Exploring criminal responsibility of PTSD patients; findings from a survey in Chinese Mainland courts
بررسی مسئولیت کیفری بیماران مبتلا به PTSD؛ یافته های یک نظرسنجی در دادگاه های سرزمین اصلی چین-2019
Background. – The Wenchuan Earthquake in Sichuan Province is China’s deadliest natural disaster in a generation; after such disturbance, a kind of mental illness named post-traumatic stress disorder (PTSD, also called delayed psychogenic reaction) raises concern in Mainland China, but probably not rapidly sufficient. Different from that in the USA, earthquake is both the reason and focus of PTSD research in China. Methods. – In order to find out the relationship between the PTSD defense and criminal responsibility in Mainland China, the authors decided to use certain academic tools and analysis judicial decisions (816 cases). The authors identified key information from government official websites. Results. – Data demonstrated that research regarding PTSD increases considerably after the Wenchuan earthquake in 2008. However, data also showed that Chinese courts are hesitant in accepting PTSD as a mental defense for criminals, despite relevant existing rules. Some legal ambiguities, such as lack of procedures or instructions for the connection between diagnosis and judgment, can be observed when courts encounter criminals with PTSD. Conclusions. – PTSD patients occur in all races, classes, religions, and nationalities and some would unfortunately be criminals. This pattern reveals concern for the boundary between the reasonable use and abuse of PTSD in view of medico-legal expertise practice. Expert testimony or opinion cannot replace the judges’ decision. Chinese courts should learn from the American Bar Association and accept the three-part analysis for forensic consideration of PTSD. Further details regarding the regulations for resolving the criminal responsibility of PTSD patients should be obtained.
Keywords: Criminal Responsibility | Legal Identification | Mainland China | Post-traumatic Stress Disorder
Identification and analysis of behavioral phenotypes in autism spectrum disorder via unsupervised machine learning
شناسایی و تجزیه و تحلیل فنوتیپ های رفتاری در اختلال طیف اوتیسم از طریق یادگیری ماشین بدون نظارت-2019
Background and objective: Autism spectrum disorder (ASD) is a heterogeneous disorder. Research has explored potential ASD subgroups with preliminary evidence supporting the existence of behaviorally and genetically distinct subgroups; however, research has yet to leverage machine learning to identify phenotypes on a scale large enough to robustly examine treatment response across such subgroups. The purpose of the present study was to apply Gaussian Mixture Models and Hierarchical Clustering to identify behavioral phenotypes of ASD and examine treatment response across the learned phenotypes. Materials and methods: The present study included a sample of children with ASD (N = 2400), the largest of its kind to date. Unsupervised machine learning was applied to model ASD subgroups as well as their taxonomic relationships. Retrospective treatment data were available for a portion of the sample (n =1034). Treatment response was examined within each subgroup via regression. Results: The application of a Gaussian Mixture Model revealed 16 subgroups. Further examination of the subgroups through Hierarchical Agglomerative Clustering suggested 2 overlying behavioral phenotypes with unique deficit profiles each composed of subgroups that differed in severity of those deficits. Furthermore, differentiated response to treatment was found across subtypes, with a substantially higher amount of variance accounted for due to the homogenization effect of the clustering. Discussion: The high amount of variance explained by the regression models indicates that clustering provides a basis for homogenization, and thus an opportunity to tailor treatment based on cluster memberships. These findings have significant implications on prognosis and targeted treatment of ASD, and pave the way for personalized intervention based on unsupervised machine learning.
Keywords: Machine learning | Autism spectrum disorder | Behavioral phenotypes | Cluster analysis | Treatment response
Perception, knowledge and attitudes of small animal practitioners regarding animal abuse and interpersonal violence in Brazil and Colombia
درک ، دانش و نگرش متخصصان حیوانات کوچک در مورد سوء استفاده از حیوانات و خشونت بین فردی در برزیل و کلمبیا-2019
Identification and report of animal abuse by veterinarians are fundamental to the promotion of animal welfare and the prosecution of this crime. Likewise, these professionals have an important responsibility to cope with the cycle of violence. This study aims to characterize the perception, knowledge, and attitudes of small animal practitioners regarding animal abuse and interpersonal violence in Brazil and Colombia. An online survey containing 27 questions was distributed to small animal practitioners of both countries. Multiple correspondence analysis (MCA) was employed to construct relationships among categorical variables and the chi-square statistic was used for testing these relationships. An important number of respondents had suspected that their patients could be victims of animal abuse (Brazil 48.1%; Colombia 64.5%). However, only a minority reported this situation to competent authorities (Brazil 32.7%; Colombia 10.8%). To receive training about veterinary forensics and/or animal welfare sciences in veterinary college was associated with identifying and denouncing animal abuse (p < .05). Deficiency in training received by veterinarians on veterinary forensic and animal welfare science in veterinary college was evident. Despite this, small animal practitioners recognize the existence of an association between animal abuse and interpersonal violence (Brazil 94.2%; Colombia 96.8%). The results highlight the need to strengthen education on animal abuse and promote the participation of veterinarians in the prosecution of this crime in Latin America.
Keywords: Veterinary education | Animal cruelty | Human-animal relationship | Companion animal maltreatment |Link theory
Protection of bio medical iris image using watermarking and cryptography with WPT
محافظت از تصویر عنبیه بیولوژیکی پزشکی با استفاده از علامت گذاری و رمزنگاری با WPT-2019
The emerging technologies in this present world is real time biometrics which recognized a specific person in a reliable manner through their distinct biological features. The most reliable biometric identification is an iris identification. The collection of iris images can be stored in the database which is hacked by the intruders. In order to prevent these databases with watermark text, a novel hybrid method is proposed which is a combination of Wavelet Packet Transform (WPT) and cryptography. This paper presents WPT for segmenting the iris image and finding the minimum energy band where the watermark text is embedded. The watermark text is the personal information of the owner of iris. Once the watermarking is done, the cryptographic key is used to encrypt the watermarked image. This way, both the image and the watermark text are prevented in an efficient manner. The quality measures of watermarked image have been analyzed and compared with other existing techniques. The proposed technique has been analyzed with blurring, salt and pepper, JPEG, cropping, Gaussian noise, rotate, speckle noise, filter, gamma, intensity and histogram equalization noises having PSNR value increased by 3.3%, 3.6%, 4.1%, 5.3%, 7.7%, 6.1%, 11.9%, 7.7%, 14.4%, 10.7% and 10.2% respectively which effectively increased the quality of image.
Keywords: Wavelet Packet Transform (WPT) | Watermarking | Cryptography | Peak Signal to Noise Ratio (PSNR) | Mean Square Error (MSE) | Normalized Cross Correlation (NCC)
Malpractice risk and medical treatment selection
خطر سوء استفاده و انتخاب درمان پزشکی-2019
Westudy howlegal and financial incentives affectmedical decisions. Using patient-level data fromItaly, weidentify the effect of a change in medical liability pressure by exploiting the geographical distribution of hospitals across court districts, where some districts increase the predictability of expected damages per injury while others do not. Using a difference-in-differences identification strategy, we show that as certainty of compensation increases, c-sections increase by 6.5 percentage points. There is no statistically significant effect on secondary health outcomes of either mothers or newborns, but the increase is higher for low-risk than high-risk mothers. The increase is driven by hospitals that have lower quality, are governed by inefficient court districts, face lower expected damages, and are paid more per c-section.
Keywords: Scheduled damages | Cesarean sections | Difference in differences
Image anomaly detection for IoT equipment based on deep learning
تشخیص ناهنجاری تصویر برای تجهیزات اینترنت اشیا بر اساس یادگیری عمیق-2019
Intelligent power grid systems is the trend of power development, since traditional methods of manually monitoring power equipment have been unable to meet the requirements of power systems. When an abnormal situation occurs in the operating environment, most monitoring devices cannot be quickly and accurately identified, which may have serious consequences. Aiming at the above problems, in this paper, we propose an anomaly detection algorithm for the monitoring environment of power IoT equipment operating environment based on deep learning from the perspective of personnel identification and fire smoke detection. The multi-stream CNN-based remote monitoring image personnel detection method and the deep convolutional neural network-based fire smoke detection method have achieved good results in personnel identification and fire smoke detection in the power equipment operating environment monitoring image, respectively. This provides a reference for monitoring image anomaly detection.
Keywords: Operating environment monitoring | Image anomaly detection | Deep learning
An ontology-based methodology for hazard identification and causation analysis
یک روش مبتنی بر هستی شناسی برای شناسایی ریسک و تجزیه و تحلیل علیت-2019
This article presents a dynamic hazard identification methodology founded on an ontology-based knowl-edge modeling framework coupled with probabilistic assessment. The objective is to develop an efficientand effective knowledge-based tool for process industries to screen hazards and conduct rapid risk esti-mation. The proposed generic model can translate an undesired process event (state of the process)into a graphical model, demonstrating potential pathways to the process event, linking causation tothe transition of states. The Semantic web-based Web Ontology Language (OWL) is used to captureknowledge about unwanted process events. The resulting knowledge model is then transformed intoProbabilistic-OWL (PR-OWL) based Multi-Entity Bayesian Network (MEBN). Upon queries, the MEBNsproduce Situation Specific Bayesian Networks (SSBN) to identify hazards and their pathways along withprobabilities. Two open-source software programs, Protégé and UnBBayes, are used. The developed modelis validated against 45 industrial accidental events extracted from the U.S. Chemical Safety Board’s (CSB)database. The model is further extended to conduct causality analysis.
Keywords:Hazard identification |Probabilistic ontology |Web ontology language | Multi-entity Bayesian network | Expert system
Detecting adverse drug reactions in discharge summaries of electronic medical records using Readpeer
بررسی عوارض جانبی دارویی در خلاصه تخلیه سوابق پزشکی الکترونیکی با استفاده از Readpeer-2019
Background: Hospital discharge summaries offer a potentially rich resource to enhance pharmacovigilance efforts to evaluate drug safety in real-world clinical practice. However, it is infeasible for experts to read through all discharge summaries to find cases of drug-adverse event (AE) relations. Purpose: The objective of this paper is to develop a natural language processing (NLP) framework to detect drug- AE relations from unstructured hospital discharge summaries. Basic procedures: An NLP algorithm was designed using customized dictionaries of drugs, adverse event (AE) terms, and rules based on trigger phrases, negations, fuzzy logic and word distances to recognize drug, AE terms and to detect drug-AE relations. Furthermore, a customized annotation tool was developed to facilitate expert review of discharge summaries from a tertiary hospital in Singapore in 2011. Main findings: A total of 33 trial sets with 50 to 100 records per set were evaluated (1620 discharge summaries) by our algorithm and reviewed by pharmacovigilance experts. After every 6 trial sets, drug and AE dictionaries were updated, and rules were modified to improve the system. Excellent performance was achieved for drug and AE entity recognition with over 92% precision and recall. On the final 6 sets of discharge summaries (600 records), our algorithm achieved 75% precision and 59% recall for identification of valid drug-AE relations. Principal conclusions: Adverse drug reactions are a significant contributor to health care costs and utilization. Our algorithm is not restricted to particular drugs, drug classes or specific medical specialties, which is an important attribute for a national regulatory authority to carry out comprehensive safety monitoring of drug products. Drug and AE dictionaries may be updated periodically to ensure that the tool remains relevant for performing surveillance activities. The development of the algorithm, and the ease of reviewing and correcting the results of the algorithm as part of an iterative machine learning process, is an important step towards use of hospital discharge summaries for an active pharmacovigilance program
Keywords: Pharmacovigilance | Text mining | Electronic medical records | Expert system | Adverse drug reaction
Improving air quality prediction accuracy at larger temporal resolutions using deep learning and transfer learning techniques
بهبود دقت پیش بینی کیفیت هوا در وضوح زمانی بزرگتر با استفاده از تکنیک های یادگیری عمیق و انتقال یادگیری-2019
As air pollution becomes more and more severe, air quality prediction has become an important approach for air pollution management and prevention. In recent years, a number of methods have been proposed to predict air quality, such as deterministic methods, statistical methods as well as machine learning methods. However, these methods have some limitations. Deterministic methods require expensive computations and specific knowledge for parameter identification, while the forecasting performance of statistical methods is limited due to the linear assumption and the multicollinearity problem. Most of the machine learning methods, on the other hand, cannot capture the time series patterns or learn from the long-term dependencies of air pollutant concentrations. Furthermore, there is a lack of methods that could generate high prediction accuracy for air quality forecasting at larger temporal resolutions, such as daily and weekly or even monthly. This paper, therefore, proposes a deep learning-based method namely transferred bi-directional long short-term memory (TL-BLSTM) model for air quality prediction. The methodology framework utilizes the bi-directional LSTM model to learn from the longterm dependencies of PM2.5, and applies transfer learning to transfer the knowledge learned from smaller temporal resolutions to larger temporal resolutions. A case study is conducted in Guangdong, China to test the proposed methodology framework. The performance of the framework is compared with other commonly seen machine learning algorithms, and the results show that the proposed TL-BLSTM model has smaller errors, especially for larger temporal resolutions
Keywords: Air quality prediction | Large temporal resolution | Deep learning | Long short-term memory | Transfer learning
Establishing structure-property localization linkages for elastic deformation of three-dimensional high contrast composites using deep learning approaches
ایجاد پیوندهای محلی سازی ساختار و خاصیت برای تغییر شکل الاستیک کامپوزیت های کنتراست بالا سه بعدی با استفاده از روشهای یادگیری عمیق-2019
Data-driven methods are attracting growing attention in the field of materials science. In particular, it is now becoming clear that machine learning approaches offer a unique avenue for successfully mining practically useful process-structure-property (PSP) linkages from a variety of materials data. Most previous efforts in this direction have relied on feature design (i.e., the identification of the salient features of the material microstructure to be included in the PSP linkages). However due to the rich complexity of features in most heterogeneous materials systems, it has been difficult to identify a set of consistent features that are transferable from one material system to another. With flexible architecture and remarkable learning capability, the emergent deep learning approaches offer a new path forward that circumvents the feature design step. In this work, we demonstrate the implementation of a deep learning feature-engineering-free approach to the prediction of the microscale elastic strain field in a given threedimensional voxel-based microstructure of a high-contrast two-phase composite. The results show that deep learning approaches can implicitly learn salient information about local neighborhood details, and significantly outperform state-of-the-art methods.
Keywords: Materials informatics | Convolutional neural networks | Deep learning | Localization | Structure-property linkages