Modelling fuzzy combination of remote sensing vegetation index for durum wheat crop analysis
مدل سازی ترکیب فازی از شاخص پوشش گیاهی سنجش از دور برای تجزیه و تحلیل محصول گندم دوروم-2019
The application of new technologies (e.g. Internet of Things, mechatronics, remote sensing) to the primary sector will reduce the production costs, limit the waste of primary materials, and reduce the release of polluting compounds into the environment. Precision agriculture (PA) has been growing in the last years thanks to industry efforts and development of applications for diagnostic purposes. Many applications in PA use vegetation indices to measure phenology parameters in terms of Leaf Area Index (LAI). In this context, the correlation of some vegetation indices were analyzed with respect to the durum wheat canopy, evaluating two different phenological stages (elongation and maturity). The results show that for the first stage of growth, the Enhanced Vegetation Index (EVI) was the best-correlated vegetation index with LAI, while the Land Surface Water Index (LSWI) was more reliable for the following stage of growth. Considering trials findings, a fuzzy expert system was developed to combine EVI and LSWI, obtaining a new combined index (Case-specific Fuzzy Vegetation Index) that better represents the LAI in comparison with the single indices. Thus, this approach could give place to a better representative vegetation index of a different biological condition of the plant. It may also serve as a reliable method for wheat yield forecasting and stress monitoring.
Keywords: Precision agriculture | LAI | Remote sensing | Crop management | Landsat images | Ecosystem services
Solving visual pollution with deep learning: A new nexus in environmental management
حل آلودگی بینایی با یادگیری عمیق: پیوند جدیدی در مدیریت محیط زیست-2019
Visual pollution is a relatively new concern amidst the existing plethora of mainstream environmental pollution, recommending the necessity for research to conceptualize, formalize, quantify and assess it from different dimensions. The purpose of this study is to create a new field of automated visual pollutant classification, harnessing the technological prowess of the 21st century for applications in environmental management. From the wide range of visual pollutants, four categories have been considered viz. (i) billboards and signage, (ii) telephone and communication wires, (iii) network and communication towers and (iv) street litter. The deep learning model used in this study simulates the human learning experience in the context of image recognition for visual pollutant classification by training and testing a convolutional neural network with several layers of artificial neurons. Data augmentation using image processing techniques and a train-test split ratio of 80:20 have been used. Training accuracy of 95% and validation accuracy of 85% have been achieved by the deep learning model. The results indicate that the upper limit of accuracy i.e. the asymptote, depends on the dataset size for this type of task. This study has several applications in environmental management. For example, the deployment of the trained model for processing of video/live footage from smartphone applications, closed-circuit television and drones/unmanned aerial vehicles can be applied for both the removal and management of visual pollutants in the natural and built environment. Furthermore, generating the ‘visual pollution score/index’ of urban regions such as towns and cities will create a new ‘metric/indicator’ in the field of urban environmental management..
Keywords: Visual pollution | Deep learning | Convolutional neural network | Image recognition | Pollutant classification | Environmental management
Intratumoural immunotherapy: activation of nucleic acid sensing pattern recognition receptors
سیستم ایمنی درمانی داخل رحمی: فعال سازی گیرنده های تشخیص الگوی سنجش اسید نوکلئیک-2019
Recently, it has become clear that the tumour microenvironment (TME) is important in cancer immunotherapy. While immune checkpoint inhibitors are effective for some patients, the heterogeneous nature and status of the TME (‘cold’ tumours) play a critical role in suppressing antitumour immunity in non-responding patients. Converting ‘cold’ to ‘hot’ tumours through modulation of the TME may enable expansion of the therapeutic efficacy of immunotherapy to a broader patient population. This paper describes advances in intratumoural immunotherapy, specifically activation of nucleic acid sensing pattern recognition receptors to modulate the TME.
Keywords: Intratumoral immunotherapy | Modulation of tumour microenvironment | Nucleic acid recognising pattern recognition | receptors | RIG-I | STING | Toll-like receptors
تولید نخاله ساختمانی به یک نگرانی درحال رشد برای شهرهای درحال رشد تبدیل شده است
سال انتشار: 2019 - تعداد صفحات فایل pdf انگلیسی: 5 - تعداد صفحات فایل doc فارسی: 14
چگونه شهرهای درحال رشد به صورت پایدار گسترش می یابند؟ تولید انبوه زباله تبدیل به یک چالش اصلی در بسیاری از شهرهای درحال رشد به ویژه در کشورهای درحال توسعه شده است. تولید سالانه زباله ساخت و تخریب در چین حدود 4/2 بیلیون تن در دهه گذشته برآورد شده است که 15 برابر بیشتر از تولید زباله جامد شهری می باشد. هند تولید زباله ساخت و تخریب کمتری نسبت به چین دارد و همچنان مقدار آن به همان بزرگی 530 میلیون تن سال 2013 می باشد. با این حال فقط حدود 5% زباله ساخت و تخریب در این کشورها مورد استفاده مجدد قرار گرفته است و بقیه آنها تاحد زیادی در مکان های انباشت زباله دفع می شوند. از آنجاییکه چین و سایر کشورهای درحال توسعه به شهری سازی خود در دهه های بعدی ادامه می دهند، مدیریت زباله ساخت و تخریب همچنان یک چالش قابل توجه برای پایداری شهری ازنظر محیط زیست، اقتصاد و ایمنی خواهد بود. بنابراین ما تولید و مدیریت زباله ساخت و تخریب و نیز چالش های پیش روی شهرهای دارای گسترش سریع را در چین و سایر کشورهای درحال توسعه به صورت انتقادی مرور کردیم. ما همچنین ویژگی های فعلی سیستمهای کلیدی زباله ساخت و تخریب را در کشورهای درحال توسعه و توسعه یافته مقایسه کردیم. توصیه هایی برای اقدامات سریع توسط سیاست گذاران در شهرهای درحال رشد ارائه می شود.
|مقاله ترجمه شده|
Deriving double dividends through linking payments for ecosystem services to environmental entrepreneurship: The case of the invasive weed Lantana camara
سود سهام مضاعف از طریق پیوند پرداخت خدمات اکوسیستم به کارآفرینی زیست محیطی: مورد تهاجمی علفهای هرز Lantana camara-2019
A payment for ecosystem services mechanism is designed to support an environmental enterprise aimed at controlling Lantana camara, an invasive weed that is costly to eradicate. A forest reserve manager engages the local community in lantana control efforts. The community converts the weeds into household durable items for sale. However, as markets for such products may not account for the environmental services generated through weed control, the enterprise could fail for want of additional financial support. The challenge addressed in this paper is to incorporate the full environmental benefits of the weed-based enterprise and provide adequate compensation to the local community. An optimal compensation mechanism is derived through linking the ecological dimension of weed growth to its impact on biodiversity values within the reserve. Results indicate that optimal payments to the community would need to take into consideration both the value addition to the environment from controlling the invasive weed and the opportunity cost of participation by the community. When there exists a risk of enterprise failure due to low profitability, higher payments by the manager are required. However, the best environmental outcomes are obtained when the manager incorporates the welfare of the local community within the utility function.
Keywords: Lantana camara | Invasive weed | Environmental enterprise | Payments for ecosystem services | Environmental service | Biodiversity conservation
Relationship among land price, entrepreneurship, the environment, economics, and social factors in the value assessment of Japanese cities
رابطه قیمت زمین ، کارآفرینی ، محیط زیست ، اقتصاد و عوامل اجتماعی در ارزیابی ارزش شهرهای ژاپن-2019
Assessing the value of local areas and cities, including examining the entrepreneurial, environmental, economic, and social dimensions of sustainability, has become key to fostering development. Such assessments can be conducted using a land-price function. This study analysed the land-price function using a geographically weighted regression model of land price, and explanatory variables related to entrepreneurial, environmental, economic, and social factors. To consider these factors, specific data scores were taken into account. A correlation analysis showed that six variables are essential for future value analyses: these are entrepreneurship, nature conservation, resource recycling, social vitality, the local governments financial viability, and environmental quality. Using these six variables to conduct preliminary regression analyses via four models, the study found that entrepreneurship, social vitality, and environmental quality have a positive impact on land prices in local areas and cities, while nature conservation, resource recycling, and the local governments financial viability have a negative impact. The result of a geographically weighted regression analysis showed that the areas around large cities in Japan have benefitted more from entrepreneurship and social vitality than have the areas around small and mid-sized cities. While large cities may be better equipped to promote environmental policies, small and mid-sized cities could promote entrepreneurship and social vitality policies using their environmental advantages
Keywords: Entrepreneurship | Environment | Economy | Society | Land-price function | Geographically weighted regression model
Time to seize the digital evolution: Adoption of blockchain in operations and supply chain management among Malaysian SMEs
زمان استفاده از تحول دیجیتالی: پذیرش بلاکچین در عملیات و مدیریت زنجیره تأمین در باره شرکتهای کوچک و متوسط مالزی-2019
This study aims to investigate the effects of relative advantage, complexity, upper management support, cost, market dynamics, competitive pressure and regulatory support on blockchain adoption for operations and supply chain management among Small-Medium Enterprises (SMEs) in Malaysia. Unlike existing studies that employed linear models with Technology Acceptance Model or United Theory of Acceptance and Use of Technology that ignores the organisational and environmental factors, we adopted the Technology, Organisation and Environment Framework that covers the technological dimensions of relative advantage and complexity, organisational dimensions of upper management support and cost and environmental dimensions of market dynamics, competitive pressure and regulatory support. Empirical data from 194 SMEs were investigated and ranked using a nonlinear non-compensatory PLS-ANN approach. Competitive pressure, complexity, cost and relative have significant effects on behavioural intention. Market dynamics, regulatory support and upper management support were insignificant predictors. SMEs often lack resources for technological investments but faces same requirements for streamlining business processes to optimise returns and blockchain presents a viable option for SMEs’ sustainability due to its features of immutability, transparency and security that have the potential to revolutionise businesses. This study contributes new knowledge to the literature on factors that affect blockchain adoption and justifications were discussed accordingly.
Keywords: Blockchain | Operations and Supply Chain Management | (OSCM) | Partial Least Squares Structural Equation | Modelling (PLS-SEM) | Artificial Neural Network analysis (ANN) | Technology, Organisation and Environment | Framework (TOE)
Operating an environmentally sustainable city using fine dust level big data measured at individual elementary schools
مدیریت یک شهر سازگار با محیط زیست با استفاده از داده های بزرگ گرد و غبار، اندازه گیری شده در مدارس ابتدایی فردی-2018
As the problem of fine dust pollution becomes increasingly serious in South Korea, the country is becoming more interested in obtaining information on fine dust levels. Fine dust level data are sufficiently local to make regional forecasting meaningless. Thus, this study proposes an alternative measurement technique to minimize differ ences between published and perceived levels of fine dusts. Owing to the large variations in the fine dust levels within urban areas, it is very difficult to provide measurements that are sufficiently area-representative. Because infants and elementary school students are more sensitive to fine dust than adults, it is useful to construct large data sets of measurements of fine dust levels at elementary schools. In Korea, the distribution of elementary schools is consistent with population density, which is useful for analyzing local differences in the fine dust levels in urban areas. This study will provide a basis for big data application to public health policy and infographics using color fuzzy model.
Keywords: Fine dust ، Big data ، Sustainable city ، Public health policy ، Infographics ، Color fuzzy model
Les big data, généralités et intégration en radiothérapie
داده های بزرگ، ژنتیک و مقیاس پذیری در محیط زیست-2018
The many advances in data collection computing systems (data collection, database, storage), diagnostic and therapeutic possibilities are responsible for an increase and a diversification of available data. Big data offers the capacities, in the field of health, to accelerate the discoveries and to optimize the management of patients by combining a large volume of data and the creation of therapeutic models. In radiotherapy, the development of big data is attractive because data are very numerous et heterogeneous (demographics, radiomics, genomics, radiogenomics, etc.). The expectation would be to predict the effectiveness and tolerance of radiation therapy. With these new concepts, still at the preliminary stage, it is possible to create a personalized medicine which is always more secure and reliable.
Keywords: Big data , Radiotherapy , Predictive model , Cancer , Genomics , Radiosensitivity
The IoT for smart sustainable cities of the future: An analytical framework for sensor-based big data applications for environmental sustainability
اینترنت اشیا برای شهرهای پایدار هوشمند از آینده: یک چارچوب تحلیلی برای کاربردهای داده های بزرگ مبتنی بر حسگر برای سازگاری با محیط زیست-2018
The Internet of Things (IoT) is one of the key components of the ICT infrastructure of smart sustainable cities as an emerging urban development approach due to its great potential to advance environmental sustainability. As one of the prevalent ICT visions or computing paradigms, the IoT is associated with big data analytics, which is clearly on a penetrative path across many urban domains for optimizing energy efficiency and mitigating en vironmental effects. This pertains mainly to the effective utilization of natural resources, the intelligent man agement of infrastructures and facilities, and the enhanced delivery of services in support of the environment. As such, the IoT and related big data applications can play a key role in catalyzing and improving the process of environmentally sustainable development. However, topical studies tend to deal largely with the IoT and related big data applications in connection with economic growth and the quality of life in the realm of smart cities, and largely ignore their role in improving environmental sustainability in the context of smart sustainable cities of the future. In addition, several advanced technologies are being used in smart cities without making any con tribution to environmental sustainability, and the strategies through which sustainable cities can be achieved fall short in considering advanced technologies. Therefore, the aim of this paper is to review and synthesize the relevant literature with the objective of identifying and discussing the state-of-the-art sensor-based big data applications enabled by the IoT for environmental sustainability and related data processing platforms and computing models in the context of smart sustainable cities of the future. Also, this paper identifies the key challenges pertaining to the IoT and big data analytics, as well as discusses some of the associated open issues. Furthermore, it explores the opportunity of augmenting the informational landscape of smart sustainable cities with big data applications to achieve the required level of environmental sustainability. In doing so, it proposes a framework which brings together a large number of previous studies on smart cities and sustainable cities, including research directed at a more conceptual, analytical, and overarching level, as well as research on specific technologies and their novel applications. The goal of this study suits a mix of two research approaches: topical literature review and thematic analysis. In terms of originality, no study has been conducted on the IoT and related big data applications in the context of smart sustainable cities, and this paper provides a basis for urban researchers to draw on this analytical framework in future research. The proposed framework, which can be replicated, tested, and evaluated in empirical research, will add additional depth to studies in the field of smart sustainable cities. This paper serves to inform urban planners, scholars, ICT experts, and other city sta keholders about the environmental benefits that can be gained from implementing smart sustainable city in itiatives and projects on the basis of the IoT and related big data applications.
Keywords: Smart sustainable cities , The IoT , Big data analytics , Sensor technology , Data processing platforms , Environmental sustainability , Big data applications , Cloud computing , Fog/edge computing