Miracle-dz/UniFAR-FTUs huggingface dataset:
A novel pitch based voice recognition model (PVRM)

The voice of each individual is unique because the fact that each person have a different size of vocal cord and the shape of the body. The length of the vocal cord decides the pitch and range of frequency of the voice of an individual. In this paper, we have made an attempt to use this unique feature of the individual for the purpose of identification and recognition. The proposed technique uses the concept of Fourier Transformation (FT) to extract the pitch of the voice and it also uses the method of noise removal and extraction of the noise free voice. The pitch for each individual is extracted and saved in the database. To recognize the voice, the proposed model makes use of neural network to train and classify the new voice.
Postural Control in Lowlanders With COPD Traveling to 3100 m: Data From a Randomized Trial Evaluating the Effect of Preventive Dexamethasone Treatment

Objective: To evaluate the effects of acute exposure to high altitude and preventive dexamethasone treatment on postural control in patients with chronic obstructive pulmonary disease (COPD). Methods: In this randomized, double-blind parallel-group trial, 104 lowlanders with COPD GOLD 1-2 age 20–75 years, living near Bishkek (760 m), were randomized to receive either dexamethasone (2 × 4 mg/day p.o.) or placebo on the day before ascent and during a 2-day sojourn at Tuja-Ashu high altitude clinic (3100 m), Kyrgyzstan. Postural control was assessed with a Wii Balance BoardTM at 760 m and 1 day after arrival at 3100 m. Patients were instructed to stand immobile on both legs with eyes open during five tests of 30 s each, while the center of pressure path length (PL) was measured. Results: With ascent from 760 to 3100 m the PL increased in the placebo group from median (quartiles) 29.2 (25.8; 38.2) to 31.5 (27.3; 39.3) cm (P < 0.05); in the dexamethasone group the corresponding increase from 28.8 (22.8; 34.5) to 29.9 (25.2; 37.0) cm was not significant (P = 0.10). The mean difference (95% CI) between dexamethasone and placebo groups in altitude-induced changes (treatment effect) was -0.3 (-3.2 to 2.5) cm, (P = 0.41). Multivariable regression analysis confirmed a significant increase in PL with higher altitude (coefficient 1.6, 95% CI 0.2 to 3.1, P = 0.031) but no effect of dexamethasone was shown (coefficient -0.2, 95% CI -0.4 to 3.6, P = 0.925), even when controlled for several potential confounders. PL changes were related more to antero-posterior than lateral sway. Twenty-two of 104 patients had an altitude-related increase in the antero-posterior sway velocity of >25%, what has been associated with an increased risk of falls in previous studies. Conclusion: Lowlanders with COPD travelling from 760 to 3100 m revealed postural instability 24 h after arriving at high altitude, and this was not prevented by dexamethasone. Trial Registration: clinicaltrials.gov Identifier: NCT02450968.
Psychopathy and Interests: Implications of Psychopathic Personality Traits for Vocational and Avocational Preferences

General personality traits and interests, both vocational and avocational, have long been considered intertwined constructs. Nevertheless, the linkages between personality disorder features, such as psychopathy, and interests are poorly understood. This study bridges this gap by examining how psychopathic traits relate to vocational and avocational interests, and to what extent these associations are distinctive to psychopathy as opposed to a broader pattern of general and abnormal personality traits. In a sample of 426 community participants, Psychopathic Personality Inventory–Revised Fearless Dominance features of psychopathy were associated with interest in a broad swath of vocational and avocational interests, whereas Self-Centered Impulsivity features were associated with realistic, artistic, enterprising, and conventional interests; most zero-order associations were in the small to medium range. Coldheartedness and the factors derived from the Levenson Self-Report Psychopathy Scale were largely unrelated to interests, although there were several notable exceptions. Narcissistic traits, as well as HEXACO (Honesty–Humility, Emotionality, Extraversion, Agreeableness, Conscientiousness, and Openness) Honesty–Humility, Extraversion, and Openness to Experience, were also related broadly to interests. The patterns of interests associated with personality disorder traits may ultimately bear practical implications for interventions as individuals seek out positions or hobbies that suit their traits.
Proximal Pathway Enrichment Analysis for Targeting Comorbid Diseases via Network Endopharmacology

The past decades have witnessed a paradigm shift from the traditional drug discovery shaped around the idea of “one target, one disease” to polypharmacology (multiple targets, one disease). Given the lack of clear-cut boundaries across disease (endo)phenotypes and genetic heterogeneity across patients, a natural extension to the current polypharmacology paradigm is to target common biological pathways involved in diseases via endopharmacology (multiple targets, multiple diseases). In this study, we present proximal pathway enrichment analysis (PxEA) for pinpointing drugs that target common disease pathways towards network endopharmacology. PxEA uses the topology information of the network of interactions between disease genes, pathway genes, drug targets and other proteins to rank drugs by their interactome-based proximity to pathways shared across multiple diseases, providing unprecedented drug repurposing opportunities. Using PxEA, we show that many drugs indicated for autoimmune disorders are not necessarily specific to the condition of interest, but rather target the common biological pathways across these diseases. Finally, we provide high scoring drug repurposing candidates that can target common mechanisms involved in type 2 diabetes and Alzheimer’s disease, two conditions that have recently gained attention due to the increased comorbidity among patients.
Cross age face recognition using deep learning model based on center symmetric LPS

The paper presents an age invariant face recognition model using center symmetric local pattern selection (CS-LPS) algorithm. The facial appearance can change as age progresses and this is one of the most challenging factors in ageing face recognition. In this paper a combination of two methods ie. , HOG and CS-LPS are used so that the structural as well as the intensity information of an image is obtained. A binary tree structure is used for dimensionality reduction. The reduced data is given to a logistic regression classifier in which the test data is also compared. The dataset used in this paper is Cross-age celebrity dataset (CACD) which includes 163, 446 images from 2000 celebrities. A significant improvement in accuracy can be seen in this method.
Study of Thai organizational commitment using a three-component model

Recently, the concept of organizational commitment has received more attention as an important factor for organizational efficiency and the motivation of members. According to several previous studies, it is assumed that higher organizational commitment leads to better organizational efficiency and higher motivation among members. The concept of the relationship between individuals and their organization has received increased attention as an important factor for organizational efficiency and the motivation of members. The purpose of this study was to investigate the influence of national character on organizational commitment. Organizational commitment among students belonging to the Thai-Nichi Institute of Technology (TNI) was measured using a three- component model. It was clarified using the attributes of organization commitment of Thai by factors analysis. As a result, 6 factors were identified. The 6 factors were named based on the loading of each factor as Emotional affection, Value of belonging, Continuance awareness, Internalization consciousness, Reflection and Utilitarian consciousness. The output of 6 factors was plotted in 15 scatter diagrams. The results clearly showed the distinctive features of Thai people. Thus, the study supposed that creating a valuable organization for Thai characteristics is important.
Dynamic query forms (DQF) using ranking models for database queries

The widespread use of databases in government, industry, and education, and the development of the Internet and World Wide Web result into large and complex databases. Modern science and scientific database also have lots of attributes. The static and automatic query forms are not able to get user required result on these huge databases. This paper proposes a query form interface, which is able to dynamically generate query forms called as Dynamic Query Forms (DQF) with the ranking model to enhance the feature with ranked form components. The DQF captures feedback and reference from the user ad those are used to rank query form components which assist the user in making decisions of choosing the highest ranked component. In this paper, we used three different ranking models 1) Probabilistic ranking model, 2) learning to rank model by using frequent pattern tree and 3) Bayesian network model. The iterative process is used to generate a query form and is guided by the user. The system automatically generates ranking lists of form components at each of the iteration by using ranking model chosen by the user. And the user can update or enhance the query form using those ranked query form component to get the desired result. After enhanced updated query form the user can submit those queries to view the result at each of the iteration. Our experimental evaluation compares between three ranking models and demonstrates effectiveness and efficiency of the system.
Wavelet analysis for upscaling of two dimensional permeability fields

We use a multiresolution analysis based on a wavelet transform to upscale two-dimensional heterogeneous permeability fields. Results are compared with conventional averaging upscaling schemes using arithmetic, geometric and harmonic averaging. It is found that the wavelet analysis is able to preserve the fine scale heterogeneous features with the increase of the coarse scale size significantly better than the averaging methods, while preserving information required for downsizing the properties of the system.
Impact of photo voltaic cells on hybrid micro grid power distribution systems

In spite of availability of natural sources, still people are now sitting on the dark zone. Because of the remote area, the grid networking system is very weak. As a result of which power stability may not be assured, which greatly effects to the day-today activities of the people in the reason. Fortunately, technology exist that can give the assurance of quality of electricity in an isolated area which is for away from the regular power network. The preset scenario is the coexistence of both AC and DC micro grids, because of the evolutions of different types of loads. The hybrid microgrid concept assume a cluster of different types of loads and distributed generatoroperating as a controllable system which maintain the quality of power and its stability to its local area. To make the world pollution free environment, renewable energy infrastructure is increasing particularly in the distribution network. It has the advantage that the power generated through distributed generation system is utilized in the grid for other users. The PV cell generates voltage at the distribution level. At the end of distribution line a 1 MVA three phase load is connected. In the absence of the generation from the PV cells, the voltages and currents at various lengths of the line are calculated with a single load of three phase 1 MVA. With the inclusion of PV cells, the improvement in voltage dip is also estimated. This estimate provides the power supplied by the PV cell and improvement in voltages. In order to determine the effect of additional load on voltage profile, a three phase load of 1 MVA was connected to intermediate point in the transmission system. This causes the dip in voltage at various nodes of the distribution line. Several combinations of source and loads have been examined to determine the effect on voltage and power. The above work has been carried out using Electro Magnetic Transient Program (EMTP) Software. In order to simulate the system, 8.98 kV, 5 km long line is divided into 5 sections of 1 km each. The sections are represented in the form of Pi-network by its series inductance, resistance and capacitance to ground. Loads are represented by R and L of appropriate magnitude and through EMTP program the calculations are done. The PV cell is represented by a source stepped up to 8.98 kV system. Both single phase and three phase lines have been examined through the above program. The results obtained for various conditions are presented in the paper.
A novel unit commitment model considering electric heating load and its effects on wind power integration

It is inevitable for traditional coal-burning heating approach to be replaced by cleaner means. Particularly, electric heating is being vigorously promoted in some countries including China. Due to the good complementary features between heat and electric power, electric heating load can be regarded as a dispatchable resource that improves the flexibility of power systems. This paper proposes a novel unit commitment model incorporating electric heating load as a type of demand response. The dispatch of electric heating load is optimized together with the schedule of generation units. A numerical study is carried out based on the IEEE 30 Bus system, which verifies the effectiveness of the proposed model. Results indicate that the total operation cost and carbon emission of power system can be reduced, and more wind power generation can be integrated, as a result of the optimal utilization of electric heating load demand response with this model.
New Problem Formulation for Optimal Demand Side Response in Hybrid AC/DC Systems

This paper proposes a new problem formulation to simplify the mathematical representation for a hybrid ac/dc domestic energy system. Instead of building the formulation from a component level, the new formulation is built at the whole system level such that all power transfers between ac/dc or dc/ac are reflected in the ac power drawn from the main grid. This is achieved by assigning the ac power drawn from the grid as a piecewise function of local dc power, each slope in the function represents the conversion efficiency, making it flexible to consider a wide range of conversion efficiencies for different system components. The new formulation substantially reduces the number of variables/constraints, improves efficiency modelling accuracy and increases the search efficacy. The piecewise functions are directly solved by a mixed integer linear programming (MILP). The performance of the proposed formulation is illustrated by the hybrid ac/dc energy systems at a primary school in the U.K. The results show that the proposed problem formulation and the MILP solution method provide an effective optimal control strategy for a mixed ac/dc domestic energy system in the presence of variable tariffs and differing conversion efficiency, achieved a cost saving of 24% increase in energy bills compared with the traditional approach.
Omega-3 Fatty Acid Supplementation During Pregnancy continued

Omega-3 fatty acids are essential and can only be obtained from the diet. The requirements during pregnancy have not been established, but likely exceed that of a nonpregnant state. Omega-3 fatty acids are critical for fetal neurodevelopment and may be important for the timing of gestation and birth weight as well. Most pregnant women likely do not get enough omega-3 fatty acids because the major dietary source, seafood, is restricted to 2 servings a week. For pregnant women to obtain adequate omega-3 fatty acids, a variety of sources should be consumed: vegetable oils, 2 low-mercury fish servings a week, and supplements (fish oil or algae-based docosahexaenoic acid). [Rev Obstet Gynecol. 2008;1(4):162-169]
Implementation of integrated converter topologies for grid-connectedphotovoltaic module

The growth of photovoltaic (PV) module generation is growing at almost an exponential rate. A PV power system with high voltage gain and the steady-state analysis are presented in this paper. For a typical photovoltaic cell, the output voltage is comparatively low, and for grid connection it required high voltage gain. Hence the proposed topology uses interleaved converter coupled with inductor coil and PWM technique as a primary conversion stage which moderate the voltage to a high dc bus voltage. In second stage a bridge inverter with bidirectional power flow strategy which interconnect grid. In addition to this for power balancing a Maximum Power Point Tracking (MPPT) system is applied gives high performance with minimum cost and high dynamic response.
Interactive fuzzy satisfying method for optimal multi-objective VAr planning in power systems

The paper presents an interactive fuzzy satisfying method for solving a multi-objective VAr planning problem by assuming that the decision maker (DM) has imprecise or fuzzy goals for each of the objective functions. Through the interaction with the DM, the fuzzy goals of the DM are quantified by eliciting corresponding membership functions. If the DM specifies the reference membership values, the minimax problem is solved for generating a corresponding global noninferior optimal solution for the DM's reference membership values. Then, by considering the current values of the membership functions as well as the objectives, the DM acts on this solution by updating the reference membership values. The interactive procedure continues until the satisfying solution for the DM is obtained. The minimax problem can be easily handled by the simulated annealing (SA) approach which can find a global optimal solution even for the solution space which is nonconvex and the objective functions which are nondifferentiable. Results of the application of the proposed method are presented.
Contribution of environmental forcings to US runoff changes for the period 1950–2010

Runoff in the United States is changing, and this study finds that the measured change is dependent on the geographic region and varies seasonally. Specifically, observed annual total runoff had an insignificant increasing trend in the US between 1950 and 2010, but this insignificance was due to regional heterogeneity with both significant and insignificant increases in the eastern, northern, and southern US, and a greater significant decrease in the western US. Trends for seasonal mean runoff also differed across regions. By region, the season with the largest observed trend was autumn for the east (positive), spring for the north (positive), winter for the south (positive), winter for the west (negative), and autumn for the US as a whole (positive). Based on the detection and attribution analysis using gridded WaterWatch runoff observations along with semi-factorial land surface model simulations from the Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP), we found that while the roles of CO2 concentration, nitrogen deposition, and land use and land cover were inconsistent regionally and seasonally, the effect of climatic variations was detected for all regions and seasons, and the change in runoff could be attributed to climate change in summer and autumn in the south and in autumn in the west. We also found that the climate-only and historical transient simulations consistently underestimated the runoff trends, possibly due to precipitation bias in the MsTMIP driver or within the models themselves.
EVA: Fair and Auditable Electric Vehicle Charging Service using Blockchain

The recent success of electric vehicles leads to unprecedentedly high peaks of demand on the electric grid at the times when most people charge their cars. In order to avoid unreasonably rising costs due to inefficient utilization of the electricity infrastructure, we propose EVA: a scheduling system to solve the valley filling problem by distributing the electricity demand generated by electric vehicles in a geographically limited area efficiently over time spans in which the electric grid is underutilized. EVA is based on a smart contract running on the Ethereum blockchain in combination with off-chain computational nodes performing the schedule calculation using the Alternating Direction Method of Multipliers (ADMM). This allows for a high degree of transparency and verifiability in the scheduling computation results while maintaining a reasonable level of efficiency. In order to interact with the scheduling system, we developed a decentralized app with a graphical frontend, where the user can enter vehicle information and future energy requirements as well as review upcoming schedules. The calculation of the schedule is performed on a daily basis, continuously providing schedules for participating users for the following day.
Perturbation Theories Based on the Density Matrix Renormalization Group

This thesis describes the development of perturbation theories based on the density matrix renormalization group (DMRG) and their applications to strongly correlated electronic systems. We introduce two types of perturbation theories based on DMRG for systems with a mixture of static and dynamic correlation. The first one uses the framework of multi-reference perturbation theory. We present a combination of the DMRG and the strongly-contracted variant of second order N -electron valence state perturbation theory (SC-NEVPT2) that uses an efficient algorithm to compute high order reduced density matrices from DMRG wave functions. To demonstrate its capability, we apply it to calculations of the chromium dimer potential energy curve at the basis set limit, and the excitation energies of the poly-p-phenylene vinylene trimer. The second one, perturbative DMRG (p-DMRG), is designed for systems with very large active spaces. In p-DMRG, a zeroth-order wavefunction with a small number of variational parameters is first obtained by a standard DMRG calculation. Then, the residual correlation is recovered by a second-order perturbative treatment. To circumvent the problem of a large bond dimension in the first order wavefunction, we use a sum of several DMRG wavefunctions and extrapolation schemes to expand the first-order wavefunction, yielding substantial savings in computational cost and memory. We also present an efficient stochastic algorithm for p-DMRG, which bypasses the difficulty in solving the first-order equation in the deterministic algorithm. The key part of the algorithm is to represent the right hand side of the first-order equation, QV̂ |Ψ0〉, through randomly sampled Slater determinants, and to use the EpsteinNesbet (EN) partitioned zeroth-order Hamiltonian. The efficiency of this algorithm is demonstrated for typical benchmark molecules, the carbon dimer and the chromium dimer. iii
Fuzzy adaptive gamma correction for contrast enhancement

This paper presents a new contrast enhancement algorithm based on fuzzy logic. The fuzzy contrast distribution (FCD) is introduced in this work. The algorithm computes FCD of the input image using fuzzy membership function and fuzzy rules. An adaptive parameter gamma is defined using FCD. The algorithm uses gamma correction with adaptive gamma to obtain the contrast-enhanced images. The performance of the proposed algorithm is compared with conventional as well as state-of-art contrast enhancement algorithms. The assessment of the experimental results is performed quantitatively and visually. The assessment of results shows that the proposed algorithm outperforms most of the existing contrast enhancement algorithms. The proposed algorithm yields good contrast, natural-looking images.