Miracle-dz/UniFAR-FTUs huggingface dataset:
Social Capital and Mental Health among Older Adults in Indonesia: A Multilevel Approach

In the social epidemiology field, social capital plays an important role as a determinant of a population’s mental health. Despite the vast number of studies on this issue, few have been conducted among the aging Indonesian population. This study is intended to investigate this association using a multilevel approach on a sample of 2,496 older adults. The data come from the 5th wave of the Indonesian Family Life Survey. The results revealed that taking into account cluster-specific random effects, social trust at the individual level predicted decreased depression among older adults only before adjusting for control variables. Neighborhood safety emerged as the only indicator of social capital that predicted reduced depression of elders after adjusting for control variables. In addition, high levels of poverty at the community level were associated with increased depression, and having completed senior high school, being healthy, and doing physical activity frequently were consistently associated with reduced depression across all models. In conclusion, living in a safe community with reduced levels of poverty might be protective factors against depression for older adults.
A linear array design based on non-uniform Chebyshev distribution for high gain low sidelobe level millimetre wave 5G applications

Antenna is the front-end element and is a major part of designing a robust communication system. The increased user density and high data rate requirements now a days can be overcome by a wideband antennas capable of having a stable radiation pattern with high gain and low sidelobe level. This study presents the design and analysis of a linear array antenna based on Chebyshev current distribution for mm-wave applications. An extensive review of the most recently published work on array design and architecture for the future generation (5G) antennas is also included. Systematic approach is followed towards the design of eight elements corporate feed for non-uniform amplitude excitation using unequal power split T-junctions. The array is designed on a Rogers RO5880 substrate with a compact size of 45 x 15 x 0.32 mm. Two design configurations have been demonstrated. The simulation results show that the first design offers an impedance bandwidth of 5% and a gain and sidelobe level (SLL) of 12 dB and -16 dB, respectively. To further enhance the antenna array performance a second array design is proposed. The modified design has an improved impedance bandwidth of 13%, also the gain and SLL have been enhanced to 14 dB and -19 dB, respectively. Results of the first array configuration have been validated by fabricating and testing the antenna prototype. The proposed array antenna is a promising candidate for the future mm-wave communication devices due to its compact structure and very good performance parameters such as high gain, wide-bandwidth and reduced sidelobe level.
Success factors of small and medium enterprises in the Malaysian furniture industry: Discerning the growth of entrepreneurs

Entrepreneurs and small and medium enterprises are the foundation of the Malaysian furniture industry. Yet, in a multi-ethnic society such as Malaysia, the success factors of entrepreneurs and small and medium enterprises (SMEs) in the furniture industry have not been studied. Therefore, this study evaluated the success factors of entrepreneurs of the Malay and Chinese ethnic groups in the furniture industry and discerned the growth trajectory of young entrepreneurs from wood science and technology programs in the furniture industry. A questionnaire-based survey was used with the assistance of relevant trade associations and universities. The results were statistically analyzed to establish the significant differences between the two ethnic groups in their perceived success factors. The results revealed that Malay entrepreneurs pay more attention to political and socio-cultural factors to gain success, whereas Chinese entrepreneurs focus on enhancing their competitiveness to remain viable. Further, young graduates showed a reduced desire to pursue a career in the furniture industry, as they deem it not environmentally sustainable in addition to limited career growth. These results suggested that current entrepreneurship development programs may need to be revised, so as to assist in producing more resilient and successful entrepreneurs in the future in the furniture industry.
The Effect of Silver Grating and Nanoparticles Grafting for LSP–SPP Coupling and SERS Response Intensification

The systematic study of structures with gold and silver nanoparticles (MeNPs) of various shapes and dielectric functions immobilized onto the silver grating is performed. The structures may serve as double resonance SERS (surface-enhanced Raman spectroscopy) systems with coupling between surface plasmon polariton (SPP) supported by the silver grating and localized surface plasmons (LSPs) excited on the grafted metal nanoparticles (MeNPs). The silver grating supports SPP excitation under the 785 nm wavelength illumination. Spherical silver and gold nanoparticles, triangular silver nanoprisms and gold nanorods are prepared and used with the aim to gradually cover the LSP excitation in the 400–850 nm wavelength range. MeNPs are grafted through the 4,4′-biphenyldithiol (BFDT) linkers. Rhodamine 6G (R6G) molecules are added onto SERS substrates and located above and between the MeNPs. Several wavelengths (470, 532, and 785 nm) are applied to probe the SERS response. Depending on the nanoparticles type and exci.
Computing and optimizing the robust strong H-inﬁnity norm of uncertain time-delay systems

: This short paper presents a method to compute and optimize the robust strong H-inﬁnity norm of linear time-invariant systems with discrete delays and uncertainties on the system matrices. Special attention will be paid to a fragility problem of the H-inﬁnity norm for systems with discrete delays in the direct feed-through terms. More speciﬁcally, for such systems the H-inﬁnity norm might be sensitive to arbitrary small delay changes. This fragility problem can be resolved by considering the strong H-inﬁnity norm, which takes into account inﬁnitesimal delay perturbations. The robust strong H-inﬁnity norm is subsequently deﬁned as the worst-case strong H-inﬁnity norm over all instances of the uncertainties and is a measure for robust performance. It can be shown that this robust strong H-inﬁnity norm is related to the robust distance to instability of an associated uncertain system described by delay diﬀerential-algebraic equations. Using this relation, the robust strong H-inﬁnity norm can be computed eﬃciently. This eﬃcient computation of the robust strong H-inﬁnity norm will be used for controller design by direct minimization of the robust strong H-inﬁnity norm as function of the controller parameters.
S YNERISE AT KDD C UP 2021: N ODE C LASSIFICATION IN M ASSIVE H ETEROGENEOUS G RAPHS

We describe our winning solution to the KDD Cup 2021 Open Benchmark Challenge. We tackle the task of academic paper classiﬁcation within a heterogeneous graph containing paper, author and institution nodes. We present an efﬁcient model based on our previously introduced algorithms: EMDE and Cleora, on top of a simplistic feed-forward neural network. Our solution can be trained on a single commodity 16 GB GPU, taking around 40 minutes per model. We achieve the 1st place with 0.7454 test accuracy on the initial leaderboard, and 0.7460 test accuracy on the ﬁnal evaluation set. We release the source code at: https://github.com/Synerise/kdd-cup-2021
Frequency of copy number variants involving the sex chromosomes in a clinical setting

Copy number variants (CNVs) are a common finding in the clinical setting and contribute to both genetic variation as well as disease. Recently, studies have described the accumulation of multiple CNVs as a disease modifying mechanism. While it has been characterized how additional CNVs may play a role in phenotype, in which ways and to what extent sex chromosomes are involved has not been fully described. We performed a secondary data analysis using the DECIPHER database on 2,273 de-identified individuals with 2 CNVs. CNVs were designated primary and secondary based on our criteria and characteristics of both CNV groups were described. Further analysis was performed identifying differences in CNVs on the sex chromosomes vs autosomes. We found that CNVs on the sex chromosome have a significant difference compared to autosomes when comparing median size (p=0.013), pathogenicity classifications (p<0.001), and variant classification (p=0.001). We identified chromosome combinations for primary and secondary CNVs, and identified the X chromosome was the most common site for a secondary CNV. Additionally, we observed the plurality of secondary CNVs fell in the same chromosome as the primary CNV. From this study, we can conclude that the X chromosome is the most common site for secondary CNVs in a clinical setting. Identification of chromosome combinations for primary and secondary CNVs is essential in explanation of complex phenotypes and highlights areas of importance of the human genome.
Multiclass Classification using Genetic Programming

— Data classification has received increasing interest lately. It is a challenging task due to uncertainty, unpredictability and inconsistency of data. This challenge increases in the case of multi-class classification. Genetic Programming (GP) has shown promising results as an efficient and robust classification strategy. For multiclass classification, multi-tree chromosome classifiers can be used, where each tree is an arithmetic expression that discriminates between one and rest of the classes. In this paper, we have emphasized fitness of an individual tree in multi-tree classifiers which adds to the fitness of whole chromosome and results in better classifier efficiency. A series of experiments have been conducted to support the efficiency of proposed algorithm and the results have been found encouraging.
PDCNN: FRAMEWORK for Potato Diseases Classification Based on Feed Foreword Neural Network

The economy is exceptionally reliant on agricultural productivity. Therefore, in domain of agriculture, plant infection discovery is a vital job because it gives promising advance towards the development of agricultural production. In this work, a framework for potato diseases classification based on feed foreword neural network is proposed. The objective of this work is presenting a system that can detect and classify four kinds of potato tubers diseases; black dot, common scab, potato virus Y and early blight based on their images. The presented PDCNN framework comprises three levels: the pre-processing is first level, which is based on K-means clustering algorithm to detect the infected area from potato image. The second level is features extraction which extracts features from the infected area based on hybrid features: grey level run length matrix and 1st order histogram based features. The attributes that extracted from second level are utilized in third level using FFNN to perform the classification process. The proposed framework is applied to database with different backgrounds, totally 120 color potato images, (80) samples used in training the network and the rest samples (40) used for testing. The proposed PDCNN framework is very effective in classifying four types of potato tubers diseases with 91.3% of efficiency.
Adversarial Multi Class Learning under Weak Supervision with Performance Guarantees

We develop a rigorous approach for using a set of arbitrarily correlated weak supervision sources in order to solve a multiclass classiﬁcation task when only a very small set of labeled data is available. Our learning algorithm provably converges to a model that has minimum empirical risk with respect to an adversarial choice over feasible labelings for a set of unlabeled data, where the feasibility of a labeling is computed through constraints deﬁned by rigorously estimated statistics of the weak supervision sources. We show theoretical guarantees for this approach that depend on the information provided by the weak supervision sources. Notably, this method does not require the weak supervision sources to have the same labeling space as the multiclass classiﬁcation task. We demonstrate the effectiveness of our approach with experiments on various image classiﬁcation tasks.
Stigmasterol and Stigmasterone from Methanol Extract of Calophyllum soulattri Burm. F. Stem Bark

Stigmasterol and Stigmasterone from Methanol Extract of Calophyllum soulattri Burm. F. Stem Bark. Calophyllum soulattri Burm. F. has been widely used for herbal medicine. Phytochemical investigation of C. soulattri contains a secondary metabolite of the steroid class. Steroid compounds have various biological activities, such as anti-inflammatory, antioxidant, antiproliferative, antibacterial, antimalarial, and anticancer. Two secondary metabolites steroids have been isolated and identified from the stem bark extract of C . soulattri . Isolation was carried out through the extraction (maceration), fractionation, and purification stages. Maceration is carried out using methanol as a solvent. Fractionation was carried out by vacuum liquid chromatography (VLC), and purification was by flash column chromatography. Identification of combined fractions and determination of pure isolates were used through thin-layer chromatography (TLC). The solvent used in the chromatography methods was a mixture of n-hexane and ethyl acetate. The structure isolates were identified by FTIR, 1 H NMR, and 13 C NMR and compared with literature data. Secondary metabolites steroids that have been isolated are identical compounds to stigmasterol and stigmasterone.
Youth , higher education , and employment : A case study of Jharkhand state

Youth, higher education, and employment: A case study of Jharkhand state Saurav Kumar This paper examines youth, higher education, and employment in the state of Jharkhand. It analyzes the implications of higher education on the employment of the region. It is based on a case study of NilamberPitamber University (a state university) located at Medininagar, Jharkhand. Data were collected online in the months of June-July using Google form questionnaire and a total of 360 students from different colleges of the university responded to the questions. The questionnaire on the quality of higher education was divided into four categories very low, low, medium, and high. In the primary survey, 21% of students responded that the quality of education is very low; 51% responded low; 24% responded to medium, and 4% responded to high. This study shows that the quality of education is very poor in the state and it has serious repercussions on its youth and their employment status in the state. Further, it shows that students are mostly inclined towards government jobs (73.89%) as found in the primary survey and they are preparing for it for a long period of time. Students unable to acquire a government job and lack of marketable skills for the corporate job have made them educated unemployed and it was concluded that these problems if not solved at its earliest, may lead to the demographic disaster in the state.
A comparative study of the effect of fluorine substitution on the photovoltaic performance of benzothiadiazole-based copolymers

Two conjugated alternating copolymers to be used as the donor materials of the active layers in polymer solar cells have been designed and synthesized via a Stille coupling reaction. The alternating structure consisted of 4,8-bis((2-ethylhexyl)oxy)benzo[1,2-b:4,5-b′]dithiophene (BDT) as the donor unit and benzo[c][1,2,5]thiadiazole (BT) or fluorinated benzo[c][1,2,5]thiadiazole (FBT) as the acceptor unit, along with a thiophene group as the π-bridge between the donor and acceptor units. Since the donor units have attached alkoxy pendant chains, both polymers were soluble in common organic solvents. UV-vis spectra of both copolymers exhibited broad absorption bands in the range of 325–900 and 380–900 nm, respectively, and corresponding low band gaps of 1.82 and 1.80 eV. After fluorination of the BT unit, the highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) energy levels of the polymer were lowered and estimated to be −5.51 and −3.71 eV, respectively. It was found that substitution of an F atom into the BT units facilitated the intramolecular charge transfer. In comparison with the nonfluorinated polymer, the photovoltaic performance of the fluorinated polymer was significantly improved due to the enhanced Jsc and Voc. Based on the ITO/PEDOT:PSS/polymer:PC61BM/LiF/Al device structure, the optimal device efficiency was obtained from a device with a blend of PBDTFBT and PC61BM at a weight ratio of 1 : 1. For this blend ratio, the values of Jsc and power conversion efficiency (PCE) obtained at room temperature are 7.98 mA cm−2 and 3.62%, respectively, under the illumination of AM 1.5 (100 mW cm−2).
Dichloro-diphenyl-trichloro-ethane (DDT) and hexachlorohexane (HCH) pesticide residues in foodstuffs from markets in Ile-Ife, Nigeria

Levels of dichlorodiphenyltrichloroethanes (DDTs) and Hexachlorocyclohexanes (HCHs) pesticide were evaluated in samples of cowpea grains and dried yam chips obtained from Ile-Ife markets, southwestern Nigeria. The objectives of the study were to establish the presence of the organochlorine pesticide residues in the foodstuffs and also to ascertain whether the residue levels are above their respective permissible levels. Randomly selected samples from highly patronized markets were subjected to standard extraction protocols. The DDT and HCH levels were determined using Gas Chromatography coupled with Electron Capture Detector (GC-ECD). The mean concentration of ϒ-HCH (Lindane) in cowpea grains was 0.085 mg/kg and 0.042 mg/kg in dried yam chips. The ρ,ρ’-DDD concentrations were 0.086 mg/kg and 0.159 mg/kg in cowpea and dried yam chips, respectively. ϒ-HCH detected in all the cowpea samples and 90% in dried yam chips samples were above the recommended EU maximum residue level (MRL) of 0.01 mg/kg. The results of this study indicated that substances that could pose serious health risks were associated, at outrageous levels, with important foodstuffs in Nigerian markets even to the point of sales and consumption. Keywords : Dichlorodiphenyltrichloroethane, hexachlorocyclohexane, pesticide, residue, cowpea grain, yam chip.
Efficient network immunization strategy based on generalized Herfindahl–Hirschman index

The topic of finding effective strategies to restrain epidemic spreading in complex networks is of current interest. A widely used approach for epidemic containment is the fragmentation of the contact networks through immunization. However, due to the limitation of immune resources, we cannot always fragment the contact network completely. In this study, based on the size distribution of connected components for the network, we designed a risk indicator of epidemic outbreaks, the generalized Herfindahl–Hirschman index (GHI), which measures the upper bound of the expected infection’s prevalence (the fraction of infected nodes) in random outbreaks. An immunization approach based on minimizing GHI is developed to reduce the infection risk for individuals in the network. Experimental results show that our immunization strategy could effectively decrease the infection’s prevalence as compared to other existing strategies, especially against infectious diseases with higher infection rates or lower recovery rates. The findings provide an efficient and practicable strategy for immunization against epidemic diseases.
Invariant measures of fractional stochastic delay reaction–diffusion equations on unbounded domains

In this paper, existence of invariant measure is mainly investigated for a fractional stochastic delay reaction–diffusion equation defined on unbounded domains. We first establish the mean-square uniform smallness of the tails of the solutions in order to overcome the non-compactness of standard Sobolev embeddings on unbounded domains. We then show the weak compactness of a family of probability distributions of the solutions by combining the Ascoli–Arzelà theorem, the uniform tail-estimates as well as the technique of dyadic division.
Dehydration measurement using sweat sensor patch and pulse sensor

Technology has increased visibly large. People find ways to easily detect their conditions. Heartbeat and dehydration level is one of the most common among people especially athletes. Athletes are drifted towards wearable technologies to track their training and recovery. Usually a dehydrated person sweats more. The sweat contains various physiological substance and health data. Based on studies sweat consists of salts and ions like [Na+], [K+], [Cl] lactate, glucose and ammonia. We are developing a wearable sweat analyser that can detect the raise in concentration of sodium and potassium. The sweat patch consist of a counter electrode created by screen printing Silver Chloride (AgCl) nanoparticles on Polydimithylsiloxane (PDMS) material and reference electrode (RE) created on the patch material by adding a platinum nanoparticle. This sweat sensor patch is connected to a microcontroller along with a temperature sensor and a Bluetooth module. The data which is collected by the sweat patch and the temperature sensor is sent via Bluetooth to mobile application were the data is displayed.
Simulation And Modelling of BCI Based Multi Purpose Wheel Chair For Paralysed People

According to the survey of Reeve foundation and WHO says, there are nearly 1 in 50 people living with paralysis, every year 5.4 million people affected. Paralysis is caused by spinal injury, stroke, multiple sclerosis, cerebral palsy and other causes like motor accidents and victim of violence. It is also called loss of muscle function in some part of the body. In this paper, we systematically survey a different techniques used for brain computer interface and also review the research on non-invasive, electroencephalography (EEG)-based BCI systems for communication and rehabilitation. Main research focus on previous techniques like deep learning or deep neural network, machine learning, Neuro plasticity, support vector machine, artifact suppression and so on. In this Differing from traditional machine learning algorithms, online sequential machine learning algorithm is empowered to learn distinct high-level representations from raw brain signals without manual feature selection. A certain MATLAB program is designed to use these motions. Our results are to exploit the accuracy rate and to generate the assistive devices for restoration of movement and communication strength for physically disabled patients in order to rehabilitate their lost motor abilities.
Medical Image Denoising Using Two-Stage Iterative Down-Up CNN and SURF Features

This paper proposes a Two-Stage Deep Iterative Down-Up Convolutional Neural Network for denoising the medical images, which more often increase and decrease the feature map resolution. The function, Speeded Up Robust Features (SURF) is employed in the proposed system to handle the problem of gradient vanishing. The SURF is a patented local feature detector and descriptor. Experiment is conducted using several different medical noise images like Computed Tomography (CT) and Ultrasound Image and found that the proposed system outperforms the current state-of-the-art consistently in image denoising methodologies.