A knowledge construction methodology to automate case-based learning using clinical documents

The case-based learning (CBL) approach has gained attention in medical education as an alternative to traditional learning methodology. However, current CBL systems do not facilitate and provide computer-based domain knowledge to medical students for solving real-world clinical cases during CBL practice. To automate CBL, clinical documents are beneficial for constructing domain knowledge. In the literature, most systems and methodologies require a knowledge engineer to construct machine-readable knowledge. Keeping in view these facts, we present a knowledge construction methodology (KCM-CD) to construct domain knowledge ontology (i.e., structured declarative knowledge) from unstructured text in a systematic way using artificial intelligence techniques, with minimum intervention from a knowledge engineer. To utilize the strength of humans and computers, and to realize the KCM-CD methodology, an interactive case-based learning system(iCBLS) was developed. Finally, the developed ontological model was evaluated to evaluate the quality of domain knowledge in terms of coherence measure. The results showed that the overall domain model has positive coherence values, indicating that all words in each branch of the domain ontology are correlated with each other and the quality of the developed model is acceptable.     

Luminescence from praseodymium doped AlN thin films deposited by RF magnetron sputtering and the effect of material structure and thermal annealing on the luminescence

Thin films of Praseodymium doped AlN are deposited on silicon (111) substrates at 77 K and 950 K by rf magnetron sputtering method. About 500–1000 nm thick films are grown at 100–200 watts RF power and 5–8 mTorr nitrogen, using a metal target of Al with Pr. X-rays diffraction results show that films deposited at 77 K are amorphous and those deposited at 950 K are crystalline. Cathodoluminescence studies are performed at room temperature and luminescence peaks are observed in a wide range from ultraviolet to infrared region. The most intense peak is obtained in green at 526 nm from amorphous films as a result from ³P₁→³H₅ transition. In crystalline films the intense peak was obtain in red at 648 nm as a result from ³P₀→³F₂ transition. Films are thermally activated at 1300 K for half an hour in a nitrogen atmosphere. Thermal activation enhances the intensity of luminescence. Two peaks at 488 nm and 505 nm merged after thermal activation, giving rise to a single peak at 495 nm.     

Autonomous mapping of HL7 RIM and relational database schema

Healthcare systems need to share information within and across the boundaries in order to provide better care to the patients. For this purpose, they take advantage of the full potential of current state of the art in healthcare standards providing interoperable solutions. HL7 V3 specification is an international message exchange and interoperability standard. HL7 V3 messages exchanged between healthcare applications are ultimately recorded into local healthcare databases, mostly in relational databases. In order to bring these relational databases in compliance with HL7, mappings between HL7 RIM (Reference Information Model) and relational database schema are required. Currently, RIM and database mapping is largely performed manually, therefore it is tedious, time consuming, error prone and expensive process. It is a challenging task to determine all correspondences between RIM and schema automatically because of extreme heterogeneity issues in healthcare databases. To reduce the amount of manual efforts as much as possible, autonomous mapping approaches are required. This paper proposes a technique that addresses the aforementioned mapping issue and aligns healthcare databases to HL7 V3 RIM specifications. Furthermore, the proposed technique has been implemented as a working application and tested on real world healthcare systems. The application loads the target healthcare schema and then identifies the most appropriate match for tables and the associated fields in the schema by using domain knowledge and the matching rules defined in the Mapping Knowledge Repository. These rules are designed to handle the complexity of semantics found in healthcare databases. The GUI allows users to view and edit/re-map the correspondences. Once all the mappings are defined, the application generates Mapping Specification, which contains all the mapping information i.e. database tables and fields with associated RIM classes and attributes. In order to enable the transactions, the application is facilitated with the autonomous code generation from the Mapping Specification. The Code Generator component focuses primarily on generating custom classes and hibernate mapping files against the runtime system to retrieve and parse the data from the data source—thus allows bi-directional HL7 to database communication, with minimum programming required. Our experimental results show 35–65% accuracy on real laboratory systems, thus demonstrating the promise of the approach. The proposed scheme is an effective step in bringing the clinical databases in compliance with RIM, providing ease and flexibility.     

Thermoelectric properties of SbNCa3 and BiNCa3 for thermoelectric devices and alternative energy applications

Thermoelectric properties of two antiperovskites SbNCa₃ and BiNCa₃ are calculated using first principles calculations. High values of Seebeck coefficients are observed for these materials. Electrical and thermal conductivities are also calculated. Increase in thermal conductivity and decrease in electrical conductivity are found with increasing temperature. The maximum values of thermal conductivity are 92×10¹⁴  W/m K s and 88×10¹⁴  W/m K s for SbNCa₃ and BiNCa₃ respectively at a temperature of 900 K. The peak values of 5×10²⁰/Ω m s and 5.2×10²⁰/Ω m s are achieved for n-type SbNCa₃ and BiNCa₃ respectively at a temperature of 300 K. Figure of merit is achieved for these materials at room temperature which shows that these materials can be useful for thermoelectric devices and alternative energy sources.     

DeepClassRooms: a deep learning based digital twin framework for on-campus class rooms

Neural Computing and Applications - A lot of different methods are being opted for improving the educational standards through monitoring of the classrooms. The developed world uses Smart...     

Optimal Deep Learning Based Intruder Identification in Industrial Internet of Things Environment

With the increased advancements of smart industries, cybersecurity has become a vital growth factor in the success of industrial transformation. The Industrial Internet of Things (IIoT) or Industry 4.0 has revolutionized the concepts of manufacturing and production altogether. In industry 4.0, powerful Intrusion Detection Systems (IDS) play a significant role in ensuring network security. Though various intrusion detection techniques have been developed so far, it is challenging to protect the intricate data of networks. This is because conventional Machine Learning (ML) approaches are inadequate and insufficient to address the demands of dynamic IIoT networks. Further, the existing Deep Learning (DL) can be employed to identify anonymous intrusions. Therefore, the current study proposes a Hunger Games Search Optimization with Deep Learning-Driven Intrusion Detection (HGSODL-ID) model for the IIoT environment. The presented HGSODL-ID model exploits the linear normalization approach to transform the input data into a useful format. The HGSO algorithm is employed for Feature Selection (HGSO-FS) to reduce the curse of dimensionality. Moreover, Sparrow Search Optimization (SSO) is utilized with a Graph Convolutional Network (GCN) to classify and identify intrusions in the network. Finally, the SSO technique is exploited to fine-tune the hyper-parameters involved in the GCN model. The proposed HGSODL-ID model was experimentally validated using a benchmark dataset, and the results confirmed the superiority of the proposed HGSODL-ID method over recent approaches.     

Biosynthesis and characterisation of nano‐silica as potential system for carrying streptomycin at nano‐scale drug delivery

A growing trend within nanomedicine has been the fabrication of self‐delivering supramolecular nanomedicines containing a high and fixed drug content ensuring eco‐friendly conditions. This study reports on green synthesis of silica nanoparticles (Si‐NPs) using Azadirachta indica leaves extract as an effective chelating agent. X‐ray diffraction analysis and Fourier transform‐infra‐red spectroscopic examination were studied. Scanning electron microscopy analysis revealed that the average size of particles formed via plant extract as reducing agent without any surfactant is in the range of 100–170 nm while addition of cetyltrimethyl ammonium bromide were more uniform with 200 nm in size. Streptomycin as model drug was successfully loaded to green synthesised Si‐NPs, sustain release of the drug from this conjugate unit were examined. Prolong release pattern of the adsorbed drug ensure that Si‐NPs have great potential in nano‐drug delivery keeping the environment preferably biocompatible, future cytotoxic studies in this connection is helpful in achieving safe mode for nano‐drug delivery.Inspec keywords: silicon compounds, nanofabrication, nanomedicine, drug delivery systems, nanoparticles, X‐ray diffraction, Fourier transform infrared spectra, scanning electron microscopyOther keywords: nanosilica, streptomycin, nanoscale drug delivery, nanomedicine, silica nanoparticles, Azadirachta indica leaves extract, X‐ray diffraction analysis, Fourier transform‐infrared spectroscopy, scanning electron microscopy, cetyltrimethyl ammonium bromide, SiO₂      

Smart Innovation Engineering: Toward Intelligent Industries of the Future

ABSTRACT

Knowledge-based engineering systems are founded upon integration of knowledge into computer systems and are one of the core requirements for the future Industry 4.0. This paper presents a system called smart innovation engineering (SIE) capable of facilitating product innovation process semi-automatically. It enhances decision-making processes using the explicit knowledge of formal decision events. The SIE system carries the promise to support the innovation processes of manufactured products in a quick and efficient way. It stores and reuses past decisional events or sets of experiences related to innovation issues, which significantly enhances innovation progression. The analysis of basic concepts and implementation method proves that SIE system is an advanced form of cyber physical systems. It is flexible, systematic, fast, and supports customization. It can play a vital role toward Industry 4.0 development.     

Inflammation,Oxidative Stress,Senescence in Atherosclerosis: Thioredoxine-1 as an Emerging Therapeutic Target

Atherosclerosis is a leading cause of cardiovascular diseases (CVD) worldwide and intimately linked to aging. This pathology is characterized by chronic inflammation, oxidative stress, gradual accumulation of low-density lipoproteins (LDL) particles and fibrous elements in focal areas of large and medium arteries. These fibrofatty lesions in the artery wall become progressively unstable and thrombogenic leading to heart attack, stroke or other severe heart ischemic syndromes. Elevated blood levels of LDL are major triggering events for atherosclerosis. A cascade of molecular and cellular events results in the atherosclerotic plaque formation, evolution, and rupture. Moreover, the senescence of multiple cell types present in the vasculature were reported to contribute to atherosclerotic plaque progression and destabilization. Classical therapeutic interventions consist of lipid-lowering drugs, anti-inflammatory and life style dispositions. Moreover, targeting oxidative stress by developing innovative antioxidant agents or boosting antioxidant systems is also a well-established strategy. Accumulation of senescent cells (SC) is also another important feature of atherosclerosis and was detected in various models. Hence, targeting SCs appears as an emerging therapeutic option, since senolytic agents favorably disturb atherosclerotic plaques. In this review, we propose a survey of the impact of inflammation, oxidative stress, and senescence in atherosclerosis; and the emerging therapeutic options, including thioredoxin-based approaches such as anti-oxidant, anti-inflammatory, and anti-atherogenic strategy with promising potential of senomodulation.     

Eco-friendly food products as source of natural colorant for wool yarn dyeing

ABSTRACT

Food products as a source of natural colorants are gaining widespread popularity around the globe due to their ayurvedic and eco-friendly nature. Current study is aimed with the utilization of food products (Saffron and madder) as a source of dye for woolen yarn. It is found that, for Saffron, irradiation of yarn for 6 min gives high color strength by dyeing for 45 min in the presence of 2% of salts using un-irradiated dye bath of 1 pH, whereas for madder, 4 min is optimal irradiation time for extract to dye irradiated yarn for 25 min in the presence of 6% of salts using the irradiated dye bath of pH 1. The mordanting carried out at optimal conditions show that using both extracts, woolen yarn dyed at optimal conditions has given good color characteristics. It is concluded that microwave radiation has not only improved natural dyeing process but also make the process more eco-friendly and sustainable as well.