Miracle-dz/UniFAR-FTUs huggingface dataset:
Precise bonding-free micromoulding of miniaturized elastic inflatable actuators

A new precise micromoulding process has been developed in order to fabricate elastic inflatable microactua-tors, having a corrugated inner cavity without requiring any additional bonding step. Micromoulds are machined using micromilling and wire electrical discharge grinding. This manufacturing process allows the out-of-plane fabrication of complex shapes of microactuators with smallest feature down to 40 μm, It is possible to fabricate miniaturized versions of actuators that are normally made at a larger scale. Particularly, this paper reports a silicone-made miniaturized elastic inflatable actuator showing a large bending deformation. The particular pattern of the inner cavity forms a corrugated structure and enables a fully curled deformation of the actuator due to the equal distribution of multiple bending points. As a demonstrator, three microactuators are used as active fingers of a soft microgripper.
Risk Minimization Routing Against Geographically Correlated Failures

Regional failures, such as natural disaster or malicious attack, have become a major threat to the construction of future reliable communication network. The regional failures usually cause a large number of disconnected nodes simultaneously and influence the network for a long time. However, a routing scheme that is resilient to such geographically correlated failures is still unexplored. In this paper, we provide a comprehensive study of the disaster resilient routing dealing with the regional failure in operational IP backbone networks. It is notable that the path with minimal risk (i.e., minimal failure probability) is not necessarily the shortest path. The main challenge of finding such paths is that regional failure is unpredictable in terms of time, location, and the affected area. To this end, in combination with the computational geometry tool, we develop effective algorithms to find the minimal risk path between end node pairs to tolerate random regional failures. We show that in contrast to the conventional shortest path, a little longer path can be more effective to the disasters. After selecting such a path as the primary path, we turn to find a secondary backup path. In contrast to the conventional single link/node failure, a regional failure disrupts a large number of network components, simultaneously. As a result, how to find backup paths for re-establishment of the corrupted paths will raise a novel fairness issue. Specifically, during the backup path allocation, we focus on routing fairness to bound the worst-case user experience. A metric is proposed based on which an ILP is formulated. The extensive simulations validate that such an issue is non-negligible in face of regional failure scenarios.
DeepFlow: Detecting Optimal User Experience From Physiological Data Using Deep Neural Networks

Flow is an affective state of optimal experience, total immersion and high productivity. While often associated with (professional) sports, it is a valuable information in several scenarios ranging from work environments to user experience evaluations, and we expect it to be a potential reward signal for human-in-the-loop reinforcement learning systems. Traditionally, flow has been assessed through questionnaires which prevents its use in online, real-time environments. In this work, we present our findings towards estimating a user's flow state based on physiological signals measured using wearable devices. We conducted a study with participants playing the game Tetris in varying difficulty levels, leading to boredom, stress, and flow. Using an end-to-end deep learning architecture, we achieve an accuracy of 67.50% in recognizing high flow vs. low flow states and 49.23% in distinguishing all three affective states boredom, flow, and stress.
An Improved SVD based Image Compression

Advent of information technology since last two to three decades led to the enormous generation of multimedia data. Digital images form a major share of multimedia data; hence a large interest over the area of image compression had been seen among researchers. Image compression deals with exploiting visual imperceptibility of human eye to encode data with much smaller number of bits for the same amount of information. Singular Value Decomposition (or SVD as it is commonly abbreviated) based image compression had been extensively studied in the past few decades. We present an improved approach over the technique using the orthonormal property of the matrices produced in SVD decomposition method. We show that we do not need to store full vectors in order to preserve the orthonormal matrices of eigenvectors instead if we store partial vectors then also we can generate the rest of the values using the orthonormal properties of the matrices of eigenvectors. We further derive a mathematical expression for the compression ratio for this scheme and extend it to derive an expression for the gain thus involved. We present the experimental results showing the PSNR, SSIM and compression ratio of the proposed scheme with that of the state of the art SVD compression scheme. The results indicate that the proposed scheme improves the compression ratio while maintain the image quality.
Performance of Kramers–Kronig Receivers in the Presence of Local Oscillator Relative Intensity Noise

There is increasing interest in low-complexity coherent optical transceivers for the use in short-reach fiber links. Amongst the simplest configurations is the heterodyne coherent receiver, using a 3-dB coupler to combine the signal with the local oscillator (LO) laser output, and a single photodiode for detection of each polarization. In this paper, through numerical simulations, we investigate the impact of signal–signal beating interference (SSBI) and LO relative intensity noise (RIN) on the performance of such coherent transceivers. We assess the performance of two methods to mitigate the SSBI: first, the use of high LO laser power, and second, the application of digital signal processing-based receiver linearization, specifically, the Kramers–Kronig (KK) scheme. The results indicate that, in the case of a RIN-free LO laser, a strong LO is effective in mitigating SSBI and achieves a similar performance to that of the KK algorithm. However, the required increase in LO-to-signal power ratio (LOSPR) is significant. For example, a 20 dB higher optimum LOSPR was observed in the 28 Gbaud dual polarization 16 QAM system at an optical signal-to-noise power ratio of 22 dB. The drawback of using such a high LOSPR is the increased penalty due to RIN-LO beating terms, which we next investigated. The lower optimum LOSPR and, consequently, the lower impact of LO RIN on the performance of the KK receiver lead to a reduction in the pre-FEC BER by over an order of magnitude for LO RIN levels above −140 dBc/Hz.
Deteriorated Cardiometabolic Risk Profile in Individuals with Excessive Blood Pressure Response to Submaximal Exercise.

BACKGROUND Early identification of individuals at increased cardiometabolic risk is an essential step to improve primary preventive interventions. Excessive maximal exercise blood pressure (EBP) has been associated with several adverse outcomes. We examined how submaximal EBP could help to identify individuals with a deteriorated cardiometabolic risk profile. METHODS Data from an observational study of 3,913 participants from a convenience sample were used. Subjects included in the analyses completed a comprehensive cardiometabolic health assessment (resting blood pressure [BP]; waist circumference; lipid profile; HbA1c; submaximal treadmill exercise test including a standardized stage [3.5 mph and 2% slope] with BP and heart rate measurements). Participants were classified on BP response at the standardized stage (Normal or Excessive Response). Excessive response was defined as systolic BP ≥80th percentile or diastolic BP ≥90 mmHg. Subjects were also classified into five resting BP subgroups according to current guidelines. RESULTS The Excessive Response group had more deteriorated cardiometabolic and cardiorespiratory profiles than the Normal Response group (P≤0.01). The Excessive Response group also showed a greater proportion of carriers of the hypertriglyceridemic waist phenotype in most resting BP subgroups (P≤0.05). Finally, excessive BP response to submaximal exercise showed an independent contribution on cardiometabolic and cardiorespiratory factors beyond age, sex and resting BP. CONCLUSIONS This study demonstrates that an excessive BP response to a submaximal exercise is associated with a deteriorated cardiometabolic risk profile beyond resting BP profile. Therefore, submaximal EBP represents a simple screening tool to better identify at-risk individuals requiring aggressive preventive lifestyle interventions.
WebRTC: your privacy is at risk

New web technologies are changing the way we interact with the web and with applications. They enable a whole new family of applications for desktop systems but also for mobile devices. The specification of HTML5 and related JavaScript APIs paved the way for rich applications backed by web technologies offering a comparable user experience to native interfaces. Web-based real-time communication (WebRTC) is the next step towards the elimination of current browser limitations. It enables a direct browser-to-browser or device-to-device communication. In this paper, we are targeting the security and privacy implications imposed by this emerging technology. We developed several attacks on WebRTC, compromising user's security and privacy, as well as the privacy of other devices in the same network. Our evaluation shows that even though WebRTC is based on a solid security basis, user's privacy and communication security can be compromised due to several design decisions. For each attack, mitigation strategies are defined where the operation and functionality of WebRTC are still maintained and user's privacy is protected.
Optimization of Database Resources Assignment in Cloud Computing Using Gravity Search Algorithm

Cloud computing of the computational model is based on the great computer networks such as the internet; these networks propose a new pattern for providing, consuming, and delivering the Information Technology (including hardware, software, information, and the other common computational resources) by using internet. Cloud computing suggests guidelines to provide Information Technology services in similar ways such as public industries (water, electricity, telephone, etc.). This means that access to Information Technology at the time of demand and based on the user’s demand level is provided for the users in a flexible and scalable way. On the other hand, the optimal resources in the cloud computing systems are considered from the fundamental challenges. Due to its large search space, this issue is from the difficult issues and the random search methods are more used for solving it. So far, different algorithms have been proposed for solving this problem. In this paper, for solving the resource assignment problem in cloud computing, we have used a meta-heuristic algorithm called GSA-RACloud. To show the performance of the proposed algorithm (GSA-RACloud), this algorithm has been compared with the ant colony algorithm (ACO). The empirical results show that the proposed algorithm has a higher performance than the compared algorithm.
Research on the Credible Capacity Model and Calculation Method of Photovoltaic Power Generation

Due to the randomness and intermittency of solar energy, photovoltaic power plants can not express actual capacity with rated output. In the power system planning, we need to evaluate the contribution of PV grid connected to the reliability of the whole system, so it is necessary to study the credible capacity of PV generation. This paper presenting an algorithm of PV credible capacity based on sequence operation theory. By referring to the analysis process of wind power credible capacity, this paper deduces an analytical model of PV credible capacity. Taking advantage of RTS-79 system, this paper analyzes several factors that can affect PV credible capacity, getting the law how this factors including the capacity of PV power, the season and the geographical position affects the PV credible capacity.
Effects of Single-Sex and Coeducational Secondary Schooling on Children's Academic Achievement

Using prospective data from an 18-year longitudinal study of a birth cohort of 657 New Zealand children, this paper examines the effects of single-sex and coeducational secondary schooling on children's academic achievement. This analysis showed a pervasive tendency for children attending single-sex schools to have greater success in the School Certificate examinations, higher Burt reading scores, greater school retention, less likelihood of leaving school without qualifications, and less exposure to unemployment than children attending coeducational schools. However, a substantial amount of this association was explained by pre-entry differences in children's academic, behavioural, social, and family functioning. None the less, even after control for selection processes, children attending single-sex schools tended to perform better than their coeducated peers across a number of educational outcomes. Possible explanations for these differences are considered, including school gender composition, school climate and traditions, and possible inadequate control for selection and confounding factors.
Examining faith-based collaboration in U.S. States’ suicide prevention guidelines

Abstract Faith-based communities (FBCs) are recognized by most states as key players within systematic suicide prevention efforts. The aim of the present study was to conduct a thematic analysis of documents that detail the suicide prevention efforts of individual states with respect to the role(s) delegated to FBCs. Thematic analysis is recognized as a useful methodology for identifying implications for policy and practice. Documents were procured from all states, with the exception of New Mexico (n = 49). The findings suggest six areas relevant to collaboration with FBCs: suicide prevention training for the FBC, suicide prevention training for individual faith leaders, community engagement, faith leaders as gatekeepers, culturally sensitive suicide prevention, and postvention support. These state guideline documents consistently affirm the importance of engaging FBCs in suicide prevention efforts and cover a range of recommendations, though generally lack specifics with respect to how FBCs can optimally engage.
Integrated Data Compression and Pulse Rate Extraction Scheme Using Differential Coding for Wireless PPG Monitoring Devices

This paper presents a novel integrated data compression and pulse rate measurement scheme for power reduction in Internet-of-Things (IoT) enabled wireless PPG monitoring devices. In the proposed scheme, the recorded photoplethysmograph (PPG) signal is compressed using the differential pulse code modulation and Huffman coding techniques and then the pulse rate (PR) is simultaneously extracted from the differential signal, which is the difference between the original and predicted signals. The proposed scheme achieves an average compression ratio, percentage root-mean-squared difference (PRD), signal-to-noise ratio (SNR), and normalized cross correlation (NCC) of 4.76, 0.19%, 27.90 dB and 0.9991 respectively for the 5 s PPG signals digitized with a sampling rate of 100 Hz and resolution of 12 bits, on the three standard PPG databases (MIT-BIH SLP, MIMIC-II and CSL). Evaluation results show that the method yields peak detection rates of 98.94% and average PR measurement error of 0–2.6 samples. The proposed integrated scheme can enable timely measurement of the pulse rate (PR) and to significantly reduce the overall power consumption and therefore it is highly suited for IoT wireless wearable PPG sensors.
The putative iron transport system SitABCD encoded on SPI1 is required for full virulence of Salmonella typhimurium

Salmonella typhimurium is an invasive pathogen that causes diseases ranging from mild gastroenteritis to enteric fever. During the infection process, S. typhimurium induces a number of virulence genes required to circumvent host defences and/or acquire nutrients in the host. We have used the in vivo expression technology (IVET) system to select for S. typhimurium genes that are induced after invasion of a murine cultured cell line. We have characterized a putative iron transporter in Salmonella pathogenicity island 1, termed sitABCD. The sitABCD operon is induced under iron‐deficient conditions in vitro and is repressed by Fur. This locus is induced in the animal specifically after invasion of the intestinal epithelium. We show that a sit null mutant is significantly attenuated in BALB/c mice, suggesting that SitABCD plays an important role in iron acquisition in the animal.
High-Precision Indoor Visible Light Positioning Using Modified Momentum Back Propagation Neural Network with Sparse Training Point

In this letter, we propose an indoor visible light positioning technique using a Modified Momentum Back-Propagation (MMBP) algorithm based on received signal strength (RSS) with sparse training data set. Unlike other neural network algorithms that require a large number of training data points to locate accurately, we have realized high-precision positioning for 100 test points with only 20 training points in a 1.8 m × 1.8 m × 2.1 m localization area. In order to verify the adaptability of the MMBP algorithm, we experimentally demonstrate two different training data acquisition methods adopting either even or arbitrary training sets. In addition, we also demonstrate the positioning accuracy of the traditional RSS algorithm. Experimental results show that the average localization accuracy optimized by our proposed algorithm is only 1.88 cm for the arbitrary set and 1.99 cm for the even set, while the average positioning error of the traditional RSS algorithm reaches 14.34 cm. Comparison indicates that the positioning accuracy of our proposed algorithm is 7.6 times higher. Results also show that the performance of our system is higher than some previous reports based on RSS and RSS fingerprint databases using complex machine learning algorithms trained by a large amount of training points.
ZAP-70 Regulates Autoimmune Arthritis via Alterations in T Cell Activation and Apoptosis

T cells play an essential role in the pathogenesis of both human rheumatoid arthritis (RA) and its murine models. A key molecule in T cell activation is ZAP-70, therefore we aimed to investigate the effects of partial ZAP-70 deficiency on the pathogenesis of recombinant human G1(rhG1)-induced arthritis (GIA), a well-established mouse model of RA. Arthritis was induced in BALB/c and ZAP-70+/− heterozygous mice. Disease progression was monitored using a scoring system and in vivo imaging, antigen-specific proliferation, cytokine and autoantibody production was measured and T cell apoptotic pathways were analyzed. ZAP-70+/− mice developed a less severe arthritis, as shown by both clinical picture and in vitro parameters (decreased T cell proliferation, cytokine and autoantibody production). The amount of cleaved Caspase-3 increased in arthritic ZAP-70+/− T cells, with no significant changes in cleaved Caspase-8 and -9 levels; although expression of Bim, Bcl-2 and Cytochrome C showed alterations. Tyrosine phosphorylation was less pronounced in arthritic ZAP-70+/− T cells and the amount of Cbl-b—a negative regulator of T cell activation—decreased as well. We hypothesize that the less severe disease seen in the partial absence of ZAP-70 might be caused by the decreased T cell activation accompanied by increased apoptosis.
ROR1 Potentiates FGFR Signaling in Basal-Like Breast Cancer

Among all breast cancer types, basal-like breast cancer (BLBC) represents an aggressive subtype that lacks targeted therapy. We and others have found that receptor tyrosine kinase-like orphan receptor 1 (ROR1) is overexpressed in BLBC and other types of cancer and that ROR1 is significantly correlated with patient prognosis. In addition, using primary patient-derived xenografts (PDXs) and ROR1-knockout BLBC cells, we found that ROR1+ cells form tumors in immunodeficient mice. We developed an anti-ROR1 immunotoxin and found that targeting ROR1 significantly kills ROR1+ cancer cells and slows down tumor growth in ROR1+ xenografts. Our bioinformatics analysis revealed that ROR1 expression is commonly associated with the activation of FGFR-mediated signaling pathway. Further biochemical analysis confirmed that ROR1 stabilized FGFR expression at the posttranslational level by preventing its degradation. CRISPR/Cas9-mediated ROR1 knockout significantly reduced cancer cell invasion at cellular levels by lowering FGFR protein and consequent inactivation of AKT. Our results identified a novel signaling regulation from ROR1 to FGFR and further confirm that ROR1 is a potential therapeutic target for ROR1+ BLBC cells.
Blind estimation of multiple digital signals transmitted over FIR channels

Using oversampling and the finite-alphabet property of digital communication signals, it is possible to blindly identify an FIR channel carrying a superposition of such signals, provided they have the same (known) period, and certain rank conditions on the data and channel matrices are satisfied. In particular, this technique allows separation of finite alphabet signals, removal of intersymbol interference, and synchronization of the signals. An algorithm is proposed and tested on simulated data. <<ETX>>
Energy Conservation in Wireless Sensor Networks Using Partly-Informed Sparse Autoencoder

The energy in wireless sensor networks is considered a scarce commodity, especially in scenarios where it is difficult or impossible to provide supplementary energy sources once the initially available energy is used up. Even in cases where energy harvesting is feasible, effective energy utilization is still a crucial step for prolonging the network lifetime. Enhancement of life-time through efficient energy management is one of the essential ingredients underlining the design of any credible wireless sensor network. In this paper, we propose a sensor selection method using a novel and unsupervised neural network structure referred to as partly-informed sparse autoencoder (PISAE) that aims to reconstruct all sensor readings from a select few. The PISAE comprises three submodules, namely: the gate (which selects the most important sensors), encoder (encodes and compresses the data from select sensors), and decoder (decodes the output of the encoder and regenerates the readings of all initial sensors). Our approach relies on the premise that many sensors are redundant because their readings are spatially and temporally correlated and are predictable from the readings of a few other sensors in the network. Thus, overall network reliability and lifetime are enhanced by putting sensors with redundant readings to sleep without losing significant information. We evaluate the efficacy of the proposed method on three benchmark datasets and compare with existing results. The experimental results indicate the superiority of our approach compared with existing approaches in terms of accuracy and lifetime extension factor.