Miracle-dz/UniFAR-FTUs huggingface dataset:
Altering Attitudes and Improving Intentions: An Evaluation of the Safe Zone Workshop at Georgia State University

Sexual prejudice, or negative attitudes toward sexual and gender minority persons, endures in U.S. culture today. A variety of interventions attempt to reduce sexual prejudice on college campuses. This doctoral dissertation project evaluates the adapted Safe Zone Workshop, a university-based sexual prejudice reduction intervention, with 82 students, faculty, and staff from Georgia State University over 13 workshop sessions. Results suggest a significant improvement from pretest to posttest in participants’ broad awareness of oppression. However, no statistically significant change was observed in quantitative data analysis from pretest to posttest in attitude (i.e., sexual prejudice); specific lesbian, gay, and bisexual oppression; knowledge, skills, openness, and support to act as an ally; or behavioral intentions to act as an ally. However, data suggest that the lack of quantitative findings may be due to ceiling effects as participants choosing to take part in the workshop likely have positive and accepting attitudes, knowledge, skills, and intentions before the workshop. Generally, participants reported that they liked specific activities, learning, and gaining resources in the workshop and disliked the physical space, specific activities, and timing of the workshop. Participants suggested ideas for recruitment and advertising as well as other activities to incorporate into and around the intervention. Further, many participants noted that they did experience change as a consequence of the workshop; they reported learning new terms, gaining knowledge, and becoming more comfortable and aware of issues related to gender and sexual minority persons. Therefore, this study adds to the extant literature in that few evaluations have been conducted of Safe Zone programs, fewer that include mixed-methods research on a university campus. Such programs may lead to better attitudes and actions of workshop attendees that may then contribute to safer and more inclusive environments for gender and sexual minority persons on campus. INDEX WORDS: Program evaluation, Mixed methods, Sexual prejudice, Intervention, Prejudice reduction, Sexual and gender minority persons ALTERING ATTITUDES AND IMPROVING INTENTIONS: AN EVALUATION OF THE SAFE ZONE WORKSHOP AT GEORGIA STATE UNIVERSITY
Position-aware Joint Entity and Relation Extraction with Attention Mechanism

Named entity recognition and relation extraction are two important core subtasks of information extraction, which aim to identify named entities and extract relations between them. In recent years, span representation methods have received a lot of attention and are widely used to extract entities and corresponding relations from plain texts. Most recent works focus on how to obtain better span representations from pre-trained encoders, but ignore the negative impact of a large number of span candidates on slowing down the model performance. In our work, we propose a joint entity and relation extraction model with an attention mechanism and position-attentive markers. The attention score of each candidate span is calculated, and most of the candidate spans with low attention scores are pruned before being fed into the span classifier, thus achieving the goal of removing the most irrelevant spans. At the same time, in order to explore whether the position information can improve the performance of the model, we add position-attentive markers to the model. The experimental results show that our model is effective. With the same pre-trained encoder, our model achieves the new state-of-the-art on standard benchmarks (ACE05, CoNLL04 and SciERC), obtaining a 4.7%-17.8% absolute improvement in relation F1.
AGROFERDURE: Intelligent Crop Recommendation System For Agriculture Crop Productivity Using Machine Learning Algorithm

Farming is the backbone of India. The yield of a modern-day farmer in India depends on many factors ranging from their economic conditions to environmental factors, and their habitat. A shortage in any of these factors would result in large scale loss of yield of farmer’s crop. To reduce losses incurred by the farmers we are trying to lend a helping hand by developing a tool for them. With the help of Machine Learning and Artificial Intelligence Technology this project provides yield improvement in various geographical areas with specific crops. Our Project collects information from the farmer and uses it to provide accurate and optimal recommendations to them which include information about soil and its nature in which the farmer wishes to cultivate. Depending upon this information our project provides the farmer with the best possible fertilizer and crop recommendation which enhances the yield. This information collected is stored in order to facilitate future use. The Technology and Algorithms used for this are Machine Learning, Regression and Recommendation Algorithms.
3D Convolutional Neural Network Framework with Deep Learning for Nuclear Medicine

Though artificial intelligence (AI) has been used in nuclear medicine for more than 50 years, more progress has been made in deep learning (DL) and machine learning (ML), which have driven the development of new AI abilities in the field. ANNs are used in both deep learning and machine learning in nuclear medicine. Alternatively, if 3D convolutional neural network (CNN) is used, the inputs may be the actual images that are being analyzed, rather than a set of inputs. In nuclear medicine, artificial intelligence reimagines and reengineers the field's therapeutic and scientific capabilities. Understanding the concepts of 3D CNN and U-Net in the context of nuclear medicine provides for a deeper engagement with clinical and research applications, as well as the ability to troubleshoot problems when they emerge. Business analytics, risk assessment, quality assurance, and basic classifications are all examples of simple ML applications. General nuclear medicine, SPECT, PET, MRI, and CT may benefit from more advanced DL applications for classification, detection, localization, segmentation, quantification, and radiomic feature extraction utilizing 3D CNNs. An ANN may be used to analyze a small dataset at the same time as traditional statistical methods, as well as bigger datasets. Nuclear medicine's clinical and research practices have been largely unaffected by the introduction of artificial intelligence (AI). Clinical and research landscapes have been fundamentally altered by the advent of 3D CNN and U-Net applications. Nuclear medicine professionals must now have at least an elementary understanding of AI principles such as neural networks (ANNs) and convolutional neural networks (CNNs).
Single‐switch, integrated DC–DC converter for high‐voltage step‐down applications

An integrated non-isolated DC-DC converter for applications that demand wide conversion range is proposed in this work. Using a technique known as graft scheme, it is possible to design a single-switch SEPIC-buck converter, where both the input current and the current supplied to the output stage composed of the filter capacitor and the load are non-pulsating when operation occurs in continuous conduction mode (CCM). Therefore, minimised electromagnetic interference (EMI) levels result as a consequence. Besides, the converter requires the use of a single active switch connected to the source reference node, without the need for isolated control circuitry, while it is possible to achieve wider conversion ranges than that provided by the classical buck converter. The qualitative and quantitative analysis of the converter is presented, from which it is possible to design and analyse it. Experimental results are discussed in detail to validate the theoretical considerations.
On the role of innovation and market structure on trade performance: is Schumpeter right?

PurposeThe goal of the paper is to examine the dynamics between innovation, market structure and trade performance. Firstly, the author first investigates the effects of innovation on trade performance. Secondly, the author then examines how market structure affect trade by classifying industries based on their innovation intensity.Design/methodology/approachThe author uses a detailed level data set of eight OECD countries in a panel of 17 industries from the STAN and ANBERD Database. The author employs both a pooled regression and a two-stage quantile regression analysis. The author first investigates the effects of innovation at the aggregate level, and then the author assesses the effects at the disaggregated or firm level.FindingsThe author finds that at the aggregate level, innovation and market size have a positive and significant effect on competitivity in most of the specifications. However, innovation is negatively associated with trade performance in the case of bilateral trade between Spain and the Netherlands. Also, the sectoral analysis provides evidence that the innovation-trade nexus depends on technological classification. The author shows that: (1) the effect of innovation activity on trade performance economic performance is lower for the high technology and high concentration (HTHC) market compared to the low technology (LT) market; (2) the impact of innovation on economic performance is ambiguous for firms in the high technology and low concentration (HTLC) market.Research limitations/implicationsAlthough the database provides a rich data set on industrial data, it fails to provide innovation output such as patent data which may underestimate the innovation activities of firms that do not have a separate R&D records. In the current context of subdue economic growth these research results have important policy implications. Firstly, the positive impact of innovation on trade performance strengthens its role for sustainable development. The negative coefficient on innovation is an indication that research intensity in some cases has not been able to create a new demand capable to boost economic performance.Practical implicationsThe market classification analysis provides new evidence that innovation in the LT market has the potential to enhance competition. Secondly, market size supports industries that are competing in the international market. Policy makers must therefore put in place incentives to encourage firms to grow in size if they want to remain globally competitive.Social implicationsSustainable development can be supported through investment in research and development in the low technology sector.Originality/valueThe study is the first as far as the author knows, to examine the impact of innovation on bilateral trade performance using industry level data from OECD countries. Secondly, the author complements the existing literature by examining how innovation activities (classified as high technological intensive or low technological intensive) affect trade performance.
Analysis and Representation of Face Robot-portrait Features

ABSTRACT Biometrics is being increasingly implemented and used in systems, mainly in security. Many works are still being carried out by researchers to improve existing biometric systems. One current important ongoing research in the field of security is the use of face biometrics in police departments to recognize criminals via portrait robot images described from crime witnesses. However, the correct identification of an individual is still a challenge. This is because the features required for the identification or the authentication might not have been accurately drawn or generated. The need of efficient robot-portrait image applications makes this study of vital importance and thus, a face recognition application using robot-portrait images is being developed in this research work. To extract and represent the features from the images, two techniques namely, Principal Component Analysis (PCA) and Local Binary Patterns (LBPs) have been adopted. To cater for the high computational load of the face features, PCA has been improved through the Gram-Schmidt orthogonalization process and the Rayleight-Quotient iteration scheme. From the results generated from the experiments conducted, it was concluded that modified PCA and LBP are two promising techniques that can be adopted to develop robot-portrait recognition applications.
Fly Ash Formation and Characteristics from (co-)Combustion of an Herbaceous Biomass and a Greek Lignite (Low-Rank Coal) in a Pulverized Fuel Pilot-Scale Test Facility

The lignite boilers are designed for lower quality fuels, and often the ash is not utilized. This work assessed the impact of combustion of an herbaceous biomass with a low-quality Greek lignite on the quality of the resulting fly ash. Test results were compared with those of fly ash samples from an industrial facility using the same fuel qualities. Inductively coupled plasma-optical (ICP) emission spectroscopy, X-ray powder diffraction (XRD), and scanning electron microscope (SEM) analyses were performed on the collected samples. Despite the significantly higher contents of K, Na and S in the biomass, at a 50% co-firing thermal share, the major and minor oxides in the fly ash were comparable to the lignite fly ash quality. This is attributed to the high ash content of the lignite, the low ash content of the biomass, and the much higher heating value of the biomass. There were improvements in fly ash performance characteristics with the herbaceous biomass in the fuel blend. The initial setting time and volume stability evaluations were improved with the biomass in the fuel blend. The work supports efforts of good practices in ash management, social responsibility, a circular economy, power plant renewable energy operations, and co-firing herbaceous biomass fuels in lignite power plants.
Udwadia–Kalaba theory for the control of bulldozer link lever

We propose to apply Udwadia–Kalaba theory to the bulldozer dynamics analysis. The bulldozer system is divided into several subsystems by this approach, which simplifies the modeling process for multi-link mechanism. Based on this approach, the constraints are classified into structure constraints and performance constraints. Structure constraints are used to set up dynamic model without regard for trajectory. Performance constraints are the desired trajectory. Then, according to the equation of motion of the unconstrained system established by Lagrange approach and system constraints which include structure and performance constraints, an explicit, closed-form analytical expression for the control force can be obtained by solving Udwadia–Kalaba equation. We demonstrate that this approach does not need to solve Lagrange multiplier, which is always difficult to obtain. However, for bulldozer link lever system, the initial conditions are difficult to satisfy the constraints in the actual situation. Thus, the problem of initial condition deviation is taken into consideration. In the end, the numerical simulations are done to prove that the trajectory of the bulldozer satisfies the designed one and the real-time forces are conveniently acquired.
Intelligent Control of Construction Manufacturing Processes using Deep Reinforcement Learning

: This paper is concerned with the development and evaluation of a reinforcement learning approach to the control of factory based construction operations. The unique challenges associated with controlling construction work is first discussed: uneven and uncertain demand, high customization, the need to fabricate work to order, and a lack of opportunity to stockpile work. This is followed by a review of computational approaches to this problem, specifically those based on heuristics and machine learning. A description is then given of a model of a factory for producing precast reinforced concrete components, and a proposed reinforcement learning strategy for training a neural network based agent to control this system. The performance of this agent is compared to that of rule-of-thumb and random policies for a series of protracted simulation production runs. The reinforcement learning method was found to be promising, outperforming the two competing strategies for much of the time. This is significant given that there is high potential for improvement of the method. The paper concludes with an indication of areas of proposed future research.
LMI Stability Condition for NCS with Packet Delay and Event-triggered Control

: This paper presents a controller design for networked control systems (NCS) with packet delay and event-triggered control. The total network delay is assumed to be an integer multiple of a fixed sampling period so that the overall system is time-varying with each model depending on the number of time delays. The design methodology is applicable to an arbitrary number of packet delays, regardless of whether the delays are random or deterministic. The methodology is applied to a simple example and Monte Carlo simulation results show that the controller stabilizes the NCS and is robust with respect to random variations in the sampling period and to changes in the probability of packet delays.
Consumer interest in upcycling techniques and purchasing upcycled clothing as an approach to reducing textile waste

ABSTRACT This study investigates the constructs of environmental concern, consumer creativity, and fashion consciousness and examines the relationships between these variables and consumer interest in learning upcycling techniques and purchasing upcycled clothing. Students (n = 120) at a Mid-Atlantic university were surveyed with an instrument created for the study using multi-item scales to measure each variable. Simple linear regression and multiple regression tests were run to measure the strength, direction, and significance of the hypothesised relationships. Results show a positive and significant relationship between interest in learning upcycling techniques with environmental concern and with consumer creativity, as well as between interest in purchasing upcycled clothing with environmental concern and with fashion consciousness. The findings can be used by brands selling upcycled apparel to successfully target creative, as well as environmentally and fashion-conscious, consumers to limit post-consumer textile waste.
Diffuser Augmented Wind Turbine (DAWT) Technologies: A Review

Diffuser Augmented Wind Turbines (DAWT) are an optimised class of wind turbines that use the theory of a Diffuser to accelerate and direct air flow onto a wind turbine rotor to drive it for higher rotations-per-minute and power output. This power output is typically measured and rated in terms of the power augmentation. Diffuser theory was pioneered in the 1970’s and has re-emerged in recent years with a range of new technological approaches to achieving laminar wind profiles, low exit pressures, improved pressure recovery, reduced blade tip losses, improved torque generation and adaptability to wind directional and speed changes. Research has been pivotal in the advancement of design and performance of DAWT’s with CFD remaining a critical analysis tool. Power augmentations have been realistically achieved within the range of 2-3 for small-medium scale turbines. In this review, ground-based Diffuser technologies have been presented primarily according to rotor type. Large-scale on-shore and off-shore concepts have been presented along with airborne technologies. Building-integrated DAWT’s are then presented with a description of some of the influential economic and technical factors that currently affect and will continue to affect the development of the DAWT industry. DAWT’s prove to have a realisable significant potential even when considered in applications across the wind turbine sector. The current DAWT industry is mostly research-based with very little commercialisation as the majority of technologies presented here are in their early developmental stages. It is expected that subsequent research and innovation in this field will be able to advance this issue to allow DAWT’s a credible chance as a key player in the wind technology sector.
DEVELOPING DIGITAL LITERACIES THROUGH CONTINUING PROFESSIONAL DEVELOPMENT

Digital technologies have much to offer education and to enable learners to interact with rich multimedia, and to communicate and collaborate. This paper argues that to harness the potential of digital technologies for learning new digital literacies skills are needed. To support teachers in the development of these literacies new and innovative forms of Continuing Professional Development are needed. The paper concludes with a description of the 7Cs of Learning Design framework, which aims to help teachers make more pedagogically informed design decisions that make appropriate use of digital technologies.
Hybrid Pulse Width Modulated Three-Phase Quasi- Z-Source Grid-Tie Photovoltaic Power System

A hybrid pulse width modulated three-phase quasi-Zsource inverter (qZSI) based grid-tie Photovoltaic (PV) power system is proposed. The hybrid pulse width modulation (HPWM) conducts PWM when the required ac output voltage is lesser than the dc source voltage, otherwise it performs pulse-amplitude modulation (PAM) for the three-phase qZSI. The low-frequency ripple voltage of dc link is utilized, resulting in reductions of quasi-Z-source inductance and capacitance, as well as power devices’ switching actions. Control strategy of the proposed system is further presented to track the maximum power points of the PV panel and to inject the extracted PV power into grid. A grid-tie current controller, combining with the plug-in repetitive control and proportional-resonant regulator, is employed to achieve strong harmonic suppression, fast convergence, and zero
Practical approach to testing the transformer differential protection for internal and external faults, CT saturation and inrush transients

This study presents a methodology that allows examining the differential protection of power transformers through computational simulations and numerical relay tests. Using that purpose, the alternative transient program (ATP) was used as a computational tool to perform several different events, which the power transformers are submitted to, and that can cause mal-operation of its differential protection, such as: external and internal faults, inrush current and saturation of current transformers (CTs). A testing system was implemented in laboratory, which consisted in simulating the power system in ATP (power transformer, CTs and faults modes) and applying the simulated signals through a programmable test set to a commercial relay to evaluate their response to these events. The main results of the behaviour of transformer protection relay under the tested transients are presented and discussed in this work.