Charging Station Planning for Electric Vehicles

Charging station (CS) planning for electric vehicles (EVs) for a region has become an important concern for urban planners and the public alike to improve the adoption of EVs. Two major problems comprising this research area are: (i) the EV charging station placement (EVCSP) problem, and (ii) the CS need estimation problem for a region. In this work, different explainable solutions based on machine learning (ML) and simulation were investigated by incorporating quantitative and qualitative metrics. The solutions were compared with traditional approaches using a real CS area of Austin and a greenfield area of Bengaluru. For EVCSP, a different class of clustering solutions, i.e., mean-based, density-based, spectrum- or eigenvalues-based, and Gaussian distribution were evaluated. Different perspectives, such as the urban planner perspective, i.e., the clustering efficiency, and the EV owner perspective, i.e., an acceptable distance to the nearest CS, were considered. For the CS need estimation, ML solutions based on quadratic regression and simulations were evaluated. Using our CS planning methods urban planners can make better CS placement decisions and can estimate CS needs for the present and the future.

Methodology of Effective Lateral Placement for Underbalanced Coiled Tubing Drilling Wells

This paper describes a methodology that has been developed to maximize lateral placement in productive reservoir intervals during underbalanced coiled tubing drilling (UBCTD) operations. UBCTD has emerged as an effective and economically viable development solution for exploiting reserves in mature gas reservoirs. In some cases, it can be a suitable solution to develop reserves in more geologically complex and heterogonous reservoirs over the conventional drilling and stimulation techniques. The methodology integrates big surface and subsurface data from multiple sources in multiple formats in real to near real-time that are normally acquired during UBCTD drilling operations. The multiple sources of data include subsurface geology, wellsite biosteering, reservoir influx, well testing and drilling, and can provide important information about the reservoirs encountered. With the aid of data analytics and an advanced visualization tool, the data is translated into in series of engineering plots that enable easier identification of productive intervals and more informed as well as efficient lateral placement decisions. This methodology has proven superior to the conventional instantaneous Productivity Index (PI) approach that is commonly used for UBCTD lateral placement. The methodology has been tested with good success in a number of recently drilled UBCTD wells in geologically complex depositional environments across carbonates and clastic reservoirs. Post flowback and pressure transient test analyses have shown significant improvement in the well deliver abilities and effective lateral lengths. Past performance from wells drilled using the PI method will be compared with wells drilled with this method.

Advanced Engineering Solutions to Achieve Best in Class Performance in Drilling Extended Reach Dual Lateral Wells in the Sultanate of Oman

A Large Omani Operator successfully achieved best in class performance in drilling extended reach dual-lateral wells in Oman. Turning the legs to achieve the required separation distance and continue drilling to the required depth through a thin fractured reservoir resulted in complex well trajectories and harsh drilling environment. This paper will focus on the newly innovative designs, engineering optimizations and utilizing lean methodology to overcome drilling risks and achieve best in class performance. Rotary Steerable system was utilized to drill the extended reach drilling (ERD) in 3D with continuous proportional steering technology. Advance modeling including lateral shocks, Torque and Drag and BHA design were as well key enablers. Logging while drilling tools supported reservoir mapping and real-time well placement decisions. To excel in lateral applications and overcome harsh drilling environment, a shallow cone tip profile with High Performance cutter bit technology was selected. A focus optimization project using lean tools was performed to map out the undercut process, visualize possible waste, perform root causes analysis and implement countermeasures to eliminate the process waste Regional benchmark showed that the performance of 11 wells drilled since the start of the campaign is located within the best 10% of the benchmark data which is marked as best in class performance. Due to the continues improvement, the campaign manages to reach a learning curve of 30%. Furthermore, the actual production from the wells was 300% more than the forecast. Using the advanced RSS and bit technologies resulted in reducing the Torque values in the lateral section by 30% which effectively increased the reservoir drilled interval by 22%. The designed BHA also managed to complete wells including multi undercuts (up to 6) in one run. One trip Whipstock System for creating the second leg is used as part of the well design. The Whipstock system which is uniquely set in the horizontal tangent section has achieved 100% success rate in setting and retrieving operations. The undercut activities have improved by 50% as a direct result of the optimization Lean project. In addition, utilizing lean methodology resulted in reducing the cost impact of the additional sidetracks (undercuts) which enabled having best reservoir quality and achieving savings over the total cost of ownership TCO. Extended Reach Dual lateral well design was utilized for the first time in PDO operations during this drilling campaign. This paper will present how advance modelling can enable the industry to deliver complex well designs. Additionally, it will introduce the company innovation in implementing the Lean philosophy to optimize the drilling operation.

Factors Associated with Attachment and Care Decisions

It is widely accepted that remaining in the community for as long as possible is preferable to placement in a care facility. For many, this can only be realized with the support of a family caregiver. Previous research on the relationship between attachment and caregiving decisions is sparse, but tends to suggest there is a relationship between attachment and the decision to assume caregiving responsibilities, but more information is needed to better understand this unique relationship. This exploratory research seeks to address gaps in our understanding by asking is attachment related to the decision to care for a parent and what factors are associated with attachment. A convenience sample of 128 individuals caring for older parents was surveyed to answer these questions. Results indicate lower attachment related avoidance was associated with greater odds of caring for a recipient in the community rather than placing the recipient in a care facility. No relationship between attachment related anxiety and placement decisions was observed. Additionally, greater levels of attachment related avoidance were observed among caregivers reporting lower levels of filial responsibility, more adverse childhood experiences, less perceived support, and greater financial stability. Findings from this study can be used to support the development of interventions to strengthen attachment between adult children and their parents before care decisions are necessary.

QoS Improvement Using In-Network Caching Based on Clustering and Popularity Heuristics in CCN

Content-Centric Networking (CCN) has emerged as a potential Internet architecture that supports name-based content retrieval mechanism in contrast to the current host location-oriented IP architecture. The in-network caching capability of CCN ensures higher content availability, lesser network delay, and leads to server load reduction. It was observed that caching the contents on each intermediate node does not use the network resources efficiently. Hence, efficient content caching decisions are crucial to improve the Quality-of-Service (QoS) for the end-user devices and improved network performance. Towards this, a novel content caching scheme is proposed in this paper. The proposed scheme first clusters the network nodes based on the hop count and bandwidth parameters to reduce content redundancy and caching operations. Then, the scheme takes content placement decisions using the cluster information, content popularity, and the hop count parameters, where the caching probability improves as the content traversed toward the requester. Hence, using the proposed heuristics, the popular contents are placed near the edges of the network to achieve a high cache hit ratio. Once the cache becomes full, the scheme implements Least-Frequently-Used (LFU) replacement scheme to substitute the least accessed content in the network routers. Extensive simulations are conducted and the performance of the proposed scheme is investigated under different network parameters that demonstrate the superiority of the proposed strategy w.r.t the peer competing strategies.

Intelligent scaling for 6G IoE services for resource provisioning

The proposed research motivates the 6G cellular networking for the Internet of Everything’s (IoE) usage empowerment that is currently not compatible with 5G. For 6G, more innovative technological resources are required to be handled by Mobile Edge Computing (MEC). Although the demand for change in service from different sectors, the increase in IoE, the limitation of available computing resources of MEC, and intelligent resource solutions are getting much more significant. This research used IScaler, an effective model for intelligent service placement solutions and resource scaling. IScaler is considered to be made for MEC in Deep Reinforcement Learning (DRL). The paper has considered several requirements for making service placement decisions. The research also highlights several challenges geared by architectonics that submerge an Intelligent Scaling and Placement module.

Using WebClass for Reduced Redundancy Testing

This article examines and reviews two types of reduced redundancy tests, namely cloze tests and C-tests, which involve completing a text from which certain units (whole words or their parts) have been removed. Assessment instruments of this kind are typically used to measure overall language proficiency, for example for the purpose of making placement decisions. The paper also discusses the development of these two measures of reduced redundancy with the help of the WebClass testing system.

Micromobility Station Placement Optimization for a Rural Setting

Micromobility is an evolving form of transportation modality that uses small human- or electric-powered vehicles to move people short distances. Planners expected that bike sharing, the first form of micromobility, would reduce traffic congestion, cut travel cost, reduce pollution, enable connectivity with other modes of transport, and promote public health. However, micromobility options also brought new challenges such as the difficulty of placement decisions to encourage adoption and to minimize conflict with other transport modes. Sound deployment decisions depend on the unique environmental characteristics and demographics of a location. Most studies analyzed deployments in high-density urban areas. This research determines the best locations for 5 new bike-sharing stations in Fargo, North Dakota, a small urban area in the rural United States. The workflow combines a geographic information system (GIS), level of traffic stress (LTS) ratings, and location-allocation optimization models. The spatial analysis considered 18 candidate station locations and eliminated those that fell within the 700-meter isochrone walking distance of the 11 existing stations. This case study demonstrates a scalable workflow that planners can repeat to achieve sustainable micromobility deployments by considering the land use, population density, activity points, and characteristics of the available pathways in their unique setting.

Parental Rights and the Temporality of Attachment: Law, Kinship, and Child Welfare in Japan

This article explores the legal norms and regulatory mechanisms in Japan that structure child welfare placement decisions, focusing specifically on the legal category of “parental rights.” It is suggested that the ways child welfare officers and caregivers understand the concept of “rights”—both those of the biological parent(s) and the child—construe kinship relationships as problems to be managed, but with a particular orientation toward what is called in the article the temporality of attachment. Child welfare caseworkers’ understandings of legal categories, processes, and forms of documentation (such as the Japanese family registry) produce particular forms of kinship that prioritize a child's possible future relationship with an absent parent, above and beyond the day-to-day relationships children might develop with alternative caregivers such as foster parents. Despite the fact that the author's Japanese interlocutors often described kinship as an immutable relationship of blood ties, the author shows how kinship is in fact produced through specific encounters between (mostly absent) parents and their children, child welfare caseworkers, and foster and institutional caregivers, scaffolded by their engagement with legal and bureaucratic regimes. The article explores what parenthood means within Japanese child welfare, both as a temporalized form of relationality and as a set of legally structured claims to the right to care.

CoLocateMe: Aggregation-based, Energy, Performance and Cost Aware VM Placement and Consolidation in Heterogeneous IaaS Clouds

In many production clouds, with the notable exception of Google, aggregation-based VM placement policies are used to provision datacenter resources energy and performance efficiently. However, if VMs with similar workloads are placed onto the same machines, they might suffer from contention, particularly, if they are competing for similar resources. High levels of resource contention may degrade VMs performance, and, therefore, could potentially increase users' costs and infrastructure's energy consumption. Furthermore, segregation-based methods result in stranded resources and, therefore, less economics. The recent industrial interest in segregating workloads opens new directions for research. In this paper, we demonstrate how aggregation and segregation-based VM placement policies lead to variabilities in energy efficiency, workload performance, and users' costs. We, then, propose various approaches to aggregation-based placement and migration. We investigate through a number of experiments, using Microsoft Azure and Google's workload traces for more than twelve thousand hosts and a million VMs, the impact of placement decisions on energy, performance, and costs. Our extensive simulations and empirical evaluation demonstrate that, for certain workloads, aggregation-based allocation and consolidation is ~9.61% more energy and ~20.0% more performance efficient than segregation-based policies. Moreover, various aggregation metrics, such as runtimes and workload types, offer variations in energy consumption and performance, therefore, users' costs.<br>