Revealing deep semantic commercial patterns: Insights from urban landscape depiction
آشکار ساختن الگوهای تجاری معنایی عمیق: بینش از منظر شهری-2020
Commercial functions play a significant role in economic growth and sustainable development. Influenced by socioeconomic changes which were investigated based on commercial behaviours using big geo-data, urban environment shows polycentric structures and further leads to more complex commercial landscapes. Thus, research considered environmental consequences of urban growth to better understand the commercialization process. However, only general commercial areas or certain commercial retail locations were investigated with the effects of urban environmental landscapes in existing studies. The decomposition of the commercial patterns such as accommodation and catering functions remains undiscussed. To fill this gap, this paper proposes a framework to decompose commercial patterns based on insight from different urban landscapes. First, we depict urban landscapes using spatial metrics, including the density of points of interest (POIs), percentage of landscape of buildings, landscape shape index of buildings and density of roads. Then, we examine the spatial distribution of the decomposed commercial patterns by delineating potential deep semantic topics. Finally, a structural equation model is utilised to determine the relations among urban landscapes and commercial patterns. The results show that four types of urban landscapes exhibit different spatial patterns, revealing various perspectives of commercial characteristics. Meanwhile, the decomposed commercial patterns, including those for catering and accommodation functions, display a heterogeneous distribution. These commercial patterns are directly affected by various configurations of buildings, POIs and roads. On this basis, suggestions are offered to improve commercial pattern development, including integrating urban landscape construction, organising the commercial pattern distribution and enhancing the harmony between the urban environment and commercial land uses.
Keywords: Urban landscape | Urban function | Commercial pattern | Structural equation model
Machine to machine performance evaluation of grid-integrated electric vehicles by using various scheduling algorithms
ارزیابی عملکرد ماشین به ماشین از وسایل نقلیه برقی شبکه یکپارچه با استفاده از الگوریتم های مختلف برنامه ریزی-2020
For smart cities, electric vehicles (EVs) are promisingly considered as a striving industry due to its pollution-less behaviours and easy-to-maintain characteristics. A seamless management system is necessary to manage the energy between EV and various parties participating in the grid operation. To facilitate the energy system in a distributed and coordinated way, a machine-to-machine (M2M) system can be considered as the key component in future intelligent transportation systems. Due to the ubiquitous range and data speed, a fourth-generation (4G) cellular-based long-term evaluation (LTE) system inspires us to select it as a potential carrier for M2M communication. However, various simulation and analytical modelling end up with the conclusion that the maximum 250 EVs can be connected under an LTE base station. These limitations or scalability limits may result in a terrible mix-up in future smart cities for over dense roads. In this paper, we measured various M2M quality of services performance for exceeding the number of EVs by using three popular algorithms (proportional fair scheduling, modified largest weighted delay first scheduling and exponential scheduling). The result shows that the proportional fair scheduler has the highest packet loss ratio (PLR) and delay time as compared to other two schedulers.
Keywords: DLS | Electric vehicle | Energy management system | EXP | M2M communication | M-LWDF | PF | PLR
Banalization discourse in sentenced persons: Some clinical aspects in the penitentiary context
گفتمان تحریم در افراد محکوم : برخی از جنبه های بالینی در زمینه زندان -2020
Objectives. – The use of the term “banalization” has become wides-pread in the judicial and penitentiary context, as a descriptiveway for the professional (penitentiary counsellor, psychologist,magistrate, etc.) to account for the gap between an institutionallysanctioned offense and the convicted person’s point of view. In thisway, we hear in our daily lives about people in the criminal jus-tice system who “banalize their actions.” However, this term lacksa clear definition and an operationality, appearing more as a gene-ral category and sometimes as a “catch-all.” This article aims toquestion the use of banalization in order to give it a more precisedefinition, in particular, regarding its psychodynamic stakes.Method. – Starting from a psychologist’s practice in a penitentiaryservice of insertion and probation, and relying on clinical materialaround banalization discourse, we propose to develop some aspectsof such discursive anchorages that are located at the crossroads of the singular subject and the social reference, or which question thenotion of defense mechanism. A detour through the works of H.Arendt will also allow us to extend the theoretical field to cate-gories of thought activity, individual responsibility, relationship toinstitution and culture (prohibition, law, norms, etc.), and will alsoilluminate the distinction between banality and banalization.Results. – Banalization discourse demonstrates, for the subject, thepsychodynamic stakes in terms of the ability to think through onesactions, to dialecticize one’s individual responsibility, and to situateoneself in a relationship with the other. There is, furthermore, asocial dimension (rules of living together, normative, instituted) atstake. From this point of view, banalization discourse involves thesubject as subject of language and of a social bond, incarnated herein the institutional judiciary and penitentiary context.Discussion. – The discourse of banalization, on the condition of beingquestioned outside of mere moral considerations or judgments,opens up a complex discursive figure for the professional, in light ofpsychodynamic determinisms, references to the institutional sym-bolic framework, and the expression of a language practice withinthe social bond. Banalization questions, from this point of view, themeaning of the sentence and the probation process put in placearound the notion of the offender’s accountability.Conclusions. – Banalization, beyond the person who minimizesher/his actions, refers to a wider clinical vision in a penitentiaryenvironment, since it touches the subjectivation of the judicialevent, the manner in which subjects are included in the social bondand what regulates it, their empathic preoccupations, their abilityto conceptualize their actions, their relationship to a common refe-rence point with its necessary limits. . . In this way, banalizationemerges as a figure of language that must be considered in a waythat goes beyond the mere description of a representation gap.
Keywords: Banalization | Discourse | Penitentiary institution | Capacity to think | Alterity | Responsibility | Social bond
A new circular business model typology for creating value from agrowaste
نوع شناسی مدل کسب و کار دایره ای جدید برای ایجاد ارزش حاصل از زراعت-2020
Shifting from a linear to a circular economy in the agrifood domain requires innovative business models, including reverse logistics, newvisions on customer-supplier relationships, and newforms of organization andmarketing strategies at the crossroads of various value chains. This research aims to identify and characterise different types of business models that create value from agricultural waste and by-products via cascading or closing loops. Conceptual and management insights into circular business models are still sparse. In total, 39 cases have been studied that convert agro-waste and by-products into valuable products via a circular economy approach. Semi-structured interviews and on-site visits of six representative cases have been done, and secondary data been collected. Data has been treatedwith content analysis. Cases are presented according to the type of organisational structure, resources, transformation processes, value propositions, key partners, customers, strategic approaches and innovation. Six types of circular business models are identified and discussed: biogas plant, upcycling entrepreneurship, environmental biorefinery, agricultural cooperative, agropark and support structure. They differ in their way of value creation and organisational form, but strongly depend on partnerships and their capacity to respond to changing external conditions. This study offers the first circular businessmodel typologywithin the agricultural domain, revealing the interconnectedness of the six different businessmodel types. It provides options for managers in positioning and adapting their business strategies. It highlights the potential of using biomass first for higher added-value products before exploiting it as energy source. Cascading biomass valorisation at a territorial level will increasingly be important for locally cooperating actors within a circular bioeconomy approach.
Keywords: Circular economy | Bioeconomy | Business models | Agro-waste valorisation | Networks
Highway crash detection and risk estimation using deep learning
تشخیص تصادف بزرگراه و تخمین ریسک با استفاده از یادگیری عمیق-2020
Crash Detection is essential in providing timely information to traffic management centers and the public to reduce its adverse effects. Prediction of crash risk is vital for avoiding secondary crashes and safeguarding highway traffic. For many years, researchers have explored several techniques for early and precise detection of crashes to aid in traffic incident management. With recent advancements in data collection techniques, abundant real-time traffic data is available for use. Big data infrastructure and machine learning algorithms can utilize this data to provide suitable solutions for the highway traffic safety system. This paper explores the feasibility of using deep learning models to detect crash occurrence and predict crash risk. Volume, Speed and Sensor Occupancy data collected from roadside radar sensors along Interstate 235 in Des Moines, IA is used for this study. This real-world traffic data is used to design feature set for the deep learning models for crash detection and crash risk prediction. The results show that a deep model has better crash detection performance and similar crash prediction performance than state of the art shallow models. Additionally, a sensitivity analysis was conducted for crash risk prediction using data 1-minute, 5-minutes and 10-minutes prior to crash occurrence. It was observed that is hard to predict the crash risk of a traffic condition, 10 min prior to a crash.
Keywords: Crash detection | Crash prediction | Deep learning
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
هوش مصنوعی قابل توضیح (XAI): مفاهیم ، طبقه بندی ها ، فرصت ها و چالش ها در برابر هوش مصنوعی مسئول-2020
In the last few years, Artificial Intelligence (AI) has achieved a notable momentum that, if harnessed appropriately, may deliver the best of expectations over many application sectors across the field. For this to occur shortly in Machine Learning, the entire community stands in front of the barrier of explainability, an inherent problem of the latest techniques brought by sub-symbolism (e.g. ensembles or Deep Neural Networks) that were not present in the last hype of AI (namely, expert systems and rule based models). Paradigms underlying this problem fall within the so-called eXplainable AI (XAI) field, which is widely acknowledged as a crucial feature for the practical deployment of AI models. The overview presented in this article examines the existing literature and contributions already done in the field of XAI, including a prospect toward what is yet to be reached. For this purpose we summarize previous efforts made to define explainability in Machine Learning, establishing a novel definition of explainable Machine Learning that covers such prior conceptual propositions with a major focus on the audience for which the explainability is sought. Departing from this definition, we propose and discuss about a taxonomy of recent contributions related to the explainability of different Machine Learning models, including those aimed at explaining Deep Learning methods for which a second dedicated taxonomy is built and examined in detail. This critical literature analysis serves as the motivating background for a series of challenges faced by XAI, such as the interesting crossroads of data fusion and explainability. Our prospects lead toward the concept of Responsible Artificial Intelligence , namely, a methodology for the large-scale implementation of AI methods in real organizations with fairness, model explainability and accountability at its core. Our ultimate goal is to provide newcomers to the field of XAI with a thorough taxonomy that can serve as reference material in order to stimulate future research advances, but also to encourage experts and professionals from other disciplines to embrace the benefits of AI in their activity sectors, without any prior bias for its lack of interpretability.
Keywords: Explainable Artificial Intelligence | Machine Learning | Deep Learning | Data Fusion | Interpretability | Comprehensibility | Transparency | Privacy | Fairness | Accountability | Responsible Artificial Intelligence
Regenerative active suspension system with residual energy for in-wheel motor driven electric vehicle
سیستم تعلیق فعال احیا کننده با انرژی باقیمانده برای موتور الکتریکی محور در چرخ-2020
The active suspension system is a practical solution to improve vehicle comfort and safety by applying controlled forces to the vehicle body and wheels. However, the widespread application of the system is significantly inhibited by their large power demands. This paper proposes a new regenerative active suspension system for the in-wheel motor driven electric vehicles. In this system, a new advance dynamic-damper mechanism with a suspended driving motor is designed. Two electromagnetic actuators are controlled to imitate the behaviors of skyhook damper and conventional shock absorber for better ride comfort and harvesting energy from the vibration of suspended driven motor, respectively. An improved boost-buck converter is employed to regulate the damping force only utilizing the feedback of current of actuators. To further improve the regenerative efficiency, a variable threshold strategy is designed for the hybrid energy storage system to keep its terminal voltage locating in high-efficiency regions, which are identified through analyzing system performance. The results indicate that the desired damping forces of actuators are precisely tracked regardless of the voltage conditions. The vehicle ride comfort and comprehensive performance are improved by 52% and 14%, respectively. In addition, the variable thresholds strategy shows higher regenerative efficiency than the fixed one. After offsetting the energy consumed by active control, the average regenerated power is 4.9, 17.7, 49.2 and 45.0W on A, B, C and D class roads, respectively. The proposed system is verified as a practical solution to simultaneously improve the dynamic and energy conservation performances of vehicles.
Keywords: Active suspension | Energy regeneration | Electromagnetic damping force control | Energy management strategy | In-wheel motor driven electric vehicle
Assessment of traffic congestion with ORESTE method under double hierarchy hesitant fuzzy linguistic environment
ارزیابی تراکم ترافیک با استفاده از روش ORESTE در محیط زبانی فازی با سلسله مراتب مضاعف-2020
With the new generation of information technology development and the promotion of the Internet, local governments turn their attention to the construction of intelligent transportation systems. More and more cities began building intelligent transportation which has been widely used to monitor urban traffic. Experts can evaluate urban traffic congestion based on the information collected from the big data of intelligent transportation. In recent two years, double hierarchy hesitant fuzzy linguistic term set has been widely used to depict explicit evaluation information, which is straightforward and broadspectrum. When evaluating traffic congestion in a city, decision makers can utilize double hierarchy hesitant fuzzy linguistic term sets to express vague information. Moreover, the ORESTE method is an applicative method which can select a reliable alternative by subdividing alternatives and reduce the loss of information in the conversion process. In this paper, we propose a double hierarchy hesitant fuzzy linguistic ORESTE method and a new score function of double hierarchy hesitant fuzzy linguistic term set. The method raises a new perspective to reduce the error from other methods and the new score function derives a robust decision-making result. Then, we apply the double hierarchy hesitant fuzzy linguistic ORESTE method to solve a practical case involving choosing the congested city by evaluating the 5S traffic congestion model. Finally, we compare the double hierarchy hesitant fuzzy linguistic ORESTE method with other methods such as the classical ORESTE method and the double hierarchy hesitant fuzzy linguistic MULTIMOORA to illustrate the advantages of our method.
Keywords: Double hierarchy hesitant fuzzy linguistic | term sets | Double hierarchy hesitant fuzzy linguistic | ORESTE method | Score function | Traffic congestion
Analyzing the Influencing Factors of Urban Thermal Field Intensity Using Big-Data-Based GIS
تجزیه و تحلیل عوامل مؤثر از شدت میدان حرارتی شهری با استفاده از GIS مبتنی بر داده های بزرگ-2020
The effects of human activities and land cover changes on urban thermal field patterns are closely related to the land surface temperature (LST) and air temperature. At present, the number of studies on the quantitative relationship between these two indexes and the effect of the observational scale on their influence is insufficient. In this study, spatial analysis methods such as geographic modeling were combined with remote sensing images, meteorological data, and points of insert and used to investigate the composition and scale of the factors influencing the temperature field in Beijing. The results showed that there are differences in the positive and negative correlations between LST and air temperature and various influencing factors. At a spatial resolution of 90 m, LST had a strong linear relationship with the average air temperature. Indicators reflecting elements of human activity, such as buildings, roads, and entertainment, were easily measured by meteorological stations at a small scale, and the natural green space ratio could also be easily captured by satellite thermal sensors at small scales. These results have substantial implications for environmental impact assessments in areas experiencing an increasing urban heat island effect due to rapid urbanization.
Keywords: land-surface temperature | thermal field pattern | POI data | GIS | air temperature
سیستم پشتیبانی از تصمیم برای خطرات و اقدامات متقابل ایمنی جاده ای اروپا
سال انتشار: 2019 - تعداد صفحات فایل pdf انگلیسی: 8 - تعداد صفحات فایل doc فارسی: 32
سیستم پشتیبانی از تصمیم درباره ایمنی جاده ای اروپا (roadsafety-dss.eu) یک سیستم نوآورانه است که شواهد و مدارک دسترس پذیری را درباره گستره وسیعی از خطرات جاده ای و اقدامات متقابل امکانپذیر فراهم می کند. این مقاله پایه و اساس علمی سیستم پشتیبانی از تصمیم را توصیف می کند. ساختار موجود در سیستم پشتیبانی از تصمیم شامل (1) یک طبقه بندی که به شناسایی عوامل خطر و اقدامات متقابل آن می پردازد و آنها را به همدیگر مرتبط می کند، (2) یک مجموعه ای از مطالعات، و (3) خلاصه هایی که تاثیرات تخمین زده شده در منابع علمی را برای هر عامل و سنجه خطر خلاصه بندی می کنند و (4) یک ابزار ارزیابی کارآمدی اقتصادی (محاسبه گر E3) می شود. سیستم پشتیبانی از تصمیم در یک ابزار نوین مبتنی بر وب با فصل مشترک بسیار انسانی اجرا می شود که به کاربران اجازه می دهد تا مرور اجمالی سریعی داشته باشند یا نتایج هر مطالعه را برطبق نیازهای مخصوص آنها عمیق تر بررسی کنند.
کلیدواژه ها: اقدامات متقابل ایمنی جاده | خطرات جاده ای | سودمندی | سیستم آنلاین | مرور | هزینه – سود
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