bluuebunny/crossref_metadata_2025 huggingface dataset:
Sociocultural and Clinical Determinants of Sexual Dysfunction in Perimenopausal Women with and Without Breast Cancer
Authors: Delgado-Enciso Osiris G. , Melnikov Valery , Hernandez-Fuentes Gustavo A. , Romero-Michel Jessica C. , Montes-Galindo Daniel A. , Guzmán-Sandoval Veronica M. , Delgado-Enciso Josuel , Ramirez-Flores Mario , Rodriguez-Sanchez Iram P. , Martinez-Fierro Margarita L. , Garza-Veloz Idalia , Sánchez-Meza Karmina , Sanchez-Ramirez Carmen A. , Meza-Robles Carmen , Delgado-Enciso Ivan
Published: November 2024

Breast cancer survivorship is a recognized risk factor for sexual dysfunction, with various clinical, sociocultural, and psychological factors potentially interacting differently across populations. This study compared sexual dysfunction, anxiety, and depression between females with breast cancer and those without, aiming to identify associated factors. A total of 362 females participated, including 227 with sexual dysfunction and 135 controls. Among them, 195 are breast cancer survivors, while 167 have no personal history of cancer. Key variables were analyzed using Student’s t-test for quantitative data and Fisher’s exact test for categorical data, while logistic regression models were used to assess the association between sexual dysfunction and various factors. Multivariate analysis revealed that, in sexually active females, breast cancer survivorship increased the odds of sexual dysfunction 2.7-fold (95% CI: 1.17–6.49; p = 0.020). Anxiety was significantly associated with sexual dysfunction, regardless of cancer status (AdOR 6.00; 95% CI: 2.50–14.43; p < 0.001). The interaction between cancer survival and anxiety further increased the odds of sexual dysfunction by more than 11-fold (AdOR 11.55; 95% CI: 3.81–35.04; p < 0.001). Additionally, obesity was found to be a protective factor among cancer survivors (AdOR 0.149; 95% CI: 0.027–0.819; p = 0.029). In conclusion, breast cancer has a significant impact on sexual function, with psychological factors like anxiety playing a crucial role. Addressing these issues requires a holistic, patient-centered approach that considers the complex interplay of physical, emotional, and sociocultural factors.
Electrochemotherapy in the Locoregional Treatment of Metastatic Colorectal Liver Metastases: A Systematic Review
Authors: Barbieri Pierluigi , Posa Alessandro , Lancellotta Valentina , Madoff David C. , Maresca Alessandro , Cornacchione Patrizia , Tagliaferri Luca , Iezzi Roberto
Published: November 2024

Background: The global incidence of secondary liver cancer is rising due to multiple risk factors, presenting significant challenges in public health. Similarly, colorectal cancer (CRC) remains a leading cause of cancer-related mortality with the development of frequent liver metastases. Surgical resection of CRC liver metastases is only suitable for a limited subset of patients, necessitating alternative nonsurgical treatments such as electrochemotherapy (ECT); Methods: This review adhered to the S.P.I.D.E.R. framework. Systematic searches of PubMed, Cochrane, and Scopus databases were conducted for studies published between 2003 and 2023, following PRISMA guidelines. Inclusion criteria were full-text clinical studies in English focusing on ECT-treated CRC liver metastases, excluding reviews, editorials, and non-clinical papers. The GRADE approach was utilized to assess evidence quality, considering study limitations, consistency, and other factors; Results: From 38 identified articles, 4 met the inclusion criteria, encompassing 78 patients and 128 treated lesions. The studies demonstrated variability in design and follow-up duration (3–11 months). Complete response (CR) rates ranged from 33.3% to 63.0%, while progression disease (PD) rates were high, ranging from 23.0% to 55.6%. Median overall survival (OS) spanned 11.3 to 29.0 months. No severe ECT-related complications were reported. Conclusions: ECT appears to be a safe and effective modality for the treatment of CRC liver metastases, especially for lesions unsuitable for other ablative techniques. Further prospective and randomized studies are essential to better define the role of ECT in managing CRC liver metastases and to compare its efficacy with other ablative methods.
Identification of Pre-Heart Failure in Early Stages: The Role of Six Stages of Heart Failure
Authors: Jankajova Monika , Singh Ram B. , Hristova Krasimira , Elkilany Galal , Fatima Ghizal , Singh Jaipaul , Fedacko Jan
Published: November 2024

Despite increased availability of effective drug therapy for treatment of heart failure (HF), the morbidity and mortality in chronic heart failure (CHF) are unacceptably high. Therefore, there is an urgent need to ascertain new imaging techniques to identify early sub-clinical forms of cardiac dysfunctions, to guide early relevant treatment. It seems that all the behavioral risk factors—such as tobacco, alcoholism, Western-type diet, sedentary behavior and obesity, emotional disorders, and sleep disorder are associated with early cardiac dysfunction, which may be identified by speckle-tracking echocardiography (STE). Cardiac remodeling can also occur chronologically in association with biological risk factors of CHF, such as diabetes mellitus (DM), hypertension, cardiomyopathy, valvular heart disease, and coronary artery disease (CAD). In these conditions, twisting and untwisting of the heart, cardiac fibrosis, and hypertrophy can be identified early and accurately with 2-Dimentional (2D) and 3D echocardiography (2D echo and 3D echo) with tissue Doppler imaging (TDI), strain imaging via STE, and cardiac magnetic resonance imaging (CMR). Both 2D and 3D echo with STE are also useful in the identification of myocardial damage during chemotherapy and in the presence of risk factors. It is possible that global longitudinal systolic strain (GLS) obtained by STE may be an accurate marker for early identification of the severity of CAD in patients with non-ST segment elevation MI. Left ventricular ejection fraction (LVEF) is not the constant indicator of HF and it is normal in early cardiac dysfunction. In conclusion, this review suggests that GLS can be a useful early diagnostic marker of early or pre-cardiac dysfunction which may be treated by suitable drug therapy of HF along with the causes of HF and adhere to prevention strategies for recurrence. In addition, STE may be a superior clinical tool in the identification of cardiac dysfunction in its early stages compared to ejection fraction (EF) based on conventional echocardiography. Therefore, it is suggested that the chances of either stalling or reversing HF are far better for patients who are identified at an early stage of the disease.
Shifts in the Boot: Understanding Inequality’s Impact on Interregional Migration Patterns in Italy
Authors: Di Pasquale Giacomo , Parazzi Elisa
Published: November 2024

Italy has long experienced a significant developmental gap between its northern and southern regions, with the latter being less developed. The 2007–2008 financial crisis accentuated this gap, leading to distinct patterns of interregional migration. This paper aims to investigate the effects of past migration flows and income inequality on interregional mobility in Italy, using a gravity model with bidirectional dyads and three different measures of inequality as dependent variables: Gini Index, Relative Poverty, and Income Ratio. Previous research has shown that living in highly unequal regions is associated with increased mistrust and anxiety about social status, contributing to unhappiness among residents. Using bilateral gross migration flows for the period 2007–2018, the study aims to control for the potential endogeneity between interregional mobility and inequality. The results indicate a positive relationship between high levels of inequality and interregional out-mobility, underscoring the need for policies aimed at reducing both horizontal and vertical inequality within and among Italian regions.
Students’ Assertiveness and Empathy Social Skills and Gender at the University of Seville
Authors: Claudel Alicia , García González Alfonso Javier
Published: November 2024

The social skills of assertiveness and empathy are valuable and useful in our daily lives, influencing the quality of our interactions and relationships. This fact underscores the need for research that examines the various factors influencing these constructs. The present study addressed gender disparities in the social skills of assertiveness and empathy in a sample of students from the University of Seville. Its objectives were to identify the gender differences in the social skills of assertiveness and empathy in university students and identify the obstacles gender posed to assertiveness and empathy. For this purpose, an exploratory study was conducted with 116 students, 56% women and 44% men. A Google questionnaire was used to collect various sociodemographic variables, and the Rathus Assertiveness Schedule (Ras, 1973), the Spanish adaptation of the Interpersonal Reactivity Index (IRI; Davis, 1980), and a questionnaire concerning students’ beliefs (Colás-Bravo y Villaciervos-Moreno, 2007) were applied. The results yielded no gender differences in the development of students’ levels of assertiveness and empathy. However, when analyzing the external factors, “socioeconomic level” and “religion” stood out. The findings of this work have relevant implications for the promotion of gender equality and the formation of public policies. Studies in this field should be extended to different educational contexts.
Solving the Chemistry Puzzle—A Review on the Application of Escape-Room-Style Puzzles in Undergraduate Chemistry Teaching
Authors: Clapson Marissa Lorrene , Schechtel Shauna , Davy Emma , Durfy Connor Skye
Published: November 2024

Active learning techniques are taking the classroom by storm. Numerous research articles have highlighted the benefits of active learning techniques on student understanding, knowledge retention, problem solving, and teamwork. One avenue to introduce active learning into the classroom is the gamification of course learning content. Educational escape rooms are one such example in which students solve a series of puzzles related to course content to “escape” within a set time frame. Escape games play an interesting role in motivating students, building communication skills and allowing for multimodal learning, having been shown to increase students’ test results and enjoyment of the course content. In lieu of the traditional escape room format, a fully immersive room(s) with classical escape room puzzles (finding items, riddles, alternative locking mechanisms) is used alongside learning activities, and educators have begun to develop truncated activities for easier applications in larger classrooms. In this review, we explore several escape room activities: immersive, paper-based, Battle Boxes, condensed escape activities, and online/virtual, providing examples of the types of puzzles included therein. We similarly discuss the creation of escape room materials and recommendations for the interested educator, as well as the learning benefits of engaging in puzzle development. Finally, we provide an overview on methods to assess active learning through escape rooms, establishing an overview of empirical evidence towards their effectiveness as a learning tool.
School Self-Evaluation in Decision Making: Perspectives from Portuguese School Leaders
Authors: Figueiredo Carla , Sampaio Marta , Marinho Paulo , Dihl Moraes Amanda , Fernandes Preciosa
Published: November 2024

This paper explores the use of school self-evaluation (SSE) conclusions in school leaders’ decision making regarding school management. Originating from the understanding that school management is a complex process that thrives when informed and substantiated by rigorous and contextualised information on school functioning, this study highlights that SSE processes are a valuable source of such information. The present study was conducted following a qualitative approach in which 18 school leaders (i.e., headteachers) from schools in Portugal were interviewed through semi-structured interviews to explore how they perceive SSE, and if and how they use it to make decisions in their schools. The results reveal that school leaders value SSE processes and the knowledge produced by them as a source of contextualised information that can be used to validate and legitimise decisions. Likewise, the results indicate that school leaders use the information provided by SSE to inform their decisions in collaborative and shared debates and reflection processes with their school communities. Nonetheless, the headteachers also report some constraints in decision-making processes. It is evident that school leaders rely on SSE results and information to substantiate their decisions. Although not generalisable, these results provide valuable insights into the topic and contribute to closing a gap in the literature concerning the effective use of SSE in school management and decision making.
Integrating Artificial Intelligence in the Sustainable Development of Agriculture: Applications and Challenges in the Resource-Based Theory Approach
Authors: Petcu Monica Aureliana , Sobolevschi-David Maria-Iulia , Curea Stefania Cristina , Moise Dumitru Florin
Published: November 2024

In the electronics sector, artificial intelligence (AI) has grown into a disruptive force that is changing how humans engage with technology and creating new opportunities. AI is expanding the capabilities of electronic devices, granting them higher intelligence, increased intuitiveness, and the ability to comprehend and react to human behavior. The purpose of this approach is to highlight the knowledge structure in artificial intelligence application in agriculture and its challenges within the European Union. A bibliometric analysis was conducted, distinguishing the following items as the main research themes: agriculture 4.0; advanced monitoring and controlling strategies in intelligent agriculture; the automation of agriculture by including practices such as cloud computing, Internet of Things (IoT), big data, blockchain, robotics and AI, information security; new skills, and responsible leadership. The regression analysis revealed that the employers’ assumption of responsibility, by ensuring opportunities for training and development of digital skills, determines the growth of added value (0.013) and its rate (0.0003). Enhancing labor productivity depends on Internet access for the integration of technologies based on artificial intelligence (1.343). An increasing employment rate of low-skilled people affects agricultural production (0.0127). The contributions of this two-dimensional approach consist in supporting the integration of digital technology in agriculture as a condition for achieving the goals of sustainable development.
Artificial-Intelligence-Based Model for Early Strong Wind Warnings for High-Speed Railway System
Authors: Gu Wei , Xing Hongyan , Yang Guoyuan , Shi Yajing , Liu Tongyuan
Published: November 2024

Wind speed prediction (WSP) provides future wind information and is crucial for ensuring the safety of high-speed railway systems (HSRs). However, the accurate prediction of wind speed (WS) remains a challenge due to the nonstationary and nonlinearity of wind patterns. To address this issue, a novel artificial-intelligence-based WSP model (EE-VMD-TCGRU) is proposed in this paper. EE-VMD-TCGRU combines energy-entropy-guided variational mode decomposition (EE-VMD) with a customized hybrid network, TCGRU, that incorporates a novel loss function: the Gaussian kernel mean square error (GMSE). Initially, the raw WS sequence is decomposed into various frequency-band components using EE-VMD. TCGRU is then applied for each decomposed component to capture both long-term trends and short-term fluctuations. Furthermore, a novel loss function, GMSE, is introduced to the training of TCGRU to analyze the WS’s nonlinear patterns and improve prediction accuracy. Experiments conducted on real-world WS data from the Beijing–Baotou railway demonstrate that EE-VMD-TCGRU outperforms benchmark models, achieving a mean absolute error (MAE) of 0.4986, a mean square error (MSE) of 0.4962, a root mean square error (RMSE) of 0.7044, and a coefficient of determination (R2) of 94.58%. These results prove the efficacy of EE-VMD-TCGRU in ensuring train operation safety under strong wind environments.
Correction: Sun et al. Multimodal Dataset Construction and Validation for Driving-Related Anger: A Wearable Physiological Conduction and Vehicle Driving Data Approach. Electronics 2024, 13, 3904
Authors: Sun Lichen , Yang Hongze , Li Bo
Published: November 2024

In the original publication [...]
A Sub-6 GHz 8 × 8 MIMO Antenna Array for 5G Metal-Frame Mobile Phone Applications
Authors: Chen Yu-Tung , Su Hsin-Lung
Published: November 2024

This article introduces a broadband sub-6 GHz 8 × 8 MIMO (multi-input multi-output) antenna array for 5G (fifth-generation) metal-frame mobile phone applications. The unique advantage of this compact antenna design is its placement in the corners of the mobile phone, allowing for significant PCB board space reduction. The proposed antenna’s 6 dB impedance bandwidth ranged from 3.3 to 6 GHz, covering the n77/78/79 and WiFi-5GHz bands. The main radiating element was an open-slot antenna coupled by a T-shaped structure connected to a 50-Ω transmission line. The size of the single-antenna element was 12.25 mm × 2.5 mm × 7 mm, and these antennas were symmetrical at four corners of the smartphone. A wide slot and neutral line were incorporated to reduce mutual coupling between adjacent antennas. The MIMO antenna array achieved isolation above 12 dB. The peak realized gain ranged from 2 to 5.28 dBi, and the total efficiency spanned 37% to 71%. The ECC (envelope correlation coefficient) was less than 0.34, and the CC (channel capacity) ranged from 33 and 41 bps/Hz.
KARAN: Mitigating Feature Heterogeneity and Noise for Efficient and Accurate Multimodal Medical Image Segmentation
Authors: Gu Xinjia , Chen Yimin , Tong Weiqin
Published: November 2024

Multimodal medical image segmentation is challenging due to feature heterogeneity across modalities and the presence of modality-specific noise and artifacts. These factors hinder the effective capture and fusion of information, limiting the performance of existing methods. This paper introduces KARAN, a novel end-to-end deep learning model designed to overcome these limitations. KARAN improves feature representation and robustness to intermodal variations through two key innovations: First, KA-MLA, a novel attention block incorporating State Space Model (SSM) and Kolmogorov–Arnold Network (KAN) characteristics into Transformer blocks for efficient, discriminative feature extraction from heterogeneous modalities. Building on KA-MLA, we propose KA-MPE for multi-path parallel feature extraction to avoid multimodal feature entanglement. Second, RanPyramid leverages random convolutions to enhance modality appearance learning, mitigating the impact of noise and artifacts while improving feature fusion. It comprises two components: an Appearance Generator, creating diverse visual appearances, and an Appearance Adjuster, dynamically modulating their weights to optimize model performance. KARAN achieves high segmentation accuracy with lower computational complexity on two publicly available datasets, highlighting its potential to significantly advance medical image analysis.
Quantile Connectedness of Uncertainty Indices, Carbon Emissions, Energy, and Green Assets: Insights from Extreme Market Conditions
Authors: Liu Tiantian , Zhang Yulian , Zhang Wenting , Hamori Shigeyuki
Published: November 2024

In this study, we investigate the volatility spillover effects across uncertainty indices (Infectious Disease Equity Market Volatility Tracker (IDEMV) and Geopolitical Risk Index (GPR)), carbon emissions, crude oil, natural gas, and green assets (green bonds and green stock) under extreme market conditions based on the quantile connectedness approach. The empirical findings reveal that the total and directional connectedness across green assets and other variables in extreme market conditions is much higher than that in the median, and there is obvious asymmetry in the connectedness measured at the extreme lower and upper quantiles. Our findings suggest that the uncertainty caused by COVID-19 has a more significant impact on green assets than the uncertainty related to the Russia–Ukraine war under normal and extreme market conditions. Furthermore, we discover that the uncertainty indices are more important in predicting green asset volatility under extreme market conditions than they are in the normal market. Finally, we observe that the dynamic total spillover effects in the extreme quantiles are significantly higher than those in the median.
Thermal Conductivity of the Binary Mixtures of N-Heptane and Fatty Acid Esters: Measurement and Correlation
Authors: Zou Qing , Chen Zhenhui , Ding Siwen , Xu Jimin , He Maogang , Liu Xiangyang
Published: November 2024

As a renewable energy source and potential substitute for fossil fuels, biodiesel plays an increasingly important role in both energy security and environmental protection. Accurate thermal conductivity data of biodiesels and their mixture with diesel are critical to engine design to achieve high combustion efficiency. This study measured the thermal conductivity of binary mixtures of heptane and biodiesel components, specifically methyl myristate, methyl laurate, and methyl caprate, over a temperature range of 298.15–328.15 K, using the two-wire 3ω method. Based on the experimental data, the effect of mass fraction, temperature, and carbon chain length of the fatty acid ester on the thermal conductivity was analyzed. The second-order Scheffé polynomial model, Flippov equation, Jamieson equation, and Chen equation were used to correlate the experimental data and compare to find a better one. The Flippov equation shows the lowest absolute average relative deviation of 0.80% for the binary mixtures of heptane with methyl myristate, methyl laurate, and methyl caprate.
A Multi-Mode Recognition Method for Broadband Oscillation Based on CS-OMP and Adaptive VMD
Authors: Gao Jinggeng , Xu Honglei , Yang Yong , Zhang Xujun , Mao Xiangde , Dong Haiying
Published: November 2024

Due to the application of power electronics and wind power generation equipment in power systems, broadband oscillation events constantly appear, which makes broadband oscillation difficult to detect due to the limitations of communication bandwidth and the sampling theorem. To ensure the safety and stability of the system, and to detect and recognize the broadband oscillation information timely and accurately, this paper presents a multi-mode recognition method of broadband oscillation based on compressed sensing (CS) and the adaptive Variational Mode Decomposition (VMD) algorithm. Firstly, the high-dimensional oscillation signal data collected by the Phasor Measurement Unit (PMU) is compressed and sampled by a Gaussian random matrix, and the obtained low-dimensional data are uploaded to the main station. Secondly, the orthogonal matching pursuit (OMP) algorithm of the master station is used to reconstruct the low-dimension signal, and the original high-dimension signal data are recovered without losing the main features of the signal. Finally, an adaptive VMD algorithm with energy loss minimization as a threshold is used to decompose the reconstructed signal, and the Intrinsic Mode Function (IMF) components with broadband oscillation information are obtained. By constructing oscillating signals with different frequencies, Gaussian white noise with a signal-to-noise ratio of 10 dB to 30 dB is added successively. After the signal is compressed and reconstructed by the proposed method, the signal-to-noise ratio can reach 18.8221 dB to 40.0794 dB, etc., and the oscillation frequency and amplitude under each signal-to-noise ratio can be accurately identified. The results show that the proposed method not only has good robustness to noise, but also has good denoising effect to noise. By using the simulation measurement model, the original oscillation signal is compressed and reconstructed, and the reconstruction error is 0.1263. The basic characteristics of the signal are restored, and the frequency and amplitude of the oscillation mode are accurately identified, which proves that the method is feasible and accurate.
Emission of Gaseous Pollutants During Combustion and Co-Combustion of Thermally Treated Municipal Solid Waste
Authors: Lasek Janusz , Głód Krzysztof , Supernok Krzysztof , Bigda Joanna
Published: November 2024

This study shows the results of the combustion of raw and torrefied municipal solid waste. The pellets made of waste were torrefied using a rotary kiln reactor. The average solid yield was 551 g/h. The ratio of solid product mass to the raw material mass (i.e., percentage solid yield, wt%) was 78%. The combustion of the waste/coal mixtures and the torrefied waste was stable, without major problems. The unstable combustion was observed during the combustion of single waste. It was probably caused by the blocking of the portion of raw pellets in the screw feeder. A significantly lower emission of SO2 (i.e., 43–114 mg/m3 STP ref. to 6 v.% O2) was observed during the single combustion of torrefied and raw waste. Significantly higher emissions of CO and H-C were observed during the combustion of single raw and torrefied waste. This higher emission should be reduced by the optimization of air distribution.
TestShark: A Passive Conformance Testing System for ISO 15118 Using Wireshark
Authors: Lee Hoguk , Shin Minho
Published: November 2024

ISO 15118 is an international standard for charging communication between electric vehicles and charging infrastructure. Among the series of ISO 15118, ISO 15118-4, ISO 15118-5, ISO 15118-9, and ISO 15118-21 (under development) define active conformance testing methodologies where the test system engages with the system under test as a primary actor and tests whether the counterparty is complying with the standard. However, such a full-fledged test system is not appropriate during system development and field testing. In this paper, a passive conformance testing approach that monitors the packets between the electric vehicle and the charger and performs conformance testing against the requirements of ISO 15118 is proposed. This novel approach exhibits practicality yet comprehensive testing capability for field testing because of its passive access to the packets with deep analysis for the conformance. TestShark, a modified packet analysis tool, showed that the passive conformance testing system can help developers and operators analyze problems in charging communication and assess the quality of implementation with respect to ISO 15118.
Decomposition Analysis of CO2 Emissions in 138 Countries During the COVID-19 Pandemic
Authors: Ito Yasunori , Fujii Hidemichi
Published: November 2024

This study uses factor decomposition analysis from the extended framework of Kaya’s identity to determine the factors influencing CO2 emissions in 138 countries worldwide from 2019–2022. The results revealed that CO2 emissions decreased due to economic stagnation caused by COVID-19 from 2019–2020. From 2020–2021, CO2 emissions increased due to economic recovery and increased dependence on fossil energy. However, the timing of recovery varies with income level. In low-income countries, economic recovery progressed more from 2021–2022 than from 2020–2021, which was the opposite of the situation in developed countries. In terms of technological factors, carbon intensity was a factor in increasing emissions in high-income countries during the recovery period, whereas fossil dependency and energy efficiency were factors in increasing emissions in upper-medium- and lower-medium-income countries, respectively. The results of the decoupling analysis indicate that lower-medium-income countries tend to exhibit undesirable decoupling. Furthermore, from an analysis of the transition of decoupling states, it is necessary to reduce the carbon intensity and fossil fuel dependency to achieve a desirable decoupling relationship between CO2 and economic development.
Life Satisfaction and Its Relation to Leisure and Self-Reported Health with an Example from Mexico
Authors: Sánchez Fernando , Chimal Abraham
Published: November 2024

Life satisfaction is a concept related to an individual’s evaluation of their life, which is determined by taking into account all of the aspects that could affect it. Such an evaluation usually takes into consideration more than just economic conditions. In this paper, we present the relationship of life satisfaction with two of its main determinants, namely, self-reported health and leisure satisfaction. To exemplify this relationship, we utilize data from Mexico.
Environmental Factor Index (EFI): A Novel Approach to Measure the Strength of Environmental Influence on DNA Methylation in Identical Twins
Authors: Takenaka Yoichi ,   Osaka Twin Research Group, Watanabe Mikio
Published: November 2024

Background/Objectives: The dynamic interaction between genomic DNA, epigenetic modifications, and phenotypic traits was examined in identical twins. Environmental perturbations can induce epigenetic changes in DNA methylation, influencing gene expression and phenotypes. Although DNA methylation mediates gene-environment correlations, the quantitative effects of external factors on DNA methylation remain underexplored. This study aimed to quantify these effects using a novel approach. Methods: A cohort study was conducted on healthy monozygotic twins to evaluate the influence of environmental stimuli on DNA methylation. We developed the Environmental Factor Index (EFI) to identify methylation sites showing statistically significant changes in response to environmental stimuli. We analyzed the identified sites for associations with disorders, DNA methylation markers, and CpG islands. Results: The EFI identified methylation sites that exhibited significant associations with genes linked to various disorders, particularly cancer. These sites were overrepresented on CpG islands compared to other genomic features, highlighting their regulatory importance. Conclusions: The EFI is a valuable tool for understanding the molecular mechanisms underlying disease pathogenesis. It provides insights into the development of preventive and therapeutic strategies and offers a new perspective on the role of environmental factors in epigenetic regulation.
Job Stress, Working Capacity, Professional Performance and Safety of Shift Workers at Forest Harvesting in the North of Russian Federation
Authors: Korneeva Yana , Shadrina Nina , Simonova Natalia , Trofimova Anna
Published: November 2024

This study describes the features, manifestations and interrelationships of job stress, working capacity, professional performance and safety in loggers with a shift work organization in the North. This study involved 402 loggers. The research methods include questionnaires and psychophysiological diagnostics of stress and working capacity as well as questionnaires on the professional performance and safety of workers. The following statistical methods were used: correlation, multivariate dispersion and multi-regression stepwise analysis. It was found that the severity of stress, speed, accuracy and operator working capacity, as well as well-being, activity and mood in logging equipment operators, timber truck drivers and maintenance specialists statistically differ significantly. Higher professional stress and a more frequent decrease in professional performance are characteristic of truck drivers. Psychological and psychophysiological job stress and working capacity parameters are associated with the professional performance parameters of loggers. According to the results of a number of multi-regression analyses, the reduced professional performance markers of loggers include the index of stress, satiety, monotony and fatigue, vegetative balance and stress (method of M. Luscher and coefficients from G.A. Aminev), operator working capacity and reaction speed (CVMR).
The Impact of Structural Indicators and Game Browsing on the Natural Regeneration of Forest–Steppe Ecosystems Dominated by Deciduous Oak Species—A Case Study from the Krupina Plateau, Slovakia
Authors: Parobeková Zuzana , Sedmáková Denisa , Pittner Ján , Sarvašová Ivana , Lukáčik Ivan
Published: November 2024

Forest–steppe ecosystems represent a heterogeneous small-patch landscape important as a biodiversity hotspot and habitat for many endangered species. In this work, we examine threatened forest–steppe communities using structural indices, browsing intensity and indices that consider herbivorous game preferences. The study aims to assess the abundance of natural regeneration and the browsing intensity in relation to different stand structures and to identify woody species threatened by selective browsing at three study sites on the Krupina Plateau in the southern part of central Slovakia. At each study site, three circular permanent research plots (PRPs), each 500 m2 in size, were established for stand structure analysis. Within each PRP, nine circular subplots with a radius of 2 m were established for detailed analysis of natural regeneration. The results of this study suggest that forest patches in the forest–steppe mosaic suffer from a long-term lack of natural regeneration, which is induced mainly by increasing aridity and ungulate pressure. Positive effects on the amount of natural regeneration were noted in association with the aggregated structure of forest patches and the presence of dead wood. Differentiated stand structure influenced the regeneration number in a negative way and at the same time significantly increased browsing intensity. In relation to a more advanced regeneration, shrubs had a protective effect against browsing. The feeding preferences of the animals can positively modulate the species composition and eliminate the imbalances in the regeneration in favor of the increasingly rare Quercus pubescens Willd.
Determination of 16 European Priority Polycyclic Aromatic Hydrocarbons in Doner Kebab Varieties Cooked Under Different Heating Sources
Authors: Akkaya Esra , Colak Hilal , Hampikyan Hamparsun , Cakmak Sancar Burcu , Akhan Meryem , Engin Ayse Seray , Cetin Omer , Bingol Enver Baris
Published: November 2024

Doner kebab is a traditional Turkish meat product produced from lamb, beef or poultry meat seasoned with a blend of spices such as salt, black pepper, cumin, thyme and/or sauces. The aim of this study was to determine 16 EU priority polycyclic aromatic hydrocarbons (PAHs) in doner kebabs cooked under four different heating sources (electricity, open gas, wood and charcoal grilling). For this purpose, 200 meat doner and 200 chicken doner kebab samples were obtained randomly from various buffets and restaurants located in Istanbul and analyzed by means of GC-MS. According to the results, benzo[a]pyrene and PAH4 levels, which are important PAH compounds as biomarkers, were significantly higher in chicken doner than in meat doner (p < 0.05). The highest occurrence of benzo[a]pyrene and PAH4 in meat and chicken doner samples was in the charcoal heating source, whereas the lowest occurrence was detected in electric grilling. In terms of all PAH compounds, cooking with an electric heating source caused the formation of fewer PAH compounds in doner kebab samples. Consequently, the fat content of fatty meat products such as doner kebab should be reduced, the contact of fat with the heating source (especially flame) and dripping of fat to the source should be prevented and overcooking of meat should be avoided.
Projecting Climate Change Impacts on Channel Depletion in the Sacramento–San Joaquin Delta of California in the 21st Century
Authors: Salehi Sohrab , Salehi Neyshabouri Seyed Ali Akbar , Schwarz Andrew , He Minxue
Published: November 2024

The Sacramento–San Joaquin Delta (Delta) is a critical hub of California’s statewide water distribution system. Located at the confluence of California’s two largest rivers, the Sacramento River and the San Joaquin River, the Delta features a complex network of braided channels and over a hundred islands, most of which are located below sea level. The Delta’s complex nature and low-lying topography make it a unique hydrological area pertinent to climate change studies. This paper aims to estimate and explore the potential effects of climate change on the hydrological features of the Delta, especially Net Channel Depletion (NCD), which is one of the main contributors to the Net Delta Outflow (NDO). Downscaled CMIP6 General Circulation Model outputs are used to generate plausible future climate data. The Delta Channel Depletion model (DCD) is used to simulate daily hydrological processes for 61 plausible future climate scenarios. Simulation models are applied to the historical period (1930–2014) and projected future periods (2016–2100). A thorough water balance is computed in the DCD simulation model, offering insights into various elements in the hydrological cycle. Key hydrological features such as crop evapotranspiration, seepage, drainage, and runoff are simulated. Potential changes in NCD, calculated as the sum of diversions and seepage minus drainage, are also examined. The study identified a wide range of increases in NCD across all scenarios in the future period relative to the average of the historical period. These increases are projected to vary from 0.3% up to 20%. Moreover, a spatial analysis conducted across diverse regions of the Delta highlights notable variations in depletion across these areas. The results of this research indicate an anticipated increased stress on water resources, necessitating the adoption of innovative strategies to manage extreme events effectively and ensure the sustainability and resilience of water resource management.
Data-Driven Approach for Intelligent Classification of Tunnel Surrounding Rock Using Integrated Fractal and Machine Learning Methods
Authors: Ma Junjie , Li Tianbin , Shirani Faradonbeh Roohollah , Sharifzadeh Mostafa , Wang Jianfeng , Huang Yuyang , Ma Chunchi , Peng Feng , Zhang Hang
Published: November 2024

The degree of rock mass discontinuity is crucial for evaluating surrounding rock quality, yet its accurate and rapid measurement at construction sites remains challenging. This study utilizes fractal dimension to characterize the geometric characteristics of rock mass discontinuity and develops a data-driven surrounding rock classification (SRC) model integrating machine learning algorithms. Initially, the box-counting method was introduced to calculate the fractal dimension of discontinuity from the excavation face image. Subsequently, crucial parameters affecting surrounding rock quality were analyzed and selected, including rock strength, the fractal dimension of discontinuity, the discontinuity condition, the in-situ stress condition, the groundwater condition, and excavation orientation. This study compiled a database containing 246 railway and highway tunnel cases based on these parameters. Then, four SRC models were constructed, integrating Bayesian optimization (BO) with support vector machine (SVM), random forest (RF), adaptive boosting (AdaBoost), and gradient boosting decision tree (GBDT) algorithms. Evaluation indicators, including 5-fold cross-validation, precision, recall, F1-score, micro-F1-score, macro-F1-score, accuracy, and the receiver operating characteristic curve, demonstrated the GBDT-BO model’s superior robustness in learning and generalization compared to other models. Furthermore, four additional excavation face cases validated the intelligent SRC approach’s practicality. Finally, the synthetic minority over-sampling technique was employed to balance the training set. Subsequent retraining and evaluation confirmed that the imbalanced dataset does not adversely affect SRC model performance. The proposed GBDT-BO model shows promise for predicting surrounding rock quality and guiding dynamic tunnel excavation and support.
Positive Solution Pairs for Coupled p-Laplacian Hadamard Fractional Differential Model with Singular Source Item on Time Variable
Authors: Li Cheng , Guo Limin
Published: November 2024

The mathematical theories and methods of fractional calculus are relatively mature, which have been widely used in signal processing, control systems, nonlinear dynamics, financial models, etc. The studies of some basic theories of fractional differential equations can provide more understanding of mechanisms for the applications. In this paper, the expression of the Green function as well as its special properties are acquired and presented through theoretical analyses. Subsequently, on the basis of these properties of the Green function, the existence and uniqueness of positive solutions are achieved for a singular p-Laplacian fractional-order differential equation with nonlocal integral and infinite-point boundary value systems by using the method of a nonlinear alternative of Leray–Schauder-type Guo–Krasnoselskii’s fixed point theorem in cone, and the Banach fixed point theorem, respectively. Some existence results are obtained for the case in which the nonlinearity is allowed to be singular with regard to the time variable. Several examples are correspondingly provided to show the correctness and applicability of the obtained results, where nonlinear terms are controlled by the integrable functions 1π(lnt)12(1−lnt)12 and 1π(lnt)34(1−lnt)34 in Example 1, and by the integrable functions θ,θ¯ and φ(v),ψ(u) in Example 2, respectively. The present work may contribute to the improvement and application of the coupled p-Laplacian Hadamard fractional differential model and further promote the development of fractional differential equations and fractional differential calculus.
Poly(Vinyl Alcohol) Drug and PVA–Drug–Surfactant Complex Organogel with Dimethyl Sulfoxide as a Drug Delivery System
Authors: Otarbayeva Sabina , Berillo Dmitriy
Published: November 2024

The relevance of active research lies in the need to develop new technologies to improve drug delivery methods for the effective treatment of wound healing. Additionally, the potential application of organogels in other areas of biomedicine, such as creating medical patches with controlled drug delivery, indicates a wide range of possibilities for using this technology. This study focuses on developing controlled drug delivery systems using organogels as carriers for ceftriaxone and ofloxacin. By selecting optimal formulations, organogels were created to immobilize the drugs, facilitating their effective and sustained release. The swelling behavior of the hydrogels was studied, showing a swelling coefficient between 16 and 32%, indicating their ability to absorb liquid relative to their weight. Drug release studies demonstrated that ceftriaxone was released 1.8 times slower than ofloxacin, ensuring a more controlled delivery. Microbiological tests confirmed that the organogels containing ofloxacin exhibited antimicrobial activity against Escherichia coli, Bacillus subtilis, and Staphylococcus aureus. However, it was a challenge to estimate activity for the model antibiotic ceftriaxone due to bacterial resistance to it. Organogel poly(vinyl alcohol) (PVA)-DMSO–alginate modifications with surfactant cetylpyridinium bromide led to the formation of a polyelectrolyte complex on the interphase, allowing further enhanced the prolonged release of the drugs. The research identified that the optimal compositions for sustained drug release were organogels with compositions PVA (10%)-PVP (1%) DMSO (50%) and PVA (10%)-DMSO (50%) formulations, illustrating the transparent nature of these organogels making them suitable for ophthalmological application. Various organogels compositions (PVA-DMSO, PVA-poly(vinylpyrrolidone)-DMSO, PVA-DMSO–alginate, PVA-DMSO-PLGA, PVA-DMSO–drug–surfactant) loaded with ceftriaxone, ofloxacin, and surfactant were prepared and characterized, highlighting their potential use in antibiotic patches for wound healing. These organogels illustrate promising results for localized treatment of infections in wounds, cuts, burns, and other skin lesions.
The Unfulfilled Potential of Synthetic and Biological Hydrogel Membranes in the Treatment of Abdominal Hernias
Authors: Manikion Kenigen , Chrysanthou Christodoulos , Voniatis Constantinos
Published: November 2024

Hydrogel membranes can offer a cutting-edge solution for abdominal hernia treatment. By combining favorable mechanical parameters, tissue integration, and the potential for targeted drug delivery, hydrogels are a promising alternative therapeutic option. The current review examines the application of hydrogel materials composed of synthetic and biological polymers, such as polyethylene glycol (PEG), polyvinyl alcohol (PVA), gelatine, and silk fibroin, in the context of hernia repair. Overall, this review highlights the current issues and prospects of hydrogel membranes as viable alternatives to the conventional hernia meshes. The emphasis is placed on the applicability of these hydrogels as components of bilayer systems and standalone materials. According to our research, hydrogel membranes exhibit several advantageous features relevant to hernia repair, such as a controlled inflammatory reaction, tissue integration, anti-adhesive-, and even thermoresponsive properties. Nevertheless, despite significant advancements in material science, the potential of hydrogel membranes seems neglected. Bilayer constructs have not transitioned to clinical trials, whereas standalone membranes seem unreliable due to the lack of comprehensive mechanical characterization and long-term in vivo experiments.
Time Series Foundation Models and Deep Learning Architectures for Earthquake Temporal and Spatial Nowcasting
Authors: Jafari Alireza , Fox Geoffrey , Rundle John B. , Donnellan Andrea , Ludwig Lisa Grant
Published: November 2024

Advancing the capabilities of earthquake nowcasting, the real-time forecasting of seismic activities, remains crucial for reducing casualties. This multifaceted challenge has recently gained attention within the deep learning domain, facilitated by the availability of extensive earthquake datasets. Despite significant advancements, the existing literature on earthquake nowcasting lacks comprehensive evaluations of pre-trained foundation models and modern deep learning architectures; each focuses on a different aspect of data, such as spatial relationships, temporal patterns, and multi-scale dependencies. This paper addresses the mentioned gap by analyzing different architectures and introducing two innovative approaches called Multi Foundation Quake and GNNCoder. We formulate earthquake nowcasting as a time series forecasting problem for the next 14 days within 0.1-degree spatial bins in Southern California. Earthquake time series are generated using the logarithm energy released by quakes, spanning 1986 to 2024. Our comprehensive evaluations demonstrate that our introduced models outperform other custom architectures by effectively capturing temporal-spatial relationships inherent in seismic data. The performance of existing foundation models varies significantly based on the pre-training datasets, emphasizing the need for careful dataset selection. However, we introduce a novel method, Multi Foundation Quake, that achieves the best overall performance by combining a bespoke pattern with Foundation model results handled as auxiliary streams.