Abstract: The pursuit of youth and attractiveness is a fundamental desire in contemporary society. For many individuals, the appearance of visible signs of aging, such as skin laxity, gravitational changes in facial and neck soft tissues, and loss of harmonious proportions, can be psychologically distressing. The choice of effective methods for addressing these changes depends on their specific characteristics, the patient's anatomical features, and the type of facial aging. The introduction of 3D imaging to determine the hyoid bone level, the volume of soft tissue structures in the cervico-mental area, ptosis of subplatysmal structures, and the volume of submandibular glands, in addition to ultrasound study of the submental projection at the preoperative stage, are key for taking the best decision regarding preoperative planning. This case report aims to illustrate how modern imaging techniques are a fundamental part of the preoperative assessment of the mid and lower third of the face to ensure the most favorable aesthetic outcomes for each patient. Key message: To attain harmonious results in surgical correction of age-related facial and neck changes, it is crucial to consider the patient's anatomical features and the extent of age-related alterations. A comprehensive facial analysis, taking into account the type of facial aging and employing a CT scan and ultrasound examination of the soft tissues in the chin region, facilitates the development of an individualized plan tailored to achieve the patient's desired outcome.

Objective: The aim of this in vitro study was to evaluate the accuracy of resin-based fixed dental restorations, namely veneers, single crowns, and four-unit fixed partial dental prosthesis (FPDs) using two different 3D printers and polymer-based materials. Materials and Methods: A standard maxillary polyurethane jaw model containing prepared teeth was scanned using an intraoral scanner. The generated STL data were used to design the restorations virtually using CAD software. Two 3D printers were utilized for the provisional digital light processing and stereolithography for the castable resin patterns. Each printer produced 10 specimens of each type of restoration, for a total of 80 restorations. The 3d-printed restorations were then 3D scanned using the same intraoral scanner and evaluated for external and internal dimensional accuracy in terms of trueness and precision. A one-way ANOVA and Two-sample T-Test were implemented to compute the precision (variability between groups) and trueness (with the designed CAD model). A level of statistical significance of p-Value

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 present grievous situation of the tuberculosis disease can be improved by efficient case management and timely follow-up evaluations. With the advent of digital technology this can be achieved by quick summarization of the patient-centric data. The aim of our study was to assess the effectiveness of the ODK Scan paper-to-digital system during testing period of three months. A sequential, explanatory mixed-method research approach was employed to elucidate technology use. Training, smartphones, application and 3G enabled SIMs were provided to the four field workers. At the beginning, baseline measures of the data management aspects were recorded and compared with endline measures to see the impact of ODK Scan. Additionally, at the end, users’ feedback was collected regarding app usability, user interface design and workflow changes. 122 patients’ records were retrieved from the server and analysed for quality. It was found that ODK Scan recognized 99.2% of multiple choice bubble responses and 79.4% of numerical digit responses correctly. However, the overall quality of the digital data was decreased in comparison to manually entered data. Using ODK Scan, a significant time reduction is observed in data aggregation and data transfer activities, however, data verification and form filling activities took more time. Interviews revealed that field workers saw value in using ODK Scan, however, they were more concerned about the time consuming aspects of the use of ODK Scan. Therefore, it is concluded that minimal disturbance in the existing workflow, continuous feedback and value additions are the important considerations for the implementing organization to ensure technology adoption and workflow improvements.

Chromium (Cr) metal has garnered significant attention in alloy systems owing to its exceptional properties, such as high melting point, low density, and superior oxidation and corrosion resistance. However, its processing capabilities are hindered by the high Ductile-Brittle Transition Temperature (DBTT). Recently, powder bed fusion-laser beam for metals (PBF-LB/M) has emerged as a promising technique, offering the fabrication of net shapes and precise control over crystallographic texture. Nevertheless, research investigating the mechanism underlying crystallographic texture development in pure Cr via PBF-LB/M still needs to be conducted. This study explores the impact of scan speed on relative density and crystallographic texture. At the optimal scan speed, an increase in grain size attributed to epitaxial growth was observed, resulting in the formation of cubic texture. Consequently, a reduction in High Angle Grain Boundaries (HAGB) was achieved, suppressing defects such as cracks and enhancing relative density up to 98.1%. Furthermore, with increasing densification, Vickers hardness also exhibited a corresponding increase. These findings underscore the efficacy of PBF-LB/M for processing metals with high DBTT properties.

Color plays an important part in human activities, affects circadian cycle and decision making process. Therefore, it is important to investigate human judgment under different color of illumination, especially, because color perception is subjective. In this study, we developed required instrumentation to control the red (R), green (G), Blue (B) and Amber (A) colored light emitting diode (LED) lamp for carrying out Magneto Encephalography (MEG) brain scan. We developed a software to generate all the colors in the visual spectrum, predefined white light combinations and saturation of an illuminated objects by using RGBA color pallet. The lamp is required to control from outside of the MEG electromagnetically shielded room with the LED lamp located inside the MEG room. Hence, USB is used for the communication mode. The feasibility of using LED lamp with MEG brain scanner has been validated for bio-medical and psychological MEG experiments.

Background: Health management is gaining importance as a business resource to enhance employee well-being. The rise of remote work during COVID-19 impacted lifestyle habits. This study explores the impact of tele-exercise intervention on remote workers' lifestyle habits, aiming to improve health and productivity. Methods: This study divided into remote (G1) and face-to-face (G2) groups. Over three months, G1 followed a 30-minute weekly exercise program. Measurements and questionnaires were collected at three time points covering areas like presenteeism, weight, sleep, stress, job satisfaction, and happiness. Data underwent Two-way repeated measures ANOVA, Autocorrelation, Correlation Analysis, and Correspondence Analysis to assess differences and relationships between G1 and G2. Results: ANOVA showed that a significant difference was observed in Daily Stress (DS) between G1 and G2, with G1 exhibiting higher DS. Autocorrelation Analyses showed for G1 that strong relationships each of BMI, Presenteeism (PST), Daily Stress (DS), Stress Relief (SR), Sleep Quality (SQ), Purpose of Work (PW), and Satisfaction of Life (SL) in the time frame. Correlation Analysis showed for G1 that the pairs of PRS and SQ, and DS and SL had strong positive relationships, and the pairs of BMI and PRS, and BMI and SQ had strong negative relationships. Autocorrelation Analyses showed for G2 that strong relationships each of SR, Stress due to Remote Work (SRW), ST, SQ, and PW in the time frame. Correspondence Analysis showed that that G1 was encompassed in a job and life satisfaction area, and G2 was encompassed in a sleep and stress for weight control area.

Consuming fish sustainably means consuming less and better, considering environmental, social, and economic impacts. This demands more knowledge to make informed decisions. In this study, the main goal was to understand how sustainability education can be used as a tool to raise awareness and promote new habits regarding the sustainable consumption of fish in 4th-grade students at primary school. During the COVID-19 pandemic, face-to-face and online learning methodologies were used in two different groups of students to understand whether the teaching method impacted learning. A sequential explanatory mixed methodology was applied, with a pre-test/post-test design, combined with focus group sessions. Between the pre and post-tests three learning modules were performed. The results showed that the students were unaware of many aspects related to fish sustainable consumption, but the developed activities significantly increased their knowledge of the subject and impacted students to change their consumption behaviours. It was also found that the construction of knowledge by the students after the activities was achieved either by face-to-face or online learning. Interviews from focus group sessions showed that students got to know new fish species, learned about the concept of minimum landing size and closure season and that fish are labelled like other types of food.

Human Face and facial parts are the most significant parts as it reveals a person’s true identity. It plays an important role in various biometric applications like crowd analysis, human tracking, photography, cosmetic surgery, etc. There are many techniques are available to detect a facial image. Among them, skin detection is the most popular one. The aim of this paper is to detect first the person's identity from facial image and finally check any spot present the the detected person. The first step is to detect the maximum skin region based on a combination method of RGB and HSV color space model. Next it is to verify the skin areas of human through machine learning approach. The Aggregated Channel Features (ACF) detector is used to identify the different facial parts like eye pairs, nose, and mouth. Bootstrap aggregation decision tree classifier is applied to classify the person’s identity based on Histogram Oriented Gradient (HOG) features value. The experimental results show that the proposed method gives the average 97% accuracy.

Nowadays, fostered by technological progress and contextual circumstances such as economic crisis and pandemic restrictions, remote education is living a growing deployment. However, this growth generated widespread doubts about the actual effectiveness of remote/online compared to face-to-face education. The present study aimed at comparing face-to-face and remote education through a multimodal neurophysiological approach. It involved forty students at a driving school, during a real classroom, experiencing both the modalities. Wearable devices to measure brain, ocular, heart and sweating activities were employed in order to analyse the students’ neurophysiological signals to obtain insights about their cognitive dimension. In particular, four parameters were considered, the Eye Blink Rate, the Heart Rate and its Variability and the Skin Conductance Level. Also, the students filled a questionnaire at the end to obtain an explicit measure of their learning performance. Data analysis showed a higher cognitive activity, in terms of attention and mental engagement, in presence with respect to remote modality. On the other hand, students by remote felt more stressed, in particular during the first part of the lesson. Analysis of questionnaires demonstrated worst performance by remote, thus suggesting a common “disengaging” behaviour when attending remote courses, thus undermining their effectiveness. In conclusion, neuroscientific tools could help to obtain insights about mental concerns, often «blind», such as attentional decreasing and stress increasing, as well as their dynamics during the lesson itself, so allowing to define proper countermeasures to emerging issues when introducing new practices into daily life.

With the increase of intelligent voice phishing and the increasing reliance on open banking systems, there has been a rise in cases where individuals' personal information has been exposed, resulting in significant financial losses for the victims. Non-face-to-face transactions in the financial sector face challenges such as customer identification, ensuring transaction integrity, and preventing transaction rejection. Blockchain-based distributed ledgers have been proposed as a solution, but their adoption is limited due to the difficulty of managing private keys and the burden of gas fees management. This paper proposes a non-face-to-face P2P real-time token payment system that minimizes the risk of key loss by storing private keys in a keystore file and database through a server-based key management module. The proposed system simplifies token creation and management through a server-based token management module and implements an automatic gas charging function for smooth token transactions. Transaction integrity and non-repudiation are ensured through a transaction confirmation module that uses transaction IDs without exposing personal information. Furthermore, advanced security measures such as blocking foreign IP access and DDoS defense are implemented to securely protect user data. The proposed system aims to provide a convenient, secure, and accessible online payment solution to the public by implementing a self-authentication function using a web application that is not limited to smart phones or application platforms.

During the COVID-19 outbreak, most university courses have been offered on online platforms. A sudden shift from face-to-face classroom learning to online formats could influence the learning effectiveness of classes. This study aims to investigate differences in the learning effectiveness of online and face-to-face lecture courses. It also explores factors that impact the effectiveness of online instruction. These factors include interactions among learners, interactions between learners and the instructor, the quality of online platforms, learners’ ability to use devices and follow instructions, and learners’ situational challenges. The study participants were 261 university students at King Mongkut’s University of Technology Thonburi in Bangkok, Thailand. All participants were enrolled in at least one lecture course, such as history, humans and the environment, the environment and development, or general philosophy, during the 2019 academic year. A questionnaire was distributed to participants after they completed these courses in May 2020. Paired simple t-test analyses were used to compare the effectiveness of online and face-to-face classes, and a multiple regression analysis was used to identify factors that impact the learning effectiveness of online classes. The results show that online classes are less effective than face-to-face courses. The multiple regression analysis also revealed that the effectiveness of online learning was significantly impacted by learners’ ability to interact with classmates during class, their ability to interact with instructors after the class, the quality of online platforms, and disturbances or distractions in learners’ environments.

Background The acknowledgment of computed tomography (CT) defined diagnosis in high prevalence northern Italy may identify more patients with Coronavirus Disease-2019 (COVID 19) infection, than RT-PCR alone. Methods We retrospectively reviewed 148 chest CT scans of oncological patients who were referred to the Radiological Unit of Policlinico S. Marco from 1st of February 2020 to 30th of April 2020, during the Covid-19 outbreak in Bergamo area. Therefore, we analyzed RT-PCR tests of these 148 patients. Results Among 32 patients with diagnosis of COVID-19 infection: 17 patients were asymptomatic or had mild symptoms (53.1%), while 15 developed severe disease (46.8%). The incidence of COVID-19 infection is 22.9%, the mortality rate is 18.8%. Severe COVID-19 disease is associated with higher median age. We did not find any correlation between disease severity and sex, smoke or cardiovascular comorbidities. Remarkably, patients who were on treatment developed milder disease than cancer patients who were not on treatment. Conclusions The acceptance of CT-defined diagnosis in high prevalence area like Bergamo highlighted a larger number of COVID-19 oncological population than RT-PCR alone, in particular asymptomatic and mild symptomatic patients. We observed that actively treated patients had milder disease, according to previous studies that suggested a protective role of immunosuppression.

When in doubt, asking a peer can be very helpful. Students learn a lot of social strategies from peers. However, stated preference studies [Newman, 1993] have found that for elementary school students with math questions they prefer to ask the teacher. In this paper, we study revealed preferences instead of stated preferences. We analyze the behavior of fourth-grade students seeking face-to-face assistance while working on an online math platform. Students start by working independently on the platform before the teacher selects two or three tutors from among those who have answered 10 questions correctly. Each student is then able to choose between the teacher or one of these tutors when requesting assistance. We study the students’ preferences over 3 years, involving 88 fourth-grade classes, 2,700 students, 1,209 sessions with classmate tutors, and a total of 16,485 requests for help when there was an option to choose between a teacher or a classmate. We found that students prefer asking classmates for help 3 times more than asking their teachers when given the choice. Furthermore, this gap increases from the first to the second semester. We also found that students prefer to request help from classmates of the same sex and of higher academic performance. In this sense, students from the two highest tertiles sought help from classmates in the same two tertiles, and students from the medium tertile prefer to seek help from students of the highest tertile. However, students in the two lowest tertiles do not prefer asking for help from students from the top tertile more than from their own tertiles.

Metal additive manufacturing technologies have large potential for future use in load bearing aerospace applications, requiring a deeper understanding of mechanical performance and influencing factors. The objective of this study is to manufacture fatigue specimens by laser powder bed fusion with comparable bulk and different as-built surface quality (without post-processing). This goal was pursued by using identical bulk/hatch scan parameters while varying contour scan parameters. The bulk quality was evaluated by density measurements according to Archimedes’ principle and tensile testing. The surfaces were investigated using the optical fringe projection method and surface quality was assessed by the areal surface texture parameters Sa (arithmetic mean height) and Sk (core height, derived from material ratio curve). The resulting mean values of max (ultimate tensile strength) were between 375 and 405 MPa, which is a variation of roughly 8%, for seven different surface conditions. It could be confirmed that the influence of contour scan variation on bulk quality is insignificant for the assessed samples.

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.

A clinical simulation web-based program, known as FIRST2ACT (Feedback Incorporating Review and Simulation Techniques to Act on Clinical Trends) is designed to increase the efficacy of cli-nicians’ actions in the recognition and immediate response to a patient’s deterioration. This study, which was nested in a larger mixed method project, used ten focus groups (n=65) of graduate, enrolled, registered nurses, Associate Nurse Unit Managers, and general manag-ers/educators/coordinators from four different institutions to investigate whether nurses felt their practice was influenced by participating in either a face-to-face or web-based simulation educa-tional programme about patient deterioration. Results indicate individuals who were less ‘tech-savvy’ appreciated the flexibility of web-based learning, which increased their confidence. Face-to-face students appreciated self-reflection through performance evaluation. While face-to-face simulations were unable to completely duplicate symptoms, they did show nurses' adaptability. Both interventions enhanced clinical practice by improving documentation and re-plies while also boosting confidence and competence. Web learners initially experienced tech-related anxiety, which gradually subsided, demonstrating healthcare professionals' resilience to new learning approaches. Overall, the study highlighted the advantages and challenges of web-based and face-to-face education in clinical practice, emphasising the importance of adapta-bility and reflective learning for healthcare professionals. Further exploration of specific topics is required to improve practice, encourage knowledge sharing among colleagues, and improve early detection of patient deterioration.

The rapid expansion of China's A-share market, coupled with the burgeoning volume of financial research reports, presents a unique challenge to traditional stock price prediction methodologies. This study introduces FAST-SCAN, a novel framework designed to leverage natural language processing (NLP) and time series analysis for the efficient and accurate prediction of stock market trends. By integrating advanced sentiment analysis, primarily through the utilization of the RoBERTa model, with dynamic time series forecasting, FAST-SCAN aims to distill actionable insights from the vast corpus of financial research reports. This approach not only enhances the speed and efficiency of financial analysis but also addresses the time-sensitive nature of market-influencing factors, thereby providing a competitive edge in stock price prediction. The framework demonstrates a notable improvement in predictive performance, achieving a 20% annualized return and a 7.19% RankIC, marking a significant advancement over conventional statistical analysis methods. Through its innovative combination of NLP and predictive analytics, FAST-SCAN paves the way for a more informed and strategic approach to investment in the A-share market, emphasizing the importance of timely, data-driven decision-making in the financial industry.

In this presented case, a 77 years old woman suffering from ongoing knee pain underwent a bone scan using 99mTc-hydroxydiphosphonate (HDP) in suspicious for bone infection. An incidental finding from this scan revealed diffuse cardiac uptake, necessitating further diagnostic procedures to exclude the possibility of cardiac amyloidosis. In the subsequent 99mTc-3,3-diphosphono-1,2-propanodicarboxylic acid (DPD) scan and SPECT images, no perceptible cardiac uptake was observed at all. Upon retrospective review of the patient’s medical records, she had received 1000mg of ferric carboxymaltose for iron-deficient anemia, the day before the 99mTc-HDP bone scan. Therefore, it is assumed that the diffuse and temporary cardiac activity was due to the transient iron overload. We present and share this bone scan images in order to avoid possible future misinterpretation of cardiac amyloidosis.

Purpose: Localization and mapping with LiDAR data is a fundamental building block for autonomous vehicles. Though LiDAR point clouds can often encode the scene depth more accurate and steadier compared with visual information, laser-based Simultaneous Localization And Mapping (SLAM) remains challengeable as the data is usually sparse, density variable and less discriminative. The purpose of this paper is to propose an accurate and reliable laser-based SLAM solution. Design/methodology/approach: The method starts with constructing voxel grids based on the 3D input point cloud. These voxels are then classified into three types to indicate different physical objects according to the spatial distribution of the points contained in each voxel. A global environment model with Partition of Unity (POU) implicit surface is maintained along the process and located voxels are merged into it from stage to stage, through scan-to-model matching implemented by Levenberg-Marquardt method. Findings: We find a laser-based SLAM method. The method uses POU implicit surface representation to build the model and is evaluated on the KITTI odometry benchmark without loop closure. Experimental results indicate that the method achieves accuracy comparable to the state-of-the-art methods. Originality/value: We propose a novel, low-drift SLAM method, which falls into a scan-to-model matching paradigm, operates on point clouds obtained from Velodyne HDL64. The method is of value to researchers developing SLAM systems for autonomous vehicles.

Face verification for unrestricted faces in the wild is a challenging task. This paper proposes a method based on two deep convolutional neural networks(CNN) for face verification. In this work, we explore to use identification signal to supervise one CNN and the combination of semi-verification and identification to train the other one. In order to estimate semi-verification loss at a low computation cost, a circle, which is composed of all faces, is used for selecting face pairs from pairwise samples. In the process of face normalization, we propose to use different landmarks of faces to solve the problems caused by poses. And the final face representation is formed by the concatenating feature of each deep CNN after PCA reduction. What's more, each feature is a combination of multi-scale representations through making use of auxiliary classifiers. For the final verification, we only adopt the face representation of one region and one resolution of a face jointing Joint Bayesian classifier. Experiments show that our method can extract effective face representation with a small training dataset and our algorithm achieves 99.71% verification accuracy on LFW dataset.

This work introduces a novel facial image capture system that utilizes computer vision technology and artificial intelligence for real-time detecting, tracking, and capturing of human faces. The objective of this study is to address the challenges posed by poor-quality facial images in biometric authentication, especially in passport photo acquisition and recognition. By combining face-aware capture technology with AES encryption for secure image storage, we present a completely open-source hardware solution that consists of a Jetson processor, a 16MP autofocus RGB camera, a custom enclosure, and a touch sensor LCD for user interaction. Pilot data collection demonstrates the system's ability to capture high-quality images, achieving a 98.98% accuracy in storing images of acceptable quality. The integration of AES encryption ensures data security, making the proposed system suitable for real-time applications in other domains beyond identity verification in passport applications, such as security systems, video conferencing, etc

Face swapping is an intriguing and intricate task in the field of computer vision. Currently, most mainstream face swapping methods employ face recognition models to extract identity features and inject them into the generation process. Nonetheless, such methods often struggle to effectively transfer identity information, result in generated results failing to achieve a high identity similarity with the source face. Furthermore, if we can accurately disentangle identity information, we can achieve controllable face swapping, thereby providing more choices to users. In pursuit of this goal, we propose a new face swapping framework (ControlFace) based on the disentanglement of identity information. We disentangle the structure and texture of the source face, encoding and characterizing them in the form of feature embeddings separately. According to the semantic level of each feature representation, we inject them into the corresponding feature mapper and fuse them adequately in the latent space of StyleGAN. Owing to such disentanglement of structure and texture, we are able to controllablely transfer parts of the identity features. Extensive experiments and comparisons with state-of-the-art face swapping methods demonstrate the superiority of our face swapping framework in terms of transferring identity information, producing high-quality face images and controllable face swapping.

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.

Recently, Artificial Intelligence (AI)-based algorithms have revolutionized the medical image segmentation processes. Thus, the precise segmentation of organs and their lesions may contribute to an efficient diagnostics process and a more effective selection of targeted therapies as well as increasing the effectiveness of the training process. Thus, AI may contribute to the automatization of the image scan segmentation process and increase the quality of the resulting 3D objects, which may lead to the generation of more realistic virtual objects. In this paper, we focus on the AI-based solutions applied in the medical image scan segmentation, and intelligent visual-content generation, i.e. computer-generated three-dimensional (3D) images in the context of Extended Reality (XR). We consider different types of neural networks used with a special emphasis on the learning rules applied, taking into account algorithm accuracy and performance, as well as open data availability. This paper also attempts to summarize the current development of AI-based segmentation methods in medical imaging and intelligent visual content generation that are applied in XR. Finally, this paper concludes with possible developments and open challenges in AI application in Extended Reality-based solutions. Finally, the future lines of research and development directions of Artificial Intelligence applications both in medical image segmentation and Extended reality-based medical solutions are discussed.

Temperature field is an essential attribute of metal additive manufacturing in view of its bearings on the prediction, control, and optimization of residual stress, part distortion, fatigue, balling effect, etc. This work provides an analytical physics-based approach to investigate the effect of scan strategy parameters including time delay between two irradiations and hatching space on thermal material properties and melt pool geometry. This approach is performed through the analysis of the distribution of material properties and temperature profile in three-dimensional space. The moving point heat source approach is used to predict the temperature field. To predict the temperature field during the additive manufacturing process some important phenomena are considered. 1) Due to the high magnitude of temperature in the presence of the laser, the temperature gradient is usually high which has a crucial influence on thermal material properties. Consequently, the thermal material properties of stainless steel grade 316L are considered to be temperature-dependent. 2) Due to the repeated heating and cooling, part usually undergoes several melting and solidification cycles. This physical phenomenon is considered by modifying the heat capacity using the latent heat of melting. 3) The multi-layer aspect of metal AM process is considered by incorporating the temperature history from the previous layer since the interaction of the successive layers has an impact on heat transfer mechanisms. 4) Effect of heat affected zone on thermal material properties is considered by the superposition of material properties in regions where the temperature fields of two consecutive irradiations have an overlap since the consecutive irradiations change the behavior of the material properties. The goals are to 1) investigate the effects of temperature-sensitive material properties and constant material properties on the temperature field. 2) Study the behavior of thermal material properties under different scan strategies. 3) Study the importance of considering the effect of heat affected zone on thermal material through the prediction of melt pool geometry. 4) Investigate the effect of hatching space on melt pool geometry. This work is purely employed physics-based analytical models to predict the behavior of material properties and temperature field under different process conditions, and no finite element modeling is used.

This article discusses the comparison between digital and traditional face-to-face coaching within the scope of shadow education institutions. While analyzing the differences and similarities between the two educational models, both their advantages and disadvantages are thoroughly discussed. In this context, interviews were conducted with students and teachers who receive education in both face-to-face and digital coaching, and the positive and negative aspects of both institutions, suitable and unsuitable courses, the future situation, and the effects on students' academic achievements were revealed. According to the results obtained from the research, it is noteworthy that students who do not receive education in digital coaching have prejudices against digitalization. Additionally, no significant difference was found between the academic achievements of students receiving education in digital coaching and those receiving education in face-to-face coaching.

Acute myocardial infarction (AMI) is a leading cause of morbidity and mortality worldwide. This study aimed to test the efficacy of a newly-developed symptom self-management program on health outcomes among inpatients post-AMI. A pilot randomised, controlled trial was utilised. A convenience sample of 90 was recruited from a tertiary hospital in Singapore. Adult inpatients with post-AMI were randomly assigned to the three groups: IManage-HSet, IManage-FF or control group. Sample size was computed using power analysis. Data were collected through self-reported questionnaires and physiological instruments. Data analyses included univariate statistics, analysis of variance and repeated measure analysis of variance. Ninety participants were recruited with 30 in each group. Participants in the IManage-HSet and IManage-FF group had significantly greater relaxation (ηp2 = 0.11), lower objective stress (ηp2 =0.14), and greater knowledge (ηp2 = 0.62) than those of the control group. The two intervention groups had lower subjective stress (ηp2 = 0.04), depression (ηp2 = 0.01), and anxiety (ηp2 = 0.03) than the control but such differences did not achieve statistical significance. Furthermore, most participants reported that they were satisfied with the programs and they provided positive comments on the contents, educational materials and relaxation practice. This pilot study supports the potential benefits and feasibility of the IManage-VR and IManage-FF interventions on stress, perceived relaxation, knowledge and patient satisfaction. Contents of the symptom management interventions could be added into current cardiac rehabilitation programs. Future RCTs with multi-centered sites and larger sample size are required to further test the intervention. This study was prospectively registered with the International Standard Randomised Controlled Trial on the 5th August 2014 with registration number ISRCTN61027816.

This paper presents the development and implementation of a Pan-Tilt-Zoom (PTZ) camera interfaced with a Jetson processor hosting Artificial Intelligence (AI) algorithms, designed to address the challenges of traditional biometric capture for first-time passport photos of young children. The system enhances the accuracy, efficiency, and adaptability of facial biometric capture by recognizing and accommodating the dynamic and diverse behaviors of children. Utilizing AI, the system detects faces and analyzes facial features using 68 landmark points, ensuring high-quality images that meet ISO standards. These technical specifications, implementation process, and performance evaluations of the developed system are provided in this paper, demonstrating the potential of the proposed AI Face-Capture to advance biometric identification and verification for young children.

"Deep fake" technologies are often associated with face-swapping algorithms based on images or videos. Although much research is focused on developing these techniques, there is still room for improvement, especially in human and visual assessment. In this study, we present an innovative approach to the problem of face replacement based on one model while preserving background features. Our model uses the Adaptive Attentional Denormalization method from FaceShifter while integrating identity features from the ArcFace and BiSeNet models to support the attribute extraction process. We used Fast GAN as a generative model to optimize and accelerate the training process on smaller data sets. Our research proves the effectiveness of the proposed approach by combining innovative methods and architectures in one coherent solution.

Currently, face recognition technologies are the most widely used methods for verifying 1an individual’s identity. Nevertheless, it has increased in popularity, raising concerns about face spoofing attacks, in which a photo or video of an authorized person’s face is used to get access to services. Based on a combination of Background Subtraction (BS) and Convolutional Neural Networks (CNN), as well as an ensemble of classifiers, we propose an efficient and more robust face spoof detection algorithm. This algorithm includes a Fully Connected (FC) classifier with a Majority Vote (MV) algorithm, which uses different face spoof attacks (e.g., printed photo and replayed video). By including a majority vote to determine whether the input video is genuine or not, the proposed method significantly enhances the performance of the Face Anti-Spoofing (FAS) system. For evaluation, we considered the MSU MFSD, REPLAY-ATTACK, and CASIA-FASD databases. The obtained results by our proposed approach are better than those obtained by state of the art methods. On the REPLAY-ATTACK database, we were able to attain a Half Total Error Rate (HTER) of 0.62% and an Equal Error Rate (EER) of 0.58%. It was possible to attain an EER of 0% on both the CASIA-FASD and the MSU FAS databases.

The science around the use of masks by the general public to impede COVID-19 transmission is advancing rapidly. Policymakers need guidance on how masks should be used by the general population to combat the COVID-19 pandemic. In this narrative review, we develop an analytical framework to examine mask usage, considering and synthesizing the relevant literature to inform multiple areas: population impact; transmission characteristics; source control; PPE; sociological considerations; and implementation considerations. A primary route of transmission of COVID-19 is via respiratory droplets, and is known to be transmissible from presymptomatic and asymptomatic individuals. Reducing disease spread requires two things: first, limit contacts of infected individuals via physical distancing and other measures, and second, reduce the transmission probability per contact. The preponderance of evidence indicates that mask wearing reduces the transmissibility per contact by reducing transmission of infected droplets in both laboratory and clinical contexts. Public mask wearing is most effective at reducing spread of the virus when compliance is high. The decreased transmissibility could substantially reduce the death toll and economic impact while the cost of the intervention is low. Given the current shortages of medical masks we recommend the adoption of public cloth mask wearing, as an effective form of source control, in conjunction with existing hygiene, distancing, and contact tracing strategies. Because many respiratory droplets become smaller due to evaporation, we recommend increasing focus on a previously overlooked aspect of mask usage: mask-wearing by infectious people ("source control") with benefits at the population-level, rather than mask-wearing by susceptible people, such as health-care workers, with focus on individual outcomes. We recommend that public officials and governments strongly encourage the use of widespread face masks in public, including the use of appropriate regulation.

Local binary pattern is one of the visual descriptors and can be used as a powerful feature extractor for texture classification. In this paper, a novel representation for face recognition is proposed, called it Bilateral Line Local Binary Patterns (BL-LBP). This scheme is an extension of Line Local Binary Patterns descriptors in the statistical learning subspace. The present bilateral descriptors are fused with an ensemble learning of calibrated SVM models. The performance of this scheme is evaluated using 5 standard face databases. It is found that it is robust against illumination variation, diverse facial expressions and head pose variations and its recognition accuracy reaches 98 percent, running on a mobile device with a processing speed of 63 ms per face. Results suggest that our proposed method can be very useful for the vision systems that have limited resources where the computational cost is critical.

In this article, we analyse the computer aid screening method of COVID19 using Xray and CT scan images. The main objective is to set an analytical closure about the computer aid screening of COVID19 among the X-ray image and CT scan image. The computer aid screening method includes deep feature extraction, transfer learning and traditional machine learning image classification approach. The deep feature extraction and transfer learning method considered 13 pre-trained CNN model. The machine learning approach includes three sets of features and three classifiers. The pre-trained CNN models are alexnet, googlenet, vgg16, vgg19, densenet201, resnet18, resnet50, resnet101, inceptionv3, inceptionresnetv2, xception, mobilenetv2 and shufflenet. The features and classifiers in machine learning approaches are GLCM, LBP, HOG and KNN, SVM, Naive bay’s respectively. In addition, we also analyse the different paradigms of classifiers. In total, the comparative analysis is carried out in 65 classification models, i.e. 13 in deep feature extraction, 13 in transfer learning and 39 in machine learning approaches. Finally, all the classification models perform better in X-ray image set compare to CT scan image set.

Purpose: This study investigated the impact of the COVID-19 pandemic on mental health, quality of life, and family functioning in a sample of the general female population, exploring difficulties encountered in managing family and work responsibilities and burden of care when taking care of a loved one. The study was moreover aimed at investigating factors capable of influencing severe depressive symptomatology in the context of socio-demographics, traumatic events, individual vulnerability, and family functioning. Method: During a weekend devoted to Mental Health initiatives (October 8-10, 2021), 211 women were interviewed (47% exposed to the 2009 L’Aquila earthquake). Results: More than 50% of the sample reported a higher complexity in managing their lives during the COVID-19 pandemic compared to their previous routine, with no statistically significant differences between working women and non-workers, although the latter obtained higher scores for depressive symptomatology and poorer quality of life. Compared to non-caregivers, women caregivers (22.3%) in charge of the care of loved ones affected by physical (10.9%) or psychiatric disabilities (11.4%) complained of a poorer quality of life, with those caring for people affected by mental disorders seeming to experience a more significant worsening. Approximately 20% of the total sample reported severe depressive symptomatology. Previous access to mental health services (O.R. 10.923), low level of education (O.R. 5.410) and difficulties in management of their everyday lives during the COVID-19 pandemic (O.R. 3.598) were found to be the main variables predictive of severe depressive psychopathology. Old age, good problem-solving skills and ability to pursue personal goals were identified as protective factors. Conclusions: The COVID-19 pandemic underlined the need for support for emotionally vulnerable women with pre-existing mental health conditions, partly reflecting the cumulative effects of traumas.

We investigated and quantified bacterial accumulation in facemasks used by the general population, using 16S rRNA (Sanger Sequencing), culture and biochemical analysis along with Rose Bengal staining. Additionally, a systematic overview of the literature on face mask contamination was undertaken. We found an average bacterial load of 4.24 × 10⁴ CFU recovered/mask, with a maximum load of 2.85 × 10⁵ CFU. This maximum is 310 times higher than the limit value for contamination of ventilation system outlet surfaces specified by the German standard VDI 6022. Biochemical and molecular identification predominantly found Staphylococcus species (80%), including Staphylococcus aureus, along with endospore-forming Bacillus spp. Literature reports also indicate contamination of masks by bacterial and fungal opportunists of the genera Acinetobacter, Aspergillus, Alternaria, Bacillus, Cadosporium, Candida, Escherichia, Enterobacter, Enterococcus, Klebsiella (including K. pneumoniae), Micrococcus, Microsporum, Mucor, Pseudomonas, Staphylococcus and Streptococcus. Bacterial counts increase linearly with wearing duration, and prolonged use may affect the skin and respiratory microbiomes, promoting consequential eye, skin, oral and airway conditions. These aspects underscore the urgent need for further research and a risk-benefit analysis in respect of mask use, particularly given their unproven efficacy in disrupting the transmission of respiratory viruses and their adverse social consequences.

Natural killer T cells (NKT) are a subset of T lymphocytes bridging innate and adaptive immunity. These cells recognize self and microbial glycolipids bound to non-polymorphic and highly conserved CD1d molecules. Three NKT cell subsets, type I, II and NKT-like expressing different antigen receptors (TCR) were described and TCR activation promotes intracellular events leading to specific functional activities. NKT can exhibit different functions depending on the secretion of soluble molecules and the interaction with other cell types. NKT cells act as regulatory cells in the defence against infections but, on the other hand, their effector functions can be involved in the pathogenesis of several inflammatory disorders due to their exposure to different microbial or self antigens, respectively. A deep understanding of the biology and functions of type I, II and NKT-like cells as well as their interplay with cell types acting in innate (Neuthrophils, Innate Lymphoid cells, Machrophages and Dendritic cells) and adaptive immunity (CD4+,CD8+ and Double Negative T cells) should be important to design potential immunotherapies for infectious and autoimmune diseases.

The cell stress response is an essential system present in every cell for responding and adapting to extracellular environmental stimulations. A major program for stress response is the heat shock factor (HSF)–heat shock protein (HSP) system that maintains proteostasis in cells and promotes cancer progression. However, less is known about how the cell stress response is regulated by alternative transcription factors. Here, we show that the SCAN domain-containing transcription factors (SCAN-TFs) are involved in repressing the stress response in cancer. SCAND1 and SCAND2 are SCAN-TFs that can hetero-oligomerize with SCAN-zinc finger transcription factors, such as MZF1(ZSCAN6), for accessing DNA and transcriptionally co-repressing target genes. We found that heat stress induced the expression of both SCAND1 and MZF1(ZSCAN6) bound to HSP90 gene promoter regions in prostate cancer cells. SCAND1 and MZF1 blocked the heat shock responsiveness of HSP90 in prostate cancer cells. Gene expression of SCAND2, SCAND1 and MZF1 was negatively correlated with HSP90 expression in patients-derived prostate adenocarcinoma. MZF1 and SCAND2 were more highly expressed in paired normal tissues than in tumor tissues in several cancer types. High expression of SCAND2, SCAND1 and MZF1 correlated with enhanced prognoses of pancreatic cancer and head and neck cancers. Additionally, high expression of SCAND2 was correlated with better prognoses of lung adenocarcinoma and sarcoma. In contrast, high expression of HSP90AA1 correlated with poorer prognoses in several cancer types. These data suggest that the stress-inducible SCAN-TFs can function as a feedback system, suppressing excessive stress response and inhibiting cancers.

The mutton sheep breeding industry has transformed significantly in recent years, from traditional grassland free-range farming to a more intelligent approach. As a result, automated sheep face recognition systems have become vital to modern breeding practices and have gradually replaced ear tagging and other manual tracking techniques. Although sheep face datasets have been introduced in previous studies, they have often involved pose or background restrictions (e.g., fixing of the subject’s head, cleaning of the face), which restrict data collection and have limited the size of available sample sets. As a result, a comprehensive benchmark designed exclusively for the evaluation of individual sheep recognition algorithms is lacking. To address this issue, this study develops a large-scale benchmark dataset, Sheepface-107, comprised of 5,350 images acquired from 107 different subjects. Images were collected from each sheep at multiple angles, including front and back views, in a diverse collection that provides a more comprehensive representation of facial features. In addition to the dataset, an assessment protocol is developed by applying multiple evaluation metrics to the results produced by three different deep learning models: VGG16, GoogLeNet, and ResNet50, which achieved F1-scores of 83.79%, 89.11%, and 93.44%, respectively. A statistical analysis of each algorithm suggested that accuracy and the number of parameters were the most informative metrics for use in evaluating recognition performance.

In today's world it is very much important to maintain the security of information and its risks. The biometric-based techniques are very much useful in these problems. Among the several kinds of biometric-based technique, face detection is much complex and much more important. Due to the age and several other problems, a human face structure changes over time, again a human has lots of expressions. Sometimes due to the lighting condition or the variation of the angle of an input device, the pattern of a human face structure also changed. As a result, the face cannot be detected properly. In this paper, a method is proposed that can detect the human faces both automatically and manually very efficiently. In manual mode, a user can select the input faces referred by the system according to their choice. In automated mode, the system detected all possible face areas using the Kohonen Self-Organizing Feature Map technique. This method reduced the complex color image into a vector quantized image with desired colors. Then a color segmentation technique is used to detect the possible face skin areas from the vector quantized image. Then the Histogram Oriented Gradient technique used to detect the feature from the faces and K-Nearest Neighbour Classifier is used to compare both face images detected by the two modes. The automated method prosed better accuracy than the manual method.

Nowadays, security is a top priority. In fact, biometrics uses cutting-edge technologies to identify terrorists and criminals. But the practice of distinguishing humans based on intrinsic physical or behavior traits goes back thousands of years. With the widespread use of computers in the late 20th century, new possibilities for digital biometrics emerged and new technologies were generously used. Among these, we remember high resolution security video cameras and drones. So, the aim of the present project is to study and explain the features of these technologies, especially the ones of the the Phantom 4 Pro+ aircraft and analyze its operating methods in order to identify human faces during live streaming of videos. For this purpose, it will be used Paul Viola and Michael Jones’ face detection algorithm, which includes Haar features and cascade classifiers to identify faces, eyes and ears of an individual.

Laser powder bed fusion (LPBF) is an additive manufacturing technique that is gaining popularity for producing metallic parts in various industries. However, parts produced by LPBF are prone to residual stress, deformation, cracks and other quality defects due to uneven temperature distribution during the LPBF process. To address this issue, in prior work, the authors have proposed SmartScan, a method for determining laser scan sequence in LPBF using a model-based and optimization-driven approach, rather than using heuristics, and applied it to simple 2D geometries. This paper presents a generalized SmartScan methodology that is applicable to arbitrary 3D geometries. This is achieved by: (1) expanding the thermal model and optimization approach used in SmartScan to multiple layers; (2) enabling SmartScan to process shapes with arbitrary contours and infill patterns within each layer and (3) providing SmartScan with global foresight to make it less myopic in its optimization. Sample 3D parts are printed using the proposed generalized SmartScan and compared to those printed using standard heuristic scan sequences. Reductions of up to 93% in temperature inhomogeneity, 87% in residual stress, and 26% in maximum deformation were observed, without significantly sacrificing print speed. However, SmartScan was found to cause minor (<5%) to significant (up to 20%) increases in surface roughness compared to the heuristic approaches, depending on the scan pattern used.

Side-scan sonar (SSS) detection is a key method in applications such as underwater environmental security and subsea resource development. The use of acoustic images for seabed target detection has gradually become a mainstream underwater detection method. However, many existing detection approaches primarily concentrate on tracking the evolution path of optical image object detection tasks, resulting in complex structures and limited versatility. To tackle this issue, we introduce a pioneering Dual-Domain Multi-Frequency Network (D2MFNet) meticulously crafted to harness the distinct characteristics of SSS image detection. In D2MFNet, aiming at the underwater detection requirements of small scenes, we introduce a novel method for optimize and improve the detection sensitivity of different frequency ranges and propose a Multi-Frequency Combined Attention Mechanism (MFCAM). This mechanism amplifies the relevance of dual-domain features across different channels and space. Moreover, recognizing that SSS images can provide richer insights after frequency domain conversion, we introduce a Dual-Domain Feature Pyramid Network (D2FPN). By incorporating frequency domain information representation, D2FPN significantly augments the depth and breadth of feature information in underwater small datasets. Our methods are seamlessly designed for integration into existing networks, offering plug-and-play functionality with substantial performance enhancements. We have conducted extensive experiments to validate the efficacy of our proposed techniques, and the results showcase their state-of-the-art performance. MFCAM improves the mAP by 16.9% in the KLSG dataset and 15.5% in the SCTD dataset. The mAP of D2FPN was improved by 8.4% in the KLSG dataset and by 9.8% in the SCTD dataset. We will make our code and models publicly available at https://dagshub.com/estrellaww00/D2MFNet.

Sustainable urban drainage systems (SuDS) such as swales are designed to collect, store and infiltrate a large amount of surface runoff water during heavy rainfall. Stormwater is known to transport pollutants, such as particle-bound Potential Toxic Elements (PTE), which are known to often accumulate in the topsoil. A portable XRF instrument (pXRF) is used to provide in situ spatial characterization of soil pollutants, specifically lead (Pb), zink (Zn) and copper (Cu). The method uses pXRF measurements of PTE along profiles with set intervals (1 meter) to cover the swale with cross-sections, across the inlet, the deepest point and the outlet. Soil samples are collected, and the In-Situ measurements are verified by the results from laboratory analyses. Stormwater is here shown to be the transporting media for the pollutants, so it is of importance to investigate areas most prone to flooding and infiltration. This quick scan method is time and cost-efficient, easy to execute and the results are comparable to any known (inter)national threshold criteria for polluted soils. The results are of great importance for all stakeholders in cities that are involved in climate adaptation and implementing green infrastructure in urban areas. However, too little is still known about the long-term functioning of the soil-based SuDS facilities.

Although mask-wearing is now widespread, knowledge of how to quantify or improve its performance remains surprisingly limited and largely based on empirical evidence. The objective of this study was to visualize the expiratory airflows from facemasks and evaluate aerosol transmission between two persons. Different visualization methods were explored, including the Schlieren optical system, laser/LED-particle imaging system, thermal camera, and vapor-SarGel system. The leakage flows and escaped aerosols were quantified using a hotwire anemometer and a particle counter, respectively. The results show that mask-wearing reduces the exhaled flow velocity from 2~4 m/s (with no facemask) to around 0.1 m/s, thus decreasing droplet transmission speeds. Cloth, surgical, and KN95 masks showed varying leakage flows at the nose top, sides, and chin. The leakage rate also differed between inhalation and exhalation. The neck gaiter has low filtration efficiency and high leakage fractions, providing low protection efficiency. There was considerable deposition in the mouth-nose area, as well as the neck, chin, and jaw, which heightened the risk of self-inoculation through spontaneous face-touching. A face shield plus surgical mask greatly reduced droplets on the head, neck, and face, indicating that double face coverings can be highly effective when a single mask is insufficient. The vapor-SarGel system provided a practical approach to study interpersonal transmission under varying close contact scenarios or with different face coverings.

A Pogorelov polytope is a combinatorial simple 3-polytope realizable in the Lobachevsky (hyperbolic) space as a bounded right-angled polytope. It has no 3- and 4-gons and may have any prescribed numbers of k-gons, k ≥ 7. Any polytope with only 5-, 6- and at most one 7-gon is Pogorelov. For any other prescribed numbers of k-gons, k ≥ 7, we give an explicit construction of a Pogorelov and a non-Pogorelov polytopes. Any Pogorelov polytope different from Löbel polytopes can be constructed from the 5- or the 6-barrel by cuttings off pairs of adjacent edges and connected sums with the 5-barrel along a 5-gon with the intermediate polytopes being Pogorelov. For fullerenes there is a stronger result. Any fullerene different from the 5-barrel and the (5, 0)-nanotubes can be constructed by only cuttings off adjacent edges from the 6-barrel with all the intermediate polytopes having 5-, 6- and at most one additional 7-gon adjacent to a 5-gon. This result can not be literally extended to the latter class of polytopes. We prove that it becomes valid if we additionally allow connected sums with the 5-barrel and 3 new operations, which are compositions of cuttings off adjacent edges. We generalize this result to the case when the 7-gon may be isolated from 5-gons.

Energy dissipation is one of the most important factors in choosing stepped spillways. However, few studies are about energy dissipation with different horizontal face angles. In this paper, the realizable k-ε turbulence model was used to study the flow field and energy dissipation rates of different stepped spillways with five horizontal face angles in the skimming flow regions. It shows that the free water surfaces and energy dissipation rates change with horizontal face angles and the energy dissipation rates initially decrease and then increase as the horizontal face angle increases. And the horizontal face angles cause the great changes of flow field.

Well-being is one of the pillars of positive psychology, which is known to have positive effects not only on the personal and professional lives of individuals, but also on teams and organizations. Understanding and promoting individual well-being is essential for staff health and long-term success, but current tools for assessing subjective well-being rely on time-consuming surveys and questionnaires, which limit the possibility of providing the real-time feedback needed to raise awareness and change individual behavior. This paper proposes a framework for understanding the process of non-verbal communication in teamwork, using video data to identify significant predictors of individual well-being in teamwork. It relies on video acquisition technologies and state-of-the-art artificial intelligence tools to extract individual, relative, and environmental characteristics from panoramic video. Statistical analysis is applied to each time series, leading to the generation of a dataset of 125 features, which are then linked to PERMA (Positive Emotion, Engagement, Relationships, Meaning, and Accomplishments) surveys developed in the context of positive psychology. Each pillar of the PERMA model is evaluated as a regression or classification problem using machine learning algorithms. Our approach was applied to a case study, where 80 students collaborated in 20 teams for a week on a team task in a face-to-face setting. This enabled us to formulate several hypotheses identifying factors influencing individual well-being in teamwork. These promising results point to interesting avenues for research, for instance fusing different media for the analysis of individual well-being in teamwork.

Nowadays, hydropower is the principal renewable; however, climate change increases extreme events such as floods, droughts, erosion, and sedimentation of rivers that produce uncertainty in hydroelectric generation. Thus, this document aims to analyze the climate change projections in the hydropower systems of Ecuador based on data from 14 projects studying the scenarios ac-cording to the Shared Socioeconomic Pathways from the Intergovernmental Panel on Climate Change. The study period starts from 2010 to 2020 with historical data, collects the tendency, defines a database year, and then projects the scenarios to 2050. The quantitative methodology uses a statistic on Ecuador's hydropower obtained inflow time series to calculate the deviation over the last years and develop a model to simulate future generation. The results show that the hydropower in Ecuador is expected to decrease considerably through 2050 due to meteorological changes. In this calculation of the Shared Socioeconomic Pathways, the selected scenarios show a reduction in SSP5 of 11.5%, SP2 of 16.2%, and SSP4 of 18.2% to 2050, concluding that the oppor-tunities for hydroelectric production facing climate change are variable, but the challenges are broad. In Ecuador, the projections of hydropower plants represent a sensitive issue of their re-ductions, especially knowing that the country had an energy grid in 2020 that depended on 87% of hydroelectric production.

Fighting against the COVID-19 pandemic caused by the SARS-CoV-2 virus is one of the most critical challenges facing the global health system today. The possibility to identify the group of persons in the cohort of people under 50 years old, who are sensitive to the COVID-disease by non-invasive methods, is a very perspective approach for estimating the epidemiological state of the human population. The study aimed to identify the features of people's faces with COVID-19 that the most correlate with disease severity could serve as one of these approaches. For this aim, 525 photos of patients' faces with different outcomes of COVID-19 disease were analyzed using the Dlib face recognition convolutional neural network pre-trained for face recognition. Face descriptor vectors were obtained using the convolutional neural network. Facial features were found that predict a person's sensitivity to the SARS-CoV-2 virus (disease severity), and the contribution of each of the features to the risk of developing a severe form of COVID in a person was found. The accuracy of the binary classification of the individual severity of the COVID-19 course using the k-nearest neighbors algorithm on the test dataset was accuracy - 84%, AUC - 0.90.

Material identification based on R_value (transparency natural logarithm ratio of low-energy to high-energy) of line-scan dual-energy X-ray transmission (DE-XRT) has a good prospect for industrial application. Unfortunately, the DE-XRT signals before attenuation cannot be directly measured, whereas their precision is very important to R_value. Therefore, a context-based signal synthesis method which takes the filtered signals that remove high-frequency noises and retain low-frequency actual fluctuations as the reference value, and takes into account the vertical (forward/column) direction and horizontal (scanning/row) direction anisotropy of line-scan images was proposed. The vertical is a time series with continuity of signal trend; the horizontal is a spatial characteristic with the fluctuation synchronization within the same row signals. The special synthesis evaluations of curve synthesis difference and surface synthesis difference were also proposed. Experimental results show that the tow evaluations are both only about 0.0007, and it only takes 35 ms to complete the surface synthesis of 119×119 pixels on the CPU with 3.4 GHz main frequency. The presented method can achieve high signal synthesis precision and calculation real-time, so as to facilitate DE-XRT material identification. It can be extended to improve the precision of numerical synthesis in other line-scan uncomplete signals.

In the current industrial landscape, increasingly pervaded by technological innovations, the adoption of optimized strategies for asset management is becoming a critical key success factor. Among the various strategies available, the “Prognostics and Health Management” strategy is able to support maintenance management decisions more accurately, through continuous monitoring of equipment health and “Remaining Useful Life” forecasting. In the present study, Convolutional Neural Network-based Deep Neural Network techniques are investigated for the Remaining Useful Life prediction of a punch tool, whose degradation is caused by working surface deformations during the machining process. Surface deformation is determined using a 3D scanning sensor capable of returning point clouds with micrometric accuracy during the operation of the punching machine, avoiding both downtime and human intervention. The 3D point clouds thus obtained are transformed into bidimensional image-type maps, i.e., maps of depths and normal vectors, to fully exploit the potential of convolutional neural networks for extracting features. Such maps are then processed by comparing 15 genetically optimized architectures with the transfer learning of 19 pre-trained models, using a classic machine learning approach, i.e., Support Vector Regression, as a benchmark. The achieved results clearly show that, in this specific case, optimized architectures provide performance far superior (MAPE=0.058) to that of transfer learning which, instead, remains at a lower or slightly higher level (MAPE=0.416) than Support Vector Regression (MAPE=0.857).

In today’s world face detection is the most important task. Due to the chromosomes disorder sometimes a human face suffers from different abnormalities. For example, one eye is bigger than the other, cliff face, different chin-length, variation of nose length, length or width of lips are different, etc. For computer vision currently this is a challenging task to detect normal and abnormal face and facial parts from an input image. In this research paper a method is proposed that can detect normal or abnormal faces from a frontal input image. This method used Fast Fourier Transformation (FFT) and Discrete Cosine Transformation of frequency domain and spatial domain analysis to detect those faces.

Face recognition is a valuable forensic tool for criminal investigators since it certainly helps in identifying individuals in scenarios of criminal activity like fugitives or child sexual abuse. It is, however, a very challenging task as it must be able to handle low-quality images of real world settings and fulfill real time requirements. Deep learning approaches for face detection have proven to be very successful but they require a large computation power and processing time. In this work, we evaluate the speed-accuracy tradeoff of three popular deep-learning-based face detectors on the WIDER Face and UFDD data sets in several CPUs and GPUs. We also develop a regression model capable to estimate the performance, both in terms of processing time and accuracy. We expect this to become a very useful tool for the end user in forensic laboratories in order to estimate the performance for different face detection options. Experimental results showed that the best speed-accuracy tradeoff is achieved with images resized to 50% of the original size in GPUs and images resized to 25% of the original size in CPUs. Moreover, performance can be estimated using multiple linear regression models with a Mean Absolute Error (MAE) of 0.113 what is very promising for the forensic field.

Inbreeding depression is expected to be more pronounced in fitness-related traits, such as pig litter size. Recent studies have suggested that the genetic determinism of inbreeding depression may be heterogeneous across the genome. Therefore, the objective of this study is to conduct a genomic scan across the pig autosomal genome to detect the genomic regions that control inbreeding depression for litter size in two varieties of Iberian pigs (Entrepelado and Retinto). The datasets consist of 2,069 (338 sows) and 2,028 (327 sows) records for litter size (Total Number Born and Number Born Alive) for the Entrepelado and Retinto varieties. All sows were genotyped using the Geneseek GGP PorcineHD 70 K. We employed the Unfavorable Haplotype Finder software to extract runs of homozygosity (ROHs) and conducted a mixed model analysis to identify highly significant differences between homozygous and heterozygous sows for each specific ROH. A total of 8 genomic regions located on SSC2, SSC5, SSC7, SSC8, and SSC13, were significantly associated with inbreeding depression, housing some relevant genes such as FSHR, LHCGR, CORIN, AQP6, and CEP120.

Introduction: CT scan of the skull in patients with head trauma play an important role in identifying patients' injuries; In moderate to severe brain injury; CT scans are arguably the best way to get information about the intracranial complications of a head injury. Researchers have cited a number of reasons for why doctors are increasingly asking for CT scans of patients. In this study; pathological findings in CT scan of patients with mild cranial trauma and level of consciousness 13-15 are examined. Method: In this study, the treatment process of 445 patients who had referred to the emergency department due to skull trauma was examined as research units; and samples were taken by simple random sampling among patients for whom CT scan of the brain was requested. In this study; the reasons for requesting a CT scan of the skull from the requesting physician were questioned through an interview at the time of request. Results: According to the tables; it can be seen that most of the patients referred to the hospital due to skull trauma for CT scan were men and were discharged in less than the first 24 hours. The age group of 21 to 30 years had the highest and the age group over 70 years had the lowest frequency and the highest percentage of request for CT scan was by doctor and the biggest injury that caused patients with mild cranial trauma to go to hospital was fall. Conclusion Based on the findings of this study, cranial trauma is one of the life-threatening factors that can cause minor or severe injuries Its range varies from superficial injuries to hematoma and intracranial hemorrhage, which is a reason to perform CT scans of patients with skull trauma. In order to carefully examine the extent of the damage to the patient and to avoid possible risks and to take the necessary measures for treatment. On the other hand, the lack of clear protocols as well as the lack of clarity and certainty of physicians in their diagnoses to perform graphs of qualified people has increased the number of unnecessary CT scans

We report a large third-order nonlinear optical response of palladium diselenide (PdSe₂) films – a two-dimensional (2D) noble metal dichalcogenide material. Both open-aperture (OA) and closed-aperture (CA) Z-scan measurements are performed with a femtosecond pulsed laser at 800 nm to investigate the nonlinear absorption and nonlinear refraction, respectively. In the OA experiment, we observe optical limiting behaviour originating from large two photo absorption (TPA) in the PdSe₂ film of β = 3.26 ×10^-8 m/W. In the CA experiment, we measure a peak-valley response corresponding to a large and negative Kerr nonlinearity of n₂ = -1.33×10^-15 m²/W – two orders of magnitude larger than bulk silicon. We also characterize the variation of n₂ as a function of laser intensity, observing that n₂ decreases in magnitude with incident laser intensity, becoming saturated at n₂ = -9.96×10^-16 m²/W at high intensities. These results verify the large third-order nonlinear optical response of 2D PdSe₂ as well as its strong potential for high performance nonlinear photonic devices.

Background: Different drugs damage the frontal cortices, particularly the prefrontal areas involved in both emotional and cognitive functions, with a consequence of decoding emotion deficits for people with substance abuse. The present study aims to explore the cognitive impairments in drug abusers through facial, body and disgust emotion recognition, expanding the investigation of emotions, processing, measuring accuracy and response velocity. Method: We enrolled 13 addicted to cocaine and 12 alcohol patients attending treatment services in Italy, comparing them with 33 matched controls. Facial emotion and body posture recognition tasks, a disgust rating task, and the Barrat Impulsivity Scale were included in the experimental assessment. Results: We found that emotional processes are differently influenced by cocaine and alcohol, suggesting that these substances impact diverse cerebral systems. Conclusion: The contribution made by the duration of consumption on emotional processing seems far less important than for cognitive processes. Drug abusers seem to be slower on elaboration of emotions and, in particular, of disgust emotion. Considering that the participants were not impaired in cognition, our data support the hypothesis that emotional impairments emerge independently from damage to cognitive functions.

Facial recognition systems are widely used systems around the globe. Their applications in real world scenarios like law enforcement, surveillance, ID card identification, information security, access control and many more has made them very important in today’s world. These systems have shown promising results and excellent performance; however, these systems need tremendous number of facial images for training before they are implemented in the real-world scenarios. The problem arises when we face lack of training images due to which the performance of such systems decreases exponentially. The problem becomes even worse when there is only one image per subject for training our facial recognition system, the probe image might contain variation like illumination, expression and disguise which are hard to be accurately recognized. Generative models have achieved great success for handling the lack of training data by generating synthetic data that resembles the original data. We have created a new method by fine tuning the basic Conditional Generative Adversarial Network (CGAN) to generate synthetic facial images provided only single image per subject. These synthetic facial images contain six variations that are smile, anger, stare, illumination and disguise. We increased the size of the dataset and then applied a convolutional neural network for facial recognition, which improved the accuracy of our system from 0.76 to 0.99.

With the continuous development of deep learning, the application of object detection based on deep neural network in the coal mine has been expanding. Simultaneously, as the production applications demand higher recognition accuracy, most research chooses to enlarge the depth and parameters of the network to improve accuracy. However, due to the limited computing resources in the coal mining face, it is challenging to meet the computation demands of a large number of hardware resources. Therefore, this paper proposes a lightweight object detection algorithm designed specifically for coal mining face, referred to as CM-YOLOv8. The algorithm introduces adaptive predefined anchor boxes tailored to the coal mining face dataset to enhance the detection performance of various targets. Simultaneously, a pruning method based on the L1 norm is designed, significantly compressing the model's computation and parameter volume without compromising accuracy. The proposed algorithm is validated on the coal mining dataset DsLMF+, achieving a compression rate of 40% on the model volume with less than a 1% drop in accuracy. Comparative analysis with other existing algorithms demonstrates its efficiency and practicality in coal mining scenarios. The experiments confirm that CM-YOLOv8 significantly reduces the model's computational requirements and volume while maintaining high accuracy.

Information-Centric Networking (ICN) is a new paradigm of network architectures that focuses on content, rather than hosts, as first-class citizens of the network. As part of these architectures, in-network storage devices are essential to provide end users with close copies of popular content, to reduce latency and improve overall experience for the user, but also to reduce network congestion and load on the content producers. To be effective, in-network storage devices, such as content storage routers should maintain copies of the most popular content objects. Adversaries that wish to reduce this effectiveness can launch cache pollution attacks to eliminate the benefit of the in-network storage device caches. Therefore, it is crucial to protect these devices and ensure the highest hit rate possible. This paper demonstrates per-face popularity approaches to reducing the effects of cache pollution and improving hit rates by normalizing assessed popularity across all faces of content storage routers. The mechanisms that were developed prevent the consumers, whether legitimate or malicious, on any single face or small number of faces from overwhelmingly influencing the content objects that remain in the cache. The results demonstrate that per-face approaches generally have much better hit rates than currently used cache replacement techniques.

In tunnel excavation with boring machines, the tunnel face is supported to avoid collapse and minimise settlement. This article proposes the use of reinforcement learning, specifically the Deep Q-Network algorithm, to predict the face support pressure. The approach is tested both analytically and numerically. By using the soil properties ahead of the tunnel face and the overburden depth as the input, the algorithm is capable of predicting the optimal tunnel face support pressure, adapting to changes in geological and geometrical conditions.

Single sample face recognition (SSFR) is a computer vision challenge. In this scenario, there is only one example from each individual on which to train the system, making it difficult to identify persons in unconstrained environments, particularly when dealing with changes in facial expression, posture, lighting, and occlusion. This paper suggests a different method based on a variant of the Binarized Statistical Image Features (BSIF) descriptor called Multi-Block Color-Binarized Statistical Image Features (MB-C-BSIF) to resolve the SSFR Problem. First, the MB-C-BSIF method decomposes a facial image into three channels (e.g., red, green, and blue), then it divides each channel into equal non-overlapping blocks to select the local facial characteristics that are consequently employed in the classification phase. Finally, the identity is determined by calculating the similarities among the characteristic vectors adopting a distance measurement of the k-nearest neighbors (K-NN) classifier. Extensive experiments on several subsets of the unconstrained Alex &amp; Robert (AR) and Labeled Faces in the Wild (LFW) databases show that the MB-C-BSIF achieves superior results in unconstrained situations when compared to current state-of-the-art methods, especially when dealing with changes in facial expression, lighting, and occlusion. Furthermore, the suggested method employs algorithms with lower computational cost, making it ideal for real-time applications.

Digital health has added numerous promising solutions to enhance the health and wellness of people with neurocognitive disorders (NCDs) and their informal caregivers: (1) Background: It is important to get a comprehensive view of currently available technologies, their outcomes, and conditions of success to inform recommendations regarding digital health solutions for people with NCDs and their caregivers. This environmental scan was performed to identify the features of existing digital health solutions relevant to the targeted population. This work reviews currently available digital health solutions and their related characteristics to develop a decision support tool for older adults living with mild or major neurocognitive disorders and their informal caregivers. This knowledge will inform the development of a decision support tool to assist older adults and their informal caregivers in their search for adequate digital health solutions according to their needs and preferences, based on trustable information.; (2) Methods: We conducted an environmental scan to identify digital health solutions from a systematic review and targeted searches in grey literature covering the regions of Canada and Europe. Technological tools were scanned based on a preformatted extraction grid. We assessed their relevance based on selected attributes and summarized findings; (3) Results: We identified 100 available digital health solutions. The majority (56%) were not specific to NCDs. Only 28% provided scientific evidence of their effectiveness. Remote patient care, movement tracking, and cognitive exercises were the most common purposes of digital health solutions. Most solutions were presented as decision aid tools, pill dispensers, apps, web, or a combination of these platforms. (4) Conclusions: This environmental scan allowed the identification of current digital health solutions for older adults with mild or major neurocognitive disorders and their informal caregivers. Findings from the environmental scan highlight the need for additional approaches to strengthen digital health interventions for the well-being of older adults with mild and major NCDs and their informal and formal healthcare providers.

This study aimed to demonstrate the potential role of Dual Energy CT in assessing Bone Mineral Density (BMD) in postmenopausal women. A retrospective study was conducted on 51 postmenopausal female patients who underwent DXA and DECT examinations for other clinical reasons. DECT images were acquired with spectral imaging using a 256-slice system. These images were processed and visualized using an HAP(fat) map. Statistical analysis was performed using the Bland-Altman method to assess agreement between DXA and DECT HAP(fat) measurements. Mean BMD, vertebral and femoral T-scores were obtained. For vertebral analysis, the Bland-Altman plot showed an inverse correlation (R2: -0.042; RMSE: 0.690) between T-scores and DECT HAP(fat) values for measurements from L1 to L4, while a good linear correlation (R2: 0.341; RMSE: 0.589) it was found for measurements at the femoral neck. In conclusion, we demonstrated the enhanced accuracy of BMD calculation through DECT, finding a statistically significant correlation only at the femoral neck where BMD results do not seem to be influenced by the overlap of the measurements on cortical and trabecular bone. This outcome could be beneficial in the future by reducing radiation exposure for patients already undergoing follow-up for chronic conditions.

This research aims to thoroughly understand the delicate interplay between stakeholders' perspectives, biometric data protection policies, and the deployment of face recognition technologies in Indonesia's dynamic digital world. Adopting a qualitative methodology, the study conducts Focus Group Discussions (FGDs) with various stakeholders, including government agencies, technology developers, regulatory agencies, civil society groups, and data subjects as end-users. Face-to-face group discussions facilitate an in-depth exploration of participants' perspectives, yielding rich qualitative data that reveals nuanced insights into the delicate balance between technological innovation, ethical considerations, and socioeconomic repercussions associated with implementing face recognition technology. The research comprehensively explains the difficulties and opportunities associated with responsible technological progress and biometric data security. The findings from the FGDs influence the development of strategies that effectively balance technical advancement with individual rights, privacy, and social well-being within the growing digital landscape of Indonesia. The regulatory function of the government seeks to bridge the gap between public expectations and technological advancements. Collaboration between academic institutions, government agencies, the private sector, and data subjects as end-users emerges as a crucial element, fostering an all-encompassing strategy integrating research, law, technology deployment, and user empowerment. This holistic approach is necessary to ensure that biometric data and facial recognition technology are utilized responsibly, thereby laying the way for Indonesia's technologically sophisticated and ethically aware digital future.

Abstract: Background: Coronary angiography CT scans play a pivotal role in diagnosing cardiovascular diseases, providing crucial information for treatment planning. However, concerns regarding radiation exposure have prompted the need for establishing region-specific diagnostic reference levels (DRLs) to ensure patient safety. This study aims to assess radiation exposure during coronary angiography CT scans in the Northeast Assam population and establish DRLs tailored to this demographic. Methods: A total of 380 patients were referred to the Primus Diagnostic Centre and Heath City Hospital, Guwahati Assam, during the study with coronary artery disturbances. Data on the technical parameters used in CT procedures were taken in 2021-2022. The aim and study's objective was Organ and surface dose to specific radiosensitive organs (Chest) estimation using software imPACT 1.0.4 from National Radiological Protection Board (NRPB) SR250 Monte Carlo dataset. Results: The study population (n = 380) comprised 190 men and 190 women with an average age of 29 to 75 years. The Mean ± SD of BMI and ED are 22.42±1.06 and 21.57±4.27 respectively. The mean DLP is 854.67, and the mean ED is 21.57. The effective doses for males (13- 27) mSv were in females (13-29) mSv. The DRL for Male population was found to be 24.26mSv.cm2 whereas for female population it was 24.69mSv.cm2. Conclusions: This study highlights the necessity of establishing tailored diagnostic reference levels for coronary angiography CT scans in the Northeast Assam population. By doing so, healthcare providers can ensure optimal image quality while minimizing radiation exposure, ultimately enhancing patient safety and quality of care. These findings have implications for radiological practice in the region and contribute to the ongoing efforts to standardize radiation doses in medical imaging procedures.

To verify the reliability of the proposed stitching linear scan method for roundness measurement of small cylindrical workpieces, measurement uncertainties of the workpieces with various dimension are conducted. The cross-sectional circle of a cylindrical workpiece is divided into several equal arcs to be measured. Thus, the arc profile can be characterized according to the measured arcs. The roundness profile of the cylindrical part can be obtained by stitching these arc profiles together. Firstly, the measurement uncertainty of the workpiece with a diameter 1.5 mm and length 5.8 mm is evaluated to be 0.095 μm which can meet the uncertainty target of 0.1 μm. Considering the uncertainty of θx and θz which are the alignment around X-axis and Z-axis respectively changing with workpiece dimension changing, relevant uncertainties are carried out. As a result, there is no limitation with respect to workpiece diameter theoretically If the alignment can reach θz = θx = ±0.1° without considering the measurable range of the machine.

Urgent environmental challenges and emerging additive manufacturing (AM) technologies push research towards more performant and new materials. In the field of metallurgy, high entropy alloys (HEAs) have recently represented a topic of intense research because of their promising properties, such as high temperature strength and stability. Moreover, this class of multi-principal element alloys (MPEAs) have opened up researcher community to unexplored compositional spaces, making prosper literature of high-throughput methodologies and tools for rapidly screening large number of alloys. However, none of the methods has been aimed to design new MPEAs for AM process known as selective laser melting (SLM) so far. Here we conducted nanoindentation testing on single scan tracks of elemental powder blends and pre-alloyed powders after ball milling of AlTiCuNb and AlTiVNb. Results show that nanoindentation can represent an effective technique to gain information about phase evolution during laser scanning, contributing to accelerate the development of new MPEAs.

With the rapid growth of the global mask market, consumers have put forward higher requirements for the functionality and ingredients of mask products. Conventional facial masks generally use ordinary non-woven fabrics as the mask base fabric, are frequently used with essence and contain preservatives. At the same time, they are susceptible to the influence of the external environment and are easily oxidized, which will cause the mask to deteriorate and skin allergic reactions. In addition, conventional facial masks exhibit poor skin fit, limited breathability, inadequate absorption of nutrient solutions, and easy to drip. The nanofiber mask prepared by electrospinning technology has a high specific surface area and high porosity, which enabling improved nutrient absorption by the skin, superior skin fit, and exceptional breathability. Nanofiber facial mask products can effectively solve consumer pain points and promote the high-end development of facial mask products. Therefore, this research combines needleless electrospinning technology to develop a novel solid-state, preservative-free, and quick-dissolving nanofiber facial mask that can be prepared on a large scale. Based on needleless electrospinning technology, this research deeply explores the process parameters and influencing mechanisms of quick-dissolving nanofiber facial masks, ultimately achieving the consistent production of nanofiber facial masks with optimal morphology. Comprehensive and evaluation of the structure and performance of nanofiber facial masks from micro and macro perspectives. The experimental results show that the mask morphology is optimal under the process conditions of using a spinning liquid of 20% collagen peptide solution, a spinning voltage of 30kV, a collection distance of 19cm, and a liquid supply speed of 130ml/h.

We investigate new graph characteristics namely total (vertex, edge) face irregularity strength of gen- eralized plane grid graphs Gmn under k-labeling Phi of type (Alpha, Beta, Gamma). The minimum integer k for which a vertex-edge labelled graph has distinct face weights is called the total face irregularity strength of the graph and is denoted by tfs(Gmn). In this article, the graphs G = (V;E; F) under consideration are simple, fi nite, undirected and planar. We will estimate the exact tight lower bounds for the total face irregularity strength of some families of generalized plane grid graphs.

Complex illumination condition is one of the most critical challenging problems for practical face recognition. In this paper, we propose a novel method to improve the illumination invariants for solving this challenge. Firstly, a new method based on the Lambert reflectance model is proposed to extract illumination invariant, which is less insensitive to complex illumination variations. Secondly, in order to repair the defects caused by process of illumination invariants extraction, Fast Mean Filter is utilized to smooth and remove noise. Lastly, for raising the richness of information in output image, a nonlinear normalization transformation is proposed. Compared with the state-of-the-arts, experimental results show that the proposed method can extract more robust illumination invariants. Apart from it, the richness of information in processed image is greater and superior performance in face recognition rate is superior.

COVID-19 is a severe global problem, and AI can play a significant role in preventing losses by monitoring and detecting infected persons in early-stage. This paper aims to propose a high-speed and accurate fully-automated method to detect COVID-19 from the patient's CT scan images. We introduce a new dataset that contains 48260 CT scan images from 282 normal persons and 15589 images from 95 patients with COVID-19 infections. At the first stage, this system runs our proposed image processing algorithm to discard those CT images that inside the lung is not properly visible in them. This action helps to reduce the processing time and false detections. At the next stage, we introduce a novel method for increasing the classification accuracy of convolutional networks. We implemented our method using the ResNet50V2 network and a modified feature pyramid network alongside our designed architecture for classifying the selected CT images into COVID-19 or normal with higher accuracy than other models. After running these two phases, the system determines the condition of the patient using a selected threshold. We are the first to evaluate our system in two different ways. In the single image classification stage, our model achieved 98.49% accuracy on more than 7996 test images. At the patient identification phase, the system correctly identified almost 234 of 245 patients with high speed. We also investigate the classified images with the Grad-CAM algorithm to indicate the area of infections in images and evaluate our model classification correctness.

A continuous spatio-temporal database of accurate soil moisture (SM) measurements is an important asset for agricultural activities, hydrologic studies, and environmental monitoring. The Advanced Microwave Scanning Radiometer 2 (AMSR2), launched in May 2012, has been providing SM data globally with a revisit period of two days. It is imperative to assess the quality of this data before performing any application. Since resources of accurate SM measurements are very limited in Puerto Rico, this research will assess the quality of the AMSR2 data by comparing with ground-based measurements and perform a downscaling technique to provide a better description of how the sensor perceives the surface soil moisture as it passes over the island. The comparison consisted of the evaluation of the mean error, root mean squared error, and the correlation coefficient. Two downscaling techniques were used and their performances were studied. The results revealed that AMSR2 products tend to underestimate. This is due to the extreme heterogeneous distributions of elevations, vegetation densities, soil types, and weather events on the island. This research provides a comprehensive study on the accuracy and potential of the AMSR2 products over Puerto Rico. Further studies are recommended to improve the AMSR2 products.

This study explored the electrocortical correlates of conscious and nonconscious perceptions of emotionally laden faces in neurotypical adult women with varying levels of autistic-like traits (Autism Spectrum Quotient - AQ). Event-related potentials (ERPs) were recorded during the viewing of backward-masked images for happy, neutral, and sad faces presented either below (16 ms - subliminal) or above the level of visual conscious awareness (167 ms - supraliminal). Sad compared to happy faces elicited larger frontal-central N1, N2, and occipital P3 waves. We observed larger N1 amplitudes to sad faces than to happy and neutral faces in High-AQ (but not Low-AQ) scorers. Additionally, High-AQ scorers had a relatively larger P3 at the occipital region to sad faces. Regardless of the AQ score, subliminal perceived emotional faces elicited shorter N1, N2, and P3 latencies than supraliminal faces. Happy and sad faces had shorter N170 latency in the supraliminal than subliminal condition. High-AQ participants had a longer N1 latency over the occipital region than Low-AQ ones. In Low-AQ individuals (but not in High-AQ ones), emotional recognition with female faces produced a longer N170 latency than with male faces. N4 latency was shorter to female faces than male faces. These findings are discussed in view of their clinical implications and extension to autism.

Three tiny bones compose the human ossicular chain: malleus, incus and stapes. Also known as auditory ossicles, they are united by joints in the middle ear cavity of the petrous part of the temporal bone. Completely developed 2 years after birth, the ossicular chain is involved in the physiological process of hearing, by which sound waves from the environment are converted into electrochemical impulses. In the last 500 years, most studies have focussed on the morphogenesis, morphological variability and clinical pathology of the ossicular chain, whilst only a few studies have added relevant knowledge to anthropology and forensic science. The auditory ossicles and the enclosing petrous bone are some of the hardest in the human skeleton. This reflects in a relative resistance to fire. In the post mortem period and in favourable circumstances, the auditory ossicles can fossilize and survive for millions of years. Post mortem loss in skeletal or decomposed remains can be ascribed to their destruction or physical displacement, by either macro-micro-faunal action and/or any other natural or artificial disturbance. Physical displacement is closely connected to the depositional environment of the skeletal remains, such as burial, entombment (sarcophagus, coffin, vault…), submersion or exposure to natural elements. Auditory ossicles can be recovered in situ, or very close to their anatomical location, when the skeletal material has been involved in an archaeological excavation. In the case of accessible or disturbed remains, scavengers may remove the tiny ossicles, and/or they can slip out of the middle ear cavity following skull movements. Entombment offers an effective protection against the displacement of the auditory ossicles, whereas aquatic submersion and aquatic movement almost invariably displace them. The preservation of the human auditory ossicles should be critically considered in the comprehensive context of any forensic investigation on human remains, since it can assist the reconstruction of their taphonomic history. Taphonomic histories of remains can add crucial information to forensic investigations (e.g., the Post Mortem Interval, PMI). The aim of this study, limited by scarce relevant literature, is to discuss the potential role of the ossicular chain, detected by post mortem imaging techniques, as a taphonomical indicator in decomposing and/or skeletonized bodies.

Parts produced by laser or electron-beam powder bed fusion (PBF) additive manufacturing are prone to residual stresses, deformations, and other defects linked to non-uniform temperature distribution during the manufacturing process. Several researchers have highlighted the important role scan sequence plays in achieving uniform temperature distribution in PBF. However, scan sequence continues to be determined offline based on trial-and-error or heuristics, which are neither optimal nor generalizable. To address these weaknesses, we have articulated a vision for an intelligent online scan sequence optimization approach to achieve uniform temperature distribution, hence reduced residual stresses and deformations, in PBF using physics-based and data-driven thermal models. This paper proposes SmartScan, our first attempt towards achieving our vision using a simplified physics-based thermal model. The conduction and convection dynamics of a single layer of the PBF process are modeled using the finite difference method and radial basis functions. Using the model, the next best feature (e.g., stripe or island) that minimizes a thermal uniformity metric is found using control theory. Simulations and experiments involving laser marking of a stainless steel plate are used to demonstrate the effectiveness of SmartScan in comparison to existing heuristic scan sequences for stripe and island scan patterns. In experiments, SmartScan yields up to 43% improvement in average thermal uniformity and 47% reduction in deformations (i.e., warpage) compared to existing heuristic approaches. It is also shown to be robust, and computationally efficient enough for online implementation.

Abstract: Background: Trauma is the leading cause of morbidity and mortality in young patients, acute surgical conditions cause morbidity and mortality in patients of all ages. Computed tomography scan (CT) assist greatly in the investigation and management of these patients with blunt and penetrating trauma, facilitating early diagnosis and expedites early appropriate treatment of these patients. Acute non-traumatic surgical conditions also mimic non-surgical pathologies and differentiating the two is important because management is different and any delay in surgical intervention results in high morbidity and some may even result in mortality. Method: Literature review of the role of multidetector CT scan (MDCT) in managing acute surgical emergencies and trauma-diagnosis in the trauma patient and patients presenting with acute surgical emergencies. The focus is on the role of the MDCT in the management of these patients. Results highlight that the use of MDCT expedites management of these patients, allowing early surgical intervention and preventing unnecessary surgical interventions where the intervention is not indicated, based on the MDCT results. Conclusion: This paper emphasizes the important role that is played by the MDCT in the management of the above patients.

Acacia dealbata Link, named Mimosa in Portugal, is an invasive hardwood species with potential for construction use, but research is limited. The available stock of small-diameter juvenile wood (sjw) logs can help reduce this gap, but tangential cracking at log ends challenges fastener connections. This study evaluated different treatments to control and reduce end-face cracking in sjw logs during air drying, an economical and environmentally friendly procedure. Extreme two-thirds of sixteen Mimosa logs were subjected to two treatments: one with longitudinal kerfs 15 mm deep along the length (2 and 3 kerfs) and the other with a hollow in the centre up to half the length (16 mm and 30 mm diameters). Over 219 days of air drying and compared with the central part, kerfing treatments significantly reduced outer wood tangential cracking (p &lt; 0.001), with the 3-kerf also reducing crack numbers (p &lt; 0.05) but increasing significantly cracks near the pith (p &lt; 0.01). The 30 mm central hollow significantly reduced central perforation cracking (p &lt; 0.05). Results suggest that a combined treatment approach could help mitigate cracking, enhancing the suitability of wild Mimosa logs for construction use.

Climate change is a big challenge for agriculture since it affects crop productivity and yield. The increase of droughts, salinity, and soil degradation are some of the main consequences of climate change. The use of microorganisms has emerged as an alternative to mitigate them. Among these microorganisms, dark septate endophytes (DSEs) have garnered more attention in recent years. Various studies show that their association with plants helps reduce the harmful effects of abiotic stresses and increases nutrient availability, allowing plants to thrive under adverse conditions. This work reviews the effect of DSEs and the subjacent mechanisms that will help plants develop a higher tolerance to climate change.

Background: The rapid flow of globalization makes the world free from barriers so that the existence of globalization gives birth to a new concept of "World Without Borders". This situation puts the character of millennials on the brink of collapse and needs to be straightened out. Based on the problems above, the researcher determines the question: How is the role of civic education in shaping millennial character in the era of globalization? Purpose: The purpose of this study is to determine the impact of the current globalization on society, as a form of knowledge of the importance of citizenship education as a shield in the era of globalization, knowing the role of civic education in shaping the character of the millennial generation. Method: The method used in this research is a literature review. The search for journals was carried out using the google scholar database with the keywords “citizenship educations”, “millennial generation”, and “globalization”. Feasibility studies are assessed based on title, abstract, and full text. Data analysis using qualitative analysis based on research findings. Results: The results of this study show the influence of globalization causes character problems in millennials increasing every year. The high nature of individualism, consumerism, and westernization shows the characteristics of Indonesia began to erode and eventually also impacted the moral damage. Therefore, prevention is necessary through citizenship education as the basis for character formation by the values of Pancasila.

Background: The rapid flow of globalization makes the world free from barriers so that the existence of globalization gives birth to a new concept of "World Without Borders". This situation puts the character of millennials on the brink of collapse and needs to be straightened out. Based on the problems above, the researcher determines the question: How is the role of civic education in shaping millennial character in the era of globalization? Purpose: The purpose of this study is to determine the impact of the current globalization on society, as a form of knowledge of the importance of citizenship education as a shield in the era of globalization, knowing the role of civic education in shaping the character of the millennial generation. Method: The method used in this research is literature review. The search for journals was carried out using the google scholar database with the keywords “citizenship educations”, “millennial generation”, and “globalization”. Feasibility studies are assessed based on title, abstract, and full text. Data analysis using qualitative analysis based on research findings. Results: The results of this study show the influence of globalization causes character problems in millennials increasing every year. The high nature of individualism, consumerism, and westernization shows the characteristics of Indonesia began to erode and eventually also impacted the moral damage. Therefore, prevention is necessary through citizenship education as the basis for character formation by the values of Pancasila.

The purpose of this study was to develop a mathematical model for non-contacting face seals to analyze how their performance is affected by thermoelastic phenomena. The model was used to solve thermal conductivity and thermoelasticity problems. The primary goal was to calculate the values of thermal deformations of the sealing rings in a non-contacting face seal with a flexibly mounted rotor (FMR) for a turbomachine. The model assumes conversion of mechanical energy into heat in the fluid film. The heat flux generated in the fluid film is transferred first to the sealing rings and then to the fluid surrounding them. An asymmetric distribution of temperature within the sealing rings leads to the occurrence of thermal stresses and, consequently, a change in the rings geometry. The model is solved analytically. The distributions of temperature fields for the sealing rings in the cross-sections are calculated using the Fourier-Bessel series as a superficial function of two variables (r,z). The thermoelasticity problems described by the Navier equations are solved by applying the Boussinesq harmonic functions and Goodier’s thermoelastic displacement potential function. The proposed method involves solving various theoretical and practical problems of thermoelasticity in FMR-type non-contacting face seals. The calculated thermal deformations of the sealing rings are used to determine the most important seal performance parameters such as the leakage rate and power loss.

The COVID-19 crisis has resulted in a shortage of personal protective equipment (PPE) [1]. COVID-19 is currently the leading cause of death in the United States[2]. Health care providers caring for COVID-19 patients or at high risk of being exposed to the SARS-CoV-2 virus benefit from a face shield to protect against aerosol droplets that could hit the face and minimize the chance of inadvertently touching the face with contaminated hands, and air filtration to filter out aerosolized SARS-CoV-2. Adapting commercially available full-faced snorkel masks has been proposed as an alternative to narrow the gap in PPE [3]. Here we explore a full-faced snorkel mask with commercially available particulate filters.

The energy production market based on hydrogen technologies is to be an innovative solution that will allow the industry to achieve climate neutrality in the future in Poland and in the world. The paper presents the idea of using hydrogen as a modern energy carrier, and devices that, in cooperation with renewable energy sources, produce the so-called green hydrogen and the applicable legal acts that allow for the implementation of the new technology were analysed. Energy transformation is inevitable, and according to reports on good practices in European Union countries, hydrogen and the hydrogen value chain (production, transport and transmission, storage, use in transport and energy) have a wide potential. Thanks to joint projects and subsidies from the EU, initiatives supporting hydrogen technologies are created, such as: hydrogen clusters, hydrogen valleys, and EU and national strategic programs set the main goals. As noted in this article, Poland is one of the leaders in hydrogen production both in the world and in Europe. Domestic tycoons from the energy, refining and chemical industries are involved in the projects. Eight hydrogen valleys that have recently been created in Poland successfully implement the assumptions of the "Polish Hydrogen Strategy until 2030 with a perspective until 2040", "Energy Policy of Poland until 2040", which are in line with the assumptions of the most important legal acts of the EU , including: the European Union's energy and climate policy, the Green Deal, the Fit for 55 Package.

As the COVID-19 pandemic begins to abate and national public health systems are treating the SARS-Cov-2 virus as endemic, many public health measures are no longer mandated, but remain recommended with voluntary participation. One of these is the wearing of fitted face masks, initially mandated to contain, or at least slow, the spread of SARS-CoV-2 which is primarily transmitted via aerosols emitted while breathing, coughing, or sneezing. While the habit of once wearing fitted face masks recedes into memory for much of the population, so does the knowledge of the various types of masks that were once en vogue. To create a record for the future, this paper provides the first comprehensive documentation of the nature and range of fitted facemasks that circulated during the COVID-19 pandemic.

Transparent materials used for facial protection equipment provide protection against microbial infections caused by viruses and bacteria, including multidrug-resistant strains. However, transparent materials used for this type of application are made of materials that do not possess antimicrobial activity. They just avoid direct contact between the person and the biological agent. Therefore, healthy people can get infected through contact of the contaminated material surfaces and this equipment constitute an increasing source of infectious biological waste. Furthermore, infected people can transmit microbial infections easily because the protective equipment do not inactivate the microbial load generated while breathing, sneezing, or coughing. In this regard, the goal of this work consisted of fabricating a transparent face shield with intrinsic antimicrobial activity that could provide extra-protection against infectious agents and reduce the generation of infectious waste. Thus, a single-use transparent antimicrobial face shield composed of polyethylene terephthalate and an antimicrobial coating of benzalkonium chloride has been developed for the next generation of facial protective equipment. The antimicrobial coating was analyzed by atomic force microscopy and field emission scanning electron microscopy with elemental analysis. This is the first facial transparent protective material capable of inactivating enveloped viruses such as SARS-CoV-2 in less than one minute of contact, and the methicillin-resistant Staphylococcus aureus and Staphylococcus epidermidis. Bacterial infections contribute to severe pneumonia associated with the SARS-CoV-2 infection, and their resistance to antibiotics is increasing. Our extra protective broad-spectrum antimicrobial composite material could also be applied for the fabrication of other facial protective tools such as such as goggles, helmets, plastic masks and space separation screens used for counters or vehicles. This low-cost technology would be very useful to combat the current COVID-19 pandemic and protect health care workers from multidrug-resistant infections in developed and underdeveloped countries.

Fabric material was coated with Viruferrin™ and tested for its inactivating properties against the pandemic severe acute respiratory syndrome 2 (SARS-CoV-2) and influenza A viruses. A statistically significant (p<0.0001) decrease in the number of infectious virus particles exposed to Viruferrin-treated fabric when compared with the cotton control for both SARS-CoV-2 and influenza A viruses was observed. For both SARS-CoV-2 and influenza A, Viruferrin-treated fabrics experienced a > 99% virus reduction without saliva after five minutes of contact when compared to the positive control at time point 0. Furthermore, the reusability of the Viruferrin treated fabric was demonstrated by stability for up to 10 washes. The level of anti-viral (SARS-CoV-2) activity remained constant from 5 to 10 washes and demonstrated a significant difference (p<0.0001) from the unwashed untreated material. Applications for this treated fabric are far-reaching, and as a biological face covering offers not only a unique 2-way protection but also is unlikely to cause onward touch transmission.

The performance of laser beams in tracking Lissajous scan trajectories is severely limited by beam jitter. To enhance the performance of fast steering mirror (FSM) control in tracking Lissajous scan trajectories, this paper proposed a fractional order active disturbance rejection controller (FOADRC) and verified its effectiveness in improving system scanning tracking accuracy. A dynamic mathematical model of fast steering mirror was studied and the design of parameters for the control mode of the closed-loop system was determined. A reduced order linear active disturbance rejection controller suitable for FSM systems was designed and the corresponding fractional order proportional differentiation (FOPD) controller were determined according to the mathematical model. The use of the designed controller enabled high-performance tracking of high-frequency Lissajous scanning curves (X-axis 500 Hz, Y-axis 350 Hz) and met the need for high-frequency repetitive scanning. The controller has the characteristics of simple implementation and low computational complexity and is suitable for closed-loop control applications in engineering.

For esophageal cancer patients treated with neoadjuvant chemoradiotherapy (nCRT), restaging using F-18-fluorodeoxyglucose (18F-FDG) positron emission tomography computed tomography (PET-CT) following nCRT can detect interval metastases, including liver metastases, in almost 10% of patients. However, in clinical practice, focal FDG liver uptake, unrelated to liver metastases, is observed after chemoradiotherapy. This radiation-induced liver injury (RILI) can potentially lead to overstaging. Liver radiation damage is also a concern during distal esophageal cancer radiotherapy due to proximity to the left liver lobe, typically included in the radiation field. Post-CRT, if FDG activity appears in the left or caudate liver lobes, thorough investigation is needed to confirm or rule out distant metastases. The increased FDG uptake in liver lobes post-CRT often presents a diagnostic dilemma. Distinguishing between radiation-induced liver disease and metastasis is vital for appropriate patient management, necessitating a combination of imaging techniques and an understanding of the factors influencing radiation response. Diagnosis involves identifying new foci of hepatic FDG avidity on PET/CT scans. Geographic regions of hypoattenuation on CT and well-demarcated regions with specific enhancement patterns on contrast-enhanced CT scan and MRI are characteristic of radiation-induced liver disease (RILD). Lack of mass effect on all three modalities (CT, MRI, PET) indicates RILD. Resolution of abnormalities on subsequent examinations also helps in diagnosing RILD. Moreover, it can also help to rule out occult metastases, thereby excluding those patients from further surgery who will not benefit from esophagectomy with curative intent.

In recent years, there has been a growing interest in the prediction of facial age due to its diverse applications in fields such as security, entertainment, and healthcare. The current scourge of underaged voting in Nigeria is a problem and this research delves into the realm of facial age prediction by employing four well-known convolutional neural network (CNN) architectures, namely VGG16, ResNet50, Mobile Net, and VGG19 to predict chronological ages from facial pictures of person. The objective is to achieve precise age estimation from facial images, utilizing two datasets: UTKFace and CASIA African Facial Datasets. The results of this investigation are noteworthy. Specifically, the VGG16 model which demonstrated remarkable performance, yielding a Mean Absolute Error (MAE) of 1.76 when applied to the UTK-Face dataset. Additionally, when utilizing Mobile-Net, an unprecedented MAE of 1.10 was achieved for the Casia Africa Face Dataset. Notably, this marks the first instance of employing the dataset for facial age detection with CNNs, and this approach outperformed previous works, yielding the lowest MAE among all the studies reviewed.

It is crucial to select the right machine learning classifier for image classification and face recog-nition. This study examines the effectiveness of four different face recognition classifiers - Support Vector Machines (SVM), Random Forest, K-Nearest Neighbors (KNN), and Neural Networks. An analysis of the Large Faces in the Wild (LFW) dataset was carried out using Principal Component Analysis (PCA). Classifiers are rigorously trained and evaluated based on the extracted features. Comparison of classifier performance is an insightful way to figure out their strengths and weaknesses. Having a visual representation of the classifier's performance gives a complete understanding of its capabilities. Through the selection of the most appropriate classifier, study results contribute to advancements in image classification, recognition, and biometric identification. The comparison study demonstrated that the Neural Network classifier was exceptionally accurate and proficient in recognizing faces from the LFW dataset when used in conjunction with PCA for feature extraction. According to the comparative analysis, the Neural Network classifier proved exceptionally accurate and proficient at identifying faces from the LFW dataset when combined with PCA for feature extraction.

An ion exchange dialysis (IED) is used in the recovery of aluminium from residue. In this papers, the face-centered central composite design (FC-CCD) of the response surface methodology (RSM) and desirability approach is used for experimental design, modelling and process optimization of a counter flow IED system. The feed concentration, feed flowrate, sweep flowrate and sweep concentration are selected as the process variables, with the Al-transport across a Nafion 117 membrane as the target response. A total of 30 experimental runs were conducted with 6 center points. The response obtained was analysed by analysis of variance (ANOVA) and fitted to a second-order polynomial model using multiple regression analysis. The actual R2 and standard deviation of the model are 0.9548 and 0.2932 respectively. The influences of significant variables are plotted on 3D surface and contour plots. The designed variables were numerically optimized by applying the desirability function to achieve the maximum Al-transport. The optimised condition values were found to be feed concentration (1600 ppm), feed flowrate (61.76%), sweep flowrate (37.50%) and sweep concentration (0.75 N) for the 80% target response at 32hrs. Overall, the model can be used to effectively predict Al-recovery using the designed system

Based on the kinematic approach of the limit analysis and slip-line theories, this paper proposes a new 2D analytical model to evaluate the collapse support pressure to ensure the face stability of a circular tunnel in purely cohesive soils driven by a shield. The normality conditions, the yield criterion and the vertical soil arching effect are considered in the analytical model. Two upper bound solutions corresponding to the ratio of the cover to the diameter (C/D) are derived from considering the mechanisms based on the motion of rigid multi-blocks. Comparisons are made with existing upper and lower bound solutions published in previous articles. The results are close to the solutions of practical engineering. The failure mechanisms proposed in this study provide a better explanation for the failure process in the heading of the tunnel face.

Face routing has been adopted in wireless sensor networks (WSNs) where topological changes occur frequently or maintaining full network information is difficult. For message forwarding in networks, a planar graph is used to prevent looping, and because long edges are removed by planarization and the resulting planar graph is composed of short edges, messages are forwarded along multiple nodes connected by them even though they can be forwarded directly. To solve this, face routing using information on all nodes within 2-hop range was adopted to forward messages directly to the farthest node within radio range. However, as the density of the nodes increases, network performance plunges because message transfer nodes receive and process increased node information. To deal with this problem, we propose a new face routing using the planar graphs of neighboring nodes to improve transfer efficiency. It forwards a message directly to the farthest neighbor and reduces loads and processing time by distributing network graph construction and planarization to the neighbors. It also decreases the amount of location information to be transmitted by sending information on the planar graph nodes rather than on all neighboring nodes. Simulation results show that it significantly improves transfer efficiency.

This research identifies the resilience factors of tax centers in Cameroon. To do so, we use binary logistic regression on data collected from tax revenue mobilization centers (TRMC). the results show that the digitization of the tax system and leadership are factors in absorbing the crisis. Access to electricity and staff at the center are the recovery factors for the level of revenue after the crisis. We recommend that public institutions improve the digitalization processes of tax administrations. The transmission channels of digitization such as access to electricity and telecommunications networks must be ensured in the tax centers. Furthermore, we propose to increase the number of tax centers or to build tax kiosks to reduce the distance between taxpayers and their centers of attachment.

From 2020 to 2023 many people around the world were forced to wear masks for large proportions of the day based on mandates and laws. We aimed to study the potential of face masks for the content and release of inanimate toxins. A scoping review of 1003 studies was performed (database search in PubMed/MEDLINE, qualitative and quantitative evaluation). Twenty-four studies were included (experimental time 17 min to 15 days) evaluating content and/or release in 631 masks (273 surgical, 228 textile and 130 N95 masks). Most studies (63%) showed alarming results with high micro- and nanoplastics (MPs and NPs) release and exceedances could also be evidenced for volatile organic compounds (VOCs), xylene, acrolein, per-/polyfluoroalkyl substances (PFAS), phthalates (including di(2-ethylhexyl)-phthalate, DEHP) and for Pb, Cd, Co, Cu, Sb and TiO₂. Of course, masks filter bacteria, dirt and plastic particles and fibers from the air we breathe and have specific indications, but according to our data they also carry risks. Depending on the application, a risk-benefit analysis is necessary. However, mask mandates during the SARS-CoV-2 pandemic have been generating an additional source of potentially harmful exposition to toxins at population level with almost zero distance to the airways.

Background: The COVID-19 transmission has been established to occur through respiratory droplets from coughing and sneezing. Health agencies have strongly recommended the use of facemask as a precaution from cross-transmission. Objective: This study investigated the prevalence of facemask use among visitors to the hospital. This study also investigated the demographic factors contributing to unacceptable facemask practice. Setting: This prospective observational study was done among hospital visitors to a district specialist hospital during COVID-19 pandemic outbreak. Methods: Individuals entering through dedicated entry point were observed for the type, category and practice of wearing personal protective equipment. Inclusion criteria for this study were any individual's ≥ 2 years old entering the treatment facility from selected entry points. Patients were categorized into two groups of acceptable and unacceptable facemask practice. The Pearson chi-square was used to test for differences in investigated variables in the univariate setting and Binary Logistic regression model was used in the multivariate setting. Main Outcome Measure: Prevalence, acceptance practice and odds ratio of unacceptance of facemask use. Results: Among 1652 individuals included in the final analysis, 1574 (96.9%) was observed wearing facemask with 1269 (72.0%) of individuals worn medical-grade facemask. However, among them, only 1397 (88.8%) individuals' facemask practice was acceptable while the reaming 177 (11.2) individuals were perceived with unacceptable facemask practice. Male individuals, Malay ethnic and high risk age group are 1.47 times (Odds Ratio: OR=1.47; 95% CI, 1.06-2.06; p=.023), 2.18 times (OR=2.18; 95% CI, 1.55-3.08; p<.001) and 1.99 times (OR=2.18; 95% CI, 1.42 - 2.77; p<.001) more likely to exhibit unacceptable facemask practice respectively. Conclusion: Extensive use of facemasks coupled with environmental hygiene measures is a public health strategy which can help to mitigate the COVID-19 epidemic impact. However, a targeted comprehensive strategy to improve compliance to proper facemask practice among the high-risk population is needed to achieve maximal protective benefit.

One of the most important problems in the presence of epidemics and pandemics is an accurate prediction and preparedness. Scholars and experts argue that future pandemics and/or epidemics are almost inevitable events and is not whether next pandemics will happen, but when a new heath emergency will emerge. Epidemiologic models for prediction of Coronavirus Disease 2019 (COVID-19) have shown many limitations because of unpredictable dynamics of the new viral agent SARS-CoV-2 in environment and society. The main goals of this study are twofold: first, the analysis of anthropogenic activities and factors that may trigger pandemic threats; second, the planning of new directions for strategies to reduce risks that a pandemic threat emerges and/or in the initial phase to reduce vast diffusion and negative impact of new viral agents that can generate hazards and problems in public health, environment and socioeconomic systems. In particular, the investigation and understanding of sources and driving factors concerning the emergence and diffusion of new pandemics have critical aspects for strategic actions of forecast, prevention and preparation of effective policy responses to cope with next pandemic crises and health emergencies. Insights here endeavor, whenever possible, to clarify these problems to increase the knowledge of the sources and factor determining the emergence of new viral agents in order to design optimal response policies to face next pandemic diseases similar to COVID-19. .

Face milling is a well known commercial process highly used in heavy industries that consumes high amount of power. Besides power issue, modern manufacturing industries are aiming for per part cost reduction keeping the product quality unimpaired. Unexpectedly if the part is rejected in any stage of manufacturing, the cost of manufacturing dramatically increases. Major cause of part rejection is excessive tool wear that imparts poor surface profile or catastrophic tool failure that causes adherence of broken tool debris onto machined surface. Furthermore, the tool wear is associated with sliding distance (frictional distance) and the tool life quantifies the cost of tools. As such, from the perspective of manufacturing industries it is imperative to optimize the surface quality parameter, cost of part, power consumption, and material removal – this is exactly what is accomplished here. By this work, it is possible to conserve power consumption, produce parts with lower cost, manufacture with uncompromising surface quality and enhanced material removal rate. Moreover, as intermediate factors of interest, the influences of sliding distance, tool life and tool flank wear on the overall machining performance are evaluated. The multi-objective optimization by Grey Relational Analysis (GRA) revealed that for improved product performance and fast manufacturing (case 1) optimum results are: feed per tooth fz = 0.25 mm/tooth, cutting speed vc = 392.6 m/min and cutting length l = 0.5 mm; for resource conservation (case 2) the optimum results are: feed per tooth fz = 0.125 mm/tooth, cutting speed vc = 392.6 m/min, cutting length l = 0.5 mm.