Miracle-dz/UniFAR-FTUs huggingface dataset:
Why do we pick similar mates, or do we?

Humans often mate with those resembling themselves, a phenomenon described as positive assortative mating (PAM). The causes of this attract broad interest, but there is little agreement on the topic. This may be because empirical studies and reviews sometimes focus on just a few explanations, often based on disciplinary conventions. This review presents an interdisciplinary conceptual framework on the causes of PAM in humans, drawing on human and non-human biology, the social sciences, and the humanities. Viewing causality holistically, we first discuss the proximate causes (i.e. the ‘how’) of PAM, considering three mechanisms: stratification, convergence and mate choice. We also outline methods to control for confounders when studying mate choice. We then discuss ultimate explanations (i.e. ‘the why’) for PAM, including adaptive and non-adaptive processes. We conclude by suggesting a focus on interdisciplinarity in future research.
Multi-label Prediction for Visual Sentiment Analysis using Eight Different Emotions based on Psychology

In visual sentiment analysis, sentiment estimation from images is a challenging research problem. Previous studies focused on a few specific sentiments and their intensities and have not captured abundant psychological human feelings. In addition, multi-label sentiment estimation from images has not been sufficiently investigated. The purpose of this research is to build a visual sentiment dataset, accurately estimate the sentiments as a multi-label multi-class problem from images that simultaneously evoke multiple emotions. We built a visual sentiment dataset based on Plutchik's wheel of emotions. We describe this ‘Senti8PW’ dataset, then perform multi-label sentiment analysis using the dataset, where we propose a combined deep neural network model that enables inputs from both hand-crafted features and CNN features. We also introduce a threshold-based multi-label prediction algorithm, in which we assume that each emotion has a probability distribution. In other words, after training our deep neural network, we predict evoked emotions for an image if the intensity of the emotion is larger than the threshold of the corresponding emotion. Extensive experiments were conducted on our dataset. Our model achieves superior results compared to the state-of-the-art algorithms in terms of subsets.
Fault Diagnosis of UAV System Base On One-Class Support Vector Machine

Given the complex structure and long failure time of the flight automation control system, which affect the aircraft's operational efficiency, a fault diagnosis scheme with a one-class support vector machine(OCSVM) optimized by an ant colony optimization(ACO) is proposed. Firstly, this paper analyses the fault characteristics of flight automation systems and constructs a noise filter. Then, a residual decision algorithm based on an improved support vector machine is proposed to judge the residuals in the case of complex flight control system output coupling. Third, experimental simulation results show that the decision algorithm takes about 0.5s for fault detection at a sampling time of 0.1s, significantly reducing fault detection time and an effective fault detection rate of greater than 90%.
Face Template Protection through Residual Learning Based Error-Correcting Codes

The leakage of the face template leads to severe security problems since the facial image is unique and irreplaceable to each individual. Many researchers have been devoted to protecting the face template. Nevertheless, to achieve high security for the face template, partial matching accuracy is usually sacrificed. The main challenge of this problem is the low inter-user variations and high intra-user variations of facial images. In this work, we propose a method integrating residual learning and error-correcting codes for face template protection. In particular, the proposed method consists of two major components: (a) a deep residual network component mapping facial images to polar codewords assigned to users, and (b) a polar decoder reducing noise brought by high intra-user variations in the predicted codewords. The proposed method is evaluated on extended Yale B, CMU-PIE, and FEI databases. It provides high security of face template and achieves a high (100%) genuine accept rate at a low false accept rate (0%) simultaneously, which outperforms most state-of-the-arts.
Thermal Radiation Detector (TRD) Modeling

This paper describes the theoretical principles of TRD operation based on thermal absorption of incident power of radiation. In the modelling procedures, the main parameters influencing on the detector features have been introduced. So the this model allow us to predict the behaviour of different types of thermal detectors. It is found that the temperature response of the detector against the frequency of incident radiation in logarithmic scale describes the TRD as a typical low pass filter characteristics. The cut off corner frequency is found to be at 1 Hz under which the temperature change attains a saturation value. In the sense that the thermal detector will detect all the incoming radiations of higher frequency.
Power Management Integrated Circuit with 90Plus Efficiency Used in AC/DC Converter

Recently, resonant AC/DC converter has been accepted by the industry. However, the efficiency will be decreased at light load. So, a novel topology with critical controlling mode combined with resonant ones is proposed in this paper. The new topology can correspond to a 90 plus percent of power converting. So，a novel topology of an state of-art integrated circuit, which can be used as power management circuit, has been designed based on the above new topology. A simulator which is specific suitable for the power controller has been founded in this work and it has been used for the simulation of the novel architecture and the proposed integrated circuit.
Research on High Power Railguns at the Naval Research Laboratory

The Naval Research Laboratory (NRL) has initiated a program focusing on railgun bore materials science. The objective of the program is to study the conditions in a high power railgun barrel during launch. An 11-MJ prime energy, 6- m long, 5-cm bore diameter railgun is being built for testing. This laboratory launcher will be used to study sliding contacts under the extreme current, temperature, and pressure conditions found in a high power railgun. Real time diagnostics coupled with extensive post-shot materials analysis will be used to understand railgun operation. Results will be compared with other high power railguns.
Unmasking the drivers and Implementation of change

This paper investigates the drivers for, and implementation of, digital transformation projects and organizational change initiatives in 20 companies in the UK. The study follows on previous research based on the Socio-Technical Toolbox [STT], specifically focusing on the activity analysis and employees’ experience and participation in the change initiative or digital transformation project. © 2021 Copyright for this paper by its authors.
Optimum design of a high-power, high-frequency transformer

A procedure for optimum design of a high-power, high-frequency transformer is presented. The procedure is based on both electrical and thermal processes in the power transformer and identifies: (a) the VA-rating of ferrite cores in relation to the operating frequency; (b) the optimum flux density in the core; and (c) the optimum current densities of the windings providing maximum transformer efficiency. Since the transformer is the major contributor to the volume and weight of the power supply, the results of transformer analysis can be used for entire power supply optimization as well. Two high-power, high-frequency transformers are optimally designed, built, and tested. Practical results show good agreement with the theory.
Metabarcoding faecal samples to investigate spatiotemporal variation in the 1 diet of the endangered Westland petrel ( Procellaria westlandica ) 2

24 As top predators, seabirds can be indirectly impacted by climate variability and commercial 25 fishing activities through changes in marine communities. However, high mobility and 26 foraging behaviour enables seabirds to exploit prey distributed patchily in time and space. 27 This capacity to adapt to environmental change can be described through the study of their 28 diet. Traditionally, the diet of seabirds is assessed through the morphological identification 29 of prey remains in regurgitates. This sampling method is invasive for the bird and limited in 30 terms of taxonomic resolution. However, the recent progress in DNA-based approaches is 31 now providing a non-invasive means to more comprehensively and accurately characterize 32 animal diets. Here, we used a non-invasive metabarcoding approach to characterize the diet 33 of the Westland petrel (Procellaria westlandica), which is an endangered burrowing species, 34 endemic to the South Island of New Zealand. We collected 99 fresh faecal samples at two 35 different seasons and in two different sub-colonies. Our aims were to describe the diet of 36 the Westland petrel, investigate seasonal and spatial variation in the petrels diet, and assess 37 potential impacts of the New Zealand fishery industry. We found that amphipods were the 38 most common prey, followed by cephalopods and fish. Our results could be the result of 39 natural foraging behaviour, but also suggest a close link between the composition of prey 40 items and New Zealand’s commercial fishing activities. In particular, the high abundance of 41 amphipods could be the result of Westland petrels feeding on discarded fisheries waste (fish 42 guts). Our results also showed significant differences in diet between seasons (before 43 hatching vs chick rearing season) and between sampling sites (two sub-colonies 1.5 km 44 apart), indicating plasticity in the foraging strategy of the Westland petrel. Due to its non45 invasive nature, metabarcoding of faecal samples can be applied to large numbers of 46 samples to help describe dietary variation in seabirds and indicate their ecological 47 requirements. In our example, dietary DNA (dDNA) provided valuable information regarding 48 the dietary preferences of an iconic species in New Zealand’s unique biodiversity. dDNA can 49 thus inform the conservation of endangered or at-risk species that have elusive foraging 50 behaviours. 51
SPEECH TO CHART: SPEECH RECOGNITION AND NATURAL LANGUAGE PROCESSING FOR DENTAL CHARTING

Typically, when using practice management systems (PMS), dentists perform data entry by utilizing an assistant as a transcriptionist. This prevents dentists from interacting directly with the PMSs. Speech recognition interfaces can provide the solution to this problem. Existing speech interfaces of PMSs are cumbersome and poorly designed. In dentistry, there is a desire and need for a usable natural language interface for clinical data entry. Objectives. (1) evaluate the efficiency, effectiveness, and user satisfaction of the speech interfaces of four dental PMSs, (2) develop and evaluate a speech-to-chart prototype for charting naturally spoken dental exams. Methods. We evaluated the speech interfaces of four leading PMSs. We manually reviewed the capabilities of each system and then had 18 dental students chart 18 findings via speech in each of the systems. We measured time, errors, and user satisfaction. Next, we developed and evaluated a speech-to-chart prototype which contained the following components: speech recognizer; post-processor for error correction; NLP application (ONYX) and; graphical chart generator. We evaluated the accuracy of the speech recognizer and the post-processor. We then performed a summative evaluation on the entire system. Our prototype charted 12 hard tissue exams. We compared the charted exams to reference standard exams charted by two dentists. Results. Of the four systems, only two allowed both hard tissue and periodontal charting via speech. All interfaces required using specific commands directly comparable to using a mouse. The average time to chart the nine hard tissue findings was 2:48 and the nine periodontal findings was 2:06. There was an average of 7.5 errors per exam. We created a speech-to-chart prototype that supports natural dictation with no structured commands. On manually transcribed exams, the system performed with an average 80% accuracy. The average time to chart a single hard tissue finding with the prototype was 7.3 seconds. An improved discourse processor will greatly enhance the prototype's accuracy. Conclusions. The speech interfaces of existing PMSs are cumbersome, require using specific speech commands, and make several errors per exam. We successfully created a speech-to-chart prototype that charts hard tissue findings from naturally spoken dental exams.
Performance improvement of a three-phase phase-locked-loop algorithm under utility voltage disturbances using non-autonomous adaptive filters

This study proposes phase-locked-loop (PLL) algorithms, which are employed to improve the performance related to the estimation of the phase angle, as well as the frequency, in three-phase systems under utility voltage disturbances. The proposed PLL schemes are based on the three-phase instantaneous active power (3pPLL), which is composed of non-autonomous adaptive filters (AFs) operating in conjunction with a positive-sequence detectors (PSDs), yielding the AF-PSD-3pPLL algorithms operating in both three-phase (abc-axis) and two-phase (αβ-axis) stationary reference frames. The AFs are used to extract the fundamental components of the utility voltages, allowing an adequate rejection of harmonic disturbances, whereas the PSDs at fundamental utility frequency are employed for rejecting the power quality problem related to the utility unbalances. A complete stability analysis of the PLL systems is carried out. In addition, to validate the theoretical development, the dynamic response and the robustness of the proposed AF-PSD-3pPLL schemes are evaluated by means of simulation and experimental tests, taking into account utility disturbances, such as voltage harmonics and unbalances, voltage sags, phase-angle jumps and frequency variations. The effectiveness of the proposed PLL schemes are emphasised by means of a comparative analysis with the conventional 3pPLL algorithm.
SAR and Computer-Aided Drug Design Approaches in the Discovery of Peroxisome Proliferator-Activated Receptor γ Activators: A Perspective

Activators of PPARγ, Troglitazone (TGZ), Rosiglitazone (RGZ), and Pioglitazone (PGZ) were introduced for treatment of Type 2 diabetes, but TGZ and RGZ have been withdrawn from the market along with other promising leads due cardiovascular side effects and hepatotoxicity. However, the continuously improving understanding of the structure/function of PPARγ and its interactions with potential ligands maintain the importance of PPARγ as an antidiabetic target. Extensive structure activity relationship (SAR) studies have thus been performed on a variety of structural scaffolds by various research groups. Computer-aided drug discovery (CADD) approaches have also played a vital role in the search and optimization of potential lead compounds. This paper focuses on these approaches adopted for the discovery of PPARγ ligands for the treatment of Type 2 diabetes. Key concepts employed during the discovery phase, classification based on agonistic character, applications of various QSAR, pharmacophore mapping, virtual screening, molecular docking, and molecular dynamics studies are highlighted. Molecular level analysis of the dynamic nature of ligand-receptor interaction is presented for the future design of ligands with better potency and safety profiles. Recently identified mechanism of inhibition of phosphorylation of PPARγ at SER273 by ligands is reviewed as a new strategy to identify novel drug candidates.
Two-dimensional pulse-to-pulse canceller applied in multiple-input-multiple-output radar

Since the spectrum of the ground clutter in the ground-based pulse radar is spatially–temporally decoupled, a simple pulse-to-pulse canceller is often used as a moving target indication filter for suppressing the ground clutter near the zero-Doppler frequency. In contrast, in the airborne phased-array radar, which is a typical single-input–multiple-output radar, the spectrum of the ground clutter is spatially–temporally coupled each other, and the two-dimensional pulse-to-pulse canceller (TDPC) for ground clutter cancellation has been developed by using the ground clutter model in the spatial–temporal domain. Based on the ground clutter model of multiple-input–multiple-output (MIMO) radar, the authors can extend the existing TDPC to more general form that can suppress the ground clutter in the MIMO radar. As the MIMO TDPC can be calculated offline by utilising the geometry knowledge of the ground clutter of MIMO radar, it can be used as an efficient and convenient ground clutter pre-filtering tool.
Recent developments in materials for use in protective clothing

This paper outlines the innovations in high functional and high performance fibres for applications in protective clothing, including fibres for flame and heat protection. It also describes some typical woven and non‐woven constructions for such applications. And presents the trends in producing smart textile materials, capable of interacting with human/environmental conditions.
Costing for Decision- Making in a Theory of Constraints Environment

Adherents to the Theory of Constraints (TOC) have made clear their antipathy to precise cost accounting. We argue that they have something in common with early writers who were concerned about the allocation of costs. However, given the lack of attention to the long-term in TOC we explore, using an action research approach over a four year period, the relevance of TOC for both long-term and short-run decisions. We argue that TOC does meet short-run decision relevance although it is not entirely adequate for longer term decisions.
Remote COPD Severity and Exacerbation Detection Using Heart Rate and Activity Data Measured from a Wearable Device

Chronic obstructive pulmonary disease (COPD) is one of the leading causes of human mortality worldwide. Traditionally, estimating COPD severity has been done in controlled clinical conditions using cough sounds, respiration, and heart rate variability, with the latter reporting insights on the autonomic dysfunction caused by the disease. Advancements in remote monitoring and wearable device technologies, in turn, have allowed for remote COPD monitoring in daily life conditions. In this study, we explore the potential for predicting COPD severity and exacerbation using a low-cost wearable device that measures heart rate and activity data. We collected smartwatch sensor data from 35 COPD patients over a period of three months. Our evaluation shows that future trajectory of the disease can be predicted using only the first few days of continuous unobtrusive wearable data collected from COPD patients. Using features extracted from wearable device an Isolation Forest was able to predict exacerbation with an area under curve (AUC) 0.69 thus showing improvement over a random choice classifier.
Identification of Motor Unit Twitch Properties in the Intact Human In Vivo

Restoring natural motor function in neurologically injured individuals is challenging, largely due to the lack of personalization in current neurorehabilitation technologies. Signal-driven neuro-musculoskeletal models may offer a novel paradigm for devising novel closed-loop rehabilitation strategies according to an individual’s physiology. However, current modelling techniques are constrained to bipolar electromyography (EMG), thereby lacking the resolution necessary to extract the activity of individual motor units (MUs) in vivo. In this work, we decoded MU spike trains from high-density (HD)-EMG to obtain relevant neural properties across multiple isometric plantar-dorsiflexion tasks. Then, we sampled MU statistical distributions and used them to reproduce MU specific activation profiles. Results showed bimodal distributions which may correspond to slow and fast MU populations. The estimated activation profiles showed a high degree of similarity to the reference torque (R2>0.8) across the recorded muscles. This suggests that the estimation of MU twitch properties is a crucial step for the translation of neural information into muscle force.Clinical Relevance— This work has multiple implications for understanding the underlying mechanism of motor impairment and for developing closed-loop strategies for modulating alpha motor circuitries in neurologically injured individuals.
Ground Effect on Aerodynamic Characteristics of Flapping-Wing Micro Air Vehicles

This paper presents the ground effect on the aerodynamic performance of a hawkmoth-like flapping-wing micro air vehicle. An unsteady aerodynamic model based on the potential-flow theory is used to determine the ground effect at several flight conditions. In this research, the program is written in FORTRAN programming language by using OpenMP library for parallel computing. It was found that the ground effect is the most significant when the flapping-wing micro air vehicle is hovering with a reduction in the lift force when the distance from the center of mass of the flapping-wing micro air vehicle to the ground is below 0.05m. For forward flight, this effect tends to decrease as the flight speed increases.
Relationship between major macroeconomic variables and economic growth: A case of Pakistan during 1976-2016

This study is an attempt to investigate the long run relationship of major macroeconomic variables with economic growth for the economy of Pakistan during 1976 – 2016.Autoregressive distributed lag (ARDL) model and bounds test have been applied to estimate the short run and long run relationship between GDP and labour force, rate of capital formation, money supply, inflation rate, trade rate, foreign direct investment (FDI) and unemployment. ARDL model indicates significant positive relationship between labour force and economic growth while negative significant with FDI and unemployment, in the long run, at 1% and 5% level.
Assessing and Mitigating Impact of Time Delay Attack against Cyber-Physical Systems

—Recent attacks against cyber-physical systems (CPSes) show that traditional reliance on isolation for security is insufﬁcient. This paper develops real-time assessment and mitigation of an attack’s impact as a system’s built-in mechanisms. We focus on a general class of attacks, which we call time delay attack , that delays the transmissions of control data packets in a linear CPS control. Our attack impact assessment, which is based on a joint stability-safety criterion, consists of (i) a machine learning (ML) based safety classiﬁcation, and (ii) a tandem stability-safety classiﬁcation that exploits a basic relationship between stability and safety, namely that an unstable system must be unsafe whereas a stable system may not be safe. The ML addresses a state explosion problem in the safety classiﬁcation, whereas the tandem structure reduces false negatives in detecting unsafety arising from imperfect ML. We apply our approach to assess the impact of the attack on power grid automatic generation control, and accordingly develop a two-tiered mitigation that tunes the control gain automatically to restore safety where necessary and shed load only if the tuning is insufﬁcient. Extensive simulations based on a 37-bus system model are conducted to evaluate the effectiveness of our assessment and mitigation approaches. We also apply our attack impact assessment approach to a thermal power plant control system consisting of two PID control loops.