One of the important concerns in the field of exercise immunology is determining the appropriate intensity and duration of exercise to prevent suppression of the immune system. Adopting a reliable approach to predict the number of white blood cells (WBCs) during exercise can help to identify the appropriate intensity and duration. Therefore, this study was designed to predict leukocyte levels during exercise with the application of a machine-learning model. We used a random forest (RF) model to predict the number of lymphocytes (LYMPH), neutrophils (NEU), monocytes (MON), eosinophils, basophils, and WBC. Intensity and duration of exercise, WBCs values before exercise training, body mass index (BMI), and maximal aerobic capacity (VO2 max) were used as inputs and WBCs values after exercise training were assessed as outputs of the RF model. In this study, the data was collected from 200 eligible people and K-fold cross-validation was used to train and test the model. Finally, model efficiency was assessed using standard statistics (root mean square error (RMSE), mean absolute error (MAE), relative absolute error (RAE), root relative square error (RRSE), coefficient of determination (R²), and Nash–Sutcliffe efficiency coefficient (NSE)). Our findings revealed that the RF model performed well for predicting the number of WBC with RMSE = 0.94, MAE = 0.76, RAE = 48.54, RRSE = 48.17, NSE = 0.76, and R² = 0.77. Furthermore, the results showed that intensity and duration of exercise are more effective parameters than BMI and VO2 max to predict the number of LYMPH, NEU, MON, and WBC during exercise. Totally, this study developed a novel approach based on the RF model using the relevant and accessible variables to predict WBCs during exercise. The proposed method can be applied as a promising and cost-effective tool for determining the correct intensity and duration of exercise in healthy people according to the body’s immune system response.
The goal of this study is to develop a technology analysis for examining the evolutionary phases of some critical quantum technologies to explain on-going technological development. Method applies S-shaped model based on logistic function that is estimated with patent data to analyze the phases of quantum technologies over the course of their technological evolution. Findings reveal that the technological cycle of recent quantum technologies has a shorter period in emergence phase and a longer period in growth and maturity phases than older quantum technologies. In particular, structure of technological cycle also shows that for quantum technologies originated after 1980, technological phase of emergence (to reach to the point of growth) is reduced to 52% of the total length of the cycle, compared to 68% of technologies originated before 1980, whereas the growth and maturity phases for technologies originated after 1980 have a higher percentage weight on the total duration of the cycle than technologies originated before 1980: growth stage is 22.78% of total duration of cycle in new technologies originated after 1980 vs. 15.76% in older technologies originated before the 1980; maturity stage is 25.32% vs. 16.08%, respectively of total technological cycle. Results here can provide theoretical implications to explain dynamics and structure of the technological evolution of emerging quantum innovations that support the technological forecasting for improving decisions of R&D investments in specific technologies that can be major sources of next technological, industrial, economic and social change.
The objective of the current study was to investigate the grief experiences of people affected by COVID-19. The study adopted a qualitative design of descriptive phenomenology. Fifteen adults who had lost a family member during the COVID-19 pandemic were selected as the sample through the purposive sampling method until theoretical saturation was achieved. Data was collected using semi-structured interviews and the Colaizzi analysis method. Six main themes (i.e., unexpressed grief, psychosomatic reactions, negative emotions, family problems, and social and occupational problems) were extracted. Data analysis showed that complex disenfranchised grief is the pervasive consequence of the COVID-19 experience. According to the findings, participants experienced disenfranchised grief during the loss of their loved ones due to the COVID-19 disease, which was a complex, painful experience accompanied by negative emotions and family, work, and social tensions. This grief is accompanied by more severe and prolonged symptoms, making it difficult for the bereaved to return to normal life. In unexpressed grieving, there are intense feelings of grief, pain, separation, despair, emptiness, low self-esteem, bitterness, or longing for the presence of the deceased. This grief originated from the conditions of quarantine and physical distance on the one hand, which required the control of the outbreak of the COVID-19 disease, and on the other hand, the cultural-religious context of the Iranian people.
This study was aimed at devising and validating a questionnaire on EFL teachers’ perceptions of reflective practice. To this end, seven experts in the field of Applied Linguistics were invited to check the face and content validity of the questionnaire with a checklist. They were also asked to rate the questionnaire items on a 5- point Likert scale. Based on the experts’ viewpoints (removing 3 items and making some revisions on 8 items) and the review of the relevant literature on teacher reflective practice, 45 items were selected and maintained for the initial scale. To validate the instrument, entailing 45 items, exploratory and confirmatory factor analyses (EFA, CFA) were used. Based on the EFA results, 3 items were removed from the scale due to their ineffective loading on the factors and scree plot indicated that 5 factors (interpersonal, intrapersonal, critical, behavioral, and strategic) had acceptable eigenvalue that corresponded to the tentative model. The result of CFA also reduced the scale to 33 items. This validated instrument can be used to determine EFL teachers’ attempts to be reflective and their perceptions of reflective practice.
Henry James’s critics suggest numerous form-conscious reasons for his serpentine meaning-making aesthetics. Seeing the undecidability of James’s proto-modernist narrative on a par with that of the baroque aesthetics, this paper cites the inscribable essence of the female body in James’s materialism as one undiscussed reason. In the theories of écriture feminine or feminine writing, the female writer’s integrity with her body serves as the source of logic for the writing practice that revolts against phallogocentric conventions. In search of a concrete example for this assertion, I celebrate the content-conscious contemporaneity between James’s What Maisie Knew and New Woman writing in light of four common motifs: the psycho-ethical analysis of (1) the protagonist’s melancholic mother-daughter relationship and (2) her quest for truth paves the way for the essentialist insight into (3) the privilege of feminine aesthetics and (4) the emancipated motion of the New Woman’s body. The secondary objective of this paper is to celebrate the ideological return of the baroque in James’s proto-modernism through such topoi as imperfect beauty, motion, and madness so as to introduce the meaning-making role of the body in his impressionistic integrity of subject and object.
Background This study aimed to investigate the effects of a combined home-based exercise program on potential indicators of severe coronavirus disease 2019 (COVID-19) in overweight middle-aged men during home quarantine caused by COVID-19. Methods Forty men (aged 45–64 years) were assigned to the exercise (EXE, n = 20) or control (CON, n = 20) groups. A 6-week combined program was carried out three days/week, starting at 20 min per session at 50% maximal heart rate (HRmax) and advancing to 45 min at 70% HRmax. Pulmonary functional and cellular stress biomarkers were measured before and after the training program. Analysis of the covariance (ANCOVA) was used for comparison between the two groups considering the baseline values. Results Thirty-six participants (EXE, n = 17; CON, n = 19) completed the research protocol. The EXE group showed post-training improvements in forced vital capacity (FVC), forced expiratory volume in 1 s (FEV1), FEV1/FVC, Vital capacity (VC), and Forced expiratory flow at 25-75% (FEF25-75) compared to the CON group (P < 0. 05). Further, the plasma levels of fibrinogen, Interleukin (IL)-6, Interleukin (IL)-1β, D-dimer, and angiotensin (Ang II) decreased in the EXE group compared to the CON group (P < 0. 05). After six weeks of the training program, leukocyte counts increased in the EXE group compared to the CON group (P < 0. 05). There was a significant positive correlation between body mass index (BMI) with cardiovascular and inflammatory biomarkers other than white blood cells (WBC) in the EXE group (P < 0.05). Conclusions The findings suggest that combined home-based exercise during home quarantine improves risk factors for severe COVID-19 in overweight middle-aged men. These improvements were further correlated with changes in BMI. Future research is required to confirm the findings of this study.
In recent years, there has been a growing interest in developing next point-of-interest (POI) recommendation systems in both industry and academia. However, current POI recommendation strategies suffer from the lack of sufficient mixing of details of the features related to individual users and their corresponding contexts. To overcome this issue, we propose a deep learning model based on an attention mechanism in this study. The suggested technique employs an attention mechanism that focuses on the pattern’s friendship, which is responsible for concentrating on the relevant features related to individual users. To compute context-aware similarities among diverse users, our model employs six features of each user as inputs, including user ID, hour, month, day, minute, and second of visiting time, which explore the influences of both spatial and temporal features for the users. In addition, we incorporate geographical information into our attention mechanism by creating an eccentricity score. Specifically, we map the trajectory of each user to a shape, such as a circle, triangle, or rectangle, each of which has a different eccentricity value. This attention-based mechanism is evaluated on two widely used datasets, and experimental outcomes prove a noteworthy improvement of our model over the state-of-the-art strategies for POI recommendation.
This study used Latent Growth Curve Modeling (LGCM) to examine the overtime patterns of the score and test-taking strategy changes in an international high-stakes standardized proficiency test. To this end, the test records of 178 Iranian IELTS repeaters were analyzed, using close-and open-ended questionnaires to measure test scores as a function of construct relevant and construct-irrelevant test-taking strategy changes. Additionally, this study explored the accountable factors for the changes in the repeat-ers' strategies. Results indicated a small and gradual increase in the test scores following an overall augmented use of test-management (TM) and a decreased employment of test-wiseness (TW) strategies. Along with contributing to IELTS validity evidence based on the repeaters' scores, this study found multiple sources to account for the changes in repeaters' test-taking strategies. Consideration of changes in repeaters' test-taking strategies by IELTS instructors and test users may add to the validity of interpretation of test scores to the intended purposes of the tests.
Objective: The driver fatigue detection using multi-channel electroencephalography (EEG) has been extensively addressed in the literature. However, the employment of a single prefrontal EEG channel should be prioritized as it provides users with more comfort. Furthermore, eye blinks from such channel can be analyzed as the complementary information. Here, we present a new driver fatigue detection method based on simultaneous EEG and eye blinks analysis using an Fp1 EEG channel. Methods: First, the moving standard deviation algorithm identifies eye blink intervals (EBIs) to extract blink-related features. Second, the discrete wavelet transform filters the EBIs from the EEG signal. Third, the filtered EEG signal is decomposed into sub-bands, and various linear and nonlinear features are extracted. Finally, the prominent features are selected by the neighbourhood components analysis and fed to a classifier to discriminate between fatigue and alert driving. In this paper, two different databases are investigated. The first one is used for parameters' tuning of proposed method for the eye blink detection and filtering, nonlinear EEG measures, and feature selection. The second one is solely used for testing the robustness of the tuned parameters. Main results: The comparison between the obtained results from both databases by the AdaBoost classifier in terms of sensitivity (90.2% vs. 87.4%), specificity (87.7% vs. 85.5%), and accuracy (88.4% vs. 86.8%) indicates the reliability of the proposed method for the driver fatigue detection. Significance: Considering the existence of commercial single prefrontal channel EEG headbands, the proposed method can be used to detect the driver fatigue in real-world scenarios.
Directed Motivational Currents (DMCs) construct proposed by Dörnyei and his colleagues [2015. “Directed Motivational Currents: Regulating Complex Dynamic Systems Through Motivational Surges.” In Motivational Dynamics in Language Learning, edited by Z. Dörnyei, P. D. MacIntyre, and A. Henry, 95–105. Bristol, UK: Multilingual Matters. doi:10.4324/9781315772714; 2016. Motivational Currents in Language Learning: Frameworks for Focused Interventions. New York: Routledge. doi:10.4324/9781315772714] identifies a particular motivational experience in which individuals engage in periods of intense and stable motivation in pursuit of a well-defined target goal. This study argued that success or failure in achieving the target goal of DMCs might have some associated experiences for those who undergo this particular type of motivation. This study, therefore, used a qualitative approach to examine various experiences that might be associated with goal un/accomplishment in DMCs. The results of the interview data analysis revealed that success or failure in achieving the well-defined target goal affected self-efficacy beliefs, language learning mindsets, mood, self-enhancement and personal approach toward learning. The implications of the study and suggestions for further research are presented.
This study investigates the relationship between dark personality traits, aggressive behavior in violent video games, and severe traffic violations among 200 driving offenders from Tehran, Iran, participating in a rehabilitation program. Participants engaged in a computerized shooting decision task, where their tendency to shoot unarmed targets (innocent victims), compared to armed targets (criminals), was used as an indicator of aggressive behavior toward innocent victims. Additionally, they completed self-report measures of narcissism, Machiavellianism, psychopathy, and sadism to evaluate the impact of Dark Tetrad personality traits on their behavior. Bivariate analyses revealed associations between Dark Tetrad personality traits and aggressive behavior in the video game with serious traffic offenses. Multivariate analyses identified Machiavellianism, sadism, and aggressive behavior in video games as significant predictors of severe traffic offenses. The results suggest that dark personality traits and aggressive behavior in video games may aid in better identifying road traffic offenders with the most severe violations. Potential implications for preventing repeated traffic offenses by tailoring rehabilitation programs are discussed.
Veterans’ quality of life (QoL) can be drastically affected by posttraumatic stress disorder (PTSD). We compared prolonged exposure therapy (PET) with metacognitive therapy (MCT) in their effects on quality of life (QoL) among veterans with post-traumatic stress disorder (PTSD). Overall, 57 veterans with PTSD were randomly assigned to three groups MCT (N = 17), PET (N = 17), and Control (N = 23). The 36-item short-form survey (SF-36) was used to evaluate QoL pretest, posttest, and after a 3-month follow-up. The MCT was based on the practice of detached mindfulness, controlling rumination/anxiety, and challenging negative beliefs about symptoms. The PET was based on in-vivo and imaginal exposure to trauma-related events, and discontinuation of avoidance-oriented coping strategies. Both MCT and PET groups significantly improved QoL at posttest and follow-up, compared with the control group (P < .001); however, the MCT and PET groups showed no significant difference at posttest (P = .644) or follow-up (P = .646). Our results support the efficacy of PET as the standard for PTSD treatment, while also signifying the effectiveness of MCT at increasing the QoL in war-related PTSD at a 3-month follow-up.
Objective: Emotional disturbances are the most common mental health problems in different populations and societies. We intend to provide the latest evidence related to the effectiveness of Acceptance and Commitment Therapy (ACT) on depression and anxiety by reviewing systematic review and meta-analysis studies published in the last three years. Method: PubMed and Google Scholar databases were systematically searched between January 1, 2019 and November 25, 2022 with relevant keywords for English systematic review and meta-analysis articles reviewing the utilization of ACT to reduce anxiety and depression symptoms. Results: 25 articles were included in our study: 14 systematic review and meta-analysis studies and 11 systematic reviews. These studies have investigated the effects of ACT on depression and anxiety in populations of children or adults, mental health patients, patients with different cancers or multiple sclerosis, people with audiological problems, parents or caregivers of children with mental or physical illnesses as well as normal people. Furthermore, they have examined the effects of ACT in individual, group, Internet, computerized, or combined delivery formats. Most of the reviewed studies reported significant effect sizes (small to large effect sizes) of ACT, regardless of the delivery method, compared to passive (placebo, waitlist) and active (treatment as usual and other psychological interventions except cognitive behavioral therapy (CBT)) controls for depression and anxiety. Conclusion: Recent literature mainly agrees on the small to moderate effect sizes of ACT on depression and anxiety symptoms in different populations.
Objective: The objective of this study was to determine the most effective coping mechanism to deal with auditory hallucinations that reduces the frequency of voice-hearing and associated distress. In the present randomized controlled trial, each of the three coping mechanisms of attentional avoidance, attentional focusing, and mindfulness were used in one group and the fourth group was the control group. Method: A total of 64 patients with schizophrenia, categorized in three groups of attentional avoidance, attentional focusing and mindfulness and one control group, were asked to listen to an ambiguous auditory task depending on the type of their coping mechanism. After determining the baseline of distress, the task was performed in duplicate for each group. After playing the auditory task for the first time, participants were asked to rate out the level of their distress and compliance with instructions, and they were asked to estimate the likely number of words they had heard. After the second time, they were asked to note the words they hear during the task and rate out their distress and compliance with instructions again at the end of the task. Results: There was a significant difference between groups in terms of distress with a medium effect size of 0.47. The post hoc analysis revealed that mindfulness group reported less distress compared to the attentional focusing group (P = 0.017) and the control group (P = 0.027). Also, a significant difference existed between groups in terms of the frequency of the identified words, with a moderately strong effect size of 0.59, and a very good statistical power of 0.99. The post hoc analysis showed that attentional avoidance (P = 0.013) and attentional focusing (P = 0.011) groups heard fewer words than the control group. Conclusion: Attention is a good target for treating psychotic patients with auditory hallucinations. Also, manipulation of attention can affect the frequency of auditory hallucinations and associated distress.
Rationing of nursing care (RONC) refers to necessary nursing tasks that nurses refuse or fail to do because of limited time, staffing level, or skill mix. As an important process factor, it affects the quality of patient care. The concept of rationing of nursing care has not yet been clearly defined and analyzed and there are different views regarding this issue. Using Walker and Avant's eight-step method, this concept analysis was conducted to analyze the meaning, attributes, dimensions, antecedents, and consequences of nursing care rationing. The literature was collected by searching in electronic databases including PubMed, ScienceDirect, Web of Science, Scopus, and Google Scholar with no date limitation. Both qualitative and quantitative studies on rationing of nursing care, which were open-access and published in English, were included in this study. Thirty-three articles were investigated in the present study. The four defining attributes of RONC included the duty of performing nursing care, dealing with problems of doing nursing care, decision-making and prioritizing, and outcome. The antecedents included nurse-related, organization-related, care-related, and patient-related antecedents. A theoretical definition and a conceptual model of RONC were developed. The attributes, antecedents, and consequences of RONC identified in this study can be used in nursing education, research, and managerial and organizational planning.
Drawing on the rhetorical/relational goal theory, this study examined the role of instructor clarity and non-verbal immediacy in affective learning through the mediation of instructor understanding. Data were gathered through close-ended questionnaires from 756 Chinese and 715 Iranian English as a foreign language (EFL) students, the factor structure and cross-cultural validity of which were supported via confirmatory factor analysis and testing measurement invariance, respectively. Path analysis results indicated that clarity and non-verbal immediacy positively predicted instructor understanding and affective learning; instructor understanding positively predicted affective learning; and understanding was a significant positive mediator in the relationship of non-verbal immediacy and clarity with affective learning. Except for the positive association of non-verbal immediacy with understanding which was significantly higher for the Iranian group, no significant difference was found between the Chinese and Iranian groups in all other associations, providing empirical support for the role of EFL teachers’ positive interpersonal communication behaviors in EFL students’ affective learning, irrespective of the cultural context.
Many studies have been conducted on designing systems based on the redundancy allocation problem (RAP). However, considering repairable warm-standby components (which are subject to failure even in an idle state) is somewhat neglected by researchers due to the complex mathematical models. One of the crucial aspects of these systems is considering the probability of failure when switching to a standby component or subsystem. This study tries to highlight these imperfect switching and switch selection strategies in the redundancy allocation designs. In this regard, this article is dedicated to developing two RAP models (a single objective and a bi-objective) with warm standby repairable components by proposing a solving approach based on the genetic algorithm (GA) and Markov chains. Since the model’s objective functions minimize the system’s mean time to failure (MTTF) and cost, we discussed how imperfect switches affect the total system’s cost and mean time to failure for the proposed RAPs. Finally, we adopted a GA and a non-dominated sorting genetic algorithm (NSGA-II) to solve the proposed models due to the models’ complexity. Solving these models clearly indicates the critical role of selecting an appropriate switching strategy on the system’s costs and reliability.
Employing various mathematical tools in machine learning is crucial since it may enhance the learning problem’s efficiency. Dynamic systems are among the most effective tools. In this study, an effort is made to examine a kind of machine learning from the perspective of a dynamic system, i.e., we apply it to learning problems whose input data is a time series. Using the discretization approach and radial basis functions, a new data set is created to adapt the data to a dynamic system framework. A discrete dynamic system is modeled as a matrix that, when multiplied by the data of each time, yields the data of the next time, or, in other words, can be used to predict the future value based on the present data, and the gradient descent technique was used to train this matrix. Eventually, using Python software, the efficacy of this approach relative to other machine learning techniques, such as neural networks, was analyzed.
Cesarean delivery continues to increase due to various reasons, considering its negative effects, our aim in this research is to investigate the behavioral intention of pregnant women who choose vaginal delivery. In this regard, the expanded Theory of Planned Behavior was used by increasing two predictor variables. About 188 pregnant women voluntarily participated in this research in some healthcare centers in Tehran County, Iran. Our results showed that this enhanced model can increase the power of the original theory. Overall, the expanded model successfully described the mode of delivery among Iranian women and explained 59.4% of the variation in the intention variable with a stronger effect. The effect of the variables added to the model was indirectly significant. Among all the variables, attitude showed the best effect on the choice of normal vaginal delivery, and after that, the variable of general health orientation had a greater effect on attitude.
Background A growing body of evidence has been paid to the cognitive impairment in patients with multiple sclerosis (MS). However, studies concerning cognitive functions in MS have also yielded conflicting results. This study investigates the attention and inhibitory control functions in patients with MS and their relationship with other clinical features, such as depression and fatigue in these patients. Methods Participants included 80 patients with MS and 60 healthy controls. The attention and inhibitory control, fatigue, and psychiatric screening in all subjects were studied, respectively with the Integrated Visual and Auditory Continuous Performance Test (IVA-CPT), Fatigue Severity Scale (FSS), and the Hospital Anxiety and Depression Scale (HADS). Results Patients with MS performed the IVA-CPT task more poorly than the healthy control group (p < 0.001). However, multiple regression analysis did not show any significant relationship between disease duration, FSS, and HADS on attention and inhibitory control. Conclusion Inhibitory control and attention are significantly impaired in patients with MS. Finding the basics of cognitive deficits in MS have potentially important clinical implications for developing better cognitive rehabilitation strategies.
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Dehkadeh-ye-Olampik, Tehran, Iran
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