Educational Games Design

Video games are widely and increasingly adopted in the educational field thanks to their inherent engaging, immersive, and adaptive capacities. Yet, one of the greatest problematic in educational games design remains how to create ludic and adaptive experiences without going astray from the targeted learning objectives. In creating adaptive educational games, modeling the learner/player is a prerequisite. This chapter highlights the importance of educational standards in learning content design and proposes an adaptive mechanism “AMEG” based on IMS learning design and artificial intelligence that model learners using game metrics and adapt the gameplay as well as the learning content. As a practical experimentation of the mechanism, MathQuests, an educational game that helps in teaching mathematical operations for first year middle school students was created.

Emberi bánásmód veszteségek idején

Humane Treatment of People Who Experience Losses. In this paper, we present the grief as a natural response to the losses that people experience. We regard grief as an adaptive mechanism to the new situation that occurs with a loss. In our success-oriented consumerist society, grief has become a taboo: fighting pain in losses became a sign of weakness and failure. This is valid also for Christians, who consider faith as the measure of successful dealing with distress. We present the positive perceptive structures that may help the grief – based on Anil Seth’s theories. Later on, we speak about the toxic positivism, which harms the grieving person and the adequate expression of condolence, which may express our participation in his/her tormented situation. Destructive attitude consists in shaming, abandoning, or betraying the grieving person, whereas constructive attitude means acceptance, condolence, and support. This paper tries to provide a practical guideline to an empathic grief counselling. Keywords: grief, loss, mourning, toxic positivism, positive perceptive structures

Ischemic Conditioning in the Right Colon and Terminal Ileum: An Experimental Rat Model

Introduction: Preoperative gastric ischemic conditioning (IC) improves the outcome of esophageal replacement gastroplasty and is associated with low morbidity. However, when the stomach cannot be used for esophageal replacement, a colonic replacement is required. The study aim was to assess the viability of right colon and terminal ileum IC in a rat model, the histological damage/recovery sequence, and determine if neovascularization is a potential adaptive mechanism. Methods: The study was conducted in Rattus norvegicus with ileocolic vascular ligation. Seven groups of animals were established (six rats per group) with groups defined by the date of their post-IC euthanasia (+1, +3, +6, +10, +15, and +21 days). Comparisons were made with a sham group. Viability of the model was defined as <10% of transmural necrosis. The evaluation of histological damage used the Chiu score in hematoxylin and eosin sections of paraffin-embedded specimens with CD31 immunohistochemical assessment of neovascularization by the median of submucosal vessel counts in five high-magnification fields. Results: Transmural colon necrosis occurred in 1/36 animals (2.78%) with no animal demonstrating transmural ileal necrosis. The maximum damage was observed in the colon on +1 day post-IC (average Chiu score 1.67, P = 0.015), whereas in the ileum, it was on days +1, +3, and +6 (average Chiu score 1.5, 1.3, and 1.17; P = 0.015, 0.002, and 0.015, respectively). In the +21-day group, histological recovery was complete in the colon in four (66.7%) of the six animals and in the ileum in five (83.3%) of six animals. There were no significant differences in quantitative neovascularization in any of the groups when compared with the sham group or when comparisons were made between groups. Conclusions: The tested animal model for IC of the colon and terminal ileum appeared to be feasible. Histological damage was maximal between the 1st and 3rd day following IC, but by day 21, recovery was complete in two-thirds of the rats. There was no evidence in this preliminary IC model that would suggest neovascularization as an adaptive mechanism.

Abnormal Respiratory Sounds Classification Using Deep CNN Through Artificial Noise Addition

Respiratory sound (RS) attributes and their analyses structure a fundamental piece of pneumonic pathology, and it gives symptomatic data regarding a patient's lung. A couple of decades back, doctors depended on their hearing to distinguish symptomatic signs in lung audios by utilizing the typical stethoscope, which is usually considered a cheap and secure method for examining the patients. Lung disease is the third most ordinary cause of death worldwide, so; it is essential to classify the RS abnormality accurately to overcome the death rate. In this research, we have applied Fourier analysis for the visual inspection of abnormal respiratory sounds. Spectrum analysis was done through Artificial Noise Addition (ANA) in conjunction with different deep convolutional neural networks (CNN) to classify the seven abnormal respiratory sounds—both continuous (CAS) and discontinuous (DAS). The proposed framework contains an adaptive mechanism of adding a similar type of noise to unhealthy respiratory sounds. ANA makes sound features enough reach to be identified more accurately than the respiratory sounds without ANA. The obtained results using the proposed framework are superior to previous techniques since we simultaneously considered the seven different abnormal respiratory sound classes.

Longitudinal single-cell analysis of a myeloma mouse model identifies subclonal molecular programs associated with progression

Molecular programs that underlie precursor progression in multiple myeloma are incompletely understood. Here, we report a disease spectrum-spanning, single-cell analysis of the Vκ*MYC myeloma mouse model. Using samples obtained from mice with serologically undetectable disease, we identify malignant cells as early as 30 weeks of age and show that these tumours contain subclonal copy number variations that persist throughout progression. We detect intratumoural heterogeneity driven by transcriptional variability during active disease and show that subclonal expression programs are enriched at different times throughout early disease. We then show how one subclonal program related to GCN2 stress response is progressively activated during progression in myeloma patients. Finally, we use chemical and genetic perturbation of GCN2 in vitro to support this pathway as a therapeutic target in myeloma. These findings therefore present a model of precursor progression in Vκ*MYC mice, nominate an adaptive mechanism important for myeloma survival, and highlight the need for single-cell analyses to understand the biological underpinnings of disease progression.

Prepare: P owe r -Awar e A p proximate Re a l-time Task Scheduling for Ene r gy-Adaptiv e QoS Maximization

Achieving high result-accuracy in approximate computing (AC) based real-time applications without violating power constraints of the underlying hardware is a challenging problem. Execution of such AC real-time tasks can be divided into the execution of the mandatory part to obtain a result of acceptable quality, followed by a partial/complete execution of the optional part to improve accuracy of the initially obtained result within the given time-limit. However, enhancing result-accuracy at the cost of increased execution length might lead to deadline violations with higher energy usage. We propose Prepare , a novel hybrid offline-online approximate real-time task-scheduling approach, that first schedules AC-based tasks and determines operational processing speeds for each individual task constrained by system-wide power limit, deadline, and task-dependency. At runtime, by employing fine-grained DVFS, the energy-adaptive processing speed governing mechanism of Prepare reduces processing speed during each last level cache miss induced stall and scales up the processing speed once the stall finishes to a higher value than the predetermined one. To ensure on-chip thermal safety, this higher processing speed is maintained only for a short time-span after each stall, however, this reduces execution times of the individual task and generates slacks. Prepare exploits the slacks either to enhance result-accuracy of the tasks, or to improve thermal and energy efficiency of the underlying hardware, or both. With a 70 - 80% workload, Prepare offers 75% result-accuracy with its constrained scheduling, which is enhanced by 5.3% for our benchmark based evaluation of the online energy-adaptive mechanism on a 4-core based homogeneous chip multi-processor, while meeting the deadline constraint. Overall, while maintaining runtime thermal safety, Prepare reduces peak temperature by up to 8.6 °C for our baseline system. Our empirical evaluation shows that constrained scheduling of Prepare outperforms a state-of-the-art scheduling policy, whereas our runtime energy-adaptive mechanism surpasses two current DVFS based thermal management techniques.

Psychological Adaptive Mechanism Maturity, Age, and Depression Symptoms in Advanced-Stage Cancer Patients

Background: Previously, we reported that the maturity of Psychological Adaptive Mechanism (PAM; alternatively, ego defense mechanism) endorsement, but not depression symptom severity, predicted 5-year survival rates in adult cancer patients and that study controlled for age as a significant variable. In this investigation, we hypothesized that greater PAM maturity would correlate significantly with age and with fewer depression symptoms in a larger sample.Methods: In this cross-section study, adult cancer outpatients (N=293) completed the Defense Style Questionnaire (DSQ), the Beck Depression Inventory (BDI), and provided additional clinical data. Spearman’s correlation and multiple regression modeling provided statistical tests of the study hypotheses.Results: Contrary to our hypothesis, DSQ PAM maturity endorsement did not correlate significantly with increasing age. Greater PAM maturity ratio on the DSQ (p&lt;0.0001) and current antidepressant use (p&lt;0.05), however, both provided inverse associations with total BDI symptom frequency (p&lt;0.01). Age was inversely associated with BDI mood (p&lt;0.0001) and somatic scores (p&lt;0.04). Items that worsened BDI symptom frequency included self-reported mood-altering anti-cancer medications and any psychiatric history. Cancer stage, time since diagnosis, and chemotherapy treatment did not correlate with DSQ or BDI scores. Multiple regression analysis found that the correlated items accounted for 17.2% of the variance in mood symptoms and 4.9% in somatic symptoms. Specifically, adaptive maturity and age associated with fewer depression symptoms, while cancer medications affecting mood, and a previous psychiatric history each predicted higher frequency of depression scores.Conclusion: The results suggest that PAM maturity likely predicts fewer depression symptoms while younger age associates with more depression symptoms in this clinical sample. Centrally, acting cancer medications, such as glucocorticoids, and any history of psychiatric disorder correlated with increased depression symptom frequencies. In this cross-section study, antidepressant medications indicated higher frequencies of depressive symptoms, likely reflecting their use in persons previously diagnosed with depression. Further research should target factors that improve PAM maturity as a potential treatment target, especially in younger age groups.

Real-Time Concept Drift Detection and Its Application to ECG Data

Prediction of cardiac disease is one the most crucial topics in the sector of medical info evaluation. The stochastic nature and the variation concerning time in electrocardiogram (ECG) signals make it burdensome to investigate its characteristics. Being evolving in nature, it requires a dynamic predictive model. With the presence of concept drift, the model performance will get worse. Thus learning algorithms require an apt adaptive mechanism to accurately handle the drifting data streams. This paper proposes an inceptive approach, Corazon Concept Drift Detection Method (Corazon CDDM), to detect drifts and adapt to them in real-time in electrocardiogram signals. The proposed methodology results in achieving competitive results compared to the methods proposed in the literature for all types of datasets like synthetic, real-world &amp; time-series datasets.