bluuebunny/crossref_metadata_2025 huggingface dataset:
Phenotypic Alterations in Erythroid Nucleated Cells of Spleen and Bone Marrow in Acute Hypoxia
Authors: Nazarov Kirill , Perik-Zavodskii Roman , Perik-Zavodskaia Olga , Alrhmoun Saleh , Volynets Marina , Shevchenko Julia , Sennikov Sergey
Published: December 2023

Hypoxia leads to metabolic changes at the cellular, tissue, and organismal levels. The molecular mechanisms for controlling physiological changes during hypoxia have not yet been fully studied. Erythroid cells are essential for adjusting the rate of erythropoiesis and can influence the development and differentiation of immune cells under normal and pathological conditions. We simulated high-altitude hypoxia conditions for mice and assessed the content of erythroid nucleated cells in the spleen and bone marrow under the existing microenvironment. For a pure population of CD71+ erythroid cells, we assessed the production of cytokines and the expression of genes that regulate the immune response. Our findings show changes in the cellular composition of the bone marrow and spleen during hypoxia, as well as changes in the composition of the erythroid cell subpopulations during acute hypoxic exposure in the form of a decrease in orthochromatophilic erythroid cells that are ready for rapid enucleation and the accumulation of their precursors. Cytokine production normally differs only between organs; this effect persists during hypoxia. In the bone marrow, during hypoxia, genes of the C-lectin pathway are activated. Thus, hypoxia triggers the activation of various adaptive and compensatory mechanisms in order to limit inflammatory processes and modify metabolism.
Investigation of Porous Ceramic Structures Based on Hydroxyapatite and Wollastonite with Potential Applications in the Field of Tissue Engineering
Authors: Cucuruz Andreia , Ghițulică Cristina-Daniela , Voicu Georgeta , Bogdan Cătălina-Alexandra , Dochiu Vasilica , Popescu Roxana Cristina
Published: December 2023

Bioceramics are the most promising materials used for hard tissue reconstruction. In this study, wollastonite/hydroxyapatite (HAp/WS)-type composite ceramic structures were synthesized with the aim of reaching a material with improved properties for use in bone tissue regeneration. The scaffolds were synthesized using a foam replica method, starting from ceramic powders with different mass ratios. These were subsequently studied and compared to identify the ideal mass ratio in terms bioactive character, appropriate mechanical properties, but also microstructural influence. The results indicate that all of the samples showed a highly porous microstructure with interconnected pores and high mineralization after 21 days of immersion in SBF. The porous structures with 90% and 70% mass contents of hydroxyapatite presented a well-defined structure and the highest values of mechanical compressive strength. Biocompatibility evaluation showed that osteoblast-like cells are able to penetrate the inner volume of the structures, exhibiting a biocompatible behavior in terms of morphological features and viability following 7 days of incubation. All results show that the porous composite ceramics with 90% and 70% mass contents of hydroxyapatite are promising materials for bone tissue regeneration.
Recent Advances in C–H Functionalization of Pyrenes
Authors: Arulananda Babu Srinivasarao , Dalal Arup , Bodak Subhankar
Published: December 2023

In recent years, transition metal-catalyzed C–H activation and site-selective functionalization have been considered to be valuable synthetic tactics to functionalize organic compounds containing multiple C–H bonds. Pyrene is one of the privileged and notorious polycyclic aromatic hydrocarbons. Pyrene and its derivatives have found applications in various branches of chemical sciences, including organic chemistry, chemical biology, supramolecular sciences, and material sciences. Given the importance of pyrene derivatives, several classical methods, including the C–H functionalization method, have been developed for synthesizing modified pyrene scaffolds. This review attempts to cover the recent developments in the area pertaining to the modification of the pyrene motif through the C–H activation process and the functionalization of C–H bonds present in the pyrene motif, leading to functionalized pyrenes.
A Systematic Review of Lipid-Focused Cardiovascular Disease Research: Trends and Opportunities
Authors: Anyaegbunam Uchenna Alex , More Piyush , Fontaine Jean-Fred , Cate Vincent ten , Bauer Katrin , Distler Ute , Araldi Elisa , Bindila Laura , Wild Philipp , Andrade-Navarro Miguel A.
Published: December 2023

Lipids are important modifiers of protein function, particularly as parts of lipoproteins, which transport lipophilic substances and mediate cellular uptake of circulating lipids. As such, lipids are of particular interest as blood biological markers for cardiovascular disease (CVD) as well as for conditions linked to CVD such as atherosclerosis, diabetes mellitus, obesity and dietary states. Notably, lipid research is particularly well developed in the context of CVD because of the relevance and multiple causes and risk factors of CVD. The advent of methods for high-throughput screening of biological molecules has recently resulted in the generation of lipidomic profiles that allow monitoring of lipid compositions in biological samples in an untargeted manner. These and other earlier advances in biomedical research have shaped the knowledge we have about lipids in CVD. To evaluate the knowledge acquired on the multiple biological functions of lipids in CVD and the trends in their research, we collected a dataset of references from the PubMed database of biomedical literature focused on plasma lipids and CVD in human and mouse. Using annotations from these records, we were able to categorize significant associations between lipids and particular types of research approaches, distinguish non-biological lipids used as markers, identify differential research between human and mouse models, and detect the increasingly mechanistic nature of the results in this field. Using known associations between lipids and proteins that metabolize or transport them, we constructed a comprehensive lipid–protein network, which we used to highlight proteins strongly connected to lipids found in the CVD-lipid literature. Our approach points to a series of proteins for which lipid-focused research would bring insights into CVD, including Prostaglandin G/H synthase 2 (PTGS2, a.k.a. COX2) and Acylglycerol kinase (AGK). In this review, we summarize our findings, putting them in a historical perspective of the evolution of lipid research in CVD.
The Effect of Plasma Spray Parameters on the Quality of Al-Ni Coatings
Authors: Wang Shenglian , Chen Shuang , Liu Ming , Huang Qinghua , Liu Zimo , Li Xin , Xu Shaofeng
Published: December 2023

The plasma spray method is widely utilized for enhancing wear, surface fatigue, and corrosion properties through coating. The mechanical and surface characteristics of the resulting coating are contingent upon various spraying parameters, including arc current, working current, spraying distance, and plasma gun traversing speed. This study investigates the impact of these manufacturing parameters on the porosity, hardness, and bond strength of a coating produced from an Al-Ni alloy applied to a Q235 steel substrate. An extensive experimental program was conducted to analyze the influence of these parameters on the coating properties. Consequently, a preferred combination of parameters, identified through a comprehensive evaluation method, yielded greater performance benefits compared to the orthogonal experimental groups.
Micro-Arc Oxidation in Titanium and Its Alloys: Development and Potential of Implants
Authors: Ming Xinwei , Wu Yan , Zhang Ziyue , Li Yan
Published: December 2023

Titanium (Ti) and its alloys are widely recognized as preferred materials for bone implants due to their superior mechanical properties. However, their natural surface bio-inertness can hinder effective tissue integration. To address this challenge, micro-arc oxidation (MAO) has emerged as an innovative electrochemical surface modification technique. Its benefits range from operational simplicity and cost-effectiveness to environmental compatibility and scalability. Furthermore, the distinctive MAO process yields a porous topography that bestows versatile functionalities for biological applications, encompassing osteogenesis, antibacterial, and anti-inflammatory properties. In this review, we undertake an examination of the underlying mechanism governing the MAO process, scrutinize the multifaceted influence of various factors on coating performance, conduct an extensive analysis of the development of diverse biological functionalities conferred by MAO coatings, and discuss the practical application of MAO in implants. Finally, we provide insights into the limitations and potential pathways for further development of this technology in the field of bone implantation.
New Polynuclear Coordination Compounds Based on 2–(Carboxyphenyl)iminodiacetate Anion: Synthesis and X-rays Crystal Structures
Authors: Martínez Sebastián , Kremer Carlos , González-Platas Javier , Mendoza Carolina
Published: December 2023

In the present work, novel polymeric copper(II) coordination compounds, namely [Cu2(cpida)(H2O)4][Cu(cpida)]·3H2O (1) (cpida3− = 2-(carboxyphenyl)iminodiacetate anion) and Na[Cu(cpida)] (3), were synthesized and characterized using infrared spectroscopy, thermogravimetric analysis, elemental analysis, and single-crystal X-ray diffraction. Compound 1 was obtained by slowly evaporating an aqueous solution of H3cpida, copper(II) sulfate, and NaOH at room temperature. The structural characterization revealed that 1 is an ionic entity formed by the [Cu(cpida)]− anion and the [Cu2(cpida)(H2O)4]+ cation, both of polymeric 1D structure. Compound 3 was prepared under similar conditions from copper perchlorate and crystallized via acetone diffusion. It is a coordination polymer formed by the [Cu(cpida)]− units, and the sodium cation is present in the structure, counterbalancing the anion charge. Depending on the crystallization conditions, it was possible to obtain other solvation forms of these structures. Starting from the conditions of compound 1, via the diffusion of ethanol, [Cu2(cpida)(H2O)4][Cu(cpida)]·H2O·1/2EtOH (2) was precipitated, while a hydrate form of compound 3 with the formula Na[Cu(cpida)]·2H2O (4) was obtained via methanol diffusion.
Cordierite-Supported Transition-Metal-Oxide-Based Catalysts for Ozone Decomposition
Authors: Chernykh Maria , Grabchenko Maria , Knyazev Alexey , Mamontov Grigory
Published: December 2023

Cordierite-based supported noble-metal-free catalysts for ozone decomposition are elaborated. The cordierite ceramic surface is pretreated with oxalic acid and NaOH, and Mn-Cu-Ni oxide catalysts are prepared by the impregnation method. The mass ratio of the supported oxides in the resulting catalysts is MnO2:CuO:NiO = 3:2:1, and their loadings are from 1.8 to 7.0 wt.%. The pretreated supports and catalysts are characterized by low-temperature N2 adsorption, scanning electron microscopy (SEM), powder X-ray diffraction analysis (XRD), and temperature-programmed reduction with H2 (TPR-H2). The catalysts are tested in ozone decomposition with high airflow rates (20 and 50 L/min) and with initial ozone concentrations of 1 and 2 ppm at temperatures in the range of 25–120 °C. It is shown that a combined treatment of cordierite with oxalic acid and NaOH leads to a developed porous structure and stabilization of supported Mn-Cu-Ni oxides in a highly dispersed state. The high activity of catalysts in ozone decomposition at room temperature and high airflow is demonstrated. The developed catalysts can be recommended for application in purification of air from the ozone because of their high catalytic activity, high mechanical stability, and relatively low weight and cost.
Machine Learning Radiomics Signature for Differentiating Lymphoma versus Benign Splenomegaly on CT
Authors: Cheng Jih-An , Lin Yu-Chun , Lin Yenpo , Wu Ren-Chin , Lu Hsin-Ying , Yang Lan-Yan , Chiang Hsin-Ju , Juan Yu-Hsiang , Lai Ying-Chieh , Lin Gigin
Published: December 2023

Background: We aimed to develop and validate a preoperative CT-based radiomics signature for differentiating lymphoma versus benign splenomegaly. Methods: We retrospectively analyzed CT studies from 139 patients (age range 26–93 years, 43% female) between 2011 and 2019 with histopathological diagnosis of the spleen (19 lymphoma, 120 benign) and divided them into developing (n = 79) and testing (n = 60) datasets. The volumetric radiomic features were extracted from manual segmentation of the whole spleen on venous-phase CT imaging using PyRadiomics package. LASSO regression was applied for feature selection and development of the radiomic signature, which was interrogated with the complete blood cell count and differential count. All p values < 0.05 were considered to be significant. Results: Seven features were selected for constructing the radiomic signature after feature selection, including first-order statistics (10th percentile and Robust Mean Absolute Deviation), shape-based (Surface Area), and texture features (Correlation, MCC, Small Area Low Gray-level Emphasis and Low Gray-level Zone Emphasis). The radiomic signature achieved an excellent diagnostic accuracy of 97%, sensitivity of 89%, and specificity of 98%, distinguishing lymphoma versus benign splenomegaly in the testing dataset. The radiomic signature significantly correlated with the platelet and segmented neutrophil percentage. Conclusions: CT-based radiomics signature can be useful in distinguishing lymphoma versus benign splenomegaly and can reflect the changes in underlying blood profiles.
Cooperative Robot Manipulators Dynamical Modeling and Control: An Overview
Authors: Ghorbanpour Amin
Published: December 2023

Robot manipulators possess the capability to autonomously execute complex sequences of actions. Their proficiency in handling challenging and hazardous tasks has led to their widespread adoption across diverse sectors, including industry, business, household appliances, rehabilitation, and many more. However, certain tasks prove to be challenging for individual robots, primarily due to constraints in their structure and limited degrees of freedom. Cooperative robot manipulators (CRMs) emerge as a compelling solution when dealing with large, heavy, or flexible payloads. The utilization of CRMs offers a host of benefits, including enhanced manipulation performance achieved through the synergy of sensing and actuation capabilities or by tapping into increased redundancy. Numerous techniques have been devised for the control and dynamical modeling of CRMs. Nevertheless, the field continues to present technical challenges and scientific inquiries. To inspire and facilitate further research and development in this realm, this review aims to consolidate the current body of knowledge pertaining to CRMs kinematics, dynamics modeling, and various control methodologies used for payload manipulation via CRMs.
Exploring Attitudinal Dimensions of Inclusive Education: Predictive Factors among Romanian Teachers
Authors: Jurca Andra Maria , Baciu Damaris , Lustrea Anca , Sava Simona , Borca Claudia Vasilica
Published: December 2023

Inclusive attitudes are considered an important predictor of the quality of educational inclusion. Child-related, teacher-related, and environment-related factors were measured over time in connection with teachers’ positive inclusive attitudes. This study aimed to contribute with insights from Romania to the comprehensive understanding of the attitudinal dimensions of inclusive education and the factors that predict it. A quantitative, non-experimental, correlational research design was undertaken in September–October 2022 to determine the factors that can significantly predict the dimensions of inclusive attitudes. A convenience sample of 1040 Romanian teachers participated in the study. The MATIES scale was used to measure the dimensions of inclusive attitudes: cognitive, affective, and behavioral. The results showed that there are a number of universally known factors that have been found to predict inclusive attitudes, like the school environment, close relationships with people with disabilities, and training in special education. Their predictive power is relatively low, ranging between 2% and 9%, suggesting the presence of unexplored influential variables and emphasizing the need for future studies to consider additional factors. The specific and significant factor for Romanian culture was found to be the need for training in special education. The data can be informative for curriculum designers, training providers, and policymakers, signaling the need for comprehensive training in special education in the initial and continuous training of all teachers.
Flipped Learning in Higher Education for the Development of Intrinsic Motivation: A Systematic Review
Authors: Alonso Rebeca Kerstin , Vélez Alexander , Martínez-Monteagudo María Carmen , Rico-González Markel
Published: December 2023

Background: Flipped learning (FL) is being considered, in terms of new educational trends, a beneficial pedagogical model in the classroom. In particular, FL and intrinsic motivation (IM) are key components to the model since they can be crucial to a high-quality education. FL for the development of IM in higher education, as well as searches for potential interventions have, thus improved over the past ten years. However, no reviews that analyze the findings and conclusions reached have been published. Consequently, the objectives of this paper were to analyze the relationship between the use of FL and the IM of students in higher education, and to identify the aspects that should be present in FL models to develop the IM that contributes to high-quality education. Methods: in accordance with PRISMA guidelines, a systematic review of PubMed, Web of Science, Scopus, and ProQuest was carried out. Results: Of the 407 studies that were initially discovered, 17 underwent a full examination in which all findings and conclusions were analyzed. After implementation, the majority of the FL interventions improved IM results. Conclusion: many key aspects have been identified that must be followed in order to intrinsically motivate students using the FL methodology.
Car Full View Dataset: Fine-Grained Predictions of Car Orientation from Images
Authors: Catruna Andy , Betiu Pavel , Tertes Emanuel , Ghita Vladimir , Radoi Emilian , Mocanu Irina , Dascalu Mihai
Published: December 2023

The orientation of objects plays an important role in accurate predictions for the tasks of classification, detection, and trajectory estimation. This is especially important in the automotive domain, where estimating an accurate car orientation can significantly impact the effectiveness of the other prediction tasks. This work presents Car Full View (CFV), a novel dataset for car orientation prediction from images obtained by video recording all possible angles of individual vehicles in diverse scenarios. We developed a tool to semi-automatically annotate all the video frames with the respective car angle based on the walking speed of the recorder and manually annotated key angles. The final dataset contains over 23,000 images of individual cars along with fine-grained angle annotations. We study the performance of three state-of-the-art deep learning architectures on this dataset in three different learning settings: classification, regression, and multi-objective. The top result of 3.39° in circular mean absolute error (CMAE) shows that the model accurately predicts car orientations for unseen vehicles and images. Furthermore, we test the trained models on images from two different datasets and show their generalization capability to realistic images. We release the dataset and the best models while publishing a web service to annotate new images.
A Fuzzy-Based Proportional–Integral–Derivative with Space-Vector Control and Direct Thrust Control for a Linear Induction Motor
Authors: Abdelwanis Mohamed I. , El-Sousy Fayez F. M. , Ali Mosaad M.
Published: December 2023

In this study, the analysis and control of a multi-phase linear induction motor loaded with a variable mechanical system are carried out. Mathematical models are established, and simulation results are analyzed for an improved proportional–integral–derivative controller with closed-loop vector control for PLIM. To make the PID controller more responsive to load thrust disturbances, a fuzzy PID load thrust observer was developed. The FPID is similarly based on space-vector modulation DTC technology to regulate the PLIM’s speed, flux, and thrust. The FPID output is used to calculate the reference thrust force, which is compared to the actual thrust value to calculate the second error. To maintain the linear speed of the PLIM at the specified reference values and at different load values, the FPID controller settings are adjusted. Four indicators were used to compare the capabilities of the FPID controller with those of the conventional PID controller in order to evaluate the performance of PLIM in both cases. These indices represent the individual SSE for each operational phase and the total SSE for the entire loading period. According to the simulation results, the FPID works better than a regular PID when used to adjust the operation of DTC-SVM to drive a PLIM to improve the overall system performance. The simulation results using MATLAB Simulink for a PLIM-drive system show that the proposed FPID control provides improved control behavior and operating performance with fast and accurate speed tracking.
The Current Progress and Future Prospects of Path Loss Model for Terrestrial Radio Propagation
Authors: Wang Jian , Hao Yulong , Yang Cheng
Published: December 2023

The radio channel model is a major factor supporting the whole life cycle of the terrestrial radio system, including the demonstration, design, validation, operation, and so on. To improve the spectrum sharing and spectral efficiency in terrestrial radio services, we analyze three types of path loss models in detail: deterministic, empirical, and semi-empirical models, to meet the requirements of path loss modeling for supporting traditional band expansion and reuse. Then, we conduct a comparative analysis based on the characteristics of the current models. Furthermore, a preview of the future terrestrial path loss modeling methods is provided, including intelligent modeling processes and multi-model hybridization methods. Finally, we look forward to the potential technology that can be used in future wireless communication, such as terahertz communication, reconfigurable intelligent surface technology, and integrated communication and sensing technology. The above research can provide a reference for the development of terrestrial radio channel modeling, promoting the technologies of terrestrial channel modeling. We hope this paper will stimulate more interest in modeling terrestrial radio channels.
Deep Learning for Channel Estimation in Physical Layer Wireless Communications: Fundamental, Methods, and Challenges
Authors: Lv Chaoluo , Luo Zhongqiang
Published: December 2023

With the rapid development of wireless communication technology, intelligent communication has become one of the mainstream research directions after the fifth generation (5G). In particular, deep learning has emerged as a significant artificial intelligence technology widely applied in the physical layer of wireless communication for achieving intelligent receiving processing. Channel estimation, a crucial component of physical layer communication, is essential for further information recovery. As a motivation, this paper aims to review the relevant research on applying deep learning methods in channel estimation. Firstly, this paper briefly introduces the conventional channel estimation methods and then analyzes their respective merits and drawbacks. Subsequently, this paper introduces several common types of neural networks and describes the application of deep learning in channel estimation according to data-driven and model-driven approaches, respectively. Then, this paper extends to emerging communication scenarios and discusses the existing research on channel estimation based on deep learning for reconfigurable intelligent surface (RIS)-aided communication systems. Finally, to meet the demands of next-generation wireless communication, challenges and future research trends in deep-learning-based channel estimation are discussed.
Determination of Energy Savings via Fuel Consumption Estimation with Machine Learning Methods and Rule-Based Control Methods Developed for Experimental Data of Hybrid Electric Vehicles
Authors: Arıkuşu Yılmaz Seryar , Bayhan Nevra , Tiryaki Hasan
Published: December 2023

In this study, a parallel hybrid electric vehicle produced within the scope of our project titled “Development of Fuel Efficiency Enhancing and Innovative Technologies for Internal Combustion Engine Vehicles” has been modeled. Firstly, a new rule-based control method is proposed to minimize fuel consumption and carbon emission values in driving cycles in the experimental model of the parallel hybrid electric vehicle produced within the scope of this project. The proposed control method ensures that the internal combustion engine (ICE) operates at the optimum point. In addition, the electric motor (EM) is activated more frequently at low speeds, and the electric motor can also work as a generator. Then, a new dataset was also created on a traffic-free racetrack with the proposed control method for fuel consumption estimation of a parallel hybrid electric vehicle using ECE-15 (Urban Driving Cycle), EUDC (Extra Urban Driving Cycle), and NEDC (New European Driving Cycle) driving cycles. The data set is dependent on 11 different input variables, which complicates the system. Afterward, the fuel estimation process is made with seven different machine learning methods (ML), and these methods are compared using the obtained data set. To avoid overfitting machine learning, two different test data sets were created. The Random Forest algorithm is the most suitable technique in terms of training and testing the fuel consumption model using correlation coefficient (R2), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) simulation appropriateness for both test datasets. Moreover, the random forest algorithm achieved an impressive accuracy of 97% and 90% for both test datasets, outperforming the other algorithms. Furthermore, the proposed method consumes 4.72 L of fuel per 100 km, while the gasoline-powered vehicle consumes 7 L of fuel per 100 km. The results show that the proposed method emits 4.69 kg less CO2 emissions. The effectiveness of the Random Forest Algorithm has been verified by both simulation results and real-world data.
A Decomposition Analysis of the Energy System Patent with Blockchain Technology
Authors: Mao Danning , Fujii Hidemichi
Published: December 2023

The energy blockchain is a platform based on blockchain technology, creating a secure, transparent, and decentralized system for peer-to-peer transactions and automated smart contracts. This platform has the ability to facilitate the exchange and management of energy resources, such as electricity or renewable energy certificates. Our research aims to clarify the growth trends of energy systems with blockchain technology throughout the world. The novelty of this study is to understand the main factor in energy blockchain patent granting using a patent decomposition analysis and log mean Divisia index analysis and discover the relative importance in the R&D shift from electricity to other technology. Additionally, the IPC for energy blockchain technology primarily focuses on configuring and managing energy systems, including electricity, gas, and water supply. We also present a comprehensive overview of how countries and companies engage with energy blockchain technology and find China leads with 59% of patents, followed by the U.S. with 20%, but their specific tech shares differ. Participants span beyond traditional energy sectors, including electric and electronic machinery, IT firms, transport manufacturers, startups, and universities dedicated to blockchain technology.
Prospects for the Use of Metal Surfaces Modified by Nanosecond Laser Radiation for Energy Applications
Authors: Glushkov Dmitriy , Paushkina Kristina , Pleshko Andrey , Zykov Ilya , Orlova Evgeniya , Feoktistov Dmitriy
Published: December 2023

Laser technologies for processing metals used as heat exchange surfaces are unrivaled to solve a number of problems in the energy industry. This is explained by the fact that after laser radiation treatment, metal surfaces gain unique surface functional properties (extreme wettability properties, high resistance to corrosion in contact with traditional coolants, high abrasive and cavitation resistance). The study of the processes of evaporation, boiling, and condensation on such surfaces is hampered by one of the unsolved problems, which is the lack of the ability to predict the configuration of microtextures, for example, in the form of micropillars and microchannels with predetermined sizes. In this work, a graphic–analytical technique based on the use of ablation spot sizes for the formation of a given configuration and microtexture dimensions on traditional structural materials of heat exchange surfaces is developed. Based on experimental data, regime maps were constructed for the formation of microtextures on the surfaces of aluminum alloy AlMg6 and steel AISI 310. The prospects for using metal surfaces with a given microtexture formed by laser radiation to intensify the phase transition of coolants and control convective flows in a droplet lying on a heated surface were assessed. The obtained results can be used in the development of spray (drip) irrigation systems to provide thermal protection for heat-stressed equipment.
The Waste-to-Energy (WtE) Technology to Support Alternative Fuels for Agriculture in the Context of Effective Solid Waste Management in the Jabodetabek Area, Indonesia
Authors: Nanda Muhammad Achirul , Sugandi Wahyu , Wijayanto Arif Kurnia , Imantho Harry , Sutawijaya Arya , Nelwan Leopold Oscar , Budiastra I Wayan , Seminar Kudang Boro
Published: December 2023

Jabodetabek has been termed Indonesia’s most populous urban area, and it is predicted to generate around 9.069 million tons/year by 2023. On the other hand, this area has promising agricultural prospects and requires a great deal of energy. Tractors, generators, and other agricultural equipment are necessary to increase productivity. Therefore, it can potentially convert waste into energy as an effective alternative fuel for agriculture. This study looked at ways to recover the waste to energy (WtE) with various technologies (incineration, gasification, pyrolysis, landfilling, and anaerobic digestion). The assessment indicators focus on the type of waste, energy production efficiency, energy produced, technological readiness, capacity, byproducts, costs, air pollution, water pollution, and soil pollution. Each WtE technology is assessed using a scale between 1 (worst) and 5 (best) on each performance indicator concerning related references. Based on the analysis, the anaerobic digester is the most attractive WtE technology to generate an alternative fuel for agriculture in the Jabodetabek area. This is an environmentally friendly and economical technology because the technology readiness level is relatively mature. Thus, this review will assist policymakers and implementing authorities in determining the potential of the right WtE technologies for effective alternative fuel generation for agriculture, particularly in the metropolitan region of Jabodetabek.
Thorough Analysis of the Reliability of Measurements on Chassis Roller Dynamometer and Accurate Energy Consumption Estimation for Electric Vehicles
Authors: Alhanouti Muhammed , Gauterin Frank
Published: December 2023

The energy consumption of the electric test vehicle on the roller dynamometer test bench is thoroughly investigated in this work. Initially, we extensively examined the relevant testing approaches on chassis dynamometer test benches. Then, we presented the main issues and limitations of this type of testing facility. After that, an experimental analysis was performed by recording the NEDC and the WLTP-class 2 standard driving cycle test data. It was found that the power consumption associated with specific test maneuvers could be accurately simulated via the roller dynamometer system. However, the experimental test results showed some disparities compared to the expected results from the simulation models. These findings supported identifying the dynamic behavior resulting from the tire and roller contact. The LuGre friction model provided the solution, incorporating friction-related physical models such as stick-slip and the Stribeck effect. In addition, adjustments were made to the LuGre model to consider the dynamic influences of the roller on the tire. Finally, we created a systematic estimation model for energy consumption by integrating the boundary conditions of the test bench, a generic model for energy estimation of electric vehicles, and the proposed tire–roller contact model. The overall model requires only the measured traction speed of the test vehicle and the auxiliary energy losses as physical inputs. The results proved that the proposed model enabled the estimation of the total power consumption with high accuracy.
Power Management Strategy of Hybrid Fuel Cell Drones for Flight Performance Improvement Based on Various Algorithms
Authors: Hyun Daeil , Han Jaeyoung , Hong Seokmoo
Published: December 2023

In recent years, there has been a growing demand for multipurpose drones that can handle surveillance, environmental monitoring, and urgent deliveries. This trend has spurred the need for increased power and longer flight times for drones. Hence, many researchers introduced various hybrid systems to enhance endurance. In particular, a hybrid system that integrates solar cells, fuel cells, and batteries can substantially enhance a drone’s endurance. However, linking multiple power sources necessitates a control strategy that prioritizes safety and durability. It is also essential to analyze the control characteristics of each component as the dynamic behavior of individual components, coupled with environmental factors, significantly impacts the overall dynamic characteristics of drone systems. This study introduces a PEMFC–battery drone model. The model’s dynamic characteristics can be evaluated based on changes in environmental conditions and the control strategies of primary components. The validity of this model is confirmed by analyzing the dynamic characteristics of drone systems. As a result, the MRAC logic applied to the flight-level control and thrust motor of the drone was found to amplify the characteristics of the underlying PI and IP controllers. These control characteristics can lead to the development of control strategies for improving the flight performance or power durability of the aircraft by being properly applied to the flight environment of the drone.
Kraft Lignin Electro-Oxidation under Ambient Temperature and Pressure
Authors: Han Jiashuai , Lin Roger , Salehi Mahdi , Farzi Amirhossein , Carkner Andrew , Liu Kefang , Abou El-Oon Omar , Ajao Olumoye , Seifitokaldani Ali
Published: December 2023

Lignin is the largest natural source of aromatic chemicals. Due to its complex polymeric structure, Kraft lignin is under-utilized and usually combusted for heat generation, thus resulting in CO2 emissions in the Kraft process. To valorize lignin with renewable electricity and to convert it into value-added aromatic chemicals, efficient electrochemical methods need to be discovered, based not only on its apparent yield of building block chemicals but also on its energy efficiency. In this study, the electro-oxidative performance of six different metals was systematically evaluated. The results showed that the synthesized Ni-based catalyst can increase the vanillin and vanillic acid apparent yield by 50–60% compared to when Ni-based catalyst is absent. We also found that the oxygen evolution reaction (OER) is more than a competing reaction since the presence of oxygen synergistically aids oxidation of the lignin to increase aromatic chemical production by 63% compared to the sum of vanillin generation from both voltage-only and oxygen-only scenarios. With the novel proposed notion of charge efficiency, we showed that utilizing a thinner layer of Ni catalyst balances the OER and the oxidative reaction of lignin, thus improving the charge efficiency for vanillin by 22%
Numerical Study on Combustion-Driven Jet Actuation for Aerodynamic Control of Airfoil Flows
Authors: Kim Taesoon , Park Suhyeon , Sohn Ilyoup
Published: December 2023

In this study, a numerical investigation is conducted on combustion-driven pulsed-jet actuation to control the flow around a lifting surface. Based on relevant experimental measurements and computations, high-speed jets are generated from the impulsive variation in pressure at the actuator boundary. A supersonic jet flow is momentarily generated by combustion in a reaction chamber of the actuator, and the flow interacts with the external flow around the lifting surface and alters the aerodynamic characteristics. The computational results indicate that the flow control performance of the jet actuation is significant at a high-incidence angle of attack, such as beyond the stall angle, whereas the impact is minimal at low angles of attack, such as in the linear lift region. Repetitive jet actuation can produce additional momentum to the external flow and alters the pressure distribution on the suction surface, particularly downstream of the actuator location. This pressure variation from the actuation yields an additional lift force on the lifting surface and reduces the amplitude of the aerodynamic moment at a given angle of attack, thus enhancing the aerodynamic performance of the airfoil.
Economic Evaluation of a 1 MWel Capacity Power-to-Biomethane System
Authors: Bai Attila , Balogh Péter , Nagy Adrián , Csedő Zoltán , Sinóros-Szabó Botond , Pintér Gábor , Prajapati Sanjeev Kumar , Singh Amit , Gabnai Zoltán
Published: December 2023

Power-to-biomethane (bio-P2M) is a novel technology that combines the long-term storage of periodically available renewable energy sources (RES) and the upgrading of biogas. This article introduces a complex economic analysis of a 1 megawatt electric (MWel) capacity bio-P2M system based on economic characteristics considered to be typical in practice. The evaluation includes an investment analysis to present the basic scenario, a sensitivity analysis and a unit cost calculation to show the economic viability, the cost structure and the possible reserves of the synthetic natural gas (SNG) as a final output. The risk analysis is executed using Monte Carlo simulation, and the final results are the mean and standard deviation of the outputs, distribution functions and probabilities. Our results show that a significant state subsidy would be needed to boost competitiveness either in terms of investment costs (44% in our calculation) or in technology development to improve technological effectiveness. Another important competitiveness issue is the full utilization of the plant and the lowest possible price for the electricity used. If both cannot be optimized at the same time, then the first one is more important. Natural gas prices and the full utilization of waste heat might result in smaller changes.
A Framework for the Estimation of Air Quality by Applying Meteorological Images: Colours-of-the-Wind (COLD)
Authors: Chan Kenneth , Matthews Paul , Munir Kamran
Published: December 2023

This paper presents a new framework, “colours-of-the-wind” (COLD), which is designed to estimate air quality based on images from meteorological cameras, data analytics techniques, and the application of deep learning. Existing air quality estimation systems mainly rely on physical monitoring stations, which are limited by the monitoring stations’ physical constraints. Instead of collecting data from environmental monitoring stations, COLD collects air quality data from meteorological cameras. This approach can collect data from any location where a camera could capture a reliable image, which is otherwise not collectable by the physical environmental monitoring station(s). This approach can also avoid bias due to the location of data collection. The system is evaluated by building a prototype based on the Air Quality Health Index from the Hong Kong Observatory. This is one of the unique data-analytic frameworks based on such air quality measurement. The COLD’s air quality estimation is also based on AQHI, the first machine learning-based estimation framework that generates AQHI as the proposed output. Experimental results suggest that the approach adopted by the COLD prototype is feasible and has some promising outcomes. The results also suggest possible parameters for the CNN model used for the training and analyses of the images.
A Deep Survey of Fish Health for the Recognition of Useful Biomarkers to Monitor Water Pollution
Authors: Orso Graziella , Imperatore Roberta , Coccia Elena , Rinaldi Gianluca , Cicchella Domenico , Paolucci Marina
Published: December 2023

The aim of the present study was to evaluate the wild freshwater fish health status using a vast array of biomarkers as predictive factors of pollutant exposure. The European eel (Anguilla anguilla) and brown trout (Salmo trutta fario), resident in rivers with different degrees of pollution in the South of Italy (Picentino River with good environmental quality and Tusciano River with low environmental quality), were examined using biometric parameters, histopathological and immunohistochemical biomarkers to evaluate the health status and a possible correlation with the water quality. Several alterations identified in the liver positively correlated with water and soil pollutants: hemorrhage (p ≤ 0.05), cytoplasmic vacuolization (p ≤ 0.01), hemosiderosis (p ≤ 0.05), irregular arrangement of hepatocytes (p ≤ 0.01), lipid accumulation (p ≤ 0.05), necrosis (p ≤ 0.01), cellular hyperplasia (p ≤ 0.05), leukocyte infiltration (p ≤ 0.01) and melanomacrophages centers (MMC) (p ≤ 0.01). In the spleen, only hemosiderosis correlated with water and soil pollutants (p ≤ 0.05). The inflammatory biomarker tumor necrosis factor α (TNFα) and ciclooxigenase 2 (COX2) responded to the environmental pollution, as well as the oxidative stress biomarkers superoxide dismutase (SOD2) and 8-Hydroxy-2′-deoxyguanosine (8-OHdG). Erythrocytic nuclear abnormalities and erythrocytic cellular abnormalities were found to be significantly higher in the blood of both the European eel (p < 0.0001) and brown trout (p < 0.001) in the Tusciano River compared with the Picentino River. Taken together, these results outline the need to increase the number of suitable biomarkers to assess fish health and reinforce the importance of employing additional biomarkers in biomonitoring programs that can be applied to evaluate water quality and in environmental assessment around the world.
Use of Measurement Tools to Validate the Health Effects of Forest Healing Programs: A Qualitative Analysis
Authors: Chun Hae-ryoung , Cho Inhyung , Choi Yoon Young , Park Sujin , Kim Geonwoo , Cho Sung-il
Published: December 2023

Research is increasingly focusing on the health-promoting effects of forest healing programs. A number of different health indicators are in use, necessitating the establishment of standardized health measurement tools and protocols for forest healing. Also, it is necessary to improve the indicators and protocols by incorporating the opinions of program participants and instructors, so we conducted a qualitative analysis based on focus group interviews (FGIs) and in-depth interviews (IDIs). We collected opinions through interviews conducted for about 1 h with 32 participants in the pilot study and three instructors of the forest healing program. We utilized the MAXQDA program, commonly employed for qualitative research, such as coding and analyzing interview transcripts and literature reviews, as part of the qualitative research process. Three researchers coded and categorized the data, and the first author and corresponding author performed the final coding and categorization. Opinions on the five mental health questionnaires, three physical health measures, and exercise behavior measures used in the forest healing program were solicited. Opinions on the measurement protocol were also solicited. Participants faced challenges in completing the mental health questionnaire due to inappropriate terminology, difficulty in providing truthful responses due to repetitive questions, and the complexity of answering exercise-type questions due to the length of the survey and the absence of clear examples. It was identified that improvements are needed in the future. Some participants commented on the need to measure blood circulation and short-term health changes, and others noted that performing measurements in large groups was difficult, such that there was a need to introduce a measurement protocol for groups. This study is the first to qualitatively evaluate the validity of health measurement tools associated with forest therapy programs. It can contribute to the establishment of standardized health indicators and protocols, as well as serve as a valuable reference for selecting measurement tools to evaluate the effectiveness of forest healing interventions.
Benchmark for Automatic Clear-Cut Morphology Detection Methods Derived from Airborne Lidar Data
Authors: Melichová Zlatica , Pekár Stano , Surový Peter
Published: December 2023

Forest harvest detection techniques have recently gained increased attention due to the varied results they provide. Correctly determining the acreage of clear-cut areas is crucial for carbon sequestration. Detecting clear-cut areas using airborne laser scanning (ALS) could be an accurate method for determining the extent of clear-cut areas and their subsequent map display in forest management plans. The shapes of ALS-detected clear-cut areas have uneven edges with protrusions that might not be readable when displayed correctly. Therefore, it is necessary to simplify these shapes for better comprehension. To simplify the shapes of ALS-scanned clear-cut areas, we tested four simplification algorithms using ArcGIS Pro 3.0.0 software: the retain critical points (Douglas–Peucker), retain critical bends (Wang–Müller), retain weighted effective areas (Zhou–Jones), and retain effective areas (Visvalingam–Whyatt) algorithms. Ground-truth data were obtained from clear-cut areas plotted in the forest management plan. Results showed that the Wang–Müller algorithm was the best of the four ALS algorithms at simplifying the shapes of detected clear-cut areas. Using the simplification algorithm reduced the time required to edit polygons to less than 1% of the time required for manual delineation.