bluuebunny/crossref_metadata_2025 huggingface dataset:
Profitability Assessment of Precision Agriculture Applications—A Step Forward in Farm Management
Authors: Karydas Christos , Chatziantoniou Myrto , Tremma Ourania , Milios Alexandros , Stamkopoulos Kostas , Vassiliadis Vangelis , Mourelatos Spiros
Published: August 2023

Profitability is not given the necessary attention in contemporary precision agriculture. In this work, a new tool, namely ProFit, is developed within a pre-existing farm management system, namely ifarma, to assess the profitability of precision agriculture applications in extended crops, as most of the current solutions available on the market respond inadequately to this need. ProFit offers an easy-to-use interface to enter financial records, while it uses the dynamic map view environment of ifarma to display the profitability maps. Worked examples reveal that profitability maps end up being quite different from yield maps in site-specific applications. The module is regulated at a 5 m spatial resolution, thus allowing scaling up of original and processed data on a zone-, field-, cultivar-, and farm-scale. A bottom-up approach, taking advantage of the full functionality of ifarma, together with a flexible architecture allowing future interventions and improvements, renders ProFit an innovative commercial tool.
Principal Hugoniots of Promethium, Terbium, Thulium, Lutetium, and Actinium in a Wide Pressure Range
Authors: Burakovsky Leonid , Preston Dean L. , Ramsey Scott D. , Sjue Sky K. , Starrett Charles E. , Baty Roy S.
Published: August 2023

We present the analytic forms of the principal Hugoniots of actinium (Ac) and the lanthanide promethium (Pm), which have both never been measured or calculated before, as well as those of terbium (Tb), thulium (Tm), and lutetium (Lu), the three least studied of the remaining lanthanides. They are based on our new analytic model of principal Hugoniot. A comparison of the five Hugoniots to our own independent theoretical calculations demonstrates very good agreement in every case, but each of the Hugoniots of Tb, Tm, and Ac from the TEFIS database, which ours are also compared to, appear to violate Johnson’s theoretical constraint 4<ηmax<7 for the maximum compression ratio ηmax, which corresponds to the Hugoniot turnaround point. Possible reason for this behavior of the TEFIS Hugoniots is briefly discussed.
The Marine Bacterial Genus Euzebya Is Distributed Worldwide in Terrestrial Environments: A Review
Authors: Gonzalez-Pimentel Jose Luis , Martin-Pozas Tamara , Jurado Valme , Laiz Leonila , Fernandez-Cortes Angel , Sanchez-Moral Sergio , Saiz-Jimenez Cesareo
Published: August 2023

The advent of molecular tools, and particularly next-generation sequencing, has dramatically changed our knowledge of the diversity of microbial life on Earth. In recent decades, many studies on different terrestrial environments have described the intriguing diversity and abundance of Euzebyales/Euzebyaceae/Euzebya, yet its role in the geochemical cycle of elements is unknown. In addition, as far as we know, no Euzebya isolates have been obtained from terrestrial niches. In this review, it is shown that Euzebya and other haloalkaliphilic bacteria can thrive under harsh conditions, such as high concentrations of sodium and/or calcium, high electric conductivity and alkaline pH, highly variable temperatures, and water fluctuations. These conditions are quasi-extreme in the studied terrestrial environments. However, the culture media used so far for isolation have failed to reproduce the original conditions of these terrestrial ecosystems, and this is likely the reason why strains of Euzebya and other bacteria that inhabit the same niche could not be isolated. It is expected that culture media reproducing the environmental conditions outlined in this review could cope with the isolation of terrestrial Euzebya and other haloalkaliphilic genera.
Low-Light Image Enhancement Method for Electric Power Operation Sites Considering Strong Light Suppression
Authors: Xi Yang , Zhang Zihao , Wang Wenjing
Published: August 2023

Insufficient light, uneven light, backlighting, and other problems lead to poor visibility of the image of an electric power operation site. Most of the current methods directly enhance the low-light image while ignoring local strong light that may appear in the electric power operation site, resulting in overexposure and a poor enhancement effect. Aiming at the above problems, we propose a low-light image enhancement method for electric power operation sites by considering strong light suppression. Firstly, a sliding-window-based strong light judgment method was designed, which used a sliding window to segment the image, and a brightness judgment was performed based on the average value of the deviation and the average deviation of the subimages of the grayscale image from the strong light threshold. Then, a light effect decomposition method based on a layer decomposition network was used to decompose the light effect of RGB images with the presence of strong light and eliminate the light effect layer. Finally, a Zero-DCE (Zero-Reference Deep Curve Estimation) low-light enhancement network based on a kernel selection module was constructed to enhance the low-light images with reduced or no strong light interference. Comparison experiments using the electric power operation private dataset and the SICE (Single Image Contrast Enhancement) Part 2 public dataset showed that our proposed method outperformed the current state-of-the-art low-light enhancement methods in terms of both subjective visual effects and objective evaluation metrics, which effectively improves the image quality of electric power operation sites in low-light environments and provides excellent image bases for other computer vision tasks, such as the estimation of operators’ posture.
Automation of the Edge Deburring Process and Analysis of the Impact of Selected Parameters on Forces and Moments Induced during the Process
Authors: Falandys Karol , Kurc Krzysztof , Burghardt Andrzej , Szybicki Dariusz
Published: August 2023

The article concerns the possibility of the automation and robotization of the process of deburring jet engine components. The paper presents the construction of a laboratory stand enabling the automation of selected production operations of typical low-pressure turbine blades. The work identifies important parameters and results of the technological process related to the removal of burrs that affect the exactness of the process. The results of the analysis of the impact of individual process parameters on the magnitude of forces and moments occurring during deburring were carried out and presented. The results of initial and detailed tests were presented. Based on the results obtained, it was noticed that doubling the rotational speed of the brush results in a linear increase in torque and an increase in the engagement of the detail in the disc brush, leading to a non-linear increase in torque. It has also been shown that with tool wear, the value of the torque generated by the rotating tool decreases. Based on the results of a comparison of manual and automated process and histogram analysis, results from an automated stand are centered more correctly inside of the required radius range. This means that the repeatability of the process is higher for an automated test stand, which is one of the key aspects of large-scale aviation component manufacturing. Additionally, it was confirmed by visual inspection that all burs had been removed correctly—the deburring operation for all tested work pieces was successful. Based on the results obtained, it was proven that introduction of an automated stand can improve working conditions (by the elimination of the progressive fatigue of employees and the possibility for injury) and allows for the elimination of the negative impact of the machining process on workers. Further areas in which the optimization of the process parameters of the edge deburring can be developed in order to reduce unit costs have also been indicated.
Building Energy Savings by Developing Complex Smart Windows and Their Controllers
Authors: Hong Seong-Ki , Choi Sang-Ho , Jeong Su-Gwang
Published: August 2023

The interest in zero-energy buildings has increased in Korea recently. Following the significant increases in cooling and lighting energy consumption in offices, various studies have been conducted to implement energy-saving measures. The purpose of this study is to reduce lighting and cooling energy consumption in the summer through the dimming control of a complex smart window system. To achieve this, the optimal dimming control algorithm has been derived and applied in simulations to analyze the energy consumption for lighting and cooling. A smart window incorporates suspended particle display glass that actively responds to changes in indoor and outdoor environments and controls light transmittance. It also includes a light-guiding glass that can actively control solar reflectance. Simulations of office buildings were conducted to develop optimal control algorithms and controllers based on solar radiation. Subsequently, we installed this complex smart window in a test room along with the developed control algorithm and controller, which responded to the amount of insolation and time. To ensure the accuracy of the experiment, we constructed separate test and reference rooms. The experimental results obtained under the same conditions showed a reduction of approximately 36.9% in cooling energy consumption in the test room compared with the reference room and a 54.5% reduction in lighting energy consumption. Furthermore, based on additional simulations and experiments, we confirmed that the application of complex smart window systems in office buildings could reduce considerably the energy consumption for cooling and lighting.
Initial Structure Design and Optimization of an Automotive Remote Head-Up Display
Authors: Ye Yu , Chen Huaixin , Wang Zhixi
Published: August 2023

Aiming at the problems of difficult construction and occlusion of the initial structure of an automotive augmented reality head-up display (AR-HUD), a method to quickly build the initial structure of an automotive AR-HUD is proposed. Firstly, the position of the mirrors in the initial structure is calculated based on the Rodrigues rotation formula. Secondly, the position of the mirrors is restricted by constraints during optimization to prevent the problem of structural occlusion. Finally, a virtual image display system with a visual distance of 7.5 m and a field of view angle of 10° × 5° is designed. The image quality analysis of the optimized system shows that the light spots in each field of view are all within Airy spots. At the spatial cutoff frequency of the virtual image plane of the optical system, the modulation transfer function (MTF) value of the full field of view is basically greater than 0.5, and the distortion is less than 1%. Finally, using the image for simulation, the results of the simulation image are satisfying, which proves the validity and feasibility of the structural design. It provides a useful reference for the structural design of the remote head-up display system.
Multi-Scale Spatial–Temporal Transformer: A Novel Framework for Spatial–Temporal Edge Data Prediction
Authors: Ming Junhao , Zhang Dongmei , Han Wei
Published: August 2023

Spatial–temporal prediction is an important part of a great number of applications, such as urban traffic control, urban traffic management, and urban traffic planning. However, real-world spatial–temporal data often have complex patterns, so it is still challenging to predict them accurately. Most existing spatial–temporal prediction models fail to aggregate the spatial features in a suitable neighborhood during fixed spatial dependencies extraction and lack adequately comprehensive time series analysis for intricate temporal dependencies. This paper proposes a novel model named multi-scale spatial–temporal transformer network (MSSTTN) to deal with intricate spatial–temporal patterns. Firstly, we develop an improved graph wavelet neural network, which learns how to pass the spatial graph signals of different frequency scales to adjust the neighborhood of feature aggregation adaptively. Then, we propose decomposing the time series into local trend-cyclical parts of various scales during time series analysis, making the model capture more reliable temporal dependencies. The proposed model has been evaluated on publicly available real-world datasets. The experimental findings indicate that the proposed model exhibits superior performance compared to conventional techniques including, spatial–temporal transformer (STTNs), GraphWaveNet, and others.
Synthesis of State/Output Feedback Event-Triggered Controllers for Load Frequency Regulation in Hybrid Wind–Diesel Power Systems
Authors: Abdelrahim Mahmoud , Almakhles Dhafer
Published: August 2023

Hybrid power systems based on renewable energy sources and diesel generators are efficient solutions for supplying electricity to remote and off-grid locations. One of the most crucial problems in hybrid power systems is frequency regulation, which is established by balancing the supplied power with the load demand using the load frequency control approach. Since most feedback signals are analog and the control setups are digital, the resulting control system is a sampled-data system, which requires careful designs for both the control law and the sampling frequency to guarantee closed-loop stability. This paper is concerned with the state-feedback load frequency regulation for hybrid wind–diesel power systems under event-triggered implementation. It is assumed that the full state measurement is available for feedback and that sensors and controllers communicate over a shared digital network. To mitigate the communication load on the network, an event-triggering mechanism is constructed by emulation, based on the time-regularization principle in the sense that each consecutive triggering instant is speared by a specified minimum dwell time. The closed-loop system is described as a hybrid dynamical system to account for mixed dynamical behaviors naturally arising in networked control systems. By means of appropriate Lyapunov functions, the closed-loop stability is ensured under the proposed triggering rule. Moreover, the enforced dwell time between transmissions ensures that the accumulation of sampling times is prevented, which is crucial for the event-triggering condition to be implementable in practice. The required conditions to apply this technique are derived in terms of a linear matrix inequality. Numerical simulations on an isolated hybrid power system were implemented to demonstrate the efficiency of the proposed method. Comparative simulations with relevant techniques in the literature were carried out, which showed that the proposed approach can produce fewer transmission numbers over the network.
Grasp Detection Combining Self-Attention with CNN in Complex Scenes
Authors: Niu Jinxing , Liu Shuo , Li Hanbing , Zhang Tao , Wang Lijun
Published: August 2023

In this paper, we present a novel approach that subtly combines the transformer with grasping CNN to achieve more optimal grasps in complex real-life situations. The approach comprises two unique designs that effectively improve grasp precision in complex scenes. The first essential design uses self-attention mechanisms to capture contextual information from RGB images, boosting contrast between key object features and their surroundings. We precisely adjust internal parameters to balance accuracy and computing costs. The second crucial design involves building a feature fusion bridge that processes all one-dimensional sequence features at once to create an intuitive visual perception for the detection stage, ensuring a seamless combination of the transformer block and CNN. These designs eliminate noise features in complex backgrounds and emphasize graspable object features, providing valuable semantic data to the subsequent grasping CNN to achieve appropriate grasping. We evaluated the approach on the Cornell and VMRD datasets. According to the experimental results, our method achieves better performance than the original grasping CNN in single-object and multi-object scenarios, exhibiting 97.7% and 72.2% accuracy on the Cornell and VMRD grasp datasets using RGB, respectively.
Study on Power Frequency Breakdown Characteristics of Nano-TiO2 Modified Transformer Oil under Severe Cold Conditions
Authors: Qin Chunxu , Huang Yongxiang , Liu Liqiang , Liang Huijuan , Shang Jiafeng , Xue Yunpeng
Published: August 2023

With 45# transformer oil as the base fluid, different concentrations of TiO2 nanomodified transformer oil were prepared via the thermal oscillation method. Nanomodified transformer oil with a concentration of 0.01 g/L was selected for evaluation via the breakdown test. Then, 0.01 g/L nanomodified transformer oil and ordinary 45# transformer oil were subjected to the variable control of different moisture contents and different temperatures, and breakdown tests under different types of electrodes were performed within the temperature range of −30 °C to 30 °C. The test results showed the following: Under severe cold conditions, the improvement effect of the breakdown voltage in different temperature ranges was different. Within the temperature range of −30 °C to −10 °C, the enhancement effect could reach 13% to 15%. Within the temperature range of −10 °C to 0 °C, the enhancement effect could reach 8% to 9%. Within the temperature range of 0 °C to 30 °C, the enhancement effect could reach 18% to 21%. Compared with the test results at high water contents, the improvement in the breakdown voltage amplitude of transformer oil by nanomaterials was more obvious at low water contents. In addition, nanomaterials could reduce the dispersion of the breakdown voltage to make the breakdown voltage more stable. Lastly, COMSOL was used to simulate the polarization process of nanoparticles under a uniform electric field and the influence of the trajectory of charged particles in the oil to further analyze the mechanism of the influence of nanoparticles in oil on the breakdown voltage of the oil gap. The simulation results showed that, when the particles were accelerated by the electric field, they moved irregularly. After adding nanoparticles, the charged particles were adsorbed by the nanoparticles when passing through the nanoparticles, which reduced the migration rate of charged particles in the oil. The breakdown voltage of the oil gap increased.
Numerical Assessment of Standard Firebrand Accumulation Curve When Transferring Temperature to Contact Surfaces
Authors: Bicelli Antonio Renato , Cantor Pedro , Arruda Mário Rui , Tiago Carlos , Bernardes de Assis Ellon , Branco Fernando
Published: August 2023

This work presents research concerning the numerical assessment of two previously measured temperatures due to firebrand accumulation on surfaces, which was determined in former thermal experimental campaigns. A 3D numerical model using thermal transient non-linear analysis is used to validate the thermal outputs of these two previous experimental campaigns, and therefore, corroborating the previous temperature vs. time curves created with a prescribed flux in the firebrand accumulation area. The firebrand thermal heat transfer to the plane surface is simulated using convection and radiation film conditions, in which a 3D non-linear, time-dependent finite element simulation is used. Then, the previous proposed standard firebrand accumulation curve, ISO 834, and external fire curve are numerically compared with the results from previous firebrand accumulation curves in a wood corner wall. Finally, the merit assessment of the proposed standard firebrand accumulation curve shows a visible improvement, which has low values and is in accordance with the experimental results in the temperature field distribution of firebrand accumulation onto a contact surface. It is fair to argue that it constitutes a point to search for an efficient design for structures at elevated temperatures due to firebrand accumulation.
Detection and Evaluation of Construction Cracks through Image Analysis Using Computer Vision
Authors: Del Savio Alexandre Almeida , Luna Torres Ana , Cárdenas Salas Daniel , Vergara Olivera Mónica Alejandra , Urday Ibarra Gianella Tania
Published: August 2023

The introduction of artificial intelligence methods and techniques in the construction industry has fostered innovation and constant improvement in the automation of monitoring and control processes at construction sites, although there are areas where more studies still need to be conducted. This paper proposes a method to determine the criticality of cracks in concrete samples. The proposed method uses a previously trained YOLOv4 neural network to identify concrete cracks. Then, the region of interest, determined by the bounding box resulting from the neural network model classification, is extracted. Finally, the extracted image is converted to negative grayscale to quantify the number of white pixels above a certain threshold, automatically allowing the system to characterize the fracture’s extent and criticality. The classification module reached a veracity between 98.36% and 99.75% when identifying five concrete crack types of failures in 1132 images. A qualitative analysis of the results obtained from the characterization module shows a promising alternative to evaluate the criticality of concrete cracks.
An Optimized Energy and Time Constraints-Based Path Planning for the Navigation of Mobile Robots Using an Intelligent Particle Swarm Optimization Technique
Authors: Raj Ravi , Kos Andrzej
Published: August 2023

Mobile robots (MRs) typically require running for many hours on one charge of the battery. Electric autonomous mobile robots (AMRs) have become increasingly common in the manufacturing process in the last few years. MRs must often complete difficult assignments while gathering information across an unknown area involving energy constraints and time-sensitive preferences. This paper estimates the information collection assignment for surveillance as a multi-objective optimization dilemma with both energy and time constraints. In this study, three main objectives during acquiring data are taken into consideration, including the greatest quantity of data acquired for surveillance, following a path where obstacles are least likely to be experienced, and traveling the smallest feasible path. To obtain the optimal path for an MR by addressing the presented issue, this approach presents an intelligent particle swarm optimization (PSO) technique that determines fitness value by simplifying the optimization task for achieving the shortest path for MR navigation. It allows particles to execute variable operations while maintaining most of the prior search information. The findings of the simulation show that this technique of the PSO algorithm can realize swift convergence and high accuracy when compared with different benchmark functions derived for PSO. A comparative discussion on various energy-efficient navigation techniques for MRs is also provided. Lastly, this study describes the possible future research directions.
Local Dependency-Enhanced Graph Convolutional Network for Aspect-Based Sentiment Analysis
Authors: Wu Fei , Li Xinfu
Published: August 2023

The task of aspect-based sentiment analysis (ABSA) is to detect the sentiment polarity toward given aspects. Contemporary methods predominantly utilize graph neural networks and incorporate attention mechanisms to dynamically connect aspect terms with their surrounding contexts, resulting in more informative feature representations. However, these methods only consider whether there are dependencies between words when introducing dependencies, ignoring that dependencies between different sentiment words have different effects. Neglecting this could introduce noise and negatively impact the model’s performance. To overcome this limitation, we introduce a novel approach called the local dependency-enhanced graph convolutional network (LDEGCN). Our method combines semantic information and dependency relationships to better capture the affective relationships between words. Specifically, we integrate sentiment knowledge from SenticNet to enrich the sentence’s dependency graph and thoroughly explore the dependency types between contexts and aspects to focus on particular dependency types. The local context weight (LCW) method is employed on the dependency-enhanced graph to emphasize the importance of local contexts, thereby mitigating the issue of long-distance dependencies. Through extensive evaluations of five public datasets, the LDEGCN model demonstrates significant improvements over mainstream models.
A New Method for Evaluating Natural Gas Pipelines Based on ICEEMDAN-LMS: A View of Noise Reduction in Defective Pipelines
Authors: Gao Yiqiong , Luo Zhengshan , Bi Aorui , Wang Qingqing , Wang Yuchen , Wang Xiaomin
Published: August 2023

As the main means of energy transportation, pipelines have the characteristics of 24 h uninterrupted transportation, low cost, strong transmission capacity, and low risk, and they have become an important device for natural gas transportation. However, due to defects caused by the pipeline itself and external forces, it is easy for natural gas leakage to cause major accidents, serious environmental pollution, and huge losses of life and property. Traditional pipeline defect detection technology can be interfered with by a large number of noise signals when collecting defect signals, and the noise reduction method is simple but has poor accuracy, so it cannot directly obtain effective pipeline defect information from the signal and cannot effectively improve the emergency intensity and emergency level of pipeline operation and maintenance management. Therefore, a new noise reduction method, ICEEMDAN-LMS, for natural gas pipelines is proposed in this paper, and the method is calculated and tested in detail. The effectiveness of the new method is verified through the analysis of a pipeline defect signal and normal signal. Moreover, the proposed method can reduce noise more effectively than conventional methods, which has clear value in engineering applications. Importantly, this noise reduction method not only provides a reliable basis for the intelligent diagnosis of pipeline defect signals, but can also be an important reference for helping management departments to make decisions and emergency plans and formulate on-site treatment programs.
Research on the Clothing Classification of the She Ethnic Group in Different Regions Based on FPA-CNN
Authors: Ding Xiaojun , Li Tao , Chen Jingyu , Ma Ling , Zou Fengyuan
Published: August 2023

In order to achieve the effective computer recognition of the She ethnic clothing from different regions through the extraction of color features, this paper proposes a She ethnic clothing classification method based on the Flower Pollination Algorithm-optimized color feature fusion and Convolutional Neural Network (FPA-CNN). The method consists of three main steps: color feature fusion, FPA optimization, and CNN classification. In the first step, a color histogram and color moment features, which can represent regional differences in She ethnic clothing, are extracted. Subsequently, FPA is used to perform optimal weight fusion, obtaining an optimized ratio. Kernel principal component analysis is then applied to reduce the dimensionality of the fused features, and a CNN is constructed to classify the She ethnic clothing from different regions based on the reduced fused features. The results show that the FPA-CNN method can effectively classify the She ethnic clothing from different regions, achieving an average classification accuracy of 98.38%. Compared to SVM, BP, RNN, and RBF models, the proposed method improves the accuracy by 11.49%, 7.7%, 6.49%, and 3.92%, respectively. This research provides a reference and guidance for the effective recognition of clothing through the extraction of color features.
Design and Implementation of a Compliant Gripper for Form Closure of Diverse Objects
Authors: Bingham Kyler C. , Hessler Matthew , Lama Safal , Deemyad Taher
Published: August 2023

This research presents a comprehensive study on the design and implementation of a flexible robotic gripper. Conventional grippers utilized in articulated robotic arms are often limited in their capabilities, being restricted to specific tasks or fixed object sizes. While soft grippers are a viable option, they have limitations in terms of grasping objects across a wide range and providing complete coverage. In this study, a novel compressible gripper is developed to enable safe and secure grasping of objects with varying sizes and shapes within a wide range. The gripper features a grasping area measuring 14 cm × 6 cm, allowing complete coverage of objects within this surface area. The current prototype with 7 cm of compressibility demonstrates the ability to grasp objects with a width difference of 7 cm with a maximum thickness of 15 cm, enabling manipulation of objects with varying widths, as defined by user-programmable parameters. The functionality of the gripper is based on the compressibility of the 3D-printed thermoplastic polyurethane (TPU) material. The flexible part of the gripper can be easily interchanged, offering versatility by accommodating different thicknesses without the need to replace the entire gripper mechanism. The gripper system operates using an open-loop control system, enhancing user-friendliness. Experimental evaluation of the gripper involved the creation and analysis of a CAD model followed by the fabrication of a prototype. The prototype exhibited exceptional performance in grasping objects of diverse sizes, shapes, and textures, demonstrating the effectiveness of the developed soft gripper system. The scalability of the soft gripper enables seamless integration with various types of articulated robotic arms, while the maximum weight limit for objects will be defined based on the robotic arms’ limitations. The research findings highlight the promising capabilities of the compressible gripper in enhancing the versatility and efficiency of robotic grasping systems, offering a significant contribution to the field of robotics.
Geosystemic Impacts of the Extreme Rainfall Linked to the El Niño 2015/2016 Event in Northern Paraná, Brazil
Authors: Montanher Otávio Cristiano , Minaki Cíntia , de Morais Eduardo Souza , de Paula Silva Juliana , Pereira Paulo
Published: August 2023

The El Niño episode between September 2015 and February 2016 caused abundant rainfall in the state of Paraná in southern Brazil. In this study, we map the greatest magnitude of this climatic event and the respective geosystem responses in the landscape. For this purpose, 32 precipitation series were analyzed (1981–2016). The parameter most indicative of extreme rainfall events during the El Niño period was the six-month accumulated precipitation. The return period (RP) of this parameter was calculated and spatialized using kriging. The results show that a longitudinal strip of 450 km by 140 km had rainfall with RP > 30 years, reaching maximums greater than 150 years. Surveys performed in the field, with aerial images, official sources and media news show a great diversity of effects caused by rainfall, such as road blockages, interruption of water and power supply, and erosion in urban and rural areas. It is concluded that the stability of regional geosystems was disrupted because of extreme rainfall values, so that the northwest region of Paraná had less resilience compared to the north-central and Pioneer North regions, due to the differences between the lithologies and soils of these regions.
Decision Support Model for Allocating Maintenance Budgets for Bridges
Authors: Alshibani Adel , Abu Saa Amin S. , Hassanain Mohammad A. , Bubshait Abdulaziz , Shash Ali
Published: August 2023

This paper introduces a newly developed decision support model for allocating a budget for bridge maintenance. The model is built based on capturing the experts’ knowledge for the prioritization of criteria for selecting a bridge maintenance plan. A literature review and interviews with a group of nine local experts were carried out to identify the criteria for bridge prioritization for maintenance. A questionnaire survey was then designed and administered, utilizing multi-criteria decision-making techniques through face-to-face meetings with local bridge maintenance managers to determine the weights of the identified criteria and develop the utility curves for each criterion. The developed model consists of four major modules: (1) bridge ranking module; (2) prioritization module of bridge structural elements; (3) optimization-based simulation module; and (4) the outputs module. The model was validated in a hypothetical network of four bridges. The validation reveals that the model can assist bridge maintenance managers in setting a bridge maintenance plan, with a high level of confidence using efficient frontier analysis technique. Utilizing frontier analysis enables comparisons of mean costs of different bridge maintenance plans against different levels of risk to enable decision-makers to make informed decisions. The result of this study indicated that bridge structural condition was the most important criterion amongst all the criteria influencing the decision of bridge prioritization for maintenance, followed by traffic and bridge location. Moreover, bridge deck and parapet, followed by bearing pads, were found to be the most important of the bridge’s structural elements.
Closely-Spaced Repetitions of CAMTA Trans-Factor Binding Sites in Promoters of Model Plant MEP Pathway Genes
Authors: Szymczyk Piotr
Published: August 2023

Previous research has demonstrated the presence of two closely spaced repetitions of the rapid stress-responsive cis-active element RSRE (G/A/C)CGCG(C/G/T) in the 5′UTR of S. miltiorrhiza2C-methyl-D-erithrytol 2,4-cyclodiphosphate synthase (MECPS) gene. The product of MECPS activity, represented by 2C-methyl-D-erithrytol 2,4-cyclodiphosphate (MECPD), indicates its retrograde regulatory role and activates CAMTA trans-factors. Since the complete activation of CAMTA trans-factors requires the cooperative interaction of CAMTA3 with CAMTA2 or CAMTA4, the closely spaced RSREs recognized by CAMTA trans-factors could be used to promote CAMTA trans-factor dimerization. The present study aims to evaluate if the occurrence of these two closely spaced RSREs in the 5′UTR is specific to S. miltiorrhiza or could be observed in other MECPS genes. An analysis of nineteen MECPS gene sequences from seven selected model plants indicated the closely spaced repetition of RSREs in the 5′UTR region of two maize (Zea mays) MECPS genes, Zm00001d051458 and Zm00001d017608. This observation suggests the potential autoregulatory function of MECPD in relation to the MECPS transcription rate. Moreover, an analysis of eighty-five promoter regions of other plastidial methyl-D-erythritol phosphate (MEP) pathway genes indicated such closely spaced RSREs in the proximal promoter of Zea mays2-C-methyl-D-erythritol 4-phosphate cytidylyltransferase (CMS) (Zm00001d012197) and Oryza sativa4-hydroxy-3-methylbut-2-enyl diphosphate reductase (HDR) (Os03t0732000-00).
Mobile Customer Satisfaction Scoring Research Based on Quadratic Dimension Reduction and Machine Learning Integration
Authors: Zeng Fei , He Yuqing , Yang Chengqin , Hu Xinkai , Yuan Yining
Published: August 2023

Customer satisfaction is a measure of the degree of satisfaction of customer experience. Among the three major operators in China, China Mobile plays an important role in the communication field. A study of customer satisfaction with China Mobile will have a significant positive impact on the sustainable development of the entire communication industry. In order to respond to customer needs accurately, a mobile customer satisfaction research method based on quadratic dimensionality reduction and machine learning integration is proposed. Firstly, the core evaluation system of impact satisfaction is established, through the integration of systematic clustering and exploratory factor analysis for quadratic dimensionality reduction. Then, unreasonable data in the core influencing factors are eliminated. Finally, the gradient-boosted decision tree (GBDT) machine learning algorithm is applied to predict satisfaction, with a prediction accuracy of up to 99%, and the highly accurate satisfaction prediction can quickly respond to customer needs and feedback to improve customer experience and satisfaction.
A Numerical Method-Based Analysis of the Structural Deformation Behaviour of a Turkish String Instrument (Cura Baglama) under Varying String Tensions
Authors: Celik H. Kursat , Gok Sevilay , Caglayan Nuri , Rennie Allan E. W.
Published: August 2023

This study focuses on the structural design analysis of a cura baglama, a traditional Turkish string instrument that does not have in place a regulated set of manufacturing standards to follow. The aim therefore is to introduce a structural deformation analysis for a sample cura baglama in three different string tensions via a numerical method-based engineering analysis technique. The three-dimensional solid model of a sample cura baglama was created using a 3D scanner and parametric 3D solid modelling software. Based on experimental frequency analysis, structural deformation analyses of the instrument were conducted using finite element method-based engineering simulation techniques. The simulation results revealed useful visual and numerical outputs related to the deformation behaviour of the instrument under pre-defined boundary conditions. A maximum deformation of 0.223 mm on the soundboard (at the D3 tune) and a maximum equivalent stress of 18.325 MPa on the bridge (at the D3 tune) were calculated. The outputs of this research contribute to further research into the usage of numerical method-based deformation simulation studies related to the standardisation, development, and preservation of such traditional string instruments.
Two-Sided Matching Decision Method of Electricity Sales Package Based on Disappointment Theory
Authors: Ruan Jianyu , Wan Yingtong , Ma Yuanqian
Published: August 2023

Under the background of the “dual carbon” targets and continuously promoted power system reform, the application of a high proportion of renewable energy is becoming increasingly widespread. All sectors of society have greater demands for more appropriate electricity sales packages to guide the behavior of power users, which will in turn help conserve energy, reduce emissions, and finally achieve low-carbon operation of the power market economy. However, the existing methods of recommending electricity sales packages fail to provide appropriate and accurate recommendations for the users lacking preference information. Therefore, this paper proposes a two-sided matching decision-making method of an electricity sales package based on disappointment theory. First of all, according to the incomplete fuzzy preference relationship provided by the power user and the electricity sales package, the respective priority weight vector is calculated, and then the subjective satisfaction matrix of the power user and the electricity sales package is calculated. Next, the adjusted satisfaction matrix is calculated by adding the influence of the theory of elation and disappointment. Then, on the basis of the adjusted satisfaction matrix, an optimization model aiming at maximizing the total satisfaction of electric power customers and electricity sales packages is established, and the optimal stable matching model of electric power customers and electricity sales packages is obtained. Lastly, taking an industrial park in Zhejiang Province as an example, using the bilateral matching method proposed in this article, the optimal matching schemes for five electric power customers and six electricity sales packages is obtained, which shows the effectiveness and rationality of the two-sided matching decision-making method of electricity sales packages based on the disappointment theory.
Anticancer Effects in HT-29 Cells and Anti-Inflammatory Effects on Mouse Splenocytes of Broccoli Cultivated with Deep Sea Water Minerals in Organic Farming
Authors: Lee Yeon-Jun , Pan Yanni , Park Seung-Hwan , Sin Sin-Il , Park Kun-Young
Published: August 2023

In this study, broccoli grown with deep sea water minerals (DSWMs) confirmed anticancer effects in HT-29 colorectal cancer cells and anti-inflammatory effects in C57BL/6 mouse splenocytes. Natural dream cultured broccoli (NB) grown with DSWMs had elevated sodium (Na) and calcium (Ca) levels and enhanced the expression of p53 and p21, both of which are associated with cell cycle arrest in HT-29 colorectal cancer cells. It also decreased the expression of Bax, Bad, Bim, Bak, caspase-9, and caspase-3. In lipopolysaccharide (LPS)-treated C57BL/6 mouse splenocytes, NB produced little nitric oxide (NO). ELISA research indicated that NB decreased IL-1β, IL-6, TNF-α, IFN-γ, and IL-12 expression while increasing NK cell activity. As a result, broccoli cultivated with deep water minerals has better anticancer and anti-inflammatory properties than conventional and organic farming.
Delamination Localization in Multilayered CFRP Panel Based on Reconstruction of Guided Wave Modes
Authors: Papanaboina Mastan Raja , Jasiuniene Elena , Samaitis Vykintas , Mažeika Liudas , Griškevičius Paulius
Published: August 2023

Multi-layered composite materials are being used in various engineering fields, such as aerospace, automobile, and wind energy, because of their superior material properties. Due to various impact loads during the service life of composite structures, different types of defects can occur, such as matrix cracking, fiber breakage, delaminations, etc. In this research, a novel SHM technique for delamination detection and localization using a minimum number of sensors is proposed. The analytical, numerical, and experimental analysis of GW was performed to increase the probability of detection and localization of delaminations in CFRP material. A new analytical model was developed, which enables identifying converted and transmitted modes in the presence of multiple GW modes. A 2D FFT-based spatial filtering was used to filter the GW modes. The dominant A0 mode was separated to inspect the delamination. Phase velocity is one of the important features in GW inspection to localize the delamination. A phase spectrum approach was developed to reconstruct the phase velocity dispersion of the GW modes in case material properties are unknown.
Research into Image Point Positioning Compensation of a High-Orbit Thermal Infrared Staring Camera
Authors: Xiao Hui , Li Chenying , Sheng Qinghong , Wang Bo , Li Jun , Ma Jianguo , Wu Fan , Zhou Wei
Published: August 2023

The sight of the high-orbit thermal infrared staring camera is concentrated and sensitive to temperature changes, and therefore its image point positioning is key to ensuring the geometric quality of the thermal infrared staring image and eliminating the errors of the imaging system in orbit. According to the geometric imaging characteristics of the high-orbit thermal infrared staring camera, internal and external positioning compensation models are proposed in this paper based on the two-dimensional pointing angle of temperature change, and four kinds of experimental schemes of image point positioning compensation are designed. The experimental results show that the method proposed in this paper has a good compensation effect on the image point positioning error.
Convolutional-Neural-Network-Based Hexagonal Quantum Error Correction Decoder
Authors: Li Aoqing , Li Fan , Gan Qidi , Ma Hongyang
Published: August 2023

Topological quantum error-correcting codes are an important tool for realizing fault-tolerant quantum computers. Heavy hexagonal coding is a new class of quantum error-correcting coding that assigns physical and auxiliary qubits to the vertices and edges of a low-degree graph. The layout of heavy hexagonal codes is particularly suitable for superconducting qubit architectures to reduce frequency conflicts and crosstalk. Although various topological code decoders have been proposed, constructing the optimal decoder remains challenging. Machine learning is an effective decoding scheme for topological codes, and in this paper, we propose a machine learning heavy hexagonal decoder based on a convolutional neural network (CNN) to obtain the decoding threshold. We test our method on heavy hexagonal codes with code distance of three, five, and seven, and increase it to five, seven, and nine by optimizing the RestNet network architecture. Our results show that the decoder thresholding accuracies are about 0.57% and 0.65%, respectively, which are about 25% higher than the conventional decoding scheme under the depolarizing noise model. The proposed decoding architecture is also applicable to other topological code families.
Detecting and Isolating Adversarial Attacks Using Characteristics of the Surrogate Model Framework
Authors: Biczyk Piotr , Wawrowski Łukasz
Published: August 2023

The paper introduces a novel framework for detecting adversarial attacks on machine learning models that classify tabular data. Its purpose is to provide a robust method for the monitoring and continuous auditing of machine learning models for the purpose of detecting malicious data alterations. The core of the framework is based on building machine learning classifiers for the detection of attacks and its type that operate on diagnostic attributes. These diagnostic attributes are obtained not from the original model, but from the surrogate model that has been created by observation of the original model inputs and outputs. The paper presents building blocks for the framework and tests its power for the detection and isolation of attacks in selected scenarios utilizing known attacks and public machine learning data sets. The obtained results pave the road for further experiments and the goal of developing classifiers that can be integrated into real-world scenarios, bolstering the robustness of machine learning applications.