Data Mining Strategies for Real-Time Control in New York City
استراتژی داده کاوی برای کنترل زمان واقعی در شهر نیویورک-2105
The Data Mining System (DMS) at New York City Department of Transportation (NYCDOT) mainly consists of four database systems for traffic and pedestrian/bicycle volumes, crash data, and signal timing plans as well as the Midtown in Motion (MIM) systems which are used as part of the NYCDOT Intelligent Transportation System (ITS) infrastructure. These database and control systems are operated by different units at NYCDOT as an independent database or operation system. New York City experiences heavy traffic volumes, pedestrians and cyclists in each Central Business District (CBD) area and along key arterial systems. There are consistent and urgent needs in New York City for real-time control to improve mobility and safety for all users of the street networks, and to provide a timely response and management of random incidents. Therefore, it is necessary to develop an integrated DMS for effective real-time control and active transportation management (ATM) in New York City. This paper will present new strategies for New York City suggesting the development of efficient and cost-effective DMS, involving: 1) use of new technology applications such as tablets and smartphone with Global Positioning System (GPS) and wireless communication features for data collection and reduction; 2) interface development among existing database and control systems; and 3) integrated DMS deployment with macroscopic and mesoscopic simulation models in Manhattan. This study paper also suggests a complete data mining process for real-time control with traditional static data, current real timing data from loop detectors, microwave sensors, and video cameras, and new real-time data using the GPS data. GPS data, including using taxi and bus GPS information, and smartphone applications can be obtained in all weather conditions and during anytime of the day. GPS data and smartphone application in NYCDOT DMS is discussed herein as a new concept. © 2014 The Authors. Published by Elsevier B.V. Selection and peer-review under responsibility of Elhadi M. Shakshu Keywords: Data Mining System (DMS), New York City, real-time control, active transportation management (ATM), GPS data
Introducing an application of an industry 4:0 solution for circular supply chain management
معرفی کاربرد راه حل صنعت 4:0 برای مدیریت حلقه تأمین دایره ای-2021
In recent years, sustainable supply chain management practices have been adopted by companies that desire to reduce the negative environmental and social impacts within their supply chains. Within this perspective, a circular approach has been developed in the supply chain literature. Circular economy models and solutions assisted by industry 4.0 technologies have been developed to transform products in the end of their life cycle into new products with different use. In this paper an industry 4.0 waste-to- energy solution is developed and applied in a pilot case study comprised by a real-world supply chain to evaluate the sustainability performance of circular supply chain management (CSCM). The ﬁndings show that redesigning supply chains for circular economy with the use of Industry 4.0 technologies, can enable circular supply chain management. Clear beneﬁts are provided linking the proposed solution to the six circular economy dimensions of the ReSOLVE model i.e. regenerate, share, optimize, loop, virtualise, and exchange. Improved availability of personnel (5% and 15%) and ﬂeet resources (15%) are identiﬁed as some of the key quantitative beneﬁts, while supply chain traceability through the full visibility and automation offered by the proposed solution, are some of the key non-quantiﬁable out- comes. The present work seeks to contribute to the existing literature by providing empirical evidence of how industry 4.0 and circular economy are applied in practice. Implications for managers and policy makers, along with the study limitations and further research paths are also presented.© 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-NDlicense (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Keywords: Circular economy | Circular supply chain management (CSCM) | Industry 4.0 | Waste-to-energy | ReSOLVE model
Adaptive Management of Multimodal Biometrics—A Deep Learning and Metaheuristic Approach
مدیریت تطبیقی بیومتریک چند حالته - یادگیری عمیق و رویکرد فرا مکاشفه ای-2021
This paper introduces the framework for adaptive rank-level biometric fusion: a new approach towards personal authentication. In this work, a novel attempt has been made to identify the optimal design parameters and framework of a multibiometric system, where the chosen biometric traits are subjected to rank-level fusion. Optimal fusion parameters depend upon the security level demanded by a particular biometric application. The proposed framework makes use of a metaheuristic approach towards adaptive fusion in the pursuit of achieving optimal fusion results at varying levels of security. Rank-level fusion rules have been employed to provide optimum performance by making use of Ant Colony Optimization technique. The novelty of the reported work also lies in the fact that the proposed design engages three biometric traits simultaneously for the first time in the domain of adaptive fusion, so as to test the efficacy of the system in selecting the optimal set of biometric traits from a given set. Literature reveals the unique biometric characteristics of the fingernail plate, which have been exploited in this work for the rigorous experimentation conducted. Index, middle and ring fingernail plates have been taken into consideration, and deep learning feature-sets of the three nail plates have been extracted using three customized pre-trained models, AlexNet, ResNet-18 and DenseNet-201. The adaptive multimodal performance of the three nail plates has also been checked using the already existing methods of adaptive fusion designed for addressing fusion at the score-level and decision- level. Exhaustive experiments have been conducted on the MATLAB R2019a platform using the Deep Learning Toolbox. When the cost of false acceptance is 1.9, experimental results obtained from the proposed framework give values of the average of the minimum weighted error rate as low as 0.0115, 0.0097 and 0.0101 for the AlexNet, ResNet-18 and DenseNet-201 based experiments respectively. Results demonstrate that the proposed system is capable of computing the optimal parameters for rank-level fusion for varying security levels, thus contributing towards optimal performance accuracy.© 2021 Elsevier B.V. All rights reserved.
Keywords: Adaptive Biometric Fusion | Ant Colony Optimization | Deep Learning | Fingernail Plate | Multimodal Biometrics | Rank-level Adaptive Fusion
Environmental hotspots analysis: A systematic framework for food supply chains and implementation case in the UK poultry industry
تجزیه و تحلیل کانون های محیطی: چارچوبی سیستماتیک برای زنجیره های تأمین مواد غذایی و موارد پیاده سازی در صنعت طیور انگلیس-2021
Environmental sustainability analyses of end-to-end food supply chain (SC) operations need to be per- formed regularly to accommodate reconﬁguration opportunities arising in the global business landscape. This research scrutinises the pertinent literature and identiﬁes the challenges of data availability, data obsolescence, computational complexity, and data speciﬁcity that associate to well-established environmental assessment methodologies, and proposes a stepwise approach that considers key players and processes for generating “close to real-time snapshots” of the main environmental hotspots for the focus ﬁrm. The applicability of the proposed systematic approach is demonstrated via an implementation at a resource-intensive sector of signiﬁcant scale, i.e., the UK poultry industry. Overall, this research con- tributes to the SC environmental sustainability management domain by guiding the mapping and identiﬁcation of environmental hotspots across end-to-end networks of operations in the form of a stepwise framework, and through articulating several research propositions.© 2021 Elsevier Ltd. All rights reserved.
Keywords: Environmental sustainability | Hotspots analysis | Food supply networks | Supply chain management | Systematic approach
Developing and solving an integrated model for production routing in sustainable closed-loop supply chain
توسعه و حل یک مدل یکپارچه برای مسیر یابی تولید در زنجیره تأمین حلقه بسته پایدار-2021
Social and environmental sustainability has gained increasing importance in today’s complex supply chains. Accordingly, an integrated model for production routing in the sustainable closed-loop supply chain is presented in the current study. A three-objective mathematical model is also proposed to minimize supply chain costs, maximize social responsibility or social beneﬁts, and ﬁnally, minimize environmental emissions. Sample trial problems are solved in three groups of the small, medium, and large size using the BCO algorithm. To prove the efﬁciency of this algorithm, its results are compared with the results using the NSGA-II algorithm in terms of comparative metrics such as quality, diversity, and spacing, as well as the runtime to the solution. According to the results, in all cases, the BCO algorithm outperformed the NSGA-II algorithm as it achieved more qualitative and near-optimal solutions. Also, the diversity metric values showed that the BCO algorithm is stronger in the exploration and extraction of the solution feasible region. The results of the metric of spacing and runtime to solution also showed that the NSGA-II algorithm achieves the solution in lower runtime than the BCO algorithm and searches solutions space in a more uniform manner.© 2021 Elsevier Ltd. All rights reserved.
Keywords: Closed-loop supply chain | Sustainability | Production routing
Trustworthy authorization method for security in Industrial Internet of Things
روش مجوز معتبر برای امنیت در اینترنت اشیا صنعتی-2021
Industrial Internet of Things (IIoT) realizes machine-to-machine communication and human–computer inter- action (HCI) through communication network, which makes industrial production automatic and intelligent. Security is critical in IIoT because of the interconnection of intelligent industrial equipment. In IIoT environment, legitimate human–computer interaction can only be performed by authorized professionals, and unauthorized access is not tolerated. In this paper, a reliable authentication method based on biological information is proposed. Specifically, the complete local binary pattern (CLPB) and the statistical local binary pattern (SLPB) are introduced to describe the local vein texture characteristics. Meanwhile, the contrast energy and frequency domain information are regarded as auxiliary information to interpret the finger vein. The distance between the features of the registration image and the test image is used to recognize the finger vein image, so as to realize identity authentication. The experiments are carried out on SDUMLA-FV database and FV-USM database, and results show that the presented method has achieved high recognition accuracy.
Keywords: Industrial Internet of Things (IIoT) | Human–computer interaction (HCI) | Biometric recognition | Comprehensive texture | Security system
Developing a two-stage model for a sustainable closed-loop supply chain with pricing and advertising decisions
در حال توسعه یک مدل دو مرحله ای برای یک زنجیره تامین حلقه بسته پایدار با تصمیمات قیمت گذاری و تبلیغات-2021
Closed-Loop Supply Chain (CLSC) has become a critical problem due to its effects on various factors including economic motivations, environmental concerns, and social impacts. Moreover, there are coordination tools, such as pricing and advertising, which impact its performance. In this paper, we offer a two-stage approach to model and solve a sustainable CLSC, taking into account pricing, green quality, and advertising. In the first stage, optimal decisions on pricing, greening, and advertising are made, while in the second stage, a fuzzy multi- objective Mixed Integer Linear Programming (MILP) model is used to maximize the total profit, reduce CO2 emissions, and improve social impacts. Suitable solution methods are introduced according to the scale of the problem. For small-scale instances, an augmented ϵ-constraint method is used to solve the problem. For large-scale instances, approximations are required, and a Lagrangian relaxation algorithm solves the problem in polynomial time. The performance of the proposed model is evaluated through various numerical examples. The results illustrate the applicability and efficiency of the model, while confirming significant improvements in sustainable objectives under optimal pricing, green quality, and advertising. Besides, the proposed Lagrangian relaxation method significantly reduces the computational time for large-scale instances, with only a 2.308% deviation from the optimal results.
Keywords: Sustainable closed-loop supply chain | Multi-objective programming | Supply chain pricing | Augmented ϵ-constraint | Lagrangian relaxation | CO2 emissions
Efficient biometric-based identity management on the Blockchain for smart industrial applications
مدیریت هویت مبتنی بر بیومتریک کارآمد در Blockchain برای کاربردهای صنعتی هوشمند-2021
In this work, we propose a new Blockchain-based Identity Management system for smart industry. First, we describe an efficient biometric-based anonymous credential scheme, which supports selective disclosure, suspension/thaw and revocation of credentials/entities. Our system provides non-transferability through a freshly computed hidden biometric attribute, which is generated using a secure fuzzy extractor during each authentication. This mechanism combined with offchain storage guarantees GDPR compliance, which is required for protecting user’s data. We define blinded (Brands) DLRep scheme to provide multi-show unlinkability, which is a lacking feature in Brands’ credential based systems. For larger organizations, we re-design the system by replacing the Merkle Tree with an accumulator to improve scalability. The new system enables auditing by adapting the standard Industrial IoT (IIoT) Identity Management Lifecycle to Blockchain. Finally, we show that the new proposal outperforms BASS, i.e. the most recent blockchain-based anonymous credential scheme designed for smart industry. The computational cost at the user-side (can be a weak IoT device) of our scheme is 8-times less than that of BASS. Thus, our system is more suitable for IIoT.© 2020 Elsevier B.V. All rights reserved.
Keywords: Identity management | Smart industry | Blockchain | Non-transferability | Biometrics | DLRep | Multi-show unlinkability | Selective disclosure | Accumulators
Towards resilient and sustainable supply of critical elements from the copper supply chain: A review
به سمت تأمین انعطاف پذیر و پایدار عناصر حیاتی از زنجیره تامین مس: یک مرور-2021
The highly specialized materials needed for the de-carbonization of energy, smart devices and the internet of things have created supply concerns of critical elements used in these applications. Several critical elements are produced as by-products from base metal mining and processing. Increasing the capture of critical elements from existing operations should lead to a more resilient and sustainable supply of these elements. Towards this goal, this paper presents a review of the distribution behavior of five critical elements (selenium, tellurium, arsenic, antimony and bismuth) through the primary copper pyrometallurgical supply chain. This review identifies gaps in the distribution/concentration data of these elements in deposits and during mineral processing. Smelter dusts, refinery slimes and electrolyte are points of enrichment that can be targeted for additional recovery of these elements. Using published data, copper smelter dusts appear to contain enough arsenic and bismuth to meet the world’s supply needs. Industrial data collected from 29 refineries and represents ~46% of the worlds electrorefining production was extrapolated to examine the contained annual content of these five elements. Copper anodes contain 7900 tones/yr of selenium, 2300 tonnes/yr of tellurium, 24,000 tones/yr arsenic, 7100tonnes/yr of antimony and 5100 tones/yr of bismuth. The selenium and tellurium contents are 2–3 times and 4–5 times more than the current world’s annual production of these elements, respectively. While technology development in the processing of smelter dusts and refinery slimes could provide important breakthroughs, government and corporate collaboration are likely needed to encourage increased recovery of selenium, tellurium, arsenic, antimony and bismuth from the primary copper pyrometallurgical supply chain.
Keywords: Critical elements | Copper | Ore | Flotation | Smelting | Refining
Biometric indices of eleven mangrove fish species from southwest Bangladesh
شاخص های بیومتریک یازده گونه ماهی حرا از جنوب غربی بنگلادش-2021
Biometric indices, i.e. i) length-weight relationships (LWRs), ii) form factor (a3.0), iii) length-frequency distributions (LFDs), and iv) condition factors (relative KR and Fulton’s KF) are considered to be very cru- cial in the assessment of fishery studies as they provide information on fish population growth and coastal habitat well-being. The study of biometric indices of mangrove fish has, however, received little attention. Our research investigates the LFDs, LWRs, a3.0, KR and KF of 395 individuals from nine families (Latidae, Engraulidae, Gobiidae, Mugilidae, Synbranchidae, Schilbeidae, Scatophagidae, Plotosidae, and Terapontidae). The LFDs showed that the lowest total length (TL) was 4.57 cm for Stolephorus tri, and highest TL was 56.20 for Monopterus cuchia. The LWRs showed that the b (allometric coefficient) values ranging from 2.01 (Plotosus canius) to 3.29 (Terapon jarbua), appeared as highly significant (P < 0.001). Moreover, the KR values ranged from 0.80 to 1.36, which indicate a good state of health of the population. Our findings could be useful in updating the FishBase (online database) and tracking mangrove fish spe- cies sustainably.© 2021 National Institute of Oceanography and Fisheries. Hosting by Elsevier B.V. This is an open accessarticle under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Keywords: Length-weight relationship | Growth | Form factor | Condition | FishBase