The biotechnological production of glycosides is an economically competitive manufacturing alternative to classical chemical synthesis. Through continuous production improvement, glycosides can now be used in low-cost products by various industries. However, many production systems still suffer from low yields. Directed evolution, coupled with a suitable screening method, can tackle this challenge. We generated glycosyltransferase mutants through error-prone-PCR and screened the library using a small-scale whole-cell biotransformation system. The screening of only 176 colonies yielded three putative candidates. Detailed investigations revealed that the reason for the increase in product titer was mainly due to different expression effects of the mutagenized genes rather than improved enzyme kinetics. In total, a 60-fold increase in product formation was achieved. Therefore, in addition to the quality of the mutant library, an efficient and stable expression system is crucial to achieve high concentrations of active enzyme and product, as formation of inclusion bodies and other inactive forms of the biocatalyst reduces productivity.
Small-to-medium businesses are always seeking affordable ways to advertise their products and services securely. With the emergence of mobile technology, it is possible than ever to implement innovative Location-based Advertising (LBS) systems using smartphones that preserve the privacy of mobile users. In this paper, we present a prototype implementation of such systems by developing a distributed privacy-preserving system, which has parts executing on smartphones as a mobile app, as well as a web-based application hosted on the cloud. The mobile app leverages Google Maps libraries to enhance the user experience in using the app. Mobile users can use the app to commute to their daily destinations while viewing relevant ads such as job openings in their neighborhood, discounts on favorite meals, etc. We developed a client-server privacy architecture that anonymizes the mobile user trajectories using a bounded perturbation strategy. A multi-modal sensing approach is proposed for modeling the context switching of the developed LBS system, which we represent as a Finite State Machine (FSM) model. The multi-modal sensing approach can reduce the power consumed by mobile devices by automatically detecting sensing mode changes to avoid unnecessary sensing. The developed LBS system is organized into two parts: the business side and the user side. First, the business side allows business owners to create new ads by providing the ad details, Geo-location, photos, and any other instructions. Second, the user side allows mobile users to navigate through the map to see ads while walking, driving, bicycling, or quietly sitting in their offices. Experimental results are presented to demonstrate the scalability and performance of the mobile side. Our experimental evaluation demonstrates that the mobile app incurs low processing overhead and consequently has a small energy footprint.
A bendable UHF RFID tag antenna using non-uniform meandered lines for retail garments in the textile industry is presented. Based on an earlier UHF RFID tag antenna using nonuniform meandered lines, the proposed tag is fully bendable and aimed to be embedded in retail garments for long-life cycles. As a result, a relatively low cost, wide band, compactness and good conjugate matching with good dipole-like read range is presented. Results showed an antenna with a wide bandwidth of 900MHz and a long read range of 10.1m making the UHF RFID tag antenna using non-uniform meandered lines a potential candidate for retail garments in bendable applications of the textile industry. Simulations are corroborated by measurements and are in fairly agreement.
Stir bar sorptive extraction (SBSE) was compared with standardized pump sampling regarding the prospects to assess airborne levels of polycyclic aromatic hydrocarbons (PAHs) in indoor environments. A historic railway water tower, which will be preserved aa technical monument for museum purposes, was sampled with both approaches because built-in insulation material was suspected to release PAHs to the indoor air. The 16 PAH on the US EPA list were quantified using gas chromatography with mass selection detection in filters from pump sampling after solvent extraction and on SBSE devices after thermal desorption. SBSE was seen to sample detectable PAH masses with excellent repeatability and a congener pattern largely similar to that observed with pump sampling. Congener patterns were however significantly different from that in the PAH source because release from the insulation material is largely triggered by the respective congener vapor pressures. Absolute masses in the ng range sampled by SBSE corresponded to airborne concentrations in the ng L -1 range determined by pump sampling. Principle differences between SBSE and pump sampling as well as prospects of SBSE as cost-effective and versatile complement of pump sampling are discussed.
In this work, a 0.9-2.4 GHz, 25 Watt output power, radio frequency (RF) power amplifier based on Class-E switchmode topology has been analyzed. A load-pull simulations method is used to optimize the power performance in the operating band. To design input and output matching networks an optimized low pass filter network was used. Simulated results of the power amplifier (PA) demonstrate wideband behavior which covers a 0.9 GHz to 2.4 GHz band with an efficiency of 32-78%, and an output power of 25 W (44 dBm), and an average gain of 20 dB. The designed PA provides attractive features associated with a wider band, high gain, and efﬁciency, which makes it a proper candidate for the mobile transmitter and cellular infrastructure applications.
In this paper, we present the design and implementation of a smart irrigation system using Internet of Things (IoT) technology, which can be used for automating the irrigation process in agricultural fields. It is expected that this system would create a better opportunity for farmers to irrigate their fields efficiently, as well as eliminating the field’s under-watering, which could stress the plants. The developed system is organized into three parts: sensing side, cloud side, and user side. We used Microsoft Azure IoT Hub as an underlying infrastructure to coordinate the interaction between the three sides. The sensing side uses a Raspberry Pi 3 device, which is a low cost, credit-card sized computer device that is used to monitor in near real-time soil moisture, air temperature and relative humidity, and other weather parameters of the field of interest. Sensors readings are logged and transmitted to the cloud side. At the cloud side, the received sensing data is used by the irrigation scheduling model to determine when and for how long the water pump should be turned on based on a user-predefined threshold. The user side is developed as an Android mobile app, which is used to control the operations of the water pump with voice recognition capabilities. Finally, this system was evaluated using various performance metrics, such as latency and scalability.
A novel approach towards developing a micro-bubble detection technology based on using a PZT transducer to induce an acoustic resonance state within the system under investigation is here presented. The concept, originally proof-of-concept tested in a cylindrical acoustic resonant chamber, has proven to be able to detect single microbubbles with diameters in the range of 390 to 600 µm in a swine thigh, with either saline solution or sheep blood as the medium in the bubble guide. It has shown to be extremely adaptable, capable of accommodating industrial pipes as well as biological specimens, resilient and extremely energy efficient, able to detect micro-bubbles with as little as 0.8 mW and potentially less.
Sentiment analysis of social media posts and texts can provide information and knowledge that is applicable in social settings, business intelligence, evaluation of citizens’ opinions in governance and mood triggered devices in Internet of Things. Feature extraction and selection is a key determinant of accuracy and computational cost of machine learning models for such analysis. Most feature extraction and selection techniques utilize bag of words such as N-grams and frequency-based algorithms especially Term Frequency-Inverse document frequency (TF-IDF). However, these approaches suffer shortcomings such as; they do not consider relationships between words, they ignore words’ characteristics and they suffer high feature dimensionality. In this paper we propose and evaluate an approach that utilizes a fixed hybrid N-gram window for feature extraction and Minimum Redundancy Maximum Relevance feature selection for sentence level sentiment analysis. The approach improves the existing feature extraction techniques specifically the N-gram by generating a tri-gram vector from words, Part of speech tags and word semantic orientation. The N-gram vector is extracted by employing a static 3-gram window identified by a lexicon where a sentiment word appears in a sentence. A blend of the words, POS tags and the sentiment orientations of the 3N-gram are used to build the feature vector. The optimal features from the vector are then selected using Minimum Redundancy Maximum Relevance (MR2) algorithm. Experiments were carried out with a publicly available yelp tweets dataset to evaluate the performance of four supervised machine learning classifiers (Naïve Bayes, K-Nearest Neighbor, Decision Tree and Support Vector Machines) when augmented with the proposed model. The results showed that the proposed model had the highest accuracy (86.85%), recall (86.85%) and precision (86.96%).
Early detection of increasing values of intraocular pressure (IOP) due to glaucoma can prevent sever ocular diseases and ultimately, prevent loss of vision. Currently, the need for an accurate, mobile measurement of intraocular pressure is unmet within the modern healthcare practices. There is a potential to utilize soundwaves as a mobile measurement method and therefore, the relationship between IOP and the reflection coefficient of sound waves is investigated. Simulations are conducted using COMSOL Multiphysics to provide theoretical confirmation of the worthiness of the experiment. An experimental demonstrated is presented to further investigate the relationship between the internal pressure of an object and its acoustic reflection coefficient. The experiment exploits the use of hydrostatic pressure to determine internal pressure, and the reflection coefficient is measured and analyzed. An initial experiment is conducted to identify the resonant frequency of the object and the optimal frequency for maximizing reflection. The experiment shows comprehensively that there is a relationship between the internal pressure of an object and its acoustic reflection coefficient, providing a confirmation of the theory that would allow mobile measurements of IOP to be conducted with the use of a smart phone.
Numerical optimization of the manufacturing process of hybrid lightweight struc- tures consisting of fiber-reinforced plastics (FRP) is of high importance. It can reduce the time to market and can also avoid the production of costly prototypes. To model the considered thermoforming process, the temperature dependent defor- mation mechanisms have to be characterized and modeled within a finite element framework. An industry-oriented approach based on the parameterization of a mate- rial model implemented in LS-DYNA is introduced. The accordingly parameterized material model for the FRP is eventually applied in the simulation of thermoforming processes to show the influence of process and material parameters on the forming behavior of the thermoplastic prepreg.
Surface discharges occurring on a porcelain bushing under DC voltage not only causes an incipient fault condition but also can degrade the pertinent location once the surface deposition layer or the insulation material gets carbonized. Naturally, it becomes important to identify and analyze the surface discharges occurring on bushing. The current practice on analyzing surface discharges initiated under DC voltage employs partial discharge test methods that focuses on counting the PD events occurring over a time span. The method is sensitive but provides no information about the possible source of fault condition. In this context, a non-conventional, pattern based partial discharge analysis method on understanding the characteristics of electrical discharges occurring on the surface of a polluted bushing under DC voltage is studied. Initially, a half-wave bridge rectifier unit that produces an uncontrolled DC voltage is selected and employed. Later, the surface of the polluted bushing is energized, and the signals initiated by the surface discharges occurring on the surface contaminated bushing are recorded. Instead of counting the PD events, the pattern manifested by the surface discharges is correlated to the AC voltage input of the rectifier. Once this is accomplished, the pertinent findings are validated on an actual bushing installed in an electrostatic precipitator unit that is applied for cleaning producer gas of a biomass gasification plant.
This paper describes an in-depth methodical approach to the development of efficient high frequency (HF) antennas for usage in radio frequency identification (RFID) systems operating at 13.56MHz. It presents brief theory relevant to RFID communication and sets up a framework within which features and requirements of antennas are linked to key design parameters such as antenna form-factor and size; RF power level, materials and communication protocol. Tuning circuits necessary to adjust the resonance and power matching characteristics of antennas for good transponder interrogation and response recovery are discussed. To validate the approaches outlined, a complete step-wise antenna design and measurement described. Common practical problems that are often encountered in such design processes are also commented on.
Due to the soaring growth of the electric vehicles and grid energy storage markets, the high-safety and high-energy-density battery storage systems are urgent needed. Lithium metal anode with highest theoretical specific capacity (3860 mA·h·g−1) and the lowest electrochemical potential (−3.04 V vs standard hydrogen electrode) is regarded as the ultimate choice for the high energy density batteries. However, its safety problems as well as the low Coulombic efficiency during the Li plating and stripping processes significantly limit the commercialization of lithium metal batteries. Recently, Li-containing alloys have demonstrated vital roles in inhibiting lithium dendrite growth, controlling interfacial reactions and enhancing the Coulombic efficiency as well as cycle life. Accordingly, in this perspective, the progresses of lithium alloys for robust, stable and dendrite free anode for rechargeable lithium metal batteries are summarized. The challenges and future focus research of lithium-containing alloys in lithium metal batteries are also discussed.
Electrochemical machining (ECM) is a method for removing metal by anodic dissolution. At the interface between the workpiece surface and an electrically conductive ﬂuid (electrolyte), the material is dissolved locally without direct physical contact to the cathodic tool. Due to the force-free nature of the process, ECM is used for machining high-strength or hard materials, such as titanium aluminides, Inconel, Waspaloy, and high nickel, cobalt, and rhenium alloys.1 However, determining suitable process parameters remains challenging due to their interacting eﬀects on working distances during the machining process. Therefore a simulation-based approach to process design substantially reduces resource and time investment to achieve the desired geometry of the ﬁnished part. This methodology requires data about the materials electrochemical properties, such as removal velocity and current eﬃciency, which have to be obtained experimentally. In this study, a methodology for acquiring and processing this data as well as the development of multiphysics simulation models is presented for two use cases: (i) manufacturing a centrifugal impeller with a diameter of 14 mm consisting of the nickel alloy Inconel 713C for use in turbomachinery and (ii) the generation of a deﬁned surface micro structure into the novel Mg-Y-Zn alloy WZ73.
Goal: Fast Fourier transform (FFT), has been the main tool for EEG spectral analysis (SPA). As EEG can show nonlinear and non-stationary behavior, FFT may at times be meaningless. A novel method was developed for analyzing nonlinear and non-stationary signals using the Hilbert-Huang transform. Methods: We compared spectral analyses of EEG using FFT with Hilbert marginal spectra (HMS) with a multivariate empirical mode decomposition algorithm. Segments of continuous 60-sec EEGs recorded from 19 leads of 47 healthy volunteers were studied. Results: HMS showed a reduction of the alpha activity (-5.64%), with increments in the beta-1 (+1.67%), and gamma (+1.38%) fast activity bands, an increment in theta (+2.14%), and in delta (+0.45%) bands, and vice versa for the FFT method. For weighted mean frequencies, insignificant mean differences (lower than 1Hz) were observed between both methods for delta, theta, alpha, beta-1 and beta-2 bands, and only for gamma band values. The HMS were 3 Hz higher than the FFT method. Conclusion: HMS may be considered a good alternative for SPA of the EEG when nonlinearity or non-stationarity may be present.
Optical cables are enormous transmission media which carries high-speed data across transatlantic, intercontinental, international boundaries and cities. The optical cable is essential in data communication. The cable has become an indispensable component in optical communications infrastructure; hence, conscious efforts are always adopted to prevent or minimize faults in the optical network infrastructure. Typically, tracing fault in the underground optical network has been difficult even though optical time-domain reflectometer (OTDR) has been used to measure the distance of faults in the underground fiber cable. The methodologies deployed in the reviewed literature indicate a vast gap between the fault distance measured by the OTDR and the actual distance of fault. This paper observed the difficulties involved in tracing the actual spot of fault in the underground optical networks. The difficulty of tracing these underground faults mostly result in an undue delay and loss of revenue. This research presents a machine learning (ML) approach to predict the actual location of a fiber cable fault in an underground optical transmission link. Linear regression in the python sci-kit learn library was used to predict the actual location of a fault in an underground optical network. The MSE and MAE evaluation matrix used provided good accuracy results of 0.061291 and 0.080143, respectively. The result obtained in this paper indicates that faults in underground optical networks can be found quickly to avoid the delays in the fault tracing process, which leads to an excessive revenue loss.
This paper presents a complete design procedure, with an optimized feeding method, of two-dimensional slotted waveguide antenna arrays (2D SWAs). For a desired sidelobe level ratio, the proposed system provides a pencil shape pattern with a narrow halfpower beamwidth, large sidelobe level ratio (SLR), and very low sidelobe levels (SLL), which makes it suitable for high power microwave applications. The radiating slotted waveguide antennas use longitudinal slots, designed for a specified slidelobe level ratio and resonance frequency. The resulting two-dimensional slotted waveguide antenna array is formed by stacking a number of similarly designed radiating SWAs, and fed with an additional SWA. The proposed feeding method uses longitudinal coupling slots rather than the conventional inclined coupling slots, which can provide better values of SLR and easily obtain very low SLLs, in comparison with the conventional systems. The feeder dimensions and slots positions are deduced from the dimensions and total number of the radiating SWAs. For a desired SLR, the slots excitation in the radiating and feeder SWAs are calculated based on a specified distribution. Then, using simplified closed-form equations and for a desired resonance frequency, the slots lengths, widths, and their distribution along the length of the radiating SWAs and feeder SWA can be found. Two examples are illustrated with different number of slots and radiating elements, and one is fabricated and tested. Chebyshev distribution is used to estimate the excitations of the SWA slots in the examples. The obtained measured and simulated results are in accordance with the design objectives.
Fully digital microscopes are becoming more and more common in surgical applications. In addition to high-resolution stereoscopic images of the operating field, which can be transmitted over long distances or stored directly, these systems offer further potentials by supporting the surgical workflow based on their fully digital image processing chain. For example, the image display can be adapted to the respective surgical scenario by adaptive color reproduction optimization or image overlays with additional information, such as the tissue topology. Knowledge of this topology can be used for computer-assisted or AR-guided microsurgical treatments and enables additional features such as spatially resolved spectral reconstruction of surface reflectance. In this work, a new method for high-resolution depth measurements in digital microsurgical applications is proposed, which is based on the principle of laser triangulation. Part of this method is a sensor data fusion procedure to properly match the laser scanner and camera data. In this context, a strategy based on RBF interpolation techniques is presented to handle missing or corrupt data, which, due to the measuring principle, can occur on steep edges and through occlusion. The proposed method is used for the acquisition of high-resolution depth profiles of various organic tissue samples, proving the feasibility of the proposed concept as a supporting technology in a digital microsurgical workflow.