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This is a paper on experimental studies performed in the 1940s at the University of Notre Dame accelerator laboratory to investigate radiation effects on materials and also to probe the efficiency of atmospheric cooling of the fireball created by a nuclear fission device. This was a critical question to be addressed during the early phases in the development of the Manhattan project, which was again raised in the film Oppenheimer, posing the question whether "a bomb can set the atmosphere on fire." This paper brings forth the formerly classified reaction studies performed at Notre Dame's Nuclear Science Laboratory to answer this question. [ABSTRACT FROM AUTHOR]

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With the progressive development of new energy technologies, high-power lithium batteries have been widely used in ship power systems due to their high-power density and low environmental pollution, and they have gradually become one of their main propulsion energy sources. However, the large-scale deployment of lithium batteries has also brought a series of safety problems to ship operations, especially the battery internal short circuit (ISC). Battery ISC faults are very hidden and unpredictable at the initial stage and often fail to be detected in time, ultimately leading to overheating, fire or even an explosion of the ship's power system. Based on this, this paper proposes a fast and accurate method for early-stage ISC fault location and detection of lithium batteries. Initially, voltage variations across the lithium battery packs are quantified using curvilinear Manhattan distances to pinpoint faulty battery units. Subsequently, the localized characteristics of voltage variance among adjacent batteries are leveraged to detect an early-stage ISC fault. Simulation results indicate that the proposed method can quickly and accurately locate the position of 5 Ω, 10 Ω and 15 Ω ISC faulty batteries within the battery pack, as well as detect the abnormal batteries in a timely manner with considerable sensitivity and reliability. [ABSTRACT FROM AUTHOR]

In today's digital era, human-computer interaction interfaces evolve and increase together with the needs of the users. However, the existing technologies have their limitations, which can hinder the efficiency of modern input devices like the Kinect sensor or other similar sensors. In this paper we improved our previous algorithm by extending it with two algorithms that aim to help telerehabilitation for individuals with movement disabilities. These two algorithms are based on the k-Nearest Neighbors, the k-Means algorithms. The algorithms are designed to accommodate the needs of the patients by adapting to their gestures based on their previous three. Using these gestures, the algorithms create multiple gesture acceptance domains around each coordinate of the gesture. Consequently, they decide whether the next user-input gesture can be considered the same movement. The accuracy of these algorithms was evaluated in three acceptance domains by comparing gesture descriptors with either the Euclidean or the Manhattan distance calculation methods. The results show that k-Nearest Neighbors algorithm yields better results in larger acceptance domains, while the k-Means algorithm can provide a better gesture acceptance rate in the smaller ones. The results show that both algorithms can be used in the telerehabilitation process, although the k-Means algorithm is more accurate than the k-Nearest Neighbors algorithm. [ABSTRACT FROM AUTHOR]

Copyright of Studia Universitatis Babes-Bolyai, Philologia is the property of Babes-Bolyai University, Cluj-Napoca, Romania and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

IoT (Internet-of-Things)-powered devices can be exploited to connect vehicles to smart city infrastructure, allowing vehicles to share their intentions while retrieving contextual information about diverse aspects of urban viability. In this paper, we place ourselves in a transient scenario in which next-generation vehicles that are able to communicate with the surrounding infrastructure coexist with traditional vehicles with limited or absent IoT capabilities. We focus on intersection management, in particular on reusing existing traffic lights empowered by a new management system. We propose an auction-based system in which traffic lights are able to exchange contextual information with vehicles and other nearby traffic lights with the aim of reducing average waiting times at intersections and consequently overall trip times. We use bid propagation to improve standard vehicle trip times while allowing emergency vehicles to free up the way ahead without needing ad hoc system for such vehicle, only an increase in their budget. The proposed system is then tested against two baselines: the classical Fixed Time Control system currently adopted for traffic lights, and an auction strategy that does not exploit traffic light coordination. We performed a large set of experiments using the well known MATSim transport simulator on both a synthetic Manhattan map and on a map we built of an urban area located in Modena, Northern Italy. Our results show that the proposed approach performs better than the classical fixed time control system and the auction strategy that does not exploit coordination among traffic lights. [ABSTRACT FROM AUTHOR]

In conventional point-line visual–inertial odometry systems in indoor environments, consideration of spatial position recovery and line feature classification can improve localization accuracy. In this paper, a monocular visual–inertial odometry based on structured and unstructured line features of vanishing points is proposed. First, the degeneracy phenomenon caused by a special geometric relationship between epipoles and line features is analyzed in the process of triangulation, and a degeneracy detection strategy is designed to determine the location of the epipoles. Then, considering that the vanishing point and the epipole coincide at infinity, the vanishing point feature is introduced to solve the degeneracy and direction vector optimization problem of line features. Finally, threshold constraints are used to categorize straight lines into structural and non-structural features under the Manhattan world assumption, and the vanishing point measurement model is added to the sliding window for joint optimization. Comparative tests on the EuRoC and TUM-VI public datasets validated the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we investigate a new problem of optimal placement of multiple finite-size rectangular facilities with known dimensions in the presence of existing rectangular facilities. This problem has applications in facility layout (re)design in manufacturing, distribution systems, services, and electronic circuit design. Three types of facility interactions are considered: between new facilities and existing facilities; between pairs of existing facilities; and between pairs of new facilities. All interactions are serviced through a finite number of input/output points located on the facility boundaries. Travel is assumed to occur according to the rectilinear (or Manhattan) metric and travel through facilities is prohibited. The objective is to find the simultaneous and non-overlapping placement of new facilities, which minimises the total weighted distance (Minisum objective) between the interacting facilities. To arrive at a solution, we divide the feasible region into sub-regions and prove that the candidates for optimal placement of the new facilities can be drawn from the sub-region boundaries. Being a continuous generalisation of the quadratic assignment problem, the solution complexity of this procedure is exponential in the number of new facilities. Our main contribution is the rigorous treatment of an important problem that unifies facility location and layout theories with minisum objective and rectilinear metrics. [ABSTRACT FROM AUTHOR]

AbstractThis practitioner paper describes an inquiry-based approach applying the College, Career, and Civic Life (C3) framework to facilitate instruction of September 11, 2001 history, and its impact on post-9/11 society. An upper elementary level inquiry teaches the Boatlift Rescue from Manhattan Island, and a secondary level inquiry addresses the concepts of loss and persistence. Enduring issues are humanity and resiliency. The featured sources are children’s and youth literature related to 9/11 history and post-9/11 society. Children’s and youth trade books offer educators a valuable starting point to teach about 9/11 and post-9/11 history. For each inquiry instructional strategies are highlighted that encourage students to make their thinking visible when reading trade books and using primary sources from 9/11 repositories. [ABSTRACT FROM AUTHOR]

The so-called Hamming distance measures the difference between two binary strings A and B. In simplified form, it measures the number of changes in A to get B. This type of distance is very useful in classical computing in applications such as error correction. It is also advantageous in quantum computing, being for example widely used in quantum machine learning. Since current quantum computers have limited resources, this type of distance is particularly attractive because it can be computed using fewer qubits and operations than other distances such as Euclidean or Manhattan distances. In this paper, two circuits for calculating Hamming distances using exclusively Clifford+T gates are presented. The aim of both circuits is to reduce the quantum cost and number of T gates needed to compute the Hamming distance. The T gate is more expensive than the other gates, so this reduction will have a significant impact on the total cost of the circuits. Furthermore, the proposed circuits are implemented using only Clifford+T gates. The circuits implemented exclusively with this group of gates are compatible with proven error detection and correction codes. [ABSTRACT FROM AUTHOR]

This paper presents a new approach for the detection of copy-move image forgery, a commonly employed technique for image manipulation. The proposed method combines a modified version of the Gabor Filter and Centre Symmetric Local Binary Pattern (CS-LBP) for feature extraction, aiming to meet the growing demand for accurate forgery detection. The process involves pre-processing the image and extracting features using the Gabor filter and CS-LBP at varying scales and orientations. Key points are matched using the Manhattan distance to identify forged regions. Classification of the forged images is achieved using Hybrid Neural Networks with Decision Tree (HNN-DT). In order to assess the performance of the presented method, diverse image datasets are used and compared to existing feature extraction techniques. The results demonstrate the effectiveness of the modified Gabor filter with CS-LBP in accurately classifying forged images. Specifically, the HNN-DT method with Gabor filter CS-LBP feature extraction surpasses HNN-DT with SURF and PCA feature extraction in terms of classification accuracy and overall performance. Evaluation on the CoMoFoD database confirms superior results compared to existing techniques, establishing the proposed method as a reliable approach for distinguishing between authentic and forged images. Consequently, it serves as a robust solution for image classification and forgery detection. The presented work focuses on detecting mainly three types of image forgery, namely retouching, splicing, and cloning. It is applicable to various image formats, including JPEG and BMP, and is designed specifically for forensic applications in image forgery detection. [ABSTRACT FROM AUTHOR]

In this paper, we will study the geometric structure on the Sierpinski networks which are skeleton networks of a connected self-similar Sierpinski carpet. Under some suitable condition, we figure out that the renormalized shortest path distance is comparable to the planar Manhattan distance, and obtain the Hausdorff dimension of Sierpinski networks. [ABSTRACT FROM AUTHOR]

Haloperidol, the first butyrophenone neuroleptic, was created in Europe by Janssen Pharmaceuticals in 1958 and was introduced swiftly throughout the continent with great enthusiasm. On September 15, 1959, at Janssen's headquarters in Belgium, teams from around Europe praised the effectiveness of haloperidol. In the United States, on the contrary, its introduction was a tremendous failure, plagued by accusations of inefficacy and patent disputes. A clinical trial in Manhattan has been blamed for this commercial failure. The results of the Manhattan trial were seen as radically different from the results obtained in continental Europe. This divide would have considerable impact not only with regard to haloperidol's path on both sides of the Atlantic, but also possibly on the practical experience and theoretical construction of psychiatry. This article tries to reconstruct the story of that trial based mainly on published papers and interviews. Exploring how societal changes and issues of gender and race shaped this process, this investigation attempts to understand and contextualize different possible reasons for this Atlantic rift. [ABSTRACT FROM AUTHOR]

Semantic relatedness is one of the most significant aspects of natural language processing. It has been identified as a critical technology for developing intelligent systems like Siri, Microsoft Ice, Cortana, and Xiaoai. In 2014, SemEval ranked SR as the top task. While many existing studies have focused on analyzing the entailment of single phrases, advancements in deep learning have made it possible to analyze complete sentences or texts. While the natural parallelism of membrane computing has shown promise for data processing, harnessing this potential to advance semantic relatedness remains an open problem yet to be tackled. This paper proposes a novel Siamese Manhattan LSTM-SNP approach (SiMaLSTM-SNP) for the SR problem. The approach uses a collaborative Word2vec and 10-Layer Attention strategy to represent and extract sentence pairs and a Siamese LSTM-SNP structure to calculate the hidden states of sentences. The multi-head self-attention layer identifies text associations and redistributes hidden state weights. The last hidden state is extracted, and the relatedness score is calculated using the Manhattan distance. The experiments demonstrate that SiMaLSTM-SNP outperforms 17 classical SR baselines and 7 novel approaches on the standard datasets SICK and STS in terms of mean square error performance. This indicates that SiMaLSTM-SNP can accurately capture the semantic distinction between two sentences and effectively preserve their semantic information. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the equivalence of connectedness for the Sierpinski-like sponge and skeleton networks, and find out the relation between the geodesic distance on the sponge and renormalized shortest path distance on the skeleton networks. Furthermore, under some assumption on the IFS, we obtain the comparability of the Manhattan distance and the geodesic distance on the sponge. [ABSTRACT FROM AUTHOR]

As a novel and economical transportation way, ride-sharing has attracted more and more passengers and drivers to participate. How to match passengers with drivers efficiently has become a key issue. Specifically, drivers are usually heterogeneous with different costs, and they may behave strategically (e.g. reveal their private cost information untruthfully) in order to make more profits. Drivers' strategic behavior may lead to inefficient matching, which results in the loss of social welfare of ride-sharing platforms and drivers. In this paper, an incentive-compatible and efficient mechanism is proposed to solve this issue, which can match passengers with drivers and determine the payments to drivers in order to maximize social welfare while ensuring drivers reveal their cost information truthfully. Specifically, an order matching algorithm with branch and bound based route planning is designed to accelerate the matching process. Meanwhile, the payments to drivers are computed based on the second-pricing algorithm. In so doing, a second-pricing based ride-sharing mechanism (SPRM) is proposed, which satisfies incentive compatibility, individual rationality, budget balance and computational efficiency. Based on the real Manhattan taxi order data and vehicle fuel consumption data, extensive experiments are conducted to evaluate the proposed mechanism. The experimental results show that SPRM can guarantee drivers' profits and improve the ratio of drivers' participation and the ratio of served orders, and eventually achieve greater social welfare than four typical benchmark approaches, GPri, ND, mT-share and mdp. The research of this paper can provide useful insights for designing efficient ride-sharing system to maximize the social welfare of all participants in the real life. • An efficient mechanism to match orders with drivers is proposed. • A branch and bound based route planning method is used to accelerate the matching. • A second pricing based mechanism is proposed to compute the payments to drivers. • The proposed mechanism can outperform benchmark approaches in the social welfare. [ABSTRACT FROM AUTHOR]

Autonomous ride-hailing platforms, such as Waymo and Cruise, are quickly expanding their services, but their interactions with the existing ride-hailing companies, such as Uber and Lyft, are rarely discussed. To fill this gap, this paper focuses on the competition between an emerging autonomous ride-hailing platform and a traditional ride-hailing platform by characterizing the equilibrium of their competition and the impact of technology transfer. In particular, we consider an autonomous ride-hailing platform that owns the AV technology and offers ride-hailing services to passengers through a fleet of AVs. In the meanwhile, it competes with a traditional ride-hailing platform that primarily relies on a fleet of human-driver vehicles (HDVs) but may rent a sub-fleet of AVs from the autonomous ride-hailing platform to complement the human-driver fleet (referred to as AV technology transfer). A game-theoretic model is formulated to characterize the competition between the autonomous ride-hailing platform and the traditional ride-hailing platform over a transportation network, encapsulating the passengers' mode choices, the drivers' job options, the network traffic flows and the strategic decisions of the platforms. An algorithm is proposed to compute the approximate Nash equilibrium of the game and conduct an ex-post evaluation on the performance of the obtained solutions. The proposed framework and solution algorithm are validated through a realistic case study for Manhattan. Based on numerical simulations, we find that technology transfer of AVs between the two platforms can lead to a win-win situation where both two platforms get a higher profit, but this comes at the cost of reduced surpluses for human drivers and passengers. In the simulation, a critical trade-off is revealed for the autonomous ride-hailing platform: it strategically forfeits some of its market share in ride-hailing services to encourage the traditional ride-hailing platform to rent more AVs, thereby increasing its rental revenue and consequently, the overall profit. Furthermore, we also find it intriguing that as AV technology improves and operational costs decrease, the traditional ride-hailing platform cannot enjoy any benefit in its profit although it has the option of leasing AVs from the autonomous ride-hailing platform at lower operational costs. Instead, it is compelled to rent a larger fleet of AVs from the autonomous ride-hailing platform at a higher rental price, consequently suffering a reduced profit. Conversely, the autonomous ride-hailing platform significantly benefits from the reduced AV operational cost by capturing a larger market share in the ride-hailing market and earning higher revenue from the AV technology transfer. • Investigated the competition between autonomous and traditional ride-hailing platforms. • A game-theoretic model to capture the incentives of players over transportation networks. • An algorithm is proposed to obtain an approximate Nash equilibrium. • Examined the impacts of AVs on traditional ride-hailing market under platform competition. [ABSTRACT FROM AUTHOR]

Focused crawler traverses the Web to only collect pages that are relevant to a particular topic, and is increasingly considered as a way to get around the scalability issues with current general-purpose search engines. But the data diversity in the Web forces these crawlers to face three significant problems: (i) inconsistency, (ii) ubiquity, and (iii) ambiguity, which causes misguidance in crawling. To handle these issues, this paper proposes a weakly supervised Gated Recurrent Unit (GRU) mechanism for an adaptive focused web crawler framework that matches semantically relevant t o p i c s and w e b p a g e c o n t e n t. This weakly supervised Gated Recurrent Unit model accepts the vector form of the t o p i c and the fetched w e b p a g e as input to produce meaningful s e m a n t i c vectors and incorporates the Manhattan distance rule to compute the topical relevance of the w e b p a g e. The proposed mechanism guides the focused crawler in downloading more relevant web pages by finding the relevant hyperlinks and omitting the irrelevant hyperlinks concerning the topic. The proposed method helps the focused crawler to semantically find, arrange, and index the web pages in a relatively narrow segment of the web to solve the inconsistency, ubiquity, and ambiguity problems of the focused crawlers. The experimental results indicate that the proposed technique outperforms the s t a t e − o f − t h e − a r t approaches in terms of h a r v e s t r a t e , p r e c i s i o n , r e c a l l , h a r m o n i c m e a n , and i r r e l e v a n c e r a t i o. In summary, the strategy described here works well and is important for focused crawlers. [ABSTRACT FROM AUTHOR]

• This study proposes a multi wavelength optical image feature fusion method for improving the effectiveness of subdural hematoma image reconstruction. • CT images of real patients was used in this study to establish a 3D three-layer tissue brain tissue model. • The average VRE and average AMD of hematoma reconstruction in the model which is using this method are only 0.66 % and 0.0052 mm−1. • Compared with the model which is directly using optical information as input, the method make the average VRE and average AMD of hematoma reconstruction respectively decreased by 80.0% and 27.8%. Diffuse optical tomography (DOT) under medical image guidance can be used to reconstruct images of subdural hematomas for monitoring purposes. However, when the shape of hematoma is irregular or the signal to noise ratio(SNR) of input signal is low, the accuracy and robustness of this method both decrease. In order to alleviate this situation and improve the reconstruction effect of cerebral hematoma images, a multi wavelength optical image feature fusion method was proposed in this paper. Experimental results show that the mean relative volume error (VRE) of the hematoma reconstruction model based on the multi-wavelength optical image feature fusion method is only 0.66 %, and the mean value of the average Manhattan distance (AMD) between the reconstructed absorption coefficient and the true absorption coefficient is 0.0052 mm−1. Compared with the model using single-wavelength optical image mapping feature extraction method, the multi-wavelength optical image feature fusion method reduces the average VRE of the model by 16.5 %, and the average AMD by 16.1 %. And compared with the model directly inputting single-wavelength optical information, its average VRE and average AMD decreased by 80.0 % and 27.8 %. This method has clinical application significance for the monitoring of subdural hematomas. [ABSTRACT FROM AUTHOR]

The paper presents a novel method for automatically creating semantic digital models for buildings in Manhattan environments from dense point cloud data. Unlike previous approaches, which rely solely on data-driven methods, our method integrates artificial intelligence with domain engineering knowledge to overcome challenges in indoor point cloud processing and geometry representation in complex layouts. A feature-based Decision Tree classifier extracts main building elements, utilized in a knowledge-based algorithm for 3D space parsing. On this basis, an optimization process generates parameterized floor plans, used to finally create volumetric digital models. The method was validated on datasets from the Technical University of Munich and Stanford University, achieving a mean accuracy of approximately 0.08 m for model placement and 0.06 m for estimating element parameters, which highlights its effectiveness for generating a building's semantic digital model. This approach underscores the potential of AI integration in digital twinning workflows for more automated solutions. [Display omitted] • Designing a hybrid bottom-up, top-down approach for automatic building digital model creation. • Align the capabilities of AI in scene understanding with domain knowledge to overcome challenges in indoor 3D reconstruction. • Developing a knowledge-based method for 3D space parsing in largescale indoor point cloud. • Reconstruction of the parametric building's floor plan using point cloud with low sensitivity to poor data quality. • Creation of dynamic parametric building digital models (LOD200) for flexible facility management and space analysis. [ABSTRACT FROM AUTHOR]

While there are well-known synthetic methods in the literature for finding the image of a point under circular inversion in l 2 -normed geometry (Euclidean geometry), there is no similar synthetic method in Minkowski geometry, also known as the geometry of finite-dimensional Banach spaces. In this study, we have succeeded in creating a synthetic construction of the circular inversion in l 1 -normed spaces, which is one of the most fundamental examples of Minkowski geometry. Moreover, this synthetic construction has been given using the Euclidean circle, independently of the l 1 -norm. [ABSTRACT FROM AUTHOR]

Quantitative trait loci (QTL) denote regions of DNA whose variation is associated with variations in quantitative traits. QTL discovery is a powerful approach to understand how changes in molecular and clinical phenotypes may be related to DNA sequence changes. However, QTL discovery analysis encompasses multiple analytical steps and the processing of multiple input files, which can be laborious, error prone, and hard to reproduce if performed manually. To facilitate and automate large-scale QTL analysis, we developed the yQTL Pipeline, where the 'y' indicates the dependent quantitative variable being modeled. Prior to the association test, the pipeline supports the calculation or the direct input of pre-defined genome-wide principal components and genetic relationship matrix when applicable. User-specified covariates can also be provided. Depending on whether familial relatedness exists among the subjects, genome-wide association tests will be performed using either a linear mixed-effect model or a linear model. The options to run an ANOVA model or testing the interaction with a covariate are also available. Using the workflow management tool Nextflow, the pipeline parallelizes the analysis steps to optimize run-time and ensure results reproducibility. In addition, a user-friendly R Shiny App is developed to facilitate result visualization. It can generate Manhattan and Miami plots of phenotype traits, genotype-phenotype boxplots, and trait-QTL connection networks. We applied the yQTL Pipeline to analyze metabolomics profiles of blood serum from the New England Centenarians Study (NECS) participants. A total of 9.1M SNPs and 1,052 metabolites across 194 participants were analyzed. Using a p-value cutoff 5e-8, we found 14,983 mQTLs associated with 312 metabolites. The built-in parallelization of our pipeline reduced the run time from ~90 min to ~26 min. Visualization using the R Shiny App revealed multiple mQTLs shared across multiple metabolites. The yQTL Pipeline is available with documentation on GitHub at https://github.com/montilab/yQTLpipeline. [ABSTRACT FROM AUTHOR]

Facial identification for surgical and non-surgical datasets is getting popular. The reason behind this popularity is the growing need of a robust facial recognition system which is consistent to occlusion, spoofing attacks and last but most important plastic surgery effects. Plastic therapies are undertaken by individuals to beautify their external appearance but it is also undertaken by impostors to commit crimes and falsify their true identities. This research work aims at developing a facial recognition system which can identify genuine and impostor pairs. The projected methodology optimizes face detection via Back-Propagation Neural Network (BPNN) and dimensionality reduction by means of Speeded Up Robust Features followed by Multi-K-Nearest-Neighbor technique. The novelty is the production of an innovative-fangled T-Database which trains the BPNN. Thus, BPNN converges faster and achieves higher recognition. The proposed scheme is not applied till date on a medically altered dataset. We have applied five distance metrics and integrated them to acquire T-Dataset, which is fed to the BPNN. This scheme is tested on surgical and non-surgical datasets and it is deduced that higher recognition is achieved with non-surgical databases as compared to surgical ones. For both surgical and non-surgical datasets, the computational cost attained is the modest. [ABSTRACT FROM AUTHOR]

This paper considers a multimodal charging network in which charging stations and battery swapping stations are jointly built to support an electric ride-hailing fleet synergistically. Our argument is based on the observation that charging an EV is a time-consuming burden, and battery swapping faces scaling issues due to its deployment costs. However, charging stations are cost-effective, making them ideal for scaling up EV fleets, while battery swapping stations offer quick turnaround and can be deployed in tandem with charging stations to improve fleet utilization and reduce operational costs. To fulfill this vision, we consider a ride-hailing platform that jointly builds charging and battery swapping stations to support an EV fleet. An optimization model is proposed to capture the platform's planning and operational decisions. In particular, the model incorporates essential components such as elastic passenger demand, spatial charging equilibrium, charging and swapping congestion, etc. The overall problem is formulated as a nonconcave program. Instead of pursuing the globally optimal solution, we establish a tight upper bound through relaxation and decomposition, allowing us to evaluate the solution optimality even in the absence of concavity. Through case studies for Manhattan, New York City, we find that joint planning of charging and battery swapping stations outperforms deploying only one of them, yielding a total profit that is 11.7% higher than swapping-only deployment under a limited budget, and 17.5% higher than charging-only deployment under a sufficient budget. These results underscore the complementary benefit between charging and battery swapping facilities. • Investigated a multimodal charging network including charging and battery swapping stations. • Proposed a nonconvex optimization framework for optimal infrastructure planning and fleet operations. • Evaluated the synergistic value of battery swapping and plug-in charging. • Plug-in charging can accelerate fleet electrification, while battery swapping can enhance fleet utilization. [ABSTRACT FROM AUTHOR]

Structural geometry constraints, such as perpendicularity, parallelism and coplanarity, are widely existing in man-made scene, especially in Manhattan scene. By fully exploiting these structural properties, we propose a monocular visual-inertial odometry (VIO) using point and line features with structural constraints. First, a coarse-to-fine vanishing points estimation method with line segment consistency verification is presented to classify lines into structural and non-structural lines accurately with less computation cost. Then, to get precise estimation of camera pose and the position of 3D landmarks, a cost function which combines structural line constraints with feature reprojection residual and inertial measurement unit residual is minimized under a sliding window framework. For geometric representation of lines, Plücker coordinates and orthonormal representation are utilized for 3D line transformation and non-linear optimization respectively. Sufficient evaluations are conducted using two public datasets to verify that the proposed system can effectively enhance the localization accuracy and robustness than other existing state-of-the-art VIO systems with acceptable time consumption. [ABSTRACT FROM AUTHOR]

Signal strength maps are of great importance for cellular system providers in network planning and operation. Accurate prediction of signal strength is important for solving problems such as link quality. In this study, Received Signal Strength (RSS) prediction model is proposed for the 900 MHz band in the Van Yüzüncü Yıl University campus environment by using machine learning regression methods such as K- Nearest Neıghbours (KNN) and Support Vector Regression (SVR) together with Geographic Information Systems. For the training of this model, signal strength values taken from the RF Spectrum Analyser at different locations and distances were used. In addition, spatial data sets such as the digital elevation model, location of base stations and measurement stations, building heights and location, and land use/cover were used in the model. The effect of these data sets on RSS power is included in the model. The model aims to predict RSS accurately, visualize the estimated signal strength, and analyze the signal field strength coverage. Different kernels from the SVR model such as Polynomial, , and Sigmoid were tested. To increase the success of the model, appropriate parameter values were selected and configured according to SVR and KNN methods. For 900 MHz, the performances of SVR and KNN models were compared and the results of the models were verified using root mean squares (RMSE). Among the measured data, the lowest prediction is found in KNN Manhattan. According to the results of the simulation was observed that the SVR model created with spatial data performs better for Signal Strength. Finally, the lowest RMSE value (1.71 dB) was obtained from the Sigmoid kernel in the best signal strength estimation SVR model. The SVR model is recommended for Campus Area signal strength estimation. [ABSTRACT FROM AUTHOR]

Link prediction in complex network is an important issue in network science. Recently, various structure-based similarity methods have been proposed. Most of algorithms are used to analyze the topology of the network, and to judge whether there is any connection between nodes by calculating the similarity of two nodes. However, it is necessary to get the extra attribute information of the node in advance, which is very difficult. Compared to the difficulty in obtaining the attribute information of the node itself, the topology of the network is easy to obtain, and the structure of the network is an inherent attribute of the network and is more reliable. The proposed method measures kinds of similarity between nodes based on non-trivial eigenvectors of Laplacian Matrix of the network, such as Euclidean distance, Manhattan distance and Angular distance. Then the classical machine learning algorithm can be used for classification prediction (two classification in this case), so as to achieve the purpose of link prediction. Based on this process, a spectral analysis-based link prediction algorithm is proposed, and named it LPbSA (Link Prediction based on Spectral Analysis). The experimental results on seven real-world networks demonstrated that LPbSA has better performance on Accuracy, Precision, Receiver Operating Curve(ROC), area under the ROC curve(AUC), Precision and Recall curve(PR curve) and balanced F Score(F-score curve) evaluation metrics than other ten classic methods. [ABSTRACT FROM AUTHOR]

We investigated the ability of three planetary boundary layer (PBL) schemes in the Weather Research and Forecasting (WRF) Model to simulate boundary layer turbulence in the "gray zone" (i.e., resolutions from 100 m to 1 km). The three schemes chosen are the well-established MYNN PBL scheme and the two newest PBL schemes added to WRF: the three-dimensional scale-adaptive turbulent kinetic energy scheme (SMS-3DTKE) and the E–ε parameterization scheme (EEPS). The SMS-3DTKE scheme is designed to be scale aware and avoid the double counting of TKE in simulations within the gray zone. We evaluated their performance using aircraft measurements obtained during three research flights immediately downwind of Manhattan, New York City, New York. The MYNN PBL scheme simulates TKE best, despite not being scale aware and slightly underestimating TKE from observations, whereas the SMS-3DTKE scheme appears to be overly scale aware for the three flights examined, in particular, when combined with the MM5 surface layer scheme. The EEPS scheme significantly underestimates TKE, mostly in the elevated layers of the boundary layer. In addition, we examined the impact of flow over tall buildings on observed TKE and found that only the windiest day showed a significant increase in TKE directly downwind of Manhattan. This impact was not reproduced by any of the model configurations, regardless of the land-use data selected, although the better resolved National Land Cover Database (NLCD) land use led to a slight improvement of the spatial distribution of TKE, implying that more explicit representation of the impact of tall buildings may be needed to fully capture their impact on boundary layer turbulence. Significance Statement: Because the majority of the world's population lives in cities, it is important to accurately simulate the atmosphere above and around these cities including the turbulence caused by tall buildings. This turbulence can significantly impact the mixing and dilution of air pollutants and other toxins in highly populated urban environments. The scale of cities often falls into what is known as the "gray zone" for turbulence modeling, which has been analyzed theoretically before but rarely in varied real-world conditions. Our analysis around New York City, New York, suggests that model turbulence schemes can match observations relatively well even at gray zone scales, although newer schemes require refinement, and all schemes tend to underestimate turbulence downwind of tall buildings. [ABSTRACT FROM AUTHOR]