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Soft particulate gels include materials we can eat, squeeze, or 3D print. From foods to bio-inks to cement hydrates, these gels are composed of a small amount of particulate matter (proteins, polymers, colloidal particles, or agglomerates of various origins) embedded in a continuous fluid phase. The solid components assemble to form a porous matrix, providing rigidity and control of the mechanical response, despite being the minority constituent. The rheological response and gel elasticity are direct functions of the particle volume fraction $\phi$: however, the diverse range of different functional dependencies reported experimentally has, to date, challenged efforts to identify general scaling laws. Here we reveal a hidden hierarchical organization of fractal elements that controls the viscoelastic spectrum, and which is associated with the spatial heterogeneity of the solid matrix topology. The fractal elements form the foundations of a viscoelastic master curve, which we construct using large-scale 3D microscopic simulations of model gels, and can be described by a recursive rheological ladder model over a range of particle volume fractions and gelation rates. The hierarchy of the fractal elements provides the missing general framework required to predict the gel elasticity and the viscoelastic response of these ubiquitous complex materials.

The advent of Low Power Wide Area Networks (LPWAN) has enabled the feasibility of wireless sensor networks for environmental traffic sensing across urban areas. In this study, we explore the usage of LoRaWAN end nodes as traffic sensing sensors to offer a practical traffic management solution. The monitored Received Signal Strength Indicator (RSSI) factor is reported and used in the gateways to assess the traffic of the environment. Our technique utilizes LoRaWAN as a long-range communication technology to provide a largescale system. In this work, we present a method of using LoRaWAN devices to estimate traffic flows. LoRaWAN end devices then transmit their packets to different gateways. Their RSSI will be affected by the number of cars present on the roadway. We used SVM and clustering methods to classify the approximate number of cars present. This paper details our experiences with the design and real implementation of this system across an area that stretches for miles in urban scenarios. We continuously measured and reported RSSI at different gateways for weeks. Results have shown that if a LoRaWAN end node is placed in an optimal position, up to 96% of correct environment traffic level detection can be obtained. Additionally, we share the l, Comment: 7 pages, accepted to Emerging Topics in Wireless (EmergingWireless) in CoNEXT 2022

The performance of physical layer secrecy approach in non-orthogonal multiple access (NOMA)-enabled heterogeneous networks (HetNets) is analyzed in this paper. A $K$-tier multi-cell HetNet is considered, comprising NOMA adopted in all tiers. The base stations, legitimate users (in a two-user NOMA setup), and passive eavesdroppers, all with single-antenna, are randomly distributed. Assuming independent Poisson point processes for node distribution, stochastic geometry approaches are exploited to characterize the ergodic secrecy rate. A lower bound on the ergodic secrecy rate along with closed-form expressions for the lower bound on the ergodic rates of the legitimate users in a special case are derived. Moreover, simpler expressions for the ergodic secrecy rate are obtained in the interference-limited regime with vanishing noise variance. The effect of multi-tier technology, NOMA, and physical layer secrecy are investigated using numerical results. The results reveal that applying HetNet to a secure multi-cell NOMA system improves the spectrum efficiency performance.

Differentiable neural architecture search (DARTS) is a popular method for neural architecture search (NAS), which performs cell-search and utilizes continuous relaxation to improve the search efficiency via gradient-based optimization. The main shortcoming of DARTS is performance collapse, where the discovered architecture suffers from a pattern of declining quality during search. Performance collapse has become an important topic of research, with many methods trying to solve the issue through either regularization or fundamental changes to DARTS. However, the weight-sharing framework used for cell-search in DARTS and the convergence of architecture parameters has not been analyzed yet. In this paper, we provide a thorough and novel theoretical and empirical analysis on DARTS and its point of convergence. We show that DARTS suffers from a specific structural flaw due to its weight-sharing framework that limits the convergence of DARTS to saturation points of the softmax function. This point of convergence gives an unfair advantage to layers closer to the output in choosing the optimal architecture, causing performance collapse. We then propose two new regularization terms that aim to prevent performance collapse by harmonizing operation selection via aligning gradients of layers. Experimental results on six different search spaces and three different datasets show that our method ($\Lambda$-DARTS) does indeed prevent performance collapse, providing justification for our theoretical analysis and the proposed remedy., Comment: Published as a conference paper at ICLR 2023

In this paper, we present ApacheJIT, a large dataset for Just-In-Time defect prediction. ApacheJIT consists of clean and bug-inducing software changes in popular Apache projects. ApacheJIT has a total of 106,674 commits (28,239 bug-inducing and 78,435 clean commits). Having a large number of commits makes ApacheJIT a suitable dataset for machine learning models, especially deep learning models that require large training sets to effectively generalize the patterns present in the historical data to future data.

In order to satisfy the delay requirements of telecommunication systems, in this paper, we present a cooperative network with the short packet transmission in the Rayleigh fading channel. The desired relay can be implemented as a two-way half-duplex (HD) or a two-way full-duplex (FD). Also, for more accurate satisfaction and reduction of communication delays, sending and receiving with short packets is considered. Effective capacity appropriately measures the transmission rate under the delay constraint. Therefore, it is considered as a performance evaluation criterion here. With a two-way relay, two nodes exchange data with each other using a relay simultaneously. The priorities and requirements of the two nodes are not necessarily the same. Therefore, to increase performance, the system is modeled and solved as a multi-objective problem. In this way, the available power in the network is divided between the relay and two nodes, and the effective capacity of the two nodes is maximized. Depending on the different conditions, the optimal amount of allocated power to relay and nodes is calculated. However, due to the complexity and time consuming calculations, an approximate method which speeds up the calculation is presented. The approximated solution has a very close performance to the optimal allocated power. Finally, various comparisons have been made in different conditions between the performance of two-way HD and two-way FD relays. The improvement of multi-objective power allocation has been shown, especially when the relay is not located in the middle of two nodes., Comment: Springer Wireless Networks

This paper describes the hardware, software framework, and experimental testing of SURENA IV humanoid robotics platform. SURENA IV has 43 degrees of freedom (DoFs), including seven DoFs for each arm, six DoFs for each hand, and six DoFs for each leg, with a height of 170 cm and a mass of 68 kg and morphological and mass properties similar to an average adult human. SURENA IV aims to realize a cost-effective and anthropomorphic humanoid robot for real-world scenarios. In this way, we demonstrate a locomotion framework based on a novel and inexpensive predictive foot sensor that enables walking with 7cm foot position error because of accumulative error of links and connections' deflection(that has been manufactured by the tools which are available in the Universities). Thanks to this sensor, the robot can walk on unknown obstacles without any force feedback, by online adaptation of foot height and orientation. Moreover, the arm and hand of the robot have been designed to grasp the objects with different stiffness and geometries that enable the robot to do drilling, visual servoing of a moving object, and writing his name on the white-board.

Advancements in miniaturisation and new capabilities of implantable devices impose a need for the development of compact, hermetic, and CMOS-compatible micro packaging methods. Gold-tin-based eutectic bonding presents the potential for achieving low-footprint seals with low permeability to moisture at process temperatures below 350\degree C. In this paper, a method for the deposition of gold-tin eutectic alloy frames via sequential electroplating from commercially available solutions, with no special fabrication process, is described in detail. Bond quality was characterised through shear force measurements, scanning electron microscopy, visual inspection, and immersion tests. Characterisation of seals geometry, solder thickness, and bonding process parameters were evaluated, along with toxicity assessment of bonding layers to the human fibroblast cells. With a successful bond yield of over 70\% and no cytotoxic effect, AuSn eutectic bonding appears as a suitable method for the protection of integrated circuitry in implantable applications., Comment: 16 pages, 12 figures, paper submitted to Journal of Micromechanics and Microengineering

Nowadays, the speed up development and use of digital devices such as smartphones have put people at risk of internet crimes. The evidence of present crimes in a computer file can be easily unreachable by changing the prefix of a file or other algorithms. In more complex cases, either file divided into different parts or the parts of a file that has information about the file type are deleted, where the file fragment recognition issue is discussed. The known files are divided into different fragments, and different classification algorithms are used to solve the problems of file fragment recognition. The issue of identifying the type of file fragment due to its importance in cybercrime issues as well as antivirus has been highly emphasized and has been addressed in many articles. Increasing the accuracy in this field on the types of widely used files due to the sensitivity of the subject of recognizing the type of file under study is the main goal of researchers in this field. Failure to identify the correct type of file will lead to deviations of the results and evidence from the main issue or failure to conclude. In this paper, first, the file is divided into different fragments. Then, the file fragment features, which are obtained from Binary Frequency Distribution, are reduced by 2 feature reduction algorithms; Sequential Forward Selection algorithm as well as Sequential Floating Forward Selection algorithm to delete sparse features that result in increased accuracy and speed. Finally, the reduced features are given to 3 Multiclass classifier algorithms, Multilayer Perceptron, Support Vector Machines, and K-Nearest Neighbor for classification and comparison of the results. The proposed recognition algorithm can recognize 6 types of useful files and may distinguish a type of file fragments with higher accuracy than the similar works done., Comment: 16 pages, 7 figures, 8 tables. Multimed Tools Appl (2021)

In this paper, we introduce the concept of j-hom-derivation, $j\in\{1,2\}$ and solve the new generalized additive-quadratic functional equations in the sense of ternary Banach algebras. Moreover, using the fixed point method, we prove its Hyers-Ulam stability.

In subterranean coal seam gas CSG reservoirs, massive amounts of small-sized coal fines are released during the production and development stages, especially during hydraulic fracturing stimulation. These coal fines inevitably cause mechanical pump failure and permeability damage due to aggregation and subsequent pore throat blockage. This aggregation behavior is thus of key importance in CSG production and needs to be minimized. Consequently, such coal fines dispersions need to be stabilized, which can be achieved by the formulation of improved fracturing fluids. Here, we thus systematically investigated the effectiveness of two additives; ethanol, 0.5 wt percent and SDBS, 0.001 and 0.01 wt percent, on dispersion stability for a wide range of conditions: pH 6 to 11, salinity of 0.1 to 0.6 M NaCl brine. Technically, the coal suspension flowed through a glass bead proppant pack, and fines retention was measured. We found that even trace amounts of sodium dodecylbenzene sulfonate SDBS i.e. 0.001 wt per cent drastically improved dispersion stability and reduced fines retention. The retention was further quantified by fractal dimensional analysis, which showed lower values for suspensions containing SDBS. This research advances current CSG applications and thus contributes to improved energy security., Comment: 12 pages, 12 figures and 4 tables

Unmanned aerial vehicles (UAVs) have been increasingly utilized in various civilian and military applications such as remote sensing, border patrolling, disaster monitoring, and communication coverage extension. However, there are still prone to several cyber attacks such as GPS spoofing attacks, distributed denial-of-service (DDoS) attacks, and man-in-the-middle attacks to obtain their collected information or to enforce the UAVs to perform their requested actions which may damage the UAVs or their surrounding environment or even endanger the safety of human in the operation field. In this paper, we propose a trust monitoring mechanism in which a centralized unit (e.g. the ground station) regularly observe the behavior of the UAVs in terms of their motion path, their consumed energy, as well as the number of their completed tasks and measure a relative trust score for the UAVs to detect any abnormal behaviors in a real-time manner. Our simulation results show that the trust model can detect malicious UAVs, which can be under various cyber-security attacks such as flooding attacks, man-in-the-middle attacks, GPS spoofing attack in real-time.

A fundamental challenge for running machine learning algorithms on battery-powered devices is the time and energy limitations, as these devices have constraints on resources. There are resource-efficient classifier algorithms that can run on these devices, but their accuracy is often sacrificed for resource efficiency. Here, we propose an ultra-low power classifier, SEFR, with linear time complexity, both in the training and the testing phases. SEFR is comparable to state-of-the-art classifiers in terms of classification accuracy, but it is 63 times faster and 70 times more energy efficient than the average of state-of-the-art and baseline classifiers on binary class datasets. The energy and memory consumption of SEFR is very insignificant, and it can even perform both train and test phases on microcontrollers. To our knowledge, this is the first multipurpose classification algorithm specifically designed to perform both training and testing on ultra-low power devices.