bluuebunny/crossref_metadata_2025 huggingface dataset:
An Innovative Heating Solution for Sustainable Agriculture: A Feasibility Study on the Integration of Phase Change Materials as Passive Heating Elements
Authors: Thaler Stephan M. , Zwatz Josef , Nicolay Pascal , Hauser Robert , Lackner Roman
Published: August 2024

In this study, we investigate an innovative option for the ecological management of agricultural land. The focus is on the use of phase change materials (PCMs) for passive temperature regulation in greenhouses and fruit crop fields in order to reduce yield losses due to unforeseen late frost events. The use of PCMs represents a novel approach to enhancing crop growth and extending growing seasons without relying on conventional energy-intensive methods, providing a stable microclimate that can protect plants from cold stress. This passive regulation of temperature helps to reduce the need for fossil fuel-based heating systems, thereby lowering greenhouse gas emissions and operational costs. The application of PCMs in agricultural settings is particularly innovative as it leverages naturally occurring temperature variations to create a self-sustaining, low-maintenance solution that aligns with the principles of sustainable farming. This approach not only improves energy efficiency but also contributes to the resilience of agricultural practices in the face of climate variability. This study focuses on the possible use of PCMs in passive heating modules for the protection of potted plants in greenhouses. Various PCMs such as paraffin, beeswax, and shea butter were tested. Experiments were then conducted, using one kind of paraffin-based PCM, in a specially designed module. In addition, an FEM simulation model (CFD) was built and tested. The model was used to perform detailed analyses of the heat transfer efficiency, fluid dynamics, and overall performance of the modules. The model can also be used for optimization purposes (e.g., efficiency improvements).
Accuracy of Implant Placement Using Digital Prosthetically-Derived Surgical Guides: A Systematic Review
Authors: Abad-Coronel Cristian , Vandeweghe Stefan , Vela Cervantes María Dolores , Tobar Lara María José , Mena Córdova Nancy , Aliaga Paulina
Published: August 2024

Dental implant placement is crucial in oral rehabilitation, requiring precision for successful outcomes. Digital technologies, including surgical guides, enhance predictability and efficiency in implant procedures. However, their impact on implant positioning accuracy is still under investigation. This systematic review aimed to evaluate the literature on implant accuracy using digital prosthetically-derived surgical guides. Registered in PROSPERO (CRD 42023483194), the review employed a PICO strategy and searched PubMed for English-language, in vivo studies from 2013 to 2023 on restrictive digital prosthetically-derived surgical guides. Two reviewers independently assessed records, with a third verifying the decisions. PRISMA guidelines were followed, yielding 24 results after excluding nine duplicates. Ten studies met the criteria after title, abstract, and keyword review, with three included after verification. These studies showed coronal deviations of 0.44 mm to 0.56 mm, apical deviations of 0.64 mm to 1.03 mm, angular deviations of 2.03° to 2.42°, and vertical deviations of 0.19 mm to 0.45 mm. Superior accuracy was noted with static guided techniques, while bilateral guides offered stability and printed guides were cost-effective. A surgical guide that comes from a planning with a primary wax-up of the prosthesis leads to the placement of a dental implant that can be functionally and esthetically rehabilitated. Further research is needed to standardize outcomes and improve implant protocols and patient outcomes.
Design of a Digital Platform for Carbon Generalized System of Preferences Communities Based on the TAO Model of Three-Way Decisions
Authors: Wei Huilan , Yang Chendan , Wen Chuanye , Wang Yanlong
Published: August 2024

The increasing carbon dioxide emissions from human activities present a significant global concern, with approximately two-thirds of greenhouse gas emissions attributed to household activities. The Carbon Generalized System of Preferences (CGSP) has emerged as a pivotal mechanism to incentivize voluntary carbon reduction in community households. This paper examines the development of a community digital management platform designed to incentivize voluntary carbon reduction at the community level, highlighting the critical role of reducing emissions in urban community life to meet carbon peak and neutrality targets. This study employs the TAO model of Three-Way Decision to establish a closed-loop operational framework for the CGSP digital platform. The platform features a Trisection mechanism to record and quantify low-carbon behaviors, an Action mechanism to classify and reward community members, and an Outcome mechanism to assess overall community carbon reduction achievements. Additionally, a user interface tailored for community users is developed to enhance platform accessibility. The proposed platform presents a practical and innovative solution for exploring emission reduction potential in urban communities. By systematically recording low-carbon behaviors, providing targeted rewards, and conducting comprehensive assessments, the platform aims to guide community residents in adopting sustainable practices. This study offers a valuable reference for the digital transformation, intelligent system construction, and development of new urban functional units within communities.
Simulation Study of Dynamic Rotation and Deformation for Plasmonic Electric Field-Skyrmions
Authors: Tang Gao , Bai Chunyan , Kanwal Saima , Zhao Zhening , Zhang Dawei
Published: August 2024

The topological properties of optical skyrmions in confined electromagnetic fields are perfectly presented through spin vectors and electric-field vectors. However, currently, electric-field optical skyrmions in surface plasmon polaritons are mostly presented in the form of a Néel type. Most control strategies involve linear directional movement, and topological manipulation methods are monotonous. We specifically propose a multi-arc symmetric slit array, which generates skyrmions from the surface plasmon polariton (SPP) field under excitation of a linearly polarized Gaussian light-source array and exhibits strong dependence processes on the rotation, deformation, and phase distribution of the incident light source. We also discuss the independence and synthesis of deformation and rotation related to phase difference and positions of regulation, respectively, which provide the possibility for rich deformations under different rotation states. Our work extends new ideas for the dynamic control of plasmonic skyrmions, which is of great significance to fields such as spin photonics and nano-positioning.
Adaptability of Swiss Stone Pine (Pinus cembra) in Two Different Environmental Conditions of Romanian Carpathians
Authors: Budeanu Marius , Popescu Flaviu , Besliu Emanuel , Apostol Ecaterina Nicoleta
Published: August 2024

Swiss stone pine (Pinus cembra) is a feasible solution for increasing the resistance of future mixed spruce–pine stands to windfall at the upper altitudinal limit of Romanian forests. This study aims to analyze the adaptability of ten full-sib families in two different environmental conditions and their evolution in time for predicting forward selection. At a seedling age of 33, evaluations were carried out for survival rate (Sr), growth (diameter at breast height—Dbh, and trees’ height), and branch traits, as well as for stem straightness. The high level of genetic variability, inheritance rate, and trait–trait correlations, registered in both trials, ensure the success of the breeding program. The Swiss stone pine shows a very good adaptation on high-sloping lands from the upper altitudinal limit of Romania’s forests, with the cross-pollinated (CP) families registering a 28.5% better survival in the Rodna trial (34° slope) compared to the Păltiniș experiment (7° slope). The consanguineous families registered only an 11% Sr in Păltiniș, while in the Rodna trial, the Sr was four times higher (in both trials, consanguineous trees were dominated). The juvenile–adult correlations of growth traits were significant, indicating that early selection could be efficient. The forward selection of the best 10% of CP families for Dbh could be applied, ensuring a 9% to 15% genetic gain.
Microbial Diversity of Biodeteriorated Limestone Cultural Heritage Assets Identified Using Molecular Approaches—A Literature Review
Authors: Suchy Hana , Zalar Polona , Macedo Maria Filomena
Published: August 2024

A significant part of our stone heritage is made of limestone. Researchers are increasingly concerned about the risk of biodeterioration of these important objects. In this article, we present an up-to-date review of the microbial diversity of biodeteriorated limestone cultural heritage (CHL). This is based on an extensive bibliographic search of the literature investigating biodiversity using culture-dependent (CD) and culture-independent (CI) techniques. In the case of the former, only articles in which microorganisms were identified using molecular tools that generate DNA sequences were selected, with the aim of providing traceable identification based on the sequences submitted to public databases. The literature search resulted in the selection of 50 articles published between 2004 and 2023. The biodiversity data obtained from the CHL were organized into the following groups: fungi (626 records), bacteria and cyanobacteria (786 and 103 records, respectively), algae (51 records), and archaea (27 records). Within each group, the microbial diversity studied was compared according to results obtained using CD and CI techniques. Of all the articles selected, 12 used both approaches, demonstrating the growing effort to discover the total microbiome of biodeteriorated cultural heritage assets.
Strength and Vibration Analysis of Axial Flow Compressor Blades Based on the CFD-CSD Coupling Method
Authors: Lin Haiwei , Bao Hong , Zhang Huaihuai , Zhao Feifei , Wang Junli
Published: August 2024

During the operational process of an axial-flow compressor, the blade structure is simultaneously subjected to both aerodynamic loads and centrifugal loads, posing significant challenges to the safe and reliable operation of the blades. Considering both centrifugal loads and aerodynamic loads comprehensively, a bidirectional CFD-CSD coupling analysis method for blade structure was established. The Navier–Stokes governing equations were utilized to solve the internal flow field of the axial-flow compressor. The conservative interpolation method was utilized to couple and solve the blade’s static equilibrium equation, and the deformation, stress distribution, and prestress modal behavior of compressor blades were mainly analyzed. The research results indicate that the maximum deformation of the blades occurred at the lead edge tip, while stress predominantly concentrated approximately 33% upward from the blade root, exhibiting a radial distribution that gradually decreased. As the rotational speed increased, the maximum deformation of the blades continuously increased. Furthermore, at a constant rotational speed, the maximum deformation of the blade exhibited a trend of first increasing and then decreasing with the increase in mass flow. In contrast, the maximum stress showed a trend of first increasing, then decreasing, and finally increasing again as the rotational speed continuously increased. Centrifugal loads are the primary factor influencing blade stress and natural frequency. During operation, the blades exhibited two resonance points, approximately occurring at 62% and 98% of the design rotational speed.
Enhancing Concurrent Emergency Response: Joint Scheduling of Emergency Vehicles on Freeways with Tailored Heuristic
Authors: Li Linwei , Tan Erlong , Gao Peng , Jin Yinli
Published: August 2024

Scheduling decisions for concurrent emergency response (CER) across multiple disaster sites presents numerous difficulties. The main challenge is to minimize human casualties while taking into account the rationality of resource allocation across different disaster sites. This paper establishes a joint scheduling model for emergency vehicles on freeways in the context of CER. The model aims to minimize the transportation time, dispatch cost, and casualty risk, by using the resource site scheduling scheme as the decision variable, addressing multiple disaster and resource sites. Specifically, a casualty risk function based on the rescue waiting time was designed to balance the competing needs among various disaster sites, enhance equitable resource allocation, and reduce the probability of casualties. To achieve global convergence in a high-dimensional solution space, a tailored heuristic algorithm called adaptive dual evolutionary particle swarm optimization (ADEPSO) is proposed. The numerical results show that the scheduling scheme proposed by the ADEPSO algorithm satisfies all constraints and demonstrates significant advantages in large-sized instances. Compared to the two basic algorithms, ADEPSO provides a more cost-effective scheme and reduces the average rescue waiting time. Moreover, integrating the casualty risk function significantly decreases the average rescue waiting time at both high- and low-priority disaster sites, thereby directly lowering the casualty risk.
Analytical Model of Mechanical Responses of Circular Tunnels Considering Rheological Behavior of Surrounding Rock and Functionally Graded Lining
Authors: Du Jianming , Zhang Xuan
Published: August 2024

The rock–lining interaction significantly affects the stability and safety of a tunnel in service. In this study, a mechanical model is proposed to explore the rock–lining interaction under hydrostatic pressure. The model takes into account the alterable mechanical property (such as the elastic modulus) of the lining in the rheological rock mass, which may be subjected to inner surface pressure along the radial direction of the highway tunnel. The alterable elastic modulus is assumed as a power function of the radius. The analytical solutions of this model are first verified by comparison with existing solutions and corresponding results are obtained by numerical simulation. Then, systematic parametric investigations are carried out to analyze the influence of the main model parameters on the radial deformation of the rock–lining interface and the normalized supporting pressure provided by the lining. The research conclusions obtained by this study can offer some valuable references for the safety evaluation of a tunnel in service.
Status-Byte-Assisted RDMA Transmission Mechanism for Optimizing Multi-Task Video Streaming in Edge Computing
Authors: Xiao Donglei , Yi Huiyue , Zhang Wuxiong , Shen Wenhui
Published: August 2024

In the context of the rapid development of edge computing, optimizing data transmission and reducing latency is crucial for efficient collaborative processing among edge servers. Traditional TCP/IP protocols are hindered by high latency and low throughput, while RDMA (Remote Direct Memory Access) technology addresses these challenges by enabling direct memory access and bypassing the operating system kernel. However, the RDMA data transmission mechanism based on sliding windows requires frequent memory status exchanges in the order of memory blocks, which can limit its ability to handle multiple concurrent tasks within a single Queue Pair (QP). To address the limitations of the traditional sliding window transmission mechanism in multi-task environments, we propose a novel RDMA data transmission mechanism that utilizes status bytes to indicate memory block utilization, which utilizes stateless server connections, and multi-task shared QP transmission strategies. In the proposed mechanism, fine-grained control over memory blocks is achieved through the status byte, thereby enabling effective multi-task real-time video stream transmission. Experimental results show that, compared to the sliding window method, the proposed status-byte-assisted RDMA transmission mechanism provides higher throughput, lower latency, and reduced resource consumption, thus enhancing system scalability and reducing CPU utilization. Moreover, this mechanism achieves more stable throughput than the sliding window method when transmitting multiple real-time video streams in edge computing scenarios, making it particularly suitable for data transmission in such environments.
Cross-Modal Adaptive Interaction Network for RGB-D Saliency Detection
Authors: Du Qinsheng , Bian Yingxu , Wu Jianyu , Zhang Shiyan , Zhao Jian
Published: August 2024

The salient object detection (SOD) task aims to automatically detect the most prominent areas observed by the human eye in an image. Since RGB images and depth images contain different information, how to effectively integrate cross-modal features in the RGB-D SOD task remains a major challenge. Therefore, this paper proposes a cross-modal adaptive interaction network (CMANet) for the RGB-D salient object detection task, which consists of a cross-modal feature integration module (CMF) and an adaptive feature fusion module (AFFM). These modules are designed to integrate and enhance multi-scale features from both modalities, improve the effect of integrating cross-modal complementary information of RGB and depth images, enhance feature information, and generate richer and more representative feature maps. Extensive experiments were conducted on four RGB-D datasets to verify the effectiveness of CMANet. Compared with 17 RGB-D SOD methods, our model accurately detects salient regions in images and achieves state-of-the-art performance across four evaluation metrics.
An AI-Powered Product Identity Form Design Method Based on Shape Grammar and Kansei Engineering: Integrating Midjourney and Grey-AHP-QFD
Authors: Wang Chenlu , Zhang Jie , Liu Dashuai , Cai Yuchao , Gu Quan
Published: August 2024

Product Identity (PI) is a strategic instrument for enterprises to forge brand strength through New Product Development (NPD). Concurrently, facing increasingly fierce market competition, the NPD for consumer emotional requirements (CRs) has become a significant objective in enterprise research and development (R&D). The design of new product forms must ensure the continuity of PI and concurrently address the emotional needs of users. It demands a high level of experience from designers and significant investment in R&D. To solve this problem, a generative and quantitative design method powered by AI, based on Shape Grammar (SG) and Kansei Engineering (KE), is proposed. The specific method is as follows: Firstly, representative products for Morphological Analysis (MA) are selected, SG is applied to establish initial shapes and transformation rules, and prompts are input into Midjourney. This process generates conceptual sketches and iteratively refines them, resulting in a set of conceptual sketches that preserve the PI. Secondly, a web crawler mines online reviews to extract Kansei words. Factor Analysis (FA) clusters them into Kansei factors, and the Grey Analytic Hierarchy Process (G-AHP) calculates their grey weights. Thirdly, after analyzing the PI conceptual sketches for feature extraction, the features are integrated with CRs into the Quality Function Deployment (QFD) matrix. Experts evaluate the relationships using interval grey numbers, calculating the optimal ranking of PI Engineering Characteristics (PIECs). Finally, professional designers refine the selected sketches into 3D models and detailed designs. Using a Chinese brand as a case study, we have designed a female electric moped (E-moped) to fit the PI and users’ emotional needs. Through a questionnaire survey on the design scheme, we argue that the proposed innovative method is efficient, applicable, and effective in balancing the product form design of PI and user emotions.
Neural Coincidence Detection Strategies during Perception of Multi-Pitch Musical Tones
Authors: Bader Rolf
Published: August 2024

Multi-pitch perception is investigated in a listening test using 30 recordings of musical sounds with two tones played simultaneously, except for two gong sounds with inharmonic overtone spectra, judging roughness and separateness as the ability to tell the two tones in each recording apart. Of the sounds, 13 were from a Western guitar playing all 13 intervals in one octave, the other sounds were mainly from non-Western instruments, comparing familiar with unfamiliar instrument sounds for Western listeners. Additionally the sounds were processed in a cochlea model, transferring the mechanical basilar membrane motion into neural spikes followed by post-processing simulating different degrees of coincidence detection. Separateness perception showed a clear distinction between familiar and unfamiliar sounds, while roughness perception did not. By correlating perception with simulation different perception strategies were found. Familiar sounds correlated strongly positively with high degrees of coincidence detection, where only 3–5 periodicities were left, while unfamiliar sounds correlated with low coincidence levels. This corresponds to an attention to pitch and timbre, respectively. Additionally, separateness perception showed an opposite correlation between perception and neural correlates between familiar and unfamiliar sounds. This correlates with the perceptional finding of the distinction between familiar and unfamiliar sounds with separateness.
Skin Cancer Diagnosis Using VGG16 and Transfer Learning: Analyzing the Effects of Data Quality over Quantity on Model Efficiency
Authors: Djaroudib Khamsa , Lorenz Pascal , Belkacem Bouzida Rime , Merzougui Hanine
Published: August 2024

The recent increase in the prevalence of skin cancer, along with its significant impact on individuals’ lives, has garnered the attention of many researchers in the field of deep learning models, especially following the promising results observed using these models in the medical field. This study aimed to develop a system that can accurately diagnose one of three types of skin cancer: basal cell carcinoma (BCC), melanoma (MEL), and nevi (NV). Additionally, it emphasizes the importance of image quality, as many studies focus on the quantity of images used in deep learning. In this study, transfer learning was employed using the pre-trained VGG-16 model alongside a dataset sourced from Kaggle. Three models were trained while maintaining the same hyperparameters and script to ensure a fair comparison. However, the quantity of data used to train each model was varied to observe specific effects and to hypothesize about the importance of image quality in deep learning models within the medical field. The model with the highest validation score was selected for further testing using a separate test dataset, which the model had not seen before, to evaluate the model’s performance accurately. This work contributes to the existing body of research by demonstrating the critical role of image quality in enhancing diagnostic accuracy, providing a comprehensive evaluation of the VGG-16 model’s performance in skin cancer detection and offering insights that can guide future improvements in the field.
Comparative Analysis of Solar Radiation Forecasting Techniques in Zacatecas, Mexico
Authors: Escalona-Llaguno Martha Isabel , Solís-Sánchez Luis Octavio , Castañeda-Miranda Celina L. , Olvera-Olvera Carlos A. , Martinez-Blanco Ma. del Rosario , Guerrero-Osuna Héctor A. , Castañeda-Miranda Rodrigo , Díaz-Flórez Germán , Ornelas-Vargas Gerardo
Published: August 2024

This work explores the prediction of daily Global Horizontal Irradiance (GHI) patterns in the region of Zacatecas, Mexico, using a diverse range of predictive models, encompassing traditional regressors and advanced neural networks like Evolutionary Neural Architecture Search (ENAS), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Meta’s Prophet. This work addressing a notable gap in regional research, and aims to democratize access to accurate solar radiation forecasting methodologies. The evaluations carried out using the time series data obtained by Comisión Nacional del Agua (Conagua) covering the period from 2015 to 2018 reveal different performances of the model in different sky conditions, showcasing strengths in forecasting clear and partially cloudy days while encountering challenges with cloudy conditions. Overall, correlation coefficients (r) ranged between 0.55 and 0.72, with Root Mean Square Error % (RMSE %) values spanning from 20.05% to 20.54%, indicating moderate to good predictive accuracy. This study underscores the need for longer datasets to bolster future predictive capabilities. By democratizing access to these predictive tools, this research facilitates informed decision-making in renewable energy planning and sustainable development strategies tailored to the unique environmental dynamics of the region of Zacatecas and comparable regions.
Thermal and Thermomechanical Analysis of Amorphous Metals: A Compact Review
Authors: Radovanović-Perić Floren , Panžić Ivana , Bafti Arijeta , Mandić Vilko
Published: August 2024

Metallic glasses are amorphous metals that are supercooled to a frozen, glassy state and lack long-range order, in contrast to conventional metal structures. The lack of a well-ordered structure largely contributes to the unique properties exhibited by these materials. However, their synthesis and processability are defined and thereby constrained by a plethora of thermal and mechanical parameters. Therefore, their broader utilization in the scientific field and particularly in the related industry is somewhat hindered by the limitations related to preparing them in higher amounts. This may be overcome by changing the approach of metal glass formation to a bottom-up approach by utilizing solid-state plasma techniques, such as spark plasma ablation. Another important aspect of amorphous metals, inherently related to their non-equilibrium metastable nature, is the necessity to understand their thermal transformations, which requires unconventional thermal analysis methods. Therefore, this minute review aims to highlight the most important conceptual parameters behind configuring and performing conventional and advanced thermal analysis techniques. The importance of calorimetry methods (differential and fast scanning calorimetry) for the determination of key thermal properties (critical cooling rate, glass-forming ability, heat capacity, relaxation, and rejuvenation) is underscored. Moreover, the contributions of thermomechanical analysis and in situ temperature-dependent structural analysis are also mentioned. Namely, all of the mentioned temperature-dependent mechanical and structural analyses may give rise to the discovery of new glass systems with low critical cooling rates.
Mapping Gaps in Sugarcane Fields in Unmanned Aerial Vehicle Imagery Using YOLOv5 and ImageJ
Authors: Yano Inacio Henrique , de Lima João Pedro Nascimento , Speranza Eduardo Antônio , da Silva Fábio Cesar
Published: August 2024

Sugarcane plays a pivotal role in the Brazilian economy as a primary crop. This semi-perennial crop allows for multiple harvests throughout its life cycle. Given its longevity, farmers need to be mindful of avoiding gaps in sugarcane fields, as these interruptions in planting lines negatively impact overall crop productivity over the years. Recognizing and mapping planting failures becomes essential for replanting operations and productivity estimation. Due to the scale of sugarcane cultivation, manual identification and mapping prove impractical. Consequently, solutions utilizing drone imagery and computer vision have been developed to cover extensive areas, showing satisfactory effectiveness in identifying gaps. However, recognizing small gaps poses significant challenges, often rendering them unidentifiable. This study addresses this issue by identifying and mapping gaps of any size while allowing users to determine the gap size. Preliminary tests using YOLOv5 and ImageJ 1.53k demonstrated a high success rate, with a 96.1% accuracy in identifying gaps of 50 cm or larger. These results are favorable, especially when compared to previously published works.
Tomato Leaf Disease Classification by Combining EfficientNetv2 and a Swin Transformer
Authors: Sun Yubing , Ning Lixin , Zhao Bin , Yan Jun
Published: August 2024

Recently, convolutional neural networks (CNNs) and self-attention mechanisms have been widely applied in plant disease identification tasks, yielding significant successes. Currently, the majority of research models for tomato leaf disease recognition rely solely on traditional convolutional models or Transformer architectures and fail to capture both local and global features simultaneously. This limitation may result in biases in the model’s focus, consequently impacting the accuracy of disease recognition. Consequently, models capable of extracting local features while attending to global information have emerged as a novel research direction. To address these challenges, we propose an Eff-Swin model that integrates the enhanced features of the EfficientNetV2 and Swin Transformer networks, aiming to harness the local feature extraction capability of CNNs and the global modeling ability of Transformers. Comparative experiments demonstrate that the enhanced model has achieved a further increase in training accuracy, reaching an accuracy rate of 99.70% on the tomato leaf disease dataset, which is 0.49~3.68% higher than that of individual network models and 0.8~1.15% higher than that of existing state-of-the-art combined approaches. The results show that integrating attention mechanisms into convolutional models can significantly enhance the accuracy of tomato leaf disease recognition while also offering the great potential of the Eff-Swin backbone with self-attention in plant disease identification.
Temperature Field Optimization for Multi-Microwave Sources Based on Collaborative Switching under Uncertain Communication
Authors: Yang Biao , Zhao Zhongwei , Zhang Haoran , Chen Yang , Chen Xiucai
Published: August 2024

The multi-microwave sources reactor can significantly reduce energy consumption and processing time with broad application prospects in industrial processing. In order to optimize the temperature field of materials, this paper proposes the heating strategy of multi-microwave sources based on collaborative switching, particularly in distributed combined heat source networks with poor communication conditions. Firstly, simplifying system control variables to enhance the design of the microwave intelligent agent system, and optimizing the time-frequency characteristics of combined power output from multi-microwave sources to emphasize the process of energy partition. Meanwhile, an event-triggered strategy reduces communication frequency and energy consumption between agents. Secondly, a fixed positive lower limit τmin is used in event-triggered to avoid Zeno behavior caused by DoS attacks. Finally, The finite element method was used with the time domain for thermal analysis. The simulation results of SiC show that the energy utilization efficiency of microwave heating equipment is increased by 4.3∼10.7%, temperature uniformity is improved by 25.6∼43.6%, and the results of the potato experiment simulation showed that the multi-microwave source collaborative switching heating strategy can effectively optimize the temperature field distribution of the material.
Molybdenum Recovery from the Copper Hydrometallurgical Extraction Route with High Content of Chloride Ions Using the Ion Exchange Technique
Authors: Tapia Jaime , Quintriqueo Angélica , Hernández José
Published: August 2024

This manuscript describes molybdenum recovery from copper Pregnant Leaching Solutions (PLSs) in the copper oxide mining industry with high contents of chloride ions. This product was recovered from the copper leaching pond solutions of the Chilean National Copper Mining Corporation (CODELCO) using the ion exchange process. This process recovered molybdenum from initial Mo concentrations of 50 mg/L using two different anion−exchange resins. The first experiments, with 1 g/L Cl−, achieved recovery yields of 90% and molybdenum concentrates as CaMoO4. However, the process was permanently halted because of the early saturation of the ion exchange resins given by high chloride concentrations (10 g/L Cl−) of the original copper PLS and the significant reagent consumption given by the low adsorption capacity. Static studies were developed to determine the adsorption isotherms, followed by continuous molybdenum recovery tests. The Langmuir adsorption parameters were determined as a function of the chloride concentration, giving absorption capacities from 180 to 250 mg Mo/gram of resin and recoveries from 63% to 90%. The breakthrough values for the DOWEX and Lewatit resins for chloride contents of 10 g/L were 180 and 245 BV, respectively, while for 1 g/L, these values were 620 and 890 BV. This allowed obtaining elution solutions of 890 mg Mo/L for the DOWEX resin and 1000 mg Mo/L for the Lewatit resin.
A Method of Dual-AGV-Ganged Path Planning Based on the Genetic Algorithm
Authors: Cai Yongrong , Liu Haibin , Li Mingfei , Ren Fujie
Published: August 2024

The genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection, and it is known for its iterative optimization capabilities in both constrained and unconstrained environments. In this paper, a novel method for GA-based dual-automatic guided vehicle (AGV)-ganged path planning is proposed to address the problem of frequent steering collisions in dual-AGV-ganged autonomous navigation. This method successfully plans global paths that are safe, collision-free, and efficient for both leader and follower AGVs. Firstly, a new ganged turning cost function was introduced based on the safe turning radius of dual-AGV-ganged systems to effectively search for selectable safe paths. Then, a dual-AGV-ganged fitness function was designed that incorporates the pose information of starting and goal points to guide the GA toward iterative optimization for smooth, efficient, and safe movement of dual AGVs. Finally, to verify the feasibility and effectiveness of the proposed algorithm, simulation experiments were conducted, and its performance was compared with traditional genetic algorithms, Astra algorithms, and Dijkstra algorithms. The results show that the proposed algorithm effectively solves the problem of frequent steering collisions, significantly shortens the path length, and improves the smoothness and safety stability of the path. Moreover, the planned paths were validated in real environments, ensuring safe paths while making more efficient use of map resources. Compared to the Dijkstra algorithm, the path length was reduced by 30.1%, further confirming the effectiveness of the method. This provides crucial technical support for the safe autonomous navigation of dual-AGV-ganged systems.
Spray Dried Cashew (Anacardium occidentale L.) Juice Ingredients as an Upcycling Strategy for Abundant Cashew Apple
Authors: Moraes Francisca Pereira de , Costa Janaína de Paula da , da Silva Edilene Souza , Rocha Patrícia Maria , Medeiros Fábio Gonçalves Macêdo de , Costa José Maria Correia da , Hoskin Roberta Targino
Published: August 2024

Spray-dried yellow cashew juice ingredients produced under different inlet temperatures (140 and 150 °C) and gum arabic (GA) addition ratios (15% and 25% w/v) were evaluated for their physicochemical and phytochemical attributes and storage stability for 56 days. All spray-dried cashew juice particles showed high solids recovery (>70%) and solubility (>90%), low water activity (<0.3), and low hygroscopicity (<10%). Spray-dried particles prepared with 15% w/v GA showed spherical shapes with a semi-crystalline structure and higher ascorbic acid concentration (>650 mg 100 g−1) and total phenolic content (>330 mg GAE 100 g−1). During storage, spray-dried cashew juice particles maintained their water activity levels within the microbiologically safe range and retained high solubility, in addition to high ascorbic (>68%) and phenolic (>55%) acid retention. Overall, we showed that spray-drying cashew juice is a feasible strategy to upcycle abundant and undervalued cashew juice into stable, phytochemical-rich ingredients for multiple applications.
Ray Tracing of the New Multi-Modal X-ray Imaging Beamline PolyX at SOLARIS National Synchrotron Radiation Centre
Authors: Kosiorowski Filip , Wróbel Paweł , Kołodziej Tomasz , Sowa Katarzyna M. , Szczerbowska-Boruchowska Magdalena , Korecki Paweł
Published: August 2024

The aim of the presented research is to evaluate the potential performance of a new bending magnet X-ray beamline—PolyX, designed for microimaging and microspectroscopy at the National Synchrotron Radiation Centre SOLARIS in Krakow. Due to the short beamline length (<15 m), PolyX uses compact polycapillary and single-bounce monocapillary optics for X-ray focusing in the 4–15 keV energy range. Polycapillary optics require a dedicated approach for an efficient simulation of X-ray propagation in multiple capillary channels. Therefore, the PolyX beamline was ray traced by combining XRT (XRayTracer) and polycap libraries. In addition, to estimate the X-ray fluorescence spectra excited by focused beams, Monte Carlo simulations were conducted using XMI-MSIM. All simulations were aimed to estimate the crucial X-ray beam properties, i.e., the flux, the spot size, and the energy spectrum, for monochromatic and polychromatic X-ray beams.
Status and Migration Activity of Lead, Cobalt and Nickel in Water and in Bottom Sediments of Lake Markakol, Kazakhstan
Authors: Ismukhanova Laura , Madibekov Azamat , Opp Christian , Zhadi Askhat , Sultanbekova Botakoz , Zhumatayev Serik
Published: August 2024

Lake Markakol is located in a metal-rich mountain area of Kazakhstan. Metal input into the lake water and in the bottom sediments can be expected. Lead, cobalt and nickel monitoring in both near-surface and deep-water layers and in bottom sediments was carried out using flame atomic absorption spectrometric analyses. Lead contamination of surface water ranging from 2.6 to 6.8 µg/L occurs in all water samples with the exception of the surface water layer. In the deep-water section concentrations reach up to 13.0–16.2 µg/L. Cobalt concentrations range from 36.8 to 67.5 µg/L in the surface layer and from 25.5 to 69.2 µg/L in the deep-water layer. High values of nickel were found in the surface and bottom layers of the water, ranging from 13.5 to 49.0 and 17.2 to 49.0 µg/L, respectively. High concentrations of lead, cobalt and nickel were identified in all samples of the bottom sediments. The lead content in bottom sediments reaches 11.3, cobalt reaches 10.3–18.0 and nickel reaches 15.0 mg kg−1. The results and their assessment can serve as a basis for future monitoring and measures to reduce pollution, restore the lake ecosystem and ensure the safety of fishery products for humans.
Techno-Economic Assessment of Coaxial HTS HVAC Transmission Cables with Critical Current Grading between Phases Using the OSCaR Tool
Authors: Musso Andrea , Cavallucci Lorenzo , Angeli Giuliano , Bocchi Marco , L’Abbate Angelo , Vitulano Lorenzo Carmine , Dambone Sessa Sebastian , Sanniti Francesco , Breschi Marco
Published: August 2024

In recent years, the scientific and industrial interest regarding alternative technologies for transmission cables has increased. These conductors should efficiently transmit significant amounts of power between grid nodes, which are expected to be particularly congested due to the projected global increase in electricity production. Superconducting cables are considered a promising solution in this context, offering the potential to transmit large amounts of energy with minimal losses and compact dimensions, thereby potentially benefiting the environment. To evaluate the feasibility of integrating superconducting cables into existing grids, techno-economic approaches should be adopted. Such techniques enable the conceptual design of a specific cable structure, allowing users to explore a wide range of operating parameters to derive optimal designs. This paper reports a comprehensive techno-economic analysis of High Voltage Alternating Current (HVAC) cables realized with High-Temperature Superconducting (HTS) tapes, with the aim to transmit extremely high-power level. The optimal coaxial design is selected using Optimization Tool for Superconducting Cable Research (OSCaR) by implementing a graded approach to the critical current of the HTS tapes used for the different phases. This optimization aims to achieve the most effective balance between the cost of the coated conductors and their electrical properties. The whole set of model equations, the user-defined parameters, and the applied constraints are detailed. The OSCaR tool is then applied to assess the impact on the optimized design of the cable system and the corresponding cost indexes of several crucial parameters, such as the maximum transmitted power, the voltage level, and the line length.
Numerical Analysis of Flood Invasion Path and Mass Flow Rate in Subway Stations under Heavy Rainfall Conditions
Authors: Lu Jia , Lin Zhiyu , Lin Hang
Published: August 2024

The occurrence of extreme weather, such as heavy rainfall and sudden increases in precipitation, has led to a notable rise in the frequency of flooding in subway stations. By conducting numerical simulations of flood disasters in subway stations under heavy rainfall conditions and gaining insights into the patterns of flood invasion inside the stations, it is possible to develop practical and feasible drainage designs for the stations. This paper employs the computational fluid dynamics (CFD) method, utilising the volume of fluid function (VOF) method and the renormalization k-ε group model within the vortex viscosity model. The complete process of flood invasion into subway stations with varying water levels (1500 mm, 2000 mm, and 2450 mm) is modelled, and the distribution of floods at different times under varying operational conditions is analysed to identify the evolutionary patterns of station flood history. The simulation calculations yielded the mass flow rate time history curve at the tunnel entrance and exit, which was then subjected to an analysis of its development trend over time. The total accumulated water in the subway station is calculated by integrating the difference in mass flow rate between the entrance and the tunnel exit, using the mass flow rate curve. In conclusion, the paper proposes drainage measures that provide valuable insights into pumping strategies when floodwaters infiltrate subway stations. The results indicate that the speed of flood spreading in subway stations increases with higher groundwater levels, and that the mass flow rate of floodwater entering the tunnels increases over time, eventually reaching a stable state. It was observed that, at certain times, the mass flow rate of floodwater into the tunnels exhibited a linear relationship with time.
Model Modification of the Soil–Water Characteristic Curve of Unsaturated Weak Expansive Soil
Authors: Ma Lina , Guo Jinran , Liang Dongfang , Ding Xiaogang , Xue Yanjin
Published: August 2024

This study evaluates the impact of compaction on the soil–water characteristic curve of unsaturated remodeled weakly expansive soils by assessing changes in soil pore structure resulting from variations in compaction. The remodeled weakly expansive soil in the Xinjiang Hami area is taken as the research subject to investigate how compaction affects microscopic pore structure using mercury intrusion testing. Subsequently, mercury intrusion porosimetry is employed to examine pore structure and distribution patterns at different dry densities. Based on the capillary principle and experimental methods (filter paper method and pressure plate method test), modified soil–water characteristic curves are obtained by fitting them with a three-parameter model law. The results indicate that higher dry density leads to an increased air intake value and significantly reduces the total volume of large pores within samples. Both the Fredlund and Xing model and the three-parameter model effectively capture the influence of initial dry density on the development pattern of the soil–water characteristic curve.
Decision Support Systems for Disease Detection and Diagnosis
Authors: Rizzi Maria
Published: August 2024

The last few years have been characterized by a large amount of research activity in the field of healthcare for both the improvement of diagnostic treatments and the development of simple, efficient, and multi-tasking applications [...]
Logging Identification Method for Reservoir Facies in Fractured-Vuggy Dolomite Reservoirs Based on AI: A Case Study of Ediacaran Dengying Formation, Sichuan Basin, China
Authors: Ye Yu , Jiang Zengzheng , Liu Xiangjun , Wang Zhanlei , Gu Yifan
Published: August 2024

As an important target for deep to ultra-deep carbonate oil and gas exploration, Fractured-Vuggy dolomite reservoirs have strong heterogeneity. Accurate characterization of reservoir facies is crucial for their exploration and exploitation. Three methods, including the unsupervised intelligent clustering method of improved Fuzzy C-means clustering Algorithm Based on Density Sensitive Distance and Fuzzy Partrition (FCM-DSDFP), the fusion method of Principal Components Analysis (PCA) dimensionality reduction and noise reduction, and the principle of clustering feature analysis are applied to identify reservoir facies based on logging data. Based on the PCA method, the logging response characteristics of the reservoir facies are excavated, and the fusion characterization data of dimensionality reduction and noise reduction are extracted. The FCM-DSDFP unsupervised intelligent clustering method, a model that approximates the subsurface conditions is established, and the reliability of the model is tested according to the elbow rule and silhouette coefficient. Combining drilling core observation, Fractured-Vuggy type, partially cemented Fractured-Vuggy type, Pore-Vuggy type, Pore Type I, Pore Type II, Tight Type I, and Tight Type II are divided in the Dengying Formation 4th Member. Fractured-Vuggy type, partially cemented Fractured-Vuggy type, Pore-Vuggy Type I, Pore-Vuggy Type II, Pore Type I, Pore Type II, and Tight Type are divided in the Dengying Formation 2nd Member, respectively. Two methods were applied to verify the reservoir facies identification results based on intelligent algorithms. The first method is to compare the identification results with the reservoir facies types identified by core observations (Well PT103 and PS13). The second method is to verify the recognition results of intelligent algorithms by utilizing the relationship between reservoir facies types and bitumen. The test results show that the accuracy of the reservoir level identification is higher than 0.9, and the applicability is better than the commonly used algorithms such as FCM and K-means.