Observed and simulated winter temperature over Gurudongmar area, North Sikkim, India

The global warming and its impact on the cryosphere is a matter of serious concern. The Sikkim and the Eastern Himalaya are a canvas of vivid landscapes and of different climate zones. The study of cryosphere needs more attention on long term climatic trends of surface air temperature. The Gurudongmar area is very much important because this area is surrounded by glaciers and as well as cold desert and TsoLhamo Lake nearby. The Gurudongmar lake (located at an altitude of 17,800 ft) has been studied by several researchers in the context of Glacial Lake Outburst Floods (GLOFs) and reported a high risk lake which is being largely affected by global warming and climate change. The present study is aimed to investigate the trend of temperature in recent past and in future time periods over the study area of Sikkim. The observed and model’s simulated gridded temperature data is considered to inkling of rising trend in winter months of December-January-February (DJF) over the study area. An increase in temperature is found for the future time period. This can be linked to the increasing hazard risk and change in local cryosphere environment.

Machine Learning-Based Model Predictive Control for Collaborative Production Planning Problem with Unknown Information

In industrial production planning problems, the accuracy of the accessible market information has the highest priority, as it is directly associated with the reliability of decisions and affects the efficiency and effectiveness of manufacturing. However, during a collaborative task, certain private information regarding the participants might be unknown to the regulator, and the production planning decisions thus become biased or even inaccurate due to the lack of full information. To improve the production performance in this specific case, this paper combines the techniques of machine learning and model predictive control (MPC) to create a comprehensive algorithm with low complexity. We collect the historical data of the decision-making process while the participants make their individual decisions with a certain degree of bias and analyze the collected data using machine learning to estimate the unknown parameter values by solving a regression problem. Based on an accurate estimate, MPC helps the regulator to make optimal decisions, maximizing the overall net profit of a given collaborative task over a future time period. A simulation-based case study is conducted to validate the performance of the proposed algorithm in terms of estimation accuracy. Comparisons with individual and pure MPC decisions are also made to verify its advantages in terms of increasing profit.

THE VALUE OF PERSONAL CREDIT HISTORY IN RISK SCREENING OF ENTREPRENEURS: EVIDENCE FROM MARKETPLACE LENDING

We explore the quality of risk assessment for entrepreneurs/small business borrowers as compared to consumers, when the same information on previous credit history is used for both segments in marketplace lending. By building several cross-sectional logistic regression and machine-learning models and applying them separately to small business loans (SBL) and consumers we can measure models’ predictive accuracy for different segments, and thus, make observations about the value of the information used for screening. We find the differences in profiles between SBL and consumers, hence they should be assessed by separate models. Yet separate SBL models do not perform well when applied to a future time period. We attribute this to the relatively low predictive value of personal credit history for entrepreneurs as compared to the consumers. We advocate the use of additional information for risk assessment of entrepreneurs, in order to improve the quality of credit screening. This should lead to improved access of small business borrowers to credit in situations when they have to compete with consumers for funding.

Testing of an Optimization-Simulation Model for Real-Time Flood Operation of River-Reservoir Systems

A new methodology was developed for the real-time determination gate control operations of a river-reservoir system to minimize flooding conditions. The methodology is based upon an optimization-simulation model approach interfacing the genetic algorithm within MATLAB with simulation software for short-term rainfall forecasting, rainfall–runoff modeling (HEC-HMS), and a one-dimensional (1D), two-dimensional (2D), and combined 1D and 2D combined unsteady flow models (HEC-RAS). Both real-time rainfall data from next-generation radar (NEXRAD) and gaging stations, and forecasted rainfall are needed to make gate control decisions (reservoir releases) in real-time so that at time t, rainfall is known and rainfall over the future time-period (Δt) to time t + Δt can be forecasted. This new model can be used to manage reservoir release schedules (optimal gate operations) before, during, and after a rainfall event. Through real-time observations and optimal gate controls, downstream water surface elevations are controlled to avoid exceedance of threshold flood levels at target locations throughout a river-reservoir system to minimize the damage. In an example application, an actual river reach with a hypothetical upstream flood control reservoir is modeled in real-time to test the optimization-simulation portion of the overall model.

Predicting Influential Users in Online Social Network Groups

The widespread adoption of Online Social Networks (OSNs), the ever-increasing amount of information produced by their users, and the corresponding capacity to influence markets, politics, and society, have led both industrial and academic researchers to focus on how such systems could be influenced . While previous work has mainly focused on measuring current influential users, contents, or pages on the overall OSNs, the problem of predicting influencers in OSNs has remained relatively unexplored from a research perspective. Indeed, one of the main characteristics of OSNs is the ability of users to create different groups types, as well as to join groups defined by other users, in order to share information and opinions. In this article, we formulate the Influencers Prediction problem in the context of groups created in OSNs, and we define a general framework and an effective methodology to predict which users will be able to influence the behavior of the other ones in a future time period, based on historical interactions that occurred within the group. Our contribution, while rooted in solid rationale and established analytical tools, is also supported by an extensive experimental campaign. We investigate the accuracy of the predictions collecting data concerning the interactions among about 800,000 users from 18 Facebook groups belonging to different categories (i.e., News, Education, Sport, Entertainment, and Work). The achieved results show the quality and viability of our approach. For instance, we are able to predict, on average, for each group, around a third of what an ex-post analysis will show being the 10 most influential members of that group. While our contribution is interesting on its own and—to the best of our knowledge—unique, it is worth noticing that it also paves the way for further research in this field.

Intensity-Duration-Frequency (IDF) Curves Under Changing Climate – A Non-Stationary Modelling Approach

<p>Anthropogenic activities have accelerated the global warming phenomena, causing a rapid change in the weather patterns, especially the extremes. Intensification of magnitude and frequency of extreme events have increased the stress on water infrastructures. Hence design methods have to be updated to build climate-resilient infrastructures. Intensity-Duration-Frequency (IDF) curves play a vital role in flood risk assessment and impact the region's socio-economic structure. In this study, a non-stationary modelling approach is proposed to develop IDF curves under changing climate using Global Climate Models (GCMs). Non-Stationary Generalized Extreme Value Distribution (NS-GEVD) location parameter is modelled as a linear function of GCM outputs.  Data used for analysis is the annual maximum daily precipitation generated at a Hyderabad city station, India using 27 GCMs of Coupled Model Intercomparison Project Phase-5 (CMIP-5).  The analysis is carried out for the baseline period of 1971 to 2005 and the future time-period of 2006 to 2100. Corrected Akaike Information Criterion test statistic is used to identify the best NS-GEVD model. The results indicate that NS-GEVD model could capture the non-stationary behaviour and predicted an average increase of 7% in extreme rainfall intensity for the future. Besides, it is observed that six GCM covariates outperform other GCMs. The outcomes of this study will benefit the city municipality, practitioners and decision-makers in identifying future risk for water infrastructures. </p>

Genetics and Epigenetics of Alzheimer’s Disease: new Cognitive Technologies and Neurocommunication

The 21st century Homo sapiens evolves and improves using new nano, bio, information and cognitive technologies that provide and focus on cognitive and creative processes. 21st century brain H. sapiens combines internal and external layered information into a single algorithm for structuring, routing, storing, and retrieving information in the present and future time period. 4P and 5P medicine, 5G medical services, next-generation sequencing and pharmacogenetics are new modern foundations of personalized medicine. The next-generation information and communication infrastructure of 5G for clients (patients) from the medical sphere is an intelligent border-cloud platform with an integrated cloud network architecture. Next-generation mobile technologies are being introduced in a modern hospital: from higher communication speeds to smart computing and additional reality. 5G medical services are a revolution in the medical industry. Neuromarketing and neurobytes, neuroimaging of consciousness and brain-machine interfaces, biorobots and biochips interact with the “external and internal hippocampus”. The result of human activity in a market economy and a new society is intellectual property. The heterogeneous nature of dementia (Alzheimer’s disease, Peak, frontal-temporal degeneration) and the various pathophysiological features of specific dementia highlight the need to develop separate algorithms based on current biomarkers specific to these diseases. Modern prognosis of Alzheimer’s disease is achieved by structural neuroimaging, cognitive testing, and biological indicators (genotype APOE-ε4) based, both on orders, standards, and clinical recommendations, as well as on the quantitative assessment of brain structures using neurointerfaces. The functioning of integrated neural systems through integration and analysis of dynamic hybrid multimodal neural EEG and fMRI information, combined with neuropsychological testing, will enable the geriatric clinician to manage healthy aging of H. sapiens.

Climate change simulation and impacts on extreme events of rainfall and storm water in the Zayandeh Rud Catchment

Nowadays, one of the most significant problems is that to recognize how the severity of heavy precipitation and floods may alter in future time in comparison with the current period. The purpose of this research is to understand the impact of future climate change on storm water and probability of maximum flood for future time period. Zayandeh rud river basin in Iran is selected as a case study. Forecast of future climatic parameters based on temperature and precipitation of the upcoming period (2006-2040) is completed with using the HadCM3 model and based on RCP 2.6, 4.5, and 8.5 emission patterns. Also, climate change model is downscaled statistically with applying LARS-WG. In the next step, the probable of maximum precipitation is measured through synoptic method and then, in order to model maximum storm water under the climate change effects, the HEC-HMS for simulating rainfall-runoff model is used. Also, the Snowmelt Runoff Model (SRM) is applied to model snow melting. The results of this research indicate the maximum of probable precipitation in the basin for the period of 2006-2040 under the scenario RCP 2.6, can rise by 5% and by the scenarios of RCP 4.5 and RCP 8.5 can decrease by 5% and 10%, respectively in comparison with the current period 1970-2005.

THE IMPACTS OF CLIMATE CHANGE ON ENERGY-WATER SYSTEMS AND ECONOMIC ANALYSIS

In order to develop a valued decision-support system for climate alteration policy and planning, recognizing the regionally-specific features of the climate change, energy-water nexus, and the history of the current and possible future climate, water and energy supply systems is necessary. This paper presents an integrated climate change, water/energy modeling platform which allows tailored climate alteration and water-energy assessments. This modeling platform is established and described in details based on particular regional circumstances. The modeling platform involves linking three different models, including the climate change model from Coupled Model Intercomparison Project Phase 5 under the most severe scenario (Representative Concentration Pathways, Water Evaluation, and Planning system and the Long-range Energy Alternatives Planning system). This is to understand the impacts of climate variability (changes in temperature and precipitation) on water and electricity consumption in Zayandeh Rud River Basin (Central Iran) for the current (1971–2005) and future time period (2006–2040). Climate models have projected that the temperature will increase by 7 °C and precipitation will decrease by 44%, it is also proposed that electricity imports will rise during a severe dry scenario in the basin, while power generation will decrease around 8%.

Analysis and Prioritization the Effective Factors on Increasing Farmers Resilience Under Climate Change and Drought

California is severely exposed to drought and damage due to the climate change and drought belt, which has a major impact on agriculture. So, after the drought crisis, there are various reactions from farmers. The extent of the damage caused by the socioeconomic, environment and the extent of the resistance of farmers to this crisis is manifested in a variety of ways. Recognizing the population’s resilience and the involved human groups is a tool for preventing a catastrophe-based increase in life-threatening areas in high-risk areas. Sometimes the inability to manage this phenomenon (especially under the climate change) leads to farmers’ desertification and agricultural land release, which itself indicates a low level of resilience and resilience to the crisis. The recent drought under the climate change condition in California and the severity of the damage sustained by farmers continue to be vulnerable. The present study seeks to prioritize and prioritize resilience of farmers to the crisis under the climate change. This study simulated drought condition with using PDSI value for current and future time period. In order to calculate PDSI values, the climatic parameters extracted from CMIP5 models and downscaled under the scenario of RCP 8.5. Also in order to understand the resilience of the agriculture activities under the climate change, this study was performed using statistical tests and data from the questionnaire completed in the statistical population of 320 farmers in the Tulare region in California. The findings of the research by t test showed that the average level of effective factors in increasing the resilience of farmers in the region is low. This is particularly significant in relation to the factors affecting government policies and support. So that only the mean of five variables is higher than the numerical desirability of the test and the other 15 variables do not have a suitable status for increasing the resilience of the farmers. Also, the results of the Vikor model showed that most of the impact on their resilience to drought and climate change was the development of agricultural insurance, the second important impact belongs to drought monitoring system, climate change and damage assessment, and variable of attention to knowledge is in third place of the important factor.