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This article aims to provide a deeper understanding of the automatic subtitling tools that are currently being used and of their context of application. Based on the assumption that AVT, in general, and subtitling, in particular, are undergoing fundamental changes, it is our aim to analyse the range of tools that allow AVT translators to enhance their productivity and their efficiency. For this purpose, we have analysed 40 different automatic subtitling tools, currently available and accessible on Internet. Through this analysis, it has been possible to observe the main features of these tools and observe their functioning. Therefore, different criteria have been established in order to systemize this extensive inventory based on which 23 categories of software dedicated to automatic subtitling have been identified. These categories have been illustrated with examples. In this study, the aim is to provide a more accurate and more systematic understanding of automated subtitling programs. The paper is addressed to AVT professionals as well as to teachers and students having an interest in the state-of-the-art of automated subtitling.

The aim of the study was to propose the Automatic Speech Recognition (ASR) technology as a means of improving access to televised and radio broadcasted government communications in English language for people who are deaf and hard of hearing (DHH) in Botswana. To gain better insight into possibilities of using ASR for information accessibility for people who are DHH, this paper evaluated the accuracy rate of the ASR technology in terms of Word Error Rate (WER) by comparing transcriptions generated by ASR software and transcriptions generated and edited by the researcher. The results of our study showed that all the audio-visual documents transcribed scored a WER of less than 15%, indicating that transcriptions were of a very high quality. Some documents even scored an impressive WER of less than 5%, meaning that the transcriptions generated could be used and understood without any human intervention. The study concludes that freely available ASR technology is adapted as a means of improving and diversifying accessibility modes to televised and radio broadcasted government communications in English language for people who are DHH in Botswana.

The clinical concepts in the information gathered from the healthcare services are categorized and standardized using medical coding. The International Classification of Diseases (ICD) includes codes for various diseases that have an impact on financing, reporting, and research. In order to provide patient care and billing, medical coding allocates a subset of ICD codes to each patient visit. Medical personnel must spend a lot of time and effort on manual medical coding, which can lead to missed revenue and claim denials. Different studies on machine learning achieved promising performance for automated medical coding. Many researchers carried out their research on ICD. But, heterogeneous mode of operations by doctors and diagnosis methods makes the medical coding as more complex one. Furthermore, the current ICD approaches did not reduce computational complexity or increase accuracy. To address these problems, a range of deep learning and machine learning approaches are tested for ICD.

The undertaken work revolves around an experimental investigation into harnessing solar energy and its conversion into thermal energy through the utilization of a parabolic concentrator. Ensuring optimal orientation concerning solar radiation is facilitated by an automatic tracking system. The prototype employed for testing primarily consists of a solar reflector, shaped as a parabola to efficiently reflect solar radiation, and an absorber comprising a coiled copper tube, designated as the focal point for concentrating the sun's rays. The attained results are significant, with temperatures surpassing 380°C recorded on the absorber's surface.

PurposeTo explore the implementation of symptom screening and supportive/palliative care referral pathway in patients newly referred to a medical oncology clinic.MethodsEighty-eight subjects were recruited in each study arm. Intervention subjects were assessed by a member of the supportive/palliative care team if they had a severity score of >3/10. Controls received normal care, including discretionary referral. Symptom severity and quality of life were assessed over 5 months. Data on survival, care setting of death (home, hospice or hospital) and long-term resource use was collected for a further 2 years.ResultsScreening led to 141 specialist supportive/palliative care visits in the intervention arm versus 9 in controls. There were no differences in symptom severity or the long-term outcomes measured. The service was however not overwhelmed. Considerations on timing and mode of administration of screening tools were revealed.ConclusionsTriaging of positive screens is an important role for oncology nurses.

The current paper aims to analyse the main features and the limitations of the online automatic subtitling platforms. Based on different applications such as: video-to-text transcription programs, machine translation programs, and text segmenters, automatic subtitling involves a complex workflow and is meant to enhance the productivity of the professional subtitler. There are very few studies about the online subtitling platforms, therefore, the analysis we carried on will provide comprehensive empirical data and will contribute to a better knowledge of these innovative systems.

While employing the Holmium YAG laser, photonic technologies can help detect urinary stones and enhance safety for the patient. Our research group recently found that continuous monitoring of the fluorescence spectra of urinary calculi suffices to distinguish between stone, tissue, and endoscope components precisely and in real time. We hereby introduce our new automatic target identification system and the results of experimental studies we conducted. In this study, we review the research on in vitro and in vivo experiments we conducted developing and characterizing a novel target system, and summarize the key features of this new technology. This new system using intraoperative autofluorescence monitoring, enables the detection of the laser’s target by analyzing the fluorescent spectra reflected from the target. The energy pulses are only emitted when a urinary stone is within reach of the laser fiber tip. Our experiments revealed that this autofluorescence-based automatic target recognition lithotripsy system delivers valuable diagnostic information to the surgeon in real time. Our system recognizes potential target structures via implemented fluorescence detection. After setting a fluorescence intensity threshold level, a feedback mode was employed that autonomously controls the Ho:YAG laser. During this procedure, the pulse emissions were controlled only by our system, not by the surgeon. The safety and effectiveness of this system has been successfully proven in animal studies. This new target system with a feedback mechanism provides certainty that even in the event of unintentional laser activation, the laser emission is blocked, thus preventing tissue damage and unnecessary heat generation. Ours is a promising approach with the potential to be used in various future urological and non-urological applications primarily to enhance patients’ safety.

Instrumental catalogues of earthquakes in the subduction zone of the Lesser Antilles are produced by local observatories and the International Seismological Centre. But none of these catalogues merge all arrival times of the first regional phases available; in addition, they have a magnitude of completeness relatively high for the entire Lesser Antilles area. As part of the Antilles Seismological Data Centre project, we produced a unified catalogue of known earthquakes from 1972 to 2012, with optimal constraints on the hypocentre locations. We re-evaluated the hypocentres with the method used in local observatories and a probabilistic method improving the distributions of arrival time residuals. We developed a simple method to select the preferred hypocentre independently of the localization algorithm, and we offer a complete catalogue including 46,703 earthquakes. Compared to other existing catalogues, we provide additional arrival times for 24,528 earthquakes. Our results highlight the variabilities of the magnitude of completeness and of the seismicity suggesting how the future analysis could infer the mechanisms of heterogeneous seismic coupling and intermediate-depth triggering.

The so-called ‘mismatch’ is a factor which experts in the forensic voice comparison field encounter regularly. Therefore, we decided to explore to what extent the automatic-speaker-recognition system’s and the earwitness’ ability to identify speakers is influenced when recordings are acquired in different languages and at different times. 100 voices in a database of 300 recordings (100 speakers recorded in three mutually mismatched sessions) were compared with an automatic-speaker-recognition software VOCALISE based on i-vectors and x-vectors, and by 39 respondents in simulated voice parades. Both the automatic and perceptual approach seem to have yielded similar results in that the less complex the mismatch type, the more successful the identification. The results point to the superiority of the x-vector approach, and also to varying identification abilities of listeners.

The paper presents an automatic control system for automatic maintenance of the desired microclimate in the storage, controlled by a computer. The state of the microclimate in the vegetable store is monitored with the product and channel temperature sensors and internal and external humidity sensors. This system will automatically control the ventilation parameters at different times of the year, and regulate humidity conditions.

Coelogyne pandurata Lindl. is an endemic orchid species of Indonesia from East Kalimantan and Papua. It is locally known as the “black orchid” due to the distinctive black coloration on its labellum with intricate green and hairy lines. Unfortunately, the population of this orchid has significantly declined in its natural habitat, rendering it a rare endangered species. Temporary immersion bioreactor systems have been proven to be efficient for endemic plant micropropagation with performance enhancements and innovations. This study aims to develop an optimized bioreactor design and innovate the automatic control performance of temporary immersion bioreactor systems based on previous research. The control system developed consists of three modules, namely Graphical User Interface (GUI) module, Sequence Control (SC) module, and Hardware Interface (HI) module. The GUI module receives information regarding time and duration of immersion and gas exchange, then the information is synchronized by the SC module, which plays the role of starting and stopping the processes, while HI module executes the order of the automatic control system in the immersion and gas exchange process. The developed bioreactor design and control system offer convenience, require less labor, and ensure precise control over the optimum conditions for black orchid micropropagation.

The article examines the use of written corrective feedback by online grammar checkers as a tool for L2 competence development. While feedback on students’ writing is considered an instrumental part in their learning process, providing comprehensive feedback may not always be possible. Grammar checkers can therefore become an interesting tool to scaffold the students’ learning process as they promote self-directed learning. For the purposes of this study, a set of authentic EFL compositions in English (n=91; C1 CEFR) was subjected to a popular Automated Writing Evaluation (AWE) system, Grammarly, in order to analyse the feedback provided, namely the types and frequency of errors and the level of accuracy in error detection. The findings determine that, while grammar checkers may be useful as a complementary tool, they are developed with native speakers in mind, so the feedback does not always meet the EFL learners’ needs.

Automatic inspection of castings with X-rays (radiographic and computed tomography) is widespread for parts that are relevant for safety or have high quality requirements. Examples in the automotive sector are aluminum wheels, chassis parts and new parts within the electric power train. Those parts are automatically inspected, which means that both the image acquisition and the evaluation of the images is done fully automatically. Today, in most industrial implementations, the generated data with a size up to several gigabytes per part is summarized to a simple good or bad decision, according to specification. All other data is dismissed, although this information can be valuable to optimize production processes and thus minimize rejects. This contribution gives an overview about the results of the project Cast Control, which is a collaboration of Fraunhofer Development Center for X-ray Technology EZRT, Fraunhofer Center for Applied Research on Supply Chain Services SCS and industry partner RONAL GROUP. RONAL GROUP is a major aluminum wheel manufacturer, mainly for the OEM market. Within the project we combined serial production data from the low pressure die casting process from a foundry of the RONAL GROUP with the data generated in the automatic X-ray inspection. After collecting a large base of sample data, we were able train a neural network for the prediction of error metrics obtained by X-ray inspection. We apply a combination of layer-wise relevance propagation and dimensionality reduction to find correlations between data of the casting machines (process and sensor) and the characteristics of anomalies detected during X-ray inspection. With this information, it is possible to adjust the casting process in an early stage – even before rejects are produced. This enables the foundry to reduce their rejects rate, which saves costs and energy and results in a better competitivenessin a better competitiveness.

The large eggplant farming land often makes it difficult for farmers to water. In addition, the water content in the soil does not meet the minimum requirement, which is 80%-90%. Hence, adequate watering with a high level of accuracy is necessary. This study aims to design and invent an automatic watering system for eggplant farming land with an area of 150 m2. The method was to design automatic sprinklers using programming and fritzing software using Arduino IDE. Soil moisture data was obtained from the soil moisture sensor and displayed on the I2c LCD so that farmers could monitor the soil moisture level. The pump used PB-WH1100BCP with a specification of 1100 W with a discharge of 145 l/minute and a head of 30 m. This automatic watering system was supplied with a 500 Wp photovoltaic solar cell with a current value of 2 A, and a voltage of 20V was obtained when the sun was shining and could work effectively for 6 hours per day. To store electric current, this system was equipped with a VRLA 12V- 200Ah battery connected in series, and the current charging was controlled by the Solar Charge Controller (SCC). It can supply electric current to the pump for 24 hours. This automatic watering system has been tested and functions well on eggplant farmland.

The process of hatching chicken eggs by farmers still uses manual methods, in the market itself there are hatching machine tools but not fully automated. So that the chicken breeding process is less effective and efficient to meet high market needs. In this study, an automatic chicken egg hatching incubator device based on the DHT 11 sensor was designed. The purpose of this design is to help farmers to hatch chicken eggs automatically. The way this tool works is to hatch chicken eggs automatically with a DHT 11 sensor that can read temperature and humidity so that temperature and humidity can be stable according to the needs obtained from the heat of incandescent lamps, fans, and water in the incubator. Then for the process of turning chicken eggs, farmers do not need to do it manually because they already use automatic racks regulated by RTC (Real Time Clock). For the egg turning schedule so that it will move the dynamo motor and the egg rack will shift to turn the eggs. As a result of this study, the well-designed device works and can produce 91% egg hatching with stable temperature and humidity and scheduled egg turning.

The paper examines aspects of developing and formalizing the task of applying computer vision methods and algorithms using OpenCV (implemented in Python version 3.13 notation) for automatic detection and classification of objects in decision support systems. A software implementation of a modular example is provided, enabling automatic detection and classification for the detection of plant diseases based on their external characteristics in decision support systems in agriculture. This approach will facilitate prompt response to plant diseases and the implementation of necessary measures for their treatment.

The object of this study is the analysis of the latest domestic and foreign technologies for the creation of automatic fire alarm systems. The subject of the study is the fire protection system of the laboratory building of the University. The purpose of the study is to design a demonstration stand for an automatic fire alarm and fire extinguishing system in the laboratory building of the University based on the analysis of the latest domestic and foreign research. The theoretical and methodological basis for the analysis of Russian and foreign technologies in fire alarm systems were legislative acts, regulatory documents, and other sources on the topic of the work. A study has been carried out on the development of a demonstration stand of an automatic fire alarm system in modern conditions using statistical data and scientific publications of recent years. In the work, the structural and schematic diagrams of the stand for demonstrating the operation of automatic fire alarm systems are designed, the rationale for choosing the base of the main elements of the alarm system is given, the characteristics of the elements and their reliability indicators are calculated.

Renewable energy is crucial for facilitating the shift towards a more sustainable society. It plays a significant role in establishing a clean energy system, mitigating greenhouse gas emissions, and enhancing the well-being of both current and future generations. The study included field observation and data analysis approaches. The study utilized IoT-based automatic fish feed spreaders, photovoltaic units, batteries, SCC (solar charge controller), Android phones, thermometers, multimeter tester, LCDs, ultrasonic sensors, and a microcontroller circuit. This study aims to acquire power, electrical energy, and efficiency measurements from solar systems employed in an Internet of Things (IoT) enabled automatic fish feeder apparatus. The results show that the power consumption needed to operate the IoT-based automatic fish feeder gadget is 48.75 watts. The peak of solar irradiance was recorded at 12.00 WIT, averaging 900.27 W/m2. The lowest intensity was at 09.00 WIT, averaging 422.78 W/m2. Photovoltaics can produce electrical power at 22.75 watts per hour, leading to a daily electrical energy output of 159.25 Wh. The highest photovoltaics efficiency was 16.37% with 465.3 W/m2 of solar irradiance, 30.7°C of temperature and 2.144 m/s of wind speed. The temperature and wind speed has significantly affected on photovoltaics efficiency.

The article is devoted to analysis of automatic control systems used to optimize current collection on high-speed railway electric rolling stock. A classification of automatic control systems has been developed. The use of automatic control systems that change the pantograph static force can significantly improve the quality of the current collection. However, their use changes the compression of the elastic elements of the panhead suspension, which can ultimately lead to its complete compression and breakaways. To solve this problem, authors propose using intra-spring pneumatic elements in panhead suspension. The automatic control system must simultaneously control the pantograph static force and the panhead suspension static characteristics. The authors proposed and described an automatic control system of the pantograph contact force and the panhead suspension static characteristics. To test the prospects of the proposed automatic control system, a three-mass mathematical model of a pantograph equipped with intra-spring pneumatic elements in panhead suspension has been developed and implemented in the MATLAB Simulink software package. The obtained results indicate the possibility of using intra-spring pneumatic elements in panhead suspension when using an automatic control system. It allows you to avoid vertical impacts causing a sharp increase in contact force and breakaways of the panhead.

While using automatic vacuum cleaners, users found dissatisfaction namely dust problem, obstacle problem, and narrow space problem. Therefore, the problems in this automatic vacuum cleaner must be fixed so that it can maximize user productivity. The methods that researchers will use to carry out this research include the participatory ergonomic which is a method for translating consumer needs into an optimal and solutive products by gain aspirations from users and stakeholders. The next method is Maynard Operation Sequence Technique which is a predetermined motion time method that aims to determine the standard time for doing work. There are also participatory usability and ergonomics methods that help this research. For the dust problem, researchers have looked for a solution to the problem, namely by adding SLAM and Artificial Intelligence logarithms to the automatic. For the narrow space problem, researchers have designed different nozzles for the automatic to use. For this exact problem, researchers have added a crevice nozzle to the automatic so that the automatic can insert suction in narrow areas such as the back of cupboards. Researchers created this feature so that users no longer need to bother cleaning dust from sofas, tables and other items in the user’s room.

Broiler chicken farm waste is in the form of spilled water and mixed feed and chicken manure which causes an unpleasant odor. These odors are called ammonia gas (NH3) and hydrogen sulfide gas (H2S), dimentyl sulfide, carbon disulfide and mercaptans. In broiler chicken coops the temperature and humidity needed to warm the chickens are around 25 – 29 °C and the required humidity is around 50-70%. To solve this problem, an odor control system is needed while maintaining the temperature and humidity of the coops. This research produces an automatic system to control chicken coop odors and maintain temperature and humidity. The experimental method in this study is to simulate the system on a miniature cage with a scale of 1: 8 and send data to thingspeak in real time. Changes in the temperature of the chicken coop in the morning and evening are directly proportional to the ammonia levels, but not during the day. However, ammonia levels increase during the day. Bamboo charcoal effectively neutralizes chicken coop odors by reducing the level of ammonia gas to 1 ppm in an average of 0.83 seconds. The results of this study have implications in the fields of automatic control and the Internet of Things (IoT).

One of the most challenging issues in the world of ship control is automatic ship berthing. In order for the berthing to be completed automatically and safely, the control systems must account for the ship’s dynamics at low speeds. Artificial neural networks (ANNs) are frequently used to address this need because of their capacity to mimic and carry out all the functions of the human brain throughout the ship berthing procedure. However, there are still certain drawbacks when employing this theory to create the automatic system for ship berthing, and this makes it more challenging to develop the control system for practical ship applications. This study reviews the pros and cons of employing ANNs along with some other deep learning algorithms in automatic ship berthing systems. Some directions for further research into automatic ship-berthing systems are also suggested.

André Masson has long been regarded as a representative figure of surrealist and automatism painting, but a closer look at his works makes it easy to find a dialogic nature with ancient Chinese calligraphy and painting. Influenced by Breton's concept of automatic writing, his works also draw on Chinese ink paintings of the Song Dynasty, abandoning perspective and playing the important role of white space. This paper tries to sort out the lineage of the emergence of Oriental consciousness in Masson's automatic paintings and explains the specific expression of Chinese painting in his art.

For the majority of people, having a pet is always simple, but caring for it will always be a pain in the neck since pet owners must devote a whole time to meeting the pet’s demands, which include food, shelter, medical care, love and attention. Nowadays, feeding is one of the most frequent issues that pet owners’ encounter due to heavy work schedule. As a result, the automatic solar-powered pet food dispenser system is useful for assisting pet owners in caring for their pet’s needs when they are away from home or on the road. This automatic pet feeder proposes a simple automatic pet food dispenser controlled using the Arduino as the main controller. DS3231 Real Time Clock (RTC) module that can assist in automatically giving the pets the right mealtime was employed. This helps the food dispensing process without the owner’s presence, which is unlike the manual pet feeding product. The project ensures that the pets are fed on schedule and that the amount of food released is controlled appropriately. Also, this project will employ solar energy as a green energy source for sustainable development. This project can feed the pets 20 grams or less pet food in a single meal, according to the servo motor rotation setting activated by the Arduino.

This paper presents a reflection on two technologies - automatic speech recognition (ASR) and Text-to-Speech (TTS) - to improve learners’ pronunciation, aiming for successful spoken communication. It sheds some light on the practical usage of these technologies, demonstrating their effectiveness, qualities, and limitations to assist teachers in deciding the most efficient digital resources applied to their students’ needs. A review of literature on previous empirical studies was carried out, with quantitative and/or qualitative studies conducted by researchers in the field, investigating teachers’ and learners' perceptions and the use of ASR and TTS as a pedagogical tool for pronunciation practice. As a result, it was concluded that a) the presented resources seem to have the potential to enhance pronunciation practice, both in terms of perception and production; b) technology can result in considerable benefits to learners, mainly as a supplement to pronunciation teaching; and c) the use of these digital resources is a way of giving learners the opportunity to focus on their specific difficulties and receive personalized feedback while becoming more autonomous in their learning process.

The emergence of artificial intelligence (AI) and natural language processing (NLP) technologies has led to the proliferation of automated systems capable of generating text. While these advancements have enhanced various fields, such as language translation and content generation, they have also given rise to concerns regarding the potential misuse of generated texts, particularly in the context of environmental preservation. This scientific article investigates the intricate relationship between automatic detection of generated texts and the environment. We examine the impact of generated texts on environmental awareness, misinformation propagation, and the role of automated detection systems in mitigating the risks associated with generated content. Our findings highlight the crucial need for robust detection mechanisms to preserve the integrity of environmental discourse and ensure sustainable decision-making.

The proliferation of artificial intelligence (AI) and natural language processing (NLP) technologies has enabled the generation of realistic and coherent texts, but it also raises concerns regarding the potential misuse of these technologies for generating misleading or malicious content. Automatic detection of generated texts is crucial in addressing this issue. This article provides a comprehensive examination of the relationship between the detection of generated texts and energy consumption, delving into the techniques, challenges, and opportunities for developing energyefficient algorithms for text detection.

Personalised Environmental Control Systems (PECS) are devices that cater to the individual needs by providing micro-climate heating, cooling, and ventilation. However, to ensure comfort, energy savings, and productivity, a comfort model based automatic control is required. For its development, thermal preference, physiological information, and data on the surrounding indoor climate were gathered from 24 subjects when using a newly developed PECS with heating, cooling, and ventilation functions. Since PECS should ensure a high level of comfort while providing energy savings through background temperature relaxation, multiple steady-state ambient temperature settings ranging from 18 to 28 °C were tested. The data were clustered according to the subject’s self-assessed general thermal preference, namely neutral, warmer, and colder. Machine learning was used to generate a cluster-based personalised comfort model using environmental, physiological, and behavioural indicators. The prediction performance of the models was 11 to 18 percent points higher than that of current group comfort models, predicted mean vote (PMV), which is independent of occupant similarities. The advantage of the personalised approach was the increased performance of the thermal comfort prediction at no expense of occupant sensitive information. Although reliant on estimates of physiological indicators, the models’ performance may be increased using real-time data acquisition.

Automatically mapping building footprints has a wide range of applications in many fields. In recent years, the automatic building extraction methods based on deep learning show an absolute advantage over the traditional image segmentation methods due to its high speed and high precision. However, the building footprint extracted by deep learning is just an irregular building mask. There is still much work to be done to transform the building mask into a vector building footprint in the usual sense. One of the most important tasks is to determine the orientation of each side of the building. Most of the current methods are based on the building mask to determine the orientation of each side of the building. The biggest disadvantage of this method is that it completely relies on the building mask which is often unsatisfactory. In this case, the article proposes a method to determine the orientation of each side of the building based on the building mask and line segments, thereby effectively avoiding the danger of relying on the building mask. Experiments show that the proposed method can achieve high-speed and high-precision automatic extraction of building footprints from remote sensing images, saving costs.

The Non Destructive Evaluation (NDE) is a fundamental process when high safety standards for the tested component are required. The evaluation may imply a combination of methods to achieve the highest Probability of Detection (POD) resulting in several data available which must be properly collected, processed, and stored for future reviews and for traceability reasons. All these requirements imply a sophisticated digital infrastructure and the usage of the most advanced Information Technology (IT) solutions to achieve computation speed, safe data processing and accessibility of the information. The Industry4.0 revolution is the result of the combination of digital technology to industrial processes for rethinking the performances in terms of automation, integration, crosslinking, availability and sharing of information. These principles can be applied in the NDE process as several products on the market are tested and monitored during the whole lifetime. The tracking of data, the secure data validation, and the need to avoid misuses and manipulations are new additional requirements for the NDE 4.0. Block chain technology, mainly known for crypto applications, is a promising technology for storing digital identity and test records offering traceability, safety and availability of the data. The present paper wants to present the Swiss Safety Center AG approach for a specific application case (retesting of gas cylinders according to ISO18119) where the guidelines and trends of NDE 4.0 have been successfully integrated in a fully automatic system, first in class for this application. The paper wants also to provides some insights about future trends and potential developments which may not be limited to this specific case but can be applied for other similar applications, e.g. in railways, manufacturing industry, automotive, aerospace and more in general in the NDE applications.

Mizuna, or Japanese mustard greens, is a highly popular plant in Indonesia, with wide market availability and high economic price, among other green vegetables. Mizuna cultivation using an automatic nutrient film technique (NFT) hydroponic system will produce better quality and quantity and efficient use of fertilizers and electricity. This study aimed to find the best concentration of nutrients to support the growth of mizuna and determine the flow time of hydroponic media suitable for mizuna cultivation. The study was designed using RCBD with two treatment factors, namely nutrient formulation (four nutrient formulations and water as control) and medium flow time (three levels of switch-off time i.e 15, 45, and 60 min). Growth observation data were analyzed using variance (ANOVA), then the multiple comparison test was continued with the Duncan multiple range test (DMRT) at α level 5%. Different nutrient sources in this study did not significantly affect the growth variables of mizuna, but mizuna grown in a nutrient medium did significantly show better growth than control. This study found that all of the flow time treatments (15, 45, and 60 minutes off) also gave statistically similar results on the growth of mizuna in the NFT hydroponic system.

The current state of the use of methods of stopping wagons on railway transport in cases of their spontaneous departure is investigated. As a result, it was found that cases of spontaneous departure of rolling stock on railways in the region of the Republic of Uzbekistan account for 4.2% of the total share of traffic safety violations and account for 19.9% of the damage caused. This article highlights that in order to prevent the above situations, after installing brake shoes through automatic braking, a device on a spontaneously departing rolling stock, taking into account the number of wagons and their state of loading, based on the development of a mathematical model for determining the stopping distance, a system operating without a human factor was created and introduced into production.

This work mainly focused on the development of the controller for performing the automatic voltage regulation (AVR) action in the most optimal way. In this regard, the tilt-integral-derivative-filter (TIDF) controller is suggested in this work. Moreover, the controller TIDF requires soft computing methodologies for its optimal functioning, and hence the donkey and smuggler optimization algorithm (DSOA) is conceived. However, the efficacy of the TIDF is demonstrated with the other controllers that emerged from the background of intelligence and fractional calculus. Further, the dominance of DSOA is also established with other soft computing techniques. The optimization of TIDF using DSOA is performed under the integral square error (ISE) index, and a comparative analysis is made among the several time domain indices to establish its dominance. Finally, the simulation analysis revealed the overall efficacy of the DSOA-tuned TIDF controller under the ISE criteria and confirmed the optimal AVR performance.

Semi-automatic, A.I.-specified stereographic scene processing is achieved by extending Blender with ChatGPT. We can generate objects and set scenes in Blender semi-automatically by invoking appropriate text prompts. Stereography techniques include anaglyphy, chromastereoscopy, and side-by-side display using binocular viewing. One can create a similar scene by copying and pasting the prompt used. It cannot always work identically, but it behaves roughly the same. First, we established Skybox and added scene Objects (Camera, Cube, etc.) in 3D view, materials, and animation with prompt. Stereographic images are created semiautomatically and compared across some qualities. The goal of this research is to validate those various stereographic techniques as rendered automatically in Blender.

The article examines the influence of car dynamics on the safety of rolling stock. When moving, loads are transferred to the components and parts of the car, which change during the movement and depend on many factors, including the operating mode of the rolling stock. Quasi-static forces of tension and compression that work when transmitting locomotive traction to the train as a whole, determine the main dynamic parameters during operation. To determine loads during movement, a specialized software device is required that allows to retrieve data while moving. To calculate loads on components and parts, it is necessary to carry out discrete analysis, which can determine stress-strain states when assessing strength. Simulation using the Femap software package can identify the initial stages of node destruction, as well as simulate the interaction of several nodes with each other. To determine the dynamic impact of an automatic coupler on a car or two cars coupled to each other, it is necessary to first consider the loads acting on the individual components and parts of which it consists, as well as conduct a study of stress concentration on the surfaces of selected components, in this case on the inner surface of the absorber mechanism’s neck.

The aim of the study is to reduce energy losses in a drum steam boiler by introducing an automatic control system realized on the basis of fuzzy adaptation of proportional-integral-derivative (PID) controller coefficients using fuzzy term-multiplications. Control algorithms of the main technological parameters of the boiler unit (air quantity, fuel quantity, steam pressure, feed water flow rate), reducing energy costs due to the maintenance of the ratio of fuel and air pressure in accordance with the mode map of the boiler unit (optimal technological process of combustion and steam formation in a steam boiler) are developed. The structural scheme of the object control is offered, in which, besides regulation of values of technological parameters, full compensation of mutual influence of all regulation circuits is made.

Smart Garden is a system design created to make work in plantations easier. Currently, most Indonesians use sling type lawn mowers because they are cheap but can causing noise and air pollution. It can also cause CTDS in users due to bad posture while using the tool. The researchers innovated an IoT-based automatic cutting machine. Where in the machine application, the machine can be moved using a smartphone connected using bluetooth. Which consists of two options, namely machine and vacuum. The REBA method are used to analyzing ergonomics for all parts of the body and a usability method which will determine the extent to which the product can achieve effectiveness, efficiency and comfort. This data collection was carried out by distributing the SUS questionnaire. The REBA results showed that the average body posture at moderate risk had a score of 7.8 and 9 which required further analysis of the bent posture even though it was still classified as moderate. Meanwhile, the results of the usability approach use a questionnaire as a reference for processing data. From this data, users can accept the presence of the Control Smart System Application and are quite satisfied with the interface used.

The paper presents the results of application of the method of automatic generation of representative and unbiased set for in-season cotton crop mapping, based on crop simulation model, previously parameterized using ground truth and satellite data. The method provided confident mapping of cotton fields without using actual ground-truth information or apriori information about their in-season phenology. Overall mapping accuracy calculated using relevant ground truth data for cotton fields has reached 95.6 %. Consideration of time series of NDVI values as a model of phase characteristics allowed using relatively simple criteria to identify typical representatives of the selected crop on the basis of analysis of their seasonal phenology and made it possible to build a reference sample for modeling and further classification.

Universitas Terbuka is a pioneer in distance learning, it is required to always innovate, especially in the mode of learning. One of learning is through virtual classes, the tutor as a teacher provides direction and assistance to students as participants. Tutor and student attendance can be written directly by the tutor or through the attendance link, this tends to have a lack of human error, either the tutor forgets to write down the participants who were present or the attendance link filled in by participants who were not present, because the link can be accessed by anyone. Based on this, we try to design an automatic presence that is carried out in a virtual class. The research result design of image database, design of user interface, and design of learning engine. The results of database testing show that every table in the database is normal and there is a logical relationship between tables. The user interface test results are proven to be able to accommodate the database and engine display deep learning method. The results of the learning engine show the creation of a deep learning method architecture in accordance with the needs of face recognition in the virtual class.

Ensuring a safe transportation process is impossible without the use of complex systems for interval regulation of train traffic. In particular, such a system is a continuous automatic cab signaling (CACS) where signals are affected by various kinds of interference, leading to system failures. To reduce the number of system failures, the authors previously proposed a quadrature signal receiver of CACS (QSR of CACS). The article presents the methodology and results of an experimental study of the quality of the functioning of the QSR of CACS on records of real signals and interference. A simulation model of the test facility has been developed in the Simulink simulation environment. The study showed that the QSR of CACS provides reliable reception of CACS code signals with a low level of interference. However, it needs to apply additional measures in it to reduce the influence of impulse noise.

Based on the literature review, the problem is formulated, which consists in the absence of detailed recommendations for the design of grinding operations for CNC cylindrical grinding machines. This paper provides a solution to the problem described above using the developed method, which includes: the formation of initial data, the formation of grinding cycles, and the calculated determination of cycle parameters.

In coal-fired power plants, how to timely inventory coal storage, in order to optimize the coal stock, and improve coal blending operation, and then carry out economic dispatch scientifically and reasonably, has always been an important topic. The traditional way of artificial method has big error, high cost and serious waste of resources. Ultrasonic measurement technology is greatly affected by time difference and dust. Measurement technology based on computer vision requires interpolation to fit the correction model. The above methods all have large measurement errors and have been eliminated by the market. This paper mainly introduces coal measuring technologies based on laser and uav tilt photography which have been researched and applied more recently. Their respective installation methods, characteristics and applicable working occasions are analyzed. They have the common characteristic that the 3d coordinate point cloud data of coal yard surface needs to be solved before the 3d model of coal yard can be generated and the coal storage amount can be calculated. In the case that the 3d coordinate point cloud data of coal yard of power plant cannot be obtained directly, 3d reconstruction can be carried out by using 2d images of coal yard in coal report, or by using neural network model, and then the coal volume can be checked. This research idea is of great significance to realize effective and accurate supervision of coal storage in power plants..

Approaching to PDCA (Plan - Do - Check - Take Action) in management of infrastructure asset requires digital transformation, sufficient data and strong database supporting management, analysis as well as creation of data-driven decision making tools. For pavements, data including condition indicators such as roughness and rutting depth are collected automatically during the survey vehicle travelling. However, pavement crack ratio and crack features of pattern and segmentation have not been detected by the system but manual in the case in Vietnam. The paper presents result of research on algorithm of statistic machine learning model in AI applying deep learning algorithm to automatically detect crack feature on pavement photos for enhancement of the performance and productivity of current survey technology. In the research, a deep architecture using convolutional neural network (CNN) for crack segmentation on gray scale images has been developed. The results show the CNN model for crack segmentation is better than other methods using traditional digital processing such as the Gabor filters or threshold and machine learning such as Adaboost.