Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of Congestion on Applications of Vehicular Ad-Hoc Network

: Technological innovations have improved the quality of life; thousands of kilometers can be traveled in hours or less. Vehicular Ad Hoc Network (VANET) has become a promising topic in research community due to its various applications like safety, infotainment, and traffic management application. VANET supports Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) communication. With an increase in the number of vehicles on the road, the number of vehicle accidents is also increasing in equivalent pace. Therefore, safety is the essence of daily life; to reduce this adverse effect vehicle to vehicle communication is predominantly required. For safety applications, vehicle broadcast two types of messages that are beacons and emergency messages. Beacons include vehicle status like position, velocity, speed, etc. are disseminated periodically. Vehicles for safety application broadcast self-information messages, which sometimes results in congestion due to increasing vehicle density. To avoid congestion, our attention is towards designing a MAC protocol that utilizes the communication medium efficiently; reliable and undelayed message reception are taken into consideration. The most challenging task is to deliver emergency messages before the deadline. VANET other applications also had made Internet services available during traveling and had given a smooth shape to traffic. The characteristics, applications, review of the challenges of VANET such as congestion is discussed. we present the existing congestion control techniques in decentralized vehicular environments also known as Decentralized Congestion Control (DCC), and a comparative analysis is performed of the schemes.
Facial-Expression-Aware Emotional Color Transfer Based On Convolutional Neural Network

Emotional color transfer aims to change the evoked emotion of the source image to that of the target image by adjusting color distribution. Most of existing emotional color transfer methods ignore the facial expression features in the image. Therefore, we propose a new facial-expression-aware emotional color transfer framework. We ﬁrstly predict the emotion label of the image through the emotion classiﬁcation network. Then, emotion labels are matched with pre-trained emotional models. Finally, we use the matched emotion model to transfer the color of the target image to the input image. Experiments demonstrate that our method outperforms the state-of-the-arts, which can successfully capture and transfer sophisticated emotion features.
PGPM_A_329787 1441..1455

1Department of Pharmacy Practice, Karpagam College of Pharmacy, Coimbatore, Tamilnadu, India; 2Karpagam Faculty of Medical Sciences and Research, Coimbatore, India; 3Department of Pharmacy Practice, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, India; 4Translational Medicine Centre, All India Institute of Medical Sciences, Bhopal, Madhya Pradesh, India; 5Department of General Medicine, All India Institute of Medical Sciences, Bhopal, Madhya Pradesh, India Abstract: Type 2 diabetes mellitus (T2DM) is the most common form of diabetes, and is rising in incidence with widespread prevalence. Multiple gene variants are associated with glucose homeostasis, complex T2DM pathogenesis, and its complications. Exploring more effective therapeutic strategies for patients with diabetes is crucial. Pharmacogenomics has made precision medicine possible by allowing for individualized drug therapy based on a patient’s genetic and genomic information. T2DM is treated with various classes of oral hypoglycemic agents, such as biguanides, sulfonylureas, thiazolidinediones, meglitinides, DPP4 inhibitors, SGLT2 inhibitors, α-glucosidase inhibitors, and GLP1 analogues, which exhibit various pharmacogenetic variants. Although genomic interventions in monogenic diabetes have been implemented in clinical practice, they are still in the early stages for complex polygenic disorders, such as T2DM. Precision DM medicine has the potential to be effective in personalized therapy for those suffering from various forms of DM, such as T2DM. With recent developments in genetic techniques, the application of candidate-gene studies, large-scale genotyping investigations, genome-wide association studies, and “multiomics” studies has begun to produce results that may lead to changes in clinical practice. Enhanced knowledge of the genetic architecture of T2DM presents a bigger translational potential. This review summarizes the genetics and pathophysiology of T2DM, candidategene approaches, genome-wide association studies, personalized medicine, clinical relevance of pharmacogenetic variants associated with oral hypoglycemic agents, and paths toward personalized diabetology.
Heuristics for Home Appliances Scheduling Problems with Energy Consumption Bounds

Many existing papers on the topic of smart grids work on scheduling of appliances considering an unlimited energy supply. However, the size of the power grid necessary to operate on periods of peak demand of the day would be large, and that would not be exploited during most of the day. Because of this, electricity providers could establish energy consumption bounds, which modify considerably appliance scheduling problems. In this paper we formulate a mathematical model for the problems we tackle, which include energy consumption bounds that depend on the time of the day. We prove that these problems are NP-hard and we later describe several heuristics we designed, explaining their foundations. Finally, we analyze the computational results obtained for instances we generated and present the best algorithms to employ according to different priorities the users might have.
Lack of Association Between 25(OH)D Levels and Incident Type 2 Diabetes in OlderWomen

RESEARCH DESIGN AND METHODS—A post hoc analysis of three nested case-control studies of fractures, colon cancer, and breast cancer that measured serum 25(OH)D levels in women participating in the Women’s Health Initiative (WHI) Clinical Trials and Observational Study who were free of prevalent diabetes at baseline. Diabetes was defined as self-report of physician diagnosis and receiving insulin or oral hypoglycemic medication. We used inverse probability weighting to make the study population representative of the WHI population as a whole. Weighted logistic regression models compared 25(OH)D levels (divided into quartiles, clinical cut points [,50, 50–,75, $75 nmol/L], or as a continuous variable) using the distribution of control subjects and adjusted for multiple confounding factors.
Frequency-based methods for the detection of damage in structures: A chronological review

Infrastructure development is a common feature of emerging countries. As a result, the design and construction of complex structures susceptible to damage is becoming increasingly common. Over the years, multiple advances in Structural Health Monitoring (SHM) haven allowed researchers and engineers to detect, locate, and quantify structural damage in critical components of civil engineering structures. Frequency-based methods have demonstrated their reliability in multiple numerical and experimental applications. This study presents a brief chronological literature review of methodologies based on frequency analysis that have been used in the detection ofstructural damage over the last forty years. It is worth noting that the paper focuses on computer-aided techniques such as artificial neuronal networks (ANN), genetic algorithms (GAs), and metaheuristics that have been employed to solve the inverse damage detection problem.
Using Fuzzy Logic for the Accurate Determination of the Compressibility Factor of Hydrocarbon Gases

Gas compressibility factor is an important thermodynamic property introduced to modify the ideal gas law to account for the real gas behavior. Precise determination of this property is the top priority for many aspects of chemical and petroleum engineering applications. After the publication of the results of the experiments conducted by Standing and Katz in 1942, their proposed chart has become an industry standard, and since then many authors have tried to develop new models by fitting their parameters to that chart. Lack of sufficient accuracy, especially near specific boundaries, as expected for applications like in numerical reservoir simulators, and the iterative nature of procedures are the most common problems of these suggested models. To defeat such problems, in this research a novel type of model is developed on the basis of fuzzy inference system. The error analysis of the generated model compared with the widely accepted Dranchuk and Abou-Kassem correlation, the artificial neural network model proposed by Kamyab and colleagues and several other well-known models demonstrate that fuzzy logic can reach much higher precisions than most of the current methods.
Finite element method analyses of ambient temperature effects on characteristics of piezoelectric motors

There have been numerous experimental reports about the environmental effects on characteristics of intelligent actuators, such as the piezoelectric motors. However, the influences of temperature coefficients of material properties, which are the fundamental reasons for the changes of motors’ output characteristics in different ambient temperature condition, are specially difficult to be acquired through experiments. Thus, the optimization for piezoelectric motors driven in extreme environments is scarce till now. This article is aimed to establish one calculating method to solve this problem. First, a theoretical model is developed for investigating the effects of ambient temperature on characteristics of piezoelectric motors by the finite element method. And then, the mechanical and electrical characteristics of a piezoelectric motor are measured to demonstrate the theoretical model. After that, the changes of critical parameters caused by the ambient temperature are discussed. Based on this, the final proportion of effects of materials’ temperature coefficients on changes of characteristics of piezoelectric motors is obtained. The results obtained by the theoretical model give useful guidelines for the optimization of piezoelectric motors operating in extreme environments.
A 333 MHz, 20 mW, 18 ps resolution digital DLL using current-controlled delay with parallel variable resistor DAC (PVR-DAC)

A new Delay Locked Loop (DLL) using a Digital-to-Analog Converter with the Parallel Variable Resister (PVR-DAC) has been developed. The PVR-DAC successfully manages the current controlled-delay element (CCDE) and achieves a fine time-based resolution. The DLL adopting PVR-DAC has been simulated. It realizes a time-based resolution of 18 ps, an operation frequency range of 143 MHz through 333 MHz, with the maximum power consumption of 20 mW at 1.5 V, and also achieves the small circuit area of 0.5 mm/sup 2/.
A Real-World Application of Fuzzy Logic and Influence Coefficients for Gas Turbine Performance Diagnostics

This paper presents an example of the use of fuzzy logic combined with influence coefficients applied to engine test-cell data to diagnose gas-path related performance faults. The approach utilizes influence coefficients, which describe the changes in measurable parameters due to changes in component condition such as compressor efficiency. Such approaches usually have the disadvantages of attributing measurement noise or sensor errors to changes in engine condition and do not have the ability to diagnose more faults than the number of measurement parameters that exist. These disadvantages usually make such methods impractical for anything but simulated data without measurement noise or errors. However, in this example, the influence coefficients are used in an iterative approach, in combination with fuzzy logic, to overcome these obstacles. The method is demonstrated using eight examples from real-world test-cell data.
Dynamic Structural Properties of Cable‐Stayed Tampico Bridge

Because of their complex behavior under wind, earthquake, and moving loads, the dynamic response of cable-stayed bridges requires special studies. Use of experimental procedures to confirm design assumptions and predict their structural performance is well known. A monitoring program was initiated in order to determine the present dynamic properties of the Tampico bridge as well as changes in structural characteristics during its lifetime of service. Results from ambient vibration and pull-back tests are presented. The structure and the experimental setup is described. The data recorded during the testing program are analyzed in detail in order to determine the structural dynamic properties of the structure. Fundamental periods and mode shapes were calculated. Damping characteristics were obtained with different approaches developed by other authors and compared to those obtained for a similar structure. The results are useful for the dynamic analysis of new cable-stayed bridges and to detect design failures in existing structures of this type.
Factors influencing the jump momentum – sprint momentum correlation: a data simulation

ABSTRACT Jump take-off momentum has previously been proposed as an alternative test to predict sprint momentum. This study used a data simulation to replicate and systematically investigate relationships reported in previous studies between body mass, vertical jump performance, and sprint performance. Results were averaged for 1000 simulated data sets in each condition, and the effects of various parameters on correlations between jump momentum and sprint momentum were determined. The ability of jump take-off momentum to predict sprint momentum is greatest under relatively high inter-individual variation in body mass and relatively low inter-individual variation in jump height. This is largely due to the increased emphasis on body mass in these situations. Even under zero or a small negative (r = −0.30) correlation between jump height and sprint velocity, the correlation between the two momenta remained very large (r ≥ 0.76) on average. There were no investigated conditions under which jump momentum was most frequently a significantly (p < 0.05) greater predictor of sprint momentum compared to simply using body mass alone. Furthermore, between-individual correlations should not be used to make inferences or predictions for within-individual applications (e.g. predicting or evaluating the effects of a longitudinal training intervention). It is recommended that any rationale for calculating and/or monitoring jump take-off momentum should be separate from its ability to predict sprint momentum. Indeed, body mass alone may be a better predictor of sprint momentum. Highlights This study replicated and systematically perturbed relationships reported in the literature to investigate factors contributing to correlations between jump momentum and sprint momentum. The ability of jump take-off momentum to predict sprint momentum is greatest under relatively high inter-individual variation in body mass and relatively low inter-individual variation in jump height. This is largely due to the increased emphasis on body mass in these situations. Even under zero or a small negative correlation between jump height and sprint velocity, the correlation between the two momenta remained very large on average. There were no investigated conditions under which jump momentum was a better predictor of sprint momentum compared to simply using body mass alone. It is recommended that any rationale for calculating and/or monitoring jump take-off momentum should be separate from its ability to predict sprint momentum. Indeed, body mass alone may be a better inter-individual predictor of sprint momentum if such a prediction were deemed necessary.
COVID-19 mental health impact among children with early brain injury and associated conditions

ABSTRACT This study describes the impact of COVID-19 among a clinical research sample of children with early brain injury and associated conditions. Between March 2020 and March 2021, 64 children and their parents participated. Children ranged in age between 3 and 14 years (M = 6 years, 3 months; SD = 2 years, 4 months) with a range of diagnoses (i.e., neonatal stroke, hypoxic ischemic encephalopathy (HIE), congenital heart disease (CHD) and preterm birth (<32 weeks)). The abbreviated CoRonavIrus Health Impact Survey (CRISIS) was completed by parents as part of their child’s routine intake for neuropsychological services. Questions included COVID-19 specific ratings of child mental health impact, child, and parent stressors, with open-ended questions regarding negative and positive COVID-19 related changes. Over 40% of parents described moderate to extreme influence of COVID-19 on their child’s mental health. Common child stressors reported included restrictions on leaving the home and social isolation. Among parents, the most common stress reported was caring for their child’s education and daily activities. Children’s mental health impact was associated with social isolation, parent mental health, COVID-19 economic concern, and number of siblings in the home. Child’s age, sex, brain injury severity, or intellectual functioning were not associated with reported COVID-19 mental health impact. Some COVID-19 positives were identified, namely increased quality family time. Findings reflect the significant pandemic mental health impact among neurologically at-risk children and their families. Implications to future clinical needs and considerations for neuropsychological practice are discussed.
Energy-efficiency region for multiple access channels

To investigate the trade-off between energy efficiency (EE) and spectral efficiency (SE) for Gaussian multiple access channels (MACs), a novel concept of the EE region is developed. The EE region for the time division multiple access system is also derived and compared. The SE–EE trade-off order is introduced to explicitly describe how fast the EE decays with SE in the high SE region. For a symmetric MAC, a constraint on SE–EE trade-off orders is derived and the Pareto optimum can be achieved by the corner points on the EE region.
Adiponectin in relation to exercise and physical performance in patients with type 2 diabetes and coronary artery disease

ABSTRACT Introduction: Adipokines, expressed by adipose tissue (AT), have been associated with metabolic disturbances and coronary artery disease (CAD). The impact of exercise training on the AT in patients suffering from both diabetes and CAD is unknown. To gain knowledge on changes in ATs’ inflammatory profile in such a population, we investigated the effects of long-term exercise on selected adipokines and their associations with physical performance and glucometabolic variables. Adiponectin was selected based on its anti-atherogenic and anti-diabetic properties and visfatin and tumour necrosis factor (TNF) for their association with atherosclerosis and metabolic disorders. Not many studies have focused on the effects of long-term exercise training on adipokines in patients with concomitant T2DM and CAD. Methods: Patients with type 2 diabetes and CAD (n = 137), 41–81 years, 17.2% females, were randomized in a 1:1 manner to an exercise group, who underwent 1 year of 150 min weekly combined strength and endurance exercise, or a control group. AT from the gluteal region and blood samples were obtained at baseline and after 12 months, along with a physical performance test, assessed by the VO2 peak. Circulating protein levels were measured by ELISA. RNA was extracted from AT and expression levels were relatively quantified by PCR. Results: After 1 year, no significant difference in the change in the investigated markers between the intervention group and the control group was observed. Changes in circulating adiponectin and VO2 peak correlated in the total population (r = 0.256, p = 0.008). At baseline, circulating adiponectin and TNF correlated inversely with insulin and with C-peptide and VO2peak, respectively (p < 0.001, all). Conclusion: In this population with concomitant diabetes and CAD, ATs’ inflammatory profile remained unchanged apparently after 1 year of exercise intervention. Changes in the VO2peak were nevertheless, related to changes in circulating adiponectin levels. Trial registration: http://www.clinicaltrials. gov NCT01232608.
Identification of KIF4A as a prognostic biomarker for esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) is the most common and aggressive tumor worldwide, and the long-term survival of these patients remains poor. Three databases (GSE17351, GSE20347, and GSE100942) were obtained from Gene Expression Omnibus, and 193 differentially expressed genes including 56 upregulated and 137 downregulated genes were identified by paired test using limma R package. Then, functional enrichments by gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses showed these genes were mainly related protein digestion and absorption, and IL-17 signaling pathway. We then constructed a protein-protein interaction network and cytoHubba module to determine the six hub genes and overall survival analysis of the six hub genes were evaluated by UALCAN and GEPIA2 analysis. Ultimately, the experimental results confirmed the KIF4A was overexpressed in the ESCC tissues and cell lines compared with the normal esophageal mucosal tissues and was linked to poor prognosis. Moreover, we also revealed that KIF4A facilitates proliferation, cell cycle, migration, and invasion of ESCC in vivo and in vitro. Overall, these findings demonstrated that KIF4A could serve as diagnostic and prognostic biomarkers and may help facilitate therapeutic targets in ESCC patients.
White matter damage due to pulsatile versus steady blood pressure differs by vascular territory: A cross-sectional analysis of the UK Biobank cohort study

Small vessel disease is associated with age, mean blood pressure (MAP) and blood pressure pulsatility (PP). We used data from the UK Biobank cohort study to determine the relative importance of MAP versus PP driving white matter injury within individual white matter tracts, particularly in the anterior and posterior vascular territory. The associations between blood pressure and diffusion indices in 27 major tracts were analysed using unadjusted and fully-adjusted general linear models and mixed-effect linear models. Blood pressure and neuroimaging data were available for 37,041 participants (mean age 64+/−7.5 years, 53% female). In unadjusted analyses, MAP and PP were similarly associated with diffusion indices in the anterior circulation. In the posterior circulation, the associations were weaker, particularly for MAP. In fully-adjusted analyses, MAP remained associated with all diffusion indices in the anterior circulation, independently of age. In the posterior circulation, the effect of MAP became protective. PP remained associated with greater mean diffusivity and extracellular free water diffusion in the anterior circulation and all diffusion indices in the posterior circulation. There was a significant interaction between PP and age. This implies discordant mechanisms for chronic white matter injury in different brain regions and potentially in the associated stroke risks.