bluuebunny/crossref_metadata_2025 huggingface dataset:
Proteomic Approach to Study the Effect of Pneumocystis jirovecii Colonization in Idiopathic Pulmonary Fibrosis
Authors: Carmona-Pírez Jonás , Salsoso Rocío , Charpentier Eléna , Olmedo Cinta , Medrano Francisco J. , Román Lucas , de la Horra Carmen , de Armas Yaxsier , Calderón Enrique J. , Friaza Vicente
Published: January 2025

Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive, and interstitial disease with an unclear cause, believed to involve genetic, environmental, and molecular factors. Recent research suggested that Pneumocystis jirovecii (PJ) could contribute to disease exacerbations and severity. This article explores how PJ colonization might influence the pathogenesis of IPF. We performed a proteomic analysis to study the profile of control and IPF patients, with/without PJ. We recruited nine participants from the Virgen del Rocio University Hospital (Seville, Spain). iTRAQ and bioinformatics analyses were performed to identify differentially expressed proteins (DEPs), including a functional analysis of DEPs and of the protein–protein interaction networks built using the STRING database. We identified a total of 92 DEPs highlighting the protein vimentin when comparing groups. Functional differences were observed, with the glycolysis pathway highlighted in PJ-colonized IPF patients; as well as the pentose phosphate pathway and miR-133A in non-colonized IPF patients. We found 11 protein complexes, notably the JAK-STAT signaling complex in non-colonized IPF patients. To our knowledge, this is the first study that analyzed PJ colonization’s effect on IPF patients. However, further research is needed, especially on the complex interactions with the AKT/GSK-3β/snail pathway that could explain some of our results.
Fluorescent Neutron Track Detectors for Boron-10 Microdistribution Measurement in BNCT: A Feasibility Study
Authors: Galuzzi Laura , Parisi Gabriele , Pascali Valeria , Niklas Martin , Bortot Davide , Protti Nicoletta , Altieri Saverio
Published: January 2025

Boron Neutron-Capture Therapy (BNCT) is a form of radiation therapy that relies on the highly localized and enhanced biological effects of the 10B neutron capture (BNC) reaction products to selectively kill cancer cells. The efficacy of BNCT is, therefore, strongly dependent on the 10B spatial microdistribution at a subcellular level. Fluorescent Nuclear Track Detectors (FNTDs) could be a promising technology for measuring 10B microdistribution. They allow the measurement of the tracks of charged particles, and their biocompatibility allows cell samples to be deposited and grown on their surfaces. If a layer of borated cells is deposited and irradiated by a neutron field, the energy deposited by the BNC products and their trajectories can be measured by analyzing the corresponding tracks. This allows the reconstruction of the position where the measured particles were generated, hence the microdistribution of 10B. With respect to other techniques developed to measure 10B microdistribution, FNTDs would be a non-destructive, biocompatible, relatively easy-to-use, and accessible method, allowing the simultaneous measurement of the 10B microdistribution, the LET of particles, and the evolution of the related biological response on the very same cell sample. An FNTD was tested in three irradiation conditions to study the feasibility of FNTDs for BNCT applications. The FNTD allowed the successful measurement of the correct alpha particle range and mean penetration depth expected for all the radiation fields employed. This work proved the feasibility of FNTD in reconstructing the tracks of the alpha particles produced in typical BNCT conditions, thus the 10B microdistribution. Further experiments are planned at the University of Pavia’s LENA (Applied Nuclear Energy Laboratory) to test the final set-up coupling the FNTD with borated cell samples.
Laguerre-Based Frequency-Limited Balanced Truncation of Discrete-Time Systems
Authors: Song Zhou , Song Qiu-Yan , Zulfiqar Umair
Published: January 2025

This paper introduces a novel model order reduction (MOR) method for linear discrete-time systems, focusing on frequency-limited balanced truncation (BT) techniques. By leveraging Laguerre functions, we develop two efficient MOR algorithms that avoid the computationally expensive generalized Lyapunov equation solvers used in traditional methods. These algorithms employ recursive formulas to calculate Laguerre expansion coefficients, which are then used to derive low-rank decomposition factors for frequency-limited controllability and observability Gramians. Additionally, we enhance the Laguerre-based low-rank MOR algorithm by incorporating a modified frequency-limited BT method, further improving its computational efficiency. Numerical simulations validate the effectiveness of the proposed approach, demonstrating significant reductions in computational complexity while maintaining accuracy in system approximation.
Radiofrequency Electromagnetic and Pulsed Magnetic Fields Protected the Kidney Against Lipopolysaccharide-Induced Acute Systemic Inflammation, Oxidative Stress, and Apoptosis by Regulating the IL-6/HIF1α/eNOS and Bcl2/Bax/Cas-9 Pathways
Authors: Balci Çağrı , Özcan Mustafa S. , Aşci Halil , Karabacak Pınar , Kuruşçu Oya , Taner Rümeysa , Özmen Özlem , Tepebaşi Muhammet Y. , İlhan İlter , Çömlekçi Selçuk
Published: January 2025

Background/Objectives: Sepsis-associated acute kidney injury caused by lipopolysaccharide (LPS) is related to hypoxia, amplification of the inflammatory response, oxidative stress, mitochondrial dysfunction, and apoptosis. This study aims to explore the protective effects of a radiofrequency electromagnetic field (RF-EMF) and a pulsed magnetic field (PMF) on acute kidney injury in rats. Materials and methods: Forty female Wistar albino rats were randomly divided into five groups (each containing eight rats): control, LPS, RF-EMF, PMF, and RF-EMF + PMF groups. Six hours after LPS application, blood and tissues were removed for histopathological, immunohistochemical, biochemical, and genetic analysis. Results: Histopathological findings, caspase-3, inducible nitric oxide synthase and tumor necrosis factor-alpha immunoexpressions, total oxidant status and oxidative stress index levels, and interleukin-6, hypoxia-inducible factor alpha, Bcl-2-associated X protein, and caspase 9 gene expression in kidney tissue and blood urine nitrogen and creatinine levels in blood were increased, whereas endothelial nitric oxide synthase and B-cell lymphoma 2 gene expression were decreased in the LPS groups. Both RF-EMF and PMF reversed all these findings and recovered renal tissues. Conclusions: Noninvasive, nontoxic, low-cost PMF and RF-EMF, both single and combined, have been demonstrated to have renoprotective anti-inflammatory, antioxidant, and antiapoptotic effects.
Association of Trace Elements with Polycystic Ovary Syndrome in Women—A Case-Control Study
Authors: Srnovršnik Tinkara , Pinter Bojana , Horvat Milena , Snoj Tratnik Janja , Falnoga Ingrid , Mazej Darja , Verdenik Ivan , Virant-Klun Irma
Published: January 2025

Objectives: There are still limited or lacking data on the association of trace elements (TEs) with polycystic ovary syndrome (PCOS). This case–control study aimed to determine levels of essential TEs (manganese (Mn), copper (Cu), zinc (Zn), selenium (Se), molybdenum (Mo)) and non-essential TEs (arsenic (As), cadmium (Cd), mercury (Hg), lead (Pb)) in urine, whole blood, and serum to investigate a possible association with kidney and liver function, endocrine and metabolic parameters, and environmental and lifestyle sources of potential exposure and provide possible recommendations. Methods: In our case–control study, women with PCOS (n = 35) and healthy controls (n = 35) underwent clinical and ultrasonographic examination, filled in questionnaires targeting general, lifestyle, and environmental information, and provided fasting venous blood samples and first morning urine for biochemical, hormonal, and TE analysis. Multiple linear regression models were used to evaluate the association between TE levels and data obtained through questionnaires. Results: In women with PCOS, lower Mo levels in whole blood (p = 0.024) and serum (p = 0.011) and higher serum Cu levels (p = 0.026) were detected when compared to healthy controls. Results of this study show that amendments in Cu and Mo levels might be related to altered kidney and liver function and disrupted hormonal balance in PCOS women. Cu levels positively correlated with leukocyte count. There was a negative correlation of Mo levels with proteinuria and luteinizing hormone levels. Regarding liver function, Mo negatively correlated with urinary bilirubin levels, and there was a positive association with alanine and aspartate aminotransferase, respectively. Dietary supplement consumption and certain diet habits appeared to be important predictors of exposure to Cu (beef consumption) or Mo (cereal and boiled vegetable consumption) and modify Mo and Cu levels in women. Conclusions: Concentrations of the chemical elements Mo and Cu in biological samples of women appear to be related to PCOS and nutrition. To our knowledge, this is a novel finding for Mo. Additional research is needed to provide more insights into the causality of the PCOS relationship with Mo and Cu in humans.
Fabrication of Highly Ordered Macropore Arrays in p-Type Silicon by Electrochemical Etching: Effect of Wafer Resistivity and Other Etching Parameters
Authors: Zhang Jing , Zhang Faqiang , Ma Mingsheng , Liu Zhifu
Published: January 2025

Macroporous silicon is a promising substrate in the field of optics, electronics, etc. In this paper, highly ordered macropore arrays were fabricated in p-type silicon wafers by electrochemical etching using a double-tank cell. The effect of the silicon resistivity, etching voltage and etching time on the pore morphology was investigated and the influence mechanism was analyzed. The pore diameter would decrease with the increase in the silicon resistivity and the decrease in the etching voltage, due to the increase in the space charge region width (SCRL). The pore depth would increase with the resistivity and the voltage. However, too high resistivity would cause insufficiency at the pore tips and too high voltage would cause pore splitting, which may cause a decrease in the pore depth. Then, the aspect ratio of 21 can be obtained on the silicon wafer with a resistivity of 50–80 Ω·cm at the etching voltage of 5 V with a maximum etching rate of about 0.92 μm/min.
Enantiopure Turbo Chirality Targets in Tri-Propeller Blades: Design, Asymmetric Synthesis, and Computational Analysis
Authors: Wang Yu , Xu Ting , Pandey Ankit , Jin Shengzhou , Yan Jasmine X. , Yuan Qingkai , Zhang Sai , Wang Jia-Yin , Liang Ruibin , Li Guigen
Published: January 2025

Enantiopure turbo chirality in small organic molecules, without other chiral elements, is a fascinating topic that has garnered significant interest within the chemical and materials science community. However, further research into and application of this concept have been severely limited by the lack of effective asymmetric tools. To date, only a few enantiomers of turbo chiral targets have been isolated, and these were obtained through physical separation using chiral HPLC, typically on milligram scales. In this work, we report the first asymmetric approach to enantiopure turbo chirality in the absence of other chiral elements such as central and axial chirality. This is demonstrated by assembling aromatic phosphine oxides, where three propeller-like groups are anchored to a P(O) center via three axes. Asymmetric induction was successfully carried out using a chiral sulfonimine auxiliary, with absolute configurations and conformations unambiguously determined by X-ray diffraction analysis. The resulting turbo frameworks exhibit three propellers arranged in either a clockwise (P,P,P) or counterclockwise (M,M,M) configuration. In these arrangements, the bulkier sides of the aromatic rings are oriented toward the oxygen atom of the P=O bond rather than in the opposite direction. Additionally, the orientational configuration is controlled by the sulfonimine auxiliary as well, showing that one of the Naph rings is pushed away from the auxiliary group (-CH2-NHSO2-tBu) of the phenyl ring. Computational studies were conducted on relative energies for the rotational barriers of a turbo target along the P=O axis and the transition pathway between two enantiomers, meeting our expectations. This work is expected to have a significant impact on the fields of chemistry, biomedicine, and materials science in the future.
Gamma Radiation in the Synthesis of Inorganic Silica-Based Nanomaterials: A Review
Authors: Baltac Andreea Simona , Mitran Raul-Augustin
Published: January 2025

Gamma radiation offers a versatile approach for the synthesis of silica-based nanomaterials, leveraging high-energy radiolysis to produce pure and finely structured composites without the need for surfactants or capping agents. This review explores the underlying mechanisms of γ-ray-induced radiolytic reduction, detailing the interaction of radiolytic species with silica matrices to synthesize metallic and hybrid nanomaterials. Emphasis is placed on the synthesis of silver and noble metal composites, which demonstrate promising properties for catalytic, antimicrobial, and sensing applications. The influence of synthesis parameters, such as dose, pH, and matrix characteristics, on nanoparticle size and yield is discussed. Emerging applications of these materials in biomedical devices and environmental technologies are presented. While γ-ray synthesis circumvents issues of contamination and scalability inherent in chemical methods, challenges such as accessibility to radiation sources and control over nanoparticle morphology remain. Future research directions are proposed, including the extension of this technique to multimetallic systems, sulfide-based nanocomposites, and hybrid materials.
Dietary Lipid Profile in Spanish Children with Overweight or Obesity: A Longitudinal Study on the Impact of Children’s Eating Behavior and Sedentary Habits
Authors: García Silvia , Ródenas-Munar Marina , Argelich Emma , Mateos David , Ugarriza Lucía , Tur Josep A. , Bouzas Cristina
Published: January 2025

Background: Dietary lipids, sedentary habits, and eating behaviors influence childhood obesity, but their interrelations remain unclear. Aim: To assess the relationships between young children’s dietary lipid profile and children’s eating behavior, as well as their sedentary habits, providing evidence-based insights that can help mitigate obesity risk in this vulnerable population. Design: A longitudinal randomized controlled trial conducted over 9 months, involving 90 children aged 2–6 years with overweight or obesity who were followed under a program that promoted healthy lifestyle habits for all participants and regularly monitored their parameters. Methods: The dietary lipid profile, eating behavior, and sedentary habits were assessed at baseline and after 9 months using validated tools, including 24 h recalls, the child eating behavior questionnaire (CEBQ), and screen time questionnaires. Results: Reductions in screen/sedentary time were significantly associated with decreased total fat (−11.1 g/day) and saturated fat (−4.3 g/day) intake, compared to smaller reductions in unchanged screen hours and increases with prolonged screen use. A positive correlation was observed between changes in dietary fat and the CEBQ domain “Desire to drink” (r = 0.528, p < 0.001), with regression analysis confirming a direct relationship. Conclusions: Managing screen time and eating behavior is crucial for improving dietary lipid profile and reducing childhood obesity risk. Strategies should include reducing sedentary behaviors, limiting sugary drinks, and promoting water as the main beverage, alongside effective nutrition education for families.
Protein Intake, Physical Performance and Body Composition in Master Athletes—A Short Scoping Review
Authors: Franzke Bernhard , Maierhofer Renate , Putz Peter
Published: January 2025

Sufficient protein intake has been shown to be advantageous for developing muscle mass, muscle strength, muscle quality, as well as for improving recovery from strenuous exercise, all of which are essential for athletic performance. Aging athletes, so-called master athletes, would benefit from evidence-based recommendations; however, studies investigating the role of their protein intake on muscle quality or performance are diverse and rare. Consequently, existing recommendations for this subpopulation of aging athletes are non-existent or speculative in nature. The aim of this short scoping review is to summarize available reports, identify common outcomes, and suggest future research directions. Literature research was carried out in PubMedMedline, SPORTDiscuss, and ScienceDirect without any restrictions regarding year of publication, type of research or sports discipline. Only observational and interventional studies with data on protein intake linked with body composition or performance outcomes were included for further analyses. We identified 12 suitable reports on master athletes with very diverse age-ranges, a broad variety of sports, and very heterogeneous outcome parameters. Seven studies investigated endurance athletes, four studies investigated multi-sports athletes, and only one study reported on strength-trained athletes. Average protein intake ranged between 1.0 and 1.9 g/kg/d. Within the few available studies, evidence tends to point towards a benefit of higher protein intakes for muscle mass and function; however, the low number of studies, combined with heterogeneity in study design and methods, limits their generalizability. Future studies are needed to build the evidence base for clear dietary recommendations respecting the specific needs of aging athletes.
Factor Structure of the Brief Coping Orientation to Problems Experienced Inventory (Brief-COPE) in Chinese Nursing Students
Authors: Cheng Cheng , Wang Qingling , Bai Jie
Published: January 2025

Background/Objectives: Coping strategies are influenced by sociocultural factors, and an understanding of how the Brief-COPE functions within the Chinese student population is important for its validity and reliability. This study aimed to explore the factor structure of the Brief Coping Orientation to Problems Experienced Inventory (Brief-COPE) in Chinese nursing students. Methods: A cross-sectional study design was adopted. A total of 284 college nursing students, aged 18 years or older, were recruited from a medical university in China using convenience sampling. Exploratory factor analysis (EFA) was conducted to identify the underlying domain structure of the Brief-COPE within those students. This study adhered to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement. Results: The Brief-COPE demonstrated robust validity, revealing eight distinct factors: positive reframing coping, avoidant and passive coping, seeking social support, self-blame and emotional distress coping, denial and deflective coping, spirituality and humor coping, avoidance and emotional release coping, and adaptive acceptance with distraction. The scale exhibited good internal consistency, as indicated by a Cronbach’s alpha of 0.759. Conclusions: The Brief-COPE is a valid tool for assessing coping strategies in Chinese nursing students. Nursing educators could benefit from training aimed at enhancing the use of appropriate strategies. Also, culturally tailored interventions, such as peer support groups and mentorship programs, could further promote coping skills and improve the emotional well-being of these students.
Method of Tissue Differentiation Based on Changes in Tissue Optical Properties Under Mechanical Stress Estimated with Optical Coherence Tomography
Authors: Sherstnev Evgeny P. , Moiseev Alexander A. , Sovetsky Aleksander A. , Shilyagin Pavel A. , Ksenofontov Sergey Y. , Gelikonov Grigory V.
Published: January 2025

This study highlights the possibility of contrasting the differences in tissues’ mechanical properties from Optical Coherence Tomography (OCT) data without maintaining phase stability during OCT data acquisition. The proposed method is based on the rate of attenuation coefficient changes under the mechanical pressure evaluation of the OCT data. It was shown, both on the calibrated synthetic and ex vivo biological samples, that the rate of attenuation coefficient changes observed corresponds to the sample’s mechanical properties and could be used to characterize the sample and distinguish it from other samples even if their optical properties before the pressure application are similar. This opens up the possibility to use an in vivo OCT-based system that can contrast mechanical properties without ensuring phase stability.
A Revised Taxonomy of the Bassia scoparia Complex (Camphorosmoideae, Amaranthaceae s.l.) with an Updated Distribution of B. indica in the Mediterranean Region
Authors: Sukhorukov Alexander P. , Wen Zhibin , Krinitsina Anastasiya A. , Fedorova Alina V. , Verloove Filip , Kushunina Maria , Léger Jean-François , Chambouleyron Mathieu , Tanji Abbès , Sennikov Alexander N.
Published: January 2025

Bassia scoparia is a widespread weedy species in the temperate regions of the world and is valued as a medicinal and ornamental plant. To date, the taxonomic concept of B. scoparia remains insufficiently studied due to a limited number of samples used in the previous phylogenetic analyses. To solve the taxonomy of the B. scoparia complex, we constructed a new phylogeny based on the nuclear ribosomal internal transcribed spacer (ITS), plastid intergenic spacer atpB-rbcL, and plastid region rpL16 intron sequences for numerous samples with diverse morphology. Our analysis revealed a close proximity and intermixed positions of the samples of the B. scoparia group with various morphology. Because of this polyphyly, we prefer to broadly delimit the species. An updated nomenclature of B. scoparia is provided including four new synonyms: Bassia angustifolia, B. littorea, Kochia albovillosa, and K. scoparia subsp. hirsutissima. In its new circumscription, B. scoparia encompasses populations with glabrous or variously hairy leaves and perianths. The original material of Kochia sieversiana, previously considered a species with hairy leaves and inflorescences, has the same diagnostic characters as in B. scoparia s.str. The correct name for more hairy-leaved plants is B. scoparia var. subvillosa. Plants with hairy perianths known as Kochia albovillosa and K. scoparia subsp. hirsutissima have a restricted distribution in Central Asia and South Siberia and have never been recorded as alien in other regions; they can be classified as a separate variety, B. scoparia var. hirsutissima. The ornamental variant of oblong or pyramidal shape may be called B. scoparia var. trichophila. Bassia scoparia is often confused with a similarly looking relative, B. indica, especially in North Africa, a region where secondary ranges of both species overlap. Phylogenetically, these species are sister groups; they share some morphological characters but have different primary distribution ranges. We traced a recent expansion of B. indica in the Mediterranean with the first record reported from the European continent (Spain) and uncovered various introduction pathways of the species in this region.
Reservoir Fluid Identification Based on Bayesian-Optimized SVM Model
Authors: Li Hongxi , Chen Mingjiang , Zhang Xiankun , Yang Bei , Zhao Bin , Li Xiansheng , Wang Huanhuan
Published: January 2025

Tight sandstone reservoirs are characterized by fine-grained rock particles, a high clay content, and a complex interplay between the electrical properties and gas content. These factors contribute to low-contrast reservoirs, where the logging responses of the gas and water layers are similar, resulting in traditional logging interpretation charts exhibiting a low accuracy in the fluid-type classification. This inadequacy fails to meet the fluid identification needs of the study area’s reservoirs and severely restricts the exploration and development of unconventional oil and gas resources. To address this challenge, this study proposes a fluid identification method based on Bayesian-optimized Support Vector Machine (SVM) to enhance the accuracy and efficiency of the fluid identification in low-contrast reservoirs. Firstly, through a sensitivity analysis of the logging responses, sensitive logging parameters such as the natural gamma, compensated density, compensated neutron, and compensated sonic logs are selected as input data for the model. Subsequently, Bayesian optimization is employed to automatically search for the optimal combination of hyperparameters for the SVM model. Finally, an SVM model is established using the optimized hyperparameters to classify and identify the following four fluid types: water layers, gas layers, gas–water layers, and dry layers. The proposed method is applied to fluid identification in the study area, and comparative experiments are conducted with the K-Nearest Neighbor (KNN), Random Forest (RF), and AdaBoost models. The classification performance of each model is systematically evaluated using metrics such as the accuracy, recall, and F1-score. The experimental results indicate that the SVM model outperforms the other models in fluid identification, achieving an average accuracy of 91.41%. This represents improvements of 16.94%, 4.39%, and 8.30% over the KNN, RF, and AdaBoost models, respectively. These findings validate the superiority of the SVM model for fluid identification in the study area and provide an efficient and feasible solution for fluid identification in tight sandstone reservoirs.
Optimization of Low-Carbon Operation in a Combined Electrical, Thermal, and Cooling Integrated Energy System with Liquid Carbon Dioxide Energy Storage and Green Certificate and Carbon Trading Mechanisms
Authors: Ma Xiaojing , Zhang Zhiqing , Chen Jie , Sun Ming
Published: January 2025

The liquid carbon dioxide energy storage system (LCES), as a highly flexible, long-lasting, and environmentally friendly energy storage technology, shows great potential for application in integrated energy systems. However, research on the combined cooling, heating, and power supply using LCES in integrated energy systems is still limited. In this paper, an optimized scheduling scheme for a low-carbon economic integrated energy system is proposed, coupling LCES with power-to-gas (P2G) technology and the green certificate/carbon trading mechanism. Mathematical models and constraints for each system component are developed, and an optimization scheduling model is constructed, focusing on the economic and low-carbon operation of the integrated energy microgrid system. The objective function aims to minimize total system costs. A case study based on a northern China park is conducted, with seven scenarios set for comparative optimization analysis. The results demonstrate that the use of the combined cooling, heating, and power LCES system reduces total costs by USD 2,706.85 and carbon emissions by 34.57% compared to the single-energy flow operation. These findings validate the effectiveness of the proposed model in optimizing system costs and reducing carbon emissions.
Indian Land Carbon Sink Estimated from Surface and GOSAT Observations
Authors: Nayagam Lorna , Maksyutov Shamil , Janardanan Rajesh , Oda Tomohiro , Tiwari Yogesh K. , Sreenivas Gaddamidi , Datye Amey , Jain Chaithanya D. , Ratnam Madineni Venkat , Sinha Vinayak , Hakkim Haseeb , Terao Yukio , Naja Manish , Ahmed Md. Kawser , Mukai Hitoshi , Zeng Jiye , Kaiser Johannes W. , Someya Yu , Yoshida Yukio , Matsunaga Tsuneo
Published: January 2025

The carbon sink over land plays a key role in the mitigation of climate change by removing carbon dioxide (CO2) from the atmosphere. Accurately assessing the land sink capacity across regions should contribute to better future climate projections and help guide the mitigation of global emissions towards the Paris Agreement. This study estimates terrestrial CO2 fluxes over India using a high-resolution global inverse model that assimilates surface observations from the global observation network and the Indian subcontinent, airborne sampling from Brazil, and data from the Greenhouse gas Observing SATellite (GOSAT) satellite. The inverse model optimizes terrestrial biosphere fluxes and ocean-atmosphere CO2 exchanges independently, and it obtains CO2 fluxes over large land and ocean regions that are comparable to a multi-model estimate from a previous model intercomparison study. The sensitivity of optimized fluxes to the weights of the GOSAT satellite data and regional surface station data in the inverse calculations is also examined. It was found that the carbon sink over the South Asian region is reduced when the weight of the GOSAT data is reduced along with a stricter data filtering. Over India, our result shows a carbon sink of 0.040 ± 0.133 PgC yr−1 using both GOSAT and global surface data, while the sink increases to 0.147 ± 0.094 PgC yr−1 by adding data from the Indian subcontinent. This demonstrates that surface observations from the Indian subcontinent provide a significant additional constraint on the flux estimates, suggesting an increased sink over the region. Thus, this study highlights the importance of Indian sub-continental measurements in estimating the terrestrial CO2 fluxes over India. Additionally, the findings suggest that obtaining robust estimates solely using the GOSAT satellite data could be challenging since the GOSAT satellite data yield significantly varies over seasons, particularly with increased rain and cloud frequency.
Weamba: Weather-Degraded Remote Sensing Image Restoration with Multi-Router State Space Model
Authors: Wu Shuang , He Xin , Chen Xiang
Published: January 2025

Adverse weather conditions, such as haze and raindrop, consistently degrade the quality of remote sensing images and affect subsequent vision-based applications. Recent years have witnessed advancements in convolutional neural networks (CNNs) and Transformers in the field of remote sensing image restoration. However, these methods either suffer from limited receptive fields or incur quadratic computational overhead, leading to an imbalance between performance and model efficiency. In this paper, we propose an effective vision state space model (called Weamba) for remote sensing image restoration by modeling long-range pixel dependencies with linear complexity. Specifically, we develop a local-enhanced state space module to better aggregate rich local and global information, both of which are complementary and beneficial for high-quality image reconstruction. Furthermore, we design a multi-router scanning strategy for spatially varying feature extraction, alleviating the issue of redundant information caused by repeated scanning directions in existing methods. Extensive experiments on multiple benchmarks show that the proposed Weamba performs favorably against state-of-the-art approaches.
Frequency-Constrained QR: Signal and Image Reconstruction
Authors: Garrett Harrison , Long David G.
Published: January 2025

Because a finite set of measurements is limited in the amount of spectral content it can represent, the reconstruction process from discrete samples is inherently band-limited. In the case of 1D sampling using ideal measurements, the maximum bandwidth of regular and irregular sampling is well known using Nyquist and Gröchenig sampling theorems and lemmas, respectively. However, determining the appropriate reconstruction bandwidth becomes difficult when considering 2D sampling geometries, samples with variable apertures, or signal to noise ratio limitations. Instead of determining the maximum bandwidth a priori, we derive an inverse method to simultaneously reconstruct a signal and determine its effective bandwidth. This inverse method is equivalent to incrementally computing a band-limited inverse using a frequency-constrained QR decomposition (FQR). Comparisons between reconstruction results using FQR and QR decompositions illustrate how FQR is less sensitive to noisy measurement errors, but it is more sensitive to high-frequency components. These methods are particularly useful in the reconstruction of remote sensing images from such as microwave radiometers and scatterometers.
Using Fracture-Induced Electromagnetic Radiation (FEMR) for In Situ Stress Analysis: A Case Study of the Ramon Crater
Authors: Das Shreeja , Frid Vladimir
Published: January 2025

This study examines fracture-induced electromagnetic radiation (FEMR) to assess tectonic stress in the Ramon Crater, a geologically “stable” area in southern Israel. With its minor seismic activity, the Ramon Crater poses unique challenges for traditional methods of stress assessment. Here, we introduce FEMR as a novel approach for detecting regional stress orientations by capturing electromagnetic pulses from micro-cracks formed under stress. These FEMR pulses provide indirect but valuable indicators of stress directions on both local and regional scales, demonstrating FEMR’s capability to detect subtle stress changes even in low-activity regions. The findings show that FEMR offers a scalable and sensitive method for mapping stress orientations in stable tectonic environments, making it a promising alternative to conventional seismic techniques. This application of FEMR opens new avenues for understanding regional stress fields in areas with limited seismicity, providing critical insights into tectonic stress behaviors that influence faulting and fracture dynamics in such stable regions.
EEG Signal Processing Techniques and Applications—2nd Edition
Authors: Wei Hua-Liang , Guo Yuzhu , He Fei , Zhao Yifan
Published: January 2025

Electroencephalography (EEG), as a well-established, non-invasive tool, has been successfully applied to a wide range of conditions due to its many evident advantages, such as economy, portability, easy operation, easy accessibility, and widespread availability in hospitals [...]
Securing IoT Sensors Using Sharding-Based Blockchain Network Technology Integration: A Systematic Review
Authors: Aslam Ammad , Postolache Octavian , Oliveira Sancho , Pereira José Dias
Published: January 2025

Sharding is an emerging blockchain technology that is used extensively in several fields such as finance, reputation systems, the IoT, and others because of its ability to secure and increase the number of transactions every second. In sharding-based technology, the blockchain is divided into several sub-chains, also known as shards, that enhance the network throughput. This paper aims to examine the impact of integrating sharding-based blockchain network technology in securing IoT sensors, which is further used for environmental monitoring. In this paper, the idea of integrating sharding-based blockchain technology is proposed, along with its advantages and disadvantages, by conducting a systematic literature review of studies based on sharding-based blockchain technology in recent years. Based on the research findings, sharding-based technology is beneficial in securing IoT systems by improving security, access, and transaction rates. The findings also suggest several issues, such as cross-shard transactions, synchronization issues, and the concentration of stakes. With an increased focus on showcasing the important trade-offs, this paper also offers several recommendations for further research on the implementation of blockchain network technology for securing IoT sensors with applications in environment monitoring. These valuable insights are further effective in facilitating informed decisions while integrating sharding-based technology in developing more secure and efficient decentralized networks for internet data centers (IDCs), and monitoring the environment by picking out key points of the data.
Real-Time Driver Drowsiness Detection Using Facial Analysis and Machine Learning Techniques
Authors: Essahraui Siham , Lamaakal Ismail , El Hamly Ikhlas , Maleh Yassine , Ouahbi Ibrahim , El Makkaoui Khalid , Filali Bouami Mouncef , Pławiak Paweł , Alfarraj Osama , Abd El-Latif Ahmed A.
Published: January 2025

Drowsy driving poses a significant challenge to road safety worldwide, contributing to thousands of accidents and fatalities annually. Despite advancements in driver drowsiness detection (DDD) systems, many existing methods face limitations such as intrusiveness and delayed reaction times. This research addresses these gaps by leveraging facial analysis and state-of-the-art machine learning techniques to develop a real-time, non-intrusive DDD system. A distinctive aspect of this research is its systematic assessment of various machine and deep learning algorithms across three pivotal public datasets, the NTHUDDD, YawDD, and UTA-RLDD, known for their widespread use in drowsiness detection studies. Our evaluation covered techniques including the K-Nearest Neighbors (KNNs), support vector machines (SVMs), convolutional neural networks (CNNs), and advanced computer vision (CV) models such as YOLOv5, YOLOv8, and Faster R-CNN. Notably, the KNNs classifier reported the highest accuracy of 98.89%, a precision of 99.27%, and an F1 score of 98.86% on the UTA-RLDD. Among the CV methods, YOLOv5 and YOLOv8 demonstrated exceptional performance, achieving 100% precision and recall with mAP@0.5 values of 99.5% on the UTA-RLDD. In contrast, Faster R-CNN showed an accuracy of 81.0% and a precision of 63.4% on the same dataset. These results demonstrate the potential of our system to significantly enhance road safety by providing proactive alerts in real time.
Advancing Sustainable Educational Practices Through AI-Driven Prediction of Academic Outcomes
Authors: Albahli Saleh
Published: January 2025

The integration of artificial intelligence (AI) into educational systems has the potential to transform academic practices and promote sustainability in education. This study explores the development and evaluation of machine learning (ML) models to predict student performance, integrating socio-demographic, academic, and behavioral data to enhance accuracy and interpretability. By leveraging advanced techniques such as convolutional neural networks (CNNs) and explainable AI (XAI), this research provides actionable insights into key factors influencing student success, such as attendance and socio-economic status. The results demonstrate that CNNs achieve exceptional predictive accuracy (99.97%) compared to traditional models, while XAI methods ensure model transparency for informed decision-making. These findings enable the design of personalized learning strategies, timely interventions, and equitable educational practices that contribute to student retention and overall institutional efficiency. This study aligns with the goals of sustainable education by emphasizing data-driven approaches to enhance learning outcomes, equity, and resource utilization.
Microbial Bioindicators for Monitoring the Impact of Emerging Contaminants on Soil Health in the European Framework
Authors: Visca Andrea , Di Gregorio Luciana , Costanzo Manuela , Clagnan Elisa , Nolfi Lorenzo , Bernini Roberta , Orgiazzi Alberto , Jones Arwyn , Vitali Francesco , Mocali Stefano , Bevivino Annamaria
Published: January 2025

Antibiotic resistance (AR) is recognized by the World Health Organization as a major threat to human health, and recent studies highlight the role of microplastics (MPs) in its spread. MPs in the environment may act as vectors for antibiotic-resistant bacteria (ARB) and antibiotic resistance genes (ARGs). Bacterial communities on the plastisphere, the surface of MPs, are influenced by plastic properties, allowing ARB to colonize and form biofilms. These biofilms facilitate the transfer of ARGs within microbial communities. This study analyzed data from the LUCAS soil dataset (885 soil samples across EU countries) using the Emu tool to characterize microbial communities at the genus/species level. Functional annotation via PICRUSt2, supported by a custom tool for Emu output formatting, revealed significant correlations between the genera Solirubrobacter, Bradyrhizobium, Nocardioides, and Bacillus with pathways linked to microplastic degradation and antibiotic resistance. These genera were consistently present in various soil types (woodland, grassland, and cropland), suggesting their potential as bioindicators of soil health in relation to MP pollution. The findings underscore MPs as hotspots for ARB and ARGs, offering new insights into the identification of bioindicators for monitoring soil health and the ecological impacts related to MP contamination.
A Review on PM2.5 Sources, Mass Prediction, and Association Analysis: Research Opportunities and Challenges
Authors: Yin Peng-Yeng
Published: January 2025

Air pollution has long been one of the most life-threatening issues which has led to massive amounts of premature human death due to fatal diseases and environmental disasters. Several Sustainable Development Goals (SDGs) set up by the United Nations coincide with the solutions for air pollution reduction. To reach a sustainable future, researchers have conducted many theoretical analyses or case studies of air pollution at different places on the globe and proposed prudent strategies for obtaining an equilibrium between socioeconomic development and air pollution reduction. This research selected a substantial number of articles and existing review papers published between 2013 and 2024 and organized these publications into subfields. This research was focused on filling the gap between existing reviews and the state-of-the-art technologies in the last decade. To be informative and contextual, this review presented a pathway for readers to comprehend the research in three contiguous phases of air pollution analysis, from compositional apportionment and mass prediction of pollution to disclosing associations between pollution concentration and natural or anthropogenic factors. At the end of this review, the author highlighted several research fields which have been overlooked in previous reviews but will be increasingly important in the future.
Research on the Development Logic and Sustainable Transformation Path of Suburban Villages from the Perspective of Relational Turn: A Case Study of Panlongling Village
Authors: Yan Ziyi , Ma Enpu , Liao Liuwen , Yang Yanru
Published: January 2025

Against the backdrop of urban–rural element flow and rural transformational development in China, suburban villages, as frontier zones connecting cities and rural areas, are pivotal in promoting urban–rural integration and fostering sustainable growth. This study takes Panlongling Village in China as a case study, utilizing theoretical methods from the perspective of the relational turn in geography to analyze the relational logic of rural socio-economic phenomena and address related conflicts. Based on data from multiple field surveys and interviews, this study identifies problems already discovered in Panlongling Village’s development, such as superficial economic enhancement, low villager participation, unidirectional benefit flow, and so on. This study proposes a hypothesis about the causes of these problems and validates this hypothesis through Actor–Network Theory, including unequal benefit distribution, insufficient technical support, inadequate governance and supervision, and a lack of self-improvement awareness among the actors. This study further offers targeted future development pathways for the village’s industrial transformation through actor–network reconstruction, taking into account the multidimensional relationships of stakeholders within the village, urban–rural relations, and socio-economic–natural interactions. It highlights the importance of a people-centric approach, shared benefits among actors, and cross-regional and cross-scale relational connections, offering insights for promoting sustainable development and the transformational development of suburban villages.
Study on the Biofilm Kinetics in Micro-Electrolysis Biological Reactors
Authors: Zhang Xiaohui , Zhang Zeya , Xu Jingyi , Pei Liang , Han Tongshun , Zhao Jianguo
Published: January 2025

The kinetic study of micro-electrolysis biotechnology not only determines the removal efficiency of a micro-electrolysis process but also influences the optimal design of a system. This paper investigates the relationship between electric field strength, pollutant degradation rate, and biofilm thickness by constructing a microporous biofilm model for pollutant removal. Additionally, the study derives equations linking electric field strength to reaction rate, pollutant effluent concentration, and biofilm thickness under both high and low pollutant influent concentrations. This work bridges the gap between macroscopic processes and periplasmic mechanisms, enhancing our understanding of pollutant removal mechanisms and facilitating process optimization. It also provides theoretical support for the sustainable development of micro-electrolysis biotechnology. Future research will focus on experimental validation and the optimization of model accuracy and flexibility to accommodate diverse treatment conditions.
Difference Analysis of Coal Carbon Emission Coefficient in China and Its Effects on Carbon Emission Calculation, Quota Allocation, and Enterprise Costs
Authors: Lei Jingyu , Chen Feng , Wang Yinchu , Liu Zilong , Xiong Xingchuang , Song Xiaoping
Published: January 2025

China is a leading producer and consumer of coal, with coal being the dominant energy source. The accurate calculation of the mass carbon emission factor (EFm) of coal is crucial as the carbon emissions from its combustion influence carbon emission assessment and policy formulation. However, discrepancies in EFm values across documents, due to varying net calorific values (NCVs), carbon contents (CCs), and carbon oxidation factors (COFs), have posed challenges for enterprises in carbon emission calculations. By analyzing different coal types, it is found that for anthracite, the EFm difference in different documents can reach 38.5%; for bituminous coal, it can reach 42.3%; and for lignite, it can reach 18.6%. These differences significantly affect carbon emission calculation accuracy, carbon allowance allocation fairness, and enterprise costs under the Carbon Border Adjustment Mechanism (CBAM). For instance, in 2023, the calculated carbon emissions of anthracite vary by over 300 million tons depending on the EFm used. To address these issues, relevant departments should establish a unified EFm release system, build a data sharing platform, and standardize enterprise testing standards to enhance the accuracy of carbon-related calculations and drive the low-carbon development of the coal industry.
Economics of Cybersecurity Investment and Information Sharing: Firm Decision Making Under Policy Constraints
Authors: Zhao Liurong , Wu Xinshuo , Li Jiao , Tong Huagang
Published: January 2025

With an increasing number of firms in cybersecurity information-sharing platforms, the potential cyber risks become a critical challenge during the exchanging of information. How to balance economic benefits and security requirements is an important topic for both firms and the government. By developing a game-theoretic model, the firms’ optimal strategies are discussed considering their absorptive capacity for security information under different policy constrains. The results show that the value of security information, intrusion loss, the level of cybersecurity vulnerability, the negative impact coefficient of platform security information disclosure, and the absorptive capacity for security information are key factors impacting firms’ decisions. The value of security information and intrusion loss are constrained by the marginal utility of cybersecurity investment and security information sharing. Firms prefer to increase their security investment or security information sharing only if the value of security information and intrusion loss are positively related to the marginal utility of cybersecurity investment or cybersecurity information sharing. Specifically, in the case without policy constrains, the optimal strategies of n firms are discussed, and it is found that they are consistent with those of two firms and that the utility of any firm in the platform decreases as the number of firms increases.