Background: Huntington’s disease is an inherited autosomal dominant trait neuro-degenerative disorder caused by changes (mutations) of a gene called huntingtin (htt) that is located on the short arm (p) of chromosome 4, CAG expansion mutation. It is characterized by unusual movements, cognitive and psychiatric disorders. Objective: This review was undertaken to apprehend biological pathways of Huntington’s disease (HD) pathogenesis and its management by nature-derived products. Natural products can be lucrative for the management of HD as it shows protection against HD in pre-clinical trials. Advanced research is still required to assess the therapeutic effectiveness of the known organic products and their isolated compounds in HD experimental models. Summary: Degeneration of neurons in Huntington’s disease is distinguished by progressive loss of motor coordination and muscle function. This is due to the expansion of CAG trinucleotide in the first exon of the htt gene responsible for neuronal death and neuronal network degeneration in the brain. It is believed that the factors such as molecular genetics, oxidative stress, excitotoxicity, mitochondrial dysfunction, neuroglia dysfunction, protein aggregation, and altered UPS leads to HD. The defensive effect of the natural product provides therapeutic efficacy against HD. Recent reports on natural drugs have enlightened the protective role against HD via antioxidant, anti-inflammatory, antiapoptotic, and neurofunctional regulation.

Several studies have concentrated on cooling the PV module temperature (T_PV) to enhance the system’s electrical output power and efficiency in recent years. In this review study, PCM-based cooling techniques are reviewed majorly classified into three techniques: (i) incorporating raw/pure PCM behind the PV module is one of the most straightforward techniques; (ii) thermal additives such as inter-fin, nano-compound, expanded graphite (EG), and others are infused in PCM to enhance the heat transfer rate between PV module and PCM; and (iii) thermal collectors that are placed behind the PV module or inside the PCM container to minimize the PCM usage. Advantageously, these techniques favor reusing the waste heat from the PV module. Further, in this study, PCM thermophysical properties are straightforwardly discussed. It is found that the PCM melting temperature (T_melt) and thermal conductivity (K_PCM) become the major concerns in cooling the PV module. Based on the literature review, experimentally proven PV-PCM temperatures are analyzed over a year for UAE and Islamabad locations using typical meteorological year (TMY) data from the National Renewable Energy Laboratory (NREL) data source in 1 h frequency.

The circular economy encourages the production and consumption of sustainable embankment geomaterials and their blends utilizing recycled waste materials in roads, railway tracks, airfields, and underground structures. Geomaterials comprising high-plastic soft expansive clay pose excessive settlement during cyclic traffic/railway/airfield loading resulting in uneven geometry of overlying layers. This paper demonstrates multiobjective optimized improvement of expansive clay (C) geotechnical characteristics by cost-effective agro-wastes additives at microlevel (by 3 to 12 rice husk ash, i.e., RHA), nanolevel (by 0.6 to 1.5 rice husk derived green nano-SiO₂, i.e., NS), and synergistic micro to nanolevel (NS-RHA). The swell potential, resilient modulus (MR), initial elastic modulus (Es), unconfined compressive strength (UCT), and California bearing ratio (CBR) of C and its blends were determined. The chemical characterization of C and its blends were conducted through Fourier transform infrared spectroscopy (FTIR) and optical microscopic tests. The outcome of this study depicted that the cost ratio for the optimized composite, i.e., (1.2 NS-9 RHA)/(9 RHA) is 1.22 whereas stiffness ratio MR (NS-RHA)/MR(RHA) and Es (NS-RHA)/Es(RHA) and strength ratio UCT(NS-RHA)/UCT(RHA) and CBR(NS-RHA)/CBR(RHA) were found to be 2.0, 1.64, 2.17, and 2.82, respectively. FTIR revealed the chemical compatibility between C, RHA, & NS from durability perspective. Cost-stiffness results of this study can be applied by geotechnical experts to economize the green stabilization of C by use of agro-waste for sustainable development.

Video surveillance in smart cities provides efficient city operations, safer communities, and improved municipal services. Object detection is a computer vision-based technology, which is utilized for detecting instances of semantic objects of a specific class in digital videos and images. Crowd density analysis is a widely used application of object detection, while crowd density classification techniques face complications such as inter-scene deviations, non-uniform density, intra-scene deviations and occlusion. The convolution neural network (CNN) model is advantageous. This study presents Aquila Optimization with Transfer Learning based Crowd Density Analysis for Sustainable Smart Cities (AOTL-CDA3S). The presented AOTL-CDA3S technique aims to identify different kinds of crowd densities in the smart cities. For accomplishing this, the proposed AOTL-CDA3S model initially applies a weighted average filter (WAF) technique for improving the quality of the input frames. Next, the AOTL-CDA3S technique employs an AO algorithm with the SqueezeNet model for feature extraction. Finally, to classify crowd densities, an extreme gradient boosting (XGBoost) classification model is used. The experimental validation of the AOTL-CDA3S approach is tested by means of benchmark crowd datasets and the results are examined under distinct metrics. This study reports the improvements of the AOTL-CDA3S model over recent state of the art methods.