Miracle-dz/UniFAR-FTUs huggingface dataset:
The Role of Moderating Variables on BOLD fMRI Response During Semantic Verbal Fluency and Finger Tapping in Active and Educated Healthy Seniors

Semantic verbal fluency is among the most employed tasks in cognitive aging research and substantial work is devoted to understanding the underlying mechanisms behind age-related differences at the neural and behavioral levels. The present investigation aimed to evaluate the role of moderating variables, such as age, sex, MMSE, and proxies of cognitive reserve (CR) on the hemodynamic response evoked by semantic verbal fluency in healthy young and healthy older adults. So far, no study has been conducted to this end. To elucidate the exclusive effect of the mentioned variables on brain activation during semantic fluency, finger tapping was included as a control task. Results showed that disregarding adjustments for age, older adults displayed important parietal activations during semantic fluency as well as during finger-tapping. Specifically, the anterior intra-parietal sulcus (IPS) and left inferior parietal lobule (IPL) were areas activated in both tasks in the older group. Younger adults, only displayed parietal activations related to age and sex when these demographics were employed as predictors. Concerning proxies of CR in semantic fluency, the only vocabulary was an important moderator in both age groups. Higher vocabulary scores were associated with lesser activation in occipital areas. Education did not show significant correlations with brain activity during semantic fluency in any of the groups. However, both CR proxies were significantly correlated to brain activations of older adults during finger tapping. Specifically, vocabulary was associated with frontal regions, while education correlated with parietal lobe and cingulate gyrus. Finally, the effects of MMSE were mostly observed on brain activation of older adults in both tasks. These findings demonstrate that the effects of moderating variables on shaping brain activation are intricate and not exclusive of complex verbal tasks. Thus, before adjusting for “nuisance variables,” their importance needs to be established. This is especially true for samples including older adults for whom a motor task may be a demanding operation due to normal age-related processes of dedifferentiation.
TreeMap: a structured approach to fine mapping of eQTL variants

Motivation Expression quantitative trait loci (eQTL) harbor genetic variants modulating gene transcription. Fine mapping of regulatory variants at these loci is a daunting task due to the juxtaposition of causal and linked variants at a locus as well as the likelihood of interactions among multiple variants. This problem is exacerbated in genes with multiple cis-acting eQTL, where superimposed effects of adjacent loci further distort the association signals. Results We developed a novel algorithm, TreeMap, that identifies putative causal variants in cis-eQTL accounting for multisite effects and genetic linkage at a locus. Guided by the hierarchical structure of linkage disequilibrium, TreeMap performs an organized search for individual and multiple causal variants. Via extensive simulations, we show that TreeMap detects co-regulating variants more accurately than current methods. Furthermore, its high computational efficiency enables genome-wide analysis of long-range eQTL. We applied TreeMap to GTEx data of brain hippocampus samples and transverse colon samples to search for eQTL in gene bodies and in 4 Mbps gene-flanking regions, discovering numerous distal eQTL. Furthermore, we found concordant distal eQTL that were present in both brain and colon samples, implying long-range regulation of gene expression. Availability TreeMap is available as an R package enabled for parallel processing at https://github.com/liliulab/treemap.
The effectiveness of trauma-informed wilderness therapy with adolescents: A pilot study.

Objective: Wilderness therapy (WT) is a complementary/integrative approach for treating struggling adolescents by using outdoor adventure activities to foster personal and interpersonal growth/well-being. Empirical support for the effectiveness of traditional WT is growing, but evidence supporting trauma-informed WT (TIWT) is lacking. This pilot study addresses that gap. Method: Between 2009 and 2019, 816 adolescents (Ages 13-17, Mage = 15.36, SD = 1.25; 41.1% female) completed the Youth-Outcome Questionnaire-SR 2.0 at intake and discharge (M = 75.02 days, SD = 28.77). Three-hundred seventy-eight adolescents also completed the Family Assessment Device-General Functioning (FAD-GF), and 253 adolescents completed two, 2.5-min segments of heart-rate-variability biofeedback (one while resting and one while using a coping skill). One-hundred eighty-nine caregivers completed the Youth-Outcome Questionnaire 2.01, and 181 caregivers completed the FAD-GF. Between 25 and 99 adolescents and caregivers also completed psychological and family measures at 6 months and 1 year postdischarge. Results: Adolescents reported experiencing improvements in psychological and family functioning. They also exhibited improvement in psychophysiological functioning (heart-rhythm coherence). Caregivers reported improvements in family functioning and their child's psychological functioning. Caregivers observed more persisting benefits in their child's psychological functioning, whereas adolescents reported more persisting benefits in family functioning. Changes in psychological and family functioning were related. There were very few differential effects on the basis of demographic factors, trauma exposure, or past and current treatment factors. Conclusion: Results of this pilot study suggest TIWT is a promising complementary/integrative intervention for improving the psychological, family, and psychophysiological functioning of struggling adolescents. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
Software Engineering Needs in the North of Sinaloa Employing the Cluster Analysis.

In the design or restructuring of a syllabus, especially in higher education, a series of factors must be considered to meet the expectations of actors involved: educational institution, students and employers. However, it is imperative to know the needs of the region regarding the area of knowledge. This article presents an analysis of the needs of Software Engineering (SE) in the northern area of the state of Sinaloa. The aim of the research is to update the syllabus of the SE career of the Facultad de Ingeniería Mochis (Sinaloa) from the Universidad Autónoma de Sinaloa (UAS). This research applied the cluster analysis with the agglomerative hierarchical classification method to a data set. The data set was obtained through a survey applied to employers in the region to determine SE common needs. The investigation determined that the learning autonomy (generic competencies) and the approach to analysis and design of the SE (specific competencies) represent the most important aspects for the area.
Patterns of Heart Failure Dyadic Illness Management

Background The ways in which patients with heart failure (HF) and their care partners work together to manage HF are often overlooked. Objective The aim of this study was to identify and compare different patterns of HF dyadic illness management. Methods This was a secondary analysis of data on HF dyads. Heart failure management was measured using patient and care partner versions of the Self-Care of HF Index and European HF Self-care Behavior Scale. Latent class modeling was used to identify patterns of HF dyadic management. Results The mean age of the 62 patients and their care partners was 59.7 ± 11.8 and 58.1 ± 11.9 years, respectively. A majority of patients (71.0%) had class III/IV HF, and a majority of the couples (95.2%) were married. Two distinct dyadic patterns were observed, 1 collaborative management type (n = 42, 67.7%) and 1 autonomous management type (n = 20, 32.3%). Dyads in the autonomous pattern were mostly female patients with male care partners; patients in this pattern also were more anxious and depressed, and reported worse relationship quality compared with collaborative dyads. Conclusion There is an engendered spectrum of collaboration in how HF patient–care partner dyads work together to manage HF that needs to be considered in clinical care and research.
Mixed-Order Sugeno Model to Predict the Resultant Force in the Milling Process for Honeycomb Sandwich

The use of composites and fibre reinforced composites is increasing as their advantages are more and more recognized. They have excellent mechanical properties at low weight. However, new problems arise in the production and cutting of such materials (e.g.: fiber pull out, fiber breakage, matrix smearing, and delamination). Numerous studies have proven that cutting forces have a significant effect on problems during cutting, so knowing and estimating how cutting forces occur is vital in the technological process planning. The development of predictive models can be used to replace the cost- and time-consuming measurements. In this paper a mixed-order Sugeno model is introduced to predict the resultant force for milling a composite honeycomb sandwich composite.
SDF-SLAM: Semantic Depth Filter SLAM for Dynamic Environments

Simultaneous Localization and Mapping (SLAM) has been widely applied in computer vision and robotics. For the dynamic environments which are very common in the real word, traditional visual SLAM system faces significant drop in localization and mapping accuracy due to the static world assumption. Recently, the semantic visual SLAM systems towards dynamic scenes have gradually attracted more and more attentions, which use the semantic information of images to help remove dynamic feature points. Existing semantic visual SLAM systems commonly detect the dynamic feature points by the semantic prior, geometry constraint or the combine of them, then map points corresponding to dynamic feature points are removed. In the visual SLAM framework, pose calculation is essentially around the 3D map points, so the essence of improving the accuracy of visual SLAM system is to build a more accurate and reliable map. These existing semantic visual SLAM systems are actually adopting an indirect way to acquire reliable map points, and several drawbacks exist. In this paper, we present SDF-SLAM: Semantic Depth Filter SLAM, a visual semantic SLAM system towards dynamic environments, which utilizes the technology of depth filter to directly judge whether a 3D map point is dynamic or not. First, the semantic information is integrated into the original pure geometry SLAM system by the semantic optical flow method to perform reliable map initialization. Second, design the semantic depth filter that satisfies the Gaussian Uniform mixture distribution to describe the inverse depth of each map point. Third, updating the inverse depth of 3D map point in a Bayesian estimation framework, and dividing the 3D map point into active one or inactive one. Last, only the active map points are utilized to achieve robust camera pose tracking. Experiments on TUM dataset demonstrate that our approach outperforms original ORB-SLAM2 and other state-of-the-art semantic SLAM systems.
Performance evaluation of fan and comb shaped plasma reactors for distribution of generated ozone in a confined space

Enhanced distribution of atmospheric surface dielectric barrier discharge (SDBD) generated ozone in air for effective decontamination is examined. This is achieved through experimental measurement and numerical prediction of the flow and ozone distribution produced by surface DBD plasma reactors kept in a closed chamber. This study provides an alternative technique for decontamination of assembly cleanroom facilities and spacecraft components. Conventional methods like dry heat microbial reduction and vapor phase hydrogen peroxide used in spacecraft related decontamination lead to thermal and chemical damage of sophisticated materials. Advantages associated with atmospheric DBD plasma decontamination like low temperatures, no organic-residuals and design flexibility make it a potential alternative to these methods. In this study, spore forming bacterial species, Bacillus subtilis, which is commonly used in planetary protection studies was used as the test organism. Significant reduction (~78%) of bacterial concentrations in the inoculated volume of air was observed using the comb shaped DBD plasma reactor. Increased reduction (~98%) was observed when the same reactor was used in conjunction with an external fan for better ozone distribution. Considering this, a recently developed DBD configuration called the fan SDBD reactor can be used instead of the external fan to obtain better ozone distribution leading to lower ozone requirements and energy consumption. For this purpose, ozone distribution in a chamber with the comb and fan SDBD reactor were compared experimentally. The fan SDBD reactor uniformly distributed ozone from the center towards the walls of the chamber with higher ozone concentrations at the center. In contrast, the comb SDBD reactor pushed the ozone towards one of the walls resulting in biased distribution with higher concentrations in one direction. These results suggest that the fan reactor is better suited for decontamination applications like sterilization of space craft components where a more uniform decontamination is preferred requiring less ozone concentrations. Preliminary simulation results of flow and ozone distribution by the comb and fan reactors up till 10 seconds of powering up are validated with experimental data. Such numerical simulations of ozone distribution resulting from different SDBD configurations can be used to develop and optimize reactor configurations for planetary protection decontamination applications.
Computational Intelligence Approach to Dynamic Blood Allocation With ABO-Rhesus Factor Compatibility Under Real-World Scenario

The supply chain management of blood products specifically deals with the aspect of efficient planning, implementing, and controlling of the in and out flow processes of blood unity in the blood bank system. Therefore, any improvement in the management of these chains would directly influence the manner in which blood and blood products are distributed to all the various sectors requiring such scarce and precious resources. Generally, the management of blood products is difficult due to the four ABO blood groups, which is further complicated by positive/negative rhesus factors. In this paper, a more simplified and robust dynamic mathematical model is presented for the efficient management of the blood bank. The corresponding sets of governing equations from an existing model are extended to cover the rhesus factors and the solution methods of the newly derived equations are subsequently investigated. In addition, a mathematical representation of the decision making process is presented as a function of the blood bank stockpile. Furthermore, in order to demonstrate the robustness of the developed model and to provide managerial insights, a new global hybrid symbiotic organisms search genetic particle swarm optimization algorithm is developed. Several numerical computations are performed using real-world datasets from the Enugu National Blood Transfusion Centre, in Nigeria, which fall within the monthly initial blood volume bounds of 300 over a period of eight years (2010 – 2018). Finally, the experimental results show that the mathematical model and metaheuristic optimization method proposed in this paper offer a better solution approach for blood allocation in dynamic environments. More so, the impact of some essential control parameters on the results are analysed to help the blood bank managers or decision makers to select accurately the desired parameters for optimal results yield.
Artificial Intellgence based Decision Making of Autonomous Vehicles Before Entering Roundabout

Decisions of entering into a roundabout or stop before are multi-criterial situations that are complex for both human drivers and for the self-driving vehicles as well. Complexity of the decision is the reason to involve artificial intelligence. Creating standards for autonomous vehicle artificial intelligence based decisions at roundabout entering situation needs deeper understanding of vehicle and traffic behaviour, parametrizing, modelling and simulation. In our research paper, we present an overview about the literature of autonomous vehicles roundabout related decisions. Based on the overviewed cases main parameters of the decision situation was determined. A commonly utilized modeling environment was used for the first simulations and data gathering for feeding the developed neural networks. In this paper, we give an overview about the status of the research, the developed roundabout traffic, the complexity of the decision and the first test results about autonomous vehicle decision before entering into the roundabout supported with artificial intelligence.
Reclaimer Scheduling in Dry Bulk Terminals

This paper studies a complex parallel scheduling problem with non-crossing constraint, sequence dependent setup times, eligibility restrictions, and precedence relationships motivated by reclaimer scheduling in dry bulk terminals. In a stockyard of any dry bulk terminal, stockpiles are handled by reclaimers. Therefore, improving the operational efficiency of reclaimers is critical for the overall performance of these terminals which are struggling with increasing workload. We study the variants of this problem with and without stacking operations. For each variant, we present a lower and an upper bound. A strong lower bound is obtained by relaxing the non-crossing constraint and solving the resulting problem to the optimality. While this relaxation still addresses a challenging scheduling problem, proposed arc-time-indexed formulation copes with the instances of practical size. We develop a novel constraint programming formulation to provide an upper bound for the problem. Computational experiments show this robust approach is able to generate near-optimal schedules for different stockyard configurations within a minute.
Classification of Proactive Personality: Text Mining Based on Weibo Text and Short-Answer Questions Text

This study focused on the topic of predicting “proactive personality”. With 901 participants selected by cluster sampling method, targeted short-answer questions text and participants’ social media post text (Weibo) were obtained while participants’ labels of proactive personality were evaluated by experts. In order to make classification, five machine learning algorithms included Support Vector Machine (SVM), XGBoost, K-Nearest-Neighbors (KNN), Naive Bayes (NB) and Logistic Regression (LR) were deployed. Seven different indicators, which include Accuracy (ACC), F1-score (F1), Sensitivity (SEN), Specificity (SPE), Positive Predictive Value (PPV), Negative Predictive Value (NPV) and Area under Curve (AUC), combined with hierarchical cross-validation were also used to make the comprehensive evaluation of models. With participants’ Weibo text and short-answer questions text, we proposed a new approach to classify individuals’ proactive personality based on text mining technology. The results showed that short-answer questions + Weibo text datasets had the best performance, followed by short-answer questions text datasets, while the outcome of Weibo text datasets were the worst. However, it is noteworthy that Weibo text has the highest average score on the SPE, which indicated that Weibo text played an important role in identifying individuals with low proactive personality. With Weibo text, SEN was also improved compared with only applying short-answer questions text. In addition, among all three datasets, the indicator SPE is always higher than SEN, indicating this text classification approach was more competent for identifying college students with low proactive personality. As for algorithms, Support Vector Machine and Logistic Regression showed steadier performance compared with other algorithms.
A study on the phylogeny and phylogeography of a marine cosmopolite diatom from the southern Black Sea

A single-cell PCR method was applied to Pseudo-nitzschia pungens strains from the southern Black Sea. Based on the aligment set of the LSU D1-D3 region, a Bayesian molecular phylogeny analysis and a parsimony network analysis were used to investigate phylogenetic clades (Clades I-III) in P. pungens and to determine the ancestral clades. The parsimony network analysis also demonstrated that ancestral haplotypes belonged to Clade II, residing around the northeastern Pacific, while Clade I was distributed globally but antitropicaly. According to the findings of this study, the Black Sea strain (Clade III) shows a global phylogeographic pattern.
Number Plate Reorganization using Image Processing and machine learning Approaches: A Review

For detecting the number plate from the moving vehicle there are many algorithm has been developed but still this area always remain evolving each every year, Now a days it is very important to apply image processing and machine learning techniques in various field license plate recognition or number plate recognition is widely used application of image processing and machine learning here in this paper we are going to discuss different methodologies used for recognition of number plate and extracting numbers and characters from number plate and also the accuracy reorganization and future scope of different available methods.
Heat Transfer Analysis of Three-Dimensional Mixed Convective Flow of an Oldroyd-B Nanoliquid over a Slippery Stretching Surface

The present article examines Soret and Dufour effects on the three-dimensional mixed convective flow of an Oldroyd-B nanoliquid. The flow is caused due to bidirectional stretching of the surface in the presence of an induced magnetic field and heat generation/absorption. Besides, concentration and thermal buoyancy impacts are inspected. The velocity slip, convective and zero nanoparticle mass flux boundary condition at the surface are taken into account. Nonlinear system of equations which are highly coupled is solved via optimal homotopy algorithm. The influence of pertinent parameters on velocity, temperature, and concentration are analyzed graphically. The impact of Dufour number is quite substantial on temperature whereas Soret number increases the concentration. To see the legitimacy of the present work, the present results are compared with the results available in the literature and noted an excellent agreement for the limiting cases.