A perspective on the enabling technologies of explainable AI-based industrial packetized energy management.

This paper reviews the key information and communication technologies that are necessary to build an effective industrial energy management system considering the intermittence of renewable sources like wind and solar †. In particular, we first introduce the concept of software-defined energy networks in the context of industrial cyber-physical systems aiming at the optimal energy resource allocation in terms of its environmental impact. The task is formulated as a dynamic scheduling problem where supply and demand must match at minute-level timescale, also considering energy storage units. The use of (explainable and trustworthy) artificial intelligence (AI), (informative) networked data, demand-side management, machine-type (wireless) communications, and energy-aware scheduling in industrial plants are explored in detail. The paper also provides a framework for understanding the complexities of managing renewable energy sources in industrial plants while maintaining efficiency and environmental sustainability.

From private to public governance: The case for reconfiguring energy systems as a commons.

The discussions around the unsustainability of the dominant socio-economic structures have yet to produce solutions to address the escalating problems we face as a species. Such discussions, this paper argues, are hindered by the limited scope of the proposed solutions within a business-as-usual context as well as by the underlying technological rationale upon which these solutions are developed. In this paper, we conceptualize a radical sustainable alternative to the energy conundrum based on an emerging mode of production and a commons-based political economy. We propose a commons-oriented Energy Internet as a potential system for energy production and consumption, which may be better suited to tackle the current issues society faces. We conclude by referring to some of the challenges that the implementation of such a proposal would entail.

Computed Tomographic Study of Remarkable Anatomic Variations in Paranasal Sinus Region and their Clinical Importance - A Retrospective Study.

INTRODUCTION: With the advent of functional endoscopic sinus surgery and coronal computed tomography (CT) imaging, more attention has been paid toward paranasal region anatomy. Detailed knowledge of anatomic variations in paranasal sinus region is critical for surgeons performing endoscopic sinus surgery as well as for the radiologist involved in the pre- and post-operative assessment. The anatomical variants with some accompanying pathologies would directly influence the success of diagnostic and therapeutic management of paranasal sinus diseases. Our study intends to explore the anatomy of paranasal air sinus through CT and to describe its variants, which may predispose to chronic sinusitis and complications in endoscopic sinonasal surgery. MATERIALS AND METHODS: This was a retrospective study carried out in a tertiary institution. Two hundred and fifty patients without paranasal sinus symptoms who presented for head CT studies and gave consent for a coronal section scan of the paranasal sinuses to be taken in addition to the axial section of the head were included in the study. The CT examination was performed with GE Hispeed-NX/I Base-2002 Dual Slice Helical CT machine. RESULTS: Among 250 study population, 100 were females and 150 males. Among these 423 cases of anatomical variants were observed. The most common anatomical variants were pneumatization of the middle nasal turbinates 30.73%. This is followed by agger nasi cells 21.64%, Haller's cells 22.91%, septal deviation 21.91%, and sphenoid sinus septation (20.18%). DISCUSSION: CT is the gold standard in the radiologic investigation of the paranasal sinuses, sinonasal lesions, and inflammatory disease or pre- and post-surgical assessment. It has the capability of disclosing in greatest detail any anatomical variations, which could be causing or precipitating the sinusitis.

Effect of coconut oil in plaque related gingivitis - A preliminary report.

BACKGROUND: Oil pulling or oil swishing therapy is a traditional procedure in which the practitioners rinse or swish oil in their mouth. It is supposed to cure oral and systemic diseases but the evidence is minimal. Oil pulling with sesame oil and sunflower oil was found to reduce plaque related gingivitis. Coconut oil is an easily available edible oil. It is unique because it contains predominantly medium chain fatty acids of which 45-50 percent is lauric acid. Lauric acid has proven anti inflammatory and antimicrobial effects. No studies have been done on the benefits of oil pulling using coconut oil to date. So a pilot study was planned to assess the effect of coconut oil pulling on plaque induced gingivitis. MATERIALS AND METHODS: The aim of the study was to evaluate the effect of coconut oil pulling/oil swishing on plaque formation and plaque induced gingivitis. A prospective interventional study was carried out. 60 age matched adolescent boys and girls in the age-group of 16-18 years with plaque induced gingivitis were included in the study and oil pulling was included in their oral hygiene routine. The study period was 30 days. Plaque and gingival indices of the subjects were assessed at baseline days 1,7,15 and 30. The data was analyzed using paired t test. RESULTS: A statistically significant decrease in the plaque and gingival indices was noticed from day 7 and the scores continued to decrease during the period of study. CONCLUSION: Oil pulling using coconut oil could be an effective adjuvant procedure in decreasing plaque formation and plaque induced gingivitis.

Improving Robustness of Deep Neural Network Acoustic Models via Speech Separation and Joint Adaptive Training.

Although deep neural network (DNN) acoustic models are known to be inherently noise robust, especially with matched training and testing data, the use of speech separation as a frontend and for deriving alternative feature representations has been shown to improve performance in challenging environments. We first present a supervised speech separation system that significantly improves automatic speech recognition (ASR) performance in realistic noise conditions. The system performs separation via ratio time-frequency masking; the ideal ratio mask (IRM) is estimated using DNNs. We then propose a framework that unifies separation and acoustic modeling via joint adaptive training. Since the modules for acoustic modeling and speech separation are implemented using DNNs, unification is done by introducing additional hidden layers with fixed weights and appropriate network architecture. On the CHiME-2 medium-large vocabulary ASR task, and with log mel spectral features as input to the acoustic model, an independently trained ratio masking frontend improves word error rates by 10.9% (relative) compared to the noisy baseline. In comparison, the jointly trained system improves performance by 14.4%. We also experiment with alternative feature representations to augment the standard log mel features, like the noise and speech estimates obtained from the separation module, and the standard feature set used for IRM estimation. Our best system obtains a word error rate of 15.4% (absolute), an improvement of 4.6 percentage points over the next best result on this corpus.

Extraction of spatial information for low-bandwidth telerehabilitation applications.

Telemedicine applications, based on two-dimensional (2D) video conferencing technology, have been around for the past 15 to 20 yr. They have been demonstrated to be acceptable for face-to-face consultations and useful for visual examination of wounds and abrasions. However, certain telerehabilitation assessments need the use of spatial information in order to accurately assess the patient's condition and sending three-dimensional video data over low-bandwidth networks is extremely challenging. This article proposes an innovative way of extracting the key spatial information from the patient's movement during telerehabilitation assessment based on 2D video and then presenting the extracted data by using graph plots alongside the video to help physicians in assessments with minimum burden on existing video data transfer. Some common rehabilitation scenarios are chosen for illustrations, and experiments are conducted based on skeletal tracking and color detection algorithms using the Microsoft Kinect sensor. Extracted data are analyzed in detail and their usability discussed.

Dentistry: by choice or chance? A questionnaire survey for pre clinical dental students

Historically, Indian education has been elitist. Currently, India has the second largest education system in the world preceded by China. In order to meet the demand for coveted dental education, private dental colleges have sprung across the country, making the access easy for the students to enroll into dental education programmes after their higher secondary education. This has lead to a suspicion whether these dental students had been forced into a course that they did not aspire of and even if they actually chose the career, are they performing as good as they expected to be once the course commences. This questionnaire survey aims at discussing these issues and to prepare the Indian education system at both school and college level to encompass and implement an orientation and motivation program to overcome this.

Endodontic Treatment of a Mandibular Second Premolar with Type IV Wiene's Root Canal: A Case Report.

This case report describes an endodontic treatment of a mandibular second premolar with type IV root canal. A 26-year-old male patient reported pain in right mandibular second premolar. Clinical examination showed a large carious lesion with pulp exposure. Radiographs showed minimal periapical changes and slight widening of periodontal ligament space. Mandibular second premolars usually have one canal. The mandibular second premolar may present large number of anatomic variations. The clinician should be aware of the configuration of the pulp system. This case presents the diagnosis and clinical management of a mandibular second premolar with two distinct canals in the apical third of root (Type IV Wiene's canal configuration), drawing particular attention to tactile examination of all the canal walls and obturating it with calamus 3D obturation system.

On Training Targets for Supervised Speech Separation.

Formulation of speech separation as a supervised learning problem has shown considerable promise. In its simplest form, a supervised learning algorithm, typically a deep neural network, is trained to learn a mapping from noisy features to a time-frequency representation of the target of interest. Traditionally, the ideal binary mask (IBM) is used as the target because of its simplicity and large speech intelligibility gains. The supervised learning framework, however, is not restricted to the use of binary targets. In this study, we evaluate and compare separation results by using different training targets, including the IBM, the target binary mask, the ideal ratio mask (IRM), the short-time Fourier transform spectral magnitude and its corresponding mask (FFT-MASK), and the Gammatone frequency power spectrum. Our results in various test conditions reveal that the two ratio mask targets, the IRM and the FFT-MASK, outperform the other targets in terms of objective intelligibility and quality metrics. In addition, we find that masking based targets, in general, are significantly better than spectral envelope based targets. We also present comparisons with recent methods in non-negative matrix factorization and speech enhancement, which show clear performance advantages of supervised speech separation.

Development of telerehabilitation application with designated consultation categories.

Telerehabilitation (TR) is getting ever more popular because it is effective in bringing rehabilitation services to rural populations by means of audiovisual systems and its initial implementation studies presented encouraging results. TR is proven to be helpful, with benefits in terms of reduced travel time, cost, and availability of specialists' support in local communities. However, TR systems that are usable under low-bandwidth network environments are rare. This article introduces the development of a TR system with customized consultation categories for users to choose from, depending on requirements. Each category, with its preset parameter values, is discussed in detail by demonstrating relevant rehabilitation exercises. A novel bandwidth adaptation algorithm is also presented for optimal utilization of the dynamic network conditions, which ensures the system functionality even under narrow-bandwidth environments. Experiment results show that the system is able to perform effectively in each consultation category while the rehabilitation exercises are being performed. The proposed algorithm is also verified for its ability to adapt the content quality and effectively utilize the network under constrained conditions. A survey conducted on the video quality of the system under low-bandwidth conditions shows encouraging results for a large scale deployment of the application.

Prevalence of archaea in chronic periodontitis patients in an Indian population.

AIM: The aim of this study was to investigate the prevalence of archaea in the subgingival crevices of patients with chronic periodontitis in an Indian population. MATERIALS AND METHODS: Thirty four chronic periodontitis patients and 16 healthy subjects were included in the study. Thirty four subgingival plaque samples were collected from chronic periodontitis patients, of which 17 samples were from deep pockets and 17 were from shallow pockets. Sixteen subgingival plaque samples were collected from healthy subjects. The presence of archaea in plaque samples was detected by polymerase chain reaction. RESULTS: Prevalence of archaea in chronic periodontitis patients was 29.4% and in healthy subjects was 11.8%, which was not a statistically significant difference. However, prevalence of archaea, in deep periodontal pockets was 47.1%, in shallow periodontal pockets was 11.8% and in healthy sulcus was 12.5%, respectively. Thus, showing a statistically significant difference between prevalence of archaea in deep periodontal pockets (47.1%) and healthy sulcus (12.5%) and also between deep periodontal pockets (47.1%) and shallow pockets (11.8%), respectively. CONCLUSION: Archaea were detected commonly in severe periodontitis suggesting that these microorganisms might be involved in the pathogenesis of periodontal diseases.

The role of binary mask patterns in automatic speech recognition in background noise.

Processing noisy signals using the ideal binary mask improves automatic speech recognition (ASR) performance. This paper presents the first study that investigates the role of binary mask patterns in ASR under various noises, signal-to-noise ratios (SNRs), and vocabulary sizes. Binary masks are computed either by comparing the SNR within a time-frequency unit of a mixture signal with a local criterion (LC), or by comparing the local target energy with the long-term average spectral energy of speech. ASR results show that (1) akin to human speech recognition, binary masking significantly improves ASR performance even when the SNR is as low as -60 dB; (2) the ASR performance profiles are qualitatively similar to those obtained in human intelligibility experiments; (3) the difference between the LC and mixture SNR is more correlated to the recognition accuracy than LC; (4) LC at which the performance peaks is lower than 0 dB, which is the threshold that maximizes the SNR gain of processed signals. This broad agreement with human performance is rather surprising. The results also indicate that maximizing the SNR gain is probably not an appropriate goal for improving either human or machine recognition of noisy speech.

Robust speech recognition from binary masks.

Inspired by recent evidence that a binary pattern may provide sufficient information for human speech recognition, this letter proposes a fundamentally different approach to robust automatic speech recognition. Specifically, recognition is performed by classifying binary masks corresponding to a word utterance. The proposed method is evaluated using a subset of the TIDigits corpus to perform isolated digit recognition. Despite dramatic reduction of speech information encoded in a binary mask, the proposed system performs surprisingly well. The system is compared with a traditional HMM based approach and is shown to perform well under low SNR conditions.