bluuebunny/crossref_metadata_2025 huggingface dataset:
A Comprehensive Microstructure-Aware Electromigration Modelling Framework; Investigation of the Impact of Trench Dimensions in Damascene Copper Interconnects
Authors: Saleh Ahmed Sobhi , Croes Kristof , Ceric Hajdin , De Wolf Ingrid , Zahedmanesh Houman
Published: October 2024

This paper presents a comprehensive simulation framework for the investigation of electromigration (EM) in nano-interconnects, with a primary focus on unravelling the influential role of microstructure. To this end, a novel approach to generate conductor metal’s microstructures is presented, whereby a predefined statistical distribution of grain sizes obtained from experimental texture analyses can be incorporated into the developed EM modelling framework. The framework considers the impact of diffusion heterogeneity through the metal texture and interfaces. As such, the intricate interplay between microstructural/interfacial properties, and the resulting atomic flux and stress distribution within nanointerconnects could be investigated. Additionally, the study advances beyond the state of the art by comprehensively simulating all stages of electromigration, including stress evolution, void nucleation, and void dynamics while considering the impact of microstructure. Specifically, the model was employed to study the impact of trench dimensions on dual damascene copper texture and its subsequent impact on electromigration aging, where the model findings were corroborated by comparing to available experimental findings. A nearly linear augmentation in normalized lifetime was detected for aspect ratios equal to or greater than 1. However, a saturation is detected for wider lines with no effective enhancement in lifetime. An aspect ratio of 1 seems to maximize EM lifetime for each cross-sectional area by fostering a bamboo-like structure, where about 2-fold of increase is estimated when going from aspect ratio 2 to 1.
Factors Determining Unique Thermal Resistance and Surface Properties of Silicone-Containing Composites
Authors: Zielecka Maria , Rabajczyk Anna
Published: October 2024

This review discusses the key factors influencing the exceptional thermal resistance and surface properties of silicone-containing composites. Silicone polymers, known for their excellent chemical and physical proper-ties, are widely used in a number of innovative products. In order to make silicone composites suitable for innovative applications, it is essential to ensure that they have both very good thermal resistance and su-perhydrophobic properties. Identification of the key factors influencing these properties enables the use of these composites in coatings, electronics and photovoltaic panels. The discussion includes the role of or-ganosilicon polymer structures and the incorporation of micro- and nano-additives to enhance the perfor-mance of these materials. Different methods for the modification and production of silicone composites are analyzed, with an emphasis on achieving thermal stability and surface superhydrophobicity simultaneously. The review highlights the growing demand for silicone-based coatings, driven by technological advances and environmental concerns. Furthermore, the role of surface modification and hierarchical surface structures in achieving these unique properties is discussed, as well as the potential applications and challenges in the development of next-generation silicone-containing materials.
Application of Invasive Bacteria for the Delivery of Anti-cancer Therapeutics
Authors: Akinsola Rasaq , Narayanan and Kumaran
Published: October 2024

Bacterial vectors for biomolecule delivery to targeted organelles, facilitating temporary or continuous protein production, have emerged as a promising approach for treating acquired and inherited diseases. This method offers a selective cancer eradication and targeting strategy with minimal side effects. Bacterial vectors provide an alternative to viral gene delivery, given their capacity to deliver large genetic materials while inducing minimal immunogenicity and cytotoxicity. Bacteria such as Bifidobacterium, Salmonella, and Clostridium have demonstrated potential for tumor-targeted biomolecule delivery. These vectors have also been used to transfer and amplify genes encoding biomolecules such as prodrug-converting enzymes, toxins, angiogenesis inhibitors, and cytokines. The microenvironment of necrotic tumors offers a unique opportunity for targeted therapy with the non-pathogenic anaerobic bacterium. For example, Clostridium sporogenes can germinate selectively in the necrotic regions upon injection as endospores, which helps to enhance the specificity of Clostridium sporogenes, resulting in tumor-specific colonization. Further, some invasive bacteria vectors engineered with the invasin gene from Yersinia Pseudotuberculosis, which enables specific binding to integrin receptors abundantly expressed in cancer cells and escape the endosome with incorporation of the Listeriolysin O gene from Listeria monocytogenes, have shown potential for application in cancer therapy. This review provides an overview of the use of the bacteria vector in cancer therapy, highlighting strategies to maximize delivery efficiency and address potential challenges.
Pregnancy-Associated Glycoproteins Profile and PAGs/P4 Ratio Biomarker to Diagnosis Pseudopregnancy in Dairy Goats
Authors: Cruz Carlos , Margatho Gisele , Simões Miguel , Simões João
Published: October 2024

Pseudopregnancy (PPG) is a common reproductive disorder in dairy goats, characterized by the accumulation of sterile fluid in the uterus and persistence of a corpus luteum, leading to temporary infertility and reduced farm efficiency. This study mainly aimed to determine the profile of pregnancy-associated glycoproteins (PAGs) in dairy goats, and to evaluate the potential of the PAGs/P4 ratio as a novel biomarker for PPG diagnosis. A total of 605 Saanen and crossbreed goats were evaluated via transabdominal ultrasonography, at 34.5 ± 0.5 days after bucks´ removal, in four intensive dairy farms, and presenting an overall incidence of 7.8%. Blood samples from PPG (n = 47), non-pregnant (NPG; negative control; n = 11) and pregnant (PG; positive control; n=10) does were collected for PAG and P4 analysis. Plasmatic levels of PAGs of PPG was similar (0.08 ± 0.02) to NPG (0.13 ± 0.04; p >0.05), but lower than PG group (1. 45 ± 0.04 S-N OD; p <0.001). PAGs/P4 ratio was lower in PPG (0.01 ± 0.11; p < 0.001)) than in NPG (0.24 ± 0.23) and PG (0.18 ± 0.23) groups. The sensitivity and specificity of this biomarker to diagnosis PPG was 98.8% and 100%, respectively. The area under the survival curve (AUC) was 99.6 for cut-off of 0.04 and 97.9% of sensibility and specificity. This study suggests that the PAGs/P4 ratio can serve as a reliable biomarker for PPG diagnosis, helping to distinguish it from pregnancy and non-pregnancy, and improving reproductive management in dairy goats.
P450 in Liver Pathology Diagnostic
Authors: Chesca Antonella A , Sandle Tim T
Published: October 2024

A specific key point in liver cirrhosis is the decreased metabolic capacity for drugs. So medicines which are metabolized by oxidative biotransformation play a great role in liver pathology. Responsible for the drug metabolism that takes place in liver during illnes, are three cytochrome P450 (P450 or CYP) gene families in liver microsomes (CYP 1, CYP2 and CYP3). In attention of various studies, is cytochrome P450. Folowing the currently aim,we try to assess the effect of liver disease on multiple CYP enzymes by use of a validated cocktail composed of medicins. Liver diseases are associated with metabolic activity changes It is important to know and to tell a little bit about different directions in cirrhosis diagnostic, including laboratory tests or management ideas.
Dynamic Valgus Correction for Nonunion of Subtrochanteric Hip Fractures
Authors: Delgado-Martinez Alberto D. , Cañada-Oya Hermenegildo
Published: October 2024

The objective is to describe a new technique for subtrochanteric hip nonunions. Three patients with a nonunion of a previously nailed subtrochanteric hip fracture were selected. The nail was withdrawn, and a simple lateral approach was performed. A hip screw was introduced through the neck, and a dynamic plate (135-150º) was applied, to increase the femoral-neck angle (valgus) and reduce the head offset at the hip. The correction was achieved just by mobilizing the nonunion site. No osteotomy was done. Fixation promoted dynamic mechanical compression and fracture healing. The mean age of patients was 57 (34-85). 2 women, and one man. Duration of surgery was 102 min (63-123). Femoral neck angle before surgery was 125º (122-128), and after surgery 147º (142-150). Offset was 50 mm (42-59) before surgery and 20 mm (19-24) after surgery. Leg length was increased by 8,6 mm (6-12). Harris hip score was improved from 37,33 (30-42) to 93,33 (88-96). All patients healed and were asymptomatic after 12 months follow-up. In conclusion, valgus correction with a dynamic hip screw is a useful technique for a nonunion of a subtrochanteric hip fracture. This zone is well vascularized, so just dynamic mechanical compression at the fracture site is needed.
Role of Lipoprotein(a) Reduction in Cardiovascular Disease
Authors: Schuth Uma Heidi , Gill Kieran , Bawamia Bilal , Telyuk Pyotr , Austin David , Zaman Azfar G
Published: October 2024

Recent studies have shown that Lipoprotein(a) (Lp(a)) is an important risk factor for a plethora of different cardiovascular diseases. It has been proven that Lp(a) levels are genetically determined, and correlate with risk of cardiovascular disease, independent of lifestyle factors. As of yet, treatment options to reduce Lp(a) levels are limited, but new research into Lp(a) reduction yields promising results. This review delves into Lp(a) biochemistry, mechanism of effect, association between Lp(a) and cardiovascular diseases, and possible therapies to minimize cardiovascular disease.
Heparin Resistance on ECMO: A Review
Authors: Li Tatyana , Zhailauova Azhar , Kuanyshbek Aidyn , Wachruschew Iwan , Tulegenov Shaimurat , Sazonov Vitaliy , Kapyshev Timur
Published: October 2024

Heparin resistance (HR) on ECMO exacerbates bleeding and thrombogenesis. So far, there is no universal definition of HR on ECMO and no unified strategy for the assessment of heparin efficacy in ECMO patients. The most frequent discrepancy in defining HR is the difference in reported doses: units per day (U/d) or per kilogram per hour (U/kg/h). Another disagreement occurs ow-ing to different ways of measuring UFH efficacy, contributing to further disagreement in a com-mon definition of HR. Due to numerous processes that start with ECMO initiation, including protein layer formation on the surface of the circuits, recruitment of immune cells, activation of complement and contact activation systems, and platelets, the assessment of pure antithrombin consumption is complicated. Moreover, there is an alternative anticoagulation performed by a serine protease inhibitor named Heparin Cofactor II, the contribution of which could also be measured on heparin consumption. Considering simultaneously launched processes of inflam-mation and thrombogenesis in response to contact with artificial surfaces on ECMO, we listed possible mechanisms contributing to additional antithrombin consumption. The effect of the flow on the platelets activation and vWF assembly was also described. We examined the literature from Pubmed and Embase to find possible definitions of heparin resistance on ECMO among pediatric and adult cohorts. We extracted 13 records with different HR assessment approaches and sug-gested our vision of delineating HR on ECMO.
Mindful Organisations: Cultivating Well-being, Performance, and Leadership in the Modern Workplace
Authors: Smith Mary C
Published: October 2024

This paper explores the burgeoning field of mindfulness in organisational contexts, examining its evolution from Buddhist practices to contemporary corporate applications. It critically analyses the implementation of mindfulness interventions in Fortune 500 companies, elucidating both the potential benefits and challenges. The research highlights the role of mindful leadership in fostering positive organisational cultures and addresses the complexities of integrating these practices within diverse corporate settings. Drawing on case studies, the paper illuminates the variability in mindfulness approaches and their impacts on employee well-being, productivity, and organisational resilience. Whilst acknowledging the promising outcomes, the study also considers critiques regarding the commodification of mindfulness and its potential to blur work-life boundaries. The concept of Microdosed Mindfulness is introduced as a nuanced strategy for overcoming resistance and facilitating gradual adoption. This comprehensive analysis provides valuable insights for organisations navigating the implementation of mindfulness practices in the contemporary world.
Causality-Driven Feature Selection for Calibrating Low-Cost Air Quality Sensors Using Machine Learning
Authors: Sooriyaarachchi Vinu , Lary David J. , Wijerante Lakitha Omal Harindha
Published: October 2024

With escalating global environmental challenges and worsening air quality, there is an urgent need for enhanced environmental monitoring capabilities. Low-cost sensor networks are emerging as a vital solution, enabling widespread and affordable deployment at fine spatial resolutions. In this context, machine learning for the calibration of low-cost sensors is particularly valuable. However, traditional machine learning models often lack interpretability and generalizability when applied to complex, dynamic environmental data. To address this, we propose a causal feature selection approach based on convergent cross-mapping within the machine learning pipeline to build more robustly calibrated sensor networks. This approach is applied in the calibration of low-cost optical particle counters, effectively reproducing the measurements of PM1 and PM2.5 as recorded by research grade spectrometers. We evaluated the predictive performance and generalizability of these causally optimized models, observing improvements in both while reducing the number of input features, thus adhering to the Occam’s razor principle. For the PM1 calibration model, the proposed feature selection reduced the mean squared error on the test set by 43.2% compared to the model with all input features, while the SHAP value-based selection only achieved a reduction of 29.6%. Similarly, for the PM2.5 model, the proposed feature selection led to a 33.2% reduction in the mean squared error, outperforming the 30.2% reduction achieved by SHAP value-based selection. By integrating sensors with advanced machine learning techniques, this approach advances urban air quality monitoring, fostering a deeper scientific understanding of microenvironments. Beyond the current test cases, this feature selection method holds potential for broader application in other environmental monitoring applications, contributing to the development of interpretable and robust environmental models.
Molecular Biotechniques for the Isolation and Identification of Microorganisms
Authors: Qurbani Karzan , Hussein Safin , Ahmed Sirwan Khalid
Published: October 2024

Microorganisms play a pivotal role in ecology, medicine, agriculture, and biotechnology. To comprehend their diversity, physiology, and functions, isolation and characterization are imperative. This review provides a concise overview of prevalent molecular biotechniques for microbial isolation. It describes the advantages, constraints, and practical applications of both culture-dependent and culture-independent approaches. Discussed methods encompass selective/differential media, 16S rRNA gene sequencing, metagenomics, fluorescence in situ hybridization (FISH), polymerase chain reaction (PCR)-based techniques, and single-cell genomics. Emerging high-throughput sequencing and single-cell isolation methods exemplify cutting-edge procedures explored herein. Integration of these molecular tools offers comprehensive insights into microbial-diversity, community structure, metabolic capabilities, and functional interplays.
Application of Deep Reinforcement Learning for Cryptocurrency Market Trend Forecasting and Risk Management
Authors: Zhao Fanyi , Zhang Mingxuan , Zhou Shiji , Lou Qi
Published: October 2024

With the gradual development and integration of artificial intelligence into various industries, there is also a great range of integration in the financial industry. Therefore, this article focuses on the trend prediction model and financial risk management problems of deep reinforcement learning (DRL), one of the largest branches of artificial intelligence, in the cryptocurrency market. In addition, in the experimental part of this paper, the artificial intelligence machine learning Long short-term memory network (LSTM) model is used to make effective time series prediction and analysis on the relevant data of the cryptocurrency market, so as to make a large-scale analysis to improve the accuracy of market trend prediction and the effectiveness of risk management. In addition, in this experiment, technology-related indicators, emotional states of financial market customers and other content related to large language models are combined. While optimizing investment strategy by using deep reinforcement learning algorithm, machine learning prediction model is also used to capture the time dependence of financial market. The experimental results also show that the predicted results are consistent with the actual value. Therefore, the model has high practical application value in predicting the time series price trend of cryptocurrency in the financial market and indicates that the integrated DRL model framework can further optimize and manage the price and trading strategy of the financial market. Future research should focus on improving the LSTM model and incorporating more features to improve prediction accuracy and adapt to market changes.
How Good Perform Logistic Regression Algorithm for Complex Gastroenterological Image Analysis. Comparative Analysis with Physicians Performace
Authors: Cristea Daniela-Maria , Sima Ioan , Iantovics László-Barna
Published: October 2024

Given the complexity and variability of gastrointestinal polyps, there is a growing need for more accurate machine-learning algorithms that can assist medical professionals in decision-making processes. In our study, we employed machine learning (ML) techniques to classify gastrointestinal polyps seen in colonoscopy images, that present a risk of colon cancer, affecting a large portion of the population. The dataset used in the research comprised 152 instances and included three types of lesions, totaling 76 polyps. The study consisted of applying Logistic Regression (LR) to classify gastrointestinal images. The principal motivation for choosing this algorithm consists in the fact that is preferred by many medical researchers. Another motivation consisted in the fact to compare the polyps classification accuracy of physicians and LR with optimized hyperparameters. The approached model performance was evaluated with the following metrics: Accuracy, Precision, Recall, F1_Score and Macro-average. We used the Multiclass Logistic Regression (mLR) classifier to classify polyps into hyperplastic (27.63%), serrated (19.74%), and adenoma lesions (52.63%). It should be noted that serrated polyps are hard to diagnose, even by physicians, because they have characteristics of both hyperplastic and adenoma ones. One of the objectives consisted in the study of the hyperparameters tunning of the Logistic Regression to find the best-fitted model. To check the influence of the data mix on the results obtained, for the two best models (LR with liblinear solver, L1 penalty and C = 0.01), it was performed a comprehensive statistical analysis. Based on an algorithm that admits obtaining accurate results. Such analyses are mistakenly applied in the scientific literature. The model with optimized hyperparameters achieved an accuracy score of 70.39%, indicating a reasonable level of prediction, surpassing both beginner and expert physicians and obtaining results similar to those obtained by Random Subspace and Random Forest. The model successfully classifies the hyperplastic and adenoma polyps but showed to have a more limited ability to predict the serrated ones.
Evaluation of DNA Mismatch Repair Proteins by Immunohistochemistry Using Two Antibodies
Authors: Lino-Silvca Leonardo S. , Salazar-Castillo Mariana , Balderas-López Scarlett F. , Martínez-Villavicencio Sabrina B. , Rivera-Moncada Luisa F. , Ortega-Martínez Hanna I , Zepeda-Najar Cesar
Published: October 2024

Background: Determining the status of DNA mismatch repair (MMR) proteins is crucial for guiding treatment decisions and predicting prognosis in patients with colorectal cancer. This study evaluates a streamlined panel utilizing two antibodies for MMR protein assessment, aiming to enhance cost-effectiveness, reduce workload, and expedite result delivery. Methods: We re-classified patients with adenocarcinoma requiring MMR determination by evaluating only PMS2 and MSH6 proteins. The diagnostic performance of the proposed two-antibody panel was compared to the traditional four-antibody panel against a polymerase chain reaction (PCR) assay used as the reference standard. Results: A total of 404 cases were analyzed, with the predominant histological type being adenocarcinoma not otherwise specified. The most common histological grade was 2, and 60.9% of cases were in clinical stage II. Comparative analysis showed no statistically significant differences between the two-antibody and four-antibody panels. Sensitivity was 95.35% for the four-antibody panel versus 90.7% for the two-antibody panel; specificity was 98.74% versus 98.11%, respectively. Additionally, the positive predictive value was 95.35% for the four-antibody panel compared to 92.86% for the two-antibody panel, and negative predictive values were 98.74% versus 97.50%. The area under the curve was 0.970 for the four-antibody panel and 0.944 for the two-antibody panel (p=0.419). Conclusion: The analysis shows that determining MMR status using only two antibodies is as effective as employing a traditional four-antibody panel, providing a viable, cost-effective alternative for clinical practice.
Quantifying the Emergence of Basic Research Capabilities in Cluster Enterprises: An Analytical Framework Based on Information Entropy
Authors: Zhang Hongsi , He Zhongbing
Published: October 2024

This study looks at how basic research capabilities develop within enterprise clusters, focusing on the complex and adaptive nature of these systems. It builds a conceptual model using systems theory and applies information entropy to measure how much these capabilities have emerged. By using information entropy, the study creates a model to gauge how research abilities are forming in enterprise clusters. To dive deeper, the China Pingmei Shenma Group was used as a case study. Data came from interviews, surveys, and text analysis. This case—focused on a state-owned enterprise cluster in China’s coal-based energy and chemical industries—highlights the key factors that influence research capability growth. These factors include support from external systems, how internal resources are used, and their renewal over time. From 2017 to 2022, changes in entropy were tracked, revealing the process of research development driven by both internal and external forces. The study shows that when a system can bring in external resources while reducing internal disorder, its research capability is greatly improved. In real-world terms, the research proves that measuring entropy is a valuable way to evaluate and enhance research abilities within enterprise clusters, offering a clear method to manage innovation systems in tech-driven industries.
Study on the Sustainability of Farmers’ Livelihoods Capital and its Impact on the Rural-urban Income Gap
Authors: Wu Yuping , Wu Yuhuan , Wang Haisong , ZHu Ning
Published: October 2024

The concept and practice of ’livelihood’ has undergone various stages in the development of Western countries. Its core has always focused on eradicating rural poverty and promoting rural development. This study utilizes the entropy value method to measure the comprehensive development level of the livelihood sustainability of farm households. Additionally, the Theil index is used to measure the presentation income gap, and multiple linear regression is utilized to test the impact of the livelihood capital sustainability of farm households on the presentation income gap. Results showed that the the sustainability of farm households’ livelihood capital has a significant negative impact on the urban-rural income gap. In other words, improving the livelihood capital of farm households can significantly reduce the urban-rural income gap. Accordingly, it is proposed that efforts be made to enhance the sustainability of farm households’ livelihoods, and to increase the contribution of the sustainable enhancement of farm households’ livelihood capital to narrowing the rural-urban income gap.
Using IoT for Cistern and Water Tank Level Real-Time Monitoring
Authors: Wister Miguel A. , Leon Ernesto , Alejandro-Carrillo Alejandro , Pancardo Pablo , Hernandez-Nolasco Jose A
Published: October 2024

This paper proposes an experimental design to publish on the Internet the measurements resulting from the use of four sensors: a sensor inside a cistern to measure the level of drinking water, a sensor inside a water tank to measure the water level, a sensor to measure the water flow or pressure brought by the pipes, and another sensor to measure the water quality. Using these sensors, data was collected and published in real-time on the Internet, and at the same time, it was recorded in a database. Several tests of filling and emptying the cistern were performed. A system analyzes, makes decisions, and acts on the data collected by the sensors. Finally, experimental results demonstrated that if the cistern perceived that there was no water in the tank, it turned on the water pump to fill the tank to 100\% of its storage capacity; while this was happening, the water level in the cistern and tank, the flow of water from the piped water, and the quality of the water could be visualized on a dashboard. In short, this proposal monitors water levels and flows through the Internet of Things.
Reinforcement Learning‐Based Model for Enterprise Financial Asset Risk Assessment and Intelligent Decision‐Making
Authors: Ju Chengru , Zhu Yida
Published: October 2024

This study discusses the application of reinforcement learning (RL) in financial asset risk assessment, especially how to improve the ability of risk prediction and asset portfolio optimization through deep reinforcement learning (DRL) model. Traditional financial risk assessment methods are often unable to effectively cope with the dynamic changes and complexities of the financial market, but RL technology provides innovative solutions for modern financial risk management by means of real-time adjustment and adaptation mechanism. This paper experimentally verifies the advantages of DRL models in improving forecasting accuracy and risk management efficiency and discusses the potential impact of AI technology in the financial sector. The findings show that despite challenges such as data quality and model interpretation, the application of AI technology provides financial institutions with more precise and flexible risk management tools, driving further development of fintech.
Solid-State Battery Innovations: Insights from a Systematic Patent Review and a Cross-Sectional Bibliometric and Thematic Analysis
Authors: Bridgelall Raj
Published: October 2024

Solid-state batteries (SSBs) hold the potential to revolutionize energy storage systems by offering enhanced safety, higher energy density, and longer life cycles compared with conventional lithium-ion batteries. However, the widespread adoption of SSBs faces significant challenges, including low charge mobility, high internal resistance, mechanical degradation, and the use of unsustainable materials. These technical and manufacturing hurdles have hindered the large-scale commercialization of SSBs, which are crucial for applications such as electric vehicles, portable electronics, and renewable energy storage. This paper provides a systematic review and cross-sectional bibliometric and thematic analysis of the global patent landscape for SSB technologies. The study classifies innovations into key problem and solution areas by meticulously examining 244 patents across multiple dimensions, including geographic distribution, inventor engagement, award latency, and technological focus. Key findings reveal progress in addressing performance, safety, material sustainability, and manufacturing scalability, with significant innovations in electrolyte, electrode, architectural, and process engineering. This research contributes a comprehensive analysis of the technological landscape, offering valuable insights into ongoing advancements and providing a roadmap for future research and development. This work will benefit researchers, industry professionals, and policymakers by highlighting the most promising areas for innovation, thereby accelerating the commercialization of SSBs, and supporting the transition toward more sustainable and efficient energy storage solutions.
A Methodology for the Identification of Key Factors in Power System Planning: The Case of Mexico
Authors: Hernandez-Hurtado Ulises , Medina-Marín Joselito , Seck-Tuoh-Mora Juan Carlos , Hernández-Romero Norberto , Martin-del-Campo Cecilia
Published: October 2024

COP21 represented a starting point for several nations to develop and implement energy transition strategies to face and mitigate climate change, making the electrical power sector crucial in achieving the established goals and commitments. This research presents a 7-step methodology to improve decision-making in energy transition strategies for the power sector by integrating an economic dispatch optimization model based on linear programming to determine vulnerable aspects of power generation and transmission in strategic planning scenarios that could jeopardize the country’s energy transition. The application of the methodology is implemented under the open-source platform Python Optimization Modelling (PYOMO) and illustrated through a case study of the Mexican Electrical Power System (SEN) during the year 2025. The case study shows that the reserve margin fluctuated due to the variable renewable energy installed despite having a vast installed capacity to supply the country’s total demand. In addition, the results showed that most of the transmission lines had a congestion frequency higher than 90% of their capacity during most of the year. Two regions were identified as the best options for reducing greenhouse gas emissions by installing new power plants. Finally, most technologies reflected an under-generation, suggesting a high dependence on some fuels to supply the Mexican demand. The model’s programming is freely available in GitHub.
Partial Reprogramming Exerts a Rejuvenating Effect on Human Mesenchymal Stem Cells Underwent Replicative Senescence in Culture
Authors: Ivanova Julia , Shorokhova Mariia , Pugovkina Natalia , Kozhukharova Irina , Alekseenko Larisa , Guriev Nikita , Kuneev Ivan , Domnina Alisa , Grinchuk Tatiana , Zemelko Victoria , Lyublinskaya Olga
Published: October 2024

Mesenchymal stem/stromal cells (MSCs) are becoming increasingly important for biomedical applications such as cell therapy, disease modeling, and drug screening. At the same time, long-term cultivation, which is necessary to prepare a sufficient amount of cellular material for therapeutic and research purposes, is accompanied by the development of replicative senescence. Partial reprogramming emerged as a novel method that shows promising results in rejuvenation of cells in vitro and in vivo, however, it has not yet been applied for human MSCs under-went replicative senescence in culture. In the present study, we subjected senescent human endometrial MSCs to partial reprogramming using Sendai virus vectors containing genes encoding Yamanaka transcription factors Oct4, Sox2, Klf4 and c-Myc. Characterization of MSCs 5 days after transduction showed the loss of key markers of senescence: the youthful morphology was restored, the expression of senescent-associated β-galactosidase and the number of double-strand DNA breaks decreased, proliferation was activated, and DNA damage response was enhanced. Further, using in vitro wound-healing assay, we demonstrated that conditioned medium from partially reprogrammed MSCs showed higher therapeutic activity than that from senescent cells. However, biosafety test revealed the presence of viral components in conditioned medium, which caused the agglutination of erythrocytes. Collectively, our data suggest that partial re-programming is a potentially effective strategy for rejuvenation of cultured MSCs in late passages, but requires the use of virus-free protocols such as chemical reprogramming.
An Improved Random Forest Approach on GAN-Based Dataset Augmentation for Fog Observation
Authors: Cao Yucan , Zhao Panpan , Xu Balin , Liang Jingshu
Published: October 2024

The monitoring of fog density is of great importance in meteorology and its applications in environment, aviation and transportation. Nowadays, vision-based fog estimation from images taken with surveillance cameras has made a great supplementary contribution to the scarcely traditional meteorological fog observation. In this paper, we propose a new Random Forest (RF) approach for image-based fog estimation. In order to reduce the impact of data imbalance on recognition, the StyleGAN2-ADA (generative adversarial network with adaptive discriminator augmentation) algorithm is used to generate virtual images to expand the data of low proportions. Key image features related to fog are extracted, and an RF method, integrated with the hierarchical and k-medoids clustering, is deployed to estimate the fog density. The experiment conducted in Sichuan in February 2024 shows that the improved RF model has achieved an average accuracy of fog density observation to 93%, 6.4% higher than the RF model without data expansion, 3%-6% higher than the VGG16, the VGG19, the ResNet50, and the DenseNet169 with or without data expansion. What is more, the improved RF method exhibits a very good convergence as a cost-effective solution.
Infective Endocarditis in Individuals with Drug Addiction: A Retrospective Cohort Study Conducted at "Dr. Carol Davila" Central Military Emergency University Hospital in Bucharest
Authors: Anton Corina Ioana , Ștefan Ion , Zamfir Mădălina , Buzilă Cosmin Alexandru , Ștefan Teodora Alexia , Streinu-cercel Adrian
Published: October 2024

Abstract: Objective: Evaluation of individuals with intravenous drug addiction who developed infective endocarditis (IE) is required for a better understanding of the management that can be applied in these cases. This study aimed to shed light on the complex interplay between intravenous drug use and IE with the ultimate goal of improving patient care and outcomes Method: The study examined the clinical outcomes of 43 patients admitted with infective endocarditis (IE) associated with injection drug use. Data was collected over an average follow-up period of 12 months, providing a substantial timeframe to assess the long-term effects of different treatment approaches. The research focused on comparing the outcomes between two distinct groups: patients who received medical treatment alone and those who underwent surgical intervention in addition to medical management. Results This study included 43 intravenous drug users who were diagnosed with infective endocarditis (IE). The mean age of the patient cohort was 31 ± 9 years with a predominance of males (74.4%). All cases involved native valve IE, with the aortic valve being the most commonly affected (62.9%), followed by the tricuspid (30.2%) and mitral valves (6.9 %). Multiple valve involvement was observed in 13.9% of the cases. The study population had a high prevalence of comorbidities, particularly HIV infection (65.1%) and hepatitis C infection (25.5%). The study also revealed that 19.3% of the patients had previously experienced IE. Mortality rates were slightly higher in patients with recurrent IE (23.2%) than in those who experienced their first episode. Transthoracic echocardiography proved to be a highly effective diagnostic tool for detecting vegetation in 95% of the cases. Microbiological analysis identified Staphylococcus aureus as the predominant causative organism, isolated from blood cultures in 55.8% of the cases. These findings highlight the complex nature of IE in intravenous drug users, emphasizing the importance of comprehensive management strategies that address both cardiac manifestations and underlying comorbidities in this high-risk population. Conclusions: Injection drug use–associated infective endocarditis presents a significant challenge in modern healthcare, with patients facing a high risk of complications and poor clinical outcomes despite current therapeutic approaches. This persistent issue underscores the complexity of treating individuals with both infectious diseases and substance use disorders. The multifaceted nature of these cases often involves immediate cardiac infection, as well as concurrent medical, psychological, and social challenges that can complicate treatment and recovery.
Magnesium Doped Hydroxyapatite Nanofibers for Medicine Applications: Characterization, Antimicrobial Activity, and Cytotoxicity Study
Authors: Alanis-Gómez Ricardo Pascual , Hernández-Rosas Fabiola , Olivares-Hernández Juan David , Rivera-Muñoz Eric Mauricio , Zapatero-Gutiérrez Araceli , Méndez-Lozano Néstor , Alanis-Gómez José Rafael , Velázquez-Castillo Rodrigo
Published: October 2024

Magnesium-doped hydroxyapatite (Mg-HAp) nanofibers show promise for medical applications due to their structural similarity to bone mineral and enhanced biological properties, such as improved biocompatibility and antimicrobial activity. This study synthesized Mg-HAp nanofibers using a microwave-assisted hydrothermal method (MAHM) to evaluate their cytotoxicity, biocompatibility, and antimicrobial efficacy compared to commercial hydroxyapatite (HAp). Characterization through X-ray diffraction (XRD), Scanning Electron Microscopy (SEM), Transmission Electron Microscopy (TEM), Energy Dispersive X-ray Spectroscopy (EDS), and Fourier Transform Infrared Spectroscopy (FTIR) confirmed the successful incorporation of magnesium, producing high-purity, crystalline nanofibers with hexagonal morphology. Rietveld refinement showed slight lattice parameter shortening, indicating Mg2+ ion integration. Cell viability assays (MTT and AlamarBlue) revealed a significant increase in fibroblast proliferation with 2% and 5% Mg-HAp concentrations compared to controls (p<0.05), demonstrating non-cytotoxicity and enhanced biocompatibility. Antimicrobial tests (disk diffusion method, 100 µg/ml) showed that Mg-HAp had strong antibacterial effects against Gram-positive and Gram-negative bacteria, and moderate antifungal activity against Candida albicans. In contrast, commercial HAp showed no antimicrobial effects. These results suggest Mg-HAp nanofibers have significant advantages as biomaterials for medical applications, particularly in preventing implant-related infections, supporting further clinical development.
Copula-Based Risk Aggregation and the Significance of Reinsurance
Authors: Dias Alexandra , Ismail Isaudin , Zhang Aihua
Published: October 2024

Insurance companies need to calculate solvency capital requirements in order to ensure that they can meet their future obligations to policyholders and beneficiaries. The solvency capital requirement is a risk management tool essential for, when extreme catastrophic events occur, resulting in a high number of possibly interdependent claims. This paper studies the problem of aggregating the risks coming from several insurance business lines and analyses the effect of reinsurance in the level of risk. Our starting point is to use a Hierarchical Risk Aggregation method, which was initially based on 2-dimensional elliptical copulas. We then propose the use of copulas from the Archimedean family and a mixture of different copulas. Our results show that a mixture of copulas can provide a better fit to the data than an individual copula and consequently avoid over or underestimating of the capital requirement of an insurance company. We also investigate the significance of reinsurance in reducing the insurance company’s business risk and its effect on diversification. The results show that reinsurance does not always reduce the level of risk, but can also reduce the effect of diversification for insurance companies with multiple business lines.
A Proposal for a Simple Subclassification of Advanced Hepatocellular Carcinoma in Systemic Treatment
Authors: Imai Norihiro , Yamamoto Takafumi , Mizuno Kazuyuki , Yokoyama Shinya , Yamamoto Kenta , Ito Takanori , Ishizu Yoji , Kuzuya Teiji , Honda Takashi , Ishikawa Tetsuya , Kawashima Hiroki
Published: October 2024

Objectives: This study focused on the presence or absence of vascular invasion and extrahepatic metastasis in hepatocellular carcinoma (HCC) and examined their impact on systemic treatment outcomes. Methods: We retrospectively analyzed 362 patients with unresectable HCC who received first-line systemic therapy. The prognostic evaluation was based on the presence of vascular invasion and extrahepatic metastasis at the time of treatment initiation. Results: Patients with vascular invasion or extrahepatic metastasis (advanced group) had significantly worse outcomes than those without these features (intermediate group), with median survival times of 434 and 658 days, respectively. Further subdivision of the advanced group into three categories—patients with only extrahepatic metastasis (m group, n = 77), patients with only vascular invasion (v group, n = 78), and patients with both vascular invasion and extrahepatic metastasis (vm group, n = 52)—revealed that the m group had significantly better outcomes than those in the other two groups, with median survival times of 649, 323, and 187 days, respectively. A comparison of the clinical backgrounds among the three groups demonstrated that the m group had significantly better liver function at the time of treatment initiation than that in the other two groups. Multivariate analysis, including performance status, Child–Pugh score, and the use of immune checkpoint inhibitors as first-line therapy, identified the m group as an independent and significant prognostic factor (hazard ratio, 0.50). Conclusions: Unresectable HCC with extrahepatic metastasis and no vascular invasion represents a novel staging category for systemic treatment.
Landslide Thickness Estimated from InSAR-derived 2D Deformation: Application to the Xiongba ancient Landslide, China
Authors: Yang Yinghui , Xu Qian , Xie Liyuan , Xu Qiang , Hu JyrChing , Chen Qiang
Published: October 2024

The thickness estimation is crucial for evaluating landslide evaluating. The traditional non-contact mass conservation method adopts three-dimensional deformation to estimate landslide thickness, which may be unsuitable due to the limitations of observations. In this study, a more feasible approach for estimating landslide thickness based on 2D deformation derived from two-track Interferometric Synthetic Aperture Radar (InSAR) observations is proposed, specifically applied to the Xiongba landslide. The comparison between the estimated thickness from the InSAR-derived 2D deformation and the measured thickness by geological and drilling methods validates the reliability of the proposed method. The mapped InSAR LOS deformation rate fields reveal two distinct deformation regions, including a frontal zone exhibiting significant deformation and a relatively stable zone. The analysis of the 2D deformation indicates that surface uplift at the front edge of the Xiongba-H2 landslide may result from the pushing of the rock-soil mass in areas of high deformation. The inversion result suggests that the thickness of the Xiongba-H2 landslide is ranging from 10 to 100 m, corresponding to the active volume of 6.17×107 m3. A thicker region is identified, at the front edge along the Jinsha River, posing the potential of further failure. It is believed that the proposed low-cost, easily implementable approach could enhances the applicability of InSAR technology for landslide motion analysis and hazard assessment.
An Econometric Analysis of the Economic and Social Preferences in Energy-Saving Behavior Using a Structural Equation Modeling Approach
Authors: Busu Cristian , Busu Mihail , Grasu Stelian , Skackauskiene Ilona , Fonseca Luis Miguel
Published: October 2024

Keywords: Structural Equation Modeling (SEM); economic and social preferences; consumer choices; econometric model; Partial Least Square (PLS).
Modelling Carbon-Based Nanomaterials (CNMs) and Derived Composites and Devices
Authors: Chiminelli Agustίn , Radović Ivan , Fasano Matteo , Fantoni Alessandro , Laspalas Manuel , Kalinić Ana , Provenzano Marina , Fernandes Miguel
Published: October 2024

A review of different modelling techniques, specifically in the framework of carbon-based nanomaterials (CNMs, including nanoparticles as graphene and carbon nanotubes - CNTs) and the composites and devices that can be derived from them, is presented. The article highlights the multiscale nature of these types of materials and systems, which require different approaches depending on the type, size, internal structure/configuration of the material and properties of interest. Far from attempting to cover the entire spectrum of models, this review examines a wide range of analysis and simulation techniques, highlighting their potential use, some of their weaknesses and strengths, and presenting the latest developments and some application examples. Specifically, this paper shows how electronic, atomistic, mesoscopic, continuum and system models can be used to study and design CNMs, to predict different properties and responses of materials, or even to study devices such as sensors. This review has been performed within the framework of COST Action EsSENce (High-performance Carbon-based composites with Smart properties for Advanced Sensing Applications – CA19118).
Machine Learning Approach to Predicting Rift Valley Fever Disease Outbreaks in Kenya
Authors: Mulwa Damaris , KAZUZURU BENEDICTO , Bett Benard
Published: October 2024

In Kenya, Rift Valley Fever (RVF) outbreaks pose significant challenges, being one of the most severe climate-sensitive zoonoses. While Machine Learning (ML) techniques have shown superior performance in time series forecasting, their application in predicting disease outbreaks in Africa remains under explored. Leveraging data from the International Livestock Research Institute (ILRI) in Kenya, this study pioneers the use of ML techniques to forecast RVF outbreaks by analysing climate data spanning from 1981 to 2010, including machine learning models. Through a comprehensive analysis of ML model performance and the influence of environmental factors on RVF outbreaks, this study provides valuable insights into the intricate dynamics of disease transmission. The XGB Classifier emerged as the top-performing model, exhibiting remarkable accuracy in identifying RVF outbreak cases, with an accuracy score of 0.997310. Additionally, positive correlations were observed between various environmental variables, including rainfall, humidity, and claypatterns, and RVFcases, underscoring the critical role of climatic conditions in disease spread. These findings have significant implications for public health strategies, particularly in RVF-endemic regions, where targeted surveillance and control measures are imperative. However, the study also acknowledges the limitations in model accuracy, especially in scenarios involving concurrent infections with multiple diseases, highlighting the need for ongoing research and development to address these challenges. Overall, this study contributes valuable insights to the field of disease prediction and management, paving the way for innovative solutions and improved public health outcomes in RVF-endemic areas and beyond.