Miracle-dz/UniFAR-FTUs huggingface dataset:
Fuzzy-IP Controller for Voltage Regulation in a Stand-Alone Microgrid System

This paper presents a method that combines fuzzy inference system and Integral-Proportional named Fuzzy-IP controller to regulate an independent microgrid voltage with a distributed energy resource unit. The unit employed photovoltaic (PV) array with DC voltage converted to three-phase AC voltage. The control design was carried out through modeling and simulation using Matlab software environment. Transfer function with the 2×2 structure using system identification has estimated the non-linear plant model. Two controllers transformed the a-b-c to the d-q axis coordinates of voltage to simplify linear control design. An oscillator is applied to set frequency according to the recommendation. The results show that the compensated case-study system using Fuzzy-IP with disturbance tracked the setpoint excellently and it regulated the voltage properly including frequency control using internal oscillator. The paper presents performance superiority of the proposed method over PI control by comparing the transient response, the mean squared error, and the root mean squared error.
FINANCIAL DEVELOPMENT AND ENVIRONMENTAL DEGRADATION IN INDONESIA: EVIDENCE FROM AUTO REGRESSIVE DISTRIBUTED LAG BOUND TESTING METHOD

Financial sector can carry a negative impact on power consumption by advancing energy efficient technologies through technological advancements in the power industry to decline toxic carbon emanations. Therefore, the emphasis of the current investigation is on identifying the empirical connection between financial advancement and carbon emission in the developing country of Indonesia. In addition, given the relevant significance, the study also aims to examine the contribution of energy consumption and economic growth of Indonesia in influencing environmental degradation in the country. The results of Auto Regressive Distributed Lag bound testing confirm a valid long-run relationship between financial advancement, energy consumption, economic growth, and carbon dioxide emission in Indonesia. The result further suggested that financial advancement, energy consumption, and economic growth have a positive and significant impact on carbon emission in both the long and short run period. The study recommends that the government needs to divert its energy and also financial institution toward the green investment and environmental friend energy utilization, which ultimately help to reduce the carbon emission level in Indonesia.
DATA MINING APPROACH FOR PREDICTING STUDENT PERFORMANCE

Although data mining has been successfully implemented in the business world for some time now, its use in higher education is still relatively new, i.e. its use is intended for identification and extraction of new and potentially valuable knowledge from the data. Using data mining the aim was to develop a model which can derive the conclusion on students' academic success. Different methods and techniques of data mining were compared during the prediction of students' success, applying the data collected from the surveys conducted during the summer semester at the University of Tuzla, the Faculty of Economics, academic year 2010-2011, among first year students and the data taken during the enrollment. The success was evaluated with the passing grade at the exam. The impact of students' socio-demographic variables, achieved results from high school and from the entrance exam, and attitudes towards studying which can have an affect on success, were all investigated. In future investigations, with identifying and evaluating variables associated with process of studying, and with the sample increase, it would be possible to produce a model which would stand as a foundation for the development of decision support system in higher education.
The Quest for a Primary EPSAS Purpose – Insights from Literature and Conceptual Frameworks

Abstract Since 2013, the European Union has aimed to harmonize the public sector accounting (PSA) of its member states by developing European Public Sector Accounting Standards (EPSAS). As a prerequisite for consistent EPSAS, a conceptual framework (CF) is to be developed which includes an explicit selection of purposes. Providing an international comparative view with the theoretical underpinning of Nobes’ classification, this paper presents a literature-based systematization of PSA purposes. Our systematization is applied to three CFs: one international (IPSAS) and two national (from the USA and Germany, respectively). Through a normative discussion, we conclude that accountability is preferable as the overall accounting purpose. This theoretical lens is used to evaluate the recent status quo of the ongoing EPSAS CF project. This paper thereby develops relevant implications for accounting theory and offers a starting point for standard setters and scholars to prioritize purposes for PSA with respect to different legal frameworks.
A Watt-Level 4G LTE CMOS Reconfigurable Power Amplifier with Efficiency Enhancement in Power Back-Off

This paper presents a reconfigurable two-stage power amplifier (PA) for use in 4G LTE unmanned aerial vehicle (UAV) applications. The PA using the TSMC bulk 65-nm CMOS process exhibits a saturated output power of 29.8 dBm, a power gain of 35.6 dB, a maximum power added efficiency (PAE) of 27.2 % at 2.5GHz and maintains PAE over 10 % in the output power’s 8 dB back-off zone as required by LTE’s PAPR specifications. The proposed reconfigurable PA architecture, which includes four sub PA cells with the power cell switching (PCS) technique, allows the high level of efficiency in back-off of output power. The four sub-PA cells are composed of three differential cascode stages, supplied by 3.3 V and implemented with the segmented bias (SB) technique to maintain the high level of PAE, reduce the DC power consumption and reconfigure the output impedance.
Adults with a diagnosis of autism: personal experiences of engaging with regional criminal justice services

This paper reports on interviews undertaken with four adults with a diagnosis of autism, about their experiences of engaging with regional Criminal Justice Services (CJS) as victims, witnesses or perpetrators of a crime. Participants were recruited through an autism-specific charity, Triple A Project (All About Autism). These interviews comprised the second phase of a two-stage project; the first phase involved interviews with members of the charity and revealed differences in perceptual frames of reference between individual participants with a diagnosis of autism and CJS professionals that led to miscommunication and misunderstanding. Key findings from the research suggest that specific challenges with lateral thought and interpretation can mean that individuals on the autism spectrum are potentially vulnerable to finding themselves implicated as perpetrators or victims of a criminal act. This research represents an important development concerning the question of autism and criminality; the researchers sought to extend the academic discourse beyond an examination of the perspectives of professionals to give voice to the perspectives of experts by experience with a diagnosis of autism.
Modeling and experimental validation of highly astigmatic laser beams

Gaussian beamlet decomposition (GBD) and Gaussian beamlet propagation (GBP) have become accepted and well know methods for physical optics modeling, implemented in many commercial optical simulation software packages. Typical applications are the modeling of diffraction problems, involving collimated or low-divergence laser beams. For these applications and many more typical textbook examples, it can be shown that GBP and GBD offer an excellent match between experimental data and modeling results. For highly astigmatic laser beams, on the other hand, no published examples can be found, where GBP and GBD were used to model the resulting irradiance distribution and compared it to experimental data. A typical representative of such a system is a line laser system, which can be found in a variety of applications. Line lasers are used e.g. as pattern generators in triangulation-based 3D measuring systems, as light curtains in machine safety applications or for shape and volume measurements in logistics automation. Already in a very early product development phase, the knowledge of the irradiance distribution is crucial to ensure product functionality and product safety. Therefore, the measured irradiance data of a typical line laser system are compared here with the modeling results of a simulation software supporting the GBD and GBP methods. Under most conditions, there is a good agreement found between the measurement and simulation data, which suggests that GBD and GBP are useful tools in the design process of line laser systems.
Children’s narrative representations of peers and teachers in 3 new classroom scenarios

ABSTRACT Narrative Story-Stem Tasks (NSST) assess young children’s perceptions of close relationship qualities via the spontaneous narratives they create in response to story beginnings using doll-like figures. Most NSST research to date has focused on family relationships. This study extends this research to children’s perceptions of relationships with teachers (caregiving and attachment) and peers (exploration, mutuality, and aggression) in school settings. Three new story-stems were created for this purpose. A racially and economically diverse sample (n = 39) was recruited from two school systems in the U.S. As predicted, girls were more likely to enact relational aggression in their narratives. Children attending the higher SES school were significantly more likely to enact representations of teachers as caregivers, and representations of parental and teacher caregiving were significantly associated. Parent-reported perceptions of their children’s teachers were significantly associated with children’s narrative representations of attachment behaviour directed toward teachers. Implications for policy and services are discussed.
GETTING UNIVERSITY STUDENTS TO FINISH WHAT THEY STARTED: DESIGN, TESTING AND ITERATION OF AN APP TO PROMOTE STUDENT RETENTION IN HIGHER EDUCATION

University student dropout is an issue of concern worldwide; lecturers, administrators and governments alike aspire to see students complete their degrees. Within academic literature, student retention has been conceptualized as a function of engagement (with engagement meaning investment in a particular learning institution and culture). In the literature, it is also known that students tend to copy the behavior of their peers: if a student’s friends leave, that student may be more likely to leave; if a student’s friend’s stay, then that student may be more likely to stay too. Aiming to reduce the dropout rate in our institution, we developed an App that highlights student activities, fosters social connections, and aims to make it easier for faculty and staff to communicate with students. We tested the App’s impact on student retention by asking: Would highlighting positive engagement and retention behaviors as well as promoting community building through the use of an App positively influence student persistence rates within a small US university? Answering this question was and is both easy and hard. Quantitatively, dropout rates are easy to measure. However, the behaviors that lead to those dropout numbers are often influenced by a series of human interactions, so qualitative data was also collected. Specifically, a design-based research framework - an iterative framework well-suited for ongoing technological innovations, and improvements - was employed. Both quantitative and qualitative data were collected through surveys, focus groups, interviews, user data, and institutional metrics. Initial results have been promising. During the first two weeks of the fall 2018 term the App was used 14,990 times by 911 distinct users with the total undergraduate enrollment being just under 1,300 students. A small increase in fall to spring semester student retention has been measured; in our current stage of research we are aiming to measure to what extent the App may have had an impact on this potentially positive development. This presentation will showcase the App, overview our data on the relationship between the App and retention, and discuss our developing features and versions of the App.
Data Assimilation in Discrete Event Simulations

Enabled by the increased availability of data, the data assimilation technique, which incorporates measured observations into a dynamical system model to produce a time sequence of estimated system states, gains popularity. The main reason is that it can produce more accurate estimation results than using either a simulation model or the measurements. Due to this benefit, the data assimilation technique has been applied in many continuous systems applications, but very little data assimilation research has been found for discrete event simulations. With the application of new sensor technologies and communication solutions, the availability of data for discrete event systems has increased as well. The increased data availability for discrete event systems but the lack of related data assimilation techniques thus motivated this work on data assimilation for discrete event simulations. Since discrete event simulations are highly nonlinear, non-Gaussian systems, particle filters are used to conduct data assimilation in discrete event simulations. However, applying particle filtering in discrete event simulations still encounters several theoretical and practical problems, such as the state retrieval problem (discrete event simulation models have a piecewise constant state trajectory, so the retrieved state was updated at a past time instant, with which inaccurate estimation results will be obtained), the variable dimension problem (the dimension of the state trajectory during a fixed time interval is random, leading to inapplicability of the standard sequential importance sampling algorithm), and the processing of non-numerical data. Therefore, this research aims to develop a particle filter based data assimilation framework for discrete event simulations, in which the aforementioned problems can be addressed.
Cumulative cultural evolution in a non-copying task in children and Guinea baboons

The unique cumulative nature of human culture has often been explained by high-ﬁdelity copying mechanisms found only in human social learning. However, transmission chain exper- iments in human and non-human primates suggest that cumulative cultural evolution (CCE) might not be dependent on high-ﬁdelity copying after all. In this study we test whether CCE is possible even with a non-copying task. We performed transmission chain experiments in Guinea baboons and children where individuals observed and reproduced visual pat- terns on touch screen devices. In order to be rewarded, participants had to avoid touching squares that were touched by a previous participant. In other words, they were regarded for innovation rather than copying. Results nevertheless ex- hibited two fundamental properties of CCE: an increase over generations in task performance and the emergence of sys- tematic structure. However, CCE arose from different mechanisms across species: children, unlike baboons, converged in behaviour over generations by copying speciﬁc patterns in a different location, thus introducing alternative copying mech- anisms into the non-copying task. We conclude that CCE can result from non-copying tasks and that there is a broad spec- trum of possible mechanisms that will lead to CCE aside from high-ﬁdelity transmission.
Combining Nonclinical Determinants of Health and Clinical Data for Research and Evaluation: Rapid Review

Background Nonclinical determinants of health are of increasing importance to health care delivery and health policy. Concurrent with growing interest in better addressing patients’ nonmedical issues is the exponential growth in availability of data sources that provide insight into these nonclinical determinants of health. Objective This review aimed to characterize the state of the existing literature on the use of nonclinical health indicators in conjunction with clinical data sources. Methods We conducted a rapid review of articles and relevant agency publications published in English. Eligible studies described the effect of, the methods for, or the need for combining nonclinical data with clinical data and were published in the United States between January 2010 and April 2018. Additional reports were obtained by manual searching. Records were screened for inclusion in 2 rounds by 4 trained reviewers with interrater reliability checks. From each article, we abstracted the measures, data sources, and level of measurement (individual or aggregate) for each nonclinical determinant of health reported. Results A total of 178 articles were included in the review. The articles collectively reported on 744 different nonclinical determinants of health measures. Measures related to socioeconomic status and material conditions were most prevalent (included in 90% of articles), followed by the closely related domain of social circumstances (included in 25% of articles), reflecting the widespread availability and use of standard demographic measures such as household income, marital status, education, race, and ethnicity in public health surveillance. Measures related to health-related behaviors (eg, smoking, diet, tobacco, and substance abuse), the built environment (eg, transportation, sidewalks, and buildings), natural environment (eg, air quality and pollution), and health services and conditions (eg, provider of care supply, utilization, and disease prevalence) were less common, whereas measures related to public policies were rare. When combining nonclinical and clinical data, a majority of studies associated aggregate, area-level nonclinical measures with individual-level clinical data by matching geographical location. Conclusions A variety of nonclinical determinants of health measures have been widely but unevenly used in conjunction with clinical data to support population health research.
Solving Travelling Salesman Problem by Using Optimization Algorithms

This paper presents the performances of different types of optimization techniques used in artificial intelligence (AI), these are Ant Colony Optimization (ACO), Improved Particle Swarm Optimization with a new operator (IPSO), Shuffled Frog Leaping Algorithms (SFLA) and modified shuffled frog leaping algorithm by using a crossover and mutation operators. They were used to solve the traveling salesman problem (TSP) which is one of the popular and classical route planning problems of research and it is considered as one of the widely known of combinatorial optimization. Combinatorial optimization problems are usually simple to state but very difficult to solve. ACO, PSO, and SFLA are intelligent meta-heuristic optimization algorithms with strong ability to analyze the optimization problems and find the optimal solution. They were tested on benchmark problems from TSPLIB and the test results were compared with each other. Keywords : Ant colony optimization, shuffled frog leaping algorithms, travelling salesman problem, improved particle swarm optimization
Influence of listener task on ratings of pleasantness for everyday sounds

Objective: To develop a method to investigate the influence of subject task on the evaluation of sound stimuli. The method is for use in future hearing aid experiments. Method: Twenty listeners with normal hearing rated real-life sound stimuli under different conditions. The sound stimuli were binaurally-recorded soundscapes with low-level target sounds mixed in. The conditions were: 1. Listening only to sound stimuli without any other tasks. This condition is similar to the method used in typical hearing aid studies. 2. An ‘auditory detection’ paradigm, where listeners detect low-level target sounds (e.g. a microwave beep) within the sound stimuli. 3. The ‘irrelevant sound’ paradigm, where listeners perform cognitive tasks (e.g. simple addition of numbers), while the sound stimuli are presented. After listening to each sound stimulus under these three conditions, listeners rated the pleasantness of the sound stimulus. Results and Conclusions: Ratings of auditory pleasantness were lower under the irrelevant sound condition and under the auditory detection condition than in the listening only condition. However, there was a large degree of variability associated with the ratings, which reduces the sensitivity of the method for use of evaluating hearing aid settings.
Variation in physicochemical and biochemical soil properties among different plant species treatments early in the restoration of a desertified alpine meadow

Soil nutrients, enzyme activities, and microbial biomass have important significance for biogeochemical cycles and are affected by vegetation restoration in degradation ecosystems. To explore the effects of replanting different plant species on soil restoration in a desertified alpine meadow on the eastern Tibetan Plateau and to optimize the restoration process with locally appropriate plant species, the soil physicochemical and biochemical properties were measured beneath the plant species Rhodiola kirilowii, Salix cupularis, and Kengyilia rigidula over two periods of vegetative growth (2 years and 5 years). The results showed that all the soil properties significantly improved over the recovery periods (p < .001) and the plant species significantly affected the patterns of soil bulk density, available resources, and biochemical properties (p < .05). However, R. kirilowii improved soil properties most significantly among the three plant species as the recovery time elapsed, and these improvements were strongly related to the greatest fresh vegetation biomass and coverage. Furthermore, R. kirilowii enhanced microbial biomass phosphorus even more, which is an important available phosphorus stock in the soil, and more effectively promoted the development of soil fungi than the two other species. Therefore, we suggest that R. kirilowii is the preferred species of the three to expedite the recovery of desertified alpine soil, and that the appropriate addition of P fertilizer and propelling the development of specific fungi groups in the soil are key to soil restoration in the desertified alpine meadow.
Towards clearer formulation of adult Autism Spectrum Disorder : exploring comorbidity and measurement

Difficulties with emotional regulation, interpersonal relationships and empathy, in Borderline Personality Disorder (BPD), have been noted to occur in Autism Spectrum Disorders (ASD). These similarities in presentation have been proposed to lead to diagnostic uncertainty, with misdiagnosis in clinical settings, as well as increased psychopathology for comorbid individuals. The current review aimed to summarise evidence highlighting the overlap between the two conditions, focusing on issues around misdiagnosis, patterns of comorbidity and specific symptomatic signatures in ASD, BPD and comorbid populations. Comprehensive literature searches were completed across three databases: Web of Science, PubMed and PsycINFO, resulting in 11 papers eligible for review. Findings confirmed significant parallels in symptomology between the two conditions, with higher levels of psychopathology seen in comorbid individuals, particularly suicidality. Comorbidity prevalence rates varied, suggesting a need for controlled epidemiological studies. Subtle differences in specific characteristics were found between groups. Clinical implications include raising awareness within psychiatric and specialist diagnostic settings of the similarities between the two conditions and conducting detailed assessments with developmental histories and measures relating to personality, interpersonal functioning and executive functioning, in order to be fully comprehensive.