A review of the desert gold jojoba (Simmondsia chinensis) whole plant,oil, and meal: Phytochemical composition,medicinal uses,and detoxification

Simmondsia chinensis (Link) C.K. Schneider (Jojoba) is a valuable shrub that can bear harsh conditions and is cultivated in many countries globally. Its prominence originates from the unique oil that constitutes more than 50% of the seeds. The great economic value of jojoba oil is highlighted in many fields, especially the cosmetic industry. The remaining meal, which is rich in proteins, constitutes a good source for cattle feeding. However, the presence of antinutritional principles in the meal limited its use and encouraged the researchers to find different ways for its detoxification. The detoxification ways of jojoba meal included physical, biological, and chemical treatments. The phytochemical composition of the oil was deeply studied, but for the remaining plant, only few studies have reported its chemical composition. Jojoba oil composed of wax esters (97%), fatty acids, fatty alcohols, sterols, and small percentage of vitamin E. Jojoba possesses a long traditional history. It has been used in folklore for treatment of cold, dysuria, and obesity. Many recent studies reported its medicinal and pharmacological properties like antioxidant, anti-inflammatory, antimicrobial, anticancer, anti-acne, anti-psoriasis, wound healing, and hepatoprotective activities. Many of these biological activities have been attributed to the presence of several phytochemicals such as simmondsin and phenolic compounds. In this review, the authors will highlight the previous phytochemical studies, medicinal applications of jojoba oil and different plant parts, and the various ways of meal detoxification.     

Natural fibers and zinc hydroxystannate 3D microspheres based composite paper sheets for modern bendable energy storage application

For modern high-tech flexible energy storage devices, it becomes important to synthesize micro-/nanostructures as per the required shape and morphology with superior physical and electro-active characteristics. This work shares the fabrication and characterization of ZnSn(OH)₆ (Zinc hydroxystannate [ZHS]) prepared by facile microwave-assisted technique and furthermore converted into flexible sheets by employing lignocelluloses (LC) known as natural fibers, collected from Carica papaya leaf petiole as a substrate to provide the flexible matrix. X-ray diffraction measurements confirm the successful crystalline structure of ZHS. Scanning electron microscopy and transmission electron microscopy showed the solid spherical structure of ZHS microspheres. Fourier transform infrared spectrometry and Raman spectroscopy confirmed the composite formation of ZHS and LC-based composite sheets (ZHS/LC sheets). Electrochemical measurements that is, cyclic voltammetry (CV), Galvanostatic charge/discharge, and electrochemical impedance (EIS) spectroscopy revealed the electroactive behavior of ZHS/LC paper sheets as working electrode for energy storage applications. CV measurements revealed the specific capacitance of 100 F/g and EIS measurements confirmed the decrease in the resistance of LC fiber after the growth of ZHS microspheres. Presented flexible ZHS based paper sheets will be highly feasible for the modern bendable/flexible/disposable energy storage applications.     

A typology of secure multicast communication over 5 G/6 G networks

International Journal of Information Security - The growth of media services, multimedia conferencing, interactive distance learning, and distributed interactive simulations is becoming more...     

Nanoarchitectonics of Niobium-Doped,Lead-Free BLT (Ba0.97La0.02Ti0.98Nb0.016O3) for Electrical Properties with Unusual d.c Bias Voltage Independence

Polycrystalline sample of Ba_0.97La_0.02Ti_0.98Nb_0.016O₃ (distinguished as BLTi_0.98Nb_0.016) has been prepared through Molten-Salt-Flux reaction route. The XRD, surface morphology, absorption spectra, impedance, and dielectric behaviors were employed to typify the prepared polycrystalline ceramic. The XRD analysis reflects that obtained perovskite having the pure-tetragonal structure with space group P4/mmm. As of the absorption spectra, the optical band gap (Eg), Urbach energy (Eu), and refractive index values have designed. The electrical properties of synthesized compound have been inspected via complex impedance spectroscopy vs. frequency (f) (10² Hz–10⁶ Hz) within the d.c-bias voltage range [0.5 V–5 V]. The fitting of the Nyquist plot exposes that both intra- and intra grains contribute to relaxation and the grain limits are more resistive and capacitive than the grains. Modulus analysis confirms that relaxation in our sample is of non-Debye nature and d.c-bias voltages dependent. Depending on the frequency, the change of ε′ can be discussed founded on the principle of interfacial polarization of the Maxwell–Wagner category. BLTi_0.98Nb_0.016 shows notable frequency independent relative studied properties, it is a potential candidate for devices.

     

Bioinspired Golden Spiral Shapes on Crushing Protection Behaviors of Tubular Structures

Advanced structure design for energy absorption is a long-lasting pursuit due to its fascinating scientific merit in mechanics and important engineering application. Curves, a type of mathematical variation in geometry, are widely adopted in the biological system and biomaterial structures after long-time nature evolution toward optimization and adaption. Inspired by nature, a novel type of tubular structure is designed with golden spiral curvature. With the variations in the hierarchy of the supporting spirals and the number of arms, a maximum of 38.8% increase is demonstrated in performance over an uncurved counterpart structure. The computational modeling reveals that the additional added golden spiral arms within the tube enhance the rigidity and increase the energy dissipation via progress buckling involved by additional materials such that the energy absorption upon crushing is significantly improved. Theoretical models then provide intrinsic underlying mechanics analysis with more intuitive design guidance for such structures. Results develop an efficient yet simple design based on widely accepted tubular structure design for impact protection, enrich the mechanistic understanding of the nonlinear buckling behaviors, and provide applicable design guidance for engineering structures.     

Sol-Gel-Derived Ordered Mesoporous High Entropy Spinel Ferrites and Assessment of Their Photoelectrochemical and Electrocatalytic Water Splitting Performance

The novel material class of high entropy oxides with their unique and unexpected physicochemical properties is a candidate for energy applications. Herein, it is reported for the first time about the physico- and (photo-) electrochemical properties of ordered mesoporous (CoNiCuZnMg)Fe₂O₄ thin films synthesized by a soft-templating and dip-coating approach. The A-site high entropy ferrites (HEF) are composed of periodically ordered mesopores building a highly accessible inorganic nanoarchitecture with large specific surface areas. The mesoporous spinel HEF thin films are found to be phase-pure and crack-free on the meso- and macroscale. The formation of the spinel structure hosting six distinct cations is verified by X-ray-based characterization techniques. Photoelectron spectroscopy gives insight into the chemical state of the implemented transition metals supporting the structural characterization data. Applied as photoanode for photoelectrochemical water splitting, the HEFs are photostable over several hours but show only low photoconductivity owing to fast surface recombination, as evidenced by intensity-modulated photocurrent spectroscopy. When applied as oxygen evolution reaction electrocatalyst, the HEF thin films possess overpotentials of 420 mV at 10 mA cm⁻² in 1 m KOH. The results imply that the increase of the compositional disorder enhances the electronic transport properties, which are beneficial for both energy applications.     

A new lossless electroencephalogram compression technique for fog computing-based IoHT networks

The ability to design smarter, more predictive healthcare solutions for use in the community (at work and at home) and in healthcare facilities has been greatly enhanced by recent developments in the Internet of Health Things (IoHT) and cyber physical systems (CPS). These data collected by such medical sensors will be sent in a large quantity to the fog gateway of IoHT networks. These data should be forwarded to far cloud for further analysis and processing. Therefore, sending all of these data over the IoHT network to the data center will impose a significant burden on the IoHT network. This paper proposed a new lossless electroencephalogram (EEG) compression technique (NeLECoT) for fog computing-based IoHT networks. It encodes the data of the patient at the fog gateway prior to sending it to the data center, thereby reducing the data's size. In the fog node, the NeLECoT combines three efficient techniques: clustering based on density-based spatial clustering of applications with noise (DBSCAN), RLE (run length encoding), and Huffman encoding (HE). The clustering based on DBSCAN separates a massive volume of captured data into small groups of closely related (or similar) data. RLE encodes clustered EEG data, and the resulting file is encoded with HE. The fog gateway then transmits the encoded file to the cloud. Numerous simulation experiments were carried out, and the findings demonstrated that the suggested NeLECoT achieved better results than the competing techniques in terms of transmitted data size, compression ratio, compression power, compression time, decompression time, and average compression power.     

Maximization of heat transfer density from radially finned tubes in cross-flow using the constructal design method

The maximization of volumetric heat transfer density from radially finned tubes in cross-flow is investigated in this study based on the constructal design method. A row of radially finned tubes is placed in cross-air flow. The tubes and the radial fins are heated at uniform temperatures and cooled by the air cross-flow. The cross-air flow is generated by a finite pressure difference. Two dimensionless pressure differences (Bejan number) are considered (Be = 10³ and Be = 10⁵). The objective function, the degrees of freedom, and the constraints in the constructal design method should be identified. The objective function is the maximization of the heat transfer density from the finned tubes. The degrees of freedom are; the fin tip-to-fin tip spacing, the number of fins, the tube diameter, the fin thickness, and the angle between the fins. The constraints are the length and height of the space occupied by the finned tubes. The pressure-driven flow and energy equations (steady, two-dimensional, and incompressible) are solved by means of the finite volume method. The ranges of the dimensionless fin tip-to-fin tip spacing are (0.2 ≤ S ≤ 1 for Be = 10³ and 0.05≤ S ≤ 0.3 for Be = 10⁵). The number of fins is changed as (N = 2, 4, 6, 8, 10, and 12). The dimensionless tube diameter is changed as (D = 0.25, 0.5, and 0.75). The dimensionless fin thickness is changed as (T = 0.001, 0.01, and 0.05). The results showed that for both (Be = 10³) and (Be = 10⁵), the highest value of the maximum volumetric heat transfer density is for (N = 2) and decreases as the number of fins increases. In addition, the minimum values of the maximum volumetric heat transfer density occur when the vertical fins exist at (N = 4, 8, and 12).     

Sailfish Optimization with Deep Learning Based Oral Cancer Classification Model

Recently, computer aided diagnosis (CAD) model becomes an effective tool for decision making in healthcare sector. The advances in computer vision and artificial intelligence (AI) techniques have resulted in the effective design of CAD models, which enables to detection of the existence of diseases using various imaging modalities. Oral cancer (OC) has commonly occurred in head and neck globally. Earlier identification of OC enables to improve survival rate and reduce mortality rate. Therefore, the design of CAD model for OC detection and classification becomes essential. Therefore, this study introduces a novel Computer Aided Diagnosis for OC using Sailfish Optimization with Fusion based Classification (CADOC-SFOFC) model. The proposed CADOC-SFOFC model determines the existence of OC on the medical images. To accomplish this, a fusion based feature extraction process is carried out by the use of VGGNet-16 and Residual Network (ResNet) model. Besides, feature vectors are fused and passed into the extreme learning machine (ELM) model for classification process. Moreover, SFO algorithm is utilized for effective parameter selection of the ELM model, consequently resulting in enhanced performance. The experimental analysis of the CADOC-SFOFC model was tested on Kaggle dataset and the results reported the betterment of the CADOC-SFOFC model over the compared methods with maximum accuracy of 98.11%. Therefore, the CADOC-SFOFC model has maximum potential as an inexpensive and non-invasive tool which supports screening process and enhances the detection efficiency.     

Supramolecular Self-Assembly Built by Hydrogen,Stacking and Br···Br Interactions in 4-((4-Bromobenzyl)Selanyl)Aniline: Structure,Hirshfeld Surface Analysis, 3D Energy Framework Approach and Global Reactivity Descriptors

Journal of Inorganic and Organometallic Polymers and Materials - A novel organoselenium compound named 4-((4-bromobenzyl)selanyl)aniline, C13H12BrNSe, (A), was synthesized via reduction of...