Detection and monitoring of airborne hazards using e-noses has been lifesaving and prevented accidents in real-world scenarios. E-noses generate unique signature patterns for various volatile organic compounds (VOCs) and, by leveraging artificial intelligence, detect the presence of various VOCs, gases, and smokes onsite. Widespread monitoring of airborne hazards across many remote locations is possible by creating a network of gas sensors using Internet connectivity, which consumes significant power. Long-range (LoRa)-based wireless networks do not require Internet connectivity while operating independently. Therefore, we propose a networked intelligent gas sensor system (N-IGSS) which uses a LoRa low-power wide-area networking protocol for real-time airborne pollution hazard detection and monitoring. We developed a gas sensor node by using an array of seven cross-selective tin-oxide-based metal-oxide semiconductor (MOX) gas sensor elements interfaced with a low-power microcontroller and a LoRa module. Experimentally, we exposed the sensor node to six classes i.e., five VOCs plus ambient air and as released by burning samples of tobacco, paints, carpets, alcohol, and incense sticks. Using the proposed two-stage analysis space transformation approach, the captured dataset was first preprocessed using the standardized linear discriminant analysis (SLDA) method. Four different classifiers, namely AdaBoost, XGBoost, Random Forest (RF), and Multi-Layer Perceptron (MLP), were then trained and tested in the SLDA transformation space. The proposed N-IGSS achieved “all correct” identification of 30 unknown test samples with a low mean squared error (MSE) of 1.42 × 10−4 over a distance of 590 m.
Construction industry concrete used heavily. The building industry relies heavily on coarse aggregate (C.A.). Due to the lack of C.A., artificial aggregate known as Ferro silica slag (F.S.S.) is the predominant alternative material. The Use of F.S.S. increases concrete strength and lowers construction costs. The strength of concrete is determined by how long it is allowed to cure. Because of improper curing, the hydration of cement problem can be solved by employing a self-curing mechanism. It’s possible to use paraffin wax Light and Heavy in Concrete have a variety of beneficial effects on S.C.C.’s fresh and hardened concrete qualities. The immersing curing agent is a material that retains Water and reduces evaporation; the self-curing admixture incorporates concrete after the standard setting of concrete on account of increased water retention capacity and compares to internally cured concrete. It gives inside assuaging, known explicitly as “self-curing concrete,” in short, less or no external curing is required in another way if outside mitigating may cause better warmth of hydration. Light molecular weight and high molecular weight are two examples of self-curing liquids that can boost the strength and serviceability of concrete. In this investigation, the percentage of paraffin wax in M25 grade concrete was altered from 0%, 0.1%, 0.5%, and 1.0% and compared to internally cured concrete(I). The replacement of coarse aggregate with the optimum amount of F.S.S. Paraffin is then added to concrete in a liquid form, resulting in varied dosages. Based on the literature, the optimum percentage of F.S.S. 40% with paraffin wax light and heavy 1.0% improvement of compression and flexure strength of concrete. Ultrasonic pulse velocity (U.P.V.) and Rapid chloride penetration tests (RCPT) were used to determine dense microstructure and enhanced durability of concrete.
Modern societies and industrial sectors are serviced through storage and distribution centres (SDCs) such as supermarkets, malls, warehouses, etc. Large quantities of supplies are stocked here, e.g., food grains, clothes, shoes, pharmaceuticals, electronics, plastics, edible oils, electrical wires/equipment, petroleum products, painting materials, etc. Fires due to the burning of these materials are categorized into six classes, viz., Class A, Class B, Class C, Class D, Class K, and Class F. A fire is extinguished better when the right type of fire retardant is used. A thumb rule on firefighting also says, “never fight a fire if you do not know what is burning”. In this paper, we have proposed an Intelligent Decision Support System (ID2S4FH) to generate a real-time ‘fire-map’ of such SDCs during a fire hazard. We have interfaced six tin-oxide-based gas sensor elements, a temperature and humidity sensor, and a particulate matter (PM) sensor with microcontrollers to capture the real-time signature patterns of the ambient air. We burned sixteen different types of materials belonging to six classes of fire and created a dataset consisting of 2400 samples. The sensor array responses were then pre-processed and analysed using various classifiers trained in different analysis space domains. Among the classifiers, four classifiers achieved ‘all correct’ identification of the fire classes of 80 unknown test samples, and the lowest mean squared error (MSE) achieved was 2.81 × 10−3. During a fire hazard, our proposed ID2S4FH can generate real-time fire maps of SDCs and help firefighters to extinguish the fire using the appropriate fire retardant.
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