Uncovering patterns of inter-urban trip and spatial interaction from social media check-in data

The article revisits spatial interaction and distance decay from the perspective of human mobility patterns and spatially-embedded networks based on an empirical data set. We extract nationwide inter-urban movements in China from a check-in data set that covers half million individuals and 370 cities to analyze the underlying patterns of trips and spatial interactions. By fitting the gravity model, we find that the observed spatial interactions are governed by a power law distance decay effect. The obtained gravity model also well reproduces the exponential trip displacement distribution. However, due to the ecological fallacy issue, the movement of an individual may not obey the same distance decay effect. We also construct a spatial network where the edge weights denote the interaction strengths. The communities detected from the network are spatially connected and roughly consistent with province boundaries. We attribute this pattern to different distance decay parameters between intra-province and inter-province trips.Comment: 20 pages, 10 figure

Hybrid prediction-optimization approaches for maximizing parts density in SLM of Ti6Al4V titanium alloy

It is well known that the processing parameters of selective laser melting (SLM) highly influence mechanical and physical properties of the manufactured parts. Also, the energy density is insufficient to detect the process window for producing full dense components. In fact, parts produced with the same energy density but different combinations of parameters may present different properties even under the microstructural viewpoint. In this context, the need to assess the influence of the process parameters and to select the best parameters set able to optimize the final properties of SLM parts has been capturing the attention of both academics and practitioners. In this paper different hybrid prediction-optimization approaches for maximizing the relative density of Ti6Al4V SLM manufactured parts are proposed. An extended design of experiments involving six process parameters has been configured for constructing two surrogate models based on response surface methodology (RSM) and artificial neural network (ANN), respectively. The optimization phase has been performed by means of evolutionary computations. To this end, three nature-inspired metaheuristic algorithms have been integrated with the prediction modelling structures. A series of experimental tests has been carried out to validate the results from the proposed hybrid optimization procedures. Also, a sensitivity analysis based on the results from the analysis of variance was executed to evaluate the influence of the processing parameter and their reciprocal interactions on the part porosity

Particle Swarm Optimization Using Multiple Neighborhood Connectivity And Winner Take All Activation Applied To Biophysical Models Of Inferior Colliculus Neurons

Age-related hearing loss is a prevalent neurological disorder, affecting as many as 63% of adults over the age of 70. The inability to hear and understand speech is a cause of much distress in aged individuals and is becoming a major public health concern as age-related hearing loss has also been correlated with other neurological disorders such as Alzheimer\u27s dementia. The Inferior Colliculus (IC) is a major integrative auditory center, receiving excitatory and inhibitory inputs from several brainstem nuclei. This complex balance of excitation and inhibition gives rise to complex neural responses, which are measured in terms of firing rate as a given parameter is varied. A major obstacle in understanding the mechanisms involved in generating normal and aberrant auditory responses is estimating the strength and tuning of excitatory and inhibitory inputs that are integrated to form the output firing of IC neurons. To better understand IC response generation, biophysically accurate, conductance-based computational models were used to recreate IC frequency tuning responses. The problem of fitting response curves in vivo was approached using particle swarm optimization, an optimization paradigm which mimics social networks of flocking birds to solve problems. A new social network modeling winner-take-all activation found in visual neuron coding was developed in which agents are divided into social hierarchies and compete for leadership rights. This social network has shown good performance in benchmark optimization problems and is used to recreate IC frequency tuning responses which can be used to further understand pathological aging in the auditory system

The transition to sustainable aviation in Northern Norway. Key drivers and barriers in a transition to sustainable aviation in Norway from a social-technical perspective, using Lofoten as a case study

This thesis investigates the key drivers and barriers of the energy transition in aviation in Northern Norway. The regional project Lofoten the Green Islands 2030 represents a radical change, which is necessary to phase out fossil fuels and reach the goals of fossil-free aviation by 2050 set by Norwegian authorities. Using literature review, observations, and interviews from key political, public, and industrial actors in Lofoten, the thesis explores the key factors influencing the niche development in the aviation regime in Norway. It also explores how the niche technology interacts with the broader aviation landscape, leaning on Geels ́ framework on the Multi-Level Perspective (Geels, 2002; Geels & Kemp, 2007). The perspectives of key actors affect the process of Lofoten the Green Islands ́ goal to halve Lofoten ́s aviation GHG (greenhouse gas) emissions by 2030. The data shows how the goals and ambitions during the transition process vary among the different actors, especially regarding the ambitious timeline. On the other hand, the actors often share the same perception of what the end goal will be. The end goal is sustainable aviation with electric-driven aircrafts on Norway ́s short- haul network where the archipelago group of Lofoten should be a national pilot. Further, the thesis reveals how the energy transition can result in radical change where both traditional actors and new stakeholders both in the air and on the ground will take on new roles and evolve along with the changes from the regime. The knowledge and communication that comes from the interaction between government, industry, academia, and population look to play an important role in the energy transition. Such political and social interaction should be arranged for and prioritized as it will strengthen each actor. This is identified in the quadruple helix model, where roles and knowledge in the transition to sustainable aviation are important. The thesis further explores a definitional sustainability discussion, as sustainability is presented as social, economic, and environmental sustainability, and the different actors have different sustainability goals with their participation in the energy transition. Finally, the thesis identifies the drivers and barriers that form a socio-technical system: society and culture, policy, industry, technology, and science. Developing a multi-level perspective understanding of the energy transition in aviation, and presenting the key drivers and barriers found in the Lofoten case, is critical to a full accounting of the challenges and opportunities that the actors of Lofoten and Norwegian aviation may face

Enhancing numerical modelling efficiency for electromagnetic simulation of physical layer components.

The purpose of this thesis is to present solutions to overcome several key difficulties that limit the application of numerical modelling in communication cable design and analysis. In particular, specific limiting factors are that simulations are time consuming, and the process of comparison requires skill and is poorly defined and understood. When much of the process of design consists of optimisation of performance within a well defined domain, the use of artificial intelligence techniques may reduce or remove the need for human interaction in the design process. The automation of human processes allows round-the-clock operation at a faster throughput. Achieving a speedup would permit greater exploration of the possible designs, improving understanding of the domain. This thesis presents work that relates to three facets of the efficiency of numerical modelling: minimizing simulation execution time, controlling optimization processes and quantifying comparisons of results. These topics are of interest because simulation times for most problems of interest run into tens of hours. The design process for most systems being modelled may be considered an optimisation process in so far as the design is improved based upon a comparison of the test results with a specification. Development of software to automate this process permits the improvements to continue outside working hours, and produces decisions unaffected by the psychological state of a human operator. Improved performance of simulation tools would facilitate exploration of more variations on a design, which would improve understanding of the problem domain, promoting a virtuous circle of design. The minimization of execution time was achieved through the development of a Parallel TLM Solver which did not use specialized hardware or a dedicated network. Its design was novel because it was intended to operate on a network of heterogeneous machines in a manner which was fault tolerant, and included a means to reduce vulnerability of simulated data without encryption. Optimisation processes were controlled by genetic algorithms and particle swarm optimisation which were novel applications in communication cable design. The work extended the range of cable parameters, reducing conductor diameters for twisted pair cables, and reducing optical coverage of screens for a given shielding effectiveness. Work on the comparison of results introduced ―Colour maps‖ as a way of displaying three scalar variables over a two-dimensional surface, and comparisons were quantified by extending 1D Feature Selective Validation (FSV) to two dimensions, using an ellipse shaped filter, in such a way that it could be extended to higher dimensions. In so doing, some problems with FSV were detected, and suggestions for overcoming these presented: such as the special case of zero valued DC signals. A re-description of Feature Selective Validation, using Jacobians and tensors is proposed, in order to facilitate its implementation in higher dimensional spaces

An Empirical Survey on Various Power Transfer Techniques in Electrical Vehicle using Wireless Mode

This study examines the current condition of the wireless power transfer (WPT) techniques in electric vehicle. The pros and downsides of wireless power transfer are explored, as well as its   classification, application, trend, benefits and impact on society. It also offers a comparison of prior research in transfer the power wireless, pointing out upcoming different kind of method, topologies, statement, and optimization methods implemented for boost the efficiency of performance of the electric vehicle system and directing researchers in the appropriate direction for future research

THEORETICAL PREDICTION AND STUDIES OF SELECTED NOVEL MATERIALS UNDER AMBIENT AND EXTREME CONDITIONS

The development of powerful computer algorithms that are specialized at exploring the energy landscape of chemical systems has revolutionized chemical physics and its derived disciplines. Such algorithms that ranges from random search to genetic algorithm are capable of uncovering a geometric configuration for a combination of chemical elements with minimum energy. The unbiased particle swarm-intelligence optimization algorithm extends the capabilities of the genetic algorithm by incorporating social intelligence through particle communication. Social communication during energy surface exploration improves the efficiency and convergence of the algorithm by preventing prediction of similar-energy structures. Particle swarm-intelligence optimization algorithm is capable of solving crystal structure problems and predicting novel crystal structures across dimensions ranging from 0D (clusters) to 3D bulk solids at specific pressure. In this study, the particle swarm-intelligence optimization algorithm was used to study and solve crystal structure problems relating to two classes of materials of industrial significance – high energy density materials and bimetallic nanoclusters. As a significant step towards solving the problem of finding a single-bonded allotrope of nitrogen, we discuss the prediction and characterization of this member of very important class of material – high energy density materials (HEDMs). A new allotrope of nitrogen formed solely by N−N single bonds is predicted to exist between 100 and 150 GPa using the metadynamics algorithm with a biased potential. The crystal structure is characterized by a distorted tetrahedral network consisting of fused N8, N10, and N12 rings. Stability of the structure is established by phonon and vibrational free energy calculations at zero and finite temperatures, respectively. The simulated x-ray diffraction pattern of the new phase is compared to the pattern of a recently synthesized nitrogen phase at the same P-T conditions and an excellent agreement is observed. This suggests the new phase is likely to form above the stability field of cubic gauche (cg) phase. The outstanding metastability of the new phase is attributed to the intrinsic stability of the sp3 bonding as well as the energetically favorable dihedral angles between N−N single bonds, in either gauche or trans conformation. The results of this work after the lab-synthesized cg phase will stimulate new research on metastable phases of nitrogen and their applications as environment-friendly HEDMs. Furthermore, in the second part of this thesis, bimetallic cluster growth is theoretically explored up to the bulk phase. Small clusters provide a unique medium between a single atom and the bulk crystal. Preliminary theoretical and experimental results show that the geometric structures and electronic properties of clusters often differ radically from those of the solid state. Here, a first-principles investigation to explore the growth mechanism of bimetallic clusters AlnAun (n=1-10) and AlAu crystal structures is carried out. It was found that the tetrahedral Al2Au2 cluster can serve as the building block to construct the subsequent nanomaterials as a function of the cluster size until the AlAu bulk. The results in this work provide a clear illustration of how structure evolve from a two-atom particle to multi-atom nanoclusters, and to 3D bulk element. Continued experimental and theoretical studies of these AlnAun clusters may lead to the discovery of how properties transform from a particle to the bulk phase which has important technological implications in electronics, engineering and catalysis

Fusing Long Short-Term Memory and Autoencoder Models for Robust Anomaly Detection in Indoor Air Quality Time-Series Data

People spend most of their time indoors by choice or by need. Carbon dioxide (CO2) accumulation can cause various adverse health effects, including vertigo, headache, and fatigue. Therefore, monitoring indoor air quality(IAQ) is necessary for various health reasons. The market is flooded with air quality monitoring devices. However, the ordinary public does not make use of them because they are expensive and difficult to obtain. Several research studies have been carried out to monitor indoor air quality with the help of the Internet of Things(IoT), which has greatly simplified the method for monitoring IAQ. In this research, we offer an improved IoT based IAQ monitoring system with AI-powered recommendations. Our suggested system relies on the Message Queuing Telemetry Transport(MQTT) protocol for communication between IoT devices. In addition, the gathered CO2 occupancy data is used together with the deep learning approach of Long Short-Term Memory and Autoencoder (LSTM-AE) to detect anomalies or outliers in CO2 concentrations.  Due to a close connection between air quality and human health and well-being, the detection of anomalies in the data of  IAQ has emerged as an essential topic of study. Anomalies requiring the observation of correlations spanning numerous data points (i.e., often referred to as long-term dependencies) were not detectable by conventional statistical and basic machine learning (ML) related techniques in the sector of  IAQ.  Hence this research uses the LSTM-AE model to address this issue.  In comparison to previous similar models, our experimental results on a generated CO2 occupancy time series reveal a robust and powerful accuracy of 99.49%