bluuebunny/crossref_metadata_2025 huggingface dataset:
Beading Mechanism and Performance of Porous Steel Slag Microbead Abrasive
Authors: Pei Jingjing , Zhang Yuzhu , Xing Hongwei , Ren Qianqian , Huo Wenqing , Wu Jinhu
Published: November 2021

The use of the gas-quenching process for preparing porous bead slag abrasive was investigated in this paper. An X-ray diffractometer, field emission scanning electron microscope, mercury intrusion porosimetry, and stereo microscope were used to analyze the microbead forming mechanism, pore structure, acid–alkali resistance, and polishing properties of porous steel slag microbead abrasives. Results show that the porous steel slag abrasives present a mono-disperse spherical shape with a hard shell and the porosity is 42.36%. The thermodynamic fractal model indicates that the fractal dimension of the abrasive is 2.226, which shows its simple pore structure. The sample has better chemical stability in the polishing fluid than in water, acid, and alkali solution. Therefore, aluminum and copper alloys are used as substrates for polishing tests. The results indicate that the abrasives could effectively improve the quality of the workpiece surface and the polishing efficiency for aluminum alloy was higher than that for copper alloy.
Strain-Gradient Crystal Plasticity Finite Element Modeling of Slip Band Formation in α-Zirconium
Authors: Sedaghat Omid , Abdolvand Hamidreza
Published: November 2021

Two methods for the determination of geometrically necessary dislocation (GND) densities are implemented in a lower-order strain-gradient crystal plasticity finite element model. The equations are implemented in user material (UMAT) subroutines. Method I has a direct and unique solution for the density of GNDs, while Method II has unlimited solutions, where an optimization technique is used to determine GND densities. The performance of each method for capturing the formation of slip bands based on the calculated GND maps is critically analyzed. First, the model parameters are identified using single crystal simulations. This is followed by importing the as-measured microstructure for a deformed α-zirconium specimen into the finite element solver to compare the numerical results obtained from the models to those measured experimentally using the high angular resolution electron backscatter diffraction technique. It is shown that both methods are capable of modeling the formation of slip bands that are parallel to those observed experimentally. Formation of such bands is observed in both GND maps and plastic shear strain maps without pre-determining the slip band domain. Further, there is a negligible difference between the calculated grain-scale stresses and elastic lattice rotations from the two methods, where the modeling results are close to the measured ones. However, the magnitudes and distributions of calculated GND densities from the two methods are very different.
Complete and Durable Response to Nivolumab in Recurrent Poorly Differentiated Pancreatic Neuroendocrine Carcinoma with High Tumor Mutational Burden
Authors: Kang Nai-Wen , Tan Kien-Thiam , Li Chien-Feng , Kuo Yu-Hsuan
Published: November 2021

Poorly differentiated pancreatic neuroendocrine carcinomas (NECs) are rare and aggressive malignancies with rapid disease progression and early widespread metastasis. Given histology similarity, they are commonly treated with platinum-based chemotherapy as small cell lung cancer (SCLC). However, no standard treatment has been established for recurrent or progressive disease. We present an Asian patient with recurrent poorly differentiated pancreatic NEC after curative surgery and adjuvant chemotherapy with cisplatin and etoposide. The tumor mutational burden (TMB) was high. The patient received chemotherapy combined with maintenance immunotherapy with nivolumab and achieved promising and durable response, suggesting TMB could be a biomarker to identify NEC patients for immune checkpoint inhibitor (ICI) treatment.
How to Diagnose and Treat CD5-Positive Lymphomas Involving the Spleen
Authors: Cabeçadas José , Nava Victor E. , Ascensao Joao L. , Gomes da Silva Maria
Published: November 2021

Patients with CD5-expressing lymphomas presenting with splenomegaly are frequently diagnosed with chronic lymphocytic leukemia. The most important differential diagnosis is mantle cell lymphoma, both in its classical and leukemic, non-nodal forms, given its prognostic and therapeutic implications. Other small B-cell neoplasms that frequently involve the spleen and occasionally express CD5 include the splenic marginal zone lymphoma, hairy cell leukemia and, rarely, lymphoplasmacytic lymphoma. The frequency of CD5 positivity depends in part on the sensitivity of the detection methods employed. Usually, a combination of morphological, immunophenotypic and molecular findings allows for a precise sub-classification of CD5-positive, low-grade B-cell lymphomas of the spleen. Some of these tumors may display a mixture of small and larger B cells, raising the possibility of more aggressive lymphomas, such as diffuse large B-cell lymphomas (DLBCL). Approximately 5–10% of DLBCL are CD5-positive and some may manifest as primary splenic lesions. When available, the morphology of DLBCL in the splenic tissue is distinctive and a leukemic picture is very rare. In conclusion, the appropriate morphological and clinical context assisted by flow cytometry panels and/or immunohistochemistry allows the differential diagnosis of CD5-positive, non-Hodgkin, B-cell lymphomas involving the spleen.
Do Preoperative Transfusions Impact Prognosis in Moderate to Severe Anaemic Surgical Patients with Colon Cancer?
Authors: Tamini Nicolò , Gianotti Luca , Darwish Shadya , Petitto Salvatore , Bernasconi Davide , Oldani Massimo , Uggeri Fabio , Braga Marco , Nespoli Luca
Published: November 2021

(1) Background: Anaemia is a common finding in patients with colon cancer and is commonly corrected by blood transfusion prior to surgery. However, the prognostic role of perioperative transfusions is still debated. The aim of the present study was to investigate the role of preoperative anaemia and preoperative blood transfusion in influencing the prognosis in colon cancer. (2) Patients and Methods: Patients undergoing elective surgery for colon cancer at a tertiary referral university hospital between January 2010 and December 2018 were included in a retrospective review of a prospectively collected database. Univariate and regression analyses were performed to identify the prognostic role of preoperative anaemia and preoperative transfusions in this homogeneous cohort of patients. (3) Results: A total of 780 patients were included in the final analysis. The estimated five-year overall survival rate was significantly worse in the anaemic group (83.8% in non-anaemic patients, 60.6% in mild anaemic patients, 61.3% in moderate anaemic patients and 58.4% in severe anaemic patients; log-rank < 0.001 vs. non-anaemic patients). Anaemic status was found to be an independent adverse prognostic factor (hazard ratio (HR): 1.46; 95% confidence interval (CI): 1.02–2.07) during multivariate analysis. Among moderate to severe anaemic patients, no significant association was found between preoperative transfusions and the risk of mortality or recurrence. (4) Conclusions: Preoperative anaemia, regardless of its severity, and not preoperative blood transfusion, was independently associated with a worse prognosis after surgery in patients with colonic cancer.
Atrial Fibrillation and Peri-Atrial Inflammation Measured through Adipose Tissue Attenuation on Cardiac Computed Tomography
Authors: Gaibazzi Nicola , Martini Chiara , Benatti Giorgio , Palumbo Alessandro Anselmo , Cacciola Giovanna , Tuttolomondo Domenico
Published: November 2021

Background: Inflammation plays a key role in atrial fibrillation (AF). Epicardial adipose tissue around the atrial wall can influence atrial morpho-functional properties. The aim of this study was to assess whether an increased quantity and/or density of adipose tissue located around the left atrium (Fat-LA) are related to AF, independently from atrial size. Methods: eighty patients who underwent AF ablation and 80 patients without history of AF were selected. The Fat-LA mass was quantified as tissue within −190 to −30 Hounsfield Units (HU) on cardiac computed tomography angiograms (CCTA), and the mean adipose tissue attenuation was assessed. Results: Adipose tissue mass was higher in patients with AF (5.42 ± 2.94 mL) versus non-AF (4.16 ± 2.55 mL, p = 0.007), but relative fat quantity did not differ after adjusting for atrial size. Mean fat density was significantly higher in AF (−69.15 HU) versus non-AF (−76.82 HU, p < 0.0001) participants. In the logistic regression models, only the addition of mean Fat-LA attenuation led to a significant improvement of the model’s chi-square (from 22.89 of the clinical model to 31.69 of the clinical and adipose tissue attenuation model, p < 0.01) and discrimination (AUC from 0.775 to 0.829). Conclusions: Fat-LA volume is significantly greater only in absolute terms in patients with AF, but this difference does not hold after adjusting for the larger LA of AF subjects. On the contrary, a higher Fat-LA density was associated with AF, independently from LA size, providing incremental value over other variables that are associated with AF.
Entanglement-Structured LSTM Boosts Chaotic Time Series Forecasting
Authors: Meng Xiangyi , Yang Tong
Published: November 2021

Traditional machine-learning methods are inefficient in capturing chaos in nonlinear dynamical systems, especially when the time difference Δt between consecutive steps is so large that the extracted time series looks apparently random. Here, we introduce a new long-short-term-memory (LSTM)-based recurrent architecture by tensorizing the cell-state-to-state propagation therein, maintaining the long-term memory feature of LSTM, while simultaneously enhancing the learning of short-term nonlinear complexity. We stress that the global minima of training can be most efficiently reached by our tensor structure where all nonlinear terms, up to some polynomial order, are treated explicitly and weighted equally. The efficiency and generality of our architecture are systematically investigated and tested through theoretical analysis and experimental examinations. In our design, we have explicitly used two different many-body entanglement structures—matrix product states (MPS) and the multiscale entanglement renormalization ansatz (MERA)—as physics-inspired tensor decomposition techniques, from which we find that MERA generally performs better than MPS, hence conjecturing that the learnability of chaos is determined not only by the number of free parameters but also the tensor complexity—recognized as how entanglement entropy scales with varying matricization of the tensor.
Victory Tax: A Holistic Income Tax System
Authors: Jacobs Donald J.
Published: November 2021

How can an income tax system be designed to exploit human nature and a free market to create a poverty free society, while balancing budgets without disproportional tax burdens? Such a tax system, with universal character, is deduced from the following guiding principles: (1) a single tax rate applies to all income types and levels; (2) the tax rate adjusts to satisfy budget projections; (3) government transfer only supplements the income of households with self-generated income below the poverty line; (4) deductions for basic living expenses, itemized investments and capital losses are allowed; (5) deductions cannot be applied to government transfer. A general framework emerges with three parameters that determine a minimum allowed tax deduction, a maximum allowed itemized deduction, and a maximum deduction defined by income percentage. An income distribution that mimics the United States, and a series of log-normal distributions are considered to quantitatively compare detailed characteristics of this tax system to progressive and flat tax systems. To minimize government dependency while maximizing after-tax income, the effective tax rate (ETR) as a function of income percentile takes the shape of the letter, V, inspiring the name victory tax, where the middle class has the lowest ETR.
An Entropy-Based Anti-Noise Method for Reducing Ranging Error in Photon Counting Lidar
Authors: Huang Mingwei , Zhang Zijing , Xie Jiaheng , Li Jiahuan , Zhao Yuan
Published: November 2021

Photon counting lidar for long-range detection faces the problem of declining ranging performance caused by background noise. Current anti-noise methods are not robust enough in the case of weak signal and strong background noise, resulting in poor ranging error. In this work, based on the characteristics of the uncertainty of echo signal and noise in photon counting lidar, an entropy-based anti-noise method is proposed to reduce the ranging error under high background noise. Firstly, the photon counting entropy, which is considered as the feature to distinguish signal from noise, is defined to quantify the uncertainty of fluctuation among photon events responding to the Geiger mode avalanche photodiode. Then, the photon counting entropy is combined with a windowing operation to enhance the difference between signal and noise, so as to mitigate the effect of background noise and estimate the time of flight of the laser pulses. Simulation and experimental analysis show that the proposed method improves the anti-noise performance well, and experimental results demonstrate that the proposed method effectively mitigates the effect of background noise to reduce ranging error despite high background noise.
Evidence of a Threshold Size for Norwegian Campsites and Its Dynamic Growth Process Implications—Does Gibrat’s Law Hold?
Authors: Valenta Robin , Idsø Johannes , Opstad Leiv
Published: November 2021

Although campsites are an important segment of the tourist sector, few applied articles have analyzed their growth path and tested Gibrat’s Law for firms within this industry. This knowledge can be of importance to the authorities when analyzing the regional impacts of growth in this sector. With government statistics from the last decade, we use a GMM framework to test the stricter version of Gibrat’s Law, which consist of three parts: the campsites’ growth trend, how they carry over success and failure, and how volatile their size is. The first and third part are rejected for Norwegian campsites, leading to a rejection of Gibrat’s Law. To see if firms of different sizes follow different dynamics, we split the sample in three parts. Here, we find evidence of a threshold size, as large campsites follow a fundamentally different dynamic than small and medium campsites. Specifically, large campsites gain no stability in revenue by further increases in size, whereas they carry over success/failure across years. The opposite is true for the rest of the sector. Gibrat’s Law is rejected on at least one count for each of the sub-samples. Lastly, we supplement the analysis with economy-wide and firm-specific variables to test further hypotheses.
Can Pedagogical Innovations Be Sustainable? One Evaluation Outlook for Research Developed in Portuguese Higher Education
Authors: Guerra Cecília , Costa Nilza
Published: November 2021

Across higher education, teachers have been inspiring themselves (and others) to apply pedagogical innovations grounded in a unifying intention: to prepare students for labour markets and societal challenges. Research-based education has been funded to promote pedagogical innovations with valuable impact on the students’ academic success and/or the teachers’ academic growth. However, there is still few documented research-based evidence that highlight how long-lasting and/or how embedded are the pedagogical innovations in academic practices, particularly when the funding period comes to an end. The purpose of this article is to discuss the extent to which funded national research-based education projects, developed in public Portuguese higher education institutions (universities and polytechnic institutes), have considered the sustainability of research results (e.g., pedagogical innovations), after funding ends. Based on a qualitative research approach, data collection included: document analysis of 39 projects’ materials and conceptions collected with 9 coordinators and 17 key participants from selected projects, through interviews and questionnaires, respectively. Content analysis of data collected showed that there are key factors that contribute to enhance and/or limit the sustainability of research results (e.g., funding and institutional support to maintain innovation). Results and recommendations are put forward to inform educational policies, funding agencies and involved actors (e.g., institution leaders, researchers, and teaching staff) to increase the sustainability of research results.
Washable, Low-Temperature Cured Joints for Textile-Based Electronics
Authors: Szalapak Jerzy , Scenev Vitalij , Janczak Daniel , Werft Lukas , Rotzler Sigrid , Jakubowska Malgorzata , von Krshiwoblozki Malte , Kallmayer Christine , Schneider-Ramelow Martin
Published: November 2021

Low-temperature die-attaching pastes for wearable electronics are the key components to realize any type of device where components are additively manufactured by pick and place techniques. In this paper, the authors describe a simple method to realize stretchable, bendable, die-attaching pastes based on silver flakes to directly mount resistors and LEDs onto textiles. This paste can be directly applied onto contact pads placed on textiles by means of screen and stencil printing and post-processed at low temperatures to achieve the desired electrical and mechanical properties below 60 °C without sintering. Low curing temperatures lead to lower power consumption, which makes this paste ecological friendly.
MarsExplorer: Exploration of Unknown Terrains via Deep Reinforcement Learning and Procedurally Generated Environments
Authors: Koutras Dimitrios I. , Kapoutsis Athanasios C. , Amanatiadis Angelos A. , Kosmatopoulos Elias B.
Published: November 2021

This paper is an initial endeavor to bridge the gap between powerful Deep Reinforcement Learning methodologies and the problem of exploration/coverage of unknown terrains. Within this scope, MarsExplorer, an openai-gym compatible environment tailored to exploration/coverage of unknown areas, is presented. MarsExplorer translates the original robotics problem into a Reinforcement Learning setup that various off-the-shelf algorithms can tackle. Any learned policy can be straightforwardly applied to a robotic platform without an elaborate simulation model of the robot’s dynamics to apply a different learning/adaptation phase. One of its core features is the controllable multi-dimensional procedural generation of terrains, which is the key for producing policies with strong generalization capabilities. Four different state-of-the-art RL algorithms (A3C, PPO, Rainbow, and SAC) are trained on the MarsExplorer environment, and a proper evaluation of their results compared to the average human-level performance is reported. In the follow-up experimental analysis, the effect of the multi-dimensional difficulty setting on the learning capabilities of the best-performing algorithm (PPO) is analyzed. A milestone result is the generation of an exploration policy that follows the Hilbert curve without providing this information to the environment or rewarding directly or indirectly Hilbert-curve-like trajectories. The experimental analysis is concluded by evaluating PPO learned policy algorithm side-by-side with frontier-based exploration strategies. A study on the performance curves revealed that PPO-based policy was capable of performing adaptive-to-the-unknown-terrain sweeping without leaving expensive-to-revisit areas uncovered, underlying the capability of RL-based methodologies to tackle exploration tasks efficiently.
Smart Glass System Using Deep Learning for the Blind and Visually Impaired
Authors: Mukhiddinov Mukhriddin , Cho Jinsoo
Published: November 2021

Individuals suffering from visual impairments and blindness encounter difficulties in moving independently and overcoming various problems in their routine lives. As a solution, artificial intelligence and computer vision approaches facilitate blind and visually impaired (BVI) people in fulfilling their primary activities without much dependency on other people. Smart glasses are a potential assistive technology for BVI people to aid in individual travel and provide social comfort and safety. However, practically, the BVI are unable move alone, particularly in dark scenes and at night. In this study we propose a smart glass system for BVI people, employing computer vision techniques and deep learning models, audio feedback, and tactile graphics to facilitate independent movement in a night-time environment. The system is divided into four models: a low-light image enhancement model, an object recognition and audio feedback model, a salient object detection model, and a text-to-speech and tactile graphics generation model. Thus, this system was developed to assist in the following manner: (1) enhancing the contrast of images under low-light conditions employing a two-branch exposure-fusion network; (2) guiding users with audio feedback using a transformer encoder–decoder object detection model that can recognize 133 categories of sound, such as people, animals, cars, etc., and (3) accessing visual information using salient object extraction, text recognition, and refreshable tactile display. We evaluated the performance of the system and achieved competitive performance on the challenging Low-Light and ExDark datasets.
Optimal Base Station Location for Network Lifetime Maximization in Wireless Sensor Network
Authors: Mukase Sandrine , Xia Kewen , Umar Abubakar
Published: November 2021

Wireless sensor networks have attracted worldwide attention in recent years. The failure of the nodes is caused by unequal energy dissipation. The reasons that cause unequal energy dissipation are, first and foremost, the distance between the nodes and the base station, and secondly, the distance between the nodes themselves. In wireless sensor networks, the location of the base station has a substantial impact on the network’s lifetime effectiveness. An improved genetic algorithm based on the crossover elitist conservation genetic algorithm (CECGA) is proposed to optimize the base station location, while for clustering, the K-medoids clustering (KMC) algorithm is used to determine optimal medoids among sensor nodes for choosing the appropriate cluster head. The idea is to decrease the communication distance between nodes and the cluster heads as well as the distance among nodes. For data routing, a multi-hop technique is used to transmit data from the nodes to the cluster head. Implementing an evolutionary algorithm for this optimization problem simplifies the problem with improved computational efficiency. The simulation results prove that the proposed algorithm performed better than compared algorithms by reducing the energy use of the network, which results in increasing the lifetime of the nodes, thereby improving the whole network.
Multi-Objective Grasshopper Optimization Based MPPT and VSC Control of Grid-Tied PV-Battery System
Authors: Chankaya Mukul , Hussain Ikhlaq , Ahmad Aijaz , Malik Hasmat , García Márquez Fausto Pedro
Published: November 2021

This article presents the control of a three-phase three-wire (3P-3W) dual-stage grid-tied PV-battery storage system using a multi-objective grass-hopper optimization (MOGHO) algorithm. The voltage source converter (VSC) control of the presented system is implemented with adaptive kernel width sixth-order maximum correntropy criteria (AKWSOMCC) and maximum power point tracking (MPPT) control is accomplished using the variable step-size incremental conductance (VSS-InC) technique. The proposed VSC control offers lower mean square error and better accuracy, convergence rate and speed as compared to peer adaptive algorithms, i.e., least mean square (LMS), least mean fourth (LMF), maximum correntropy criteria (MCC), etc. The adaptive Gaussian kernel width is a function of the error signal, which changes to accommodate and filter Gaussian and non-Gaussian noise signals in each iteration. The VSS-InC based MPPT is provided with a MOGHO based modulation factor for better and faster tracking of the maximum power point during changing solar irradiation. Similarly, an optimized gain conventional PI controller regulates the DC bus to improve the power quality, and DC link stability during dynamic conditions. The optimized DC-link generates an accurate loss component of current, which further improves the VSC capability of fundamental load current component extraction. The VSC is designed to perform multi-functional operations, i.e., harmonics elimination, reactive power compensation, load balancing and power balancing at point of common coupling during diverse dynamic conditions. The MOSHO based VSS-InC, and DC bus performance is compared to particle swarm optimization (PSO) and genetic algorithm (GA). The proposed system operates satisfactorily as per IEEE519 standards in the MATLAB simulation environment.
Advances in Public Transport Platform for the Development of Sustainability Cities
Authors: Corchado Juan M. , Larriba-Pey Josep L. , Chamoso-Santos Pablo , De la Prieta Pintado Fernando
Published: November 2021

There is high and varied mobility in modern societies which requires a complex transport system that adapts to social needs and guarantees the movement of people and goods in an economically efficient and safe way [...]
Jamming and Anti-Jamming Strategies of Mobile Vehicles
Authors: Dubosarskii Gleb , Primak Serguei
Published: November 2021

Anti-jamming games have become a popular research topic. However, there are not many publications devoted to such games in the case of vehicular ad hoc networks (VANETs). We considered a VANET anti-jamming game on the road using a realistic driving model. Further, we assumed the quadratic power function in both vehicle and jammer utility functions instead of the standard linear term. This makes the game model more realistic. Using mathematical methods, we expressed the Nash equilibrium through the system parameters in single-channel and multi-channel cases. Since the network parameters are usually unknown, we also compared the performance of several reinforcement learning algorithms that iteratively converge to the Nash equilibrium predicted analytically without having any information about the environment in the static and dynamic scenarios.
Robust Control Optimization Based on Actuator Fault and Sensor Fault Compensation for Mini Motion Package Electro-Hydraulic Actuator
Authors: Van Nguyen Tan , Tran Huy Q. , Nguyen Khoa Dang
Published: November 2021

In recent years, electro-hydraulic systems have been widely used in many industries and have attracted research attention because of their outstanding characteristics such as power, accuracy, efficiency, and ease of maintenance. However, such systems face serious problems caused simultaneously by disturbances, internal leakage fault, sensor fault, and dynamic uncertain equation components, which make the system unstable and unsafe. Therefore, in this paper, we focus on the estimation of system fault and uncertainties with the aid of advanced fault compensation techniques. First, we design a sliding mode observer using the Lyapunov algorithm to estimate actuator faults that produce not only internal leakage fault but also disturbances or unknown input uncertainties. These faults occur under the effect of payload variations and unknown friction nonlinearities. Second, Lyapunov analysis-based unknown input observer model is designed to estimate sensor faults arising from sensor noises and faults. Third, to minimize the estimated faults, a combination of actuator and sensor compensation fault is proposed, in which the compensation process is performed due to the difference between the output signal and its estimation. Finally, the numerical simulations are performed to demonstrate the effectiveness of the proposed method obtained under various faulty scenarios. The simulation results show that the efficiency of the proposed solution is better than the traditional PID controller and the sensor fault compensation method, despite the influence of noises.
Multi-Channel Profile Based Artificial Neural Network Approach for Remaining Useful Life Prediction of Electric Vehicle Lithium-Ion Batteries
Authors: Ansari Shaheer , Ayob Afida , Hossain Lipu Molla Shahadat , Hussain Aini , Saad Mohamad Hanif Md
Published: November 2021

Remaining useful life (RUL) is a crucial assessment indicator to evaluate battery efficiency, robustness, and accuracy by determining battery failure occurrence in electric vehicle (EV) applications. RUL prediction is necessary for timely maintenance and replacement of the battery in EVs. This paper proposes an artificial neural network (ANN) technique to predict the RUL of lithium-ion batteries under various training datasets. A multi-channel input (MCI) profile is implemented and compared with single-channel input (SCI) or single input (SI) with diverse datasets. A NASA battery dataset is utilized and systematic sampling is implemented to extract 10 sample values of voltage, current, and temperature at equal intervals from each charging cycle to reconstitute the input training profile. The experimental results demonstrate that MCI profile-based RUL prediction is highly accurate compared to SCI profile under diverse datasets. It is reported that RMSE for the proposed MCI profile-based ANN technique is 0.0819 compared to 0.5130 with SCI profile for the B0005 battery dataset. Moreover, RMSE is higher when the proposed model is trained with two datasets and one dataset, respectively. Additionally, the importance of capacity regeneration phenomena in batteries B0006 and B0018 to predict battery RUL is investigated. The results demonstrate that RMSE for the testing battery dataset B0005 is 3.7092, 3.9373 when trained with B0006, B0018, respectively, while it is 3.3678 when trained with B0007 due to the effect of capacity regeneration in B0006 and B0018 battery datasets.
A Review of the Geomechanics Aspects in Space Exploration
Authors: Knez Dariusz , Zamani Mohammad
Published: November 2021

From the 2000s onwards, unprecedented space missions have brought about a wealth of novel investigations on the different aspects of space geomechanics. Such aspects are related to the exploratory activities such as drilling, sampling, coring, water extraction, anchoring, etc. So far, a whole range of constitutive research projects on the plate tectonics, morphology, volcanic activities and volatile content of planetary bodies have been implemented. Furthermore, various laboratory experiments on extraterrestrial samples and their artificial terrestrial simulants are continually conducted to obtain the physical and mechanical properties of the corresponding specimens. Today, with the space boom being steered by diverse space agencies, the incorporation of geomechanics into space exploration appreciably appears much needed. The primary objective of this article is to collate and integrate the up-to-date investigations related to the geomechanical applications in space technologies. Emphasis is given to the new and future applications such as planetary drilling and water extraction. The main impetus is to provide a comprehensive reference for geoscience scientists and astronauts to quickly become acquainted with the cutting-edge advancements in the area of space geomechanics. Moreover, this research study also elaborates on the operational constraints in space geomechanics which necessitate further scientific investigations.
Studies on Energy Consumption of Electric Light Commercial Vehicle Powered by In-Wheel Drive Modules
Authors: Szewczyk Piotr , Łebkowski Andrzej
Published: November 2021

This article presents the results of energy consumption research for an electric light commercial vehicle (eLCV) powered by a centrally located motor (4 × 2 drive system) or motors placed in the vehicle’s wheels (4 × 4 drive system). For the considered constructions of electric drive systems, mathematical models of 4 × 2 and 4 × 4 drive systems were developed in the Modelica simulation environment, based on real data. Additionally, the influence of changes in the vehicle loading condition on the operation of the motor mounted in the wheel and the energy consumption of the drive module was investigated. On the basis of the conducted research, a comparative analysis of energy consumption by electric drive systems in 4 × 2 and 4 × 4 configurations was carried out for selected test cycles. The tests carried out with the Worldwide harmonized Light vehicles Test Cycles (WLTC) test cycle showed a roughly 6% lower energy consumption by the 4 × 4 drive system compared to the 4 × 2 configuration.
The Potential of Using Renewable Energy Sources in Poland Taking into Account the Economic and Ecological Conditions
Authors: Niekurzak Mariusz
Published: November 2021

The aim of the manuscript was to present the collective results of research on the profitability of using various renewable sources in Poland with the greatest development potential. In the paper, the economic parameters of various investment projects were determined and calculated, i.e., Net Capital Value (NPV), Internal Rate of Return (IRR) and the Period of Return on Invested Capital (PBT). The economic assessment of the use of RES technologies was supplemented with the assessment of environmental benefits. The ecological criterion adopted in the study was the assessment of the potential and costs of reducing greenhouse gas emissions as a result of replacing fossil fuels with renewable energy technologies. On the basis of the constructed economic model to assess the profitability of investments, it has been shown that the analyzed projects will start to bring, depending on their type and technical specification, measurable economic benefits in the form of a reduction in the amount of energy purchased on an annual basis and environmental benefits in the form of reduction of carbon dioxide emissions to the atmosphere. Moreover, the calculations show a high potential for the use of certain renewable sources in Poland, which contributes to the fulfillment of energy and emission obligations towards the EU. The analyzes and research of the Polish energy market with the use of the presented models have shown that the project is fully economically justified and will allow investors to make a rational decision on the appropriate selection of a specific renewable energy source for their investment. The presented economic models to assess the profitability of investments in renewable energy sources can be successfully used in other countries and can also be a starting point for a discussion about the direction of energy development. Due to the lack of collective, original and up-to-date research on the domestic market, the manuscript provides the reader with the necessary knowledge regarding the legitimacy of using renewable energy sources, investment and environmental profitability.
Effects of Clay Minerals and External Pressures on Imbibition in Shales
Authors: Lu Li , Li Jianting , Zhang Xuhui , Li Yingjun , Ma Fujian
Published: November 2021

Imbibition is an important mechanism of recovery during waterflooding and low flow-back during fracking in shale reservoirs. Experiments were carried out to study the development of imbibition in shale samples. The effects of clay minerals, especially the illite and IS, were mainly investigated and discussed. The imbibition under different pressures was conducted and compared. The influence of clay minerals on imbibition in shale is significant and complex. It is shown that the low content of illite and IS and small capillary force lead to small imbibition mass and speed. Formation of new micro fractures due to the swelling of clay minerals can cause the permeability to increase and the imbibition to be speeded up. The pore structure, the content of IS, and the capillary force affect the imbibition process significantly. The external pressure obviously affects the imbibition speed and the final imbibition mass. The content of clay minerals is more important to the formation of new micro fractures than the external pressure. There is a peak in the curve of displacement efficiency versus the content of either clay minerals or illite and IS. The effect of illite and IS more remarkable.
Comparison of Baseline Load Forecasting Methodologies for Active and Reactive Power Demand
Authors: Segovia Edgar , Vukovic Vladimir , Bragatto Tommaso
Published: November 2021

Forecasting the electricity consumption is an essential activity to keep the grid stable and avoid problems in the devices connected to the grid. Equaling consumption to electricity production is crucial in the electricity market. The grids worldwide use different methodologies to predict the demand, in order to keep the grid stable, but is there any difference between making a short time prediction of active power and reactive power into the grid? The current paper analyzes the most usual forecasting algorithms used in the electrical grids: ‘X of Y’, weighted average, comparable day, and regression. The subjects of the study were 36 different buildings in Terni, Italy. The data supplied for Terni buildings was split into active and reactive power demand to the grid. The presented approach gives the possibility to apply the forecasting algorithm in order to predict the active and reactive power and then compare the discrepancy (error) associated with forecasting methodologies. In this paper, we compare the forecasting methodologies using MAPE and CVRMSE. All the algorithms show clear differences between the reactive and active power baseline accuracy. ‘Addition X of Y middle’ and ‘Addition Weighted average’ better follow the pattern of the reactive power demand (the prediction CVRMSE error is between 12.56% and 13.19%) while ‘Multiplication X of Y high’ and ‘Multiplication X of Y middle’ better predict the active power demand (the prediction CVRMSE error is between 12.90% and 15.08%).
Economic Model and Risk Analysis of Energy Investments Based on Cogeneration Systems and Renewable Energy Sources
Authors: Gołębiewski Michał , Galant-Gołębiewska Marta
Published: November 2021

Today, distributed energy production is a key activity supporting energy systems in many countries around the world. Applicable regulations, fees and subsidies encourage entrepreneurs to look for solutions that will reduce operating costs and limit their negative impact on the natural environment. In the article, it was decided to carry out a technical and economic analysis and investment risk analysis for the distributed production of electricity and heat based on natural gas. Six scenarios were taken into account, depending on the number of gas engines, the use of the photovoltaic installation and the Organic Rankine Cycle (ORC) system. It has been shown that the most advantageous of the presented solution is the use of a system adjusted to the power of an industrial plant (return on investment in 4th year). The least beneficial for the investor are solutions aimed at the use and resale of energy supplemented with photovoltaic panels and an ORC system. An investment risk analysis and a sensitivity analysis were also performed. It shows how changes in electricity and gas prices and the environmental fee affect the profitability of investments. It has been shown that solutions with variable power are characterised by the lowest investment risk. The summary indicates the possible activities leading to greater economic efficiency. Such actions will be forced in the future by the market, political and environmental situation. Analyses such as these will allow entrepreneurs to thoroughly prepare for the European Union energy modernization process.
Optimal Pricing, Advertising, Production, Inventory and Investing Policies in a Multi-Stage Sustainable Supply Chain
Authors: Pan Jia-Liang , Chiu Chui-Yu , Wu Kun-Shan , Yang Chih-Te , Wang Yen-Wen
Published: November 2021

In this paper, the study of a sustainable production–inventory model with price and advertisement dependent on demand considering carbon emission reduction technology is investigated. The aim of this paper is to determine the optimal appropriate pricing, advertising, production, inventory, and capital investment decisions under various carbon emission policies to maximize the joint total profit of a multi-stage supply chain system. Various theoretical results and an algorithm are provided to verify and obtain the optimal solution of the problem. Further, the model is verified by numerical examples, and the robustness check of parameter variation is also analyzed. Finally, some management implications for decision makers are drawn from numerical examples. In summary, this study puts forward more realistic modeling hypothesis, which is beneficial to the academic research, and the research results can provide relevant decision makers with a model for managing a sustainable supply chain.
Decoupling Analysis of Greenhouse Gas Emissions from Economic Growth: A Case Study of Tunisia
Authors: Dahmani Mounir , Mabrouki Mohamed , Ragni Ludovic
Published: November 2021

The study examined the impact of different factors on greenhouse gas (GHG) emissions, by applying the extended STIRPAT model and decoupling analysis for Tunisia for the period 1990–2018. Furthermore, the study utilizes Tapio decoupling model, and the Auto-Regressive Distributed Lag (ARDL) bounds test approach to examine the relationship between the variables of greenhouse gas (GHG) emissions, economic growth, energy consumption, urbanization, innovation, and trade openness. The findings validated an inverted U-shape relationship between GDP and GHG emissions. In addition, we find that the consumption of renewable energy contributes to the reduction of GHG emissions in the long run. The findings call authority for the adaption of the regulatory framework relating to energy management, energy efficiency and the development of renewable energies, as well as to initiate energy market reforms, implement mitigation strategies and encourage investments in clean energies.
Design and Evaluation of High-Temperature Well Cementing Slurry System Based on Fractal Theory
Authors: Zheng Guanyi , Guo Xiaoyang , Li Zaoyuan , Sun Jinfei
Published: November 2021

The efficient development of oil and gas resources is inseparable from the progress of drilling technology and the safety of the long life cycle of wellbore. At present, exploration and development is expanding to deep and ultra-deep areas. The long life cycle safety of deep and ultra-deep wells is mainly realized by the sealing performance of cement slurry. Additionally, the accumulation degree of cement slurry particles is closely related to sealing performance. Based on fractal theory, an accumulation model of continuous distribution of additive material particles was designed, which can determine the range of fractal dimension necessary to realize the tight stacking and guide the proportion of solid admixture. The formulation of high temperature-resistant cement slurry was prepared by designing the ratio of solid admixture and optimizing the high temperature-resistant liquid admixture. The evaluation of engineering and temperature resistance of the cement slurry proves the rationality of the accumulation model, which can be applied to the design of a high temperature cementing slurry system in deep and ultra-deep wells.
Laminar Burning Velocities of Hydrogen-Blended Methane–Air and Natural Gas–Air Mixtures, Calculated from the Early Stage of p(t) Records in a Spherical Vessel
Authors: Mitu Maria , Razus Domnina , Schroeder Volkmar
Published: November 2021

The flammable hydrogen-blended methane–air and natural gas–air mixtures raise specific safety and environmental issues in the industry and transportation; therefore, their explosion characteristics such as the explosion limits, explosion pressures, and rates of pressure rise have significant importance from a safety point of view. At the same time, the laminar burning velocities are the most useful parameters for practical applications and in basic studies for the validation of reaction mechanisms and modeling turbulent combustion. In the present study, an experimental and numerical study of the effect of hydrogen addition on the laminar burning velocity (LBV) of methane–air and natural gas–air mixtures was conducted, using mixtures with equivalence ratios within 0.90 and 1.30 and various hydrogen fractions rH within 0.0 and 0.5. The experiments were performed in a 14 L spherical vessel with central ignition at ambient initial conditions. The LBVs were calculated from p(t) data, determined in accordance with EN 15967, by using only the early stage of flame propagation. The results show that hydrogen addition determines an increase in LBV for all examined binary flammable mixtures. The LBV variation versus the fraction of added hydrogen, rH, follows a linear trend only at moderate hydrogen fractions. The further increase in rH results in a stronger variation in LBV, as shown by both experimental and computed LBVs. Hydrogen addition significantly changes the thermal diffusivity of flammable CH4–air or NG–air mixtures, the rate of heat release, and the concentration of active radical species in the flame front and contribute, thus, to LBV variation.
Significance of Anisotropic Thermal Expansion in High Speed Electric Machines Employing NdFeB Permanent Magnets
Authors: Kumar Ram , La Rocca Antonino , Vakil Gaurang , Gerada David , Gerada Chris , Fernandes Baylon G.
Published: November 2021

Many high speed applications employ a surface permanent magnet (PM) machine topology with a retaining sleeve due to its robustness and ability to achieve high overall peripheral speeds as well as efficiencies. One often overlooked feature in the mechanical design of such machines, which has not achieved sufficient attention to date is the anisotropic thermal expansion of rare earth magnets, the degree of which varies for different magnet technologies. This paper investigates the effects of the aforementioned on the mechanical design of a high speed PM spindle machine with NdFeB magnets. The maximum allowable interference is found to be limited by the working temperature of the magnets while the minimum required interference is increased due to their anisotropic thermal expansion. Based on this, appropriate conditions are formulated to integrate a Neodymium Iron Boron (NdFeB) PM in high speed rotors. These modifications considering the shaft together with the magnet anisotropic thermal expansion are included in a proposed rotor design and validated using simulations in ANSYS mechanical environment.
Research on a Solar Hybrid Trigeneration System Based on Exergy and Exergoenvironmental Assessments
Authors: Cavalcanti Eduardo J. C. , Ferreira João Victor M. , Carvalho Monica
Published: November 2021

The environmental performance of a combined cooling, heating, and power system is analyzed in this study at a component-level using a SPECO-based exergoenvironmental analysis. The engine consumes natural gas and produces 168.6 kW net power. The waste heat is recovered by a LiBr-H2O absorption chiller and a heat exchanger, which are used for cooling and heating purposes. The energy system is assisted by a solar field. An environmental Life Cycle Assessment quantifies the environmental impacts of the system, and these data are combined with exergy evaluations. The highest total environmental impact rate, 23,740.16 mPt/h, is related to the internal combustion engine, of which pollutant formation is the primary source of environmental impact. Compared with a non-renewable energy system, the solar-assisted trigeneration system decreased the environmental impact per exergy unit of chilled water by 10.99%. Exergoenvironmental performance can be further improved by enhancing the exergy efficiency of the solution pump and high-pressure generator (HG), and by employing a treatment to remove nitrogen oxides in the reciprocating engine.