Miracle-dz/UniFAR-FTUs huggingface dataset:
Application of Water Quality Index and Multivariate Statistical Techniques to Assess and Predict of Groundwater Quality with Aid of Geographic Information System

In this study, the groundwater quality and spatial distribution of the Basra province in the south of Iraq was assessed and mapped for drinking and irrigation purposes. Groundwater samples (n = 41) were collected from deep wells in the study area to demonstrate, estimate and model the Water Quality Index (WQI). The analysis of water samples integrated with GIS-based IDW technique was used to express the spatial variation in the study area with consideration of WQI. The physicochemical parameters, including pH, sodium (Na + ), electrical conductivity (EC), chloride (Cl - ), total dissolved solids (TDS), calcium (Ca 2+ ), nitrate (NO 3- ), sulfate (SO 42- ), magnesium (Mg 2+ ), and bicarbonate (HCO 3- ) were identified for groundwater quality assessment. The results of calculated WQI classify groundwater into three sorts. The results of WQI showed that 2.5%, 2.5% and 95% of the groundwater samples were classified as poor/very poor/unsuitable for drinking, respectively. The GIS tools integrated with statistical techniques are utilized for spatial distribution and description of water quality. Correlation analysis of groundwater data revealed that some parameters have actually a relationship that is strong with the other parameters and they share a common source of origin. Multivariate statistical techniques, especially cluster analysis (CA) and factor analysis (FA), were applied for the evaluation of spatial variations of forty-one selected groundwater samples. Cluster analysis confirmed that some different locations of wells have comparable sourced elements of water pollution, whereas factor analysis yielded three factors which are accountable for groundwater quality variations, clarifying more than 72% of the total variance of the data and permitted to group the preferred water quality. Multi-Layer Perceptron (MLP) models were applied in modeling the water quality index. Comparing different result values of the MLP network suggested that the values of MSE and r for the selected model are 0.1940 and 0.9998, respectively. Finally, it can be revealed that the MLP network precisely predicted the output, i.e. the WQI values.
3-V Input, 70-V Output, Fully Integrated Hybrid Charge Pump

A novel integrated-circuit charge-pump (CP) voltage multiplier conceived for high multiplying factors is proposed. The solution exploits a mixed architecture which merges both Cockcroft-Walton and Dickson topologies, by using on-chip 12-V poly-poly capacitors and 3.3-V CMOS transistors, maximizing the output voltage at the minimum silicon size. The proposed CP has been designed to supply a fully integrated driver for miniaturized motors in Micro Electro Mechanical Systems (MEMS) technology. It is conceived to boost the power-supply voltage to values higher than those that either a Dickson (D), Fibonacci (F), Cockroft-Walton (CW) or Serial-Parallel (SP) fully integrated CPs can generate in the selected technology process. Indeed, the output voltage of the former two CPs is limited by voltage constraints of on-chip capacitors while the multiplication efficiency of the latter two is impacted by high values of on-chip stray capacitors. Specifically, the target > 70 V output voltage has to be internally generated starting from the provided minimum power-supply voltage of 3 V. Furthermore, a mathematical model is proposed to evaluate the open load output voltage and the equivalent output resistance.
Unmatched Preconditioning of the Proximal Gradient Algorithm

This work addresses the resolution of penalized least-squares problems using the proximal gradient algorithm (PGA). PGA can be accelerated by preconditioning strategies. However, typical effective choices of preconditioners may correspond to intricate matrices that are not easily inverted, leading to increased complexity in the computation of the proximity step. To relax these requirements, we propose an unmatched preconditioning approach where two metrics are used in the gradient step and the proximity step. We provide convergence conditions for this new iterative scheme and characterize its limit point. Simulations for tomographic image reconstruction from undersampled measurements show the benefits of our approach for various simple choices of metrics.
On the Design and Development of Musculoskeletal Bipedal Robots

Bio-inspired musculoskeletal bipedal robots with tendon driven series elastic actuation including biarticular structures have the potential to outperform rigidly actuated robots. But the design, the control and the tuning of these bio-inspired robots are more complex than for their rigidly actuated counterparts. In this thesis new approaches to solving the problems arising from the bio-inspired design of robots are proposed and evaluated using a prototype series of musculoskeletal bipedal robots developed in the BioBiped project. This includes a systematic approach to tuning of hardware and software parameters in a hardware-in-the-loop optimization process with increased efficiency through the use of expert knowledge.
Implementing therapeutic community as a rehabilitation intervention for the imprisoned narcotic drug offenders with substance use disorder: special reference to Sri Lanka

Abstract Narcotic offenders with substance use disorder (SUD) have become a severe burden around the world including Sri Lanka. Prisons and other rehabilitation centers are threatened by overcrowding, due to recidivism, rearrest, and re-incarceration of narcotic offenders with SUD and limited capacities available in these centers. The therapeutic community (TC), owing to its comprehensive approach, long history of use, proven efficacy, and more importantly its relatability to Asian culture, has made it more appropriate for narcotic offenders with SUD sentenced to imprisonment, to subdue SUD while serving their prison sentence. TC is a rehabilitation intervention found by Tom Main in 1946 and a proper understanding of its core concept and elements is important when implementing a TC program. However, challenges are inevitable that need to be overcome when implementing a prison TC program. This paper intended to provide an insight to consider TC as a rehabilitation intervention for the imprisoned narcotic drug offenders with SUD. A special reference is provided to Sri Lanka, given its current requirement for an appropriate rehabilitation model for the prison community suffering from SUD.
Accuracy and processing speed trade-offs in classical and quantum SVM classifier exploiting PRISMA hyperspectral imagery

ABSTRACT Quantum machine learning (QML) focuses on machine learning models developed explicitly for quantum computers. Availability of the first quantum processor led to further research, particularly exploring possible practical applications of QML algorithms in the remote sensing field. The demand for extensive field data for remote sensing applications has started creating bottlenecks for classical machine learning algorithms. QML is becoming a potential solution to tackle big data problems as it can learn from fewer data. This paper presents a QML model based on a quantum support vector machine (QSVM) to classify Holm Oak trees using PRISMA hyperspectral Imagery. Implementation of quantum models was carried on a quantum simulator and a real-time superconducting quantum processor of IBM. The performance of the QML model is validated in terms of dataset size, overall accuracy, number of qubits, training and predicting speed. Results were indicative that (i) QSVM offered 5% higher accuracy than classical SVM (CSVM) with 50 samples and ≥12 qubits/feature dimensions whereas with 20 samples at 16 Qubits/feature dimension, (ii) training time for QSVM at maximum accuracy was 284 s with 50 samples and with 20 samples was 53.68 s and (iii) predicting time for 400 pixels using the QSVM model trained with 50 samples dataset was 5243 s whereas with 20 samples dataset was 2845 s. Results were indicative that QML offers better accuracy but lack training and predicting speed for hyperspectral data. Another observation is that predicting speed of QSVM depends on the number of samples used to train the model.
The Effect of Top Management Support on Environmental Sustainability. Does Supplier Development Matter in the Ghanaian Cocoa Industry?

One of the most challenging issues for stakeholders and researchers in the cocoa industry has been developing sustainability in the cocoa supply chain. This study was conducted to explore the effect of top management support on environmental sustainability in Ghana’s cocoa supply chain, as well as indicate the indirect role of supplier development in this chain. The study was conducted using survey data from 630 Cocoa farmers in Ghana, and it employed both exploratory and confirmatory factor analyses to assess the validity and reliability of the study constructs. The Partial Least Square-Structural Equation Modeling (PLS-SEM) was used in testing the hypotheses. The findings demonstrate that both top management and supplier development are significant predictors of environmental sustainability in Ghana’s cocoa supply chain. The result also showed a significant effect of top management support on supplier development, and it further indicated that supplier development is very important as such, it cannot be ignored in the effort to improve environmental sustainability in Ghana’s cocoa supply chain. The mediating analysis explored in this study revealed that supplier development partially mediates the relationship between top management support and environmental sustainability. The findings of the study showed that an optimal level of environmental sustainability in the cocoa supply chain can be achieved by combining top management support and supplier development. The outcome of the study dwells on cross-sectional data and it covered the views of the farmers at a specific period of time. Meanwhile, using a cross- sectional strategy limits the study’s capability to examine the implications of GBSR in ensuring sustainability over a period of time. However, a longitudinal approach that follows farmers over a time period could be used to offer much more insight into (the implications of GBSR on cocoa supply chain sustainability in Ghana).
Geometric morphometric comparison of the oral jaw and lower pharyngeal jaw of the closely-related cichlid, Vieja bifasciata, V. breidohri and V. hartwegi (Cichliformes: Cichlidae)

Systematics of the genus Vieja is complex, with many morphologically similar species that have little genetic differentiation. Examination of additional morphological traits can be useful for clarifying their taxonomic status and evolutionary history. We analyzed morphological variation of bones in the oral jaw and lower pharyngeal jaw to determine if these structures permit delimitation of species and to study possible functional implications. Morphological differences were quantified from canonical variates analysis, MANOVA, and paired comparisons. Differences in the number of pharyngeal teeth in the lower pharyngeal jaw were determined using an ANCOVA. The shape of the premaxilla and lower pharyngeal jaw are best for differentiating between these closely related-species. Although the morphological variation between V. breidohri and V. hartwegi were most similar, the shape of the lower pharyngeal jaw contrasts in morphospace. Vieja bifasciata possesses more teeth in the lower pharyngeal jaw. Variation in this bones may effect jaw biomechanics. Morphological differences are in contrast with the little genetic differentiation observed. The geographic isolation of V. bifasciata is likely related to its morphological differentiation. The common ancestry of V. breidohri and V. hartwegi probably explains their small morphological divergence.
Reducing of specific carcinogenic disinfection by-products compounds from drinking water using chlorine dioxide as alternative for chlorine

Chlorination has signiﬁcantly reducing the risk of pathogenic dirt but could pose a chemical hazard to human health due to formation of various disinfection by-products (DBPs). Chlorine dioxide is one of the most intriguing oxidants since it combines a high oxidation capacity with a minimal generation of harmful byproducts like chlorinated organics when used. As a result, it is widely utilized in the disinfection of drinking water and, more recently, in the disinfection of surfaces and structures. Trihalomethanes (THMs) that react with chlorine and organic materials are suspected carcinogens. THMs, haloacetic acids (HAAs), haloacetonitriles (HANs), and halogenated ketones are the most common disinfection by-products (DBPs). Cancer, abortion, poor birth weight, and congenital impairments are all increased by exposure to these by-products. This study aimed to highlight the ability to use chlorine dioxide as alternative for chlorine to reducing carcinogenic hazardous organic material formation. The study resulted that both the concentration of chlorine and chlorine dioxide is one of the major parameters in hazardous organic material formation.
Antenna Theory & Design

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Effects of anthropogenic activities on bird diversity in an urban waterbody (Bhoj Wetland), Bhopal, Madhya Pradesh, India

Tropical forests being rich in biodiversity are becoming increasingly fragmented and degraded, hence are declined due to human intervention, ultimately threatening survival of birds. The present study was envisaged to highlight the impact of anthropogenic activities on avifauna. Our study revealed habitat destruction (60%) being the most detrimental human activity on avifauna diversity habitats due to timber extraction, livestock grazing, firewood and medicinal herbs, however, hunting of birds for continuous use (15%) only affected limited bird species. Two areas were taken protected areas (possessing low cultivation, overgrazing, and logging) and second unprotected areas (having higher levels of intervention). Farms around the wetlands increased bird diversity. The current study demands strict actions on illegal forest exploitation to ensure survival of avifauna diversity in Bhoj Wetland, Bhopal. Proper management not only will improve the current situation for its dweller species, but also will attract more species in future.
Exogenous GA3 promotes flowering in Paphiopedilum callosum (Orchidaceae) through bolting and lateral flower development regulation

Abstract Paphiopedilum orchids have a high ornamental value, and their flower abundance and timing are both key horticultural traits regulated by phytohormones. All one-flowered Paphiopedilum have additional lateral buds in the apical bract that fail to develop. In this study, an exogenous gibberellin (GA3) application promoted flowering of Pathiopedilum callosum by inducing its early bolting instead of the floral transition of dominant flowers. Applying GA3 effectively promoted lateral flower differentiation, resulting in a two-flowered inflorescence. GA-promoted lateral flower formation involved GA interacting with indole-3-acetic acid (IAA) and cytokinins (CTKs), given the decreased CTK content and downregulated expression of CTK synthesis genes, the increased IAA content and downregulated expression of IAA degradation, and the upregulated expression of transport genes. Further, GA acted via PcDELLA, PcTCP15, and PcXTH9 expressed in stage 5 to promote bolting, and via expression of PcAP3, PcPI, and PcSEP to promote flowering. This study provides insight into mechanisms regulating flower development of P. callosum.
Brand-consumer relationship of female millennials toward K-beauty

For the last decade, the Korean beauty market has skyrocketed in terms of sales. Despite differences in usage steps, culture, and geography, Korean beauty products are the fastest-growing market segment in Indonesia. In addition, they are aimed primarily at millennial consumers. Therefore, this study looked at the effect of the brand-consumer relationship on the experience of Korean beauty products in Indonesia. The research sample consisted of millennial female users. As a result, 255 valid responses were obtained. Using SEM Lisrel, the study revealed that all study hypotheses were accepted. All the variables had a significant influence on each other. Notably, brand commitment had the strongest impact, and it was significantly related to and contributed more to brand love. The contribution of this brand-consumer relationship stems from the fact that brand commitment is more consistent in achieving brand love than brand experience. Furthermore, it was found that brand experience is more subjective, resulting in irrational consumer behavior toward brand love.
Consumers' perspectives and experiences of prebiotics and probiotics for gut health in chronic kidney disease.

OBJECTIVE Nutrition supplementation, including prebiotics and probiotics, is a therapeutic strategy for modulating the gut microbiome in chronic kidney disease (CKD). However, the acceptability of gut-targeted supplements in this population remains largely unexplored. This study aims to describe the perceptions of nutrition supplementation, and the acceptability and experiences of pre- and probiotics in adults with stage 3 to 4 CKD. METHODS Semi-structured interview study of adults with stage 3 to 4 CKD (n=30), aged 41-80 (mean 68) years, who completed a 12-month prebiotic and probiotic intervention or placebo, were interviewed between January and March 2019. Interviews were transcribed verbatim and analyzed thematically. RESULTS Five themes were identified: integrating and sustaining routine supplementation (flexibility in prescription of prebiotics and probiotics, fitting in with regular routines); striving for health benefits (hoping to improve kidney health, hoping to improve general health, confirming health benefits); facilitating pre- and probiotic supplementation (perceiving pre- and probiotics as safe, side-effects from taking pre- and probiotics); empowering knowledge (valuing the opportunity to increase knowledge of gut health); considerations for future use (questioning credibility of health claims, average palatability of prebiotic powder, cost concerns). CONCLUSIONS Adults with stage 3 to 4 CKD found pre- and probiotic supplements to be acceptable and complementary gut-targeted supplements. Individual preferences for nutrition supplementation should be considered alongside health knowledge to enhance uptake and adherence in practice.
Rheological Interpretation of the Slump Test

Abstract We examine the validity of the slump test for predicting the yield stress of polymeric suspensions and mineral suspensions. First we propose a modification of this test: in order to make measurements on fluids with high yield stress (of the order of several hundreds of Pascal) we add a mass at the sample top. From detailed observations of the slump in time we show that, for polymeric suspensions (hair gel and sewage sludges), two critical stresses can be distinguished which almost exactly correspond to the two critical stresses (respectively corresponding to a regime change and to the asymptotic slump) observed in rheometry during creep tests. Thus the slump test appears as a practiceful and relevant means to determine the intrinsic properties of these fluids. For mineral suspensions it is shown that the flow abruptly stops after a short time, a behaviour in agreement with the results of rheometrical tests carried out by progressively decreasing the applied stress. In that case the slump also appears to significantly depend on the procedure and cannot be related to a single property of the material.
Regression analysis of correlation between learning time and academic performance

If students get high grades, they can apply for better schools or find better jobs. Therefore, it is important to explore which factors can affect their academic performance. Our intuition and experience tell us that learning time should be positively correlated with achievement. Therefore, the purpose of this study is to find the correlation between learning time and academic performance. We choose to learn time as the independent variable and final score as the dependent variable. This study mainly uses the simple linear regression model, a mathematical regression model, to determine the correlation between variables. We use Rstudio to make this model compute the least-squares parameter β0, β1 and get the fitted line. Finally, several methods, including histogram of residuals, normal Q-Q plot and plot of residuals vs. fitted values, are used to assess whether the fitted model is accurate. The conclusion is that there is a slight negative relationship between learning time and final scores. Besides, the simple linear regression model is appropriate. Therefore, the data collected in this study do not support additional learning time to improve test scores.
TAGUCHI METHOD OPTIMIZATION OF THE TOTAL POWER CONSUMPTION DURING CHIPBOARD CUTTING WITH THE PANEL SAW MACHINE

This work presents Taguchi method used in order to optimize overall power consumption of panel saw machine Holzma HPP 300 during sawing of laminated particleboards. Single boards and gathered in package of two and three sheets with thickness 18 mm were cut. There were used three feed speeds, namely: 50, 100 and 120 m/min. Influence of two factors (amount of sheets machined in one cycle and feed speed) on overall power consumption during cutting. Moreover, optimum values of this parameters within analyzed parameter range were pointed out. Feed speed turned out not to be important regards to overall power consumption during machining at all. However, amount of boards in package sawed in one cycle was in great importance.
Urdu Named Entity Recognition System Using Deep Learning Approaches

Named entity recognition (NER) is a fundamental part of other natural language processing tasks such as information retrieval, question answering systems and machine translation. Progress and success have already been achieved in research on the English NER systems. However, the Urdu NER system is still in its infancy due to the complexity and morphological richness of the Urdu language. Existing Urdu NER systems are highly dependent on manual feature engineering and word embedding to capture similarity. Their performance lags if the words are previously unknown or infrequent. The feature-based models suffer from complicated feature engineering and are often highly reliant on external resources. To overcome these limitations in this study, we present several deep neural approaches that automatically learn features from the data and eliminate manual feature engineering. Our extension involved convolutional neural network to extract character-level features and combine them with word embedding to handle out-of-vocabulary words. The study also presents a tweets dataset in Urdu, annotated manually for five named entity classes. The effectiveness of the deep learning approaches is demonstrated on four benchmarks datasets. The proposed method demonstrates notable progress upon current state-of-the-art NER approaches in Urdu. The results show an improvement of 6.26% in the F1 score.