Entire Procedure Research Articles

A Real-Time Defect Detection Strategy for Additive Manufacturing Processes Based on Deep Learning and Machine Vision Technologies.

Nowadays, additive manufacturing (AM) is advanced to deliver high-value end-use products rather than individual components. This evolution necessitates integrating multiple manufacturing processes to implement multi-material processing, much more complex structures, and the realization of end-user functionality. One significant product category that benefits from such advanced AM technologies is 3D microelectronics. However, the complexity of the entire manufacturing procedure and the various microstructures of 3D microelectronic products significantly intensified the risk of product failure due to fabrication defects. To respond to this challenge, this work presents a defect detection technology based on deep learning and machine vision for real-time monitoring of the AM fabrication process. We have proposed an enhanced YOLOv8 algorithm to train a defect detection model capable of identifying and evaluating defect images. To assess the feasibility of our approach, we took the extrusion 3D printing process as an application object and tailored a dataset comprising a total of 3550 images across four typical defect categories. Test results demonstrated that the improved YOLOv8 model achieved an impressive mean average precision (mAP50) of 91.7% at a frame rate of 71.9 frames per second.

Application of Machine Learning Algorithm for Defect Analysis in Semiconductors Using High Resolved Scanning Acoustic Microscopy

Scanning Acoustic Microscopy is a dedicated powerful technology in NDT to analyze and characterize bonded interfaces for different technologies. As the complexity of the semiconductor package grows, even seemingly insignificant faults become quite important. Main applications include detection of voids, inclusions and delaminated areas in bonding interfaces and thickness variations in layer structures. The SAM operates with the pulse reflection method. The acoustic lens transforms high frequency electromagnetic vibrations that are transmitted inside the lens as a planar parallel wave field. Through the coupling medium, which is water, the cavity concentrates the sound field on the sample. The sound pulses reflected from the sample are gathered up by the acoustic lens and are converted into electromagnetic pulses by the transducer. These pulses are reconstructed into images with defined gray values. Pixels with certain gray levels are used to depict pulses.New developments in SAM combine high throughput inspection and high resolution down to the sub-micrometer range.The wafer bow is quickly eliminated by altering the array transducer focus point throughout the entire scanning procedure. New techniques for SAM's bonded interface protection keep the wafer almost completely dry throughout inspection. In order to discover and categorize failures with the least amount of data, research focuses on deep learning networks and scanning acoustic microscopes for automated failure assessment. High speed automated wafer-level inspections were made possible by the development of image and signal-based machine learning techniques. These operations can be carried out at both the wafer level and the die level without the need for sample preparation.The automatic handling, scanning, drying, and separation of good wafers from defected wafers based on acoustic data made possible by technological developments in SAM systems. Despite the high sensitivity, minute variations in the reflected signal that would point to a flaw can be lost during image reconstruction. More emphasis is being placed on creating AI-based algorithms for highly accurate automated signal interpretation for failure detection as a result of recent breakthroughs in AI (Artificial Intelligence). Since it requires less human work, automated failure analysis is becoming more and more popular for the detection and qualification of wafer bond integrity. This new technique trains the network for automated fault prediction using raw A-Scan data. In the first phase, die level prediction is reviewed and in the later phase, a wafer-level high volume inspection is performed. Combining SAM with an A-Scan-based Deep Learning approach primarily benefits non-destructive analysis with less labelling effort and training time. With minimal human effort, this process flow is perfect for automated high-volume inspection.SAM also uses image based machine learning approach for automated defect analysis. The U-net machine-learning algorithm serves as the foundation for the integrated image analysis program. Deep Learning technologies is integrated into a new modular image analysis (MIA) architecture with built-up integrated pipeline for Label Studio Software-based Deep Learning activities (labelling, training, analysis). The built-up pipeline is used to identify, train, and test various classes of defect in a bonded wafer. This method employs model confidence to manually mark marginal and defective predictions.High resolution and high throughput analysis for the defect characterization and defect analysis of various types of bonded wafers is improved in scanning acoustic microscopes using the array transducers, signal processing methods like SAFT, Wavelet filtering techniques, Artificial intelligence methods based on both image and signal. With all these advanced technologies, Scanning Acoustic Microscopy is capable of inspection of wafer bond integrity, TSV characterization, 3D package evaluation, Wafer Level Chip Scale Packaging (WL-CSP) and Crack inspection. For the metrology of mass production, from initial research and development, our equipment services and software algorithms assist manufacturers of nano electronics in better defect analysis and material characterization. Next Generation transducers were designed and developed in particular for WL-CSP applications.

Microheater with copper nanofiber network via electrospinning and electroless deposition

In this report, we present the development of a copper nanofiber network-based microheater, designed for applications in electron microscopes, gas sensing, and cell culture platforms. The seed layer, essential for electroless deposition, was fabricated through the electrospinning of a palladium-contained polyvinylpyrrolidone solution followed by a heat treatment. This process minimized the contact resistance between nanofibers. We successfully fabricated a microheater with evenly distributed temperature by controlling the electrospinning time, heat treatment conditions, and electroless deposition time. We assessed the electrical and thermal characteristics of the microheater by examining the nanofiber density, sheet resistance, and transmittance. The microheater’s performance was evaluated by applying current, and we verified its capacity to heat up to a maximum of 350 °C. We further observed the microheater’s temperature distribution at varying current levels through an infrared camera. The entire manufacturing procedure takes place under normal pressure, eliminating the need for masking or etching processes. This renders the method easily adaptable to the mass production of microdevices. The method is expected to be applicable to various materials and sizes and is cost-effective compared to commercially produced microheaters developed through microelectromechanical system processes, which demand complex facilities and high cost.

Findings from the Use of Spinal Anesthesia in the Laparoscopic Treatment of Extrauterine Pregnancy: Could It Represent an Alternative to General Anesthesia?

Introduction: Minimally invasive procedures performed in laparoscopy, such as salpingectomy for ectopic pregnancy, can be combined with a minimally invasive anesthesia. The aim of this study was to assess the feasibility and the intraoperative and postoperative outcomes of laparoscopic surgery for ectopic pregnancy under spinal anesthesia (SA) compared to general anesthesia (GA) from the point of view of the surgeon, anesthesiologist, and patient. Methods: A retrospective cohort study was performed at DAI Materno Infantile of AOU Federico II of Naples, analyzing all medical records of women who met the inclusion criteria between April 2020 and April 2023. Eighty-two women (35 under SA in group A and 47 under GA in group B) undergone elective or emergency laparoscopic salpingectomy for ectopic tubal or ovarian pregnancy were included. Results: Patients in group A reported less pain at 0 h (adjusted mean difference: −1.5; 95% CI: −2.3 to −0.7; p < 0.001) and after 6 h (adjusted mean difference: −1.1; 95% CI: −2.0 to −0.3; p = 0.01) while no statistically significant differences between the two groups at 12 and 24 h after surgery. No differences were observed among the type of analgesic and during the postoperative observation time, except for paracetamol at 0 h in group B. A faster resumption of bowel motility, patient’s mobilization, and a shorter hospital stay were observed in group A compared to group B. Also greater odds of returning faster to daily activities emerged in group A (adjusted OR: 5.39; 95% CI: 1.77–16.37). A greater number of patients in group A were satisfied with the entire procedure compared to those of group B (33 [94.3%] vs. 37 [78.7%]). The general surgeon satisfaction was always very good or excellent in group A. Finally, all surgical steps were well tolerated in group A. Conclusion: In specific settings, SA is a feasible and safe procedure for the laparoscopic treatment of ectopic pregnancy.

Evaluation Methodology of the Railway Stations Using the AHP Method in the Transport Hubs from the Freight Transport Point of View

Currently, it is necessary to support not only public passenger transport at the expense of individual car transport but also to ensure the modal split of goods from road transport to railway transport. Moreover, it is important to modernize the railway infrastructure, especially hubs and other important railway stations in important settlements and big cities. Therefore, it is necessary to constantly invest in railway lines as well as railway stations. The contribution deals with the determination of the methodology for the evaluation of railway stations in freight transport based on current scientific publications and the AHP method. Its main goal is to determine the size of the peak on the network-railway station on the infrastructure manager’s railway network. One of the benefits is the subsequent determination of the next procedure from the given peak in terms of operation, considering the economic complexity of the entire procedure. The methodology is preceded by an extensive analysis of input data in Slovakia and abroad. This methodology defines the parameters or factors that influence decisions for a particular railway freight station. Subsequently, based on the proposed methodology, a practical application is also developed, within which four railway stations on the ŽSR network are evaluated. In a broader sense, the contribution also points to improving the quality of railway infrastructure in cities.

Surgical Feasibility Study on Cadaver for Vascularized Wrist Joint Transplantation

Despite modern advancements in the treatment of late stages of wrist joint degeneration, few reliable options exist for patients requiring motion-preserving reconstruction of their radiocarpal and midcarpal joints. Vascularized composite allotransplantation (VCA) could be considered an option for wrist reconstruction in the future. The goal of this study was to describe the relevant anatomy and design a potential surgical technique for wrist VCA. Anatomic studies were performed on 17 human upper extremities. The arterial system of each cadaver was injected with latex dye or radiographic contrast. After injecting a contrast medium visible on a computerized tomography (CT) scan, the initial three specimens were examined using microCT. This confirmed joint vascular patency and allowed for the dissection of the other specimens that were injected with latex for the study of joint vascularization and the design of the wrist VCA. We then outlined a donor and recipient surgical technique for transplant based on recipient CT scans. Customized cutting guides were designed for the transplant procedure. After the procedure, we performed angiography of the VCA to determine the vascularity of the transplant. Using a combined volar and dorsal approach, we were able to perform a complete wrist VCA procedure. After the completed transplant procedure, angiographic imaging of the specimens demonstrated that the flap dissection and transplantation preserved the nutrient endosteal supply to the distal end of the radius and ulna, as well as to the carpal bones and the metacarpal bases. The dissection of the donor, recipient, and the entire vascularized joint transplant procedure served to illustrate the anatomical feasibility of the cadaveric surgical technique. This establishes an anatomic basis for the possibility of future human clinical applications. This study helps investigate the anatomical feasibility of a wrist VCA.

Application of a rapid and sensitive RPA-CRISPR/Cas12a assay for naked-eye detection of Haemophilus parasuis

BackgroundHaemophilus parasuis (H. parasuis) is a gram-negative bacterial pathogen that causes severe infections in swine, resulting in substantial economic losses. Currently, the majority of H. parasuis detection methods are impractical for on-site application due to their reliance on large instruments or complex procedures. Thus, there is an urgent need to develop a rapid, visually detectable, and highly sensitive detection method, especially under resource-limited environments and field conditions. ResultsIn this study, we established a naked eye assay for highly sensitive detection by combining recombinase polymerase amplification (RPA) with CRISPR/Cas12a technology. Positive samples exhibited a clear red color visible to the naked eye, while negative samples appeared blue. We achieved a remarkable sensitivity, detecting H. parasuis down to a single copy, with no cross-reactivity with other bacteria. In a mouse model, our assay detected H. parasuis infection nearly 8 h earlier than traditional PCR. Compared to qPCR, our detection results were 100 % accurate. To enhance point-of-care applicability and mitigate the risk of aerosol contamination from uncapping, we consolidated RPA and CRISPR/Cas12a cleavage into a single-tube reaction system. This integrated approach was validated with 20 clinical lung samples, yielding results consistent with those obtained from qPCR. The entire procedure, from DNA extraction to detection, was completed in 35 min. SignificanceWe present an RPA-CRISPR/Cas12a assay suitable for the early and resource-efficient diagnosis of H. parasuis infections. Its simplicity and visual detection are advantageous for field diagnostics, representing a substantial develpoment in the diagnosis of H. parasuis.

Risk Factors in Administering Spinal Anesthesia: A Comprehensive Review.

Numerous advantages, including a quick start and consistent anesthesia, are provided by spinal anesthesia, a method often utilized in contemporary medicine for various surgical operations. However, it has some hazards, just like any medical procedure.With an emphasis on identifying and assessing the risk factors associated with administering spinal anesthesia, the review analyzes published literature and clinical investigations carried out in the field of anesthesia. Various key factors, including technique-related procedural and patient-related aspects, can influence the effectiveness of spinal anesthesia. Among these factors are age, sex, body mass index, concurrent conditions (such as cardiovascular disease, diabetes, and respiratory problems), pre-existing neurological issues, allergies, and a history of adverse responses to anesthesia drugs. Additionally, the chance of problems might be increased by physical abnormalities or malformations in the spinal canal and vertebral column. The safety and effectiveness of spinal anesthesia depend significantly on procedural factors, such as the type and dosage of anesthesia agents administered and the patient's position and alignment maintained during the entire surgical procedure and the injection rate. Increased risks can also be caused by inadequate monitoring and a slow response to unfavorable circumstances. Risk factors related to the technique include the expertise and competency of the anesthesiologist or medical professional carrying out the procedure. Inadequate post-procedure monitoring, inadvertent dural puncture, and improper needle placement might lead to complications during or after the spinal anesthesia administration. This review emphasizes the need for a complete preoperative assessment, suitable patient selection, and rigorous procedural planning to reduce the likelihood of problems during the administration of spinal anesthesia. It also emphasizes the significance of ongoing monitoring and timely management of adverse events to guarantee patient safety and the best results. Healthcare professionals may put preventative measures in place and follow best practices to limit possible consequences efficiently by recognizing the risk factors associated with spinal anesthesia. This review helps encourage safer anesthesia practices and improvepatient care as medical knowledge and technology advance. However, further study and evidence-based recommendations are required to enhance patient outcomes and risk assessment.

Role of tourist-chatbot interaction on visit intention in tourism: the mediating role of destination image

ABSTRACT The current research examined the link between the informativeness of tourist-chatbot interaction, destination image, and visit intention in two studies. In the first study, 111 participants were asked to interact with ChatGPT about a destination (i.e. Batumi) for 5–10 min. The conceptual model was analyzed using the Structural Equation Modelling framework. Findings suggested that the informativeness of tourist-chatbot interaction would increase destination image and visit intention. Destination image was also directly and positively related to visit intention. Specifically, destination image mediated the link between the informativeness of tourist-chat bot interaction and visit intention. A second study (N = 184) was conducted, in which the entire procedure was the same as the first study, to test the replicability of the current findings. Consequently, all results remained consistent with the first study. This is the first study to show the mediating role of destination image in the link between human-machine interaction and visit intention in tourism research. Thus, the findings would expand the current understanding regarding the role of human-machine interaction on attitude and behaviour.

Metabolomic and lipidomic landscape of porcine kidney associated with kidney perfusion in heart beating donors and donors after cardiac death

Transplant centers are currently facing a lack of tools to ensure adequate evaluation of the quality of the available organs, as well as a significant shortage of kidney donors. Therefore, efforts are being made to facilitate the effective use of available organs and expand the donor pool, particularly with expanded criteria donors. Fulfilling a need, we aim to present an innovative analytical method based on solid-phase microextraction (SPME) - chemical biopsy. In order to track changes affecting the organ throughout the entire transplant procedure, porcine kidneys were subjected to multiple samplings at various time points. The application of small-diameter SPME probes assured the minimal invasiveness of the procedure. Porcine model kidney autotransplantation was executed for the purpose of simulating two types of donor scenarios: donors with a beating heart (HBD) and donors after cardiac death (DCD). All renal grafts were exposed to continuous normothermic ex vivo perfusion. Following metabolomic and lipidomic profiling using high-performance liquid chromatography coupled to a mass spectrometer, we observed differences in the profiles of HBD and DCD kidneys. The alterations were predominantly related to energy and glucose metabolism, and differences in the levels of essential amino acids, purine nucleosides, lysophosphocholines, phosphoethanolamines, and triacylglycerols were noticed. Our results indicate the potential of implementing chemical biopsy in the evaluation of graft quality and monitoring of renal function during perfusion.

Estimation of Postinfusion Resource Use and Total Costs of Care for Patients with R/R Follicular Lymphoma (FL) Receiving Lisocabtagene Maraleucel (liso-cel) in the TRANSCEND FL Study

Background: Liso-cel, an autologous, CD19-directed, 4-1BB CAR T cell product administered at equal target doses of CD8 + and CD4 + CAR + T cells, has previously demonstrated clinically meaningful responses in patients with R/R FL in the global phase 2 TRANSCEND FL study (NCT04245839). Limited evidence is published on estimates of health care resource utilization (HCRU) and cost of postinfusion care among patients with large B-cell lymphoma (LBCL) treated with liso-cel (Palomba ML, et al. Leuk Lymphoma 2021), and no evidence is currently available for patients with R/R FL. This analysis evaluated postinfusion HCRU and estimated cost of care across a 6-month period among patients treated with liso-cel in the TRANSCEND FL study. Methods: In this retrospective analysis, patient-level HCRU data from the TRANSCEND FL study database, including hospitalizations and length of hospital stay (LOS) for the entire hospitalization period postinfusion (standard inpatient [IP] and ICU days), diagnostics (eg, laboratory work and imaging), procedures (eg, dialysis and intubation), and medications (eg, oncology supportive care, prophylactics, and other AE management), were analyzed from the day of liso-cel infusion through 6 months of follow-up. Postinfusion costs (not including the cost of liso-cel) were estimated using a microcosting methodology in which HCRU in the 6-month period was identified and unit costs sourced from public databases or literature (adjusted to 2023 US dollars [USD] using the Consumer Price Index) were applied to each HCRU. Facility costs were sourced from the Healthcare Cost and Utilization Project National Inpatient Sample (2020) databases (Dasta JF, et al. Crit Care Med 2005), and the US Centers for Medicare and Medicaid Services (CMS) Hospital Outpatient Prospective Payment System (OPPS; 2023). Diagnostic and procedure costs were obtained from the CMS OPPS (2023), clinical laboratory fee schedule (2023), physician fee schedule (2023), durable medical fee schedule (2023), and Dasta 2005. Medication cost data were obtained from IBM ® Micromedex ® RED BOOK ® (2023) using wholesale acquisition cost and were uniformly applied across sites of care. Average total cost by month after liso-cel infusion was calculated among patients with ongoing status in that month. Patients censored due to data cutoff were excluded. Results: A total of 130 patients were treated with liso-cel in the TRANSCEND FL study, primarily in the fourth-line setting (45.4%). These patients had a median (range) age of 60 (23‒80) years and were predominantly male (83/130 [63.8%]) and White (69/130 [53.1%]); 45 (34.6%) had unknown race. Among patients who received liso-cel infusions, the estimated mean 6-month postinfusion total cost of care was $89,516 (Figure 1). Most 6-month costs ($77,088 [86.1%]) were incurred in Month 1 after infusion and were primarily driven by facility costs ($66,683 [74.5%]), namely standard IP hospitalization and ICU stays. HCRU and LOS (including standard IP hospitalization and ICU days) are shown in Table 1. Almost all patients were hospitalized in the standard IP setting (128/130 [98.5%]). Few patients were admitted to the ICU (4/130 [3.1%]), and 3/130 (2.3%) had an emergency department visit. Median (range) hospitalization time was 14 (3‒45) days for standard IP and 5 (3‒7) days for ICU stays. Some patients received corticosteroids (30/130 [23.1%]) or tocilizumab (31/130 [23.8%]), and few required intravenous immunoglobulin (11/130 [8.5%]), vasopressors (4/130 [3.1%]), lumbar puncture (2/130 [1.5%]), or mechanical ventilation (1/130 [0.8%]). No patients received dialysis. Conclusions: The majority of costs in the 6-month period after liso-cel infusion among patients with R/R FL in the TRANSCEND FL study were incurred in Month 1 and were facility related (IP and ICU hospitalizations and LOS). These findings, consistent with those previously reported for patients with LBCL treated with liso-cel, support that a single infusion of liso-cel is associated with significantly less HCRU and costs after the initial treatment period in the first month.

Analysis of UAV Flight Patterns for Road Accident Site Investigation

Unmanned Aerial Vehicles (UAVs) offer a promising solution for road accident scene documentation. This study seeks to investigate the occurrence of systematic deformations, such as bowling and doming, in the 3D point cloud and orthomosaic generated from images captured by UAVs along an horizontal road segment, while exploring how adjustments in flight patterns can rectify these errors. Four consumer-grade UAVs were deployed, all flying at an altitude of 10 m while acquiring images along two different routes. Processing solely nadir images resulted in significant deformations in the outputs. However, when additional images from a circular flight around a designated Point of Interest (POI), captured with an oblique camera axis, were incorporated into the dataset, these errors were notably reduced. The resulting measurement errors remained within the 0–5 cm range, well below the customary error margins in accident reconstruction. Remarkably, the entire procedure was completed within 15 min, which is half the estimated minimum duration for scene investigation. This approach demonstrates the potential for UAVs to efficiently record road accident sites for official documentation, obviating the need for pre-established Ground Control Points (GCP) or the adoption of Real-Time Kinematic (RTK) drones or Post Processed Kinematic (PPK) technology.

Norwood-Sano Procedure With Neo-aortic Valve Repair on the Beating Heart

Norwood operation for patients with hypoplastic left heart syndrome or its variants may be associated with prolonged period of myocardial ischemia, particularly in those, who may require associated neo-aortic valve repair. Using the myocardial perfusion technique described herein, the heart could remain beating in normal sinus rhythm during the entire procedure, including neo-aortic valve repair and root reconstruction, and the need for cardioplegic heart arrest can be eliminated.

Design, synthesis, in vitro acetylcholinesterase, butyrylcholinesterase activities, and in silico molecular docking study of oxindole-oxadiazole hybrid analogues

Alzheimer's disease is a neurological disorder characterized by cognitive dysfunction and deficits in activities of daily living. There is no drug available for Alzheimer's at the moment, and the inhibition of acetylcholinesterase and butyrylcholinesterase enzymes are a good target to treat Alzheimer's disease. In this study, a series of new oxindole-oxadiazole hybrid analogues (1–18) have been synthesized and evaluated for acetylcholinesterase and butyrylcholinesterase inhibitory activities. All compounds showed a varied degree of inhibition, ranging from 0.80 ± 0.20 to 12.50 ± 0.40 µM (against acetylcholinesterase) and 1.10 ± 0.20 to 20.30 ± 0.10 µM (against butyrylcholinesterase), as compared to the standard inhibitor donepezil (IC50 values of 2.16 ± 0.12 & 4.5 ± 0.11 µM, respectively). In the case of acetylcholinesterase, analogue 17 (IC50 = 0.80 ± 0.20 µM) while in case of butyrylcholinesterase, analogue 5 (IC50 = 1.10 ± 0.20 µM) was found the most potent among the whole series. Structure-activity relationship has been established for all compounds, which mainly depended upon the nature, number, position, and electron-donating or -withdrawing effect of the substituent(s) on the phenyl ring. A molecular docking study was carried out to investigate the interaction of the most potent compounds with the active site of enzymes. The stability of each drug-like hit in complex with acetylcholinesterase and butyrylcholinesterase was determined by computing their RMSD, RMSF analysis. Among all the compounds, compound 17 and compound 5 respectively, revealed a good interactions and stability during the entire simulation procedure.

An unsupervised real-time spike sorting system based on optimized OSort

Objective. The OSort algorithm, a pivotal unsupervised spike sorting method, has been implemented in dedicated hardware devices for real-time spike sorting. However, due to the inherent complexity of neural recording environments, OSort still grapples with numerous transient cluster occurrences during the practical sorting process. This leads to substantial memory usage, heavy computational load, and complex hardware architectures, especially in noisy recordings and multi-channel systems. Approach. This study introduces an optimized OSort algorithm (opt-OSort) which utilizes correlation coefficient (CC), instead of Euclidean distance as classification criterion. The CC method not only bolsters the robustness of spike classification amidst the diverse and ever-changing conditions of physiological and recording noise environments, but also can finish the entire sorting procedure within a fixed number of cluster slots, thus preventing a large number of transient clusters. Moreover, the opt-OSort incorporates two configurable validation loops to efficiently reject cluster outliers and track recording variations caused by electrode drifting in real-time. Main results. The opt-OSort significantly reduces transient cluster occurrences by two orders of magnitude and decreases memory usage by 2.5–80 times in the number of pre-allocated transient clusters compared with other hardware implementations of OSort. The opt-OSort maintains an accuracy comparable to offline OSort and other commonly-used algorithms, with a sorting time of 0.68 µs as measured by the hardware-implemented system in both simulated datasets and experimental data. The opt-OSort’s ability to handle variations in neural activity caused by electrode drifting is also demonstrated. Significance. These results present a rapid, precise, and robust spike sorting solution suitable for integration into low-power, portable, closed-loop neural control systems and brain–computer interfaces.

Customized virtual reality naturalistic scenarios promoting engagement and relaxation in patients with cognitive impairment: a proof-of-concept mixed-methods study

Being immersed in a natural context has a beneficial and pervasive impact on well-being. Virtual Reality (VR) is a technology that can help expose people to naturalistic scenarios virtually, overcoming obstacles that prevent them from visiting real natural environments. VR could also increase engagement and relaxation in older adults with and without cognitive impairment. The main aim of this study is to investigate the feasibility of a customized naturalistic VR scenario by assessing motion-sickness effects, engagement, pleasantness, and emotions felt. Twenty-three individuals with a diagnosis of cognitive impairment living in a long-term care home participated in our study. At the end of the entire VR experimental procedure with older adults, five health staff operators took part in a dedicated assessment phase focused on evaluating the VR procedure's usability from their individual perspectives. The tools administered were based on self-reported and observational tools used to obtain information from users and health care staff professionals. Feasibility and acceptance proved to be satisfactory, considering that the VR experience was well-tolerated and no adverse side effects were reported. One of the major advantages emerged was the opportunity to deploy customized environments that users are not able to experience in a real context.Trial Registration: National Institute of Health (NIH) U.S. National Library of Medicine, ClinicalTrials.gov NCT05863065 (17/05/2023).

Distributed runtime verification of metric temporal properties

Distributed Systems are often composed of geographically separated components, where the clocks may not be perfectly synchronized. As such verifying the correctness of such system properties are a major challenge and are of utmost importance. In this paper, we describe a centralized runtime monitoring technique for distributed system. First, we propose a generalized runtime verification technique for verifying partially synchronous distributed computations for the metric temporal logic (MTL) by exploiting bounded-skew clock synchronization. Second, we introduce a progression-based formula rewriting scheme for monitoring MTL specifications which employs SMT solving techniques and report experimental results. Third, we also quantify each event according to the possible time of occurrence and calculate the probabilistic guarantee for generating the verification verdict. Lastly, we have implemented the entire procedure and report on extensive synthetic experimental results using UPPAAL, a set of cross-chain transactions implemented on Ethereum and an Industrial Control System of a water treatment plant.

Surrogate-based optimization for the worst-case prediction regarding a flexible structure impacted by wind gusts

The paper is a follow-up of the recent study on the assessment of discrete wind gust parameters impacting a flexible lightweight structure as a first step towards the evaluation of the worst-case scenario caused by strong wind gusts (JWEIA 231, 105207, 2022). The present study goes beyond by suggesting an optimization framework which allows to determine the worst-case scenario automatically. For this purpose, a stochastic response surface algorithm with a surrogate model based on radial basis functions is chosen. The algorithm relies on costly evaluations of the objective function, which consist of CPU-time intensive fully coupled fluid–structure interaction (FSI) high-fidelity simulations including the pre- and post-processing of the results. Besides the parallelization of the coupled FSI solver, a parallel version of the optimization algorithm allows to carry out several costly evaluations simultaneously. The Metric Stochastic Response Surface algorithm determines the worst case fast. Then, it continues to explore the optimization space to ensure that the global extremum is reached. A sensitivity study on relevant parameters of the optimization algorithm is conducted. Typically, for the present FSI setup, an optimization run takes one week with 6 evaluations in parallel to compute 100 different configurations. The worst case is found after about one third of the evaluations. The increase of parallel evaluations drastically reduces the wall-clock time, but the worst case is found later after half of the evaluations. This later finding is due to the parallel nature of the algorithm. Finally, the various sources of uncertainties that arise throughout the entire procedure are assessed and discussed.

Not Just Non-Consensual Pelvic Exams

Not Just Non-Consensual Pelvic Exams

BCD-net: Stain separation of histological images using deep variational Bayesian blind color deconvolution

Histological images are often tainted with two or more stains to reveal their underlying structures. Blind Color Deconvolution (BCD) techniques separate colors (stains) and structural information (concentrations), which is useful for the processing, data augmentation, and classification of such images.Classical analytical BCD methods are typically computationally expensive in two distinct ways. First, estimating the colors and concentrations corresponding to a given image is a time-consuming process. Second, the entire estimation procedure must be performed independently for each image.In contrast, Deep Learning (DL) methods involve high training costs, but once trained, they are able to directly process unseen images. The application of DL to BCD has been limited by the absence of extensive databases containing ground truth color and concentrations. In this work, we propose BCD-Net, a deep variational Bayesian neural network for stain separation and concentration estimation. Under this framework, we address the challenge of lacking ground truth data by leveraging Bayesian modeling and inference techniques.We propose to use a prior distribution on the stain colors, and a simple flat prior on the concentrations. BCD-Net is trained by maximizing the evidence lower bound of the observed images. The loss function comprises two essential components: fidelity to the observed images and the Kullback-Leibler divergence between the estimated posterior distribution of colors and the selected prior.The model is trained, validated, and tested on two multicenter databases: Camelyon-17 and Warwick stain separation benchmark. The proposed approach is tested on image reconstruction, stain separation, and cancer classification. It performs well when contrasted with classical non-amortized methods and offers a substantial computational time advantage. This marks a significant step forward in the application of DL techniques to address BCD and paves the way for new approaches.