A visible-light-active BiFeO<Subscript>3</Subscript>/ZnS nanocomposite for photocatalytic conversion of greenhouse gases

Given the changes in environmental conditions in the world, photocatalytic conversion of greenhouse gases is of great interest today. Our aim was to increase the photocatalytic efficiency of BiFeO₃/ZnS (p-n heterojunction photocatalyst) by varying the molar ratio of ZnS to perovskite structure of BiFeO₃ using hydrothermal synthesis. The results of X-ray diffraction (XRD), scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), FT-IR spectroscopy showed the small crystal size and suitable distribution of ZnS particles on the BiFeO₃ structure. The results of UV-visible, and photoluminescence (PL) spectroscopy analyses showed the good behavior of p-n heterostructure in absorption of visible light and lowering electron-hole recombination. The best visible light photocatalytic efficiency of CO₂ reduction, 24.8%, was obtained by an equimolar ratio of BiFeO₃/ZnS.     

An Efficient Eco-Friendly Synthesis of Pyran Annulated Heterocyclic Systems under Conventional Heating and Microwave Irradiation in Solvent-Free Conditions

1,4-Diazabicyclo[2.2.2]octane (DABCO) has been used as an efficient and reusable solid base catalyst for the rapid and green synthesis of pyran annulated heterocyclic ring systems by the condensation reaction of various activated CH-acids and tetracyanoethylene under conventional heating and microwave irradiation in solvent-free conditions. Excellent yield, very short reaction time (2–5 min), operatinal simplity, easy work-up procedure, avoidance of hazardous or toxic catalysts, and organic solvents are the main advantages of this green methodology which makes it more economic than the other conventional methods.     

Data generative machine learning model for the assessment of outdoor thermal and wind comfort in a northern urban environment

Predicting comfort levels in cities is challenging due to the many metric assessment. To overcome these challenges, much research is being done in the computing community to develop methods capable of generating outdoor comfort data. Machine Learning (ML) provides many opportunities to discover patterns in large datasets such as urban data. This paper proposes a data-driven approach to build a predictive and data-generative model to assess outdoor thermal comfort. The model benefits from the results of a study, which analyses Computational Fluid Dynamics (CFD) urban simulation to determine the thermal and wind comfort in Tallinn, Estonia. The ML model was built based on classification, and it uses an opaque ML model. The results were evaluated by applying different metrics and show us that the approach allows the implementation of a data-generative ML model to generate reliable data on outdoor comfort that can be used by urban stakeholders, planners, and researchers.     

A new pricing constrained single-product inventory-production model in perishable food for maximizing the total profit

In this paper, a new constrained single-product pricing and inventory model is presented. The goal of the proposed model is to decide on the prices as well as on the inventory and production decisions in order to maximize the total profit. The developed model is a nonlinear programming model which is solved by using hybrid genetic algorithm (HGA) and simulated annealing. Comparison of results obtained from the two algorithms shows that HGA is better. To enhance the performance of our algorithms, we apply the Taguchi experimental design method to tune their parameters. Finally, some recommendations for future developments are presented.     

Effects of Conditioning During Amygdalar Inactivation on Training-Induced Neuronal Plasticity in the Medial Geniculate Nucleus and Cingulate Cortex in Rabbits (Oryctolagus cuniculus).

This study addressed the amygdala's role in avoidance conditioning in rabbits (Oryctolagus cuniculus). Intra-amygdalar muscimol infusion before 60 or 120 conditioning trials blocked training-induced neuronal activity (TIA) in the medial geniculate (MG) nucleus. One hundred twenty trials with muscimol blocked TIA permanently, during conditioning with muscimol and then later without muscimol; 60 trials with muscimol blocked TIA only when muscimol was present. Cingulate cortical TIA was blocked only when muscimol was present. Behavioral learning did not occur with muscimol, but later learning was facilitated (i.e., savings occurred) in rabbits initially given muscimol plus training. These results define the time period wherein amygdalar processes initiate TIA in the MG nucleus and suggest that distinct forms of amygdalar processes induce TIA in the MG nucleus and cingulate cortex. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Intelligent Sound-Based Early Fault Detection System for Vehicles

An intelligent sound-based early fault detection system has been proposed for vehicles using machine learning. The system is designed to detect faults in vehicles at an early stage by analyzing the sound emitted by the car. Early detection and correction of defects can improve the efficiency and life of the engine and other mechanical parts. The system uses a microphone to capture the sound emitted by the vehicle and a machine-learning algorithm to analyze the sound and detect faults. A possible fault is determined in the vehicle based on this processed sound. Binary classification is done at the first stage to differentiate between faulty and healthy cars. We collected noisy and normal sound samples of the car engine under normal and different abnormal conditions from multiple workshops and verified the data from experts. We used the time domain, frequency domain, and time-frequency domain features to detect the normal and abnormal conditions of the vehicle correctly. We used abnormal car data to classify it into fifteen other classical vehicle problems. We experimented with various signal processing techniques and presented the comparison results. In the detection and further problem classification, random forest showed the highest results of 97% and 92% with time-frequency features.     

Robust estimation of surface properties and interpolation of shadow/specularity components

The Polynomial Texture Map framework (PTM) extends the simple model of image formation from the Lambertian variant of Photometric Stereo (PST) to more general reflectances and to more complex-shaped surfaces. It forms an alternative method for apprehending object color, albedo, and surface normals. Here we consider solving such a model in a robust version, not to date attempted for PTM, with the upshot that both shadows and specularities are identified automatically without the need for any thresholds. Instead of the linear model used in few-source PST for Lambertian surfaces, PTM adopts a higher degree polynomial model. PTM has two aims: interpolation of images for new lighting directions, and recovery of surface properties. Here we show that a robust approach is a good deal more accurate in recovering surface properties. For new-lighting interpolation, we demonstrate that a simple radial basis function interpolation can accurately interpolate specularities as well as attached and cast shadows even with a medium-sized image set, with no need for reflectance sharing across pixels or extremely large numbers of interpolation coefficients.     

A novel approach to accelerate the convergence speed of a stochastic multi-agent system using recurrent neural nets

One problem in the design of multi-agent systems is the difficulty of predicting the occurrences that one agent might face, also to recognize and to predict their optimum behavior in these situations. Therefore, one of the most important characteristic of the agent is their ability during adoption, to learn, and correct their behavior. With consideration of the continuously changing environment, the back and forth learning of the agents, the inability to see the agent’s action first hand, and their chosen strategies, learning in a multi-agent environment can be very complex. On the one hand, with recognition to the current learning models that are used in deterministic environment that behaves linearly, which contain weaknesses; therefore, the current learning models are unproductive in complex environments that the actions of agents are stochastic. Therefore, it is necessary for the creation of learning models that are effective in stochastic environments. Purpose of this research is the creation of such a learning model. For this reason, the Hopfield and Boltzmann learning algorithms are used. In order to demonstrate the performance of their algorithms, first, an unlearned multi-agent model is created. During the interactions of the agents, they try to increase their knowledge to reach a specific value. The predicated index is the number of changed states needed to reach the convergence. Then, the learned multi-agent model is created with the Hopfield learning algorithm, and in the end, the learned multi-agent model is created with the Boltzmann learning algorithm. After analyzing the obtained figures, a conclusion can be made that when learning impose to multi-agent environment the average number of changed states needed to reach the convergence decreased and the use of Boltzmann learning algorithm decreased the average number of changed states even further in comparison with Hopfield learning algorithm due to the increase in the number of choices in each situation. Therefore, it is possible to say that the multi-agent systems behave stochastically, the more closer they behave to their true character, the speed of reaching the global solution increases.