bluuebunny/crossref_metadata_2025 huggingface dataset:
A 55 nm CMOS RF Transmitter Front-End with an Active Mixer and a Class-E Power Amplifier for 433 MHz ISM Band Applications
Authors: Yuan Huazhong , Zhou Ranran , Wang Peng , Xu Hui , Wang Yong
Published: November 2023

In order to meet the increasing demands of wireless communication for ISM bands, a 433 MHz transmitter RF front-end is designed using a 55 nm low-power CMOS technology. The circuits consist of an active mixer, a driver amplifier and a class-E power amplifier (PA). A double-balanced Gilbert active mixer is designed to realize binary phase-shift keying (BPSK) modulation. The driver is used to preamplify the modulated RF signals. The class-E PA adopts a parallel four-branch cascode structure to control the output power level. The load network of the PA is implemented through an off-chip circuit, in which a finite DC-feed inductance load network is selected to reduce the power loss. The mixer and driver are designed with a 1.2 V supply voltage, while the PA is operated at a 1.8 V supply voltage. The area of the chip is 0.206 mm × 0.089 mm, and the measured results show that it achieves a maximum output power of 2.7 dBm, with a total power consumption of 6.72 mW. At a drain efficiency (DE) of 34.5%, an S22 less than −10 dB over the frequency ranges from 393.79 MHz to 455.70 MHz can be measured for the PA. With 192 kbps BPSK data modulated at 433 MHz, the measured EVM is about 0.83% rms.
Post-Impact Stabilization during Lane Change Maneuver
Authors: Park Yeayoung , Gim Juhui , Ahn Changsun
Published: November 2023

This study addresses challenges in vehicle collisions, especially in non-front or non-rear impacts, causing rapid state changes and a loss of control. Electronic Stability Control (ESC) can stabilize a vehicle in minor impact cases, but it cannot effectively handle major collision cases. To overcome this, our research focuses on Post-Impact Stabilization Control (PISC). Existing PISC methods face issues like misidentifying collisions during cornering maneuvers due to assumptions of straight driving, rendering them ineffective for lane change accidents. Our study aims to design PISC specifically for cornering and lane change maneuvers, predicting collision forces solely from the ego vehicle’s data, ensuring improved collision stability control. We employ the unscented Kalman filter to estimate collision forces and develop a sliding mode controller with an optimal force allocation algorithm to counter the disturbances caused by collisions and stabilize the vehicle. Rigorous validation through simulations and tests with a driving simulator demonstrates the feasibility of our proposed methodology in effectively stabilizing vehicles during collision accidents, particularly in lane change situations.
The Influence of Augmented Reality (AR) on the Motivation of High School Students
Authors: Amores-Valencia Antonio , Burgos Daniel , Branch-Bedoya John W.
Published: November 2023

Augmented reality (AR) is a technology whose presence has increased in the field of education in recent years. However, its role in secondary education has not been thoroughly explored. Therefore, this research aims to analyse the influence of AR on the motivation of students at this stage while considering gender and previous information and communication technology (ICT) experience. This research uses a quantitative methodology that follows Keller’s Attention, Relevance, Confidence and Satisfaction (ARCS) motivational model. We implemented this instructional design model for a sample of 321 students from the same educational centre. They were divided into two categories: an experimental group (n = 159) and a control group (n = 162). The control group were studied in a slide-based learning environment, while the experimental group worked with an AR mobile application. For data collection, we used the Instructional Materials Motivation Survey (IMMS). The results showed that the students who used AR displayed greater motivation, highlighting great interest in the integration of this technology into the learning process. However, no significant differences were obtained in the motivation of the students according to gender and previous experience with the use of ICT. In conclusion, this research shows that the use of AR improves motivation in secondary education.
A Review of AI-Based Cyber-Attack Detection and Mitigation in Microgrids
Authors: Beg Omar , Khan Asad , Rehman Waqas , Hassan Ali
Published: November 2023

In this paper, the application and future vision of Artificial Intelligence (AI)-based techniques in microgrids are presented from a cyber-security perspective of physical devices and communication networks. The vulnerabilities of microgrids are investigated under a variety of cyber-attacks targeting sensor measurements, control signals, and information sharing. With the inclusion of communication networks and smart metering devices, the attack surface has increased in microgrids, making them vulnerable to various cyber-attacks. The negative impact of such attacks may render the microgrids out-of-service, and the attacks may propagate throughout the network due to the absence of efficient mitigation approaches. AI-based techniques are being employed to tackle such data-driven cyber-attacks due to their exceptional pattern recognition and learning capabilities. AI-based methods for cyber-attack detection and mitigation that address the cyber-attacks in microgrids are summarized. A case study is presented showing the performance of AI-based cyber-attack mitigation in a distributed cooperative control-based AC microgrid. Finally, future potential research directions are provided that include the application of transfer learning and explainable AI techniques to increase the trust of AI-based models in the microgrid domain.
Analysis of Tariffs and the Impact on Voltage Variations in Low-Voltage Grids with Smart Charging and Renewable Energy
Authors: Jung Daniel , Sundström Christofer
Published: November 2023

The rapid increase in electric vehicles (EVs) and installed photovoltaic systems (PV) has resulted in new challenges for electric systems, e.g., voltage variations in low-voltage grids. Grid owners cannot directly control the power consumption of the end consumers. However, by the design of transparent tariffs, economic incentives are introduced for the end consumers to adjust their EV charging patterns. In this work, the main objective is to design a time-of-use pricing tariff to reduce the voltage variations in a low-voltage grid when introducing PVs and EVs with smart charging. Data from an existing low-voltage grid and hourly data from household power consumption, together with models of PV and EV charging, are used to simulate the voltage fluctuations based on the modified electric consumption. The results show that a time-of-use pricing tariff taking into consideration maximum peak power is important to reduce grid voltage variations. Another observation is that the use of economic incentives, such as subsidies when selling power from the household, combined with V2G technology can be economical for households but increases the voltage variations in the grid.
Technical Impacts of Virtual Clean Hydrogen Plants: Promoting Energy Balance and Resolving Transmission Congestion Challenges
Authors: Do Gyeong-Taek , Son Eun-Tae , Oh Byeong-Chan , Kim Hong-Joo , Ryu Ho-Sung , Cho Jin-Tae , Kim Sung-Yul
Published: November 2023

This paper presents the VCHP platform as a solution to address PV curtailment and line congestion in scenarios of increasing renewable energy penetration. Solar PV generation profiles and load profiles were generated for three scenarios (2025, 2030, and 2035) using data provided by KPX. Modifications were made to the IEEE 30 Bus model to accurately reflect the Korean power system, including the introduction of PCA and LCA at relevant buses. Line congestion was evaluated using metrics such as TUR, STUR, and TLR. The research findings indicate that integrating the VCHP platform in all scenarios effectively alleviates line congestion, as shown by the TUR remaining below 1. Importantly, the reduction in line losses exceeds the decrease in power flow, demonstrating the effectiveness of VCHP in reducing power losses. The results suggest that as renewable energy sources increase, line congestion issues may arise in the existing power system. However, integrating the proposed VCHP platform is a valuable solution for effectively utilizing surplus PV energy and improving the stability of the power grid. The adoption of such a platform can significantly enhance the operation and management of the power system.
Stacked Ensemble Regression Model for Prediction of Furan
Authors: Faraji Mohammad Amin , Shooshtari Alireza , El-Hag Ayman
Published: November 2023

Furan tests provide a non-intrusive and cost-effective method of estimating the degradation of paper insulation, which is critical for ensuring the reliability of power grids. However, conducting routine furan tests can be expensive and challenging, highlighting the need for alternative methods, such as machine learning algorithms, to predict furan concentrations. To establish the generalizability and robustness of the furan prediction model, this study investigates two distinct datasets from different geographical locations, Utility A and Utility B. Three scenarios are proposed: in the first scenario, a round-robin cross-validation method was used, with 75% of the data for training and the remaining 25% for testing. The second scenario involved training the model entirely on Utility A and testing it on Utility B. In the third scenario, the datasets were merged, and round-robin cross-validation was applied, similar to the first scenario. The findings reveal the effectiveness of machine learning algorithms in predicting furan concentrations, and particularly the stacked generalized ensemble method, offering a non-intrusive and cost-effective alternative to traditional testing methods. The results could significantly impact the maintenance strategies of power and distribution transformers, particularly in regions where furan testing facilities are not readily available.
A Survey on Recent Applications of Artificial Intelligence and Optimization for Smart Grids in Smart Manufacturing
Authors: Hsu Chao-Chung , Jiang Bi-Hai , Lin Chun-Cheng
Published: November 2023

To enable highly automated manufacturing and net-zero carbon emissions, manufacturers have invested heavily in smart manufacturing. Sustainable and smart manufacturing involves improving the efficiency and environmental sustainability of various manufacturing operations such as resource allocation, data collecting and monitoring, and process control. Recently, a lot of artificial intelligence and optimization applications based on smart grid systems have improved the energy usage efficiency in various manufacturing operations. Therefore, this survey collects recent works on applications of artificial intelligence and optimization for smart grids in smart manufacturing and analyzes their features, requirements, and challenges. In addition, potential trends and further challenges for the integration of smart grids with renewable energies for smart manufacturing, applications of 5G and B5G (beyond 5G) technologies in the SG system, and next-generation smart manufacturing systems are discussed to provide references for further research.
DelayNet: Enhancing Temporal Feature Extraction for Electronic Consumption Forecasting with Delayed Dilated Convolution
Authors: Anh Le Hoang , Yu Gwang-Hyun , Vu Dang Thanh , Kim Hyoung-Gook , Kim Jin-Young
Published: November 2023

In the face of increasing irregular temperature patterns and climate shifts, the need for accurate power consumption prediction is becoming increasingly important to ensure a steady supply of electricity. Existing deep learning models have sought to improve prediction accuracy but commonly require greater computational demands. In this research, on the other hand, we introduce DelayNet, a lightweight deep learning model that maintains model efficiency while accommodating extended time sequences. Our DelayNet is designed based on the observation that electronic series data exhibit recurring irregular patterns over time. Furthermore, we present two substantial datasets of electricity consumption records from South Korean buildings spanning nearly two years. Empirical findings demonstrate the model’s performance, achieving 21.23%, 43.60%, 17.05% and 21.71% improvement compared to recurrent neural networks, gated-recurrent units, temporal convolutional neural networks and ARIMA models, as well as greatly reducing model complexity and computational requirements. These findings indicate the potential for micro-level power consumption planning, as lightweight models can be implemented on edge devices.
Scalable Inverse Uncertainty Quantification by Hierarchical Bayesian Modeling and Variational Inference
Authors: Wang Chen , Wu Xu , Xie Ziyu , Kozlowski Tomasz
Published: November 2023

Inverse Uncertainty Quantification (IUQ) has gained increasing attention in the field of nuclear engineering, especially nuclear thermal-hydraulics (TH), where it serves as an important tool for quantifying the uncertainties in the physical model parameters (PMPs) while making the model predictions consistent with the experimental data. In this paper, we present an extension to an existing Bayesian inference-based IUQ methodology by employing a hierarchical Bayesian model and variational inference (VI), and apply this novel framework to a real-world nuclear TH scenario. The proposed approach leverages a hierarchical model to encapsulate group-level behaviors inherent to the PMPs, thereby mitigating existing challenges posed by the high variability of PMPs under diverse experimental conditions and the potential overfitting issues due to unknown model discrepancies or outliers. To accommodate computational scalability and efficiency, we utilize VI to enable the framework to be used in applications with a large number of variables or datasets. The efficacy of the proposed method is evaluated against a previous study where a No-U-Turn-Sampler was used in a Bayesian hierarchical model. We illustrate the performance comparisons of the proposed framework through a synthetic data example and an applied case in nuclear TH. Our findings reveal that the presented approach not only delivers accurate and efficient IUQ without the need for manual tuning, but also offers a promising way for scaling to larger, more complex nuclear TH experimental datasets.
Investigation of the Impact of Castor Biofuel on the Performance and Emissions of Diesel Engines
Authors: Zheng Fangyuan , Cho Haeng Muk
Published: November 2023

Fossil fuel is a non-renewable fuel, and with the development of modern industry and agriculture, the storage capacity of fossil fuels is constantly decreasing. In this study, a systematic study and analysis were conducted on the combustion characteristics, engine performance, and exhaust emission characteristics of castor biodiesel–diesel blends and pure diesel fuel in different proportions at different speeds of a single-cylinder four-stroke diesel engine under constant load. The castor biodiesel required for the experiment is generated through an ester exchange reaction and mixed with diesel in proportion to produce biodiesel–diesel blends. The experimental results show that as an oxygenated fuel with a higher cetane number, the CO, HC, and smoke emissions of diesel and B80 blend fuel at 1800 rpm were reduced by 16.9%, 31.6%, and 68%, respectively. On the contrary, the NOx and CO2 emissions increased by 17.3% and 34.6% compared to diesel at 1800 rpm. In addition, due to its high viscosity and low calorific value, the brake thermal efficiency and brake-specific fuel consumption of the biodiesel–diesel blends are slightly lower than those of diesel, but the biodiesel–diesel blends exhibit lower exhaust gas temperatures. Comparing B80 and diesel fuel at 1800 rpm, the BSFC of diesel at 1800 rpm is 3.12 kg/W·h, whereas for B80 blended fuel, it increases to 4.2 kg/W·h, and BTE decreases from 25.39% to 21.33%. On the contrary, B60 blended fuel exhibits a lower exhaust emission temperature, displaying 452 °C at 1800 rpm. Based on the experimental results, it can be concluded that castor biodiesel is a very promising clean alternative fuel with low waste emissions and good engine performance.
Rollover Prevention Model for Stratified Liquefied Natural Gas in Storage Tanks
Authors: Włodek Tomasz , Łaciak Mariusz
Published: November 2023

At least 24 liquefied natural gas (LNG) rollover incidents have been reported since 1960. During rollover, because of the heat ingress through the tank walls, a stratified LNG may be suddenly homogenized while releasing massive amounts of vapor. It can result in an overpressure in the tank and significant amounts of potentially explosive LNG vapor being vented out. Both of these factors represent considerable hazards. Rollover is a physical mixing process in a single tank with two or more different cells of LNG of different compositions, temperatures, and densities that can manifest in large boil-off rates. It can exceed venting equipment capacities, and vapor pressure in tank increases rapidly and in extreme cases can lead to tank damage. This paper presents numerical approach for determination of time of rollover occurrence in storage tank. The presented model is based on the energy balance of the stratified cryogenic liquid and the gas phase as separate three thermodynamic systems in the storage tank. As a result of proposed model, for the adopted assumptions and cylindrical tank volume of 78,500 m3, the approximate time of the rollover occurrence was determined for two cases. In the first case, for heavier LNG, the rollover phenomenon will occur 193.25 h after the start of the calculations from the assumed initial conditions. In the second case, for light LNG with a higher initial liquid level in the tank, the rollover will occur after 150.25 h.
Enhanced Thermal Properties of Phase Change Materials through Surfactant-Functionalized Graphene Nanoplatelets for Sustainable Energy Storage
Authors: Fikri M. Arif , Suraparaju Subbarama Kousik , Samykano M. , Pandey A. K. , Rajamony Reji Kumar , Kadirgama K. , Ghazali M. F.
Published: November 2023

Phase change materials (PCMs) are increasingly gaining prominence in thermal energy storage due to their impressive energy storage capacity per unit volume, especially in applications with low and medium temperatures. Nevertheless, PCMs have significant limitations regarding their ability to conduct and store heat, primarily due to their inadequate thermal conductivity. One potential solution for improving the thermal conductivity of PCMs involves the inclusion of nanoparticles into them. However, a recurring issue arises after several thermal cycles, as most nanoparticles have a tendency to clump together and settle at the container’s base due to their low interfacial strength and poor compatibility. To address this challenge, including surfactants such as sodium dodecylbenzene sulfonate (SDBS) has emerged as a prevalent and economically viable approach, demonstrating a substantial impact on the dispersion of carbon nanoparticles within PCMs. The foremost objective is to investigate the improvement of thermal energy storage by utilizing graphene nanoplatelets (GNP), which are dispersed in A70 PCM at various weight percentages (0.1, 0.3, 0.5, 0.7, and 1.0), both with and without the use of surfactants. The findings indicate a remarkable enhancement in thermal conductivity when GNP with surfactants is added to the PCM, showing an impressive increase of 122.26% with a loading of 1.0 wt.% compared to conventional PCM. However, when 1.0 wt.% pure GNP was added, the thermal conductivity only increased by 48.83%. Additionally, the optical transmittance of the composite containing ASG-1.0 was significantly reduced by 84.95% compared to conventional PCM. Furthermore, this newly developed nanocomposite exhibits excellent stability, enduring 1000 thermal cycles and demonstrating superior thermal and chemical stability up to 257.51 °C. Due to its high thermal stability, the composite NePCM is an ideal candidate for preheating in industrial and photovoltaic thermal (PVT) applications, where it can effectively store thermal energy.
Active Autonomous Open-Loop Technique for Static and Dynamic Current Balancing of Parallel-Connected Silicon Carbide MOSFETs
Authors: Giannopoulos Nektarios , Ioannidis Georgios , Vokas Georgios , Psomopoulos Constantinos
Published: November 2023

Silicon carbide (SiC) MOSFETs tend to become one of the main switching elements in power electronics applications of medium- and high-power density. Usually, SiC MOSFETs are connected in parallel to increase power rating. Unfortunately, unequal current sharing between power devices occurs due to mismatches in the technical parameters between devices and the layout of the power circuit. This current imbalance causes different current stress upon power switches, raising concerns about power system reliability. For over a decade, various methods and techniques have been proposed for balancing the currents between parallel-connected SiC MOSFETs. However, most of these methods cannot be implemented unless the deviation between the technical parameters of semiconductor switches is known. This requirement increases the system cost because screening methods are extremely costly and time-consuming. In addition, most techniques aim at suppressing only the transient current imbalance. In this paper, a simple but innovative current balancing technique is proposed, without the need of screening any power device. The proposed technique consists of an open-loop system capable of balancing the currents between two parallel-connected SiC MOSFETs, with the aid of two active gate drivers and an FPGA, actively and independently of the cause. Experimental test results validate that the proposed open-loop method can successfully achieve suppression of current imbalance between parallel-connected SiC MOSFETs, proving its durability and validity level.
Wind and Solar Energy Generation Potential Features in the Extreme Northern Amazon Using Reanalysis Data
Authors: dos Reis Jean , Bose Nícolas , Amorim Ana , de Almeida Dantas Vanessa , Bezerra Luciano , Oliveira Leonardo , Emiliavaca Samira , Matos Maria , Pereira Nickollas , Lima Raniere , de Medeiros Antonio
Published: November 2023

This article examines the potential for wind and solar energy generation in the state of Amapá, Brazil, using ERA5 data from between 1991 and 2020. Key metrics considered include wind power density, capacity factor, photovoltaic potential, and concentrated solar power output. Analyses revealed pronounced wind speeds offshore during summer and in continental regions during spring. Solar irradiance was notably higher in the spring. Differences in wind potential were observed between northern and southern offshore areas. Concentrated solar power efficiency and photovoltaic potential were influenced by location and cloud cover, respectively. Overall, summer presents the best offshore wind energy potential, while spring is optimal for onshore solar energy in Amapá. This study underscores the importance of understanding local climatic patterns when planning energy installations in the region.
Current and Future Role of Natural Gas Supply Chains in the Transition to a Low-Carbon Hydrogen Economy: A Comprehensive Review on Integrated Natural Gas Supply Chain Optimisation Models
Authors: Yusuf Noor , Al-Ansari Tareq
Published: November 2023

Natural gas is the most growing fossil fuel due to its environmental advantages. For the economical transportation of natural gas to distant markets, physical (i.e., liquefaction and compression) or chemical (i.e., direct and indirect) monetisation options must be considered to reduce volume and meet the demand of different markets. Planning natural gas supply chains is a complex problem in today’s turbulent markets, especially considering the uncertainties associated with final market demand and competition with emerging renewable and hydrogen energies. This review study evaluates the latest research on mathematical programming (i.e., MILP and MINLP) as a decision-making tool for designing and planning natural gas supply chains under different planning horizons. The first part of this study assesses the status of existing natural gas infrastructures by addressing readily available natural monetisation options, quantitative tools for selecting monetisation options, and single-state and multistate natural gas supply chain optimisation models. The second part investigates hydrogen as a potential energy carrier for integration with natural gas supply chains, carbon capture utilisation, and storage technologies. This integration is foreseen to decarbonise systems, diversify the product portfolio, and fill the gap between current supply chains and the future market need of cleaner energy commodities. Since natural gas markets are turbulent and hydrogen energy has the potential to replace fossil fuels in the future, addressing stochastic conditions and demand uncertainty is vital to hedge against risks through designing a responsive supply chain in the project’s early design stages. Hence, hydrogen supply chain optimisation studies and the latest works on hydrogen–natural gas supply chain optimisation were reviewed under deterministic and stochastic conditions. Only quantitative mathematical models for supply chain optimisation, including linear and nonlinear programming models, were considered in this study to evaluate the effectiveness of each proposed approach.
Easy and Scalable Syntheses of Li1.2Ni0.2Mn0.6O2
Authors: Prosini Pier Paolo , Aurora Annalisa , Della Seta Livia , Paoletti Claudia
Published: November 2023

Solid-state and sol-gel syntheses were selected as easy and scalable methods to prepare a lithium-rich cathode material for lithium-ion batteries. Among the extended family of layered oxides, Li1.2Ni0.2Mn0.6O2 was chosen for its low nickel content and the absence of cobalt. Both synthesis methods involved two heating steps at different temperatures, 600 and 900 °C. The first step is needed to decompose the metal acetates, which were selected as precursors, and the second step is needed to crystallise the material. To obtain a material with well-defined defects, the rate of heating and cooling was carefully controlled. The materials were characterised by X-ray diffraction, SEM coupled with EDS analysis, and thermal analysis and were finally tested as cathodes in a lithium semi cell. The solid-state synthesis allowed us to obtain better structural characteristics with respect to the sol-gel one in terms of a well-formed hexagonal layer structure and a reduced Li+/Ni2+ disorder. On the other hand, the sol-gel method produced a material with a higher specific capacity. The performance of this latter material was then evaluated as a function of the discharge current, highlighting its good rate capabilities.
Semi-Empirical Models for Stack and Balance of Plant in Closed-Cathode Fuel Cell Systems for Aviation
Authors: Donateo Teresa
Published: November 2023

In recent years, there has been a growing interest in utilizing hydrogen as an energy carrier across various transportation sectors, including aerospace applications. This interest stems from its unique capability to yield energy without generating direct carbon dioxide emissions. The conversion process is particularly efficient when performed in a fuel cell system. In aerospace applications, two crucial factors come into play: power-to-weight ratio and the simplicity of the powerplant. In fact, the transient behavior and control of the fuel cell are complicated by the continuously changing values of load and altitude during the flight. To meet these criteria, air-cooled open-cathode Proton Exchange Membrane (PEM) fuel cells should be the preferred choice. However, they have limitations regarding the amount of thermal power they can dissipate. Moreover, the performances of fuel cell systems are significantly worsened at high altitude operating conditions because of the lower air density. Consequently, they find suitability primarily in applications such as Unmanned Aerial Vehicles (UAVs) and Urban Air Mobility (UAM). In the case of ultralight and light aviation, liquid-cooled solutions with a separate circuit for compressed air supply are adopted. The goal of this investigation is to identify the correct simulation approach to predict the behavior of such systems under dynamic conditions, typical of their application in aerial vehicles. To this aim, a detailed review of the scientific literature has been performed, with specific reference to semi-empirical and control-oriented models of the whole fuel cell systems including not only the stack but also the complete balance of plant.
Multi-Omics Analysis of Gene and microRNA Expression in Diploid and Autotetraploid Poplar under Drought Stress by Transcriptome, microRNA, and Degradome Sequencing
Authors: Han Qiang , Du Kang , Xia Yufei , Kang Xiangyang
Published: November 2023

Drought-induced forest death has become a global phenomenon, which is hindering the development of sustainable forestry. Polyploidy breeding has been considered as an effective method of genetic improvement for tree stress resistance. However, the response mechanisms of tetraploid poplars to drought stress are unclear. In this study, based on high-throughput sequencing of transcriptome, small RNA, and degradome for these samples, which selected three genotypes of tetraploid poplars and their counterpart diploids for drought stress and rewatering trial in the experiment, we performed multi-omics analyses to investigate the distinction in drought resistance between tetraploid and diploid. A total of 3391 differentially expressed genes (DEGs) were found from the Dro-Di vs. CK-Di, 3753 DEGs from the Re-Di vs. Dro-Di, 3857 DEGs from the Dro-Te vs. CK-Te, and 4177 DEGs from the Re-Te vs. Dro-Te. Of the above DEGs, 1646 common-DEGs were identified significantly related to drought-stress response, 2034 common-DEGs related to rewater response, 158 and 114 common-DEGs showed opposite expression patterns between diploid and tetraploid, implying that these DEGs might play important roles in response to drought stress as a result of differences in ploidy. Additionally, 586 known miRNAs and 72 novel miRNAs were identified through analysis of 18 small RNA libraries, among which eight common-miRNAs were significantly related to drought-stress response, and four were related to rewater response. The degradome sequencing analysis revealed that 154 target transcripts for 24 drought-stress-associated differentially expressed miRNAs (DEmiRs), and 90 for 12 rewatering-associated DEmiRs were identified in the tetraploid based on both degradome and TargetFinder analyses. These findings provide valuable information for further functional characterization of genes and miRNAs in response to drought stress in Populus polyploidy, and potentially contribute to drought-resistant breeding of polypoid in the future.
An Efficient and Light Transformer-Based Segmentation Network for Remote Sensing Images of Landscapes
Authors: Chen Lijia , Chen Honghui , Xie Yanqiu , He Tianyou , Ye Jing , Zheng Yushan
Published: November 2023

High-resolution image segmentation for landscape applications has garnered significant attention, particularly in the context of ultra-high-resolution (UHR) imagery. Current segmentation methodologies partition UHR images into standard patches for multiscale local segmentation and hierarchical reasoning. This creates a pressing dilemma, where the trade-off between memory efficiency and segmentation quality becomes increasingly evident. This paper introduces the Multilevel Contexts Weighted Coupling Transformer (WCTNet) for UHR segmentation. This framework comprises the Mult-level Feature Weighting (MFW) module and Token-based Transformer (TT) designed to weigh and couple multilevel semantic contexts. First, we analyze the multilevel semantics within a local patch without image-level contextual reasoning. It avoids complex image-level contextual associations and eliminates the misleading information carried. Second, MFW is developed to weigh shallow and deep features for enhancing object-related attention at different grain sizes from multilevel semantics. Third, the TT module is introduced to couple multilevel semantic contexts and transform them into semantic tokens using spatial attention. Then, we can capture token interactions and obtain clearer local representations. The suggested contextual weighting and coupling of single-scale patches empower WCTNet to maintain a well-balanced relationship between accuracy and computational overhead. Experimental results show that WCTNet achieves state-of-the-art performance on two UHR datasets of DeepGlobe and Inria Aerial.
Genome-Wide Identification and Expression Analysis of the Walnut C-Repeat Binding Factor Gene Family under Low-Temperature Stress
Authors: Liu Ningfang , Du Hao , Xue Yansheng , Liao Yongling , Zhang Weiwei , Ye Jiabao , Wang Qijian , Xu Feng
Published: November 2023

The walnut (Juglans regia) is a nut with a high nutritional value and has been recognized throughout the world as an economically important woody plant. However, the walnut is vulnerable to abiotic stresses, especially low-temperature freezes, which can severely impede their growth and development, resulting in substantial financial losses. The CBF (C-repeat binding factor) gene, a unique plant transcription factor classified within the AP2/ERF (ethylene response factor) family, plays a vital role in the process of plants coping with abiotic stress, particularly low temperatures. This study utilized bioinformatics techniques to identify eight JrCBF genes within the walnut genome, distributed across six chromosomes. The upstream promoter sequences of these genes are rich in cis-regulatory elements related to hormonal responses and non-biotic stresses. Transcriptome data and qRT-PCR analysis revealed that JrCBF1 and JrCBF2 were significantly upregulated under low temperatures. JrCBF1 and JrCBF2 also responded positively to high-temperature stress. Under drought stress, JrCBF5 and JrCBF8 had a significant difference in their expression relative to other genes. To gain further insights into their functionality, subcellular localization experiments were conducted on JrCBF1 and JrCBF2, confirming their nuclear localization. These results provide valuable insights into the specific functions of CBF gene in enhancing the resistance of walnut to abiotic stress.
The Bulk Density and Cohesion of Submicron Particles Emitted by a Residential Boiler When Burning Solid Fuels
Authors: Gregorovičová Eva , Pospíšil Jiří , Sitek Tomáš
Published: November 2023

Particles emitted from combustion sources have a negative impact on human health and the environment. The solid fuel boilers that are used in households are a significant source of air pollution. The present study analyzes particulate matter (PM) produced during the combustion of wood pellets, wood logs, and coke in a residential boiler. The captured particles’ particle size distribution, bulk density, and cohesion are identified. A cascade impactor was used to capture the particles, and an optical digital microscope was used to determine the bulk density and cohesion (angle of repose) of the particles. The results show that the highest particle mass concentration emitted by the boiler falls within the interval of 89–146 nm, with the combustion of wood logs producing the most particles and coke combustion producing the least. The bulk density of particles of 51–595 nm (impactor stages 4–8) ranges from 320 to 785 kg/m3 for wood logs, 372–1108 kg/m3 for wood pellets, and 435–1330 kg/m3 for coke. The PM1 particles (impactor stage 4) have the highest bulk density. In the particle size range studied, the bulk density of particles decreases significantly with increasing particle size.
Nurturing Sustainability and Health: Exploring the Role of Short Supply Chains in the Evolution of Food Systems—The Case of Poland
Authors: Drejerska Nina , Sobczak-Malitka Wioleta
Published: November 2023

Over the last few decades, short food supply chains and local food markets, where farmers either sell their products directly to consumers or use a limited number of intermediaries, have developed worldwide in rural and urban areas. They complement conventional, often globalized, long food chains where small farmers have little bargaining power, and consumers cannot link the food they buy to a known agricultural producer or geographical area where the food is produced. The advantage of direct sales is that producers can obtain a higher price while consumers have easier access to fresh and seasonal food products. The main aim of the paper is to identify and characterize the spatial concentration of local food systems in Poland and their importance in sustainable development and food policy for healthy eating. As part of this study, an analysis of the statistical data of the Central Statistical Office for 2021 was carried out. Data obtained from the Chief Veterinary Inspectorate as of mid-2021 were analyzed to discuss the topic in detail. Descriptive methods and comparative analyses were used to understand regional differences. Absolute and proportional values were used for the research to enable better comparisons between regions, using the traditional method used in spatial structure studies, i.e., the distribution index (number of entities per 1000 inhabitants). The analysis identified spatial differences and possible implications for food policy and regional development. In addition, data on the number of marketplaces in Polish regions in 2022 were used. The study results indicated that short supply chains in the Polish food system contribute to increasing the availability of healthy local products, which may improve consumer health. However, despite these benefits, the results revealed challenges such as the limited production scale of local suppliers and the need to adapt to changing market conditions.
Sage Essential Oil as an Antimicrobial Agent against Salmonella enterica during Beef Sous Vide Storage
Authors: Gál Robert , Čmiková Natália , Kačániová Miroslava , Mokrejš Pavel
Published: November 2023

Sous-vide is a process comprising vacuum-sealing food, heating it to the desired temperature, and circulating it in a water bath in a sous vide machine. This cooking technique is increasingly common in homes and catering establishments due to its simplicity and affordability. However, manufacturers and chef’s recommendations for low-temperature and long-term sous-vide cooking in media raise food safety concerns, particularly when preparing beef tenderloin. In this study, Salmonella enterica was found to be inactivated by heat and sage essential oil (EO) in beef samples from musculus psoas major that had been sous vide processed. To determine whether heat treatment was likely to increase the sous vide efficiency, S. enterica and sage EO were mixed. After being vacuum-packed and injected with S. enterica, the samples were cooked at 50–65 °C through the sous vide technique for the prescribed time. On days 1, 3, and 6, the amounts of S. enterica, total bacteria, and coliform bacteria were measured in the control and treated groups of beef processed sous vide. Mass spectrometry was used to identify bacterial isolates on different days. On each day that was measured, a higher number of all the microbiota was found in the samples exposed to 50 °C for 5 min. The most frequently isolated microorganisms from both groups of samples were Pseudomonas fragi (17%), Pseudomonas cedrina (8%), and Proteus vulgaris (8%); in the treated group, also S. enterica (21%), Pseudomonas fragi (13%), and Pseudomonas veronii (6%). After the heat treatment of samples at 65 °C for 20 min, the total count of bacteria and coliform bacteria was zero. It has been shown that adding sage essential oil (EO) in combination with sous vide processing technique leads to the stabilization and safety of beef tenderloin.
Lactobacillus paracasei Relieves Constipation by Acting on the Acetic Acid-5-HT-Intestinal Motility Pathway
Authors: Wang Linlin , Yang Shurong , Mei Chunxia , Tang Nan , Wang Jialiang , Yu Qiangqing , Wang Gang , Wu Gaojue , Zhao Jianxin , Chen Wei
Published: November 2023

Constipation is a major health concern worldwide and requires effective and safe treatment options. In this study, we selected ten strains of two species of lactobacilli to identify whether they were effective against constipation induced by loperamide administration in BALB/c mice. Monitoring of constipation-related indicators indicated that Lactobacillus paracasei (L. paracasei) mainly acted on the whole intestinal peristalsis to relieve constipation. Furthermore, through the detection of biological, chemical, mechanical, and immune barriers in mice, it was discovered that L. paracasei changed the relative abundance of bacteria related to the levels of acetic acid and 5-hydroxytryptamine (5-HT) (such as by increasing the relative abundance of Odoribacter and Clostridium, and reducing the relative abundance of Mucispirillum, Ruminococcus, Coprobacillus, and Dorea), increased the concentration of acetic acid in the intestine, which stimulated enterochromaffin cells, promoted 5-HT synthesis in the colon, enhanced intestinal motility, and relieved constipation. In conclusion, this study provides a theoretical foundation for the development of personalized products for the treatment of constipation.
Identification of Hub Genes and Target miRNAs Crucial for Milk Production in Holstein Friesian Dairy Cattle
Authors: Roudbari Zahra , Mokhtari Morteza , Ebrahimpour Gorji Abdolvahab , Sadkowski Tomasz , Sadr Ayeh Sadat , Shirali Masoud
Published: November 2023

Dairy milk production is a quantitative trait that is controlled by many biological and environmental factors. This study employs a network-driven systems approach and clustering algorithm to uncover deeper insights into its genetic associations. We analyzed the GSE33680 dataset from the GEO database to understand the biological importance of milk production through gene expression and modules. In this study, we employed CytoNCA and ClusterONE plugins within Cytoscape for network analysis. Moreover, miRWalk software was utilized to detect miRNAs, and DAVID was employed to identify gene ontology and pathways. The results revealed 140 up-regulated genes and 312 down-regulated genes. In addition, we have identified 91 influential genes and 47 miRNAs that are closely associated with milk production. Through our examination of the network connecting these genes, we have found significant involvement in important biological processes such as calcium ion transit across cell membranes, the BMP signaling pathway, and the regulation of MAPK cascade. The conclusive network analysis further reveals that GAPDH, KDR, CSF1, PYGM, RET, PPP2CA, GUSB, and PRKCA are closely linked to key pathways essential for governing milk production. Various mechanisms can control these genes, making them valuable for breeding programs aiming to enhance selection indexes.
The RODI mHealth app Insight: Machine-Learning-Driven Identification of Digital Indicators for Neurodegenerative Disorder Detection
Authors: Giannopoulou Panagiota , Vrahatis Aristidis G. , Papalaskari Mary-Angela , Vlamos Panagiotis
Published: November 2023

Neurocognitive Disorders (NCDs) pose a significant global health concern, and early detection is crucial for optimizing therapeutic outcomes. In parallel, mobile health apps (mHealth apps) have emerged as a promising avenue for assisting individuals with cognitive deficits. Under this perspective, we pioneered the development of the RODI mHealth app, a unique method for detecting aligned with the criteria for NCDs using a series of brief tasks. Utilizing the RODI app, we conducted a study from July to October 2022 involving 182 individuals with NCDs and healthy participants. The study aimed to assess performance differences between healthy older adults and NCD patients, identify significant performance disparities during the initial administration of the RODI app, and determine critical features for outcome prediction. Subsequently, the results underwent machine learning processes to unveil underlying patterns associated with NCDs. We prioritize the tasks within RODI based on their alignment with the criteria for NCDs, thus acting as key digital indicators for the disorder. We achieve this by employing an ensemble strategy that leverages the feature importance mechanism from three contemporary classification algorithms. Our analysis revealed that tasks related to visual working memory were the most significant in distinguishing between healthy individuals and those with an NCD. On the other hand, processes involving mental calculations, executive working memory, and recall were less influential in the detection process. Our study serves as a blueprint for future mHealth apps, offering a guide for enhancing the detection of digital indicators for disorders and related conditions.
Rubella Vaccine Uptake among Women of Childbearing Age in Healthcare Settings
Authors: Ferrari Cristiana , Somma Giuseppina , Gentili Sandro , Manili Gianmarco , Mauro Gaetano , Treglia Michele , Trabucco Aurilio Marco , Magrini Andrea , Coppeta Luca
Published: November 2023

Background: Rubella is a contagious viral infection that occurs most often in children and young adults. Rubella is the leading vaccine-preventable cause of birth defects. Rubella infection in pregnant women may cause fetal death or congenital defects known as congenital rubella syndrome. There is no specific treatment for rubella, but the disease is preventable by vaccination with an efficacy of over 95%. Vaccination coverage is still below the recommended levels and many cases have occurred worldwide. The COVID-19 pandemic has had a negative impact on the immunization programs and the quality of disease surveillance worldwide. Operators of the healthcare setting are at increased risk of infection due to their work duties and should receive preventive vaccination or serologic protection to work in a healthcare setting. Aims: To evaluate the serological evidence of rubella IgG antibodies in female healthcare operators of childbearing age, to assess the risk of a breakthrough infection and the need for an additional dose of vaccine. Methods: We collected age and antibody titers from 449 young female operators aged <50 years who underwent the periodic surveillance at the Occupational Medicine Unit of the Policlinico Tor Vergata, Rome, from January to July 2022. Subjects were considered immune if the anti-rubella IgG titer was >11.00 IU/mL. Results: The rate of serologically unprotected subjects was 9.13% (41/449). The mean age of protected subjects was 26.93 years, while the mean age of unprotected subjects was 28.24 years. Age did not correlate with mean titer on statistical analysis (p = 0.10). The acceptance rate among unprotected operators was 31.7%. A positive attitude towards vaccination was found in 11/28 (39.3%) of the unvaccinated subjects, while a negative tendency was found in 2/28 (7.1%) of these subjects; most of the unvaccinated operators 15/28 (53.6%) prefer to postpone the administration of the vaccine. When compared with a similar population from the pre-pandemic period, the actual proportion of immune female subjects was not significantly different from that found in 2019 (90.87% vs. 90.3%). Conclusions: Protection against rubella was suboptimal among female healthcare workers of childbearing age. Acceptance of the rubella vaccine among these operators was low. Most of those who were hesitant intended to postpone the vaccination, while a minority had negative attitudes toward vaccination. A policy of mandatory vaccination policy should overcome the reluctance of operators.