Search results for: innovative techniques

Authors: Diptiman Dinda, Shyamal Kumar Saha

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In the 21st century, sensitive and selective detection of trace amounts of explosives has become a serious problem. Generally, nitro compound and its derivatives are being used worldwide to prepare different explosives. Recently, TNP (2, 4, 6 trinitrophenol) is the most commonly used constituent to prepare powerful explosives all over the world. It is even powerful than TNT or RDX. As explosives are electron deficient in nature, it is very difficult to detect one separately from a mixture. Again, due to its tremendous water solubility, detection of TNP in presence of other explosives from water is very challenging. Simple instrumentation, cost-effective, fast and high sensitivity make fluorescence based optical sensing a grand success compared to other techniques. Graphene oxide (GO), with large no of epoxy grps, incorporate localized nonradiative electron-hole centres on its surface to give very weak fluorescence. In this work, GO is functionalized with 2, 6-diamino pyridine to remove those epoxy grps. through SN2 reaction. This makes GO into a bright blue luminescent fluorophore (DAP/rGO) which shows an intense PL spectrum at ∼384 nm when excited at 309 nm wavelength. We have also characterized the material by FTIR, XPS, UV, XRD and Raman measurements. Using this as fluorophore, a large fluorescence quenching (96%) is observed after addition of only 200 µL of 1 mM TNP in water solution. Other nitro explosives give very moderate PL quenching compared to TNP. Such high selectivity is related to the operation of FRET mechanism from fluorophore to TNP during this PL quenching experiment. TCSPC measurement also reveals that the lifetime of DAP/rGO drastically decreases from 3.7 to 1.9 ns after addition of TNP. Our material is also quite sensitive to 125 ppb level of TNP. Finally, we believe that this graphene based luminescent material will emerge a new class of sensing materials to detect trace amounts of explosives from aqueous solution.

Keywords: graphene, functionalization, fluorescence quenching, FRET, nitroexplosive detection

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Authors: Danlei Yang, Luofeng Huang

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The integration of digital twin technology with renewable energy systems offers an innovative approach to predicting and optimising performance throughout the entire lifecycle. A digital twin is a continuously updated virtual replica of a real-world entity, synchronised with data from its physical counterpart and environment. Many digital twin companies today claim to have mature digital twin products, but their focus is primarily on equipment visualisation. However, the core of a digital twin should be its model, which can mirror, shadow, and thread with the real-world entity, which is still underdeveloped. For a floating solar energy system, a digital twin model can be defined in three aspects: (a) the physical floating solar energy system along with environmental factors such as solar irradiance and wave dynamics, (b) a digital model powered by artificial intelligence (AI) algorithms, and (c) the integration of real system data with the AI-driven model and a user interface. The experimental setup for the floating solar energy system, is designed to replicate real-ocean conditions of floating solar installations within a controlled laboratory environment. The system consists of a water tank that simulates an aquatic surface, where a floating catamaran structure supports a solar panel. The solar simulator is set up in three positions: one directly above and two inclined at a 45° angle in front and behind the solar panel. This arrangement allows the simulation of different sun angles, such as sunrise, midday, and sunset. The solar simulator is positioned 400 mm away from the solar panel to maintain consistent solar irradiance on its surface. Stability for the floating structure is achieved through ropes attached to anchors at the bottom of the tank, which simulates the mooring systems used in real-world floating solar applications. The floating solar energy system's sensor setup includes various devices to monitor environmental and operational parameters. An irradiance sensor measures solar irradiance on the photovoltaic (PV) panel. Temperature sensors monitor ambient air and water temperatures, as well as the PV panel temperature. Wave gauges measure wave height, while load cells capture mooring force. Inclinometers and ultrasonic sensors record heave and pitch amplitudes of the floating system’s motions. An electric load measures the voltage and current output from the solar panel. All sensors collect data simultaneously. Artificial neural network (ANN) algorithms are central to developing the digital model, which processes historical and real-time data, identifies patterns, and predicts the system’s performance in real time. The data collected from various sensors are partly used to train the digital model, with the remaining data reserved for validation and testing. The digital twin model combines the experimental setup with the ANN model, enabling monitoring, analysis, and prediction of the floating solar energy system's operation. The digital model mirrors the functionality of the physical setup, running in sync with the experiment to provide real-time insights and predictions. It provides useful industrial benefits, such as informing maintenance plans as well as design and control strategies for optimal energy efficiency. In long term, this digital twin will help improve overall solar energy yield whilst minimising the operational costs and risks.

Keywords: digital twin, floating solar energy system, experiment setup, artificial intelligence

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Authors: Hadjer Sadoune, Liza Lamini, Scherazade Krim, Amel Djouadi, Rachida Rihani

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Deep neural networks are highly regarded for their accuracy in predicting intricate fermentation processes. Their ability to learn from a large amount of datasets through artificial intelligence makes them particularly effective models. The primary obstacle in improving the performance of these models is to carefully choose the suitable hyperparameters, including the neural network architecture (number of hidden layers and hidden units), activation function, optimizer, learning rate, and other relevant factors. This study predicts biogas production from real wastewater treatment plant data using a sophisticated approach: hybrid Bayesian optimization with a tree-structured Parzen estimator (BO-TPE) for an optimised deep neural network (DNN) model. The plant utilizes an Upflow Anaerobic Sludge Blanket (UASB) digester that treats industrial wastewater from soft drinks and breweries. The digester has a working volume of 1574 m3 and a total volume of 1914 m3. Its internal diameter and height were 19 and 7.14 m, respectively. The data preprocessing was conducted with meticulous attention to preserving data quality while avoiding data reduction. Three normalization techniques were applied to the pre-processed data (MinMaxScaler, RobustScaler and StandardScaler) and compared with the Non-Normalized data. The RobustScaler approach has strong predictive ability for estimating the volume of biogas produced. The highest predicted biogas volume was 2236.105 Nm³/d, with coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE) values of 0.712, 164.610, and 223.429, respectively.

Keywords: anaerobic digestion, biogas production, deep neural network, hybrid bo-tpe, hyperparameters tuning

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Authors: Galal Omer, Onisimo Mutanga, Elfatih M. Abdel-Rahman, Elhadi Adam

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Threatened tree species (TTS) play a significant role in ecosystem functioning and services, land use dynamics, and other socio-economic aspects. Such aspects include ecological, economic, livelihood, security-based, and well-being benefits. The development of techniques for mapping and monitoring TTS is thus critical for understanding the functioning of ecosystems. The advent of advanced imaging systems and supervised learning algorithms has provided an opportunity to classify TTS over fragmenting landscape. Recently, vegetation maps have been produced using advanced imaging systems such as WorldView-2 (WV-2) and robust classification algorithms such as support vectors machines (SVM) and artificial neural network (ANN). However, delineation of TTS in a fragmenting landscape using high resolution imagery has widely remained elusive due to the complexity of the species structure and their distribution. Therefore, the objective of the current study was to examine the utility of the advanced WV-2 data for mapping TTS in the fragmenting Dukuduku indigenous forest of South Africa using SVM and ANN classification algorithms. The results showed the robustness of the two machine learning algorithms with an overall accuracy (OA) of 77.00% (total disagreement = 23.00%) for SVM and 75.00% (total disagreement = 25.00%) for ANN using all eight bands of WV-2 (8B). This study concludes that SVM and ANN classification algorithms with WV-2 8B have the potential to classify TTS in the Dukuduku indigenous forest. This study offers relatively accurate information that is important for forest managers to make informed decisions regarding management and conservation protocols of TTS.

Keywords: artificial neural network, threatened tree species, indigenous forest, support vector machines

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Authors: Jean-François Pouilly

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This study addresses the growing complexity of embedded systems and the critical need for secure, reliable software. Traditional cybersecurity testing methods, often conducted late in the development cycle, struggle to keep pace. This talk explores how formal methods, integrated with advanced analysis tools, empower C/C++ developers to 1) Proactively address vulnerabilities and bugs, which includes formal methods and abstract interpretation techniques to identify potential weaknesses early in the development process, reducing the reliance on penetration and fuzz testing in later stages. 2) Streamline development by focusing on bugs that matter, with close to no false positives and catching flaws earlier, the need for rework and retesting is minimized, leading to faster development cycles, improved efficiency and cost savings. 3) Enhance software dependability which includes combining static analysis using abstract interpretation with full context sensitivity, with hardware memory awareness allows for a more comprehensive understanding of potential vulnerabilities, leading to more dependable and secure software. This approach aligns with industry best practices (ISO2626 or ISO 21434) and empowers C/C++ developers to deliver robust, secure embedded systems that meet the demands of today's and tomorrow's applications. We will illustrate this approach with the TrustInSoft analyzer to show how it accelerates verification for complex cases, reduces user fatigue, and improves developer efficiency, cost-effectiveness, and software cybersecurity. In summary, integrating formal methods and sound Analyzers enhances software reliability and cybersecurity, streamlining development in an increasingly complex environment.

Keywords: safety, cybersecurity, ISO26262, ISO24434, formal methods

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Authors: Sreejani Barua, P. P. Srivastav

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Formulation of new functional food products for diabetes patients and obsessed people is a challenge for food industries till date. Starch is a certainly happening, ecological, reasonable and profusely obtainable polysaccharide in plant material. In the present scenario, there is a great interest in modifying starch functional properties without destroying its granular structure using different modification techniques. Resistant starch (RS) contains almost zero calories and can control blood glucose level to prevent diabetes. The current study focused on modification of mung bean starch which is a good source of legumes carbohydrate for the production of functional food. Heat moisture treatment (HMT) of mung starch was conducted at moisture content of 10-30%, temperature of 80-120 °C and time of 8-24 h.The content of resistant starch after modification was significantly increased from native starches containing RS 7.6%. The design combinations of HMT had been completed through Central Composite Rotatable Design (CCRD). The effects of HMT process variables on the yield of resistant starch was studied through Rapid Surface Methodology (RSM). The highest increase of resistant starch was found up to 34.39% when treated the native starch with 30% m.c at 120 °C temperature for 24 h.The functional properties of both native and modified mung bean starches showed that there was a reduction in the swelling power and swelling volume of HMT starches. However, the solubility of the HMT starches was higher than that of untreated native starch and also observed change in structural (scanning electron microscopy), X-Ray diffraction (XRD) pattern, blue value and thermal (differential scanning calorimetry) properties. Therefore, replacing native mung bean starch with heat-moisture treated mung bean starch leads to the development of new products with higher resistant starch levels and functional properties.

Keywords: Mung bean starch, heat moisture treatment, functional properties, resistant starch

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Authors: Alessandra Swiny, Michalis Georgiou, Yiorgos Hadjichristou

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This paper focuses on the questions raised through the work of Unit 5: ‘In/Out of crisis, emergent and adaptive’; an architectural research-based studio at the University of Nicosia. It focusses on sustainable architectural and urban explorations tackling with the ever growing crises in its various types, phases and locations. ‘Great crisis situations’ are seen as ‘great chances’ that trigger investigations for further development and evolution of the built environment in an ultimate sustainable approach. The crisis is taken as an opportunity to rethink the urban and architectural directions as new forces for inventions leading to emergent and adaptive built environments. The Unit 5’s identity and environment facilitates the students to respond optimistically, alternatively and creatively towards the global current crisis. Mark Wigley’s notion that “crises are ultimately productive” and “They force invention” intrigued and defined the premises of the Unit. ‘Weather and nature are coauthors of the built environment’ Jonathan Hill states in his ‘weather architecture’ discourse. The weather is constantly changing and new environments, the subnatures are created which derived from the human activities David Gissen explains. The above set of premises triggered innovative responses by the Unit’s students. They thoroughly investigated the various kinds of crisis and their causes in relation to their various types of Terrains. The tools used for the research and investigation were chosen in contradictive pairs to generate further crisis situations: The re-used/salvaged competed with the new, the handmade rivalling with the fabrication, the analogue juxtaposed with digital. Students were asked to delve into state of art technologies in order to propose sustainable emergent and adaptive architectures and Urbanities, having though always in mind that the human and the social aspects of the community should be the core of the investigation. The resulting unprecedented spatial conditions and atmospheres of the emergent new ways of living are deemed to be the ultimate aim of the investigation. Students explored a variety of sites and crisis conditions such as: The vague terrain of the Green Line in Nicosia, the lost footprints of the sinking Venice, the endangered Australian coral reefs, the earthquake torn town of Crevalcore, and the decaying concrete urbanscape of Athens. Among other projects, ‘the plume project’ proposes a cloud-like, floating and almost dream-like living environment with unprecedented spatial conditions to the inhabitants of the coal mine of Centralia, USA, not just to enable them to survive but even to prosper in this unbearable environment due to the process of the captured plumes of smoke and heat. Existing water wells inspire inversed vertical structures creating a new living underground network, protecting the nomads from catastrophic sand storms in the Araoune of Mali. “Inverted utopia: Lost things in the sand”, weaves a series of tea-houses and a library holding lost artifacts and transcripts into a complex underground labyrinth by the utilization of the sand solidification technology. Within this methodology, crisis is seen as a mechanism for allowing an emergence of new and fascinating ultimate sustainable future cultures and cities.

Keywords: adaptive built environments, crisis as opportunity, emergent urbanities, forces for inventions

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Authors: Ruqaiya Khalil, Saman Usmani, Zaheer Ul-Haq

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The accurate characterization of ligand binding affinity is indispensable for designing molecules with optimized binding affinity. Computational tools help in many directions to predict quantitative correlations between protein-ligand structure and their binding affinities. Molecular dynamics (MD) simulation is a modern state-of-the-art technique to evaluate the underlying basis of ligand-protein interactions by characterizing dynamic and energetic properties during the event. Autoimmune diseases arise from an abnormal immune response of the body against own tissues. The current regimen for the described condition is limited to immune-modulators having compromised pharmacodynamics and pharmacokinetics profiles. One of the key player mediating immunity and tolerance, thus invoking autoimmunity is Interleukin-2; a cytokine influencing the growth of T cells. Molecular dynamics simulation techniques are applied to seek insight into the inhibitory mechanisms of newly synthesized compounds that manifested immunosuppressant potentials during in silico pipeline. In addition to estimation of free energies associated with ligand binding, MD simulation yielded us a great deal of information about ligand-macromolecule interactions to evaluate the pattern of interactions and the molecular basis of inhibition. The present study is a continuum of our efforts to identify interleukin-2 inhibitors of both natural and synthetic origin. Herein, we report molecular dynamics simulation studies of Interluekin-2 complexed with different antagonists previously reported by our group. The study of protein-ligand dynamics enabled us to gain a better understanding of the contribution of different active site residues in ligand binding. The results of the study will be used as the guide to rationalize the fragment based synthesis of drug-like interleukin-2 inhibitors as immune-modulators.

Keywords: immuno-modulators, MD simulation, protein-ligand interaction, structure-based drug design

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Authors: Mariam Jaber, Abdullah Yahya, Mohammad Alkhedher

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The shift toward sustainable energy solutions increasingly focuses on hydrogen, recognized for its potential as a clean energy carrier. Despite its benefits, hydrogen storage poses significant challenges, primarily due to its low energy density and high volatility. Among the various solutions, pressure vessels designed for hydrogen storage range from Type I to Type V, each tailored for specific needs and benefits. Notably, Type V vessels, with their all-composite, liner-less design, significantly reduce weight and costs while optimizing space and decreasing maintenance demands. This study focuses on optimizing Type V hydrogen storage tanks by examining how different shapes affect performance in drop tests—a crucial aspect of achieving ISO 15869 certification. This certification ensures that if a tank is dropped, it will fail in a controlled manner, ideally by leaking before bursting. While cylindrical vessels are predominant in mobile applications due to their manufacturability and efficient use of space, spherical vessels offer superior stress distribution and require significantly less material thickness for the same pressure tolerance, making them advantageous for high-pressure scenarios. However, spherical tanks are less efficient in terms of packing and more complex to manufacture. Additionally, this study introduces toroidal vessels to assess their performance relative to the more traditional shapes, noting that the toroidal shape offers a more space-efficient option. The research evaluates how different shapes—spherical, cylindrical, and toroidal—affect drop test outcomes when combined with various composite materials and layup configurations. The ultimate goal is to identify optimal vessel geometries that enhance the safety and efficiency of hydrogen storage systems. For our materials, we selected high-performance composites such as Carbon T-700/Epoxy, Kevlar/Epoxy, E-Glass Fiber/Epoxy, and Basalt/Epoxy, configured in various orientations like [0,90]s, [45,-45]s, and [54,-54]. Our tests involved dropping tanks from different angles—horizontal, vertical, and 45 degrees—with an internal pressure of 35 MPa to replicate real-world scenarios as closely as possible. We used finite element analysis and first-order shear deformation theory, conducting tests with the Abaqus Explicit Dynamics software, which is ideal for handling the quick, intense stresses of an impact. The results from these simulations will provide valuable insights into how different designs and materials can enhance the durability and safety of hydrogen storage tanks. Our findings aim to guide future designs, making them more effective at withstanding impacts and safer overall. Ultimately, this research will contribute to the broader field of lightweight composite materials and polymers, advancing more innovative and practical approaches to hydrogen storage. By refining how we design these tanks, we are moving toward more reliable and economically feasible hydrogen storage solutions, further emphasizing hydrogen's role in the landscape of sustainable energy carriers.

Keywords: hydrogen storage, drop test, composite materials, type V tanks, finite element analysis

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Authors: Layan Moussa, Darine Salam, Samir Mustapha

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Heavy metal contamination in agricultural soils in Lebanon poses serious environmental and health problems. Intensive efforts are employed to improve existing quantification methods of heavy metals in contaminated environments since conventional detection techniques have shown to be time-consuming, tedious, and costly. The implication of hyperspectral remote sensing in this field is possible and promising. However, factors impacting the efficiency of hyperspectral imaging in detecting and quantifying heavy metals in agricultural soils were not thoroughly studied. This study proposes to assess the use of hyperspectral imaging for the detection of Ni in agricultural clay soil collected from the Bekaa Valley, a major agricultural area in Lebanon, under different contamination levels and soil moisture content. Soil samples were contaminated with Ni, with concentrations ranging from 150 mg/kg to 4000 mg/kg. On the other hand, soil with background contamination was subjected to increased moisture levels varying from 5 to 75%. Hyperspectral imaging was used to detect and quantify Ni contamination in the soil at different contamination levels and moisture content. IBM SPSS statistical software was used to develop models that predict the concentration of Ni and moisture content in agricultural soil. The models were constructed using linear regression algorithms. The spectral curves obtained reflected an inverse correlation between both Ni concentration and moisture content with respect to reflectance. On the other hand, the models developed resulted in high values of predicted R2 of 0.763 for Ni concentration and 0.854 for moisture content. Those predictions stated that Ni presence was well expressed near 2200 nm and that of moisture was at 1900 nm. The results from this study would allow us to define the potential of using the hyperspectral imaging (HSI) technique as a reliable and cost-effective alternative for heavy metal pollution detection in contaminated soils and soil moisture prediction.

Keywords: heavy metals, hyperspectral imaging, moisture content, soil contamination

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Authors: Arkadiusz Zurek

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The potential of using drones is visible in many areas of logistics, especially in terms of their use for monitoring and control of many processes. The technologies implemented in the last decade concern new possibilities for companies that until now have not even considered them, such as warehouse inventories. Unmanned aerial vehicles are no longer seen as a revolutionary tool for Industry 4.0, but rather as tools in the daily work of factories and logistics operators. The research problem is to develop a method for measuring the weight of goods in a selected link of the clothing supply chain by drones. However, the purpose of this article is to analyze the causes of errors in traditional measurements, and then to identify adverse events related to the use of drones for the inventory of a heap of textiles intended for production purposes. On this basis, it will be possible to develop guidelines to eliminate the causes of these events in the measurement process using drones. In a real environment, work was carried out to determine the volume and weight of textiles, including, among others, weighing a textile sample to determine the average density of the assortment, establishing a local geodetic network, terrestrial laser scanning and photogrammetric raid using an unmanned aerial vehicle. As a result of the analysis of measurement data obtained in the facility, the volume and weight of the assortment and the accuracy of their determination were determined. In this article, this work presents how such heaps are currently being tested, what adverse events occur, indicate and describes the current use of photogrammetric techniques of this type of measurements so far performed by external drones for the inventory of wind farms or construction of the station and compare them with the measurement system of the aforementioned textile heap inside a large-format facility.

Keywords: drones, unmanned aerial system, UAS, indoor system, security, process automation, cost optimization, photogrammetry, risk elimination, industry 4.0

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Authors: Ainouna Bouziane

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The ability of Electron Tomography to recover the 3D structure of catalysts, with spatial resolution in the subnanometer scale, has been widely explored and reviewed in the last decades. A variety of experimental techniques, based either on Transmission Electron Microscopy (TEM) or Scanning Transmission Electron Microscopy (STEM) have been used to reveal different features of nanostructured catalysts in 3D, but High Angle Annular Dark Field imaging in STEM mode (HAADF-STEM) stands out as the most frequently used, given its chemical sensitivity and avoidance of imaging artifacts related to diffraction phenomena when dealing with crystalline materials. In this regard, our group has developed a methodology that combines image denoising by undecimated wavelet transforms (UWT) with automated, advanced segmentation procedures and parameter selection methods using CS-TVM (Compressed Sensing-total variation minimization) algorithms to reveal more reliable quantitative information out of the 3D characterization studies. However, evaluating the accuracy of the magnitudes estimated from the segmented volumes is also an important issue that has not been properly addressed yet, because a perfectly known reference is needed. The problem particularly complicates in the case of multicomponent material systems. To tackle this key question, we have developed a methodology that incorporates volume reconstruction/segmentation methods. In particular, we have established an approach to evaluate, in quantitative terms, the accuracy of TVM reconstructions, which considers the influence of relevant experimental parameters like the range of tilt angles, image noise level or object orientation. The approach is based on the analysis of material-realistic, 3D phantoms, which include the most relevant features of the system under analysis.

Keywords: electron tomography, supported catalysts, nanometrology, error assessment

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Authors: Nkechinyere Joy Olawuyi, Babajide Samuel Afolabi, Bola Ibitoye

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This study collected a preprocessed dataset of chest radiographs and formulated a deep neural network model for detecting abnormalities. It also evaluated the performance of the formulated model and implemented a prototype of the formulated model. This was with the view to developing a deep neural network model to automatically classify abnormalities in chest radiographs. In order to achieve the overall purpose of this research, a large set of chest x-ray images were sourced for and collected from the CheXpert dataset, which is an online repository of annotated chest radiographs compiled by the Machine Learning Research Group, Stanford University. The chest radiographs were preprocessed into a format that can be fed into a deep neural network. The preprocessing techniques used were standardization and normalization. The classification problem was formulated as a multi-label binary classification model, which used convolutional neural network architecture to make a decision on whether an abnormality was present or not in the chest radiographs. The classification model was evaluated using specificity, sensitivity, and Area Under Curve (AUC) score as the parameter. A prototype of the classification model was implemented using Keras Open source deep learning framework in Python Programming Language. The AUC ROC curve of the model was able to classify Atelestasis, Support devices, Pleural effusion, Pneumonia, A normal CXR (no finding), Pneumothorax, and Consolidation. However, Lung opacity and Cardiomegaly had a probability of less than 0.5 and thus were classified as absent. Precision, recall, and F1 score values were 0.78; this implies that the number of False Positive and False Negative is the same, revealing some measure of label imbalance in the dataset. The study concluded that the developed model is sufficient to classify abnormalities present in chest radiographs into present or absent.

Keywords: transfer learning, convolutional neural network, radiograph, classification, multi-label

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Authors: Huixin Wei, Shibin Wang, Linan Li, Lei Zhou, Xinhao Tu

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Facial skin is a biomedical material with complex mechanical properties of anisotropy, viscoelasticity, and hyperelasticity. The mechanical properties of facial skin are crucial for a number of applications including facial plastic surgery, animation, dermatology, cosmetic industry, and impact biomechanics. Skin is a complex multi-layered material which can be broadly divided into three main layers, the epidermis, the dermis, and the hypodermis. Collagen fibers account for 75% of the dry weight of dermal tissue, and it is these fibers which are responsible for the mechanical properties of skin. Many research on the anisotropic mechanical properties are mainly concentrated on in vitro, but there is a great difference between in vivo and in vitro for mechanical properties of the skin. In this study, we presented a method to measure the mechanical properties of facial skin in vivo. Digital image correlation (DIC) and indentation tests were used to obtain the experiment data, including the deformation of facial surface and indentation force-displacement curve. Then, the experiment was simulated using a finite element (FE) model. Application of Computed Tomography (CT) and reconstruction techniques obtained the real tissue geometry. A three-dimensional FE model of facial skin, including a bi-layer system, was obtained. As the epidermis is relatively thin, the epidermis and dermis were regarded as one layer and below it was hypodermis in this study. The upper layer was modeled as a Gasser-Ogden-Holzapfel (GOH) model to describe hyperelastic and anisotropic behaviors of the dermis. The under layer was modeled as a linear elastic model. In conclusion, the material properties of two-layer were determined by minimizing the error between the FE data and experimental data.

Keywords: facial skin, indentation test, finite element, digital image correlation, computed tomography

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Authors: M. Johnson, R. Faggian, V. Sposito

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A participatory and engaged approach is key in connecting agricultural managers to sustainable agricultural systems to support and optimize production in Victoria’s food bowl. A sustainable intensification (SI) approach is well documented globally, but participation rates amongst Victorian farmers is fragmentary, and key outcomes and implementation strategies are poorly understood. Improvement in decision-support management tools and a greater understanding of the productivity gains available upon implementation of SI is necessary. This paper reviews the current understanding and uptake of SI practices amongst farmers in one of Victoria’s premier food producing regions, the Goulburn Broken; and it spatially analyses the potential for this region to adapt to climate change and optimize food production. A Geographical Information Systems (GIS) approach is taken to develop an interactive decision-support tool that can be accessible to on-ground agricultural managers. The tool encompasses multiple criteria analysis (MCA) that identifies factors during the construction phase of the tool, using expert witnesses and regional knowledge, framed within an Analytical Hierarchy Process. Given the complexities of the interrelations between each of the key outcomes, this participatory approach, in which local realities and factors inform the key outcomes and help to strategies for a particular region, results in a robust strategy for sustainably intensifying production in key food producing regions. The creation of an interactive, locally embedded, decision-support management and education tool can help to close the gap between farmer knowledge and production, increase on-farm adoption of sustainable farming strategies and techniques, and optimize farm productivity.

Keywords: agriculture, decision-support management tool, Geographic Information System, GIS, sustainable intensification

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Authors: Nurah Saleh Alfares

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An aim of TS in EFL is to help learners to understand how they learn, which could help them in using the target language with other learners in language classrooms, and in their social life. The early researchers have criticised the system of teaching methods in EFL applied in Saudi schools, as they claim that it does not produce students who are highly proficient in English. Some of them suggested that enhancing learners’ TS would help to improve the learners’ proficiency of using the EFL. The textbook in Saudi schools is the central material for teachers to follow in the EFL classroom. Thus, this study is investigating the main issues that could promote TS in Saudi EFL: the textbook and the teachers. The purposes of the study are: to find out the extent to which the tasks in the textbook have the potential to support teachers in promoting TS; to discover insights into the nature of classroom activities that teachers use to encourage TS from the textbook and to explore the teachers’ views on the role of the textbook in promoting TS in the English language. These aims will improve understanding of the connection between the potential of the textbook content and the participants’ theoretical knowledge and their teaching practice. The investigation employed research techniques including the following: (1) analysis of the textbook; (2) questionnaire for EFL teachers; (3) observation for EFL classroom; (4) interviews with EFL teachers. Analysis of the third intermediate grade textbook has been undertaken, and six EFL teachers from five intermediate schools were involved in the study. Data analysis revealed that 36.71 % of the tasks in the textbook could have the potential to promote TS, and 63.29 % of the tasks in the textbook could not have the potential to promote TS. Therefore, the result of the textbook analysis showed that the majority of the tasks do not have the potential to help teachers to promote TS. Although not all teachers of the observed lessons displayed behaviour helpful to promote TS, teachers, who presented potential TS tasks in their lesson encouraged learners’ interaction and students’ engagement more than teachers who presented tasks that did not have the potential to promote TS. Therefore, the result of the teachers’ data showed that having a textbook that has the potential to promote TS is not enough to develop teaching TS in Saudi EFL since teachers’ behaviour could make the task more or less productive.

Keywords: English as a Foreign Language, metacognitive skills, textbook, thinking skills

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Authors: Milton Manyangadze, Joseph Govha, T. Bala Narsaiah, Ch. Shilpa Chakra

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The availability and access to fresh water is today a serious global challenge. This has been a direct result of factors such as the current rapid industrialization and industrial growth, persistent droughts in some parts of the world, especially in the sub-Saharan Africa as well as population growth. Growth of the chemical processing industry has also seen an increase in the levels of pollutants in our water bodies which include heavy metals among others. Heavy metals are known to be dangerous to both human and aquatic life. As such, they have been linked to several diseases. This is mainly because they are highly toxic. They are also known to be bio accumulative and non-biodegradable. Lead for example, has been linked to a number of health problems which include damage of vital internal body systems like the nervous and reproductive system as well as the kidneys. From this background therefore, the removal of the toxic heavy metal, Pb2+ from waste water was investigated using nano silica hollow spheres (NSHS) as the adsorbent. Synthesis of NSHS was done using a three-stage process in which CaCO3 nanoparticles were initially prepared as a template. This was followed by treatment of the formed oxide particles with NaSiO3 to give a nanocomposite. Finally, the template was destroyed using 2.0M HCl to give NSHS. Characterization of the nanoparticles was done using analytical techniques like XRD, SEM, and TGA. For the adsorption process, both thermodynamic and equilibrium studies were carried out. Thermodynamic studies were carried out and the Gibbs free energy, Enthalpy and Entropy of the adsorption process were determined. The results revealed that the adsorption process was both endothermic and spontaneous. Equilibrium studies were also carried out in which the Langmuir and Freundlich isotherms were tested. The results showed that the Langmuir model best described the adsorption equilibrium.

Keywords: characterization, endothermic, equilibrium studies, Freundlich, Langmuir, nanoparticles, thermodynamic studies

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Authors: G. C. Joshi, M. Paul, B. K. Kalita, V. Ranga, J. S. Rawat, P. S. Rawat

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Being a habitat of the global village, every place has established connection through the strength and power of social media piercing through the political boundaries. Social media is a digital platform, where people across the world can interact as it has advantages of being universal, anonymous, easily accessible, indirect interaction, gathering and sharing information. The power of social media lies in the intensity of sharing extreme opinions or feelings, in contrast to the personal interactions which can be easily mapped in the form of Sentiment Mapping. The easy access to social networking sites such as Facebook, Twitter and blogs made unprecedented opportunities for citizens to voice their opinions loaded with dynamics of emotions. These further influence human thoughts where social media plays a very active role. A recent incident of public importance was selected as a case study to map the sentiments of people through Twitter. Understanding those dynamics through the eye of an ordinary people can be challenging. With the help of R-programming language and by the aid of GIS techniques sentiment maps has been produced. The emotions flowing worldwide in the form of tweets were extracted and analyzed. The number of tweets had diminished by 91 % from 25/08/2017 to 31/08/2017. A boom of sentiments emerged near the origin of the case, i.e., Delhi, Haryana and Punjab and the capital showed maximum influence resulting in spillover effect near Delhi. The trend of sentiments was prevailing more as neutral (45.37%), negative (28.6%) and positive (21.6%) after calculating the sentiment scores of the tweets. The result can be used to know the spatial distribution of digital penetration in India, where highest concentration lies in Mumbai and lowest in North East India and Jammu and Kashmir.

Keywords: sentiment mapping, digital literacy, GIS, R statistical language, spatio-temporal

Procedia PDF Downloads 151

Authors: N. G. Teik Hiang, Fathyah Hashim

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This is a case of how strategic management techniques can be used to help resolving problems faced by an ageing Small and Medium Enterprise (SME). Strategic way of resolving problems had been proven to be possible in this case despite general thought that strategic management is useful mostly for large corporations. Small and Medium Enterprises (SMEs) can also use strategic management in managing their business and determining their future cause of action and strategies in order to survive in this ever competent world. Strategic orientation is the key to survival and development of small and medium enterprises. In order to adapt to the fierce market competition, ageing SMEs should improve competitiveness and operational efficiency. They must therefore establish a sense of strategic management to improve the strategic management skills, combined with its own unique characteristics, and work out practical strategies to develop core competitiveness of enterprises in the fierce market competition in order to be sustainable. In this case, internal strengths and weaknesses of an SME had been identified. Strategic internal factors and external factors had been classified and further utilized to formulate potential strategies to encounter various problems faced by the SME. These strategies had been further match to take advantages of the opportunities and to overcome the weaknesses and minimize the threats it is facing. Tan, a consultant who was given the opportunity to formulate a plan for the business started with the environmental scanning (internal and external environmental analysis), assessing strengths and weaknesses for the company, strategies generation, analysis and evaluation. He had numerous discussions with the owner of the business and the senior management in order to match the key internal and external factors to formulate alternative strategies for solving the problems that the company facing. Some of the recommendations or solutions are generated from the inspiration of the owner of the business who is a very enterprising and experience businessman.

Keywords: strategic orientation, strategic management, SME, core competitiveness, sustainable

Procedia PDF Downloads 419

Authors: C. Brierley, H. El-Farahaty, A. Farhan

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The Leeds Parallel Corpus of Arabic-English Constitutions is a parallel corpus for the Arabic legal domain. Analysis of legal language via Corpus Linguistics techniques is an important development. In legal proceedings, a corpus-based approach to disambiguating meaning is set to replace the dictionary as an interpretative tool, and legal scholarship in the States is now attuned to the potential for Text Analytics over vast quantities of text-based legal material, following the business and medical industries. This trend is reflected in Europe: the interdisciplinary research group in Computer Assisted Legal Linguistics mines big data collections of legal and non-legal texts to analyse: legal interpretations; legal discourse; the comprehensibility of legal texts; conflict resolution; and linguistic human rights. This paper focuses on ‘dignity’ as an important aspect of the overarching concept of human rights in current constitutions across the Arab world. We have compiled a parallel, Arabic-English raw text corpus (169,861 Arabic words and 205,893 English words) from reputable websites such as the World Intellectual Property Organisation and CONSTITUTE, and uploaded and queried our corpus in Sketch Engine. Our most challenging task was sentence-level alignment of Arabic-English data. This entailed manual intervention to ensure correspondence on a one-to-many basis since Arabic sentences differ from English in length and punctuation. We have searched for morphological variants of ‘dignity’ (رامة ك, karāma) in the Arabic data and inspected their English translation equivalents. The term occurs most frequently in the Sudanese constitution (10 instances), and not at all in the constitution of Palestine. Its most frequent collocate, determined via the logDice statistic in Sketch Engine, is ‘human’ as in ‘human dignity’.

Keywords: Arabic constitution, corpus-based legal linguistics, human rights, parallel Arabic-English legal corpora

Procedia PDF Downloads 183

Authors: Oshienemen Albert

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Human’s activities could be pointed as the root cause of almost all environmental damages/ disasters as we contribute to the activities that are currently damaging the ozone layers (global warming), unusual environmental changes and extreme weather conditions (climate change) in recent times. Nigeria just as every other disaster-prone nation is faced with different types of disasters and environmental calamities, starting from terrorist displacement disasters, flood, drought and oil spill hazards. Oil spillage as an environmental disaster has great consequences not just on the environment but on human health, economy and the entire populace that might be involved, which deem necessary to look into the root causes of the incidents and how it can be curtailed. The different incidents of oil spillages and other oil production consequent on the environment is alarming in the Nigerian context and cannot be overemphasized without a critical investigation and synthesis. This paper investigates the root causes of environmental pollution induced by oil spill hazards from petroleum activities within Niger Delta communities of effects and detailed the potential solutions to reduce the causal factors and reoccurrence of the incidents. This study adopts a desk-based approach, interviews with key members of communities which consist of chiefs, youth leaders, and key women within the high environmental damaged communities. Also, Interviews were conducted with environmental expertise representatives from the oil and gas sectors and representatives from oil spill-related agency. Data were analyzed using thematic techniques. The study shows different influencing factors of sabotage and vandalism of oil facilities as such; marginalization, deprivation of resources utility and resource derivation principles were identified as major contributors to vandalism and sabotage act. The study proposed potential strategies to curtail the root causes of sabotage and vandalism as the major causes of environmental devastations in Nigeria.

Keywords: environment, oil spill hazards, Niger delta, Nigeria

Procedia PDF Downloads 191

Authors: Zakia Hbellaq

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The Mediterranean region is considered a hotbed for climate change. Morocco is a semi-arid Mediterranean country facing water shortages and poor water quality. Its limited water resources limit the activities of various economic sectors. Most of Morocco's territory is in arid and desert areas. The potential water resources are estimated at 22 billion m3, which is equivalent to about 700 m3/inhabitant/year, and Morocco is in a state of structural water stress. Strictly speaking, the Kingdom of Morocco is one of the “very riskiest” countries, according to the World Resources Institute (WRI), which oversees the calculation of water stress risk in 167 countries. The surprising results of the Institute (WRI) rank Morocco as one of the riskiest countries in terms of water scarcity, ranking 3.89 out of 5, thus occupying the 23rd place out of a total of 167 countries, which indicates that the demand for water exceeds the available resources. Agriculture with a score of 3.89 is most affected by water stress from irrigation and places a heavy burden on the water table. Irrigation is an unavoidable technical need and has undeniable economic and social benefits given the available resources and climatic conditions. Irrigation, and therefore the agricultural sector, currently uses 86% of its water resources, while industry uses 5.5%. Although its development has undeniable economic and social benefits, it also contributes to the overfishing of most groundwater resources and the surprising decline in levels and deterioration of water quality in some aquifers. In this context, REUSE is one of the proposed solutions to reduce the water footprint of the agricultural sector and alleviate the shortage of water resources. Indeed, wastewater reuse, also known as REUSE (reuse of treated wastewater), is a step forward not only for the circular economy but also for the future, especially in the context of climate change. In particular, water reuse provides an alternative to existing water supplies and can be used to improve water security, sustainability, and resilience. However, given the introduction of organic trace pollutants or, organic micro-pollutants, the absorption of emerging contaminants, and decreasing salinity, it is possible to tackle innovative capabilities to overcome these problems and ensure food and health safety. To this end, attention will be paid to the adoption of an integrated and attractive approach, based on the reinforcement and optimization of the treatments proposed for the elimination of the organic load with particular attention to the elimination of emerging pollutants, to achieve this goal. , membrane bioreactors (MBR) as stand-alone technologies are not able to meet the requirements of WHO guidelines. They will be combined with heterogeneous Fenton processes using persulfate or hydrogen peroxide oxidants. Similarly, adsorption and filtration are applied as tertiary treatment In addition, the evaluation of crop performance in terms of yield, productivity, quality, and safety, through the optimization of Trichoderma sp strains that will be used to increase crop resistance to abiotic stresses, as well as the use of modern omics tools such as transcriptomic analysis using RNA sequencing and methylation to identify adaptive traits and associated genetic diversity that is tolerant/resistant/resilient to biotic and abiotic stresses. Hence, ensuring this approach will undoubtedly alleviate water scarcity and, likewise, increase the negative and harmful impact of wastewater irrigation on the condition of crops and the health of their consumers.

Keywords: water scarcity, food security, irrigation, agricultural water footprint, reuse, emerging contaminants

Procedia PDF Downloads 160

Authors: Seongjun Kim, Jinwooung Kim, Sung-Ah Kim

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The development of information and communication technology also affects human cognition and thinking, especially in the field of design, new techniques are being tried. In architecture, new design methodologies such as machine learning or data-driven design are being applied. In particular, these methodologies are used in analyzing the factors related to the value of real estate or analyzing the feasibility in the early planning stage of the apartment housing. However, since the value of apartment buildings is often determined by external factors such as location and traffic conditions, rather than the interior elements of buildings, data is rarely used in the design process. Therefore, although the technical conditions are provided, the internal elements of the apartment are difficult to apply the data-driven design in the design process of the apartment. As a result, the designers of apartment housing were forced to rely on designer experience or modular design alternatives rather than data-driven design at the design stage, resulting in a uniform arrangement of space in the apartment house. The purpose of this study is to propose a methodology to support the designers to design the apartment unit plan with high consumer preference by deriving the correlation and importance of the floor plan elements of the apartment preferred by the consumers through the machine learning and reflecting this information from the early design process. The data on the pre-sale competition rate and the elements of the floor plan are collected as data, and the correlation between pre-sale competition rate and independent variables is analyzed through machine learning. This analytical model can be used to review the apartment unit plan produced by the designer and to assist the designer. Therefore, it is possible to make a floor plan of apartment housing with high preference because it is possible to feedback apartment unit plan by using trained model when it is used in floor plan design of apartment housing.

Keywords: apartment unit plan, data-driven design, design methodology, machine learning

Procedia PDF Downloads 268

Authors: Parth Gupta, Ujjawal Singh, Shashank Kumar, Mansi Chandra, Arnab Sarkar

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Microfluidic devices have emerged as indispensable tools across various scientific disciplines, offering precise control and manipulation of fluids at the microscale. Their efficacy in flow-based research, spanning engineering, chemistry, and biology, relies heavily on the geometric design of microfluidic channels. This work introduces a novel approach to optimise these channels through Response Surface Methodology (RSM), departing from the conventional practice of addressing one parameter at a time. Traditionally, optimising microfluidic channels involved isolated adjustments to individual parameters, limiting the comprehensive understanding of their combined effects. In contrast, our approach considers the simultaneous impact of multiple parameters, employing RSM to efficiently explore the complex design space. The outcome is an innovative microfluidic channel that consumes an optimal sample volume and minimises flow time, enhancing overall efficiency. The relevance of geometric parameter optimization in microfluidic channels extends significantly in biomedical engineering. The flow characteristics of porous materials within these channels depend on many factors, including fluid viscosity, environmental conditions (such as temperature and humidity), and specific design parameters like sample volume, channel width, channel length, and substrate porosity. This intricate interplay directly influences the performance and efficacy of microfluidic devices, which, if not optimized, can lead to increased costs and errors in disease testing and analysis. In the context of biomedical applications, the proposed approach addresses the critical need for precision in fluid flow. it mitigate manufacturing costs associated with trial-and-error methodologies by optimising multiple geometric parameters concurrently. The resulting microfluidic channels offer enhanced performance and contribute to a streamlined, cost-effective process for testing and analyzing diseases. A key highlight of our methodology is its consideration of the interconnected nature of geometric parameters. For instance, the volume of the sample, when optimized alongside channel width, length, and substrate porosity, creates a synergistic effect that minimizes errors and maximizes efficiency. This holistic optimization approach ensures that microfluidic devices operate at their peak performance, delivering reliable results in disease testing. A key highlight of our methodology is its consideration of the interconnected nature of geometric parameters. For instance, the volume of the sample, when optimized alongside channel width, length, and substrate porosity, creates a synergistic effect that minimizes errors and maximizes efficiency. This holistic optimization approach ensures that microfluidic devices operate at their peak performance, delivering reliable results in disease testing. A key highlight of our methodology is its consideration of the interconnected nature of geometric parameters. For instance, the volume of the sample, when optimized alongside channel width, length, and substrate porosity, creates a synergistic effect that minimizes errors and maximizes efficiency. This holistic optimization approach ensures that microfluidic devices operate at their peak performance, delivering reliable results in disease testing.

Keywords: microfluidic device, minitab, statistical optimization, response surface methodology

Procedia PDF Downloads 68

Authors: M. R. Akbari, M. Sadeghi, R. Faghihi, M. A. Mosleh-Shirazi, A. R. Khorrami-Moghadam

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Proton radiotherapy (PRT) is becoming an established treatment modality for cancer. The localized tumors, the same as undifferentiated thyroid tumors are insufficiently handled by conventional radiotherapy, while protons would propose the prospect of increasing the tumor dose without exceeding the tolerance of the surrounding healthy tissues. In spite of relatively high advantages in giving localized radiation dose to the tumor region, in proton therapy, secondary neutron production can have significant contribution on integral dose and lessen advantages of this modality contrast to conventional radiotherapy techniques. Furthermore, neutrons have high quality factor, therefore, even a small physical dose can cause considerable biological effects. Measuring of this neutron dose is a very critical step in prediction of secondary cancer incidence. It has been found that FLUKA Monte Carlo code simulations have been used to evaluate dose due to secondaries in proton therapy. In this study, first, by validating simulated proton beam range in water phantom with CSDA range from NIST for the studied proton energy range (34-54 MeV), a proton therapy in thyroid gland cancer was simulated using FLUKA code. Secondary neutron dose equivalent of some organs and tissues after the target volume caused by 34 and 54 MeV proton interactions were calculated in order to evaluate secondary cancer incidence. A multilayer cylindrical neck phantom considering all the layers of neck tissues and a proton beam impinging normally on the phantom were also simulated. Trachea (accompanied by Larynx) had the greatest dose equivalent (1.24×10-1 and 1.45 pSv per primary 34 and 54 MeV protons, respectively) among the simulated tissues after the target volume in the neck region.

Keywords: FLUKA code, neutron dose equivalent, proton therapy, thyroid gland

Procedia PDF Downloads 425

Authors: Muqadis Shazia Rajpar, Lawrence Mitelberg, Rubaiat S. Ahmed, Jemer Garrido, Rukhsana Hossain, Sergey M. Motov

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Distraction techniques have been used as a means to reduce pain, anxiety, and stress in various healthcare settings to facilitate care and make visits less unpleasant. Using virtual reality (VR) in the pediatric emergency setting can be a valuable, effective, and safe non-pharmacological alternative to the current standard of care for pain and anxiety management in pediatric patients. Our pilot study aimed to evaluate the effectiveness of a VR-based intervention as an alternative distraction modality to alleviate pain and anxiety associated with pediatric emergency department (ED) visits and acute pain conditions. The pilot study period was from November 16 to December 9, 2022, for pediatric ED visits for pain, anxiety, or both. Patients were selected based on a novel VR protocol to receive the VR intervention with the administration of pre and post-intervention surveys concerning pain/anxiety ratings and pain scores (Wong-Baker FACES/NRS). Descriptive statistics, paired t-test, and a Fisher Exact Test were used for data analysis, assuming a p-value of 0.05 for significance. A total of 33 patients (21 females, 12 males), ages 5-20 (M = 10.5, SD = 3.43) participated in this study – 12 patients had pain, 2 patients had anxiety, and 19 patients had both pain and anxiety. There was a statistically significant decrease in post-intervention pain scores of less than one point on the rating scale (6.48 vs. 5.62, p < .001). There was a statistically significant reduction in the percentage of patients suffering from “considerable” or “great” pain after the VR intervention (51.6% to 42.3%, p < .001). Similarly, we noticed an increase in the number of patients with “slight” or “moderate” pain post–VR intervention (48.4% to 57.7%, p < .001). Lastly, we demonstrated a decrease in anxiety among patients after utilizing VR (63.6% vs. 36.4%, p < .001). To conclude, VR can alleviate pain and anxiety in pediatric patients and be a useful non-pharmacological tool in the emergency setting.

Keywords: anxiety, emergency room, pain management, pediatric emergency medicine, virtual reality

Procedia PDF Downloads 88

Authors: Musaab Mohammed Ahmed Ali, Vladimir Vodichev

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work deals with an universal control technique or single controller for linear and nonlinear stabilization and tracing control systems. These systems may be structured as SISO and MIMO. Parameters of controlled plants can vary over a wide range. Introduced a novel control systems design method, construction of stable platform orbits using derivative balance, solved transfer function stability preservation problem of linear system under partial substitution of a rational function. Universal controller is proposed as a polar system with the multiple orbits to simplify design procedure, where each orbit represent single order of controller transfer function. Designed controller consist of proportional, integral, derivative terms and multiple feedback and feedforward loops. The controller parameters synthesis method is presented. In generally, controller parameters depend on new polynomial equation where all parameters have a relationship with each other and have fixed values without requirements of retuning. The simulation results show that the proposed universal controller can stabilize infinity number of linear and nonlinear plants and shaping desired previously ordered performance. It has been proven that sensor errors and poor performance will be completely compensated and cannot affect system performance. Disturbances and noises effect on the controller loop will be fully rejected. Technical and economic effect of using proposed controller has been investigated and compared to adaptive, predictive, and robust controllers. The economic analysis shows the advantage of single controller with fixed parameters to drive infinity numbers of plants compared to above mentioned control techniques.

Keywords: derivative balance, fixed parameters, stable platform, universal control

Procedia PDF Downloads 136

Authors: Salah Kayed Kayed, Jessica Hong Yang

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This study investigates, whether earnings management in the audited financial statements is associated with tone management in the narrative sections of the annual report in the UK. Earnings management and narrative disclosure are communication strategies used from managers to communicate with investors or other users. Because earnings management and narrative disclosure stem from managers, they can exploit this by doing manipulation in their earnings, and simultaneously disclosing qualitative text (narrative information) in their reports as a tone of words, which will affect users’ perception, and hence users will be misinformed. The association between earnings and tone management can be explained by the self-serving, through cognitive reference points, theory. The sample period lasts from 2010 to 2015, and the sample comprises all non-financial firms that consider under FTSE 350 in any year during the sample period. A list of words from previous research is used to measure the tone in the narrative sections of the annual report. Because this study focuses on the managerial strategic choice and the subjective issues that come from management, it uses the abnormal tone to capture the managerial discretion on tone, and a number of different discretionary accruals proxies to measure earnings management, where accruals management is considered as a manipulation tool from managers to change the users' perception. This research is motivated to fulfil the literature gap by examining the association between earnings and tone management. Moreover, if firms that apply earnings management use tone management to mislead investors, it is beneficial for investors, policy makers, standard setters, or other users to know whether there is an association between earnings management and tone management. Clearly, we believe that this study is fundamental in the accounting context, where it evaluates the communication strategies that are used in firms' financial reports. Consistent with prior research, it is expected that tone management is positively associated with earnings management. This means that firms that use earnings management have incentives to manipulate in their narrative disclosure through tone of words, to reflect a good perception for users, which will conceal the earnings management techniques used in their reporting.

Keywords: earnings management, FTSE 350, narrative disclosure, tone management

Procedia PDF Downloads 278

Authors: Raquib Ahsan, Md. Mahir Asif, Md. Zahidul Alam

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A huge portion of old masonry buildings in Bangladesh are vulnerable to earthquake. In most of the cases these buildings contain unreinforced masonry wall which are most likely to be subjected to earthquake damages. Due to deterioration of mortar joint and aging, shear resistance of these unreinforced masonry walls dwindle. So, retrofitting of these old buildings has become an important issue. Among many researched and experimented techniques, ferrocement retrofitting can be a low cost technique in context of the economic condition of Bangladesh. This study aims at investigating the behavior of ferrocement retrofitted unconfined URM walls under different types of cyclic loading. Four 725 mm × 725 mm masonry wall units were prepared with bricks jointed by stretcher bond with 12.5 mm mortar between two adjacent layers of bricks. To compare the effectiveness of ferrocement retrofitting a particular type wire mesh was used in this experiment which is 20 gauge woven wire mesh with 12.5 mm × 12.5 mm square opening. After retrofitting with ferrocement these wall units were tested by applying cyclic deformation along the diagonals of the specimens. Then a comparative study was performed between the retrofitted specimens and control specimens for both partially reversed cyclic load condition and cyclic compression load condition. The experiment results show that ultimate load carrying capacities of ferrocement retrofitted specimens are 35% and 27% greater than the control specimen under partially reversed cyclic loading and cyclic compression respectively. And before failure the deformations of ferrocement retrofitted specimens are 43% and 33% greater than the control specimen under reversed cyclic loading and cyclic compression respectively. Therefore, the test results show that the ultimate load carrying capacity and ductility of ferrocement retrofitted specimens have improved.

Keywords: cyclic compression, cyclic loading, ferrocement, masonry wall, partially reversed cyclic load, retrofitting

Procedia PDF Downloads 240

Authors: Shimayali Kaushal, Nitesh Priyadarshi, Nitin Kumar Singhal

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Food borne bacterial species have been identified as major pathogens in most of the severe pathogen-related diseases among humans which result in great loss to human health and food industry. Conventional methods like plating and enzyme-linked immune sorbent assay (ELISA) are time-consuming, laborious and require specialized instruments. Nanotechnology has emerged as a great field in case of rapid detection of pathogens in recent years. The AuNRs material has good electro-optical properties due to its larger light absorption band and scattering in surface plasmon resonance wavelength regions. By exploiting the sugar-based adhesion properties of microorganism, we can use the glycoconjugates capped gold nanorods as a potential nanobiosensor to detect the foodborne pathogen. In the present study, polyethylene glycol (PEG) coated gold nanorods (AuNRs) were prepared and functionalized with different types of carbohydrates and further characterized by UV-Visible spectrophotometry, dynamic light scattering (DLS), transmission electron microscopy (TEM). The reactivity of above said nano-biosensor was probed by lectin binding assay and also by different strains of foodborne bacteria by using spectrophotometric and microscopic techniques. Due to the specific interaction of probe with foodborne bacteria (Escherichia coli, Pseudomonas aeruginosa), our nanoprobe has shown significant and selective ablation of targeted bacteria. Our findings suggest that our nanoprobe can be an ideal candidate for selective optical detection of food pathogens and can reduce loss to the food industry.

Keywords: glyco-conjugates, gold nanorods, nanobiosensor, nanoprobe

Procedia PDF Downloads 136