Construction of consistent neural network empirical physical formulas for detector counts in neutron exit channel selection

Proper selection of neutron exit channels following heavy-ion reactions is important in nuclear structure physics. A knowledge of detector counts versus number of neutron interaction points per event can be useful in this selection. In this paper, we constructed layered feedforward neural networks (LFNNs) consistent empirical physical formulas (EPFs) to estimate the detector counts versus number of neutron interaction points per event. The LFNN-EPFs are of explicit mathematical functional form. Therefore, by various suitable operations of mathematical analysis, these LFNN-EPFs can be used to derivate further physical functions which might be potentially relevant to neutron exit channel selection.     

A novel material for lightweight concrete production

This paper presents the results of an experimental study on the effects of using recycled waste expanded polystyrene foam (EPS), as a potential aggregate in lightweight concrete. In this study, thermally modified waste EPS foams have been used as aggregate. Modified waste expanded polystyrene aggregates (MEPS) were obtained by heat treatment method by keeping waste EPS foams in a hot air oven at 130 °C for 15 min. Effects of MEPS aggregate on several properties of concrete were investigated. For this purpose, six series of concrete samples were prepared. MEPS aggregate was used as a replacement of natural aggregate, at the levels of 0%, 25%, 50%, 75%, and 100% by volume. The density of MEPS is much less than that of natural aggregate; MEPS concrete becomes a lightweight concrete with a density of about 900–1700 kg/m³. The 28-d compressive strengths of MEPS concrete range from 12.58 MPa to 23.34 MPa, which satisfies the strength requirement of semi-structural lightweight concrete.     

A review of cryogenic cooling in machining processes

The cooling applications in machining operations play a very important role and many operations cannot be carried out efficiently without cooling. Application of a coolant in a cutting process can increase tool life and dimensional accuracy, decrease cutting temperatures, surface roughness and the amount of power consumed in a metal cutting process and thus improve the productivity. In this review, liquid nitrogen, as a cryogenic coolant, was investigated in detail in terms of application methods in material removal operations and its effects on cutting tool and workpiece material properties, cutting temperature, tool wear/life, surface roughness and dimensional deviation, friction and cutting forces. As a result, cryogenic cooling has been determined as one of the most favourable method for material cutting operations due to being capable of considerable improvement in tool life and surface finish through reduction in tool wear through control of machining temperature desirably at the cutting zone.     

Biomass-based hydrogen production: A review and analysis

In this study, various processes for conversion of biomass into hydrogen gas are comprehensively reviewed in terms of two main groups, namely (i) thermo-chemical processes (pyrolysis, conventional gasification, supercritical water gasification (SCWG)), and (ii) biological conversions (fermentative hydrogen production, photosynthesis, biological water gas shift reactions (BWGS)). Biomass-based hydrogen production systems are discussed in terms of their energetic and exergetic aspects. Literature studies and potential methods are then summarized for comparison purposes. In addition, a biomass gasification process via oxygen and steam in a downdraft gasifier is exergetically studied for performance assessment as a case study. The operating conditions and strategies are really important for better performance of the system for hydrogen production. A distinct range of temperatures and pressures is used, such as that the temperatures may vary from 480 to 1400 °C, while the pressures are in the range of 0.1–50 MPa in various thermo-chemical processes reviewed. For the operating conditions considered the data for steam biomass ratio (SBR) and equivalence ratio (ER) range from 0.6 to 10 and 0.1 to 0.4, respectively. In the study considered, steam is used as the gasifying agent with a product gas heating value of about 10–15 MJ/Nm³, compared to an air gasification of biomass process with 3–6 MJ/Nm³. The exergy efficiency value for the case study system is calculated to be 56.8%, while irreversibility and improvement potential rates are found to be 670.43 and 288.28 kW, respectively. Also, exergetic fuel and product rates of the downdraft gasifier are calculated as 1572.08 and 901.64 kW, while fuel depletion and productivity lack ratios are 43% and 74.3%, respectively.     

Performance assessment of a geothermally heated building

This study deals with an exergetic performance evaluation of a geothermally heated building. This building used in the analysis has a volume of 1147.03 m³ and a net floor area of 95.59 m², while indoor and exterior air temperatures are 20 and 0 °C, respectively. The geothermal heating system used for the heat production was constructed in the Ozkilcik heating center, Izmir, Turkey. Thermal water has a pressure of 6.8 bar, a temperature of 122 °C and a mass flow rate of 54.73 kg/s, while it is reinjected at 3.2 bar and 72 °C. The system investigated feeds three regions. Among these, the Ozkanlar region has supply/return pressure and temperature values of 4.6/3 bar and 80/60 °C, respectively. Energy and exergy flows are studied to quantify and illustrate exergy destructions in the overall system. Total exergy input rate to the system is found to be 9.92 kW and the largest exergy destruction rate occurs in the primary energy transformation at 3.85 kW.     

Type 2 Diabetes Risk Prediction Using Deep Convolutional Neural Network Based-Bayesian Optimization

Diabetes mellitus is a long-term condition characterized by hyperglycemia. It could lead to plenty of difficulties. According to rising morbidity in recent years, the world’s diabetic patients will exceed 642 million by 2040, implying that one out of every ten persons will be diabetic. There is no doubt that this startling figure requires immediate attention from industry and academia to promote innovation and growth in diabetes risk prediction to save individuals’ lives. Due to its rapid development, deep learning (DL) was used to predict numerous diseases. However, DL methods still suffer from their limited prediction performance due to the hyperparameters selection and parameters optimization. Therefore, the selection of hyper-parameters is critical in improving classification performance. This study presents Convolutional Neural Network (CNN) that has achieved remarkable results in many medical domains where the Bayesian optimization algorithm (BOA) has been employed for hyperparameters selection and parameters optimization. Two issues have been investigated and solved during the experiment to enhance the results. The first is the dataset class imbalance, which is solved using Synthetic Minority Oversampling Technique (SMOTE) technique. The second issue is the model's poor performance, which has been solved using the Bayesian optimization algorithm. The findings indicate that the Bayesian based-CNN model superbases all the state-of-the-art models in the literature with an accuracy of 89.36%, F1-score of 0.88.6, and Matthews Correlation Coefficient (MCC) of 0.88.6.     

Simultaneous removal of C, N, P from cheese whey by jet loop membrane bioreactor (JLMBR)

The membrane bioreactor (MBR) used in this study consisted of a jet loop bioreactor (aerobic high rate system) and a membrane separation unit (microfiltration). Jet loop membrane bioreactor (JLMBR) system is a high performance treatment system. High organic loading rates can be achieved with a very small footprint. The JLMBR is a compact biological treatment system which requires much smaller tank volumes than conventional activated sludge system. Solid-liquid separation is performed with a membrane. The JLMBR system, of 35 L capacity, was operated continuously for 3 months with a sludge age of 1.1-2.8 days and chemical oxygen demand (COD) loads of 3.5-33.5 kg COD m(-3) day(-1). The mean concentration values of COD, total nitrogen (TN) and PO(4)3- in cheese whey (CW) were found as 78,680 mg L(-1), 1125 mg L(-1) and 378 mg L(-1), respectively. Ninety-seven percent COD removal rate was obtained at the sludge age (Thetac) of 1.6 days and volumetric loads of 22.2 kg COD m(-3) day(-1). TN removal was obtained as 99% at the loading rates of 17-436 g TN m(-3) day(-1). PO4(3-) removals were between 65 and 88% for the loading of 30-134 gPO4(3-) m(-3) day(-1). The system could simultaneously remove the COD, TN and PO(4)3- at high efficiencies. The sludge flocks were highly motile, dispersed and had poor settling properties.     

A simulated annealing algorithm based approach for balancing and sequencing of mixed-model U-lines

It is known that two interrelated problems called as line balancing and model sequencing should be solved simultaneously for an efficient implementation of a mixed-model U-shape assembly line in a JIT (Just in Time) environment. On the other hand, three versions of assembly line balancing problem can be identified: Type I, Type II, and Type E. There are only two articles ( Kara, Ozcan, & Peker, 2007a and Hamzadayi & Yildiz, 2012) related to simultaneous balancing and sequencing of mixed-model U-lines for minimizing the number of stations (Type 1 problem) by ignoring the fixed model sequence in the current literature. In this paper, a simulated annealing algorithm is proposed for solving a problem of type 1 by ignoring the fixed model sequence. Accordingly, simulated annealing based fitness evaluation approach proposed by Hamzadayi and Yildiz (2012) is enhanced by adding the tabu list, and inserted into the proposed algorithm. Implementation difficulties experienced in meta-heuristics based on solution modification for solving these types of problems are demonstrated. ‘Absolute deviation of workloads’ (ADW) is quite frequently used as performance criteria in the literature. It is found that ADW is an insufficient performance criterion for evaluating the performance of the solutions, and this is showed by means of an illustrative example. The parameters of the proposed algorithm are reviewed for calibrating the algorithm by means of Taguchi design of experiments. Performance of the proposed approach is tested through a set of test problems. The results of computational experiments indicate that the proposed approach is an effective method in solving simultaneous line balancing/model sequencing problems for mixed-model U-lines for minimizing the number of stations.     

St. Lucie cherry,yellow jasmine,and madder berries as novel natural sensitizers for dye‐sensitized solar cells

Natural dyes extracted from fruits, vegetables, flowers, and leaves are considered as promising alternative sensitizers to replace synthetic dyes for dye‐sensitized solar cells (DSSCs). Generally, solar activity of natural dyes stem from anthocyanin pigment. Carbonyl, carboxyl, and hydroxyl groups present in the anthocyanin molecule improve the adsorption ability of dye on TiO₂ and therefore facilitate charge transfer. Here, for the first time, novel natural dyes extracted from St. Lucie cherry, yellow jasmine, and madder berries are reported to act as sensitizer in DSSCs. These novel natural dye extracts are prepared by dissolving related fruits in ethanol. The ingredient of the dyes is identified by FT‐IR spectroscopy. Accordingly, FT‐IR spectrum reveals that novel natural dye extracts exhibit all the characteristic peaks of anthocyanin pigment. Specifically, St. Lucie cherry consists of more distinct carbonyl group than other sources. Also, photoanodes composed of three TiO₂ layers are prepared by using a spin‐coating method. Then, they are immersed into natural dyes and analyzed by conducting UV‐Vis spectroscopy. Compared with bare TiO₂, natural dye–loaded photoanodes demonstrate far higher absorption ability in the visible region. After fabrication of devices with different novel natural dye sensitizers, current‐voltage characteristics and electrochemical impedance spectroscopy measurements are performed. The best power conversion efficiency (PCE) of 0.19% is obtained by sensitization of St. Lucie cherry with an open‐circuit voltage (V_oc) of 0.56 V, short‐circuit current density (J_sc) of 181 μA cm^?2, and fill factor (FF) of 0.55. Furthermore, St. Lucie cherry–sensitized devices show the lowest charge transfer and highest recombination resistances. This result can be attributed to the obvious carbonyl group exhibited by St. Lucie cherry.