Opioids are highly addictive substances with a relapse rate of over 90%. While preclinical models of chronic opioid exposure exist for studying opioid dependence, none recapitulate the relapses observed in human opioid addiction. The mechanisms associated with opioid dependence, the accompanying withdrawal symptoms and the relapses that are often observed months or years after opioid dependence are poorly understood. Therefore, we developed a novel model of chronic opioid exposure whereby the level of administration is self-directed with periods of behavior acquisition, maintenance and then extinction alternating with reinstatement. This profile arguably mirrors that seen in humans, with initial opioid use followed by alternating periods of abstinence and relapse. Recent evidence suggests that dietary interventions that reduce inflammation, including omega-3 fatty acids such as docosahexaenoic acid (DHA), may reduce substance misuse liability. Using the self-directed intake model, we characterize the observed profile of opioid use and demonstrate that a diet enriched in polyunsaturated fat acids (PUFAs) ameliorates oxycodone-seeking behaviors in the absence of drug availability and reduces anxiety. Guided by the major role gut microbiota have on brain function, neuropathology, and anxiety, we profile the microbiome composition and the effects of chronic opioid exposure and DHA supplementation. We demonstrate that withdrawal of opioids led to a significant depletion in specific microbiota genera whereas DHA supplementation increased microbial richness, phylogenetic diversity, and evenness. Lastly, we examined the activation state of microglia in the striatum and found that DHA supplementation reduced the basal activation state of microglia. These preclinical data suggest that a diet enriched in PUFAs could be used as a treatment to alleviate anxiety induced opioid-seeking behavior and relapse in human opioid addiction.

The quantization error (QE) from Self-Organizing Map (SOM) output after learning is exploited in this studies. SOM learning is applied on time series of spatial contrast images with variable relative amount of white and dark pixel contents, as in monochromatic medical images or satellite images. It is proven that the QE from the SOM output after learning provides a reliable indicator of potentially critical changes in images across time. The QE increases linearly with the variability in spatial contrast contents of images across time when contrast intensity is kept constant. The hitherto unsuspected capacity of this metric to capture even the smallest changes in large bodies of image time series after using ultra-fast SOM learning is illustrated on examples from SOM learning studies on computer generated images, MRI image time series, and satellite image time series. Linear trend analysis of the changes in QE as a function of the time an image of a given series was taken gives proof of the statistical reliability of this metric as an indicator of local change. It is shown that the QE is correlated with significant clinical, demographic, and environmental data from the same reference time period during which test image series were recorded. The findings show that the QE from SOM, which is easily implemented and requires computation times no longer than a few minutes for a given image series of 20 to 25, is useful for a fast analysis of whole series of image data when the goal is to provide an instant statistical decision relative to change/no change between images.

Owing to the nondiffracting, self-accelerating, and self-healing properties, Airy beams of different nature have become a subject of immense interest in the past decade. Their interesting properties have opened doors to many diverse applications. Consequently, the questions of how to properly design spatial manipulation of Airy beams or how to implement them in different setups have become important and timely in the development of various optical devices. Here, based on our previous work, we present a short review on the spatial control of Airy beams, including the interactions of Airy beams in nonlinear media, beam propagation in harmonic potential, and the dynamics of abruptly autofocusing Airy beams in the presence of a dynamic linear potential. We demonstrate that under the guidance of nonlinearity and external potential, the trajectory, acceleration, structure, and even the basic properties of Airy beams can be adjusted to suit specific needs. We describe other fascinating phenomena observed with Airy beams, such as self-Fourier transformation, periodic inversion of Airy beams, and the appearance of spatial solitons in the presence of nonlinearity. These results have promoted the development of Airy beams, and have been utilized in various applications, including particle manipulation, self-trapping, and electronic matter waves.

In wireless sensor networks (WSNs), each sensor node can estimate the global parameter from the local data in distributed manner. This paper proposed a robust diffusion estimation algorithm based on minimum error entropy criterion with self-adjusting step-size, which are referred to as diffusion MEE-SAS (DMEE-SAS) algorithm. The DMEE-SAS algorithm has fast speed of convergence and is robust against non-Gaussian noise in the measurements. The detailed performance analysis of the DMEE-SAS algorithm is performed. By combining the DMEE-SAS with diffusion minimum error entropy (DMEE) algorithms, an Improving DMEE-SAS algorithm is proposed, in non-stationary environment where tracking is very important. The Improving DMEE-SAS algorithm can avoid insensitivity of the DMEE-SAS algorithm due to the small effective step-size near the optimal estimator, and obtain a fast convergence speed. Numerical simulations are given to verify the effectiveness and advantages of these proposed algorithms.

In this paper, the new optical wave solutions of truncated M-fractional perturbed Kundu-Eckhaus model with full non-linearity are obtained by utilizing the expa function technique and modified extended tanh expansion function technique. The solutions are in the form of dark soliton, bright soliton, singular solitons and other form of solutions. The gained solutions are helpful for the further development of concerned model. The obtained results have also been presented graphically in both two-dimensional and three-dimensional formats to discuss the dynamical features as well as the parametric dependence of the constructed solutions. The study offers a highly spectacular and acceptable techniques to combine various intriguing wave demonstrations for more sophisticated models of the modern day.

We try to solve three decades-old physics challenges. List all elementary particles. Describe dark matter. Describe mechanisms that govern the rate of expansion of the universe. We propose new modeling. The modeling uses extensions to harmonic oscillator mathematics. The modeling points to all known elementary particles. The modeling suggests new particles. Based on those results, we do the following. We explain observed ratios of dark matter amounts to ordinary matter amounts. We suggest details about galaxy formation. We suggest details about inflation. We suggest aspects regarding changes in the rate of expansion of the universe. We interrelate the masses of some elementary particles. We interrelate the strengths of electromagnetism and gravity. Our work seems to offer new insight regarding applications of harmonic oscillator mathematics. Our work seems to offer new insight regarding three branches of physics. The branches are elementary particles, astrophysics, and cosmology.

We develop and apply new physics theory. The theory suggests specific unfound elementary particles. The theory suggests specific constituents of dark matter. We apply those results. We explain ratios of dark matter amounts to ordinary matter amounts. We suggest details about galaxy formation. We suggest details about inflation. We suggest aspects regarding changes in the rate of expansion of the universe. The theory points to relationships between masses of elementary particles. We show a relationship between the strength of electromagnetism and the strength of gravity. The mathematics basis for matching known and suggesting new elementary particles extends mathematics for harmonic oscillators.

Objectives: The aim of this study was to determinate the validity and the reliability of the self-administered Timed Up and Go (TUG) test, a gold standard test for fall risk screening, when compared to the traditional face-to-face assessment carried out by a physiotherapist. Methods: A total of 37 community-dwelling adults, mean age 61.78±6.88, 73% female, who took part in fall risk screening actions in the central region of Portugal, were assessed. The protocol included sociodemographic and history of falls questions, the Self-Efficacy for Exercise questionnaire, the Activities and Participation Profile Related to Mobility (PAPM) and three functional tests, namely the 10-Meter Walking Speed (10-MWS), TUG and 30 Seconds Sit to Stand (30s STS) tests. Within an interval of 18-24 hours after the face-to-face moment, the participants were instructed to self-administer the TUG test at home. The validity and reliability of self-administered TUG test were examined using the limits of agreement, clinically acceptable limit, intra-class correlation coefficients (ICC), paired t-tests and Pearson´s coefficient correlation (r). Results: The limits of agreement for self-administered assessment were within the clinically acceptable limits. The average result of the face-to-face TUG test and self-administered TUG test was 7.47 ± 2.45 and 7.57 ± 3.10 seconds, respectively. When comparing the two evaluations, they were strongly associated (r=0.716, p<0.001), with an excellent ICC of 0.82 (0.65-0.91), for a 95% confidence interval. Conclusion: The use of the self-administered TUG test for the screening of risk of fall, using low-cost technology, appears to be valid and reliable in community-dwelling adults aged 50 and over.

This paper proposes an innovative postulate based on experimental observations, namely, that the speed of light can vary between two inertial reference systems. This postulate is defined as the principle of relative change in the speed of light, which refreshes the concept of absolute speed of light in traditional physics. By integrating the principle of the invariance of the speed of light with the principle of relativity in relativity theory, several important conclusions are derived: space and time must be capable of and proportionally expand and contract. Between two inertial reference systems with different relative speeds of light, objects have an energy difference of k^2 times, which induces a natural tendency for objects to move towards a state of lower energy. The acceleration is only related to the relative change rate of the speed of light in vacuum space and is independent of the mass of the object. The kinetic energy obtained by the object comes from the reduction of internal energy of the object, emphasizing the key role of energy conversion in this process. The paper provides a new theoretical perspective for explaining the physical mechanisms of universal gravitation and dark energy.

In Brain-Computer Interface Motor Imagery (BCI-MI) systems, Convolutional Neural Networks (CNNs) have traditionally dominated as the deep learning method of choice, demonstrating significant advancements in state-of-the-art studies. Recently, Transformer models with attention mechanisms have emerged as a sophisticated technique, enhancing the capture of long-term dependencies and intricate feature relationships in BCI-MI. This research investigates the performance of EEG-TCNet and EEG-Conformer models, which are trained and validated using various hyperparameters and bandpass filters during preprocessing to assess improvements in model accuracy. Additionally, this study introduces the EEG-TCNTransformer, a novel model that integrates the convolutional architecture of EEG-TCNet with a series of self-attention blocks employing a multi-head structure. The EEG-TCNTransformer achieves an accuracy of 82.97% without the application of bandpass filtering. The source code of EEG-TCNTransformer is available on GitHub.

Biological systems have a unique ability inherited through their genome. It allows them to build, operate, and manage a society of cells with complex organizational structures where autonomous components execute specific tasks and collaborate in groups to fulfill systemic goals with shared knowledge. The system receives information from various senses, makes sense of what is being observed, and acts using its experience, while the observations are still in progress. We use the General Theory of Information (GTI) to implement a digital genome, specifying the operational processes that design, deploy, operate, and manage a cloud-agnostic distributed application that is independent of IaaS and PaaS infrastructure, which provides the resources required to execute the software components. The digital genome specifies the functional and non-functional requirements that define the goals and best-practice policies to evolve the system using associative memory and event-driven interaction history to maintain stability and safety while achieving the system’s objectives. We demonstrate a structural machine, cognizing oracles, and knowledge structures derived from GTI used for designing, deploying, operating, and managing a distributed video streaming application with autopoietic self-regulation that maintains structural stability and communication among distributed components with shared knowledge while maintaining expected behaviors dictated by functional requirements.

Edvard Munch, a founder of Expressionism was pre-occupied with depicting his subjective experience and the most prolific painter of self-portraits since Rembrandt. Based on evidence that faces and self-related images are more salient than other objects it is plausible that self-portraits, in which the subject and object are the same, reflect the artist’s state of mind. Although he suffered from a variety of physical and psychological illness Munch was not diagnosed with any specificity in his lifetime. Posthumous diagnoses include schizophrenia, anxiety, bipolar and other disorders. Recent research has revealed altered visual perception in such patients. The present study empirically analysed three stylistic elements of Munch’s painted self-portraits and portraits: contrast, colour and fractal dimension and his painted productivity, to determine if variations correlate with critical life events and if so whether they indicate states of mind. It found significant increases in contrast and colour brightness and persistent high complexity during critical periods supporting the conclusion that Munch is diagnostically best described as suffering from early onset schizophrenia and comorbid social anxiety disorder.

Glass and ceramics have a fundamental and crucial role in our lives due to their properties and aesthetic decoration. However, they create serious environmental problems, mainly due to their high occupation of landfills and harmful emissions. Both wastes could be utilized to reduce the natural resources' adverse environmental effects and exhaustion. With increasing environmental concerns to reduce solid waste as much as possible, the concrete industry has adopted several methods to achieve this goal. Hence this study examines the performance of self-compacted concrete (SCC) utilizing various percentages of recycled waste materials such as those deposited from glass and ceramic industries. The idea of utilizing recycled waste materials in concrete manufacturing has gained massive attention due to their impressive results in rheological and mechanical state. Recycled glass (RG) and ceramic waste powder (CWP) were utilized to replace fine aggregate and cement, respectively. Five mixes were designed, including the control mix, and the other four mixes with different dosages of RG and CWP as fine aggregate and cement replacement ranging between 5 to 25%. Mixes were tested for both rheological and mechanical properties to evaluate their compilment with SCC requirements as per codes and guidelines. The results revealed that 20%CWP or less as cement replacement and 10% or less of RG as fine aggregate replacement would provide suitable rheological properties along with mechanical ones. Utilizing recycled glass and ceramic waste powder provides strength like the mix designed with natural resources, which helps us structure economically and environmentally friendly.

Digital machine intelligence has evolved from its inception in the form of computation of numbers to AI, which is centered around performing cognitive tasks that humans can perform, such as predictive reasoning or complex calculations. The state of the art includes tasks that are easily described by a list of formal, mathematical rules or a sequence of event-driven actions such as modeling, simulation, business workflows, interaction with devices, etc., and also tasks that are easy to do “intuitively”, but are hard to describe formally or as a sequence of event-driven actions such as recognizing spoken words or faces. While these tasks are impressive, they fall short in applying common sense reasoning to new situations, filling in information gaps, or understanding and applying unspoken rules or norms. Human intelligence uses both associative memory and event-driven transaction history to make sense of what they are observing fast enough to do something about it while they are still observing it. In addition to this cognitive ability, all bio-logical systems exhibit autopoiesis and self-regulation. In this paper, we demonstrate how machine intelligence can be enhanced to include both associative memory and event-driven transaction history to create a new class of knowledge-based assistants to augment human intelligence. The digital assistants use global knowledge derived from the Large Language Models to bridge the knowledge gap between various participants interacting with each other. We use the general theory of information and schema-based knowledge representation to create the memory and history of various transactions involved in the interactions.

Chronic conditions like type 2 diabetes mellitus (T2DM), arterial hypertension (HTN), and obesity are major global health issues, causing significant morbidity and mortality. In Latin America, these conditions contribute to millions of premature deaths annually, posing challenges exacerbated by limited healthcare access and poor primary care quality, particularly in Mexico. Complementary medicine presents a potential solution, offering minimally invasive techniques to improve physical, mental, and spiritual well-being. Effective treatment adherence, crucial for positive outcomes, is influenced by self-efficacy, the ability to manage one's behavior in response to medical conditions. However, treatment adherence rates remain low, indicating a pressing public health concern. This study aimed to explore how perception of complementary medicine and treatment adherence predict self-efficacy among individuals with chronic conditions in Mexico. Analyzing data from 113 participants revealed significant correlations between variables, with treatment adherence positively associated with self-efficacy and perception of holistic medicine negatively correlated with self-efficacy. Regression analysis showed that incorporating perception of complementary medicine and treatment adherence significantly explained self-efficacy levels, suggesting the potential of complementary therapies in holistic healthcare approaches. However, collinearity between variables highlights the need for further research to better understand these relationships and their implications for improving healthcare outcomes in Mexico. Integrating complementary therapies with conventional treatments could enhance overall well-being and treatment adherence, offering promising avenues for addressing chronic conditions in the region. These findings underscore the importance of comprehensive healthcare approaches that integrate both conventional and complementary modalities to optimize health outcomes and quality of life for individuals with chronic conditions in Mexico and beyond.

This paper studies the flexural behavior of the bridge deck link slabs made with the Polyvinyl Alcohol-Strain Hardening Cementitious Composites (PVA-SHCC). Tensile and flexural properties of the PVA-SHCC with four volume fractions, i.e., 0%, 1%, 1.5%, and 2%, were evaluated first. Next, using the similarity theory, composite models with a geometric similarity ratio of 1: 5 were designed to represent the bridge deck with the link slabs. The models considered three materials for link slabs, including concrete, cement mortar and self-compacting PVA-SHCC; and two different curing ages at 7 and 56 days. Bending tests were performed to investigate the flexural behavior of the models. Based on the fractal theory, cracking characteristics of the models with different types of link slabs were compared, and the relationship between Df and the flexural behavior of the models were studied. Numerical models were built to correlate with the results from the bending tests. It is illustrated that the flexural behavior of the self-compacting PVA-SHCC link slab is better than that of concrete and cement mortar link slabs, where the crack initiation and propagation can be postponed.

Integrating a grid-connected battery into a Renewable Energy Community boosts Collective-Self-Consumption of photovoltaic produced energy and allow to perform Energy Arbitrage in the day-ahead and intraday electricity market. However, is it a cost-effective investment? The proposed analysis relay on Net Present Value assessment. Ageing affects are integrated and sensitivity analyses are conducted: considering community customers number, electricity mar-ket prices, battery cost, size and the decision to engage in energy arbitrage. Each scenario under-goes a 20-year hourly simulation using an ageing aware rolling-horizon 24-hours-looking-ahead scheduling, optimized with Mixed-integer linear Programming. Forecast errors, provid-ing of ancillary services, real time control and grid constrains are not taken into account. Simulations conducted on the Italian market indicates that a battery dedicated solely to collec-tive self-consumption is not economically appealing. However, integrating Energy Arbitrage, despite impact on battery lifespan, halves the payback period and enhances the attractiveness of larger battery investments. The Net Present Value is contingent upon battery size, customer number and market prices. However, if battery cost does not exceed 200 €/kWh the investment become cost-effective across all scenarios.

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

(1) Background: Mindfulness-based interventions (MBIs) have shown interesting preliminary ef-fects over self-regulation processes in early adolescence. However, interventions usually integrate various types of practices with no clarity about the specific effect of each of them over attentional, emotional, and behavioral regulation. The objective of this research was to evaluate the effect of two MBIs, one focused on classic attentional practices and another one focused on the recognition and expression of emotions, over attentional, emotional, and behavioral self-regulation in early adolescents. (2) Method: An experimental method was carried out. A sample of 70 children be-tween 8 and 12 years old were randomized and assigned to three experimental conditions: 1) MBI with a focus on attentional practices, 2) MBI with a focus on recognition and expression of emotions, 3) control group, waiting list. The programs lasted 8 weeks, with a weekly 1-hour online synchronic session, plus home practices. Children were evaluated before starting the in-tervention and at the end of the 8-week program. The assessed variables were: 1) Mindfulness; 2) Emotional Regulation; 3) Attentional Regulation and 4) Behavioral regulation. (3) Results: Chil-dren that participated in both programs, increased their mindfulness and emotional and be-havioral regulation scores. Only children who participated in the program with a focus on at-tention showed significant changes in their ability to self-regulate attention. (4) Conclusions: online MBIs, with attention to external and internal stimuli practices, can be a good strategy to strengthen self-regulation skills of attention, emotions, and behavior in early adolescence.

This study investigated whether reciprocal collaborative pedagogy significantly impacts Indonesian electrical engineering polytechnic students in reducing reading anxiety and improving self-efficacy in ESP reading comprehension skills. Fifty Polytechnic students were equally assigned to two groups. Control groups learned in the traditional reciprocal teaching method individually. Experimental group engaged in reciprocal collaborative pedagogy. Each group received a 6-week reading strategy training. E-learning platforms were used in this study as a complementary tool to distribute ESP reading materials, announcements, and the experiment details being taught. The quantitative and qualitative results demonstrated that the tasks in reciprocal collaborative pedagogy positively affects Indonesian electrical engineering polytechnic students' learning experience and outcomes. This pedagogy promotes a productive learning environment through peer interaction, active engagement, and peer group discussions of the whole learning activities, reducing feelings of anxiety and building confidence in ESP-specialized text.

Printed circuit boards (PCBs) make up 3 to 5% of all electronic waste. The metal content of spent PCBs can reach 40%. They usually contain valuable metals - Ag, Au, Pd, as well as other metals such as Cu, Sn, Pb, Cd, Cr, Zn, Ni and Mn. However metallic part of whole PCB is 40-60% including Cu layers between the fiberglass-polymer layers. The paper describes economics of the the valuable metals (Ag, Au, Pd) containing concentrate preparation from raw PCB. Considered the influence of pre-treatment method of PCB before the extraction of valuable metals on the extraction self-cost change. The disintegration method is based on the high-energy impact of the particles of the material to be ground, thus causing the separation of the metallic components of the PCB. In the course of the work, single and double direct grinding by the method of disintegration was studied. For the calculation the test batch 10’000 kg of two types of PCB were taken for estimation of the self costs and potential profit in case of complete valuable metals (Ag, Au, Pd) plus Cu extraction. Is shown that from 10’000 kg of studied PCB is possible to obtain 1144 and 1644 kg of metal-rich concentrate, which further should be subjected to electro-hydrochlorination for metals leaching. The main findings shows, that despite of high content of Cu in studied PCB, the commercial value is insignificant in total income from the Ag, Au and Pd sale. Was established correlation of the self-cost decrease after separative disintegration of PCB by metal content increase (by specific metals such as Au, Ag, Pd and also Cu) with the metal potential yield after extraction.

Objective The aim of this study was to examine the impact of a modern lifestyle on college female students' health, specifically focusing on their hip and knee joint mobility. The objective of this study was to assess the influence of hydraulic resistance training with the self-myofascial release technique on improving joint mobility in female college students. The training also aimed to compare the effectiveness of hydraulic resistance exercise with and without the self-myofascial release technique on hip and knee joint mobility.Methods The study used a randomized controlled trial design to recruit 30 female college students aged 18-25 through campus advertisements and announcements. Medical conditions or injuries that might have affected participation was screened. Participants were indiscriminately allocated to Group A (Hydraulic Resistance Training with Self-Myofascial Technique) or Group B (Hydraulic Resistance Training without Self-Myofascial Technique). The baseline assessments included joint range of motion (ROM) measurements using a goniometer. Certified trainers supervised the training sessions, and the data were analyzed using SD and mean using SPSS. ResultsThis study aimed to evaluate the impact of hydraulic resistance training with self-myofascial release on hip and knee joint mobility in female college students. The results showed that both groups showed improved hip and knee flexion with and without the self-myofascial release technique. However, the Group A using the self-myofascial release technique (Group A) showed a more significant enhancement, as indicated by the smaller p value compared to Group B. It has been suggested that the self-myofascial release technique may improve hip and knee joint mobility. ConclusionThis study concluded that Hydraulic Resistance Training with Self-Myofascial Release (HSMR) could improve hip and knee joint mobility among female college students. These findings suggest that integrating the self-myofascial release technique into training regimens can improve joint mobility. However, further research with larger sample sizes and extended intervention periods was needed to understand the long-term effects and sustainability of these improvements.

The study aimed to investigate how distributed leadership influences adolescents' social and emotional competence, considering the roles of student-centered instructional practices and teacher self-efficacy. Utilizing survey data from 7,246 Suzhou adolescents enrolled in the SESS program, the study employed multilevel structural equation modeling for analysis. The findings indicated a positive correlation between distributed leadership and adolescents' social and emotional competence, as evaluated by teachers. Moreover, distributed leadership was observed to exert an indirect impact on adolescents' self-assessment and teacher-rated social and emotional competence. This influence operated through several mediators: student-centered instructional practices, teacher self-efficacy, and the sequential mediating pathway of student-centered instructional practices → teacher self-efficacy. This study underscores the significance of supportive elements in fostering students' social and emotional competence. It offers valuable insights into the everyday practices of teachers, principals, and administrators. Additionally, it sheds light on how distributed leadership contributes to enhancing adolescents' overall social and emotional development.

This study explores the interplay between web-based professional development, teachers' self-efficacy, resilience, and work values in the context of the Davao Region. The investigation reveals significant findings in three key areas: web-based professional development, teachers' resilience, and teachers' work values. The survey indicates that web-based professional development significantly impacts teachers' self-efficacy, with interaction self-efficacy registering the highest mean score. Moreover, teachers' resilience is manifested across emotional, motivational, and social dimensions, with emotional resilience scoring the highest mean. Teachers' work values, encompassing physical facilities, social environment, job security, new challenges, professional growth, personal growth, and interpersonal relationships, all demonstrate high descriptive equivalents. The study establishes a significant positive correlation between web-based professional development and teachers' self-efficacy, as well as a significant relationship between teachers' resilience and their work values. Furthermore, both web-based professional development and teachers' resilience exert a significant influence on teachers' work values.The conclusions drawn from the study highlight the prominence of web-based professional development, teachers' resilience, and work values in the educational landscape of the Davao Region. The findings underscore the interconnectedness of these factors and their crucial role in shaping the teaching profession. In light of these results, recommendations are made to inform policy decisions and enhance professional development programs for teachers in the region. The study encourages a holistic understanding of the relationships between web-based professional development, self-efficacy, resilience, and work values, aiming to contribute to the continuous improvement of education quality in the Davao Region. Additionally, future research is suggested to delve deeper into specific aspects of teachers' general self-efficacy, social resilience, and interpersonal relationships for a more comprehensive understanding.

Nucleobase-containing molecules are compounds essential in biology due to the fundamental role of nucleic acids, and in particular G-quadruplex DNA and RNA in life. Moreover, some molecules different from nucleic acids isolated from different vegetal sources or microorganisms show nucleobase moieties in their structure. Nucleoamino acids and peptidyl nucleosides belong to this molecular class. Closely related to the above, nucleopeptides also known as nucleobase-bearing peptides are chimeric derivatives of synthetic origin and more rarely isolated from plants. Herein the self-assembly properties of a vast number of structures, belonging to the nucleic acid and nucleo amino acid/nucleopeptide family, is explored in the light of the recent scientific literature. Moreover, several technologically relevant properties such as the hydrogelation ability of some of the nucleobase-containing derivatives are reviewed in order to open the door to future experimental investigations of newly-inspired nucleobase-driven hydrogels. Nucleobase-containing molecules, such as mononucleosides, DNA, RNA, quadruplex (G4)-forming oligonucleotides and nucleopeptides, are paramount in gel and hydrogel formation owing to their distinctive molecular attributes and ability to self-assemble in biomolecular nanosystems with the most diverse applications in different fields of biomedicine and nanotechnology. In fact, these molecules and their gels present numerous advantages, underscoring their significance and applicability in both material science and biomedicine. Their versatility, capability for molecular recognition, responsiveness to stimuli, biocompatibility, and biodegradability collectively contribute to their prominence in modern nanotechnology and biomedicine. In this review, we emphasize the critical role of nucleobase-containing molecules of different nature in pioneering novel materials with multifaceted applications highlighting their potential in therapy, diagnostics, and new nanomaterials fabrication as required in order to address the numerous current biomedical and nanotechnological challenges.

Self-consolidating concrete (SCC) is highly workable concrete that flows under minimal vibrations into congested reinforcement and sophisticated formworks. The properties of SCC are achieved by incorporating chemical admixtures. In this study, we analyzed if SCC could be designed with a cheap, easily accessible plant extract as a bio-admixture known as aloe vera mucilage (AVM). The main aim is to determine the effect of AVM on SCC prepared from ordinary Portland cement (OPC) and blended cement LC3, consisting of clinker (50%), calcined waste clay (30%), limestone (15%), and gypsum (5%). The AVM is applied in different dosages of up to 10%. The findings showed that the LC3 had a lower rate of consistency, lower slump values, and higher initial and final setting times compared to OPC. The V-funnel and L-box values were reduced with increased plasticizer dosing. Additionally, the OPC samples with both plasticizers achieved higher compressive strength than LC3 in the 7, 14, and 28 curing ages. However, the 2.5 % dosage of AVM showed some increase in compressive strength in both OPC and LC3 samples. Overall, AVM is comparable to the commercial plasticizer used as both influence the reduction of yield stress with an increase in slump flow.

Text annotation is an essential element of the natural language processing approaches. The manual annotation process performed by humans has several drawbacks, such as subjectivity, slowness, fatigue, and possibly carelessness. In addition, annotators may annotate ambiguous data. So, we developed the concept of automated annotation to get the best annotations using several machine-learning approaches. The proposed approach is based on an ensemble algorithm of meta-learners and meta-vectorizer techniques. The approach employs a semi-supervised learning technique for automated annotation, aimed at detecting hate speech. This involves leveraging various machine learning algorithms, including Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbors (KNN), and Naive Bayes (NB), in conjunction with Word2Vec and TF-IDF text extraction methods. The annotation process is performed using 13,169 Indonesian YouTube comments data. The proposed model used a Stemming approach using data from Sastrawi and also new data of 2,245 words. Semi-supervised learning uses 5%, 10%, and 20% of labeled data as compared to performing labeling based on 80% of the datasets. In semi-supervised learning, the model learns from the labeled data, which provides explicit information, and the unlabeled data, which offers implicit insights. This hybrid approach enables the model to generalize and make informed predictions even when limited labeled data is available, ultimately enhancing its ability to handle real-world scenarios with scarce annotated information. In addition, the proposed method uses a variety of thresholds for matching words labeled with hate speech ranging from 0.6, 0.7, 0.8, and 0.9. The experiment showed that the KNN-Word2ec model has the best accuracy value of 96.9% with a scenario of 5%:80%:0.9. However, several other methods have also accuracy above 90%, such as SVM and DT based on both text extraction methods in several test scenarios.

Eutrophication is a major environmental issue with many negative consequences, such as hypoxia and harmful cyanotoxins production. Monitoring coastal eutrophication is a crucial, especially for island countries like the Republic of Cyprus, which are economically dependent on the touristic sector. Additionally, the open-sea aquaculture industry in Cyprus has been exhibiting an increase in the last decades and environmental monitoring to identify possible signs of eutrophication is mandatory according to the legislation. Therefore, in this modelling study, two different types of Artificial Neural Networks (ANNs) are developed based on in situ-data collected from stations located in the coastal waters of Cyprus. Theses ANNs aim to model the eutrophication phenomenon based on two different data-driven modelling procedures. Firstly, the self-organizing map (SOM) ANN examines several water quality parameters (specifically water temperature, salinity, nitrogen species, ortho-phosphates, dissolved oxygen and electrical conductivity) interactions with the Chlorophyll-a parameter. The SOM model enables us to visualize the monitored parameters relationships and to comprehend complex biological mechanisms related to Chlorophyll-a production. A second feed-forward ANN model is also developed for predicting the Chlorophyll-a levels. Based on this ANN model, several scenarios associated to the eutrophication-related water quality parameters can be extracted. The combination of these two ANNs models is considered a holistic modelling approximation for the identification of eutrophication scenarios, since it enables not only the prediction of the Chlorophyll-a parameter levels, but also the “capturing” of hidden biological mechanisms associated with algal production.

"Finding fresh water in the ocean of data." is a challenge that all deep learning domains struggle with, especially in the area of hyperspectral image analysis. As hyperspectral remote sensing technology advances by leaps and bounds, there are increasing amounts of hyperspectral images(HSIs) can be available. Whereas in fact, these unlabeled HSIs are powerless to be used as material to driven a supervised learning task due to the extremely expensive labeling costs and some unknown regions. Although learning-based methods have achieved remarkable performance due to their superior ability to represent features, at the cost, these methods are complex, inflexible and tough to carry out transfer learning. In this paper, we propose the "Instructional Mask AutoEncoder"(IMAE), which is a simple and powerful self-supervised learner for HSI classification that uses a transformer-based mask autoencoder to extract the general features of HSIs through a self-reconstructing agent task. Moreover, we utilize the metric learning to perform an instructor which can direct the model focus on the human interested region of the input so that we can alleviate the defects of transformer-based model such as local attention distraction, lack of inductive bias and tremendous training data requirement. In downstream forward propagation, instead of global average pooling, we employ a learnable aggregation to put the tokens into fullplay. The obtained results illustrate that our method effectively accelerates the convergence rate and promotes the performance in downstream task.

Self-medication with natural products during disease outbreaks in developing countries poses significant public health challenges yet remains relatively unexplored in the published literature. This study aims to assess the implications of such practices, address the state and quality of evidence, identify gaps in the literature, explore determinants and contributing factors, elucidating the implications, and propose possible solutions. Using a modified systematic literature review methodology, 20 articles were selected from 1,683 reports after applying rigorous selection criteria and quality assessment using the AXIS critical evaluation tool. The findings revealed a dearth of literature concerning the implications of self-medication with natural products during disease outbreaks, highlighting the need for further investigation in this domain. Key determinants included gender, acquisition, and ideological factors, while common side effects comprised diarrhoea, stomach pain, sweating, headache, and nausea/vomiting. Notably, self-medication with natural products was associated with reduced communication with healthcare providers, leading to underreported side effects. It also creates a false sense of safety which may potentially exacerbate the spread of the prevailing disease. In conclusion, the rampant use of natural products for self-medication necessitates increased awareness among the population, encouraging individuals to seek professional medical care and support when faced with disease outbreaks.

This article presents a state-of-the-art literature review to understand people’s perceptions and opinions of Autonomous Vehicles (AVs) and the factors that influence AV adoption. A strategic literature search was conducted to select articles for this review. Most of the articles were published within the last five years and they used a household questionnaire survey to collect data. Mostly, they used statistical and econometric methods to evaluate the factors that affect people’s intentions to adopt AVs. The results show that psychological factors often appear as the most important internal factors of people’s willingness to adopt AVs. Additionally, other internal factors such as the socioeconomic profile of individuals and their household, and knowledge and familiarity with AV technologies would affect AV adoption tendencies. User attributes also indirectly affect AV adoption by influencing the psychological factors of users. We identify several critical external factors such as opportunities (e.g., safety and security, low congestion, energy use) and challenges (e.g., system failure, privacy breach, and legal issues), while another influential group includes transportation factors (e.g., travel mode, distance, and time), urban form (e.g., urban/rural, density, land use diversity), affinity to new technology, and the institutional regulatory environment. We discuss some recommendations for policy makers, auto industries, and private stakeholders to formulate policies and strategies to increase market share of AVs. Finally, we identify some limitations of previous studies and provide a blueprint for future research on AV adoption.

The impact of early maternal age on several outcomes (e.g. mental health issues, delinquency, aggression, impulsivity, victimization and interpersonal difficulties) has been studied since the late 1980s. Research has demonstrated links between exposure to adverse childhood experiences (ACEs) and trauma related outcomes such as substance abuse, interpersonal and self-directed violence, and sexual risk-taking, to name a few. The current study analyzed the incidence of ACE exposure and mental health outcomes (e.g. suicidal behaviors or self-harm, psychiatric hospitalizations, and substance use) as related to early maternal age in youth known to have experienced trafficking or sexual abuse. General demographics and incidence of various experiences and clinical presentations (e.g. ACE exposure, age of mother at birth of youth and age at first birth), and history of self-harm, substance use and psychiatric hospitalization were examined in a sample of 225 youth referred for services subsequent to experiences of sex trafficking or exploitation. Relationships between ACE exposure and maternal age related to mental health outcomes was also examined. Trafficked and exploited youth reported rates of ACE exposure and poor mental health outcomes at far greater rates than the general population. Significant findings were related to early maternal age and engaging in self-harm or suicidal behaviors. Previously anticipated mediational analyses were not conducted due to the sample size and lack of correlations. Findings highlight the significant ACE exposure and high needs of exploited, trafficked and abused youth and related mental health outcomes, emphasizing the need for early and comprehensive preventive and therapeutic interventions.

This study investigated the effects of flamenco dance on mood and subjective well-being in older women. In a broader context, the question was raised about how participation in dance activities could influence the emotional well-being of this demographic group. Thirty-four older women, aged between 62 and 79 years, with a mean age of 70.11 years (SD = 5.13), voluntarily participated in the study after providing informed consent. All of them were enrolled in a 12-week flamenco dance workshop conducted by the Autoestima Flamenca Association, with one two-hour session per week. Mood measurements (sadness, anxiety, anger, and joy) were taken biweekly before and after each session, and subjective well-being assessments were conducted before and after the intervention. The results revealed significant improvements in the overall mood of the participants, as well as an increase in their subjective well-being. These improvements were observed at both the individual and group levels. In conclusion, participation in flamenco dance demonstrated a positive impact on the mood and subjective well-being of older women. These findings support the idea that physical and artistic activities, such as flamenco dance, can play an important role in promoting emotional well-being in this age group.

Recently, there is a growing need for sensors that can operate autonomously, without the need for an external power source. This is especially important in applications where conventional power sources, such as batteries, are impractical or difficult to replace. Self-powered sensors have emerged as a promising solution to this challenge, offering a range of benefits such as low cost, high stability, and environmental friendliness. One of the most promising self-powered sensor technologies is L-S TENG, which stands for the liquid-solid triboelectric nanogenerator. This technology works by harnessing the mechanical energy generated by external stimuli such as pressure, touch, or vibration, and converting it into electrical energy that can be used to power sensors and other electronic devices. Therefore, self-powered based on L-S TENG, which provides numerous benefits such as rapidly responding, portability, cost-effectiveness, and miniaturization, is critical for increasing living standards and optimizing industrial processes. In this review paper, the working principle with three basic modes has been briefly introduced firstly. After that, affecting parameters to L-S TENG are reviewed based on the properties of the liquid and solid phases. With different working principles, L-S TENG has designed a lot of structure that works as a self-powered sensor for pressure/force change, liquid flow motion, concentration, and chemical detection or biochemical sensing. Moreover, the continuous output signal of TENG plays an important role in a real-time sensor that is vital to the growth of the Internet of Things.

The research proposes a self-directed paradigm with self-design using the existing technology with LMS modules, discussions, and self-tests. The research establishes its criteria for assurance of teaching and learning known as 7x2C for the compliance of content, and appearance along with other criteria for the analysis. The above technology of web and LMS is tested for a business course that is technology-oriented in creating an experiential learning system for Covid-19 awareness. Similarly, among other ideas, an environment for educating learners about diabetes and obesity has been designed. The project is known as Sunchoke with its theme of Grow, Eat, and Heal. The educators can use their technology and apply self-directed pedagogy to transform this approach to their customized ways by using their content and rubrics.

(1) Background: The 2002 Budapest Open Access Initiative recommended on self-archiving of scientific articles in open repositories as the “green road” to open access. Twenty years later, only one part of the researchers deposits their publications in open repositories; moreover, one part of the repositories’ content is not based on self-archived deposits but on mediated nonfaculty contributions. The purpose of the paper is to provide more empirical evidence on this situation and to assess the impact on the future of the green road. (2) Methods: We analyzed the contributions on the French national HAL repository from more than 1,000 laboratories affiliated to the ten most important French research universities, with a focus on 2020, representing 14,023 contributor accounts and 166,939 deposits. (3) Results: We identified seven different types of contributor accounts, including deposits from nonfaculty staff and import flows from other platforms. Mediated nonfaculty contribution accounts for at least 48% of the deposits. We also identified difference between institutions and disciplines. (4) Conclusions: Our empirical results reveal a transformation of open repositories from self-archiving and direct scientific communication towards research information management. Repositories like HAL are somewhere in the middle of the process. The paper describes data quality as the main issue and major challenge of this transformation.

Supramolecular structures are of great interest due to their applicability in various scientific and industrial fields. These fields of scientific action include promising applications i biomedical areas, in sensing, and in conductive and electrical phases. The sensible definition of supramolecular molecules is being set by investigators, who, because of the different sensitivities of their methods and observational timescales, may have different views as to what constitute these supramolecular structures. Besides, diverse polymers can be found to offer unique avenues for multifunctional systems with properties in medicine industrial applications. Aspects of this review provide different conceptual strategies to address molecular design, properties, and potential applications of self-assembly materials and the use of metal coordination as a feasible and useful strategy for constructing complex supramolecular structures. In this order of appreciations and ideas order, it is that this review report involves classic topics such as hydrogels that range from their relationship with drug delivery systems, to metallo-supramolecular materials, through applications in ophthalmic systems, in applications that require adhesiveness and electrically conductive hydrogels. This review also addresses systems that are largely based on hydrogel chemistry and the enormous opportunities to design very specific structures for applications that demand enormous specificity.

User experience and user requirements are two independent approaches. User requirements address the customer's requirements and expectations for the solution, whereas user experience encompasses all aspects of how the user interacts with and uses the software. The software product should be easy to use and has usable features. Moreover, the additional value for the software is if the product has an attractive design or working environment that is in line with user behaviors, it can occur if integrate software requirements and user experience. Integration escalates developer productivity by focusing on features that meet the user's needs and expectations. That integration improves efficiency in software development by identifying and addressing problems that may arise during the development process, saving developer time and effort in developing software. The usage context of integration of user experience and user requirements in UX Journey contributes increase developer productivity and self-efficacy in developing software by focusing development on features that match the user needs, as well as increasing efficiency in overcoming problems that arise during the development process. UX Journey makes developers feel more confident in their ability to develop quality software.

Here I describe an overlooked form of non-biological paralife (i.e., near-life) that has been evolving on Earth for millions of years, and is currently in the final stages of transitioning into a new form of life. Any consideration of non-biological life or paralife is complicated by the fact that there is no consensus among biologists for the definition of life. This ambiguity has caused disagreement about whether subcellular reproduction systems like viruses are a form of life, despite having genomes, mutations, heritable phenotypes and system-improving evolution. To resolve this problem, I develop a definition of life that is entirely functional and independent of any of the structural idiosyncrasies of biological life on Earth: an order-generating system controlled by internally-encoded information that perpetuates itself by functioning to counteract its entropic decay. Using this definition, subcellular transposons, plasmids, and viruses are paralife because they match the definition of life in all ways except that they induce their order-generating functioning by a living host rather through their own self-sustaining production system. Using this functional definition of life, I show that utility- products (UPs) like fabricated hand tools are part of induced-reproduction systems that have features equivalent to biological genomes, mutations, heritable phenotypes, and a process of system-improving evolution. The perceived benefit of utility-products causes them to induce their reproduction by a biological life-form (humans). For these reasons, human utility products are functionally just as close to being a form of life as subcellular transposons, plasmids, and viruses, i.e., they are Utility-Product paralife (UP-parlife). I also show that some forms of UP-paralife are currently evolving into mechanical life that is capable of both self- sustaining reproduction and system-improving evolution without outside assistance. This transition requires the development of a high level of factory and/or UP automation and artificial intelligence (AI) that is capable of complex reasoning, imagination and creativity. Finally, I consider the influence of UP-life and UP-paralife on the development of the level of structural complexity in the universe, and I briefly speculate about how these non-biological forms of life and paralife will influence the expansion of scientific knowledge about the universe.

Refugee subject position is an evolving topic of contention in the world today with increased migrant and refugee mobilities. Urban refugee spaces are often segregated in the form of colonies, ethnic villages, even ghettos, embodying institutionalised discourses of apathy and violence. These spaces only occupy the cracks and margins of the normative, formal city, as appropriations of inhospitable natural terrains and urban systems. The paper discusses how refugees compete for resources for survival as “bio-political” subjects and are often held summarily responsible for causing ecological stress in host environments. After the 1947 Partition of the Indian subcontinent, millions of Hindu Bengali refugees from East Pakistan flooded the Eastern Indian states of West Bengal, Assam and Tripura. Kolkata particularly drew millions for better livelihood prospects. Facing government apathy and local violence, East-Bengali refugees appropriated the urban fringes of Kolkata and claimed their right to urban space through the political act of squatting or Jabar-Dakhal. The intent of this paper is to investigate and map the spatial distribution of East-Bengali refugee squatters and elaborate on how they transformed the terrain and distributed resources through self-management tactics. This spatial history case-study attempts to uncover locational data from archival government records, existing academic literature and fieldwork to visualise where the 145 pre-1950 and the 123 post-1950 Jabar-Dakhal colonies were located in the Kolkata Metropolitan Area. This case of successful refugee self-settlement is qualitatively read in relation to the major areas of ecological stress in Kolkata. One of the UN sustainable development goals is to make cities and human settlements ‘inclusive, safe, resilient and sustainable’. This paper hopes to encourage further studies of urban refugee self-settlement and local integration as a viable but complex socio-political-environmental process.

As a mediator variable, self-confidence is one of the most effective elements of the decision-making process of consumer behaviour. This research has studied the effects of different aspects of consuming fashion on the self-confidence and behaviour of consumers in Tehran’s clothing market. This study has considered the acceptance of new products, interest in mode and fashion, utilitarianism, and personal taste in its analysis. This research aims to understand the fashion buying behaviour amongst Iranian consumers in consideration of their attitude towards self-confidence and aspects of fashion consumption. The statistic sample is 400 consumers from Tehran’s clothing market who have been chosen based on the random availability procedure. The primary tool in this research was a questionary used to testify the assumptions and a model fit created by using structural equations and factor analysis. This research showed that the interest in mode and fashion, personal taste, utilitarianism, and new products positively impact self-confidence. In addition, the positive impact of self-confidence on fashion buying behaviour was confirmed.

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

Introduction. Self-sampling has the potential to increase cervical cancer screening (CCS) among women living with HIV (WLWH) in low and middle-income countries (LMICs). However, our understanding of how HPV self-collection studies have been conducted in WLWH is limited. The purpose of this scoping review was to examine the extent to which the HPV self-sampling has been applied among WLWH in LMICs. Method: We conducted multiple searches in several databases for articles published between 2000 and January 2022. With the combination of keywords relating to HPV self-sampling, LMICs, and WLWH, we retrieved over 9,000 articles. We used pre-defined inclusion and exclusion criteria to select relevant studies for this review. Once a study met the inclusion criteria, we created a table to extract each study’s characteristics and classified them under common themes. We used a qualitative descriptive approach to summarize the scoping results. Results: A total of 12 articles were included in the final review. Overall, 3,178 women were enrolled in those studies and 2,105 (66%) of them were WLWH. The self-sampling participation rate was 92.6%. The findings of our study show that 43% of the WLWH in 8 of the studies reviewed tested positive for high-risk HPV (hr-HPV) genotypes, indicating 4 out of 10 WLWH in the studies are at risk of cervical cancer. The prevalence of the hr-HPV in WLWH was 18% higher than that of HIV-negative women. Most women in the study found the self-sampling experience acceptable, easy to use, convenient, and comfortable. Self-sampling performance in detecting hr HPV genotypes is comparable to clinician-performed sampling. However, limited access (i.e., affordability, availability, transportation), limited knowledge about self-screening, doubts about the credibility of self-sampling results, and stigma remain barriers to wide acceptance and implementation of self-sampling. In conclusion, the findings of this review highlight that (a) cervical cancer is a threat to every sexually active woman but for WLWH the threat increases, (b) self-sampling laboratory performance is similar to clinician performed sampling, (c) self-sampling is associated with an increase in cervical cancer screening uptake and (d) WLWH reported a positive experience with self-sampling. However, personal, environmental, and structural barriers challenge the application of self-sampling in LMICs, and these need to be addressed. Keywords: keyword 1; keyword 2; keyword 3 (List three to ten pertinent keywords specific to the article yet reasonably common within the subject discipline.)

This work proposes a new theoretical approach for the next generation of extremely efficient motors, whose impact will be substantial for future sustainable technological development. Building on well-known physical concepts, this approach introduces the concept of equilibrium of mutual retention, upon which a device remains in an equilibrium state between movement and attraction-repulsion. This new theoretical approach showed promising results in computer simulation experiments, indicating that the equilibrium of mutual retention can allow for a new way of modeling an extremely efficient motor. Finally, a theoretical efficiency analysis showed the need to expand the limits of some physical concepts already established.

Universities have increasingly incorporated a third mission into their strategic planning. In addition to teaching and research, they have emphasised the training of entrepreneurs. However, there is still a lot of work to be done, as this process is facing resistance. The Entrepreneurial University covers all disciplines, including faculties of education. However, it has been shown that entrepreneurship tends to be more related to the faculties of economics and engineering, with a lesser presence in the faculties of education for various reasons: they consider entrepreneurship to be alien to their teaching role, there is a lack of entrepreneurial culture, and the objective of the Entrepreneurial University is unknown. The aim of this study is to analyse the level of entrepreneurship in Spanish faculties and schools of education. Forty deans and heads of education faculties in Spain took part in the survey. The results indicated a sufficient level of entrepreneurship; the dimensions related to active methodologies, and mission and strategy were the most developed, whereas entrepreneurship funding and entrepreneurship training for faculty employees were the least developed areas. Some deans noted that entrepreneurship was alien to their professional performance, although courses and good practices for the development of entrepreneurial initiative are gradually being implemented.

Teacher burnout has been revealed to be one of the most common negative consequence of the COVID-19 pandemic The purpose of this study was to identify distinct psychological resources and burnout risk profiles of teachers and to examine their association with Kolb’s Educator Roles and the professional experience. Methods: The survey data were collected from 330 preschool and primary school (84 males, Mage = 38.3, SD = 9.14) teachers using a convenience sampling method. Results: The two-step cluster analysis revealed two distinct profiles. The first profile, ‘High psychological resources, no burnout risk’, was characterised by absent symptoms of burnout and increased levels of well-being, self-control, and positive emotionality. The second profile, ‘Moderate psychological resources, mild burnout’, was associated with medium levels of well-being, self-control and positive emotionality accompanied by mild burnout. Our findings highlighted that cluster 1 had a significantly higher score for the Facilitator role and cluster 2 for the Expert and Coach roles. In addition, teachers with less professional experience were more likely to belong to cluster 1, taking into account their good skills on digital literacy. Conclusions: These findings help to provide new insights into the explanation of teacher burnout and the design of intervention programmes.

Alphaviruses, flaviviruses, measles viruses and rhabdoviruses are enveloped single-stranded RNA viruses, which have been engineered as expression vector systems for recombinant protein expression and vaccine development. Due to the presence of non-structural genes encoding the replicase complex, a 200,000-fold amplification of viral RNA occurs in the cytoplasm of infected cells providing extreme transgene expression levels, which is why they are named self-replicating RNA viruses. Expression of surface proteins of pathogens causing infectious disease and tumor antigens provide the basis for vaccine development against infectious diseases and cancer. The self-replicating RNA viral vectors can be administered as replicon RNA, recombinant viral particles, or layered DNA/RNA replicons. Self-replicating RNA viral vectors have been applied for vaccine development against influenza virus, HIV, hepatitis B virus, human papilloma virus, Ebola virus and recently coronaviruses, especially SARS-CoV-2 the causative agent of the COVID-19 pandemic. Measles virus and rhabdovirus vector-based SARS-CoV-2 vaccine candidates have been subjected to clinical trials. Moreover, RNA vaccine candidates based on self-amplifying alphaviruses have also been evaluated in clinical settings. Various cancers such as brain, breast, lung, ovarian, prostate cancer and melanoma have also been targeted for vaccine development. Robust immune responses and protection have been demonstrated in animal models. Clinical trials have shown good safety and target-specific immune responses. Ervebo, the VSV-based vaccine against Ebola virus disease has been approved for human use.

An introductory course of Arka Dhyana, also known as Intuitive Meditation (IM), consisting of five sessions, was offered to an international audience via Zoom technology. Participants were shown how to connect to their deeper self, essence or soul by bringing their I-ego-awareness from the thinking mind, often associated with the frontal part of the brain, to 19 energetic stations in the body including the heart centre. In this limited study, evaluation was both process and outcome orientated and included HeartMath (HM) Inner Balance or emWave2 electronic technology to measure mean coherence and achievement before and after each session. A highly significant increase in both coherence and achievement in six participants was found, which was also reflected in a reported increase in wellbeing related to feelings in qualitative statements indicating changes in levels of consciousness and individual transformative experiences as predicted by the Theory of the Six Main Levels of Consciousness.

The market for autonomous vehicles (AV) is expected to experience significant growth over the coming decades and to revolutionize the future of transportation and mobility. The AV is a vehicle that is capable of perceiving its environment and perform driving tasks safely and efficiently with little or no human intervention and is anticipated to eventually replace conventional vehicles. Self-driving vehicles employ various sensors to sense and perceive their surroundings and, also rely on advances in 5G communication technology to achieve this objective. Sensors are fundamental to the perception of surroundings and the development of sensor technologies associated with AVs has advanced at a significant pace in recent years. Despite remarkable advancements, sensors can still fail to operate as required, due to for example, hardware defects, noise and environment conditions. Hence, it is not desirable to rely on a single sensor for any autonomous driving task. The practical approaches shown in recent research is to incorporate multiple, complementary sensors to overcome the shortcomings of individual sensors operating independently. This article reviews the technical performance and capabilities of sensors applicable to autonomous vehicles, mainly focusing on vision cameras, LiDAR and Radar sensors. The review also considers the compatibility of sensors with various software systems enabling the multi-sensor fusion approach for obstacle detection. This review article concludes by highlighting some of the challenges and possible future research directions.

In this paper a novel and flexible solution for fault prediction based on data collected from Supervisory Control and Data Acquisition (SCADA) system is presented. Generic fault/status prediction is offered by means of a data driven approach based on a self-organizing map (SOM)and the definition of an original Key Performance Indicator (KPI). The model has been assessed on a park of three photovoltaic (PV) plants with installed capacity up to 10 MW, and on more than sixty inverter modules of three different technology brands. The results indicate that the proposed method is effective in predicting incipient generic faults in average up to 7 days in advance with true positives rate up to 95%. The model is easily deployable for on-line monitoring of anomalies on new PV plants and technologies, requiring only the availability of historical SCADA data, fault taxonomy and inverter electrical datasheet.

The increasingly used approach of combining different simulation software in testing of automated driving systems (ADS) increases the need for potential and convenient software designs. Recently developed co-simulation platforms (CSP) provide the possibility to cover the high demand on testing kilometers for ADS by combining vehicle simulation software (VSS) with traffic flow simulation software (TFSS) environments. The emphasis on the demand of testing kilometers is not enough to choose a suitable CSP. The complexity level of the used vehicle, object, sensors and environment models is essential for valid and representative simulation results. Choosing a suitable CSP raises the question of how the test procedures should be defined and constructed and what the relevant test scenarios are. Parameters of the ADS, the environments, objects, sensors in VSS as well as traffic parameters in TFSS can be used to define and generate test scenarios. In order to generate a large number of scenarios in a systematic and automated way, suitable and appropriate software designs are required. In this paper we present a software design for CSP based on the Model-View-Controller (MVC) design pattern and implementation of a complex CSP for virtual testing of ADS. Based on this design, an implementation of a CSP is presented using the VSS from IPG Automotive called CarMaker and the TFSS from PTV Group called Vissim. The results have shown that the presented CSP design and the implementation of the co-simulation can be used to generate relevant scenarios for testing of ADS.

We previously showed that, based on the frequency and distribution of specific cell types, rainbow trout (RT) intestinal mucosa is divided in two regions that form a complex nonlinear 3D pattern and have a different renewal rate. This work had two aims. First, to investigate whether the unusual distribution of cell populations reflects a similar distribution of functional activities. To this end, we determined the protein expression pattern of three well defined enterocytes functional markers: Peptide Transporter 1 (PepT1), Sodium-Glucose/Galactose Transporter 1 (SGLT-1) and fatty acid-binding protein 2 (Fabp2). Second, to characterize the structure of RT intestinal stem cells (ISC) niche and to determine whether the different proliferative rates correlate with a different organization and/or extension of the stem cells population. We studied the expression and localization of well-characterized mammal ISC markers: LGR5, HOPX, SOX9, NOTCH1, DLL1 and WNT3A. Our results indicate that morphological similarity is associated with similar function only between the first portion of the mid-intestine and the apical part of the complex folds in the second portion. Mammal ISC markers are all expressed in RT but their localization is completely different suggesting also substantial functional differences. Finally, higher renewal rates are supported by a more abundant ISC population.

We propose a novel transfer learning method for speech emotion recognition allowing us to obtain promising results when only few training data is available. With as low as 125 examples per emotion class, we were able to reach a higher accuracy than a strong baseline trained on 8 times more data. Our method leverages knowledge contained in pre-trained speech representations extracted from models trained on a more general self-supervised task which doesn’t require human annotations, such as the wav2vec model. We provide detailed insights on the benefits of our approach by varying the training data size, which can help labeling teams to work more efficiently. We compare performance with other popular methods on the IEMOCAP dataset, a well-benchmarked dataset among the Speech Emotion Recognition (SER) research community. Furthermore, we demonstrate that results can be greatly improved by combining acoustic and linguistic knowledge from transfer learning. We align acoustic pre-trained representations with semantic representations from the BERT model through an attention-based recurrent neural network. Performance improves significantly when combining both modalities and scales with the amount of data. When trained on the full IEMOCAP dataset, we reach a new state-of-the-art of 73.9% unweighted accuracy (UA).

Influence of particle size on the mechanical strength of alkali activated material from waste refractory monolithic was investigated in this study. Precursor was chemically and mineralogically analysed, separated on 4 fractions and alkali activated with Na-water glass. Alkali activated materials were thoroughly investigated under SEM and XRD to evaluate the not predicted differences in mechanical strength. Time dependence of curing temperature on mechanical strength was investigated in the sample prepared from a fraction that caused the highest compressive strength.

Within the task of replacing bronze journal bearings with aluminum the processes taking part on friction surface of high-alloyed aluminum alloys working with steel are investigated. The surface and subsurface layer of experimental aluminum bearings were studied before and after tribological tests by scanning electron microscopy including energy-dispersive analysis. Both structural and composition changes in surface layer are shown. It has been revealed that secondary structures are formed on the surface during friction process and include all the chemical elements of the tribosystem which is a consequence of its self-organization. The interaction behavior of some chemical elements of tribological system is discussed.

The increasing number of registered road crashes involving cyclists during the last decade, and the high proportion of road crashes resulting in severe injuries and fatalities among cyclists constitutes a global issue for community health, urban development, and sustainability. Nowadays, the incidence of many risk factors for road crashes of cyclists remains largely unexplained. Given the importance of this issue, the present study has been conducted with the aim of determining relationships between infrastructural, human factors and safety outcomes of cyclists. Objectives: This study aimed, first, to examine the relationship between key infrastructural and human factors present in cycling, bicycle-user characteristics, and their self-reported experience with road crashes. And second, to determine whether a set of key infrastructural and human factors may predict their self-reported road crashes. Methods: For this cross-sectional study, a total of 1064 cyclists (38.8% women, 61.2% men; M = 32.8 years of age) from 20 different countries across Europe, South America and North America, participated in an online survey composed of four sections: demographic data and cycling-related factors, human factors, perceptions on infrastructural factors, and road crashes suffered. Results: The results of this study showed significant associations between human factors, infrastructural conditions and self-reported road crashes. Also, a logistic regression model found that self-reported road crashes of cyclists could be predicted through variables such as age, riding intensity, risky behaviors, and problematic user/infrastructure interactions. Conclusions: The results of this study suggest that self-reported road crashes of cyclists are influenced by features related to the user and their interaction with infrastructural characteristics of the road.

Highly flexible manufacturing systems require continuous run-time (self-) optimization of processes with respect to various parameters, e.g. efficiency, availability, energy consumption etc. A promising approach for achieving (self-) optimization in manufacturing systems is the usage of the context sensitivity approach. Thereby the Cyber-Physical Systems play an important role as sources of information to achieve context sensitivity. In this paper it is demonstrated how context sensitivity can be used to realize a holistic solution for (self-) optimization of discrete flexible manufacturing systems, by making use of Cyber-Physical System integrated in manufacturing systems/processes. A generic approach for context sensitivity, based on self-learning algorithms, is proposed aiming at a various manufacturing systems. The new solution is propos encompassing run-time context extractor and optimizer. Based on the self-learning module both context extraction and optimizer are continuously learning and improving their performance. The solution is following Service Oriented Architecture principles. The generic solution is developed and then applied to two very different manufacturing processes. This paper proposes a holistic solution to achieve context sensitivity for Flexible Manufacturing Systems, whereby the knowledge created by applying the context sensitivity approach can be used for (self-) optimization of manufacturing processes.

Cerebellar ataxia, neuronopathy, and vestibular areflexia syndrome (CANVAS) is caused by biallelic pathogenic expansions, or compound heterozygosity with other pathogenic variants in the RFC1 gene. CANVAS is estimated to be underdiagnosed, both because of the lack of formal diagnostic criteria and molecular challenges that translate to lesser access and high cost of routine testing. Our aim was to address the need for making CANVAS genetic testing routine, by designing a streamlined two-step PCR consisting of a short-allele screening PCR and a confirmatory PCR with fragment capillary electrophoresis detection. Exome sequencing of RFC1 was additionally foreseen to resolve potential compound heterozygosity cases. Specificity of our approach was evaluated using ataxia patients with known non-CANVAS diagnoses, and optimized using Southern blot confirmed CANVAS patients. We evaluated our approach by testing patients consecutively referred for clinically suspected CANVAS using first the two-step PCR, followed by exome sequencing. Our approach was able to accurately identify negative and confirm positive cases in prospectively collected suspected CANVAS patients presenting with at least three typical clinical signs. The proposed testing approach provides an alternative method able to clearly distinguish between CANVAS negative and positive cases and can be easily incorporated into the genetic diagnostic laboratory workflow.

A model of space-time is presented, whose origin is set before the appearance of any universe. It uses concepts of previous work, a central one being that the information about the laws of nature is stored locally everywhere in space such that all physical processes obey them. Since information storage is bound to matter or energy, space is assumed to consist of dark energy (DE), which thus assumes an importance far beyond its function as a stopgap for an energy deficit in our universe ( U ). The primordial state (PS) is a timeless and relatively large space of 3 dimensions described by a Wheeler DeWitt (WDW) equation, whose solution is interpreted as an arrangement of very many space quanta and possesses properties that remind of the time-honored concept of the unmoved mover. The comparison of WDW solutions belonging to different mass densities of the DE favors one in which the space quanta are mini black holes. This allows the space quanta to be imagined as mass points which are kept at a distance compatible with the quantum volume by the Pauli principle or something equivalent. Triggered by a dramatic increase in probability, the PS is followed by a time-controlled sequence of quantized expansion states (ES i ). The concomitant emergence of time occurs in a lightning huge crash, consuming almost all DE of the PS. The subsequent quantized ES i emerge from a WDW equation, novelly derived from a solution of the modified cosmic momentum equation, which classically describes an exponential space expansion (CS). The huge volme of the ES i is also used as size of the PS. Information is assumed to be geometrically stored in arrangements of space quanta similar to how genetic information is stored in DNA molecules. During the CS information must be transferred from already existing "information quanta" to new ones. This requires time, slows down the expansion and is taken into account by a friction term in the cosmological momentum equation thus modified. Since the Friedmann-Lemaître (FL) equation remains fully valid, the model is widely based on recognized equations, so that some results may be understood as interpretations.

The absolute time and relative time are defined in terms of the 4-D Euclidean space. Our universe is the 3-D x₁x₂x₃ quantized photon space which follows the absolute time simultaneity when the universe moves along the absolute time axis of ct. The length expansion of Dx = gDx₀ is derived under the condition of the absolute time (ct) simultaneity. From the similarity between this length expansion of Dx = gDx₀ and the energy increasing of E = gE₀, it is assumed that the energy is proportional to the particle size of Dx. The extension of this assumption to the 4-D Euclidean space gives the new definition that the particle energy (E) is the 4-D volume. Then, the particle mass energy is defined as E= mc² = cDtDV = gE₀. The masses of the elementary particles are originated from the 4-D warped volume of the photon space because the particle is the warped photon space with the velocity of v < c. Therefore, the Higgs boson concept in the standard model (SM) is not needed in the present 3-D quantized space model (TQSM). The scalar boson with the spin of zero, photon with the spin of 1 and graviton with the spin of 2 are the two-boson states. Therefore, the observed Higgs boson is reinterpreted as the two-boson state of the scalar boson with the spin of zero. The cosmic muon observation and twin paradox are explained by using the absolute time and relative time. The relative time is the observed time in the twin paradox and cosmic muon observation. In the twin paradox, a person who travels the long distance is more aged than a person on the earth in terms of the relative time ages because of the space and time conversion effect (STCE effect) of the moving space distance (x). But twins are in the same ages in terms of the absolute time without STCE effect. The fast-moving cosmic muon has the expanded half-life from the time expansion of the relative time. Also, the quantum entanglement and Pauli exclusion principle are explained. The quantum base of the photon space line connects two entangled particles. Two particles and quantum base system is fluctuated along the absolute time axis by the time clicking when one particle is measured. Another particle is instantly selected by the time clicking. This is called as the quantum entanglement. The photons which are the flat photon space with the constant speed of c along the space axis and absolute time axis have the 4-D photon velocity of c_eff = 2^0.5 c. Total 10-D Euclidean space including three 3-D quantized spaces and one absolute time axis is required for the electric charges (EC), lepton charges (CC) and color charges (CC) of the elementary particles. The 3-D photon space is very stiff along the absolute time (ct) axis and very soft along the space axes. The Coulomb force through the photons (2EM waves) of the space fluctuations is much stronger than the Gravitational force through the gravitons (G waves) of the time fluctuations between two electrons.

Background: Although cervical cancer (CC) is highly preventable through appropriate screening methods like the Papanicolaou (Pap) test, which enables early detection of malignant and precancerous lesions, access to such screening has not been equitable across social groups. Sex workers and people with records of incarceration are among the most under-screened populations in Ontario. Little is known about the acceptability and feasibility of HPV self-sampling (HPV-SS) as an alternative cervical cancer screening method for these groups. This online, community-based mixed-methods pilot study aimed to address this knowledge gap. Methods Eighty-four under- and never-screened sex workers and ex-prisoners aged 25–69 years and residing in the Greater Toronto Area, were recruited by community peer associates. Participants completed an online survey and viewed short videos about CC and screening with Pap and HPV-SS. Those who opted for HPV-SS conducted the test at one of two collaborating organizations. Results The median age of participants was 36.5 years. Most had limited knowledge about CC and screening. Approximately 13% identified as non-binary, and 5% as two-spirit or trans men, with the majority having completed secondary education. Of participants, 88% chose HPV-SS, and one-third tested positive for high-risk HPV types. The ability to self-sample without judgment from healthcare providers was noted as a key advantage. However, there was a need for training on proper HPV-SS techniques. Conclusion To improve cervical cancer screening among sex workers, increasing awareness through participatory community co-creation of sexual health education is essential. Additionally, offering HPV-SS as a screening option is crucial, given its demonstrated acceptability and feasibility within this population, many of whom lack a primary care provider and face discriminatory attitudes in healthcare settings.

Crowd abnormal behavior recognition is one of the research hotspots in computer vision. Its goal is to use computer vision technology and abnormal behavior detection models to accurately perceive, predict, and intervene in potential abnormal behaviors of the crowd, and monitor the status of the crowd system in public places in real-time, to effectively prevent and deal with public security risks and ensure public life safety and social order. To this end, focusing on the crowd abnormal behavior recognition technology in the computer vision system, a systematic review study of its theory and cutting-edge technology is conducted. First, the crowd level and abnormal behaviors in public places are defined, and the challenges faced by crowd abnormal behavior recognition are expounded. Then, from the dimensions based on traditional methods and based on deep learning, the mainstream technologies of abnormal behavior recognition are discussed, and the design ideas, advantages, and limitations of various methods are analyzed. Next, the mainstream software tools are introduced to provide a comprehensive reference for the technical framework; Secondly, typical abnormal behavior datasets at home and abroad are sorted out, and the characteristics of these datasets are compared in detail from multiple perspectives such as scale, characteristics and uses, and the performance indicators of different algorithms on the datasets are compared and analyzed; Finally, the full text is summarized and the future development direction of crowd abnormal behavior recognition technology is prospected.

This research paper investigates the enhancement of strength and performance of self-compacting concrete (SCC) for integration in Department of Public Works and Highways (DPWH) projects using a value engineering approach. The study explores key elements of SCC's strength and performance, including mix design optimization, construction practices, and durability considerations. Strategies to optimize SCC's strength, durability, and workability are discussed, emphasizing the benefits of SCC in improving construction efficiency and quality control within DPW infrastructure. The study also identifies challenges and gaps in SCC utilization, such as standardized performance verification and cost considerations, and proposes future directions for advancing SCC technology in civil infrastructure development.

The significance of digital sustainability in donation-based crowdfunding platforms lies in its ability to encourage environmentally aware and socially responsible practices in the sphere of online philanthropy. Fundraising has been revolutionized by the emergence of donation-based crowdfunding platforms. This study explores the various factors influencing individuals' attitudes towards these platforms, particularly focusing on perceived usefulness, ease of use, credibility, and self-efficacy. The study examines how these factors affect donation intentions and electronic word-of-mouth intentions, utilizing data from 326 college students. The findings confirm that perceived usefulness, ease of use, credibility, and self-efficacy play crucial roles in shaping attitudes. Furthermore, these attitudes significantly impact both donation intentions and electronic word-of-mouth intentions. Adjustments to the structural model highlight the interconnectedness of these factors, improving the model's accuracy. The results provide practical guidance for platform administrators, campaign creators, and stakeholders involved in online philanthropy, emphasizing the need for user-friendly designs, trust-building, and fostering user confidence for successful crowdfunding efforts.

Such rapidly changing external conditions as the COVID-19 pandemic and the war in Ukraine, being pure crisis phenomena, not only determine the geopolitical and geoeconomic situation on the global markets, but also cause structural changes in individual countries. Under these conditions, the level of food self-sufficiency becomes particularly important, as it is one of the factors determining the food security of any country. The aim of the research is to assess Ukraine's food self-sufficiency compared to the European Union and Poland. The self-sufficiency ratio (SSR) index was used to assess food self-sufficiency. This is the ratio of the volume of production of the most important products of plant and animal origin to the volume of domestic consumption. Domestic consumption was defined as the volume of domestic production increased by the volume of imports, reduced by the volume of exports and also included changes in domestic stock levels. The surveyed time frame is 2019–2021. The analyzed data were taken from the State Statistics Service of Ukraine, FAOSTAT, USDA and Statistics Poland. A comparative analysis of self-sufficiency for the main agri-food sectors of Ukraine, the EU, and Poland showed significant differences. Taking into account the production potential and food self-sufficiency indicators of Ukraine, Ukraine's possible accession to the EU can be expected to have a major impact on the EU's supply and demand situation, as it may pose a threat to many EU agri-food sectors.

Quinacridone (QA) and 2,9-dimethylquinacridone (DQA) are synthetic substances suitable as a hard, abrasion-resistant, self-organizing gliding layer on ice, snow, and water. For sustainable use, a large number of parameters must be considered to demonstrate that these non-biogenic substances and their by-products and degradation products are harmless to humans and the environment in the quantities released. For this task, available experimental data are used and supplemented for all tautomers by numerous relevant physical, chemical, toxicological and ecotoxicological estimated values based on various Quantitative Structure Activity Relationship (QSAR) methods. On the one hand, the low solubility of QA and DQA leads to stable gliding layers and thus low abrasion and uptake by plants, animals, and humans. On the other hand, the four hydrogen bond forming functional groups per molecule allow nanoparticle decomposition and enzymatic degradation in natural environments. All available data justify a sustainable use of QA and DQA as a gliding layer. The assessment of the toxicological properties is complemented with an investigation of the size and morphology of DQA particles as well as field tests indicating an excellent performance as a gliding layer on snow.

Stroke causes disability and significantly affects patient quality of life. Post-stroke rehabilitation of upper limb function is crucial, as it affects daily activities and individual autonomy. Traditional rehabilitation methods often require frequent visits to specialized centers, which can be costly and challenging. This study investigated the effectiveness of a home-based self-training device, "Kenko Yusuri," in improving upper limb function in patients with chronic stroke. This multicenter prospective intervention study used a quasi-randomized controlled trial design. Ninety-three outpatients from five hospitals in Japan were enrolled and assigned to either the intervention group who used the "Kenko Yusuri" device or the control group who underwent conventional rehabilitation. All patients received botulinum toxin type A (BoNT-A) injections to treat upper limb spasticity. The primary outcome measure was the Fugl-Meyer Assessment (FMA) of upper-extremity motor function. Secondary outcomes included the modified Ashworth scale (MAS) score, range of motion (ROM), and chronic pain assessments. The intervention group demonstrated significantly greater improvements in FMA total scores than the control group. Patients with moderate paralysis experienced the greatest benefits from the intervention. There were no significant between-group differences in MAS scores or ROM measurements. Pain and wrist ROM showed improvement in the intervention group. Home-based training with a shaking device significantly improved upper limb function in patients with moderate chronic post-stroke paralysis. This approach aligns with the principles of use-dependent plasticity and offers a feasible and cost-effective alternative to traditional rehabilitation methods.

This study presents the chemical synthesis, purification, and characterization of a novel non-natural synthetic amino acid. The compound was synthesized in solution, purified, and characterized using NMR spectroscopy, polarimetry, and melting point determination. Dynamic Light 22 Scattering (DLS) analysis demonstrated its ability to form aggregates with an average size of 391 nm, extending to low micrometric sizes. Furthermore, cellular biological assays revealed its ability to enhance fibroblast cell growth, highlighting its potential for tissue regenerative applications. Circular dichroism (CD) spectroscopy showed the ability of the synthetic amino acid to bind serum albumins (using bovine serum albumin (BSA) as a model), and CD deconvolution provided insights into the changes in secondary structures of BSA upon interaction with the amino acid ligand. Additionally, molecular docking using HDOCK software elucidated the most likely binding mode of the ligand inside the BSA structure. We also performed in silico oligomerization of the synthetic compound in order to obtain a model of aggregate to investigate computationally. More in detail, in the dimer formation achieved by molecular self-docking, two distinct poses were observed, corresponding to the lowest and comparable energies, with one pose exhibiting a quasi-coplanar arrangement characterized by a close alignment of two aromatic rings from the synthetic amino acids within the dimer, suggesting the presence of π-π stacking interactions. In contrast, the second pose displayed a non-coplanar configuration, with the aromatic rings oriented in a staggered arrangement, indicating distinct modes of interaction. Both poses were further utilized in the self-docking procedure. Notably, iterative molecular docking of amino acid structures resulted in the formation of higher-order aggregates, with a model of a 512-mer aggregate obtained through self-docking procedures. This model of aggregate presented a cavity capable of hosting therapeutic cargoes and biomolecules, rendering it a potential scaffold for cell adhesion and growth in tissue regenerative applications. Overall, our findings highlight the potential of this synthetic amino acid for tissue regenerative therapeutics and provide valuable insights into its molecular interactions and aggregation behavior.

In the face of escalating climate concerns and the push for sustainable development, the global shift towards renewable and decentralized energy systems presents new challenges and opportunities. This study investigates integrating decentralized energy production, particularly photovoltaic (PV) systems, into national energy planning, aiming to optimize energy strategies that balance local production and consumption with national objectives. By analyzing the Swiss energy model, the research employs the EnergyScope and REHO models to assess the strategic implications of decentralized versus centralized energy systems. Results show that a decentralized approach can significantly reduce PV installation needs to 35 GW, about 23% of potential capacity, and decrease annual system costs by 10% to CHF 1230 per capita. This strategy emphasizes local consumption, minimizes grid reinforcement demands, and leverages economic advantages while addressing overproduction challenges through effective energy storage and grid management. Conclusions underline the strategic value of combining centralized and decentralized methods for resilient and sustainable energy planning. The study contributes to the discourse on energy policy and infrastructure planning, advocating for a hybrid model that accommodates both local conditions and broader energy objectives, urging further research into climate impacts and technology integration for a comprehensive energy future.

This study proposed to use wearable EEG to study the impact of vipassana mindfulness practice on beginner's physiological and neurological responses. Mindfulness meditation has been shown to have numerous benefits, including stress reduction, improved attention and cognitive function, and enhanced emotional regulation. The roots of mindfulness meditation in Buddhism emphasize cultivating present-moment awareness and insight. However, the neural mechanisms together with physiological mechanism and their interaction underlying these benefits remain poorly understood. Method: One middle age man (50 years old) participated in this longitudinal study using wearable EEG to investigate the neurophysiological change during a one-month consecutive vipassana mindfulness practice. Both EEG and heart rate responses were recorded each time, before (5 minutes), during (20 minutes), and after (5 minutes) meditation sessions. The EEG and HR data were analyzed by Matlab and EEGlab to examine changes in brainwave activity and heart rate. Results: alpha peak frequency (APF) is significantly lower after mindfulness meditation, as shown by comparison of APF before mindfulness (pre-5) and during mindfulness practice (med-20), and comparison between APF of pre-5 and after mindfulness practice (post-5). The same is with the beta peak power. While for theta peak frequency, it is significant lower during mindfulness meditation (med-20), when comparing to either pre-5 or post-5. See figure 1a. However, peak power index is not so sensitive, and it is more obvious in theta wave band. That is, the theta power peak is significantly lower after meditation, as can be observed in comparison between pre-5 and med-20, also between pre-5 and med-20. See figure 1b. STATE Heart-mean (bpm) Heart-std(bmp) pre-5 69.88 3.88 med-20 68.36 2.81 post-5 69.55 4.13 t-value test (pre-med) 0.0694 0.0003 t-value test (pre-post) 0.7934 0.3656 t-value test (post-med) 0.3193 0.0001 Table 1. The difference of Heart rate (mean and SD) in before (pre-5), during (med-20) and after (post-5)mindfulness meditation. We find significant change in standard deviation (SD) of heart rate between meditation and non-meditation mental states, as shown in table 1. The study provides insights into the underlying neurophysiological mechanisms of mindfulness meditation using wearable EEG devices. It demonstrates that in term of EEG measurement, spectrum frequency can be more sensitive than spectrum power; also heart rate variability as measured by SD is more sensitive to mean of HR. The proposed study contributes to the literature by using wearable EEG devices to collect data in a more accessible and convenient manner.

Lipophilic oligonucleotide derivatives are a potent approach to the intracellular delivery of nucleic acids. The binding of these derivatives to serum albumin is a major determinant of their fate in the body, as its structure contains several sites of high affinity for hydrophobic compounds. This study focuses on the features of self-association and non-covalent interactions with human serum albumin of novel self-penetrating oligonucleotide derivatives. The study revealed that the introduction of a triazinyl phosphoramidate modification bearing two dodecyl groups at the 3' end region of the oligonucleotide sequence has a negligible effect on its affinity for the complementary sequence. Dynamic light scattering verified that the amphiphilic oligonucleotides under study can self-assemble into micelle-like particles ranging from 8 to 15 nm in size. The oligonucleotides with dodecyl groups form stable complexes with human serum albumin with a dissociation constant of approximately 10-6 M. The oligonucleotide micelles are simultaneously destroyed upon binding to albumin. Using an electrophoretic mobility shift assay and affinity modification, we examined the ability of DNA duplexes containing triazinyl phosphoramidate oligonucleotides to interact with Ku antigen and PARP1, as well as the mutual influence of PARP1 and albumin or Ku antigen and albumin upon interaction with DNA duplexes. These findings, together with the capability of dodecyl-containing derivatives to effectively penetrate different cells, such as HEK293 and T98G, indicate that the oligonucleotides under study can be considered as a platform for the development of therapeutic preparations with a target effect.

Background: Coronavirus disease (COVID-19) has caused one of the worst global pandemics in recent decades. It has disrupted education systems worldwide, leading to a forced shift from traditional face-to-face to blended or fully distanced learning, requiring a higher level of student readiness for self-directed learning (SDL) and a more internal locus of control (LOC). Objective: This study explored the relationship between locus of control and level of readiness for SDL among Saudi nursing students and whether the COVID-19 pandemic has impacted this relation-ship. Methods: a cross-sectional correlational descriptive study was conducted to survey 277 Saudi nursing students enrolled in the bachelor program at one of the reputable universities in Saudi Arabia. An E-questionnaire containing two scales, the Self-Directed Learning Readiness Scale for Nursing Education, and the Locus of Control Scale, was used to collect data in addition to the selected participants’ characteristics. Results: Nursing students had a moderate-to-low level of readiness for SDL (mean = 144.0), and the majority had an external LOC. There was a significant association between the locus of control and level of readiness for self-directed learning (r = 0.19*, p = 0.001), and the internal locus of control was more significantly associated with self-directed learning (r = 0.22*, p = 0.0001) than with external locus of control. Conclusion: The study findings indicate a propensity of respondents towards an external locus of control, whereas most of the respondents’ reported levels of readiness ranged between low and moderate across all dimensions of self-directed learning. This study was not registered.

Conclusion: When applied to the air-abraded Filtek™ Bulk Fill Flowable Restorative composite surface, 10-MDP-containing adhesives perform better than 10-MDP-free adhesives. The binding strength was unaffected by the 10-MDP-containing adhesives' chemical makeup. The bond strength endurance of adhesives containing 10-MDP decreases with age. Keywords: keyword 1; keyword 2; keywords: low-viscosity bulk fill composite, universal adhesives, self-etch adhesive, 10-MDP, aging, bond strength.

Disassembly is a pivotal technology to enable the circularity of electric vehicle batteries by applying circular economy strategies that extend the life cycle of their components, like remanufacturing and repurposing, or by using efficient recycling to reintegrate the gained materials in producing new battery systems. This paper aims to develop a multi-method self-configuring simulation model for investigating disassembly scenarios while considering the battery design as well as the configuration and layout of the disassembly station. We demonstrate the developed model in a case study using a battery of Mercedes-Benz and the automated disassembly station of the project DeMoBat at Fraunhofer IPA. Furthermore, we introduce two disassembly scenarios, the component-oriented and the accessibility-oriented disassembly, and compare them using the simulation model to determine several indicators, such as the frequency of tool change, the number and distribution of robot routes, the tool utilization, and the disassembly time.

Algal-bacterial granular sludge, a new biological technology, has been widely recognized due to its highly effective pollutant treatment and energy efficiency. This study investigated the effects of environmental concentrations of Cr(VI) (0.5-2.5 mg/L) on the performance of algal-bacterial gran-ular sludge and self-defensive responses after 90 days of cultivation. The results showed that Cr(VI) affected chemical oxygen demand (COD), ammonia-N and phosphate removal with different trends being apparent. A linear decline in COD removal was observed, whereas an initial de-creasing and then increasing ammonia-N and phosphate removal took place. Algal-bacterial granular sludge effectively removed Cr(VI) from wastewater through biological adsorption and reduction, showing the potential to treat Cr(VI)-contaminated wastewater. Cr(VI) affected the community abundance of the algal-bacterial granular sludge, in which Chlorophyceae and cya-nobacteria were vulnerable under Cr(VI)-induced stress. To reduce the toxicity of Cr(VI), over-produced EPS-PN and antioxidant enzymes (MDA, SOD and CAT) acted as self-defensive responses to resist oxidative damage. This study showed that algal-bacterial granular sludge can remove 00.5 mg/L of Cr(VI) without performance loss. It is hoped that this study can provide useful information for improved engineering feasibility of algal-bacterial granular sludge.

DFT-D3 calculations based on structural X-ray diffraction data obtained for 3-oxo-camphorsulfonyl imine (1), camphorsulfonyl chloride (2) and seven camphor sulfonimines (O2SNC10H13NR, L1-L7) from which L2 (R=4-OHC6H4), L4 (R=4-ClC6H4) and L6 (R=3,5-(CH3)2C6H3) are new, provide information into the intra and inter molecular interactions with concomitant elucidation of the supramolecular arrangement of the compounds. The DFT D3 calculations performed in small clusters of 2 or 3 molecular units reproduce the interactions observed by X-ray analyses showing that as a general trend the structural arrangement of the molecules is driven by electronic rather than by packing parameters. In all compounds the self-assembly of 3D structures involves the sulfonyl imine group ( NSO2) either to establish hydrogen bonds, through the oxygen atoms or non-classic oxygen-aliphatic hydrogen or non-bonding interactions (NBI) which also involve the sulfonyl oxygens. Interestingly, the camphor sulfonimine compounds (L2, L3) having protic groups (R=C6H4X: X=OH, L2 or X=NH2, L3) at the aromatic imine substituents (=NR) present an extra π-π stacking which is absent in the other compounds aromatic derivatives. The X-ray analysis shows that all the reported camphor sulfonimine compounds display the E configuration with respect to the imine substituent (R). The study of the redox behavior of the compounds by cyclic voltammetry allows an insight into the solution properties of the compounds and the rationalization of the molecular interactions that stand in the solid and solution states. Camphor sulfonimine compounds (L) display appropriate binding atoms to coordinate transition metals. The herein results show that monodentate coordination through the nitrogen atom of the sulfonimine 5-membered ring to the {Ag(NO3)} metal center is favored. When this imine nitrogen atom is not itself involved in the Organic Framework DFT-D3 calculations shows that the complexation does not affect the non-covalent interactions that are reproduced in the MOF structure.

In swarm-robotics foraging, the purpose of task allocation is to adjust the number of active foraging robots dynamically based on the task demands and changing environment. It is a difficult challenge to generate self-organized foraging behavior in which each robot can adapt to environmental changes. To complete the foraging task efficiently, this paper presents a novel self-organized task allocation strategy known as the dynamic response threshold model (DRTM). To adjust the behavior of the active foraging robots, the proposed DRTM newly introduces the traffic flow density, which can be used to evaluate the robot density. First, the traffic flow density and the amount of obstacle avoidance are used to adjust the threshold which determines the tendency of a robot to respond to a stimulus in the environment. Then, each individual robot uses the threshold and external stimulus to calculate the foraging probability that determines whether or not to go foraging. Simulation and physical experiments are carried out to evaluate the performance of the proposed method. The experimental results show that the DRTM is superior to and more efficient than the adaptive response threshold model (ARTM) in swarm foraging.

Material selection for product design is a complex task. Thus, one of the objectives of this work is to analyze, understand and promote the importance of selection of materials to conceive quality products with help of designers' that promote green self-identity in the early stage of the conception of new products. A questionnaire was sent to professional designers and engineers. 38 responses were validated, this being the sample of our study. The aspects that influence the complex selection of materials and the final quality of the products through the design and production process is presented. As a result of the answers carried out with product designers who work in the market, with students who are graduating and with some engineers, a new approach for the selection of materials was developed. Based on a collection of main ideas from traditional and non-traditional methods of material selection, seeking to group the maximum requirements of both methods, inspired by the "Canvas" model on the basic modular methodology, a new model for a project of new product is presented. Our study focuses on the selection of materials, since this aspect is one of the most relevant steps in early stage of the prototyping phase of new products, with a view to reducing CO² from the air in the atmosphere that we all breathe. The classification of materials is complex, due to the diversity of available options. The novelty of this model is that all properties of the newly designed product, such as technical, aesthetic, productive, environmental and other properties, are all grouped in a model, which serves as an innovative support. Thus, the designer has at his disposal a tool that can help him in the selection of the best material for the products he designs. This study intends to make a contribution in the field of material selection, quality and design of new products, promoting a green self-identity for designers in the initial phase of product design. Consequently, all consumers in search of a sustainable planet will profit from it.

This study compares the effects of lecture-based instruction and process-oriented guided inquiry learning (POGIL)-based instruction on the self-efficacy performance of Grade 12 students. A quasi-experimental, pretest-posttest design was adopted to investigate and assess the impact of POGIL-based instruction versus lecture-based instruction on students’ performance as measured by three types of cognitive outcomes; Knowing, Applying and Reasoning (KAR). Two government high schools in Alain were selected as research sites, one for the boys and one for the girls. The total number of participants was approximately 110 students (N=110); 54 were assigned to treatment groups (25 girls and 29 boys), and 56 were assigned to control groups (27 girls and 29 boys). The treatment group was taught a circular motion unit in physics using POGIL-based instruction, while the control group was taught the unit using lecture-based instruction. The study's findings showed statistically significant differences between students of the control group and the treatment group in favour of the latter regarding their science performance, self-efficacy and science-related attitudes. Moreover, positive correlations were found between participants’ performance at the KAR test, self-efficacy and scientific attitudes towards scientific inquiry after the intervention. In conclusion, it is recommended to shift teaching towards POGIL-based instruction due to its positive impact on students’ performance and self-efficacy. It is also suggested to replicate the study to include government and private schools, elementary and high schools, teachers and advisors.

Body Expression (BE) has been defined in the past decades as a discipline within Physical Education (PE) with very particular characteristics and a strong emotional component. In this study, a programme of this discipline was applied to university Physical Activity and Sports Sciences (PASS) students from six consecutive academic years: three prior to and three during the pandemic. A pre-post design was used to determine how the BE programme affected the university students' self-concept (SC). To do so, a questionnaire with a multidimensional approach to this construct was administered, whose dimensions were closely related to the BE programme's characteristics. The results revealed significant improvements in the final SC, compared to the initial SC. Men reported lower SC values than women before the programme implementation, but higher at the end. Therefore, the change was larger in men, so the programme may have had an equalising effect between groups. It was also verified that the pandemic had particularly affected women.

Quantitative differential phase contrast (qDPC) imaging has become an important method of optical measurement and life science research in microscopy because of its high reconstruction resolution and non-invasive, high-contrast and quantitative imaging of biological samples. Despite the continuous development of the principle and algorithm, the frame rate of the existing qDPC algorithm is still much lower than that of camera acquisition, so it is hardly applied to real-time image the fast-moving biological samples. In this paper, based on color-coded multiplexing strategy, a compact real-time quantitative phase imaging system is designed to realize multi-mode imaging. The system employs a programmable LED array to illuminate directly, and the phase reconstruction algorithm is deployed in the graphics processing unit (GPU) of the laptop to accelerate the calculation. The system can achieve high-speed quantitative phase imaging of non-stained biological samples, and the frame rate can reach 60fps. The device has the advantages of compact structure, low cost and portability. Thus, it is suitable for mobile medical applications.

In remote sensing applications, one of the key points is the acquisition, real-time pre-processing and storage of information. Due to the large amount of information present in the form of images or videos, compression of this data is necessary. Compressed sensing (CS) is an efficient technique to meet this challenge. It consists in acquiring a signal, assuming that it can have a sparse representation, using a minimal number of non-adaptive linear measurements. After this CS process, a reconstruction of the original signal must be performed at the receiver. Reconstruction techniques are often unable to preserve the texture of the image and tend to smooth out its details. To overcome this problem, we propose in this work, a CS reconstruction method that combines the total variation regularization and the non-local self-similarity constraint. The optimization of this method is performed by the augmented Lagrangian which avoids the difficult problem of non-linearity and non-differentiability of the regularization terms. The proposed algorithm, called denoising compressed sensing by regularizations terms (DCSR), will not only perform image reconstruction but also denoising. To evaluate the performance of the proposed algorithm, we compare its performance with state-of-the-art methods, such as Nesterov's algorithm, group-based sparse representation and wavelet-based methods, in terms of denoising, and preservation of edges, texture and image details, as well as from the point of view of computational complexity. Our approach allows to gain up to 25% in terms of denoising efficiency, and visual quality using two metrics: PSNR and SSIM.

We reveal the significance of plasmonic nanoparticle’s (NP) shape and its surface morphology en route to an efficient self-assembled plasmonic nanoparticle cluster. A simplified model is simulated in the form of free-space dimer and trimer nanostructures (NPs in shape of sphere, cube, and disk). A ~ 200 % to ~ 125% raise in near field strength (gap mode enhancement) is observed for spherical NPs in comparison with cubical NPs (from 2 nm to 8 nm gap sizes). Full-width three-quarter maximum reveals better broad-spectral optical performance in a range of ~ 100 nm (dimer) and ~ 170 nm (trimer) from spherical NPs as compared to a cube (~ 60 nm for dimer and trimer). These excellent properties for sphere-based nanostructures are merited from its dipole mode characteristics.

In light of the self-simulation hypothesis, a simple form implementation of the principle of efficient language is discussed in a self-referential geometric quasicrystalline state sum model in three dimensions. Emergence is discussed in context of geometric state sum models.

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

Solar cell layers technology has achieved global standing in the solar cell layers deposition process, and it covers the innovative methods and techniques in significant applications. Recent solar cell layers technology has an advanced interest in a refined approach to enhance performance and highlights the importance of recent proficient procedures for manufacturing. For example, the application is used to search for novel materials for solar cells' layers to clarify the current energy crisis. The technological process and various types of solar cells depend on climate change. Among them, layers of solar cells and silicon wafer solar cells are very encouraging. Solar cell layers technology has led to solar cells being a more reasonable active option in design and production. The productivities facilitated by new solar cells still need to be enhanced for the various processes involved in the additional enhancement from Copper Indium Gallium Selenide (CIGS) microfilms to solar cell crystal structure dye-sensitized solar cells. The hydrophobic coating works as an anti-dust coating, enhancing efficiency and decreasing the cost of cleaning solar cells. In Saudi Arabia Majmaah City, most solar projects are in dry regions, where the dusty weather reduces solar cell efficiency. Therefore, combining these two properties and applying an anti-reflective and superhydrophobic coating will increase solar cell efficiency by 20%. Solar cells' crystal structure results are substituted with layers or new materials to balance environmental impact and toxic nature.

The notion of hierarchy is a key characteristic of any complex system. This paper explores which notions of hierarchy are being used in the field of management and organization studies. Four distinct types of hierarchy are identified: a ladder of formal decision-making authority, a ladder of achieved status, a self-organized ladder of responsibility, and an ideology-based ladder. A social mechanism-based perspective serves to define and distinguish these four types. Subsequently, the typology is further developed by comparing the four hierarchy types in terms of their tacit/explicitness, (in)transitivity, and behavior- versus cognition-centeredness. This review article contributes to the literature by dissecting the general metaphor of hierarchy into four different constructs and their social mechanisms, which serves to create a typology of the various ways in which complex systems can be characterized as being hierarchical. This typology can inform future research drawing on any type of hierarchy.

Climate change is likely to have a complex effect on the growth of plants, their phenology, plant-pollinator interactions, and reproductive success. Therefore, we tested the impact of three key factors (temperature, water, and nitrogen supply) on traits, pollination, and seed production in Sinapis alba (Brassicaceae). We grew the plants in different combinations of temperature, water, and nitrogen supplementation, measured multiple vegetative and floral traits, and assessed the response of pollinators in the field. We also evaluated the effect of growing conditions on seed set in plants exposed to pollinators and hand-pollinated plants. Our results show that water stress impaired vegetative growth, decreased flower production, reduced visitation by pollinators and seed set, while nitrogen availability played an important role in nectar production. Temperature modulated the effect of water and nitrogen availability on vegetative and floral traits and strongly affected flowering phenology and flower production. We demonstrated that changes in temperature, water, and nitrogen availability induce changes in plant vegetative and floral traits which impact flower visitation and consequently plant reproduction. Climate change, particularly increasing temperature combined with reduced precipitation, thus may impact plant-pollinator interactions with negative consequences for the reproduction of wild plants and insect-pollinated crops.

Since the emergence of the quantified self movement, users aim at health behavior change, but only those who are sufficiently motivated and competent with the tools will succeed. Our literature review shows that theoretical models for quantified self exist but they are too abstract to guide the design of effective user support systems. Here, we propose principles linking theory and implementation to arrive at a hierarchical model for an adaptable and personalized self-quantification system for physical activity support. We show that such a modeling approach should include a multi-factors user model (activity, context, personality, motivation), a hierarchy of multiple time scales (week, day, hour), and a multi-criteria decision analysis (user activity preference, user measured activity, external parameters). This theoretical groundwork, which should facilitate the design of more effective solutions, has now to be validated by further empirical research.

In this article we explore the limiting behavior of the universal prior distribution obtained when applied over multiple meta-level hierarchy of programs and output data of a computational automata model. We were motivated to alleviate the effect of Solomonoff's assumption that all computable functions or hypotheses of the same length are equally likely, by weighing each program in turn by the algorithmic probability of their description number encoding. In the limiting case, the set of all possible program strings of a fixed-length converges to a distribution of self-replicating quines and quine-relays - having the structure of a constructor. We discuss how experimental algorithmic information theory provides insights towards understanding the fundamental metrics proposed in this work and reflect on the significance of these result in digital physics and the constructor theory of life.

Intranasal (i.n.) administration became an alternative strategy to bypass the blood-brain barrier and improve drug bioavailability in the brain. The main goal of this work was to preliminarily study the biodistribution of mixed amphiphilic mucoadhesive nanoparticles made of chitosan-g-poly(methyl methacrylate) and poly(vinyl alcohol)-g-poly(methyl methacrylate) and ionotropically crosslinked with sodium tripolyphosphate in the brain after intravenous (i.v.) and i.n. administration to Hsd:ICR mice. After i.v. administration, the highest nanoparticle accumulation was detected in the liver, among other peripheral organs. After i.n. administration of a 10-times smaller nanoparticle dose, the accumulation of the nanoparticles in off-target organs was much lower than after i.v. injection. In particular, the accumulation of the nanoparticles in the liver was 20 times lower than by i.v. When brains were analyzed separately, intravenously administered nanoparticles accumulated mainly in the “top” brain, reaching a maximum after 1h. Conversely, in i.n. administration, nanoparticles were detected in the “bottom” brain and the head (maximum reached after 2 h) owing to their retention in the nasal mucosa and could serve as a reservoir from which the drug is released and transported to the brain over time. Overall results indicate that i.n. nanoparticles reach similar brain bioavailability, though with a 10-fold smaller dose, and accumulate in off-target organs to a more limited extent and only after redistribution through the systemic circulation. At the same time, both administration routes seem to lead to differential accumulation in brain regions and thus, they could be beneficial in the treatment of different medical conditions.

Dentin bonding is a key in restorative dentistry. Herein, we developed self-etching two-bottle adhesive system containing 10-methacryloyloxidecyl dihydrogen phosphate monomer (MDP) and the physical, mechanical, and biocompatible properties were evaluated. The characteristics of MDP were analyzed using nuclear magnetic resonance (NMR). The water sorption and solubility, the shear-bond strengths to dentin and enamel, and cytotoxicity tests were performed. The newly blended experimental group showed the lowest thickness and water sorption and solubility values. The shear bond strength to enamel and dentin were comparable to control groups (the three other products) all test groups showed 60% of cell viability. In this study, the properties of the newly synthesized adhesive are comparable with the others. The fundamental goal of this study is to get the MDP patent released, as it is intended for domestic production. For this purpose, this dentin adhesive was developed and compared with the commercial product.

In our recent works we reported that physical and chemical characteristics of serum can vary in relation to the psychic activity of an individual depending on whether it is oriented to stress or relaxation. We wandered if these observations could be accompanied by an appreciable modification of the Ph, electric conductivity and Delayed Luminescence of the same serum samples. Our preliminary data may suggest that the serum pH could significantly increase during a Relaxation Response intervention while electric conductivity seems to decrease. Moreover, Delayed Luminescense could vary in the same subject according to the Relaxation Response practice. According to our proof of concept study, we postulate the appearance of a coherent system within the blood samples analyzed after the Relaxation Response. Further researches and some technical development are needed to support our preliminary findings.

In the author's opinion, many global problems that face humanity - in the fields of education, medicine, management etc can be tackled more effectively if the cyclic nature of self-reproducing systems – including living beings – is taken into account. Summarizing the main physiological findings of the last decades on "adaptation reactions", one can very roughly say that the way of action which is effective in the sense of productive activity of people happens at the same time to be healthy, and it gives the participants of the process the feeling of happiness. The present paper represents a very short overview of the contemporary concepts of the adaptation reactions based on the fundamental understanding of their cyclic nature due to general properties of self-reproducing systems. One interesting feature of self-reproduction cycles is its first "phase of orientation" which was not discussed in detail in the past but plays a key role in the whole cycle.

Hierarchical honeycomb patterns were manufactured with the breath-figures self-assembly by drop-casting on the silicone-oil lubricated glass substrates. Silicone oil promoted spreading of the polymer solutions. The process was carried out with the industrial grade polystyrene and polystyrene with the molecular weight Mw=35.000. Both of polymers gave rise to the patterns, built from micro- and nano-scaled pores. Ordering of the pores was quantified with the Voronoi tessellations and calculating the Voronoi entropy. Measurement of the apparent contact angles evidenced the Cassie - Baxter wetting regime of the porous films.

A new circuit model of the self-powered device for heart rate measurement is presented in this paper. This device consists of piezoelectric energy harvester (PEH), power management circuit (PMC) with energy storage, microcontroller, Photoplethysmography (PPG) sensor, and Wi-Fi module. The PEH is placed under the insole to harvest the pressure energy from human foot-step to generate ac power. In our model, a PEH is represented by sine voltage source, where its parameters were taken from experiments with 20 volunteers. The PMC is simplified by a switch with gain δ placed in series with the main circuit. The model of the main circuit is RC elements in parallel, where C is the capacitance of the storage device, and R is the equivalent parallel resistance of the microcontroller, PPG sensor, and Wi-Fi modules, respectively. The value of R depends on the power and current absorbed by those modules during sleep, deep sleep, sense, and transmit modes which collected from the datasheet. Finally, the proposed circuit model of the self-powered device was built and simulated in SPICE. The simulation results were compared with the laboratory experiment using commercial devices. Based on the results, the proposed model had small gaps compared to the real self-powered devices in terms of average current, voltage, power and efficiency.

Health of migrant is a widely studied topic. It has been argued that migrant health may deteriorate over time. Though migrants are ‘a hard to reach’ population in survey data, this paper builds on a unique dataset provided by Médecins du Monde from five countries. We study self perceived health (SPH) in connection with socio-economic and demographic factors and length of stay. Results show different results for men and women. Asylum seekers compared to other documented migrants have a worse health. Migrants with better living conditions tend to be in better health. Employment and stable accommodation has a positive effect on SPH. Women from poorer countries have a better physical SPH after 3 months of residing in the host country. This paper contributes widely to knowledge of health of migrants. Contrarily to other evidence, health of migrants tends to improve for some migrants.

2-Hydroxypropyl-β-cyclodextrin (HPβCD) has unique properties to enhance the stability and the solubility of low water-soluble compounds by inclusion complexation. Understanding of the structural properties of HPβCD and its derivatives based on the number of 2-hydroxypropyl (HP) substituents at the a-D-glucopyranose subunits is rather important. In this work, replica exchange molecular dynamics simulations were performed to investigate the conformational changes of single- and double-sided HP-substitution called as 6-HPβCDs and 2,6-HPβCDs, respectively. The results show that glucose subunits in both 6-HPβCDs and 2,6-HPβCDs have lower chance to flip than in βCD. Also, HP groups are occasionally blocking the hydrophobic cavity of HPβCDs, thus hindering the drug inclusion. We found that HPβCDs with high number of HP-substitutions are more likely to be blocked, while HPβCDs with double-sided HP-substitution are even more probable to be blocked. Overall, 6-HPβCDs with three and four HP-substitutions are highlighted as the most suitable structures for guest encapsulation based on our conformational analyses such as structural distortion, radius of gyration, circularity and cavity self-closure of the HPβCDs.

It is shown that structuring at the microlevel is the intrinsic property of water and aqueous solutions. At room conditions water (including "ultrapure" one) and aqueous solutions are dispersed systems in which microcrystals of NaCl, surrounded by a layer of hydrated water (average diameter - 10-15 microns), are "elementary microparticles", which form the basis of the dispersed phase. Possible ways of formation of these microparticles and their evolution in the process of evaporation of unstructured part of water - dispersion medium - are considered. It is shown, in particular, that they are present in the air as aerosol contaminants. When the ionic strength of the solution increases, the water-salt particles coagulate, forming a new phase - coacervates, remaining on the substrate after evaporation of the liquid part of the water. The aggregates of coacervate structures, formed in a liquid medium, are disordered during heating, which can cause a change in a number of physicochemical properties of water at the temperatures of 50°-60°C range that have not been correctly explained in the framework of atomic-molecular concepts.

Nowadays, the research and development of on-chip LiDAR sensors for vehicle collision avoidance is growing very fast. Therefore, the assessment of the reliability in obstacle detection using the information provided by LiDAR sensors has become a key issue to be explored by the scientific community. This paper presents the design and implementation of a self-tuning method in order to maximize the reliability of an Internet-of-Things sensors network and to minimize the number of sensors to localize with the required accuracy obstacles by a detection threshold. In order to achieve this goal, models that predict accuracy (i.e., prediction error) for object localization using data collected by LIDAR sensors are designed and implemented in Webots Automobile 3D simulation tool. The approach is based on combining different techniques. Firstly, point-cloud clustering technique and an error prediction model library composed by a multilayer perceptron neural network with backpropagation, k-nearest neighbors and linear regression are explored. Secondly the above-mentioned techniques for modeling are also combined with a supervised and reinforcement machine learning technique, Q-learning in order to minimize the detection threshold. In addition, a IoT driving assistance simulated scenario with a LiDAR sensor network is designed in order to validate the prediction model and the optimal configuration of the sensor network to guarantee reliability in obstacle localization. The results demonstrate that the self-tuning method is appropriate to increase the reliability of the sensor network whereas minimizing the detection threshold