Miracle-dz/UniFAR-FTUs huggingface dataset:
Green synthesis, characterisation of Au and Ag nanoparticles by various bioextracts and their usability at graphite electrode modification

In this study, the biosynthesis of Au and Ag nanoparticles (AuNp and AgNp) with a green chemistry approach was performed by using bioextracts of various fruits and vegetables (Lycopersicon esculentum (LE), Cucumis sativus (CS), Malus domestica (MD), Cucurbita pepo (CP), Apiumgraveolens var. rapaceum (AVR), Prunus cerasifera (PC), Oleraceae var. botrytis (OVB)). In the determination of optimum experimental conditions, the parameters such as the type and concentration of bioextract, concentration of metal ion solutions, the ratio of metal ion solution to bioextract, pH, reaction time and temperature were found to be significant. The characterisation of AuNp and AgNp synthesized by providing the most appropriate experimental conditions was performed by UV-Vis. , SEM, EDX, XPS and FTIR techniques. The characterisation study showed that, AuNp and AgNp were successfully synthesized and detailed information was obtained about their stabilities, sizes, homogeneities etc. In the final step, the usability of the green synthesized nanoparticles in the modification of pencil graphite electrodes (PGE) was investigated. Electrochemical characterisation of AuNp/GE and AgNp/GE electrodes was performed using cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) techniques. The obtained results and calculated electrochemical parameters showed that these modified electrodes have better conductivity, electron transfer rate and electrocatalytic activity.
Copperas-coated zeolite as an efficient adsorbent for cadmium removal from synthetic wastewater

Water pollution by cadmium is a major global issue as it tends to bioaccumulate and not biode - gradable in the environment. Exposure to this heavy metal even at low concentrations can cause many serious health effects to humans. This study aimed to investigate the efficiency of integrated copperas and zeolite as adsorbents for cadmium removal in an aqueous solution. Both materi als were integrated by simple immersion and heating methods for 4 h and their properties were each characterized in terms of morphology and functional group analysis via SEM and Fourier-transform infrared spectroscopy, respectively. In the first stage of evaluation, the performance of the adsorbents to remove cadmium was conducted through batch adsorption experiments on natu ral zeolite and integrated copperas/zeolite. Copperas/zeolite appeared to be a better adsorbent and was selected for further use in batch adsorption experiments at different dosages, initial concen -trations, and pH. The highest cadmium removal (99%) was obtained at pH 7, 4.0 g/L dosage, and 0.5 mg/L initial cadmium concentration. The adsorption study was well fitted with the Langmuir and Freundlich isotherm models and indicated both homogenous and heterogeneous adsorp - tion. The findings can provide an essential pathway for developing sustainable and cost-effective adsorbents for cadmium removal in water and wastewater treatment.
Perceived gendered expectations: a challenge for Generation Z women

ABSTRACT This study analyzes Generation Z women’s perceptions of gendered stereotypes about STEM and through social media. Generation Z is currently an age group still in school or on the verge of deciding for a career path, where STEM subjects are an option. Moreover, they are the most digitally active cohort, extensively engaging with social media. Several corporate as well as feminist technoscience studies attribute a possible influence of digitization on gender equality. Qualitative interviews with women living in Germany born between 1995 and 2001 show the prevalence of gendered stereotypes about interest in STEM, which holds women back from deciding for a STEM career path. In addition, idealized images of female beauty on social media exert pressure on women within Generation Z. Such gendered expectations create challenges for Generation Z women in their everyday lives and career tracks.
Multiple Instance Learning Networks for Stock Movements Prediction with Financial News

A major source of information can be taken from financial news articles, which have some correlations about the fluctuation of stock trends. In this paper, we investigate the influences of financial news on the stock trends, from a multi-instance view. The intuition behind this is based on the news uncertainty in random news occurrences and the lack of annotation for every single financial news. Under the scenario of Multiple Instance Learning (MIL) where training instances are arranged in bags, and a label is assigned for the entire bag instead of instances, we develop a flexible and adaptive multi-instance learning model and evaluate its ability in directional movement forecast of Standard & Poor’s 500 index on financial news dataset. Specifically, we treat each trading day as one bag, with certain amounts of news happening on each trading day as instances in each bag. Experiment results demonstrate that our proposed multiinstance-based framework gains outstanding results in terms of the accuracy of trend prediction, compared with other state-of-art approaches and baselines.
Deep Reinforcement Learning-based Construction Robots Collaboration for Sequential Tasks

—The integration of robots into the construction industry shows promise in addressing challenges such as stagnant productivity and low efficiency. Recently, an increasing amount of research develops construction robots based on reinforcement learning (RL). However, most existing RL-based construction robots are trained to conduct specific tasks individually without cooperation. This paper proposes an approach that utilizes two RL-based construction robots (an unmanned ground vehicle and a robot arm) to collaboratively finish the task of window panel transport and installation in sequence without human intervention. Our experiment results show that the two construction robots can successfully collaborate to finish all tasks in an end-to-end manner after they are trained separately with a success rate of 79.6%.
Designing Faster CM OS Subthreshold Circuits U sing Transistor Sizing and Parallel Transistor Stacks

Subthreshold region of operation in digital CMOS circuits provides an ideal lowpower solution for many applications that need tremendously low-energy operation. However, this advantage comes at the cost of speed, so enhancing the speed of sub­ threshold circuits can expand their application spectrum. This thesis deals with the techniques for speed improvement of subthreshold circuits to expand the domain of subthreshold circuits at no or minimal energy cost. The first part of the thesis examines the effect of PMOS-to-NMOS width ratio on frequency of operation in the subthreshold region. Analytical and simulation re­ sults illustrate that in this region the frequency attains its maximum at the optimum PMOS-to-NMOS ratio independent of the supply voltage. Using this optimum value leads to designing circuits for highest speed and nearly lowest energy in the subthresh­ old region. These results reveal that the minimum sizing doesn’t give the minimum energy per cycle in all cases. Parallel Transistor Stacks (PTS) technique has been shown to be effective for improving the speed of digital circuits operating in the subthreshold region, which comes at the cost of power consumption and area. However, our experience shows that using PTS is not beneficial in all cases. In the second part of the thesis, we present a methodology to identify whether or not using PTS is beneficial (or not) in a particular CMOS technology and to determine what transistor sizing can be employed to maximize the circuit speed. Our technique is based on analyzing the current-over-capacitance (COC) ratio of PMOS and NMOS transistors. The results of incorporating the proposed methodology in a 4-bit comparator and a 19-stage inverter ring oscillator, using 90 nm CMOS technology, illustrate 26% and 40% extra improvement compared with the blind use of PTS, respectively.
Development of an IoT system for fire-fighting in smart homes

: This paper shows an example of an IoT system for fire-fighting in smart homes. As support to this system, web application for tracking of sensors detection and warnings is shown. The devices that register smoke and flame and inform fire departments are implemented. The web application should enable early detection of fire and extinguish early fire while informing the house owner of the situation via SMS or via GSM call. All data is followed in real time through web application which is used by fire department. Aim of this system is to detect and prevent fire disasters in real time.
Partial discharge and corona theory and measurement

PARTIAL DISCHARGE AND CORONA THEORY AND MEASUREMENT Fuchs, Steven Paul University of Dayton, 1993 Advisor: Dr. Frank A. Scarpino A clear understanding of the processes of partial discharge and corona is an important tool in the design of high-voltage insulation systems. With a knowledge of the conditions under which different types of discharge activity are likely to occur, a designer can take measures to ensure against premature system failures due to partial discharge or corona activity. Also important to the design process is an understanding of the role and limitations of testing in diagnosing the condition of a development or production component. It is especially important that testing be conducted in a manner that provides legitimate indication of the condition of the component without causing damage that could affect future performance or life.
Perceptual, Affective and Cognitive Factors of L2 Pronunciation and Foreign Accent A Survey with Italian University Students Majoring in Languages

This contribution investigates the opinions of 372 first-year students from Ca’ Foscari University of Venice about pronunciation and foreign accent in the languages chosen as main subjects. Although a native-like accent is highly valued, students do not simply equate good and accent-free pronunciation. They are confident in assessing the pronunciation of other L2 speakers but show considerable uncertainty about self-assessment and pronunciation deficits. L2 pronunciation is mostly associated with pleasure for them. The main predictor of responses is proficiency level, followed by target languages, linguistic biography and motivation for enrolling in the course.
Exploring Weak-Supervision and Generative Models for Semantic Segmentation

Semantic segmentation is the task of assigning each pixel in an image an object class. Accurately assigning semantic labels requires precisely pinpointing the borders of objects, which requires a much higher localization accuracy than other high-level visual recognition tasks such as image classification or object detection. This constraint naturally suggests the use of probabilistic graphical models to model uncertainty in predictions combined with robust and powerful deep convolutional approaches to obtain accurate and well-localized segmentation maps.
The review of critical success factors of enterprise resource planning system implementation

Enterprise resource planning systems integrate all functional units within an organization which gives the firm competitive advantage over its competitors. The purpose of this study is to systematically appraise current and previous literature on enterprise resource planning, and investigate the critical factors that determine their successful implementation. The aim of this paper is to review prior studies on Enterprise Resource Planning (ERP) from 2002 to 2016 including implementation benefit/success, factors affecting implementation and implementation successes of ERP. The study employed the use of Systematic Literature Review (SLR). Finally, the study explains the findings of this review regarding contemporary studies on the ERP implementation and factors determining it and in turn highlights implication, conclusion and suggestions for future research study.
CORRELATION BETWEEN SEX AND ADDITIVE ON BLOOD BIOCHEMISTRY OF BROILER

The correlation between sex and additive on blood biochemistry, and gut morphometric of broiler was investigated. First experiment were day-old-chicks of Ross 308 placed among control and experimental. The first experiment were used 56 Ross 308 broiler with aged 35 days including 28 males and 28 females as control, and 28 males and 28 females supplemented with or without probiotics. The second experiment were used nonlinear models of Gompertz models to predict between sex and probiotic effect on the growth performance. The data analyses were uses two-way of variance between probiotic and sex (male-female). At the end experiment (35 aged d) thus probiotic and sex were significant difference (p > 0.05) on live weight (g), glucose (mg/dL), and villus height (µm). Data were statistically analyses using of SAS University version 4.0 red hat (64-bit). To sum up, the interaction between group × sex was significant for body weight (g), villus height (µm) and trends reduce amount of glucose (mg/dL) of broiler and non-linear models were effective to comparison between in vivo and predicted models.
Olfactory deafferentation generates gamma activity in the hippocampus during the wake-sleep cycle in the armadillo ( Chaetophractus villosus )

Background: There are recent evidence of sleep-dependent changes in olfactory system structure and function which contribute to odor memory and perception. Objective: The aim of the present study is to study the effect of the elimination of the olfactory mucosa on the electrical activity of the hippocampus, olfactory bulb, olfactory tubercle, pyriform cortex and frontal cortex during wakefulness and sleep. Methodology: Fifteen adult armadillos chronically prepared for electrographic recordings were employed in this study. Some animals were subjected to peripheral olfactory deafferentation by removal of the olfactory mucosa. They were studied during wakefulness and sleep phases. Results: The lack of peripheral olfactory input causes invasion in all the structures mentioned by very conspicuous and almost continuous bursts of high amplitude gamma activity (32 Hz) during slow wave sleep and paradoxical sleep. Conclusions: The hippocampus, together with some olfactory structures participates in an outstanding phenomenon produced by the suppression of olfactory receptors. The appearance of continuous bursts of gamma activity is in sharp contrast to other published studies because it is entirely independent from olfactory input. The relevance of this phenomenon is discussed considering the so called non olfactory functions of the olfactory bulb and the participation of the hippocampus in emotion, learning and memory. The importance of studying the effects of peripheral olfactory deafferentation during sleep is emphasized in view of possible derivations of interest for the interpretation of some pathological conditions. The significance of these findings is discussed.
Running head: MACHINE VISION IN A MOBILE ROBOT Machine Vision: Navigation and Object Recognition in a Mobile Robot

The engineering goals of this project were to create a mobile robotic system capable of automated obstacle avoidance and realtime object recognition. Specifically, the robot should be able to construct a visual model of an object from a set of training images, detect and identify said object within a realtime environment, and navigate safely to the object's position. Several different algorithms, such as Simultaneous Localization and Mapping (SLAM), Point-Line Iterative Corresponding Point (PLICP), Speeded-Up Robust Features (SURF), and linear triangulation were used in order to create and iteratively refine a working prototype of the robot. The robot's navigation capabilities were tested by timing the robot as it planned and followed a path to five predefined goals around the lab area, and its object recognition capabilities were tested by requesting the robot to identify several types of items in five different images. The project is not currently complete, but the robot has at least partially met all of its engineering goals; it is able to consistently and efficiently identify, locate, and navigate to a given object after viewing images of it. This is in stark contrast to the first iteration of the system's first iteration, where none of the goal points were reached due to map corruption.
Naming and Shaming or Compliance Tracking? International Water Treaties and Upstream-Downstream Monitoring along European Rivers

International rivers are the classic case of asymmetric externalities. These externalities create divided interests over cooperation. Can downstream states reduce these negative effects by naming and shaming upstream perpetrators through scientific measurements of the water quality close to the border? Or do downstream states report measurements to keep track of upstream compliance with existing agreements? The unequal distribution of monitoring stations over space and time provokes an explanation for why certain states monitor and report more others. Theorising that there is an endogenous relationship between monitoring and cooperation, I operationalise monitoring as a directed one-mode network based on geo-referenced gauging stations and the cooperation network as a two-mode network. To empirically study between network effects, I use a Stochastic Actor Oriented Model. The results suggest that downstream monitoring increases cooperation, however, that shared treaties are not conducive for monitoring.
“Nonparametric Bayes Modeling of Populations of Networks”

Durante, Dunson and Vogelstein (DDV) are to be congratulated on producing an accessible paper on flexibly modeling population distributions for networks. The framework has its roots in the hierarchical projection model of Hoff et al. (2002). In that model, the nodes are represented by unknown directions in an underlying “social space” and, conditional on the directions, the ties are independent. Specifically:
Enhancing BIM Methodology with VR Technology

Building information modeling (BIM) is defined as the process of generating, storing, managing, exchanging, and sharing building information. In the construction industry, the processes and technologies that support BIM are constantly evolving, making the BIM even more attractive. A current topic that requires attention is the integration of BIM with virtual reality (VR) where the user visualizes a virtual world and can interact with it. By adding VR, the BIM solution can address retrieving and presenting information and increasing efficiency on communication and problem solving in an interactive and col - laborative project. The objective of this chapter is to report the improvement of BIM uses with the addition of interactive capacities allowed by VR technology. A bibliographic and software research was made to support the study. Revit plugin; Enscape Revit plugin; Augment; Stingray; web3D; EON
Weight Gain and Dyslipidemia in Early Adulthood Associate with Polycystic Ovary Syndrome: 1 Prospective Cohort Study

Context: Obesity affects the majority of women with polycystic ovary syndrome (PCOS), but previous studies are inconsistent about the prevalence of obesity and the importance of weight gain in 48 the development of the syndrome. 49 Objective: To explore the association between weight, weight gain, hyperandrogenism and PCOS 50 from adolescence to late adulthood. Design: A prospective Northern Finland Birth Cohort 1966 study including 5889 females born in 52 1966 and followed at the ages of 14, 31 and 46 years. 53 Setting: General community. Participants: Women presenting both oligo/amenorrhea (OA) and hirsutism (H) at age 31 (N=125) or with formally diagnosed PCOS by age 46 (N=181) were compared with women without PCOS symptoms or diagnosis (n=1577). Interventions: Women with OA+H at 31 or diagnosis of PCOS by age 46 had the highest BMI at all 62 ages compared with the controls. Increase of BMI between ages 14 and 31, but not between 31 and 63 46, was greater in with isolated OA ( P =.006), OA+H ( P =.001) diagnosis of PCOS ( BMI 1.00-1.10], (OR=1.08 [95%CI: 1.03- 1.14]), (OR=1.05 [95%CI: 1.00-1.09]) triglycerides (OR=1.48 67
Modeling of Eccentricity and Performance of Three-Phase Induction Motors

: This paper modelled the eccentricity in three-phase induction motors and the accompanying performance. The geometry of three-phase asynchronous induction motor was utilized to calculate dynamic electromagnetic torque and inductances for the purpose of performance improvement. The position-dependent variation of the mechanical energy was applied to the rotor through the air gap which generates the mutual inductance across the stator-rotor circuitry. The entire six-by-six inductance matrix modelled for the non-concentric three-phase induction motor was analytically differentiated and used to model the torque equation for the eccentric motor. The approach was simulated in MATLAB and ratified with an eccentricity degree of zero (non-existent) which tallied and rhymed with that gotten for a healthy induction motor. Results obtained show that an eccentric motor rotor does not accelerate uniformly, but glitches and flickers intermittently until it attains a maximum speed of about 320 rad/sec, whereas for the healthy motor, the rotor accelerates uniformly from rest to peak giving rise to a load-less motor speed of 0.33sec. For eccentricity degrees greater than 0.7, the air gap function for a non-concentric three-phase induction motor goes higher than 1.0 at some points and hits zero at lowest values thereby validating that the displacement of the rotor position from the normal is directly proportional to the degree of eccentricity.
Joint Convexity of Error Probability in Blocklength and Transmit Power in the Finite Blocklength Regime

To support ultra-reliable and low-latency services for mission-critical applications, transmissions are usually carried via short blocklength codes, i.e., in the so-called finite blocklength (FBL) regime. Different from the infinite blocklength regime where transmissions are assumed to be arbitrarily reliable at the Shannon’s capacity, the reliability and capacity performances of an FBL transmission are impacted by the coding blocklength. The relationship among reliability, coding rate, blocklength and channel quality has recently been characterized in the literature, considering the FBL performance model. In this paper, we follow this model, and prove the joint convexity of the FBL error probability with respect to blocklength and transmit power within a region of interest, as a key enabler for designing systems to achieve globally optimal performance levels. Moreover, we apply the joint convexity to general use cases and efficiently solve the joint optimization problem in the setting with multiple users. We also extend the applicability of the proposed approach by proving that the joint convexity still holds in fading channels, as well as in relaying networks. Via simulations, we validate our analytical results and demonstrate the advantage of leveraging the joint convexity compared to other commonly-applied approaches.
Facial Expression Recognition using Deep Convolutional Neural Network

- Human Facial Expression Recognition (FER) is always been a challenging task for researchers. Face expressions play an important role in non-verbal communication. Nowadays, deep learning is achieving great attention in this area. But it is a tedious task to build a simple and effective architecture in deep learning. The need is to develop an architecture which is fast to train and achieves good accuracy. Therefore, for detecting human facial expressions deep learning model using a convolutional neural network(CNN) is used in image classification as these models are effective and achieve high accuracy and effictiveness for the facial expression recognition problems. This work is proposed to acquire results improvement on FER-201, JAFFE, and CK+ dataset. Python platform is used for implementation and execution of the proposed work.