Miracle-dz/UniFAR-FTUs huggingface dataset:
Fluctuating Asymmetry as a Method of Assessing Environmental Stress in Two Predatory Carabid Species within Mediterranean Agroecosystems

Fluctuating asymmetry (FA) is used in assessing the effect of environmental stress on the development stability of individuals by measuring small random deviations from perfect bilateral symmetry. Here, we checked for FA on two predatory carabid beetles, Pterostichus melas and Poecilus koyi, in order to evaluate species response to agricultural practices within Mediterranean agroecosystems, as well as FA as a method. The samples were collected in vineyards and olive groves, both under integrated pest management (IPM) and ecological pest management (EPM), and in pristine habitats in the Mediterranean region of Croatia. Geometric morphometrics (GMMs) were used to analyze the pronotum and abdomen shape variations and left–right asymmetries of each population. In respect to the FA measurements, analyzed species responded differently, with P. koyi displaying a lower intensity of FA than P. melas. On the other hand, P. melas beetles from vineyards showed a higher intensity of FA compared with populations from pristine habitats and olive groves. Accordingly, FA pointed out olive groves as potentially less adverse habitats to predatory carabids, keeping in mind the different levels of asymmetry between the two species. Our study singled out P.melas as a more suitable species for further research, in the effect that different agricultural practices can have their impact on non-target invertebrates analyzed by measuring the FA.
Iterative Receiver Design for Probabilistic Constellation Shaping in ISI Channel

This paper investigates the receiver design for probabilistic constellation shaping signaling over inter-symbol interference channel. The key component performing the constellation shaping is an adjustable distribution matcher, and the probabilistic shaping system is capable to adapt to variable data rates by adjusting the distribution match rate rather than the modulation or coding mode. In this paper, we resort to distinct techniques to derive two iterative receivers operating in time domain. Shaped-BCJR is a trellis-based solution where forward/backward algorithm together with turbo iteration is used to compute the posteriori probability in which the nonuniform a priori symbol probability initializes the calculation. Another receiver is based on linear filtering where expectation propagation provides a Gaussian approximation of the posteriori probability of each symbol. This receiver structure is represented by a factor graph and we detail the derivation of messages exchanging between adjacent nodes. The Bayesian equalization in each EP iteration brings unacceptable computational burden. An efficient block-wise matrix inversion strategy is proposed to tackle this problem, significantly reducing the computational complexity with little performance loss. Simulation results show that the proposed algorithms remarkably outperform LMMSE solutions and traditional EP based algorithms. Proposed matrix inversion strategy can also be used to improve the performance of other filter-type solutions.
First Demonstration of 4-Stacked Ge0.915Sn0.085 Wide Nanosheets by Highly Selective Isotropic Dry Etching with High S/D Doping and Undoned Channels

The undoped stacked GeSn channels without parasitic Ge channels are realized by a radical-based highly selective isotropic dry etching. Heavily doped Ge sacrificial layers can reduce S/D resistance and the undoped GeSn channels can increase the channel mobility. SS=89m V /dec and $\mathrm{I}_{\mathrm{O}\mathrm{N}}=42\mu 1$ per stack ($10.5\mu A$. per sheet) at $\mathrm{V}_{\mathrm{OV}}\mathrm{V}_{\mathrm{DS}}=-0.5\mathrm{V}$ are achieved for the undoped 4-stacked 12nm-thick nanosheets with 120nm gate length and the width larger than 50nm. The etching selectivity and the channel uniformity are highly improved by the dry etching as compared to H202 wet etching. Both dry etching and undoped channel are essential to obtain stacked wide nanosheets with high performance.
Deep Air Quality Forecasts: Suspended Particulate Matter Modeling With Convolutional Neural and Long Short-Term Memory Networks

Public health risks arising from airborne pollutants, <italic>e.g</italic>. , Total Suspended Particulate (<italic>TSP</italic>) matter, can significantly elevate ongoing and future healthcare costs. The chaotic behaviour of air pollutants posing major difficulties in tracking their three-dimensional movements over diverse temporal domains is a significant challenge in designing practical air quality systems. This research paper builds a deep learning hybrid <italic>CLSTM</italic> model where convolutional neural network (<italic>CNN</italic>) is amalgamed with the long short-term memory (<italic>LSTM</italic>) network to forecast hourly <italic>TSP</italic>. The <italic>CNN</italic> model entails a data processer including feature extractors that draw upon statistically significant antecedent lagged predictor variables, whereas the <italic>LSTM</italic> model encapsulates a new feature mapping scheme to predict the next hourly <italic>TSP</italic> value. The hybrid <italic>CLSTM</italic> model is comprehensively benchmarked and is seen to outperform an ensemble of five machine learning models. The efficacy of the <italic>CLSTM</italic> model is elucidated in model testing phase at study sites in Queensland, Australia. Using performance metrics, visual analysis of <italic>TSP</italic> simulations relative to observations, and detailed error analysis, this study ascertains the <italic>CLSTM</italic> model’s practical utility for air pollutant forecasting systems in health risk mitigation. This study captures a feasible opportunity to emulate air quality at relatively high temporal resolutions in global regions where air pollution is a considerable threat to public health.
Influence of various thickness values of the cladding and barrier layers on the performance of 462 nm In0.02Ga0.98N QW lasers

For 462 nm In0.02Ga0. 98N quantum well (QW) lasers, we analyzed, by means of simulations with LASTIP software, the influence of various thickness values of the cladding and barrier layers on the main performance parameters (threshold current density, slope efficiency, external differential quantum efficiency, radiative, Auger, Shockley-Read-Hall (SRH) and stimulated recombination and potential distribution in the structure) of such devices, with the purpose of finding out if these thickness variations have an effect on the performance of these lasers, if this effect consists in a performance degradation or improvement and for evaluating the extent of these structure performance variations.
Passenger Travel Behavior in Public Transport Corridor After the Operation of Urban Rail Transit: A Random Forest Algorithm Approach

A competitive relationship has been generated between urban rail transit and bus transit since the operation of the former. Despite different roles in providing services in public transport corridor, they affect each other in actual situations. In terms of urban transportation planning and policy formulation, it is necessary to explore and master the rules of passengers’ travel mode choice from different means. In order to study their choices between the rail transit and the bus transit after the operation of the former, taking Xiamen, China as an example, this article analyzed the overall travel features of passenger flow before and after the operation of rail transit by using the public transit IC card data from two consecutive weeks in November 2017 and November 2018. Some features of travel distance, travel time, travel cost, whether to travel in peak hours, the number of collinear stations between bus transit and rail transit or that of rail transit stations are sorted out. With random forest algorithm, a model is set up for the travel mode choice of passengers after urban rail transit is put into use to find out the impact of different travel features. The result shows that travel cost is the most crucial factor that affects passengers’ decisions, followed by the number of collinear stations between bus transit and rail transit or that of rail transit stations, travel time and travel distance. Whether to travel in peak hours have less impact on their choices. This study is constructive for cities in the stage of facing competition between newly opened rail transit and bus transit and support transportation decision-making.
Asymmetric Cascaded Multilevel Inverter with Capacitor-based Half-bridge Cells and Reduced Number of Components

This study proposes an asymmetric multilevel inverter (MI) entitled as the Asymmetric Cascaded Half-bridge MI (ACHB-MI). The proposed ACHB-MI is structured by cascading multiple capacitor-based half-bridge cells with nonequal dc sources and one ancillary cell. A half-bridge cell can only develop a specific voltage level with either a negative or positive polarity, and thus the cascaded half bridge cells can generate certain odd or even voltage levels. Consequently, an ancillary cell is added to generate the entire needed negative, positive and zero voltage levels. The proposed ACHB-MI reduces the total number of switches, the conduction power loss, and the cost. The performance and feasibility of the proposed ACHB-MI are approved through simulation and experimental verification.
Soft Open Point in Distribution Networks

The main objective of this article is to present a comprehensive review of soft open point (SOP), an emerging power electronics technology to maximize future distribution networks’ (DNs) resiliency and flexibility as well as increase hosting capacity for distributed energy resources like photovoltaics and electric vehicles. The SOP is currently an active area of research and ongoing development of new control techniques for SOP and optimization algorithm for the optimal use of SOP in DNs produces new techniques until DN operators use it comprehensively in their systems. The motivation for this work is to present the research that has been completed for the SOP and summarize the duties of SOP in DNs according to the literature and propose advanced duties for SOP according to modern standards. Finally, future research directions are mentioned to pave the way for research in the coming years to drive the DNs towards more flexibility and ‘Robust’ from controllability, stability, and protection structure point of view.
Proposal and Experimental Demonstration of Reservoir Computing using Hf0.5Zr0.5O2/Si FeFETs for Neuromorphic Applications

We propose a new AI calculation scheme by reservoir computing utilizing the memory effect and nonlinearity of ferroelectric gate MOSFETs (FeFETs) for neuromorphic applications. The task operations of time-series data are experimentally demonstrated by taking time responses of the drain current for gate voltage input as the virtual nodes. A high ability to classify input data is experimentally verified.
Distributed Constrained Optimization with Linear Convergence Rate

This paper considers the consensus optimization problems with identical convex constraint sets, via local computation and communication under an undirected graph. To solve the problem, we propose algorithm combining projection operation, gradient tracking technique and consensus method. With the help of the strong convexity assumption and l-smooth assumption, the proposed algorithm with fixed stepsize is proved to converge linearly to the optimal solution under a connected graph and an assumption on the communication weight matrix. We establish explicit theoretical estimates for the convergence rate. The results are also demostrated by numerical experiments.
Feasibility of Permanent Magnet Fault Tolerant Machines for Aircraft Starter/Generator Systems

With the development of the electrification of aircrafts, the demand for electrical power is growing. In order to reconcile the growing electrical power and the lightweight requirement of aircrafts, high power density permanent magnet starter-generator (PMSG) systems are considered as a competitive solution. By analyzing the requirements of the starter-generator (SG) systems and reviewing the state-of-the-art of permanent magnet fault tolerant (FT) machines, the topology feasibility of permanent magnet FT machines in safety-critical aerospace starter-generator systems are evaluated. As mature and compromise candidates, dual three-phase topology feasibility and fault tolerance of varied slot/pole combination have been investigated in this paper.
Comprehensive Classification of Internet Background Noise

Internet background noise (IBN, also known as Internet background radiation) is unsolicited network packets. For example, packets addressed to non-existing host. There are several reasons for background noises: untargeted scanning of global network in search of vulnerable hosts, network devices misconfiguration, backscatters of DDoS attacks with IP-spoofing technology, traces of worms propagation etc. We study background noises with «dark» collector – it is the trap that records all incoming packets and does not respond to them or do any other activity (term constructed in opposite to «honeypot»). In this research, we captured about ten millions packets of background noise. All captured packets was divided into groups according to character combination of flags and parameters in network, transport and application headers. For each group of packets the reason of appearing in the background noise was suggested and statistical characteristic was given. We suppose that different kinds of noises should be study separately. It will allow to study global networks threats trends by changing frequency of appearing packets from such groups. Based on the given classification, what may be developed are the dashboards for global network monitoring as well as the triggers of new kinds of attacks.
Heuristic-Based Address Clustering in Bitcoin

With the emergence of decentralized cryptocurrencies such as Bitcoin, it has become very difficult for law enforcement to detect suspicious activities, identify users and obtain transaction records for criminals who utilize the pseudoanonymity provided by the cryptocurrency system. Address clustering aims to break such pseudoanonymity by linking addresses that are controlled by the same user based on the information available from the blockchain, such as transaction graphs. There are already two widely used heuristics for Bitcoin address clustering. One is based on the multiple input addresses of transactions. The other is based on one-time change addresses. By reconsidering the one-time change address-based heuristic from the perspective of address reuse, we propose a new heuristic that detects one-time change addresses by eliminating addresses that are reused later as non-change addresses. As a result, this heuristic works for transactions whose one-time change addresses cannot be identified by the previous two heuristics. The experimental results for different scales of Bitcoin transaction data show that the proposed heuristic has a 0.33% mean contribution to the ratio of address reduction in addition to the contribution of the multiple input addresses and one-time change address heuristics.
3D Printing as a Technology Enabler for Electrical Machines: Manufacturing and Testing of a Salient Pole Rotor for SRM

Among the various technology enablers for modern electrical machines, additive manufacturing plays a key role. The advantage of having a precise control of the shape of ferromagnetic structures, whilst achieving good electromagnetic performance, fits well with the design requirements of rotating electrical machines. To a certain extent, some of the physical properties of the material can be “tuned”, allowing for quick trade-off studies (i.e., prototyping), as opposed to conventional manufacturing techniques. Despite being considered an enabling technology, 3D printing of soft magnetic materials for electric motors is still at an embryonic stage. This work, thus, aims in providing an initial proof of concept. For the purpose, a switched reluctance machine is chosen as a case study. Its rotor core is additively manufactured through selective laser melting. Its performances are compared to those of an identical commercial motor featuring a laminated rotor core, via in-depth experimental tests. Initial results show that the 3D printed machine can actually develop the rated power, but with an efficiency reduction.
Real-Time Simulation of Three-Phase Current Source Inverter using Sub-Cycle Averaging Method

Current Source Inverters (CSI) are used to drive permanent magnet synchronous machines (PMSM) in high power applications, such as wind power generation and hybrid vehicles. Compared to conventional Voltage Source Inverters (VSI) the modulation scheme for the CSI is more complex and can be difficult to test directly on real hardware prototypes. In Hardware-In-the-Loop (HIL) simulations, the actual plant is replaced with a dynamic model which allows to test the modulation and control scheme in a safe environment. Due to the big number of switching devices in a CSI, it is challenging to implement the dynamic model on a real-time platform. This paper presents a CSI model with high fidelity and accuracy that can run in both variable-step desktop simulations and fixed-step real-time simulations.
Understanding the Emergence of Collaborative Problem-Solving Practices in Enterprise Social Media: The Roles of Social Factors

Firms are increasingly introducing enterprise social media (ESM) technologies to nurture the virtual community of practice, offering a prospect for collaborative problem-solving (CPS) within organizations. Based on a unique dataset including 10101 actual problem-solving scenarios across 519 ESM groups, our objective is to identify the relationships between many social factors and the occurrence of CPS by drawing on different social theories. Via multilevel logistic regression, we found that the quality and decentralized degree of social interactions in an ESM group can increase the likelihood of CPS; one user’s social similarity with other members of the group and the user’s user classes are positively related to the likelihood of CPS. Finally, social ties’ strength between the user and other members in the ESM group has a U-shaped association with the likelihood of CPS. These findings can provide companies with meaningful implications for using ESM sites to support CPS.
Multi-Agent Reinforcement Learning for Adaptive Routing: A Hybrid Method using Eligibility Traces

Packet routing in communication networks is a natural problem for sequential decision-making. Previously, many heuristic methods are proposed based on domain knowledge. Most of them rely on the understanding of the network environment and the algorithms are sensitive to the model of the networks. However, in practice it is intractable to model the dynamics of a real network perfectly, since many features of the network such as traffic load and network topology are irregularly changing with time and the changes are hard to predict. Hence, model-free approach becomes more promising in dealing with a complex dynamic environment. In this work, we consider a model-free method by leveraging reinforcement learning techniques. We propose a multi-agent reinforcement learning framework for adaptive routing in communication networks, which takes advantage of both the real-time Q-learning and the actor-critic methods. Provided a global feedback signal, the routers (agents) act independently but are able to learn cooperative behaviors to reduce packet delivery time. Our algorithm is robust to some dynamic changes in the network and each agent learns an adaptive policy to route packets. Simulation results demonstrate that our proposed algorithm outperforms some existing benchmark algorithms.
Comparing Robust Cost Functions for Bathymetric Point Cloud Registration

Scan matching is an established method of identifying loop closure in bathymetric SLAM. Newly developed subsea optical scanners offer much greater resolution compared to imaging sonar, but have a limited field of view. This naturally leads to higher outlier rates when performing scan registration, decreasing the success rate of traditional iterative closest point (ICP) algorithms. This work evaluates the effectiveness of different robust cost functions for outlier rejection on field data collected using an AUV-mounted optical scanner.
Experimental and simulated data at fragment productions in 100 MeV proton-induced reaction on 232Th

This paper shows the results from the experiment that was done in JINR (Joint Institute for Nuclear Research) Dubna. Thorium foil between two aluminum foils was irradiated inside Phasotron with 100 MeV protons. As a result - gamma spectra was obtained from the Th and aluminum foils and residual nuclei cross-sections were evaluated from the reaction. Also there was made a simulation analysis of the beam distribution inside the Th foil and particle escape from the target. The experimental cross-sections were compared with calculations made in Monte-Carlo simulation (MCNP 6.1).
Short-Term Load Forecasting With Long Short-Term Memory: A Case Study of Java-Bali System

Short-term Load Forecasting (STLF) plays an important role in power system operation. It will be used for manage power balance between the dynamic power demand and power supply. This research presents a Long Short-term Memory (LSTM) and Recurrent Neural Network (RNN) for short-term load forecasting Java-Bali power system. We compare the performance of these network architecture models using Mean Average Percentage Error (MAPE) and Root Mean Squared Error (RMSE) to choose the best models for development Java-Bali power system operationalization in the future. The result show LSTM can forecast better than RNN due to vanishing and exploding gradient condition. The best LSTM model has MAPE 5,67% and RMSE 1683,09MW.
End Effects and Geometric Compensation in a Linear Permanent Magnet Synchronous Generator with Buried Topology

Electricity production from ocean waves with different design topologies is a topic of big research interest. Many of such topologies are based on submerged linear generators which inherently introduce end forces. In this paper, the detent force is investigated for two different winding patterns using Maxwell stress tensor in a finite element software. Induced voltage is also investigated for active stator area and the unequal contributions due to stator ends. Two ways of overcoming the end forces are further investigated: The first method reduces the magnetic flux difference when the translator is surrounded by stator and air, respectively. The second aims at countering the end forces at both ends for full active stator area. The first investigated end effect compensation decreased the end effects but increased the attraction forces. The second end effect compensation decreased the end forces during full active stator area with little effect on the attraction forces.