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1 |
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 |
مقاله انگلیسی |
2 |
Community-based “Piggy-back Network” utilizing Local Fixed & Mobile Resources supported by Heterogeneous Wireless & AI-based Mobility Prediction
"شبکه Piggy-back" مستقر در جامعه با استفاده از منابع محلی ثابت و موبایل پشتیبانی شده توسط بی سیم ناهمگن و پیش بینی تحرک مبتنی بر هوش مصنوعی-2020 This paper proposes a concept to construct a
community-based cross-industrial data/contents delivery platform
named “Piggy-back network,” which utilizes alreadyexisting
local fixed and mobile resources in a smart city. These
fixed and mobile resources are assumed to equip store-carryforwarding-
based (SCF-based) wireless data sharing functions,
i.e., a short-range but extremely high-speed millimeter-wave
device, a mid/long-range but low-speed microwave device, and
data storage buffer. It is discussed that the data delivery
performance of such SCF-based platform could exceed the one
when using wired/wireless infrastructure directly, and it will
be significantly improved if an AI-based mobility prediction
engine recommends the human drivers or the driving controllers
of future automated driving vehicles to detour and/or stop by
some specific locations. It is theoretically shown that the mobile
resources can potentially deliver high-volume data with shorter
time than using wired/wireless network infrastructures under
some conditions. The real commercial mobilities’ trajectory data
obtained experimentally in the city of Kakogawa, Japan, are
analyzed and the potential of the data delivery performance is
estimated. Index Terms: Piggy-back network | community-based cross-industrial data/contents delivery platform | Store-Carry- Forwarding | Opportunistic Network | AI-based mobility prediction |
مقاله انگلیسی |
3 |
Intelligent handover decision scheme using double deep reinforcement learning
طرح تصمیم گیری واگذاری هوشمند با استفاده از یادگیری تقویتی عمیق دو برابر-2020 Handovers (HOs) have been envisioned to be more challenging in 5G networks due to the inclusion of
millimetre wave (mm-wave) frequencies, resulting in more intense base station (BS) deployments.
This, by its turn, increases the number of HOs taken due to smaller footprints of mm-wave BSs
thereby making HO management a more crucial task as reduced quality of service (QoS) and quality of
experience (QoE) along with higher signalling overhead are more likely with the growing number of
HOs. In this paper, we propose an offline scheme based on double deep reinforcement learning (DDRL)
to minimize the frequency of HOs in mm-wave networks, which subsequently mitigates the adverse
QoS. Due to continuous and substantial state spaces arising from the inherent characteristics of the
considered 5G environment, DDRL is preferred over conventional Q-learning algorithm. Furthermore,
in order to alleviate the negative impacts of online learning policies in terms of computational
costs, an offline learning framework is adopted in this study, a known trajectory is considered in
a simulation environment while ray-tracing is used to estimate channel characteristics. The number of
HO occurrence during the trajectory and the system throughput are taken as performance metrics. The
results obtained reveal that the proposed method largely outperform conventional and other artificial
intelligence (AI)-based models. Keywords: Double deep reinforcement learning | Handover management | Millimetre-wave communication |
مقاله انگلیسی |
4 |
Explainable AI: A Hybrid Approach to Generate Human-Interpretable Explanation for Deep Learning Prediction
هوش مصنوعی قابل توضیح: رویکرد ترکیبی برای ایجاد توضیح قابل تفسیر توسط انسان برای پیش بینی یادگیری عمیق-2020 With massive computing power and data explosion as catalysts, Artificial Intelligence (AI) has finally come out of research labs to become a ground-breaking technology. Businesses are seeing its value in a wide range of applications and therefore looking for ways to make AI an integral part of their decision-making processes. However, to trust an AI model prediction or to take downstream action based on a prediction outcome, one needs to understand the reasons for the prediction. With deep neural networks increasingly becoming the algorithm of choice for models, generation of such reasons has become more challenging. Deep neural networks are highly nested non-linear models that learn patterns in the data through complex combinations of inputs. Their complex architecture makes it very difficult to decipher the exact reasons for their prediction. Due to this lack of transparency, businesses are not able to utilize this technology in many applications. To increase the adoption of deep learning models, explainability is critical in building trust in the solution and in guiding downstream actions in business applications.
In this paper we aim to create human-interpretable explanations for predictions from deep learning models. We propose a hybrid of two prior approaches, integrating clustering of the network’s hidden layer representation [2] with TREPAN decision tree [1], both of which uniquely deconstruct a neural network. Our aim is to visualize flow of information within the deep neural network using factors that make sense to humans, even if the underlying model uses more complex factors. This enables generation of human interpretable explanations (or, reasons codes) for each model outcome at an individual instance level. We demonstrate the new approach on credit card default prediction given by a deep feed forward neural network model. We compare and contrast this new integrated approach with three different approaches, based on the results we obtained from experimentation. Keywords: Deep Learning | Neural Network | Explainable AI | TREPAN | Clustering | Reason Code | Comprehensibility | Fidelity | LIME |
مقاله انگلیسی |
5 |
Reversible Inactivation of Different Millimeter- Scale Regions of Primate IT Results in Different Patterns of Core Object Recognition Deficits
غیرفعال شدن برگشت پذیر مناطق مختلف مقیاس میلیمتر نتایج پیرامون IT در الگوهای مختلف نقص تشخیص اشیاء اصلی-2019 Extensive research suggests that the inferior temporal
(IT) population supports visual object recognition
behavior. However, causal evidence for this
hypothesis has been equivocal, particularly beyond
the specific case of face-selective subregions of IT.
Here, we directly tested this hypothesis by pharmacologically
inactivating individual, millimeter-scale
subregions of IT while monkeys performed several
core object recognition subtasks, interleaved trialby
trial. First, we observed that IT inactivation
resulted in reliable contralateral-biased subtaskselective
behavioral deficits. Moreover, inactivating
different IT subregions resulted in different patterns
of subtask deficits, predicted by each subregion’s
neuronal object discriminability. Finally, the similarity
between different inactivation effects was tightly
related to the anatomical distance between corresponding
inactivation sites. Taken together, these
results provide direct evidence that the IT cortex
causally supports general core object recognition
and that the underlying IT coding dimensions are
topographically organized. |
مقاله انگلیسی |
6 |
A feature adaptive image watermarking framework based on Phase Congruency and Symmetric Key Cryptography
یک چارچوب واترمارک سازی تصویر سازگار با ویژگی مبتنی بر فاز جمع و رمزنگاری کلید متقارن-2019 In this paper, a Phase Congruency based digital color image watermarking algorithm is proposed which
provides a higher degree of robustness against attacks and excellent imperceptibility. Here, Phase
Congruency has been used to detect the local feature regions of the host image and then the watermark
has been infused into it using a new technique called ‘Adaptive a-b Blending’. An accurate Human Visual
System modeling has been incorporated via Lifting Wavelet Transform to take the full advantage of perceptual
watermarking. The coefficients of the a-b blending are selected adaptively based on the Phase
Congruency feature map of the host image. Furthermore, the watermark is secured with a cryptographic
algorithm called Arnold’s Cat Map to prohibit further eavesdropping. From rigorous testing, results indicate
that our approach is robust against various geometric, non-geometric and combined attacks while
maintaining a sublime imperceptibility Keywords: Digital Watermarking | Phase Congruency | a-b Blending | Arnold’s Cat Map | Lifting Wavelet Transform | Cryptography |
مقاله انگلیسی |
7 |
A better last-minute hotel deal via app? Cross-channel price disparities between HotelTonight and OTAs
یک رزرو هتل دقیقه آخری بهتر از طریق برنامه های کاربردی: ناهمخوانی های بین کانالی بین قیمت هرشب هتل و OTA ها-2018 To better understand hotels cross-channel pricing mechanisms, this study aims to investigate various factors behind price discounts (as a type of price disparity) from a popular last-minute hotel deal app, HotelTonight, compared to major online travel agency (OTA) websites. Using pricing data collected from the HotelTonight app and OTA websites in six U.S. cities, we estimate several regression models to examine price discounts. The results reveal that after controlling for other variables, price discounts are largely shaped by online reputation metrics (i.e., relative review valence and volume on TripAdvisor compared to on HotelTonight), complimentary access to services with high marginal variable costs, and uncertainty in the room type offered. However, hotels’ market power does not explain price discounts. Lastly, implications are discussed.
keywords: Last-minute deal |Price disparity |Online reputation |Hotel |Tonight |
مقاله انگلیسی |
8 |
Combined data mining/NIR spectroscopy for purity assessment of lime juice
طیف سنجی تلفیقی داده ها / NIR برای ارزیابی خلوص از آب لیمو-2018 This paper reports the data mining study on the NIR spectrum of lime juice samples to determine their
purity (natural or synthetic). NIR spectra for 72 pure and synthetic lime juice samples were recorded in
reflectance mode. Sample outliers were removed using PCA analysis. Different data mining techniques for
feature selection (Genetic Algorithm (GA)) and classification (including the radial basis function (RBF)
network, Support Vector Machine (SVM), and Random Forest (RF) tree) were employed. Based on the
results, SVM proved to be the most accurate classifier as it achieved the highest accuracy (97%) using
the raw spectrum information. The classifier accuracy dropped to 93% when selected feature vector by
GA search method was applied as classifier input. It can be concluded that some relevant features which
produce good performance with the SVM classifier are removed by feature selection. Also, reduced spec
tra using PCA do not show acceptable performance (total accuracy of 66% by RBFNN), which indicates that
dimensional reduction methods such as PCA do not always lead to more accurate results. These findings
demonstrate the potential of data mining combination with near-infrared spectroscopy for monitoring
lime juice quality in terms of natural or synthetic nature.
Keywords: NIR spectroscopy ، Genetic algorithm ، Support vector machine ، Random forest ، Radial basis function network |
مقاله انگلیسی |
9 |
Total integrated performance excellence system (TIPES): A true north direction for a clinical trial support center
سیستم جامع عملکرد سیستم یکپارچه (TIPES): یک جهت شمال واقعی برای مرکز پشتیبانی کارآزمایی بالینی-2018 This paper presents the quality journey taken by a Federal organization over more than 20 years. These efforts
have resulted in the implementation of a Total Integrated Performance Excellence System (TIPES) that combines
key principles and practices of established quality systems. The Center has progressively integrated quality
system frameworks including the Malcom Baldrige National Quality Award (MBNQA) Framework and Criteria
for Performance Excellence, ISO 9001, and the Organizational Project Management Maturity Model (OPM3), as
well as supplemental quality systems of ISO 15378 (packaging for medicinal products) and ISO 21500 (guide to
project management) to systematically improve all areas of operations. These frameworks were selected for
applicability to Center processes and systems, consistency and reinforcement of complimentary approaches, and
international acceptance. External validations include the MBNQA, the highest quality award in the US, con
tinued registration and conformance to ISO standards and guidelines, and multiple VA and state awards. With a
focus on a holistic approach to quality involving processes, systems and personnel, this paper presents activities
and lessons that were critical to building TIPES and establishing the quality environment for conducting clinical
research in support of Veterans and national health care.
Keywords: Integration of quality systems ، Quality hierarchy ، Baldrige criteria ، ISO 9001 and 15378 standards ، ISO 21500 guideline standards ، Organizational project management maturity ، model |
مقاله انگلیسی |
10 |
Meso-DTA Traffic Model Technology for Evaluating Effectiveness and Quality of the Organization of Traffic in Large Cities
فن آوری مدل ترافیک Meso-DTA برای ارزیابی اثربخشی و کیفیت سازمان ترافیک در شهرهای بزرگ-2017 A scientific approach to assessing the quality of the organization of traffic in large cities requires use of new methods and the
most advanced information technologies. Developing the mathematical apparatus and software for modeling traffic flows allows
a systematic approach to the analysis of effectiveness of measures to improve traffic management.
This scientific article finds the answer to creating an original methodology for assessing the quality of traffic management on the
basis of the parameters of mesoscopic models and theories of qualimetry in the conditions of high load of the street and road
networks in large cities.
Keywords: traffic management | meso-DTA | DTAlite / NEXTA | mesoscopic transport model | qualimetry | quality of traffic management | traffic management efficiency |
مقاله انگلیسی |