Recent research emphasises the importance of Artificial Intelligence applications as supporting tools for students in higher education. Simultaneously, an intensive exchange of views has started in the public debate in the international educational community. However, for a more proper use of these applications, it is necessary to investigate the factors that explain their intention and actual use in the future. With the Unified Theory of Acceptance and Use of Technology (UTAUT2) model, this work analyses the factors influencing students' use and intention to use artificial intelligence technology. For this purpose, a sample of 199 Greek students at the School of Humanities and Social Sciences from the University of Patras participated in a survey. The findings highlight that expected performance, habit, and enjoyment of these Artificial Intelligence applications are key determinants influencing teachers' intentions to use them. Moreover, behavioural intention, habit, and facilitating conditions explain the usage of these Artificial Intelligence applications. The study did not reveal any moderating effects. The limitations, practical implications, and proposed directions for future research based on these results are discussed.

This study examined the relationships between attitude, subjective norms, perceived behavioral control, and behavioral intention through the perceived value of Pilates participants. The participants of this study were 301 Pilates students using Pilates studios located in Busan, Ulsan, and Gyeongbuk Province. The participants were selected by convenient sampling, a non-probability sampling method. The collected data were analyzed using descriptive analysis, reliability analysis, correlation analysis, confirmatory factor analysis, and structural equation modeling analysis using SPSS 22.0 and AMOS 24.0. The results can be summarized as follows. First, among the three subfactors of perceived value, functional and social values had a positive effect on attitude. Second, attitude, subjective norms, and perceived behavioral control, which are three main components of the Theory of Planned Behavior, had a positive effect on the behavioral intention for Pilates. These results suggest that Pilates instructors require meticulous planning and efforts to develop an environment for promoting spontaneous interest and participation to attract students and engage their attention and interest in the field.

As an emerging model, self-service fitness centers are reshaping the consumer fitness experience globally. Given the crucial role of customer loyalty in the sustainable operation and management of fitness service enterprises, this study constructs a structural model incorporating service quality, service convenience, customer satisfaction, word-of-mouth intentions, and repatronage intentions. The model is designed to elucidate the factors and mechanisms impacting customer loyalty at self-service fitness centers and further investigates how these relationships are affected by service methods, either with staff or without staff. The study employs PLS-SEM to empirically analyze 552 customer questionnaires from four self-service fitness centers in first-tier cities in China. The results reveal positive correlations among the constructs in the structural model, emphasizing the important position of raising service quality standards and reducing the time and effort expended by customers in the management of customer loyalty. Concurrently, service convenience was found to play a significant driving role in customers' perceived service quality. Through the chain-mediating effect of service quality and customer satisfaction, service convenience exerts a considerable positive impact on word-of-mouth intentions and repatronage intentions. Moreover, the study reveals significant differences in these relationships when considering service methods, either with staff or without staff.

Neuropsychiatric disorders are globally public-health concern, in which diagnosis might be based on symptoms that often vary across individuals. Schizophrenia is one of the major neuropsychiatric disorders, which may affect millions worldwide. Detection of predictive biomarkers is the most intensively developing approach for the disease. However, the biochemical alterations have not been comprehensively distinguished up to the present time. In addition, there is less confidence of finding a specific biomarker for neuropsychiatric disorders including schizophrenia, but rather a specific characteristic behavioral pattern. Maternal immune activation has been considered to be one of the important factors for the development of neuropsychiatric disorders. Here, the mouse model of neuropsychiatric disorders has been built, in which poly-I:C, sodium dextran sulfate (DSS) and κ-carrageenan (CGN) were used for the maternal immune activation during the pregnancy of individuals. Subsequently, we challenged to link some of biochemical changes of p62 and GLAST in the offspring mice brain to the alteration of several pathological behaviors. Consequently, it has been shown that autophagy might be involved in the development of neuropsychiatric disorders including schizophrenia. To our knowledge, this is the first report of the significant correlation between pathological behavior and the biochemical alterations in neuropsychiatric disorder model animals. The relative ease of conducting these evaluation-tasks would make them useful for testing novel therapeutics designed to ameliorate the symptoms of several psychiatric disorders.

Definition: Most multicellular animals have a nervous system that is based on the following three components: 1) Sensory cells gather information and send it to processing units; 2) the processing units use the information to decide on what action to take; and 3) effector neurons activate the appropriate muscles. Due to the importance of making the right decisions, evolution made profound advances in the processing units. I shall review present knowledge regarding the evolution of neurological tools for making decisions, here referred to as strategies or algorithms. Consciousness can be understood as a particularly sophisticated strategy. It may have evolved to allow for the use of feelings as a ‘common currency’ to evaluate behavioral options. The advanced cognitive capacity of species such as humans further improved the usefulness of consciousness, yet in biological terms it does not seem to be an optimal, fitness-enhancing strategy. A model for the gradual evolution of consciousness is presented. There is a somewhat arbitrary cutoff as to which animals have consciousness but based on current information it seems reasonable to restrict the term to amniotes.

Abstract: The aim of the present study was to identify the age and sex-related associations between marital status with PA and TV time. We used data from Vigitel, an annual telephone survey started in 2006 in Brazil. We applied a complex sample logistic regression model to estimate the odds for PA and TV time comparing marital statuses according to age and sex subgroups, independent of obesity, hypertension, diabetes, self-assessed poor health, and smoking. Our sample included 561,837 individuals with a TV time > 3 h/day (prevalence = 25.2%) and PA > 150 min/week (prevalence = 35%). Compared to single individuals, married men and women were less likely to watch TV more than 3 h/day in participants > 30 years old. When compared to single, married participants were less likely to do more than 150 min of PA/week at younger age groups. Married women older than 40 years were more likely to do more than 150 min of PA/week than the single ones, while there were no differences among married men by age group. In conclusion, married individuals are less likely to spend more than 3 hours a day watching TV than single individuals. Single men and women were more likely to do more than 150 min of PA/week at younger age groups and married women older than 40 years were more likely to do 150 min of PA/week than single women.

Oncology clinical pharmacists are uniquely positioned to make interventions to increase patient activation and engagement. To accomplish this goal, pharmacists can target health system-related, provider-related, and patient-related factors to help enhance patient-centered care and drive behavioral health changes. Interventions that pharmacists must tackle include educating team members and patients on the medication acquisition process, communicating urgency of treatment, optimizing workflows, facilitating guideline recommendations, preventing, and managing treatment toxicities, and promoting patient self-advocacy through education and shared decision-making. As crucial members of the healthcare team, oncology clinical pharmacists can simplify highly complex treatment regimens to facilitate and optimize patients’ ownership of their care. This review will focus on the example of venetoclax treatment in acute myeloid leukemia to demonstrate the impact that pharmacists provide that leads to behavioral change of patients and clinicians.

Chronic exposure to noise can cause several extraordinary effects and involve all the systems of the human organism. In addition to cardiovascular, gastrointestinal and immune effects, the data in the literature show alterations in behavioral disturbances, in memory capacity and cognitive performance. Through this systematic review, the authors try to find out which are the main neurobehavioral alterations, in case of occupational exposure to noise. Literature review included articles published in the major databases (PubMed, Cochrane Library, Scopus), using a combina-tion of some relevant keywords. This online search yielded 4434 references; after selection, the authors analyzed 41 articles (4 narrative reviews and 37 original articles). From this analysis, it appears that main symptoms are related to psychological distress, annoyance, sleep disturbances, cognitive performance. Regarding tasks, the most frequent employments concern school staff, followed by employees from various industrial sectors and office workers. Although the causes are still widely debated, it is essential to protect these workers against chronic exposure to noise. In fact, in addition to a hearing loss, they can manifest many other related discomforts over time and compromise their full working capacity, as well as expose them to a greater risk of accidents or absences from work.

Georgia is ranked fifth highest among states for rates of HIV diagnosis. About 4% of persons living with HIV infection in the United States reside in Georgia, and almost 19% of these people do not know their HIV status. The present study examined the trends and associated factors of HIV testing among adults in Georgia between 2010 and 2014 by analyzing data of the Behavioral Risk Factor Surveillance System (BRFSS). A total of 30,791 persons aged ≥18 years were identified who responded to the question “Have you ever been tested for HIV?” Overall, there were 11,543 respondents who had been tested for HIV, with a decrease in percentage from 49.4% in 2010 to 43.7% in 2014 (p<0.001). Factors associated with HIV testing were being black (p<0.001), being younger than 55 years (p<0.001), single (p=0.02), attaining education level above high school (P<0.001), engaging in HIV high-risk behaviors (p<0.001), and not having healthcare coverage (p=0.03). Overall in Georgia, there has been a decline in the temporal trend of HIV testing, and more than half of adults have never been tested for HIV. For reducing HIV transmission in Georgia, enhancing access and utilization of HIV testing should be a public health priority.

Cartoons in the era of digital media are among the most prevalent medium of entertainment for the children, parents also encourage them to view such contents to engage the children which make an area need to be explained exhaustively by adopting new approaches. There are lot of studies in the past conducted to explain the impacts of the cartoons on the children however, it is remarkable fact that there is lack of the studies in the literature which directly address the observation about the above mentioned impacts in view of the teacher’s. This study deals with the educational, emotional, behavioral and Religious etc. impacts of cartoons on kids in view of teachers of the primary schools as they are considered as the one who can observer these impacts in a profound way. of the of Multan. Different areas of impacts have been analyzed in this study by using survey technique and analysis is conducted and presented in the teacher’s views in this study to examine what they feel that how cartoons are effecting the personalities of the children.

Background Scientific literature in psychology considers personality as an organization of ways of being, knowing, and acting that ensures unity, coherence and continuity, stability and planning of the individual's relations with the world (Caprara & Gennaro, 1994). Personality is the outcome of two distinct constructs: Temperament and Character. So, personality is the result of the interaction between biological aspects (temperament) and the experiences that anybody experiences in his own life, interacting with the environment (Cloninger et al., 1993). Scientific literature now largely agrees that adversity in the early years of life has broad long-term consequences on the neuroendocrine, immune, and metabolic systems (Kim et al., 2019), as well as on neuroplasticity and neuronal morphology that alter the overall evolution of (van Bodegom, et al., 2017; Edwards et al., 2010). Here we analyze the hypothesis that maternal environmental factors (air and chemical pollutants, maternal health, eating behaviors, maternal and fetal stresses, caregivers' behaviors, etc.) can cause epigenetic alterations in the offspring in the prenatal and perinatal period that shape the trait's personality of the offspring themselves and that orient part of their character in adult life. Methods The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement was used to develop this systematic review. PubMed, Scopus, PsycINFO, and Biosis databases were searched for primary research articles published between 2010 and 2023 looking for English language articles containing the term "prenatal" and any of the following terms: "anxiety"; "brain development"; "depression"; "gestation"; "maternal health"; "maternal stress"; "mental health"; "newborn health"; "perinatal programming"; "pregnancy"; "prenatal stress"; "resilience to stress"; "transgenerational epigenetic inheritance"; "personality"; "personality trait"; "environmental epigenetics". Retrieved articles were screened and a subset of relevant abstracts was then selected for a more detailed evaluation. The final studies selected consisted of those resources that directly evaluated the relationship between maternal environmental factors and the personality traits of the offspring. Results and Conclusions One hundred and eleven studies were identified but only three met the primary question. There is evidence to support the hypothesis of correlations between environmental exposure during the intrauterine fetal period and personality traits of the newborn. Limits: multiple limitations were found based on the lack of research on this specific topic, although such a field of research can be important for developing effective strategies for primary prevention.

Optic neuritis (ON) is the most common cause of vision loss in young adults. It manifests as acute or subacute vision loss, often accompanied by retrobulbar discomfort or pain on eye movements. Typical ON is associated with Multiple Sclerosis and is generally mild and steroid-responsive, whereas atypical forms are characterized by unusual features, such as optic disc edema, poor treatment response and bilateral involvement, and are often associated with autoantibodies against aquaporin-4 (AQP4) and Myelin Oligodendrocyte Glycoprotein (MOG). However, in rare cases AQP4 and MOG antibodies will come back negative, plunging the clinician in a diagnostic conundrum. Seronegative ON warrants a broad differential diagnosis, including autoantibody-associated, granulomatous, and systemic disorders. These rare forms need to be identified promptly, as their management and prognosis is greatly different. The scope of this review is to describe possible, rarer etiologies of ON when AQP4 and MOG antibodies are negative and discuss their diagnosis and treatment.

The hybrid method proposed in the study, ANFIS-GWO, combines the Adaptive Neuro-Fuzzy Inference System (ANFIS) with Grey Wolf Optimization (GWO) for the diagnosis of liver disorders. ANFIS is a powerful tool that combines the advantages of neural networks and fuzzy logic to create a hybrid model capable of handling complex and uncertain data. GWO is a metaheuristic optimization algorithm inspired by the social behaviour of grey wolves. In the ANFIS-GWO method, the hyperparameters of ANFIS are optimized using GWO. This optimization process aims to fine-tune the ANFIS model based on the available dataset, which consists of 7 characteristic attributes and 354 samples related to liver diseases. By adopting the hyper-parameters, the ANFIS-GWO method enhances the overall performance and accuracy of the diagnostic system. To evaluate the effectiveness of the ANFIS-GWO intelligent medical system, the study employs classification accuracy, sensitivity, and specificity analysis. Classification accuracy measures the overall correctness of the system in predicting liver disease cases. Sensitivity refers to the system’s ability to correctly identify individuals with liver disorders, while specificity measures its ability to correctly identify those without liver disorders. Experimental results demonstrate that the performance of the ANFIS-GWO method surpasses that of traditional Fuzzy Inference Systems (FIS) and ANFIS models that do not undergo an optimization phase. This suggests that the integration of GWO optimization significantly improves the diagnostic accuracy of the ANFIS model for liver disease diagnosis.

UPF3B is a constituent of the classical nonsense-mediated mRNA decay (NMD) pathway that degrades both the aberrant transcripts and a set of physiological transcripts. In higher eukaryotes, UPF3B have significant biochemical functions in diverse cellular processes including NMD and translation. UPF3B plays a crucial role in neuronal development and differentiation. Next-generation sequencing technologies identified several loss-of-function mutations in the UPF3B gene that results in neuro-developmental disorders in humans. To uncover the mechanistic role of UPF3B in neuronal functions, we have generated the UPF3B-knockout mammalian cell line model system using CRISPR-Cas9 gene editing method. RNA-Sequencing Analysis of cellular transcriptome from UPF3B-KO cells identified specific genes involved in cell growth and neuronal functions. Altered expression of genes related to the axon guidance pathway delineated the UPF3B function to regulate the neuron-specific genes. Functional enrichment analysis identified the genes involved in the disorders related to mental health and intellectual disability. Our study has the potential to identify the direct players of intellectual disability and will have broader implications.

There is evidence that chronic fatigue spectrum disorders (CFAS-D) including Myalgic Encephalomyelitis (ME), chronic fatigue syndrome (CFS) and chronic fatigue with physiosomatic symptoms including when due to comorbid medical disease are characterized by neuroimmune and neuro-oxidative biomarkers. The present study was performed to delineate the protein-protein interaction (PPI) network of CFAS-D and to discover the pathways, molecular patterns and domains enriched in their PPI network. We performed network, enrichment and annotation analysis using differentially expressed proteins and metabolics, which we established in CFAS-D patients. PPI network analysis revealed that the backbone of the highly connective CFAS-D network comprises NFKB1, CTNNB1, ALB, peroxides, NOS2, TNF, and IL6, and that the network comprises interconnected immune-oxidative-nitrosative and Wnt/catenin subnetworks. MultiOmics enrichment analysis shows that the CFAS-D network is highly significantly associated with cellular (antioxidant) detoxification, hydrogen peroxide metabolic process, peroxidase and oxidoreductase activity, IL10 anti-inflammatory signaling, and neurodegenerative, canonical Wnt, the catenin complex, cadherin domains, cell-cell junctions and TLR2/4 pathways; and the transcription factors NF-κB and RELA. The top-10 DOID annotations of the CFAS-D network include four intestinal, three immune system disorders, cancer and infectious disease. Custom GO term annotation analysis revealed that the CFAS-D network is associated with a response to a toxic substance, lipopolysaccharides, bacterium or virus. In conclusion, CFAS-D may be triggered by a variety of stimuli and their effects are mediated by aberrations in the cross-talks between redox, NF-κB, and Wnt/catenin signaling pathways leading to dysfunctions in multicellular organismal homeostatic processes.

A meta-analysis showed a significant association between activated immune-inflammatory and nitro-oxidative (IO&NS) pathways and suicide attempts (SA). There are no data whether suicidal ideation (SI) is accompanied by activated IO&NS pathways and whether there are differences between SA and SI. The current study searched PubMed, Google Scholar, and Web of Science, for articles published from inception until May 10, 2021, and systematically reviewed and meta-analyzed the association between recent SA/SI (< 3 months) and IO&NS biomarkers. We included studies which compared psychiatric patients with and without SA and SI and controls (either healthy controls or patients without SA or SI) and used meta-analysis (random-effect model with restricted maximum-likelihood) to delineate effect sizes with 95% confidence intervals (CI). Our search included 59 studies comprising 4.034 SA/SI cases and 12.377 controls. Patients with SA/SI showed activated IO&NS pathways (SMD: 0.299; CI: 0.200; 0.397) when compared to controls. The immune profiles were more strongly associated with SA than with SI, particularly when compared to healthy controls, as evidenced by activated IO&NS pathways (SMD: 0.796; CI: 0.503; 1.089), an immune-inflammatory response (SMD: 1.409; CI: 0.637; 1.462), inflammation (SMD: 1.200; CI: 0.584; 1.816), and neurotoxicity (SMD: 0.904; CI: 0.431; 1.378). The effects sizes of the IO&NS, immune-inflammatory response and inflammatory profile were significantly greater in SA than in SI. In conclusion: increased neurotoxicity due to inflammation and nitro-oxidative stress and lowered neuroprotection may explain at least in part why psychiatric patients show increased SA and SI. The IO&NS pathways are more pronounced in recent SA than in SI.

Background: Suicide attempts (SA) frequently occur in patients with mood disorders and schizophrenia, which are both accompanied by activated immune-inflammatory and nitro-oxidative (IO&NS) pathways. Methods: We searched PubMed, Google Scholar, and Web of Science, for articles published from inception until February 1, 2021. We included studies that compared blood biomarkers in psychiatric patients with (SA+) and without SA (SA-) and heathy controls and we combined different IO&NS biomarkers into immune, inflammatory, and neurotoxic profiles and used meta-analysis (random-effect model with restricted maximum-likelihood) to delineate effect sizes with 95% confidence interval (CI).Findings: Our search included 51 studies comprising 4.945 SA+ patients and 24.148 controls. We stratified the control group into healthy controls and SA- patients. SA+ patients showed significantly (p<0.001) increased immune activation (SMD: 1.044; CI: 0.599-1.489), inflammation (SMD: 1.109; CI: 0.505, 1.714), neurotoxicity (SMD: 0.879; CI: 0.465, 1.293), and lowered neuroprotection (SMD: 0.648; CI: 0.354, 0.941) as compared with healthy controls. When compared with SA- patients, those with SA+ showed significant (p<0.001) immune activation (SMD: 0.290; CI: 0.183, 0.397), inflammation (SMD: 0.311; CI: 0.191, 0.432), and neurotoxicity (SMD: 0.315; CI: 0.198, 0.432), and lowered neuroprotection (SMD: 0.341; CI: 0.167, 0.515). Patients with current, but not lifetime, SA showed significant (p<0.001) levels of inflammation and neurotoxicity as compared with controls. Conclusions: Patients with immune activation are at a higher risk of SA which may be explained by increased neurotoxicity due to inflammation and nitro-oxidative stress. This meta-analysis discovered new biomarkers of SA and therapeutic targets to treat individuals with SA.

End-stage renal disease (ESRD) is associated with fatigue and physio-somatic symptoms. The aims of this study are to delineate the associations between severity of fatigue and physio-somatic symptoms and glomerular filtration rate, inflammatory biomarkers, and Wnt/catenin-pathway proteins. The Wnt-pathway related proteins β-catenin, Dickkopf-related protein 1 (DKK1), R-spondin-1, and sclerostin were measured by ELISA technique in 60 ESRD patients and 30 controls. The Fibromyalgia and Chronic Fatigue Syndrome (FF) Rating Scale was used to assess severity of FF symptoms. ESRD is characterized by a significant increase in the total FF score, muscle tension, fatigue, sadness, sleep disorders, GI symptoms, and a flu-like malaise. The total FF score was significantly correlated with serum levels of urea, creatinine, phosphate, and copper (positively), and β-catenin, eGFR, hemoglobin, albumin, and zinc (inversely). The total FF score was associated with the number of total dialysis and weekly dialysis sessions, and these dialysis characteristics were more important in predicting FF scores than eGFR measurements. Partial Least Squares analysis showed that the FF score comprised two factors which are differently associated with biomarkers: a) 43.0% of the variance in fatigue, GI symptoms, muscle tension, sadness, and insomnia is explained by hemoglobin, albumin, zinc, β-catenin, and R-spondin-1; and b) 22.3% of the variance in irritability, concentration and memory impairments by increased copper and cations/chloride ratio, and male sex. ESRD patients show high levels of fatigue and physio-somatic symptoms which are associated with hemodialysis and mediated by dialysis-induced changes in inflammatory pathways, the Wnt/catenin pathway, and copper.

Recently, a breakdown of paracellular and vascular pathways and activated neuroimmune and oxidative pathways were established in (deficit) schizophrenia. The aim of the current study was to delineate a) the differences in these pathways between stable phase, first (FES) and multiple (MES) episode schizophrenia, and b) the pathways that determine the behavioral-cognitive-physical-psychosocial (BCPS)-worsening in FES/MES. This study included 21 FES and 58 FES patients and 40 healthy controls and measured indicants of serum IgA to C1q, and leaky gut, immune activation, and oxidative stress toxicity (OSTOX) biomarkers. We constructed a BCPS-worsening index by extracting a latent vector from symptomatic, neurocognitive, and quality of life data. FES patients showed significantly higher IgA to C1q, cadherin, catenin, plasmalemma vesicle-associated protein, and IgA/IgM to Gram-negative bacteria than FES patients and controls. In FES patients, the BCPS-worsening score was predicted (48.7%) by IgA to Klebsiella pneumoniae and lowered paraoxonase 1 activity. In MES patients, the BCPS-worsening score was explained (42.7%) by increased tumor necrosis factor-α, OSTOX, and number of episodes. In schizophrenia, 34.0% of the variance in the BCPS-worsening score was explained by IgA to K. pneumoniae, OSTOX, and number of episodes. Increased IgA to K. pneumoniae was the single best predictor of residual psychotic symptoms in FES and MES. This study delineated different mechanistic processes in FES, including breakdown of adherens junctions, bacterial translocation, and C1q circulating immune complexes; and FES, including immune and oxidative stress neurotoxic pathways. FES and MES comprise different staging subtypes, i.e., FES and MES with and without worsening.

The hybridization of machine learning methods with soft computing techniques is an essential approach to improve the performance of the prediction models. Hybrid machine learning models, particularly, have gained popularity in the advancement of the high-performance control systems. Higher accuracy and better performance for prediction models of exergy destruction and energy consumption used in the control circuit of heating, ventilation, and air conditioning (HVAC) systems can be highly economical in the industrial scale to save energy. This research proposes two hybrid models of adaptive neuro-fuzzy inference system-particle swarm optimization (ANFIS-PSO), and adaptive neuro-fuzzy inference system-genetic algorithm (ANFIS-GA) for HVAC. The results are further compared with the single ANFIS model. The ANFIS-PSO model with the RMSE of 0.0065, MAE of 0.0028, and R² equal to 0.9999, with a minimum deviation of 0.0691 (KJ/s), outperforms the ANFIS-GA and single ANFIS models.

Major depression (MDD) is accompanied by higher serum IgM/IgA responses to LPS of Gram-negative bacteria, suggesting increased bacterial translocation and gut dysbiosis. Gut dysbiosis may occur in bipolar disorder (BD) and there are differences between MDD and BD type 1 (BP1) and -2 (BP2) in nitro-oxidative stress biomarkers associated with leaky gut. This study examines serum IgM/IgA responses directed to LPS of 6 Gram-negative bacteria in 29 BP1, 37 BP2, 44 MDD and 30 healthy individuals. MDD plus BD was best discriminated from controls by increased IgM/IgA responses to Pseudomonas aeruginosa. BP1 patients showed higher IgM responses to Morganella morganii as compared with MDD and BP2 patients. Patients with melancholia showed higher IgA responses to Citrobacter koseri as compared to controls and non-melancholic depression. The total score on the Hamilton Depression Rating Scale was significantly associated with IgA responses, especially C. koseri. IgG responses to oxidized low-density lipoprotein were significantly associated with signs of increased bacterial translocation. In conclusion, not only MDD but also BP1 and BP2 are accompanied by an immune response due to the increased load of plasma LPS of gut commensal bacteria while these aberrations in the gut-brain axis are most pronounced in BP1 and patients with melancholic features. Activated oxidative stress pathways and autoimmune responses to oxidative specific epitopes in mood disorders may be driven by a breakdown in gut paracellular, transcellular and/or vascular pathways. If replicated, drugs that protect the integrity of the gut barrier may offer novel therapeutic opportunities for BP1 and MDD.

A simple in vitro biosensor for the detection of β-amyloid 42 in phosphate-buffer saline (PBS) and undiluted human serum was fabricated and tested based on our platform sensor technology. The bio-recognition mechanism of this biosensor was based on the effect of the interaction between antibody and antigen of β-amyloid 42 to the redox couple probe of K₄Fe (CN) ₆ and K₃Fe (CN) ₆. Differential pulse voltammetry (DPV) served as the transduction mechanism measuring the current output derived from the redox coupling reaction. The biosensor was a three-electrode electrochemical system, and the working and counter electrodes were 50 nm thin gold film deposited by sputtering technique. The reference electrode was a thick-film printed Ag/AgCl electrode. Laser ablation technique was used to define the size and structure of the biosensor. Cost-effective roll-to-roll manufacturing process was employed in the fabrication of the biosensor making it simple and relatively inexpensive. Self-assembled monolayers (SAM) of 3-Mercaptopropionic acid (MPA) was employed to covalently immobilize the thiol group on the gold working electrode. A carbodiimide conjugation approach using N-(3-dimethylaminopropyl)-N’-ethylcarbodiimide hydrochloride (EDC) and N–hydroxysuccinimide (NHS) was undertaken for cross-linking antibody of β-amyloid 42 to the carboxylic groups on one end of the MPA. The antibody concentration of β-amyloid 42 used was 18.75µg/mL. The concentration range of β-amyloid 42 in this study was from 0.0675µg/mL to 0.5µg/mL for both PBS and undiluted human serum. DPV measurements showed excellent response in this antigen concentration range. Interference study of this biosensor was carried out in the presence of Tau protein antigen. Excellent specificity of this β-amyloid 42 biosensor was demonstrated without interference by other species such as T-tau protein.

Waste heat recovery stands out as a promising technique to tackle both energy shortages and environmental pollution. Currently, this valuable resource, generated from processes like fuel combustion or chemical reactions, is often dissipated into the environment, despite its potential to significantly contribute to the economy. To harness this untapped potential, a travelling-wave thermo-acoustic generator has been designed and subjected to comprehensive experimental analysis. Fifty-two data corresponding to different working conditions of the system were extracted to build ANN, ANFIS and ANN-PSO models. Evaluation of performance metrics reveals that the ANN-PSO model demonstrates the highest predictive accuracy (R^2=0.9959), particularly in relation to output voltage. This research demonstrates the potential of machine learning techniques for the analysis of thermo-acoustic systems. By doing so, it is possible to get an insight into nonlinearities inherent to thermo-acoustic systems. This advancement empowers researchers to forecast the performance characteristics of alternative configurations with a heightened level of precision.

Nucleotide-binding domain like receptor protein 3 (NLRP3) inflammasome in the kidney and the heart is increasingly being suggested to play a key role in mediating inflammation. In the kidney, NLRP3 activation has been associated with the progression of diabetic kidney disease. In the heart, activation of the NLRP3 inflammasome has been related to enhanced release of interleukin-1β (IL-1β) and the subsequent induction of atherosclerosis and heart failure. Apart from their glucose-lowering effects, SGLT-2 Inhibitors have been documented to attenuate activation of the NLRP3, thus, resulting in the constellation of an anti-inflammatory milieu. In this review, we will focus on the interplay between SGLT-2 Inhibitors and the inflammasome in the kidney, the heart and the neurons in the context of diabetes mellitus and its complications.

Pharmacists must acquire the necessary knowledge to provide better drug therapy to patients. In other words, educational programs need to facilitate behavioral changes among pharmacists. Therefore, this study aimed to evaluate how the behavior of pharmacists changed after attending a palliative care educational program. We conducted a web-based questionnaire survey examining the behavior of pharmacists regarding palliative care before participating in the program, two months after participating in the program, and eight months after participating in the program, to determine the behavior and changes over time. For all questions, scores were higher at two and eight months after attending the program than before attending the program (P &lt;0.05). In addition, no significant difference was observed between two and eight months after attending the program for all questions (P =0.504-1.000). The knowledge gained from the educational program was used to repeatedly intervene with patients with cancer, to address the various symptoms they experienced and maintain their behavior. The proven effectiveness of this program serves as a stepping stone for a nationwide rollout across Japan’s 47 prefectures.

Intergenerational integration has given rise to a novel aging paradigm known as all-age communities, which is garnering international attention. In China, the aging population and the implementation of the three-child policy have resulted in increased demand for retirement and childcare services among residents in older neighborhoods. Consequently, there is a pressing need to retrofit these older neighborhoods to accommodate all-age living arrangements given the high demand they generate. Therefore, this study undertakes research interviews with residents and constructs an exploratory theoretical model rooted in established theory. To assess the significance of our model, we employ Smart PLS 3.0 based on 297 empirical data points. Our findings indicate that anxiety has a significant negative effect on payment behavior; objective perception, willingness to pay, and government assistance exert significant positive effects on payment behavior. By comprehensively analyzing the mechanisms underlying residents’ payment behavior, this study provides valuable insights to the government for promoting the aging process within communities and formulating effective transformation policies.

This study was conducted to investigate bit use in equestrian sports, focusing on welfare and performance as bit use has been subject of debate. The multi-faceted study combines a literature review, survey, and video analyses. Research on physiological and behavioral effects of bit use, discussing concerns and benefits, was evaluated. The survey involved 250 equestrians, gathering data on experiences, opinions, and practices regarding bit usage. Results demonstrate that different bits are used during competitions and that riders prioritize factors like horse satisfaction, consistent connection, and chewing behavior when riding at home, while show use focusses on factors like control. Video analyses showed high conformity between veterinary expert and show jumper, indicating reliability and validity. Pelham and loose ring bits were most used, while 3-ring and full cheek bits were associated with the most aversive movements, particularly opening of the mouth. Other defense movements included putting ears back, tail swishing, and head tilting. The study highlighted the need for understanding bit preferences in various settings and cautioned against generalizing bit usage based solely on show observations. It also emphasized the potential for developing new designs that prioritize horse comfort and responsiveness, to enhance communication and partnership between horses and riders.

The behavioral variant of frontotemporal dementia (bvFTD) has a devastating effect on multiple domains of daily living. The purpose of this PRISMA-compliant systematic review is to summarize the most important factors associated with functional impairment in this clinical group by critically analyzing the existing literature spanning the period from 2000 to 2023. To be included in the review, the study had to investigate any kind of correlates of functional status in bvFTD patients, using a previously validated instrument of functional assessment. Out of 40 articles assessed for eligibility, 18 met the inclusion criteria. The anatomical pattern of cerebral atrophy at baseline appeared to be the strongest predictor of the rate of functional decline over time, with the frontal-dominant anatomical subtype being associated with a faster rate of functional impairment. Additionally, executive dysfunction as well as apathy appeared to contribute significantly to functional disability in bvFTD patients. A comparative examination of bvFTD in relation to other clinical subtypes of FTD and other types of dementia in general suggests that it’s the predominant atrophy of the frontal lobes along with the subsequent unique combination of cognitive and neuropsychiatric manifestations that account for the pronounced functional limitations observed in these individuals, even from the early stages of the disease.

Building on recent directions in religion-related social and political science, our essay addresses a need for location-specific and religion-specific scientific research that might contribute directly to local and regional interreligious peacemaking. Over the past 11 years, our US-Pakistani research team has conducted research of this kind: a social scientific method for diagnosing the probable near-future behavior of religious stakeholder groups toward other groups. Integrating features of ethnography, linguistics, and semiotics, the method enables researchers to read a range of ethno-linguistic signals that appear uniquely in the discourses of religious groups. Examining the results, we observe, firstly, that our religion and location-specific science identifies features of religious group behavior that are inevident in broader, social scientific studies of religion and conflict; we observe, secondly, that our science integrates constative and performative elements: it seeks facts and it serves a purpose. We conclude that strictly constative, fact-driven sciences may fail to detect certain crucial features of religious stakeholder group behavior.

Global demand for sleep-tracking wearables, or consumer sleep technologies (CSTs), is steadily increasing. CST marketing campaigns often feature a scientific component, but the scientific relevancy and monetary value of CST features within the sleep research community remains unquantified. Sleep medicine experts were recruited through social media and nonprobability sampling techniques to complete a survey identifying sleep metrics and device features that are most desirable to the scientific community. A hypothetical purchase task (HPT) estimated economic valuation for devices with different features by price. Forty-six (N=46) respondents with an average of 10±6 years’ experience conducting research in real-world settings completed the online survey. Total sleep time was ranked as the most important measure of sleep followed by objective sleep quality while sleep architecture/depth and diagnostic information were ranked as least important. Experts preferred wrist-worn devices that could reliably determine sleep episodes as short as 20 minutes. Economic value was greater for hypothetical devices with longer battery life. These data set a precedent to determine how scientific relevance of a product impacts the potential market value of a CST device. This is the first known attempt to establish consensus opinion or economic valuation for scientifically-desirable CST features and metrics using expert elicitation.

The paper describes an algorithm for semantic representation of behavioral contexts relative to a dichotomic decision alternative. The contexts are represented as quantum qubit states in two-dimensional Hilbert space visualized as points on the Bloch sphere. The azimuthal coordinate of this sphere functions as a one-dimensional semantic space in which the contexts are accommodated according to their subjective relevance to the considered uncertainty. The contexts are processed in triples defined by knowledge of a subject about a binary situational factor. The obtained triads of context representations function as stable cognitive structure at the same time allowing a subject to model probabilistically-variative behavior. The developed algorithm illustrates an approach for quantitative subjectively-semantic modeling of behavior based on conceptual and mathematical apparatus of quantum theory.

In this paper, we consider a manufacturer who produces and sells a kind of innovative product in the monopoly market environment. Because the life cycle of innovative product is usually shorter than its procurement lead time, one unique demand quantity (scenario) will occur in the selling season, thus there is only one chance for the manufacturer to determine both optimal production quantity and optimal sale price. Considering this one-time feature of such a decision problem, a price-setting newsvendor model for innovative products is proposed. Different to the existing price-setting newsvendor models, the proposed models determine the optimal production quantity and sale price based on some specific state (scenario) which is most applicable for the manufacturer. The theoretical analysis provides managerial insights into the manufacturers’ behaviors in a monopoly market of an innovative product and several phenomena in the luxury goods market are well explained.

The importance of detecting and diagnosing the imminence of aneurysm rupture before it occurs cannot be overstated. However, this process is often time-consuming and challenging, with variability in conclusions not uncommon among experts. Furthermore, current state-of-the-art models in medical image segmentation suffer from limited availability of large annotated datasets for training, exacerbated by an immense data imbalance. In most segmentation tasks, 3D segmentation is preferred due to its capacity to leverage more contextual information. However, these models require more data, which is constrained by the aforementioned issues. In the realm of current diagnosis models, those lacking geographic context are notably ineffective. To address these issues, the aim is to develop a model pipeline to accurately detect cerebral intracranial aneurysms and diagnose rupture imminence through 3D magnetic resonance angiography (MRA) and tabular input, augmented with the implementation of neuro-symbolic ai. The utilization of data fusion to tackle the lack of context in tabular models is proposed, leveraging extracted geographical features from a segmentation model. To enhance segmentation results and tackle the data imbalance in aneurysm segmentation, a two-stage model pipeline that extracts contextual geographic features before aneurysm segmentation is suggested. Segmentation results are additionally improved through ensemble techniques and novel preprocessing techniques. Finally, a neuro-symbolic aspect is introduced to enhance model diagnosis, interpretability and performance. The multi-step Integrated Neuro-Symbolic Analysis Pipeline for Cerebral Aneurysm Rupture Diagnosis is introduced.

As the name implies, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a single-stranded RNA virus and a member of the corona virus family, primarily affecting the upper respiratory system and the lungs. Like many other respiratory viruses, SARS-CoV-2 can spread to other organ systems. Apart from causing diarrhea, another most common but debilitating complication caused by the SARS-CoV-2 is neurological symptoms and cognitive difficulties, which occur in up to two thirds of hospitalized covid patients and ranging from shortness of concentration, overall declined cognitive speed to executive or memory function impairment. Neuro-cognitive dysfunction and “brain fog” are frequently present in COVID-19 cases, which can last several months after the infection, leading to disruption of daily life. Cumulative evidence suggests that SARS-CoV-2 affects vasculature in the extra pulmonary systems directly or indirectly, leading to impairment of endothelial function and even multi-organ damage. The post COVID-19 long-lasting neurocognitive impairments have not been studied fully; and the underlying mechanism remains elusive. In this review, we summarize the current understanding of the effects of COVID-19 on vascular dysfunction and how vascular dysfunction leads to cognitive impairment in patients.

Ursolic and oleanolic acids are secondary plant metabolites that are known to be involved in the plant defence system against water loss and pathogens. Nowadays these triterpenoids are also regarded as potential pharmaceutical compounds and there is mounting experimental data that either purified compounds or triterpenoid-enriched plant extracts exert various beneficial effects, including anti-oxidative, anti-inflammatory and anticancer, on model systems of both human or animal origin. Some of those effects have been linked to the ability of ursolic and oleanolic acids to modulate intracellular antioxidant systems and also inflammation- and cell death-related pathways. Therefore, our aim was to review the current knowledge about the distribution of ursolic and oleanolic acids in plants, bioavailability and pharmacokinetic properties of these triterpenoids and their derivatives, and to discuss their neuromodulatory effects in vitro and in vivo.

One of the most crucial and economically beneficial tasks for energy customer is peak load curtailment. On account of the fast response of renewable energy resources (RERs) such as photovoltaic (PV) units and battery energy storage system (BESS), this task is closer to be efficiently implemented. Depends on the customer peak load demand and energy characteristics, the feasibility of this strategy may warry. When adaptive neuro-fuzzy inference system (ANFIS) is exploited for forecasting, it can provide many benefits to address the above-mentioned issues and facilitate its easy implementation, with short calculating time and re-trainability. This paper introduces a data driven forecasting method based on fuzzy logic for optimized peak load reduction. First, the amount of energy generated by PV is forecasted using ANFIS which conducts output trend, and then, the BESS capacity is calculated according to the forecasted results. The trend of the load power is then decomposed in Cartesian plane into two parts, left and right from load peak, searching for BESS capacity equal. Network switching sequence over consumption is provided by a fuzzy logic controller (FLC) with respect to BESS capacity and PV energy output. Finally, to prove the effectiveness of the proposed ANFIS-based peak shaving method, offline digital time-domain simulations have been performed on a real-life practical test micro grid system in MATLAB/Simulink environment and the results have been experimentally verified by testing on a practical micro grid system with real-life data obtained from smart meter and also, compared with several previously-reported methods.

It is widely known that vitamin D receptors have been found in neurons and glial cells and their highest expression is in the hippocampus, hypothalamus, thalamus and subcortical grey nuclei, and substantia nigra. The vitamin D helps the regulation of neurotrophin, neural differentiation and maturation, through the control operation of growing factors synthesis (ie NGF and GDNF), the trafficking of the septo-hyppocampal pathway, and the control of the synthesis process of different neuromodulators (such as Ach, DA, and GABA). Based on these assumptions, we have written this review in order to summarize the potential role of vitamin D in neurological pathologies. The work could be titanic, and might result very fuzzy and even incoherent, if we would not have conjectured to taper our first intentions and devoted our interests towards three mainstreams: demyelinating pathologies, vascular syndromes and neurodegeneration. Due to the lack of effective therapeutic options, a part from the disease modifying strategies, the role of different risk factors should be investigated in neurology, as far as their correction may lead to the improvement of the cerebral conditions. We have explored the relationships between the gene-environmental influence and long term vitamin D deficiency, as a risk factor for the development of different types of neurological disorders, along with the role and the rationale of therapeutic trials with vitamin D implementation.

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

Pure silver as well as silver compounds have been used as antiseptics for centuries. Nowadays, the most frequently applied form of silver is a nanoparticle form. Several studies demonstrate the ability of silver nanoparticles and silver salts to accumulate in the brain of laboratory ani-mals as well as the ability of silver nanoparticles to influence behavioral and cognitive functions of laboratory mammals. For the first time, the influence of a silver salt, in particular, silver citrate on the change dynamics of behavioral and cognitive functions of laboratory mice at the daily oral exposure during 30, 60, 120 and 180 days is investigated in the present research. We observed the anxiety increase after 30 days of the exposure as well as the ability of mice to adapt to the prolonged oral exposure of silver citrate manifested in the anxiety decrease, increase of locomotor activity and the tendency of long-term contextual memory improvement. Increase of locomotor activity and memory improvement might be associated with the sensitivity increase and regarded as compensatory mechanism to anxiety increase. The observed phenomenon is classified as a hormetic effect. A comparison with the analogous study implemented polyvi-nylpyrrolidone coated silver nanoparticles is carried out. It was concluded that the nanoform is more neurotoxic.

The leaves and stems of Bauhinia guianensis Aubl. are used in traditional Amazonian phytotherapy for the treatment of pain and inflammation. This study investigates the anti-inflammatory and antinociceptive effects of hydroethanolic extracts from B. guianensis Aubl. leaves and stems (HELBg and HESBg, respectively) in in vivo models of inflammation and hyperalgesia. Danio rerio experimental animals were submitted to the acute inflammation test, induced by intraperitoneal (ip.) administration of carrageenan 20 μg / animal (abdominal edema), the groups were previously treated orally with saline solution 2 μl / animal (SS), Dimethyl sulfoxide 2 μl / animal (DMSO), Indomethacin 10 mg/kg, HELBg 100 mg/kg and HESBg 100 mg/kg, n = 12 per experimental group, to evaluate inhibition of edema and alterations histopathology of the liver, intestine and kidney of these animals. The antinociceptive effect was observed from the body curvature index and the behavioral responses of Danio rerio, after an experimental protocol for the induction of hyperalgesia, by ip. administration of 10 μl / animal of 2.5% acetic acid, the animals were previously orally treated with saline solution 2 μl/animal (SS), dimethyl sulfoxide 2 μl/animal (DMSO), morphine 2.5 mg/kg, HELBg 100 mg/kg and HESBg 100 mg/kg, n = 5 per experimental group. In carrageenan-induced edema, the group treated with HESBg inhibited edema formation over the 3 hours of the experiment. Maximum edema was inhibited by 54% (p &lt; 0.05) when compared to the control group. Both HELBg and HESBg prevented body curvature index changes (t (df = 3 8) =, 6.96 and t (df = 3, 8) = 6.61, respectively, both p &lt; 0.0001). In the behavioral parameters sensitive to antinociceptive pharmacological modulation, due to the abdominal constriction induced by acetic acid, the administration of HELBg and HESBg resulted in an improvement in swimming activity, by increasing the distance covered (F (df = 3, 16) = 6.50 and F (df = 3, 16)  = 7.72, respectively, both p &lt; 0.0001), decrease in freezing time (F (df = 3, 16)  = 2.04 and F (df = 3, 16)  = 1.28, respectively, both p &lt; 0.0059), increase in the number of ascents to the upper area of the tank (F (df = 3, 16)  = 33.02 and F (df = 3, 16)  = 35.62, respectively, both p &lt; 0.0009 ) and decreased time spent in that area (F (df = 3, 16)  = 101.19 and F (df = 3, 16)  = 103.59, respectively, both p &lt; 0.0038). It is reasonable to suppose that both extracts modulated the variations induced by carrageenan and acetic acid through the inhibition of prostaglandin biosynthesis, decreasing the release of inflammatory mediators and the sensitization of peripheral nociceptors and, consequently, the perception of pain. These results suggest that HELBg and HESBg have anti-inflammatory and antinociceptive activities, probably of peripheral origin and linked to the inhibition of prostaglandin biosynthesis.

The present study investigates the decision-making process of investors on the Pakistan Stock Exchange with regard to portfolio construction, explicitly focusing on the incorporation of ESG concerns. The present study employed SmartPLS and structural equation modeling (SEM) to quantitatively assess data received from a sample of 393 out of 400 respondents. Based on the available data, investors participating in the Pakistan Stock Exchange are notably impacted by ESG aspects. The findings of this study hold significance for emerging economy firms, regulators, and investors, both in terms of theoretical and practical ramifications. The study's findings demonstrate a clear indication of investors' significant emphasis on ESG matters. Furthermore, it provides valuable insights for stakeholders who are involved in the ever-evolving realm of sustainable finance within developing countries

The leaves and stems of Bauhinia guianensis Aubl. are used in traditional Amazonian phytotherapy for the treatment of pain and inflammation. This study investigates the anti-inflammatory and antinociceptive effects of hydroethanolic extracts from B. guianensis Aubl. leaves and stems (HELBg and HESBg, respectively) in in vivo models of inflammation and hyperalgesia. Danio rerio experimental animals were submitted to the acute inflammation test, induced by intraperitoneal (ip.) administration of carrageenan 20 μg / animal (abdominal edema), the groups were previously treated orally with saline solution 2 μl / animal (SS), Dimethyl sulfoxide 2 μl / animal (DMSO), Indomethacin 10 mg/Kg, HELBg 100 mg/Kg and HESBg 100 mg/Kg, n = 12 per experimental group, to evaluate inhibition of edema and alterations histopathology of the liver, intestine and kidney of these animals. The antinociceptive effect was observed from the body curvature index and the behavioral responses of Danio rerio, after an experimental protocol for the induction of hyperalgesia, by ip administration. of 10 μl / animal of 2.5% acetic acid, the animals were previously orally treated with saline solution 2 μl/animal (SS), dimethyl sulfoxide 2 μl/animal (DMSO), morphine 2.5 mg/Kg, HELBg 100 mg/Kg and HESBg 100 mg/Kg, n = 5 per experimental group. In carrageenan-induced edema, the group treated with HESBg inhibited edema formation over the 3 hours of the experiment. Maximum edema was inhibited by 54% (p &lt; 0.05) when compared to the control group. Both HELBg and HESBg prevented body curvature index changes (t (df = 3 8) =, 6.96 and t (df = 3, 8) = 6.61, respectively, both p &lt; 0.0001). In the behavioral parameters sensitive to antinociceptive pharmacological modulation, due to the abdominal constriction induced by acetic acid, the administration of HELBg and HESBg resulted in an improvement in swimming activity, by increasing the distance covered (F (df = 3, 16) = 6.50 and F (df = 3, 16)  = 7.72, respectively, both p &lt; 0.0001), decrease in freezing time (F (df = 3, 16)  = 2.04 and F (df = 3, 16)  = 1.28, respectively, both p &lt; 0.0059), increase in the number of ascents to the upper area of the tank (F (df = 3, 16)  = 33.02 and F (df = 3, 16)  = 35.62, respectively, both p &lt; 0.0009 ) and decreased time spent in that area (F (df = 3, 16)  = 101.19 and F (df = 3, 16)  = 103.59, respectively, both p &lt; 0.0038). It is reasonable to suppose that both extracts modulated the variations induced by carrageenan and acetic acid through the inhibition of prostaglandin biosynthesis, decreasing the release of inflammatory mediators and the sensitization of peripheral nociceptors and, consequently, the perception of pain. These results suggest that HELBg and HESBg have anti-inflammatory and antinociceptive activities, probably of peripheral origin and linked to the inhibition of prosta-glandin biosynthesis

Background: Increasing number of long-term gastrointestinal (GI) cancer survivors highlights the importance of understanding factors that contribute to their health-related quality of life (HRQoL). We investigated the risk factors of HRQoL, in-cluding demographics, clinical characteristics, and social and behavioral determinants of health (SBDH). Methods: Adult GI cancer survivors (n = 3,201) in the BRFSS surveys from 2014-2021 (except for 2015) were analyzed. Unadjusted/adjusted logistic regression was used. Results: The majority were female (54%) and White (78%), with a median age of 67. Survivors who were 65 years or older, diagnosed with colorectal cancer, or who had fewer comorbidities were more likely to report significantly better HRQoL. Significant social factors of poor HRQoL were being unmarried, racial and ethnic minorities, low socioeconomic status, and poor health care access. Significant behavioral factors of poor HRQoL were lack of physical activity, heavy alcohol consumption, and current smoking, with lack of physical activity being the most significant factor. Conclusions: The SBDH have a critical role in HRQoL. Future studies are warranted to develop a tailored survivorship intervention, such as physical rehabilitation and to ex-plore machine learning/artificial intelligence predictive models to identify cancer sur-vivors at a high risk of developing poor HRQoL.

Silver in different forms is used for medical purposes from ancient times. It is not yet well known, which form of silver is more biocompatible and less toxic. Here we considered silver nanoparticles and silver citrate. Also, the relationships of neurotoxicity of silver compaunds with ageing factor is not yet described. To assess the role of nanoform in neurotoxicity of silver and role of ageing a long-term experiment was conducted. We had four control groups of intact mice and four experimental groups which were exposed to silver nanoparticles and silver citrate for two months. Four groups of mice were introduced into the experiment since the age of five months to assess ageing factors. It was shown that the nanoform does play a certain role in neurotoxicity of silver. Silver citrate seems to be a more preferable silver compound. Ageing can be regarded as a positive factor that neutralizes toxic action of silver compounds. It may be due to the development of physiological/cognitive functions with the age as well as adaptation to unnatural content in the individual cages that is definitely stressful for mice.

The field of applied behavior analysis has been directly involved in both research and applications of behavioral principles to improve the lives of captive zoo animals. Thirty years ago, Forthman and Ogden (1992) wrote one of the first papers documenting some of these efforts. Since that time, considerable work has been done using behavioral principles and procedures to guide zoo welfare efforts. The current paper re-examines and updates Forthman and Ogden’s original points, with attention to the five categories they detailed: (1) promotion of species-typical behavior, (2) reintroduction and repatriation of endangered species, (3) animal handling, (4) pest control, and (5) animal performances. In addition, we outline three current and future directions for behavior analytic endeavors: (i) experimental analyses of behavior and the zoo, (ii) applied behavior analysis and the zoo, and (iii) within-subject methodology and the zoo. The goal is to provide a framework that can guide future behavioral research in zoos, as well as create applications based on these empirical evaluations.

Process (or business process) management systems fulfill defining, executing, monitoring and managing process models deployed on process-aware enterprises. Accordingly, the functional formation of the systems is made up of three subsystems such as modeling subsystem, enacting subsystem and mining subsystem. In recent times, the mining subsystem has been becoming an essential subsystem. Many enterprises have successfully completed the introduction and application of the process automation technology through the modeling subsystem and the enacting subsystem. According as the time has come to the phase of redesigning and reengineering the deployed process models, from now on it is important for the mining subsystem to cooperate with the analyzing subsystem; the essential cooperation capability is to provide seamless integrations between the designing works with the modeling subsystem and the redesigning work with the mining subsystem. In other words, we need to seamlessly integrate the discovery functionality of the mining subsystem and the analyzing functionality of the modeling subsystem. This integrated approach might be suitable very well when those deployed process models discovered by the mining subsystem are complex and very large-scaled, in particular. In this paper, we propose an integrated approach for seamlessly as well as effectively providing the mining and the analyzing functionalities to the redesigning work on very large-scale and massively parallel process models that are discovered from their enactment event logs. The integrated approach especially aims at analyzing not only their structural complexity and correctness but also their animation-based behavioral properness, and becomes concretized to a sophisticated analyzer. The core function of the analyzer is to discover a very large-scale and massively parallel process model from a process log dataset and to validate the structural complexity and the syntactical and behavioral properness of the discovered process model. Finally, this paper writes up the detailed description of the system architecture with its functional integration of process mining and process analyzing. And more precisely, we excogitate a series of functional algorithms for extracting the structural constructs as well as for visualizing the behavioral properness on those discovered very large-scale and massively parallel process models. As experimental validation, we apply the proposed approach and analyzer to a couple of process enactment event log datasets available on the website of the 4TU.Centre for Research Data.

Deep behavioral covariates (DBCs) introduced in this perspective form a new class of covariates that have the potential to enhance the performance of predictive models and improve analytics in clinical decision support applications. DBCs can measure how engaged a patient tends to be and how he or she tends to respond to events, and they may be highly predictive of the patient’s outcomes for a planned treatment. DBCs may potentially serve as a standard to measure patient engagement and activation and may form highly efficient mechanisms for improving patient outcomes.

1) Background: It has been more than a decade since the concern about addictive use of the Internet and mobile phones was first expressed and its possible inclusion into the lists of mental disorders has been a popular topic of discussion recently, thus it seems to be a fitting moment to investigate the evolution of this issue over time. The aim of the present study was to analyze the evolution of the perception of problematic Internet and smartphone use in young people over the period 2006–2017; 2) Methods: To this end, a questionnaire on Internet use habits and the CERI and CERM questionnaires on negative consequences of Internet and smartphone use were administered to a sample of 792 university students and compared with data from former studies over the period 2006–2017; 3) Results: The perception of problematic Internet and mobile phone use has increased over the last decade, social networks are considered responsible for this increase, and females are the ones perceived to be more affected than males. The degree to which participants agree with the statement “I am addicted to the Internet” can be used as a screening item for problematic use.

Multiple Sclerosis (MS) is a neurodegenerative disease that mainly affects young adults. The high prevalence of the disease and its impact on patients' quality of life led to priority being given to the etiopathogenesis of the disease to identify possible modifiable factors and consequent effective intervention strategies. Recent hypotheses suggest that the etiopathogenesis of MS is multifactorial and includes factors related to the immune system, neuroinflammation and neuro-degeneration. In this scenario, sleep has a close - although indirect - relationship with MS, through its relationship with each of those factors. In particular, the role of cytokines in immune system impairment and neuroinflammation, mitochondrial dysfunction and oxidative stress in neurodegeneration all are associated with sleep in different forms. Furthermore, melatonin in relation with vitamin D has a potential therapeutic effect in MS sleep-related problems. Given the growing interest of research in the mechanisms underlying MS and therapies able to alleviate MS-related symptoms at all stages of the disease, a more in-depth study of the role of sleep disturbances and the factors intrinsically related to sleep and MS could be useful both to investigate the etiopathogenetic factors of MS and to develop potential non-invasive intervention strategies for MS treatment.

The emotion of disgust protects individuals against pathogens, and it has been found that during pregnancy, it is elevated. Physiological mechanisms discussed in relation to these changes include immune markers and progesterone levels. The aim of this study is to assess the association between steroids and disgust in pregnancy. In a sample of 179 pregnant women, we have analyzed blood serum steroid concentrations and measured disgust sensitivity by text-based questionnaires in the first and third trimester. We found positive correlations between disgust sensitivity and the levels of C19 steroids, including testosterone, but also its precursors in the Δ5 pathway (androstenediol, DHEA, and their sulfates) and the Δ4 pathway (androstenedione). Furthermore, positive correlations between disgust sensitivity and 5α/β-reduced C19 steroid metabolites were consistently present in both trimesters. In the first trimester, we found a positive association between disgust sensitivity and 17-hydroxypregnanolone as well as some estrogens. In the third trimester, we have observed positive associations between disgust sensitivity and cortisol as well as immunoprotective Δ5 C19 7α/β-hydroxy-steroids. Our study has thus shown that disgust sensitivity is positively correlated with immunomodulatory steroids, and in the third trimester, steroids may reflect potential maternal anxiety-related symptoms.

Cervical cancer is one of the ailments that endangers the health of women all over the world and causes infertility. Fortunately, this disease can be avoided. Both the results and participation rates of the current preventative strategy remain poor. Hence, preventative or early detection methods remain challenging and open. In gynecology and computer science fields, few studies on identifying cervical cancer are based on behavior risk factor and machine learning. Moreover, various social and behavioral aspects might often make it more difficult to detect cervical cancer especially in low‑ and middle‑income countries (LMICs). Accordingly, focus on predicting the presence of cervical cancer through behavioral risk factors is the basic structure of this research. We proposed an approach to classify cervical cancer from a social-behavioral perspective using logistic regression to predict cervical cancer. We got significantly improved outcomes than the existing methods. Moreover, we used feature engineering to formulate less dimensionality as a pre-processing step and optimized parameters to be prepared for classification phase. The results proved that using the logistic regression with regularization type L1 had a promising performance. The accuracy of Logistic Regression (LR) L1 was 97.2%. Such results support the proposed work as a reliable classification predictive tool.

This study aimed to explore what factors affect teachers’ acceptance and instructional use of ICT in Chinese higher vocational colleges. Grounded in the modified UTAUT model, the current study investigated the direct and indirect effects of teachers’ performance expectancy, effort expectancy, external conditions, and behavioral intentions on using ICT in teaching. A total of 6087 teachers from 219 vocational colleges in 28 provinces in China participated in a large-scale survey. Structural equation modeling revealed that the teachers’ psychological perceptions (including performance expectancy, effort expectancy, and intention to use ICT technology) and the external support conditions (including professional development support, infrastructures, the climate of organizational reform and innovation, and teacher performance assessment mechanisms) significantly directly affect the use of ICT in Chinese higher vocational college teachers’ teaching practice. Moreover, this study confirmed the mediating role of teachers’ intention to use ICT in teaching in the relationship between teachers’ psychological perceptions and ICT instructional usage behavior. However, there were differences in the significance of these variables in the chain effect of teachers’ intention to use ICT. These findings expand our understanding of the factors influencing ICT use in teaching among VET teachers in China and thus provide practical implications for higher vocational college managers to promote teachers’ ICT teaching behaviors.

Despite the strong links between sleep, circadian rhythms, and mental health, sleep education has been neglected in mental healthcare provider training programs. The current pilot study examined the potential efficacy and acceptability of a sleep education workshop for trainee psychologists, called the Sleep Psychology Workshop. Eleven students completing their Master of Psychology degrees (90% female, 24.4 ± 1.6 years old) attended the Sleep Psychology Workshop as part of their Health Psychology course, delivered as three, two-hour lectures (total six hours). Trainees’ sleep psychology knowledge quiz scores (% correct) demonstrated significant improvement from pre- (M = 60%, SD = .09) to post-workshop (M = 79%, SD = .08), t (6) = -5.18, p = .002. Trainees also reported increased self-efficacy to use common sleep-related assessment instruments and empirically supported interventions to manage sleep and circadian rhythm disturbances, along with increased confidence to manage insomnia disorder in clinical practice (all p<.02). Trainees also endorsed the workshop as an acceptable sleep education program for trainee psychologists via a post-workshop feedback survey, focus group, and six-month follow-up survey. This pilot study provides preliminary evidence for the Sleep Psychology Workshop as an effective and acceptable sleep education program for trainee psychologists.

Understanding how plants change their root foraging strategy in the presence of neighbors is of paramount importance for plant ecology and agriculture. The root tragedy of the common (RToC) is a plant behavior predicted by game theory models in which competing plants forage for soil resources inefficiently. The RToC is generally assumed to be induced by non-self root recognition, and researchers consider root overproliferation and reduced fitness with respect to a plant growing solo as the trace left by plants engaging in an RToC in experiments and model results. Herein, I first challenge both notions, and argue that the RToC is a suboptimal phenotypically plastic response of plants that is based in soil resource information exclusively. Second, I discuss how this new perspective carries important implications for the design of experiments investigating the physiological mechanisms underlying observable plant root responses. Finally, I discuss why placing the RToC theory in the context of more general root research is fundamental: The RToC represents a mechanistic foundation for understanding the belowground behavior of plants interacting with neighbors, and a spatially explicit approach to RToC may produce more comprehensive results.

Less aligned emphasis has been given to the COVID-19 infodemic coordinating with the COVID-19 outbreak. Global profusion of tangled monikers and hashtags has found their ways in daily communication and contributed to backlash against Chinese. Official naming efforts against infodemic should be meet with a fair share of identification. Based on brief critical reviews on previous multifarious naming practices, we punctuate heuristic introspection in scientific conventions and sociocultural paradigms. Infodemiological analysis promises to articulate that people around the globe are divided in their favor stigmatized monikers in the public and scientific communities because of perceptual bias. There is no positive correlation between the degree of infection in their territories and collective perceptual bias to COVID-19. The official portfolio “COVID-19” and “SARS-CoV-2” has not become de facto standard usages, but full-fledged official names are excepted to duly contribute to the resilience of negative perceptual bias and collective behavioral propensities.

This study examines the evolution of the two-factor authentication method and its adaptability to the online education system. Two-factor authentication is a security measure used especially in areas where information such as banking is valuable. Parallel to technological developments, it has developed as much as daily. It aims to take security one step forward because it is composed of two phases. Today, banking, IOT devices, public transport tickets and many other areas are used. Two-factor authentication methods against security attacks in the field of information are also being updated. In recent years, new technologies such as biometric (iris pattern, retinal pattern, etc.) or behavioral biometry (location tracking, walking information, touch speed etc.) were studied. Instead of physically studying somewhere like going to a course in modern society, online trainings become more advantageous. Most of these online trainings are given certificates such as participation certificate, success certificate, etc. The main problem here is whether the person who is being certified is true. In the study conducted in line with these details, there is a proposal for the application of the Mimic Control Method with Sound Intensity (MCMSI) method for on-line training by examining the two-factor authentication techniques up to the day.

To better understand farmers’ refusal and corresponding negative emotions to pay agricultural water fee under current policy in rural China, this paper applies mental accounting, a behavioral economics framework, to explore how the governmental policies of reform of rural taxes and fees, direct agricultural subsidy programs and agricultural water fee individually influence farmers’ decisions in paying agricultural water fee. Using fieldwork data from 577 farmers and 20 water managers in Sichuan, we explore farmers’ information processing regarding paying agricultural water fee via three sequential mental accounting processes, with the associated underlying principles and measures behind each process. We find that the information processing in three mental accounting scenarios related to the agricultural water fee elucidates farmers’ observed behaviors in rural China. Generally, in the three mental accounting scenarios, two conditional intuitive expectations and nine conditional intuitive preferences are formed, however, the conditions of those expectations or preferences cannot be matched with the facts due to the reform of rural taxes and fees, the direct agricultural subsidy programs and the internal attributes of agricultural water fee, which interpret those negative behaviors in rural China. Additionally, this paper offers a view into how previous policies create negative psychological externalities (such as farmers’ psychological dependence on the government) through mental accounting to negatively influence agents’ subsequent decision making; it highlights the significance of underlying mental factors and information processing of negative behaviors in policymaking for managing or conserving common pool resources.

Background: The role played by the non-dominant parietal lobe in motor cognition, attention and spatial awareness networks has potentiated the use of awake surgery. When this is not feasible, asleep monitoring and mapping techniques should be used to achieve an onco-functional balance. Objective: This study aims to assess the feasibility of a dual-strip method to obtain direct cortical stimulation for continuous real-time cortical monitoring and subcortical mapping of motor and visual pathways simultaneously in parietal lobe tumour surgery. Methods: Single-centre prospective study between May’19-November’20 of patients with intrinsic non-dominant parietal-lobe tumours. Two subdural strips were used to simultaneously map and monitor motor and visual pathways. Results: Fifteen patients were included. With regards to motor function, a large proportion of patients had abnormal interhemispheric resting motor threshold ratio (iRMTr) (71.4%), abnormal Cortical Excitability Score (CES) (85.7%), close distance to the corticospinal tract – Lesion-To-Tract Distance (LTD) – 4.2mm, Cavity-To-Tract Distance (CTD) – 7mm and intraoperative subcortical distance - 6.4mm. Concerning visual function, the LTD and CTD for optic radiations (OR) were 0.5mm and 3.4mm, respectively; the mean intensity for positive subcortical stimulation of OR was 12mA±2.3mA and 5/6 patients with deterioration of VEPs>50% had persistent hemianopia and transgression of ORs. 12 patients remained stable, one patient had a de-novo transitory hemiparesis, and two showed improvements in motor symptoms. A higher iRMTr for lower limbs was related with a worse motor outcome (p=0.013) and a longer CTD to OR was directly related with a better visual outcome (p=0.041). At 2 weeks after hospital discharge, all patients were ambulatory at home and all proceeded to have oncological treatment. Conclusion: We propose motor and visual function boundaries for asleep surgery of intrinsic non-dominant parietal tumours. Pre-operative abnormal cortical excitability of the motor cortex, deterioration of the VEP recordings and CTD<2mm from the OR were related to poorer outcomes.

Development of effective treatments for high-grade glioma (HGG) is hampered by 1) the blood-brain barrier (BBB), 2) an infiltrative growth pattern, 3) rapid development of therapeutic resistance, and, in many cases, 4) dose-limiting toxicity due to systemic exposure. Convec-tion-enhanced delivery (CED) has the potential to significantly limit systemic toxicity and in-crease therapeutic index by directly delivering homogenous drug concentrations to the site of disease. In this review, we present clinical experiences and preclinical developments of CED in the setting of high-grade gliomas.

As new treatment modalities are being explored in neuro-oncology, viruses are emerging as a promising class of therapeutics. Virotherapy consists of introduction of either wild-type or engineered viruses to the site of disease, where they exert anti-tumor effect. These viruses can either be non-lytic, in which case they are used to deliver gene therapy, or lytic, which induce tumor cell lysis and subsequent host immunologic response. Replication-competent viruses can then go on to further infect and lyse neighboring glioma cells. This treatment paradigm is being explored extensively in both preclinical and clinical studies for a variety of indications. Virus-based therapies are advantageous due to the natural susceptibility of glioma cells to viral infection, which improves therapeutic selectivity. Furthermore, lytic viruses expose glioma antigens to the host immune system and subsequently stimulate an immune response that specifically targets tumor cells. This review surveys the current landscape of oncolytic virotherapy clinical trials in high-grade glioma, summarizes preclinical experiences, identifies challenges associated with this modality across multiple trials, and highlights potential to integrate this therapeutic strategy into promising combinatory approaches.

Songorine (SON) is a diterpenoid alkaloid from Aconitum plants. Preparations of Aconitum roots have been employed in traditional oriental herbal medicine, however, their mechanisms of action are still unclear. Since GABA-receptors are possible brain targets of SON, we investigated which subtypes of GABA-receptors contribute to the effects of SON, and how SON affects anxiety-like trait behavior and psychomotor cognitive performance of rats. First, we investigated the effects of microiontophoretically applied SON alone and combined with GABA-receptor agents picrotoxin and saclofen on neuronal firing activity in various brain areas. Next, putative anxiolytic effects of SON (1.0-3.0 mg/kg) were tested against the GABA-receptor positive allosteric modulator refer-ence compound diazepam (1.0-5.0 mg/kg) in the elevated zero maze (EOM). Furthermore, basic cognitive effects were assessed in a rodent version of the psychomotor vigilance task (PVT). Local application of SON predominantly inhibited the firing activity of neurons. This inhibitory effect of SON was successfully blocked by GABA(A)-receptor antagonist picrotoxin but not by GABA(B)-receptor antagonist saclofen. Similar to GABA(A)-receptor positive allosteric modulator diazepam, SON increased the time spent by animals in the open quadrants of the EOM without any signs of adverse psychomotor and cognitive effects observed in the PVT. We showed that, under in vivo conditions SON acts as a potent GABA(A)-receptor agonist and effectively decreases anxiety without observable side effects. The present findings facilitate the deeper understanding of the mechanism of action and the widespread pharmacological use of diterpene alkaloids in various CNS indications.

The purpose of this Individually Randomized Group Treatment Trial was to compare an Acceptance and Commitment Therapy-based (ACT) group intervention and a Cog-nitive Behavioral Therapy-based (CBT) group intervention for weight loss maintenance in a sample of adult patients with obesity seeking treatment for weight loss. 155 over-weight adults (BMI: Kg/m2= 43.8[6.8]) attending a multidisciplinary rehabilitation program for weight loss were randomized into two conditions: ACT and CBT. Demo-graphical, physical, and clinical data were assessed at the beginning of the program (t0), at discharge (t1), and at 6-month follow-up (t2). The following measures were ad-ministered: The Acceptance and Action Questionnaire-II (AAQ-II) and the Clinical Outcome in Routine Evaluation-Outcome Measure (CORE-OM). Generalized linear mixed models were performed to assess differences between groups. Moderation ef-fects for gender and eating disorders (ED) have been considered. From baseline to dis-charge no significant differences between interventions were found, with the only ex-ception of an improvement in the CORE-OM total score and in the CORE-OM subjective well-being subscale for those in the CBT condition. From discharge to follow-up ACT group participants showed significant results in terms of weight loss maintenance, CORE-OM total score, and CORE-OM and AAQ-II’s wellbeing, symptoms, and psy-chological problems subscales. Gender moderated the effects of time and intervention on the CORE-OM’ subscale reporting the risk for self-harm or harm others. The pres-ence of an eating disorder moderated the effect of time and intervention on the CORE-OM total score, on the CORE-Om’ symptoms and psychological problems sub-scales, and on the AAQ-II. Patients who received the ACT intervention were more likely to achieve a ≥5% weight loss from baseline to follow-up and to maintain the weight loss after discharge. The ACT intervention was thus effective in maintaining weight loss over time.

This paper examines whether classical extensional mereology is adequate for formalizing the whole-parts relation in quantum chemical systems. Although other philosophers have argued that classical extensional and summative mereology does not adequately formalize whole-parts relation within organic wholes and social wholes, such critiques often assume that summative mereology is appropriate for formalizing the whole-parts relation in inorganic wholes such as atoms and molecules. However, my discussion of atoms and molecules as they are conceptualized in quantum chemistry will establish that standard mereology cannot adequately fulfill this task, since the properties and behavior of such wholes are context-dependent and cannot simply be reduced to the summative properties of their parts. To the extent that philosophers of chemistry have called for the development of an alternative mereology for quantum chemical systems, this paper ends by proposing behavioral mereology as a promising step in that direction. According to behavioral mereology, considerations of what constitutes a part of a whole is dependent upon the observable behavior displayed by these entities. Thus, relationality and context-dependence are stipulated from the outset and this makes behavioral mereology particularly well-suited as a mereology of quantum chemical wholes. The question of which mereology is appropriate for formalizing the whole-parts relation in quantum chemical systems is relevant to contemporary philosophy of chemistry, since this issue is related to the more general question of the reducibility of chemical wholes to their parts and of the reducibility of chemistry to physics, which have been of central importance within the philosophy of chemistry for several decades. More generally, this paper puts contemporary discussions of mereology within the philosophy of chemistry into a broader historical and philosophical context. In doing so, this paper also bridges the gap between formal mereology, conceived as a branch of formal ontology, and ‘applied’ mereology, conceived as a branch of philosophy of science.

The managers’ strategic behavior of commercial real estate in a competitive environment is discussed. The means of this research are methods of mathematical and experimental game theory. Illustrates an example of a decision-theoretic game problem with two players engaged in the management of competing shopping centers. The strategies of these players are determined by the costs of the development. Since the number of visitors is constantly and changing the quality of shopping centers, managers actually pull visitors from each other, the game-theoretical formulation of the problem of management of shopping centers corresponds to the zero-sum game. The model example shows that with the help of mathematical modeling it is possible to determine the theoretical expected behavior of agents. The model of the software module created specifically for experimental research is described. The experiments are designed to compare the theoretically predicted behavior with the real actions of people. The novelty is that at significant costs of the Manager for the development and promotion of shopping and entertainment centers, the strategies of managers are resistant to each other's actions, in contrast to changes in the external environment is shown.

The accurate prediction of the solar Diffuse Fraction (DF), sometimes called the Diffuse Ratio, is an important topic for solar energy research. In the present study, the current state of Diffuse Irradiance research is discussed and then three robust, Machine Learning (ML) models, are examined using a large dataset (almost 8 years) of hourly readings from Almeria, Spain. The ML models used herein, are a hybrid Adaptive Network-based Fuzzy Inference System (ANFIS), a single Multi-Layer Perceptron (MLP) and a hybrid Multi-Layer Perceptron-Grey Wolf Optimizer (MLP-GWO). These models were evaluated for their predictive precision, using various Solar and Diffuse Fraction (DF) irradiance data, from Spain. The results were then evaluated using two frequently used evaluation criteria, the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE). The results showed that the MLP-GWO model, followed by the ANFIS model, provided a higher performance, in both the training and the testing procedures.

Climate change impacts and adaptations is subject to ongoing issues that attract the attention of many researchers. Insight into the wind power potential in an area and its probable variation due to climate change impacts can provide useful information for energy policymakers and strategists for sustainable development and management of the energy. In this study, spatial variation of wind power density at the turbine hub-height and its variability under future climatic scenarios are taken under consideration. An ANFIS based post-processing technique was employed to match the power outputs of the regional climate model with those obtained from the reference data. The near-surface wind data obtained from a regional climate model are employed to investigate climate change impacts on the wind power resources in the Caspian Sea. Subsequent to converting near-surface wind speed to turbine hub-height speed and computation of wind power density, the results have been investigated to reveal mean annual power, seasonal, and monthly variability for a 20-year period in the present (1981-2000) and in the future (2081-2100). The results of this study revealed that climate change does not affect the wind climate over the study area, remarkably. However, a small decrease was projected for future simulation revealing a slightly decrease in mean annual wind power in the future compared to historical simulations. Moreover, the results demonstrated strong variation in wind power in terms of temporal and spatial distribution when winter and summer have the highest values of power. The findings of this study indicated that the middle and northern parts of the Caspian Sea are placed with the highest values of wind power. However, the results of the post-processing technique using adaptive neuro-fuzzy inference system (ANFIS) model showed that the real potential of the wind power in the area is lower than those of projected from the regional climate model.

This paper presents a method to detect and classify the high impedance fault that occur in the medium voltage distribution network using discrete wavelet transform (DWT) and adaptive neuro-fuzzy inference system (ANFIS). The network is designed using Matlab software and various faults such as high impedance, symmetrical and unsymmetrical fault have been applied to study the effectiveness of the proposed ANFIS classifier method. This is achieved by training the ANFIS classifier using the features (standard deviation values) extracted from the three phase fault current signal by DWT technique for various cases of fault with different values of fault resistance in the system. The success and discrimination rate obtained for identifying and classifying the high impedance fault from the proffered method is 100% whereas the values are 66.7% and 85% respectively for conventional fuzzy based approach. The results indicate that the proposed method is more efficient to identify and discriminate the high impedance fault accurately from other power system faults in the system.

The recent availability of commercial platforms for behavioral analysis in zebrafish larvae based on video-tracking technologies has exponentially increased the number of studies analyzing different behaviors in this model organism to assess neurotoxicity. Among the most commonly used assays in zebrafish larvae are basal locomotor activity (BLA) and visual motor response (VMR). However, the effect of different intrinsic and extrinsic factors that can significantly alter the outcome of these assays is still not well understood. In this work we have analyzed in 143 larvae the influence of age (5-8 days post-fertilization), time of day (8:00, 10:00, 12:00, 14:00; 16:00, 18:00, and 20:00 h), and experiment (three experiments performed at different days) on BLA and VMR results (4,004 analyses for each behavior). Results from both behaviors were adjusted to a Random-Effects linear regression model using generalized least squares (GLS), including in the model the effect of the three variables, the second-way interactions between them and the three-way interaction. The results presented in this manuscript show a specific effect of all three intrinsic factors and their interactions on both behaviors, supporting the view that the most stable time period for performing these behavioral assays is from 10:00 am to 04:00 pm, with some differences depending on age of the larva and the behavioral test.

AbstractThe emergence of AI-powered deepfakes, manipulatively realistic synthetic media, raises unprecedented challenges for human information processing and decision-making. This paper explores the cognitive and emotional consequences of exposure to deepfakes, delving into how these digitally fabricated experiences can reshape perception, trust, and social interactions. By reviewing existing research and outlining potential methodological approaches, this paper aims to establish a framework for investigating the multifaceted impact of deepfakes on the human brain.BackgroundThe digital age has ushered in an era of unprecedented information abundance and accessibility. Yet, this abundance breeds vulnerability, particularly with the emergence of AI-powered deepfakes. These hyper-realistic synthetic media creations present a potent threat to the fundamental trust and integrity of information sources. Deepfakes have infiltrated various domains, from entertainment and social media to politics and journalism, blurring the lines between truth and fabrication. Consequently, understanding the cognitive and emotional consequences of encountering deepfakes becomes crucial for navigating this increasingly deceptive landscape.ObjectivesThis research aims to: Investigate how exposure to deepfakes impacts various cognitive functions, including perception, attention, memory, and decision-making. Elucidate the emotional responses elicited by deepfakes and their underlying neural mechanisms. Evaluate the role of individual differences, such as susceptibility to deception and emotional sensitivity, in mediating the impact of deepfakes. Develop a comprehensive framework for understanding the multifaceted influence of deepfakes on the human brain. Results (anticipated)This research is expected to yield insights into: The specific cognitive and emotional processes altered by deepfakes, providing evidence for potential mechanisms of deception and manipulation. The neural circuitry involved in detecting and responding to deepfakes, leading to the development of more effective detection methods. The influence of individual differences (e.g., age, cognitive style) on susceptibility to deepfakes, informing targeted interventions and educational efforts.ConclusionBy deciphering the impact of deepfakes on the human brain, this research will offer valuable tools for mitigating the harmful consequences of misinformation and promoting responsible use of this powerful technology. Ultimately, understanding how deepfakes manipulate our cognitive and emotional landscape is not merely a scientific pursuit, but a critical step towards ensuring a future where truth prevails in the digital world.

This study examined the effect of the media usage environment, particularly TV and SNS devices, on advertisement acceptance, lifestyle, and psychological satisfaction. Hypothesis pathways and comparative structural mediating effects were analyzed. Media and Consumer Research public data were used for empirical analysis. A total of 6,640 people who participated in sports activities were included in the study. The results demonstrated that TV and SNS device usage environments had similar effects on advertisement acceptance. While TV did not influence the pursuit of personal life, SNS device use environments affected the pursuit of personal life. SNS device use environments had a greater effect on individual psychological satisfaction than TV. Adoption had a mediating effect on the influence of media usage environments on the pursuit of personal life. The pursuit of a personal life had a significant mediating effect on the influence of media use environments on psychological stability. This study revealed no differences in the effects of new and traditional media on advertisement acceptance, while new media had a greater influence on lifestyle and psychological satisfaction.

Keywords: Interspecific competition; Character displacements, Behavioral syndrome; Foraging; Pecking; Probing; Sweeping; Ballistic transport, Surface tension transport; Lingual transport

Artificial intelligence (AI) applications in different fields are developing rapidly, among which AI painting technology, as an emerging technology, has received wide attention from users for its creativity and efficiency. This study aimed to investigate the factors that influence user acceptance of the use of AIBPS by proposing an extended model that combines the Extended Technology Acceptance Model (ETAM) with the AI-based Painting System (AIBPS).A questionnaire was administered to 528 Chinese participants, using validated factor analysis data and Structural Equation Modeling (SEM) was used to test the hypotheses. The findings showed that hedonic motivation (HM) and perceived trust (PE) had a positive effect (+) on users' perceived usefulness (PU) and perceived ease of use (PEOU), while previous experience (PE) and technical features (TF) had no effect (-) on users' perceived usefulness (PU). This study provides an important contribution to the literature on AIBPS and the evaluation of systems of the same type, which helps to promote the sustainable development of AI in different domains and provides a possible space for further extension of TAM, thus helping to improve the user experience of AIBPS. The results of the study provide insights for system developers and enterprises to better motivate users to use AIBPS.

As one of the world's most populous countries, Indonesia is struggling with the composition of its people, the majority of whom are unemployed productive-age individuals with low education levels. Another issue is the high poverty rate, which is followed by issues of gender inequality. The government cannot solve these social issues on its own. There is a need for community support to solve these problems through sustainable social entrepreneurship activities. Using the Theory of Planned Behavior and Outcome Expectation, this study will determine the factors that influence the interest of individuals known as the Z and Millennial generation, aged 18 to 34 years old, in becoming social entrepreneurs. This study applied a quantitative approach with a judgmental sampling technique to a sample of Generation Z and Millennials who have ever participated in social activities but have not yet started their own business. The study gathered 210 respondents, who were then analyzed using the PLS-SEM method with multiple group analysis generation-based comparison. According to the findings, the Millennial Generation's Attitude Toward Behavior, Subjective Norms, and Outcome Expectation all had a positive impact on Social Entrepreneurial Intention. Meanwhile, Subjective Norms had no effect on Social Entrepreneurial Intention in the Z Generation. Only attitude toward behavior, perceived behavioral control, and outcome expectation have a positive effect on the development of social entrepreneurial intentions. Furthermore, the findings of this study confirm that subjective norms influence attitude toward behavior and Perceived Behavioral Control in both Generation Z and millennials.

ETS exposure has been shown as an important environmental risk factor in vulnerable population groups. The aim of the project is to analyze the relationships among the ETS and behavior and health in 6−15-year-old children in Slovakia. The status of physical and mental health of children in relation to exposure to tobacco smoke was examined in the representative group of 1478 school children. The methods used, included anonymous questionnaires filled in by parents, Columbia Impairment Scale (CIS), Behavior Problem Index (BPI) and anthropometry. The prevalence of passive smoking is the highest in the capital (27%) and southern cities. Significant relationships have been confirmed between ETS and the age, socio-economic status, the incompleteness of the family, the level of mother's education and the significantly higher prevalence of respiratory diseases (26.7%).The relationships of ETS with emotional (CIS scores ≥16) and behavioral functions (BPI score ≥14) were significant in children exposed to mother‘s and father‘s smoking at home. In the multivariate analysis this association was not significant; the factors such as income and completeness of the family were dominant. The results confirmed the impact of ETS and social factors on health state and health behavior and could be the argumentation for legislative changes.

Background: Glioblastoma is the most common primary brain neoplasm in adults, with still a poor prognosis despite a constant effort to improve patients’ survival. Some neuroradiological volumetric parameters seem to play a predictive role on Overall Survival (OS) and Progression Free Survival (PFS). The aim of this study is to analyze the impact that the volumetric areas of contrast-enhancing tumor and perineoplastic edema have on survival of patients treated for glioblastoma; Methods: A series of 87 patients who underwent surgery was retrospectively analyzed; OS and PFS were considered as the end points of the study. For each patient a multidisciplinary revision was conducted in collaboration with the Neuroradiology and Neuro-Oncology board. A manual and semi-automatic measurement were adopted to perform the radiological evaluation: contrast Enhancement Preoperative (CE-PTV) and Postoperative Tumor Volume (CE-RTV), Edema/Infiltration Preoperative (T2/FLAIR-PV) and Postoperative Volume (T2/FLAIR-RV); necrosis volume inside the tumor (NV); total tumor volume, including necrosis (TV); Results: The median OS value was 9 months and the median PFS value was 4 m; the mean values were respectively 12,3 m and 6,9 m. Multivariate analysis showed that the OS related factors were: adjuvant chemo-radiotherapy (p &lt; 0,0001), CE-PTV &lt; 15 cm³ (p=0,03), surgical resection &gt; 95% (p=0,004) and the presence of a “pseudo-capsulated” radiological morphology (p=0,04); Conclusions: maximal safe resection is one of the most relevant predictive factors for patients’ survival. The semi-automatic pre-operative MRI evaluation could play a key role in prognostically categorizing these tumors.

Virtual reality (VR) describes a family of technologies which immerse users in sensorily-stimulating virtual environments. Such technologies have increasingly found applications in the treatment of neurological and mental health disorders. Depression, anxiety, and other mood abnormalities are of concern in the growing elderly population – especially those who reside in long-term care facilities (LTCFs). The transition from the familiar home environment to the foreign LTCF introduces a number of stressors that can precipitate depression. However, recent studies reveal that VR therapy (VRT) can promote positive emotionality and improve cognitive abilities in the elderly, both at home and in LTCFs. VR thus holds potential in allowing elderly individuals to gradually adapt to their new environments – thereby mitigating the detrimental effects of place attachment and social exclusion. Nevertheless, while the current psychological literature is promising, the implementation of VR in LTCFs faces many challenges. LTCF residents must gain trust in VR technologies, care providers require training to maximize the positive effects of VRT, and decision makers must evaluate both the opportunities and obstacles in adopting VR. Here, we concisely review the implications of depression related to place attachment in LTCFs, and explore the potential therapeutic applications of VR.

In this paper, we look at how Artificial Swarm Intelligence can evolve using evolutionary algorithms that try to minimize the sensory surprise of the system. We will show how to apply the free-energy principle, borrowed from statistical physics, to quantitatively describe the optimization method (sensory surprise minimization), which can be used to support lifelong learning. We provide our ideas about how to combine this optimization method with evolutionary algorithms in order to boost the development of specialized Artificial Neural Networks, which define the proprioceptive configuration of particular robotic units that are part of a swarm. We consider how optimization of the free-energy can promote the homeostasis of the swarm system, i.e. ensures that the system remains within its sensory boundaries throughout its active lifetime. We will show how complex distributed cognitive systems can be build in the form of hierarchical modular system, which consists of specialized micro-intelligent agents connected through information channels. We will also consider the co-evolution of various robotic swarm units, which can result in development of proprioception and a comprehensive awareness of the properties of the environment. And finally, we will give a brief outline of how this system can be implemented in practice and of our progress in this area.

This study evaluates audiogenic epilepsy in Krushinsky-Molodkina rats using a wireless EEG system to overcome the limitations of traditional methods that restrict animal movement. Nine male KM rats, prone to audiogenic seizures, were implanted with electrodes in areas critical for seizure activity (inferior colliculus, ventrolateral periaqueductal gray, dorsal area of the secondary auditory cortex, and motor cortex). This setup allowed for the observation of seizures without movement restriction. Results showed that targeted neural intervention via electrode implantation reduced the intensity of convulsive seizures in half of the subjects, suggesting a potential therapeutic approach. The severity of seizures was influenced by the pre-phonostimulation EEG background activity in specific brain regions, identifying them as key in the genesis and generalization of seizures. Observations of dual generalized seizure waves and induced hyperkinesis challenge conventional epilepsy models and suggest new directions for research and therapy development.

Opioid use disorder has increased in the world in recent years and Iran ranks first in terms of opium smoking in the world. In fact, opium smoking by traditional methods, in addition to dependence on morphine, is a behavioral addiction in Iran. The body position during opium smoking is usually non-standard and non-ergonomic. Over time, changes in muscle tissue, tendons and ligaments, and the connective tissue go from elasticity to neuromuscular plasticity. This study aims to compare sway back posture and musculoskeletal problems between opium smokers and non-drug users. In this comparative and cross-sectional study, 80 opium smokers were compared with 74 non-drug users in terms of sway angle (sway back posture), and musculoskeletal complaints (NORDIC questionnaire) and the association of possible risk factors was investigated. Data were analyzed by Kolmogorov-Smirnov, Correlation Coefficient and paired-t tests using SPSS version 23. There were significant differences between two groups in terms of sway back posture (P=0.007), and neck (<0.001), upper back (<0.001) and lower back (0.006) musculoskeletal complaints. Homelessness, the lifetime duration of opium smoking (in months), and the duration of daily opium smoking (in minutes) had significant correlation with sway back posture and musculoskeletal complaints. Homelessness was the strongest predictor and had strongest correlation. Overall, an increase in the duration of sitting in non-ergonomic positions could lead to increase in sway angle and sway back posture and increase in musculoskeletal complaints of neck, upper back and lower back due to the non-neutral posture of opium smoking. It seems that plasticity and structural and functional adaptations of the posture occurs due to opium smoking and further studies are recommended.

This study investigates the specific factors affecting the usage intention of distributed ledger technology (DLT), such as availability, diversity and economic value from the perspective of a unified theory of technology acceptance. Users of DLT in public and private sectors were surveyed. Availability and economic value affect performance expectancy, while availability, diversity, and profitability have an influence on effort expectancy. Performance expectancy and transparency have a positive effect on the intention to use DLT which in turn exerts a positive effect on usage behavior. This study provides implications for researchers in that it attempts to investigate the factors affecting the usage intention of DLT based on the extended unified theory of acceptance and encompassing diverse industries that adopt DLT, such as the public, financial, medial, and logistics sectors.

Non-pharmacologic options like the ketogenic diet (KD) and intermittent fasting (IF) are practical nutritional interventions with minor reported side effects like gastrointestinal symptoms, dyslipidemia, and hypomagnesemia for various medical ailments. In conjunction with IF, KD shows promise in weight loss, diabetes management, cardiovascular disorders, polycystic ovarian syndrome, cancer, and chronic neurological disorders. Based on prior research, we have examined the mechanism of action of KD and IF and their effect on neurological diseases, cancer, and obesity. We have also suggested evidence-based recommendations for the safer practice of KD and IF. Despite potential benefits, long-term adherence to KD poses challenges. Periodic KD implementation may thus benefit newly diagnosed overweight or obese patients with type 2 diabetes mellitus, aiding blood glucose and lipid management while promoting weight loss. KD is a high-fat and low-carbohydrate diet with a ratio of fat to carbohydrates and protein being 4:1 or 3:1, and thus, for peripheral tissues and the brain, fatty acids become the mandatory source of cellular energy. Ketone bodies have been used as the primary energy source during fasting. KD has been utilized as an effective treatment for refractory epilepsy since the 1920s. Evidence of the neuroprotective role of KD in diseases like epilepsy, stroke, traumatic brain injury, Alzheimer’s disease, and other neurological diseases has been noted. Since the 1960s, KD has become a popular method for obesity treatment. In addition, KD has been suggested as a potent anticancer therapy when used alone or as an adjuvant. KD may increase tumor cell sensitivity when combined with classic chemotherapy and radiotherapy. Thus, the probability that modifying the diet can help manage obesity, cancer, and chronic neurological disease without depending on pharmacological treatment and their serious side effects for a lifetime is promising and requires further investigation. KD holds promise as a potential adjunctive therapy in various neurological disorders, offering new avenues for treatment and neuroprotection. IF has shown potential in slowing the progression of neurodegenerative diseases such as Alzheimer's and Parkinson's by promoting antioxidant defense and suppressing inflammation. KD and IF show promise in cancer therapy by targeting altered cancer cell metabolism. Additionally, KD may enhance the effects of standard treatments like chemotherapy and radiotherapy. Some of the most robust reports of keto's possible benefits have come from glioblastoma, a very aggressive brain cancer. KD has also shown strong evidence for its effectiveness in weight loss, mainly attributed to its appetite-suppressing action in ketosis. However, long-term adherence to KD can be challenging, and periodic KDs may help manage blood glucose and lipid levels in overweight or obese patients with type 2 diabetes mellitus. Likewise, IF may be more effective than regular calorie restriction for achieving weight loss goals when combined with exercise programs. More prospective human studies are warranted to evaluate both KD and IF's potential therapeutic effectiveness and safety.

Results from a behavioral economic laboratory experiment are used to enhance our understanding of public health decisions made during the COVID-19 pandemic. The identification of systematic biases from optimal decision theory found in controlled experiments could help inform public policy design for future public health crises. The laboratory and the shelter-in-place decisions made during COVID-19 included elements of risk, uncertainty and ambiguity. The lab findings found individuals adopt different decision rules depending on both personal attributes and on the context and environment in which the decision task is conducted. Key observations to consider in the context of the COVID-19 decision environment include the importance of past experience, the ability to understand and calculate the odds of each action, the size and differences in economic payoffs given the choice, the value of information received, and how past statistical independent outcomes influence future decisions. The academic space encompassing both public health and behavioral economics is small, yet important, particularly in the current crisis. The objective of continued research in this area would be to develop a more representative model of decision-making processes, particularly during crisis, that would serve to enhance future public health policy design.

Neurodevelopmental disorders are characterized by an amalgam of genetic and epigenetic faults implicating neuronal protein-related loss-of-function and gain-of-function respectively, hence their inclusion into the category of complex diseases. Neurodevelopmental disorders are regarded as autism spectrum disorder due to this reason; that the risk factors and phenotype constitute a spectrum of faults and protein overactivity. One example of an epigenetic factor leading to the onset of neurodevelopmental delays constitutes a set of immune response-based dysregulations, as well as the excessive growth of the adaptive immune memory of the fetus or the baby following significant infectious disease during pregnancy, as well as in babies aged 0-2, given the existence of profound links between developmental immunity and developmental neurology. Given that genetic information very likely represents a form of energy, the intake of excessive pathogen-related information may transiently overload the adaptive immune memory similarly to the manner functional pathogens do whilst inducing significant illness, and if events as such occur during important stages of neuro-immunological development, then there could often be irreversible effects upon the rate of development in neurological networks that normally have a major importance in the modulation of the related cognitive and behavioral phenotype. The full extent of evidence may only be obtained via a thorough study of the applications of physical laws into biology and medicine, given the foundational role of the physical matter in the development and maintenance of life. A common misconception is that neurodevelopmental delays are characterized solely by an accumulation of protein loss-of-function incidences in the central nervous system area, rather than a disruption of neuronal region developmental rates. For example, an overexpression of neuronal proteins related to cognition factors that is accompanied by an underexpression of proteins related to social behavior is phenotypically manifested in people with sharper rational thinking and poorer communication skills, with an impact on the abilities of decision-making as well, and this tends to be regarded as a classical example of the clinical manifestation of neurodevelopmental delays. More severe forms of autism spectrum disorder tend to implicate a more generalistic impact upon the developmental rates of key human behaviors, including those related with the development and maintenance of social and intimate relationships. Likewise, more severe implications upon regular developmental rates tend to bring a major impact upon the healthy function of related functions. As a result, lower functioning forms of neurodevelopmental delays bring fresh concerns with regards to a possible uncanny increase of incidences of deviated sexuality during years of adulthood, and in the worst cases, such deviations may lead to the planning and development of criminal activity, particularly in individuals who are or become prone to breaking the moral code that established the civilized society. What holds the concerns valid is the exponential increase in the number of lower functioning autism cases throughout the world and the major signs that such an increase will continue with a full force. One major solution to this potential future problem would be a much higher investment into awareness of the exact genetic and environmental factors that directly or indirectly result in the onset of lower-functioning autism and of the current research efforts to develop the matching support for people found in situations as such. The most important aim of future research with regards to such a scenario would be a separation of disease and disability with choices made to step into more severe forms of disease and into areas that are outside of the established moral circle.

This article investigated the Automatic Generation Control(AGC) of multi-area multi-source interconnected systems with hydropower plants, thermal power plants, and wind energy. Adaptive Neuro-fuzzy controller integrated with the cascaded proportional-integral-derivative with filter (PIDF-PIDF) is a new cascaded controller (ANF-PIDF-PIDF) that has been presented as a secondary controller for applied hybrid power systems. The recent Skill Optimization Algorithm (SOA) is employed to optimize PIDF- PIDF controller parameter gains and the Adaptive Neuro-Fuzzy controller's inputs and output scaling factors. SOA is used to update the controller parameters with integral square error (ISE) employed as the objective function. A 1% step load disturbance was considered simultaneously in all three areas. The controller's performance is evaluated and compared with and without considering the effects of wind energy sources and non-linearity for ANF-PIDF-PIDF, PIDF-PIDF, and PIDF and it was determined that the ANF-PIDF-PIDF was the most efficient. The dynamic system performance is also compared with parallel high voltage direct current (HVDC) tie-lines. The investigation clearly shows that incorporating HVDC tie-line with multi-area, multi-source provides better dynamic performance in maximum amplitude, oscillation, and settling time. Additionally, sensitivity analysis is done and the optimum controller gains does not need to be reset to uncertain values in system loading conditions. All simulation results were evaluated using MATLAB 2016b.

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.

Since its emergence, COVID-19 has caused a great impact in health and social terms. Governments and health authorities have attempted to minimize this impact by enforcing different mandates. Recent studies have addressed the relationship between various socioeconomic variables and compliance level to these interventions. However, little attention has been paid to what constitutes people's response and whether people behave differently when faced with different interventions. Data collected from different sources show very significant regional differences across the United States. In this paper, we attempted to shed light on the fact that a response may be different depending on the health system capacity and each individuals’ social status. For that, we analyzed the correlation between different societal variables (i.e. education, income levels, population density, etc.) along with healthcare capacity related variables (i.e. hospital occupancy rates, percentage of essential workers, etc.) with regards to people's level of compliance with three main governmental mandates in the United States: mobility restrictions, mask adoption, and vaccine participation. Our aim was to isolate the most influential variables impacting behavior in response to these policies. We found that there was a strong relationship between individuals' educational levels and political preferences with respect to compliance with each of these mandates.

This article focuses on developing a continuous authentication system using behavioral biometrics to recognize users accessing computing devices. The user’s distinct behavioral biometric is captured through keystroke dynamics, and reward-based reinforcement learning (RL) ideas are applied to recognize them throughout the session. The suggested system adds an extra layer of security to traditional authentication methods, forming a robust continuous authentication system that can be added to static authentication systems. The methodology involves training a RL model to detect unusual user typing patterns and flag suspicious activity. Each user has an agent trained on their historical data, which is preprocessed and used to create episodes for the agent to learn from. The environment involves fetching observations and randomly corrupting them to learn out-of-order behavior. The observation vector includes both running features and summary features. The re-ward function is binary and minimalistic. The Principal Component Analysis (PCA) model is used to encode the running features, and the Double Deep Q-Network (DDQN) algorithm with a fully connected neural network is used as the policy net. The evaluation achieved an average training accuracy and EER (equal error rate) of 94.7% and 0.0126 and test accuracy and ERR of 81.06% and 0.0323 for all users when the number of encoder features was increased. Therefore, it is concluded that by continuously learning and adapting to changing behavior patterns, this approach can provide more secure and personalized authentication, lowering the possibility of unauthorized access and cyberattacks. Overall, the use of reinforcement learning and behavioral biometrics for continuous authentication has the potential to significantly enhance security in the digital age and are effective in identifying each user.

Spinal Cord (SC) Anatomy is often assimilated to a morphologically encapsulated neural entity, but its functional anatomy remains only partially understood. We hypothesized that it could be possible to reexplore SC neural networks by performing live electrostimulation mapping, based on “super-selective” Spinal Cord Stimulation (SCS), originally designed as a therapeutical tool to address chronic refractory pain. As a starting point, we initiated a systematic SCS lead programming approach using live electrostimulation mapping on a chronic refractory perineal pain patient, previously implanted with multicolumn SCS at the level of the conus medullaris (T12-L1). It appeared possible to (re)explore the classical anatomy of the conus medullaris using statistical correlations of paresthesia coverage mappings, resulting from 165 different electrical configurations tested. We highlighted that sacral dermatomes were not only located more medially but also deeper than lumbar dermatomes at the level of the conus medullaris, in contrast with classical anatomical descriptions of SC somatotopical organization. As we were finally able to find a morpho-functional description of ‘Philippe-Gombault’s triangle’, in 19th century historical textbooks of neuroanatomy, matching remarkably with these conclusions, the concept of “Neuro-Fiber-Mapping” was introduced.

Accurate prediction of mercury content emitted from fossil-fueled power stations is of utmost importance for environmental pollution assessment and hazard mitigation. In this paper, mercury content in the output gas of power stations’ boilers was predicted using an adaptive neuro-fuzzy inference system (ANFIS) method integrated with particle swarm optimization (PSO). The input parameters of the model include coal characteristics and the operational parameters of the boilers. The dataset has been collected from 82 power plants and employed to educate and examine the proposed model. To evaluate the performance of the proposed hybrid model of ANFIS-PSO model, the statistical meter of MARE% was implemented, which resulted in 0.003266 and 0.013272 for training and testing, respectively. Furthermore, relative errors between acquired data and predicted values were between -0.25% and 0.1%, which confirm the accuracy of the model to deal nonlinearity and representing the dependency of flue gas mercury content into the specifications of coal and the boiler type.

Solar energy is a renewable resource of energy that is broadly utilized and has the least emissions among the renewable energies. In this study, machine learning methods of artificial neural networks (ANNs), least squares support vector machines (LSSVM), and neuro-fuzzy are used for advancing prediction models for thermal performance of a photovoltaic-thermal solar collector (PV/T). In the proposed models, the inlet temperature, flow rate, heat, solar radiation, and the sun heat have been considered as the inputs variables. Data set has been extracted through experimental measurements from a novel solar collector system. Different analyses are performed to examine the credibility of the introduced approaches and evaluate their performance. The proposed LSSVM model outperformed ANFIS and ANNs models. LSSVM model is reported suitable when the laboratory measurements are costly and time-consuming, or achieving such values requires sophisticated interpretations.

The assessment of welfare and disease progression in animal models is critical. Most tools rely on evaluating individual subjects, whereas social behaviors, also sensitive to acute illness, chronic diseases, or mental health, are scarcely monitored because of their complexity, are invasive, and time-consuming. We propose the evaluation of social nesting, a species-typical behavior naturally occurring in standard housing conditions, for such behavioral monitoring. We provide an example of its use to evaluate social deficits and the long-term effects of neonatal sensorial stimulation in male and female adult 3xTg-AD mice for Alzheimer's disease compared to sex- and age-matched NTg counterparts with normal aging. Social nesting was sensitive to genotype (worse in 3xTg-AD mice), sex (worse in males), profile, and treatment (distinct temporal patterns, time to observe the maximum score and incidence of the perfect nest). Since social nesting can be easily included in housing routines, this neuroethological approach can be useful for animal's welfare, monitoring the disease's progress, and evaluating potential risk factors and effects of preventive/therapeutical strategies. Finally, the non-invasive, painless, simple, short time and low-cost features of this home-cage monitoring are advantages that make social nesting feasible to be successfully implemented in most animal department settings.

Loyalty is one of the greatest intangible assets that any organization can possess and improving client loyalty is a primary marketing goal that can have a significant financial impact on any business. This quantitative study examined the mediating role of communication on the relationship between satisfaction and loyalty (attitudinal and behavioral) in veterinary clinics, along with the moderating roles of trust, commitment, perceived value, and relational characteristics. Responses collected from 351 pet-owners through social media were analyzed using descriptive and inferential statistics. The results show that attitudinal loyalty (AL) has a strong positive relationship with communication at multiple points in a veterinary clinic whereas the relationship with behavioral loyalty was not as clear. Additional findings suggest that AL, which is influenced by trust in the veterinarian, communication from staff members and commitment, has a strong positive relationship with behavioral intentions, increases the number of products and services that a pet-owner consumes at his or her primary veterinary clinic, and attenuates the role of cost in receiving veterinary care. These findings can help veterinary clinic owners and managers in developing and implementing relationship strategies that improve pet-owner loyalty. The article that follows is a synopsis of the author’s dissertation.

Predicting change from multivariate time series has relevant applications ranging from medical to engineering fields. Multisensory stimulation therapy in patients with dementia aims to change the patient’s behavioral state. For example, patients who exhibit a baseline of agitation may be paced to change their behavioral state to relaxed. This study aims to predict changes in behavioral state from the analysis of the physiological and neurovegetative parameters to support the therapist during the stimulation session. In order to extract valuable indicators for predicting changes, both handcrafted and learned features were evaluated and compared. The handcrafted features were defined starting from the CATCH22 feature collection, while the learned ones were extracted using a Temporal Convolutional Network, and the behavioral state was predicted through Bidirectional Long Short-Term Memory Auto-Encoder, operating jointly. From the comparison with the state-of-the-art, the learned features-based approach exhibits superior performance with accuracy rates of up to 99.42% with a time window of 70 seconds and up to 98.44% with a time window of 10 seconds.

The hybridization of conventional thermal power plants by incorporation of renewable energy systems has witnessed widespread adoption in recent years. This trend aims not only to mitigate carbon emissions but also to enhance the overall efficiency and performance of these power generation facilities. However, calculating the performance of such intricate systems using fundamental thermodynamic equations proves to be both laborious and time-intensive. Nevertheless, possessing accurate and real-time insights into their performance is of utmost significance to ensure optimal plant operation, facilitate decision-making, and streamline power production planning. In this paper, we delve into the application of machine learning techniques, particularly hybrid methodologies, to predict the performance of power plants. Specifically, we employ three approaches: the Adaptive Neuro-Fuzzy Inference System (ANFIS), the ANFIS optimized via Particle Swarm Optimization (ANFIS-PSO), and the ANFIS optimized through Genetic Algorithm (ANFIS-GA). These techniques are alternatively applied to a complex hybrid thermal power plant, namely an Integrated Solar Combined Cycle Power Plant (ISCCPP). This ISCCPP comprises a steam turbine section, a gas turbine section, and a concentrated solar power system utilizing troughs as collectors. The energy transfer fluid, in conjunction with exhaust from the gas turbine section, heats water within a vessel or steam generator, producing steam to propel the steam turbine's rotor. Electricity generation is facilitated by both gas and steam turbines, which are coupled to a generator. The findings of this study underscore the accuracy and appropriateness of ANFIS-based models in evaluating and predicting the performance of intricate hybrid power plants. The ANFIS model exhibited an impressive overall coefficient of correlation of 0.9991. Notably, the integration of evolutionary algorithms (PSO and GA) into the ANFIS framework elevated model performance, yielding correlation coefficients of 0.9994 for ANFIS-PSO and 0.9997 for ANFIS-GA. It is noteworthy that ANFIS-GA demonstrated superior predictive capability. The modeling framework developed in this research offers valuable support to designers, energy managers, and decision-makers. It provides a robust and dependable tool to assess the performance of hybrid thermal power plants, which are poised for a global surge in numbers amid the ongoing energy transition.

The combination of artificial intelligence algorithms and numerical methods has recently become popular in the prediction of macroscopic and microscopic hydrodynamics parameters of bubble column reactors. The multi inputs and outputs machine learning can cover small phase interactions or large fluid behavior in industrial domains. This numerical combination can develop the smart multiphase bubble column reactor with the ability of low-cost computational time. It can also decrease case studies for the optimization process when big data is appropriately used during learning. There are still many model parameters that need to be optimized for a very accurate artificial algorithm, including data processing and initialization, the combination of inputs and outputs, number of inputs and model tuning parameters. For this study, we aim to train four inputs big data during learning process by an adaptive neuro-fuzzy inference system or adaptive-network-based fuzzy inference system (ANFIS) method, and we consider the superficial gas velocity as one of the input variables, while for the first time, one of the computational fluid dynamics (CFD) outputs named gas velocity is used as an output of the artificial algorithm. The results show that the increasing number of input variables improves the intelligence of the ANFIS method up to , and the number of rules during learning process has a significant effect on the accuracy of this type of modeling. The results also show that propper selection of model parameters results in more accuracy in prediction of the flow characteristics in the column structure.

Background: COVID-19 pandemic has pulled us all a few steps back, were we never shake hands or hug each other when we meet our friends and family after a gap, but instead we greet them by saying Namaste and joining our hands together. As we all know, COVID-19 spreads through air and the only way to shield ourselves is by maintaining a safe distance from one another. Methodology: In order to conduct a meta-analysis on the number of COVID-19 cases in Kerala and India, the data was retrieved from various sites hosted by the government bodies. The data for analysis was collected from May 2020 to July 2020. The average number of days required to reach every 5000 fresh cases were also calculated using this data. COVID-19 has affected all the economy holistically regardless of financial, behavioral, or societal aspects. Conclusion: Lifting of the lockdown in a step by step process keeping in mind the necessities for the nation was a thoughtful act, but the people who mistook this opportunity and did not remain in quarantine after coming from abroad was recognized as the reasons behind the sudden and uncontrolled rise in the number of COVID-19 cases in Kerala, India. The government authorities had no other option but to lift the restrictions to reduce the economic burdens that had already affected the daily wage worker and farmers prompting them to give up their lives.

In the present work, a novel and the robust computational investigation is carried out to estimate solubility of different acids in supercritical carbon dioxide. Four different algorithms such as radial basis function artificial neural network, Multi-layer Perceptron (MLP) artificial neural network (ANN), Least squares support vector machine (LSSVM) and adaptive neuro-fuzzy inference system (ANFIS) are developed to predict the solubility of different acids in carbon dioxide based on the temperature, pressure, hydrogen number, carbon number, molecular weight, and acid dissociation constant of acid. In the purpose of best evaluation of proposed models, different graphical and statistical analyses and also a novel sensitivity analysis are carried out. The present study proposed the great manners for best acid solubility estimation in supercritical carbon dioxide, which can be helpful for engineers and chemists to predict operational conditions in industries.

Solar energy is a renewable resources of energy which is broadly utilized and have the least pollution impact between the available alternatives of fossil fuels. In this investigation, machine leaening approaches of neural networks (NN), neuro-fuzzy and least squares support vector machine (LSSVM) are used to build the models for prediction of the thermal performance of a photovoltaic-thermal solar collector (PV/T) by estimating its efficiency as an output of the model while inlet temperature, flow rate, heat, solar radiation, and heat of sun are input of the designed model. Experimental measurements was prepared by designing a solar collector system and 100 data extracted. Different analyses are also performed to examine the credibility of the introduced approaches revealing great performance. The suggested LSSVM model represented the best performance regarding the mean squared error (MSE) of 0.003 and correlation coefficient (R²) value of 0.99, respectively.

Electricity demand prediction is vital for energy production management and proper exploitation of the present resources. Recently, several novel machine learning (ML) models have been employed for electricity demand prediction to estimate the future prospects of the energy requirements. The main objective of this study is to review the various ML models applied for electricity demand prediction. Through a novel search and taxonomy, the most relevant original research articles in the field are identified and further classified according to the ML modeling technique, perdition type, and the application area. A comprehensive review of the literature identifies the major ML models, their applications and a discussion on the evaluation of their performance. This paper further makes a discussion on the trend and the performance of the ML models. As the result, this research reports an outstanding rise in the accuracy, robustness, precision and the generalization ability of the prediction models using the hybrid and ensemble ML algorithms.

Neurodegeneration is a major problem in neuroscience and medicine as it causes and worsens many neurological and psychiatric disorders. It involves gradual loss of neurons at different levels of the brain, affecting cognition, emotion, behavior, consciousness, and attention. Understanding the causes and consequences of neurodegeneration is crucial for identifying the risk factors, biomarkers, and treatments. However, the current therapies are mostly symptomatic and ineffective. Therefore, new and innovative methods, such as noninvasive brain stimulation (NIBS), are needed to modulate brain activity and plasticity in a safe and reversible manner. This field is rapidly developing, with a focus on exploring new clinical applications, mechanisms, and combinations of NIBS and drugs. This special issue covers the latest progress and challenges in the study of neurodegeneration in cognitive impairment and mood disorders from the experimental, clinical, and translational perspectives. It addresses the complexity and diversity of these conditions, the lack of disease-modifying treatments, the ethical and practical issues of conducting research in vulnerable and diverse populations, the integration and interpretation of data from multiple sources and modalities, and the development and validation of novel methods such as NIBS, artificial intelligence, and drug repurposing. Collaborative efforts among various stakeholders are needed to improve our knowledge and treatment of neurodegeneration underlying cognitive impairment and mood disorders.