Miracle-dz/UniFAR-FTUs huggingface dataset:
Integrating soft sensor systems using conductive thread

We are part of a growing community of researchers who are developing a new class of soft machines. By using mechanically soft materials (MPa modulus) we can design systems which overcome the bulk-mechanical mismatches between soft biological systems and hard engineered components. To develop fully integrated soft machines—which include power, communications, and control sub-systems—the research community requires methods for interconnecting between soft and hard electronics. Sensors based upon eutectic gallium alloys in microfluidic channels can be used to measure normal and strain forces, but integrating these sensors into systems of heterogeneous Young’s modulus is difficult due the complexity of finding a material which is electrically conductive, mechanically flexible, and stable over prolonged periods of time. Many existing gallium-based liquid alloy sensors are not mechanically or electrically robust, and have poor stability over time. We present the design and fabrication of a high-resolution pressure-sensor soft system that can transduce normal force into a digital output. In this soft system, which is built on a monolithic silicone substrate, a galinstan-based microfluidic pressure sensor is integrated with a flexible printed circuit board. We used conductive thread as the interconnect and found that this method alleviates problems arising due to the mechanical mismatch between conventional metal wires and soft or liquid materials. Conductive thread is low-cost, it is readily wetted by the liquid metal, it produces little bending moment into the microfluidic channel, and it can be connected directly onto the copper bond-pads of the flexible printed circuit board. We built a bridge-system to provide stable readings from the galinstan pressure sensor. This system gives linear measurement results between 500–3500 Pa of applied pressure. We anticipate that integrated systems of this type will find utility in soft-robotic systems as used for wearable technologies like virtual reality, or in soft-medical devices such as exoskeletal rehabilitation robots.
Mindfulness with children: a content analysis of evidence-based interventions from a developmental perspective

ABSTRACT Objective As the number of mindfulness-based interventions (MBIs) for children has been constantly growing, there is a benefit to be derived for a comprehensive study that gathers what mindfulness activities have been reported to be effective at particular developmental stages, and how these practices have been modified to make them age-appropriate. Method To address this problem, the content of 26 (cluster-) randomised controlled studies was analysed to identify any quantitative and qualitative differences. Results The duration of MBIs varied greatly ranging from 4 to 25 weeks, 8 to 144 sessions, and 3 to 45 h, with session duration from 3 to 90 min, which is an important factor to take into account. However, there were no age group differences due to number of sessions or program length. MBIs for early childhood were more likely to use mindful movement practice, psychoeducation, and story-based context. In case of other components, such as breathing awareness or working with thoughts and emotions, qualitative differences were found between early and middle childhood regarding how the activities were implemented age appropriately. Conclusions The present study offers practical implications and distinguishes differences in mindfulness activities for early and middle childhood. Important developmental considerations have been raised for future program developers and practitioners. KEY POINTS What is already known about this topic: (1) Mindfulness-based interventions for children have gained popularity in the last decade. (2) Adult mindfulness program components have been modified to make them developmentally appropriate for children. (3) There is a lack of clarity concerning age-appropriate components for children in different developmental stages and how these components were modified. What this topic adds: (1) Early childhood mindfulness programs used mindful movement and body practices, story-based context, and psychoeducation more frequently than middle childhood programs. (2) Age–related qualitative differences were identified between younger and older children regarding many mindfulness components and activities, such as breathing awareness or psychoeducation. (3) The lack of mindful movement practices and psychoeducation in many middle childhood programs raises some important development–related questions which might be considered by future program developers and practitioners.
Dynamic Thermal Rating Forecasting Methods: A Systematic Survey

Dynamic Thermal Rating (DTR) allows optimum electric power line rating use. It is an intelligent grid technology predicting changes in line rating due to changing physical and environmental conditions. This study performed a meta-analysis of DTR forecasting methods by classifying the methods, implementing them, and comparing their outputs for a 24hr forecast lead time. It implemented deep learning methods of Recurrent Neural Network (RNN), Ensemble Means forecasting and Convolution Neural Network (CNN). RNN uses the initial outcome of a specific neural network layer as feedback to the network to predict the layer’s outcome. Ensemble Means forecasting is a Monte-Carlo simulation process producing random, equally viable forecasting solutions. On the other hand, CNN uses unsupervised learning to predict features with minimal errors. This survey systematically implements Quantile Regression (QR), RNN, CNN and Ensemble means forecasting. Point error metrics and probabilistic error metrics of sharpness, skill, and bias were used in the methods’ evaluation. All methods tested prove to be efficient, but 50th percentile QR appears more conservative, secure and less error-prone. It achieved between 35% - 45% line capacity utilization over the Static Thermal Rating (STR). On average, judging by the error metrics of all methods, 50th percentile quantile regression proves highly reliable and provides a better conviction in our choice of DTR forecasting.
Edge-based Heart Disease Prediction Device using Internet of Things

Internet of Things connects things for anywhere, anytime and anyone communication. It has wide applications that include healthcare, industry, transportation and smart cities. Internet of things enabled health applications have been developed in the past decade to measure vital signs viz., heart rate, oxygen level, accelerometer and ECG. These vital signs enable early detection of chronic diseases saving the life of people. However, many of the existing techniques aim at developing cloud-based processing for these devices. This increases the latency and consumes bandwidth for real-time critical applications. This also leaks the privacy of the patient's data. This paper aims at proposing an edge-based heart disease prediction device using the Internet of Things. The prototype was developed using raspberry pi connecting temperature, pulse rate and accelerometer sensor. The measured value is tested using a pre-trained machine learning model to understand the condition of the patient. The system was also tested under various cases and shows 88% of accuracy.
3D Object Detection from Point Cloud Based on Deep Learning

In order to study the modern 3D object detection algorithm based on deep learning, this paper studies the point-based 3D object detection algorithm, that is, a 3D object detection algorithm that uses multilayer perceptron to extract point features. This paper proposes a method based on point RCNN. A three-stage 3D object detection algorithm improves the accuracy of the algorithm by fusing image information. The algorithm in this paper integrates the information and image information of the three stages well, which improves the information utilization of the whole algorithm. Compared with the traditional 3D target detection algorithm, the structure of the algorithm in this paper is more compact, which effectively improves the utilization of information.
Broadband conical beam antenna in planar environment

In this article, a substrate‐truncated microstrip circular patch antenna with shorting vias is proposed for X‐band applications. The bandwidth of the designed antenna is substantially increased by making two slots—one circular and another annular ring—at the top of the structure which actually helps in bringing two individual resonating frequencies closer to each other. The antenna is simulated using the Ansoft HFSS, and various parameters are optimized for better performance. The deigned structure is finally fabricated and tested, and the measured data fairly agree with the simulated results. The measured relative impedance bandwidth (|S11| < −10 dB) is found to be 28.5% (8.9‐11.85 GHz). The proposed antenna is behaving like a monopole with the radiated beam of conical shaped in the entire operating band having a maximum gain of 7.2 dBi.
Image analysis using convolutional neural network to detect bird species

Recognition for bird species is one of the most challenging tasks in visualizing a very abundant bird species. It is a subfield of object recognition and is meant to differentiate sub-categories. Furthermore, it aims to categorize the data, such as images. Therefore, identifying the bird species through images helps us acquire a more accurate result. Thus, we have developed a deep learning model to help users identify bird species. Meanwhile, deep convolutional neural networks have been very effective in dealing with images of having different dimensions and thus succeeding at tasks like object detection and classification, learning various features like edges, colors, etc. Thus, using the neural network model, we have taken 200 different species of birds in the form of images to classify. First, birds images were learned by a convolutional neural network (CNN) to identify the dominant features in the images. Then, we used the softmax function to obtain a probability distribution of bird characteristics. Finally, to obtain a more accurate result, we compare our GoogleLeNet model with the VGG16.
Robust Deep Compressive Sensing With Recurrent-Residual Structural Constraints

Existing deep compressive sensing (CS) methods either ignore adaptive online optimization or depend on costly iterative optimizer during reconstruction. This work explores a novel image CS framework with recurrent-residual structural constraint, termed as R<inline-formula><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula>CS-NET. The R<inline-formula><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula>CS-NET first progressively optimizes the acquired samplings through a novel recurrent neural network. The cascaded residual convolutional network then fully reconstructs the image from optimized latent representation. As the first deep CS framework efficiently bridging adaptive online optimization, the R<inline-formula><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula>CS-NET integrates the robustness of online optimization with the efficiency and nonlinear capacity of deep learning methods. Signal correlation has been addressed through the network architecture. The adaptive sensing nature further makes it an ideal candidate for color image CS via leveraging channel correlation. Numerical experiments verify the proposed recurrent latent optimization design not only fulfills the adaptation motivation, but also outperforms classic long short-term memory (LSTM) architecture in the same scenario. The overall framework demonstrates hardware implementation feasibility, with leading robustness and generalization capability among existing deep CS benchmarks.
Silver Nanoparticles Containing Fucoidan Synthesized by Green Method Have Anti-Trypanosoma cruzi Activity

The brown seaweed Spatoglossum schröederi synthesizes three bioactive fucoidans, the most abundant of which is fucan A. This fucoidan was extracted and its identity was confirmed by chemical analysis, Fourier-transform infrared spectroscopy (FTIR), and agarose gel electrophoresis. Thereafter, silver nanoparticles containing fucan A (AgFuc) were produced using an environmentally friendly synthesis method. AgFuc synthesis was analyzed via UV–vis spectroscopy and FTIR, which confirmed the presence of both silver and fucan A in the AgFuc product. Dynamic light scattering (DLS), X-ray diffraction, scanning electron microscopy, and atomic force microscopy revealed that the AgFuc particles were ~180.0 nm in size and spherical in shape. DLS further demonstrated that AgFuc was stable for five months. Coupled plasma optical emission spectrometry showed that the AgFuc particles contained 5% silver and 95% sugar. AgFuc was shown to be more effective in inhibiting the ability of parasites to reduce MTT than fucan A or silver, regardless of treatment time. In addition, AgFuc induced the death of ~60% of parasites by necrosis and ~17% by apoptosis. Therefore, AgFuc induces damage to the parasites’ mitochondria, which suggests that it is an anti-Trypanosoma cruzi agent. This is the first study to analyze silver nanoparticles containing fucan as an anti-Trypanosoma cruzi agent. Our data indicate that AgFuc nanoparticles have potential therapeutic applications, which should be determined via preclinical in vitro and in vivo studies.
Developing a Scale to Investigate the Dissatisfaction with the Face and Body

The purpose of this study was to develop a psychometric scale to measure dissatisfaction with one’s face. In Study 1, 1,002 people aged 17–42 years using a 7-point Likert scale for a draft scale. Exploratory factor analysis revealed that the scale consisted of 27 items and a 3-factor structure (Avoidance, Fear, and Obsessive thinking), named Face Dissatisfaction Scale (FDS). Study 2 examined sex differences in dissatisfaction with the face and body. The results showed that females were more dissatisfied with faces than males; however, there was no sex difference in body dissatisfaction. Males were more dissatisfied with bodies than faces; however, there was no difference in females. We found that face dissatisfaction was positively associated with dysmorphic concerns and obsessive-compulsive tendencies. Moreover, the FDS showed high goodness-of-fit, reliability, and validity. The study suggests that FDS is a useful psychometric tool to seek unsatisfied people with their faces.
Inference of a Boolean Network From Causal Logic Implications

Biological systems contain a large number of molecules that have diverse interactions. A fruitful path to understanding these systems is to represent them with interaction networks, and then describe flow processes in the network with a dynamic model. Boolean modeling, the simplest discrete dynamic modeling framework for biological networks, has proven its value in recapitulating experimental results and making predictions. A first step and major roadblock to the widespread use of Boolean networks in biology is the laborious network inference and construction process. Here we present a streamlined network inference method that combines the discovery of a parsimonious network structure and the identification of Boolean functions that determine the dynamics of the system. This inference method is based on a causal logic analysis method that associates a logic type (sufficient or necessary) to node-pair relationships (whether promoting or inhibitory). We use the causal logic framework to assimilate indirect information obtained from perturbation experiments and infer relationships that have not yet been documented experimentally. We apply this inference method to a well-studied process of hormone signaling in plants, the signaling underlying abscisic acid (ABA)—induced stomatal closure. Applying the causal logic inference method significantly reduces the manual work typically required for network and Boolean model construction. The inferred model agrees with the manually curated model. We also test this method by re-inferring a network representing epithelial to mesenchymal transition based on a subset of the information that was initially used to construct the model. We find that the inference method performs well for various likely scenarios of inference input information. We conclude that our method is an effective approach toward inference of biological networks and can become an efficient step in the iterative process between experiments and computations.
Research on bus electrodynamic force of 750KV Micro-loss economical fault current limiter under power frequency short-circuit

The short-circuit current will bring serious threat to the security and stability of power systems. Under the action of first half wave of short-circuit current, the electrodynamic force analysis of the bus is an important issue in its design and manufacture. In this paper, the finite element method is used to study the electrodynamic force of bus in the first half wave of short circuit current. Firstly, the domain decomposition method is used in the simulation, and the whole calculation area is partitioned into blocks. Different grid methods are used to improve the efficiency of finite element calculation. And we can obtain the distribution of the magnetic field of the flexible bus and hard bus under the first half wave of short-circuit current. And then, the electrodynamic force of the bus can be further calculated by using the calculated magnetic field. Finally, according to the electrodynamic force on the bus, the maximum stress and displacement of the bus are analyzed. Also, the variation characteristics of stress and displacement under different support numbers are discussed. The research results of this paper will provide an important theoretic and practical value for the design and manufacture of 750 kV economical fault current limiter.
"This Patriarchal, Machista and Unequal Culture of Ours": Obstacles to Confronting Conflict-Related Sexual Violence

Abstract:Prior research has established that conflict-related sexual violence against women is anchored in patriarchal norms and practices that assert gendered hierarchies. What remains relatively underresearched, however, is how patriarchal structures shape individual, social, and institutional responses to conflict-related sexual violence and its victims. This article sets out to shed light on this question, identifying different social and institutional processes that impede efforts to confront conflict-related sexual violence. The analysis of interviews with Colombian civil society activists illustrates how patriarchal norms and practices normalize sexual violence in society, but also ostracize, stigmatize, and ultimately seek to silence its victims. This risks obliterating conflict-related sexual violence from the political map and severely undermines the pursuit of justice. Power imbalances disadvantaging and further marginalizing the victims permeate these processes. Civil society organizations play an important role in reclaiming power for the victims, by overcoming disabling silences, making sexual violence visible, and confronting harmful patriarchal practices.
An LSTM network-based real-time noise suppression tool for conducting audio and video conferences

Background: A real-time noise suppression tool is an essential requirement of today's time. By using such a tool, audio and video conferences can be conducted noiselessly, even in the absence of a quiet office environment. The tool's methodology includes a signal pre-processing phase and a network computations phase followed by statistical analysis. In the pre-processing phase, various signal transformation methods are analyzed comparatively. Then, the most suitable one is applied to the input audio signal to extract its main frequencies. Afterward, these extracted features are used for training the deep neural network. Results: Many sample audio signals are fed to this tool, and it gives their cleaned versions with suppressed background noise. Statistical analysis for particular sample audio shows the amplitude difference between the two speech signals graphically. Conclusion: It is found that the Short-time Fourier Transform method is better amongst all other methods for extracting signal features due to its lossless nature. The backbone of the Long Short-term Memory Network facilitates this tool to retain any extended sequential information. An overall T statistic value of -94.920 for sample audio 1 and -208.843 for sample audio 2 solidifies the confidence in the accuracy of this tool.
Plasma Promotes Fungal Cellulase Production by Regulating the Levels of Intracellular NO and Ca2+

For the industrial-scale production of useful enzymes by microorganisms, technological development is required for overcoming a technical bottleneck represented by poor efficiency in the induction of enzyme gene expression and secretion. In this study, we evaluated the potential of a non-thermal atmospheric pressure plasma jet to improve the production efficiency of cellulolytic enzymes in Neurospora crassa, a filamentous fungus. The total activity of cellulolytic enzymes and protein concentration were significantly increased (1.1~1.2 times) in media containing Avicel 24–72 h after 2 and 5 min of plasma treatment. The mRNA levels of four cellulolytic enzymes in fungal hyphae grown in media with Avicel were significantly increased (1.3~17 times) 2–4 h after a 5 min of plasma treatment. The levels of intracellular NO and Ca2+ were increased in plasma-treated fungal hyphae grown in Avicel media after 48 h, and the removal of intracellular NO decreased the activity of cellulolytic enzymes in media and the level of vesicles in fungal hyphae. Our data suggest that plasma treatment can promote the transcription and secretion of cellulolytic enzymes into the culture media in the presence of Avicel (induction condition) by enhancing the intracellular level of NO and Ca2+.
Security and Privacy Challenges for Intelligent Internet of Things Devices 2022 TADW: Traceable and Antidetection Dynamic Watermarking of Deep Neural Networks

Deep neural networks (DNN) with incomparably advanced performance have been extensively applied in diverse fields (e.g., image recognition, natural language processing, and speech recognition). Training a high-performance DNN model requires a lot of training data and intellectual and computing resources, which bring a high cost to the model owners. Therefore, illegal model abuse (model theft, derivation, resale or redistribution, etc.) seriously infringes model owners’ legitimate rights and interests. Watermarking is considered the main topic of DNN ownership protection. However, almost all existing watermarking works apply solely to image data. They do not trace the unique infringing model, and the adversary easily detects these ownership verification samples (trigger set) simultaneously. This paper introduces TADW, a dynamic watermarking scheme with tracking and antidetection abilities in the deep learning (DL) textual domain. Specifically, we propose a new approach to construct trigger set samples for antidetection and innovatively design a mapping algorithm that assigns a unique serial number (SN) to every watermarked model. Furthermore, we implement and detailedly evaluate TADW on 2 benchmark datasets and 3 popular DNNs. Experiment results show that TADW can successfully verify the ownership of the target model at a less than 0.5% accuracy cost and identify unique infringing models. In addition, TADW is excellently robust against different model modifications and can serve numerous users.