Miracle-dz/UniFAR-FTUs huggingface dataset:
Low-Cost and Energy-Efficient Alternatives for Home Automation using IoT

Due to the COVID-19 pandemic, the world's population has undergone different changes in which an impact on the use of the internet stands out from daily aspects such as education, commerce, health, among others; which has made us highly dependent on its usage. In this regard, the term Internet of Things (IoT) has been increasingly recognized for its contribution to improving people's life quality, through objects that are integrated and connected to the Internet. This document aims to introduce alternatives for IoT-based home automation making use of NodeMCU, Sinric Pro, and smart voice assistants such as Google Home and Alexa. The proposals are efficient in terms of easy implementation and reduce electricity consumption by around 30%. This document is research that helps families improve their energy efficiency and daily productivity through IoT.
3D shape modeling based on low-dimension data

3D modeling is a long-standing task in computer vision. However, it is very difficult to directly obtain the complete 3D data. Thus, recovering 3D model from low-dimension information is an important issue. Researchers have developed many methods to solve this problem. This paper presents comprehensive state-of-the-art works on this problem. We firstly introduce the image-driven 3D modeling methods, including single-image-based and multi-image-based ways. Then we summarize the completion-based methods, which is to build a 3D shape from fragmentary observation, such as partial point cloud. Quantitative and qualitative comparisons of the mentioned methods are also presented.
Comparative Study of Cage Subsidence in Single-Level Lateral Lumbar Interbody Fusion

We investigated the incidence and clinical features of cage subsidence after single-level lateral lumbar interbody fusion (LLIF). We studied a retrospective cohort of 59 patients (34 males, 25 females; mean age, 68.9 years) who received single-level LLIF. Patients were classified into subsidence and no-subsidence groups. Cage subsidence was defined as any violation of either endplate, classified using radiographs and computed tomography (CT) images. After one year, we compared patient characteristics, surgical parameters, radiological findings, pain scores, and fusion status. We also compared the Hounsfield unit (HU) endplate value obtained on CT preoperatively. Twenty patients (33.9%) had radiographic evidence of interbody cage subsidence. There were significant differences between the subsidence and no-subsidence groups in sex, cage height, fusion rate, and average HU value of both endplates (p < 0.05). There were no significant differences in age, height, weight, or body mass index. Moreover, there were no significant differences in global alignment and Numerical Rating Scale change in low back pain, leg pain, and numbness. Despite suggestions that patients with lower HU values might develop cage subsidence, our results showed that cage subsidence after single-level LLIF was not associated with low back pain, leg pain, or numbness one year post-operation.
Who cares more about the environment?: An empirical study in Vietnam

Environmental problems have become one of the top concerns in Vietnam since 2016. This study aimed to investigate what groups of people were most concerned about the environment and to what extent their concerns differed from each other. Logistic regression models were used to perform the analyses. The analysis on the gender - environmental concern relationship indicated that males were more likely to be concerned about the environment than females. The age - environmental concern relationship suggested higher concern levels of younger people as compared to older people. In the examination of the social class - environmental concern relationship, people who were wealthier, well-educated, and had greater occupational prestige tended to have more concerns. People with higher education levels also expressed higher levels of concern toward the environment. People with postgraduate degrees reported the highest level of concern, while people with no formal education showed the least concern. From the perspective of the political involvement - environmental concern relationship, it was shown that while people who were not a member of any organization were the least environmentally concerned, the members of university alumni associations were the most concerned. Finally, the exploration of the residence - environmental concern relationship pointed out that Kinh people were more environmentally concerned than Vietnamese ethnic minority groups, and people in the North Central Coast paid the highest attention to the environment in Vietnam.
Improved Observability In Distribution Grids using Correlational Measurements

This paper develops a novel pseudo-measurement construct termed a Correlational Measurement (CM) to improve observability and state estimation accuracy. CMs utilize the correlation between similar classes of loads and DERs. The modifications required to accommodate CMs in traditional node voltage or branch current based state estimators are derived. Additionally, an observability analysis framework for distribution systems with DERs is presented to seamlessly integrate CMs. The framework assembles an observability test and an observable island identification procedure from the literature along with a novel observability metric. Finally, an on-line CM parameter estimation procedure is presented. Simulations on the IEEE 123 bus test system and real field data from Pecan Street demonstrate the utility of the proposed CMs and the observability analysis framework in improving observability and state estimation accuracy.
Automated chondrocyte viability analysis of articular cartilage based on deep learning segmentation and classification of two-photon microscopic images

Chondrocyte viability is an important measure to consider when assessing cartilage health. Dye-based cell viability assays are not suitable for in vivo or long-term studies. We have introduced a non-labeling viability assay based on the assessment of high-resolution images of cells and collagen structure using two-photon stimulated autofluorescence and second harmonic generation microscopy. By either the visual or quantitative assessment, we were able to differentiate living from dead chondrocytes in those images. However, both techniques require human participation and have limited throughputs. Throughput can be increased by using methods for automated cell-based image processing. Due to the poor image contrast, traditional image processing methods are ineffective on autofluorescence images produced by nonlinear microscopes. In this work, we examined chondrocyte segmentation and classification using Mask R-CNN, a deep learning approach to implement automated viability analysis. It has been demonstrated an 85% accuracy in chondrocyte viability assessment with proper training. This study demonstrates that automated and highly accurate image analysis is achievable with the use of deep learning methods. This image processing approach can be helpful to other imaging applications in clinical medicine and biological research.
A Simple Closed-Loop Test Based Control of Boost Converter Using Internal Model Control and Direct Synthesis Approach

In this article, a simple controller design method for output voltage control of dc–dc boost converter (BC) has been proposed. A simple closed-loop step test with proportional gain $k_{0}$ is performed. BC open-loop transfer function is obtained from the closed-loop step response test data, which does not require information of BC circuit parameters. Furthermore, the controller is designed from the open-loop model of the BC through the internal model control (IMC) scheme and direct synthesis (DS) approach. The robust stability analysis of both the control schemes has been carried out. The proposed modeling technique and controller design methods have been validated in the experimental setup. Both the controller design techniques work satisfactorily. Superior transient performance of the IMC scheme in comparison to the DS scheme has been observed.
Opportunities for Human Factors Measures in Military Operational Test and Evaluation

Military Operational Test and Evaluation (OT&E) is conducted under operationally realistic conditions across a full spectrum of military operations to determine the readiness of a system to be fielded. OT&E seeks to assess the operational effectiveness and suitability of the system under test. Operational effectiveness measures the overall ability of a system to enable mission accomplishment when used by warfighters in a representative operational environment. Operational effectiveness seeks to evaluate organization, doctrine, and tactics. Operational suitability measures the overall ability of a system to be placed in field use. Operational suitability assesses reliability, availability, and human factors. To date, little effort has been placed on evaluating the warfighters' performance as part of the overall ability of the system to enable mission accomplishment. In fact, any warfighter performance issues are normally reported by exception, which means that it is not evaluated unless a problem is identified during testing (i.e., there is no formal plan to evaluate the warfighter performance component of overall system performance). Most analysis focused on the human factor has been done through the use of opinion surveys. Although, opinion surveys can provide insight into how well a warfighter likes a new system, they do not provide information on the new system's impacts on the warfighters' cognitive capabilities and ultimate performance, nor on the impact on the performance of a team of warfighters. Over many years, the human factors domain has developed measures of cognition (mental workload, situation awareness, decision making, etc.) and teamwork (collaboration, coordination, shared SA, social network analysis, etc.) that should be applied during military OT&E. These types of human factor measures can be used to evaluate both operational effectiveness and suitability, providing a method of analyzing the human component in mission effectiveness and the impact of the system under test on the human component. Without objectively evaluating the human component of the overall system, it is impossible to truly evaluate operational effectiveness and suitability. This panel will present the need for more objective, comprehensive, and automatically-collected human performance measurements in military OT&E, and earlier in the development and testing, especially in relation to command and control (C2) systems, as well as touch upon the potentially useful human factor measures currently available and how to select the correct ones to use.
“Cute Goddess is Actually an Aunty”: The Evasive Middle-Aged Woman Streamer and Normative Performances of Femininity in Video Game Streaming

In this paper the focus is on the representations of “middle-aged” or “aging” women streamers in western media. I analyze discussions in Western online media around a case of Chinese DouYu live-streamer. “Qiaobiluo Dianxia,” as her streamer name goes, became a topic in Western media after a glitch in her live stream revealed her to be a middle-aged woman, rather than young woman she was assumed to be. The discussions are analyzed with critical discourse analysis. It is argued that the aging bodies of women, both their presence and absence, should be read and understood through toxic gaming culture and geek masculinity and the hegemonic discourse they constitute.
Fruit Recognition Based on YOLOX*

Pattern recognition is an urgent problem to be solved in the field of computer vision. In this paper, the network of fruit recognition based on YOLOX is studied. Due to the problem of slow training speed and low accuracy in the classical algorithms, the de-coupling detection head is optimized in YOLOX to overcome the above shortcomings. In terms of data enhancement, a new method combining Mosaic and MixUp is proposed. Through experimental verification, the method proposed in this paper has a great improvement over related algorithms such as YOLO v 5, the accuracy is 98.6%, which is increased 5.2%.
Machine Learning Algorithms for Flow Pattern Classification in Pulsating Heat Pipes

Owing to their simple construction, cost effectiveness, and high thermal efficiency, pulsating heat pipes (PHPs) are growing in popularity as cooling devices for electronic equipment. While PHPs can be very resilient as passive cooling systems, their operation relies on the establishment and persistence of slug/plug flow as the dominant flow regime. It is, therefore, paramount to predict the flow regime accurately as a function of various operating parameters and design geometry. Flow pattern maps that capture flow regimes as a function of nondimensional numbers (e.g., Froude, Weber, and Bond numbers) have been proposed in the literature. However, the prediction of flow patterns based on deterministic models is a challenging task that relies on the ability of explaining the very complex underlying phenomena or the ability to measure parameters, such as the bubble acceleration, which are very difficult to know beforehand. In contrast, machine learning algorithms require limited a priori knowledge of the system and offer an alternative approach for classifying flow regimes. In this work, experimental data collected for two working fluids (ethanol and FC-72) in a PHP at different gravity and power input levels, were used to train three different classification algorithms (namely K-nearest neighbors, random forest, and multilayer perceptron). The data were previously labeled via visual classification using the experimental results. A comparison of the resulting classification accuracy was carried out via confusion matrices and calculation of accuracy scores. The algorithm presenting the highest classification performance was selected for the development of a flow pattern map, which accurately indicated the flow pattern transition boundaries between slug/plug and annular flows. Results indicate that, once experimental data are available, the proposed machine learning approach could help in reducing the uncertainty in the classification of flow patterns and improve the predictions of the flow regimes.
HashFormer: Vision Transformer Based Deep Hashing for Image Retrieval

Deep image hashing aims to map an input image to compact binary codes by deep neural network, to enable efficient image retrieval across large-scale dataset. Due to the explosive growth of modern data, deep hashing has gained growing attention from research community. Recently, convolutional neural networks like ResNet have dominated in deep hashing. Nevertheless, motivated by the recent advancements of vision transformers, we propose a pure transformer-based framework, called as HashFormer, to tackle the deep hashing task. Specifically, we utilize vision transformer (ViT) as our backbone, and treat binary codes as the intermediate representations for our surrogate task, i.e., image classification. In addition, we observe that the binary codes suitable for classification are sub-optimal for retrieval. To mitigate this problem, we present a novel average precision loss, which enables us to directly optimize the retrieval accuracy. To the best of our knowledge, our work is one of the pioneer works to address deep hashing learning problems without convolutional neural networks (CNNs). We perform comprehensive experiments on three widely-studied datasets: CIFAR-10, NUSWIDE and ImageNet. The proposed method demonstrates promising results against existing state-of-the-art works, validating the advantages and merits of our HashFormer.
Development of a solar powered micro air vehicle

The design of a solar powered micro air vehicle is presented. The paper describes the process followed and assumptions made during the design. The study concludes that it is feasible to build a solar powered micro air vehicle with a maximum linear dimension less than 0.38 m (15 in.). Construction of and flight tests of the prototype vehicle (Sunbeam I) are described. The battery-powered prototype had a mass of 71 gm, surface area of 0.051 m, and flew at a velocity of less than 10 m/s with 3.5 W of power. It is projected that on a good day (near noon during the summer months) the solar version of the plane will be able to fly on solar power alone.
Psychoacoustic Approaches for Harmonic Music Mixing

The practice of harmonic mixing is a technique used by DJs for the beat-synchronous and harmonic alignment of two or more pieces of music. In this paper, we present a new harmonic mixing method based on psychoacoustic principles. Unlike existing commercial DJ-mixing software, which determines compatible matches between songs via key estimation and harmonic relationships in the circle of fifths, our approach is built around the measurement of musical consonance. Given two tracks, we first extract a set of partials using a sinusoidal model and average this information over sixteenth note temporal frames. By scaling the partials of one track over ±6 semitones (in 1/8th semitone steps), we determine the pitch-shift that maximizes the consonance of the resulting mix. For this, we measure the consonance between all combinations of dyads within each frame according to psychoacoustic models of roughness and pitch commonality. To evaluate our method, we conducted a listening test where short musical excerpts were mixed together under different pitch shifts and rated according to consonance and pleasantness. Results demonstrate that sensory roughness computed from a small number of partials in each of the musical audio signals constitutes a reliable indicator to yield maximum perceptual consonance and pleasantness ratings by musically-trained listeners.
Design Optimization of Modular Permanent Magnet Machine with Triple Three-Phase for Aircraft Starter Generator

Permanent magnet (PM) electrical machine has far-reaching impacts in aviation electrification due to the continuous development in high power density and high efficiency electrical drives. The primary barrier to acceptance of permanent magnet machines for safety-critical starter-generator systems is its low fault-tolerance capability and low reliability (for the conventional designs). This article investigates a modular triple three-phase PM starter-generator comprehensively, including the tradeoff of fault-tolerant topology, optimization design process, analysis of electromagnetic (highlight the post-fault analysis) and thermal behavior, respectively. The triple three-phase segmented topology proposed meet the fault-tolerant requirement along with complete electrical, magnetic, and thermal isolation. There would be cost penalty on the proposed topology, but it gets offset by the ease of manufacturing of coils and their insertion. The stator gaps are introduced while doing segmentation which would provide unique opportunity to provide innovative cooling solutions. The multi-objectively optimization method is employed to search an optimal candidate of modular-designed machine. The detailed electromagnetic analysis of this candidate has been performed, while the post-fault performances have been highlighted, especially under the short-circuit scenarios. After that, the thermal behavior of this modular machine has been studied by the means of Computational Fluid Dynamics (CFD). By a parametric analysis, the varied size of segment gap seems no effect to the thermal management. Moreover, the housing water jacket cooling method is chosen to make the machine work safely. In all, this modular-designed triple three-phase machine meet the requirement for an aircraft application with complete fault tolerance and increased reliability.
Reference evapotranspiration estimation using long short‐term memory network and wavelet‐coupled long short‐term memory network

Evapotranspiration (ET) is a vital component of the hydrological cycle, and accurate estimation of reference evapotranspiration (ET0) is of great importance in agriculture water resources planning and management. In this study, long short‐term memory network (LSTM), artificial neural network (ANN), extreme learning machine (ELM), and their wavelet‐coupled models were used for daily ET0 estimation. For comparison purposes, this study also investigated the ET0 estimation capability of three typical empirical models, that is, the Hargreaves–Samani (HS) equation, Penman (PM) equation, and Priestley–Taylor (PT) equation. Daily meteorological data were obtained from two weather stations, Beijing and Baoding, located in the northern part of the North China Plain. Results demonstrated that single machine learning (ML) models, for example, ANN, ELM, LSTM, are steadier and generally have better overall performance than wavelet‐coupled ML models. Besides, the LSTM model performed best among the single ML models and was far superior to the HS, PM, and PT models. The LSTM model helped the root mean square error (RMSE) and mean absolute error (MAE) of the ANN and ELM models decrease by 5%–69% and 8%–72%, respectively. Moreover, the LSTM model helped the RMSE and MAE of the empirical models decrease by 6%–83% and 5%–84%, respectively.
Pulmonary Protein Oxidation and Oxidative Stress Modulation by Lemna minor L. in Progressive Bleomycin-Induced Idiopathic Pulmonary Fibrosis

Bleomycin (BLM) administration is associated with multifunctional proteins inflammations and induction of idiopathic pulmonary fibrosis (IPF). Lemna minor L. extract, a free-floating monocot macrophyte possesses antioxidant and anti-inflammatory potential. The aim of the study was to examine the protective effect of L. minor extract on lung protein oxidation and oxidative stress modulation by BLM-induced pulmonary fibrosis in Balb/c mice. For this purpose, the protein carbonyl content, advanced glycation end product, nitroxide protein oxidation (5-MSL), and lipid peroxidation (as MDA and ROS), in lung cells were examined. The histological examinations, collagen deposition, and quantitative measurements of IL-1β, IL-6, and TNF in lung tissues and blood were investigated. Intraperitoneal, BLM administration (0.069 U/mL; 0.29 U/kg b.w.) for 33 days, caused IPF induction in Balb/c mice. Pulmonary combining therapy was administered with L. minor at dose 120 mg/mL (0.187 mg/kg b.w.). L. minor histologically ameliorated BLM induced IPF in lung tissues. L. minor significantly modulated (p < 0.05) BLM-alterations induced in lung hydroxyproline, carbonylated proteins, 5-MSL-protein oxidation. Oxidative stress decreased levels in antioxidant enzymatic and non-enzymatic systems in the lung were significantly regulated (p < 0.05) by L. minor. L. minor decreased the IL-1β, IL-6, and TNF-α expression in lung tissues and plasma. The L. minor improves the preventive effect/defense response in specific pulmonary protein oxidation, lipid peroxidation, ROS identifications, and cytokine modulation by BLM-induced chronic inflammations, and could be a good antioxidant, anti-inflammatory, and anti-fibrotic alternative or IPF prevention involved in their pathogenesis.