Miracle-dz/UniFAR-FTUs huggingface dataset:
A Distributed Framework for EA-Based NAS

Evolutionary Algorithms (EA) are widely applied in Neural Architecture Search (NAS) and have achieved appealing results. Different EA-based NAS algorithms may utilize different encoding schemes for network representation, while they have the same workflow. Specifically, the first step is the initialization of the population with different encoding schemes, and the second step is the evaluation of the individuals by the fitness function. Then, the EA-based NAS algorithm executes evolution operations, e.g., selection, mutation, and crossover, to eliminate weak individuals and generate more competitive ones. Lastly, evolution continues until the max generation and the best neural architectures will be chosen. Because each individual needs complete training and validation on the target dataset, the EA-based NAS always consumes significant computation and time inevitably, which results in the bottleneck of this approach. To ameliorate this issue, this article proposes a distributed framework to boost the computation of the EA-based NAS algorithm. This framework is a server/worker model where the server distributes individuals requested by the computing nodes and collects the validated individuals and hosts the evolution operations. Meanwhile, the most time-consuming phase (i.e., individual evaluation) of the EA-based NAS is allocated to the computing nodes, which send requests asynchronously to the server and evaluate the fitness values of the individuals. Additionally, a new packet structure of the message delivered in the cluster is designed to encapsulate various network representations and support different EA-based NAS algorithms. We design an EA-based NAS algorithm as a case to investigate the efficiency of the proposed framework. Extensive experiments are performed on an illustrative cluster with different scales, and the results reveal that the framework can achieve a nearly linear reduction of the search time with the increase of the computational nodes. Furthermore, the length of the exchanged messages among the cluster is tiny, which benefits the framework expansion.
On the Use of Textual and Visual Data from Online Social Networks for Predicting Community Health

Social media has been increasingly utilized as a powerful sensor for a wide range of healthcare applications from infectious disease monitoring, psychiatric disorder detection to public health prediction. However, most existing social media-based approaches have been so far based on textual data mining, while imagery information, that has been shown as an effective indicator of expression, status and experiences, is still under-explored. In this paper, we investigate the use of two typical data sources from social media: text and images, in prediction of population health outcomes. Specifically, we propose two types of population health representations extracted from social media photos, in particular color histograms - a hand-crafted visual feature set, and automatic features learned from a deep convolutional neural network. For evaluation, we benchmark the proposed visual feature sets and existing well-known textual features including language style features and content-based features. To deal with the problem of weakly-labeled data, the multi-instance learning technique was applied to reduce probable classification bias. These features are evaluated in the task of the U.S. county health outcome prediction based on a large-scale dataset collected from Flickr, and the Behavioral Risk Factor Surveillance System. We have found that imagery information, that has been investigated in studies on emotion analysis, is also an informative indicator for population health outcomes. These experiments along with in-depth analysis will serve as a technical documentation for future research on population health analysis through the lens of social media.
Alternating Direction Method of Multipliers for TOA-Based Positioning Under Mixed Sparse LOS/NLOS Environments

For positioning system based on wireless sensor networks, NLOS errors are one of the main factors to degrade localization performance of an algorithm, about which lots of research results and analysis have been published in previous literatures to enhance localization performance. However, those literatures have neglected computational time, another important index to performance. To decrease computational time and improve localization accuracy simultaneously, we firstly consider NLOS errors as outliers and transform the TOA-based localization problem into a sparse optimization one in LOS-dominating environment. Then, we introduce sparse technology into line-of-sight/none-line-of-sight (LOS/NLOS) scenarios formulating a L1-norm minimization problem, and solve it by alternating direction method of multipliers (ADMM) with a strategy of iterative adaptive. Monte Carlo simulation results show that our method has advantages of high computation speed and positioning accuracy under mixed sparse LOS/NLOS scenarios.
Sustainable Supply Chain Management Practices and Firm Performance: Evidence from Small and Medium Enterprises of Pakistan

The purpose of this research is to demonstrate if sustainable supply chain practices can lead to a positive corporate performance, in recent research internal efficiency has been emphasized over external effectiveness. This research explores two internal and two external management variables. Furthermore, the implications of Sustainable Supply Chain Management incorporation with regard to its related internal and external practices have not been explored in the literature with regards to developing countries and small and medium enterprises context. It is also noted that recognition of the economic benefits obtained from sustainability programs implemented can have managerial and practical implications but needs more evidences. Structural equation modelling approach is used for analysis whereby a theoretical model is adapted from previously published research. A purposive multi-industry approach to sampling was adopted to select environment friendly firms. After validity and reliability of data, the sample equation was analyzed using structural modelling for relationship effects. Firms can learn from the results to align their stance towards sustainable advancements as a competitive advantage.
Improvement of Autonomous Vehicles Trust Through Synesthetic-Based Multimodal Interaction

Trust is the key factor for people to accept autonomous vehicles(AVs). Existing studies have reported that multimodal interaction would enhance people’s trust in AVs. However, these researches mainly focus on the superposition effect between sensory channels, and lack on the research of correlation between different sensory channels and its influence on AVs trust. Therefore, we innovatively introduce synesthesia theory for the research of improving AVs trust. We present an AVs multimodal interaction model based on audio-visual synesthesia theory, and finally prove that the model has a definite effect on improving AVs trust by experiments. Firstly, 82 participants are recruited and assigned into two groups: Group A (non-synesthesia group) and Group B (synesthesia group). They conduct an experimental driving experienced normal traffic conditions (NTC) (turning, traffic lights, over and limit speed prompts) and emergency traffic condition (ETC) (sudden braking of the car in front, temporary lane change, pedestrian thrusting) while completing a secondary task. Then, we conduct a survey (questionnaire and interviews) to evaluate the attitude about trust, technical competence, situation management and perceived ease of use after participants finished experimental driving. The results demonstrate that synesthetic-based multimodal interaction (SBMI) can more effectively remind people of relevant information especially under ETC. SBMI model is more effective than single information stimulus or non-synesthetic audio-visual information stimulus not only in terms of information transmission efficiency and effect, but also in terms of output response/ action. The results also show that SBMI contributes to the improvement of AVs trust. These findings provide evidence on the importance of SBMI to the improvement of AVs trust. The findings of this study will be helpful to the future design of AVs interaction system.
Design of a Portable Photovoltaic I–V Curve Tracer Based on the DC–DC Converter Method

The I–V curve tracer is an instrument that captures the current–voltage characteristics of photovoltaic (PV) generators under real operating conditions. Such device can be used to evaluate their performance and identify faults or aging effects, but its high costs have prevented it from being widely used in the industry. This article thus proposes the design of a low-cost, portable I–V curve tracer based on the classic dc–dc Ćuk converter for PV modules of up to 300 W. In order to obtain a compact device, some design techniques are proposed, including circuit reduction, high switching frequency operation, specification of the optimum load resistor power rating, and improved measurement distribution because of a nonlinear duty cycle variation. Experimental curves were compared with those acquired from commercial PV tracers to demonstrate its accuracy. In addition, the physical parameters of the well-known single-diode model have been extracted and compared with data provided by manufacturers to identify aging effects and module performance.
Sustainable framework of Lean manufacturing implementation during supporting programs

According to several research the implementation of lean brings important profits consequently progressively more Moroccan SMEs are oriented to pick this assistance from national supporting programs; however, the presence of issues prohibits the beneficiation and causes the drop of implemented practices. This article aims to investigate the status of some Moroccan small and medium enterprises SMEs which adopt the proposed program of Moroccan initiative of performances improvements INMAA by the implementation of lean manufacturing. The results show the level of each practice and the faced issues that could be avoid by the proposed framework of supporting programs for a sustainable lean manufacturing implementation in SMEs.
DRF: Disentangled Representation for Visible and Infrared Image Fusion

In this article, we propose a novel decomposition method by applying disentangled representation for visible and infrared image fusion (DRF). According to the imaging principle, we perform the decomposition depending on the source of information in the visible and infrared images. More concretely, we disentangle the images into the scene- and sensor modality (attribute)-related representations through the corresponding encoders, respectively. In this way, the unique information defined by the attribute-related representation is closer to the information captured by each type of sensor individually. Thus, the problem of inappropriate extraction of unique information can be alleviated. Then, different strategies are applied for the fusion of these different types of representations. Finally, the fused representations are fed into the pretrained generator to generate the fusion result. The qualitative and quantitative experiments on the publicly available TNO and RoadScene data sets demonstrate the comparable performance of our DRF over the state of the art in terms of both visual effect and quantitative metrics.
In-depth Life Cycle Cost Analysis of a Li-ion Battery-based Hybrid Diesel-Electric Multiple Unit

This study analyzes the life cycle costs of railway projects involving hybrid diesel-electric multiple units, focusing on the influence of lithium-ion battery technologies and energy management strategies. Specifically, 3 lithium-ion battery technologies and 6 energy management strategies are proposed, leading to a sensitivity analysis composed of 18 cases. In addition, for each case an approach for the optimal sizing of the diesel generator and lithium-ion battery is proposed. A scenario based on a real railway line is introduced and the results are compared to a traditional diesel-electric multiple unit. Potential life cycle cost savings of 16.0% are obtained when deploying a global optimization-based energy management strategy and LTO batteries.
Synthesis and Structural Characteristics of Organic Aerogels with Different Content of Lignin

Lignin can be used as a cheap raw material to prepare organic and carbon aerogels based upon the sol-gel reactions. Different concent lignin organic aerogels have been prepared by sol–gel polymerization of formaldehyde with resorcinol, following by the process of RF-gel production and subsequent freeze drying. The resulting materials were characterized by FT-IR analysis, nitrogen adsorption, transmission electron microscopy and Scanning electron micrographs. The structural properties organic aerogel depend on the amount of lignin added to the sol-gel system. and the LRF aerogels obtained in the experimental have an open cell structure with continuous porosity and homogeneous spherical particles.
A Dataset and Benchmark Towards Multi-Modal Face Anti-Spoofing Under Surveillance Scenarios

Face Anti-spoofing (FAS) is a challenging problem due to complex serving scenarios and diverse face presentation attack patterns. Especially when captured images are low-resolution, blurry, and coming from different domains, the performance of FAS will degrade significantly. The existing multi-modal FAS datasets rarely pay attention to the cross-domain problems under deployment scenarios, which is not conducive to the study of model performance. To solve these problems, we explore the fine-grained differences between multi-modal cameras and construct a cross-domain multi-modal FAS dataset under surveillance scenarios called GREAT-FASD-S. Besides, we propose an Attention based Face Anti-spoofing network with Feature Augment (AFA) to solve the FAS towards low-quality face images. It consists of the depthwise separable attention module (DAM) and the multi-modal based feature augment module (MFAM). Our model can achieve state-of-the-art performance on the CASIA-SURF dataset and our proposed GREAT-FASD-S dataset.
Gradients in N‐cycling attributes along forestry and agricultural land‐use systems are indicative of soil capacity for N supply

Indicators of soil quality associated with N‐cycling were assessed under different land‐use systems (native forest – NAT, reforestation with Araucaria angustifolia or Pinus taeda and agricultural use – AGR) to appraise the effects on the soil potential for N supply. The soil total N ranged from 2 to 4 g/kg (AGR and NAT, respectively), and the microbial biomass N ranged from 80 to 250 mg/kg, being higher in NAT and A. angustifolia, and lower in P. taeda and AGR sites. Activities of asparaginase (ca. 50–200 mg NH4+‐N/kg per h), glutaminase (ca. 200–800 mg NH4+‐N/kg per h) and urease (ca. 80–200 mg NH4+‐N/kg/h) were also more intense in the NAT and A. angustifolia‐reforested soils, indicating greater capacity for N mineralization. The NAT and AGR soils showed the highest and the lowest ammonification rate, respectively (ca. 1 and 0.4 mg NH4+‐N/kg per day), but the inverse for nitrification rate (ca. 12 and 26%), indicating a low capacity for N supply, in addition to higher risks of N losses in the AGR soil. A multivariate analysis indicated more similarity between NAT and A. angustifolia‐reforested sites, whilst the AGR soil was different and associated with a higher nitrification rate. In general, reforestation with the native species A. angustifolia had less impact than reforestation with the exogenous species P. taeda, considering the soil capacity for N supply. However, AGR use caused more changes, generally decrease in indicators of N‐cycling, showing a negative soil management effect on the sustainability of this agroecosystem.
The kinetics of hydrolysis

The rates of hydrolysis of methyl chloride, methyl bromide and methyl iodide have been measured in the absence of the vapour phase at 10° intervals between 30 and 90°C. In this region, the energy of activation at first decreases with a rise in temperature, and then passes through a minimum value. Taking the energy of crystalline methyl bromide at the absolute zero of temperature as being itself zero, the partial heat content of the critically activated solute has been evaluated, and found to be constant. The complicated course followed by the energy of activation appears to be due to the complex temperature variation of the average heat content of the normal solute, which, in the region examined, rises as the temperature is raised, and passes through a maximum value.
Electromagnetic Field Imaging in Arbitrary Scattering Environments

In this article, we propose a method to reconstruct the total electromagnetic field in an arbitrary two-dimensional scattering environment without any prior knowledge of the incident field or the permittivities of the scatterers. However, we assume that the region between the scatterers is homogeneous and that the approximate geometry describing the environment is known. Our approach uses field measurements and a compressive sensing inspired algorithm to estimate the incident field and the tangential electric and magnetic fields on the scatterers’ surfaces. These estimates are then used to predict the field everywhere using Huygens’ principle. Further, we identify the best measurement locations in the environment, which reduces the estimation error to approximately half of the error obtained when using random locations. We show that in an indoor scenario with up to four scattering objects, the total electric field is recovered with less than 10% error when the number of measurements is just 0.3 times the number of unknowns in which the problem is formulated. The formulated problem is solved using ‘Total field - Compressive sensing based subspace optimization method’ – an algorithm that leverages the sparsity of the tangential fields in known transformation domains to obtain an optimal solution.
Participation of Every Generator to Loads, Currents, and Power Losses

Among other things, in a detailed analysis of circumstances in power systems of great importance is the knowledge on contribution of individual generators to loads, and to currents and power losses in network elements. This work proposes an efficient method for obtaining these details. The method uses results of a power flow calculation, utilizes specific power system model, and applies the superposition theorem. The method does not contain problematic assumptions or simplifications and it is easy to apply. It allows getting important facts enabling us to establish transparent and non-discriminatory relationships between network operators, electricity producers and consumers.
Grid Impact of Electric Vehicle Fast Charging Stations: Trends, Standards, Issues and Mitigation Measures - An Overview

With growing concern on climate change, widespread adoption of electric vehicles (EVs) is important. One of the main barriers to EV acceptance is range anxiety, which can be alleviated by fast charging (FC). The main technology constraints for enabling FC consist of high-charging-rate batteries, high-power-charging infrastructure, and grid impacts. Although these technical aspects have been studied in literature individually, there is no comprehensive review on FC involving all the perspectives. Moreover, the power quality (PQ) problems of fast charging stations (FCSs) and the mitigation of these problems are not clearly summarized in the literature. In this paper, the state-of-the-art technology, standards for FC (CHAdeMO, GB/T, CCS, and Tesla), power quality issues, IEEE and IEC PQ standards, and mitigation measures of FCSs are systematically reviewed.
Quantification of Single-Cell Cortical Tension Using Multiple Constriction Channels

This article presents a microfluidic system with multiple constriction channels in parallel capable of characterizing cortical tension of single cells in a continuous manner. Single cells are forced to travel through constriction channels (cross-sectional area smaller than single cells) in a quasi-static manner with front/rear membrane curves of deformed cells captured. Then the front/rear membrane curves are translated into cortical tension based on a home-developed mechanical model illustrating the relationship among cortical tension, cell deformation, aspiration pressure and geometrical parameters of constriction channels. Based on this microfluidic platform, cortical tension with sample sizes as large as thousands of single HL-60 cells were quantified for the first time where a variety of experimental conditions were used for comparison. Specifically, comparable values of cortical tension were obtained from both front and rear membrane portions of single cells while aspiration pressure rather than channel length can affect the quantified values of cortical tensions to an extent. As a demonstration, the microfluidic system was used to process HL-60 cells under a variety of cell treatments, producing higher cortical tension for the cells treated with paraformaldehyde for fixation and lower cortical tension for the cells treated with cytochalasin D for cytoskeleton compromise in comparison to wild-type counterparts. In summary, the developed microfluidic system can quantify cortical tension from single cells in a continuous fluid flow, which may function as an enabling tool in the field of single-cell analysis.
Hot-Probe Method for Majority Charge Carrier Determination in Methylammonium Lead Halide Perovskites

Perovskites, due to their promising capabilities, are among the major candidates to substitute early-generation silicon solar cells. Despite their outstanding electrical and optical properties, actual comprehension of their behavior requires further studies and also investigation tools. It is shown that vacancy traps in organic–inorganic perovskites induce different polarities, where lead and methylamine vacancies posses p- and n-type polarity, respectively. Referred to as the self-doping property, different molar ratios of chemical precursors lead to controllable polarity and intrinsic doping. In other reports, thermal annihilation is also shown to convert p-type perovskite to n-type. According to the broad range of synthesized perovskites and their current application and promising future in optoelectronic devices, a simple, quick, and dependable method for trap concentration and major carrier-type determination is essential. Hot probe is a simple, affordable, and fast method for extracting polarity type in bulk and thin-film semiconductors. So far, this method and its variations have been applied for measuring conductivity, polarity, and impurity concentration in diverse ranges of semiconductors. In this method, free carriers are generated by raising the temperature of hot-probe contact and induced diffusion from this contact toward the other cold contact is studied. An equilibrium state is reached as a result of the formed built-in electric field between the cold and hot electrodes and also temperature elevation of this cold electrode, which act as diffusion dampers. Here, different types of organic–inorganic perovskites are synthesized and their electrical behavior is evaluated by the hot-probe method. Finally, it is concluded that the hot-probe approach is a dependable, fast, and accurate method to be applied in the field of perovskite material evaluations.