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Numerous works that have demonstrated the study and enhancement of switching properties of ZnO-based RRAM devices are discussed. Several native point defects that have a direct or indirect effect on ZnO are discussed. The use of doping elements, multi-layered structures, suitable bottom and top electrodes, controlling the deposition materials, and the impact of hybrid structure for enhancing the switching dynamics are discussed. The potentials of ZnO-based RRAM for invisible and bendable devices are also covered. ZnO-based RRAM has the potential for possible application in bio-inspired cognitive computational systems. Thus, the synapse capability of ZnO is presented. The sneak-path current issue also besets ZnO-based RRAM crossbar array architecture. Hence, various attempts to subdue the bottleneck have been shown and discussed in this article. Interestingly, ZnO provides not only helpful memory features. However, it demonstrates the ability to be used in nonvolatile multifunctional memory devices. Also, this review covers various issues like the effect of electrodes, interfacial layers, proper switching layers, appropriate fabrication techniques, and proper annealing settings. These may offer a valuable understanding of the study and development of ZnO-based RRAM and should be an avenue for overcoming RRAM challenges.

Chemicals and pesticides contamination in the food, drinking water, and environment ecosystem have become one of the most serious problems for human public health in the world due to their large amount used and wide application in the agriculture industry. Therefore, the detection and analysis of contamination in the food and drinking water by using techniques that are simple and suitable for fast screening are important. This review gives an overview of the last trends and recent advances biosensors for chemicals and pesticides detection based on electrochemical, optical and mechanical transducers strategies. Furthermore, the biosensors are classified according to their immobilized biorecognition elements including aptamer, antibodies, enzymes, and molecularly imprinted polymers. The implementation of nanomaterials such as graphene, carbon nanotubes, and metals nanoparticles are also emphasized and discussed in this review, these nanomaterials provides remarkable features to the biosensors such highly sensitive and accurate which allowing efficient pesticides detection. In addition to highlighting and summarizing various novel sensors, this review also provided some drawbacks, challenging, prospects as well as the current efforts to enhanced optical sensors.

In this paper, a novel hybrid model based on the constraint online sequential extreme learning machine (COSELM) classifier with adaptive weighted sparse representation classification (WSRC) and k nearest neighbor (KNN) is proposed for the WiFi-based indoor positioning system. It is referred to as A Fast-Accurate-Reliable Localization System (AFARLS). AFARLS exploits the speed advantage of COSELM to reduce the computational cost, and the accuracy advantage of WSRC to enhance the classification performance, by utilizing KNN as the adaptive sub-dictionary selection strategy. The understanding is that the original extreme learning machine (ELM) is less robust against noise, while sparse representation classification (SRC) and KNN suffer a high computational burden when using the over-complete dictionary. AFARLS unifies their complementary strengths to resolve each other’s limitation. In large-scale multi-building and multi-floor environments, AFARLS estimates a location that considers the building, floor, and position (longitude and latitude) in a hierarchical and sequential approach according to a discriminative criterion to the COSELM output. If the classifier result is unreliable, AFARLS uses KNN to achieve the best relevant sub-dictionary. The sub-dictionary is fed to WSRC to re-estimate the building and the floor, while the position is predicted by the ELM regressor. AFARLS has been verified on two publicly available datasets, the EU Zenodo and the UJIIndoorLoc. The experimental results demonstrate that AFARLS outperforms the state-of-the-art algorithms on the former dataset, and it provides near state-of-the-art performance on the latter dataset. When the size of the dataset increases remarkably, AFARLS shows that it can maintain its real-time high-accuracy performance.

Environmental monitoring for the detection and regulation of toxic gases is of paramount importance. This study proposes the modeling and fabrication of MEMS devices based on the standard Polysilicon Multi-Users MEMS Process (PolyMUMPs) for acetone gas detection. Titanium dioxide ( $TiO_{2}$ ) nanoparticles has been incorporated onto the top sensor’s surface, amplifying its detection sensitivity. The study delves into Electrothermal mechanisms that has integrated with the embedded microheater to induce thermal forces. This enhancement significantly improves the acetone gas detection capabilities of the device with high sensitivity and low detection limit. Results include the displacement amplitude of the uncoated PolyMUMPs sensor, with exploration of the coated device’s behavior under different heating voltages. Electrothermal actuation is employed, and the output voltage is measured using an MS3110 universal capacitive readout circuit. Comparisons of uncoated and coated devices has been investigated, demonstrating the influence of the sensing material that improve the detection performance. This investigation delves into the impact of varying coating thickness on the PolyMUMPs sensor’s performance. The mass sensitivity of the device is found to be 3.8574 mHz/pg, while the detection limit indicates that the device can detect up to 44 part per billion (ppb) at resonance frequency of 7.627 kHz.