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The purpose of this paper is to investigate how well hotel website load time performance compared against customer expectation benchmarks. In a competitive market, service interactions are important. As customers move to mobile devices, the time to load a website is a critical part of the service delivery. Long load times can lead to poor service experiences, customer frustration and lost business. Hotel website load times on both mobile and desktop devices were examined and compared to service expectations.

The study used an online service to assess and compare website load performance using both desktop and mobile devices for 259 international hotel company and sub-brand websites.

The time to load hotel websites was significantly slower on mobile devices compared to desktops. Load times on both platforms exceeded 3 s, which is considered best practice. Long load times represent a service gap and can cause dissatisfaction resulting in a potential customer abandoning the website for a competitor’s site, thus affecting sales.

While the population for the study was robust in size and contained most of the major hotel companies worldwide, it was not exhaustive. Data also represent a snapshot and will change over time. Load times vary based on test location, access device and network traffic. Additionally, web page load times and customer expectations will change as technology evolves.

Increased use of mobile devices for hotel reservations increases the importance of mobile service delivery. This is the first known study to measure hotel website load times for mobile devices, and to examine both mobile and desktop performance against best practice. The results of this study highlight a service gap, which can lead to loss of business. Given the consistency of the results, the authors suspect that this is an issue that has not been recognized within the industry. This study is valuable because it exposes an issue of website design not generally addressed in the hospitality industry, even though tools are available to monitor site performance.

Organizations regularly use budgets as benchmarks for performance, and budgets represent a key control feature for almost every organization (Brown and Solomon (1993)). Research has demonstrated that outcome effects are pervasive in performance evaluation processes, and that performance evaluators do not interpret situational information consistently. An experiment is conducted to examine the effects of situational information on managers’ performance and ability attributions under conditions of favorable and unfavorable financial outcomes. The findings indicate that when financial outcomes are unfavorable, outcome effects dominate the performance evaluation process, and situational information has little effect on performance evaluations. The results of cognitive load manipulations indicate that situational information is not ignored, but rather discounted when financial outcomes are favorable.

The aim of this paper is to present a novel multifactor structural optimisation method incorporating reliability performance.

This research addresses structural optimisation problems in which the design is required to satisfy multiple performance criteria, such as strength, stiffness, mass and reliability under multiple loading cases simultaneously. A MOST technique is extended to accommodate the reliability‐related optimisation. Structural responses and geometrical sensitivities are analysed by a FE method, and reliability performance is calculated by a reliability loading‐case index (RLI). The evaluation indices of performances and loading cases are formulated, and an overall performance index is presented to quantitatively evaluate a design.

The proposed method is applicable to multi‐objective, multi‐loading‐case, multi‐disciplinary and reliability‐related optimisation problems. The applications to a star‐like truss structure and a raised‐access floor panel structure confirmed that the method is highly effective and efficient in terms of structural optimisation.

A systematic method is proposed. The optimisation method combines the MOST technique with a RLI (a new alternative route to calculate the reliability index at multiple loading cases) using a parametric FE model.

Load balancing is an important issue for a heterogeneous distributed computing system environment that has been proven to be a nondeterministic polynomial time hard problem. This paper aims to propose a resource-aware load balancing (REAL) model for a batch of independent tasks with a centralized load balancer to make the solution appropriate for a practical heterogeneous distributed environment having a migration cost with the objective of maximizing the level of load balancing considering bandwidth requirements for migration of the tasks.

To achieve the effective schedule, load balancing issues should be addressed and tackled through efficient workload distribution. In this approach, the migration has been carried out in two phases, namely, initial migration and best-fit migration. Using the best-fit policy in migrations helps in the possible performance improvement by minimizing the remaining idle slots on underloaded nodes that remain unentertained during the initial migration.

The experimental results reveal that the proposed model exhibits a superior performance among the other strategies on considered parameters such as makespan, average utilization and level of load balancing under study for a heterogeneous distributed environment.

Design of the REAL model and a comparative performance evaluation with LBSM and ITSLB have been conducted by using MATLAB 8.5.0.

The purpose of this paper is to introduce a new design optimization technique for a surface mounted permanent magnet (SMPM) machine to increase sensorless performance at high loadings by compromising with torque capability.

An SMPM parametric machine model was created and analysed by finite element analysis (FEA) software by means of the Matlab environment. Eight geometric parameters of the machine were optimized using genetic algorithms (GAs). The outer volume of the machine, namely copper loss per volume, was kept constant. In order to prevent sensorless performance loss at high loading, an optimization process was realized using two loading stages: maximum torque with minimum ripple at nominal load and maximum self-sensing capability at twice load. In order to show the effectiveness of the proposed technique, the obtained results were compared with the classical one-stage optimization realized for each loading condition separately.

With the proposed technique, fairly good performance results of the optimization were obtained when compared with the one-stage optimizations. Using the proposed technique, sensorless performance of the motor was highly increased by compromising torque capability for high loading. Additionally, this paper shows that the self-sensing properties of a SMPM machine should be considered at the design stage of the machine.

In related literature, design optimization studies for the sensorless capability of SMPM motor are very few. By increasing optimization performance, new proposed technique provides to achieve good result at high load for sensorless performance compromising torque capability.

To develop structured guidelines for the synthesis of dynamic force simulators that are required for the testing of high speed aerospace actuators. To provide realistic and proven solutions at both test bench hardware and control design levels.

The state of the art in control design applied to load simulators in mainly based on complex controllers and does not take into account practical considerations. The objective of the present work is to provide generic preliminary design rules to ensure that the test bench architectures (frame, power transmission and control) and the components specifications are consistent with the targeted performance. Once selected the appropriate power transmission architecture, a linear approach is used as a foundation to generate design rules. Then, preliminary design is achieved thanks to the introduction, as early as possible, of the unavoidable technological defects.

A step‐by‐step methodology allows the designer to select the controller architecture and to specify components with special care to their consistency with the required dynamic performance. The linear then practical approach generates key rules that can be used in the very early phase of the test bench design.

Practical considerations on the components static and dynamic limitations are introduced progressively to make the natural test bench performance as consistent as possible with the performance requirements. Consequently, the controller becomes simpler to design and robust.

Increasing load factor is crucial for transport efficiency and may benefit shippers because of its potential to reduce both environmental impact and transportation costs. The purpose of this paper is to explore how shippers can increase load factor in their road transport by identifying opportunities for logistics action and influences on load factor performance measures created by such opportunities.

A case study is performed of the outgoing goods flow from the central warehouse of a large retailer in Sweden. Data are collected from interviews with the shipper and its contracted freight forwarder, as well as from archival sources and visual observations, and applied to produce a framework.

Logistics actions that can increase load factor are identified and categorised according to packaging efficiency, loading efficiency and booking efficiency, all of which are linked to logistics variables and specific performance measures in the framework. Visual observations of volumetric load factor in vehicles indicate room for improvement via, for example, making lead times more flexible.

The framework’s principles can be used to support shippers in finding opportunities to increase load factor.

The framework clarifies the concept of load factor as a whole by explaining each logistics action’s contribution to increasing load factor, as well as the actions’ combined effect in the context of a shipper and its purchased transport share.

Light-Gauge Steel Frame (LSF) structures are popular in building construction due to their lightweight, easy erecting and constructability characteristics. However, due to steel lipped channel sections negative fire performance, cavity insulation materials are utilized in the LSF configuration to enhance its fire performance. The applicability of lightweight concrete filling as cavity insulation in LSF and its effect on the fire performance of LSF are investigated under realistic design fire exposure, and results are compared with standard fire exposure.

A Finite Element model (FEM) was developed to simulate the fire performance of Light Gauge Steel Frame (LSF) walls exposed to realistic design fires. The model was developed utilising Abaqus subroutine to incorporate temperature-dependent properties of the material based on the heating and cooling phases of the realistic design fire temperature. The developed model was validated with the available experimental results and incorporated into a parametric study to evaluate the fire performance of conventional LSF walls compared to LSF walls with lightweight concrete filling under standard and realistic fire exposures.

Novel FEM was developed incorporating temperature and phase (heating and cooling) dependent material properties in simulating the fire performance of structures exposed to realistic design fires. The validated FEM was utilised in the parametric study, and results exhibited that the LSF walls with lightweight concrete have shown better fire performance under insulation and load-bearing criteria in Eurocode parametric fire exposure. Foamed Concrete (FC) of 1,000 kg/m3 density showed best fire performance among lightweight concrete filling, followed by FC of 650 kg/m3 and Autoclaved Aerated Concrete (AAC) 600 kg/m3.

The developed FEM is capable of investigating the insulation and load-bearing fire ratings of LSF walls. However, with the availability of the elevated temperature mechanical properties of the LSF wall, materials developed model could be further extended to simulate the complete fire behaviour.

LSF structures are popular in building construction due to their lightweight, easy erecting and constructability characteristics. However, due to steel-lipped channel sections negative fire performance, cavity insulation materials are utilised in the LSF configuration to enhance its fire performance. The lightweight concrete filling in LSF is a novel idea that could be practically implemented in the construction, which would enhance both fire performance and the mechanical performance of LSF walls.

Limited studies have investigated the fire performance of structural elements exposed to realistic design fires. Numerical models developed in those studies have considered a similar approach as models developed to simulate standard fire exposure. However, due to the heating phase and the cooling phase of the realistic design fires, the numerical model should incorporate both temperature and phase (heating and cooling phase) dependent properties, which was incorporated in this study and validated with the experimental results. Further lightweight concrete filling in LSF is a novel technique in which fire performance was investigated in this study.

The advancements in the cloud computing has gained the attention of several researchers to provide on-demand network access to users with shared resources. Cloud computing is important a research direction that can provide platforms and softwares to clients using internet. However, handling huge number of tasks in cloud infrastructure is a complicated task. Thus, it needs a load balancing (LB) method for allocating tasks to virtual machines (VMs) without influencing system performance. This paper aims to develop a technique for LB in cloud using optimization algorithms.

This paper proposes a hybrid optimization technique, named elephant herding-based grey wolf optimizer (EHGWO), in the cloud computing model for LB by determining the optimal VMs for executing the reallocated tasks. The proposed EHGWO is derived by incorporating elephant herding optimization (EHO) in grey wolf optimizer (GWO) such that the tasks are allocated to the VM by eliminating the tasks from overloaded VM by maintaining the system performance. Here, the load of physical machine (PM), capacity and load of VM is computed for deciding whether the LB has to be done or not. Moreover, two pick factors, namely, task pick factor (TPF) and VM pick factor (VPF), are considered for choosing the tasks for reallocating them from overloaded VM to underloaded VM. The proposed EHGWO decides the task to be allocated in the VM based on the newly derived fitness functions.

The minimum load and makespan obtained in the existing methods, constraint measure based LB (CMLB), fractional dragonfly based LB algorithm (FDLA), EHO, GWO and proposed EHGWO for the maximum number of VMs is illustrated. The proposed EHGWO attained minimum makespan with value 814,264 ns and minimum load with value 0.0221, respectively. Meanwhile, the makespan values attained by existing CMLB, FDLA, EHO, GWO, are 318,6896 ns, 230,9140 ns, 1,804,851 ns and 1,073,863 ns, respectively. The minimum load values computed by existing methods, CMLB, FDLA, EHO, GWO, are 0.0587, 0.026, 0.0248 and 0.0234. On the other hand, the proposed EHGWO with minimum load value is 0.0221. Hence, the proposed EHGWO attains maximum performance as compared to the existing technique.

This paper illustrates the proposed LB algorithm using EHGWO in a cloud computing model using two pitch factors, named TPF and VPF. For initiating LB, the tasks assigned to the overloaded VM are reallocated to under loaded VMs. Here, the proposed LB algorithm adapts capacity and loads for the reallocation. Based on TPF and VPF, the tasks are reallocated from VMs using the proposed EHGWO. The proposed EHGWO is developed by integrating EHO and GWO algorithm using a new fitness function formulated by load of VM, migration cost, load of VM, capacity of VM and makespan. The proposed EHGWO is analyzed based on load and makespan.

This paper aims to demonstrate the efficacy of fuzzy logic controller (FLC) over proportional integral (PI) and sliding mode controller (SMC) for maintaining flat voltage profile at the load bus of a single-generator-based micro-grid system using STATCOM.

A STATCOM is used to improve the voltage profile of the load bus. The performance of the STATCOM is evaluated by using three different controllers: PI controllers, FLCs and SMCs. The performance comparison of the controllers is done with different dc bus voltages, different load bus voltage references, various loads such as R-L loads and dynamic loads.

A comparative analysis is done between the performances of the three different controllers. The comparative study culminates that FLC is found to be superior than the other proposed controllers. SMC is a close competitor of fuzzy controller.

Design of fuzzy logic and SMCs for a STATCOM implemented in a single-generator-based micro-grid system is applied.