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In this research, a novel and efficient nanocatalyst, namely, palladium immobilized onto 1,4-bis(pyrid-4-yl)benzene grafted to n-propyl-functionalized Fe3O4@MCM-41 nanoparticles (PBPBPMF), was synthesized and introduced. Then, its catalytic activity was tested in the synthesis of bis(pyrazolyl)methane (14 examples, 90–97%, 9–25 min, solvent-free, 60 °C) and biphenyl (13 examples, 80–98%, 10–120 min, PEG-400, 70 °C) derivatives. Bis(pyrazolyl)methane derivatives were synthesized via a one-pot pseudo-five-component reaction of aldehydes, ethyl acetoacetate, and phenylhydrazine, and biphenyl derivatives were obtained by a Stille coupling reaction. Transmission electron microscopy (TEM), energy-dispersive X-ray spectroscopy (EDS), scanning electron microscopy (SEM), N2 sorption isotherms, vibrating sample magnetometer (VSM), inductively coupled plasma atomic emission spectrometer (ICP-OES), X-ray diffraction (XRD), thermogravimetric analysis (TGA), Fourier transform infrared spectroscopy (FT-IR), and elemental mapping analyses were used to identify the catalyst. The PBPBPMF is a heterogeneous catalyst with several advantages, including a large specific surface area, ease of purification, and separation from the reaction mixture; furthermore, the nanocatalyst was recycled six times without appreciably altering its catalytic properties. [ABSTRACT FROM AUTHOR]

Context : Phographene and its family member structures are of the newly proposed semiconductors for detection of chemicals. That is, in this project, the potential of using α-phographene (α-POG) both for adsorption and detection of five types of the most important air pollutant gases containing SO2, AsH3, CF3H, NO2, and CO2 species were investigated. The results of the time dependent density functional theory (TD-DFT) calculations indicate that during the adsorption of NO2, and SO2 by the sorbent, big redshifts occur (up to 866.2 nm, and 936.5, respectively) resulting in considerable changes in the orbitals and the electronic structures of the systems. Moreover, the results of the thermodynamic calculations reveal that α-POG could selectively adsorb SO2, NO2, and AsH3 gases (with different orders), but it could not adsorb the two other gases.Finally, the outcome of the band gap calculations shows that between all mentioned gases, α-POG could selectively detect the presence of SO2, and then NO2; while, this nanosheet could not sense the existence of AsH3, CF3H, or CO2 gases. Methods: All of the calculations were carried out by using the Gaussian 03 quantum chemical package. In addition, the physiochemical parameters were extracted from the output files for further calculations. Studies on all saddle points and the following calculations were performed applying the B3LYP/6-311g(d,p) level of theory. [ABSTRACT FROM AUTHOR]

This study developed a model to compensate for both active and passive power in an electric microgrid with an optimal power flow to allow a detailed analysis of its power flow, which encourages the integration of renewable energy sources (RESs). Mixture integer linear programming (MILP) is proposed, with distributed generation (DG) integrated into the existing bars. It is developed in the GAMS tool to minimize operating costs in the microgrid as an objective function, to guarantee reliability, and quality in the microgrid. The algorithm calculates an optimal solution that meets all requirements for reliability and quality in the microgrid. Finally, a comparison of data between the two systems without and with DG was performed, showing an improvement in voltage profiles, reduced active power losses in the lines, a better power factor (PF), and improved angular behavior; that also adheres to the restrictions set forth at a minimum operating cost. A nine-bar model has been used to model the problem by IEEE standards. [ABSTRACT FROM AUTHOR]

Objective: Dementia is a broad term referring to a decline in problem-solving abilities, language skills, memory, and other cognitive functions to a degree that it significantly disrupts everyday activities. The underlying cause of dementia is the impairment or loss of nerve cells and their connections within the brain. The particular symptoms experienced are contingent upon specific regions of the brain affected by this damage. In this research, we aimed to investigate the nonlinear dynamics of the mixed demented brain compared to healthy subjects using electroencephalogram (EEG) analysis. Method: For this purpose, EEG was recorded from 66 patients with mixed dementia and 65 healthy subjects during rest. After signal preprocessing, sample entropy and Katz fractal dimension analyses were applied to the preprocessed EEG data. Analysis of variance with repeated measures was utilized to compare the nonlinear dynamics of brain activity between dementia and healthy states and partial correlation analysis was employed to explore the relationship between EEG complexity measures and cognitive and neuropsychiatric symptoms of patients. Results: Based on repeated measures ANOVA, there was a significant main effect between groups for both Katz fractal dimension (F = 4.10, P = 0.01) and sample entropy (F = 4.81, P = 0.009) measures. Post hoc comparisons revealed that EEG complexity was significantly reduced in dementia mainly in the occipitoparietal and temporal areas (P < 0.05). MMSE scores were positively correlated with EEG complexity measures, while NPI scores were negatively correlated with EEG complexity measures, mainly in the occipitoparietal and temporal areas (P < 0.05). Moreover, using a KNN classifier, all significant complexity measures yielded the best classification performance with an accuracy of 98.05%, sensitivity of 97.03% and specificity of 99.16% in detecting dementia. Conclusion: This study demonstrated a unique dynamic system within the brain impacted by dementia that results in more predictable patterns of cortical activity mainly in the occipitoparietal and temporal areas. These abnormal patterns were associated with patients' cognitive capacity and neuropsychiatric symptoms. [ABSTRACT FROM AUTHOR]

Context: The abilities of Co-Al18P18, Ni-Al21N21, Fe-B24N24, Mn-B27P27, Ti-C60 and Cu-Si72 as catalysts for N2-RR to create the NH3 are investigated by theoretical levels. The ∆Eadoption and ∆Eformation of Co-Al18P18, Ni-Al21N21, Fe-B24N24, Mn-B27P27, Ti-C60 and Cu-Si72 are investigated. The ∆Eadsorption of N2-RR intermediates and ΔGreaction of reaction steps of N2-RR on Co-Al18P18, Ni-Al21N21, Fe-B24N24, Mn-B27P27, Ti-C60 and Cu-Si72 are examined. In acceptable mechanisms, the *NN → *NNH step is potential limiting step and *NN → *NNH step in enzymatic mechanism is endothermic reaction. The ∆Greaction of *NHNH2 → *NH2NH2 step on Co-Al18P18, Ni-Al21N21, Fe-B24N24, Mn-B27P27, Ti-C60 and Cu-Si72 are -0.904, -0.928, -0.860, -0.882, -0.817 and -0.838 eV, respectively. The Co-Al18P18 and Ni-Al21N21 have the highest ∆Greaction values for reaction steps of N2-RR. Finally, it can be concluded that the Co-Al18P18, Ni-Al21N21, Fe-B24N24 and Mn-B27P2 have acceptable potential for N2-RR by acceptable pathways. Methods: The structures of Co-Al18P18, Ni-Al21N21, Fe-B24N24, Mn-B27P27, Ti-C60 and Cu-Si72 and N2-RR intermediates are optimized by PW91PW91/6-311+G (2d, 2p) and M06-2X/cc-pVQZ as theoretical levels in GAMESS software. The convergence for force set displacement of Co-Al18P18, Ni-Al21N21, Fe-B24N24, Mn-B27P27, Ti-C60 and Cu-Si72 and N2-RR intermediates are 1.5 × 105 Hartree/Bohr and 6.0 × 10-5 Angstrom. The Opt = Tight and MaxStep = 30 are considered to optimize Co-Al18P18, Ni-Al21N21, Fe-B24N24, Mn-B27P27, Ti-C60 and Cu-Si72 and N2-RR intermediates. The frequencies of Co-Al18P18, Ni-Al21N21, Fe-B24N24, Mn-B27P27, Ti-C60 and Cu-Si72 and N2-RR intermediates are calculated. [ABSTRACT FROM AUTHOR]

This paper explores the application of a machine learning approach to predict equipment failure rates in power distribution networks, motivated by the significant impact of power outages on citizens' daily lives and the economy. In this research, data on equipment failure rates and maintenance records were collected from power distribution networks in Baghdad, Iraq. The collected data underwent preprocessing, and features were extracted to train Adaptive NeuroFuzzy Inference System (ANFIS) and Periodic Autoregressive Moving Average (PARMA) time series models. To initiate the project, information regarding blackouts that occurred between January 2018 and December 2021 was retrieved from the database. The RMSE index results for the PARMA time series and ANFIS model are 3.518 and 2.264, respectively, demonstrating the superior performance of the ANFIS model in predicting equipment failure rates and its potential for future predictions. This study highlights the ANFIS model's capacity to anticipate equipment failure rates, potentially enhancing maintenance efficiency and reducing power outages in Baghdad. The error mean square was employed to evaluate the proposed models' error rate. [ABSTRACT FROM AUTHOR]