Adipose‐Derived Biogenic Nanoparticles for Suppression of Inflammation

Extracellular vesicles secreted from adipose‐derived mesenchymal stem cells (ADSCs) have therapeutic effects in inflammatory diseases. However, production of extracellular vesicles (EVs) from ADSCs is costly, inefficient, and time consuming. The anti‐inflammatory properties of adipose tissue‐derived EVs and other biogenic nanoparticles have not been explored. In this study, biogenic nanoparticles are obtained directly from lipoaspirate, an easily accessible and abundant source of biological material. Compared to ADSC‐EVs, lipoaspirate nanoparticles (Lipo‐NPs) take less time to process (hours compared to months) and cost less to produce (clinical‐grade cell culture facilities are not required). The physicochemical characteristics and anti‐inflammatory properties of Lipo‐NPs are evaluated and compared to those of patient‐matched ADSC‐EVs. Moreover, guanabenz loading in Lipo‐NPs is evaluated for enhanced anti‐inflammatory effects. Apolipoprotein E and glycerolipids are enriched in Lipo‐NPs compared to ADSC‐EVs. Additionally, the uptake of Lipo‐NPs in hepatocytes and macrophages is higher. Lipo‐NPs and ADSC‐EVs have comparable protective and anti‐inflammatory effects. Specifically, Lipo‐NPs reduce toll‐like receptor 4‐induced secretion of inflammatory cytokines in macrophages. Guanabenz‐loaded Lipo‐NPs further suppress inflammatory pathways, suggesting that this combination therapy can have promising applications for inflammatory diseases.     

High Security for De-Duplicated Big Data Using Optimal SIMON Cipher

Cloud computing offers internet location-based affordable, scalable, and independent services. Cloud computing is a promising and a cost-effective approach that supports big data analytics and advanced applications in the event of forced business continuity events, for instance, pandemic situations. To handle massive information, clusters of servers are required to assist the equipment which enables streamlining the widespread quantity of data, with elevated velocity and modified configurations. Data deduplication model enables cloud users to efficiently manage their cloud storage space by getting rid of redundant data stored in the server. Data deduplication also saves network bandwidth. In this paper, a new cloud-based big data security technique utilizing dual encryption is proposed. The clustering model is utilized to analyze the Deduplication process hash function. Multi kernel Fuzzy C means (MKFCM) was used which helps cluster the data stored in cloud, on the basis of confidence data encryption procedure. The confidence finest data is implemented in homomorphic encryption data wherein the Optimal SIMON Cipher (OSC) technique is used. This security process involving dual encryption with the optimization model develops the productivity mechanism. In this paper, the excellence of the technique was confirmed by comparing the proposed technique with other encryption and clustering techniques. The results proved that the proposed technique achieved maximum accuracy and minimum encryption time.     

A Novel Deep Neural Network for Intracranial Haemorrhage Detection and Classification

Data fusion is one of the challenging issues, the healthcare sector is facing in the recent years. Proper diagnosis from digital imagery and treatment are deemed to be the right solution. Intracerebral Haemorrhage (ICH), a condition characterized by injury of blood vessels in brain tissues, is one of the important reasons for stroke. Images generated by X-rays and Computed Tomography (CT) are widely used for estimating the size and location of hemorrhages. Radiologists use manual planimetry, a time-consuming process for segmenting CT scan images. Deep Learning (DL) is the most preferred method to increase the efficiency of diagnosing ICH. In this paper, the researcher presents a unique multi-modal data fusion-based feature extraction technique with Deep Learning (DL) model, abbreviated as FFE-DL for Intracranial Haemorrhage Detection and Classification, also known as FFEDL-ICH. The proposed FFEDL-ICH model has four stages namely, preprocessing, image segmentation, feature extraction, and classification. The input image is first preprocessed using the Gaussian Filtering (GF) technique to remove noise. Secondly, the Density-based Fuzzy C-Means (DFCM) algorithm is used to segment the images. Furthermore, the Fusion-based Feature Extraction model is implemented with handcrafted feature (Local Binary Patterns) and deep features (Residual Network-152) to extract useful features. Finally, Deep Neural Network (DNN) is implemented as a classification technique to differentiate multiple classes of ICH. The researchers, in the current study, used benchmark Intracranial Haemorrhage dataset and simulated the FFEDL-ICH model to assess its diagnostic performance. The findings of the study revealed that the proposed FFEDL-ICH model has the ability to outperform existing models as there is a significant improvement in its performance. For future researches, the researcher recommends the performance improvement of FFEDL-ICH model using learning rate scheduling techniques for DNN.     

Optimal Confidential Mechanisms in Smart City Healthcare

Smart City Healthcare (SHC²) system is applied in monitoring the patient at home while it is also expected to react to their needs in a timely manner. The system also concedes the freedom of a patient. IoT is a part of this system and it helps in providing care to the patients. IoT-based healthcare devices are trustworthy since it almost certainly recognizes the potential intensifications at very early stage and alerts the patients and medical experts to such an extent that they are provided with immediate care. Existing methodologies exhibit few shortcomings in terms of computational complexity, cost and data security. Hence, the current research article examines SHC² security through Light Weight Cipher (LWC) with Optimal S-Box model in PRESENT cipher. This procedure aims at changing the sub bytes in which a single function is connected with several bytes’ information to upgrade the security level through Swam optimization. The key contribution of this research article is the development of a secure healthcare model for smart city using SHC² security via LWC and Optimal S-Box models. The study used a nonlinear layer and single 4-bit S box for round configuration after verifying SHC² information, constrained by Mutual Authentication (MA). The security challenges, in healthcare information systems, emphasize the need for a methodology that immovably concretes the establishments. The methodology should act practically, be an effective healthcare framework that depends on solidarity and adapts to the developing threats. Healthcare service providers integrated the IoT applications and medical services to offer individuals, a seamless technology-supported healthcare service. The proposed SHC² was implemented to demonstrate its security levels in terms of time and access policies. The model was tested under different parameters such as encryption time, decryption time, access time and response time in minimum range. Then, the level of the model and throughput were analyzed by maximum value i.e., 50 Mbps/sec and 95.56% for PRESENT-Authorization cipher to achieve smart city security. The proposed model achieved better results than the existing methodologies.     

In situ MAS NMR investigations of molecular sieves and zeolite-catalyzed reactions

Solid-state MAS NMR is a powerful technique to study heterogeneous catalysts and the way by which they operate. In situ MAS NMR has been demonstrated to be a powerful method to understand reaction mechanisms, to study the nature, dynamics and reactivity of surface intermediates and active sites, and to characterize structural modifications in the catalyst itself, in particular when using ¹³C strategically labelled substrates. In this paper, three examples selected from our own work are used to illustrate the potential of in situ MAS NMR. They are the formation of cumene and its isomerization to n-propylbenzene on zeolite H-ZSM-11, the activation of propane at low temperature and the alkylation of benzene with propane on zeolite H-ZSM-5, and the characterization of the aluminophosphate molecular sieve VPI-5 structure with temperature. Studies of the alkylation of benzene with propene confirmed that cumene was the primary reaction product. The undesired n-propylbenzene by-product results from the intermolecular reaction between cumene and benzene, enhanced by molecular shape-selective effects in medium pore size zeolites (e.g., H-ZSM-11). It explains why large pore zeolites, e.g., zeolite Beta, are used commercially today for this process. Propane can be activated at low temperature (ca. 573 K) on bifunctional medium pore size zeolites possessing intimately related acidic Brønsted sites and a dehydrogenation function provided by Ga or Zn species. In Ga/H-ZSM-5 catalysts, at 573 K, the activation of propane was shown to occur via a protonated pseudocyclopropane (PPCP) intermediate (or transition state). The latter evolves in a manner that can be formally described by the formation of CH ₃ ⁺ , C₂H ₂ ⁺ , and C₃H ₇ ⁺ carbenium ion intermediates. These species can react with olefins, alkanes, or other electron-rich molecules such as benzene. The primary reaction products of the reaction of propane with benzene are n-propylbenzene (in small amount), ethylbenzene and toluene. Their subsequent reactions lead eventually to toluene and xylenes as the final products. In the structural characterization of VPI-5, ²⁷Al, ³¹P, and ²⁷Al nutation MAS NMR spectra show that, at 294 K, fully hydrated VPI-5 contains three equally populated Al and P crystallographic sites and that one-third of Al is 6-coordinate. The VPI-5 structure then belongs to the P6₃ space group. Above 353 K, VPI-5, fully or partially hydrated, undergoes a structural transformation to a higher framework symmetry, i.e., the P6₃cm space group. The transformation occurs at nearly the same temperature in both cases, indicating that the breakdown of the hydrogen-bonded helical water structure inside the VPI-5 pores is not a factor in the process.     

Structure and conductivity of yttria and scandia‐doped zirconia crystals grown by skull melting

In this paper a detailed study of the (ZrO₂)_1‐x(Y₂O₃)_x (x=0.025–0.15), (ZrO₂)_1‐x(Sc₂O₃)_x (x = 0.06 – 0.11) and (ZrO₂)_1‐x‐y(Sc₂O₃)_x(Y₂O₃)_y (x=0.07 – 0.11; y=0.01 – 0.04) solid solution crystals grown by skull melting technique is presented. The structure, phase composition, and ion conductivity of the obtained crystals were investigated by X‐ray diffraction, transmission electron microscopy, Raman scattering spectroscopy, and impedance spectroscopy. Maximum conductivity as (ZrO₂)_1‐x(Y₂O₃)_x and (ZrO₂)_1‐x(Sc₂O₃)_x solid solution crystals is observed for the compositions containing 10 mol% stabilizing oxide, and the conductivity of 10ScSZ is ~3 times higher than for 10YSZ. Experiments on crystal growth (ZrO₂)_1‐x‐y(Sc₂O₃)_x(Y₂O₃)_y solid solutions showed that uniform, transparent crystals 7Sc3YSZ, 7Sc4YSZ, 8Sc2YSZ, 8Sc3YSZ, 9Sc2YSZ, 9Sc3YSZ, 10Sc1YSZ, and 10Sc2YSZ are single phase crystal containing t″ phase. It is established that a necessary condition of melt growth of (ZrO₂)_1‐x‐y(Sc₂O₃)_x(Y₂O₃)_y single‐phase crystals is the total concentration of the stabilizing oxides from 10 to 12 mol%. The addition of Y₂O₃ affects the (ZrO₂)_1‐x‐y(Sc₂O₃)_x(Y₂O₃)_y solid solution conductivity different ways and depends on the Sc₂O₃ content in the starting composition. The effects of structure, phase composition, concentration, and type of stabilizing oxides on the electrical characteristics of obtained crystals are discussed.     

Structural,morphological and magnetic characterization of metal-chitosan/poly (vinyl amine) complexes

Cu²⁺ and Ni²⁺ complexes of chitosan/poly(vinyl amine) (CS/PVAm) composites were prepared. The metal-CS/PVAm complexes were characterized by FT-IR spectroscopy, SEM-EDX, X-ray diffraction (XRD), and magnetic moment determination. FT-IR spectra of the metal-CS/PVAm complexes showed the characteristic bands of anhidroglucose unit were affected by the metal complexation, and new bands assigned to Me–N and Me-O bonds were observed. SEM images of the surface of metal-composite complexes show the influence of metal ion on the morphology of the complexes, the strong binding of Cu²⁺ and Ni²⁺, involving most of the amino groups, leading to a dense surface structure. The chemical composition on the surface of metal-CS/PVAm complexes was determined from EDX measurements. XRD provided information about the amorphous or crystalline nature of the composite and metal-composite complexes. Using magnetic susceptibility method, the oxidation degree of metal ions from the polymer phase, the homogeneous distribution of the ligand groups from the volume of the CS/PVAm beads, and the existence of the antiferromagnetic interactions between the metal ions were determined.     

Thermocapillary Convection Problem for Two Compressible Immiscible Fluids

A study is made of the motion of two compressible barotropic fluids separated by a closed interface where the surface tension depending on the temperature is taken into account. We have the so-called Marangoni effect which plays the leading role in thermodynamics under low gravity. Local (in time) unique solvability of the problem is obtained in Hölder spaces of functions with power-like decay at infinity. After the passage to Lagrangian coordinates, we arrive at a nonlinear, noncoercive initial boundary—value problem which is equivalent to the original one for small time. We establish the existence theorem for this problem on the basis of the solvability of the problem for two compressible fluids with constant surface tension coefficient on the interface. All results are obtained under some restrictions on fluid densities and viscosities which mean that the fluids are not so different from each other.     

Study of active millimeter-wave image speckle reduction by Hadamard phase pattern illumination

Active millimeter-wave images typically exhibit characteristic speckle noise, due to the coherence of artificial millimeter-wave sources. We study the Hadamard speckle contrast reduction (SCR) technique, which has been successfully used in laser projection systems, in the context of millimeter-wave imaging. We show the impact of Hadamard pattern order and size and of image and pattern resolution on speckle reduction efficiency. Practical limitations of Hadamard pattern implementations and their effect on speckle reduction efficiency are also discussed.