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Salah satu proses dalam produksi kerupuk yang memerlukan pengembangan yaitu proses pengeringan yang masih konvensional dengan pengeringan di tempat terbuka yang bergantung dari sinar matahari. Dalam pengeringan konvensional terdapat beberapa permasalahan yaitu panas yang fluktuatif, kebersihan yang tidak terjaga, memerlukan tempat yang cukup luas dan pada saat musim hujan proses pengeringan kerupuk terhenti. Dari permasalahan proses pengeringan yang ada saat ini, tujuan penelitian ini merancang alat pengeringan kerupuk yang tidak bergantung pada panas cahaya matahari, lebih hemat dalam penggunaan energi, lebih besar kapasitasnya, alat lebih ergonomis, pengguna tidak terpapar langsung dengan sinar matahari serta tidak mudah menimbulkan kelelahan. Dengan perkembangan teknologi, menuntut adanya inovasi untuk menciptakan alat pengering kerupuk sebagai pengganti pengeringan secara konvensional. Alat pengering kerupuk “Smart Fuse Water Dryer “ menggunakan microcontroller sebagai pengontrol dalam proses pengeringan, yaitu mengontrol suhu dan lama waktu proses pengeringan secara elektronik dan automatic. Hal ini akan lebih mudah untuk mengeringkan kerupuk tanpa harus menunggu cuaca panas. Perbandingan kinerja alat “Smart Fuse Water Dryer” hasil pengeringan selama 4 jam sedangkan menggunakan alat konvesional selama 8 jam. Dari segi sistem menggunakan alat ini sistemnya otomatis sedangkan yang konvensional secara manual. Dari segi posturk kerja dengan REBA dengan menggunakan alat skor nya 4 sedangkan yang konvensional skornya 9 yang artinya perlu segera diperbaikan. Dari segi lahan pengeringan yang semula membutuhkan 600 x 1000 cm sekarang hanya memerlukan 95 x 100 cm. Dari segi peningkatan ouput dengan menggunakan alat menghasilkan 70 kg sedangkan yang konvensional menghasilkan 35 Kg.

This paper presents an invention that relates to a device for the automatic inflation of inflatable tires, suitable as an accessory for tires subjected in operation to the cyclical phenomena of heating and cooling, which allows the harnessing of the thermal energy accumulated in their structure, in that it is provided with an actuation mechanism that allows its thermo-mechanical conversion. [ABSTRACT FROM AUTHOR]

Subtypes of automatically maintained self‐injurious behavior (SIB) have been defined based on response patterns observed during the functional analysis, which are thought to reflect each subtype's distinct mechanisms. Current practice for identifying subtypes involves using structured criteria to identify whether SIB is automatically maintained, followed by the application of additional criteria to identify the subtype. We describe a series of studies directed at simplifying these methods to facilitate wider application of the subtyping model in research and practice. In Studies 1 and 2, we demonstrate the accuracy of modified criteria using the level of differentiation of SIB across the play and no‐interaction conditions at distinguishing between two subtypes (i.e., Subtypes 1 and 2). We then demonstrate visual analysis (without structured criteria) can accurately identify SIB as automatically maintained in Study 3, which can be used in combination with level of differentiation to enable application of the subtyping model in practice. [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.)

Automotive display image enhancement may be utilized to enhance display visibility under various lighting environments typically encountered in the automotive cockpit. Image enhancement methods may be combined with automatic luminance control methods to yield a new class of display visibility product. [ABSTRACT FROM AUTHOR]

Introduction: Electrode length selection based on case-related cochlear parameters is becoming a standard pre-operative step for cochlear implantation. The manual measurement of the parameters is often time-consuming and may lead to inconsistencies. Our work aimed to evaluate a novel, automatic measurement method. Materials and Methods: A retrospective evaluation of pre-operative HRCT images of 109 ears (56 patients) was conducted, using a development version of the OTOPLAN® software. Inter-rater (intraclass) reliability and execution time were assessed for manual (surgeons R1 and R2) vs. automatic (AUTO) results. The analysis included A-Value (Diameter), B-Value (Width), H-Value (Height), and CDLOC-length (Cochlear Duct Length at Organ of Corti/Basilar membrane). Results: The measurement time was reduced from approximately 7 min ± 2 (min) (manual) to 1 min (AUTO). Cochlear parameters in mm (mean ± SD) for R1, R2 and AUTO, respectively, were A-value: 9.00 ± 0.40, 8.98 ± 0.40 and 9.16 ± 0.36; B-value: 6.81 ± 0.34, 6.71 ± 0.35 and 6.70 ± 0.40; H-value: 3.98 ± 0.25, 3.85 ± 0.25 and 3.76 ± 0.22; and the mean CDLoc-length: 35.64 ± 1.70, 35.20 ± 1.71 and 35.47 ± 1.87. AUTO CDLOC measurements were not significantly different compared to R1 and R2 (H0: Rx CDLOC = AUTO CDLOC: p = 0.831, p = 0.242, respectively), and the calculated intraclass correlation coefficient (ICC) for CDLOC was 0.9 (95% CI: 0.85, 0.932) for R1 vs. AUTO; 0.90 (95% CI: 0.85, 0.932) for R2 vs. AUTO; and 0.893 (95% CI: 0.809, 0.935) for R1 vs. R2. Conclusions: We observed excellent inter-rater reliability, a high agreement of outcomes, and reduced execution time using the AUTO method. [ABSTRACT FROM AUTHOR]

In this study, the improved overlap distance is used as a criterion in order to build clusters for interval data. This distance has shown the suitability, and given an outstanding advantage in evaluating the similarity for intervals with a lot of the considered data sets. Based on the overlap distance, we propose the Automatic Clustering Algorithm for Interval data (ACAI). One of the best advantages of the proposed algorithm is that ACAI figure out simultaneously the appropriate number of groups, and factors in every group. The proposed algorithm can be effectively performed through a Matlab procedure. Based on the extracted intervals from texture of images, we have applied ACAI to recognize the images, an interesting and challenging issue at present. Experimental data sets including the differences of the characteristics as well as the number of elements has shown the reasonableness of the proposed algorithm, and its advantages in comparing to the surviving ones. From the image recognition problem, this research has shown prospect in practical applications for many fields. [ABSTRACT FROM AUTHOR]

To quantify a qualitative screening tool for the early recognition of sepsis in children with fever either visiting the emergency department or already admitted to hospital. Prospective observational study including febrile patients under 18 years of age. Sepsis diagnosis was the main outcome. A multivariable analysis was performed with 4 clinical variables (heart rate, respiratory rate, disability, and poor skin perfusion). The cut-off points, odds ratio, and coefficients of these variables were identified. The quantified tool was then obtained from the coefficients. The area under the curve (AUC) was obtained and internal validation was performed using k-fold cross-validation. Two hundred sixty-six patients were included. The multivariable regression confirmed the independent association of the 4 variables with the outcome. The quantified screening tool yielded an excellent AUC, 0.825 (95%CI 0.772–0.878, p < 0.001), for sepsis prediction. Conclusion: We successfully quantified a sepsis screening tool, and the resulting model has an excellent discriminatory power. What is Known: • Screening tests have to be based only on clinical variables that needs minimum technological support. • The current Sepsis Code is a qualitative screening tool. What is New: • The current screening tool was quantified using four clinical variables, weighted according to the deviation from normality and differentiated according to the age of the patient. • The resulting model has an excellent discriminatory power in identifying septic patients among febrile pediatric patients. [ABSTRACT FROM AUTHOR]

An artificial phototropism system with autonomous intelligence is critical for the development of an autonomous light‐harvesting system. However, a current technology for artificial phototropism light‐harvesting system with the motor system is capable of accurate direction with high efficiency but still limited to bulky and expensive. Inspired by nature, artificial phototropism systems based on photo‐responsive smart soft material liquid crystal elastomer (LCE) are developed to follow the light direction but are still limited by a low payload that is not suitable for practical application. Herein, an untethered automatic light‐harvesting system with artificial phototropism is proposed. This system combines the advantages of the LCE actuator and electrical control system and is capable of adjusting the solar cell facing the sun automatically through controls of bending and rotation of the LCE tubular actuator to maximize the absorption of solar energy. Compared with the traditional mini photovoltaic panels, the absorption efficiency of solar energy by the light‐harvesting system is increased by 27.68% for the whole day and 230.15% in the morning or dawn sunlight with a small incident angle. The proposed untethered automatic light‐harvesting system is compact, inexpensive, and can provide a large payload, showing great application potential in solar automation and robot systems, and energy‐saving fields. [ABSTRACT FROM AUTHOR]

Pests cause heavy crop losses, so it is vital to conduct early pest management and control in precision agriculture. In general, pest monitoring is a foundation for early pest management and control. Conventional pest monitoring using manual sampling and detection is time consuming and labour intensive. Therefore, many studies have explored how to achieve automatic pest monitoring. However, few works have focused on automatic monitoring of flying vegetable insect pests. To close this gap, this study developed an automatic monitoring scheme for flying vegetable insect pests based on two hypotheses: (1) yellow sticky traps could provide reliable information to assess population density of flying vegetable insect pests, and (2) a computer-vision-based detector could accurately detect pests in images. Specifically, yellow sticky traps were exploited to sample flying vegetable insect pests, and an RGB camera was adopted to capture yellow-sticky-trap images; and a computer-vision-based detector called "YOLO for Small Insect Pests" (YOLO-SIP) was used to detect pests in captured images. The hypotheses were tested by using the Heuristics engineering method, installing yellow sticky traps and RGB cameras in vegetable fields, constructing a manually labelled image dataset, and applying YOLO-SIP to the constructed dataset with the mean average precision (mAP), average mean absolute error (aMAE), and average mean square error (aMSE) metrics. Experiments showed that the proposed scheme captured yellow-sticky-trap images automatically and obtained an mAP of 84.22%, an aMAE of 0.422, and an aMSE of 1.126. Thus, the proposed scheme is promising for the automatic monitoring of flying vegetable insect pests. [ABSTRACT FROM AUTHOR]

Medication non-adherence or mismanagement in medicines schedules such as missing doses, taking the wrong amounts, or having medicines at incorrect times is a serious problem, especially in elderly patients or patients with serious illnesses, and may lead to deadly consequences. This paper proposed a smart medical box that dispenses not only medicines at prescribed schedules but also has a basic health monitoring system for the patient's temperature, oxygen level, and heart rate detection, thus relieving the patient from visiting a doctor. This device is Raspberry Pi-controlled, having an added security feature of biometric recognition so that the medicine is dispensed to the correct patient. Moreover, the user is notified once their medicine has been dispensed via SMS. The main aim of this project was to keep the device cost-effective, user-friendly, simple, and beneficial for the elderly population. [ABSTRACT FROM AUTHOR]

The zebrafish embryos were widely employed in genetics, development and drug discovery studies as miniatured animal models. Sorting of two-color fluorescent embryos is often required in large-scale experiments but it is challenging to manually sort with high efficiency. Here, we reported a high-throughput sorting system for two-color fluorescent zebrafish embryos. The embryos can be automatically loaded from a sample pool and sorted based on the average fluorescent intensity. The two-color fluorescent signals were split into two lines and detected by an area array camera. The system achieves the sorting of 100 embryos in less than 10[Formula: see text]min with an accuracy of greater than 95%.

In the course of the COVID-19 pandemic and the implementation of associated non-pharmaceutical containment measures, the need for continuous monitoring of the mental health of populations became apparent. When the pandemic hit Germany, a nationwide Mental Health Surveillance (MHS) was in conceptual development at Germany’s governmental public health institute, the Robert Koch Institute. To meet the need for high-frequency reporting on population mental health we developed a prototype that provides monthly estimates of several mental health indicators with smoothing splines. We used data from the telephone surveys German Health Update (GEDA) and COVID-19 vaccination rate monitoring in Germany (COVIMO). This paper provides a description of the highly automated data pipeline that produces time series data for graphical representations, including details on data collection, data preparation, calculation of estimates, and output creation. Furthermore, statistical methods used in the weighting algorithm, model estimations for moving three-month predictions as well as smoothing techniques are described and discussed. Generalized additive modelling with smoothing splines best meets the desired criteria with regard to identifying general time trends. We show that the prototype is suitable for a population-based high-frequency mental health surveillance that is fast, flexible, and able to identify variation in the data over time. The automated and standardized data pipeline can also easily be applied to other health topics or other surveys and survey types. It is highly suitable as a data processing tool for the efficient continuous health surveillance required in fast-moving times of crisis such as the Covid-19 pandemic.

Copyright of Rolling Stock (1002-7602) is the property of Rolling Stock 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.)

In the command system related to the operation and maintenance of urban rail vehicles, data analysis is the basis for real-time vehicle monitoring and state analysis. Due to the different module equipment and signal points involved in different types of vehicles, data protocols of different vehicle types are different.Data parsing by writing data parsing code is of heavy workload and cannot be reused for other vehicle types, so it is difficult to support large-scale project applications. This paper designs an automatic data analysis module for urban rail vehicles, which combines a general data analysis engine with a dynamically configurable database signal point table. Users only needs to make a signal point table according to the data protocol, and the data analysis engine automatically completes the data analysis according to the signal point table. It has the characteristics of reusable core code of data analysis engine, clear structure of signal point table and simple development of analysis function, etc. The module is applied and promoted in the urban rail vehicle intelligent maintenance and derivative system. Experimental and application results show that, after using the automatic data analysis module, the project development efficiency is increased by 5.6 times, the accuracy of data analysis is increased by 0.24% and the efficiency and accuracy of data parsing are improved.

Today's technological developments are growing rapidly, but there are still jobs that are done manually and semi-manually. This paper aims to develop a control system to coordinate the work of tools using an electric motor, which is very helpful in the process of cutting onions in large quantities and is more efficient in spending production costs because the tool designed is an automatic onion cutter. This research method begins with the collection of materials and data related to onion cutters, production of onion cutters, and testing of onion cutters. The measurement results of the tool show that every component in the automatic onion cutter works well and optimally; the measurement process is carried out properly and thoroughly. This optimal work result is supported by Arduino Uno. The Arduino Uno microcontroller acts as a tool controller. In the main system, the onion cutter is controlled by the Arduino Uno with the Arduino programming language. This automatic onion cutting system will work when the onion you want to cut has been inserted into a cross-section that has been equipped with an ultrasonic sensor, and then the onion cutting process is carried out. Compared with the manual method, the cutting time is almost the same. It's just that the advantages of slicing with this tool, the resulting slices are relatively more uniform. Besides that, another advantage is that it doesn't hurt the eyes of the onion slicer. So that this tool can be used by small entrepreneurs and onion farmers to increase productivity and the community's economy.

The gate is a means of dividing the land between the house and the road. The gate also serves for security and comfort as a place to live. As a home security, the gate is made high and equipped with a safety lock. With a gate, you will feel comfortable because other people who come can't enter the house directly, and it's often difficult or overwhelmed when opening or closing the gate. In the case of a gate that has a large size and is also heavy, this will overwhelm us so that a lot of energy and time is needed. The automatic gate control device designed is an electric motor that makes it easier for people to open or close the gate by remote control while at home or at work. Based on the results of the design that has been made, the simulation of the automatic sliding gate control device is expected to solve the problems encountered in the field where the dimensions of the gate are 1500mm x 1000mm x 80mm with a weight of 27kg, the electric motor used is reversible AC which has a speed of 1300 RPM, a power of 60 watts and a 1: 60 gearbox which has become 1 part, the speed of the electric motor is transmitted by a gear with a diameter of 78mm, so that the average gate opening and closing speed is 12.52 and 13.21s with a difference of 0.69s, with the The controller used is the AK-T02 module and the AKJ027 remote can work up to a distance of 10 m. It is hoped that making this control device simulation can be a reference in making automatic gate control devices for homes and other gates.

Automatic extraction of surface water is of great significance to the study of the global water cycle and the dynamic management of water resources. At present, the water extraction accuracy of high-resolution multi-spectral remote sensing images has been greatly improved. But it is still affected by the shadow of the mountains and the shadow of the tall buildings inside the city. The spectral information of shadow is basically consistent with the spectral information of water, so the accuracy of any traditional index extraction of water will be questioned by users. Or the user must adjust threshold parameters many times to obtain good extraction results, which is contradictory to fast and large-area remote sensing monitoring. To solve the above problems, this paper firstly introduces the thermal infrared band at the data end for pre-treatment. Then, a lightweight neural network (EDCM) combining the most advanced lightweight image classification model and semantic segmentation model is proposed, which is specially used for fast, large area and automatic water extraction. Multi-scale training of samples using lightweight convolutional networks aims to extract multi-scale context information. The newly constructed model was tested in three highly heterogeneous scenarios, and the results showed that the trained EDCM model achieved the highest accuracy in all selected test areas, reaching more than 95.28%. The EDCM model can be used for the high-precision extraction of surface water in complex areas.

Abstract An artificial phototropism system with autonomous intelligence is critical for the development of an autonomous light‐harvesting system. However, a current technology for artificial phototropism light‐harvesting system with the motor system is capable of accurate direction with high efficiency but still limited to bulky and expensive. Inspired by nature, artificial phototropism systems based on photo‐responsive smart soft material liquid crystal elastomer (LCE) are developed to follow the light direction but are still limited by a low payload that is not suitable for practical application. Herein, an untethered automatic light‐harvesting system with artificial phototropism is proposed. This system combines the advantages of the LCE actuator and electrical control system and is capable of adjusting the solar cell facing the sun automatically through controls of bending and rotation of the LCE tubular actuator to maximize the absorption of solar energy. Compared with the traditional mini photovoltaic panels, the absorption efficiency of solar energy by the light‐harvesting system is increased by 27.68% for the whole day and 230.15% in the morning or dawn sunlight with a small incident angle. The proposed untethered automatic light‐harvesting system is compact, inexpensive, and can provide a large payload, showing great application potential in solar automation and robot systems, and energy‐saving fields.

We describe an automated assessment process for team-coding assignments based on DevOps best practices. This system and methodology includes the definition of Team Performance Metrics measuring properties of the software developed by each team, and their correct use of DevOps techniques. It tracks the progress on each of metric by each group. The methodology also defines Individual Performance Metrics to measure the impact of individual student contributions to increase in Team Performance Metrics. Periodically scheduled reports using these metrics provide students valuable feedback. This process also facilitates the process of assessing the assignments. Although this method is not intended to produce the final grade of each student, it provides very valuable information to the lecturers. We have used it as the main source of information for student and team assessment in one programming course. Additionally, we use other assessment methods to calculate the final grade: written conceptual tests to check their understanding of the development processes, and cross-evaluations. Qualitative evaluation of the students filling relevant questionnaires are very positive and encouraging. [ABSTRACT FROM AUTHOR]

The Box and Blocks Test (BBT) is a widely used outcome measure for manual dexterity assessments in neurological rehabilitation. The BBT score is based on the maximum number of cubes that a person is able to displace during a 60s time window. In this paper, a low-cost instrumented system to automatically obtain the number of cubes using proximity sensors is presented. For that purpose, the central partition of the BBT was sensorized, aiming to minimise the employed sensors and minimally alter the physical BBT box. The counting system, connected to the mobile app, allows for the self-administration of the test as users only need to follow the presented instructions. Firstly, the methodology used to automate the test scoring is presented, including the sensors' description and the prototype design. Then, the obtained success rate in cube counting is shown, with an average of 98% in trials with five healthy users. Finally, the conclusions and future work are shown. The results support the use of automated methods for upper limb assessment, providing more objective results and additional information about user performance. [ABSTRACT FROM AUTHOR]

We describe the control and interfacing of a fluorometer designed for aerial drone-based measurements of chlorophyll- a using an Arduino Nano 33 BLE Sense board. This 64 MHz controller board provided suitable resolution and speed for analog-to-digital (ADC) conversion, processed data, handled communications via the Robot Operating System (ROS) and included a variety of built-in sensors that were used to monitor the fluorometer for vibration, acoustic noise, water leaks and overheating. The fluorometer was integrated into a small Uncrewed Aircraft System (sUAS) for automated water sampling through a Raspberry Pi master computer using the ROS. The average power consumption was 1.1 W. A signal standard deviation of 334 µV was achieved for the fluorescence blank measurement, mainly determined by the input noise equivalent power of the transimpedance amplifier. An ADC precision of 130 µV for 10 Hz chopped measurements was achieved for signals in the input range 0-600 mV. Graphical Abstract [ABSTRACT FROM AUTHOR]

In the context of brain tumour response assessment, deep learning-based three-dimensional (3D) tumour segmentation has shown potential to enter the routine radiological workflow. The purpose of the present study was to perform an external evaluation of a state-of-the-art deep learning 3D brain tumour segmentation algorithm (HD-GLIO) on an independent cohort of consecutive, post-operative patients. For 66 consecutive magnetic resonance imaging examinations, we compared delineations of contrast-enhancing (CE) tumour lesions and non-enhancing T2/FLAIR hyperintense abnormality (NE) lesions by the HD-GLIO algorithm and radiologists using Dice similarity coefficients (Dice). Volume agreement was assessed using concordance correlation coefficients (CCCs) and Bland–Altman plots. The algorithm performed very well regarding the segmentation of NE volumes (median Dice = 0.79) and CE tumour volumes larger than 1.0 cm3 (median Dice = 0.86). If considering all cases with CE tumour lesions, the performance dropped significantly (median Dice = 0.40). Volume agreement was excellent with CCCs of 0.997 (CE tumour volumes) and 0.922 (NE volumes). The findings have implications for the application of the HD-GLIO algorithm in the routine radiological workflow where small contrast-enhancing tumours will constitute a considerable share of the follow-up cases. Our study underlines that independent validations on clinical datasets are key to asserting the robustness of deep learning algorithms. [ABSTRACT FROM AUTHOR]

Correctly identifying sleep stages is essential for assessing sleep quality and treating sleep disorders. However, the current sleep staging methods have the following problems: (1) Manual or semi-automatic extraction of features requires professional knowledge, which is time-consuming and laborious. (2) Due to the similarity of stage features, it is necessary to strengthen the learning of features. (3) Acquisition of a variety of data has high requirements on equipment. Therefore, this paper proposes a novel feature relearning method for automatic sleep staging based on single-channel electroencephalography (EEG) to solve these three problems. Specifically, we design a bottom–up and top–down network and use the attention mechanism to learn EEG information fully. The cascading step with an imbalanced strategy is used to further improve the overall classification performance and realize automatic sleep classification. The experimental results on the public dataset Sleep-EDF show that the proposed method is advanced. The results show that the proposed method outperforms the state-of-the-art methods. The code and supplementary materials are available at GitHub: https://github.com/raintyj/A-novel-feature-relearning-method. [ABSTRACT FROM AUTHOR]

When we say that the termination of the employment contract by unilateral will is still in its infancy in the context of the Labor Code, and the developments you are experiencing in the field of termination in general, from the hearing procedure, compensation, adaptation of the various means of proof, and the consequent fire, and the judiciary because of its chain in The adaptation of laws is obligatory to interpret the new legal rule, which the legislator came up with in the Labor Code, and in the absence of a rule that includes the dispute presented to him in light of the legal requirements, he will have to make due diligence in relation to it, and this results from the fact that the approved law, as previously mentioned, is a new law. The issue of termination of the employment contract is of great importance, and it is one of the central issues around which discussions revolve around most of the files that are brought before the courts in the field of labor disputes. [ABSTRACT FROM AUTHOR]

We describe a waterproof, lightweight (1.3 kg), low-power (∼1.1 W average power) fluorometer operating on 5 V direct current deployed on a small uncrewed aircraft system (sUAS) to measure chlorophyll and used for triggering environmental water sampling by the sUAS. The fluorometer uses a 450 nm laser modulated at 10 Hz for excitation and a standard photodiode and transimpedance amplifier for the detection of fluorescence. Additional detectors are available for measuring laser intensity and light scattering. Control of the fluorometer and communication between the fluorometer and the Raspberry Pi 4B computer controlling the sampler were provided by an Arduino microcontroller using the robot operating system (ROS). Calibrations were based on standards of dissolved chlorophyll extracted from Chlorella powder (a widely available dietary supplement). The detection limit for chlorophyll from these calibrations was found to be 0.2 μg per liter of water for a single 0.1 s differential measurement. The detection limit decreases with the square root of the integration time as expected. Detection limits increase by a factor of two to three when mounted in the sUAS due to electrical noise; sUAS acoustic noise and vibration do not appear to contribute significantly. Graphical Abstract [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to investigate the accuracy of automatic tooth finish line registration compared to manual registration with regard to various finish line configurations and dental computer-aided design (CAD) software. Methods: Finish line registrations were performed on 15 digital tooth models with different finish line configurations (edge roundness radius = 0 mm, 0.2 mm, and 0.4 mm; edge angle = 30°, 60°, 90°, 120°, and 150°) using automatic and manual methods for designing virtual copings (N = 150). The discrepancies between the registered finish line extracted from the copings and the actual finish line segmented from the digitized tooth model were measured. Three-way analysis of variance (ANOVA) and post-hoc analyses with Bonferroni correction (α = 0.05) were used to analyze the results. Results: The finish line configurations, registration methods, and CAD software interacted with the accuracy of the registered finish line (p = 0.001). The automatic finish line registration method exhibited larger error values than the manual method, especially at high finish line edge roundness and obtuse edge angles for both EXOCAD and R2CAD software (p < 0.001). The difference in dental CAD software affected the registration accuracy in the automatic method (p < 0.001), but not in the manual method (p = 0.676). Conclusions: Finish line registration errors may occur when the automatic registration method is applied to the indistinct edge of tooth preparation. The accuracy of the automatic finish line registration could differ according to the CAD software program. [ABSTRACT FROM AUTHOR]

Currently, Indonesia and the whole world are being hit by the Covid-19 pandemic which has an impact on various fields of life. It affects all sectors, including the education sector. The government through the Ministry of Education and Culture makes a policy in education in terms of the learning process. Teaching and learning activities that were initially carried out face to face become distance learning which was carried out at home. In this study, a systematic literature review is conducted on automatic assessment of essay answers. Various previous studies discuss the essay answer scoring system that has been developed using various methods. We synthesize the results to enrich our understanding of the automated essay exam scoring system. The expected result of this research is that it can contribute to further research related to the automated essay exam scoring system, especially in terms of considering methods and dataset forms. [ABSTRACT FROM AUTHOR]

Many catfish cultivators in Indonesia have failed due to lack of attention to the quality of the water used in aquaculture ponds. Water quality is very influential on the catfish farming system with biofloc technology, water has the most important role for the survival of catfish. This research periodically measures the level of turbidity and pH in the water, then the readings from the sensor will be processed by Arduino Uno which will be displayed on the LCD. In addition, this tool can also schedule the provision of nutrients as desired and is able to perform automatic water changes according to the conditions of the aquaculture pond, if the pH and turbidity conditions of the pond exceed the existing threshold, it will activate the drain pump and water increase. The accuracy of the acidity (pH) sensor has an accuracy rate of 96.21% which is very good, with a standard deviation of 2.49. The turbidity sensor has a standard deviation of 0.133. So this tool is designed to make it easier for cultivators to take care of the pond. The hope is that by making this tool it can overcome or overcome the mortality rate of catfish among cultivators. [ABSTRACT FROM AUTHOR]

Electric fans are mechanical devices that move air and other gases vital to human comfort and safety in industrial processes, whereas disinfection measures are needed to limit the possible contamination of viruses like the Covid-19 Virus. The Automatic Disinfectant Fan uses a microcontroller device, sensors, and electronic components to automatically switch on the electric fan when a human presence is detected. When no human presence is detected, the fan automatically sprays disinfectant for a predetermined period. The weighted mean was used to characterize and analyze the data collected. The Automatic Disinfectant Fan was evaluated with a mean score of 4.09, which was described as "agree" in terms of functional suitability, performance efficiency, usability, reliability, and portability. The evaluation of the Automatic Disinfectant Fan's system design and implementation showed that it had achieved its goal, and potential advancements for the system are suggested. [ABSTRACT FROM AUTHOR]

The shift from manual weather measurements to automation is almost inevitable. When switching to AWS (Automatic Weather Station), WMO requires parallel data testing between automatic and manual measurements to be performed. The purpose of this paper is to conduct a parallel test of AWS data using a simple statistical test that has been applied to three main weather parameters, namely temperature, pressure, humidity, rainfall, and wind direction and speed. The months of January and June were used as samples to represent the character of the wet and dry seasons in the Makassar monsoon area. The results of the analysis show that during the rainy season, only pressure and temperature are identical and homogeneous. Meanwhile, in the dry season, apart from these two parameters, humidity and wind speed are also homogeneous and rainfall is a non-homogeneous parameter in January and June. Both AWS and manual observations show that the influence of land-sea winds in Makassar is very strong. Considering that there are inhomogeneous parameters, it is highly recommended to test for a longer time, taking into account the season, the influence of other global phenomena, the effect of missing data and incorrect data testing various methods of homogeneity and characteristics in each place and their effect on forecasts. [ABSTRACT FROM AUTHOR]

An automatic railway gate control system using an Arduino UNO and IR sensors is proposed in this study. The manually operated rail gates can be converted into automatic control of railway gates using different types of sensors. These are mostly needed where the track passes over a city, neighborhood, or redundant gateways of the passage regions. The gates are currently manually operated by gatekeepers according to the existing system, depending on signals obtained from the control centre. Accidents are most likely to occur in such cases, causing severe loss of human lives and property near rail crossing zones. The programmed railway gate regulator tracks the movement of the train and detects obstacles, thereby notifying the server motor to open or close the gate automatically. This study also suggests the effective use of real-time information on the train positions. The main objective of this study is to safeguard efficiency, quality, time management, public safety and automation. [ABSTRACT FROM AUTHOR]

Abstract Correctly identifying sleep stages is essential for assessing sleep quality and treating sleep disorders. However, the current sleep staging methods have the following problems: (1) Manual or semi-automatic extraction of features requires professional knowledge, which is time-consuming and laborious. (2) Due to the similarity of stage features, it is necessary to strengthen the learning of features. (3) Acquisition of a variety of data has high requirements on equipment. Therefore, this paper proposes a novel feature relearning method for automatic sleep staging based on single-channel electroencephalography (EEG) to solve these three problems. Specifically, we design a bottom–up and top–down network and use the attention mechanism to learn EEG information fully. The cascading step with an imbalanced strategy is used to further improve the overall classification performance and realize automatic sleep classification. The experimental results on the public dataset Sleep-EDF show that the proposed method is advanced. The results show that the proposed method outperforms the state-of-the-art methods. The code and supplementary materials are available at GitHub: https://github.com/raintyj/A-novel-feature-relearning-method .

The scientific research process generally starts with the examination of the state of the art, which may involve a vast number of publications. Automatically summarizing scientific articles would help researchers in their investigation by speeding up the research process. The automatic summarization of scientific articles differs from the summarization of generic texts due to their specific structure and inclusion of citation sentences. Most of the valuable information in scientific articles is presented in tables, figures, and algorithm pseudocode. These elements, however, do not usually appear in a generic text. Therefore, several approaches that consider the particularity of a scientific article structure were proposed to enhance the quality of the generated summary, resulting in ad hoc automatic summarizers. This paper provides a comprehensive study of the state of the art in this field and discusses some future research directions. It particularly presents a review of approaches developed during the last decade, the corpora used, and their evaluation methods. It also discusses their limitations and points out some open problems. The conclusions of this study highlight the prevalence of extractive techniques for the automatic summarization of single monolingual articles using a combination of statistical, natural language processing, and machine learning techniques. The absence of benchmark corpora and gold standard summaries for scientific articles remains the main issue for this task.

Background/Aims Endoscopists frequently encounter musculoskeletal injuries, with a notable prevalence in distal upper extremities. These injuries are often caused by the substantial forces, awkward postures, and repetitive movements required in endoscopic procedures. A significant, yet underexplored, risk factor for these injuries is repetitive changing of endoscopic instruments during endoscopy. In this study, an automatic endoscopic instrument insertion and transfer device was developed. We aimed to objectively analyze the hazardous angles of the endoscopist's wrist joint during instrument changes and evaluate the ergonomic benefits of the automatic instrument insertion machine. Methods We developed an automated instrument insertion and transfer device named “INSERTrument” and evaluated its performance across a range of endoscopic instruments. The machine's performance was validated by performing endoscopic submucosal dissection procedures on pigs. Key outcomes included the number of wrist snaps, total instrument change time, and the percentage of time spent in high-risk wrist postures, which were measured using inertial measurement units (IMUs) attached to the endoscopists. Results INSERTrument significantly reduced the number of wrist snaps by over 80% across all tested instruments. In vivo ESD experiments further confirmed these findings, with the INSERTrument group showing significantly fewer wrist snaps and reduced instrument insertion times. Additionally, the time spent in high-risk wrist postures was markedly lower in the INSERTrument group compared to the conventional group. Conclusion The use of INSERTrument significantly reduces the ergonomic strain associated with endoscopic instrument changes, particularly by minimizing repetitive wrist movements and high-risk postures. These findings suggest that INSERTrument could improve the ergonomics of endoscopic procedures, potentially reducing the incidence of musculoskeletal injuries among endoscopists. Further studies are warranted to explore the long-term benefits and clinical implications of this device in routine endoscopic practice. [ABSTRACT FROM AUTHOR]

Although the independent within-person association between physical activity and sedentary behaviour with valence in daily life has been extensively studied, few studies have used compositional data analysis to examine how different movement behaviour compositions are related to valence in daily life. This study aimed to examine the within-person association between wake-time movement behaviour compositions with affective valence and the extent to which replacing time spent sedentary with physical activity was associated with valence within individuals in daily life. A 7-day ecological momentary study design was used whereby 94 Canadian university students (M age = 19.45, SD = 2.21, 78.7 % female) reported on affective valence using an adapted version of the Feeling Scale at 7 randomly timed prompts each day. In addition, activPAL accelerometers were worn continuously by participants on their right thigh for the duration of the study to determine time spent engaging in sedentary behaviours and physical activity. Compositional data analysis with isotemporal substitution models were used to examine the within-person association between movement behaviour compositions and affective valence. The within-person association between movement behaviours and affective valence was weak (r2 = 0.013). Nevertheless, engaging in less sedentary time than usual and instead engaging in physical activity was significantly related to more positive affective valence. Considering light intensity physical activity (LPA) and moderate-to-vigorous intensity physical activity (MVPA) separately, replacing time spent sedentary with time engaged in MVPA and LPA both had a significant positive association on affective valence, although the association with MVPA was stronger than the association with LPA. The results provide unique insights into how replacing sedentary time with physical activity in daily life, especially MVPA, may be associated with more feelings of pleasure. These results may be useful to help inform the development of just-in-time adaptive interventions. • Replacing time sedentary with physical activity related to positive valence. • Replacing sedentary time with MVPA has stronger association with valence than LPA. • Breaking up sedentary time with physical activity may be an effective strategy to promote positive affect. [ABSTRACT FROM AUTHOR]

Termites can cause severe economic damage in households, reservoirs, agriculture, and forests. Monitoring termite activity plays an important role in integrated termite management. A device for the automatic monitoring of termites with high accuracy and reliability is highly demanded. Here, we introduce Dekan electromagnetic induction with non-looping (DEMINL) method and a novel wireless system for the monitoring of termite activities. The system comprises of a termite bait station and a handheld reader. While the wood pieces in the bait station were consumed by termites, the device could generate alarm signals, which are sent to a signal reader (receiver) through radio frequency identification (RFID) and displayed on the handheld reader’s screen. Field tests at three locations over a two-year period showed an accurate detection rate of 97.5–98.5%. The DEMINL-based device demonstrated high accuracy and durability in detecting termites, making it suitable for termite monitoring in households, crop fields, forests, and reservoirs.

Proper waste separation is essential for recycling. However, it can be challenging to identify waste materials accurately, especially in real-world settings. In this study, a systematic literature review (SLR) was carried out to identify the physical enablers (sensors and computing devices), datasets, and machine learning (ML) algorithms used for waste identification in indirect separation systems. This review analyzed 55 studies, following the Kitchenham guidelines. The SLR identified three levels of autonomy in waste segregation systems: full, moderate, and low. Edge computing devices are the most widely used for data processing (9 of 17 studies). Five types of sensors are used for waste identification: inductive, capacitive, image-based, sound-based, and weight-based sensors. Visible-image-based sensors are the most common in the literature. Single classification is the most popular dataset type (65%), followed by bounding box detection (22.5%). Convolutional neural networks (CNNs) are the most commonly used ML technique for waste identification (24 out of 26 articles). One of the main conclusions is that waste identification faces challenges with real-world complexity, limited data in datasets, and a lack of detailed waste categorization. Future work in waste identification should focus on deployment and testing in non-controlled environments, expanding system functionalities, and exploring sensor fusion.

Crop recognition with high accuracy at a large scale is hampered by the spatial heterogeneity of crop growth characteristics under the complex influence of environmental conditions. With the aim to automatically realize large-scale crop classification with high accuracy, this study proposes an automatic crop classification strategy considering spatial heterogeneity (ACCSH) by combining the geographic detector technique, random forest average accuracy model, and random forest classification model. In ACCSH, spectral and textural indexes that can quantify crop growth characteristics and environmental variables with potential driving effects are first calculated. Next, an adaptive spatial heterogeneity mining method based on the geographic detector technique is proposed to mine spatial homogeneous zones adaptively with significant differentiation of crop growth characteristics. Subsequently, in view of the differences in crop growth characteristics and key classification indexes between spatial homogeneous zones, correlation analysis, and random forest average accuracy are combined to optimize classification indexes independently within each zone. Finally, random forest is used to classify the target crop in each spatial homogeneous zone separately. The proposed ACCSH is applied to automatically recognize crop types, specifically wheat and corn, in northern France. Results show that kappa coefficients of wheat and corn using ACCSH are 15% and 26% higher than those of classifications at the global scale, respectively. In addition, the index optimization strategy in ACCSH shows apparent superiority. Kappa coefficients of wheat and corn are 5–18% and 9–42% higher than those of classifications based on non-optimized indexes, respectively. In general, ACCSH can automatically realize crop classification with a high precision that suggests its reliability.