Enhanced efficiency of AgAlO2/g-C3N4 binary composite to degrade organic pollutants for environmental remediation under visible light irradiation.

This study explores the utilization of semiconductor-based photocatalysts for environmental remediation through photocatalytic degradation, harnessing solar energy for effective treatment. The primary focus is on the application of photocatalytic technology for the degradation of 2-chlorophenol and methylene blue, critical pollutants requiring remediation. The research involves the synthesis of binary AgAlO2/g-C3N4 nanocomposites through an exchange ion method, subsequent calcination, and sonication. This process enhances the transfer of photogenerated electrons from AgAlO2 to g-C3N4, resulting in a significantly increased reductive electron charge on the surface of g-C3N4. The photocatalytic activity of the synthesized composites is comprehensively examined in the degradation of 2-chlorophenol and methylene blue through detailed crystallographic, electron-microscopy, photoemission spectroscopy, electrochemical, and spectroscopic characterizations. Among the various composites, AgAlO2/20% g-C3N4 emerges as the most active photocatalyst, achieving an impressive 98% degradation of methylene blue and 97% degradation of 2-chlorophenol under visible light. Notably, AgAlO2/20% g-C3N4 surpasses bare AgAlO2 and bare g-C3N4, exhibiting 1.66 times greater methylene blue degradation and constant rate (k) values of 20.17 × 10-3 min-1, 4.18 × 10-3 min-1 and 3.48 × 10-3 min-1, respectively. The heightened photocatalytic activity is attributed to the diminished recombination rate of electron-hole pairs. Scavenging evaluations confirm that O2•- and h+ are the primary photoactive species steering methylene blue photodegradation over AgAlO2/g-C3N4 in the visible region. These findings present new possibilities for the development of efficient binary photocatalysts for environmental remediation.

Lethal Complications of Meningiomas: A Case Series.

Background: Meningiomas are the most prevalent type of primary intracranial tumor in adults, comprising nearly one-third of all intracranial tumors. They are typically benign, slow-growing, and asymptomatic but may cause neurological symptoms as they expand due to mass effect. Classification is determined by World Health Organization (WHO) grades 1 to 3 following pathological examination corresponding to benign, atypical, and anaplastic (malignant), respectively, reflecting their rate of growth and risk of recurrence. The vast majority are WHO grade 1 and their slow growth permits timely presentation for elective resection; however, meningiomas in vulnerable locations and coexisting morbidities can result in sudden death. Objectives: We present a series of four adult patients with meningiomas which resulted in death, including a case of fatal seizure, midline hemorrhagic meningioma, postresection meningitis, and compression of the cerebellum. Research design: Retrospective review of the authors' cases was conducted. Available pathology, medical, and autopsy records including gross images were reviewed in each case. The inclusion criteria were adult patients (>18 years old) and that the cause of death had to include meningioma. Subjects: The four patients included a 61-year-old male, an 84-year-old female, a 62-year-old male, and a 37-year-old female. Measures: Qualitative; autopsy reports describing cause of death and pathology report findings including gross and microscopic analysis. Conclusions: Meningiomas are often benign in nature but can rarely result in death. Size and location of the tumor and risk factors are contributory. Autopsy examination can be instrumental in identifying the cause and mechanism of deaths associated with meningiomas.

Clinical Implications of Pituitary Adenomas Exhibiting Dual Transcription Factor Staining: A Case Series of 27 Patients.

OBJECTIVE: According to the 2017 World Health Organization classification of neuro-endocrine tumors, pituitary adenomas (PAs) are classified according to immunoexpression of the pituitary-specific transcription factors (TFs). A small subset of PAs exhibit multiple TF staining on immunohistochemistry and we present a series of 27 pathologically-confirmed cases of dual TF staining PAs (dsTF-PAs), and report clinically relevant implications. METHODS: A retrospective chart review of a multi-institutional database of patients with PAs surgically resected between 2008-2021 was performed. PAs expressing immunopositivity 2+ TFs. Patient demographics, neuro-imaging characteristics, histopathologic findings, and clinical data were collected. RESULTS: Twenty-seven patients had pathologically verified dsTF-PAs, of whom 17 were female (63%), with ages ranging from 20-84 years. Twenty-three (85.2%) patients harbored functional PAs, with acromegaly being the most common functional subtype (86.4%). The most common combination of TFs within a single tumor was PIT-1/SF-1 (85.2%). Six PAs exhibited Knosp cavernous sinus invasion grades of 3 or 4 and the Ki-67 labeling index was ≥3% in 6 patients (24.0%) and all stained for PIT-1/SF-1. Hormonal remission was achieved in 78% of functional dsTF-PAs. No PAs showed evidence of recurrence or progression over the mean follow-up period of 28.5 months. CONCLUSIONS: PAs exhibiting dsTF-PAs represent a small but clinically relevant diagnostic subset of PAs according to the 2021 World Health Organization criteria, as a majority are GH-producing. Precise classification using TF staining plays a key role in understanding the biology of these tumors. Favorable outcomes can be achieved in this subset of PAs with evolving TF classification.

Photocurable Carbon Nanotube/Polymer Nanocomposite for the 3D Printing of Flexible Capacitive Pressure Sensors.

A photocurable resin/carbon nanotube (CNT) nanocomposite was fabricated from aligned CNTs in an acrylic matrix. The conductivity of the nanocomposite increased rapidly and then stabilized when the CNT content was increased up to and beyond the percolation threshold. Various structures were created using a digital light processing (DLP) 3D printer. Various polymeric dispersants (SMA-amide) were designed and synthesized to improve the CNT dispersion and prevent aggregation. The benzene rings and lone electron pairs on the dispersant interacted with aromatic groups on the CNTs, causing the former to wrap around the latter. This created steric hindrance, thereby stabilizing and dispersing the CNTs in the solvent. CNT/polymer nanocomposites were created by combining the dispersant, CNTs, and a photocurable resin. The CNT content of the nanocomposite and the 3D printing parameters were tuned to optimize the conductivity and printing quality. A touch-based human interface device (HID) that utilizes the intrinsic conductivity of the nanocomposite and reliably detects touch signals was fabricated, enabling the free design of sensors of various styles and shapes using a low-cost 3D printer. The production of sensors without complex circuitry was achieved, enabling novel innovations.

Data-driven prediction of continuous renal replacement therapy survival.

Continuous renal replacement therapy (CRRT) is a form of dialysis prescribed to severely ill patients who cannot tolerate regular hemodialysis. However, as the patients are typically very ill to begin with, there is always uncertainty as to whether they will survive during or after CRRT treatment. Because of outcome uncertainty, a large percentage of patients treated with CRRT do not survive, utilizing scarce resources and raising false hope in patients and their families. To address these issues, we present a machine-learning-based algorithm to predict if patients will survive after being treated with CRRT. We use information extracted from electronic health records from patients who were placed on CRRT at multiple institutions to train a model that predicts CRRT survival outcome; on a held-out test set, the model achieved an area under the receiver operating curve of 0.929 (CI=0.917-0.942). Feature importance, error, and subgroup analyses identified consistently, mean corpuscular volume as a driving feature for model predictions. Overall, we demonstrate the potential for predictive machine-learning models to assist clinicians in alleviating the uncertainty of CRRT patient survival outcomes, with opportunities for future improvement through further data collection and advanced modeling.

Tumor characteristics guiding selection of channel-based versus open microscopic approaches for resection of atrial intraventricular meningiomas: patient series.

BACKGROUND: Atrial intraventricular meningiomas (AIMs) are relatively rare and typically deep-seated tumors that can mandate resection. Compared with transsulcal or transcortical open microscopic approaches, port- or channel-based exoscopic approaches have facilitated a less invasive alternative of tumor access and resection. The authors present a case series of seven patients with AIMs who underwent open microscopic versus channel-based exoscopic resection by a senior neurosurgeon at their institution between 2012 and 2022 to understand patient and tumor features that lent themselves to selection of a particular approach. OBSERVATIONS: In the patients harboring three AIMs selected for channel-based resection, the average AIM diameter (2.9 vs 5.2 cm) was smaller, the AIMs were deeper from the cortical surface (2.5 vs 1.1 cm), and the patients had a shorter average postoperative length of stay (3.3 vs 5.8 days) compared with the four patients who underwent open resection. Gross-total resection was achieved in all cases. Complications for both groups included transient homonymous hemianopsia and aphasia. No recurrences were identified over the follow-up period. LESSONS: The authors demonstrate that channel-based exoscopic resection is safe and effective for AIMs 3 cm in diameter and over 2 cm deep. This may help guide neurosurgeons in future approach selection based on tumor features, including size/volume, location, and depth from cortical surface.

Higher Prehospital Blood Glucose Levels Associated With Return of Spontaneous Circulation in Out-of-Hospital Non-traumatic Cardiac Arrests.

OBJECTIVE: Cardiac arrest leads to an array of metabolic disturbances. We aimed to investigate the association between prehospital blood glucose levels (BGLs) and rates of return of spontaneous circulation (ROSC) in non-traumatic out-of-hospital cardiac arrests (OHCAs). METHODS: A retrospective analysis of adult non-traumatic OHCAs within Kalamazoo County, MI, from January 2018 to May 2020 using the Michigan Emergency Medical Services Information System database was performed. Demographic data, Utstein variables, and BGLs (hypoglycemia < 70 mg/dL, euglycemia 70-120 mg/dL, and hyperglycemia >120 mg/dL) were abstracted. Chi-square and Wilcoxon rank sum tests were used to evaluate the relationship between BGL and ROSC. RESULTS: In total, 314 patients met the inclusion criteria. The mean age was 62.9 years. ROSC was achieved in 147 (46.8%) patients. Fifty (15.9%), 75 (23.9%), and 189 (60.2%) patients were hypoglycemic, euglycemic, and hyperglycemic, respectively. An association was found between the glycemic group and ROSC (P < .0001), with an estimated odds of ROSC being 77% lower (95% confidence interval, 46%-90%) for hypoglycemic than euglycemic or hyperglycemic patients. The point difference between median ROSC-yes BGL (median [interquartile range] = 160 mg/dL [110-225 mg/dL]) was 33 mg/dL (95% CI, 13-51 mg/dL) greater than the ROSC-no group (median [interquartile range] = 127 mg/dL [75-190 mg/dL], P = .001). CONCLUSION: Non-traumatic OHCA patients achieving ROSC had a significantly higher prehospital BGL than the ROSC-no group. Further study is warranted to investigate the role intra-arrest BGL may have as a prognostic marker for ROSC.

Comparison of preoperative versus postoperative treatment dosimetry plans of single-fraction stereotactic radiosurgery for surgically resected brain metastases.

OBJECTIVE: Stereotactic radiosurgery (SRS) for operative brain metastasis (BrM) is usually administered 1 to 6 weeks after resection. Preoperative versus postoperative timing of SRS delivery related to surgery remains a critical question, as a pattern of failure is the development of leptomeningeal disease (LMD) in as many as 35% of patients who undergo postoperative SRS or the occurrence of radiation necrosis. As they await level I clinical data from ongoing trials, the authors aimed to bridge the gap by comparing postoperative with simulated preoperative single-fraction SRS dosimetry plans for patients with surgically resected BrM. METHODS: The authors queried their institutional database to retrospectively identify patients who underwent postoperative Gamma Knife SRS (GKSRS) after resection of BrM between January 2014 and January 2021. Exclusion criteria were prior radiation delivered to the lesion, age < 18 years, and prior diagnosis of LMD. Once identified, a simulated preoperative SRS plan was designed to treat the unresected BrM and compared with the standard postoperative treatment delivered to the resection cavity per Radiation Therapy Oncology Group (RTOG) 90-05 guidelines. Numerous comparisons between preoperative and postoperative GKSRS treatment parameters were then made using paired statistical analyses. RESULTS: The authors' cohort included 45 patients with a median age of 59 years who were treated with GKSRS after resection of a BrM. Primary cancer origins included colorectal cancer (27%), non-small cell lung cancer (22%), breast cancer (11%), melanoma (11%), and others (29%). The mean tumor and cavity volumes were 15.06 cm3 and 12.61 cm3, respectively. In a paired comparison, there was no significant difference in the planned treatment volumes between the two groups. When the authors compared the volume of surrounding brain that received 12 Gy or more (V12Gy), an important predictor of radiation necrosis, 64% of patient plans in the postoperative SRS group (29/45, p = 0.008) recorded greater V12 volumes. Preoperative plans were more conformal (p < 0.001) and exhibited sharper dose drop-off at the lesion margins (p = 0.0018) when compared with postoperative plans. CONCLUSIONS: Comparison of simulated preoperative and delivered postoperative SRS plans administered to the BrM or resection cavity suggested that preoperative SRS allows for more highly conformal lesional coverage and sharper dose drop-off compared with postoperative plans. Furthermore, V12Gy was lower in the presurgical GKSRS plans, which may account for the decreased incidence of radiation necrosis seen in prior retrospective studies.

Integration of Multivariate Statistical Control Chart and Machine Learning to Identify the Abnormal Process Parameters for Polylactide with Glass Fiber Composites in Injection Molding; Part I: The Processing Parameter Optimization for Multiple Qualities of Polylactide/Glass Fiber Composites in Injection Molding.

This paper discusses the mixing of polylactide (PLA) and glass fiber which use injection molding to produce a functional composite material with glass fiber properties. The injection molding process explores the influence of glass fiber ratio, melt temperature, injection speed, packing pressure, packing time and cooling time on the mechanical properties of composite. Using the orthogonal table planning experiment of the Taguchi method, the optimal parameter level combination of a single quality process is obtained through main effect analysis (MEA) and Analysis of variance (ANOVA). Then, the optimal parameter level combination of multiple qualities is obtained through principal component analysis (PCA) and data envelopment analysis (DEA), respectively. It is observed that if all the quality characteristics of tensile strength, hardness, impact strength and bending strength are considered at the same time, the optimal process conditions are glass fiber addition 20 wt %, melt temperature 185 °C, injection speed 80 mm/s, holding pressure 60 MPa, holding time 1 s and cooling time 15 s, and the corresponding mechanical properties are tensile strength 95.04 MPa, hardness 86.52 Shore D, impact strength 4.4408 J/cm2, bending strength 119.89 MPa. This study effectively enhances multiple qualities of PLA/GF composite.

Endoscopic endonasal approach for MRI-Negative Cushing's microadenoma.

A 54-year-old male with a history of diabetes mellitus type 2 for 12 years and hypertension was seen in the clinic due to poorly controlled diabetes. Inferior petrosal sinus sampling (IPSS) confirmed Cushing's disease with primary adrenocorticotropic hormone (ACTH)-secreting pituitary adenoma on the right. However, 3T and subsequent 7T MRI showed no visible tumor. An endoscopic transsphenoidal approach was selected to explore the pituitary gland and resect the presumed microadenoma. Tumor was identified in the lateral recess along the right medial cavernous sinus wall and gross-total resection (GTR) was performed. The normal pituitary gland was preserved, and the patient went into remission. The video can be found here: https://stream.cadmore.media/r10.3171/2023.4.FOCVID2324.

Synthesis of novel Type-II MnNb2O6/g-C3N4 Mott-Schottky heterojunction photocatalyst: Excellent photocatalytic performance and degradation mechanism of fluoroquinolone-based antibiotics.

Fluoroquinolone antibiotics have been encountered in aquatic environments in quantities giving rise to significant concern recently. To cope with this problem, it is necessary to design a semiconductor photocatalyst having excellent photocatalytic efficiency to eliminate the antibiotics. The heterojunction is a likely situate where the efficiency of relevant photocatalyst can be strengthened. In this study, the performance of MnNb2O6/g-C3N4 (MNO/g-CN) composites in the photocatalytic degradation of ciprofloxacin (CIP) and tetracycline-HCl (TCH) antibiotics was explored. Enhanced photocatalytic activity of MNO/g-CN was found to be owing to electron's shifting between the MNO, and g-CN sheets, which promotes the formation of photo-generated e⁻/h⁺ pairs. This shows a low-waste, high-performance material exists to eradicate CIP and TCH from wastewater. Further, the structural, photochemical and light interacted properties of the MNO/g-CN photocatalyst, prepared by solvothermal method and sonication, were described using photochemical, physiochemical and electrochemical approaches. The synthesized photocatalyst owes its particular efficiency to its methodical photo-degradation of CIP and TC using visible light. The optimum composite 15% MNO/g-CN evinced the greatest photocatalytic efficiency with CIP and TCH photo-degradation of 94.10%, and 98.50%, respectively, and degradation mechanism were investigated using LC-MS spectroscopy. The suitable photocatalytic activity is ascribed to lower the recombination's rate of e⁻/h⁺ pairs. The scavenging evaluations proved that the h+ and •O2- were two major photoactive species accomplishing the CIP and TCH photodegradation over MNO/g-CN under visible region. Our findings pave the way for the construction of efficient binary photocatalysts for antibiotic restitution.

Complete, Fully Automatic Detection and Classification of Benign and Malignant Breast Tumors Based on CT Images Using Artificial Intelligent and Image Processing.

Breast cancer is the most common type of cancer in women, and early detection is important to significantly reduce its mortality rate. This study introduces a detection and diagnosis system that automatically detects and classifies breast tumors in CT scan images. First, the contours of the chest wall are extracted from computed chest tomography images, and two-dimensional image characteristics and three-dimensional image features, together with the application of active contours without edge and geodesic active contours methods, are used to detect, locate, and circle the tumor. Then, the computer-assisted diagnostic system extracts features, quantifying and classifying benign and malignant breast tumors using a greedy algorithm and a support vector machine. The study used 174 breast tumors for experiment and training and performed cross-validation 10 times (k-fold cross-validation) to evaluate performance of the system. The accuracy, sensitivity, specificity, and positive and negative predictive values of the system were 99.43%, 98.82%, 100%, 100%, and 98.89% respectively. This system supports the rapid extraction and classification of breast tumors as either benign or malignant, helping physicians to improve clinical diagnosis.

In Vivo Automatic and Quantitative Measurement of Adult Human Larynx and Vocal Fold Images.

INTRODUCTION: Qualitative laryngoscopy belongs to a diagnostic routine. Nevertheless, quantitative morphometric measurements of laryngeal structures remain challenging. This study aimed to introduce a special laser projection device that can facilitate computer-assisted digitalized analysis and provide important quantitative information for diagnostics and treatment planning. MATERIALS AND METHODS: The laryngeal images were captured with our device, which contained two parallel laser beams in order to provide the scaling reference. The maximum length of the vocal fold during respiration and vibration (phonation), vocal width at midpoint, total fold area, maximum cross-sectional area of the glottic space, and maximum vocal fold angle were determined and calculated. These parameters were analyzed and compared on the basis of age, sex, body height, body weight and body mass index. RESULTS: A total of 87 subjects were enrolled in this study, comprising 39 males and 48 females. The age range for all subjects was 21 to 80 years old. The maximum value of the glottic area and vocal angle showed no significant gender difference. Both the respiration and vibration vocal fold length was significantly longer in males than in females. The vocal width revealed no gender difference, but the fold area during both respiration and phonation was significantly larger in men than in women. As for the respiration-to-vibration ratio of the vocal length, there was a trend, but without statistical significance (P = 0.06), toward a higher length compression ratio in men than in women. Meanwhile, age was found to have a strong relationship with vocal width during phonation. The width of vibration vocal fold decreased with aging significantly. CONCLUSION: Our innovative module can provide reference parameters, which makes it possible to directly estimate the objective absolute values of relevant laryngeal structures. Our non-invasive approach can be used during routine laryngoscopy and the findings easily documented. In future, we can extend its clinical application to measure subtle laryngeal or hypopharyngeal changes, which are difficult to objectively quantify.

Albert Feng: father, friend, scientist, innovator (1944-2021).

Albert S.-H. Feng was an outstanding family man and brilliant scientist, with a creative mind, a gift for dealing with people of all types, and a warm, personable demeanor. He was blessed with many talents, making him a sought-after colleague and collaborator. His love for people and travel took him to many destinations around the world where he studied the neuroethology of frog and bat communication both in the field and in the lab. He has made many fundamental contributions to our understanding of the vertebrate auditory system. These include characterizing the "delay-tuned" neurons in the bat midbrain underlying target detection, and in discovering several terrestrial amphibians in which the upper limit of hearing extends well into the ultrasonic range, forever changing our long-held perception of frogs as "low-frequency animals".

Patient-level thyroid cancer classification using attention multiple instance learning on fused multi-scale ultrasound image features.

For patients with thyroid nodules, the ability to detect and diagnose a malignant nodule is the key to creating an appropriate treatment plan. However, assessments of ultrasound images do not accurately represent malignancy, and often require a biopsy to confirm the diagnosis. Deep learning techniques can classify thyroid nodules from ultrasound images, but current methods depend on manually annotated nodule segmentations. Furthermore, the heterogeneity in the level of magnification across ultrasound images presents a significant obstacle to existing methods. We developed a multi-scale, attention-based multiple-instance learning model which fuses both global and local features of different ultrasound frames to achieve patient-level malignancy classification. Our model demonstrates improved performance with an AUROC of 0.785 (p<0.05) and AUPRC of 0.539, significantly surpassing the baseline model trained on clinical features with an AUROC of 0.667 and AUPRC of 0.444. Improved classification performance better triages the need for biopsy.

Improvement in Injection Molding Quality Performance with Innovative Cyclone Mixers Used in Polypropylene with Spherical Silicon Dioxide Composites.

This research proposes an innovative design of a new cyclone mixer for the quality of polymer materials, and it presents a systematic optimization model of process parameters for plastic injection molding. Thermo gravimetric analysis (TGA) and differential scanning calorimetry (DSC) were used to determine the appropriate thermal properties of processing in order to select appropriate control factors and level values for a Taguchi orthogonal array. The injection molding machine was used to make sample test pieces for tensile strength, hardness and impact strength. Significant factors were found by the signal-to-noise (S/N) ratio with an analysis of variation (ANOVA), and the single-quality optimal parameter combination was obtained. The reproducibility of the experiment was evaluated, and various quality weights were evaluated by principal components analysis (PCA). The multi-quality optimal parameter combination was found, and the comprehensive scores were compared. Finally, the process capability indices were combined with a multi-process capability analysis chart (MPCAC) to compare the process yields of cyclone mixing and screw mixing. The mechanical properties of products were evaluated to verify the performance of cyclone mixing and to provide perfect information for the injection molding quality performance of cyclone mixing and screw mixing. It was concluded that the overall quality of the cyclone mixing products is 42.72, and the total quality of the screw mixing products is 41.85. The total number of defects for the cyclone mixing is 9659 ppm, and that of the screw mixing is 10688 ppm. It can be seen that, for the overall product quality performance, cyclone mixing can be applied in the plastic injection molding process instead of screw mixing.

Au Nanorods on Carbon-Based Nanomaterials as Nanohybrid Substrates for High-Efficiency Dynamic Surface-Enhanced Raman Scattering.

Gold nanorods (AuNRs) with different aspect ratios were prepared by the seed-mediated growth method and combined with three carbon-based nanomaterials of multiple dimensions (i.e., zero-dimensional (0D) carbon black (CB), one-dimensional (1D) carbon nanotubes (CNTs), and two-dimensional (2D) graphene oxide (GO)). The AuNR/carbon-based nanomaterial hybrids were utilized in dynamic surface-enhanced Raman scattering (D-SERS). First, cetyltrimethylammonium bromide (CTAB) was used to stabilize and coat the AuNRs, enabling them to be dispersed in water and conferring a positive charge to the surface. AuNR/carbon-based nanomaterial hybrids were then formed via electrostatic attraction with the negatively charged carbon-based nanomaterials. Subsequently, the AuNR/carbon-based nanomaterial hybrids were utilized as large-area and highly sensitive Raman spectroscopy substrates. The AuNR/GO hybrids afforded the best signal enhancement because the thickness of GO was less than 5 nm, which enabled the AuNRs adsorbed on GO to produce a good three-dimensional hotspot effect. The enhancement factor (EF) of the AuNR/GO hybrids for the dye molecule Rhodamine 6G (R6G) reached 1 × 107, where the limit of detection (LOD) was 10-8 M. The hybrids were further applied in D-SERS (detecting samples transitioning from the wet state to the dry state). During solvent evaporation, the system spontaneously formed many hotspots, which greatly enhanced the SERS signal. The final experimental results demonstrated that the AuNR/GO hybrids afforded the best D-SERS signal enhancement. The EF value for R6G reached 1.1 × 108 after 27 min, with a limit of detection of 10-9 M at 27 min. Therefore, the AuNR/GO nanohybrids have extremely high sensitivity as molecular sensing elements for SERS and are also very suitable for the rapid detection of single molecules in water quality and environmental management.

Design of Acrylate-Terminated Polyurethane for Nylon Seamless Bonding Fabric Part I: Design of the End-Capping Thermoplastic Polyurethane Adhesive with Acrylate Copolymer.

This series of studies aims to design acrylate-terminated polyurethanes for use in nylon seamless bonded fabrics. The first part used N,N-dimethylacrylamide (DMAA) and methyl methacrylate (MMA) to replace the chain extender in polyurethane synthesis as end-capping agent to synthesize thermoplastic polyurethane (TPU) adhesive. The molecular weight of the TPU is controlled to further influence the mechanical and processing properties of the polyurethane. Here, polytetramethylene ether glycol (PTMG) and 4,4-methylene diphenyl diisocyanate (MDI) were polymerized, and then a blocking agent was added thereto. The results show that the characteristic peaks of benzene ring and carbamate of TPU adhesive are at 1596 cm-1 and 1413 cm-1, respectively, while the characteristic peaks of DMAA are at 1644 cm-1 and 1642 cm-1 in the FT-IR spectrum. There is an absorption peak -N=C=O- which is not shown near 2268 cm-1, which proves that the structure of TPU contains the molecular structure of capping agent, PTMG and MDI. When the DMAA concentration in the capping agent was increased from 3.0 wt% to 10 wt%, the -C=O (H-bond) area percentage of hydrogen bonds formed at 1711 cm-1 increased from 41.7% to 57.6%, while the -NH (H bond) produced at 3330 cm-1 increased from 70% to 81%. These phenomena suggest that increasing the concentration of DMAA capping agent can effectively promote the formation of complex supramolecular network structures by hydrogen bonding in TPU. The content and concentration of the capping agent affects the molecular weight of the TPU. Chain growth is terminated when molecular weight growth can be effectively controlled and reduced. It was observed in thermal analysis that with increasing DMAA concentration in the molecular structure, the concentration of capping agent in TPU, hydrogen bonding force between hard segments, melting point (Tmh) and melting enthalpy (ΔH) all increased the capping agent. The pyrolysis temperature of TPU is increased by 10-20 °C.

Automated Optical Inspection for Defect Identification and Classification in Actual Woven Fabric Production Lines.

This paper presents a turnkey integrated system that can be operated in real time for real textile manufacturers. Eight types of defects in woven fabric, including stain, broken end, broken weft, hole, nep, double pick, kinky weft and float can be recognized and classified. First, an image is captured by a CMOS industrial camera with a pixel size of 4600 × 600 above the batcher at 20 m/min. After that, the four-stage image processing procedure is applied to detect defects and for classification. Stage 1 is image pre-processing; the filtration of the image noise is carried out by a Gaussian filter. The light source is corrected to reduce the uneven brightness resulting from halo formation. The improved mask dodging algorithm is used to reduce the standard deviation of the corrected original image. Afterwards, the background texture is filtered by an averaging filter, and the mean value is corrected for histogram shifting, so that this system is robust to the texture and color changes of woven fabric. The binary segmentation threshold is determined using the mean value and standard deviation of an image with a normal sample. Stage 2 uses adaptive binarization for separation of the background and defects and to filter the noise. In Stage 3, the morphological processing is used before the defect contour is circled, i.e., four features of each block, including the defect area, the aspect ratio of the defect, the average gray level of the defect and the defect orientation, which are calculated according to the range of contour. The image defect recognition dataset consists of 2246 images. The results show that the detection success rate is 96.44%, and the false alarm rate is 3.21%. In Stage 4, the defect classification is implemented. The support vector machine (SVM) is used for classification, 230 defect images are used as training samples, and 206 are used as test samples. The experimental results show that the overall defect recognition rate is 96.60%, providing that the software and hardware equipment designed in this study can implement defect detection and classification for woven fabric effectively.

Multiscale generative model using regularized skip-connections and perceptual loss for anomaly detection in toxicologic histopathology.

Background: Automated anomaly detection is an important tool that has been developed for many real-world applications, including security systems, industrial inspection, and medical diagnostics. Despite extensive use of machine learning for anomaly detection in these varied contexts, it is challenging to generalize and apply these methods to complex tasks such as toxicologic histopathology (TOXPATH) assessment (i.e.,finding abnormalities in organ tissues). In this work, we introduce an anomaly detection method using deep learning that greatly improves model generalizability to TOXPATH data. Methods: We evaluated a one-class classification approach that leverages novel regularization and perceptual techniques within generative adversarial network (GAN) and autoencoder architectures to accurately detect anomalous histopathological findings of varying degrees of complexity. We also utilized multiscale contextual data and conducted a thorough ablation study to demonstrate the efficacy of our method. We trained our models on data from normal whole slide images (WSIs) of rat liver sections and validated on WSIs from three anomalous classes. Anomaly scores are collated into heatmaps to localize anomalies within WSIs and provide human-interpretable results. Results: Our method achieves 0.953 area under the receiver operating characteristic on a real-worldTOXPATH dataset. The model also shows good performance at detecting a wide variety of anomalies demonstrating our method's ability to generalize to TOXPATH data. Conclusion: Anomalies in both TOXPATH histological and non-histological datasets were accurately identified with our method, which was only trained with normal data.