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BACKGROUND: Reference to an intrinsic healing mechanism or an inner healer is commonplace amongst psychedelic drug-using cultures. The inner healer refers to the belief that psychedelic compounds, plants or concoctions have an intrinsically regenerative action on the mind and brain, analogous to intrinsic healing mechanisms within the physical body, for example, after sickness or injury. AIMS: Here, we sought to test and critique this idea by devising a single subjective rating item pertaining to perceived inner healing effects. METHODS: The item was issued to 59 patients after a single high (25 mg, n = 30) or placebo (1 mg, n = 29) dose of psilocybin in a double-blind randomised controlled trial of psilocybin for depression. RESULTS: Inner healer scores were higher after the high versus placebo dose of psilocybin (t = 3.88, p

Background: The increasing integration of AI in chest X-ray evaluation holds promise for enhancing diagnostic accuracy and optimizing clinical workflows. However, understanding its performance in real-world clinical settings is essential. Objectives: In this study, we evaluated the sensitivity (Se) and specificity (Sp) of an AI-based software (Arterys MICA v29.4.0) alongside a radiology resident in interpreting chest X-rays referred from the emergency department (ED), using a senior radiologist's assessment as the gold standard (GS). We assessed the concordance between the AI system and the resident, noted the frequency of doubtful cases for each category, identified how many were considered positive by the GS, and assessed variables that AI was not trained to detect. Methods: We conducted a retrospective observational study analyzing chest X-rays from a sample of 784 patients referred from the ED at our hospital. The AI system was trained to detect five categorical variables—pulmonary nodule, pulmonary opacity, pleural effusion, pneumothorax, and fracture—and assign each a confidence label ("positive", "doubtful", or "negative"). Results: Sensitivity in detecting fractures and pneumothorax was high (100%) for both AI and the resident, moderate for pulmonary opacity (AI = 76%, resident = 71%), and acceptable for pleural effusion (AI = 60%, resident = 67%), with negative predictive values (NPV) above 95% and areas under the curve (AUC) exceeding 0.8. The resident showed moderate sensitivity (75%) for pulmonary nodules, while AI's sensitivity was low (33%). AI assigned a "doubtful" label to some diagnoses, most of which were deemed negative by the GS; the resident expressed doubt less frequently. The Kappa coefficient between the resident and AI was fair (0.3) across most categories, except for pleural effusion, where concordance was moderate (0.5). Our study highlighted additional findings not detected by AI, including 16% prevalence of mediastinal abnormalities, 20% surgical materials, and 20% other pulmonary findings. Conclusions: Although AI demonstrated utility in identifying most primary findings—except for pulmonary nodules—its high NPV suggests it may be valuable for screening. Further training of the AI software and broadening its scope to identify additional findings could enhance its detection capabilities and increase its applicability in clinical practice. [ABSTRACT FROM AUTHOR]

Induction motors are widely used machines in a variety of applications as primary components for generating rotary motion. This is mainly due to their high efficiency, robustness, and ease of control. Despite their high robustness, these machines can experience failures throughout their lifespan due to various mechanical, electrical, and environmental factors. To prevent irreversible failures and all the implications and costs associated with breakdowns, various methodologies have been developed over the years. Many of these methodologies have focused on analyzing various physical quantities, either during start-up transients or during steady-state operations. This involves the use of specific techniques depending on the focus of the methodology (start-up transients or steady-state) to obtain optimal results. In this regard, it is of great importance to develop methods capable of separating and detecting the start-up transient of the motor from the steady state. This will enable the development of automatic diagnostic methodologies focused on the specific operating state of the motor. This paper proposes a methodology for the automatic detection of start-up transients in induction motors by using magnetic stray flux signals and processing by means of statistical indicators in time-sliding windows, the calculation of variances with a proposed method, and obtaining optimal values for the design parameters by using a Particle Swarm Optimization (PSO). The results obtained demonstrate the effectiveness of the proposed method for the start-up and steady-state regimes automatic separation, which is validated on a 0.746 kW induction motor supplied by a variable frequency drive (VFD). [ABSTRACT FROM AUTHOR]

Copyright of Automobile Technology & Material is the property of Automobile Technology & Material Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

[Introduction] In order to address the issues of high costs and occupation of limited road space in urban ground distribution, we build an underground logistics delivery networks, enabling efficient, automated, and intelligent logistics transportation. [Method] We aimed to achieve efficient and economical logistics distribution while keeping costs under control by using advanced information technology. [Result] This paper envisages an underground smart logistic system—"Way+". This system, based on underground municipal infrastructure, introduces information technology, breakthrough planning architecture theory and intelligent manufacturing technology, by building an underground high-speed intelligent channel network specialized for freight, so as to realize automatic and intelligent delivery of goods [Conclusion] This paper provides innovative route for new retail and construction of future green smart cities.

The Brain Tumor Segmentation (BraTS) Challenge has been a main driver of the development of deep learning (DL) algorithms and provides by far the largest publicly available expert-annotated brain tumour dataset but contains solely preoperative examinations. The aim of our study was to facilitate the use of the BraTS dataset for training DL brain tumour segmentation algorithms for a postoperative setting. To this end, we introduced an automatic conversion of the three-label BraTS annotation protocol to a two-label annotation protocol suitable for postoperative brain tumour segmentation. To assess the viability of the label conversion, we trained a DL algorithm using both the three-label and the two-label annotation protocols. We assessed the models pre- and postoperatively and compared the performance with a state-of-the-art DL method. The DL algorithm trained using the BraTS three-label annotation misclassified parts of 10 out of 41 fluid-filled resection cavities in 72 postoperative glioblastoma MRIs, whereas the two-label model showed no such inaccuracies. The tumour segmentation performance of the two-label model both pre- and postoperatively was comparable to that of a state-of-the-art algorithm for tumour volumes larger than 1 cm3. Our study enables using the BraTS dataset as a basis for the training of DL algorithms for postoperative tumour segmentation.

Correctly torquing bone screws is important to achieve good patient outcomes. An automated system has been previously proposed that may allow more objective torquing of bone screws. Here the system is tested vs. a static torque limit in pig bone. 5 screws were first inserted until stripping to calibrate the dynamic and static limits. Then 6 were inserted limited to 80% of the mean stripping torque. Then 7 were inserted using 80% of the dynamic model-predicted stripping torque. The pull-out strength of the dynamic and static insertions were compared and no statistically significant difference was found. Future work should consider at using larger screws and different bone samples, with more repetitions to try and obtain a statistically significant result.

Abstract Background To describe the algorithm and investigate the efficacy of a novel systematic review automation tool “the Deduplicator” to remove duplicate records from a multi-database systematic review search. Methods We constructed and tested the efficacy of the Deduplicator tool by using 10 previous Cochrane systematic review search results to compare the Deduplicator’s ‘balanced’ algorithm to a semi-manual EndNote method. Two researchers each performed deduplication on the 10 libraries of search results. For five of those libraries, one researcher used the Deduplicator, while the other performed semi-manual deduplication with EndNote. They then switched methods for the remaining five libraries. In addition to this analysis, comparison between the three different Deduplicator algorithms (‘balanced’, ‘focused’ and ‘relaxed’) was performed on two datasets of previously deduplicated search results. Results Before deduplication, the mean library size for the 10 systematic reviews was 1962 records. When using the Deduplicator, the mean time to deduplicate was 5 min per 1000 records compared to 15 min with EndNote. The mean error rate with Deduplicator was 1.8 errors per 1000 records in comparison to 3.1 with EndNote. Evaluation of the different Deduplicator algorithms found that the ‘balanced’ algorithm had the highest mean F1 score of 0.9647. The ‘focused’ algorithm had the highest mean accuracy of 0.9798 and the highest recall of 0.9757. The ‘relaxed’ algorithm had the highest mean precision of 0.9896. Conclusions This demonstrates that using the Deduplicator for duplicate record detection reduces the time taken to deduplicate, while maintaining or improving accuracy compared to using a semi-manual EndNote method. However, further research should be performed comparing more deduplication methods to establish relative performance of the Deduplicator against other deduplication methods.

To comprehensively meet the test requirements for the common mode rejection ratio (CMRR) across different ECG-particular standards, this paper presents the design of an ECG CMRR automatic test system. The hardware component primarily consists of a test signal generation module, an automatic control network (which includes a resistance-capacitance network control module and a polarization voltage control module), and a noise level switching module. The software portion enables automatic control and user interaction. Experimental results indicate that the system is stable, reliable, and highly automated, capable of satisfying the test requirements of various ECG-particular standards, thus demonstrating a promising application prospect.

Abstract Background Accurate measurement of hemoglobin concentration is essential for various medical scenarios, including preoperative evaluations and determining blood loss. Traditional invasive methods are inconvenient and not suitable for rapid, point-of-care testing. Moreover, current models, due to their complex parameters, are not well-suited for mobile medical settings, which limits the ability to conduct frequent and rapid testing. This study aims to introduce a novel, compact, and efficient system that leverages deep learning and smartphone technology to accurately estimate hemoglobin levels, thereby facilitating rapid and accessible medical assessments. Methods The study employed a smartphone application to capture images of the eye, which were subsequently analyzed by a deep neural network trained on data from invasive blood test data. Specifically, the EGE-Unet model was utilized for eyelid segmentation, while the DHA(C3AE) model was employed for hemoglobin level prediction. The performance of the EGE-Unet was evaluated using statistical metrics including mean intersection over union (MIOU), F1 Score, accuracy, specificity, and sensitivity. The DHA(C3AE) model’s performance was assessed using mean absolute error (MAE), mean-square error (MSE), root mean square error (RMSE), and R^2. Results The EGE-Unet model demonstrated robust performance in eyelid segmentation, achieving an MIOU of 0.78, an F1 Score of 0.87, an accuracy of 0.97, a specificity of 0.98, and a sensitivity of 0.86. The DHA(C3AE) model for hemoglobin level prediction yielded promising outcomes with an MAE of 1.34, an MSE of 2.85, an RMSE of 1.69, and an R^2 of 0.34. The overall size of the model is modest at 1.08 M, with a computational complexity of 0.12 FLOPs (G). Conclusions This system presents a groundbreaking approach that eliminates the need for supplementary devices, providing a cost-effective, swift, and accurate method for healthcare professionals to enhance treatment planning and improve patient care in perioperative environments. The proposed system has the potential to enable frequent and rapid testing of hemoglobin levels, which can be particularly beneficial in mobile medical settings. Trial Registration The clinical trial was registered on the Chinese Clinical Trial Registry (No. ChiCTR2100044138) on 20/02/2021.

Introduction: Shooting simulations provide an excellent opportunity to train use-of-force decisions in controlled environments. Recently, military and law enforcement organizations have expressed a growing desire to integrate physiological measurement into simulations for training and feedback purposes. Although participants can easily wear physiological monitors in these scenarios, direct implementation into training may not be simple. Theoretical problems exist in the ultra-short heart rate variability windows associated with use-offorce training, and practical problems emerge as existing scenario libraries at training organizations were not designed for physiological monitoring. Methods: The current study explored the challenges and possibilities associated with direct implementation of physiological monitoring into an existing library of firearms training scenarios. Participants completed scenarios in a shooting simulator using existing military training scenarios while wearing a device to monitor their heart rate. Results: The results revealed lower heart rate variability (approximately 6%) occurred in scenarios where participants did not have to fire weapons, indicating that don't-shoot scenarios may actually impose more cognitive stress on shooters. Additional evidence further demonstrated how both behavioral and physiological factors could be used concomitantly to predict unintentionally firing on non-hostile actors. However, behavioral measures were more predictive (e.g., χ = .221) than physiological measures (e.g., χ = -.132) when the latter metrics were limited to specific scenarios. Qualitative results suggest that simply applying physiological monitoring to existing shooting simulations may not yield optimal results because it would be difficult to directly integrate physiological measurement in a meaningful way without re-designing some elements of the simulations, the training procedure, or both. Discussion: Future use-of-force shooting simulations should consider designing novel scenarios around the physiological measurement rather than directly implementing physiological assessments into existing libraries of scenarios. [ABSTRACT FROM AUTHOR]

The Brain Tumor Segmentation (BraTS) Challenge has been a main driver of the development of deep learning (DL) algorithms and provides by far the largest publicly available expert-annotated brain tumour dataset but contains solely preoperative examinations. The aim of our study was to facilitate the use of the BraTS dataset for training DL brain tumour segmentation algorithms for a postoperative setting. To this end, we introduced an automatic conversion of the three-label BraTS annotation protocol to a two-label annotation protocol suitable for postoperative brain tumour segmentation. To assess the viability of the label conversion, we trained a DL algorithm using both the three-label and the two-label annotation protocols. We assessed the models pre- and postoperatively and compared the performance with a state-of-the-art DL method. The DL algorithm trained using the BraTS three-label annotation misclassified parts of 10 out of 41 fluid-filled resection cavities in 72 postoperative glioblastoma MRIs, whereas the two-label model showed no such inaccuracies. The tumour segmentation performance of the two-label model both pre- and postoperatively was comparable to that of a state-of-the-art algorithm for tumour volumes larger than 1 cm3. Our study enables using the BraTS dataset as a basis for the training of DL algorithms for postoperative tumour segmentation. [ABSTRACT FROM AUTHOR]

The pottery wheel machine is an essential piece of equipment in ceramics manufacturing. This paper presents the design of an intelligent pottery wheel machine aimed at addressing the issues of low clay material utilization and the challenges of pottery throwing operations. Traditional techniques require exceptional craftsmanship and proficient mechanical operation, and existing pottery wheel machines still demand attention to equipment usage and clay material knowledge. The intelligent pottery wheel machine integrates central positioning, automatic clay feeding, and clay storage. The pull mechanism stores and precisely feeds clay, regulating usage and minimizing excess. The automated settings of the clay storage and power push system replace the need for manual assessment of clay density and quality, ensuring balanced and high-quality clay extrusion. The continuous feeding setting allows for automatic addition of clay, saving replenishment time and reducing the labor intensity of adding clay. The lifting mechanism ensures that the clay body remains centered, avoiding repeated manual adjustments and allowing users to focus on creating pottery rather than spending excessive time mastering the centering technique. The machine's lightweight, modular design reduces clay waste, making the process more sustainable. By saving clay materials and improving throwing efficiency, it enhances the success rate of throwing. [ABSTRACT FROM AUTHOR]

This review systematically examines the recent research from the past decade on diverse path-planning algorithms tailored for stereotactic neurosurgery applications. Our comprehensive investigation involved a thorough search of scholarly papers from Google Scholar, PubMed, IEEE Xplore, and Scopus, utilizing stringent inclusion and exclusion criteria. The screening and selection process was meticulously conducted by a multidisciplinary team comprising three medical students, robotic experts with specialized knowledge in path-planning techniques and medical robotics, and a board-certified neurosurgeon. Each selected paper was reviewed in detail, and the findings were synthesized and reported in this review. The paper is organized around three different types of intervention tools: straight needles, steerable needles, and concentric tube robots. We provide an in-depth analysis of various path-planning algorithms applicable to both single and multi-target scenarios. Multi-target planning techniques are only discussed for straight tools as there is no published work on multi-target planning for steerable needles and concentric tube robots. Additionally, we discuss the imaging modalities employed, the critical anatomical structures considered during path planning, and the current status of research regarding its translation to clinical human studies. To the best of our knowledge and as a conclusion from this systematic review, this is the first review paper published in the last decade that reports various path-planning techniques for different types of tools for minimally invasive neurosurgical applications. Furthermore, this review outlines future trends and identifies existing technology gaps within the field. By highlighting these aspects, we aim to provide a comprehensive overview that can guide future research and development in path planning for stereotactic neurosurgery, ultimately contributing to the advancement of safer and more effective neurosurgical procedures. [ABSTRACT FROM AUTHOR]

Background: To describe the algorithm and investigate the efficacy of a novel systematic review automation tool "the Deduplicator" to remove duplicate records from a multi-database systematic review search. Methods: We constructed and tested the efficacy of the Deduplicator tool by using 10 previous Cochrane systematic review search results to compare the Deduplicator's 'balanced' algorithm to a semi-manual EndNote method. Two researchers each performed deduplication on the 10 libraries of search results. For five of those libraries, one researcher used the Deduplicator, while the other performed semi-manual deduplication with EndNote. They then switched methods for the remaining five libraries. In addition to this analysis, comparison between the three different Deduplicator algorithms ('balanced', 'focused' and 'relaxed') was performed on two datasets of previously deduplicated search results. Results: Before deduplication, the mean library size for the 10 systematic reviews was 1962 records. When using the Deduplicator, the mean time to deduplicate was 5 min per 1000 records compared to 15 min with EndNote. The mean error rate with Deduplicator was 1.8 errors per 1000 records in comparison to 3.1 with EndNote. Evaluation of the different Deduplicator algorithms found that the 'balanced' algorithm had the highest mean F1 score of 0.9647. The 'focused' algorithm had the highest mean accuracy of 0.9798 and the highest recall of 0.9757. The 'relaxed' algorithm had the highest mean precision of 0.9896. Conclusions: This demonstrates that using the Deduplicator for duplicate record detection reduces the time taken to deduplicate, while maintaining or improving accuracy compared to using a semi-manual EndNote method. However, further research should be performed comparing more deduplication methods to establish relative performance of the Deduplicator against other deduplication methods. [ABSTRACT FROM AUTHOR]

This research aims to design and test the feasibility of an automatic system duck egg incubator, with 100% local ingredients (TKDN) and affordable for small duck egg hatching industries. Duck egg hatching continues to develop along with advances in technology and the increasing need for duck eggs and cut duck meat. The innovation of the automatic duck egg incubator machine is an effort to help the small duck egg hatching industry to increase production in a more efficient and ergonomic manner. In this research, the creation of an automatic duck egg incubator machine uses stages including problem identification, formulation and refinement of ideas, selection of design concepts, analysis and creation of working drawings, manufacture of tool prototypes, testing and refinement of tool prototypes, then feasibility tests for temperature stability and stability are carried out. humidity, and hatchability tests carried out at the small duck egg hatchery industry UD. Putra Jember, Puger, Jember. The result of this research is an automatic duck egg incubator with a capacity of 5000 eggs, with an LPG heating source. The feasibility test showed stable temperature and humidity fluctuations and produced a hatchability of 82%. [ABSTRACT FROM AUTHOR]

The paper is focused on ensuring an automatic reliable operation of aviation engine in a wide range of modes using wireless data transmission. This also helps to reduce the weight of it's control system, the mass and dimensions of the modular construction of the engine. It is possible only on the basis of complex study of the transition processes of the aviation engine with the mutual influence of the course, from the theoretical point of view. Firstly, it is necessary to define method for start up as the process of engine transition from a state of rest in ground conditions or autorotation mode in flight to minimum stable operation mode. Then, in process start up for the initial spin up of the engine rotor, fuel supply and ignition a special starting system based on the Wireless Distributed Automatic Control System must be used in the combustion chamber. In practical application, this study can be used to create the new generation of aviation gas turbine engines and their control systems for subsonic and supersonic aircraft, identification their models based on the diagnosing the state of the engine according to dynamic ones parameters. [ABSTRACT FROM AUTHOR]

The integration of camera and LiDAR technologies has the potential to significantly enhance construction robots' perception capabilities by providing complementary construction information. Structured light cameras (SLCs) are a desirable alternative as they provide comprehensive information on construction defects. However, fusing these two types of information depends largely on the sensors' relative positions, which can only be established through extrinsic calibration. This paper introduces a novel calibration algorithm considering a customized board for SLCs and repetitive LiDARs, which are designed to facilitate the automation of construction robots. The calibration board is equipped with four symmetrically distributed hemispheres, whose centers are obtained by fitting the spheres and adoption with the geometric constraints. Subsequently, the spherical centers serve as reference features to estimate the relationship between the sensors. These distinctive features enable our proposed method to only require one calibration board pose and minimize human intervention. We conducted both simulation and real-world experiments to assess the performance of our algorithm. And the results demonstrate that our method exhibits enhanced accuracy and robustness. [ABSTRACT FROM AUTHOR]

Background: Accurate measurement of hemoglobin concentration is essential for various medical scenarios, including preoperative evaluations and determining blood loss. Traditional invasive methods are inconvenient and not suitable for rapid, point-of-care testing. Moreover, current models, due to their complex parameters, are not well-suited for mobile medical settings, which limits the ability to conduct frequent and rapid testing. This study aims to introduce a novel, compact, and efficient system that leverages deep learning and smartphone technology to accurately estimate hemoglobin levels, thereby facilitating rapid and accessible medical assessments. Methods: The study employed a smartphone application to capture images of the eye, which were subsequently analyzed by a deep neural network trained on data from invasive blood test data. Specifically, the EGE-Unet model was utilized for eyelid segmentation, while the DHA(C3AE) model was employed for hemoglobin level prediction. The performance of the EGE-Unet was evaluated using statistical metrics including mean intersection over union (MIOU), F1 Score, accuracy, specificity, and sensitivity. The DHA(C3AE) model's performance was assessed using mean absolute error (MAE), mean-square error (MSE), root mean square error (RMSE), and R^2. Results: The EGE-Unet model demonstrated robust performance in eyelid segmentation, achieving an MIOU of 0.78, an F1 Score of 0.87, an accuracy of 0.97, a specificity of 0.98, and a sensitivity of 0.86. The DHA(C3AE) model for hemoglobin level prediction yielded promising outcomes with an MAE of 1.34, an MSE of 2.85, an RMSE of 1.69, and an R^2 of 0.34. The overall size of the model is modest at 1.08 M, with a computational complexity of 0.12 FLOPs (G). Conclusions: This system presents a groundbreaking approach that eliminates the need for supplementary devices, providing a cost-effective, swift, and accurate method for healthcare professionals to enhance treatment planning and improve patient care in perioperative environments. The proposed system has the potential to enable frequent and rapid testing of hemoglobin levels, which can be particularly beneficial in mobile medical settings. Trial Registration: The clinical trial was registered on the Chinese Clinical Trial Registry (No. ChiCTR2100044138) on 20/02/2021. [ABSTRACT FROM AUTHOR]

This article focuses on the ongoing debate on the definition of autonomous weapon systems. This debate is introduced using two approaches to the definition of autonomy: (1) autonomy as the human – machine relation, and (2) autonomy as the complexity of a machine’s decision-making capabilities. The aim of the text is to critically review the current state of the debate and analyze the key issues related to each approach to defining autonomous weapons. I argue that in the current discourse, neither of these approaches offer definitions that could become the basis for regulating autonomous weapon systems. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Traditional methods of watering plants based on timers have been shown to be wasteful when compared to sensor based watering. While sensor based irrigation systems do exist, current implementations are both costly and needlessly complex. Our goal was to design an irrigation sensor system is cheap, easy to implement, and saves more water than traditional watering systems. Our approach was to create a sensor node network that is both reliable and cost effective when installing it. With this approach we were able to create a dependable tree like sensor network that could read moisture data from soil and have it visualized in an Android application. In regards to our Android application, we were able to implement features such as sensor calibration in order to obtain accurate readings. Our tree like network was also able to be consistent in being able to send data packets back and forth with each sensor having a reach of up to 100 meters and being able to have a total of 3125 radio modules in use. In conclusion we were able to create a reliable and cost effective irrigation system that could be used for both farms and home gardens. We were able to meet our initial goals which were to create an user friendly application that could be used to read and visualize data as well creating a sensor network that is low-power and is low cost.  Advisor: Professor Sitao Huang, Department of Electrical Engineering and Computer Science

University food environments have a strong influence on the dietary choices of students and staff. The aim of this study was to assess the food environment at a large university in Sydney, Australia. Data were collected between March and July 2022 from 27 fixed food outlets and 24 vending machines. The healthiness of the food environment was evaluated using the Healthy Food and Drink in NSW Health Facilities for Staff and Visitors Framework ('Framework'), which assesses food environment parameters including the availability, placement, and promotion of 'Everyday' (healthy) and 'Occasional' (less healthy) products. Each parameter was evaluated overall and across each food outlet type. Across all outlets, Everyday foods and drinks made up 43.9% of all products. Only two outlets met the Framework's product availability benchmark of ≥75% Everyday foods and drinks. A total of 43 outlets (84.3%) sold sugary drinks as part of their product range. Occasional products made up 68.4%, 53.3%, and 59.9% of all items for sale at checkout areas, countertops, and eye-level shelves, respectively. Finally, 79.7% of meal deals included Occasional products. Our findings highlight the need to improve the availability, placement, and promotion of foods and drinks sold at a major university campus in Sydney, Australia.

Millions of ionograms are acquired annually to monitor the ionosphere. The accumulated data contain untapped information from a range of locations, multiple solar cycles, and various geomagnetic conditions. In this study, we propose the application of deep convolutional neural networks to automatically classify and scale high-latitude ionograms. A supervised approach is implemented and the networks are trained and tested using manually analyzed oblique ionograms acquired at a receiver station located in Skibotn, Norway. The classification routine categorizes the observations based on the presence or absence of E- and F-region traces, while the scaling procedure automatically defines the E- and F-region virtual distances and maximum plasma frequencies. Overall, we conclude that deep convolutional neural networks are suitable for automatic processing of ionograms, even under auroral conditions. The networks achieve an average classification accuracy of 93% ± 4% for the E-region and 86% ± 7% for the F-region. In addition, the networks obtain scientifically useful scaling parameters with median absolute deviation values of 118 kHz ± 27 kHz for the E-region maximum frequency and 105 kHz ± 37 kHz for the Fregion maximum O-mode frequency. Predictions of the virtual distance for the E- and F-region yield median distance deviation values of 6.1 km ± 1.7 km and 8.3 km ± 2.3 km, respectively. The developed networks may facilitate EISCAT 3D and other instruments in Fennoscandia by automatic cataloging and scaling of salient ionospheric features. This data can be used to study both long-term ionospheric trends and more transient ionospheric features, such as traveling ionospheric disturbances. [ABSTRACT FROM AUTHOR]

Understanding OLED life characteristics is crucial towards implementation of OLED technology into automotive applications. The use of the Arrhenius equation together with the Inverse Power Law may be utilized to estimate the improvement in OLED life when automatic luminance control methods are utilized. [ABSTRACT FROM AUTHOR]

The paper is focused on ensuring an automatic reliable operation of aviation engine in a wide range of modes using wireless data transmission. This also helps to reduce the weight of it’s control system, the mass and dimensions of the modular construction of the engine. It is possible only on the basis of complex study of the transition processes of the aviation engine with the mutual influence of the course, from the theoretical point of view. Firstly, it is necessary to define method for start up as the process of engine transition from a state of rest in ground conditions or autorotation mode in flight to minimum stable operation mode. Then, in process start up for the initial spin up of the engine rotor, fuel supply and ignition a special starting system based on the Wireless Distributed Automatic Control System must be used in the combustion chamber. In practical application, this study can be used to create the new generation of aviation gas turbine engines and their control systems for subsonic and supersonic aircraft, identification their models based on the diagnosing the state of the engine according to dynamic ones parameters.

IntroductionShooting simulations provide an excellent opportunity to train use-of-force decisions in controlled environments. Recently, military and law enforcement organizations have expressed a growing desire to integrate physiological measurement into simulations for training and feedback purposes. Although participants can easily wear physiological monitors in these scenarios, direct implementation into training may not be simple. Theoretical problems exist in the ultra-short heart rate variability windows associated with use-of-force training, and practical problems emerge as existing scenario libraries at training organizations were not designed for physiological monitoring.MethodsThe current study explored the challenges and possibilities associated with direct implementation of physiological monitoring into an existing library of firearms training scenarios. Participants completed scenarios in a shooting simulator using existing military training scenarios while wearing a device to monitor their heart rate.ResultsThe results revealed lower heart rate variability (approximately 6%) occurred in scenarios where participants did not have to fire weapons, indicating that don't-shoot scenarios may actually impose more cognitive stress on shooters. Additional evidence further demonstrated how both behavioral and physiological factors could be used concomitantly to predict unintentionally firing on non-hostile actors. However, behavioral measures were more predictive (e.g., β = .221) than physiological measures (e.g., β = −.132) when the latter metrics were limited to specific scenarios. Qualitative results suggest that simply applying physiological monitoring to existing shooting simulations may not yield optimal results because it would be difficult to directly integrate physiological measurement in a meaningful way without re-designing some elements of the simulations, the training procedure, or both.DiscussionFuture use-of-force shooting simulations should consider designing novel scenarios around the physiological measurement rather than directly implementing physiological assessments into existing libraries of scenarios.

With the development of the circular economy and low-carbon society, the large-scale application of construction solid waste in buildings, such as recycled concrete, is becoming imperative. Accurately predicting the carbonation depth of recycled concrete is of great significance. Quantitatively analyzing the impact of each parameter on carbonation and elucidating the relationships between these parameters present challenges in predicting the carbonation of recycled concrete. In this study, different machine learning models and prediction equation models were applied and compared to predict the carbonation depth of 576 datasets associated with recycled concrete. The machine learning models used include Automation Machine Learning (AutoML), LightGBM, CatBoost, Neural Networks, Extra Trees, Random Forest, XGBoost, KNN (K-Nearest Neighbor). The results indicate that the machine learning method shows higher accuracy than the traditional equation, the AutoML model exhibits the best prediction accuracy among the investigated machine learning models, and carbonation test results further verified the favorable carbonation depth prediction effects of AutoML model. Furthermore, SHAP (Shapley Additive Explanations) was utilized to quantitatively analyze and explain the prediction results. The results demonstrate that carbonation time and the water to cement (W/C) ratio of recycled concrete have the most significant impact on the carbonation depth of recycled concrete buildings.

PurposeTo assess the generalizability of a deep learning-based algorithm to segment the ellipsoid zone (EZ).MethodsThe dataset consisted of 127 spectral-domain optical coherence tomography volumes from eyes of participants with USH2A-related retinal degeneration enrolled in the RUSH2A clinical trial (NCT03146078). The EZ was segmented manually by trained readers and automatically by deep OCT atrophy detection, a deep learning-based algorithm originally developed for macular telangiectasia Type 2. Performance was evaluated using the Dice similarity coefficient between the segmentations, and the absolute difference and Pearson's correlation of measurements of interest obtained from the segmentations.ResultsWith deep OCT atrophy detection, the average (mean ± SD, median) Dice similarity coefficient was 0.79 ± 0.27, 0.90. The average absolute difference in total EZ area was 0.62 ± 1.41, 0.22 mm2 with a correlation of 0.97. The average absolute difference in the maximum EZ length was 222 ± 288, 126 µm with a correlation of 0.97.ConclusionDeep OCT atrophy detection segmented EZ in USH2A-related retinal degeneration with good performance. The algorithm is potentially generalizable to other diseases and other biomarkers of interest as well, which is an important aspect of clinical applicability.

Renewal is a relapse phenomenon that refers to the recurrence of a previously reduced behavior following a change in stimulus conditions. Muething et al. (2022) examined the phenomenology of renewal among individuals with automatically maintained challenging behavior treated at an outpatient clinic. We replicated their findings by retrospectively examining renewal across various topographies of automatically maintained behavior treated at an inpatient hospital, and we extended their work by also examining differences across subtypes of automatically maintained self‐injurious behavior. The prevalence of renewal was comparable to that observed by Muething et al., supporting the notion that automatically maintained challenging behavior is susceptible to relapse phenomena. Furthermore, renewal was twice as likely to occur for individuals with Subtype 2 versus Subtype 1 self‐injurious behavior, providing additional evidence of behavioral differentiation between subtypes. Our findings suggest that even after apparent stability in treatment, practitioners should remain vigilant for the recurrence of automatically maintained behavior during generalization. [ABSTRACT FROM AUTHOR]

Natural Language Processing (NLP) has emerged as a valuable approach to assist in solving complex challenges in educational settings. This study explores NLP techniques, particularly sentence embedding models, to evaluate the flexibility dimension of divergent thinking during an open-ended collaborative creative (cocreative) process. The methodology involved a case study in which 25 secondary education students participated. The students worked in five collaborative groups to solve a real-life challenge through a cocreative process. During this study, we focus on evaluating flexibility, defined as the shift from one semantic category to another, grounded in the semantic similarity of ideas. Initially, we measured semantic similarity with eight-sentence embedding models and experts. We also conducted a correlation analysis of the experts and sentence embedding models to choose one highly correlated model. Subsequently, we evaluated the flexibility of ideas in creative techniques using experts and the high-correlated sentence embedding model. The results disclose that among the eight applied sentence embedding models to evaluate the semantic similarity of open-ended ideas, the Universal Sentence Encoder Transformer (USE-T) is highly correlated with experts. Moreover, USE-T strongly aligns with experts' evaluation to evaluate flexibility. These results will be valuable for designing and providing immediate feedback during cocreation, enabling AI-driven support to foster innovative solutions to real-world challenges. [ABSTRACT FROM AUTHOR]

Operational pollen monitoring networks have developed across Europe, and the world more generally, in response to the increasing prevalence of pollen allergy and asthma. Routine pollen observations are in large part currently still based on time-intensive manual techniques developed in the 1950s. These methods suffer from low temporal resolution and long delays in data availability. Recent technological developments are revolutionising the field making real-time high-temporal resolution measurements possible. This paper describes the rationale behind the EUMETNET AutoPollen programme, which aims to develop a prototype automatic pollen monitoring network across Europe. We provide a brief description of the current state-of-the-art, then an overview of new technologies, and finally the main tasks of the AutoPollen programme. [ABSTRACT FROM AUTHOR]

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Forensic gender identification based on teeth is needed to identify incomplete victims, only bones and teeth. Previous studies identified gender by manually or semi-automatically measuring mandibular parameters based on landmark points. Gender identification, especially for victims of mass disasters, requires accuracy, so it takes longer, especially if there are many parameters to be measured. In addition, the observer's manual or semi-automatic measurements may give different results. This study proposes a new automatic approach to generate ten mandibular landmark points from panoramic radiographic images for gender identification. We propose a step, namely determining the centroid point of the mandibular image and using linear regression to predict ten mandibular landmark points such as the two condyle, two gonion, four ramus, and two body of the mandible. This study obtained panoramic radiographic images from the Academic Dental Hospital, Universitas Airlangga. We calculated the distance between expert mandibular landmark points and the predicted results to evaluate performance. The prediction of the landmark with the smallest average distance is the lower point of the mandibular body 1 pixel. In comparison, the predicted landmark with the most extended average length is a gonion of 10 pixels. [ABSTRACT FROM AUTHOR]

This research aims to assist farmers in controlling plant pests, birds and insect, by applying an appropriate electronic technology. The system operates in an automatic and remote-controlled modes using IoT technology. We used a solar powered systems that are environmentally friendly with zero carbon emissions, as an energy source for the systems. The test results showed that the bird pest control system worked well according to the automatic and remote-control modes design. We compare those two methods to evaluate the most effective between those two methods. Bird pest control using two actuators, sound and movement, outsmart control using only sound by success ratio 70:50. The insect pest control system worked well with the remote-control mode through Telegram application on farmers’ smartphones.

Millions of ionograms are acquired annually to monitor the ionosphere. The accumulated data contain untapped information from a range of locations, multiple solar cycles, and various geomagnetic conditions. In this study, we propose the application of deep convolutional neural networks to automatically classify and scale high-latitude ionograms. A supervised approach is implemented and the networks are trained and tested using manually analyzed oblique ionograms acquired at a receiver station located in Skibotn, Norway. The classification routine categorizes the observations based on the presence or absence of E− and F-region traces, while the scaling procedure automatically defines the E− and F-region virtual distances and maximum plasma frequencies. Overall, we conclude that deep convolutional neural networks are suitable for automatic processing of ionograms, even under auroral conditions. The networks achieve an average classification accuracy of 93% ± 4% for the E-region and 86% ± 7% for the F-region. In addition, the networks obtain scientifically useful scaling parameters with median absolute deviation values of 118 kHz ±27 kHz for the E-region maximum frequency and 105 kHz ±37 kHz for the F-region maximum O-mode frequency. Predictions of the virtual distance for the E− and F-region yield median distance deviation values of 6.1 km ± 1.7 km and 8.3 km ± 2.3 km, respectively. The developed networks may facilitate EISCAT 3D and other instruments in Fennoscandia by automatic cataloging and scaling of salient ionospheric features. This data can be used to study both long-term ionospheric trends and more transient ionospheric features, such as traveling ionospheric disturbances.

This study focuses on developing an automated feeding tool for fish farming in Pesawaran, a region known for its aquaculture potential. Aquaculture, crucial for global food needs, requires efficient and sustainable management, especially in feeding, a critical factor in fish growth and health. Traditional manual feeding methods are time-consuming and prone to errors, affecting fish productivity and growth. The research aimed to enhance feed management efficiency, minimize feeding errors, and improve the sustainability and productivity of fish farming in Pesawaran. The initial phase involved analyzing the needs of fish farmers, environmental factors, and fish species. The design of the automation tool emphasized ergonomics, reliability, and ease of use. The Rapid Application Development (RAD) method was employed, focusing on quick and iterative development. This method was applied at the Pokdakan Pemuda Tani RPL in Negeri Sakti Village, Gedong Tataan District, Pesawaran Regency, from July to November 2023. The application of RAD in designing the Pokdakan Pesawaran fish-feeding automation tool yielded positive outcomes. The fast, responsive development process, which actively involved users, led to a practical solution well-received by the fish farming community. This research demonstrates the value of RAD principles in providing practical, locally relevant solutions and guiding the development of adaptive, user-oriented aquaculture technology.

The pottery wheel machine is an essential piece of equipment in ceramics manufacturing. This paper presents the design of an intelligent pottery wheel machine aimed at addressing the issues of low clay material utilization and the challenges of pottery throwing operations. Traditional techniques require exceptional craftsmanship and proficient mechanical operation, and existing pottery wheel machines still demand attention to equipment usage and clay material knowledge. The intelligent pottery wheel machine integrates central positioning, automatic clay feeding, and clay storage. The pull mechanism stores and precisely feeds clay, regulating usage and minimizing excess. The automated settings of the clay storage and power push system replace the need for manual assessment of clay density and quality, ensuring balanced and high-quality clay extrusion. The continuous feeding setting allows for automatic addition of clay, saving replenishment time and reducing the labor intensity of adding clay. The lifting mechanism ensures that the clay body remains centered, avoiding repeated manual adjustments and allowing users to focus on creating pottery rather than spending excessive time mastering the centering technique. The machine’s lightweight, modular design reduces clay waste, making the process more sustainable. By saving clay materials and improving throwing efficiency, it enhances the success rate of throwing.

This review systematically examines the recent research from the past decade on diverse path-planning algorithms tailored for stereotactic neurosurgery applications. Our comprehensive investigation involved a thorough search of scholarly papers from Google Scholar, PubMed, IEEE Xplore, and Scopus, utilizing stringent inclusion and exclusion criteria. The screening and selection process was meticulously conducted by a multidisciplinary team comprising three medical students, robotic experts with specialized knowledge in path-planning techniques and medical robotics, and a board-certified neurosurgeon. Each selected paper was reviewed in detail, and the findings were synthesized and reported in this review. The paper is organized around three different types of intervention tools: straight needles, steerable needles, and concentric tube robots. We provide an in-depth analysis of various path-planning algorithms applicable to both single and multi-target scenarios. Multi-target planning techniques are only discussed for straight tools as there is no published work on multi-target planning for steerable needles and concentric tube robots. Additionally, we discuss the imaging modalities employed, the critical anatomical structures considered during path planning, and the current status of research regarding its translation to clinical human studies. To the best of our knowledge and as a conclusion from this systematic review, this is the first review paper published in the last decade that reports various path-planning techniques for different types of tools for minimally invasive neurosurgical applications. Furthermore, this review outlines future trends and identifies existing technology gaps within the field. By highlighting these aspects, we aim to provide a comprehensive overview that can guide future research and development in path planning for stereotactic neurosurgery, ultimately contributing to the advancement of safer and more effective neurosurgical procedures.