Blast vibration dependence on total explosives weight in open-pit blasting

It is well established that the blast design parameters, namely explosives weight per delay, distance between the blast site and monitoring location, delay interval, total explosives detonated in a blasting round, velocity of detonation (VOD) of explosives, burden, spacing, explosive column length, top stemming length, number of decks and their length, transmitting media and its geology, and scattering in the delay time of detonators, influence blast-induced vibrations. A study was conducted to assess the effect of total weight of explosive detonated in the blast in ground on the magnitude of blast vibrations at four big coal open-cast mines in India keeping all the parameters constant as stated above. Accordingly, experimental as well as production blasts were conducted at drag line and shovel benches. The results revealed that the magnitude of blast vibrations was influenced by the total amount of explosive detonated in a blast in ground at shorter distances regardless of maximum explosives weight per delay. This paper describes the result of a study carried out to investigate these effects at open-cast projects in India. The study involved 60 blasts with varying blast designs and 498 vibration data were recorded

A Case of Trichotillomania With Comorbid Depression And Anxiety

BACKGROUND: Trichotillomania also called hair-pulling or compulsive hair-pulling disorder is a psychiatric condition that involves recurrent, irresistible urges to pull out hair from their scalps, eyebrows, or other areas of the body.[1] It is an uncontrollable urge that is present after an anxiety-provoking situation and which causes severe distress it can interfere with one’s social, occupational functioning.[6] The term trichotillomania was coined by the French dermatologist Francois Henri Hallopeou in the year 1889.[2] The lifetime prevalence of trichotillomania is estimated to be between 0.6% and 4.0% of the overall population with a 1% prevalence in gender-wise.[5] The mean age at diagnosis is between 9 and 13 years, the symptoms can be pulling out hair repeatedly breaking off pieces of hair, eating or keeping hair, feeling relieved after pulling hair.[2]. Associated symptoms included sadness, lack of attention and concentration, lack of interest in doing daily activities, which affect the socio- functional aspects of the person. The comorbid conditions or the distress is mainly leading the person for consultation in ICD-10 and DSM IV, trichotillomania is classified under impulse control disorder in DSM V it is under obsessive-compulsive and related disorders. [1] CASE DESCRIPTION: A 20-year-old adult female was referred from the Department of Dermatology presented with a history of hair pulling, hair loss and anxiety, sadness related to her hair-pulling behavior. She had these symptoms for the past 4 years. The reason for referral was that the comorbid anxiety, and their history taking suggestive of hair pulling associated with anxiety. After collecting the detailed history and psychological assessment, it was confirmed as a case of Trichotillomania, the comorbid condition are depression and anxiety. CONCLUSIONS: This case report presents trichotillomania the assessment indicated as a moderate level of anxiety and depression, so it is very essential for a detailed investigation, and also both pharmacotherapy and psychotherapy are very essential for the complete recovery of the patient

Deep learning to filter SMS spam

The popularity of short message service (SMS) has been growing over the last decade. For businesses, these text messages are more effective than even emails. This is because while 98% of mobile users read their SMS by the end of the day, about 80% of the emails remain unopened. The popularity of SMS has also given rise to SMS Spam, which refers to any irrelevant text messages delivered using mobile networks. They are severely annoying to users. Most existing research that has attempted to filter SMS Spam has relied on manually identified features. Extending the current literature, this paper uses deep learning to classify Spam and Not-Spam text messages. Specifically, Convolutional Neural Network and Long Short-term memory models were employed. The proposed models were based on text data only, and self-extracted the feature set. On a benchmark dataset consisting of 747 Spam and 4,827 Not-Spam text messages, a remarkable accuracy of 99.44% was achieved

ADIC: Anomaly Detection Integrated Circuit in 65nm CMOS utilizing Approximate Computing

In this paper, we present a low-power anomaly detection integrated circuit (ADIC) based on a one-class classifier (OCC) neural network. The ADIC achieves low-power operation through a combination of (a) careful choice of algorithm for online learning and (b) approximate computing techniques to lower average energy. In particular, online pseudoinverse update method (OPIUM) is used to train a randomized neural network for quick and resource efficient learning. An additional 42% energy saving can be achieved when a lighter version of OPIUM method is used for training with the same number of data samples lead to no significant compromise on the quality of inference. Instead of a single classifier with large number of neurons, an ensemble of K base learner approach is chosen to reduce learning memory by a factor of K. This also enables approximate computing by dynamically varying the neural network size based on anomaly detection. Fabricated in 65nm CMOS, the ADIC has K = 7 Base Learners (BL) with 32 neurons in each BL and dissipates 11.87pJ/OP and 3.35pJ/OP during learning and inference respectively at Vdd = 0.75V when all 7 BLs are enabled. Further, evaluated on the NASA bearing dataset, approximately 80% of the chip can be shut down for 99% of the lifetime leading to an energy efficiency of 0.48pJ/OP, an 18.5 times reduction over full-precision computing running at Vdd = 1.2V throughout the lifetime.Comment: 1

Is this question going to be closed? : Answering question closibility on Stack Exchange

Community question answering sites (CQAs) are often flooded with questions that are never answered. To cope with the problem, experienced users of Stack Exchange are now allowed to mark newly-posted questions as closed if they are of poor quality. Once closed, a question is no longer eligible to receive answers. However, identifying and closing subpar questions takes time. Therefore, the purpose of this paper is to develop a supervised machine learning system that predicts question closibility, the possibility of a newly posted question to be eventually closed. Building on extant research on CQA question quality, the supervised machine learning system uses 17 features that were grouped into four categories, namely, asker features, community features, question content features, and textual features. The performance of the developed system was tested on questions posted on Stack Exchange from 11 randomly chosen topics. The classification performance was generally promising and outperformed the baseline. Most of the measures of precision, recall, F1-score, and AUC were above 0.90 irrespective of the topic of questions. By conceptualizing question closibility, the paper extends previous CQA research on question quality. Unlike previous studies, which were mostly limited to programming-related questions from Stack Overflow, this one empirically tests question closibility on questions from 11 randomly selected topics. The set of features used for classification offers a framework of question closibility that is not only more comprehensive but also more parsimonious compared with prior works

Jaw Morphology and Vertical Facial Types: A Cephalometric Appraisal

Aims and objectives: To evaluate the maxillary and mandibular morphology in different vertical facial types and to implicate the achieved results into diagnosis and treatment planning of patients requiring orthodontic treatment. Materials and methods: The present study is conducted on a sample of 120 subjects comprising of 60 males and 60 females in the age range of 18 to 25 years. The lateral head cephalograms of the subjects were divided into three groups, i.e. group I (hypodivergent), group II (normodivergent) and group III(hyperdivergent) with regard to vertical facial type by using the following three parameters, i.e. SN-MP (facial divergence angle), overbite depth indicator (ODI) and Jarabak ratio or facial height ratio (FHR). Differences among the groups and between genders were assessed by means of variance analysis and Newman- Keuls post hoc test. Results: Maxillary and mandibular anterior alveolar and maxillary postalveolar height was found to be greater for hyperdivergent group in comparison to others. Hyperdivergent facial types posseslong and narrow symphysis along with greater antegonial notch depth whereas hypodivergent showed an opposite tendency. Hyperdivergent facial types generally have a smaller maxillary area as compared to other facial types. However, total mandibular area does not vary among different vertical facial types. Sexual dichotomy was found with maxillary anterior alveolar and basal height, mandibular posterior alveolar and basal height, mandibular length, symphyseal depth, depth of the antegonial notch, symphyseal area and ext/total symphyseal area ratio. Conclusion: Vertical facial type may be related to the morphological and dentoalveolar pattern of both maxilla and mandible. Determination of this relationship may be of great help from diagnostic as well as therapeutic aspects of many vertical malocclusion problems

Analysis of community question‐answering issues via machine learning and deep learning: State‐of‐the‐art review

Over the last couple of decades, community question-answering sites (CQAs) have been a topic of much academic interest. Scholars have often leveraged traditional machine learning (ML) and deep learning (DL) to explore the ever-growing volume of content that CQAs engender. To clarify the current state of the CQA literature that has used ML and DL, this paper reports a systematic literature review. The goal is to summarise and synthesise the major themes of CQA research related to (i) questions, (ii) answers and (iii) users. The final review included 133 articles. Dominant research themes include question quality, answer quality, and expert identification. In terms of dataset, some of the most widely studied platforms include Yahoo! Answers, Stack Exchange and Stack Overflow. The scope of most articles was confined to just one platform with few cross-platform investigations. Articles with ML outnumber those with DL. Nonetheless, the use of DL in CQA research is on an upward trajectory. A number of research directions are proposed

PLAS-5k: Dataset of Protein-Ligand Affinities from Molecular Dynamics for Machine Learning Applications

Computational methods and recently modern machine learning methods have played a key role in structure-based drug design. Though several benchmarking datasets are available for machine learning applications in virtual screening, accurate prediction of binding affinity for a protein-ligand complex remains a major challenge. New datasets that allow for the development of models for predicting binding affinities better than the state-of-the-art scoring functions are important. For the first time, we have developed a dataset, PLAS-5k comprised of 5000 protein-ligand complexes chosen from PDB database. The dataset consists of binding affinities along with energy components like electrostatic, van der Waals, polar and non-polar solvation energy calculated from molecular dynamics simulations using MMPBSA (Molecular Mechanics Poisson-Boltzmann Surface Area) method. The calculated binding affinities outperformed docking scores and showed a good correlation with the available experimental values. The availability of energy components may enable optimization of desired components during machine learning-based drug design. Further, OnionNet model has been retrained on PLAS-5k dataset and is provided as a baseline for the prediction of binding affinities

Changes in hypertension prevalence, awareness, treatment and control rates over 20 years in National Capital Region of India: results from a repeat cross-sectional study.

BACKGROUND AND OBJECTIVES: Despite being one of the leading risk factors of cardiovascular mortality, there are limited data on changes in hypertension burden and management from India. This study evaluates trend in the prevalence, awareness, treatment and control of hypertension in the urban and rural areas of India's National Capital Region (NCR). DESIGN AND SETTING: Two representative cross-sectional surveys were conducted in urban and rural areas (survey 1 (1991-1994); survey 2 (2010-2012)) of NCR using similar methodologies. PARTICIPANTS: A total of 3048 (mean age: 46.8±9.0 years; 52.3% women) and 2052 (mean age: 46.5±8.4 years; 54.2% women) subjects of urban areas and 2487 (mean age: 46.6±8.8 years; 57.0% women) and 1917 (mean age: 46.5±8.5 years; 51.3% women) subjects of rural areas were included in survey 1 and survey 2, respectively. PRIMARY AND SECONDARY OUTCOME MEASURES: Hypertension was defined as per Joint National Committee VII guidelines. Structured questionnaire was used to measure the awareness and treatment status of hypertension. A mean systolic blood pressure <140 mm Hg and diastolic blood pressure <90 mm Hg was defined as control of hypertension among the participants with hypertension. RESULTS: The age and sex standardised prevalence of hypertension increased from 23.0% to 42.2% (p<0.001) and 11.2% to 28.9% (p<0.001) in urban and rural NCR, respectively. In both surveys, those with high education, alcohol use, obesity and high fasting blood glucose were at a higher risk for hypertension. However, the change in hypertension prevalence between the surveys was independent of these risk factors (adjusted OR (95% CI): urban (2.3 (2.0 to 2.7)) rural (3.1 (2.4 to 4.0))). Overall, there was no improvement in awareness, treatment and control rates of hypertension in the population. CONCLUSION: There was marked increase in prevalence of hypertension over two decades with no improvement in management