bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluation of the Mechanical Properties of Different Dental Resin-Based Materials After Submersion in Acidic Beverages
Authors: Brânzan Răzvan Constantin , Tărăboanță Ionuț , Ghiorghe Cristina Angela , Stoleriu Simona , Cârlescu Vlad , Tărăboanță-Gamen Andra Claudia , Andrian Sorin
Published: December 2024

Background: The aim of this study was to evaluate the influence of acidic beverages on the mechanical properties of various dental resin-based materials. Methods: A total number of 160 samples were prepared using four types of resin-based materials—Group A (n = 40): flowable composite, Group B (n = 40): heavy-flow composite, Group C (n = 40): resin-based sealant and Group D (n = 40): nano-hybrid composite. Then, the samples were distributed into four subgroups according to the submersion solution: a (n = 10): artificial saliva, b (n = 10): coffee, c (n = 10): cola and d (n = 10): red wine. The Vickers microhardness, Young’s modulus of elasticity and scratch resistance were assessed using a CETR UMT-2 tribometer. Results: The obtained results showed that 14-day submersion of the resin-based materials in coffee, cola and red wine solutions significantly (p < 0.05) decreased the microhardness values (VHN), Young’s modulus of elasticity and scratch resistance. Fourteen days of storage in coffee decreased the microhardness values of flow resin from 117.5 to 81.59 VHN (p < 0.001) whereas the values of the nanohybrid resin decreased from 125.5 to 89.4 (p < 0.001). The elasticity modulus of the heavy flow resin showed a decline from 15.57 to 10.50 GPa after 14 days’ submersion in coffee (p < 0.001), and from 21.29 to 13.10 GPa for the nanohybrid resin after immersion in cola (p < 0.001). For the scratch test, the resin-based sealant showed a significant decrease after 14 days of storage in coffee, from 0.34 to 0.02 units. Conclusions: The submersion of conventional nanohybrid, flowable, heavy-flow composite resins and resin-based sealants in coffee, cola and red wine solutions changes the mechanical properties (Young’s modulus of elasticity, Vickers microhardness and scratch resistance). The most resistant resin-based material to acid attack was the conventional nanohybrid composite resin, followed by heavy flow resin, flowable resin and resin-based sealant.
Sporadic Burkitt Lymphoma First Presenting as Painful Gingival Swellings and Tooth Hypermobility: A Life-Saving Referral
Authors: Papadopoulou Erofili , Kouri Maria , Velonis Dimitrios , Andreou Anastasia , Georgaki Maria , Damaskos Spyridon , Piperi Evangelia , Delli Konstantina , Karoussis Ioannis K. , Vlachou Antonia , Avgerinou Georgia , Kattamis Antonis , Nikitakis Nikolaos G.
Published: December 2024

Background: Burkitt lymphoma (BL) is an aggressive non-Hodgkin lymphoma (NHL), subdivided into endemic, sporadic, and immunodeficiency-associated forms. While jaw lesions are common in endemic BL, they are infrequent in sporadic cases, only rarely constituting the first manifestation of the disease. The aim of this study is to present a rare pediatric case of sporadic BL first manifesting as gingival swellings and tooth hypermobility and provide a review of all the published sporadic BL case reports as the first sign of disease. Case report: An 11-year-old Caucasian female was referred for the evaluation of hypermobility of posterior lower teeth, associated with painful gingival swellings of 20 days duration. Clinical examination revealed right facial asymmetry and bilateral prominent swellings of the posterior lower gingiva. A panoramic radiograph revealed ill-defined radiolucent lesions in the posterior mandible bilaterally. On computed tomography, soft-tissue masses were identified along the mandibular ramus extending into the maxillary sinus bilaterally. The histopathologic and immunohistochemical analyses of the lesions led to a diagnosis of Burkitt lymphoma (BL). The patient underwent a full staging work-up, revealing bone marrow involvement and widespread disease. A multi-chemotherapy regimen was initiated with the regression of oral lesions and symptoms within a few weeks and complete disease remission after nine chemotherapy cycles. The patient remains free of disease 11 years later. Conclusions: This case underscores the critical importance of the timely diagnosis and life-saving referral of rapidly growing jaw lesions, which may represent the first sign of an underlying lymphoreticular malignancy with aggressive course, such as BL.
Analysis of the Relationship Between Body Mass Index (BMI) and Dento-Skeletal Maturation: A Cross-Sectional Case-Control Study
Authors: Verdecchia Alessio , Torre Inmaculada Coronado de la , Diaz Ivan Menéndez , Sanz Veronica García , Mesa Yolanda García , Cobo Teresa , Gallardo Vanessa Paredes
Published: December 2024

Background/Objectives: The aim of this cross-sectional study was to establish if there is a relationship between body mass index (BMI) and skeletodental development in young obese patients in comparison with normal-weight patients. Methods: The sample consisted of 178 individuals (115 normal weight, 37 overweight and 26 obese), aged 6 to 16 years, with a mean biological age of 11.96 ± 2.44 years. Dental maturation (dental age) was determined by using Demerjian’s method; craniofacial growth pattern, skeletal and dental class were determined by using cephalometric measurements; skeletal maturation was determined by using Baccetti’s method. Data were statistically analyzed. Results: According to Demirjian’s method, the mean dental age of the overweight and obese subjects was significantly higher than that of the normal-weight subjects (p = 0.001 and p = 0.02, respectively). A higher rate of dental class III was detected in the overweight group (p = 0.002). Concerning cephalometric records, statistically significant differences (p = 0.018) were observed in the distribution of SNA and SNB values, higher values being found in obese subjects. No difference was detected in the distribution of the ANB angle between the groups. As regards craniofacial growth pattern, no significant differences were found between the groups. Children with obesity presented more advanced skeletal maturation, reaching statistically significant differences (p = 0.02), in comparison with the normal-weight subjects. Conclusions: In conclusion, obese children showed increased tooth and skeletal development in comparison to the normal-weight subjects. These findings may be of interest for orthodontists, who should consider weight status when performing orthodontic treatment in children and adolescents.
Entropy Production in an Electro-Membrane Process at Underlimiting Currents—Influence of Temperature
Authors: Maroto Juan Carlos , Muñoz Sagrario , Barragán Vicenta María
Published: December 2024

The entropy production in the polarization phenomena occurring in the underlimiting regime, when an electric current circulates through a single cation-exchange membrane system, has been investigated in the 3–40 °C temperature range. From the analysis of the current–voltage curves and considering the electro-membrane system as a unidimensional heterogeneous system, the total entropy generation in the system has been estimated from the contribution of each part of the system. Classical polarization theory and the irreversible thermodynamics approach have been used to determine the total electric potential drop and the entropy generation, respectively, associated with the different transport mechanisms in each part of the system. The results show that part of the electric power input is dissipated as heat due to both electric migration and diffusion ion transports, while another part is converted into chemical energy stored in the saline concentration gradient. Considering the electro-membrane process as an energy conversion process, an efficiency has been defined as the ratio between stored power and electric power input. This efficiency increases as both applied electric current and temperature increase.
Kinetic Theory with Casimir Invariants—Toward Understanding of Self-Organization by Topological Constraints
Authors: Yoshida Zensho
Published: December 2024

A topological constraint, characterized by the Casimir invariant, imparts non-trivial structures in a complex system. We construct a kinetic theory in a constrained phase space (infinite-dimensional function space of macroscopic fields), and characterize a self-organized structure as a thermal equilibrium on a leaf of foliated phase space. By introducing a model of a grand canonical ensemble, the Casimir invariant is interpreted as the number of topological particles.
Flexible Threshold Quantum Homomorphic Encryption on Quantum Networks
Authors: Tang Yongli , Guo Menghao , Li Binyong , Geng Kaixin , Yu Jinxia , Qin Baodong
Published: December 2024

Currently, most quantum homomorphic encryption (QHE) schemes only allow a single evaluator (server) to accomplish computation tasks on encrypted data shared by the data owner (user). In addition, the quantum computing capability of the evaluator and the scope of quantum computation it can perform are usually somewhat limited, which significantly reduces the flexibility of the scheme in quantum network environments. In this paper, we propose a novel (t,n)-threshold QHE (TQHE) network scheme based on the Shamir secret sharing protocol, which allows k(t≤k≤n) evaluators to collaboratively perform evaluation computation operations on each qubit within the shared encrypted sequence. Moreover, each evaluator, while possessing the ability to perform all single-qubit unitary operations, is able to perform arbitrary single-qubit gate computation task assigned by the data owner. We give a specific (3, 5)-threshold example, illustrating the scheme’s correctness and feasibility, and simulate it on IBM quantum computing cloud platform. Finally, it is shown that the scheme is secure by analyzing encryption/decryption private keys, ciphertext quantum state sequences during transmission, plaintext quantum state sequence, and the result after computations on the plaintext quantum state sequence.
The Tariff Liberalisation Policy Nexus with Non-Tariff Measures: Panel Model Evidence in the SA–EU Fruit Products Trade
Authors: Muchopa Chiedza L.
Published: December 2024

Higher levels of quota granted can induce and increase exports, but the impact is not the same across all tariff lines. Answers are sought to the question of how the level of exports changes as the quota size of tariff rate quotas changes, thus enabling the investigation of whether unilateral quotas granted to South Africa by the European Union have influenced fruit products’ export flows in the presence of non-tariff measures. Drawing on panel data regression techniques, this study observes five fruit products’ tariff rate quotas repeatedly from 2004 to 2021. It also incorporates a variable to capture non-tariff measures based on the data from the WTO I-TIP database. The findings indicate a positive relationship between quota size and exports, further showing that for a given quota size, the increase in exports is small in the presence of non-tariff measures. These findings draw attention to future trade reforms that focus on seeking the expansion of quota size for the most productive tariff lines in terms of export growth while aiming for the simultaneous reduction of non-tariff measures and tariff rates.
A Gamified Active-Learning Proposal for Higher-Education Heterogeneous STEM Courses
Authors: Duran Mario J. , Aciego Juan J. , Gonzalez-Prieto Ignacio , Carrillo-Rios Juan , Gonzalez-Prieto Angel , Claros-Colome Alicia
Published: December 2024

Both industry and academia have recently highlighted the need to provide students with 21st-century abilities that involve soft skills and flexible technical competencies. To this end, active learning (AL) helps scholars to be more autonomous, proactive, self-regulated, sociable and creative. A full AL implementation typically requires costly resources and institutional support. However, it is possible to maintain a traditional teaching structure and include low-cost AL activities that enhance the students’ performance and satisfaction. This proposal suggests a methodology where lectures become more dynamic, the practical teaching is student-centered, the lab teaching is fully open, and the learner’s progress is gamified. A full subject design is presented in a comprehensive manner aiming for satisfying courses with a heterogeneous students’ profile. All activities are carefully selected with a social constructivist approach in order to promote critical thinking, social interaction and students’ engagement. The assessment of the course design implementation is performed considering multiple aspects, such as satisfaction, performance, social relationships and self-regulation. Results confirm the adequacy of the proposal to develop technical skills, excite non-technical competences and motivate scholars with an inclusive approach.
The Disruptive Use of Artificial Intelligence (AI) Will Considerably Enhance the Tourism and Air Transport Industries
Authors: Florido-Benítez Lázaro , del Alcázar Martínez Benjamín
Published: December 2024

The main objective of this paper is to illustrate the use of artificial intelligence (AI) in the tourism and air transport industries to improve tourists’ experiences, as well as provide a definition of the AI concept closest to both sectors. In order to examine and demonstrate the body of literature on AI and its application to the travel and tourism industry. This study also presents the findings of a literature review using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) approach in conjunction with a systematic literature review using the Web of Science (WoS) database. This approach enabled us to construct a novel AI concept in the context of tourism. This research found that AI technology offers new and creative opportunities for tourists due to this innovative tool that promotes and empowers travel and tourism organisations’ products and services. AI has helped to outline travel planning for tourists, made it easier to discover new experiences, and streamlined the booking process. The reality is that AI methods and applications are changing and improving passengers and tourists’ experiences in tourism cities and the air transport sector. Moreover, it is necessary to highlight that one of AI technology’s greatest strengths lies in the immediacy of response and advice that swiftly help tourists plan their trips, tours, detailed itineraries, and flight bookings at the same moment. This research is an antecedent attempt to define AI technology in the tourism and air transport context and to illustrate its virtues and shortcomings to improve tourists’ experiences in cities and the operational efficiency of organisations.
Synthesis of the Large-Scaled 64 × 64 Thinned Array Using the Branch and Bound Technique with Convex Optimization for Satellite Communication
Authors: Li Xuelian , Wang Yan , Zhang Chuansong
Published: December 2024

This paper presents the synthesis of a 64 × 64 thinned array, using the branch and bound technique with convex optimization and a low sidelobe level for satellite communication. The branch and bound technology decomposes optimization into several subproblems. The convex optimization transforms the optimized variable binary [0, 1] of the thinned array into a continuous variable, i.e., from 0 to 1. Based on the branch and bound technique with convex optimization, the fast and accurate convergence of the synthesis of large-scale thinned array was achieved, and 2400 elements were selected for a full 64 × 64 array with a sidelobe level that satisfies the system requirements. The proposed thinned array with 2400 elements was simulated via full-wave 3D simulation. A prototype of the proposed thinned array was fabricated and measured. The simulated and measured results verified the effectiveness of the branch and bound technique with convex optimization.
Foul Detection for Table Tennis Serves Using Deep Learning
Authors: Yang Guang Liang , Nguyen Minh , Yan Wei Qi , Li Xue Jun
Published: December 2024

Detecting serve fouls in table tennis is critical for ensuring fair play. This paper explores the development of foul detection of table tennis serves by leveraging 3D ball trajectory analysis and deep learning techniques. Using a multi-camera setup and a custom dataset, we employed You Only Look Once (YOLO) models for ball detection and Transformers for critical trajectory point identification. We achieved 87.52% precision in detecting fast-moving balls and an F1 score of 0.93 in recognizing critical serve points such as the throw, highest, and hit points. These results enable precise serve segmentation and robust foul detection based on criteria like toss height and vertical angle compliance. The approach simplifies traditional methods by focusing solely on the ball motion, eliminating computationally intensive pose estimation. Despite limitations such as a controlled experimental environment, the findings demonstrate the feasibility of artificial intelligence (AI)-driven referee systems for table tennis games, providing a foundation for broader applications in sports officiating.
Optimization of Video Surveillance System Deployment Based on Space Syntax and Deep Reinforcement Learning
Authors: Li Bingchan , Li Chunguo
Published: December 2024

With the widespread deployment of video surveillance devices, a large number of indoor and outdoor places are under the coverage of cameras, which plays a significant role in enhancing regional safety management and hazard detection. However, a vast number of cameras lead to high installation, maintenance, and analysis costs. At the same time, low-quality images and potential blind spots in key areas prevent the full utilization of the video system’s effectiveness. This paper proposes an optimization method for video surveillance system deployment based on space syntax analysis and deep reinforcement learning. First, space syntax is used to calculate the connectivity value, control value, depth value, and integration of the surveillance area. Combined with visibility and axial analysis results, a weighted index grid map of the area’s surveillance importance is constructed. This index describes the importance of video coverage at a given point in the area. Based on this index map, a deep reinforcement learning network based on DQN (Deep Q-Network) is proposed to optimize the best placement positions and angles for a given number of cameras in the area. Experiments show that the proposed framework, integrating space syntax and deep reinforcement learning, effectively improves video system coverage efficiency and allows for quick adjustment and refinement of camera placement by manually setting parameters for specific areas. Compared to existing coverage-first or experience-based optimization, the proposed method demonstrates significant performance and efficiency advantages.
FIR: Achieving High Throughput and Fast Recovery in a Non-Volatile Memory Online Transaction Processing Engine
Authors: Wei Jianhao , Zhang Qian , Xiang Yiwen , Gong Xueqing
Published: December 2024

Existing databases supporting Online Transaction Processing (OLTP) workloads based on non-volatile memory (NVM) have not fully leveraged hardware characteristics, resulting in an imbalance between throughput and recovery performance. In this paper, we conclude with the reason why existing designs fail to achieve both: placing indexes on NVM results in numerous random writes and write amplification for index updates, leading to a decrease in system performance. Placing indexes on dynamic random access memory (DRAM) results in much time consumption for rebuilding indexes during recovery. To address this issue, we propose FIR, an NVM OLTP Engine with the fast rebuilding of the DRAM indexes, achieving instant system recovery while maintaining high throughput. Firstly, we design an index checkpoint strategy. During recovery, the indexes are quickly rebuilt by the bottom-up algorithm with index checkpoints. Then, to achieve instant recovery of the entire engine after rebuilding indexes, we optimize the existing log-free design by leveraging time-ordered storage, which significantly reduces the number of NVM writes. We also implement garbage collection based on data redistribution, enhancing system availability. The experimental results demonstrate that FIR achieves 98% of the performance of state-of-the-art OLTP Engine when running TPCC and YCSB. And the recovery speed of FIR is 43.6×–54.5× faster, achieving near-instantaneous recovery.
MPB-UNet: Multi-Parallel Blocks UNet for MRI Automated Brain Tumor Segmentation
Authors: Chahbar Fatma , Merati Medjeded , Mahmoudi Saïd
Published: December 2024

Brain tumor segmentation in Magnetic Resonance Imaging (MRI) is crucial for accurate diagnosis and treatment planning in neuro-oncology. This paper introduces a novel multi-parallel blocks UNet (MPB-UNet) architecture for automated brain tumor segmentation. Our approach enhances the standard UNet model by incorporating multiple parallel processing paths, inspired by the human visual system’s multi-scale processing capabilities. We integrate Atrous Spatial Pyramid Pooling (ASPP) to effectively capture multi-scale contextual information. We evaluated our proposed architecture using the publicly available Low-Grade Glioma (LGG) Segmentation Dataset. This comprehensive collection comprises 3929 axial slices of FLAIR MRI sequences from 110 patients, each slice paired with a corresponding segmentation mask. Our model demonstrated superior performances on this dataset compared with existing state-of-the-art methods, highlighting its effectiveness in accurate tumor delineation. We provide a comprehensive analysis of the model’s performance, including visual results and comparisons with other architectures. This work contributes to advancing automated brain tumor segmentation techniques, potentially improving diagnostic accuracy and efficiency in clinical settings. The proposed multi-parallel blocks UNet shows promise for integration into clinical workflows and opens avenues for future studies in medical image analysis. Our model achieves strong performances across multiple metrics: 99.86% accuracy, 99.86% precision, 99.86% sensitivity, 99.86% specificity, 99.80% Dice Similarity Coefficient (DSC), and 92.17% Average Intersection over Union (IoU).
An Internet of Things—Supervisory Control and Data Acquisition (IoT-SCADA) Architecture for Photovoltaic System Monitoring, Control, and Inspection in Real Time
Authors: He Wei , Baig Mirza Jabbar Aziz , Iqbal Mohammad Tariq
Published: December 2024

The Internet of Things (IoT) serves as a key component to enhance operational efficiency and decision-making in the context of supervisory control and data acquisition (SCADA) systems. Featuring the improved system robustness and real-time parameters, including images of the load, a new design of SCADA system monitoring for a photovoltaic (PV) system based on dual IoT platforms is proposed in this paper. Two voltage sensors collect the voltages of the PV module and the battery, while three current sensors accumulate the current data from the PV module, the battery, and the load. ESP32-E assembles the data and then transmits them to the Arduino Cloud via MQTT for real-time display and ESP32-S3 via HTTP. The relay and the load are controlled by ESP32-E to turn ON/OFF based on the battery voltage as well. In addition, ESP32-S3 forwards the received data to ThingSpeak for advanced analysis, data storage, and real-time display via HTTP. The load images are also displayed on a camera web server built by ESP32-S3. Successfully monitoring for over 20 days, the proposed system demonstrated its robustness and versatility even during the downtime of the Arduino Cloud, with a one-day voltage measurement ranging to a maximum of 13 V and current ranging from zero amperes to 4.42 amperes. To add to this system, it incorporates visual load monitoring features, which are unseen in traditional systems.
SGSAFormer: Spike Gated Self-Attention Transformer and Temporal Attention
Authors: Gao Shouwei , Qin Yu , Zhu Ruixin , Zhao Zirui , Zhou Hao , Zhu Zihao
Published: December 2024

Spiking neural networks (SNNs), a neural network model structure inspired by the human brain, have emerged as a more energy-efficient deep learning paradigm due to their unique spike-based transmission and event-driven characteristics. Combining SNNs with the Transformer model significantly enhances SNNs’ performance while maintaining good energy efficiency. The gating mechanism, which dynamically adjusts input data and controls information flow, plays an important role in artificial neural networks (ANNs). Here, we introduce this gating mechanism into SNNs and propose a novel spike Transformer model, called SGSAFormer, based on the Spikformer network architecture. We introduce the Spike Gated Linear Unit (SGLU) module to improve the Multi-layer perceptron (MLP) module in SNNs by adding a gating mechanism to enhance the model’s expressive power. We also incorporate Spike Gated Self-Attention (SGSA) to strengthen the network’s attention mechanism, improving its ability to capture temporal information and dynamic processing. Additionally, we propose a Temporal Attention (TA) module, which selects new filters for the input data along the temporal dimension and can substantially reduce energy consumption with only a slight decrease in accuracy. To validate the effectiveness of our approach, we conducted extensive experiments on several neuromorphic datasets. Our model outperforms other state-of-the-art models in terms of performance.
Analysis and Design of Low-Power Piezoelectric Energy Harvesting Circuit for Wearable Battery-Free Power Supply Devices
Authors: Pandiev Ivaylo , Antchev Hristo , Kurtev Nikolay , Tomchev Nikolay , Aleksandrova Mariya
Published: December 2024

Improving microelectronic technologies has created various micro-power electronic devices with different practical applications, including wearable electronic modules and systems. Furthermore, the power sources for wearable electronic devices most often work with electrical energy obtained from the environment without using standard batteries. This paper presents the structure and electrical parameters of a circuit configuration realized as a prototype of a low-power AC-DC conversion circuit intended for use as a power supply device for signal processing systems that test various biomedical parameters of the human body. The proposed prototype has to work as a wearable self-powered system that transfers electrical energy obtained through mechanical vibrations in the piezoelectric generator. The obtained electrical energy is used to charge a single low-voltage supercapacitor, which is used as an energy storage element. The proposed circuit configuration is realized with discrete components consisting of a low-voltage bridge rectifier, a low-pass filter, a DC-DC step-down (buck) synchronous converter, a power-controlling system with an error amplifier, and a window detector that produces a “power-good” signal. The power-controlling system allows tuning the output voltage level to around 1.8 V, and the power dissipation for it is less than 0.03 mW. The coefficient of energy efficiency achieved up to 78% for output power levels up to 3.6 mW. Experimental testing was conducted to verify the proposed AC-DC conversion circuit’s effectiveness, as the results confirmed the preliminary theoretical analyses and the derived analytical expressions for the primary electrical parameters.
Optimal Allocation of Community Distributed Energy and Storage Based on Regional Autonomous Balance and Sharing Mechanism
Authors: Liu Jiangping , Wang Jing , Cui Xue , Liu Peng , Tong Pingzheng , Dang Xuehan
Published: December 2024

In the context of new power systems, the rapid development of distributed renewable energy and the drive of dual carbon targets have prompted community-level clean energy and energy storage configuration to become the key to improving energy efficiency and reducing carbon emissions. Based on the regional autonomy balance and sharing mechanism, this paper establishes a community distributed energy and energy storage optimization configuration model. With the goal of minimizing the total operating cost of the community, the established model is linearized by using the Big-M method and the McCormick method and transformed into a mixed integer linear programming model that is easy to solve. In order to comprehensively evaluate the comprehensive benefits of the established optimization scheme, this paper introduces the indicators of clean energy self-consumption rate, load self-supply rate, static investment payback period, and static CO2 investment payback period from the aspects of energy utilization, the economy, and the environment. Finally, a calculation example analysis is conducted, and the results show that, compared with the scenario where energy storage is configured separately and distributed energy resources are not shared, the configuration strategy proposed in the article can reduce the energy storage configuration capacity by 46.6% and the distributed energy configuration capacity by 21.1%. Investment costs can be reduced by 15.6%. At the same time, 91.75% of distributed energy self-consumption and 96.80% of load self-supply are achieved, reducing grid interaction and promoting regional autonomy and balance. The static CO2 investment payback period is also significantly shortened, and the carbon emission reduction effect is significant, providing an important reference for community energy system optimization planning and green and low-carbon development.
Numerical Methodologies for the Analysis of Horizontal-Axis Floating Offshore Wind Turbines (F-HAWTs): A State-of-the-Art Review
Authors: Akbari Zadeh Naghmeh , Ryan Peter , Kennedy David M. , O’Rourke Fergal
Published: December 2024

In recent decades, wind turbine installations have become a popular option to meet the world’s growing demand for energy. Both onshore and offshore wind turbines form pivotal components of the electricity sector. Onshore wind energy is now a mature technology, with significant experience gained by wind farm developers and operators over the last couple of decades. However, as a more recent enterprise, the offshore wind industry still requires significantly more development before the technologies and operations reach maturity. To date, floating platforms at sea have been utilised extensively for the oil and gas industry. While a lot of the expertise and technology is transferable to the floating offshore wind industry, significant development work remains; for example, there is significant work required due to the different device types. Compared to floating oil and gas platforms, floating wind turbine platforms have a higher centre of gravity, which influences their performance and complexity. The successful large-scale development of floating offshore wind farms will require significant expertise and learning from the onshore wind, oil, and gas sectors. There are a wide range of software packages available to predict the operational behaviour of floating offshore wind turbines. In spite of this, it is still extremely difficult to create a fully coupled model of a floating wind turbine that can accurately and comprehensively model the turbine aerodynamics, hydrodynamics, servodynamics, structural dynamics, and mooring dynamics. This paper presents details on various fully coupled and uncoupled software packages and methodologies utilised to simulate floating offshore wind turbine performances. Various kinds of mooring systems, floating wind turbines, analysis methods, and experimental validation methods are comprehensively described. This paper serves as a reliable methodological guideline for researchers and wind industry professionals engaged in the design/analysis of wind farm projects.
Rules-Based Energy Management System for an EV Charging Station Nanogrid: A Stochastic Analysis
Authors: Danielsson Gabriel Henrique , da Silva Leonardo Nogueira Fontoura , da Paixão Joelson Lopes , Abaide Alzenira da Rosa , Neto Nelson Knak
Published: December 2024

The article presents the development of a Rules-Based Energy Management System for a nanogrid that serves an electric vehicle charging station. This nanogrid is composed of photovoltaic generation, a wind turbine, a battery energy storage system, and a fast electric vehicle charger. The objective is to prioritize the use of renewable energy sources, reducing costs and promoting energy efficiency. The methodology includes forecasting models based on an Artificial Neural Network for photovoltaic generation, a parametric estimation for wind generation, and a Monte Carlo simulation to predict the energy consumption of electric vehicles. The developed algorithm makes decisions every 15 min, considering variables such as energy tariff, battery state of charge, renewable generation forecast, and energy consumption forecast. The results showed that the system adequately balances energy generation, consumption, and storage, even under forecasting uncertainties. The use of the Monte Carlo simulation was crucial for evaluating the financial impacts of forecast errors, enabling robust decision-making. This energy management system proved to be effective and sustainable for nanogrids dedicated to electric vehicle charging, with the potential to reduce operational costs and increase energy reliability and the use of renewable energy sources.
Design and Research on the Variable Polar Distance of the Double-Sided Linear Induction Motor for Electromagnetic Catapult
Authors: Liu Xijun , Zhao Hao , Ai Houlong , Chen Zelin
Published: December 2024

According to the special technical requirements of carrier-based aircraft catapults, this paper describes the design of a variable pole distance bilateral linear induction motor. When the traditional constant pole motor is used as the catapult of carrier-based aircraft, the current frequency continues to increase during the catapult process, which greatly aggravates the burden of the motor. Therefore, we propose a variable pole length primary double-sided linear induction motor structure. Compared with the traditional constant pole motor structure, this structure can gradually increase the pole distance with an increase in speed when the current frequency remains unchanged. In contrast, the variable pole distance method with a current frequency of 200 Hz has a pole distance of 0.262 m when the displacement is 10 m, and the pole distance increases to 0.352 m when the displacement is 100 m. By maintaining a constant current frequency, this method effectively reduces the control complexity at high speed. Through the theoretical analysis and research calculation conducted on the designed motor, a finite element simulation model was also established by ANSYS 14.0, and the influence of the change in the pole distance on the performance of the motor was analyzed. The magnetic field line and magnetic density distribution of the motor are simulated and analyzed, and the validity of the theoretical calculation is verified.
Shifts in the Soil Microbial Community and Enzyme Activity Under Picea crassifolia Plantations and Natural Forests
Authors: Zheng Yunyou , Fan Qiuyun , Geng Yuqing , Chen Lin , Han Xiang , Wu Weitai , Shi Famiao
Published: December 2024

Soil microbes are crucial for regulating biogeochemical cycles and maintaining forest ecosystem sustainability; however, the understanding of microbial communities and enzyme activity under natural and plantation forests in plateau regions remains limited. Using soil samples from 15-, 30-, and 50-year-old Picea crassifolia plantations and a natural forest (NF) in eastern Qinghai, China, this study assessed physicochemical properties, microbial communities, and enzyme activity across three soil layers. Microbial composition was characterized using the phospholipid fatty acid (PLFA) method, which is sensitive to structural changes. The PLFAs of bacteria, fungi, and actinomycetes accounted for 58.31%–74.20%, 8.91%–16.83%, and 3.41%–10.41% of the total PLFAs in all forests, respectively. There were significant differences between the NF and plantations, with the NF exhibiting higher PLFA abundance and enzyme activities than plantations, except for fungal PLFAs. PLFAs in plantations increased with the plantation age. However, the fungi-to-bacteria ratio was lower in the NF than in plantations. Finally, a redundancy analysis revealed that soil properties influence microbial composition and enzyme functionality significantly. These findings highlight the influence of stand age on microbial communities and structure, offering valuable insights for forest management practices aimed at conserving natural forests.
Spectral Weaver: A Study of Forest Image Classification Based on SpectralFormer
Authors: Yu Haotian , Li Xuyang , Xu Xinggui , Li Hong , Fan Xiangsuo
Published: December 2024

In forest ecosystems, the application of hyperspectral (HS) imagery offers unprecedented opportunities for refined identification and classification. The diversity and complexity of forest cover make it challenging for traditional remote-sensing techniques to capture subtle spectral differences. Hyperspectral imagery, however, can reveal the nuanced changes in different tree species, vegetation health status, and soil composition through its nearly continuous spectral information. This detailed spectral information is crucial for the monitoring, management, and conservation of forest resources. While Convolutional Neural Networks (CNNs) have demonstrated excellent local context modeling capabilities in HS image classification, their inherent network architecture limits the exploration and representation of spectral feature sequence properties. To address this issue, we have rethought HS image classification from a sequential perspective and proposed a hybrid model, the Spectral Weaver, which combines CNNs and Transformers. The Spectral Weaver replaces the traditional Multi-Head Attention mechanism with a Channel Attention mechanism (MCA) and introduces Centre-Differential Convolutional Layers (Conv2d-cd) to enhance spatial feature extraction capabilities. Additionally, we designed a cross-layer skip connection that adaptively learns to fuse “soft” residuals, transferring memory-like components from shallow to deep layers. Notably, the proposed model is a highly flexible backbone network, adaptable to both hyperspectral and multispectral image inputs. In comparison to traditional Visual Transformers (ViT), the Spectral Weaver innovates in several ways: (1) It introduces the MCA mechanism to enhance the mining of spectral feature sequence properties; (2) It employs Centre-Differential Convolutional Layers to strengthen spatial feature extraction; (3) It designs cross-layer skip connections to reduce information loss; (4) It supports both multispectral and hyperspectral inputs, increasing the model’s flexibility and applicability. By integrating global and local features, our model significantly improves the performance of HS image classification. We have conducted extensive experiments on the Gaofen dataset, multispectral data, and multiple hyperspectral datasets, validating the superiority of the Spectral Weaver model in forest hyperspectral image classification. The experimental results show that our model achieves 98.59% accuracy on multispectral data, surpassing ViT’s 96.30%. On the Jilin-1 dataset, our proposed algorithm achieved an accuracy of 98.95%, which is 2.17% higher than ViT. The model significantly outperforms classic ViT and other state-of-the-art backbone networks in classification performance. Not only does it effectively capture the spectral features of forest vegetation, but it also significantly improves the accuracy and robustness of classification, providing strong technical support for the refined management and conservation of forest resources.
Analysis of a Commercial Red Wine Fermentation Dataset with a Wine Kinetic Model
Authors: Nelson James , Coleman Robert , Gravesen Patrick , Silacci Michael , Velasquez Alaina , Marinelli Kimberlee , Boulton Roger
Published: December 2024

The adoption of sensors to monitor wine fermentation enables the collection of large datasets that relate the initial juice chemistry, density and temperature patterns during fermentation to fermentation outcomes. Wine kinetic models are now being applied to commercial fermentations in real time to identify abnormal or sluggish fermentations. In this work, 222 red wine fermentations from five harvests at two commercial wineries were evaluated by a wine fermentation model. The model parameters, initial juice chemistries and fermentation outcomes were analyzed for trends and relationships between them. While the fermentations with higher initial assimilable nitrogen concentrations had higher maximum fermentation rates, this did not guarantee successful fermentation outcomes in the tailing stage of the fermentation. Neither the initial, final, minimum and maximum temperatures, nor the initial pH, titratable acidity, measured yeast-assimilable nitrogen and primary amino nitrogen concentrations had any significant correlation with the maximum fermentation rate or successful completion of the fermentation. These results suggest that the initial juice-assimilable nitrogen measurements for these juices are of limited use in predicting slower and incomplete fermentation outcomes.
Energy-Efficient Federated Learning for Internet of Things: Leveraging In-Network Processing and Hierarchical Clustering
Authors: Baqer M.
Published: December 2024

Federated learning (FL) has emerged as a promising solution for the Internet of Things (IoT), facilitating distributed artificial intelligence while ensuring communication efficiency and data privacy. Traditional methods involve transmitting raw sensory data from IoT devices to servers or base-stations for processing, resulting in significant communication overhead. This overhead not only increases energy consumption but also diminishes device longevity within IoT networks. By focusing on model updates rather than raw data transmission, FL reduces the volume of data communicated to the base-station; however, FL still faces challenges due to the multiple communication rounds required for convergence. This research introduces an innovative approach that leverages the in-network processing capabilities of IoT devices by integrating a hierarchical clustering routing protocol with FL. This approach enhances energy efficiency through single-round pattern recognition, minimizing the need for multiple communication rounds to achieve convergence. It is envisaged that the proposed approach will prolong the lifespan of IoT devices and maintain high accuracy in event detection, all while ensuring robust data privacy.
Heterotis niloticus (African Bonytongue) Domestication: Potentials, Efforts, and Challenges
Authors: Ogunji Johnny , Evulobi Onyedikachi , Iheanacho Stanley , Wuertz Sven
Published: December 2024

Heterotis niloticus is a fascinating freshwater fish species popularly referred to as the African bonytongue. It offers great potential for aquaculture due to its rapid growth, flesh quality, adaptability to low oxygen environments, and high market value. Domestication efforts for this species have been initiated to optimize its production and meet the increasing global demand for sustainable protein sources. This review explores the current state of H. niloticus domestication and highlights the key challenges and opportunities. Despite their promising potential, several obstacles remain, including the development of efficient hatchery techniques, optimization of feed formulations, and disease control. Overcoming these challenges is essential to promote the full potential of H. niloticus aquaculture and contribute to improved food security. Future research should focus on refining breeding programs, improving water quality management, and exploring innovative aquaculture systems. Policy support is key to driving investments, innovation, conservation efforts, and the sustainable development of H. niloticus aquaculture.
Broccoli, Artichoke, Carob and Apple By-Products as a Source of Soluble Fiber: How It Can Be Affected by Enzymatic Treatment with Pectinex® Ultra SP-L, Viscozyme® L and Celluclast® 1.5 L
Authors: Ayuso Pablo , Peñalver Rocío , Quizhpe Jhazmin , Rosell María de los Ángeles , Nieto Gema
Published: December 2024

Dietary fiber (DF), and especially soluble dietary fiber (SDF), is a nutrient of particular interest today because of its anti-inflammatory role and its ability to reduce cardiovascular risk. Therefore, the enhancement of SDF in foods using different techniques has become a promising field of research. In order to prove the possibility of increasing this SDF content, the effects of different commercial enzymes (Pectinex® Ultra SP-L, Viscozyme® L and Celluclast® 1.5 L) were tested on a variety of carob (CE), artichoke (ARE), apple (APE) and broccoli (BE) by-product extracts. Enzymatic treatment significantly affected SDF content in all by-products, showing the greatest increases for CE, ARE and APE using Celluclast® 1.5 L, while Viscozyme® L obtained the best results after application in BE. On the other hand, positive results were reported in the solubility, WHC and FAC of the by-products due to the enzymatic treatment, being increased in all extracts analyzed. Moreover, a general increase in antioxidant capacity (FRAP, ABTS and DPPH) was observed after enzymatic treatment. Finally, high yields were obtained after the application of the enzymatic processes, reaching values of 80–85% for each food by-product. These results evidenced a potential revalorization of carob, artichoke, apple and broccoli by-products after enzymatic treatment, improving its nutritional and physicochemical properties, revealing a possible application as a higher value-added ingredient.
Prevalence of Antibiotic Resistance Genes in Differently Processed Smoothies and Fresh Produce from Austria
Authors: Galazka Sonia , Vigl Valerie , Kuffner Melanie , Dielacher Irina , Spettel Kathrin , Kriz Richard , Kreuzinger Norbert , Vierheilig Julia , Woegerbauer Markus
Published: December 2024

Plant-derived foods are potential vehicles for microbial antibiotic resistance genes (ARGs), which can be transferred to the human microbiome if consumed raw or minimally processed. The aim of this study was to determine the prevalence and the amount of clinically relevant ARGs and mobile genetic elements (MGEs) in differently processed smoothies (freshly prepared, cold-pressed, pasteurized and high-pressure processed) and fresh produce samples (organically and conventionally cultivated) to assess potential health hazards associated with their consumption. The MGE ISPps and the class 1 integron-integrase gene intI1 were detected by probe-based qPCR in concentrations up to 104 copies/mL in all smoothies, lettuce, carrots and a single tomato sample. The highest total (2.2 × 105 copies/mL) and the most diverse ARG and MGE loads (16/26 targets) were observed in freshly prepared and the lowest prevalences (5/26) and concentrations (4.1 × 103 copies/mL) in high-pressure-processed (HPP) smoothies. BlaCTX-M-1-15 (1.2 × 105 c/mL) and strB (6.3 × 104 c/mL) were the most abundant, and qacEΔ1 (95%), blaTEM1 (85%), ermB and sul1 (75%, each) were the most prevalent ARGs. QnrS, vanA, sat-4, blaKPC, blaNDM-1 and blaOXA-10 were never detected. HPP treatment reduced the microbial loads by ca. 5 logs, also destroying extracellular DNA potentially encoding ARGs that could otherwise be transferred by bacterial transformation. The bacterial microbiome, potential pathogens, bacterial ARG carriers and competent bacteria able to take up ARGs were identified by Illumina 16S rRNA gene sequencing. To reduce the risk of AMR spread from smoothies, our data endorse the application of DNA-disintegrating processing techniques such as HPP.