Search results for: active distribution network (ADN)

Authors: Safayat Ali Shaikh

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Software has been developed for optimal cropping pattern in an irrigation project considering land constraint, water availability constraint and pick up flow constraint using modified Simplex Algorithm. Artificial Neural Network Models (ANN) have been developed to predict rainfall. AR (1) model used to generate 1000 years rainfall data to train the ANN. Simulation has been done with expected rainfall data. Eight number crops and three types of soil class have been considered for optimization model. Area under each crop and each soil class have been quantified using Modified Simplex Algorithm to get optimum net return. Efficacy of the software has been tested using data of large irrigation project in India.

Keywords: artificial neural network, large irrigation project, modified simplex algorithm, optimal cropping pattern

Procedia PDF Downloads 189

Authors: Wassim Jerbi, Abderrahmen Guermazi, Hafedh Trabelsi

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The optimum use of coverage in wireless sensor networks (WSNs) is very important. LEACH protocol called Low Energy Adaptive Clustering Hierarchy, presents a hierarchical clustering algorithm for wireless sensor networks. LEACH is a protocol that allows the formation of distributed cluster. In each cluster, LEACH randomly selects some sensor nodes called cluster heads (CHs). The selection of CHs is made with a probabilistic calculation. It is supposed that each non-CH node joins a cluster and becomes a cluster member. Nevertheless, some CHs can be concentrated in a specific part of the network. Thus, several sensor nodes cannot reach any CH. to solve this problem. We created an O-LEACH Orphan nodes protocol, its role is to reduce the sensor nodes which do not belong the cluster. The cluster member called Gateway receives messages from neighboring orphan nodes. The gateway informs CH having the neighboring nodes that not belong to any group. However, Gateway called (CH') attaches the orphaned nodes to the cluster and then collected the data. O-Leach enables the formation of a new method of cluster, leads to a long life and minimal energy consumption. Orphan nodes possess enough energy and seeks to be covered by the network. The principal novel contribution of the proposed work is O-LEACH protocol which provides coverage of the whole network with a minimum number of orphaned nodes and has a very high connectivity rates.As a result, the WSN application receives data from the entire network including orphan nodes. The proper functioning of the Application requires, therefore, management of intelligent resources present within each the network sensor. The simulation results show that O-LEACH performs better than LEACH in terms of coverage, connectivity rate, energy and scalability.

Keywords: WSNs; routing; LEACH; O-LEACH; Orphan nodes; sub-cluster; gateway; CH’

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Authors: Marwa Ragab, Eman El-Kimary

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Chiral separation and analysis of omeprazole and naproxen enantiomers in tablets were achieved using high-performance liquid chromatographic method with diode array detection (HPLC-DAD). Kromasil Cellucoat chiral column was used as a stationary phase for separation and the eluting solvent consisted of hexane, isopropanol and trifluoroacetic acid in a ratio of: 90, 9.9 and 0.1, respectively. The chromatographic system was suitable for the enantiomeric separation and analysis of active isomers of the drugs. Resolution values of 2.17 and 3.84 were obtained after optimization of the chromatographic conditions for omeprazole and naproxen isomers, respectively. The determination of S-isomers of each drug in their dosage form was fully validated.

Keywords: chiral analysis, esomeprazole, S-Naproxen, HPLC-DAD

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Authors: Nikolaos Papadimas, Joanna Bennett, Amir Sakhaei, Timothy Dodwell

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Composite modeling of consolidation processes is playing an important role in the process and part design by indicating the formation of possible unwanted prior to expensive experimental iterative trial and development programs. Composite materials in their uncured state display complex constitutive behavior, which has received much academic interest, and this with different models proposed. Errors from modeling and statistical which arise from this fitting will propagate through any simulation in which the material model is used. A general hyperelastic polynomial representation was proposed, which can be readily implemented in various nonlinear finite element packages. In our case, FEniCS was chosen. The coefficients are assumed uncertain, and therefore the distribution of parameters learned using Markov Chain Monte Carlo (MCMC) methods. In engineering, the approach often followed is to select a single set of model parameters, which on average, best fits a set of experiments. There are good statistical reasons why this is not a rigorous approach to take. To overcome these challenges, A hierarchical Bayesian framework was proposed in which population distribution of model parameters is inferred from an ensemble of experiments tests. The resulting sampled distribution of hyperparameters is approximated using Maximum Entropy methods so that the distribution of samples can be readily sampled when embedded within a stochastic finite element simulation. The methodology is validated and demonstrated on a set of consolidation experiments of AS4/8852 with various stacking sequences. The resulting distributions are then applied to stochastic finite element simulations of the consolidation of curved parts, leading to a distribution of possible model outputs. With this, the paper, as far as the authors are aware, represents the first stochastic finite element implementation in composite process modelling.

Keywords: data-driven , material consolidation, stochastic finite elements, surrogate models

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Authors: Harshit Mittal, Neeraj Garg

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Hand symbol recognition is a pivotal component in the domain of computer vision, with far-reaching applications spanning sign language interpretation, human-computer interaction, and accessibility. This research paper discusses the approach with the integration of the Canny Edge algorithm and convolutional neural network. The significance of this study lies in its potential to enhance communication and accessibility for individuals with hearing impairments or those engaged in gesture-based interactions with technology. In the experiment mentioned, the data is manually collected by the authors from the webcam using Python codes, to increase the dataset augmentation, is applied to original images, which makes the model more compatible and advanced. Further, the dataset of about 6000 coloured images distributed equally in 5 classes (i.e., 1, 2, 3, 4, 5) are pre-processed first to gray images and then by the Canny Edge algorithm with threshold 1 and 2 as 150 each. After successful data building, this data is trained on the Convolutional Neural Network model, giving accuracy: 0.97834, precision: 0.97841, recall: 0.9783, and F1 score: 0.97832. For user purposes, a block of codes is built in Python to enable a window for hand symbol recognition. This research, at its core, seeks to advance the field of computer vision by providing an advanced perspective on hand sign recognition. By leveraging the capabilities of the Canny Edge algorithm and convolutional neural network, this study contributes to the ongoing efforts to create more accurate, efficient, and accessible solutions for individuals with diverse communication needs.

Keywords: hand symbol recognition, computer vision, Canny edge algorithm, convolutional neural network

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Authors: Mizan Ahmed, Srikanth Venkatesan

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Geological and tectonic framework indicates that Bangladesh is one of the most seismically active regions in the world. The Bengal Basin is at the junction of three major interacting plates: the Indian, Eurasian, and Burma Plates. Besides there are many active faults within the region, e.g. the large Dauki fault in the north. The country has experienced a number of destructive earthquakes due to the movement of these active faults. Current seismic provisions of Bangladesh are mostly based on earthquake data prior to the 1990. Given the record of earthquakes post 1990, there is a need to revisit the design provisions of the code. This paper compares the base shear demand of three major cities in Bangladesh: Dhaka (the capital city), Sylhet, and Chittagong for earthquake scenarios of magnitudes 7.0MW, 7.5MW, 8.0MW and 8.5MW using a stochastic model. In particular, the stochastic model allows the flexibility to input region specific parameters such as shear wave velocity profile (that were developed from Global Crustal Model CRUST2.0) and include the effects of attenuation as individual components. Effects of soil amplification were analysed using the Extended Component Attenuation Model (ECAM). Results show that the estimated base shear demand is higher in comparison with code provisions leading to the suggestion of additional seismic design consideration in the study regions.

Keywords: attenuation, earthquake, ground motion, Stochastic, seismic hazard

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Authors: Ayesha S. Rahman, Khaled Haddad, Ataur Rahman

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At-site flood frequency analysis is used to estimate flood quantiles when at-site record length is reasonably long. In Australia, FLIKE software has been introduced for at-site flood frequency analysis. The advantage of FLIKE is that, for a given application, the user can compare a number of most commonly adopted probability distributions and parameter estimation methods relatively quickly using a windows interface. The new version of FLIKE has been incorporated with the multiple Grubbs and Beck test which can identify multiple numbers of potentially influential low flows. This paper presents a case study considering six catchments in eastern Australia which compares two outlier identification tests (original Grubbs and Beck test and multiple Grubbs and Beck test) and two commonly applied probability distributions (Generalized Extreme Value (GEV) and Log Pearson type 3 (LP3)) using FLIKE software. It has been found that the multiple Grubbs and Beck test when used with LP3 distribution provides more accurate flood quantile estimates than when LP3 distribution is used with the original Grubbs and Beck test. Between these two methods, the differences in flood quantile estimates have been found to be up to 61% for the six study catchments. It has also been found that GEV distribution (with L moments) and LP3 distribution with the multiple Grubbs and Beck test provide quite similar results in most of the cases; however, a difference up to 38% has been noted for flood quantiles for annual exceedance probability (AEP) of 1 in 100 for one catchment. These findings need to be confirmed with a greater number of stations across other Australian states.

Keywords: floods, FLIKE, probability distributions, flood frequency, outlier

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Authors: Chun-Shien Lu

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Privacy-preserving query has recently received considerable attention in the signal processing and multimedia community. It is also a critical step in wireless sensor network for retrieval of sensitive data. The purposes of privacy-preserving query in both the areas of signal processing and sensor network are the same, but the similarity and difference of the adopted technologies are not fully explored. In this paper, we first review the recently developed methods of privacy-preserving query, and then describe in a comprehensive manner what we can learn from the mutual of both areas.

Keywords: encryption, privacy-preserving, search, security

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Authors: Chompunut Jantarasorn, Chutima Prommak

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This paper presents an intensive measurement studying of the network performance analysis when IEEE 802.11g Wireless Local Area Networks (WLAN) coexisting with IEEE 802.15.4 Wireless Personal Area Network (WPAN). The measurement results show that the coexistence between both networks could increase the Frame Error Rate (FER) of the IEEE 802.15.4 networks up to 60% and it could decrease the throughputs of the IEEE 802.11g networks up to 55%.

Keywords: wireless performance analysis, coexistence analysis, IEEE 802.11g, IEEE 802.15.4

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Authors: Elackiya Sithamparanathan

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Chemical agents used during the Sri Lankan civil war continue to threaten human and environmental health as affected areas are re-settled. Bioremediation is a cost-effective and eco-friendly approach to degrading chemical agents, and has greater public acceptance than chemical degradation. Baseline data on contaminant distribution, environmental parameters, and indigenous microbes are required before bioremediation can commence. The culture and isolate of suitable microbes and enzymes should be followed by laboratory trials, before field application and long-term monitoring of contaminant concentration, soil parameters, microbial ecology, and public health to monitor environmental and public health. As local people are not aware of the persistence of warfare chemicals and do not understand the potential impacts on human health, community awareness programs are required. Active community participation, and collaboration with international and local agencies, would contribute to the success of bioremediation and the effective removal of chemical agents in war affected areas of Sri Lanka.

Keywords: bioremediation, environmental protection, human health, war affected regions in Sri Lanka

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Authors: Yang Zhou, Heli Sun, Jianbin Huang, Jizhong Zhao, Shaojie Qiao

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Traffic prediction is a fundamental problem in urban environment, facilitating the smart management of various businesses, such as taxi dispatching, bike relocation, and stampede alert. Most earlier methods rely on identifying the intrinsic spatial-temporal correlation to forecast. However, the complex nature of this problem entails a more sophisticated solution that can simultaneously capture the mutual influence of both adjacent and far-flung areas, with the information of time-dimension also incorporated seamlessly. To tackle this difficulty, we propose a new multi-phase architecture, DSTDS (Dual Spatial-Temporal Data Scheme for traffic prediction), that aims to reveal the underlying relationship that determines future traffic trend. First, a graph-based neural network with an attention mechanism is devised to obtain the static features of the road network. Then, a multi-granularity recurrent neural network is built in conjunction with the knowledge from a grid-based model. Subsequently, the preceding output is fed into a spatial-temporal super-resolution module. With this 3-phase structure, we carry out extensive experiments on several real-world datasets to demonstrate the effectiveness of our approach, which surpasses several state-of-the-art methods.

Keywords: traffic prediction, spatial-temporal, recurrent neural network, dual data scheme

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Authors: Jing Zhang

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To reduce the carbon footprint of mankind and become more sustainable is one of the major challenges in our era. Internet of Things (IoT) mainly resolves three problems: Things to Things (T2T), Human to Things, H2T), and Human to Human (H2H). Borrowing the classification of IoT, we can find carbon prints of industries also can be divided in these three ways. Therefore, monitoring the routes of generation and circulation of products may help calculate product carbon print. This paper does not consider any technique used by IoT itself, but the ideas of it look at the connection of products. Carbon prints are like a gene or mark of a product from raw materials to the final products, which never leave the products. The contribution of this paper is to combine the characteristics of IoT and the methodology of network science to find a way to calculate the product's carbon footprint. Life cycle assessment, LCA is a traditional and main tool to calculate the carbon print of products. LCA is a traditional but main tool, which includes three kinds.

Keywords: product carbon footprint, Internet of Things, network science, life cycle assessment

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Authors: Sanaz Kavianpour, Zuraini Ismail, Bharanidharan Shanmugam

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Social Network Sites (SNSs) can be served as an invaluable platform to transfer the information across a large number of individuals. A substantial component of communicating and managing information is to identify which individual will influence others in propagating information and also whether dissemination of information in the absence of social signals about that information will be occurred or not. Classifying the final audience of social data is difficult as controlling the social contexts which transfers among individuals are not completely possible. Hence, undesirable information diffusion to an unauthorized individual on SNSs can threaten individuals’ privacy. This paper highlights the information diffusion in SNSs and moreover it emphasizes the most significant privacy issues to individuals of SNSs. The goal of this paper is to propose a privacy-preserving model that has urgent regards with individuals’ data in order to control availability of data and improve privacy by providing access to the data for an appropriate third parties without compromising the advantages of information sharing through SNSs.

Keywords: anonymization algorithm, classification algorithm, information diffusion, privacy, social network sites

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Authors: Patsorn Worawattananutai, Srisopa Ruangnoo, Arunporn Itharat

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Hibiscus sabdariffa (HS) leaves are vegetables which are extensively used as blood tonic and laxatives in Thai traditional medicine. They are popularly used as healthy sour soup for prevention of chronic diseases such as cancer. Therefore, the cytotoxic activity of different extracts of fresh and dried Hibiscus sabdariffa leaves were investigated via the sulforhodamine B (SRB) assay against three types of women’s cancer cell lines, namely the human cervical adenocarcinoma cell line (HeLa), the human ovarian adenocarcinoma cell line (SKOV-3), and the human breast adenocarcinoma cell line (MCF-7). Extraction methods were squeezing, boiling with water and maceration with 95% or 50% ethanol. The 95% ethanolic extracts of Hibiscus sabdariffa dry leaves (HSDE95) showed the highest cytotoxicity against all types of women’s cancer cell lines with the IC50 values in range 7.51±0.33 to 12.13±1.85 µg/ml. Its IC50 values against SKOV-3, HeLa and MCF-7 were 7.51±0.33, 9.44±1.41 and 12.13±1.85 µg/ml, respectively. In these results, this extract can be classified as “active” according to the NCI guideline which indicated that IC50 values of the active cytotoxic plant extracts have to be beneath 20 µg/ml. Thus, HSDE95 was concluded to be a potent cytotoxic drug for all women’s cancer cells. This extract should be further investigated to isolate active compounds against women’s cancer cells.

Keywords: breast adenocarcinoma, cervical adenocarcinoma, cytotoxic activity, Hibiscus sabdariffa, ovarian adenocarcinoma

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Authors: Ashiraliyev SHavkat

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Relevance of the topic: Diabetes mellitus in overweight people development and absence of treatment measures. Objective: to give patients the correct instructions on proper nutrition, to organize a network of preventive and therapeutic measures. Materials and methods: Multidisciplinary Tashkent Medical Academy. As a result of objective observations in patients who applied to the clinic, 28 11 overweight patients had to type 2 diabetes. Diabetesmellituswasdiagnosed. Results: 11.5 mmol / L on an empty stomach in the morning. EDT yes. Pathogenesis: fat content in the diet of patients with diabetes mellitus. Carbohydrate foods make up 60%. Eating disorders and physical inactivity As a result, the accumulation of glucose in the form of fat increases, and this is constantly in the blood, which led to an increase in the number of fatty acids. Clinic: Frequent fasting in 11 patients (hypothalamus). Associated with glucose deficiency), drinking 8-9 liters of water per day of blood in 7 people Systolic pressure 150 diastolic pressures 100. Sensation of ants in 3 people and poor eyesight in 5 people. Conclusion: Explain to patients that nutritional guidelines should be followed. Assign active movement in accordance with the energy entering the body.

Keywords: mellitus, diabetes, pathogenesis, clinic

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Authors: A. M. John-Otumu, M. M. Rahman, O. C. Nwokonkwo, M. C. Onuoha

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Online social media networks have long served as a primary arena for group conversations, gossip, text-based information sharing and distribution. The use of natural language processing techniques for text classification and unbiased decision-making has not been far-fetched. Proper classification of this textual information in a given context has also been very difficult. As a result, we decided to conduct a systematic review of previous literature on sentiment classification and AI-based techniques that have been used in order to gain a better understanding of the process of designing and developing a robust and more accurate sentiment classifier that can correctly classify social media textual information of a given context between hate speech and inverted compliments with a high level of accuracy by assessing different artificial intelligence techniques. We evaluated over 250 articles from digital sources like ScienceDirect, ACM, Google Scholar, and IEEE Xplore and whittled down the number of research to 31. Findings revealed that Deep learning approaches such as CNN, RNN, BERT, and LSTM outperformed various machine learning techniques in terms of performance accuracy. A large dataset is also necessary for developing a robust sentiment classifier and can be obtained from places like Twitter, movie reviews, Kaggle, SST, and SemEval Task4. Hybrid Deep Learning techniques like CNN+LSTM, CNN+GRU, CNN+BERT outperformed single Deep Learning techniques and machine learning techniques. Python programming language outperformed Java programming language in terms of sentiment analyzer development due to its simplicity and AI-based library functionalities. Based on some of the important findings from this study, we made a recommendation for future research.

Keywords: artificial intelligence, natural language processing, sentiment analysis, social network, text

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Authors: Rama Munajat

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Discourse information levels may be viewed from either a background-foreground distinction or a multi-level perspective, and cross-linguistic studies on this area suggest that each information level is marked by a specific linguistic device. In this sense, Sundanese, spoken in Indonesia’s West Javanese Province, further differentiates the background and foreground information into ordinary and significant types. This paper will report an ongoing shift from particle-to-active voice marking in the way Sundanese signals foregrounding events. The shift relates to decades of contact with Bahasa Indonesia (Indonesia’s official language) and linguistic compatibility between the two surface marking strategies. Representative data analyzed include three groups of short stories in both Sundanese and Bahasa Indonesia (Indonesian) published in three periods: before 1945, 1965-2006, and 2016-2019. In the first group of Sundanese data, forward-moving events dominantly appear in particle KA (Kecap Anteuran, word-accompanying) constructions, where the KA represents different particles that co-occur with a special group of verbs. The second group, however, shows that the foregrounded events are more frequently described in active-voice forms with a subject-predicate (SP) order. Subsequently, the third offers stronger evidence for the use of the SP structure. As for the Indonesian data, the foregrounding events in the first group occur in verb-initial and passive-voice constructions, while in the second and third, the events more frequently appear in active-voice structures (subject-predicate sequence). The marking shift above suggests a structural influence from Indonesian, stemmed from generational differences among authors of the Sundanese short stories, particularly related to their education and language backgrounds. The first group of short stories – published before 1945 or before Indonesia's independence from Dutch – were written by native speakers of Sundanese who spoke Indonesian as a foreign language and went through the Dutch education system. The second group of authors, on the other hand, represents a generation of Sundanese native speakers who spoke Indonesian as a second language. Finally, the third group consists of authors who are bilingual speakers of both Sundanese and Indonesian. The data suggest that the last two groups of authors completed the Indonesian education system. With these, the use of subject-predicate sequences to denote foregrounding events began to appear more frequently in the second group and then became more dominant in those of the third. The coded data also signify that cohesion, coherence, and pragmatic purposes in Particle KA constructions are intact in their respective active-voice structure counterparts. For instance, the foregrounding events in Particle KA constructions occur in Sentence-initial KA and Pre-verbal KA forms, whereas those in the active-voice are described in Subject-Predicate (SP) and Zero-Subject active-voice patterns. Cross-language data further demonstrate that the Sentence-initial KA and the SP active-voice structures each contain an overt noun phrase (NP) co-referential with one of the entities introduced in a preceding context. Similarly, the pre-verbal KA and Zero-Subject active-voice patterns have a deleted noun phrase unambiguously referable to the only one entity previously mentioned. The presence and absence of an NP inform a pragmatic strategy to place prominence on topic/given and comment/new information, respectively.

Keywords: discourse analysis, foregrounding marking, pragmatics, language contact

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Authors: Jingpeng Gao, Xinrui Mao, Zhibin Deng

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Aiming at the low recognition rate on the composite signal modulation in low signal to noise ratio (SNR), this paper proposes a modulation recognition algorithm based on ICanny-CNN. Firstly, the radar signal is transformed into the time-frequency image by Choi-Williams Distribution (CWD). Secondly, we propose an image processing algorithm using the Guided Filter and the threshold selection method, which is combined with the hole filling and the mask operation. Finally, the shallow convolutional neural network (CNN) is combined with the idea of the depth-wise convolution (Dw Conv) and the point-wise convolution (Pw Conv). The proposed CNN is designed to complete image classification and realize modulation recognition of radar signal. The simulation results show that the proposed algorithm can reach 90.83% at 0dB and 71.52% at -8dB. Therefore, the proposed algorithm has a good classification and anti-noise performance in radar signal modulation recognition and other fields.

Keywords: modulation recognition, image processing, composite signal, improved Canny algorithm

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Authors: J. P. Borah, R. D. Raland

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Hyperthermia is one of many techniques used destroys cancerous cell. It uses the physical methods to heat certain organ or tissue delivering an adequate temperature in an appropriate period of time, to the entire tumor volume for achieving optimal therapeutic results. Magnetic Metal ferrites nanoparticles (MFe₂O₄ where M = Mn, Zn, Ni, Co, Mg, etc.) are one of the most potential candidates for hyperthermia due to their tunability, biocompatibility, chemical stability and notable ability to mediate high rate of heat induction. However, to obtain the desirable properties for these applications, it is important to optimize their chemical composition, structure and magnetic properties. These properties are mainly sensitive to cation distribution of tetrahedral and octahedral sites. Among the ferrites, zinc ferrite (ZnFe₂O₄) and Manganese ferrite ((MnFe₂O₄) is one of a strong candidate for hyperthermia application because Mn and zinc have a non-magnetic cation and therefore the magnetic property is determined only by the cation distribution of iron, which provides a better platform to manipulate or tailor the properties. In this talk, influence of doping and surfactant towards cation re-distribution leading to an enhancement of magnetic properties of ferrite nanoparticles will be demonstrated. The efficiency of heat generation in association with the enhanced magnetic property is also well discussed in this talk.

Keywords: magnetic nanoparticle, hyperthermia, x-ray diffraction, TEM study

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Authors: Abdelkader Nouri, M’hamed Bouslama, Faouzi Saidi, Hassan Maaref, Michel Gendry

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Self-organized InAs quantum dots (QDs) have been grown on 3,1% InP (110) lattice mismatched substrate by Solid Source Molecular Beam Epitaxy (SSMBE). Stranski-Krastanov mode growth has been used to create self-assembled 3D islands on InAs wetting layer (WL). The optical quality depending on the temperature and power is evaluated. In addition, Atomic Force Microscopy (AFM) images shows inhomogeneous island dots size distribution due to temperature coalescence. The quantum size effect was clearly observed through the spectra photoluminescence (PL) shape.

Keywords: AFM, InAs QDs, PL, SSMBE

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Authors: Wissal Ben Arfi, Lubica Hikkerova, Jean-Michel Sahut

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This study examines interfirm networks in the digital transformation era, and in particular, how tacit knowledge codification affects innovation success within digital platforms. Hence, one of the most important features of digital transformation and innovation process outcomes is the emergence of digital platforms, as an interfirm network, at the heart of open innovation. This research aims to illuminate how digital platforms influence inter-organizational innovation through virtual team interactions and knowledge sharing practices within an interfirm network. Consequently, it contributes to the respective strategic management literature on new product development (NPD), open innovation, industrial management, and its emerging interfirm networks’ management. The empirical findings show, on the one hand, that knowledge conversion may be enhanced, especially by the socialization which seems to be the most important phase as it has played a crucial role to hold the virtual team members together. On the other hand, in the process of socialization, the tacit knowledge codification is crucial because it provides the structure needed for the interfirm network actors to interact and act to reach common goals which favor the emergence of open innovation. Finally, our results offer several conditions necessary, but not always sufficient, for interfirm managers involved in NPD and innovation concerning strategies to increasingly shape interconnected and borderless markets and business collaborations. In the digital transformation era, the need for adaptive and innovative business models as well as new and flexible network forms is becoming more significant than ever. Supported by technological advancements and digital platforms, companies could benefit from increased market opportunities and creating new markets for their innovations through alliances and collaborative strategies, as a mode of reducing or eliminating uncertainty environments or entry barriers. Consequently, an efficient and well-structured interfirm network is essential to create network capabilities, to ensure tacit knowledge sharing, to enhance organizational learning and to foster open innovation success within digital platforms.

Keywords: interfirm networks, digital platform, virtual teams, open innovation, knowledge sharing

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Authors: Eshwara Prasad Koorapati, R. Raman Goud, Swadesh Kumar Singh

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In the present work forming limit diagrams and strain distribution profile diagrams for extra deep drawing steel at room and elevated temperatures have been determined experimentally by conducting stretch forming experiments by using designed and fabricated warm stretch forming tooling setup. With the help of forming Limit Diagrams (FLDs) and strain distribution profile diagrams the formability of Extra Deep Drawing steel has been analyzed and co-related with mechanical properties like strain hardening coefficient (n) and normal anisotropy (r−).Mechanical properties of EDD steel from room temperature to 4500 C were determined and discussed the impact of temperature on the properties like work hardening exponent (n) anisotropy (r-) and strength coefficient of the material. Also, the fractured surfaces after stretching have undergone the some metallurgical investigations and attempt has been made to co-relate with the formability of EDD steel sheets. They are co-related and good agreement with FLDs at various temperatures.

Keywords: FLD, micro hardness, strain distribution profile, stretch forming

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Authors: Amir Mabhout, Toktam Ghafarian, Amirhossein Farzin, Zahra Makki, Sajjad Alizadeh, Amirhossein Ghavi

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The high number of Netflix subscribers makes it attractive for data scientists to extract valuable knowledge from the viewers' behavioural analyses. This paper presents a set of statistical insights into viewers' viewing history. After that, a deep learning model is used to predict the future watching behaviour of the users based on previous watching history within the Netflixlatte data pool. Netflixlatte in an aggregated and anonymized data pool of 320 Netflix viewers with a length 250 000 data points recorded between 2008-2022. We observe insightful correlations between the distribution of viewing time and the COVID-19 pandemic outbreak. The presented deep learning model predicts future movie and TV series viewing habits with an average loss of 0.175.

Keywords: data analysis, deep learning, LSTM neural network, netflix

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Authors: Trisiladi Supriyanto

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This study aims to establish a concept of rate of profit on Islamic banking that can create economic justice and stability in the Islamic Financial Market (Banking and Capital Markets). A rate of profit that creates economic justice and stability can be achieved through its role in maintaining the stability of the financial system in which there is an equitable distribution of income and wealth. To determine the role of the rate of profit as the basis of the profit sharing system implemented in the Islamic financial system, we can see the connection of rate of profit in creating financial stability, especially in the asset-liability management of financial institutions that generate a stable net margin or the rate of profit that is not affected by the ups and downs of the market risk factors, including indirect effect on interest rates. Furthermore, Islamic financial stability can be seen from the role of the rate of profit on the stability of the Islamic financial assets value that are measured from the Islamic financial asset price volatility in the Islamic Bond Market in the Capital Market.

Keywords: economic justice, equitable distribution of income, equitable distribution of wealth, rate of profit, stability in the financial system

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Authors: Ilaria Manca, Ilaria Manca, Francesca Pettinau, Ignazia Mocci, Elisabetta M. Usai, Barbara Pittau

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Lamotrigine is a phenyltriazine used in the treatment of epilepsy and bipolar disorder type I. The purpose of this study was to test and compare various solid forms of immediate release (IR) lamotrigine products, at different strenghts, in order to study their disaggregation and dissolution behavior. IR products are designed to release their active substance promptly after administration. Concentration of hydrochloric acid in gastric juice is about 0.1-0.001 M, so FDA (Food and Drug Administration) recommends, for lamotrigine regular tablets, dissolution tests in HCl 0.1 M.Toinvestigate the pH dependency of drug release in the entire gastrointestinal tract, we worked at two additional media with different pH values (4.5 and 6.8), that reflect conditions in it. To afford acceptable dissolution rates, tablets must disintegrate. Disaggregation of constituent particles increases the surface area and substantially increases the dissolution rate. For this reason availability of an active substance from tablets depends on its ability to disintegrate fast in dissolution media. pH of gastrointestinal fluid affects drug absorption by conditioning its solubility and dissolution, but also tablet disintegration may be influenced by it. To obtain information about the quantitative relationship between different mixture components, Nuclear Magnetic Resonance (NMR) spectroscopy was used. We also investigate tablet hardness. The investigation carried out confirms pH 1.2 as the ideal environment for the immediate availability of the active substance.

Keywords: dissolution, disaggregation, Lamotrigine, bioequivalence

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Authors: Debasis Sengupta, Sudipta Das

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The class of life distributions with decreasing mean residual life (DMRL) is well known in the field of reliability modeling. It contains the IFR class of distributions and is contained in the NBUE class of distributions. While upper and lower bounds of the reliability distribution function of aging classes such as IFR, IFRA, NBU, NBUE, and HNBUE have discussed in the literature for a long time, there is no analogous result available for the DMRL class. We obtain the upper and lower bounds for the reliability function of the DMRL class in terms of first order finite moment. The lower bound is obtained by showing that for any fixed time, the minimization of the reliability function over the class of all DMRL distributions with a fixed mean is equivalent to its minimization over a smaller class of distribution with a special form. Optimization over this restricted set can be made algebraically. Likewise, the maximization of the reliability function over the class of all DMRL distributions with a fixed mean turns out to be a parametric optimization problem over the class of DMRL distributions of a special form. The constructive proofs also establish that both the upper and lower bounds are sharp. Further, the DMRL upper bound coincides with the HNBUE upper bound and the lower bound coincides with the IFR lower bound. We also prove that a pair of sharp upper and lower bounds for the reliability function when the distribution is increasing mean residual life (IMRL) with a fixed mean. This result is proved in a similar way. These inequalities fill a long-standing void in the literature of the life distribution modeling.

Keywords: DMRL, IMRL, reliability bounds, hazard functions

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Authors: Hassam Muazzam

Abstract:

This paper devises an autonomous self-driven vehicle that is capable of taking a disabled person to his/her desired location using three different power sources (gasoline, solar, electric) without any control from the user, avoiding the obstacles in the way. The GPS co-ordinates of the desired location are sent to the main processing board via a GSM module. After the GPS co-ordinates are sent, the path to be followed by the vehicle is devised by Pythagoras theorem. The distance and angle between the present location and the desired location is calculated and then the vehicle starts moving in the desired direction. Meanwhile real-time data from ultrasonic sensors is fed to the board for obstacle avoidance mechanism. Ultrasonic sensors are used to quantify the distance of the vehicle from the object. The distance and position of the object is then used to make decisions regarding the direction of vehicle in order to avoid the obstacles using artificial neural network which is implemented using ATmega1280. Also the vehicle provides the feedback location at remote location.

Keywords: autonomous self-driven vehicle, obstacle avoidance, desired location, pythagoras theorem, neural network, remote location

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Authors: Adamu Zubairu Mohammed, Fransis Oforum Arimoro, Salihu Maikudi Ibrahim, Y. I. Auta, T. I. Arowosegbe, Y. Abdullahi

Abstract:

The present study was conducted to evaluate the effect of depth on the distribution of zooplankton and some physicochemical parameters in Tungan Kawo Lake (Wushishi dam). Water and zooplankton samples were collected from the surface, 3.0 meters deep and 6.0 meters deep, for a period of 24 hours for six months. Standard procedures were adopted for the determination of physicochemical parameters. Results have shown significant differences in the pH, DO, BOD Hardness, Na, and Mg. A total of 1764 zooplankton were recorded, comprising 35 species, with cladocera having 18 species (58%), 14 species of copepoda (41%), 3 species of diptera (1.0%). Results show that more of the zooplankton were recorded in the 3.0 meters-deep region compared to the two other depts and a significant difference was observed in the distribution of Ceriodaphnia dubia, Daphnia laevis, and Leptodiaptomus coloradensis. Though the most abundant zooplankton was recorded in the 3.0 meters deep, Leptodiaptomus coloradesnsis, which was observed in the 6.0 meters deep as the most individual observed, this was followed by Daphnia laevis. Canonical correspondence analysis between physicochemical parameters and the zooplankton indicated a good relationship in the Lake. Ceriodaphnia dubia was found to have a good association with oxygen, sodium, and potassium, while Daphnia laevis and Leptodiaptomus coloradensis are in good relationship with magnesium and phosphorus. It was generally observed that this depth does not have much influence on the distribution of zooplankton in Wushishi Lake.

Keywords: zooplankton, standard procedures, canonical correspondence analysis, Wushishi, canonical, physicochemical parameter

Procedia PDF Downloads 73

Authors: J. S. Saini, P. P. K. Sandhu

Abstract:

The multi-hop nature of Wireless Mesh Networks and the hasty progression of throughput demands results in multi- channels and multi-radios structures in mesh networks, but the main problem of co-channels interference reduces the total throughput, specifically in multi-hop networks. Quality of Service mentions a vast collection of networking technologies and techniques that guarantee the ability of a network to make available desired services with predictable results. Quality of Service (QoS) can be directed at a network interface, towards a specific server or router's performance, or in specific applications. Due to interference among various transmissions, the QoS routing in multi-hop wireless networks is formidable task. In case of multi-channel wireless network, since two transmissions using the same channel may interfere with each other. This paper has considered the Destination Sequenced Distance Vector (DSDV) routing protocol to locate the secure and optimised path. The proposed technique also utilizes the Lempel–Ziv–Welch (LZW) based lossless data compression and intra cluster data aggregation to enhance the communication between the source and the destination. The use of clustering has the ability to aggregate the multiple packets and locates a single route using the clusters to improve the intra cluster data aggregation. The use of the LZW based lossless data compression has ability to reduce the data packet size and hence it will consume less energy, thus increasing the network QoS. The MATLAB tool has been used to evaluate the effectiveness of the projected technique. The comparative analysis has shown that the proposed technique outperforms over the existing techniques.

Keywords: WMNS, QOS, flooding, collision avoidance, LZW, congestion control

Procedia PDF Downloads 319

Authors: Sina Moradi, Sanly Liu, Christopher W. K. Chow, John Van Leeuwen, David Cook, Mary Drikas, Soha Habibi, Rose Amal

Abstract:

In recent years, chloramine has been widely used for both primary and secondary disinfection. However, a major concern with the use of chloramine as a secondary disinfectant is the decay of chloramine and nitrification occurrence. The management of chloramine decay and the prevention of nitrification are critical for water utilities managing chloraminated drinking water distribution systems. The detection and monitoring of nitrification episodes is usually carried out through measuring certain water quality parameters, which are commonly referred to as indicators of nitrification. The approach taken in this study was to collect water samples from different sites throughout a drinking water distribution systems, Tailem Bend – Keith (TBK) in South Australia, and analyse the samples by high performance size exclusion chromatography (HPSEC). We investigated potential association between the water qualities from HPSEC analysis with chloramine decay and/or nitrification occurrence. MATLAB 8.4 was used for data processing of HPSEC data and chloramine decay. An increase in the absorbance signal of HPSEC profiles at λ=230 nm between apparent molecular weights of 200 to 1000 Da was observed at sampling sites that experienced rapid chloramine decay and nitrification while its absorbance signal of HPSEC profiles at λ=254 nm decreased. An increase in absorbance at λ=230 nm and AMW < 500 Da was detected for Raukkan CT (R.C.T), a location that experienced nitrification and had significantly lower chloramine residual (<0.1 mg/L). This increase in absorbance was not detected in other sites that did not experience nitrification. Moreover, the UV absorbance at 254 nm of the HPSEC spectra was lower at R.C.T. than other sites. In this study, a chloramine residual index (C.R.I) was introduced as a new indicator of chloramine decay and nitrification occurrence, and is defined based on the ratio of area underneath the HPSEC spectra at two different wavelengths of 230 and 254 nm. The C.R.I index is able to indicate DS sites that experienced nitrification and rapid chloramine loss. This index could be useful for water treatment and distribution system managers to know if nitrification is occurring at a specific location in water distribution systems.

Keywords: nitrification, HPSEC, chloramine decay, chloramine residual index

Procedia PDF Downloads 281