OpenAI has managed to turn 100 million heads in two months towards their new Language model tool (LM) ChatGPT. The third generation Generative Pretrained Transformer (GPT-3) has the capacity to tackle simple to complex and sophisticated problems, while providing reasoning behind its generated answers. ChatGPT can be used to increase productivity and improve efficiency and face challenging problems. Programming mobile applications is a challenging task that requires professional software engineers, skills and abilities to be developed. The following paper takes a case study approach to assess how novice app developers can use ChatGPT to generate Java scripts that will be used in Android studio to create a functional application. The results after, many iterations and ongoing conversations with ChatGPT managed to create an application for the anticipated function. Important insights have been drawn from the case study that could set the ground for any novice user seeking to create applications using Java scripting and Android Studio.

Learning programming is a complex and challenging task for many students. It in-volves both understanding theoretical concepts and acquiring practical skills. Hence, analyzing learners’ data from online learning environments alone fails to capture the full breadth of stu-dents’ actions if part of their learning process takes place elsewhere. Moreover, existing studies on learning analytics applied to programming education have mainly relied on frequency analysis to classify students according to their approach to programming or to predict academic achieve-ment. However, frequency analysis provides limited insights into the individual time-related characteristics of the learning process. The current study examines students’ strategies when learning programming, combining data from the learning management system and from an au-tomated assessment tool. To gain an in-depth understanding of students’ learning process as well as of the types of learners, we used learning analytics methods that account for the temporal order of learning actions. Our results show that students have special preferences for specific learning resources when learning programming, namely slides that support search, and copy and paste. We also found that videos are relatively less consumed by students, especially while working on programming assignments. Lastly, students resort to course forums to seek help only when they struggle.

The study purpose was to train and validate a deep-learning approach to detect micro-scale streetscape features related to pedestrian physical activity. This work innovates by combining computer vision techniques with Google Street View (GSV) images to overcome impediments to conducting audits (e.g., time, safety, and expert labor cost). The EfficientNETB5 architecture was used to build deep-learning models for eight micro-scale features guided by the Microscale Audit of Pedestrian Streetscapes-Mini tool: sidewalks, sidewalk buffers, curb cuts, zebra and line crosswalks, walk signals, bike symbols, and streetlights. We used a train--correct loop, whereby images were trained on a training dataset, evaluated using a separate validation dataset, and trained further until acceptable performance metrics were achieved. Further, we used trained models to audit participant (N=512) neighborhoods in the WalkIT Arizona trial. Correlations were explored between micro-scale features and GIS-measured- and participant reported-macro-scale walkability. Classifier precision, recall, and overall accuracy were all >84%. Total micro-scale was associated with overall macro-scale walkability (r=0.300,p<.001). Positive associations were found between model-detected and self-reported sidewalks (r=0.41,p<.001) and sidewalk buffers (r=0.26,p<.001). Computer vision model results suggest an alternative to trained human raters, allowing for audits of hundreds or thousands of neighborhoods for population surveillance or hypothesis testing.

Visual Question Generation (VQG) from images is a rising research topic in both fields of natural language processing and computer vision. Although there are some recent efforts towards generating questions from images in the open domain, the VQG task in the medical domain has not been well-studied so far due to the lack of labeled data. In this paper, we introduce a goal-driven VQG approach for radiology images called VQGRaD that generates questions targeting specific image aspects such as modality and abnormality. In particular, we study generating natural language questions based on the visual content of the image and on additional information such as the image caption and the question category. VQGRaD encodes the dense vectors of different inputs into two latent spaces, which allows generating, for a specific question category, relevant questions about the images, with or without their captions. We also explore the impact of domain knowledge incorporation (e.g., medical entities and semantic types) and data augmentation techniques on visual question generation in the medical domain. Experiments performed on the VQA-RAD dataset of clinical visual questions showed that VQGRaD achieves 61.86% BLEU score and outperforms strong baselines. We also performed a blinded human evaluation of the grammaticality, fluency, and relevance of the generated questions. The human evaluation demonstrated the better quality of VQGRaD outputs and showed that incorporating medical entities improves the quality of the generated questions. Using the test data and evaluation process of the ImageCLEF 2020 VQA-Med challenge, we found that relying on the proposed data augmentation technique to generate new training samples by applying different kinds of transformations, can mitigate the lack of data, avoid overfitting, and bring a substantial improvement in medical VQG.

Fall prediction using machine learning has become one of the most fruitful and socially relevant applications of computer vision in gerontological research. Since its inception in the early 2000s, this subfield has proliferated into a robust body of research underpinned by various machine learning algorithms (including neural networks, support vector machines, and decision trees) as well as statistical modeling approaches (Markov chains, Gaussian mixture models, and hidden Markov models). Furthermore, some advancements have been translated into commercial and clinical practice, with companies in various stages of development capitalizing on the aging population to develop new commercially available products. Yet despite the marvel of modern machine learning-enabled fall prediction, little research has been conducted to shed light on the security and privacy concerns that such systems pose for older adults. The present study employs an interdisciplinary lens in examining privacy issues associated with machine learning fall prediction and exploring the implications of these models in elderly care and the Internet of Medical Things (IoMT). Ultimately, a justice-informed set of best practices rooted in social geroscience is suggested to help fall prediction researchers and companies continue to advance the field while preserving elderly privacy and autonomy.

The objective of this paper is to assess the potential for potable water savings due to rainwater use in a precast concrete factory in southern Brazil. The economic feasibility and the rainwater quality were also assessed. The current water consumption, future water demand and rainwater demand in the factory were estimated. The future demand considered was two times higher than the current water consumption since there are plans to increase the production. Three scenarios were then simulated using the computer programme Netuno. The ideal rainwater tank capacity, the potential for potable water savings and the economic feasibility analysis for each scenario were estimated. Samples of rainwater were collected in the factory and tested for quality for manufacturing precast concrete. For a rainwater tank capacity equal to 25,000 litres, the potential for potable water savings for the first scenario was 55.4%, but the first scenario was considered economically unfeasible. For the same tank capacity, the second and third scenarios presented viable results regarding potable water savings and payback. As for the rainwater quality, it was proven to be adequate for manufacturing precast concrete. The main conclusion is that rainwater can be used to manufacture precast concrete in the factory studied herein.

Holographic projection is a simple projection because it enlarges or reduces reconstructed images without using a zoom lens. However, one major problem associated with this projection is the deterioration of image quality as the reconstructed image enlarges. In this paper, we propose a time-division holographic projection, in which the original image is divided into blocks and the holograms of each block are calculated. Using a digital micromirror device (DMD), the holograms were projected at high speed to obtain the entire reconstructed image. However, the holograms on the DMD need to be binarized, thereby causing uneven brightness between the divided blocks. We correct this by controlling the displaying time of each hologram. Additionally, combining both the proposed and noise reduction methods, the image quality of the reconstructed image was improved. Results from the simulation and optical reconstructions show we obtained a full-color reconstruction image with reduced noise and uneven brightness.

Susceptible, infective, recovered, and hospitalized/isolated individuals are placed on the cells of a nxn square lattice, where each cell is occupied by a single individual or is vacant. At discrete time units (typically one day each) all susceptibles and infectives execute a random movement and when a coincidence of the two types occurs the susceptible is converted to infective status according to some probability. Infectives are labelled by the number of days since originally infected. At each time increment the age label of the infectives is increased by one unit. When the label reaches a number like 15 or 20 days the susceptibles recover with some probability or become isolated/hospitalized. Upon reaching some age the latter types either recover or die. Probabilities for the movements and conversions from one status to another are implemented by random numbergeneration. Simulations were carried out to investigate the effect of several probability and age parameters, the size of population (proportional to nxn) and density (related to fraction of occupied cells), and the size of the movements. Mid-term gradual conversion of susceptibles to isolated was explored as intervention policy.

Recent improvements in deepfake creation have made deepfake videos more realistic. Moreover, open-source software has made deepfake creation more accessible, which reduces the barrier to entry for deepfake creation. This could pose a threat to the people’s privacy. There is a potential danger if the deepfake creation techniques are used by people with an ulterior motive to produce deepfake videos of world leaders to disrupt the order of countries and the world. Therefore, research into the automatic detection of deepfaked media is essential for public security. In this work, we propose a deepfake detection method using upper body language analysis. Specifically, a many-to-one LSTM network was designed and trained as a classification model for deepfake detection. Different models were trained by varying the hyperparameters to build a final model with benchmark accuracy. We achieved 94.39% accuracy on the deepfake test set. The experimental results showed that upper body language can effectively detect deepfakes.

In this paper, the columnar supramolecular aggregates of photosensitive star-shaped azobenzenes with benzene-1,3,5-tricarboxamide core and azobenzene arms are analysed theoretically applying a combination of computer simulation techniques. Without a light stimulus, the trans-stars build one-dimensional columns of stacked molecules during the first stage of the noncovalent association. These columnar aggregates represent the structural elements of more complex experimentally observed morphologies -- fibers, spheres, gels and others. Upon UV light exposure, the azobenzene arms isomerise from thermodynamically stable planar trans- to a metastable kinked cis-state influencing the aggregate morphology. Here, we determine the most favourable mutual orientations of the \textit{trans}-stars in the stack in terms of (i) the pi-pi distance between the cores lengthwise the aggregate, (ii) the star slipped displacements and (iii) the rotation promoting the helical twist and chirality of the aggregate by calculating the binding energy diagrams using density functional theory. The model predictions are further compared with available experimental data. The intermolecular forces responsible for the stability of the stacks made of trans-azobenzene stars in crystals are quantified using Hirshfeld surface analysis. Finally, to characterize the self-assembly mechanism of such stars in solution, we calculate the hydrogen bond lengths, the normalized dipole moment and the binding energies as the functions of the columnar length using molecular dynamics trajectories, and conclude about the cooperative nature of this process.

Cells receive external stimuli to incur structural and functional damages. On scanning acoustic microscopy (SAM), speed-of-sound (SOS), attenuation-of-sound (AOS), and thickness values are plotted on the screen to create cellular images, which are related to stiffness, viscosity, and cell size, respectively. The obtained digital data compared using statistical analysis. We aimed to investigate the effects of anticancer drugs, acidic fluids, and heat effects on the cells by using SAM. Anticancer drug cisplatin induced cancer cell apoptosis/necrosis and regeneration in culture, causing elevated SOS, reduced AOS, and thickness. During a more prolonged incubation, the SAM values fluctuated differently between the cisplatin-treated and untreated cells. The tannic and acetic acid and microwave stimuli induced SOS and AOS elevations. These stimuli altered the cell size, number, differentiation, viscosity, and stiffness, which corresponded well to the fluctuation of the SOS and AOS values after incubation. Different anticancer drugs interacted with cancer cells to induce the characteristic alterations of the SAM values. These structural and mechanical alterations induced in cells was difficult to observe on light microscopy. Cellular damages were statistically compared between different stimuli and time-lapse cellular changes were observed using a SAM analysis. SAM is a useful modality to evaluate cellular damage.

Deep convolutional neural networks (CNNs) have been widely used in various medical imaging tasks. However, due to the intrinsic locality of convolution operation, CNNs generally cannot model long-range dependencies well, which are important for accurately identifying breast cancer from unregistered multi-view mammograms. This motivates us to leverage the architecture of Vision Transformers to capture long-range relationships of multiple mammograms from the same patient. For this purpose, we employ local Transformer blocks to separately learn patch relationships within a specific view (CC/MLO) of one side (right/left) of mammogram. The outputs from different views and sides are concatenated and fed into global Transformer blocks, to jointly learn patch relationships between two different views of the left and right breasts. To evaluate the proposed model, we retrospectively assembled a dataset involving 949 sets of CC and MLO view mammograms, which include 470 malignant cases and 479 normal or benign cases. We trained and evaluated the model using a five-fold cross-validation method. Without any arduous preprocessing steps (e.g., optimal window cropping, chest wall or pectoral muscle removal, two-view image registration, etc.), our two-view Transformer-based model achieves lesion classification accuracy of 77.0% and area under ROC curve (AUC = 0.814), outperforming state-of-the-art multi-view CNNs by 3.1% and 3%, respectively. Meanwhile, the new two-view model improves mammographic case classification accuracies of two single-view models by 7.4% (CC) and 4.5% (MLO), respectively. The promising results unveil the great potential of using Transformers to develop high-performing computer-aided diagnosis (CADx) schemes of mammography.

This paper proposes a multifractal model, with the aim of providing a possible explanation for the locality phenomenon that appears in the estimation of the Hurst exponent in stationary second order temporal series representing self-similar traffic flows in current high-speed computer networks. It is shown analytically that this phenomenon occurs if the network flow consists of several components with different Hurst exponents.

Sustainability is the core concern of every business; The exploration of avenues to maintain sustainability while staying competitive with high level of productivity remains a vital endeavor. Production flexibility is a key area that can enhance the sustainability of manufacturing industries as it can ensure product availability, scalability, agility/fault tolerance as well as disaster recovery potentials. Technological advancements have provided avenues where companies can enhance virtually all aspects of their operations for efficiency, effectiveness, and productivity. This paper uses both quantitative and qualitative research approach to identify the capability requirements for smart and effective production management and subsequent analysis is done using Multi-Criteria Decision-Making methodology to identify and rank various industry 4.0 technologies and concepts that can provide these smart capabilities in in manufacturing industries to aid the businesses to achieve sustainability with production flexibility. The paper identifies over 12 smart capabilities and 9 Industry 4.0 Technologies which are applicable to production management. It also compares results from the analytics of historical I4.0 implementation as discussed in literatures with the current state as deduces from survey feedbacks from various manufacturing industries.

This paper presents a novel architecture for detecting mathematical formulas in document images, which is an important step for reliable information extraction in several domains. Recently, Cascade Mask R-CNN networks have been introduced to solve object detection in computer vision. In this paper, we suggest a couple of modifications to the existing Cascade Mask R-CNN architecture: First, the proposed network uses deformable convolutions instead of conventional convolutions in the backbone network to spot areas of interest better. Second, it uses a dual backbone of ResNeXt-101, having composite connections at the parallel stages. Finally, our proposed network is end-to-end trainable. We evaluate the proposed approach on the ICDAR-2017 POD and Marmot datasets. The proposed approach demonstrates state-of-the-art performance on ICDAR-2017 POD at a higher IoU threshold with an f1-score of 0.917, reducing the relative error by 7.8%. Moreover, we accomplished correct detection accuracy of 81.3% on embedded formulas on the Marmot dataset, which results in a relative error reduction of 30%.

Objective A trained T1 class Convolutional Neural Network (CNN) model will be used to examine its ability to successfully identify motor imagery when fed pre-processed electroencephalography (EEG) data. In theory, and if the model has been trained accurately, it should be able to identify a class and label it accordingly. The CNN model will then be restored and used to try and identify the same class of motor imagery data using much smaller sampled data in an attempt to simulate live data. Approach PyCharm, a Python platform, will be used to house and process the CNN. The raw data used for the training of the CNN will be sourced from the PhysioBank website. The EEG signal data will then be pre-processed using Brainstorm software that is a toolbox used in conjunction with MATLAB. The sample data used to validate and test the trained CNN, will be also be extracted from Brainstorm but in a much smaller size compared to the training data which is comprised of thousands of images. The sample size would be comparable to a person wearing a Brain Computer Interface (BCI), offering approximately 20 seconds of motor imagery signal data. Results The raw EEG data was successfully extracted and pre-processed. The deep learning model was trained using the extracted image data along with their corresponding labels. After training, it was able to accurately identify the T1 class label at 100 percent. The python coding was then modified to restore the trained model and feed it test sample data in which it was found to recognise 6 out of 10 lines of T1 signal image data. The result suggested that the initial training of the model required a different, more varying approach, so that it would be able to detect varying sample signal image data. The outcome of which could mean that the model could be used in applications for multiple patients wearing the same BCI hardware to control a device or interface.

The designs of two large arenas of different epochs are discussed, which are the most famous antique amphitheater the Roman Colosseum (Italy) and the modern Gazprom Arena stadium (St. Petersburg, Russia). 3D-computer models of the arenas have been built and evacuation of people has been simulated in the Sigma FS software (Russia). The arena designs are analyzed in terms of organizing pedestrian evacuation, a comparative analysis is made, and the specific characteristics affecting the evacuation time are established.

The Study proposes possible solution to enhance Password Authentication using Association Technique based on the Ecological theory of memory and data at the Presbyterian university College Ghana. The study used a deductive research approach and employed two empirical Studies using the non-probability sampling technique where few respondents were selected in categories out of the populace by means of openness in other to get similar categories of respondents with different age groups and education background. The two Empirical study also used a quasi-experimental approach which structure incorporate observation, experimental treatment and timing. The First empirical study carried out an investigation to identify existing Password authentication Technique used by End Users, as well as their behavior in password utilization through a self-completed questionnaires which was analyzed using SPSS version 21 . The Second empirical study was an experiment to compared three kinds of password constructions that is own set, modified dictionary, and association against one another to see which of them would be best meet the ecological theory of memory and data which aims at creating a secured password that is easy to recall. The computation and evaluation of password construction was done using My1login Password meter whiles Levenshtein Distance String Edit Software was also used to compute the memorability of all given password. Across-tabulation was then employed out of the experiment using SPSS version 21. The result from the analysis revealed that the majority of the respondents do have weak passwords and also have few passwords which they even end up sharing with families and friends and reuse it. This confirms statements made by researchers that human being is the weakest connection in information system securities. To maintain Confidentiality, Availability and Integrity of data, the study therefore recommended the use of the Association Password technique which makes it easier to develop a much secured password that is difficult to crack but easy remember.

Incorporating substantial sustainable development issues into teaching and learning is the ultimate task of Education for Sustainable Development (ESD). The purpose of our study is to identify the confused students who have failed to master the skill(s) given by the tutors as a homework using Intelligent Tutoring System (ITS). We have focused ASSISTments, an ITS in this study and scrutinized the skill-builder data using machine learning techniques and methods. We used seven candidate models that include: Naïve Bayes (NB), Generalized Linear Model (GLM), Logistic Regression (LR), Deep Learning (DL), Decision Tree (DT), Random Forest (RF), and Gradient Boosted Trees (XGBoost). We trained, validated and tested learning algorithms, performed stratified cross-validation and measured the performance of the models through various performance metrics i.e., ROC (Receiver Operating Characteristic), Accuracy, Precision, Recall, F-Measure, Sensitivity & Specificity. We found GLM, DT & RF are high accuracies achieving classifiers. However, other perceptions such as detection of unexplored features that might be related to the forecasting of outputs can also boost the accuracy of the prediction model. Through machine learning methods, we identified the group of students which were confused attempting the homework exercise and can help students foster their knowledge, and talent to play a vital role in environmental development.

The prompt and accurate diagnosis of breast lesions, including the distinction between cancer, non-cancer, and suspicious cancer, plays a crucial role in the prognosis of breast cancer. In this paper, we introduce a novel method based on feature extraction and reduction for detection of breast cancer in mammography images. First, we extract features from multiple pre-trained convolutional neural network (CNN) models, and then concatenate them. The most informative features are selected based on their mutual information with the target variable. Subsequently, the selected features can be classified using a machine learning algorithm. We evaluate our approach using four different machine learning algorithms, and our results demonstrate that the neural network-based classifier yields an accuracy as high as 92% for the RSNA dataset which is a new dataset that provides two views and additional features such as age. We compare our proposed algorithm with state-of-the-art methods and demonstrate its superiority, particularly in terms of accuracy and sensitivity. For the MIAS dataset, we achieve an accuracy as high as 94.5%, and for the DDSM dataset, an accuracy of 96% is attained. These results highlight the effectiveness of our method in accurately diagnosing breast lesions and surpassing existing approaches.

Objective tools to track medication adherence are lacking. A tool to monitor pill intake that can be implemented in mHealth apps without the need for additional devices was developed. We propose a pill intake detection tool that uses digital image processing to analyze images of a blister to detect the presence of pills. The tool uses the circular Hough transform as a feature extraction technique and is therefore primarily useful for the detection of pills with a round shape. This pill detection tool is composed of two steps. First, the registration of a full blister and storing of reference values in a local database. Second, the detection and classification of taken and remaining pills in similar blisters, to determine the actual number of untaken pills. In the registration of round pills in full blisters, 100% of pills in gray blisters or blisters with a transparent cover were successfully detected. In counting of untaken pills in partially opened blisters, 95.2% of remaining and 95.1% of taken pills were detected in gray blisters, while 88.2% of remaining and 80.8% of taken pills were detected in blisters with a transparent cover. The proposed tool provides promising results for the detection of round pills. However, the classification of taken and remaining pills need to be further improved, in particular for the detection of pills with non-oval shapes.

Fractal behavior and long-range dependence are widely observed in measurements and characterization of traffic flow in high-speed computer networks of different technologies and coverage levels. This paper presents the results obtained when applying fractal analysis techniques on a time series obtained from traffic captures coming from an application server connected to the Internet through a high-speed link. The results obtained show that traffic flow in the dedicated high-speed network link have fractal behavior when the Hurst exponent is in the range of 0.5, 1, the fractal dimension between 1, 1.5, and the correlation coefficient between –0.5, 0. Based on these results, it is ideal to characterize both the singularities of the traffic and its impulsiveness during a fractal analysis of temporal scales. Finally, based on the results of the time series analyses, the fact that the traffic flows of current computer networks exhibit fractal behavior with a long-range dependency is reaffirmed.

Objective: Radiomics and deep transfer learning are two popular technologies used to develop computer-aided detection and diadnosis (CAD) schemes of medical images. This study aims to investigate and compare advantages and potential limitations of applying these two technologies in developing CAD schemes. Methods: A relatively large and diverse retrospective dataset including 3,000 digital mammograms is assembled in which 1,496 images depict malignant lesions and 1,054 images depict benign lesions. Two CAD schemes are developed to classify breast lesions. The first scheme is developed using four steps namely, applying an adaptive multi-layer topographic region growing algorithm to segment lesions, computing initial radiomics features, applying a principal component algorithm to generate an optimal feature vector, and building a support vector machine classifier. The second CAD scheme is built based a pre-trained residual net architecture (ResNet50) as a transfer learning model to classify breast lesions. Both CAD schemes are trained and tested using a 10-fold cross-validation method. Several score fusion methods are also investigated to classify breast lesions. CAD classification performance is evaluated by the area under ROC curve (AUC). Results: ResNet50 model-based CAD scheme yields AUC = 0.85±0.02, which is significantly higher than radiomics feature-based CAD scheme with AUC = 0.77±0.02 (p < 0.01). Additionally, fusion of classification scores generated by two CAD schemes does not further improve classification performance. Conclusion: This study indicates that using deep transfer learning is more efficient to develop CAD schemes and enables to yield higher lesion classification performance than CAD schemes developed using radiomics-based technology.

A brain-computer interface for controlling elements commonly used at home is presented in this paper. It includes the electroencephalography device needed to acquire signals associated to the brain activity, the algorithms for artefact reduction and event classification, and the communication protocol.

An extension of the models used to generate fractal traffic flows is presented by means of the formulation of a model that considers the use of one-dimensional chaotic maps. Based on the disaggregation of the temporal series generated by the model, a valid explanation of behavior of the values of Hurst exponent is proposed and the feasibility of their control from the parameters of the proposed model is shown.

This article explores the required amount of time series points from a high-speed traffic network to accurately estimate the Hurst exponent. The methodology consists in designing an experiment using estimators that are applied to time series, followed by addressing the minimum amount of points required to obtain accurate estimates of the Hurst exponent in real-time. The methodology addresses the exhaustive analysis of the Hurst exponent considering bias behavior, standard deviation, mean square error, and convergence using fractional Gaussian noise signals with stationary increases. Our results show that the Whittle estimator successfully estimates the Hurst exponent in series with few points. Based on the results obtained, a minimum length for the time series is empirically proposed. Finally, to validate the results, the methodology is applied to real traffic captures in a high-speed network based on the IEEE 802.3ab standard.

Cognitive deterioration caused by illness or aging often occurs before symptoms arise, and their timely diagnosis is crucial to reducing its medical, personal, and societal impacts. Brain-Computer Interfaces (BCIs) stimulate and analyze key cerebral rhythms, enabling reliable cognitive assessment that can accelerate diagnosis. The BCI system presented analyzes Steady-State Visually Evoked Potentials (SSVEPs) elicited in subjects of varying age to detect cognitive aging, predict its magnitude, and identify its relationship with SSVEP features (band power and frequency detection accuracy), which were hypothesized to indicate cognitive decline due to aging. The BCI system was tested with subjects of varying age to assess its ability to detect aging-induced cognitive deterioration. Rectangular stimuli flickering at theta, alpha, and beta frequencies were presented to subjects, and frontal and occipital EEG responses were recorded. These were processed to calculate detection accuracy for each subject and calculate SSVEP band power. A neural network was trained using the features to predict cognitive age. The results showed potential cognitive deterioration through age-related variations in SSVEP features. Frequency detection accuracy declined after age group 20–40 and band power, throughout all age groups. SSVEPs generated at theta and alpha frequencies, especially 7.5 Hz, were the best indicators of cognitive deterioration. Here, frequency detection accuracy consistently declined after age group 20-40 from an average of 96.64% to 69.23%. The presented system can be used as an effective diagnosis tool for age related cognitive decline.

Bell type inequalities are proven using oversimplified probabilistic models and/or counterfactual definiteness (CFD). If setting-dependent variables describing measuring instruments are correctly introduced none of these inequalities may be proven. In spite of this a belief in a mysterious quantum nonlocality is not fading. Computer simulations of Bell tests allow studying different scenarios how the experimental data might have been created. They allow also to generate outcomes of various counterfactual experiments such as repeated or simultaneous measurements performed in different settings on the same ‘’ photon-pair” etc. They allow reinforcing or relaxing CFD- compliance and /or to study the impact of various “photon identification procedures” mimicking those used in real experiments. Using a specific setting- dependent identification procedure data samples consistent with quantum predictions may be generated. It reflects an active role of instruments during the measurement process. Each setting dependent data samples are consistent with specific setting –dependent probabilistic models which may not be deduced using non-contextual local realistic or stochastic hidden variables. In this paper we discuss the results of these simulations. Since the data samples are generated in a locally causal way, these simulations provide additional strong arguments for closing the door on quantum nonlocality

Based on a local causal model of the dynamics of curved discrete spacetime, a causal model of quantum field theory in curved discrete spacetime is described. At the elementary level, space(-time) is assumed to consists of interconnected space points. Each space point is connected to a small discrete set of neighbor space points. Density distribution of the space points and the lengths of the space point connections depend on the distance from the gravitational sources. This leads to curved spacetime in accordance with general relativity. Dynamics of spacetime (i.e., the emergence of space and the propagation of space changes) dynamically assigns "in-connections" and "out-connections" to the affected space points. Emergence and propagation of quantum fields (including particles) are mapped to the emergence and propagation of space changes by utilizing identical paths of in/out-connections. Compatibility with standard quantum field theory (QFT) requests the adjustment of the QFT techniques (e.g., Feynman diagrams, Feynman rules, creation/annihilation operators), which typically apply to three in/out connections, to n > 3 in/out connections. In addition, QFT computation in position space has to be adapted to a curved discrete space-time.

One of the critical tools for early detection and subsequent evaluation of the incidence of lung diseases is chest radiography. This study presents a real-world implementation of a convolutional neural network (CNN) based Carebot Covid app to detect COVID-19 from chest X-ray (CXR) images. Our proposed model takes the form of a simple and intuitive application. Used CNN can be deployed as a STOW-RS prediction endpoint for direct implementation into DICOM viewers. The results of this study show that the deep learning model based on DenseNet and ResNet architecture can detect SARS-CoV-2 from CXR images with precision of 0.981, recall of 0.962 and AP of 0.993.

Adhesion systems are very important in robots for infrastructure inspection (especially in vertical walls). They present the challenge of optimizing the ratio vacuum/power consumption in battery-powered robots. In this paper a CFD (Computer Fluid Dynamics) modelling and optimization process of a robot adhesion system is carried out to determine the best performing configuration in terms of vacuum and power consumption. Analytical and numerical models were developed to predict the behaviour of the system for different configurations. The models were validated, using test rig measurements, by calibrating an arbitrary defined inlet height that simulates the leakage flow. Then, different geometric parameters were varied to determine the best performing configuration based on the vacuum/power consumption ratio value. The model presented in the paper was capable of predicting the behaviour of the system for different configurations, with a margin of error of 15% for the vacuum prediction and a 25% for the motor power calculation. Finally, the model was used to optimize parameters of the system, like the number of blades of the impeller. The adhesion system was conceived for the modular autonomous climbing legged robot ROMERIN.

Purpose: The use of smart devices has increased greatly in the last ten years with users reaching out to the possibility to do more with them especially in the networking front. In this context there is a need to understand the connection between users’ social demographic factors and their way to related to their smart devices. Objective: This study was designed to evaluate the senso of belonging of a community in order to evaluate intangible benefits that employees may gain from a more immerse relationship with their devices. Method: We used a dataset of 586 anonymous respondent of an existing survey designed for capturing the relationships that humans develop with their smart devices. In particular, we investigate the relationships with smart device and particular background variables of the respondents using a chi-square test. Results: The study showed that there is a significant relationship between users’ sex and smart device type and their dependency on smart device. Male tends to think that smart device (in general) enables them to connect with a larger community. At the same time, female using smart phones feels more connected more to large community than when using other smart devices. Conclusion: This study provided several significant findings that confirm and strength previous literature works on the subject. In addition, socio demographics variables (like gender) as well as the type of smart device present a correlation between the smart device users and their tendency to stay in touch with a larger community.

The objective of this study was aimed to study the sensory processes of the “human-computer interaction” model when classifying visual images with an incomplete set of signs based on the analysis of early, middle, late and slow components of event-related potentials (ERPs). 26 healthy subjects (men) aged 20-22 years were investigated. ERPs in 19 monopolar sites according to the 10/20 system were recorded. Discriminant and factor analysis were applied. The component N450 is the most specialized indicator of the perception of unrecognizable (oddball) visual images. The amplitude of the ultra-late components N750 and N900 is also higher under conditions of presentation of the oddball image, regardless of the location of the registration points. The results of the study are discussed in the light of the paradigm of the P300 wave application in brain-computer interface systems, as well as with the peculiarities in brain pathology. Promising directions for the development of studies of the “Brain Computer Interface” (BCI) P300 systems are to increase the throughput of information flows. To extend the application of the P300 ERPs to multiple modalities, the underlying physiological mechanisms and responses of the brain for a particular sensory system and mental function must be carefully examined.

Thanks to the continued development of experimental structural biology and the half-a-century old Protein Data Bank, 2021 saw a big step forward in the development of protein structure prediction with deep learning algorithms. Recently, DeepMinds AlphaFold has determined the structures of ∼ 200 million proteins from 1 million species. The speed of this progress raise the question of what becomes possible for computational drug discovery and design when we have a systems-wide account of the structures and motions of most proteins. Therefore, this article puts forward the concept of a general intermolecular binding affinity calculator (GIBAC): Kd = f(molA, molB, envPara), towards the acceleration of traditional computer-aided drug design (CADD) and artificial intelligence-integrated drug discovery (AIDD), for both small molecules and biologics such as therapeutic proteins.

Computer vision is considered as an ally to solve business problems that require human intervention, intelligence and criteria. This topic of research has evolved in XXI century at faster peace, delivering various alternatives from open source until commercial platforms. With so many options and market growing, it result difficult to make a decision on which one to use, or even worse, realize it was not suited for different scenarios. In this paper we analyze five options selected arbitrarily and tested on a dataset of 755 images to detect persons in an image, using object detectors. We analyze elapsed time to process an image, error with observations by humans, number of persons detected, correlation of time and person density, object detected size and F1 Score, considering precision and recall. As we found there are score ties and similar behaviors among options available, we introduce a novel index that takes in consideration the number of persons and their pixel size, to propose the Vision Acuity Index of Computer Vision. The results demonstrate this is a good option to serve as indicator to make decisions. Also, this index proposed have a potential to be expanded for different business use cases, and to measure new proposed algorithms in the future along with the traditional metrics used previously.

The growth of solid particles during liquid phase sintering was modeled by the Cellular Automata method. The binary phase diagram and Fickian approach for the diffusion process were applied to simulate the chemical composition variation in liquid and solid phases during sintering. The Oswald-Ripening effect was considered during the dissolution of the solid phase in the liquid phase. It is used to define the probability of solid-phase dissolution by the liquid phase and develop the model to simulate the alloy with solid solubility. So, the microstructure could be modeled in the liquid phase sintering process.

In order to design efficient and sustainable office spaces and to automate lighting, heating and air circulation in these facilities, solving the challenge of occupancy prediction is crucial. In office spaces where large areas need to be observed, multiple sensors must be used for full coverage. In these cases, it is normally important to keep the costs low, but also to make sure that the privacy of the people who use such environments are preserved. Low-cost and low-resolution heat (thermal) sensors can be very useful to build solutions that address these concerns. However, they are extremely sensitive to noise artifacts which might be caused by heat prints of the people who left the space or by other objects which are either using electricity or exposed to sunlight. There are some earlier solutions for occupancy prediction that employ low-resolution heat sensors, however, they have not addressed nor compensate for such heat artifacts. Therefore, in this paper, we present a low-cost and low-energy consuming smart space implementation to predict the number of people in the environment based on whether their activity is static or dynamic in time. We use a low-resolution (8×8) and non-intrusive heat sensor to collect data from an actual meeting room. We propose two novel workflows to predict the occupancy; one based on computer vision and one based on machine learning. Besides comparing the advantages and disadvantages of these different workflows, we use several state-of-the-art explainability methods in order to provide a detailed analysis of the algorithm parameters and how the image properties influence the resulting performance. Furthermore, we analyze noise resources which affect the heat sensor data. We hope that our analysis brings light into understanding how to handle very low-resolution heat images in these environments. The presented workflows could be used in various domains and applications other than smart offices, where occupancy prediction is essential, e.g., for elderly care.

Convolutional neural network (CNN), a class of artificial neural network (ANN) is attracting interests of researchers in all research domain. CNN was invented for computer vision. They have also shown to be useful for semantic parsing, sentence modeling and other natural language processing related tasks. Here in this paper we discuss the basics of CNN models and their scope to provide a reference/baseline to the researchers interested in using CNN models in their research.

As the Autonomous Vehicle (AV) industry is rapidly advancing, classification of non-motorized (vulnerable) road users (VRUs) becomes essential to ensure their safety and to smooth operation of road applications. The typical practice of non-motorized road users’ classification usually takes numerous training time and ignores the temporal evolution and behavior of the signal. In this research effort, we attempt to detect VRUs with high accuracy be proposing a novel framework that includes using Deep Transfer Learning, which saves training time and cost, to classify images constructed from Recurrence Quantification Analysis (RQA) that reflect the temporal dynamics and behavior of the signal. Recurrence Plots (RPs) were constructed from low-power smartphone sensors without using GPS data. The resulted RPs were used as inputs for different pre-trained Convolutional Neural Network (CNN) classifiers including constructing 227×227 images to be used for AlexNet and SqueezeNet; and constructing 224×224 images to be used for VGG16 and VGG19. Results show that the classification accuracy of Convolutional Neural Network Transfer Learning (CNN-TL) reaches 98.70%, 98.62%, 98.71%, and 98.71% for AlexNet, SqueezeNet, VGG16, and VGG19, respectively. The results of the proposed framework outperform other results in the literature (to the best of our knowledge) and show that using CNN-TL is promising for VRUs classification. Because of its relative straightforwardness, ability to be generalized and transferred, and potential high accuracy, we anticipate that this framework might be able to solve various problems related to signal classification.

Intracortical microelectrodes that can be implanted have the ability to capture fast-changing neuron action potentials in the living brain. Despite this potential, many obstacles must still be overcome for reliable, long-term, high-quality recordings and accurate analysis of brain activity. These challenges include improving recording quality, enhancing recording stability, increasing recording capacity, and adding multi-functional features. One potential approach to enhance implantable microelectrodes involves the advancement of materials, refinement of implantation methodologies, and augmentation of the number of sites for recording. However, these challenges are still being actively researched, and advancements in microelectrode technology are underway. The difficulties with implantable microelectrodes are evaluated, and solutions are presented from different perspectives. The latest advancements in microelectrode technology are reviewed and condensed, and future possibilities are explored.

As people grow a custom to effortless outdoor navigation there is a rising demand for similar possibility indoors as well. Unfortunately, indoor localization, being one of the necessary requirements for navigation, continues to be problem without a clear solution. In this article we are proposing a method for an indoor positioning system using a single image. This is made possible using small preprocessed database of images with known control points as the only preprocessing needed. Using feature detection with SIFT algorithm we can look through the database and find image which is the most similar to the image taken by user. Pair of images is then used to find coordinates of database image using PnP problem. Furthermore, projection and essential matrices are determined allowing for the user image localization ~ determining the position of the user in indoor environment. Benefits of this approach lies in the single image being the only input from user and no requirements for new onsite infrastructure and thus enables a simpler realization for the building management.

The feasibility of a steady-state visual evoked potential (SSVEP) brain-computer interface (BCI) with a single flicker stimulus for multiple-target decoding has been demonstrated in a number of recent studies. The single-flicker BCIs have mainly employed the direction information for encoding the targets, i.e. different targets are placed at different spatial directions relative to the flicker stimulus. The present study explored whether visual eccentricity information can also be used to encode target for the purpose of increasing the number of targets in the single-flicker BCIs. A total number of 16 targets were encoded, placed at eight spatial directions, and two eccentricities (2.5° and 5°) relative to a 12 Hz flicker stimulus. Whereas distinct SSVEP topographies were elicited when participants gazed at targets of different directions, targets of different eccentricities were mainly represented by different signal-to-noise ratios (SNRs). Using a canonical correlation analysis-based classification algorithm, simultaneous decoding of both direction and eccentricity information was achieved, with an average offline 16-class accuracy of 66.8±16.4% averaged over 12 participants and a best individual accuracy of 90.0%. Our results demonstrate a single-flicker BCI with a substantially increased target number towards practical applications.

The use of commodity polymers such as polypropylene (PP) is key to open new market segments and applications for the additive manufacturing industry. Technologies such as powder-bed fusion (PBF) can process PP powder; however, much is still to learn concerning process parameters for reliable manufacturing. This study focusses in the process-property relationships of PP using laser-based PBF. The research presents an overview of the intrinsic and the extrinsic characteristic of a commercial PP powder as well as fabrication of tensile specimens with varying process parameters to characterize tensile, elongation at break, and porosity properties. The impact of key process parameters, such as power and scanning speed are systematically modified in a controlled design of experiment. The results were compared to the existing body of knowledge; the outcome is to present a process window and optimal process parameters for industrial use of PP. The computer tomography data revealed a highly porous structure inside specimens ranging between 8.46% and 10.08%, with porosity concentrated in the interlayer planes in the build direction. The results of the design of experiment for this commercial material show a narrow window of 0.122 ≥ Ev ≥ 0.138 J/mm³ led to increased mechanical properties while maintaining geometrical stability.

Lung cancer, globally recognized as the leading cause of cancer-related mortality, presents a pressing need for innovative treatment strategies. Increasing evidence points towards the potential of monoamine oxidase (MAO) inhibitors in modulating cancer progression, primarily due to their role in regulating cellular metabolism and apoptotic pathways. This extraordinary study delves into the multifaceted therapeutic implications of MAO inhibitors in lung cancer management, elucidating their ability to pave the way for clinical advancement via novel targets and pathways. Capitalizing on recent advances in molecular biology, this research explores the molecular mechanisms underlying the anticancer actions of MAO inhibitors. By examining their impact on cell proliferation, migration, invasion, and apoptosis in lung cancer cell lines, we provide insight into the intricate signaling pathways and biochemical processes modulated by these compounds. The study also investigates the synergistic effects of MAO inhibitors in combination with established chemotherapeutic agents, elucidating their potential to enhance treatment efficacy and reduce cytotoxicity. Furthermore, we delve into the implications of MAO inhibitors on the tumor microenvironment, evaluating their influence on angiogenesis, immune regulation, and the epithelial-mesenchymal transition. This comprehensive analysis enables the identification of novel molecular targets, which hold promise for the development of advanced therapeutics tailored for specific lung cancer subtypes. To substantiate our findings, in this review, we thoroughly analyze the existing literature using state-of-the-art bioinformatics tools." in vivo orthotopic lung cancer models, and clinical patient cohorts. This robust and multidisciplinary approach permits the generation of clinical evidence supporting the integration of MAO inhibitors in lung cancer management. Conclusion: This ground-breaking study establishes the therapeutic potential of monoamine oxidase inhibitors in lung cancer management by unearthing novel pathways and targets for clinical advancement. The outcomes open doors for the development of innovative treatment strategies and inspire continued research efforts to combat the global burden of lung cancer.

Functional connectivity (FC) is a potential candidate that can increase the performance of brain-computer interfaces (BCIs) in the elderly because of its compensatory role in neural circuits. However, it is difficult to decode FC by current machine learning techniques because of a lack of its physiological understanding. To investigate the suitability of FC in BCI for the elderly, we propose the decoding of lower- and higher-order FCs using a convolutional neural network (CNN) in six cognitive-motor tasks. The layer-wise relevance propagation (LRP) method describes how age-related changes in FCs impact BCI applications for the elderly compared to younger adults. Seventeen younger (24.5±2.7 years) and twelve older (72.5±3.2 years) adults were recruited to perform tasks related to hand-force control with or without mental calculation. CNN yielded a six-class classification accuracy of 75.3% in the elderly, exceeding the 70.7% accuracy for the younger adults. In the elderly, the proposed method increases the classification accuracy by 88.3% compared to the filter-bank common spatial pattern (FBCSP). LRP results revealed that both lower- and higher-order FCs were dominantly overactivated in the prefrontal lobe depending on task type. These findings suggest a promising application of multi-order FC with deep learning on BCI systems for the elderly.

Manual interpretation of these huge amounts of image volumes are susceptible to inter-reader variability and human error. Thus, accurate automated CAD scheme is highly desirable in clinical pathological diagnosis. In this research, plethora of machine learning paradigms (e.g. feature extraction, dimensionality reduction and supervised classification methods) were explored, evaluated, compared and analyzed to identify the optimal pathway for brain MR images (normal vs neoplastic) binary classification task. External validation dataset was used to test the generalizability of the optimal predictive models implemented. Relevant and informative features were selected to construct cross-validated decision tree and eventually simple rule set was built based on the decision tree. The experimental results show that almost all pattern recognition paradigms achieve high accuracy with careful selection of number of attributes. LDA+ELM with 55 features are the optimal pipelines which achieve perfect classification when training and test data are of same source; and achieving (accuracy=97.5%, AUC=0.989, sensitivity=95% and specificity=100%) under balanced test dataset; (accuracy=99.5%, AUC=0.988, sensitivity=95% and specificity=100%). Cross-validated decision tree model also shows comparable performance: accuracy=98.8%, AUC=99.1%, sensitivity=99.6% and specificity=98.2%. Three highly relevant and robust attributes are visualized and selected for construction of decision tree models and finally a rule sets are read directly off the decision tree. This rule sets can potentially serve as fast and accurate classification algorithm.

Gyroscopes are fascinating instruments with a history of about 200 years. When J.G.F. Bohnenberger presented his machine to his students in 1810 at the University of Tuebingen, Germany, nobody could have foreseen that this fascinating development would be used for complex orientation and positioning. At the University of Stuttgart, Germany, a collection of 160 exhibits is available and in transition for a sustainable future. Here, the systems are digitized in 2D, 2.5D and 3D and are made available for a world-wide community using OpenAccess platforms. The technologies being used are Computed Tomography, Computer Vision, Endoscopy and Photogrammetry. The workflows for combining voxel representations and colored point clouds are described, to create Digital Twins of the tangible assets. Advantages and disadvantages are discussed und work for near future is outlined in this new and challenging field of Tech Heritage digitization.

Background: Processing whole-slide images (WSI) to train neural networks can be intricate and laborious. We developed an open-source library covering recurrent tasks in processing of WSI and in evaluating the performance of the trained networks for classification tasks. Methods: Two histopathology use-cases were selected. First we aimed to train a CNN to distinguish H&amp;E-stained slides obtained from neuropathologically classified low-grade epilepsy-associated dysembryoplastic neuroepithelial tumor (DNET) and ganglioglioma (GG). The second project we trained a convolutional neural network (CNN) to predict the hormone expression of pituitary adenoms only from hematoxylin and eosin (H&amp;E) stained slides. In the same approach, we addressed the issue to also predict clinically silent corticotroph adenoma. We included four clinico-pathological disease conditions in a multilabel approach. Results: Our best performing CNN achieved an area under the curve (AUC) of 0.97 for the receiver operating characteristic (ROC) for corticotroph adenoma, 0.86 for silent corticotroph adenoma and 0.98 for gonadotroph adenoma. Our DNET-GG classifier achieved an AUC of 1.00 for the ROC curve. All scores were calculated with the help of our library on predictions on a case basis. Conclusions: Our comprehensive library is most helpful to standardize the work-flow and minimize the work-burden in training CNN. It is also compatible with fastai. Indeed, our new CNNs reliably extracted neuropathologically relevant information from the H&amp;E staining only. This approach will supplement the clinico-pathological diagnosis of brain tumors, which is currently based on cost-intense microscopic examination and variable panels of immunohistochemical stainings.

Mice are one of the frequently used animal models in science research whose behavioral characteristics can provide much valuable information in biology, neuroscience, and pharmacology. Nowadays, artificial intelligence is widely used in mice behavior analysis. Integrated AI systems such as ChatGPT and VisualGPT are already available, and we discuss the feasibility of MiceGPT to help researchers identify and classify mouse behavior more easily. We review the applications of mice behavior analysis, analyze the tasks of deep learning on these applications based on an AI pyramid, and finally summarize the AI approaches to solve these tasks. Based on these summaries, we propose three MiceGPT architectures to demonstrate the theoretical feasibility of MiceGPT.

The use of visual signals in horticulture has attracted significant attention and encompassed a wide range of data types such as 2D images, videos, hyperspectral images, and 3D point clouds. These visual signals have proven to be valuable in developing cutting-edge computer vision systems for various applications in horticulture, enabling plant growth monitoring, pest and disease detection, quality and yield estimation, and automated harvesting. However, unlike other sectors, developing deep learning computer vision systems for horticulture encounters unique challenges due to the limited availability of high-quality training and evaluation datasets necessary for deep learning models. This paper investigates the current status of vision systems and available data in order to identify the high-quality data requirements specific to horticultural applications. We analyse the impact of the quality of visual signals on the information content and features that can be extracted from these signals. To address the identified data quality requirements, we explore the usage of a deep learning-based super-resolution model for generative quality enhancement of visual signals. Furthermore, we discuss how these can be applied to meet the growing requirements around data quality for learning-based vision systems. We also present a detailed analysis of the competitive quality generated by the proposed solution compared to cost-intensive hardware-based alternatives. This work aims to guide the development of efficient computer vision models in horticulture by overcoming existing data challenges and paving a pathway forward for contemporary data acquisition.

This code provides computational facilities to simulate current versus time during the charging of Li-ion cells at desire constant voltage by considering multiscale physical phenomena. This code only considers a powder of active materials (at microscale or nanoscale) and a small part of electrolyte around it as a half cell. Then it is extended to a complete cell by applying correct boundary conditions. This code is very useful by modifying code parameters to understand the effect of the complex shape of active materials powder (surface area and powder size), kind of electrolyte, and the applied voltages on the charging response of Li-ion cell. As a summary, a microscale approach to the design of Li-ion cells has been provided via this code.

Deep Neutral Networks (DNNs) were initially proposed towards the midst of the 20th century, motivated by the neural structure and mechanism of the human brain. Thanks to major ad-vancements in computational resources, during the past decade DNN-based systems have demonstrated unprecedented performance in terms of accuracy and speed. However, recent work has shown that such models may not be sufficiently robust during the inference process. Furthermore, due to the data-driven learning nature of DNNs, designing interpretable and gen-eralizable networks is a major challenge, especially in critical applications, such as medical Computer Aided Diagnostics (CAD) and other medical imaging tasks, including classification, regression and reconstruction. Within this context, a line of physics-driven approaches for deep learning has recently emerged, aimed at improving the stability and generalization capacity of DNNs for medical imaging applications. In this paper, we review recent work focused on phys-ics-driven or prior-information learning for a variety of imaging modalities and medical applica-tions. We discuss how the inclusion of domain-related knowledge into the learning process and networks’ design supports their stability and generalization capability. In addition, we highlight current and future challenges within this scope.

Diffuse interstitial lung diseases (DILD) are a heterogeneous group of over 200 entities, some with dramatical evolution and poor prognostic. Because of their overlapping clinical, physiopathological and imagistic nature, successful management requires early detection and proper progression evaluation. This paper tests a complex networks (CN) algorithm for imagistic aided diagnosis fitness for the possibility of achieving relevant and novel DILD management data. 65 DILD and 31 normal high resolution computer tomography (HRCT) scans were selected and analyzed with the CN model. The algorithm is showcased in two case reports and then statistical analysis on the entire lot shows that a CN algorithm quantifies progression evaluation with a very fine accuracy, surpassing functional parameters’ variations. The CN algorithm can also be successfully used for early detection, mainly on the ground glass opacity Hounsfield Units band of the scan.

Background Bone fracture fixation surgery is one of the most commonly performed surgical procedures in the orthopedic field. However, fracture healing complications occur frequently, and the choice of the most optimal surgical approach often remains challenging. In the last years, computational tools have been developed with the aim to assist preoperative planning procedures of bone fracture fixation surgery. Objectives The aims of this review are (1) to provide a comprehensive overview of the state-of-the-art in computer-assisted preoperative planning of bone fracture fixation surgery, (2) to assess the clinical feasibility of the existing virtual planning approaches, and (3) to assess their clinical efficacy in terms of clinical outcomes as compared to conventional planning methods. Methods A literature search was performed in the MEDLINE-PubMed, Ovid-EMBASE, Ovid-EMCARE, Web of Science, and Cochrane libraries to identify articles reporting on the clinical use of computer-assisted preoperative planning of bone fracture fixation. Results 79 articles were included to provide an overview of the state-of-the art in virtual planning. While patient-specific geometrical model construction, virtual bone fracture reduction, and virtual fixation planning are routinely applied in virtual planning, biomechanical analysis is rarely included in the planning framework. 21 of the included studies were used to assess the feasibility and efficacy of computer-assisted planning methods. The reported total mean planning duration ranged from 22 to 258 minutes in different studies. Computer-assisted planning resulted in reduced operation time (Standardized Mean Difference (SMD): -2.19; 95% Confidence Interval (CI): -2.87, -1.50), less blood loss (SMD: -1.99; 95% CI: -2.75, -1.24), decreased frequency of fluoroscopy (SMD: -2.18; 95% CI: -2.74, -1.61), shortened fracture healing times (SMD:-0.51; 95% CI: -0.97, -0.05) and less postoperative complications (Risk Ratio (RR): 0.64, 95% CI: 0.46, 0.90). No significant differences were found in hospitalization duration. Some studies reported improvements in reduction quality and functional outcomes but these results were not pooled for meta-analysis, since the reported outcome measures were too heterogeneous. Conclusions Current computer-assisted planning approaches are feasible to be used in clinical practice and have been shown to improve clinical outcomes. Including biomechanical analysis into the framework has the potential to further improve clinical outcome.

This study's main target was to analyze and design rectangular edges supported two ways solid slabs by using ES EN 1992-1-1:2015. Slab design is often carried out either manually or with the use of design and analytic software. The researcher sees that some software cannot accept some countries' standard codes. For example, currently in Ethiopia analysis and design of two-way solid slab is done using readily available Excel sheet template. But working with this might have many problems, firstly the structure that is already analyzed by SAP or SAFE or any other international software application that uses international codes but which cannot design structure using ES EN 1992; for instant euro codes and designed by excel sheet can create failure and uneconomical analysis and design result. In this paper, the slab is designed and analyzed based on the chosen concrete grade, chosen reinforcement bar diameter, chosen steel grade for design and analysis of slabs calculations like load, moment, shear, and deflection checking using the moment coefficient method for analysis and design and Microsoft Visual Basic 2010 for coding. All input values are given by the International Standard units and are also used to represent output values. Using manual calculations delays time and mostly the result is not correct. But using this Computer program can increase computation accuracy and save time. The procedure the researcher followed is, first the manual calculation has been done and then SADSE2021 has been done. The result is that both are 99.9% identical, and the disadvantage of this method is that it cannot be used to determine the detailed drawing.

In this paper, we propose an imagined speech-based brain wave pattern recognition using deep learning. Multiple features were extracted concurrently from eight-channel Electroencephalography (EEG) signals. To obtain classifiable EEG data with fewer number of sensors, we placed the EEG sensors on carefully selected spots on the scalp. To decrease the dimensions and complexity of the EEG dataset and to avoid overfitting during the deep learning algorithm, we utilized the wavelet scattering transformation. A low-cost 8-channel EEG headset was used with MATLAB 2023a to acquire the EEG data. The Long-Short Term Memory Recurrent Neural Network (LSTM-RNN) was used to decode the identified EEG signals into four audio commands: Up, Down, Left, and Right. Wavelet scattering transformation was applied to extract the most stable features by passing the EEG dataset through a series of filtration processes. Filtration has been implemented for each individual command in the EEG datasets. The proposed imagined speech-based brain wave pattern recognition approach achieved a 92.50% overall classification accuracy. This accuracy is promising for designing a trustworthy imagined speech-based Brain-Computer Interface (BCI) future real-time systems. For better evaluation of the classification performance, other metrics were considered, and we obtained 92.74%, 92.50% and 92.62% for precision, recall, and F1-score, respectively.

Melanoma is considered to be the most aggressive form of skin cancer. Due to the similar shape of malignant and benign cancerous lesions, doctors spend considerably more time when diagnosing these findings. At present, the evaluation of malignancy is performed primarily by invasive histological examination of the suspicious lesion. Developing an accurate classifier for early and efficient detection can minimize and monitor the harmful effects of skin cancer and increase patient survival rates. This paper proposes a multi-class classification task using the CoAtNet architecture, a hybrid model that combines the depthwise convolution matrix operation of traditional convolutional neural networks with the strengths of Transformer models and self-attention mechanics to achieve better generalization and capacity. The proposed multi-class classifier achieves an overall precision of 0.901, recall 0.895, and AP 0.923, indicating high performance compared to other state-of-the-art networks.

There is a need for cost-effective and non-invasive methods of monitoring feeding behavior in livestock operations, considering the significant impact of feed costs on economic efficiency and assisting in detecting health issues of group-fed animals. This paper proposes using deep learning-based computer vision techniques to detect pen-fed beef cattle feeding behavior using Mask Region-based Convolutional Neural Network (RCNN). A deep learning model was pre-trained on the Common Objects in Context (COCO) dataset to generate cattle instance segmentation. Manually defined feed bunk polygons are compared with these segmentation masks to derive feeding time for each bunk. A full day’s worth of video data and the corresponding physical sensor data are collected for the experiment. By benchmarking the computer vision detected data with physical ground truth over random time segments from morning to evening (thus various lighting conditions), the optimal thresholds for Mask RCNN are determined to be 0.7 for bounding boxes and 0.1 for masks. Using these parameters. The reports suggest that the computer vision system achieved a precision of 87.2% and a recall of 89.1%, signifying precise detection of feeding events. Our study, to the best of our knowledge, was one of the first investigations of instance segmentation on feeding time sense, which combines deep learning methods with traditional computer vision logistics, reporting on feeding time data collection and processing, camera testing and adjustment, and performance evaluation. Future research directions include computer vision applied in feed grading and animal re-identification for individual production analysis.

Mental stress state recognition using electroencephalogram (EEG) signals for real-life applications needs a conventional wearable device. This requires an efficient number of EEG channels and an optimal feature set. The main objective of the study is to identify an optimal feature subset that can best discriminate mental stress states while enhancing the overall performance. Thus, multi-domain feature extraction methods were employed, namely, time domain, frequency domain, time-frequency domain, and network connectivity features, to form a large feature vector space. To avoid the computational complexity of high dimensional space, a hybrid feature selection (FS) method of minimum Redundancy Maximum Relevance with Particle Swarm Optimization and Support Vector Machine (mRMR-PSO-SVM) is proposed to remove noise, redundant, and irrelevant features and keep the optimal feature subset. The performance of the proposed method is evaluated and verified using four datasets, namely EDMSS, DEAP, SEED, and EDPMSC. To further consolidate, the effectiveness of the proposed method is compared with that of the state-of-the-art heuristic methods. The proposed model has significantly reduced the features vector space by an average of 70% in comparison to the state-of-the-art methods while significantly increasing overall detection performance.

Tracking and early identification of suspected cases are essential to control and prevent potential COVID-19 outbreaks. One of the most popular techniques used to track this disease is the use of Infrared cameras to identify individuals with elevated body temperatures. However, they are limited by their inability to be implemented in open public settings such as public parks or even outdoor recreational centers. This limits their ability to effectively track possible COVID-19 patients as open public recreational places such as parks, concert venues and other public venues are hotspots for the spreading of the virus. Other technological solutions such as thermal scanners require an individual to perform the actual testing as they are not individual standalone technologies. This method of testing can potentially cause the transmission of the virus between the tester and the individual getting tested. As can be seen, an alternative solution is essential to solving this issue. In this study, we aim to present the system, design and potential scope of a non-invasive system that can diagnose and identify potential COVID-19 patients using thermal and optical images of the individual using drone technology. The proposed system (COVIDRONE) combines multi-modal machine intelligence, computer vision and real-time monitoring to enable scalable monitoring. The system will also involve the use of machine learning algorithms for better and more accurate diagnosis. We envisage that development of such technologies may help in developing technological solutions to combat infectious disease threats in the future pandemics.

Purpose The gradual increase in geriatric issues and global imbalance of the ratio between patients and healthcare professionals has created a demand for intelligent systems with the least error-prone diagnosis results to be used by less medically trained persons and save clinical time. This paper aims at investigating the development of an image-based colourimetric analysis. The purpose of recognising such tests is to support wider users to begin a colourimetric test to be used at homecare settings, telepathology, etc. Design/methodology/approach The concept of an automatic colourimetric assay detection is delivered by utilising two cases. Training Deep Learning (DL) models on thousands of images of these tests using transfer learning, this paper i) classifies the type of the assay, and ii) classifies the colourimetric results. Findings This paper demonstrated that the assay type can be recognised using DL techniques with 100% accuracy within a fraction of a second. Some of the advantages of the pre-trained model over the calibration-based approach are robustness, readiness and suitability to deploy for similar applications within a shorter period of time. Originality/value To the best of our knowledge, this is the first attempt to provide Colourimetric Assay Type Classification (CATC) using DL. Humans are capable to learn thousands of visual classifications in their life. Object recognition may be a trivial task for humans, due to photometric and geometric variabilities along with the high degree of intra-class variabilities it can be a challenging task for machines. However, transforming visual knowledge into machines, as proposed, can support non-experts to better manage their health and reduce some of the burdens on experts.

Recent digital transformations have significantly impacted the current society, economy, and culture. This includes adoption of rapidly emerging technologies, such as artificial intelligence (AI), immersive technology (e.g., virtual, augmented, mixed and extended reality (VR/AR/MR/XR)), cloud services, and internet of things, in our day-to-day life. `Metaverse’ has emerged as a technology that incorporates many of these transforming technologies to deliver personalised immersive experience related to specific application areas. A potential major beneficiary of Metaverse is the educational ecosystem, where highly immersive learning experiences can be delivered to students, catering to their personal learning needs and train them on life skills such as empathy, ethical qualities, and communication skills. The present-day Metaverse allows physical engagement, enabling users to utilise limb movements to interact with the presented materials. However, ethical concerns and technical limitations impede the widespread implementation of the Metaverse in the real-world. This paper provides a comprehensive survey of the existing literature covering the architecture, type and components of Metaverse, followed by practical aspects of metaverse implementation. The paper is concluded by discussing on the open challenges and issues along with their mitigation strategies and future research directions after the detailed usecase of Metaverse in Education.

In recent years, due to the advancement of machine learning, object detection has become a mainstream task in the computer vision domain. The first phase of object detection is to find the regions where objects can exist. With the improvement of deep learning, traditional approaches such as sliding windows and manual feature selection techniques have been replaced with deep learning techniques. However, object detection algorithms face a problem when performing in low light, challenging weather, and crowded scenes like any other task. Such an environment is termed a challenging environment. This paper exploits pixel-level information to improve detection under challenging situations. To this end, we exploit the recently proposed hybrid task cascade network. This network works collaboratively with detection and segmentation heads at different cascade levels. We evaluate the proposed methods on three complex datasets of ExDark, CURE-TSD, and RESIDE and achieve an mAP of 0.71, 0.52, and 0.43, respectively. Our experimental results assert the efficacy of the proposed approach.

Cloud-based CNC is an emerging paradigm of Industry 4.0 where computer numerical control (CNC) functionalities are moved to the cloud and provided to manufacturing machines as a service. Among many benefits, C-CNC allows manufacturing machines to leverage advanced control algorithms running on cloud computers to boost their performance at low cost, without need for major hardware upgrades. However, a fundamental challenge of C-CNC is how to guarantee safety and reliability of machine control given variable Internet quality of service, especially on public Internet networks. We propose a three-tier redundant architecture to address this challenge. We then prototype tier one of the architecture on a 3D printer successfully controlled via C-CNC over public Internet connections, and discuss follow-on research opportunities.

The use of Attitude and Heading Reference Systems (AHRS) for orientation estimation is now common practice in a wide range of applications, e.g., robotics and human motion tracking, aerial vehicles and aerospace, gaming and virtual reality, indoor pedestrian navigation and maritime navigation. The integration of the high-rate measurements can provide very accurate estimates, but these can suffer from errors accumulation due to the sensors drift over longer time scales. To overcome this issue, inertial sensors are typically combined with additional sensors and techniques. As an example, camera-based solutions have drawn a large attention by the community, thanks to their low-costs and easy hardware setup; moreover, impressive results have been demonstrated in the context of Deep Learning. This work presents the preliminary results obtained by DOES , a supportive Deep Learning method specifically designed for maritime navigation, which aims at improving the roll and pitch estimations obtained by common AHRS. DOES recovers these estimations through the analysis of the frames acquired by a low-cost camera pointing the horizon at sea. The training has been performed on the novel ROPIS dataset, presented in the context of this work, acquired using the FrameWO application developed for the scope. Promising results encourage to test other network backbones and to further expand the dataset, improving the accuracy of the results and the range of applications of the method.

Motor imagery(MI)-based Brain-Computer Interfaces (BCI) have shown increased potential for the rehabilitation of stroke patients; nonetheless, their implementation in clinical practice has been restricted due to their low accuracy performance. To date, although a lot of research has been made in benchmarking and highlighting the most valuable classification algorithms in BCI configurations, most of them are using offline data and not from real BCI performance during the closed-loop (or online) sessions. Since rehabilitation training relies on the availability of an accurate feedback system, we surveyed articles of current and past EEG-based BCI frameworks who report the online classification of the movement of two upper limbs in both healthy volunteers and stroke patients. We found that the recently developed Deep Learning methods do not outperform the traditional machine learning algorithms. In addition, patients and healthy subjects exhibit similar classification accuracy in current BCI configurations. Lastly, in terms of neurofeedback modality, Functional Electrical Stimulation (FES) yielded the best performance compared to non-FES systems.

Increased screen time during the COVID-19 pandemic and the accelerated incorporation of technology into daily practices have directly contributed to the increased prevalence of eye disease. The human eye is not built for continual technological use and must adjust to exposure to digital screens. Overexposure to technology can result in eye strain and an increased risk of eye diseases such as myopia with complications later in life, including retinal tears, cataracts, and macular degeneration. As vision and ocular diseases become more prevalent, the study of eye diseases, including their causes and treatments, is especially relevant. There is a need to prevent ocular diseases before they become a burden to individuals, their families, and their communities by better understanding the pathophysiology of ocular diseases. Several ocular diseases are thought to be caused by imbalances in reactive oxygen species (ROS) and autophagy. ROS refers to a class of highly reactive oxygen-containing molecules that can undergo damaging reactions with other molecules in the cell. Autophagy is a cellular process of self-eating whereby damaged, harmful, or dead material in cells is broken down to maintain cellular homeostasis. This paper discusses the role of ROS and autophagy in the pathogenesis of computer vision syndrome (CVS), digital eye strain (DES), myopia, cataracts, glaucoma, diabetic retinopathy (DR), age-related macular degeneration (AMD), optic nerve crush injury (OCN), optic nerve gliomas, and retinoblastoma. Lifestyle changes such as limiting screen time, ensuring adequate exposure to evening sunlight, and using blue light protection measures or protective eyewear are important in the prevention of eye disease. Nutrition also contributes significantly to eye health. A balanced diet rich in vitamins and antioxidants may help to prevent ROS and autophagy imbalance-induced eye disease. Medical and surgical treatments become necessary when preventative measures fail. This paper also addresses how government measures to decrease the onset of eye disease, including targeted programs to increase access to fresh vegetables and fruits in food deserts, are a critical macro-level avenue to reduce eye disease prevalence in the US, which according to the National Eye Institute, costs $139 billion yearly.

The human mandible's cancellous bone, which is characterized by its unique porosity and directional sensitivity to external forces, is crucial for sustaining biting stress. Traditional CAD models fail to fully represent bone's anisotropic structure and thus depend on simple isotropic assumptions. For our research, we use the latest versions of nTopology and Creo Parametric software to make biomimetic Voronoi lattice models that accurately reflect the complex geometry and mechanical properties of trabecular bone. The porosity of human cancellous bone is accurately modeled in this work using biomimetic Voronoi lattice models. The porosities range from 70% to 95%, which can be achieved by changing the pore sizes to 1.0 mm, 1.5 mm, 2.0 mm, and 2.5 mm. Finite element analysis (FEA) was used to examine the displacements, stresses, and strains acting on dental implants with a buttress thread, abutment, retaining screw, and biting load surface. The results show that the Voronoi model accurately depicts the complex anatomy of the trabecular bone in the human jaw, compared to standard solid block models. The ideal pore size for biomimetic Voronoi lattice trabecular bone models is 2 mm, taking into account both the von Mises stress distribution over the dental implant, screw retention, cortical bone, cancellous bone, and micromotions. This pore size displayed balanced performance by successfully matching natural bone's mechanical characteristics. Advanced finite element analysis (FEA) improves biomechanical understanding of how bones and implants interact by creating more accurate models of biological problems and dynamic loading situations. This makes biomechanical engineering better.

Research on Traffic Light Recognition (TLR) has grown in recent years, primarily driven by the growing interest in autonomous vehicles development. Machine Learning algorithms have been widely used to that purpose. Mainstream approaches, however, require large amount of data to properly work, and as a consequence, a lot of computational resources. In this paper we propose the use of Expert Instruction (IE) as a mechanism to reduce the amount of data required to provide accurate ML models for TLR. Given an image of the exterior scene taken from the inside of the vehicle, we stand the hypothesis that the picture of a traffic light is more likely to appear in the central and upper regions of the image. Frequency Maps of traffic light location were thus constructed to confirm this hypothesis. The frequency maps are the result of a manual effort of human experts in annotating each image with the coordinates of the region where the traffic light appears. Results show that EI increased the accuracy obtained by the classification algorithm in two different image datasets by at least 15%. Evaluation rates achieved by the inclusion of EI were also higher in further experiments, including traffic light detection followed by classification by the trained algorithm. The inclusion of EI in the PCANet achieved a precision of 83% and recall of 73% against 75.3% and 51.1%, respectively, of its counterpart. We finally presents a prototype of a TLR Device with that expert model embedded to assist drivers. The TLR uses a smartphone as a camera and processing unit. To show the feasibility of the apparatus, a dataset was obtained in real time usage and tested in an Adaptive Background Suppression Filter (AdaBSF) and Support Vector Machines (SVMs) algorithm to detect and recognize traffic lights. Results show precision of 100% and recall of 65%.

To solve two key problems in the identification of people who are infected with COVID-19: the first problem is that the identification accuracy is not high enough. The second problem is that present identification method such as nucleic acid testing is expensive in many countries. Methods: So, I decided to design a fast identification method for COVID-19 patients which is based on deep learning. After the model (CoughNet) learns more than 6,000 cough spectrograms of both COVID-19 patients and normal people, the accuracy rate of identification of COVID-19 patients and normal people is higher than 99% in the test set. Structure: This paper is mainly divided into three parts: the first part introduces the main background and research status of the research; The second part introduces the research methods; The third part introduces the specific process of the experiment.

In the last decades, the automatic recognition and interpretation of brain waves acquired by electroencephalographic (EEG) technologies have undergone remarkable growth, leading to a consequent rapid development of Brain Computer Interfaces (BCIs). EEG-based BCIs are non-invasive systems that allow communication between a human being and an external device interpreting brain activity directly. Thanks to the advances in neurotechnologies, and especially in the field of wearable devices, BCIs are now also employed outside medical and clinical applications. Within this context, this paper proposes a systematic review of EEG-based BCIs, focusing on one of the most promising paradigms based on Motor Imagery (MI) and limiting the analysis to applications that adopt wearable devices. This review aims to evaluate the maturity levels of these systems, both from the technological and computational points of view. The selection of papers has been performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), leading to 84 publications considered in the last ten years (from 2012 to 2022). Besides technological and computational aspects, this review also aims at systematically list experimental paradigms and available datasets in order to identify benchmarks and guidelines for the development of new applications and computational models.

Objective: Design and validate a wireless, low-cost, easy-to-use, mobile, dry-electrode headset for scalp electroencephalography (EEG) recordings for closed-loop brain-computer (BCI) interface and internet-of-things (IoT) applications. Approach: The EEG-based BCI headset was designed from commercial off-the-shelf (COTS) components using a multi-pronged approach that balanced interoperability, cost, portability, usability, form factor, reliability, and closed-loop operation. Main Results: The adjustable headset was designed to accommodate 90\% of the population. A patent-pending self-positioning dry electrode bracket allowed for vertical self-positioning while parting the user's hair to ensure contact of the electrode with the scalp. In the current prototype, five EEG electrodes were incorporated in the electrode bracket spanning the sensorimotor cortices bilaterally, and three skin sensors were included to measure eye movement and blinks. An accelerometer provides monitoring of head movements. The EEG amplifier operates with 24 bits resolution up to 500 Hz sampling frequency, and can communicate with other devices using 802.11 {b/g/n/WiFi}. It has high SNR and CMRR (121 dB and 110 dB, respectively), and low input noise. In closed-loop BCI mode, the system can operate at 40 Hz, including real-time adaptive noise cancellation and 512 MB of processor memory. It supports LabVIEW as backend coding language and JS, CSS, HTML as front-end coding languages, and includes training and optimization of Support Vector Machine (SVM) neural classifiers. Extensive bench testing supports the technical specifications and human subject pilot testing of a closed-loop BCI application to support upper-limb rehabilitation provides proof-of-concept validation for device use at both the clinic and at home. Significance: The low-cost, accessibility, usability, interoperability, and programmability of the proposed closed-loop BCI system provides a low-cost solution for BCI and neurorehabilitation research and IoT applications.

This research aims to introduce a model, SectraBank, designed to mitigate cyber fraud attacks, specifically targeting SIM Swapping and Fake App schemes in mobile banking users. A quantitative methodology with an experimental design was meticulously employed to examine the rise of cyber fraud within mobile banking. The study involved a sample of 100 professionals with advanced knowledge in Information and Communication Technologies (ICTs). Surveys were utilized to measure awareness of threats and perceptions of security before and after the implementation of the proposed model. SectraBank leverages unique user identifiers such as fingerprint authentication and the International Mobile Equipment Identity (IMEI) number, in addition to geolocation and One Time Passwords (OTP) for validating electronic transactions in mobile banking. The effectiveness of the proposed model was notable, achieving a 96.1% efficacy rate in mitigating Fake App attacks and a 90.2% success rate in countering SIM Swapping attacks. These findings underscore the potential of SectraBank as a robust solution to enhance cybersecurity measures in the realm of mobile banking.

If a clear and valid no-go for Einsteinian hidden parameters is real, it must in no way be possible to violate the CHSH with a local hidden variables based computer simulation. In the paper we show that with the use of a modied Glauber-Sudarshan method it is possible to violate the CHSH.The criterion value comes close to the quantum value and is approximately 2.4. The proof (POC) is presented with the use of an R computer program. The important snippets of the code are discussed and the complete code is presented in an appendix.

Tuberculosis (TB) disease still remain a major global threat due to the growing number of drug-resistant species and global warming. Despite the fact that there are new molecular diagnostic approaches, however, majority of developing countries and remote clinics depends on conventional approaches such as Tuberculin test, microscopic examinations and radiographic imaging (Chest X-ray). These techniques are hindered by several challenges which can lead to miss-diagnosis especially when interpreting large number of sample cases. Thus, in order to reduce workload and prevent miss-diagnosis, scientists incorporated computer-aided technology for detection of medical images known as Computer aided Detection (CADe) or Diagnosis (CADx). The use of AI-powered techniques has shown to improve accuracy, sensitivity, specificity. In this review, we discussed about the epidemiology, pathology, diagnosis and treatment of tuberculosis. The review also provides background information on Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Transfer Learning (TL) and their applications in detection of tuberculosis from both microscopic slide images and X-ray images. The review also proposed an IoT/AI powered system which allows transfer of results obtained from DL models with end users through internet networks. The concept of futuristic diagnosis, limitations of current techniques and open research issues are also discussed.

Computer simulation relies on well-defined experimental supported theories, models, and numerical methods. Computer simulation is an alternative to fill the gaps where the experiment is pricy or limited in engineering. Computer simulation is a high-fidelity computational technique that can work as an engineering innovation tool to try imaginative ideas beyond the available technologies. The accuracy of current computer simulation results is reliable enough to accelerate innovation for new materials and mechanical technologies without experimental validation. So, we may need to urge relying on computer simulation results without any experimental validation to accelerate engineering innovations. In addition, using high-fidelity computer simulation for engineering innovation acceleration without validation will be promoted if it is credited as a new classification. This subject and a related justification's classification in engineering have been discussed.

The Brain-Computer Interface (BCI) technology has emerged as a groundbreaking innovation with profound implications across diverse domains, particularly in healthcare. By establishing a direct communication pathway between the human brain and external devices, BCI systems offer unprecedented opportunities for diagnosis, treatment, and rehabilitation, thereby reshaping the landscape of medical practice. However, despite its immense potential, the widespread adoption of BCI technology in clinical settings faces several challenges. These include the need for robust signal acquisition and processing techniques, ensuring user safety and privacy, addressing ethical considerations, and optimizing user training and adaptation. Overcoming these challenges is crucial to unleashing the complete potential of BCI technology in healthcare and realizing its promise of personalized, patient-centric care. This review work underscores the transformative potential of BCI technology in revolutionizing medical practice. This paper offers a comprehensive analysis of medical-oriented BCI applications by exploring the various uses of BCI technology and its potential to transform patient care.

Deepfakes are manipulated or altered images, or video, that are created using deep learning models with high levels of photorealism. The two popular methods of producing a deepfake are based on either convolutional neural networks (CNN), or autoencoders. Deepfakes created using CNN comparatively show higher qualities of realism, yet oftentimes leave artifacts and distortions in the generated media that can be detected using machine learning and deep learning algorithms. In recent years, there has been an influx of periocular image and video data because of the increase usage of face masks. By wearing masks, much of what is used for facial recognition is hidden, leaving only the periocular region visible to an observer. This loss of vital information leads to easier misidentification of media, allowing deepfakes to less likely be identified as fake. In this work, feature extraction methods, such as Scale-Invariant Feature Transform (SIFT), Histogram of Oriented Gradients (HOG), and CNN, are used to train an ensemble deep learning model to detect deepfakes in videos on a frame-by-frame level based on the periocular region. Our proposed model is able to distinguish original and manipulated images with accuracies around 98.9 percent, which is an improvement to previous works by combining SIFT and HOG for deepfake detection in convolutional neural networks.

The present study illustrates a brain-computer interface designed and developed to be wearable, portable, and user-friendly. Eight dry electroencephalographic sensors were adopted to acquire the brain activity associated with motor imagery. Multimodal feedback in extended reality was exploited to improve the detection of neurological phenomena. Twenty-seven healthy subjects used the proposed system in five sessions to investigate the effects of feedback on motor imagery. The sample was divided into two equal-sized groups: the "neurofeedback" group, which performed motor imagery while receiving feedback, and the "control" group, which performed motor imagery with no feedback. Several questionnaires were administrated to participants aiming to investigate the usability of the proposed system and individual’s ability to imagine movements. The highest mean classification accuracy across subjects of control group was about 62 % with 3 % associated type A uncertainty, and 69 % with 3 % uncertainty for neurofeedback group. Moreover, in some cases the results were significantly higher for the neurofeedback group. The perceived usability by all participants was high. Overall, the study highlights the advantages and the pitfalls of using a wearable brain-computer interface with dry sensors. Notably, the results and the perceived usability pave the way for the employment of the proposed system in tele-rehabilitation.

This study presents review of 7580 papers in 13 academic journals, published from January 2015 to January 2020. After a detailed analysis of all papers, 46 papers were further selected showing research on a student population aged between 6 and 15 years old. In order for the paper to be included in the research, the condition was that the paper deals with teaching at least one of the following content areas: programming languages, game design, computer thinking (CT), algorithmic thinking and robotics programming. This study shows the representation of the listed content area in reviewed papers for the specified time period as well as a detailed analysis of the selected papers. Available data about study, participants and education level, country (first author), learning domain, teaching tools, research questions, hypothesis, pre/post-test results, interviews, control groups, course duration, research design, previous experience, project or grant and re-search purpose in detected papers were analysed. In addition, impact of studying some of the listed content areas on student learning performance, motivation, attitude and perception were investigated.

Document deskewing is a fundamental problem in document image processing. While existing methods have limitations, such as Hough Line Transformation that can deskew images upside down, and Deep Learning models that require huge amounts of human labour and computational resources and still fail to deskew while taking care of orientation, OCR-based methods also struggle to read text when it is tilted. In this paper, we propose a novel, simple, cost-effective deep learning method for fixing the skew and orientation of documents. Our approach reduces the search space for the machine learning model to predict whether an image is upside down or not, avoiding the huge search space of predicting an angle between 0 and 360. We finetuned a MobileNetV2 model, which was pre-trained on imagenet, using only 200 images and achieve good results. This method is useful for automation-based tasks, such as data extraction using OCR technology, and can greatly reduce manual labour.

Eric Lord has observed: “In nature, helical structures arise when identical structural subunits combine sequentially, the orientational and translational relation between each unit and its predecessor remaining constant.” This paper proves Lord’s Observation. Constant-time algorithms are given for the segmented helix generated from the intrinsic properties of a stacked object and its conjoining rule. Standard results from screw theory and previous work are combined with corollaries of Lord’s observation to allow calculations of segmented helices from either transformation matrices or four known consecutive points. The construction of these from the intrinsic properties of the rule for conjoining repeated subunits of arbitrary shape is provided, allowing the complete parameters describing the unique segmented helix generated by arbitrary stackings to be easily calculated. Free-libre open-source interactive software and a website is provided which performs this computation for arbitrary prisms along with interactive 3D visualization . We prove that any subunit can produce a toroid-like helix or a maximally-extended helix, forming a continuous spectrum based on joint-face normal twist. This software, website and paper, taken together, compute, render, and catalog an exhaustive “zoo” of 28 uniquely-shaped platonic helices, such as the Boerdijk-Coxeter tetrahelix and various species of helices formed from dodecahedra.

The increasing use of deep learning techniques to manipulate images and videos, commonly referred to as "deepfakes," is making more and more challenging to differentiate between real and fake content. While various deepfake detection systems have been developed, they often struggle to detect deepfakes in real-world situations. In particular, these methods are often unable to effectively distinguish images or videos when these are modified using novel techniques which have not been used in the training set. In this study, we carry out an analysis of different deep learning architectures in an attempt to understand which is more capable of better generalizing the concept of deepfake. According to our results, it appears that Convolutional Neural Networks (CNNs) seem to be more capable of storing specific anomalies and thus excel in cases of datasets with a limited number of elements and manipulation methodologies. The Vision Transformer, conversely, is more effective when trained with more varied datasets, achieving more outstanding generalization capabilities than the other methods analysed. Finally, the Swin Transformer appears to be a good alternative for using an attention-based method in a more limited data regime. All the analyzed architectures seem to have a different way to look at deepfakes but since in a real-world environment, the generalization capability is essential, based on the carried out experiments the Vision Transformer seems to provide superior performances.

Computer vision methods have shown to be effective in classifying garbage into recycling categories for waste processing, existing methods are costly, imprecise, and unclear. To tackle this issue, we introduce MWaste, a mobile application that uses computer vision and deep learning techniques to classify waste materials as trash, plastic, paper, metal, glass or cardboard. Its effectiveness was tested on various neural network architectures and real world images, achieving an average precision of 92% on the test set. This app can help combat climate change by enabling efficient waste processing and reducing the generation of greenhouse gases caused by incorrect waste disposal.

Since the release of ChatGPT, numerous studies have highlighted the remarkable performance of ChatGPT, which often rivals or even surpasses human capabilities in various tasks and domains. However, this paper presents a contrasting perspective by demonstrating an instance where human performance excels in typical tasks suited for ChatGPT, specifically in the domain of computer programming. We utilize the IEEExtreme Challenge competition as a benchmark — a prestigious, annual international programming contest encompassing a wide range of problems with different complexities. To conduct a thorough evaluation, we selected and executed a diverse set of 102 challenges, drawn from five distinct IEEExtreme editions, using three major programming languages: Python, Java, and C++. Our empirical analysis provides evidence that contrary to popular belief, human programmers maintain a competitive edge over ChatGPT in certain aspects of problem-solving within the programming context. In fact, we found that the average score obtained by ChatGPT on the set of IEEExtreme programming problems is 3.9 to 5.8 times lower than the average human score, depending on the programming language. This paper elaborates on these findings, offering critical insights into the limitations and potential areas of improvement for AI-based language models like ChatGPT.

Virtual reality software might be challenging to utilize for beginners and unskilled professionals who do not have a programming or 3D modeling background. Concurrently, there is a knowledge gap in software project design for intuitive virtual reality authoring tools, which were supposed to be easier to use. These tools are frequently insufficient due to a lack of support and standard operating procedures. Adopting the Design Science Research paradigm, this study aims to evaluate the validity of fourteen design guidelines for the development of intuitive virtual reality authoring tools as an artifact. While a previous study completed the first steps of the Design Science Research, by identifying problems, defining solution objectives, and developing and demonstrating the design guidelines, this work seeks to qualitatively evaluate their application in a practical experiment. A group of engineering students with no prior experience in creating virtual worlds were tasked with examining the design guidelines while using the NVIDIA Omniverse Enterprise as an exemplary use case and responding to a questionnaire and a focus group interview about how they perceived these guidelines. A correlation analysis confirmed that most guidelines scores behaved as expected and were ranked according to the use-case functionality. The participants understood the guidelines' definition and could decide if they agreed or disagreed with their presence during the experiment. We conclude that, in accordance with the Design Science Research, the proposed artifact is useful, i.e., the design guidelines for virtual reality authoring tools perform what they are designed to do and are operationally reliable in accomplishing their goals.

Macrophytes and seagrasses play a crucial role with a variety of the functions in the marine ecosystem and respond synchronized-promptly to the changing climate, followed by ecological status. Monitoring of the seagrasses was one of the paramount topics at the marine environment. One of the fast monitoring techniques is the vegetation acoustics with the advantages compared to the other remote sensing techniques. The acoustic method alone is ambitious to sea-truth identities of the backscatterers at the sea. Therefore, a package of computer programs was developed to identify, estimate leaf biometrics (leaf length and biomass) of one of the common seagrasses, Posidonia oceanica. Some troubleshooting in the acoustical data were fixed and then solved to reach the estimates regarding to the matters encountered for the vegetation as well as fisheries and plankton acoustics. One of the trouble was the “Lost” bottom which occurred during the data collection and post-processing due to occurrence of the acoustical noises, reverberations, interferences and intense scatterers suchlike fish schools. Another problem to remove was the occurrence as studying the near-bottom echoes belonging to submerged vegetations such as seagrasses, followed by spurious echoes. Last one was recognition of the seagrass to estimate leaf length and biomass calibrating the sheaths/vertical rhizome of the seagrass and establishing relationships between acoustical units and biometrics, respectively. As a consequence, an autonomous package of the code written in language MatLab was developed to perform all processes, and the name of the package was abbreviated as POSIBIOM, POSIdonia BIOMass. This study presented the algorithms, methodology, acoustics-biometrics relation, and mapping of the biometrics for the first time, and discussed advantages and disadvantages of the package compared to the software purposed for the bottom types, habitat and vegetation acoustic. The future studies were recommended to improve the package.

Artificial intelligence (AI) and 3D printing (3DP) play critical roles in what is known as the 4th Industrial Revolution by developing data- and machine-intelligence-based integrated production technologies. This shift has led to increasingly complex design requirements and fabrication methods, posing several challenges for implementing design, fabrication process, and architectural quality standardization. This paper systematically reviews AI techniques applied in diverse stages of 3DP production in architecture. The research goals are to (1) offer a comprehensive critical analysis of the body of literature; (2) identify and categorize approaches to integrating AI in the production of 3D-printed structures; (3) identify and discuss challenges and opportunities of AI integration in architectural production of 3DP structures; and (4) identify gap and provide recommendations for future research. The findings indicate that topics related to AI applications are current and trending, receiving increasing attention from researchers in general and particularly those working in machine and deep learning, artificial neural networks, and pattern recognition. However, the potential of the application of AI in architectural production using additive manufacturing still needs to be explored. Considering this, the paper emphasizes the necessity of redefining traditional field boundaries and opening new opportunities in the sector.

Cycle times at workstations in offsite construction factories fluctuate widely due to various influencing factors. Consequently, relying on average rates, such as length per unit of time, for estimating cycle times proves to be inaccurate, often leading to significant deviations between production schedules and actual operations. To address this issue, this study proposes an estimation system that leverages machine-learning-based prediction, statistical methods, 3D simulation, and computer vision to predict cycle times at the workstation level. Testing of the system on a semi-automated wood-wall framing workstation in a panelized construction factory shows that it reduces the mean absolute error and sum of errors by approximately 36% and 68%, respectively, compared to the fixed rate method. The results also highlight the efficacy of using computer vision data for training machine-learning models for cycle time estimation, the importance of identifying and understanding the factors influencing cycle times, and the impact of random delays on the accuracy of cycle time estimation systems.

Early detection of breast cancer, one of the most common cancers in women worldwide, raises survival rates and lowers the cost of treatment. Although breast cancer detection and classification (CAD) tools have improved, some issues and restrictions still require further research. The creation of breast cancer CAD systems, particularly with deep learning models, was significantly impacted by recent advancements in machine learning and image processing techniques. The current deep learning-based CAD system to identify and categorize masses in mammography is presented in a structured manner in this survey. The survey provides the most popular breast cancer CAD system evaluation metrics, publicly available mammographic datasets, and current image modalities. The survey examines the available literature, emphasizing its strengths and limitations. The survey also emphasizes the difficulties and drawbacks of the current methods for classifying and detecting breast cancer. We point out research gaps and make suggestions for further study. This systematic review may be beneficial for clinicians using CAD systems to diagnose breast cancer early on and for researchers looking for knowledge gaps and making more contributions to the field.

As machine learning and computer vision techniques and methods continue to advance, the collection of naturalistic traffic data from video feeds is becoming more and more feasible. That is especially true for the case of bicycles, for which the collection of naturalistic data is not achievable in the traditional vehicle approach. This study describes a research effort that aims to extract naturalistic cycling data from a video dataset for use in safety and mobility applications. The used videos come from a dataset collected in a previous Virginia Tech Transportation Institute study in collaboration with SPIN in which continuous video data at a non-signalized intersection on the Virginia Tech campus was recorded. The research team applied computer vision and machine learning techniques to develop a comprehensive framework for the extraction of naturalistic cycling trajectories. In total, this study resulted in the collection and classification of 619 bicycle trajectories based on their type of interactions with other road users. The results confirm the success of the proposed methodology in relation to extracting the locations, speeds, and accelerations of the bicycles at a high level of precision. Furthermore, preliminary insights into the acceleration and speed behavior of bicyclists around motorists are determined.

Virtual reality experiences are frequently created using game engines, yet they are not simple for novices and unskilled professionals who don’t have programming and 3D modeling skills. Concurrently, there is a knowledge gap in software project design for intuitive virtual reality authoring tools, which were supposed to be easier to use. This work compiles design guidelines derived from a systematic literature review to contribute in the development of more intuitive virtual reality authoring tools. We searched the Scopus and Web of Science databases for works published between 2018 and 2021 and discovered 14 papers. We compiled 14 requirement and feature design guidelines, such as Visual Programming, Immersive Authoring, Reutilization, Sharing and Collaboration, Metaphors, and Movement Freedom, among others. The collected guidelines have the potential to either guide the development of new authoring tools or to evaluate the intuitiveness of existing tools. Furthermore, they can also support the development of the metaverse, since virtual content creation is one of its bases.

This paper introduces an innovative approach to lipreading, leveraging a web application designed to generate subtitles for videos where the speaker's mouth is visible and a comprehensive literature review that precedes the discussion, encompassing a thorough examination of various lipreading methods employed over the past decade. Our method employs a powerful deep learning model, featuring a 3D-convolution network and bidirectional LSTM, enabling accurate sentence-level predictions based solely on visual lip movements. With an impressive accuracy of 97%, our model is trained using pre-segmented lips regions, transformed into animated GIFs for effective pre-training. This work stands as a significant contribution to the evolving landscape of lipreading research, offering a practical and accurate solution for real-world applications.

Semi-structured complex numbers H was a number set developed to enable division by zero in ordinary algebraic equations. Its utility has been shown in mathematics and engineering. However, very little has been done to show its usefulness in computer science. Consequently, the aim of this paper was to show the utility of semi-structured complex numbers in computer science by developing a division by zero calculator. First two computer programs were written, one for a standard (STD) calculator and the other for a division by zero (DBZ) calculator. The programs were fed 20000 randomly generated arithmetic equations of varying lengths and the space and time complexity associated with processing these equations were measured and compared to determine the efficiency of each calculator. In the process, three major contributions were made: (1) A representation for semi-structured complex numbers that enables it to be easily used by a computer was developed; (2) It was demonstrated that the DBZ calculator outperforms the STD calculator in terms of efficiency; and, (3) It was shown that the number set H reduced the amount of error handling required to run a computer program. These results provide a firm foundation to advance the number set as a useful tool in computer science.

Attempts to use computers to aid in the detection of breast malignancies date back more than 20 years. Despite significant interest and investment, this has historically led to minimal or no significant improvement in performance and outcomes with traditional computer aided detection. However, recent advances in artificial intelligence and machine learning are now starting to deliver on the promise of improved performance. There are at present more than 20 FDA approved AI applications for breast imaging, but adoption and utilization are widely variable and overall low. Breast imaging is unique and has aspects that create both opportunities and challenges for AI development and implementation. Breast cancer screening programs worldwide rely on screening mammography to reduce the morbidity and mortality of breast cancer and many of the most exciting research and available AI applications focus on cancer detection for mammography. There are however multiple additional potential applications for AI in breast imaging including decision support, risk assessment, breast density quantitation, workflow and triage, quality evaluation, response to neoadjuvant chemotherapy assessment, and image enhancement. In this review the current status, availability, and future directions of investigation of these applications are discussed, as well as the opportunities and barriers to more widespread utilization.

A single paragraph of about 200 words maximum. Electroencephalography (EEG) accumulates the electrical activities of human brain. It is an easy and cost effective tool to characterize motor imagery (MI) task used in brain computer interface (BCI) implementation. The MI task is represented by short time trial of multichannel EEG. In this paper, the raw EEG is decomposed into a finite set of narrowband signals obtained from individual EEG channels using Fourier transformation based bandpass filter. Each of the subband signals represents narrowband rhythmic components which characterize the brain activities related to motor imagery. The subband signals are arranged to extent the dimension of EEG trial in spatial domain. The spatial features are extracted from the set of extended trials using common spatial pattern (CSP). An optimum number of features are used to classify the motor imagery tasks represented by EEG trials. Artificial neural network is used to classify MI tasks. The performance of the proposed method is evaluated using two publicly available benchmark datasets. The experimental results show that it performs better than the recently developed algorithms.

Human Action Recognition is widely used in multiple fields to recognize activities and extract spatial and temporal information. This paper focuses on analyzing various methods as well as provides extensive knowledge of the foundational concepts of the HAR. A dataset is also very crucial for any research study so we discussed the popular dataset and its features in this paper.

Research has been growing on object detection using semi-supervised methods in past few years. We examine the intersection of these two areas for floor-plan objects to promote the research objective of detecting more accurate objects with less labelled data. The floor-plan objects include different furniture items with multiple types of the same class, and this high inter-class similarity impacts the performance of prior methods. In this paper, we present Mask R-CNN based semi-supervised approach that provides pixel-to-pixel alignment to generate individual annotation masks for each class to mine the inter-class similarity. The semi-supervised approach has a student-teacher network that pulls information from the teacher network and feeds it to the student network. The teacher network uses unlabeled data to form pseudo-boxes, and the student network uses both unlabeled data with the pseudo boxes and labelled data as ground truth for training. It learns representations of furniture items by combining labelled and unlabeled data. On the Mask R-CNN detector with ResNet-101 backbone network, the proposed approach achieves mAP of 98.8%, 99.7%, and 99.8% with only 1%, 5% and 10% labelled data, respectively. Our experiment affirms the efficiency of the proposed approach as it outperforms the fully supervised counterpart using only 10% of the labels.

Quality control is a critical component in industrial manufacturing, directly influencing efficiency, product reliability, and ultimately, customer satisfaction. In the dynamic environment of industrial manufacturing, traditional methods of inspection may not adequately meet the evolving complexity, necessitating innovative approaches to bolster precision and productivity. In this study, we explore the application of mixed reality (MR) technology for real-time quality control in the assembly process. Our methodology involved the integration of smart glasses with a server-based image recognition system, designed to conduct real-time component analysis. The innovative aspect of our study lies in the harmonization of MR and computer vision algorithms, providing immediate visual feedback to inspectors and thereby improving the speed and accuracy of defect detection. YOLOv8 have been adopted in this study for detection object model. The project implementation occurred in a controlled environment to enable a comprehensive evaluation of the system functionality, the identification of possible problems and improvements in the system performance. The results indicated the viability of mixed reality as a powerful tool for enhancing traditional inspection processes. The fusion of MR and computer vision offers possibilities for future advancements in industrial quality control, paving the way for more efficient and reliable manufacturing ecosystems.

Uncontrolled and fast cell proliferation is the cause of brain tumors. Early cancer detection is vitally important in order to save many lives. Brain tumors can be divided into several categories depending on the kind, place of origin, pace of development, and stage of progression; as a result, tumor classification is crucial for targeted therapy. The aim of brain tumor segmentation is to accurately delineate the areas of the brain. A specialist with a thorough understanding of brain illnesses must manually identify the proper type of brain tumor. Additionally, processing a lot of images takes time and is tiresome. Therefore, automatic segmentation and classification techniques are required to speed up and enhance the diagnosis of brain tumors. Tumors can be quickly and safely detected by brain scans using imaging modalities, including computed tomography (CT), magnetic resonance imaging (MRI), and others. Machine learning (ML) and artificial intelligence (AI) have shown promise in the development of algorithms that aid in automatic classification and segmentation utilizing various imaging modalities. This review discussed various types of brain tumors, publicly accessible datasets, enhancement methods, segmentation, feature extraction, classification, machine learning techniques, and deep learning, learning through transfer, for the study of brain tumors.