bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effect of droplet transfer mode on composition homogeneity of twin-wire plasma arc additively manufactured titanium aluminide
Authors: Xin Jianwen , Wu Dongsheng , Chen Haiyao , Wang Lin , Zhou Wenlu , Wu Kanglong , Zhang Yuelong , Shen Chen , Hua Xueming , Li Fang 
Published: July 2022

Abstract
Twin-wire plasma arc additive manufacturing (TW-PAAM) is an innovative process for titanium aluminide manufacture which is low in equipment cost and high in efficiency. However, as an in-situ alloying process, the composition homogeneity is typically a source of concern. The present work investigates this subject by observing the element input mode at various welding wire positions with a high-speed camera system and particle tracking method. It is found that when the welding wires are in a high position, the droplet enters the molten pool from the tip of the titanium wire, and the alloy element input interval is long, resulting in the formation of bands with high aluminium content at the edge of the deposition layer. When the welding wires are close to the workpiece surface, the droplet transfer mode switches to a mixture of dual wire-bridge transfer and Ti wire-bridge transfer. The diameter and transfer interval of the droplets decrease, as well as the heterogeneity of the deposition layer. The conclusion establishes a correlation between droplet transfer mode and composition homogeneity, which is beneficial for optimizing the TW-PAAM titanium aluminide fabrication process.
A machine learning framework for Major depressive disorder (MDD) detection using non-invasive EEG signals
Authors: Bashir Nayab , NAREJO SANAM , Soomro bushra Naz , Ismail Fatima , Anjum Muhammad Rizwan , butt Aisha , Anwar sadia , Prasad Ramjee 
Published: July 2022

Abstract
According to World Health Organization (WHO) report, every 40 seconds a person attempts suicide globally. Depression, one of the world’s most prevailing diseases has become a reason behind these suicides. It is believed that early diagnosis of major depressive disorder (MDD) can reduce the adversity of this heinous deformity. For few years various machine learning and advanced neurocomputing techniques are being utilized in Electroencephalogram (EEG) based detection of multiple neurological diseases. In the proposed study, an EEG based screening of MDD is presented while using various Machine Learning and one Deep Learning approach. The majority of previous EEG based MDD decoding research has concentrated on a limited features. It was necessary to conduct in-depth comparisons of different approaches, besides more detailed feature-based EEG analysis. This research starts with the creation of a complete feature-based framework, which is then further compared against the state of the art end to end techniques. The K-nearest neighbors (KNN) model outperformed the other models and gained an accuracy of 87.5%. While long short term memory (LSTM) model acquired an accuracy of 83.3%. This study can further support in clinical diagnosis of multiple stages of MDD and can attempt to provide an early intervention.
Estimation of distance distributions between Gd3+ radical pairs with a significant zero- field splitting from pulsed EPR DEER data
Authors: Misra Sushil K. , Salahi Hamid Reza 
Published: July 2022

Abstract
The numerical method, using full diagonalization of the spin-Hamiltonian matrix, to calculate DEER (Double Electron-Electron Resonance) signal, based on the double rotating-frames (DRF) technique, taking into account the zero-field splitting of the Gd3+ ion with spin S=7/2 is exploited here to calculate the kernel signals for the various distances, r, between two coupled Gd3+ radicals, distributed randomly in a biological system. These are then used to estimate the probabilities of the distance distribution, P(r), between the various Gd3+ radical pairs, separated by the distances, r. This is accomplished by using Tikhonov regularization, as implemented in the software DeerAnalysis [Jeschke et al. Appl. Magn. Reson., 30(3), pp.473-498 (2006)], but using the kernel signals calculated here by the DRF technique for different r values. This procedure is successfully illustrated by applying it to calculate the distance distribution probabilities, P(r) from the reported experimental four-pulse DEER data for a sample of Gd ruler 1_5 in D2O/glycerol-d_8 (i) at Q-band [Doll et al., J. Magn. Reson. 259, pp.153-162] and (ii) at W-band [Dalaloyan et al., Phys. Chem. Chem. Phys., 17(28), pp.18464-18476 (2015)]. Significant differences in the distance-distribution probabilities are found between those calculated here using the DRF-calculated kernel signals for Gd3+ with spin 7/2 with significant ZFS from those calculated by the use of analytical kernel signals for spin-½ system without ZFS, using hard pulses.
Evaluation of a quantitative PCR-based method for chimerism analysis of Japanese donor/recipient pairs
Authors: Ikeda Kazuhiko , Minakawa Keiji , Ono Satoshi , Watanabe Mao , Sato Yuka , Takahashi Saki , Odawara Sho , Kawabata Kinuyo , Ueda Koki , Nollet Kenneth , Sano Hideki , Ikezoe Takayuki , Kikuta Atsushi 
Published: July 2022

Abstract
Chimerism analysis is a surrogate indicator of graft rejection or relapse after allogeneic hematopoietic stem cell transplantation (HSCT). Although short tandem repeat PCR (STR-PCR) is the usual method, limited sensitivity and technical variability are matters of concern. Quantitative PCR-based methods to detect single nucleotide polymorphisms (SNP-qPCR) are more sensitive, but their informativity and quantitative accuracy are highly variable. For accurate and sensitive chimerism analysis, a set of KMR kits (GenDx, Utrecht, Netherlands), based on detection of insertions/deletions (indels) by qPCR, have been developed. Here, we investigated informativity and validated the accuracy of KMR kits in Japanese donor/recipient pairs and virtual samples of DNA mixtures representative of Japanese genetic diversity. We found that at least one recipient-specific marker among 39 KMR-kit markers was informative in all of 65 Japanese donor/recipient pairs. Moreover, the percentage of recipient chimerism estimated by KMRtrack correlated well with ratios of mixed DNA in virtual samples and with the percentage of chimerism in HSCT recipients estimated by STR-PCR/in-house SNP-qPCR. Moreover, KMRtrack showed better sensitivity with high specificity when compared to STR-PCR to detect recipient chimerism. Chimerism analysis with KMR kits can be a standardized, sensitive, and highly informative method to evaluate the graft status of HSCT recipients.
Understanding the Correlation of Parkinson’s dataset with Multivariate Analysis Packages
Authors: Bagchi Preenon , A Rajha Priya 
Published: July 2022

Abstract
Parkinson’s dataset available in open source was used in this work.Inferential statistical analysis is used here to inspect each unit from this dataset and to test a hypothesis depending on the sample data. From the analysis inferences could be extracted by applying probability and make generalizations about the whole data. This method can also be used in experimental and quasi-experimental research design or in program outcome evaluation. During the analysis, the whole population of the sample data was considered while making deductions. Specifically, multivariate analysis was used which involves more than two dependent variables, this method helps in reduction and simplification of data without losing its details. Packages like ggplot2, psych, corrgram, performance analysis, ggcorrplot, ggpubr and RColorBrewer Packages were used for interpreting the data.
Analysis of the wall thickness distortion in tube hydro-forging
Authors: Chen Gang , Chu Guan-Nan , Liu Peixing , Gao Jun 
Published: July 2022

Abstract
The tensile deformation is the most common forming principle in the sheet forming process, but there is a new tube forming process - tube hydro-forging has been developed based on the principle of compression deformation. In addition to the instabilities problem, a new surface defect - thickness distortion was found in the compression deformation. In this paper, the wall thickness distortion is analyzed from the point of view of mechanics and deformation. It is revealed that in the Low Pressure Tube Hydroforming process, a bending moment is exist and leads to some small wrinkles in the straight wall. In the Tube Hydro-forging, the small wrinkles would develop to the thickness distortion with the increase of compressive deformation. The wall thickness and compressive strain are two major effect factors. It is verified by experiments and simulation. This study helps to recognize the new surface phenomenon in compressive deformation.
Seasonal variations of water quality and phytoplankton diversity affect the feeding ecology of hilsa fish in the Meghna River estuary zones in Bangladesh
Authors: Shaha Dinesh Chandra , Hasan Jahid , Kundu Sampa Rani , Yus Fatimah Md , Salam Mohammad Abdus , Khan Murshida , Haque Farhana , Ahmed Minhaz , Rahman Mohammad Jalilur , Wahab Md. Abdul 
Published: July 2022

Abstract
The tropical estuarine ecosystem is fascinating for studying the dynamics of water quality and phytoplankton diversity due to its frequently changing hydrological conditions. Most importantly, phytoplankton is the main supplier of ω3 polyunsaturated fatty acids (PUFA) in the coastal food web for fish as they could not synthesize PUFA. This study evaluated seasonal variations of water quality parameters in the Meghna River estuary (MRE), explored how phytoplankton diversity changes according to hydro-chemical parameters, and identified the major phytoplankton groups as the main source of PUFA for hilsa fish. Ten water quality indicators including temperature, dissolved oxygen, pH, salinity, dissolved inorganic nitrogen (DIN=nitrate, nitrite, ammonia) and phosphorus, dissolved silica and chlorophyll-a were evaluated. In addition, phytoplankton diversity was assessed in the water and hilsa fish gut. Principal component analysis (PCA) was used to analyze the spatio-temporal changes in the water quality conditions, and the driving factors in the MRE. Four main components were extracted and explained 75.4% variability of water quality parameters. The most relevant driving factors were dissolved oxygen, salinity, temperature, and DIN (nitrate, nitrite and ammonia). These variabilities in physicochemical parameters and dissolved inorganic nutrients caused seasonal variations in two major groups of phytoplankton. Peak abundance of chlorophyta (green algae) occurred in water in nutrient-rich environments (nitrogen and phosphorus) during the wet (36 %) season, while bacillariophyta (diatoms) were dominant during the dry (32%) season that depleted dissolved silica. Thus, the decrease of green algae and increase of diatoms in the dry season indicated the potential link to seasonal changes of hydro-chemical parameters. The green algae (53.7 %) were the dominant phytoplankton group in the hilsa gut content followed by diatoms (22.6 %) and both are contributing as the major source of PUFAs for hilsa fish according to electivity index as they contain the highest amounts of PUFAs (60 and 28% respectively).
Experimental, Computational, and Data-Driven Study of the Effects of Selective Laser Melting (SLM) Process Parameters on Single-Layer Raster Scanning Surface Characteristics
Authors: Fotovvati Behzad , Rauniyar Santosh , Arnold Jobe A. , Chou Kevin 
Published: July 2022

Abstract
In this study, the influence of selective laser melting (SLM) parameters on the surface characteristics of “single-layer” raster scanning deposits, with Ti6Al4V powder, is thoroughly investigated through experimental, computational, and data-driven approaches. In layer-wise manufacturing processes, such as SLM, the evaluation and understanding of the surface characteristics of individual layers of materials are important; however, there is limited work in the literature on the “single-layer” surface characteristics in SLM. Single-layer samples are designed on a smooth surface on top of semi-cylinders and fabricated using different levels of SLM processing parameters, i.e., laser power, scan speed, and hatch spacing, in different numbers of scan lines, i.e., 5, 10, 20, and 40. The fabricated sample surface data are acquired using non-contact optical interferometry. The computational approach utilizes the discrete element method (DEM) for the powder bed distribution, as well as computational fluid dynamics (CFD) for simulating the laser-powder interaction. One of the main contributions of this study is to investigate and provide predictive models for the correlations between the surface roughness of raster scans and SLM process parameters through experiments and computational work. In addition, a method for comparing the surface characteristics of the thermo-fluid simulation and experimental results is presented. A technique is employed to extract the surface roughness from the simulated results. The discussion on the experiment-simulation comparison challenges provides a deep insight into the criteria selection for experiment-simulation comparison in the SLM process. Moreover, an artificial neural network was trained using the back-propagation method and K-fold cross-validation, as well as regularization techniques to avoid overfitting. The laser power proved to be the most significant parameter, having a contribution of 48.3%, in determining the surface roughness with a unique trend affected by the spattering of powder particles. The simulation results showed noticeable deviations from the experiments in cases with extreme parameters, e.g., too high or too low values of power, speed, or hatch spacing. The machine learning algorithm achieves reasonable predictability of the single-layer surface roughness, showing a root mean square error and a coefficient of determination of 0.9589 µm and 98.78%, respectively, for a separate testing dataset.
A novel method for identification of candidate genes for Alzheimer’s disease using network topology measure and intelligent based deep learning models
Authors: Gnanadesigan Naveen Sundar , Dhanasegar Narmadha , Ramasamy Manjula Devi , Loganathan Ashok Kumar , Muthusamy Suresh , Panchal Hitesh , Thangaraj Kokilavani , Ravindaran Ashok Kumar 
Published: July 2022

Abstract
Background: Alzheimer's disease (AD) is a neurological illness that causes short-term memory loss. There are currently no viable therapeutic therapies for this condition that can totally cure it. The source of Alzheimer's disease is unknown, although genetic factors are thought to have a role in the illness's development, with about 70% of the disease's risk attributed to the vast number of genes associated. Despite the discovery of a number of potential AD susceptibility genes through genetic association studies, there is greater challenge to identify unidentified AD-associated genes and drug targets in order to gain a good insight of the disease-causing mechanisms of Alzheimer's disease and develop effective AD therapeutics.The proposed CG-DC model brings an accuracy of 96% for ANN model, 87.3% for KNN classifier, 86% for SVM classifier, 85.3% than Decision Tree. It is clearly visible; the proposed network topology model performs good for ANN classifier than other existing models. Similarly, the model also brings a sensitivity measure of 97% for ANN model, 84% for KNN classifier, 84.2% for SVM classifier and 84% for Decision tree classifier.Results: In the proposed research work, a novel network topology measure (DC-GC) and intelligent based machine learning models are used for identifying candidate genes from protein-protein interaction and sequence features of genes.Conclusions:The proposed approach DC-GC based network topology measure provides remarkable improvement than existing centrality measures. This helps in the identification of disease associated candidate genes, disease treatment and suitable drug development.
First record of grey rockcod Lepidonotothen squamifrons larvae in the Amundsen Sea, Antarctic: implication for a new spawning ground and early life characteristics
Authors: Guo Huilin , Zhang Chi , Duan Mi , Li Jianchao , Zhang Wenchao , Tian Yongjun 
Published: July 2022

Abstract
Grey rockcod, Lepidonotothen squamifrons is widespread throughout the Antarctic, however, its early life information is little known. Highly abundant larvae of grey rockcod were collected during a zooplankton survey conducted in the Amundsen Sea polynyas in March of 2022. This is the first record of grey rockcod larvae found at high latitudes of the Southern Ocean. The otolith microstructure analysis provided more insight into the early life characteristics of grey rockcod. A total of 41 individuals with a standard length of 9.6–15.3 mm were estimated of 6–11 days. Multiple primordia were first recorded in the otolith. Gompertz model was well fitted to the age–length data, indicating a mean daily growth rate of 0.66 mm day− 1. Larval hatching occurred at a size of 4.08 mm and was concentrated on a short period, at early March. Compared with the population at lower latitude, grey rockcod in the Amundsen Sea polynyas showed later hatch time, smaller hatch size, and faster growth rate. Therefore, we speculate that Amundsen Sea polynyas are potential spawning grounds for grey rockcod. The revealed spatial differences in early life characteristics could contribute to understanding the life history strategy and adaption of grey rockcod.
An evaluation of the Compassion, Inclusion, and Engagement initiative: learning from PWLE and communities across British Columbia
Authors: Maguet Sally , LaLiberte Nancy , Moore Laura , Milkovich Tammy , Burmeister Charlene , Scow Marnie , Sproule Wendy , Dove Naomi , Martens Sheila 
Published: July 2022

Abstract
Background: The Compassion, Inclusion and Engagement initiative (CIE) was a social contact intervention that operated in British Columbia between 2015 and 2021. The primary objective of CIE was to increase the participation of people with lived experience of substance use (PWLE) in the planning, design, implementation, and evaluation of harm reduction supports and services. Case presentation: CIE used the developmental evaluation methodology Outcome Mapping to define and measure progress toward it’s goals. Developmental evaluation emphasizes learning in contrast to other forms of evaluation which are often more focused on determining the value or success of a project or program based on predetermined criteria. Outcome Mapping is a relational practice which acknowledges that change is achieved by an initiative’s partners and the role of the initiative is to provide access to resources, ideas and opportunities that can facilitate and support change .Conclusions: Through the implementation and evaluation of CIE, it became clear that directly supporting PWLE facilitated more meaningful and lasting change than solely working to improve the health and social services that supported them. The impacts of the CIE initiative extend far beyond the outcomes of any of the dialogues it facilitated and are largely the result of an increase in social capital. CIE engagements created the opportunity for change by inviting people most affected by the toxic drug supply together with those committed to supporting them, but their ability to bring about systemic change was limited. Both PWLE and service providers noted the lack of support to attend CIE engagements, lack of support for actions that came from those engagements, and lack of PWLE inclusion in decision-making by health authorities as limiting factors for systemic change. The lack of response at a systemic level, often results in PWLE carrying the burden of responding to toxic drug poisonings, often without resources, support, or compensation.
Effect of Heteroatom Doping on the Electrocatalytic properties of Graphene Quantum Dots for Oxygen Reduction Reaction
Authors: Goswami Mrigaraj , Mandal Sneha , Pillai Vijayamohanan K. 
Published: July 2022

Abstract
Oxygen Reduction is an important reaction involved in a diverse variety of energy storage devices and also in many chemical and biological processes. However, the high cost of suitable catalysts like Platinum, Rhodium, and Iridium proves to be a major obstacle for its commercialization. Consequently, many new materials have emerged in recent years such as various forms of carbon, carbides, nitrides, core-shell particles, and Transition metal complexes as alternatives to Platinum and other noble metals for Oxygen Reduction Reaction (ORR). Among these, Graphene Quantum Dots (GQDs) as metal-free alternatives have captured universal attention, since electrocatalytic properties can be tuned not only by size and functionalization but by heteroatom doping also. We discuss electrocatalytic properties of GQDs (approximate size 3–5 nm) with specific dopants such as N and S prepared by solvothermal routes. Cyclic Voltammetry shows benefits of doping as lowering of the onset potentials while Galvanostatic Tafel polarization measurements show a clear difference in the apparent Tafel slope, along with enhanced exchange current densities, suggesting higher rate constants.
Clinical and molecular characterisation of Vibrio furnissii isolates from patients with diarrhoea
Authors: ZHOU Yan-yan , Zheng Nan , Li YU , Jian-rong SU 
Published: July 2022

Abstract
Background: Vibrio furnissii is an emerging human pathogen closely related to Vibrio fluvialis that causes acute gastroenteritis. However, the distribution of virulence factors, antibiotic resistance and molecular epidemiological features of V. furnissii isolates in China remain unknown. Results: We collected seven V. furnissii strains from 1,985 stool samples from people over 14 years old with acute diarrhoea at a general hospital in Beijing, China between April and October 2018. Blood agar plates with 20 μg/mL ampicillin helped to isolate more V. furnissii compared with only using TCBS ( thiosulfate-citrate-bile salts-sucrose agar). Phylogenetic analysis based on SNPs (single-nucleotide polymorphisms) was used to assess the relatedness of isolates. Virulence genes T2SS, T3SS, vfh, hupO and vfp were detected in all V. furnissii strains, and T6SS also occurred at a high frequency of 85.7%. High resistance to cefazolin and tetracycline was found in the V. furnissii isolates at respective rates of 100.0% and 57.1%, and intermediate resistance to ampicillin/sulbactam and imipenem was found at respective rates of 85.7% and 85.7%. Of the tested strains, 42.9% showed resistance to at least three antibiotics. Transposon islands with concentrations of resistance genes occurred in two strains. Phylogenetic analysis separated the V. furnissii strains into three major clades. Conclusion: This study provided detailed bioinformatic, antibiotic susceptibility and clinical features of V. furnissii in China. However, continued and enhanced laboratory-based surveillance is needed for prevention and control of V. furnissii infection.