bluuebunny/crossref_metadata_2025 huggingface dataset:
Real-Time Semantic Data Integration and Reasoning in Life- and Time-Critical Decision Support Systems
Authors: Soularidis Andreas , Kotis Konstantinos Ι. , Vouros George A.
Published: January 2024

Natural disasters such as earthquakes, floods, and forest fires involve critical situations in which human lives and infrastructures are in jeopardy. People are often injured and/or trapped without being able to be assisted by first responders on time. Moreover, in most cases, the harsh environment jeopardizes first responders by significantly increasing the difficulty of their mission. In such scenarios, time is crucial and often of vital importance. First responders must have a clear and complete view of the current situation every few seconds/minutes to efficiently and timely tackle emerging challenges, ensuring the safety of both victims and personnel. Advances in related technology including robots, drones, and Internet of Things (IoT)-enabled equipment have increased their usability and importance in life- and time-critical decision support systems such as the ones designed and developed for Search and Rescue (SAR) missions. Such systems depend on efficiency in their ability to integrate large volumes of heterogeneous and streaming data and reason with this data in (near) real time. In addition, real-time critical data integration and reasoning need to be performed on edge devices that reside near the missions, instead of using cloud infrastructure. The aim of this paper is twofold: (a) to review technologies and approaches related to real-time semantic data integration and reasoning on IoT-enabled collaborative entities and edge devices in life- and time-critical decision support systems, with a focus on systems designed for SAR missions and (b) to identify open issues and challenges focusing on the specific topic. In addition, this paper proposes a novel approach that will go beyond the state-of-the-art in efficiently recognizing time-critical high-level events, supporting commanders and first responders with meaningful and life-critical insights about the current and predicted state of the environment in which they operate.
A*–Ant Colony Optimization Algorithm for Multi-Branch Wire Harness Layout Planning
Authors: Yang Feng , Wang Ping , Zhang Renjie , Xing Shuyu , Wang Zhenlin , Li Ming , Fang Qiang
Published: January 2024

The planning of multi-branch cable harness layouts holds significant practical importance in aircraft industrial contexts, yet it has received limited attention in prior research. This study aims to address the matter concerning the significance of managing multiple constraints and preventing loops. It formulates the problem as an optimization problem in 3D free-form space and resolves it using an extended A* path planning approach in combination with the ant colony optimization algorithm. Initially, a feasible search space for wiring is established through the repair and simplification of the input CAD model. Subsequently, the topology of a multi-branched wiring harness is identified, taking into account industrial requirements related to cable physics, turning, support, bundling, and electromagnetic compatibility constraints. Specifically, the disassembly or merging of branches and loops is employed to avoid wire loops. Ultimately, we propose an A*–ant colony optimization algorithm (A*-ACO) with an enhanced heuristic function for neighboring points, incorporating a concentration increment model. Experimental tests illustrate the effectiveness of this approach in minimizing wire loops and reducing the total cable layout cost, considering factors such as length, bundling, and turning costs. It results in a reduction of 67.0%, 68.5%, and 51.1% compared to A*, ACO, and manual wiring methods, respectively.
Enhancing Object Detection in Smart Video Surveillance: A Survey of Occlusion-Handling Approaches
Authors: Ouardirhi Zainab , Mahmoudi Sidi Ahmed , Zbakh Mostapha
Published: January 2024

Smart video surveillance systems (SVSs) have garnered significant attention for their autonomous monitoring capabilities, encompassing automated detection, tracking, analysis, and decision making within complex environments, with minimal human intervention. In this context, object detection is a fundamental task in SVS. However, many current approaches often overlook occlusion by nearby objects, posing challenges to real-world SVS applications. To address this crucial issue, this paper presents a comprehensive comparative analysis of occlusion-handling techniques tailored for object detection. The review outlines the pretext tasks common to both domains and explores various architectural solutions to combat occlusion. Unlike prior studies that primarily focus on a single dataset, our analysis spans multiple benchmark datasets, providing a thorough assessment of various object detection methods. By extending the evaluation to datasets beyond the KITTI benchmark, this study offers a more holistic understanding of each approach’s strengths and limitations. Additionally, we delve into persistent challenges in existing occlusion-handling approaches and emphasize the need for innovative strategies and future research directions to drive substantial progress in this field.
Reinforcement Learning-Driven Bit-Width Optimization for the High-Level Synthesis of Transformer Designs on Field-Programmable Gate Arrays
Authors: Jang Seojin , Cho Yongbeom
Published: January 2024

With the rapid development of deep-learning models, especially the widespread adoption of transformer architectures, the demand for efficient hardware accelerators with field-programmable gate arrays (FPGAs) has increased owing to their flexibility and performance advantages. Although high-level synthesis can shorten the hardware design cycle, determining the optimal bit-width for various transformer designs remains challenging. Therefore, this paper proposes a novel technique based on a predesigned transformer hardware architecture tailored for various types of FPGAs. The proposed method leverages a reinforcement learning-driven mechanism to automatically adapt and optimize bit-width settings based on user-provided transformer variants during inference on an FPGA, significantly alleviating the challenges related to bit-width optimization. The effect of bit-width settings on resource utilization and performance across different FPGA types was analyzed. The efficacy of the proposed method was demonstrated by optimizing the bit-width settings for users’ transformer-based model inferences on an FPGA. The use of the predesigned hardware architecture significantly enhanced the performance. Overall, the proposed method enables effective and optimized implementations of user-provided transformer-based models on an FPGA, paving the way for edge FPGA-based deep-learning accelerators while reducing the time and effort typically required in fine-tuning bit-width settings.
Towards Fully Autonomous UAV: Damaged Building-Opening Detection for Outdoor-Indoor Transition in Urban Search and Rescue
Authors: Surojaya Ali , Zhang Ning , Bergado John Ray , Nex Francesco
Published: January 2024

Autonomous unmanned aerial vehicle (UAV) technology is a promising technology for minimizing human involvement in dangerous activities like urban search and rescue missions (USAR), both in indoor and outdoor. Automated navigation from outdoor to indoor environments is not trivial, as it encompasses the ability of a UAV to automatically map and locate the openings in a damaged building. This study focuses on developing a deep learning model for the detection of damaged building openings in real time. A novel damaged building-opening dataset containing images and mask annotations, as well as a comparison between single and multi-task learning-based detectors are given. The deep learning-based detector used in this study is based on YOLOv5. First, this study compared the different versions of YOLOv5 (i.e., small, medium, and large) capacity to perform damaged building-opening detections. Second, a multitask learning YOLOv5 was trained on the same dataset and compared with the single-task detector. The multitask learning (MTL) was developed based on the YOLOv5 object detection architecture, adding a segmentation branch jointly with the detection head. This study found that the MTL-based YOLOv5 can improve detection performance by combining detection and segmentation losses. The YOLOv5s-MTL trained on the damaged building-opening dataset obtained 0.648 mAP, an increase of 0.167 from the single-task-based network, while its inference speed was 73 frames per second on the tested platform.
Three-Dimensional Digitization of Documentation and Perpetual Preservation of Cultural Heritage Buildings at Risk of Liquidation and Loss—The Methodology and Case Study of St Adalbert’s Church in Chicago
Authors: Milosz Marek , Kęsik Jacek , Montusiewicz Jerzy
Published: January 2024

This paper presents a three-dimensional (3D) digitization methodology for documenting the appearance and geometry of cultural heritage buildings using modern tools for data acquisition and processing. This work presents a method combining laser scanning of building dimensions with photographic texture acquisition techniques in order to develop accurate, photo-realistic 3D models. This work also presents a method for digital reconstruction of the elements of interiors. This case study presents the process and result of scanning the church of St Adalbert in Chicago along with its inner finishing elements, achieved during the interventional 3D scanning of a historical building interior. The obtained colorized point cloud has 3.5 billion points and a volume of 65 GB. Its creation took nearly 3.5 h on a high-end computer. The generated textured mesh model has 1.6 billion triangles and a volume of 60 GB. The analysis of the results showed that the elements of the church furnishings were reproduced with very high accuracy. The developed 3D model of the interior, appearance and dimensions of the church is its perpetual documentation. It can be used for various purposes, such as popularizing the appearance on the Internet, scientific research on interior artefacts, creating files for VR and shooting high-resolution films.
Intelligent Design Optimization for Traction and Steering Motors of an Autonomous Electric Shuttle under Driving Scenarios
Authors: Demir Uğur , Ehsani Mehrdad , Demir Pelin , Akinci Tahir Cetin
Published: January 2024

Electrified autonomous vehicles have become quite popular and have a wide range of applications. The traction and steering motors to be used on an electrified autonomous vehicle are designed considering the lateral and longitudinal forces in the environment where the vehicle operates, and they are selected with extra safety margins and “over-engineering” features. This causes wastage of rare earth elements, along with both cost and energy inefficiencies. For autonomous shuttle vehicles, traction and steering performances can be analyzed based on driving scenarios. The reference speed and steering signals for the selected driving scenarios were run on a dynamic vehicle model and the minimum performance requirements for the traction and steering motors were determined. Then, the determined design parameters by DoE (Design of Experiments) were trained in two different ANN (Artificial Neural Networks) models created for motor models. The trained ANN models were run according to the minimum performance criteria and predicted motor models with new design parameters for the traction and steering motors. The performance results of the predicted traction and steering motor models showed a significant improvement in terms of the minimum performance requirements.
Power Generation Mix Optimization under Auction Mechanism for Carbon Emission Rights
Authors: Zhao Erdong , Chen Jianmin , Lan Junmei , Liu Liwei
Published: January 2024

As the international community attaches importance to environmental and climate issues, carbon dioxide emissions in various countries have been subject to constraints and limits. The carbon trading market, as a market tool to reduce greenhouse gas emissions, has gone through a development process from a pilot carbon market to a national carbon market in China. At present, the industries included in the national carbon market are mainly the electric power industry, and the carbon emissions of the electric power industry account for about 40% of the national carbon emissions. According to the construction history of foreign carbon markets, China’s future carbon quota allocation will gradually transition from free allocation to auction allocation, and the auction mechanism will bring a heavy economic burden to the electric power industry, especially the thermal power generation industry. Therefore, this study takes Guangdong Province as an example to optimize the power generation mix with the objective of minimizing the total economic cost after the innovative introduction of the carbon quota auction mechanism, constructs an optimization model of the power generation mix based on the auction ratio by comprehensively applying the system dynamics model and the multi-objective linear programming model, systematically researches the power generation structure under different auction ratios with the time scale of months, and quantitatively evaluates the economic inputs needed to reduce the greenhouse gas emissions. The results of the study show that after comprehensively comparing the total economic cost, renewable energy development, and carbon emissions, it is the most scientific and reasonable to set the auction ratio of carbon allowances at 20%, which achieves the best level of economic and environmental benefits.
Performance Analysis of Floating Structures in Solar-Powered Desalination
Authors: Kaviti Ajay Kumar , Akkala Siva Ram , Pohořelý Michael , Sikarwar Vineet Singh
Published: January 2024

Solar desalination employs direct sunrays in order to evaporate water vapor and collect the condensed water, making it an effective solution to combat water scarcity. In this experimental study, a solar still with a floating absorber is placed on the water, which acts as a heat absorber and is used to stop the heat conducting to the condensed water present in the still. Stainless steel, with thickness of 0.05 mm and dimensions of 500 mm × 500 mm, is used; this is coated with a Cr-Mn-Fe oxide nanocoating, and a wooden frame is attached to the sheet in order to maintain the balance, allowing the still to float at a constant depth on the water. The experiment is conducted on three different levels of water (3 cm, 4 cm, and 5 cm) using a conventional solar still (CSS) and a modified solar still (MSS) under the same climatic circumstances. The total distillate for depths of 3 cm, 4 cm, and 5 cm are 390 mL, 385 mL, and 385 mL, respectively for the MSS; the depths were 250 mL, 220 mL, and 205 mL, respectively, for the CSS. Upon comparison, the MSS performed better than the CSS by 56% at the 3 cm depth of water, 75% at the 4 cm depth of water, and 87% at the 5 cm depth of water. It was deduced that desalinated water for the MSS was 15.6% more cost-effective than for the CSS, and it was also 81% more cost-effective than packaged drinking water in India.
Predictive Modeling of Renewable Energy Purchase Prices Using Deep Learning Based on Polish Power Grid Data for Small Hybrid PV Microinstallations
Authors: Pikus Michał , Wąs Jarosław
Published: January 2024

In the quest for sustainable energy solutions, predicting electricity prices for renewable energy sources plays a pivotal role in efficient resource allocation and decision making. This article presents a novel approach to forecasting electricity prices for renewable energy sources using deep learning models, leveraging historical data from the power system operator (PSE). The proposed methodology encompasses data collection, preprocessing, feature engineering, model selection, training, and evaluation. By harnessing the power of recurrent neural networks (RNNs) and other advanced deep learning architectures, the model captures intricate temporal relationships, weather patterns, and demand fluctuations that impact renewable energy prices. The study demonstrates the applicability of this approach through empirical analysis, showcasing its potential to enhance energy market predictions and aid in the transition to more sustainable energy systems. The outcomes underscore the importance of accurate renewable energy price predictions in fostering informed decision making and facilitating the integration of renewable sources into the energy landscape. As governments worldwide prioritize renewable energy adoption, this research contributes to the arsenal of tools driving the evolution towards a cleaner and more resilient energy future.
A Machine Learning-Based Electricity Consumption Forecast and Management System for Renewable Energy Communities
Authors: Matos Miguel , Almeida João , Gonçalves Pedro , Baldo Fabiano , Braz Fernando José , Bartolomeu Paulo C.
Published: January 2024

The energy sector is currently undergoing a significant shift, driven by the growing integration of renewable energy sources and the decentralization of electricity markets, which are now extending into local communities. This transformation highlights the pivotal role of prosumers within these markets, and as a result, the concept of Renewable Energy Communities is gaining traction, empowering their members to curtail reliance on non-renewable energy sources by facilitating local energy generation, storage, and exchange. Also in a community, management efficiency depends on being able to predict future consumption to make decisions regarding the purchase, sale and storage of electricity, which is why forecasting the consumption of community members is extremely important. This study presents an innovative approach to manage community energy balance, relying on Machine Learning (ML) techniques, namely eXtreme Gradient Boosting (XGBoost), to forecast electricity consumption. Subsequently, a decision algorithm is employed for energy trading with the public grid, based on solar production and energy consumption forecasts, storage levels and market electricity prices. The outcomes of the simulated model demonstrate the efficacy of incorporating these techniques, since the system showcases the potential to reduce both the community electricity expenses and its dependence on energy from the centralized distribution grid. ML-based techniques allowed better results specially for bi-hourly tariffs and high storage capacity scenarios with community bill reductions of 9.8%, 2.8% and 5.4% for high, low, and average photovoltaic (PV) generation levels, respectively.
Calibration Techniques for Water Content Measurements in Solid Biofuels
Authors: Kjeldsen Henrik , Østergaard Peter Friis , Strauss Helena , Nielsen Jan , Tallawi Bayan , Georgin Eric , Sabouroux Pierre , Nielsen Jan G. , Hougaard Jens Ole
Published: January 2024

This paper presents methodologies and equipment for SI-traceable inline measurements of water content (a critical quality parameter) in solid biofuels. Inline measurement systems for water content are commonly used at CHP plants, providing continuous real-time data. However, the accuracy of these systems is in most cases unsatisfactory, mainly because the systems are not calibrated representatively for the relevant material, and until now, calibrations traceable to the SI system have not been available. To provide reliable and accurate inline water content data, new procedures and equipment for calibrating measurement systems were developed. Two reference methods for the determination of water content were developed; one measures the airflow and dewpoint of desorbed water in the air passing a test sample, while the other uses a P2O₅-sensor. Additionally, a transfer standard based on a cavity resonance sensor was developed for fast onsite calibration of the inline sensor at the power plant. This new instrument allows for quick and accurate measurements. The transfer standard is made metrologically traceable to the primary measurement standards. The entire system was demonstrated by calibrating an inline microwave-based (MW) sensor at the CHP plant of VERDO in Randers, Denmark. Thus, a complete metrological traceability chain was established from an industrial to a primary standard.
A Refined DER-Level Transient Stability Prediction Method Considering Time-Varying Spatial–Temporal Correlations in Microgrids
Authors: Zhao Huimin , He Lili , Peng Yelun , Shuai Zhikang , Zhang Zhixue , Hu Liang
Published: January 2024

The transient responses of distributed energy resources (DERs) in a microgrid are dynamically correlated in spatial and temporal dimensions. Hence, the transient stability prediction in microgrids would require an effective modeling of time-varying correlations and the mining of spatial–temporal features of electrical data. This paper proposes a refined DER-level transient stability prediction method for microgrids considering the time-varying spatial–temporal correlations of DERs. First, the spatial–temporal dynamic correlation of DERs was extracted and modeled by an attention-based mechanism. Then, a spatial–temporal graph convolution network was proposed to predict the dynamics of unstable DERs and the instability severity trend in a microgrid. The TSP model consisted of three parts: (1) several stacked spatial–temporal convolution modules to simultaneously mine the spatial–temporal dynamic features of microgrids, (2) an unstable DER identification module to predict the microgrid system stability and identify unstable DERs, and (3) an instability severity trend prediction module for DERs in a microgrid. The test results on a realistic 16-bus 10-DER microgrid demonstrated that the proposed prediction method possessed the desirable reliability and interpretability and outperformed the state-of-the-art baselines in unstable DER identifications and DER instability severity trend predictions.
Recent Advances in Self-Powered Electronic Skin Based on Triboelectric Nanogenerators
Authors: Feng Qingyang , Wen Yuzhang , Sun Fengxin , Xie Zhenning , Zhang Mengqi , Wang Yunlu , Liu Dongsheng , Cheng Zihang , Mao Yupeng , Zhao Chongle
Published: January 2024

Human skin, the body’s largest organ, plays a crucial role in perceiving mechanical stimulation and facilitating interaction with the external environment. Leveraging the unique attributes of human skin, electronic skin technology aimed at replicating and surpassing the capabilities of natural skin holds significant promise across various domains, including medical care, motion tracking, and intelligent robotics. In recent research, triboelectric nanogenerators have emerged as a compelling solution for addressing the energy challenge in electronic skins. Triboelectric nanogenerators harness the combination of the triboelectric effect and electrostatic induction to efficiently convert mechanical energy into electrical power, serving as self-powered sensors for electronic skins, which possess the advantages of self-powered operation, cost-effectiveness, and compatibility with a wide range of materials. This review provides an introduction to the working principles and the four operational modes of triboelectric nanogenerators, highlighting the functional features of electronic skins, such as stretchability, self-healing, and degradability. The primary focus is on the current applications of self-powered electronic skins based on triboelectric nanogenerators in medical care, motion tracking, and machine tactile recognition. This review concludes by discussing the anticipated challenges in the future development of self-powered electronic skins based on triboelectric nanogenerators. This review holds practical significance for advancing the practical use of self-powered electronic skins based on triboelectric nanogenerators and offers valuable guidance for individuals interested in pursuing scientific and healthy endeavors.
Costs of Coal Abatement for Residential Heating to Reduce Urban Air Pollution in Asian Russia: Evidence from Krasnoyarsk
Authors: Syrtsova Ekaterina A. , Ivantsova Ekaterina D. , Miskiv Alexandra S. , Zander Evgeniya V. , Pyzhev Anton I.
Published: January 2024

Heat and electricity generation are major sources of air pollution in many large cities worldwide. In Siberian cities, the heat supply significantly contributes to air pollution, as coal remains the primary energy fuel in most of Asian Russia. Krasnoyarsk, the second-largest city in the macro-region by population, serves as a clear example of urban air pollution. In recent years, public authorities have implemented several measures to reduce pollutant emissions. These measures include modernizing thermal power stations, replacing inefficient small boiler houses, and converting residential heating to more environmentally friendly types of fuel. However, our analysis shows that these policies have not yet resulted in a significant transition away from using coal for heating. One of the primary reasons is that alternative fuels are unable to compete with coal in terms of price. The proposal suggests transitioning private households to pellet heating. Our goal is to evaluate the potential environmental and economic impacts of this measure. We estimate the necessary subsidy size for the successful implementation of both initiatives. In conclusion, converting households from coal to pellet heating can reduce the emissions of nitrogen oxides, carbon monoxide, sulfur oxides, and suspended particles by 0.2%, 8.5%, 4.4%, and 2.7%, respectively, of the total pollutant emissions in Krasnoyarsk. Furthermore, this conversion can provide economic benefits by supporting local pellet producers who heavily rely on export markets. If practically implemented, the proposed approach could solve one of the most significant development issues of one of the largest Russian cities within a few years, with only 2.5% of the annual city budget expenditure.
Selected Issues, Methods, and Trends in the Energy Consumption of Industrial Robots
Authors: Sękala Agnieszka , Blaszczyk Tomasz , Foit Krzysztof , Kost Gabriel
Published: January 2024

Industrial robots, like all machines, require energy to operate, which is why energy efficiency in industrial robotics has been a subject of consideration in recent years in many scientific and industrial centers. Interest in the topic is especially noticeable in Industry 4.0. Research on energy efficiency stems from the emergence of new possibilities in terms of making strategic decisions related to manufacturing processes. As energy-efficient production is an essential part of sustainable development, the energy efficiency of industrial robots must be considered. The need to reduce costs while maintaining quality and increasing production efficiency has necessitated the implementation of modern solutions aimed at reducing electricity consumption. The rational use of electrical energy, especially in the industrial sector, significantly reduces production costs and, consequently, contributes to a company’s profits and competitiveness. This article aims to provide an overview of energy efficiency issues based on recently published articles. This article discusses the appropriate selection of robots, their programming, energy-efficient trajectory planning, and the monitoring of the operation of the robotic system to minimize energy consumption. Typical industrial applications of robots are also mentioned and discussed.
Detection and Diagnostics of Bearing and Gear Fault under Variable Speed and Load Conditions Using Heterogeneous Signals
Authors: Bouzouidja Mahfoud , Soualhi Moncef , Soualhi Abdenour , Razik Hubert
Published: January 2024

In industrial applications, rotating machines operate under real-time variable speed and load regimes. In the presence of faults, the degradation of critical components is accelerated significantly. Therefore, robust monitoring algorithms able to identify these faults become crucial. In the literature, it is hard to find comprehensive monitoring systems that include variable speed and load regimes with combined gearbox faults using electrical and vibration signals. For this purpose, a novel signal processing methodology including a geometric classification technique is proposed. This methodology is based on using different types of sensors such as current, voltage and vibration sensors with a regime normalization, which allows the grouping of different regimes belonging to the same health state. It consists of reducing dispersion between the class observations and separating other classes representing different health states including the variation in speed and load. Then, a peripheral threshold is proposed in our classifier to diagnose new health states. To verify the effectiveness of the methodology, current, voltage and vibration data from a gearbox system are collected under variable speed and load levels.
Multi-Objective Optimization Design of Cycloid-Pin Gears Based on RV Reducer Precision Transmission Performance
Authors: Zhao Yunda , Han Zhenhua , Tan Qifeng , Shan Wentao , Li Rirong , Wang Hao , Du Youwu
Published: January 2024

This paper aims to realize multi-objective optimization of cycloid-pin gears to improve the positioning accuracy and load-carrying capacity of the rotary vector (RV) reducer, via the consideration of backlash, transmission error, and torsional stiffness. Initially, the analytical models of the RV transmission backlash and transmission error are developed by using both purely geometrical and equivalent model methods individually. Based on the generalized Hooke’s law, a torsion angle model is established to characterize the torsional stiffness of the system, utilizing methods such as Hertzian contact theory and bearing stiffness models. Subsequently, employing the Monte Carlo method, extremum method, and quality loss function, mapping objective functions for dimensional accuracy (tolerance) and transmission performance (backlash, transmission error, and torsional stiffness) are constructed. The geometry dimensions, dimensional accuracy, and modification of the cycloid-pin gear are considered as design variables to create a multi-objective optimization model. The improved Parallel Adaptive Genetic Algorithm using Deferential Evolution (PAGA-DE) is used for multi-objective solutions. Through example calculations, the impact of cycloid-pin gear parameters on transmission performance before and after optimization is determined. The reliability of backlash after optimization within 1.5′ reaches 99.99%, showing an increase of 8.24%. The reliability of transmission error within 1′ reaches 98.52%, demonstrating an increase of 1.35%. The torsional angle is reduced by 8.9% before optimization. The results indicate that the proposed multi-objective optimization design method for cycloid-pin gears can achieve the goal of improving the transmission performance of the RV reducer.
Experimental Study on Rejuvenation of Aged Power Cables via Simulation Operation
Authors: Xie Yue , Zhang Yu , Lou Yihui , Zhang Jinming
Published: January 2024

Thermal effects play a crucial role in the evolution of insulation performance in power cables during long-term operation. Before the experiments, crosslinked polyethylene (XLPE) sheets and cables were thermally aged at 105 °C for up to 180 days. Then, the heat treatments on XLPE sheets and cables were conducted in three stages. Firstly, the aged sheets were subjected to heat treatment with a temperature range of 90 to 115 °C at intervals of 5 °C, with each step lasting for 20 h. Secondly, a 7-year-serviced cable underwent simulated cable operation at the same temperature as the XLPE sheets. Thirdly, two 15- and 30-year-serviced cables were treated at temperatures ranging from 90 to 105 °C, adhering to the same intervals as the second stage. The differential scanning calorimetry (DSC), cross-linking degree, DC conduction, and breakdown strength were measured. The results show that both heat treatment methods are effective in enhancing crystallization characteristics and conductivity for XLPE sheets and aged cables, and the optimum values were achieved at decreasing temperatures as the aging period extended. Moreover, the heat treatment on retired cables yielded similar results, suggesting that a heat treatment resembling cable operation at higher temperatures would initially be beneficial for cable rejuvenation.
Numerical Study of Supercritical Opposed Wall-Fired Boiler Furnace Temperature and High-Temperature Heating Surface Stress under Variable Load Operation
Authors: Du Jiajun , Li Yilong , Zhao Yonggang , Da Yaodong , Che Defu
Published: January 2024

The opposed wall-fired boiler is widely used in Chinese power plants due to its adaptability. However, deviations from design conditions can cause the reduction of combustion efficiency and combustion stability, and the overheating of heating surfaces. This study conducted numerical simulations on a 600 MW supercritical opposed wall-fired boiler at 75%, 50%, and 30% Turbine Heat Acceptance (THA) load conditions. The variation of temperature field and heat flux in the furnace under different loads, and parameters such as distributions of heat flux, temperature, and the stress of the high-temperature heating surface are analyzed. Results indicate that reducing the load from 75% to 30% THA lowers the furnace outlet temperature from 1158 to 1009 K and reduces the average gas temperature of the high-temperature heating surface from 1800 to 1570 K. Under a high load, the maximum heat flux concentrates on the side water-cooled wall of the combustion zone. However, when the load decreases, the heat absorption shifts towards the main combustion zone. Furthermore, under a high load, the average wall temperature of the high-temperature heating surface remains at 1600 K with a uniform temperature distribution. In contrast, when the load drops to 30% THA, significant temperature differences appear on the heating surface, with a maximum difference of 400 K. This leads to excessive expansion and slagging on the high-temperature heating surface, particularly in the middle and lower sections, due to the increased stress. These findings offer valuable insights for optimizing the combustion characteristics of opposed wall-fired boilers and preventing overtemperature explosions on the platen heating surface.
Inversion Study of Nitrogen Content of Hyperspectral Apple Canopy Leaves Using Optimized Least Squares Support Vector Machine Approach
Authors: Hou Kaiyao , Bai Tiecheng , Li Xu , Shi Ziyan , Li Senwei
Published: January 2024

The rapid and accurate estimation of the nitrogen content of fruit trees helps to achieve a precise management of orchards. Hyperspectral data were collected from leaves of apple tree canopies at different fertility stages through field experiments to investigate the relationship between the nitrogen content and spectral reflectance of apple canopy leaves. Two different preprocessing methods, Savitzky–Golay (SG) smoothing and multiple scattering correction (MSC), were used to extract the feature bands by combining the successive projection method (SPA) and the competitive adaptive weighting algorithm–partial least squares (CARS-PLS). The reflectance values of the feature bands screened via these two methods were used as inputs to construct the multi-factor inversion models of apple canopy leaf nitrogen content based on the long- and short-term memory (LSTM) network, the support vector regression (SVR) and the Least Squares Support Vector Machine Regression (RIME-LSSVM). The study compared the ability of three algorithmic models to estimate leaf nitrogen content, and the results showed that the model constructed with the reflectance values of the characteristic bands screened by the CARS-PLS algorithm as inputs was more effective in predicting the nitrogen content of leaves. Furthermore, the accuracy of the model constructed using RIME-LSSVM was significantly higher than that of the model constructed using the long- and short-term memory network and support vector regression, in which the coefficient of determination of the test set (R-squared) is 0.964 and the root-mean-squared error (RMSE) is 0.052. Finally, the CARS-PLS algorithm combined with the RIME-LSSVM model has a higher prediction accuracy. The study demonstrated the feasibility and reliability of hyperspectral techniques for the estimation of nitrogen content of apple leaves in the Aksu region.
Will “Tall Oaks from Little Acorns Grow”? White Oak (Quercus alba) Biology in the Anthropocene
Authors: Abbott Albert , Staton Margaret , Lhotka John , DeWald Laura , Zhebentyayeva Tetyana , Kapoor Beant , Thomas Austin , Larson Drew , Hadziabdic Denita , DeBolt Seth , Nelson C. , Carlson John
Published: January 2024

Quercus alba L., also known as white oak, eastern white oak, or American white oak, is a quintessential North American species within the white oak section (Quercus) of the genus Quercus, subgenus Quercus. This species plays a vital role as a keystone species in eastern North American forests and plays a significant role in local and regional economies. As a long-lived woody perennial covering an extensive natural range, Q. alba’s biology is shaped by a myriad of adaptations accumulated throughout its natural history. Populations of Q. alba are crucial repositories of genetic, genomic, and evolutionary insights, capturing the essence of successful historical adaptations and ongoing responses to contemporary environmental challenges in the Anthropocene. This intersection offers an exceptional opportunity to integrate genomic knowledge with the discovery of climate-relevant traits, advancing tree improvement, forest ecology, and forest management strategies. This review provides a comprehensive examination of the current understanding of Q. alba’s biology, considering past, present, and future research perspectives. It encompasses aspects such as distribution, phylogeny, population structure, key adaptive traits to cyclical environmental conditions (including water use, reproduction, propagation, and growth), as well as the species’ resilience to biotic and abiotic stressors. Additionally, this review highlights the state-of-the-art research resources available for the Quercus genus, including Q. alba, showcasing developments in genetics, genomics, biotechnology, and phenomics tools. This overview lays the groundwork for exploring and elucidating the principles of longevity in plants, positioning Q. alba as an emerging model tree species, ideally suited for investigating the biology of climate-relevant traits.
Effect of the Vacuum Impregnation Process on Water Absorption and Nail-Holding Power of Silica Sol-Modified Chinese Fir
Authors: Tao Mengxue , Liu Xia , Xu Wei
Published: January 2024

The application of fast-growing Chinese fir (Cunninghamia lanceolata) is limited due to low dimensional stability and weak mechanical strength. Silica sol can effectively improve fast-growing fir wood’s physical and mechanical properties. In order to clarify the influence of impregnation process parameters on the modification effect, the effect of the vacuum impregnation variants (e.g., pre-vacuum time, pre-vacuum pressure, pressurization time, and pressurization pressure) was discussed using the orthogonal test approach. The optimal modification process was determined by comparing the water absorption and nail-holding power under different modification processes. The range analysis and variance analysis methods were used to study the correlation between process factors and the performance of the modified wood. The results showed that the water absorption and nail-holding power of fast-growing fir wood were significantly improved via vacuum impregnating with silica sol. The optimum process parameters for water absorption and nail-holding power of fast-growing fir as the pre-vacuum time was 30 min, the pre-vacuum pressure was −0.08 MPa, the pressurization time was 3 h, and the pressurization pressure was 1.2 MPa.
Thiamine Deficiency M74 Developed in Salmon (Salmo salar) Stocks in Two Baltic Sea Areas after the Hatching of Large Year-Classes of Two Clupeid Species—Detected by Fatty Acid Signature Analysis
Authors: Vuorinen Pekka J. , Käkelä Reijo , Pakarinen Tapani , Heinimaa Petri , Ritvanen Tiina , Nikonen Soili , Rokka Mervi , Keinänen Marja
Published: January 2024

Lipid-related thiamine (vitamin B1) deficiency of Baltic salmon (Salmo salar), the M74 syndrome, is generally caused by feeding on abundant young sprat (Sprattus sprattus) in the Baltic Proper, the main foraging area of these salmon. In 2014, a strong year-class of sprat was hatched in the Baltic Proper, and a strong herring (Clupea harengus) year-class was hatched in the Gulf of Bothnia, where herring is the dominant salmon prey. The fatty acid (FA) signatures of prey fish in muscle or eggs of second sea-year spawners suggested that 27% of wild River Simojoki and 68% of reared River Dal salmon remained in the Gulf of Bothnia in 2014 instead of continuing to the Baltic Proper. In 2016, 23% of the M74 females of the River Simojoki and 58% of the River Dal originated from the Gulf of Bothnia, and 13% and 16%, respectively, originated from the Baltic Proper. Some salmon from the River Neris in the southern Baltic Proper had also been feeding in the Gulf of Bothnia. In general, low free thiamine (THIAM) concentration in eggs was associated with high lipid content and high docosahexaenoic acid (DHA, 22:6n−3) and n−3 polyunsaturated FA (n−3 PUFA) concentrations in muscle but not in eggs. A higher THIAM concentration and lower proportions of DHA and n−3 PUFAs in Arctic Ocean salmon eggs, despite higher egg lipid content, indicated that their diet contained fewer fatty fish than the Baltic salmon diet. Hence, M74 originated by foraging heavily on young fatty sprat in the Baltic Proper or herring in the Gulf of Bothnia.
Toxic Impact of Dietary Cadmium on Bioaccumulation, Growth, Hematological Parameters, Plasma Components, and Antioxidant Responses in Starry Flounder (Platichthys stellatus)
Authors: Jung Hyeok-Chan , Kim Jun-Hwan , Kang Ju-Chan
Published: January 2024

Starry flounders (Platichthys stellatus) (average weight—114.69 ± 13 g, length—19.0 ± 0.21 cm) underwent a 4-week exposure to varying dietary cadmium (Cd) concentrations: 0, 10, 20, 40, 60, and 80 mg/kg. Bioaccumulation patterns were revealed in the intestine, kidney, liver, and gills in descending order of accumulation. Significant declines in growth indicators—body weight gain (BWG), specific growth rate (SGR), and feed efficiency ratio percentage (FER)—were evident at 80 mg Cd/kg for 2 weeks and beyond 60 mg Cd/kg for 4 weeks. Hematological parameters, including red blood cell (RBC) count, hematocrit (Ht), and hemoglobin (Hb), remarkably reduced at 80 mg Cd/kg for both 2 and 4 weeks. The total plasma protein reduced significantly after exposure to Cd for 2 and 4 weeks, alongside increased glucose levels, glutamic oxaloacetic transaminase (GOT), and glutamic pyruvate transaminase (GPT). Considerable increases in antioxidant responses—superoxide dismutase (SOD), catalase (CAT), glutathione S-transferase (GST), and glutathione (GSH) levels—were observed after exposure to Cd for 2 and 4 weeks. P. stellatus exhibits a high accumulation of dietary Cd in specific tissues. Moreover, concentrations above 60 mg Cd/kg adversely affected the growth performance, hematological parameters, plasma components, and antioxidant responses.
Characterization and Preliminary Safety Evaluation of Akkermansia muciniphila PROBIO
Authors: Ma Xin , Tian Meng , Yu Xueping , Liu Ming , Li Bin , Ren Dayong , Wang Wei
Published: January 2024

In addition to providing certain health advantages to the host, a bacterial strain must possess a clearly defined safety profile to be regarded as a probiotic. In this study, we present a thorough and methodical assessment of the safety of a novel strain of bacteria, Akkermansia muciniphila PROBIO, which was isolated from human feces. Firstly, we examined the strain’s overall features, such as its gastrointestinal tolerance and its physiological and biochemical traits. Next, we verified its genotoxic properties through bacterial reverse mutation and in vitro mammalian cell micronucleus assays. The drug sensitivity of A. muciniphila PROBIO was subsequently examined through an analysis of its antibiotic resistance genes. Additionally, the toxicological impact was verified through acute and sub-chronic toxicity studies. A genome-based safety assessment was conducted to gain further insights into gene function, including potential virulence factors and pathogenic properties. Finally, we assessed whether moxifloxacin resistance in A. muciniphila PROBIO is transferred using in vitro conjugation experiments. A. muciniphila PROBIO exhibited superior gastrointestinal tolerance, with no observed hematological or histopathological abnormalities. Moreover, the outcomes pertaining to mutagenic, clastogenic, or toxic impacts were found to be negative, even at exceedingly high dosages. Moreover, no adverse effects associated with the test substance were observed during the examination of acute and sub-chronic toxicity. Consequently, it was plausible to estimate the no-observed-adverse-effect level (NOAEL) to be 6.4 × 1011 viable bacteria for an average individual weighing 70 kg. Additionally, only three potential drug resistance genes and one virulence factor gene were annotated. A. muciniphila PROBIO is naturally resistant to moxifloxacin, and resistance does not transfer. Collectively, the data presented herein substantiate the presumed safety of A. muciniphila PROBIO for its application in food.
Dietary Tomato Pectin Attenuates Hepatic Insulin Resistance and Inflammation in High-Fat-Diet Mice by Regulating the PI3K/AKT Pathway
Authors: Sun Jing , Wu Kongyan , Wang Pan , Wang Yubin , Wang Dan , Zhao Wenting , Zhao Yuanyuan , Zhang Chunhong , Zhao Xiaoyan
Published: January 2024

Chronic metabolic disease is a serious global health issue, which is accompanied by impaired insulin resistance. Tomato pectin (TP) is a naturally soluble complex hetero-polysaccharide with various biological functions. However, the impact of TP on hepatic insulin resistance in a high-fat diet (HFD) and its potential mechanism remains largely unknown. The results revealed that TP treatment significantly decreased the liver weight, hepatic fat accumulation and hepatic injury in HFD-fed mice. TP also improved fasting blood glucose levels and glucose tolerance in HFD-fed mice. The underlying mechanisms involved in the inflammation, oxidative stress and insulin signaling in the liver were also investigated by RT-qPCR and western blot, which indicated that TP ameliorated hepatic insulin resistance by regulating the PI3K/AKT/GSK-3β pathway, increasing the expression of GLUT4, decreasing the expression of PECK and G6P as well as restoring antioxidant activities and suppressing the inflammation statues in HFD-fed mice. Our data showed that dietary TP has profound effects on hepatic insulin resistance, inflammation and oxidative stress, demonstrating that TP might be a promising therapeutic agent against insulin resistance and related chronic metabolic disease.
Data-Driven Field Representations and Measuring Processes
Authors: Hong Wanrong , Zhu Sili , Li Jun
Published: January 2024

Natural mathematical objects for representing spatially distributed physical attributes are 3D field functions, which are prevalent in applied sciences and engineering, including areas such as fluid dynamics and computational geometry. The representations of these objects are task-oriented, which are achieved using various techniques that are suitable for specific areas. A recent breakthrough involves using flexible parameterized representations, particularly through neural networks, to model a range of field functions. This technique aims to uncover fields for computational vision tasks, such as representing light-scattering fields. Its effectiveness has led to rapid advancements, enabling the modeling of time dependence in various applications. This survey provides an informative taxonomy of the recent literature in the field of learnable field representation, as well as a comprehensive summary in the application field of visual computing. Open problems in field representation and learning are also discussed, which help shed light on future research.