Investigating a Machine Learning Approach to Predicting White Pixel Defects in Wafers-A Case Study of Wafer Fabrication Plant F.

CMOS image sensor (CIS) semiconductor products are integral to mobile phones and photographic devices, necessitating ongoing enhancements in efficiency and quality for superior photographic outcomes. The presence of white pixels serves as a crucial metric for assessing CIS product performance, primarily arising from metal impurity contamination during the wafer production process or from defects introduced by the grinding blade process. While immediately addressing metal impurity contamination during production presents challenges, refining the handling of defects attributed to grinding blade processing can notably mitigate white pixel issues in CIS products. This study zeroes in on silicon wafer manufacturers in Taiwan, analyzing white pixel defects reported by customers and leveraging machine learning to pinpoint and predict key factors leading to white pixel defects from grinding blade operations. Such pioneering practical studies are rare. The findings reveal that the classification and regression tree (CART) and random forest (RF) models deliver the most accurate predictions (95.18%) of white pixel defects caused by grinding blade operations in a default parameter setting. The analysis further elucidates critical factors like grinding load and torque, vital for the genesis of white pixel defects. The insights garnered from this study aim to arm operators with proactive measures to diminish the potential for customer complaints.

Infodemiology of Influenza-like Illness: Utilizing Google Trends' Big Data for Epidemic Surveillance.

Background: Influenza-like illness (ILI) encompasses symptoms similar to influenza, affecting population health. Surveillance, including Google Trends (GT), offers insights into epidemic patterns. Methods: This study used multiple regression models to analyze the correlation between ILI incidents, GT keyword searches, and climate variables during influenza outbreaks. It compared the predictive capabilities of time-series and deep learning models against ILI emergency incidents. Results: The GT searches for "fever" and "cough" were significantly associated with ILI cases (p < 0.05). Temperature had a more substantial impact on ILI incidence than humidity. Among the tested models, ARIMA provided the best predictive power. Conclusions: GT and climate data can forecast ILI trends, aiding governmental decision making. Temperature is a crucial predictor, and ARIMA models excel in forecasting ILI incidences.

Analyzing Monthly Blood Test Data to Forecast 30-Day Hospital Readmissions among Maintenance Hemodialysis Patients.

Background: The increase in the global population of hemodialysis patients is linked to aging demographics and the prevalence of conditions such as arterial hypertension and diabetes mellitus. While previous research in hemodialysis has mainly focused on mortality predictions, there is a gap in studies targeting short-term hospitalization predictions using detailed, monthly blood test data. Methods: This study employs advanced data preprocessing and machine learning techniques to predict hospitalizations within a 30-day period among hemodialysis patients. Initial steps include employing K-Nearest Neighbor (KNN) imputation to address missing data and using the Synthesized Minority Oversampling Technique (SMOTE) to ensure data balance. The study then applies a Support Vector Machine (SVM) algorithm for the predictive analysis, with an additional enhancement through ensemble learning techniques, in order to improve prediction accuracy. Results: The application of SVM in predicting hospitalizations within a 30-day period among hemodialysis patients resulted in an impressive accuracy rate of 93%. This accuracy rate further improved to 96% upon incorporating ensemble learning methods, demonstrating the efficacy of the chosen machine learning approach in this context. Conclusions: This study highlights the potential of utilizing machine learning to predict hospital readmissions within a 30-day period among hemodialysis patients based on monthly blood test data. It represents a significant leap towards precision medicine and personalized healthcare for this patient group, suggesting a paradigm shift in patient care through the proactive identification of hospitalization risks.

An International Federal Hyperledger Fabric Verification Framework for Digital COVID-19 Vaccine Passport.

The COVID-19 virus has been spreading worldwide on a large scale since 2019, and the most effective way to prevent COVID-19 is to vaccinate. In order to prove that vaccination has been administered to allow access to different areas, paper vaccine passports are produced. However, paper vaccine passport records are vulnerable to counterfeiting or abuse. Previous research has suggested that issuing certificates digitally is an easier way to verify them. This study used the consortium blockchain based on Hyperledger Fabric to upload the digital vaccine passport (DVP) to the blockchain network. In order to enable collaboration across multiple systems, networks, and organizations in different trust realms. Federated Identity Management is considered a promising approach to facilitate secure resource sharing between collaborating partners. Therefore, the international federal identity management architecture proposed in this study enables inspectors in any country to verify the authenticity of the DVP of incoming passengers using the consortium blockchain. Through practical construction, the international federal Hyperledger verification framework for the DVP proposed in this study has shown the feasibility of issuing a global DVP in safety analysis and efficacy testing.

Dysarthria Speech Detection Using Convolutional Neural Networks with Gated Recurrent Unit.

In recent years, due to the rise in the population and aging, the prevalence of neurological diseases is also increasing year by year. Among these patients with Parkinson's disease, stroke, cerebral palsy, and other neurological symptoms, dysarthria often appears. If these dysarthria patients are not quickly detected and treated, it is easy to cause difficulties in disease course management. When the symptoms worsen, they can also affect the patient's psychology and physiology. Most of the past studies on dysarthria detection used machine learning or deep learning models as classification models. This study proposes an integrated CNN-GRU model with convolutional neural networks and gated recurrent units to detect dysarthria. The experimental results show that the CNN-GRU model proposed in this study has the highest accuracy of 98.38%, which is superior to other research models.

Cluster Analysis of US COVID-19 Infected States for Vaccine Distribution.

Since December 2019, COVID-19 has been raging worldwide. To prevent the spread of COVID-19 infection, many countries have proposed epidemic prevention policies and quickly administered vaccines, However, under facing a shortage of vaccines, the United States did not put forward effective epidemic prevention policies in time to prevent the infection from expanding, resulting in the epidemic in the United States becoming more and more serious. Through "The COVID Tracking Project", this study collects medical indicators for each state in the United States from 2020 to 2021, and through feature selection, each state is clustered according to the epidemic's severity. Furthermore, through the confusion matrix of the classifier to verify the accuracy of the cluster analysis, the study results show that the Cascade K-means cluster analysis has the highest accuracy. This study also labeled the three clusters of the cluster analysis results as high, medium, and low infection levels. Policymakers could more objectively decide which states should prioritize vaccine allocation in a vaccine shortage to prevent the epidemic from continuing to expand. It is hoped that if there is a similar epidemic in the future, relevant policymakers can use the analysis procedure of this study to determine the allocation of relevant medical resources for epidemic prevention according to the severity of infection in each state to prevent the spread of infection.

Correction: Evaluation of a novel biodegradable thermosensitive keto-hydrogel for improving postoperative pain in a rat model.

[This corrects the article DOI: 10.1371/journal.pone.0186784.].

Evaluation of a novel biodegradable thermosensitive keto-hydrogel for improving postoperative pain in a rat model.

This study evaluates the sustained analgesic effect of ketorolac-eluting thermosensitive biodegradable hydrogel in the plantar incisional pain model of the rat hind-paw. A ketorolac-embedded 2, 2'-Bis (2-oxazolin) (BOX) linking methoxy-poly(ethylene glycol) and poly(lactide-co-glycolide) (mPEG-PLGA) diblock copolymer (BOX copolymer) was synthesized as keto-hydrogel based on optimal sol-gel phase transition and in vitro drug release profile. The effect of keto-hydrogel on postoperative pain (POP) was assessed using the established plantar incisional pain model in hind-paw of rats and compared to that of ketorolac solution. Pain and sensory threshold, as well as pain scoring, were evaluated with behavioral tests by means of anesthesiometer and incapacitance apparatus, respectively. Pro-inflammatory cytokine levels (TNF-α, IL-6, VEGF, and IL-1ß) around incisional wounds were measured by ELISA. Tissue histology was assessed using hematoxylin and eosin and Masson's trichrome staining. Ten mg/mL (25 wt%) keto-hydrogel showed a sol-gel transition at 26.4°C with a 10-day sustained drug release profile in vitro. Compared to ketorolac solution group, the concentration of ketorolac in tissue fluid was higher in the keto-hydrogel group during the first 18 h of application. Keto-hydrogel elevated pain and sensory threshold, increased weight-bearing capacity, and significantly reduced the levels of TNF-α, IL-6, and IL-1ß while enhanced VEGF in tissue fluid. Histologic analysis reveals greater epithelialization and collagen deposition around wound treated with keto-hydrogel. In conclusion, our study suggests that keto-hydrogel is an ideal compound to treat POP with a secondary gain of improved incisional wound healing.

An APN model for Arrhythmic beat classification.

MOTIVATION: Changes in the normal rhythm of a human heart may result in different cardiac arrhythmias, which may be immediately fatal or cause irreparable damage to the heart sustained over long periods of time. Therefore, the ability to automatically identify arrhythmias from ECG recordings is important for clinical diagnosis and treatment. In this article, classification by using associative Petri net (APN) for personalized ECG-arrhythmia-pattern identification is proposed for the first time in literature. RESULTS: A rule-based classification model and reasoning algorithm of APN are created for ECG arrhythmias classification. The performance evaluation using MIT-BIH arrhythmia database shows that our approach compares well with other reported studies.

Spinal cord NMDA receptor-mediated activation of mammalian target of rapamycin is required for the development and maintenance of bone cancer-induced pain hypersensitivities in rats.

UNLABELLED: Mammalian target of rapamycin (mTOR) controls mRNA translation and is critical for neuronal plasticity. However, how it participates in central sensitization underlying chronic pain is unclear. Here, we show that NMDA receptors are required for the functional role of spinal cord mTOR in bone cancer pain induced by injecting prostate cancer cells (PCCs) into the tibia. Intrathecal rapamycin, a specific mTOR inhibitor, dose dependently attenuated the development and maintenance of PCC-induced mechanical allodynia and thermal hyperalgesia. Rapamycin alone did not affect locomotor activity and acute responses to thermal or mechanical stimuli. Phosphorylation of mTOR and p70S6K (a downstream effector) was increased time dependently in L(4-5) dorsal horn and transiently in L(4-5) dorsal root ganglions on the ipsilateral side after PCC injection, although total expression of mTOR or p70S6K was not changed in these regions. The increases in dorsal horn were abolished by intrathecal infusion of DL-AP5, an NMDA receptor antagonist. Moreover, NMDA receptor subunit NR1 colocalized with mTOR and p70S6K in dorsal horn neurons. These findings suggest that PCC-induced dorsal horn activation of the mTOR pathway participates in NMDA receptor-triggered dorsal central sensitization under cancer pain conditions. PERSPECTIVE: The present study shows that inhibition of spinal mTOR blocks cancer-related pain without affecting acute pain and locomotor function. Given that mTOR inhibitors are FDA-approved drugs, mTOR in spinal cord may represent a potential new target for preventing and/or treating cancer-related pain.

Accuracy of a new body length-based formula for predicting tracheal tube size in Chinese children.

BACKGROUND: Variations in body weight and length (height) in children in the same age group have increased. Traditional age-based formulas often fail to predict the correct endotracheal tube (ETT) size. In our previous study, we devised a new length-based formula as follows: ETT internal diameter (ID) (mm) = 2 + (body length in cm/30). The current study was undertaken to assess the accuracy of this formula in Chinese children. METHODS: The ETT size was selected according to this length-based formula for 336 children who required tracheal intubation during general anesthesia. Incidences of tube change were recorded. Statistical analysis was performed using the chi-square test for differences in accuracy between age groups and body length groups. RESULTS: The length-based formula predicted a suitable ETT size in 277 (82.4%) of 336 subjects. There were 59 (17.6%) reintubations. Only 5 (1.49%) patients needed two tube changes when the correct ETT size was 1 mm larger or smaller than predicted. There were no statistically significant differences between age groups or length groups. CONCLUSIONS: The length-based formula ID (mm) = 2 + body length in cm/30 has high accuracy in predicting the appropriate ETT size in Chinese children.

Role of spinal cord alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors in complete Freund's adjuvant-induced inflammatory pain.

Spinal cord alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors (AMPARs) mediate acute spinal processing of nociceptive and non-nociceptive information, but whether and how their activation contributes to the central sensitization that underlies persistent inflammatory pain are still unclear. Here, we examined the role of spinal AMPARs in the development and maintenance of complete Freund's adjuvant (CFA)-induced persistent inflammatory pain. Intrathecal application of two selective non-competitive AMPAR antagonists, CFM-2 (25 and 50 microg) and GYKI 52466 (50 microg), significantly attenuated mechanical and thermal hypersensitivities on the ipsilateral hind paw at 2 and 24 h post-CFA injection. Neither CFM-2 nor GYKI 52466 affected the contralateral basal responses to thermal and mechanical stimuli. Locomotor activity was not altered in any of the drug-treated animals. CFA-induced inflammation did not change total expression or distribution of AMPAR subunits GluR1 and GluR2 in dorsal horn but did alter their subcellular distribution. The amount of GluR2 was markedly increased in the crude cytosolic fraction and decreased in the crude membrane fraction from the ipsilateral L4-5 dorsal horn at 24 h (but not at 2 h) post-CFA injection. Conversely, the level of GluR1 was significantly decreased in the crude cytosolic fraction and increased in the crude membrane fraction from the ipsilateral L4-5 dorsal horn at 24 h (but not at 2 h) post-CFA injection. These findings suggest that spinal AMPARs might participate in the central spinal mechanism of persistent inflammatory pain.

Comparison of patient-controlled epidural analgesia and continuous epidural infusion for labor analgesia.

BACKGROUND: In recent years, patient-controlled epidural analgesia (PCEA) has been developed as an attractive alternative to continuous epidural infusion (CEI) for labor pain control. PCEA is still not popular for labor pain control in Taiwan and disparities may exist between different ethnic and cultural groups toward the attitude of labor pain control. The aim of this study was to investigate whether there were any differences between PCEA and CEI in the maintenance of epidural analgesia for Taiwanese parturients undergoing spontaneous delivery. METHODS: We collected data of 179 parturient requests for epidural labor analgesia. They were allocated into two groups with PCEA (n = 81) or CEI (n = 98) for maintenance with the same solution of 0.08% ropivacaine and 2 microg/mL fentanyl mixture. The demographic characteristics, epidural maintenance methods, dosage requirements, obstetrical outcomes, intervention of inadequate analgesia or side effects, and the quality of labor analgesia of parturient were also analyzed. RESULTS: There were no differences in demographic characteristics, duration of 1st and 2nd stages, delivery methods, fetal Apgar scores, local anesthetics usage, and analgesic qualities between the PCEA and CEI groups. There were also more requirements for intervention by the anesthesiologist due to inadequate analgesia in the CEI group. CONCLUSION: The results of this study provided further evidence that PCEA is a highly effective method of the control of labor pain, which was highly accepted by women in labor. In a busy obstetric unit, this could potentially improve parturient satisfaction and reduce the workloads of clinicians and nurses.

Intubation conditions with low dose rocuronium under sevoflurane induction for children.

BACKGROUND: During short surgical procedures and when there is a need to avoid the use of anticholinesterase at the end of surgery, the use of a smaller intubation dose of neuromuscular blocking drug is preferred. The aim of this study was to evaluate tracheal intubation conditions using smaller doses of rocuronium for children under sevoflurane induction. METHODS: Eighty American Society of Anesthesiologists classification physical status I or II children were enrolled. After mask induction with sevoflurane with nitrous oxide for 3 minutes, 0.3 mg/kg of rocuronium was given. Intubation was performed 60 or 90 seconds thereafter. Study group A included children aged 1 to 3 years and 90 seconds between rocuronium injection and intubation. Group B included children aged 1 to 3 years who had 60 seconds between rocuronium injection and intubation. Group C included children aged 4 to 6 years who had 90 seconds between rocuronium injection and intubation. Group D included children aged 4 to 6 years who had 60 seconds between rocuronium injection and intubation. Intubation conditions were judged based on the scoring of ease of jaw opening and laryngoscopy, position of the vocal cords, and degree of straining after tracheal intubation. RESULTS: All 80 children underwent successful tracheal intubation without laryngospasm or any complications. Intubation conditions were judged as optimal in all children in group A, 95% in group B, 80% in group C, and 65% in group D. CONCLUSIONS: A total of 0.3 mg/kg of rocuronium was sufficient for tracheal intubation for children 1 to 6 years old under sevoflurane induction. To guarantee optimal intubation conditions for elder children, allow 90 seconds waiting time after rocuronium administration was recommended.