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Artificial intelligence (AI) is used in tasks that usually require human intelligence. The motivation behind this study is the growing interest in deploying AI in public spaces, particularly in autonomous vehicles such as flying drones, to address challenges in navigation and control. The primary challenge lies in developing a robust, cost-effective system capable of autonomous navigation in real-world environments, handling obstacles, and adapting to dynamic conditions. To tackle this, we propose a novel approach integrating machine learning (ML) algorithms, specifically, reinforcement learning (RL), with a comprehensive simulation and testing framework. Reinforcement learning machine algorithms designed to solve problems requiring optimization of the solution for the highest possible reward were used. It was assumed that the algorithms do not have to be created from scratch, but they need a well-defined training environment that will appropriately reward or punish the actions taken. This study aims to develop and implement a novel approach to autonomous drone navigation using machine learning (ML) algorithms. The primary innovation lies in the comprehensive integration of ML algorithms with a real-world drone control system, encompassing both simulations and real-world testing. A vital component of this approach is creating a multi-stage training environment that accurately replicates actual flight conditions and progressively increases the complexity of scenarios, ensuring a robust evaluation of algorithm performance. This research also introduces a new approach to optimizing system cost and accessibility. It involves using commercially available, cost-effective drones and open-source or free simulation tools, significantly reducing entry barriers for potential users. A critical aspect of this study is to assess whether affordable components can provide sufficient accuracy and stability without compromising system quality. The authors developed a system capable of autonomously determining optimal flight paths and controlling the drone, allowing it to avoid obstacles and respond to dynamic conditions in real time. The performance of the trained algorithms was confirmed through simulations and real-world flights, which allowed for assessing their usefulness in practical drone navigation scenarios. [ABSTRACT FROM AUTHOR]

The process of developing software is intricate and time-consuming. Resource estimation is one of the most important responsibilities in software development. Since it is currently the only acceptable metric, the functional size of the program is used to generate estimating models in a widely accepted manner. On the other hand, functional size measurement takes time. The use of artificial intelligence (AI) to automate certain software development jobs has gained popularity in recent years. Software functional sizing and estimation is one area where artificial intelligence may be used. In this study, we investigate how to apply the concepts and guidelines of the COSMIC method to measurements using ChatGPT 4o, a large language model (LLM). To determine whether ChatGPT can perform COSMIC measurements, we discovered that ChatGPT could not reliably produce accurate findings. The primary shortcomings found in ChatGPT include its incapacity to accurately extract data movements, data groups, and functional users from the text. Because of this, ChatGPT's measurements fall short of two essential requirements for measurement: accuracy and reproducibility. [ABSTRACT FROM AUTHOR]

We developed and tested an automated measurement platform which can fit multiple samples for their investigation in transmission mode using terahertz time-domain spectroscopy. The temperature inside the platform can be varied to simulate different storage conditions of the samples, in our case, pharmaceuticals. As a proof-of-concept, the setup was successfully tested to monitor the crystallization process of amorphous nifedipine, as a model drug, at 24 °C, 30 °C and 35 °C for over 144 h. To the best of our knowledge, this is the first study to follow the crystallization of nifedipine with quasi-continuous measurements over a time frame of several days. The influence of the storage temperature on the crystallization rate was monitored including the appearance of polymorphic intermediate states of nifedipine throughout the process. The platform developed in combination with terahertz time-domain spectroscopy is a helpful tool for deepening the understanding of the crystallization behavior of amorphous and polymorphic materials and can be, for example, of great importance for the development of novel amorphous pharmaceutical formulations. [ABSTRACT FROM AUTHOR]

Study Design. Single-center, double-blinded, prospective crossover randomized controlled trial. Objective. To clinically validate the efficacy of nighttime braces designed automatically by a generative design algorithm to treat idiopathic scoliosis (IS). The tested hypothesis was the clinical equivalence of immediate in-brace correction for the new automatically generated brace design versus a standard Providence-type brace. Summary of Background Data. Documented efficacy of brace treatment varies between centers, and depends on the empirical expertise of the treating orthotist. Our group previously developed a fully automated generative brace design algorithm that leverages a patient-specific finite-element model (FEM) to optimize brace geometry and correction before its fabrication. Methods. Fifty-eight skeletally immature patients diagnosed with IS, aged between 10 and 16 years were recruited. All patients received both a nighttime brace automatically generated by the algorithm (test) and a Providence-type brace designed by an expert orthotist (control). Radiographs were taken for each patient with both braces in a randomized crossover approach to evaluate immediate in-brace correction. Results. The targeted 55 patients (48 females, 7 males) completed the study. The immediate Cobb angle correction was 57%±19 (test) versus 58%±21 (control) for the main thoracic (MT) curve, whereas it was 89%±25 (test) versus 87%±28 (control) for the thoracolumbar/lumbar (TLL) spine. The immediate correction with the test brace was noninferior to that of the Control brace (P<0.001). The order in which the braces were tested did not have a residual effect on the immediate correction. Conclusion. The fully automated generative brace design algorithm proves to be clinically relevant, allowing for immediate inbrace correction equivalent to that of braces designed by expert orthotists. Patient 2 years follow-up will continue. This method's integration could help design and rationalize the design of braces for the treatment of IS. [ABSTRACT FROM AUTHOR]

Generating aerial shoreline segmentation masks can be a daunting task, often requiring manual labeling or correction. This is further problematic because neural segmentation models require decent and abundant data for training, requiring even more manpower to automate the process. In this paper, we propose utilizing Unmanned Surface Vehicles (USVs) in an automated shoreline segmentation system on satellite imagery. The remotely controlled vessel first collects above- and underwater shoreline information using light detection and ranging (LiDAR) and multibeam echosounder (MBES) measuring instruments, resulting in a geo-referenced 3D point cloud. After cleaning and processing these data, the system integrates the projected map with an aerial image of the region. Based on the height values of the mapped points, the image is segmented. Finally, post-processing methods and the k-NN algorithm are introduced, resulting in a complete binary shoreline segmentation mask. The obtained data were used for training U-Net-type segmentation models with pre-trained backbones. The InceptionV3-based model achieved an accuracy of 96% and a dice coefficient score of 93%, demonstrating the effectiveness of the proposed system as a source of data acquisition for training deep neural networks. [ABSTRACT FROM AUTHOR]

Characterizing the contemporary world as massively entropic and pointing to the proletarianization of human beings, Bernard Stiegler claims that we need to “bifurcate”. This paper clarifies what he means by bifurcation and examines the conditions necessary for its occurrence. After explaining how Stiegler’s general organology provides a framework for his assessment of our present, the paper focuses on how humans can become capable of producing bifurcations. Emphasizing that bifurcation must occur in relation to technology, the paper identifies it as an inventive dis-closure of a current system that transforms both technology and us through the creation of knowledge. It then shows that although the problem of our time is primarily one of automatization, we must deal with it through internalization rather than disautomatization. Our very humanity is being disrupted because we are not realizing the possibilities offered by the development of technology, and this can only be achieved if we internalize technology. The process of internalizing technology is itself bifurcative and enables us to overcome our proletarianization. [ABSTRACT FROM AUTHOR]

Camera‐traps are valuable tools for estimating wildlife population density, and recently developed models enable density estimation without the need for individual recognition. Still, processing and analysis of camera‐trap data are extremely time‐consuming. While algorithms for automated species classification are becoming more common, they have only served as supporting tools, limiting their true potential in being implemented in ecological analyses without human supervision. Here, we assessed the capability of two camera‐trap based models to provide robust density estimates when image classification is carried out by machine learning algorithms.We simulated density estimation with Camera‐Trap Distance Sampling (CT‐DS) and Random Encounter Model (REM) under different scenarios of automated image classification. We then applied the two models to obtain density estimates of three focal species (roe deer Capreolus capreolus, red fox Vulpes vulpes and Eurasian badger Meles meles) in a reserve in central Italy. Species detection and classification was carried out both by the user and machine learning algorithms (respectively, MegaDetector and Wildlife Insights), and all outputs were used to estimate density and ultimately compared.Simulation results suggested that the CT‐DS model could provide robust density estimates even at poor algorithm performances (down to 50% of correctly classified images), while the REM model is more unpredictable and depends on multiple factors. Density estimates obtained from the MegaDetector output were highly consistent for both models with the manually labelled images. While Wildlife Insights' performance differed greatly between species (recall: badger = 0.15; roe deer = 0.56; fox = 0.75), CT‐DS estimates did not vary significantly; on the contrary, REM systematically overestimated density, with little overlap in standard errors.We conclude that CT‐DS and REM models can be robust to the loss of images when machine learning algorithms are used to identify animals, with the CT‐DS being an ideal candidate for applications in a fully unsupervised framework. We propose guidelines to evaluate when and how to integrate machine learning in the analysis of camera‐trap data for density estimation, further strengthening the applicability of camera‐traps as a cost‐effective method for density estimation in (spatially and temporally) extensive multi‐species monitoring programmes. [ABSTRACT FROM AUTHOR]

The article examines the impact of 16 key parameters of the Georgian economy on economic inequality, using the Perelman model and Ricci flow mathematical methods. The aim of the study is to conduct a deep analysis of the impact of socio-economic challenges and technological progress on the dynamics of the Gini coefficient. The article examines the following parameters: income distribution, productivity (GDP per hour), unemployment rate, investment rate, inflation rate, migration (net negative), education level, social mobility, trade infrastructure, capital flows, innovative activities, access to healthcare, fiscal policy (budget deficit), international trade (turnover relative to GDP), social protection programs, and technological access. The results of the study confirm that technological innovations and social protection programs have a positive impact on reducing inequality. Productivity growth, improving the quality of education and strengthening R&D investments increase the possibility of inclusive development. Sensitivity analysis shows that social mobility and infrastructure are important factors that affect economic stability. The accuracy of the model is confirmed by high R² values (80-90%) and the statistical reliability of the Z-statistic (

Abstract Camera‐traps are valuable tools for estimating wildlife population density, and recently developed models enable density estimation without the need for individual recognition. Still, processing and analysis of camera‐trap data are extremely time‐consuming. While algorithms for automated species classification are becoming more common, they have only served as supporting tools, limiting their true potential in being implemented in ecological analyses without human supervision. Here, we assessed the capability of two camera‐trap based models to provide robust density estimates when image classification is carried out by machine learning algorithms. We simulated density estimation with Camera‐Trap Distance Sampling (CT‐DS) and Random Encounter Model (REM) under different scenarios of automated image classification. We then applied the two models to obtain density estimates of three focal species (roe deer Capreolus capreolus, red fox Vulpes vulpes and Eurasian badger Meles meles) in a reserve in central Italy. Species detection and classification was carried out both by the user and machine learning algorithms (respectively, MegaDetector and Wildlife Insights), and all outputs were used to estimate density and ultimately compared. Simulation results suggested that the CT‐DS model could provide robust density estimates even at poor algorithm performances (down to 50% of correctly classified images), while the REM model is more unpredictable and depends on multiple factors. Density estimates obtained from the MegaDetector output were highly consistent for both models with the manually labelled images. While Wildlife Insights' performance differed greatly between species (recall: badger = 0.15; roe deer = 0.56; fox = 0.75), CT‐DS estimates did not vary significantly; on the contrary, REM systematically overestimated density, with little overlap in standard errors. We conclude that CT‐DS and REM models can be robust to the loss of images when machine learning algorithms are used to identify animals, with the CT‐DS being an ideal candidate for applications in a fully unsupervised framework. We propose guidelines to evaluate when and how to integrate machine learning in the analysis of camera‐trap data for density estimation, further strengthening the applicability of camera‐traps as a cost‐effective method for density estimation in (spatially and temporally) extensive multi‐species monitoring programmes.

Abstract Background Title-abstract screening in the preparation of a systematic review is a time-consuming task. Modern techniques of natural language processing and machine learning might allow partly automatization of title-abstract screening. In particular, clear guidance on how to proceed with these techniques in practice is of high relevance. Methods This paper presents an entire pipeline how to use natural language processing techniques to make the titles and abstracts usable for machine learning and how to apply machine learning algorithms to adequately predict whether or not a publication should be forwarded to full text screening. Guidance for the practical use of the methodology is given. Results The appealing performance of the approach is demonstrated by means of two real-world systematic reviews with meta analysis. Conclusions Natural language processing and machine learning can help to semi-automatize title-abstract screening. Different project-specific considerations have to be made for applying them in practice.

Abstract Defining “dance” is challenging, because many distinct classes of human movement may be considered dance in a broad sense. Although the most obvious category is rhythmic dancing to a musical beat, other categories of expressive movement such as dance improvisation, pantomime, tai chi, or Japanese butoh suggest that a more inclusive conception of human dance is needed. Here we propose that a specific type of conscious awareness plays an overarching role in most forms of expressive movement and can be used to define dance (in the broad sense). We can briefly summarize this broader notion of dance as “mindful movement.” However, to make this conception explicit and testable, we need an empirically verifiable characterization of “mindful movement.” We propose such a characterization in terms of predictive coding and procedural learning theory: mindful movement involves a “suspension” of automatization. When first learning a new motor skill, we are highly conscious of our movements, and this is reflected in neural activation patterns. As skill increases, automatization and overlearning occurs, involving a progressive suppression of conscious awareness. Overlearned, habitual movement patterns become mostly unconscious, entering consciousness only when mistakes or surprising outcomes occur. In mindful movement, this automatization process is essentially inverted or suspended, reactivating previously unconscious details of movement in the conscious workspace, and crucially enabling a renewed aesthetic attention to such details. This wider perspective on dance has important implications for potential animal analogs of human dance and leads to multiple lines of experimental exploration.

In this research, we study the types and features of digital automatized solutions which are connected with knowledge and information systems management in various sectors of the economy. It is demonstrated that at the current stage of the formation of the digital economy, there have formed various directions (vectors) that cover e-commerce, high-tech industry, logistics, transport, etc. Despite many advantages of automatization in the considered directions, there are still problems with adapting ICTs and a global threat of the growth of unemployment. A comprehensive solution to this problem will determine the further socioeconomic development of countries. The objective of this research is to systematise the key directions (vectors) of automatization in the management of knowledge and information systems in the conditions of digitalization of economic sectors and infrastructure. The methodological framework of this research is comprised of frame analysis, statistical method, and trend method. The scientific novelty of this research is connected with the substantiation of perspectives of further development of automatized digital solutions in the digital economy given the existing social threats.

This study presents an ultrasonic non-destructive method with convolutional neural networks (CNN) used for the detection of interface defects in adhesively bonded dissimilar structures. Adhesive bonding, as the weakest part of such structures, is prone to defects, making their detection challenging due to various factors, including surface curvature, which causes amplitude variations. Conventional non-destructive methods and processing algorithms may be insufficient to enhance detectability, as some influential factors cannot be fully eliminated. Even after aligning signals reflected from the sample surface and interface, in some cases, due to non-parallel interfaces, persistent amplitude variations remain, significantly affecting defect detectability. To address this problem, a proposed method that integrates ultrasonic NDT and CNN, and which is able to recognize complex patterns and non-linear relationships, is developed in this work. Traditional ultrasonic pulse-echo testing was performed on adhesive structures to collect experimental data and generate C-scan images, covering the time gate from the first interface reflection to the time point where the reflections were attenuated. Two classes of datasets, representing defective and defect-free areas, were fed into the neural network. One subset of the dataset was used for model training, while another subset was used for model validation. Additionally, data collected from a different sample during an independent experiment were used to evaluate the generalization and performance of the neural network. The results demonstrated that the integration of a CNN enabled high prediction accuracy and automation of the analysis process, enhancing efficiency and reliability in detecting interface defects. [ABSTRACT FROM AUTHOR]

Defining "dance" is challenging, because many distinct classes of human movement may be considered dance in a broad sense. Although the most obvious category is rhythmic dancing to a musical beat, other categories of expressive movement such as dance improvisation, pantomime, tai chi, or Japanese butoh suggest that a more inclusive conception of human dance is needed. Here we propose that a specific type of conscious awareness plays an overarching role in most forms of expressive movement and can be used to define dance (in the broad sense). We can briefly summarize this broader notion of dance as "mindful movement." However, to make this conception explicit and testable, we need an empirically verifiable characterization of "mindful movement." We propose such a characterization in terms of predictive coding and procedural learning theory: mindful movement involves a "suspension" of automatization. When first learning a new motor skill, we are highly conscious of our movements, and this is reflected in neural activation patterns. As skill increases, automatization and overlearning occurs, involving a progressive suppression of conscious awareness. Overlearned, habitual movement patterns become mostly unconscious, entering consciousness only when mistakes or surprising outcomes occur. In mindful movement, this automatization process is essentially inverted or suspended, reactivating previously unconscious details of movement in the conscious workspace, and crucially enabling a renewed aesthetic attention to such details. This wider perspective on dance has important implications for potential animal analogs of human dance and leads to multiple lines of experimental exploration. [ABSTRACT FROM AUTHOR]

Background: Title-abstract screening in the preparation of a systematic review is a time-consuming task. Modern techniques of natural language processing and machine learning might allow partly automatization of title-abstract screening. In particular, clear guidance on how to proceed with these techniques in practice is of high relevance. Methods: This paper presents an entire pipeline how to use natural language processing techniques to make the titles and abstracts usable for machine learning and how to apply machine learning algorithms to adequately predict whether or not a publication should be forwarded to full text screening. Guidance for the practical use of the methodology is given. Results: The appealing performance of the approach is demonstrated by means of two real-world systematic reviews with meta analysis. Conclusions: Natural language processing and machine learning can help to semi-automatize title-abstract screening. Different project-specific considerations have to be made for applying them in practice. [ABSTRACT FROM AUTHOR]

We explore the automatized transition of point cloud data into a Building Information Model (BIM), an essential task for reconstructions, demolition plans, or auditing. In this study, we use point cloud data generated by 3D laser scanning. The research elaborates on the process of segregating and segmenting floor slabs from point clouds and the developed algorithms categorize point cloud subsets based on different height parameters (z-coordinates). Once slabs are identified, a function is applied to create envelopes for each slab level, accommodating any convex or concave shape. The resulting geometry, along with relevant auxiliary data, is exported in the Industry Foundation Classes (IFC) format, a standard for BIM data exchange. The exported entities can then be further processed using prevalent software such as Revit or ArchiCAD. [ABSTRACT FROM AUTHOR]

This study investigated tbe degree to wbicb differences in tbe effects of external, internal and holistic focus of attention relate to movement automaticity by comparing performance between tbe preferred and non-preferred band m a karate puncbing task. Thirteen experienced karate players Cage 35.91£16.6 years) completed reverse punches (i.e., gyaku-zuki) on a boxing bag witi) external, internal and holistic focus instructions using botb their preferred and non-preferred band. Results confirmed tbe advantage of an external focus relative to an internal focus. No differences were found witb holistic foms. In addition, for peak wrist velocity tbis differential attentional focus effect only occurred iii tbe preferred band. For impact force, no difference of attentional focus was found between tbe two bands. These observations are consistent witb tbe constrained-action bypotbesis, entailing tbat an external focus delivers superior performance compared to an internal focus for movement control tbat is (more) automatized, as per preferred band. [ABSTRACT FROM AUTHOR]

Nowadays pathology laboratories are worldwide facing a digital revolution, with an increasing number of institutions adopting digital pathology (DP) and whole slide imaging solutions. Despite indeed providing novel and helpful advantages, embracing a whole DP workflow is still challenging, especially for wide healthcare networks. The Azienda Zero of the Veneto Italian region has begun a process of a fully digital transformation of an integrated network of 12 hospitals producing nearly 3 million slides per year. In the present article, we describe the planning stages and the operative phases needed to support such a disruptive transition, along with the initial preliminary results emerging from the project. The ultimate goal of the DP program in the Veneto Italian region is to improve patients' clinical care through a safe and standardized process, encompassing a total digital management of pathology samples, easy file sharing with experienced colleagues, and automatic support by artificial intelligence tools. [ABSTRACT FROM AUTHOR]

This article describes a module and method for measuring the surface tension of liquid solders implemented on a measuring device as part of an integrated platform for automatic measurement of brazebility parameters at high temperatures. A concept for constructing a test stand is presented, with a description of the individual functional blocks. The developed stand allows for testing of the solder's surface tension. The surface tension is one of the parameters that describe the thermodynamics of interfacial reactions and the structure of newly created joints. Determining the physicochemical interactions between liquid and solid substances is crucial for various industrial processes in fields such as metallurgy, electronics, and aviation, mainly where soldering and brazing technologies are employed. A series of bubble experiments in solder for a ceramic capillary is carried out to verify the proposed method using the developed system. One of these experiments is described in this article. [ABSTRACT FROM AUTHOR]

Introduction: Metadata extraction from digitized slides or whole slide image files is a frequent, laborious, and tedious task. In this work, we present a tool to automatically extract all relevant slide information, such as case number, year, slide number, block number, and staining from the macro-images of the scanned slide.We named the tool Babel fish as it helps translate relevant information printed on the slide. It is written to contain certain basic assumptions regarding, for example, the location of certain information. This can be adapted to the respective location. The extracted metadata can then be used to sort digital slides into databases or to link them with associated case IDs from laboratory information systems. Material and methods: The tool is based on optical character recognition (OCR). For most information, the easyOCR tool is used. For the block number and cases with insufficient results in the first OCR round, a second OCR with pytesseract is applied.Two datasets are used: one for tool development has 342 slides; and another for one for testing has 110 slides. Results: For the testing set, the overall accuracy for retrieving all relevant information per slide is 0.982. Of note, the accuracy for most information parts is 1.000, whereas the accuracy for the block number detection is 0.982. Conclusion: The Babel fish tool can be used to rename vast amounts of whole slide image files in an image analysis pipeline. Furthermore, it could be an essential part of DICOM conversion pipelines, as it extracts relevant metadata like case number, year, block ID, and staining.

The article deals with the complexity of the workflow used when integrated circuits are implemented using 2D electronic design automation (EDA) tools and adapting the workflow for 3D integrated circuits. If some issues are not identified promptly in the workflow, the working time to produce an integrated circuit is increased. By analyzing and refining workflow, identifying bottlenecks and early problems in a design, the optimization process will ensure an error-minimized trajectory from synthesis to final design prepared for manufacturing. Considering all the details at every stage of the working flow will help the backend designer to solve problems faster and save time. It was created detailed scripts for automatization of the process at every stage including floorplan, power plan, placement, clock tree synthesis, routing and all physical and logical analysis. The workflow was optimized using loops at all levels, extracting important information at every level and improving the process of working. Moreover, optimization techniques contribute significantly to precision and quality in design implementation. This working flow can be used to implement 3D integrated circuits with automated 2D tools from Synopsis.

This study revisits the roles of different aspects of phonological vocabulary knowledge in second language (L2) listening. Japanese learners of English (n = 114) completed the TOEIC Listening test and three phonological vocabulary tests assessing (a) ability to recognize the meanings of aural forms (meaning recognition), (b) ability to recall the meanings of aural forms (meaning recall), and (c) ability to spontaneously judge the appropriate use of word meanings in sentential contexts (lexicosemantic judgment task [LJT]). Among the three measures, the LJT best predicted the participants’ ability to access the target words during real‐life L2 listening comprehension of monologues and conversations (measured via TOEIC). Structural equation modeling demonstrated that the LJT was distinct from both meaning recognition and recall and revealed their different associations with listening comprehension scores. In line with the skill acquisition theory, we propose that the LJT reflects automatized knowledge, whereas meaning recognition and recall represent declarative knowledge. [ABSTRACT FROM AUTHOR]

Background: Patients with lower extremity burn injuries have decreased gait function. Gait dysfunctions are compensated by activation of executive areas such as the prefrontal cortex (PFC). Although robot-assisted gait training (RAGT) can improve gait function, the training mechanisms of RAGT are unknown. We aimed to determine the clinical effects of RAGT in patients with burns and investigate their underlying mechanisms. Methods: This single-blind, randomized controlled trial involved 54 patients with lower extremity burns. The RAGT group underwent RAGT using SUBAR® and conventional training. The control (CON) group underwent only conventional training. The primary outcome was cortical activity measured using a functional near-infrared spectroscopy device before and after 8 weeks of training to confirm the compensatory effect of gait dysfunction. The secondary outcomes were the functional ambulation category (FAC) to evaluate gait performance, 6-min walking test (6 MWT) distance to measure gait speed, isometric force and range of motion (ROM) of lower extremities to evaluate physical function, and the visual analog scale (VAS) score to evaluate subjective pain during gait. Results: PFC activation during the gait phase in the RAGT group decreased significantly compared with that of the CON. The VAS score decreased and FAC score improved after 8 weeks of training in both groups. The 6 MWT scores, isometric strengths (the left knee flexor and bilateral ankle plantar flexors), and the ROMs (the extensions of bilateral hip and bilateral knee) of the RAGT group were significantly improved compared with those of the CON. RAGT improved gait speed, lower extremity ROMs, and lower extremity muscles strengths in patients with burns. Conclusions: The improvement in gait speed and cerebral blood flow evaluation results suggests that the automatization of gait is related to the treatment mechanism during RAGT. [ABSTRACT FROM AUTHOR]

Three-dimensional printing has become a fast-growing industry. The first phase of this technology is the design of a 3D object to personalize it and optimize its production. This paper explores the procedural generation of the 3D model. The article aims to present the method of procedurally generating 3D objects in Python. Procedural content generation is the automated creation of content using algorithms. Most often, as part of procedural generation, a small number of input parameters and pseudo-random processes are used to generate content that will meet the requirements. The programming techniques for object customization in Python optimize the manufacturing process. Moreover, procedural generation speeds up the model design, and if developers use 3D scanning methods and artificial intelligence, production can be personalized, which is in line with the concept of Industry 4.0. [ABSTRACT FROM AUTHOR]

In connection with the need to obtain a properly made and cut material and the appearance of the surface layer, new manufacturing technologies were used for tests, namely the laser cutting technology. This article describes the laboratory stand built for the purpose of research, as well as the possibility of using laser cutting on several sample materials (polymer films), together with an indication of the results obtained. The idea was to elaborate on the cutting technology that will be proper for manufacturing the desired type of spacers for ion-exchange membranes separating while maintaining the required level of product quality and chemical purity. The latter criterion was the basic one, due to the scope of use of the manufactured elements. This article also describes the problem encountered during the construction of the stand or during the research. The last part of this article describes the further steps of the research that will be carried out in the future along with a discussion and summary of the research performed. It is important from the point of view of the development of production technology, but also because of the characteristics of materials for the production of surface layers and coatings resistant to mechanical or thermal wear used in industry. The introduction of innovative solutions is also aimed at studying the improvement of the economics of the production of materials that are significant, in particular, for small- and medium-sized enterprises. [ABSTRACT FROM AUTHOR]

In this research, we study the types and features of digital automatized solutions which are connected with knowledge and information systems management in various sectors of the economy. It is demonstrated that at the current stage of the formation of the digital economy, there have formed various directions (vectors) that cover e-commerce, high-tech industry, logistics, transport, etc. Despite many advantages of automatization in the considered directions, there are still problems with adapting ICTs and a global threat of the growth of unemployment. A comprehensive solution to this problem will determine the further socioeconomic development of countries. The objective of this research is to systematise the key directions (vectors) of automatization in the management of knowledge and information systems in the conditions of digitalization of economic sectors and infrastructure. The methodological framework of this research is comprised of frame analysis, statistical method, and trend method. The scientific novelty of this research is connected with the substantiation of perspectives of further development of automatized digital solutions in the digital economy given the existing social threats. [ABSTRACT FROM AUTHOR]

THE PURPOSE. Consider the issue of automation of modern sea vessels with a high degree of electrification. The purpose of the study is to analyze the electrical power system of the AION tanker to determine the possible occurrence of abnormal phenomena during parallel operation of generator sets and methods for eliminating them. METHODS. The article analyzes the electrical power system and automatic control devices of the AION tanker. Particular attention is paid to the study of systems that ensure the production and distribution of electrical energy between consumers. The widespread use of modern computer-based automatic control systems in ship power plants has been noted. The extensive possibilities for controlling a ship's electrical power plant in various daily or emergency situations are shown. The software and hardware of the ship's Power Management System allows you to reduce the consumption of fuel, oil and other important resources by automatically regulating the load on diesel generators, timing the supply of fuel to the cylinders, and controlling exhaust and starting valves. RESULTS. Experimental oscillograms of studies of the parallel operation of diesel generator sets were obtained, in which power exchange oscillations were recorded. The amplitude of power exchange oscillations reaches 40 % of the steady-state value, and their period is 600 - 800 ms, depending on the operating mode of the ship's electrical power system. During thecollection of these oscillograms, the ship's load is turned on and no starts or stops of any powerful consumers are made, that is, a quasi-steady process takes place. CONCLUSION. It is proposed to introduce a function for eliminating power exchange oscillations into the ship power plant control system. To implement it, it is necessary to add a block that receives information from all parallel operating diesel generator sets and adaptively adjusts the settings of the frequency regulators.

Artificial intelligence (AI) is used in tasks that usually require human intelligence. The motivation behind this study is the growing interest in deploying AI in public spaces, particularly in autonomous vehicles such as flying drones, to address challenges in navigation and control. The primary challenge lies in developing a robust, cost-effective system capable of autonomous navigation in real-world environments, handling obstacles, and adapting to dynamic conditions. To tackle this, we propose a novel approach integrating machine learning (ML) algorithms, specifically, reinforcement learning (RL), with a comprehensive simulation and testing framework. Reinforcement learning machine algorithms designed to solve problems requiring optimization of the solution for the highest possible reward were used. It was assumed that the algorithms do not have to be created from scratch, but they need a well-defined training environment that will appropriately reward or punish the actions taken. This study aims to develop and implement a novel approach to autonomous drone navigation using machine learning (ML) algorithms. The primary innovation lies in the comprehensive integration of ML algorithms with a real-world drone control system, encompassing both simulations and real-world testing. A vital component of this approach is creating a multi-stage training environment that accurately replicates actual flight conditions and progressively increases the complexity of scenarios, ensuring a robust evaluation of algorithm performance. This research also introduces a new approach to optimizing system cost and accessibility. It involves using commercially available, cost-effective drones and open-source or free simulation tools, significantly reducing entry barriers for potential users. A critical aspect of this study is to assess whether affordable components can provide sufficient accuracy and stability without compromising system quality. The authors developed a system capable of autonomously determining optimal flight paths and controlling the drone, allowing it to avoid obstacles and respond to dynamic conditions in real time. The performance of the trained algorithms was confirmed through simulations and real-world flights, which allowed for assessing their usefulness in practical drone navigation scenarios.

In the last decades, the determination of trace elements in biological materials has emerged as an important area of study because of its relevance to human health and the environment. Inductively coupled plasma mass spectrometry (ICP-MS) has proven to be a powerful tool for trace element analysis, owing to its high sensitivity and ability to determine several elements in a single measurement. However, given the complex nature of biological matrices and the presence of elements, most of them at ultratrace levels, it becomes crucial to complement ICP-MS with preconcentration techniques to increase the sensitivity and selectivity of analytical methods. This article presents an exhaustive overview of liquid- and solid-phase preconcentration techniques used in combination with ICP-MS for trace element determination in different biological samples from 2000 to the present. An in-depth discussion of the advances on the application of state-of-the-art solvents and materials in trace element extraction and preconcentration is presented. Special attention is given to different strategies for elemental speciation analysis, employing both chromatographic and non-chromatographic techniques. The role of automation in these methodologies is also described. Finally, future trends and challenges related to this topic are discussed. [ABSTRACT FROM AUTHOR]

Background: The use of 3D planning to guide corrective osteotomies of the lower extremity is increasing in clinical practice. The use of computer-tomography (CT) data acquired in supine position neglects the weight-bearing (WB) state and the gold standard in 3D planning involves the manual adaption of the surgical plan after considering the WB state in long-leg radiographs (LLR). However, this process is subjective and dependent on the surgeons experience. A more standardized and automated method could reduce variability and decrease costs. Purpose: The aim of the study was (1) to compare three different three-dimensional (3D) planning modalities for medial open-wedge high tibial osteotomy (MOWHTO) and (2) to describe the current practice of adapting NWB CT data after considering the WB state in LLR. The purpose of this study is to validate a new, standardized approach to include the WB state into the 3D planning and to compare this method against the current gold standard of 3D planning. Our hypothesis is that the correction is comparable to the gold standard, but shows less variability due compared to the more subjective hybrid approach. Methods: Three surgical planning modalities were retrospectively analyzed in 43 legs scheduled for MOWHTO between 2015 and 2019. The planning modalities included: (1) 3D hybrid (3D non-weight-bearing (NWB) CT models after manual adaption of the opening angle considering the WB state in LLR, (2) 3D NWB (3D NWB CT models) and (3) 3D WB (2D/3D registration of 3D NWB CT models onto LLR to simulate the WB state). The pre- and postoperative hip-knee-ankle angle (HKA) and the planned opening angle (°) were assessed and differences among modalities reported. The relationship between the reported differences and BMI, preoperative HKA (LLR), medial meniscus extrusion, Outerbridge osteoarthritis grade and joint line convergence angle (JLCA) was analyzed. Results: The mean (std) planned opening angle of 3D hybrid did not differ between 3D hybrid and 3D WB (0.4 ± 2.1°) (n.s.) but was higher in 3D hybrid compared to 3D NWB (1.1° ± 1.1°) (p = 0.039). 3D WB demonstrated increased preoperative varus deformity compared to 3D NWB: 6.7 ± 3.8° vs. 5.6 ± 2.7° (p = 0.029). Patients with an increased varus deformity in 3D WB compared to 3D NWB (> 2 °) demonstrated more extensive varus alignment in LLR (p = 0.009) and a higher JLCA (p = 0.013). Conclusion: Small intermodal differences between the current practice of the reported 3D hybrid planning modality and a 3D WB approach using a 2D/3D registration algorithm were reported. In contrast, neglecting the WB state underestimates preoperative varus deformity and results in a smaller planned opening angle. This leads to potential under correction in MOWHTO, especially in patients with extensive varus deformities or JLCA. Clinical Relevance: Incorporating the WB state in 3D planning modalities has the potential to increase accuracy and lead to a more consistent and reliable planning in MOWHTO. The inclusion of the WB state in automatized surgical planning algorithms has the potential to reduce costs and time in the future. [ABSTRACT FROM AUTHOR]

Modal tracking plays a vital role in structural health monitoring since changes in modal parameters help us understand a structure's dynamic characteristics and may reflect the potential deterioration of structural performance. Although numerous modal parameter estimation (MPE) methods exist, it is not guaranteed that an MPE process will exclude all spurious modes and not lose any physical modes every time over a long-term monitoring period. Relatively large damping of a structure, poor data quality, and significant changes in structural modal parameters may make the estimated modal parameters spurious, missing, or misclassified. It makes long-term modal tracking semiautomated or manual, which constrains timely downstream applications such as anomaly detection, condition assessment, and decision making. This research aims to propose a long-term continuous automatic modal tracking algorithm based on Bayesian inference even when the modal parameters, damping, and data quality change significantly. Bayesian inference is used to determine the physical modes from the results of existing MPE methods. Both the modes identified from the most recent response set and the modal probability model from multiple previous response sets are considered in the Bayesian model to better determine the physical modes from the results of MPE. Moreover, the proposed algorithm requires only three extra hyperparameters compared to general modal tracking algorithms, and they can be quickly determined by a grid search method. The performance of the proposed algorithm is verified by a numerical example and a real-world civil structure Z24 Bridge benchmark. [ABSTRACT FROM AUTHOR]

At the Polytechnic of Viseu (PV) [1] , the number of students has been increasing [2] , and it is hoped that this trend continues, for the sake of the literacy of the population that serves in its area of coverage and for the reduction of the desertification of the interior region in which is inserted. In the case of Students with Specific Educational Needs (SSEN) who attend the PV (SSENPV), it is extremely important to develop procedures that minimize the anxiety brought about by change for these students, as well as to facilitate their adaptation, to make the period of permanence in Higher Education (HE) an inclusive period, generating well-being, promoting academic success, and facilitating the transition to active life. To combat this phenomenon and since the PV is a Higher Education Institution (HEI) that is guided by equity in its community, in particular the student community, the SSENPV census is a crucial measure insofar as it is necessary to implement procedures, which must respect and obey individual specificities. It is also intended that, regarding access to information on the platform, it will allow reducing asymmetries between students as well as access to services. To respond to this reality, within the scope of the Inova & Includes project. IPV I2 [3] , a group of researchers, in partnership with the degree course in Computer Engineering at the Superior School of Technology and Management of Viseu (ESTGV) – curricular unit of "Project", developed an integrated management platform for SSENPV. This platform, which is intended to be a contribution to true equity in education in the PV, is based on the support of social impact in different dimensions, which translates into the implementation of the following profiles: • Informative Profile: dissemination of legislation and other relevant information on Specific Educational Needs (SEN), ensuring centralized and accessible information management and streamlining procedures and support measures. • Academic Profile: registration and updating of data on the SSENPV. • Technical Evaluation and Follow-up Profile: registration of the SSENPV procedural evaluation, with automatic sending of technical evaluations to authorized users. [ABSTRACT FROM AUTHOR]

Three-dimensional (3D) deformity assessment and leg realignment planning is emerging. The aim of this study was to (1) validate a novel 3D planning modality that incorporates the weight-bearing (WB) state (3D WB) by comparing it to existing modalities (3D non-weight-bearing (NWB), 2D WB) and (2) evaluate the influence of the modality (2D vs. 3D) and the WB condition on the measurements. Three different planning and deformity measurement protocols were analyzed in 19 legs that underwent medial open-wedge high tibial osteotomy (HTO): (1) a 3D WB protocol, after 2D/3D registration of 3D CT models onto the long-leg radiograph (LLR) (3D WB), (2) a 3D NWB protocol based on the 3D surface models obtained in the supine position (3D NWB), and (3) a 2D WB protocol based on the LLR (2D WB). The hip–knee–ankle angle (HKA), joint line convergence angle (JLCA), and the achieved surgical correction were measured for each modality and patient. All the measurement protocols demonstrated excellent intermodal agreement for the achieved surgical correction, with an ICC of 0.90 (95% CI: 0.76–0.96)) (p < 0.001). Surgical correction had a higher mean absolute difference compared to the 3D opening angle (OA) when measured with the WB protocols (3D WB: 2.7 ± 1.8°, 3D NWB: 1.9 ± 1.3°, 2D WB: 2.2 ± 1.3°), but it did not show statistical significance. The novel planning modality (3D WB) demonstrated excellent agreement when measuring the surgical correction after HTO compared to existing modalities. [ABSTRACT FROM AUTHOR]

For the calibration of linear scales, comparators are generally used. Comparators are devices that enable the movement of an evaluation apparatus over a calibrated scale along a linear base with high precision. The construction of a comparator includes a movable carriage that carries the device for the evaluation of the position of the given edge of the line scale relative to the beginning of the scale. In principle, it involves a camera capturing the scale of the measurer, where the position of the camera's projection center is measured using an interferometer. This article addresses the development of a comparator assembled from low-cost components, as well as the description of systematic influences related to the movement of individual parts of the system, such as the inclination and rotation of the camera and directional and height deviations during the carriage's movement. This article also includes an evaluation of the edge of the given scale with subpixel accuracy, addressing distortion elimination and excluding the influences of impurities or imperfections on the scale. The proposed solution was applied to linear-scale measurers, such as leveling rods with coded and conventional scales and measuring tapes. The entire process of measurement and evaluation was automated. [ABSTRACT FROM AUTHOR]

The relevance of the chosen topic arises from the growing need to explore the automation of treasury systems in enterprises, given the rapidly changing external and internal environments of companies. This study investigates both the theoretical and practical aspects of automation, focusing on how new systems can adapt flexibly to these dynamic conditions. The research is rooted in a detailed examination of methodologies for change management during the implementation of automated treasury systems, ensuring that such transitions align with broader business strategies. The study’s methodology includes a thorough analysis of existing theoretical frameworks in the field of cash flow system automation, particularly in treasury management. Additionally, it draws on practical experiences from companies such as Severstal, JSC USC, and SMART-HOLDING, which have implemented systems like 1C, Finance 360°, and the integrated "Treasury" project. These cases provide valuable insights into the challenges and effective solutions for developing efficient cash flow management systems. The findings demonstrate that automated treasury systems are crucial for companies to manage their cash flows effectively. They enable firms to maintain control over key economic indicators while providing the flexibility needed to respond to fluctuating external and internal factors. Through the use of sophisticated methodological tools, these systems support strategic financial management and help companies stay competitive in a fast-paced environment. The conclusions of this study are highly relevant for students and professionals engaged in the study or practice of cash flow management and treasury operations in various organizational settings. The insights offered emphasize the importance of a well-implemented treasury automation system in ensuring financial stability and achieving long-term business success. Furthermore, the study presents practical recommendations for effectively implementing and adapting these systems to meet the evolving demands of the global business landscape.

The development of grain storage warehouses has low high-tech coverage because of the special features of grain products. Traditional grain warehouses are facing problems including limited land resources, increased labor costs, and poorer health indicators. Accompanied by the development of information technology and the Internet of Things, automated three-dimensional warehouses have been widely used in industry with the advantages of unmanned, informationized, intensive, high-speed and seamless. In the field of grain warehousing, the finished grain three-dimensional warehouse based on mechatronics, automation, control and logistics is expected to solve the problem of grain warehousing. The traditional grain storage industry will be driven by new technology to ensure national food security.

This study presents an ultrasonic non-destructive method with convolutional neural networks (CNN) used for the detection of interface defects in adhesively bonded dissimilar structures. Adhesive bonding, as the weakest part of such structures, is prone to defects, making their detection challenging due to various factors, including surface curvature, which causes amplitude variations. Conventional non-destructive methods and processing algorithms may be insufficient to enhance detectability, as some influential factors cannot be fully eliminated. Even after aligning signals reflected from the sample surface and interface, in some cases, due to non-parallel interfaces, persistent amplitude variations remain, significantly affecting defect detectability. To address this problem, a proposed method that integrates ultrasonic NDT and CNN, and which is able to recognize complex patterns and non-linear relationships, is developed in this work. Traditional ultrasonic pulse-echo testing was performed on adhesive structures to collect experimental data and generate C-scan images, covering the time gate from the first interface reflection to the time point where the reflections were attenuated. Two classes of datasets, representing defective and defect-free areas, were fed into the neural network. One subset of the dataset was used for model training, while another subset was used for model validation. Additionally, data collected from a different sample during an independent experiment were used to evaluate the generalization and performance of the neural network. The results demonstrated that the integration of a CNN enabled high prediction accuracy and automation of the analysis process, enhancing efficiency and reliability in detecting interface defects.

This article describes a module and method for measuring the surface tension of liquid solders implemented on a measuring device as part of an integrated platform for automatic measurement of brazebility parameters at high temperatures. A concept for constructing a test stand is presented, with a description of the individual functional blocks. The developed stand allows for testing of the solder’s surface tension. The surface tension is one of the parameters that describe the thermodynamics of interfacial reactions and the structure of newly created joints. Determining the physicochemical interactions between liquid and solid substances is crucial for various industrial processes in fields such as metallurgy, electronics, and aviation, mainly where soldering and brazing technologies are employed. A series of bubble experiments in solder for a ceramic capillary is carried out to verify the proposed method using the developed system. One of these experiments is described in this article.

According to [1]-[3], the healthcare systems are suffering from severe personnel shortages with a lack of medical care workers, operating room nurses, and surgical technicians. This deficiency will become worse in the upcoming decades. Automatization of highly standardized procedures could be a promising approach to address this issue. We designed a new concept for the automated handover and return of surgical instruments to the surgeon in an operating theater scenario to relieve operating room personnel from their workload. In this setup, the surgical instruments are delivered pre-sorted in an upright position by the sterilization unit in an instrument basket or drum. A single-use, doublelayered, brushy conveyor belt system mounted on a robot arm acts as the connector between pick-position (instrument basket) and individually pre-definable hand-over-position. The system is placed next to the table inside the operation room (OR). Based on voice commands, the conveyor intake moves over to the selected instrument and delivers it to the surgeon in a fast and short motion. After use, the surgeon can feed the instrument into the intake at the handover position and place it back in the instrument basket. The robot arm allows precise maneuvering of the intake and output position. The concept was realized as a minimal-viable product and will be evaluated for further improvement.

Abstract Background Written medical examinations consist of multiple-choice questions and/or free-text answers. The latter require manual evaluation and rating, which is time-consuming and potentially error-prone. We tested whether natural language processing (NLP) can be used to automatically analyze free-text answers to support the review process. Methods The European Board of Radiology of the European Society of Radiology provided representative datasets comprising sample questions, answer keys, participant answers, and reviewer markings from European Diploma in Radiology examinations. Three free-text questions with the highest number of corresponding answers were selected: Questions 1 and 2 were “unstructured” and required a typical free-text answer whereas question 3 was “structured” and offered a selection of predefined wordings/phrases for participants to use in their free-text answer. The NLP engine was designed using word lists, rule-based synonyms, and decision tree learning based on the answer keys and its performance tested against the gold standard of reviewer markings. Results After implementing the NLP approach in Python, F1 scores were calculated as a measure of NLP performance: 0.26 (unstructured question 1, n = 96), 0.33 (unstructured question 2, n = 327), and 0.5 (more structured question, n = 111). The respective precision/recall values were 0.26/0.27, 0.4/0.32, and 0.62/0.55. Conclusion This study showed the successful design of an NLP-based approach for automatic evaluation of free-text answers in the EDiR examination. Thus, as a future field of application, NLP could work as a decision-support system for reviewers and support the design of examinations being adjusted to the requirements of an automated, NLP-based review process. Clinical relevance statement Natural language processing can be successfully used to automatically evaluate free-text answers, performing better with more structured question-answer formats. Furthermore, this study provides a baseline for further work applying, e.g., more elaborated NLP approaches/large language models. Key points • Free-text answers require manual evaluation, which is time-consuming and potentially error-prone. • We developed a simple NLP-based approach — requiring only minimal effort/modeling — to automatically analyze and mark free-text answers. • Our NLP engine has the potential to support the manual evaluation process. • NLP performance is better on a more structured question-answer format. Graphical Abstract