Preface

This paper examines the degree of multidisplinary cooperation for Geographic Information Science (GIS) education programs that award GIS-related degrees or certificates at US colleges and universities. We classified departments and courses into ten major disciplines using Dewey Decimal Classification. In the 2007–2008 academic year, approximately 40 per cent of GIS education programs related to multiple disciplines and nearly 20 per cent were involved with more than three disciplines. Geography was the major provider of GIS education programs, but the ratio between geography-related discipline and other disciplines combined was approximately 1:3. Fostering multidisciplinary GIS education programs should strengthen geography in general as well as GIS education.

Person, time, place is a mantra recited often when describing infectious disease epidemiology. To date sexual health research has been largely focused on individual demographic characteristics, sexual networks and behaviours....

From August 4th to 9th 2013, the “IGU Kyoto Regional Conference 2013,” (hereafter “Kyoto Conference”), will be held at the Kyoto International Conference Center in Kyoto. With the IGU as the parent organization, more than 1,000 researchers and graduate students from all over the world are expected to participate. A research conference of this size for geographers has not been held in Japan for 33 years since the 24th International Geographical Congress in Tokyo in 1980. It was decided at the IGU General Assembly held in Tunis in August 2008 to hold the regional conference in Kyoto. Organized by the National Committee of Japan for the IGU, the Science Council of Japan has been added as a joint sponsoring organization. In August 2010, the Organizing Committee of the IGU Kyoto Regional Conference 2013, which plays a major role in promoting preparatory activities, was inaugurated. The Tokyo Geographical Society is also participating in planning as a joint sponsoring organization, carrying out preparatory activities and providing financial support. The main theme of the Kyoto Conference is “Traditional Wisdom and Modern Knowledge for the Earth’s Future.” Regarding the main subject, according to the Organizing Committee, more than 1,000 research presentations are expected to be given on geography from a cultural perspective on the dynamics of economic spaces, global transformation and population flows, geographic information science (GIS), biodiversity, natural disasters, land use/land cover change (LUCC), land degradation and desertification, sustainable agricultural systems, geo-parks, geography education, the Geography Olympiad, water sustainability, and others. In addition to general research presentations, a diverse program of special lectures, commissioned sessions, academic exhibitions, corporate exhibitions, excursions, and field trips, as well as public citizen lectures are being prepared. In April 2013 a special postage stamp will also be issued.

Convolutional Neural Networks (CNNs) are vulnerable to misclassifying images when small perturbations are present. With the increasing prevalence of CNNs in self-driving cars, it is vital to ensure these algorithms are robust to prevent collisions from occurring due to failure in recognizing a situation. In the Adversarial Self-Driving framework, a Generative Adversarial Network is implemented to generate realistic perturbations in an image that cause a classifier CNN to misclassify data. This perturbed data is then used to train the classifier CNN further. The Adversarial Self-driving framework is applied to an image classification algorithm to improve the classification accuracy on perturbed images and is later applied to train a self-driving car to drive in a simulation. A small-scale self-driving car is also built to drive around a track and classify signs. The Adversarial Self-driving framework produces perturbed images through learning a dataset, as a result removing the need to train on significant amounts of data. Experiments demonstrate that the Adversarial Self-Driving framework identifies situations where CNNs are vulnerable to perturbations and generates new examples of these situations for the CNN to train on. The additional data generated by the Adversarial Self-driving framework provides sufficient data for the CNN to generalize to the environment. Therefore, it is a viable tool to increase the resilience of CNNs to perturbations. Particularly, in the real-world self-driving car, the application of the Adversarial Self-Driving framework resulted in an 18 % increase in accuracy, and the simulated self-driving model had no collisions in 30 minutes of driving.

The secondary structure of proteins is significant for studying the three-dimensional structure and functions of proteins. Several models from image understanding and natural language modeling have been successfully adapted in the protein sequence study area, such as Long Short-term Memory (LSTM) network and Convolutional Neural Network (CNN). Recently, Gated Convolutional Neural Network (GCNN) has been proposed for natural language processing and reduces latency while achieving high levels of sentence scoring. Conditionally Parameterized Convolution (CondConv), which use extra sample-dependant modules to conditionally adjust the convolutional network, have achieved great success in the image processing area. In this paper, we propose a novel Conditionally Parameterized Convolutional network (CondGCNN) which utilize the power of both CondConv and GCNN, and we leverage an ensemble encoder to combine the capabilities of both LSTM and CondGCNN to encode protein sequences to obtain better sequential features from proteins. In addition, due to the similarity between the image segmentation problem and the secondary structure prediction problem, we propose an ASP network (Atrous Spatial Pyramid Pooling (ASPP) based network) as the secondary structure generator in our proposed framework. We have conducted extensive ablation studies over each component in the proposed model to verify its effectiveness. Extensive experiments show that the proposed method can achieve higher performance on protein secondary structure prediction task than existing methods on CB513, Caspll and CASP12 datasets. Our method is expected to be useful for protein structure and further protein functions prediction.

The secondary structure of proteins is significant for studying the three-dimensional structure and functions of proteins. Several models from image understanding and natural language modeling have been successfully adapted in the protein sequence study area, such as Long Short-term Memory (LSTM) network and Convolutional Neural Network (CNN). Recently, Gated Convolutional Neural Network (GCNN) has been proposed for natural language processing. It has achieved high levels of sentence scoring, as well as reduced the latency. Conditionally Parameterized Convolution (CondConv) is another novel study which has gained great success in the image processing area. Compared with vanilla CNN, CondConv uses extra sample-dependant modules to conditionally adjust the convolutional network. In this paper, we propose a novel Conditionally Parameterized Convolutional network (CondGCNN) which utilizes the power of both CondConv and GCNN. CondGCNN leverages an ensemble encoder to combine the capabilities of both LSTM and CondGCNN to encode protein sequences by better capturing protein sequential features. In addition, we explore the similarity between the secondary structure prediction problem and the image segmentation problem, and propose an ASP network (Atrous Spatial Pyramid Pooling (ASPP) based network) to capture fine boundary details in secondary structure. Extensive experiments show that the proposed method can achieve higher performance on protein secondary structure prediction task than existing methods on CB513, Casp11, CASP12, CASP13, and CASP14 datasets. We also conducted ablation studies over each component to verify the effectiveness. Our method is expected to be useful for any protein related prediction tasks, which is not limited to protein secondary structure prediction.

The 2022 Australasian Road Safety Conference, was held in conjunction with Trafinz NZ. With the restrictions of the COVID-19 pandemic mainly behind, the organising committee were pleased to present the proceedings for the first hybrid format for this conference. Conference attendees were able to attend in person (at the Te Pae conference centre in Ōtautahi Christchurch) or virtually, including live streamed plenary sessions. This is the seventh conference in the series that commenced with amalgamation of the Road Safety Research and Education Conference and the Australasian College of Road Safety Conference. It is the first time the combined conference has been in New Zealand. This conference was a unique opportunity for everyone involved in road safety including researchers, practitioners, policymakers, police, educators, advocates and community groups to meet, present and discuss their work. These proceedings describe research, educational and policing program implementation and policy and management strategies related to all aspects of road safety and especially related to the conference theme of Changing Today for Tomorrow. Over 600 delegates from 18 countries will be attending the hybrid conference. This conference covered a comprehensive range of topics including speed, infrastructure and road design, education, licensing, vehicle design, impairment due to alcohol, drugs and mobile phones. The conference plenaries also covered the impacts of climate change on future transport systems and how this might impact on road safety. There was also a special plenary session on safety issues for Indigenous people groups across New Zealand and Australia and how we can work with these communities to achieve vision zero. The conference also presented both face-to-face and virtual symposium sessions including the following important topics: Integrating road safety into local Government, Automated Enforcement, Implementing Speed Management in Victoria and New Zealand, Gig economy road safety, Changing the way we think about older Australian drivers, Managing the safety of ageing heavy vehicle drivers, How safe is your dinner and Redeveloping Bike Education. Authors of accepted Extended Abstracts and Full Papers represent international and local institutions from all aspects of their respective communities including research centres, private companies, government agencies and community groups. These Extended Abstracts provide an indication of the important work being done in Australia, New Zealand and internationally as part of the United Nations, One UN Vision for Road Safety to reduce the number of crashes on the road by 50 percent by 2030. The Conference Organising Committee allowed two manuscript types for the conference: ‘Extended Abstracts’ and peer-reviewed ‘Full Papers’. Using a similar format to the previous successful conference in 2021, the Conference Scientific sub-Committee initially called for submissions in the form of Extended Abstracts (approx. 1 to 3 pages). Each Extended Abstract was reviewed by two independent expert peer reviewers on the following selection criteria: content consistent with the conference theme, novelty of information or data, clarity, relevance to practice or policy, scientific merit, and interest to audience. Over 200 Extended Abstract manuscripts were accepted for face-to-face (F2F), virtual (around 80) and poster sessions. To accommodate more presentations into the four F2F conference streams some authors were offered a rapid-fire presentation slot of 4 minutes, with the option to provide a longer virtual presentation or poster. Authors were also provided the option of submitting a Full Paper, which is HERDC* compliant. Based on the outcome of the peer review of their Extended Abstract, some authors who requested extension of their submissions into Full Papers for a run on into the Journal of Road Safety, were provided that opportunity by the two peer reviewers. The submitted Full Paper subsequently underwent a further review by three independent peer reviewers for inclusion into the Journal of Road Safety (JRS). There were a record number of Full Paper submissions of which nine of twenty four submitted have so far been fully peer-reviewed and accepted as ‘In-Press’ submissions for publication in the JRS. DOI links to these full papers are included into these proceedings. For the second time in the conference series the ACRS2022 partnered with Monash University’s Monash Art, Design and Architecture (MADA) to link Poster authors with final year graphic design students and alumni. Like at the ARSC2021 this gave authors an opportunity to develop high quality visual communications of their poster content. The Authors who chose this option that were matched with a MADA graphic design student and successfully completed the Poster, have had their Poster attached to their Extended Abstract pdfs in these proceedings. The Poster authors were also provided a 4 minute oral rapid-fire presentation slot in a concurrent podium session, followed by a 30 minute poster session, where attendees could ask questions.

Today, Tesla, Google, Uber, and GM are all trying to create their own self-driving cars that can run on real-world roads and Dominos Pizza, Amazon and Walmart decided to create them own proprietary delivery vehicles for commercial use. Intelligent logistics services using self-driving technology, in contrast with the traditional last-mile logistics services, provide a viable alternative for lowering delivery costs and improving quality. Many analysts predict that within the next 5 years, we will start to have fully autonomous cars running in our cities, and within 30 years, nearly ALL cars will be fully autonomous [1]. Wouldn't it be cool to build your very own self-driving car using some of the same techniques the big guys use? I have built my own prototype for a self-driving autonomous car from scratch that uses Deep Neural Network. I built enable the car to detect and follow lanes, recognize, and respond to signs also, it can detect post code box for commercial use. I also used Computer Vision and Deep Learning software needed to recognize the signs, post code box and road lanes. I used python as main tool and second tool is OpenCV powerful computer vision package. To construct a self-driving car, this project proposes an interesting methodology that combines Machine Learning, Image Processing, and IoT concepts. With the help of image processing, the input image is prepared. In order to process the photos, two major models were used. In the first model, I used image processing to recognize road lanes, causing the automobile to move to the right or left to stay within the same lane. The photos of the side of the road right after the lane line, on the other hand, are the region of interest in the second model. The picture of the region of interest is sent into the Convolutional Neural Network in this model, which processes the image. The output of the Neural Network assists in making targeted judgments. After that, the controller just sends the proper signal, and the autonomous vehicle's computer reacts accordingly.

The research and education in Geographic Information Science and Technology (GISc & T) at Louisiana State University (LSU, www.lsu.edu) has been necessitated by its geographical location in the sou...

Geographic information systems and science: today and tomorrow

The international Coastal Dynamics conference has rapidlybecome one of the most important meetings for scientists ac-tive in the field of nearshore sciences and coastal evolution.The first event took place in 1994, and since 2001, the con-ference is held every 4 years, typically in Europe althoughonce in Japan in 2009. The Coastal Dynamics conferenceseries advances the community’s understanding of recent ap-plied and basic research concerning coastal waves and cur-rents, interactions between wind, water and sediments, andmorphology changes in the coastal zone. A wide range ofenvironments (with and without structures) are consideredsuch as sandy, rocky, and muddy coasts, inlets, and estuaries.The conference documents research and applications treatingthese coastal dynamics at the short, medium, and large/longspatial and temporal scales.The 7th International Conference on Coastal DynamicswasheldinJune23–26,2013,inthecityofArcachon,France.ItwasjointlyhostedbytheUniversityofBordeaux,theCentreNationaldelaRechercheScientifique(CNRS)andtheServiceHydrographique et Oceanographique de la Marine (SHOM),chaired by Dr. Philippe Bonneton and Dr. Thierry Garlan.Approximately 270 researchers from 27 countries attendedthe conference. At the conference, we had 137 oral and 51poster presentations, each associated with a full paper in theconference proceedings volume. The contributions were se-lected among more than 300 submitted abstracts, peerreviewed by an international technical abstract review com-mittee. During the conference, three keynote lectures weregiven by Gerben Ruessik, Huib de Swart and FabriceArdhuin. The conference also comprised two short coursesand two field trips in the Arcachon lagoon and adjacentbeaches. A gala dinner took place at the prestigious ChateauSmith-Haut-Lafitte. During the dinner, the organizing com-mittee honoured Prof. Peter Nielsen as theCoastalDynamics’13 coastal award winner, for his significant contri-bution in the field of coastal sediment transport. The organiz-ing committee also announced the Coastal Dynamics’13 beststudent presentation winners, Anouk de Bakker, MelissaMoulton and Gerad Dam and the Coastal Dynamics’13 beststudent poster winner Thibaud Revil-Baudard.For this Topical Collection, we had thus invited 15 papersthat were presented at the conference based on (1) the overallimpressionduringthepresentationattheconferenceincludingcommentsfromtheaudience,(2)ourownadditionalreviewofthe candidate papers and (3) scores and comments from thefirst abstract review round. The authors of these 15 paperswere further invited to submit a full scientific paper to meetthe Ocean Dynamics guidelines and to be evaluated throughits regular peer-review process. All 15 papers did pass thereviewround,asanticipated,giventhecriteriaoftheinvitationprocess.Roland and Ardhuin (2014) review the recent improve-ments in forcing fields, physical parametrizations andnumerical techniques of spectral wave models, which cannow provide highly accurate wave hindcasts and forecasts.Arns et al. (2015) propose a methodology to estimate region-alized return water levels at ungauged sites, illustrated by anapplicationtothenortherncoastofGermany.Twopapersdealwith modelling of interactions between hydrodynamics and

Introduction Descriptive epidemiologic and geographic analysis utilizing geographic information science (GIS) has been used to determine the utilization of trauma systems and to spatially describe patterns of trauma and crime. We examined the relationship between spatial components of criminality and injuries in order to evaluate the optimal trauma center location and determine a correlation between reported violent crime and trauma center utilization. Methods All adult trauma and violent crime (VC) encounters in a defined area over a single year were included. Geospatial statistics pattern analysis tools of Median Center (MC) and the Average Nearest Neighbor analysis (ANNa) were used to determine if mapped points occurred in complete spatial randomness or were clustered in a significant pattern. Results ANNa of VC resulted in a z-score of –20.54 and a p-value of <0.001, indicating a <1% likelihood that violent crimes were distributed randomly. Further ANNa yielded a zscore of –5.67 and p-value of <0.001 for injuries. Our trauma center is 1.45 miles from the MC of VC and 2.28 miles from the MC for injuries. Spatial autocorrelation failed to demonstrate a direct relationship between criminality and trauma center utilization with a z-score of 0.030 and p-value of 0.98. Conclusion While not statistically significant, the spatial trends of violent crime and trauma center utilization demonstrated a clear pattern. GIS is a powerful tool for the trauma director, and examination of the local regional patterns of trauma should be undertaken by health systems to assist with optimizing outreach, expansion, and response times.

Geographic information science and technology education has evolved into a multidisciplinary undertaking supporting academic and professional qualifications and professions. With strong conceptual foundations in geography, motivated by application domains including planning, ecology, engineering, and infrastructure management, and facilitated by technologies and computer science, educational programs and underlying curricula are now offered worldwide. This entry explores the academic backgrounds, curriculum initiatives, and models for study programs in, for example, geoinformatics, including traditional frameworks as well as continuing education, distance learning, and noncredit and informal learning. Best practice by worldwide institutions and actors is well documented through collective experience as a foundation for developing new directions accommodating novel technological and conceptual developments.

Image Captioning is one of the emerging topics of research in the field of AI. It uses a combination of Computer Vision (CV) and Natural Language Processing (NLP) to derive features from the image, use this information to identify objects, actions, their relationships, and generate a description for the image. It is most important concept in artificial intelligence applied in the fields like aid to the blind, self-driving cars, and many more. This paper we demonstrates a concise state of art image captioning and its method for caption generation using deep learning concepts. We also determine the approach for image caption generation using Convolutional Neural Network (CNN) and Generative Adversarial Network (GAN) model in deep learning framework. Using this approach system intelligent enough to create sentences for images. It uses the encoder-decoder architecture, where CNN is used for image vector generation and LSTM is used for the generation of a logical sentence using the NLP concepts. Finally, we evaluate the proposed system experimental analysis with numerous existing systems and show the effeteness of system.

Advanced Materials XIV is a compilation of peer reviewed papers presented at the 14th International Symposium on Advanced Materials (ISAM 2015) held from October 12- 16th 2015 at the National Centre for Physics, Islamabad, Pakistan. In the role of Secretary ISAM-2015, I feel honored that the symposium ended on a positive note. The challenges of the present day industry entail an indepth knowledge of the science behind the processes involving advanced materials. It is this need that ISAM, along with similar other forums, endeavors to address.The five day deliberations of ISAM 2015, consisted of 29 technical sessions and 2 poster sessions, inclusive of a poster competition on nanostructures which displayed 150 research papers depicting various aspects of nanomaterials. In all 307 papers were presented, inclusive of 61 contributory, invited and oral presentations. The symposium also hosted 3 parallel panel discussions led by renowned scientists and eminent researchers from foreign as well as local institutes.The ultimate aim of this proceeding is to archive various findings in the field of advanced materials. I hope that the technical data available in this publication proves its worth to scientists and researchers working in this area of science.I wish to acknowledge the Institute of Physics (IOP) Publishing UK, for accepting the research papers from ISAM-2015 for publication in the IOP Conference Series: Materials Science and Engineering. The proceeding will be available on the IOP website as an online open access document.I am profoundly thankful to the Symposium Chairman for his firm support and for the wisdom with which he guided the Organizing Committee of ISAM 2015 to make it a successful event. On the same note, I must sincerely acknowledge the truly commendable efforts of the organizing committee. My gratitude to all our distinguished participants, session chairs/co-chairs, and reviewers for their active role in the symposium. Last yet not the least, my thankfulness goes to all our sponsors for financing the event.