GIS for all: exploring the barriers and opportunities for underexploited GIS applications

Geographical Information Systems have been existed since the early 1960s, but evidence suggests that adoption of GIS technologies still remains relatively low in many sectors. We will explore both the barriers that affect the utilisation of GIS and opportunities to overcome these barriers. As part of this exploration we performed a literature review, collected responses from quantitative questionnaire survey and interviewed a range of technical and domain experts. Having analysed and collated the results of these studies we have identified ways forward for future research and development to facilitate wider spread adoption and exploitation of GIS applications. Our discussion focuses on the importance of open-source GIS software, open data and cloud computing as key mediators for breaking the barriers and promoting the wider appropriation of GIS based solutions

GIS for All: Exploring the Barriers and Opportunities for Underexploited GIS Applications

Geographical Information Systems have been existed since the early 1960s, but evidence suggests that adoption of GIS technologies still remains relatively low in many sectors. We will explore both the barriers that affect the utilisation of GIS and opportunities to overcome these barriers. As part of this exploration we performed a literature review, collected responses from quantitative questionnaire survey and interviewed a range of technical and domain experts. Having analysed and collated the results of these studies we have identified ways forward for future research and development to facilitate wider spread adoption and exploitation of GIS applications. Our discussion focuses on the importance of open-source GIS software, open data and cloud computing as key mediators for breaking the barriers and promoting the wider appropriation of GIS based solutions

Leisure Preference and Corporate Tax Planning

Using a novel cross-country measure of leisure preference to quantify managerial effort aversion, we examine its relation to corporate tax avoidance, and document a negative association between the two. The result is stronger for firms located in countries with a more complex tax system, and for firms with less access to tax consulting services — situations in which corporate tax planning can be especially onerous. Finally, tax planning appears to be one mechanism mediating the negative relation between leisure preference and firm value, implying that effort aversion is a source of agency costs that impedes value-enhancing tax planning activities

Microcantilever Q Control Via Capacitive Coupling

We introduce a versatile method to control the quality factor Q of a conducting cantilever in an atomic force microscope (AFM) via capacitive coupling to the local environment. Using this method, Q may be reversibly tuned to within ~ 10% of any desired value over several orders of magnitude. A point-mass oscillator model describes the measured effect. Our simple Q control module increases the AFM functionality by allowing greater control of parameters such as scan speed and force gradient sensitivity, which we demonstrate by topographic imaging of a VO$_{2}$ thin film in high vacuum.Physic

Use of Numerical Groundwater Modeling to Evaluate Uncertainty in Conceptual Models of Recharge and Hydrostratigraphy

Numerical groundwater models are based on conceptualizations of hydrogeologic systems that are by necessity developed from limited information and therefore are simplifications of real conditions. Each aspect (e.g. recharge, hydrostratigraphy, boundary conditions) of the groundwater model is often based on a single conceptual model that is considered to be the best representation given the available data. However, the very nature of their construction means that each conceptual model is inherently uncertain and the available information may be insufficient to refute plausible alternatives, thereby raising the possibility that the flow model is underestimating overall uncertainty. In this study we use the Death Valley Regional Flow System model developed by the U.S. Geological Survey as a framework to predict regional groundwater flow southward into Yucca Flat on the Nevada Test Site. An important aspect of our work is to evaluate the uncertainty associated with multiple conceptual models of groundwater recharge and subsurface hydrostratigraphy and quantify the impacts of this uncertainty on model predictions. In our study, conceptual model uncertainty arises from two sources: (1) alternative interpretations of the hydrostratigraphy in the northern portion of Yucca Flat where, owing to sparse data, the hydrogeologic system can be conceptualized in different ways, and (2) uncertainty in groundwater recharge in the region as evidenced by the existence of several independent approaches for estimating this aspect of the hydrologic system. The composite prediction of groundwater flow is derived from the regional model that formally incorporates the uncertainty in these alternative input models using the maximum likelihood Bayesian model averaging method. An assessment of the joint predictive uncertainty of the input conceptual models is also produced. During this process, predictions of the alternative models are weighted by model probability, which is the degree of belief that a model is more plausible given available prior information (expert opinion) and site measurements (hydraulic head and groundwater flux). The results indicate that flow simulations in Yucca Flat are more sensitive to hydrostratigraphic model than recharge model. Furthermore, posterior model uncertainty is dominated by inter-model variance as opposed to intra-model variance, indicating that conceptual model uncertainty has greater impact on the results than parametric uncertainty. Without consideration of conceptual model uncertainty, uncertainty in the flow predictions would be significantly underestimated. Incorporation of the uncertainty in multiple conceptual models renders the groundwater flow model predictions more scientifically defensible

Technical note: Towards more realistic 4DCT(MRI) numerical lung phantoms.

BACKGROUND Numerical 4D phantoms, together with associated ground truth motion, offer a flexible and comprehensive data set for realistic simulations in radiotherapy and radiology in target sites affected by respiratory motion. PURPOSE We present an openly available upgrade to previously reported methods for generating realistic 4DCT lung numerical phantoms, which now incorporate respiratory ribcage motion and improved lung density representation throughout the breathing cycle. METHODS Density information of reference CTs, toget her with motion from multiple breathing cycle 4DMRIs have been combined to generate synthetic 4DCTs (4DCT(MRI)s). Inter-subject correspondence between the CT and MRI anatomy was first established via deformable image registration (DIR) of binary masks of the lungs and ribcage. Ribcage and lung motions were extracted independently from the 4DMRIs using DIR and applied to the corresponding locations in the CT after post-processing to preserve sliding organ motion. In addition, based on the Jacobian determinant of the resulting deformation vector fields, lung densities were scaled on a voxel-wise basis to more accurately represent changes in local lung density. For validating this process, synthetic 4DCTs, referred to as 4DCT(CT)s, were compared to the originating 4DCTs using motion extracted from the latter, and the dosimetric impact of the new features of ribcage motion and density correction were analyzed using pencil beam scanned proton 4D dose calculations. RESULTS Lung density scaling led to a reduction of maximum mean lung Hounsfield units (HU) differences from 45 to 12 HU when comparing simulated 4DCT(CT)s to their originating 4DCTs. Comparing 4D dose distributions calculated on the enhanced 4DCT(CT)s to those on the original 4DCTs yielded 2%/2 mm gamma pass rates above 97% with an average improvement of 1.4% compared to previously reported phantoms. CONCLUSIONS A previously reported 4DCT(MRI) workflow has been successfully improved and the resulting numerical phantoms exhibit more accurate lung density representations and realistic ribcage motion

An overview: Reaping off the benefits of flexible working arrangements (FWAs) to both employer and employees

The Movement Control Order (MCO) was introduced as a Malaysian preventive measure against the COVID-19 virus outbreak. These drastic changes have compelled the majority of Malaysia's workforce to adopt flexible working arrangements (FWAs) which was not widely practised prior to pandemic crisis. Prior to the pandemic, FWAs is not widely practised in Malaysia. Therefore, this paper provides an overview summary from the existing FWAs related studies and reaping off the benefits of FWAs to both employer and employees. Through the realisation of the benefits of FWAs on both an individual and an organisational level, the significance of FWAs can be convincingly demonstrated, and employees and employers should not be hesitant to embrace FWAs while also adjusting to these prospects for future business success

Detailed Case Studies

Wireless body area networks (WBANs) are one of the key technologies that support the development of pervasive health monitoring (remote patient monitoring systems), which has attracted more attention in recent years. These WBAN applications requires stringent security requirements as they are concerned with human lives. In the recent scenario of the corona pandemic, where most of the healthcare providers are giving online services for treatment, DDoS attacks become the major threats over the internet. This chapter particularly focusses on detection of DDoS attack using machine learning algorithms over the healthcare environment. In the process of attack detection, the dataset is preprocessed. After preprocessing the dataset, the cleaned dataset is given to the popular classification algorithms in the area of machine learning namely, AdaBoost, J48, k-NN, JRip, Random Committee and Random Forest classifiers. Those algorithms are evaluated independently and the results are recorded. Results concluded that J48 outperform with accuracy of 99.98% with CICIDS dataset and random forest outperform with accuracy of 99.917, but it takes the longest model building time. Depending on the evaluation performance the appropriate classifier is selected for further DDoS detection at real-time