Miracle-dz/UniFAR-FTUs huggingface dataset:
Teachers’ experience and regulation of anger and fear in the school context

Abstract The study focuses on the experience and regulation of teachers’ anger and fear in the school context, as emotions play an important role in teachers’ professional work. The sample comprised 101 Slovenian primary teachers, aged from 25 to 61 years (M = 41.36; SD = 10.28). The results indicate that the teachers rarely experienced anger and fear at work and at home, although anger was experienced more frequently than fear. Anger and fear were triggered in specific circumstances: the majority of situations triggering anger involved students, while the majority of situations triggering fear involved parents. The teachers employed various strategies to regulate anger and fear, most of which were used moderately. Some differences emerged: situation modification, experiential response modulation and physical activation were used more frequently to regulate anger than fear, while suppression was used more frequently to regulate fear that anger. As emotions represent an important part of school life, the results may prove valuable to all involved in the school context.
Culture, Training, Technological Application and Crime Investigation Effectiveness across Commercial Crime Investigative Units in Nigeria: An Empirical Analysis

fighting corruption. in line with the implementation of the recommendations of the Justice KayodeEsho Commission of Inquiry, of institutions Abstract: Crime investigation is an important law enforcement activity.It is one of the least studied law enforcement functions. Generally, criminal investigation is viewed as a ‘truth finding’ process at the end of which the crime is solved, and offenders are caught. This study therefore investigated culture, training, technological application and crime investigation effectiveness across commercial crime investigative units in Nigeria. This study used a descriptive survey design of ex-post-facto type. One thousand and two hundred participants, selected through a combination of purposive and stratified random sampling techniques were drawn from commercial crime investigating units of the Police, EFCC, Customs and NDLEA in Lagos State. Data were collected using two adopted and three adapted instruments. The reliability coefficients yielded by the instruments were: Agency Culture Inventory, 0.88; Training Effectiveness Scale, 0.73; Technology Application Inventory, 0.91; and Commercial Crime Investigators Effectiveness Scale, 0.81. Three null hypotheses were tested at 0.05 level of significance, while data were analyzed using of means, standard deviation and Multiple Regression Analysis. The findings of this study indicated the results indicated significant mean differences in the influence of agency culture (F (4, 1179) = 4.765, p = . 000), training (F (4, 1179) = 8.306, p = . 007), and technological application (F (4, 1179) = 5.160, p = . 000) on commercial crime investigators' effectiveness based on agency type. It is therefore concluded that agency culture, training and application of technology has substantial impact on law enforcement practices. In addition, any positive relationship that exists between agency culture, training and application of technology by the agency improves the quality of investigation and performance of law enforcement agencies.
Measurement-Based Analysis of MIMO Communications over the PCS Spectrum

The objective of this thesis is to design a system to analyze the real-world performance of space-time codes for narrowband multiple-input multiple-output (MIMO) communications. In theory, space-time codes, which are a form of coded modulation for high-rate multiple antenna systems, have been shown to bring about significant performance increases in terms of bit error probability over conventional uncoded systems. However, until now, not many experiments have verified the power of these codes in practice; most of the major preceding work has focused on understanding the nature of the MIMO channel and channel estimation ([1], [2], [3], [4]). This thesis goes through the process of designing a MIMO system in conjunction with the Signals and Systems group at Lincoln Laboratories for an indoor field test at Lincoln Labs. In this field test, space-time coding schemes were tested for scenarios with up to 8 fixed transmit antennas and 10 fixed receive antennas. Preliminary analysis of the experimental data seems to suggest that space-time codes are indeed capable of achieving their theoretical potential. Thesis Supervisor: Vahid Tarokh Title: Associate Professor Acknowledgments I First and foremost, I am deeply grateful to my advisors, Vahid Tarokh at MIT and Dan Bliss at Lincoln Labs, for taking me on this project and for their kindness, enthusiasm, patience, and encouragement. I have truly learned an enormous amount under their guidance this past year. I am indebted to my wise graduate student mentor Julius Kusuma, who gave me perspective and answered the questions I would have been embarrassed to ask otherwise. I also thank Dr. Il-Min Kim for breaking (and fixing) my code several times throughout the year. I am grateful for group leader Gary Hatke for hiring me and the research staff at Lincoln Labs, including Ali Yegulalp and Nick Chang, for their help with channel estimation, and Keith Forsythe. I am of course also indebted to Lincoln's over-extended hardware and technical staff, including Sean Tobin, Jeff Nowak, Lee Duter, John Mann, Peter Priestner, and Bob Devine, for carrying out the experiment on which this thesis is based. Finally, I thank my parents and brother, without whom none of this would have been possible in the first place. This work was sponsored by the Defense Advanced Research Projects Agency under Air Force Contract F19268-00-C-0002. Opinions, interpretations, conclusions, and recommendations are those of the author and are not necessarily endorsed by the United States Government.
Dataset-Driven Unsupervised Object Discovery for Region-Based Instance Image Retrieval

Instance image retrieval could greatly benefit from discovering objects in the image dataset. This not only helps produce more reliable feature representation but also better informs users by delineating query-matched object regions. However, object classes are usually not predefined in a retrieval dataset and class label information is generally unavailable in image retrieval. This situation makes object discovery a challenging task. To address this, we propose a novel dataset-driven unsupervised object discovery framework. By utilizing deep feature representation and weakly-supervised object detection, we explore supervisory information from within an image dataset, construct class-wise object detectors, and assign multiple detectors to each image for detection. To efficiently construct object detectors for large image datasets, we propose a novel “base-detector repository” and derive a fast way to generate the base detectors. In addition, the whole framework is designed to work in a self-boosting manner to iteratively refine object discovery. Compared with existing unsupervised object detection methods, our framework produces more accurate object discovery results. Different from supervised detection, we need neither manual annotation nor auxiliary datasets to train object detectors. Experimental study demonstrates the effectiveness of the proposed framework and the improved performance for region-based instance image retrieval.
Price Index Modeling and Risk Prediction of Sharia Stocks in Indonesia

This study aimed to predict the JKII (Jakarta Islamic Index) price as a price index of sharia stocks and predict the loss risk. This study uses geometric Brownian motion (GBM) and Value at Risk (VaR; with the Monte Carlo Simulation approach) on the daily closing price of JKII from 1 August 2020–13 August 2021 to predict the price and loss risk of JKII at 16 August 2021–23 August 2021. The findings of this study were very accurate for predicting the JKII price with a MAPE value of 2.03%. Then, using VaR with a Monte Carlo Simulation approach, the loss risk prediction for 16 August 2021 (one-day trading period after 13 August 2021) at the 90%, 95%, and 99% confidence levels was 2.40%, 3.07%, and 4.27%, respectively. Most Indonesian Muslims have financial assets in the form of Islamic investments as they offer higher returns within a relatively short time. The movement of all Islamic stock prices traded on the Indonesian stock market can be seen through the Islamic stock price index, namely the JKII (Jakarta Islamic Index). Therefore, the focus of this study was predicting the price and loss risk of JKII as an index of Islamic stock prices in Indonesia. This study extends the previous literature to determine the prediction of JKII price and the loss risk through GBM and VaR using a Monte Carlo simulation approach.
Human-Machine Collaborative Telerobotics: Computer Assistance for Manually Controlled Telesurgery and Teleoperation

Teleoperated robots are controlled by human operators and allow us to act in environments that are inaccessible for reasons of safety, scale, or remoteness. Unfortunately, due to lack of tactile sense and dexterity and proprioception, task performance can suffer compared to direct object manipulation with the hands. Still, use of human controlled robot s is the only viable option for these dangerous or hard-to-reach applications, since robots do not yet have the capability to autonomously perform most unstructured tasks. This thesis presents new contributions in telerobotics that show the way to improved access and control. The target application domain is telerobotic surgery. A novel surgical robotic system is presented, the Raven II, which forms the basis of a collaborative telerobotic research network with open open source softwar e and tool interface. The robot is now in use at more than a dozen institutions and is a shared platform for developing and sharing new hardware, software, and methods. Collaborating on telerobotics research requires agreement on a teleoperation protocol. To this end a new data interface was developed and tested for compatibility among fourteen master-slave teleoperation robots. Results of this study suggest avenues for a common internet standard for teleoperation robots. To study performance with this system, beyond purely manual control, two types of computer assistance are studied: virtual fixtures and mixed autonomy. Virtual fixtures are spatially varying environment features that provide motion control assistance . Mixed autonomy combines autonomous robotic motion with human control or commands. In this work, a new mixed autonomy method is presented based on a division of the workspace into regions that are either safe for computer assisted control, or require pure human control. The new method was evaluated experimentally and compared with novel virtual fixture implementations and pure manual control. Experimental evaluation used uni- and bi-manual peg-in-hole object grasping and manipulation tasks, and several metrics were reported. The aim of these experiments was to determine when and how computer assistance functions will help improve human control of robots. Results showed that performance on some sub-components of object manipulation was improved by the addition of computer assistance. However a performance detriment was measured, due to a mismatch of state information when control is switched from pure hu man to mixed human-computer operation. Results of this research demonstrate ways to improve many high-value tasks being carried out today under telerobotic operation; successful application of this research may literally save lives in the operating room, disaster relief situations, nuclear environments and other critical applications.
Large-scale physical modeling of broken solitary waves impacting elevated coastal structures

ABSTRACT Amongst extreme hydrodynamic events are bore- and surge-type flow motions that are observed in the context of storm surges induced by tropical cyclones, but also occur when tsunami or flash floods strike. Coastal houses built on elevated pile foundations have suffered less damages in recent extreme hydrodynamic events since the water could pass beneath the floor slabs decreasing the exertion of forces onto structures. To date, research pertaining to horizontal and vertical forces on elevated structures is still scarce. Specifically, previous research may not be applicable to cases of bore-type inundation interacting with elevated coastal structures. This work hence aims to model non-elevated and elevated coastal structure, and to deepen insight into forces with a focus on the structural elevation. For this purpose, large-scale experimental tests were performed on a uniform 1:15 slope in combination with an adjacent horizontal plane. Idealized residential buildings on a length scale of 1:5 were designed to simulate loading conditions of broken solitary waves on slab-on-grade and elevated buildings. A wide range of horizontal forces between 0.1 and 10 , vertical forces between 0.5 and 7.5 and overturning moments up to 4.5 were measured. In accordance with the experimental results, design equations were derived.
Low One-Photon Absorption for 3D Microfabrication

Direct Laser Writing (DLW) opened the doors for the efficient and customizable fabrication of both 2D and 3D microand nanostructures. Currently, Two-Photon Polymerization (TPP) is the ubiquitous DLW method; however, its use is cost inhibitive. Low One-Photon Absorption (LOPA) provides a cheap avenue for DLW and yields comparable 2D and 3D structures in their size and complexity. The underlying absorption process of LOPA is largely unknown, and thus require further investigation in order to optimize low one-photon polymerization methods. This work seeks to elucidate this underlying process, as well as demonstrate LOPA's use as an alternative to TPP.
An Offloading Algorithm based on Markov Decision Process in Mobile Edge Computing System

In this paper, an offloading algorithm based on Markov Decision Process (MDP) is proposed to solve the multi-objective offloading decision problem in Mobile Edge Computing (MEC) system. The feature of the algorithm is that MDP is used to make offloading decision. The number of tasks in the task queue, the number of accessible edge clouds and Signal-Noise-Ratio (SNR) of the wireless channel are taken into account in the state space of the MDP model. The offloading delay and energy consumption are considered to define the value function of the MDP model, i.e. the objective function. To maximize the value function, Value Iteration Algorithm is used to obtain the optimal offloading policy. According to the policy, tasks of mobile terminals (MTs) are offloaded to the edge cloud or central cloud, or executed locally. The simulation results show that the proposed algorithm can effectively reduce the offloading delay and energy consumption.
Novel Network for Medicinal Leaves Identification

From the ancient days, plant leaves are used to cure various infectious diseases. Even today, herbal leaves are preferred by medicinal experts for treating cancer, asthma, heart problems, etc. The recognition of these herbal plants is based on the visual perception of villagers. There are many kinds of species that seem to be very similar in color and shape. There is a high probability of human error in the identification of such plants. It is inevitable to correctly identify the species of plants to treat the patients. Therefore, a smart plant classification system is essentially required to eliminate human error. This research work develops a hybrid system that is based on deep convolutional neural networks. The system is named as AousethNet which is a modification of AlexNet by replacing its classifier namely, SoftMax with the Majority vote classifier. It is trained to predict the plant species from a huge number of leaf samples from four datasets namely Mendeley, D-Leaf, Flavia, and Folio. Typically, the performance of AousethNet with Mendeley dataset attained an accuracy of 99.89%, precision 98.61%, and very less recognition time of 0.087 s/image. This system is found to have good feature extraction and strong discrimination ability compared with the original version of AlexNet.
Smart solution for reducing COVID-19 risk using internet of things

The article exposes a smart device designed for mitigating the coronavirus disease (COVID-19) risk using the internet of things. A portable smart alerting device is designed for ensuring safety in public places which can alert people when the guidelines given by the government were not followed and alert health authorities when any abnormalities found. By doing so, the spread of this fatal disease can be stopped. The modules of the proposed system include the face mask detection module, social distance alerting module, crowd detection and analysis module, health screening module and health assessment module. The proposed system can be placed in any public entrances to monitor people without human intervention. Firstly, the human face images are captured for face mask check, then the crowd analysis of the particular entrance where the person is entering is performed, thereafter health screening of the person is done and the values were imported to the health assessment module to check for any abnormalities. Finally, after all the conditions were met the door is opened automatically. The smart device can be installed and effectively used in many scenarios such as malls, stores, crowded places and campuses to avoid the risk of spread of the coronavirus.
Vegetable Oil Based Compressor Oil-optimising of Tribological Characteristics

ABSTRACT This study explored the use of vegetable oil-based polyolester of rapeseed oil as a potential biolubricant for reciprocating air compressor. Rapeseed oil-based pentaerythritol ester (PE) was formulated via ultrasound-assisted transesterification process. Response surface methodology (RSM) based D-optimal design of experiments was used to analyse the tribological parameters, such as disc rotational speed, load and compressor oils on various tribological behaviours, namely, coefficient of friction (COF) and specific wear rate (SWR) using a pin-on-disc tribometer. The least specific wear rate of 1.85 × 10−5 mm3/Nmand COF of 0.0393 was recorded at optimal conditions. The surface morphology analysis of the tested pin using SEM/EDX and tribo-film formation analysis of disc surface using XPS technique disclosed that blended compressor oil (PE75), i.e., 75% by volume of PE and 25% by volume of synthetic compressor oil (SAE30) has numerous capabilities as environmental friendly compressor oil.
Extra-Intestinal Manifestations of Celiac Disease: What Should We Know in 2022?

Celiac disease (CD) is a chronic, small-intestinal, immune-mediated enteropathy due to gluten exposition in genetically predisposed individuals. It occurs in about 1% of the population and often remains an underdiagnosed condition. This could be due to the fact that the adult population often lacks the classical signs and symptoms of CD, manifesting only atypical symptoms. In this review we analyzed the main extra-intestinal manifestations of CD which include cutaneous and endocrinological disorders, abnormal liver function tests, and neuropsychiatric features. When CD is not diagnosed and therefore is not treated with a gluten-free diet (GFD), it can predispose to severe complications, not only gastrointestinal. Thus, it is important for clinicians to quickly recognize the atypical manifestations of CD, considering that an early diagnosis can significantly impact on a patient’s prognosis.
Computational Studies of Coinage Metal Anion M− + CH3X (X = F, Cl, Br, I) Reactions in Gas Phase

We characterized the stationary points along the nucleophilic substitution (SN2), oxidative insertion (OI), halogen abstraction (XA), and proton transfer (PT) product channels of M− + CH3X (M = Cu, Ag, Au; X = F, Cl, Br, I) reactions using the CCSD(T)/aug-cc-pVTZ level of theory. In general, the reaction energies follow the order of PT > XA > SN2 > OI. The OI channel that results in oxidative insertion complex [CH3–M–X]− is most exothermic, and can be formed through a front-side attack of M on the C-X bond via a high transition state OxTS or through a SN2-mediated halogen rearrangement path via a much lower transition state invTS. The order of OxTS > invTS is inverted when changing M− to Pd, a d10 metal, because the symmetry of their HOMO orbital is different. The back-side attack SN2 pathway proceeds via typical Walden-inversion transition state that connects to pre- and post-reaction complexes. For X = Cl/Br/I, the invSN2-TS’s are, in general, submerged. The shape of this M− + CH3X SN2 PES is flatter as compared to that of a main-group base like F− + CH3X, whose PES has a double-well shape. When X = Br/I, a linear halogen-bonded complex [CH3−X∙··M]− can be formed as an intermediate upon the front-side attachment of M on the halogen atom X, and it either dissociates to CH3 + MX− through halogen abstraction or bends the C-X-M angle to continue the back-side SN2 path. Natural bond orbital analysis shows a polar covalent M−X bond is formed within oxidative insertion complex [CH3–M–X]−, whereas a noncovalent M–X halogen-bond interaction exists for the [CH3–X∙··M]− complex. This work explores competing channels of the M− + CH3X reaction in the gas phase and the potential energy surface is useful in understanding the dynamic behavior of the title and analogous reactions.
Growth Inhibition and Apoptotic Effect of Pine Extract and Abietic Acid on MCF-7 Breast Cancer Cells via Alteration of Multiple Gene Expressions Using In Vitro Approach

In vitro anti-proliferative activity of Pinus palustris extract and its purified abietic acid was assessed against different human cancer cell lines (HepG-2, MCF-7 and HCT-116) compared to normal WI-38 cell line. Abietic acid showed more promising IC50 values against MCF-7 cells than pine extract (0.06 µg/mL and 0.11 µM, respectively), with insignificant cytotoxicity toward normal fibroblast WI-38 cells. Abietic acid triggered both G2/M cell arrest and subG0-G1 subpopulation in MCF-7, compared to SubG0-G1 subpopulation arrest only for the extract. It also induced overexpression of key apoptotic genes (Fas, FasL, Casp3, Casp8, Cyt-C and Bax) and downregulation of both proliferation (VEGF, IGFR1, TGF-β) and oncogenic (C-myc and NF-κB) genes. Additionally, abietic acid induced overexpression of cytochrome-C protein. Furthermore, it increased levels of total antioxidants to diminish carcinogenesis and chemotherapy resistance. P. palustris is a valuable source of active abietic acid, an antiproliferative agent to MCF-7 cells through induction of apoptosis with promising future anticancer agency in breast cancer therapy.