Search Results (645)

Assessing the quality of urban street space can provide suggestions for urban planning and construction management. Big data collection and machine learning provide more efficient evaluation methods than traditional survey methods. This study intended to quantify the urban street spatial quality based on street view image segmentation. A case study was conducted in the Second Ring Road of Changsha City, China. Firstly, the road network information was obtained through OpenStreetMap, and the longitude and latitude of the observation points were obtained using ArcGIS 10.2 software. Then, corresponding street view images of the observation points were obtained from Baidu Maps, and a semantic segmentation software was used to obtain the pixel occupancy ratio of 150 land cover categories in each image. This study selected six evaluation indicators to assess the street space quality, including the sky visibility index, green visual index, interface enclosure index, public–facility convenience index, traffic recognition, and motorization degree. Through statistical analysis of objects related to each evaluation indicator, scores of each evaluation indicator for observation points were obtained. The scores of each indicator are mapped onto the map in ArcGIS for data visualization and analysis. The final value of street space quality was obtained by weighing each indicator score according to the selected weight, achieving qualitative research on street space quality. The results showed that the street space quality in the downtown area of Changsha is relatively high. Still, the level of green visual index, interface enclosure, public–facility convenience index, and motorization degree is relatively low. In the commercial area east of the river, improvements are needed in pedestrian perception. In other areas, enhancements are required in community public facilities and traffic signage. Full article

Binary As_xSe_100−x alloys from the border of a glass-forming region (65 < x < 70) subjected to nanomilling in dry and dry–wet modes are characterized by the XRPD, micro-Raman scattering (micro-RS) and revised positron annihilation lifetime (PAL) methods complemented by a disproportionality analysis using the quantum–chemical cluster modeling approach. These alloys are examined with respect to tetra-arsenic biselenide As₄Se₂ stoichiometry, realized in glassy g-As₆₅Se₃₅, glassy–crystalline g/c-As₆₇Se₃₃ and glassy–crystalline g/c-As₇₀Se₃₀. From the XRPD results, the number of rhombohedral As and cubic arsenolite As₂O₃ phases in As-Se alloys increases after nanomilling, especially in the wet mode realized in a PVP water solution. Nanomilling-driven amorphization and reamorphization transformations in these alloys are identified by an analysis of diffuse peak halos in their XRPD patterning, showing the interplay between the levels of a medium-range structure (disruption of the intermediate-range ordering at the cost of an extended-range one). From the micro-RS spectroscopy results, these alloys are stabilized by molecular thioarsenides As₄Se_n (n = 3, 4), regardless of their phase composition, remnants of thioarsenide molecules destructed under nanomilling being reincorporated into a glass network undergoing a polyamorphic transition. From the PAL spectroscopy results, volumetric changes in the wet-milled alloys with respect to the dry-milled ones are identified as resulting from a direct conversion of the bound positron–electron (Ps, positronium) states in the positron traps. Ps-hosting holes in the PVP medium appear instead of positron traps, with ~0.36–0.38 ns lifetimes ascribed to multivacancies in the As-Se matrix. The superposition of PAL spectrum peaks and tails for pelletized PVP, unmilled, dry-milled, and dry–wet-milled As-Se samples shows a spectacular smoothly decaying trend. The microstructure scenarios of the spontaneous (under quenching) and activated (under nanomilling) decomposition of principal network clusters in As₄Se₂-bearing arsenoselenides are recognized. Over-constrained As_6·(2/3) ring-like network clusters acting as pre-cursors of the rhombohedral As phase are the main products of this decomposition. Two spontaneous processes for creating thioarsenides with crystalline counterparts explain the location of the glass-forming border in an As-Se system near the As₄Se₂ composition, while an activated decomposition process for creating layered As₂Se₃ structures is responsible for the nanomilling-driven molecular-to-network transition. Full article

The studies on strategies for improving restoration times in electrical distribution systems are extensive. They have theoretically explored the application of mathematical models, the implementation of remotely controlled systems, and the use of digital simulators. This research aims to connect conceptual studies and the implementation of improvements and impact assessment in electrical distribution systems in developing countries, where distribution technologies vary widely, by employing a comprehensive methodology. The proposed research examines the restoration times for faults in substations within general distribution networks in the central–western region of Mexico. The study comprises these stages: (a) diagnosing the electrical supply, demand, and infrastructure; (b) analyzing the electrical restoration time and the restoration index of the substations; and (c) providing recommendations and implementing pilot tests for improvements in the identified critical substations. The results revealed 12 analysis zones, including 120 distribution substations, 150 power transformers, and 751 medium voltage circuits. Among the substations, 73% have ring connections, 15% have TAP connections, and 12% have radial connections. Additionally, 27% of the substations rely on only a single distribution line. The study identified areas with significant challenges in restoring electricity supply, particularly focusing on power transformers: 32 transformers with permanent power line failures requiring load transfer via medium voltage; 67 transformers requiring optimized restoration maneuvers due to specific characteristics; and 4 areas with opportunities to enhance the reliability of the power supply through remote-controlled link systems. The analysis resulted in the installation of 145 remote link systems, which improved restoration rates by over 40%. This approach is expected to be replicated throughout Mexico to identify improvements needed in the national distribution system. Full article

A digitizer is considered one of the fundamental components of an earthquake monitoring system. In this paper, we design and implement a high accuracy seismic digitizer. The implemented digitizer consists of several blocks, i.e., the analog-to-digital converter (ADC), GPS receiver, and microprocessor. Three finite impulse response (FIR) filters are used to decimate the sampling rate of the input seismic data according to user needs. A graphical user interface (GUI) has been designed for enabling the user to monitor the seismic waveform in real time, and process and adjust the parameters of the acquisition unit. The system casing is designed to resist harsh conditions of the environment. The prototype can represent the three component sensors data in the standard MiniSEED format. The digitizer stream seismic data from the remote station to the main center is based on TCP/IP connection. This protocol ensures data transmission without any losses as long as the data still exist in the ring buffer. The prototype was calibrated by real field testing. The prototype digitizer is integrated with the Egyptian National Seismic Network (ENSN), where a commercial instrument is already installed. Case studies shows that, for the same event, the prototype station improves the solution of the ENSN by giving accurate timing and seismic event parameters. Field test results shows that the event arrival time and the amplitude are approximately the same between the prototype digitizer and the calibrated digitizer. Furthermore, the frequency contents are similar between the two digitizers. Therefore, the prototype digitizer captures the main seismic parameters accurately, irrespective of noise existence. Full article

The sun’s activity role in climate change has become a topic of debate. According to data from the IPCC, the global average temperature has shown an increasing trend since 1850, with an average increase of 0.06 °C/decade. Our analysis of summer temperature records from five weather stations in northern Fennoscandia (65°–70.4° N) revealed an increasing trend, with a range of 0.09 °C/decade to 0.15 °C/decade. However, due to the short duration of instrumental records, it is not possible to accurately assess and predict climate changes on centennial and millennial timescales. In this study, we used the Finnish super-long (~7600 years) tree-ring chronology to create a climate prediction for the 21st century. We applied a method that combines a long short-term memory (LSTM) neural network with the continuous wavelet transform and wavelet filtering in order to make climate change predictions. This approach revealed a significant decrease in tree-ring growth over the near term (2063–2073). The predicted decrease in tree-ring growth (and regional temperature) is thought to be a result of a new grand solar minimum, which may lead to Little Ice Age-like climatic conditions. This result is significant for understanding current climate processes and assessing potential environmental and socio-economic risks on a global and regional level, including in the area of the Arctic shipping routes. Full article

This study introduces an integrated structural optimization design method based on a BP neural network and NSGA-II multi-objective genetic algorithm. Initially, a two-dimensional axisymmetric finite element model of the Giant Magnetostrictive Actuator (GMA) was established, and the coupling simulation of the electromagnetic field, structural field, and temperature field was conducted to obtain the GMA’s performance parameters. Subsequently, the structural parameters of the GMA magnetic circuit, including the magnetic conducting ring, magnetic conducting sidewall, magnetic conducting body, and coil, were used as inputs, and the axial magnetic induction intensity, uniformity of axial magnetic induction intensity, and coil loss on the Giant Magnetostrictive Material (GMM) rod were used as outputs to establish a back propagation (BP) neural network model. This model delineated the nonlinear relationship between structural parameters and performance parameters. Then, the BP-NSGA-II algorithm was applied to perform multi-objective optimization on the actuator’s structural parameters, resulting in a set of Pareto optimal non-dominated solutions, from which a set of optimal solutions was obtained using the entropy weight method. Finally, simulation analysis of this optimal solution was conducted, indicating that under a 5 A power supply excitation, the maximum axial magnetic induction intensity on the optimized GMM rod increased from 0.87 T to 1.12 T; the uniformity of axial magnetic induction intensity improved from 93.1% to 96.5%; and the coil loss decreased from 7.79 × 10⁴ W/m³ to 4.97 × 10⁴ W/m³. Based on the optimization results, a prototype actuator was produced, and the test results of the prototype’s output characteristics proved the feasibility of this optimization design method. Full article

Significant progress has been made in the research of all-optical neural networks in recent years. In this paper, we theoretically explore the properties of a neural system composed of semiconductor ring lasers (SRLs). Our study demonstrates that external optical signals generated by a tunable laser (TL) are injected into the first semiconductor ring laser photonic neuron (SRL1). Subsequently, the responses of SRL1 in the clockwise (CW) and counterclockwise (CCW) directions are unidirectionally injected into the CW and CCW directions of the second semiconductor ring laser photonic neuron (SRL2), respectively, which then exhibits similar spiking inhibition behaviors. Numerical simulations reveal that the spiking inhibition behavior of the SRL response can be precisely controlled by adjusting the perturbation time and intensity of the external injection signal, and this behavior is highly repeatable. Most importantly, we successfully achieve the stable transmission of these responses between the two SRL photonic neurons. These inhibition behaviors are analogous to those of biological neurons, but with a response speed reaching the sub-nanosecond level. Additionally, we indicate that SRL photonic neurons undergo a refractory-period-like phenomenon when subjected to two consecutive perturbations. These findings highlight the immense potential for the design and implementation of future all-optical neural networks, providing critical theoretical foundations and support for them. Full article

Globalization and informatization have exerted far-reaching impacts on the spatial connection and development of urban systems. This study, concerning the network of an urban system based on the space of flows, supplements the insufficiency on the micro-level in macro-urban network research. Taking Nanjing as an example, this study explores the characteristics of the network of the urban system from the perspective of people flow, refining the granularity of the analysis to the township- and street-level spatial units using mobile phone data. The findings are as follows: (1) There is a multicenter layered network pattern, with the main urban area being the core of the network, while Dongshan Street and Moling Street, as secondary centers, form a joint development pattern with the main urban area. (2) The spatial differentiation is significant. The spatial heterogeneity of “centralization in the central region, delayering in the north, and hierarchization in the south” is obvious. The net people inflow nodes are mainly concentrated in the main urban area and its surroundings, while the net outflow nodes are mostly located on the edge of the city. Moreover, the nodes to the south of Yangtze River are advantageous in urban resource control. (3) The phenomenon of “double shadow circle” appears in the ring of the main city and the ring of the municipal area. Moreover, the northern district experiences a serious outflow of population. (4) The effect of policy intervention is beginning to show. Improved levels of development of street and township units such as Jiangbei New District show the positive influence of national strategy on regional development. Full article

The steric hindrance in molecules of 1-(2,4,6-trimethylphenyl)ethanone and 1-(2,4,6-triisopropylphenyl)ethanone were shown to substantially differentiate the options of synthesis of the respective alcohols. The former was obtained with a yield of 12% with a mild reducing agent, i.e., NaBH₄, as well as in vapor phase transfer hydrogenation (22% yield at 673 K) over MgO, whereas the latter was not formed at all under those conditions. The only agent that was able to reduce both ketones was LiAlH₄. The single crystals of the two alcohols were obtained and their structures were determined. The symmetry of the 0-D hydrogen-bonded networks of molecules in these crystals was analyzed. It was shown that the methyl substituent allows the molecules to form hexameric rings, whereas the isopropyl-substituted molecules formed tetrameric ones. In both cases, there were two types of rings in the cell, but four types of molecules forming tetramers and only three types of molecules in the hexamers. These structures were compared to similar structures formed by other molecules found in the Cambridge Structural Database via hydrogen bonding. Moreover, the single crystal of 1-(2,4,6-triisopropylphenyl)ethanone was obtained to explain if either the hydrogen bonding or the presence of isopropyl groups influences the angles in the molecules. Full article

Spatial community detection is a method that divides geographic spaces into several sub-regions based on spatial interactions, reflecting the regional spatial structure against the background of human mobility. In recent years, spatial community detection has attracted extensive research in the field of geographic information science. However, mining the community structures and their evolutionary patterns from spatial interaction data remains challenging. Most existing methods for spatial community detection rely on representing spatial interaction networks in Euclidean space, which results in significant distortion when modeling spatial interaction networks; since spatial community detection has no ground truth, this results in the detection and evaluation of communities being difficult. Furthermore, most methods usually ignore the dynamics of these spatial interaction networks, resulting in the dynamic evolution of spatial communities not being discussed in depth. Therefore, this study proposes a framework for community detection and evolutionary analysis for spatial interaction networks. Specifically, we construct a spatial interaction network based on network science theory, where geographic units serve as nodes and interaction relationships serve as edges. In order to fully learn the structural features of the spatial interaction network, we introduce a hyperbolic graph convolution module in the community detection phase to learn the spatial and non-spatial attributes of the spatial interaction network, obtain vector representations of the nodes, and optimize them based on a graph generation model to achieve the final community detection results. Considering the dynamics of spatial interactions, we analyze the evolution of the spatial community over time. Finally, using taxi trajectory data as an example, we conduct relevant experiments within the fifth ring road of Beijing. The empirical results validate the community detection capabilities of the proposed method, which can effectively describe the dynamic spatial structure of cities based on human mobility and provide an effective analytical method for urban spatial planning. Full article

Cyclodextrins are macrocyclic rings composed of glucose residues. Due to their remarkable structural properties, they can form host–guest inclusion complexes, which is why they are frequently used in the pharmaceutical, cosmetic, and food industries, as well as in environmental and analytical chemistry. This review presents the reports from 2011 to 2023 on the quantitative structure–activity/property relationship (QSAR/QSPR) approach, which is primarily employed to predict the thermodynamic stability of inclusion complexes. This article extensively discusses the significant developments related to the size of available experimental data, the available sets of descriptors, and the machine learning (ML) algorithms used, such as support vector machines, random forests, artificial neural networks, and gradient boosting. As QSAR/QPR analysis only requires molecular structures of guests and experimental values of stability constants, this approach may be particularly useful for predicting these values for complexes with randomly substituted cyclodextrins, as well as for estimating their dependence on pH. This work proposes solutions on how to effectively use this knowledge, which is especially important for researchers who will deal with this topic in the future. This review also presents other applications of ML in relation to CD complexes, including the prediction of physicochemical properties of CD complexes, the development of analytical methods based on complexation with CDs, and the optimisation of experimental conditions for the preparation of the complexes. Full article

A novel, compact, monopole apple-shaped, triple-band metamaterial-printed wearable antenna backed by a uniplanar compact electromagnetic bandgap (UC-EBG) structure is introduced in this paper for wearable wireless and medical body area network (WBAN/MBAN) applications. A tri-band UC-EBG structure has been utilized as a ground plane to minimize the impact of antenna radiation on the human body and improve antenna performance for the proposed wearable antenna. Metamaterial triangular complementary split ring resonators (TCSRRs) are incorporated into the antenna and UC-EBG structure, resulting in a compact UC-EBG-backed antenna with an overall size of 39 × 39 × 2.84 mm³ (0.41 λ_g × 0.41 λ_g × 0.029 λ_g). The printed textile antenna operates at 2.45 GHz for the wireless local area network (WLAN), 3.5 GHz for 5G new radio (NR), and 5.8 GHz for the industrial, scientific, and medical (ISM) bands with improved gain and high-efficiency values. Furthermore, the performance of the antenna is analyzed on the human body, where three models of curved body parts are considered: a child’s arm (worst case) with a 40 mm radius, an adult’s arm with a 60 mm radius, and an adult’s leg with a 70 mm radius. The results demonstrate that the proposed antenna is an attractive candidate for wearable healthcare and fitness monitoring devices and other WBAN/MBAN applications due to its compact size, high performance, and low SAR values. Full article

Detection and enumeration of coliform bacteria using traditional methods and current molecular techniques against E. coli usually involve long processes with less sensitivity and specificity to distinguish between viable and non-viable bacteria for microbiological water analysis. This approach involves developing and validating an immunosensor comprising ring resonators functionalized with specific antibodies surrounded by a network of microchannels as an alternative method for detecting and indirectly enumerating Escherichia coli in samples of water for consumption. Different ELISA assays were conducted to characterize monoclonal and polyclonal antibodies selected as detection probes for specific B-galactosidase enzymes and membrane LPS antigens of E. coli. An immobilization control study was performed on silicon nitride surfaces used in the immunosensor, immobilized with the selected antibodies from the ELISA assays. The specificity of this method was confirmed by detecting as few as 10 CFU/mL of E. coli from viable and non-viable target bacteria after applying various disinfection methods to water samples intended for human consumption. The 100% detection rate and a 100 CFU/mL Limit of Quantification of the proposed method were validated through a comprehensive assessment of the immunosensor-coupled microfluidic system, involving at least 50 replicates with a concentration range of 10 to 10⁶ CFU/mL of the target bacteria and 50 real samples contaminated with and without disinfection treatment. The correlation coefficient of around one calculated for each calibration curve obtained from the results demonstrated sensitive and rapid detection capabilities suitable for application in water resources intended for human consumption within the food industry. The biosensor was shown to provide results in less than 4 h, allowing for rapid identification of microbial contamination crucial for ensuring water monitoring related to food safety or environmental diagnosis and allowing for timely interventions to mitigate contamination risks. Indeed, the achieved setup facilitates the in situ execution of laboratory processes, allowing for the detection of both viable and non-viable bacteria, and it implies future developments of simultaneous detection of pathogens in the same contaminated sample. Full article

To study the damage characteristics and evolution law of granite under the influence of temperature and loading rate, uniaxial compression tests at different loading rates were performed for granite samples at 30 °C, 40 °C, 60 °C, and 80 °C. The damage characteristics and evolution law of granite were studied in conjunction with acoustic emission ringing, acoustic emission b values, acoustic emission signaling constellations, and fractal dimensions. The results showed the following: (1) At a low loading rate (0.005 mm/s), the peak stress of the granite increases gradually as the temperature increases. At high loading rates (0.01 mm/s and 0.02 mm/s), the peak stress of the granite first decreases, then increases as the temperature increases. The elastic modulus at different loading rates decreases first, then increases as the temperature increases. The elastic modulus of the granite is the lowest at 40 °C; (2) At the same temperature, specimens subjected to higher loading rates exhibit accelerated internal damage progression, with a precipitous decrease in the b-value occurring earlier. At a loading rate of 0.005 mm/s, the total number of acoustic emission ring-down events is at least 1.7 times greater than that at other loading rates, indicating a more complex development of internal cracks in the granite specimens. Moreover, as the temperature increases under a constant loading rate, the concentration of acoustic emission ring-downs intensifies during the unstable crack development and failure phases, accompanied by progressively larger fluctuations in the b-value; (3) Under the same temperature conditions, the acoustic emission signals in the specimens loaded at 0.005 mm/s and 0.01 mm/s propagate from one end to the other. At 0.02 mm/s, emissions appear simultaneously at both ends of the specimen and converge toward the center. The proportion of damage and energy associated with compression differs with temperature; at 30 °C and 40 °C, damage and energy are concentrated in the early phase of compression, whereas at 60 °C and 80 °C, they are more significant during the later stages; and (4) The fractal dimension of the granite specimens generally decreases, then undergoes a phase of fluctuating growth, followed by a decline. With increasing loading rates at a constant temperature, the compaction of the granite specimens decreases; the orderliness of the crack patterns gradually increases. As the temperature increases, the distribution of cracks during the compaction and failure phases becomes more orderly. During the crack initiation and development phases, the dispersion of cracks and the formation of crack networks are more pronounced. These findings provide a theoretical reference for understanding the microscale damage and failure mechanisms of granite under varying loading rates and temperatures. Full article

Tinnitus is the perception of sounds like ringing or buzzing in the ears without any external source, varying in intensity and potentially becoming chronic. This study aims to enhance the understanding and treatment of tinnitus by analyzing a dataset related to tinnitus therapy, focusing on electroencephalography (EEG) signals from patients undergoing treatment. The objectives of the study include applying various preprocessing techniques to ensure data quality, such as noise elimination and standardization of sampling rates, and extracting essential features from EEG signals, including power spectral density and statistical measures. The novelty of this research lies in its innovative approach to representing different channels of EEG signals as new graph network representations without losing any information. This transformation allows for the use of Graph Neural Networks (GNNs), specifically Graph Convolutional Networks (GCNs) combined with Long Short-Term Memory (LSTM) networks, to model intricate relationships and temporal dependencies within the EEG data. This method enables a comprehensive analysis of the complex interactions between EEG channels. The study reports an impressive accuracy rate of 99.41%, demonstrating the potential of this novel approach. By integrating graph representation and deep learning, this research introduces a new methodology for analyzing tinnitus therapy data, aiming to contribute to more effective treatment strategies for tinnitus sufferers. Full article