Physico‐mechanical and decay resistance properties of chemically modified tropical wood material

In this present research, several kinds of selected tropical light hardwoods were chemically modified with benzene diazonium salt to improve their physico‐mechanical and decay resistance properties. Benzene diazonium salt underwent a coupling reaction with wood which was confirmed through fourier transform infrared spectroscopic analysis. The compressive modulus of the treated wood increased, whereas modulus of rupture was shown to decrease on treatment. Water absorption was also found to decrease considerably after modification. The modified wood samples had higher hardness (Shore D) value compared to that of the control ones. The wood was exposed to two types of fungi; white‐rot (Polyporus versicolor) and brown‐rot (Postia placenta), for 12 weeks and then decay was assessed through weight loss percentage (%). A significant improvement was found in the modified wood compared to the control wood. © 2012 Wiley Periodicals, Inc. J. Appl. Polym. Sci., 2013     

New emissions targeting strategy for site utility of process industries

A new procedure for environmental targeting of co-generation system is presented. The proposed method is based on the concepts of pinch technology for total site targeting of fuel, power, steam, environmental impacts and total annualized cost with considering emissions taxes. This approach provides a consistent, general procedure for determining mass flow rates and efficiencies of the applied turbines. This algorithm utilizes the relationship of entropy with enthalpy and isentropic efficiency. Also, the life cycle assessment (LCA) as a well-known tool for analyzing environmental impacts on a wide perspective with reference to a product system and the related environmental and economic impacts have been applied. In this regard, a damage-oriented impact analysis method based on Eco-indicator 99 and footprints analysis was considered. In addition, the present work demonstrates the effect of including both sensible and latent heating of steam in the extended Site Utility Grand Composite Curve (ESUGCC). It is shown that including sensible heating allows for better thermal matching between the processes. Furthermore, the other representation YSUGCC as the other form of Site Utility Grand Composite has been proposed. Two case studies were used to illustrate the usefulness of the new environmental targeting method.     

Experimental Evaluation of Trilateration-Based Outdoor Localization with LoRaWAN

Long Range Wide Area Network (LoRaWAN) in the Internet of Things (IoT) domain has been the subject of interest for researchers. There is an increasing demand to localize these IoT devices using LoRaWAN due to the quickly growing number of IoT devices. LoRaWAN is well suited to support localization applications in IoTs due to its low power consumption and long range. Multiple approaches have been proposed to solve the localization problem using LoRaWAN. The Expected Signal Power (ESP) based trilateration algorithm has the significant potential for localization because ESP can identify the signal’s energy below the noise floor with no additional hardware requirements and ease of implementation. This research article offers the technical evaluation of the trilateration technique, its efficiency, and its limitations for the localization using LoRa ESP in a large outdoor populated campus environment. Additionally, experimental evaluations are conducted to determine the effects of frequency hopping, outlier removal, and increasing the number of gateways on localization accuracy. Results obtained from the experiment show the importance of calculating the path loss exponent for every frequency to circumvent the high localization error because of the frequency hopping, thus improving the localization performance without the need of using only a single frequency.     

Study on oil fouling in a double pipe heat exchanger with mitigation by a surfactant

Fouling of oils on heat exchanger surfaces and pipelines is a common problem in a variety of industrial applications. This is because the oil deposits on the heat transfer surface causes an increase in pressure drop and a decrease in heat exchanger efficiency. In the current work, oil fouling in double pipe heat exchanger was investigated and mitigated using a surface‐active agent for the flow of a dispersion fluid containing different dispersed oil fractions in water. The effect of the dispersed oil fraction (5%vol and 10%vol) and temperature (35°C‐55°C) on the oil fouling rate was studied and discussed under turbulent flow conditions for both hot and cold fluids. Different amounts of alkylbenzene sulfonate as a surfactant were added to reduce the fouling rate under turbulent flow. It was found that the fouling thermal resistance (R_f) increases when the fluid temperature decreases. The higher the dispersed oil fraction, the higher the R_f for all temperatures due to higher oil deposition. Addition of 0.2%vol to 0.5%vol of alkylbenzene sulfonate caused an appreciable reduction in R_f depending on oil fraction and Reynolds number. The mitigation percent was higher for a lower Reynolds number, reaching up to 96%.     

Geographic information systems-based land suitability assessment for switchgrass cultivation in marginal lands: a case study for İzmir-Turkey

Clean Technologies and Environmental Policy - Energy crop cultivation is considered the main factor in the rapid increase in the share of biomass energy in the future global energy supply. However,...     

A Highly Accurate Dysphonia Detection System Using Linear Discriminant Analysis

The recognition of pathological voice is considered a difficult task for speech analysis. Moreover, otolaryngologists needed to rely on oral communication with patients to discover traces of voice pathologies like dysphonia that are caused by voice alteration of vocal folds and their accuracy is between 60%–70%. To enhance detection accuracy and reduce processing speed of dysphonia detection, a novel approach is proposed in this paper. We have leveraged Linear Discriminant Analysis (LDA) to train multiple Machine Learning (ML) models for dysphonia detection. Several ML models are utilized like Support Vector Machine (SVM), Logistic Regression, and K-nearest neighbor (K-NN) to predict the voice pathologies based on features like Mel-Frequency Cepstral Coefficients (MFCC), Fundamental Frequency (F₀), Shimmer (%), Jitter (%), and Harmonic to Noise Ratio (HNR). The experiments were performed using Saarbrucken Voice Database (SVD) and a privately collected dataset. The K-fold cross-validation approach was incorporated to increase the robustness and stability of the ML models. According to the experimental results, our proposed approach has a 70% increase in processing speed over Principal Component Analysis (PCA) and performs remarkably well with a recognition accuracy of 95.24% on the SVD dataset surpassing the previous best accuracy of 82.37%. In the case of the private dataset, our proposed method achieved an accuracy rate of 93.37%. It can be an effective non-invasive method to detect dysphonia.     

Classification analysis of burnout people's brain images using ontology-based speculative sense model

Burnout is a state of exhaustion that results from prolonged, excessive workplace stress. This can be examined with the biological explications of burnout and physical consequences and classified against prolonged vigorous activities. The research aims to classify burnout people's brain images against prolonged emotional activities using ontology analysis of treatment and prevention and intermediate layers formation based on a speculative sense model. In this segment, the Ontology analysis of Treatment and prevention and intermediate layers formation based on a hypothetical sense model is employed for burnout people's classification analysis. The methodology is performed in the platform of ontology creation and performs the classification analysis. The calculation analysis found the result, and the brain images were classified. The classification analysis of burnout people's brain images, separation of prolonged vigorous activities, and the ontology creation for treatment and prevention against burnout people's brain images were obtained. The analysis received the result, and the results of the precision, recall, storage, computation time, specificity, and classification of burnout people's brain images were obtained. Furthermore, all these Ontology analysis of Treatment and prevention and intermediate layers formation based on a hypothetical sense model had the prediction sensitivity (SN) over 50% and specificity (SP) over 90%. The Classification of Burnout People's Brain performance comparison shows that the proposed system is much more successful than existing methods, especially on a scoring accuracy of 98%.     

Thermal and Mechanical Properties of Glass–Ceramics Based on Slate and Natural Raw Materials

Silicon - In recent years, utilization of raw materials was increased to produce natural low-cost glass–ceramic materials with distinct properties and appropriate for various applications....     

3D Printing of Small-Scale Soft Robots with Programmable Magnetization

Soft magnetic structures having a non-uniform magnetization profile can achieve multimodal locomotion that is helpful to operate in confined spaces. However, incorporating such magnetic anisotropy into their body is not straightforward. Existing methods are either limited in the anisotropic profiles they can achieve or too cumbersome and time-consuming to produce. Herein, a 3D printing method allowing to incorporate magnetic anisotropy directly into the printed soft structure is demonstrated. This offers at the same time a simple and time-efficient magnetic soft robot prototyping strategy. The proposed process involves orienting the magnetized particles in the magnetic ink used in the 3D printer by a custom electromagnetic coil system acting onto the particles while printing. The resulting structures are extensively characterized to confirm the validity of the process. The extent of orientation is determined to be between 92% and 99%. A few examples of remotely actuated small-scale soft robots that are printed through this method are also demonstrated. Just like 3D printing gives the freedom to print a large number of variations in shapes, the proposed method also gives the freedom to incorporate an extensive range of magnetic anisotropies.