizational challenges; it also has proved that the database systems are vital in maintaining operational integrity, decision-making, and adapting to unforeseen global disruptions.
A Robust Cross-Sectional Assessment of the Impacts of COVID-19 Pandemic on the Prevalence of Female Genital Mutilation Among 0–14 Years Old Girls in Nigeria
Authors: Visée Corentin , Morlighem Camille , Nnanatu Chibuzor Christopher
Published: January 2025

Background: Female genital mutilation (FGM) is a human rights violation that still affects more than 3 million girls aged 0-14 years each year. To achieve the Sustainable Development Goal 2030 agenda, efforts have been made at the local, national and international levels to end the practice by the year 2030. However, the recent COVID-19 pandemic may have reversed the progress made due to increased rates of early marriage of girls, violence against children and school closures during lockdowns. Although some surveys have examined changes in FGM prevalence over the COVID-19 period, changes at the national and sub-national levels among 0-14 years old girls have not been quantified. ObjectivesThis study aimed to understand the potential impacts of the COVID-19 pandemic on the likelihood of FGM among girls aged 0-14 years, and whether it affected progress towards the elimination of FGM. Design We used Bayesian hierarchical regression models implemented within the integrated nested Laplace Approximations (INLA) frameworks.MethodsWe modelled the likelihood and prevalence of FGM among girls aged 0-14 years before and after the COVID-19 pandemic in Nigeria, with respect to individual and community-level characteristics, using Bayesian hierarchical models. We used the 2018 Demographic and Health Survey as the pre-COVID-19 period and the 2021 Multiple Indicator Cluster Survey as the post-COVID-19 period. ResultsAt the state level, FGM prevalence varied geographically and increased by 23% and 27% in the northwestern states of Katsina and Kana respectively. There were 11% increase in Kwara and 14% increase in Oyo. However, at the national level the prevalence of FGM was found to decrease from 19.5% to 12.3% between 2018 and 2021. Cultural factors were identified as the key drivers of FGM among 0-14 years old girls in Nigeria. The changes in the likelihood of girls undergoing FGM across the two time periods also varied across ethnic and religious groups following COVID-19 pandemic.ConclusionOur findings highlight that FGM is still a social norm in some states/regions and groups in Nigeria, thereby highlighting the need for a continued but accelerated FGM interventions throughout the country.
Machine Learning Algorithmic Models for the Authentication of Albanian Mono-Cultivar Olive Oils
Authors: Topi Ardiana , Hudhra Daniel Thimi , Topi Dritan
Published: January 2025

Distinguished for its nutritional benefits and high economic value, olive oil has faced issues with adulteration and fraud. As production increases, the need to identify olive oil by specific cultivars and regions has become more pressing. Analyzing chemical data related to the origin and olive cultivars will facilitate advanced quality control and authenticity practices for Albanian olive oil, enhancing its competitiveness as an organic product. While traditional empirical methods have been relied upon to detect olive oil fraud and evaluate quality, this study pioneers a modern approach using machine learning algorithms to differentiate authentic products from counterfeits. Establishing effective mechanisms and best practices to trace product origins and quality indicators will raise awareness about the risks of adulteration to both consumers' health and the broader food industry.
To enhance the precision of origin predictions, the importance of data pre-processing steps—especially the normalization process following the isolation of independent features from the target variable- particularly crucial for distance-based algorithms like kNN that improve accuracy. Furthermore, performance metrics for all algorithms were evaluated, including k-Nearest Neighbors, Logistic Regression, Support Vector Machines, implement hyperparameter tuning techniques, and the best-performing model. Application of supervised machine-learning methods to categorize Albanian olive oils (OO) according to their chemical composition, aiding in identifying their geographical and cultivar origin. Our results indicate an accuracy of 62.5%, constrained by the limitations of the current dataset; however, we intend to expand the dataset in the future.
Computational Offloading and Time Allocation Policies in Mobile Edge Computing and Mobile Cloudlet
Authors: Ahsan Isna , Butt Mahmil , Ali Zaiwar , Alawad Mohamad A. , Alenezi Abdulmajeed M. , Khan Sheroz , Yahya And Muhammad
Published: January 2025

Wireless-powered Mobile Edge Computing (MEC) has been proving to be an auspicious paradigm to enhance the data processing competency of low-powered networks in light of the increasing need for diagnostic information retrieval. Applications of dividing a given load into smaller units and then executing each unit independently by different processors are a class of tasks that require a pressing need of parallel and distributed processing. However, it is challenging to decide whether the units will be offloaded to the edges of a cloud (MEC) or through a concept of Mobile Device Cloudlet (MDC) where a User Equipage (UE) with finite resources prefer to offload its units to a foreign UE. The network of the client UE and foreign UE is known as a cloudlet. Furthermore, the cost function developed assigns equal weights to the factors to optimise the analysis of policies which in actual fact is not persuasive. There is a need to improve energy efficiency of UEs and consider the latency dilema in cloud computing due to distant communication between UEs and remote cloud centres. To address these problems, our paper proposes an Offloading and Time Allocation Policy using MDC and MEC (OTPMDC) that implements whether a task should be offloaded through MEC or MDC in conjunction with the time allocation judgement for the UE to harvest energy and transmit information. To address the gap of the second issue, our goal is to train an intelligent deep learning-based decision-making algorithm that will choose an optimal set of applications based on the energy in the UE. We have formulated a cost function by considering the above policies that will generate an extensive dataset from which the algorithm will select the optimal sets and train a deep learning network. The obtained simulation results depict that performance of UEs is improved.
Novel Pathogenic Variant of the TRRAP Gene Detected in a Hungarian Family with Autosomal Dominant Non-Syndromic Hearing Loss
Authors: Nagy Nikoletta , Szalenko-Tőkés Ágnes , Pál Margit , Bokor Barbara Anna , Nagy Roland , Jarabin János András , Róvó László , Széll Márta
Published: January 2025

Autosomal dominant non-syndromic hearing loss (ADNSHL) is a genetically heterogenic condition. The transformation/transcription domain associated protein (TRRAP) gene has been recently associated with ADNSHL and only four variants of the gene have so far been reported in this disease. (1) Here, we report on a Hungarian ADNSHL family, in which the affected individuals exhibited sensorineural hearing loss with similar clinical symptoms, including initial impaired high frequencies that subsequently affected speech and lower frequencies. (2) Whole exome sequencing and screening of the shared genetic variants of the affected individuals was performed. (3) Our results revealed a novel heterozygous missense variant (NM_001244580.2, c.5360A>G, p.Lys1787Arg) in the TRRAP gene. This variant is completely co-segregated with hearing impairment. It is present in a heterozygous form in the affected mother and daughter, but not carried by any unaffected family members. (4) This study highlights the importance of elucidating the germline genetic background of ADNSHL, which may help to predict individual risk and the risk of family members. This will improve prevention, screening and therapeutic measures for each patient and hearing loss-prone families.
Toward Reliable Solar Flare Forecasting: Exploring Multi-Wavelength Data from the Solar Dynamics Observatory
Authors: Popowicz Adam
Published: January 2025

Solar flares, originating from sudden energy releases in the Sun’s atmosphere, pose significant risks to spaceborne and terrestrial technological systems, including satellite operations, communications networks, and power grids. Accurate solar flare forecasting is therefore essential for mitigating these impacts and advancing space weather prediction capabilities. In this study, we present a comprehensive deep-learning-based approach utilizing multi-channel observations from the Solar Dynamics Observatory (SDO), a spaceborne remote sensing platform dedicated to solar monitoring. Our analysis focuses on classifying solar flares under three scenarios: C vs. 0, M vs. C, and M vs. 0, leveraging ten distinct image channels spanning photospheric magnetograms and extreme ultraviolet (EUV) wavelengths. We trained and evaluated three modern convolutional neural network architectures—ResNet50, GoogLeNet, and DenseNet121—using the True Skill Score (TSS) and Gini coefficient to assess performance. The results highlight the superior predictive power of magnetogram data, with additional contributions from EUV channels such as 94 and 211 Å. This work underscores the utility of combining multi-spectral solar observations with state-of-the-art deep learning architectures to capture subtle pre-flare signatures and improve flare prediction accuracy. Furthermore, the methodology and open dataset provide a reproducible benchmark for advancing solar flare forecasting, supporting the broader remote sensing and space weather research communities.
Rebuttal to: Sound Power of Onshore Wind Turbines and Its Spectral Distribution
Authors: Verhoeven Harrie
Published: January 2025

This paper will review the preprint submitted to this server about the development of sound power of wind turbines. Using information available in the scientific record, it will be shown:1. that the methods are not suited for the goal of the authors by reinterpreting their presented results;2. the results are not supported by data from the scientific record; 3. The discussion is based on a selected and small sample of the scientific literature;4. The conclusions in the discussed paper are not supported by the results.All this is supported by a review of the relevant literature, with examples of opposing interpretations.
Machine Learning Discoveries of FANCD2-X Synergy in etc-1922159 Treated Colorectal Cancer Cells
Authors: Sinha Shriprakash
Published: January 2025

Fanconi anemia complementation group D2 (FANCD2) is one of the members of FA complementation group, that is activated in response to DNA damage and is involved in DNA repair and regulation of genomic stability. FANCD2 works with other members of FANC group along with other proteins/genes, to carry out its required functionality. Further, disruption of FA/BRCA pathway has been implicated in cancer progression. In colorectal cancer (CRC) cells treated with ETC-1922159, FANCD2 was found to be down regulated along with other genes. A recently developed search engine ranked combinations of FANCD2-X (X, a particular gene/protein) at 2nd order level after drug administration. Some of these combinations have been tested and established in wet lab, however many have been pointed out by the search engine that are yet to be explored/tested. These rankings reveal which FANCD2-X combinations might be working synergistically in CRC. In this research work, I cover combinations of FANCD2 with, REV3 like DNA directed polymerase zeta catalytic subunit (REV3L), Bloom syndrome RecQ like helicase (BLM), MRE11 homolog double strand break repair nuclease (MRE11A), Wnt family member 10B (WNT10B), ubiquitin like with PHD and ring finger domains 1 (UHRF1), hes family bHLH transcription factor 1 (HES1), BRCA DNA repair associated (BRCA), RAD51 recombinase (RAD51), ERCC excision repair endonuclease non-catalytic subunit (ERCC), KIAA, X-ray repair cross com- plementing (XRCC), structural maintenance of chromosomes (SMC), WD repeat do- main containing (WDR), ubiquitin conjugating enzyme E2 (UBE2), cell division cycle (CDC), importin (IPO), aldehyde dehydrogenase family member (ALDH), H2A variant histone (H2A), heat shock protein (HSP), cyclin dependent kinase (CDK), dynein axonemal heavy chain (DNAH), forkhead box (FOX), ring finger protein (RNF), E2F transcription factor (E2F) and small nucleolar RNA host gene (SNHG) family.
Research on Data Security Approaches in Spring Boot for Rest API
Authors: Batyrbekova Saikal , Esenalieva Gulzada
Published: January 2025

In the modern educational process, the relevance of informatization and cybersecurity threats cannot be overstated. As highlighted by Gulzada Esenalieva [1], the rapid development of information technologies and the widespread digitalization in education have introduced both innovative communication methods and significant cybersecurity challenges. These challenges include filtering unwanted information, preventing unauthorized access, combating cyberterrorism, and managing the use of social networks. Pedagogical support for students in developing safe online behavior is increasingly important in this context.Spring Boot provides a comprehensive set of tools to secure web applications, offering robust security and authentication mechanisms essential for safeguarding REST APIs. This study delves into Spring Boot’s capabilities in areas such as data encryption, Cross-Site Request Forgery (CSRF) protection [6, 8], and context security, drawing parallels with the broader concerns of cybersecurity in the educational environment.
Effects of Dietary Intervention on Lipid Profile: A Systematic Review
Authors: Santhosh Kumar Haripriya , Kumari Aasta , Aakash Fnu , Sah Sabita , Panjiyar Binay
Published: January 2025

Background Dietary interventions significantly influence lipid profiles, which are key risk factors for cardiovascular disease. Understanding the impact of various dietary patterns can guide effective strategies to improve lipid profiles and reduce cardiovascular risk. Objectives This systematic review aims to evaluate the effectiveness of dietary interventions in improving lipid profiles. Methods ● Search Strategy: Articles published up to July 31, 2023, were identified using PubMed and Google Scholar. ● Inclusion Criteria: Studies examining the relationship between dietary interventions and lipid profiles. ● Review Process: The review followed PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Results ● Impact of Dietary Composition: ○ Diets high in saturated fats and cholesterol were linked to elevated low-density lipoprotein (LDL) cholesterol levels.(Sacks et al., 2017). ○ Diets rich in fruits, vegetables, whole grains, and lean proteins were associated with reduced LDL cholesterol and increased high-density lipoprotein (HDL) cholesterol.(Rodríguez-López et al., 2021). ○ Ketogenic diets, characterized by low carbohydrate and high fat, showed a potential increase in LDL cholesterol.(Schwingshackl & Hoffmann, 2014). ● Effectiveness of Specific Diets: ○ The Mediterranean diet and the Dietary Approaches to Stop Hypertension (DASH) diet emerged as effective dietary patterns for improving lipid profiles.(Davis et al., 2017; Rodríguez-López et al., 2021) ○ These diets prioritize plant-based foods, lean proteins, and healthy fats while minimizing saturated and trans fats.(Sacks et al., 2017). ● Individual Variability: ○ The effectiveness of dietary interventions varies based on factors such as age, sex, and medical history.(Berryman et al., 2017; Maki et al., 2018). Conclusion Dietary interventions are non-invasive and cost-effective strategies to improve lipid profiles and reduce cardiovascular disease risk. However, these interventions should be tailored to individual needs and conducted under the guidance of healthcare professionals. Further research is required to assess the long-term effects of dietary modifications on lipid profiles.
Machine Learning Discoveries of RHNO1-X Synergy in ETC-1922159 Treated Colorectal Cancer Cells
Authors: Sinha Shriprakash
Published: January 2025

In response to DNA damage and replication stress, RHINO RAD9-HUS1-RAD1 interacting nuclear orphan (RHNO1), interacts with RAD9-HUS1-RAD1 (9-1-1) clamp and TOPBP1, to activate ATR signaling pathway. Recently, it has been found to be implicated in cancer as it is often overexpressed. In colorectal cancer (CRC) cells treated with ETC-1922159, RHNO1 was found to be down regulated along with other genes. A recently developed search engine ranked combinations of RHNO1-X (X, a particular gene/protein) at 2nd order level after drug administration. Some of these combinations have been tested in wet lab, however many have been pointed out by the search engine that are yet to be explored/tested. These rankings reveal which RHNO1-X combina- tions might be working synergistically in CRC. In this research work, I cover combinations of RHNO1 with possible members of DNA topoisomerase (TOP), nei like DNA glycosylase (NEIL), flap structure-specific endonuclease 1 (FEN1), tumor protein p53 (TP53), ATR serine/threonine kinase (ATR), cell division cycle (CDC), forkhead box (FOX) and bone morphogenetic protein (BMP) family.
Revolutionizing Tooth Regeneration: Innovations from Stem Cells to Tissue Engineering
Authors: Alsuraifi Ali , Mouzan Mohammed M. , Ali Abd Alrahman A. , Algzaare Abdullah , Aqeel Zaid , Ezzat Dina , Ayad Abdullah
Published: January 2025

Purpose: Tooth regeneration research aims to bridge historical milestones with cutting-edge molecular insights and biomaterial strategies to revolutionize regenerative dentistry. It focuses on decoding the genetic underpinnings and molecular signaling pathways that govern tooth morphogenesis. This understanding is crucial for advancing the development of innovative and effective dental restoration techniques that can potentially replace conventional methods. Methods: This comprehensive review integrates historical perspectives with contemporary scientific inquiries, emphasizing the role of advanced techniques such as single-cell sequencing, induced pluripotent stem cells (iPSCs), and organoid culture in unraveling the complexities of tooth development. Additionally, this review explores the use of proteomics, phosphoproteomics, and genetic engineering systems in studying molecular signaling pathways involved in tooth development and morphogenesis. Results: This review highlights the pivotal role of induced pluripotent stem cells (iPSCs) in tooth regeneration, shedding light on their transformative applications. Furthermore, the integration of advanced computational analysis with single-cell combinatorial indexing RNA sequencing (sci-RNA-seq) has unveiled previously unknown populations of epithelial support tissues, offering profound insights into dental tissue differentiation. Additionally, this review discusses the advances in biomaterials and bioengineering approaches, emphasizing the intricacies of neurovascular integration, which is crucial for successful tooth regeneration. Conclusion: This comprehensive review provides a holistic understanding of tooth regeneration, emphasizing the historical milestones, molecular insights, and biomaterial strategies that have shaped the field. The integration of advanced techniques and innovative approaches holds promise for transformative applications in regenerative dentistry, paving the way for future advancements in tooth regeneration.
Optimization of Clinical Trial Strategies for Anti-HER2 Drugs Based on Bayesian Optimization and Deep Learning
Authors: Li Tingxuan
Published: January 2025

The optimization of clinical trial strategies for anti-HER2 drugs is imperative for the acceleration of the development of effective cancer therapeutics. This study proposes a sophisticated framework that integrates Bayesian optimization and deep learning to enhance the design and execution of clinical trials of anti-HER2 drugs. Utilizing established Bayesian models, the proposed approach integrates deep neural networks to model complex patient response dynamics and to fuse diverse clinical and genomic data. The study introduces a novel hybrid acquisition function that enables adaptive experimental design and real-time decision-making by leveraging the uncertainty quantization of Bayesian optimization and the feature extraction capability of deep learning. Furthermore, the model employs multi-task learning to simultaneously optimize multiple trial objectives, such as efficacy, safety, and cost-effectiveness, thus providing a comprehensive optimization strategy. Ensuring the robustness and preventing overfitting in high-dimensional data environments is achieved by employing ensemble methods and regularization techniques. The experimental results demonstrated that the optimized trial strategy presented in this study enhanced the prediction accuracy of treatment outcomes by 25% in comparison with conventional trial design methods.
Small Intestinal Bacterial Overgrowth Is a Predictor of Overt Hepatic Encephalopathy in Patients with Liver Cirrhosis
Authors: Sakamaki Akira , Yokoyama Kunihiko , Yamazaki Hanako , Wakabayashi Takuya , Igarashi Takanori , Kojima Yuichi , Tominaga Kentaro , Abe Hiroyuki , Tsuchiya Atsunori , Kamimura Kenya , Yokoyama Junji , Terai Shuji
Published: January 2025

Objective: Liver cirrhosis (LC) progression induces intestinal microbiota abnormalities, such as small intestinal bacterial overgrowth (SIBO), and these changes lead to the inflow of gut pathogens and their degradation products into the vessels, causing cirrhotic complications such as hepatic encephalopathy (HE). Methods: To clarify the relationship between the development of overt HE and SIBO, we conducted a three-year observation after the assessment of SIBO in patients with LC. Results: In the analysis of 107 patients with a mean follow-up duration of 29.4 months, 31 were diagnosed with SIBO and 30 with covert HE. In the Cox multivariate regression analysis for prognosis, the Child-Pugh score, blood urea nitrogen level, and the Union for International Cancer Control (UICC) stage of hepatocellular carcinoma were derived using the following five factors: white blood cell count, blood urea nitrogen level, Child-Pugh score, UICC stage, and serum aspartate aminotransferase level (p = 0.001, hazard ratio [HR] 3.733, 95% confidence interval [CI] 1.592 – 8.754, p = 0.001, HR 1.076, 95% CI 1.030 – 1.123, and p < 0.001, HR 2.767, 95% CI 1.780 – 4.302, respectively). Furthermore, in the Cox multivariate regression analysis for overt HE development, covert HE and methane-producing SIBO were derived using the following four factors: methane-producing SIBO, UICC stage, covert HE, and serum ammonia level (p = 0.038, HR 5.008, 95% CI 1.096 – 22.892 and p = 0.006, HR 8.597, 95% CI 1.881 – 39.291, respectively). Conclusion: M-SIBO positivity was a significant predictor of overt HE.
A Novel Biplex Onchocerca volvulus Rapid Diagnostic Test: Feasibility and Comparison with the Ov16 SD Bioline Rapid Diagnostic Test Among 3–9-Year-Old Children in Maridi, South Sudan
Authors: Hadermann Amber , Jada Stephen Raimon , Lubbers Carlotte , Amaral Luís-Jorge , Biamonte Marco , De Souza Dziedzom Komi , Bol Yak Yak , Siewe Fodjo Joseph Nelson , Colebunders Robert
Published: January 2025

The DDTD biplex A RDT prototype was easy to use in the field but the evaluation of its performance relative to the commercially available Ov16 SD Bioline RDT will require additional research. The high Ov16 seroprevalence suggests high ongoing O. volvulus transmission around the Maridi Dam. Therefore, strengthening the onchocerciasis elimination program in Maridi should be a public health priority.
Advancing Diabetes Care The Role of Personalized Medicine in Type 2 Diabetes Management
Authors: Hamza Naeem , Li Chen Lin , Ahmed Nuaman , Zainaba Naeema
Published: January 2025

Personalized medicine represents a groundbreaking approach to diabetes management, leveraging individual genetic, metabolic,and environmental factors to optimize treatment and improve outcomes. This review explores the advancements and applicationsofpersonalized medicine in diabetes, highlighting its role in risk prediction, therapeutic strategies, and the integration of digital healthtechnologies. Genomic research has identified polymorphisms affecting drug efficacy, enabling the customization of treatmentslikemetformin and sulfonylureas. Metabolomic profiling has uncovered biomarkers, such as α-hydroxybutyrate, which predict insulinresistance and provide opportunities for early intervention. Personalized nutrition, informed by glycemic response studies, furthersupports tailored dietary strategies to enhance glucose homeostasis. Despite these innovations, challenges persist, includingthecomplexity of integrating multi-omics data, cost barriers, and ethical concerns related to data privacy and equitable access. Mobilehealth technologies and artificial intelligence have emerged as promising tools to overcome these hurdles, facilitatingreal-timedecision-making and improving patient engagement. However, more extensive longitudinal studies are essential to validatethesafetyand efficacy of personalized approaches across diverse populations. This review emphasizes the transformative potentialofpersonalized medicine in revolutionizing diabetes care by overcoming current limitations and utilizing technological advancements,personalized interventions can greatly improve patient outcomes and expand the use of precision healthcare in managingchronicdiseases.
Sacred Slavery in Education: A Comparative Analysis of Neoliberal Policies’ Impact on Moral Education and Family Relationships in the United States, Finland, and Chile
Authors: Alhemyari Talal , Maldonado Adriana Isabel , Fatani Abdulaziz
Published: January 2025

This study examines the transformative impact of neoliberal educational policies on moral education and family relationships through a comparative analysis of educational systems in the United States, Finland, and Chile. The research introduces the concept of "sacred slavery" to analyze how market-driven educational paradigms bind students to economic imperatives at the expense of moral development and family cohesion. Through Critical Discourse Analysis of educational policies, quantitative analysis of PISA data (2018-2022), and case studies, the study reveals significant correlations between the degree of market-oriented education policies and approaches to moral education. Analysis of PISA well-being indicators demonstrates that students in Finland report substantially higher life satisfaction (85th percentile) compared to their counterparts in the United States (33rd percentile) and Chile (28th percentile). These variations correlate with the presence of moral and ethical content in curricula. The United States and Chile, characterized by highly marketized systems, exhibit clear signs of "sacred slavery," potentially sacrificing comprehensive moral education for economic objectives. The findings suggest the need for post-secular educational frameworks that balance market considerations with holistic moral development, contributing to ongoing debates about education's role in shaping ethical citizens.
A Framework for Bridging Perceived and Actual Quality Through Automation: Strengthening Data Reliability and Governance
Authors: Podobnikar Tomaž
Published: January 2025

Following spatial data capture, stakeholders often invest significant resources to meet technical specifications. This challenge arises largely from varying interpretations of established standards, resulting in data that fails to meet the requirements for ingestion into the enterprise geospatial ecosystem. A key issue lies in the discrepancy between perceived data quality – how stakeholders understand or interpret the performance of the data, which is aligned with technical specifications – and actual data quality, which reflects objective performance when properly measured. The proposed data quality management (DQM) framework addresses this discrepancy by focusing on key aspects of spatial data quality, with an automated program playing a central role in bridging this divide. The framework enhances stakeholder communication and significantly improves the reliability of data governance by providing a comprehensive evaluation of data quality. This evaluation with the outputs combining error presentation through statistics, georeferenced files, and visualization enables rapid interpretation and error resolution. When applied to planned land use (PLU) data, this solution improved efficiency, enhanced overall data quality, and ensured seamless integration into the enterprise Spatial information system. This resulted in a higher level of maturity in data quality management.
Deriving Coastal Sea Surface Current by Integrating a Tide Model and Hourly Ocean Color Satellite Data
Authors: Chen Songyu , Shen Fang , Li Renhu , Zhang Yuan , Li Zhaoxin
Published: January 2025

Sea surface currents (SSC) play a pivotal role in material transport, energy exchange, and ecosystem dynamics in coastal marine environments. While traditional methods to obtain wide-range SSC, such as satellite altimetry, often struggle with limited performance in coastal regions due to waveform contamination, deriving SSC from sequential ocean color data using the Maximum Cross Correlation (MCC) has emerged as a promising approach. In this study, an enhanced MCC method called the Tide-Restricted Maximum Cross Correlation (TRMCC) is proposed and implemented on hourly ocean color data obtained from the Geostationary Ocean Color Imager II (GOCI-II) to derive SSC in coastal seas and turbid estuaries. Cross-comparison over three years with buoy data, high-frequency radar, and numerical model products shows that the TRMCC is capable of obtaining high-resolution SSC with good accuracy in coastal and estuarine areas. Both large-scale ocean circulation patterns in seas and fine-scale surface current structures in estuaries can be effectively captured. The deriving accuracy, especially in coastal and estuarine areas, can be significantly improved by integrating tidal current data into the MCC workflow, and the influence of invalid data can be minimized by using a flexible reference window size and the Normalized Cross-Correlation in the Fourier Domain technique. Seasonal SSC structure in Bohai Sea and diurnal SSC variation in the Yangtze River Estuary were depicted via satellite method for the first time. Our study highlights the vast potential of the TRMCC to be able to improve understanding of current dynamics in complex coastal regions.
The Organization for Human Brain Mapping Time Machine: A Freely Accessible Archive of Annual Meeting Talks on YouTube
Authors: Wearn Alfie , Sitek Kevin R. , Valk Sofie L. , Forkel Stephanie J.
Published: January 2025

Since 1997, the Organization for Human Brain Mapping (OHBM) Annual Meeting has provided a critical platform for sharing the latest developments in brain mapping. For over a decade OHBM has recorded presentations and made them accessible to its members through the OnDemand platform. As of 2024, this content is now openly and freely accessible to brain mapping enthusiasts worldwide - without the need to be a society member. Limited access to cutting-edge scientific content solely to paying members contradicts the longstanding ethos of OHBM, which has championed open science principles and practices for many years. In this paper, we introduce the OHBM Time Machine, a collaborative initiative undertaken by the Program, Education and Communications Committees of OHBM. The project seeks to create a free and permanent archive of all recorded Annual Meeting content, accessible to members and non-members alike. We outline the ongoing efforts to migrate all content dating back to 2013 to the OHBM YouTube channel and provide guidelines to support hosting future Annual Meeting recordings. Additionally, we discuss the benefits and challenges associated with this initiative, explaining why making this content publicly available will not diminish the value of attending the Annual Meetings. Instead, it is expected to enhance interest and awareness of OHBM as a world-leading organization of brain mapping experts. The OHBM Time Machine will provide an unparalleled educational resource, establishing a lasting record of scientific progress and the evolution of critical topics within the field of brain mapping.
A Signal Pattern Extraction Method Useful for Monitoring the Condition of Actuated Mechanical Systems Operating in Steady State Regimes
Authors: Munteanu Adriana , Horodinca Mihaita , Bumbu Neculai-Eduard , Dumitras Catalin Gabriel , Chitariu Dragos-Florin , Mihai Constantin-Gheorghe , Khdair Mohammed , Oancea Lucian
Published: January 2025

The aim of this paper is to present an approach to condition monitoring of actuated mechanical systems (e.g. machine tools) operating in a steady state regime. The signals generated by the sensors placed on a mechanical system (e.g. a lathe gearbox) operating in a steady state regime are mainly a sum of many periodic components with different amplitudes and frequencies, sometimes mixed with a considerable amount of noise. Each periodic component (consisting of a fundamental sine wave and some harmonics) usually characterizes the state of a rotating part (e.g. a shaft, a belt, a bearing, etc.). This paper proposes a simple pattern extraction (recognition) method to find the time-domain description for the significant periodic components within these signals. This method is based on the averaging of a number of periodically selected signal samples. It acts as a kind of numerical multiple narrow bands pass numerical filtering. Some results prove the availability of this method for condition monitoring, applied in this work on three different signals: the active electrical power absorbed by an asynchronous AC electric motor that acts a lathe headstock gearbox, vibration of this gearbox and instantaneous angular speed of the output spindle.
Mission and 1-Year Experience of a Kidney-Heart Outpatient Service: A Patient-Centered Management Model
Authors: Carollo Caterina , Evola Salvatore , Sorce Alessandra , Cirafici Emanuele , Bennici Miriam , Mulè Giuseppe , Geraci Giulio
Published: January 2025

Everywhere, independently of the care setting, there is an urgent, unsatisfied need to take care of patients with combined kidney and heart disease. In May 2023 we launched the Kidney-Heart Outpatient Service in the Nephrology and Dialysis Unit of “Paolo Giaccone” University Hospital in Palermo. The clinical mission of our Kidney-Heart Service is to provide a coordinated, multi- disciplinary care for non hospitalized patients with coexisting kidney and heart disease, with the aim to significantly reduce admissions and readmissions in hospital, improving patient outcomes and optimizing utilization of economic resources. In this letter we present the clinical objectives and services provided of this new service and the 1-year results.
Comprehensive Bioinformatics Analysis of Glycosylation-Related Genes and Potential Therapeutic Targets in Colorectal Cancer
Authors: Chuang Po-Kai , Chang Kai-Fu , Chang Chih-Hsuan , Chen Ting-Yu , Wu Yueh-Jung , Lin Hui-Ru , Wu Chi-Jen , Wu Cheng-Chun , Ho Yu-Cheng , Lin Chih-Chun , Yuan Chien-Han , Wang Chih-Yang , Lee Yung-Kuo , Chen Tung-Yuan
Published: January 2025

Colorectal cancer (CRC) is a leading cause of cancer-related deaths worldwide, charac-terized by high incidence and poor survival rates. Glycosylation, a fundamental post-translational modification, influences protein stability, signaling, and tumor pro-gression, with aberrations implicated in immune evasion and metastasis. This study investigates the role of glycosylation-related genes (Glycosylation-RGs) in CRC using machine learning and bioinformatics. Data from The Cancer Genome Atlas (TCGA) and the Molecular Signatures Database (MSigDB) were analyzed to identify 67 differentially expressed Glycosylation-RGs. These genes were used to classify CRC patients into two subgroups with distinct survival outcomes, highlighting their prognostic value. Weighted Gene Coexpression Network Analysis (WGCNA) revealed key modules as-sociated with CRC traits, including pathways like glycan biosynthesis and PI3K-Akt signaling. A machine learning-based prognostic model demonstrated strong predictive performance, stratifying patients into high- and low-risk groups with significant sur-vival differences. Additionally, the model revealed correlations between risk scores and immune cell infiltration, providing insights into the tumor immune microenvironment. Drug sensitivity analysis identified potential therapeutic agents, including Trametinib, SCH772984, and Oxaliplatin, showing differential efficacy between risk groups. These findings enhance our understanding of glycosylation in CRC, identifying it as a critical factor in disease progression and a promising target for future therapeutic strategies.
Myocarditis in Puppies Associated with Parvovirus Infections in Western Romania
Authors: Stancu Constantin Adrian , Degi János , Hulea Anca Sofiana , Luca Iasmina , Marc Simona , PASCA Sorin Aurelian , Voia Octavian Sorin , Marcu Adela
Published: January 2025

Perinatal parvoviral infection in puppies leads to necrotizing myocarditis, resulting in either high mortality or gradual heart damage. We believe that canine parvovirus 2 (CPV-2) myocardial infection is not being fully recognized as a cause of myocarditis, cardiac injury, and fibrosis repair in young dogs, despite the significant reduction in the epidemic of canine parvoviral myocarditis due to extensive vaccination. This study analyzed formalin-fixed, paraffin-embedded tissues obtained from 40 canine cases aged less than two years, spanning the period from 2019 to 2021. The objective was to test for the presence of CPV 2. Cardiac necrosis, inflammation, or fibrosis were observed in the diagnosed cases, but the age-matched controls did not have any cardiac abnormalities and tested positive for CPV. The presence of myocardial CPV-2 was detected in young dogs with mild myocarditis, suggesting that the period of vulnerability to myocarditis in the heart may be greater than what has been previously documented. CPV-2 was found in dogs who had extensive myocardial fibrosis, indicating that CPV-2 had caused previous injury to the heart muscle. Although vaccinations are common, these findings indicate that CPV-2 continues to be a major contributor to cardiac injury in dogs.
Machine Learning Based Modeling to Estimate Potential Distribution of Current Remerging hMPV in Asia Regions
Authors: Wibowo Andri
Published: January 2025

Recently hMPV has been reported across countries in Asia regions. Among those countries, hMPV has been reported in several locations and in subcontinent. Considering the records of hMPV in this subcontinent, this study aims to use a machine learning model to estimate the potential distribution of current remerging hMPV in Asian regions using bioclimatic variables. The mean temperature of the driest quarter, followed by the precipitation seasonality and the annual mean temperature, were the climate variables that have the most contribution to the suitable areas of hMPV. Based on the model, areas with potential areas have preferences in temperate regions with north latitude of 210-230 and east longitude of 720-750. While there is an area covering north latitude of 130-170 and east longitude of 730-750, indicating potential preferences in tropical regions. In spite of hMPV preferences in temperate regions, potential distribution to tropical regions, mainly in Southeast Asia, should be vigilant.
Assessment of the Effects and Contributions of Natural and Human Factors on the Nutrient Status of Typical Lakes and Reservoirs in the Yangtze River Basin
Authors: Zeng Yangbo , Wang Ziteng , Zhao Qianyu , Huang Nannan , Li Jiayue , Wang Jie , Sun Fuhong
Published: January 2025

This study investigated the relative contributions of natural and anthropogenic factors to the nutrient status of 33 representative lakes and reservoirs in the Yangtze River Basin. Using national water quality monitoring data, remote sensing imagery, GIS, the InVEST model, and RDA, we analyzed the Spatiotemporal differences of total nitrogen (TN), total phosphorus (TP), the ratio of TN to TP (TN/TP), trophic level index (TLI), and habitat quality (HQ). Results revealed significant spatial heterogeneity in lake nutrient status, with upstream reservoirs exhibiting better water quality than their midstream and downstream counterparts. Over time, there is a decreasing trend in nutrient loads in lakes and reservoirs, yet the risk of eutrophication remains high. The middle and lower reaches of lakes and reservoirs face more severe eutrophication pressure. The contribution rates of natural factors and human activities to TN and TP in lakes and reservoirs are 19.1% and 35.0%, respectively. The main driving factors are livestock and poultry breeding volume, habitat quality, and urbanization, with contribution rates of 13.0%, 9.8%, and 0.2%, respectively. The contribution rates of natural factors and human activities to TN/TP and TLI of lakes and reservoirs are 19.8% and 15.5%, respectively. Actual Evapotranspiration (7.8%), habitat quality (7.3%), and hydraulic retention time (3.1%) were key drivers for the shifts of TN/TP and TLI. Management strategies should therefore control agricultural nitrogen fertilizer inputs upstream, industrial and agricultural non-point source pollution in the midstream, and enhanced wastewater treatment alongside population density and economic development control in the downstream areas. This research provides a crucial scientific basis for the ecological environment protection and sustainable utilization of water resources in the Yangtze River Basin.
An Empirical Study on the Construction of Green Airports and the Control of Aviation Pollution
Authors: Deng Shiguo , Zhou Shuolei , Zhang Li , Zhao Jiani
Published: January 2025

In the era of globalization and information technology, the aviation industry has experienced rapid growth. However, the increase in flight numbers has exacerbated environmental issues such as exhaust emissions and noise pollution, raising significant concerns across society. This paper aims to explore the current state of environmental pollution within the aviation industry and propose solutions to promote the development of green airports and effective pollution control measures. This study primarily employs literature analysis. Initially, a preliminary evaluation index system was established to represent various aspects of aviation pollution. The system was then refined and optimized using the entropy weight method. Subsequently, kernel density estimation and Moran index methods are applied to analyze the temporal and spatial trends of the evaluation indicators. An empirical study is conducted to investigate the degree of endogenous correlation and lag effects among the indices. The results show that: (1) Regional Neutrality in Pollution Indicators. The spatial autocorrelation test reveals a lack of significant spatial correlation among the studied aviation environmental pollution indicators, indicating that these variables maintain a degree of regional neutrality. (2) Impact of Cargo Throughput on Pollution. The PVAR model analysis highlights that cargo throughput has a significant self-impact on aviation environmental pollution, indicating that monitoring and managing cargo operations could be crucial in predicting and mitigating future pollution levels.