Textile wastewater accommodates many toxic organic contaminants which could potentially threaten the ecosystem if left untreated. Methylene blue is a toxic, nondegradable, cationic dye which is reportedly found in significant amounts in the textile effluent stream as it is widely used to dye silk and cotton fabrics. This study reports an investigation of methylene blue removal using a composite membrane fabricated using chitosan and graphene oxide. The fabricated composite membrane was characterized using Scanning Electron Microscopy, FTIR Spectroscopy, Raman Spectroscopy, UV vis spectroscopy, and X ray Diffraction. The isotherm modelling conducted confirmed a maximum adsorptive capacity of 179 mg/g which was well fitted with the Langmuir isotherm model indicating a homogenous monolayer adsorption.
Demand for organic food is rapidly increasing. This is despite several constraints in the process of producing organic foods. Producers should aim to maximize their profit by catering to the rising demand of this niche market. Manufacturing cost per unit is comparatively higher in the organic chain compared to the conventional chain. Hence, producers must make critical decisions when supplying their products to different markets. A Python-based Linear Programming optimization model tested using Google optimization Tools has been developed to identify the optimum delivery volume that should be supplied to each market which has been identified in this empirical study. The designed model aims to maximize profits by minimizing unsold products and postharvest waste. The developed model can guide producers who operate in the organic perishable supply chain to gain market benefits in short food supply chains. There is a lack of research on sustainability aspects in agricultural coordination and applications in supply network performance. Therefore, this study fills this gap by addressing the issue of postharvest waste in the distribution process of organic vegetables and fruits. The model can be extended into other product variants to validate the model’s applicability under different market scenarios.
Due to the increase in electricity demand and the rapid depletion of fossil fuels, energy management has become a critical issue over the last two decades. Thus, researchers, utility suppliers, governments, and policymakers are working in tandem to develop novel solutions. In recent years, solutions based on Intrusive Load Monitoring (ILM) and Non-intrusive Load Monitoring (NILM) have garnered the interest of many researchers. However, NILM systems are less difficult to implement and more cost-effective than ILM systems. Even though available NILM-based solutions can identify single-state devices with acceptable accuracy, identifying the various operating states of multi-state devices remains a problem. This research work proposes a novel supervised learning algorithm to correctly identify the operating states of multi-state residential devices. Results obtained through extensive simulations indicate that the proposed algorithm can achieve device and state identification accuracy of 93 percent and 91 percent, respectively.
Mobile users may experience frequent service outages or poor quality of service in rural areas due to the scarcity of base stations in the proximity. Therefore, a cost effective and flexible solution is introduced in this paper with unmanned aerial vehicles (UAVs) and terrestrial base stations (TBSs), coexisting under a stochastic network with the combination of millimeter waves and non-orthogonal access (NOMA) techniques. This is achieved by deploying both the UAVs (millimeter wave frequencies) and TBSs (sub-6 GHz frequencies) in the same system, and selecting the transmitter based on a user association scheme. The maximum signal-to-interference-plus-noise ratio (SINR) and the closest transmitter user association schemes are compared using extensive simulations to identify the most suitable scheme in terms of the outage probability and the achievable downlink rate. The results show that the closest transmitter user association policy results in lower outage probability and higher downlink rates for UAV-assisted millimeter wave NOMA Networks.
The adequacy evaluation of modern renewable-rich power systems tends to be a computationally challenging task due to variations of renewable power generation. Recently, more computationally efficient evolutionary algorithms and swarm intelligence-based methods are utilized for evaluating the adequacy of power systems. In this paper, the authors have proposed a wind and solar integrated composite system adequacy evaluation framework using an Evolutionary Swarm Algorithm (ESA). The system failure states which have a higher probability of occurrence are explored using the ESA to estimate the adequacy indices of the system. The wind and solar power generation are modeled using a clustering-based method considering their annual effective power output. Moreover, the correlation between the system load and renewable power generation is modeled in the adequacy evaluation framework. Using the proposed framework, several case studies are conducted on the IEEE Reliability Test System to analyze the impact of integrating renewables on the adequacy of composite systems. The results show that wind generation tends to improve system reliability than solar due to its higher availability. In addition, the equivalent capacities of wind and solar generators are found to be 125MW and 215MW against a 50MW hydro generator.
Visual Question Answering (VQA) is a computer vision task in which a system produces an accurate answer to a given image and a question that is relevant to the image. Medical VQA can be considered as a subfield of general VQA, which focuses on images and questions in the medical domain. The VQA model’s most crucial task is to learn the question-image joint representation to reflect the information related to the correct answer. Medical VQA remains a difficult task due to the ineffectiveness of question-image embeddings, despite recent research on general VQA models finding significant progress. To address this problem, we propose a new method for training VQA models that utilizes adversarial learning to improve the question-image embedding and illustrate how this embedding can be used as the ideal embedding for answer inference. For adversarial learning, we use two embedding generators (question–image embedding and a question-answer embedding generator) and a discriminator to differentiate the two embeddings. The questionanswer embedding is used as the ideal embedding and the question-image embedding is improved in reference to that. The experiment results indicate that pre-training the question-image embedding generation module using adversarial learning improves overall performance, implying the effectiveness of the proposed method.
The traditional way of using pen and paper to take notes is getting over by the touch screen devices. These devices provide more options to the users to enhance their productivity while taking notes. The ability to recognize and validate the words written on the touch screens facilitates further capabilities to the users. Hence, in this paper, we describe a segmentation-based approach combined with an n-gram model for the recognition and validation of the Sinhala words written on touch screens. We compare the results of 6 commonly used machine learning models to find the best performing classifier for recognizing individual characters of words. The classifiers are trained to identify 19 different Sinhala characters. Based on the results, Convolution Neural Network (CNN) based word classifier stands ahead of other classifiers.
Soft growing robots are a novel concept that uses fluid flow to increase in length at the tip. Using effective steering mechanisms, soft-growing robots can grow along the desired path. Series pneumatic artificial muscles (sPAMs) are used as steering actuators in contemporary soft-growing robots. This paper presents the experimental evaluation of the effect of the sPAM configuration on the bending of a soft-growing robot. The paper also includes the design and fabrication of the robot body and the sPAMs, the experimental setup, and the analysis of the observed results. The soft-growing robot body and sPAMs are made of low-density polyethylene (LDPE). The effect of four sPAM configurations (n sPAMs in m groups) on the bending angle and the blocked force is experimentally evaluated. Experimental results show that the bending angle is proportional to the number of sPAMs in a group (Bending angle reduces from 33° to 12° as the number of sPAMs in the group is reduced from 6 to 2). The blocked force applied by the robot tip remains constant over all the tested sPAM configurations. Hence, by varying sPAM configurations, a designer will be able to change the bending angle of a soft-growing robot without affecting tip forces.
When people are getting old, some gait abnormalities may have happened in their walking patterns. It means, there may be slight differences in their physical performance. Due to the complexity of that evaluation, a machine learning algorithm can be used to cluster the gait patterns. Kohonen Maps (KM) and mini-batch k-means (MBKM) have been combined to cluster the gait parameters according to the age groups to identify the principal gait characteristics which are affected to the walking pattern. Dataset is consisting of 180 gait data based on the data which have been gained through the inertial measurement unit (IMU). When analysing the results, the proposed algorithm is showing low computational cost and time which is more efficient. As well the results have been proved that the cadence is the most important and affected gait parameter when caused to a walking pattern of a person when he or she is getting older. These results provide clues for the health professionals to identify and evaluate the difficulties of walking patterns of patients according to age.
A conceptual model development is a primary mechanism for defining dependencies among parameters. Here, the integration of concepts linked with Multi-Hazard Early Warning mechanism is captured. This became more significant when implementing Disaster Risk Reduction strategies emphasized by the Sendai Framework for Disaster Risk Reduction 2015-2030. Such a warning mechanism is required to ensure that the public at risk is timely alerted and adequately informed. In proper understanding of this mechanism, the conceptual model development is a significant approach. Here, the activity level sequencing was determined with the analytical illustration of the activity concentration and stakeholder level. Recent studies were considered in conceptual model development. For further verification, the reviews obtained from the pilot expert survey were considered. The developed model was checked for applicability considering disaster situations such as Indian Ocean Tsunami in 2004, Cyclone Fani in 2019 and Meethotamulla garbage dump failure in 2017. Here, the activity level concentration variation is categorized based on stakeholder levels which define on the international to community level are captured along with time change. Based on the idealized conceptual framework, the policymakers and associated stakeholders can use this in integrating the guidelines and policy framework which are targeted at Disaster Risk Reduction.
The wet chemical synthesis of Nano-Hydroxyapatite HAP [Ca10(PO 4 ) 6 (OH) 2 ] derived from precursors Ca(OH) 2 and H 3 PO 4 was experimented using a kinetic model derived based on the classical nucleation theory. The model gives a mathematical formulation for the nucleation rate in terms of the process variables of the wet chemical synthesis namely supersaturation, temperature, and interfacial tension. Only the effect of supersaturation for nano formation was studied in the experimental work of this study. The different supersaturations for five different samples were initiated by changing the precursor concentrations keeping the Ca/P molar ratio at 1.6 to 1.7 in the solution being mixed. Finally, the model was statistically and experimentally validated using Fourier Transform Infrared Spectroscopy (FTIR) analysis, and data obtained by laser particle analyzer. This model can be potentially used to synthesize Nano-Hydroxyapatite particles in a quantitative manner changing the supersaturation of the wet medium by precursor concentrations.
Rapid assessment of building vulnerability and risk is very useful, especially if based on sound engineering principles as opposed to expert opinion alone. A tsunami relative risk index (TRRI) has recently been proposed for hospital buildings based on such an approach. This study extends the concept to reinforced concrete school buildings. Two typical plan forms of school buildings were explored, each of two and three storey height. The criterion for overall structural failure was the shear capacity of columns; for scour, the number of footings undermined; and for debris impact, the shear capacity of corner columns. Of the parameters explored, the inundation depth and flow velocity were found to have the greatest influence on TRRI, while building type, building height and flow direction had much smaller influence. Debris impact was the governing risk at low inundation depths (around 1m), with scour at medium depths (around 3m) and overall structural shear failure at higher depths (around 5m).
Lithium-ion Batteries (LIBs) have come a long way with various improvements to make them more efficient, compact, and safe while simultaneously enhancing the energy density and cycle life. If it is possible to improve the technicalities to lower the cell cost by indicating some potential solutions, the economic issues in LIBs automotive applications can be addressed. This study intends to approach a bottleneck solution for pure Electric Vehicle (EV) cost reduction. The BatPaC 5.0 modeling tool is used to examine different cell chemistries (NMC811-Graphite(Gr), NCA-Gr, LFP-Gr, LMO-Gr, and 50%/50%NMC532/LMO-Gr) and determine the accuracy of the hypothesis made on the effect of positive electrode coating thickness of LIBs, on the cell cost, gravimetric energy density and volumetric energy density in high volume production. Using the above assumption, it is obtained that doubling the coating thickness of the positive electrode from 60 to 120 μm, reduces the cost in all cell types. But the highest by ~20% in LFP-Gr. And it emerges that increasing the positive electrode coating thickness of LIBs, lowers the cell cost whilst improving the gravimetric energy density and volumetric energy density. Therefore, the positive electrode coating thickness can be considered a crucial parameter in cell cost reduction.
This study describes a procedure in which a compound has been formed by pyrolyzed rice husk at high temperature and subsequent grinding, which is intended to be used as a substitutional filler for carbon black in natural rubber compounds. This process has been investigated in order to develop a better value addition for rice husk which is a material source that is abundantly available in rice cultivating countries including Sri Lanka. The study has investigated effects on rheological and curing behavior, strength and elongation, hardness and resilience when using rice husk carbon black (RHCB) compared to regular N330 carbon black. The results indicated that non-modified RHCB does not contribute to strength improvement, but it is non-detrimental on elasticity and resilience of the compound.
In deep excavations, it is necessary to guarantee stability against catastrophic failure and to ensure that the deformations in the surrounding are within acceptable limits. Excavations done above the groundwater table can be supported with simple structures such as soldier pile walls. But the stability of the structure is affected by the infiltration of rainwater. If the infiltration of rainwater can be reduced the construction of a deep vertical excavation support system can be optimized. A Capillary Barrier (CB) which consists of a fine layer lying on top of a coarse layer at the ground level can cut off the infiltration into the lower layers. In this research study, initially, attempts were made to establish the critical parameters through parametric studies. A laboratory model of a Capillary Barrier was constructed with instrumentation and a rainfall pattern was applied. Experimental results were verified with GeoStudio, 2012 SEEP/W software and there was a very good agreement. A deep excavation supported by a soldier pile wall in an unsaturated soil was modelled thereafter with Midas GTS NX 3D software and the effectiveness of the capillary barrier in optimizing the design of the support system during a prolonged rainfall was illustrated.
Manufacturers and brand owners apply sentiment analysis techniques on customer reviews to identify customer opinions on their products and services. Sentiment analysis at the document level or sentence level does not provide a complete view of the customer opinion because customers may express their opinion on different aspects of the product or service within a single review. This issue has inspired aspect-level opinion mining. Two core tasks are involved with aspect-level opinion mining: aspect detection and aspect-based sentiment analysis. This research is aimed at the first task - aspect detection. The focused domain is sportswear apparel, which has been largely overlooked in the field of opinion mining. Accordingly, this paper presents a new dataset produced with manual annotations by domain experts, according to a newly defined aspect taxonomy. This research compares the performance of a set of pre-trained language models for the considered task, and achieves state-of the-art performance for sportswear apparel reviews using a novel ensemble method.
An increasing number of institutions are converting from traditional verification to online digital verification of user documents. In Sri Lanka, this requires clean digital images of documents such as the National Identity Card (NIC), driver’s license etc. which often have background textures and reflective surfaces. Due to human error, uneven natural light and reflectance properties, such document images contain cast shadows which pose a difficulty to further processing. The NIC dataset itself is unique in nature. It has some properties unique to a document image, i.e., dark letters on a light background, and some properties unique to a natural image, i.e., background object textures. Therefore, the target domain or nature of dataset itself is a novelty. For such domain, we propose a shadow removal mechanism based on Dual Hierarchical Aggregation Network (DHAN) and VGG-19 (Convolutional network by Visual Geometry Group) object detection deep learning model. Since previous research is not already done on this specific target area, we do not have a direct benchmark to compare our proposed methodology. Hence, we have experimented our dataset with already existing state of the art models in both shadow removal for document images and shadow removal for natural images. Our proposed model reflects the typical backbone architecture for shadow removal models for natural images when removing shadows while preserving background textures. Our architecture can be directly utilised or added to an already-existing image processing pipeline. Although our target domain is relatively new, for comparison purpose we went comparing our model with a close relative which is shadow removal on natural images. Our architecture results in an overall quality improvement of 12% and 63% improvement in output resolution when compared with the state-of-the-art architecture in shadow removal for natural images.
Steel brackets have a renowned potential of being used in bridge constructions as a load-bearing element. However, the excessive material usage in bracket manufacturing will lead to expensive constructions, increased energy consumption and a rise in carbon footprint. To circumvent these challenges, this paper demonstrates a novel approach for producing an optimum and sustainable steel bracket for pedestrian bridge construction. Topology optimization is used as the tool of choice in this work, which has a proven record of arriving at the highest stiffness to weight ratio. This study uses an existing steel bridge bracket in Castleford Foot Bridge, England as a study case. The bracket is optimized under several volume fractions and ultimately, the optimum design is selected based on both simulation results and practical considerations. It is shown that a considerable amount of material could be saved without sacrificing the strength and stiffness requirement of the bridge bracket. Without a loss of generality, the selected optimal design is manually extracted to a Computer-Aided Design (CAD) software for further post-processing and analysis.
Plastic waste management is a growing concern worldwide since permanent solutions are costly for third-world countries. This study explores storing plastic waste in concrete as a partial replacement of the fine aggregate to produce a useful building material. To resolve the material's lack of compressive strength, this study used HDPE chemically treated with Sodium Hypochlorite (NaOCl) and partially replaced the cement with silica fume at 7.5%, and 10% to improve the concretes Interfacial Transition Zones (ITZ). This research tests the workability, density, and compressive strength, and completes a microstructure analysis of this material to determine if structural lightweight concrete (SLWC) can be produced. The results obtained indicate that adding silica fume with chemically treated aggregate increased the compressive strength by 1.9% and 7.4% respectively in comparison to the control. Through the statistical analysis, these additions were then shown to make a significant difference in the concrete's strength. The microstructure analysis too confirmed that the quality of the ITZs had improved in these mixtures. However, the workability of these 2 mixtures was reduced by 77.4%. The study concludes that although the concrete isn't lightweight, its compressive strength can be improved to match that of conventional structural concrete.
Transportation is one of the main aspects of a country’s economy. Most economic sectors are laid upon detrimental results due to an unorganized transportation network. This is a crucial issue faced by developing countries. There is no doubt that highways should be built in order to maximize the throughput of the transportation network; nevertheless, expansion of existing roads is also not applicable in countries like Sri Lanka due to its ceasing land area with increasing population. Thus it is essential to switch to a more efficient, technologically advanced approach to solve this issue. In addition to the typical congestion scenarios, the prevailing pandemic situation has realized the importance of prioritizing ambulances when it is caught amidst a traffic jam. Pedestrians are another vital part of the road network. Effective and safe pedestrian crossing will ensure the reduction of road accidents while improving the existing heavy traffic. A smart traffic monitoring system integrated to control the traffic signals is the ideal solution in this context. This paper proposes a smart adaptive traffic monitoring and control system to detect vehicles and pedestrians and prioritize emergency vehicles. A new Convolutional Neural Network is trained with YOLOV3 architecture to achieve 91.3% detection precision.
