Initiatives for the Application of Restorative Programs in Tehran Juvenile Courts

The present study has answered the this question: How and on what initiative do judges provide the context for the application of restorative programs in juvenile criminal courts? Restorative interpretations of legal provisions such as referral to mediation in all Ta'zirat offenses, weakening of the constituent elements of the crime, use of the legal capacity of a conditional or suspended pardon, and obtaining the plaintiff's consent after the announcement of the end of the proceedings and before the verdict; are Judicial initiatives. Also, the replacement of similar institutions with unimpeded legal establishments such as the establishment of the Peace Council and the social work unit, the referral of mediation to counter with closed- case policy _to believe the number of closed case as the criterion for the efficiency of judicial system instead of quality of decision making_ and the cooperation with the lawyers of the Association for the Protection of Children's Rights to solve the problems caused by the undesirable quality of defense lawyers are structural initiatives to avoid obstacles

Horizon-scale tests of gravity theories and fundamental physics from the Event Horizon Telescope image of Sagittarius A∗^*

Horizon-scale images of black holes (BHs) and their shadows have opened an unprecedented window onto tests of gravity and fundamental physics in the strong-field regime. We consider a wide range of well-motivated deviations from classical General Relativity (GR) BH solutions, and constrain them using the Event Horizon Telescope (EHT) observations of Sagittarius A$^*$ (Sgr A$^*$), connecting the size of the bright ring of emission to that of the underlying BH shadow and exploiting high-precision measurements of Sgr A$^*$'s mass-to-distance ratio. The scenarios we consider, and whose fundamental parameters we constrain, include various regular BHs, string-inspired space-times, violations of the no-hair theorem driven by additional fields, alternative theories of gravity, novel fundamental physics frameworks, and BH mimickers including well-motivated wormhole and naked singularity space-times. We demonstrate that the EHT image of Sgr A$^*$ places particularly stringent constraints on models predicting a shadow size larger than that of a Schwarzschild BH of a given mass, with the resulting limits in some cases surpassing cosmological ones. Our results are among the first tests of fundamental physics from the shadow of Sgr A$^*$ and, while the latter appears to be in excellent agreement with the predictions of GR, we have shown that a number of well motivated alternative scenarios, including BH mimickers, are far from being ruled out at present.Comment: 82 pages, 47 figures, 50+ models tested. v3: fixed a few figures, clarified several points, included various analytical expressions for shadow sizes within the different models, added a few references, included a summary table (Table II). Version accepted for publication in Classical and Quantum Gravit

Learning-based Vehicle Diagnostic and Prognostic System Utilizing Natural Language Processing

Initial fault detection and diagnostics are essential elements to improve the efficiency, safety, and stability of vehicle operation. Diagnostics can make direct and indirect financial impacts on service and support entities in place for the vehicle. Remote diagnostics can reduce vehicle downtime in service centers and increase customer satisfaction, primarily when conducting over-the-air updates or telephone lines. In order to troubleshoot a vehicle, specific tools can be used to look up failure codes stored in vehicle controllers or manually gather failure symptoms through customer service hotlines and remote service technicians. The overall gathering of data, deciphering, and execution of any repairs still consumes precious time and may suffer from potential human errors. Recently, numerous studies have investigated data-driven approaches to improve vehicle diagnostics using available vehicle data. This study investigates a machine learning pipeline to improve automated vehicle diagnostics and prognostics. Using Natural Language Processing (NLP), we demonstrate a comprehensive model to extract the customer and agent interactions from repair-service call transcriptions. This dissertation applies Machine Learning (ML) algorithms to identify accurate failure reports and claims. Also, it classifies the service requests to the proper service department and utilizes the historical service information along with current customer claims to identify possible failed vehicle parts. First, NLP techniques are used to automate the task of crucial information extraction from free-text failure reports (generated within customers' calls to the service department). We have introduced an NLP taxonomy in the automotive domain since known NLP techniques had a weak performance on such texts. We have shown that domain-based NLP processing and feature extraction can help to extract meaningful information from the reports. Deep learning algorithms are employed to validate service requests and filter vague or misleading claims. Various classification algorithms are implemented to classify service requests so that valid service requests can be directed to the relevant service department. We proposed to employ Bidirectional Long Short-term Memory (BiLSTM), along with Convolution Neural Network (CNN) model, which shows more than 18% performance improvement in validating service requests compared to average technicians' capabilities. Furthermore, using domain-based NLP techniques at preprocessing and feature extraction stages along with CNN-BiLSTM-based request validation enhanced the performance of the Gradient Tree Boosting (GTB) service classification model. The performance parameter of the Receiver Operating Characteristic Area Under the Curve (ROC-AUC) reached 0.82. Next, we performed automated failure classification on extracted data to route the claims to the proper service departments. By introducing optimized feature extraction and classification methods, requests can be forwarded to the correct departments with 80% accuracy. This method exceeds the 60% baseline accuracy for an average customer service technician. NLP analysis can also generate technical information from the text report for vehicle and component prognostics that have not been previously studied. Finally, we proposed a novel network structure that employs a multi-variant high-dimensional Markov chain to predict the possible failed component of the next service interval to enhance the CNN-LSTM model performance. The Markov model takes advantage of historical records to identify the most efficient CNN kernels in the network structure. The proposed model significantly improved data classification efficiency in correlated historical records such as vehicle service reports. Compared to conventional CNN-LSTM models, the introduced model demonstrated significant performance enhancement of 8% accuracy, 9% sensitivity, 11% specificity, 10% precision, and 12% f-score by reducing the false positive cases in customer claim classification

Improvement of mesenchymal stem cell differentiation into the endoderm lineage by four step sequential method in biocompatible biomaterial

Introduction: The goal of the study described here, was to investigate the potential of umbilical cord derived mesenchymal stem cell (UC-MSCs) into hepatocyte like cells in a sequential 2D and 3D differentiation protocols as alternative therapy. Methods: Mesenchymal stem cells (MSCs) were isolated from the umbilical cord (UC) and CD markers were analyzed by flow cytometry. For hepatic differentiation of UC-MSCs, cells were induced with a sequential 4-step protocol in 3D and 2D culture system. Urea concentration and albumin secretion into the culture medium was quantified by ELISA. Gene expression levels of AFP, ALB, and CK18 were determined by RT-PCR. Data were statistically analyzed by the SPSS software. The difference between the mean was considered significant when p < 0.05. Results: Growth factor dependent morphological changes from elongated fibroblast-like cells to round epithelial cell morphology were observed in 2D culture. Cell proliferation analysis showed round-shaped morphology with clear cytoplasm and nucleus on the alginate scaffold in 3D culture. The mean valuses of albumin production and urea secretion were significantly higher in the 3D Culture system when compared with the 2D culture (p = 0.005 vs p = 0.001), respectively. Treatment of cells with TSA in the final step of differentiation induced an increased expression of CK18 and a decreased expression of αFP in both the 3D and 2D cultures (p = 0.026), but led to a decreased albumin gene expression, and an increased expression in the 2D culture (p = 0.001). Conclusion: Findings of the present study indicated that sequential exposure of UC-MSCs with growth factors in 3D culture improves hepatic differentiation