Packet Squeezing of Random Access with 5G Real-Time Services for Internet of Things

Wireless Personal Communications - Random Access techniques are many, most of which are designed for a limited number of mobile nodes. However, in a 5G Internet of Things environment, the design of...     

MicroRNAs,Parkinson’s Disease,and Diabetes Mellitus

Parkinson’s disease (PD) is a neurodegenerative disorder that affects 1% of the population over the age of 60. Diabetes Mellitus (DM) is a metabolic disorder that affects approximately 25% of adults over the age of 60. Recent studies showed that DM increases the risk of developing PD. The link between DM and PD has been discussed in the literature in relation to different mechanisms including mitochondrial dysfunction, oxidative stress, and protein aggregation. In this paper, we review the common microRNA (miRNA) biomarkers of both diseases. miRNAs play an important role in cell differentiation, development, the regulation of the cell cycle, and apoptosis. They are also involved in the pathology of many diseases. miRNAs can mediate the insulin pathway and glucose absorption. miRNAs can also regulate PD-related genes. Therefore, exploring the common miRNA biomarkers of both PD and DM can shed a light on how these two diseases are correlated, and targeting miRNAs is a potential therapeutic opportunity for both diseases.     

Mixed phase bioceramics in the CaMgSi2O6 – MoO3 system: Mechanical properties and in-vitro bioactivity

Mixed phase materials in the quasi binary diopside (CaMgSi₂O₆) – molybdite (MoO₃) system were synthesized by a precipitation method. Materials were fabricated with diopside to molybdite ratios of 1:0, 10:1, 5:1, 2:1 and 1:1. XRD, SEM and EDS results show that alongside the initial diopside phase, phases such as calcium molybdate CaMoO₄, rod-like enstatite MgSiO₃ and cristobalite SiO₂ formed as the molybdite content increased, and diopside was entirely absent at the highest molybdite content. At lower Mo content, mixed phase materials showed higher hardness and slower biodegradation in SBF relative to pristine diopside, while maintaining reasonable hydroxyapatite (HAp) formation capability. In contrast, materials with higher molybdite content exhibited lower hardness and bioactivity. The variation in the mechanical and bioactive performance could be attributed to the presence of bulk CaMoO₄, acting as a reinforcement, and rod-like MgSiO₃ with a highly porous and fragile structure. The trend of hardness is not consistent to the proportion of the component phases could be attributed to morphologies, interfaces, and densities of the samples. Both of secondary phases had poorer HAp deposition compared to pure diopside, indicating the MoO₃ addition lowered mixed phase CaMgSi₂O₆ – MoO₃ bioceramics’ ability to form Hap. The results suggest that moderate addition of molybdite to diopside would be an effective pathway towards crystalline bioceramics with enhanced performance.     

Identifying catalyst layer compositions of proton exchange membrane fuel cells through machine-learning-based approach

Membrane electrode assembly (MEA) is considered a key component of a proton exchange membrane fuel cell (PEMFC). However, developing a new MEA to meet desired properties, such as operation under low-humidity conditions without a humidifier, is a time- and cost-consuming process. This study employs a machine-learning-based approach using K-nearest neighbor (KNN) and neural networks (NN) in the MEA development process by identifying a suitable catalyst layer (CL) recipe in MEA. Minimum redundancy maximum relevance and principal component analysis were implemented to specify the most important predictor and reduce the data dimension. The number of predictors was found to play an essential role in the accuracy of the KNN and NN models although the predictors have self-correlations. The KNN model with a K of 7 was found to minimize the model loss with a loss of 11.9%. The NN model constructed by three corresponding hidden layers with nine, eight, and nine nodes can achieve the lowest error of 0.1293 for the Pt catalyst and 0.031 for PVA as a good additive blending in the CL of the MEA. However, even if the error is low, the prediction of PVA seems to be inaccurate, regardless of the model structure. Therefore, the KNN model is more appropriate for CL recipe prediction.     

Hydrogen production via ammonia decomposition catalyzed by Ni/M–Mo–N (M = Ni,Co) bimetallic nitrides

This study demonstrates the significant improvement in NH₃ decomposition using Ni-decorated M–Mo–N-based catalysts (M = Co and Ni) compared with conventional catalysts. Catalysts are prepared using a mixture of the corresponding metal salts and hexamethylenetetramine, and the impregnation method is used to decorate the Ni-particles on the catalysts. Among all the samples, 10 wt% Ni-decorated Co₃Mo₃N exhibits the highest NH₃ conversion rate (71%) at 500 °C, and the performance remains stable for 30 h of long-term testing. According to the gas chromatography measurements, the H₂/N₂ ratio is approximately 3 in all cases, which is consistent with the theoretical value. X-ray photoelectron spectroscopy results show that Co₃Mo₃N possesses the highest NH₃ conversion efficiency because of the weaker binding energy of Mo–N. Furthermore, Co₃Mo₃N exhibits a stronger Lewis acidity and higher NH₃ decomposition, which is attributed to the easy breaking of the N–H bond on the Co₃Mo₃N surface.     

Temperature dependent negative permittivity in solid solutions Sr2Mn1-xSnxO4 (x = 0, 0.3, 0.5)

A few compositions of the system Sr₂Mn_1-xSn_xO₄ (x = 0.0, 0.3, 0.5) were synthesized in the air by the solid-state ceramic route. A change in the sign (positive to negative) of the permittivity above a particular temperature (T_C) is observed at all the measured frequencies. The negative permittivity was analyzed by the Drude-Lorentz model. It was found that negative permittivity is caused by the plasma oscillations of thermally excited free charge carriers. Analysis of XPS spectra confirmed the presence of mixed-valence states of both Mn (Mn⁴⁺ and Mn³⁺) and Sn (Sn⁴⁺ and Sn²⁺) ions. The UV–vis.-IR spectroscopy results indicated generation of a large number of defect states in the forbidden bandgap region of Sr₂MnO₄ on the substitution of Sn at Mn site. Synthesized samples are promising metamaterials for radio frequency (10 Hz -2 MHz) region applications due to the high-temperature plasmonic behavior.     

Grain orientation evolution and multi-scale interfaces enhanced thermoelectric properties of textured Sr0.9La0.1TiO3 based ceramics

Sr_0.9La_0.1TiO₃ based textured ceramics (SLTT-S3T) with a texture fraction of 0.81 are successfully fabricated by the reactive template grain growth method, in which Sr_0.9La_0.1TiO₃/20 wt%Ti was used as matrix and 10 wt% plate-like Sr₃Ti₂O₇ template seeds were used as templates. The phase transition, microstructure evolution, and the anisotropic thermoelectric properties of SLTT-S3T ceramics were investigated. The results show that the ceramics are mainly composed of Sr_0.9La_0.1TiO₃ and rutile TiO₂ phases. Grains grow with a preferred orientation along (h00). A maximum ZT of 0.26 at 1073 K was achieved in the direction perpendicular to the tape casting direction. The low lattice thermal conductivity of 1.9 W/(m K) at 1073 K was obtained decreased by 34%, 40%, and 38% compared with non-textured, SrTiO₃ and Sr_0.9La_0.1TiO₃ ceramics prepared by the same process, can be attributed to the enhanced phonon scattering by the complex multi-scale boundaries and interfaces. This work provides a strategy of microstructural design for thermoelectric oxides to decrease intrinsic lattice thermal conductivity and further regulate thermoelectric properties via texture engineering.     

Characteristics and gas sensor applications of ZnO-Perovskite heterostructure

In this study, zinc oxide (ZnO) nanofibers were prepared using the electrospinning method, and the effects of different spinning voltages and annealing temperatures on the fiber structure were tested. La_0.8Sr_0.2FeO₃ (LSFO) perovskite film was prepared by a sol-gel method. Then we dip LSFO on ZnO nanofiber and grow it on the interdigital gold electrode substrate for gas sensors. The results show that the ZnO/LSFO heterostructure gas sensor has a good sensing response to H₂S gas and exhibits good gas selectivity. The best gas response is 52.17% under 4 ppm H₂S and work temperature 200°C, which has good recovery and reproducibility.