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.

Introduction: In an era of increasing public awareness regarding the pivotal role of nutrition in the onset and progression of chronic degenerative diseases, there is a growing demand for information and assistance pertaining to the intricate relationship between diet and disease. Nutritional practices, therefore, emerge as a fundamental element in the prevention and management of these maladies, necessitating a proactive response from healthcare professionals (HCP). This study sought to evaluate the impact of participation in a Culinary Medicine (CM) program on Mediterranean diet adherence, food management skills, culinary proficiency, and counseling confidence among HCP. Methods: A mixed-methods pilot implementation program (PIP) was meticulously designed and executed, engaging a cohort of 20 HCP recruited from Hospital Clinic Barcelona. The program unfolded at the Alícia Foundation kitchen-lab and encompassed a series of interactive, hands-on sessions that delved into the realms of gastronomy, food management, culinary skills, and culinary diet therapy, with a specific focus on prevalent diseases and their prevention, treatment, and care. Tailored questionnaires were administered to all participants both before and after the PIP. Results: Of the 20 participants, 14 successfully completed the study, comprising 86% women, 86% aged above 40, 71% nurses, and 7% medical doctors. Interestingly, 86% of the participants had previously received nutrition training, whereas only 14% had received culinary training. Following the completion of the program, there was a remarkable improvement in adherence to the Mediterranean diet (p < 0.01). However, no statistically significant improvements were observed in food management and culinary skills or counseling confidence (p > 0.5). Despite the absence of statistical significance, there was a noteworthy trend towards greater advocacy for dietary changes before resorting to medication, along with enhanced confidence in providing nutritional and culinary guidance to patients. Conclusion: The findings from this study underscore the potential of hands-on CM training as a vital component of nutrition education for HCP. While statistically significant improvements in certain domains were not observed, the program did demonstrate its ability to influence attitudes, knowledge, and behaviors related to healthy cooking and eating for both HCP themselves and their patients. Given these promising trends, future studies with a larger sample size must be performed to further elucidate the impact of CM training on HCP and its potential benefits for public health.

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

Currently, the use of medications during pregnancy in Latin America is common; studies with Latin American samples find that nearly 90% of pregnant women use some medication at least once during pregnancy. Furthermore, approximately 21% of the respondents self-medicated. Worldwide, the prevalence of this disease is 32%. The most common medications are nonsteroidal anti-inflammatory drugs and analgesics. Many of these drugs are available over the counter without a prescription. For this reason, a systematic review of the literature on the adverse effects on the woman and the fetus was performed. Objective: To determine the adverse effects associated with the use of nonsteroidal anti-inflammatory drugs (NSAIDs) on pregnant women and fetuses during pregnancy. Methods: Systematic literature review. The following search strategy was used for the databases: Science Direct, PubMed, Scopus and Web of Science, through September 3, 2022. The population of pregnant women without previous comorbidities was defined. The intervention of interest was the use of NSAIDs during pregnancy. The outcomes of interest were adverse effects for the mother or fetus (weight of the newborn, congenital malformations present in the fetus, systemic effects in the pregnant woman during her gestation period). The types of studies included clinical trials, cohort studies and case‒control studies. Results: 1 study included 2,985 pregnant women without previous comorbidities. The use of aspirin more frequently during pregnancy stands out. The adverse effects on the fetus were preterm birth <37 weeks, low birth weight < 2500 g and increased admission to the neonatal intensive care unit. No adverse effects were found for the mother. Conclusions: There are not enough data indicating that the use of NSAIDs during pregnancy is safe. Some studies show that the use of NSAIDs during pregnancy could be related to problems in the development and growth of the fetus and health effects for the mother during pregnancy, so it is advisable to use these drugs under medical prescription and for the shortest possible time. in the gestation stage.

The study examines the behavior of the Mechanically Stabilized Earth (MSE) wall using available reinforcement materials in Danang, Vietnam. The MSE was reinforced by the self-fabricated galvanized steel grids using CB300V steel with 3 cm ribs. The backfill soil is a sandy clay soil from the local area with a low cohesion. A full-scale model with full instrumentation was installed to investigate the distribution of tensile forces along the reinforcement layers. The highest load that caused the wall to collapse due to internal instability (reinforcement rupture) was 302 kN/m2, which is 15 times the design load of 20 kN/m2. The failure surface within the reinforced soil had a parabolic sliding shape which is similar to the theoretical studies. At the failure load level, the maximum lateral displacement at the top of the wall facing was small (3.9 mm) which is significantly lower than the allowable displacements for the retaining wall. Furthermore, a numerical model using FLAC software was applied to simulate the performance of the MSE wall. The modeling results are in good agreement with the physical model. Thus, the self-fabricated galvanized steel grids could be confidentially used in combination with the local backfill soil for the MSE walls.

This paper deals with a method of calculating the self and mutual inductances of various air coils located arbitrarily in space. Known elementary solutions (the Biot-Savart formulas) were used to determine the magnetic field of infinitely thin current loops and infinitely thin wires of finite length linking magnetically other coils. Unlike commonly used algorithms, these elementary solutions were not extensively transformed analytically but were used to perform calculations by direct numerical integration. This enabled very quick and accurate obtaining of the self-inductance values as well as determining the dependence of mutual inductances on the position of both coils. This method allows analysis of different coil configurations (misaligned coils, inclined to each other, etc.) that other methods do not cover. It also enables the determination of the forces acting on the coils, as well as the calculation of the magnetic field distribution from any coil configuration. The obtained results were compared with those presented by other authors.

Background: Oscillation and pulmonary expansion (OPE) therapy can decrease postoperative pulmonary complications in a general surgical population, but its effect after cardiac surgery has not been reported, to our knowledge. We hypothesized that using an OPE device after cardiac surgery before extubation would decrease pulmonary complications. Methods: This retrospective cohort study included adults undergoing elective open cardiac surgery at our institution from January 2018 through January 2019, who had an American Society of Anesthesiologists score of 3 or greater. For mechanically ventilated patients after cardiac surgery, a new OPE protocol was adopted July 1, 2019, comprising an initial 10-minute OPE treatment administered in-line with the ventilator circuit, then continued treatments for 48 hours after extubation. Severe postoperative respiratory complications (primary outcome measure) included need for antibiotics, increased supplemental oxygen use, and prolonged hospital length of stay (LOS). Demographic, clinical, and outcomes data were compared for patients before (usual care with postextubation hyperinflation) and after protocol implementation (OPE). Results: Of 104 patients, 54 patients received usual care, and 50 received OPE. Usual-care recipients had more men (74% vs 62%; P=.19) and were older (median, 70 vs 67 years; P=.009) than OPE recipients. The OPE group had a significantly shorter hospital LOS than the usual-care group (mean, 6.2 vs 7.4 days; P=.04). Other measures improved with OPE but did not reach significance: shorter ventilator duration (mean, 0.6 vs 1.1 days with usual care; P=.06) and shorter LOS in the intensive care unit (mean, 2.7 vs 3.4 days; P=.06). On multivariate analysis, intensive care unit LOS was significantly shorter for the OPE group (mean difference, 0.85 days; 95% CI, 1.65 to 0.06; P=.04). The OPE group had a lower percentage of postoperative complications (10% vs 20%). Conclusion: OPE therapy after cardiac surgery is associated with decreased ICU and hospital LOS.

Soil nitrite (NO2‒) is an important reactive intermediate in many nitrogen transformation processes, but it is unstable under acidic conditions. Canonical extraction method of soil NO2‒ with potassium chloride (KCl) solution greatly underestimates its concentration. In order to reflect the concentration more accurately, we optimized the extraction method of soil NO2‒ in this study. Moreover, soil ammonium (NH4+) and nitrate (NO3‒) were also systematically investigated to achieve efficient extraction of soil inorganic nitrogen. The results showed that un-buffered KCl significantly underestimated soil NO2‒ concentration compared to DIW. The highest recovery of NO2‒ was obtained by extracting with DIW at 10 min of oscillation for three soils. Compared with DIW, the concentration of NH4+ and NO3‒ in soil extracted from KCl solution increased significantly. Furthermore, the soil inorganic nitrogen content of extracts stored at 4°C for one day were closer to directly measurements of fresh samples than the other storage methods. Overall, the recommend analysis method for soil NO2‒ was extracted by DIW, oscillated for 10 min, and filtered by a 0.45 µm filter, and soil NH4+ and NO3‒ were extracted with KCl solution and oscillated for 30 min. The extract should be stored at 4°C and analyzed within 24 hours.

In this paper, an ultracompact combined sensor for displacement and angle synchronous measurement is proposed based on self-imaging effect of optical microgratings. Using a two-grating structure, linear and angular displacement can be measured by detecting the change of phase and amplitude of the optical transmission respectively within one single structure in meantime. The optical transmitted properties of the two-grating structure are investigated in both theory and simulation. Simulated results indicate that, the optical transmission changes in a sinusoidal relationship to the input linear displacement. Meanwhile, the amplitude of the curve decreases with an input pitch angle, indicating the ability for synchronous measurement within one single compact structure. The synchronous measurement of the linear displacement and the angle is also demonstrated experimentally. The results show a resolution down to 4nm for linear displacement measurement, and a sensitivity of 0.26mV/arcsec within a range of ±1° for angle measurement. Benefitting from a simple common-path structure without using optical components including reflectors and polarizers, the sensor shows an ultrahigh compactness for multiple-degrees-of-freedom measuring, indicating the great potential for this sensor in the fields such as integrated mechanical positioning and semiconductor fabrication.

This work deals with a novel porous TiNi-based material for ophthalmic orbital implants that was produced by high-temperature synthesis (SHS) and possesses a specific microstructure of its surface pores, which makes it attractive for biomedical use in implants. After preparation via SHS, the obtained porous TiNi material was etched in acidic environment, to get rid of its surface Ti2Ni secondary-phase particles. This was found to improve the material’s surface morphology, adding micro-roughness to its macro-rough pores. As a result, cell growth tests conducted on the material demonstrated improved cell adhesion and growth kinetics on such a porous material with improved roughness. Finally, the material was tested in vivo as an ophthalmic orbital implant, demonstrating good biocompatibility, good degree of biointegration with surrounding eyeball tissues, and no signs of rejection after as long as 180 days. Thus, the novel porous TiNi-based material shows promise for its use in ophthalmic implantology, for instanse for manufacturing musculoskeletal stumps of the eyeball after evisceration, as it is biocompatible, has a high tissue-implant integration potential and demonstrates reduced risks of exposure and rejection of the implant.

Postpartum depression is a significant health issue during the postpartum period and not only affects the woman but also has important repercussions for the newborn. Group support interventions during the postpartum period have proven effective in helping women cope with depression and improving breastfeeding rates. The aim of this study was to assess the effectiveness of a midwife-led breastfeeding support group intervention on breastfeeding rates, postpartum depression and general self-efficacy. This was a multicentric cluster randomized controlled trial with control and intervention groups and was not blinded. It was conducted in Andalusia (southern Spain) from October 2021 to May 2023. A total of 382 women participated in the study. Results showed a significant difference in exclusive breastfeeding rates at 4 months postpartum between the groups (p &lt; 0.001). Additionally, there was a lower mean score on the Edinburgh Postnatal Depression Scale in the intervention group (12.49 ± 3.6 vs 13.39 ± 4.0; p = 0.044). Similarly, higher scores of general self-efficacy were observed among breastfeeding women at 2 and 4 months postpartum (p = 0.002 and p &lt; 0.001, respectively). In conclusion, midwife-led breastfeeding support groups enhanced self-efficacy, prolonged breastfeeding and reduced postpartum depression 4 months after giving birth.

The Reactive Spark Plasma Sintering (R-SPS) method is compared in this work with the two-steps SHS-SPS route, based on combination of the Self-propagating High-temperature Synthesis (SHS) with the SPS process, for the fabrication of dense (Hf0.2Mo0.2Ti0.2Ta0.2Nb0.2)B2-SiC and (Hf0.2Mo0.2Ti0.2Ta0.2Zr0.2)B2-SiC ceramics. A multiphasic and inhomogeneous product, containing various borides, is obtained at 2000°C/20min by R-SPS, from transition metals, B4C and Si. In contrast, if the same precursors are first reacted by SHS, and then processed by SPS under the optimized condition of 1800°C/20min, the desired ceramics are successfully attained. Both High-Entropy-Boride/SiC products display densities > 97%, uniform microstructures and contained only small amounts of residual oxides (<2.4 wt.%). The presence of SiC makes the sintering temperature milder, i.e. 150°C below to that needed by the corresponding additive-free system. Fracture toughness is also markedly improved, particularly when considering the Nb-containing system processed at 1800°C/20min, whereas KIC progressively decreases (from 7.35 to 5.36 MPa m1/2) as the SPS conditions become more severe. SiC addition is found to inhibit the volatilization of metal oxides like MoO3 formed during oxidation experiments, thus avoiding mass loss in the ceramics. The benefits above also likely take advantage of the fact that the two composite constituents are synthesized in parallel, according to the SHS-SPS approach, rather than being produced separately and combined subsequently, so that strong interfaces between them are formed.

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 presents an in-depth analysis and evaluation of self-healing networks as an innovative solution to combat the escalating threat of ransomware attacks. Recognizing the limitations of traditional network security methods in the face of advanced cyber threats, the research focuses on the development and implementation of self-healing networks, characterized by their dynamic, intelligent response systems. Utilizing a combination of artificial intelligence and machine learning algorithms, these networks demonstrate remarkable adaptability and resilience. Through rigorous simulations, the study examines the efficacy of self-healing networks in detecting, isolating, and recovering from ransomware attacks, with an emphasis on their ability to learn and evolve from each incident. The research highlights the strengths and limitations of these networks and discusses their potential applications in various sectors. The findings suggest that self-healing networks, with their proactive and adaptive defense mechanisms, represent a significant advancement in cybersecurity strategies, offering a robust solution against a range of cyber threats. Future work is recommended to further enhance the capabilities of self-healing networks, particularly in the areas of speed, detection accuracy, and quantum-resistant security measures.

In an outstanding article published in 2008, Suzuki obtained a nice generalization of the Banach contraction principle from which derived a characterization of metric completeness. Although Suzuki’s theorem has been successfully generalized and extended in several directions and contexts, we here show by means of a simple example that the problem of achieving, in an obvious way, its full extension to the framework of w-distances does not have an emphatic response. Motivated by this fact we introduce the concept of presymmetric w-distance on metric spaces, we give some properties and examples of this new structure and show that it provides a reasonable setting to obtain a real and hardly forced w-distance generalization of Suzuki’s theorem. This is realized in our main result, which is a fixed point theorem that involves presymmetric w-distances and certain contractions of Suzuki-type. We also discuss the relationship between our main result and the well-known w-distance full generalization of the Banach contraction principle, due to Suzuki and Takahashi. Connected to this approach we prove another fixed point result that compare with our main result through some examples. Finally, we state a characterization of metric completeness by using our fixed point results.

In today’s competitive landscape, achieving customer-centricity is paramount for the sustainable growth and success of organisations. This research is dedicated to understanding customer preferences in the context of the Internet of Things (IoT) and employs a two-part modeling ap-proach tailored in this digital era. In the first phase, we leverage the power of the Self-Organizing Map (SOM) algorithm to segment IoT customers based on their connected device usage patterns. This segmentation approach reveals three distinct customer clusters, with the second cluster demonstrating the highest propensity for IoT device adoption and usage. In the second phase, we introduce a robust Decision Tree methodology designed to prioritize various factors influencing customer satisfaction in the IoT ecosystem. We employ the Classification and Regression Tree (CART) technique to analyze 17 key questions that assess the significance of factors impacting IoT device purchase decisions. By aligning these factors with the identified IoT customer clusters, we gain profound insights into customer behaviour and preferences in the rapidly evolving world of connected devices. This comprehensive analysis delves into the factors contributing to customer retention in the IoT space, with a strong emphasis on crafting logical marketing strategies, en-hancing customer satisfaction, and fostering customer loyalty in the digital realm. Our research methodology involves surveys and questionnaires distributed to 207 IoT users, categorizing them into three distinct IoT customer groups. Leveraging analytical statistical methods, regression analysis, and IoT-specific tools and software, this study rigorously evaluate the factors influencing IoT device purchases. Importantly, this approach not only effectively clusters the IoT Customer Relationship Management (IoT-CRM) dataset but also provides valuable visualizations that are essential for understanding the complex dynamics of the IoT customer landscape. Our findings underscore the critical role of logical marketing strategies, customer satisfaction, and customer loyalty in enhancing customer retention in the IoT era. This research makes a significant contri-bution to businesses seeking to optimize their IoT -CRM strategies and capitalize on the oppor-tunities presented by the IoT ecosystem.

Gastric outlet obstruction (GOO) poses a common and challenging clinical scenario, characterized by mechanical blockage in the pylorus, distal stomach, or duodenum, resulting in symptoms such as nausea, vomiting, abdominal pain, and early satiety. Its diverse etiology encompasses both benign and malignant disorders. The spectrum of current treatment modalities extends from conservative approaches to more invasive interventions, incorporating procedures like surgical gastroenterostomy (SGE), self-expandable metallic stents (SEMS) placement, and the advanced technique of endoscopic ultrasound-guided gastroenterostomy (EUS-GE). While surgery is favored for longer life expectancy, stents are preferred in malignant gastric outlet stenosis. The novel EUS-GE technique, employing a lumen-apposing self-expandable metal stent (LAMS), combines the immediate efficacy of stents with the enduring benefits of gastroenterostomy. Despite its promising outcomes, EUS-GE is a technically demanding procedure requiring specialized expertise and facilities.

Neurodegeneration is becoming one of the leading causes of death worldwide as the population expands and grows older. There is a growing desire to understand the mechanisms behind prion proteins as well as the prion-like proteins that make up neurodegenerative diseases (NDs) including Alzheimer’s Disease (AD). Both amyloid β (Aβ) and hyperphosphorylated tau (p-tau) proteins behave in ways similar to that of the infectious form of the prion protein, PrPSc, such as aggregating, seeding, and replicating under not yet fully understood mechanisms, thus the designation of prion-like. This review aims to highlight the shared mechanisms between prion-like proteins and prion proteins in the structural variations associated with aggregation and disease development. These mechanisms are largely focusing on the dysregulation of protein homeostasis, self-replication, and protein aggregation, and how this knowledge could contribute to diagnoses and treatments for the given NDs.

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.

This study aimed to expand and inform the growing body of research on Postpartum Depression, from a netnographic standpoint, for the social media platform of Pinterest. More specifically, it aimed to assess the type of content present on Pinterest regarding Postpartum Depression shared by women in 2018 and 2021, as well as examine the subjective experience of women who have experienced this mood disorder. Data were collected following a search using the keywords “Postpartum Depression” on Pinterest on two separate timestamps (2018 and 2021); pins were limited to those in English or French. Master themes were identified among all pins and those identified as “self-disclosure” were subsequently further analyzed. Self-disclosure data were separately analysed using thematic analysis, specifically adopting a deductive thematic approach for the 2021 data set stemming from the coding scheme created from analysis of the 2018 data. Major themes were identified, and an analysis of frequency was performed for each sub-theme, along with qualitative examples associated with each theme. A total of 483 Pinterest pins from two different time points of 2018 (169 pins) and 2021 (314 sample pins) were analyzed. Five primary categories were identified in both 2018 and 2021, with five additional categories being identified in the 2021 dataset. Comparative results from 2018 and 2021 self-disclosure pins show individuals’ varying yet similar subjective manifestations of PPD, the potential need to adopt a multidisciplinary treatment approach, and the societal fears, such as mental health stigma, that are playing a part in help-seeking behavior hesitancies. Our findings provide imperative insight into individuals’ subjective experiences with PPD; experiences that involve a sensitive and vulnerable topic. Netnographic research, along with Pinterest, must be taken into consideration for future work delving into women’s health issues, including those surrounding perinatal mental health.

his quantitative study investigates teachers' perceptions of self-efficacy during the COVID-19 pandemic and explores the correlation between these perceptions and the preservice training they received. The research addresses the cognitive connection between teachers' current self-efficacy, particularly their satisfaction with and appreciation of preservice lecturers. Utilizing a retrospective lens, the study reveals a significant correlation between teachers' evaluation of their preservice training, especially their appreciation of lecturers, and their present self-efficacy. The findings highlight that teachers, amidst the challenges of the pandemic, evaluated their self-efficacy at a remarkably high level. This underscores their resilience during a period of unprecedented uncertainty, demanding substantial personal and professional adaptability. The nuanced interplay observed suggests that teachers' sense of self-efficacy serves as a predictive variable of their mental and professional resilience when confronting uncertainty and navigating rapid and profound changes, as exemplified by the exigencies of the COVID-19 pandemic.

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.

In our latest studies with the help of the self-similar Ansatz we derived new type of 1 solutions for the regular diffusion equation which are much more complex than the well-known 2 Gaussian and error functions. These solutions contain additional Kummer’s M and Kummer’s U 3 functions with quadratic argument. In the present treaties we perform an analogous analysis for the 4 regular diffusion equation which has a complex diffusion coefficient. Formally, it is equivalent to 5 the free Schrödinger equation however it is far from being trivial how the solutions can be given 6 quantum mechanical interpretation. We investigate both the one dimensional Cartesian and the 7 spherical symmetric equations. We find solutions which fulfill the L2 integrability criteria therefore 8 might have quantum mechanical relevance in the future.

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.

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.

This article introduces a new physically based Lyapunov function difinition, which can be used as acoustic energy for finite amplitude pressure oscillations in reacting systems especially in combustion chambers. Reacting flow is seen as an open, non-equilibrium (parameters are distributed unevenly locally) thermodynamic system. This Lyapunov function is defined as the maximum mechanical work, which could be extracted by a heat engine from studied system, if this system would be disconnected from inlet and outlet and from any other surrounding environment, and the engine could transfer to the surrounding environment only mechanical work.

This paper presents the design and measurement of an IEEE 802.11ad standard compatible RF transceiver for 60GHz wireless communication system. In addition to the traditional half-duplex (HD) mode, this work supports full-duplex (FD) operation to deliver better channel utilization and faster response time for the system. The isolation between the transmitter and receiver from the architecture design to system integration for FD operation has been fully considered. A digital self-interference cancellation (DSIC) is implemented in MATLAB to verify the FD performance. The super-heterodyne architecture with an intermediate frequency (IF) of 12GHz is designed to suppress the image frequencies without using extra filters. A flexible phase-locked loop (PLL) synthesizer provides a local oscillator (LO) frequency with a 2kHz resolution. Other than the time division duplex (TDD) mode used in the conventional 60GHz system, a wide bandwidth baseband digital variable gain amplifier (DVGA) with a 3dB bandwidth of more than 4GHz also supports frequency division duplex (FDD) operation. The transceiver chip is fabricated in Tower Jazz 0.18µm SiGe BiCMOS process. With on-board antenna, the transceiver covers all four channels in 802.11ad standard with MCS-12 (7.04Gbps under 1.76GSym/s and 16-QAM) under 1.5m. In the proposed system design, the RF frontend based self-interference (SI) suppression from the local transmitter to receiver LNA is around 54dB. To achieve a practical FD application, the SI is further suppressed with the help of digital SI compensation. The measured power consumption for transmitter and receiver configuration is 194mW and 231mW, respectively, in HD mode and 398mW for FDD or FD operation mode.

Premature failure and degradation of layers are the main problems for transportation infrastruc-ture. Addressing these issues necessitates implementing structural health monitoring (SHM) for transportation layers. This strategic approach is instrumental in mitigating maintenance expenses, prolonging the operational lifespan, and preventing accidents resulting from abrupt layer col-lapse. To this end, this research investigated the stress/strain and damage detection capabilities of a self-sensing cementitious composite developed for potential utilization in the construction of an intelligent subgrade layer. The prepared self-sensing cementitious composite consisted of 10% cement and hybrid conductive fillers, including multiwalled carbon nanotubes (MWCNTs) and graphene nanoplatelets (GNPs) in sand. Initial findings reveal that the electrical resistivity of the composite is significantly affected by the concentration of MWCNTs/GNPs, with a minimum concentration of more than 0.5% needed to achieve a responsive cementitious composite. More-over, the piezoresistive analysis indicates that an increase in the concentration of MWCNTs/GNPs and stress levels leads to an improvement in the stress/strain sensing perfor-mance. When the self-sensing cementitious composite is subjected to equivalent stress levels, variations in the FCR exhibit an increasing trend with decreasing resilient modulus, stemming from a decrease in stiffness due to the increased concentration of MWCNTs/GNPs. Additionally, the EIS analysis demonstrates a contraction for the Nyquist curves under compressive ramp loading prior to failure, followed by the expansion of these curves postfailure. Scanning electron microscopy (SEM) images visually showcase the bridging effects of MWCNTs and the filling ef-fects of GNPs within the composite structure.

The evolution of sentence representation learning, especially with parse tree encoders, has shown remarkable progress. Traditional approaches predominantly rely on recursive encoding of tree structures, which impedes parallel processing capabilities. Additionally, these methods often overlook the significance of dependency tree arc labels. To overcome these limitations, we introduce the Syntax-Enhanced Transformer (SET), incorporating a novel dual-attention mechanism that integrates relation-focused attention alongside traditional self-attention. This design effectively encodes both dependency and spatial positional relationships within sentence dependency trees. Our approach innovatively incorporates syntactic information into the Transformer framework without compromising its inherent parallelizability. The SET demonstrates superior or comparable performance to contemporary methods across various sentence representation tasks, significantly enhancing computational efficiency.

A Berry geometrical phase is identified in a strongly metastable system containing dynamically responsive clathrate hydrate structures within a crystal-fluid material. High energy degeneracy in the associated chemistry produces local stability and false vacuum conditions that lead to non-extensive and non-additive contributions in the fundamental thermodynamic relation. The reciprocating action of a piston expander confirms a net energy gain despite the crystal-fluid material maintaining almost constant density. Application of Ginzburg-Landau theory and the scaling laws reveals a coherence length and a penetration depth for a macro-scale dual superconductor. The coherence length determines non-extensive volume changes whilst its inverse gives the Higgs mass. The penetration depth determines the extent of QCD vacuum suppression whilst its inverse gives the vector boson mass together with indirect manifestation as non-additive hyperbolic curvature. External pressure perturbations of the low-energy system initiate ‘rolling’ critical responses that see energy and momentum conserved across a synchronized U(2) symmetry group whilst a complex gauge field is also exposed. Simultaneous emergence of the Ginzburg-Landau superconducting phase transition is consistent with gauge-invariant coupling of this scalar field to the Yang-Mills action in QCD. The discovery of an energy gap in the gradient energy term of the system Lagrangian is associated with a critical correlation length revealed in the transition from a gapped to a gapless superconducting state. Together with the emergence and absorption of the Higgs-like scalar field, a mechanism for describing the QCD mass gap arises.

The purpose of this study is to analyze the self-regulated learning and the ability to write scientific papers of students after learning with the Project-Based Digital Science Module (DSM). This research is a mixed methods by analyzing quantitatively and qualitatively. The research instrument used to obtain data is using a product assessment sheet and a self-regulated learning questionnaire. Data analysis uses N-Gain to determine the increase in self-regulated learning scores and student scientific writing skills scores, and uses linear regression analysis to determine the effect of self-regulated learning on student scientific writing skills. The results obtained showed N-Gain score of learning independent is 0,65 and scientific writing ability is 0,94, then significant’s score from linear regression by using SPSS is 0,22 < 0,05. This result showed an increase in self-regulated learning and the ability to write scientific papers of students on each indicator after learning using the Project-Based Digital Science Module (DSM). In addition, based on the results of data analysis, it can be seen that there is an influence of self-regulated learning on the ability to write scientific papers of students after learning using the Project-Based Digital Science Module.

The COVID-19 pandemic has disrupted healthcare worldwide, potentially impacting disease management. This cross-sectional study assessed the influence of COVID-19 on diabetes self-management in 341 Saudis with type 2 diabetes using the Diabetes Self-Management Questionnaire (DSMQ). The DSMQ contains 16 items across 4 domains assessing glucose management, dietary control, physical activity, and healthcare use. Three additional items measured the perceived overall impact of COVID-19. Total DSMQ scores were significantly higher in patients aged &gt;60 years versus younger groups. Scores were significantly lower in patients diagnosed for 1-5 years versus longer durations. Patients on insulin had higher glucose management subscores than oral medication users. Overall, DSMQ scores were higher than the pre-pandemic Saudi population and Turkish post-pandemic findings. DSMQ results suggest that while COVID-19 negatively impacted some self-management domains, Saudi patients upheld relatively good diabetes control during the pandemic. Further research is warranted on specific barriers to optimize diabetes care during public health crises.

The aim of article is to design a calm water resistance predictor based on Machine Learning Tools and development of a systematic series for battery-driven catamaran hull forms. Regression Trees (RT), Support Vector Machines (SVM), and Artificial Neural Network (ANN) regression models are applied for dataset training on developed systematic series of catamarans. A hullform optimization was implemented for various catamarans including dimensional and hull coefficient parameters based on resistance and structural weight reduction and battery performance improvement. This paper provides a diverse database of catamaran hullform. Hence, an automated Matlab program was coded for geometry generation and cost function evaluation. Design distribution based on Lackenby transformation fulfills all design space and sequentially a novel self-blending method reconstructs new hullforms based on two parents blending. Finally, a machine learning approach was conducted on generated data of case study. This study shows that ANN algorithm correlates well with the measured resistance. Accordingly, a general and unique tool is proposed for optimized and desired design in first design stage.

Hox gene clusters are crucial in Embryogenesis. It was observed that some Hox genes are located in order along the telomeric to centromeric direction of the DNA sequence: Hox1, Hox2, Hox3…. These genes are expressed in the same order in the ontogenetic units of the Drosophila embryo along the Anterior-Posterior axis. The two entities (genome and embryo) differ significantly in linear size and in-between distance. This strange phenomenon was named Spatial Collinearity (SP). Later, it was observed that, particularly in the Vertebrates, a Temporal Collinearity (TC) coexists: first is Hox1 expressed, later Hox2 and even later Hox3,…,. According to a Biophysical Model (BM), pulling forces act at the anterior end of the cluster while a cluster fastening applies at the posterior end. Hox clusters are irreversibly elongated along the force direction. During Evolution, the elongated Hox clusters are broken at variable lengths thus split clusters may be created. An Empirical Rule was formulated distinguishing development due to a complete Hox cluster from development due to split Hox clusters. BM can ‘explain’ this Empirical Rule. In a spontaneous mutation where the cluster fastening is dismantled, a minimal pulling force can automatically shift the cluster inside the Hox activation domain. This cluster translocation can probably explain the absence of Temporal Collinearity in Drosophila.

The benefits of event-driven architecture (EDA) derive from how systems and components are loosely coupled, which can facilitate independent development and deployment of systems, improved scaling and fault tolerance, and integration with external systems, especially in comparison to monolithic architectures. With the advent of new technologies such as containers, and microservices, a new generation of distributed event streaming platforms are commonly used in event-driven architecture for efficient event-driven communication. However, the asynchronous and distributed nature of EDA poses several problems that include handling failures, the dependence of an end-to-end transaction on individual component stability, etc. In this paper, we describe a new approach to designing self-regulating distributed applications with autopoietic and cognitive workflow management. This approach is based on the new science of information processing structures derived from the General Theory of Information. Just as a genome enables self-organizing and self-regulating biological structures, a digital genome enables a specific software application with several components, the ability to use distributed resources and self-regulate the evolution of the system based on functional and non-functional requirements, and best-practice policies that maintain the stability, safety, and survival under non-deterministic fluctuations in the demand for resources. In addition, cognitive workflow management assures end-to-end transaction delivery.

Contemporary diagnostic practice in psychiatry relies on the tick-box approach of diagnostic manuals. In light of the obvious limitations of diagnostic systems, narrative formulations, whether in the form of biological, psychodynamic or behavioural cognitive ones, have been proposed. Their approach, which can be caricatured as one of drawing arrows, enriches our clinical tools and practice but in their explicitly theoretical orientation, as well as expanding the field of discovery, they also constrain it. In two previous papers I summarized the resulting limitations, with specific reference to the patient’s experience and proposed further enriching clinical perspicacity by attending to images and parataxis. The specific intent of attending to the patient’s experiences through a process that explicitly eschews narrativization, but allows clinical phenomena to “hang loose”, is to facilitate a process of clinical discovery and healing through practices of co-production between patient and clinician. In the present paper, I expand the range of phenomena that may assist in this co-production through adding attention to dreams to that of images and parataxis. In particular, I turn to the work of the Italian novelist Guido Morselli. Morselli agrues that the self is created through bundling experiences together through acts of “will” in order to establish our personal narrative. In doing so, however, the “will” forbids access to phenomena that do not fit the dominant narrative. Experience is therefore impoverished. Dream consciousness is a type of consciousness not harnessed by the constraints of narrativity. It allows a slipping of the narrative vigilance which bundles phenomena together to create a coherent narrative and thus offers a richer menu of potential experiences, including “bizarre” images that can serve to challenge our usual self-narrative and offer a new perspective on ourselves.

Estimating the Remaining Useful Life (RUL) of aircraft engines holds a pivotal role in enhancing safety, optimizing operations, and promoting sustainability, thus being a crucial component of modern aviation management. Precise RUL predictions offer valuable insights into an engine’s condition, enabling informed decisions regarding maintenance and crew scheduling. In this context, we propose a novel RUL prediction approach in this paper, harnessing the power of Bi-directional LSTM and Transformer architectures, known for their success in sequence modeling, such as natural languages. We adopt the encoder part of the full Transformer as the backbone of our framework, integrating it with a self-supervised denoising autoencoder that utilizes Bidirectional LSTM for improved feature extraction. Within our framework, a sequence of multivariate time series sensor measurements serves as the input, initially processed by the Bidirectional LSTM autoencoder to extract essential features. Subsequently, these feature values are fed into our Transformer encoder backbone for RUL prediction. Notably, our approach simultaneously trains the autoencoder and Transformer encoder, different from the naive sequential training method. Through a series of numerical experiments carried out on the C-MAPSS datasets, we demonstrate that the efficacy of our proposed models either surpasses or stands on par with that of other existing methods.

The prevalence of depression and anxiety among healthcare workers (HCWs) during the Covid-19 pandemics is high. The aim of the study is to identify the importance of self-esteem and occupational factors in association with the incidence of depression and anxiety among HCWs through a longitudinal cohort study during the Covid-19 pandemics in Latvia. Participants were interviewed repeatedly seven times during the Covid-19 pandemics. 322 participants were included in the data analysis for depression and 352 for anxiety. Low self-esteem and working in the general practitioner's office is associated with the incidence of depression and anxiety. Undergraduate education and direct contact with Covid-19 patients are associated with the incidence of depression. The risk of anxiety decreases by 1% each year among HCWs. Our study shows the importance of personality factors of HCWs during the Covid-19 pandemics.

The contemporary landscape of education is witnessing a paradigm shift towards innovative instructional methods, with the flipped learning approach gaining considerable attention. The purpose of this research was to investigate the impact of the flipped learning approach on students' perception and acceptance throughout an entire semester in the 'Introduction to Programming with Java' course, using a research design with a quantitative approach, ultimately aiming to inform educational practice and advance our knowledge of innovative teaching methods in higher education. This study was conducted at a university with 174 students involved, divided into two groups, 87 students in the experimental group and 87 students in the control group. The data collected through the scales were analyzed by using descriptive and inferential statistical analysis techniques in statistical software. At the end of the measurements, the technology acceptance level and self-directed learning perceptions of engineering students who received education with flipped learning were high. The results suggest that educators should consider students' readiness for self-directed learning when implementing the flipped learning approach and focus on creating an environment that supports their autonomy and engagement. This research offers valuable guidance for instructors, curriculum designers, and educational policymakers seeking to enhance the effectiveness of flipped learning in higher education courses.

Matter grows and self-assembles to produce complex structures such as virus capsids, carbon fullerenes, proteins, glasses, etc. Due to its complexity, performing pen-and-paper calculations to explain and describe such assemblies is cumbersome. Many years ago, Richard Kerner presented a pen-and-paper path integral approach to understand self-organized matter. Although successfully addressed many important problems including the yield of fullerene formation, the glass transition temperature of doped chalcogenide glasses, the fraction of boroxol rings in B2O3 glasses, the first theoretical explanation for the empirical recipe of window and Pyrex glass and the understanding of virus capsid self-assembly, still is not the primary choice when tackling similar problems. The reason lies in the fact that it diverges from mainstream approaches based on the energy landscape paradigm and non-equilibrium thermodynamics. In this context, a critical review is presented, demonstrating that the Richard Kerner method is, in fact, a clever way to identify relevant configurations. Its equations are simplified, common physical sense versions to those found in the energy landscape kinetic equations. Subsequently, the utilization of equilibrium Boltzmann factors in the transition Markov chain probabilities is analyzed within the context of local two-level energy landscape models kinetics. This analysis demonstrates that their use remains valid when the local energy barrier between reaction coordinate states is small compared to the thermal energy. This finding places the Richard Kerner model on par with other more sophisticated methods and, hopefully, will promote its adoption as an initial and useful choice for describing the self-agglomeration of matter.

Advances in biomimetic triboelectric nanogenerators (TENGs) have significant implications for electronic skin (e-skin) and human-machine interaction (HMI). Emphasizing the need to mimic complex functionalities of natural systems, particularly human skin, TENGs leverage triboelectricity and electrostatic induction to bridge the gap in traditional electronic devices' responsiveness and adaptability. The exploration begins with an overview of TENGs' operational principles and modes, transitioning into structural and material biomimicry inspired by plant and animal models, proteins, fibers, and hydrogels. Key applications in tactile sensing, motion sensing, and intelligent control within e-skins and HMI systems are highlighted, showcasing TENGs' potential in revolutionizing wearable technologies and robotic systems. This review also addresses the challenges in performance enhancement, scalability, and system integration of TENGs. It points to future research directions, including optimizing energy conversion efficiency, discovering new materials, and employing micro-nanostructuring techniques for enhanced triboelectric charges and energy conversion. The scalability and cost-effectiveness of TENG production, pivotal for mainstream application, are discussed along with the need for versatile integration with various electronic systems. The review underlines the significance of making bioinspired TENGs more accessible and applicable in everyday technology, focusing on compatibility, user comfort, and durability. Conclusively, it underscores the role of bioinspired TENGs in advancing wearable technology and interactive systems, indicating a bright future for these innovations in practical applications.

Self-compacting concrete (SCC) is a high-quality construction solution combining high fluidity, passing and filling ability with improved mechanical properties and durability. In the present study, the effect of incorporating alternative waste materials, such as two by-products of the ce-ment industry, namely cement kiln dust (CKD) and by-pass dust (BPD), into SCC as partial re-placement for traditional filler material was investigated. The produced compositions were compared with reference mixtures containing only marble powder (MP) as a filler. A series of tests including specific test methods for wet SCC, compressive, flexural and tensile splitting strength tests as well as drying shrinkage determination were conducted to evaluate the quality of the produced SCC in terms of fresh and hardened SCC properties. The use of alternative fine filler materials resulted in a high-performance sustainable SCC of low cement content. Specifi-cally, incorporating CKD into the SCC enhanced its rheological behavior and improved slightly its mechanical properties, while the use of BPD led to SCC mixtures of adequate rheological characteristics but with highly upgraded mechanical and physical properties

We report a detailed investigation of vibrational modes, structure, and dynamics of glutathione (GSH) solutions using ultrasonic relaxation spectroscopy, FT-IR vibrational spectroscopy and electronic absorption measurements. The experimental data were analyzed in view of density functional theory (DFT) and molecular docking calculations. Three distinct Debye-type relaxation processes are resolved in the acoustic spectra, which are assigned to conformational changes between GSH conformers, self-association of GSH, and protonation processes. The standard volume changes for each process were estimated both experimentally and theoretically revealing a close resemblance among them. The higher the effect of the relaxation process in the structure, the greater the induced volume changes. From the temperature dependence of specific acoustic parameters, the thermodynamic characteristics of each process were determined. The experimental FT-IR spectra were compared with the corresponding theoretically predicted vibrational spectra revealing that the GSH dimers and extended conformers dominate the structure of GSH solutions in the high concentration region. The absorption spectra in the ultraviolet region confirmed the gradual aggregation mechanism that takes place in the aqueous GSH solutions. The results of the present study were discussed and analyzed in the framework of the current phenomenological status of the field.

The present paper presents findings from the follow-up of 185 of these nurses who had been involved in the front-line care of COVID-19 patients in urban centres in Northern England, during and after the “second wave” of COVID patients in November, 2020. Measures of adjustment, stress (including PTSD), and Maslowian self-actualization were added to earlier measures of personality, adjustment, work-life stress, and career intention. Cluster analysis identified three types in the 2020-2023 cohort: I ‘Actualizing Professionals’; (N=62); II ‘Strong Professionals’ (N=56); III ‘Highly Stressed Nurses’ (N=45). These latter very stressed nurses included all nurses (N=16) identified as having PTSD symptoms.

Carbonization of a biopolymer (lignin) in self-propagating high-temperature synthesis (SHS) led to the production of 2D graphene structures. With the help of modern analytical methods (Raman spectroscopy, X-ray diffraction) and electron microscopy, the resulting product was confirmed to have a few-layer 2D graphene structure. The predicted photovoltaic properties of the resulting few-layer graphene were implemented for laser ignition of a model pyrotechnic composition based on porous silicon. A phenomenological model of the formation mechanism of 2D graphene structures under the conditions of the SHS process is proposed.

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.

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.

We used a mixed-method single-case experimental research design to examine the effect of modelling (peer versus non-peer) on exercise self-efficacy in stroke survivors who participated in a community-based exercise program. Quantitative data were obtained using a ABCA design: (A1) no model/baseline 1 (3 weeks); (B) peer model (6 weeks); (C) non-peer model (6 weeks); and (A2) no model/baseline 2 (3 weeks). Four participants completed self-efficacy questionnaires after each weekly session. Qualitative data were obtained using researcher diaries and two semi-structured interviews: after B and A2. Based on quantitative and qualitative results, participants reported higher exercise self-efficacy in the model conditions, with ratings appearing highest for the non-peer model. This finding could be due to a lack of full integration of the peer model and low feelings of similarity. Modelling in general could help people recovering from a stroke increase their exercise self-efficacy, but non-peer models may not be most advantageous.

Laser scanning 3D imaging technology, because it can get accurate three-dimensional surface data, has been widely used in the search for wrecks and rescue operations, underwater resource development, and other fields. At present, the conventional underwater rotating laser scanning imaging system maintains a relatively fixed light window. However, in low-light situations underwater, the rotation of the scanning device causes some degree of water fluctuation, which warps the light strip data that the system sensor receives about the object's surface. To solve the problem, this research studies an underwater 3D scanning and imaging system that makes use of a fixed-light window and a spinning laser (FWLS). A refraction error compensation algorithm is investigated that is based on the fundamentals of linear laser scanning imaging and the dynamic refraction mathematical model is established by the motion of the imaging device. During the imaging process, the incident angle between the laser and the light window varies due to the scanning mode of the system. The experimental results show that the reconstruction radius error is reduced by 60% (from 2.5 mm to about 1 mm) when the measurement data for a standard sphere with a radius of 20 mm are compensated. Moreover, the compensated point cloud data exhibits a higher degree of correspondence with the model of the standard spherical point cloud. This study has a specific reference value for the refractive error analysis of an underwater laser scanning imaging system, and it provides certain research ideas for the subsequent refractive error analysis of various scanning imaging modalities.

(1) Background: Prevention of acute cardiovascular events in patients with cardiovascular diseases (CVDs) requires promoting health-protective behaviors (e.g., physical activity) and preventing health-compromising behaviors (sitting). Digital health interventions provide much potential. Based on multiple behavioral change theory, an intervention (reCardial app) was evaluated, and the following hypotheses were tested: H1: Health behaviors (physical activity, sitting) and disease self-management (self-care maintenance, self-care confidence) are closely interrelated. H2: Change in health behaviors and disease self-management indicators over time is more pronounced in reCardial app-users. H3: In the intervention group, changes in systolic and diastolic blood pressure indicate a positive trajectory. (2) Methods: A study with the design of a randomized controlled trial over 12 weeks was conducted. 40 CVD-patients were randomized equally to the Intervention Group (35% women; Mage=60.6years) and Control Group (45% women; Mage=61.5years). (3) Results: Findings support H1 with significant correlations between health behaviors (r=0.45-0.63**), and disease self-management (r=-0.32-0.54**), H2 with Eta²=0.21 (not statistically significant) and H3 partially with d=0.101 for systolic blood pressure but not regarding diastolic blood pressure. (4) Conclusions: Replications are needed with larger samples and more objective measures. The app can help prevent and manage CVD by means of promoting health-protective behaviors and preventing health-compromising behaviors. Taking different behaviors into account can increase intervention effects and with that support of users.

Abstract: Arterial hypertension is the main preventable risk factor for premature death and stroke worldwide and anxiety is a real public health problem. Stress is the common denominator for both cases. In the last 3 years, the aggressive exposure of the population to negative emotions (fear of disease in a pandemic, fear of war, financial instability) was the trigger factor. The psycho-neuro-endocrine mechanisms include the hypothalamic-pituitary-adrenal axis and induce sympathetic-parasympathetic imbalance, hypercortisolemia, endothelial dysfunction, oxidative stress, inflammation, cytokine growth, and atherosclerosis. The individual’s vulnerability to stress depends on many factors, but the key role in the perception of stress belongs to immunogenic personality traits. Among them, self-efficacy is a protective factor against stress. Our research aimed to evaluate the connections between psycho-emotional factors and hypertension, using psychometric tests, to identify vulnerability to stress, perceived stress level, anxiety, self-efficacy, and psychobehavioral type, for 215 patients, 104 hypertensive and 111 non-hypertensive patients. The results of the study confirmed that there is a statistically significant difference, which requires psychological screening measures to identify patients vulnerable to stress, as well as medical education courses for doctors in the field of psychology.

The nanocomposites with reversible cross-linking covalent bonds were prepared by reacting furfurylamine (FA)-modified diglycidyl ether of bisphenol A (DGEBA) and furfuryl-functionalized aniline trimer-modified graphene (TFAT-G) with bismaleimide (BMI) via Diels-Alder (DA) reaction. The successful synthesis of the TFAT modifier is confirmed by NMR hydrogen spectroscopy and IR spectroscopy tests. The structure and properties of TFAT-G epoxy nanocomposites are characterized by SEM, DSC, tensile and resistivity CES. The results show that TFAT-G was uniformly dispersed in the resin, and 1wt% TFAT-G composites increase to 233% for tensile strength, 63% for elongation at break, 66.1% for modulus and 7.8℃ for Tg. Overall, the graphene/self-healing resin nanocomposites have both good self-healing function and electrical conductivity by adding 1% modified graphene and still maintain the original 83% strength and 89% electrical conductivity after one cycle of heating repair.

Ultra high molecular weight polyethylene (UHMWPE) is a thermoplastic high-performance polymer in high demand for biomedicine, ship and machine building, anthropomorphic robots and smart prostheses. Highly oriented UHMWPE fibers possess record specific strength and may also be used for the fabrication of self-reinforced PE-PE composites (SRPEC). The temperature evolution study of the small-angle X-ray scattering (SAXS) signature of the supramolecular structure of UHMWPE can help reveal their important role in the mechanism of the shape memory effect in SRPEC. The laboratory SAXS–WAXS beamline XEUSS 3.0 was used for in situ studies of the nanostructure parameters in unidirectional SRPEC. Namely, the radius of gyration and the dimensionality factor were extracted from 2D SAXS patterns using several fitting algorithms. Significant changes of these parameters occurred in temperature ranges that correspond to the initiation of the shape memory effect and melting. The relationship between the material anisotropy and 2D SAXS patterns are discussed in the context of the supramolecular structure evolution.

Non-suicidal self-injurious behavior (NSSI), prevalent in patients with non-psychotic mental disorders (NPMD), is associated with numerous adverse outcomes. Despite active research into the clinical and psychological aspects of NSSI, the underlying biological mechanisms remain obscure. Early adverse experiences are believed to induce long-lasting changes in neuroendocrine mechanisms of stress control playing a key role in NSSI development. The aim of the study was to evaluate parameters potentially predicting development of NSSI in female patients with NPMD. Eighty female patients over 18 years with NPMD and suicidal ideation (40 with and 40 without NSSI) and 48 age matching women without evidence of mental illness (healthy controls) were enrolled. Diagnostic interviews and self-report measures were used to assess childhood maltreatment, presence, frequency, and characteristics of suicidal and self-injurious thoughts and behaviors, the Beck Depression Inventory scale to assess severity of depression. Hypothalamic-pituitary-adrenal axis markers, hormones, and neurotrophic factors were measured in blood serum. The likelihood of developing NSSI in patients with NPMD was associated with early adverse family history and elevated adrenocorticotropic hormone levels. Dysregulation of hypothalamic-pituitary-adrenal axis as a result of early chronic stress experiences may represent critical biological mechanism promoting the development of NSSI behaviors in patients with NPMD.

As distinct human societies, three unique intentional communities are investigated regarding their preferred consensus decision-making practices. It is Identified that each has adopted a different form of consensus decision-making to solve potential group-wide interpersonal conflict. The individual attributes of these three consensus decision-making practices are considered, both from the perspective of maintaining group stability and in relation to individual member's mental health. The communities are a Canadian self-directed public senior elementary and secondary school, an annual English conference for those self-identifying as on the autistic spectrum, and a self-producing Korean popular music (K-pop) group. It is found that the intentional community and participants’ mental health are sustained regarding each of the three consensus decision-making practices. Nevertheless, the resulting decisions generate various stresses within the communities, both as a whole and concerning the individual members. To retain group cohesion and maintain individual mental health, these stresses must be recognized and understood by participants. The novel finding of this research is that, dependent on the time available for decision-making, and the members’ perspective adopted, intentional communities might practice more than one form of consensus decision-making and still support both group cohesion and individual mental health, maintaining the democracy of these distinct societies.

In air-breathing proton exchange membrane fuel cells (Air PEM FCs), high rate of water evaporation from the cathode might influence the resistance of the membrane electrode assembly (MEA), which is highly dependent on the water content of the Nafion membrane. We propose a dead-end hydrogen anode as a means of intermediate storage of water/humidity for self-humidification of the membrane. Such inflatable bag integrated with the PEM FC has a potential in blimp applications for anode self-humidification. A dynamic numerical water balance model, validated by experimental measurements, is derived to predict the effect of MEA configuration, membrane’s hydration state, and water transfer rate at the anode on MEA resistance and performance. The experimental setup included humidity measurements, polarization and electrochemical impedance spectroscopy tests to quantify the effect of membrane hydration on its resistance in a lightweight MEA integrated with an inflatable dead-end hydrogen storage bag. Varying current density (20 mA, 40 mA and 60 mA) and cathode humidity (20, 50 and 80%) were examined and compared with the numerical results. The validated model predicts that the hydration state of the membrane and water transfer rate at the anode can be increased by using a thin membrane and thicker gas diffusion layer.

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.

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.

Traumatic brain injury (TBI) is among the leading causes of mortality and long-term impairment globally. TBI has a dynamic pathology encompassing a variety of metabolic and molecular events that occur in two phases, primary and secondary. An external forceful blow to the brain initiates the primary phase, which is followed by a secondary phase that involves the release of calcium ions (Ca2+) and the initiation of a cascade of inflammatory processes, including mitochondrial dysfunction, rise in oxidative stress, activation of glial cells, and damage to the blood-brain barrier (BBB), resulting in paracellular leakage. There is currently no FDA-approved drug for TBI, but existing approaches rely on delivering small and macromolecular treatments, which are severely constrained by the BBB, poor retention, off-target toxicity, and complex pathology of TBI. Therefore, there is a demand for innovative and alternative therapeutics with effective delivery tactics for diagnosis and treatment of TBI. Tissue engineering and use of biomaterials is one such alternative approach. With this approach, neuronal stem cell therapy is combined with synthetically generated tissue materials such as hydrogels, self-assembling peptides, and electrospun nanofibers, which may induce neurite outgrowth, differentiation of human neural stem cells, and nerve gap bridging in TBI. This review examines tissue engineering and the use of biomaterials as potential treatments for TBI, including their synthesis, mechanisms of action, and limitations. The review also discusses challenges facing tissue engineering and biomaterial technology including survival rate of transplanted stem cells and the development of biodegradable, biocompatible, and mechanically flexible biomaterials. A better understanding of the mechanisms and drawbacks of these novel therapeutic approaches will help guide the design of future TBI therapies.

Biological function depends on the composition as well as structure of the organism, that is the organization of interactions between its parts. In multicellular organisms, the complex communication between cells is crucial for the coordination of cellular functions (growth, differentiation, apoptosis), compartmentalization of cellular processes within the organism, and the integration of cells into functioning higher levels of structural organization. Here we focus on the interaction among cells, as the basic level of biological organization, capable of a remarkable degree of autonomy and self-maintenance. While a wealth of data on putative cell interactions has recently become available, its biological interpretation depends on expectations we have for the structure of these interaction networks. This contribution is an attempt at formulating basic questions we may ask about cell interaction data. We build on the understanding that cells fulfill two general functions: the integrity-maintaining and the organismal service function. We derive the expected patterns of cell interactions considering two intertwined aspects: the immediate functional and the evolutionary. Based on these, we propose several guidelines for analysis and interpretation of transcriptional cell-interactome data.

The present paper reports an exact approach quantifying the electromagnetic contribution to the anomalous magnetic moment occurring in isolated system comprised of non-composite particle carrying elementary electric charge. Essential averaging procedure and regularization of the electromagnetic field potentials necessary when quantifying the electromagnetic self-interactions and when deriving equations of motion without singularities and obeying the conservation laws are thoroughly discussed. The study shows that the dynamics of the considered system is associated to unique classical transcendental equations of motion satisfied by the particle's velocity and the electromagnetic contribution to the anomalous $g$-factor known from the quantum electrodynamics. The equations of motion lead to an exact analytical expression for the anomalous $g$-factor that provides more accurate result than that calculated with the aid of quantum electrodynamics. It matches the experimentally measured value reported in the literature to a one part per trillion. We obtain a_e=0.00115965218000(65) thus reveling the potential of non-perturbative, non-probability methods in predicting the electron's anomalous g-factor.

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.

Although adults and children differ in self-versus-other perception, the developmental perspective on this discriminative ability is missing. We compared neural activation of self-vs-others in 39 participants of 4 different age groups (4 yo. to adulthood). Two brain regions, the MCC and right postcentral gyrus, exhibited respectively non-linear and gradual gains in discriminative abilities for self-vs-others stimuli. These regions play critical roles in self-referential processing, empathy, and social cognition. Nine brain regions showed linear increase for others-vs-self stimuli. These regions are associated with multisensory processing, somatosensory skills, complex visual stimuli, self-awareness, empathy, theory of mind, and social recognition. Understanding the neural basis of self-vs-others discrimination during development can inform when and how social contexts support learning processes during childhood and adolescence.

The extent of micro-phase separation in polyurethanes (PUs) depended on the molecular weight, the chemical nature and the structure of the polyol/soft segments. Balanced properties in PUs were obtained by using blends of polyols during synthesis. No previous studies have been devoted to understand the interactions between the soft segments that affect the properties of the PUs. In this study, different blends of two polyols of different nature (polyester – PE - and polycarbonate diol - CD) and similar molecular weight were prepared and their structural, thermal, surface, viscoelastic and self-adhesion properties were assessed. PE showed larger number of structural repeating units and higher number of polar groups than CD, but the carbonate-carbonate interactions in CD were stronger than the ester-ester interactions in PE. The blending of CD and PE imparted synergic structural properties, particularly in the ones containing less than 50 % PE, they were associated to the disrupt of carbonate-carbonate interactions in CD and the formation of new ester-carbonate and hydroxyl-carbonate interactions. Therefore, higher glass transition temperatures, a new diffraction peak at 2θ = 24°, one additional thermal degradation at 426-436 °C, and less steeper decline of the storage moduli to lower temperature were found in CD+PE blends with less than 50 wt.% PE. Furthermore, the different interactions between the polyol chains in the blends were also evidenced on their surface properties, and all CD+PE blends showed self-adhesion property which seemed related to the existence of ester-carbonate and carbonate-carbonate interactions.

In the present study, an epoxy-modified silica nano-composite coating was deposited on an aluminum substrate and ACSR conductor. For this purpose, super-hydrophobic modified silica nanoparticles based on the TEOS and MTES precursors were prepared. Then, the modified silica nanoparticles were added to the epoxy resin solution. The coating deposition on an aluminum substrate and ACSR conductors was performed by a spraying method. The structure, morphology, and chemical analysis of the nanoparticle’s surface were studied with X-ray diffractometer (XRD), Transmission electron microscopy (TEM), and Attenuated Total Reflectance Fourier Transform Infrared (ATR-FTIR) methods. The water contact angles on the prepared samples were measured by a water contact angle analyzer method. Icing tests were performed at a constant operating temperature of -14 °C. Also, the self-cleaning behavior of the samples was evaluated with an iron oxide powder. The results indicated that the epoxy-modified silica nano-composite coating showed hydrophobic (CA, 141°), anti-icing, and self-cleaning properties.

Resistive random-access memory has emerged as a promising non-volatile memory technology, garnering substantial attention due to its potential for high operational performance, low power consumption, and scalability. Two-dimensional nanostructured materials play a pivotal role in RRAM devices, offering enhanced electrical properties and physical attributes that contribute to overall device improvement. In this study, self-rectifying switching behavior in RRAM devices is proposed based on molybdenum disulfide nanocomposites decorated with palladium on SiO2/Si substrates. The integration of Pd and MoS2 at the nanoscale effectively mitigates leakage currents decreasing from cross-talk in the RRAM array, eliminating the need for a separate selector device. The successful demonstration of the expected RRAM switching behavior follows the application of a Pd nanoparticle coating. The fabricated Pd-MoS2 synaptic device showed a high current ratio for forward/reverse current higher than 60 at a low resistance state and observed a memory on/off ratio of 103 performing stable resistance switching behavior.

X-rays production through betatron radiation emission from electron bunches is a valuable resource for several research fields. The EuAPS (EuPRAXIA Advanced Photon Sources) project, within the framework of the EuPRAXIA project, aims to provide 1-10 keV photons (soft X-rays) developing a compact plasma-based system designed to exploit self-injection processes that occur in highly nonlinear laser-plasma interaction (LWFA) to drive electron betatron oscillations. Since the emitted radiation spectrum, intensity, angular divergence, and possible coherence strongly depend on the properties of the self-injected beam, accurate preliminary simulations of the process are necessary to evaluate the optimal diagnostic device specifications. Dedicated tools for these tasks are under development: electron trajectories from particle-in-cell (PIC) simulations are currently undergoing numerical analysis through the calculation of retarded fields and spectra for various plasma and laser parameter combinations. At the same time, an analytical approach is being examined to obtain an expression for the emitted spectrum, assuming linear or quadratic electron energy variation. Code structure and some trajectories analysis results are presented.

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

The impact of the COVID-19 pandemic and the quarantine measures on children and adolescents’ hospitalization psychiatric has been poorly studied, especially in Italy, the first European country to adopt social restrictions. To fill this gap, this retrospective study examined changes in admissions to our child and adolescent psychiatry ward from the emergency department during the two COVID-19 quarantine periods in Italy (March to June 2020; October 2020 to January 2021) compared with two reference periods (defined as 12 months prior to the respective quarantine periods). Specifically, we examined data on the number of inpatients and the distribution of psychopathological disorders (i.e., psychosis, mood disorder) and self-injurious behaviors. The results revealed significantly fewer admissions during the first quarantine period relative to the first reference period and, conversely, significantly more admissions during the second quarantine period relative to the second reference period. Of note, the results showed an increased frequency of mood disorders, non-suicidal self-injurious behaviors and suicidal ideation during the quarantine periods, compared to the reference periods. The findings underline the need to focus on the psychopathological profile of children and adolescents with patient-tailored therapeutic interventions and suggest the importance of developing psychological healthcare services for future emergency periods..

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

Many relevant health social actors have voiced concerns about the danger of self-medication in times of growing use of over-the-counter medicines and, in some contexts, unregulated selling of them. Previous research has examined the incidence of parental self-medication as well as the use and abuse of antibiotics without professional advice. However, these studies have limited evidence on the role of family doctors and the perceived severity of self-medication in the case of parents. Based on Health Belief Model, our research tested the effects of exposure to medical information on the perceived level of self-treating the children without medical advice. Specifically, we aimed to assess whether exposure to information warning about the risks of treating children without a medical prescription influences parents' attitudes toward administering medicines to their children without medical advice. The results showed that our respondents evaluated the practices of self-medication negatively, especially when it came to treating their children without medical advice. However, their attitudes towards self-medication varied depending on their beliefs about administering certain medications. Additionally, the results showed that the perceived level of satisfaction with the family physician could be a significant factor in controlling and avoiding the treatment of children without professional advice.

The differential-equation of second order for space-time oscillation in flat tangential-spacetime of Minkowski-type can be written as an energy-equation. For this energy-equation the analogon of damping energy in two coupled Minkoswki-spacetimes with ftl can be calculated and formulated over Lamberts W-function or via Euler-equation.

Self-supervised learning is a method that learns general representation from unlabeled data. Masked image modeling (MIM), one of the generative self-supervised learning methods, has drawn attention showing state-of-the-art performance on various downstream tasks, though showing poor linear separability resulting from the token-level approach. In this paper, we propose a contrastive learning-based multi-view masked autoencoder for MIM, exploiting an image-level approach by learning common features from two different augmented views. We strengthen MIM by learning long-range global patterns from contrastive loss. Our framework adopts simple encoder-decoder architecture, learning rich and general representation by following a simple process: 1) two different views are generated from an input image with random masking and by contrastive loss, we can learn semantic distance of the representations generated by an encoder. By applying a high mask ratio, 80%, it works as strong augmentation and alleviates the representation collapse problem. 2) With reconstruction loss, decoder learns to reconstruct an original image from the masked image. We assess our framework by several experiments on benchmark datasets of image classification, object detection, and semantic segmentation. We achieve 84.3% fine-tuning accuracy on ImageNet-1K classification and 76.7% in linear probing, exceeding previous studies and show promising results on other downstream tasks. Experimental results demonstrate that our work can learn rich and general image representation by applying contrastive loss to masked image modeling.

This article is a scoping review analyzing advances in experimental studies on bacterial self-healing of concrete. From Scopus, ScienceDirect, Web of Science and Google Scholar search engine databases, 54 studies were selected for analysis, and an increasing trend of publications since 2015 was found, with India as the country with the largest research presence. Different ma-terials are used as replacement or admixture in concrete, such as fibers, particles and recycled material from construction waste. It was found that the most commonly used bacteria are Bacillus subtilis and Bacillus sphaericus, which can improve the strength of concrete by generating calcite and sealing cracks. In addition, it was found that the presence of materials such as fibers, ashes and construction recyclates can protect bacteria in an alkaline environment. The designs usually in-clude more than two experimental groups and a control. In summary, this study highlights the importance of bacterial self-healing in concrete and provides information on advances in this field of research.

This study proposes self-calibration of capacitor mismatch errors for high-resolution pipeline analog-to-digital converters (ADCs). The proposed calibration circuit recursively amplifies the capacitor mismatch error by re-utilizing a multiplying digital-to-analog converter in a pipeline stage without increasing the circuit complexity, and the amplified error voltage is converted into digital code by utilizing the remaining pipeline stages. Error correction is performed by subtracting the digital code from the ADC output during normal operation. A prototype of a 12-bit pipeline ADC is fabricated in a 0.18 µm standard CMOS process. The ADC comprises eight 1.5-bit stages, followed by a 4-bit flash ADC as the final stage; the capacitor mismatch errors in the first two pipeline stages are corrected by utilizing the proposed self-calibration technique. At a sampling rate of 30 MS/s, the measured differential and integral nonlinearities improve from +0.82/-0.75 and +1.12/-1.79 to +0.45/-0.41 and +0.47/-0.91 after calibration, respectively. Further, the measured dynamic performances improve significantly with calibration. For an input frequency of 2.09 MHz, the ADC achieves a spurious-free dynamic range (SFDR) and signal-to-noise and distortion ratios (SNDR) of 69.3dB and 63.9dB before calibration, respectively. After calibration, the SFDR and SNDR increase to 84.1dB and 68.9dB, respectively, and the effective number of bits is approximately 11.1bit. The power consumption of the ADC is 35mW under 1.8V supply, and the resulting figure of merit is 514fJ/conversion step.

The brief paper utilises Zimbabwe as a case study to discuss the effects of El Nino in Southern Africa. It offers potential adaptation and mitigation measures for farmers to prepare for the forecasted El Nino-influenced rainy season 2023/24 and the future. To reduce climate and weather hazards connected with El Nino, the brief report suggests anticipatory action methods be applied in southern Africa, using Zimbabwe as a case study. To protect farmers' livelihoods and enhance drought readiness for the forthcoming agricultural seasons, the paper suggests a degree of strategic, tactical, and operational decision-making that the agriculture industry should adhere to. It emphasised the significance of providing farmers with knowledge and advice regarding drought and heat stress, including cultivating crop varieties and livestock and sufficient fire safety precautions. The brief paper calls to advocate for anticipatory action to avert El Nino in Southern Africa.

This article describes the impact of restricting the air intake in industrial 250 m3 WEMCO flotation cells at Los Pelambres concentrator. The influence of this air restriction on the hydrodynamic and metallurgical performance of this type of machine was evaluated. The experiments were conducted in single flotation cells and in entire rougher banks. In all cases, the gas holdup was measured to estimate the effectiveness of the obstruction system to decrease the air concentration. In single cells, axial profiles for solid percentage and particle size were evaluated. In addition, a mass balance was conducted to assess the copper recovery and concentrate features. In individual cells, the air restriction led to a decrease in the gas holdup. However, this slight change was enough to obtain a more stable froth zone and a better solids suspension. The latter was observed as a higher P80 below the pulp-froth interface, a less diluted pulp at this level, a slightly higher Cu recovery, and a coarser concentrate product. A mineralogical analysis of the concentrate sample also showed the presence of coarser liberated Cu-sulfide particles. The results in single cells suggested an improvement in the recovery of coarse particles by a more intense solids suspension. The air intake was also restricted in 3 rougher banks to assess the impact of the air obstruction on the overall performance of the respective circuit. Eleven out of fourteen cells were operated with air restriction, which led to a significant recovery improvement of 0.9%-1.6% (absolute) with a 95% confidence level. Size-by-size mass balances were also conducted for the rougher circuits, which proved that the recovery improvements were justified by the simultaneous increase in the recovery of coarse and fine particles. Thus, a restriction of the air intake showed that the decrease in the gas holdup (and in the bubble surface area flux) was compensated by a better solids suspension and a higher turbulence in the impeller. The former promotes the recovery of coarse particles, whereas the latter improves the interaction between bubbles and fine particles. Further developments are being made to implement a regulatory control strategy to control the air intake in self-aspirated flotation cells, and to use this approach for optimizing industrial flotation banks.

Background and Objective: Intimate relationships hold significant importance during emerging adulthood. This study aimed to investigate the critical determinants of perceived well-being in friendship relationships during emerging adulthood.Method: Using a quantitative approach, a sample of 726 undergraduate students from the Ferdowsi University of Mashhad were selected through convenience sampling. They completed the Interpersonal Behaviors Questionnaire (IBQ), the Compassionate and self-image goals scale (CSIGS), the Basic Need Satisfaction in Relationships Scale (BNS-RS), the Interpersonal Needs Questionnaire (INQ-10), Friendship Self-Regulation Questionnaire (SRQ-F), Interpersonal Behaviors Questionnaire-Self (IBQ-self), State Level Subjective Vitality Scale (SVS-SL), the Perceived Relationship Quality Component (PRQC), and the Basic Empathy Scale (BES). Data analysis was conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM).Results: The results indicated that the critical determinants in predicting perceived well-being in friendship relationships were, in order, goals in friendship relationships, friendship self-regulation, perceived support of basic psychological needs by friends, satisfied basic psychological needs, frustrated psychological needs, and supporting the basic psychological needs of friends.Conclusion: The findings demonstrate that self-determination theory (SDT) provides a coherent framework for explaining perceived well-being in friendships.

Living organisms are active open systems far from thermodynamic equilibrium. The ability to behave actively corresponds to dynamical metastability: minor but supercritical internal or external effects may trigger major substantial actions such as gross mechanical motion, dissipating internally accumulated energy reserves. Gaining selective advantage from beneficial use of activity requires a consistent combination of sensual perception, memorised experience, statistical or causal prediction models, and resulting favourable decisions on actions. This information processing chain originated from mere physical interaction processes prior to life, here denoted as structural information exchange. From there, the self-organised transition to symbolic information processing marks the beginning of life, evolving by novel purposivity of trial-and-error feedback and accumulation of symbolic information. The emergence of symbols and prediction models can be described as a ritualisation transition, a symmetry-breaking kinetic phase transition of the 2^nd kind previously known from behavioural biology. The related new symmetry is the neutrally stable arbitrariness, conventionality or code invariance of symbols with respect to their meaning. The meaning of such symbols is given by the structural effect they ultimately unleash, directly or indirectly, by deciding on which actions to take. The early genetic code represents the first symbols. The genetically inherited symbolic information is the first prediction model for activities sufficient for survival under the condition of environmental continuity, sometimes understood as a “final causality” property of the model.

Machinery degradation assessment can offer meaningful prognosis and health management in-formation. Although numerous machine prediction models based on artificial intelligence have emerged in recent years, they still face a series of challenges: (1) Many models continue to rely on manual feature extraction. (2) Deep learning models still struggle with long sequence prediction tasks. (3) Health indicators are inefficient for remaining useful life (RUL) prediction with cross-operational environments when dealing with high-dimensional datasets as inputs. This research proposes a health indicator construction methodology based on a transformer self-attention transfer network (TSTN). This methodology can directly deal with the high-dimensional raw dataset and keep all the information without missing when the signals are taken as the input of the diagnosis and prognosis model. First, we design an encoder with a long-term and short-term self-attention mechanism to capture crucial time-varying information from a high-dimensional dataset. Second, we propose an estimator that can map the embedding from the encoder output to the estimated degradation trends. Then, we present a domain dis-criminator to extract invariant features from different machine operating conditions. The case studies with the FEMTO-ST bearing dataset and the Monte Carlo method for RUL prediction during the degradation process are conducted. The experiment results fully exhibited the signif-icant advantages of the proposed method compared to other state-of-the-art techniques.

Oxytocin (OXT), a pro-social peptide, is increasingly recognized as a potential protective substance against drug addiction. In the context of ethanol, previous researches have shown OXT properties to reduce self-administration, alleviate motor impairment in rodents, and reduce craving in humans. However, its role in behavioral sensitization, a neuroadaptive response resulting from repeated drug exposure linked to increased drug incentive, remains unexplored. OXT is also associated with modulating the Hypothalamic-Pituitary-Adrenal (HPA) axis, with corticosterone well-known as a significant factor in behavioral sensitization development. This study aimed to investigate the effects of Carbetocin (CBT), an analogue of OXT, on the expression of behavioral sensitization to ethanol and concurrent alterations in plasma corticosterone levels in male and female Swiss mice. We also aimed to confirm previous studies on OXT impact on ethanol consumption in male mice, but with a focus on CBT, using the two-bottle choice model and drinking in the dark (DID) methodology. For the sensitization study, mice received either ethanol (1.8 g/kg, ip) or saline treatments daily for 15 consecutive days, followed by treatment with carbetocin (0.64 mg/kg, ip) or a vehicle for 6 days. Subsequently, on day 22, all animals underwent an ethanol challenge to assess the expression of behavioral sensitization. Plasma corticosterone levels were measured on days 21 and 22. CBT effectively prevented the expression of ethanol-induced behavioral sensitization in both male and female subjects, with no alterations detected in corticosterone levels. In the ethanol consumption study, following an initial phase of ethanol acquisition, mice underwent a 6-day treatment with CBT i.p or saline before being reexposed to ethanol. We also found a reduction in ethanol consumption due to CBT treatment. In conclusion, carbetocin emerges as a promising and effective intervention in mitigating ethanol-induced behavioral sensitization and reducing ethanol intake, highlighting its potential significance in alcohol addiction treatment.

This study uses explainable artificial intelligence to analyze major predictors of poor self-rated health, dementia and their comorbidity, which include various aspects of social support. Data came from the Korean Longitudinal Study of Ageing (2016-2018), with 5527 participants aged 56 or more. The dependent variables were: (1) poor self-rated health (SRH) with 5 categories (very good, good, middle, poor, very poor); (2) dementia (no, yes); and (3) their comorbidity with 4 categories (SRH very good, good or middle, dementia no; SRH very good, good or middle, dementia yes; SRH poor or very poor, dementia no; SRH poor or very poor, dementia yes). The 49 demographic, socioeconomic and health-related predictors were included. The accuracy and area under the receiver operating characteristic curve (AUC) were compared for logistic regression and the random forest. Logistic regression and the random forest registered similar AUCs with the range of 0.80-0.87. Based on random forest variable importance, 18 out of top-20 predictors were identical for SRH, dementia and their comorbidity: previous SRH, life satisfaction - health, age, body mass index, income, chronic diseases, life satisfaction - economic, life satisfaction - overall, grandchildren alive, friendship meeting, brothers/sisters alive, class identity, children alive, social activity - friendship, education, children in weekly contact, religion and drinker. However, some variations were found among the three dependent variables, i.e., previous SRH for comorbidity (1st) and SRH (1st) compared to dementia (11th), grandchildren alive for SRH (6th) and dementia (4th) compared to comorbidity (9th), chronic diseases for comorbidity (6th) compared to SRH (10th) and dementia (10th). In terms of Shapley Additive Explanation (SHAP) values, the probability of dementia is expected to decrease by 5.57% in case a predictor grandchildren alive is included to the model. This study highlights the importance of social support for the prediction of poor self-rated health, dementia and their comorbidity. Specifically, the promotion of family support and friendship activity for elders would be vital for the prevention and management of their self-rated health and dementia.

Three novel iron(II) coordination polymers, [Fe(H2O)2(ttmb)2](ClO4)2·4H2O (1), [Fe(H2O)2(ttmb)2](BF4)2·4H2O (2), [Fe(ttmb)2(NCS)2] (3), were synthesized with the linker 1,3,5-tris(1H-1,2,4-triazol-1-yl)methyl)benzene (ttmb). The single-crystal structures show that all three compounds form a double chain structure with the adjacent iron atoms being bridged by two ttmb linkers. The iron(II) ions are octahedrally surrounded by four N4 donor atom from the 1,2,4-triazol-1-yl groups of four different ttmb linkers which form an equatorial plane and two trans-coordinated aqua ligands in 1 and 2 or isothiocyanato ligands in 3 in the axial positions. In view of the neutral bridging ttmb linker, there is a counter-anion in all structures; uncoordinated ClO4– and BF4–in 1 and 2 or coordinated NCS–in 3. Compounds 1 and 2 are isostructural. Interestingly, the ttmb linker only utilizes two of its three potentially coordinating triazole groups. All iron(II) coordination networks are colorless or have a light-yellow color, being indicative of the high-spin state.

Self-selecting the intensity of aerobic training is a perspective that allows practitioners to develop autonomy (choice of pace), improve physical fitness and achieve weight loss. To compare the effect of four aerobic training protocols on cardiometabolic and psycho-affective responses in physically inactive adult men and women. The sample consisted of 11 individuals aged 56 ± 10 years and BMI 29.2 ± 2.4 kg/m2 performed four different treadmill aerobic training protocols, including: 1-individual with self-selected intensity (ISS), 2-individual with prescribed intensity (IPI) (64-76% of HR max), 3-small group with self-select. selected intensity (SGS) and 4-small group prescribed intensity (SGP) (64-76% of HR max). Heart rate (HR), lactate (LACT), perceived exertion (RPE), affective response (FS), activation scale (FAS), enjoyment (PACES) and intention to repeat the session (INT) were analyzed. Statistical analysis used ANOVA with Tukey's post hoc test (p&lt;0.05). HR, LACT and RPE analyzes did not show significant differences between protocols. The FS showed a smaller decline in the SI protocol (p&lt;0.05), and the intention to repeat the training session was greater in the SI protocol (p&lt;0.05). Conclusion: The data allow us to conclude that the SI training protocol produced superior results in affective response, pleasure and intention to repeat the session.

In this paper, we critically examine the burgeoning role of advanced computational methodologies in deciphering the complex tapestry of farm animal behaviors and emotions. Leveraging digital imaging and artificial intelligence, we unearth nuanced behavioral patterns and micro-expressions, offering predictive insights into animal emotional states. Sound vocalization analysis, often overlooked, emerges as a pivotal tool, decoding intricate communicative nuances and emotional undertones. Cognitive tests, including mirror and bias assessments, challenge long-standing perceptions, revealing surprising depths of animal self-awareness and cognitive sophistication. However, the paper also underscores the imperative of integrating these tools with a profound understanding of animal psyche, ensuring technology serves as an enhancer, not a replacement, of traditional observational methods. This research not only highlights the transformative potential of cognitive computing in animal welfare but also calls for a judicious application, ensuring technology augments, not undermines, the intrinsic value of human-animal interactions and understanding.

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.

Alcohol use disorder (AUD) is a chronic and relapsing biopsychosocial condition that leads to a wide range of medical, psychological, social, economic, and personal issues. The study investigated the predictors of quality of life (QoL) among male patients seeking treatment for AUD based on attention, cognitive instability, motor impulsiveness, perseverance, and self-control dimensions of trait impulsivity. The study also investigated the potential mediating effect of anxiety and alcohol use severity in this relationship. After an initial assessment, participants underwent comprehensive evaluations, including sociodemographic data, clinical history, and QoL domains. Evaluations also covered AUD severity, impulsivity dimensions, cognitive functions, craving intensity, depression, anxiety and self-efficacy. Multiple linear regressions were employed to identify associations between independent variables and QoL. Attention dimension of impulsivity and anxiety symptoms significantly predicted physical health QoL. Self-control dimension and severity of alcohol use disorder predicted psychological QoL. Indirect effect of attention dimension on physical health was significant [effect =-1.082,95% CI (-2.008, -0.3598)].The significance of impulsivity in influencing QoL for individuals with AUD has profound clinical implications. These findings underscore the importance of addressing anxiety and impulsivity in managing AUD, given their considerable impact, particularly on QoL, ultimately shaping treatment outcomes.

The resonance spectrum of surface resistance derivative in quasi-two dimensional layered organic conductors has been calculated numerically for the out-of-plane and in-plane magnetic field. Not all but some of the resonances in a given series of transitions are present in the spectra. They are observed at the positions corresponding to the maxima in the surface resistance derivative curve and are in a good agreement with previously calculated magnetic field resonance positions. For tilted magnetic fields, there are more resonances present in the spectrum than in the case of a non-tilted field. Apart from the resonances corresponding to the transitions between the adjacent surface states there also appear resonances involving the transitions between the more distant surface states. This is correlated with the specific topology of Fermi surface of the organic conductors. The presented calculations will allow correct interpretation of the experimental curves for the resonance spectrum, and also to obtain information on the surface properties of these materials for their possible implementation in fabrication of devices based on organic molecular conductors.

Block copolymers are a class of materials particularly interesting for their ability to self-assemble into ordered structures. In this context, the coupling between environment and dynamics is particularly relevant, given that movements at the molecular level influence various properties of macromolecules. Mixing the polymer with a second macromolecule appears to be an easy way to study these relationships. In this work, we studied a system based on two diblock copolymers of methyl methacrylate as first block and 3-methyl-4-[6-(methylacryloyloxy)-hexyloxy]-4’-pentyloxy azobenzene as second block and their blends with PMMA. The relaxational properties of these systems were investigated by electron spin resonance (ESR) spectroscopy sensitive to nanometric length-scales. The results of the study on the blends were related to the dynamic behavior of the copolymer. The study evidenced the presence of fast and slow molecular sites for the molecular reorientation at nanoscale, modulated by the ability of block copolymer to form supramolecular structures. For blends, the heterogeneities at nanoscale are still detected. However it was found that the presence of the PMMA as the major component of the blends modified their dynamic behaviour.

The technical capabilities of modern Industry 4.0 and Industry 5.0 are rather vast and growing exponentially daily. The present-day Industrial Internet of Things (IIoT) combines manifold underlying technologies that require real-time interconnections and communications among heterogeneous devices. Smart cities are established with sophisticated designs and control for seamless Machine-to-Machine (M2M) communication to optimize resources, costs, performances, and energy distribution. All the sensory devices within a building interact to maintain a sustainable climate for residents and intuitively optimize the energy distribution to optimize energy production. However, it encompasses quite a few challenges for devices that lack compatible and interoperable designs. Conventional solutions are restricted to limited domains or rely on engineers to design and deploy translators for each pair of ontologies. This is a costly process in terms of engineering efforts and computational resources. The issue persists that a new device with a different ontology must be integrated into an existing IoT network. We propose a self-learning model that can determine the taxonomy of devices given their ontology meta-data and structural information. The model finds matches between two distinct ontologies using the Natural Language Processing (NLP) approach for learning linguistic contexts. Then, by visualizing the ontology network as a knowledge graph, it is possible to learn the structure of the meta-data and understand the device's message formulation. Finally, it can align entities of both ontology graphs similar in context and structure. Furthermore, the model performs dynamic M2M translation without requiring extra engineering or hardware efforts.

The research community has shown significant interest in the aqueous synthesis of nanomaterials due to its ability to eliminate the need for complex organic solvents. This synthesis approach aligns with the principles of green chemistry, attracting considerable attention. Aqueous solution technology in fabricating nanostructures has gained recognition for its potential to create ultrasensitive, low-energy, and ultrafast optoelectronic devices. This report focuses on synthesizing lead iodide (PbI2) nanoplates using a water-based solution technique and fabricating a planar photodetector. The photodetectors with a planar type of device structure (ITO/PbI2 NPs/Au) demonstrated a remarkable photosensitivity of 3.9×103 and a photoresponsivity of 0.51 mA/W at a wavelength of 405 nm. Notably, the asymmetrical output properties of ITO/PbI2 NPs/Au detector deliver additional evidence of the effective creation of a Schottky contact. Thus, the photodetector exhibited a photoresponse even at 0 V bias, leading to the realization of self-powered photodetectors. Additionally, the device exhibited a rapid photoresponse of 0.21/0.38 s (−5 V) in the visible range. This study has expanded the horizons for the aqueous-phase synthesis of nanoplate nanostructures, enabling the large-area fabrication of high-performance photodetectors.

Background Coronavirus disease 2019 (COVID-19) almost ceased to spread across the globe, but post Covid psychological experiences are rarely documented. It is essential to understand the individual psychological needs and challenges regarding working during the covid-19 pandemic and resilience and psychological health to continuing work in the aftermath of this crisis. Objective To explore the psychological transformation of staff nurses after COVID-19 Methods A phenomenological and purposive sampling approach was employed. Registered nurses working at the largest health organization in Qatar includes 14 health facilities were invited to participate in this study. 17 nurses who were working in the facilities and were deployed during the first, second and third wave of Covid 19 participated in the interview. Data were collected through semi-structured face-to-face interviews using quarantine measures. The interview data were analyzed using inductive thematic analysis. Results The psychological transformation of participants was summarized into three themes fear zone, Learning zone and growth zone. These themes were classified into 10 sub-themes Fear and anxiety, Influence of media and news, Frequent change in policies and guidelines were the fear zone factors .Secondly Coping mechanism, teamwork, establishing compassionate care, social acceptance, personal and professional benefits helps them learn psychologically. Thirdly growth factors such as confident & resilient, Sense of purpose and Change life perception and meaning Conclusion Working during COVID-19 imposed many challenges on nurses such as workload and stress. However, the different waves of the pandemic may increase the resilience, confidence and life meaning in nurses. Their previous experiences in handling difficult situations during the pandemic may make the nurses become more confident in dealing with stressful situations and working under pressure.

Previous studies on the mechanisms underlying willed actions reported that the premotor cortex may be involved in the construction of motor awareness. However, its exact role is still under investigation. Here we investigated the role of the dorsal premotor cortex (PMd) in motor awareness by modulating its activity applying inhibitory rTMS to PMd, before a specific motor awareness task (under three conditions: without stimulation, after rTMS and after sham stimulation). During the task, subjects had to trace straight lines to a given target, receiving a visual feedback of the line trajectories on a computer screen. Crucially, in most trials the trajectories on the screen were deviated and to produce straight lines, subjects had to correct their movements towards the opposite direction. After each trial, participants were asked to judge whether the line seen on the computer screen corresponded to the line actually drawn. Results show that participants in the no stimulation condition did not recognize the perturbation until 14 degrees of deviation. Importantly, active, but not sham, rTMS significantly modulated motor awareness, decreasing the amplitude of the angle at which participants became aware of the trajectory correction. These results suggest that PMd plays a crucial role in action self- monitoring.

The phenomenon of ordered self-assembly on solid substrates is a topic of interest in both fundamental surface science research and its applications in nanotechnology. The regulation and control of two-dimensional (2D) self-assembled supra-molecular structures on surfaces have been realized through applying external stimuli. By utilizing scanning tunneling microscopy (STM), researchers can investigate detailed phase transition process about self-assembled monolayers (SAMs), providing insight into the interplay between intermolecular weak interactions and substrate-molecule interactions, which govern the formation of molecular self-assembly. This review will discuss the structural transition of self-assembly probed by STM in response to external stimuli and provide state-of-art methods such as tip induced confinement for the alignment of SAM domains and selective chirality. Finally, we discuss the challenges and opportunities in the field of self-assembly and STM.

"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-phase modulation (SPM) broadening of laser spectra was studied in a transmission mode in natural and synthetic diamonds at variable laser wavelengths (515 and 1030 nm), pulse energies and widths (0.3 - 12 ps, positively chirped pulses), providing their filamentary propagation. Besides the monotonous SPM broadening of the laser spectra versus pulse energy, more pronounced for the (sub)picosecond pulsewiths and more doped natural diamond, periodical low-frequency modulation was observed in the spectra at the shorter laser pulsewidths, indicating dynamic Bragg filtering of the supercontinuum due to ultrafast plasma and nanoplasmonic effects. Damping of broadening and ultramodulation for the longer picosecond pulsewidths was related to thermalized electron-hole plasma regime established for the laser pulsewidths longer, than 2 ps. Unexpectedly, at higher pulse energies and corresponding longer microfilaments, the number of spectral modulation features increases, indicating dynamic variation of the periods in the longitudinal plasma Bragg gratings along the filaments due to prompt secondary laser-plasmon interactions.

Research on image-inpainting tasks has mainly focused on enhancing performance by augmenting various stages and modules. However, this trend does not consider the increase in the number of model parameters and operational memory, which increases the burden on computational resources. To solve this problem, we propose a Parametric Efficient Image InPainting Network (PEIPNet) for efficient and effective image-inpainting. Unlike other state-of-the-art methods, the proposed model has a one-stage inpainting framework in which depthwise and pointwise convolutions are adopted to reduce the number of parameters and computational costs. To generate semantically appealing results, we selected three unique components: spatially-adaptive denormalization (SPADE), dense dilated convolution module (DDCM), and efficient self-attention (ESA). The SPADE was adopted to conditionally normalize activations according to the mask to distinguish between damaged and undamaged regions. The DDCM was employed at every scale to overcome the gradient-vanishing obstacle and gradually fill-in pixels by capturing global information along the feature maps. The ESA was utilized to obtain clues from unmasked areas by extracting long-range information. In terms of efficiency, our model has the lowest operational memory compared with other state-of-the-art methods. Both qualitative and quantitative experiments demonstrate the generalized inpainting of our method on three public datasets: Paris StreetView, CelebA, and Places2.

In the realm of mathematics education, self-explanation stands as a crucial learning mechanism, allowing learners to articulate their comprehension of intricate mathematical concepts and strategies. As digital learning platforms grow in prominence, there are mounting opportunities to collect and utilize mathematical self-explanations. However, these opportunities are met with challenges in automated evaluation. Automatic scoring of mathematical self-explanations is crucial for preprocessing tasks, including the categorization of learner responses, identification of common misconceptions, and the creation of tailored feedback and model solutions. Nevertheless, this task is hindered by the dearth of ample sample sets. Our research introduces a semi-supervised technique using the Language Learning Model (LLM), specifically its Japanese variant, to enrich datasets for the automated scoring of mathematical self-explanations. We rigorously evaluated the quality of self-explanations across five datasets, ranging from human-evaluated originals to ones devoid of original content. Our results show that combining LLM-based explanations with mathematical material significantly improves the model's accuracy. Interestingly, there's an optimal limit to how much synthetic self-explanation data can bene-fit the system. Exceeding this limit doesn't further improve outcomes. This study thus highlights the need for careful consideration when integrating synthetic data into solutions, especially within the mathematics discipline.

The objectives of this study are threefold. First, we evaluate the applicability of the information–motivation–behavioral skills (IMB) model for predicting organic food purchase intention (OFPI) in adults with chronic conditions. Second, we examine the indirect effects of organic food knowledge, attitudes toward organic food purchase, and subjective norms on OFPI through self-efficacy. Third, we examine whether these indirect effects are moderated by gender and educational attainment. Data were collected from Indian adults with chronic conditions using a self-administered questionnaire. The results show significant indirect effects of organic food knowledge, attitude toward organic food purchase, and subjective norm on OFPI through self-efficacy. Moreover, the mediating effect of knowledge was moderated by gender and education attainment, with the effect being stronger for females and among individuals with a lower level of education. Organic food marketers, social marketers, and public health agencies promoting organic food consumption to people with chronic conditions should aim to increase their confidence in comprehending organic food. This study contributes to the literature by assessing the applicability of Fisher and Fisher’s (1992) IMB model in understanding behavioral intention toward organic food.