Distance and similarity measures on belief and plausibility under q-rung orthopair fuzzy sets with applications.

Belief and plausibility functions based on evidence theory (ET) have been widely used in managing uncertainty. Various generalizations of ET to fuzzy sets (FSs) have been reported in the literature, but no generalization of ET to q-rung orthopair fuzzy sets (q-ROFSs) has been made yet. Therefore, this paper proposes a novel, simple, and intuitive approach to distance and similarity measures for q-ROFSs based on belief and plausibility functions within the framework of ET. This research addresses a significant research gap by introducing a comprehensive framework for handling uncertainty in q-ROFSs using ET. Furthermore, it acknowledges the limitations inherent in the current state of research, notably the absence of generalizations of ET to q-ROFSs and the challenges in extending belief and plausibility measures to certain aggregation operators and other generalizations including Hesitant fuzzy sets, Bipolar fuzzy sets, Fuzzy soft sets etc. Our contribution lies in the proposal of a novel approach to distance and similarity measures for q-ROFSs under ET, utilizing Orthopairian belief and plausibility intervals (OBPIs). We establish new similarity measures within the generalized ET framework and demonstrate the reasonability of our method through useful numerical examples. Additionally, we construct Orthopairian belief and plausibility GRA (OBP-GRA) for managing daily life complex issues, particularly in multicriteria decision-making scenarios. Numerical simulations and results confirm the usability and practical applicability of our proposed method in the framework of ET.

Investigation of the potential of pomegranate peel as a treatment option for heavy metal contaminated wastewater: Experimental and modeling approaches.

Heavy metals can cause serious environmental and human health problems, and their removal from wastewater is critical to protect our planet and communities. This study investigated the ability of crushed pomegranate peel to remove mercury and cadmium ions from contaminated water as a function of different experimental parameters. The experimental results showed that the pH of the solution influenced the adsorptive removal of heavy metals, with the best performance observed at pH 4.8. Optimization studies and process balance modeling were performed to optimize the process for commercial use. The performance of pomegranate peel was compared with that of other materials, and the highest adsorption capacities for both cadmium (Ca (II)) and mercury (Hg (II)) ions were observed to be 89.59 and 42.125 mg/g, respectively. The results were interpreted using the Langmuir model, which provided the best fit to describe the behavior of the process.

Cobalt-doped zinc oxide based memristors with nociceptor characteristics for bio-inspired technology.

Neuromorphic computing is a new field of information technology, which is inspired by the biomimetic properties of the memristor as an electronic synapse and neuron. If there are electronic receptors that can transmit exterior impulses to the internal nervous system, then the use of memristors can be expanded to artificial nerves. In this study, a layer type memristor is used to build an artificial nociceptor in a very feasible and straightforward manner. An artificial nociceptor is demonstrated here through the fabrication and characterization of a cobalt-doped zinc oxide (CZO)/Au based memristor. In order to increase threshold switching performance, the surface effects of the CZO layer are eliminated by adding cobalt cobalt-doped zinc oxide (CZO) layer between the P++-Si and Au electrodes. Allodynia, hyperalgesia, threshold, and relaxation are the four distinct nociceptive behaviours that the device displays based on the strength, rate of relapse, and duration of the external stimuli. The electrons that are trapped in or released from the CZO layer's traps are responsible for these nociceptive behaviours. A multipurpose nociceptor performance is produced by this type of CZO-based device, which is crucial for artificial intelligence system applications such as neural integrated devices with nanometer-sized characteristics.

Experimental and theoretical study of improved mesoporous titanium dioxide perovskite solar cell: The impact of modification with graphene oxide.

The present study serves experimental and theoretical analyses in developing a hybrid advanced structure as a photolysis, which is based on electrospun Graphene Oxide-titanium dioxide (GO-TiO2) nanofibers as an electron transfer material (ETMs) functionalized for perovskite solar cell (PVSCs) with GO. The prepared ETMs were utilized for the synthesis of mixed-cation (FAPbI3)0.8(MAPbBr3)0.2. The effect of GO on TiO2 and their chemical structure, electronic and morphological characteristic were investigated and discussed. The elaborated device, namely ITO/Bl-TiO2/3 wt% GO-TiO2/(FAPbI3)0.8(MAPbBr3)0.2/spiro-MeTAD/Pt, displayed 20.14% disposition and conversion solar energy with fill factor (FF) of 1.176%, short circuit current density (Jsc) of 20.56 mA/cm2 and open circuit voltage (VOC) 0.912 V. The obtained efficiency is higher than titanium oxide (18.42%) and other prepared GO-TiO2 composite nanofibers based ETMs. The developed materials and device would facilitate elaboration of advanced functional materials and devices for energy storage applications.