A DEMATEL analysis of smart city determinants

Substantial population growth, increasing urbanization, overloaded infrastructure, more energy consumption, environmental impacts, and investment in technological developments are major challenges in city management and development. Municipalities seek to improve their citizens’ wellbeing and quality of life, but the need to find a balance between these complex factors makes decision-making processes more difficult. Experts must analyze the determinants of smart cities, which are urban centers intended to be socially involved, habitable, and economically and environmentally sustainable, and that rely strongly on technology and innovation. Based on multiple-criteria decision analysis (MCDA) principles, this study combines cognitive mapping techniques and the decision-making trial and evaluation laboratory (DEMATEL) method to develop a multicriteria model that can help managers analyze smart city determinants in a collaborative manner. The research included identifying these determinants and their respective cause-and-effect relationships using an expert panel’s experience and knowledge related to the subject under analysis. The results were validated by both the panel and the city councilor responsible for mobility and municipal projects within Évora City Council, Portugal, which has implemented smart city strategies. The latter specialist verified that the proposed evaluation system can be used as a tool to facilitate smart city collaborative decision-making processes. The study’s main advantages and limitations are also analyzed and discussed.     

Rapid degradation of metamitron and highly complex mixture of pollutants using MIL-53(Al) integrated combustion synthesized TiO2

In the present work, we report a new approach for the synthesis of TiO₂-MIL-53(Al) (MIL: Matériaux de l′Institut Lavoisier) nano-composite. The integration of microwave synthesized metal organic framework (MOF) with combustion synthesized metal oxide is reported for the first time. The synthesized materials were characterized using an X-ray diffractometer, Fourier transform infrared spectrometer, scanning electron microscope, energy dispersive X-ray spectroscope, energy dispersive X-ray fluorescence, porosimeter, and UV–visible diffuse reflectance spectroscope. The diffraction pattern of the composite indicated the presence of TiO₂ as well as MIL-53(Al). Furthermore, the presence of anatase–rutile mixed phase of TiO₂ in the synthesized material was observed. The Fourier transform infrared spectrum was in support of XRD observations. A significantly low surface area and porosity of the composite material, compared to its parent components, was observed. The surface micrograph image of the composite depicted its nano-sheet type morphology. The energy dispersive X-ray analysis confirmed the presence of Ti, Al, C and O in the composite material. The composite material exhibited a narrow band-gap compared to the pristine MIL and TiO₂.As an application, the photocatalytic activity of synthesized material was determined by the degradation of metamitron. The application part is also one of the novelties of the present work. The effect of catalyst loading, initial concentration of metamitron and presence of inorganic ions on the photocatalytic degradation was investigated. The reusability of the composite material was evaluated. A plausible mechanism of photocatalytic degradation is proposed based on radical scavenging experiments. To the best of our knowledge, to date, there is no study on the application of TiO₂-MOF composite for the photocatalytic degradation of herbicide. Further, the synthesized material was successfully used for the photocatalytic degradation of a complex mixture of pollutants comprising metamitron, methylene blue, rhodamine B and 2, 4-dichlorophenol. This is a first of its kind study to demonstrate the application of TiO₂-MOF composite for photocatalytic degradation of a highly complex mixed pollutant.     

Efficient reliability analysis of dynamic k-out-of-n phase-AND mission systems

Motivated by real-world applications of satellites and wireless sensor networks, this paper models and evaluates a dynamic k-out-of-n phase-AND mission system (k/n-PAMS). The mission task conducted by a k/n-PAMS involves multiple consecutive phases; the mission is successful as long as the task is successful in any of the phases. Due to factors, such as scheduled maintenance, location changes in task execution during different phases, and resource sharing with other tasks, the total number of available components n for the considered mission task and the required number of working components k may change from phase to phase. In addition, due to varying load and working environments, component failure time distributions are also phase dependent. This paper proposes an analytical modeling approach based on multivalued decision diagrams (MDDs) for assessing reliability of the considered k/n-PAMS. The approach encompasses a new and fast MDD model generation algorithm that considers behaviors of all the mission phases simultaneously based on node labeling. As demonstrated through empirical studies on k/n-PAMSs with different sizes (different numbers of phases and different numbers of system components), the proposed algorithm is more efficient than the traditional phase-by-phase model generation method.     

Novel preparation and high electrical performance effect of Mn-doped ultra-high surface area activated carbon (USAC) as an additive for Ni hybrid capacitors

This paper reports the synthesis of various molar concentrations of manganese (Mn)-doped Ultra-High Surface area Activated Carbon (USAC) additives and their efficient use as cathode materials for supercapacitors. We synthesized the nanoparticles via a novel and facile dip-coating process and characterized them in detail by various analytical techniques. The SEM, EDAX, and XPS results showed that the Mn ions were successfully substituted on the USAC additives’ layered structure without any structural changes. The long cyclic stability of the as-prepared Mn-doped USAC additives was tested as a cathode material for supercapacitors at different current densities. The detailed experimental results showed that the Mn dopant content crucially determines the electrochemical performances of the USAC additives. Electrochemical measurements showed that the MnCEP-S600HTT with 0.10 mol% molar concentration of Mn dopant gives the best cycling performances. It delivers a discharge capacity of 262.9 mAh g^?1 after 100 cycles. Further increasing the current density to 1000 mA g^?1 allowed it to still maintain 253.6 mAh g^?1 after 200 cycles. We confirmed that the structure of Mn-doped USAC additives is an important pole to improve the structural stability and electrochemical properties.     

An Enhanced Jacobi Precoder for Downlink Massive MIMO Systems

Linear precoding methods such as zero-forcing (ZF) are near optimal for downlink massive multi-user multiple input multiple output (MIMO) systems due to their asymptotic channel property. However, as the number of users increases, the computational complexity of obtaining the inverse matrix of the gram matrix increases. For solving the computational complexity problem, this paper proposes an improved Jacobi (JC)-based precoder to improve error performance of the conventional JC in the downlink massive MIMO systems. The conventional JC was studied for solving the high computational complexity of the ZF algorithm and was able to achieve parallel implementation. However, the conventional JC has poor error performance when the number of users increases, which means that the diagonal dominance component of the gram matrix is reduced. In this paper, the preconditioning method is proposed to improve the error performance. Before executing the JC, the condition number of the linear equation and spectrum radius of the iteration matrix are reduced by multiplying the preconditioning matrix of the linear equation. To further reduce the condition number of the linear equation, this paper proposes a polynomial expansion precondition matrix that supplements diagonal components. The results show that the proposed method provides better performance than other iterative methods and has similar performance to the ZF.     

A theoretical study of the ability of 2D monolayer Au (111) to activate gas molecules

The adsorption and activation of gas molecules are investigated substantially in solid-gas heterogeneous catalysis. Here we investigated the interaction between gas molecules and unique two-dimensional monolayer Au (111) structure using density functional theory. It is found that CO₂, H₂O, N₂ and CH₄ molecules are weakly adsorbed on the surface with the adsorption energies between ?0.150 and ?0.250 eV due to van der Waals interaction. While CO, NO, NO₂, and NH₃ molecules are adsorbed more stably with the adsorption energies between ?0.300 and ?0.470 eV. Especially, the bond length of CO is stretched by 0.038 Å and the bond angle of NO₂ is obviously enlarged by 10.460°. The activation originates from the rearrangement of molecule orbitals and the orbitals hybridization between the partial orbitals of gas molecules and Au-5d orbitals. The fundamental analyses of adsorption mechanism and electronic properties may provide guidance for the applications of two-dimensional monolayer metal catalysis.PACSnumbers 73.22.-f, 73.61.-r     

Laser beam melting of aluminum alloys with hull-core build strategy by using an initial meso-scale simulation

Laser beam melting (LBM) of aluminum alloys is gaining a wide popularity in different industrial applications as an alternative technology for the production of individual and complex parts. A long build time and the high amount of experimental work for optimizing or finding new process parameters are two of the current challenges for reaching an industrial maturity. This paper proposes an efficient way to determine new process parameters for aluminum alloy aluminum-silicon10-magnesium with highest build-up rates by using a 3D finite element model on the mesoscopic level. High laser power in combination with the hull-core build strategy was used to increase the build-up rate without impairing the part accuracy. The influences of high laser power, laser diameter and scan speed on the melt pool were studied by using a thermal simulation of single laser tracks. Based on the simulation results the process window could be derived and was tested on a laser beam melting (LBM) system. The achieved reduction of the build time of up to 31 % without loss in part accuracy proved the novel approach for the prediction of the required process window as an efficient method to reduce costly and time-consuming experimental work.     

Factors influencing the use of artificial intelligence in government: Evidence from China

Some studies have discussed the potential and challenges related to the use of artificial intelligence (AI) in government. However, there are few empirical studies that have examined factors that influence the use of AI in government. By collecting policy documents and empirical data from the government, IT enterprises, and the public in China, we identified the influencing factors in the three stages of government adoption, implementation, and decision-making. The research results show that the influencing factors of government application of AI are different at different stages and with different stakeholders’ backgrounds.     

Face Recognition Based on Gabor Feature Extraction Followed by FastICA and LDA

Over the past few decades, face recognition has become the most effective biometric technique in recognizing people’s identity, as it is widely used in many areas of our daily lives. However, it is a challenging technique since facial images vary in rotations, expressions, and illuminations. To minimize the impact of these challenges, exploiting information from various feature extraction methods is recommended since one of the most critical tasks in face recognition system is the extraction of facial features. Therefore, this paper presents a new approach to face recognition based on the fusion of Gabor-based feature extraction, Fast Independent Component Analysis (FastICA), and Linear Discriminant Analysis (LDA). In the presented method, first, face images are transformed to grayscale and resized to have a uniform size. After that, facial features are extracted from the aligned face image using Gabor, FastICA, and LDA methods. Finally, the nearest distance classifier is utilized to recognize the identity of the individuals. Here, the performance of six distance classifiers, namely Euclidean, Cosine, Bray-Curtis, Mahalanobis, Correlation, and Manhattan, are investigated. Experimental results revealed that the presented method attains a higher rank-one recognition rate compared to the recent approaches in the literature on four benchmarked face datasets: ORL, GT, FEI, and Yale. Moreover, it showed that the proposed method not only helps in better extracting the features but also in improving the overall efficiency of the facial recognition system.