Visualization system to identify structurally vulnerable links in OHT railway network in semiconductor FAB using betweenness centrality.

In semiconductor fabrication (FAB), wafers are placed into carriers known as Front Opening Unified Pods (FOUPs), transported by the Overhead Hoist Transport (OHT). The OHT, a type of Automated Guided Vehicle (AGV), moves along a fixed railway network in the FAB. The routes of OHTs on the railway network are typically determined by a Single Source Shortest Path (SSSP) algorithm such as Dijkstra's. However, the presence of hundreds of operating OHTs often leads to path interruptions, causing congestion or deadlocks that ultimately diminish the overall productivity of the FAB. This research focused on identifying structurally vulnerable links within the OHT railway network in semiconductor FAB and developing a visualization system for enhanced on-site decision-making. We employed betweenness centrality as a quantitative index to evaluate the structural vulnerability of the OHT railway network. Also, to accommodate the unique hierarchical node-port structure of this network, we modified the traditional Brandes algorithm, a widely-used method for calculating betweenness centrality. Our modification of the Brandes algorithm integrated node-port characteristics without increasing computation time while incorporating parallelization to reduce computation time further and improve usability. Ultimately, we developed an end-to-end web-based visualization system that enables users to perform betweenness centrality calculations on specific OHT railway layouts using our algorithm and view the results through a web interface. We validated our approach by comparing our results with historically vulnerable links provided by Samsung Electronics. The study had two main outcomes: the development of a new betweenness centrality calculation algorithm considering the node-port structure and the creation of a visualization system. The study demonstrated that the node-port structure betweenness centrality effectively identified vulnerable links in the OHT railway network. Presenting these findings through a visualization system greatly enhanced their practical applicability and relevance.

Video domain adaptation for semantic segmentation using perceptual consistency matching.

Unsupervised domain adaptation (UDA) aims to transfer knowledge in previous and related labeled datasets (sources) to a new unlabeled dataset (target). Despite the impressive performance, existing approaches have largely focused on image-based UDA only, and video-based UDA has been relatively understudied and received less attention due to the difficulty of adapting diverse modal video features and modeling temporal associations efficiently. To address this, existing studies use optical flow to capture motion cues between in-domain consecutive frames, but is limited by heavy compute requirements and modeling flow patterns across diverse domains is equally challenging. In this work, we propose an adversarial domain adaptation approach for video semantic segmentation that aims to align temporally associated pixels in successive source and target domain frames without relying on optical flow. Specifically, we introduce a Perceptual Consistency Matching (PCM) strategy that leverages perceptual similarity to identify pixels with high correlation across consecutive frames, and infer that such pixels should correspond to the same class. Therefore, we can enhance prediction accuracy for video-UDA by enforcing consistency not only between in-domain frames, but across domains using PCM objectives during model training. Extensive experiments on public datasets show the benefit of our approach over existing state-of-the-art UDA methods. Our approach not only addresses a crucial task in video domain adaptation but also offers notable improvements in performance with faster inference times.

Evaluation of clinical factors predicting dysphagia in patients with traumatic and non-traumatic cervical spinal cord injury: a retrospective study.

Introduction: Dysphagia is a common complication in patients with cervical spinal cord injury (C-SCI) and can cause various pulmonary complications, such as aspiration pneumonia and mechanical airway obstruction increasing mortality and morbidity. This study evaluated the clinical factors that predict dysphagia in patients with traumatic and non-traumatic C-SCI. Methods: Ninety-eight patients with C-SCI were retrospectively enrolled in this study and were divided into those with and without dysphagia. Clinical factors such as age, sex, tracheostomy, spinal cord independence measure, pulmonary function test (PFT) including forced vital capacity (FVC), forced expiratory volume in 1 s (FEV1) and FVC/FEV1, American Spinal Cord Injury Association score, Berg Balance Scale, and surgical approach were investigated retrospectively. Results: Multivariate logistic regression analysis revealed that FVC and the presence of tracheostomy were significantly correlated with dysphagia in patients with C-SCI (p < 0.05). FVC and the presence of tracheostomy are useful tools for detecting dysphagia in patients with C-SCI. Conclusion: Considering the results of our study, early PFTs, especially FVC, in patients with C-SCI and early initiation of dysphagia management and treatment in patients with C-SCI and tracheostomy will be advantageous in lowering the mortality and morbidity due to pulmonary aspiration in these patients.

Deep learning algorithm for automatically measuring Cobb angle in patients with idiopathic scoliosis.

PURPOSE: The Cobb angle is a standard measurement to qualify and track the progression of scoliosis. However, the Cobb angle has high inter- and intra-observer variability. Consequently, its measurement varies with vertebrae and may even differ when the same vertebra is measured. Therefore, it is not constant and differs with measurements. This study aimed to develop a deep learning model that automatically measures the Cobb angle. The deep learning model for identifying vertebrae on spine radiographs was developed. METHODS: The dataset consisted of 297 images that were divided into two subsets for training and validation. Two hundred and twenty-seven images (76.4%) were used to train the model, while 70 images (23.6%) were used as the validation dataset. Absolut error between the measurements by the observer and developed deep learning model and intraclass correlation coefficient (ICC). RESULTS: The average absolute error between the measurements was 1.97° with a standard deviation of 1.57°. In addition, 95.9% of the angles had an absolute error of less than 5°. The ICC was calculated to assess the model's reliability further. The ICC was 0.981, indicating excellent reliability. CONCLUSIONS: The authors believe the model will be useful in clinical practice by relieving clinicians of the burden of having to manually compute the Cobb angle. Further studies are needed to enhance the accuracy and versatility of this deep learning model.

Experience from a single-center study on multimodal medication therapy for patients with complex regional pain syndrome.

BACKGROUND: Despite the application of various therapeutic methods, pain caused by complex regional pain syndrome (CRPS) is not sufficiently managed and often progresses to a chronic stage. For the systematic and effective treatment of CRPS, we developed an algorithm for multimodal medication therapy based on the established pathophysiology of CRPS to control CRPS-related pain. OBJECTIVE: In this study, we present the outcomes of our novel algorithm for multimodal medication therapy for patients with CRPS, consisting of three major components: multimodal oral medication, intravenous ketamine, and intravenous lidocaine therapy. METHODS: We retrospectively investigated patients with CRPS who received multimodal therapy. Pain severity scores were evaluated using a numerical rating scale at four time points (P1, pain at initial consultation; P2, pain after oral medication; P3, pain after ketamine treatment; and P4, pain after lidocaine treatment). The effect of the multimodal medication therapy algorithm on pain management was evaluated at each time point. RESULTS: In patients with CRPS, multimodal oral medication, intravenous ketamine, and intravenous lidocaine therapies led to significantly improved pain control (p< 0.05). Additionally, the combination of these three therapies (through the multimodal medication therapy algorithm) resulted in significant pain relief in patients with CRPS (p< 0.05). CONCLUSIONS: Our multimodal medication therapy algorithm effectively controlled pain in patients with CRPS. However, further prospective studies with large sample sizes and randomized controlled trials are needed for more accurate generalization.

Analysis of the effect of dust barriers on particulate matter dispersion from a construction site using CFD simulation.

Urban construction activities generate particulate matter (PM), which poses a severe threat to urban residents' quality of life. The complex topography of urban areas and turbulent air flow make it difficult to predict PM dispersion and establish control measures. This study predicted PM dispersion from construction sites to urban areas under four wind directions using CFD simulations and determined optimal dust barrier heights for reducing PM exposure. This study showed that PM exposure on residents depends on wind directions in the urban area up to 2.3 times, still a flexible approach to determine optimal barrier heights based on stakeholder preferences was effective in all wind directions. Accurate PM dispersion predictions and control measures are crucial for improving urban air quality and residents' well-being.

Searching for a Home Port in a Polyvectic World: Molecular Analysis and Global Biogeography of the Marine Worm Polydora hoplura (Annelida: Spionidae).

The spionid polychaete Polydora hoplura Claparède, 1868 is a shell borer widely occurring across the world and considered introduced in many areas. It was originally described in the Gulf of Naples, Italy. Adult diagnostic features are the palps with black bands, prostomium weakly incised anteriorly, caruncle extending to the end of chaetiger 3, short occipital antenna, and heavy sickle-shaped spines in the posterior notopodia. The Bayesian inference analysis of sequence data of four gene fragments (2369 bp in total) of the mitochondrial 16S rDNA, nuclear 18S, 28S rDNA and Histone 3 has shown that worms with these morphological features from the Mediterranean, northern Europe, Brazil, South Africa, Australia, Republic of Korea, Japan and California are genetically identical, form a well-supported clade, and can be considered conspecific. The genetic analysis of a 16S dataset detected 15 haplotypes of this species, 10 of which occur only in South Africa. Despite the high genetic diversity of P. hoplura in South Africa, we tentatively propose the Northwest Pacific, or at the most the Indo-West Pacific, as its home region, not the Atlantic Ocean or the Eastern Pacific Ocean. The history of the discovery of P. hoplura around the world appears to be intimately linked to global shipping commencing in the mid-19th century, followed by the advent of the global movement of commercial shellfish (especially the Pacific oyster Magallana gigas) in the 20th century, interlaced with continued, complex dispersal by vessels and aquaculture. Given that P. hoplura has been detected in only a few of the 17 countries where Pacific oysters have been established, we predict that it may already be present in many more regions. As global connectivity through world trade continues to increase, it is likely that novel populations of P. hoplura will continue to emerge.

Deep-learning-based blood pressure estimation using multi channel photoplethysmogram and finger pressure with attention mechanism.

Recently, several studies have proposed methods for measuring cuffless blood pressure (BP) using finger photoplethysmogram (PPG) signals. This study presents a new BP estimation system that measures PPG signals under progressive finger pressure, making the system relatively robust to errors caused by finger position when using the cuffless oscillometric method. To reduce errors caused by finger position, we developed a sensor that can simultaneously measure multi-channel PPG and force signals in a wide field of view (FOV). We propose a deep-learning-based algorithm that can learn to focus on the optimal PPG channel from multi channel PPG using an attention mechanism. The errors (ME ± STD) of the proposed multi channel system were 0.43±9.35 mmHg and 0.21 ± 7.72 mmHg for SBP and DBP, respectively. Through extensive experiments, we found a significant performance difference depending on the location of the PPG measurement in the BP estimation system using finger pressure.

Contrastive encoder pre-training-based clustered federated learning for heterogeneous data.

Federated learning (FL) is a promising approach that enables distributed clients to collaboratively train a global model while preserving their data privacy. However, FL often suffers from data heterogeneity problems, which can significantly affect its performance. To address this, clustered federated learning (CFL) has been proposed to construct personalized models for different client clusters. One effective client clustering strategy is to allow clients to choose their own local models from a model pool based on their performance. However, without pre-trained model parameters, such a strategy is prone to clustering failure, in which all clients choose the same model. Unfortunately, collecting a large amount of labeled data for pre-training can be costly and impractical in distributed environments. To overcome this challenge, we leverage self-supervised contrastive learning to exploit unlabeled data for the pre-training of FL systems. Together, self-supervised pre-training and client clustering can be crucial components for tackling the data heterogeneity issues of FL. Leveraging these two crucial strategies, we propose contrastive pre-training-based clustered federated learning (CP-CFL) to improve the model convergence and overall performance of FL systems. In this work, we demonstrate the effectiveness of CP-CFL through extensive experiments in heterogeneous FL settings, and present various interesting observations.

Association between Anterior Surgical Approach and Dysphagia Severity in Patients with Cervical Spinal Cord Injury.

INTRODUCTION: Early detection and management of dysphagia are essential for preventing aspiration pneumonia and reducing mortality in patients with cervical spinal cord injury (C-SCI). In this study, we identified risk factors for dysphagia in patients with C-SCI by analyzing the correlation between the clinical factors and the severity of dysphagia, not the presence or absence of dysphagia. Combined with the analysis results of previous studies, we thought that this additional analysis method could more accurately reveal the risk factors for dysphagia in patients with C-SCI. METHODS: The presence and severity of dysphagia in patients with C-SCI was evaluated using a modified videofluoroscopic dysphagia scale (mVDS) and penetration-aspiration scale (PAS). All included patients with C-SCI performed a video fluoroscopic swallowing study (VFSS). Clinical factors such as age, sex, the presence of tracheostomy, spinal cord independence measure (SCIM), pulmonary function test (PFT), including forced vital capacity (FVC), forced expiratory volume in 1 s (FEV1), FVC/FEV1, maximal inspiratory pressure (MIP), and maximal expiratory pressure (MEP), American Spinal Cord Injury Association (ASIA) score, Berg Balance Scale (BBS), and operation method were investigated. RESULTS: In the multivariate regression analysis, the anterior surgical approach was the only clinical factor that had a significant correlation in both mVDS and PAS, which represents the severity of dysphagia in C-SCI patients (p < 0.05). CONCLUSION: The anterior surgical approach was correlated with the severity of dysphagia in patients with C-SCI. Considering this, as one of the risk factors affecting dysphagia in patients with C-SCI, surgical method may also need to be considered. Additionally, we recommend that clinicians should pay particular attention to the potential for development of dysphagia in patients who received anterior cervical surgery. However, further prospective studies with larger sample sizes are needed for more accurate generalization.

Dental radiology reporting status and recording frequency of reporting items in Korea.

Purpose: This study investigated the current dental radiology reporting methods and the recording rate of 10 mandatory reporting items in Korea. Materials and Methods: An original online survey created using Google Forms was distributed to dental practitioners. The survey asked about the participants' age, experience, workplace, use of radiologic equipment, radiology reporting methods, and recording reporting items. Results: In total, 354 responses were analyzed. Radiologic reporting in dental charts was the most commonly used method for each modality. Four out of 10 mandatory items were recorded at a high rate, but the remaining 6 items had substantially lower recording rates, often below 50%. The participants who reported radiographic findings through other separate methods had higher item scores than those who wrote findings in dental charts (P<0.05). Conclusion: Radiologic societies and dental associations should encourage the use of separate reports for radiographic examinations. Education regarding radiology reports and the justification for reporting items should be reinforced in dental schools, training courses on radiology, and the continuing education curriculum.

Ice-Crystal-Templated "Accordion-Like" Cellulose Nanofiber/MXene Composite Aerogels for Sensitive Wearable Pressure Sensors.

Exfoliated MXene nanosheets are integrated with cellulose nanofibers (CNFs) to form composite aerogels with high electric conductivity. The combination of CNFs and MXene nanosheets forms a unique "accordion-like" hierarchical architecture with MXene-CNF pillared layers through ice-crystal templating. Benefiting from the special "layer-strut" structure, the MXene/CNF composite aerogels have low density (50 mg/cm3), excellent compressibility and recoverability, as well as superior fatigue resistance (up to 1000 cycles). When being used as a piezoresistive sensor, the composite aerogel exhibits high sensitivity upon different strains, stable sensing performance with various compressive frequencies, broad detection range, and quick responsiveness (0.48 s). Moreover, the piezoresistive sensors are shown to have an excellent real-time sensing ability for human motions such as swallowing, arm bending, walking, and running. The composite aerogels also have a low environmental impact with the natural biodegradability of CNFs. The designed composite aerogels can serve as a promising sensing material for developing next-generation sustainable and wearable electronic devices.

A Simple Micromilled Microfluidic Impedance Cytometer with Vertical Parallel Electrodes for Cell Viability Analysis.

Microfluidic impedance cytometry has been demonstrated as an effective platform for single cell analysis, taking advantage of microfabricated features and dielectric cell sensing methods. In this study, we present a simple microfluidic device to improve the sensitivity, accuracy, and throughput of single suspension cell viability analysis using vertical sidewall electrodes fabricated by a widely accessible negative manufacturing method. A microchannel milled through a 75 µm platinum wire, which was embedded into poly-methyl-methacrylate (PMMA), created a pair of parallel vertical sidewall platinum electrodes. Jurkat cells were interrogated in a custom low-conductivity buffer (1.2 ± 0.04 mS/cm) to reduce current leakage and increase device sensitivity. Confirmed by live/dead staining and electron microscopy, a single optimum excitation frequency of 2 MHz was identified at which live and dead cells were discriminated based on the disruption in the cell membrane associated with cell death. At this frequency, live cells were found to exhibit changes in the impedance phase with no appreciable change in magnitude, while dead cells displayed the opposite behavior. Correlated with video microscopy, a computational algorithm was created that could identify cell detection events and determine cell viability status by application of a mathematical correlation method.

Synthesis of MAPbBr3 -Polymer Composite Films by Photolysis of DMF: Toward Transparent and Flexible Optical Physical Unclonable Functions (PUFs) with Hierarchical Multilevel Complexity.

Physical entities with inherent randomness have been investigated as anti-counterfeiting labels based on physical unclonable functions (PUFs). Herein, a transparent and flexible optical PUF label associated with multilevel complexity is demonstrated by taking advantage of the optical properties of hierarchical morphologies of the composite film composed of metal halide perovskite nanoparticles (MAPbBr3 NPs) and the intrinsic spinodal-decomposition-like phase separation of polymer blend (PMMA/PS blend). Due to the combinatorial effects of the photolysis synthesis of MAPbBr3 and the thermodynamic instability of the PMMA/PS blend, randomized patterns emerge at two-level scales. These patterns are intrinsically non-deterministic, and therefore, the PUF labels from the multilevel random patterns are challenging to replicate. This is mainly attributed to random spot patterns (higher-level patterns) confined within intricate bicontinuous patterns (lower-level patterns).

Comparison of the clinical usefulness of structured and free-text reports for interpretation of jaw lesions on cone beam computed tomography images.

OBJECTIVE: This study compared the clinical usefulness of structured reports (SRs) and free-text reports (FTRs) of lesions depicted on cone beam computed tomography (CBCT) images from the perspectives of report providers and receivers. STUDY DESIGN: In total, 36 CBCT images of jaw lesions obtained between February 2020 and August 2020 were evaluated. A working group of 3 oral and maxillofacial radiologists (OMRs) established a reporting system and prepared reports. Evaluation group I (2 OMRs) wrote SRs and FTRs for each case and assessed the reporting process for the criteria of convenience and organization. Evaluation group II (3 general practitioners [GPs] and 3 oral and maxillofacial surgeons [OMSs]) assessed the reports for the criteria of productivity, consistency, and organization. A 5-point Likert scale was used to assess the usefulness of each report. Scores were statistically compared according to report type with the paired Wilcoxon signed-rank test. RESULTS: The SRs scored significantly higher for all criteria as assessed by evaluation group I and the GPs of group II (P < .001). The FTRs scored significantly higher for productivity and organization as assessed by the OMSs of group II (P = .005 for both criteria). CONCLUSIONS: The clinical usefulness of reports may differ according to roles of the report recipients in diagnosis and treatment.

Erratum: Development of diagnostic molecular markers for marker-assisted breeding against bacterial wilt in tomato.

[This corrects the article DOI: 10.1270/jsbbs.20027.].

Risk factors for external root resorption of maxillary second molars associated with third molars.

Purpose: This study aimed to evaluate the diagnostic performance of panoramic images compared to cone-beam computed tomography (CBCT) imaging for maxillary third molar (M3)-associated external root resorption (ERR), and to identify the risk factors of ERR on panoramic images. Materials and Methods: The study population was composed of all patients who underwent panoramic imaging at Dankook University Dental Hospital from May to October 2019. In total, 397 cases of maxillary M3s in 247 patients (147 men and 100 women) were included. The diagnostic accuracy of ERR in panoramic images compared to CBCT images was evaluated using the chi-square test. To identify risk factors for ERR, dental records and panoramic findings were evaluated by logistic regression analysis. Results: The diagnostic accuracy of ERR on panoramic images was 0.79 compared to CBCT images (P<0.05). Superimposition of M3s onto second molars (M2) was associated with an approximately 33 times higher risk of ERR than separated M3s (P<0.05). Impacted M3s showed a 5 times higher risk of ERR than erupted M3s (P<0.05). Conclusion: ERR related to M3s is a common clinical condition, and superimposition of M3 onto M2 on panoramic images was the most important risk factor for ERR. It seemed that CBCT examinations for maxillary M3s might be indicated for ERR diagnosis especially if panoramic radiographs show superimposition of M3 onto M2. Impaction itself was also a risk factor, and it should be carefully examined.

A digital workflow for pair matching of maxillary anterior teeth using a 3D segmentation technique for esthetic implant restorations.

We investigated a state-of-the-art algorithm for 3D reconstruction with a pair-matching technique, which enabled the fabrication of individualized implant restorations in the esthetic zone. This method compared 3D mirror images of crowns and emergence profiles between symmetric tooth pairs in the anterior maxilla using digital slicewise DICOM segmentation and the superimposition of STL data. With the outline extraction of each segment provided by 100 patients, the Hausdorff distance (HD) between two point sets was calculated to identify the similarity of the sets. By using HD thresholds as a pair matching criterion, the true positive rates of crowns were 100, 98, and 98%, while the false negative rates were 0, 2, and 2% for central incisors, lateral incisors, and canines, respectively, indicating high pair matching accuracy (> 99%) and sensitivity (> 98%). The true positive rates of emergence profiles were 99, 100, and 98%, while the false negative rates were 1, 0, and 2% for central incisors, lateral incisors, and canines, respectively, indicating high pair matching accuracy (> 99%) and sensitivity (> 98%). Therefore, digitally flipped contours of crown and emergence profiles can be successfully transferred for implant reconstruction in the maxillary anterior region to optimize esthetics and function.

A practical evaluation of machine learning for classification of ultrasound images of ovarian development in channel catfish (Ictalurus punctatus).

Machine learning is a powerful tool to improve efficiency of industrial processes, but it has not yet been well utilized in aquacultural and hatchery applications. The goal of the present study was to evaluate the feasibility of using a broad array of machine learning approaches (testing of > 200 vectorization and model combinations, reporting on 20) to classify ultrasound images spanning annual ovarian development (i.e., from undeveloped to mature) of channel catfish (Ictalurus punctatus). The specific objectives were to: 1) establish dataset preprocessing to standardize image features; 2) develop and train image classification models with deep learning methods; 3) develop and train models with traditional machine learning methods; 4) compare performance of deep learning and traditional methods on two classification problems (2-class and 5-class), and 5) propose insights to deploy models in practical aquaculture applications for research and hatchery use. A total of 931 ultrasound images of catfish ovaries were used to train and evaluate models for a 2-class problem (as a 'yes' or 'no' answer) to support hormone-injection decisions for spawning management in hatcheries, and a 5-class problem for classifying gonadal development stages for research. By using feature extraction, cropping, dimension reduction, and histogram normalization, a preprocessing method was created to standardize images to develop traditional (i.e., vector input), and deep learning convolutional neural network (CNN) (i.e., image input) approaches. Traditional machine learning models with image classification achieved 100% median accuracy on the 2-class problem (with the models RN-50 and RN-152), and 96% median accuracy for the 5-class problem (with VGG-19 image vectorization). The deep learning approach for the 2-class problem had a median accuracy of > 98% for 15models. The 5-class deep learning models produced a steady increase in median accuracy with training net size, achievable through expansion of the dataset. These models can be developed further, but traditional models (using CNN architectures to simply calculate image vectors) outperformed the deep learning approach. These models can be directly applicable to aquaculture in hatcheries and reproductive biology research, in addition to a wide variety of other image-based applications.

A Fabricated Force Glove That Measures Hand Forces during Activities of Daily Living.

Understanding hand and wrist forces during activities of daily living (ADLs) are pertinent when modeling prosthetics/orthotics, preventing workplace-related injuries, and understanding movement patterns that make athletes, dancers, and musicians elite. The small size of the wrist, fingers, and numerous joints creates obstacles in accurately measuring these forces. In this study, 14 FlexiForce sensors were sewn into a glove in an attempt to capture forces applied by the fingers. Participants in this study wore the glove and performed grasp and key turn activities. The maximal forces produced in the study were 9 N at the distal middle finger phalanx and 24 N at the distal thumb phalanx, respectively, for the grasp and key turn activities. Results from this study will help in determining the minimal forces of the hand during ADLs so that appropriate actuators may be placed at the appropriate joints in exoskeletons, orthotics, and prosthetics.