bluuebunny/crossref_metadata_2025_split huggingface dataset:
The low efficiency of the Fall and early Winter cooling does not balance the overall Sea Surface Temperature warming in the Mediterranean Cilician Basin
Authors: Deliceirmak Cagri , Salihoglu Ilkay 
Published: September 2024

Abstract

The continuous and significant rise of the sea surface temperature (SST) in the Eastern Mediterranean Sea over the past three decades is not uniform both in temporal and spatial scales. This study aimed to investigate the temporal evolution of the SST in the Cilician Basin on monthly to decadal scales within two partially overlapping periods (1983–2023 and 1995–2023) by utilising three datasets obtained with different methodologies. “The High-Resolution Daily Optimum Interpolation SST” (OISST V2.1), the fifth generation reanalysis (ERA5) “monthly averaged data on single-level”, and the available in-situ observations (1995–2023) were utilised to compare all datasets and analyse the temporal evolution of the SST in the region. The annual mean SST results of the OISST (0.043 ℃/year) and In-Situ (0.042 ℃/year) datasets were almost identical. In both datasets, the highest warming is recorded in fall, and June is recorded with a decreasing trend. In the ERA5 dataset, the SST is increased by 0.054 ℃/year, and the highest warming is recorded in summer. Continuous and consecutive positive SST anomalies have been observed since 2013. The highest three annual mean SSTs since 1983 were recorded in the last six years, and 2018 was the warmest year in the studied period. On the decadal scale, a cooling of the SST was recorded between 1983 and 1993, and the highest warming occurred in the following decade, between 1993 and 2003. The rise of the In-Situ SST in November and December was drastic, especially in 2017 and 2018. This excessive warming (low-efficient cooling) in the fall/early winter is of particular concern as it may affect the physical/biochemical characteristics of the water column, hence the circulation and coastal ecosystem.
Research on Path Smoothing Optimization based on Improved RRT-Connect Algorithm and third-order Bezier curve
Authors: Zheng Zhu-an , Xie Shuangjian , Ye Zimo , Zheng Xiangyu , Yu Zhiwei 
Published: September 2024

Abstract

Targeting the deficiencies of the original RRT-Connect path planning algorithm in dealing with obstacle avoidance, planning efficiency and path smoothing in static environments, this paper proposes an improved path optimization method that fuses the RRT-Connect path planning algorithm with the Greedy search strategy and adopts the third-order Bezier curve for path smoothing. First, the Greedy strategy is integrated into the path planning process of the original RRT-Connect algorithm to guide the search direction, make the path search goal-oriented, reduce the time of path planning, and improve the efficiency. Second, the generated paths are smoothed with third-order Bezier curves, and the control point selection strategy is used to set reasonable values of sampling variables to reduce the complexity of path smoothing of RRT-Connect algorithm, and to reduce the phenomena such as sharp turns and folding back. Then, the smooth path with continuous curvature is generated to ensure that the intelligent vehicle can perform path tracking. Finally, the algorithm before and after optimization is simulated and analyzed to check the performance of the algorithm and view the effect of path smoothing after optimization. The results show that the global path length of the optimized algorithm is reduced by an average of 14.41% and the path planning time is shortened by an average of 32.78% compared with the original RRT-Connect algorithm in complex environments. The method proposed in this paper is efficient, with short planning paths and smoother generated paths.
Neurocognitive function in pediatric bipolar disorder and disruptive mood dysregulation disorder and at risk for bipolar disorder
Authors: Sivri Isranur Yenen , Menderes Ayşe Selma Y. , Can Kazım Cihan , Beğli Seda , Çöp Esra , Taş Demet 
Published: September 2024

Abstract

Objective: Neurocognitive function studies in Pediatric Bipolar Disorder (BPD) and Disruptive Mood Dysregulation Disorder (DMDD) are limited, despite substantial literature on their clinical presentation. This study aims to comparatively evaluate the neuropsychological characteristics of children and adolescents diagnosed with BPD, DMDD, and those at risk for bipolar disorder.
Method: We administered the neurocognitive battery to 120 participants aged 8-18 with BPD (n = 30), at-risk for bipolar disorder (n= 25), DMDD (n = 36) and healthy controls (n = 29). The neurocognitive battery included the Continuous Performance Test, Wisconsin Card Sorting Test, Stroop Color and Word Test, Trait Making Test A and B, California Verbal Learning Test-Child version.
Results: There were significant impairments in sustained attention, processing speed, cognitive flexibility, response prevention, verbal memory, and working memory in the BPD and DMDD groups, and in sustained attention in the at-risk group compared with controls. Impairments in verbal memory remained in the BPD and DMDD groups after controlling for current IQ score. After excluding ADHD diagnoses, deficits in sustained attention persisted in the at-risk group versus controls.
Conclusion: This study is the first to directly compare the neurocognitive profiles of children and adolescents with DMDD, BPD, and at risk for BD. The study highlights significant neurocognitive impairments within BPD and DMDD. The persistence of sustained attention deficits after excluding ADHD diagnoses suggests that sustained attention may be an endophenotype in the at-risk group.
Partial white mold resistance in a Brazilian-adapted common bean panel
Authors: Silva Givanildo Rodrigues , Gilio Thiago Alexandre Santana , Gonçalves-Vidigal Maria Celeste , Araújo Kelly Lana , Barelli Marco Antonio Aparecido , Souza Lorenna Lopes , Neves Leonarda Grillo , Pastor-Corrales Marcial 
Published: September 2024

Abstract

The pathogen Sclerotinia sclerotiorum (Lib.) de Bary is a fungus that causes white mold (WM) in many crops, and it is one of the greatest phytosanitary problems that compromises the productivity and quality of common bean (Phaseolus vulgaris L.). This study aimed to characterize a panel composed of common bean lines (BLs) from Brazilian farmers with WM resistance using two methods/tests under controlled conditions. The “straw test” (ST - Terán et al., 2006) and “seedling straw test” (SST - Arkwazee & Myers, 2017) were used to screen the panel. The disease score (DS) and relative disease progress (RDP) were calculated from consecutive evaluations to obtain the area under the disease progress curve (AUDPC). In addition, the phenotypic means were used to identify genomic regions associated with the WM reaction using the genome-wide association study (GWAS) approach. In total, fifteen accessions (eleven Mesoamerican and four Andean) were selected showing high to moderate resistance, and three regions were identified on chromosomes Pv01, Pv02 and Pv03, coinciding with previously reported quantitative trait loci (QTLs), additionally, twelve genes were indicated for validation. We identified putative regions and genes contributing to physiological resistance to WM in a well-adapted common bean panel. The regions indicated in this panel that are adapted to the Brazilian climate may be important in common bean breeding programs.
Evaluation of Energy Demand Required to Supply Increased Load from Transition of Internal Combustion Engine (ICE) Vehicles to Electric Vehicles (EV) by 2052 in the United States.
Authors: Sankhwar Pravin 
Published: September 2024

Abstract

With the rising need to transition from fossil fuel consumption to renewables, the transportation industry is foreseeing large-scale adoption of electric vehicles (EV). From various studies, oil will last until or around 2052 [1] [2]. Utilizing the US Federal Highway Administration (FHWA) published data on number of registered vehicles by each state, an estimated quantity of vehicles by type classified as automobiles, buses, trucks, and motorcycles was obtained. When average mileage of each vehicle was factored in, to project generation required, the need of preparation for future energy generation using renewable energy was presented. A roof-top solar photovoltaic system is easy to install on unoccupied roof space for US house owners. Obtaining the capacity of such roof-top solar PV system acts as a good decision-making criterion for both house owners and developers. How well EV cars performed against internal combustion engine (ICE) cars is another positive indication for this transition. With limitations on battery size and thus increased frequency for drivers to return for charging installation, additional fast chargers (level 3) at existing gasoline stations is an option that requires further assessment.
Effects of yoga on mental health in university students: Protocol for a systematic review
Authors: Lemes Uitairany do Prado , Borges Cezimar Correia , Silva Gusthavo Ribeiro , Chaveiro Neuma 
Published: September 2024

Abstract

Background
College students are more likely to develop symptoms of mental disorders and lose quality of life due to the competitive environment and high demands for performance. The consequences of high levels of stress, symptoms of anxiety, depression and low quality of life during undergraduate studies can lead to increased consumption of psychotropic medications, substance abuse and low academic and professional performance. A growing body of evidence points to non-pharmacological strategies as a possibility to address this problem, and the practice of Yoga is one of the possibilities. Therefore, the objective of this review is to verify the effects of the practice of Yoga on the variables anxiety, perceived stress and quality of life in college students.
Design:
Systematic review and possibility of meta-analysis.
Methods
The study will be conducted by two independent reviewers on the basis of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) rules. Searches will be performed in the PEDro, LILACS, Web of Science, and PUBMED search engines. The searches begin in the second half of 2024, starting in September. Studies published between 2014 and 2024 with an experimental design in English, Spanish, or Portuguese will be included. The methodological quality assessment will be performed via the Revised Tool for Risk of Bias in Randomized Trials (RoB 2 tool). If possible on the basis of the results found, a random-effects meta-analysis by DerSimonian and Laird will be applied to calculate pooled relative risks for binary data and pooled weighted mean differences or standardized mean differences for continuous data.
Discussion
We hope that this review contributes to the discussion and creation of intra-academic actions to promote a healthier and more productive university environment.
Systematic review registration:
PROSPERO, CRD42024506075.
Traditional knowledge on living fences in Andean linear plantations of southwestern Colombia
Authors: Fernández German Marino Rivera , Delgado-Vargas Iván Andrés 
Published: September 2024

Abstract

Linear plantations are a common practice in several countries as living fences, delimiting agroecosystems. They comprise multipurpose perennial woods that provide assets, protection, and supply services. However, not much is known about some traditional communities like the Andean region, including the composition of species and the social and ecological benefits of the species used. This study aimed to analyze the traditional knowledge about living fences systems including the species composition and their uses in rural communities of Alto Casanare, Colombia. Field measurements, interviews, guided tours, and species sampling were carried out to describe traditional practices and forestry species. The use values were estimated through the coefficient of importance of the species. A total of 26 farms were found, covering an area of 9283.3 m. In these systems 30 species were identified, belonging to 23 families. Alnus acuminata was the most representative species, presenting multipurpose uses for both economic and ecological purposes. The transmission of knowledge about the used species is very heterogeneous among the farmers. Exchanging this information would increase the species versatility, and the socioeconomic aspects of these communities, increasing the agrobiodiversity and ecological services provided by living fences. The description of the transmission of knowledge provides a better understanding of the relationship of these communities with the natural resources, pointing out creative approaches to achieve local environment conservation in these agroecosystems and promoting socioeconomic development.
miR-Cabiner: A universal microRNA sensing platform based on self- stacking cascaded bicyclic DNA circuit-mediated CRISPR/Cas12a
Authors: Deng Ruijia , Sheng Jing , Xie Zuowei , Yang Hongzhao , Yang Sha , Xie Shuang , Tang Xiaoqi , Zhao Shuang , Dong Haohao , Chen Ming , Chang Kai 
Published: September 2024

Abstract

Background
CRISPR/Cas12a-based diagnostics enable great potential in the sensing nucleic acid, but its application was limited by the sequence dependence.
Results
A platform termed miR-Cabiner (a universal miRNA sensing platform based on self-stacking cascaded bicyclic DNA circuit-mediated CRISPR/Cas12a) is demonstrated that is sensitive and universal for analyzing miRNAs. This platform combines catalytic hairpin assembly (CHA) and hybrid chain reaction (HCR) into a unified circuit and finally cascaded to CRISPR/Cas12a. Compared to the CHA-Cas12a and HCR-Cas12a systems, miR-Cabiner exhibits a significantly higher reaction rate. Panels of miRNAs (miR-130a, miR-10b, miR-21, miR-1285), which are associated with diagnosis, staging, and prognosis of breast cancer were designed to demonstrate the universality of miR-Cabine. Four miRNAs can be detected to fm-level by simply tunning the sequence in CHA components. Additionally, miRNA panel analysis also shows high accuracy in practical sample.
Conclusion
This universally applicable platform for detecting miRNA may serve as an excellent tool for clinical diagnosis.
Arduino Ear: A Low-Cost IoT System for Real-Time Environmental Monitoring and Protection
Authors: Barath M.K , sagayaraj R 
Published: September 2024

Abstract

Acoustic sources offer a valuable but underutilized reservoir of environmental data. This paper explores the use of Arduino-based sensors to map noise pollution and monitor ecosystems. The system utilizes an ATmega328P chip, ESP8266 Wi-Fi module, and sound sensor to capture and transmit sound data. By analysing variations in sound intensity, the system can detect potential threats like illegal sand mining. The data can be uploaded to cloud platforms for visualization and analysis, enabling authorities to take swift action. This project contributes to creating a sustainable environment by promoting acoustic monitoring and ecosystem protection.
Skyrmion nanodomains in ferroelectric-antiferroelectric solid solutions
Authors: Wen Zheng , Zheng Weijie , Ma Xingyue , Pang Zhentao , Ren Yifeng , Chen Hongying , Xu Jibo , Zheng Chunyan , liu jianyi , Liu Xiaohui , Deng Yu , Nie Yuefeng , Wu Di , Bellaiche L. , Yang Yurong 
Published: September 2024

Abstract

Polar skyrmions have demonstrated rich physics and exotic properties for developing novel functionalities in the next-generation nanoelectronics. However, to date, skyrmion nanodomains exist only in a few material systems, such as ferroelectric/dielectric superlattices, free-stranding PbTiO3/SrTiO3 epitaxial bilayers, and ultrathin Pb(Zr,Ti)O3/SrTiO3/Pb(Zr,Ti)O3 sandwiches. These heterostructures are fabricated with elaborately designed boundary conditions to meet the delicate energy balance for stabilizing the topological phases. This critical requirement limits broad applications of skyrmions in electronic devices. Here, we show widespread skyrmion nanodomains in ferroelectric-antiferroelectric solid solutions, which are composed of ferroelectric PbTiO3 and one of antiferroelectric PbSnO3 [Pb(Ti1-x,Snx)O3], PbHfO3 [Pb(Ti1-x,Hfx)O3], and PbZrO3 [Pb(Ti1-x,Zrx)O3], respectively. The skyrmionic textures are formed by engineering the dipole-dipole and antiferrodistortive-dipole couplings in the competition between ferroelectric and antiferroelectric polar orderings, which allows the stabilization of topological phases in both bulk and film forms. A phase diagram is built for ceramics of the three series of solid solutions, revealing composition-dependent domain configurations and stabilization regions of the skyrmion nanodomains. In addition, the non-trivial domains also exhibit improved switching characters, reversible writing/erasure, and long-term retention for electrical manipulation of polar configurations. These findings open a new avenue for the investigation and exploitation of polar skyrmions in ferroelectric-based materials, providing opportunities in topological electronics.
Electrifying Industrial Hydrogen Peroxide Production via Interfacial Molecular Mediation
Authors: Aziz Michael , Xi Dawei , Wu Yuheng , Li Yuli , Liu Richard 
Published: September 2024

Abstract

Hydrogen peroxide (H2O2) is predominantly produced via the centralized thermocatalytic hydrogenation-anthraquinone oxidation (t-AO) process, a conventional nonaqueous method. Electrifying such nonaqueous processes with improved selectivity remains a significant challenge. Here, we present a multi-phase electrochemical anthraquinone autoxidation (e-AO) system that leverages an interfacial hydrogen atom transfer reaction facilitated by a heterogeneous molecular catalytic process. This design enables aqueous electrochemical reactions with over 97% efficiency at high current densities (> 200 mA cm−2), using only carbon electrodes. The aqueous-nonaqueous interfacial hydrogen atom transfer, operating with nearly 100% selectivity through a quinhydrone intermediate, eliminates waste caused by over-reduction of anthraquinones in conventional t-AO processes. Our approach combines the benefits of aqueous electrochemistry with those of the traditional t-AO process while addressing issues like unwanted electrolyte migration in aqueous systems and anthraquinone over-reduction in t-AO. This strategy enables the continuous production of low-cost, high-purity H2O2 solutions (> 10 wt.%) and promotes the electrification and decentralization of nonaqueous chemical processes.
Outbreaks, Metastases and Homomorphisms: Phylogenetic Inference of Migration Histories of Heterogeneous Populations under Evolutionary and Structural Constraints
Authors: Kuzmin Kiril , Schmidt Henri , Snir Sagi , Raphael Benjamin , Skums Pavel 
Published: September 2024

Abstract

Many human diseases, including viral infections and cancers, are driven by the evolutionary dynamics of heterogeneous populations of genomic variants. A major type of evolutionary behavior of these populations is migration including viral transmissions and cancer metastatic spread. A common strategy for migration pathways reconstruction involves constructing a phylogenetic tree of observed genotypes and inferring its ancestral states corresponding to migration sites. Key challenges here include determining the conditions when a phylogenetic tree topology reflects the underlying migration tree structure, and balancing computational tractability, flexibility, and biological realism of inference algorithms and models.

In this study, we address these challenges using the powerful machinery of graph homomorphisms, a mathematical concept that describes how one graph can be mapped onto another while preserving its structure. We investigate how structural constraints on migration patterns and migration tree topologies influence the relationship between phylogenies and migration trees, characterize trees compatible with a given phylogeny and propose a series of algorithms to assess whether given phylogenetic and migration trees are compatible under various migration scenarios.

Leveraging our findings, we present a framework for inferring migration trees by sampling potential trees from a prior random tree distribution and identifying a subsample compatible with a given phylogeny. By varying prior tree distributions, this approach expands upon several existing models, offering a versatile strategy applicable to a variety of biological processes. We validate our methodology using simulated datasets and real data from studies of viral outbreaks and cancer metastasis, demonstrating its effectiveness across different contexts.
Optimizing stainless steel tensile strength analysis: through data exploration and machine learning design with Streamlit
Authors: Leni Desmarita , Karudin Arwizet , Abbas Muhammad Rabiu , Sharma Jai Kumar , Adriansyah Adriansyah 
Published: September 2024

The use of Exploratory Data Analysis (EDA) and machine learning in material science has rapidly advanced in recent years. EDA enables researchers to thoroughly explore and analyze material datasets, while machine learning allows for the development of predictive models capable of understanding complex patterns within the data. This study aims to develop an optimization tool to enhance the analysis of tensile strength in stainless steel by leveraging integrated data exploration and machine learning approaches within the Streamlit framework. The developed tool consists of four main features: data visualization, correlation analysis, 3D visualization, and machine learning. The developed machine learning model has 14 input variables, including chemical elements and heat treatment temperatures. In this research, the machine learning features comprise three models: Decision Tree, Random Forest, and Artificial Neural Network. The research findings indicate that the optimization tool can automatically display stainless steel tensile strength data using available pandas profiling in the visualization feature. The correlation feature can illustrate the relationship between chemical elements and heat treatment temperatures concerning stainless steel tensile strength. The 3D visualization feature can be utilized to identify optimal values of chemical elements and heat treatment temperatures according to desired tensile strength. Meanwhile, the machine learning feature can accurately predict stainless steel tensile strength based on chemical composition and heat treatment temperatures. This is evident from the performance evaluation metrics of the Random Forest model, which achieved MAE of 10.36, RMSE of 14.44, and R-squared of 0.97