Actual Scenario Research Articles

Daylighting and energy performance of window with transparent insulation slats combined with building shading in the hot-summer and cold-winter zone

The shading from surrounding buildings significantly affects the energy and daylighting performance of transparent insulation materials (TIM) systems. In previous studies, the performance of TIM systems was primarily discussed in ideal situations without considering the influence of surrounding buildings. However, this is not realistic in actual urban scenarios. This study presents a case study conducted in Changsha to evaluate and compare the energy and daylighting performance of the window with transparent insulation slats (WTIS) and normal double glazing (NDG). The study considers the varying degrees of building shading effects. The results show that windows facing west exhibit the best energy efficiency, while windows facing south have the worst. WTIS achieves a higher Useful Daylight Illuminance (UDI) when building shading effects are not significant, whereas NDG achieves a higher UDI when building shading effects are significant. Despite increasing lighting energy consumption by 69.8 % to 84.3 %, WTIS consistently outperforms NDG in terms of total energy savings. Furthermore, strategically utilizing or deactivating WTIS according to recommended periods during winter can enhance the total solar gain for the building by approximately 22.3 %. This study provides valuable recommendations for the application of WTIS systems and the design of buildings in the hot-summer and cold-winter zone.

Continuous charging assignment algorithm for heterogeneous robot clusters based on E-CARGO

Forming robot clusters is a common means to improve work efficiency and ensure personnel safety in intelligent logistics warehousing. However, designing a reasonable charging sequence and time for each robot in the cluster poses a major challenge for management personnel. In actual scenarios, the working and charging processes of the robots must be considered simultaneously. This study also considers multiple constraints on robot battery levels and available workstations under different parameters to ensure each team operates normally at any given time. Additionally, to enhance cooperation efficiency and alleviate the workload of management personnel, the problem is transformed from conventional one-time assignments to adaptive continuous assignments within fixed time periods. Accordingly, the ACP-GRAC problem is proposed. Firstly, a group role assignment model with conflicts is introduced to resolve assignment conflicts caused by mismatches between job skills and position requirements. Then, energy information is shared among robots during the charging process, adding adaptive cooperation concepts to address continuous charging issues. Finally, the E-CARGO model is utilized to formalize the ACP-GRAC problem, and the HCCA algorithm is designed for its solution. Furthermore, a classification backtracking strategy is proposed to simultaneously address the constraint of continuous operation at any given time and the problem of constraint violations caused by the HCCA algorithm’s pursuit of maximum team performance. Moreover, since the classification backtracking strategy can compute the theoretical minimum remaining energy of the robot before departing for charging, it further optimizes the overall work efficiency. Additionally, this study addresses the issue of low cluster work efficiency due to the unreasonable initial planning sequence of the algorithm. In the experimental section, comparisons are made with recent excellent heuristic algorithms and deep reinforcement learning algorithms. Experimental results demonstrate that B-HCCA exhibits significant advantages in both solution efficiency and solution quality when addressing adaptive cooperation problems with group role assignment conflicts.

Street landscape environment design based on visual technology and entertainment robots: Computer simulation gamification landscape design

In recent years, with the development of social economy and the innovation of science and technology, the street landscape environment design based on visual technology and entertainment robot has been widely concerned. This paper aims to explore how to use computer simulation and gamification design methods to enhance the entertainment and attractiveness of street landscapes. In this paper, the basic principles of computer vision and V2X vehicle networking are deeply studied, and a series of simulation experiments and data analysis are carried out in combination with actual scenarios to verify the feasibility and effectiveness of V2X vehicle networking mode based on computer vision in urban street landscape design. V2X vehicle networking mode through real-time information exchange with road infrastructure, other vehicles, pedestrians, etc., can obtain a large number of urban streetscape design related data. Studies have shown that this design approach can increase people’s interest and engagement in street landscapes, and increase the entertainment experience and sense of interaction of urban residents.

Continual learning for seizure prediction via memory projection strategy

Despite extensive algorithms for epilepsy prediction via machine learning, most models are tailored for offline scenarios and cannot handle actual scenarios where data changes over time. Catastrophic forgetting(CF) for learned electroencephalogram(EEG) data occurs when EEG changes dynamically in the clinical setting. This paper implements a continual learning(CL) strategy Memory Projection(MP) for epilepsy prediction, which can be combined with other algorithms to avoid CF. Such a strategy enables the model to learn EEG data from each patient in dynamic subspaces with weak correlation layer by layer to minimize interference and promote knowledge transfer. Regularization Loss Reconstruction Algorithm and Matrix Dimensionality Reduction Algorithm are introduced into the core of MP. Experimental results show that MP exhibits excellent performance and low forgetting rates in sequential learning of seizure prediction. The forgetting rate of accuracy and sensitivity under multiple experiments are below 5%. When learning from multi-center datasets, the forgetting rates for accuracy and sensitivity decrease to 0.65% and 1.86%, making it comparable to state-of-the-art CL strategies. Through ablation experiments, we have analyzed that MP can operate with minimal storage and computational cost, which demonstrates practical potential for seizure prediction in clinical scenarios.

Modification of LiMn2O4 Cathodes to Boost Kinetics Match via rGO for High-Performance Rocking-Chair Lithium-Ion Capacitors.

The rocking-chair lithium-ion capacitors (RLICs), composed of a battery-type cathode and capacitive-type anode, alleviates the issue of increased internal resistance caused by electrolyte consumption during the cycling process of the lithium-ion capacitors (LICs). However, the poor conductivity of cathode materials and the mismatch between the cathode and anode are the key issues that hinder its commercial application. In this work, a modification simplification strategy is proposed to tailor the conductivity of the cathode and matching characteristic with the anode. The in situ grown lithium manganate (LMO) is featured with a three-dimensional conductive network constructed by reduced graphene oxide (rGO). The optimized LMO/rGO composite cathode demonstrates an excellent rate performance, lithium-ion diffusion rate, and cycling performance. After assembling an RLICs with activated carbon (AC), the RLICs exhibits an energy density of as high as 239.11 Wh/kg at a power density of 400 W/kg. Even at a power density of 200 kW/kg, its energy density can maintain at 39.9 Wh/kg. These excellent electrochemical performances are mainly attributed to the compounding of LMO with rGO, which not only improves the conductivity of the cathode but also realizes a better matching with the capacitive-type anode. This modification strategy provides a reference for the further development of energy storage devices suitable for actual production conditions and application scenarios.

Multi-scale damage characterisation of semi-flexible pavements under freeze-thaw cycles

This study aims to investigate the failure mechanism of semi-flexible pavements (SFP) under freeze-thaw conditions and to provide theoretical support for optimizing the material properties. By simulating the freeze-thaw damage process of SFP in actual service scenarios and combining various experimental methods, the strength characteristics and microstructural change rules of SFP in the freeze-thaw process are systematically analyzed. The study used freeze-thaw tests to simulate the real environment, combined with indirect tensile tests (IDT) and three-point bending tests to evaluate the mechanical properties of SFP. To further investigate the failure mechanism, scanning electron microscopy (SEM) was used to observe the interfacial changes between asphalt and early-strength sulphoaluminate cement (JGM301). Additionally, CT scanning was used to capture the structural changes of SFP during freeze-thaw cycles. The results showed that freeze-thaw cycles significantly and negatively affected the split tensile strength of SFP. SEM observations revealed that cracks appeared at the asphalt-cement interface (ITZ), the cement-SBS bond weakened, and the structure of the cement itself became loose. CT revealed structural changes such as cracking at the interface of the two-phase material, creation of new voids, and linkage of old voids during freeze-thawing of the SFP. In summary, the reduction in adhesion at the cement-asphalt interface and the frost swelling of JGM301 are the main reasons for the damage of SFP in freeze-thaw cycles. This study provides an important basis for an in-depth understanding of the freeze-thaw damage mechanism of SFP and optimization of its performance.

Fault diagnosis of rolling bearing based on adaptive attention network and federated learning

Abstract For rolling bearings in actual industrial scenarios, the problem of low diagnostic accuracy is caused by the large difference in data distribution under different working conditions and the complexity of working conditions with a lot of redundant information, in this paper, a combination of the Federated Adaptive Attention Network (FAAN) based fault diagnostic model is proposed. Firstly, the fault information is randomly divided into multiple sequences, by utilizing dual convolutional layers and adaptive attention mechanism to process abundant vibration data, it enables accurate identification of fault information distribution in the original signal while removing superfluous information nd fusing characteristics to improve diagnostic accuracy. Furthermore, a federated learning model incorporating attention mechanism is proposed. It performs asynchronous updates based on local data distribution, improving the efficiency and accuracy of data analysis uploads, and enhancing the model’s generalization capability. Simulation experiments have been carried out using the datasets from Case Western Reserve University and Jiangnan University, and after comparative analysis, the proposed method has better performance and generalization ability.

Gaussian second derivative blur kernels for image deblurring

Image blur is a common image degradation phenomenon that occurs in case of camera movement and lens defocusing. In order to deblur the image, point spread function (PSF) model is often used to match the real blur kernel, but currently the matching effect is poor. In this paper, an image restoration technique based on the Gaussian Second Derivative (GSD) fitted PSF is proposed to address this problem. Firstly, a GSD model is proposed. The actual image Line spread function (LSF) extracted by knife-edge method is modeled as a linear combination of a series of Gaussian second derivatives. Then, an improved annihilation filtering algorithm is proposed. The algorithm is used to estimate the parameters of the GSD model, so as to remove the errors in the actual complex scenarios. Finally, the image PSF is obtained by using the LSF curve fitted and corrected by the GSD model, and combined with the existing Richardson-Lucy (R-L) algorithm, the clear image is finally restored. Experimental results show that compared with the existing image restoration strategies based on other PSF models, this method can effectively improve the image restoration accuracy.

Monitoring of interface separation damage in buckling-restrained steel plate shear walls using piezoelectric based smart aggregates under cyclic loading

Interface debonding damage is a critical issue for many prefabricated concrete components. Various studies have employed different methods to detect debonding behaviors and provided quantitative damage indexes. In this paper, we propose a novel monitoring approach for detecting interface debonding damage in Engineered Cementitious Composite (ECC) panel-restrained composite steel plate shear walls (SPSWs) using piezoelectric ceramics (PZT). Four SPSWs were designed with different types of concrete, steel plate thicknesses, and bolt arrangements for the cyclic loading experiment. Energy data was collected for time-domain and wavelet packet analysis. The root mean square deviation (RMSD) was then calculated to provide a quantitative damage index for the specimens. The highest damage indices for each specimen were 0.770, 0.963, 0.988, and 1, respectively, which corresponded well with the actual damage scenarios. The experiments verified that using PZT can accurately monitor debonding damage in SPSWs.

Development of a correction coefficient for radiographic evaluation of the alveolar bone crest: a pilot study.

To analyze the differences in cusp height on radiographs, establishing proportional relationships between cusp and alveolar bone crest (ABC) measurements. The goal of this study was to develop a correction coefficient by considering this proportion. Twenty-one artificial teeth, molars and premolars, and bovine ribs were used. Interproximal radiographs were taken with the aid of a positioner. The vertical angles used were: 0°, + 5°, and + 10°, and processed using three spatial resolutions measured in line pairs per mm (lp/mm): 20, 25 and 40. The Perio filter was applied to each image, in addition to the original one. Combinations of angle, resolution, and filter were made. Eighteen images were analyzed by three specialists, resulting in 252 measurements for each evaluator, totaling 756 measurements. The overall variability of the measurements can be explained mainly by the variation in tooth anatomy. The 0° 25 lp/mm Perio filter method was the closest one to the actual clinical scenario for both cusps and ABC. The correction factor managed to explain 71.45% of the errors. The variation in vertical angulation interferes with cusp and ABC measurements, and the angulation at 0º and spatial resolution of 25 lp/mm showed better results. The use of correction coefficients allowed approaching actual measurement values. More accurate ABC height measurements are essential even in radiographic exams that do not meet the standard of excellence because the need to repeat radiographic exams is then eliminated.

Evaluating the Mechanisms and Efficiency of Johkasou Systems for Decentralized Domestic Effluent Treatment: A Review

Johkasou systems have emerged as quintessential examples of decentralized wastewater treatment technologies due to their compact design, easy operation, and robust resistance to mechanical impact attributes that are particularly effective in mitigating and treating rural domestic wastewater. Although the efficiency of the Johkasou process in removing nitrogen and phosphorus has been well-documented, a comprehensive synthesis of the underlying mechanisms and influencing factors is still elusive. This review seeks to elucidate these aspects by detailing the biogeochemical pathways involved in nitrogen and phosphorus removal, characterizing the key microbial consortia, and addressing the potential accumulation of nitrous oxide (N2O). Furthermore, the review critically examines the impact of various media used in Johkasou systems on nutrient removal efficacy, with a particular emphasis on nitrogen. It also proposes a range of practical adjustments to design parameters, including dissolved oxygen (DO), pH, temperature, and hydraulic retention time (HRT), to enhance process performance. Finally, the practical implementation of Johkasou systems and their integration with ancillary processes in actual domestic sewage treatment scenarios are synthesized, providing a theoretical foundation for advancing Johkasou methodologies in rural areas.

Dataspace Integration for Agrobiodiversity Digital Twins with RO-Crate

A severe impact of human-induced climate change is the global decrease in crop yields, threatening the United Nations sustainable development goal (SDG) to end hunger, achieve food security, improve nutrition, and promote sustainable agriculture (SDG 2: “Zero Hunger Goal”). Decision-making on appropriate mitigation strategies requires data integration from heterogeneous data-rich contexts including Earth observation (EO), biodiversity monitoring, gene banks as well as socio-economic data. Accordingly, large-scale initiatives for thematic data spaces, such as the European projects Destination Earth (DestinE) and the Green Deal Data Space, have been launched. Underutilized crops and crop wild relatives (CWR) can contribute to mitigating the impacts of climate change since their greater genetic diversity allows adaptation of agro-food sources to extreme weather events such as flooding and drought or emerging crop pests. Within the framework of Biodiversity Digital Twin (BioDT), a prototype digital twin for crop genetic resources modeling is under development. This prototype combines the scanning of genetic resources of CWRs and traditional cultivars for promising pest resistance and abiotic tolerance traits with advanced species distribution modeling and actual climate scenarios from EO. The aim is to generate habitat suitability maps and identify potential areas where relevant populations are potentially growing (Chala et al. 2024). The talk gives an overview of data models used to facilitate the required interoperability of both Earth models and environmental data within and across different data spaces such as the DestinE Data Lake (Duatis Juarez et al. 2023). Based on our findings from the use case of agrobiodiversity digital twins, we will discuss approaches for processing machine-actionable digital objects (Fig. 1) containing workflow information (i.e., in the Common Workflow Language format, Crusoe et al. 2022), and for sharing resulting research data in a FAIR (Findable, Accessible, Interoperable, and Reusable) manner by leveraging on so-called webby FAIR Digital Objects involving Research Object Crate (RO-Crate, Soiland-Reyes et al. 2022), FAIR Signposting (Soiland-Reyes et al. 2024) and schema.org and its Bioschemas extension respectively (Castro et al. 2020),

Effective approaches for mining correlated and low-average-cost patterns

High-utility pattern mining is a data-mining research field that focuses on identifying patterns that exceed a specified threshold for utility values. These utility values primarily indicate positive benefits such as profits. However, in actual scenarios, certain applications and demands require the discovery of patterns at a lower cost. Hence, the concept of low-average-cost itemset mining is introduced to evaluate patterns based on their average costs. This is different from high-utility pattern mining, as the cost is minimized. In this study, we focus on extracting patterns with strong correlations and low average costs. Because existing techniques depend on the support metric to devise cost-based pruning strategies, these strategies are ineffective in extracting correlated and low-average-cost patterns. Therefore, we introduce a novel lower bound to efficiently limit the search space for patterns. Experimental analyses are conducted on eight databases to assess the performance of the mining algorithm in terms of execution time, memory usage, pruning effectiveness, and scalability. Additionally, the effects of different pruning techniques and item-sorting orders on the performance of the algorithm are investigated.

Cognitive Hybrid Deep Learning-based Multi-modal Sentiment Analysis for Online Product Reviews

Recently the field of sentiment analysis has gained a lot of attraction in literature. The idea that a machine can dynamically spot the text’s sentiments is fascinating. In this paper, we propose a method to classify the textual sentiments in Twitter feeds. In particular, we focus on analyzing the tweets of products as either positive or negative. The proposed technique utilizes a deep learning schema to learn and predict the sentiment by extracting features directly from the text. Specifically, we use Convolutional Neural Networks with different convolutional layers. Further, we experiment with LSTMs and try an ensemble of multiple models to get the best results. We employ an n-gram-based word embeddings approach to get the machine-level word representations. Testing of the method is conducted on real-world datasets. We have discovered that the ensemble technique yields the best results after conducting experiments on a huge corpus of more than one million tweets. To be specific, we get an accuracy of 84.95%. The proposed method is also compared with several existing methods. An extensive numerical investigation has revealed the superiority of the proposed work in actual deployment scenarios.

Multi-modal incomplete label information three-way bidirectional decision-making: Applications of disease assessment

Disease assessment involves two stages of decision-making process. The first process is to infer the development trend of the disease through the existing judgment conditions, which is to make a positive estimation (prediction) decision. The second process is to update the judgment conditions for different disease states and optimize the clinical assessment evidence by fusing disease progression data, that is, to make a reverse evaluation (optimization) decision by reviewing historical diagnostic and treatment data to summarize the best judgment conditions for the disease characteristics. Therefore, we construct a three-way bidirectional decision method for multi-modal incomplete labeling information, considering the mixed data features with no labels and labels, and the bidirectional decision features of positive prediction and reverse optimization. Specifically, the method first introduces semi-supervised learning (SSL) into the rough sets, constructs a rough sets model based on cosine similarity relation, generates pseudo-labels for incomplete information, and explains the positive prediction decision process principle. Second, the gradient space is constructed on the predictive information system by the three-way decision-making principle, the loss function formed by the combination of KL divergence and cross entropy loss function is used to find the direction of the fastest decline in attribute information value, and the optimal decision rule and the automatic updating strategy of the threshold value are obtained. At the same time, the principle of the reverse optimization decision process is explained. Finally, multimodal information from pathological tissue images is used to evaluate clinical progress problem. A pathological tissue images dataset includes 2000 colorectal cancer and 300 kidney disease cases used for simulation experiments. The proposed three-way bidirectional decision-making model can be widely applied to actual decision-making scenarios for evaluating clinical progress of different diseases. Compared with previous studies, this method can realize objective calculation and automatic update of threshold and loss values and effectively solve complex prediction and optimization decision-making problems.

Potential of ChatGPT in academic research: exploring innovative thinking skills

ABSTRACT Academics, scholars, and learners are increasingly turning to AI-based language models like ChatGPT for a variety of academic and non-academic applications including essay writing, speech creation, literature summarizing, and idea production. However, the use of ChatGPT in academic research has sparked debate and raised concerns about its influence on research and publishing. By offering an actual scenario and suggestions, this study intends to shed light on the practical implementation of ChatGPT in academic research. The dataset for this study includes 3,860 students from Thailand's basic and secondary school levels. The survey data gathered focuses on investigating innovative thinking skills. The findings suggest that ChatGPT might be a useful technique for generating first ideas in academic studies. However, it is crucial for researchers to exercise caution as they may encounter certain challenges during the process. As a result, while using ChatGPT in academic research, researchers must be aware of these issues and make educated judgments appropriately. Given the possible uses and ramifications of ChatGPT, the academic community must develop detailed protocols governing its usage in research and subsequent publishing. These rules will ensure the proper and ethical use of ChatGPT in the academic setting.

Study to Assess the Effectiveness of Live Demonstration on Knowledge Regarding Surgical Management of Inguinal Hernia Repair among Nursing Students in Selected College of Nursing, Hubli

Introduction: Teaching skills are essential for learning needs of the students, the teaching is aided by the various technique of teaching. Live demonstration is a technique of educating students in actual scenario with demonstration of the content of topic, here the teacher performs the in presence of the students the topic of discussion. In the present study the technique of live demonstration practiced to improve the knowledge of nursing students in surgical management of inguinal hernia repair included pre, intra and post operative management of surgery. Objectives: the study designed to assess the effectiveness of live demonstration on knowledge regarding surgical management of inguinal hernia among nursing students. Methodology: one group pretest posttest design used for 156 nursing students selected by simple random sampling technique, a structured knowledge questionnaire was designed having content validity of 0.89 and reliability of 0.92 used to collect and assess knowledge of nursing students on surgical management of inguinal hernia repair, pretest was conducted following which a live demonstration on periopertive management of inguinal hernia repair was demonstration in class room and by live video visuals from operation theater while performing surgery following which the post operative care was demonstrated till the stabilization of the patient. The posttest was conducted to assess the gained knowledge using same questionnaire used in pretest. The collected data was analyzed using SPSS 16.0 version statistical analysis and the findings were tabulated and computed. Results: The findings of the study revealed that, the pretest knowledge of the nursing students regarding surgical management of inguinal hernia repair was inadequate, out of 156 nursing students, assessed 145 students had inadequate knowledge, and following live demonstration the knowledge, improved to adequate to almost all the students signifying that live demonstration is effective method of teaching learning activity. The t value was 69.56 for 0.05 level of significance. Conclusion: teaching is an art, which include the colors of various techniques to make classroom live and living. Live demonstration is the best method, which bring in the actual experience and spot learning of the students and the retention of the content discussed in live demonstration is high along with ability to compare, judge and reason the care and outcome of the learning. The study recommends that the nursing education should be equipped with live in demonstration, which brings students out of imagination of content and picture memorization from books and articles. Live demonstration should be included in teaching learning activity of the student’s class learning.

Static output feedback LFC for semi‐Markov type interconnected multi‐area power systems: A non‐fragile PI strategy

AbstractThis article addresses the static output feedback load frequency control (LFC) problem for semi‐Markov type interconnected multi‐area power systems (IMAPSs) through non‐fragile proportional‐integral (PI) strategy. Primarily, by roundly exploiting the dynamical properties and considering random mutations of the IMAPSs, the semi‐Markov jump process (MJP) is elaborately scheduled for establishing the dynamical model with precision. Subsequently, on account of the characteristic of geographical isolation, a memory‐like sampled‐data LFC mechanism considering time‐varying transmission delay for the semi‐Markov jump IMPASs is formulated accordingly. Additionally, the non‐fragile issue is explored for the PI controller gain fluctuations to meet the actual operation scenario. After that, a mode‐dependent two‐side polynomial‐type looped Lyapunov functional is constructed for enhancing flexibility. Then, in virtue of the stochastic analysis technique, some relaxed conditions guaranteeing stochastic stability with prescribed performance are thereby derived in the shape of linear matrix inequalities (LMIs). Ultimately, a numerical example is carried out to validate the efficacy of the developed control methodology.

Intraoral ageing of aligners and attachments: Adverse effects on clinical efficiency and release of biologically-active compounds.

The clinical application of aligners is accompanied by the ageing of the polymer appliances and the attachments used, which may result in inefficiency in reaching the predicted range of tooth movement, and release of compounds and microplastics in the oral cavity as a result of the friction, wear and attrition of the aligner and composite attachment. The purpose of this review is to present the mechanism and effects of in vivo ageing; describe the hydrolytic degradation of aligners and enzymatic degradation of composite attachments; examine the ageing pattern of aligners in vivo, under actual clinical scenarios; and identify a link to the discrepancy between predicted and actual clinical outcome. Lastly, strategies to deal with three potentially critical issues associated with the use of aligners, namely the necessity of weekly renewal, the dissimilar mechanical properties of aligner and attachment resulting in wear and plastic deformation of the aligner, and the development of integuments and biofilms with microbial colonization of the appliance, are discussed.

Experimental study on the dynamic response of sintered NdFeB and secondary development verification of its Zhu-Wang-Tang damage constitutive model

Currently, there is relatively limited research on the dynamic response of sintered neodymium iron boron (sintered NdFeB). In order to reveal the dynamic response of sintered NdFeB, a Hopkinson pressure bar experimental platform is constructed to study its dynamic response. The results of the study indicate the necessity of establishing a dynamic constitutive model which can reflect its the strain rate sensitivity. Therefore, combining the Weibull distribution with the Zhu-Wang-Tang constitutive model, a constitutive model with damage for sintered NdFeB is created and this model is extended to three-dimensional form, expressing its incremental formulation. Finally, to verify the accuracy of this model in practical applications, the VUMAT subroutine is utilized to compile the damage constitutive relationship of sintered NdFeB. A Hopkinson pressure bar computational model is established, and the results show good consistency between the simulation and the experiment. This study provides a reference for the practical application of the damage constitutive model in actual scenarios.