bluuebunny/crossref_metadata_2025 huggingface dataset:
AI-Based Breast Cancer Detection System
Authors: Moursi Amro Sayed Mahmoud , Abumadi Abdulrahman , Qidwai Uvais
Published: February 2025

This work addresses the critical need for early detection of breast cancer, a significant health concern worldwide. Through a combination of advanced deep learning and machine learning techniques, we offer a comprehensive solution to enhance breast cancer detection accuracy. By leveraging state-of-the-art convolutional neural networks (CNNs) like GoogLeNet, AlexNet, and ResNet18, alongside traditional classifiers such as k-nearest neighbors (KNN) and support vector machine (SVM), we ensure robust prediction capabilities. Our preprocessing methods significantly improve input data quality, leading to promising detection accuracies. For instance, ResNet-18 achieved impressive results, outperforming other models. Furthermore, our integration of these algorithms into a user-friendly MATLAB application ensures easy access for medical professionals, facilitating timely diagnosis and treatment. This work represents a vital step towards more effective breast cancer diagnosis, underscoring the importance of early intervention for improved patient outcomes.
New Concept of Colonoscopy Assisted by a Microwave-Based Accessory Device: First Clinical Experience
Authors: Ortiz Oswaldo , Sendino Oriol , Rivadulla Silvia , Garrido Alejandra , Neira Luz María , Sanahuja Josep Martí , Sesé Pilar , Guardiola Marta , Fernández-Esparrach Glòria
Published: February 2025

Background/Objectives: Microwave imaging can obtain anatomical and functional images of the colon and has demonstrated to detect polyps based on their dielectric properties. This study aims to evaluate the feasibility, safety and performance of micro-wave-based colonoscopy for diagnosis of polyps in humans. Methods: single-center, prospective, observational study. Patients referred for elective outpatient diagnostic co-lonoscopy were included. A device provided with microwave antennas was attached to the tip of a conventional colonoscope. The primary outcomes were rate of cecal intubation, adverse events, mural injuries and performance metrics for detection of polyps. Secondary outcomes were: patients’ subjective feedback, procedural time and perception of difficulty by the endoscopist. Results: 15 patients were enrolled. Cecal intubation rate was 100% with a mean time of 12.7±4.9 min (range 4-22). Use of the device did not affect the endoscopic image and polypectomy was successfully performed in all cases. In a scale from 0 (not difficult) to 4 (very difficult), the maneuverability during the insertion was considered < 2 in the 86.7% (13/15) of colonoscopies. Only 16 incidents were reported in 14 patients: 11 (67%) superficial hematomas, 2 minor rectal bleedings, 1 anal fissure, 1 rhinorrhea and 1 headache. Most of the patients (94%) referred no discomfort or minimal discomfort before discharge (Gloucester score 1 and 2, respectively). In the six patients with 23 polyps used for the performance analysis, the sensitivity and specificity were 86.9% and 72.0%, respectively. Conclusions: microwave-based colonoscopy is safe and feasible and has the potential of detecting polyps
Acute Suppurative and Subacute Thyroiditis: from Diagnosis to Management
Authors: Toschetti Tommaso , Parenti Cecilia , Ricci Ilaria , Addati Irene , Diona Sonia , Esposito Susanna , Street Maria Elisabeth
Published: February 2025

Background: Acute Suppurative Thyroiditis (AST) and Subacute Thyroiditis (SAT) are two distinct inflammatory conditions of the thyroid gland with different clinical presentation, treatment and that recognize different causes. AST is a rare but serious bacterial infection, often associated with congenital anomalies in children, whereas SAT is a self-limiting, post-viral condition that causes temporary thyroid dysfunction. Methods: A comprehensive literature review was conducted using PubMed and UpToDate, including systematic reviews, meta-analyses, case series, and case reports. Studies focusing on epidemiology, pathophysiology, clinical presentation, diagnosis, and treatment were selected, with special attention to pediatric cases. Results: AST accounts for fewer than 1% of thyroid diseases and is more common in children, with pyriform sinus fistulas being present in 21% of cases. It presents with fever, painful neck swelling, and complications such as abscess formation and airway obstruction. Early recognition and prompt management with broad-spectrum antibiotics, ultrasound-guided aspiration, or surgical drainage are crucial. In contrast, SAT can occur at any age but is most common in adult women and follows typically a viral infection. It presents with anterior neck pain and transient thyrotoxicosis and is generally managed with non-steroidal anti-inflammatory drugs or corticosteroids in severe cases. Accurate differential diagnosis is essential to prevent unnecessary interventions. Conclusions: Although rare, both AST and SAT require timely diagnosis and tailored treatment strategies to avoid complications. Advances in imaging and early detection of congenital anomalies have improved AST outcomes, while SAT remains a self-limiting condition that primarily requires symptom management. Further research is needed to better understand risk factors, pathogenesis, and optimal treatment approaches, particularly in pediatric populations and resource-limited settings.
Utilizing Predictive Analytics for Real-Time Risk Mitigation in Drilling Fluid Systems
Authors: William Barnty , Mabel Emmanuel
Published: February 2025

In the oil and gas industry, drilling operations face complex and dynamic challenges that require real-time monitoring and proactive decision-making to ensure safety, operational efficiency, and environmental protection. Drilling fluid systems, which are essential for maintaining well stability, cooling equipment, and preventing blowouts, can be prone to risks such as pressure anomalies, viscosity changes, and contamination. This article explores the role of predictive analytics in mitigating these risks in real-time. By integrating advanced sensors and machine learning models, predictive analytics can detect anomalies, forecast potential failures, and suggest corrective actions before critical issues arise. The study highlights how real-time data collected from drilling fluid systems can be analyzed to predict equipment malfunctions, fluid imbalances, and hazardous events, ultimately reducing incidents and improving safety outcomes. The article also discusses the challenges of data integration, system accuracy, and operator training, while emphasizing the potential for predictive analytics to enhance decision-making and operational resilience in high-risk drilling environments. The findings suggest that by leveraging predictive analytics, drilling operations can achieve more reliable, cost-effective, and safer outcomes, paving the way for future advancements in risk management and fluid system optimization.
Chordal Metric Formula Between Generalized Singular Values of Grassmann Matrix Pairs by Riemannian Optimization Models
Authors: Wang Yujie , Kang Wen , Zhu Lei
Published: February 2025

In this paper, we present a new explicit formula for the sum of the chordal distance between the generalized singular values of Grassmann matrix pairs, based on Riemannian optimization models. The new formulas involve small-size unitary matrices and real orthogonal matrices derived from Riemannian optimization models. We then apply Newton’s method on Riemannian manifolds to efficiently solve the variable matrices involved. The new explicit formulas provide significant improvements over the existing theoretical and computational upper bounds.
Efficacy and Safety of Echinacea purpurea in Treating Upper Respiratory Infections and Complications of Otitis Media in Children: Systematic Review and Meta-Analysis
Authors: Pham Thi-Phuong-Thao , Vu Thi-Mai-Hoa , Doan Pham-Minh-Khue , Nguyen Thi-Thuy-Duong , Bui Thi-Thu-Thuy , Ha Thi-Hong-Linh , Hoang Thi-Kim-Quy , Taufani Indra Putra , Ha Hai-Anh
Published: February 2025

Background: Echinacea purpurea is widely utilized in dietary supplements, nutraceuticals, and herbal medicine, yet its effectiveness and safety for children remain debated. Objective: To evaluate the effectiveness and safety of E. purpurea in the treatment of upper respiratory tract infection (URTI) and otitis media (OM) in children. Data sources: Embase, Cochrane Central Register of Controlled Trials (CENTRAL), ClinicalTrials.gov, PubMed from January 2000 to December 2023. Methods: This systematic review and meta-analysis were conducted and reported according to the PRISMA 2020 Checklist and Cochrane Training 2022 guidelines. Results: 9 randomized controlled trials with 1518 participants in the treatment group, 1651 participants in the placebo control group, met the selection criteria. Products derived from E. purpurea are highly diverse, utilizing different plant parts such as roots, leaves, and flowers, available in various dosage forms like capsules and extracts, and manufactured in numerous countries worldwide. Risk of bias was assessed according to the PEDro scale with 3 studies of excellent quality, 5 studies of high quality and 1 study of moderate quality.The meta-analysis found that E. purpurea significantly benefits URTI, reducing treatment duration (SMD = -0.19, 95% CI [-0.30; -0.09], p < 0.01), incidence (RR = 0.81, 95% CI [0.75; 0.87]), and antibiotic use (RR = 0.18, 95% CI [0.13; 0.25], p < 0.01). However, adverse events were moderately increased (RR = 1.38, 95% CI [1.08; 1.78]), though generally mild. For OM, E. purpurea lowered episode incidence (RR = 0.56, 95% CI [0.44; 0.73], p < 0.01) but did not shorten treatment duration (p = 0.10). Its effectiveness may be limited due to OM's bacterial etiology, often requiring antibiotics. While E. purpurea may help reduce recurrence, it is unlikely to speed recovery. Further research on optimal formulations and dosages is needed.The publication bias of pooled results as assessed by the funnel plot showed that the studies had certain bias, largely due to heterogeneity between studies. Conclusion: The meta-analysis of nine studies found that E. purpurea was effective in reducing the duration of treatment, the incidence of episodes, and antibiotic usage for URTI in children, with some positive effects on OM. This study systematically evaluates the efficacy and safety of E. purpurea in treating URTI and OM in children, providing evidence-based insights to guide clinical decisions and inform healthcare professionals and parents considering its use. However, the safety profile remains unclear, particularly regarding adverse events. Further research is required to standardize methodologies and confirm these results.
The Effect of Technical Fouls on Momentum Change in Basketball: A Comparison of Regular Season vs. Playoffs in the NBA
Authors: Tenenbaum Gershon , K. Maymon Yaniv , Ben-Zion Tomer , Lev Assaf
Published: February 2025

Spanning two decades (2000–2021), this study delves into how TF-induced momentum shifts differ between regular season and playoff matchups, factoring in home-court advantage and team score status at the time of the call. Analyzing 4,196 cases of TFs called against coaches, we employ big data analytics to uncover distinct patterns in momentum shifts and their strategic implications. Using advanced statistical modeling, we identify how these effects vary across game contexts, demonstrating how big data enhances decision-making in competitive sports. Logistic regression revealed a significant season-by-location interaction (p < .03). The findings revealed that, in the regular season, TFs in home games were associated with a 44%-win rate, compared to 28% in away games. However, in the playoffs, this shifted to 50% at home and 23% away. These results provide valuable insights into the TF-momentum shift phenomenon. Leveraging game analytics to identify patterns in TF-related momentum shifts can help coaches make more informed decisions, including pinpointing the optimal moments for TFs and other strategic actions.
What School Teachers and Students Think About Artificial Intelligence
Authors: Miranda Sergio , Vegliante Rosa , Marzano Antonio
Published: February 2025

This research compares teachers' and students' opinions on the use of artificial intelligence (AI) in education. The paper explores the potential benefits of AI, such as personalizing learning and increasing efficiency, along with ethical and practical concerns. The study collected data through a questionnaire administered to 184 participants mainly active in the Campania Region, Italy. The results show an openness to AI as a support tool, but with reservations about replacing human teachers and traditional teaching materials. The analysis highlights the need for a balanced approach that values the human role and considers the ethical implications of AI in the education of the future. The research contributes to a better understanding of how industry actors perceive AI, highlighting the importance of its conscious and responsible integration.
Bioinspired Soft Machines: Engineering Nature's Grace into Future Innovations
Authors: Singh Ajay Vikram , Ansari Mohammad Hasan Dad , Dey Arindam K. , Laux Peter , Samal Shailesh Kumar , Malgaretti Paolo , Mohapatra Soumya Ranjan , Busse Madleen , Suar Mrutyunjay , Tisato Veronica , Gemmati Donato
Published: February 2025

This article explores the transformative advances in soft machines, where biology, materials science, and engineering have converged. We discuss the remarkable adaptability and versatility of soft machines, drawing inspiration from nature's elegant solutions. From the intricate movements of octopus tentacles to the resilience of an elephant trunk, nature provides a wealth of inspiration for designing robots capable of navigating complex environments with grace and efficiency. Central to this advancement is the ongoing research in bioinspired materials, which serve as the building blocks for creating soft machines with lifelike behaviors and adaptive capabilities. By fostering collaboration and innovation, we can unlock new possibilities in soft machines, shaping a future where robots seamlessly integrate into and interact with the natural world, offering solutions to humanity's most pressing challenges.
Manganese Poisoning Induced by Total Parenteral Nutrition in the ICU: A Case Report and Review
Authors: Seijas-Martínez-Echevarría Victoria , Martinez Manzanal Rita , Mena Perez Ester , Nuñez Valentin Pilar , Ruiz-Martin Guadalupe
Published: February 2025

Introduction: Manganese is an essential trace element for humans. It has been recognized as a potential occupational toxic but its danger as toxic in patients under parenteral nutrition is often forgotten. Case report: 73-year-old man, logged 210 days in Intensive Care Unit (ICU), receiving total parenteral nutrition (TPN) for a month, current disease chorea-type movements in the head and neck and left hemi body. Diagnostic tests: Magnetic resonance image findings suggest manganese deposit, total blood manganese concentration (34 µg. L-1) (reference range: less than 13 µg. L-1). Diagnosis: Abnormal movements can be caused by manganese poisoning due to parenteral nutrition and are associated with liver failure in the ICU. Discussion and Conclusions: After a prolonged stay in ICU, assessing liver failure caused by septic and protracted parenteral nutrition, manganese poisoning should be considered as a cause of chorea-type movements.
Data-Driven Education: The Role of IoT and AR in Enhancing Teaching Strategies
Authors: Heston Richard , Adams Grace
Published: February 2025

The integration of data-driven technologies in education has revolutionized traditional teaching methodologies, offering innovative solutions to enhance learning experiences. This paper explores the role of the Internet of Things (IoT) and Augmented Reality (AR) in transforming educational strategies by providing real-time data analytics, personalized learning environments, and interactive content. IoT-enabled smart classrooms facilitate automated attendance tracking, adaptive learning systems, and real-time performance monitoring, enabling educators to tailor instruction to individual student needs. Meanwhile, AR enhances engagement by overlaying digital content onto physical environments, fostering immersive and experiential learning. The synergy between IoT and AR contributes to a more responsive, efficient, and student-centric education system. This study examines case studies and empirical evidence to highlight the effectiveness of these technologies in improving learning outcomes and teacher effectiveness.
Decoding Pension Funds: Sustainability Indicators for Annual Report Analysis
Authors: Medeiros Leticia Martins , Macagnan Clea Beatriz , Seibert Rosane Maria
Published: February 2025

The pension funds' growth highlights the need to emphasize fiduciary duty and investment sustainability, considering the current and future participant's interests (priority stakeholders) and the systemic risk reduction (environmental, social, economic, and governance effects). Therefore, this study builds sustainability indicators based on the interests of pension fund stakeholders. The methodology was carried out in five stages: the first consisted of analyzing annual information reports to create a preliminary list of indicators; the second involved examining specific legislation on pension fund disclosure and identifying mandatory information; the third involved submitting the updated list to experts, and the fourth involved submitting it to priority stakeholders for evaluation and validation. After updates, the indicators list was evaluated using Principal Component Analysis. All these stages allowed the triangulation of information and the creation of the final list containing 48 sustainability indicators for pension funds, with information requested by priority stakeholders. This allows regulators to adjust disclosure rules, including those required by stakeholders and good governance practices. It also allows pension funds to identify the indicators required by stakeholders, reducing the information asymmetry. The adoption of the list of indicators promotes trust, legitimacy, and sustainability for pension funds.
A Fuzzy System for the Quality Assessment of Educational Multimedia Edition Design
Authors: Senkivskyy Vsevolod , Sikora Liubomyr , Lysa Nataliia , Kudriashova Alona , Pikh Iryna
Published: February 2025

The quality of educational multimedia edition design is determined by a set of characteristics that affect perception, readability and communication efficiency. Predicting the quality of multimedia edition design is based on a comprehensive analysis of characteristics that affect the aesthetics, functionality and adaptability of a multimedia product. Within the framework of this study, the goal is to develop a fuzzy system for predicting the quality of educational multimedia edition design. An approach to determining an integral quality indicator based on fuzzy logic is proposed, which ensures that the influence of various factors that cannot be characterized exclusively by numerical parameters is taken into account. A multilevel model of fuzzy logical inference is constructed, representing the hierarchical dependency between quality factors. Membership functions for linguistic variables are formed and their weight coefficients are determined using pairwise comparison matrices. The developed approach contributes to making informed management decisions in the process of creating multimedia products. The use of fuzzy logic methods allows one to assess the design quality even under conditions where the parameters are subjective or do not have clear numerical characteristics. Thus, quality prediction provides the opportunity to identify the design weaknesses at the stage of its development, optimize the process of creating multimedia editions and increase the efficiency of their use in educational and professional environments. Further research aimed at integrating artificial intelligence for automated updating of the knowledge base and expanding the system by introducing additional assessment criteria is considered to be promising.
Retrieval-Free Suggestion Question Generation via Large Language Models
Authors: Taylor Charles
Published: February 2025

This paper addresses the challenge of ambiguous and poorly formulated user queries in Retrieval-Augmented Generation (RAG) based conversational systems. Current RAG systems often struggle to provide satisfactory responses to such queries, hindering user experience. To mitigate this issue, we propose a novel approach for suggestion question generation that moves beyond traditional retrieval-based methods. Our method leverages the inherent knowledge and generative capabilities of Large Language Models (LLMs) to directly generate relevant and helpful suggestion questions, without explicit document retrieval during inference. We train our models on a dedicated dataset of user queries and curated suggestion questions using a supervised learning strategy. Extensive experiments, comparing our approach against zero-shot, few-shot, and RAG-based baselines, demonstrate the superior performance of our LLM-driven method in terms of correctness, relevance, and helpfulness, further validated by human evaluations. Ablation studies and error analysis provide deeper insights into the effectiveness and limitations of our approach. The results highlight the potential of purely generative models for user query refinement and suggest a paradigm shift in suggestion question generation for conversational AI.
Evaluation of the Impact of Social Support on the Functioning of Patients Under Home Nursing Care
Authors: Kopcych Bożena Ewa , Falkowski Paweł , Costa Daniela Patricia Santos
Published: February 2025

The type of non-professional or professional support received affects the quality of life of the patient and his caregivers. Social support is the type of interaction that is taken by the patient and his caregivers in a problematic, difficult, stressful or critical situation. Aim The aim of the study was to assess the impact of social support on the functioning of patients under nursing home care. Material and methods The study included 148 chronically ill patients under nursing home care. The study used the diagnostic survey method, the research technique was a questionnaire containing basic data about the respondent and the Social Support Scale (SWS) by Krystyna Kmiecik-Baran. Results The need to continue the causal treatment at home means that the main source of support for care beneficiaries are nurses who provide medical services at the patient's home, supported by doctors and family members of the patient. Patients rated the information support provided by nurses as the highest at 14.3 points and emotional support at 13.3 points. In the opinion of the surveyed patients, the value-added support provided was the lowest rated category by patients, 9.74 points. Instrumental support was also rated very poorly by the respondents (10.17 points). Conclusions Patients under home care highly appreciated the support provided to them by the nursing staff. Social support for a chronically ill person who requires constant care and care by the nursing staff is a form of direct impact that relieves stress and tension, minimizes the effects of the disease, directly affects the course of treatment and care, and prevents stigmatization.
A Study on the Impact of Financialization of the Economy and the Income Gap between Urban and Rural Residents: Evidence from China
Authors: Chen Zhuang , Jiao Fangyi
Published: February 2025

The financialization of the economy is the process of industrial capital moving towards monetization and virtualization, appropriating the value of workers and enriching the capital stock of the rich class through the financial cycle, resulting in income differentiation. This paper uses China's provincial data from 2003 to 2022 to comprehensively evaluate the degree of financialization of the economy and examine its impact on the income gap between urban and rural residents, and finds that: financialization of the economy has curbed the convergence of the income gap between urban and rural residents, and regional heterogeneity is obvious, while expanding the wage income gap and contracting the property income gap; the income gap between urban and rural residents is related to the degree of financialization of the economy in the region, and is also affected by the spillover effect of neighboring regions, called "Club Convergence" Effect. Considering the development of financialization of the economy from a multi-dimensional perspective, relying on the policy-based financial system, deepening inclusive financial services in the countryside, and giving full play to regional advantages will alleviate the income imbalance between urban and rural areas, and benefit people's livelihoods and well-being.
A High-Efficiency Frequency Multiplier with Triangular-Resistance Phase Interpolation
Authors: Ding Yuyang , Wang Chen , Wang Xukun , Huang Chunli , Zhou Bo
Published: February 2025

A high-efficiency frequency multiplier is presented in 65-nm CMOS with a core area of 0.06 mm2. A low-cost five-segment triangular-resistance phase interpolation scheme is proposed. By performing resistive interpolation on four-path orthogonal triangular signals, 10-fold frequency multiplication is achieved within the input frequency range of 12-20 MHz. The prototype only includes a quadrature square-wave generator, four orthogonal square-triangular converters, and the proposed four-path five-segment triangular-resistance phase interpolators, with the acceptable frequency deviation. The presented design achieves an output power of -9.8 dBm, with an input power of -2.0 dBm and the power consumption of 0.45 mW from a 1.2-V supply, which obtains a frequency multiplication efficiency up to 9.6%. The proposed mechanism could be extended to accomplish a configurable multiplication factor.
Forage Yield, Nutrient Composition, and In-Vitro Digestibility of Ten Intermediate Maturing Cowpea (Vigna Unguiculate) Genotypes Under Various Ecology of Eastern Oromia, Ethiopia
Authors: Dinkale Tamrat , Debela Muleta , Gadisa Birmaduma
Published: February 2025

The experiment was conducted in West Hararghe Zone, Ethiopia. The trial was conducted at mid to lowland agroecologies for two years to evaluate cowpea genotypes' agronomic performance and nutritional quality under different environments. Ten genotypes and two standard checks were used as experimental treatments in RCBD with three replications. All agronomic and yield parameters showed statistically significant (p<0.05) variation among genotypes but did not show significant (P>0.05) differences among the genotypes in Ash, CP, NDF, ADF, ADL, and in vitro dry matter digestibility. The overall mean of 50% flowering days of three locations ranged from 40.14 to 73.67 and the mean of days to maturity ranged from 107.75 to 129.3 and the shortest days to maturity recorded for kenketi variety at all locations over the years. The highest dry matter yield was recorded from genotype ILRI#25368 and the lowest from kenketi variety and the highest grain yield was recorded from genotype ILRI#11988 (24.30 quintal/ha) followed by ILRI#25368 (23.36 quintal/ha) whereas the lowest grain yield recorded from genotype ILRI#9341 (14.05quintal/ha). The most disease-susceptible genotype was ILRI#11988 (2.44) and the most disease-tolerant genotype was ILRI#25367 (1.22) followed by 25368 (1.39). The most pests-affected genotypes/variety was Kenketi (2.78) and the most pests (aphids) tolerant genotype was ILRI#9359 (1.11). Further, research should be conducted on production performance under farmer’s conditions, awareness of its importance and stage of utilization, and conservation techniques of these genotypes should be conducted for wider cultivation in the area.
Developing a Predictive Model for Significant Prostate Cancer Detection in Prostatic Biopsies from Seven Clinical Variables: Is Machine Learning Superior to Logistic Regression?
Authors: Morote Juan , Miró Berta , Hernando Patricia , Paesano Nahuel , Picola Natàlia , Muñoz-Rodriguez Jesús , Ruiz-Plazas Xavier , Muñoz-Rivero Marta V. , Celma Ana , García-de Manuel Gemma , Servian Pol , Abascal José M. , Trilla Enrique , Méndez Olga
Published: February 2025

This study compares machine learning and logistic regression algorithms in developing a predictive model for sPCa using the seven predictive variables from the Barcelona (BCN-MRI) predictive model. A cohort of 5,005 men suspected of having PCa who underwent MRI, and targeted and/or systematic biopsies was used for training, validation and testing. A Feedforward Neural Network (FNN) SimpleNet-based model (GMV) and a logistic regression-based model (BCN) were developed. The models were evaluated for discrimination ability, net benefit and clinical utility. Both models demonstrated strong predictive performance. The GMV model achieved an area under the curve of 0.88 in training and 0.85 in test cohorts (95% CI: 0.83-0.90), while the BCN model reached 0.85 and 0.84 (95% CI: 0.82-0.87), respectively (p > 0.05). The GMV model exhibited superior recall, making it more suitable for clinical scenarios prioritizing sensitivity, whereas the BCN model demonstrated higher precision and specificity, optimizing the reduction of unnecessary biopsies. Both models provided net benefit over biopsying all men, reducing unnecessary procedures by 27.5-29% and 27-27.5% of prostate biopsies at 95% sensitivity, respectively (p > 0.05). Both, machine learning and logistic regression-based models exhibited high and comparable similar clinical performance in sPCa detection using a limited dataset.
Amorphization of Ticagrelor Through Preparation with Cyclodextrins – Solid State Characterization
Authors: Marin Loredana-Maria , Ozon Emma Adriana , Anuța Valentina , Sârbu Iulian , Skotnicki Marcin , Lulek Janina , Drăgănescu Doina
Published: February 2025

Background/Objectives: Approximately 90% of newly developed active pharmaceutical ingre-dients (APIs) exhibit low solubility in aqueous environments. The formation of cyclodextrin (CD)-guest inclusion complexes (ICs) is one method of increasing the solubility of poorly soluble drugs. In contrast to its crystalline state, a compound is more soluble in its amorphous solid state. Methods: The purpose of this work was to investigate how the solid-state characteristics of ti-cagrelor (TICA) are affected by complexation with specific cyclodextrins. TICA inclusion com-plexes containing three different types of CDs were prepared by freeze-drying, co-precipitation, and kneading methods. Solid-state characterization of pure ingredients, physical mixtures (PMs) and TICA-CD ICs was performed using: scanning electron microscopy (SEM), powder X-ray diffraction analysis (PXRD), thermogravimetric analysis (TGA), differential scanning calorimetry (DSC) and temperature-modulated differential scanning calorimetry (TMDSC). Results: The freeze-drying method was the most effective in terms of obtaining TICA in the amorphous state. Using the kneading technique, the complexes with lower crystallinity were obtained compared to pure ticagrelor. Since almost pure crystalline ticagrelor was found in the mixes produced by co-precipitation, it may be said that the procedure was unsuccessful. Conclusions: Therefore, our findings suggest that kneading and, particularly, freeze-drying techniques are useful approaches for preparing TICA–CD amorphous ICs.
Influenza Vaccination and Cardiovascular Outcomes in Patients with Coronary Artery Diseases, A placebo-controlled random-ized study; IVCAD
Authors: Dehesh Mohammadmoein , Gholamin Sharareh , Razavi Seyed-Mostafa , Skandari Ali , Vakili Hossein , Rahnavardi Azari Mohammad , Wang Yunzhi , Gough Ethan , Keshtkar-Jahromi Maryam
Published: February 2025

Background/Objectives: Influenza infection is associated with cardiovascular mor-bidity and mortality, but effect of influenza vaccination on cardiovascular outcomes is not fully understood. This clinical trial aimed to investigate correlation between car-diovascular outcomes and influenza vaccine (FluVac) in coronary artery diseases (CAD) subjects. Methods: This was a randomized single blinded placebo-controlled trial. Enrolled CAD subjects received 0.5 ml of 2007-2008 trivalent FluVac (15µg he-magglutinin of each of Solomon Islands/3/2006 (H1N1), Wisconsin/67/2005 (H3N2), and Malaysia/2506/2004 (B)). Subjects were followed up at 1 month (hemagglutinin (HA) antibody titers), and at 12 months post-vaccination for evaluation of outcomes (influenza-like episodes, acute coronary syndrome (ACS), myocardial infarction (MI), coronary revascularization, and death). Results: 278 eligible CAD subjects were ran-domized to receive either FluVac (n = 137) or placebo (n = 141) of which consequently 131 and 135 subejcts completed the study. Cardiovascular deaths (3/131 [2.29%] vs. 3/135 [2.22%]) and all-cause deaths (4/131 [3.05%] vs. 4/135 [2.96%]) were similar in both groups. Adverse cardiovascular events, including ACS, MI, and coronary revas-cularization were less frequent in the vaccine group, but did not reach statistical sig-nificance. Magnitude of antibody change and serologic response (≥4-fold HI titer rise) of all three antibodies were significantly higher in the vaccine group compared to the placebo but did not correlate with cardiovascular outcomes in the FluVac group. Con-clusions: Influenza vaccine may improve cardiovascular outcomes, though this im-provement is not correlated with post-vaccination antibody titers. Despite controver-sies, Influenza vaccination is recommended in CAD population. (ClinicalTrials.gov NCT00607178)
Proactive IT Management with a Predictive Maintenance System
Authors: Giwa Yusuff , Akinmuyisitan Taiwo , Adebesin Adedayo , Sanni Jacob
Published: February 2025

This research addresses the challenge of predictive maintenance in IT systems by developing and evaluating models that accurately identify and classify failure types, crucial for preemptive action in maintenance. To achieve this, we designed a hybrid CNN-LSTM model to capture complex temporal and spatial patterns in system data, essential for detecting nuanced failure dynamics. This model was benchmarked against traditional models, including Logistic Regression, Decision Trees, Random Forests, and Support Vector Machines (SVM). We used the SMOTE technique to address class imbalances, which improved the training process and enabled the model to detect minority failure types more effectively. Our CNN-LSTM hybrid model achieved a notable accuracy of 92.4%, outperforming conventional models by better leveraging multi-dimensional data attributes. Confusion matrices showed high precision across most failure classes, though the model’s lowest precision was 86% for random failures. ROC and Precision-Recall curves with areas consistently above 0.90 further validated the model’s robustness. Although effective, the class imbalance issue still impacted precision and recall for infrequent failure types. Significant predictors, including rotational speed and process temperature, were identified, highlighting their role in failure detection. We improve IT systems’ predictive maintenance with such hybrid approach, which is a good candidate for handling complex failure modes. In conclusion, this paper has proposed a CNN-LSTM hybrid model that will enhance IT predictive maintenance by detecting and classifying failure types accurately leading to the application of aiding IT managers with optimum maintenance strategies.
Hardware-Centric Exploration of the Discrete Design Space in Transformer-LSTM Models for Wind Speed Prediction on Memory-Constrained Devices
Authors: Aslam Laeeq , Zou Runmin , Awan Ebrahim Shahzad , Hussain Sayyed Shahid , Shaki Kashish Ara , Wani Mudasir Ahmad , Asim Muhammad
Published: February 2025

Wind is one of the most important resources in the renewable energy basket. However, questions regarding wind as a sustainable solution, especially when it faces challenges such as upfront cost, visual impact, noise pollution and bird collisions. These challenges arise from commercial windmills whereas for domestic small-scale windmills these challenges are limited. On the other hand, accurate wind speed prediction (WSP) is crucial for optimizing power management in renewable energy systems. Existing research focuses on proposing model architectures and optimizing hyper-parameters to improve model performance. This approach often results in larger models, which are hosted on cloud servers. Such models face challenges, including bandwidth utilization leading to data delays, increased costs, security risks, concerns about data privacy and the necessity for continuous internet connectivity. Such resources are not available for domestic windmills. To overcome these obstacles, this work proposes a transformer model integrated with Long Short-Term Memory (LSTM) units, optimized for memory-constrained devices (MCD). A contribution of this research is the development of a novel cost function that balances the reduction of mean square error with the constraints of model size. This approach enables model deployment on low-power devices, avoiding the challenges of cloud-based deployment. The model, with its tuned hyper-parameters, outperforms recent methodologies in terms of mean squared error, mean absolute error, model size and R-squared scores across three different datasets. This advancement leads the way for more dynamic and secure on-device wind speed prediction (WSP) applications, representing a step forward in renewable energy management.
Smartphone-Based Analysis for Early Detection of Aging Impact on Gait and Stair Negotiation: A Cross Sectional Study
Authors: Hayek Roee , Brown Rebecca T. , Gutman Itai , Baranes Guy , Springer Shmuel
Published: February 2025

Aging is associated with gradual mobility decline, often undetected until it affects daily life. This study investigates the potential of smartphone-based accelerometry to detect early age-related changes in gait and stair performance in middle-aged adults. Eighty-eight participants were divided into four age groups: young (20-35 years), early middle-aged (45-54 years), late middle-aged (55-65 years), and older adults (65-80 years). They completed single-task, cognitive, and physical dual-task gait assessments and stair negotiation tests. While single-task walking did not reveal early changes, cognitive dual-task cost (DTC) of stride time variability deteriorated in late middle age. A strong indicator of early mobility changes was movement similarity, measured using dynamic time warping (DTW), which declined from early middle age for both cognitive DTC and stair negotiation. These findings highlight the potential of smartphone-based assessments, particularly movement similarity, to detect subtle mobility changes in midlife, allowing for targeted interventions to promote healthy aging.
A Proof of the Collatz Conjecture via Boundedness and Cycle Uniqueness
Authors: Nwankpa Amarachukwu
Published: February 2025

We prove the Collatz Conjecture. Our proof demonstrates that all Collatz sequences are bounded and converge to the 4→2→1 cycle through two key arguments. Boundedness is established via a novel asymptotic analysis leveraging refined congruence restrictions modulo 12. This modulo 12 analysis reveals deterministic residue class transitions that preclude unbounded growth. Cycle uniqueness is proven using a novel product equation and prime factorization. This combination of asymptotic boundedness and algebraic cycle uniqueness provides a complete resolution to the Collatz Conjecture.
Chitosan Combined with Methanolic Plants Extracts: Antifungal Activity, Phytotoxicity and Acute Toxicity
Authors: de Gante-de la Maza Sofía , Plascencia-Jatomea Maribel , Cortez-Rocha Mario Onofre , Sánchez-Mariñez Reyna Isabel , Meneses-Sagrero Salvador Enrique , Galvez-Iriqui Alma Carolina , Lopez-Meneses Ana Karenth
Published: February 2025

Anthracnose is a disease caused by phytopathogenic fungi such as Colletotrichum siamense that attacks plants and fruits causing great postharvest losses. Different alternatives for the control of this fungus have been studied, and in the present study we evaluated the in vitro antifungal activity of chitosan mixed with methanolic extracts of Baccharis glutinosa and Jacquinia macrocarpa as well as toxicity in different models. Using the radial growth technique, it was observed that the mycelial development of C. siamense was altered and reduced during exposure to the different treatments evaluated during the first hours of incubation, indicating a fungistatic effect. While the cell viability, by colorimetric assay using the XTT salt, showed alteration since the chitosan reduced proliferation by 50 %, while the plant extracts and their mixtures with chitosan reduced approximately 40 % indicating cell damage, which was confirmed by fluorescence microscopy. In addition, toxicity tests showed that the extract of J. macrocapra affected the germination percentage of Lactuca sativa seeds while radicle length was reduced in all treatments, except with chitosan alone. The larval survival test for Artemia salina with the extracts indicated their potential toxicity by causing up to 60% mortality. The results indicate that the extracts of B. glutinosa and J. macrocarpa mixed with chitosan are a good option for the control of C. siamense, but at the concentrations used they show a toxic effect in the models evaluated.
Analysis of Hybrid Fractional Differential Inclusion with Impulses in Ordered Banach Algebras
Authors: Zhai Chengbo , Zhang Lili
Published: February 2025

In this article, we are devoted to investigating a class of fractional differential inclusions with impulses in a concrete ordered Banach algebra. The existence results of solutions for the considered problem are derived by applying related hybrid fixed point theorem of multi-valued maps. A simple example is provided to illustrate and validate our proposed results.
Circular Economy Strategy Selection Through a Digital Twin Approach
Authors: Rinaldi Marta , Caterino Mario , Fera Marcello , Abbate Raffaele , Daniele Umberto , Macchiaroli Roberto
Published: February 2025

This study investigates the impact of different reverse logistics (RL) strategies on the economic and environmental performance of a system within the rubber flooring sector. A simulation tool was developed to replicate the behavior of a real production system, focusing on the transition from linear to circular processes. By considering multiple factors influencing system performance, this research offers an overview of the sustainability of various RL strategies and provides realistic es-timates for different scenarios. Three key factors were identified to evaluate each strategy’s re-sponse: transportation distance, flooring thickness, and returned flooring quality. The findings suggest that the environmental advantage generally favors on-site inspections at the customer’s location to assess the returned product's condition, regardless of distance. However, centralizing inspections at the manufacturer’s facility is more economically advantageous when distances are short, particularly when the company prioritizes recycling over other CE practices. Based on these results, practical implications and guidelines are proposed to help companies balance cost-effectiveness with sustainability, optimizing their operations within a circular economy framework.
Germin: relevant Allergen in Lemon, Orange and Mandarin
Authors: Zamarro Parra M. Soledad , Martínez-Gomaríz Montserrat , Hernández Alan , Alcover Javier , Dobski Isabel , Rodriguez David , Palacios Ricardo , Carbonell Antonio
Published: February 2025

Orange allergy is estimated to account for 3% of food allergies. Cit s 1 (germin-like protein) and Cit s 2 (profilin) have been identified as major allergens. Also, Cyt s 3 (lipid transfer protein) and Cyt s 7 (gibberellin-regulated protein) have been described as orange allergens. This study focuses on the unusual occurrence of orange allergy in five patients. Materials and methods: We report five cases of patients with immediate allergic manifestations after ingestion of orange. Skin prick-tests to common aeroallergens, and different foods. Prick by prick with orange, lemon pulp mandarin (peel, pulp and seeds). Anaphylaxis score; Total IgE, Serum-specific IgE and food allergy quality of life questionnaire. Allergenic extracts were prepared from orange (Citrus sinensis), lemon (Citrus lemon) and mandarin (Citrus reticulata) peel and pulp. Protein characterization was determined by Bradford and SDS-PAGE and sera by immunoblot. Selected proteins were identified by peptide fingerprinting. Results: A 23 kD band was recognized by all 5 patients in orange (pulp and peel), lemon (peel) and mandarin (pulp and peel), having already been annotated in IUIS in the case of orange as germin (Cit s 1). To send the 23 kDa bands for peptide fingerprinting, SDS-PAGE of the 3 fruits was performed and as a positive control we used the 23 kDa band from orange. Together, the 23 kDa bands from orange, lemon and mandarin peel were cut out and sent for peptide fingerprint analysis, yielding the germin like protein sequence in all cases. Conclusions: A band of 23 kDa (germin-like protein) appears common in orange (annotated in IUIS as germin), lemon and tange-rine peel. Not previously described in IUIS in lemon and mandarin and confirmed as germin. Germin was in the peel of the 3 fruits and in the orange pulp, but not in lemon or mandarin.
Failure Modes and Effect Analysis of Turbine Units of Pumped Hydro Energy Storage Systems
Authors: Todorov Georgi , Kralov Ivan , Kamberov Konstantin , Sofronov Yavor , Zlatev Blagovest Nikolov , Zahariev Evtim
Published: February 2025

In the present paper subject of the investigations is the reliability assessment of the single-stage reversible Hydropower Unit No. 3 (HU3) in the Bulgarian Pumped Hy-dro-Electric Storage (PHES) plant “Chaira”, which processes the waters of the “Belmeken” dam and “Chaira” dam. Preceding destruction of HU4 and its virtual simulation, analysis and the conclusions for the rehabilitation and safety provided the information for the possible processes in HU3. Detailed analysis of the consequences of prolonged use of HU3 was carried out. The Supervisory Control and Data Acquisition (SCADA) system records were studied. Fault Tree Analysis (FTA) is applied to determine the component relationships and subsystem failures that can lead to an undesired primary event. The functional structure of the system was depicted as a causal chain of failure effects. The probability of system failure was estimated based on the failure probabilities of the primary events. The effects of static loads, dynamic loads and low-cycle loads were investigated. Based on the experience and the investigations of the HU4 and its damages, as well as of the failures in the stay vanes of HU3 it is recommended to organize monitoring for water ingress into the drainage holes, which will allow detecting failures in a timely manner.
Feasibility Study on the Use of Virtual Reality with Evocative and Aesthetic Content for AD Patients
Authors: Carlomagno Francesco , Bevilacqua Vitoantonio , Brunetti Antonio , Sibilano Elena , Delussi Marianna , Lippolis Mariangela , Diomede Raffaele , Brattico Elvira
Published: February 2025

Recent advancements in technology have introduced novel therapeutic interventions for Alzheimer's Disease (AD). This study evaluates the feasibility of a Virtual Reality (VR) intervention for patients with mild to moderate cognitive decline, aiming to improve mood, evoke autobiographical memories, and enhance overall well-being. The intervention leverages personalized and emotionally evocative VR sessions paired with culturally and aesthetically tailored music. Seven elderly participants (6 with mild cognitive declining and 1 with moderate cognitive decline) were exposed to immersive 360-degree videos depicting familiar and non-familiar scenarios. Results demonstrated high levels of immersion and positive emotional responses, with notable memory recall linked to the multi-sensory stimuli. Despite minor side effects, such as fatigue or anxiety, participants generally found the experience engaging and meaningful. This feasibility study lays the groundwork for future large-scale trials and underscores the potential of VR as a complementary tool in cognitive rehabilitation strategies for AD.