In response to escalating environmental concerns and the surge in eco-friendly sports activities, this study investigates the determinants of sustained intention to participate in plogging, a prac-tice that combines jogging and litter collection. The study seeks to discern the effects of autonomy, competence, and relatedness experiences, as suggested by Self-Determination Theory, on the intent to persist in plogging. Additionally, the study probes the moderating role of eco-friendly attitudes, thereby propelling the confluence of sports and environmental research. The research methodology encompassed surveying plogging participants and analyzing their responses. The validation of these relationships was executed using multi-group structural equation modeling. The results revealed that autonomy and competence did not significantly sway extrinsic motivation, but relatedness surfaced as the most influential factor. This suggests that plogging, primarily viewed as a volunteering activity, serves more as a prosocial behavior that bolsters relationships rather than a means to enhance physical competence. The study also found that the value participants derived from plogging and their intention to continue varied based on their eco-friendly attitudes. The study concludes that voluntary participation and socializing are the core values of plogging, and understanding these values can foster healthier and more sustainable behaviors. The findings offer valuable insights for academic, practical, and policy perspectives in promoting eco-friendly sports activities.

In the study, it was aimed to develop a valid and reliable scale in order to determine the self-efficacy perceptions of classroom teachers in the field of mathematics learning difficulties. In the development process of the scale, the basic resources and the scale development studies in the literature were examined. First, a pool of substances was created. Content validity was ensured by taking the opinions of 21 expert participants in the 24 question areas determined in the item pool. Content validity index was used in content validity. In this analysis, two questions were removed from the scale. Preliminary application was made with 44 participating teachers who are experts in their fields and exploratory factor analysis was started. For exploratory factor analysis, 359 people consisting of classroom teachers in Sakarya province were studied. In the explanatory factor analysis, 5 items were removed from the scale by looking at the factor load. The factor loadings for the 17 items in the scale vary between 0.508 and 0.976. Among the comparative fit indices obtained as a result of confirmatory factor analysis, RMSEA=0.067 and Cmin/DF=1.568 were calculated. The Cronbach alpha reliability coefficient of the measurements is 0.970. The data obtained were analyzed with statistical methods using SPSS 25, AMOS 24 and Microsoft Excel programs. The validity and reliability criteria of the scale developed within the scope of the study were met.

Research for novel vaccine adjuvants remains a critical need to improve the immune responses to a recombinant vaccine antigen. Emulsion adjuvants such as AS03 and MF59, particularly in the area of Influenza vaccines, have shown antigen dose sparing and allowed reduced immunizations. It was previously demonstrated that these emulsion adjuvants can be formulated using a simpler, low shear process of self-emulsification. The role of alpha tocopherol as an immunomodulator in emulsion adjuvants is evident from the success of AS03 in the on-going covid-19 pandemic. Although, it was a challenge to formulate alpha tocopherol with a low shear process to get closer to the AS03 composition, the self-emulsified version referred as self-emulsifying adjuvant systems (SE-AS) showed comparable immune responses to AS03 when co-administered with a Quadrivalent influenza virus (QIV) vaccine. In this paper, we first optimized the SE-AS with alpha tocopherol referred to as SE-AS44 to allow sterile filtration. We compared the in vitro cytokine profile with self-emulsifying adjuvant 160 (SEA160), a squalene-only self-emulsified adjuvant with composition similar to MF59. We compared SE-AS44 and SEA160 co-administered with a recombinant cytomegalovirus (CMV) pentamer antigen, which is a less immunogenic antigen, in vivo to compare the antibody and T-cell responses, in different adjuvanted groups, in C57BL/6 mice.

Self-healing is the capability of materials to repair themselves after damage has occurred, usually by interaction between molecules or chains. Physical and chemical processes are applied for the preparation of self-healing systems. There are different approaches for these systems such as heterogeneous systems, shape memory effects, hydrogen bonding or covalent-bond interaction, diffusion and flow dynamics. Self-healing mechanisms can occur in particular by heat and light exposure or by reconnection without direct effect. The applications of these systems display an increasing trend in both R&D and industry sectors. Moreover, self-healing systems and their energy storage applications are currently getting great importance. This review aims to provide general information on recent developments in self-healing materials and their energy applications in view of the critical importance of self-healing systems for lithium-ion batteries (LIBs). In the first part of the review, an introduction about self-healing mechanisms and design strategies of self-healing materials is given. Then, selected important healing materials in the literature for the anodes of LIBs are mentioned in the second part. The results and future perspectives are stated in the conclusion section.

Background: HIV self-testing (HIVST) is one of the recommended approaches for HIV testing services, particularly for helping reach populations who would not normally access facility-based HIV testing. HIVST must be tailored to different populations to ensure uptake. Objective: The main objective of this study was to develop an acceptable HIVST delivery strategy to help improve urban men’s engagement with HIV services. Methods: We invited key stakeholders for urban men’s HIV services to participate in a co-creation workshop aimed at developing HIVST delivery approaches for urban men, using eThekwini municipality as a study setting. We conducted purposive sampling to include health care users and health care providers, representing a range of views across the public sector and voluntary sector. We employed the Nominal Group Technique (NGT) method for data collection. The NGT workshop was conducted in two consecutive phases: phase one was focused on determining barriers for men’s engagement with the current/facility-based HIV testing services; phase two was aimed at determining HIVST delivery strategies. We used the results of the NGT to design a tailored HIVST strategy for urban men in eThekwini District. Results: Participants identified the following psychological factors as the most important barriers to uptake of HIV testing services by urban men: stigma, ignorance about the importance of testing and testing process as well as fear of positive test results. Key stakeholders suggested internal motivation strategies as a potentially effective approach to support HIVST delivery strategy. Guided by the NGT results, we designed a HIVST delivery strategy that is supported by a risk communication approach Conclusion: We designed an evidence-based risk communication mobile health (mHealth) strategy coupled with SARS COV-2 self-testing tailored to improve men’s uptake of HIVST. A follow-up study to evaluate the feasibility of implementing these approaches is recommended.

Our brains process information using a layered hierarchical network architecture, with abundant connections within each layer and sparse long-range connections between layers. As these long-range connections are mostly unchanged after development, each layer has to locally self-organize in response to new inputs to enable information routing between the sparse in- and output connections. Here we demonstrate that this can be achieved by a well-established model of cortical self-organization based on a well-orchestrated interplay between several plasticity processes. After this self-organization, stimuli conveyed by sparse inputs can be rapidly read out from a layer using only very few long-range connections. To achieve this information routing, the neurons that are stimulated form feed-forward projections into the unstimulated parts of the same layer and get more neurons to represent the stimulus. Hereby, the plasticity processes ensure that each neuron only receives projections from and responds to only one stimulus such that the network is partitioned into parts with different preferred stimuli. Along this line, we show that the relation between the network activity and connectivity self-organizes to a biologically plausible regime. Finally, we argue how the emerging connectivity may minimize the metabolic cost for maintaining a network structure under the above described constraints.

This proposal describes the design and development of an interoperable application that supports green open access with long-term sustainability and improved user experience of article deposit. Introduction: The lack of library resources and unfriendly repository user interface are two significant barriers that hinder green open access. Tasked to implement the open access mandate, librarians at an American research university developed a comprehensive system called Easy Deposit 2 to automate the support workflow of green open access. Implementation: Easy Deposit 2 is a web application that is able to harvest newly publications, outreach for manuscript on behalf of the library, and facilitate self-archiving to IR. It is developed and maintained by the library and integrated with the IR. Results and Discussion: The article deposit rate is about 25% with Easy Deposit 2, which increases significantly comparing to the previous period. It also serves as a local database for faculty publications with open access status. The lesson learned is that library cannot rely on a single commercial provider for publication data due to mismatched priorities. Conclusion: Recent IT developments provides new opportunities of innovation like Easy Deposit 2 in supporting open access. Academic librarians are vital in promoting "openness" in scholarly communication such as transparency and diversity in the sharing of publication data.

The study aimed to understand the experiences of adolescent smokers who participated in a smoking cessation program based on self-reflection, and to develop the theoretical basis for constructing similar programs. The participants, ten students from middle and high schools located in cities G and S, were interviewed after the program ended. Data were collected from August to November 2019 and analyzed using a phenomenological approach to understand participant experiences in depth. The analysis revealed five major themes: ‘Uniqueness of the Program,’ ‘Perception of Smoking Cessation,’ ‘Positive Reflection on Life,’ ‘Understanding Others,’ and ‘A Search for Hope and Vision in Life.’ The findings revealed that the smoking cessation program facilitated the development of self-identity and vision, which may indirectly strengthen the motivation for adolescent smokers to quit smoking. These findings suggest the need for a life-changing smoking cessation program that enhances self-concept and self-esteem. Moreover, it highlights the importance of follow-up research to ensure effectiveness, and the need to develop programs with creative content.

While some researchers have suggested that the self-employment (SE) sector is a haven during a financial Crisis, others believe that SE is not necessarily the desired outcome, but an indicator that the labor market is tightening for some groups. Few researchers have compared the SE sector before and after the occurrence of a significant financial Crisis, especially in developed countries. This paper analyzes the determinants of entry into self-employment during the 2008 Spanish Crisis. Using data from the Encuesta de Presupuesto Familiar (EPF), results show that although the rate of SE did not experience a significant change during this time the Crisis affected people differently based on gender, with being females more affected than males. Results also suggest differences between Comunidades Autonomas in how the self-employment sector behaved during the Crisis.

Due to limitation of the binding energy of a self-gravitating matter, the radius of a body is at least twice larger than the Schwarzschild radius. The total energy is adsorbed at the body surface, giving rise of a size-dependent surface tension. Since the Hawking temperature appears to be the critical one, the black holes possess zero surface tension. Microscopic neutrino stars are also introduced.

This study aimed to know the conditioning factors of the transition process to the self-care of women diagnosed with HIV/AIDS. This qualitative study was carried out from June to September 2015 with seven seropositive women, users of a specialized service in sexually transmitted diseases in the municipality of Imperatriz, Maranhão State, Brazil. For the data collection, an individual interview was used, and data analysis was performed by content analysis delineated by Hsieh and Shannon (2005). The resources that influence the self-care in the transition process of women with HIV/AIDS are represented by personal conditioning factors, such as the meaning they attribute to the living with the disease, personal attitudes and cultural beliefs, socioeconomic status, preparation and knowledge about the disease, and by conditioning factors found in the community and society. The transition theory can provide important insights about the resources present in the adaptation process of women diagnosed with HIV so that they can perform their self-care satisfactorily.

To better understand the shape memory materials and self-healing materials, a new series of liquid-crystalline shape memory polyurethane (LC-SMPU) composites, named SMPU-OOBAm, were successfully prepared by incorporating 4-octyldecyloxybenzoic acid (OOBA) into the PEG-based SMPU. The effect of OOBA on the structure, morphology and properties has been carefully investigated. The results demonstrate that SMPU-OOBAm have liquid crystalline properties, triple-shape memory properties and self-healing properties. The incorporated OOBA promotes the crystallizability of both soft and hard segments of SMPU, and the crystallization rate of the hard segment of SMPU decreases when the OOBA-content increases. Additionally, the SMPU-OOBAm forms a two-phase separated structure (SMPU phase and OOBA phase), and it shows two-step modulus changes upon heating. Therefore, the SMPU-OOBAm shows triple-shape memory behavior, and the shape recovery ratio decreases with an increase in the OOBA content. Finally, SMPU-OOBAm shows self-healing properties. The new mechanism can be ascribed to the heating-induced “bleeding” of OOBA in the liquid crystalline state and the subsequent re-crystallization upon cooling. This successful combination of liquid crystalline properties, triple-shape memory properties and self-healing properties make the SMPU-OOBAm composites with many promise applications in smart optical devices, smart electronic devices and smart sensors.

Two polyurethanes (PUs) were similarly synthesized by reacting a cycloaliphatic isocyanate with 1,4 butanediol and two polyols of different nature (polyester, polycarbonate diol) with molecular weights of 1000 Da. The PU made with polycarbonate diol polyol (YCD) was the only showing intrinsic self-healing at 20 °C. For assessing the mechanism of intrinsic self-healing of YCD, a structural characterization by molecular weights determination, infrared and X-ray photoelectronic spectroscopies, differential scanning calorimetry, X-ray diffraction, thermal gravimetric analysis and dynamic mechanical thermal analysis was carried out. It was concluded that the self-healing at 20 °C of YCD was due to non-covalent dynamic exchange interactions among the carbonate groups of the soft segments. Therefore, the chemical nature of the polyol played a key role in developing PUs with intrinsic self-healing at 20 °C.

This work is the first part of a series of papers on a comprehensive analysis of the processes of prebiotic self-organization and protophotosynthesis on the surface of semiconductor minerals and in systems of natural dispersed semiconductors. Comprehensive analysis within the framework of the "semiconductor world" concept allows integrating a variety of models on a single physical basis - from ZnS world and FeS world (based on inorganic semiconductors) to the PAH world and aromatic world (including organic semiconductors). Thus, we do not put forward a new alternative hypothesis of chemical evolution - a new "chemical world", but only integrate the evolutionary and geochemical criteria of different "chemical worlds" into a single "physical world", which gives one the opportunity to reconstruct and predict the directions of chemical evolution according to the uniform principles of physical chemistry. In the first part of this series we consider photoinduced self-organization and "photo-controlled" evolution of the early protobiological systems performing solar energy conversion on the surface of dispersed semiconductor minerals capable of (photo)catalyzing and photosensitizing prebiological processes. The latter include a transition from the elementary cycles of photocatalytic reactions on the surface of semiconductors to protophotometabolic cycles and protophotosynthesis, from photophysical processes on the surface of mineral semiconductors to photoinduced membrane potentials, from photocontrolled sorption on the surface of such minerals to the formation of photosensitive protomembranes and selection of photosynthetic structures. The evolutionary approach to the analysis of protobiological mechanisms and protobioenergetics within the framework of the "semiconductor world" concept provides a new approach to the study of the last common point of divergence of protobiological systems, where the emergence principles of different energy supply schemes (like the energy source-specific photoautotrophy and substrate-specific chemoautotrophy) merge at the physicochemical level. The proposed integrating scheme is consistent with most biological, geological, and physicochemical concepts, which ensures its complete cross-checking and internal consistency.

In 2015, the Self-Action Leadership Theory—a qualitative, nomological expansion of self-leadership theory rooted in atmospheric and astronomical metaphor aimed at expanding the personal freedom of individuals, organizations, and nations by bolstering the existential growth of individuals through a series of Maslow-esque stages of holistic, personal development. This article introduces an accompanying, practitioner-based Model of Self-Action Leadership (SAL) aimed at the implicit enhancement of a holistic range of administrative processes through explicit training, mentoring, and coaching in the model’s general and universally-applicable principles and practices. The SAL model produces an original construct of personal leadership practice that builds upon the extant self-leadership academic canon, which dates back to 1983 (Manz, 1983). It also provides an analogue to four of the five core processes of Project Management by positioning a self-action leader (an individual) as the ongoing “project” at hand. The SAL Model is rooted in action research and was developed through a variety of self-oriented, action research projects in conjunction with a comprehensive, qualitative, analytical autoethnographic study of a scholar’s life experiences.

Text CAPTCHAs are crucial security measures employed on global websites to deter unauthorized intrusions. The presence of anti-attack features incorporated into text CAPTCHAs limits the effectiveness of breaking them, despite CAPTCHA recognition being an effective method for assessing their security. This study introduces a novel color augmentation technique called Variational Color Shift (VCS) to boost the recognition accuracy of different networks. VCS generates a color shift range of every input image and then resamples the image within that range to generate a new image, thus expanding the number of samples of the original dataset to improve training effectiveness. In contrast to Random Color Shift (RCS) which treats the color offsets as hyperparameters, VCS uses the estimated offsets to reparametrize the points sampling from the unit uniform distribution to reconstruct new image pixels, which makes the offsets learnable. To reduce the computational effort of VCS, we also propose two variants of VCS: Sim-VCS and Dilated-VCS. In addition, to solve the overfitting problem caused by many disturbances in CAPTCHAs, we propose an Auto Encoder (AE) based on a Large Separable Kernel Attention (AE-LSKA) to replace the convolutional module in the text CAPTCHA recognizer. This new module employs an AE) to compress the interference while expanding the receptive field using Large Separable Kernel Attention (LSKA), which reduces the impact of local interference on the model training and improves the overall perception of characters. The experimental results show that the recognition rate of the model after integrating the AE-LSKA module is improved by at least 15 percentage points on both M-CAPTCHA and P-CAPTCHA datasets. In addition, experimental results demonstrate that color augmentation using VCS is more effective in enhancing recognition.

Despite various interventions in road safety work, fatal and severe road traffic accidents (RTAs) remain a significant challenge leading to human suffering and economic costs. Understanding the multicausal nature of RTAs, where multiple conditions and factors interact, is crucial for developing effective prevention measures in road safety work. This study investigates the multivariate statistical analysis of co-occurring conditions in RTAs, focusing on single-vehicle accidents with single occupancy and personal injury on Austrian roads outside built-up areas from 2012 to 2019. The aim is to detect recurring combinations of accident-related variables, referred to as blackpatterns (BPs), using the Austrian RTA database. The study proposes Fisher’s exact test to estimate the relationship between an accident-related variable and fatal and severe RTAs (severe casualties). In terms of pattern recognition, the study develops the maximum combination value (MCV) of accident-related variables, a procedure to search through all possible combinations of variables to find the one that has the highest frequency. The accident investigation proceeds with the application of pattern recognition methods, including binomial logistic regression and a newly developed method, the PATTERMAX-method, created to accurately detect and analyse variable-specific BPs in RTA data. Findings indicate significant BPs contributing to severe accidents. The combination of binomial logistic regression and the PATTERMAX-method appears to be a promising approach to investigate severe accidents, providing both insights into detailed variable combinations and their impact on accident severity.

The sleeping and grazing behaviors of horses play a crucial role in their health and welfare. In the horse industry, with the prevalent trend of individual single-stall farming in stable, accurately identifying various behaviors of horses within stables is imperative. This study has established a spatio-temporal dataset for horse postures and behaviors, proposed an algorithm utilizing a SE-SlowFast network with an enhanced loss function. This algorithm achieves automatic recognition of multiple postures and behaviors of horse simultaneously. The model developed in this study demonstrates high accuracy in identifying three postures within the stable: Standing, Crouching, and Lying, achieving respective accuracies of 92.73%, 91.87%, and 92.58%. Additionally, it accurately recognizes two behaviors: Sleeping and Grazing, achieving best accuracies of 93.56% and 98.77%. The overall best accuracy of the model is 93.9%. Experiments on video recognition confirm that the suggested model recognizes horse video data accurately. This study marks the first time computer vision technology has been used in the horse farming industry, achieving non-intrusive recognition of multiple horse postures and behaviors. This study provides valuable data support for livestock managers to evaluate the health conditions of horses, enhances the welfare of horses in stables, and contributes to the advancement of modern horse husbandry practices.

This paper proposes a novel facial expression recognition network called the Lightweight Facial Network with Spatial Bias (LFNSB). The LFNSB model balances model complexity and recognition accuracy. It has two key components: a lightweight feature extraction network (LFN) and a Spatial Bias (SB) module for aggregating global information. The LFN introduces combined channel operations and depthwise convolution techniques, effectively reducing the number of parameters while enhancing feature representation capability. The Spatial Bias module enables the model to focus on local facial features while also capturing the dependencies between different facial regions. Additionally, a novel loss function called Cosine-Harmony Loss is designed. This function optimizes the relative positions of feature vectors in high-dimensional space, resulting in better feature separation and clustering. Experimental results on the AffectNet and RAF-DB datasets show that the proposed LFNSB model performs excellently in facial expression recognition tasks. It achieves high recognition accuracy while significantly reducing the number of parameters, thus substantially lowering model complexity.

Machine Learning (ML) has revolutionized healthcare by enhancing diagnostic capabilities because of its ability to analyze large datasets and detect minor patterns often overlooked by humans. This is beneficial, especially in pain recognition, where patient communication may be limited. However, ML models often face challenges such as memorization and sensitivity to adversarial examples. Regularization techniques like mixup, which trains models on convex combinations of data pairs, address these issues by enhancing model generalization. While mixup has proven effective in image, speech, and text datasets, its application to time-series signals like electrodermal activity (EDA) is less explored. This research uses ML for pain recognition with EDA signals from the BioVid Heat Pain Database to distinguish pain by applying mixup regularization to manually extracted EDA features and using a Support Vector Machine (SVM) for classification. Results showed that this approach achieved an average accuracy of 75.87% using leave-one-subject-out cross-validation (LOSOCV) compared to 74.61% without mixup. This demonstrates the mixup’s efficacy in improving ML model accuracy for pain recognition from EDA signals. This study highlights the potential of mixup in ML as a promising approach to enhance pain assessment in healthcare.

In genomics analysis, with the development of sequencing technology and the rapid growth of data volume, how to effectively identify and analyze important information in genomic data from massive data has become an important challenge. Identify key patterns and features in genomic data through advanced community testing methods. First, genomic data often contains a lot of noise and redundant information that needs to be processed through steps such as normalization, filtering, and dimensionality reduction. Normalization makes data at different scales comparable, filtering out data points with low quality or missing values, and dimensionality reduction reduces data dimensions through principal component analysis (PCA) and retains the main information. The gene co-expression network establishes the connection relationship between genes by calculating the expression similarity matrix between genes, and applies the Louvain algorithm to maximize the modularity of the network, aggregate nodes layer by layer, form a community structure, and identify the complex relationship and potential structure between genes. Finally, we utilize the cancer dataset to evaluate the proposed model. From our extensive experimental results, we can observe that Louvain's algorithm indicates outperformances and accuracy in the recognition and analysis of genomic data.

In this study, we investigate how dataset composition influences the performance and bias of age estimation models across different ethnic groups. We utilized pre-trained Convolutional Neural Networks (CNNs) such as VGG19, fine-tuning them on two datasets: UTKFace and APPA-REAL. UTKFace comprises 23,705 samples (12,391 males, 11,314 females) including 10,078 White, 4,526 Black, and 3,434 Asian individuals. APPA-REAL consists of 7,591 samples (3,818 males, 3,773 females) with 6,686 White, 231 Black, and 674 Asian individuals. We adjusted dataset composition by oversampling minority groups and reducing samples from the majority group to evaluate effects on model performance, measured using Mean Absolute Error (MAE) and standard deviation. Our findings demonstrate that oversampling alone does not guarantee equitable performance across ethnicities; reducing samples from the majority group often resulted in more balanced performance, evidenced by lower MAE standard deviations. These results underscore the importance of a nuanced approach to achieve fairness, including combining various sampling techniques. This study emphasizes the critical need for tailored dataset compositions to mitigate bias and suggests exploring more refined data processing methods and algorithmic adjustments for enhanced model fairness and accuracy in facial recognition technologies.

Named Entity Recognition (NER) serves as a fundamental and pivotal stage in the development of various knowledge-based support systems, including knowledge retrieval and question answering systems. In the domain of pig diseases, Chinese NER models encounter several challenges such as the scarcity of annotated data, domain-specific vocabulary, diverse entity categories, and ambiguous entity boundaries.To address these challenges, a corpus, labeled datasets and a lexicon specific to pig diseases for Chinese named entity recognition were constructed. Subsequently, a Pig Disease Chinese Named Entity Recognition (PDCNER) model was proposed. The model integrates external lexicon knowledge of pig disease by employing Lexicon-enhanced BERT and enhance feature representation by incorporating contrastive learning. Experimental results show that the model achieved the best recognition results, with a precision of 86.92%, a recall of 85.08%, and an F1-score of 85.99% respectively. Furthermore, the model exhibits robustness and generalizability across few-shot and publicly available datasets. Experimental results illustrate the proposed model could effectively identify Chinese named entities of pig diseases, outperforming several existing baseline methods. Moreover, the proposed model can be extended to other animal disease domains, such as chicken and cattle, thereby facilitating seamless adaptation for named entity identification across diverse contexts.

Lysine plays a crucial role in promoting development, enhancing immune function, and improving the function of central nervous system tissues. The two configurational isomers of amino acids have significantly different effects. Currently, methods for chiral recognition of lysine all have drawbacks such as expensive equipment and complicated detection processes, whereas fluorescence analysis boasts high sensitivity, strong selectivity, and simple operation. In this study, we synthesized four novel Binaphthyl-Amine (BINAM)-based fluorescent probes capable of specifically identifying the L-configuration of lysine among the twenty amino acids constituting human proteins. The enantiomeric fluorescence enhancement ratio (ef or ΔIL/ΔID) reached up to 15.29, demonstrating high enantioselectivity. We also evaluated the probe's recognition ability under different pH, reaction times, and metal ion conditions, as well as the Limit of Detection of the probe. Our findings indicate that this probe is a highly stable tool for detecting chiral lysine.

Caring for dolphins is a delicate process that requires experienced caretakers to pay close attention to their behavioral characteristics. However, caretakers may sometimes lack experience or not be able to give their full attention, which can lead to misjudgment or oversight. To address these issues, a dolphin behavior analysis system has been designed to assist caretakers in making accurate assessments. The study utilized image preprocessing techniques to reduce sunlight reflection in the pool and enhance the outline of dolphins, making it easier to analyze their movements. The dolphins were divided into 11 keypoints using an open-source tool called DeepLabCut, which helped accurately mark various body parts for skeletal detection. The AquaAI Dolphin Decoder (ADD) was then used to analyze six dolphin behaviors. To improve behavior recognition accuracy, the Long Short-Term Memory (LSTM) neural network was introduced. The ADD and LSTM models were integrated to form the ADD-LSTM system. Several classification models, including unidirectional and bidirectional LSTM, GRU, and SVM, were compared. The results showed that the ADD module combined with a double-layer bidirectional LSTM method achieved high accuracy in dolphin behavior analysis. The accuracy rates for each behavior were over 90%.

Scientific research in the study of Human-Robot Interactions (HRI) has advanced significantly in recent years. Social humanoid robots have undergone severe testing and have been implemented in a variety of settings, including educational institutions, healthcare facilities and senior care centers. Humanoid robots have also been evaluated across diverse population groups. However, the research on different children-groups is still scarce, especially deaf children. This feasibility study includes both hearing children and deaf children, and aim to assess the interaction between children and a humanoid robot without the use of sounds and voices. In addition, an experiment was conducted where the children were asked to watch a video and then assess whether the NAO humanoid robot responded to the emotions shown in the video in a congruent or incongruent way. The findings indicated that there was no difference between the children who were present-ed with congruent emotions in the NAO as opposed to the children who were presented with in-congruent emotions in the robot. Only the ability to predict emotion in videos and gender were significant predictors of guessing the correct emotion in robot. Although no significantly differ-ence was found between hearing and deaf children, this feasibility study is intended to lay the groundwork for future research in this unexplored field of study.

RFID based technology innovated a new field of wireless sensing, which has been applied in posture recognition, object localization and the other sensing fields, the existing sensing methods often require tags to be attached to dynamic targets (because RFID signals are more sensitive to moving target), significantly limiting their applications. It is a challenging task to sensing a static target without tag attached in NLOS (Non-Line-of-Sight). We utilized RFID technologies to sense the static foreign objects in agricultural products, and taken metal, rock, rubber and clod as sensing targets that are common in agricultural. By deploying a tag matrices to create a sensing region, we observed the signal variations before and after the appearance of the targets in this sensing region, determined the targets’ positions and their types. Here, we buried the targets in the media of seedless cotton and wheat, and detected static targets with a non-contact method in the NLOS. Researches illustrated that by deploying appropriate tag matrices and adjusting the angle of a single RFID antenna, the matrices signals can be sensitive to the static targets’ positions and their properties,i.e. matrices signals vary with different targets and their positions. Specifically, we achieved a 100% success rate in locating metallic targets, while the success rate for clods was the lowest at 86%. We achieved a 100% recognition rate for the types of all the four objects.

Due to the recent trend of unmanned economy, retail stores have gradually reduced their service and cashier manpower. The retail product recognition becomes one of key issues for unmanned shopping. Although the success of deep neural networks has made object recognition feasible in a variety of applications, it still struggles to perform well on a large number of classes of retail products. In this paper, we propose an improved ensemble learning-based approach for retail product recognition. In the proposed approach, the object classification network is first improved through feature extraction and block attention. An ensemble model is then built by integrating multiple network models and using loss selection as model weights. In the experiments, the feasibility of our ensemble learning-based approach has been evaluated through many production items. The results demonstrate the effectiveness of the proposed approach compared with previous retail product recognition methods.

Machine learning (ML) systems that use sensors obtain observations from sensors and use them to recognize and interpret the current situation. These systems are susceptible to sensor-based adversarial example attacks (AEs); if some sensors are vulnerable and can be compromised by an attacker, the attacker can change the output of the system by changing the values of the sensors. The detection of compromised sensors is important to defend the system against sensor-based AEs, because we can check the sensors and replace them by detecting the sensors used by the attacker. In this paper, we propose a method to detect the sensors used in sensor-based AEs by utilizing the features of the attack that cannot be avoided. In this method, we introduced a model called the feature-removable model (FRM), which allows us to select the features used as inputs into the model. Our method detects the sensors used in sensor-based AEs by finding the inconsistencies between the outputs of the FRM obtained by changing the selected features. We evaluated our method using a human activity recognition model with sensors attached to the user’s chest, wrist, and ankle. We demonstrate that our method can accurately detect sensors used by the attacker and achieves an average Recall of Detection of 0.92, and the average Precision of Detection is 0.72.

An algorithm is proposed for discriminating the fatigue state of air traffic controllers based on ap-plying multispeech feature fusion using an FSVM to voice data, and for extracting eye-fatigue-state discrimination features based on PERCLOS eye data. For the speech algorithm and an eye-fatigue index, a new controller fatigue-state evaluation index based on the entropy weight method is proposed based on decision-level fusion of fatigue discrimination results for speech and the eyes. Experimental results show that the fatigue-state recognition accuracy rate was 84.81% for the fatigue state evaluation index, which was 3.36% and 1.86% higher than those for speech and eye assessments, respectively. The comprehensive fatigue evaluation index provides important reference values for controller scheduling and mental-state evaluations.

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

Sea cucumber detection represents a significate step in underwater environmental perception, which is an indispensable part of the intelligent subsea fishing system. However, various complex factors such as water turbidity declines the clarity of underwater images, presenting a challenge to vision-based underwater target detection. Therefore, accurate, real-time and lightweight detection models are required. First of all, the development of subsea target detection is summarized in this presented work. Besides, since target detection methods based on deep learning such as YOLOv5 and DETR, which are respectively examples of one-stage and anchor-free deep leaning methods, have been increasingly applied in underwater detection scenarios. Based on the analysis of state-of-the-art underwater sea cucumber detection approaches and aiming to provide a reference for practical subsea identification, the sea cucumber detection based on the YOLOv5 and DETR are investigated and compared in detail. For each approach, the detection experiment is carried out on the derived dataset which contains a wide variety of sea cucumber sample images. The compared experiments demonstrate that the overall outperformance of YOLOv5 in terms of low computing consumption and high precision, particularly in detection of small and dense features. Nevertheless, the DETR exhibits rapid development and holds promising prospects in underwater object detection applications, owing to its relatively simple architecture and ingenious attention mechanism.

Issues of fairness and consistency in Taekwondo poomsae evaluation have emerged owing to the lack of an objective evaluation method. This study proposes a three-dimensional (3D) convolutional neural network (CNN)-based action recognition model for the objective evaluation of Taekwondo poomsae. The model exhibits robust recognition performance regardless of variation in perspective by reducing the discrepancies between training and test images. The model uses 3D skeletons of the poomsae unit action collected using a full-body motion-capture suit to generate synthesized two-dimensional (2D) skeletons from the desired perspective. This approach aids in obtaining 2D skeletons from diverse perspectives as part of the training dataset and ensures consistent recognition performance regardless of the viewpoint. The model was trained using 2D skeletons projected from diverse viewpoints, and its performance was evaluated using various test datasets, including projected 2D skeletons and RGB images captured from various viewpoints. Comparison of the performance of the proposed model with that of previously reported action recognition models demonstrated the superiority of the model, underscoring its effectiveness in recognizing and classifying Taekwondo poomsae actions.

Identification of plants is a challenging task which aims to identify the family, genus, and species level according to morphological features. Automated deep learning-based computer vision algorithms are widely used for identifying plants and can help users to narrow down the possibilities. However, numerous morphological similarities between and within species make the classification difficult. In this paper, we tested a custom convolution neural network (CNN) and vision transformer (ViT) based models using the PyTorch framework to classify plants. We used a large dataset of 88K and 16K images for classifying plants at genus and species levels respectively. Our results show that for classifying plants at the genus level, ViT models perform better compared to CNN-based models ResNet50 and ResNet-RS-420, and other state-of-the-art CNN-based models suggested in previous studies on a similar dataset. The ViT model achieved top accuracy of 83.3% for classifying plants at the genus level. ViT models also perform better for classifying plants at the species level compared to CNN-based models ResNet50 and ResNet-RS-420, with a top accuracy of 92.5%. We show that the correct set of augmentation techniques plays an important role in classification success.

To achieve an acceptable level of security on the web, the Completely Automatic Public Turing test to tell Computer and Human Apart (CAPTCHA) was introduced as a tool to prevent bots from doing destructive actions such as downloading or signing up. Mobile devices have small screens, and therefore, using the common CAPTCHA methods (e.g. text CAPTCHAs) in these devices raises usability issues. To introduce a reliable, secure, and usable CAPTCHA that is suitable for mobile devices, this paper introduces a hand gesture recognition CAPTCHA based on applying Genetic Algorithm (GA) principles on Multi-Layer Perceptron (MLP). The proposed method improves the performance of MLP-based hand gesture recognition. It has been trained and evaluated on 2201 videos of the IPN Hand dataset, and MSE and RMSE benchmarks report the index values of 0.0018 and 0.0424, respectively. Comparison with the related works shows a minimum of 1.79% fewer errors, and experiments produced a sensitivity of 93.42% and accuracy of 92.27% – 10.25% and 6.65% improvement compared to the MLP implementation.

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

Target recognition mainly includes three approaches: optical image-based, echo detection-based, and passive signal analysis-based methods. Among them, the passive signal-based method is closely integrated with practical applications due to its strong environmental adaptability. Based on passive radar signal analysis, we design an "end-to-end" model that cascades a noise estimation network with a recognition network to identify working modes in noise environment. The noise estimation network is implemented based on U-Net, which adopts a method of feature extraction and reconstruction to adaptively estimate the noise mapping level of the sample, which can help the recognition network to reduce noise interference. Focusing on the characteristics of radar signal, the recognition network is realized based on Multi-Scale Convolutional Attention Network (MSCANet). Firstly, the deep group convolution is used to isolate the channel interaction in the shallow network. Then, through the multi-scale convolution module, finer-grained features of the signal are extracted without increasing the complexity of the model. Finally, the self-attention mechanism is used to suppress the influence of low-correlation and negative-correlation channels and spaces. This method overcomes the problem that the conventional method is seriously disturbed by noise. We validated the proposed method in 81 kinds of noise environments, achieving an average accuracy of 94.65%. Additionally, we discussed the performance of six machine learning algorithms and four deep learning algorithms. Compared to these methods, proposed MSCANet achieved an accuracy improvement of approximately 17%. Our method demonstrates better generalization and robustness.

Symbolic semantic understanding of staff images is an important part in music information retrieval. Due to the complex composition of staff symbols and the strong semantic correlation between symbol spaces, it is difficult to understand the pitch and duration of each note during performances. In this paper, we design a semantic understanding system for optical staff symbols. The system uses the YOLOv5 to implement optical staff’s low-level semantic understanding stage, which understands the pitch and duration in natural scales and other symbols that affect the pitch and duration. The proposed note encoding reconstruction algorithm is used to implement high-level semantic understanding stage. Such algorithm understands the logical, spatial, and temporal relationships between natural scales and other symbols based on music theory, and outputs digital codes for the pitch and duration of main notes during performances. The model is trained with a self-constructed SUSN dataset. Experimental results of YOLOv5 show that the precision is 0.989 and the recall is 0.972. For the system, the error rate is 0.031 and the omission rate is 0.021. The paper concludes by analysing the causes of semantic understanding errors and offers recommendations for further research. The results of this paper provide a method for multimodal music artificial intelligence applications such as notation recognition through listening, intelligent score flipping and automatic performance.

Fine-grained image classification remains an ongoing challenge in the computer vision field, which is particularly intended to identify objects within sub-categories. It is a difficult task since there is a minimal and substantial intra-class variance. The current methods address the issue by first locating selective regions with Region Proposal Networks (RPN), object localization, or part localization, followed by implementing a CNN Network or SVM classifier to those selective regions. This approach, however, makes the process simple by implementing a single-stage end-to-end feature encoding with a localization method, which leads to improved feature representations of individual tokens/regions by integrating the transformer encoder blocks into the Yolov5 backbone structure. These Transformer Encoder Blocks, with their self-attention mechanism, effectively captured the global dependencies and enabled the model to learn relationships between distant regions. This improved the model ability to understand context and captured long-range spatial relationships in the image. We also replaced the Yolov5 detection heads with three transformer heads at the output for object recognition using the discriminative and informative features maps from transformer encoder blocks. We established the potential of the single stage detector for the fine-grained image recognition task, by achieving state of the art 93.4% accuracy, as well as outperforming the existing Yolov5 model. The effectiveness of our approach is assessed using the Stanford car dataset, which includes 16,185 images of 196 different classes of vehicles with significantly identical visual appearances.

In offshore drilling, accidents such as gas invasion, overflow, and kicks are inevitable, and they can escalate into blowouts and other catastrophic events that result in casualties and substantial economic losses. Therefore, maintaining drilling safety requires accurate monitoring of gas invasion and overflow. The majority of overflow monitoring methods currently utilized at drilling sites are threshold-based. However, monitoring parameters acquired during actual drilling operations frequently contain noise signals, making it difficult for threshold-based methods to strike a balance between improving accuracy and minimizing false positives. In this paper, Pattern Recognition-based Kick Detection (PRKD) is proposed as a novel method for diagnosing overflow in offshore drilling. This method utilizes the overflow evolution process by integrating multi-phase flow calculations, data filtering theory, pattern recognition theory, the Bayesian framework, and other theoretical models. The PRKD effectively detects and monitors gas intrusion and overflow based on single parameters by analyzing the shape and wave characteristics of the curves. The case analysis demonstrates that the proposed method for monitoring drilling overflow achieves high precision while maintaining a low false positive rate. By combining advanced computational techniques with pattern recognition algorithms, the PRKD improves the accuracy and reliability of kick detection, enabling proactive responses to potential risks, protecting the environment and human lives, and optimizing drilling operations.

In recent years, with the popularity of smart mobile devices, the interaction between devices and users, especially in the form of voice interaction, has become increasingly important. If smart devices can understand more users' emotional states through voice data, more customized services can be provided for users. This paper proposes a novel machine learning model for speech emotion recognition, which combines convolutional neural networks (CNN), long short-term memory neural networks (LSTM), and deep neural networks (DNN), called CLDNN. To make the designed system can recognize the audio signal closer to the human auditory system does, this article uses the Mel frequency cepstral coefficients (MFCCs) of audio data as the input of the machine learning model. First, the MFCCs of the voice signal is extracted as the input of the model, and the feature values of the data are calculated using several local feature learning blocks (LFLB) composed of one-dimensional CNN. Because the audio signals are time-series data, the feature values obtained from LFLBs then input into LSTM layer to enhance the learning on time-series level. Finally, fully connected layers are used for classification and prediction. Three databases RAVDESS, EMO-DB and IEMOCAP are used for the experiments in this paper. The experimental results show that the proposed method can improve the accuracy compared to other related researches in speech emotion recognition.

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

The growth in online child exploitation material is a significant challenge for European Law Enforcement Agencies (LEAs). One of the most important sources of such online information corresponds to audio material that needs to be analyzed to find evidence in a timely and practical manner. That is why LEAs require a next-generation AI-powered platform to process audio data from online sources. We propose the use of speech recognition and keyword spotting to transcribe audiovisual data and to detect the presence of keywords related to child abuse. The considered models are based on two of the most accurate neural-based architectures to date: Wav2vec2.0 and Whisper. The systems are tested under an extensive set of scenarios in different languages. Additionally, keeping in mind that obtaining data from LEAs is very sensitive, we explore the use of federated learning to have more robust systems for the addressed application, while maintaining the privacy of the data to LEAs. The considered models achieved a word error rate between 11% and 25%, depending on the language. In addition, the systems are able to recognize a set of spotted words with true positives rates between 82% and 98%, depending on the language. Finally, federated learning strategies show that they can maintain and even improve the performance of the systems when compared to centralized trained models. The proposed systems sit the basis for an AI-powered platform for automatic analysis of audio in the context of forensic applications within child abuse. The use of federated learning is also promising for the addressed scenario, where data privacy is an important issue to be managed.

The promised dataset was obtained from the daily Turkish words and phrases pronounced by various people in the videos posted on YouTube. The purpose of collecting the dataset is to provide detection of the spoken word by recognizing patterns or classifying lip movements with supervised, unsupervised, semi-supervised learning and machine learning algorithms. Most of the datasets related with lip reading consist of people recorded on camera with fixed backgrounds and the same conditions, but the dataset presented here consists of images compatible with machine learning models developed for real-life challenges. It contains a total of 2335 instances taken from TV series, movies, vlogs, and song clips on YouTube. The images in the dataset vary due to factors such as the way people say words, accent, speaking rate, gender and age. Furthermore, the instances in the dataset consist of videos with different angles, shadows, resolution, and brightness that are not created manually. The most important feature of our lip reading dataset is that we contribute to the non-synthetic Turkish dataset pool, which does not have wide dataset varieties. Machine learning studies can be carried out in many areas, such as the defense industry and social life, with this dataset.

The emerging advancements in separation and classification of various biological matters (e.g., living cells and proteins) using magnetic levitation (MagLev) technology have proven to be effective for improving disease diagnostics. MagLev technique has the capacity to detect and separate useful diagnostic biomarkers from biocomplex environments (e.g., blood and plasma), minimizing the unpleasant daunting task of sample preparations and labeling procedures. Here, we demonstrate the capability of this technique combined with image analysis and machine learning approaches for discriminating the various types of multiple sclerosis (MS) as an important model disease. To arrive at a systematic expert system, we combined robust statistical analysis with machine learning to (1) detect and remove outliers from the raw MagLev image datasets; then (2) process the images and output a low dimensional representation of massive data without losing the main statistical features; and finally (3) predict the MS clinical disease type (Relapsing-Remitting, Primary–Progressive, or Secondary–Progressive) using a classifier. This is expected to improve MS diagnostics since the current practices rely solely on clinical observation and central nervous system imaging, making management approaches are often reactional and inefficient. Thus, there is a need to identify the disease type early on. MagLev is expected to improve MS diagnostics, thereby aiding in prognosis and guiding adequate treatment choices before the patient exhibits signs of permanent neurological deficits.

The body of scientific literature continues to grow annually. Over 1.5 million abstracts of biomedical publications were added to the PubMed database in 2021. Therefore, developing cognitive systems that provide a specialized search for information in scientific publications based on subject area ontology and modern artificial intelligence methods is urgently needed. We previously developed a web-based information retrieval system, ANDDigest, designed to search and analyze information in the PubMed database using a customized domain ontology. This paper presents an improved ANDDigest version that uses fine-tuned PubMedBERT classifiers to enhance the quality of short name recognition for molecular-genetics entities in PubMed abstracts on eight biological object types: cell components, diseases, side effects, genes, proteins, pathways, drugs, and metabolites. This approach increased average short name recognition accuracy by 13%. The new ANDDigest version (01.2022) has a web interface and is freely available to users at https://anddigest.sysbio.ru/.

Data augmentation techniques recently gained more adoption in speech processing, including speech emotion recognition. Although more data tends to be more effective, there may be a trade-off in which more data will not provide a better model. This paper reports experiments on investigating the effects of data augmentation in speech emotion recognition. The investigation aims at finding the most useful type of data augmentation and the number of data augmentations for speech emotion recognition. The experiments are conducted on the Japanese Twitter-based emotional speech corpus. The results show that for speaker-independent data, two data augmentations with glottal source extraction and silence removal exhibited the best performance among others, even with more data augmentation techniques. For the text-independent data (including speaker and text-independent), more data augmentations tend to improve speech emotion recognition performances. The results highlight the trade-off between the number of data augmentation and the performance of speech emotion recognition showing the necessity to choose a proper data augmentation technique for a specific application.

Facial expressions play a key role in interpersonal communication when it comes to negotiating our emotions and intentions, as well as interpreting those of others. Research has shown that we can connect to other people better when we exhibit signs of empathy and facial mimicry. However, the relationship between empathy and facial mimicry is still debated. Among the factors contributing to the difference in results across existing studies is the use of different instruments for measuring both empathy and facial mimicry, as well as often ignoring the differences across various demographic groups. This study first looks at the differences in empathetic abilities of people across different demographic groups based on gender, ethnicity and age. The empathetic ability is measured based on the Empathy Quotient capturing a balanced representation of both emotional and cognitive empathy. Using statistical and machine learning methods, the study then investigates the correlation between the empathetic ability and facial mimicry of subjects in response to images portraying different emotions displayed on a computer screen. Unlike the existing studies measuring facial mimicry using electromyography, this study employs a technology detecting facial expressions based on video capture and deep learning. This choice was made in the context of increased online communication during and post the COVID-19 pandemic. The results of this study confirm the previously reported difference in the empathetic ability between females and males. However, no significant difference in the empathetic ability was found across different age and ethnic groups. Furthermore, no strong correlation was found between empathy and facial reactions to faces portraying different emotions shown on a computer screen. Overall, the results of this study can be used to inform the design of online communication technologies and tools for training empathy team leaders, educators, social, and health care providers.

As recommended in the ICH Guidelines (S5-R2 and S6-R1), and based on bioethical concerns, we chose bovine embryos (BE) to check the in vitro embryo development considering the use of different amounts of rHGAL-1 as supplementations of in vitro embryo culture (IVP) mediums. Based on procedures for commercial BE in vitro production, using oocytes aspirated from slaughterhouse ovaries, the rHGAL-1 supplementation performed in two experiments (#01 on the oocyte maturation - IVM medium supplemented and experiment #2 on culture step IVC, supplemented SOF medium). There were IVP commercial procedures done, with 3 IVP batches per experiment and distributed the oocytes in four groups of treatment (one control group and three different dosages of rHGAL-1 to supplement both IVM and SOF mediums, using (2, 20 and 40µg.mL-1 respectively). A total of 962 (experiment 1) and 1,213 (experiment 2) oocytes were aspirated and submitted to IVP procedure. There was no damage to in vitro bovine embryos growth, considering cleavage percentage (%CLE), blastocysts development on day 7 (BlD7, BxD7, BhD7), or hatching blastocysts maturation on day 8 (BhD8%), regardless of rHGAL-1 supplementation. The immunohistochemistry assay with D8 embryos cultivated with rHGAL-1 supplementation on the culture medium (SOF medium) could demonstrate the presence of exogenous GAL-1, distributed in mass cell and trophoblastic cells, and the profile observed is dependent of exogenous supplementation and it was more evident in hatched embryos. The findings reassure the use of a reasonable amount of rHGAL-1 for in vitro embryonic development and make using rHGAL-1 in assisted reproduction in humans more reliable and safer.

We present improvements in n-best rescoring of code-switched speech achieved by n-gram augmentation as well as optimised pretraining of long short-term memory (LSTM) language models with larger corpora of out-of-domain monolingual text. In addition, we consider the application of large pretrained transformer-based architectures. Our experimental evaluation is performed on an under-resourced corpus of code-switched speech comprising four bilingual code-switched sub-corpora, each containing a Bantu language (isiZulu, isiXhosa, Sesotho, or Setswana) and English. We find in our experiments that, by combining n-gram augmentation with the optimised pretraining strategy, speech recognition errors are reduced for each individual bilingual pair by 3.51% absolute on average over the four corpora. Importantly, we find that even speech recognition at language boundaries improves by 1.14% even though the additional data is monolingual. Utilising the augmented n-grams for lattice generation, we then contrast these improvements with those achieved after fine-tuning pretrained transformer-based models such as distilled GPT-2 and M-BERT. We find that, even though these language models have not been trained on any of our target languages, they can improve speech recognition performance even in zero-shot settings. After fine-tuning on in-domain data, these large architectures offer further improvements, achieving a 4.45% absolute decrease in overall speech recognition errors and a 3.52% improvement over language boundaries. Finally, a combination of the optimised LSTM and fine-tuned BERT models achieves a further gain of 0.47% absolute on average for three of the four language pairs compared to M-BERT. We conclude that the careful optimisation of the pretraining strategy used for neural network language models can offer worthwhile improvements in speech recognition accuracy even at language switches, and that much larger state-of-the-art architectures such as GPT-2 and M-BERT promise even further gains.

This paper describes the successful application of the Transformer model used in the natural language processing and vision tasks as a means of processing the time series of signals from gyroscope and accelerometer sensors for the classification of human activities. The Transformer model is based on deep neural networks with many layers which can generalize well on signals. All measured signals come from a smartphone placed in a waist bag. Activity prediction is sequence-to-sequence, each time step of the signal is assigned a designation of the performed activity. Emphasis is placed on attention mechanisms, which express individual dependencies between signal values within a time series. In comparison with another recent result, the recognition precision was improved from 89.67 percent to 99.2 percent. The transformer model should in the future be included among the top options in machine learning methods for human activity recognition.

The multi-modal data fusion method based on IA-net and CHMM technical proposed is designed to solve the problem that the incompleteness of target behavior information in complex family environment leads to the low accuracy of human behavior recognition.The two improved neural networks(STA-ResNet50、STA-GoogleNet)are combined with LSTM to form two IA-Nets respectively to extract RGB and skeleton modal behavior features in video. The two modal feature sequences are input CHMM to construct the probability fusion model of multi-modal behavior recognition.The experimental results show that the human behavior recognition model proposed in this paper has higher accuracy than the previous fusion methods on HMDB51 and UCF101 datasets. New contributions: attention mechanism is introduced to improve the efficiency of video target feature extraction and utilization. A skeleton based feature extraction framework is proposed, which can be used for human behavior recognition in complex environment. In the field of human behavior recognition, probability theory and neural network are cleverly combined and applied, which provides a new method for multi-modal information fusion.

The ability to efficiently recognize the emotions on others’ faces is something that most of us take for granted. Children with callous-unemotional (CU) traits and impulsivity/conduct problems (ICP), such as attention-deficit hyperactivity disorder, have been previously described as being “fear blind”. This is also associated with looking less at the eye regions of fearful faces, which are highly diagnostic. Previous attempts to intervene into emotion recognition strategies have not had lasting effects on participants’ fear recognition abilities. Here we present both (a) additional evidence that there is a two-part causal chain, from personality traits to face recognition strategies using the eyes, then from strategies to rates of recognizing fear in others; and (b) a pilot intervention that had persistent effects for weeks after the end of instruction. Further, the intervention led to more change in those with the highest CU traits. This both clarifies the specific mechanisms linking personality to emotion recognition and shows that the process is fundamentally malleable. It is possible that such training could promote empathy and reduce the rates of antisocial behavior in specific populations in the future.

With the exponential growth in today’s technology and its expanding areas of application it has become vital to incorporate it in education. One such application is Knowledge Discovery in Databases (KDD) which is a subset of data mining. KDD deals with extracting useful information and meaningful patterns from the database that were not known before. This study is a detailed application of KDD and focuses on analyzing why a particular set of students performed better than others and what factors influenced the results. The study is conducted on a dataset of 480 students and across 16 different features. The authors implemented 4 major classification techniques namely Logistic Regression, Decision Tree, Random Forest and XGB classifier. Obtaining the key features from the top performing ML algorithms that have a major impact on the performance of the student, the study takes these features as a baseline for further analysis. Further data analysis highlights patterns in the data. The study concludes that there are a lot of non-academic factors that influence the overall performance of a student and should be taken into consideration by universities and other relevant bodies.

Emotion recognition defined as identifying human emotion and is directly related to different fields such as human-computer interfaces, human emotional processing, irrational analysis, medical diagnostics, data-driven animation, human-robot communi- cation and many more. The purpose of this study is to propose a new facial emotional recognition model using convolutional neural network. Our proposed model, “ConvNet”, detects seven specific emotions from image data including anger, disgust, fear, happiness, neutrality, sadness, and surprise. This research focuses on the model’s training accuracy in a short number of epoch which the authors can develop a real-time schema that can easily fit the model and sense emotions. Furthermore, this work focuses on the mental or emotional stuff of a man or woman using the behavioral aspects. To complete the training of the CNN network model, we use the FER2013 databases, and we test the system’s success by identifying facial expressions in the real-time. ConvNet consists of four layers of convolution together with two fully connected layers. The experimental results show that the ConvNet is able to achieve 96% training accuracy which is much better than current existing models. ConvNet also achieved validation accuracy of 65% to 70% (considering different datasets used for experiments), resulting in a higher classification accuracy compared to other existing models. We also made all the materials publicly accessible for the research community at: https://github.com/Tanoy004/Emotion-recognition-through-CNN.

Retinopathy is a human eye disease that causes changes in retinal blood vessels that leads to bleed, leak fluid and vision impairment. Symptoms of retinopathy are blurred vision, changes in color perception, red spots, and eye pain. In this paper, a new methodology based on Convolutional Neural Networks (CNN) is developed and proposed to diagnose and give a decision about the presence of retinopathy. The CNN model is trained by different images of eyes that have retinopathy and those which do not have retinopathy. The performance of the proposed model is compared with the related methods of DREAM, KNN, GD-CNN and SVM. Experimental results show that the proposed CNN performs better.

Recognition of the human interaction on the unconstrained videos taken from cameras and remote sensing platforms like a drone is a challenging problem. This study presents a method to resolve issues of motion blur, poor quality of videos, occlusions, the difference in body structure or size, and high computation or memory requirement. This study contributes to the improvement of recognition of human interaction during disasters such as an earthquake and flood utilizing drone videos for rescue and emergency management. We used Support Vector Machine (SVM) to classify the high-level and stationary features obtained from Convolutional Neural Network (CNN) in key-frames from videos. We extracted conceptual features by employing CNN to recognize objects from first and last images from a video. The proposed method demonstrated the context of a scene, which is significant in determining the behaviour of human in the videos. In this method, we do not require person detection, tracking, and many instances of images. The proposed method was tested for the University of Central Florida (UCF Sports Action), Olympic Sports videos. These videos were taken from the ground platform. Besides, camera drone video was captured from Southwest Jiaotong University (SWJTU) Sports Centre and incorporated to test the developed method in this study. This study accomplished an acceptable performance with an accuracy of 90.42%, which has indicated improvement of more than 4.92% as compared to the existing methods.

Indoor scene recognition has great significance for intelligent applications such as mobile robots, location-based services (LBS) and so on. Wherever we are or whatever we do, we are under a specific scene. The human brain can easily discern a scene with a quick glance. However, for a machine to achieve this purpose, on one hand, it often requires plenty of well-annotated data which is time-consuming and labor-intensive. On the other hand, it is hard to learn effective visual representations due to large intra-category variation and inter-categories similarity of indoor scenes. To solve these problems, in this paper, we adopted an unsupervised visual representation learning method which can learn from unlabeled data with a Siamese Convolutional Neural Network (Siamese ConvNet) and graph-based constraints. Specifically, we first mined relationships between unlabeled samples with a graph structure. And then, these relationships can be used as supervision for representation learning with a Siamese network. In this method, firstly, a k-NN graph would be constructed by taking each image as a node in the graph and its k nearest neighbors are linked to form the edges. Then, with this graph, cycle consistency and geodesic distance would be considered as criteria for positive and negative pairs mining respectively. In other words, by detecting cycles in the graph, images with large differences but in the same cycle can be considered as same category (positive pairs). By computing geodesic distance instead of Euclidean distance from one node to another, two nodes with large geodesic distance can be regarded as in different categories (negative pairs). After that, visual representations of indoor scenes can be learned by a Siamese network in an unsupervised manner with the mined pairs as inputs. In order to evaluate the proposed method, we tested it on two scene-centric datasets, MIT67 and Places365. Experiments with different number of categories have been conducted to excavate the potential of proposed method. The results demonstrated that semantic visual representations for indoor scenes can be learned in this unsupervised manner. In addition, with the learned visual representations, indoor scene recognition models trained with the learned representations and a few of labeled samples can achieve competitive performance compared to the state-of-the-art approaches.

We describe a selective and sensitive fluorescence platform for the detection of trinitrophenol (TNP) based on competitive host–guest recognition between pyridine-functionalized pillar[6]arene (PCP6) and probe (acridine orange, AO) that used PCP6-functionalized reduced graphene (PCP6-rGO) as the receptor. TNP is an electron-deficient and negative molecule which is captured by PCP6 via electrostatic interactions and π-π interactions. Therefore, a selective and sensitive fluorescence sensor for TNP detection is developed. It has a low detection limit of 0.0035 μM (S/N=3) and a wider linear response of 0.01−5.0 and 5.0−125.0 for TNP. The sensing platform is also used to test TNP in two water and soil samples with satisfying results. This suggests that this approach has potential applications for the determination of TNP.

This paper serves a survey and empirical evaluation of the state-of-the-art in activity recognition methods using accelerometers. We examine research that has focused on the selection of activities, the features that are extracted from the accelerometer data, the segmentation of the time-series data, the locations of accelerometers, the selection and configuration trade-offs, the test/retest reliability, and the generalisation performance. Furthermore, we study these questions from an experimental platform and show, somewhat surprisingly, that many disparate experimental configurations yield comparable predictive performance on testing data. Our understanding of these results is that the experimental setup directly and indirectly defines a pathway for context to be delivered to the classifier, and that, in some settings, certain configurations are more optimal than alternatives. We conclude by identifying how the main results of this work can be used in practice, specifically in experimental configurations in challenging experimental conditions.

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.

Objectives: The aim of this study was to determine why and how adults with attention deficit hyperactivity disorder (ADHD) experience variable impairment and identify the processes and strategies adults with ADHD use to develop positive self-regulation skills. Methods: A mixed cohort of 11 participants (6 female and 5 male) from a university, an adult ADHD clinic and an ADHD support group in the UK were interviewed online between September 2021 and February 2022. Data were collected and analysed simultaneously, inspired by constructivist grounded theory methodology. Results: Participants described a “polar awareness of difference” from others in terms of engagement and ADHD characteristics, and a “polar awareness of consciousness” experienced as the states of chaotic attention and hyperfocus, both of which impact core perceptions of self. Using an infinity paradigm, the results demonstrate unskilled attempts to self-regulate within and between these states using self-absorption or self-transcendence strategies including their inherent challenges and energetic cost. Our results further indicate that at the centre of this dynamic paradigm creative awareness strategies exist, which exemplify polarity awareness and the regulation of that awareness supported by an authentic inner compass (AIC). Conclusions: This paper presents the empirical foundation for the ADHD Creative Awareness Theory (CAT) – a new theory for understanding the experience of ADHD consciousness and environmental engagement. Practice implications are explored, and recommendations include use of the CAT as a framework for understanding and development of inherent self-regulation skills for adults with ADHD.

In vitro circular RNA (circRNA) preparation methods have been gaining a lot of attention recently as several reports suggest that circRNAs are more stable with better performances in cells and in vivo than linear RNAs in various biomedical applications. Self-splicing ribozymes are considered a major in vitro circRNA generation method for biomedical applications due to their simplicity and efficiency in circularization of gene of interest. This review summarizes updates and discusses recently developed self-circularization methods based on the self-splicing ribozyme such as group I and II intron ribozymes and pros and cons of each method to prepare circRNA in vitro.

Nanometer-curved surfaces are abundant in biological systems as well as in nano-sized technologies. Properly functionalized polymer-grafted nanoparticles (PGNs) adhere to surfaces with different geometries and curvatures. This work explores some of the energetic and mechanical characteristics of the adhesion of PGNs to surfaces with positive, negative and zero curvatures using Coarse-Grained Molecular Dynamics (CGMD) simulations. Our calculated free energies of binding of the PGN to the curved and flat surfaces as a function of separation distance show that curvature of the surfaces critically impacts the adhesion strength. We find that the flat surface is the most adhesive, and the concave surface is the least adhesive surface. This somewhat counterintuitive finding suggests that while a bare nanoparticle is more likely to adhere to a positively curved surface than a flat surface, grafting polymer chains to the nanoparticle surface inverts this behavior. Moreover, we studied the rheological behavior of PGN upon separation from the flat and curved surfaces under external pulling force. The results presented herein can be exploited in drug delivery and self-assembly applications.

The goal of this work is to show that the underlying symmetries of effective field theory can be traced to the onset of self-similarity. In particular, we argue that the scale-free structure of fractal geometry lies at the heart of invariance principles in classical and Quantum Field Theory.

This paper introduces the self-supervised pretext task of rare visual token enhancement: given an image, the model is trained to push the rarest visual tokens far from all the others. This pretext task slightly-but-consistently improves baseline performances for both registration and few-shot change detection on OSCD, LEVIR-CD and S2looking.

Background: Exercise rehabilitation programs are important for long-term health and wellbeing among people with cardiac and pulmonary disease. De-spite this, many people struggle to maintain their physical activity once reha-bilitation ends. This repeated measures study tracked changes in physical ac-tivity behaviour and motivation during and after completing a communi-ty-based exercise rehabilitation program. Methods: Cardiac and pulmonary exercise rehabilitation patients (N=31), com-pleted six once-monthly measures of physical activity (MET·min), self-determined motivation, intention, and habit strength for rehabilitation ex-ercise (within rehabilitation sessions) and lifestyle physical activity (outside of rehabilitation sessions). Linear regression and random effects models with es-timated marginal means were used to test for associations between physical activity motivation and behaviour and change during and post-rehabilitation. Results: Overall physical activity decreased after rehabilitation (823 MET·min) despite patients becoming more self-determined for lifestyle physical activity during rehabilitation. More self-determined motivation, stronger intentions, and stronger habits were associated with more lifestyle physical activity be-haviour. However, none of these motivation variables were significantly asso-ciated with rehabilitation exercise behaviour. Conclusions: Among community-based cardiac and pulmonary rehabilitation patients, physical activity levels decreased following exercise rehabilitation programs. The findings revealed clear distinctions in the motivation of reha-bilitation exercise compared to lifestyle physical activity. Exercise rehabilitation programs might improve the longevity of outcomes by integrating approaches to enhance lifestyle physical activity beyond the clinic.

Self-healing, stretchable and moldable hydrogels have a great potential application in tissue engineering and soft robotics. Despite great success in reported hydrogels, it is still a great challenge to construct the moldable hydrogels with an ultrafast self-healing performance. Herein, the dynamic double network (DN) composite hydrogels (PBLH) with ultrafast self-healing, stretchable and moldable were successfully constructed by poly (vinyl alcohol) (PVA), borate, ε-poly-L-lysine (EPL) and hyaluronic acid (HA) based on an efficient one-pot method. Fourier transform infrared spectroscopy, X-Ray diffraction and rheological measurements confirmed the formation of dynamic double networks among PVA, B, EPL and HA through the cross-linking of dynamic borate bonds, electrostatic interaction and hydrogen bonding. Having fabricated the dynamic double network structure, the damage gap of the composite hydrogels can heal within 1 min, presenting an excellent self-healing ability. Simultaneously, the DN hydrogels can be molded to various shapes and the length of the DN hydrogels can be stretched to 15 times than original length. In addition, the DN hydrogels exhibited an excellent antibacterial property against Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli). Our results illustrated that the DN composite hydrogels not only retain the advantages of traditional hydrogels, but also possess ultrafast self-healing, outstanding stretchable and antibacterial properties, presenting a prospective candidate for constructing biomedical materials.

Recently, a large number of Transformer-based solutions have emerged for the trajectory prediction task, but there are shortcomings in the effectiveness of Transformers in trajectory prediction. Specifically, while position encoding preserves some of the ordering information, the self-attention mechanism at the core of the Transformer has its alignment invariance that leads to the loss of temporal information, which is crucial for trajectory prediction. For this reason, we design a simple and efficient strategy for temporal information extraction and prediction of trajectory sequences using the self-attention mechanism and linear layers. Experimental results show that the strategy we designed can effectively combine the advantages of the linear layer and self-attention mechanisms, compensating for the shortcomings of the Transformer. Additionally, we conducted an empirical study to explore the effectiveness of the linear layer and sparse self-attention mechanisms in trajectory prediction.

The concept of intelligence has many applications, such as coatings and cyber security. Smart coatings have the ability to sense and/or response to external stimuli and generally interact with their environment. Self-healing coatings represent a significant advance in improving material durability and performance using microcapsules and nanocontainers loaded with self-healing agents, catalysts, corrosion inhibitors, and water-repellents. These smart coatings can repair damage on their own and restore mechanical properties without external intervention and are inspired by biological systems. Properties that are affected by either momentary or continuous external stimuli in smart coatings can be related to corrosion, fouling, fungal, self-healing, pie-zoelectric, microbiological, etc. These coating properties can be obtained via combinations of either organic or inorganic polymer phases, additives and pigments. In this article, a review on the advancements of micro/nanocapsules for self-healing coatings is reported from the aspect of ex-trinsic self-healing ability. The concept of extrinsic self-healing coatings is based on the use of capsules or multichannel vascular systems loaded with healing agents/inhibitors. The outcome is that self-healing coatings exhibit improved properties compared to traditional coatings. It is mentioned that self-healing anticorrosive coating not only has enhanced passive barrier function, but also realizes active defense. As a result, there is a significant improvement of the service life and overall performance of the coating. Future research should be devoted to refining self-healing mechanisms and developing cost-effective solutions for a wide range of industrial applications.

Video self-supervised learning (VideoSSL) offers significant potential for reducing annotation costs and enhancing a wide range of downstream tasks in video understanding. The ultimate goal of VideoSSL is to achieve human-level video intelligence across a spectrum of tasks, from low-level tasks such as pixel temporal correspondence to high-level complex spatio-temporal tasks like action recognition. However, most existing VideoSSL methods focus on isolated aspects of this spectrum and fail to integrate different levels of task complexity. Our study presents the first comprehensive survey that connects all families of VideoSSL methods. We provide a detailed review of the full spectrum of VideoSSL, from low to high levels, by conceptually linking their self-supervised learning objectives and including a comprehensive categorization. Our extensive evaluation highlights the strengths and limitations of each SSL objective across various downstream task families. We also detail the challenges in current VideoSSL research such as data curation, interpretability, deployment, and privacy concerns, an area that previous surveys have not thoroughly explored. In addressing these challenges, we recognize the strengths of existing methods in addressing these challenges and outline future directions for research.

Competent authorities of many countries, including Belgium, impose control measures (preventing wild bird access to feeders and water facilities, indoor confinement of captive birds, or fencing off outdoor ranges with nets) to professional and non-professional keepers of birds to prevent the spread of avian influenza (AI). Flemish laying hen farmers (FAR, n = 33) and private keepers of captive birds (PRI, n = 263) were surveyed about their opinion on, and compliance with, AI-measures legally imposed during the most recent high-risk period before this survey in 2021. Participants answered questions on a 5-point Likert scale (1 = the worst, 3 = neutral, and 5 = the best). FAR indicated better compliance with the AI-measures compared to PRI, except for confinement with nets. PRI and FAR perceived the level of compliance with AI-measures by other private bird keepers to be lower compared to themselves. FAR regarded the AI-measures as more effective than PRI. To prevent the spread of AI more effectively, national authorities could focus on information campaigns explaining to private bird keepers, in particular, the need for the various control measures that they impose, implement alternative control measures that have broader support or implement stricter enforcement of the control measures.

The phenomenon of emergence in complex systems is vividly exemplified by the coordinated behaviors of ant colonies, where apparent chaos evolves into order. This article delves into the essence of complexity and emergence through interdisciplinary research spanning biology, computer science, and information theory. It begins by examining the foundational principles of swarm intelligence, revealing how simple ant behaviors culminate in sophisticated collective intelligence. The discussion then transitions to the philosophical dimensions of emergent phenomena and the principles of self-organization that underpin systemic order. Key concepts such as ant colony optimization algorithms and stigmergy are synthesized, highlighting their relevance in both natural and artificial systems. The broader societal implications are considered through the lens of collective intelligence, drawing parallels between ant colonies and human social structures. Complementary online resources and multimedia content enrich the narrative, providing accessible insights and dynamic representations of ant intelligence. This convergence of diverse perspectives underscores the remarkable capacity for simplicity to generate complexity, culminating in a profound understanding of collective wisdom.

Our study focused on investigating the impact of Instagram use on body satisfaction and self-esteem among participants aged 20 to 40 years (N = 95) through a cross-sectional survey design in which the Rosenberg Self-Esteem Scale, the Body Shape Questionnaire (BSQ), and ad hoc designed questions were administered. The results highlight the influence of Instagram on body dissatisfaction (p = .005) but not on self-esteem (p = .211). Gender was not found to be an influential factor (p = .173). However, it was found that greater body satisfaction correlates with higher self-esteem and vice versa (p < .001). Finally, we concluded that the number of hours users in the sample spend on Instagram is related to greater body dissatisfaction but not to lower self-esteem.

Background: This study aimed to examine the obesity¬ stress, weight control behavior, and self-control in overweight and obese female university students, and identify the mediating effect of self-control in the relationship between obesity stress and weight control behavior. Methods: This study employed a descriptive correlational design. A total of 109 female university students who were overweight and obese participated in the study. Data were analyzed using descriptive statistics, Pearson's correlation coefficient analysis, and Bootstrapping using Hayes' PROCESS macro for mediation. Results: Significant relationships between obesity stress and weight control behavior (r=0.25, p<0.001), obesity stress and self-control (r=-0.36, p<0.001), and weight control behavior and self-control (r=.26, p<.001) were observed. Self-control showed indirect mediating effect on the relationship between obesity stress and weight control behavior. Conclusion: Therefore, development of programs and strategies for improving self-control is necessary in order to decrease obesity among female university students.

Tungsten and its alloys are considered as a key component in high temperature environment. Self-passivating W-Si-xY alloys with different Y contents made from the powders milled for different times were prepared by mechanical alloying (MA) and spark plasma sintering (SPS). Their microstructures and oxidation resistance were investigated. It is found that the oxidation resistance of the alloys with low Y contents (0Y and 2Y) made from 20 h-milled powders is worse than that of the alloys made from 4 h-milled powders. It is because the protective WO3/SiO2 composite layer is discontinuous. While the alloys with high Y contents (4Y and 6Y) made from 4 h and 20 h-milled powders show good oxidation resistance. Even, the thickness of the oxide layer of the alloys with 4Y and 6Y made from 20 h-milled powders after oxidation at 1000 ℃ for 2 h is 84.0 μm and 70.7 μm, less than that made from 4 h-milled powders. This is due to the size and distribution of W5Si3, Y2Si2O7 aggregates and W-Y-O melt. The synergistic effects of Y contents and milling time on the microstructures and oxidation resistance of the alloys are revealed.

With the development of electricity spot markets, accurate electricity load forecasting enables power generation companies to supply the right amount of electricity, Significantly avoid power waste. As a result, time series forecasting in the field of power can bring significant benefits. Previously, the Informer model successfully introduced the Transformer into long time series forecasting(LTSF) by proposing the ProbSparse self-attention mechanism, which solved the inherent problem of high memory complexity in self-attention. Recent research has further demonstrated the potential of the self-attention for mining complex dependencies. However, the limited amount of historical data has become one of the main challenges in applying deep learning techniques to power LSTF tasks. Previous reserches often add a large number of time covariates to provide more information. In this paper, to address this issue, (i) we design a simple but effective Season-aware Block to enhance the model’s ability to mine artificial prior information in temporal covariates; (ii) we conduct experiments using the provincial power data of Zhejiang Province, China, from 2019 to 2022, and our model outperforms other models, achieving a 19 percent MSE relative improvement; (iii) we conduct ablation experiments to assess the efficacy of the Season-aware Block in extracting temporal periodic features. Furthermore, we elucidate the underlying reasons for the effectiveness of both the self-attention mechanism and the Season-aware Block through visualization experiments.

Background and Objectives: Although there has been growing interest in the association of ADHD with anxiety, depression, suicidal ideation, and self-esteem, there is a lack of consistent results and these associations remain unclear. This study investigated the differential patterns of anxiety, depression, suicidal ideation, and self-esteem with regard to the presence of ADHD. Materials and Methods: The subjects were divided into a patient group and a control group. The patient group included 49 patients diagnosed with ADHD at the department of psychiatry of one university hospital. For the control group, 3,727 students were invited to complete a questionnaire. Of the 1,717 students who responded to the questionnaire, 245 were selected for patient-controlled studies. All the subjects were instructed to complete the Beck Suicide Ideation Scale, Children's Depression Inventory, Beck’s Depression Inventory, State-Trait Anxiety Inventory, and Rosenberg Self-Esteem Scale. Results: ADHD subjects experienced more anxiety, depression, and suicidal ideation as well as lower self-esteem compared to the control group comprising participants without ADHD. Conclusions: These findings suggest that ADHD symptoms may contribute to greater suicidal ideation through experienced depression, anxiety, and low self-esteem in school-aged children. Therefore, appropriate ADHD symptom management is necessary to reduce suicidal ideation among school-aged children with ADHD symptoms.

cGAS is a cytosolic DNA sensor that activates innate immune responses through production of the second messenger 2’3’-cGAMP, which activates the adaptor STING. cGAS senses dsDNA in a length-dependent but sequence-independent manner, and it cannot discriminate self-DNA from foreign DNA. In normal physiological conditions, cellular DNA is sequestered in the nucleus by nuclear envelope and in mitochondria by mitochondrial membrane. When self-DNA leaks into the cytosol during cellular stress, or during mitosis, the cGAS can be exposed to self-DNA and be activated. Recently, many studies have investigated how cGAS keeps inactive and avoids to be aberrantly activated by self-DNA. Thus, the aim of this narrative review is to summarize the mechanisms by which cGAS avoids to sense self-DNA under normal physiological conditions.

Keywords: FTY720, self-assembled nanoparticles, intracerebral hemorrhage, neuroprotection

Inspired by nature's examples such as lotus leaves and insect wings, superhydrophobic surfaces have been extensively studied and engineered. However, achieving superoleophobic surfaces, which repel oils, has proven to be more challenging due to the oils' lower surface tension than water. This article presents a novel, cost-effective technique for fabricating superoleophobic surfaces with enhanced durability. We demonstrate the effectiveness of this technique by using Perfluorooctyltriethoxysilane (PFOTES) and silicon dioxide nanoparticles on stainless steel mesh substrate. The resulting surfaces exhibit remarkable superoleophobic properties by incorporating re-entrant structures, low surface energy, and high surface roughness, making them ideal for oil-repellency applications.

In this unprecedented era of computer-supported and distance learning, it is critical to rethink the way students are supported through vital skills like self-regulation and peer collaboration. A variety of digital media has emerged, allowing learners autonomy while requiring the ability to effectively regulate different components of the educational process for long-term academic success. We reviewed collaboration, communication, and self-regulation in digital learning environments as part of online learning. We discussed how these abilities are reflected in highly cited content. High-impact factor studies were identified using advanced software Herzing Publish or Perish to retrieve highly cited research publications metrics. The search resulted in 272 records from 2010 to 2022, 192 duplicates were identified to remove and 48 JCR articles were shortlisted for review. We examined and reflected on sufficient connections to make firm judgments about correlations of concepts. The review identified prominent themes surfaced in the high impact factor academic publication e.g., digital learning environment, interaction, engagement, feedback, self-efficacy, motivation, meta-cognition, achievement, technology, blended learning, and orientation of ICT. Many studies support different features of SRL &amp; PCL through different tools, access to educators or feedback, encouragement, and through peers. However, all of them discuss the processes that students use to self-regulate learning in these situations. It is suggested to examine whether the effectiveness of certain SRL processes varies by online learning mode.

This paper, which is one of the few in the world dealing with this topic, presented the psychologist's work based on the concepts of mentalization and internal working models (IWMs) of attachment, with an adolescent girl who was prone to nonsuicidal self-injury. Gaga, a student in the first grade of high school, had visible scars from cutting her hands on the inner sides of both forearms. Gaga told the psychologist that her mother believes that this is an attempt to draw attention to her problems, and even a suicide attempt. Together with Gaga, the psychologist made a formulation of her mental difficulties. The main aim of the psychologist's work with Gaga was to control unpleasant impulses and emotions, so as to eliminate self-injury. This aim has been achieved through three global tasks: 1) that Gaga, in the relationship with the psychologist, builds her IWMs as positive, so that she can see herself as a person who is worthy of the love and support of close people; 2) to develop a stable capacity for mentalization; 3) to apply this acquired skills in the interpretation of her affective experiences. The mentalizing approach in working with Gaga was based on an attitude of emotional warmth, acceptance and authentic interest in her inner world. The psychologist's work with Gaga led to her better self-control, which resulted in the cessation of her self-injury. In her work with a psychologist, Gaga acquired positive IWMs, which she transferred into other significant interpersonal relationships.

Consciousness is usually perceived as a state of being aware of one’s environment as well as self. Despite its omnipresence in our life, understanding this concept is challenging. This has given rise to several theories attempting to explain the nature of consciousness, as well as hard and soft problems of consciousness. In fact, the boundaries of consciousness defined by these theories are a topic of continued discussion, particularly in light of the recent advances in artificial intelligence (AI). Some of these theories consider consciousness as a simple integration of information while others purport the need for an agency in the process of integration for an entity to be considered conscious. Some theories consider consciousness as a graded entity and some equate consciousness with content of awareness. In this work, major theories of consciousness are reviewed and compared, focusing on awareness, attention, and sense of self. These findings are interpreted in relation to AI in order to ascertain what makes AI distinct from natural intelligence.

Consciousness is usually interpreted as a state of being aware of one’s environment as well as self, while awareness is understood as knowledge of something. Despite their semantic differences, in philosophy, these terms are often used interchangeably, as is the case of the hard problem of consciousness proposed by Chalmers, which in fact is the hard problem of awareness. Trilogy paradigm of consciousness (or simply “trilogy”) offers a new paradigm where consciousness is the result of a unique interaction between awareness and the decision-making process. By conferring the input of awareness to the decision-making process, a new mental function of awareness-based choice selection (ABCS) or true free will emerges. Likewise, application of the power of decision-making to the process of awareness gives rise to discretionary selection of information for awareness (DSIA) or intentional attention. The intertwined actions of ABCS and DSIA comprise “I” which is the faculty of our consciousness and is what sets natural intelligence (NI) apart from artificial intelligence (AI). Based on trilogy, mind is an unconscious compilation of all mental function excluding ABCS and DSIA that are the essence of consciousness. As humans, we are a union of “I,” our minds, and our bodies.

Peru has one of the highest informal employment rates in Latin America (73%). Previous studies have shown higher prevalence of poor self-perceived health (P-SPH) in informal workers compared to formal. The study’s aim is to analyse the role of working conditions in the association between informality and SPH in urban working population in Peru. We conducted a cross-sectional study based on 3098 workers from the working conditions survey of Peru 2017. Prevalence of P-SPH and exposure to poor working conditions were calculated separated by formal and informal employment and stratified by sex. Poisson regression models were used to assess the association between P-SPH and informal employment, with its prevalence ratios (PR) crude and adjusted for working conditions. Informal employment was 76% in women and 66% in men. Informal workers were more exposed to poor working conditions than formals and reported worse SPH. Workers in informal employment showed higher risk of P-SPH than formals: PR 1.38 [95% CI: 1.16 – 1.64] in women and PR 1.27 [95% CI: 1.08 – 1.49] in men. Adjustment by working conditions weakened the association in both sexes. In women, this association was only partially explained by worse working conditions 1.23 [95% CI: 1.04 – 1.46]. The negative effect on informal workers’ health is partially explained by poor working conditions. However, there is a part of the effect explained by informality per se.

Background: This study consists of a brief psychological intervention, which uses the Self-Regulation Therapy (SRT, procedure based on suggestion and classical conditioning), to improve coping with stress and emotionality by reproducing the positive effects of illegal drugs: cannabis, cocaine, ecstasy. Method: 15 volunteers (8 males, 7 females), with a mean age of 24.67 (SD = 4.43), underwent intervention to improve their coping with stress and emotionality using SRT. They carried out pre- and post-intervention scores for 10 days and during a 4-week fol-low-up. The employed instruments were: COPE (Coping Skills Inventory) and PNAS (Positive and Negative Affect Schedule). Results: SRT was superior to non-intervention for the 4 coping strategies (2= .829, .453, .411 and .606) and for positive (2= .371) and negative emotionality (2= .419). An improvement in scores was evidenced in the follow-up scores compared to the pre-intervention measures. Conclusions: This study shows for the first time that it is possible to use illegal drugs, considered harmful to public health, to improve young people’s coping capacity and emotionality by reproducing their positive effects with SRT.

Background & Aims: Globally, liver diseases account for 2 million deaths per year. For those with advanced liver disease the only curative approach is liver transplantation. However, less than 10% of those in need get a liver transplant due to limited organ availability. To circumvent this challenge, there has been a great focus in generating a bioengineered liver. Despite its essential role in liver functions, a functional biliary system has not yet been developed. In this framework, exploration of epithelial cell self-organogenesis and microengineering-driven geometrical cell confinement allow to envision the bioengineering of a functional biomimetic intrahepatic biliary tract. Approach: Three-dimensional (3D) bile ducts were built in vitro by restricting cell adhesion to two-dimensional (2D) patterns to guide cell self-organization. Tree shapes mimicking the configuration of the human biliary system were micropatterned on glass slides, restricting cell attachment to these areas. Different tree geometries and culture conditions were explored to stimulate self-organogenesis of normal rat cholangiocytes (NRCs) used as a biliary cell model, either alone or in coculture with human umbilical endothelial cells (HUVECs). Results: Pre-seeding the micropatterns with HUVECs promoted luminogenesis with higher efficiency to yield functional branched biliary tubes. Lumen formation, apico-basal polarity, and preservation of the cholangiocyte phenotype were confirmed. Moreover, intact and functional biliary structures were detached from the micropatterns for further manipulation. Conclusion: This study presents physiologically relevant 3D biliary duct networks built in vitro from 2D micropatterns. This opens opportunities for investigating bile duct organogenesis, physiopathology, and drug testing.

(1)Background: This report was an effective evaluation of using multimedia educational program for self-care and quality of life in patients with a stoma at postoperative and established a simple threshold for enterostomy self-care skills.(2)Methods:108 patients with enterostomy were randomly assigned to receive either the intervention multimedia education program (including information about preparation for enterostomy care, cleaning method, manual bag replacement steps and precautions). The primary outcome was self-care ability and quality of life (defined as overall enterostomy self-care ability and overall quality of life), which was assessed at 2 weeks after intervention via an interview.(3)Results: Compared with those who in the conventional stoma education program, patients received the multimedia education program significantly improved their overall self-care ability and quality of life in statistically. The threshold of the enterostomy self-care skill scale was 20 points, and its corresponding sensitivity was 77.8%, and the specificity was 75.7%.(4)Conclusions: According to our study, the multimedia education program could enhance self-care ability of home care and quality of life in patients with enterostomy.

The advancement of bioconcrete over cementitious composites has brought us to the application of microbes in the field of construction materials. Certain microbes like bacteria, algae, and fungi have been discussed in the review. The purpose of applying these microbes in the matrix is mainly to enhance the concrete’s strength and other properties such as durability, resistance, and self-healing ability. As these microbes are able to induce calcite biomineralizations, the process is also known as Microbiologically Induced Calcite Precipitation (MICP). Some known microorganisms with their mentioned ability are Bacillus subtilis and Bacillus cohnii (bacteria), Chlorella vulgaris and Spirulina platensis (algae), and Trichoderma reesei, Aspergillus niger, and Neurospora crassa (fungi). The paper provides a “state-of-the-art” review of research into the effects of bioconcrete and discusses the overall methodologies of every medium with their physiological, physicochemical and bioengineering properties in the light of recent researches done so far in the same field.

There is growing evidence on the relevance of self-efficacy for well-being and functioning among individuals with psychotic disorders, but specific self-efficacy for coping with stress has rarely been investigated. This study explored the outcomes of an intervention for the improvement of coping resources based on a training in coping skills and coping with stress self-efficacy (CSSE). Fourteen adult volunteers who were diagnosed with schizophrenia (n=12) or schizoaffective disorder (n=2) were matched in clinical and sociodemographic characteristics and randomly assigned to the study groups. The intervention group received the training along with their pharmacological therapy; the control group received their prescribed drug therapy. Participants completed self-reports on CSSE, perceived successful daily functioning based on coping skills, and clinical status (BPRS-E). Trained patients showed a significant increase in CSSE and reported greater successful functioning status, and significant improvements in their clinical status were also observed. All these enhancements remained at 3-month and 6-month follow-ups. Control participants showed no significant changes. Moreover, the intervention condition interacted with CSSE and perceived coping functioning in explaining improvements in clinical status: in the treatment group, greater CSSE translated into enhanced daily functioning, and this improvement predicted better clinical status. These findings stress the relevance of promoting coping resources in psychotic disorders and provide preliminary evidence for the potential benefits of CSSE.

Proteins and DNA exhibit key physical chemical properties that make them advantageous for building nanostructures with outstanding features. Both DNA and protein nanotechnology have growth notably and proved to be fertile disciplines. The combination of both types of nanotechnologies is helpful to overcome the individual weaknesses and limitations of each one, paving the way for the continuing diversification of the structural nanotechnologies. Recent studies have implemented a synergistic combination of both biomolecules to assemble unique and sophisticate protein-DNA nanostructures. These hybrid nanostructures are highly programmable and display remarkable features that create new opportunities to build in the nanoscale. This review focuses on the strategies deployed to create hybrid protein-DNA nanostructures. Here, we will discuss strategies such as polymerization, spatial directing and organizing, coating, rigidizing or folding DNA into particular shapes or moving parts. The enrichment of structural DNA nanotechnology by incorporating protein nanotechnology has been clearly demonstrated and still shows a large potential to create useful and advanced materials with cell-like properties or dynamic systems. It can be expected that structural protein-DNA nanotechnology will open new avenues in the fabrication of nano-assemblies with unique functional applications and enrich the toolbox of bionanotechnology.

Transition from primary to secondary school is more successful when students’ learning is consistent. Students are also more likely to enjoy the school, engage with learning, and have a high academic achievement in the secondary school when they feel motivated. This is a critical aspect especially in cases when global pandemics situations allow only the online schooling opportunity. Students that are away from school lack the traditional sources of motivation and self-regulated learning skills, thus research is needed to identify other important factors that can be developed in remote settings. The aim of this study was to find out how students perceive their experience with the transition from primary to secondary school and how such a transition influences students’ self-regulated learning (SRL) and motivation. Self-reported data were collected during the COVID-19 breakout from a total of N=80, 6th and 7th grade students aged 12-14 years old. Results showed that students had a successful transition, especially when they are supported by their parents and teachers. Next, Bivariate Pearson Correlation analysis indicated that students’ perceptions about their experience with the transition from primary to secondary school and their self-regulated learning and motivation are significantly correlated. No gender differences were found among all main study variables.

Background and Objectives: In Libya, prescription medicines can easily be dispensed without a prescription, as self-medication with the subsequent of potential misuse and unnecessary risk for patients. This systematic review and meta-analysis explored the prevalence of self-medication among citizens in Libya. Methods: We systematically searched PubMed, ISI Web of Knowledge, EMBASE, Medline, Scopus, google scholar and ResearchGate databases for studies published in Libya reporting the prevalence of non-prescription use of medicines in community pharmacies, targeting studies published from Jan 2008 until Dec 2019. A random meta-analysis was performed to analyze pooled estimates of non-prescription usage of medicines. Results: Out of 63 articles identified, a total of 13 papers from 7 cities were met the inclusion criteria and involved a total of 4741 subjects. The overall pooled proportion of self-medication of drugs was 53.6% (95% CI: 0.93% - 1.08%), with low heterogeneity and the P-value of the whole population tasted was found to be = 1.000 (df = 12). Seven out of the 13 studies reported data on self-medication for antibiotics without prescription. The records ranged from 15.3% (95% CI 0.61–1.65) in Misurata to 76.6% (95% CI 0.80–1.25) in Tripoli. Conclusion: Self-medication use of medicines among Libyan population is a common phenomenon involving a high proportion use of antibiotics. This misuse of medications could enhance the development and spread of antibiotic resistance.

The Photovoltaic modules are usually installed on the ground which exposes it to surface deposition of foreign particles. In the Middle East and North Africa region, the primary culprit is dust and sand. They form an insulating and opaque layer on the surface of the glass, which obstructs its heat transfer and optical properties, thereby reducing the overall yield efficiency of the solar panel. Cleaning of this layer is critical to the operation of the solar panel and often requires great effort and energy on a large-scale solar array. In this paper, we propose a novel self-cleaning mechanism for solar panels, with an understanding of the structural integrity of the Photovoltaic laminate and application of external mechanical vibration. By applying an external source of vibration, the solar panels vibrate, excites its fundamental frequencies and cleans by its own. The method is analyzed using finite element analysis method and tested using experiments. Our simulation results based on IEC 61215 show that the maximum principal stress and deformation in the critical layers is within limits. Our experimental results prove the proposed theory is feasible and can be extended to large scale solar arrays. Our proposed method is retrofittable and could save money, energy and effort in cleaning the solar arrays, which can replace current techniques.

Nanoparticle (NP)–surfactants formed by the self-assembly of NPs and end-functionalized polymers at the hydrophilic/hydrophobic interface have a wide range of applications in many fields. In this study, the influence of density of amino groups, NPs dimension and pH on the interaction between end-functionalized polymers and NPs were extensively investigated. Single amino-terminated polystyrene (PS-NH2, Mw ≈ 0.6k, 2.5k, 3.5k, 3.9k) and diamino-terminated polystyrene (H2N-PS-NH2, Mw ≈ 1.1k, 2.8k) were prepared using reversible addition–fragmentation chain transfer polymerization and atom transfer radical polymerization. NPs with different dimensions (zero-dimensional carbon dots with sulfonate groups, one-dimensional cellulose nanocrystals with sulfate groups and two-dimensional graphene with sulfonate groups) in the aqueous phase were added into the toluene phase containing the aminated PS. The influence of pH and the molecular weight of amino-terminated PS on the interfacial tension between two phases were investigated. The results indicate that aminated PS exhibited the strongest interfacial activity after compounding with sulfonated NPs at a pH of 3. Terminating PS with amino groups on both ends leads to better performance in in reducing the water/toluene interfacial tension than modifying the molecular structure of PS on a single end. The dimension of sulfonated NPs also contributed significantly to the reduction of the water/toluene interfacial tension. The minimal interfacial tension was 4.49 mN/m after compounding PS-NH2 with sulfonated zero-dimensional carbon dots. Molecular dynamics simulation on the evolution of the water/toluene interface in the presence of sulfonated carbon dots and H2N-PS-NH2 revealed that these opposite charged substances moved towards the interface in an extreme short time and orderly assembled in a thermodynamic equilibrium.

In this paper, first the Self-Written Waveguide (SWW) process in wet photopolymer media (liquid solutions), are examined for three examples: single-, counter-, and co-fibers exposure. Then the SWWs formed inside solid material are examined including the effects of manipulating the alignment of the fibers. In all cases high precision measurements are used to position the fiber optic cables (FOCs) before exposure using a microscope. The self-writing process is indirectly monitored by observing (imaging) the light emerging from the side of the material sample during SWW formation. In this way the optical waveguide trajectories formed in an Acrylamide/Polyvinyl Alcohol (AA/PVA) a photopolymer material (sensitized at 532 nm) are examined. First the transmission of light by this material is characterized. Then the bending and merging of the waveguides which occur are investigated. The predictions of our model are shown to qualitatively agree with the observed trajectories. The largest index changes taking place at any time during the exposure, i.e. during SWW formation, are shown to take place at the positions where the largest exposure light intensity is present. Typically, such maxima exist close to the input face and the first maximum is referred to as the location of the Primary Eye. Other local maxima also appear further along the SWW and are referred to as Secondary Eyes, i.e. deeper within the material.

The work shows a single case intervention based on the motivational coaching model the “Cantón’s Giraffe”, theoretically and experimentally proven in a 20-years-old dancesport practitioner, who considered improving her execution with her dance partner. Methodological triangulation was used [34], combining a test-retest quantitative measure (Using Ryff Psychological Wellbeing Scales; the Rosenberg Self-Esteem Questionnaire and an ad-hoc instrument for the motivational force assessment) and subjective analysis of the responses from the interviews conducted. A three-phase intervention protocol was used [33]: identification, intervention, and follow-up. The results show an improvement in reevaluation scores, an increase in the subjective evaluation, and participant satisfaction and perceived resources raised at the end of the intervention. These results are in line with previous similar studies.

Hydrogels incorporated with hydrophobic motifs have received considerable attention to recapitulate the cellular microenvironments, specifically for the bio-mineralization of a 3D matrix. Introduction of hydrophobic motifs into a hydrogel often results in irregular arrangement of the motifs, and further phase separation of hydrophobic domains, but limited efforts have been made to resolve this challenge in the hydrophobically-modified hydrogel. Therefore, this study presents an advanced integrative strategy to incorporate hydrophobic domains regularly in a hydrogel by self-assembling of polymer cross-linkers, building blocks of a hydrogel. Self-assemblies between polymer cross-linkers were examined as micro-domains to incorporate hydrophobic motifs in a hydrogel. The self-assembled structures in a pre-gelled solution were confirmed with the fluorescence analysis and the hydrophobicity of a hydrogel could be tuned by incorporating the motifs in a controlled manner. Overall, the results of this study would greatly serve to tuning performance of a wide array of hydrophobically-modified hydrogels in drug delivery, cell therapies and tissue engineering.

In recent decades, organizational research has given special attention to the mechanisms promoting the health and wellbeing of nursing professionals. In this context, self-esteem is a personal resource which influences wellbeing at work and psychological wellbeing of nurses. The purpose of this study was to analyze the mediating role of eating in the effect of sleep quality on self-esteem in nursing professionals. The sample of 1073 nurses were administered the Rosenberg General Self-Esteem Scale, the Pittsburgh Sleep Quality Index and the Three-Factor Eating Questionnaire-R18. The results show that poor sleep quality and type of eating directly and indirectly affect self-esteem. More so, poor sleep quality deteriorated self-esteem through emotional eating, and even though emotional eating facilitated disinhibited eating, this relationship had no significant effects on self-esteem. The findings of this study suggest that hospital management should implement employee health awareness programs on the importance of healthy sleep and design educational interventions for improving the quality of their diet.