Search results for: complex trauma

Authors: Nkechi Neboh, Josiah Adeyemo, Abimbola Enitan, Oludayo Olugbara

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Evolutionary algorithms are techniques extensively used in the planning and management of water resources and systems. It is useful in finding optimal solutions to water resources problems considering the complexities involved in the analysis. River basin management is an essential area that involves the management of upstream, river inflow and outflow including downstream aspects of a reservoir. Water as a scarce resource is needed by human and the environment for survival and its management involve a lot of complexities. Management of this scarce resource is necessary for proper distribution to competing users in a river basin. This presents a lot of complexities involving many constraints and conflicting objectives. Evolutionary algorithms are very useful in solving this kind of complex problems with ease. Evolutionary algorithms are easy to use, fast and robust with many other advantages. Many applications of evolutionary algorithms, which are population based search algorithm, are discussed. Different methodologies involved in the modeling and simulation of water management problems in river basins are explained. It was found from this work that different evolutionary algorithms are suitable for different problems. Therefore, appropriate algorithms are suggested for different methodologies and applications based on results of previous studies reviewed. It is concluded that evolutionary algorithms, with wide applications in water resources management, are viable and easy algorithms for most of the applications. The results suggested that evolutionary algorithms, applied in the right application areas, can suggest superior solutions for river basin management especially in reservoir operations, irrigation planning and management, stream flow forecasting and real-time applications. The future directions in this work are suggested. This study will assist decision makers and stakeholders on the best evolutionary algorithm to use in varied optimization issues in water resources management.

Keywords: evolutionary algorithm, multi-objective, reservoir operation, river basin management

Procedia PDF Downloads 491

Authors: Jing-Ma

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Digital transformation (DT) is an important way for manufacturing enterprises to shape new competitive advantages, and how to choose an effective DT strategy is crucial for enterprise growth and sustainable development. Rooted in strategic change theory, this paper incorporates the dimensions of managers' digital cognition, organizational conditions, and external environment into the same strategic analysis framework and integrates the dynamic QCA method and PSM method to study the antecedent grouping of the DT strategy mode of manufacturing enterprises and its impact on corporate profitability based on the data of listed manufacturing companies in China from 2015 to 2019. We find that the synergistic linkage of different dimensional elements can form six equivalent paths of high-level DT, which can be summarized as the proactive change mode of resource-capability dominated as well as adaptive response mode such as industry-guided resource replenishment. Capacity building under complex environments, market-industry synergy-driven, forced adaptation under peer pressure, and the managers' digital cognition play a non-essential but crucial role in this process. Except for individual differences in the market industry collaborative driving mode, other modes are more stable in terms of individual and temporal changes. However, it is worth noting that not all paths that result in high levels of DT can contribute to enterprise profitability, but only high levels of DT that result from matching the optimization of internal conditions with the external environment, such as industry technology and macro policies, can have a significant positive impact on corporate profitability.

Keywords: digital transformation, strategy mode, enterprise profitability, dynamic QCA, PSM approach

Procedia PDF Downloads 24

Authors: K. Sathishkumar, V. Thiagarasu

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Due to recent advances in DNA microarray technology, it is now feasible to obtain gene expression profiles of tissue samples at relatively low costs. Many scientists around the world use the advantage of this gene profiling to characterize complex biological circumstances and diseases. Microarray techniques that are used in genome-wide gene expression and genome mutation analysis help scientists and physicians in understanding of the pathophysiological mechanisms, in diagnoses and prognoses, and choosing treatment plans. DNA microarray technology has now made it possible to simultaneously monitor the expression levels of thousands of genes during important biological processes and across collections of related samples. Elucidating the patterns hidden in gene expression data offers a tremendous opportunity for an enhanced understanding of functional genomics. However, the large number of genes and the complexity of biological networks greatly increase the challenges of comprehending and interpreting the resulting mass of data, which often consists of millions of measurements. A first step toward addressing this challenge is the use of clustering techniques, which is essential in the data mining process to reveal natural structures and identify interesting patterns in the underlying data. This work presents an analysis of several clustering algorithms proposed to deals with the gene expression data effectively. The existing clustering algorithms like Support Vector Machine (SVM), K-means algorithm and evolutionary algorithm etc. are analyzed thoroughly to identify the advantages and limitations. The performance evaluation of the existing algorithms is carried out to determine the best approach. In order to improve the classification performance of the best approach in terms of Accuracy, Convergence Behavior and processing time, a hybrid clustering based optimization approach has been proposed.

Keywords: microarray technology, gene expression data, clustering, gene Selection

Procedia PDF Downloads 323

Authors: Milan Joshi, Harsh Agrawal, Pallaw Mishra, Sanand Sule

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Predicting electricity usage is a crucial aspect of organizing and controlling sustainable energy systems. The task of forecasting electricity load is intricate and requires a lot of effort due to the combined impact of social, economic, technical, environmental, and cultural factors on power consumption in communities. As a result, it is important to create strong models that can handle the significant non-linear and complex nature of the task. The objective of this study is to create and compare three machine learning techniques for predicting electricity load for both the day ahead and intraday, taking into account various factors such as meteorological data and social events including holidays and festivals. The proposed methods include a LightGBM, FBProphet, combination of FBProphet and LightGBM for day ahead and Motifs( Stumpy) based on Mueens algorithm for similarity search for intraday. We utilize these techniques to predict electricity usage during normal days and social events in the Himachal Region. We then assess their performance by measuring the MSE, RMSE, and MAPE values. The outcomes demonstrate that the combination of FBProphet and LightGBM method is the most accurate for day ahead and Motifs for intraday forecasting of electricity usage, surpassing other models in terms of MAPE, RMSE, and MSE. Moreover, the FBProphet - LightGBM approach proves to be highly effective in forecasting electricity load during social events, exhibiting precise day ahead predictions. In summary, our proposed electricity forecasting techniques display excellent performance in predicting electricity usage during normal days and special events in the Himachal Region.

Keywords: feature engineering, FBProphet, LightGBM, MASS, Motifs, MAPE

Procedia PDF Downloads 72

Authors: A. Aldawsari

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The complex issue of women working in a mixed-gender work environment has its roots in social and cultural factors. This research was done to identify and explore the social and cultural challenges Saudi women face in a mixed-gender work environment in Saudi Arabia. Over the years, Saudi women in mixed-gender work environments in Saudi Arabia have been of interest in various research areas, especially within the context of a hospital work environment. This research, which involves a female researcher interacting one-on-one with Saudi women, will address this issue as well as the effect of the 2030 Vision in Saudi Arabia, and it will aim to include several new fields of work environments for women in Saudi Arabia. The aim of this research is to examine the perceptions of Saudi women who work in a mixed gender environment regarding the general empowerment of women in these settings. The objective of this research is to explore the cultural and social challenges that influence Saudi women's rights to work in a mixed-gender environment in Saudi Arabia. The significance of this research lies in the fact that there is an urgency to resolve issue of female employment in Saudi Arabia, where Saudi women still suffer from inequality in employment opportunity. Although the Saudi government is seeking to empower women by integrating them into a mixed-gender work environment, which is a key goal and prominent social change advocated for in the 2030 Vision, this same goal is one of the main challenges in the face of achieving female empowerment. The methodology section focuses on appropriate methods that can be used to study the effect of social and cultural challenges on the employment of women. It then determines the conditions and limitations of the research by applying a qualitative research approach to the investigation and analysing the data collected from the interviews. A statistical analysis tool, such as NVivo, will be used for the qualitative analysis of the interviews. The study found that the factor most responsible for creating social and cultural challenges is family—whether close family or distant family—more so than tribe or community.

Keywords: women, work, mixed-gender, environment

Procedia PDF Downloads 132

Authors: F. Ambreen, A.Naheed

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Osteoarthritis (OA) is a degenerative disorder affecting millions of people worldwide. Nitric oxide (NO) was found to play a catabolic role in the development of osteoarthritis. It is a toxic free radical gas generated during the metabolism of L-arginine by the enzyme Nitric oxide synthase (NOS). Inducible Nitric Oxide Synthase (iNOS) is one of the isoform of NOS, and its overexpression leads to the excessive formation of NO that results in pathophysiological joint conditions. Several synthetic anti-inflammatory drugs and inhibitors are present to date, but all showed side effects and complications. Therefore, the pursuit of natural disease-modifying drugs remains a top priority. Curcumin is an active component of turmeric, and the past few decades have witnessed intense research devoted to the antioxidant and anti-inflammatory properties of curcumin. The present study focused on curcumin and its derivatives in the search for new iNOS inhibitors for the treatment of osteoarthritis. We conducted a molecular docking study on curcumin and its four derivatives; cyclocurcumin, tetrahydrocurcumin, demethoxycurcumin and curcumin monoglucoside with iNOS using CLC Drug discovery work bench 3.02. We selected two co-crystallized ligands for this study; tetrahydrobiopterin and N-omega-propyl-L-arginine present in complex with the enzyme iNOS. Results showed the best binding affinity of N-omega-propyl-L-arginine with cyclocurcumin and curcumin monoglucoside that exhibit binding energies of -65.2 kcal/mol and -68 kcal/mol respectively. Whereas with tetrahydrobiopterin, best binding scores of -64.7 kcal/mol and -62.2 kcal/mol were found with tetrahydrocurcumin and demethoxycurcumin respectively. This information could open doors of research for the designing of novel drugs using herbs such as curcumin for the treatment of inflammatory joint diseases.

Keywords: curcumin, iNOS, molecular docking, osteoarthritis

Procedia PDF Downloads 129

Authors: M. C. Pardo-Ferreira, J. C. Rubio-Romero, M. Martínez-Rojas

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Resilience Engineering is a new paradigm of safety management that proposes to change the way of managing the safety to focus on the things that go well instead of the things that go wrong. Many complex and high-risk sectors such as air traffic control, health care, nuclear power plants, railways or emergencies, have applied this new vision of safety and have obtained very positive results. In the construction sector, safety management continues to be a problem as indicated by the statistics of occupational injuries worldwide. Therefore, it is important to improve safety management in this sector. For this reason, it is proposed to apply Resilience Engineering to the construction sector. The Construction Phase Health and Safety Plan emerges as a key element for the planning of safety management. One of the key tools of Resilience Engineering is the Resilience Assessment Grid that allows measuring the four essential abilities (respond, monitor, learn and anticipate) for resilient performance. The purpose of this paper is to develop a questionnaire based on the Resilience Assessment Grid, specifically on the ability to learn, to assess whether a Construction Phase Health and Safety Plans helps companies in a construction site to implement this ability. The research process was divided into four stages: (i) initial design of a questionnaire, (ii) validation of the content of the questionnaire, (iii) redesign of the questionnaire and (iii) application of the Delphi method. The questionnaire obtained could be used as a tool to help construction companies to evolve from Safety-I to Safety-II. In this way, companies could begin to develop the ability to learn, which will serve as a basis for the development of the other abilities necessary for resilient performance. The following steps in this research are intended to develop other questions that allow evaluating the rest of abilities for resilient performance such as monitoring, learning and anticipating.

Keywords: resilience engineering, construction sector, resilience assessment grid, construction phase health and safety plan

Procedia PDF Downloads 137

Authors: Sang-Keun Moon, Hong-Rok Lim, Jin-O Kim

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This paper presents a Power Quality (PQ) modeling and filtering processes for the distribution system disturbances using recognition learning methods. Typical PQ waveforms with mathematical applications and gathered field data are applied to the proposed models. The objective of this paper is analyzing PQ data with respect to monitoring, discriminating, and evaluating the waveform of power disturbances to ensure the system preventative system failure protections and complex system problem estimations. Examined signal filtering techniques are used for the field waveform noises and feature extractions. Using extraction and learning classification techniques, the efficiency was verified for the recognition of the PQ disturbances with focusing on interactive modeling methods in this paper. The waveform of selected 8 disturbances is modeled with randomized parameters of IEEE 1159 PQ ranges. The range, parameters, and weights are updated regarding field waveform obtained. Along with voltages, currents have same process to obtain the waveform features as the voltage apart from some of ratings and filters. Changing loads are causing the distortion in the voltage waveform due to the drawing of the different patterns of current variation. In the conclusion, PQ disturbances in the voltage and current waveforms indicate different types of patterns of variations and disturbance, and a modified technique based on the symmetrical components in time domain was proposed in this paper for the PQ disturbances detection and then classification. Our method is based on the fact that obtained waveforms from suggested trigger conditions contain potential information for abnormality detections. The extracted features are sequentially applied to estimation and recognition learning modules for further studies.

Keywords: power quality recognition, PQ modeling, waveform feature extraction, disturbance trigger condition, PQ signal filtering

Procedia PDF Downloads 186

Authors: Dharshika Rajalingam, Jeffery W. Peng

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Acinetobacter baumannii is a challenging threat to global health, recognized as a multidrug-resistant pathogen. -lactamase is one of the principal resistant mechanisms developed by A. baumannii to survive against -lactam antibiotics. OXA24/40 is one of the types of -lactamases, a well-documented carbapenem hydrolyzing class D -lactamases (CHDL). It was revealed that OXA24/40 showed resistivity against doripenem, one of the carbapenems, by two different mechanisms as hydrolysis and -lactonization. Furthermore, it undergoes genetic mutations to broaden the -lactamase activity to survive against antibiotic environments. One of the crucial characterizations of prokaryotes to develop adaptation is post-translational modification (PTM), mainly phosphorylation. However, the PTM of OXA24/40 is an unknown feature, and the impact of PTM on antibiotic resistivity is yet to be explored. We approached these hypotheses using NMR and MS techniques and found that the OXA24/40 could be phosphorylated in vitro. The Ser81 at the active STFK motif of OXA24/40 of catalytic pocket was identified as the site of phosphorylation using 1D 31P NMR experiment, whereas S81 is required to form an acyl-enzyme complex between enzyme and -lactam antibiotics. The activity of completely phosphorylated OXA24/40 wild type against doripenem revealed that the phosphorylation of active Ser inactivates the -lactamases activity of OXA24/40. The 1D 1H CPMG NMR-based activity assay of phosphorylated OXA24/40 against doripenem confirmed that both deactivating mechanisms are inhibited by phosphorylation. Carbamylated Lysine at the active STFK motif is one of the critical features of CHDL required for the acylation and deacylation reactions of the enzyme. The 1D 13C NMR experiment confirmed that the K84 of phosphorylated OXA24/40 is de-carbamylated. Phosphorylation of OXA24/40 affects both active S81 and carbamylated K84 of OXA24 that are required for the resistivity of -lactamase. So, phosphorylation could be one of the reasons for the genetic mutation of OXA24/40 for the development of antibiotic resistivity. Further research can lead to an understanding of the effect of phosphorylation on the clinical mutants of the OXA24-like -lactamase family on the broadening of -lactamase activity.

Keywords: OXA24/40, phosphorylation, clinical mutants, resistivity

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Authors: Amir Ali, Laura Yael Mendoza

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The application of nanomaterials is becoming the most effective strategy of elicitation to produce a desirable level of plant biomass with complex medicinal compounds. This study was designed to check the influence of phytosynthesized iron nanoparticles (FeNPs) on physical growth characteristics, antioxidant status, and production of steviol glycosides of in vitro grown Stevia rebaudiana. Effect of different concentrations of iron nanoparticles replacement of iron sulfate in MS medium (stock solution) on invitro stevia plant growth following positive control (MS basal medium), negative control (iron sulfate devoid medium), iron sulfate devoid MS medium and supplemented with FeNPs at different concentrations (5.6 mg/L, 11.2 mg/L, 16.8 mg/L, 22.4 mg/L) was evaluated. The iron deficiency leads to a drastic reduction in plant growth. In contrast, applying FeNPs leads to improvement in plant height, leave diameter, improved leave morphology, etc., in a concentration-dependent manner. Furthermore, the stress caused by FeNPs at 16.8 mg/L in cultures produced higher levels of total phenolic content (3.7 ± 0.042 mg/g dry weight: DW) and total flavonoid content (1.9 ± 0.022 mg/g DW and antioxidant activity (78 ± 4.6%). In addition, plants grown in the presence of FeNPs at 22.4 mg/L resulted in higher enzymatic antioxidant activities (SOD = 3.5 ± 0.042 U/mg; POD = 2.6 ± 0.026 U/mg; CAT = 2.8 ± 0.034 U/mg and APx = 3.6 ± 0.043 U/ mg), respectively. Furthermore, exposure to a higher dose of FeNPs (22.4 mg/L) exhibited the maximum amount of stevioside (stevioside: 4.6 ± 0.058 mg/g (DW) and rebaudioside A: 4.9 ± 0.068 mg/g DW) as compared to other doses. The current investigation confirms the effectiveness of FeNPs in growth media. It offers a suitable prospect for commercially desirable production of S. rebaudiana biomass with higher sweet glycosides profiles in vitro.

Keywords: cell culture, stevia, iron nanoparticles, antioxidants

Procedia PDF Downloads 96

Authors: Ammarah Irum, Muhammad Ali Tahir

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Document-level sentiment analysis in Urdu is a challenging Natural Language Processing (NLP) task due to the difficulty of working with lengthy texts in a language with constrained resources. Deep learning models, which are complex neural network architectures, are well-suited to text-based applications in addition to data formats like audio, image, and video. To investigate the potential of deep learning for Urdu sentiment analysis, we implemented five different deep learning models, including Bidirectional Long Short Term Memory (BiLSTM), Convolutional Neural Network (CNN), Convolutional Neural Network with Bidirectional Long Short Term Memory (CNN-BiLSTM), and Bidirectional Encoder Representation from Transformer (BERT). In this study, we developed a hybrid deep learning model called BiLSTM-Single Layer Multi Filter Convolutional Neural Network (BiLSTM-SLMFCNN) by fusing BiLSTM and CNN architecture. The proposed and baseline techniques are applied on Urdu Customer Support data set and IMDB Urdu movie review data set by using pre-trained Urdu word embedding that are suitable for sentiment analysis at the document level. Results of these techniques are evaluated and our proposed model outperforms all other deep learning techniques for Urdu sentiment analysis. BiLSTM-SLMFCNN outperformed the baseline deep learning models and achieved 83%, 79%, 83% and 94% accuracy on small, medium and large sized IMDB Urdu movie review data set and Urdu Customer Support data set respectively.

Keywords: urdu sentiment analysis, deep learning, natural language processing, opinion mining, low-resource language

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Authors: Rahmi Aksoy

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The western part of Konya consists of an area of block faulted basin and ranges. Present day topography is characterized by alternating elongate mountains and depressions trending east-west. A number of depressions occur in the region. One of the large depressions is the E-W trending Kızılören-Küçükmuhsine (KK basin) basin bounded on both sides by normal faults and located on the west of the Konya city. The basin is about 5-12 km wide and 40 km long. Ranges north and south of the basin are composed of undifferentiated low grade metamorphic rocks of Silurian-Cretaceous age and smaller bodies of ophiolites of probable Cretaceous age. The basin fill consists of the upper Miocene-lower Pliocene fluvial, lacustrine, alluvial sediments and volcanic rocks. The younger and undeformed Plio-Quaternary basin fill unconformably overlies the older basin fill and is composed predominantly of conglomerate, mudstone, silt, clay and recent basin floor deposits. The paleostress data on the striated fault planes in the basin indicates NW-SE extension and associated with an NE-SW compression. The eastern end of the KK basin is cut and terraced by the active Konya fault zone. The Konya fault zone is NE trending, east dipping normal fault forming the western boundary of the Konya depression. The Konya depression consists mainly of Plio-Quaternary alluvial complex and recent basin floor sediments. The structural data gathered from the Konya fault zone support normal faulting with a small amount of dextral strike-slip tensional tectonic regime that shaped under the WNW-ESE extensional stress regime.

Keywords: central Anatolia, fault kinematics, Kızılören-Küçükmuhsine basin, Konya fault zone, neotectonics

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Authors: Jacky Liu

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This paper develops a team sports outcome prediction system with potential for wide-ranging applications across various disciplines. Despite significant advancements in predictive analytics, existing studies in sports outcome predictions possess considerable limitations, including insufficient feature engineering and underutilization of advanced machine learning techniques, among others. To address these issues, we extend the Sports Cross Industry Standard Process for Data Mining (SRP-CRISP-DM) framework and propose a unique, comprehensive predictive system, using National Basketball Association (NBA) data as an example to test this extended framework. Our approach follows a holistic methodology in feature engineering, employing both Time Series and Non-Time Series Data, as well as conducting Explanatory Data Analysis and Feature Selection. Furthermore, we contribute to the discourse on target variable choice in team sports outcome prediction, asserting that point spread prediction yields higher profits as opposed to game-winner predictions. Using machine learning algorithms, particularly XGBoost, results in a significant improvement in predictive accuracy of team sports outcomes. Applied to point spread betting strategies, it offers an astounding annual return of approximately 900% on an initial investment of $100. Our findings not only contribute to academic literature, but have critical practical implications for sports betting. Our study advances the understanding of team sports outcome prediction a burgeoning are in complex system predictions and pave the way for potential profitability and more informed decision making in sports betting markets.

Keywords: machine learning, team sports, game outcome prediction, sports betting, profits simulation

Procedia PDF Downloads 102

Authors: Muhammad Asif ShakoorI, Maogang He, Aamir Shahzad

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Dusty (complex) plasmas contained micro-sized charged dust particles in addition to ions, electrons, and neutrals. It is typically low-temperature plasma and exists in a wide variety of physical systems. In this work, the effects of an external electric field on the diffusion coefficient and share viscosity are investigated through equilibrium molecular dynamics (EMD) simulations in three-dimensional (3D) strongly coupled (SC) dusty plasmas (DPs). The effects of constant and varying normalized electric field strength (E*) have been computed along with different combinations of plasma states on the diffusion of dust particles using EMD simulations. Diffusion coefficient (D) and share viscosity (η) along with varied system sizes, in the limit of varying E* values, is accounted for an appropriate range of plasma coupling (Γ) and screening strength (κ) parameters. At varying E* values, it is revealed that the 3D diffusion coefficient increases with increasing E* and κ; however, it decreases with an increase of Γ but within statistical limits. The share viscosity increases with increasing E*and Γ and decreases with increasing κ. New simulation results are outstanding that the combined effects of electric field and screening strengths give well-matched values of Dandη at low-intermediate to large Γ with varying small-intermediate to large N. The current EMD simulation outcomes under varying electric field strengths are in satisfactory well-matched with previous known simulation data of EMD simulations of the SC-DPs. It has been shown that the present EMD simulation data enlarged the range of E* strength up to 0.1 ≤ E*≤ 1.0 in order to find the linear range of the DPs system and to demonstrate the fundamental nature of electric field linearity of 3D SC-DPs.

Keywords: strongly coupled dusty plasma, diffusion coefficient, share viscosity, molecular dynamics simulation, electric field strength

Procedia PDF Downloads 191

Authors: Ibrahim Magaji

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WhatsApp messaging platform brings about new opportunities for users to participate in unique storytelling experiences and audience engagement, particularly to Students of Mass communication who receive training to report events and issues accurately and objectively in accordance with official controls. Also, the complex nature of society today made it possible to use the WhatsApp platform that revolutionizes the means of sharing information, ideas, and experiences. This paper examined the WhatsApp messaging platform and how it influenced the academic performance of students in the Department of Mass Communication, Abdu Gusau Polytechnic, Talata Mafara. It used in-depth interview techniques and focus group discussion with students, as well as the use of published materials as well as unpublished materials to gather related and relevant data. Also, the paper used procedures involved to analyze long interview content. This procedure includes observation of a useful utterance, development of expanded observation, the examination of the interconnection of observed comments, collective scrutiny of observation for patterns and themes, and review and analysis of the themes across all interviews for development of the thesis. The result revealed that the majority of students used WhatsApp messenger for making friends and chatting. Also, the students experienced negative effects such as poor grammar and spelling, less study time, and poor academic performance because of active participation in the use of WhatsApp messaging platform. Surprisingly, there was a high addiction rate among students in the usage of WhatsApp messenger. However, other students experienced an improvement in their readings skills as a result of participation in the use of the platform. Also, students shared ideas, discussed, and shared examination questions among themselves on WhatsApp messenger.

Keywords: WhatsApp messenger, students, participation, group

Procedia PDF Downloads 133

Authors: Paulu Saramge Shalika Nirupani Seram

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The study delves into the complex dynamics of social networks and how they affect paddy farmers’ adoption of agricultural technologies, which are included in Yaya Development program, Weedy rice program and Good Agricultural Practices (GAP) program in Matara district. Identify the social networks among the farmers of ongoing Extension Programs in Matara district, examine the farmers’ adoption level to the ongoing extension programs in Matara district, analyze the impacts of social networks for the adoption to the technologies of ongoing extension programs and give suggestions and recommendations to improve the social network of paddy farmers in Matara District for ongoing extension programs are the objectives of this research. A structured questionnaire survey was conducted with 25 farmers from Matara-North (Wilpita), 25 farmers from Matara-Central (Kamburupitiya), and 25 farmers from Matara-South (Malimbada). UCINET (Version -6.771) software was used for social network analysis, and other than that, descriptive statistics and inferential statistics were used to analyze the findings. Matara-North has the highest social network density, and Matara-South has the lowest social network density according to the social network analysis. Dissemination of intensive technologies requires the most prominent actors of the social network, and in Matara district, agricultural instructors have the highest ability to disseminate technologies. The influence of actors in the social network, the trustworthiness of AI officers, and the trust of indigenous knowledge about paddy cultivation have a significant effect on the technology adoption of farmers. The research endeavors to contribute a nuanced understanding of the social networks and agricultural technology adoption in Matara District, offering practical insights for stakeholders involved in agricultural extension services.

Keywords: agricultural extension, paddy cultivation, social network, technology adoption

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Authors: Perdana Rakhmana Putra, Hansen Wijaya, Sri Budiyani, Muhamad Natsir, Alexis badai Samudra

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The Palembang Sub-Basin (PSB) located in southern part of South Sumatera basin (SSB) consist of half-graben complex trending N-S to NW-SE. These geometries are believe as an impact of strike-slip regime developed in Eocene-Oligocene. Generally, most of the wells in this area produced hydrocarbon from late stage of syn-rift sequences called Lower Talang Akar (LTAF) and post-rift sequences called Batu Raja Formation (BRF) and drilled to proved hydrocarbon on structural trap; three-way dip anticline, four-way dip anticline, dissected anticline, and stratigraphy trap; carbonate build-up and stratigraphic pinch out. Only a few wells reached the deeper syn-rift sequences called Lahat Formation (LAF) and Lemat Formation (LEF). The new interpretation of subsurface data was done by the tectonostratigraphy concept and focusing on syn-rift sequence. Base on seismic characteristic on basin centre, it divided into four sequences: pre-rift sequence, rift initiation, maximum rift and late rift. These sequences believed as a new exploration target on PSB mature basin. This paper will demonstrate the paleo depositional setting during Paleogene and exploration play concept of syn-rift sequence in PSB. The main play for this area consists of stratigraphic and structure play, where the stratigraphic play is Eocene-Oligocene sediment consist of LAF sandstone, LEF-Benakat formation, and LAF with pinch-out geometry. The pinch-out, lenses geometry and on-lap features can be seen on the seismic reflector and formed at the time of the syn-rift sequence. The structural play is dominated by a 3 Way Dip play related to reverse fault trap.

Keywords: syn-rift, tectono-stratigraphy, exploration play, basin center play, south sumatera basin

Procedia PDF Downloads 196

Authors: M. M. Hefnawy, A. M. A. Homoda, M. A. Abounassif, A. M. Alanazia, A. Al-Majed, Gamal A. E. Mostafa

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PVC membrane sensors using different approach e.g. ion-pair, ionophore, and Schiff-base has been used as testing membrane sensor. Analytical applications of membrane sensors for direct measurement of variety of different ions in complex biological and environmental sample are reported. The most important step of such PVC membrane sensor is the sensing active material. The potentiometric sensors have some outstanding advantages including simple design, operation, wide linear dynamic range, relative fast response time, and rotational selectivity. The analytical applications of these techniques to pharmaceutical compounds in dosage forms are also discussed. The construction and electrochemical response characteristics of Poly (vinyl chloride) membrane sensors for moxifloxacin HCl (MOX) are described. The sensing membranes incorporate ion association complexes of moxifloxacin cation and sodium tetraphenyl borate (NaTPB) (sensor 1), phosphomolybdic acid (PMA) (sensor 2) or phosphotungstic acid (PTA) (sensor 3) as electroactive materials. The sensors display a fast, stable and near-Nernstian response over a relative wide moxifloxacin concentration range (1 ×10-2-4.0×10-6, 1 × 10-2-5.0×10-6, 1 × 10-2-5.0×10-6 M), with detection limits of 3×10-6, 4×10-6 and 4.0×10-6 M for sensor 1, 2 and 3, respectively over a pH range of 6.0-9.0. The sensors show good discrimination of moxifloxacin from several inorganic and organic compounds. The direct determination of 400 µg/ml of moxifloxacin show an average recovery of 98.5, 99.1 and 98.6 % and a mean relative standard deviation of 1.8, 1.6 and 1.8% for sensors 1, 2, and 3 respectively. The proposed sensors have been applied for direct determination of moxifloxacin in some pharmaceutical preparations. The results obtained by determination of moxifloxacin in tablets using the proposed sensors are comparable favorably with those obtained using the US Pharmacopeia method. The sensors have been used as indicator electrodes for potentiometric titration of moxifloxacin.

Keywords: potentiometry, PVC, membrane sensors, ion-pair, ionophore, schiff-base, moxifloxacin HCl, sodium tetraphenyl borate, phosphomolybdic acid, phosphotungstic acid

Procedia PDF Downloads 439

Authors: Chahrazed Zouaoui

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In recent years, the volume of geospatial information is increasing due to the evolution of communication technologies and information, this information is presented often by geographic information systems (GIS) and stored on of spatial databases (BDS). The classical data mining revealed a weakness in knowledge extraction at these enormous amounts of data due to the particularity of these spatial entities, which are characterized by the interdependence between them (1st law of geography). This gave rise to spatial data mining. Spatial data mining is a process of analyzing geographic data, which allows the extraction of knowledge and spatial relationships from geospatial data, including methods of this process we distinguish the monothematic and thematic, geo- Clustering is one of the main tasks of spatial data mining, which is registered in the part of the monothematic method. It includes geo-spatial entities similar in the same class and it affects more dissimilar to the different classes. In other words, maximize intra-class similarity and minimize inter similarity classes. Taking account of the particularity of geo-spatial data. Two approaches to geo-clustering exist, the dynamic processing of data involves applying algorithms designed for the direct treatment of spatial data, and the approach based on the spatial data pre-processing, which consists of applying clustering algorithms classic pre-processed data (by integration of spatial relationships). This approach (based on pre-treatment) is quite complex in different cases, so the search for approximate solutions involves the use of approximation algorithms, including the algorithms we are interested in dedicated approaches (clustering methods for partitioning and methods for density) and approaching bees (biomimetic approach), our study is proposed to design very significant to this problem, using different algorithms for automatically detecting geo-spatial neighborhood in order to implement the method of geo- clustering by pre-treatment, and the application of the bees algorithm to this problem for the first time in the field of geo-spatial.

Keywords: mining, GIS, geo-clustering, neighborhood

Procedia PDF Downloads 375

Authors: Arya Farjand

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This study delves into the Early Pleistocene fauna of the Yuanmou Basin, highlighting two significant findings. The first is the discovery of exceptionally well-preserved canid coprolites, which provide a rare glimpse into the diet and ecological niche of these ancient carnivores. The analysis of these coprolites has revealed a diet rich in diverse prey species, suggesting a complex food web and a dynamic ecological environment. This discovery not only sheds light on the dietary habits of these canids but also offers broader insights into the region's ecological dynamics during the Early Pleistocene. Additionally, the preservation of these coprolites allows for detailed study of the carnivore's role in the ecosystem, including their interactions with other species and the overall health of the environment. The second major finding is the identification of a mustelid species, Eirictis yuanmouensis, from the same fossil horizon as the coprolites. This discovery is crucial for understanding the diversity and evolution of Mustelidae in the region. The detailed analysis of cranial and dental morphology of Eirictis yuanmouensis indicates unique adaptations that suggest a specialized ecological niche. This finding, in conjunction with the coprolite analysis, provides a comprehensive view of the ecological niches occupied by both mustelids and hyenas, enhancing our understanding of their adaptations and interactions within this paleoenvironment. The study's significance is further amplified by the analysis of pollen data from the same horizon, which indicates a paleoenvironment characterized by rapid climatic changes and a dominant semiarid climate. This combination of faunal and floral data paints a detailed picture of the Early Pleistocene environment in the Yuanmou Basin, offering valuable insights into the interactions between different carnivore species and their adaptation strategies in response to changing environmental conditions.

Keywords: Yuanmou Basin, coprolite, Hyaena, eirictis yuanmouensis, early pleistocene

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Authors: Gerald Gwamba, Bo Zhou, Yajun Song, Dong Changyin

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The challenges that sand production presents to the oil and gas industry, particularly while working in poorly consolidated reservoirs, cannot be overstated. From restricting production to blocking production tubing, sand production increases the costs associated with production as it elevates the cost of servicing production equipment over time. Production in reservoirs that present with high viscosities, flow rate, cementation, clay content as well as fine sand contents is even more complex and challenging. As opposed to the one-factor at a-time testing, investigating the interactive effects arising from a combination of several factors offers increased reliability of results as well as representation of actual field conditions. It is thus paramount to investigate the conditions leading to the onset of sanding during production to ensure the future sustainability of hydrocarbon production operations under viscous conditions. We adopt the Design of Experiments (DOE) to analyse, using Taguchi factorial designs, the most significant interactive effects of sanding. We propose an optimized regression model to predict the drawdown time at sand production. The results obtained underscore that reservoirs characterized by varying (high and low) levels of viscosity, flow rate, cementation, clay, and fine sand content have a resulting impact on sand production. The only significant interactive effect recorded arises from the interaction between BD (fine sand content and flow rate), while the main effects included fluid viscosity and cementation, with percentage significances recorded as 31.3%, 37.76%, and 30.94%, respectively. The drawdown time model presented could be useful for predicting the time to reach the maximum drawdown pressure under viscous conditions during the onset of sand production.

Keywords: factorial designs, DOE optimization, sand production prediction, drawdown time, regression model

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Authors: Raghavi Janaswamy, Saraswathi K. Vasudev

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Music is ubiquitous in human lives. Ever since the fetus hears the sound inside the mother’s womb and later upon birth, the baby experiences alluring sounds, the curiosity of learning emanates and evokes exploration. Music is an education than mere entertainment. The intricate balance between music, education, and entertainment has well been recognized by the scientific community and is being explored as a viable tool to understand and improve human cognition. There are seven basic swaras (notes) Sa, Ri, Ga, Ma, Pa, Da, and Ni in the Carnatic music system that are analogous to C, D, E, F, G, A, and B of the western system. The Carnatic music builds on the conscious use of microtones, gamakams (oscillation), and rendering styles that evolved over centuries and established its stance. The complex but erudite raga system has been designed with elaborate experiments on srutis (musical sounds) and human perception abilities. In parallel, ‘rasa’- the emotions evoked by certain srutis and hence the ragas been solidified along with the power of language in combination with the musical sounds. The Carnatic music branches out as Kalpita sangeetam (pre-composed music) and Manodharma sangeetam (improvised music). This article explores the Manodharma sangeetam and its subdivisions such as raga alapana, swara kalpana, neraval, and ragam-tanam-pallavi (RTP). The intrinsic mathematical strategies in it’s practice methods toward improvising the music have been explored in detail with concert examples. The techniques on swara weaving for swara kalpana rendering and methods on the alapana development are also discussed at length with an emphasis on the impact on the human cognitive abilities. The articulation of the outlined conscious practice methods not only helps to leave a long-lasting melodic impression on the listeners but also onsets cognitive developments.

Keywords: Carnatic, Manodharmam, music cognition, Alapana

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Authors: Shuxin Li, Peihao Song, Haixiao Hu, Dongfeng Cao

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The interactive deformation and failure mechanisms, including local bucking/delamination propagation and global bucking, are investigated in this paper with numerical simulation and validation with experimental results. Three dimensional numerical models using ABAQUS brick elements combined with cohesive elements and contact elements are developed to simulate the deformation and failure characteristics of composite laminates with and without delamination under compressive loading. The zero-thickness cohesive elements are inserted on the possible path of delamination propagation, and the inter-laminate behavior is characterized by the mixed-mode traction-separation law. The numerical simulations identified the complex feature of interaction among local buckling and/or delamination propagation and final global bucking for composite laminates with delamination under compressive loading. Firstly there is an interaction between the local buckling and delamination propagation, i.e., local buckling induces delamination propagation, and then delamination growth further enhances the local buckling. Secondly, the interaction between the out-plan deformation caused by local buckling and the global bucking deformation results in final failure of the composite laminates. The simulation results are validated by the good agreement with the experimental results published in the literature. The numerical simulation validated with experimental results revealed that the degradation of the load capacity, in particular of the compressive strength of composite structures with delamination, is mainly attributed to the combined local buckling/delamination propagation effects. Consequently, a simple field-bolt repair approach that can hinder the local buckling and prevent delamination growth is explored. The analysis and simulation results demonstrated field-bolt repair could effectively restore compressive strength of composite laminates with delamination.

Keywords: cohesive elements, composite laminates, delamination, local and global bucking, field-bolt repair

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Authors: Bao Trang Thi Nguyen, Stephen Moore

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A large number of Vietnamese lecturers leave their home institutions every year to pursue an education in Australia and in other countries and most of whom return home to careers back in the Vietnamese work context. However, to the authors’ best knowledge, there is little empirical knowledge about these Vietnamese returnees. Much less is about how these overseas-trained returnees continue doing research while taking a lecturing role, though research has recently received growing heightened attention in Vietnamese Higher Education institutions and returnees are an important source of human resources. The research is mixed-methods in nature with questionnaires and interviews as the main instruments of data collection. Seven-six Vietnamese returnees working from a broad range of disciplines from different higher education institutions in central Vietnam completed a questionnaire on their perceived constraints and affordances in teaching and continuing doing research upon return from their overseas education. Twenty-five of these returnees took part in a subsequent in-depth interview which lasted from 30 minutes to an hour, which further seeks understanding of their lived individual experiences and stories. The overall results show that time constraint, heavy teaching loads, and varied administrative and familial roles are among inhibiting factors. However, these factors were more constraining for some returnees more than others. Their motivations to do research varied, from passion to work pressure and self-perceived responsibilities. Above all, these were mediated by personal, institutional and disciplinary contexts. The paper argues for a nuanced understanding of returnee academics’ life as complex and layered with the multiple identities they associated themselves with and the differing trajectories they embarked on as to what they perceived important as a university lecturer. Implications for Higher Education management and administration and professional development are addressed.

Keywords: Vietnamese overseas-trained returnees, higher education, teaching, doing research, constraints, affordances

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Authors: Li Yin, Xiaoqin Wang

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Background and purpose: Cancer diagnosis is part of a complex stochastic process, in which patients' personal and social characteristics influence the choice of diagnosing methods, diagnosing methods, in turn, influence the initial assessment of cancer stage, the initial assessment, in turn, influences the choice of treating methods, and treating methods in turn influence cancer outcomes such as cancer survival. To evaluate diagnosing methods, one needs to estimate and test the causal effect of a regime of cancer diagnosis and treatments. Recently, Wang and Yin (Annals of statistics, 2020) derived a new general formula, which expresses these causal effects in terms of the point effects of treatments in single-point causal inference. As a result, it is possible to estimate and test these causal effects via point effects. The purpose of the work is to estimate and test causal effects under various regimes of cancer diagnosis and treatments via point effects. Challenges and solutions: The cancer stage has influences from earlier diagnosis as well as on subsequent treatments. As a consequence, it is highly difficult to estimate and test the causal effects via standard parameters, that is, the conditional survival given all stationary covariates, diagnosing methods, cancer stage and prognosis factors, treating methods. Instead of standard parameters, we use the point effects of cancer diagnosis and treatments to estimate and test causal effects under various regimes of cancer diagnosis and treatments. We are able to use familiar methods in the framework of single-point causal inference to accomplish the task. Achievements: we have applied this method to stomach cancer survival from a clinical study in Sweden. We have studied causal effects under various regimes, including the optimal regime of diagnosis and treatments and the effect moderation of the causal effect by age and gender.

Keywords: cancer diagnosis, causal effect, point effect, G-formula, sequential causal effect

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Authors: Ali Raouli, Amin Chegenizadeh, Hamid Nikraz

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The Perth metropolitan area and its surrounding regions have been expanding rapidly in recent decades and it is expected that this growth will continue in the years to come. With this rapid growth and the resulting increase in population, consideration should be given to strategic planning and modelling for the future expansion of Perth. The accurate estimation of projected traffic volumes has always been a major concern for the transport modelers and planners. Development of a reliable strategic transport model depends significantly on the inputs data into the model and the calibrated parameters of the model to reflect the existing situation. Trip distribution is the second step in four-step modelling (FSM) which is complex due to its behavioral nature. Gravity model is the most common method for trip distribution. The spatial separation between the Origin and Destination (OD) zones will be reflected in gravity model by applying deterrence functions which provide an opportunity to include people’s behavior in choosing their destinations based on distance, time and cost of their journeys. Deterrence functions play an important role for distribution of the trips within a study area and would simulate the trip distances and therefore should be calibrated for any particular strategic transport model to correctly reflect the trip behavior within the modelling area. This paper aims to review the most common deterrence functions and propose a calibrated deterrence function for work trips within the Perth Metropolitan Area based on the information obtained from the latest available Household data and Perth and Region Travel Survey (PARTS) data. As part of this study, a four-step transport model using EMME software has been developed for Perth Metropolitan Area to assist with the analysis and findings.

Keywords: deterrence function, four-step modelling, origin destination, transport model

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Authors: Jakub Zembrzuski, Bartosz Sobczyk, Mikołaj MIśkiewicz

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Besides designing new constructions, engineers all over the world must face another problem – maintenance, repairs, and assessment of the technical condition of existing bridges. To solve more complex issues, it is necessary to be familiar with the theory of finite element method and to have access to the software that provides sufficient tools which to enable create of sometimes significantly advanced numerical models. The paper includes a brief assessment of the technical condition, a description of the in situ non-destructive testing carried out and the FEM models created for global and local analysis. In situ testing was performed using strain gauges and displacement sensors. Numerical models were created using various software and numerical modeling techniques. Particularly noteworthy is the method of modeling riveted joints of the crossbeam of the viaduct. It is a simplified method that consists of the use of only basic numerical tools such as beam and shell finite elements, constraints, and simplified boundary conditions (fixed support and symmetry). The results of the numerical analyses were presented and discussed. It is clearly explained why the structure did not fail, despite the fact that the weld of the deck plate completely failed. A further research problem that was solved was to determine the cause of the rapid increase in values on the stress diagram in the cross-section of the transverse section. The problems were solved using the solely mentioned, simplified method of modeling riveted joints, which demonstrates that it is possible to solve such problems without access to sophisticated software that enables to performance of the advanced nonlinear analysis. Moreover, the obtained results are of great importance in the field of assessing the operation of bridge structures with an orthotropic plate.

Keywords: bridge, diagnostics, FEM simulations, failure, NDT, in situ testing

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Authors: Jean Hugé, Francisco Benitez-Capistros, Giorgia Camperio-Ciani

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Addressing conservation conflicts requires the consideration of multiple stakeholders' perspectives and knowledge claims, in order to inform complex and possibly contentious decision-making dilemmas. Hence, a better understanding of why people in particular contexts act in a particular way in a conservation conflict is needed. First, this contribution aims at providing and applying an approach to map and interpret the diversity of subjective viewpoints with regard to iconic species in conservation conflicts. Secondly, this contribution aims to feed the reflection on the possible consequences of the diversity of perspectives for the future management of wildlife (in particular iconic species), based on case studies in Galapagos and Malaysia. The use of the semi-quantitative Q methodology allowed us to identify various perspectives on conservation in different social-ecological contexts. While the presence of iconic species may lead to a more passionate and emotional debate, it may also provide more opportunities for finding common ground and for jointly developing acceptable management solutions that will depolarize emergent, long-lasting or latent conservation conflicts. Based on the research team’s experience in the field, and on the integration of ecological and social knowledge, methodological and management recommendations are made with regard to conservation conflicts involving iconic wildlife. The mere presence of iconic wildlife does not guarantee its centrality in conservation conflicts, and comparisons will be drawn between the cases of the giant tortoises (Chelonoidis spec.) in Galapagos, Ecuador and the Milky Stork (Mycteria cinerea) in western peninsular Malaysia. Acknowledging the diversity of viewpoints, reflecting how different stakeholders see, act and talk about wildlife management, highlights the need to develop pro-active and resilient strategies to deal with these issues.

Keywords: conservation conflicts, Q methodology, Galapagos, Malaysia, giant tortoise, milky stork

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Authors: Khalid Iqbal

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This work presents a comparison of an anthropomorphic PRESAGE® dosimeter and radiochromic film measurements with a commercial treatment planning system to determine the feasibility of PRESAGE® for 3D dosimetry in breast IMRT. An anthropomorphic PRESAGE® phantom was created in the shape of a breast phantom. A five-field IMRT plan was generated with a commercially available treatment planning system and delivered to the PRESAGE® phantom. The anthropomorphic PRESAGE® was scanned with the Duke midsized optical CT scanner (DMOS-RPC) and the OD distribution was converted to dose. Comparisons were performed between the dose distribution calculated with the Pinnacle3 treatment planning system, PRESAGE®, and EBT2 film measurements. DVHs, gamma maps, and line profiles were used to evaluate the agreement. Gamma map comparisons showed that Pinnacle3 agreed with PRESAGE® as greater than 95% of comparison points for the PTV passed a ± 3%/± 3 mm criterion when the outer 8 mm of phantom data were discluded. Edge artifacts were observed in the optical CT reconstruction, from the surface to approximately 8 mm depth. These artifacts resulted in dose differences between Pinnacle3 and PRESAGE® of up to 5% between the surface and a depth of 8 mm and decreased with increasing depth in the phantom. Line profile comparisons between all three independent measurements yielded a maximum difference of 2% within the central 80% of the field width. For the breast IMRT plan studied, the Pinnacle3 calculations agreed with PRESAGE® measurements to within the ±3%/± 3 mm gamma criterion. This work demonstrates the feasibility of the PRESAGE® to be fashioned into anthropomorphic shape, and establishes the accuracy of Pinnacle3 for breast IMRT. Furthermore, these data have established the groundwork for future investigations into 3D dosimetry with more complex anthropomorphic phantoms.

Keywords: 3D dosimetry, PRESAGE®, IMRT, QA, EBT2 GAFCHROMIC film

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Authors: Farideh Halakou, Emel Sen, Attila Gursoy, Ozlem Keskin

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Protein–Protein Interactions (PPIs) mediate major biological processes in living cells. The study of PPIs as networks and analyze the network properties contribute to the identification of genes and proteins associated with diseases. In this study, we have created the sub-networks of brain and lung metastasis from primary tumor in breast cancer. To do so, we used seed genes known to cause metastasis, and produced their interactions through a network-topology based prioritization method named GUILDify. In order to have the experimental support for the sub-networks, we further curated them using STRING database. We proceeded by modeling structures for the interactions lacking complex forms in Protein Data Bank (PDB). The functional enrichment analysis shows that KEGG pathways associated with the immune system and infectious diseases, particularly the chemokine signaling pathway, are important for lung metastasis. On the other hand, pathways related to genetic information processing are more involved in brain metastasis. The structural analyses of the sub-networks vividly demonstrated their difference in terms of using specific interfaces in lung and brain metastasis. Furthermore, the topological analysis identified genes such as RPL5, MMP2, CCR5 and DPP4, which are already known to be associated with lung or brain metastasis. Additionally, we found 6 and 9 putative genes that are specific for lung and brain metastasis, respectively. Our analysis suggests that variations in genes and pathways contributing to these different breast metastasis types may arise due to change in tissue microenvironment. To show the benefits of using structural PPI networks instead of traditional node and edge presentation, we inspect two case studies showing the mutual exclusiveness of interactions and effects of mutations on protein conformation which lead to different signaling.

Keywords: breast cancer, metastasis, PPI networks, protein conformational changes

Procedia PDF Downloads 244