Miracle-dz/UniFAR-FTUs huggingface dataset:
Bandwidth Enhancement of Square Microstrip Antennas Using Dual Feed Line Techniques

This study proposes a new design of wide bandwidth microstrip antennas using dual feed line techniques. To obtain the optimal impedance bandwidth (IBW) and Axial Ratio Bandwidth (ARBW), several iterations were performed by controlling the dimensions and length of the dual feed line. From the simulation results, the proposed antenna obtained IBW of 0.4GHz or 17% and ARBW of 0.38GHz or 15% at an operating frequency of 2.5 GHz. The gain of the proposed antenna was 5.73dB with a directional radiation pattern. The dual feedline technique successfully improved IBW up to 254.16% compared with the single feed technique. This study would be useful especially for bandwidth optimization of microstrip antennas. 
Pre-Service Teachers Self-Perceived Training Needs Associated with Program Design and Management and Leadership and SAE Development

Teacher educators face the perpetual challenge of providing pre-service teachers with the most pertinent pedagogical and content-related knowledge and skills to ensure their success in the field of education. Using a modified version of a Borich needs assessment instrument, we assessed the agricultural education training needs of agricultural education pre-service teachers (n = 69) at Iowa State University related to program design and management and leadership and Supervised Agricultural Experiences (SAE) development. The General Program Standards for Agricultural Education, developed by the Iowa Council on Agricultural Education, served as a guide for assessing the areas of pre-service teachers’ training needs. Mean weighted discrepancy scores (MWDS) were calculated for needs assessment items which served as a means to rank the areas of needs expressed by the Iowa State University agricultural education pre-service teachers. The areas of training needs were assessed as a whole and by grade classification. Overall, all 25 items included in the instrument were indicated as an area of need and the calculated MWDS ranged from 2.29 – 9.05, and variance of training needs was discovered between preservice teachers of various grade classifications. Implications for agricultural education teacher educators and recommendations were discussed.
Performance properties of particleboard panels modified with nanocellulose/boric acid

The physical and mechanical properties of particleboard panels were evaluated relative to various loading levels of nanocellulose (NC) and boric acid (BA) in the urea-formaldehyde (UF) resin used for panel production. The results showed that NC and BA reinforcement substantially affected the performance properties of the particleboard panels. It was determined that using 3% NC and 3% BA in the panels afforded the best results relative to thickness swelling (TS), water absorption (WA), moisture content (MC), modulus of rupture (MOR), modulus of elasticity (MOE), and internal bonding strength (IB). It was concluded that the performance properties of the particleboard panels could be enhanced by adjusting the loading levels of NC and BA. The study also showed that it is possible to apply NC and BA as modifiers for the formaldehyde resin in the process of manufacturing particleboard panels. The NC and BA reinforcement techniques could be used to develop novel furniture components and interior design materials.
Passive control of magneto-nanomaterials transient flow subject to non-linear thermal radiation

Present investigation is concerned with mixed convection flow of Williamson nanoliquid over an unsteady slandering stretching sheet. Aspects of non-linear thermal radiation, Brownian diffusion and thermophoresis effects are addressed. Non-linear stretching surface of varying thickness induce the flow. Novel features of combined zero mass flux and convective conditions are accounted. Use of appropriate transformations results into the non-linear ODEs. Computations for the convergent solutions are provided. Graphs are designed for interpretations to quantities. Nusselt number and surface drag are computationally inspected. Our computed results indicate that attributes of nanoparticles and non-linear thermal radiation enhance the temperature distribution.
Influence of 6-Week Pooled Soccer Plyometric and Sprint Training on Speed and Agility amongst Underweight

The objective of this inquiry was to know the impact of pooled soccer, plyometric, and sprint drill on speed and agility among underweight males. 26 underweight male subjects were chosen with a BMI (kg/m2) of less than 18.5. The ages of the subjects ranged from 18-22 years. The physical fitness tests administered before and after the training program was 20m sprint, 40m sprint, and agility. The training plan was engaged for six weeks, 45M per exercise phase, two days per week. The combined workout regime was given as per the schedule, plyometric training combined with sprints for 25 minutes followed by a soccer game for 20 minutes. To compare pre-and post-test, a statistical paired t-test was utilized for all the selected variables. Data was analyzed using SPSS Version 16. The outcome of the current study indicated that the participants had enhanced significantly when prior testing was equated with later testing in 20m, 40m, and agility by (3.41 ± 0.18 vs. 3.18 ± 0.25 Sec; P < 0.001), by 4% (6.20 ± 0.46 vs. 5.93 ± 0.36 Sec; P < 0.001), and by 3.8% (18.10 ± 0.82 vs. 17.36 ± 0.74 Sec; P < 0.001) respectively. It was established that the impact of pooled soccer, plyometric, and sprint drill for underweight participants had shown significant performance in all the chosen fitness components from earlier to later tests, which was very promising and noteworthy.
Quality management in gene expression analysis

Necessary considerations for the establishment of a quality-managed RT-PCR workflow for the analysis of differential gene expression are introduced. Relevant quality management systems are considered and a basic work programme for the implementation of a laboratory quality management systems. In conformance with ISO 17025 is discussed. A general methodology for the implementattion of preventive measures for laboratory processus is presented.
Dimer radical cation of 4‐thiouracil: a pulse radiolysis and theoretical study

Pulse radiolysis with optical absorption detection has been used to study the reactions of hydroxyl radical (OH•) with 4-thiouracil (4TU) in aqueous medium. The transient absorption spectrum for the reaction of OH• with 4TU is characterized by λmax 460 nm at pH 7. A second-order rate constant k(4TU+OH) of 1.7 × 1010 M−1 s−1 is determined via competition kinetics method. The transient is envisaged as a dimer radical cation [4TU]2•+, formed via the reaction of an initially formed radical cation [4TU]•+ with another 4TU. The formation constant of [4TU]2•+ is 1.8 × 104 M−1. The reactions of dibromine radical ion (Br2•−) at pH 7, dichlorine radical ion (Cl2•−) at pH 1, and azide radical (N3•) at pH 7 with 4TU have also produced transient with λmax 460 nm. Density functional theory (DFT) studies at BHandHLYP/6–311 + G(d,p) level in aqueous phase showed that [4TU]2•+ is characterized by a two-centerthree electron (2c-3e) [−S∴S−] bond. The interaction energy of [−S∴S−] bond in [4TU]2•+ is −13.01 kcal mol−1. The predicted λmax 457 nm by using the time-dependent DFT method for [4TU]2•+ is in agreement with experimental λmax. Theoretical calculations also predicted that compared with [4TU]2•+, 4-thiouridine dimer is more stable, whereas 4-thiothymine dimer is less stable. Copyright © 2013 John Wiley & Sons, Ltd.
Discrete particle simulations of micro-structured membrane-assisted fluidized beds

is introductory chapter gives an overview of the research on the micro-structured membrane-assisted fluidized beds presented in this thesis. e research background and motivation, introducing the novel reactor concept of the micro-structured membrane-assisted fluidized bed reactor as well as the following investigations, are briefly described. Subsequently, the research objectives are discussed and the research approaches are introduced. e chapter is concluded with an outline of the thesis.
FORAGE YIELD OF SORGHUM UNDER WATER DEFICIT AND FOLIAR APPLICATION OF ZINC SULPHATE AND SALICYLIC ACID

The effect of foliar application of zinc sulphate and salicylic acid on forage yield and some qualitative and physiological characteristics of sorghum bicolor Var. Speedfeed, under water deficit conditions, was studied as split-plot experiment based on randomized complete block design with three replications at University of Urmia Research Farm in 2013. Water deficit stress as main plot consisted of three levels (irrigation at the 90%, 70% and 50% of soil field capacity) and foliar applications as sub plot with five levels including: no foliar application (control), foliar application of salicylic acid (0.5 and 1mM) and zinc sulphate (300 and 3000 mg.l) Which were applied in the 5 to 6 leaf stage. The highest digestibility (64.76 %) was obtained from the foliar application of zinc sulphate 3000 mg.l at all levels of water deficit. Maximum crude protein and ash contents (13.35% and 8.32%) were achieved with foliar application of zinc sulphate 3000 mg.l in control and moderate water deficit. Severe water deficit decreased hay yield by 21.11% in comparison with control. Both foliar application of zinc sulphate 3000 mg/l and salicylic acid 0.5 mM with average of 1571.53 g.m produced the highest hay yield. Therefore, these treatments that led to significant improvements in the hay yield quality and quantity are recommended as the best treatments of this research
An Improved Numerical DBSCAN Algorithm Based on Non-IIDness Learning

In clustering algorithm research, objects, attributes and other aspects of data sets are usually considered to be independent and identically distributed; that is, each object is assumed to be an independent and uniformly distributed individual with no impacts between objects. However, objects in real life are often neither independently nor identically distributed; that is, they are non-IID, leading to a complex coupling relationship between objects, and objects interact with each other. The results of a clustering algorithm under an independent and identical distribution may be incomplete or even misleading. To make the results of the DBSCAN algorithm as accurate as possible, an improved numerical DBSCAN algorithm based on non-IIDness learning is proposed in this paper. The algorithm calculates the coupling relationship between objects to obtain the potential relationship between objects and determines the parameters Eps and MinPts by the distribution characteristics of the data. Experiments on large-scale real and synthetic data sets show that the algorithm achieves a higher accuracy than the original DBSCAN algorithm and the main algorithms that improved upon it.
Suitability of open digital species records for assessing biodiversity patterns in cities: a case study using avian records

Openly available species observation records on various online platforms achieve good coverage in urban areas. Thus, such digital data could provide a basis for biodiversity assessments in cities. Here, we investigated suitability of open digital species occurrence data, compared with systematically field surveyed data, in Freiburg, Germany (a Western city) and Dhaka, Bangladesh (a global-South city). We focused on resident bird species richness as an indicator of local biodiversity. We collected avian records for urban areas from ‘ornitho.de’ in Freiburg and ‘gbif.org’ in Dhaka. Additionally, we conducted point count surveys at several urban locations in both cities. Using these records, we prepared three grid (cell size 250 m × 250 m) based datasets—open digital dataset (i.e. records compiled from well-surveyed grid cells), field surveyed dataset (i.e. records of systematic bird surveys) and combined dataset (i.e. digital data and field data combined). We compared the relationship of resident bird richness with different habitat factors by applying linear regression models, separately using each of the three datasets. We assessed suitability of data from online platforms by comparing the variables retained after model selection based on digital data versus field surveyed data. We found that field surveyed data and combined data did not alter general understanding of the key driving factors of bird richness patterns we obtained from open digital data. This held for both case examples, Freiburg vs. Dhaka, respectively. This suggests that open digital data from well-surveyed urban locations can provide a suitable basis to assess drivers of biodiversity patterns within cities.
Modeling and Control of Probabilistic Fuzzy Discrete Event Systems

We investigate modeling and control of probabilistic fuzzy discrete event systems (PFDES). PFDES is a new type of fuzzy discrete event systems. It allows the use of probabilities to describe the chances of occurrences of different events. Our new model for PFDES consists of a fuzzy automaton and a crisp automaton that specifies what sequences of events can occur and their probabilities of occurrences. Based on the new model, optimal control is designed using an on-line and limited lookahead method. Control is calculated one step at a time, after an occurrence of an event. At each step, a lookahead window of <inline-formula><tex-math notation="LaTeX">$N$</tex-math></inline-formula> events is constructed. The performance measures for all states in the window are determined, which is a function of fuzzy states. Control is calculated to maximize the expected performance measure after the occurrences of <inline-formula><tex-math notation="LaTeX">$N$</tex-math></inline-formula> events. To reduce computational complexity, a “dynamic-programming” approach is proposed. We prove that the control obtained is optimal. Examples are given in the paper to illustrate the results.
Development of Prediction Models for Acute Myocardial Infarction at Prehospital Stage with Machine Learning Based on a Nationwide Database

Models for predicting acute myocardial infarction (AMI) at the prehospital stage were developed and their efficacy compared, based on variables identified from a nationwide systematic emergency medical service (EMS) registry using conventional statistical methods and machine learning algorithms. Patients in the EMS cardiovascular registry aged >15 years who were transferred from the public EMS to emergency departments in Korea from January 2016 to December 2018 were enrolled. Two datasets were constructed according to the hierarchical structure of the registry. A total of 184,577 patients (Dataset 1) were included in the final analysis. Among them, 72,439 patients (Dataset 2) were suspected to have AMI at prehospital stage. Between the models derived using the conventional logistic regression method, the B-type model incorporated AMI-specific variables from the A-type model and exhibited a superior discriminative ability (p = 0.02). The models that used extreme gradient boosting and a multilayer perceptron yielded a higher predictive performance than the conventional logistic regression-based models for analyses that used both datasets. Each machine learning algorithm yielded different classification lists of the 10 most important features. Therefore, prediction models that use nationwide prehospital data and are developed with appropriate structures can improve the identification of patients who require timely AMI management.
Stock Price Prediction Using Predictive Error Compensation Wavelet Neural Networks

: Machine Learning (ML) algorithms have been widely used for financial time series prediction and trading through bots. In this work, we propose a Predictive Error Compensated Wavelet Neural Network (PEC-WNN) ML model that improves the prediction of next day closing prices. In the proposed model we use multiple neural networks where the first one uses the closing stock prices from multiple-scale time-domain inputs. An additional network is used for error estimation to compensate and reduce the prediction error of the main network instead of using recurrence. The performance of the proposed model is evaluated using six different stock data samples in the New York stock exchange. The results have demonstrated significant improvement in forecasting accuracy in all cases when the second network is used in accordance with the first one by adding the outputs. The RMSE error is 33% improved when the proposed PEC-WNN model is used compared to the Long Short-Term Memory (LSTM) model. Furthermore, through the analysis of training mechanisms, we found that using the updated training the performance of the proposed model is improved. The contribution of this study is the applicability of simultaneously different time frames as inputs. Cascading the predictive error compensation not only reduces the error rate but also helps in avoiding overfitting problems.
The Geopolitical Threat Index: A Text-Based Computational Approach to Identifying Foreign Threats

Few concepts figure more prominently in the study of international politics than threat. Yet scholars do not agree on how to identify and measure threats or systematically incorporate leaders’ perceptions of threat into their models. In this research note, we introduce a text-based strategy and method for identifying and measuring elite assessments of international threat from publicly available sources. Using semi-supervised machine learning models, we show how text sourced from newspaper articles can be parsed to discern arguments that distinguish threatening from non-threatening states, and to measure and track variation in the intensity of foreign threats over time. To demonstrate proof of concept, we use news summaries from The New York Times from 1861 to 2017 to create a geopolitical threat index (GTI) for the United States. We show that the index successfully matches periods in US history that historians identify as high and low threat and correctly identifies countries that have posed a threat to US security at different points in its history. We compare and contrast GTI with traditional indicators of international threat that rely on measures of material capability and interstate behavior.
Food phytochemicals against Helicobacter pylori

Alexandru Mandici1, Alina-Alexandra Covrig2, Irina Macovei3 1 Department of Pharmacognosy, Faculty of Pharmacy, Grigore T. Popa University of Medicine and Pharmacy, Iași, Romania 2 Department of Oncology, Regional Institute of Oncology, Iași, Romania 3 Department of Drug analysis, Faculty of Pharmacy, Grigore T. Popa University of Medicine and Pharmacy, Iași, Romania Ref: Ro J Pharm Pract. 2021;14(1) DOI: 10.37897/RJPhP.2021. 1.2
Single and multi-exciton dynamics in nanoscale semiconductors

Recent advancements in the synthesis and fabrication of nanoscale semiconductors and optoelectronic devices are revolutionizing an array of industries. Nonlinear spectroscopy is a powerful tool for studying the unique optical and electronic properties of these semiconductors. This thesis describes experiments using a unique multi-dimensional spectrometer to study three semiconductors: double-walled J-aggregate nanotubes, cuprous oxide, and monolayer transition metal dichalcogenides. First, we use two-dimensional correlation spectroscopy and cryogenic tunneling electron microscopy to correlate the excitonic and structural properties of J-aggregate nanotubes in solution. We observe weak coupling between inner and outer wall excitons in both isolated and bundled nanotubes. We also use two-dimensional rephasing spectroscopy to measure the homogeneous and inhomogeneous linewidths in solution at 295 K and in a glass at 10 K and determine dynamic and static disorder dominate, respectively. In addition, we observe photo-induced damage and recovery of nanotubes in the solid state. Quantum process tomography is used to unambiguously elucidate the single-exciton dynamics of J-aggregate nanotubes in solution. Inversion of spectroscopic signals from eight transient grating experiments completely characterizes the time evolution of the singleexciton density matrix by determining the process matrix. We confirm the weak coupling of inner and outer wall excitons and observe no contributions from non-secular processes. Second, we use two-quantum spectroscopy to make the first experimental observation of two-exciton correlations (i.e. biexcitons) in cuprous oxide. The direct measurement of twoexciton correlations supports the proposed mechanism of biexciton-Auger recombination for the efficient suppression of the Bose-Einstein condensation of excitons. Third, we use two-dimensional rephasing spectroscopy to observe substantial inhomogeneous broadening due to large static disorder in monolayer transition metal dichalcogenides. We also use two-dimensional correlation spectroscopy to reveal interactions of excitons with in-plane optical and acoustic phonons. Lastly, we use oneand two-quantum spectroscopy to
Identification of Leptospira in water by Fe-Pd-doped polyaniline nanocomposite thin film

Leptospirosis disease was caused by rat urine which contains the genus Leptospira bacteria. In this study, the fabrication of Pd-Fe-doped polyaniline nanocomposite thin films for the determination of the genus Leptospira bacteria thin films has been investigated. Pd-Fe-doped polyaniline nanocomposite thin films were fabricated by sol–gel spin coating method. The electrode sensors were immersed in the Leptospira solution. The resulting materials were investigated using field-emission scanning electron microscopy, atomic force microscopy, transmission electron microscopy, and current–voltage measurement. The atomic force microscopy images show the specific morphology films’ structure for Leptospira detection, whereas the field-emission scanning electron microscopy image shows the irregularity of clump nanoparticles in thin film surfaces. Transmission electron microscopy result shows that metal alloy (Fe-Pd) embedded in the polymer matrix. Current–voltage measurement with and without incubation of the thin film into Leptospira solution was done to show the relationship between concentration bacteria versus current. The result shows that polyaniline-Fe0.4-Pd0.6 nanocomposite thin film has higher sensitivity in detecting Leptospira, where it has performed with the highest percentage of the sensitivity of 16.9%. Besides that, selectivity tests were conducted to distinguish the existence of Leptospira, Pseudomonas aeruginosa, and Staphylococcus aureus bacteria. These results confirm the potentials of polyaniline metal alloys’ nanocomposite thin films to be used for Leptospira bacteria detection in water.
Disease Avoidance Motives Trade-Off Against Social Motives, Especially Mate-Seeking, to Predict Social Distancing: Evidence From the COVID-19 Pandemic

A range of studies have sought to understand why people’s compliance with social distancing varied during the COVID-19 pandemic. Recent theory suggests that pathogen avoidance behavior is based not only on perceived risk but on a trade-off between the perceived costs of pathogen exposure and the perceived benefits of social contact. We hypothesized that compliance with social distancing may therefore be explained by a trade-off between pathogen avoidance and various social motives such as mate-seeking. Two studies conducted during the COVID-19 pandemic showed that social distancing was positively associated with disease avoidance motives but negatively associated with social motives, especially mating motives. These associations remained after controlling for predictors identified by previous research, including risk perception and personality. Findings indicate that people who are more interested in seeking new romantic partners (e.g., young men) may be less inclined to socially distance and be more at risk of pathogen transmission.