Urban Air Mobility: Viability of Hub-Door and Door-Door Movement by Air

Owing to a century of innovation in connected and automated aircraft design, for the rst time in history, air transport presents a potential competitive alternative to road, for hub-to-door and door-to-door urban services. In this article, we study the viability of air transport, for moving people and goods in an urban area, based on three metrics - enroute travel time, fuel cost and carbon dioxide (CO2) emissions. We estimate the metrics from emission standards and operational assumptions on vehicles based on current market data and compare electric air travel to gasoline road travel. For passenger movement, air is faster than road for all distances. It fares better on fuel cost and emissions only for longer distances (specic transition distances are stated in the text). For consolidated movement of goods, air is at par with road. Finally, for movement of unconsolidated goods, air again fares better than road on all three metrics. It is also noteworthy that these results are based on a road friendly urban design. Changes in design that facilitate easier access to air based hub-to-door and door-to-door services, would only make the case stronger for Urban Air Mobility (UAM), especially with connected and automated aircraft, as the next revolution in urban transportation

Metabolic scavenging by cancer cells: when the going gets tough, the tough keep eating

Cancer is fundamentally a disease of uncontrolled cell proliferation. Tumour metabolism has emerged as an exciting new discipline studying how cancer cells obtain the necessary energy and cellular ‘building blocks’ to sustain growth. Glucose and glutamine have long been regarded as the key nutrients fuelling tumour growth. However, the inhospitable tumour microenvironment of certain cancers, like pancreatic cancer, causes the supply of these nutrients to be chronically insufficient for the demands of proliferating cancer cells. Recent work has shown that cancer cells are able to overcome this nutrient insufficiency by scavenging alternative substrates, particularly proteins and lipids. Here, we review recent work identifying the endocytic process of macropinocytosis and subsequent lysosomal processing as an important substrate-acquisition route. In addition, we discuss the impact of hypoxia on fatty acid metabolism and the relevance of exogenous lipids for supporting tumour growth as well as the routes by which tumour cells can access these lipids. Together, these cancer-specific scavenging pathways provide a promising opportunity for therapeutic intervention

Efficacy of Wii Fit Plus Strength Training in Older Adults Dwelling in an Assisted Living Facility

TACSM Abstract Efficacy of Wii Fit Plus Strength Training in Older Adults Dwelling in an Assisted Living Facility Chandrasekhar Bulusu MHS (PT), Sandor Dorgo Ph.D., C.S.C.S Ross Moore Fitness Center; Department of Interdisciplinary Health Sciences; University of Texas at El Paso; El Paso, TX Category: Doctoral ABSTRACT Wii fit plus strength training alone to assess physical fitness in older adults is not evident. The purpose of the study was to assess whether regular strength training utilizing the Wii Fit Plus device improves physical fitness in assisted living facility dwelling older adults compared to a non-exercising control group over a twelve-week period.17 volunteering older adults (mean ± SD age: 79.62±8.10; BMI: 32.3±7.65) were recruited from an assisted living facility and randomly assigned to the Wii exercise group (Wii) or the non-exercising control group (Control). Subjects in Wii performed three exercises with three sets of ten repetitions two times per week. Each exercise session was about 30 minutes. Control subjects were asked to continue their normal lifestyle. Pre-, Mid-, and Post-test sessions were conducted to assess potential changes in the subjects’ physical fitness. Assessments included dynamic handgrip strength, 30-second chair stand, 30-second arm curl, timed up and go (TUG), and gallon jug transfer tests. Data were analyzed using the general linear mixed model with alpha level set at p0.07). For the TUG test a significant group-by-time interaction was observed (p=0.005), as the Wii group showed a significant improvement from pre- to post-test (p=0.01) while the control group did not (p=0.08). Our results indicate that the Wii strength training did not improve physical fitness in older adults when compared to the non-exercising older adults, although fitness improvement trends point in that direction. The utilization of Wii Fit Plus exercises for 30-minutes twice weekly over a 12-week period were inadequate to elicit significant changes in fitness measures. Further research is necessary to evaluate if higher intensity or frequency of Wii exercises may be effective for assisted living facility dwelling older adults

An experimental study on the buoyancy-driven motion of air bubbles in square channels

The motion of drops and bubbles in confined domains is encountered in several applications such as oil recovery, solvent extraction, paper-making, and microfluidics, among others. In this thesis, the motion of air bubbles in square capillaries moving under the influence of gravity is studied at finite Reynolds numbers. The steady shapes, deformations, film thickness, and velocities of the bubbles as a function of the bubble size are determined experimentally. The bulk fluid phase is either Newtonian, viscoelastic, or a surfactant solution. Bubbles rising in a Newtonian fluid are nearly spherical at lower bubble volumes and become prolate losing their fore and aft symmetry at larger bubble volumes. At lower bulk viscosities, a reentrant cavity develops at the rear of bubble. The critical viscosity at which this shape transition occurs depends on the size of the capillary. The terminal velocity of bubbles increases with volume for small bubble volumes. Even at small bubble volumes, the terminal velocity of the bubbles is much less than the Hadamard-Rybczinski velocity of a spherical bubble with the same volume. (Abstract shortened by UMI.)

ON ROBUST MACHINE LEARNING IN THE PRESENCE OF ADVERSARIES

In today\u27s highly connected world, the number of smart devices worldwide has increased exponentially. These devices generate huge amounts of real-time data, perform complicated computational tasks, and provide actionable information. Over the past decade, numerous machine learning approaches have been widely adopted to infer hidden information from this massive and complex data. Accuracy is not enough when developing machine learning systems for some crucial application domains. The safety and reliability guarantees on the underlying learning models are critical requirements as well. This in turn necessitates that the learned models be robust towards processing corrupted data. Data can be corrupted by adversarial attacks where the attack may consist of data taking arbitrary values adversely affecting the efficiency of the algorithm. An adversary can replace samples with erroneous or malicious samples such as false labels or arbitrary inputs. In this dissertation, we refer to this type of attack as attack on data. Moreover, with the rapid increase in the volume of the data, storing and processing all this data at a central location becomes computationally expensive. Therefore, utilizing a distributed system is warranted to distribute tasks across multiple machines (known as distributed learning). Improvement of the efficiency of the optimization algorithms with respect to computational and communication costs along with maintaining a high level of accuracy is critical in distributed learning. However, an attack can occur by replacing the transmitted data of the machines in the system with arbitrary values that may negatively impact the performance of the learning task. We refer to this attack as attack on devices. The aforementioned attack scenarios can significantly impact the accuracy of the results, thereby, negatively impacting the expected model outcome. Hence, the development of a new generation of systems that are robust to such adversarial attacks and provide provable performance guarantees is warranted. The goal of this dissertation is to develop learning algorithms that are robust to such adversarial attacks. In this dissertation, we propose learning algorithms that are robust to adversarial attacks under two frameworks: 1) supervised learning, where the true label of the samples are known; and 2) unsupervised learning, where the labels are not known. Although neural networks have gained widespread success, theoretical understanding of their performance is lacking. Therefore, in the first part of the dissertation (Chapter 2), we try to understand the inner workings of a neural network. We achieve this by learning the parameters of the network. In fact, we generalize the estimation procedure by considering the robustness aspect along with the parameter estimation in the presence of adversarial attacks (attack on data). We devise a learning algorithm to estimate the parameters (weight matrix and bias vector) of a single-layer neural network with rectified linear unit activation in the unsupervised learning framework where each output sample can potentially be an arbitrary outlier with a fixed probability. Our estimation algorithm uses gradient descent algorithms along with the median-based filter to mitigate the effect of the outliers. We further determine the number of samples required to estimate the parameters of the network in the presence of the outliers. Combining the use of distributed systems to solve large-scale problems with the recent success of deep learning, there has been a surge of development in the field of distributed learning. In fact, the research in this direction has been further catalyzed by the development of federated learning. Despite extensive research in this area, distributed learning faces the challenge of training a high-dimensional model in a distributed manner while maintaining robustness against adversarial attacks. Hence, in the second part of the dissertation (Chapters 3 and 4), we study the problem of distributed learning in the presence of adversarial nodes (attack on nodes). Specifically, we consider the worker-server architecture to minimize a global loss function under both the learning frameworks in the presence of adversarial nodes (Byzantines). Each honest node performs some computation based only on its own local data, then communicates with the central server that performs aggregation. However, an adversarial node may send arbitrary information to the central server. In Chapter 3, we consider robust distributed learning under the supervised learning framework. We propose a novel algorithm that combines the idea of variance-reduction with a filtering technique based on vector median to mitigate the effect of the Byzantines. We prove the convergence of the approach to a first-order stationary point. Further, in Chapter 4, we consider robust distributed learning under the unsupervised learning framework (robust clustering). We propose a novel algorithm that combines the idea of redundant data assignment with the paradigm of distributed clustering. We show that our proposed approaches obtain constant factor approximate solutions in the presence of adversarial nodes

Architecture and Implementation of a Trust Model for Pervasive Applications

Collaborative effort to share resources is a significant feature of pervasive computing environments. To achieve secure service discovery and sharing, and to distinguish between malevolent and benevolent entities, trust models must be defined. It is critical to estimate a device\u27s initial trust value because of the transient nature of pervasive smart space; however, most of the prior research work on trust models for pervasive applications used the notion of constant initial trust assignment. In this paper, we design and implement a trust model called DIRT. We categorize services in different security levels and depending on the service requester\u27s context information, we calculate the initial trust value. Our trust value is assigned for each device and for each service. Our overall trust estimation for a service depends on the recommendations of the neighbouring devices, inference from other service-trust values for that device, and direct trust experience. We provide an extensive survey of related work, and we demonstrate the distinguishing features of our proposed model with respect to the existing models. We implement a healthcare-monitoring application and a location-based service prototype over DIRT. We also provide a performance analysis of the model with respect to some of its important characteristics tested in various scenarios

Security in heterogeneous wireless networks

The proliferation of a range of wireless devices, from the cheap low power resource starved sensor nodes to the ubiquitous cell phones and PDA\u27s has resulted in their use in many applications. Due to their inherent broadcast nature Security and Privacy in wireless networks is harder than the wired networks. Along with the traditional security requirements like confidentiality, integrity and non-repudiation new requirements like privacy and anonymity are important in wireless networks. These factors combined with the fact that nodes in a wireless network may have different resource availabilities and trust levels makes security in wireless networks extremely challenging. The functional lifetime of sensor networks in general is longer than the operational lifetime of a single node, due to limited battery power. Therefore to keep the network working multiple deployments of sensor nodes are needed. In this thesis, we analyze the vulnerability of the existing key predistribution schemes arising out of the repeated use of fixed key information through multiple deployments. We also develop SCON, an approach for key management that provides a significant improvement in security using multiple key pools. SCON performs better in a heterogeneous environment. We present a key distribution scheme that allows mobile sensor nodes to connect with stationary nodes of several networks. We develop a key distribution scheme for a semi ad-hoc network of cell phones. This scheme ensures that cell phones are able to communicate securely with each other when the phones are unable to connect to the base station. It is different from the traditional ad hoc networks because the phones were part of a centralized network before the base station ceased to work. This allows efficient distribution of key material making the existing schemes for ad hoc networks ineffective. In this thesis we present a mechanism for implementing authenticated broadcasts which ensure non-repudiation using identity based cryptography. We also develop a reputation based mechanism for the distributed detection and revocation of malicious cell phones. Schemes which use the cell phone for secure spatial authentication have also been presented

Detection of Lightweight Directory Access Protocol Query Injection Attacks in Web Applications

The Lightweight Directory Access Protocol (LDAP) is a common protocol used in organizations for Directory Service. LDAP is popular because of its features such as representation of data objects in hierarchical form, being open source and relying on TCP/IP, which is necessary for Internet access. However, with LDAP being used in a large number of web applications, different types of LDAP injection attacks are becoming common. The idea behind LDAP injection attacks is to take advantage of an application not validating inputs before being used as part of LDAP queries. An attacker can provide inputs that may result in alteration of intended LDAP query structure. LDAP injection attacks can lead to various types of security breaches including (i) Login Bypass, (ii) Information Disclosure, (iii) Privilege Escalation, and (iv) Information Alteration. Despite many research efforts focused on traditional SQL Injection attacks, most of the proposed techniques cannot be suitably applied for mitigating LDAP injection attacks due to syntactic and semantic differences between LDAP and SQL queries. Many implemented web applications remain vulnerable to LDAP injection attacks. In particular, there has been little attention for testing web applications to detect the presence of LDAP query injection attacks. The aim of this thesis is two folds: First, study various types of LDAP injection attacks and vulnerabilities reported in the literature. The planned research is to critically examine and evaluate existing injection mitigation techniques using a set of open source applications reported to be vulnerable to LDAP query injection attacks. Second, propose an approach to detect LDAP injection attacks by generating test cases when developing secure web applications. In particular, the thesis focuses on specifying signatures for detecting LDAP injection attack types using Object Constraint Language (OCL) and evaluates the proposed approach using PHP web applications. We also measure the effectiveness of generated test cases using a metric named Mutation Score

The Patient-Centric Blockchain

A revolution is brewing in the healthcare marketplace. In the early nineties, the World Wide Web initiated a new era for the use of the Internet in the consumption of healthcare services. This eventually led to the Big Data movement (Erevelles et al. 2016), which initiated a non-linear transformation in healthcare analytics and developed into a dominant paradigm in the healthcare marketplace. However, the World Wide Web architecture was never designed to support a marketplace in healthcare or, for that matter, a marketplace of any other kind. It was primarily designed for the sharing of information and was even referred to as the “information superhighway” in its early days. Despite this, over the years, the World Wide Web has evolved into a foundation (Erevelles et al. 2003) for the healthcare marketplace, widely utilized by the healthcare community. This has resulted in major breakdowns in patient trust, security, and privacy, among other problems, which additionally, have contributed to already sharply rising healthcare costs. For the first time, with the emergence of the blockchain, the healthcare community may finally have a platform specifically designed for the sharing of value (Erevelles et al. 2022). Healthcare is critical for almost everyone and faces potentially catastrophic crises. Blockchain’s value proposition is strong and distinct: greater trust, security, privacy, authenticity, and disintermediation in the healthcare marketplace. Yet, despite its potential impact, relatively little academic thought has been given to consumer-focused solutions in the healthcare marketplace. To fill this gap, the authors propose a game-theoretic framework for a patient-centric blockchain, and present an initial theoretical framework, with key foundational premises and propositions, that may help in the evolution of a blockchain-centric healthcare marketplace. This research makes multiple unique research contributions to the literature involving blockchain and healthcare consumption. First, we propose a framework for a patient-centric healthcare blockchain and present a theoretical foundation for healthcare consumption using blockchain technology. Second, we develop a set of propositions based on blockchain-centric logic that could provide theoretical guidelines that could help researchers identify potential research problems and develop solutions for these problems in the future. Third, we propose a hybrid blockchain-based healthcare framework as an initial practical step for the implementation of healthcare blockchains in the shorter term. This research is likely the first to develop a theoretical framework for blockchain-centric logic in a healthcare setting, as well as to identify related technological, behavioral, and managerial issues in the processes involved. Without a doubt, considerable further research is needed to better explore various important theoretical and behavioral questions that may arise. It would be reasonable to conclude, however, that this research provides a crucial first step for the further development of a critical technology that is expected to radically transform healthcare marketplaces and patient behavior in the future