Siting park-and-ride facilities using a multi-objective spatial optimization model

Park-and-ride services are an important component of many public transportation systems in the United States. Locating park-and-ride facilities is an essential step when planning for these services. In this research we focus on three major siting/modeling concerns that need to be addressed when siting park-and-ride facilities: covering as much potential demand as possible, locating park-and-ride facilities as close as possible to major roadways, and siting such facilities in the context of an existing system. Unfortunately, existing models do not enable each of these concerns to be simultaneously addressed. This paper presents a multi-objective spatial optimization model for integrating these considerations. This model is applied for siting park-and-ride facilities in Columbus, Ohio. Application results show the usefulness of the developed model in supporting transit planning in an urban region.     

Giant Anomalous Hall and Nernst Conductivities in Magnetic All-d Metal Heusler Alloys

All-d Heuslers are a category of novel compounds combining versatile functionalities such as caloric responses and spintronics with enhanced mechanical properties. Despite the promising transport properties (anomalous Hall (AHC) and anomalous Nernst (ANC) conductivities) shown in the conventional Co₂XY Heuslers with p-d hybridization, the all-d Heuslers with only d-d hybridization open a new horizon to search for new candidates with outstanding transport properties. In this work, the AHC and ANC are evaluated for thermodynamically stable ferro/ferri-magnetic all-d-metal regular Heusler compounds based on high-throughput first-principles calculations. It is observed that quite a few materials exhibit giant AHCs and ANCs, such as cubic Re₂TaMn with an AHC of 2011 S cm^-1, and tetragonal Pt₂CrRh with an AHC of 1966 S cm^-1 and an ANC of 7.50 A m^-1K^-1. Comprehensive analysis on the electronic structure reveals that the high AHC can be attributed to the occurrence of the Weyl nodes or gapped nodal lines in the neighborhood of the Fermi level. The correlations between such transport properties and the number of valence electrons are also thoroughly investigated, which provides a practical guidance to tailor AHC and ANC via chemical doping for transverse thermoelectric applications.     

Design and development of bioinspired calcium phosphate nanoparticles of MTX: pharmacodynamic and pharmacokinetic evaluation

The aim of this investigation is the management of rheumatoid arthritis (RA) by developing methotrexate-loaded calcium phosphate nanoparticles (MTX-CAP-NP) and to evaluate pharmacokinetic and pharmacodynamic behavior in adjuvant induced arthritis model. The nanoparticles were synthesized by wet precipitation method and optimized by Box-Behnken experimental design. MTX-CAP-NPs were characterized by TEM, FTIR, DSC and XRD studies. The particle size, zeta potential and entrapment efficiency of the optimized nanoparticles were found to be 204.90?±?64?nm, ?11.58?±?4.80?mV, and 88.33?±?3.74%, respectively. TEM, FTIR, DSC and XRD studies revealed that the developed nanoparticles were nearly spherical in shape and the crystalline structure of CAP-NP was not changed after MTX loading. The pharmacokinetic studies revealed that MTX-CAP-NP enhanced bioavailability of MTX by 2.6-fold when compared to marketed formulation (FOLITRAX-10). Under pharmacodynamic evaluation, arthritic assessment, radiography and histopathology studies revealed that CAP has ability to regenerate cartilage and bone therefore, together with MTX, MTX-CAP-NPs have shown significant reduction in disease progression. The overall work demonstrated that the developed nanodelivery system was well tolerated and more effective than the marketed formulation.     

Design of dual‐band microstrip patch antenna with right‐angle triangular aperture slot for energy transfer application

This work focusing on the dual‐band antenna design with rectifying circuit for energy transfer system technology for enhancement gain performance. The air gap technique is applied on this microstrip antenna design work to enhance the antenna gain. The work begins with designing and analyzing the antenna via the CST Microwave Studio software. After validation on acceptable performance in simulation side is obtained, the return loss, S₁₁ of the antenna is measured using vector network analyzer equipment. The rectifier circuit is used to convert the captured signal to DC voltage. This projected dual‐band antenna has successfully accomplished the target on return loss of ?44.707 dB and ?32.163 dB at dual resonant frequencies for 1.8 GHz and 2.4 GHz, respectively. This proposed antenna design benefits in low cost fabrication and has achieved high gain of 6.31 dBi and 7.82 dBi for dual‐band functioning frequencies.     

The effect of computer-assisted cooperative learning methods and group size on the EFL learners’ achievement in communication skills

This study explored the effect of cooperative learning small group size and two different instructional modes (positive interdependence vs. individual accountability) on English as a Foreign Language (EFL) undergraduate learners’ communication skills (speaking and writing) achievement in computer-based environments. The study also examined the effects of disclosing/blinding the participants’ identities while interacting around computers on their post-test. The findings of the study revealed that the computer-based environment enabled the participants to blind their identities and reduce their anxiety from face-to-face debate, and so was very helpful in developing their communication skills. The use of the individual accountability mode was quite useful compared with the positive interdependence mode as it enabled all group members to perform their roles significantly. The 5-student group also significantly outperformed other groups of 2-7 members on the post-test communication skills. In conclusion, the findings of this study bring us a step closer to understanding the technique of cooperative language learning and group size.     

Disaster-survivable cloud-network mapping

Cloud-computing services are provided to consumers through a network of servers and network equipment. Cloud-network (CN) providers virtualize resources [e.g., virtual machine (VM) and virtual network (VN)] for efficient and secure resource allocation. Disasters are one of the worst threats for CNs as they can cause massive disruptions and CN disconnection. A disaster may also induce post-disaster correlated, cascading failures which can disconnect more CNs. Survivable virtual-network embedding (SVNE) approaches have been studied to protect VNs against single physical-link/-node and dual physical-link failures in communication infrastructure, but massive disruptions due to a disaster and their consequences can make SVNE approaches insufficient to guarantee cloud-computing survivability. In this work, we study the problem of survivable CN mapping from disaster. We consider risk assessment, VM backup location, and post-disaster survivability to reduce the risk of failure and probability of CN disconnection and the penalty paid by operators due to loss of capacity. We formulate the proposed approach as an integer linear program and study two scenarios: a natural disaster, e.g., earthquake and a human-made disaster, e.g., weapons-of-mass-destruction attack. Our illustrative examples show that our approach reduces the risk of CN disconnection and penalty up to 90 % compared with a baseline CN mapping approach and increases the CN survivability up to 100 % in both scenarios.     

A framework for mining interesting high utility patterns with a strong frequency affinity

High utility pattern (HUP) mining is one of the most important research issues in data mining. Although HUP mining extracts important knowledge from databases, it requires long calculations and multiple database scans. Therefore, HUP mining is often unsuitable for real-time data processing schemes such as data streams. Furthermore, many HUPs may be unimportant due to the poor correlations among the items inside of them. Hence,the fast discovery of fewer but more important HUPs would be very useful in many practical domains. In this paper, we propose a novel framework to introduce a very useful measure, called frequency affinity, among the items in a HUP and the concept of interesting HUP with a strong frequency affinity for the fast discovery of more applicable knowledge. Moreover, we propose a new tree structure, utility tree based on frequency affinity (UTFA), and a novel algorithm, high utility interesting pattern mining (HUIPM), for single-pass mining of HUIPs from a database. Our approach mines fewer but more valuable HUPs, significantly reduces the overall runtime of existing HUP mining algorithms and is applicable to real-time data processing. Extensive performance analyses show that the proposed HUIPM algorithm is very efficient and scalable for interesting HUP mining with a strong frequency affinity.