Simulating Problem Difficulty in Arithmetic Cognition Through Dynamic Connectionist Models

The present study aims to investigate similarities between how humans and connectionist models experience difficulty in arithmetic problems. Problem difficulty was operationalized by the number of carries involved in solving a given problem. Problem difficulty was measured in humans by response time, and in models by computational steps. The present study found that both humans and connectionist models experience difficulty similarly when solving binary addition and subtraction. Specifically, both agents found difficulty to be strictly increasing with respect to the number of carries. Another notable similarity is that problem difficulty increases more steeply in subtraction than in addition, for both humans and connectionist models. Further investigation on two model hyperparameters --- confidence threshold and hidden dimension --- shows higher confidence thresholds cause the model to take more computational steps to arrive at the correct answer. Likewise, larger hidden dimensions cause the model to take more computational steps to correctly answer arithmetic problems; however, this effect by hidden dimensions is negligible.Comment: 7 pages; 15 figures; 5 tables; Published in the proceedings of the 17th International Conference on Cognitive Modelling (ICCM 2019

ANASYSIS OF ISOMETRICITY OF THE ANTERIOR CRUCIATE LIGAMENT DURING KNEE FLEXION-EXTENSION FOR OPTIMAL LIGAMENT RECONSTRUCTION

Anterior cruciate ligament (ACL) is liable to a major injury that often results in a functional impairment requiring surgical reconstruction. The success of reconstruction depends on such factors as attachment positions, initial tension of ligament and surgical methods of fixation. The purpose of this study is to find isometric area of the substitute during flexion/extension and to simulate successful ACL reconstruction position using MADYMO(MAthematical DYnamic MOdel) software

The Effect of Competitive Advantage and Human Advantage on Industrial Competitive Strategy (Case Study: Smis in Gorontalo Province)

Small and Medium Industries (SMIs) have a strategic role in the Indonesian economy, as they earn 61.9 percent of the foreign exchange which goes to make up the nation\u27s Gross Domestic Product, and nationally they are able to absorb 97 percent of the workforce. The Global Competitiveness Report also notes that SMIs serve as the business units that affect every nation\u27s competitiveness. Considering this strategic role, the selection of a competitive strategy for these SMIs is absolutely necessary. Through an in-depth literature review, this study aims to explore what variables influence the competitive strategy of industries, particularly the SMIs. By using a Systematic Literature Review (SLR) with a total of 31 main literature (articles, papers and books), this study has found two dominant factors that influence industrial competitive strategy: Competitive advantage and human advantage, which are subsequently developed into six independent variables (construct variables), i.e. cost, delivery, product quality, product variety, know-how and innovativeness, with a total of 44 indicators. The results of measurements of the sample of SMIs in Gorontalo Province, using Structural Equation Modeling, found that both competitive advantage and human advantage jointly influence 40.2 percent of the industrial competitive strategies. These results indicate that competitive strategies, such as creating products with unique features, on-time delivery, flexibility in production, and employee involvement in the innovations, are indispensable to SMIs in order for them to produce quality products and be able to maintain their advantage

Good Glycemic Control Is Associated with Better Survival in Diabetic Patients on Peritoneal Dialysis: A Prospective Observational Study

BACKGROUND: The effect of glycemic control after starting peritoneal dialysis (PD) on the survival of diabetic PD patients has largely been unexplored, especially in Asian population. METHODS: We conducted a prospective observational study, in which 140 incident PD patients with diabetes were recruited. Patients were divided into tertiles according to the means of quarterly HbA1C levels measured during the first year after starting PD. We examined the association between HbA1C and all-cause mortality using Cox proportional hazards models. RESULTS: The mean age was 58.7 years, 59.3% were male, and the mean follow-up duration was 3.5 years (range 0.4-9.5 years). The mean HbA1C levels were 6.3%, 7.1%, and 8.5% in the 1(st), 2(nd), and 3(rd) tertiles, respectively. Compared to the 1(st) tertile, the all-cause mortality rates were higher in the 2(nd) [hazard ratio (HR), 4.16; 95% confidence interval (CI), 0.91-18.94; p = 0.065] and significantly higher in the 3(rd) (HR, 13.16; 95% CI, 2.67-64.92; p = 0.002) tertiles (p for trend = 0.005), after adjusting for confounding factors. Cardiovascular mortality, however, did not differ significantly among the tertiles (p for trend = 0.682). In contrast, non-cardiovascular deaths, most of which were caused by infection, were more frequent in the 2(nd) (HR, 7.67; 95% CI, 0.68-86.37; p = 0.099) and the 3(rd) (HR, 51.24; 95% CI, 3.85-681.35; p = 0.003) tertiles than the 1(st) tertile (p for trend = 0.007). CONCLUSIONS: Poor glycemic control is associated with high mortality rates in diabetic PD patients, suggesting that better glycemic control may improve the outcomes of these patients

Recalibration and validation of the Charlson Comorbidity Index in acute kidney injury patients underwent continuous renal replacement therapy

Background Comorbid conditions impact the survival of patients with severe acute kidney injury (AKI) who require continuous renal replacement therapy (CRRT). The weights assigned to comorbidities in predicting survival vary based on type of index, disease, and advances in management of comorbidities. We developed a modified Charlson Comorbidity Index (CCI) for use in patients with AKI requiring CRRT (mCCI-CRRT) and improved the accuracy of risk stratification for mortality. Methods A total of 828 patients who received CRRT between 2008 and 2013, from three university hospital cohorts was included to develop the comorbidity score. The weights of the comorbidities were recalibrated using a Cox proportional hazards model adjusted for demographic and clinical information. The modified index was validated in a university hospital cohort (n = 919) using the data of patients treated from 2009 to 2015. Results Weights for dementia, peptic ulcer disease, any tumor, and metastatic solid tumor were used to recalibrate the mCCI-CRRT. Use of these calibrated weights achieved a 35.4% (95% confidence interval [CI], 22.1%–48.1%) higher performance than unadjusted CCI in reclassification based on continuous net reclassification improvement in logistic regression adjusted for age and sex. After additionally adjusting for hemoglobin and albumin, consistent results were found in risk reclassification, which improved by 35.9% (95% CI, 23.3%–48.5%). Conclusion The mCCI-CRRT stratifies risk of mortality in AKI patients who require CRRT more accurately than does the original CCI, suggesting that it could serve as a preferred index for use in clinical practice

Effective connectivity during working memory and resting states: A DCM study

Although the relationship between resting-state functional connectivity and task-related activity has been addressed, the relationship between task and resting-state directed or effective connectivity – and its behavioral concomitants – remains elusive. We evaluated effective connectivity under an N-back working memory task in 24 participants using stochastic dynamic causal modelling (DCM) of 7 T fMRI data. We repeated the analysis using resting-state data, from the same subjects, to model connectivity among the same brain regions engaged by the N-back task. This allowed us to: (i) examine the relationship between intrinsic (task-independent) effective connectivity during resting (Arest) and task states (Atask), (ii) cluster phenotypes of task-related changes in effective connectivity (Btask) across participants, (iii) identify edges (Btask) showing high inter-individual effective connectivity differences and (iv) associate reaction times with the similarity between Btaskand Arestin these edges. We found a strong correlation between Arestand Ataskover subjects but a marked difference between Btaskand Arest. We further observed a strong clustering of individuals in terms of Btask, which was not apparent in Arest. The task-related effective connectivity Btaskvaried highly in the edges from the parietal to the frontal lobes across individuals, so the three groups were clustered mainly by the effective connectivity within these networks. The similarity between Btaskand Arestat the edges from the parietal to the frontal lobes was positively correlated with 2-back reaction times. This result implies that a greater change in context-sensitive coupling – from resting-state connectivity – is associated with faster reaction times. In summary, task-dependent connectivity endows resting-state connectivity with a context sensitivity, which predicts the speed of information processing during the N-back task