bluuebunny/crossref_metadata_2025 huggingface dataset:
BRAQUE: Bayesian Reduction for Amplified Quantization in UMAP Embedding
Authors: Dall’Olio Lorenzo , Bolognesi Maddalena , Borghesi Simone , Cattoretti Giorgio , Castellani Gastone
Published: February 2023

Single-cell biology has revolutionized the way we understand biological processes. In this paper, we provide a more tailored approach to clustering and analyzing spatial single-cell data coming from immunofluorescence imaging techniques. We propose Bayesian Reduction for Amplified Quantization in UMAP Embedding (BRAQUE) as an integrative novel approach, from data preprocessing to phenotype classification. BRAQUE starts with an innovative preprocessing, named Lognormal Shrinkage, which is able to enhance input fragmentation by fitting a lognormal mixture model and shrink each component towards its median, in order to help further the clustering step in finding more separated and clear clusters. Then, BRAQUE’s pipeline consists of a dimensionality reduction step performed using UMAP, and a clustering performed using HDBSCAN on UMAP embedding. In the end, clusters are assigned to a cell type by experts, using effects size measures to rank markers and identify characterizing markers (Tier 1), and possibly characterize markers (Tier 2). The number of total cell types in one lymph node detectable with these technologies is unknown and difficult to predict or estimate. Therefore, with BRAQUE, we achieved a higher granularity than other similar algorithms such as PhenoGraph, following the idea that merging similar clusters is easier than splitting unclear ones into clear subclusters.
A Well-Posed Fractional Order Cholera Model with Saturated Incidence Rate
Authors: Baba Isa Abdullahi , Humphries Usa Wannasingha , Rihan Fathalla A.
Published: February 2023

A fractional-order cholera model in the Caputo sense is constructed. The model is an extension of the Susceptible–Infected–Recovered (SIR) epidemic model. The transmission dynamics of the disease are studied by incorporating the saturated incidence rate into the model. This is particularly important since assuming that the increase in incidence for a large number of infected individualsis equivalent to a small number of infected individualsdoes not make much sense. The positivity, boundedness, existence, and uniqueness of the solution of the model are also studied. Equilibrium solutions are computed, and their stability analyses are shown to depend on a threshold quantity, the basic reproduction ratio (R0). It is clearly shown that if R0<1, the disease-free equilibrium is locally asymptotically stable, whereas if R0>1, the endemic equilibrium exists and is locally asymptotically stable. Numerical simulations are carried out to support the analytic results and to show the significance of the fractional order from the biological point of view. Furthermore, the significance of awareness is studied in the numerical section.
From Digital Collection to Open Access: A Preliminary Study on the Use of Digital Models of Local Culture
Authors: Chang Chia-Ling , Lin Chin-Lon , Hsu Chi-Hsien , Sun Yikang
Published: February 2023

In the past, most cultural content was in a passive state of protection. In recent years, with the popularity of digital printing and the emergence of the concept of open-source sharing, it provides a new idea for cultural preservation. Using cultural elements from the Taitung region as a sample, this study established an open-source database, and completed the production of 60 digital models and the archiving of related materials. Based on the concept of open-source sharing, our research hopes that this database can be applied in more places. Through surveys, it could be concluded that, when the models are designed in parts and are easy to print and display, it is more conductive for the models to be used in promotions and applications. It is expected that each township will have its own localized 3D model database. Through the open-source localized digital model’s unrestricted and free features, under the influence of COVID-19, it can also allow people from all over the world who cannot visit these places in person to print the local cultural content remotely, so as to have a three-dimensional under-standing of Taiwanese cultural objects. It is expected that the localized 3D model databases will help promote local cultural improvement and move towards local innovation.
Australian Primary School Students’ Knowledge of the Agricultural Industry
Authors: Cosby Amy , Fogarty Eloise S. , Manning Jaime
Published: February 2023

The teaching of agriculture is critical to ensure students understand where their food and fibre originate, as well as the industry’s contribution to the economy. This research examines the agricultural knowledge of Australian primary school students. Grade 4–6 students (n = 2392) from 75 schools were surveyed in 2021. The students’ mean knowledge score was 8.7 out of 13 (SD = 2.1). The students exhibited a reasonable understanding of animal-derived products compared to plant-derived products, particularly when the plant product has undergone substantial processing. However, the students lacked an understanding of modern animal production systems, including the use of technologies on farms. This research identifies the gap in students’ agricultural knowledge and provides insight to the industry to design and implement programs to improve their understanding.
Super Twisting Sliding Mode Control with Compensated Current Controller Dynamics on Active Magnetic Bearings with Large Air Gap
Authors: Vennemann Jonah , Brasse Romain , König Niklas , Nienhaus Matthias , Grasso Emanuele
Published: February 2023

Due to their unique properties, like no mechanical contact and therefore no wear and no lubrication needed, Active Magnetic Bearings (AMBs) have been a dynamic field of research in the past decades. The high non-linearities of AMBs generate many challenges for the control of the otherwise unstable system, thus they need to be addressed to deliver the performance that modern applications require. Integrating the current controller dynamics into the model of a position controller in a cascading control loop helps to improve the performance of the control loop compared to a plain current controlled schema. Further, this nested control loop guarantees the predefined current dynamics of the current controller, tuned according to an industrial criterion. The systems dynamics are modelled and the proposed controller is validated experimentally on a physical test bench. The experimental results show a performant position control with a nested and explicit current controller on an AMB, even with a large air gap and star-connected coils. The trajectory range of the rotor was reduced by 87% to ±20 μm, compared to a plain current-controlled model. The proposed control strategy lays the foundation for further research, especially concerning sensorless position estimation techniques since these usually have limited bandwidth and benefit from a predefined current dynamic.
Fractal Loaded, Novel, and Compact Two- and Eight-Element High Diversity MIMO Antenna for 5G Sub-6 GHz (N77/N78 and N79) and WLAN Applications, Verified with TCM Analysis
Authors: Addepalli Tathababu , Kumar Maragani Satish , Jetti Chandrasekhar Rao , Gollamudi Naveen Kumar , Kumar Bandi Kiran , Kulkarni Jayshri
Published: February 2023

A novel compact fractal loaded two- and eight-element multiple input multiple output (MIMO) with strong diversity is designed for 5G Sub 6 GHz and WLAN applications. The suggested antenna is designed and manufactured on inexpensive FR4 dielectric material with small size of 72 mm × 72 mm × 1.6 mm (0.792λ × 0.792λ × 0.0176λ, where λ is calculated at a lower operating frequency). The proposed layout features a partially grounded, protruding T-shaped stub on the underside of the substrate and a set of fractally loaded circular patch antenna elements on the top. Four triangular slots on the substrate and a T-shaped stub on the ground are employed to produce good isolation over the intended bands. The proposed antenna has a frequency range of (3.3–6.0) GHz, making it compatible with the 5G sub-6 GHz bands and the WLAN band thanks to its high isolation of above 15 dB and good impedance matching characteristics. Good agreement is observed between the antenna results and the theory of characteristic mode analysis approach. The designed antenna is well suited for 5G sub-6 GHz and WLAN communication applications due to its low ECC (0.005), total active reflection coefficient (TARC) (−10 dB), mean effective gain (MEG) (−3 dB), and diversity gain (DG) (−10 dB), channel capacity losses (CCL) (0.05), peak gain (>2.5 dBi), radiation efficiency (>95%), and stable boresight radiation patterns.
Analytical Model of an Energy Detector for Ultra-Wideband Chaotic Communications
Authors: Andreyev Yuri
Published: February 2023

For the ultra-wideband chaotic radio pulses an analytical model of an energy detector is designed and closed-form expressions for the optimal threshold and the detection probabilities are derived. The analytical solution is compared with the results of the numerical simulation. The model proves to be well-suited for the chaotic radio pulses with large base B (or processing gain, or the number of freedom degrees), namely, B ≥ 100. Potential applications areas of the proposed model are ultra-wideband communications with chaotic pulse carriers, e.g., ultra-wideband systems of 802.15.x standards, and multi-element chaotic communication systems.
Classifying Brain Tumors on Magnetic Resonance Imaging by Using Convolutional Neural Networks
Authors: Gómez-Guzmán Marco Antonio , Jiménez-Beristaín Laura , García-Guerrero Enrique Efren , López-Bonilla Oscar Roberto , Tamayo-Perez Ulises Jesús , Esqueda-Elizondo José Jaime , Palomino-Vizcaino Kenia , Inzunza-González Everardo
Published: February 2023

The study of neuroimaging is a very important tool in the diagnosis of central nervous system tumors. This paper presents the evaluation of seven deep convolutional neural network (CNN) models for the task of brain tumor classification. A generic CNN model is implemented and six pre-trained CNN models are studied. For this proposal, the dataset utilized in this paper is Msoud, which includes Fighshare, SARTAJ, and Br35H datasets, containing 7023 MRI images. The magnetic resonance imaging (MRI) in the dataset belongs to four classes, three brain tumors, including Glioma, Meningioma, and Pituitary, and one class of healthy brains. The models are trained with input MRI images with several preprocessing strategies applied in this paper. The CNN models evaluated are Generic CNN, ResNet50, InceptionV3, InceptionResNetV2, Xception, MobileNetV2, and EfficientNetB0. In the comparison of all CNN models, including a generic CNN and six pre-trained models, the best CNN model for this dataset was InceptionV3, which obtained an average Accuracy of 97.12%. The development of these techniques could help clinicians specializing in the early detection of brain tumors.
High Performance Network Intrusion Detection System Using Two-Stage LSTM and Incremental Created Hybrid Features
Authors: Han Jonghoo , Pak Wooguil
Published: February 2023

Currently, most network intrusion detection systems (NIDSs) use information about an entire session to detect intrusion, which has the fatal disadvantage of delaying detection. To solve this problem, studies have been proposed to detect intrusions using only some packets belonging to the session but have limited effectiveness in increasing the detection performance compared to conventional methods. In addition, space complexity is high because all packets used for classification must be stored. Therefore, we propose a novel NIDS that requires low memory storage space and exhibits high detection performance without detection delay. The proposed method does not need to store packets for the current session and uses only some packets, as in conventional methods, but achieves very high detection performance. Through experiments, it was confirmed that the proposed NIDS uses only a small memory of 25.8% on average compared to existing NIDSs by minimizing memory consumption for feature creation, while its intrusion detection performance is equal to or higher than those of existing ones. As a result, this method is expected to significantly help increase network safety by overcoming the disadvantages of machine-learning-based NIDSs using existing sessions and packets.
Service Performance Evaluation of Operating Loess Railway Tunnel Based on Bayesian Network
Authors: Yang Yandong , Zhang Qian , Xu Fang , Du Mingyuan , Hou Linyan , Hou Lili
Published: February 2023

Due to the particularity of loess engineering geology, loess railway tunnel accidents occur frequently. Based on the theoretical basis of risk management, this study evaluated the service performance of loess railway tunnels. Based on the improved TOPSIS method, the indirect proximity degree of each risk factor was compared, and the appropriate service performance evaluation index was selected. Based on the ISM model and the causality graph modification method, the dependency relationship between nodes was obtained and the Bayesian network evaluation model was constructed. By constructing the database, the EM algorithm was used for data learning, and the model was trained and verified, and the overall accuracy (ACC) and F1 value are used to comprehensively evaluate the training and prediction effect of the model. Finally, the evaluation model was applied to a tunnel case. The results show that the established Bayesian network model has a high accuracy of 92%, which is easy to operate, effective and practical, and it is also applicable in situations of incomplete index statistical data.
Quality-Aware Task Allocation for Mobile Crowd Sensing Based on Edge Computing
Authors: Li Zhuo , Li Zecheng , Zhang Wei
Published: February 2023

In the field of mobile crowd sensing (MCS), the traditional client–cloud architecture faces increasing challenges in communication and computation overhead. To address these issues, this paper introduces edge computing into the MCS system and proposes a two-stage task allocation optimization method under the constraint of limited computing resources. The method utilizes deep reinforcement learning for the selection of optimal edge servers for task deployment, followed by a greedy self-adaptive stochastic algorithm for the recruitment of sensing participants. In simulations, the proposed method demonstrated a 20% improvement in spatial coverage compared with the existing RBR algorithm and outperformed the LCBPA, SMA, and MOTA algorithms in 41, 42, and 48 tasks, respectively. This research contributes to the optimization of task allocation in MCS and advances the integration of edge computing in MCS systems.
The Link between Digitization and the Sustainable Development in European Union Countries
Authors: Ionescu-Feleagă Liliana , Ionescu Bogdan-Ștefan , Stoica Oana Cristina
Published: February 2023

Digitalization and sustainable development represent two highly topical subjects, each of them being primarily debated in the literature. Although it is evident that digitalization brings new opportunities and challenges for the management of organizations and for meeting their sustainable strategies, there are relatively few studies analyzing the relationship between the two. Therefore, the objective of the present study is to analyze the relationship between digitalization and sustainable development in the European Union (EU) countries, between 2019 and 2021, before and during the COVID-19 pandemic. On this matter, the link between Digital Economy and Society Index (DESI), on one side, and Sustainable Development Goal Index (SDG Index) and Spillover Index (SS Index), on the other side, has been analyzed using correlations. While DESI refers to the monitoring of digital advancement of the EU member states, SDG Index and SS Index are based on many indicators and give a multidimensional perspective regarding sustainable development. Results show a positive and significant relationship between DESI and SDG Index, and the effect gradually decreases during the analyzed period. Regarding the relationship between DESI and SS Index, the correlation was negative, but significant in 2020 and 2019 only. At the level of each geographical region, the relationship between DESI and SDG Index was positive, with a larger effect in the Northern and Western region and the Southern region compared to the Central and Eastern region, but this effect was not statistically significant. In addition, the relationship between DESI and SS Index was negative and significant at the level of the Central and Eastern region and the Southern region but positive and not statistically significant at the level of the Northern and Western region. In addition to previous papers on this field, this study adds the analysis between digitalization and the Spillover Index. In addition, while previous studies seek to identify the relationship between digitalization and sustainability at a single country level or region, this study presents the correlation analysis at the EU level and compares the results for each EU region. Following the results, our study gives arguments for reshaping the concept of sustainable development given that digitalization is becoming an essential business component.
SRFPI-LADRC Based Control Strategy for Off-Grid Single-Phase Inverter: Design, Analysis, and Verification
Authors: Lin Liaoyuan , Li Haoda , Zhu Kai , Shi Lingling
Published: February 2023

Linear active disturbance rejection control (LADRC) has been extensively used in various areas due to its excellent disturbance suppression capability. When LADRC is applied to a single-phase inverter for tracking a sinusoidal reference signal, there is an inherent tracking inaccuracy problem. The steady-state error can be removed with the synchronous reference frame proportional-integral (SRFPI) control, which generates two orthogonal signals. In this paper, a modified control method based on compound SRFPI and LADRC for an off-grid single-phase inverter is put forward, where both output signals of SRFPI are employed as the reference signals of LADRC. Furthermore, a selective harmonic compensation method is performed by paralleling multiple SRFPI controllers to further reduce the selective harmonic components. Detailed theoretical analyses including system stability, robustness, performance of voltage tracking error and disturbance rejection are presented, which indicate that this organic combination fuses the merits of both SRFPI and LADRC without complicating the control design. Additionally, contrast experiments are conducted to demonstrate its effectiveness and superiority. These findings demonstrate that the system realizes a slight voltage tracking error and steady-state error, rapid dynamic response, and low total harmonic distortion (THD), especially under highly nonlinear load conditions.
Vision-Based UAV Landing with Guaranteed Reliability in Adverse Environment
Authors: Ge Zijian , Jiang Jingjing , Pugh Ewan , Marshall Ben , Yan Yunda , Sun Liang
Published: February 2023

Safe and accurate landing is crucial for Unmanned Aerial Vehicles (UAVs). However, it is a challenging task, especially when the altitude of the landing target is different from the ground and when the UAV is working in adverse environments, such as coasts where winds are usually strong and changing rapidly. UAVs controlled by traditional landing algorithms are unable to deal with sudden large disturbances, such as gusts, during the landing process. In this paper, a reliable vision-based landing strategy is proposed for UAV autonomous landing on a multi-level platform mounted on an Unmanned Ground Vehicle (UGV). With the proposed landing strategy, visual detection can be retrieved even with strong gusts and the UAV is able to achieve robust landing accuracy in a challenging platform with complex ground effects. The effectiveness of the landing algorithm is verified through real-world flight tests. Experimental results in farm fields demonstrate the proposed method’s accuracy and robustness to external disturbances (e.g., wind gusts).
Image Steganalysis of Low Embedding Rate Based on the Attention Mechanism and Transfer Learning
Authors: Liu Shouyue , Zhang Chunying , Wang Liya , Yang Pengchao , Hua Shaona , Zhang Tong
Published: February 2023

In recent years, some research results have been achieved in the field of image steganalysis. However, there are still problems of difficulty in extracting steganographic features from images with low embedding rates and unsatisfactory detection performance of steganalysis. In this paper, we propose an image steganalysis method based on the attention mechanism and transfer learning. The method constructs a network model based on a convolutional neural network, including a preprocessing layer, a transposed convolutional layer, an ordinary convolutional layer, and a fully connected layer. We introduce the efficient channel attention module after the ordinary convolutional layer to focus on the steganographic region of the image, capture the local cross-channel interaction information, realize the adaptive adjustment of feature weights, and enhance the ability to extract steganographic features. Meanwhile, we apply the transfer learning method to use the training model parameters of high embedding rate images as the initialization parameters of the training model of the low embedding rate to achieve feature migration and further improve the steganalysis performance of the low embedding rate. The experimental results show that compared to the typical Xu-Net and Yedroudj-Net models, the detection accuracy of the proposed method is improved by 16.36% to 30.66% and by 35.59 to 37.83% for the embedding rates of 0.05 bpp, 0.1 bpp, and 0.2 bpp, respectively. Compared to the state-of-the-art Shen-Net model with low embedding rates, the detection accuracy is improved by 3.43% to 6.41%. This demonstrates the higher detection performance of the proposed method for steganalysis of low embedding rate images.
A Novel Approach for Contextual Clustering and Retrieval of Behavior Trees to Enrich the Behavior of Social Intelligent Agents
Authors: Jamjoom Mona , Ahmed Nada , Abbas Safia , Hodhod Rania , El-Sheikh Mohamed , Ullah Zahid
Published: February 2023

Recently, many works have been carried out to find effective ways that can allow for plausibly effective interactions of social intelligent agents (SIAs) in unpredictable environments in a reasonable time. Behavior trees (BTs) allow for knowledge to be modeled as a graph representation and provide a way for SIAs to effectively interact with the received information. BTs can store past social experiences that can then be used by SIAs to provide adequate human-like interactions when facing new social situations (query). One challenge appears when a social agent with vast past experiences—represented as a forest of BTs—tries to retrieve a similar BT to learn from in order to provide plausible interactions in the current situation in a cost-effective manner. Cognitive scripts with their inherent temporal structure can address this challenge where they can facilitate the use of contextual retrieval techniques on BTs represented as cognitive scripts. This paper introduces novel hybrid retrieval techniques that use agglomerative hierarchical clustering (H-clustering) and similarity-based algorithms: map-and-reduce and least common parent (LCP) to effectively retrieve similar BTs to a specific query BT in a reasonable time. The model groups BTs, represented as cognitive scripts, into compact clusters that can then be used to retrieve the most similar BT to a query one in real time without noticeable delay. A comparison was done between the performance of the proposed hybrid-retrieval techniques using a semi-structured dataset of cognitive scripts. The results showed that H-clustering-map-and-reduce is more cost-effective than H-clustering-LCP as it allowed for a low average retrieval time of 8 × 10−3 s compared to 3.1 s, respectively.
Low-Cost Hardware in the Loop for Intelligent Neural Predictive Control of Hybrid Electric Vehicle
Authors: Essa Mohamed El-Sayed M. , Lotfy Joseph Victor W. , Abd-Elwahed M. Essam K. , Rabie Khaled , ElHalawany Basem M. , Elsisi Mahmoud
Published: February 2023

The design and investigation of an intelligent controller for hardware-in-the-loop (HIL) implementation of hybrid electric vehicles (HEVs) are proposed in this article. The proposed intelligent controller is adopted based on the enhancement of a model predictive controller (MPC) by an artificial neural network (ANN) approach. The MPC-based ANN (NNMPC) is proposed to control the speed of HEVs for a simulation system model and experimental HIL test systems. The HIL is established to assess the performance of the NNMPC to control the velocity of HEVs in an experimental environment. The real-time environment of HIL is implemented through a low-cost approach such as the integration of an Arduino Mega 2560 and a host Lenovo PC with a Core i7 @ 3.4 GHz processor. The NNMPC is compared with a proportional–integral (PI) controller, a classical MPC, and two different settings of the ANN methodology to verify the efficiency of the proposed intelligent NNMPC. The obtained results show a distinct behavior of the proposed NNMPC to control the speed of HEVs with good performance based on the distinct transient response, minimum error steady state, and system robustness against parameter perturbation.
How Applicable Are Turbulence Assumptions Used in the Tidal Energy Industry?
Authors: Naberezhnykh Alyona , Ingram David , Ashton Ian , Culina Joel
Published: February 2023

As tidal current and marine hydro-kinetic energy converters start to be deployed in pre-commercial arrays, it is critical that the design conditions are properly characterised. Turbulence is known to influence fatigue loads and power production, so developers use turbulence models to generate unsteady flows in order to simulate device performance. Most such models construct a synthetic flow field using a combination of measured parameters and theoretical assumptions. The majority in use today are based on atmospheric flow conditions and may have limited applicability in tidal environments. In the present work, we compare key turbulence model assumptions (which are recommended by the tidal turbine standards and are used in design software) to turbulence measurements from two tidal test sites in Scotland and Canada. Here, we show that the two sites have different levels of conformity to theoretical models, with significant variability within nearby locations at the same site. The agreement with spectral models is shown to be depth-dependent. The vertical component spectrum is better represented by the Kaimal model, while the streamwise spectrum is better represented by the von Kármán model. With the exception of one site, the shear profiles follow a power law, although with a different exponent to that commonly assumed. Both sites show significant deviations from the theoretical length scales and isotropy ratios. Such deviations are likely to misrepresent the loads experienced by a device. These results highlight the turbulence characteristics at real deployment sites, which are not well represented by current models, and, hence, which must be determined using field measurements.
Regenerative Braking Algorithm for Parallel Hydraulic Hybrid Vehicles Based on Fuzzy Q-Learning
Authors: Ning Xiaobin , Wang Jiazheng , Yin Yuming , Shangguan Jiarong , Bao Nanxin , Li Ning
Published: February 2023

The use of regenerative braking systems is an important approach for improving the travel mileage of electric vehicles, and the use of an auxiliary hydraulic braking energy recovery system can improve the efficiency of the braking energy recovery process. In this paper, we present an algorithm for optimizing the energy recovery efficiency of a hydraulic regenerative braking system (HRBS) based on fuzzy Q-Learning (FQL). First, we built a test bench, which was used to verify the accuracy of the hydraulic regenerative braking simulation model. Second, we combined the HRBS with the electric vehicle in ADVISOR. Third, we modified the regenerative braking control strategy by introducing the FQL algorithm and comparing it with a fuzzy-control-based energy recovery strategy. The simulation results showed that the power savings of the vehicle optimized by the FQL algorithm were improved by about 9.62% and 8.91% after 1015 cycles and under urban dynamometer driving schedule (UDDS) cycle conditions compared with a vehicle based on fuzzy control and the dynamic programming (DP) algorithm. The regenerative braking control strategy optimized by the fuzzy reinforcement learning method is more efficient in terms of energy recovery than the fuzzy control strategy.
Theoretical and Experimental Studies of Combined Heat and Power Systems with SOFCs
Authors: Iliev Iliya Krastev , Filimonova Antonina Andreevna , Chichirov Andrey Alexandrovich , Chichirova Natalia Dmitrievna , Pechenkin Alexander Vadimovich , Vinogradov Artem Sergeevich
Published: February 2023

The article presents an overview of experimental layout design solutions and the general operation scheme of combined heat and power systems with a high-temperature solid oxide fuel cell (SOFC). This system is an environmentally friendly and energy-saving way to produce electricity and heat. The use of high-temperature SOFCs makes it possible to obtain an electrical efficiency of 45–55%. Combining the electrochemical and mechanical system can increase the total efficiency by up to 60–65% in a hybrid power plant. This article discusses the structure and relationship between the components of a hybrid power plant and various modification options for efficient power generation. The technological schemes for existing and tested hybrid power plants with an SOFC and gas turbine are presented and described in detail. When designing a hybrid power plant, the key factors are the choice of design, heat source, and fuel-reforming method; the design of a solid oxide fuel cell and the number of modules in a stack; selecting devices for generating electricity with the development of cogeneration or trigeneration cycles (for possible use in thermal power plants and for the energy supply of social facilities); the direction of material flows within the system; pressure and tightness; and the interconnection of the hybrid power system elements. Researchers have accumulated and described in scientific papers extensive experience in designing, theoretical research, and numerical modeling of hybrid power plants with high-temperature SOFCs. It is shown that experimental hybrid power plants based on SOFCs of the megawatt class are in operation. Hybrid systems with an SOFC are designed only for the kilowatt power class. Trigeneration systems with a steam turbine exist only in the form of theoretical calculations. Trigeneration systems show the highest electrical efficiency, but the highest construction and service costs. Systems based on high-temperature SOFCs can be used for autonomous systems, and in combination with gas and steam turbines only at thermal power plants. Experimental laboratory studies are limited by the high cost of installations and the difficulties of testing the possibility of using combined heat and power systems on an industrial scale. Therefore, a more detailed study of the relationship between the units of a combined heat and power system is recommended in order to achieve the high efficiency indicators obtained from theoretical studies.
Production of Bio-Oil from Thermo-Catalytic Decomposition of Pomegranate Peels over a Sulfonated Tea Waste Heterogeneous Catalyst: A Kinetic Investigation
Authors: Rehman Nafees Ur , Nisar Jan , Ali Ghulam , Ahmad Ali , Shah Afzal , Farooqi Zahoor H. , Muhammad Faisal
Published: February 2023

In this study, the pyrolysis procedure was used to extract oil from pomegranate peels (PP) utilizing biomass-derived sulfonated tea waste as a catalyst. FTIR, SAA, SEM, and XRD were used to characterize the catalyst. Thermo-catalytic decomposition was carried out in a salt bath reactor and the bio-oil composition was determined through GC-MS. The oil obtained from virgin PP was observed to contain compounds in the range of C5–C13, whereas from the catalyzed reaction it was found to be rich in C5–C23. For the calculation of kinetic parameters, TG analysis was performed of virgin PP and with the catalyst at different heating rates. TG/DTG indicated weight loss in four steps. The first weight loss below 100 °C is due to the physically adsorbed water molecule evaporation. The second weight loss is attributed to hemicellulose decomposition and the third one to cellulose degradation. The fourth weight loss is due to lignin degradation. Kissinger model was used for measuring the activation energy (Ea) of the decomposition reaction. The activation energy of hemicellulose, cellulose, and lignin for non-catalytic reactions was observed as 199, 249, and 299 kJmol−1, while in the case of the loaded tea waste catalyst, the Ea was reduced to 122, 163, and 207 kJmol−1, respectively, confirming the effectiveness of the catalyst. From these findings, it can be concluded that sulfonated tea waste catalyst has not only lowered the pyrolysis temperature and Ea but also brought a change in oil quality by enhancing value-added compounds in the bio-oil.
Modelling Climatically Suitable Areas for Mahogany (Swietenia macrophylla King) and Their Shifts across Neotropics: The Role of Protected Areas
Authors: Herrera-Feijoo Robinson J. , Torres Bolier , López-Tobar Rolando , Tipán-Torres Cristhian , Toulkeridis Theofilos , Heredia-R Marco , Mateo Rubén G.
Published: February 2023

Mahogany (Swietenia macrophylla King) is a species with great economic interest worldwide and is classified as vulnerable to extinction by the IUCN. Deforestation and climate change are the main hazards to this species. Therefore, it is vital to describe possible changes in distribution patterns under current and future climatic conditions, as they are important for their monitoring, conservation, and use. In the current study, we predict, for the very first time, the potential distribution of Mahogany based on data that reflect the total distribution of the species, climatic and edaphic variables, and a consensus model that combines the results of three statistical techniques. The obtained model was projected to future climatic conditions considering two general circulation models (GCM), under two shared socioeconomic pathways (SSP245 and SSP585) for 2070. Predictions under current climatic conditions indicated wide adequate areas in Central American countries such as Mexico and demonstrated a coverage of up to 28.5% within the limits of the protected areas. Under future scenarios, drastic reductions were observed in different regions, particularly in Venezuela, Perú, and Ecuador, with losses of up to 56.0%. On the other hand, an increase in suitable areas for the species within protected areas was also detected. The results of this study are certainly useful for identifying currently unrecorded populations of Mahogany, as well as for identifying locations that are likely to be suitable both now and in the future for conservation management planning. The methodology proposed in this work is able to be used for other forest species in tropical zones as a tool for conducting dynamic conservation and restoration strategies that consider the effects of climate change.
Modelling the Development of Above-Ground Biomass Energy Reserves of Four Economically Important Coniferous Woody Species
Authors: Petráš Rudolf , Mecko Julian , Kukla Ján , Kuklová Margita , Hnilička František , Hniličková Helena , Pivková Ivica
Published: February 2023

The goal of renewable energy is to replace energy production from fossil fuels. In that sense, forest biomass is essential renewables. This article presents the results of the development of energy reserves in fractions, increments and the total above-ground biomass of coniferous stands (spruce, fir, pine, larch) during their economic cycle. The experimental material comes from 22 forest stands located mainly in Central Slovakia, to a lesser extent also in Western and Eastern Slovakia. Energy reserves of coniferous stands were calculated based on the volume production of above-ground biomass fractions taken from mathematical models of yield tables and average values of their basic density and calorific value were determined. The research showed that as the age of the stands increased, the share of energy in the wood fraction increased, while it decreased in the bark fraction, and especially the branch fraction. The curves constructed in relation to the age of the stand and site index have a very similar shape to the curves of the total current annual energy increment of coniferous stands. The energy reserves of stands grew faster at the age of 40 to 80 years than at the age of 80 to 140 years. Spruce had the highest total mean energy increment, followed by fir, larch and pine. As the age of the stands increases, the energy reserves of the increments slightly decrease and the efficiency of solar energy significantly decreases. It peaks practically at the age of reaching the maximum annual energy increment.
MaxEnt Modeling for Predicting Suitable Habitat for Endangered Tree Keteleeria davidiana (Pinaceae) in China
Authors: Zhang Qin , Shen Xiangbao , Jiang Xiaolong , Fan Tingting , Liang Xiaocui , Yan Wende
Published: February 2023

Understanding species response to climate change is essential for the conservation and utilization of species resources under rapid climate change in the future. In this study, the present and future suitable distribution range of Keteleeria davidiana, a tertiary relict gymnosperm, was predicted based on the maximum entropy model (MaxEnt). A total of 158 occurrence records were collected after removing the duplicated records. Six low-correlation climate variables were used to predict species distributions. The three key climate factors that affect the distribution of K. davidiana were temperature seasonality (34.96%), mean temperature of the coldest quarter (28.30%) and precipitation seasonality (13.58%). The most suitable zone of the temperature seasonality for K. davidiana was between 377.4 and 843.4. The highly suitable area was located in the mountainous regions of central and southeast China, which accounted for 13.39% of the whole study area. With climate warming in the future, the highly suitable distribution area of K. davidiana was estimated to decrease by 35% (SSP1-2.6 scenario) or 85% (SSP5-8.5 scenario). This study has provided a sufficient scientific basis for the future in situ and ex situ conservation of K. davidiana.
Tomato Processing By-Products Valorisation through Ohmic Heating Approach
Authors: Coelho Marta C. , Ghalamara Soudabeh , Campos Débora , Ribeiro Tânia Bragança , Pereira Ricardo , Rodrigues António S. , Teixeira José A. , Pintado Manuela
Published: February 2023

Tomato by-products from processing industries have a higher potential to be reused as a source of bioactive compounds. Reliable national data on tomato by-products and physicochemical characterisation that will inform and find effective planning on tomato waste management in Portugal is absent. To help obtain this knowledge, selected Portugal companies were recruited to obtain representative samples of by-products generation, and physicochemical composition was evaluated. Furthermore, an environmental-friendly method (the ohmic heating (OH) method, which allows the recovery of bioactive compounds in absence of hazardous reagents) was also used and compared with conventional methods to explore new safe value-added ingredients. Total antioxidant capacity and total and individual phenolic compounds were also evaluated by spectrophotometric and high-performance liquid chromatography (HPLC), respectively. Tomato processing by-products have revealed a higher potential since both collected samples from companies were rich in protein (between 16.3 to 19.4 g/100 g DW, with fibre content ranging from 57.8 to 59.0 g/100 g DW). In addition, these samples contain 17.0 g/100 g of fatty acids (mainly polyunsaturated, monounsaturated and saturated, such as linoleic, oleic, and palmitic acid, respectively). Also, they present mainly chlorogenic acid and rutin as phenolic compounds. After understanding its composition, the OH was applied to determine added-value solutions to tomato by-products. With extractions, two types of fractions were obtained, namely liquid fraction rich in phenols, free sugars, and carotenoids and a solid fraction rich in fibre bound to phenols and carotenoids. This treatment has been shown to have the ability to preserve carotenoids, such as lycopene relative to conventional methods. Nevertheless, new molecules were identified by LC-ESI-UHR-OqTOF-MS analysis, such as phene-di-hexane and N-acethyl-D-tryptophan. According to the results, the OH boosts the potential of tomato by-products and can be directly introduced into the process, contributing to the circular economy and zero by-products.
Identification of Key Antioxidants of Free, Esterified, and Bound Phenolics in Walnut Kernel and Skin
Authors: Wu Shutian , Mo Runhong , Wang Ruohui , Li Qingyang , Shen Danyu , Liu Yihua
Published: February 2023

Walnut is a natural source of antioxidants. Its antioxidant capacity is determined by the distribution and composition of phenolics. The key phenolic antioxidants in various forms (free, esterified, and bound) in walnut kernel (particularly seed skin) are unknown. The phenolic compounds in twelve walnut cultivars were analyzed using ultra-performance liquid chromatography coupled with a triple quadrupole mass spectrometer in this study. A boosted regression tree analysis was used to identify the key antioxidants. Ellagic acid, gallic acid, catechin, ferulic acid, and epicatechin were abundant in the kernel and skin. The majority of phenolic acids were widely distributed in the free, esterified, and bound forms in the kernel but more concentrated in bound phenolics in the skin. The total phenolic levels of the three forms were positively correlated with antioxidant activities (R = 0.76–0.94, p < 0.05). Ellagic acid was the most important antioxidant in the kernel, accounting for more than 20%, 40%, and 15% of antioxidants, respectively. Caffeic acid was responsible for up to 25% of free phenolics and 40% of esterified phenolics in the skin. The differences in the antioxidant activity between the cultivars were explained by the total phenolics and key antioxidants. The identification of key antioxidants is critical for new walnut industrial applications and functional food design in food chemistry.
Deterministic and Fractional-Order Co-Infection Model of Omicron and Delta Variants of Asymptomatic SARS-CoV-2 Carriers
Authors: Faridi Waqas Ali , Asjad Muhammad Imran , Ahmad Shabir , Iftene Adrian , Abd El-Rahman Magda , Sallah Mohammed
Published: February 2023

The Delta and Omicron variants’ system was used in this research study to replicate the complex process of the SARS-CoV-2 outbreak. The generalised fractional system was designed and rigorously analysed in order to gain a comprehensive understanding of the transmission dynamics of both variants. The proposed dynamical system has heredity and memory effects, which greatly improved our ability to perceive the disease propagation dynamics. The non-singular Atangana–Baleanu fractional operator was used to forecast the current pandemic in order to meet this challenge. The Picard recursions approach can be used to ensure that the designed fractional system has at least one solution occupying the growth condition and memory function regardless of the initial conditions. The Hyers–Ulam–Rassias stability criteria were used to carry out the stability analysis of the fractional governing system of equations, and the fixed-point theory ensured the uniqueness of the solution. Additionally, the model exhibited global asymptotically stable behaviour in some conditions. The approximate behaviour of the fatal virus was investigated using an efficient and reliable fractional numerical Adams–Bashforth approach. The outcome demonstrated that there will be a significant decline in the population of those infected with the Omicron and Delta SARS-CoV-2 variants if the vaccination rate is increased (in both the symptomatic and symptomatic stages).
Progress in and Prospects of Genome Editing Tools for Human Disease Model Development and Therapeutic Applications
Authors: Phan Hong Thi Lam , Kim Kyoungmi , Lee Ho , Seong Je Kyung
Published: February 2023

Programmable nucleases, such as zinc finger nucleases (ZFNs), transcription activator-like effector nucleases (TALENs), and clustered regularly interspaced short palindromic repeats (CRISPR)/Cas, are widely accepted because of their diversity and enormous potential for targeted genomic modifications in eukaryotes and other animals. Moreover, rapid advances in genome editing tools have accelerated the ability to produce various genetically modified animal models for studying human diseases. Given the advances in gene editing tools, these animal models are gradually evolving toward mimicking human diseases through the introduction of human pathogenic mutations in their genome rather than the conventional gene knockout. In the present review, we summarize the current progress in and discuss the prospects for developing mouse models of human diseases and their therapeutic applications based on advances in the study of programmable nucleases.
A Chemoptogenetic Tool for Spatiotemporal Induction of Oxidative DNA Lesions In Vivo
Authors: Han Suhao , Sims Austin , Aceto Anthony , Schmidt Brigitte F. , Bruchez Marcel P. , Gurkar Aditi U.
Published: February 2023

Oxidative nuclear DNA damage increases in all tissues with age in multiple animal models, as well as in humans. However, the increase in DNA oxidation varies from tissue to tissue, suggesting that certain cells/tissues may be more vulnerable to DNA damage than others. The lack of a tool that can control dosage and spatiotemporal induction of oxidative DNA damage, which accumulates with age, has severely limited our ability to understand how DNA damage drives aging and age-related diseases. To overcome this, here we developed a chemoptogenetic tool that produces 8-oxoguanine (8-oxoG) at DNA in a whole organism, Caenorhabditis elegans. This tool uses di-iodinated malachite green (MG-2I) photosensitizer dye that generates singlet oxygen, 1O2, upon fluorogen activating peptide (FAP) binding and excitation with far-red light. Using our chemoptogenetic tool, we are able to control generation of singlet oxygen ubiquitously or in a tissue-specific manner, including in neurons and muscle cells. To induce oxidative DNA damage, we targeted our chemoptogenetic tool to histone, his-72, that is expressed in all cell types. Our results show that a single exposure to dye and light is able to induce DNA damage, promote embryonic lethality, lead to developmental delay, and significantly reduce lifespan. Our chemoptogenetic tool will now allow us to assess the cell autonomous versus non-cell autonomous role of DNA damage in aging, at an organismal level.