A Study on the Impact of the Adhesion of Penicillium expansum on the Physicochemical Surface Properties of Cedar Wood

The sessile drop technique was used to investigate the evolution of the physicochemical properties of cedar wood as a function of contact time with the Penicillium expansum spores. The most important finding showed that the impact of different contact periods (2, 4, 6, 8, 10, and 24 hr) on the wood surface were very indicative. In fact, after 2 hr of contact, the results have shown a significant impact of the bioadhesion of spores to the substrate on both the hydrophobic character (θ_W = 108.5°; ΔGiwi = ?28.25 mJ/m²), the electron donor (γ^? = 13.63 mJ/m²), and the electron acceptor (γ⁺ = 4.35 mJ/m²) parameters that were significantly reduced compared to the initial wood (θ_W = 118.5°; ΔGiwi = ?6.29 mJ/m²; γ^? = 32.1 mJ/m²; and γ⁺ = 9.1 mJ/m²). In addition, this decrease of parameters continued over time to stabilize after 10 hr of contact. Indeed, after 24 hr, the acid/base properties were almost zero and the contact angle with water decreased to 30°. Moreover, it was found that the coefficient of correlation (r²) was strong between the contact angle with water, the surface energy, and the electron acceptor character with the contact time parameter with values (r² = 0.65), (r² = 0.79), and (r² = 0.68), respectively.     

Electronic spectra of VO2+ in some tutton salts

Optical absorption spectra of VO²⁺ in ammonium magnesium sulphate hexahydrate, potassium magnesium sulphate hexahydrate and potassium zinc sulphate hexahydrate single crystals have been studied. The observed bands in all three crystals have been attributed to the VO²⁺ ion in C_4v symmetry. The octahedral and tetragonal field parameters and the molecular orbital coefficients have been evaluated.     

Deep Learning Based Process Analytics Model for Predicting Type 2 Diabetes Mellitus

Process analytics is one of the popular research domains that advanced in the recent years. Process analytics encompasses identification, monitoring, and improvement of the processes through knowledge extraction from historical data. The evolution of Artificial Intelligence (AI)-enabled Electronic Health Records (EHRs) revolutionized the medical practice. Type 2 Diabetes Mellitus (T2DM) is a syndrome characterized by the lack of insulin secretion. If not diagnosed and managed at early stages, it may produce severe outcomes and at times, death too. Chronic Kidney Disease (CKD) and Coronary Heart Disease (CHD) are the most common, long-term and life-threatening diseases caused by T2DM. Therefore, it becomes inevitable to predict the risks of CKD and CHD in T2DM patients. The current research article presents automated Deep Learning (DL)-based Deep Neural Network (DNN) with Adagrad Optimization Algorithm i.e., DNN-AGOA model to predict CKD and CHD risks in T2DM patients. The paper proposes a risk prediction model for T2DM patients who may develop CKD or CHD. This model helps in alarming both T2DM patients and clinicians in advance. At first, the proposed DNN-AGOA model performs data preprocessing to improve the quality of data and make it compatible for further processing. Besides, a Deep Neural Network (DNN) is employed for feature extraction, after which sigmoid function is used for classification. Further, Adagrad optimizer is applied to improve the performance of DNN model. For experimental validation, benchmark medical datasets were used and the results were validated under several dimensions. The proposed model achieved a maximum precision of 93.99%, recall of 94.63%, specificity of 73.34%, accuracy of 92.58%, and F-score of 94.22%. The results attained through experimentation established that the proposed DNN-AGOA model has good prediction capability over other methods.     

Localization of isotropic and anisotropic wireless sensor networks in 2D and 3D fields

Telecommunication Systems - Internet of Things (IoT) has changed the way people live by transforming everything into smart systems. Wireless Sensor Network (WSN) forms an important part of IoT....     

A finite element modeling-based approach to predict vibrations transmitted through different body segments of the operator within the workspace of a small tractor

Agricultural tractor drivers are subjected to high levels of whole-body vibrations and hand arm vibrations during most part of the farm activities due to unevenness of field surface, uneasy posture, improper workplace design, moving parts of the tractor, and other unavoidable circumstances. The comfort level of the operator inside a dynamic tractor is dependent on the level of vibration generated inside the different human body segments. In the present study, a finite element modeling was proposed to predict vertical vibrations (Z-axis) and frequencies at the different body segments of the seated small tractor operator. The forces required for different controls of the tractor were measured to be used as input parameters in the finite element modeling. The maximum mean forces of the brake (172.8 N) and clutch (153.2 N) were used as the input parameters for the simulation study. The simulated results were validated with the field measured values of vertical accelerations at selected body segments of the operator. The simulation could successfully predict vertical vibrations at selected points of interest (i.e., foot, leg, thigh, lower arm, upper arm, back, and head) except the chest of the body, as the buttock of the operator model was fixed (degree of freedom is equal to zero) in the simulation. The obtained results were compared with the international standards ISO 2631-1 (1985/1997) and ISO 5349-1 (2001) to assess the vibration characteristics at the different body segments of the operator. The foot, leg, lower arm, and upper arm of the operator were subjected to vertical vibration frequencies from 10 to 200 Hz. Most of the resonance of vertical accelerations occurred in one-third octave bands of 20–80 Hz frequencies. The thigh, chest, back, and head of the operator were exposed to vibration frequencies below 40 Hz during field operation. At these parts of the body, the vertical acceleration resonated at lower frequencies, between 2 and 8 Hz.     

Plasma immersion ion implantation for SOI synthesis: SIMOX and ion-cut

We have demonstrated feasibility to form silicon-on-insulator (SOI) substrates using plasma immersion ion implantation (PIII) for both separation by implantation of oxygen and ion-cut. This high throughput technique can substantially lower the high cost of SOI substrates due to the simpler implanter design as well as ease of maintenance. For separation by plasma implantation of oxygen wafers, secondary ion mass spectrometry analysis and cross-sectional transmission electron micrographs show continuous buried oxide formation under a single-crystal silicon overlayer with sharp Si/SiO₂ interfaces after oxygen plasma implantation and high-temperature (1300°C) annealing. Ion-cut SOI wafer fabrication technique is implemented for the first time using PIII. The hydrogen plasma can be optimized so that only one ion species is dominant in concentration and there are minimal effects by other residual ions on the ion-cut process. The physical mechanism of hydrogen induced silicon surface layer cleavage has been investigated. An ideal gas law model of the microcavity internal pressure combined with a two-dimensional finite element fracture mechanics model is used to approximate the fracture driving force which is sufficient to overcome the silicon fracture resistance.     

Cyclopropenoid fatty acids of six seed oils from malvaceae

The seeds ofAlyogine hakeifolia, Alyogine huegelii,Gossypium australe, Hibiscus coatesii, Lawrencia viridigrisea andRadyera farragei (Malvaceae) contained 13.5-18.6% oil. Linoleic acid predominated (60.0-68.2%) in the component fatty acids of all the oils, followed by palmitic (9.9-18.1%) and oleic acids (7.8-15.8%). Cyclopropene fatty acids, malvalic and sterculic, were present in small concentrations (1.0-4.4%, 0.1-1.5% respectively). Dihydrosterculic acid was present in small quantities (trace-2.1%). *To whom correspondence should be addressed at Department of Chemistry, P.O. Box 320, University of Papua New Guinea, Papua, New Guinea.     

Detection of wave groups from the motion behaviour of a discus buoy

A scale modeled discus data buoy was tested for its motion characteristics under the action of wave groups of different frequencies in a wave tank. The wave groups were theoretically simulated based on the methodology of Xu et al., (1993, Statistical simulation of wave groups, Applied Ocean Research, 15, 217–226) and then converted to paddle displacement signals to drive the wave maker. The motion responses of the buoy model were measured using potentiometers as well as with a non-contact motion tracking camera system. The details of the model, instrumentation, testing conditions and the analysis are presented. The displacement and wave elevation time histories were analysed through statistical, envelope, wavelet transform and phase-time approaches to detect the wave groupiness. The effect of groupiness parameters on the surge, heave and pitch motions of the buoy are detailed in this paper.