Environmental Processes Research Articles

Secure Patient Data Monitoring and Efficient Routing Optimization using a Hyperelliptic Curve Cryptography with Fuzzy-based Priority in WBSN

Aims and Background: Wireless Body Sensor Network (WBSN) technology is one of the major research areas in the medical and entertainment industries. A wireless sensor network (WSN) is a dense sensor network that senses environmental conditions, processes, and outgoing data at the sink node. A WBSN develops patient monitoring systems that provide the flexibility and mobility needed to monitor patient health. In data communications, it is difficult to find flexible optical routing paths, switching capabilities, and packet processing in the composition of optical networks. Information-centric networks (ICNs) are a new network model and are different from information- centric models. The priority of the information-centric model is the communication network. Objectives: In the existing literature, such methods are typically developed using computationally expensive procedures, such as bilinear pairing, elliptic curve operations, etc., which are unsuitable for biomedical devices with limited resources. Using the concept of hyperelliptic curve cryptography (HECC), we propose a new solution: a smart card-based two-factor mutual authentication scheme. In this new scheme, HECC’s finest properties, such as compact parameters and key sizes, are utilized to enhance the real-time performance of an IoT-based TMIS system. Methodology: A fuzzy–based Priority Aware Data Sharing (FPADS) method is introduced to schedule the priority data and monitor the transmission length. The child node adjusts the transmission speed of the cluster head with the help of a fuzzy logic controller (FLC). Results: The proposed model estimated the traffic load of the child node and the priority of the different amounts of data to be transmitted. The principle of scheduling data packets to be developed is based on the precedence of the data with the lowest transmit length in the network. Conclusion: The proposed FPADS performance increases in terms of scheduling time utilisation, traffic distribution, and mean delay. Simulations have been done using NS2, and the outcomes have shown that the proposed methodology is efficient and improves the overall QoS of the system.

An Effective Algorithm Based on Sequence and Property Information for N4-methylcytosine Identification in Multiple Species

Abstract: N4-methylcytosine (4mC) is one of the most important epigenetic modifications, which plays a significant role in biological progress and helps explain biological functions. Although biological experiments can identify potential 4mC sites, they are limited due to the experimental environment and labor-intensive process. Therefore, it is crucial to construct a computational model to identify the 4mC sites. Some computational methods have been proposed to identify the 4mC sites, but some problems should not be ignored, such as those presented as follows: (1) a more accurate algorithm is required to improve the prediction, especially for Matthew’s correlation coefficient (MCC); (2) easier method is needed for clinical research to design medicine or treat disease. Considering these aspects, an effective algorithm using comprehensible encoding in multiple species was proposed in this study. Since nucleotide arrangement and its property information could reflect the sequence structure and function, several feature vectors have been developed based on nucleotide energy information, trinucleotide energy information, and nucleotide chemical property information. Besides, feature effect has been analyzed to select the optimal feature vectors for multiple species. Finally, the optimal feature vectors were inputted into the CatBoost algorithm to construct the identification model. The evaluation results showed that our study obtained the highest MCC, i.e., 2.5%~11.1%, 1.4%~17.8%, 1.1%~7.6%, and 2.3%~18.0% higher than previous models for the A. thaliana, C. elegans, D. melanogaster, and E. coli datasets, respectively. These satisfactory results reflect that the proposed method is available to identify 4mC sites in multiple species, especially for MCC. It could provide a reasonable supplement for biological research.

Toward the European Health Data Space: The IMPaCT-Data secure infrastructure for EHR-based precision medicine research

Background:Art. 50 of the proposal for a Regulation on the European Health Data Space (EHDS) states that “health data access bodies shall provide access to electronic health data only through a secure processing environment, with technical and organizational measures and security and interoperability requirements”. Objective:To identify specific security measures that nodes participating in health data spaces shall implement based on the results of the IMPaCT-Data project, whose goal is to facilitate the exchange of electronic health records (EHR) between public entities based in Spain and the secondary use of this information for precision medicine research in compliance with the General Data Protection Regulation (GDPR). Data and methods:This article presents an analysis of 24 out of a list of 72 security measures identified in the Spanish National Security Scheme (ENS) and adopted by members of the federated data infrastructure developed during the IMPaCT-Data project. Results:The IMPaCT-Data case helps clarify roles and responsibilities of entities willing to participate in the EHDS by reconciling technical system notions with the legal terminology. Most relevant security measures for Data Space Gatekeepers, Enablers and Prosumers are identified and explained. Conclusion:The EHDS can only be viable as long as the fiduciary duty of care of public health authorities is preserved; this implies that the secondary use of personal data shall contribute to the public interest and/or to protect the vital interests of the data subjects. This condition can only be met if all nodes participating in a health data space adopt the appropriate organizational and technical security measures necessary to fulfill their role.

Similar assembly state discriminator for reinforcement learning-based robotic connector assembly

In practice, the process of robot assembly in an unstructured environment faces difficulties due to the presence of unpredictable environmental errors related to vision and pose. Therefore, to minimize the uncertain environmental errors during the robotic assembly process in an unstructured environment, several studies have considered a reinforcement learning (RL)-based approach. However, if assembly parts are changed, it becomes difficult to apply RL-based methods to assemble various parts because additional learning may be required. Especially in the case of connector assembly, fine-tuning is essential because the shape changes depending on the type of connector. In this study, we propose a similar assembly state discriminator that transforms the state information (force, velocity, and RGB image) of reinforcement learning into generalized features to respond various types of connector assembly tasks. This method processes the data to include essential features for assembly regardless of connector type. By learning the RL model with the processed data using this method, the RL model trained for a specific connector can be efficiently applied to other types of connectors without fine-tuning. The assembly success rate for the 7 types of connectors (Harting, HDMI, USB, power, air jack, banana plug and PCIE) using the proposed method was demonstrated to be over 96 %.

Advanced Electrode Materials Dedicated for Electroanalysis

The development of advanced electrode materials has significantly enhanced the capabilities of electrochemical devices, enabling their application in diverse fields such as environmental monitoring, medical diagnostics, food safety, and industrial processes [...]

Examining the Land Use and Land Cover Impacts of Highway Capacity Expansions in California Using Remote Sensing Technology

Highway capacity expansion has wide impacts on land use and land cover (LULC). Cost–benefit analyses and environmental review processes for roadway capacity expansion and maintenance decisions do not comprehensively consider the LULC impacts. This study examined land cover changes directly associated with highway expansion in California, and the relationship between land use and the vegetation impacts of highway projects using satellite remote sensing data. The methodology involves a geospatial analysis of normalized difference vegetation index (NDVI) data in 18 sites across California before and after highway expansion project completion. We accounted for seasonality and included a set of control sites. Findings indicate that the impacts of highway expansion on changes in NDVI are diverse, stressing the importance of the environmental context around each individual project site. Sites that are located near less-developed areas with more extensive natural vegetation (e.g., sprawled areas or exurbs), show a significant decline in NDVI values. Virtually all sites with insignificant changes in NDVI after highway expansion are located in areas that already exhibit heavy urban development (e.g., Los Angeles, San José) or are otherwise located near large expanses of bare, non-vegetated earth. Also, project sites that experienced multiple types of construction (i.e., adding more lanes, widening sections, bridge renovation, etc.) were more likely to exhibit decreasing NDVI values compared with project sites that only experienced one type of construction. Decisions about highway construction and capacity expansion should consider the context and the full environmental impacts, including land use and land cover changes over time.

Tipping points in coupled human–environment system models: a review

Abstract. Mathematical models that couple human behavior to environmental processes can offer valuable insights into how human behavior affects various types of ecological, climate, and epidemiological systems. This review focuses on human drivers of tipping events in coupled human–environment systems where changes to the human system can abruptly lead to desirable or undesirable new human–environment states. We use snowball sampling from relevant search terms to review the modeling of social processes – such as social norms and rates of social change – that are shown to drive tipping events, finding that many affect the coupled system depending on the system type and initial conditions. For example, tipping points can manifest very differently in human extraction versus human emission systems. Some potential interventions, such as reducing costs associated with sustainable behavior, have intuitive results. However, their beneficial outcomes via less obvious tipping events are highlighted. Of the models reviewed, we found that greater structural complexity can be associated with increased potential for tipping events. We review generic and state-of-the-art techniques in early warning signals of tipping events and identify significant opportunities to utilize digital social data to look for such signals. We conclude with an outline of challenges and promising future directions specific to furthering our understanding and informing policy that promotes sustainability within coupled human–environment systems. Non-technical summary. Mathematical models that include interactions between humans and the environment can provide valuable information to further our understanding of tipping points. Many social processes such as social norms and rates of social change can affect these tipping points in ways that are often specific to the system being modeled. Higher complexity of social structure can increase the likelihood of these transitions. We discuss how data are used to predict tipping events across many coupled systems.

A cooperative learning-aware dynamic hierarchical hyper-heuristic for distributed heterogeneous mixed no-wait flow-shop scheduling

The distributed heterogeneous mixed scheduling mode in the manufacturing systems emphasizes the cooperation between factories for the entire production cycle, which poses enormous challenges to the processing and assignment of jobs. Discrepancies in the processing environment and types of machines of each factory during various production stages cause diverse processing paths and scheduling. The distributed heterogeneous mixed no-wait flow-shop scheduling problem with sequence-dependent setup time (DHMNWFSP-SDST), abstracted from the industrial scenarios, is addressed in this paper. The mathematical model of DHMNWFSP-SDST is established. A cooperative learning-aware dynamic hierarchical hyper-heuristic (CLDHH) is proposed to solve the DHMNWFSP-SDST. In CLDHH, a cooperative initialization method is developed to promote diversity and quality of solutions. A hierarchical hyper-heuristic framework with reinforcement learning (RL) is designed to select the algorithm component automatically. Estimation of Distribution Algorithm (EDA) guides the upper-layer RL to select four neighborhood structures. A dynamic adaptive neighborhood switching constructs the lower-layer RL to accelerate exploitation with the dominant sub-neighborhoods. An elite-guided hybrid path relinking achieves local enhancement. The experimental results of CLDHH and six state-of-the-art algorithms on instances indicate that the proposed CLDHH is superior to the state-of-the-art algorithms in solution quality, robustness, and efficiency.

NGO Activism and Anti‐Corruption Disclosures: An Empirical Study of Emerging Economy Multinational Companies

AbstractBuilding on social movement theory, we investigate particular stakeholder contexts, including non‐governmental organization (NGO) activism, within which emerging economy multinational companies (EMNCs) are more likely to disclose anti‐corruption information. By leveraging Transparency International's unique dataset on anti‐corruption disclosures by EMNCs, we find that EMNCs disclose more anti‐corruption information in countries with a lower level of government oppression of NGOs, greater transparency of NGOs, as well as a greater number of anti‐corruption NGOs operating in such markets. EMNCs with a higher level of internationalization exhibit more anti‐corruption disclosures when they are based in countries with a higher level of NGO transparency and a lower level of government oppression of NGOs. Anti‐corruption disclosures of EMNCs are more responsive to corruption scandals when they are headquartered in less repressive countries and countries with a higher level of NGO transparency. This study provides original insights into whether and how the NGO‐driven social movement process in institutional environments plays a role in influencing the anti‐corruption disclosure and transparency practices of EMNCs. We argue that, within the context of emerging economies, unless we consider the interactions of NGO activism with internationalization and corruption scandals, we will be unable to fully understand anti‐corruption disclosure practices by EMNCs.

Heavy metal exposure reduces larval gut microbiota diversity of the rice striped stem borer, Chilo suppressalis.

Cadmium (Cd), a widely distributed environmental pollutant in agroecosystems, causes negative effects on crops and herbivores through bottom-up processes. The gut microbial community of an insect can play a critical role in response to metal stress. To understand how microbiota affect the stress responses of organisms to heavy metals in agroecosystems, we initially used 16S rRNA sequencing to characterize the larval gut microbiota of Chilo suppressalis, an important agricultural pest, exposed to a diet containing Cd. The species richness, diversity, and composition of the gut microbial community was then analyzed. Results revealed that while the richness (Chao1 and ACE) of gut microbiota in larvae exposed to Cd was not significantly affected, diversity (Shannon and Simpson) was reduced due to changes in species distribution and relative abundance. Overall, the most abundant genus was Enterococcus, while the abundance of the genera Micrococcaceae and Faecalibaculum in the control significantly superior to that in Cd-exposed pests. Phylogenetic investigation of microbial communities by the reconstruction of unobserved states (PICRUSt) showed that the intestinal microorganisms appear to participate in 34 pathways, especially those used in environmental information processing and the metabolism of the organism. This study suggests that the gut microbiota of C. suppressalis are significantly impacted by Cd exposure and highlights the importance of the gut microbiome in host stress responses and negative effects of Cd pollution in agroecosystems.

Influence of in-flight thermal dynamics of metal powder on directed energy deposition process

During the laser directed energy deposition (LDED) process, the in-flight thermal dynamics of metal powders lead to the formation of detrimental by-products such as fumes, flames, and metal vapors. In this work, we use a high-speed camera to investigate the nuanced thermal stage changes of the powders, examining how gas flow rates and laser beam powers influence them. We identify distinct stages in the powder's journey: heating up, fusion and vaporization, flaming, tailing, and accelerated falling. Notably, the latter stages adversely affect both the processing environment and surface quality. Control over gas flow rate and laser beam power dictates the trajectory, duration, and intensity of tailing. Additionally, laser irradiation accelerates the falling speed of particles by 6.8–22.8% compared to non-irradiated ones. Higher laser beam powers increase vaporization-induced propulsion, culminating in escalated particle velocities. Optimal gas flow rates enhance the surface quality by significantly minimizing high-temperature spatter. Through this work, the in-flight thermal dynamic behaviors of metal particles and their impact on LDED processing are explained.

Fe(III)-mediated changes in microalgae-associated bacterial communities and dissolved organic matter characteristics: A case study for Chlorococcum sp. GD

Trivalent iron ions (Fe(III)) have important effects on aquatic ecosystems, especially on the functional diversity and stability of microorganisms and ecosystems. Here, a cultivable microalgae (Chlorococcum sp. GD) from the natural environment (Shanxi, China) were isolated and identified. A combined high-throughput 16S rRNA gene amplicon sequencing/excitation-emission matrix coupled with parallel factor (EEM-PARAFAC) analysis was used to integrated the results of Fe(III) influence on Chlorococcum sp. GD bacterial community and dissolved organic matter (DOM) characteristics through laboratory experiments. Due to the addition of Fe(III) in the form of ferric nitrate (Fe(NO3)3·9H2O), complexed with EDTA to maintain solubility and bioavailability, the bacterial community was altered. This led to a decrease in the relative abundance of Proteobacteria and Actinobacteria and an increase in the relative abundance of Cyanobacteria, especially under excessive Fe(III) treatment. In addition, the relative contribution of bacterial community dispersal limitation and homogenizing dispersal was 100 %, which may lead to differences in their local adaptation and ecological processes with homogenization of bacterial diversity and loss of function. Excessive Fe(III) caused a significant increase in the abundance of genes involved in the carbon cycle (p < 0.01) and a significant decrease in genes involved in the nitrogen cycle (p < 0.01), further affecting the overall regulatory network of gene expression. This led to an increase in the abundance of genes involved in metabolism, cellular processes, environmental information processing, genetic information processing, and human diseases. The moderate amount of Fe(III) promoted the production of microbial components and accelerated the degree of DOM humification. It is also worth mentioning that excessive Fe(III) inhibited DOM degradation. Overall, this work explored the characteristics of bacterial community and DOM changes in Fe(III)-stressed Chlorococcum sp. GD as an example, which contributes to an in-depth understanding of microbial community diversity and element cycling in aquatic ecosystems.

Livestock and poultry breeding farms as a fixed and underestimated source of antibiotic resistance genes.

The excessive use of antibiotics, disinfectants, and drugs in livestock and poultry breeding has resulted in a rise in the presence of antibiotic resistance genes (ARGs). Antibiotic-resistant bacteria (ARB) and ARGs have been widely found in animal feces, farm wastewater, and farm air. ARGs can not only spread across media through adsorption and migration, but also transfer resistance across bacterial genera through horizontal gene transfer. Livestock breeding has become a fixed and unavoidable source of ARGs in the environment. Existing technologies for controlling ARGs, such as composting, disinfection, and sewage treatment, are not efficient in removing ARB and ARGs from waste. Furthermore, the remaining ARGs still possess a strong capacity for dissemination. At present, antibiotics used in animal husbandry are difficult to replace in a short period of time. The growth and potential risks of resistance genes in livestock and poultry breeding sources in the receiving environment are not yet clear. In this paper, we summarize the current situation of ARGs in the livestock and poultry breeding environment. We also explain the key environmental processes, main influencing factors, and corresponding ecological risks associated with ARGs in this environment. The advantages and disadvantages of current technologies for the removal of ARGs are primarily discussed. There is a particular emphasis on clarifying the spatiotemporal evolution patterns and environmental process mechanisms of ARGs, as well as highlighting the importance and urgency of developing efficient pollution control technologies.

Effect of water on CO2 capture and phase change in non-aqueous 1,4-butanediamine/ethylene glycol biphasic absorbents: Role of hydrogen bond competition

Non-aqueous liquid–solid biphasic absorbents show significant potential for reducing energy consumption in CO2 capture. However, the high hygroscopic nature of amine compounds and the approximately 10 % to 20 % water vapor in natural gas plants’ flue gases challenge their efficiency and stability. This study investigated water’s effects on the CO2 absorption/desorption performance, phase-change behavior, and products of the non-aqueous absorbent 1,4-Butanediamine (BDA)/ethylene glycol (EG). Experimental results show that water significantly impacts absorption/desorption performance. The maximum CO2 loading and regeneration efficiency were 1.4199 mol·mol−1 and 89.85 %, respectively. Water leads to the gradual disappearance of phase changes, with solid-phase CO2 loading decreasing from 0.008 to 0.004 mol·g−1. Quantitative NMR demonstrates that carbamates and CO32-/HCO3- are the main CO2-related products in aqueous systems, with their relative proportions affected by water content. Quantum chemical calculations and molecular dynamics simulations reveal that water enhances absorption efficiency by participating in the catalytic environment and reaction process. Water-induced hydrogen bonding competition is the leading cause of weakened interactions between species. Specifically, H-bonds between carbamate/protonated amines were decreased by 23.94 %. The enhanced solvation effect reduces aggregation of the key products, contributing to the gradual disappearance of phase changes. This study provides insights into water’s effect on non-aqueous biphasic absorbents at the molecular level, offering a theoretical foundation for the development and optimization of absorbents suitable for use with steam-containing flue gases.

Scatterometric defect measurements – uncertainty assessment by means of a virtual instrument and a statistical analysis

Abstract Defects in nanostructured surfaces have to be detected in or close to the manufacturing process in the production environment. For this purpose, scatterometry promises a non-contact approach with in-process capability. However, the achievable measurement uncertainty with a scatterometric measurement principle is difficult to assess by means of experiments. Especially for nano-surfaces with stochastic features,a large sample size is required. In addition, the influence of the natural uncertainty due to the inherent surface stochastics, which causes an ultimate uncertainty limit, remains hidden. Therefore, a virtual experimentation in combination with a statistical evaluation is proposed for the uncertainty assessment of scatterometric defect measurements of nanostructured surface. One virtual experiment calculates the scattered light distribution from a randomized modelled surface. For the uncertainty assessment, (a) multiple defective surfaces with the same defect grade are modelled, (b) the surfaces’ scattered light distributions are simulated, (c) the signal processing is applied for obtaining the virtual measurement results of the defect grade, and (d) the uncertainty is determined. The proposed virtual method is realized and demonstrated for defective ZnO nanograss surfaces, where the vacancy of nanograss is the studied defect as an example. As central results for the exemplarily studied defect grade measurement, the determined achievable mean uncertainty due to the nanostructure randomness is 3.2 %, and the effect of the detector shot noise is negligibly small. Furthermore, the proposed method is universally applicable for any type of nanostructure/defect, and for any scatterometric principle, to clarify the respective potential for the reliable detection of nanostructure defects.&amp;#xD;

Tsallis q-Statistics Fingerprints in Precipitation Data across Sicily

Precipitation patterns are critical for understanding the hydrological and climatological dynamics of any region. Sicily, the largest island in the Mediterranean sea, with its diverse topography and climatic conditions, serves as an ideal case study for analyzing precipitation data, to gain insights into regional water resources, agricultural productivity, and climate change impacts. This paper employs advanced statistical physics methods, particularly Tsallis q-statistics, to analyze sub-hourly precipitation data from 2002 to 2023, provided by the Sicilian Agrometeorological Informative System (SIAS). We investigate several critical variables related to rainfall events, including duration, depth, maximum record, and inter-event time. The study spans two decades (2002–2012 and 2013–2023), analyzing the distributions of relevant variables. Additionally, we examine the simple returns of these variables to identify significant temporal changes, fitting these returns with q-Gaussian distributions. Our findings reveal the scale-invariant nature of precipitation events, the presence of long-range interactions, and memory effects, characteristic of complex environmental processes.

Theoretical solution of vortex splitting due to islands effect on the vortex

Islands, acting as transitional areas between land and sea, significantly influence their surrounding environments. Studying the changes in vortex structure resulting from the interaction between islands and mesoscale vortices is crucial for understanding the dynamic characteristics and ecological processes of the marine environment. Previous theoretical studies have shown that in a domain without boundaries, due to the conservation of angular momentum, a vortex cannot split by itself. This paper establishes the conditions for the splitting of an anticyclonic vortex when it collides with two square islands. By linking the initial and final states of islands effect on the vortex and utilizing conserved quantities such as angular momentum and mass, along with the slow variation approximation, a nonlinear theoretical solution is constructed. The analysis shows that when the interaction between the vortex and the two islands leads to vortex splitting, the length of the islands, the vortex radius, and the distance between the islands must satisfy a certain condition O(LR)∼O(wR)∼O(1). These results provide support for subsequent analyses of the impact of various parameters on vortex structure when the North Brazil Current (NBC) ring encounters the Lesser Antilles in the tropical western Atlantic.

Evaluation of working conditions in the production system

The paper analyzes methods for assessing professional risk, emphasizing that the primary method is job certification. Job certification establishes the compliance of the workplace with regulatory characteristics for specific risk factors in the production environment. The analysis reveals that the existing methodology for job certification does not provide insights into changes in the parameters of hazardous and harmful factors in the production environment, and consequently, changes in working conditions between planned certifications. The study advocates for a comprehensive assessment of the collective impact of all factors in the production environment and work processes on the human body. This is achieved through hygienic risk factor assessment. The paper highlights the appropriateness of quantitatively assessing working conditions in points, considering the simultaneous influence of multiple hazardous factors. A procedure for point-based assessment of working conditions is proposed, and the applicability of Haber's Law to determine the cumulative dose of a harmful factor in the body is demonstrated.

Head and neck complications of cancer therapies: taste and smell.

Sensory deficits affect awareness of the environment and information processing, leading to dysfunction that may have significant consequences. Deterioration of taste and/or smell sensation has been linked to impaired nutritional intake, and overall decreased quality of life (QoL). Recent data suggest that loss of these senses is also associated with cognitive decline and worse overall cancer treatment prognosis. Cancer therapies have commonly been associated with sensory deterioration. We review these associations with taste and smell in light of new findings and discuss potential prophylactic and therapeutic modalities for taste and smell function.

Improving Rebar Twist Prediction Exploiting Unified-Channel Attention-Based Image Restoration and Regression Techniques.

Recent research has made significant progress in automated unmanned systems utilizing Artificial Intelligence (AI)-based image processing to optimize the rebar manufacturing process and minimize defects such as twisting during production. Despite various studies, including those employing data augmentation through Generative Adversarial Networks (GANs), the performance of rebar twist prediction has been limited due to image quality degradation caused by environmental noise, such as insufficient image quality and inconsistent lighting conditions in rebar processing environments. To address these challenges, we propose a novel approach for real-time rebar twist prediction in manufacturing processes. Our method involves restoring low-quality grayscale images to high resolution and employing an object detection model to identify and track rebar endpoints. We then apply regression analysis to the coordinates obtained from the bounding boxes to estimate the error rate of the rebar endpoint positions, thereby determining the occurrence of twisting. To achieve this, we first developed a Unified-Channel Attention (UCA) module that is robust to changes in intensity and contrast for grayscale images. The UCA can be integrated into image restoration models to more accurately detect rebar endpoint characteristics in object detection models. Furthermore, we introduce a method for predicting the future positions of rebar endpoints using various linear and non-linear regression models. The predicted positions are used to calculate the error rate in rebar endpoint locations, determined by the distance between the actual and predicted positions, which is then used to classify the presence of rebar twisting. Our experimental results demonstrate that integrating the UCA module with our image restoration model significantly improved existing models in Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) metrics. Moreover, employing regression models to predict future rebar endpoint positions enhances the F1 score for twist prediction. As a result, our approach offers a practical solution for rapid defect detection in rebar manufacturing processes.