bluuebunny/crossref_metadata_2025 huggingface dataset:
A New Method for Complex Impedance Measurement of Power Transformers via a Continuous Wavelet Transform
Authors: Gómez-Luna Eduardo , Candelo-Becerra John E. , Vasquez Juan C.
Published: December 2024

The Fourier transform is widely accepted as the time-to-frequency conversion procedure, although it has some limitations. Currently, measurements in the time domain are usually transient (non-periodic waveforms) within a finite window time and discrete (non-continuous) sampled signals. The accuracy of the Fourier transform decreases as the window time and sampling frequency decrease. This is where the wavelet transform proves to be a valuable tool in this analysis. This paper presents a novel method for estimating the complex electrical impedance of power transformers by analyzing transient electrical signals with the continuous wavelet transform. The great importance of knowing the complex electrical impedance of the transformer is that it allows knowing the state and condition of the internal parts, such as the core and the windings, whose behavior depends on the frequency with which the transformer is fed. The wavelet transform is employed in the proposed method to improve the analysis of the frequency response (FRA), following the same procedure commonly used with the Fourier transform. The proposed method is validated by performing an experimental test on a 28 MVA power transformer. The results show that the new method using the continuous wavelet transform is a power tool that enhances the extraction of the total electrical impedance curve (magnitude–phase) compared to the Fourier transform. This enables real-time frequency response analysis in transformers, facilitating accurate diagnosis.
Review of Social Sustainability Assessments of Electricity Generating Systems
Authors: Lemence Allen Lemuel G. , Cravioto Jordi , McLellan Benjamin C.
Published: December 2024

In the quest for sustainable development, the energy sector must address the three pillars of sustainability: economic, environmental, and social. However, compared to the other pillars, research on social sustainability has been relatively limited. A systematic literature review was conducted covering 143 peer-reviewed articles (after initial screening) to explore the current state and opportunities in the social sustainability assessment of electricity generating systems. The contributions of this research are two-fold: first is the analysis of the elements of social sustainability assessment, particularly the explicit and implicit definitions of social sustainability as well as the roles and nature of involvement of stakeholders. Second is the analysis of the intersections among framework elements. The insights provided serve as a valuable resource in conducting social sustainability assessments of electricity generating systems as well as inform researchers and stakeholders concerning future research directions.
The Prediction of Coalbed Methane Layer in Multiple Coal Seam Groups Based on an Optimized XGBoost Model
Authors: Zhao Weiguang , Sang Shuxun , Han Sijie , Cheng Deqiang , Zhou Xiaozhi , Guo Zhijun , Zhao Fuping , Zhang Jinchao , Gao Wei
Published: December 2024

The prediction of the optimal coalbed methane (CBM) layer plays a significant role in the efficient development of CBM in multiple coal seam groups. In this article, the XGBoost model optimized by the tree-structured Parzen estimator (TPE) algorithm was established to automatically predict the optimal CBM layer in complex multi-coal seams of the Dahebian block in Guizhou Province, China. The research results indicate that the TPE XGBoost model has higher evaluation metrics than traditional machine learning models, with higher accuracy and generalization ability. The optimal coalbed methane layer predicted by the model for the Dacong 1–3 well is the 11th coal seam. In addition, the interpretation results of the model indicate that sonic (AC) and caliper logging (CAL) are relatively important in determining the optimal CBM layer. The favorable layers for coalbed methane development are distributed in coal seams with developed fractures and high gas content. The TPE-XGBoost model can help us objectively analyze the significance of different types of logging, quickly predict the optimal layer in complex multiple coal seam groups, and greatly reduce costs and subjective impact. It provides a new approach to predict the best CBM layer in multiple coal seam groups in the Guizhou Province in the southwest of China.
The Wideband Oscillatory Localization Method Based on Combining Compressed Sensing and Graph Attention Networks
Authors: Gao Jinggeng , Yang Yong , Xu Honglei , Xie Yingzhou , Zhou Chen , Dong Haiying
Published: December 2024

Due to the increasing integration of new energy sources, the power system now exhibits low inertia, in which the broadband oscillation problem is increasingly significant in the face of the strong coupling of complex and variable power systems, and the current lack of uniform and effective mathematical models and analysis methods. To solve this major problem, a broadband oscillation localization method based on the combination of compressed perception and graph attention network (GAT) is proposed. The method firstly uses the principle of compression perception to compress and transmit the oscillation time series data of the sub-station, reconstructs the compressed signal at the master station and aggregates the grid topology and node characteristic information to effectively reduce the redundancy of the oscillation data; reconstruction error is only 0.031, takes into account the balance of the samples and the effectiveness of the computation, and adopts the multi-attention mechanism and the cross-entropy loss function to improve the performance of the model training. Finally, the offline training and online evaluation model based on the GAT algorithm is constructed, and the accuracy of the model is up to 98.5%; and the results show that the method has a high positioning accuracy and a certain anti-noise ability at the same time.
Study of Cooling Characteristics of Axisymmetric Tail Nozzle
Authors: Zeng Hao , Xi Rui , Li Yiwen , Hou Xingyao , Fu Changqin
Published: December 2024

In order to reduce the infrared radiation intensity of supersonic tail nozzles and in response to the increasingly severe battlefield infrared environment, simulations were conducted on axisymmetric expanding tail nozzles to study the effects of air, liquid nitrogen, and dry ice cold flows at different flow rates on the nozzle wall temperature. The results show that when the dry ice flow rate is increased by 1 kg/s, the maximum temperature of the wall surface in the expansion section decreases by about 40 K. At a cold flow rate of 5% in the 0° detection direction, the intensity of infrared radiation was reduced by 20.8% for the liquid nitrogen cold flow and 26.3% for the dry ice cold flow, compared to the cold flow of injected air. The IR suppression of the tail nozzle was significant in the range from α = 0 to 50°. Compared to cooling air, the maximum IR radiation intensity was reduced by 26.5% for dry ice and 20.4% for liquid nitrogen. When the flow rate of the injected cold stream was increased by 4%, the intensity of the infrared radiation from the nozzle was reduced by 52.6%, 55.8%, and 66.2% for the injected air, liquid nitrogen, and dry ice cold streams, respectively.
A Mini Review of the Impacts of Machine Learning on Mobility Electrifications
Authors: Noor ali Kimiya , Hemmati Mohammad , Miraftabzadeh Seyed Mahdi , Mohammadi Younes , Bayati Navid
Published: December 2024

Electromobility contributes to decreasing environmental pollution and fossil fuel dependence, as well as increasing the integration of renewable energy resources. The increasing interest in using electric vehicles (EVs), enhanced by machine learning (ML) algorithms for intelligent automation, has reduced the reliance on. This shift has created an interdependence between power, automatically, and transportation networks, adding complexity to their management and scheduling. Moreover, due to complex charging infrastructures, such as variations in power supply, efficiency, driver behaviors, charging demand, and electricity price, advanced techniques should be applied to predict a wide range of variables in EV performance. As the adoption of EVs continues to accelerate, the integration of ML and especially deep learning (DL) algorithms will play a pivotal role in shaping the future of sustainable transportation. This paper provides a mini review of the ML impacts on mobility electrification. The applications of ML are evaluated in various aspects of e-mobility, including battery management, range prediction, charging infrastructure optimization, autonomous driving, energy management, predictive maintenance, traffic management, vehicle-to-grid (V2G), and fleet management. The main advantages and challenges of models in the years 2013–2024 have been represented for all mentioned applications. Also, all new trends for future work and the strengths and weaknesses of ML models in various aspects of mobility transportation are covered. By discussing and reviewing research papers in this field, it is revealed that leveraging ML models can accelerate the transition to electric mobility, leading to cleaner, safer, and more sustainable transportation systems. This paper states that the dependence on big data for training, the high uncertainty of parameters affecting the performance of electric vehicles, and cybersecurity are the main challenges of ML in the e-mobility sector.
An Investigation of Structural Integrity and Dynamic Response of CSB in Case of Slanted Contact with RV Outlet Nozzle
Authors: Hieu Pham Quang , Namgung Ihn
Published: December 2024

One of the challenges in the current reactor is the gap between the outlet nozzle of the Reactor Vessel (RV) and the outlet opening of the Core Support Barrel (CSB). This gap causes bypass flow, reducing the overall efficiency. To address this issue, RV and CSB designs were modified with various configurations. Through finite element analysis, this study evaluated the structural integrity and dynamic response of an RV and CSB with these modified designs. The structural integrity was assessed against ASME code to determine the limits of design parameter changes that met code requirements. Additionally, the natural vibration characteristics of the CSB and RV were analyzed to evaluate improvements in the seismic response by modal analysis. The findings revealed that the CSB design in the case of 3-degree slanted contact with the RV outlet nozzle provided the most optimized results. Moreover, modal analysis indicated a substantial enhancement in seismic response, with the dominant CSB mode frequencies increasing by 30%. This shift, from the 15–20 Hz range to the 20–30 Hz range, is especially noteworthy given the concentration of seismic energy within the 1–20 Hz band.
Towards Sustainability: Understanding Norway’s Ecological Footprint Through the Framework of the Environmental Kuznets Curve
Authors: Georgescu Irina , Nica Ionuț , Kinnunen Jani
Published: December 2024

With rising concerns about environmental sustainability, examining the economic-environmental nexus is essential. This study investigates the relationship between economic growth (GDP), renewable energy consumption (RENC), urbanization (URB), and foreign direct investment (FDI) with Norway’s ecological footprint from 1990 to 2023. Guided by the Environmental Kuznets Curve (EKC) hypothesis, we aim to understand how these factors influence Norway’s environmental impact over time. Using an Autoregressive Distributed Lag (ARDL) model, the study captures both the short- and long-term effects of these variables on the ecological footprint, and model stability is confirmed through the Cumulative Sum of Squares (CUSUM) test. The findings reveal a U-shaped EKC pattern, where initial economic growth reduces the ecological footprint, but beyond a certain GDP threshold, environmental pressures increase. Renewable energy consumption is shown to lower the ecological footprint significantly, supporting Norway’s sustainability goals. Conversely, FDI is associated with increased environmental impact, potentially due to resource-intensive projects. These insights underscore the need for balanced policies that harmonize economic development with environmental preservation. Policymakers are encouraged to foster green FDI, prioritize renewable energy investments, and implement sustainable urban planning to maintain Norway’s trajectory toward ecological sustainability.
Advancements and Future Prospects in the Hydraulic Fracturing of Geothermal Reservoirs
Authors: Shan Kun , Zou Qinqin , Li Chongshuai , Yu Ziwang
Published: December 2024

Reservoir reconstruction is a critical challenge in many significant underground energy projects, such as enhanced geothermal systems, oil shale extraction, and shale gas development. Effectively reconstructing geothermal reservoirs can significantly enhance the exploitation and production capacity of geothermal resources. However, this process requires stringent technical standards and varies with different geological conditions across regions, necessitating tailored reconstruction strategies. This review offers a comprehensive examination of hydraulic fracturing within geothermal reservoirs, covering the geological and physical characteristics inherent to these systems, the effects of injection methods and thermal stimulation on hydraulic fracturing processes, and the assessment and optimization of transformation effects, as well as environmental implications and risk management considerations. We explore the influence of various injection modes on hydraulic fracturing dynamics. Moreover, we compare the differences between hydraulic fracture propagation with and without thermal effects. Additionally, we summarize optimization strategies for reservoir reconstruction. Finally, we discuss several challenges and potential future directions for development, offering insights into possible advancements. This review is of substantial significance for both research and commercial applications related to hydraulic fracturing in geothermal reservoirs.
A Study on Wind Collection Effect of Vertical Axis Windmills
Authors: Hosoe Tadashi , Yukita Kazuto
Published: December 2024

In recent years, global warming caused by greenhouse gasses such as carbon dioxide has become a concern. This has resulted in increased focus on environmentally friendly power systems. Consequently, renewable energy power generation methods, such as wind and solar power generation, have attracted attention. Wind power generation is expected to significantly increase in the future. However, in many inland areas in Japan, the average wind speed remains 6 m/s or less. In this study, we proposed the introduction of winglets and wind collectors (used in aircraft wings) into straight-wing vertical-axis wind turbines to improve their power generation efficiency. Field tests were conducted to confirm the effectiveness of the proposed method. Using winglets and wind collectors, the wind turbine rotation speed was increased at low wind speeds, which facilitated the generation of power. Moreover, it was confirmed that a wind turbine equipped with the proposed winglets and wind collectors could capture wind without its dispersal as it passed through the turbine.
Revealing the NO Formation Kinetics for NH3/CH4 Blends Under Dual-Flame and Premixed Swirl Flame Configurations
Authors: Wang Siqi , Chong Cheng Tung , Sheykhbaglou Soroush , Ng Jo-Han , Tian Bo , Valera-Medina Agustin
Published: December 2024

Ammonia stands out as a promising zero-carbon fuel and an efficient hydrogen carrier, offering great promise for industrial applications in gas turbines and boilers. However, different combustion modes significantly influence the flame structure and combustion characteristics of ammonia. In this study, two distinct fuel injection strategies were employed in a model combustor: ammonia and methane, under fully premixed and dual-flame combustion modes. Numerical simulations were performed to analyze the flame structure, velocity fields, and temperature distribution, complemented by planar flow field, flame OH* chemiluminescence, and NO emission measurements. Findings reveal that with an increasing NH3 ratio, the flame front becomes more elongated with more pronounced temperature fluctuations at the swirler exit. Particularly, at 50% NH3, a significant reduction in flame temperature is observed, notably at a height of 30 mm from the burner. For dual flames, the reaction NH2 + O ↔ HNO + H was less significant compared to its effect in premixed flames, whereas the H + O2 ↔ O + OH reaction demonstrated the highest sensitivity coefficient. An increase in the NH3 ratio correspondingly led to a reduction in NO consumption reaction rates, heightening the sensitivity coefficient for NO inhibition, and providing critical insights into ammonia combustion optimization.
The Conversion of Ethanol to Syngas by Partial Oxidation in a Non-Premixed Moving Bed Reactor
Authors: Dorofeenko Sergei , Podlesniy Dmitry , Polianczyk Eugene , Salganskaya Marina , Tsvetkov Maxim , Yanovsky Leonid , Zaichenko Andrey
Published: December 2024

An experimental investigation into the conversion of ethanol to syngas by partial oxidation in a non-premixed counterflow moving bed filtration combustion reactor was carried out. Regimes of conversion depending on the mass flow rates of fuel and air (separate feeding), as well as a granular solid heat carrier, were studied. Depending on the mass flow rate of the heat carrier, two combustion modes were realized—reaction trailing and intermediate—with different temperature patterns in the gas preheating, combustion, and cooling zones along the reactor. The product gas composition is far from the predictions of the equilibrium model; it contains substation fractions of methane and ethylene. Combustion temperature and conversion are limited by the relatively high level of heat loss from the laboratory-scale reactor. The effect of the heat loss can be reduced by enhancing the absolute flow rate of the reactants.
Developing an Optimized Energy-Efficient Sustainable Building Design Model in an Arid and Semi-Arid Region: A Genetic Algorithm Approach
Authors: Ayoobi Ahmad Walid , Inceoğlu Mehmet
Published: December 2024

The building sector is a major contributor to resource consumption, energy use, and greenhouse gas emissions. Sustainable architecture offers a solution, leveraging Building Energy Modeling (BEM) for early-stage design optimization. This study explores the use of genetic algorithms for optimizing sustainable design strategies holistically. A comprehensive analysis and optimization model was developed using genetic algorithms to individually optimize various sustainable strategies. The optimized strategies were then applied to a pre-existing building in Kabul City, a region facing significant environmental challenges. To enhance accuracy, this study integrated energy simulations with Computational Fluid Dynamics (CFD). This research combines genetic algorithms with energy simulation and CFD analysis to optimize building design for a specific climate. Furthermore, it validates the optimized strategies through a real-world case study building. Optimizing the Window-to-Wall Ratio (WWR) and shading devices based on solar exposure significantly improved the building’s energy performance. South (S)-facing single windows and specific combinations of opposing and adjacent windows emerged as optimal configurations. The strategic optimization of building component materials led to substantial energy savings: a 58.6% reduction in window energy loss, 78.3% in wall loss, and 69.5% in roof loss. Additionally, the optimized pre-existing building achieved a 48.1% reduction in cooling demand, a 97.5% reduction in heating demand, and an overall energy reduction of 84.4%. Improved natural ventilation and controlled solar gain led to a 72.2% reduction in peak-month CO2 emissions. While this study focused on applicable passive design strategies, the integration of advanced technologies like Phase Change Materials (PCMs), kinetic shading devices, and renewable energy systems can further improve building performance and contribute to achieving net-zero buildings.
Assessing the Value of Information in an Augmented Reality City Experience
Authors: Rusho Yonit , Richter Ganit , Raban Daphne Ruth
Published: December 2024

Information is an experience good, meaning that its value emerges upon use and varies based on individual perceptions. Augmented reality (AR) is a technology designed to deliver immersive informational experiences. This study investigates the impact of AR on the perceived value of information by people experiencing information as consumers or producers. Using Google Maps, we developed an AR mobile map called Tour-It-Yourself (TIY) for city navigation. The TIY app facilitates content consumption and production around local points of interest engagingly and interactively. We discuss the development of the research tool and results from two user studies (N = 37 teenagers and N = 51 students, respectively). Further, we highlight the economic valuation aspect by examining the effects of AR on Willingness-to-Pay and Willingness-to-Accept payment for information. The results indicate a significant main effect of participant experience (consumer/producer) and AR technology on the perceived evaluation of information. Moreover, the addition of AR technology to information improves knowledge regarding points of interest for producers. These findings contribute to our understanding of how AR shapes information value perception and have implications for designing interactive location-based information systems, benefiting tourism, education, and business, as well as fostering a sense of belonging in cities.
Hierarchical Federated Learning-Based Intrusion Detection for In-Vehicle Networks
Authors: Althunayyan Muzun , Javed Amir , Rana Omer , Spyridopoulos Theodoros
Published: December 2024

Intrusion detection systems (IDSs) are crucial for identifying cyberattacks on in-vehicle networks. To enhance IDS robustness and preserve user data privacy, researchers are increasingly adopting federated learning (FL). However, traditional FL-based IDSs depend on a single central aggregator, creating performance bottlenecks and introducing a single point of failure, thereby compromising robustness and scalability. To address these limitations, this paper proposes a Hierarchical Federated Learning (H-FL) framework to deploy and evaluate the performance of the IDS. The H-FL framework incorporates multiple edge aggregators alongside the central aggregator, mitigating single-point failure risks, improving scalability, and efficiently distributing computational load. We evaluate the proposed IDS using the H-FL framework on two car hacking datasets under realistic non-independent and identically distributed (non-IID) data scenarios. Experimental results demonstrate that deploying the IDS within an H-FL framework can enhance the F1-score by up to 10.63%, addressing the limitations of edge-FL in dataset diversity and attack coverage. Notably, H-FL improved the F1-score in 16 out of 24 evaluated scenarios. By enabling the IDS to learn from diverse data, driving conditions, and evolving threats, this approach substantially strengthens cybersecurity in modern vehicular systems.
Impact of Metabolites from Foodborne Pathogens on Cancer
Authors: Mafe Alice N. , Büsselberg Dietrich
Published: December 2024

Foodborne pathogens are microorganisms that cause illness through contamination, presenting significant risks to public health and food safety. This review explores the metabolites produced by these pathogens, including toxins and secondary metabolites, and their implications for human health, particularly concerning cancer risk. We examine various pathogens such as Salmonella sp., Campylobacter sp., Escherichia coli, and Listeria monocytogenes, detailing the specific metabolites of concern and their carcinogenic mechanisms. This study discusses analytical techniques for detecting these metabolites, such as chromatography, spectrometry, and immunoassays, along with the challenges associated with their detection. This study covers effective control strategies, including food processing techniques, sanitation practices, regulatory measures, and emerging technologies in pathogen control. This manuscript considers the broader public health implications of pathogen metabolites, highlighting the importance of robust health policies, public awareness, and education. This review identifies research gaps and innovative approaches, recommending advancements in detection methods, preventive strategies, and policy improvements to better manage the risks associated with foodborne pathogens and their metabolites.
A Meta-Analysis on the Antimicrobial Effectiveness of Ozonated Water Treatments for Fresh Produce Washing—Effect of Ozonation Methods
Authors: Hong Haknyeong , Rizzi Marissa Faye , Wang Danhui , McLandsborough Lynne , Lu Jiakai
Published: December 2024

Due to the lack of a pathogen-killing process, foodborne outbreaks from contaminated fresh produce have been increasing worldwide. Hence, it is increasingly recognized that the washing step with sanitizers is important to control microbial contamination. Ozonated water is suggested as a substitute for chlorine-based sanitizers, addressing concerns about the effectiveness and environmental impact of chlorine-based sanitizers. However, using ozone as a sanitizer in the fresh produce washing process is still challenging because of its unstable and inconsistent antimicrobial effectiveness under various testing conditions. A meta-analysis was focused on the comparison of antimicrobial effectiveness between different ozonation methods commonly adopted in laboratory settings, including stationary pre-ozonated water, agitated pre-ozonated water, and sparging. The meta-analysis showed that the sparging method results in the highest microbial log reduction compared to other methods. We further developed meta-regression models based on three ozonation methods to identify key processing variables influencing the antimicrobial effectiveness of ozonated water. Attempts were made to link key processing variables to ozone stability and the mass transport phenomena involved in the washing process. This research will contribute to designing and developing a washing process to increase fresh produce safety by identifying key factors in each ozonation method and facilitate interlaboratory comparison studies.
Addition of Carrot Pomace to Enhance the Physical, Sensory, and Functional Properties of Beef Patties
Authors: Richards Jordan , Lammert Amy , Madden Jack , Cahn Anna , Kang Iksoon , Amin Samir
Published: December 2024

The global challenge of food waste necessitates innovative solutions, such as incorporating carrot pomace, a nutrient-rich by-product of carrot juice production, into beef patties to enhance their nutritional and functional properties. This study evaluated beef patties with carrot pomace added at 0%, 1.0%, 3.0%, and 4.2%, analyzing the proximate composition, pH, color, cooking yield, water-holding capacity (WHC), texture, and sensory attributes. Adding 3.0% and 4.2% pomace significantly reduced the moisture content by 5.5% and 3.3%, respectively, and decreased redness by 40% in the 4.2% patties. The cooking yield increased by 13.9% and 22.8%, and WHC improved by 8.5% and 15.7%, respectively, with these additions. The textural properties showed substantial reductions in hardness, cohesiveness, gumminess, and chewiness, particularly at 4.2%. The sensory evaluation indicated no significant differences in appearance, aroma, taste, or overall liking for patties with up to 3% pomace. In comparison, patties with 4.2% pomace received lower scores for overall liking and firmness. These findings suggest that incorporating up to 3% carrot pomace in beef patties enhances their functional properties and dietary fiber content without compromising the sensory quality. This offers a sustainable and practical approach to food waste valorization.
Exploring the Role of CCNF Variants in Italian ALS Patients
Authors: Bisogni Giulia , Conte Amelia , Costantino Umberto , Lattante Serena , Bernardo Daniela , Lucioli Gabriele , Patanella Agata Katia , Cimbolli Paola , Del Giudice Elda , Vettor Federica , Marangi Giuseppe , Doronzio Paolo Niccolò , Zollino Marcella , Sabatelli Mario
Published: December 2024

Objectives: Variants in Cyclin F (CCNF) have been associated to amyotrophic lateral sclerosis (ALS) and/or frontotemporal dementia (FTD) in a group of cases. The objectives of this study were to determine the contribution of CCNF in a large cohort of Italian ALS patients, to look for genotype-phenotype correlation of the mutations and to evaluate the CCNF-associated clinical features. Methods: We applied next-generation sequencing technologies on 971 unrelated Italian ALS patients and we filtered results to look for variants in CCNF gene. Results: We identified 13 rare missense variants in 16 index cases (2 familial and 14 sporadic), with a cumulative mutational frequency of 1.6%. The most prevalent variant was p.Phe197Leu, found in three patients. The clinical presentation was heterogeneous, with a classic phenotype in eight patients, upper motor neuron dominant (UMN-D) phenotype in four patients, and flail arm in four patients. Clinical evaluation for cognitive impairment was performed in 13 patients using the Edinburgh Cognitive and Behavioural ALS Screen (ECAS) test, demonstrating that almost half of the patients (n = 6) had variable degrees of frontal dysfunction. Discussion: In our cohort, we observed CCNF variants in 1.6% of patients (16/971), a percentage similar to that found in other series. Clinical presentation is heterogeneous, but CCNF variants are significantly associated to cognitive impairment. Conclusions: Our study expands the CCNF genetic variant spectrum in a large cohort of Italian ALS patients. Further studies are needed to assess genotype-phenotype associations of CCNF variants and to specify the role of each variant, which are quite common, especially in sALS patients.
Prenatal Maternal Psychological Stress (PMPS) and Its Effect on the Maternal and Neonatal Outcome: A Retrospective Cohort Study
Authors: Aldinger Joana Kathleen , Abele Harald , Kranz Angela
Published: December 2024

Background/Objectives: Prenatal psychology studies show that stress, depression, and psychological stress during pregnancy can have a significant impact on maternal and fetal health and are highly prevalent. The aim of the study was to compare maternal and neonatal short-term outcomes in pregnant women* (the asterisk (*) is used at the appropriate places in this text to indicate that all genders are included) with a history of prenatal maternal psychological stress (PMPS) with those of pregnant women* not exposed to PMPS to determine differences and identify risk factors. Methods: Statistical tests for differences and relative risks between the groups were carried out with the perinatal data of University Hospital Tübingen from 2022 using IBM SPSS. Results: The study shows that PMPS has significant negative effects on various parameters, including the rate of premature births, preeclampsia, induction of birth, birth duration, and fetal asphyxia, as well as the birth weight of the children and their Apgar values (an assessment of newborn health scored shortly after birth). In addition, the risk of PMPS increases in women* with stillbirths and two or more previous miscarriages. However, the practical relevance must be critically scrutinized and confirmed by bigger studies. Conclusions: PMPS has a significant impact on the maternal and neonatal birth outcomes and must be identified as a risk factor in pregnancy. There is still a need for further research with larger samples, more balanced groups, and multivariate regression models to generate detailed, more transferable results and a deeper insight into the significant effects of PMPS and the role midwives can play in helping it.
Benefits of Early Integrated and Vocational Rehabilitation in Breast Cancer on Work Ability, Sick Leave Duration, and Disability Rates
Authors: Kovacevic Nina , Žagar Tina , Homar Vesna , Pelhan Bojan , Sremec Marko , Rozman Tina , Besic Nikola
Published: December 2024

Objectives: Vocational rehabilitation plays a vital role in helping breast cancer survivors overcome physical, psychological, and occupational challenges, enabling a smoother return to work and improving quality of life. The aim of this study was to evaluate the effect of vocational rehabilitation as part of early integrated rehabilitation compared to conventional rehabilitation on sick leave duration, work ability, and disability rates. Methods: The study was designed as a prospective, interventional study. We enrolled 435 breast cancer patients, 211 patients in the control group, and 224 in the intervention group. The control group received the conventional rehabilitation as offered to breast cancer patients before the pilot study on individualized, integrated rehabilitation, while patients in the intervention group were referred for additional treatments and vocational rehabilitation. Results: There were no differences between the control and the intervention group of patients in terms of patient demographics, tumor size, disease stage, or oncologic treatment. However, compared to the control group, the intervention group had 50 days shorter sick leave (p = 0.002), better work ability (p < 0.001), and a lower proportion of patients with disabilities (p < 0.001) and better work ability (p < 0.001) one year after the beginning of cancer treatment. Vocational rehabilitation was likely associated with shorter sick leave (p < 0.069). Conclusions: Integrated rehabilitation was associated with shorter sick leave, and vocational rehabilitation was likely associated with shorter sick leave. Integrated rehabilitation was associated with improved work ability and disability rate.
A Dynamic Algorithm for Measuring Pedestrian Congestion and Safety in Urban Alleyways
Authors: Lee Jiyoon , Kang Youngok
Published: December 2024

This study presents an algorithm for measuring Pedestrian Congestion and Safety on alleyways, wherein pedestrians and vehicles share limited space, making traditional pedestrian density metrics inadequate. The primary objective is to provide a more accurate assessment of congestion and safety in these shared spaces by incorporating both pedestrian and vehicle interactions, unlike traditional methods that focus solely on pedestrians, regardless of road type. Pedestrian Congestion was calculated using Time to Collision (TTC)-based safety occupation areas, while Pedestrian Safety was assessed by accounting for both physical and psychological safety through proxemics, which measures personal space violations. The algorithm dynamically adapts to changing vehicle and pedestrian movements, providing a more accurate assessment of congestion compared to existing methods. Statistical validation through t-tests and K-S (Kolmogorov–Smirnov) tests confirmed significant differences between the proposed method and traditional pedestrian density metrics, while Bland–Altman analysis demonstrated agreement between the two methods. The experimental results reveal that Pedestrian Congestion and Safety varied with time and location, capturing the spatio-temporal characteristics of alleyways. Visual comparisons of Pedestrian Congestion, Safety, and Density further validated that the proposed algorithm provides a more accurate reflection of real-world conditions compared to traditional pedestrian density metrics. These findings highlight the algorithm’s ability to measure real-time changes in congestion and safety, incorporate psychological discomfort into safety calculations, and offer a comprehensive analysis by considering both pedestrian and vehicle interactions.
Granularity Optimization of Travel Trajectory Based on Node2vec: A Case Study on Urban Travel Time Prediction
Authors: Dong Hui , Pan Xiao , Chen Xiao
Published: December 2024

Intersections are known to cause significant changes in traffic states. However, existing link-level trajectory optimization methods often overlook intersection information, making it challenging to preserve key traffic state features during the optimization process. To address this limitation, a novel approach is proposed that integrates node2vec and K-means algorithms. First, the role of intersections in linking road segments is considered. The node2vec algorithm is employed to capture the deep spatial similarity between links while weakening the adjacency relationship between links before and after intersections. This process generates feature representations for each link. Subsequently, clustering centers are initialized at the intersections, and K-means clustering is applied based on these link feature representations. Through this method, consecutive links within a trajectory that belong to the same cluster are merged, thus optimizing the granularity of the trajectory. Finally, experimental analysis and validation are conducted using link-level travel trajectory data from Shenzhen. The results demonstrate that, under optimal conditions, the mean absolute error (MAE), the mean absolute percentage error (MAPE), and the root mean square error (RMSE) values are reduced by 8.91%, 9.44%, and 8.96%, respectively, while computational efficiency is increased by 30.08%. The proposed trajectory granularity optimization method, which accounts for the existence of intersections, not only effectively retains the key traffic state features from the original trajectory but also significantly reduces training time while improving the model’s prediction accuracy.
Advancing Agricultural Land Suitability in Urbanized Semi-Arid Environments: Insights from Geospatial and Machine Learning Approaches
Authors: Sathiyamurthi Subbarayan , Subbarayan Saravanan , Ramya Madhappan , Sivasakthi Murugan , Gobi Rengasamy , Qaysi Saleh , Praveen Kumar Sivakumar , Lee Jinwook , Alarifi Nassir , Wahba Mohamed , M. Youssef Youssef
Published: December 2024

Rising food demands are increasingly threatened by declining crop yields in urbanizing riverine regions of Southern Asia, exacerbated by erratic weather patterns. Optimizing agricultural land suitability (AgLS) offers a viable solution for sustainable agricultural productivity in such challenging environments. This study integrates remote sensing and field-based geospatial data with five machine learning (ML) algorithms—Naïve Bayes (NB), extra trees classifier (ETC), random forest (RF), K-nearest neighbors (KNN), and support vector machines (SVM)—alongside land-use/land-cover (LULC) considerations in the food-insecure Dharmapuri district, India. A grid searches optimized hyperparameters using factors such as slope, rainfall, temperature, texture, pH, electrical conductivity, organic carbon, available nitrogen, phosphorus, potassium, and calcium carbonate. The tuned ETC model showed the lowest root mean squared error (RMSE = 0.15), outperforming RF (RMSE = 0.18), NB (RMSE = 0.20), SVM (RMSE = 0.22), and KNN (RMSE = 0.23). The AgLS-ETC map identified 29.09% of the area as highly suitable (S1), 19.06% as moderately suitable (S2), 16.11% as marginally suitable (S3), 15.93% as currently unsuitable (N1), and 19.21% as permanently unsuitable (N2). By incorporating Landsat-8 derived LULC data to exclude forests, water bodies, and settlements, these suitability estimates were adjusted to 19.08% (S1), 14.45% (S2), 11.40% (S3), 10.48% (N1), and 9.58% (N2). Focusing on the ETC model, followed by land-use analysis, provides a robust framework for optimizing sustainable agricultural planning, ensuring the protection of ecological and social factors in developing countries.
mirSNPs as Potential Colorectal Cancer Biomarkers: A Systematic Review
Authors: Alves dos Santos Katiusse , Costa Alves de Sousa Lourdes Maria , Costa de Souza Karla Simone , Amigo Olalla Maroñas , Luchessi André Ducati , Silbiger Vivian Nogueira
Published: December 2024

Colorectal cancer (CRC) is the third most common neoplasm in the world and the second with the highest mortality rate. Single nucleotide polymorphisms (SNPs) in microRNA (miRNA) genes known as mirSNPs may be related to dysregulated miRNA expression in several neoplasms. This systematic review aims to investigate studies that investigate SNPs located in regions of miRNA genes that influence their expression and are associated with CRC, as well as their potential as biomarkers for the disease, based on the available literature. For this, searches were performed in public databases, including MEDLINE/PubMed, Embase, Web of Science, and Scopus. The rigorous review of the PRISMA 2020 guidelines and the methodological quality of these studies was assessed using the Newcastle–Ottawa scale and the Mixed Methods Assessment Tool. Of the 175 studies identified, 26 were considered eligible: 18 of them highlighted mirSNPs as potential biomarkers of risk and prognosis for CRC; 4 studies suggested a protective role; 1 study linked mirSNPs to treatment; 3 studies found no relevant evidence. These results highlight the importance of conducting further research on the topic, given the potential of these biomarkers to contribute to risk assessment, prognosis, and the development of therapeutic strategies for patients with CRC.
20-Hydroxyecdysone Mediates Reproductive Diapause in Galeruca daurica via Ecdysone Receptor EcR and Nuclear Hormone Receptor HR3
Authors: Li Ling , Yao Zhihan , Pang Baoping , Li Yanyan
Published: December 2024

20-hydroxyecdysone (20E) signaling plays an important role in regulating insect growth, development, and reproduction. However, the effect of 20E on reproductive diapause and its regulatory mechanisms have not been fully understood. Galeruca daurica is a new pest in the Inner Mongolia grasslands, and it aestivates in an obligatory reproductive diapause form. In this study, the complete open reading frame (ORF) sequence of the ecdysone receptor (EcR) was cloned from G. daurica. Application of 20E promoted the expression of EcR, nuclear hormone receptor (HR3), and vitellogenin (Vg), whereas it reduced the expression of fatty acid synthase (FAS) and lipid content, leading to delayed diapause entry in female adults. Silencing EcR or HR3 by RNAi increased the expression of FAS and lipid content, whereas it reduced the expression of Vg, inducing reproductive diapause. These results indicate that 20E may mediate reproductive diapause via a conserved EcR/HR3 cascade in G. daurica.
Soy Protein and Safflower-Seed Oil Attenuate Inflammation and Immune Dysfunction in Rats with Hyperuricemia
Authors: Liu Yi-Fang , Wu Yi-Chen , Yang Yu , Lo Hui-Chen
Published: December 2024

A plant-based diet is considered a promising approach for managing hyperuricemia (HUA). This study examined the effects of soy protein and plant-based oils on HUA-induced inflammation and immune dysfunction. Male Wistar rats, induced with HUA using oxonic acid and uric acid (UA), were fed casein or soy protein with palm or safflower oil (2 × 2 factorial design) for 8 weeks. HUA rats had lower serum albumin and T cell percentages in peripheral blood leukocytes (PBLs) and splenocytes, along with increased leukocyte counts and spleen weights, compared to healthy rats (p < 0.05). Soy protein improved HUA-induced reductions in albumin, while safflower-seed oil ameliorated reductions in albumin, plasma interleukin (IL)-4, and T-suppressor splenocytes, and mitigated elevated serum UA, plasma IL-6, and B leukocytes (two-way ANOVA, p < 0.05). In PBL, soy protein alleviated HUA-induced decreases in TNF-α, casein and palm oil increased IL-6, and casein further reduced IFN-γ production. Under Con A stimulation, casein and safflower-seed oil alleviated decreases in IL-6 and IL-10, respectively, while under LPS stimulation, casein further increased TNF-α production. In splenocytes, soy protein and safflower-seed oil reduced HUA-induced increases in TNF-α and increased IL-10, and safflower-seed oil increased IL-6 production. Under Con A stimulation, soy protein and safflower-seed oil reduced TNF-α and increased IL-10 production in splenocytes. The findings suggest that soy protein and safflower-seed oil may counteract HUA-related inflammation, alleviate monocyte activation, and enhance Th2 immune response in HUA. A plant-based diet rich in soy protein and safflower-seed oil may help manage HUA and associated inflammation and immune dysfunction.
Circulating Cell-Free Microbial DNA Signatures and Plasma Soluble CD14 Level Are Associated with Clinical Outcomes of Anti-PD-1 Therapy in Advanced Melanoma Patients
Authors: Drymel Bernadeta , Tomela Katarzyna , Galus Łukasz , Olejnik-Schmidt Agnieszka , Mackiewicz Jacek , Kaczmarek Mariusz , Mackiewicz Andrzej , Schmidt Marcin
Published: December 2024

An accumulating number of studies suggest the potential of circulating cell-free microbial DNA (cfmDNA) as a non-invasive biomarker in various diseases, including cancers. However, its value in the prediction or prognosis of clinical outcomes of immune checkpoint inhibitors (ICIs) is poorly explored. The circulating cfmDNA pool may also reflect the translocation of various microbial ligands to the circulatory system and may be associated with the increased release of soluble CD14 (sCD14) by myeloid cells. In the present study, blood samples were collected from advanced melanoma patients (n = 66) before and during the anti-PD-1 therapy (approximately 3 and 12 months after the start). Then, V3-V4 16S rRNA gene sequencing was performed to analyze the circulating cfmDNA extracted from plasma samples. Moreover, the concentration of plasma sCD14 was measured using ELISA. As a result, the differences in the circulating cfmDNA profiles were found between patients with favorable and unfavorable clinical outcomes of the anti-PD-1 and baseline signatures correlated with progression-free survival and overall survival. Moreover, there was a higher concentration of plasma sCD14 in patients with unfavorable clinical outcomes. High baseline sCD14 level and its increase during the therapy prognosticated worse survival outcomes. Taken together, this preliminary study indicates the potential of circulating cfmDNA signatures and plasma sCD14 levels as biomarkers of clinical outcomes of ICIs.