bluuebunny/crossref_metadata_2025 huggingface dataset:
Prediction of outpatient rehabilitation patient preferences and optimization of graded diagnosis and treatment based on XGBoost machine learning algorithm
Authors: Fan Xuehui , Ye Ruixue , Gao Yan , Xue Kaiwen , Zhang Zeyu , Xu Jing , Zhao Jingpu , Feng Jun , Wang Yulong
Published: January 2025

Background

The Department of Rehabilitation Medicine is key to improving patients’ quality of life. Driven by chronic diseases and an aging population, there is a need to enhance the efficiency and resource allocation of outpatient facilities. This study aims to analyze the treatment preferences of outpatient rehabilitation patients by using data and a grading tool to establish predictive models. The goal is to improve patient visit efficiency and optimize resource allocation through these predictive models.

Methods

Data were collected from 38 Chinese institutions, including 4,244 patients visiting outpatient rehabilitation clinics. Data processing was conducted using Python software. The pandas library was used for data cleaning and preprocessing, involving 68 categorical and 12 continuous variables. The steps included handling missing values, data normalization, and encoding conversion. The data were divided into 80% training and 20% test sets using the Scikit-learn library to ensure model independence and prevent overfitting. Performance comparisons among XGBoost, random forest, and logistic regression were conducted using metrics, including accuracy and receiver operating characteristic (ROC) curves. The imbalanced learning library’s SMOTE technique was used to address the sample imbalance during model training. The model was optimized using a confusion matrix and feature importance analysis, and partial dependence plots (PDP) were used to analyze the key influencing factors.

Results

XGBoost achieved the highest overall accuracy of 80.21% with high precision and recall in Category 1. random forest showed a similar overall accuracy. Logistic Regression had a significantly lower accuracy, indicating difficulties with nonlinear data. The key influencing factors identified include distance to medical institutions, arrival time, length of hospital stay, and specific diseases, such as cardiovascular, pulmonary, oncological, and orthopedic conditions. The tiered diagnosis and treatment tool effectively helped doctors assess patients’ conditions and recommend suitable medical institutions based on rehabilitation grading.

Conclusion

This study confirmed that ensemble learning methods, particularly XGBoost, outperform single models in classification tasks involving complex datasets. Addressing class imbalance and enhancing feature engineering can further improve model performance. Understanding patient preferences and the factors influencing medical institution selection can guide healthcare policies to optimize resource allocation, improve service quality, and enhance patient satisfaction. Tiered diagnosis and treatment tools play a crucial role in helping doctors evaluate patient conditions and make informed recommendations for appropriate medical care.
How social integration affects the income of relocated households: evidence from China
Authors: Zhao Chuangxin , Tang Manping , Wang Changxiang
Published: January 2025

Ensuring the income of relocated households is of great theoretical and practical significance for improving their livelihoods and sustainable development. This article is based on micro survey data of relocated farmers in Sichuan Province, China, and analyzes the impact of social integration on the income level and structure of relocated farmers from both theoretical and empirical perspectives. The results show that: social integration has a significant positive effect on the household income level of relocated farmers, and the effect of social integration on household income decreases with the increase of income level. From the perspective of income structure, social integration improves the operating income, wage income and property income of relocated households, but the impact of social integration on transfer income is not obvious. Non-agricultural employment ability, information acquisition and life satisfaction are the mechanisms through which social integration affects household income. There are differences in the effect of social integration on household income among different groups of relocated farmers. Finally, according to the conclusion, the paper puts forward relevant countermeasures and suggestions to improve the level of social integration, improve the non-agricultural employment ability of relocated farmers, improve the quality of information acquisition and life satisfaction, and provide differentiated support for different groups.
Exploring factors that influence the behavior response to novel object tests in young thoroughbred horses: investigating sex, test site and auction history
Authors: Klitzing Lara , Kirsch Katharina , Schindler Maria , Merle Roswitha , Hoffmann Gundula , Thöne-Reineke Christa , Wiegard Mechthild
Published: January 2025

Introduction

The novel object test is one of the three most common fear tests in veterinary science and employed in several different species. Although having been applied in several different studies in horses, it is surprising that there is no standardized test procedure available for these kinds of tests.

Methods

This study investigated the performance of the novel object test on 42 young Thoroughbred horses to determine the effect of sex (mare or stallion), test sites (round pen or paddock) and whether the horses had previously participated in an auction or not on the behavior during the novel object test.

Results

Differences in horses’ behavior during the novel object test were primarily attributed to the test sites. The animals showed significant (p < 0.05) intra-individual differences in the novel object test in the round pen and in the paddock. Sex did not affect the direct interaction with the novel object. The horses that had not participated in an auction seemed to actively perceive the novel object more quickly, so that the latency to first fixation on the object was significantly shorter.

Discussion

In order to obtain comparable results, it is recommended that novel object tests should be performed at the same location and under identical conditions. Furthermore, it is important to consider the individual behavior of each horse.
Grounding Grid Electrical Impedance Imaging Method Based on an Improved Conditional Generative Adversarial Network
Authors: Zhu Ke , Luo Donghui , Fu Zhengzheng , Xue Zhihang , Bu Xianghang
Published: January 2025

The grounding grid is an important piece of equipment to ensure the safety of a power system, and thus research detecting on its corrosion status is of great significance. Electrical impedance tomography (EIT) is an effective method for grounding grid corrosion imaging. However, the inverse process of image reconstruction has pathological solutions, which lead to unstable imaging results. This paper proposes a grounding grid electrical impedance imaging method based on an improved conditional generative adversarial network (CGAN), aiming to improve imaging precision and accuracy. Its generator combines a preprocessing module and a U-Net model with a convolutional block attention module (CBAM). The discriminator adopts a PatchGAN structure. First, a grounding grid forward problem model was built to calculate the boundary voltage. Then, the image was initialized through the preprocessing module, and the important features of ground grid corrosion were extracted again through the encoder module, decoder module and attention module. Finally, the generator and discriminator continuously optimized the objective function and conducted adversarial training to achieve ground grid electrical impedance imaging. Imaging was performed on grounding grids with different corrosion conditions. The results showed a final average peak signal-to-noise ratio of 20.04. The average structural similarity was 0.901. The accuracy of corrosion position judgment was 94.3%. The error of corrosion degree judgment was 9.8%. This method effectively improves the pathological problem of grounding grid imaging and improves the precision and accuracy, with certain noise resistance and universality.
Factors Influencing Precision Agriculture Technology Adoption Among Small-Scale Farmers in Kentucky and Their Implications for Policy and Practice
Authors: Pandeya Shreesha , Gyawali Buddhi R. , Upadhaya Suraj
Published: January 2025

The increasing pressure on food security and environmental sustainability has emphasized the importance of effective farm resource usage. Precision agriculture technologies (PATs) have been considered as one of the solutions to these challenges. Multiple stakeholders and agencies working in the agriculture sector have implemented various initiatives to facilitate their adoption. Despite numerous initiatives, the adoption of PATs in small farms is shallow in the United States. It is important to understand what socio-economic and demographic factors influence their decision-making regarding PAT adoption. This research aimed to provide actionable insights that can help farmers overcome existing challenges and capitalize on the benefits of advanced agricultural practices, ultimately contributing to the resilience and sustainability of the agricultural sector. This study used a mixed approach (a combination of mail, in-person, and focus group discussion) to investigate factors influencing PAT adoption by small-scale farmers in Kentucky. The data were analyzed using the binary logistic regression method. The results revealed that farm size and longer years of farming experience increased the likelihood of PAT adoption, whereas farmers’ age negatively affected adoption. Other demographic variables, such as gender, income, and education, did not influence adoption significantly. To promote the adoption of PATs among small farmers in Kentucky, policies should focus on supporting younger farmers and building PATs suitable for operating in small farms and reducing barriers. Furthermore, providing targeted training and resources to small-scale farmers can help to promote the adoption of these technologies, thereby improving agricultural efficiency and sustainability.
Bio-Recycling Hazelnut Shells to Improve Antioxidant Properties of Lentinus tigrinus Sporophore
Authors: Desiderio Anthea , Pedrosa Mariana C. , Heleno Sandrina A. , Carocho Marcio , Rodrigues Daniele B. , Buratti Simone , Soffientini Irene , Ratto Daniela , Savino Elena , Rossi Paola
Published: January 2025

Lentinus tigrinus is a wood-decay fungus known for its nutritional, culinary, and medicinal benefits. It contains bioactive compounds like polyphenols, terpenes, and flavonoids that exhibit antioxidant, anti-microbial, and anti-inflammatory effects. These natural antioxidants are increasingly recognized for their potential to prevent oxidative damage linked to aging and chronic diseases. This study investigates the antioxidant activity of hydroalcoholic extracts obtained from L. tigrinus sporophores cultivated on hazelnut shells (Lt1_HS), a waste material rich in phenolic compounds, and sporophores cultivated on sawdust (Lt1_S). Bioactivity tests, including DPPH, TBARS, MTT, and DCFH-DA assays, were performed to assess the hydroalcoholic extracts’ efficiency. The results showed that all the extracts contained various bioactive compounds, primarily polyphenols. Notably, the caffeoylquinic acids present in HS and Lt1_HS are linked to anti-peroxidant effects. Biological analyses demonstrated that the Lt1_HS extract has higher anti-peroxidant activity (IC50 0.77 ± 0.01 mg/mL) compared to Lt1_S (IC50 1.36 ± 0.01 mg/mL) and reduces the accumulation of reactive oxygen species in HaCaT cells by 80%. However, the specific bioactive compounds responsible for these antioxidant effects are still unclear, and further analysis will be conducted. Additionally, this study promotes recycling hazelnut shells as a valuable substrate for fungal cultivation, supporting sustainable waste management.
Organic Fertilization Leads to N Limitation Rather than P Limitation in Both Vegetable Soils
Authors: Li Qingshan , Xu Mengqian , Xu Lingying , Wu Xingwang , Zhang Yuqin , Xin Jia , Shen Yazhen , Gao Jichao
Published: January 2025

Organic amendments are widely used to enhance soil fertility and nutrient cycling in greenhouse cultivation, but their effectiveness can vary depending on their origin and composition. This study investigated the impact of four organic materials (rice husk, coconut coir, biochar, and sheep manure) on nutrient cycling and enzyme activities in two of greenhouse tomato soils. The distribution of soil nutrients and enzyme activities was analyzed under controlled conditions during a pot experiment. The addition of organic amendments, regardless of their source, significantly altered the microbial resource allocation, reducing the carbon limitation while increasing the nitrogen demand. The effect on soil nutrient pools was largely determined by the chemical composition of the amendments. In clayey soils, biochar and rice husk additions most effectively promoted enzyme activities related to carbon, nitrogen, and phosphorus acquisition. Under sandy soil conditions, sheep manure increased carbon and phosphorus acquisition enzymes, while biochar most effectively enhanced nitrogen acquisition enzymes. Biochar emerged as a particularly effective amendment, enhancing organic carbon sequestration across different soil types. The chemical composition of the amendments, specifically, the content of carboxyl C, aromatic C, and O-alkyl C, played a crucial role in influencing soil nutrient limitations. In clayey soils, the mean C:N:P ratios for CK, T1, T2, T3, and T4 treatments were 1:1.375:0.625, 1:1.244:0.662, 1:0.839:0.610, 1:1.161:0.689, and 1:1.038:0.549, respectively. In sandy soils, the ratios were 1:1.117:0.698, 1:1.18:0.75, 1:1.096:0.731, 1:1.217:0.689, and 1:1.06:0.669, respectively. These findings suggest that the addition of organic amendments can improve nutrient retention and enzyme activities, but their effects on soil nutrient pools are influenced by both the composition of the amendments and the soil texture. This research enhances our understanding of organic amendments and soil nutrient transformations, and aids in optimizing the application of organic materials for improved soil management in greenhouse cultivation.
Transcriptome Analysis Reveals Key Genes Involved in the Response of Triticum urartu to Boron Toxicity Stress
Authors: Uyar Gul Sema , Pandey Anamika , Hamurcu Mehmet , Vyhnanek Tomas , Harmankaya Mustafa , Topal Ali , Gezgin Sait , Khan Mohd. Kamran
Published: January 2025

The domestication and breeding of wheat genotypes through the years has led to the loss in their genetic variation, making them more prone to different abiotic stresses. Boron (B) toxicity is one of the stresses decreasing the wheat cultivars’ yield in arid and semi-arid regions around the world. Wild wheat progenitors, such as Triticum urartu Thumanian ex Gandilyan, possess a broader gene pool that harbors several genes conferring tolerance to various biotic and abiotic stresses. Unfortunately, T. urartu is not well-explored at the molecular level for its tolerance towards B toxicity in soil. In this study, for the first time, we compared the transcriptomic changes in the leaves of a high B-tolerant T. urartu genotype, PI662222, grown in highly toxic B (10 mM B in the form of boric acid) with the ones grown in the control (3.1 μM B) treatment in hydroponic conditions. The obtained results suggest that several mechanisms are involved in regulating the response of the studied T. urartu genotype toward B toxicity. All the growth parameters of T. urartu genotype, including root–shoot length, root fresh weight, and root–shoot dry weight, were less affected by high boron (10 mM) as compared to the boron-tolerant bread wheat cultivar. With a significant differential expression of 654 genes, 441 and 213 genes of T. urartu genotype were down- and upregulated, respectively, in the PI662222 leaves in high B in comparison to the control treatment. While key upregulated genes included those encoding RNA polymerase beta subunit (chloroplast), ATP synthase subunit gamma, chloroplastic, 60S ribosomal protein, and RNA-binding protein 12-like, the main downregulated genes included those encoding photosystem II protein D, ribulose bisphosphate carboxylase small subunit, and peroxidase 2-like. Interestingly, both Gene Ontology enrichment and KEGG pathways emphasized the possible involvement of the genes related to the photosynthetic process and apparatus in the high B tolerance of the T. urartu genotype. The further functional characterization of the identified potential T. urartu genes will facilitate their utilization in crop improvement programs for B toxicity stress.
Effects of Elevated CO2 on Maize Physiological and Biochemical Processes
Authors: Khan Pirzada , Aziz Tariq , Jan Rahmatullah , Kim Kyung-Min
Published: January 2025

Maize (Zea mays) is a critical global crop, serving as a source of food, livestock feed, and industrial raw materials. Climate changes, driven by rising atmospheric carbon dioxide (CO2) levels, have substantial effects on maize physiology, growth, and nutrient content. This review investigates the impact of elevated CO2 on maize, with a particular focus on photosynthesis enhancement as it improves water use efficiency (WUE), which can lead to increased biomass production. Despite this, elevated CO2 results in a decreased concentration of essential nutrients, including nitrogen, phosphorus, potassium, and folate. The reduction in folate, which is vital for both plant development and human nutrition, poses challenges, especially for population heavily reliant on maize. Additionally, biofortification through traditional breeding and genetic engineering is proposed as a strategy to enhance folate level in maize to mitigate nutritional deficiencies. Elevated CO2 stimulates lignin production, improving stress resistance and carbon sequestration capacity. However, the increase in guaiacyl-rich lignin may negatively affect biomass degradability and efficiency in biofuel production. The findings emphasize the importance of balancing maize’s stress resilience, nutrient profile, and lignin composition to address future climate challenges. This balance is essential for optimizing maize cultivation for food security, biofuel production, and environmental sustainability.
Ecological and Functional Changes in the Hindgut Microbiome of Holstein Cows at High Altitudes
Authors: Chen Gong , Lu Haibo , Huang Shangzhen , Zhang Congcong , Ma Xiaojuan , Li Bin , Hou Lingling , Xu Qing , Wang Yachun
Published: January 2025

The extreme environmental conditions of the Qinhai–Tibetan Plateau (QTP) challenge livestock survival and productivity, yet little is known about how high-altitude environments impact the gut microbiota of dairy cows. To fill this gap, we systematically investigated the differences in the hindgut microbiome between 87 plateau Holstein cows and 72 plain Holstein cows using 16S rRNA gene sequencing. Our analysis revealed that the hindgut microbiota of the plateau group exhibited lower species richness but higher evenness than that in the plain group. Additionally, significant separation in hindgut microbiota composition between the two groups was observed based on altitude, while parity, days in milk, and age did not show a comparable impact. Moreover, altitude had a lasting impact on bacterial communities and their co-occurrence networks, resulting in reduced microbial interactions and lower modularity in the plateau group. Furthermore, we identified four key microbial taxa, the Bacteroidaceae and Rikenellaceae families, as well as the Prevotella and Treponema genera, which were associated with the regulation of carbohydrate digestion and energy metabolism and might help the Holstein cows adapt to the plateau environment. Our findings provide insights into strategies for enhancing the adaptability of dairy cows to high-altitude environments through microbiota modulation, which could ultimately contribute to improving livestock management and sustainability in these extreme environments.
Sources and Transmission Routes of Carbapenem-Resistant Pseudomonas aeruginosa: Study Design and Methodology of the SAMPAN Study
Authors: van Veen Anneloes , Shahab Selvi N. , Rijfkogel Amber , Voor in ’t holt Anne F. , Klaassen Corné H. W. , Vos Margreet C. , Saharman Yulia Rosa , Karuniawati Anis , Zelli Silvia , De Lorenzis Desy , Menchinelli Giulia , De Angelis Giulia , Sanguinetti Maurizio , Kemper Merel , de Jong Anniek E. E. , Mohammadi Sima , Renaud Valentine , Kukavica-Ibrulj Irena , Potvin Marianne , Nguyen Guillaume Q. , Gauthier Jeff , Levesque Roger C. , Schmitt Heike , Severin Juliëtte A.
Published: January 2025

Background/Objectives: The global spread of carbapenem-resistant Pseudomonas aeruginosa (CRPA) warrants collaborative action. Guidance should come from integrated One Health surveillance; however, a surveillance strategy is currently unavailable due to insufficient knowledge on the sources and transmission routes of CRPA. The aim of the SAMPAN study (“A Smart Surveillance Strategy for Carbapenem-resistant Pseudomonas aeruginosa”) is to develop a globally applicable surveillance strategy. Methods: First, an international cross-sectional study will be conducted to investigate CRPA in clinical and environmental settings in Rotterdam (The Netherlands), Rome (Italy), and Jakarta (Indonesia). Screening cultures and risk factor questionnaires will be taken from healthy individuals and patients upon hospital admission. Clinical CRPA isolates will also be included. Additionally, samples will be taken twice from wet hospital environments and monthly from the hospitals’ (drinking) water system, hospital and municipal wastewater treatment plants, and receiving rivers. Whole-genome sequencing will be performed to characterize CRPA isolates and determine the genetic relatedness among the isolates from different reservoirs. Findings from the cross-sectional study, combined with expert elicitation using a Delphi method, will serve as the input for the surveillance strategy. Conclusions: The SAMPAN study will provide a broader understanding of the sources and transmission routes of CRPA. Therewith, the development of a globally applicable smart surveillance strategy will be made possible, delivering information that is needed to guide actions against the spread of CRPA.
Ferroptosis Transcriptional Regulation and Prognostic Impact in Medulloblastoma Subtypes Revealed by RNA-Seq
Authors: Desterke Christophe , Fu Yuanji , Bonifacio-Mundaca Jenny , Monge Claudia , Pineau Pascal , Mata-Garrido Jorge , Francés Raquel
Published: January 2025

Medulloblastoma (MB) is the most common malignant brain tumor in children, typically arising during infancy and childhood. Despite multimodal therapies achieving a response rate of 70% in children older than 3 years, treatment remains challenging. Ferroptosis, a form of regulated cell death, can be induced in medulloblastoma cells in vitro using erastin or RSL3. Using two independent medulloblastoma RNA-sequencing cohorts (MB-PBTA and MTAB-10767), we investigated the expression of ferroptosis-related molecules through multiple approaches, including Weighted Gene Co-Expression Network Analysis (WGCNA), molecular subtype stratification, protein–protein interaction (PPI) networks, and univariable and multivariable overall survival analyses. A prognostic expression score was computed based on a cross-validated ferroptosis signature. In training and validation cohorts, the regulation of the ferroptosis transcriptional program distinguished the four molecular subtypes of medulloblastoma. WGCNA identified nine gene modules in the MB tumor transcriptome; five correlated with molecular subtypes, implicating pathways related to oxidative stress, hypoxia, and trans-synaptic signaling. One module, associated with disease recurrence, included epigenetic regulators and nucleosome organizers. Univariable survival analyses identified a 45-gene ferroptosis prognostic signature associated with nutrient sensing, cysteine and methionine metabolism, and trans-sulfuration within a one-carbon metabolism. The top ten unfavorable ferroptosis genes included CCT3, SNX5, SQOR, G3BP1, CARS1, SLC39A14, FAM98A, FXR1, TFAP2C, and ATF4. Patients with a high ferroptosis score showed a worse prognosis, particularly in the G3 and SHH subtypes. The PPI network highlighted IL6 and CBS as unfavorable hub genes. In a multivariable overall survival model, which included gender, age, and the molecular subtype classification, the ferroptosis expression score was validated as an independent adverse prognostic marker (hazard ratio: 5.8; p-value = 1.04 × 10−9). This study demonstrates that the regulation of the ferroptosis transcriptional program is linked to medulloblastoma molecular subtypes and patient prognosis. A cross-validated ferroptosis signature was identified in two independent RNA-sequencing cohorts, and the ferroptosis score was confirmed as an independent and adverse prognostic factor in medulloblastoma.
Effects of Anthropomorphic Driving Vehicles on Traffic Flow
Authors: Ma Xiaoyi , Hu Xiaowei , Schramm Dieter , Wang Shufen
Published: January 2025

Autonomous driving has many positive impacts, such as improving driver and passenger safety, comfort, and traffic efficiency, but all these advantages are based on people’s trust and acceptance of this mode of driving. Anthropomorphic driving can enhance the trust and comfort of drivers and passengers and is seen as a feasible measure to increase people’s acceptance of autonomous driving. This paper reports the microscopic traffic simulation of three scenarios around a frequently congested intersection, using non-automated vehicles, autonomous driving vehicles, and anthropomorphic driving vehicles to explore their impact on traffic efficiency. The result shows that, compared to non-automated vehicles, both autonomous vehicles and anthropomorphic driving vehicles can improve traffic efficiency during congested periods, increase traffic volume per unit time, reduce the total travel time and time loss, and have a higher average speed. Compared to autonomous vehicles, anthropomorphic driving has a shorter total travel time and a similar time loss. In terms of average speed, anthropomorphic driving performed better than autonomous driving in terms of both congested and non-congested times. In summary, compared to non-automated vehicles, autonomous driving vehicles have a positive effect on improving traffic efficiency, while, compared to autonomous driving, anthropomorphic driving has more advantages in increasing traffic efficiency and reducing traffic congestion.
Development of Novel Nomograms to Predict 5- and 7-Year Biochemical-Recurrence-Free Survival in High-Risk Prostate Cancer Patients After Carbon-Ion Radiotherapy and Androgen Deprivation Therapy
Authors: Utsumi Takanobu , Suzuki Hiroyoshi , Wakatsuki Masaru , Kobayashi Kana , Okato Atsushi , Nakajima Mio , Aoki Shuri , Sumiya Taisuke , Ichikawa Tomohiko , Akakura Koichiro , Tsuji Hiroshi , Yamada Shigeru , Ishikawa Hitoshi
Published: January 2025

Background: The aim of this study was to develop nomograms predicting 5- and 7-year biochemical-recurrence (BCR)-free survival in high-risk prostate cancer (PCa) patients treated with carbon-ion radiotherapy (CIRT) and androgen deprivation therapy (ADT). Methods: We retrospectively evaluated 785 high-risk PCa patients treated with CIRT and ADT. Based on the least absolute shrinkage and selection operator model, two nomograms predicting 5- and 7-year BCR-free survival were developed and internally validated. The ability of each nomogram to predict BCR-free survival was determined by calculating the area under the survival curve (AUC). Results: The 5- and 7-year BCR-free survival rates were 92.1% and 89.3%, respectively. Age, prostate-specific antigen level, clinical T stage, and Gleason score were incorporated into the nomogram predicting 5-year BCR-free survival. In addition to these variables, the percentage of positive biopsy cores was also added to the nomogram predicting 7-year BCR-free survival. The AUC value of each nomogram showed suboptimal-to-good discrimination. Conclusions: We developed the first nomograms accurately predicting BCR-free survival in high-risk PCa patients treated with CIRT and ADT. These nomograms will enable adequate understanding and explanation of BCR-free survival to patients when clinicians use them.
Transportation Mode Selection Using Reinforcement Learning in Simulation of Urban Mobility
Authors: Taş Mehmet Bilge Han , Özkan Kemal , Sarıçiçek İnci , Yazici Ahmet
Published: January 2025

Transportation mode selection is pivotal for navigating through cities plagued by heavy traffic congestion. This plays a crucial role in ensuring the efficient utilization of time and resources to achieve the desired objectives. Given the complex dynamics of urban mobility, strategically selecting a transportation mode can significantly mitigate delays and enhance overall productivity in densely populated areas. The objective of this study is to find the most efficient result among various transportation modes to make deliveries from different points on a university campus. To solve this problem, reinforcement learning was used and tested on the simulation environment SUMO. Traffic density was increased by using an equal number of different transportation modes, such as driving, cycling, motorbiking, and walking. Various traffic densities were generated, and different reward models were applied to select the best means of transportation. Various probability distributions were used as reward models to avoid the unfair distribution caused by how near or how far the road is when moving from random points to the destination region. As a result of the models created using the applied reward–penalty functions, it was determined that the best means of transportation in areas with a low traffic density is cycling, and in areas with high traffic density, the optimal mode of transportation is motorbiking.
Sustainable Development in the Digital Age: Harnessing Emerging Digital Technologies to Catalyze Global SDG Achievement
Authors: Bocean Claudiu George
Published: January 2025

The digital revolution, characterized by rapid technological advancements, presents a unique opportunity to accelerate progress towards the United Nations’ Sustainable Development Goals (SDGs). This research explores the transformative potential of cutting-edge digital technologies—including artificial intelligence, big data analytics, cloud computing, and the Internet of Things—in fostering sustainable development across economic, social, and environmental dimensions. Our study employs a rigorous empirical approach to quantify the impact of digital innovation on SDG achievement within the European Union. Utilizing the Digital Economy and Society Index (DESI) as a comprehensive measure of technological progress, we apply structural equation modeling to emphasize the complex interplay between digital advancement and sustainable development indicators. A key focus of our analysis is the mediating role of economic performance, measured by GDP per capita, in the relationship between digital technology adoption and SDG progress. This nuanced examination provides insights into how economic factors influence the effectiveness of digital solutions in addressing global challenges. Our findings underscore the need for adaptive policies that harness the power of digital technologies while addressing potential challenges and ensuring inclusive growth.
Design of Four-Plate Parallel Dynamic Capacitive Wireless Power Transfer Coupler for Mobile Robot Wireless-Charging Applications
Authors: Bae Hongguk , Park Sangwook
Published: January 2025

A detailed theoretical design of an electric resonance-based coupler for dynamic wireless power transfer (DWPT) at the mobile robot level is presented. The scattering matrix of the coupler was derived by transforming and multiplying transmission matrices for each circuit network in a practical equivalent circuit that accounted for loss resistance. This theoretical approach was validated through equivalent circuit models, yielding results consistent with 3D full-wave simulations and showing an error rate of less than 1%. Additionally, a null-power point characteristic, where efficiency sharply decreases when the receiver moves outside the transmitter’s range, was observed. The detailed theoretical design of the practical equivalent circuit for electric resonance-based dynamic WPT couplers is expected to contribute to the design of couplers for various specifications in future applications.
The Weather On-Demand Framework
Authors: Rögnvaldsson Ólafur , Stanislawska Karolina , Hackerott João A.
Published: January 2025

This paper describes the Weather On-Demand (WOD) forecasting framework which is a software stack used to run operational and on-demand weather forecasts. The WOD framework is a distributed system for the following: (1) running the Weather Research and Forecast (WRF) model for data assimilation and forecasts by triggering either scheduled or on-demand jobs; (2) gathering upstream weather forecasts and observations from a wide variety of sources; (3) reducing output data file sizes for permanent storage; (4) making results available through Application Programming Interfaces (APIs); (5) making data files available to custom post-processors. Much effort is put into starting processing as soon as the required data become available, and in parallel where possible. In addition to being able to create short- to medium-range weather forecasts for any location on the globe, users are granted access to a plethora of both global and regional weather forecasts and observations, as well as seasonal outlooks from the National Oceanic and Atmospheric Administration (NOAA) in the USA through WOD integrated-APIs. All this information can be integrated with third-party software solutions via WOD APIs. The software is maintained in the Git distributed version control system and can be installed on suitable hardware, bringing the full flexibility and power of the WRF modelling system to the user in a matter of hours.
Modified Heisenberg Commutation Relations, Free Schrödinger Equations, Tunnel Effect and Its Connections with the Black–Scholes Equation
Authors: Contreras González Mauricio , Ortiz Herrera Roberto , González Suárez José
Published: January 2025

This paper explores the implications of modifying the canonical Heisenberg commutation relations over two simple systems, such as the free particle and the tunnel effect generated by a step-like potential. The modified commutation relations include position-dependent and momentum-dependent terms analyzed separately. For the position deformation case, the corresponding free wave functions are sinusoidal functions with a variable wave vector k(x). In the momentum deformation case, the wave function has the usual sinusoidal behavior, but the energy spectrum becomes non-symmetric in terms of momentum. Tunneling probabilities depend on the deformation strength for both cases. Also, surprisingly, the quantum mechanical model generated by these modified commutation relations is related to the Black–Scholes model in finance. In fact, by taking a particular form of a linear position deformation, one can derive a Black–Scholes equation for the wave function when an external electromagnetic potential is acting on the particle. In this way, the Scholes model can be interpreted as a quantum-deformed model. Furthermore, by identifying the position coordinate x in quantum mechanics with the underlying asset S, which in finance satisfies stochastic dynamics, this analogy implies that the Black–Scholes equation becomes a quantum mechanical system defined over a random spatial geometry. If the spatial coordinate oscillates randomly about its mean value, the quantum particle’s mass would correspond to the inverse of the variance of this stochastic coordinate. Further, because this random geometry is nothing more than gravity at the microscopic level, the Black–Scholes equation becomes a possible simple model for understanding quantum gravity.
Anterior Cruciate Ligament Mechanical Response to Load in the Setting of Changes to the Medial Meniscus
Authors: Hussain Angela , Madraswala Muffaddal , Koh Jason , Amirouche Farid
Published: January 2025

The anterior cruciate ligament (ACL) is a major ligament in the knee joint, and its function is crucial for both the movement and stability of the knee. Our research takes a novel approach by investigating the effect of meniscus tears on the ACL, how such tears will impact the stress on the ACL, and its overall compensation in response to the changes in the meniscus. Hypothesis/Purpose: This study aims to investigate how the ACL compensates for the change in knee joint stability and contact pressures due to partial horizontal cleavage tears (HCTs) in the meniscus, such as partial meniscectomy and partial transplantation on knee joint stability and contact pressures. We hypothesize that HCTs will increase contact pressures and decrease joint stability, thereby inducing compensatory stress on the anterior cruciate ligament (ACL). Method: Seven freshly frozen human cadaveric knees were used in a study to investigate the effects of different meniscal conditions and surgical interventions on the meniscus itself. Four testing scenarios were established: intact knees, knees with partial horizontal cleavage tears (HCTs) of the meniscus, knees with partial meniscectomy, and knees with partial transplantation. Axial loading was applied, and the medial meniscus contact pressures were measured at 0° and 30° of flexion. Additionally, a mathematical 3D finite element model was created to evaluate the behavior of the ACL under different meniscus scenarios, which could not have been measured experimentally. Results: ACL contact pressure and stress analysis across various meniscal conditions demonstrated substantial variability. Horizontal cleavage tears (HCTs) resulted in heightened contact pressures and diminished joint stability, as evidenced by increased ACL stress attributed to compensatory mechanisms in the presence of meniscal tears. Conversely, transplantation procedures exhibited a mitigating effect, maintaining joint mechanics closer to intact conditions and minimizing alterations in ACL forces. These trends persisted at 30 degrees of knee flexion, where significant increases in ACL forces were observed in partial and complete HCT conditions. Conclusions: This study uncovers the biomechanical impacts of meniscal injuries, demonstrating how the ACL compensates for various meniscus conditions. In contrast, transplantation and repair conditions only slightly increase the stress on the ACL, putting much less strain on the ACL and supporting structures of the knee joint than an unrepaired tear.
Digital Image Analysis of Vertebral Body S1 and Its Ossification Center in the Human Fetus
Authors: Grzonkowska Magdalena , Bogacz Katarzyna , Żytkowski Andrzej , Szkultecka-Dębek Monika , Kułakowski Michał , Janiak Michał , Rogalska Agnieszka , Baumgart Mariusz
Published: January 2025

Objectives: The aim of the present study was to examine the growth dynamics of the first sacral vertebra and its ossification center in the human fetus, based on their linear, planar, and volumetric parameters. Methods: The examinations were carried out on 54 human fetuses of both sexes (26 males and 28 females) aged 18–30 weeks of gestation, which had been preserved in 10% neutral formalin solution. Using CT, digital image analysis software, 3D reconstruction, and statistical methods, the size of the first sacral vertebra and its ossification center was evaluated. Results: The first sacral vertebra and its ossification center grew proportionately according to fetal weeks. Conclusions: The numerical data obtained from computed tomography and the growth patterns of the body of the first sacral vertebra and its ossification center may serve as age-specific normative intervals relevant for gynecologists, obstetricians, pediatricians, and radiologists during fetal ultrasound screening. Our findings on the growth of the body of the first sacral vertebra and its ossification center may be useful in daily clinical practice, particularly in ultrasonic monitoring of normal fetal growth and in screening for congenital defects and skeletal dysplasias.
Loneliness in Middle-Aged and Older Adults: Effects of Social Environments
Authors: Murtazina Inna , Krupina Kristina , Strizhitskaya Olga
Published: January 2025

Loneliness is a common subjective condition that is associated with distress and negative outcomes for psychosocial functioning and well-being, and it is grounded in destructive or inadequate social functioning. Social interactions are considered one of the key factors determining loneliness, and similarly to social interactions, loneliness can occur in different domains. While a solid body of research is focused on loneliness as a general condition, there are few studies that investigate loneliness from a multidimensional perspective, particularly combining general and domain-specific loneliness. In the present study, we conceptualized loneliness as a complex phenomenon. We focused on the associations between different types of loneliness and the characteristics of social environments. The participants were 140 adults aged 45–73 (58.9% females). The methods involved the Multidimensional Inventory of Loneliness Experience, the Social and Emotional Loneliness Scale (SELSA-S), the “Sociotropy—Self-Sufficiency” Questionnaire, and the assessment of demographic characteristics (age and sex). To test our hypothesis, we applied regression path modeling. The results showed that general loneliness predicted both family and non-family loneliness. We also found that general loneliness increased experiences of social uncertainty, while non-family loneliness decreased positive relations with others. No age effects were found. An effect of sex was found for social uncertainty and positive relations with others.
Addressing Missing Data in Slope Displacement Monitoring: Comparative Analysis of Advanced Imputation Methods
Authors: Lee Seungjoo , Kim Yongjin , Ji Bongjun , Kim Yongseong
Published: January 2025

Slope displacement monitoring is essential for assessing slope stability and preventing catastrophic failures, particularly in geotechnically sensitive areas. However, continuous data collection is often disrupted by environmental factors, sensor malfunctions, and communication issues, leading to missing data that can compromise analysis accuracy and reliability. This study addresses these challenges by evaluating advanced machine learning models—SAITS, ImputeFormer, and BRITS (Bidirectional Recurrent Imputation for Time Series)—for missing data imputation in slope displacement datasets. Sensors installed at two field locations, Yangyang and Omi, provided high-resolution displacement data, with approximately 34,000 data points per sensor. We simulated missing data scenarios at rates of 1%, 3%, 5%, and 10%, reflecting both random and block missing patterns to mimic realistic conditions. The imputation performance of each model was evaluated using Mean Absolute Error, Mean Squared Error, and Root Mean Square Error to assess accuracy and robustness across varying levels of data loss. Results demonstrate that each model has distinct advantages under specific missingness patterns, with the ImputeFormer model showing strong performance in capturing long-term dependencies. These findings underscore the potential of machine learning-based imputation methods to maintain data integrity in slope displacement monitoring, supporting reliable slope stability assessments even in the presence of significant data gaps. This research offers insights into the optimal selection and application of imputation models for enhancing the quality and continuity of geotechnical monitoring data.
Multiple Myeloma Cells Shift the Fate of Cytolytic ILC2s Towards TIGIT-Mediated Cell Death
Authors: Drommi Fabiana , Calabrò Alessia , Pezzino Gaetana , Vento Grazia , Freni Josè , Costa Gregorio , Cavaliere Riccardo , Bonaccorsi Irene , Allegra Alessandro , Ferlazzo Guido , De Pasquale Claudia , Campana Stefania
Published: January 2025

Background: Growing evidence attests to the multifaceted roles of group 2 innate lymphoid cells (ILC2s) in cancer immunity. They exhibit either pro- or anticancer activity depending on tumor type but their function in Multiple Myeloma (MM) is still not elucidated. Methods: The bone marrow (BM) and peripheral blood (PB) of patients (pts) with MM or precancerous conditions were collected, and specific properties of ILC2 subsets were assessed by flow cytometry. Results: By dissecting ILC2s according to c-Kit marker, we observed that NKp30 and NKG2D were mainly confined to c-Kithi ILC2s, while levels of DNAM-1 was significantly higher in fully mature c-Kitlo cells. Among the total MM-associated ILC2s (MM-ILC2s), we observed a significant increase in c-the Kitlo subset, but the expression of DNAM-1 in these cells was significantly reduced, especially in BM. Interestingly, MM-ILC2s from PB expressed granzyme B (GZMB), but its expression was impaired in BM-ILC2s. Accordingly, MM cells were susceptible to killing by MM-ILC2s derived from PB while eluding ILC2 surveillance in BM. Indeed, in MM-ILC2s derived from BM, the downregulation of DNAM-1 is accompanied by the upregulation of TIGIT, which mediate cell death in ILC2s upon recognition of the cognate ligands expressed by MM cells. These ILC2 changes appeared in clinical precursor conditions and eventually accumulated with disease progression. Conclusions: MM-ILC2s can act as cytolytic immune effectors that are fully competent in PB. However, MM cells shift ILC2 fate towards cell death in BM via the upregulation of TIGIT, thereby representing a potential therapeutic target to restore ILC2 antitumor activity.
DNA Methylation Profile in Buffy Coat Identifies Methylation Differences Between Cirrhosis with and Without Hepatocellular Carcinoma
Authors: Lim Hyeyeun , El-Serag Hashem B. , Luster Michelle , Grove Megan L. , Byun Jinyoung , Jung Yuri , Han Younghun , Boerwinkle Eric , Amos Christopher I. , Thrift Aaron P.
Published: January 2025

Background/Objectives: Cirrhosis is the precursor to most cases of hepatocellular carcinoma (HCC). Understanding the mechanisms leading to the transition from cirrhosis to HCC and identifying key biomarkers is crucial to developing effective screening strategies and reducing HCC-related mortality. DNA methylation is associated with gene inactivation and plays an important role in physiological and pathological processes; however, its role in cirrhosis progression to HCC is unknown. Methods: We performed genome-wide DNA methylation profiling using Illumina Infinium MethylationEPI BeadChip in pre-diagnostic samples from 22 cirrhosis patients who subsequently developed HCC and 22 cirrhosis patients who remained HCC-free during an average 4-year follow-up. In a secondary analysis, we examined a subset of patients without hepatitis C virus (HCV) infection. Results: We identified three differentially methylated positions (DMPs) located in ADAM12 (cg13674437) and PSD3 (cg06758847 and cg24595678) that show a strong association with HCC risk (lower median vs. higher median hazards ratio (HR): HR cg13674437 = 0.34, 95% CI = 0.14–0.83; HR cg06758847 = 4.89, 95% CI = 1.79–13.33; HR cg24595678 = 11.19, 95% CI = 3.27–38.35). After excluding all HCV-active patients from our analysis, the HR for the DMPs remained significant. Conclusions: In conclusion, the findings in this study support the theory that buffy coat-derived DNA methylation markers could be used to identify biomarkers among cirrhosis patients at high risk for HCC before clinical symptoms appear. A further study with a large prospective cohort is required to validate these findings.