bluuebunny/crossref_metadata_2025 huggingface dataset:
Does the Treatment of Pelvic Venous Insufficiency Really Not Influence Lower Limb Venous Disease?
Authors: Szary Cezary , Wilczko Justyna , Bodziony Anna , Celejewski Krzysztof , Swieczkowski-Feiz Siavash , Napierala Marcin , Plucinska Dominika , Zawadzki Michal , Leszczynski Jerzy , Grzela Tomasz
Published: July 2023

Pelvic venous insufficiency is a common problem in multiparous women. Besides burdensome symptoms, it correlates with the development of venous disease in the lower limbs. Therefore, the sequential treatment of abdominal/pelvic before leg veins could improve treatment effectiveness. The medical records of 243 patients with venous disease who were subjected to sequential treatment were analyzed retrospectively. The symptoms and patient satisfaction were assessed using dedicated questionnaires, both before and after treatment. Clinical effectiveness was verified using a Doppler scan, both before and after treatment. Among 243 analyzed cases, 195 underwent whole treatment; however, 48 women after embolization did not require further intervention. The total-symptom-score change (11.6 vs. 13.0, respectively) and the satisfaction score (1.6 vs. 1.5, respectively) did not differ between groups. After embolization, some patients, besides symptoms improvement, experienced reflux reduction and, hence, might avoid further intervention. A better explanation for this beneficial effect of the sequential/descending approach requires further studies.
Image Splicing Compression Algorithm Based on the Extended Kalman Filter for Unmanned Aerial Vehicles Communication
Authors: Liang Yanxia , Zhao Meng , Liu Xin , Jiang Jing , Lu Guangyue , Jia Tong
Published: July 2023

The technology of unmanned aerial vehicles (UAVs) is extensively utilized across various domains due to its exceptional mobility, cost-effectiveness, rapid deployment, and flexible configuration. However, its limited storage space and the contradiction between the large amount of image data returned and the limited bandwidth in emergency scenarios necessitate the development of a suitable UAVs image compression technology. In this research, we propose an image splicing compression algorithm based on the extended Kalman filter for UAV communication (SEKF-UC). Firstly, we introduce the concept of image splicing to enhance the compression ratio while ensuring image quality, leveraging the characteristics of deep learning-based compression, which are relatively unaffected by the size of the compressed data. Secondly, to address the issue of slow processing speed of deep neural networks (DNN) caused by the substantial volume of data involved in image splicing, we incorporate an extended Kalman filter module to expedite the process. Simulation experiments show that our proposed algorithm outperforms existing methods in multiple ways, achieving a significant compression ratio improvement, from 2:1 to 25:1, with a marginal reduction of 6.5% in structural similarity (SSIM) compared to the non-spliced approach. Moreover, for deep neural networks (DNN), our method incorporating the extended Kalman filter module achieves the same error level with only 30 iterations—a significant reduction compared to the traditional BP algorithm’s requirement of over 4000 iterations—while improving the average network operation speed by an impressive 89.76%. Additionally, our algorithm excels in image quality, with peak signal-to-noise ratio (PSNR) improving by 92.7% and SSIM by 42.1%, at most, compared to other algorithms. These results establish our algorithm as a highly efficient and effective solution, suitable for various image processing and data compression applications.
TMD Design by an Entropy Index for Seismic Control of Tall Shear-Bending Buildings
Authors: Wang Yumei , Qu Zhe
Published: July 2023

This study proposes a new arrangement-tuning method to maximize the potential of tuned mass dampers (TMDs) in decreasing the seismic responses of tall buildings. The method relies on a Grammian-based entropy index with the physical meaning of covariance responses to white noise without the involvement of external inputs. A twelve-story RC frame-shear wall building was used as an example to illustrate the method. Indices were computed for the building with TMDs placed on different stories and tuning to different modes and were compared with responses to white noise (colored) time histories. Results showed that greater index reduction cases agree well with greater story-drift reductions cases, despite the differences in the time step of the white noises and structural model types (pure shear vs. shear-bending), and the optimal TMD is not necessarily the traditional “roof—1st mode tuning” case. Comparisons were also made for the shear-bending building under seven earthquake excitations. It is found that, though TMDs are not full-band effective controllers, the index-selected TMDs still perform the best in three out of seven earthquakes. So, the proposed internal-property-based entropy index provides a good controller design for large-scale structures under unpredictable none-stationary excitations.
Fault Diagnosis for Rolling Bearing of Combine Harvester Based on Composite-Scale-Variable Dispersion Entropy and Self-Optimization Variational Mode Decomposition Algorithm
Authors: Jiang Wei , Shan Yahui , Xue Xiaoming , Ma Jianpeng , Chen Zhong , Zhang Nan
Published: July 2023

Because of the influence of harsh and variable working environments, the vibration signals of rolling bearings for combine harvesters usually show obvious characteristics of strong non-stationarity and nonlinearity. Accomplishing accurate fault diagnosis using these signals for rolling bearings is a challenging subject. In this paper, a novel fault diagnosis method based on composite-scale-variable dispersion entropy (CSvDE) and self-optimization variational mode decomposition (SoVMD) is proposed, systematically combining the nonstationary signal analysis approach and machine learning technology. Firstly, an improved SoVMD algorithm is developed to realize adaptive parameter optimization and to further extract multiscale frequency components from original signals. Subsequently, a CSvDE-based feature learning model is established to generate the multiscale fault feature space (MsFFS) of frequency components for the improvement of fault feature learning ability. Finally, the generated MsFFS can serve as the inputs of the Softmax classifier for fault category identification. Extensive experiments on the vibration datasets collected from rolling bearings of combine harvesters are conducted, and the experimental results demonstrate the more superior and robust fault diagnosis performance of the proposed method compared to other existing approaches.
Thermodynamic Analysis and Improvement Potential of Helium Closed Cycle Gas Turbine Power Plant at Four Loads
Authors: Mrzljak Vedran , Poljak Igor , Jelić Maro , Prpić-Oršić Jasna
Published: July 2023

This paper presents thermodynamic and improvement potential analyses of a helium closed-cycle gas turbine power plant (Oberhausen II) and dominant plant components at four loads. DESIGN LOAD represents optimal operating conditions that cannot be obtained in exploitation but can be used as a guideline for further improvements. In real plant exploitation, the highest plant efficiency is obtained at NOMINAL LOAD (31.27%). Considering all observed components, the regenerator (helium-helium heat exchanger) is the most sensitive to the ambient temperature change. An exact comparison shows that the efficiency decrease of an open-cycle gas turbine power plant during load decrease is approximately two and a half or more times higher in comparison to a closed-cycle gas turbine power plant. Plant improvement potential related to all turbomachines leads to the conclusion that further improvement of the most efficient turbomachine (High Pressure Turbine—HPT) will increase whole plant efficiency more than improvement of any other turbomachine. An increase in the HPT isentropic efficiency of 1% will result in an average increase in whole plant efficiency of more than 0.35% at all loads during plant exploitation. In the final part of this research, it is investigated whether the additional heater involvement in the plant operation results in a satisfactory increase in power plant efficiency. It is concluded that in real exploitation conditions (by assuming a reasonable helium pressure drop of 5% in the additional heater), an additional heating process cannot be an improvement possibility for the Oberhausen II power plant.
Coordinated Dispatch Optimization between the Main Grid and Virtual Power Plants Based on Multi-Parametric Quadratic Programming
Authors: Yang Guixing , Xu Mingze , Wang Weiqing , Lei Shunbo
Published: July 2023

Virtual power plants (VPPs) are a critical technology for distribution systems that can integrate various renewable energy resourcescontrollable loads and energy storage systems into one specific power plant through a distributed energy management system. This paper proposes a coordinated dispatch optimization model between the main grid and VPPs aiming to minimize both the power generation cost and total system active loss. When the time of the equivalent dispatching model is not divisible due to the existence of a time coupling constraint inside the VPPs, this model can obtain the global optimal solution through iteration between the main grid and the VPPs. By employing multi-parametric quadratic programming to obtain accurate critical domains and optimal cost functions, the convergence speed and stability are significantly improved. Additionally, a reactive power and voltage optimization technique leveraging the generalized Benders decomposition is presented for the coordination of the main grid and the VPPs. Moreover, the impact of distributed energy resource (DER) clusters on the main grid was studied, from which we proved that the proposed approach can expeditiously abate energy production expenditure and system active dissipation whilst enhancing the system equilibrium.
Comparative Study of Passivity, Model Predictive, and Passivity-Based Model Predictive Controllers in Uninterruptible Power Supply Applications
Authors: Hussain Shafquat , Bakeer Abualkasim , Mohamed Ihab S. , Marchesoni Mario , Vaccaro Luis
Published: July 2023

Voltage source converters are widely used in distributed generation (DG) and uninterruptible power supply (UPS) applications. This paper aims to find the controller that performs best when model changes occur in the system, showing insensitivity to parameter variations. A comparison of the finite control set model predictive controller (FCS-MPC), interconnection and damping assignment passivity-based controller (IDA-PBC), and passivity-based model predictive control (PB-MPC) reveals that the PB-MPC provides high resistance to these unexpected LC filter changes in the converter. The second aim of the paper is to reduce the total harmonic distortion (THD) of the output voltage of the three-phase voltage source inverter (VSI). A high total harmonic distortion (THD) value exists in the voltage waveform of the three-phase voltage source inverter (VSI), feeding a non-linear load. A MATLAB simulation was performed using three control techniques for a three-phase VSI feeding: linear load, unbalanced load, and non-linear load. The PB-MPC performs better than the FCS-MPC and IDA-PBC in terms of having a low THD value in the output voltage of the converter under all types of applied loads, improving the THD by up to 30%, and having low variation in THD with mismatched filter parameters, as shown in the bar charts in the results section. Overall, the PB-MPC controller improves the robustness under parameter mismatch and reduces the computational burden. PB-MPC reduces the THD value because it integrates power shaping and the injection of damping resistances into the VSI.
Application of Nonlinear Optimization for Fault Location in Transmission Lines with Series Compensation Protected by Varistors
Authors: Rocha Simone Aparecida , Cardoso Rodrigo Tomas Nogueira , Da Silveira Eduardo Gonzaga , Luiz Alex-Sander Amavel
Published: July 2023

This paper introduces a novel approach to fault localization in transmission lines with series compensation using nonlinear optimization. The objective function minimization provides information on the fault distance and the current at which the protective varistor starts conducting. Terminal source data, the maximum current capacity of the capacitor, and the operating curve of the metal oxide varistor protection element are not required. The proposed method successfully met the predefined objectives, with a maximum error of 1.13% in simulated scenarios. In validating the method using real cases from a Brazilian energy concessionaire, the errors were below 0.5%.
A Review of Solar Power Scenario Generation Methods with Focus on Weather Classifications, Temporal Horizons, and Deep Generative Models
Authors: Kousounadis-Knousen Markos A. , Bazionis Ioannis K. , Georgilaki Athina P. , Catthoor Francky , Georgilakis Pavlos S.
Published: July 2023

Scenario generation has attracted wide attention in recent years owing to the high penetration of uncertainty sources in modern power systems and the introduction of stochastic optimization for handling decision-making problems. These include unit commitment, optimal bidding, online supply–demand management, and long-term planning of integrated renewable energy systems. Simultaneously, the installed capacity of solar power is increasing due to its availability and periodical characteristics, as well as the flexibility and cost reduction of photovoltaic (PV) technologies. This paper evaluates scenario generation methods in the context of solar power and highlights their advantages and limitations. Furthermore, it introduces taxonomies based on weather classification techniques and temporal horizons. Fine-grained weather classifications can significantly improve the overall quality of the generated scenario sets. The performance of different scenario generation methods is strongly related to the temporal horizon of the target domain. This paper also conducts a systematic review of the currently trending deep generative models to assess introduced improvements, as well as to identify their limitations. Finally, several research directions are proposed based on the findings and drawn conclusions to address current challenges and adapt to future advancements in modern power systems.
Review of Fault Detection and Diagnosis Techniques for AC Motor Drives
Authors: Gultekin Muhammed Ali , Bazzi Ali
Published: July 2023

Condition monitoring in electric motor drives is essential for operation continuity. This article provides a review of fault detection and diagnosis (FDD) methods for electric motor drives. It first covers various types of faults, their mechanisms, and approaches to detect and diagnose them. The article categorizes faults into machine faults, power electronics (PE) faults, DC link capacitor faults, and sensor faults, and discusses FDD methods. FDD methods for machines are categorized as statistical methods, machine-learning methods, and deep-learning methods. PE FDD methods are divided into logic-based, residual-based, and controller-aided methods. DC link capacitor and sensor faults are briefly explained. Machine and PE faults are listed and presented as tables for easy comparison and fast referencing. Most papers are selected from the past five years but older references are added when necessary. Finally, a discussion section is added to reflect on current trends and possible future research areas.
Extended Recursive Three-Step Filter for Linear Discrete-Time Systems with Dual-Unknown Inputs
Authors: Dong Shigui , Wang Na , Wang Xueyan , Lu Zihao
Published: July 2023

This paper proposes two new extended recursive three-step filters for linear discrete systems with dual-unknown inputs, which can simultaneously estimate unknown input and state. Extended recursive three-step filter 1 (ERTSF1) introduces an innovation for obtaining the estimates of the unknown input in the measurement equation, then derives the estimates of the unknown input in the state equation. After that, it uses the already obtained estimates of the dual-unknown inputs to correct the one-step prediction of the state, and finally, it obtains the minimum-variance unbiased estimate of the system state. Extended recursive three-step filter 2 (ERTSF2) establishes a unified innovation feedback model, then applies linear minimum-variance unbiased estimation to obtain the estimates of the system state and the dual-unknown inputs to refine a more concise recursive filter. Numerical Simulation Ex-ample demonstrates the effectiveness and superiority of the two filters in this paper compared with the traditional method. The battery state of charge estimation results demonstrate the effectiveness of ERTSF2 in practical applications.
Recent Design and Synthesis Strategies for High-Performance Supercapacitors Utilizing ZnCo2O4-Based Electrode Materials
Authors: Aruchamy Kanakaraj , Balasankar Athinarayanan , Ramasundaram Subramaniyan , Oh Tae
Published: July 2023

ZnCo2O4 has emerged as a promising electrode material for supercapacitor applications due to its unique properties and potential for high-performance energy storage. As a transition metal oxide, ZnCo2O4 offers eco-friendly characteristics and favorable diffusion properties, making it an attractive candidate for sustainable energy storage systems. However, the poor conductivity and low surface area of ZnCo2O4 have posed challenges for its optimal utilization in supercapacitors. Various innovative approaches have been explored to overcome these limitations, including the development of ZnCo2O4 with different morphologies such as core-shell and porous structures. This review work aims to provide a comprehensive analysis of diverse synthesis methods employed in recent studies, including hydrothermal growth, solvothermal synthesis, wet chemical methods, and miscellaneous synthesis techniques, each offering unique advantages and influencing the properties of the synthesized materials. The synthesis conditions, such as precursor concentrations, temperature, annealing time, and the incorporation of dopants or additional materials, were found to play a crucial role in determining the electrochemical performance of ZnCo2O4-based supercapacitor electrodes. Core-shell heterostructures based on ZnCo2O4 exhibited versatility and tunability, with the choice of shell material significantly impacting the electrochemical performance. The incorporation of different materials in composite electrodes, as well as doping strategies, proved effective in enhancing specific capacitance, stability, surface area, and charge transfer characteristics. Controlled synthesis of ZnCo2O4 with diverse morphologies and porosity was crucial in improving mechanical strength, surface area, and ion diffusion capabilities. The findings provide valuable insights for the design and engineering of high-performance supercapacitor electrodes based on ZnCo2O4, and suggest exciting avenues for further exploration, including advanced characterization techniques, novel doping strategies, scale-up of synthesis methods, and integration into practical supercapacitor devices. Continued research and development in this field will contribute to the advancement of energy storage technologies and the realization of efficient and sustainable energy storage systems.
Tracking-Integrated CPV Technology: State-of-the-Art and Classification
Authors: Ceballos Maria A. , Pérez-Higueras Pedro J. , Fernández Eduardo F. , Almonacid Florencia
Published: July 2023

Concentrator photovoltaic (CPV) technology offers an alternative to conventional photovoltaic systems, focusing on the concentration of solar radiation through the optics of the system onto smaller and more efficient solar cells. CPV technology captures direct radiation and requires precise module orientation. Traditional CPV systems use robust and heavy solar trackers to achieve the necessary alignment, but these trackers add to the installation and operating costs. To address this challenge, tracking-integrated CPV systems have been developed, eliminating the need for conventional trackers. These systems incorporate tracking mechanisms into the CPV module itself. This review presents a detailed classification of existing designs in the literature and provides an overview of this type of system with different approaches to integrated tracking including tracking concentrator elements, using external trackers, or employing internal trackers (the most researched). These approaches enable the automatic adjustment of the CPV system components to follow the movement of the Sun. The various tracking-integrated systems have different designs and performance characteristics. Significant progress has been made in developing tracking-integrated CPV systems with the aim to make CPV technology more competitive and expand its applications in markets where traditional CPV has been excluded.
Decentralized Emergency Control of AC Power Grid Modes with Distributed Generation
Authors: Fishov Alexander , Osintsev Anatoly , Ghulomzoda Anvari , Marchenko Andrey , Kokin Sergey , Safaraliev Murodbek , Dmitriev Stepan , Zicmane Inga
Published: July 2023

Large-scale development of small-scale generation, and facilities based on this, with their integration into existing distribution networks, qualitatively change the modes and tasks of network management and transform previously passive electric networks into active ones. Features of parameters and modes of small-scale generation, insufficient observability and manageability in centralized management determine the need to use a decentralized multi-agent control of the modes of such networks. First of all, this applies to emergency management, which includes automatic restoration of the integrity and normal operation of the network. This paper presents a set of specialized methods for emergency management of active power grid modes and the results of a study of their effectiveness on mathematical and physical models that confirm the feasibility of using decentralized emergency management and network recovery management. In particular, this includes: a method of emergency proactively balanced separation of grid energy districts along one of the a priori fixed network cross-sections in the event of disturbances with the transition to island mode, and a method for two-stage restoration of the integrity and normal network mode with decentralized synchronization of active parts on remote network switches. In the case of the decentralized remote synchronization of active parts, it is proposed to use special control of the excitation and speed of generators to create conditions for the successful operation of automatic reclosing devices with synchronization detection. It is essential for emergency management in active networks with small generation to reject the concept of ensuring the reliability of power supply through maintaining the integrity of the network in favor of the concept of an emergency-balanced breakdown of the network into balanced areas with the subsequent automatic restoration of integrity. To conduct research on the physical model, a prototype of distributed system emergency automation has been developed that does not use data transmission tools, which ensures its high cybersecurity and the feasibility of decentralized management.
An Intermodular Active Balancing Topology for Efficient Operation of High Voltage Battery Packs in Li-Ion Based Energy Storage Systems: Switched (Flying) DC/DC Converter
Authors: Ceylan Murat , Balikci Abdulkadir
Published: July 2023

To meet the load voltage and power requirements for various specific needs, a typical lithium–ion battery (LIB) pack consists of different parallel and series combinations of individual cells in modules, which can go as high as tens of series and parallel connections in each module, reaching hundreds and even thousands of cells at high voltage (HV) levels. The inhomogeneity among the cells and modules results in voltage imbalances during operation and reduces the overall system efficiency. In this work, a robust and flexible active balancing topology is presented. It can not only mitigate the charge imbalance within a module, i.e., intramodular equalization, but also help to balance the state of charge (SoC) level of the modules in a high voltage pack, i.e., intermodular equalization, which is an often-overlooked topic. The proposed concept was proven by experimental verification on parallel and series configurations of cells in realistically sized modules and practical battery management system (BMS) hardware, when the LIB was both idle and under load.
Assessment of Leaf Area and Biomass through AI-Enabled Deployment
Authors: Shadrin Dmitrii , Menshchikov Alexander , Nikitin Artem , Ovchinnikov George , Volohina Vera , Nesteruk Sergey , Pukalchik Mariia , Fedorov Maxim , Somov Andrey
Published: July 2023

Leaf area and biomass are important morphological parameters for in situ plant monitoring since a leaf is vital for perceiving and capturing the environmental light as well as represents the overall plant development. The traditional approach for leaf area and biomass measurements is destructive requiring manual labor and may cause damages for the plants. In this work, we report on the AI-based approach for assessing and predicting the leaf area and plant biomass. The proposed approach is able to estimate and predict the overall plants biomass at the early stage of growth in a non-destructive way. For this reason we equip an industrial greenhouse for cucumbers growing with the commercial off-the-shelf environmental sensors and video cameras. The data from sensors are used to monitor the environmental conditions in the greenhouse while the top-down images are used for training Fully Convolutional Neural Networks (FCNN). The FCNN performs the segmentation task for leaf area calculation resulting in 82% accuracy. Application of trained FCNNs to the sequences of camera images allowed the reconstruction of per-plant leaf area and their growth-dynamics. Then we established the dependency between the average leaf area and biomass using the direct measurements of the biomass. This in turn allowed for reconstruction and prediction of the dynamics of biomass growth in the greenhouse using the image data with 10% average relative error for the 12 days prediction horizon. The actual deployment showed the high potential of the proposed data-driven approaches for plant growth dynamics assessment and prediction. Moreover, it closes the gap towards constructing fully closed autonomous greenhouses for harvests and plants biological safety.
Niche and Interspecific Association of Dominant Tree Species in Spruce–Fir Mixed Forests in Northeast China
Authors: Yuan Shuai , Wang Xinjie
Published: July 2023

This study focuses on the natural coniferous and broad-leaved mixed forest dominated by Picea asperata and Abies fabri in the Jingouling Forest Farm of Northeast China. Specifically, we place emphasis on examining the effects of different thinning intensities. By comparing the niche characteristics and interspecific associations of dominant tree species under various thinning intensity conditions, our aim is to gain deeper insights into the patterns of resource utilization by species and the interplay of tree species in the forest canopy. Based on plot survey data, analysis methods such as niche breadth, niche overlap index, Pearson correlation coefficient, and Spearman’s rank correlation coefficient were used to analyze the niche and interspecific relationship characteristics of the dominant tree species in the community. The results indicate that among the four selective cutting intensities, the tree species with the highest importance value in all cases is Abies fabri, with an average importance value of 30.29%. Additionally, Picea asperata exhibits the widest niche breadth among the tree species, with a value of 4.59. The selective thinning in this study resulted in a reduction of average species niche overlap in the community compared to the control plots. There were both positive and negative interspecies associations observed, but they were statistically insignificant. Few pairs showed significant correlations, with the positive-to-negative ratio of Pearson coefficients decreasing as the selective cutting intensity increased. The Spearman rank correlation analysis revealed a positive-to-negative ratio exceeding one for species pairs in the community under both light and heavy-cutting conditions, with no significant negative correlations observed. In summary, selective cutting disturbance can effectively increase the importance value of the dominant tree species, Abies fabri, in the community. However, it leads to a reduction in the niche breadth of all tree species present in the forest. Moderate selective cutting is considered a more suitable intensity, as it promotes the maintenance of biodiversity and yields higher forest management benefits in the mixed forest of Picea asperata and Abies fabri in Northeast China.
Estimation of the Overmature Wood Stock and the Projection of the Maximum Wood Mobilization Potential up to 2100 in Hungary
Authors: Borovics Attila , Mertl Tamás , Király Éva , Kottek Péter
Published: July 2023

The demand for woody biomass as a key raw material of the developing circular bioeconomy is expected to increase. This has led to the need of increased timber productivity and the search for new procurement methods, new assortments, and innovative supply chains. Timber is regarded as a climate-friendly resource, which can contribute to climate change mitigation through long-term carbon storage and through the substitution of fossil products and fossil fuels. Thus, it is of high importance to assess the amount of timber that can be harvested without compromising sustainability concerns. In this paper, we examined the amount of the wood stock accumulated in overmature stands in Hungary. We define overmature stands being those stands where the actual age of the stand is over its cutting age prescribed by the forest authority. According to our results, 11.5% of the standing volume in Hungary is overmature, and the wood stock of overmature stands has increased by more than 250% in the last 40 years. The importance of the overmature forests is enormous, as they represent an unused wood stock reserve, which could be available to meet the growing demand for timber. In our study, we also conducted a simple yield table-based projection on the maximum amount of timber available for harvest in the period 2020–2100 based on the data of the national forestry database and the cutting ages prescribed by the forest authority in the forest management plans. According to our results, even without new afforestation, more timber becomes available for harvest annually in the 2020–2100 period than the level of the average harvests of the last five historic years. In the 2020–2050 period, an additional 56% of timber is projected to become available for harvest as a maximum. This means a maximum additional potential of 4059 thousand m3, even without the harvesting of the stands, which were already overmature in the starting year of the projection. In the first part of the projection period, industrial wood available for harvest is forecasted to be above the average historic level of industrial wood production. However, in the second part of the projection period, the industrial wood yield shows a decreasing tendency and even drops below the 2017–2021 average. The decreasing availability of industrial roundwood in the second part of the projection period points out the importance of innovation in the wood industry. The inclusion of drought tolerant species, which are nowadays less used for industrial purposes seems inevitable in the production of high-quality wood products.
RIS-Assisted Fixed NOMA: Outage Probability Analysis and Transmit Power Optimization
Authors: Kumaravelu Vinoth Babu , Imoize Agbotiname Lucky , Soria Francisco R. Castillo , Velmurugan Periyakarupan Gurusamy Sivabalan , Thiruvengadam Sundarrajan Jayaraman , Do Dinh-Thuan , Murugadass Arthi
Published: July 2023

Reconfigurable intelligent surface (RIS)-assisted non-orthogonal multiple access (NOMA) has the ability to overcome the challenges of the wireless environment like random fluctuations, shadowing, and mobility in an energy efficient way when compared to multiple input-multiple output (MIMO)-NOMA systems. The NOMA system can deliver controlled channel gains, improved coverage, increased energy efficiency, and enhanced fairness in resource allocation with the help of RIS. RIS-assisted NOMA will be one of the primary potential components of sixth-generation (6G) networks, due to its appealing advantages. The analytical outage probability expressions for smart RIS-assisted fixed NOMA (FNOMA) are derived in this paper, taking into account the instances of RIS as a smart reflector (SR) and an access point (AP). The analytical and simulation findings are found to be extremely comparable. In order to effectively maximize the sum capacity, the formulas for optimal powers to be assigned for a two-user case are also established. According to simulations, RIS-assisted FNOMA surpasses FNOMA in terms of outage and sum capacity. With the aid of RIS and the optimal power assignment, RIS-AP-FNOMA offers ≈62% improvement in sum capacity over the FNOMA system for a signal-to-noise ratio (SNR) of 10 dB and 32 elements in RIS. A significant improvement is also brought about by the increase in reflective elements.
Evaluation of the Relationship between Spatio-Temporal Variability of Vegetation Condition Index (VCI), Fire Occurrence and Burnt Area in Mount Kenya Forest Reserve and National Park
Authors: Nyongesa Kevin W. , Pucher Christoph , Poletti Claudio , Vacik Harald
Published: July 2023

Climate change, vegetation dynamics, human activities and forest management influence the occurrence of fires. This study investigated the spatio-temporal variability of the Vegetation Condition Index (VCI) and its influence on fire occurrence in three different land use types in Mount Kenya Forest Reserve and National Park (MKFRNP): National Park (NP), Forest Stations (FS) and Farmlands (FL). The study used MODIS satellite data to obtain the Normalized Difference Vegetation Index (NDVI), the VCI, the number of fires and the burnt area. The specific objectives of this research were (i) to examine the spatio-temporal variability of VCI, fire occurrence and burnt area in MKFRNP from 2003 to 2018 and (ii) to explore the relationship between VCI, fire occurrence and burnt area in different areas of the MKFRNP (NP, FS and FL). The findings show that even though fires occur throughout the year in MKFRNP, most of the fires occur during dry seasons. The relationship between spatio-temporal fire occurrence and VCI distribution is different for each land use type. In the FL, the probability of fire ignition and the number of fires per month was more or less the same irrespective of the VCI because of the traditional use of fire as a land management tool. However, the probability of fire ignition and the number of fires per month is high in the NP and FS when the VCI is below 50% (drought), especially in the dry seasons, when and where the impact of meteorological conditions and climate have much more impact than human activities. In addition to the efforts already made by communities, KFS and KWS in the fire fighting and monitoring system, satellite data can be useful to acquire accurate and timely information on the VCI and the likely spatio-temporal occurrence of fires in order to be prepared in the most fire-prone periods and improve fire management, the planning of resources and fire suppression activities in MKFRNP.
Development, Analysis, and Sensory Evaluation of Improved Bread Fortified with a Plant-Based Fermented Food Product
Authors: Cabello-Olmo Miriam , Krishnan Padmanaban G. , Araña Miriam , Oneca Maria , Díaz Jesús V. , Barajas Miguel , Rovai Maristela
Published: July 2023

In response to the demand for healthier foods in the current market, this study aimed to develop a new bread product using a fermented food product (FFP), a plant-based product composed of soya flour, alfalfa meal, barley sprouts, and viable microorganisms that showed beneficial effects in previous studies. White bread products prepared with three different substitution levels (5, 10, and 15%) of FFP were evaluated for physical characteristics (loaf peak height, length, width), color indices (lightness, redness/greenness, yellowness/blueness), quality properties (loaf mass, volume, specific volume), protein content, crumb digital image analysis, and sensory characteristics. The results revealed that FFP significantly affected all studied parameters, and in most cases, there was a dose–response effect. FFP supplementation affected the nutritional profile and increased the protein content (p < 0.001). The sensory test indicated that consumer acceptance of the studied sensory attributes differed significantly between groups, and bread with high levels of FFP (10 and 15% FFP) was generally more poorly rated than the control (0%) and 5% FFP for most of the variables studied. Despite this, all groups received acceptable scores (overall liking score ≥ 5) from consumers. The sensory analysis concluded that there is a possible niche in the market for these improved versions of bread products.
G-RRDB: An Effective THz Image-Denoising Model for Moldy Wheat
Authors: Jiang Yuying , Chen Xinyu , Ge Hongyi , Jiang Mengdie , Wen Xixi
Published: July 2023

In order to solve the problem of large image noise and unremarkable features caused by factors such as fluctuations in the power of a light source during the terahertz image acquisition of wheat, this paper proposes a THz image-denoising model called G-RRDB. Firstly, a module called Ghost-LKA is proposed by combining a large kernel convolutional attention mechanism module with a Ghost convolutional structure, which improves the characteristics of the network to acquire a global sensory field. Secondly, by integrating a spatial attention mechanism with channel attention, an attention module called DAB is proposed to enhance the network’s attention to important features. Thirdly, the Ghost-LKA module and DAB module are combined with the baseline model, thus proposing the dense residual denoising network G-RRDB. Compared with traditional denoising networks, both the PSNR and SSIM are improved. The prediction accuracy of G-RRDB is verified through the classification of the VGG16 network, achieving a rate of 92.8%, which represents an improvement of 1.7% and 0.2% compared to the denoised images obtained from the baseline model and the combined baseline model with the DAB module, respectively. The experimental results demonstrate that G-RRDB, a THz image-denoising model based on dense residual structure for moldy wheat, exhibits excellent denoising performance.
Camellia japonica Flowers as a Source of Nutritional and Bioactive Compounds
Authors: Pereira Antia G. , Cassani Lucia , Liu Chao , Li Ningyang , Chamorro Franklin , Barreira João C. M. , Simal-Gandara Jesus , Prieto Miguel A.
Published: July 2023

In recent decades, plants have strengthened their relevance as sources of molecules potentially beneficial for health. This underpinning effect also arises from the extensive research that has been conducted on plants that are typically undervalued, besides being scarcely used. This is the case with Camellia japonica in Galicia (NW Spain), where, despite its abundance, it is exclusively used for ornamental purposes and has been studied only for its proximate composition. Thus, the present study was conducted on several additional parameters in the flowers of eight C. japonica varieties. Our results show that camellia has a high nutritional value, with carbohydrates as the most abundant macronutrients followed by a moderate protein content (4.4–6.3 g/100 g dry weight) and high levels of polyunsaturated fatty acids (especially ω-3 fatty acids, which represent 12.9–22.7% of the total fatty acids), raising its potential for use for nutritional purposes. According to the thermochemical characterization and elemental composition of camellia, the raw material has poor mineralization and low nitrogen content, but high percentages of volatile matter and high carbon-fixation rates, making it a promising alternative for biofuel production. Furthermore, preliminary analysis reveals a high concentration of different bioactive compounds. As a result of these findings, camellias can be used as food or functional ingredients to improve the nutritional quality of food formulations.
Image Enhancement Model Based on Fractional Time-Delay and Diffusion Tensor
Authors: Yao Wenjuan , Huang Yi , Wu Boying , Zhou Zhongxiang
Published: July 2023

Image enhancement is one of the bases of image processing technology, which can enhance useful features and suppress useless information of images according to the specified task. In order to ensure coherent enhancement for images with oriented flow-like structures, we propose a nonlinear diffusion system model based on time-fractional delay. By combining the nonlinear isotropic diffusion equation with fractional time-delay regularization, we construct a structure tensor. Meanwhile, the introduction of source terms enhances the contrast of the image, making it effective for denoising images with high-level noise. Based on compactness principles, the existence of weak solutions for the model is proved by using the Galerkin method. In addition, various experimental results verify the enhancement ability of the proposed model.
Asymptotic and Pinning Synchronization of Fractional-Order Nonidentical Complex Dynamical Networks with Uncertain Parameters
Authors: Wang Yu , He Xiliang , Li Tianzeng
Published: July 2023

This paper is concerned with the asymptotic and pinning synchronization of fractional-order nonidentical complex dynamical networks with uncertain parameters (FONCDNUP). First of all, some synchronization criteria of FONCDNUP are proposed by using the stability of fractional-order dynamical systems and inequality theory. Moreover, a novel controller is derived by using the Lyapunov direct method and the differential inclusion theory. Next, based on the Lyapunov stability theory and pinning control techniques, a new group of sufficient conditions to assure the synchronization for FONCDNUP are obtained by adding controllers to the sub-nodes of networks. At last, two numerical simulations are utilized to illustrate the validity and rationality of the acquired results.
Enhancing Therapeutic Efficacy of Curcumin: Advances in Delivery Systems and Clinical Applications
Authors: Omidian Hossein , Wilson Renae L. , Chowdhury Sumana Dey
Published: July 2023

Curcumin, a potent active compound found in turmeric and Curcuma xanthorrhiza oil, possesses a wide range of therapeutic properties, including antibacterial, anti-inflammatory, antioxidant, and wound healing activities. However, its clinical effectiveness is hindered by its low bioavailability and rapid elimination from the body. To overcome these limitations, researchers have explored innovative delivery systems for curcumin. Some promising approaches include solid lipid nanoparticles, nanomicelle gels, and transdermal formulations for topical drug delivery. In the field of dentistry, curcumin gels have shown effectiveness against oral disorders and periodontal diseases. Moreover, Pickering emulsions and floating in situ gelling systems have been developed to target gastrointestinal health. Furthermore, curcumin-based systems have demonstrated potential in wound healing and ocular medicine. In addition to its therapeutic applications, curcumin also finds use as a food dye, contraception aid, corrosion-resistant coating, and environmentally friendly stain. This paper primarily focuses on the development of gel compositions of curcumin to address the challenges associated with its clinical use.
Special Issue “Genetics and Epigenetics of Neuromuscular Diseases”
Authors: Coppedè Fabio
Published: July 2023

Neuromuscular disorders (NMDs) include several hereditary or acquired conditions that impair the neuromuscular system and muscle function [...]
Epidemiological Overview of Urogenital Gonorrhea in Mexico (2003–2020)
Authors: Loyola-Cruz Miguel Ángel , Fernández-Sánchez Verónica , Durán-Manuel Emilio Mariano , Calzada-Mendoza Claudia Camelia , Castro-Escarpulli Graciela , Quijano-Soriano María Fernanda , Nicolás-Sayago Liliana , Razo-Blanco Hernández Dulce Milagros , Villegas-Castañeda Marcela , Cárdenas-Cantero Alejandro , Cureño-Díaz Mónica Alethia , Paredes-Mendoza Marianela , Cruz-Cruz Clemente , Bello-López Juan Manuel
Published: July 2023

In Mexico, urogenital gonorrhea (UG) is one of the main sexually transmitted diseases notifiable by health systems around the world. Epidemiological data on sexually transmitted infections (STIs) in Mexico indicated that UG was “under control” until 2017. However, international epidemiological reports indicate the increase in incidence due to several factors, including an increase during the first year of the COVID-19 pandemic. These factors suggest that this phenomenon may occur in developing countries, including Mexico. Therefore, the aim of this study was to analyze national surveillance data on UG from 2003–2019 and the first year of the COVID-19 pandemic. An epidemiological study of cases and incidence of UG (2003–2020) was performed in the annual reports issued by the General Directorate Epidemiology in Mexico. Cases and incidence were classified and analyzed by year, sex, age group, and seasons (by temperature). Distribution of UG was carried out using heat maps for the whole country. Ultimately, a seasonal and correlation analysis was performed for UG cases versus temperature. The results showed that the distribution of cases and incidence by sex showed that there was no variation over 14 years. From 2016 onward, a significant increase in UG was observed before the pandemic. During the first year of the pandemic, a significant increase was observed in females aged 24–44 years. A heterogeneous distribution of UG was identified; however, border states were ranked among the top states with elevated incidences and cases. Lastly, the occurrence of UG was associated with temperature, related to summer. The information presented is intended to be useful to promote prevention and to contribute to visualize the distribution of UG over the last 18 years for decision making, and to show one of the consequences of the collapse of epidemiological surveillance of UG during the first year of the COVID-19 pandemic.