Search

ObjectivesThe aims are to explore the factors influencing Bangladeshi patients' decision-making process and their satisfaction level toward medical tourism in India.MethodsThe study used a quantitative research approach with a cross-sectional survey. Data were collected from the patients or their relatives (N = 388) who would have decided to travel to India for medical and treatment purposes at the Chittagong Indian visa center (IVAC). Data were collected using a structured, pre-tested, and facilitator-administered questionnaire, which mainly included the social demographic characteristics, health status, medical tourism information and medical tourism index. Hierarchical regression analysis was performed to explore the factors influencing their satisfaction level toward medical tourism in India.ResultsMore than three-fourths of the participants had visited India for self-treatment. Of the participants, 14% were cardiology patients, and 13% suffered from cancer. The relatives were the key source of information regarding medical tourism for more than one-fourth of the respondents. India's availability of well-experienced doctors, hospital/medical facilities with high standards, well-trained doctors, reputable doctors, and quality treatments and medical materials were top-ranked items. Regression results depict that facility and services appeared as the strongest factor (β = 0.24, t = 4.71, p < 0.001) followed by tourism destination factor (β = 0.16, t = 3.11, p = 0.002), medical tourism costs factor (β = 0.16, t = 3.24, p = 0.001) and country environment factor (β = 0.15, t = 2.69, p = 0.007).ConclusionsWe found that the factor related to facility and services is one of the strongest predictors in our models. Therefore, home countries must strengthen the health care providers' advanced professional training, including service attitudes. Moreover, it is important to lessen the language barrier, reduce the airfare for medical tourists, and make the treatment cost more affordable for patients.

While most deep learning architectures are built on convolution, alternative foundations such as morphology are being explored for purposes such as interpretability and its connection to the analysis and processing of geometric structures. The morphological hit-or-miss operation has the advantage that it considers both foreground information and background information when evaluating the target shape in an image. In this article, we identify limitations in the existing hit-or-miss neural definitions and formulate an optimization problem to learn the transform relative to deeper architectures. To this end, we model the semantically important condition that the intersection of the hit and miss structuring elements (SEs) should be empty and present a way to express Don't Care (DNC), which is important for denoting regions of an SE that are not relevant to detecting a target pattern. Our analysis shows that convolution, in fact, acts like a hit-to-miss transform through semantic interpretation of its filter differences. On these premises, we introduce an extension that outperforms conventional convolution on benchmark data. Quantitative experiments are provided on synthetic and benchmark data, showing that the direct encoding hit-or-miss transform provides better interpretability on learned shapes consistent with objects, whereas our morphologically inspired generalized convolution yields higher classification accuracy. Finally, qualitative hit and miss filter visualizations are provided relative to single morphological layer.

Fuzzy integrals (FIs) are powerful aggregation operators that fuse information from multiple sources. The aggregation is parameterized using a fuzzy measure (FM), which encodes the worths of all subsets of sources. Since the FI is defined with respect to an FM, much consideration must be given to defining the FM. However, in practice this is a difficult task—the number of values in an FM scales as $2^n$ , where $n$ is the number of input sources, thus manually specifying an FM quickly becomes tedious. In this article, we review an automatic, data-supported method of learning the FM by minimizing a sum-of-squared error objective function in the context of decision-level fusion of classifiers using the Choquet FI. While this solves the specification problem, we illuminate an issue encountered with many real-world data sets; i.e., if the training data do not contain a significant number of all possible sort orders, many of the FM values are not supported by the data. We propose various regularization strategies to alleviate this issue by pushing the learned FM toward a predefined structure; these regularizers allow the user to encode knowledge of the underlying FM to the learning problem. Furthermore, we propose another regularization strategy that constrains the learned FM's structure to be a linear order statistic. Finally, we perform several experiments using synthetic and real-world data sets and show that our proposed extensions can improve the learned FM behavior and classification accuracy. A previously proposed visualization technique is employed to simultaneously quantitatively illustrate the FM as well as the FI.

Zadeh's extension principle (ZEP) is a fundamental concept in fuzzy set (FS) theory that enables crisp mathematical operation on FSs. A well-known shortcoming of ZEP is that the height of the output FS is determined by the lowest height of the input FSs. In this article, we introduce a generalized extension principle (GEP) that eliminates this weakness and provides flexibility and control over how membership values are mapped from input to output. Furthermore, we provide a computationally efficient point-based FS representation. In light of our new definition, we discuss two approaches to perform aggregation of FSs using the Choquet integral. The resultant integrals generalize prior work and lay a foundation for future extensions. Last, we demonstrate the extended integrals via a combination of synthetic and real-world examples.

The modern era of machine learning is focused on data-driven solutions. While this has resulted in astonishing leaps in numerous applications, explainability has not witnessed the same growth. The reality is, most machine learning solutions are black boxes. Herein, we focus on data/information fusion in machine learning. Specifically, we explore four eXplainable Artificial Intelligence (XAI) questions relative to Choquet integral; (i) what is the quality of our inputs and their interactions, (ii) how is the information being combined, (iii) what is the quality of our training data (and thus our learned models), and (iv) what trust do we place in an output? Previously, we derived an initial set of indices for (i)–(iv) on the premise of perfect knowledge. Herein, we make XAI more accurate by taking into consideration what the machine learned. A combination of synthetic data and real-world experiments from remote sensing for fusing deep learners in the context of classification are explored. Our approach leads to performance gain, insights into what was learned, and it helps us realize better future solutions.

Mathematical morphology has been explored in deep learning architectures, as a substitute to convolution, for problems like pattern recognition and object detection. One major advantage of using morphology in deep learning is the utility of morphological erosion and dilation. Specifically, these operations naturally embody interpretability due to their underlying connections to the analysis of geometric structures. While the use of these operations results in explainable learned filters, morphological deep learning lacks attribution modeling, i.e., a paradigm to specify what areas of the original observed image are important. Furthermore, convolution-based deep learning has achieved attribution modeling through a variety of neural eXplainable Artificial Intelligence (XAI) paradigms (e.g., saliency maps, integrated gradients, guided backpropagation, and gradient class activation mapping). Thus, a problem for morphology-based deep learning is that these XAI methods do not have a morphological interpretation due to the differences in the underlying mathematics. Herein, we extend the neural XAI paradigm of saliency maps to morphological deep learning, and by doing, so provide an example of morphological attribution modeling. Furthermore, our qualitative results highlight some advantages of using morphological attribution modeling.

Essential oils are mostly used in aromatherapy and their popularity has grown rapidly for the last decade. However, the industry has a substantial low-value waste stream, because downstream industries require therapeutic-grade oil. These waste stream oils can be used in the transport and agricultural sectors. This study investigated the influence of various essential oil blends on the emission characteristics of a multi-cylinder diesel engine. Orange, eucalyptus and tea tree oil were blended with diesel at 5% and 10% by volume, neat diesel and a 10% waste cooking biodiesel-diesel blend were also tested for comparison. The major constituents of orange oil and eucalyptus oil are limonene and 1,8-cineole respectively, and the main constituents of tea tree oil are terpinen-4-ol, γ-terpinene and α-terpinene. Orange oil contains negligible amounts of oxygen, whereas eucalyptus oil and tea tree oil contain 8.4% and 5.4% respectively. Compared to neat diesel, all the essential oil blends exhibited similar or slightly higher density, similar heating value, lower viscosity, flash point, and cetane number, and higher surface tension. However, only orange oil and eucalyptus oil blends exhibited oxidation stability above the minimum standards. Interestingly, blending eucalyptus oil increased the oxidation stability of diesel. Tea tree oil blends emitted the most carbon monoxide (CO) while orange oil and eucalyptus oil blends emitted the least CO and nitrogen oxide (NOX). Although eucalyptus oil and tea tree oil blends contain similar levels of oxygen, they exhibited opposite NOX emission trends, which might be attributed to the dissimilar and complex bonding of oxygen molecules into the structures. Particle number emission of essential oils were load dependent, however, all essential oil belnds emitted higher particulate mass at all loads.

Steganography is the art of hiding secret data in a medium whereas visual-cryptography is a cryptographic technique where a full image is encrypted. The security of image steganography depends on the secret key set which is used for embedding the secret image. If the secret key is exposed, the secret image can be retrieved. To solve this security issue we propose a new type of steganography method using visual cryptography. In this method, text and image are used as a secret message and image for the cover object. 24-bit RGB color images are used as both secret and cover images. In this method, we use a new image namely share1 which converts a secret image to a totally different image called share2 image. Typically, knowing the extraction method, people can retrieve the secret message easily. A pseudorandomly generated image is used as a key for visual encryption and this ensures the extra layer of security. By this proposed method, we ensure more secured image steganography.

Steganography is the art of hiding secret data in a medium. The medium can be an image, video, or an audio file, which is intended to raise no suspicion. Steganography tools typically generate an identical cover image after concealing the secret data in the original image. In this paper, popular steganography tools that are implemented with different embedding algorithms have been compared based on empirical results. Firstly, a set of diverse cover and secret images has been selected as an input set. A number of image quality metrics are applied to the resulting images for comparison. It is found that OpenStego and S-Tools perform the best among seven steganography tools. This comparative analysis gives an insight into the problems and flaws of those tools. Implications for future research and software development for enhancing steganography tools are also discussed based on the experimentation results.

Deep convolutional neural networks (DCNN) have been the dominant methodology in the field of computer vision over the last decade, using various architectural organizations of successive convolutional layers to extract and assemble low level image features into visual component detectors. One of the tradeoffs that have been made as the community has migrated to deep neural models is the loss of explainability and understanding of which salient visual components are being recognized by a model for a particular task. However, there exists a significant heritage in the remote sensing community that has developed advanced algorithms to analyze the signal and structural characteristics of anthropogenic features. One such approach is the use of morphological image processing techniques to extract objects from imagery and aid in the structural analysis of shapes. In particular, the differential morphological profile (DMP) has had great success extracting object shapes, while naturally grouping the extracted shapes into scale ranges. In this research, we present a novel architecture that integrates an explicit (definable and explainable) scaled object extraction into the network architecture, allowing shallower convolutional layers and lower complexity neural models. The architecture is evaluated on a challenging remote sensing dataset of object classes, providing insights to this approach and illuminating future directions of integrating morphology into neural architectures for enhanced explainability.

Fuzzy integrals are useful general purpose aggregation operators, but they can be difficult to understand and visualize in practice. The interaction between an exponentially increasing number of variables–2n fuzzy measure variables for n inputs–makes it hard to understand what exactly is going on in a high dimensional space. We propose a new visualization scheme based on a weighted indicator matrix to better understand the inner workings of an arbitrary fuzzy measure. We provide ways of viewing the Shapley and interaction indices, as well as an optional data coverage histogram. This approach can give insight into which sources are the most relevant in the overall aggregation and decision making process, and it provides a way to visually compare fuzzy measures and subsequently integrals.

Artificial neural networks are a dominant force in our modern era of data-driven artificial intelligence. The adaptive neuro fuzzy inference system (ANFIS) is a neural network based on fuzzy logic versus a more traditional premise like convolution. Advantages of ANFIS include the ability to encode and potentially understand machine learned neural information in the pursuit of explainable, interpretable, and ultimately trustworthy artificial intelligence. However, real-world data is almost always imperfect, e.g., incomplete or noisy, and ANFIS is not naturally robust. Specifically, ANFIS is susceptible to over inflated uncertainty, poor antecedent (fuzzy set) data alignment, degenerate optimization conditions, and hard to interpret logic, to name a few factors. Herein, we explore the use of possibilistic clustering to identify outliers, specifically typicality degrees, to increase the robustness of ANFIS; or any fuzzy logic neuron/network. Experiments are presented that demonstrate the need and quality of the proposed solutions in the pursuit of robust interpretable machine learned neuro fuzzy logic solutions.

The Choquet integral (ChI) is a parametric nonlinear aggregation function defined with respect to the fuzzy measure (FM). To date, application of the ChI has sadly been restricted to problems with relatively few numbers of inputs; primarily as the FM has $2^N$ variables for $N$ inputs and $N(2^{N-1}-1)$ monotonicity constraints. In return, the community has turned to density-based imputation (e.g., Sugeno $\lambda$ -FM) or the number of interactions (FM variables) are restricted (e.g., $k$ -additivity). Herein, we propose a new scalable data-driven way to represent and learn the ChI, making learning computationally manageable for larger $N$ . First, data supported variables are identified and used in optimization. Identification of these variables also allows us recognize future ill-posed fusion scenarios; ChIs involving variable subsets not supported by data. Second, we outline an imputation function framework to address data unsupported variables. Third, we present a lossless way to compress redundant variables and associated monotonicity constraints. Finally, we outline a lossy approximation method to further compress the ChI (if/when desired). Computational complexity analysis and experiments conducted on synthetic datasets with known FMs demonstrate the effectiveness and efficiency of the proposed theory.

Microalgae feedstock is recognised as one of the most promising resources for producing triglycerides which is subsequently converted to biodiesel. However, the large-scale technology required to generate biodiesel from microalgae is still in its early stages of development. Microalgae research to date may be placed into four broad categories: (i) growth, (ii) harvesting, (iii) oil extraction and (iv) fuel properties for engine performance and emissions. More than 1000 manuscripts have been published on the first category with progressively less on subsequent groups. Finally, effects of microalgae methyl esters on engine performance have only been reported in 9 scientific articles. This review will place extraction techniques and engine performance of microalgae biodiesel in the context of the preceding two categories and examine the practical problems associated with fuel properties, engine performance and emissions. Considering energy consumption, toxicity, and time, many of the extraction techniques used in the laboratory show moderate potential for commercial scale. An important finding is that variation of conditions in the first three categories can have a significant effect on biofuel quality which can cause fuel properties to be out of standard and/or adversely affect engine performance and emissions.

This study explored the activity of moving vehicle and public transportation, as a next stage of smart device use, and its evolution with data science. Smart devices have been enabled to help people carry out their daily activities. Some smart device operating systems have special apps to make it easy for users to record information about walking, running, jogging, and stepping through the Activity Recognition process, even recording physiological information. Advanced smart devices can now be used to record information about moving vehicles, as it has begun by enabling paid advertising on personal motor vehicles. However, with advancements in smart device technology, as analyzed and proposed by this paper, people will be able to utilize this technology with new data science methods to analyze far more information related to moving vehicles and public transportation. In this paper, we have proposed a method to recognize the vehicle that the subject is currently using by recorded accelerometer and gyroscope sensor data embedded in a smart device. In other words, we have implemented a machine learning model Artificial Neural Network to classify vehicles from the sensor data.

Herein, a generalization of the ordered weighted average (OWA) is put forth relative to pattern recognition. The resultant linear order statistic neuron (LOSN) is unique in that it bridges fuzzy sets, specifically fuzzy data/information aggregation, with neural networks. This article discusses the gradient descent-based optimization and geometric interpretation of the LOSN. An advantage is that the LOSN is an efficient shared weight encoding of N! perceptrons, relative to N inputs. Open source codes are provided to facilitate reproducible research. Experiments are conducted to both validate the method and show its non-linear geometric expression.

The Choquet integral (ChI) is a proven tool for information aggregation. In prior work, we showed that learning a ChI from data results in missing variables. Herein, we explore two ways to transfer a known ChI from a source domain to a new under sampled target domain. The first method is based on regularization and it listens to the full source domain ChI. The second method optimizes what we can observe (target domain supported variables) and missing variables are the only thing migrated from the source domain. Synthetic experiments, aka we know the truth, are used to show the behavior of these methods with respect to transfering between ChIs.

For autonomous vehicles 3D, rotating LiDAR sensors are often critically important towards the vehicle’s ability to sense its environment. Generally, these sensors scan their environment, using multiple laser beams to gather information about the range and the intensity of the reflection from an object. LiDAR capabilities have evolved such that some autonomous systems employ multiple rotating LiDARs to gather greater amounts of data regarding the vehicle’s surroundings. For these multi–LiDAR systems, the placement of the sensors determine the density of the combined point cloud. We perform preliminary research regarding the optimal LiDAR placement strategy on an off–road, autonomous vehicle known as the Halo project. We use the Mississippi State University Autonomous Vehicle Simulator (MAVS) to generate large amounts of labeled LiDAR data that can be used to train and evaluate a neural network used to process LiDAR data in the vehicle. The trained networks are evaluated and their performance metrics are then used to generalize the performance of the sensor pose. Data generation, training, and evaluation, was performed iteratively to perform a parametric analysis of the effectiveness of various LiDAR poses in the Multi–LiDAR system. We also, describe and evaluate intrinsic and extrinsic calibration methods that are applied in the multi–LiDAR system. In conclusion we found that our simulations are an effective way to evaluate the efficacy of various LiDAR placements based on the performance of the neural network used to process that data and the density of the point cloud in areas of interest.

Essential oils are obtained from the non-fatty parts of a plant, such as the roots, bark, leaves, stems and flowers. These oils are mainly used in the natural medicine sector due to claimed health benefits, as well as the flavouring and fragrance sector, and the market has experienced rapid growth in recent years. The high quality required of the products leads to a very significant low-value waste stream, which is available for use in the transport and agricultural sectors. The use of essential oils in the compression ignition (CI) engine is a concept that has not yet been explored thoroughly. This paper analyses the available literature on the effect of essential oils and their blends on the performance, combustion characteristics and emission parameters of the CI engine. Regarding their properties, essential oils have similar properties to neat diesel. Engine performance using several essential oils and their blends improve brake thermal efficiency (BTE) and reduce brake-specific fuel consumption. A significant reduction in particulate matter (PM) emissions along with reduced hydrocarbon and carbon monoxide emissions has been reported in the literature. Furthermore, essential oils and their blends increase peak cylinder pressure and heat release rate (HRR) compared to neat diesel and biodiesel. However, most of the literature reported increased emissions of nitrogen oxides attributed to the lower cetane number and higher oxygen content. Due to having a low cetane number, essential oils can be used in CI engines by blending with either diesel or biodiesel. The rapid growth of the essential oil sector increases the likelihood of their utilization in CI engines in the future.

The Sensor Analysis and Intelligence Laboratory (SAIL) at Mississippi State University's (MSU's) Center for Advanced Vehicular Systems (CAVS) and the Social, Therapeutic and Robotic Systems Lab (STaRS) at MSU's Computer Science and Engineering department have designed and implemented a modular platform for automated sensor data collection and processing, named the Hydra. The Hydra is an open-source system (all artifacts and code are published to the research community), and it consists of a modular rigid mounting platform (sensors, processors, power supply and conditioning) that utilize the Picatinny rail (a standardized mounting system originally developed for firearms) as a rigid mounting system, a software platform utilizing the Robotic Operating System (ROS) for data collection, and design packages (schematics, CAD drawings, etc.). The Hydra system streamlines the assembly of a configurable multi-sensor system. This system is motivated to enable researchers to quickly select sensors, assemble them as an integrated system, and collect data (without having to recreate the Hydras hardware and software). Prototype results are presented from a recent data collection on a small robot during a SWAT-robot training.

The Canada 150 Conference on Migration of Bengalis was triggered by our academic as well as personal desires to establish broadly the history of migration of Bengalis or Bangla-speaking people to Canada. As long-time researchers on Asian immigrants in Canada, and through our involvement in the Metropolis Research Project, we realized that there was hardly any published material on Canadian Bengalis. Therefore, in 2017, on the eve of Canada’s 150th anniversary, we took the opportunity to celebrate and document the history and contemporary trends of Bengali immigrants in Canada. ISBN: 9781772870442

What deep learning lacks at the moment is the heterogeneous and dynamic capabilities of the human system. In part, this is because a single architecture is not currently capable of the level of modeling and representation of the complex human system. Therefore, a heterogeneous set of pathways from sensory stimulus to cognitive function needs to be developed in a richer computational model. Herein, we explore the learning of multiple pathways–as different deep neural network architectures–coupled with appropriate data/information fusion. Specifically, we explore the advantage of data-driven optimization of fusing different deep nets–GoogleNet, CaffeNet and ResNet–at a per class (neuron) or shared weight (single data fusion across classes) fashion. In addition, we explore indices that tell us the importance of each network, how they interact and what aggregation was learned. Experiments are provided in the context of remote sensing on the UC Merced and WHU-RS19 data sets. In particular, we show that fusion is the top performer, each network is needed across the various target classes, and unique aggregations (i.e., not common operators) are learned.

The structural features of fatty acids in biodiesel, including degree of unsaturation, percentage of saturated fatty acids and average chain length, influence important fuel properties such as cetane number, iodine value, density, kinematic viscosity, higher heating value and oxidation stability. The composition of fatty acid esters within the fuel should therefore be in the correct ratio to ensure fuel properties are within international biodiesel standards such as ASTM 6751 or EN 14214. This study scrutinises the influence of fatty acid composition and individual fatty acids on fuel properties. Fuel properties were estimated based on published equations, and measured according to standard procedure ASTM D6751 and EN 14214 to confirm the influences of the fatty acid profile. Based on fatty acid profile-derived calculations, the cetane number of the microalgal biodiesel was estimated to be 11.6, but measured 46.5, which emphasises the uncertainty of the method used for cetane number calculation. Multi-criteria decision analysis (MCDA), PROMETHEE–GAIA, was used to determine the influence of individual fatty acids on fuel properties in the GAIA plane. Polyunsaturated fatty acids increased the iodine value and had a negative influence on cetane number. Kinematic viscosity was negatively influenced by some long chain polyunsaturated fatty acids such as C20:5 and C22:6 and some of the more common saturated fatty acids C14:0 and C18:0. The positive impact of average chain length on higher heating value was also confirmed in the GAIA plane.

The current study investigates the opportunity of using Licella biofuel as a partly renewable fuel provided by Licella P/L. Hereafter this fuel will be referred to as Licella biofuel. The renewable component of the Licella biofuel was made from the hydrothermal conversion of Australian pinus radiata wood flour using Licella’s proprietary Cat-HTR™ technology. The diesel-soluble component of the hydrothermal product was extracted into road diesel to give a blended fuel containing approximately 30% renewable material with the balance from diesel. This was further blended with a regular diesel fuel (designated R0) to give fuels for testing containing 5%, 10% and 20% renewable fuel (designated R5, R10 and R20). Some of the key fuel properties were measured for R30 and compared with those of regular diesel fuel. The engine experiment was conducted on a four-cylinder turbocharged common rail direct injection diesel engine. All experiments were performed with a constant speed and five different engine loads. Exhaust emissions including particulate matter (PM) mass and numbers, nitric oxide (NO), total unburnt hydrocarbon (THC), carbon dioxide (CO2) and performance parameters including brake power (BP), indicated power (IP), brake mean effective pressure (BMEP), indicated mean effective pressure (IMEP), mechanical efficiency (ME), brake thermal efficiency (BTE) and brake specific energy consumption (BSEC) were investigated for all four blends (R0, R5, R10 and R20). Among other engine parameters, in-cylinder pressure, heat release rate (HRR) and pressure (P) versus volume (V) diagrams were also investigated for the four fuel blends.

La and Nd co-doped bismuth titanate Bi3 (LaxNd1−x)Ti3O12 (x = 0.4, 0.5, and 0.6) [BLNT] samples were prepared by solid-state reaction method. XRD pattern of calcined powder of BLNT ceramics indicates a single phase of bismuth-layered perovskite structure with no visible impurity phase. The sintered BLNT ceramics showed a uniform microstructure having spherical type grain. Dielectric constant was increased by co-doping due to increase in lattice distortion. Dielectric loss, AC conductance, and leakage current were drastically reduced due to La and Nd co-doping. Better performance was observed for the composition Bi3 (LaxNd1−x)Ti3O12, (x = 0.5).

In this study, the biodiesel properties and effects of blends of oil methyl ester petroleum diesel on a CI direct injection diesel engine is investigated. Blends were obtained from the marine dinoflagellate Crypthecodinium cohnii and waste cooking oil. The experiment was conducted using a four-cylinder, turbo-charged common rail direct injection diesel engine at four loads (25%, 50%, 75% and 100%). Three blends (10%, 20% and 50%) of microalgae oil methyl ester and a 20% blend of waste cooking oil methyl ester were compared to petroleum diesel. To establish suitability of the fuels for a CI engine, the effects of the three microalgae fuel blends at different engine loads were assessed by measuring engine performance, i.e. mean effective pressure (IMEP), brake mean effective pressure (BMEP), in cylinder pressure, maximum pressure rise rate, brake-specific fuel consumption (BSFC), brake thermal efficiency (BTE), heat release rate and gaseous emissions (NO, NOx,and unburned hydrocarbons (UHC)). Results were then compared to engine performance characteristics for operation with a 20% waste cooking oil/petroleum diesel blend and petroleum diesel. In addition, physical and chemical properties of the fuels were measured. Use of microalgae methyl ester reduced the instantaneous cylinder pressure and engine output torque, when compared to that of petroleum diesel, by a maximum of 4.5% at 50% blend at full throttle. The lower calorific value of the microalgae oil methyl ester blends increased the BSFC, which ultimately reduced the BTE by up to 4% at higher loads. Minor reductions of IMEP and BMEP were recorded for both the microalgae and the waste cooking oil methyl ester blends at low loads, with a maximum of 7% reduction at 75% load compared to petroleum diesel. Furthermore, compared to petroleum diesel, gaseous emissions of NO and NOx, increased for operations with biodiesel blends. At full load, NO and NOx emissions increased by 22% when 50% microalgae blends were used. Petroleum diesel and a 20% blend of waste cooking oil methyl ester had emissions of UHC that were similar, but those of microalgae oil methyl ester/petroleum diesel blends were reduced by at least 50% for all blends and engine conditions. The tested microalgae methyl esters contain some long-chain, polyunsaturated fatty acid methyl esters (FAMEs) (C22:5 and C22:6) not commonly found in terrestrial-crop-derived biodiesels yet all fuel properties were satisfied or were very close to the ASTM 6751-12 and EN14214 standards. Therefore, Crypthecodinium cohnii- derived microalgae biodiesel/petroleum blends of up to 50% are projected to meet all fuel property standards and, engine performance and emission results from this study clearly show its suitability for regular use in diesel engines.

Numerous applications in engineering are plagued by incomplete data. The subject explored in this article is how to extend the fuzzy integral (FI), a parametric nonlinear aggregation function, to missing data. We show there is no universally correct solution. Depending on context, different types of uncertainty are present and assumptions are applicable. Two major approaches exist, use just observed data or model/impute missing data. Three extensions are put forth with respect to just use observed data and a two step process, modeling/imputation and FI extension, is proposed for using missing data. In addition, an algorithm is proposed for learning the FI relative to missing data. The impact of using and not using modeled/imputed data relative to different aggregation operators-selections of underlying fuzzy measure (capacity)-are also discussed. Last, a case study and data-driven learning experiment are provided to demonstrate the behavior and range of the proposed concepts.

The fuzzy integral (FI) is a nonlinear aggregation operator whose behavior is defined by the fuzzy measure (FM). As an aggregation operator, the FI is commonly used for evidence fusion where it combines sources of information based on the worth of each subset of sources. One drawback to FI-based methods, however, is the specification of the FM. Defining the FM manually quickly becomes too tedious since the number of FM terms scales as 2n, where n is the number of sources; thus, an automatic method of defining the FM is necessary. In this paper, we review a data-driven method of learning the FM via minimizing the sum-of-squared error (SSE) in the context of decision-level fusion and propose an extension allowing knowledge of the underlying FM to be encoded in the algorithm. The algorithm is applied to real-world and toy datasets and results show that the extension can improve classification accuracy. Furthermore, we introduce a visualization strategy to simultaneously show the quantitative information in the FM as well as the FI.

Countless challenges in engineering require the intelligent combining (aka fusion) of data or information from multiple sources. The Choquet integral (ChI), a parametric aggregation function, is a well-known tool for multi-source fusion, where source refers to sensors, humans and/or algorithms. In particular, a selling point of the ChI is its ability to model and subsequently exploit rich interactions between inputs. For a task with N inputs, the ChI has 2N interaction variables. Therefore, the ChI becomes intractable quickly in terms of storage and its data-driven learning. Herein, we study the properties of an efficient to store, compute, and ultimately optimize version of the ChI based on a binary fuzzy measure (BFM). The BFM is further motivated by empirical observations in the areas of multi-sensor fusion and hyperspectral image processing. Herein, we provide a deeper understanding of the inner workings, capabilities and underlying philosophy of a BFM ChI (BChI). We also prove that two fuzzy integrals, the ChI and the Sugeno integral, are equivalent for a BFM. Furthermore, only a small subset of BFM variables need be stored, which reduces the BChI to a relatively simple look up operation.

In 2014, global demand for essential oils was 165 kt and it is expected to grow 8.5% per annum up to 2022. Every year Australia produces approximately 1.5k tonnes of essential oils such as tea tree, orange, lavender, eucalyptus oil, etc. Usually essential oils come from non-fatty areas of plants such as the bark, roots, heartwood, leaves and the aromatic portions (flowers, fruits) of the plant. For example, orange oil is derived from orange peel using various extraction methods. Having similar properties to diesel, essential oils have become promising alternate fuels for diesel engines. The present study explores the opportunity of using sweet orange oil in a compression ignition engine. Blends of sweet orange oil-diesel (10% sweet orange oil, 90% diesel) along with neat diesel fuel were used to operate a six-cylinder diesel engine (5.9 litres, common rail, Euro-III, compression ratio 17.3:1). Some key fuel properties such as: viscosity, density, heating value, and surface tension are presented. Engine performance (brake specific fuel consumption) and emission parameters (CO, NOX, and Particulate Matter) were measured to evaluate running with the blends. The engine was operated at 1500 rpm (maximum torque condition) with different loads. The results from the property analysis showed that sweet orange oil-diesel blend exhibits lower density, viscosity and surface tension and slightly higher calorific value compared to neat diesel fuel. Also, from the engine test, the sweet orange oil-diesel blend exhibited slightly higher brake specific fuel consumption, particulate mass and particulate number; however, the blend reduced the brake specific CO emission slightly and brake specific NOX emission significantly compared to that of neat diesel.

Hyperspectral imaging is a powerful technology that is plagued by large dimensionality. Herein, we explore a way to combat that hindrance via non-contiguous and contiguous (simpler to realize sensor) band grouping for dimensionality reduction. Our approach is different in the respect that it is flexible and it follows a well-studied process of visual clustering in high-dimensional spaces. Specifically, we extend the improved visual assessment of cluster tendency and clustering in ordered dissimilarity data unsupervised clustering algorithms for supervised hyperspectral learning. In addition, we propose a way to extract diverse features via the use of different proximity metrics (ways to measure the similarity between bands) and kernel functions. The discovered features are fused with $l_{\infty}$-norm multiple kernel learning. Experiments are conducted on two benchmark datasets and our results are compared to related work. These datasets indicate that contiguous or not is application specific, but heterogeneous features and kernels usually lead to performance gain.

Scabies is one of the commonest diseases among all age groups. Topical permethrin is a widely used treatment option for scabies. Ivermectin is a newer oral agent for the treatment of scabies. This study was done to compare the efficacy of permithrin and oral Ivermectin in the treatment of Scabies in patient suffering from Diabetes mellitus. This comparative clinical trial was carried out in the outpatient department of Dermatology & Venereology, Diabetic Association Medical College Hospital (DAMCH), Faridpur from January 2012 to December 2012. A total 60 cases were enrolled purposively and divided into 2 groups. Group I received 2 doses oral Ivermectin and group II treated with 5% permethirn cream. Patients were followed up at the 3rd and 4th week. Total 86.6% patients of Ivermectin group and 90% of permethrin group were cured. Though permethrin showed somewhat more effective, the difference was not statistically significant. The study found that both ivermectin and permethrin were similarly effective in the treatment of scabies in patients suffering from Diabetes Mellitus.Faridpur Med. Coll. J. 2014;9(2): 70-72

Physical and chemical properties of biodiesel are influenced by structural features of the fatty acids, such as chain length, degree of unsaturation and branching of the carbon chain. This study investigated if microalgal fatty acid profiles are suitable for biodiesel characterization and species selection through Preference Ranking Organisation Method for Enrichment Evaluation (PROMETHEE) and Graphical Analysis for Interactive Assistance (GAIA) analysis. Fatty acid methyl ester (FAME) profiles were used to calculate the likely key chemical and physical properties of the biodiesel [cetane number (CN), iodine value (IV), cold filter plugging point, density, kinematic viscosity, higher heating value] of nine microalgal species (this study) and twelve species from the literature, selected for their suitability for cultivation in subtropical climates. An equal-parameter weighted (PROMETHEE-GAIA) ranked Nannochloropsis oculata , Extubocellulus sp. and Biddulphia sp. highest; the only species meeting the EN14214 and ASTM D6751-02 biodiesel standards, except for the double bond limit in the EN14214. Chlorella vulgaris outranked N. oculata when the twelve microalgae were included. Culture growth phase (stationary) and, to a lesser extent, nutrient provision affected CN and IV values of N. oculata due to lower eicosapentaenoic acid (EPA) contents. Application of a polyunsaturated fatty acid (PUFA) weighting to saturation led to a lower ranking of species exceeding the double bond EN14214 thresholds. In summary, CN, IV, C18:3 and double bond limits were the strongest drivers in equal biodiesel parameter-weighted PROMETHEE analysis.

Physical and chemical properties of biofuel are influenced by structural features of fatty acid such as chain length, degree of unsaturation and branching of the chain. A simple and reliable calculation method to estimate fuel property is therefore needed to avoid experimental testing which is difficult, costly and time consuming. Typically in commercial biodiesel production such testing is done for every batch of fuel produced. In this study 9 different algae species were selected that were likely to be suitable for subtropical climates. The fatty acid methyl esters (FAMEs) of all algae species were analysed and the fuel properties like cetane number (CN), cold filter plugging point (CFPP), kinematic viscosity (KV), density and higher heating value (HHV) were determined. The relation of each fatty acid with particular fuel property is analysed using multivariate and multi-criteria decision method (MCDM) software. They showed that some fatty acids have major influences on the fuel properties whereas others have minimal influence. Based on the fuel properties and amounts of lipid content rank order is drawn by PROMETHEE-GAIA which helped to select the best algae species for biodiesel production in subtropical climates. Three species had fatty acid profiles that gave the best fuel properties although only one of these (Nannochloropsis oculata) is considered the best choice because of its higher lipid content.

Multiple kernel learning (MKL) is an elegant tool for heterogeneous fusion. In support vector machine (SVM) based classification, MK is a homogenization transform and it provides flexibility in searching for high-quality linearly separable solutions in the reproducing kernel Hilbert space (RKHS). However, performance often depends on input and kernel diversity. Herein, we explore a new way to extract diverse features from hyperspectral imagery using different proximity measures and band grouping. The output is fed to l p -norm MKL for feature-level fusion, where larger p's are preferred for diverse vs sparse solutions. Preliminary results on benchmark data indicates that l p -norm MKSVM of diverse features and kernels leads to noticeable performance gain.

Herein, we explore both a new supervised and unsupervised technique for dimensionality reduction or multispectral sensor design via band group selection in hyperspectral imaging. Specifically, we investigate two algorithms, one based on the improved visual assessment of clustering tendency (iVAT) and the other based on the automatic extraction of “blocklike” structure in a dissimilarity matrix (CLODD algorithm). In particular, the iVAT algorithm allows for identification of non-contiguous band groups. Experiments are conducted on a benchmark data set and results are compared to existing algorithms based on hierarchical and c-means clustering. Our results demonstrate the effectiveness of the proposed method.

PurposeThe purpose of this paper is to examine the environmental disclosure initiatives of Niko Resources Ltd – a Canada‐based multinational oil and gas company – following the two major environmental blowouts at a gas field in Bangladesh in 2005. As part of the examination, the authors particularly focus on whether Niko's disclosure strategy was associated with public concern pertaining to the blowouts.Design/methodology/approachThe authors reviewed news articles about Niko's environmental incidents in Bangladesh and Niko's communication media, including annual reports, press releases and stand‐alone social responsibility report over the period 2004‐2007, to understand whether news media attention as proxy for public concern has an impact on Niko's disclosure practices in relation to the affected local community in Bangladesh.FindingsThe findings show that Niko did not provide any non‐financial environmental information within its annual reports and press releases as a part of its responsibility to the local community which was affected by the blowouts, but it did produce a stand‐alone report to address the issue. However, financial environmental disclosures, such as the environmental contingent liability disclosure, were adequately provided through annual reports to meet the regulatory requirements concerning environmental persecutions. The findings also suggest that Niko's non‐financial disclosure within a stand‐alone report was associated with the public pressures as measured by negative media coverage towards the Niko blowouts.Research limitations/implicationsThis paper concludes that the motive for Niko's non‐financial environmental disclosure, via a stand‐alone report, reflected survival considerations: the company's reaction did not suggest any real attempt to hold broader accountability for its activities in a developing country.Originality/valueThis is the first known paper that investigates a multinational company's disclosure behavior in relation to environmental incidents which occurred in a local community.

A magneto-rheological (MR) fluid damper is a semi-active control device that has recently begun to receive more attention in the vibration control community. However, the inherent nonlinear nature of the MR fluid damper makes it challenging to use this device to achieve high damping control system performance. The development of an accurate modeling method for a MR fluid damper is necessary because of its unique characteristics. Our goal was to develop an alternative method for modeling an MR fluid damper by using a self tuning fuzzy (STF) method based on neural technique. The behavior of the researched damper is directly estimated through a fuzzy mapping system. To improve the accuracy of the STF model, a back propagation and a gradient descent method are used to train online the fuzzy parameters to minimize the model error function. A series of simulations were done to validate the effectiveness of the suggested modeling method when compared with the data measured from experiments on a test rig with a researched MR fluid damper. Finally, modeling results show that the proposed STF interference system trained online by using neural technique could describe well the behavior of the MR fluid damper without need of calculation time for generating the model parameters.

Microalgae are considered to be one of the most viable biodiesel feedstocks for the future due to their potential for providing economical, sustainable and cleaner alternatives to petroleum diesel. This study investigated the particle emissions from a commercially cultured microalgae and higher plant biodiesels at different blending ratios. With a high amount of long carbon chain lengths fatty acid methyl esters (C20 to C22), the microalgal biodiesel used had a vastly different average carbon chain length and level of unsaturation to conventional biodiesel, which significantly influenced particle emissions. Smaller blend percentages showed a larger reduction in particle emission than blend percentages of over 20%. This was due to the formation of a significant nucleation mode for the higher blends. In addition measurements of reactive oxygen species (ROS), showed that the oxidative potential of particles emitted from the microalgal biodiesel combustion were lower than that of regular diesel. Biodiesel oxygen content was less effective in suppressing particle emissions for biodiesels containing a high amount of polyunsaturated C20–C22 fatty acid methyl esters and generated significantly increased nucleation mode particle emissions. The observed increase in nucleation mode particle emission is postulated to be caused by very low volatility, high boiling point and high density, viscosity and surface tension of the microalgal biodiesel tested here. Therefore, in order to achieve similar PM (particulate matter) emission benefits for microalgal biodiesel likewise to conventional biodiesel, fatty acid methyl esters (FAMEs) with high amounts of polyunsaturated long-chain fatty acids (≥C20) may not be desirable in microalgal biodiesel composition.

In this paper, we explore a new way to learn an aggregation operator for fusion based on a combination of one or more labeled training data sets and information from one or more experts. One problem with learning an aggregation from training data alone is that it often results in solutions that are overly complex and expensive to implement. It also runs the risk of over-fitting and the quality of that solution is based in large on the size and diversity of the data employed. On the other hand, learning an aggregation based on only expert opinion can be overly subjective and may not result in desired performance for some given task. In order to overcome these shortcomings, we explore a new way to combine both of these important sources. However, conflict between data sets, experts or a combination of the two, can (and often do) occur and must be addressed. Herein, weighted Goal programming, an approach from multi-criteria decision making (MCDM), is employed to learn the fuzzy measure (FM) relative to the Choquet integral (CI) for data/information fusion. This framework provides an interesting way in which we can set the priority order of any number of combination of these two sources. Furthermore, it provides a mechanism to preserve the monotonicity constraints of the FM. We demonstrate results from synthetic experiments across a range of different conflicting and combination of sources scenarios.

Commercially viable carbon–neutral biodiesel production from microalgae has potential for replacing depleting petroleum diesel. The process of biodiesel production from microalgae involves harvesting, drying and extraction of lipids which are energy- and cost-intensive processes. The development of effective large-scale lipid extraction processes which overcome the complexity of microalgae cell structure is considered one of the most vital requirements for commercial production. Thus the aim of this work was to investigate suitable extraction methods with optimised conditions to progress opportunities for sustainable microalgal biodiesel production. In this study, the green microalgal species consortium, Tarong polyculture was used to investigate lipid extraction with hexane (solvent) under high pressure and variable temperature and biomass moisture conditions using an Accelerated Solvent Extraction (ASE) method. The performance of high pressure solvent extraction was examined over a range of different process and sample conditions (dry biomass to water ratios (DBWRs): 100%, 75%, 50% and 25% and temperatures from 70 to 120 °C, process time 5–15 min). Maximum total lipid yields were achieved at 50% and 75% sample dryness at temperatures of 90–120 °C. We show that individual fatty acids (Palmitic acid C16:0; Stearic acid C18:0; Oleic acid C18:1; Linolenic acid C18:3) extraction optima are influenced by temperature and sample dryness, consequently affecting microalgal biodiesel quality parameters. Higher heating values and kinematic viscosity were compliant with biodiesel quality standards under all extraction conditions used. Our results indicate that biodiesel quality can be positively manipulated by selecting process extraction conditions that favour extraction of saturated and mono-unsaturated fatty acids over optimal extraction conditions for polyunsaturated fatty acids, yielding positive effects on cetane number and iodine values. Exceeding biodiesel standards for these two parameters opens blending opportunities with biodiesels that fall outside the minimal cetane and maximal iodine values.

Magneto-rheological (MR) fluid damper is a semi-active control device that has recently received more attention by the vibration control community. But inherent nonlinear hysteresis character of magneto-rheological fluid dampers is one of the challenging aspects for utilizing this device to achieve high system performance. So the development of accurate model is necessary to take the advantage their unique characteristics. Research by others has shown that a system of nonlinear differential equations can successfully be used to describe the hysteresis behavior of the MR damper. The focus of this paper is to develop an alternative method for modeling a damper in the form of centre average fuzzy interference system, where back propagation learning rules are used to adjust the weight of network. The inputs for the model are used from the experimental data. The resulting fuzzy interference system is satisfactorily represents the behavior of the MR fluid damper with reduced computational requirements. Use of the neuro-fuzzy model increases the feasibility of real time simulation.

The authors regret an error in table 1. The correct table is presented below: The authors would like to apologise for any inconvenience caused.