bluuebunny/crossref_metadata_2025 huggingface dataset:
Advanced Monte Carlo for Acquisition Sampling in Bayesian Optimization
Authors: Garcia-Barcos Javier , Martinez-Cantin Ruben
Published: January 2025

Optimizing complex systems usually involves costly and time-consuming experiments, where selecting the experiments to perform is fundamental. Bayesian optimization (BO) has proved to be a suitable optimization method in these situations thanks to its sample efficiency and principled way of learning from previous data, but it typically requires that experiments are sequentially performed. Fully distributed BO addresses the need for efficient parallel and asynchronous active search, especially where traditional centralized BO faces limitations concerning privacy in federated learning and resource utilization in high-performance computing settings. Boltzmann sampling is an embarrassingly parallel method that enables fully distributed BO using Monte Carlo sampling. However, it also requires sampling from a continuous acquisition function, which can be challenging even for advanced Monte Carlo methods due to its highly multimodal nature, constrained search space, and possibly numerically unstable values. We introduce a simplified version of Boltzmann sampling, and we analyze multiple Markov chain Monte Carlo (MCMC) methods with a numerically improved log EI implementation for acquisition sampling. Our experiments suggest that by introducing gradient information during MCMC sampling, methods such as the MALA or CyclicalSGLD improve acquisition sampling efficiency. Interestingly, a mixture of proposals for the Metropolis–Hastings approach proves to be effective despite its simplicity.
State-Dependent Phillips Curve
Authors: Kim Hyun Hak , Lee Na Kyeong
Published: January 2025

We propose a state-dependent Phillips curve (PC) where the regime has changed endogenously. Using this framework, a free-standing PC is constructed. This study tests the robustness of the model, various types of inflation, slack measures, and various expectation measures. The PC is found to work strongly during recessionary periods but becomes weaker once an economy recovers. The latent factors that determine the regimes are highly correlated with the uncertainty measure. During recessionary periods, the uncertainty becomes negatively more certain and strengthens the relationship between inflation and labor market slack.
SAM-Iris: A SAM-Based Iris Segmentation Algorithm
Authors: Jiang Jian , Zhang Qi , Wang Caiyong
Published: January 2025

The Segment Anything Model (SAM) has made breakthroughs in the domain of image segmentation, attaining high-quality segmentation results using input prompts like points and bounding boxes. However, utilizing a pretrained SAM model for iris segmentation has not achieved the desired results. This is mainly due to the substantial disparity between natural images and iris images. To address this issue, we have developed SAM-Iris. First, we designed an innovative plug-and-play adapter called IrisAdapter. This adapter allows us to effectively learn features from iris images without the need to comprehensively update the model parameters while avoiding the problem of knowledge forgetting. Subsequently, to overcome the shortcomings of the pretrained Vision Transformer (ViT) encoder in capturing local detail information, we introduced a Convolutional Neural Network (CNN) branch that works in parallel with it. This design enables the model to capture fine local features of iris images. Furthermore, we adopted a Cross-Branch Attention mechanism module, which not only promotes information exchange between the ViT and CNN branches but also enables the ViT branch to integrate and utilize local information more effectively. Subsequently, we adapted SAM for iris image segmentation by incorporating a broader set of input instructions, which included bounding boxes, points, and masks. In the CASIA.v4-distance dataset, the E1, F1, mIoU, and Acc of our model are 0.34, 95.15%, 90.88%, and 96.49%; in the UBIRIS.v2 dataset, the E1, F1, mIoU, and Acc are 0.79, 94.08%, 88.94%, and 94.97%; in the MICHE dataset, E1, F1, mIoU, and Acc were 0.67, 93.62%, 88.66%, and 95.03%. In summary, this study has improved the accuracy of iris segmentation through a series of innovative methods and strategies, opening up new horizons and directions for large-model-based iris-segmentation algorithms.
Bifurcation and Chaos in DCM Voltage-Fed Isolated Boost Full-Bridge Converter
Authors: Gong Renxi , Xu Jiawei , Liu Tao , Qin Yan , Wei Zhihuan
Published: January 2025

The isolated boost full-bridge converter (IBFBC) is a DC–DC conversion topology that achieves a high boost ratio and provides electrical isolation, making it suitable for applications requiring both. Its operational dynamics are often intricate due to its inherent characteristics and the prevalent usage of nonlinear switching elements, leading to bifurcation and chaos. Chaos theory was employed to investigate the impact of changes in the voltage feedback coefficient K and input voltage E on the dynamic behavior of the IBFBC when operating in discontinuous conduction mode (DCM). Based on an analysis of its operating principles, a discrete iterative mapping model of the system in DCM is constructed using the stroboscopic mapping method. The effects of two control parameters, namely the proportional coefficient and input voltage, on system performance are studied using bifurcation diagrams, fold diagrams, Poincaré sections, and Lyapunov exponents. Simulation experiments are conducted using time-domain and waveform diagrams to validate the discrete mapping model and confirm the correctness of the theoretical analysis. The results indicate that when the IBFBC operates in DCM, its operating state is influenced by the voltage feedback coefficient K and input voltage E. Under varying values of K and E, the system may operate in a single-period stable state, multi-period bifurcation, or chaotic state, exhibiting typical nonlinear behavior.
Transmission Line Modeling-Based Position Sensorless Control for Permanent Magnet Synchronous Machines
Authors: Xiao Dianxun , Hu Kun , Li Chengrui
Published: January 2025

Position sensorless control has been widely used in permanent magnet synchronous motor (PMSM) drives in low-cost applications or in the fault-tolerance control of position sensors. Conventional sensorless control methods often adopt a back electromagnetic force (EMF)-based position observer, which results in bandwidth reduction in signal processing and lower estimation accuracy. This paper introduces a numerical solution based on transmission line modeling (TLM) to obtain the back EMF. The TLM method is used for the numerical calculation of electromagnetics due to the clear algorithm structure, robust convergence and stability, and easy implementation in dynamic circuit analyses. This paper first analyzes the 2D TLM method techniques. Then, a new application of TLM theory in position sensorless control of PMSMs is put forward. The proposed TLM-based sensorless control scheme can estimate the back EMF without decreasing the bandwidth, thereby enhancing the dynamic performance of the sensorless control. All numerical results are implemented using the proposed approach, which validates the effectiveness of the proposed method.
LSTM vs. Prophet: Achieving Superior Accuracy in Dynamic Electricity Demand Forecasting
Authors: Albahli Saleh
Published: January 2025

Accurate electricity demand forecasting is critical for improving energy efficiency, maintaining grid stability, reducing operational costs, and promoting sustainability. This study presents a novel hybrid forecasting model that integrates Long Short-Term Memory (LSTM) networks and Prophet models, leveraging their complementary strengths through a dynamic weighted ensemble methodology. The LSTM component captures nonlinear dependencies and long-term temporal patterns, while Prophet models seasonal trends and event-driven fluctuations. The hybrid model was evaluated using a comprehensive dataset of hourly electricity consumption from Ontario, Canada, achieving a Root Mean Square Error (RMSE) of 65.34, Mean Absolute Percentage Error (MAPE) of 7.3%, and an R2 of 0.98. These results demonstrate significant improvements over standalone LSTM, Prophet, and other State-of-the-Art methods, highlighting the hybrid model’s adaptability and superior accuracy. This study underscores the practical implications of the hybrid approach, particularly in energy grid management and resource optimization, setting a new benchmark for time series forecasting in the energy sector.
Evaluation and Reduction of Energy Consumption of Railway Train Movement on a Straight Track Section with Reduced Freight Wagon Mass
Authors: Bulakh Maryna
Published: January 2025

This paper presents an evaluation and reduction of energy consumption during railway train movement on a straight track section with reduced freight wagon mass. A theoretical model was developed to simulate energy consumption based on input parameters, including train speed, track gradient, section length, travel time, and train mass. The results indicate that energy consumption increases by 18.9% as speed rises to 90 km/h and as gradients increase to 2.0‰, while energy consumption decreases by 14.5% on a descending gradient of 1.5‰, which corresponds to the expected dynamics of railway trains. These results are supported by experiments showing that the MAPE error does not exceed 1.9%, which can confirm the accuracy of the developed model. A comprehensive analysis of the potential reduction in energy consumption with reduced freight wagon mass was also conducted. Using a freight wagon design with a reduced mass of 2.3% allows for a reduction in energy consumption by 8–89 kW·h, depending on the length of the section and the speed of movement.
Optimum Energy Flow from Source in Transient State
Authors: Hawron Konrad , Rozegnał Bartosz , Sułowicz Maciej
Published: January 2025

This article presents an extended algorithm for one of the most important tasks in source–receiver optimal cooperation—minimization of the source’s RMS current value while ensuring the required active power is supplied to the receiver. The current state of the art ensures optimal steady-state cooperation. The presented extension introduces an approximated transient state to the optimization equations. By using an extended algorithm, it is possible to provide the optimal current not only in a steady state but also at any time during the transient state. The influence of the assumed transients—finite and permanent—on the EMF and optimal current signals is shown. The obtained results are compared with conventional calculation methods to show the advantage of the presented algorithm. The developed algorithm was also tested on measurement data obtained from a real-world test setup that reflects an actual section of the power system. The article also demonstrates, using a measurement example, how the proposed current optimization process during fault conditions in the power system can contribute to reducing short circuit-related losses.
Identification of Oil Vertical Migration Through Tectonic Fractures: A New Insight into the Oil Migration Process in the Central Ordos Basin, China
Authors: Ren Yiwei , Wang Aiguo , Du Yanjun , Li Chunyu , Wang Bianyang , Li Zilong , Wang Jie , Wei Jinxiang
Published: January 2025

Identifying oil migration through tectonic fractures in stable cratons is always challenging due to limited technical approaches. Here, we provide a case study showing that combined geochemical and geological analyses are a robust tool for identifying oil vertical migration through tectonic fractures. The core samples and crude oils from the Mesozoic petroleum system in the Zhijing area were investigated by an integrated analysis including petrography, biomarkers, nitrogen compounds, and spatial distribution. The tectonic fractures in the Yanchang Formation (YcF) are more developed compared to the Yan’an Formation (YaF), influencing oil migration patterns and reservoir distribution. Eighty-one percent of the tectonic fractures are filled with calcite cement, while only two percent of the tectonic fractures contain solid bitumen. The YaF and YcF oils originating from the same oil source are at the same maturity level. However, the YaF oils exhibit disordered nC18- distribution and “mismatches” at n-undecane and a decrease in toluene/n-heptane ratios, which resulted from the phase fractionation caused by the vertical migration through tectonic fractures. The spatial distributions of 1/4-MC, 1,8/2,7-DMC, and Ts/Tm values in the YaF oils are irregular. Furthermore, the areas occupied by the YaF oil reservoirs exhibit an absence of YcFs, and vice versa. These distribution patterns can be explained by oil vertical migration through tectonic fractures. A series of pieces of evidence indicates that the oils migrated vertically through tectonic fractures to the YaF. This study offers new insights into the oil migration process within the Mesozoic petroleum system of the central Ordos Basin and serves as guidance for identifying oil migration through tectonic fractures in stable cratons.
Optimization of Well Patterns in Offshore Low-Permeability Thin Interbedded Reservoirs: A Numerical Simulation Study in the Bozhong Oilfield, China
Authors: Wu Guangai , Ma Yingwen , Cao Yanfeng , Zhang Anshun , Liu Wei , Wang Jinghe , Yang Xinyi
Published: January 2025

Offshore low-permeability thin interbedded reservoirs contain significant oil reserves and are crucial for future development. However, due to the high cost and operational challenges associated with offshore fracturing, large-scale fracturing common in onshore fields is uneconomical. Furthermore, offshore low-permeability reservoirs often have sparse well placement and wide well spacing, in contrast to onshore low-permeability fields, which leads to low recovery. Additionally, there is a lack of comprehensive theory on optimizing the well patterns and fracture networks to maximize net income, highlighting the need for further research. This study tackles these issues in a low-permeability thin interbedded reservoir in the Bozhong Oilfield by using reservoir numerical simulation. First, fracture parameters, including fracture half-length and conductivity, are optimized for different well patterns. Subsequently, well pattern optimization is conducted under fractured conditions, targeting maximum net income under various conditions. The results indicate that when fractures are confined to a single reservoir layer and the main reservoir layer accounts for less than 36% of the development section, fractured directional well patterns yield a higher net income. Conversely, when fractures penetrate all reservoir layers, fractured horizontal wells with closer fracture spacing a higher number of fractures are the most profitable option, particularly in offshore fields with large well spacing. The findings provide critical insights into optimizing well patterns and fracture network designs for offshore low-permeability thin interbedded reservoirs.
Data-Driven Technologies for Energy Optimization in Smart Buildings: A Scoping Review
Authors: Billanes Joy Dalmacio , Ma Zheng Grace , Jørgensen Bo Nørregaard
Published: January 2025

Data-driven technologies in smart buildings offer significant opportunities to enhance energy efficiency, sustainability, and occupant comfort. However, the existing literature often lacks a holistic examination of the technological advancements, adoption barriers, and business models necessary to realize these benefits. To address this gap, this scoping review synthesizes current research on these technologies, identifies factors influencing their adoption, and examines supporting business models. Inspired by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a structured search of the literature across four major databases yielded 112 relevant studies. The key technologies identified included big data analytics, Artificial Intelligence, Machine Learning, the Internet of Things, Wireless Sensor Networks, Edge and Cloud Computing, Blockchain, Digital Twins, and Geographic Information Systems. Energy optimization is further achieved through integrating renewable energy resources and advanced energy management systems, such as Home Energy Management Systems and Building Energy Management Systems. Factors influencing adoption are categorized into social influences, individual perceptions, cost considerations, security and privacy concerns, and data quality issues. The analysis of business models emphasizes the need to align technological innovations with market needs, focusing on value propositions like cost savings and efficiency improvements. Despite the benefits, challenges such as high initial costs, technical complexities, security risks, and user acceptance hinder their widespread adoption. This review highlights the importance of addressing these challenges through the development of cost-effective, interoperable, secure, and user-centric solutions, offering a roadmap for future research and industry applications.
Cryptocurrencies Transit to a Carbon Neutral Environment: From Fintech to Greentech Through Clean Energy and Eco-Efficiency Policies
Authors: Koemtzopoulos Dimitrios , Zournatzidou Georgia , Ragazou Konstantina , Sariannidis Nikolaos
Published: January 2025

Fintech prioritizes the progression of issues related to environmental conservation and the consequences of climate change. This study is among the first investigations exploring the relationship between fintech and sustainable energy. It presents potential financial models that might be developed to assist companies in remaining operational via the use of renewable and clean energy sources. We employ a bibliometric analysis as the statistical methodology to address the study topic. We extract bibliometric data from the Scopus database employing the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) approach, thereafter analyzing the data with the R statistical programming language and the bibliometric applications Biblioshiny and VOSviewer. The results of the research indicate that fintech companies are committed to achieving carbon neutrality and investing in strategies such as environmental, social, and corporate governance (ESG) which may help them reduce their carbon footprint and enhance their eco-efficiency. In contrast to the United Kingdom, which is frequently regarded as the world’s preeminent financial center, Chinese fintech enterprises appear to demonstrate a more fervent dedication to the improvement of their ecological transition. However, the results, ultimately, emphasize the transition of fintech to an alternative paradigm, namely greentech. Greentech is a new fintech-dependent paradigm which will help cryptocurrencies and fintech reduce their environmental impact and promote carbon-neutral financial institutions via investment. Greentech aims to decarbonize the financial industry by investing in renewable resources and clean energy, therefore enhancing the sector’s environmental sustainability.
Relationship Between Climate Changes and Forest Dynamics Along Altitudinal Gradients at Doi Suthep-Pui National Park, Northern Thailand
Authors: Marod Dokrak , Thinkampheang Sathid , Phumphuang Wongsatorn , Yarnvudhi Arerut , Thongsawi Jakkaphong , Kachina Panida , Nakashizuka Tohru , Kurokawa Hiroko , Hermhuk Sutheera
Published: January 2025

Climate change has become a prominent topic, particularly regarding plant adaptation and migration within mountain ecosystems. This study aimed to clarify forest dynamics by focusing on sapling and tree stages in relation to microclimate changes across a permanent plot along an altitudinal gradient. In 2012, a permanent transect plot (50 m × 600 m) was established at the forest ecotone between the deciduous dipterocarp forest (DDF) and lower montane forest (LMF). All trees with a diameter at breast height ≥ 1 cm were tagged, measured, and identified. Monitoring occurred every 2 years between 2012, 2014, 2016, and 2018, with a final census conducted in 2023. Microclimatic factors were recorded automatically, and rainfall data were obtained from a meteorological station. The dynamics of all trees were analyzed in relation to micro-climate changes. Our results showed that environmental changes significantly affected species coexistence, particularly in the forest ecotone between the DDF and LMF, where intermediate environmental factors were observed. High mortality rates were observed in the dominant evergreen species, with significant positive correlations with decreased rainfall and increased temperature, suggesting that drought conditions strongly influence tree mortality. In contrast, the deciduous species showed increased density, particularly in the forest ecotone zone, indicating their better adaptation to drought conditions and their potential to occupy the forest ecotone at a higher rate than LMF species. Understanding the ecological niches of coexisting species along altitudinal gradients is crucial for developing effective restoration programs in mountainous ecosystems.
Coppice and Coppice-with-Standard Stands Systems: Implications for Forest Management and Biodiversity
Authors: Ghanbari Sajad , Álvarez-Álvarez Pedro , Esmaili Ayeshe , Sasanifar Samira , Sadeghi Seyed Mohmmad Moein , Sefidi Kiomars , Eastin Ivan
Published: January 2025

Examining forest stand structures is crucial for effective forest management, as it provides essential insights into current conditions and informs future strategies. Coppice systems, a historic forest management practice with centuries of documented use across various regions, play a vital role in supporting unique flora and fauna, making them integral to conservation efforts. This study has two primary objectives: (i) to evaluate how various forest management approaches impact species composition and structural characteristics of forest stands, and (ii) to assess and compare diversity within these stands using a range of indices. In this research, two management systems in Iran’s Arasbaran forests were compared: the traditional coppice system and the coppice-with-standard (CWS) stands system. Fieldwork was conducted in 24 sample plots for each management system, where quantitative indicators and biodiversity indices were employed to evaluate and compare stand characteristics. The findings revealed that the CWS system exhibited higher mean values for tree height, diameter at the breast height (DBH), and basal area compared to the coppice system. Coppice stands had a sprout clump density of 546 per hectare, primarily composed of Quercus macranthera, while the CWS stands had a combined tree and sprout clump density of 421 per hectare. Diversity assessments showed that species diversity, as measured by Pielou’s index, was higher in the coppice system (1.42) than in the CWS system (1.01). However, species richness, represented by the Menhinik index, was lower in both systems, with values of 0.31 for the coppice system and 0.19 for the CWS system. These results suggest that the CWS system is more effective in promoting growth and stand development, whereas the coppice system may better support biodiversity. The findings have practical implications for forest managers and policymakers in Iran and other regions with similar forest ecosystems. For instance, if the objective is to enhance biodiversity and ecosystem resilience, the coppice system—with its higher diversity indices—may be the preferred choice. On the other hand, if the aim is to boost timber production while maintaining a baseline level of biodiversity, the CWS system could be more suitable.
Response of Non-Structural Carbohydrates and Carbon, Nitrogen and Phosphorus Stoichiometric Characteristics of Ochroma lagopus Leaves to Nitrogen Addition
Authors: Chen Gang , Zhao Zhijuan , Li Zhiqi , Duan Guihe , Wen Chengjing , Du Guanben , Wu Junwen
Published: January 2025

The response of non-structural carbohydrates and stoichiometric characteristics of Ochroma lagopus to nitrogen addition is currently unclear. In this study, a 2-year-old O. lagopus was selected, and seven nitrogen addition treatments were set up to investigate the effects of nitrogen addition on the non-structural carbohydrates and stoichiometric characteristics. O. lagopus tree height and diameter increased by 2.00%–14.00% and 3.74%–16.93%, respectively. Nitrogen addition significantly increased leaf soluble sugar, starch, and NSC (non-structural carbohydrates) contents of O. lagopus forests. Their changing trends showed a first increasing and then decreasing trend with increasing fertiliser application in both urea and slow-release fertilisation treatments. Nitrogen addition significantly increased soil N content, leaf N content, and leaf N/P in O. lagopus forests, all of which increased with the increase in urea and slow-release fertiliser application; while leaf P content in O. lagopus forests was significantly lower in nitrogen addition treatments compared with CK (no fertiliser treatment), and decreased with the increase in urea and slow-release fertiliser application. O. lagopus leaf NSC had highly significant positive correlations with soil N, leaf N content, and N/P, and significant negative correlations with leaf P content and C/N. As a result, the application of both urea and slow-release fertiliser increased the soil N content, improved N uptake and utilisation, and promoted the growth of O. lagopus, with the application of slow-release fertiliser at 450 g/plant being the best treatment, and the slow-release fertiliser being superior to urea.
The Detection of Pest Contaminants in Chocolate Using Visible-Near-Infrared Single-Pixel Imaging Technology
Authors: Taketoshi Hidemasa , Inagaki Tetsuya , Tsuchikawa Satoru , Ma Te
Published: January 2025

Food safety is gaining increasing attention worldwide. Currently, low-density organic foreign objects such as insects are extremely challenging to detect using conventional metal detectors and X-ray inspection systems. This study aimed to develop a visible-near-infrared single-pixel imaging (Vis-NIR-SPI) method to detect small insects inside food. The advantages of Vis-NIR light include its ability to analyze samples non-destructively and measure multiple components simultaneously and quickly, while SPI is robust against dark noise, high scattering, and high equipment costs. The experimental results demonstrated that (1) the newly designed system effectively reduces scattering effects from the highly scattering sample (intralipid 20%), allowing for the capture of information beyond the capabilities of a charge-coupled-device camera; (2) insects positioned behind ham and bread were readily detectable using the imaging reconstruction algorithm; and (3) even for chocolate samples with very high light absorption, only 1 uncontaminated sample out of 100 was mistakenly classified as contaminated, yielding an overall accuracy of 99%. This high level of accuracy underscores the potential of the Vis-NIR-SPI method to provide reliable detection while maintaining sample integrity. Furthermore, this method is cost-effective, offering a practical and efficient solution to improve quality control processes and consumer trust in the food industry.
Physicochemical Properties, Antioxidant Activity, and Flavor Profile of Strawberry Fruit-Based Novel Drinking Jelly Made with Gracilaria fisheri Seaweed as a Gelling Agent at Varying Concentrations
Authors: Charoenphun Narin , Noonim Paramee , Lekjing Somwang , Nitikornwarakul Chawakwan , Pham Nam Hoang , Venkatachalam Karthikeyan
Published: January 2025

Gracilaria fisheri (GF) is a red seaweed that is widely found in Southeast Asia and has gained attention for its potential bioactive compounds and versatile applications in food products. This study explored the potential of GF as a natural gelling agent in the development of sustainable strawberry-based drinking jelly. By utilizing GF at varying concentrations (0.2 (S1), 0.4 (S2), 0.6 (S3), 0.8 (S4), and 1.0% (S5)), the impact on the physicochemical, textural, phytochemical, and flavor profiles of the strawberry concentrate-based drinking jelly was examined. The results demonstrated that GF concentration significantly affected the color characteristics, structural development, and flavor profiles of the drinking jelly samples. Increasing GF levels progressively enhanced the lightness (L*) and redness (a*) values while reducing the yellowness (b*), with optimal visual appeal achieved in the S4 samples compared to others. Microscopical observations revealed that gel matrix development improved with GF concentrations up to 0.8% (S4), transitioning from a sparse, liquid-like structure at lower levels to a compact, over-gelated network at 1.0% (S5). Physicochemical parameters, including pH, total soluble solid (TSS), and TSS/titratable acidity (TA) ratios, increased with GF levels, contributing to a sweeter, less acidic product, while water activity (aw) decreased, enhancing jelly stability. Viscosity and sulfate content increased significantly with GF concentration, indicating improved gel strength but reduced fluidity. Phytochemical analysis revealed that ascorbic acid (AsA) and total phenolic content (TPC) decreased progressively with higher GF levels, leading to a reduction in antioxidant activity (DPPH and ABTS). Volatile compound analysis identified alcohols, esters, and aldehydes as dominant contributors to the flavor profile. 1-Octanol (waxy, citrus-like) and methyl anthranilate (grape-like, sweet) increased substantially, while minor groups such as terpenoids and phenolic compounds contributed floral and woody notes. The findings suggest that S4 samples strike the optimal balance between texture, color, flavor, and antioxidant properties, achieving a cohesive, visually appealing, and flavorful drinking jelly suitable for commercial applications.
Accounting for the Compositional Nature of Geochemical Data to Improve the Interpretation of Their Univariate and Multivariate Spatial Patterns: A Case Study from the Campania Region (Italy)
Authors: Pacifico Lucia Rita , Guarino Annalise , Iannone Antonio , Albanese Stefano
Published: January 2025

This study investigates the application of Compositional Data Analysis (CoDA) and multivariate statistical techniques to geochemical data from the soils of the Campania region. The dataset examined includes 3571 soil samples analyzed for 37 chemical elements. Principal Component Analysis (PCA) was employed to reduce the dataset’s dimensionality and identify key relationships between elements. The first PCA identified groups of highly correlated variables, which were then reduced to 20 representative elements for a second PCA. The three most significant principal components (PC1, PC2, and PC3) explained approximately 65% of the total variability. PC1 (accounting for 29.97% of variability) revealed an anticorrelation between Ti, La, and Sc with Au, Hg, and Ag, with positive scores primarily located in the inland Apennine areas. PC2 (21.8%) was dominated by Na, K, and Cu, with positive scores corresponding to volcanic deposits, aligning with the dispersion patterns of historical Vesuvian eruption products. PC3 (11%) was associated with Ca and S, with higher scores found in the alluvial plains and inland areas. These results demonstrate the efficacy of CoDA in minimizing spurious correlations and uncovering latent relationships between elements, thereby enhancing the interpretation of natural and anthropogenic processes influencing soil variability in the region.
State of the Art of CFD-DEM Coupled Modeling and Its Application in Turbulent Flow-Induced Soil Erosion
Authors: Xu Jun , Wang Fei , Abegaz Ruth
Published: January 2025

Fluid–soil interaction plays a pivotal role in various geotechnical engineering applications, as it significantly influences processes such as erosion, sediment transport, and soil stability. Modeling fluid–soil particle interactions in these contexts presents substantial challenges due to the inherent complexity of the interactions occurring across multiple characteristic scales. The primary challenge lies in the vast disparities in magnitude between these scales, which demand sophisticated modeling techniques to accurately capture the intricate dynamics involved. Coupled fluid–soil particle models have emerged as essential tools for understanding the mechanisms underlying fluid–soil interactions. Among these, the CFD-DEM (computational fluid dynamics–discrete element method) approach has gained significant attention. This method provides an effective compromise between high-resolution sub-particle fluid modeling and coarser mesh-based techniques for fluids and particles. By doing so, CFD-DEM facilitates large-scale simulations while maintaining computational efficiency, making it a promising solution for studying fluid–soil interactions in complex geotechnical scenarios. This review highlights the application of CFD-DEM models in geotechnical engineering, with a specific focus on soil erosion processes and the critical role of turbulent flow. It explores various fluid–soil particle interaction computational mechanisms and their implications for erosion dynamics, emphasizing several key aspects, including the following: laminar vs. turbulent flow models: understanding the distinctions between flow regimes is critical for accurately predicting fluid-induced soil particle movement. Shear stress effects: the influence of flow-induced shear stress on the detachment of soil particles is analyzed, particularly in erosion-prone environments. Sediment transport mechanisms: factors such as particle size, density, and water velocity are examined for their roles in governing sediment transport. Knowledge gaps and future directions: these involve identifying unresolved issues in current fluid–soil interaction models, with an emphasis on improving the accuracy and scalability of CFD-DEM simulations. By delving into these aspects, the review aims to advance the understanding of fluid–soil interactions and provide insights into optimizing modeling techniques for geotechnical engineering applications. It also outlines future research directions to bridge existing knowledge gaps, emphasizing the importance of integrating advanced turbulence modeling and computational strategies to enhance the predictive capabilities of fluid–soil interaction frameworks.
Borderline Personality Symptoms, Body Modification, and Emotional Regulation
Authors: Avon Victoria , Gullo Nathalie , Walker D. Catherine
Published: January 2025

Many people with BPD (borderline personality disorder) experience emotional dysregulation and thus engage in NSSI (non-suicidal self-injury), potentially in the pursuit of emotional regulation. However, research is lacking on whether body modifications (piercings, tattoos, etc.) are linked to BPD in a similar way to NSSI. In the current study, we hypothesized (1) that body modifications are associated with BPD symptoms, (2) that emotional regulation and self-expression motivations for body modifications specifically account for variance in BPD symptoms, and (3) that NSSI craving correlates with body modification craving. Participants (N = 199, ages 18–67, located in the USA) were surveyed on BPD symptomatology, NSSI craving, emotional regulation abilities, and the presence of body modifications. The extent of tattooing (number of tattoos and percentage of body surface covered) was not significantly associated with BPD symptomatology, but the number of piercings was. Individuals with higher BPD symptomatology were not more likely to report emotional regulation and self-expression as motivations for obtaining body modifications. NSSI craving scores were significantly positively correlated with body modification craving scores. Body modification may be an alternative method of emotional regulation to NSSI in individuals with BPD, which clinicians may want to consider when treating those with BPD and NSSI.
IL-4, TSLP and IL-31 Cytokine Profiles as Related to Psychometric Measures in Patients with Mastocytosis
Authors: Romantowski Jan , Fabiańczyk Kinga , Skrzypkowska Maria , Cubała Wiesław J. , Trzonkowski Piotr , Niedoszytko Marek
Published: January 2025

Mastocytosis is a rare neoplastic disease of the bone marrow. Common symptoms like urticaria, diarrhea, bronchspasm and flushing are caused by mast cell degranulation and are mostly based on mast cell mediator release and Th2 type inflammation that occurs frequently in these patients. Psychological disorders are more prevalent in patients with systemic mastocytosis, though little is known about the mechanism behind this. The aim of the study was to investigate the Th2 cytokine (IL-4, TSLP, IL-31 and IL-33) profile in patients with mastocytosis in relation to classic degranulation symptoms and the psychometric measures of cognition and distress symptoms.In total, 115 patients diagnosed with mastocytosis were enrolled. Mini-Mental State Examination (MMSE) was performed for all subjects. Other variables: Quality of life in mastocytosis, a mood assessment commonly used in systemic mastocytosis by a certified rater—the Hamilton-17 Depression Scale, Pruritus Visual Analog Score, serum tryptase concentration and bone marrow biopsy results (archival) were also analyzed/included. Serum concentrations of IL-4, TSLP, IL-33 and IL-31 were analyzed as primary outcomes. For comparison with continuous variables linear regression was used. The mean MMSE result was 27.9. Regression analysis did not reveal significant correlation between the IL-4 (p = 0.82), IL-31 (p = 0.24) and TSLP (p = 0.37) serum concentrations and MMSE. The IL-33 concentration analysis resulted in 0 for all patients (was not detected). No significant effect was observed with other endpoints as well. One in four patients with mastocytosis presents cognitive decline. This impairment does not correlate with Il-4, TSLP, nor IL-31 serum protein concentrations.
The MADS-Box Transcription Factor EjAGL18 Negatively Regulates Malic Acid Content in Loquat by Repressing EjtDT1
Authors: Chi Zhuoheng , Wang Luwei , Hu Qiankun , Yi Guangquan , Wang Shuming , Guo Qigao , Jing Danlong , Liang Guolu , Xia Yan
Published: January 2025

Malic acid is the major organic acid in loquat fruit, contributing to the sourness of fruit and affecting fruit flavor. However, the transcriptional regulation of malic acid in loquat is not well understood. Here, we discovered a MADS-box transcription factor (TF), EjAGL18, that regulated malic acid accumulation in loquat. EjAGL18 is a nucleus-localized TF without transcriptional activity. The expression of EjAGL18 increased during fruit ripening, opposite to the accumulation pattern of malic acid in loquat. The transient overexpression of EjAGL18 in loquat fruit downregulated malic acid accumulation and the transcriptional level of the tonoplast dicarboxylate transporter EjtDT1. Conversely, silencing EjAGL18 in loquat fruit upregulated the malic acid content and EjtDT1 expression level. Dual-luciferase assays and yeast one-hybrid experiments further confirmed that EjAGL18 could bind to the promoter of EjtDT1 and repress its transcriptions. Furthermore, the transient overexpression of EjtDT1 in loquat fruit increased the malic acid content. These results revealed that EjAGL18 negatively regulates malic acid content by repressing EjtDT1 in loquat. This study broadens the understanding of the MADS-box TF’s regulatory mechanisms in malic acid and provides new insights into fruit flavor improvement in loquat.
Cholesterol Attenuates the Pore-Forming Capacity of CARC-Containing Amphipathic Peptides
Authors: Oleynikov Ilya P. , Firsov Alexander M. , Azarkina Natalia V. , Vygodina Tatiana V.
Published: January 2025

Artificial peptides P4, A1 and A4 are homologous to amphipathic α-helical fragments of the influenza virus M1 protein. P4 and A4 contain the cholesterol recognition sequence CARC, which is absent in A1. As shown previously, P4 and A4 but not A1 have cytotoxic effects on some eukaryotic and bacterial cells. This might be caused by the dysfunction of cholesterol-dependent cellular structures, inhibition of the respiratory chain, or disruption of the membrane. Here, we analyzed the latter hypothesis by studying the uncoupling effect of the peptides on asolectin membranes. The influence of A4 on Δψ pre-formed either by the valinomycin-dependent K+ diffusion or by the activity of membrane-built cytochrome c oxidase (CcO) was studied on (proteo)liposomes. Also, we investigated the effect of P4, A1 and A4 on liposomes loaded with calcein. It is found that A4 in a submicromolar range causes an immediate and complete dissipation of diffusion Δψ across the liposomal membrane. Uncoupling of the CcO-containing proteoliposomes requires an order of magnitude of higher peptide concentration, which may indicate the sorption of A4 on the enzyme. The presence of cholesterol in the membrane significantly weakens the uncoupling. Submicromolar A4 and P4 cause the release of calcein from liposomes, indicating the formation of membrane pores. The process develops in minutes and is significantly decelerated by cholesterol. Micromolar A1 induces pore formation in a cholesterol-independent manner. We conclude that the peptides P4, A4 and, in higher concentrations, A1 form pores in the asolectin membrane. The CARC-mediated interaction of A4 and P4 with cholesterol impedes the peptide oligomerization necessary for pore formation. The rapid uncoupling effect of A4 is apparently caused by an increase in the proton conductivity of the membrane without pore formation.
Residual Traditional Risk in Non-Traditional Atherosclerotic Diseases
Authors: Biscetti Federico , Polito Giorgia , Rando Maria Margherita , Nicolazzi Maria Anna , Eraso Luis H. , DiMuzio Paul J. , Massetti Massimo , Gasbarrini Antonio , Flex Andrea
Published: January 2025

Individuals with chronic inflammatory and immune disorders are at an increased risk of atherosclerotic events and premature cardiovascular (CV) disease. Despite extensive literature exploring the relationship between “non-traditional” atherosclerotic conditions and CV risk, many aspects remain unresolved, including the underlying mechanisms promoting the “non-traditional CV risk”, the development of an innovative and comprehensive CV risk assessment tool, and recommendations for tailored interventions. This review aims to evaluate the available evidence on key “non-traditional” CV risk-enhancer conditions, with a focus on assessing and managing CV risk factors. We conducted a comprehensive review of 412 original articles, narrative and systematic reviews, and meta-analyses addressing the CV risk associated with “non-traditional” atherosclerotic conditions. The analysis examined the underlying mechanisms of these relationships and identified strategies for assessing and mitigating elevated risk. A major challenge highlighted is the difficulty in quantifying the contribution of individual risk factors and disease-specific elements to CV risk. While evidence supports the cardiovascular benefits of statins beyond lipid lowering, such as pleiotropic and endothelial effects, current guidelines lack specific recommendations for the use of statins or other therapies targeting non-traditional CV risk factors. Additionally, the absence of validated cardiovascular risk scores that incorporate non-traditional risk factors hinders accurate CV risk evaluation and management. The growing prevalence of “non-traditional CV risk-enhancer conditions” underscores the need for improved awareness of CV risk assessment and management. A thorough understanding of all contributing factors, including disease-specific elements, is crucial for accurate prediction of cardiovascular disease (CVD) risk. This represents an essential foundation for informed decision-making in primary and secondary prevention. We advocate for future research to focus on developing innovative, disease-specific CV risk assessment tools that incorporate non-traditional risk factors, recognizing this as a promising avenue for translational and clinical outcome research.
Non-Hypertensive Effects of Aldosterone
Authors: Ekman Natalia , Grossman Ashley B. , Nieckarz Anna , Jędrzejewski Łukasz , Wolf Jacek , Dworakowska Dorota
Published: January 2025

Aldosterone, the primary adrenal mineralocorticoid hormone, as an integral part of the renin–angiotensin–aldosterone system (RAAS), is crucial in blood pressure regulation and maintaining sodium and potassium levels. It interacts with the mineralocorticoid receptor (MR) expressed in the kidney and promotes sodium and water reabsorption, thereby increasing blood pressure. However, MRs are additionally expressed in other cells, such as cardiomyocytes, the endothelium, neurons, or brown adipose tissue cells. Therefore, aldosterone (especially aldosterone excess) may have other major impacts besides simply regulating blood pressure and circulating ion balance. Recent studies have reported a non-hypertensive impact on the cardiovascular, immune, and metabolic systems, a pro-oxidant effect, and a pro-fibrotic effect. In this review, we emphasise the non-hypertension-related effects of aldosterone, including advances in our understanding of the cellular mechanisms by which aldosterone mediates its cellular effects. We also summarise potential clinical complications related to both the hypertensive and non-hypertensive effects of aldosterone.
Associations Between Diabetes Mellitus and Neurodegenerative Diseases
Authors: Szablewski Leszek
Published: January 2025

Diabetes mellitus (DM) and neurodegenerative diseases/disturbances are worldwide health problems. The most common chronic conditions diagnosed in persons 60 years and older are type 2 diabetes mellitus (T2DM) and cognitive impairment. It was found that diabetes mellitus is a major risk for cognitive decline, dementia, Parkinson’s disease (PD), Alzheimer’s disease (AD), Huntington’s disease (HD), amyotrophic lateral sclerosis (ALS) and other neurodegenerative disorders. Different mechanisms of associations between these diseases and diabetes mellitus have been suggested. For example, it is postulated that an impaired intracellular insulin signaling pathway, together with hyperglycemia and hyperinsulinemia, may cause pathological changes, such as dysfunction of the mitochondria, oxidative stress inflammatory responses, etc. The association between diabetes mellitus and neurodegenerative diseases, as well as the mechanisms of these associations, needs further investigation. The aim of this review is to describe the associations between diabetes mellitus, especially type 1 (T1DM) and type 2 diabetes mellitus, and selected neurodegenerative diseases, i.e., Alzheimer’s disease, Parkinson’s disease, Huntington’s disease and amyotrophic lateral sclerosis. Suggested mechanisms of these associations are also described.
Ecometabolomics of Loggerhead Sea Turtles (Caretta caretta): The Impact of Age on Metabolomic Profiles
Authors: Marín-García Pablo Jesús , García-Párraga Daniel , Crespo-Picazo Jose Luis , Llobat Lola , Cambra-López María , Bordignon Francesco , Pascual Juan José , Larsen Torben , Hedemann Mette Skou
Published: January 2025

To investigate the impact of age on the metabolomic profile of loggerhead sea turtles (Caretta caretta), this study analyzed 100 plasma samples of individuals across two age groups—50 post-hatchlings and 50 juveniles—from various locations along the Mediterranean coastline. Both targeted and untargeted metabolomic analyses were performed on the samples. Our results demonstrated a significant age-related effect on the metabolomic profiles in both analyses. Specifically, post-hatchling turtles exhibited increased levels of urea (p < 0.001), triglyceride (p = 0.0003), cholesterol (p < 0.001), lysoPE (18:1/0:0) (p < 0.001), 7-HDoHE (p = 0.0121), pyrrolidinebutanoic acid (p < 0.001), formiminoglutamic acid (p < 0.001), pyroglutamic acid (p < 0.001), lysoPC (0:0/20:4) (p < 0.001), lysoPE (22:6/0:0) (p < 0.001), 1-acyl-sn-glycero-3-phosphocholine (p < 0.001), DL-homocysteine (p < 0.001) and gamma-Glutamyltyrosine (p < 0.001). Conversely, post-hatchlings showed reduced levels of total protein (p < 0.001), glucose (p = 0.0002), uric acid (p < 0.001), inorganic phosphorus (p = 0.0018) and calcium (p = 0.0410) compared with juveniles. These findings suggest significant physiological changes between the age groups, likely due to differentiated feeding patterns. Further research is needed to better understand the metabolic profiles and complex physiological and nutritional interactions of this species.