bluuebunny/crossref_metadata_2025_split huggingface dataset:
Technological overlap and international cooperation in green innovation activities——Evidence from Chinese environmental technology patents
Authors: Lin Jie , Li Jianbin 
Published: October 2024

Abstract

North-South cooperation in green innovation activities is an essential avenue for promoting global climate governance. This study examines the impact of technological overlap on the scale of international cooperation in environmental technologies (ETs) between Chinese and foreign geographical units. The results show that technological overlap significantly increases the scale of international cooperation in ETs. The main international cooperation partners are located in the G7 countries, with the United States being the most significant partner. Technological overlap has not only promoted China to send many patent inventors to other countries but also encouraged China to introduce more foreign inventors. Further research reveals a significant interaction effect between the scale of existing inventor cooperation, local technological advantages, and technological overlap, which expands the scale of international cooperation. This paper calls for strengthened ET cooperation between developed and developing countries to address climate change.
Diffraction separation method using shapeDTW and median-mean filter
Authors: Sheng Tongjie , Zhao Jingtao , Yang Jie , Chen Zongnan 
Published: October 2024

Abstract

The subsurface small-scale geological structures are manifested as diffractions in seismic data. Diffraction imaging provides high-resolution details of discontinuities such as faults, collapse columns, and karst caves. However, this high-resolution information is often obfuscated by strong reflections, necessitating their removal prior to diffraction imaging. Here, we propose a novel diffraction separation method based on shape dynamic time warping (shapeDTW) and median-mean filter. The shapeDTW is an effective time series alignment method that utilizes the distance between temporal points within a neighborhood as the alignment criterion, which can accurately align strong energy events in seismic data. We implement shapeDTW to construct flattened reflection gathers, in which the reflections are aligned and therefore behaves as horizontal events with consistent strong amplitudes, while the diffractions appear as non-horizontal weak events. Leveraging this difference in shape and amplitude, the median-mean filter can effectively extract the reflections from flattened reflection gathers. Diffractions are separated from seismic data by subtracting the extracted reflections. The synthetic data experiment confirms the feasibility of the proposed method in eliminating strong reflected waves while preserving weak diffracted waves related to karst caves in seismic data with low signal-to-noise ratio. The field data application further illustrates its effectiveness in removing strong high-slope reflections, highlighting small-scale fracture-related detailed features.
Curved-layer slicing and continuous path planning for multi-axis printing of fiber-reinforced composite structures
Authors: Li Yamin , Shi Chenyang 
Published: October 2024

Abstract

Fiber-reinforced composite (FRC) additive manufacturing technologies have successfully addressed the limitations of traditional autoclave forming, significantly enhancing design freedom. However, planning continuous printing paths that align with a defined fiber orientation vector field in an FRC structure remains a challenge. This paper introduces a comprehensive framework for multi-axis curved-layer printing of 3D FRC parts. First, we propose a multi-axis curved layer slicing method based on deformed space mapping, which ensures that the sliced curved layers are as parallel to the fiber orientations as possible. Next, we develop a vector-field-driven printing path planning method for each curved layer. This method guarantees that the generated paths are aligned with the designated fiber orientations while also ensuring continuous material deposition. Additionally, we present an algorithm for generating support structures specifically for curved layers to prevent material collapse during the printing process. The effectiveness of the proposed curved-layer slicing and printing path planning methods is validated through extensive experiments and simulations, demonstrating their potential to enhance the performance and versatility of FRC additive manufacturing.
Optimizing Water Quality in Dead-Ends of Water Distribution Networks Using Passive Control Methods: A Computational Fluid Dynamics Simulation and Predictive Modeling Study
Authors: Li Kunyi , Gao Jinliang , Wu Wenyan , Tian Yuan , Qi Shihua , Yu Jingyang , Chen Jianxun , Ding Yanchen 
Published: October 2024

Abstract

Dead-ends are points in water distribution networks (WDNs) where water flow is almost stagnant, leading to pipe corrosion and microbial growth, which degrade water quality. Controlling the contaminant dispersion in dead-ends is crucial for maintaining water quality. This research examined the use of passive control methods, specifically leading-edge spoilers, to improve water quality in dead-ends of WDNs. Computational fluid dynamics (CFD) was used to simulate and analyze the effect of spoilers on fluid flow and contaminant dispersion under different flow conditions. The results showed that spoilers significantly reduced contaminant concentrations and sped up water quality restoration. The optimal spoiler configuration was found to be twice the boundary layer thickness in height and angled at 30°, achieving control rates of 58% and 61.90% at low and high velocities, respectively. Predictive models developed from CFD data confirmed the effectiveness of spoilers in reducing YC town's WDN contaminants, and laboratory experiments demonstrated their minimal impact on hydraulic efficiency. Spoilers offer a cost-effective and environmentally friendly solution for improving water quality in WDNs.
Impact of zinc caproate supplementation on growth performance, intestinal health, anti-inflammatory activity, and Zn homeostasis in weaned piglets challenged with Escherichia coli K88
Authors: Xu Jilong , Qiao Hanzhen , Gan Liping , Wang Peng , Zhao Yifeng , Lei Zetian , Chou Yixuan , Hou Chenrui , Li Mengqi , Wang Jinrong 
Published: October 2024

Abstract

Background Enterotoxigenic Escherichia coli (ETEC) is one of the primary causes of diarrhea in piglets, creating substantial economic losses in the swine farming industry worldwide. This study aimed to investigate the impact of zinc caproate (ZnCA) on the intestinal health, growth performance, inflammatory status, and Zn homeostasis of weaned piglets challenged with ETEC K88. In total, 48 weaned piglets (Duroc × Landrace × Yorkshire, 7.78 ± 0.19 kg, 28 d) were selected for a 21-d experiment. Each experimental treatment consisted of six replicate pens with two piglets each. The treatment conditions were as follows: 1) a basal diet (CON), 2) a basal diet + ETEC K88 (NC), 3) a basal diet + 2500 mg/kg Zn oxide (ZnO) + ETEC K88 (PC), and 4) a basal diet + 1600 mg/kg ZnCA + ETEC K88 (ZnCA).Results The addition of 1600 mg/kg ZnCA to the diet of post-weaning piglets effectively enhanced growth performance and nutrient digestibility and reduced the incidence of diarrhea and inflammatory reactions caused by ETEC K88 infection. These therapeutic effects were comparable to those of pharmacological doses of ZnO. In terms of improving intestinal health and Zn homeostasis in post-weaning piglets challenged with ETEC K88, the effectiveness of 1600 mg/kg ZnCA surpassed that of pharmacological doses of ZnO.Conclusions Overall, under the experimental conditions of this study, ZnCA exhibited the potential to reduce the pharmacological dosage of ZnO while improving intestinal health and Zn homeostasis in weaned piglets.
Risk assessment of gross alpha-beta activity concentration in drinking water in Kuwait: Baseline measurements and review
Authors: Al-Shammari Hanadi F. , AL-Jarba Mashael A. , Al-Shammeri Jehan M. , Al-Ateeqi Salim , Ismael Anfal , Al-Boloushi Aisha 
Published: October 2024

Abstract

This study focused on measuring gross alpha and beta radiation in Kuwait's drinking water sources, including local and imported bottled water and public-piped water, to ensure safety and increase consumer confidence. The assessment aimed to establish a baseline for gross alpha and gross beta activity levels and validate a rapid emergency detection method, following World Health Organization (WHO) guidelines. Results showed alpha and beta gross radiation concentrations, and the total effective doses were within safe limits set by WHO across all water types. Specifically, local bottled water had 0.034 ± 0.002 Bq/L for alpha, 0.078 ± 0.04 Bq/L for beta, and 0.046 ± 0.022 mSv/y for total effective dose; imported bottled water had 0.031 ± 0.001 Bq/L for alpha, 0.066 ± 0.02 Bq/L for beta, and 0.04 ± 0.01 mSv/y for total effective dose; and public-piped water had 0.029 ± 0.0004 Bq/L for alpha, 0.041 ± 0.001 Bq/L for beta, and 0.026 ± 0.001 mSv/y for total effective dose. The measured gross alpha and beta values, which affect the total (annual) effective dose, were below the WHO reference levels for gross activity (0.05 ± 0.01 Bq/L for alpha, 1 ± 0.13 Bq/L for beta, and 0.1 mSv/y for the annual effective dose), indicating that the water is acceptable for human consumption without any safety concerns. However, three bottled water samples presented higher gross beta activity concentrations than the others, and these samples require specialized nuclide analysis via advanced methods to determine radionuclide content, as it influences the estimated effective dose.
Pan-Cancer Analysis of hnRNPAB: Implications in Tumor Progression, Prognosis, and Immune Microenvironment Modulation
Authors: Wu Rui-Ze , Sun Qian-Qian , Fu Yao , Xin Rui , Wu Yong-Hui , Pan Yu-Lin 
Published: October 2024

Abstract

Background: hnRNPAB, a member of the hnRNP protein family, is involved in mRNA cytoplasmic localization, transport, and the regulation of transcription, metabolism, and splicing. It is associated with malignant progression and metastasis in liver cancer and lung adenocarcinoma. However, a systematic pan-cancer analysis exploring its role in diagnosis, prognosis, and immune prediction is lacking.
Methods: This study evaluated hnRNPAB expression across 33 cancers and its association with immune infiltration using UCSC Xena, TIMER, GEPIA, BioGPS, and ARCHS4 databases. Methylation levels were analyzed using UALCAN and MethSurv. Survival analysis was performed with GEPIA and MethSurv platforms, and mutation analysis was conducted via cBioPortal. Protein interaction networks were constructed using STRING and Cytoscape, and functional enrichment analyses were performed with KEGG and GSVA. Immune infiltration was assessed using TIMER, CIBERSORT, and quanTIseq, and the relationship between hnRNPAB and cancer-associated fibroblasts (CAFs) was analyzed using TIMER2.0.
Results: hnRNPAB was significantly overexpressed in various cancers, with high expression correlating with poor prognosis. Mutation analysis revealed that amplification mutations of hnRNPAB were associated with worse survival rates. Low methylation of hnRNPAB was linked to cancer progression. Additionally, hnRNPAB was involved in cell cycle regulation, mTORC1, and PI3K-AKT signaling pathways. Immune cell infiltration analysis demonstrated a significant association between hnRNPAB and CAF infiltration, affecting immune therapy outcomes.
Conclusion: This study highlights the association between hnRNPAB overexpression and poor prognosis across multiple cancers, particularly in kidney and liver cancers. hnRNPAB promotes tumor growth and metastasis by regulating immune cell infiltration and CAF activity. The study also explores its mutations and methylation status, suggesting its potential as a therapeutic target or biomarker in cancer metabolism reprogramming and immune evasion.
Evaluation of Risk Factors Associated with Epicardial Fat Tissue Thickness İn Patients with Stage 4 and Stage 5 Chronic Kidney Disease
Authors: Ince Hasan , Yılmaz Zülfükar , Karabulut Aziz , Aydın Emre , Yıldırım Yaşar , Kara Ali Veysel , Kadiroğlu Ali Kemal , Aydın Fatma Yılmaz 
Published: October 2024

Abstract

Purpose: Chronic kidney disease (CKD) is a global public health issue, often associated with high mortality and morbidity, especially due to cardiovascular diseases. Epicardial adipose tissue (EAT), the visceral fat surrounding the heart, has been recognized as a significant factor in cardiovascular risk. This study aims to assess the relationship between EAT thickness and body composition in patients with stage 4 and 5 CKD usingbioelectrical impedance analysis (BIA).
Methods: The study included 80 patients with stage 4 and 5 CKD who were not on dialysis. Body composition was measured using BIA, and EAT thickness was assessed using transthoracic echocardiography. Exclusion criteria included heart failure, morbid obesity, pregnancy, and pacemaker use, among others. Various demographic, clinical, and biochemical parameters were also recorded.
Results: Patients with stage 5 CKD had significantly higher EAT thickness compared to stage 4 CKD patients. EAT thickness showed a positive correlation with age, BMI, blood pressure, C-reactive protein (CRP), and triglyceride levels, and a negative correlation with albumin and HDL levels. Multivariate analysis revealed that increased systolic blood pressure, fat tissue mass, low HDL, and low albumin were independent predictors of EAT thickness.
Conclusion: EAT thickness is higher in advanced CKD patients and is associated with several cardiovascular risk factors. Measuring EAT thickness in CKD patients using non-invasive methods like echocardiography could be valuable in predicting cardiovascular risks. Addressing the factors that contribute to increased EAT thickness may improve clinical outcomes for CKD patients.
Fuzzy Analytical Hierarchy Process Based Complexity Index for Investment Casting
Authors: Maheta Nikunj , Sata Amit , Shukla Minal , Jarboui S. , Mobarsa Divya 
Published: October 2024

Abstract

The investment casting process is widely acknowledged as a manufacturing process for producing industrial castings with precise tolerances, excellent surface finish, and intricate designs across various sectors. However, the complexity of these castings has been predominantly described qualitatively, and a quantitative measurement of complexity has not been extensively studied. This research focuses on identifying specific characteristics that contribute to the complexity of manufacturing investment castings and developing a quantitative measurement of complexity. An in-person industrial survey conducted in a significant investment casting cluster, which accounts for approximately 25% of India's investment casting foundries, identified 212 meta-attributes that correspond to 52 attributes, further delineating 19 elements and 3 factors: geometry, features, and manufacturability. The Fuzzy Analytical Hierarchy Process (FAHP) was employed to ascertain the relative weights of the factors, components, attributes, and meta-attributes utilized in calculating the complexity index. The established complexity index was evaluated through various industrial castings and shown its utility for designers in using the investment casting technique for the production of intricate castings. This research corresponds with the principles of Design for Manufacturing (DFM) and Design for Investment Casting (DFIC) by offering a quantitative assessment of complexity in the investment casting process.
Computer Vision and Reinforcement Learning in Traffic Systems: Prioritizing Efficient Urban Mobility and Gridlock Prevention
Authors: Eeswaran Aadithyaa , Duvvuri Sravya , Naidu Bhavana , N. Lalithamani 
Published: October 2024

Abstract

Traffic monitoring
and traffic light management are crucial for city planning, effective policy-making,
reducing travel times, pollution control, and quality of life. Solutions to this problem
must be reliable, cost-efficient, and extremely scalable, to support the mega-cities of the
modern world. This work proposes an end-to-end framework and implementation for
traffic monitoring and traffic light synchronization and control. The proposed framework supports
monitoring the number of traffic vehicles at each traffic signal, emergency vehicle detection and classification, emergency routing and an analytics dashboard to visualize traffic flow. An ML-driven traffic light control algorithm is proposed to synchronize traffic lights and reduce waiting times, especially in scenarios
where it may be difficult to model the traffic system mathematically.
Enhancing Lung Cancer Detection: Segmentation and Classification of CT Images Using U- Net with Pretrained Backbones and BIR Model
Authors: Golkarieh Alireza , Bayrami Farhad , Lashaki Reza Ahmadi 
Published: October 2024

Abstract

Objective:
The aim of this study is to improve the segmentation and classification of lung regions from CT images of 78 chest cancer patients, focusing on enhancing the accuracy in detecting cancerous tissues. This research evaluates the performance of different U-Net backbone models (VGG16, ResNet50, Xception) in segmentation and employs a novel BIR-enhanced CNN model for classifying lung injury severity.
Methodology:
A U-Net model with three different backbones—VGG16, ResNet50, and Xception—was utilized for lung region segmentation. Preprocessing techniques such as CLAHE (Contrast Limited Adaptive Histogram Equalization) were applied to enhance contrast and image quality, followed by resizing to 128x128 pixels and normalization. For classification, a BIR-enhanced CNN model was employed to assess lung injury severity. The models were evaluated across multiple metrics, including accuracy, recall, F1 score, Intersection over Union (IoU), and Dice coefficient.
Results:
Among the models, VGG16 achieved the highest performance, with an accuracy of 0.9836 ± 0.0177, recall of 0.9696 ± 0.0737, F1 score of 0.9363 ± 0.0832, IoU of 0.8893 ± 0.1178, and Dice coefficient of 0.9363 ± 0.0832 in segmentation tasks. For the classification of lung injury severity, the BIR-enhanced CNN model, also utilizing VGG16, achieved a classification accuracy of 97.83%.
Conclusion:
This study demonstrates the significant impact of preprocessing on segmentation and classification performance. The U-Net model with the VGG16 backbone not only provides highly accurate segmentation of lung regions but also highlights cancerous areas effectively. The integration of the BIR model further improves classification accuracy, indicating that this combination offers an effective approach for lung cancer detection and diagnosis.
ATIC, A Potential Drug Target for Constipation: Evidence from Mendelian Randomization Integrating GWAS and eQTL Data
Authors: Shen Pan , Luo Qinghua , Ding Yifan , Xu Zhiqiang , Ge Wei , Liao Wu , Li Chaofeng , Gao Xinhai , Wang Jiawen , Xie Wenli , Zhang Leichang 
Published: October 2024

Abstract

Introduction: Constipation is a prevalent chronic gastrointestinal disorder. Current treatments primarily focus on short-term relief, leaving significant challenges for achieving long-term improvement. Therefore, identifying novel therapeutic targets is crucial.
Material and methods: We employed Mendelian randomization (MR) analysis, using cis-expression quantitative trait locus (cis-eQTL) data from the eQTLGen Consortium and summary data for constipation (ncase = 15,902; ncontrol = 395,721). We performed a replication of the MR analysis using data from the FinnGen study (ncase = 44,590; ncontrol = 409,143). Multiple methods, including Bayesian colocalization, Summary-data Mendelian Randomization (SMR) analysis, heterogeneity testing, Heterogeneity in Dependent Instrument (HEIDI), and horizontal pleiotropy testing, were applied to rigorously validate these findings and assess the likelihood of shared causal variants.
Results: The primary MR analysis identified eight druggable genes significantly associated with constipation risk (P < 0.05/2534, FDR corrected), with ATIC and PPIL1 demonstrating consistent associations in replication cohorts (P < 0.05). Sensitivity analyses confirmed the robustness of these findings, with no indication of heterogeneity. Co-localization analysis suggested that ATIC is likely to share causal variants with constipation susceptibility (PP.H4 = 0.91). Subsequent SMR analysis further confirmed a significant association between ATIC and constipation (PSMR = 8.76 × 10-4), while passing the HEIDI test (PHEIDI = 3.46 × 10-1). ATIC was linked to a lower risk of constipation (OR = 0.855, 95% CI = 0.811 – 0.902, PFDR = 1.86 × 10-5).
Conclusions: Our study identified the ATIC gene as a highly promising drug target for reducing the risk of constipation, laying the groundwork for future drug development.
A Comparative Study of Low Power MOS Design Approaches and Impact on VDTA Performance
Authors: SEKRETER Pelin DOĞAN , UYGUR Atilla , ALÇI Mustafa 
Published: October 2024

Abstract

This paper investigates the Voltage Differencing Transconductance Amplifier (VDTA), a universal active element, through the use of new approaches, such as Bulk-Driven MOS (BDMOS), Floating Gate MOS (FGMOS), and Dynamic Threshold MOS (DTMOS) transistors, which perform well at low supply voltages. The Voltage Differencing Transconductance Amplifier (VDTA) is simulated to illustrate these methods. The utilized VDTA’s variant operates at a low supply voltage of ±0.2 V, with a remarkably low power dissipation of approximately 6 nW. This efficiency is primarily due to the inherent low-voltage characteristics of the MOS transistor configurations employed. The functionality and effectiveness of the utilized VDTA designs are verified through comprehensive simulations using LTSPICE, where the VDTA is applied in basic filter circuits to test its performance. The results confirm the viability of these advanced transistor techniques in enhancing the performance of VDTAs in low-power analog circuits, offering valuable contributions to the field of low-voltage, low-power electronic design.
Exploring the thermoeconomic of converting 4-stroke combustion motorcycles to 2-stroke expansion models
Authors: Deymi-Dashtebayaz Mahdi , Tayyeban Edris 
Published: October 2024

Abstract

The rise in population has led to an upsurge in the consumption of fossil fuels across various sectors. The transportation sector, as one of the important sectors in the consumption of fossil fuels, is a notable contributor to the production of pollutants and greenhouse gases. However, the use of energy sources is constrained by their inherent inconsistency. In this current research, the initial step involved identifying three prevalent motorcycle types in Tehran, the largest and most polluted city in Iran. Then, with changes in the mechanism and timing of fluid entry and exit into the cylinder, they changed from a 4-stroke combustion engine to a two-stroke expansion engine with compressed air fuel. Following the motorcycle modification, the study delved into a comprehensive examination. This included thermodynamic modelling, analysis of power and torque production, assessment of compressed air consumption, exploration of initial acceleration dynamics, and determination of the distance travelled. Furthermore, in the concluding phase, the research quantified the annual savings in pollutant production and reduced fuel consumption for Tehran. The calculated reductions stand at 15,750 kg for CO, 5,523 kg for HC, 231 kg for NOx, 114,996 kg for CO2, and a substantial 92,400 liters for fuel. These findings underscore the positive environmental impact and efficiency gains achieved through the modified engine system.