Smart Behavior Research Articles

Shorting Fees, Private Information, and Equity Mispricing

We document that stock lenders are informed about market conditions and pursue revenue maximization by setting premiums or offering discounts on stock lending fees. Using a model of supply and demand in the equity lending market, we illustrate the effect of stock borrowers' private information on the elasticity of shorting demand. Smart lenders respond to demand elasticity and increase their revenues through premiums or discounts on lending fees. Empirically, decomposing stock loan fees into intrinsic fee and premium or discount, we confirm lenders' market awareness, showing that premiums and discounts among difficult-to-borrow stocks both lead to increased lending revenues. This smart lending behavior has new implications about informed shorting, short interest, and transaction costs in the equity lending market.

The preparation and electrorheological behavior of bowl-like titanium oxide nanoparticles.

Bowl-like titanium oxide nanoparticles were successfully prepared by a simple solvothermal method using absolute ethanol and isopropanol as the cosolvent. Ostwald ripening coupled with the inner-stress-induce effect were assumed to play an important role in the formation of this unique bowl-like morphology. The morphological evolution from solid nanosphere to bowl-like nanoparticle was investigated by scanning electron microscopy (SEM) and transmission electron microscopy (TEM). Besides, the structural characteristics of the as-synthesized TiO2 nanoparticles were confirmed by X-ray diffraction (XRD), Fourier transform infrared (FT-IR) and thermogravimetric analysis (TGA). Moreover, a rotational rheometer was operated to examine the electrorheological (ER) effect. Excellent ER properties were achieved when the TiO2 particles were dispersed in silicone oil under an external electric field.

Smart and simple design of seismic resistant reinforced concrete frame

Abstract In this paper a simple and a more sophisticated design procedure to design moment resisting concrete frames, is presented. This methodology allows to design structures having a smart behaviour when subjected to seismic excitation. In fact, the structure develops the maximum number of dissipative zones by means of a particular collapse mechanism: the global one. The proposed procedure is based on the application of the kinematic theorem of the plastic collapse through the evaluation of the sum of the plastic moments of the columns required, at each storey, to prevent undesired failure modes such as soft-storey mechanism. In this work the authors show how the classical design methodology based on the beam-column hierarchy criterion does not allow to obtain a global mechanism. Beam-column hierarchy criterion, commonly suggested by seismic codes, appears only as a very rough approximation when compared to Theory of Plastic Mechanism Control (TPMC) and its theoretical background. By applying this classic methodology, if a global mechanism is obtained, the design of the column section and its reinforcement are not optimized. In fact, only with the theory already recalled we can obtain the minimum of section columns able to provide the development of a global mechanism. Significant improvements proposed by this approach include, among others, the possibility to account for different amount of reinforcement, not only at the top and bottom of the beam section, but also at the beam ends (left and right). A practical application of the TPMC process for the design of a multi-storey frame is presented, with push-over analysis that investigate the actual collapse mechanism of the designed structure. All the obtained results confirm the capability of the design procedure to achieve a collapse mechanism of global type. The importance of this theory and therefore of the design structures is the possibilities to maximize the energy dissipation capacity and global ductility because all the dissipative zones are involved in the corresponding yielding pattern.

Human interaction with IoT-based smart environments

This paper describes concepts, design, implementation, and performance evaluation of a 3D-based user interface for accessing IoT-based Smart Environments (IoT-SE). The generic interaction model of the described work addresses some major challenges of Human-IoT-SE-Interaction such as cognitive overload associated with manual device selection in complex IoT-SE, loss of user control, missing system image or over-automation. To address these challenges we propose a 3D-based mobile interface for mixed-initiative interaction in IoT-SE. The 3D visualization and 3D UI, acting as the central feature of the system, create a logical link between physical devices and their virtual representation on the end user's mobile devices. By so doing, the user can easily identify a device within the environment based on its position, orientation, and form, and access the identified devices through the 3D interface for direct manipulation within the scene. This overcomes the problem of manual device selection. In addition, the 3D visualization provides a system image for the IoT-SE, which supports users in understanding the ambience and things going on in it. Furthermore, the mobile interface allows users to control the amount and the way the IoT-SE automates the environment. For example, users can stop or postpone system triggered automatic actions, if they don't like or want them. Users also can remove a rule forever. By so doing, users can delete smart behaviors of their IoT-SE. This helps to overcome the automation challenges. In this paper, we present the design, implementation and evaluation of the proposed interaction system. We chose smart meeting rooms as the context for prototyping and evaluating our interaction concepts. However, the presented concepts and methods are generic and could be adapted to similar environments such as smart homes. We conducted a subjective usability evaluation (ISO-Norm 9241/110) with 16 users. All in one the study results indicate that the proposed 3D-User Interface achieved a good high score according to the ISO-Norm scores.

Self Sensing Concrete using Carbon Fibre for Health Monitoring of Structures under Static loading

Background/Objective: To find an easy method to health monitor structures. Method: Self sensing or smart behavior has been observed in mortar or concrete with the addition of small amounts (0.2 to 0.5% by volume of cement) of short (5mm length) carbon fibres. It is seen that there is an increase in electrical resistance on loading upto crack propagation or fracture. On reaching the inelastic stage, the resistance change is not reversible. Findings: A method is developed which can be used in place of, often used strain gage technique or fibre optic technique for health monitoring of structures. There is an increase in resistance during fibre pull out in the elastic range. The change in elastic resistance was measured by a four probe method and was seen to be reversible for elastic deformation. Also, the crack propagation and fibre breakage of the specimen can be identified by irreversible resistance change. The stress vs strain and resistance vs strain graphs when plotted show similarity. Application: This phenomenon can be made use of to find the real time weight of vehicles in traffic and finding stress values of a loaded structure etc.

Smart Gating Multi-Scale Pore/Channel-Based Membranes.

Smart gating membranes are important and promising in membrane science and technology. Rapid progress in developing smart membranes is transforming technology in many different fields, from energy and environmental to the life sciences. How a specific smart behavior for controllable gating of porous membranes can be obtained, especially for nano- and micrometer-sized multi-scale pore/channel-based membrane systems is addressed.

Animal Hairs as Water-stimulated Shape Memory Materials: Mechanism and Structural Networks in Molecular Assemblies.

Animal hairs consisting of α-keratin biopolymers existing broadly in nature may be responsive to water for recovery to the innate shape from their fixed deformation, thus possess smart behavior, namely shape memory effect (SME). In this article, three typical animal hair fibers were first time investigated for their water-stimulated SME, and therefrom to identify the corresponding net-points and switches in their molecular and morphological structures. Experimentally, the SME manifested a good stability of high shape fixation ratio and reasonable recovery rate after many cycles of deformation programming under water stimulation. The effects of hydration on hair lateral size, recovery kinetics, dynamic mechanical behaviors and structural components (crystal, disulfide and hydrogen bonds) were then systematically studied. SME mechanisms were explored based on the variations of structural components in molecular assemblies of such smart fibers. A hybrid structural network model with single-switch and twin-net-points was thereafter proposed to interpret the water-stimulated shape memory mechanism of animal hairs. This original work is expected to provide inspiration for exploring other natural materials to reveal their smart functions and natural laws in animals including human as well as making more remarkable synthetic smart materials.

A Survey on Internet of Things for SmartVehicles

In a Smart City, all objects need to be smart hence would have embedded processors and capability to communicate with each other through wired or wireless connections .These increasingly intelligent objects would provide safe and convenient environment through growing interconnection and interoperability, which is also termed as Internet of Things (IoT), Within the objectives of IoT also lies the vehicle to smart object communications, vehicles play an important role for safe and convenient travel. The main vision of the Internet of Things (IoT) is to equip reallife physical objects with computing and communication abilities so that they can interact with each other for the social good. As one of the important members of IoT, Internet of Vehicles (IoV) has seen rapid development in communication technologies. Now, vehicles can easily exchange safety, efficiency, comfort-related information with other vehicles and infrastructures using vehicular ad hoc networks (VANETs). Where vehicles are the key social entities in the machine-to-machine vehicular social networks. The near real time applications offer safe and efficient travel of the vehicle users, and the offline data ensures smart behaviour of the vehicles and data analysis for the transport authorities. We have identified the social structures of IoV components, their relationships, and the interaction types. We have mapped VANETs components into IoT architecture reference model to offer better integration of IoV with other IoT domains. Finally, we provide the implementation details and the experimental study to demonstrate the efficiency of the proposed system as well as include different application scenarios for various user groups, practical deployment of the proposed system and using it to collect real life IoV related multi-modal sensory information in city or urban areas can be another interesting direction We envision that the IoV would be an integral part of intelligent transport systems in the future smart cities.

Magnetic field and stress Induced strain in CoNiAl Ferromagnetic Shapememory Alloy

Ferromagnetic shapememory alloys (FSMAs) are versatile and best materials for the application as sensors and actuators due to their smart behavior and faster response than the ordinary SMAs. Because of the ductility induced by the gamma phase precipitates and large range of martensitic and magnetic transformation temperatures CoNiAl FSMA is getting more importance and study of it is more interesting. We have made a CoNiAl alloy with the composition Co35Ni35Al30 by arc melting method. The sample was annealed at 1200°C/24h and quenched in water. It was characterized by XRD, SEM EDAX, DSC, and magnetization. From the XRD and SEM, the sample possesses two phases at room temperature (both β and γ). The Single phase (β) Co35Ni35Al30 alloy shows very sharp martensitic transition, but has extremely low ductility at room temperature. The presence of γ solid solution (disordered F.C.C. structure) as a second phase in the CoNiAl system renders the β-phase ductile at room temperature and makes it useful for practical applications. The transformation temperatures of the present CoNiAl alloy were obtained from DSC and four probe measurements. These were found to be TMs = 175 K, TMf = 153 K, TAs = 185 K and TAf = 205 K and Tc(β) = 225 K The magnetic field induced strain was measured at room temperature using NI 6210 data logger. M-H Curves at different temperatures (80 K, 150 K, 175 K, 200 K, 250 K, 300 K, 350 K and 400 K) were recorded using VSM. Stress induced strain at different stress levels was studied by Vibrating reed method (VBR). In the present paper we are presenting the magnetic field induced strain and stress induced strain near the martensitic transformation temperature of the sample. How the sample strain in the presence of magnetic field strength and stress level affect the behaviour of the sample in the vicinity of the martensitic transformation temperature.

Biosmart materials the future of dentistry: A review

By definition and general agreement, smart materials are materials that have properties which may be altered in a controlled fashion by stimuli, such as stress, temperature, moisture, pH, and electric or magnetic fields. Thus, smart materials respond to stimuli by altering one or more of their properties. These properties have a beneficial application in various fields including dentistry. Shape memory alloys, zirconia, and smart seal are examples of materials exhibiting a smart behavior in dentistry. There is a strong trend in material science to develop new materials. These materials would potentially allow new and ground-breaking dental therapies with a significantly enhanced clinical outcome of treatments. With the help of this review we can have a better understanding of these materials.

Self-Recovering Section of RPV Steel Radiation Embrittlement Kinetics as Indication of Material Smart Behavior

Influence of neutron irradiation on reactor pressure vessel (RPV) steel degradation are examined with reference to the possible reasons of the substantial experimental data scatter and furthermore -nonstandard (non-monotonous) and oscillatory embrittlement behavior. In our glance this phenomenon may be explained by presence of the wavelike recovering component in the embrittlement kinetics. We suppose that the main factor affecting steel anomalous embrittlement is fast neutron intensity (dose rate or flux), flux effect manifestation depends on state-of-the-art fluence level. At low fluencies radiation degradation has to exceed normative value, then approaches to normative meaning and finally became sub normative. Data on radiation damage change including through the ex-service RPVs taking into account chemical factor, fast neutron fluence and neutron flux were obtained and analyzed. In our opinion controversy in the estimation on neutron flux on radiation degradation impact may be explained by presence of the wavelike component in the embrittlement kinetics. Therefore flux effect manifestation depends on fluence level. At low fluencies radiation degradation has to exceed normative value, then approaches to normative meaning and finally became sub normative. As a result of dose rate effect manifestation peripheral RPV’s zones in some range of fluencies have to be damaged to a large extent than situated closely to core. Moreover as a hypothesis we suppose that at some stages of irradiation damaged metal have to be partially restored by irradiation i.e. neutron bombardment. Nascent during irradiation structure undergo occurring once or periodically transformation in a direction both degradation and recovery of the initial properties. According to our hypothesis at some stagem(s) of metal structure degradation neutron bombardment became recovering factor. Therefore self-recovering section of RPV steel radiation embrittlement kinetics is an indication of material smart behavior. As a result oscillation arise that intern lead to enhanced data scatter. Disclosure of the steel degradation oscillating is a sign of the steel structure cyclic self-recovery transformation as it take place in self-organization processes. This assumption has received support through the discovery of the similar “anomalous” data in scientific publications and by means of own additional experiments. Data obtained stimulate looking-for ways to management of the structural steel radiation stability (for example, by means of nano-structure modification for radiation defects annihilation intensification) for creation of the intelligent self-recovering material. Expected results: - radiation degradation theory and mechanisms development, - more adequate models of the radiation embrittlement elaboration, - methods and facility development for usage data of the accelerated materials irradiation for forecasting of their capacity for work in realistic (practical) circumstances of operation, - search of the ways for creating of the stable under neutron irradiation self-recovery smart materials.

Capacitive Energy Conversion With Circuits Implementing a Rectangular Charge-Voltage Cycle—Part 1: Analysis of the Electrical Domain

Capacitive kinetic energy harvesters (KEH) employ conditioning circuits which achieve a dynamic biasing of the transducer's variable capacitor. This paper, composed of two articles Part 1 and Part 2, proposes a unified theory describing electrical and electromechanical properties of an important and wide class of conditioning circuits: those implementing a rectangular charge-voltage cycle. The article Part 1 introduces a basic configuration of conditioning circuit implementing an ideal rectangular QV cycle, and discusses its known practical implementations: the Roundy charge pump with different flyback mechanisms, and configurations based on the Bennet's doubler. In Part 1, the analysis is done in the electrical domain, without accounting for electromechanical coupling, while in Part 2, the full electromechanical system is analyzed. An optimization approach common to all configurations is proposed. A comparison is made between different topologies and operation modes, based on the maximal energy converted in one cycle under similar electrical and mechanical conditions. The last section discusses practical implementation of circuits with smart and adaptive behavior, and presents experimental results obtained with state-of-the art MEMS capacitive KEH devices.

Continuous Gesture Recognition using HLAC and Low-Dimensional Space

This study proposes a continuous gesture recognition using information about features of motions obtained from continuous gesture images through higher order correlation feature coefficient and principal component analysis (PCA). The proposed method, first, separates two-dimensional silhouette gesture region from a continuous input image that includes a human body image. Information about 35 features are extracted using the higher order local auto correlation coefficient in the divided image and a low-dimensional gesture space is composed using PCA. The model feature value reflected in the gesture space is composed of symbols of certain conditions through clustering algorithm so as to be used as the input symbol of a hidden marker model, and a random input motion is recognized as the relevant gesture model with the highest probability value. The proposed method has less computation workload than the previous geometric feature-based method or appearance-based method and shows a high recognition rate with using the minimum information, so it is very suitable for the construction of a real-time system. In addition, the recognized gesture can be used input data of various applications like dynamic motion type game by mapping to an operating system. And the human motions can be used the objects of observation, such as smart home and behavior analysis in special spaces.

A stabilized piezolaminated nine-nodded shell element formulation for analyzing smart structures behaviors

ABSTRACTAn explicit hybrid stabilization method is utilized together with a reduced order integration scheme to stabilize spurious zero energy modes from the sub-integrated degenerated shell element. This stabilization is achieved after employing appropriate contravariant higher order stress modes. The relevant finite element formulation of the piezolaminated nine-nodded shell element is then derived to analyze smart structures behaviors. To show the capabilities of the presented formulation, it has been implemented in a finite element code. The developed code is used to analyze some typical problems. The results are compared with those obtained from other schemes in the literature and experiments.

Automated Cognitive Health Assessment From Smart Home-Based Behavior Data.

Smart home technologies offer potential benefits for assisting clinicians by automating health monitoring and well-being assessment. In this paper, we examine the actual benefits of smart home-based analysis by monitoring daily behavior in the home and predicting clinical scores of the residents. To accomplish this goal, we propose a clinical assessment using activity behavior (CAAB) approach to model a smart home resident's daily behavior and predict the corresponding clinical scores. CAAB uses statistical features that describe characteristics of a resident's daily activity performance to train machine learning algorithms that predict the clinical scores. We evaluate the performance of CAAB utilizing smart home sensor data collected from 18 smart homes over two years. We obtain a statistically significant correlation ( r=0.72) between CAAB-predicted and clinician-provided cognitive scores and a statistically significant correlation ( r=0.45) between CAAB-predicted and clinician-provided mobility scores. These prediction results suggest that it is feasible to predict clinical scores using smart home sensor data and learning-based data analysis.

Critical analysis of smart environment sensor data behavior pattern based on sequential data mining techniques

Purpose – Behavioral pattern mining for intelligent system such as SmEs sensor data are vitally important in many applications and performance optimizations. Sensor pattern mining (SPM) is also dynamic and a hot research issue to pervasive and ubiquitous of smart technologies toward improving human life. However, in large-scale sensor data, exploring and mining pattern, which leads to detect the abnormal behavior is challenging. The paper aims to discuss these issues. Design/methodology/approach – Sensor data are complex and multivariate, for example, which data captured by the sensors, how it is precise, what properties are recorded or measured, are important research issues. Therefore, the method, the authors proposed Sequential Data Mining (SDM) approach to explore pattern behaviors toward detecting abnormal patterns for smart space fault diagnosis and performance optimization in the intelligent world. Sensor data types, modeling, descriptions and SPM techniques are discussed in depth using real sensor data sets. Findings – The outcome of the paper is measured as introducing a novel idea how SDM technique’s scale-up to sensor data pattern mining. In the paper, the approach and technicality of the sensor data pattern analyzed, and finally the pattern behaviors detected or segmented as normal and abnormal patterns. Originality/value – The paper is focussed on sensor data behavioral patterns for fault diagnosis and performance optimizations. It is other ways of knowledge extraction from the anomaly of sensor data (observation records), which is pertinent to adopt in many intelligent systems applications, including safety and security, efficiency, and other advantages as the consideration of the real-world problems.

Price Promotion Literacy and Numeracy: How Well Do Consumers Understand Price Promotion Communication?

Consumer literacy is a skill that combines literacy and numeracy abilities applied to shopping tasks. It influences consumers’ success in making financially sound decisions. Failure to understand price promotion signage, a very common occurrence in modern retail, might result in confusion and eventually lead to sub-optimal decisions. This study describes the results of two large-scale consumer surveys conducted in the United States and Australia that tested consumer understanding of the most commonly used price promotion communications.Of the U.S. and Australian consumers, 25% and 23%, respectively, could not accurately perform literacy and numeracy tasks related to price promotion information. Regression modeling shows that young consumers with low incomes had the most difficulties in understanding the information on price promotion signage. Thus, this group is the most vulnerable to retail price manipulations. However, this group of future consumers could benefit the most from making financially sound purchase decisions. Our results call for national educational efforts in developing young consumers’ consumer literacy to empower them for independent purchase decisions and smart spending behaviors.

Twice as smart behavior of tert-butylthiacalix[4]arene derivative in glassy and crystalline form

A studied tert-butylthiacalix[4]arene derivative with four N-(2-acetoxyethyl)carbamoylmethoxy substituents on its lower rim in partial-cone configuration (calixarene 1) can remember its previous treatment in three essentially different ways by the formation either of a molecular glass or two metastable polymorphs after heating or the removal of an included guest molecule. Guest-induced memory is very selective with a polymorph created only after the release of a few included guests among a large series of those studied and is detected via an exothermic transition. Along with ordinary properties, like glass transition, curing and cold crystallization, the molecular glass from 1 is selective due to its ability to crystallize in solvent vapors and vapor mixtures over a well-defined concentration range. Being cooperative, this property may be used for the visual detection of ethanol content in water solution when it reaches a threshold value.

Our building is smarter than your building: The use of competitive rivalry to reduce energy consumption and linked carbon footprint

This research is located within the smart city discourse and explores the linkage between smart buildings and an intelligent community, employing the University of Cape Town as a case study. It is also situated within the research stream of Green Information Systems, which examines the confluence between technology, people, data and processes, in order to achieve environmental objectives such as reduced energy consumption and its associated carbon footprint. Since approximately 80% of a university’s carbon footprint may be attributed to electricity consumption and as the portion of energy used inefficiently by buildings is estimated at 33% an argument may be made for seeing a campus as a “living laboratory” for energy consumption experiments in smart buildings. Integrated analytics were used to measure, monitor and mitigate energy consumption, directly linked to carbon footprinting. This paper examines a pilot project to reduce electricity consumption through a smart building competition. The lens used for this research was the empirical framework provided by the International Sustainable Campus Network/Global University Leadership Forum Charter. Preliminary findings suggest a link between the monitoring of smart buildings and behaviour by a segment of the intelligent community in the pursuit of a Sustainable Development strategy.

Salt and pH sensitive semi‐interpenetrating polyelectrolyte hydrogels poly(HEMA‐co‐METAC)/PEG and its BSA adsorption behavior

ABSTRACTA novel semi‐interpenetrating poly(2‐hydroxyethyl methacrylate) (pHEMA) based polyelectrolyte hydrogel [p(HEMA‐co‐METAC)/PEG] was prepared by copolymerizing HEMA with the cationic monomer 2‐methacryloyloxyethyltrimethyl ammonium chloride (METAC) in the presence of polyethylene glycol (PEG) with different content and molecular weight (MW 4000 and 400). The chemical structure of the gels was confirmed by FT‐IR spectroscopy, morphology study was performed by scanning electron microscope (SEM), thermal stability was revealed by thermogravimetric analysis (TGA), and the mechanical properties were determined by electronic universal testing machine. Swelling studies showed introduction of cationic monomer METAC led to high water content, and the obvious salt and pH sensitive properties were observed which proved the smart behavior of the semi‐interpenetrating polymer networks (IPNs) gels. In addition, the effect of temperature and some important biological solution on swelling behavior were reported. Cytotoxicity test demonstrated that synthesized gels owned satisfactory cytocompatibility and were convenient for the application as biomaterials. Finally, the weak bovine serum albumin (BSA) adsorption on semi‐IPNs by introducing METAC and controlling the content of PEG in gels demonstrated that they were of good protein resistance effect in biomedical applications. © 2014 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2015, 132, 41537.