bluuebunny/crossref_metadata_2025 huggingface dataset:
Coronary Artery Disease in Patients Hospitalized for Peripheral Artery Disease: A Nationwide Analysis of 1.8 Million Patients
Authors: Helmer Magnus , Scheurig-Muenkler Christian , Brandt Verena , Tesche Christian , Bette Stefanie , Schwarz Florian , Kroencke Thomas , Decker Josua A.
Published: March 2023

Purpose: Coronary artery disease (CAD) and peripheral artery disease (PAD) are highly prevalent in society. This nationwide analysis aimed to evaluate the trends of in-hospital treatment of patients admitted due to PAD with and without concomitant CAD, to determine the prevalence and risk factors of concomitant CAD in patients with PAD. Methods: Using data from the German Federal Statistical Office, we included all admissions for PAD (with and without concomitant CAD) in Germany between 2009 and 2018. Baseline patient characteristics, outcomes and comorbidities were analyzed. Elixhauser comorbidity groups and the linear van Walraven comorbidity score (vWs) were calculated to assess the comorbidity burden. Results: Of all 1,793,517 patients hospitalized for PAD, a total of 21.8% (390,259) had concomitant CAD, increasing from 18.6% in 2009 to 24.4% in 2018. Patients with accompanying CAD showed higher in-hospital mortality (3.7 vs. 2.6%), more major amputations (9.0 vs. 7.7%) and more comorbidities (Elixhauser score: 4.2 vs. 3.2 and vWs: 9.1 vs. 6.1), resulting in higher costs (median: EUR 4541 vs. EUR 4268 per case). More advanced stages of PAD were associated with multi-vessel CAD (10% of all patients with PAD Fontaine IV showed 3-vessel CAD) and the prevalence of multi-vessel CAD increased predominantly in patients with advanced PAD. Conclusion: One in four patients hospitalized for PAD had concomitant CAD, showing an increase over time with an additional medical and economic burden for hospitals compared with patients without CAD.
A CAD System for Lung Cancer Detection Using Hybrid Deep Learning Techniques
Authors: Alsheikhy Ahmed A. , Said Yahia , Shawly Tawfeeq , Alzahrani A. Khuzaim , Lahza Husam
Published: March 2023

Lung cancer starts and spreads in the tissues of the lungs, more specifically, in the tissue that forms air passages. This cancer is reported as the leading cause of cancer deaths worldwide. In addition to being the most fatal, it is the most common type of cancer. Nearly 47,000 patients are diagnosed with it annually worldwide. This article proposes a fully automated and practical system to identify and classify lung cancer. This system aims to detect cancer in its early stage to save lives if possible or reduce the death rates. It involves a deep convolutional neural network (DCNN) technique, VGG-19, and another deep learning technique, long short-term memory networks (LSTMs). Both tools detect and classify lung cancers after being customized and integrated. Furthermore, image segmentation techniques are applied. This system is a type of computer-aided diagnosis (CAD). After several experiments on MATLAB were conducted, the results show that this system achieves more than 98.8% accuracy when using both tools together. Various schemes were developed to evaluate the considered disease. Three lung cancer datasets, downloaded from the Kaggle website and the LUNA16 grad challenge, were used to train the algorithm, test it, and prove its correctness. Lastly, a comparative evaluation between the proposed approach and some works from the literature is presented. This evaluation focuses on the four performance metrics: accuracy, recall, precision, and F-score. This system achieved an average of 99.42% accuracy and 99.76, 99.88, and 99.82% for recall, precision, and F-score, respectively, when VGG-19 was combined with LSTMs. In addition, the results of the comparison evaluation show that the proposed algorithm outperforms other methods and produces exquisite findings. This study concludes that this model can be deployed to aid and support physicians in diagnosing lung cancer correctly and accurately. This research reveals that the presented method has functionality, competence, and value among other implemented models.
Factors Associated with the Effectiveness of Regimens for the Treatment of Tuberculosis in Patients Coinfected with HIV/AIDS: Cohort 2015 to 2019
Authors: de Resende Natália Helena , de Miranda Silvana Spíndola , Reis Adriano Max Moreira , de Pádua Cristiane Aparecida Menezes , Haddad João Paulo Amaral , da Silva Paulo Vitor Rozario , da Silva Dirce Inês , Carvalho Wânia da Silva
Published: March 2023

(1) Background: Infection with the Human Immunodeficiency Virus (HIV) is a significant challenge for tuberculosis (TB) control, with increasing mortality rates worldwide. Moreover, the loss to follow-up is very high, with low adherence to treatment, resulting in unfavorable endpoints. This study aimed to analyze the effectiveness of TB treatment in patients coinfected with HIV/AIDS and its associated factors. (2) Methods: Patients coinfected with TB and HIV/AIDS at a Reference Hospital for infectious diseases were followed up for a maximum of one year from the start of TB treatment until cure or censorship (death, abandonment, and transfer) from 2015 to 2019. The Cox proportional model was used to identify risk factors for effectiveness. (3) Results: Of the 244 patients included in the cohort, 58.2% (142/244) had no treatment effectiveness, 12.3% (30/244) died, and 11.1% (27/244) abandoned treatment. Viral suppression at the onset of TB treatment (HR = 1.961, CI = 1.123–3.422), previous use of Antiretroviral Therapy (HR = 1.676, CI = 1.060–2.651), new cases (HR = 2.407, CI = 1.197–3.501), not using illicit drugs (HR = 1.763, CI = 1.141–2.723), and using the basic TB regimen (HR = 1.864, CI = 1.084–3.205) were significant variables per the multivariate Cox regression analysis. (4) Conclusion: TB treatment for most TB patients coinfected with HIV/AIDS was not effective. This study identified that an undetectable viral load at the beginning of the disease, previous use of ART, not using illicit drugs and not having previously taken anti-TB treatment are factors associated with successful TB treatment.
Evaluation of an Innovative Rosette Flight Plan Design for Wildlife Aerial Surveys with UAS
Authors: Linchant Julie , Lejeune Philippe , Quevauvillers Samuel , Vermeulen Cédric , Brostaux Yves , Lhoest Simon , Michez Adrien
Published: March 2023

(1) Regular wildlife abundance surveys are a key conservation tool. Manned aircraft flying transects often remain the best alternative for counting large ungulates. Drones have cheaper and safer logistics, however their range is generally too short for large-scale application of the traditional method. Our paper investigates an innovative rosette flight plan for wildlife census, and evaluates relevance of this sampling protocol by comparing its statistical performance with transects, based on numerical simulations. (2) The UAS flight plan consisted in two rosettes of 6 triangular “petals” spread across the survey area, for a theoretical sampling rate of 2.95%, as opposed to a 20.04% classic sampling protocol with systematic transects. We tested the logistics of our survey design in Garamba National Park. We then modeled theoretical population distributions for both antelopes and buffaloes. We calculated animal densities in the simulated footprints of the theoretical rosette and transect flight plans. We also tested aggregating results for 2, 3 and 4 repetitions of the same rosette flight plan to increase the sampling rate. (3) Simulation results showed that the coefficient of variation associated with density estimates decreases with the number of repetitions of the rosette flight plan, and aggregating four repetitions is enough to give antelope densities with acceptable accuracy and precision while staying at a lower sampling rate. Buffalo densities displayed much higher variability and it shows the significant impact of gregariousness on density estimate accuracy and precision. (4) The method was found to be inappropriate for highly aggregative species but efficient for species that disperse widely and more randomly in their environment. Logistics required to perform a full survey in the field remain time- and resources-intensive. Therefore, we recommend it for remote parks facing difficulties to organize manned aerial counts. Lower costs and developments such as solar UASs offer interesting future perspectives.
Deep Reinforcement Learning Based Computation Offloading in UAV-Assisted Edge Computing
Authors: Zhang Peiying , Su Yu , Li Boxiao , Liu Lei , Wang Cong , Zhang Wei , Tan Lizhuang
Published: March 2023

Traditional multi-access edge computing (MEC) often has difficulty processing large amounts of data in the face of high computationally intensive tasks, so it needs to offload policies to offload computation tasks to adjacent edge servers. The computation offloading problem is a mixed integer programming non-convex problem, and it is difficult to have a good solution. Meanwihle, the cost of deploying servers is often high when providing edge computing services in remote areas or some complex terrains. In this paper, the unmanned aerial vehicle (UAV) is introduced into the multi-access edge computing network, and a computation offloading method based on deep reinforcement learning in UAV-assisted multi-access edge computing network (DRCOM) is proposed. We use the UAV as the space base station of MEC, and it transforms computation task offloading problems of MEC into two sub-problems: find the optimal solution of whether each user’s device is offloaded through deep reinforcement learning; allocate resources. We compared our algorithm with other three offloading methods, i.e., LC, CO, and LRA. The maximum computation rate of our algorithm DRCOM is 142.38% higher than LC, 50.37% higher than CO, and 12.44% higher than LRA. The experimental results demonstrate that DRCOM greatly improves the computation rate.
Remote Sensing Image of The Landsat 8–9 Compressive Sensing via Non-Local Low-Rank Regularization with the Laplace Function
Authors: Li Guibing , Jin Weidong , Miao Jiaqing , Tan Ying , Li Yingling , Zhang Weixuan , Li Liang
Published: March 2023

Utilizing low-rank prior data in compressed sensing (CS) schemes for Landsat 8–9 remote sensing images (RSIs) has recently received widespread attention. Nevertheless, most CS algorithms focus on the sparsity of an RSI and ignore its low-rank (LR) nature. Therefore, this paper proposes a new CS reconstruction algorithm for Landsat 8–9 remote sensing images based on a non-local optimization framework (NLOF) that is combined with non-convex Laplace functions (NCLF) used for the low-rank approximation (LAA). Since the developed algorithm is based on an approximate low-rank model of the Laplace function, it can adaptively assign different weights to different singular values. Moreover, exploiting the structural sparsity (SS) and low-rank (LR) between the image patches enables the restored image to obtain better CS reconstruction results of Landsat 8–9 RSI than the existing models. For the proposed scheme, first, a CS reconstruction model is proposed using the non-local low-rank regularization (NLLRR) and variational framework. Then, the image patch grouping and Laplace function are used as regularization/penalty terms to constrain the CS reconstruction model. Finally, to effectively solve the rank minimization problem, the alternating direction multiplier method (ADMM) is used to solve the model. Extensive numerical experimental results demonstrate that the non-local variational framework (NLVF) combined with the low-rank approximate regularization (LRAR) method of non-convex Laplace function (NCLF) can obtain better reconstruction results than the more advanced image CS reconstruction algorithms. At the same time, the model preserves the details of Landsat 8–9 RSIs and the boundaries of the transition areas.
Skew Constacyclic Codes over a Non-Chain Ring
Authors: Köroğlu Mehmet Emin , Sarı Mustafa
Published: March 2023

In this paper, we investigate the algebraic structure of the non-local ring Rq=Fq[v]/⟨v2+1⟩ and identify the automorphisms of this ring to study the algebraic structure of the skew constacyclic codes and their duals over this ring. Furthermore, we give a necessary and sufficient condition for the skew constacyclic codes over Rq to be linear complementary dual (LCD). We present some examples of Euclidean LCD codes over Rq and tabulate the parameters of Euclidean LCD codes over finite fields as the Φ-images of these codes over Rq, which are almost maximum distance separable (MDS) and near MDS. Eventually, by making use of Hermitian linear complementary duals of skew constacyclic codes over Rq and the map Φ, we give a class of entanglement-assisted quantum error correcting codes (EAQECCs) with maximal entanglement and tabulate parameters of some EAQECCs with maximal entanglement over finite fields.
An Explicit-Correction-Force Scheme of IB-LBM Based on Interpolated Particle Distribution Function
Authors: Liu Bowen , Shi Weiping
Published: March 2023

In order to obtain a better numerical simulation method for fluid–structure interaction (FSI), the IB-LBM combining the lattice Boltzmann method (LBM) and immersed boundary method (IBM) has been studied more than a decade. For this purpose, an explicit correction force scheme of IB-LBM was proposed in this paper. Different from the current IB-LBMs, this paper introduced the particle distribution function to the interpolation process from the fluid grids to the immersed boundary at the mesoscopic level and directly applied the LBM force models to obtain the interface force with a simple form and explicit process. Then, in order to ensure the mass conservation in the local area of the interface, this paper corrected the obtained interface force with the correction matrix, forming the total explicit-correction-force (ECP) scheme of IB-LBM. The results of four numerical tests were used to verify the order of accuracy and effectiveness of the present method. The streamline penetration is limited and the numerical simulation with certain application significance is successful for complex boundary conditions such as the movable rigid bodies (free oscillation of the flapping foil) and flexible deformable bodies (free deformation of cylinders). In summary, we obtained a simple and alternative simulation method that can achieve good simulation results for engineering reference models with complex boundary problems.
What Is Heat? Can Heat Capacities Be Negative?
Authors: Roduner Emil
Published: March 2023

In the absence of work, the exchange of heat of a sample of matter corresponds to the change of its internal energy, given by the kinetic energy of random translational motion of all its constituent atoms or molecules relative to the center of mass of the sample, plus the excitation of quantum states, such as vibration and rotation, and the energy of electrons in excess to their ground state. If the sample of matter is equilibrated it is described by Boltzmann’s statistical thermodynamics and characterized by a temperature T. Monotonic motion such as that of the stars of an expanding universe is work against gravity and represents the exchange of kinetic and potential energy, as described by the virial theorem, but not an exchange of heat. Heat and work are two distinct properties of thermodynamic systems. Temperature is defined for the radiative cosmic background and for individual stars, but for the ensemble of moving stars neither temperature, nor pressure, nor heat capacities are properly defined, and the application of thermodynamics is, therefore, not advised. For equilibrated atomic nanoclusters, in contrast, one may talk about negative heat capacities when kinetic energy is transformed into potential energy of expanding bonds.
Explicit Expressions for Most Common Entropies
Authors: Nadarajah Saralees , Kebe Malick
Published: March 2023

Entropies are useful measures of variation. However, explicit expressions for entropies available in the literature are limited. In this paper, we provide a comprehensive collection of explicit expressions for four of the most common entropies for over sixty continuous univariate distributions. Most of the derived expressions are new. The explicit expressions involve known special functions.
Effect of the Shadow Economy on Tax Reform in Developing Countries
Authors: Gnangnon Sena Kimm
Published: March 2023

The present analysis has examined the effect of the shadow economy on tax reform in developing countries. The first type of tax reform is the “structural tax reform” (STR) characterized by large episodes of tax revenue mobilization, identified by Akitoby et al. (2020) [Tax revenue mobilization episodes in developing countries, Policy Design and Practice 3: 1–29] using the narrative approach that allows obtaining the precise nature and exact timing of major tax actions in several areas of tax policy and revenue administration that truly led to increases in tax revenue. The second type of tax reform is referred to as “tax transition reform” (TTR) and reflects the reform of the tax revenue structure that involves the reduction of its dependence on international trade tax revenue at the benefit of domestic tax revenue. The analysis has used various estimators and shown that the shadow economy reduces the likelihood of STR (notably in low-income countries), including in several tax policy areas and in the revenue administration area. The shadow economy also undermines the TTR process in countries whose tax revenue structure is strongly dependent on international trade tax revenue. Finally, it fosters the TTR process in countries that enjoy greater trade openness.
Reliable Resource Allocation and Management for IoT Transportation Using Fog Computing
Authors: Atiq Haseeb Ullah , Ahmad Zulfiqar , uz Zaman Sardar Khaliq , Khan Muhammad Amir , Shaikh Asad Ali , Al-Rasheed Amal
Published: March 2023

Resource allocation in smart settings, more specifically in Internet of Things (IoT) transportation, is challenging due to the complexity and dynamic nature of fog computing. The demands of users may alter over time, necessitating more trustworthy resource allocation and administration. Effective resource allocation and management systems must be designed to accommodate changing user needs. Fog devices don’t just run fog-specific software. Resource and link failures could be brought on by the absence of centralised administration, device autonomy, and wireless communication in the fog environment. Resources must be allocated and managed effectively because the majority of fog devices are battery-powered. Latency-aware IoT applications, such as intelligent transportation, healthcare, and emergency response, are now pervasive as a result of the enormous growth of ubiquitous computing. These services generate a large amount of data, which requires edge processing. The flexibility and services on-demand for the cloud can successfully manage these applications. It’s not always advisable to manage IoT applications exclusively in the cloud, especially for latency-sensitive applications. Thus, fog computing has emerged as a bridge between the cloud and the devices it supports. This is typically how sensors and IoT devices are connected. These neighbouring Fog devices control storage and intermediary computation. In order to improve the Fog environment reliability in IoT-based systems, this paper suggests resource allocation and management strategy. When assigning resources, latency and energy efficiency are taken into account. Users may prioritise cost-effectiveness over speed in a fog. Simulation was performed in the iFogSim2 simulation tool, and performance was compared with one of the existing state-of-the-art strategy. A comparison of results shows that the proposed strategy reduced latency by 10.3% and energy consumption by 21.85% when compared with the existing strategy.
Active Disturbance Rejection Control of Bearingless Permanent Magnet Synchronous Motor Based on Genetic Algorithm and Neural Network Parameters Dynamic Adjustment Method
Authors: Wang Xin , Zhu Huangqiu
Published: March 2023

In order to solve the problem of poor control performance, caused by fixed parameters of the active disturbance rejection control (ADRC) in bearingless permanent magnet synchronous motors (BPMSM), a dynamic parameters adjustment method of ADRC, based on a genetic algorithm and back-propagation neural network (GA-BPNN), is proposed. Firstly, the ADRC control models of motor-side and suspension-side are established, according to the motor speed formula and suspension force formula. Secondly, the BPNN algorithm is used to dynamically adjust the parameters of the ADRC, and the operation processes of BPNN are deduced, according to the chain rule. Thirdly, in order to avoid the problem of getting out of control, caused by the convergence failure of BPNN, a GA based on floating point coding is used to optimize the initial value of BPNN. Finally, these methods are integrated to form a BPMSM control system, based on the GA-BPNN-ADRC, and the effectiveness is verified on an experimental platform. The experimental results, show that the proposed method reduces the failure probability of the system from 35.61% to 0%, and the anti-interference ability and dynamic performance of the speed and displacement of the control system are significantly improved.
An Air Pollutant Forecast Correction Model Based on Ensemble Learning Algorithm
Authors: Ma Jianhong , Ma Xiaoyan , Yang Cong , Xie Lipeng , Zhang Weixing , Li Xuexiang
Published: March 2023

In recent years, air pollutants have become an important issue in meteorological research and an indispensable part of air quality forecasting. To improve the accuracy of the Chinese Unified Atmospheric Chemistry Environment (CUACE) model’s air pollutant forecasts, this paper proposes a solution based on ensemble learning. Firstly, the forecast results of the CUACE model and the corresponding monitoring data are extracted. Then, using feature analysis, we screen the correction factors that affect air quality. The random forest algorithm, XGBoost algorithm, and GBDT algorithm are employed to correct the prediction results of PM2.5, PM10, and O3. To further optimize the model, we introduce the grid search method. Finally, we compare and analyze the correction effect and determine the best correction model for the three air pollutants. This approach enhances the precision of the CUACE model’s forecast and improves our understanding of the factors that affect air quality. The experimental results show that the model has a better prediction error correction effect than the traditional machine learning statistical model. After the algorithm correction, the prediction accuracy of PM2.5 and PM10 is increased by 60%, and the prediction accuracy of O3 is increased by 70%.
Water System Safety Analysis Model
Authors: Tchórzewska-Cieślak Barbara , Pietrucha-Urbanik Katarzyna
Published: March 2023

The operation of a water supply system (WSS) is inextricably linked with the possibility of different types of failure. It is very common for these failures to be random in nature. The results of reliability studies carried out in many water supply systems revealed, for example, the possibility of incidental water pollution, power supply issues, failure in machinery, damage to water plants, or natural disasters. As a result of the WSS failure, we deal with a state of threat to safety (TSS) or a state of loss of safety (LSS). Using Markov processes, we developed a failure model of the WSS to determine the possibility that the system may find itself in different states of safety. As a result, a mathematical model using Markov processes has been proposed for each of these distinct states of safety (complete safety state—CSS; threat to safety state—TSS; and loss of safety state—LSS). The proposed approach in the water supply system will limit emergency states by optimizing working and repair times. Reducing losses in the water supply system is crucial to reduce and optimize energy consumption for water production and distribution.
DSO-Aggregator Demand Response Cooperation Framework towards Reliable, Fair and Secure Flexibility Dispatch
Authors: Venizelou Venizelos , Tsolakis Apostolos C. , Evagorou Demetres , Patsonakis Christos , Koskinas Ioannis , Therapontos Phivos , Zyglakis Lampros , Ioannidis Dimosthenis , Makrides George , Tzovaras Dimitrios , Georghiou George E.
Published: March 2023

Unlocking flexibility on the demand side is a prerequisite for balancing supply and demand in distribution networks with high penetration levels of renewable energy sources that lead to high volatility in energy prices. The main means of fully gaining access to the untapped flexibility is the application of demand response (DR) schemes through aggregation. Notwithstanding, to extract the utmost of this potential, a combination of performance-, financial-, and technical-related parameters should be considered, a balance rarely identified in the state of the art. The contribution of this work lies in the introduction of a holistic DR framework that refines the DR-related strategies of the aggregator towards optimum flexibility dispatch, while facilitating its cooperation with the distribution system operator (DSO). The backbone of the proposed DR framework is a novel constrained-objective optimisation function which minimises the aggregator’s costs through optimal segmentation of customer groups based on fairness and reliability aspects, while maintaining the distribution balance of the grid. The proposed DR framework is evaluated on a modified IEEE 33-Bus radial distribution system where a real DR event is successfully executed. The flexibility of the most fair, reliable and profitable sources, identified by the developed optimisation function, is dispatched in an interoperable and secure manner without interrupting the normal operation of the distribution grid.
An Overview of Geological CO2 Sequestration in Oil and Gas Reservoirs
Authors: Askarova Aysylu , Mukhametdinova Aliya , Markovic Strahinja , Khayrullina Galiya , Afanasev Pavel , Popov Evgeny , Mukhina Elena
Published: March 2023

A tremendous amount of fossil fuel is utilized to meet the rising trend in the world’s energy demand, leading to the rising level of CO2 in the atmosphere and ultimately contributing to the greenhouse effect. Numerous CO2 mitigation strategies have been used to reverse this upward trend since large-scale decarbonization is still impractical. For multiple reasons, one of the optimal and available solutions is the usage of old depleted oil and gas reservoirs as objects for prospective CO2 utilization. The methods used in CO2 underground storage are similar to those used in oil exploration and production. However, the process of CO2 storage requires detailed studies conducted experimentally and numerically. The main goal of this paper is to present an overview of the existing laboratory studies, engineering and modeling practices, and sample case studies related to the CCS in oil and gas reservoirs. The paper covers geological CO2 storage technologies and discusses knowledge gaps and potential problems. We attempt to define the key control parameters and propose best practices in published experimental and numerical studies. Analysis of laboratory experiments shows the applicability of the selected reservoirs focusing on trapping mechanisms specific to oil and gas reservoirs only. The current work reports risk control and existing approaches to numerical modeling of CO2 storage. We also provide updates on completed and ongoing CCS in oil and gas reservoir field projects and pilots worldwide.
Cooling Water for Electricity Production in Poland: Assessment and New Perspectives
Authors: Kędra Mariola
Published: March 2023

Sustainable development requires a holistic approach to natural resources and ecosystems to avoid their degradation. Cooling water—water used for cooling in industrial or manufacturing processes and then returned at elevated temperature to a local river or lake—is a common cause of thermal pollution. The purpose of the analysis was to assess how much cooling water is currently abstracted to generate electricity in Poland, what the dynamics of this abstraction in the last 20 years (2000–2019) were, and to what extent this abstraction affects the available freshwater resources in the country and in individual river basins. Moreover, the latest plans for the development of the electricity sector in Poland were analyzed to determine how the implementation of these plans may affect cooling water abstractions and the condition of Poland’s freshwater resources. Trend analysis was performed in order to assess the strength of linear trends in the studied time series. The results show that in Poland from 2000–2019, nearly 75% of water abstracted from surface resources was cooling water used to produce electricity. The dynamics of cooling water abstraction show a clear downward trend of 54.5 million m3 annually, despite a significant increase in electricity production. This decline is likely to continue over the next 20 years, with the major unknown being the planned introduction of nuclear power as an energy source.
Influence of the In-Cylinder Catalyst on the Aftertreatment Efficiency of a Diesel Engine
Authors: Andrych-Zalewska Monika , Chlopek Zdzislaw , Pielecha Jacek , Merkisz Jerzy
Published: March 2023

The article discusses the use of a catalyst inside the cylinder, the task of which is to reduce exhaust emissions from a diesel engine. The catalyst (platinum) applied to the glow plugs provided an additional method of exhaust aftertreatment. Due to their usage, especially in urban driving, passenger cars are characterized by small mileage between individual trips, so they often operate from a cold engine start and work at a low engine temperature, which leads to reduced catalytic reactor efficiency. For this reason, the efficiency of the internal catalyst was tested in relation to the efficiency of the external reactor. This efficiency was determined based on exhaust emission measurements (before and after the catalytic reactor) in two stages: stage 1: idling of a hot engine, and stage 2: simulation of the NEDC test (valid for the selected test object). The tests were carried out on an engine dynamometer, where the traffic conditions from the type-approval test carried out on a chassis dynamometer could be replicated. The tests were carried out on a Euro 4 1.3 JTD MultiJet diesel engine. The results (measurement of carbon monoxide, hydrocarbons, and the number of particles) related to the assessment of the effect the catalyst in the cylinder were discussed. The obtained catalytic reactor efficiency results, regardless of the type of research, indicated that it achieved the highest efficiency in reducing the concentration of hydrocarbons, and the lowest—in relation to the number of solid particles (as that is not its primary function). It is particularly significant that the in-cylinder catalytic converter was most efficient during the cold engine start, which happens frequently in urban driving. The efficiency of the diesel oxidation catalytic reactor (DOC) during the engine start-up and warm-up phases with the use of standard glow plugs reached values of 31.3%, 34.1% and 14.3%, respectively, for carbon monoxide, hydrocarbons and the particle number. On the other hand, the determined efficiency of the DOC in a setup with the modified glow plugs was 28.9%, 35.7% and 12.5%, respectively. The proposed solution can be used to improve the combustion quality in internal combustion engines used in hybrid vehicles, which are characterized by frequent engine starts and stops. In addition, it is possible to use such a solution retroactively in traditional vehicles powered by an internal combustion engine, which could result in an improvement in their emission class through what is called retrofitting.
Increasing Technical Efficiency of Renewable Energy Sources in Power Systems
Authors: Smolarz Andrzej , Lezhniuk Petro , Kudrya Stepan , Komar Viacheslav , Lysiak Vladyslav , Hunko Iryna , Amirgaliyeva Saltanat , Smailova Saule , Orazbekov Zhassulan
Published: March 2023

This paper presents a method for refining the forecast schedule of renewable energy sources (RES) generation by its intraday adjustment and investigates the measures for reserving RES with unstable generation in electric power systems (EPSs). Owing to the dependence of electricity generation by solar and wind power plants (PV and WPPs, respectively) on natural conditions, problems arise with their contribution to the process of balancing the power system. Therefore, the EPS is obliged to keep a power reserve to compensate for deviations in RES from the planned generation amount. A system-wide reserve (mainly the shunting capacity of thermal and hydroelectric power plants) is used first, followed by other means of power reserve: electrochemical, hydrogen, or biogas plants. To analyze the technical and economic efficiency of certain backup means, mathematical models based on the theory of similarity and the criterion method were developed. This method is preferred because it provides the ability to compare different methods of backing up RES generation with each other, assess their proportionality, and determine the sensitivity of costs to the capacity of backup methods with minimal available initial information. Criterion models have been formed that allow us to build dependencies of the costs of backup means for unstable RES generation on the capacity of the backup means. It is shown that, according to the results of the analysis of various methods and means of RES backup, hydrogen technologies are relatively the most effective. The results of the analysis in relative units can be clarified if the current and near-term price indicators are known.
Potential and Most Promising Second-Life Applications for Automotive Lithium-Ion Batteries Considering Technical, Economic and Legal Aspects
Authors: Michelini Emanuele , Höschele Patrick , Ratz Florian , Stadlbauer Michael , Rom Werner , Ellersdorfer Christian , Moser Jörg
Published: March 2023

Electric vehicle (EV) batteries, i.e., currently almost exclusively lithium-ion batteries, are removed from the vehicle once they no longer meet certain requirements. However, instead of being disposed of or recycled, the removed batteries can be used in another, less demanding application, giving them a “second life”. Research in the field of second-life batteries (SLBs) is still at an early stage and, to better understand the “second life” concept and the related challenges, potential second-life applications need to be identified first. Using a detailed study of the scientific literature and an interview with field experts, a list of potential second-life applications was drafted. Afterwards, a technical, economic, and legal evaluation was conducted to identify the most promising options. The findings of this research consisted of the identification of 65 different mobile, semi-stationary and stationary second-life applications; the applications selected as most promising are automated guided vehicles (AGVs) and industrial energy storage systems (ESSs) with renewable firming purposes. This research confirms the great potential of SLBs indicating that second-life applications are many and belong to a broad spectrum of different sectors. The applications identified as most promising are particularly attractive for the second-life use of batteries as they belong to fast-growing markets.
Role of Non-Renewable Energy Efficiency and Renewable Energy in Driving Environmental Sustainability in India: Evidence from the Load Capacity Factor Hypothesis
Authors: Alola Andrew Adewale , Özkan Oktay , Usman Ojonugwa
Published: March 2023

Policymakers and environmental scientists have proposed numerous measures toward achieving a sustainable environment. Some of these measures include the efficient use of energy and a clean energy transition. This study empirically investigates the role of non-renewable energy efficiency and renewable energy utilization in driving environmental sustainability in India over the period from 1965 to 2018. Using the approach of the Dynamic Autoregressive Distributed Lag (DyARDL) simulations, the empirical evidence shows that non-renewable energy efficiency and renewable energy utilization promote environmental sustainability through an increase in the load capacity factor. The effects of financial development and trade impede environmental sustainability through a decrease in the load capacity factor. The results further show that the relationship between income and load capacity factor is characterized by an inverted U-shape. This suggests that the load capability curve (LCC) hypothesis is not valid for India. Given the overall findings of this study, it is suggested that policymakers should promote energy efficiency and renewable energy technologies as the ultimate policy measure to mitigate the accumulation of CO2 emissions and other significant climatic changes in India.
Analysis of Forest Biomass Wood Briquette Structure According to Different Tests of Density
Authors: Roman Kamil , Rzodkiewicz Witold , Hryniewicz Marek
Published: March 2023

X-ray technology is capable of non-destructively testing solid wood samples. The prepared wood briquette samples were identified by X-ray technology. The studies assessed the effect of biomass briquette structure by observing wood chip fractions under an X-ray. Study results show that X-ray technology is an effective tool for analyzing biomass wood-based materials, e.g., density, improving wood products quality and performance. The measurements are consistent with the true density, and chemical properties were measured from chosen material. In the article, a coefficient representing the picture density and true density of briquettes was proposed, and the comparison of both densities was based on the empirical measurements. Probably through an application of the conversion factor, the process of determining material densities could be simplified, cheaper, and quicker. Due to the conducted research, X-ray technology is an effective tool for improving wood products’ quality and performance. Combining X-ray technology with laboratory test results can provide quick and easy analysis. For example, the density comparison of shredded forest residues was defined. Based on the results, the mean value of the conversion factor was about 0.6. In addition, the observed results were compared with the doctoral research. Higher durability was found in briquettes with lower fractions of f1 (31–68%) compared to the higher f2, f3, and f4 (6–37%), which was approved in the spectrum picture.
Populations of Saccharomyces cerevisiae in Vineyards: Biodiversity and Persistence Associated with Terroir
Authors: González Magalí Lucía , Chimeno Selva Valeria , Sturm María Elena , Becerra Lucía Maribel , Lerena María Cecilia , Rojo María Cecilia , Combina Mariana , Mercado Laura Analía
Published: March 2023

The origin terroir provides distinctive characteristics for wines, in relation to soil, climate, oenological practices, etc. Hence, the characterization of each wine region by multiple aspects would allow differentiation of its wines. Several approaches at different scales have studied terroir microbiological fingerprints: from global microbiome analysis up to intraspecific Saccharomyces biodiversity. Mature grapes are the primary source of yeasts, and S. cerevisiae is a key wine fermentative species. Malbec is the emblematic Argentinean variety and is mainly cultivated in the “Zona Alta del Rio Mendoza” (ZARM). In this work, the diversity of S. cerevisiae grape populations was studied at three vintages in two Malbec vineyards of the ZARM, to evaluate their annual diversity and behavior in different vintages. Rarefaction of classical ecological indices was applied for a statistically adequate biodiversity analysis. A total of 654 S. cerevisiae isolates were differentiated by Interdelta-PCR. Each yeast grape population showed a unique composition of S. cerevisiae strains; however, a narrow genetic relationship was found in each vineyard. A slight increase in the initial diversity and a stabilization in the diversity of S. cerevisiae populations were confirmed. These results add to the discussion about the contribution of yeasts to the terroir microbiological concept, and its limitations and stability over the time.
Microencapsulation of Bifidobacterium breve to Enhance Microbial Cell Viability in Green Soybean Yogurt
Authors: Naklong Kanokorn , Therdtatha Phatthanaphong , Sumonsiri Nutsuda , Leksawasdi Noppol , Techapun Charin , Rachtanapun Pornchai , Taesuwan Siraphat , Nunta Rojarej , Khemacheewakul Julaluk
Published: March 2023

Bifidobacteria, a major bacterial group, have several beneficial impacts on health, such as enhancing the intestinal flora by limiting the colonization of pathogenic microorganisms and stimulating the immune system. As a result, bifidobacteria have been extensively included in various food products. In this study, Bifidobacterium breve TISTR 2130 was microencapsulated using an emulsion technique with sodium alginate and calcium lactate in green soybean milk as wall materials. This study found that microbeads prepared with 2.0% (w/v) sodium alginate and 2.0% (w/v) calcium lactate had the highest microencapsulation efficiency (MEE) of 99.8% ± 0.07%. In addition, the viability of microencapsulated B. breve TISTR 2130 and free cells was evaluated following a simulated gastrointestinal treatment. Microencapsulated B. breve TISTR 2130 showed higher cell viability than free cells under the simulated gastrointestinal conditions. The viability reduction of free cells dropped substantially to zero after 1 h of incubation in simulated gastrointestinal juice (SIJ), while the viable cell count of microencapsulated B. breve TISTR 2130 remained greater than 5 log CFU/mL and the survival rate was greater than 64% at the end of the sequential digestion. During refrigerated storage of green soybean yogurt (GSY) fortified with microencapsulated B. breve, the viability of B. breve TISTR 2130, syneresis, and acidity decreased, while the pH and viscosity increased. Microencapsulated B. breve TISTR 2130 has the potential to be used as a probiotic fortification in GSY since the viability remained above the recommended minimal limit of 6 log CFU/mL for 10 days during refrigerated storage. The present study demonstrated that the optimized microencapsulated B. breve TISTR 2130 sodium alginate matrix could survive the human gastrointestinal tract to provide health benefits and the possibility of incorporation into functional foods.
The Interaction between a Liquid Combustion Front and a Fire Barrier Made of CO2 Hydrate
Authors: Gaidukova Olga , Donskoy Igor , Misyura Sergey , Morozov Vladimir , Volkov Roman
Published: March 2023

This paper presents experimental research into the propagation of a liquid fuel combustion front interacting with a fire barrier made of CO2 hydrate and ice. The combustible liquids studied here were kerosene, gasoline, Diesel fuel, oil, petroleum, and alcohol. The experiments with gas hydrate involved fire barriers based on powder and tablets. Heat and mass transfer and phase transitions in the area between the fire barrier and the combustion front were found to play a fundamental role. The liquid fuel combustion fronts propagate at a velocity ranging from 0.1 m/s to 3 m/s under natural convection. Forced convection leads to 2- to 5-fold changes in the flame propagation velocities. According to our experiments, 2–4 cm is the minimum width of a CO2 hydrate fire barrier for stopping the flame combustion front. We also determined the contribution of the gas hydrate dissociation to fire suppression and identified the conditions of the combustion front stoppage. The dimensionless processing of experimental data made them scalable to industrial applications. Finally, the experimental findings were also used to develop physical and mathematical models predicting the necessary and sufficient amount of CO2 hydrate in a fire barrier to provide the effective deceleration and stoppage of a flame combustion front.
Turbulence via Intermolecular Potential: Viscosity and Transition Range of the Reynolds Number
Authors: Abramov Rafail V.
Published: March 2023

Turbulence in fluids is an ubiquitous phenomenon, characterized by spontaneous transition of a smooth, laminar flow to rapidly changing, chaotic dynamics. In 1883, Reynolds experimentally demonstrated that, in an initially laminar flow of water, turbulent motions emerge without any measurable external disturbance. To this day, turbulence remains a major unresolved phenomenon in fluid mechanics; in particular, there is a lack of a mathematical model where turbulent dynamics emerge naturally from a laminar flow. Recently, we proposed a new theory of turbulence in gases, according to which turbulent motions are created in an inertial gas flow by the mean field effect of the intermolecular potential. In the current work, we investigate the effect of viscosity in our turbulence model by numerically simulating the air flow at normal conditions in a straight pipe for different values of the Reynolds number. We find that the transition between laminar and turbulent flow in our model occurs, without any deliberate perturbations, as the Reynolds number increases from 2000 to 4000. As the simulated flow becomes turbulent, the decay rate of the time averaged Fourier spectrum of the kinetic energy in our model approaches Kolmogorov’s inverse five-thirds law. Both results are consistent with experiments and observations.
Fungicide Dissipation Kinetics and Dietary Exposure Risk Assessment in Squash Fruit and Leaf
Authors: An Dai , Ko Rakdo , Kim Jinchan , Lee Kwanghun , Lee Ji-Ho
Published: March 2023

The dissipation behavior and dietary exposure risk assessment of four fungicides (dimethomorph, mandipropamid, myclobutanil, and metalaxyl) was performed in fruits and leaves of squash grown under greenhouse conditions. Squash fruit and leaf samples were randomly collected at 0, 3, 5, 7, and 14 days after the last pesticide application. Analysis was performed using ultra-high performance liquid chromatography coupled with tandem mass spectrometry (UHPLC-MS/MS). The quick, easy, cheap, effective, rugged, and safe (QuEChERS) method was used for sample preparation. Recovery rates at two spiked levels (0.01 and 0.1 mg/kg) were found to be in the range of 76.4%–101.9% for the analyzed pesticides and their relative standard deviations were ≤4%. Pesticide half-lives were 2.1 and 4.9 days for dimethomorph, 4.6 and 8.1 days for mandipropamid, 4.7 and 8.2 days for myclobutanil, and 2.7 and 5 days for metalaxyl in squash fruit and leaf, respectively. Regarding the total surveyors, hazard quotient values for squash fruit and leaf were ≤1.03 × 10−3 and ≤2.39 × 10−3, respectively. These values in the case of true consumers were ≤3.14 × 10−3 and ≤3.91 × 10−1, respectively.