Hand gestures are an essential part of human-to-human communication and interaction, and therefore for technical applications, the aim is increasingly to achieve interaction between humans and computers that is as natural as possible, for example by means of natural language or hand gestures. In the context of human-machine interaction research, these methods are consequently being explored more and more. However, the realization of natural communication between humans and computers is a major challenge. In the field of hand gesture recognition, research approaches are being pursued that use additional hardware such as special gloves to classify gestures with high accuracy. Recently, deep learning techniques using artificial neural networks have been increasingly proposed for the problem of gesture recognition without using such tools. In this context, in our approach convolutional neural network (CNN) will be explored in detail for the task of hand gesture recognition. CNN is a deep neural network that can be used in the field of visual object processing and classification. The goal of this work is to recognize ten types of static hand gestures in front of complex backgrounds and different hand sizes based on raw images without the use of extra hardware. We achieved good results with a CNN network architecture consisting of seven layers. Through data augmentation and skin segmentation, a significant increase of the model accuracy was achieved. On public benchmarks the ten gestures have been classified almost perfectly with a testing accuracy of 96.5%

This paper introduces a multi-class hand gesture recognition model developed to identify a set of defined hand gesture sequences in two-dimensional RGB video recordings. The work presents an action detection classifier that looks at both appearance and spatiotemporal parameters of consecutive frames. The classifier utilizes a convolutional-based network combined with a long-short-term memory unit. To leverage the need for a large-scale dataset, the model uses an available dataset to then adopt a technique known as transfer learning to fine-tune the model on the hand gestures of relevance. Validation curves performed over a batch size of 64 indicate an accuracy of 93.95% (± 0.37) with a mean Jaccard index of 0.812 (± 0.105) for 22 participants. The presented model illustrates the possibility of training a model with a small set of data (113,410 fully labelled frames). The proposed pipeline embraces a small-sized architecture that could facilitate its adoption.

This paper presents a system developed for the assistance with a collaborative robot in hand-assisted laparoscopic surgery (HALS). The system includes a sensing glove with piezoresistive sensors which capture continuously the flexion degree of the surgeon's fingers. These data are analyzed using an algorithm that detects and recognize the selected movements. This information is sent as commands to the collaborative robot throughout the surgical operation. The bending patterns, speed and execution times of the movements are modelled in a pre-phase in which it will extract all the necessary information for later detection during the motion execution. The results obtained with 10 different volunteers show a high degree of accuracy and a low false discovery rate.

Gesture recognition technology has been quickly developed in the field of human-computer interaction. The multiple-input multiple-output (MIMO) radar has been widely adopted in gesture recognition because of its notable spatial resolution. In this work, a highly accurate MIMO radar-based hand gesture recognition algorithm with very low complexity is proposed. To make the proposed system applicable in the industry and work well even when the size of the training data is limited, we applied several low-complexity adaptive signal processing methods to extract features and reduce the noise effect. First, spectrum analysis is applied to range-Doppler maps (RDMs) and a cell-averaging constant false alarm rate (CA-CFAR) with mirror filters is applied to improve the robustness to noise. Afterward, the features related to the distance, speed, direction, and the elevation angle of the moving object are determined by the proposed adaptive signal analysis techniques and the random forest is applied for classification. The proposed system has the capability to precisely distinguish and identify eight motions, including waving, moving to the left or right, patting, pushing, pulling, and rotating clockwise or anticlockwise, with an accuracy of 95%. Experiments demonstrate the capability of the proposed hand gesture recognition system to classify different movements precisely.

In order to improve real-time performance of gesture recognition by micro-Doppler map of mmWave Radar, the point cloud based gesture recognition for mmWave Radar is proposed in this paper.Two steps are carried out for mmWave Radar based gesture recognition. The first step is to estimate the point cloud of the gestures by 3D-FFT and the peak grouping. The second step is to train the TRANS-CNN model by combining the multi-head self-attention and the 1D-convolutional network, so as to extract the features in the point cloud data at a deeper level to categorize the gestures. In the experiments, TI mmWave Radar sensor IWR1642 is used as benchmark to evaluate the feasibility of the proposed approach. The results show that the accuracy of the gesture recognition reaches 98.5%. In order to prove the effectiveness of our approach, a simply 2Tx2Rx Radar sensor is developed in our lab and the accuracy of recognition reaches 97.1%. The results show that our proposed gesture recognition approach achieves the best performance in real-time with limited training data, in comparison with the existing methods.

Introduction Blunt carotid injuries (BCI) in pediatric trauma are quite rare. Due to the low number of cases, only a few reports and studies have been conducted on this topic. This review will discuss how frequent BCI/ BCVI on pediatric patients after blunt trauma is, what routine diagnostics look like, if a CT/ CTA scan on pediatric patients after blunt trauma is always necessary and if there are any negative health effects? Methods This literature review includes reviews, systematic reviews, case reports and original studies in english language between 1999 and 2020 that have dealt with pediatric blunt trauma and the diagnostics of BCI and BCVI. Furthermore, publications on the risk of radiation exposure on children were included in the study. For literature research, Medline (PubMed) and the Cochrane library were used. Results Pediatric BCI/ BCVI, shows an overall incidence between 0.03 – 0.5% of confirmed BCI/ BCVI cases due to pediatric blunt trauma. 1.1 – 3.5% of pediatric blunt trauma patients underwent CTA to detect BCI. Only 0.17 – 1.2% of all CTA scans shows a positive diagnosis for BCI. In children, the median volume CT dose index on a non-contrast head CT is 33 milligray, a computed tomography angiography needs at least 138 mGy. A cumulative doses of about 50 mGy almost triples the risk of leukemia, and doses of about 60 mGy triples the risk of brain cancer. Discussion Knowing that a BCI could have extensive neurologic consequences for children, it is necessary to evaluate routine pediatric diagnostics after blunt trauma. Computed tomography scans (CT) and computed tomography angiography (CTA) are mostly used in routine BCI diagnostics. However, since radiation exposure in children should be as low as reasonably achievable, it should be asked if other diagnostic methods could be used to identify risk groups. Trauma guidelines and clinical scores like the McGovern score are reflect established BCI screening options, as well as using duplex ultrasound.

Leader-follower autonomy (LFA) systems have so far only focused on vehicles following other vehicles. Though there have been several decades of research into this topic, there has not yet been any work on human-vehicle leader-follower systems. We present a system in which an autonomous vehicle — our ACTor 1 platform — can follow a human leader who controls the vehicle through gestures. We successfully developed a modular pipeline that uses artificial intelligence/deep learning to recognize hand-and-body gestures from a user in view of the vehicle’s camera and translate those gestures into physical action by the vehicle. We demonstrate our work using our ACTor 1 platform, a modified Polaris Gem 2 electric vehicle. This work has numerous applications, such as material transport in industrial contexts.

Wearable technology has been proposed as a potential tool to change the way of human life, such as the smart bracelet and the Google Glass. In the wearable technology, the inertial sensor has great significance in tracking the object movements. The paper focused on detecting the movements of user’s finger based on the inertial sensor to give the control signals. Firstly, the attitude matrix, which represented the transformation relation of carrier coordinate system and the navigation coordinate system, was obtained. Secondly, the attitude matrix was expressed based on the quaternions. Thirdly, the finger gesture was processed by the attitude matrix to get the attitude angle. Finally, the robot was controlled by attitude angle to make the moving action. The experimental results showed the detection of the finger movement is effective.

A single water pollution by dyes is a huge environmental problem; there is a necessity to produce new decolorization methods that are effective, cost-attractive, and suitable in industrial use. Magnetic chitosan polymers offer the advantage of easy separation from the dye solution. In this work, a Chitosan-Fe polymer was synthesized, characterized, and tested for removal of the azo dye Direct Red 83:1 from water. The polymer was characterized in terms of surface morphology (SEM), elemental analysis (EA), differential scanning calorimetry (DSC), thermal gravimetric analysis (TGA), infrared spectrophotometry (IR), X-ray powder diffraction (XRD) and density functional theory (DFT). The reported results hint that 1 g and pH 7.0 were the best conditions to carry out both kinetic and isotherm models. To understand the adsorption behavior of Direct Red 83:1 onto the polymers, adsorption rate and maximum adsorption capacity were calculated using kinetic tests and isotherm curves respectively. The kinetic data and mechanism of the adsorption process were analyzed by three models and the equilibrium data by three adsorption isotherms. The results indicated that the adsorption process follows a pseudo-second-order kinetics (R2= 0.999) and fits the Temkin isotherm (R2= 0.946), indicating that the adsorption occurred on heterogeneous surfaces. The newly synthesized Chitosan-Fe polymer exhibited good adsorption properties and easy separation of cleaned water.

Bioenergy produced from perennial feedstocks such as woody biomass could serve as an opportunity to strengthen local and regional economies and also jointly produce various environmental services. In order to assess the potential for biomass- based bioenergy, it’s essential to characterize the interest that potential biomass suppliers have in such an endeavor. In the U.S. Great Plains region, this largely means assessing relevant perceptions of farmers and ranchers. We conducted a series of farmer and rancher oriented focus groups in North Dakota, South Dakota, Nebraska and Kansas to qualitatively explore opinions about the role that trees can play in agriculture and interest in woody biomass systems within existing Northern Great Plains (NGP) farms and ranches. Our findings suggest that farmer and ranchers generally value the role that trees, or tree-based practices like windbreaks can play in agriculture particularly on marginal farmland in terms of conservation or crop protection. Yet relative to the potential of trees as a biomass crop there is a distinct lack of knowledge and skepticism. Farmers and ranchers also noted variable degrees of risk concern and uncertainty regarding investing in tree-based systems, as well as a number of perceived external market related constraints to integrating trees within their managed systems. Most of the participants recognized that if biomass production or an increase in tree planting and management in general were to expand in the NGP region, government programs would likely be required to provide much needed technical guidance and financial incentives. As the NGP regional bioeconomy continues to emerge and expand, private and public investment relative to niche bioenergy feedstocks such as woody biomass should address the type of information needs that farmers and ranchers have relative to integrating biomass production into existing farm and ranch systems.

Large datasets play a crucial role in the progression of surgical robotics, facilitating advancements in the fields of surgical task recognition and automation. Moreover, public datasets enable the comparative analysis of various algorithms and methodologies, thereby assessing their effectiveness and performance. The ROSMA (Robotics Surgical Maneuvers) dataset provides 206 trials of common surgical training tasks performed with the da Vinci Research Kit (dVRK). In this work, we extend ROSMA dataset with two annotated subsets: ROSMAT24, which contains bounding box annotations for instrument detection, and ROSMAG40, which contains high and low level gesture annotations. We propose an annotation method that provides independent labels for the right-handed tool and the left-handed tool. For instrument identification, we validate our proposal with a YOLOv4 model in two experimental scenarios. We demonstrate the generalization capabilities of the network to detect instruments in unseen scenarios. For gesture segmentation, we propose two label categories: high-level annotations that describe gestures at a maneuvers level, and low-level annotations that describe gestures at a fine-grain level. To validate this proposal, we have designed a recurrent neural network based on a bidirectional long-short term memory layer. We present results for four cross-validation experimental setups, reaching up to a 77.35% mAP.

Human-to-human communication via the computer is mainly done using a keyboard or microphone. In the field of Virtual Reality (VR), where the most immersive experience possible is desired, the use of a keyboard contradicts this goal, while the use of a microphone is not always desirable (e.g. silent commands during task force training) or simply not possible (e.g. if the user has a hearing loss). Data gloves help to increase immersion within the VR as they correspond to our natural interaction. At the same time, they offer the possibility to accurately capture hand shapes, such as those used in non-verbal communication (e.g. thumbs up, okay gesture, ...) and in sign language. In this paper, we present a hand shape recognition system using Manus Prime X data gloves, including data acquisition, data preprocessing, and data classification to enable nonverbal communication within VR. We investigate the impact on accuracy and classification time of using an Outlier Detection and a Feature Selection approach in our data preprocessing. To obtain a more generalized approach, we also studied the impact of artificial Data Augmentation, i.e., we create new artificial data from the recorded and filtered data to augment the training dataset. With our approach, 56 different hand shapes could be distinguished with an accuracy of up to 93.28%. With a reduced number of 27 hand shapes, an accuracy of up to 95.55% could be achieved. Voting Meta-Classifier (VL2) has proven to be the most accurate, albeit slowest, classifier. A good alternative is Random Forest (RF), which was even able to achieve better accuracy values in a few cases and was generally somewhat faster. Outlier Detection has proven to be a effective approach, especially in improving classification time. Overall, we have shown that our hand shape recognition system using data gloves is suitable for communication within VR.

Chloroplasts are ancient organelles responsible for photosynthesis and various biosynthetic functions essential to most life on Earth. Many of these functions require tightly controlled regulatory processes to maintain homeostasis at the protein level. One such regulatory mechanism is the ubiquitin-proteasome system whose fundamental role is increasingly emerging in chloroplasts. In particular, the role of E3 ubiquitin ligases as determinants in the ubiquitination and degradation of specific intra-chloroplast proteins. Here, we highlight recent advances in understanding the roles of plant E3 ubiquitin ligases in chloroplast function.

Background: Extracellular Vesicles are pivotal mediators in intercellular communication, facilitating the exchange of biological information among healthy, pathological and tumor cells. Between the diverse subtypes of extracellular vesicles, exosomes have unique properties, clinical and therapeutical applications, even towards cancer. Breast cancer ranks as one of the most prevalent malignancies across the globe. Both the tumor core and its surrounding microenvironment engage in a complex, orchestrated interaction that facilitates the cancer’s growth and spread. Methods: The most valuable and significant PubMed Literature about extracellular vesicles and adipose–derived stem cells exosomes was selected and analyzed in order to report their biological properties and potential applications, in particular on the Triple–Negative Breast Cancer. Results: Adipose–derived stem cells exosomes represent a potential tool to target the tumor core of the Triple–Negative Breast Cancer, its microenvironment and surrounding tissues including metastasis. The authors are developing a supportive Triple–Adipose–derived stem cells Exosomes [T.A.E.] Technology against breast cancer in order to perform a selective preconditioning of the cancer before or while the traditional oncological treatments are running. Conclusion: The possibility to exploit the impact of Adipose–derived stem cells exosomes within the T.A.E. Technology against the Triple–Negative breast cancer is real, highlighting a new era for supportive measures against breast cancer.Keywords: Extracellular vesicles; Exosomes; Adipose–derived stem cells, Tumor microenvironment, Triple–Negative Breast Cancer, T.A.E. Technology.

The key links of face recognition are digital image preprocessing, facial feature extraction and pattern recognition, this article aimed at the current problem of slow speed and low recognition accuracy of face recognition , from the above three key links, on the basic of analyzing the therories of Fractional Differential Masks Operator (FDMO), Principal Component Analysis (PCA) and Support Vector Machine (SVM), design a kind of FDMO+PVA+SVM coupling algorithm that applies to face recognition to improve the speed and accuracy of it. To realize FDMO+PCA+SVM coupling algorithm, first, we should apply FDMO to face image processing binary marginalization, the purpose is getting face contour; Then, we apply PCA to get the feature of face image which is disposed by binary marainalization. At last, we can apply One-Against All of the SVM classifier and LibSVM software package to realize face recognition. Beside, the article with nine different coupling algorithm design four groups of experimental reaults on the ORL face database verified by comparative analysic FDMO+PCA+SVM coupling algorithm in the superiority of face recognition accuracy and speed.

The electroencephalogram (EEG) is a record of brain activity. Brain Computer Interface (BCI) technology formed by the EEG signal has become one of the hotspots at present. How to extract the feature signal of EEG is the most basic research of BCI technology. In this paper, A new method of recognizing fatigue, conscious, concentrated state of human brain is proposed by the combination of discrete wavelet transform and the neural network based on EEG signal. First of all, the law signal is preprocessed by the wavelet denoising method because the law EEG signal contains a large number of high frequency noise, which is decomposed into multi-layer high frequency signal and low frequency signal. thus, δ wave, θ wave, α wave, β wave are obtained by the wavelet transform. And then, frequency band energy of the different wave is regards as the feature signal of EEG. In the experiment, the feature signal is classified by radial basic function (RBF) and annealed chaotic competitive learning network (ACCLN). RBF and ACCLN networks are trained with 500 sets of sample data and are tested by 100 sets of samples in different mental states. The experimental results show that the average accuracy of RBF network under three conditions are 88.75%, 88.25%, 88.5%, respectively, and the correct rate of ACCLN network is 97%, 98%, 98%, respectively.

The present paper describes the development of a prosthetic hand based on the human hand anatomy. The hand phalanges are printed by using 3D printed with Polylactic Acid material. One of the main contributions is the investigation on the prosthetic hand joins; the proposed design enables to create personalized joins that allow the prosthetic hand a high level of movement by increasing the degrees of freedom of the fingers. Moreover, the driven wire tendons show a progressive grasping movement, being the friction of the tendons with the phalanges very low. Another important point is the use of force sensitive resistors for simulating the hand touch pressure. These are used for the grasping stop simulating touch pressure of the fingers. Surface Electromyogram (EMG) sensors allow the user to control the prosthetic hand grasping start. Their use may provide the prosthetic hand the possibility of classification of the hand movements. The practical results included in the paper prove the importance of the soft joins for the object manipulation and to get adapted to the object surface. Finally, the force sensitive sensors allow the prosthesis to actuate with more naturalness by adding conditions and classifications to the Electromyogram sensor.

Precise individual tree or sample-based inventories derived from 3D point cloud data of mobile laser scanning can improve our comprehensive understanding of the structure, function, resilience, biodiversity, and ecosystem services of urban forests. This study assessed the performance of a hand-held laser scanning system for the extraction of tree position, diameter at breast height (DBH) and tree height (H) in an urban area. A total of 2083 trees of 13 species from 34 plots were analyzed. The results showed that the registration of tree positions using ground control points (GCPs) demonstrated high accuracy, with errors consistently below 0.4 m, except for a few instances. The extraction accuracy of DBH for all trees and individual species remained consistently high, with a total root mean square error (RMSE) of 2.06 cm (6.89%) and a bias of 0.62 cm (2.07%). Notably, broad-leaved trees outperformed coniferous trees, with RMSE and bias values of 1.86 cm (6%) and 0.76 cm (2.46%) compared to 2.54 cm (9.46%) and 0.23 cm (0.84%). Tree height extraction accuracy varied significantly among different species, with R2 values ranging from 0.65 to 0.92. Generally, both DBH and height were underestimated. Linear mixed effects models (LME) were applied to evaluate factors affecting the performance of HLS with the plot as a random factor. LME analysis revealed that plant type and terrain significantly influenced the accuracy of DBH and H derived from HLS data. Other fixed factors of plot area, tree density and trajectory length didn’t have significance. With a large sample size, we concluded that the HLS demonstrated sufficient accuracy in extracting individual tree parameters in urban forests.

Motion related human activity recognition using wearable sensors can potentially enable various useful daily applications. So far, most studies view it as a stand-alone mathematical classification problem without considering the physical nature of human motions. Consequently, they suffer from data dependencies and encounter the dimension disaster problem and the over-fitting issue, and their models are never human-readable. In this study, we start from a deep analysis on natural physical properties of human motions, and then propose a useful feature selection method to quantify each feature's classification contribution capability. On one hand, the "dimension disaster" problem can be avoid to some extent, due to the affined dimension of key features; On the other hand, over-fitting issue can be depressed since the knowledge implied in human motions are nearly invariant, which compensates the possible data inadequacy. The experiment results indicate that the proposed method performs superior to those adopted in related works, such as decision tree, k-NN, SVM, neural networks.

Coxsackievirus A6 (CV-A6) has emerged as the predominant causative agent of hand, foot, and mouth disease (HFMD) in young children. Since the declaration of coronavirus disease 2019 (COVID-19) as a global pandemic, the incidence of infectious diseases, including HFMD, has decreased markedly. When social mitigation was relaxed during the COVID-19 pandemic in 2022, re-emergence of HFMD was observed in Gwangju, South Korea, and seasonal characteristics of the disease appeared to have changed. To investigate the molecular characteristics of enterovirus (EV) associated with HFMD during 2022, 277 specimens were collected. Children aged under 5 years accounted for the majority of affected individuals. EV detection and genotyping were performed using real-time RT-PCR and nested RT-PCR followed by sequence analysis. EV detection rate was found to be 82.3%, and the main genotype identified was CV-A6. Sixteen CV-A6 samples were selected for whole genome sequencing. According to phylogenetic analysis, all CV-A6 strains from this study belonged to the sub-genotype D3 clade based on VP1 sequences. Analysis of 3D polymerase phylogeny showed that only the recombinant RF-A group was identified. In conclusion, circulating EV types should be continuously monitored to understand pathogen emergence and evolution during the post-pandemic era.

In pattern recognition domain, deep architectures are widely used nowadays and they have achieved fine grades. However, these deep architectures need special demands, especially big datasets and GPU. Aiming to gain better grades without deep networks, we propose a simplified algorithm framework using fusion features extracted from the salient areas of faces. Furthermore, the proposed algorithm has achieved a better result than some deep architectures. For extracting more effective features, this paper firstly defines the salient areas on the faces. This paper normalizes the salient areas of the same location in the faces to the same size, therefore it can gain more similar features from different subjects. LBP and HOG features are extracted from the salient areas, fusion features’ dimensions are reduced by Principal Component Analysis( PCA) and we apply softmax to classify the six basic expressions at once. This paper proposes a salient areas definitude method which uses peak expressions frames to compare with their neutral faces. This paper also proposes and applies the idea of normalizing the salient areas to align the specific areas which express the different expressions. This makes the salient areas found from different subjects have the same size. Besides, gamma correction method is firstly applied on LBP features in our algorithm framework which improves our recognition rates significantly. By applying this algorithm framework, our research has gained state-of-the-art performances on CK+ database and JAFFE database.

In this comprehensive review, we delve into the realm of intention recognition within the context of digital forensics and cybercrime. The rise of cybercrime has become a major concern for individuals, organizations, and governments worldwide. Digital forensics is a field that deals with the investigation and analysis of digital evidence in order to identify, preserve, and analyze information that can be used as evidence in a court of law. Whereas, Intention recognition is a subfield of artificial intelligence that deals with the identification of agents’ intentions based on their actions and change of states. In the context of cybercrime, intention recognition can be used to identify the intentions of cybercriminals and even to predict their future actions. Employing a meticulous six-step systematic review approach, we curated research articles from reputable journals and categorized them into three distinct modeling approaches: logic-based, classical machine learning-based, and deep learning-based. Notably, intention recognition has transcended its historical confinement to network security, now addressing critical challenges across various subdomains, including social engineering attacks, AI black box vulnerabilities, and physical security. While deep learning emerges as the dominant paradigm, its inherent lack of transparency poses unique challenges in the digital forensics landscape. We advocate for hybrid solutions that blend deep learning’s power with interpretability. Furthermore, we propose the creation of a comprehensive taxonomy to precisely define intention recognition, paving the way for future advancements in this pivotal field.

This paper describes the first results of an Image Recognition Based Location (IRBL) for mobile application focusing on the procedure to generate a Database of range images (RGB-D). In an indoor environment, to estimate the camera position and orientation, a prior spatial knowledge of the surrounding is needed. In order to achieve this objective a complete 3D survey of two different environment (Bangbae metro station of Seoul and E.T.R.I. building in Daejeon – Republic of Korea) was performed using LiDAR (Light Detection And Ranging) instrument and the obtained scans were processed in order to obtain a spatial model of the environments. From this, two databases of reference images were generated using a specific software realized by the Geomatics group of Politecnico di Torino (ScanToRGBDImage). This tool allow to generate synthetically different RGB-D images) centered in the each scan position in the environment. Later, the external parameters (X, Y, Z, ω, φ, κ) and the range information extracted from the DB images retrieved, are used as reference information for pose estimation of a set of acquired mobile pictures in the IRBL procedure. In this paper the survey operations, the approach for generating the RGB-D images and the IRB strategy are reported. Finally the analysis of the results and the validation test are described.

Prompt removal of misfolded membrane proteins and misassembled membrane protein complexes is essential for membrane homeostasis. However, the elimination of these toxic proteins from the hydrophobic membrane environment has high energetic barriers. Transmembrane FtsH is the only known ATP-dependent protease responsible for this task, unlike other well-studied soluble ATP-dependent proteases. The mechanisms by which FtsH recognizes, unfolds, translocates, and proteolyzes its substrates remain unclear. Here, we report the crystal structures of the Thermotoga maritima FtsH periplasmic domain (PD) in an associative trimeric state at a 1.5-1.95 Å resolution. We also describe the pH-dependent oligomerization states of the isolated PD using dynamic light scattering. These observations help us understand how FtsH recognizes membrane-anchored misfolded proteins.

In this paper, we proposed the fusion of two projection based face recognition algorithms: local binary Patterns in DCT domain and singular value decomposition (SVD) characterized by its simplicity and efficiently. Standard databases ORL are used to test the experimental results which prove that proposed system achieves more accurate face recognition as compared to individual method.

The use of haptic devices in rehabilitation of impaired limbs has become rather popular, given the proven effectiveness in promoting recovery. In a standard framework, such devices are used in rehabilitation centers, where patients interact with virtual tasks, presented on a screen. To track their sessions, kinematic/dynamic parameters or performance scores are recorded. However, as Internet access is now available at almost every home, and in order to reduce the hospitalization time of the patient, the idea of doing rehabilitation at home is gaining wide consent. Medical care programs can be synchronized with the home rehabilitation device; patient data can be sent to the central server that could redirect to the therapist laptop (tele-healthcare). The controversial issue is that the recorded data do not actually represent the clinical conditions of the patients according to the medical assessment scales, forcing them to frequently undergo clinical tests at the hospital. To respond to this demand, we propose the use of a bilateral master/slave haptic system that could allow the clinician, who interacts with the master, to assess remotely and in real time the clinical conditions of the patient that uses the home rehabilitation device as the slave. In this paper, we describe a proof of concept to highlight the main issues of such an application, limited to one degree of freedom, and to the measure of the stiffness and range of motion of the hand.

Research on bidirectional human-machine interfaces will enable the smooth interaction with robotic platforms in contexts ranging from industry to tele-medicine and rescue. This paper introduces a bidirectional communication system to achieve multisensory telepresence during the gestural control of an industrial robotic arm. We complement the gesture-based control by means of a tactile-feedback strategy grounding on a spiking artificial neuron model. Force and motion from the robot are converted in neuromorphic haptic stimuli delivered on the user’s hand through a vibro-tactile glove. Untrained personnel participated in an experimental task benchmarking a pick-and-place operation. The robot end-effector was used to sequentially press six buttons, illuminated according to a random sequence, and comparing the tasks executed without and with tactile feedback. The results demonstrated the reliability of the hand tracking strategy developed for controlling the robotic arm, and the effectiveness of a neuronal spiking model for encoding hand displacement and exerted forces in order to promote a fluid embodiment of the haptic interface and control strategy. The main contribution of this paper is in presenting a robotic arm under gesture-based remote control with multisensory telepresence, demonstrating for the first time that a spiking haptic interface can be used to effectively deliver on the skin surface a sequence of stimuli emulating the neural code of the mechanoreceptors beneath.

Human Activity Recognition (HAR) technology enables continuous behavior monitoring, particularly valuable in healthcare. This study investigates the viability of using an ear-worn motion sensor for classifying daily activities, including lying, sitting/standing, walking, ascending stairs, descending stairs, and running. Fifty healthy participants (between 20 and 47 years old) engaged in these activities while under monitoring. Various machine learning algorithms, ranging from interpretable shallow models to state-of-the-art deep learning approaches designed for HAR, were employed for classification. Results demonstrate the ear sensor's efficacy, with deep learning models achieving 98% accuracy rate of classification. The obtained classification models are agnostic regarding which ear the sensor is worn and robust against moderate variations in sensor orientation (e.g., due to differences in auricle anatomy), meaning no initial calibration of the sensor orientation is required. The study underscores the ear's efficacy as a suitable site for monitoring human daily activity and suggests its potential for combining HAR with in-ear vital signs monitoring. This approach offers a practical method for comprehensive health monitoring by integrating sensors in a single anatomical location. This integration facilitates individualized health assessments, with potential applications in tele-monitoring, personalized health insights, and optimizing athletic training regimes.

Background: Microscopic examination of acid-fast stained sputum smears for detecting mycobacterial acid-fast bacilli (AFB) remains the most economical and readily available method for laboratory diagnosis of tuberculosis (TB). However, this conventional approach has limitations, including low sensitivity and labor-intensive procedures. Methods: An automated microscopy system incorporating artificial intelligence (AI) and machine learning for AFB identification was evaluated. The study was conducted at an Infectious Disease Hospital in Jiangsu Province, China, utilizing an intelligent microscope system (TB-Scan, Wellgen Medical, Kaohsiung). A total of 1,000 sputum smears were included in the analysis, with the system capturing digital microscopic images and employing an image recognition model to automatically identify and classify AFB. Referee technicians provided the gold standard for discrepant results. Results: The automated system demonstrated an overall accuracy of 95.00% (950/1,000), sensitivity of 91.24% (177/194), and specificity of 95.91% (773/806). Notably, the system identified 21 smears as positive that were previously reported as negative, with referee technicians confirming 17 of these as true positive and retracting the test results accordingly. Recalculating the performance, the accuracy increased to 96.70% (967/1,000), sensitivity to 91.94% (194/211), and specificity to 97.97% (773/789), with a false negative rate of 8.06% (17/211) and a false positive rate of 2.03% (16/789). Conclusions: The incorporation of AI and machine learning into an automated microscopy system demonstrated the potential to enhance the sensitivity and efficiency of AFB detection in sputum smears compared to conventional manual microscopy. This approach holds promise for widespread application in TB diagnostics and potentially other fields requiring labor-intensive microscopic examination.

Over the past decades, overweight and obesity has become a global epidemic and the leading threat for death. To prevent the serious risk, an overweight or obese individual must apply a long-term weight-management strategy to control food intake and physical activities, which is however, not easy. Recently, with the advances of information technology, more and more people can use wearable devices and smartphones to obtain physical activity information, while they can also access various health-related information from Internet online social networks (OSNs). Nevertheless, there is a lack of an integrated approach that can combine these two methods in an efficient way. In this paper, we address this issue and propose a novel mobile-social framework for health recognition and recommendation, namely, H-Rec². The main ideas of H-Rec² include (1) to recognize the individual's health status using smartphone as a general platform, and (2) to recommend physical activity and food intake based on personal health information, life science principles, and health-related information obtained from OSNs. To demonstrate the potentials of the H-Rec² framework, we develop a prototype that consists of four important components: (1) an activity recognition module that senses physical activity using accelerometer, (2) a health status modeling module that applies a novel algorithm to generate personalized health status index, (3) a restaurant information collection module that collects relevant information from OSN, and (4) a restaurant recommendation module that provides personalized and context-aware recommendation. To evaluate the prototype, we conduct both objective and subjective experiments, which confirm the performance and effectiveness of the proposed system.

Good hand hygiene has proven to be essential in reducing the uncontrolled spread of human pathogens. Cold atmospheric plasma (CAP) may provide an alternative to disinfecting hands with ethanol-based handrubs when hand washing facilities are unavailable. CAP can be safely applied to the skin if the energy is well controlled. In this study, RF and DC plasma sources was built with a pin-to-mesh electrodes configuration inside a fused silica tube of 5 mm of inner diameter. Microbiological assays based on EN 13697:2015+A1:2019 using Escherichia coli DSM 682 or Staphylococcus epidermidis DSM 20044 were used to examine the antimicrobial effect of various plasma conditions. Metal and silicone disks that model skin were used as inoculation matrices. The prototype air radio frequency (RF) CAP achieved significant disinfection in the MHz range on stainless steel and silicone substrates. This is equivalent to half the performance of direct current CAP, which is only effective on conductive substrates. Using only electricity and air CAP could, with further optimization, replace or complement current hand disinfection methods, and mitigate the economic burden of public health crises in the future.

Bionic robotic hand can perform many movements similar to human hand. But there is still a significant gap in manipulation between robot and human hand. It is necessary to understand the finger kinematics and motion patterns of human hand to improve the performance of robotic hand. This study aimed to comprehensively investigate normal hand motion patterns by evaluating the kinematics of hand grip and release in healthy individuals. The data corresponding to rapid grip and release were collected from the dominant hand of 22 healthy people by sensory glove. The kinematics of 14 finger joints were analyzed, including the dynamic range of motion (ROM), peak velocity, joint sequence and finger sequence. The results show that the proximal interphalangeal (PIP) joint had a largest dynamic ROM than metacarpophalangeal (MCP) and distal interphalangeal (DIP) joints. Besides, the PIP joint had the highest peak velocity, both in flexion and extension. For joint sequence, the PIP joint moved prior to the DIP or MCP joints during flexion, while extension started in DIP or MCP joints, followed by PIP joint. Regarding the finger sequence, the thumb started to move before four fingers, and stopped moving after the fingers during both grip and release. This study explored the normal motion patterns in hand grip and release, which provided kinematic reference for the design of robotic hand and thus contribute to its development.

Parkinson’s Disease (PD) is a neurodegenerative and progressive disease that impacts the nerve cells in the brain and varies from person to person. The exact cause of PD is still unknown, and the diagnosis of PD does not include a specific objective test with certainty. Although deep learning has made great progress in medical neuroimaging analysis, these methods are very susceptible to biases present in neuroimaging datasets. An innovative decorrelated deep learning technique is introduced to mitigate class bias and scanner bias while simultaneously focusing on finding distinguishing characteristics in resting-state functional MRI (rs-fMRI) data, which assist in recognizing the PD with good accuracy. The decorrelation function reduces the non-linear correlation between features and bias in order to learn bias-invariant features. The Parkinson’s Progression Markers Initiative (PPMI) dataset, referred to as a single scanner imbalanced dataset in this study used to validate our method. The imbalanced dataset problem affects the performance of the deep learning framework by overfitting to the majority class. To resolve this problem, we propose a new Decorrelated Convolutional Neural Networks (DcCNN) framework by applying decorrelation-based optimization to Convolutional Neural Networks(CNN). An analysis of evaluation metrics comparisons shows that integrating the decorrelation function boosts the performance of PD recognition by removing class bias. Specifically, our DcCNN model performs significantly better than existing traditional approaches to tackle the imbalance problem. Finally, the same framework can be extended to create scanner invariant features without significantly impacting the performance of a model. The obtained dataset is a multi-scanner dataset which leads to scanner bias due to the differences in acquisition protocols and scanners. The multi-scanner dataset is a combination of two datasets, namely PPMI and FTLDNI - frontotemporal lobar degeneration neuroimaging initiative (NIFD) dataset. The results of t-distributed stochastic neighbor embedding (t-SNE) and scanner classification accuracy of our proposed Feature Extraction-DcCNN (FE-DcCNN) model validated the effective removal of scanner bias. Our method achieves an average accuracy of 77.80% on a multi-scanner dataset for differentiating PD from healthy control, which is superior to the DcCNN model trained on a single scanner imbalanced dataset.

The global use of alcohol based hand sanitizers (ABHS) as a means of controlling the transmission of infectious disease increased dramatically in 2020 as governments and public health agencies across the world advocated hand hygiene as a preventative measure during the COVID-19 pandemic. Although the performance of these products is most commonly defined as a function of their alcohol concentration, they are multifaceted products in which an interplay of several factors is important in determining efficacy. The hand sanitizer tetrahedron, is a novel concept that considers both ABHS formulation factors and product performance factors from a multi-dimensional perspective. The four faces of the tetrahedron represent input/formulation factors: 1) the type and amount of alcohol, 2) inactive ingredients, 3) the type of formulation/delivery system and 4) manufacturing practices. The four corners of the tetrahedron represent output/product performance factors: 1) efficacy, 2) sensory characteristics, 3) usage, usability and compliance and 4) product safety/adverse effects. All factors are of importance to ensuring the effectiveness and utility of these products.

Classification of electromyographic signals has a wide range of applications, from clinical diagnosis of different muscular diseases to biomedical engineering, where their use as input control of prosthetic devices has become a hot topic of research. Challenge of classifying this signals relies on the accuracy of the proposed algorithm and the possibility of its implementation on hardware. This paper consider the problem of electromyography signal classification, solved with the proposed signal processing and feature extraction stages, with focus lying on the signal model and time domain characteristics for better classification accuracy. The proposal considers a simple preprocessing technique that produces signals suitable for feature extraction, and the Burg reflection coefficients to form learning and classification patterns. These coefficients yield a competitive classification rate compared to used time domain features. Sometimes, the feature extraction from electromyographic signals showed that procedure can omit less useful traits for machine learning models. Using feature selection algorithms provides a higher classification performance with as fewer traits as possible. Algorithms achieved a high classification rate up to 100% with low pattern dimensionality, with other kinds of uncorrelated attributes for hand movement identification.

Due to Motor Neurone Diseases, a large population remains disabled worldwide, negatively impacting their independence and quality of life. This typically involves a weakness in the hand and forearm muscles, making it difficult to perform fine motor tasks such as writing, buttoning a shirt, or gripping objects. This project presents a vision-enabled rehabilitation hand exoskeleton to assist disabled persons in their hand movements. The design goal was to create an accessible tool to help with a simple interface requiring no training. This prototype is built on a commercially available glove where a camera and embedded processor were integrated to help open and close the hand, using air pressure, thus grabbing an object. An accelerometer is also implemented to detect the characteristic hand gesture to release the object when desired. This passive vision-based control differs from active EMG-based designs as it does not require individualized training. Continuing the research will reduce the cost, weight, and power consumption to facilitate mass implementation.

As climate changes, our daily life has been much influenced by abnormal meteorological phenomena such as heavy rainfall, heat wave, heavy snowfall, and fine dust. Atmospheric air quality is worsening day by day and indoor air quality is also affected by interconnected daily activities throughout the inside and outside of buildings and houses. Nowadays, pollutants from various sources are emitted, transformed by sunlight, vapor, and ozone and transported into the city from country to country. Due to these reasons, there have been high demands to monitor the transportation of particulate matters and improve air quality. Monitoring of pollutants and identification of type and its concentration enables us to track and control its generation and consequently find out the solution. However, monitoring of pollutants, especially, particulate matter generation and its transportation is still not fully operated in atmospheric air due to its open nature and meteorological factors. Even though indoor air is relatively easy to monitor and control than outdoor in the aspect of specific volume and contaminant source, but it still needs to consider the meteorological parameters because indoor air is not fully separated from the outdoor air flow and contaminants transportation. In this study, optical approach using spectral sensor was attempted to reveal the feasibility of wavelength and chromaticity values of reflected light from specific particles. From the analysis of reflected light of various particulate matters according to different liquid additives, parameter studies were performed to investigate which experimental conditions can contribute to the enhanced selective sensing of particulate matters. Five different particulate matters such as household dust, soil, talc powder, gypsum powder and yellow pine tree pollen were utilized and observed to elucidate the relationship between property of particulate matter and detected light spectrum. Applicable approaches to assist current particle matter sensors and improve the selective sensing were suggested.

Water resources, especially safe, potable water, are limited for many Haitians. In areas where shallow groundwater is available, many household water needs such as laundry, bathing, and cooking are supplied by hand-dug wells. In order to better understand the water quality and prevalence of these household wells, 35 hand-dug wells were surveyed and sampled near the Hôpital Albert Schweitzer (HAS) in Deschapelles, Haiti. Water samples were collected and tested for fecal coliform and E. coli using the IDEXX Colilert-18 method. Of the samples collected, 89 percent were determined unsafe to use as a drinking water source based on the World Health Organization standard of 1.0 colony-forming unit (cfu) Escherichia Coli (E. coli) per 100 mL. 66 percent of the wells exceeded recreational/body contact standards for the state of Michigan (130 cfu/100 mL). Some of these wells were deemed suitable for conversion to a new well type called In-Situ Filtration (ISF) wells. ISF wells are installed with an internal sand filter pack, PVC casing, pump, and cap which seals the well from surface contamination and provides additional water treatment as water is pumped. Previous ISF installations have reduced E. coli to safe drinking water levels within 90 days.

Olive picking is one of the most common social agricultural activities in many regions of Andalusia where the predominant crop is the traditional olive grove. The machinery used includes shakers, blowers and an essential low-cost type: hand-rake sweepers. The latter are generally used by the women of the squads to sweep the olives that fall from the trees. This article is focused on the design and optimisation of a hand-rake sweeper, in terms of durability and cost, for the picking of olives and other fruits, such as almonds, which are currently the main alternative to non-perennial crops in Andalusia. A parametric design of a hand-rake sweeper was created for this application using the design software CATIA, and its most vulnerable points were analysed in terms of effectiveness with varying design parameters, conducting usage simulations with ANSYS for a light material such as polypropylene. The maximum von Mises stress of the whole structure was 155.81 MPa. Using ANSYS, the dimension parameters of the hand-rake sweeper structure were optimised. The modified design was analysed again, showing a reduction of maximum tensions of 10.06%, as well as a decrease in its maximum elongations (0.0181 mm).

The evolution of manual grooming and its implications have received little attention in the quest to understand the origins of simian primates and their social and technical intelligence. All simians groom manually, whereas prosimians groom orally despite comparable manual dexterity between some members of the two groups. Simians also exhibit a variable propensity for the manipulation of inanimate, non-food objects, which has culminated in tool making and tool use in some species. However, lemuriform primates also seem capable of tool use with training. Furthermore, lemuriforms appear to understand the concept of a tool and use their own body parts as “tools”, despite not using inanimate objects. This suggests that prosimian primates are pre-adapted for proprioceptive object manipulation and tool use, but do not express these cognitive abilities by default. This essay explores the paleontological, anatomical, cognitive, ethological, and neurological roots of these abilities and attempts to explain this behavioural divide between simians and prosimians. Common misconceptions about early primate evolution and captive behaviours are addressed, and chronological inconsistencies with Machiavellian Intelligence are examined. A “licking to picking” hypothesis is also proposed to explain a potential link between manual grooming and object manipulation, and to reconcile the inconsistencies of Machiavellian Intelligence. Bayesian decision theory, the evolution of the parietal cortex and enhanced proprioception, and analogies with behavioural changes resulting from artificial selection may help provide new insights into the minds of both our primate kin and ourselves.

Hand gestures may play an important role in medical applications for health care of elderly people, where providing a natural interaction for different requests can be executed by making specific gestures. In this study we explored three different scenarios using a Microsoft Kinect V2 depth sensor then evaluated the effectiveness of the outcomes. The first scenario utilized the default system embedded in the Kinect V2 sensor, which depth metadata gives 11 parameters related to the tracked body with five gestures for each hand. The second scenario used joint tracking provided by Kinect depth metadata and depth threshold together to enhance hand segmentation and efficiently recognize the number of fingers extended. The third scenario used a simple convolutional neural network with joint tracking by depth metadata to recognize five categories of gestures. In this study, deaf-mute elderly people execute five different hand gestures to indicate a specific request, such as needing water, meal, toilet, help and medicine. Then, the requests were sent to the care provider’s smartphone because elderly people could not execute any activity independently. The system transferred these requests as a message through the global system for mobile communication (GSM) using a microcontroller.

Technological advancements in the Internet of Things (IoT), unmanned aerial vehicles (UAVs), and novel technological tools have transformed disaster management practices from localized approaches to a global framework. These innovations necessitate a unified understanding and application of diverse methodologies at an international level, ensuring they are accessible and operational across different sectors. This integration is crucial not only for technical personnel but also for medical teams, who play a pivotal role in disaster response and management. By fostering a comprehensive grasp of these methods, we can enhance coordination, efficiency, and effectiveness in addressing disasters on a global scale. The authors aimed to provide a comprehensive overview of emerging techniques that are driving shifts in strategic approaches and treatment technologies. Recognized as a domain of continuous research and development, this overview, by necessity, remains an incomplete snapshot of an evolving field. It highlights the dynamic nature of technological and strategic advancements, underscoring the importance of ongoing investigation and innovation to stay abreast of current trends and future directions. The article also highlights the importance of international collaboration beyond societal and national boundaries A single paragraph of about 200 words maximum. For research articles, abstracts should give a pertinent overview of the work. We strongly encourage authors to use the following style of structured abstracts, but without headings: (1) Background: Place the question addressed in a broad context and highlight the purpose of the study; (2) Methods: briefly describe the main methods or treatments applied; (3) Results: summarize the article’s main findings; (4) Conclusions: indicate the main conclusions or interpretations. The abstract should be an objective representation of the article and it must not contain results that are not presented and substantiated in the main text and should not exaggerate the main conclusions.

During the hand-eye calibration process of the manipulator, the Euclidean distance error of the calibration results cannot be calculated because the true values of the hand-eye conversion matrix cannot be obtained. In this paper, a new method of projection error analysis is presented. Error analysis is carried out by using a priori knowledge that the location of AR markers is fixed in the calibration process. The coordinates of the AR marker center point are reprojected to the pixel coordinate system, and then compared with the true pixel coordinates of the AR marker center point obtained by corner detection or manual labeling in order to obtain the Euclidean distance between the two coordinates as the basis for error analysis. Experimental results show that the proposed optimization method can greatly improve the accuracy of hand-eye calibration results.

Significant progress has been made in understanding the computational and neural architecture that mediates eye and hand movements made in isolation. However, less is known about the mechanisms that control these movements when they are coordinated. Here, we outline our computational approaches using accumulation-to-threshold and race-to-threshold models to elucidate the mechanisms that initiate and inhibit these movements. We suggest that, depending on the behavioral context, the initiation and inhibition of coordinated eye-hand movements can operate in two modes- coupled and decoupled. The coupled-mode operates when the task context requires a tight coupling between the effectors; a common command initiates both effectors, and a unitary inhibitory process is responsible for stopping them. Conversely, the decoupled mode operates when the task context demands weaker coupling between the effectors; separate commands initiate the eye and hand, and separate inhibitory processes are responsible for stopping them. We hypothesize that higher-order control processes assess the behavioral context and choose the most appropriate mode. This computational architecture can explain heterogeneous results observed across many studies that have investigated the control of coordinated eye-hand movements and may also serve as a general framework to understand the control of complex multi-effector movements.

Electromyographic (EMG) signals provide information about a person's muscle activity. For hand movements, in particular, the execution of each gesture involves the activation of different combinations of the forearm muscles, which generate distinct electrical patterns. Conversely, the analysis of these muscle activation patterns, represented by EMG signals, allows recognizing which gesture is being performed. In this study, we aimed to implement an automatic identification system of hand or wrist gestures based on supervised Machine Learning (ML) techniques. We trained different computational models and determined which of these showed the best capacity to identify six hand or wrist gestures and generalize between different subjects. We used an open access database containing recordings of EMG signals from 36 subjects. Among the results obtained, we highlight the performance of the Random Forest model, with an accuracy of 95.39%, and the performance of a convolutional neural network with an accuracy of 94.77%.

The research deals with the particularities of the initiation in the sport of kin ball, of 94 students (54 boys and 40 girls), without a history of practicing the sport intensively, and the measure of the influence of some components of the motor capacity. Kin ball is a sport that appeared in Canada, in an academic setting and can become an alternative sport in the common university curricula in Romania. The research was carried out in 4 research groups in 2019 and 2022, two of boys and two of girls, similarly, before and after the declaration of the acute phase of the Covid-19 Pandemic, generating physical activity constraints. The originality of the research refers to the initiation, unprecedented in Romania, in the Kin ball sport, through 20 training sessions, carried out in 10-day stages and the evaluation of some motor skills on the subjects. During the experiment, the following components were tested pre- and post-intervention Kin-Ball: palmar muscle strength using the dynamometer, general resistance by running as long as possible for 2 min and general coordination function - body balance, by the Matorin test. The statistical calculation included the application of the Kolmogorov-Smirnov test, depending on which parametric or non-parametric statistical significance tests were applied and the estimation of the size of the intervention effect. The results highlighted particularities of initiation in Kin ball, but also significant differences (p<0.05) in all measurements performed in 2022. The tests in 2019 recorded both significant and statistically insignificant differences, also highlighted by the calculation of the d indicator of to Cohen. The lower initial level of all motor components measured in 2022 and their significant improvement represent aspects that can be understood as collateral effects of the lack of movement during the pandemic.

In developed countries, especially the big-sized ones like Australia and the USA, a car is almost an inevitable necessity to carry out daily activities. Due to this, used cars have become a great alternative to brand new cars because of their cost effectiveness. In this work, estimation of prices of used cars based on numerous factors is studied statistically. Data is based on prices of used cars sold across Australia. Statistical methods like correlation and permutation tests using linear regression model, exact tests and non-parametric bootstrapping is implemented to study the relationship of price with mileage and year of manufacture of the car using p-values and null hypothesis. Predictions are also made on the price by calculating a 95% confidence interval (CI) of median prices in small portions of the dataset. The study presents potential ideas for understanding correlation between variables and parameters in business studies.

Gesture recognition has always been a technique to decrease the distance between the physical and the digital world. In this work, we introduce an Arduino based vehicle system which no longer require manual controlling of the cars. The proposed work is achieved by utilizing the Arduino microcontroller, accelerometer, RF sender/receiver, and Bluetooth. Two main contributions are presented in this work. Firstly, we show that the car can be controlled with hand-gestures according to the movement and position of the hand. Secondly, the proposed car system is further extended to be controlled by an android based mobile application having different modes (e.g., touch buttons mode, voice recognition mode). In addition, an automatic obstacle detection system is introduced to improve the safety measurements to avoid any hazards. The proposed systems are designed at lab-scale prototype to experimentally validate the efficiency, accuracy, and affordability of the systems. We remark that the proposed systems can be implemented under real conditions at large-scale in the future that will be useful in automobiles and robotics applications.

Superantigens are unconventional antigens which recognise immune receptors outside the usual binding sites e.g. complementary determining regions (CDRs), to elicit a response within the target cell. T-cell superantigens crosslink T-cell receptors and MHC Class II molecules on antigen-presenting cells, leading to lymphocyte recruitment, induction of cytokine storms and T-cell anergy or apoptosis among many other effects. B-cell superantigens, on the other hand, bind immunoglobulin receptors on B-cells affecting opsonisation, IgG-mediated phagocytosis, and drive B-cells into apoptosis. Here, through a review of the structural basis for recognition of immune receptors by superantigens, we show that their binding interfaces share specific physicochemical characteristics when compared with other protein-protein interaction complexes. Given that antibody-binding superantigens have been exploited extensively in industrial antibody purification, these observations could facilitate further protein engineering to optimize the use of superantigens in this and other areas of biotechnology.

It is a challenging problem that air-written Arabic letters has received a lot of attention in the past decades when compared to commonly spoken languages like English languages. To fill this gap, we propose a strong model that brings together machine learning (ML) and optical character recognition (OCR) methods. The model applied several ML methods, (i.e., Neural Networks (NN), Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM), with deep convolutional neural networks (CNNs) such as VGG16, VGG19, and SqueezeNet for effective feature extraction. Our study utilizes the AHAWP dataset, which consists of varied writing styles and variations in hand signs, to train and evaluate the model. Preprocessing systems are applied to improve data quality by reduction bias. Besides, OCR methods are combined into our model to sequestrate individual letters from continuous air-written gestures and refine recognition results. Results of this study show that the proposed model has achieved the extreme accuracy of 88.8% using NN with VGG16. This study presents an inclusive approach that combines ML, deep CNNs, and OCR methods to address the issue of Arabic in air writing recognition research.

A large part of our understanding of the biological substrates of sex-recognition and mate-recognition is derived by studying animal models. In performing those tasks, rodents rely mostly of pheromones and other olfactory cues, whereas humans rely mostly on visual cues. That may hinder the translation of rodents’ biology to humans’ biology, especially at the neural-networks level, where those cues traverse different networks in humans and rodents brains. That may be called the “pheromonal-visual gap”. A theoretical model presented here addresses those issues. The model merges observations from humans and model-animals, as reported in specific scientific reports, and general biological principles that are accepted by the scientific community. The model suggests that the voices of men and women are the innate cues based on which humans learn to use visual cues in sex-recognition and mate-recognition. Children learn the two tasks in associative learning mechanisms, by being immersed in their community, and observing adult role-models in innocuous, non-sexual scenarios. The model proposes that the human medial-geniculate-nucleus (MGN) is the analog of the rodents’ accessory-olfactory-bulb (AOB) and the main-olfactory-bulb (MOB), and that the human MASH pathway (MGN, amygdala, bnST, hypothalamus) is the analog of the rodents’ VNOP (Vomeronasal-organ-pathway). Considering the differences in the pathways should facilitate the translation from rodents’ brain nuclei and tracks to humans’. Also, the model hypothesizes that innate direct and indirect connections between auditory centers, e.g., MGN, and sex-control centers, e.g., hypothalamus, vary across three groups of children, and those variations determine the individual’s mate-recognition that emerges at puberty.

The emergence of the COVID-19 pandemic has propelled the use of alcohol-based hand sanitizers to the fore as a SARS-CoV-2 control measure. To be effective these products must comply with relevant quality parameters such as alcohol concentration, methanol limits and purity. The current study was designed to determine the quality of alcohol-based hand sanitizer products in the Nairobi metropolitan area. For this purpose, 74 commercially marketed samples were collected and subjected to analysis by gas chromatography. Only three samples (4.1%) complied with the regulatory specifications for alcohol content, methanol limits and pH. Five samples (6.8%) complied with the specification for alcohol content but did not meet methanol or pH limits. A total of 44 (59.5%) samples had methanol levels that exceeded threshold limits. Eleven samples (14.9%) were found with methanol substitution (i.e., methanol, instead of ethanol or isopropanol, was the main alcohol component). The results show that users of alcohol-based hand sanitizers are being exposed to substandard and falsified products which in addition to being non-efficacious pose harm due to unacceptable levels of toxic impurities. Regular, routine post-market surveillance is needed to prevent such products from reaching the market.

In this paper, we propose two novel methods for robot-world/hand-eye calibration and provide a comparative analysis against six state-of-the-art methods. We examine the calibration problem from two alternative geometrical interpretations, called hand-eye and robot-world-hand-eye, respectively. The study analyses the effects of specifying the objective function as pose error or reprojection error minimization problem. We provide three real and three simulated datasets with rendered images as part of the study. In addition, we propose a robotic arm error modeling approach to be used along with the simulated datasets for generating a realistic response. The tests on simulated data are performed in both ideal cases and with pseudo-realistic robotic arm pose and visual noise. Our methods show significant improvement and robustness on many metrics in various scenarios compared to state-of-the-art methods.

Falls at height are among the major causes of fatal accidents at work, especially in the construction sector. Maintaining balance while performing a tool with both hands generates physical efforts and mental loading that needs to be managed for workers’ safety and health. The purpose of the study is to compare perceived (dis)comfort (overall and localized), the ergonomic risk and the perceived effort through the acquired postures that subjects assumed during the execution of simple and common tasks both on a ladder and on the ground. 26 subjects were asked to perform four tasks in both conditions: top screwing, frontal screwing, object picking from the bottom forward, and object picking from the bottom sideways. Subjective and objective data were gathered for assessing subjective experience (discomfort and effort, perceived) and ergonomic risk. Results highlighted few ergonomic risk differences among “on ladder” and “on ground” tasks, while subjective data reveal significant differences in different scenarios. Main results regard knees and neck areas as the most critical ones.

The article proposes to use a radio holography for the detection and identification of unmanned aerial vehicles (UAVs).

Neurodegenerative illnesses, such as Parkinson’s disease (PD), have a substantial impact on the overall well-being of those who are affected. This study investigates and contrasts the capabilities of convolutional neural networks (CNN) in detecting Parkinson’s disease (PD) by utilising hand-drawn images alongside wave and spiral images as input data. This study employs pre-trained CNN models, specifically MobileNet, ResNet50, EfficientNet-B1, and InceptionV3, to classify Parkinson’s disease (PD). The findings demonstrate that MobileNet surpasses other architectural designs, as evidenced by the F1-Score of the four classes: Spiral Normal (0.87), Spiral Parkinson (0.86), Wave Normal (0.97), and Wave Parkinson (0.97). MobileNet has also shown a remarkable accuracy of 0.92 in diagnosing Parkinson’s disease. The result demonstrates the efficacy of MobileNet in extracting features from images. The results of this study enhance the application of deep learning methods in the early detection of PD, as well as help indicate the effectiveness of patient therapy and exercise, promising better patient outcomes through timely intervention and treatment.

American Sign Language is a popular language for deaf individuals. Communication is made easier for these people through sign language. However, in a digital era like today, there is a need for these people to be able to communicate online, and even get help from technology to communicate in person with non sign language speakers. This research will present a program able to translate American sign language to plain English. This study aims to use the OpenCV library to recognize hand signals, also a trained model to identify images so that the program can then translate them to words and letters. The program uses a data set of over 2000 images which will be in this case the largest data set available. With over 90\% of accuracy it results in a basic computer program with the largest data set available that would make possible for users to communicate with a wide variety of words and expressions.

Vein segmentation and projection correction constitute the core algorithms of auxiliary venipuncture device, responding to the accurate venous positioning to assist puncture and reduce the number of punctures and pain of patients. This paper proposes an improved U-Net for segmenting vein and a coaxial correction for image alignment in the self-build vein projection system. The proposed U-Net is embedded by Gabor convolution kernels in the shallow layers to enhance segmentation accuracy. Additionally, to mitigate the semantic information loss caused by channel reduction, the network model is lightweighted by the mean of replacing conventional convolutions with inverted residual blocks. During the visualization process, a method that combining coaxial correction and homography matrix is proposed to address the non-planarity of the dorsal hand in this paper. First, use a hot mirror to adjust the light paths of both projector and camera to be coaxial, and then align the projected image with dorsal hand using a homography matrix. Using this approach, the device requires only a single calibration before use. With the implementation of the improved segmentation method, an accuracy rate of 95.12% is achieved by the dataset. The intersection over union ratio between the segmented and original images is reached at 90.07%. The entire segmentation process is completed in a 0.09 second, and the largest distance error of vein projection onto dorsal hand is 0.53mm. Experiments show that the device has reached practical accuracy and has the value of research and application.

This study investigated the relationship between wrist flexion and the dynamics of the swing of a croquet mallet. Twenty seven subjects participated in a study which used 3D motion capture equipment and high speed and high-definition video to determine if there is a correlation between the lateral twist of a croquet player’s swing and the flexion of the wrist during that swing. The study found a significant correlation between the amount of flexion of the wrist from the start of the stroke to the top of the backswing and the twist of the mallet head at the top of the backswing (r=0.330, p<0.01). The methodology and findings are relevant to all sports where minimising wrist flexion is favourable for improving consistency of stroke making. Additionally, reducing wrist motion in stroke-making may reduce the incidence of wrist pain and injury in croquet, further supporting recommendations to reduce the amount of wrist flexion during the croquet swing.

Enteroviruses are members of Pichornaviridae family consisting of human enterovirus group A, B, C, and D as well as nonhuman enteroviruses. Human enterovirus type 71 (EV71) has emerged as a major cause of viral encephalitis Hand, foot, and mouth disease (HFMD) in children worldwide especially in the Asia‐Pacific region. EV71 and coxsackievirus A16 are two viruses responsible for HFMD which are members of group A enterovirus. The identified EV71 receptors provide useful information for understanding viral replication and tissue tropism. Host factors interact with the internal ribosome entry site (IRES) of EV71 to regulate viral translation. However, the specific molecular features of the EV71 genome that determine virulence remain unclear. Although an EV71 vaccine has been currently approved, there is no effective therapy for treating EV71 infected patients. Therefore, understanding the host-pathogen interaction could provide the knowledge in viral pathogenesis and further benefit in the anti-viral therapy development. The aim of this study was to investigate the latest findings about the interaction of viral ligands to the host receptor as well as the activation of immune related signalling pathways for the activation of innate immunity and involvement of different cytokines and chemokines in the host pathogen interaction of EV71 along with interaction of viral proteins, mainly 2A and 3C protease, and Interferons production/signaling pathway and their inhibitory effects.

The canonical decomposition of a bipartite graph is a new decompositionmethod that involves three operators, parallel, series and K+S. The class of weak bisblit graphs is totally decomposable with respect of these operators, and the class of bicographs is totally decomposable with respect of parallel and series operators. We prove in this paper that the class of bipartite (P_6,C_6)-free graphs is exactly the class of bipartite graphs that are totally decomposable with respect of parallel and K+S operators. We simplify the recognition algorithm of weak-bisplit graphs to adepte with bipartite (P_6,C_6)-free graphs. As a result of this adapted algorithm, we prsent efficient solutions in this class of graphs for two optimization graph problems, the first is the maximm balanced bi-clique problem and the the second is the maximum independent set problem.

The research on the distribution of rural buildings is one of the fundamental works of urban-rural development in Vietnam. Adopting Mask R-CNN deep learning framework and collecting sub-meter remote sensing images, this research used a remote sensing interpretation model of rural buildings trained based on East Asian characteristics of rural buildings and successfully recognized about 2.87 million rural buildings in 34 Vietnamese provincial administrative districts with a total area of rural buildings of 2,492 million square meters. The reliability of the identification results was verified by manual detection and quantitative statistics, and a multi-scale database of rural buildings in Vietnam based on individual rural buildings was created. Based on the database, this paper analyzes the distribution characteristics of rural buildings and summarizes characteristics of rural buildings distribution at the country, regional, and provincial scales. The identification results lay the foundation for the next study of urban-rural relations in Southeast Asia and the construction of a basic database on villages.

Indoor scene recognition and semantic information can be helpful for social robots. Recently, in the field of indoor scene recognition, researchers have incorporated object-level information and shown improved performances. This paper demonstrates that scene recognition can be performed solely using object-level information in line with these advances. A state-of-the-art object detection model was trained to detect objects typically found in indoor environments and then used to detect objects in scene data. These predicted objects were then used as features to predict room categories. This paper successfully combines approaches conventionally used in computer vision (YOLO) and Term Frequency-Inverse Document Frequency (TF-IDF). These approaches could be further helpful in the field of embodied research and dynamic scene classification, which we elaborate on.

The ability to express and recognize emotion via facial expressions is well known to change with age. The present study investigated the differences in the facial recognition and facial expression of the elderly (n = 57) and the young (n = 115) and measure how each group uses different facial muscles for each emotion with Facial Action Coding System (FACS). In facial recognition task, the elderly did not recognize facial expressions better than young people and reported stronger feelings of fear and sad from photographs. In making facial expression task, the elderly rated all their facial expressions as stronger than the younger, but in fact, they expressed strong expressions in fear and anger. Furthermore, the elderly used more muscles in the lower face when making facial expressions than younger people. These results help to understand better how the facial recognition and expression of the elderly change, and show that the elderly do not effectively execute the top-down processing concerning facial expression.

Nowadays, speed up development and use of digital devices such as smartphones have put people at risk of internet crimes. The evidence of present crimes in a computer file can be easily unreachable by changing the prefix of a file or other algorithms. In more complex cases, either file divided into different parts or the parts of a file that has information about the file type are deleted, where the file fragment recognition issue is discussed. The known files are divided into different fragments, and different classification algorithms to solve the problems of file fragment recognition. A confusion matrix measures the accuracy of type recognition. In the present study, first, the file is divided into different fragments. Then, the file fragment features, which are obtained from Binary Frequency Distribution (BFD), are reduced by 2 feature reduction algorithms; Sequential Forward Selection algorithm (SFS) as well as Sequential Floating Forward Selection algorithm (SFFS) to delete sparse features that result in increased accuracy and speed. Finally, the reduced features are given to 3 classifier algorithms, Multilayer Perceptron (MLP), Support Vector Machines (SVM), and K-Nearest Neighbor (KNN) for classification and comparison of the results. In this paper, we proposed the algorithm of file type recognition that can recognize 6 types of useful files ( pdf, txt, jpg, doc, html, exe), which may distinguish a type of file fragments with higher accuracy than the similar works done.

The dominant paradigm holds that spontaneous and therapeutically induced anti-tumor responses are mediated mainly by CD8 T cells and directed against tumor-specific antigens (TSAs). The presence of specific TSAs on cancer cells can only be proven by mass spectrometry analyses. Bioinformatic predictions and reverse immunology studies cannot provide this type of conclusive evidence. Most TSAs are coded by unmutated non-canonical transcripts that arise from cancer-specific epigenetic and splicing aberrations. When searching for TSAs, mass spectrometry analyses must therefore interrogate not only the canonical reading frame of annotated exome but all reading frames of the entire translatome. The majority of aberrantly expressed TSAs (aeTSAs) derive from unstable short-lived proteins that are good substrates for direct MHC I presentation but poor substrates for cross-presentation. This is an important caveat because cancer cells are poor antigen-presenting cells and the immune system therefore depends on cross-presentation by dendritic cells (DCs) to detect the presence of TSAs. We therefore postulate that, in the untreated host, most aeTSAs are undetected by the immune system. We present evidence suggesting that vaccines inducing direct aeTSA presentation by DCs represent an attractive strategy for cancer treatment.

Face recognition is one of the most active research fields of computer vision and pattern recognition, with many practical and commercial applications including identification, access control, forensics, and human-computer interactions. However, identifying a face in a crowd raises serious questions about individual freedoms and poses ethical issues. Significant methods, algorithms, approaches, and databases have been proposed over recent years to study constrained and unconstrained face recognition. 2D approaches reached some degree of maturity and reported very high rates of recognition. This performance is achieved in controlled environments where the acquisition parameters are controlled, such as lighting, angle of view, and distance between the camera-subject. However, if the ambient conditions (e.g., lighting) or the facial appearance (e.g., pose or facial expression) change, this performance will degrade dramatically. 3D approaches were proposed as an alternative solution to the problems mentioned above. The advantage of 3D data lies in its invariance to pose and lighting conditions, which has enhanced recognition systems efficiency. 3D data, however, is somewhat sensitive to changes in facial expressions. This review presents the history of face recognition technology, the current state-of-the-art methodologies, and future directions. We specifically concentrate on the most recent databases, 2D and 3D face recognition methods. Besides, we pay particular attention to deep learning approach as it presents the actuality in this field. Open issues are examined and potential directions for research in facial recognition are proposed in order to provide the reader with a point of reference for topics that deserve consideration.

This paper propose an original approach of achieving a Cymatics based visual perception of isolated speech commands. The idea is to smartly combine the effective speech processing and analysis methods with the phenomena of Cymatics. In this context, an effective approach for automatic isolated speech based message recognition is proposed. The incoming speech segment is enhanced by applying the appropriate pre-emphasis filtering, noise thresholding and zero alignment operations. The Mel-Frequency Cepstral coefficients (MFCCs), Delta coefficients and Delta-Delta coefficients are extracted from the enhanced speech segment. Later on, the Dynamic Time Warping (DTW) technique is employed to compare these extracted features with the reference templates. The comparison outcomes are used to make the classification decision. The classification decision is transformed into a methodical excitation. Finally, this excitation is converted into the systematic visual perceptions via the phenomenon of Cymatics. The system functionality is tested with an experimental setup and results are presented. The approach is novel and can be employed in various applications like visual art, encryption, education, archeology, architecture, integration of impaired people, etc.

Abstract: Physicians, physical therapists, and occupational therapists have traditionally assessed hand motor function in hemiplegic patients but often struggle to evaluate complex hand move-ments. To address this issue, in 2019, we developed Fahrenheit, a device and algorithm that uses infrared camera image processing to estimate hand paralysis. However, due to Fahrenheit's de-pendency on specialized equipment, we conceived a simpler solution: developing a smartphone app that integrates MediaPipe. The objective of this study was to measure hand movements in stroke patients using both MediaPipe and Fahrenheit and to assess their criterion-related validity. The analysis revealed moderate-to-high correlations between the two methods. Consistent results were also observed in the peak angle and velocity comparisons across severity stages. Because Fahrenheit determines finger recovery status based on these measures, it has the potential to transfer this function to MediaPipe. This study highlighted the potential use of MediaPipe in pa-ralysis estimation applications.

Objectives: Evaluate hand-held echocardiography (HHE) performed and interpreted by trained Advanced Practice Providers (APPs) on hospitalized CHF patients for image quality and interpretation, by comparing against expert echocardiographer and SE findings. Background: Congestive heart failure (CHF) is associated with increased hospital admissions and mortality. While standard echocardiogram (SE) is the gold standard for cardiac assessment, it is not readily available. Hospitalized CHF patients require rapid assessment for expedited treatment. Methods: Over 6 months, five trained APPs performed HHE on hospitalized CHF patients and interpreted: a) Left ventricular (LV) size, b) LV ejection fraction (LVEF), and c) right atrial pressure (RAP). The study echocardiographer reviewed and blindly interpreted HHE images and compared with APPs and SE findings. Kappa-statistics determined the degree of agreement between APPs and study echocardiographer interpretation of HHE images and SE. Results: 80 CHF patients [age 73±14 years, 58% males; LVEF (by SE) 45±19%; 36.3% body mass indexes ≥30 kg/m2] were enrolled. HHE interpretation by APPs had good agreement for LVEF (kappa 0.79) with study echocardiographer and SE (kappa 0.74), and good agreement for RAP (kappa 0.67) with study echocardiographer. Correlation between absolute LVEF interpretation by study echocardiographer on HHE and SE was r=0.88 (p<0.0001). Conclusions: Trained APPs obtained diagnostic-quality HHE images and interpreted LV function and RAP in CHF patients, with good agreement with the study echocardiographer. LVEF by HHE correlated with LVEF by SE. Our study suggests trained APPs can use HHE to evaluate LVEF and RAP in CHF patients leading to expedited and optimized treatment.

For technical or medical applications, the knowledge of the exact kinematics of the human hand is key to utilizing its capability to handle and manipulate objects and to communicate with other humans or machines. The optimal relationship between the number of measurement parameters, measurement accuracy as well as complexity, usability and cost of the measuring systems is hard to find. Biomechanic assumptions, the concepts of a biomechatronic system and the mechatronic design process as well as commercially available components are used to develop a sensorized glove. The proposed wearable can measure 14 of 15 angular values of a simplified hand model introduced in this paper. Additionally, five contact pressure values at the fingertips and inertial data of the whole hand with a degree of freedom of six are gathered. Due to the modular design and a hand size examination based on anthropometric parameters, the concept of the wearable is applicable for a large variety of hand sizes and adaptable to different use cases. Validations show a combined root-mean-square error of 0.99° to 2.38° for the measurement of all joint angles at one finger, surpassing the human perception threshold and the current state of the art in science and technology for comparable systems.

The study aimed to estimate the prevalence and correlates of oral hygiene (OH) and hand hygiene (HH) behaviour among school adolescents in three Caribbean countries. In all, 7,476 school adolescents (median age 14 years), from Dominican Republic, Suriname and Trinidad and Tobago responded the cross-sectional “Global School-based Student Health Survey” (GSHS) in 2016-2017. The prevalence poor OH (tooth brushing <2 times/day) was 16.9%, poor HH (not always before meals) was 68.2%, poor HH (not always after toilet) was 28.4% and poor HH (not always with soap) was 52.7%. In adjusted logistic regression analysis, current cannabis use, inadequate fruit and vegetable intake, poor mental health and low parental support increased the odds for poor OH. Rarely or sometimes experiencing hunger, trouble from alcohol use, inadequate fruit and vegetable intake, poor mental health, and low parental support were associated with poor HH (before meals, and/or after toilet, and/or with soap). The survey showed poor OH and HH behaviour practices. Several sociodemographic factors, health risk behaviours, poor mental health and low parental support were found associated with poor OH and/or HH behaviour that can assist with tailoring OH and HH health promotion.

Machine learning is a fundamental aspect of artificial intelligence that involves the development of algorithms and models that allow computers to learn and make predictions or decisions without explicit programming. With the development of neural networks, back-propagation algorithms and deep learning, machine learning has made breakthroughs in the fields of image recognition, natural language processing and handwriting recognition using machine learning techniques. The advent of deep learning has revolutionized the field of handwriting recognition, using convolutional neural networks, recurrent neural networks, and sequence-to-sequence models to provide solutions that go beyond machine learning methods and significantly improve the accuracy and robustness of handwriting recognition systems. But challenges remain, including the need for large labelled datasets, computational resources and addressing potential biases. As research in deep learning techniques continues to drive handwriting recognition closer towards realisability, machine learning approaches remain at the forefront.

Pneumonia is a leading cause of death worldwide, and one of the most significant approaches to diagnose pneumonia is Chest X-ray (CXR) since it was used in clinical scenes. Convolutional neural networks (CNNs) have been widely used in computer vision community. Along with the development of CNNs, we want to make use of CNNs to recognize CXR of people who get pneumonia and make classification. It is important, especially during epidemic period. In this paper, we present a new type of residual learning framework, PEPX-Resnet, which makes use of a type of lightweight residual, and apply this network to CXR dataset. The result shows that PEPX-Resnet is easier to optimize and can have better results, especially for COVID-19 cases. PEPX-Resnet could reach higher accuracy, f1 score and some other evaluations for CXR dataset.

Over the last decade, the field of Human Attribute Recognition (HAR) has dramatically changed, mainly due to the improvements brought by deep learning solutions. This survey reviews the progress obtained in HAR, considering the transition from the traditional hand-crafted to deep-learning approaches. The most relevant works on the field are analyzed concerning the advances proposed to address the HAR's typical challenges. Furthermore, we outline the applications and typical evaluation metrics used in the HAR context. Finally, we provide a comprehensive review of the publicly available datasets for the development and evaluation of novel HAR approaches.

Significant progress has made in pattern recognition technology. However, one obstacle that has not yet overcome is the recognition of words in the Brahmi script, specifically the recognition of characters, compound characters, and word because of complex structure. For this kind of complex pattern recognition problem, it is always difficult to decide which feature extraction and classifier would be the best choice. Moreover, it is also true that different feature extraction and classifiers offer complementary information about the patterns to be classified. Therefore, combining feature extraction and classifiers, in an intelligent way, can be beneficial compared to using any single feature extraction. This study proposed the combination of HOG +zonal density with SVM to recognize the Brahmi words. Keeping these facts in mind, in this paper, information provided by structural and statistical based features are combined using SVM classifier for script recognition (word-level) purpose from the Brahmi words images. Brahmi word dataset contains 6,475 and 536 images of Brahmi words of 170 classes for the training and testing, respectively, and the database is made freely available. The word samples from the mentioned database are classified based on the confidence scores provided by support vector machine (SVM) classifier while HOG and zonal density use to extract the features of Brahmi words. Maximum accuracy suggested by system is 95.17% which is better than previously suggested studies.

Hand tracking enables controller-free interaction with virtual environments, which can, compared to traditional handheld controllers, make virtual reality (VR) experiences more natural and immersive. As naturalness hinges on both technological and user-based features, fine-tuning the former while assessing the latter can be used to increase usability. For a grab-and-place use case in immersive VR, we compared a prototype of a camera-based hand tracking interface (Leap Motion) with customized design elements to the standard Leap Motion application programming interface (API) and a traditional controller solution (Oculus Touch). Usability was tested in 32 young healthy participants, whose performance was analyzed in terms of accuracy, speed and errors as well as subjective experience. We found higher performance and overall usability as well as overall preference for the handheld controller compared to both controller-free solutions. While most measures did not differ between the two controller-free solutions, the modifications made to the Leap API to form our prototype led to a significant decrease in accidental drops. Our results do not support the assumption of higher naturalness for hand tracking but suggest design elements to improve the robustness of controller-free object interaction in a grab-and-place scenario.

With the rapid development of big data, artificial intelligence, and Internet technologies, the human-human contact and human-machine interaction have produced an explosive growth of voice data. Rapidly identifying the speaker's identity and retrieving and managing his or her speech data in the massive amount of speech data has become a major challenge for intelligent speech applications in the field of information security. This research proposes a vocal recognition technique based on information adversarial training for speaker identity recognition in massive audio and video, and speaker identification when oriented to the information security domain. The experimental results show that the method projects data from different scene channels all onto the same space and dynamically generates interactive speaker representations. It solves the channel mismatch problem and effectively improves the recognition of speaker's voice patterns across channels and scenes. It is able to separate overlapping voices when multiple people speak at the same time and reduce speaker separation errors. It realizes speaker voice recognition for information security field and achieves 89% recall rate in massive database, which has practical application value for intelligent application field.

Taekwondo has evolved from a traditional martial art into an official Olympic sport. This study introduces a novel action recognition model tailored for Taekwondo unit actions, utilizing joint motion data acquired via wearable inertial measurement unit (IMU) sensors. The utilization of IMU sensor-measured motion data facilitates the capture of the intricate and rapid movements characteristic of Taekwondo techniques. The model, underpinned by a conventional convolutional neural network (CNN)-based image classification framework, synthesizes action images to represent individual Taekwondo unit actions. These action images are generated by mapping joint motion profiles onto the RGB color space, thus encapsulating the motion dynamics of a single unit action within a solitary image. To further refine the representation of rapid movements within these images, a time-warping technique was applied, adjusting motion profiles in relation to the velocity of the action. The effectiveness of the proposed model was assessed using a dataset compiled from 40 Taekwondo experts, yielding remarkable outcomes: an accuracy of 0.998, precision of 0.983, recall of 0.982, and an F1-score of 0.982. These results underscore the time-warping technique's contribution to enhancing feature representation, as well as the proposed method's scalability and effectiveness in recognizing Taekwondo unit actions.

Hand gesture recognition has many valuable applications in engineering and health care. This study proposes a novel model which can accurately distinguish hand gestures using forearm muscles' surface electromyogram (sEMG) signals. A deep learning algorithm with hyper parameters impacting the final model’s accuracy and a convolutional neural network (CNN) were employed in the recognition stage. The number of convolutional layers, kernels per layer, and neurons in the dense layer were selected for optimization, while the remaining parameters, such as the learning rate, batch size, and number of epochs, were chosen based on trial and error and prior knowledge. The optimal values for the selected hyperparameters were obtained using a genetic algorithm to achieve maximum recognition accuracy. The UC2018 Dual-Myo database was used for training and testing the model based on EMG signals characterizing the activity of eight different hand gestures. The final structure of the model consisted of two convolutional layers with 131 and 28 kernels, a dense layer with 111 neurons, and a softmax layer with eight neurons. Upon optimizing the hyperparameters using the genetic algorithm, the accuracy of the proposed model increased from 91.86% to 96.4% at best and 95.3% on average in real-time applications and 99.6% in an offline mode. Future work is warranted towards improving the architecture and the computational cost.

Cryptococcus neoformans is a multidrug-resistant human pathogenic yeast responsible for infections in immunocompromised patients. Here, Itraconazole (ITR), a commercial antifungal drug with low effectiveness against C. neoformans, was combined with different synthetic peptides Mo-CBP3-PepII, RcAlb-PepII, RcAlb-PepIII, PepGAT, and PepKAA. The mechanisms of action responsible for the synergistic effect were evaluated for the best combinations by Fluorescence Microscopy (FM). The synthetic peptides enhanced the activity of ITR by 10-fold against C. neoformans. Our results demonstrated that the combinations could induce pore formation in the membrane and overaccumulation of ROS on C. neoformans cells. Our findings indicate that our peptides successfully potentialize the activity of ITR by reducing it by 10-fold to reach antifungal activity against C. neoformans. Therefore, synthetic peptides are potential molecules to act as co-adjuvant agents in treating Cryptococcal infections.

Sign language recognition technology can help people with hearing impairments to communicate with those who are hearing impaired. At present, with the rapid development of society, deep learning technology also provided certain technical support for sign language recognition work. In sign language recognition tasks, the use of traditional convolutional neural networks to extract spatio-temporal features from sign language videos suffers from insufficient feature extraction, resulting in low recognition rates. Nevertheless, video-based sign language datasets are very large and require a lot of computational resources for training and generalisation must be ensured, which poses a challenge for recognition. In this paper, we have presented a video-based sign language recognition method based on resnet and lstm. As the number of network layers increases, the ResNet network can effectively solve the granularity explosion problem and obtain better time series features. We use the Resnet convolutional network as the backbone model. At the initialisation stage, we obtain sign language features using ResNet; then, the learned feature space is used as the input of LSTM network to obtain long sequence features. The experimental results show that the accuracy of the above model is better than the mainstream model, and it can effectively extract the spatio-temporal features in sign language videos and improve the recognition rate of sign language actions.

Multiple sclerosis is a chronic, autoimmune disease that mainly affects the central nervous system, including the brain, spinal cord, and optic nerve. This disease can cause clinical symptoms such as cognitive decline, muscle weakness, spasms, and fatigue in patients, and the onset tends to be younger. Current medication can only prevent or alleviate symptoms, so early diagnosis of this disease can increase patients' chances of treatment. Although the use of nuclear magnetic resonance detection can improve the efficiency of early auxiliary diagnosis, it still requires experienced doctors to spend too much time and energy on comprehensive judgment. To reduce the time cost and improve the efficiency of multiple sclerosis diagnosis, this article proposes a detection and recognition algorithm for multiple sclerosis based on wavelet entropy and self-adaptive particle swarm optimization. Firstly, a triple discrete wavelet transform is performed on the brain image of multiple sclerosis, and then 10 wavelet entropies are extracted from the decomposed wavelet subbands, which can reduce the feature dimensions of the image; Then, the algorithm of self-adaptive particle swarm optimization is used to optimize the feedforward neural network, in order to obtain the optimal connection weights and thresholds during the training process. The result of the model is with an average sensitivity of 92.29±1.89, specificity of 92.54±0.67, precision of 92.48±0.59, accuracy of 92.42±0.88, and F1 score of 84.85±1.74, Matthews correlation coefficient of 92.37±0.96, and Fowlkes223 Mallows Index of 92.38±0.96. The experimental results indicate that this algorithm has a very important data support role in detecting multiple sclerosis.

Sign language recognition is essential in hearing-impaired people’s communication. Sign language recognition is an important concern in computer vision and has been developed with rapid progress in image recognition technology. However, sign language recognition using a general monocular camera has problems with occlusion and recognition accuracy in sign language recognition. In this research, we aim to improve accuracy by using a 2-axis bending sensor as an aid in addition to image recognition. We aim to achieve higher recognition accuracy by acquiring hand keypoint information of sign language actions captured by a monocular RGB camera and adding sensor assist. To improve sign language recognition, we need to propose new AI models. In addition, the amount of dataset is small because it uses the original data set of our laboratory. To learn using sensor data and image data, we used MediaPipe, CNN, and BiLSTM to perform sign language recognition. MediaPipe is a method for estimating the skeleton of the hand and face provided by Google. In addition, CNN is a method that can learn spatial information, and BiLSTM can learn time series data. Combining the CNN and BiLSTM methods yields higher recognition accuracy. We will use these techniques to learn hand skeletal information and sensor data. Additionally, the 2-axis Bending sensor glove data support training AI model. Using these methods, we aim to improve the recognition accuracy of sign language recognition by combining sensor data and hand skeleton data. Our method performed better than using skeletal information, achieving 96.5% accuracy in Top-1.

Glycans play important roles as recognition molecules on cell surfaces in living organisms due to their remarkable structural diversity. Carbohydrates exist in numerous isomeric forms and can adopt diverse structures through various branching patterns. Despite their relatively small molecular weights, they exhibit extensive structural diversity. On the other hand, lectins, also known as carbohydrate-binding proteins, not only recognize and bind to the diverse structures of glycans but also induce various biological reactions based on structural differences. Initially discovered as hemagglutinins in plant seeds, lectins have been found to play significant roles in cell recognition processes in higher vertebrates. However, our understanding of lectins in marine animals, particularly marine invertebrates, remains limited. Recent studies have revealed that marine animals possess novel lectins with unique structures and glycan recognition mechanisms not observed in known lectins. Of particular interest is their role as pattern recognition receptors in the innate immune system, where they recognize glycan structures of pathogens. Furthermore, lectins serve as toxins for self-defense against foreign enemies. Recent discoveries have identified various pore-forming proteins containing lectin domains in fish venoms and skins. These proteins utilize lectin domains to bind target cells, triggering oligomerization and pore formation in the cell membrane. These findings have spurred research into the new functions of lectins and lectin domains. In this review, we present recent findings on the diverse structures and functions of lectins in marine animals.

In the context of Shared Autonomous Vehicles, the need to monitor the environment inside the car will be crucial. This article focuses on the application of deep learning algorithms to detect objects, namely lost/forgotten items to inform the passengers, and aggressive items to monitoring if violent actions may arise between passengers. For object detection algorithms was used public datasets (COCO and TAO) to train state-of-the-art algorithms, such as YOLOv5. For violent action detection was used the MoLa InCar dataset to train on state-of-the-art algorithms such as I3D, R(2+1)D, SlowFast, TSN and TSM. At the end an embedded automotive solution was used to demonstrate both methods running in real-time.

Satellite communication (Satcom) is an artificial geostationary satellite that facilitates a wide range of telecommunications. Considering its quality of service (QoS) and security is crucial in government/military applications. The most challenging situation for efficient Satcom is radio frequency interference (RFI) environment. Thus, it is necessary to ensure that transmissions are incorruptible or at least sense the quality of its spectrum. This paper presents a new method to recognize received signal characteristics using a hierarchical classification in a multi-layer perceptron neural network. We consider signal modulation and the type of RFI as the characteristics of a real-time video stream transmitted in the direct broadcast satellite. Four different modulation types are investigated in this study. Moreover, the combination of the communication signal with various kinds of interference and their effects on the classification method widely have been analyzed. Besides, two robust feature selection techniques have been developed to reduce the data-set dimensional, which leads to optimizing the classification process. The results show that the Genetic Algorithm (GA) slightly outperforms Principal Component Analysis (PCA) for feature selection. Furthermore, the robustness of the proposed techniques is assessed to detect unknown signals at different signal to noise ratios.

Recent state-of-the-art scene text recognition methods have primarily focused on horizontal text in images. However, in several Asian countries, including China, large amounts of text in signs, books, and TV commercials are vertically directed. Because the horizontal and vertical texts exhibit different characteristics, developing an algorithm that can simultaneously recognize both types of text in real environments is necessary. To address this problem, we adopted the direction encoding mask (DEM) and selective attention network (SAN) methods based on supervised learning. DEM contains directional information to compensate in cases that lack text direction; therefore, our network is trained using this information to handle the vertical text. The SAN method is designed to work individually for both types of text. To train the network to recognize both types of text and to evaluate the effectiveness of the designed model, we prepared a new synthetic vertical text dataset and collected an actual vertical text dataset (VTD142) from the Web. Using these datasets, we proved that our proposed model can accurately recognize both vertical and horizontal text and can achieve state-of-the-art results in experiments using benchmark datasets, including the street view test (SVT), IIIT-5k, and ICDAR. Although our model is relatively simple as compared to its predecessors, it maintains the accuracy and is trained in an end-to-end manner.

Accurate determination of the concentration of alcohols and their metabolites is important in forensics and in several life science areas. A new method has been developed to quantify alcohols and their oxidative products using a headspace gas chromatography- mass spectrometer and isotope-labelled internal standards. This work demonstrates the application of this method, specifically in quantifying the concentration of oxidative products of alcohol and other minor alcohols found in hand sanitizers, which have become an essential household item since the COVID-19 pandemic. Apart from the major component, the minor alcohols found in hand sanitizers include methanol, isopropanol, and n-propanol. In ethanol-based hand sanitizers, a significant amount of acetaldehyde was observed as an oxidation product, while in isopropanol-based hand sanitizers, acetone was observed. The concentration of acetaldehyde in ethanol-based hand sanitizers significantly increased with storage time and temperature, whereas no such increase of acetone concentration was observed in isopropanol-based hand sanitizers with storage time and temperature.

The increase in life expectancy, according to the World Health Organization, is a fact, and with it rises the incidence of age-related neurodegenerative diseases. The most recurrent symptoms are those associated with tremors resulting from Parkinson's Disease (PD) or Essential Tremors (ET). The main alternatives for the treatment of these patients are medication and surgical intervention, which sometimes have restrictions and side effects. Through computer simulations in Matlab software, this work investigates the performance of adaptive algorithms based on least mean squares (LMS) to suppress tremors in upper limbs, especially in the hands. The signals resulting from pathological hand tremors, related to PD, present components at frequencies that vary between 3~Hz and 6~Hz, with the more significant energy present in the fundamental and second harmonics, while physiological hand tremors, referred to as ET, vary between 4~Hz and 12~Hz. We simulated and used these signals as reference signals in adaptive algorithms, Filtered-x Least Mean Square (Fx-LMS), Filtered-x Normalized Least Mean Square (Fx-NLMS), and a hybrid Fx-LMS\&NLMS purpose. Our results showed that the vibration control provided by the Fx-LMS\&LMS algorithm is the most suitable for physiological tremors. For pathological tremors, we have used a proposed algorithm with a filtered sinusoidal input signal, Fsinx-LMS, which presented the best results in this specific case.

A hand-held laser-induced breakdown spectroscopy device was used to acquire spectral emission data from laser-induced plasmas created on the surface of cerium-gallium alloy samples with Ga concentrations ranging from 0 to 3 weight percent. Ionic and neutral emission lines of the two constituent elements were then extracted and used to generate calibration curves relating the emission line intensity ratios to the gallium concentration of the alloy. The Ga I 287.4 nm emission line was determined to be superior for the purposes of Ga detection and concentration determination.A limit of detection below 0.25% was achieved using a multivariate regression model of the Ga I287.4 nm line ratio versus two separate Ce II emission lines. This LOD is considered a conservative estimation of technique’s capability given the type of the calibration samples available and low power( 5 mJ per 1 ns pulse) and resolving power (λ/∆λ= 4000) of this handheld device. Nonetheless, the utility of the technique is demonstrated via a detailed mapping analysis of the surface Ga distribution of a Ce-Ga sample which reveals significant heterogeneity resulting from the sample production process.

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.

This article introduces a novel approach, termed the Asymmetric Probabilistic Tsetlin (APT) Machine, which incorporates the Stochastic Point Location (SPL) algorithm with the Asymmetric Steps technique into the Tsetlin Machine (TM). APT introduces stochasticity into the state transitions of Tsetlin Automata (TA) by leveraging the SPL algorithm, thereby enhancing pattern recognition capabilities. To enhance random search processes, we introduced a decaying normal distribution into the procedure. Meanwhile, the Asymmetric Steps approach biases state transition probabilities towards specific input patterns, further elevating operational efficiency. The efficacy of the proposed approach is assessed across diverse benchmark datasets for classification tasks. The performance of APT is compared with traditional machine learning algorithms and other Tsetlin Machine models, including the Asymmetric Tsetlin (AT) Machine, characterized by deterministic rules for Asymmetric transitions, and the Classical Tsetlin (CT) Machine, employing deterministic rules for symmetric transitions. Strikingly, the introduced APT methodology demonstrates highly competitive outcomes compared to established machine learning methods. Notably, both APT and AT exhibit state-of-the-art performance, surpassing the Classical Tsetlin Machine, emphasizing the efficacy of asymmetric models for achieving superior outcomes. Remarkably, APT exhibits even better performance than AT, particularly in handling complex datasets.

In this paper we use quantum machine learning to detect and classify ocular diseases across age related macular degradation, cataract, diabetic, glaucoma, hypertension, and patological myopia categories versus a control group. We analyze fundus imagery from 1000 patients. Early findings indicate there may be benefit in terms of accuracy and loss function minimization of 2.07% and 1.979x respectively compared to a similar method implemented using traditional computers.

While the desire to uncover the neural correlates of consciousness has taken numerous directions, self-face recognition has been a constant in attempts to isolate aspects of self-awareness. The neuroimaging revolution of the 1990’s bought about systematic attempts to isolate the underlying neural basis self-face recognition. These studies, including some of the first fMRI (functional Magnetic Resonance Imaging) studies, revealed a right hemisphere bias for self-face recognition in a diverse set of regions including the insula, the Dorsal Frontal Lobe, the Temporal Parietal Junction and Medial Temporal Cortex. Confirmation of these data (which are correlational) was provided by TMS (Transcranial Magnetic Stimulation) and patients in which direct inhibition or ablation of right hemisphere regions leads to a disruption or absence of self-face recognition. These data are consistent with a number of theories including a right hemisphere dominance for self-awareness and/or a right hemisphere specialization for identifying significant social relationships including to oneself.

The speech entailed in human voice comprises essentially para-linguistic information used in many voice-recognition applications. Gender voice-recognition is considered one of the pivotal parts to be detected from a given voice, a task that involves certain complications. In order to distinguish gender from a voice signal, a set of techniques have been employed to determine relevant features to be utilized for building a model from a training set. This model is useful for determining the gender (i.e, male or female) from a voice signal. The contributions are involved in two folds: (i) providing analysis information about well-known voice signal features using a prominent dataset, (ii) studying various machine learning models of different theoretical families to classify the voice gender, and (iii) using three prominent feature selection algorithms to find promisingly optimal features for improving classification models. Experimental results show the importance of sub-features over others, which are vital for enhancing the efficiency of classification models performance. Experimentation reveals that the best recall value is equal to 99.97%; 99.7% of two models of Deep Learning (DL) and Support Vector Machine (SVM) and with feature selection the best recall value is 100% for SVM techniques.

The purpose of this study was to quantify characteristics of bimanual movement intensity during 30-hour Hand Arm Bimanual Intensive Therapy (HABIT) and bimanual performance (activities and participation) in real-world using accelerometers in children with unilateral cerebral palsy (UCP). Twenty-five children with UCP participated in a 30-hour HABIT program. Data was collected from bilateral wrist-worn accelerometers during 30-hours HABIT to quantify the movement intensity, and three days pre- and post-HABIT to assess real-world performance gains. Movement intensity and performance gains were measured using six standard accelerometer-derived variables. Bimanual capacity (body function and activities) was assessed using standardized hand function tests. We found that accelerometer variables increased significantly during HABIT, indicating increased bimanual symmetry and intensity. Post-HABIT, children demonstrated significant improvements in all accelerometer metrics, reflecting real-world performance gains. Children also achieved significant and clinically relevant changes in the hand capacity following HABIT. Therefore, our findings suggest that accelerometers can objectively quantify bimanual movement intensity during HABIT. Moreover, HABIT enhances hand function as well as activities and participation in real-world situation in children with UCP.

Millions of children in developing countries face preventable deaths due to inadequate vaccination and malnutrition, in part due to insufficient monitoring and the absence of official identification. A reliable fingerprint recognition system can be a practical solution to address this issue. However, the scarcity of longitudinal fingerprint datasets for young children leads to unresolved questions regarding the earliest age for fingerprint biometric use, the frequency of enrollment required for reliable recognition, and the methods to accommodate agerelated changes. A few recent studies introduced high-resolution fingerprint scanners and showed promising recognition performance for young children. However, these studies were conducted on a small dataset over a shorter period with limited diversity; further evaluation of their finding is essential. This study assessed the effectiveness of a high-resolution contactless scanner in a controlled, diverse longitudinal dataset of children (0-15 years). Our results indicate that infants can be enrolled at five days old and reliably recognized after two months with a TAR= 100% @ FAR = 0.1%, and children aged 4-15 years can be recognized after one year with a TAR= 98.72% @ FAR = 0.1%.

Human activity recognition (HAR) using wearable sensors is gaining popularity in the healthcare domain, enabling continuous monitoring and timely interventions. The conventional HAR systems are centralised and need data sharing with a server for processing and model training. However, this data sharing leads to various challenges, such as privacy concerns, increased communication and storage costs, inefficient computational algorithms, data scarcity due to unwillingness to share, and difficulty achieving high accuracy and personalised models simultaneously. To overcome these challenges, this paper introduces a privacy and energy-aware federated learning (FL) framework for model training without data sharing. The proposed scheme incorporates spiking neural networks (SNNs), which are particularly suited for resource-constrained devices due to their event-driven information processing. The key idea is to synergise the strengths of SNNs and long short-term memory (LSTM) to create a hybrid model for robust HAR. The proposed hybrid spiking LSTM (S-LSTM) model uses the LSTM as an input layer to capture temporal dependencies efficiently, followed by spiking layers that replace the typical activation using leaky integrate-and-fire neurons. We employ the combination of surrogate gradient learning and backpropagation through time (BPTT) to update the weights of SNN layers in a supervised manner. The performance of the proposed model is evaluated using two publicly available datasets for both indoor and outdoor scenarios. The results show that the proposed S-LSTM performs much better than spike convolutional neural networks (S-CNN) and simple CNN, achieving the accuracy of 97% and 87% for indoor and outdoor datasets, respectively. Personalisation, also known as fine-tuning the global model using local data, is crucial in HAR. It allows the model to adapt to individual behaviour and preferences, making predictions more accurate. The results also show that personalisation improves the performance of participants up to 9% on average. Additionally, analysis with 50% random client participation reduces communication costs by 50% with minimal impact on accuracy.

This paper presents a novel approach for the recognition of multiple sclerosis (MS) using wavelet entropy and a Particle Swarm Optimization (PSO)-based neural network. MS is a complex neurological disorder with diverse clinical manifestations, often challenging to diagnose accurately. In this study, we leverage wavelet entropy (WE) as a feature extraction method to capture intricate patterns within brain imaging data. These extracted features are then employed to train a neural network model optimized through PSO to enhance classification accuracy. Experimental results on a dataset comprising MS and healthy control subjects demonstrate the effectiveness of our proposed approach, viz., WE-PSONN, which achieves a sensitivity of 91.95±1.15, a specificity of 92.36±0.88, a precision of 92.28±0.88, an accuracy of 92.16±0.90. The combination of advanced signal processing techniques and machine learning optimization holds promise for improving the early diagnosis and management of MS, offering potential benefits to both patients and healthcare providers.