bluuebunny/crossref_metadata_2025_split huggingface dataset:
Energy poverty and household welfare: evidence in Togo
Authors: LITAABA-AKILA Djinta , Koriko Moubarak , EZIN Yéhomè Afi 
Published: October 2023

Abstract
This paper assesses the effects of energy poverty on household welfare in Togo. To achieve this objective, this paper uses firstly the logit method to assess the determinants of energy poverty and the two least squares method to analyze its impact on household welfare and data from the Harmonized Household Living Conditions Survey (EHCVM, 2018). Our results show that energy poverty reduces household welfare: a 1% increase in energy poverty is associated with a 0.27% decrease in welfare. This general finding is robust to other ways of measuring well-being and energy poverty. Furthermore, we find that educational attainment is an important channel through which energy poverty reduces well-being. Our results underscore the need for policy decisions to address energy poverty, especially when it comes to improving household welfare.
JEL: I31, I32, O13, Q41
Ostracods response to environmental anthropogenic disruption in a neotropical karstic lake in southern Mexico
Authors: Chávez-Lara Claudia Magali , Caballero Margarita , Prado Blanca 
Published: October 2023

Abstract
Ostracods are highly sensitive to environmental changes and represent a powerful tool to assess anthropogenic impact. In this work we present ostracod and ephippium records of Lake San Lorenzo sediments dating from 1956 to 2013. Diatom and geochemical data presented by Caballero et al. (2022) are also considered for the discussion. The ostracod assemblage consists of Darwinula stevensoni, Cypridopsis okeechobei, Cytheridella ilosvayi and two unidentified species. We suggest that one of them could be a variant of C. okeechobei with different ornamentation developed as a response of increasing salinity caused by anthropogenic catchment disruption around 1981. The second unidentified species (Unknown 2) could belong to the Subfamily Cypridopsinae Kaufmann, 1900. This species rapidly adapted to the new environmental conditions of enhanced salinity, high nutrients level and variable oxygen, whereas the other ostracods were absent in the sediments dating from 1982 to 1992. Overall, D. stevensoni, C. okeechobei and C. ilosvayi are benthic species that occur in shallow waters or littoral zones with rich vegetation. However, we suggest C. ilosvayi was the most sensitive to oxygen variations as it is only present in the record in 1978. The lack of ostracod preservation in the sedimentary record, especially during 1995–2013 could be result of anoxic lake bottom conditions or dissolution caused by high organic matter content during periods of anthropogenic catchment disturbance, from 1970–1985 and from 1990–2000. Finally, we suggest analysis of C. okeechobei in natural populations and laboratory studies need to be assessed to determine if this species develops ornamentation changes when salinity conditions increase. Moreover, analysis and description of soft parts and live specimens of Unknown 2 will be necessary to determine this species.
Gated Fusion Adaptive Graph Neural Network for Urban Road Traffic Flow Prediction
Authors: Xiong Liyan , Yuan Xinhua , Hu Zhuyi , Huang Xiaohui , Huang Peng 
Published: October 2023

Abstract
Accurate prediction of traffic flow plays an important role in maintaining traffic order and traffic safety, which is a key task in the application of intelligent transportation systems (ITS). However, the urban road network has complex dynamic spatial correlation and nonlinear temporal correlation, and achieving accurate traffic flow prediction is a highly challenging task. Traditional methods use sensors deployed on roads to construct the spatial structure of the road network and capture spatial information by graph convolution. However, they ignore that the spatial correlation between nodes is dynamically changing, and using a fixed adjacency matrix cannot reflect the real road spatial structure. To overcome these limitations, this paper proposes a new spatial-temporal deep learning model: gated fusion adaptive graph neural network (GFAGNN). GFAGNN first extracts long-term dependencies on raw data through stacking expansion causal convolution, Then the spatial features of the dynamics are learned by adaptive graph attention network and adaptive graph convolutional network respectively, Finally the fused information is passed through a lightweight channel attention to extract temporal features. The experimental results on two public data sets show that our model can effectively capture the spatiotemporal correlation in traffic flow prediction. Compared with GWNET-conv model on METR-LA dataset, the three indexes in the 60-minute task prediction improved by 2.27%,2.06% and 2.13%, respectively.
Fabrication of graphene oxide /Cadmium sulphide quantum dots electrochemical sensor for sensitive detection of dopamine and tyrosine
Authors: Mohammed Hasheena , Annapragada Ratnamala , Mohammed Noorjahaan , Gavini Deepthi Reddy , Trukhanov Alex , Trukhanov Sergei 
Published: October 2023

Abstract
The present study we report the fabrication of GO-CdS quantum dots modified on glassy carbon electrode (GO-CdS/GCE) and is applied to monitor the levels of dopamine and tyrosine using electrochemical signal enhancement. GO-CdS quantum dots were prepared by using ultrasonication method followed by hydrothermal synthesis in a facile approach. The prepared quantum dots were analyzed by XRD, SEM, Raman spectroscopy, XPS, TEM and pore size distribution methods. The fabricated sensor performance was investigated by Electrochemical impedance spectroscopy (EIS). Cyclic voltammetry(CV) and Chronoamperometric methods. The results showed excellent selectivity and sensitivity with limit of detection of dopamine was observed to be 24nM.
Hitchhiking actions: Erroneous saccade co-execution is independent of stimulus-response translation ease
Authors: Kürten Jens , Raettig Tim , Huestegge Lynn 
Published: October 2023

Abstract

Recent multiple action control studies have demonstrated difficulties with single-action (vs. dual-action) execution when accompanied by the requirement to inhibit a prepotent additional response. Such a dual-action performance benefit is typically characterized by frequent erroneous co-executions of the currently unwarranted response. Here, we investigated whether the frequency of inhibitory failures is affected by the ease of stimulus-response (S-R) translation. Participants switched between executing a single saccade, a single manual button press, and a saccadic-manual dual action on a trial-by-trial basis. We measured inhibitory failures in single action trials (e.g., saccades executed in single manual trials). Importantly, we employed three different stimulus modes that varied in S-R translation ease (peripheral square > central arrow > central shape). This hierarchy was reflected by increasing RT levels. Critically, however, the frequency of saccadic inhibitory failures was not affected by S-R translation ease. Our results rule out explanations related to capacity conflicts (between inhibitory control and S-R translation demands) as well as accounts related to the time available for the completion of inhibitory processes. Instead, the findings suggest an “action hitchhiker effect” based on enhanced activation of the oculomotor system, eventually yielding the execution of unwarranted, holistic dual-action compounds.
Translating GWAS Findings to Inform Drug Repositioning Strategies for COVID-19 Treatment
Authors: Tsai Ming-Ju , Jeong Sohyun , Yu Fangtang , Chen Ting-Fu , Li Peng-Hsuan , Juan Hsueh-Fen , Huang Jia-Hsin , Hsu Yi-Hsiang 
Published: October 2023

Abstract

We developed a computational framework that integrates Genome-Wide Association Studies (GWAS) and post-GWAS analyses, designed to facilitate drug repurposing for COVID-19 treatment. The comprehensive approach combines transcriptomic-wide associations, polygenic priority scoring, 3D genomics, viral-host protein-protein interactions, and small-molecule docking. Through GWAS, we identified nine druggable host genes associated with COVID-19 severity and SARS-CoV-2 infection, all of which show differential expression in COVID-19 patients. These genes include IFNAR1, IFNAR2, TYK2, IL10RB, CXCR6, CCR9, and OAS1. We performed an extensive molecular docking analysis of these targets using 553 small molecules derived from five therapeutically enriched categories, namely antibacterials, antivirals, antineoplastics, immunosuppressants, and anti-inflammatories. This analysis, which comprised over 20,000 individual docking analyses, enabled the identification of several promising drug candidates. All results are available via the DockCoV2 database (https://dockcov2.org/drugs/). The computational framework ultimately identified nine potential drug candidates: Peginterferon alfa-2b, Interferon alfa-2b, Interferon beta-1b, Ruxolitinib, Dactinomycin, Rolitetracycline, Irinotecan, Vinblastine, and Oritavancin. While its current focus is on COVID-19, our proposed computational framework can be applied more broadly to assist in drug repurposing efforts for a variety of diseases. Overall, this study underscores the potential of human genetic studies and the utility of a computational framework for drug repurposing in the context of COVID-19 treatment, providing a valuable resource for researchers in this field.
Mechanisms involved in the positive effects of high zinc on growth of Sedum alfredii
Authors: Gao Yuxiao , Yu Haiyue , Lin Haizhong , Lu Lingli 
Published: October 2023

Abstract

Background and Aims:Zinc (Zn) is an essential micronutrient for plant growth, and excessive Zn in the soil can inhibit normal plant growth, adversely affecting plant health.Sedum alfrediiHance, a native Zn/Cd hyperaccumulator plant species in China, has strong tolerance and ability to accumulate heavy metals. Previous studies have reported that Zn may have a positive effect onS. alfrediigrowth, rather than causing toxicity. Thus, exploring the growth-promoting mechanisms of high Zn concentrations onS. alfrediimay provide a theoretical reference for phytoremediation technologies.Methods:In this study, the hyperaccumulating ecotype (HE) and non-hyperaccumulating ecotype (NHE) ofS. alfrediias research subjects, we used metabolomics, metabolomics, and transcriptomics techniques to explore.Results:At 250 μM Zn, HE root biomass and Zn concentration in roots increased, while being reduced in NHE. Nano-XRF analysis showed that Zn was located in the cortical intercellular space in HE roots, whereas Zn was scattered in the root cells in NHE. The content of auxin in HE roots was increased by 1.26 times and the auxin oxidase activity decreased by 2.94 times after exposure to 100 μM Zn for 12 h, while NHE exhibited the opposite trend. Transcriptomic results showed that HE up-regulated genes were related to carbohydrate metabolism, nitrogen metabolism, carbon fixation, and tryptophan biosynthesis; down-regulated genes were related to lipid metabolism and linoleic acid metabolism.Conclusion:We further screened key gene resources, laying a foundation for the molecular mechanism underlying the positive effect of high Zn concentration on HE growth.
Monitoring root rot in flat-leaf parsley via machine vision by unsupervised multivariate analysis of morphometric and spectral parameters
Authors: Agarwal Avinash , Colwell Filipe de Jesus , Rogdriguez Julian Bello , Sommer Sarah , Galvis Viviana Andrea Correa , Hill Tom , Boonham Neil , Prashar Ankush 
Published: October 2023

Abstract
Use of vertical farms is increasing rapidly as it enables year-round crop production, made possible by fully controlled growing environments situated within supply chains. However, intensive planting and high relative humidity make such systems ideal for the proliferation of fungal pathogens. Thus, despite the use of bio-fungicides and enhanced biosecurity measures, contamination of crops does happen, leading to extensive crop loss, necessitating the use of high-throughput monitoring for early detection of infected plants. In the present study, progression of foliar symptoms caused by Pythium irregulare-induced root rot was monitored for flat-leaf parsley grown in an experimental hydroponic vertical farming setup. Structural and spectral changes in plant canopy were recorded non-invasively at regular intervals using a 3D multispectral scanner. Five morphometric and nine spectral features were selected, and different combinations of these features were subjected to multivariate data analysis via principal component analysis to identify temporal trends for early disease detection. Combining morphometric and spectral features enabled a clear segregation of healthy and diseased plants at 4–7 days post inoculation (DPI), whereas use of only morphometric or spectral features allowed this at 7–9 DPI. Minimal datasets combining the six most effective features also resulted in effective grouping of healthy and diseased plants at 4–7 DPI. This suggests that selectively combining morphometric and spectral features can enable accurate early identification of infected plants, thus creating the scope for improving high-throughput crop monitoring in vertical farms.
Socio-economic Determinants of Gender-based Violence [GBV]: SDG Analytics on the Global GBV Scenario With Special Reference to Technology Facilitated Gender-based Violence and Adolescent Birth Rates
Authors: Daniel Chandra Pauline , Block Adam , Knapp Kenneth , Immanuel Gifty 
Published: October 2023

Abstract
Gender-based violence (GBV) is a multifaceted problem. The most rapidly increasing modern form of violence is the intertwined epidemic of ‘Technology Facilitated GBV’ and sexual violence against women and girls. It was critical to understand the impact of technology and COVID-19 pandemic, on GBV exposures for the year 2021. A retrospective cross-sectional research design was adopted using linear regression analysis (univariate and multivariate) on SDG 5.2 with a set of global indicators, to elicit the socioeconomic determinants of GBV. Phase-I results exposed the top-four socio-economic determinants of GBV: Gender Development Index, Gross National Income, Human Development Index and Gender Inequality Index. Phase-II results indicated that the unethical use of technology was highest among the urban male internet users. The ICT-progress increased the incidence of TF GBV due to unethical use of technology. Phase-III:A country-specific case study on Mexico revealed the efficiency of utilizing an inter-agency approach, advocacy and legislation, to control digital violence and adolescent birth-rates. Phase-IV: Predictive analytics applied on SDG 5.2 led to the development of a GBV prevention business model and TF GBV disruptor strategy. In conclusion, women and girls, the key populations and female population living with disabilities, were most vulnerable and disproportionately impacted by GBV. The ICT progress was found to increase vulnerabilities and impact global health and productivity. There is an immediate need to empower women and girls, enhance economic autonomy, integrate holistic interagency interventions, mitigate cybersecurity threats with a sustained monitoring effort to eliminate digital and sexual violence against women and girls, by 2030.
Light and shadow of Watch-and-wait strategy in rectal cancer. Oncological result, clinical outcomes after salvage surgery and cost effectiveness analysis. Experience of a single centre.
Authors: Ferri Valentina , Vicente Emilio , Quijano Yolanda , Duran Hipolito , Diaz Eduardo , Fabra Isabel , Malave Luis , Ruiz Pablo , Costantini Giulia , Pizzuti Giada , Cubillo Antonio , Rubio Maria Carmen , Canamaques Lina Garcia , Alfonsel Javier Nunez , Caruso Riccardo 
Published: October 2023

Abstract
Background: Watch and wait strategy is a potential option for patients with rectal cancer that obtain a complete clinic response after neoadjuvant therapy.
The aim of this study is to analyse long-term oncological outcomes and perform a cost-effectiveness analysis in patients undergoing Watch and wait strategy for rectal cancer.
Material and Methods: Data of patients treated with Watch and Wait strategy were prospectively collected from January 2015 to January 2020. A control group was created, matched 1:1 from a pool of 480 patients undergoing total mesorectal excision . An independent company carried out the financial analysis.
Clinical and oncological outcomes were analysed in both groups. Outcome parameters included surgical and follow-up costs, quality-adjusted life years (QALYs) and the incremental cost per QALY gained or the incremental cost-effectiveness ratio (ICER).
Results: 40 patients were included in the WW group and 40 patients in the surgical group. During a median follow-up period of 36 months, metastasis-free survival (MFS) and overall survival (OS) were similar in the two groups. In the WW group, 9 (22%)local regrowthwere detected in the first two years. The permanent stoma rate was slightly higher after salvage surgery in the WW group compared to the upfront surgical group (48.5% vs 20%, p<0.01). The cost-effectiveness analysis was slightly better for the WW group especially in low rectal cancer with respect to medium-high rectal cancer (ICER= -108642.1 vs ICER= -42423).
Conclusions: WW strategy in locally advanced rectal cancer offers similar oncological outcomes respect to the surgical group and excellent result in quality of life and cost, especially in low rectal cancer. Nonetheless salvage surgery may by characterized by desfavorable functional outcome. Therefore a careful selection of patients is mandatory.
Development and validation of a nomogram for surgical site infection after gastrectomy in gastric cancer
Authors: Peng Yiyun , Yang Guoyuan , Huang Yalong , Lin Hao , Ma Xiaolong , Ma Yuqi , Ma Yuntao 
Published: October 2023

Abstract
Background
One of the most frequent problems following surgery for stomach cancer is surgical site infection (SSI). Still, a major difficulty is figuring out how to anticipate it and prevent it. The aim of this study was to investigate the risk factors for SSI after gastric cancer surgery and to develop an individualized predictive nomogram.
Method
Data were collected from 763 gastric cancer patients after surgery in Gansu Provincial People's Hospital and the First Hospital of Lanzhou University (601 cases in the training cohort and 162 cases in the validation cohort). The risk variables of postoperative surgical site infection in gastric cancer were identified using logistic regression, and a nomogram was created.
Result
Factor analysis showed that age (P = 0.002), operation time (P < 0.001), operation method (P < 0.001), total gastrectomy (P = 0.013), and tumor diameter (P = 0.017) were independent predictors of SSI. The area under the curve of the nomogram training cohort and validation cohort constructed based on the above factors were 0.834 and 0.798, respectively. calibration plots in the validation cohort based on the five predictors showed good agreement between the actual probability and the probability predicted by the column line graph. The model provided good fit and calibration in decision curve analysis with positive net benefit.
Conclusions
This nomogram has good predictive ability for postoperative SSI in gastric cancer. It can serve as a guide for choosing surgical techniques and perioperative care, and it can offer patients tailored and accurate care.
A feed direction cutting force prediction model and analysis for ceramic matrix composites C/SiC based on rotary ultrasonic profile milling
Authors: Amin Muhammad , Saleem Waqas , Rathore Muhammad Faisal , Li Qilin , Israr Asif 
Published: October 2023

Abstract
Ceramic matrix composites have immense applications in the aerospace, aircraft, automobile, and defense industries. Carbon fiber-reinforced ceramic matrix composites (C/SiC) are used for critical applications due to their superior properties. However, these materials have stringent heterogeneity, anisotropy, and varying thermal properties that affect machining quality and process efficiency. Developing a cutting force prediction model and analyzing machining parameters is essential. In this study, a mechanistic-based feed direction cutting force prediction model for rotary ultrasonic profile milling of C/SiC composites is developed and validated experimentally. The practical and simulated results closely matched each other. The mean error and the standard deviation were recorded at 1.358% and 6.003, respectively. The parametric sensitivity analysis showed that the cutting force decreased with increased cutting speed, whereas the same increased with increased feed rate and depth of cut. The proposed cutting force for rotary ultrasonic profile milling of C/SiC composites can be applied to predict cutting forces and optimize the machining process.
Analysis and Simulation of Improved Seismic Data using Adaptive Processing by National Data Centre Iraq
Authors: Shamkhi Yasameen Hameed , Mahmood Mohammed Shakir , Salman Mohammed Oudah 
Published: October 2023

Abstract

All States Parties have convenient access to all International Monitoring System (IMS) data, International Data Center (IDC) products, and all applications and scientific studies programs used in the IDC of the Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO). Integrating machine learning with seismic exploration is crucial for obtaining accurate and essential information about subsurface formations' stratigraphic structure, lithology, and porosity. However, there still needs to be a clear understanding of which algorithm produces the most accurate earthquake detection. Consequently, this study aims to perform a comparative analysis of the effectiveness of LSTM, CNN, MLP, and SVM algorithms in earthquake detection. This study has used various earthquake datasets from Arabian Sea earthquake seismic event on 26 October 2022 at 23:00:07 which was detected by two IMS monitoring technologies and non-IMS which is implemented in this work is the inclusion of analysis data using HA1 IMS (hydroacoustic station) with event location integration with seismic data for stations near that seismic event. As well as seismic event which was also studied and evaluated in Turkey, detected by IMS and non-IMS stations in IRIS on 23 November 2022 at 01:08:15 and analyzed the performance of each algorithm on these datasets by applying numerous performance metrics related to accuracy, precision, recall, F1-score, and others. The output performance results have demonstrated that the CNN network outperforms all other algorithms. In conclusion, this study provides a comprehensive evaluation of the existing literature on digital signal processing techniques employed in the analysis and detection of seismic waves.