Synthesis and Characterization of Mechanical Properties of AA8014 + Si3N4/ ZrO2 Hybrid Composites by Stir Casting Process

Lightweight materials are extremely needed for the manufacturing of industrial parts and are used in aerospace, automobile body shops, biomedical instruments, etc. Aluminium alloy is one of the light-weight materials, and it fulfills the industrial demands based on their natural strength/stiffness, enhanced temperature permanence, superior wear, and corrosion resistance. This experimental work considered aluminium alloy (AA8014) with reinforced particles of silicon nitride (Si3N4) and zirconium dioxide (ZrO2) for preparing aluminium hybrid composites. Hybrid composites are prepared by a stir casting process involving different process parameters. L27 orthogonal array is used for optimizing the stir casting parameters with the assistance of the statistical Taguchi approach. Stir casting parameters are the percentage of reinforcement (4%, 6%, and 8%), stir speed (400 rpm, 500 rpm, and 600 rpm), stir time (20 min, 25 min, and 30 min), and molten temperature (700 oC, 800 oC, and 900 oC). Mechanical performance such as wear and microhardness of the hybrid composites is evaluated. Minimum wear and higher microhardness are encountered at a percentage of reinforcement = 6%, stir speed = 400 rpm, stir time = 30 min, and molten temperature = 900°C. In wear analysis, the percentage of reinforcement highly influences the wear properties (7.06% contribution). In microhardness analysis, molten temperature parameter is the extreme influencer (11.15% contribution).

Reengineering Cracker-Dryer Machine Based on Fan speed and Temperature Considering Drying Time, Final Weight and Energy Consumption Factor

Redesign and building a cracker drying machine should give priority to several factors to find the optimal point, one of which is by conducting experimental experiments. This research was carried out to find out from the tool and also pay attention to several things, such as temperature and fan speed. If one of these factors is ignored, it will not produce dry crackers and the dried crackers are less than optimal. The Independent parameters used in this research are temperature and fan speed. Dependent parameters used in this research are concerning drying time, final weight, and energy consumption. Design experiment using Factorial 3x3. Fan speed has 3 levels, they are 1000rpm, 1500rpm and 2000rpm. The Temperature parameter has 3 levels, they are 16, 19, and 21oC. Based on the design of experiment results, it was found that the drying time response for the combination that has the highest ranking is at a temperature of 19oC with a 2000rpm fan speed resulting in a drying time of 182,677 minutes.

Olive ridley (Lepidochelys olivacea) laying eggs habitat mapping in Penimbangan Beach, Bali Island

Penimbangan beach is one of the tourist destinations located in Buleleng Regency, Bali Province, Indonesia. This beach is also a nesting place for one of the sea turtle species in Indonesia, which is olive ridley (Lepidochelys olivacea). The problem that exists on the island of Bali today is most of the land in coastal areas were experienced land degradation. This land degradation occurs due to human activities or natural factors, it harms the turtle nesting habitat around the coastal area of Bali Island. Conservation of nesting turtle habitat is needed through spatial analysis using Geographic Information Systems (GIS). This study uses the parameters of sand particle, beach slope, width beach, humidity, and temperature parameter to determine the suitability of the turtle nesting habitat. Penimbangan beach area which is very suitable for turtle nesting locations has an area of 163,45 m2, suitable for laying eggs 4.886,44 m2, and not suitable for laying eggs 10.201,64 m2. The map of the suitability for laying turtle eggs is dominated by areas that are not suitable because the width of the beach is not too long and the humidity is still relatively high.

The Trend of Temperature Changes in Iranian Metropolises in the Last Three Decades

Global warming is a challenging issue among the world's climatologists, embraced even by politicians and the common people. In this study, the trend of temperature changes in 31 metropolises of Iran, which are generally the provincial centres of the country, has been studied in a statistical period of 35 years (1985-2019). We found that there are more temperature changes in all metropolises of Iran in winter than other seasons. In this season, the maximum temperature parameter shows a significant increase, as evidenced in Ardabil with about 9 degrees Celsius. Also, the highest amount of temperature increase in all seasons has occurred in the western and northwestern part of Iran, being of mildly cold to extremely cold climate in Iran. In general, the temperature changes more intensively from the south to the north regions. It is interesting that in the southern and coastal cities of Iran, such as Ilam, Bushehr and Shiraz, in some months, the temperature keeps decreeing. The increase in temperature of metropolises has been much more regular for the maximum temperature than the minimum temperature. Strangely, unlike all the months in November, the maximum temperature has decreased in most cities in Iran.

AN ASSESSMENT OF VARIOUS NANOADDITIVES AND TRIBO-CORROSION WITH WASTE COOKING BIODIESEL FUELED IN A DIESEL ENGINE

The waste cooking biodiesel's steady-state coefficient of friction rate of fuel blends are B90 (18.2%), B60 (7.2%), B20 (16.72%), B10 (30.8%), and diesel (38.77%) higher compared with B40 fuel blend and wear scar diameter of the fuel blends from B40 to B100 had a minimal range of 0.5mm. The flash temperature parameter results higher from B40 to B100 fuel blends, and the corrosion rate was minimal for B40 and B50 fuel blends. Afterward, the fuel blend B40 (40% WCO+60% Diesel fuel) was chosen as fuel, along with Cerium (25ppm), Zinc (25ppm), and Titanium nanoparticles (25ppm) were selected as fuel additives. The B40+D60+Titanium (25ppm) blend resulted in improved BTE and 3.83% lowered BSEC comparison with diesel fuel. Then the fuel blend, B40+D80+Titanium (25ppm), resulted in 2.08% reduced HC, 36.36% CO, and 16.25% smoke emissions, along with marginally 8.5% higher NOx emissions comparison with diesel fuel. Also, the fuel blend, B40+D80+Titanium (25ppm) combustions characteristics are the equivalent trend of cylinder pressure (58.82 bar) and HRR (66.65 J/deg CA) related to diesel fuel at peak load.

The Use of Thermal Imaging in the Evaluation of Temperature Effects of Radiotherapy in Patients after Mastectomy—First Study

The aim of the study was to evaluate the temperature parameter of the breast area in patients undergoing radiotherapy at various intervals. The relationship between temperature changes on the patient’s skin and the time after the end of radiotherapy was studied. Measurements with a thermal imaging camera were performed in a group of twelve volunteers. Six of them were healthy women who did not have thermal asymmetry between the breasts, whereas six were diagnosed with breast cancer and underwent mastectomy due to the advanced stage of the disease. The patients were qualified for radiation therapy. Thermographic examinations were performed before treatment, two months later and then six months after the end of the treatment. Temperature differences between the healthy breasts and the treated areas were assessed. Additionally, the correlation between a patient’s skin temperature changes and the time after the end of radiotherapy was analyzed. The highest skin temperature increase (1.47 °C) was observed 6 months after the end of RT compared to the measurement before treatment. It seems that thermovision may bring a new tool for quantitative analyses of the temperature effects of radiotherapy.

The Impact of Changes in Exhaust Temperature on the Power Output and Heat Rate of a Gas Turbine with a Capacity of 238 MW

The exhaust temperature parameter is one of the parameters that need to be considered in maintaining the performance of the gas turbine. The purpose of this study is to analyze the effect of changes in exhaust temperature on power output and heat rate. The data used is the actual design data of the M701 gas turbine. This data is used in building the model using the GateCycle software. The modeling simulation results are then validated using the actual design data. To see the impact of changes in exhaust temperature, data from the latest gas turbine performance results are used. This study concludes that changes in exhaust temperature parameters of 1OC have an impact on changes in power output of 0.273% and heat rate of 0.047%.

Implementasi Monitoring Suhu dan Kelembaban pada Budidaya Jamur Tiram dengan IOT

Oyster mushroom commodity has been widely used as raw material for processed food products, monitoring of temperature and humidity intensively, the quality will be well maintained. Monitoring at Rumah Jamur Barokah full 24 hours, so it will be difficult if done manually. This is the reason why it is necessary to design monitoring tools in maintaining the ideal temperature and humidity with the concept of the Internet of Things. This research aims to design temperature and humidity monitoring tools (IoT), and how much the increase value of time efficiency and effectiveness after the tool is implemented. The blackbox testing method is performed for performance feasibility testing, by comparing thermo-hygrometers and sensors. The result of the tool performance test at the temperature parameter obtained an error value 0.33 0C and the DHT22 tolerance limit ± 0.5 0C. In the humidity parameter obtained an error of 1.8% RH and the DHT22 tolerance limit ± 2% RH, so the DHT22 sensor feasible to use. In the analysis of efficiency calculation obtained a result of 77.95% for improved time efficiency. In the analysis of the quality effectiveness value of oyster mushrooms is less than ideal, getting an increase in value by 143% to 163%. Keywords: blackbox testing, cultivation, internet of things (iot), monitoring, oyster mushroom

SSDAN: Multi-Source Semi-Supervised Domain Adaptation Network for Remote Sensing Scene Classification

We present a new method for multi-source semi-supervised domain adaptation in remote sensing scene classification. The method consists of a pre-trained convolutional neural network (CNN) model, namely EfficientNet-B3, for the extraction of highly discriminative features, followed by a classification module that learns feature prototypes for each class. Then, the classification module computes a cosine distance between feature vectors of target data samples and the feature prototypes. Finally, the proposed method ends with a Softmax activation function that converts the distances into class probabilities. The feature prototypes are also divided by a temperature parameter to normalize and control the classification module. The whole model is trained on both the unlabeled and labeled target samples. It is trained to predict the correct classes utilizing the standard cross-entropy loss computed over the labeled source and target samples. At the same time, the model is trained to learn domain invariant features using another loss function based on entropy computed over the unlabeled target samples. Unlike the standard cross-entropy loss, the new entropy loss function is computed on the model’s predicted probabilities and does not need the true labels. This entropy loss, called minimax loss, needs to be maximized with respect to the classification module to learn features that are domain-invariant (hence removing the data shift), and at the same time, it should be minimized with respect to the CNN feature extractor to learn discriminative features that are clustered around the class prototypes (in other words reducing intra-class variance). To accomplish these maximization and minimization processes at the same time, we use an adversarial training approach, where we alternate between the two processes. The model combines the standard cross-entropy loss and the new minimax entropy loss and optimizes them jointly. The proposed method is tested on four RS scene datasets, namely UC Merced, AID, RESISC45, and PatternNet, using two-source and three-source domain adaptation scenarios. The experimental results demonstrate the strong capability of the proposed method to achieve impressive performance despite using only a few (six in our case) labeled target samples per class. Its performance is already better than several state-of-the-art methods, including RevGrad, ADDA, Siamese-GAN, and MSCN.

Convective Heat Transfer of a Hybrid Nanofluid over a Nonlinearly Stretching Surface with Radiation Effect

The flow of the hybrid nanofluid (copper–titanium dioxide/water) over a nonlinearly stretching surface was studied with suction and radiation effect. The governing partial differential equations were then converted into non-linear ordinary differential equations by using proper similarity transformations. Therefore, these equations were solved by applying a numerical technique, namely Chebyshev pseudo spectral differentiation matrix. The results of the flow field, temperature distribution, reduced skin friction coefficient and reduced Nusselt number were deduced. It was found that the rising of the mass flux parameter slows down the velocity and, hence, decreases the temperature. Further, on enlarging the stretching parameter, the velocity and temperature increases and decreases, respectively. In addition, it was mentioned that the radiation parameter can effectively control the thermal boundary layer. Finally, the temperature decreases when the values of the temperature parameter increases.