The effect of mobile technology as an active student response system on the acquisition of U.S. history content of secondary students with specific learning disabilities

Students with specific learning disabilities (SLD) typically learn less history content than their peers without disabilities and show fewer learning gains. Even when they are provided with the same instructional strategies, many students with SLD struggle to grasp complex historical concepts and content area vocabulary. Many strategies involving technology have been used in the past to enhance learning for students with SLD in history classrooms. However, very few studies have explored the effectiveness of emerging mobile technology in K-12 history classrooms. ^ This study investigated the effects of mobile devices (iPads) as an active student response (ASR) system on the acquisition of U.S. history content of middle school students with SLD. An alternating treatments single subject design was used to compare the effects of two interventions. There were two conditions and a series of pretest probesin this study. The conditions were: (a) direct instruction and studying from handwritten notes using the interactive notebook strategy and (b) direct instruction and studying using the Quizlet App on the iPad. There were three dependent variables in this study: (a) percent correct on tests, (b) rate of correct responses per minute, and (c) rate of errors per minute. ^ A comparative analysis suggested that both interventions (studying from interactive notes and studying using Quizlet on the iPad) had varying degrees of effectiveness in increasing the learning gains of students with SLD. In most cases, both interventions were equally effective. During both interventions, all of the participants increased their percentage correct and increased their rate of correct responses. Most of the participants decreased their rate of errors. ^ The results of this study suggest that teachers of students with SLD should consider a post lesson review in the form of mobile devices as an ASR system or studying from handwritten notes paired with existing evidence-based practices to facilitate students’ knowledge in U.S. history. Future research should focus on the use of other interactive applications on various mobile operating platforms, on other social studies subjects, and should explore various testing formats such as oral question-answer and multiple choice. ^

iGrooving: A Generative Music Mobile Application for Runners

iGrooving is a generative music mobile application specifically designed for runners. The application’s foundation is a step-counter that is programmed using the iPhone’s built-in accelerometer. The runner’s steps generate the tempo of the performance by mapping each step to trigger a kick-drum sound file. Additionally, different sound files are triggered at specific step counts to generate the musical performance, allowing the runner a level of compositional autonomy. The sonic elements are chosen to promote a meditative aspect of running. iGrooving is conceived as a biofeedback-stimulated musical instrument and an environment for creating generative music processes with everyday technologies, inspiring us to rethink our everyday notions of musical performance as a shared experience. Isolation, dynamic changes, and music generation are detailed to show how iGrooving facilitates novel methods for music composition, performance and audience participation.

Electrochemical Immunosensing of Cortisol in an Automated Microfluidic System Towards Point-of-Care Applications

This dissertation describes the development of a label-free, electrochemical immunosensing platform integrated into a low-cost microfluidic system for the sensitive, selective and accurate detection of cortisol, a steroid hormone co-related with many physiological disorders. Abnormal levels of cortisol is indicative of conditions such as Cushing’s syndrome, Addison’s disease, adrenal insufficiencies and more recently post-traumatic stress disorder (PTSD). Electrochemical detection of immuno-complex formation is utilized for the sensitive detection of Cortisol using Anti-Cortisol antibodies immobilized on sensing electrodes. Electrochemical detection techniques such as cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) have been utilized for the characterization and sensing of the label-free detection of Cortisol. The utilization of nanomaterial’s as the immobilizing matrix for Anti-cortisol antibodies that leads to improved sensor response has been explored. A hybrid nano-composite of Polyanaline-Ag/AgO film has been fabricated onto Au substrate using electrophoretic deposition for the preparation of electrochemical immunosening of cortisol. Using a conventional 3-electrode electrochemical cell, a linear sensing range of 1pM to 1µM at a sensitivity of 66µA/M and detection limit of 0.64pg/mL has been demonstrated for detection of cortisol. Alternately, a self-assembled monolayer (SAM) of dithiobis(succinimidylpropionte) (DTSP) has been fabricated for the modification of sensing electrode to immobilize with Anti-Cortisol antibodies. To increase the sensitivity at lower detection limit and to develop a point-of-care sensing platform, the DTSP-SAM has been fabricated on micromachined interdigitated microelectrodes (µIDE). Detection of cortisol is demonstrated at a sensitivity of 20.7µA/M and detection limit of 10pg/mL for a linear sensing range of 10pM to 200nM using the µIDE’s. A simple, low-cost microfluidic system is designed using low-temperature co-fired ceramics (LTCC) technology for the integration of the electrochemical cortisol immunosensor and automation of the immunoassay. For the first time, the non-specific adsorption of analyte on LTCC has been characterized for microfluidic applications. The design, fabrication technique and fluidic characterization of the immunoassay are presented. The DTSP-SAM based electrochemical immunosensor on µIDE is integrated into the LTCC microfluidic system and cortisol detection is achieved in the microfluidic system in a fully automated assay. The fully automated microfluidic immunosensor hold great promise for accurate, sensitive detection of cortisol in point-of-care applications.

Enhanced 3-Dimensional Carbon Nanotube Based Anodes for Li-ion Battery Applications

A prototype 3-dimensional (3D) anode, based on multiwall carbon nanotubes (MWCNTs), for Li-ion batteries (LIBs), with potential use in Electric Vehicles (EVs) was investigated. The unique 3D design of the anode allowed much higher areal mass density of MWCNTs as active materials, resulting in more amount of Li+ ion intake, compared to that of a conventional 2D counterpart. Furthermore, 3D amorphous Si/MWCNTs hybrid structure offered enhancement in electrochemical response (specific capacity 549 mAhg-1). Also, an anode stack was fabricated to further increase the areal or volumetric mass density of MWCNTs. An areal mass density of the anode stack 34.9 mg/cm2 was attained, which is 1,342% higher than the value for a single layer 2.6 mg/cm2. Furthermore, the binder-assisted and hot-pressed anode stack yielded the average reversible, stable gravimetric and volumetric specific capacities of 213 mAhg-1 and 265 mAh/cm3, respectively (at 0.5C). Moreover, a large-scale patterned novel flexible 3D MWCNTs-graphene-polyethylene terephthalate (PET) anode structure was prepared. It generated a reversible specific capacity of 153 mAhg-1 at 0.17C and cycling stability of 130 mAhg-1 up to 50 cycles at 1.7C.

Trajectory Privacy Preservation in Mobile Wireless Sensor Networks

In recent years, there has been an enormous growth of location-aware devices, such as GPS embedded cell phones, mobile sensors and radio-frequency identification tags. The age of combining sensing, processing and communication in one device, gives rise to a vast number of applications leading to endless possibilities and a realization of mobile Wireless Sensor Network (mWSN) applications. As computing, sensing and communication become more ubiquitous, trajectory privacy becomes a critical piece of information and an important factor for commercial success. While on the move, sensor nodes continuously transmit data streams of sensed values and spatiotemporal information, known as ``trajectory information". If adversaries can intercept this information, they can monitor the trajectory path and capture the location of the source node. This research stems from the recognition that the wide applicability of mWSNs will remain elusive unless a trajectory privacy preservation mechanism is developed. The outcome seeks to lay a firm foundation in the field of trajectory privacy preservation in mWSNs against external and internal trajectory privacy attacks. First, to prevent external attacks, we particularly investigated a context-based trajectory privacy-aware routing protocol to prevent the eavesdropping attack. Traditional shortest-path oriented routing algorithms give adversaries the possibility to locate the target node in a certain area. We designed the novel privacy-aware routing phase and utilized the trajectory dissimilarity between mobile nodes to mislead adversaries about the location where the message started its journey. Second, to detect internal attacks, we developed a software-based attestation solution to detect compromised nodes. We created the dynamic attestation node chain among neighboring nodes to examine the memory checksum of suspicious nodes. The computation time for memory traversal had been improved compared to the previous work. Finally, we revisited the trust issue in trajectory privacy preservation mechanism designs. We used Bayesian game theory to model and analyze cooperative, selfish and malicious nodes' behaviors in trajectory privacy preservation activities.

Acceptance of smartphone-based mobile shopping:mobile benefits, customer characteristics, perceived risk and the impact of application context

Despite their generally increasing use, the adoption of mobile shopping applications often differs across purchase contexts. In order to advance our understanding of smartphone-based mobile shopping acceptance, this study integrates and extends existing approaches from technology acceptance literature by examining two previously underexplored aspects. Firstly, the study examines the impact of different mobile and personal benefits (instant connectivity, contextual value and hedonic motivation), customer characteristics (habit) and risk facets (financial, performance, and security risk) as antecedents of mobile shopping acceptance. Secondly, it is assumed that several acceptance drivers differ in relevance subject to the perception of three mobile shopping characteristics (location sensitivity, time criticality, and extent of control), while other drivers are assumed to matter independent of the context. Based on a dataset of 410 smartphone shoppers, empirical results demonstrate that several acceptance predictors are associated with ease of use and usefulness, which in turn affect intentional and behavioral outcomes. Furthermore, the extent to which risks and benefits impact ease of use and usefulness is influenced by the three contextual characteristics. From a managerial perspective, results show which factors to consider in the development of mobile shopping applications and in which different application contexts they matter.

Collaborative filtering and deep learning based hybrid recommendation for cold start problem

Recommender systems (RS) are used by many social networking applications and online e-commercial services. Collaborative filtering (CF) is one of the most popular approaches used for RS. However traditional CF approach suffers from sparsity and cold start problems. In this paper, we propose a hybrid recommendation model to address the cold start problem, which explores the item content features learned from a deep learning neural network and applies them to the timeSVD++ CF model. Extensive experiments are run on a large Netflix rating dataset for movies. Experiment results show that the proposed hybrid recommendation model provides a good prediction for cold start items, and performs better than four existing recommendation models for rating of non-cold start items.

Integration of process planning and scheduling using mobile-agent based approach in a networked manufacturing environment

Effective and efficient implementation of intelligent and/or recently emerged networked manufacturing systems require an enterprise level integration. The networked manufacturing offers several advantages in the current competitive atmosphere by way to reduce, by shortening manufacturing cycle time and maintaining the production flexibility thereby achieving several feasible process plans. The first step in this direction is to integrate manufacturing functions such as process planning and scheduling for multi-jobs in a network based manufacturing system. It is difficult to determine a proper plan that meets conflicting objectives simultaneously. This paper describes a mobile-agent based negotiation approach to integrate manufacturing functions in a distributed manner; and its fundamental framework and functions are presented. Moreover, ontology has been constructed by using the Protégé software which possesses the flexibility to convert knowledge into Extensible Markup Language (XML) schema of Web Ontology Language (OWL) documents. The generated XML schemas have been used to transfer information throughout the manufacturing network for the intelligent interoperable integration of product data models and manufacturing resources. To validate the feasibility of the proposed approach, an illustrative example along with varied production environments that includes production demand fluctuations is presented and compared the proposed approach performance and its effectiveness with evolutionary algorithm based Hybrid Dynamic-DNA (HD-DNA) algorithm. The results show that the proposed scheme is very effective and reasonably acceptable for integration of manufacturing functions.

Peptidomimetic ligands for tuning integrin-mediated signalling: rational design, synthesis and theranostic applications

Integrins are α/β-heterodimeric transmembrane adhesion receptors that mediate cell-cell and cell-ECM interactions. Integrins are bidirectional signalling receptors that respond to external signals (“outside-in” signalling) and in parallel, transduce internal signals to the matrix (“inside-out” signalling), to regulate vital cellular functions including migration, survival, growth and differentiation. Therefore, dysregulation of these tightly regulated processes often results in uncontrolled integrin activation and abnormal tissue expression that is responsible for many diseases. Because of their important roles in physiological and pathological events, they represent a validated target for therapeutic and diagnostic purposes. The aim of the present Thesis was focused on the development of peptidic ligands for α4β1 and αvβ3 integrin subtypes, involved in inflammatory responses (leukocytes recruitment and extravasation) and cancer progression (angiogenesis, tumor growth, metastasis), respectively. Following the peptidomimetic strategy, we designed and synthesized a small library of linear and cyclic hybrid α/β-peptidomimetics based on the phenylureido-LDV scaffolds for the treatment of chronic inflammatory autoimmune diseases. In order to implement a fast and non-invasive diagnostic method for monitoring the course of the inflammatory processes, a flat glass-surface of dye-loaded Zeolite L-crystal nanoparticles was coated with bioactive α4β1-peptidomimetics to detect specific integrin-expressing cells as biomarkers of inflammatory diseases. Targeted drug delivery has been considered a promising alternative to overcome the pharmacokinetic limitations of conventional anticancer drugs. Thus, a novel Small-Molecule Drug Conjugate was synthesized by connecting the highly cytotoxic Cryptophycin to the tumor-targeting RGDfK-peptide through a protease-cleavable linker. Finally, in view to making the peptide synthesis more sustainable and greener, we developed an alternative method for peptide bonds formation employing solvent-free mechanochemistry and ultra-mild minimal solvent-grinding conditions in common, inexpensive laboratory equipment. To this purpose, standard amino acids, coupling agents and organic-green solvents were used in the presence of nanocrystalline hydroxyapatite as a reusable, bio-compatible inorganic basic catalyst.

Advanced Functional Organic-Inorganic Hybrid (Nano)Materials: from Theranostics to Organic Electronics and Additive Manufacturing

This work is going to show the activities performed in the frame of my PhD studies at the University of Bologna, under the supervision of Prof. Mauro Comes Franchini, at the Department of Industrial Chemistry “Toso Montanari”. The main topic of this dissertation will be the study of organic-inorganic hybrid nanostructures and materials for advanced applications in different fields of materials technology and development such as theranostics, organic electronics and additive manufacturing, also known as 3D printing. This work is therefore divided into three chapters, that recall the fundamentals of each subject and to recap the state-of-the-art of scientific research around each topic. In each chapter, the published works and preliminary results obtained during my PhD career will be discussed in detail.

Joints and innovative components for automotive applications

The research activities described in this thesis were focused on two main topics: the study of shaft-hub joint performance, with particular regard to interference-fitted and adhesively bonded connection, and the fatigue characterization of additively processed metal alloys. The research on interference-fitted shaft-hub joints dealt with some studies in the field of fretting fatigue. Rotating bending fatigue tests were performed on different materials by not conventional specimens to determine the fatigue properties of interference-fitted joints and to investigate the fretting fatigue phenomenon, which led to novel and original results. In adhesively bonded and interference-fitted shaft-hub connections (called hybrid joints) the synergic effect of anaerobic adhesive and interference has the capability of improving the joint strength. However, the adhesive contribution depends on several factors. Therefore, its behavior was investigated for different coupling pressure, coupling procedure, operating temperature and joint design. The study on additively manufactured metal alloy deals with rotating banding fatigue tests. AlSi10Mg and Maraging Stainless Steel CX were involved in the campaign for their wide applicability in Automotive. Build direction, heat and surface treatments were considered as input parameters. Fatigue results were interpreted by statistical method and microscopy analyses in order to determine the effectiveness and the beneficial or detrimental effects of the considered factors. Fracture mode and microstructure were investigated by fractographic and micrographic analyses

Development of a Predictive Control Function for a Plug-in Hybrid Electric Vehicle with Electrically-heated Catalyst

This master thesis work is focused on the development of a predictive EHC control function for a diesel plug-in hybrid electric vehicle equipped with a EURO 7 compliant exhaust aftertreatment system (EATS), with the purpose of showing the advantages provided by the implementation of a predictive control strategy with respect to a rule-based one. A preliminary step will be the definition of an accurate powertrain and EATS physical model, starting from already existing and validated applications. Then, a rule-based control strategy managing the torque split between the electric motor (EM) and the internal combustion engine (ICE) will be developed and calibrated, with the main target of limiting tailpipe NOx emission by taking into account EM and ICE operating conditions together with EATS conversion efficiency. The information available from vehicle connectivity will be used to reconstruct the future driving scenario, also referred to as electronic horizon (eHorizon), and in particular to predict ICE first start. Based on this knowledge, an EATS pre-heating phase can be planned to avoid low pollutant conversion efficiencies, thus preventing high NOx emission due to engine cold start. Consequently, the final NOx emission over the complete driving cycle will be strongly reduced, allowing to comply with the limits potentially set by the incoming EURO 7 regulation. Moreover, given the same NOx emission target, the gain achieved thanks to the implementation of an EHC predictive control function will allow to consider a simplified EATS layout, thus reducing the related manufacturing cost. The promising results achieved in terms of NOx emission reduction show the effectiveness of the application of a predictive control strategy focused on EATS thermal management and highlight the potential of a complete integration and parallel development of involved vehicle physical systems, control software and connectivity data management.

Fabrication and characterization of hybrid ferromagnetic-organic heterostructures for spintronics application

Recent research in the field of organic spintronics highlighted the peculiar spin-dependent properties of the interface formed by an organic semiconductor (OSC) chemisorbed over a 3d ferromagnetic metal, also known as spinterface. The hybridization between the molecular and metallic orbitals, typically π orbitals of the molecule and the d orbitals of the ferromagnet, give rise to spin dependent properties that were not expected by considering the single components of interfaces, as for example the appearance of a magnetic moment on non-magnetic molecules or changes in the magnetic behavior of the ferromagnet. From a technological viewpoint these aspects provide novel engineering schemes for spin memory and for spintronics devices, featuring unexpected interfacial magnetoresistance, spin-filtering effects and even modulated magnetic anisotropy. Applications of these concepts to devices require nevertheless to transfer the spinterface effects from an ideal interface to room temperature operating thin films. In this view, my work presents for the first time how spinterface effects can be obtained even at room temperature on polycrystalline ferromagnetic Co thin films interfaced with organic molecules. The considered molecules were commercial and widely used in the field of organic electronics: Fullerene (C60), Gallium Quinoline (Gaq3) and Sexithiophene (T6). An increase of coercivity, up to 100% at room temperature, has been obtained on the Co ultra-thin films by the deposition of an organic molecule. This effect is accompanied by a change of in-plane anisotropy that is molecule-dependent. Moreover the Spinterface effect is not limited to the interfacial layer, but it extends throughout the whole thickness of the ferromagnetic layer, posing new questions on the nature of the 3d metal-molecule interaction.

Functional engineering of hybrid heterostructures for application in electronic, optical and optoelectronic nanostructured devices

Interfacing materials with different intrinsic chemical-physical characteristics allows for the generation of a new system with multifunctional features. Here, this original concept is implemented for tailoring the functional properties of bi-dimensional black phosphorus (2D bP or phosphorene) and organic light-emitting transistors (OLETs). Phosphorene is highly reactive under atmospheric conditions and its small-area/lab-scale deposition techniques have hampered the introduction of this material in real-world applications so far. The protection of 2D bP against the oxygen by means of functionalization with alkane molecules and pyrene derivatives, showed long-term stability with respect to the bare 2D bP by avoiding remarkable oxidation up to 6 months, paving the way towards ultra-sensitive oxygen chemo-sensors. A new approach of deposition-precipitation heterogeneous reaction was developed to decorate 2D bP with Au nanoparticles (NP)s, obtaining a “stabilizer-free” that may broaden the possible applications of the 2D bP/Au NPs interface in catalysis and biodiagnostics. Finally, 2D bP was deposited by electrospray technique, obtaining oxidized-phosphorous flakes as wide as hundreds of µm2 and providing for the first time a phosphorous-based bidimensional system responsive to electromechanical stimuli. The second part of the thesis focuses on the study of organic heterostructures in ambipolar OLET devices, intriguing optoelectronic devices that couple the micro-scaled light-emission with electrical switching. Initially, an ambipolar single-layer OLET based on a multifunctional organic semiconductor, is presented. The bias-depending light-emission shifted within the transistor channel, as expected in well-balanced ambipolar OLETs. However, the emitted optical power of the single layer-based device was unsatisfactory. To improve optoelectronic performance of the device, a multilayer organic architecture based on hole-transporting semiconductor, emissive donor-acceptor blend and electron-transporting semiconductor was optimized. We showed that the introduction of a suitable electron-injecting layer at the interface between the electron-transporting and light-emission layers may enable a ≈ 2× improvement of efficiency at reduced applied bias.

Hybrid lead-halide perovskites as novel materials for thin film direct and flexible ionizing radiation detectors

Ionizing radiations are important tools employed every day in the modern society. For example, in medicine they are routinely used for diagnostic and therapy. The large variety of applications leads to the need of novel, more efficient, low-cost ionizing radiation detectors with new functionalities. Personal dosimetry would benefit from wearable detectors able to conform to the body surfaces. Traditional semiconductors used for ionizing radiation direct detectors offer high performance but they are intrinsically stiff, brittle and require high voltages to operate. Hybrid lead-halide perovskites emerged recently as a novel class of materials for ionizing radiation detection. They combine high absorption coefficient, solution processability and high charge transport capability, enabling efficient and low-cost detection. The deposition from solution allows the fabrication of thin-film flexible devices. In this thesis, I studied the detection properties of different types of hybrid perovskites, deposited from solution in thin-film form, and tested under X-rays, gamma-rays and protons beams. I developed the first ultraflexible X-ray detector with exceptional conformability. The effect of coupling organic layers with perovskites was studied at the nanoscale giving a direct demonstration of trap passivation effect at the grain boundaries. Different perovskite formulations were deposited and tested to improve the film stability. I report about the longest aging studies on perovskite X-ray detectors showing that the addition of starch in the precursors’ solution can improve the stability in time with only a 7% decrease in sensitivity after 630 days of storage in ambient conditions. 2D perovskites were also explored as direct detector for X-rays and gamma-rays. Detection of 511 keV photons by a thin-film device is here demonstrated and was validated for monitoring a radiotracer injection. At last, a new approach has been used: a 2D/3Dmixed perovskite thin-film demonstrated to reliably detect 5 MeV protons, envisioning wearable dose monitoring during proton/hadron therapy treatments.