Sign Language Recognition (SLR) aims to bridge speech-impaired and general communities by recognizing signs from given videos. Researchers still face challenges developing efficient SLR systems because of the video’s complex background, light illumination, and subject structures. Recently many researchers developed a skeleton-based sign language recognition system to overcome the subject and background variation of hand gesture signs. However, skeleton-based SLR is still under exploration due to the lack of information and annotations on hand key points. More recently, researchers included body and face information with the hand gesture for the SLR, but their performance and efficiency are unsatisfactory. We proposed a Multi-Stream Graph-based Deep Neural Network (SL-GDN) for a skeleton-based SLR system to overcome the problems. The main purpose of the proposed SL-GDN approach is to improve the efficiency and performance of the SLR system with a low computational cost based on the human body pose in the form of 2D landmark locations. In the procedure, firstly, we constructed a skeleton graph based on the selected 27 whole-body key points among 67 key points to solve the inefficiency problems. Then we proposed multi-stream SL-GDN to extract features from the whole-body skeleton graph for four streams. Finally, we concatenated the four different features and applied a classification module to refine the feature and recognize corresponding sign classes. Our data-driven and graph construction method increases the system’s flexibility and brings high generability to adapt various data samples. We used three large-scale benchmark SLR datasets to evaluate the proposed model: WLASL, AUTSL and CSL. The demonstrated performance accuracy table proved the superiority of the proposed model, and we believe this will be considered a great invention in the SLR domain.

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.

In Mexico, the incorporation of deaf people into education has been lacking since only 14% of the deaf population in the age group between 3 and 29 years access education with the support of a hearing aid. Additionally, those who have been incorporated frequently face inappropriate educational strategies which poorly develop the use of Mexican Sign Language (MSL) and therefore academical success and opportunities for insertion in the workplace are difficult. This research explores a novel mexican sign language lexicon video dataset containing the dynamical gestures most frequently used by MSL. Each gesture consists of a set of different versions of videos under uncontrolled conditions. MX-ITESO-100 data set is composed of a lexicon of 100 gestures and 5,000 videos from three participants with different grammatical elements. Additionally, the data set is evaluated in a two-step neural network model with an accuracy greater than 99%. and thus serves as a benchmark for future training of machine learning models in computer vision systems. Finally, this research provides an inclusive environment within society and organizations in particular for people with hearing impairment.

In this work, we show that a late fusion approach to multi-modality in sign language recognition improves the overall ability of the model in comparison to the singular approaches of Computer Vision (88.14%) and Leap Motion data classification (72.73%). With a large synchronous dataset of 18 BSL gestures collected from multiple subjects, two deep neural networks are benchmarked and compared to derive a best topology for each. The Vision model is implemented by a CNN and optimised MLP and the Leap Motion model is implemented by an evolutionary optimised deep MLP topology search. Next, the two best networks are fused for synchronised processing which results in a better overall result (94.44%) since complementary features are learnt in addition to the original task. The hypothesis is further supported by application of the three models to a set of completely unseen data where a multi-modality approach achieves the best results relative to the single sensor method. When transfer learning with the weights trained via BSL, all three models outperform standard random weight distribution when classifying ASL, and the best model overall for ASL classification was the transfer learning multi-modality approach which scored 82.55% accuracy.

The current spreading novel coronavirus SARS-CoV-2 is highly infectious and pathogenic and has attracted global attention. Recent studies have found that SARS-CoV-2 and SARS-CoV share around 80% of homology and use the same cell entry receptor, ACE2. These inspired us to study other receptors of SARS-CoV, which may be used for SARS-CoV-2 binding as well. In this study, we screened the gene expression of three receptors (ACE2, DC-SIGN and L-SIGN) in four datasets of normal lung tissue from lung adenocarcinoma patients and two single-cell RNA sequencing datasets from normal lung and bronchial epithelial cells separately. No significant difference in gene expression of these three receptors were found between gender groups (male vs female). We found higher gene expression of DC-SIGN in elder with age>60 and higher gene expression of L-SIGN in Caucasian than Asian. Similar to ACE2, we observed significantly higher DC-SIGN gene expression in the lungs of smokers, especially former smokers. However, smokers upregulate ACE2 and DC-SIGN gene expression in different cell types. In the whole lung, ACE2 is actively expressed in remodeled Alveolar Type II cells of former smokers, while DC-SIGN is largely expressed in monocytes of former smokers and dendritic cells of current smokers. In bronchial epithelium, no obvious gene expression of DC-SIGN and L-SIGN was observed while ACE2 was found to be actively expressed in goblet cells of current smokers and club cells of non-smokers. In conclusion, our findings may indicate that smokers, especially former smokers, and people over 60 have higher risk and are more susceptible to SARS-CoV-2 infection. Also, this study provides hints on possible SARS-CoV-2 pathogenicity mechanisms in lung infection.

Aim: The paper aims at defining the role of abductive reasoning in the reader’s interpretation of English fiction narrative text. Three research questions are defined as follows: (1) what is the nature of sign interpretation in its application to textual analysis? (2) what linguistic factors determine the use of abduction in the interpretation of signs? (3) how to apply abductive reasoning in the process of reading and interpretation in EFL teaching practice? Abduction is viewed here as a type of reasoning in the three-componential semiotic model of argument and as a deductive hypothesis, responsible for implicit meaning processing (Charles Peirce). Materials and Methods: The paper states the four-stage process of abduction to be a basic inquiry method of the reader on his way to fiction world interpretation. By providing a step-by-step analysis of patterns of abductive reasoning in a short story “Happy Endings” by Margaret Atwood, the paper conducts a textual analysis of narratives in terms of subjectivity theory of communication, reflecting the mechanisms of reader’s manipulation with information as a dynamic semiotic process of interpretation, limited by habit (final interpretant). Results: of the research of the mental operations employed by the reader while processing textual information proved a strong interrelation of reading with writing, and mental sub-processes and operations. As the empirical research shows, the process of conceptualization demands a higher level of cognitive maturity on the part of the reader/writer, as it presupposes “knowledge transforming” operations as opposed to “knowledge telling” strategy (Paltridge et. al. 2009: 20). To represent this process schematically, scholars assign the reader/interpretant the central role in the process of triadic sign interpretation, as he makes the further interpretation possible by a reference to the environment (Scheibmayer 2004: 305). The interpretant (I) and Representamen (R1) refer to the same object (O); as Representamen (R2) stands in the same relation to object, represented by Representamen (R1) and to the system (O2), where it acquires the functions of the observer (Sonnenhauser, 2008: 331). Conclusions: The conclusions coming from this research lead to the recognition of the second-level (or third level) observer as a source of subjectivity. And subjectivity, in its turn, arises from the difference in interpretation of signs recognized and established by the observer (Maturana & Varela, 1980). Thus, the process of differentiation by the observer is expected to fix the possible existence of other meanings, produced by the relations of the interpretant to the environment. This is the notion of thirdness. And, therefore, “sign situation”, plays the role of marking the pairs of differentiation in semiotic interpretation of signs. And it is this differential potential of indexical components of signs, and not their relatedness of meaning, which makes communication dynamic.

The deaf impairment is among the most substantial health problem worldwide, that can lead to various personal, economical, and social crisis. Therefore, it is critical for developing an efficient way to facilitate communication between deaf-dumb impaired and normal people. Herein, we have rationally designed a new digitally computerized and mobile smart system as an efficient communication tool between deaf impaired and normal Arabian people. This is based on two main steps, including creating a digital output for the hand gestures using gloves flex sensors equipped with a three-axis accelerometer that is controlled using a microcontroller. The digital results are compared to that in a words-based “database”, where Arabs use expressions not alphabet in their communication. The second step is translation or conversion the outputs of the first stage into written texts and voices. The newly developed system allows Arabian deaf to translate words of ordinary people into gestures using a speech recognition system with an impressive accuracy over 90 % without the needing for a webcam, colored gloves, and/or online translator. The presented system can be used on any android or windows.

Background: Machine learning can analyze vast amounts of data and make predictions for events in the future. Our group created machine learning models for vital sign predictions. To transport the information of these predictions without numbers and numerical values and make them easily usable for human caregivers, we aimed to integrate them into the Philips Visual-Patient-avatar, an avatar-based visualization of patient monitoring. Methods: We conducted a computer-based simulation study with 70 participants in three European university hospitals. We validated the vital sign prediction visualizations by testing their identification by anesthesiologists and intensivists. Each prediction visualization consisted of a condition (e.g., blood pressure low) and an urgency (a visual indication of the timespan in which the condition is expected to occur). To obtain qualitative user feedback, we also conducted standardised interviews and derived statements that participants later rated in an online survey. Results: The mixed logistic regression model showed 77.9% (95%CI 73.2-82.0%) correct identification of prediction visualizations (i.e. condition and urgency both correctly identified) and 93.8% (95%CI 93.7-93.8%) for conditions only (i.e. without considering urgencies). Forty-nine of 70 participants completed the online survey. The online survey participants agreed that the prediction visualizations were fun to use (32/49, 65.3%), and that they could imagine working with them in the future (30/49, 61.2%). They also agreed that identifying the urgencies was difficult (32/49, 65.3%). Conclusions: This study found that care providers correctly identified >90% of the conditions (i.e. without considering urgencies). The accuracy of identification decreased when considering urgencies in addition to conditions. Therefore, in future development of the technology, we will focus on either only displaying conditions (without urgencies) or improve the visualizations of urgency to enhance usability for human users.

This comprehensive study focused on the standard taxi sign used in Ireland, its impact on drag production, fuel expenses, and CO2 emissions. Experimental analysis revealed that the conventional taxi sign significantly increased drag, especially when mounted on streamlined vehicles such as saloon cars, due to flow separation issues on the rear roof and rear windshield. Longitudinal reorientation of the sign offered a 14-fold reduction in drag increase compared to the traditional placement. It was found that positioning the sign in the middle of the roof offered the greatest fuel efficiency. Furthermore, the study estimated that implementing longitudinal repositioning on all Irish taxi signs could save drivers approximately 832 per year and reduce national CO2 emissions by a substantial 22,464 tonnes annually. Comparative analyses with international taxi signs demonstrated that the Irish sign had significantly larger drag contributions, emphasizing the need for improved aerodynamics. To address the inherent drag issue, the study explored novel appendable devices and proposed alternative taxi sign designs. Among the tested solutions, a magnet-mounted front ramp proved the most effective, reducing total drag by nearly 30%. Additionally, a motorized flip-up taxi sign design demonstrated a remarkable 40% reduction in drag. Finally, a newly proposed taxi sign design, featuring longitudinal positioning and pointed triangular front and rear faces, resulted in a minimal 4.3% increase in vehicle drag compared to the baseline car.

The gradient descent algorithm has become the standard algorithm for computing the extreme values of functions, but for multivariate functions this algorithm is mostly ineffective. This is because the convergence rate of each element is inconsistent in most cases. However, in this paper, we found that the gradient sign is a self-optimizing operator, ensuring that the convergence rate is consistent across all elements. This also explains, from an optimization perspective, the success of the Fast Gradient Sign Method (FGSM) in generating adversarial samples that are indistinguishable from the normal input, but can easily fool neural networks. We also found that the fractional order gradient is also self-optimizing, and that the convergence speed of this algorithm can be controlled by adjusting the order of the gradient. Experiments suggest that this algorithm not only generates adversarial samples faster than other algorithms, but that a single source image can generate many such samples. This algorithm is also more effective than others at generating adversarial samples from simple images.

Several types of soft tissue sarcomas have peripheral infiltrative growth characteristics called tail-like lesions. The efficacy of neoadjuvant therapy for tumors with tail-like lesions has not been elucidated. From 2012 to 2019, we analyzed 36 patients with soft tissue sarcoma with tail-like lesions treated with neoadjuvant therapy, including chemotherapy, radiotherapy, or both. The ef-fect of neoadjuvant therapy on the tail sign was investigated by analyzing the change in tail-like lesions during neoadjuvant therapy and histological responses. The median length of the tail-like lesion reduced from 29.5 mm at initiation to 19.5 mm after neoadjuvant therapy. The extent of shrinkage in tail-like lesion was related to the histopathological responses in the main part. Com-plete disappearance of the tail-like lesion was observed in 12 patients; however, it was not related to achieving a microscopically negative margin. The oncologic outcomes did not significantly differ between cases with complete disappearance of tail-like lesions or not. This study indicated the shrinkage of tail-like lesions did not have significant effect on complete resection or improve-ment of clinical outcomes. A more comprehensive evaluation is needed to elaborate on the surgi-cal strategy.

C-type lectin CD209/DC-SIGN and CD209L/L-SIGN proteins are distinct cell adhesion and pathogen recognition receptors that mediate cellular interactions and recognize a wide range of pathogens including, viruses such as SARS, SARS-CoV-2, bacteria, fungi and parasites. Pathogens exploit CD209L family proteins to promote infection and evade the immune recognition system. CD209L and CD209 are widely expressed in SARS-CoV-2 target organs and can contribute to infection and pathogenesis. CD209L family receptors are highly susceptible to alternative splicing and genomic polymorphism, which may influence virus tropism and transmission in vivo. The carbohydrate-recognition domain (CRD) and the neck/repeat region represent the key features of CD209L family proteins that are also central to facilitating cellular ligand interactions and pathogen recognition. While, the neck/repeat region is involved in oligomeric dimerization, the CRD recognizes the mannose containing structures present on specific glycoproteins such as those found on the SARS-CoV-2 spike protein. Considering the role of CD209L and related proteins in diverse pathogen recognition, this review article discusses the recent advances in the cellular and biochemical characterization of CD209 and CD209L and their roles in viral uptake, which has important implications in understanding the host-pathogen interaction, the viral pathobiology and driving vaccine development of SARS-CoV-2.

The clinical features of increasing muscle weakening are shared by a diverse set of illnesses known as muscular dystrophies. In early childhood, males with Duchenne muscular dystrophy (DMD) exhibit proximal muscle weakening and calf enlargement, making it the most frequent X-linked disease of muscular dystrophy in children. Development of motor skills is typically delayed, and wheelchair confinement eventually leads to early death from heart or lung problems. In terms of function, ambulation, quality of life, and life expectancy, treatment approaches like corticosteroid therapy and intermittent positive pressure breathing have improved. This is a case report of a 6-year-old kid who had early ambulatory stage Duchenne Muscular Dystrophy and complained to the neuro-physiotherapy department about frequent falls, walking difficulties, and dyspnea when walking. Other grievances included being unable to get off the ground and having trouble ascending stairs. He had physiotherapy and dynamic neuromuscular stabilization for his complaints. The main goals of treatment were educating the family, respiratory training, preventing ankle plantar flexors, maintaining strength, balance and gait training.

The accommodative response of the human eye is predominantly driven by foveal vision but reacts also to off-foveal stimuli. Here, we report on monocular accommodation measurements using parafoveal and perifoveal annular stimuli centered around the fovea and extending up to 8 degree radial eccentricity for young emmetropic and myopic subjects. The stimuli were presented through a sequence of random defocus step changes induced by a pupil-conjugated tunable lens. A Hartmann-Shack wavefront sensor with an infrared beacon was used to measure real-time changes of ocular aberrations up to and including the 4th radial order across a 3 mm pupil at 20 Hz. Our findings show a significant reduction in accommodative response with increased radial eccentricity.

This article propose a didactic, through games, tool based on information and communication technologies, in order to eliminate possible communication barriers and to promote the inclusion of students with hearing impairment in Physical Education classes. To this end, a dossier of traditional games has been developed. These are structured in turn into objectives, materials, organisation, graphic description and a QR code for each game. These codes are linked to different videos hosted on the YouTube platform, in which the explanation of the games, mentioned above, can be visualised graphically using sign language. The whole creative process is described in the article, as well as possible pedagogical applications of the use of the tools created for this purpose in other educational contexts.

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.

In this work we study the recently developed parametrized partition function formulation and show how we can infer the thermodynamic properties of fermions based on numerical simulation of bosons and distinguishable particles at various temperatures. In particular, we show that in the three dimensional space defined by energy, temperature and the parameter characterizing parametrized partition function, we can map the energies of bosons and distinguishable particles to fermionic energies through constant-energy contours. We apply this idea to both noninteracting and interacting Fermi systems and show it is possible to infer the fermionic energies at all temperatures, thus providing a practical and efficient approach to obtain thermodynamic properties of large fermion systems with numerical simulation. As an example, we present energies for up to 50 noninteracting fermions and up to 20 interacting fermions at all temperatures and show good agreement with the analytical result for noninteracting case.

The meridian line is a basic instrument for positional astronomy, it was used to study the motion of Sun, Moon, planets and the position of stars by measuring position and time of their passage through the meridian plane. The accuracy of such positions was dependent on precise theories of the atmospheric refraction (Cassini, 1655 and Laplace, 1825) and by the use of reference marks present originally on the meridian line, and now cancelled by the centuries. From October 27, 2018 the new pinhole of the meridian line in the Basilica of S. Maria degli Angeli in Rome (1702) is a circle 25 mm wide and 6.11 mm thick and its position is fixed, in order to perform a series of observations of astrometric quality, the IGEA campaign. The comparison of the observed positions of the meridian passages of the Sun, Southern and Northern limbs, with the ephemerides of Calsky.org and Stellarium 0.20.2 for the Sun are examined for the dates of the ingresses into the zodiacal signs, when the ecliptic longitude is exactly 0°/180° (Aries and Libra, spring and fall equinox), 30°/150° (Taurus, Virgo), 60°/120° (Gemini, Leo), 90° (Cancer), 330°/210° (Pisces, Scorpio), 300°/240° (Aquarius/Sagittarius), 270° (Capricorn). The former geometrical calibration of the marks present on the line, with a total station, is compared with another calibration done with a metal and laser meter. The first star on the floor of the Basilica representing the position of the Sun on August 20, 1702 when the pope Clement XI visited the meridian line, financed by him, has been calibrated with the solar image. The present pinhole is 4.4±0.1 mm South with respect to the original one of 1702.

This study aimed on evaluating the potential diagnostic value of a novel, sonographic, B-Flow Imaging (BFI)-based sign (“flashlight sign”, FLS) for the detection of wall-adherent, floating arterial structures (WAFAS). The FLS, characterized by a fast moving, very bright, intraluminal signal was detected in 28 patients with WAFAS. We divided this cohort into three subgroups, depending on the affected vascular segments: (1) peripheral arteries (n = 10), (2) native abdominal aorta (n = 8), and (3) abdominal aorta after endovascular aortic repair (EVAR; n = 10). Clinical characteristics were analyzed and BFI-findings were compared with contrast enhanced ultrasound (CEUS) and computed tomography angiography (CTA). Seven patients (25%) suffered from arterial embolism downstream to the FLS (EVAR, n = 4; native abdominal aorta, n = 1; peripheral arteries, n = 2). WAFAS of the abdominal aorta (native or after EVAR), as indicated by the FLS, were visible by CEUS and CTA in 60% and 93.3%, respectively. Based on the so far largest cohort of patients with WAFAS, we propose a clinically useful, BFI-based sonographic sign for detection of these so far underrated arterial pathologies in the abdominal aorta and the peripheral arteries.

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.

The aim of this report is to evaluate the segmental rib index (RI) from T1-T12 spinal level in mild and moderate idiopathic scoliosis (IS) curves of thoracic, thoracolumbar and lumbar type by gender. The relationship of segmental RI to the frontal plane radiological deformity presented as the Cobb angle and to the posterior truncal surface deformity presented as the scoliometric readings of Angle of Trunk Rotation (ATR) in these patients is also assessed. Any statistical significant rela-tionship of these parameters would be very important for biomechanical relations of the rib cage (RC) deformity presented as the rib hump deformity (RHD) and the deformity in the spine and it would thus provide valuable information about scoliogeny. The segmental rib index (RI) is presented in 83 boys and girls with mild and moderate IS. The measurements include the scoliometric readings for truncal asymmetry (TA), the Cobb angle assessment and the segmental RI from T1-T12. The statistical package SPSS 23 was used for statistical analysis. The TA is documented and the Cobb angle is presented by gender and curve type. The segmental RI of thoracic, thoracolumbar and lumbar curves are presented for the first time. The correlations of the segmental RI to surface deformity presented as the rib hump deformity (RHD) in all IS patients and particularly in thoracic curves, to Cobb angle by gender and age and the comparison of the segmental RI index of asymmetric but not scoliotic children to the scoliotic peers by curve (in thoracic, thoracolumbar, lumbar curves) in boys and girls are presented. The findings emphasize the significant protagonistic role of the thoracic asymmetry in relation to the spinal deformity mainly in girls for the thoracic and in boys for the thoracolumbar curves. The cut-off point of age of the examined scoliotics is 14 years, that is when the RI shows stronger correlation with the spinal deformity, namely when the thoracic deformity is decisively effective to the developing thoracic spinal deformity, in terms of Cobb angle. In summary, the results of this study provide aetiological implications for IS, as far as the role of thorax is con-cerned.

Cloth wearing seems so natural that everyone is self-deemed knowledgeable and has some expert opinions about it. However to clearly explain the physics involved, and hence to make predictions for clothing design or selection, turns out as demonstrated below to be quite challenging even for experts. Cloth is a multiphased, porous and anisotropic material system and usually in multilayers. Unlike ordinary engineering heat transfer problems, the human body acts as an internal heat source in a clothing situation, thus forming a temperature gradient between body and ambient, and the sign of this gradient often changes as the ambient temperature varies. Our body also perspires and the sweat evaporates, an effective body cooling process via phase change. To bring all the variables into analysis quickly escalates into a formidable task. This work attempts to unravel the problem from a physics perspective, focusing on several rarely noticed yet critically important mechanisms involved so as to offer a clear and accurate depiction of the principles in clothing thermal comfort.

This comprehensive analysis thoroughly examines the topic of adversarial attacks in artificial intelligence (AI), providing a detailed overview of the various methods used to compromise machine learning models. It explores different attack techniques, ranging from the simple Fast Gradient Sign Method (FGSM) to the intricate Carlini and Wagner Attack (C&W), emphasising the wide range of adversarial approaches and their intended goals. The discussion also distinguishes between targeted and non-targeted attacks, highlighting the adaptability and versatility of these malicious efforts. Additionally, the study delves into the realm of black-box attacks, revealing the capability of adversarial strategies to compromise models even with limited knowledge. Real-life examples illustrate the tangible consequences and potential dangers of adversarial attacks in various fields such as self-driving cars, multimedia, and voice assistants. These cases highlight the difficulties in ensuring the legitimacy and dependability of AI-powered technologies and programs. The article stresses the importance of ongoing research and innovation to address the growing difficulties posed by advanced methods like deepfakes and disguised voice commands in preserving the security of AI systems. This study provides valuable insights on how different adversarial strategies and defence mechanisms interact within AI. The results emphasise the urgent need for stronger and more secure AI models to combat the increasing number of adversarial threats in today's AI landscape. These findings can guide future research and innovations in developing more resilient AI technologies that can better withstand various adversarial vulnerabilities and challenges.

This paper introduces an innovative nanotechnology-based approach that provides a pathway to enhance the energy release efficiency of nanoenergetic materials (nEMs) by harnessing self-synchronized collective atomic vibrations and phonon wave resonance within the transition domains between nanocomponents, without altering the material composition. The key innovation involves incorporating finely-tuned 2D-ordered linear-chain carbon-based multilayer nano-enhanced interfaces as programmable nanodevices into the transition domains using advanced multistage processing and assembly techniques. These programmable nanodevices enable precise control over self-synchronized collective atomic vibrations and phonon wave propagation, leading to synergistic effects. To activate and optimize these effects, a combination of various methods is employed, including energy-driven initiation of allotropic phase transformations, surface acoustic wave-assisted micro/nano-manipulation, heteroatom doping, directed self-assembly using high-frequency electromagnetic fields, and data-driven inverse design approaches. By leveraging a data-driven inverse design strategy and uncovering hidden structure-property relationships, we maximize energy release efficiency using the carbon nanomaterials genome approach derived from multifactorial neural network-based predictive models. This approach not only unlocks new functionalities in nEMs but also improves environmental performance and safety levels. By pioneering transformative pathways for nEMs through harnessing phonon wave resonance in low-dimensional nanocarbon transition interfaces, this research brings significant advancements in the field.