Miracle-dz/UniFAR-FTUs huggingface dataset:
A Port-Hamiltonian Approach to Complete Vehicle Energy Management: A Battery Electric Vehicle Case Study

In this paper, we present a modelling approach to vehicle energy management based on Port-Hamiltonian systems representations. We consider a network of interconnected port-Hamiltonian systems that describes the powertrain components and auxiliaries in the vehicle. This description is suitable to obtain a systematic approach to formulate a decomposable optimal control problem for Complete Vehicle Energy Management. A physically insightful cost function that describes the total energy consumption of the vehicle is proposed in terms of internal energy and losses of each system connected to the network. Taking advantage of the modularity of the proposed formulation, we present a distributed optimization algorithm to find solutions to the energy management problem. To illustrate this modelling methodology, we consider a case study in which the energy consumption of a battery electric vehicle is optimized.
Impact of Azimuth and Elevation Half Power Beam Width on Human Blockage scenarios in mmWave Channels

The use of millimeter wave (mmWave) is the most important candidate for the next generation of mobile wireless networks. A recent research study has shown that millimeter wave frequencies are affected by “blockage” which caused by obstacles such as humans, vehicles, buildings. However, there is a lack of analytical models to characterize these blockages and the resulting effect on signal reliability. In this work, we have investigated and analyzed the effect of human blockage in the case of 28, 38, 60 and 73 GHz of mmWave channels for different scenarios, mainly for different TX/RX antenna HPBW (half power beam width) of 7°, 10°,15°, 30° in azimuth/Elevation with the NYUSIM channel models which is developed by researchers at New York University (NYU).
Specificity Analysis of Genome Based on Statistically Identical K-Words With Same Base Combination

Goal: Individual characteristics are determined through a genome consisting of a complex base combination. This base combination is reflected in the k-word profile, which represents the number of consecutive k bases. Therefore, it is important to analyze the genome-specific statistical specificity in the k-word profile to understand the characteristics of the genome. In this paper, we propose a new k-word-based method to analyze genome-specific properties. Methods: We define k-words consisting of the same number of bases as statistically identical k-words. The statistically identical k-words are estimated to appear at a similar frequency by statistical prediction. However, this may not be true in the genome because it is not a random list of bases. The ratio between frequencies of two statistically identical k-words can then be used to investigate the statistical specificity of the genome reflected in the k-word profile. In order to find important ratios representing genomic characteristics, a reference value is calculated that results in a minimum error when classifying data by ratio alone. Finally, we propose a genetic algorithm-based search algorithm to select a minimum set of ratios useful for classification. Results: The proposed method was applied to the full-length sequence of microorganisms for pathogenicity classification. The classification accuracy of the proposed algorithm was similar to that of conventional methods while using only a few features. Conclusions: We proposed a new method to investigate the genome-specific statistical specificity in the k-word profile which can be applied to find important properties of the genome and classify genome sequences.
DRA-IG: The Balance of Performance Isolation and Resource Utilization Efficiency in Network Slicing

Network slicing (NS) is a promising technology of 5G that provides customized end-to-end network service to multi-tenant. How to improve resource utilization efficiency with guarantee of performance isolation in a shared infrastructure is one of the main challenges in resource allocation problem of NS. To address this challenge, we characterize the degree of performance isolation based on the relationship between the requested resource amount, the allocated resource amount, and the time-varying network loads. We propose a dynamic resource allocation problem with probabilistic isolation guarantee (DRAIG), which is formulated as a chance constrained program. As the true probability distribution of network loads is usually unknown, we use the Conditional Valuate-at-Risk (CVaR) measure to provide a distributionally robust formulation that approximate the basic chance constraints of DRA-IG in a data-driven manner. We estimate the second-order moment of network loads by the periodic history information. Then, we further reformulate the distributionally robust optimization problem as a tractable semidefinite programming (SDP). Finally, numerical evaluation verifies the effectiveness of the proposed method.
Congestion Detection and Control for 6TiSCH Networks in IIoT Applications

In the context of Industrial Internet of Things (IIoT), the 6TiSCH working group has been created with the aim to enable IPv6 over the IEEE 802.15. 4e Time-Slotted Channel Hopping (TSCH) mode. The Routing Protocol for Low power and lossy networks (RPL) is introduced as the de-facto routing protocol for 6TiSCH networks. However, RPL is primarily designed to handle moderate traffic loads, whereas, during specific events in industrial applications, high traffic rates cause congestion problems at particular intermediate nodes while other nodes are underutilized. Accordingly, packets are dropped due to buffer overflow, which in turn degrades the network performance in terms of packet loss and delay. In this paper, we introduce a congestion detection and control mechanism to reliably handle high traffic load in 6TiSCH networks. The proposed method comprises two parent selection mechanisms to adapt to dynamic traffic load in the network. Congestion is detected through monitoring of the queue backlog level of each node and new parent nodes are selected accordingly to balance the load in the network. Moreover, a new routing metric is defined that considers the queue occupancy while selecting the new parent node. Performance evaluations are carried out to prove the effectiveness of the proposed method and the results show that with a marginal increase in the average delay, our proposal improves the performance of the standard RPL under heavy traffic load conditions by at least 60% and 74% in terms of the packet delivery and queue loss, respectively.
Design of Frequency-Reconfigurable Triband Dipole Antenna Using Capacitive Loading

In this work, a novel frequency-reconfigurable printed dipole antenna is reported. By embedding a capacitor in the capacitively loaded loop (CLL), we can easily generate and control an additional resonant frequency F3 besides the main resonant frequency of the basic dipole antenna F1= 2.3GHz (LTE band) and the CLL element F2 = 1.51GHz (GPS band). The proposed antenna achieves a frequency reconfigurable band with tuning range from 1.7 to 2.03 GHz (AWS, DCS and UMTS bands) when varying the capacitance values from 2 to 8pF with good gain and efficiency. The presented antenna has a compact size of 40 × 70 mm2. The proposed design is validated by simulations, displaying good results of impedance mismatch, gain pattern and radiation efficiency. The reconfigurability, range tuning and compact size makes the presented antenna well suitable for the future 5G systems to achieve dynamic spectrum access and deal with the spectrum shortage problem.
Performance Portability Evaluation of OpenCL Benchmarks across Intel and NVIDIA Platforms

We evaluate the capabilities of vendor-provided OpenCL implementations for performance portability across multiple computing platforms. The Rodinia benchmark suite is used for this evaluation. We apply the metric defined by Pennycook et al., and we use roofline efficiency from the Roofline performance model as the “performance efficiency” in the metric’s definition. We found that the delivered performance portability is similar for several benchmarks, even if the roofline-based performance efficiencies across platforms are very different among the benchmarks. To help distinguish between these instances, we extend the metric by adding the standard deviation of the performance efficiencies for each benchmark. We argue that the standard deviation gives additional insight into performance portability assessment since it adds the performance variability across platforms. Additionally, we discuss the challenges to measure performance portability associated with algorithms and system software. In terms of algorithms, we need to carefully construct the benchmarks and appropriately use the concurrency available on a platform. In terms of system software, we depend on the vendor performance tools to support the desired programming model and runtime to be able to measure the metrics of interest.
Deviated Pursuit based Cooperative Simultaneous Interception against Moving Targets

This work focuses on achieving cooperative simultaneous interception against moving targets using the concepts of deviated pursuit guidance strategy. Unlike most existing salvo guidance strategies which use estimates of time-to-go, based on proportional navigation guidance, the present strategy uses exact expression for time-to-go to ensure simultaneous interception. The guidance command is derived considering nonlinear engagement kinematics. Weighted consensus in time-to-go over a pseudo-undirected graph is used to intercept a moving target cooperatively. It has been shown that through a judicious choice of these weights, the achievable set of interception times expands. Simulations are provided to vindicate the effectiveness of the proposed strategy.
A New Algorithm for Social Inference Using Position Information

With the development of relationship inference technology, the available social data increases with the popularity of Internet and information technology. This paper focuses on the social relation, which is one of the most sensitive information that can be inferred from the association between location data and user data. Besides, the paper proposes a new random walk method based on node weight, which relies on PageRank(PR) to reallocate node weight and limit the range of random walk to improve the accuracy of prediction. Compared with the existing optimal method, the proposed inference method uses SkipGram neural network model to embed the graph. Finally, a holistic relationship mining algorithm based on node weight is proposed. The advantages of this algorithm can be effectively applied to scenarios with high accuracy requirements for relationship inference. The node PR value is used to constrain the random walk range in the selection of the first step random walk link, which effectively solves the problem of graph embedding based on the current situation. Extensive experiments are performed on the real datasets of three different cities to acquire the performance of the proposed algorithm. The evaluations show that the new algorithm improves the accuracy of relationship prediction in the field of relationship inference.
Interocular ND filter suppression: Eccentricity and luminance polarity effects

The depth and extent of interocular suppression were measured in binocularly normal observers who unilaterally adapted to neutral density (ND) filters (0, 1.5, 2, and 3 ND). Suppression was measured by dichoptically matching sectors of a ring presented to the adapted eye to a fixed contrast contiguous ring presented to the non-adapted eye. Other rings of alternating polarity were viewed binocularly. Rings were defined by luminance (L), luminance with added dynamic binary luminance noise (LM), and contrast modulating the same noise (CM). Interocular suppression depth increased with increasing ND, nearing significance (p = 0.058) for 1.5 ND. For L and LM stimuli, suppression depth across eccentricity (±12° visual field) differed for luminance increment (white) versus luminance decrement (black) stimuli, potentially confounding eccentricity results. Suppression for increment-only (white) luminance stimuli was steeper centrally and extended across the visual field, but was deeper for L than for LM stimuli. Suppression for decrement-only (black) luminance stimuli revealed only central suppression. Suppression was deeper with CM than LM stimuli, suggesting that CM stimuli are extracted in areas receiving predominantly binocular input which may be more sensitive to binocular disruption. Increment (white) luminance stimuli demonstrate deeper interocular suppression in the periphery than decrement (black) stimuli, so they are more sensitive to changes in peripheral suppression. Asymmetry of suppression in the periphery for opposite polarity luminance stimuli may be due to interocular receptive field size mismatch as a result of dark adaptation separately affecting ON and OFF pathways. Clinically, measurement of suppression with CM stimuli may provide the best information about post-combination binocularity.
Decision Tree and Support Vector Machine for Anomaly Detection in Water Distribution Networks

Drinking water quality monitoring is essential these days as the available water may be affected by pollution and can cause several diseases. Hence, it's necessary to prevent any intrusion into water distribution systems and to detect pollution momentarily. To resolve concerns on intrusion detection we have various machine learning algorithms for classification but choosing the best one is an important task. For selecting the best algorithm for our water quality monitoring system, we conducted an experimental study on machine learning algorithms. In this experimental study, we analyzed the performance of the famous classification algorithms in the literature namely Decision Tree and Support Vector Machines using a real dataset retrieved from a Tunisian water treatment station.
A Loewner Matrix Based Convex Optimization Approach to Finding Low Rank Mixed Time/Frequency Domain Interpolants

We consider the problem of finding the lowest order stable rational transfer function that interpolates a set of given noisy time and frequency domain data points. Our main result shows that exploiting results from rational interpolation theory allows for recasting this problem as minimizing the rank of a matrix constructed from the frequency domain data (the Loewner matrix) along with the Hankel matrix of time domain data, subject to a semidefinite constraint that enforces stability and consistency between the time and frequency domain data. These results are applied to a practical problem: identifying a system from noisy measurements of its time and frequency responses. The proposed method is able to obtain stable low order models using substantially smaller matrices than those reported earlier and consequently in a fraction of the computation time.
Influence of Drought on Mitochondrial Activity, Photosynthesis, Nocturnal Acid Accumulation and Water Relations in the CAM Plants Prenia sladeniana(ME-type) and Crassula lycopodioides(PEPCK-type)

Abstract The activity of photosynthesis and mitochondrial respiration, nocturnal organic acid accumulation and water relations were investigated in Prenia sladeniana L. Bol. [malic enzyme (ME)-type] and Crassula lycopodioides Lam. [phosphoenolpyruvate carboxykinase (PEPCK)-type] to compare the physiological responses to water deficit in crassulacean acid metabolism (CAM) plants differing in their decarboxylating enzyme systems. Withholding water inhibited daytime gas exchange within 2 d while night time CO 2 gain and malic acid accumulation remained relatively unchanged in both species. In P. sladeniana , maximum photochemical efficiency (F v /F m ) and photosynthetic electron transport declined to nearly the same degree as CO 2 supply was restricted during drought. Despite limited CO 2 availability, photosynthetic activity was largely unaffected in C. lycopodioides , as were mitochondrial properties. There is no indication of a drought-induced increase in the capability to totally oxidize malate, yielding 4 CO 2 , in either species. Nevertheless, the enhanced ratio of malate to glycine oxidation may have increased the in vivo capability for malate oxidation in P. sladeniana . Although pressure potential was maintained throughout the experiment in both species, active osmotic adaptation occurred only in P. sladeniana. The observed decrease in photosynthetic and mitochondrial activity may have resulted from the large increase in osmotic concentration in this species.
Inter‐specific and intra‐specific trait variation along short environmental gradients in an old‐growth temperate forest

Question When can we assume that inter-specific trait variation is higher than intra-specific trait variation in plant community ecology? Location Old-growth deciduous forest in the Gault Nature Reserve, Mount St. Hilaire (Quebec, Canada,45′32.957″ N, 73′08.884″ W), with a shorter environmental gradient than normally exists in community studies. Methods We measured 15 functional traits on all tree saplings occurring in 39 (5–7-m radius) sample plots. Using variance decomposition from mixed models and from sums of squares of community-weighted traits, we determined the relative importance of temporal, spatial, inter-specific, intra-specific and intra-individual variation in each trait. In total, we collected trait information on 3317 leaves from 786 twigs sampled on 422 saplings. Results For 12 of 15 traits, over 50% of the total variance existed between species and inter-specific variation was always the most important source of variation. However for 14 traits, intra-specific and environmental variation represented up to 28% of the total variation. Variation in community-weighted trait means was mostly generated by changes in species composition, but intra-specific trait variation was the dominate cause for leaf nitrogen, specific leaf area and tree branching. The intra-individual and temporal sources of variation were not important. Conclusions Trait variation should be dominated by inter-specific differences in most studies since they involve systems with more pronounced environmental gradients, higher species richness and more β-diversity than used here. However, in studies with short gradients, or when using more plastic traits, it will be necessary to measure trait values for each site. It is important to quantify the β-diversity of the environmental gradients in question in order to compare results across studies.
DWT-based Wind Speed Forecasting Using Artificial Neural Networks in the region of Annaba

Global demand for electrical energy is in constant increase all over the world, leading to new kinds of energy from renewable resources, namely solar and wind power. Consequently, precise wind prediction is very important for efficient management of grid-connected wind farms. This article examines the use of wavelet analysis combined with neural networks to predict wind speed. The wavelet transform is employed to smooth the wind speed time series for better prediction using neural networks. Using the wind speed data of the region of Annaba situated in the east of Algeria, the obtained results show the db4 wavelet with 5-level decomposition outperforms all other wavelet families in terms of forecasting accuracy.
Work in progress: Co-creation as a method for the identification of new functionalities for the customization of students learning styles for virtual learning platforms

Currently, there are many shortcomings in educational methodologies in a specific area. Several studies have found that this deficiency is since educators implement a single learning style to a population ignoring that each individual has different cognitive strategies in terms of learning. For this reason, a research team from University of Medellin established a strategic alliance with KUEPA and its educational platform, in which the aim is to restructure that educational platform to be able to intelligently assign a specific learning style to different students that interact with it. For the selection of the best methodology to accomplish that goal, several co-creation sessions were organized with the actors involved in the research study. As a result of the sessions, a set of new functionalities were proposed to be integrated into the platform. In addition, artificial intelligence has been found to be a key element to achieve the purpose of this research study under adaptive components based on learning methodologies.
Biological Therapies that Target Inflammatory Cytokines to Treat Uveitis

Uveitis is a leading cause of blindness that presents a considerable challenge given that our understanding of the mechanisms of disease is still evolving. Both innate and adaptive immunity play a role in disease and mediators of these responses can serve as therapeutic targets. TNF- α and IL-1 β inflammatory cytokines are central mediators of immunity and are involved in the dysregulated inflammatory response during uveitis. Because toxicity limits the use of steroids and other steroid-sparing agents, biologics that target a specific cell type or pathway are being explored for the treatment of autoimmune uveitis. This chapter begins with a broad overview of the aberrant immune response resulting in uveitis, and highlights key mediators such as TNF- α , IL-1 β , IL-6, and IL-17 and their potential use as therapeutic targets. Most biological agents discussed in this review have not been FDA-approved for uveitis. However, favorable outcomes in early trials and FDA approval of these drugs for the treatment of other autoimmune diseases associated with uveitis support the potential for these biological agents in the management of uveitis. This review aims to provide an updated report on the efficacy of biologics that target TNF- α , IL-1 β , IL-6, and IL-17 for the treatment of autoimmune uveitis.
YouTube Data Collection Using Parallel Processing

Several studies have identified social media platforms as significant data sources to study human behaviors and gain situational awareness about various events or crises. YouTube, being one of the largest social media platforms, provides a Data API that enables data collection on YouTube channels and videos which can be used in these studies. Current sequential methods for processing YouTube Data API requests are time consuming. In this paper we developed an implementation that utilizes Python’s multiprocessing to process YouTube Data API request in parallel. Our tests indicate multiprocessing improves the performance by 400%. These improvements reduce computation time through utilization of multi-threaded CPU architecture.
Semi-global state synchronization for multi-agent systems subject to actuator saturation and unknown nonuniform input delay

This paper studies semi-global state synchronization of homogeneous networks with diffusive full-state coupling subject to actuator saturation and unknown nonuniform input delay. We assume that agents are at most critical unstable, that is the agents have all its eigenvalues in the closed left-half complex plane. The communication network is associated with an undirected and weighted graph. In this paper, we derive an upper bound for the input delay tolerance, which explicitly depends on the agent dynamics. Moreover, for any unknown delay less than the upper bound, we propose a linear static protocol for MAS based on a low-gain methodology such that state synchronization is achieved among agents for any initial conditions in a priori given compact set.