bluuebunny/crossref_metadata_2025 huggingface dataset:
Delimitation of Endangered Telmatobius Species (Anura: Telmatobiidae) of the Chilean Salt Puna
Authors: Fibla Pablo , Sáez Paola A. , Lobos Gabriel , Rebolledo Nicolás , Véliz David , Pastenes Luis , del Pozo Talía , Méndez Marco A.
Published: December 2024

Clarifying the taxonomic status and distribution of endangered species is crucial to their conservation. In this study, we contrasted different lines of evidence (morphology, mtDNA, and nucDNA: microsatellites and SNP) to clarify the taxonomic status of endangered Telmatobius water frog species and unidentified populations that inhabit the Salt Puna in Chile. We studied population differentiation and species divergence by performing morphometric, population genetic and species delimitation analyses. The results confirmed the species status of Telmatobius fronteriensis and T. philippii, as they exhibited morphometric, mitochondrial and genomic SNP divergence. Although Bayes factor delimitation analysis indicated that the Telmatobius populations of Ascotán and Carcote could represent a new species, their few mitochondrial differences and similar morphology with respect to T. philippii suggested otherwise. Instead, they can be considered an evolutionarily significant unit of T. philippii that has differentiated from the type locality. These results extend the geographic distribution of T. philippii, which is categorized as critically endangered by the IUCN.
Comparative Whole-Genome Analysis of Production Traits and Genetic Structure in Baiyu and Chuanzhong Black Goats
Authors: Luo Jing , Min Qi , Sun Xueliang , Guo Xinyu , Song Meijun , Zeng Xuehui , Guo Jiazhong , Zhang Hongping , Han Yanguo , Li Li
Published: December 2024

Natural selection and artificial breeding are crucial methods for developing new animal groups. The Baiyu black goats and Chuanzhong black goats are indigenous goat breeds from distinct ecological regions in Sichuan Province, with dramatically different growth and reproductivity. This study aimed to systematically elucidate the differences in production performance and genetic traits between Baiyu black goats and Chuanzhong black goats. We quantified growth and reproductive attributes for both breeds. Furthermore, we conducted a comprehensive analysis of genetic diversity, population structure, and selection signatures using whole-genome resequencing data. This dataset included 30 individuals from the Baiyu black goat breed, 41 from the Chuanzhong black goat breed, and an additional 59 individuals representing Chengdu grey goats, Tibetan cashmere goats, and Jianchang black goats, totaling 130 individuals across five goat breeds. The comparative analysis of production performance revealed that the weight and body size of Chuanzhong black goats were significantly higher than those of Baiyu black goats (p < 0.01). At the same time, the average kidding rate and kid-weaning survival rate of Chuanzhong black goats were also notably superior to those of Baiyu black goats (p < 0.01). The Baiyu black goats exhibited a more abundant genetic diversity and distinct genetic differences compared to the Chuanzhong black goat, according to an analysis grounded on genomic variation. The Baiyu black goats are more closely related to Tibetan cashmere goats, whereas Chuanzhong black goats share a closer genetic relationship with Chengdu grey goats. Additionally, we employed the π, Fst, and XP-EHH methodologies to identify genes related to immunity (TRIM10, TRIM15, TRIM26, and TRIM5), neurodevelopment (FOXD4L1, PCDHB14, PCDHB4, PCDHB5, PCDHB6, and PCDHB7), reproduction (BTNL2 and GABBR1), body size (NCAPG, IBSP, and MKNK1), and meat quality traits (SUCLG2 and PGM5). These results provide a theoretical basis for further resource conservation and breeding improvement of the Baiyu black goat and Chuanzhong black goat.
The Relationship Between Soil and Gut Microbiota Influences the Adaptive Strategies of Goitered Gazelles in the Qaidam Basin
Authors: Wang Yiran , Li Bin , Xu Bo , Qin Wen
Published: December 2024

The gut microbiota is integral to the health and adaptability of wild herbivores. Interactions with soil microbiota can shape the composition and function of the gut microbiota, thereby influencing the hosts’ adaptive strategies. As a result, soil microbiota plays a pivotal role in enabling wild herbivores to thrive in extreme environments. However, the influence of soil microbiota from distinct regions on host’s gut microbiota has often been overlooked. We conducted the first comprehensive analysis of the composition and diversity of gut and soil microbiota in goitered gazelles across six regions in the Qaidam Basin, utilizing source tracking and ecological assembly process analyses. Significant differences were observed in the composition and diversity of soil and gut microbiota among the six groups. Source tracking analysis revealed that soil microbiota in the GangciGC (GC) group contributed the highest proportion to fecal microbiota (8.94%), while the Huaitoutala (HTTL) group contributed the lowest proportion (1.80%). The GC group also exhibited the lowest α-diversity in gut microbiota. The observed differences in gut microbial composition and diversity among goitered gazelles from six regions in the Qaidam Basin were closely tied to their adaptive strategies. Ecological assembly process analysis indicated that the gut microbiota were primarily influenced by stochastic processes, whereas deterministic processes dominated most soil microbial groups. Both the differences and commonalities in gut and soil microbiota play essential roles in enabling these gazelles to adapt to diverse environments. Notably, the utilization pattern of soil microbiota by gut microbiota did not align with regional trends in gut microbial α-diversity. This discrepancy may be attributed to variations in environmental pressures and the gut’s filtering capacity, allowing gazelles to selectively acquire microbiota from soil to maintain homeostasis. This study highlights the significant regional variation in gut and soil microbiota diversity among goitered gazelle populations in the Qaidam Basin and underscores the critical role of soil-derived microbiota in their environmental adaptation.
Evaluation of Metronidazole Resistance of Vaginal Swab Isolates from South African Women Treated for Bacterial Vaginosis
Authors: Schwebs Timo , Kieninger Ann-Katrin , Podpera Tisakova Lenka , Oberbauer Vera , Berdaguer Rocío , Mtshali Andile , Mzobe Gugulethu , Rompalo Anne , Mindel Adrian , Letsoalo Marothi , Garrett Nigel , Ngcapu Sinaye , Corsini Lorenzo
Published: December 2024

Background/Objectives: The high recurrence rate of bacterial vaginosis (BV) after antibiotic treatment is at least partially attributed to resistant bacteria. The CAPRISA 083 (CAP083) study investigated the influence of metronidazole (MTZ) treatment on the vaginal microbiome in 56 South African women diagnosed with BV. To explore the etiology of recurrent BV in this cohort, we retrospectively analyzed vaginal swabs collected in CAP083 before and after MTZ treatment. Methods: We isolated over 1200 bacterial strains, including Gardnerella, Lactobacillus, Prevotella, and Fannyhessa, and determined the minimum inhibitory concentration (MIC) of MTZ and the resistance status according to CLSI and EUCAST guidelines. Results: At baseline, 64% (CLSI) of Gardnerella isolates were resistant to MTZ, rising to 80% after MTZ treatment by the 12-week visit. Lactobacillus species consistently exhibited resistance of 100%, while Fannyhessea vaginae maintained resistance rates of 78–91% across visits. Prevotella strains varied, showing two susceptible isolates at baseline and one resistant isolate at the 6-week visit. Susceptible and resistant Gardnerella isolates were often isolated from the same swab, and 70% (CLSI) of participants had at least one resistant Gardnerella strain already at baseline. Sensitive Gardnerella isolates were not a predictor of an MTZ-mediated reduction in Gardnerella abundance. Conclusions: Our data indicate that the 23% cure rate in CAP083 was associated with a combination of a high share of MTZ-resistant bacteria at baseline, a potentially insufficient MTZ dose regimen, and a constantly high average abundance of Gardnerella. Future research should explore novel therapeutic strategies to enhance treatment efficacy and combat antibiotic resistance.
Prevalence of Antimicrobial Resistance Among the WHO’s AWaRe Classified Antibiotics Used to Treat Urinary Tract Infections in Diabetic Women
Authors: Hamdan Ahmad , AbuHaweeleh Mohannad N. , Al-Qassem Leena , Kashkoul Amira , Alremawi Izzaldin , Hussain Umna , Khan Sara , ElBadway Menatalla M. S. , Chivese Tawanda , Farooqui Habib H. , Zughaier Susu M.
Published: December 2024

Background and Objectives: Diabetes is linked to a higher risk of urinary tract infections (UTIs) in women, often leading to recurrent antibiotic treatments. Frequent antibiotic use for UTIs can contribute to antimicrobial resistance (AMR), a critical public health threat that increases treatment failure. This study investigated the prevalence of AMR and its associated factors among women with UTIs, comparing those with and without diabetes. Results: The study population had a mean age of 52 years (SD = 23) for the women without diabetes and 68 years (SD = 14) for those with diabetes. Resistance was highest for cefazolin and levofloxacin in the Access and Watch antibiotic groups, while ciprofloxacin was the most frequently prescribed antibiotic. AMR prevalence was 35.7% among the women with diabetes and 21.3% among those without. After adjustment, AMR was significantly associated with both uncomplicated diabetes (OR 1.14, 95% CI 1.08–1.21) and complicated diabetes (OR 1.54, 95% CI 1.45–1.64), as well as with higher numbers of prescribed antibiotics (OR 277.39, 95% CI 253.79–303.17). Methods: Using a cross-sectional cohort from the Physionet database, we analyzed data on 116,902 female participants treated for UTIs, including their antibiotic exposure, diabetes status, comorbidities, and hospital admission details. Antimicrobials were classified per the WHO’s AWaRe criteria. The primary outcome was AMR identified in urine cultures, and the association with diabetes status was evaluated using multivariable logistic regression. Conclusions: Our findings highlight the need for focused antimicrobial stewardship in women with diabetes to reduce the AMR rates in this vulnerable group.
Monophosphate Derivatives of Luteolin and Apigenin as Efficient Precursors with Improved Oral Bioavailability in Rats
Authors: Wu Sydney , Wang Shang-Ta , Chen Guan-Yuan , Hsu Chen , Chen Yi-Hsin , Tsai Hsin-Ya , Weng Te-I , Chen Chien-Li , Wu Yi-Fang , Su Nan-Wei
Published: December 2024

Luteolin (Lut) and apigenin (Apn), flavones present in various edible plants, exhibit diverse antioxidant and pharmacological activities but have limited in vivo efficacy due to low water solubility and poor bioavailability. Here, we generated luteolin and apigenin monophosphate derivatives (LutPs and ApnPs) individually via microbial biotransformation. We then characterized their physicochemical properties and evaluated their in vitro and in vivo pharmacokinetics and bioavailability. Both LutPs and ApnPs showed enhanced solubility and dissolution and remained stable in simulated gastrointestinal conditions. Additionally, they efficiently reverted to parental forms via alkaline phosphatase in Caco-2 cells. Following oral administration in rats, LutPs and ApnPs exhibited higher plasma exposure to both aglycone and conjugated forms compared to Lut and Apn. Notably, the in vivo biotransformation of Apn to Lut was observed in all apigenin-related groups. Our study suggests that flavone monophosphates are effective alternatives with enhanced bioavailability, providing insights for the potential application of emerging bioactive nutraceuticals.
From Adipose to Ailing Kidneys: The Role of Lipid Metabolism in Obesity-Related Chronic Kidney Disease
Authors: Xu Wenchao , Zhu Yuting , Wang Siyuan , Liu Jihong , Li Hao
Published: December 2024

Obesity has emerged as a significant public health crisis, closely linked to the pathogenesis and progression of chronic kidney disease (CKD). This review explores the intricate relationship between obesity-induced lipid metabolism disorders and renal health. We discuss how excessive free fatty acids (FFAs) lead to lipid accumulation in renal tissues, resulting in cellular lipotoxicity, oxidative stress, and inflammation, ultimately contributing to renal injury. Key molecular mechanisms, including the roles of transcriptional regulators like PPARs and SREBP-1, are examined for their implications in lipid metabolism dysregulation. The review also highlights the impact of glomerular and tubular lipid overload on kidney pathology, emphasizing the roles of podocytes and tubular cells in maintaining kidney function. Various therapeutic strategies targeting lipid metabolism, including pharmacological agents such as statins and SGLT2 inhibitors, as well as lifestyle modifications, are discussed for their potential to mitigate CKD progression in obese individuals. Future research directions are suggested to better understand the mechanisms linking lipid metabolism to kidney disease and to develop personalized therapeutic approaches. Ultimately, addressing obesity-related lipid metabolism disorders may enhance kidney health and improve outcomes for individuals suffering from CKD.
Quantum PSO-Based Optimization of Secured IRS-Assisted Wireless-Powered IoT Networks
Authors: Afridi Abid , Hameed Iqra , Koo Insoo
Published: December 2024

In this paper, we explore intelligent reflecting surface (IRS)-assisted physical layer security (PLS) in a wireless-powered Internet of Things (IoT) network (WPIN) by combining an IRS, a friendly jammer, and energy harvesting (EH) to maximize sum secrecy throughput in the WPIN. Specifically, we propose a non-line-of-sight system where a hybrid access point (H-AP) has no direct link with the users, and a secure uplink transmission scheme utilizes the jammer to combat malicious eavesdroppers. The proposed scheme consists of two stages: wireless energy transfer (WET) on the downlink (DL) and wireless information transmission (WIT) on the uplink (UL). In the first phase, the H-AP sends energy to users and the jammer, and they then harvest energy with the help of the IRS. Consequently, during WIT, the user transmits information to the H-AP while the jammer emits signals to confuse the eavesdropper without interfering with the legitimate transmission. The phase-shift matrix of the IRS and the time allocation for DL and UL are jointly optimized to maximize the sum secrecy throughput of the network. To tackle the non-convex problem, an alternating optimization method is employed, and the problem is reformulated into two sub-problems. First, the IRS phase shift is solved using quantum particle swarm optimization (QPSO). Then, the time allocation for DL and UL are optimized using the bisection method. Simulation results demonstrate that the proposed method achieves significant performance improvements as compared to other baseline schemes. Specifically, for IRS elements N = 35, the proposed scheme achieves a throughput of 19.4 bps/Hz, which is 85% higher than the standard PSO approach and 143% higher than the fixed time, random phase (8 bps/Hz) approach. These results validate the proposed approach’s effectiveness in improving network security and overall performance.
From Waste to Strength: Applying Wastepaper, Fungi and Bacteria for Soil Stabilization
Authors: Golovkina Darya A. , Zhurishkina Elena V. , Saitova Alina T. , Bezruchko Mikhail V. , Lapina Irina M. , Kulminskaya Anna A.
Published: December 2024

Biocementation is a promising soil stabilization technology that relies on microbiologically induced calcite precipitation (MICP). The addition of wastepaper was found to enhance the mechanical strength of biocemented soil. This study examined the effects of incorporating wastepaper into biocemented soil, focusing on the use of the ureolytic bacterium Bacillus licheniformis DSMZ 8782 and the yeast-like fungus Scytalidium candidum 3C for soil stabilization. The optimal wastepaper content was determined to be 2%, as it did not disrupt the uniform distribution of CaCO3 and contributed to improved soil strength. The combination of bacteria and fungi significantly increased the unconfined compression strength of samples containing 2% wastepaper (161.1 kPa) compared to untreated soil (61 kPa) and bacteria-only treatments (66.5 kPa), showing improvements of 2.6 and 2.4 times, respectively. Furthermore, we demonstrated that adding fungal biomass without wastepaper significantly improved the compressive strength, achieving a value of 236.6 kPa—nine times higher than untreated soil (26.4 kPa) and four times higher than soil treated with bacteria alone (60.6 kPa). This study identifies the optimal wastepaper content and highlights the potential of combining fungal and bacterial biomass for biocementation in soil stabilization.
New Mixed Skyhook and Displacement–Velocity Control for Improving the Effectiveness of Vibration Isolation in the Lateral Suspension System of a Railway Vehicle
Authors: Shiao Yaojung , Huynh Tan-Linh
Published: December 2024

Demands for increasing the velocity and load carrying capacity of railway vehicles are a challenge to the passive suspension systems used for isolating the lateral vibrations of the carbody of a railway vehicle, especially under a wide range of vibration frequencies. Semiactive suspension systems, especially systems with a magnetorheological damper (MRD), have been investigated as promising alternatives. Many control algorithms have been developed for fine-tuning the damping force generated by MRDs, but they have been ineffective in isolating carbody vibrations at or around the resonance frequencies of the carbody and bogie. This study aims to develop a mixed control algorithm for a new skyhook (SH) control and a new displacement–velocity (DV) control to improve the effectiveness of vibration isolation in resonance frequency regions while producing high performance across the remaining frequencies. The damping coefficient of the new SH controller depends on the vibration velocity of the components of the suspension system and the skyhook damping variable, whereas that of the new DV controller depends on the velocity and displacement of the components of the suspension system and the stiffness variable. The values of the skyhook damping variable and stiffness variable were identified from the vibration velocity of the carbody using the trial and error method. The results of a numerical simulation problem indicated that the proposed control method worked effectively at low frequencies, similar to the conventional SH–DV controller, whereas it significantly improved ride comfort at high frequencies; at the resonance frequency of the bogie (14.6 Hz), in particular, it reduced the vibration velocity and acceleration of the carbody by 50.85% and 45.39%, respectively, compared with the conventional mixed SH–DV controller. The simplicity and high performance of the new mixed SH–DV control algorithm makes it a promising tool to be applied to the semiactive suspension of railway vehicles in real-world applications.
A Dual-Task Approach for Onset Time Picking and the Detection of Microseismic Waveforms Based on Deep Learning
Authors: Zhang Hang , Li Ruoyu , Ma Chunchi , Cheng Xiaobing , Meng Simeng , Huang Zhenxing , Li Di
Published: December 2024

Construction projects in deep underground engineering are associated with the recording of massive amounts of diversified signals during real time and continuous microseismic monitoring given the complexity and specificity of the construction environment. Before the analysis of source information and further prediction of possible disasters, it is generally necessary to perform onset time picking and detection of microseismic signals. To improve the accuracy and efficiency of these tasks, this paper proposes an advanced deep dual-task neural network, which sequentially integrates the two processes. In this method, a score map is used to label the onset time of micro-fracture waveforms to improve the picking accuracy. The proposed model can simultaneously handle the onset time picking and detection tasks of microseismic signals to achieve optimal performance. Based on the similarity of data structures, the output from the onset time picking section is imported into the detection section to classify different types of microseismic waveforms. The onset time picking and detection procedures can be seamlessly integrated, where the score map of the onset time can help improve the detection accuracy. The results show that this method has a good performance for the onset time picking and detection of microseismic waveforms that are polluted by noises of various types and intensities. A comparison of the proposed method with existing methods and applications in underground engineering projects helped demonstrate the excellent performance of this method. The proposed approach can accelerate the automatic processing of microseismic signals and has significant potential for the exploration of seismology and earthquake research.
Extending WSN Lifetime with Enhanced LEACH Protocol in Autonomous Vehicle Using Improved K-Means and Advanced Cluster Configuration Algorithms
Authors: Yoon Cheolhee , Cho Seongsoo , Lee Yeonwoo
Published: December 2024

In this paper, we propose an enhanced clustering protocol that integrates an improved K-means with a Mobility-Aware Cluster Head-Election Scored (IK-MACHES) algorithm, designed for extending the lifetime and operational efficiency of Wireless Sensor Network (WSN) with mobility. Variety approaches applying Low Energy Adaptive Clustering Hierarchy (LEACH) often struggle to manage optimal energy distribution due to their static clustering and limited cluster head (CH) selection criteria, primarily focusing on the proximity of residual energy or distance. Thus, this paper proposes an algorithm that takes into account both the residual energy of sensor nodes and the distance between the cluster’s central point to the base station (BS), which ultimately enhances the network’s lifetime. Additionally, our approach incorporates mobility considerations, enhancing the adaptability of the mobility environments, such as autonomous vehicular networks. Our proposed method first constructs the cluster’s configuration and then elects the CH applying an improved K-means clustering algorithm—one of the machine learning methods—integrated with a proposed IK-MACHES mechanism. Three CH scoring strategies in the proposed IK-MACHES protocol evaluate the residual energy of the nodes, their distance to the BS and the cluster central point, and relative node’s mobility. The simulation results demonstrate that the proposed approach improves performance in terms of the first node dead (FND) and 80% alive nodes metrics with mobility, compared to other LEACH protocols such as classical LEACH, LEACH-B, Improved-LEACH, LEACH with K-means, Particle Swarm Optimization (PSO), and LEACH-GK protocol, thereby enhancing network lifetime through optimal CH selection.
Influence of the Sound Source’s Position on the Stage on the Speech Perception in the Auditorium
Authors: Brachmański Stefan , Kozłowski Piotr
Published: December 2024

Constant efforts to achieve the best possible speech intelligibility during theatre performances were the motivation for the research presented in this article. The acoustic conditions in a theatre hall depend not only on the design of the room acoustics, but also on the stage decoration and the positioning of the sound source (actor) on stage. The ACR (Absolute Category Rating) method recommended by the ITU was used to investigate the influence of the decorations and the position of the speaker on stage on the subjective evaluation of the listeners’ perception of speech. Subjective tests were carried out in situ and in the laboratory based on recordings made in the facility that was the subject of the measurements. An extensive analysis of the results was carried out, taking into account individual aspects of the tests, such as the type of decoration, speaker position, listener position, and the way the recording was made. Based on the discussion of the results, the conclusions are presented.
Natural Language Processing Tools and Workflows for Improving Research Processes
Authors: Khan Noel , Elizondo David , Deka Lipika , Molina-Cabello Miguel A.
Published: December 2024

The modern research process involves refining a set of keywords until sufficiently pertinent results are obtained from acceptable sources. References and citations from the most relevant results can then be traced to related works. This process iteratively develops a set of keywords to find the most relevant literature. However, because a keyword-based search essentially samples a corpus, it may be inadequate for capturing a broad or exhaustive understanding of a topic. Further, a keyword-based search is dependent upon the underlying storage and retrieval technology and is essentially a syntactical search rather than a semantic search. To overcome such limitations, this paper explores the use of well-known natural language processing (NLP) techniques to support a semantic search and identifies where specific NLP techniques can be employed and what their primary benefits are, thus enhancing the opportunities to further improve the research process. The proposed NLP methods were tested through different workflows on different datasets and each workflow was designed to exploit latent relationships within the data to refine the keywords. The results of these tests demonstrated an improvement in the identified literature when compared to the literature extracted from the end-user-given keywords. For example, one of the defined workflows reduced the number of search results by two orders of magnitude but contained a larger percentage of pertinent results.
Construction of Nicotinamide Mononucleotide-Loaded Liposomes and Their In Vitro Transport Across the Blood–Brain Barrier
Authors: Wang Tiantian , Wu Qi , Wang Lihong , Lan Tao , Yun Zhenyu , Zhao Lin , Wu Xi
Published: December 2024

Nicotinamide mononucleotide (NMN) possesses a variety of physiological functions and has therapeutic effects on cardio-cerebral diseases, senile degenerative diseases, neurodegenerative diseases, delayed aging, etc. However, its ability to cross the blood–brain barrier (BBB) and the mechanism of its transport have not been reported. In this study, we used the immortalized hCMEC/D3 cell line to construct an in vitro monolayer cell BBB model, evaluated its ability to cross the blood–brain barrier, and explored the mechanism by carrying out transport and efflux experiments on NMN. The ability of NMN to cross the BBB was investigated by preparing NMN-loaded liposomes conjugated with ANG peptide and RVG peptide. The results showed that the transmembrane transport ability of NMN was moderate, and the transport mechanism was passive transport relying on the concentration difference. The trans-BBB ability of ANG peptide coupled with NMN could be highly significantly improved.
Model Decomposition-Based Approach to Optimizing the Efficiency of Wireless Power Transfer Inside a Metal Enclosure
Authors: Kusnins Romans , Tjukovs Sergejs , Eidaks Janis , Gailis Kristaps , Pikulins Dmitrijs
Published: December 2024

This paper describes a numerically efficient method for optimizing the high power transfer efficiency (PTE) of a resonant cavity-based Wireless Power Transfer (WPT) system for the wireless charging of smart clothing. The WPT system under study unitizes a carbon steel closet intended to store smart clothing overnight as a resonant cavity. The WPT system is designed to operate at 865.5 MHz; however, the operating frequency can be adjusted over a wide range. The main reason behind choosing a resonant cavity-based WPT system is that it has several advantages over the competitive WPT methods. Specifically, in contrast to its Far-field Power Transfer (FPT) and Inductive Power Transfer (IPT) counterparts, resonant cavity-based WPTs do not exhibit path loss and significant PTE sensitivity to the distance between the Tx and Rx coils and misalignment, respectively. The non-uniformity of the fields within the closet is addressed by using an optimized Yagi-like transmitting antenna with an additional element affecting the waveguide mode phases. The changes in the mode phases increase the volume inside the cavity, where the PTE values are higher than 50% (the high PTE region). In the present study, the model decomposition method is adapted to substantially accelerate the process of finding the optimal WPT system parameters. Additionally, the decomposition method explains the mechanism responsible for extending the high PTE region. The generalized scattering matrices are computed using the full-wave simulator Ansys HFSS for three sub-models. Then, the calculated S matrices are combined to evaluate the system’s PTE. The decomposition method is validated against full-wave simulations of the original WPT system’s model for several different parameter value combinations. The simulated results obtained for a sub-optimal model are experimentally verified by measuring the PTE of a real-life closet-based WPT system. The measured and calculated results are found to be in close agreement with the maximum measured PTE, as high as 60%.
A Variational Neural Network Based on Algorithm Unfolding for Image Blind Deblurring
Authors: Gong Shaoqing , Wang Yeran , Yang Guangyu , Wei Weibo , Zhao Junli , Pan Zhenkuan
Published: December 2024

Image blind deblurring is an ill-posed inverse problem in image processing. While deep learning approaches have demonstrated effectiveness, they often lack interpretability and require extensive data. To address these limitations, we propose a novel variational neural network based on algorithm unfolding. The model is solved using the half quadratic splitting (HQS) method and proximal gradient descent. For blur kernel estimation, we introduce an L0 regularizer to constrain the gradient information and use the fast fourier transform (FFT) to solve the iterative results, thereby improving accuracy. Image restoration is initiated with Gabor filters for the convolution kernel, and the activation function is approximated using a Gaussian radial basis function (RBF). Additionally, two attention mechanisms improve feature selection. The experimental results on various datasets demonstrate that our model outperforms state-of-the-art algorithm unfolding networks and other blind deblurring models. Our approach enhances interpretability and generalization while utilizing fewer data and parameters.
From Recruitment to Retention: AI Tools for Human Resource Decision-Making
Authors: Madanchian Mitra
Published: December 2024

HR decision-making is changing as a result of artificial intelligence (AI), especially in the areas of hiring, onboarding, and retention. This study examines the use of AI tools throughout the lifecycle of an employee, emphasizing how they enhance the effectiveness, customization, and scalability of HR procedures. These solutions streamline employee setup, learning, and documentation. They range from AI-driven applicant tracking systems (ATSs) for applicant selection to AI-powered platforms for automated onboarding and individualized training. Predictive analytics also helps retention and performance monitoring plans, which lowers turnover, but issues such as bias, data privacy, and ethical problems must be carefully considered. This paper addresses the limitations and future directions of AI while examining its disruptive potential in HR.
Evaluating Ionospheric Total Electron Content (TEC) Variations as Precursors to Seismic Activity: Insights from the 2024 Noto Peninsula and Nichinan Earthquakes of Japan
Authors: Nayak Karan , Romero-Andrade Rosendo , Sharma Gopal , López-Urías Charbeth , Trejo-Soto Manuel Edwiges , Vidal-Vega Ana Isela
Published: December 2024

This study provides a comprehensive investigation into ionospheric perturbations associated with the Mw 7.5 earthquake on the Noto Peninsula in January 2024, utilizing data from the International GNSS Service (IGS) network. Focusing on Total Electron Content (TEC), the analysis incorporates spatial mapping and temporal pattern assessments over a 30-day period before the earthquake. The time series for TEC at the closest station to the epicenter, USUD, reveals a localized decline, with a significant negative anomaly exceeding 5 TECU observed 22 and 23 days before the earthquake, highlighting the potential of TEC variations as seismic precursors. Similar patterns were observed at a nearby station, MIZU, strengthening the case for a seismogenic origin. Positive anomalies were linked to intense space weather episodes, while the most notable negative anomalies occurred under geomagnetically calm conditions, further supporting their seismic association. Using Kriging interpolation, the anomaly zone was shown to closely align with the earthquake’s epicenter. To assess the consistency of TEC anomalies in different seismic events, the study also examines the Mw 7.1 Nichinan earthquake in August 2024. The results reveal a prominent negative anomaly, reinforcing the reliability of TEC depletions in seismic precursor detection. Additionally, spatial correlation analysis of Pearson correlation across both events demonstrates that TEC coherence diminishes with increasing distance, with pronounced correlation decay beyond 1000–1600 km. This spatial decay, consistent with Dobrovolsky’s earthquake preparation area, strengthens the association between TEC anomalies and seismic activity. This research highlights the complex relationship between ionospheric anomalies and seismic events, underscoring the value of TEC analysis as tool for earthquake precursor detection. The findings significantly enhance our understanding of ionospheric dynamics related to seismic events, advocating for a comprehensive, multi-station approach in future earthquake prediction efforts.
High-Spatial Resolution Maps of PM2.5 Using Mobile Sensors on Buses: A Case Study of Teltow City, Germany, in the Suburb of Berlin, 2023
Authors: Renard Jean-Baptiste , Becker Günter , Nodorft Marc , Tavakoli Ehsan , Thiele Leroy , Poincelet Eric , Scholz Markus , Surcin Jérémy
Published: December 2024

Air quality monitoring networks regulated by law provide accurate but sparse measurements of PM2.5 mass concentrations. High-spatial resolution maps of the PM2.5 mass concentration values are necessary to better estimate the citizen exposure to outdoor air pollution and the sanitary consequences. To address this, a field campaign was conducted in Teltow, a midsize city southwest of Berlin, Germany, for the 2021–2023 period. A network of optical sensors deployed by Pollutrack included fixed monitoring stations as well as mobile sensors mounted on the roofs of buses and cars. This setup provides PM2.5 pollution maps with a spatial resolution down to 100 m on the main roads. The reliability of Pollutrack measurements was first established with comparison to measurements from the German Environment Agency (UBA) and modelling calculations based on high-resolution weather forecasts. Using these validated data, maps were generated for 2023, highlighting the mean PM2.5 mass concentrations and the number of days per year above the 15 µg.m−3 value (the daily maximum recommended by the World Health Organization (WHO) in 2021). The findings indicate that PM2.5 levels in Teltow are generally in the good-to-moderate range. The higher values (hot spots) are detected mainly along the highways and motorways, where traffic speeds are higher compared to inner-city roads. Also, the PM2.5 mass concentrations are higher on the street than on the sidewalks. The results were further compared to those in the city of Paris, France, obtained using the same methodology. The observed parallels between the two datasets underscore the strong correlation between traffic density and PM2.5 concentrations. Finally, the study discusses the advantages of integrating such high-resolution sensor networks with modelling approaches to enhance the understanding of localized PM2.5 variability and to better evaluate public exposure to air pollution.
Determining the PM10 Pollution Sources near the Copper Smelter in Bor, Serbia
Authors: Kovačević Renata , Radović Bojan , Manojlović Dragan , Urošević Tamara , Apostolovski-Trujić Tatjana , Tasić Viša , Jovašević-Stojanović Milena
Published: December 2024

The EPA Positive Matrix Factorization (PMF) 5.0 model was applied to determine the sources and characteristics of PM10 collected near the copper smelter in Bor, Serbia, from September 2009 to July 2010. For a better understanding of the industrial sources of PM10 pollution, the dataset was divided into four observation periods: heating season (HS), non-heating season (NHS), copper smelter in work (SW), and copper smelter out of work (SOW). The daily limit for the PM10 fraction of 50 μg/m3 was exceeded on one-sixth of days in the NHS, about half the days in the HS, and about one-third of days during the SOW and SW period. The nine different sources of PM10 were identified: fuel combustion, industrial dust, dust from tailings, storage and preparation of raw materials, secondary nitrate, Cu smelter, traffic, cadmium, and plant for the production of precious metals. The contribution of factors related to the activities in the copper smelter complex to the total mass of PM10 was 83.1%. When the copper smelter was out of work the contribution of all the factors related to PM10 pollution from the copper smelter to the total mass of the PM10 was 2.3-fold lower, 35.8%, compared with the period when the copper smelter was in work. This study is the first attempt to use PMF receptor modeling to determine the air pollution sources and their contribution to ambient air pollution in the city of Bor, Serbia.
Integrating Statistical Methods and Machine Learning Techniques to Analyze and Classify COVID-19 Symptom Severity
Authors: Raddad Yaqeen , Hasasneh Ahmad , Abdallah Obada , Rishmawi Camil , Qutob Nouar
Published: December 2024

Background/Objectives: The COVID-19 pandemic, caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), led to significant global health challenges, including the urgent need for accurate symptom severity prediction aimed at optimizing treatment. While machine learning (ML) and deep learning (DL) models have shown promise in predicting COVID-19 severity using imaging and clinical data, there is limited research utilizing comprehensive tabular symptom datasets. This study aims to address this gap by leveraging a detailed symptom dataset to develop robust models for categorizing COVID-19 symptom severity, thereby enhancing clinical decision making. Methods: A unique tabular dataset was created using questionnaire responses from 5654 individuals, including demographic information, comorbidities, travel history, and medical data. Both unsupervised and supervised ML techniques were employed, including k-means clustering to categorize symptom severity into mild, moderate, and severe clusters. In addition, classification models, namely, Support Vector Machine (SVM), Adaptive Boosting (AdaBoost), eXtreme Gradient Boosting (XGBoost), random forest, and a deep neural network (DNN) were used to predict symptom severity levels. Feature importance was analyzed using the random forest model for its robustness with high-dimensional data and ability to capture complex non-linear relationships, and statistical significance was evaluated through ANOVA and Chi-square tests. Results: Our study showed that fatigue, joint pain, and headache were the most important features in predicting severity. SVM, AdaBoost, and random forest achieved an accuracy of 94%, while XGBoost achieved an accuracy of 96%. DNN showed robust performance in handling complex patterns with 98% accuracy. In terms of precision and recall metrics, both the XGBoost and DNN models demonstrated robust performance, particularly for the moderate class. XGBoost recorded 98% precision and 97% recall, while DNN achieved 99% precision and recall. The clustering approach improved classification accuracy by reducing noise and dimensionality. Statistical tests confirmed the significance of additional features like Body Mass Index (BMI), age, and dominant variant type. Conclusions: Integrating symptom data with advanced ML models offers a promising approach for accurate COVID-19 severity classification. This method provides a reliable tool for healthcare professionals to optimize patient care and resource management, particularly in managing COVID-19 and potential future pandemics. Future work should focus on incorporating imaging and clinical data to further enhance model accuracy and clinical applicability.
A Hybrid Transformer-Convolutional Neural Network for Segmentation of Intracerebral Hemorrhage and Perihematomal Edema on Non-Contrast Head Computed Tomography (CT) with Uncertainty Quantification to Improve Confidence
Authors: Tran Anh T. , Desser Dmitriy , Zeevi Tal , Abou Karam Gaby , Dierksen Fiona , Dell’Orco Andrea , Kniep Helge , Hanning Uta , Fiehler Jens , Zietz Julia , Sanelli Pina C. , Malhotra Ajay , Duncan James S. , Aneja Sanjay , Falcone Guido J. , Qureshi Adnan I. , Sheth Kevin N. , Nawabi Jawed , Payabvash Seyedmehdi
Published: December 2024

Intracerebral hemorrhage (ICH) and perihematomal edema (PHE) are key imaging markers of primary and secondary brain injury in hemorrhagic stroke. Accurate segmentation and quantification of ICH and PHE can help with prognostication and guide treatment planning. In this study, we combined Swin-Unet Transformers with nnU-NETv2 convolutional network for segmentation of ICH and PHE on non-contrast head CTs. We also applied test-time data augmentations to assess individual-level prediction uncertainty, ensuring high confidence in prediction. The model was trained on 1782 CT scans from a multicentric trial and tested in two independent datasets from Yale (n = 396) and University of Berlin Charité Hospital and University Medical Center Hamburg-Eppendorf (n = 943). Model performance was evaluated with the Dice coefficient and Volume Similarity (VS). Our dual Swin-nnUNET model achieved a median (95% confidence interval) Dice = 0.93 (0.90–0.95) and VS = 0.97 (0.95–0.98) for ICH, and Dice = 0.70 (0.64–0.75) and VS = 0.87 (0.80–0.93) for PHE segmentation in the Yale cohort. Dice = 0.86 (0.80–0.90) and VS = 0.91 (0.85–0.95) for ICH and Dice = 0.65 (0.56–0.70) and VS = 0.86 (0.77–0.93) for PHE segmentation in the Berlin/Hamburg-Eppendorf cohort. Prediction uncertainty was associated with lower segmentation accuracy, smaller ICH/PHE volumes, and infratentorial location. Our results highlight the benefits of a dual transformer-convolutional neural network architecture for ICH/PHE segmentation and test-time augmentation for uncertainty quantification.
Effect of Hard- and Soft-Density Insoles on the Postural Control of Adults over 65 Years of Age: A Cross over Clinical Trial
Authors: Martínez-Córcoles Vicenta , Becerro-de-Bengoa-Vallejo Ricardo , Calvo-Lobo César , Pérez-Boal Eduardo , Losa-Iglesias Marta Elena , Rodríguez-Sanz David , Casado-Hernández Israel , Martínez-Jiménez Eva María
Published: December 2024

Background: there is a high risk of falls in older adults. One of the factors contributing to fall episodes is advancing age due to deterioration of the proprioceptive system. Certain clinical procedures improve balance and posture, such as the use of insoles. Objective: the objective of this study was to evaluate the impact of hard and soft insoles on static foot balance in healthy older adults compared to barefoot people. Methods: a clinical trial was conducted with a sample size of 150 healthy adults (69 male and 81 women) with a mean age of 69.03 ± 3.82 years. Postural control was evaluated in different conditions of barefoot balance with hard and soft insoles. Results: All tests were statistically significant (p < 0.001). The test with soft insoles presented higher stabilometric values and presented worse postural control compared to the barefoot and hard insoles in all eyes-open and eyes-closed conditions. Conclusions: Hard and soft insoles show no improvement in postural control compared to barefoot standing. The findings suggest that soft insoles may result in impaired balance during standing. The density of the materials in the insoles emerges as a significant factor influencing postural control.
Isolating and Identifying One Strain with Lead-Tolerant Fungus and Preliminary Study on Its Capability of Biosorption to Pb2+
Authors: Li Wanna , Zhao Liujing , Liu Cui , Bai Xingpeng , Xu Chenyuan , An Fengqiu , Sun Feilong
Published: December 2024

To obtain lead-resistant microorganisms as potential strains for bioremediation, in this study, a strain of fungus with high resistance to lead was isolated and domesticated from lead-contaminated soil, which was cultured and molecularly biologically identified as the genus Sarocladium Pb-9 (GenBank No. MK372219). The optimal incubation time of strain Pb-9 was 96 h, the optimal incubation temperature was 25 °C, and the optimal incubation pH was 7. The strain Pb-9 had a good adsorption effect on Pb2+ at a lead concentration of 2000 mg/L; scanning electron microscopy (SEM) observed that the spores of the Pb-9 strain appeared to be wrinkled and deformed under Pb2+ stress, and XRD analysis showed that the mycelium of Pb-9 adsorbed Pb2+; Fourier transform infrared spectroscopy (FTIR) analysis showed that the Pb-9 strain might produce substances such as esters and polysaccharides under the treatment of different Pb2+ concentrations. The above results showed that strain Pb-9 has good resistance and adsorption capacity to lead. Therefore, it has potential application value in the bioremediation of environmental heavy metal pollution, and this study provides a fundamental basis for the bioremediation of lead pollution in the environment.
Core–Shell PLGA Nanoparticles: In Vitro Evaluation of System Integrity
Authors: Kovshova Tatyana , Malinovskaya Julia , Kotova Julia , Gorshkova Marina , Vanchugova Lyudmila , Osipova Nadezhda , Melnikov Pavel , Vadekhina Veronika , Nikitin Alexey , Ermolenko Yulia , Gelperina Svetlana
Published: December 2024

The objective of this study was to compare the properties of core–shell nanoparticles with a PLGA core and shells composed of different types of polymers, focusing on their structural integrity. The core PLGA nanoparticles were prepared either through a high-pressure homogenization–solvent evaporation technique or nanoprecipitation, using poloxamer 188 (P188), a copolymer of divinyl ether with maleic anhydride (DIVEMA), and human serum albumin (HSA) as the shell-forming polymers. The shells were formed through adsorption, interfacial embedding, or conjugation. For dual fluorescent labeling, the core- and shell-forming polymers were conjugated with Cyanine5, Cyanine3, and rhodamine B. The nanoparticles had negative zeta potentials and sizes ranging from 100 to 250 nm (measured using DLS) depending on the shell structure and preparation technique. The core–shell structure was confirmed using TEM and fluorescence spectroscopy, with the appearance of FRET phenomena due to the donor–acceptor properties of the labels. All of the shells enhanced the cellular uptake of the nanoparticles in Gl261 murine glioma cells. The integrity of the core–shell structures upon their incubation with the cells was evidenced by intracellular colocalization of the fluorescent labels according to the Manders’ colocalization coefficients. This comprehensive approach may be useful for the selection of the optimal preparation method even at the early stages of the core–shell nanoparticle development.