Defect Rate Research Articles

Research on Node Fault Diagnosis in Wireless Sensor Networks Based on a Constrained Data Maximum Entropy BN Parameter Learning Algorithm

Currently, power communication wireless sensor networks (WSNs) exhibit the characteristics of uncertainty and complexity. Furthermore, the application environment is more complex, resulting in nodes damaging easily, power communication breakdown, and economic losses. Dynamic monitoring of power communication WSN nodes is necessary. To this end, a constraint data maximum entropy Bayesian network (BN) parameter learning algorithm is used to solve the problem of small data sample size in monitoring and improve the quality of power communication WSN node fault diagnosis. After the latest verification, it is found that the correct rate of WSN node fault diagnosis under small data set is as follows: in normal conditions, the fault rate is 96%, the sensor fault is 94%, the power supply fault is 100%, the wireless communication fault is 90%, and the processor fault is 91%. It can be seen that even under small sample data sets, better diagnostic accuracy can be brought.

Late Quaternary slip rate of the Northern Margin Fault of the Huaizhuo Basin in the North China Block and its seismological implications

Late Quaternary slip rate of the Northern Margin Fault of the Huaizhuo Basin in the North China Block and its seismological implications

AlN/FeNi Microwave-Attenuating Ceramics with High-Efficiency Thermal Conductivity and Microwave Absorption

The integration, miniaturization, and high frequency of microwave vacuum electronics put forward higher requirements for heat-conducting and wave-absorbing integrated materials. However, these materials must balance the dispersion and isolation of wave-absorbing components to optimize absorption while maintaining the continuity of thermal conductivity pathways with low defect rates and minimal interfaces. This presents a significant challenge in achieving both high thermal conductivity and efficient wave absorption simultaneously. Here, AlN/FeNi microwave-attenuating ceramics were synthesized via non–pressure sintering in a nitrogen atmosphere. The influence of FeNi content (0–20 wt%) on the density, phase composition, microstructure, microwave-absorption properties and thermal conductivity of the composites was investigated. AlN/FeNi composites consist primarily of an AlN phase with FeNi0.0578, Fe, AlYO3, and Al5Y3O12 as secondary phases, and the microstructure is uniform and dense. As the FeNi content rises from 0 to 20 wt%, the density of the composites sintered at 1800 °C × 2 h increases from 3.3 to 3.7 g/cm3. Their X-band (2–18 GHz) dielectric constant goes up from 6.5 to 8.5, the dielectric loss factor rises from 0.1 to 0.9, and thermal conductivity diminishes from 130 to 123 W/m·K. Upon reaching an FeNi content of 20 wt%, the composite achieves a minimum reflection loss of −39.1 dB at 9.5 GHz, with over 90% absorption across an effective absorption bandwidth covering 2.5 GHz. It exhibits excellent impedance matching, electromagnetic wave-attenuation properties, a relative density of 98.6%, and a thermal conductivity of 123 W m−1 K−1. The prepared AlN/FeNi composites, with integrated outstanding microwave-absorption capabilities and thermal conductivity, holds great promise for applications in 5G communications, aerospace, and artificial intelligence.

Industry 4.0 techniques applied to the improvement of the cutting process of Lithium-Ion battery terminals

The transition to Industry 4.0 has brought significant advancements in production, especially in the manufacturing of lithium-ion batteries, through the application of Artificial Intelligence (AI). This study focuses on the development of an intelligent system to improve the cutting of battery terminals, using AI algorithms to optimize precision and reduce defects. The main challenge is the imprecision in the cutting process, which results in a high rate of defective products and increases production costs due to material waste. The application of machine learning and real-time data analysis enabled automatic adjustments in the cutting process, creating a flexible and adaptable manufacturing environment. As a result, there was a significant reduction in the defect rate of the batteries, along with an increase in the quality and uniformity of the terminals. These efficiency gains demonstrate the potential of AI to improve precision and reduce costs while meeting quality requirements. The integration of AI in the cutting process not only improves quality and sustainability but also opens up new possibilities for other applications in Industry 4.0. The study demonstrates that technological innovations in flexible manufacturing processes can generate a significant competitive advantage for the renewable energy and storage sector.

On the Issue of the Radon Activity of Tectonic Faults on the Qatar Peninsula

Purpose. The paper aims at providing the modern evaluation of radon activity in identified tectonic faults of the earth's crust based on archival data of radon measurements in groundwater which previously have been performed in the northern and eastern parts of the Qatar Peninsula. Methodology. The paper presents an additional extended justification for the application of an innovative technique for these conditions to identify the activity rate of tectonic faults in the rock foundation under the geological conditions of the Qatar Peninsula based on third-party measurements of radon in groundwater and using own universal Evaluation scale. Findings. A modern analysis of previously obtained results of radon measurements in groundwater of the Qatar Peninsula allowed us to suggest the orientation of tectonic fault directions in its northern and eastern parts and to additionally reveal the rate of their radon activity within the peninsula with a preliminary magnitude evaluation of displacement along the faults. A similar assumption was also proposed in the western part of the UAE adjacent to the peninsula, where the Barakah Nuclear Power Plant (BNPP) is located. This assumption is based on a certain similarity in the geological and tectonic conditions of the region. Originality. For the first time, the authors proposed the location of tectonic faults within the northern and eastern parts of the Qatar Peninsula according to radonometry performed for other purposes. We estimated their radon activity and the magnitude of movement along the faults. This implies increased safety during the construction and operation of various facilities, including a high degree of responsibility, particularly pipeline systems and transport communications that are important for the country's economy. Practical value. This technique can also be used in seismic monitoring for construction sites and in adjacent territories of other countries where similar explorations have not been conducted before.

Log truck crash rates associated with 43-t weight permits in Georgia, USA

ABSTRACT In response to the COVID-19 pandemic and supply chain challenges, the State of Georgia, USA, issued temporary permits (March 2020–March 2023) allowing permit holders to haul up to 43,091 kg gross vehicle weight, compared to 38,102 kg for log trucks without a permit. The objectives of this study were to compare log truck crashes before and after permit availability, compare performance of permit holders to non-permit holders, and identify opportunities to improve timber transportation safety. Vehicle crash reports (DOT-523) were collected from the Georgia Department of Transportation for log truck crashes occurring between 2017 and 2022. Data from the Georgia Department of Public Safety and Federal Motor Carrier Safety Administration were used to assess the safety records of permit holders. This study found no evidence that heavier trucks had a meaningful impact on timber transportation safety. Log truck crashes began increasing in 2018 and rose through 2021. There were no significant changes in the percentage of injury or tow-away collisions or damage to log trucks during crashes. Crash and vehicle defect rates among permit holders were comparable to non-permit holders. Operating newer log trucks, improving maintenance, allowing interstate highway access for loaded log trucks, and improving driver education and performance could improve timber transportation safety.

Sulfur Vacancies Limit the Open-Circuit Voltage of Sb2S3 Solar Cells.

Antimony sulfide (Sb2S3) is a promising candidate as an absorber layer for single-junction solar cells and the top subcell in tandem solar cells. However, the power conversion efficiency of Sb2S3-based solar cells has remained stagnant over the past decade, largely due to trap-assisted nonradiative recombination. Here we assess the trap-limited conversion efficiency of Sb2S3 by investigating nonradiative carrier capture rates for intrinsic point defects using first-principles calculations and Sah-Shockley statistics. Our results show that sulfur vacancies act as effective recombination centers, limiting the maximum light-to-electricity efficiency of Sb2S3 to 16%. The equilibrium concentrations of sulfur vacancies remain relatively high, regardless of growth conditions, indicating the intrinsic limitations imposed by these vacancies on the performance of Sb2S3.

Optimizing Low-Pressure Die Casting of A356 Aluminum Alloy Wheel Rims: A Novel Side Core With Integrated Cooling System

This study explores the development of a novel side core design with an integrated cooling system for the low-pressure die casting (LPDC) of A356 aluminum alloy wheel rims in Indonesia. The objective is to reduce cycle time and minimize casting defects. The methodology involves 3D modeling using SolidWorks and simulations performed with MAGMAsoft to evaluate filling and solidification behavior, air pressure, and air entrapment. Notably, this is the first application of a cooling system in Side cores within the Indonesian automotive industry, motivated by the need to improve production efficiency and product quality, as current manufacturing processes suffer from long cycle times and high defect rates. Simulation results show a significant reduction in cycle time by 72 seconds, improving both efficiency and product quality. This study demonstrates the effectiveness of integrating cooling systems into side cores to enhance the LPDC process.

LIDD-YOLO: A Lightweight Industrial Defect Detection Network

Abstract Surface defect detection is crucial in industrial production, and due to the conveyor speed, real-time detection requires 30 to 60 Frames Per Second, which exceeds the capability of most existing methods. This demand for high FPS has driven the need for lightweight detection models. Despite significant advancements in deep learning-based detection that have enabled single-stage models such as the YOLO series to achieve relatively fast detection, existing methods still face challenges in detecting multi-scale defects and tiny defects on complex surfaces while maintaining detection speed. This study proposes a lightweight single-stage detection model called Lightweight Industrial Defect Detection Network with improved YOLO architecture for high-precision and real-time industrial defect detection. Firstly, we propose the Large Separable Kernel Spatial Pyramid Pooling module, which is a spatial pyramid pooling structure with a separable large kernel attention mechanism, significantly improving the detection rate of multi-scale defects and enhancing the detection rate of small target defects. Secondly, we improved the Backbone and Neck structure of YOLOv8n with Dual convolutional kernel Convolution and enhanced the faster implementation of Cross Stage Partial Bottleneck with 2 Convolutions (C2f) module in the Neck structure with Ghost Convolution and Decoupled Fully Connected (DFC) attention, reducing the computational and parameter overhead of the model while ensuring detection accuracy. Experimental results on the NEU-DET steel defect datasets and PCB defect datasets demonstrate that compared to YOLOv8n, LIDD-YOLO improves the recognition rate of multi-scale defects and small target defects while meeting lightweight requirements. LIDD-YOLO achieves a 3.2% increase in mean Average Precision (mAP) on the NEU-DET steel defect dataset, reaching 79.5%, and a 2.6% increase in mAP on the small target PCB defect dataset, reaching 93.3%. Moreover, it reduces the parameter count by 20.0% and Floating Point Operations by 15.5%, further meeting the requirements for lightweight and high-precision industrial defect detection models.

Apple Defect Detection in Complex Environments

Aiming at the problem of high false detection and missed detection rate of apple surface defects in complex environments, a new apple surface defect detection network: space-to-depth convolution-Multi-scale Empty Attention-Context Guided Feature Pyramid Network-You Only Look Once version 8 nano (SMC-YOLOv8n) is designed. Firstly, space-to-depth convolution (SPD-Conv) is introduced before each Faster Implementation of CSP Bottleneck with 2 convolutions (C2f) in the backbone network as a preprocessing step to improve the quality of input data. Secondly, the Bottleneck in C2f is removed in the neck, and Multi-scale Empty Attention (MSDA) is introduced to enhance the feature extraction ability. Finally, the Context Guided Feature Pyramid Network (CGFPN) is used to replace the Concat method of the neck for feature fusion, thereby improving the expression ability of the features. Compared with the YOLOv8n baseline network, mean Average Precision (mAP) 50 increased by 2.7% and 1.1%, respectively, and mAP50-95 increased by 4.1% and 2.7%, respectively, on the visible light apple surface defect data set and public data set in the self-made complex environments.The experimental results show that SMC-YOLOv8n shows higher efficiency in apple defect detection, which lays a solid foundation for intelligent picking and grading of apples.

Are assisted reproductive technology pregnancies more likely to be exposed to teratogenic medication? A whole-population study.

Assisted reproductive technology (ART) pregnancies are at greater risk of birth defects than non-ART pregnancies. Teratogenic medication exposure is a potential cause of birth defects that has not been compared between ART and non-ART pregnancies. To determine whether the prevalence of exposure to teratogenic medicines during pregnancy varies by conception method (ART and three non-ART groups: ovulation induction (OI), subfertile untreated, and fertile naturally conceiving). We linked state and commonwealth datasets for all live and stillbirths (≥20 weeks) in Western Australia with a conception date ≥1 July 2012 and date of birth ≤31 December 2014. We calculated the prevalence of exposure to teratogenic medicines (Therapeutic Goods Association Category D/X) across conception groups for the: (i) first trimester, and (ii) second and third trimesters. We identified 2041 ART, 590 OI, 2063 subfertile and 52 987 fertile pregnancies (57 681). The overall prevalence of exposure to Category D/X medicines was 0.8% in the first trimester, and 0.7% in the second and third trimesters. Category X medicines exposure was <0.5% for all conception groups and trimesters. The first trimesters of ART and OI pregnancies were more often exposed to Category D medicines than subfertile and fertile pregnancies, (ART = 4.9%, OI = 2.0% vs subfertile = 1.3%, fertile = 0.6%) as were later trimesters (ART = 3.4%, OI = 1.4% vs subfertile = 0.9%, fertile = 0.6%). The overall prevalence of exposure to teratogenic medicines is low; however, exposure was greatest in pregnancies arising from ART and may be a modest contributing factor to the higher rate of birth defects observed among ART babies.

Bearing Fault Diagnosis Method Based on Osprey–Cauchy–Sparrow Search Algorithm-Variational Mode Decomposition and Convolutional Neural Network-Bidirectional Long Short-Term Memory

To solve the problem of the low diagnosis rate of early weak faults of rolling bearings, a novel bearing fault diagnosis method based on Variational Mode Decomposition (VMD) and convolutional neural network (CNN)−Bidirectional Long Short-Term Memory (BiLSTM) was proposed. Based on the basic Sparrow Search Algorithm, the tent chaotic mapping, the Osprey Optimization Algorithm, and the Cauchy mutation were used to enhance the global search ability of the algorithm. To improve the accuracy of fault diagnosis, the BiLSTM layer is introduced into CNN to preserve the global and local features to the maximum extent. The experimental results show that VMD avoids the end effect problem in Empirical Mode Decomposition (EMD). The accuracy rate of the diagnosis model based on CNN-BILSTM reached 97.6667%, which was higher than that of the common diagnosis model.

Influence of Supplier Relationship Management on Product Quality

Purpose: The aim of the study was to analyze the influence of supplier relationship management on product quality. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low-cost advantage as compared to field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: effective Supplier Relationship Management (SRM) practices, such as performance monitoring, collaboration, and trust-building, significantly enhance product quality. These practices reduce defect rates, improve compliance, and boost customer satisfaction. Strong supplier relationships foster innovation, align quality objectives, and streamline processes, making SRM a critical enabler for achieving consistent quality standards and maintaining competitive advantage in dynamic markets. Unique Contribution to Theory, Practice and Policy: Resource-Based View (RBV), Transaction Cost Economics (TCE) Theory and Stakeholder Theory in supply chain and management maybe used to anchor future studies influence of supplier relationship management on product quality. Theories should incorporate variables such as cultural, technological, and economic differences, providing a more globally relevant understanding of SRM's influence on product quality. Firms should adapt SRM practices to their industry-specific needs. For instance, green supply chains might prioritize sustainability metrics, while high-tech sectors may emphasize innovation partnerships. Policymakers should promote industry-wide standards for supplier evaluations and quality compliance to ensure consistency and accountability across supply chains

Risk Identification and Management Analysis at Company X Using FMEA and EPQ Methods

This study aims to identify and manage risks in Company X’s instant noodle production process using Failure Mode and Effects Analysis (FMEA) and Economic Production Quantity (EPQ) methods. FMEA is used to analyze potential failure modes, while EPQ optimizes production quantities to minimize storage and overproduction costs. The research identifies key risks such as overproduction, machinery failures, and packaging defects. Data collected includes production volumes, raw material usage, and defect rates. FMEA reveals that overproduction is the primary risk, leading to high storage costs. EPQ calculations are performed to determine the optimal monthly production for three instant noodle variants, resulting in significant cost savings. The study concludes that implementing EPQ alongside FMEA minimizes risks and reduces inventory management costs, highlighting the importance of systematic risk management in production optimization. This approach helps Company X enhance efficiency while cutting purchasing and costs expenses by Rp. 3,900,000.00 per month.

Programming Model for Enterprise Multi-Process Decision Optimization

This paper addresses critical decision-making challenges faced by enterprises during the production process, particularly focusing on efficient quality inspection, inspection and disassembly decisions, defective rate control, and cost minimization. In the current dynamic market environment, existing algorithms demonstrate insufficient adaptability, hindering their effectiveness in addressing complex production scenarios. To overcome these limitations, we propose a novel genetic algorithm that incorporates dynamic change adjustment through a 0-1 programming model. This approach enhances the algorithm's flexibility, enabling enterprises to develop scientifically sound and rational production decision schemes tailored to varying market conditions. Our empirical analysis reveals that the average production cost for enterprises utilizing this method is 32.7, indicating significant potential for cost reduction while maintaining product quality. The findings contribute to the existing literature by providing a robust theoretical framework that underscores the importance of adaptive algorithms in improving production efficiency and decision-making processes in an ever-evolving market landscape.

Minimal Defect Detection on End Face of Lithium Battery Shells

Lithium batteries represent a pivotal technology in the advancement of renewable energy, and their enhanced performance and safety are vital to the attainment of sustainable development goals. To solve the issue of the high missed detection rate of minimal defects on end face of lithium battery shells, a novel YOLO-based Minimal Defect Detection algorithm, named YOLO-MDD, is proposed. Firstly, aimed at the problem of insufficient data, a dataset of defects on the end face of lithium battery shells is constructed and annotated. Secondly, a YOLO-MDD network which includes a feature extraction module and a four-scale detection head for detecting defects of various scales is improved. Here, deformable convolution and an attention module are ingeniously embedded into the backbone of YOLO to capture more detailed and accurate information on object defects, and the four-scale head is used to handle the significant differences in the size and shape of defects on lithium battery shells. Finally, a hybrid loss including localization loss with normalized Wasserstein distance (NWD), classification loss, and confidence loss is designed to optimize our model to further enhance its sensitivity to minimal defects. The experimental results show that the proposed YOLO-MDD has a mean average precision of 80% for the defect detection of the lithium battery shells, especially with a minimal defect rust spots mean average precision of 74.1% and a recall rate of 71.5%, which is superior compared with other mainstream detection algorithms and provides the technical support necessary to achieve the goals of energy and environmental sustainability.

Design of Supplier Selection of Fresh Fruit Bunches (FFB) of the Palm Oil Industry

The research aims to identify the optimal strategy for selecting suppliers, analyse criteria priorities, and rank the best suppliers in PT X, Paser Regency. The research method uses quantitative approach with data collection through questionnaires based on the ANP (Analytical Network Process) model. The sample was determined using purposive sampling of three employees of PT X. The analysis was conducted in three stages: descriptive, ANP using super decision software, and TOPSIS to rank the FFB suppliers. The results on ANP showed that the design of selection of FFB suppliers in PT X is based on 5 criteria and 16 sub-criteria. Price criteria include payment method, low price, easy agreement, and price stability. The claim policy criteria include quick response, assistance, and warranty. Quality criteria involve technical conformity, quality consistency, and defect rate. Geographical location criteria consider climate, distance, road conditions, and delivery time. The delivery criterion prioritises the sub criteria of the accuracy of the number of deliveries. The conclusion from TOPSIS showed that the highest ranking of FFB suppliers is given to UD. Naila Naufal Jaya, superior on price, quality, delivery, geographic location, and warranty criteria.

SFACIF: A safety function attack and anomaly industrial condition identified framework

High-stakes process industries require a harmonious relationship between the Safety Instrumented System (SIS) and the Basic Process Control System (BPCS) to guarantee the safety and stability of operations. As security threats to SIS intensify, the imperative to fortify it against cyber-attacks has never been more critical. SIS activates safety functions to bring the process to a safe state or shut it down under anomaly idustrial conditions. This raises two critical questions for SIS security: (1) how to differentiate between genuine industrial anomalies and data injected by attackers to prevent unnecessary shutdowns and economic losses; and (2) how to distinguish between attackers’ replayed data and normal operational data to avoid casualties resulting from delayed shutdowns. In addressing these challenges, we introduce SFACIF, a framework designed to effectively identify safety function attacks and anomaly industrial conditions. Inspired by advanced two out of three voting mechanisms and process monitoring technologies, our approach encompasses several innovative strategies. Initially, a deep learning-based time series prediction method is employed to generate benchmark data. Next, potential issues are identified by detecting deviations through pairwise comparisons between the predicted benchmark data, SIS observations, and BPCS observations. To account for the higher fault rates in BPCS and the presence of process noise, we apply a modified sliding window residual statistical method for analysis. Lastly, we introduce a novel coding scheme to interpret the results of the three-way comparison, enabling the identification of safety function attacks and anomaly industrial conditions. To validate the efficacy of SFACIF, we devised a physical simulation platform that mirrors real-world industrial environments, facilitating a rigorous assessment of our framework under operational conditions. The performance metrics underscore the superior capability of SFACIF, which achieved 99% accuracy and 1% false alarm rate. These results not only attest to the ability of SFACIF to accurately differentiate between various attack vectors but also highlight its proficiency in discerning between authentic and manipulated data.

A New Patterning Method of SnO2-Based Transparent Conductive Oxide Films By Two-Step Wet Processes Using Reduction in Gluconic Acid Aqueous Solution

In order to realize a sustainable, recycling-oriented society, it is intrinsically important to develop new technologies to substitute rare elements with elements abundant in resoueces, to save energy, to introduce renewable energy, or recyce the devices.Transparent conductive oxide (TCO) films are essential materials for transparent electrodes in clean energy production sources (solar cells) and energy-saving devices (liquid crystal displays, light-emitting diodes, etc.). Indium tin oxide (ITO) is currently the most widely used TCO film. However, ITO is mainly composed of indium, a rare-earth. From the perspective of the SDGs, there is a strong need to promote the use of alternative materials to ITO, that are mainly composed of elements abundant in resources and can also be recycled.Tin dioxide (SnO2) is a TCO film in practical use during more than half a century. It is mainly composed of tin (Sn), which is abundant in resources, and has excellent weatherability and chemical resistance. It is used in solar cells, heater glass to prevent fogging, and viewing windows of analyzers. If SnO2 can replace ITO, it will have a very large impact on the realization of a recycling society.TCO films are generally patterned when used as transparent electrodes. ITO films can be easily patterned by wet processes using commercially available etchants the photoresist masks fabricated by photolithography. In contrast, SnO2 films are quite difficult to pattern by wet processes due to its high chemical resistance. SnO2 films are mainly patterned by dry processes, such as laser processing or sandblasting. However the former is expensive in cost, and the latter may cause substrates to break after shipping. If SnO2-based TCO films can be patterning by wet process, it will directly lead to improved throughput, cost reduction, and lower product defect rate, and will be a step forward toward the major goal of replacing ITO.We developed a new patterning method of SnO2-based TCO films by two-step wet processes. In the first step, the SnO2 was reduced to metallic Sn by applying voltage in gluconic acid aqueous solution (Figure 1). The reduction to Sn was confirmed by X-ray diffraction measurements. Cross-sectional observation by a scanning electron microscope (SEM) revealed that reduction proceeds from the surface of the SnO2 film toward the inside.In the second step, Sn was removed by acid, base, or electrolysis. The complete removal of the SnO2 film was confirmed by SEM and energy-dispersive X-ray spectroscopy. In addition, transmittance and reflectance of the glass substrate after removal of SnO2 film were measured.It was found that patterning SnO2-based TCO films in the micrometer order scale is possible by combining the developed two-step method with the photoresist masks fabricated by photolithography. Figure 1

Rapid biphasic decay of intact and defective HIV DNA reservoir during acute treated HIV disease

Despite antiretroviral therapy (ART), HIV persists in latently-infected cells (the HIV reservoir) which decay slowly over time. Here, leveraging >500 longitudinal samples from 67 people living with HIV (PLWH) treated during acute infection, we developed a mathematical model to predict reservoir decay from peripheral CD4 + T cells. Nonlinear generalized additive models demonstrated rapid biphasic decay of intact DNA (week 0-5: t1/2 ~ 2.83 weeks; week 5-24: t1/2 ~ 15.4 weeks) that extended out to 1 year. These estimates were ~5-fold faster than prior decay estimates among chronic treated PLWH. Defective DNA had a similar biphasic pattern, but data were more variable. Predicted intact and defective decay rates were faster for PLWH with earlier timing of ART initiation, higher initial CD4 + T cell count, and lower pre-ART viral load. In this study, we advanced our limited understanding of HIV reservoir decay at the time of ART initiation, informing future curative strategies targeting this critical time.