Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiple-input multiple-output dual-band dual-circularly polarised SIW cavity-backed slot antenna for satellite and 5G systems

ABSTRACT A dual-band 2 1 multiple-input-multiple-output (MIMO) antenna based on substrate integrated waveguide (SIW) cavity for both satellite (in K-band) and 5 G applications is proposed. Initially, the SIW cavity consisting of metallic vias on all four sides is implemented. For MIMO configuration, this cavity is divided into two smaller SIW cavities using a row of metallic vias and excited using the coaxial feed. By etching an asymmetric cross-slot (AXS) with tuned dimensions on top of the SIW cavity, a dualsense of circular polarisation (CP) in both frequency bands is obtained. The fabricated prototype of the proposed SIW antenna shows impedance bandwidth (IBW) of 15.36% (22.60–26.36 GHz; BandI) and 4.64% (29.04–30.42 GHz; BandII). The completely overlapping axial-ratio bandwidth (ARBW) with IBW of 15.16% (22.62–26.33 GHz) and 3.10% (29.20–30.12 GHz) with a peak gain of 12.47 and 7.71 dBi is obtained in BandI and Band II, respectively. The proposed antenna shows front-to-back ratio (FBR) of more than 20 dB in both resonating frequency bands. The measured results of the proposed MIMO antenna agree well with the simulation.
The motivations, backgrounds, and practices of business as mission practitioners: Insights from an international survey

“Business as mission” (BAM) was introduced to the Christian missions community during the 2004 Lausanne conference in Pattaya, Thailand. Since then, interest in BAM has grown exponentially, and with it, demand for support services, training, and financing. For all the attention, however, scholarly research in this area has not kept pace, and there remain many unknowns about the personal and contextual factors that contribute to the impact and sustainability of a BAM enterprise. Seeking to address that gap, this paper builds on insights from the social entrepreneurship literature that enables us to construct a typology that distinguishes between four basic types of BAM practitioner. While all four seek to advance the cause of Christ using business, and thus fit the basic definition of BAM, each brings a different combination of motivations, backgrounds, and practices. The relevance of those differences is then explored using survey data collected from 119 self-defined BAM practitioners around the world. We also identify several essential business practices that are correlated with strong economic and social impact. Implications for further research are discussed.
Performance Evaluation of Solar-Powered Atmospheric Water Harvesting Using Different Glazing Materials in the Tropical Built Environment: An Experimental Study

Water scarcity is a global issue, and its severity is expected to worsen in the near future, prompting further efforts to find new sources of freshwater. Solar-Powered Atmospheric Water Harvesting (SPAWH) is a promising passive approach for atmospheric water generation. This study aims to examine the thermal performance of different glazing materials and water production in SPAWH. The research consists of two phases: a laboratory test of various glazing materials and an experimental study to assess system efficiency in producing water in the tropics. The preliminary results indicated that glass demonstrated better thermal performance than acrylic in the lab, with higher thermal conductivity and less heat loss. The experimental findings showed that the maximum water produced by the proposed SPAWH (60 cm length, 60 cm width and 30 cm height) placed on a 30° tilt angle using glass (3 mm) and acrylic (3 mm) was 0.61 L/m2/day and 0.44 L/m2/day, respectively. The cost analysis revealed that produced water costs $0.18/kg for glass and $0.40/kg for acrylic, respectively. Atmospheric water could be harvested using SPAWH in the tropics, which would help to provide new opportunities for sustainable water supplies and development in these regions.
Recent Progress in Non-Enzymatic Electroanalytical Detection of Pesticides Based on the Use of Functional Nanomaterials as Electrode Modifiers

This review presents recent advances in the non-enzymatic electrochemical detection and quantification of pesticides, focusing on the use of nanomaterial-based electrode modifiers and their corresponding analytical response. The use of bare glassy carbon electrodes, carbon paste electrodes, screen-printed electrodes, and other electrodes in this research area is presented. The sensors were modified with single nanomaterials, a binary composite, or triple and multiple nanocomposites applied to the electrodes’ surfaces using various application techniques. Regardless of the type of electrode used and the class of pesticides analysed, carbon-based nanomaterials, metal, and metal oxide nanoparticles are investigated mainly for electrochemical analysis because they have a high surface-to-volume ratio and, thus, a large effective area, high conductivity, and (electro)-chemical stability. This work demonstrates the progress made in recent years in the non-enzymatic electrochemical analysis of pesticides. The need for simultaneous detection of multiple pesticides with high sensitivity, low limit of detection, high precision, and high accuracy remains a challenge in analytical chemistry.
Biology and DNA barcode analysis of Coleophora lessinica Baldizzone, 1980 and Coleophora impalella Toll, 1961 (Lepidoptera, Coleophoridae) with description of their larval cases

Host plants and cases of several species in the genus Coleophora Hübner, 1822 have remained unknown until now, while the latter structures provide important characters for identification. Host plants and cases of Coleophora lessinica Baldizzone, 1980 and Coleophora impalella Toll, 1961 were discovered by the authors and are presented here for the first time. New data on the distribution and biology of the two species in Hungary are given. Coleophora lessinica is recorded as new for the fauna of Romania. DNA barcode sequencing was performed for both Coleophora species and loaded into the BOLD System and to GenBank.
Screening and identification of osteoarthritis related differential genes and construction of a risk prognosis model based on bioinformatics analysis

Background Searching for the production mechanism of synovial lesions helps to find precise therapeutic targets and improve prognosis. The previous identification and screening of differential genes in osteoarthritis (OA) pathogenesis were well combined to further build a risk prognosis model of OA, which is beneficial to the diagnosis and treatment of patients with OA. Methods The synovia-related chip data sets GSE82107, GSE12021, GSE55457, and GSE55235 were downloaded from the public database of Gene Expression Omnibus (GEO), and 40 cases of synovial tissues of OA and 36 cases of normal synovial tissues were included. R software was used to screen differentially expressed genes (DEGs), Gene Ontology (GO) functional enrichment, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The STRING online analysis tool and Cytoscape software were used to further screen key genes, and a prognostic model of OA susceptibility risk was constructed. Results The results showed 1,921 differential genes, including 762 upregulated genes and 1,159 downregulated genes, which were mainly involved cell growth, cell adhesion, skeletal muscle growth, iron ion binding, ubiquitin protein ligase binding, and hormone receptor binding. Co-acquisition based on 10 key target genes of the protein interaction network, containing CTNNB1, GSK3B, STAT1, RHOC, HDAC9, PSEN1, KDM5C, BACE1, JAK3, and CUL1. The area under the concentration-time curve (AUC) was used to evaluate the prognostic model of OA risk, and the curve results showed that the prognostic model had high accuracy and validity (AUC =0.690). Conclusions Bioinformatics analysis was applied to screen out the DEG profiles of OA. This may provide functional predictions to provide new ideas for treatment of the disease and may be a biological marker for its diagnosis and a potential target for treatment. The construction of the risk and prognosis model is beneficial to the risk assessment of rehabilitation function recovery of patients with OA, the evaluation of the severity of the disease and the subsequent treatment guidance.
Effects of stocking density on growth performance, digestive function and immune performance of Matou goats

Abstract The objective of this study was to examine the effects of different stocking densities on Matou goats. Thirty-six female Matou goats were randomly assigned into three experimental treatments including the low density (LD, 0.67 goat/m2), the medium density (MD, 1 goat/m2) and the high density (HD, 2 goats/m2) group. Compared with LD group, the average daily weight gain (ADG) of HD group decreased (p < .05). The length of duodenal and ileal villi in HD group was significantly lower than that in LD group (p < .05). The rumen pH and concentration of total volatile fatty acid (VFA), propionate butyrate and ammonia-nitrogen increased in HD group (p < .05). The acetate/propionate ratio was the highest in MD group (p < .05). The mRNA expression level of agouti-related protein gene (AgRP) in hypothalamus of LD was higher than that in HD group (p < 0.05). Proopiomelanocortin (POMC) mRNA expression level was the highest in MD group (p<.05) and the lowest in HD group (p < .05). An increase in growth hormone receptor (GHR), insulin-like growth factor 1 (IGF1) and claudin-1 mRNA expression level were noticed in HD group (p<.05). Compared with LD group, the serum total protein (TP), albumin (Alb), globulin (Glb) IgA and IgG levels in HD group decreased (p < .05). Glucocorticoid (GCS), β-endorphin (β-EP) and cortisol levels in serum were also significantly increased in HD group compared with LD group (p < .05). These results showed that stocking density affected rumen environment and high stocking density resulted in a decrease in growth, digestive and immune function of Matou goats.
Positive school environment, sustainable behaviour and well-being among higher education students (Ambiente escolar positivo, conducta sostenible ybienestar en estudiantes de educación superior)

ABSTRACT A positive school environment (PSE) could enhance student well-being and promote sustainable behaviour (SB). However, there is little research on PSEs in universities. In order to explore the relationships between PSE, SB and well-being, and to test the psychometric properties of the instruments used, we conducted research with asample of 285 undergraduate and graduate students (M = 21.63 years of age; SD = 3.91). We calculated descriptive statistics (univariate by scale/item and Cronbach’s alpha coefficients) and inferential statistics (Spearman’s rho coefficient, confirmatory factor analysis, covariances between constructs, also testing amodel of structural equations). The findings indicated that the scales were reliable (α > .60) and that, for the most part, they showed convergent and discriminatory validity. Furthermore, the expected theoretical relationships were confirmed: PSE was directly and positively related to SB and well-being, and the latter was also significantly affected by SB. This evidence could help to formulate public policies and programmes aimed at creating positive university environments.
Reproductive Performance of Saanen Goats After PGF2α Intramuscular Injection in Correlation to Body Weight

An experiment was conducted at Kalikesur Farm, Kedung Banteng, Banyumas to investigate the reproductive performance of Saanen dairy goats after PGF2α injection on different body weights. This experiment included twenty female Saanen goats at 2-3 years of age, 2-3 kidding periods, and varying body weights of 30 to 55 kg. All goats were intramuscularly injected by 2 ml PGF2α (LutalyseTM, Pharmacia and Upjohn Company, Pfizer Inc) in a double injection pattern, with 11-day interval to perform estrous synchronization. The variables on reproductive performance such as estrus onset, length of estrus, and intensity of estrus were measured and monitored twice a day at 6-10 am and 3-5 pm. Descriptive statistics and correlation analysis were conducted. Results demonstrated that body weight performed strong correlations with estrus intensity (r = 0.66) while the time for the first estrus (onset) has a negative moderate correlation with body weight (r = -0.47), a negative weak correlation was recorded between body weight and length of estrus (r = -0.17). Based on the results obtained, it can be concluded that the body weight of goats in the estrus synchronization program can be used to be an indicator of reproductive performance, especially in estrus intensity. This can help farmers to predict estrus behavior after PGF2α synchronization.
Back Focal Plane Confocal Ptychography

This paper illustrates how the amplitude and phase of back focal plane distribution can be recovered in a confocal microscope system from several intensity images in the image plane. These will have a wide range of uses for imaging and sensing. We believe this method is complementary to the V(z) technique where the sample is defocused. The field generated in the back focal plane may be processed by virtual propagation which averages noise in a highly efficient manner. In this paper, we demonstrate that the phase information on the back focal plane can be recovered using ptychography with no need to modify the optical configuration and employ an interferometer. This phase information plays a crucial role in sensitivity of surface plasmons resonance biosensing systems.
Prospects and Pitfalls of Machine Learning in Nutritional Epidemiology

Nutritional epidemiology employs observational data to discover associations between diet and disease risk. However, existing analytic methods of dietary data are often sub-optimal, with limited incorporation and analysis of the correlations between the studied variables and nonlinear behaviours in the data. Machine learning (ML) is an area of artificial intelligence that has the potential to improve modelling of nonlinear associations and confounding which are found in nutritional data. These opportunities notwithstanding, the applications of ML in nutritional epidemiology must be approached cautiously to safeguard the scientific quality of the results and provide accurate interpretations. Given the complex scenario around ML, judicious application of such tools is necessary to offer nutritional epidemiology a novel analytical resource for dietary measurement and assessment and a tool to model the complexity of dietary intake and its relation to health. This work describes the applications of ML in nutritional epidemiology and provides guidelines to avoid common pitfalls encountered in applying predictive statistical models to nutritional data. Furthermore, it helps unfamiliar readers better assess the significance of their results and provides new possible future directions in the field of ML in nutritional epidemiology.
Inductively-powered Implantable Integrated Circuits for Amperometric Brain Chemistry Monitoring

Inductively-Powered Implantable Integrated Circuits for Amperometric Brain Chemistry Monitoring Nima Soltani Doctor of Philosophy Graduate Department of Electrical and Computer Engineering University of Toronto 2016 This dissertation presents the design of inductively-powered and inductive-powering circuits for monitoring implantable brain chemistry. This includes optimal design of implantable integrated circuits for amperometric measurement of potassium and sodium ion concentrations, inductive powering links, power transfer systems, as well as the data transmission circuits to send the sensory data to a nearby computer. First, a comprehensive tutorial and design guide on low-absorption inductive links is presented, as needed to optimally power the implanted chip. The conventional model of inductive power transfer is studied from a specific-absorption-rate (SAR) point of view. The model is analyzed in an optimization problem to find the optimal geometries of the transmitting and receiving coils that result in the highest delivered-to-absorbed power ratio (in tissue). These results are then compared with the results based on power-transfer-efficiency (PTE) optimization, which concludes that the two are distinct. A complete design of an inductive powering system is then presented for in vivo freelymoving rodent experiments. The system offers a significantly improved ratio of the power delivered to the power absorbed in the tissue, a low-cost scalable architecture to cover an arbitrarily large area, as well as a low-cost power receiver localization technique based on an impedance tracking algorithm. To communicate data from the high output datarate implantable sensory microarray to a
Predictive Risk Modeling to Identify Homeless Clients at Risk for Prioritizing Services using Routinely Collected Data

Abstract For most homelessness service providers, the number of clients who are eligible for long-term housing outstrips the availability. This study uses a cohort of housing assessments taken from a mid-size county in the US and machine learning methods to train a Predictive Risk Model (PRM) that identifies clients who would experience multiple adversities in the future. The PRM outperforms the Vulnerability Index-Service Prioritization Decision Assistance Tool (VI-SPDAT) in flagging clients at the greatest risk of adversities. The proposed method can be readily used by any Continuum of Care (CoC) that holds electronic housing assessments and service records.
Retinal blood vessels segmentation using Wald PDF and MSMO operator

ABSTRACT Glaucoma, hypertension, obesity, diabetes, etc. are the widely spread severe diseases nowadays. Glaucoma, hypertension, and diabetes are the main cause of vision loss, whereas diabetes, obesity, and hypertension are the basis of several other highly dangerous diseases. Blood vessels of a retina contain information about these critical diseases. Health professionals use this information to detect and diagnose these diseases. Hence, it is essential to segment retinal blood vessels. Many researchers have proposed different matched filter methods to segment retinal blood vessels, but their matched filter kernels are not appropriate to vessel intensity profile due to which performances of these methods are relatively low. Also, the quality of retinal images directly affects the accuracy of segmentation. Therefore, retinal images must be of good quality. Existing methods for retinal image enhancement are less efficient. The proposed method introduces the Multiscale switching morphological operator (MSMO) with Wald probability distribution function-based new matched filter for better enhancement and segmentation of retinal vessels in this paper. The proposed method is tested on two known datasets: DRIVE and STARE. The average value of the precision, specificity, accuracy, MSE and MAD of the proposed method are 83.74%, 98.80%, 95.58%, 0.0442, 0.0366 for DRIVE and 71.24%, 97.67%, 94.89%, 0.0511, 0.015 for STARE dataset. Results demonstrate significant improvement in the performance of the proposed method over existing state-of-the-art methods. The causes of improved performance are enhanced retinal images due to using a better pre-processing method and close matching of Wald matched filter kernel with vessel intensity profile.
Application of residual sludges from wastewater treatment technologies for construction of biofertiliser

To stimulate plant development in phytoremediation or in the cultivation of non-food crops in potentially contaminated soils, a biotechnologically created product could be applied. The aim of this study was to explore the possibility of creation of biofertiliser, based on activated sludge combined with bacterial strain with detoxifying and plant growth promoting properties. The presented study is focused on the effect of phenol in the following concentrations: 5 mg/l, 100 mg/l, 250 mg/l, 500 mg/l and 1000 mg/l on the metabolic activity of Brevibacillus laterosporusBT271. The gradual increased concentration of phenol was used to study the metabolic activity of mineralised activated sludge and B. laterosporusBT271. The CTC/DAPI staining showed high activity of the bacteria even at the highest concentration. The greatest amount of biomass was accumulated at 5 mg/l phenol (4.44 × 107 cells/ml). At this toxicant concentration, a total dehydrogenase activity of 5.72 × 10-4 μg H+/ml*min was found. Studies of the metabolic activity of microorganisms in experiments involving a combination of mineralised activated sludge, B. laterosporusBT271 and phenol at three concentrations (5 mg/l, 250 mg/l and 1000 mg/l) showed the highest value for dehydrogenase activity in the variant with average phenolic concentration (up to 6.39 × 10-6 μg H+/ml*min. The results proved the detoxification potential of B. laterosporusBT271 when different concentrations of phenol were present. The combination of a mineralised activated sludge and selected highly active biodegrading B. laterosporusBT271 showed valuable properties of detoxification and metabolic activity and keep these potentials up to 1000mg/l phenol.
Predicting Mental Health Status in Remote and Rural Farming Communities: Computational Analysis of Text-Based Counseling

Background Australians living in rural and remote areas are at elevated risk of mental health problems and must overcome barriers to help seeking, such as poor access, stigma, and entrenched stoicism. e-Mental health services circumvent such barriers using technology, and text-based services are particularly well suited to clients concerned with privacy and self-presentation. They allow the client to reflect on the therapy session after it has ended as the chat log is stored on their device. The text also offers researchers an opportunity to analyze language use patterns and explore how these relate to mental health status. Objective In this project, we investigated whether computational linguistic techniques can be applied to text-based communications with the goal of identifying a client’s mental health status. Methods Client-therapist text messages were analyzed using the Linguistic Inquiry and Word Count tool. We examined whether the resulting word counts related to the participants’ presenting problems or their self-ratings of mental health at the completion of counseling. Results The results confirmed that word use patterns could be used to differentiate whether a client had one of the top 3 presenting problems (depression, anxiety, or stress) and, prospectively, to predict their self-rated mental health after counseling had been completed. Conclusions These findings suggest that language use patterns are useful for both researchers and clinicians trying to identify individuals at risk of mental health problems, with potential applications in screening and targeted intervention.