با سلام خدمت کاربران عزیز، به اطلاع می رساند ترجمه مقالاتی که سال انتشار آن ها زیر 2008 می باشد رایگان بوده و میتوانید با وارد شدن در صفحه جزییات مقاله به رایگان ترجمه را دانلود نمایید.
Mobile phone network data reveal nationwide economic value of coastal tourism under climate change
ارزش اقتصادی داده های شبکه تلفن همراه در سراسر جهان از گردشگری ساحلی در اثر تغییر آب و هوا-2020
The technology-driven application of big data is expected to assist policymaking towards sustainable development; however, the relevant literature has not addressed human welfare under climate change, which limits the understanding of climate change impacts on human societies. We present the first application of unique mobile phone network data to evaluate the current nation-wide human welfare of coastal tourism at Japanese beaches and project the value change using the four climate change scenarios. The results show that the projected national economic value loss rates are more significant than the projected national physical beach loss rates. Our findings demonstrate regional differences in recreational values: most southern beaches with larger current values would disappear, while the current small values of the northern beaches would remain. These changes imply that the ranks of the beaches, based on economic values, would enable policymakers to discuss management priorities under climate change.
Keywords: Adaptation | Beach recreation | Big data | Climate change | Coastal tourism | Ecosystem services | Travel cost method | Sea level rise
Life in riverine islands in Bangladesh: Local adaptation strategies of climate vulnerable riverine island dwellers for livelihood resilience
زندگی در جزایر رودخانه ای در بنگلادش: استراتژی های سازگاری محلی ساکنان جزیره رودخانه ای آسیب پذیر در برابر آب و هوا برای تاب آوری معیشت-2020
Adaptation is a key tool to reduce the climate change vulnerability of rural people whose livelihood is dependent on agriculture. An appropriate policy and strategy cannot be effective without a proper understanding of peoples’ climate change perception. This study intends to explore the local adaptation strategies of the riverine island (char) dwellers in the face of climate change hazards through a survey of 374 char dwellers living in the flood and riverbank erosion prone geographically isolated areas in Bangladesh. The study reveals almost no difference between the perception of char dwellers and the observed data on climate change. It further reports that the climate impacts make the char households a vulnerable community and minimize their livelihood resilience. A number of local adaptation strategies are adapted by char dwellers in the face of climate change effects which enhance their livelihood resilience. The study further reveals that homestead gardening, changing cropping pattern, tree plantation and migration are the most common strategies adapted by char dwellers. The study suggests that continuous development program and riverine island-based disaster management projects should be executed through an effective monitoring for enhancing char dweller’s livelihood resilience.
Keywords: Climate change | Vulnerability | Disaster management | Resilience | Sandbar
Intrusive acceleration strategies for uncertainty quantificationfor hyperbolic systems of conservation laws
استراتژی های شتابزنی سرعتی برای تعیین کمیت عدم اطمینان برای سیستم هایپربولیک قوانین حفاظت-2020
Methods for quantifying the effects of uncertainties in hyperbolic problems can be divided into intrusive and non-intrusive techniques. Non-intrusive methods allow the usage of a given deterministic solver in a black-box manner, while being embarrassingly parallel. On the other hand, intrusive modifications allow for certain acceleration techniques. Moreover, intrusive methods are expected to reach a given accuracy with a smaller number of unknowns compared to non-intrusive techniques. This effect is amplified in settings with high dimensional uncertainty. A downside of intrusive methods is the need to guarantee hyperbolicity of the resulting moment system. In contrast to stochastic-Galerkin (SG), the Intrusive Polynomial Moment (IPM) method is able to maintain hyperbolicity at the cost of solving an optimization problem in every spatial cell and every time step. In this work, we propose several acceleration techniques for intrusive methods and study their advantages and shortcomings compared to the non-intrusive Stochastic Collocation method. When solving steady problems with IPM, the numerical costs arising from repeatedly solving the IPM optimization problem can be reduced by using concepts from PDE-constrained optimization. Integrating the iteration from the numerical treatment of the optimization problem into the moment update reduces numerical costs, while preserving local convergence. Additionally, we propose an adaptive implementation and efficient parallelization strategy of the IPM method. The effectiveness of the proposed adaptations is demonstrated for multi-dimensional uncertainties in fluid dynamics applications, resulting in the observation of requiring a smaller number of unknowns to achieve a given accuracy when using intrusive methods. Furthermore, using the proposed acceleration techniques, our implementation reaches a given accuracy faster than Stochastic Collocation.
Keywords: Uncertainty quantification | Hyperbolic conservation laws | Intrusive | Stochastic-Galerkin | Collocation | Intrusive Polynomial Moment Method
Co-production of knowledge and adaptation to water scarcity in developing countries
تولید دانش و سازگاری با کمبود آب در کشورهای در حال توسعه-2020
Dwindling of freshwater resources is a harsh reality of the arid and semi-arid regions of the world and climate change is expected to deteriorate their situation through major reduction of freshwater supplies. Co-production of knowledge, through active negotiation of experts, government and local stakeholders has been used as a strategy to adapt to water scarcity. However, in many developing countries, co-production of knowledge is not common and adaptation efforts rarely reflects the plurality of involved knowledge sources and actors. Given the urgent need of transition towards water-efficient agricultural practices, the Iran’s government applied the knowledge co-production approach and implemented an integrated participatory crop management (IPCM) project in the Bakian village, Fars province. The objectives of this study were to analyze the knowledge coproduction process, identify the factors contributing to adoption of the co-produced knowledge and investigate the corresponding social, economic and environmental impacts. A mixed-method research was conducted comprising a case study on 19 informants selected using purposive sampling and a survey of 150 rice producers selected through systematic random sampling. The results indicated the relevance and pertinence of knowledge co-production in recognizing the real problems of the rice producers and suggesting some potential adaptive strategies. Though a wide range of natural, financial, technical, institutional and structural constraints restricted adoption of the proposed adaptive strategies, application of the co-produced knowledge significantly increased water productivity, ensured higher yields and farm-based sustainable livelihoods, and enhanced resilience of the farm households under water scarcity. Some recommendations and implications are offered to increase adaptation of farm families to water scarcity.
Keywords: Co-production of knowledge | Adaptation | Water scarcity | Climate change | Integrated participatory crop management | Impact assessment
A biomorphic neuroprocessor based on a composite memristor-diode crossbar
یک پردازشگر عصبی بیومورفیک بر اساس یک قطر کامپوزیت دیود ممیستور-2020
A concept of biomorphic neuroprocessor that implements hardware spiking neural network for traditional tasks of information processing and can simulate operation of brain cortical column or its fragment is proposed. The key units of hardware neural network are memory and logic matrices, previously developed on the basis of composite memristor-diode crossbar. These matrices provide high element integration and energy efficiency compared to known neuroprocessors and individual matrices. Such efficiency is achieved by application of mixed analogdigital computations, including those that use memristors integrated in composite memristor-diode crossbars. Neuron electrical model was constructed on the basis of these matrices and the Hodgkin-Huxley biomorphic model for neuron membrane. Unlike existing neural networks with synapses based on discrete memristors, the generation of new association was demonstrated in memristor-diode crossbar by SPICE modeling of associative self-learning processes. The adaptation procedure for biomorphic neural network program to neuroprocessor hardware is defined. In essence, presented neuroprocessor is a prototype of new generation computers with artificial intelligence.
Keywords: Biomorphic neuroprocessor | Biomorphic software and hardware electrical | models of neuron | Composite memristor-diode crossbar | Memory matrix | Logic matrix | Associative self-learning
A novel intelligent option price forecasting and trading system by multiple kernel adaptive filters
رویکرد پیش بینی قیمت و گزینه سیستم تجاری با فیلترهای انطباقی چند هسته ای-2020
Derivatives such as options are complex financial instruments. The risk in option trading leads to the demand of trading support systems for investors to control and hedge their risk. The nonlinearity and non-stationarity of option dynamics are the main challenge of option price forecasting. To address the problem, this study develops a multi-kernel adaptive filters (MKAF) for online option trading. MKAF is an improved version of the adaptive filter, which employs multiple kernels to enhance the richness of nonlinear feature representation. The MKAF is a fully adaptive online algorithm. The strength of MKAF is that the weights to the kernels are simultaneous optimally determined in filter coefficient updates. We do not need to design the weights separately. Therefore, MKAF is good at tracking nonstationary nonlinear option dynamics. Moreover, to reduce the computation time in updating the filter, and prevent overadaptation, the number of kernels is restricted by using coherence-based sparsification, which constructs a set of dictionary and uses a coherence threshold to restrict the dictionary size. This study compared the new method with traditional ones, we found the performance improvement is significant and robust. Especially, the cumulated trading profits are substantially increased
Keywords: Artificial intelligence | Adaptive filter | Multiple Kernel Machine | Big data analysis | Data mining | Financial forecasting
Self-reorganization of neuronal activation patterns in the cortexunder brain-machine interface and neural operant conditioning
خود سازماندهی مجدد الگوهای فعال سازی عصبی در رابط دستگاه مغز قشر مغز و تهویه عمل عصبی-2020
In this review, we describe recent experimental observations and model simulations in the research sub-ject of brain-machine interface (BMI). Studies of BMIs have applied decoding models to extract functionalcharacteristics of the recorded neurons, and some of these have more focused on adaptation based onneural operant conditioning. Under a closed loop feedback with the environment through BMIs, neuronalactivities are forced to interact directly with the environment. These studies have shown that the neuronensembles self-reorganized their activity patterns and completed a transition to adaptive state within ashort time scale. Based on these observations, we discuss how the brain could identify the target neu-rons directly interacting with the environment and determine in which direction the activities of thoseneurons should be changed for adaptation. For adaptation over a short time scale, the changes of neuronensemble activities seem to be restricted by the intrinsic correlation structure of the neuronal network(intrinsic manifold). On the other hand, for adaptation over a long time scale, modifications to the synapticconnections enable the neuronal network to generate a novel activation pattern required by BMI (exten-sion of the intrinsic manifold). Understanding of the intrinsic constraints in adaptive changes of neuronalactivities will provide the basic principles of learning mechanisms in the brain and methodological cluesfor better performance in engineering and clinical applications of BMI.
Keywords:Oscillology | Brain-machine Interface | Neural operant conditioning | BMI | Intrinsic manifold
Divergent agricultural water governance scenarios: The case of Zayanderud basin, Iran
سناریوهای حاکم بر آب کشاورزی واگرا: پرونده حوضه زاینده رود ، ایران-2020
There is an urgent need to consider adaptation strategies for agricultural water resources in response to the evergrowing demand for freshwater around the world. This is especially poignant in arid and semi-arid regions, like the Middle East and North Africa (MENA) where water resources have been extremely limited historically. Today, water resources are declining due to a variety of factors, including climate change, population growth and changing food preferences. Research on this topic typically seeks to assess the impact of discreet alternative interventions in isolation. However, it is necessary to analyze the broader factors affecting agricultural water management as interconnected components of a complex water governance system within a specific geographic context. This research uses an exploratory, formative scenario planning approach to a) identify important adaptation strategies, b) use those adaptation strategies to construct a small set of coherent, plausible and diverse regional agricultural water governance scenarios, and c) analyze future scenarios of the Zayandehroud watershed in Iran in the year 2040. The research shares five scenarios that exemplify divergent adaptation and mitigation approaches to agriculture water demand in Zayandehroud watershed, including adhering to the status quo. Each scenario embodies different economic and political priorities to reveal how those priorities impact the ecological, social, and economic sustainability of this watershed. These scenarios provide insights into the longterm implications of near-term decisions about water and food security, resilience of local communities and the ecological integrity of the regional watershed. This research explores the conceptual relationships between components of the water governance system and demonstrates an approach to analyzing alternative constellations of factors that will impact agricultural water management. Policy-makers can make more effective policies if they consider how to transform the broader system of regional water governance, rather than only evaluating discrete agricultural water management projects on a project-by-project basis.
Keywords: Adaptive governance | Scenario planning | Water market | Rural development | Local governance | Land use planning
Models for estimating daily photosynthetically active radiation in oceanic and mediterranean climates and their improvement by site adaptation techniques
مدل های تخمین روزانه اشعه فتوسنتزی فعال در آب و هوای اقیانوسی و مدیترانه و بهبود آنها توسط تکنیک های سازگاری سایت-2020
In this work Photosynthetically Active Radiation (PAR) in oceanic and mediterranean climates is modeled. Twenty-two different models have been developed and tested: eleven Multilinear Regression (MR) models and eleven Artificial Neuron Network (ANN) models, using combinations of variables such as Global Horizontal Irradiance (GHI), Global Extraterrestrial Irradiance (G0), Temperature (T) and Relative Humidity (RH). Data provided by Satellite Application Facility on Climate Monitoring (CM SAF) are used to develop and train the models, while the models have been validated using field data from four stations located in Spain, covering the different study climates. According to the results, zones with different climate conditions need different models, both for the case of MR and ANN. The results show the need of including the GHI in all models in order to obtain accurate estimates; in fact, the presence of more variables only improves slightly the results in mediterranean climate, while in oceanic climate no improvement is observed. On the other hand, comparing MR and ANN models, ANN models did not show better results than those of MR models in no one of the cases studied. Regarding the climate, both types of models are clearly better for the mediterranean case than for the oceanic one. In order to improve the performance of the model for oceanic climate a correction based on the site adaptation technique was carried out. The good results obtained by this technique fully justify its use. The best proposed models provide better performance than other models which are restricted to certain locations. Besides, the clustering technique based on the PAR variable, used in this work, allows obtaining useful models for a whole region. Finally, another advantage of this methodology is that there is no need of ground measurements for its development, except for the site adaptation technique
Keywords: Photosynthetically active radiation | Site adaptation technique | Global horizontal irradiance | Artificial neuron network | Multilinear regression
Financing coastal resilience by combining nature-based risk reduction with insurance
تأمین اعتبار انعطاف پذیری ساحلی با ترکیب ریسک مبتنی بر طبیعت و بیمه-2020
The increasing impacts of climate hazards combined with the loss of coastal habitats require urgent solutions to manage risk. Storm losses continue to grow and much of them are uninsured. These losses represent an increasing burden to individuals, businesses, and can jeopardize national development goals. Pre-hazard mitigation is cost effective, but both the public and private sector struggle to finance up-front investments in it. This article explores a resilience solution that combines risk transfer (e.g., insurance) with risk reduction (e.g., hazard mitigation), which have often been treated as two separate mechanisms for disaster risk management. The combined mechanism could help align environmental and risk management goals and create opportunities for public and private investment in nature-based projects. We assessed this resilience insurance with hypothetical cases for coral reef restoration. Under conservative assumptions, 44% of the initial reef restoration costs would be covered just by insurance premium reductions in the first 5 years, with benefits amounting>6 times the total costs over 25 years. We also test the sensitivity to key factors such as project cost, risk reduction potential, insurance structure, economic exposure and discount rates. The resilience insurance mechanism is applicable to many coastlines and can help finance nature-based adaptation.
Keyword: Resilience insurance