bluuebunny/crossref_metadata_2025_split huggingface dataset:
Spontaneous Droplet Uranium Extraction from Salt Lake Water Natural Falling
Authors: Xue Xinyu , Jin Xianchun , Lei Lin , Du Jinyan , Wang Fuqiang , LONG Zhihe , He Yongping , Xing Lili , Zhao Xiaochong 
Published: February 2025

Abstract

Uranium as the nuclear energy fuel is critical for clean and efficient power generation, and its limited terrestrial reserves and environmentally harmful mining practices necessitate the development of alternative resources and sustainable uranium extraction technologies. In this study, we introduce an innovative droplet uranium extraction (DUE) approach, in which naturally falling salt lake water interacts with a three-layer Al-FEP-CTS@CF film, driving an in-situ electrochemical process for spontaneous uranium extraction without external power consumption. The superhydrophobic fluorinated ethylene propylene (FEP) layer induces solid-liquid interfacial interactions, converting falling water kinetic energy into electricity. At the same time, the selective reduction of uranium ions takes place at chitosan (CTS) functionalized adsorption sites on carbon felt (CF), precipitating out uranium peroxide hydrate. The DUE method achieves a high uranium extraction efficiency of 58% for the first droplet of 1000 mg/L uranium aqueous solution and maintains efficiency above 55% for subsequent droplets. The DUE method has an extraction capacity of 1250.6 mg/g, surpassing conventional adsorption methods by ~ 26 times. Field experiments in real salt lake environment with high-salinity condition validate the scalability and adaptability of DUE, successfully extracting final products of MgU2O7 and Na2U7O22 from brine water flows. This zero-consumption technology provides a scalable, economical, and environmentally sustainable way for uranium resource exploitation, and can conveniently extend to other aqueous mineral extraction.
One-Sentence Summary: We introduce a zero-consumption and scalable Droplet Uranium Extraction (DUE) method that uses naturally falling salt lake water to drive in-situ electrochemical process for spontaneous uranium extraction, successfully validated in high-salinity salt lake environments with the final products of MgU2O7 and Na2U7O22 from brine flows.
Molecule-Probed Raman Spectroscopy for Femtogram-per-Liter Level Per- and Polyfluoroalkyl Substances Detection
Authors: Li Bo , Zhao Liang , Hu Jiayue , Gong Chenchi , Dyke Alexis , Cao Han , Wu Jianlei , Bracaglia Laura , Liu Ling , Xu Wenqing 
Published: February 2025

Abstract

Per- and poly-fluoroalkyl substances (PFAS) have received significant attention due to their persistence in the environment. Because of their accumulative nature, even trace amounts can adversely impact human health and ecosystems. Here, we present Molecule-Probed Raman Spectroscopy (MPRS), an ultra-sensitive, low-cost, and fast method that can achieve the femtogram-per-liter detection of PFAS, surpassing any existing methods by at least four orders of magnitude. In contrast to existing Raman Spectroscopy monitoring the spectrum of PFAS, MPRS monitors changes in the Raman spectrum of molecular probes, methyl group (-CH3) on polydimethylsiloxane, upon PFAS capture. MPRS succeeds in detecting multiple individual PFAS in water and monitoring PFAS in complex matrices such as surface water and human blood. We also demonstrated the feasibility of on-site monitoring of PFAS using a portable Raman spectrometer. Beyond its transformative detection capability, MPRS establishes a new analyte detection paradigm, paving the way for innovative material systems and instruments.
Global, Regional, and National Burden and Future Projections of Genital Herpes Incidence among Women of Childbearing Age
Authors: Kang Yutong , Feng Yanying , Yuan Qiheng , Xu Xuanming , Zheng Meiqin , Wang Yajie , Li Zhenjun 
Published: February 2025

Abstract

Objectives
Genital herpes (GH), primarily caused by herpes simplex virus 2, imposes a significant burden on women of childbearing age (WCBA), raising the risk of pregnancy-related complications. Despite the high global burden, comprehensive studies on trends in GH incidence among WCBA are lacking.
Methods
Using GBD 2021 data, this study analyzed GH incidence in WCBA, explored Socio-demographic Index (SDI) associations and inequality trends, applied decomposition analysis, and predicted future trends with Age-period-cohort (APC) and Bayesian APC (BAPC) models.
Results
Between 1990 and 2021, global GH cases in WCBA increased from 14.6 million to 22 million, while the age-standardized incidence rate (ASIR) saw a modest annual increase of 0.045%. Regional variations were observed, with low-middle SDI regions showing continued growth in ASIR. Unfavorable period effects were exhibited in low-middle SDI regions. Population growth was identified as the main driver of morbidity trends, with emerging health inequalities over time. By 2030, global GH cases in WCBA are expected to reach 23.6 million, with persistent challenges in low-SDI regions such as Angola, the Democratic Republic of the Congo, Congo, and Equatorial Guinea.
Conclusion
GH incidence among WCBA is on the rise, underlining the need for focused management strategies, particularly in low-middle SDI regions.
Robust phase sensing via a nonlinear parity-time symmetric system
Authors: Huang Qing-An , Chen Dong-Yan , Dong Lei 
Published: February 2025

Abstract

Parity-time symmetric systems featuring loss and gain manifest as a form of non-Hermitian Hamiltonian. The inclusion of a nonlinear saturable gain can eliminate the imaginary part of frequency eigenvalues and suppress noise. Consequently, it has been employed to enhance sensing near an exceptional point (EP). A perturbation to the off-diagonal elements of the Hamiltonian leads the frequency eigenvalue to scale with the square-root of the perturbation near an EP. This, however, is associated with a lowered scaling factor, potentially diminishing the sensitivity boosted by the square-root singularity. When a perturbation is introduced to the diagonal elements of the Hamiltonian, the frequency eigenvalue exhibits a cube-root singularity in a two-level system. Nevertheless, the response has a relatively small scaling factor and the cube-root singularity comes at the expense of the sensing dynamic range. An entirely different approach we propose here is to detect the phase difference between the loss and gain resonator instead of focusing on detecting the frequency. We demonstrate theoretically and experimentally that the phase difference reveals a cube-root singularity with an enlarged scaling factor over a wide dynamic range. Experimental results for our wearable capacitive temperature sensors show that the sensitivity of EP phase sensing has enhanced by an order of magnitude across a broad dynamic range relative to that of EP frequency sensing. These findings resolve a longstanding debate on the efficacy of EP-based sensors and pave the way for their practical applications.
Quantitative CT-based biomarkers for predicting Renal cell carcinoma subtypes: a comparison of Dual-Energy CT, Perfusion CT, and CT texture parameters
Authors: SAH ANJALI , GOSWAMI SNEHA , GUPTA AMIT , GARG SANIL , YADAV NEEL , DAS CHANDAN J 
Published: February 2025

Abstract

Purpose
To evaluate and compare the diagnostic performance of CT texture analysis (CTTA), perfusion CT (PCT), and dual-energy CT (DECT) in distinguishing between clear-cell renal cell carcinoma (ccRCC) and non-ccRCC.
Methods
This retrospective study included 66 patients with RCC (52 ccRCC and 14 non-ccRCC) who underwent DECT and PCT imaging before surgery between January 2017 and December 2022. Two independent radiologists measured DECT parameters (iodine concentration and iodine ratio) and PCT parameters (blood flow, blood volume, mean transit time, and time to peak) using circular ROIs placed on tumors. For CTTA, the largest tumor cross-section in the corticomedullary phase was manually annotated using the "labelme" tool, and texture features were extracted with Python libraries including "scipy" and "numpy." Multivariate logistic regression analysis was performed to assess the ability of PCT, DECT, and CTTA models to predict tumor subtypes.
Results
All three imaging modalities demonstrated high diagnostic accuracy, with F1 scores of 0.9107, 0.9358, and 0.9348 for PCT, DECT, and CTTA, respectively. Inter-reader agreement for PCT and DECT parameters was excellent (Pearson correlation > 0.85). None of the three models were significantly different (p > 0.05). While each modality could effectively differentiate between ccRCC and non-ccRCC, higher iodine ratio (IR) on DECT and increased entropy on CTTA were independent predictors of ccRCC, with F1 scores of 0.9345 and 0.9272, respectively (p < 0.001). The combined ML model integrating DECT, PCT, and CTTA parameters yielded the highest diagnostic accuracy, with an F1 score of 0.954.
Conclusions
The diagnostic accuracy of PCT, DECT, and CTTA in distinguishing between ccRCC and non-ccRCC tumors was equivalent and high. However, among these three methods, only IR on DECT and entropy on CTTA were identified as independent predictors of the RCC subtype; hence, these two quantitative markers may be more applicable in clinical practice.
Clinical relevance:
Accurate, non-invasive biomarkers are essential to differentiate RCC subtypes, aiding in prognosis and guiding targeted therapies, particularly in ccRCC, where treatment options differ significantly.
An Enhanced Bidirectional Projection Approach for Fuzzy Credibility Multi-Attribute Group Decision Making: Application in Sports Equipment Supplier Selection
Authors: Liu Longfei 
Published: February 2025

Abstract

This article will introduce the fuzzy credibility bidirectional projection (BP) method and some important basic theories of fuzzy credibility set (FCS). Firstly, we reviewed the definition, operation rules, score function, and comparison methods of fuzzy credibility numbers (FCNs). In addition, in order to better integrate the fuzzy credibility evaluation information of multiple experts, we introduced two aggregation operators, such as FCNWAA and FCNWGA. Then, we proposed the concept of the module of FCS and introduced it into the BP method. Incorporating bounded rationality of decision-makers, we applied cumulative prospect theory (CPT) to this multi-attribute group decision-making (MAGDM) method. We employed the minimum identification information principle (MIIP) to integrate the entropy weight method with the method based on the removal effects of criteria (MEREC), alongside subjective weights, to determine decision attribute weights. This approach balances the subjective input of decision-makers with the significance of objective data, making it more applicable to real-world scenarios. Detailed calculation steps for this MAGDM technique are provided. Finally, we propose the MAGDM technique based on fuzzy credibility bidirectional projection method to selection of sports equipment suppliers (SSES) issue with fuzzy credibility information. This paper evaluates the effectiveness of the MAGDM technique by comparing it with the FCNWAA, FCNWGA, and FCDWG operators.
Effect Of Educational Intervention on The Knowledge of Health Workers Regarding Exposures to Bloodborne Pathogens: A Review
Authors: Jamiu Ishola Garba Olalekan , Alsaidi Nahla Abduljaleel 
Published: February 2025

Abstract

Introduction: Healthcare workers (HCWs) face a multitude of occupational hazards, including the risk of bloodborne infections resulting from sharp instrument injuries and exposure of skin and mucous membranes to contaminated blood and body fluids (BBF). Existing empirical evidence, albeit limited, indicates a deficiency in the knowledge of occupational safety among health workers, consequently raising concerns about their adherence to safety-related practices. Nonetheless, numerous studies have demonstrated that enhanced knowledge regarding safety precautions can mitigate the risk of exposure to diseases transmitted through blood and body fluids.
Objective: The purpose of this study was to find out the effectiveness of educational intervention in improving knowledge of universal precautions among Health workers about exposures to blood pathogens. Specifically, it examines literature published between 2010 and 2024, Methodology: the study conducted a literature review on published articles about the effectiveness of educational programs on knowledge of health workers about safety measures against exposure to blood pathogens.
Results: there were disparities in baseline knowledge levels among healthcare workers. While some studies indicated poor knowledge pre-intervention, others reported relatively high levels, particularly regarding certain aspects such as injection safety or HIV guidelines. However, Educational interventions significantly improved workers' knowledge regarding bloodborne pathogens and reduced exposure risks.
Conclusion: However, all studies highlighted the effectiveness of educational interventions in improving healthcare workers' knowledge of bloodborne pathogens. Significant increases in knowledge scores were observed post-intervention across various studies, indicating the efficacy of educational programs in enhancing understanding and awareness.
An Efficient Fire Detection Algorithm Based on Mamba Space State Linear Attention
Authors: Li Yuming , Wang Yongjie , Shao Xiaorui , Zheng Anbo 
Published: February 2025

Abstract

The Mamba model, as a State Space Model (SSM), enhances the global receptive field and feature extraction capabilities of object detection models through an architecture inspired by Recurrent Neural Networks (RNNs). Compared to traditional Convolutional Neural Networks (CNNs) and Transformers, it excels in handling complex scale variations and multi-view interference, offering a novel approach for object detection tasks in dynamic environments such as fire detection. Therefore, this paper proposes an efficient attention mechanism, the Efficient-Mamba-Attention (EMA) module, which enhances input features through adaptive average pooling and SSM modules. By integrating the EMA module with the YOLOv9 architecture, a highly efficient fire detection algorithm is proposed, particularly suitable for complex scenarios. Additionally, the model introduces the ConvNeXtV2 block to strengthen the backbone network, compensating for the limitations of SSM models in local feature modeling. Finally, the introduction of dynamic non-monotonic focusing and distance penalty mechanisms optimizes the loss function, improving the accuracy of the detection bounding boxes and significantly enhancing the precision and robustness of the fire alarm tasks. Comparative experiments demonstrate that the proposed network excels in terms of AP50 and FPS, achieving an AP50 of 91.0% on the large-scale fire dataset (dataset1), and 87.2% on the small-scale fire dataset (dataset2), with FPS reaching 71 on dataset1 and 64 on dataset2. This demonstrates that the proposed method maintains high detection performance while achieving outstanding efficiency.
Impact of Rainfall Variability on Crop Intensification and Diversification in the Coastal Region of Bangladesh
Authors: Islam H. M. Touhidul , Ahmed Sharif , Kamruzzaman Mohammad , Kumar Virender , Bhandari Humnath 
Published: February 2025

Abstract

The coastal regions of Bangladesh are highly vulnerable to climate change. Crop intensification and diversification in these areas are lower than in other parts of the country due to climate change and multiple stress environments. With other factors, high rainfall variability is one of the regions that impact agricultural production and productivity. The current study investigates the impact of rainfall variability on crop intensification and diversification in the coastal region of Bangladesh. Based on the historical climate data (1980-2020) of six weather stations, this study examines the onset (ON) and withdrawal (WD) of monsoon precipitation by forward and backward accumulation technique and the probability of dry and wet spells utilizing the Markov Chain (MC) probability model. The average ON and WD of monsoon precipitation were found at the 21st‒22nd and 43rd‒44th standard meteorological weeks (SMWs), respectively for all the stations. Extreme rainfall events have increased in recent periods (2001–2020) compared to earlier periods (1981–2000) in the rainfall months (Jun-Oct), potentially contributing to the recurrent flash floods and waterlogging in coastal Bangladesh in that time. The probabilities of dry spells are elevated in the early (1st‒17th SMWs) and late (43rd‒52nd SMWs) of the year. The region's higher likelihood of wet weeks (18th–42nd SMWs) experiencing precipitation ranges from 40‒100%. We also analyzed the effect of rainfall variability on aus (pre-monsoon rice) production, which has great potential in this area. Additionally, the frequency and return periods of extreme rainfall events of different thresholds during the dry months (Nov-Apr) were analyzed to assess the risks of cultivating rabi/non-rice crops due to increased precipitation and waterlogging and to evaluate the potentiality of rabi crops. We found that higher return periods of extreme events might delay the sowing of rabi crops and have adverse effects, particularly during their maturity phases. This study emphasizes the optimizing sowing windows of rice and non-rice crops, and cropping systems innovations based on variable rainfall patterns that will foster sustainable agricultural intensification and diversification in the coastal regions of Bangladesh.
Dynamic Token Masking in Spiking Neural Network
Authors: Fang Yuetong , Wang Ziqing , Zhou Deming , Ren Hongwei , Zhou Shibo , Xu Renjing 
Published: February 2025

Abstract

Spiking Neural Networks (SNNs) offer an energy-efficient alternative to conventional artificial neural networks (ANNs). Although ANN-to-SNN conversion has emerged as a promising direction for developing high-performance spike-driven models with reduced training complexity, it still faces the challenge of maintaining energy efficiency in converted SNNs. In this paper, we address these limitations by introducing a dynamic spiking token mixer, inspired by the strong information redundancy present in the spike self-attention mechanism. Our approach effectively replicates the selective processing capabilities of self-attention through dynamic token masking (DynMask), with layer-specific masking ratios customized according to both spatial and temporal significance. Comprehensive results establish DynMask as a practical step toward efficient deep learning systems. Specifically, DynMask achieves performance gains of up to +3.23% on ImageNet-1K while narrowing the accuracy gaps to as low as +0.02% compared to ANNs, with simultaneous energy consumption reductions of up to 44×. Moreover, our approach successfully extends to complex vision tasks that remain largely unexplored in SNN literature, including COCO detection and ADE20K segmentation.
In silico Study of Antiviral Phytochemicals for the Potential Drug Development Against Wild-type and Omicron Variants of SARS-CoV-2
Authors: Dutta Somenath , Sri Pushan Shilpa , Ghosh Rohan , Jose Maria , Pritam Manisha 
Published: February 2025

Aims:
To identify potential phytochemical-based drugs for both wild-type and Omicron
variants of SARS-CoV-2 using virtual screening and molecular dynamic simulation.

Background:
Coronavirus disease 2019 (COVID-19) is an infectious viral disease caused by
SARS-CoV-2. Since 2019, multiple variants have been reported from all over the world and the
emergence of new variants of SARS-CoV-2 is a major concern.

Objective:
To identify potential phytochemicals that can be used as drugs against different variants
of SARS-CoV-2.

Methods:
In our present study, we have selected 594 phytochemicals and performed virtual
screening to identify potential drug candidates. The screening commenced with molecular docking
techniques with both ACE2 (Human) and Spike protein (wild-type and Omicron variant),
followed by prediction of pharmacokinetics parameters and toxicity. The Schrodinger tools,
Swiss ADME, and ProTox-II accomplish the analysis. Further, molecular dynamics simulation,
binding free energy calculation and meta-dynamics study was performed for best protein-ligand
complexes of both proteins using GROMACS and gmx_MMPBSA to validate the stability of the
docked complexes.

Results:
we have identified 6 and 4 drugs as spike protein inhibitors for wild-type and Omicron
variants, respectively. 6 drugs were identified as ACE2 receptor inhibitors. We have identified
silymarin as a common drug inhibitor for both pathogen (Wild-type, and Omicrons spikes) as
well as host (human ACE2) protein that reflects its ability to inhibit the host-pathogen interaction
and prevent infection.

Conclusion:
We have found some potential Phytochemicals that can be used against different
variants of SARS-CoV-2 such as silymarin.
Recent Advancements in Breast Cancer Therapies and Biomarkers: Mechanisms and Clinical Significance
Authors: Husain Arbab , Khan Hamda , Jamale e Fatima  , Siddiqui Shirjeel Ahmad , Singh Akash Deep , Husain Adil , Pandey Shivam , Kant Ravi , Khanam Afreen , Pandey Pratibha , Khan Fahad 
Published: February 2025

Abstract:
Breast cancer remains a critical health concern, requiring continual innovation in treatment to
improve patient outcomes. The continuous obstacles in therapy and the need for novel techniques underline
the importance of making advances in this discipline. Precision medicine has emerged as a viable method,
with genomic profiling and molecular subtyping allowing for targeted therapy based on distinct breast
cancer subtypes. This strategy is supplemented by advances in early detection and screening, with
technologies like liquid biopsy promising earlier and more accurate diagnosis. The introduction of
immunotherapy has transformed breast cancer treatment by using the body's immune system to combat
cancer. Recent discoveries, particularly combination medicines, attempt to circumvent resistance
mechanisms and improve treatment success. Furthermore, including lifestyle therapies such as nutrition,
exercise, and psychological support has been demonstrated to reduce breast cancer risk and strengthen
survivability rates. Survivorship programs serve an important role in comprehensive care by addressing
long-term needs and enhancing survivors' quality of life. Investigating innovative therapeutic approaches,
such as developing cancer vaccines, epigenetic modulators, and RNA interference (RNAi) therapy, provides
new treatment options. Fostering collaboration among healthcare personnel through shared decision-making
and tumor committees is essential for the integration of multidisciplinary care, which ensures patientcentered
care. Although advancements have been made, there are still numerous obstacles to overcome in
the implementation of these future directions. To effectively confront these obstacles, it is imperative to
capitalize on opportunities for innovation and collaboration. It is imperative to address ethical, social, and
economic factors in the advancement of breast cancer care to ensure that innovations are equitable and
accessible. In conclusion, the future of breast cancer management is bright since substantial improvements
are on the verge of turning patient treatment into a completely different experience. For these breakthroughs
to become a reality, it is necessary to maintain research efforts, advocate for them, and work together. The
dedication to innovation and the joint effort to overcome current problems are the two important factors that
will determine whether or not breast cancer treatment and surviving will have a better future.