This case study provides feasibility analysis of adapting Spiking Neural Networks (SNN) based Structural Health Monitoring (SHM) system to explore low-cost solution for inspection of structural health of damaged buildings which survived after natural disaster that is, earthquakes or similar activities. Various techniques are used to detect the structural health status of a building for performance benchmarking, including different feature extraction methods and classification techniques (e.g., SNN, K-means and artificial neural network etc.). The SNN is utilized to process the sensory data generated from full-scale seven-story reinforced concrete building to verify the classification performances. Results show that the proposed SNN hardware has high classification accuracy, reliability, longevity and low hardware area overhead.

Networks-on-Chip (NoC) is the most modular and scalable solution for next generation hardware communication where significant data traffic loads are shared across many communication paths. One key challenge in maximising NoC performance is traffic congestion. The management of congestion at the earliest stage can significantly minimize the impact on NoC throughput. Prediction of NoC congestion offers a pre-emptive strategy in maximising NoC throughput. This paper proposes a novel spiking neural network (SNN) approach to prediction of traffic congestion. The proposed SNN exploits the temporal nature of the traffic to identify congestion patterns. The proposed SNN explores two models and both are trained and evaluated to predict local congestion 30 clock cycles in advance of occurring. Results shows that the SNN predictor utilizes 9 times less hardware area than previous approaches and can achieved up to 96.59% in accuracy.

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Eastern herbal medicines (HMs) are plant-derived naturally occurring substances with minimum or no industrial processing that have long been used in traditional medicine. Aflatoxins are frequent contaminants of plants. Therefore, these mycotoxins are likely to contaminate HMs and pose a health risk to individuals using them on a regular basis as preventive or curative treatments of various diseases. The present study aimed to determine aflatoxin levels in the most popular Pakistani HM formulations and to assess the health risk associated with the intake of aflatoxins. A total of 400 samples of HM formulations collected from four districts of Punjab were analyzed for the quantification of aflatoxins, out of which 52.5% were found to be contaminated. The average daily dose (ADD) of AFB1 and AFs through the intake of HM formulations ranged between 0.00483 and 0.118 ng/kg bw/day and between 0.00579 and 1.714 ng/kg bw/day, respectively. The margin of exposure (MOE) and population cancer risk ranged from 99.49 to 29378.8 and from 0.00011 to 0.0325 liver cancer cases/105 individuals/year (0.0075–2.455 liver cancer cases/105 individuals/75 years), respectively. Despite the low exposure to aflatoxins from HM formulations in the four studied Punjab (Pakistan) districts, the frequent contamination of the analyzed samples suggests that official measures should be considered to manage the associated risk.

Aggregation operators (AOs) are utilized to overcome the influence of uncertain and vague information in different fuzzy environments. A multi-attribute decision-making (MADM) technique plays a vital role in several fields of different environments such as networking analysis, risk assessment, cognitive science, recommender systems, signal processing, and many more domains in ambiguous circumstances. In this article, we elaborated the notion of Aczel–Alsina t-norm (TNM) and t-conorm (TCNM) under the system of complex Pythagorean fuzzy (CPyF) sets (CPyFSs). Some basic operational laws of Aczel–Alsina TNM and TCNM are established including Aczel–Alsina sum, product, scalar multiplication, and power operations based on CPyFSs. We established several AOs of CPyFSs such as CPyF Aczel–Alsina weighted average (CPyFAAWA), and CPyF Aczel–Alsina weighted geometric (CPyFAAWG) operators. The proposed CPyFAAWA and CPyFAAWG operators are symmetric in nature and satisfy the properties of idempotency, monotonicity, boundedness and commutativity. To solve an MADM technique, we established an illustrative example to select a suitable candidate for a vacant post in a multinational company. To see the advantages of our proposed AOs, we compared the results of existing AOs with the results of newly established AOs.