bluuebunny/crossref_metadata_2025 huggingface dataset:
Compressive Sensing via Variational Bayesian Inference under Two Widely Used Priors: Modeling, Comparison and Discussion
Authors: Shekaramiz Mohammad , Moon Todd K.
Published: March 2023

Compressive sensing is a sub-Nyquist sampling technique for efficient signal acquisition and reconstruction of sparse or compressible signals. In order to account for the sparsity of the underlying signal of interest, it is common to use sparsifying priors such as Bernoulli–Gaussian-inverse Gamma (BGiG) and Gaussian-inverse Gamma (GiG) priors on the components of the signal. With the introduction of variational Bayesian inference, the sparse Bayesian learning (SBL) methods for solving the inverse problem of compressive sensing have received significant interest as the SBL methods become more efficient in terms of execution time. In this paper, we consider the sparse signal recovery problem using compressive sensing and the variational Bayesian (VB) inference framework. More specifically, we consider two widely used Bayesian models of BGiG and GiG for modeling the underlying sparse signal for this problem. Although these two models have been widely used for sparse recovery problems under various signal structures, the question of which model can outperform the other for sparse signal recovery under no specific structure has yet to be fully addressed under the VB inference setting. Here, we study these two models specifically under VB inference in detail, provide some motivating examples regarding the issues in signal reconstruction that may occur under each model, perform comparisons and provide suggestions on how to improve the performance of each model.
A New Nonlinear Dynamic Speed Controller for a Differential Drive Mobile Robot
Authors: Hameed Ibrahim A. , Abbud Luay Hashem , Abdulsaheb Jaafar Ahmed , Azar Ahmad Taher , Mezher Mohanad , Jawad Anwar Ja’afar Mohamad , Abdul-Adheem Wameedh Riyadh , Ibraheem Ibraheem Kasim , Kamal Nashwa Ahmad
Published: March 2023

A disturbance/uncertainty estimation and disturbance rejection technique are proposed in this work and verified on a ground two-wheel differential drive mobile robot (DDMR) in the presence of a mismatched disturbance. The offered scheme is the an improved active disturbance rejection control (IADRC) approach-based enhanced dynamic speed controller. To efficiently eliminate the effect produced by the system uncertainties and external torque disturbance on both wheels, the IADRC is adopted, whereby all the torque disturbances and DDMR parameter uncertainties are conglomerated altogether and considered a generalized disturbance. This generalized disturbance is observed and cancelled by a novel nonlinear sliding mode extended state observer (NSMESO) in real-time. Through numerical simulations, various performance indices are measured, with a reduction of 86% and 97% in the ITAE index for the right and left wheels, respectively. Finally, these indices validate the efficacy of the proposed dynamic speed controller by almost damping the chattering phenomena and supplying a high insusceptibility in the closed-loop system against torque disturbance.
Reviving the Dynamics of Attacked Reservoir Computers
Authors: Cao Ruizhi , Guan Chun , Gan Zhongxue , Leng Siyang
Published: March 2023

Physically implemented neural networks are subject to external perturbations and internal variations. Existing works focus on the adversarial attacks but seldom consider attack on the network structure and the corresponding recovery method. Inspired by the biological neural compensation mechanism and the neuromodulation technique in clinical practice, we propose a novel framework of reviving attacked reservoir computers, consisting of several strategies direct at different types of attacks on structure by adjusting only a minor fraction of edges in the reservoir. Numerical experiments demonstrate the efficacy and broad applicability of the framework and reveal inspiring insights into the mechanisms. This work provides a vehicle to improve the robustness of reservoir computers and can be generalized to broader types of neural networks.
Necessary Condition of Self-Organisation in Nonextensive Open Systems
Authors: Afsar Ozgur , Tirnakli Ugur
Published: March 2023

In this paper, we focus on evolution from an equilibrium state in a power law form by means of q-exponentials to an arbitrary one. Introducing new q-Gibbsian equalities as the necessary condition of self-organization in nonextensive open systems, we theoretically show how to derive the connections between q-renormalized entropies (ΔS˜q) and q-relative entropies (KLq) in both Bregman and Csiszar forms after we clearly explain the connection between renormalized entropy by Klimantovich and relative entropy by Kullback-Leibler without using any predefined effective Hamiltonian. This function, in our treatment, spontaneously comes directly from the calculations. We also explain the difference between using ordinary and normalized q-expectations in mean energy calculations of the states. To verify the results numerically, we use a toy model of complexity, namely the logistic map defined as Xt+1=1−aXt2, where a∈[0,2] is the map parameter. We measure the level of self-organization using two distinct forms of the q-renormalized entropy through period doublings and chaotic band mergings of the map as the number of periods/chaotic-bands increase/decrease. We associate the behaviour of the q-renormalized entropies with the emergence/disappearance of complex structures in the phase space as the control parameter of the map changes. Similar to Shiner-Davison-Landsberg (SDL) complexity, we categorize the tendencies of the q-renormalized entropies for the evaluation of the map for the whole control parameter space. Moreover, we show that any evolution between two states possesses a unique q=q* value (not a range for q values) for which the q-Gibbsian equalities hold and the values are the same for the Bregmann and Csiszar forms. Interestingly, if the evolution is from a=0 to a=ac≃1.4011, this unique q* value is found to be q*≃0.2445, which is the same value of qsensitivity given in the literature.
RNNCon: Contribution Coverage Testing for Stacked Recurrent Neural Networks
Authors: Du Xiaoli , Zeng Hongwei , Chen Shengbo , Lei Zhou
Published: March 2023

Recurrent Neural Networks (RNNs) are applied in safety-critical fields such as autonomous driving, aircraft collision detection, and smart credit. They are highly susceptible to input perturbations, but little research on RNN-oriented testing techniques has been conducted, leaving a threat to a large number of sequential application domains. To address these gaps, improve the test adequacy of RNNs, find more defects, and improve the performance of RNNs models and their robustness to input perturbations. We aim to propose a test coverage metric for the underlying structure of RNNs, which is used to guide the generation of test inputs to test RNNs. Although coverage metrics have been proposed for RNNs, such as the hidden state coverage in RNN-Test, they ignore the fact that the underlying structure of RNNs is still a fully connected neural network but with an additional “delayer” that records the network state at the time of data input. We use the contributions, i.e., the combination of the outputs of neurons and the weights they emit, as the minimum computational unit of RNNs to explore the finer-grained logical structure inside the recurrent cells. Compared to existing coverage metrics, our research covers the decision mechanism of RNNs in more detail and is more likely to generate more adversarial samples and discover more flaws in the model. In this paper, we redefine the contribution coverage metric applicable to Stacked LSTMs and Stacked GRUs by considering the joint effect of neurons and weights in the underlying structure of the neural network. We propose a new coverage metric, RNNCon, which can be used to guide the generation of adversarial test inputs. And we design and implement a test framework prototype RNNCon-Test. 2 datasets, 4 LSTM models, and 4 GRU models are used to verify the effectiveness of RNNCon-Test. Compared to the current state-of-the-art study RNN-Test, RNNCon can cover a deeper decision logic of RNNs. RNNCon-Test is not only effective in identifying defects in Deep Learning (DL) systems but also in improving the performance of the model if the adversarial inputs generated by RNNCon-Test are filtered and added to the training set to retrain the model. In the case where the accuracy of the model is already high, RNNCon-Test is still able to improve the accuracy of the model by up to 0.45%.
Development of Spatial Abilities of Preadolescents: What Works?
Authors: Szabó Tibor , Babály Bernadett , Pataiová Helena , Kárpáti Andrea
Published: March 2023

Creation in space and perception of spatial relations are important workplace skills that can be successfully developed in public education through the cooperation of art education, mathematics, and technology, the STEAM model that has been successfully employed in higher education. In a longitudinal school-based experiment, we enhanced the spatial skills of preadolescents through a STEAM-based educational programme. To design our methods of intervention in mathematics, visual arts, and information technology, we used the structure of spatial skills identified in previous studies. Developmental methods involved two- and three-dimensional visualisation tasks in real-life and virtual learning environments. We employed a pre- and post-hoc assessment design using a spatial ability task sequence standardised in Hungary that proved to be valid for assessing Slovak students as well. Authentic assessment tasks included real-life situations when visualisations of spatial relations were used for orientation and creation. Pre-test results showed significant differences between school classes: we identified high achievers and middle-to-low achievers. Our developmental programme was aimed at personalised talent development in the high-achieving classes and the general improvement of the performance of low achievers, raising insufficient pre-test results to an average post-test performance.
A Cryogenic 8-Bit 32 MS/s SAR ADC Operating down to 4.2 K
Authors: Huang Yajie , Luo Chao , Guo Guoping
Published: March 2023

This paper presents a cryogenic 8-bit 32 MS/s successive approximation register (SAR) analog-to-digital converter (ADC) which operates down to 4.2 K. This work uses a modified liquid helium temperature (LHT) SMIC 0.18 μm CMOS technology to support the post-layout simulation. The proposed architecture adopts an offset-promoted dynamic comparator, waveform shaping circuit and true single-phase clock (TSPC) based sar logic circuit to achieve high realizing frequency and low power dissipation. At 1.8-V supply, 1.7 V input amplitude and 32 MS/s sampling frequency, the ADC achieves a power consumption of 2.4 mW and a signal-to-noise and distortion ratio (SNDR) of 47.7 dB, obtaining a figure of merit (FOM) of 378 fJ/conversion-step. The layout area of the ADC is about 0.253 mm2.
Out-of-Distribution (OOD) Detection and Generalization Improved by Augmenting Adversarial Mixup Samples
Authors: Gwon Kyungpil , Yoo Joonhyuk
Published: March 2023

Deep neural network (DNN) models are usually built based on the i.i.d. (independent and identically distributed), also known as in-distribution (ID), assumption on the training samples and test data. However, when models are deployed in a real-world scenario with some distributional shifts, test data can be out-of-distribution (OOD) and both OOD detection and OOD generalization should be simultaneously addressed to ensure the reliability and safety of applied AI systems. Most existing OOD detectors pursue these two goals separately, and therefore, are sensitive to covariate shift rather than semantic shift. To alleviate this problem, this paper proposes a novel adversarial mixup (AM) training method which simply executes OOD data augmentation to synthesize differently distributed data and designs a new AM loss function to learn how to handle OOD data. The proposed AM generates OOD samples being significantly diverged from the support of training data distribution but not completely disjoint to increase the generalization capability of the OOD detector. In addition, the AM is combined with a distributional-distance-aware OOD detector at inference to detect semantic OOD samples more efficiently while being robust to covariate shift due to data tampering. Experimental evaluation validates that the designed AM is effective on both OOD detection and OOD generalization tasks compared to previous OOD detectors and data mixup methods.
Crash Recovery Techniques for Flash Storage Devices Leveraging Flash Translation Layer: A Review
Authors: Alahmadi Abdulhadi , Chung Tae Sun
Published: March 2023

The flash storage is a type of nonvolatile semiconductor device that is operated continuously and has been substituting the hard disk or secondary memory in several storage markets, such as PC/laptop computers, mobile devices, and is also used as an enterprise server. Moreover, it offers a number of benefits, including compact size, low power consumption, quick access, easy mobility, heat dissipation, shock tolerance, data preservation during a power outage, and random access. Different embedded system products, including digital cameras, smartphones, personal digital assistants (PDA), along with sensor devices, are currently integrating flash memory. However, as flash memory requires unique capabilities such as “erase before write” as well as “wear-leveling”, a FTL (flash translation layer) is added to the software layer. The FTL software module overcomes the problem of performance that arises from the erase before write operation and wear-leveling, i.e., flash memory does not allow for an in-place update, and therefore a block must be erased prior to overwriting upon the present data. In the meantime, flash storage devices face challenges of failure and thus they must be able to recover metadata (as well as address mapping information), including data after a crash. The FTL layer is responsible for and intended for use in crash recovery. Although the power-off recovery technique is essential for portable devices, most FTL algorithms do not take this into account. In this paper, we review various schemes of crash recovery leveraging FTL for flash storage devices. We illustrate the classification of the FTL algorithms. Moreover, we also discuss the various metrics and parameters evaluated for comparison with other approaches by each scheme, along with the flash type. In addition, we made an analysis of the FTL schemes. We also describe meaningful considerations which play a critical role in the design development for power-off recovery employing FTL.
Intrusion Detection on AWS Cloud through Hybrid Deep Learning Algorithm
Authors: R M Balajee , M K Jayanthi Kannan
Published: March 2023

The network security and cloud environment have been playing vital roles in today’s era due to increased network data transmission, the cloud’s elasticity, pay as you go and global distributed resources. A recent survey for the cloud environment involving 300 organizations in North America with 500 or more employees who had spent a minimum of USD 1 million on cloud infrastructure, as per March 2022 statistics, stated that 79% of organizations experienced at least one cloud data breach. In the year 2022, the AWS cloud provider leads the market share with 34% and a USD 200 billion cloud market, proving important and producing the motivation to improve the detection of intrusion with respect to network security on the basis of the AWS cloud dataset. The chosen CSE-CIC-IDS-2018 dataset had network attack details based on the real time attack carried out on the AWS cloud infrastructure. The proposed method here is the hybrid deep learning based approach, which uses the raw data first to do the pre-processing and then for normalization. The normalized data have been feature extracted from seventy-six fields to seven bottlenecks using Principal Component Analysis (PCA); those seven extracted features of every packet have been categorized as two-way soft-clustered (attack and non-attack) using the Smart Monkey Optimized Fuzzy C-Means algorithm (SMO-FCM). The attack cluster data have been further provided as inputs for the deep learning based AutoEncoder algorithm, which provides the outputs as attack classifications. Finally, the accuracy of the results in intrusion detection using the proposed technique (PCA + SMO-FCM + AE) is achieved as 95% over the CSE-CIC-IDS-2018 dataset, which is the highest known for state-of-the-art protocols compared with 11 existing techniques.
Secure Decision Fusion in ISAC-Oriented Distributed Wireless Sensing Networks with Local Multilevel Quantization
Authors: Zhang Guomei , Sun Hao , Yu Jiayue
Published: March 2023

Distributed deployment for integrated sensing and communication (ISAC) can improve the sensing accuracy by exploring spatial diversity for covering the target state. However, secure fusion and limited energy consumption are still challenges for wireless-transmission-based distributed ISAC. In this paper, a secure decision-fusion scheme under energy constraint is proposed. First, the local likelihood ratios (LRs) of the local observations at sensing nodes are quantified at multiple levels corresponding to a multiple phase-shift keying (MPSK) constellation, in order to retain more sensing information. Second, an antieavesdropping scheme, which randomly rotates the constellation based on the main channel information between the nodes and ally fusion center (AFC), is proposed to confuse the data fusion of the eavesdropping fusion center (EFC). In addition, the local quantization thresholds and the rotating threshold are optimized to realize the perfect security under energy constraint and maximum rotation angle of π. In addition, the optimized rotation angle is discussed under a relaxed security requirement of the EFC in exchange for reducing the AFC error. Performance evaluation results show that the AFC error probability of the proposed scheme with a two-bit quantization and soft fusion outperforms the single-bit case and three-bit case by above 3 dB and about 0.5 dB at the error probability of 10−2, respectively. The former gain is just contributed by the more local information kept with two-bit against single-bit quantization. However, for the three-bit case, the advantage of more levels of quantization is eliminated by the worse transmission of denser constellation over a noisy channel. Moreover, the proposed scheme outperforms the conventional channel-aware encryption method under a stricter energy constraint and higher signal noise ratio (SNR).
Design and Implementation of Low Parasitic Inductance Bias Circuit for High-Power Pulsed Power Amplifiers
Authors: Fu Chao , Fang Wenrao , Fan Ruyu , Wang Lulu , Huang Wenhua , Zhang Yuchuan , Liu Changkun
Published: March 2023

This article presents a wideband bias circuit with low parasitic inductance for high-power pulsed amplifiers. The proposed bias circuit works similarly to the traditional bias circuit in that it can ensure the transmission of microwaves from the power amplifier to the load while preventing the transmission of microwaves from the power amplifier to the power supply. By making the bias line shorter and the transmission line wider than the traditional bias circuit, the proposed bias circuit reduces its parasitic inductance. The reduction of parasitic parameters is critical for reducing the drain voltage overshoot of the high-power pulse power amplifier and ensuring its safety. The simulation results demonstrate that the proposed bias circuit has a lower parasitic inductance and a wider bandwidth. To validate the theory and simulation results, the traditional and the proposed bias circuits are fabricated using microstrip circuits. Both the simulation and experimental results indicate that the proposed bias circuit has a one-third lower parasitic inductance than the traditional bias circuit. Furthermore, the proposed bias circuit has a wider bandwidth.
The Benefits of Combining Mixed Virtual Reality Exergaming with Occupational Therapy for Upper Extremity Dexterity
Authors: Roman Nadinne , Baseanu Cozmin , Tuchel Vlad Ionut , Nicolau Cristina , Repanovici Angela , Manaila Adina , Minzatanu Diana , Miclaus Roxana Steliana
Published: March 2023

Virtual Reality (VR) is expanding worldwide in education, training, gaming, and healthcare to achieve distinct outcomes. This paper aimed to identify to what extent physiotherapy based on mixed VR technology and occupational therapy serves the functionality of healthy subjects’ upper extremities; it also analyzed the benefits of this protocol for therapy guidelines in hand dexterity re-habilitation. Fifteen VR exergames and occupational therapy sessions were performed for 60 min in three weeks with a sample of sixteen people. The Jebsen Taylor Hand Function Test Scale (JTHFTS), Nine-Hole Peg test (9 Hole), Box and Blocks Test (BBT), and Timed Functional Test for the Arm and Shoulder (TFAST) were used for pre and post-therapy assessment. Linear regression was used to identify healthy subjects’ main tasks, predicting upper extremity dexterity. The results of pre- and post-exercise assessments suggested significant improvements for both upper extremities, dominant and non-dominant. BBT, the nine-hole peg test, and FAST showed significant differences in pre- and post-therapy, favoring prophylactical exercises. Hence, in the linear regression results for the dominant hand, five models emerged as potential predictors for upper extremity agility. The capacity to pick up large light objects seemed to bring the most critical influence on hand dexterity. However, regarding the non-dominant hand, the results suggested that writing ability was the most potent predictor of dexterity. In this respect, the protocol used in this research can be used as a guideline for further upper extremity dexterity training since VR exergames combined with occupational therapy can bring essential contributions to upper limb proprioception and dexterity functioning.
Flight Delay Prediction Model Based on Lightweight Network ECA-MobileNetV3
Authors: Qu Jingyi , Chen Bo , Liu Chang , Wang Jinfeng
Published: March 2023

In exploring the flight delay problem, traditional deep learning algorithms suffer from low accuracy and extreme computational complexity; therefore, the deep flight delay prediction algorithm is difficult to directly deploy to the mobile terminal. In this paper, a flight delay prediction model based on the lightweight network ECA-MobileNetV3 algorithm is proposed. The algorithm first preprocesses the data with real flight information and weather information. Then, in order to increase the accuracy of the model without increasing the computational complexity too much, feature extraction is performed using the lightweight ECA-MobileNetV3 algorithm with the addition of the Efficient Channel Attention mechanism. Finally, the flight delay classification prediction level is output via a Softmax classifier. In the experiments of single airport and airport cluster datasets, the optimal accuracy of the ECA-MobileNetV3 algorithm is 98.97% and 96.81%, the number of parameters is 0.33 million and 0.55 million, and the computational volume is 32.80 million and 60.44 million, respectively, which are better than the performance of the MobileNetV3 algorithm under the same conditions. The improved model can achieve a better balance between accuracy and computational complexity, which is more conducive mobility.
Knock-Prediction System for Kerosene Engines Using In-Cylinder Pressure Signal
Authors: Xu Zhixin , Cao Guangzhou , Wei Minxiang , Zhao Zhuowen , Xing Zhiyu , Ding Yuzhang
Published: March 2023

Piston engines fueled by kerosene have a strong application prospect in special vehicles and small aircrafts, but the low amount of octane in kerosene fuel causes its knock combustion phenomenon to be particularly serious. A knock will deteriorate the power and economy of the engine and will damage the engine in serious cases. Therefore, knocking is the key problem with kerosene engines. We propose a knock-prediction system for kerosene engines based on in-cylinder pressure signals. Firstly, the intrinsic mode function (IMF) caused by knock resonance is extracted from the in-cylinder pressure signal via empirical mode decomposition (EMD) and a time–frequency domain analysis. A time-domain statistical analysis (TDSA) combined with a principal component analysis (PCA) is used to extract features from the IMF. Finally, the data collected from the test bench are trained by a support vector machine to obtain the knock-prediction model. Compared with other technical combinations for training, the proposed scheme achieved more accurate results in knock prediction. Considering the working characteristics of kerosene engines, a slight knock can increase the power of a kerosene engine. Therefore, some incorrectly predicted cycles (slight-knock cycles) do not affect the normal operation of the engine.
Comparison of Power Coefficients in Wind Turbines Considering the Tip Speed Ratio and Blade Pitch Angle
Authors: Castillo Oscar Carranza , Andrade Viviana Reyes , Rivas Jaime José Rodríguez , González Rubén Ortega
Published: March 2023

This paper presents a review of the power and torque coefficients of various wind generation systems, which involve the real characteristics of the wind turbine as a function of the generated power. The coefficients are described by mathematical functions that depend on the trip speed ratio and blade pitch angle of the wind turbines. These mathematical functions are based on polynomial, sinusoidal, and exponential equations. Once the mathematical functions have been described, an analysis of the grouped coefficients according to their function is performed with the purpose of considering the variations in the trip speed ratio for all the coefficients based on sinusoidal and exponential functions, and with the variations in the blade pitch angle. This analysis allows us to determine the different coefficients of power and torque used in wind generation systems, with the objective of developing algorithms for searching for the point of maximum power generated and for the active control of wind turbines with variations in the blade pitch angle.
An Experimental Study on the Quantitative and Qualitative Characteristics of Tar Formed during Ex Situ Coal Gasification
Authors: Wiatowski Marian
Published: March 2023

Over the three-day gasification test of a large coal block with oxygen in atmospheric pressure conditions, the yield and composition of the tar collected was investigated. The tar was sampled approximately every 7 h into sorption tubes directly from the reactor outlet. Sand, with a moisture content of 11%, was used as an insulating material to simulate the environment of the gasified coal seam. Light aromatic hydrocarbons (BTEX), phenols, and polycyclic aromatic hydrocarbons (PAHs) were determined in the tar. The results that were obtained were recalculated into the concentrations of the individual components of the tar and its mass stream in the process gas. The residence time of the tar in the reactor, its molar mass, and the H/C ratio were also calculated. As the reaction progressed, the water that was contained in the wet sand started to react with the gasified coal, which significantly affected the composition and amount of the obtained process gas and the produced tar. Due to an increase in the amount of generated gases and steam, the residence time of the tar vapours in the reactor decreased as the gasification progressed, ranging from approximately 1 s at the beginning of the process to 0.35 s at the end. The obtained tar was characterised by a high average content of BTEX fractions at approximately 82.6%, PAHs at 14.7%, and phenols at 2.7%. Benzene was the dominant BTEX compound, with a concentration of 83.7%. The high content of the BTEX compounds, especially benzene, was a result of secondary processes taking place in the tar (hydrocracking and steam reforming), and as a result of which, in the presence of hydrogen and steam, the heavier components of the tar were transformed into lighter ones. The total yield of the tar from this UCG (underground coal gasification) process—calculated per 1 ton of gasified coal—was 1.8% (counted on the basis of the analysed tar composition). Comparing this result to the efficiency of the classic coking process, the tar yield was about three times lower.
Detailed Thermo-Economic Assessment of a Heat Pump for Industrial Applications
Authors: Martínez-Rodríguez Guillermo , Díaz-de-León Cristobal , Fuentes-Silva Amanda L. , Baltazar Juan-Carlos , García-Gutiérrez Rafael
Published: March 2023

Heat pump electricity costs grow with a power relationship as the evaporator temperature in the cycle decreases. The thermo-economic study of a solar thermal-assisted heat pump and storage system determines the minimum cost of the coupled system for an evaporator temperature. Through two case studies, one for the dairy industry and the other for 2G bioethanol production, the coupled system was evaluated for different temperatures in the evaporator, from 30 to 90 °C, and the minimum cost of the coupled system was determined. For the dairy industry, the lowest levelized total cost of a heat pump (LCOE) at 50 °C is 0.0799 USD/kWh. The evaluation carried out allowed us to determine the best operating conditions of the heat pump: 50 °C in the evaporator, COP = 4.2, and the work of the compressor of 211.3 kW. In the case of 2G anhydrous bioethanol production, the lowest levelized energy cost is 0.0409 USD/kWh for an evaporator temperature of 30 °C and a payback of 1.8 years. The study carried out makes it possible to guarantee the supply of the heat load at the operating temperature of the process and determines the impact of environmental conditions on the cost of the heat pump assisted with solar thermal energy.
Optimization of Conductive Fins to Minimize UO2 Fuel Temperature and Radial Temperature Gradient
Authors: Paaren Kyle M. , Medvedev Pavel , Mariani Robert
Published: March 2023

To further the development of low-enriched uranium fuels, precedence has been placed on delivering the same amount of power while lowering the fuel temperature and radial temperature gradient. To address this, modeling efforts have resulted in a novel design featuring conductive fins of varying thermal conductivities and geometries inserted into the fuel matrix. These conductive inserts were not allowed to exceed 6% of the original fuel volume. This constraint was imposed due to other designs displacing 10% of fuel volume. A parametric study was performed that consisted of 2.56 million BISON simulations involving varying fin characteristics (i.e., fin thermal conductivity, number, and geometry) to determine the optimal geometric configuration for a desired amount of fuel volume displaced. The results from this study show that the thickness and length of each fin affect the fuel temperature and temperature gradient more than varying the number and thermal conductivity of the fins. The parametric study resulted in the development of an optimized combination to produce the lowest peak fuel temperature, lowest radial temperature gradient, and highest temperature reduction for the amount of original fuel volume displaced. The simulations presented in this work will eventually be compared with irradiation experiments of similar fuel designs at Idaho National Laboratory’s Advanced Test Reactor.
Experimental Study of the Degradation Characteristics of LiFePO4 and LiNi0.5Co0.2Mn0.3O2 Batteries during Overcharging at Low Temperatures
Authors: Zhang Xiaoning , Sun Pengfei , Wang Shixue , Zhu Yu
Published: March 2023

Battery overcharging can occur due to capacity and internal resistance variations among cells or battery management system failure that both accelerate battery degradation, which is more likely at low temperatures because of the large polarization effect. This study experimentally investigated the battery degradation characteristics during charging of LiFePO4 (LFP)/Graphite batteries at voltages of 3.65–4.8 V and Li(Ni0.5Co0.2Mn0.3)O2 (NCM)/Graphite batteries at 4.2–4.8 V at −10 °C with currents of 0.2–1 C. The results showed that the LFP cell capacities decreased linearly with an increasing number of cycles, while the NCM cell capacities faded in three trends with an increasing number of cycles under different conditions with linear fading, accelerated fading, and decelerated fading. The incremental capacity curves and differential voltage curves showed that the LFP cell degradation was mainly caused by the loss of lithium inventory (LLI), with some effect from the loss of active material (LAM). In the NCM cells, both the LLI and LAM significantly contributed to the degradation. Combined with internal battery morphology observations, the LAM mainly occurred at the anode, and the main side reactions leading to the LLI with lithium plating and solid electrolyte interface growth also occurred at the anode.
Validation of a Holistic System for Operational Analysis and Provision of Ancillary Services in Active Distribution Networks
Authors: Papadopoulos Theofilos A. , Pippi Kalliopi D. , Barzegkar-Ntovom Georgios A. , Kontis Eleftherios O. , Nousdilis Angelos I. , Athanasiadis Christos L. , Kryonidis Georgios C.
Published: March 2023

The advent of distributed renewable energy sources (DRESs) has led to the progressive transformation of traditional distribution networks to active components of the power system. This transformation, however, may jeopardize the reliable grid operation due to the advent of new technical problems, such as network overloading, over-/under-voltage events, abnormal frequency deviation and dynamic instability. In this challenging scenery, the installation of a modern measuring infrastructure has created new sources of data and information that facilitate the provision of ancillary services (ASs) via measurement-based analysis. The ACTIVATE (ancillary services in active distribution networks based on monitoring and control techniques) project aims to design innovative AS solutions for power system operators. These solutions aim to tackle the technical issues emerged by the ever-increasing DRES penetration and their volatile nature. In this context, in ACTIVATE, a holistic system is proposed comprising centralized and decentralized control features to enhance the overall network performance. Additionally, a network monitoring system is designed to support a number of online and offline dynamic analysis applications by exploiting measurements obtained at the transmission, primary and secondary distribution network. This paper presents a validation of the overall system, which is performed by using simulation and power-hardware-in-the-loop results in combined transmission and distribution network models.
The Ethanol Market and Its Relation to the Price of Agricultural Commodities
Authors: Hoeckel Paulo Henrique , Alvim Augusto Mussi , Pontes José Pedro , Dias João
Published: March 2023

In this article, we seek to present the synthesis of the global ethanol market in the United States (US) and Brazil by using caloric equivalence to empirically characterize the elasticity of supply and demand. We also seek to evaluate the relationship between world ethanol production and prices of agricultural commodities (wheat, rice, corn, soybeans and sugar) from 1981 to 2016 using climate-induced yield shocks as instrumental variables. The main results for the world market indicate that the production and demand for ethanol respond elastically to changes in commodity prices. The world ethanol production has no significant relationship with food prices. However, evaluation of the ethanol market and its interaction with the agricultural commodities market confirms the hypothesis that Brazilian ethanol is weakly related to the price of food.
Case Study of Cost-Effective Electrification of Long-Distance Line-Haul Trucks
Authors: Karlsson Johannes , Grauers Anders
Published: March 2023

This paper investigates the economic consequences of a haulage company replacing its line-haul diesel trucks with battery-electric ones. It also examines how large truck batteries should be, whether the haulage companies should use public fast chargers to complement their own, and whether public fast chargers have the potential to be profitable. The potential extra cost of losing payload capacity is estimated and there is an investigation of whether a charge-point operator should meet the peak demand for charging. The case under analysis is designed to represent a typical line-haul service between terminals in a major logistics system, with the finding that, in this case, a transition to battery-electric trucks seems cost effective for the company. Moreover, it is advisable for the company to use public fast chargers and these will likely become profitable given that the utilisation factor of the investigated public fast chargers may realistically exceed 20%.
Identification of Pine Wilt Disease Infected Wood Using UAV RGB Imagery and Improved YOLOv5 Models Integrated with Attention Mechanisms
Authors: Zhang Peng , Wang Zhichao , Rao Yuan , Zheng Jun , Zhang Ning , Wang Degao , Zhu Jianqiao , Fang Yifan , Gao Xiang
Published: March 2023

Pine wilt disease (PWD) is a great danger, due to two aspects: no effective cure and fast dissemination. One key to the prevention and treatment of pine wilt disease is the early detection of infected wood. Subsequently, appropriate treatment can be applied to limit the further spread of pine wilt disease. In this work, a UAV (Unmanned Aerial Vehicle) with a RGB (Red, Green, Blue) camera was employed as it provided high-quality images of pine trees in a timely manner. Seven flights were performed above seven sample plots in northwestern Beijing, China. Then, raw images captured by the UAV were further pre-processed, classified, annotated, and formed the research datasets. In the formal analysis, improved YOLOv5 frameworks that integrated four attention mechanism modules, i.e., SE (Squeeze-and-Excitation), CA (Coordinate Attention), ECA (Efficient Channel Attention), and CBAM (Convolutional Block Attention Module), were developed. Each of them had been shown to improve the overall identification rate of infected trees at different ranges. The CA module was found to have the best performance, with an accuracy of 92.6%, a 3.3% improvement over the original YOLOv5s model. Meanwhile, the recognition speed was improved by 20 frames/second compared to the original YOLOv5s model. The comprehensive performance could well support the need for rapid detection of pine wilt disease. The overall framework proposed by this work shows a fast response to the spread of PWD. In addition, it requires a small amount of financial resources, which determines the duplication of this method for forestry operators.
Species Distribution Modelling under Climate Change Scenarios for Maritime Pine (Pinus pinaster Aiton) in Portugal
Authors: Alegria Cristina , Almeida Alice M. , Roque Natália , Fernandez Paulo , Ribeiro Maria Margarida
Published: March 2023

To date, a variety of species potential distribution mapping approaches have been used, and the agreement in maps produced with different methodological approaches should be assessed. The aims of this study were: (1) to model Maritime pine potential distributions for the present and for the future under two climate change scenarios using the machine learning Maximum Entropy algorithm (MaxEnt); (2) to update the species ecological envelope maps using the same environmental data set and climate change scenarios; and (3) to perform an agreement analysis for the species distribution maps produced with both methodological approaches. The species distribution maps produced by each of the methodological approaches under study were reclassified into presence–absence binary maps of species to perform the agreement analysis. The results showed that the MaxEnt-predicted map for the present matched well the species’ current distribution, but the species ecological envelope map, also for the present, was closer to the species’ empiric potential distribution. Climate change impacts on the species’ future distributions maps using the MaxEnt were moderate, but areas were relocated. The 47.3% suitability area (regular-medium-high), in the present, increased in future climate change scenarios to 48.7%–48.3%. Conversely, the impacts in species ecological envelopes maps were higher and with greater future losses than the latter. The 76.5% suitability area (regular-favourable-optimum), in the present, decreased in future climate change scenarios to 58.2%–51.6%. The two approaches combination resulted in a 44% concordance for the species occupancy in the present, decreasing around 30%–35% in the future under the climate change scenarios. Both methodologies proved to be complementary to set species’ best suitability areas, which are key as support decision tools for planning afforestation and forest management to attain fire-resilient landscapes, enhanced forest ecosystems biodiversity, functionality and productivity.
Predicting the Habitat Suitability for Angelica gigas Medicinal Herb Using an Ensemble Species Distribution Model
Authors: Jung Jong Bin , Park Go Eun , Kim Hyun Jun , Huh Jeong Hoon , Um Yurry
Published: March 2023

The distribution shift of forest species due to the fact of climate change may negatively affect ecosystem services including the production of medicinal resources. Climate change impact assessments of habitat range changes are essential to sustainably manage forest resources. A change in the habitat suitability due to the fact of climate change was predicted for Angelica gigas, which has high economic value among forest medicinal resources in South Korea. The habitat suitability was predicted by an ensemble species distribution model that combined the results of nine single algorithm models using the committee averaging method. A total 168 occurrence data and 10 environmental variables were used for the single algorithm models. The area under the receiver operation characteristic curve (AUC) and true skill statistic (TSS) were applied to evaluate the models’ performance, and the contribution of the environmental variables was calculated as an important value for each single algorithm model. Climate change scenarios were projected to predict future habitat suitability. The future suitable habitat for A. gigas was gradually reduced to the high mountain regions of the eastern part of South Korea regardless of the climate change scenarios. The main environmental variable was the annual mean temperature, and the rise in temperature due to the fact of climate change was found to have a negative effect on the habitat suitability for A. gigas. The decline in the habitat suitability for A. gigas, a major forest medicinal resource, is expected to result in the reduction in its production. Therefore, it is required to establish adaptation measures to mitigate the negative impact of this decrease, such as protecting the natural habitats of A. gigas.
Long-Term Climate Sensitivity of Resin-Tapped and Non-Resin-Tapped Scots Pine Trees Based on Tree Ring Width and Blue Intensity
Authors: Jakubowski Marcin , Dobroczyński Marek
Published: March 2023

The resin tapping of pine trees in Poland ended in the early 1990s. However, we can still find individual trees, and sometimes larger groups of trees, that were tapped. This study focused on the effect of the mechanical wounding of trees during resin tapping on the growth and climatic sensitivity of pine trees. The study concerned a 160-year-old pine stand in northwestern Poland in which resin tapping was last performed in the 1970s. All the trees had remained standing because of their high quality, which had destined them for seed collection. The stand included both resin-tapped (RT) and non-RT (NRT) trees. Our study was based on a dendrochronological analysis of two signals—annual tree ring widths (TRWs) and their delta blue intensity (DBI). We observed a significant increase in annual TRW after resin tapping had ceased, alongside a decrease in the DBI. The temporal stability in growth response was examined using daily climatic correlations from 1921 to 2021. It was found that the climatic sensitivity of RT and NRT pines was similar. There were differences in only some of the years, most while resin tapping was occurring, and then approximately 20 years after the resin tapping had ceased. However, these were small differences that mainly related to the strength of the correlation. It was also discovered that we can obtain different types of information from the study of TRWs and DBI.
Revelation for the Influence Mechanism of Long-Chain Fatty Acid Ethyl Esters on the Baijiu Quality by Multicomponent Chemometrics Combined with Modern Flavor Sensomics
Authors: Wu Yashuai , Chen Hao , Huang He , Chen Fangyuan , Hong Jiaxin , Zhao Dongrui , Zhang Chunsheng , Zhao Zhigang , Wang Shimin , Ao Ran , Sun Baoguo
Published: March 2023

Long-chain fatty acid ethyl ester (LCFAEEs) is colorless and has a weak wax and cream aroma. It can be used as an intermediate for the synthesis of emulsifiers, and stabilizers and be applied in the production of flavor essence. It is also an important trace component in Baijiu and is attributed to making a contribution to the quality of Baijiu, but its distribution in Baijiu has not been clear, and its influence mechanisms on Baijiu quality have not been systematically studied. Therefore, the distribution of LCFAEEs for Baijiu in different years (2014, 2015, 2018, and 2022), different grades (premium, excellent, and level 1; note: here Baijiu grade classification was based on Chinese standard (GB/T 10781) and enterprise classification standard), and different sun exposure times (0, 6, 12, 20, 30, and 50 days) was uncovered. Thus, in this study, the effect of LCFAEEs on the quality of Baijiu was comprehensively and objectively proven by combining modern flavor sensomics and multicomponent chemometrics. The results showed that with the increase in Baijiu storage time, the concentration of LCFAEEs increased significantly in Baijiu (4.38–196.95 mg/L, p < 0.05). The concentration of LCFAEEs in level 1 Baijiu was significantly higher than that in excellent and premium Baijiu (the concentration ranges of ET, EP, EO, E9, E912, and E91215 were: 0.27–2.31 mg/L, 0.75–47.41 mg/L, 0.93–1.80 mg/L, 0.98–12.87 mg/L, 1.01–27.08 mg/L, and 1.00–1.75 mg/L, respectively, p < 0.05). With the increase in sun exposure time, the concentration of LCFAEEs in the Baijiu first increased significantly and then decreased significantly (4.38–5.95 mg/L, p < 0.05). As the flavor sensomics showed, the concentrations of LCFAEEs in Baijiu bodies were significantly correlated with the Baijiu taste sense (inlet taste, aroma sensation in the mouth), as well as with the evaluation after drinking (maintaining taste) (p < 0.05, r > 0.7). Based on the above, LCFAEEs are critical factors for Baijiu flavor thus, it is essential to explore a suitable concentration of LCFAEEs in Baijiu to make Baijiu’s quality more ideal.