Evaluation of Usage of Different Diagnostic Aids for Oral Cancer by Oral and Maxillofacial Surgeons: An Original Research.

Objective: The purpose of this study was to assess how oral and maxillofacial surgeons used various diagnostic tools for oral cancer. Materials and Methods: A cross-sectional methodology was used, and a standardized questionnaire was given to oral and maxillofacial surgeons randomly chosen sample. The questionnaire gathered information on demographics and the use of diagnostic tools. Data analysis methods included Chi-square testing and descriptive statistics. Results: The study included 200 oral and maxillofacial surgeons in total. The most often used diagnostic tool (95%) was visual inspection, followed by toluidine blue staining (48%) and brush biopsy (32%). Less frequently used were newer methods like optical coherence tomography (12.5%) and autofluorescence imaging (15%). No significant correlations between demographic factors and patterns of use of diagnostic tools were found by Chi-square tests. Conclusion: The results show that oral and maxillofacial surgeons frequently use brush biopsy, toluidine blue staining, and ocular evaluation. However, there is a need for more widespread adoption of cutting-edge technologies. By removing obstacles and offering training opportunities, one can increase the use of diagnostic tools, improving patient outcomes and the diagnosis of oral cancer.

Task Offloading Strategy for Unmanned Aerial Vehicle Power Inspection Based on Deep Reinforcement Learning.

With the ongoing advancement of electric power Internet of Things (IoT), traditional power inspection methods face challenges such as low efficiency and high risk. Unmanned aerial vehicles (UAVs) have emerged as a more efficient solution for inspecting power facilities due to their high maneuverability, excellent line-of-sight communication capabilities, and strong adaptability. However, UAVs typically grapple with limited computational power and energy resources, which constrain their effectiveness in handling computationally intensive and latency-sensitive inspection tasks. In response to this issue, we propose a UAV task offloading strategy based on deep reinforcement learning (DRL), which is designed for power inspection scenarios consisting of mobile edge computing (MEC) servers and multiple UAVs. Firstly, we propose an innovative UAV-Edge server collaborative computing architecture to fully exploit the mobility of UAVs and the high-performance computing capabilities of MEC servers. Secondly, we established a computational model concerning energy consumption and task processing latency in the UAV power inspection system, enhancing our understanding of the trade-offs involved in UAV offloading strategies. Finally, we formalize the task offloading problem as a multi-objective optimization issue and simultaneously model it as a Markov Decision Process (MDP). Subsequently, we proposed a task offloading algorithm based on a Deep Deterministic Policy Gradient (OTDDPG) to obtain the optimal task offloading strategy for UAVs. The simulation results demonstrated that this approach outperforms baseline methods with significant improvements in task processing latency and energy consumption.

[Production Risk Analysis and Key Points for Field Inspection of Medical Electron Linear Accelerator].

The medical electron linear accelerator（LINAC） has the characteristics of complex system structure, many core components and high precision control requirements, which puts forward higher requirements for product quality control and regulation. This study puts forward the main points of field inspection through the analysis of the technical characteristics and production risk of LINAC, combined with the requirements of the good manufacturing practice of medical devices. It has certain reference significance for quality management personnel and field inspectors.

Image testing based on biomedical drug loading in cardiovascular drug care simulation for coronary heart disease.

Coronary heart disease is a common cardiovascular disease, and its therapeutic effect is affected by the distribution and absorption of drugs in the body. Biomedical drug-carrying image testing technology can provide a quantitative assessment of drug distribution and absorption in the body. This study aims to explore the application of biomedical drug-carrying image testing technology in the simulation of cardiovascular drug care in coronary heart disease, so as to provide reference for the optimization of drug treatment plan and individualized treatment. The study collected clinical data and medication regiments of patients with coronary heart disease. Then, the imaging examination of patients was carried out by selecting appropriate drug loading markers using the biomedical drug loading image examination technology. Then quantitative analysis was used to process the image data to quantitatively evaluate the distribution and absorption of drugs in the cardiovascular system. The quantitative data of drug distribution and absorption in patients with coronary heart disease have been obtained successfully by means of biomedical imaging. These data reveal the dynamic changes of drugs in the cardiovascular system, and help doctors optimize drug therapy, improve treatment effectiveness, and achieve personalized treatment.

Sustained release gel based on CT image inspection for treatment of diabetes fundus macular lesions.

Currently, slow-release gel therapy is considered to be an effective treatment for fundus macular disease, but the lack of effective evaluation methods limits its clinical application. Therefore, the purpose of this study was to investigate the application and clinical effect of slow-release gel based on CT image examination in the treatment of diabetic fundus macular disease. CT images of fundus macular lesions were collected in a group of diabetic patients. Then the professional image processing software is used to process and analyze the image and extract the key parameters. A slow-release gel was designed and prepared, and applied to the treatment of diabetic fundus macular disease. CT images before and after treatment were compared and analyzed, and the effect of slow-release gel was evaluated. In a certain period of time after treatment, the lesion size and lesion degree of diabetic fundus macular disease were significantly improved by using slow-release gel therapy with CT image examination. No significant adverse reactions or complications were observed during the treatment. This indicates that the slow-release gel based on CT image examination is a safe, effective and feasible treatment method for diabetic fundus macular disease. This method can help improve the vision and quality of life of patients, and provide a new idea and plan for clinical treatment.

Visual inspection problem-solving strategies at different experience levels.

Airport security screening is a visual inspection task comprising search and decision. Problem solving is used to support decision making. However, it is not well understood. This study investigated how airport security screeners employ problem solving during x-ray screening, and how strategies change with experience. Thirty-nine professional security screeners were observed performing x-ray screening in the field at an Australian International Airport. Video and eye-tracking data were collected and analysed to explore activity phases and problem-solving strategies. Less-experienced screeners performed more problem solving and preferred problem-solving strategies that rely on visual examination without decision support or that defer decision making, compared to more-experienced screeners, who performed efficient and independent strategies. Findings also show that screeners need more time to develop problem-solving skills than visual scanning skills. Screeners would benefit from problem-solving support tools and intensified training and mentorship within the first six months of experience to advance problem-solving competencies.

Reinforcement learning for intensive care medicine: actionable clinical insights from novel approaches to reward shaping and off-policy model evaluation.

BACKGROUND: Reinforcement learning (RL) holds great promise for intensive care medicine given the abundant availability of data and frequent sequential decision-making. But despite the emergence of promising algorithms, RL driven bedside clinical decision support is still far from reality. Major challenges include trust and safety. To help address these issues, we introduce cross off-policy evaluation and policy restriction and show how detailed policy analysis may increase clinical interpretability. As an example, we apply these in the setting of RL to optimise ventilator settings in intubated covid-19 patients. METHODS: With data from the Dutch ICU Data Warehouse and using an exhaustive hyperparameter grid search, we identified an optimal set of Dueling Double-Deep Q Network RL models. The state space comprised ventilator, medication, and clinical data. The action space focused on positive end-expiratory pressure (peep) and fraction of inspired oxygen (FiO2) concentration. We used gas exchange indices as interim rewards, and mortality and state duration as final rewards. We designed a novel evaluation method called cross off-policy evaluation (OPE) to assess the efficacy of models under varying weightings between the interim and terminal reward components. In addition, we implemented policy restriction to prevent potentially hazardous model actions. We introduce delta-Q to compare physician versus policy action quality and in-depth policy inspection using visualisations. RESULTS: We created trajectories for 1118 intensive care unit (ICU) admissions and trained 69,120 models using 8 model architectures with 128 hyperparameter combinations. For each model, policy restrictions were applied. In the first evaluation step, 17,182/138,240 policies had good performance, but cross-OPE revealed suboptimal performance for 44% of those by varying the reward function used for evaluation. Clinical policy inspection facilitated assessment of action decisions for individual patients, including identification of action space regions that may benefit most from optimisation. CONCLUSION: Cross-OPE can serve as a robust evaluation framework for safe RL model implementation by identifying policies with good generalisability. Policy restriction helps prevent potentially unsafe model recommendations. Finally, the novel delta-Q metric can be used to operationalise RL models in clinical practice. Our findings offer a promising pathway towards application of RL in intensive care medicine and beyond.

External inspection approaches and involvement of stakeholders' views in inspection following serious incidents - a qualitative mixed methods study from the perspectives of regulatory inspectors.

OBJECTIVE: The objective was to gain knowledge about how external inspections following serious incidents are played out in a Norwegian hospital context from the perspective of the inspectors, and whether stakeholders' views are involved in the inspection. METHODS: Based on a qualitative mixed methods design, 10 government bureaucrats and inspectors situated at the National Board of Health Supervision and three County Governors in Norway, were strategically recruited, and individual semi-structured interviews were conducted. Key official government documents were selected, collected, and thematically analyzed along with the interview data. RESULTS: Our findings overall demonstrate two overarching themes: Theme (1) Perspectives on different external inspection approaches of responding and involving stakeholders in external inspection following serious incidents, Theme (2) Inspectors' internal work practices versus external expectations. Documents and all participants reported a development towards new approaches in external inspection, with more policies and regulatory attention to sensible involvement of stakeholders. Involvement and interaction with patients and informal caregivers could potentially inform the case complexity and the inspector's decision-making process. However, stakeholder involvement was sometimes complex and challenging due to e.g., difficult communication and interaction with patients and/or informal caregivers, due to resource demands and/or the inspector's lack of experience and/or relevant competence, different perceptions of the principle of sound professional practice, quality, and safety. The inspectors considered balancing the formal objectives and expectations, with the expectations of the public and different stakeholders (i.e. hospitals, patients and/or informal caregivers) a challenging part of their job. This balance was seen as an important part of the continuous development of ensuring public trust and legitimacy in external inspection processes. CONCLUSIONS AND IMPLICATIONS: Our study suggests that the regulatory system of external inspection and its available approaches of responding to a serious incident in the Norwegian setting is currently not designed to accommodate the complexity of needs from stakeholders at the levels of hospital organizations, patients, and informal caregivers altogether. Further studies should direct attention to how the wider system of accountability structures may support the internal work practices in the regulatory system, to better algin its formal objectives with expectations of the public.

Simulation of defect detection for the buried petroleum pipe by the X-ray backscatter imaging.

Ensuring the integrity, safety, and compliance of oil pipelines is crucial for operators. Two significant factors affecting pipeline integrity are metal loss due to corrosion or air gouging and the presence of cracks. This paper introduces an X-ray backscatter nondestructive testing model on a smart pig(an intelligent robot) for internal inspection of petroleum pipelines. The paper investigates the X-ray source and detection system, essential components of the backscatter imaging technology, to provide technical guidance for model design. The imaging system's layout is modeled using GEANT4, and its performance in detecting defects of various formats on pipe walls is studied. The research demonstrates the feasibility of using X-ray backscatter imaging for pipeline inspection and provides insights into different defect detection. The findings contribute to designing and optimizing the robot's scanning head for effective pipeline inspection.

P3b correlates of inspection time.

Both P3b and the inspection time (IT) are related with intelligence, yet the P3b correlates of IT are not well understood. This event-related potential study addressed this question by asking participants (N = 28) to perform an IT task. There were three IT conditions with different levels of discriminative stimulus duration, i.e., 33 ms, 67 ms, and 100 ms, and a control condition with no target presentation (0 ms condition). We also measured participants' processing speed with four Elementary Cognitive Tests (ECTs), including a Simple Reaction Time task (SRT), two Choice Reaction Time tasks (CRTs), and a Pattern Discrimination task (PD). Results revealed that an increase in P3b latency with longer duration of the discriminative stimulus. Moreover, the P3b latency was negatively correlated with the accuracy of the IT task in the 33 ms condition, but not evident in the 67 and 100 ms conditions. Furthermore, the P3b latency of the 33 ms condition was positively correlated with the RT of the SRT, but not related with the RTs of CRTs or PD. A significant main effect of duration on the amplitude of P1 was also found. We conclude that the present study provides the neurophysiological correlates of the IT task, and those who are able to accurately perceive and process very briefly presented stimuli have a higher speed of information process, reflected by the P3b latency, yet this relationship is more obvious in the most difficult condition. Combined, our results suggest that P3b is related with the closure of a perceptual epoch to form the neural representation of a stimulus, in support of the "context closure" hypothesis.

Passive Electroluminescence and Photoluminescence Imaging Acquisition of Photovoltaic Modules.

In photovoltaic power plant inspections, techniques for module assessment play a crucial role as they enhance fault detection and module characterization. One valuable technique is luminescence. The present paper introduces a novel technique termed passive luminescence. It enhances both electroluminescence and photoluminescence imaging acquisition in photovoltaic power plants under normal operation in high irradiance conditions. This technique is based on the development of an electronic board, which allows the polarity of the module to be changed, enabling the current generated by the photovoltaic string to be injected into the module and producing electroluminescence effects. Additionally, the board can bypass the module and set an open circuit, inducing photoluminescence emission using sunlight as an excitation source. The proper coordination of the board and an InGaAs camera with a bandpass filter has allowed for the integration of a lock-in technique, which has produced electroluminescence and photoluminescence pictures that can be used for fault detection.

Integrated Automatic Optical Inspection and Image Processing Procedure for Smart Sensing in Production Lines.

An integrated automatic optical inspection (iAOI) system with a procedure was proposed for a printed circuit board (PCB) production line, in which pattern distortions and performance deviations appear with process variations. The iAOI system was demonstrated in a module comprising a camera and lens, showing improved supportiveness for commercially available hardware. The iAOI procedure was realized in a serial workflow of image registration, threshold setting, image gradient, marker alignment, and geometric transformation; furthermore, five operations with numerous functions were prepared for image processing. In addition to the system and procedure, a graphical user interface (GUI) that displays sequential image operation results with analyzed characteristics was established for simplicity. To demonstrate its effectiveness, self-complementary Archimedean spiral antenna (SCASA) samples fabricated via standard PCB fabrication and intentional pattern distortions were demonstrated. The results indicated that, compared with other existing methods, the proposed iAOI system and procedure provide unified and standard operations with efficiency, which result in scientific and unambiguous judgments on pattern quality. Furthermore, we showed that when an appropriate artificial intelligence model is ready, the electromagnetic characteristic projection for SCASAs can be simply obtained through the GUI.

Balancing the Efficiency and Sensitivity of Defect Inspection of Non-Patterned Wafers with TDI-Based Dark-Field Scattering Microscopy.

In semiconductor manufacturing, defect inspection in non-patterned wafer production lines is essential to ensure high-quality integrated circuits. However, in actual production lines, achieving both high efficiency and high sensitivity at the same time is a significant challenge due to their mutual constraints. To achieve a reasonable trade-off between detection efficiency and sensitivity, this paper integrates the time delay integration (TDI) technology into dark-field microscopy. The TDI image sensor is utilized instead of a photomultiplier tube to realize multi-point simultaneous scanning. Experiments illustrate that the increase in the number of TDI stages and reduction in the column fixed pattern noise effectively improve the signal-to-noise ratio of particle defects without sacrificing the detecting efficiency.

Substrate types and applications of MXene for surface-enhanced Raman spectroscopy.

Surface-enhanced Raman spectroscopy (SERS) has been widely used in the analysis of analytes because of its unique fingerprint characteristics, high sensitivity, and fast detection response. MXene is widely used in SERS studies among the various substrates due to its ultra-high chemical stability, excellent conductivity, hydrophilicity, and low fabrication cost. This mini-review summarizes MXene's research in the SERS field from two aspects. We reviewed MXene materials used as SERS substrates alone and combined with noble metal particles primarily. Subsequently, we outlined representative applications of MXene-based SERS in biomedicine, food safety, and environmental monitoring. Moreover, we discussed the technical bottleneck and the prospect of future development in this field.

Correlation between variant call accuracy and quality parameters in comprehensive cancer genomic profiling tests.

Background: Comprehensive genomic profiling (CGP) tests have been widely utilized in clinical practice. In this test, the variant list automatically output from the data analysis pipeline often contains false-positive variants, although the correlation between the quality parameters and prevalence of false-positive variants remains unclear. Methods: We analyzed 125 CGP tests performed in our laboratory. False-positive variants were manually detected via visual inspection. The quality parameters of both wet and dry processes were also analyzed. Results: Among the 125 tests, 52 (41.6%) required more than one correction of the called variants, and 21 (16.8%) required multiple corrections. A significant correlation was detected between somatic false-positive variants and quality parameters in the wet (ΔΔCq, pre-capture library peak size, pre-capture library DNA amount, capture library peak size, and capture library concentration) and dry processes (total reads, mapping rates, duplication rates, mean depth, and depth coverage). Capture library concentration and mean depth were strong independent predictors of somatic false-positive variants. Conclusions: We demonstrated a correlation between somatic false-positive variants and quality parameters in the CGP test. This study facilitates gaining a better understanding of CGP test quality management.

An Integrated Framework for Image Acquisition, Processing, and Analysis Procedures for Automated Damage Evaluation of Concrete Surfaces.

Concrete surface cracks serve as early indicators of potential structural threats. Visual inspection, a commonly used and versatile concrete condition assessment technique, is employed to assess concrete degradation by observing signs of damage on the surface level. However, the method tends to be qualitative and needs to be more comprehensive in providing accurate information regarding the extent of damage and its evolution, notwithstanding its time-consuming and environment-sensitive nature. As such, the integration of image analysis techniques with artificial intelligence (AI) has been increasingly proven efficient as a tool to capture damage signs on concrete surfaces. However, to improve the performance of automated crack detection, it is imperative to intensively train a machine learning model, and questions remain regarding the required image quality and image collection methodology needed to ensure the model's accuracy and reliability in damage quantitative analysis. This study aims to establish a procedure for image acquisition and processing through the application of an image-based measurement approach to explore the capabilities of concrete surface damage diagnosis. Digitizing crack intensity measurements were found to be feasible; however, larger datasets are required. Due to the anisotropic behavior of the damage, the model's ability to capture crack directionality was developed, presenting no statistically significant differences between the observed and predicted values used in this study with correlation coefficients of 0.79 and 0.82.

[Evaluation of pharmaceutical practice in hospitals: An exploratory study comparing processes in France and Quebec]. / Évaluation de la pratique pharmaceutique en établissement de santé : une étude exploratoire comparant les processus en France et au Québec.

CONTEXT: To ensure compliance with the legal and normative framework, the professional orders, which supervise the practice of pharmacy have established a professional inspection process. In addition to this process, other external organizations also require an accreditation, authorization, certification or validation process for professional practices. OBJECTIVE: The main objective is to identify and compare the methods of evaluating pharmaceutical practice in hospitals in France and Quebec. RESULTS: In order to identify and compare the methods of evaluating pharmaceutical practice in hospitals in France and Quebec, our approach allowed us to identify more organizations in France (i.e., High Authority of Health, Regional Health Agencies, the National Order of Pharmacists, the Nuclear Safety Agency, the Biomedicine Agency and the National Agency for the Safety of Medicines and Health Products) than in Quebec (i.e., Accreditation Canada, Ordre des pharmaciens du Québec and Health Canada). The study highlights the legal framework and applicable standards, the surveyors, the evaluation methods and the particularities for the evaluation of the hospital, the pharmacy department and the members of the department. CONCLUSIONS: This study highlights the evaluation processes of pharmaceutical practice in hospitals. In France, as in Quebec, we recognize the importance of the drug circuit in the hospital, the development and operation of a pharmacy department and the practice of pharmacy. While there are more similarities for the assessment of the drug circuit in hospitals and pharmacy department, important differences are observed for the assessment of individuals. We believe that the publication of a comparative analysis can contribute to discussions and exchanges to benefit from the best practices of each country.

Sniffing behavior of semi free-ranging Barbary macaques (Macaca sylvanus).

Olfaction is one of the evolutionarily oldest senses and plays a fundamental role in foraging and social interactions across mammals. In primates, the role of olfaction is now well recognized, but better investigated in strepsirrhine and platyrrhine primates than in catarrhines. We observed the sniffing behavior of semi-free ranging Barbary macaques, Macaca sylvanus, at Affenberg Salem, Germany, to assess how frequently macaques sniff and in which contexts, and how sniffing is affected by sex and age. Focal observations of 24 males and 24 females aged 1-25 years showed that Barbary macaques sniffed, on average, 5.24 times per hour, with more than 80% of sniffs directed at food. Irrespective of the context, younger individuals sniffed more often than older ones. Females' sniffs were more often directed at food than male sniffs, while males sniffed more often than females in a social context. Sniffs at conspecifics occurred primarily in a sexual context, with 70% of social sniffs directed at female anogenital swellings performed by males. Of the observed 176 anogenital inspections, 51 involved sniffing of the swelling. Olfactory inspections were followed by copulation significantly less often than merely visual inspections, suggesting that anogenital odors may play a role in male mating decisions, but the role of olfaction in sexual interactions warrants further investigations. In sum, results show that Barbary macaques routinely use olfaction during feeding, but also in a socio-sexual context, corroborating the relevance of the olfactory sense in the lives of catarrhine primates.

Visual perceptual processing is unaffected by cognitive fatigue.

Cognitive fatigue (CF) can lead to an increase in the latency of simple reaction time, although the processes involved in this delay are unknown. One potential explanation is that a longer time may be required for sensory processing of relevant stimuli. To investigate this possibility, the current study used a visual inspection time task to measure perceptual processing speed before and after a CF (math and memory) or non-fatiguing (documentary film) intervention. Subjective fatigue and simple reaction time significantly increased following the CF, but not the non-fatiguing intervention, confirming that CF was induced. Conversely, there was no effect of CF on inspection time task performance. It was therefore concluded that the speed of perceptual processing is not significantly impacted by CF, and thus is unlikely to underlie CF-related reaction time increases. Instead, increases in simple reaction time latency in CF may be due to delays in response preparation or initiation.

A Novel Framework for Image Matching and Stitching for Moving Car Inspection under Illumination Challenges.

Vehicle exterior inspection is a critical operation for identifying defects and ensuring the overall safety and integrity of vehicles. Visual-based inspection of moving objects, such as vehicles within dynamic environments abounding with reflections, presents significant challenges, especially when time and accuracy are of paramount importance. Conventional exterior inspections of vehicles require substantial labor, which is both costly and prone to errors. Recent advancements in deep learning have reduced labor work by enabling the use of segmentation algorithms for defect detection and description based on simple RGB camera acquisitions. Nonetheless, these processes struggle with issues of image orientation leading to difficulties in accurately differentiating between detected defects. This results in numerous false positives and additional labor effort. Estimating image poses enables precise localization of vehicle damages within a unified 3D reference system, following initial detections in the 2D imagery. A primary challenge in this field is the extraction of distinctive features and the establishment of accurate correspondences between them, a task that typical image matching techniques struggle to address for highly reflective moving objects. In this study, we introduce an innovative end-to-end pipeline tailored for efficient image matching and stitching, specifically addressing the challenges posed by moving objects in static uncalibrated camera setups. Extracting features from moving objects with strong reflections presents significant difficulties, beyond the capabilities of current image matching algorithms. To tackle this, we introduce a novel filtering scheme that can be applied to every image matching process, provided that the input features are sufficient. A critical aspect of this module involves the exclusion of points located in the background, effectively distinguishing them from points that pertain to the vehicle itself. This is essential for accurate feature extraction and subsequent analysis. Finally, we generate a high-quality image mosaic by employing a series of sequential stereo-rectified pairs.