Efficient Antenna Array Beamforming with Robustness against Random Steering Mismatch

This paper deals with the problem of using antenna sensors for adaptive beamforming in the presence of random steering mismatch. We present an efficient adaptive array beamformer with robustness to deal with the considered problem. The robustness of the proposed beamformer comes from the efficient designation of the steering vector. Using the received array data vector, we construct an appropriate correlation matrix associated with the received array data vector and a correlation matrix associated with signal sources. Then, the eigenvector associated with the largest eigenvalue of the constructed signal correlation matrix is designated as an appropriate estimate of the steering vector. Finally, the adaptive weight vector required for adaptive beamforming is obtained by using the estimated steering vector and the constructed correlation matrix of the array data vector. Simulation results confirm the effectiveness of the proposed method.

The Role of Initiator in the Synthesis of Poly(Methyl Methacrylate)-Layered Silicate Nanocomposites through Bulk Polymerization

The structure-property relationship and initiator effect on bulk polymerized poly(methyl methacrylate) (PMMA)–oragnomodified layered silicate nanocomposites was investigated. In this study, we used 2, 2'-azobis (4-methoxy-2,4-dimethyl valeronitrile and benzoyl peroxide initiators for bulk polymerization. The bulk polymerized nanocomposites’ morphology was investigated by X-ray diffraction and transmission electron microscopy. The type of initiator strongly influences the physiochemical properties of the polymer nanocomposite. The thermal degradation of PMMA in the presence of nanofiller was studied. 5 wt% weight loss temperature (T5d) increased as compared to pure PMMA. The peak degradation temperature increased for the nanocomposites. Differential scanning calorimetry and dynamic mechanical analysis were performed to investigate the glass transition temperature and the nature of the constrained region as the reinforcement mechanism respectively. Furthermore, the optical properties such as UV-Vis and Total Luminous Transmission of nanocomposites are examined.

A Numerical Study on Semi-Active Control of a Bridge Deck under Seismic Excitation

This study investigates the benefits of implementing the semi-active devices in relation to passive viscous damping in the context of seismically isolated bridge structures. Since the intrinsically nonlinear nature of semi-active devices prevents the direct evaluation of Laplace transforms, frequency response functions are compiled from the computed time history response to sinusoidal and pulse-like seismic excitation. A simple semi-active control policy is used in regard to passive linear viscous damping and an optimal non-causal semi-active control strategy. The control strategy requires optimization. Euler-Lagrange equations are solved numerically during this procedure. The optimal closed-loop performance is evaluated for an idealized controllable dash-pot. A simplified single-degree-of-freedom model of an isolated bridge is used as numerical example. Two bridge cases are investigated. These cases are; bridge deck without the isolation bearing and bridge deck with the isolation bearing. To compare the performances of the passive and semi-active control cases, frequency dependent acceleration, velocity and displacement response transmissibility ratios Ta(w), Tv(w), and Td(w) are defined. To fully investigate the behavior of the structure subjected to the sinusoidal and pulse type excitations, different damping levels are considered. Numerical results showed that, under the effect of external excitation, bridge deck with semi-active control showed better structural performance than the passive bridge deck case.

Piezoelectric Micro-generator Characterization for Energy Harvesting Application

This paper presents analysis and characterization of a piezoelectric micro-generator for energy harvesting application. A low-cost experimental prototype was designed to operate as piezoelectric micro-generator in the laboratory. An input acceleration of 9.8m/s2 using a sine signal (peak-to-peak voltage: 1V, offset voltage: 0V) at frequencies ranging from 10Hz to 160Hz generated a maximum average power of 432.4μW (linear mass position = 25mm) and an average power of 543.3μW (angular mass position = 35°). These promising results show that the prototype can be considered for low consumption load application as an energy harvesting micro-generator.

Climate Change and Food Security: The Legal Aspects with Special Focus on the European Union

Dangerous of climate change is now global problem and as such has a strategic priority also for the European Union. Europe and European citizens try to do their best to cut greenhouse gas emissions, moreover they substantially encourage other nations and regions to follow the same way. The European Commission and a number of Member States have developed adaptation strategies in order to help strengthen EU's resilience to the inevitable impacts of climate change. The EU has long been a driving force in international negotiations on climate change and was instrumental in the development of the UN Framework Convention on Climate Change. As the world's leading donor of development aid, the EU also provides substantial funding to help developing countries tackle climate change problem. Global warming influences human health, biodiversity, ecosystems but also many social and economic sectors. The aim of this paper is to focus on impact of claimant change on for food security. Food security challenges are directly related to globalization, climate change. It means that current and future food policy is exposed to all cross-cutting and that must be linked with environmental and climate targets, which supposed to be achieved. In the 7th EAP —The new general Union Environment Action Program to 2020, called “Living well, within the limits of our planet” EU has agreed to step up its efforts to protect natural capital, stimulate resource efficient, low carbon growth and innovation, and safeguard people’s health and wellbeing– while respecting the Earth’s natural limits.

Modern Spectrum Sensing Techniques for Cognitive Radio Networks: Practical Implementation and Performance Evaluation

Spectrum underutilization has made cognitive radio a promising technology both for current and future telecommunications. This is due to the ability to exploit the unused spectrum in the bands dedicated to other wireless communication systems, and thus, increase their occupancy. The essential function, which allows the cognitive radio device to perceive the occupancy of the spectrum, is spectrum sensing. In this paper, the performance of modern adaptations of the four most widely used spectrum sensing techniques namely, energy detection (ED), cyclostationary feature detection (CSFD), matched filter (MF) and eigenvalues-based detection (EBD) is compared. The implementation has been accomplished through the PlutoSDR hardware platform and the GNU Radio software package in very low Signal-to-Noise Ratio (SNR) conditions. The optimal detection performance of the examined methods in a realistic implementation-oriented model is found for the common relevant parameters (number of observed samples, sensing time and required probability of false alarm).

Improving the Performance of Gas Turbine Power Plant by Modified Axial Turbine

Computer-based optimization techniques can be employed to improve the efficiency of energy conversions processes, including reducing the aerodynamic loss in a thermal power plant turbomachine. In this paper, towards mitigating secondary flow losses, a design optimization workflow is implemented for the casing geometry of a 1.5 stage axial flow turbine that improves the turbine isentropic efficiency. The improved turbine is used in an open thermodynamic gas cycle with regeneration and cogeneration. Performance estimates are obtained by the commercial software Cycle – Tempo. Design and off design conditions are considered as well as variations in inlet air temperature. Reductions in both the natural gas specific fuel consumption and in CO2 emissions are predicted by using the gas turbine cycle fitted with the new casing design. These gains are attractive towards enhancing the competitiveness and reducing the environmental impact of thermal power plant.

Influence of Infrared Radiation on the Growth Rate of Microalgae Chlorella sorokiniana

Nowadays, the progressive decrease of primary natural resources and ongoing upward trend in terms of energy demand, have resulted in development of new generation technological processes which are focused on step-wise production and residues utilization. Thus, microalgae-based 3rd generation bioeconomy is considered one of the most promising approaches that allow production of value-added products and sophisticated utilization of residues biomass. In comparison to conventional biomass, microalgae can be cultivated in wide range of conditions without compromising food and feed production, and thus, addressing issues associated with negative social and environmental impacts. However, one of the most challenging tasks is to undergo seasonal variations and to achieve optimal growing conditions for indoor closed systems that can cover further demand for material and energetic utilization of microalgae. For instance, outdoor cultivation in St. Petersburg (Russia) is only suitable within rather narrow time frame (from mid-May to mid-September). At earlier and later periods, insufficient sunlight and heat for the growth of microalgae were detected. On the other hand, without additional physical effects, the biomass increment in summer is 3-5 times per week, depending on the solar radiation and the ambient temperature. In order to increase biomass production, scientists from all over the world have proposed various technical solutions for cultivators and have been studying the influence of various physical factors affecting biomass growth namely: magnetic field, radiation impact, and electric field, etc. In this paper, the influence of infrared radiation (IR) and fluorescent light on the growth rate of microalgae Chlorella sorokiniana has been studied. The cultivation of Chlorella sorokiniana was carried out in 500 ml cylindrical glass vessels, which were constantly aerated. To accelerate the cultivation process, the mixture was stirred for 15 minutes at 500 rpm following 120 minutes of rest time. At the same time, the metabolic needs in nutrients were provided by the addition of micro- and macro-nutrients in the microalgae growing medium. Lighting was provided by fluorescent lamps with the intensity of 2500 ± 300 lx. The influence of IR was determined using IR lamps with a voltage of 220 V, power of 250 W, in order to achieve the intensity of 13 600 ± 500 lx. The obtained results show that under the influence of fluorescent lamps along with the combined effect of active aeration and variable mixing, the biomass increment on the 2nd day was three times, and on the 7th day, it was eight-fold. The growth rate of microalgae under the influence of IR radiation was lower and has reached 22.6·106 cells·mL-1. However, application of IR lamps for the biomass growth allows maintaining the optimal temperature of microalgae suspension at approximately 25-28°C, which might especially be beneficial during the cold season in extreme climate zones.

Case Study on Innovative Aquatic-Based Bioeconomy for Chlorella sorokiniana

Over the last decade due to climate change and a strategy of natural resources preservation, the interest for the aquatic biomass has dramatically increased. Along with mitigation of the environmental pressure and connection of waste streams (including CO2 and heat emissions), microalgae bioeconomy can supply food, feed, as well as the pharmaceutical and power industry with number of value-added products. Furthermore, in comparison to conventional biomass, microalgae can be cultivated in wide range of conditions without compromising food and feed production, thus addressing issues associated with negative social and the environmental impacts. This paper presents the state-of-the art technology for microalgae bioeconomy from cultivation process to production of valuable components and by-streams. Microalgae Chlorella sorokiniana were cultivated in the pilot-scale innovation concept in Hamburg (Germany) using different systems such as race way pond (5000 L) and flat panel reactors (8 x 180 L). In order to achieve the optimum growth conditions along with suitable cellular composition for the further extraction of the value-added components, process parameters such as light intensity, temperature and pH are continuously being monitored. On the other hand, metabolic needs in nutrients were provided by addition of micro- and macro-nutrients into a medium to ensure autotrophic growth conditions of microalgae. The cultivation was further followed by downstream process and extraction of lipids, proteins and saccharides. Lipids extraction is conducted in repeated-batch semi-automatic mode using hot extraction method according to Randall. As solvents hexane and ethanol are used at different ratio of 9:1 and 1:9, respectively. Depending on cell disruption method along with solvents ratio, the total lipids content showed significant variations between 8.1% and 13.9 %. The highest percentage of extracted biomass was reached with a sample pretreated with microwave digestion using 90% of hexane and 10% of ethanol as solvents. Proteins content in microalgae was determined by two different methods, namely: Total Kejadahl Nitrogen (TKN), which further was converted to protein content, as well as Bradford method using Brilliant Blue G-250 dye. Obtained results, showed a good correlation between both methods with protein content being in the range of 39.8–47.1%. Characterization of neutral and acid saccharides from microalgae was conducted by phenol-sulfuric acid method at two wavelengths of 480 nm and 490 nm. The average concentration of neutral and acid saccharides under the optimal cultivation conditions was 19.5% and 26.1%, respectively. Subsequently, biomass residues are used as substrate for anaerobic digestion on the laboratory-scale. The methane concentration, which was measured on the daily bases, showed some variations for different samples after extraction steps but was in the range between 48% and 55%. CO2 which is formed during the fermentation process and after the combustion in the Combined Heat and Power unit can potentially be used within the cultivation process as a carbon source for the photoautotrophic synthesis of biomass.

3D Numerical Investigation of Asphalt Pavements Behaviour Using Infinite Elements

This article presents the main results of three-dimensional (3-D) numerical investigation of asphalt pavement structures behaviour using a coupled Finite Element-Mapped Infinite Element (FE-MIE) model. The validation and numerical performance of this model are assessed by confronting critical pavement responses with Burmister’s solution and FEM simulation results for multi-layered elastic structures. The coupled model is then efficiently utilised to perform 3-D simulations of a typical asphalt pavement structure in order to investigate the impact of two tire configurations (conventional dual and new generation wide-base tires) on critical pavement response parameters. The numerical results obtained show the effectiveness and the accuracy of the coupled (FE-MIE) model. In addition, the simulation results indicate that, compared with conventional dual tire assembly, single wide base tire caused slightly greater fatigue asphalt cracking and subgrade rutting potentials and can thus be utilised in view of its potential to provide numerous mechanical, economic, and environmental benefits.

Reading and Teaching Poetry as Communicative Discourse: A Pragma-Linguistic Approach

Language is communication on several discourse levels. The target of teaching a language and the literature of a foreign language is to communicate a message. Reading, appreciating, analysing, and interpreting poetry as a sophisticated rhetorical expression of human thoughts, emotions, and philosophical messages is more feasible through the use of linguistic pragmatic tools from a communicative discourse perspective. The poet's intention, speech act, illocutionary act, and perlocutionary goal can be better understood when communicative situational context as well as linguistic discourse structure theories are employed. The use of linguistic theories in the teaching of poetry is, therefore, intrinsic to students' comprehension, interpretation, and appreciation of poetry of the different ages. It is the purpose of this study to show how both teachers as well as students can apply these linguistic theories and tools to dramatic poetic texts for an engaging, enlightening, and effective interpretation and appreciation of the language. Theories drawn from areas of pragmatics, discourse analysis, embedded discourse level, communicative situational context, and other linguistic approaches were applied to selected poetry texts from the different centuries. Further, in a simple statistical count of the number of poems with dialogic dramatic discourse with embedded two or three levels of discourse in different anthologies outweighs the number of descriptive poems with a one level of discourse, between the poet and the reader. Poetry is thus discourse on one, two, or three levels. It is, therefore, recommended that teachers and students in the area of ESL/EFL use the linguistics theories for a better understanding of poetry as communicative discourse. The practice of applying these linguistic theories in classrooms and in research will allow them to perceive the language and its linguistic, social, and cultural aspect. Texts will become live illocutionary acts with a perlocutionary acts goal rather than mere literary texts in anthologies.

Estimation of PM2.5 Emissions and Source Apportionment Using Receptor and Dispersion Models

Source apportionment using Dispersion model depends primarily on the quality of Emission Inventory. In the present study, a CMB receptor model has been used to identify the sources of PM2.5, while the AERMOD dispersion model has been used to account for missing sources of PM2.5 in the Emission Inventory. A statistical approach has been developed to quantify the missing sources not considered in the Emission Inventory. The inventory of each grid was improved by adjusting emissions based on road lengths and deficit in measured and modelled concentrations. The results showed that in CMB analyses, fugitive sources - soil and road dust - contribute significantly to ambient PM2.5 pollution. As a result, AERMOD significantly underestimated the ambient air concentration at most locations. The revised Emission Inventory showed a significant improvement in AERMOD performance which is evident through statistical tests.

Performance Assessment of Carrier Aggregation-Based Indoor Mobile Networks

The intelligent management and optimisation of radio resource technologies will lead to a considerable improvement in the overall performance in Next Generation Networks (NGNs). Carrier Aggregation (CA) technology, also known as Spectrum Aggregation, enables more efficient use of the available spectrum by combining multiple Component Carriers (CCs) in a virtual wideband channel. LTE-A (Long Term Evolution–Advanced) CA technology can combine multiple adjacent or separate CCs in the same band or in different bands. In this way, increased data rates and dynamic load balancing can be achieved, resulting in a more reliable and efficient operation of mobile networks and the enabling of high bandwidth mobile services. In this paper, several distinct CA deployment strategies for the utilisation of spectrum bands are compared in indoor-outdoor scenarios, simulated via the recently-developed Realistic Indoor Environment Generator (RIEG). We analyse the performance of the User Equipment (UE) by integrating the average throughput, the level of fairness of radio resource allocation, and other parameters, into one summative assessment termed a Comparative Factor (CF). In addition, comparison of non-CA and CA indoor mobile networks is carried out under different load conditions: varying numbers and positions of UEs. The experimental results demonstrate that the CA technology can improve network performance, especially in the case of indoor scenarios. Additionally, we show that an increase of carrier frequency does not necessarily lead to improved CF values, due to high wall-penetration losses. The performance of users under bad-channel conditions, often located in the periphery of the cells, can be improved by intelligent CA location. Furthermore, a combination of such a deployment and effective radio resource allocation management with respect to user-fairness plays a crucial role in improving the performance of LTE-A networks.

Distances over Incomplete Diabetes and Breast Cancer Data Based on Bhattacharyya Distance

Missing values in real-world datasets are a common problem. Many algorithms were developed to deal with this problem, most of them replace the missing values with a fixed value that was computed based on the observed values. In our work, we used a distance function based on Bhattacharyya distance to measure the distance between objects with missing values. Bhattacharyya distance, which measures the similarity of two probability distributions. The proposed distance distinguishes between known and unknown values. Where the distance between two known values is the Mahalanobis distance. When, on the other hand, one of them is missing the distance is computed based on the distribution of the known values, for the coordinate that contains the missing value. This method was integrated with Wikaya, a digital health company developing a platform that helps to improve prevention of chronic diseases such as diabetes and cancer. In order for Wikaya’s recommendation system to work distance between users need to be measured. Since there are missing values in the collected data, there is a need to develop a distance function distances between incomplete users profiles. To evaluate the accuracy of the proposed distance function in reflecting the actual similarity between different objects, when some of them contain missing values, we integrated it within the framework of k nearest neighbors (kNN) classifier, since its computation is based only on the similarity between objects. To validate this, we ran the algorithm over diabetes and breast cancer datasets, standard benchmark datasets from the UCI repository. Our experiments show that kNN classifier using our proposed distance function outperforms the kNN using other existing methods.

Multidimensional Compromise Programming Evaluation of Digital Commerce Websites

Multidimensional compromise programming evaluation of digital commerce websites is essential not only to have recommendations for improvement, but also to make comparisons with global business competitors. This research provides a multidimensional decision making model that prioritizes the objective criteria weights of various commerce websites using multidimensional compromise solution. Evaluation of digital commerce website quality can be considered as a complex information system structure including qualitative and quantitative factors for a multicriteria decision making problem. The proposed multicriteria decision making approach mainly consists of three sequential steps for the selection problem. In the first step, three major different evaluation criteria are characterized for website ranking problem. In the second step, identified critical criteria are weighted using the standard deviation procedure. In the third step, the multidimensional compromise programming is applied to rank the digital commerce websites.

Role-Governed Categorization and Category Learning as a Result from Structural Alignment: The RoleMap Model

The paper presents a symbolic model for category learning and categorization (called RoleMap). Unlike the other models which implement learning in a separate working mode, role-governed category learning and categorization emerge in RoleMap while it does its usual reasoning. The model is based on several basic mechanisms known as reflecting the sub-processes of analogy-making. It steps on the assumption that in their everyday life people constantly compare what they experience and what they know. Various commonalities between the incoming information (current experience) and the stored one (long-term memory) emerge from those comparisons. Some of those commonalities are considered to be highly important, and they are transformed into concepts for further use. This process denotes the category learning. When there is missing knowledge in the incoming information (i.e. the perceived object is still not recognized), the model makes anticipations about what is missing, based on the similar episodes from its long-term memory. Various such anticipations may emerge for different reasons. However, with time only one of them wins and is transformed into a category member. This process denotes the act of categorization.

X-Ray Fluorescence Molecular Imaging with Improved Sensitivity for Biomedical Applications

X-ray Fluorescence Molecular Imaging (XFMI) holds great promise as a low-cost molecular imaging modality for biomedical applications with high chemical sensitivity. However, for in vivo biomedical applications, a key technical bottleneck is the relatively low chemical sensitivity of XFMI, especially at a reasonably low radiation dose. In laboratory x-ray source based XFMI, one of the main factors that limits the chemical sensitivity of XFMI is the scattered x-rays. We will present our latest findings on improving the chemical sensitivity of XFMI using excitation beam spectrum optimization. XFMI imaging experiments on two mouse-sized phantoms were conducted at three different excitation beam spectra. Our results show that the minimum detectable concentration (MDC) of iodine can be readily increased by five times via excitation spectrum optimization. Findings from this investigation could find use for in vivo pre-clinical small-animal XFMI in the future.

CRYPTO COPYCAT: A Fashion Centric Blockchain Framework for Eliminating Fashion Infringement

The fashion industry represents a significant portion of the global gross domestic product, however, it is plagued by cheap imitators that infringe on the trademarks which destroys the fashion industry's hard work and investment. While eventually the copycats would be found and stopped, the damage has already been done, sales are missed and direct and indirect jobs are lost. The infringer thrives on two main facts: the time it takes to discover them and the lack of tracking technologies that can help the consumer distinguish them. Blockchain technology is a new emerging technology that provides a distributed encrypted immutable and fault resistant ledger. Blockchain presents a ripe technology to resolve the infringement epidemic facing the fashion industry. The significance of the study is that a new approach leveraging the state of the art blockchain technology coupled with artificial intelligence is used to create a framework addressing the fashion infringement problem. It transforms the current focus on legal enforcement, which is difficult at best, to consumer awareness that is far more effective. The framework, Crypto CopyCat, creates an immutable digital asset representing the actual product to empower the customer with a near real time query system. This combination emphasizes the consumer's awareness and appreciation of the product's authenticity, while provides real time feedback to the producer regarding the fake replicas. The main findings of this study are that implementing this approach can delay the fake product penetration of the original product market, thus allowing the original product the time to take advantage of the market. The shift in the fake adoption results in reduced returns, which impedes the copycat market and moves the emphasis to the original product innovation.

Dynamic Variation in Nano-Scale CMOS SRAM Cells Due to LF/RTS Noise and Threshold Voltage

The dynamic variation in memory devices such as the Static Random Access Memory can give errors in read or write operations. In this paper, the effect of low-frequency and random telegraph noise on the dynamic variation of one SRAM cell is detailed. The effect on circuit noise, speed, and length of time of processing is examined, using the Supply Read Retention Voltage and the Read Static Noise Margin. New test run methods are also developed. The obtained results simulation shows the importance of noise caused by dynamic variation, and the impact of Random Telegraph noise on SRAM variability is examined by evaluating the statistical distributions of Random Telegraph noise amplitude in the pull-up, pull-down. The threshold voltage mismatch between neighboring cell transistors due to intrinsic fluctuations typically contributes to larger reductions in static noise margin. Also the contribution of each of the SRAM transistor to total dynamic variation has been identified.

Sanitary Measures in Piggeries, Awareness and Risk Factors of African Swine Fever in Benue State, Nigeria

A study was conducted to determine the level of compliance with sanitary measures in piggeries, and awareness and risk factors of African swine fever in Benue State, Nigeria. Questionnaires were distributed to 74 respondents consisting of piggery owners and attendants in different piggeries across 12 LGAs to collect data for this study. Sanitary measures in piggeries were observed to be generally very poor, though respondents admitted being aware of ASF. Piggeries located within a 1 km radius of a slaughter slab (OR=9.2, 95% CI - 3.0-28.8), piggeries near refuse dump sites (OR=3.0, 95% CI - 1.0-9.5) and piggeries where farm workers wear their work clothes outside of the piggery premises (OR=0.2, 95% CI - 0.1-0.7) showed higher chances of ASFV infection and were significantly associated (p < 0.0001), (p < 0.05) and (p < 0.01), and were identified as potential risk factors. The study concluded that pigs in Benue State are still at risk of an ASF outbreak. Proper sanitary and hygienic practices is advocated and emphasized in piggeries, while routine surveillance for ASFV antibodies in pigs in Benue State is strongly recommended to provide a reliable reference data base to plan for the prevention of any devastating ASF outbreak.