bluuebunny/crossref_metadata_2025 huggingface dataset:
Seasonal Changes in the Elemental Composition of Five Valuable Fish Species (Sparidae) from Bozcaada, North Aegean Sea: A Health Risk and Nutritional Benefit Assessment
Authors: Yuvka İlknur , Kosker Ali Rıza , Durmus Mustafa , Ucar Yılmaz , Ozogul Yesim
Published: January 2025

This study investigates the seasonal variations in the elemental composition of five economically valuable Sparidae fish species from Bozcaada, North Aegean: red seabream (Pagrus major), gilthead seabream (Sparus aurata), saddled seabream (Oblada melanura), white seabream (Diplodus sargus), and common dentex (Dentex dentex), with a focus on both essential minerals and toxic metals. Fish samples (n = 10 per species per season) were collected across four seasons, and their weights and lengths were recorded. The concentrations of elements such as calcium, potassium, magnesium, phosphorus, copper, iron, manganese, zinc, chromium, nickel, selenium, cadmium, and mercury were analyzed using Inductively Coupled Plasma Mass Spectrometry (ICP-MS). The elemental concentrations varied as follows: Ca (11,388.46–55,470.76), K (17,230.83–27,594.86), Mg (1436.02–2326.73), Na (1962.30–7847.41), P (13,112.11–15,516.57), Fe (107.61–282.00), Cu (36.44–59.13), Mn (6.19–19.87), Zn (98.67–256.26), Cr (4.54–11.96), Ni (6.33–13.89), Se (0.82–7.33), Cd (0.08–0.32), and Hg (0.08–1.50) mg/kg. Health risk assessments, including Estimated Weekly Intake (EWI), Target Hazard Quotient (THQ), and Cancer Risk (CR), were calculated for both adult and child consumers. The results showed that while the essential minerals remained within safe limits, seasonal variations in the concentrations of toxic metals could pose potential health risks, particularly with frequent consumption. This research provides valuable insights into balancing the nutritional benefits and safety of fish from Bozcaada, offering recommendations for informed consumption and public health policies aimed at optimizing benefits while minimizing risks.
Numerical Approximations and Fractional Calculus: Extending Boole’s Rule with Riemann–Liouville Fractional Integral Inequalities
Authors: Mateen Abdul , Haider Wali , Shehzadi Asia , Budak Hüseyin , Bin-Mohsin Bandar
Published: January 2025

This paper develops integral inequalities for first-order differentiable convex functions within the framework of fractional calculus, extending Boole-type inequalities to this domain. An integral equality involving Riemann–Liouville fractional integrals is established, forming the foundation for deriving novel fractional Boole-type inequalities tailored to differentiable convex functions. The proposed framework encompasses a wide range of functional classes, including Lipschitzian functions, bounded functions, convex functions, and functions of bounded variation, thereby broadening the applicability of these inequalities to diverse mathematical settings. The research emphasizes the importance of the Riemann–Liouville fractional operator in solving problems related to non-integer-order differentiation, highlighting its pivotal role in enhancing classical inequalities. These newly established inequalities offer sharper error bounds for various numerical quadrature formulas in classical calculus, marking a significant advancement in computational mathematics. Numerical examples, computational analysis, applications to quadrature formulas and graphical illustrations substantiate the efficacy of the proposed inequalities in improving the accuracy of integral approximations, particularly within the context of fractional calculus. Future directions for this research include extending the framework to incorporate q-calculus, symmetrized q-calculus, alternative fractional operators, multiplicative calculus, and multidimensional spaces. These extensions would enable a comprehensive exploration of Boole’s formula and its associated error bounds, providing deeper insights into its performance across a broader range of mathematical and computational settings.
History of Biological Databases, Their Importance, and Existence in Modern Scientific and Policy Context
Authors: Danielewski Mikołaj , Szalata Marlena , Nowak Jan Krzysztof , Walkowiak Jarosław , Słomski Ryszard , Wielgus Karolina
Published: January 2025

With the development of genome sequencing technologies, the amount of data produced has greatly increased in the last two decades. The abundance of digital sequence information (DSI) has provided research opportunities, improved our understanding of the genome, and led to the discovery of new solutions in industry and medicine. It has also posed certain challenges, i.e., how to store and handle such amounts of data. This, coupled with the need for convenience, international cooperation, and the possibility of independent validation, has led to the establishment of numerous databases. Spearheaded with the idea that data obtained with public funds should be available to the public, open access has become the predominant mode of accession. However, the increasing popularity of commercial genetic tests brings back the topic of data misuse, and patient’s privacy. At the previous United Nations Biodiversity Conference (COP15, 2022), an issue of the least-developed countries exploiting their natural resources while providing DSI and the most-developed countries benefitting from this was raised. It has been proposed that financial renumeration for the data could help protect biodiversity. With the goal of introducing the topic to those interested in utilizing biological databases, in this publication, we present the history behind the biological databases, their necessity in today’s scientific world, and the issues that concern them and their content, while providing scientific and policy context in relation to United Nations Biodiversity Conference (COP16, 21.10—1.11.24).
Interpretable Machine Learning Insights into the Factors Influencing Residents’ Travel Distance Distribution
Authors: Si Rui , Lin Yaoyu , Yang Dongquan , Guo Qijin
Published: January 2025

Understanding intra-urban travel patterns through quantitative analysis is crucial for effective urban planning and transportation management. In previous studies, a range of distribution functions were modeled to lay the groundwork for human mobility research. However, few studies have explored the nonlinear relationships between travel distance patterns and environmental factors. Using travel distance data from ride-hailing services, this research divides a study area into 1 × 1 km grid cells, modeling the best travel distance distribution and calculating the coefficients of each grid. A machine learning framework (Extreme Gradient Boosting combined with Shapley Additive Explanations) is introduced to interpret the factors influencing these distributions. Our results emphasize that the travel distance of human movement tends to follow a log-normal distribution and exhibits spatial heterogeneity. Key factors affecting travel distance distributions include the distance to the city center, bus station density, land use entropy, and the density of companies. Most environmental variables exhibit nonlinear and threshold effects on the log-normal distribution coefficients. These findings significantly advance our understanding of ride-hailing travel patterns and offer valuable insights into the spatial dynamics of human mobility.
Fine-Grained Arabic Post (Tweet) Geolocation Prediction Using Deep Learning Techniques
Authors: Elteir Marwa K.
Published: January 2025

Leveraging Twitter data for crisis management necessitates the accurate, fine-grained geolocation of tweets, which unfortunately is often lacking, with only 1–3% of tweets being geolocated. This work addresses the understudied problem of fine-grained geolocation prediction for Arabic tweets, focusing on the Kingdom of Saudi Arabia. The goal is to accurately assign tweets to one of thirteen provinces. Existing approaches for Arabic geolocation are limited in accuracy and often rely on basic machine learning techniques. Additionally, advancements in tweet geolocation for other languages often rely on distinct datasets, hindering direct comparisons and assessments of their relative performance on Arabic datasets. To bridge this gap, we investigate eight advanced deep learning techniques, including two Arabic pretrained language models (PLMs) on one constructed dataset. Through a comprehensive analysis, we assess the strengths and weaknesses of each technique for fine-grained Arabic tweet geolocation. Despite the success of PLMs in various tasks, our results demonstrate that a combination of Convolution Neural Network (CNN) and Long Short-Term Memory (LSTM) layers yields the best performance, achieving a test accuracy of 93.85%.
Improved RPCA Method via Fractional Function-Based Structure and Its Application
Authors: Pan Yong-Ke , Peng Shuang
Published: January 2025

With the advancement of oil logging techniques, vast amounts of data have been generated. However, this data often contains significant redundancy and noise. The logging data must be denoised before it is used for oil logging recognition. Hence, this paper proposed an improved robust principal component analysis algorithm (IRPCA) for logging data denoising, which addresses the problems of various noises in oil logging data acquisition and the limitations of conventional data processing methods. The IRPCA algorithm enhances both the efficiency of the model and the accuracy of low-rank matrix recovery. This improvement is achieved primarily by introducing the approximate zero norm based on the fractional function structure and by adding weighted kernel parametrization and penalty terms to enhance the model’s capability in handling complex matrices. The efficacy of the proposed IRPCA algorithm has been verified through simulation experiments, demonstrating its superiority over the widely used RPCA algorithm. We then present a denoising method tailored to the characteristics of logging data and based on the IRPCA algorithm. This method first involves the segregation of the original logging data to acquire background and foreground information. The background information is subsequently further separated to isolate the factual background and noise, resulting in the denoised logging data. The results indicate that the IRPCA algorithm is practical and effective when applied to the denoising of actual logging data.
Tinea Capitis Induced by Barber Shaving: Isolation of Trichophyton tonsurans
Authors: Addari Giampaolo , Corbeddu Marialuisa , Mugheddu Cristina , Chessa Mariangela , Vivanet Grazia , Ferreli Caterina , Atzori Laura
Published: January 2025

Background/Objective: Tinea capitis is a common scalp fungal infection with significant implications for public health, particularly in regions where proper hygiene and access to healthcare are limited. Emerging evidence suggests that this disease, particularly in young male individuals, may be related to certain unsanitary practices in barbershop settings, such as the use of contaminated shaving equipment. To increase awareness of the risk of scalp dermatophyte infections by disclosing different cases of tinea capitis that had arisen shortly after hairdressing procedures and providing a comprehensive review of the existing literature. Patients and Methods: 10 cases of young, adult male patients developed tinea capitis after haircuts carried out at different local barbershops in Sardinia. A collection of data regarding age, sex, location of the disease, clinical manifestations as well as direct microscopy and cultural investigations were performed. Results: Clinical manifestations varied among patients, exhibiting both non-inflammatory and inflammatory features, cultural investigations were positive for Trichophyton tonsurans. Patients were treated with griseofulvin or terbinafine in combination with topical antimycotics. Two cases out of the ten patients developed scarring alopecia. Conclusions: Outbreaks of T. tonsurans-induced tinea capitis, linked to hairdressing, have been recorded in many different countries. Timely diagnosis and therapy are crucial, since any delay can result in disease dissemination and potential complications such as scarring alopecia, particularly in the inflammatory forms.
Unmet Needs in the Management of Chronic Kidney Disease-Associated Pruritus and the Characteristics of the Ideal Treatment: A Spanish Cross-Sectional Survey from a Multidisciplinary Perspective
Authors: De Sequera Patricia , Martínez-Sesmero José M. , Romo Isabel , Calvo Ana , Aceituno Susana , Ruiz-Andrés Olga , Julián-Mauro Juan C.
Published: January 2025

Objectives: Chronic kidney disease-associated pruritus (CKD-aP) is underdiagnosed and not fully understood by healthcare professionals, which leads to poor patient management and impacts patients’ quality of life (QoL). The aim of this study was to analyse unmet needs in CKD-aP management and explore the attributes/characteristics that the ideal CKD-aP treatment should have from the perspective of a group of nephrologists, hospital pharmacists, nurses, patient representatives, and regional health authorities in Spain. Methods: A descriptive, cross-sectional study was conducted using an e-survey including ad hoc questions (6-point Likert scale) related to unmet needs in CKD-aP and best–worst scaling (BWS) to prioritise the attributes/characteristics of the ideal CKD-aP treatment. The survey was developed from a literature review, a patient focus group, and a multidisciplinary expert committee. Results: A total of 21 people participated, and it was considered, among other aspects, that CKD-aP had a significant impact on patient QoL (4.29/5), but the diagnosis rate and knowledge level of agents involved, as well as current treatment efficacy and safety, were low (1.71/5, 2.19/5, 1.91/5, and 2.67/5, respectively). The attributes “improves overall QoL (physical and mental)”, “reduces itch with statistical significance”, and “treatment is supported by clinical development/high evidence and has AEMPS (Spanish Agency for Medicines and Medical Devices)-approved indication for pruritus” were selected as the most valued attributes. There was a positive balance between best–worst scores (86-5, 71-2, and 78-13 points, respectively). Conclusions: The results show the need to undertake actions to drive relevant changes in current clinical practice to improve CKD-aP diagnosis and management.
Low-Waste Technology for High-Precision Connecting Rod Forging Manufacturing
Authors: Dudkiewicz Łukasz , Hawryluk Marek
Published: January 2025

This study refers to the application of an advanced tool in the form of numerical modelling in order to develop a low-waste hot die forging technology to produce a connecting rod forging. The technology aims at ensuring a limited amount of the charge material is necessary to produce one forging, as well as minimizing forging forces, and thus the electric energy consumption. The study includes a verification of the current production technology, which constituted the basis for the construction and development of a numerical model. A new construction of the forging tools was developed, with an additional pre-roughing pass (0X). The new process consists of die forging in the pre-roughing pass (0X), the roughing pass (1X) and the finishing impression (2X). Numerical modelling was subsequently conducted with the use of the Forge 3.0 NxT software. A detailed analysis was conducted on the accuracy of the tool impression filling (including the pre-roughing pass) by the deformed material, the distribution of temperatures for the forgings and the plastic deformations, as well as the courses of forging forces and energy. The results were verified under industrial conditions and compared with the forgings obtained in the previous technology (a roughing pass and a finishing impression). As a result of introducing the pre-roughing pass 0X, the forces were distributed between three impressions, including the especially developed pre-roughing pass. It was confirmed that the abovementioned changes in terms of forging tool construction had a positive effect on relieving the roughing pass and the finishing impression as well as limiting the charge material, and they also lowered the process energy consumption by 10%. This study also validated the relevance of using FE modelling to verify processes under virtual conditions before being implemented under industrial conditions. Therefore, the proposed approach based on multi-variant numerical simulations can be successfully used to improve other manufacturing processes in terms of reducing energy and material consumption and increasing tool service life.
MCBA-MVACGAN: A Novel Fault Diagnosis Method for Rotating Machinery Under Small Sample Conditions
Authors: Huang Wenhan , Zhang Xiangfeng , Jiang Hong , Shao Zhenfa , Bai Yu
Published: January 2025

In complex industrial scenarios, high-quality fault data of rotating machinery are scarce and costly to collect. Therefore, small sample fault diagnosis needs further research. To solve this problem, in this work is proposed a minimum variance auxiliary classifier generation adversarial network based on a multi-scale convolutional block attention mechanism. Firstly, the multi-scale convolutional block attention mechanism is designed to extract multi-scale information and perform weighted fusion to enhance the ability of the model to capture effective features. Secondly, the minimum variance term is designed to minimize the variance of sample distribution, so that the generated samples are distributed more evenly in the feature space, avoiding the problem of pattern collapse. Finally, the objective function is reconstructed by independent classification loss to improve the ability of model data generation. Experimental results on CWRU and gearbox datasets validate the effectiveness and reliability of the proposed method.
Bulk Low-Inertia Power Systems Adaptive Fault Type Classification Method Based on Machine Learning and Phasor Measurement Units Data
Authors: Senyuk Mihail , Beryozkina Svetlana , Zicmane Inga , Safaraliev Murodbek , Klassen Viktor , Kamalov Firuz
Published: January 2025

This research focuses on developing and testing a method for classifying disturbances in power systems using machine learning algorithms and phasor measurement unit (PMU) data. To enhance the speed and accuracy of disturbance classification, we employ a range of ensemble machine learning techniques, including Random forest, AdaBoost, Extreme gradient boosting (XGBoost), and LightGBM. The classification method was evaluated using both synthetic data, generated from transient simulations of the IEEE24 test system, and real-world data from actual transient events in power systems. Among the algorithms tested, XGBoost achieved the highest classification accuracy, with 96.8% for synthetic data and 85.2% for physical data. Additionally, this study investigates the impact of data sampling frequency and calculation window size on classification performance. Through numerical experiments, we found that increasing the signal sampling rate beyond 5 kHz and extending the calculation window beyond 5 ms did not significantly improve classification accuracy.
Dissolution Mechanism of YbOF in (LiF-CaF2)eut. Molten Salt
Authors: Luo Linsheng , Sun Kailei , Wang Xu
Published: January 2025

The dissolution mechanism of YbOF in a fluoride-containing (LiF-CaF2)eut. molten salt is the basis for analyzing the structure of the resulting medium and optimizing the electrolytic preparation of rare-earth Yb alloys. In this study, isothermal saturation was used to analyze solubility changes of YbOF in the (LiF-CaF2)eut. system. Quantum chemical and molecular dynamics ab initio methods were used to study the basic properties of the components of the (LiF-CaF2)eut.-YbOF system and the microscopic structural changes during the dissolution process. In addition, structural changes in the YbOF-saturated (LiF-CaF2)eut. system were analyzed by combining cryogenic-temperature Raman spectroscopy with experimental methods. The results show the solubility of YbOF increased linearly in the temperature range of 1073–1323 K. As the melting temperature exceeded 1073 K, LiF and CaF2 gradually dissociated into Li+, Ca2+, and F−. In the initial stages of YbOF dissolution (1073–1173 K), the Yb–F bond was less stable than the Yb–O bond; YbOF dissociated into YbO+ and F− in this temperature range. When the temperature was increased above 1173 K, YbO+ further dissociated into Yb3+ and O2−. Overall, the dissolution of YbOF did not affect the main structure of the (LiF-CaF2)eut. system.
Generation of Hydrogen Peroxide in Beer and Selected Strong Alcoholic Beverages
Authors: Rak Małgorzata , Mendys Dawid , Płatek Aleksandra , Sitarz Oskar , Stefaniuk Ireneusz , Bartosz Grzegorz , Sadowska-Bartosz Izabela
Published: January 2025

The generation of hydrogen peroxide has been documented in various plant-based beverages, such as coffee, tea, herbal infusions and wine, as well in energy drinks containing ascorbate and in plant-based food. There are no data in the literature on the presence and generation of hydrogen peroxide in beer and strong alcoholic beverages containing plant material. This study aimed to examine whether beer and selected strong alcoholic beverages (brandy, whisky and fruit liqueurs) contain hydrogen peroxide. The presence of hydrogen peroxide was found in freshly opened brandy, whisky, liqueurs and most diluted beers; subsequent incubation in an air atmosphere led to the generation of hydrogen peroxide. The presence of the electron paramagnetic resonance (EPR) signal of the semiquinone radical and the generation of the superoxide radical demonstrated in selected alcoholic beverages by the superoxide dismutase-inhibitable reduction of Nitrotetrazolium Blue and oxidation of dihydroethidium are in agreement with the two-step mechanism of generation of hydrogen peroxide by the autoxidation of phenolics. These results broaden the list of beverages containing and producing hydrogen peroxide.
A Feature-Enhanced Small Object Detection Algorithm Based on Attention Mechanism
Authors: Quan Zhe , Sun Jun
Published: January 2025

With the rapid development of AI algorithms and computational power, object recognition based on deep learning frameworks has become a major research direction in computer vision. UAVs equipped with object detection systems are increasingly used in fields like smart transportation, disaster warning, and emergency rescue. However, due to factors such as the environment, lighting, altitude, and angle, UAV images face challenges like small object sizes, high object density, and significant background interference, making object detection tasks difficult. To address these issues, we use YOLOv8s as the basic framework and introduce a multi-level feature fusion algorithm. Additionally, we design an attention mechanism that links distant pixels to improve small object feature extraction. To address missed detections and inaccurate localization, we replace the detection head with a dynamic head, allowing the model to route objects to the appropriate head for final output. We also introduce Slideloss to improve the model’s learning of difficult samples and ShapeIoU to better account for the shape and scale of bounding boxes. Experiments on datasets like VisDrone2019 show that our method improves accuracy by nearly 10% and recall by about 11% compared to the baseline. Additionally, on the AI-TODv1.5 dataset, our method improves the mAP50 from 38.8 to 45.2.
A Cause Analysis Model of Nuclear Accidents in Marine Nuclear Power Plants Based on the Perspective of a Socio-Technical System
Authors: Zhao Fang , Shu Ruihua , Xu Shoulong , Zou Shuliang
Published: January 2025

Marine nuclear power plants (MNPPs) represent items of forward-looking high-end engineering equipment combining nuclear power and ocean engineering, with unique advantages and broad application prospects. When a nuclear accident occurs, it causes considerable economic losses and casualties. The traditional accident analysis of nuclear power plants only considers the failure of a single system or component, without considering the coupling between the system and the operator, the environment, and other factors. In this study, the cause mechanism of nuclear accidents in MNPPs is analyzed from the perspective of a social technology system. The causal analysis model is constructed by using the internal core causal analysis (e.g., technical control) and external stimulation causal analysis (e.g., social intervention) of accidents, after which the mechanism of the coupled evolution of each influencing factor is analyzed. A Bayesian network inference model is used to quantify the coupling relationship between the factors that affect the deterioration of nuclear accidents. The results show that the main influencing factors are pump failure, valve failure, insufficient response time, poor psychological state, unfavorable sea conditions, unfavorable offshore operating environments, communication failure, inappropriate organizational procedures, inadequate research and design institutions, inadequate regulatory agencies, and inadequate policies. These 12 factors have a high degree of causality and are the main factors influencing the deterioration of the small break loss of coolant accident (SBLOCA). In addition, the causal chain that is most likely to influence the development of SBLOCA into a severe accident is obtained. This provides a theoretical basis for preventing the occurrence of marine nuclear power accidents.
Solution of the Capacity-Constrained Vehicle Routing Problem Considering Carbon Footprint Within the Scope of Sustainable Logistics with Genetic Algorithm
Authors: Palamutçuoğlu Bedrettin Türker , Çavuşoğlu Selin , Çamlı Ahmet Yavuz , Virlanuta Florina Oana , Bacalum Silviu , Züngün Deniz , Moisescu Florentina
Published: January 2025

One of the important problems of sustainable logistics is routing vehicles in a sustainable manner, the green vehicle routing problem, or vehicle routing problems which aim to reduce CO2 emissions. In the literature research, it was seen that these problems were solved with heuristic, metaheuristic, or hyper-heuristic methods and hybrid approaches since they are in the NP-hard class. This work presents a parallel multi-process genetic algorithm that incorporates problem-specific genetic operators to minimize CO2 emissions in the capacity-constrained vehicle routing problem. Unlike previous research, the algorithm combines parallel computing with tailored genetic operators in order to enhance the diversity of solutions and speed up convergence. Genetic algorithm models were developed to minimize total distance, CO2 emissions, and both objectives simultaneously. Two genetic algorithm models were developed to minimize total distance and CO2 emissions. Experimental results using the reference CVRP examples such as A-n32-k5 and B-n44-k7 show that the proposed approach reduces CO2 emissions by 1.2% more than hybrid artificial bee colony optimization, 1.3% more than ant colony optimization, and 4% more than the traditional genetic algorithm. Experimental results using benchmark CVRP instances demonstrate that the proposed approach outperforms hybrid artificial bee colony optimization, ant colony optimization, and traditional genetic algorithms for most of the test cases. This is done by exploiting multi-core processors, and the parallel architecture has improved computational efficiency; the modules compare and update solutions against the global optimum. Results obtained show that prioritizing CO2 emissions as the only objective yields better results compared to multi-objective models. This study makes two significant contributions to the literature: (1) it introduces a novel parallel genetic algorithm framework optimized for CO2 emission reduction, and (2) it provides empirical evidence underscoring the advantages of emission-focused optimization in CVRP.
Structural Equation Models to Determine the Relationship Between Startup Incubation Stages and the Graduation Rate of Incubators in Spain
Authors: Asensio-Ciria Ana , De-Pablos-Heredero Carmen , Blanco Jiménez Francisco José , Montes Botella José Luis , Martínez Antón García
Published: January 2025

Business incubators contribute to the growth of a country, and it is of great interest to deepen knowledge of the impact of incubation phases on the results of incubators to evaluate the effectiveness of developed incubation programs. The objective of this research was to propose a model that quantitatively related different incubation phases to the graduation rate of business incubators in Spain. A sample of 88 incubators was obtained. The survey included 42 items identified in different phases (spreading entrepreneurship, 9 items; pre-incubation, 9 items; basic incubation, 9 items; advanced incubation, 6 items; and graduation, 9 items) and four hypotheses relating to the existence of a positive influence from the startup incubation phases on the incubators results. These were validated by using a structural equation model (SEM) with five latent variables. Three of the four proposed hypotheses that linked startup pre-incubation (H2), basic incubation (H3), and advanced incubation (H4) with graduation rates in Spanish incubators were accepted. These startup incubation stages showed a positive influence on the startup graduation rate. The advanced incubation stage had a very strong relationship with the graduation rate (β = 0.543). Furthermore, a strong indirect effect between business incubation and the graduation rate, explaining 71% of the success of the incubators, was found. Proposals for improvement in each incubation phase to enhance the results of the business incubators are provided. Furthermore, future challenges that should be incorporated into the development of incubator programs, such as the social focus, the implementation of a training and monitoring model, an increase in network businesses, the internationalization of incubators with a globalized approach, the sustainability of the startup’s approach, and the transfer focus, are raised. Given the high variability of Spanish incubators and the wide sampling range, the model could be extended to other contexts with similar behavior within the sample range.
Marble Powder as a Sustainable Cement Replacement: A Review of Mechanical Properties
Authors: Abbas Moutaman M. , Muntean Radu
Published: January 2025

The sudden increase in industrialization has reduced the availability of natural building materials and triggered the growing awareness of sustainable practices within the construction industry. The study presented here deals with marble powder, which is one of the by-products obtained from the marble industry, as a cement replacement in concrete mixtures. The main aims will be to investigate the impact of marble powder waste materials on the mechanical properties of concrete and to promote the recycling of various industrial wastes for environmental sustainability. Material testing was conducted with the levels of substitution of marble powder for cement ranging from 0% to 50%, and the resulting concrete was evaluated for compressive and tensile strength over different curing periods. The results show that concrete compressive strength and tensile strength are most efficiently improved when marble powder replacement is up to 10–15%, attaining its full potential after 28 days. Beyond this replacement level of 15%, the mechanical properties decrease, suggesting that higher substitution levels may not be effective. This paper consolidates findings, provides a novel comparative analysis, and addresses key challenges regarding the use of marble powder, providing room for the future industrial development of supplementary cementitious materials (SCMs), eventually leading to sustainability in the construction sector.
A Science Mapping Perspective of the Spectrum of Giant Panda Research (1929–2022)
Authors: Li Zhongrong , Wu Yan
Published: January 2025

Nowadays, new data-driven science paradigms have been revolutionizing the way of acquiring knowledge and its derivatives. In addition, understanding the knowledge structure of a specific domain is valuable to the application/transformation of science in practice, especially in the era where biodiversity is given top priority. This study combines both performance analysis and science mapping to detect, quantify, and visualize the predominant themes covered by research on giant pandas and their conservation, collected from the Web of Science during the period of 1929–2022. In total, 1456 publications were analyzed with 2090 valid keywords. Twenty-four main themes were detected using co-word analysis. The most productive themes were WOLONG-NATURE-RESERVE, CLIMATE-CHANGE, CARNIVORA, NATURE-RESERVE, and ECOSYSTEM-SERVICES, according to the number of published documents, and the most impacting ones were WOLONG-NATURE-RESERVE, CARNIVORA, CLIMATE-CHANGE, ECOSYSTEM-SERVICES, DNA, and BACTERIA according to the citations, whereas the h-index indicates that the top six themes were: WOLONG-NATURE-RESERVE, CARNIVORA, CLIMATE-CHANGE, ECOSYSTEM-SERVICES, NATURE-RESERVE, and AILURUS-FULGENS. As a result, this indicates worthwhile prospects for the future study of giant panda conservation, which will focus more on the following hot themes: GUT-MICROBIOTA, ECOSYSTEM-SERVICES, HABITAT, and CLIMATE-CHANGE. Meanwhile, WOLONG still takes center stage in giant panda research. Therefore, by building a more comprehensive conceptual structure of giant panda research, this study contributes to current and future developments in this field.
Intelligent Manufacturing and Pollution Emissions from Chinese Manufacturing Firms: Theories and Mechanisms
Authors: Zi Shurong , Pan Ziyuan , Chen Yanjie , Liu Xinheng , Ban Qi
Published: January 2025

Energy conservation and emission reduction are essential for China’s economy to achieve sustainable development. This paper integrates industrial robots and pollution emissions into a heterogeneous firm model to theoretically analyze the impact and mechanism of industrial robots on pollution emissions of listed manufacturing firms. Using data from a sample of listed manufacturing firms over the period 2012–2022, an econometric model is constructed and empirically tested. The results of the study show that industrial robots have had a significant impact on the emission intensity of the manufacturing industry through technological progress and innovation effects, while firms’ pollution levels, regional environmental regulatory intensity, ownership type, and factor intensity type have heterogeneous effects on abatement. In view of this, the application of industrial robots should be promoted according to local conditions, the development of differentiated environmental regulatory policies, and targeted measures to give full play to the emission reduction effect of industrial robots, to synergistically promote a high level of ecological environmental protection and sustainable economic and social development by accelerating the greening and intelligent transformation of China’s manufacturing enterprises.
Introduction to the Topic of AI and IoT for Promoting Green Operation and Sustainable Environment
Authors: Zhang Nan , Liu Wei , Wu Chia-Huei
Published: January 2025

In the current era, green operation has become a key strategic direction for firm development, with its core being the realization of a win–win situation for economic and environmental benefits through environmentally friendly production processes [...]
Evaluation of Cognitive Functions in People Living with HIV Before and After COVID-19 Infection
Authors: Basoulis Dimtrios , Mastrogianni Elpida , Eliadi Irene , Platakis Nikolaos , Platis Dimitris , Psichogiou Mina
Published: January 2025

Background: Cognitive function decline is a problem in aging people living with HIV (PLWHIV). COVID-19 infection is associated with neuropsychiatric manifestations that may persist. The aim of our study was to evaluate cognitive function in PLWHIV before and after COVID-19 infection. Methods: This was a prospective observational study conducted at “Laiko” General Hospital from July 2019 to July 2024. The Montreal Cognitive Assessment (MOCA) scale was used to evaluate cognitive functions. Results: 116 virally suppressed PLWHIV participated (mean age: 47.6 years, 91.4% male); 60 underwent repeated evaluation after the pandemic at a median interval of 3.1 years. The median MOCA score was 24 (22–26), with 35.3% scoring within normal limits. A negative correlation was observed between MOCA scores and age (ρ = −0.283, p = 0.002), but not with a CD4 count at diagnosis (ρ = 0.169, p = 0.071) or initial HIV RNA load (ρ = 0.02, p = 0.984). In the subgroup with repeated testing, MOCA was correlated with the CD4 count (ρ = 0.238, p = 0.069 in the first and ρ = 0.319, p = 0.014 second test). An improvement in performance was observed (median score increase from 24 to 25, p = 0.02). Conclusions: MOCA can detect early changes in cognitive function in PLWHIV. Further studies are required to determine the role of COVID-19 over time.
A Systematic Review of Medical Treatments for Benign Prostatic Hyperplasia in Dogs: Evaluating Strategies for Reproductive Function Preservation
Authors: Posastiuc Florin Petrișor , Constantin Nicolae Tiberiu , Domain Guillaume , Soom Ann Van , Diaconescu Alexandru Ilie , Codreanu Mario Darius
Published: January 2025

Benign prostatic hyperplasia (BPH) is a common condition in intact male dogs and a leading cause of subfertility. While surgical and chemical castration are effective treatments for BPH, managing this condition in breeding dogs is challenging due to the desire to preserve reproductive function. This study systematically reviews medical treatments for BPH that maintain breeding potential. Thus, a comprehensive literature search was conducted across four databases (CAB Direct, Scopus, PubMed, and Web of Science) to identify peer-reviewed studies published between 2000 and 2024, following PRISMA guidelines. Specific inclusion and exclusion criteria were applied, and a risk of bias assessment was performed using a modified Cochrane tool. A total of 35 studies were included, evaluating therapies such as finasteride, osaterone acetate, tamoxifen, and others, with outcomes including prostate size, sperm quality, hormonal levels, and tissue morphology. Evidence suggests that osaterone acetate and finasteride are acceptable treatments for BPH in breeding dogs, in spite of a still debatable effect on some sperm characteristics. Therapies like tamoxifen and acyline should be excluded due to their detrimental effects on reproductive function. Further research is required for apparently promising approaches concerning tadalafil, anastrazole, mepartricin, and U. fissa.
Monnaie et commerce en Romanie génoise d’après une minute notariale inédite
Authors: Mirea Andrei
Published: January 2025

L’article offre une étude de cas portant sur l’histoire commerciale et monétaire de la Méditerranée orientale et de la mer Noire au cours de la dernière décennie du XIVe siècle à partir d’une minute notariale inédite provenant des riches archives de Gênes. Le document principal autour duquel s’articule cet exposé est représenté par une minute notariale datée du 7 juin 1417, rédigée à Gênes, faisant partie intégrante du dossier juridique relatif au litige entre les marchands génois Lodisio Bavosi et Bartolomeo Mosca. Après avoir entrepris une enquête visant à rassembler toutes les informations disponibles sur les acteurs principaux du procès et à esquisser brièvement leurs biographies, une analyse des enjeux du conflit opposant les deux marchands a été effectuée. Le même document fournit des détails d’une importance particulière pour l’histoire économique de la région concernée, notamment des transactions commerciales, des types de marchandises, des monnaies rares, des prix, des taux de change et des unités de mesure, que l’auteur a cherché à replacer dans leur contexte historique.Mots-clés : Romanie génoise; Gênes; Péra; Licostomo; Lodisio Bavosi; Bartolomeo Mosca; mer Noire; minute notariale.
The Slavonic Epistles of Basarab the Elder to the Transylvanian Saxons (1474–1476)
Authors: Mirea Andrei
Published: January 2025

The Slavonic letters dispatched by the voivodes of Wallachia to Brașov, where they remain preserved to this day, constitute a major and invaluable source for the fifteenth-century history of Wallachia. In this article, our primary objective is to readdress the undated Slavonic epistolary corpus attributed to Basarab the Elder, who ruled Wallachia with several interruptions between 1473 and 1477. These epistles were addressed by the Wallachian voivode to the Saxon town of Brașov, while the answers of the Saxon authorities to the Wallachian lord did not generally survive. A diligent analysis of the clerks’ handwriting habits alongside a comparative investigation with other types of documentary evidence, such as charters, might not only suggest new reasoned dates for these Slavonic letters, but also could enlarge other research horizons, such as the Romanian-Ottoman relations, the history of audiences and of medieval political discourse, the political thought of the Wallachian lords, the careers of the scribes working in the chancellery, or the functional dynamics of the princely chancellery. Our essay aims to revisit this epistolary corpus through several analytical methods highlighting the usefulness and the potential of such approaches for future research.Keywords: Basarab the Elder, Wallachia, Slavonic letters, chancellery, Brașov, the Romanian-Ottoman relations, medieval handwriting.
Skatole Alleviates Osteoarthritis by Reprogramming Macrophage Polarization and Protecting Chondrocytes
Authors: Wang Weiyun , Chu Yaru , Lu Yunkun , Xu Jie , Zhao Weixuan , Liang Zhuo , Guo Xueqiang , Xi Lingling , Han Tao , Shen Yaping , Song Wenjuan , Tang Yanhua , Wen Mengnan , Qian Zhuang , Wang Lei , Fan Zhenlin , Zhou Guangdong , Ren Wenjie
Published: January 2025

Osteoarthritis (OA) is the most prevalent joint disease, yet effective disease-modifying OA drugs (DMOADs) remain elusive. Targeting macrophage polarization has emerged as a promising avenue for OA treatment. This study identified skatole through high-throughput screening as an efficient modulator of macrophage polarization. In vivo experiments demonstrated that skatole administration markedly reduced synovitis and cartilage damage in both destabilization of medial meniscus (DMM)-induced OA mice and monosodium iodoacetate (MIA)-induced OA rats. Mechanistically, skatole activated signal transducer and activator of transcription 6 (Stat6) signaling, promoting M2 macrophage polarization, while inhibiting nuclear factor-κB (NFκB) and mitogen-activated protein kinase (MAPK) signaling pathways to suppress M1 polarization. RNA-sequencing analysis, targeted metabolomics, and mitochondrial stress tests further revealed that skatole treatment shifted macrophages toward oxidative phosphorylation for energy production. Additionally, it up-regulated genes associated with glutathione metabolism and reactive oxygen species (ROS) pathways, reducing intracellular ROS production. The CUT&Tag assay results indicated that the downstream transcription factor p65 of NFκB can directly bind to gene loci related to inflammation, oxidative phosphorylation, and glutathione metabolism, thereby modulating gene expression. This regulatory process is inhibited by skatole. At the chondrocyte level, conditional medium from skatole-treated M1 macrophages balanced anabolism and catabolism in mouse chondrocytes and inhibited apoptosis. In IL1β-treated chondrocytes, skatole suppressed inflammation and catabolism without affecting apoptosis or anabolism. Overall, skatole maintains immune microenvironment homeostasis by modulating macrophage polarization in joints and preserves cartilage function by balancing chondrocyte anabolism and catabolism, effectively alleviating OA. These findings suggest skatole’s potential as a DMOAD.
ЭМОЦИОНАЛЬНЫЙ ИНТЕЛЛЕКТ: ПОНЯТИЕ, МОДЕЛИ И ФОРМИРОВАНИЕ
Authors: Губарева Л.И. , Чянкаева З.А. , Соловьев А.Г. , Литвинова Л.В.
Published: January 2025

В статье представлены концепции и модели эмоционального интеллекта, а также структурно-функциональные основы и нейрофизиологические механизмы формирования эмоций. Должное внимание уделено факторам, влияющим на формирование эмоционального интеллекта и как следствие на успешность деятельности. Показаны перспективы дальнейших исследований в области поиска мозговых коррелятов эмоционального интеллекта и их взаимосвязи с другими адаптационными системами организма и свойствами личности, так как для людей, работающих по профессии типа «человек-человек», «человек-машина», эмоциональный интеллект является профессионально важным качеством.

he article presents concepts and models of emotional intelligence, as well as structural and functional foundations and neurophysiological mechanisms of emotion formation. Due attention is paid to factors influencing the formation of emotional intelligence and, as a consequence, the success of activities. The prospects for further research in the field of searching for brain correlates of emotional intelligence and their relationship with other adaptive systems of the body and personality property's are shown, since for people working in a profession such as "person-person", "person-machine" emotional intelligence is a professionally important quality.
АСИММЕТРИЯ РИТМА СЕРДЦА И СИТУАТИВНАЯ СОНЛИВОСТЬ ОТ ВЕЧЕРА К НОЧИ: ВКЛАД ФАКТОРА ОБЩЕЙ ДНЕВНОЙ СОНЛИВОСТИ
Authors: Демарева В.А.
Published: January 2025

Настоящее исследование посвящено анализу взаимосвязи между дневной сонливостью, асимметрией сердечного ритма (АСР) и ситуативной сонливостью в вечерне-ночные часы. Актуальность работы обусловлена важностью понимания механизмов автономной регуляции организма и разработки методов мониторинга сонливости, что особенно важно для предотвращения ошибок в критически важных сферах, таких как транспорт и сменная работа. Цель исследования — изучить влияние дневной сонливости на динамику АСР в вечерне-ночной период, а также выявить корреляции между ситуативной сонливостью и АСР. В эксперименте приняли участие 50 здоровых добровольцев, разделенных на группы по уровню дневной сонливости. Использовались субъективные шкалы для оценки сонливости и точные методы анализа АСР. Результаты показали, что у участников с более высокой дневной сонливостью наблюдается снижение показателей АСР с наступлением биологической ночи, что вероятно связано с увеличением физической и когнитивной усталости. В группе с высокой дневной сонливостью также были выявлены значимые отрицательные корреляции между ситуативной сонливостью и изменчивостью сердечного ритма. Эти данные могут быть использованы для создания систем мониторинга усталости и сонливости в различных областях, таких как транспорт, сменная работа и спорт.

This study focuses on analyzing the relationship between daytime sleepiness, heart rate asymmetry (HRA), and situational sleepiness during the evening-night hours. The relevance of the work is determined by the importance of understanding the mechanisms of autonomic regulation in the body and developing methods for sleepiness monitoring, which is especially crucial for preventing errors in critical areas such as transportation and shift work. The aim of the study was to investigate the effect of daytime sleepiness on the dynamics of HRA during the evening-night period and to identify correlations between situational sleepiness and HRA. The experiment involved 50 healthy volunteers, divided into groups based on their level of daytime sleepiness. Subjective sleepiness scales were used for assessment, along with precise methods for analyzing HRA. The results showed that participants with higher daytime sleepiness exhibited a decrease in HRA indicators with the onset of biological night, which is likely associated with increased physical and cognitive fatigue. In the high daytime sleepiness group, significant negative correlations were also found between situational sleepiness and heart rate variability. These findings could be used to develop systems for monitoring fatigue and sleepiness in various fields such as transportation, shift work, and sports.