Sustainable Water Supply and Demand Management in Semi-arid Regions: Optimizing Water Resources Allocation Based on RCPs Scenarios

Climate as one of the key factors in water resources management affects the amount of water in the hydrological cycle, which subsequently impacts the level of water availability. Considering the challenges that the South Alborz Region, Iran is currently facing in supplying water for various consumers; in this study, the climate change adaptation scenarios are investigated for sustainable water supply and demand. This study uses a procedure in which five different adaptation approaches, under RCPs scenarios, were established using the WEAP model to assess the impacts of various adaptation strategies on increasing the balance between water supply and demand over current and 2020s accounts. The findings suggest an imbalance between supply and demand in the current situation with the greatest imbalance in domestic use while the minimum in the industrial sector. The results of assessing adaptive scenarios show that various scenarios have different effects on balancing the water supply and demand by different consumers; on the other hand, the scenarios that directly affect domestic water demand have the greatest effect on minimizing the gap between supply and demand in the region; therefore, the options for decreasing the population demand along with diminishing the losses in the domestic water distribution network are the most effective alternatives for balancing supply and demand under all of the climate scenarios. The findings of this research indicate that adaptive management with the focus on restricting demand helps actively management of water resources in the regions with scarce water resources.

     

Modeling of dynamic micro-milling cutting forces

This paper investigates the mechanistic modeling of micro-milling forces, with consideration of the effects of ploughing, elastic recovery, run-out, and dynamics. A ploughing force model that takes the effect of elastic recovery into account is developed based on the interference volume between the tool and the workpiece. The elastic recovery is identified with experimental scratch tests using a conical indenter. The dynamics at the tool tip is indirectly identified by performing receptance coupling analysis through the mathematical coupling of the experimental dynamics with the analytical dynamics. The model is validated through micro end milling experiments for a wide range of cutting conditions.     

Energy Efficient Monitoring in Sensor Networks

We study a set of problems related to efficient battery energy utilization for monitoring applications in a wireless sensor network with the goal to increase the sensor network lifetime. We study several generalizations of a basic problem called Set k-Cover. The problem can be described as follows: we are given a set of sensors, and a set of targets to be monitored. Each target can be monitored by a subset of the sensors. To increase the lifetime of the sensor network, we would like to partition the sensors into k sets (or time-slots), and activate each set of sensors in a different time-slot, thus extending the battery life of the sensors by a factor of k. The goal is to find a partitioning that maximizes the total coverage of the targets for a given k. This problem is known to be NP-hard. We develop an improved approximation algorithm for this problem using a reduction to Max k-Cut. Moreover, we are able to demonstrate that this algorithm is efficient, and yields almost optimal solutions in practice.     

Proximate and fatty acid compositions and sensory acceptability of Hispanic consumers towards rib‐eye steaks from forage‐finished steers

Proximate and fatty acid compositions and sensory acceptability of rib‐eye steaks (fresh and 6 months frozen‐stored) from three forage‐finished steers [S1 (bermudagrass + ryegrass, etc.); S2 (bermudagrass + ryegrass + berseem, etc.); S3 (bermudagrass+berseem+soybean+brown midrib sorghum, etc.)] and one commercial steak (C), cooked by one‐sided and/or two‐sided grilling, were evaluated. All forage‐finished steaks had lower [omega‐6/omega‐3] ratios than C. Sensory liking was assessed by Hispanic consumers. Raw C steak had higher fat and lower protein contents than others (S1, S2 and S3). Concerning raw steaks, S3 had higher liking scores for overall appearance and fat appearance than others. Two cooking methods did not cause significant differences in sensory liking. Juiciness and overall liking of cooked C and S3 (one‐sided grilling) steaks were not significantly different. Purchase intent (after knowing health benefits of forage‐finished steaks) increased from 62.0–73.8% to 69.8–85.7%. Forage‐finished steaks showed a potentially healthier lipid profile than grain‐finished steaks and had market potential towards Hispanic population.     

Tool wear monitoring of micro-milling operations

The mechanical removal of materials using miniature tools, known as micro-mechanical milling processes, has unique advantages in creating miniature 3D components using a variety of engineering materials, when compared with photolithographic processes. Since the diameter of miniature tools is very small, excessive forces and vibrations significantly affect the overall quality of the part. In order to improve the part quality and longevity of tools, the monitoring of micro-milling processes is imperative. This paper examines factors affecting tool wear and a tool wear monitoring method using various sensors, such as accelerometers, force and acoustic emission sensors in micro-milling. The signals are fused through the neuro-fuzzy method, which then determines whether the tool is in good shape or is worn. An optical microscope is used to observe the actual tool condition, based upon the edge radius of the tool, during the experiment without disengaging the tool from the machine. The effectiveness of tool wear monitoring, based on a number of different sensors, is also investigated. Several cutting tests are performed to verify the monitoring scheme for the miniature micro-end mills.     

Analysis of Flood Risk Management Strategies Based on a Group Decision Making Process via Interval-Valued Intuitionistic Fuzzy Numbers

The sustainability concept has influenced decision-making patterns in various managerial fields. Watershed-based flood risk management (FRM), as an extremely complex multidisciplinary issue, has to deal with various conflicting, sustainable development criteria. The following measures through a novel structure were considered to address the complexity and uncertainty of watershed-based FRM: (1) the FRM strategies were formulated via matching internal strategic factors against external ones through Strength-Weakness-Opportunity-Threat (SWOT-TOWS) matrix; (2) a Multi Attribute Group Decision Making (MAGDM) process was applied to prioritize the strategies with consideration of sustainable development attributes; (3) the Interval-Valued Intuitionistic Fuzzy Numbers (IVIFNs) were merged into TOWS matrix as a novel procedure to overcome the uncertainty of the judgments. According to pairwise comparisons of the seven proposed attributes, the urgency of need was obtained as the first-ranked attribute. The overall prioritization of 10 TOWS-based strategies with IVIFNs revealed that the highest priority among the strategies belonged to implementation of social learning process to raise the residents’ awareness concerning water and soil conservation plus sustainable agriculture with the aid of NGOs. Throughout the present study, SWOT-TOWS matrix not only could properly identify the fundamental strategic factors but also developed the mitigation strategies including structurally and non-structurally-based measures. In addition to the IVIFNs, pairwise comparisons performed appropriately to evaluate the attributes’ weights. Finally, the proposed IVIFN-based MAGDM technique allows the analysts to aggregate several individual exact numerical scores of an attribute into an IVIFN through a simple, practical and straightforward group decision making mechanism.     

Analytical and Numerical Investigations of Unsteady Graphene Oxide Nanofluid Flow Between Two Parallel Plates

In this research, different analytical methods were applied to characterize thermal behavior of unsteady graphene oxide–water nanofluid flow between two parallel moving plates. First of all, partial differential equations (PDEs) were transformed to a system of nonlinear ordinary differential equations (ODEs) using similarity solution. Then, collocation method (CM), least square method (LSM) and Galerkin method (GM) were used to solve the system of ODEs and determine velocity and temperature distribution functions. In addition, effects of moving parameter, concentration, Eckert and Prandtl numbers on nanofluid velocity and temperature profiles were examined. Next, using numerical solution of the obtained system of differential equations, the results obtained from the analytical solutions were validated with that of the numerical solution. The validation results indicated high and appropriate accuracy of the analytical solutions compared to the numerical one.