A New Supervised Clustering Framework Using Multi Discriminative Parts and Expectation–Maximization Approach for a Fine-Grained Animal Breed Classification (SC-MPEM)

Neural Processing Letters - Fine-grained image classification is active research in the field of computer vision. Specifically, animal breed classification is an arduous task due to the challenges...     

Characterization of the Survival Influential Genes in Carcinogenesis

The genes influencing cancer patient mortality have been studied by survival analysis for many years. However, most studies utilized them only to support their findings associated with patient prognosis: their roles in carcinogenesis have not yet been revealed. Herein, we applied an in silico approach, integrating the Cox regression model with effect size estimated by the Monte Carlo algorithm, to screen survival-influential genes in more than 6000 tumor samples across 16 cancer types. We observed that the survival-influential genes had cancer-dependent properties. Moreover, the functional modules formed by the harmful genes were consistently associated with cell cycle in 12 out of the 16 cancer types and pan-cancer, showing that dysregulation of the cell cycle could harm patient prognosis in cancer. The functional modules formed by the protective genes are more diverse in cancers; the most prevalent functions are relevant for immune response, implying that patients with different cancer types might develop different mechanisms against carcinogenesis. We also identified a harmful set of 10 genes, with potential as prognostic biomarkers in pan-cancer. Briefly, our results demonstrated that the survival-influential genes could reveal underlying mechanisms in carcinogenesis and might provide clues for developing therapeutic targets for cancers.     

Modification of highly brittle polystyrene sulfonic acid-co-maleic acid crosslinked sodium alginate membrane into flexible membranes by the incorporation of dibutyl phthalate as a plasticizer for pervaporation separation

To convert highly brittle into flexible membrane, the polystyrene sulfonic acid-co-maleic acid crosslinked sodium alginate (PSSAMA/NaAlg) membrane was modified by incorporating the different weight% of dibutyl phthalate (DBP) as a plasticizer. The effect of DBP content on the physico-chemical properties of the membranes was thoroughly examined. The membranes exhibited lower glass transition temperatures with increasing the plasticizer content in the matrix of PSSAMA/NaAlg. The separation performance of the membranes for water/isopropanol and water/1,4-dioxane was studied at different temperatures. Among the modified membranes, the membrane containing 6 wt% of DBP exhibited the highest separation factors of 24,129 with a flux of 13.57 × 10⁻² kg/m² hr and 23,353 with a flux of 12.99 × 10⁻² kg/m² hr for water/isopropanol and water/1,4-dioxane at 30°C, respectively. From the temperature-dependent diffusion and permeation values, the Arrhenius activation parameters were estimated. The estimated activation energy values for permeation of water (E_pw) and isopropanol (E_pIPA) were, respectively, ranged between 12.09 and 8.79, and 42.52 and 32.79 kJ/mol. A negative heat of sorption (ΔH_s) values was obtained for all the membranes, suggesting that Langmuir's mode of sorption was predominant. Based on the results, it is concluded that the modified membranes demonstrated excellent pervaporation performance for the separation of water/isopropanol and water/1,4-dioxane.     

120 keV Ar ion-induced red and blue shift of SPR Wavelength of Au nanoparticles in fullerene C60

Journal of Materials Science: Materials in Electronics - Au nanoparticles are synthesized in a fullerene C60 matrix using the thermal co-evaporation technique. Fullerene C60 is chosen due to its...     

Enhanced Mechanical Energy Harvesting in Triboelectric Nanogenerator by the Reinforcement of Polypyrrole-Decorated rGO Sheets in PDMS

Energy harvesting triboelectric nanogenerators (TENGs) to scavenge unused mechanical energy have received significant attention in this decade. Herein, the development of reduced graphene oxide (rGO):polypyrrole (PPy) hybrid-modified polydimethylsiloxane (PDMS) as TENG for various device applications is reported. The bulk of PDMS is altered by different fillers such as rGO, PPy, and the binary hybrids of rGO and PPy with varying weight ratios. Among various PDMS composites, 1 wt% of 1:8 rGO:PPy–PDMS composite exhibits higher TENG responses than other PDMS composite. The superior TENG performances of 1 wt% 1:8 rGO:PPy–PDMS composite are attributed to the formation of intensified negative charges inside the PDMS matrix. This charge intensification in the composite is due to various mechanisms, including the charge trapping ability of rGO:PPy filler, microcapacitor formation by introducing hybrid filler in the system with proper conducting networks, and the electron-donating nature of PPy conducting polymer. A 3D stacked device proposed using 1 wt% 1:8 rGO:PPy–PDMS composite delivered a short-circuit current of 16 μA and an open-circuit potential of 60 V by simple palm pressing. Also, the ability of the stacked device for charging/powering portable devices and light-emitting diodes is demonstrated.     

Secure and Optimal LOADng Routing for IoT with Composite Routing Metric

Security is the one of the major challenges for routing the data between the source and destination in an Internet of Things (IoT) network. To overcome this challenge, a secure Lightweight On-demand Ad hoc Distance-vector—Next Generation (LOADng) Routing Protocol is proposed in this paper. As the LOADng protocol is the second version of Ad Hoc On-Demand Distance Vector (AODV) protocol, it retains most of the basic functionality and characteristics of AODV. During the route discovery process, the cyclic shift transposition algorithm (CSTA) is used to encrypt the control packets of the LOADng protocol to improve its security. CSTA approach only derives transposition and substitution without product cipher with respect to input data. Besides this, for choosing the best probable path between the source and destination, routing metrics such as link quality Indicator (LQI), hop count (HC) and queue length (QL) are included in the control packets. The data is then securely sent using CSTA using the optimal secure path selected. Experimental Results depict that the proposed secure and optimal LOADng (SO-LOADng) using CSTA encryption obtains better throughput, delivery ratio encryption time and decryption time than the existing state-of-art approaches.     

Reconfiguration of Amorphous Complex Oxides: A Route to a Broad Range of Assembly Phenomena,Hybrid Materials,and Novel Functionalities

Reconfiguration of amorphous complex oxides provides a readily controllable source of stress that can be leveraged in nanoscale assembly to access a broad range of 3D geometries and hybrid materials. An amorphous SrTiO₃ layer on a Si:B/Si_1?xGe_x:B heterostructure is reconfigured at the atomic scale upon heating, exhibiting a change in volume of ≈2% and accompanying biaxial stress. The Si:B/Si_1?xGe_x:B bilayer is fabricated by molecular beam epitaxy, followed by sputter deposition of SrTiO₃ at room temperature. The processes yield a hybrid oxide/semiconductor nanomembrane. Upon release from the substrate, the nanomembrane rolls up and has a curvature determined by the stress in the epitaxially grown Si:B/Si_1?xGe_x:B heterostructure. Heating to 600 °C leads to a decrease of the radius of curvature consistent with the development of a large compressive biaxial stress during the reconfiguration of SrTiO₃. The control of stresses via post-deposition processing provides a new route to the assembly of complex-oxide-based heterostructures in 3D geometry. The reconfiguration of metastable mechanical stressors enables i) synthesis of various types of strained superlattice structures that cannot be fabricated by direct growth and ii) technologies based on strain engineering of complex oxides via highly scalable lithographic processes and on large-area semiconductor substrates.     

Fitness adaptive deer hunting-based region growing and recurrent neural network for melanoma skin cancer detection

This proposal aims to enhance the accuracy of a dermoscopic skin cancer diagnosis with the aid of novel deep learning architecture. The proposed skin cancer detection model involves four main steps: (a) preprocessing, (b) segmentation, (c) feature extraction, and (d) classification. The dermoscopic images initially subjected to a preprocessing step that includes image enhancement and hair removal. After preprocessing, the segmentation of lesion is deployed by an optimized region growing algorithm. In the feature extraction phase, local features, color morphology features, and morphological transformation-based features are extracted. Moreover, the classification phase uses a modified deep learning algorithm by merging the optimization concept into recurrent neural network (RNN). As the main contribution, the region growing and RNN improved by the modified deer hunting optimization algorithm (DHOA) termed as Fitness Adaptive DHOA (FA-DHOA). Finally, the analysis has been performed to verify the effectiveness of the proposed method.