Local Processing Research Articles

Asynchronous Privacy-Preservation Federated Learning Method for Mobile Edge Network in Industrial Internet of Things Ecosystem

The typical industrial Internet of Things (IIoT) network system relies on a real-time data upload for timely processing. However, the incidence of device heterogeneity, high network latency, or a malicious central server during transmission has a propensity for privacy leakage or loss of model accuracy. Federated learning comes in handy, as the edge server requires less time and enables local data processing to reduce the delay to the data upload. It allows neighboring edge nodes to share data while maintaining data privacy and confidentiality. However, this can be challenged by a network disruption making edge nodes or sensors go offline or experience an alteration in the learning process, thereby exposing the already transmitted model to a malicious server that eavesdrops on the channel, intercepts the model in transit, and gleans the information, evading the privacy of the model within the network. To mitigate this effect, this paper proposes asynchronous privacy-preservation federated learning for mobile edge networks in the IIoT ecosystem (APPFL-MEN) that incorporates the iteration model design update strategy (IMDUS) scheme, enabling the edge server to share more real-time model updates with online nodes and less data sharing with offline nodes, without exposing the privacy of the data to a malicious node or a hack. In addition, it adopts a double-weight modification strategy during communication between the edge node and the edge server or gateway for an enhanced model training process. Furthermore, it allows a convergence boosting process, resulting in a less error-prone, secured global model. The performance evaluation with numerical results shows good accuracy, efficiency, and lower bandwidth usage by APPFL-MEN while preserving model privacy compared to state-of-the-art methods.

Test suite optimization under multi-objective constraints for software fault detection and localization: Hybrid optimization based model

Testing and debugging have been the most significant steps of software development since it is tricky for engineers to create error-free software. Software testing takes place after coding with the goal of finding flaws. If errors are found, debugging would be done to identify the source of the errors so that they may be fixed. Detecting as well as locating defects are thus two essential stages in the creation of software. We have created a unique approach with the following two working phases to generate a minimized test suite that is capable of both detecting and localizing faults. In the initial test suite minimization process, the cases were generated and minimized based on the objectives such as D-score and coverage by the utilization of the proposed Blue Monkey Customized Black Widow (BMCBW) algorithm. After this test suite minimization, the fault validation is done which includes the process of fault detection and localization. For this fault validation, we have utilized an improved Long Short-Term Memory (LSTM). At 90% of the learning rate the accuracy of the presented work is 0.97%, 2.20%, 2.52%, 0.97% and 2.81% is better than the other extant models like AOA, COOT, BES, BMO and BWO methods. The results obtained proved that our Blue Monkey Customized Black Widow Optimization-based fault detection and localization approach can provide superior outcomes.

Feature engineering and deep learning-based approach for event detection in Medical Internet of Things (MIoT)

Medical Internet of Things (MIoT) is becoming increasingly mainstream as their applications continue to grow. These applications include remote healthcare monitoring, drug storage, alarm systems, and medical wearable devices. Remote patient monitoring, especially using wearable devices, has improved the health and protection of numerous people, while avoiding unnecessary hospital visits. Ideally, healthcare monitoring systems should accurately and timely detect if the patient is entering a critical condition, and alert the concerned doctor accordingly. However, in practice, there are two kinds of alarms raised by such systems, namely, events and anomalies. Events are non-erroneous data that do not conform to the expected values, indicating abnormality in the patient’s condition. On the other hand, anomalies are erroneous data that may result from faulty sensors, electromagnetic interference, malicious attacks, or hardware tampering. These anomalies lead to false alarms which affect the decision making process and may cause danger to the patient’s life. Traditionally, wearable MIoTs, measuring different vitals, frequently communicate with the patient’s phone, or a local processing unit (LPU), in order to analyze and process the collected data. However, the increase in processing capabilities of IoT devices has made on-device event detection possible. This adds an extra layer of defense while avoiding unnecessary communication with LPU. In this paper, a healthcare monitoring system is proposed which compromises of two layers - (1) an online lightweight approach, based on multivariate long-short term memory (LSTM) autoencoder, embedded in each MIoT, that is able to detect data abnormality and alarms the LPU, (2) a correlation technique, at the LPU, that differentiates between anomalies and events, and alerts the patient’s doctor only in case of an event. Since a single sensor reading does not provide enough clinical insight to detect the full length of an event, a feature engineering approach is implemented to extract interpretable statistical, dynamic and physiological features that provide the clinical insight needed. The proposed approach is simulated using Medical Information Mart for Intensive Care (MIMIC) dataset for various vital signs, such as heart rate, systolic and diastolic blood pressure. It is compared with four benchmarks, where the results show the robustness of the model in differentiating between anomalies and events, with event detection sensitivity and specificity above 93%.

Robust loop closure detection and relocalization with semantic-line graph matching constraints in indoor environments

Loop closure detection (LCD) plays an essential role in the Simultaneous Localization and Mapping (SLAM) process, effectively reducing cumulative trajectory errors. However, conventional LCD methods often encounter challenges when dealing with variations in illumination, changes in viewpoint, and environments with weak textures. This is due to their reliance on low-level geometric or image features. To address these issues, we propose a robust LCD method named SL-LCD, which integrates semantic information and line features to fully leverage the semantic content and line attributes within indoor scenes, thereby establishing a reliable feature correspondence between query images and loop closure images. For the retrieval of candidate closed-loop images, we construct a semantic-line-segment topological graph and introduce a graph matching algorithm to perform the LCD task. This approach fully exploits image features and spatial information to achieve closed-loop detection in complex indoor scenes. Furthermore, we present a semantic voxel-based generalized ICP (SVGICP) closed-loop relocalization algorithm tailored for challenging and complex indoor scenes, enhancing the accuracy of closed-loop relocalization in such scenarios. Experimental results demonstrate that the SL-LCD algorithm proposed in this paper surpasses state-of-the-art methods, accurately detecting closed loops, and effectively eliminating trajectory drift.

Monitoring and analysis of physical activity and health conditions based on smart wearable devices

The rapid growth of the Internet of Things (IoT) brings sweeping changes in various industries. Healthcare industries have become a prime example where the Internet of Healthcare Things (IoHT) is making significant progress, particularly in how we approach real-time patient care. Traditional systems for monitoring older people and people with special needs are frequently expensive, require a large workforce, and fall short of providing real-time data. This paper introduces the “3-Tier Health Care Architecture,” an integrated approach to mitigating these issues. This architecture capitalizes on IoHT technologies and is constructed around three principal tiers: Sensor, Fog, and Cloud. The Sensor Tier employs Health Metrics Acquisition Units (HMAUs) fitted with an nRF5340 Development Kit, capturing an extensive range of health-related metrics via wearable sensors. These metrics are then relayed to the Local Processing Units (LPUs) in Fog Tier, which operates on Raspberry Pi Zero 2 W microprocessors for the initial data processing before forwarding to the cloud. The Cloud Tier uses a hybrid CNN-LSTM Machine Learning (ML) model to perform Real-Time Healthcare Monitoring (RTHM) status assessments and includes an Early Warning System for immediate alert issuance. The proposed architecture is resilient, scalable, and efficient, serving as a fortified and all-encompassing solution for RTHM. This enables quick medical interventions, thus elevating healthcare quality and potentially life-saving.

Social media distractions alter behavioral and neural patterns to global-local attention: The moderation effect of fear of missing out

Social media, including platforms such as Instagram or WeChat, has transformed several aspects our lives, ranging from global social interaction towards disrupted attentional patterns. Here, we explored how social media becomes a salient distractor intruding into global and local attention and the underlying neural pathways by means of functional magnetic resonance imaging (fMRI). Thirty-eight participants underwent a global-local attention paradigm combined with online social information distraction (SD), online nonsocial information distraction (NSD), and without information disturbance (baseline: BL). Results showed that SD decreased accuracy during both global and local attention processing. In contrast, NSD primarily decreased accuracy during global attention processing. On the neural level the SD effects were mirrored in decreased engagement of a core posterior default mode network node (DMN), i.e. the left precuneus. The effects at the network level were mirrored by the core region of the posterior DMN. At the same time, there was decreased activation of the Higher Visual Network during local attention processing. Variations in trait fear of missing out (FoMO) moderated the relationship between precuneus engagement and behavioral accuracy during local attention processing specifically during SD. Findings suggest that – compared to non-social media distraction - social online media information disrupts attention via the posterior DMN and stronger engagement of the higher visual cortex. Overall, this study provides valuable insights into the potential attention consequences of online social information intrusion and the neural mechanism underlying local attention disturbance in individuals with elevated levels of trait FoMO.

Sports-Related Gastrointestinal Disorders: From the Microbiota to the Possible Role of Nutraceuticals, a Narrative Analysis.

Intense physical exercise can be related to a significant incidence of gastrointestinal symptoms, with a prevalence documented in the literature above 80%, especially for more intense forms such as running. This is in an initial phase due to the distancing of the flow of blood from the digestive system to the skeletal muscle and thermoregulatory systems, and secondarily to sympathetic nervous activation and hormonal response with alteration of intestinal motility, transit, and nutrient absorption capacity. The sum of these effects results in a localized inflammatory process with disruption of the intestinal microbiota and, in the long term, systemic inflammation. The most frequent early symptoms include abdominal cramps, flatulence, the urge to defecate, rectal bleeding, diarrhea, nausea, vomiting, regurgitation, chest pain, heartburn, and belching. Promoting the stability of the microbiota can contribute to the maintenance of correct intestinal permeability and functionality, with better control of these symptoms. The literature documents various acute and chronic alterations of the microbiota following the practice of different types of activities. Several nutraceuticals can have functional effects on the control of inflammatory dynamics and the stability of the microbiota, exerting both nutraceutical and prebiotic effects. In particular, curcumin, green tea catechins, boswellia, berberine, and cranberry PACs can show functional characteristics in the management of these situations. This narrative review will describe its application potential.

Enhanced strength-ductility synergy in a gradient pseudo-precipitates heterostructured Al-2.5%Mg alloy: Design, fabrication, and deformation mechanism

Heterostructures of alloyed composites, comprising heterogeneous domains with dramatically different constitutive properties, hold remarkable potential to expand the realm of material design systems and resolve the trade-off between strength and ductility. This study introduces an innovative materials design method for synthesizing gradient pseudo-precipitates heterostructure (GPHS) in non-heat-treatable Al-2.5%Mg alloys. Utilizing cost-effective mild steel as both the diffusion source and protective layer, this heterostructure is achieved through pin-less friction stir-assisted cyclic localized deformation process. Exogenous Fe atoms diffuse across the interface by friction stir-induced heat conduction, forming Fe-Al second-phase particles in the Al alloy matrix. A rapid inter-diffusion mechanism is activated in conjunction with dense dislocation walls, grain boundaries, and sub-structures, resulting in the formation of pseudo-precipitates. These pseudo-precipitates are ultimately dispersed in a gradient distribution throughout the entire thickness of the Al alloy matrix induced by localized incremental deformation. The GPHSed Al-2.5%Mg alloy exhibits an enhanced synergy of strength and ductility, with a uniform elongation increase from 11 % to 21.2 %, while maintaining the strength. Multiple strengthening and hardening mechanisms, such as solid solution strengthening, dislocation hardening, and second phase strengthening, work synergistically to promote mechanical performance. Notably, the hetero-deformation between hard pseudo-precipitates and soft Al alloy matrix induces additional strain hardening, leading to high ductility. This work provides a fresh perspective on the design and fabrication of high-performance alloys with advanced heterostructures, especially for non-heat-treatable alloys.

Advancing Drone Operations through Lightweight Blockchain and Fog Computing Integration: A Systematic Review

This paper presents a systematic literature review investigating the integration of lightweight blockchain and fog computing technologies to enhance the security and operational efficiency of drones. With a focus on critical applications such as military surveillance and emergency response, this review examines how the combination of blockchain’s secure, decentralized ledger and fog computing’s low-latency, localized data processing can address the unique challenges of drone operations. By compiling and analyzing current research, this study highlights innovative approaches and solutions that leverage these technologies to improve data integrity, reduce communication latency, and facilitate real-time decision-making in drone missions. Our findings underscore the significant potential of this technological integration to advance the capabilities and reliability of drones in high-stakes scenarios.

Two Prediction Error Systems in the Nonlemniscal Inferior Colliculus: "Spectral" and "Nonspectral".

According to the predictive processing framework, perception emerges from the reciprocal exchange of predictions and prediction errors (PEs) between hierarchically organized neural circuits. The nonlemniscal division of the inferior colliculus (IC) is the earliest source of auditory PE signals, but their neuronal generators, properties, and functional relevance have remained mostly undefined. We recorded single-unit mismatch responses to auditory oddball stimulation at different intensities, together with activity evoked by two sequences of alternating tones to control frequency-specific effects. Our results reveal a differential treatment of the unpredictable "many-standards" control and the predictable "cascade" control by lemniscal and nonlemniscal IC neurons that is not present in the auditory thalamus or cortex. Furthermore, we found that frequency response areas of nonlemniscal IC neurons reflect their role in subcortical predictive processing, distinguishing three hierarchical levels: (1) nonlemniscal neurons with sharply tuned receptive fields exhibit mild repetition suppression without signaling PEs, thereby constituting the input level of the local predictive processing circuitry. (2) Neurons with broadly tuned receptive fields form the main, "spectral" PE signaling system, which provides dynamic gain compensation to near-threshold unexpected sounds. This early enhancement of saliency reliant on spectral features was not observed in the auditory thalamus or cortex. (3) Untuned neurons form an accessory, "nonspectral" PE signaling system, which reports all surprising auditory deviances in a robust and consistent manner, resembling nonlemniscal neurons in the auditory cortex. These nonlemniscal IC neurons show unstructured and unstable receptive fields that could result from inhibitory input controlled by corticofugal projections conveying top-down predictions.

OnceNAS: Discovering efficient on-device inference neural networks for edge devices

Edge Intelligence (EI) offers an attractive approach for local AI processing at the network edge for privacy protection and reduced transmission, but deploying resource-intensive neural networks on edge devices remains a challenge. The neural architecture search (NAS) technique, known for its automation and minimal manual intervention, serves as a pivotal tool for EI. However, existing methods typically concentrate on optimizing resource consumption for specific hardware, leading to hardware-specific neural architectures with limited generalizability. In response, we propose OnceNAS, a novel method that designs and optimizes on-device inference neural networks for resource-constrained edge devices. OnceNAS simultaneously optimizes for parameter count and inference latency in addition to inference accuracy, producing lightweight neural networks while maintaining their inference performance. Meanwhile, we introduce an efficient evaluation strategy that can simultaneously assess multiple metrics. Experimental results demonstrate the effectiveness of OnceNAS, achieving high-performing architectures with substantial size reduction (10.49x) and speedup (5.45x). As a result, OnceNAS offers practical value by generating efficient on-device inference neural architectures for resource-constrained edge devices, facilitating real-world applications like autonomous driving and smart healthcare. Furthermore, we contribute DARTS-Bench, an open-source dataset providing candidate architectures with hardware-related information and a user-friendly API, facilitating future research in lightweight NAS.

Localization of asparagus spears using time-of-flight imaging for robotic harvesting

PurposeTime of flight (ToF) imaging is a promising emerging technology for the purposes of crop identification. This paper aim to presents localization system for identifying and localizing asparagus in the field based on point clouds from ToF imaging. Since the semantics are not included in the point cloud, it contains the geometric information of other objects such as stones and weeds other than asparagus spears. An approach is required for extracting the spear information so that a robotic system can be used for harvesting.Design/methodology/approachA real-time convolutional neural network (CNN)-based method is used for filtering the point cloud generated by a ToF camera, allowing subsequent processing methods to operate over smaller and more information-dense data sets, resulting in reduced processing time. The segmented point cloud can then be split into clusters of points representing each individual spear. Geometric filters are developed to eliminate the non-asparagus points in each cluster so that each spear can be modelled and localized. The spear information can then be used for harvesting decisions.FindingsThe localization system is integrated into a robotic harvesting prototype system. Several field trials have been conducted with satisfactory performance. The identification of a spear from the point cloud is the key to successful localization. Segmentation and clustering points into individual spears are two major failures for future improvements.Originality/valueMost crop localizations in agricultural robotic applications using ToF imaging technology are implemented in a very controlled environment, such as a greenhouse. The target crop and the robotic system are stationary during the localization process. The novel proposed method for asparagus localization has been tested in outdoor farms and integrated with a robotic harvesting platform. Asparagus detection and localization are achieved in real time on a continuously moving robotic platform in a cluttered and unstructured environment.

Lower Extremity Bypass for Occlusive Disease: A Brief History

ObjectivesThis is a narrative review that aims to highlight key advancements which led to the current state of lower extremity bypass surgery. It focuses on key contributors during the last century who have driven the standardization of surgical treatment of peripheral arterial occlusive disease. MethodsA narrative review was conducted utilizing available resources in the scientific and historical literature to track landmark achievements in the development of modern lower extremity bypass surgery for occlusive disease, focusing primarily on the last century of advancement. ResultsSeveral critical conceptual, technological, and technical landmarks were identified as critical components of modern lower extremity bypass surgery. This includes fundamental development in the techniques of vascular anastomosis led by Carrel and others, a developing understanding of vascular occlusive disease as a localized and segmental process with broad implementation of the techniques of arteriography, and the development of safe thromboendarterectomy aided by the development and utilization of heparin for anti-coagulation. These factors led to the first femoral to popliteal artery bypass by Jean Kunlin in 1948. From here, advances in vascular prosthetic material pioneered by Voorhees and others, alternative vascular conduits, increasing acceptance of tibial revascularization, and dispelling the myth of diabetic “small vessel” disease broadened revascularization options for patients with complex patterns of occlusive disease and those who have limited conduit availability. ConclusionsModern lower extremity bypass surgery for occlusive disease arose steadily over the course of a century driven by complex problem-solving in the pathophysiological understanding of atherosclerosis, technical developments in vascular anastomosis and arteriography, and evolution in conduit materials and pharmacologic therapy. Future advancements in bypass surgery are targeted on solving the complex problems of anastomotic intimal hyperplasia, expanding technology for alternative vascular conduits, ongoing optimization of risk factors and scrutiny of outcomes to make patient-centered, evidence-based decisions regarding revascularization strategy.

TTool: A Supervised Artificial Intelligence-Assisted Visual Pose Detector for Tool Heads in Augmented Reality Woodworking

We present TimberTool (TTool v2.1.1), a software designed for woodworking tasks assisted by augmented reality (AR), emphasizing its essential function of the real-time localization of a tool head’s poses within camera frames. The localization process, a fundamental aspect of AR-assisted tool operations, enables informed integration with contextual tracking, facilitating the computation of meaningful feedback for guiding users during tasks on the target object. In the context of timber construction, where object pose tracking has been predominantly explored in additive processes, TTool addresses a noticeable gap by focusing on subtractive tasks with manual tools. The proposed methodology utilizes a machine learning (ML) classifier to detect tool heads, offering users the capability to input a global pose and utilizing an automatic pose refiner for final pose detection and model alignment. Notably, TTool boasts adaptability through a customizable platform tailored to specific tool sets, and its open accessibility encourages widespread utilization. To assess the effectiveness of TTool in AR-assisted woodworking, we conducted a preliminary experimental campaign using a set of tools commonly employed in timber carpentry. The findings suggest that TTool can effectively contribute to AR-assisted woodworking tasks by detecting the six-degrees-of-freedom (6DoF) pose of tool heads to a satisfactory level, with a millimetric positional error of 3.9 ± 1 mm with possible large room for improvement and 1.19 ± 0.6° for what concerns the angular accuracy.

“I’m on a First Name Basis with Everybody at the EPA”: Measuring Community Impact of Air Pollution from a Hemp Processing Plant

Air pollution is comprised of a complex mixture of various gases and particulate matter that can be damaging to human health after both short-term and long-term exposure (Bergstra et al., 2018; Sass et al., 2017). This research examined the biopsychosocial impact of exposure to air pollution in a rural community sample using a mixed methods approach. Residents from this rural community engaged a university affiliated environmental health science center to understand and assist with potential health risks associated with a local hemp processing plant. Researchers from the center conducted a mixed methods study that involved in-depth interviews and administration of standardized scales on physical and mental health with seven community residents. Results indicate that there were a range of mild to moderate physical health symptoms and higher than average levels of anxiety/distress. Themes from the interviews included physical health concerns, support from government officials, community support/alienation, impact on relationships, emotional distress, forced relocation and loss, and confounding effect of COVID-19. Implications for community practice to support the biopsychosocial needs of those experiencing environmental exposure or risk are discussed.

APPROACH TO THE AUTOMATIC CREATION OF AN ANNOTATED DATASET FOR THE DETECTION, LOCALIZATION AND CLASSIFICATION OF BLOOD CELLS IN AN IMAGE

Context. The paper considers the problem of automating the creation of an annotated dataset for further use in a system for detecting, localizing and classifying blood cells in an image using deep learning. The subject of the research is the processes of digital image processing for object detection and localization.
 Objective. The aim of this study is to create a pipeline of digital image processing methods that can automatically generate an annotated set of blood smear images. This set will then be used to train and validate deep learning models, significantly reducing the time required by machine learning specialists.
 Method. The proposed approach for object detection and localization is based on digital image processing methods such as filtering, thresholding, binarization, contour detection, and filling. The pipeline for detection and localization includes the following steps: The given fragment of text describes a process that involves noise reduction, conversion to the HSV color model, defining a mask for white blood cells and platelets, detecting the contours of white blood cells and platelets, determining the coordinates of the upper left and lower right corners of white blood cells and platelets, calculating the area of the region inside the bounding box, saving the obtained data, and determining the most common color in the image; filling the contours of leukocytes and platelets with said color; defining a mask for red blood cells; defining the contours of red blood cells; determining the coordinates of the upper left and lower right corners of red blood cells; calculating the area of the region within the bounding box; entering data about the found objects into the dataframe; saving to a .csv file for future use. With an unlabeled image dataset and a generated .csv file using image processing libraries, any researcher should be able to recreate a labeled dataset.
 Results. The developed approach was implemented in software for creating an annotated dataset of blood smear images
 Conclusions. The study proposes and justifies an approach to automatically create a set of annotated data. The pipeline is tested on a set of unlabelled data and a set of labelled data is obtained, consisting of cell images and a .csv file with the attributes “file name”, “type”, “xmin”, “ymin”, “xmax”, “ymax”, “area”, which are the coordinates of the bounding box for each object. The number of correctly, incorrectly, and unrecognised objects is calculated manually, and metrics are calculated to assess the accuracy and quality of object detection and localisation.

A Study of Major Depressive Disorder Based on Resting-State Multilayer EEG Function Network

Depression is a complex mental disease with its pathological mechanism unclear. To depict the complete picture of the abnormal information interaction in a depressed brain, this study is the first to apply fully connected multilayer brain functional (FCMBF) network framework and proposed composite FCMBF (CFCMBF) network framework, combined with graph theory to analyze the within-frequency coupling (WFC) and cross-frequency coupling (CFC) of sensor-layer and source-layer electroencephalography (EEG) signals in relevant subjects. Results showed that in the sensor-layer FCMBF network, depressive patients showed significantly reduced functional connectivity, as well as abnormal global and local information processing abilities of the network, and these network properties were significantly correlated with depressive symptoms. In addition, from the perspective of depression recognition, we found that the sensor-layer CFCMBF network could achieve better classification accuracy, especially when using the overlapping degree of node under the right center region, its accuracy could reach <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$86.88\% \pm 9.25 \%$</tex-math> </inline-formula> . More importantly, the construction of the CFCMBF network has higher time efficiency and less information loss, since it not only measures the WFC and CFC between brain region representative signals (BRRSs) extracted from different brain regions, but also measures these two couplings between all nodes within each brain region. Although the FCMBF network contains more complete information by calculating WFC and CFC between all nodes distributed in each region, it will result in an enormous computational cost. In summary, this study proved the utility of multilayer brain network in revealing the abnormal brain interaction patterns of depression, and our proposed method might provide methodological support for efficient depression recognition research based on multilayer brain networks.

Experimental and Density Functional Theory Study of Inhibitors on Cobalt Corrosion for Chemical Mechanical Planarization Process

In the localized and global chemical mechanical planarization (CMP) process of Co barrier layers, corrosion inhibitors play a crucial role in the removal rate (RR) of Co and the post-polishing surface quality. In this study, quantum chemical calculations were employed to investigate the molecular reactivity of benzotriazole (BTA), 5-methyl benzotriazole (TTA), and 2,2’-{[(methyl-1H-benzotriazol-1-yl)methyl]imino}bis-ethanol (TT-LYK), and their inhibitory performance was predicted to follow the order of TT-LYK > TTA > BTA. The experimental results validated the accuracy of the predicted order under static conditions. However, the performance order of the inhibitors shifted to TTA > BTA > TT-LYK due to varying degrees of damage caused by mechanical friction to the inhibitory film. Nevertheless, all three inhibitors are capable of effectively inhibiting the Co RR to meet industrial requirements. Ultimately, considering the outstanding inhibitory performance of TT-LYK and its ease of removal during post-cleaning processes, TT-LYK is determined as the most promising inhibitor for Co barrier layer CMP.

Evolutionary Multitasking for Costly Task Offloading in Mobile-Edge Computing Networks

The offloading of computation-intensive tasks to an edge server near resource-constrained mobile devices can provide improved application performance and user experience. However, with the rapid growth of mobile devices connected to the edge server, it is challenging to directly obtain an optimal task offloading scheme due to increasing computational cost and problem scale. In this study, we model the costly task offloading problem (CTOP) in mobile edge computing networks to achieve efficient joint optimization of energy consumption and processing latency for mobile devices. Inspired by the success of evolutionary multitasking in solving complex optimization problems by leveraging the experience of simple optimization problems, we develop a novel multitasking framework whose effectiveness is demonstrated in solving the CTOP. In this framework, auxiliary tasks are created to optimize the local processing overhead and the edge processing overhead of task offloading. On this basis, we propose an effective multitask evolutionary algorithm that includes segmented knowledge transfer and auxiliary task update. Specifically, source and extended decision variables are considered as different knowledge to be utilized, while the auxiliary tasks are allowed to be updated dynamically. Related knowledge that is learned from cheap and simple auxiliary tasks promotes the evolutionary search for CTOP. Experimental results verify the effectiveness of knowledge transfer. Compared to existing multitasking and single-tasking algorithms, the proposed algorithm shows competitive performance in CTOP instances and achieves better comprehensive performance in terms of energy consumption and processing latency.

Adaptive Local Flaw Detection Based on Magnetic Flux Leakage Images With a Noise Distortion Effect for Steel Wire Ropes

Steel wire rope (SWR) is widely used in industrial scenarios because of its high strength and toughness. To avoid accidents that local flaws (LFs) can cause, SWRs should be inspected regularly. Magnetic flux leakage (MFL) image detection, which is a common SWR inspection method, is inevitably influenced by shaking noise and strand noise. Although noise-reduction methods for these two types of noise have been proposed, the phenomenon of noise distortion has not received enough attention. This paper investigates the noise distortion phenomenon on the MFL image and finds that the distorted noise severely affects the performance of existing noise-feature-oriented (NFO) denoising methods. To solve this problem, we adopt a target-feature-oriented (TFO) denoising method. Specifically, the removal of noise is avoided, instead, an LF-enhancement-based denoising process is proposed. Moreover, to localize LFs in denoised images, a three-stage adaptive localization method mainly based on disjoint region analysis is proposed. The experiment results show that the proposed TFO denoising method significantly improves the denoising performance for images that distorted noise influences. In addition, the proposed adaptive localization method improves the intelligence of the localization process and has better localization performance for images with distorted noise.