Search results for: state machine

Authors: Alina Mirza, Sumrin M. Kabir, Shahzad A. Sheikh

Abstract:

The Orthogonal Frequency Division Multiplexing (OFDM) with high data rate, high spectral efficiency and its ability to mitigate the effects of multipath makes them most suitable in wireless application. Impulsive noise distorts the OFDM transmission and therefore methods must be investigated to suppress this noise. In this paper, a State Space Recursive Least Square (SSRLS) algorithm based adaptive impulsive noise suppressor for OFDM communication system is proposed. And a comparison with another adaptive algorithm is conducted. The state space model-dependent recursive parameters of proposed scheme enables to achieve steady state mean squared error (MSE), low bit error rate (BER), and faster convergence than that of some of existing algorithm.

Keywords: OFDM, impulsive noise, SSRLS, BER

Procedia PDF Downloads 457

Authors: R. K. Chaurasiya, N. D. Londhe, S. Ghosh

Abstract:

The study of the electrical signals produced by neural activities of human brain is called Electroencephalography. In this paper, we propose an automatic and efficient EEG signal classification approach. The proposed approach is used to classify the EEG signal into two classes: epileptic seizure or not. In the proposed approach, we start with extracting the features by applying Discrete Wavelet Transform (DWT) in order to decompose the EEG signals into sub-bands. These features, extracted from details and approximation coefficients of DWT sub-bands, are used as input to Principal Component Analysis (PCA). The classification is based on reducing the feature dimension using PCA and deriving the support-vectors using Support Vector Machine (SVM). The experimental are performed on real and standard dataset. A very high level of classification accuracy is obtained in the result of classification.

Keywords: discrete wavelet transform, electroencephalogram, pattern recognition, principal component analysis, support vector machine

Procedia PDF Downloads 638

Authors: V. Rashtchi, H. Bizhani, F. R. Tatari

Abstract:

This paper presents a new online loss minimization for an induction motor drive. Among the many loss minimization algorithms (LMAs) for an induction motor, a particle swarm optimization (PSO) has the advantages of fast response and high accuracy. However, the performance of the PSO and other optimization algorithms depend on the accuracy of the modeling of the motor drive and losses. In the development of the loss model, there is always a trade off between accuracy and complexity. This paper presents a new online optimization to determine an optimum flux level for the efficiency optimization of the vector-controlled induction motor drive. An induction motor (IM) model in d-q coordinates is referenced to the rotor magnetizing current. This transformation results in no leakage inductance on the rotor side, thus the decomposition into d-q components in the steady-state motor model can be utilized in deriving the motor loss model. The suggested algorithm is simple for implementation.

Keywords: induction machine, loss minimization, magnetizing current, particle swarm optimization

Procedia PDF Downloads 632

Authors: Azita Ramezani, Ghazal Mashhadiagha, Bahareh Sanabakhsh

Abstract:

This study researches the combination of Random. Forest classifiers with large language models (LLMs) and natural language processing (NLP) to improve diagnostic accuracy in chest X-ray analysis using the Indiana University dataset. Utilizing advanced NLP techniques, the research preprocesses textual data from radiological reports to extract key features, which are then merged with image-derived data. This improved dataset is analyzed with Random Forest classifiers to predict specific clinical results, focusing on the identification of health issues and the estimation of case urgency. The findings reveal that the combination of NLP, LLMs, and machine learning not only increases diagnostic precision but also reliability, especially in quickly identifying critical conditions. Achieving an accuracy of 99.35%, the model shows significant advancements over conventional diagnostic techniques. The results emphasize the large potential of machine learning in medical imaging, suggesting that these technologies could greatly enhance clinician judgment and patient outcomes by offering quicker and more precise diagnostic approximations.

Keywords: natural language processing (NLP), large language models (LLMs), random forest classifier, chest x-ray analysis, medical imaging, diagnostic accuracy, indiana university dataset, machine learning in healthcare, predictive modeling, clinical decision support systems

Procedia PDF Downloads 43

Authors: Kasim Görenekli, Ali Gülbağ

Abstract:

This study presents a comprehensive analysis of water consumption patterns in Kocaeli province, Turkey, utilizing various machine learning approaches. We analyzed data from 5,000 water subscribers across residential, commercial, and official categories over an 80-month period from January 2016 to August 2022, resulting in a total of 400,000 records. The dataset encompasses water consumption records, weather information, weekends and holidays, previous months' consumption, and the influence of the COVID-19 pandemic.We implemented and compared several machine learning models, including Linear Regression, Random Forest, Support Vector Regression (SVR), XGBoost, Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU). Particle Swarm Optimization (PSO) was applied to optimize hyperparameters for all models.Our results demonstrate varying performance across subscriber types and models. For official subscribers, Random Forest achieved the highest R² of 0.699 with PSO optimization. For commercial subscribers, Linear Regression performed best with an R² of 0.730 with PSO. Residential water usage proved more challenging to predict, with XGBoost achieving the highest R² of 0.572 with PSO.The study identified key factors influencing water consumption, with previous months' consumption, meter diameter, and weather conditions being among the most significant predictors. The impact of the COVID-19 pandemic on consumption patterns was also observed, particularly in residential usage.This research provides valuable insights for effective water resource management in Kocaeli and similar regions, considering Turkey's high water loss rate and below-average per capita water supply. The comparative analysis of different machine learning approaches offers a comprehensive framework for selecting appropriate models for water consumption prediction in urban settings.

Keywords: mMachine learning, water consumption prediction, particle swarm optimization, COVID-19, water resource management

Procedia PDF Downloads 15

Authors: Pham Quang Huy

Abstract:

State budget is considered as an effective tool for controlling, adjusting and regulating the market economy of any countries. To ensure that the activities of the state in the fields of politics, economy and society has been efficiency, it requires major sources of certain budget. These financial funds are formed from tax revenues and tax revenues beyond. Therefore, the Governments need to have an accounting regime to manage the receipt, expenditure which are suitable for recording a full range of items. From that, it can help to increase the transparency and accountability in budget system. One of the main requirements in Vietnamese policies is to improve that accounting system of revenues and expenditures which can provide many reports to meet the information required of government and users, as well as directions to the trends of international standards requirements. By using quantitative research methods and analytical models to exploring factors, the main purpose of this article is to identify the factors affecting budget accounting and providing some direction for Vietnamese public sector in the future. The results indicated that Vietnam budget accounting has been impacted by seven factors and aims to implement three main orientations in the public sector units.

Keywords: state budget, accounting, IPSAS, budget management, government, public sector

Procedia PDF Downloads 270

Authors: Houxiang Zhu, Chun Liang

Abstract:

The CRISPR-Cpf1 system has been successfully applied in genome editing. However, target efficiency of the CRISPR-Cpf1 system varies among different gRNA sequences. The published CRISPR-Cpf1 gRNA data was reanalyzed. Many sequences and structural features of gRNAs (e.g., the position-specific nucleotide composition, position-nonspecific nucleotide composition, GC content, minimum free energy, and melting temperature) correlated with target efficiency were found. Using machine learning technology, a support vector machine (SVM) model was created to predict target efficiency for any given gRNAs. The first web service application, CRISPR-DT (CRISPR DNA Targeting), has been developed to help users design optimal gRNAs for the CRISPR-Cpf1 system by considering both target efficiency and specificity. CRISPR-DT will empower researchers in genome editing.

Keywords: CRISPR-Cpf1, genome editing, target efficiency, target specificity

Procedia PDF Downloads 262

Authors: Shreya Banerjee, Prasanta K. Panigrahi

Abstract:

The three-way distributive entanglement is shown to be related to the parallelism of vectors. Using a measurement-based approach a set of 2−dimensional vectors is formed, representing the post-measurement states of one of the parties. These vectors originate at the same point and have an angular distance between them. The area spanned by a pair of such vectors is a measure of the entanglement of formation. This leads to a geometrical manifestation of the 3−tangle in 2−dimensions, from inequality in the area which generalizes for n− qubits to reveal that the n− tangle also has a planar structure. Quantifying the genuine n−party entanglement in every 1|(n − 1) bi-partition it is shown that the genuine n−way entanglement does not manifest in n− tangle. A new quantity geometrically similar to 3−tangle is then introduced that represents the genuine n− way entanglement. Extending the formalism to 3− qutrits, the nonlocality without entanglement can be seen to arise from a condition under which the post-measurement state vectors of a separable state show parallelism. A connection to nontrivial sum uncertainty relation analogous to Maccone and Pati uncertainty relation is then presented using decomposition of post-measurement state vectors along parallel and perpendicular direction of the pre-measurement state vectors. This study opens a novel way to understand multiparty entanglement in qubit and qudit systems.

Keywords: Geometry of quantum entanglement, Multipartite and distributive entanglement, Parallelism of vectors , Tangle

Procedia PDF Downloads 153

Authors: Tigist Girma Kidane, Yalew Dessalegn Asfaw

Abstract:

Abstract: The current investigation aims to measure the longitudinal and transversal three-point bending tests of bamboo fibres polypropylene composites (BFPPCs) for the application of the automobile industry. Research has not been done on the properties of Ethiopian bamboo fibres for the utilization of composite development. The samples of bamboo plants have been harvested in 3–groups of age, 2–harvesting seasons, and 3–regions of bamboo species. Roll milling machine used for the extraction of bamboo fibres which has been developed by the authors. Chemical constituents measured using gravimetric methods. Unidirectional bamboo fibres prepreg has been produced using PP and hot press machine, then BFPPCs were produced using 6 layers of prepregs at automatic hot press machine. Age, harvesting month, and bamboo species have a statistically significant effect on the longitudinal and transverse flexural strength (FS), modulus of elasticity (MOE), and failure strain at α = 0.05 as evaluated by one-way ANOVA. 2–yrs old of BFPPCs have the highest FS and MOE, whereas November has the highest value of flexural properties. The highest to the lowest FS and MOE of BFPPCs has measured in Injibara, Mekaneselam, and Kombolcha, respectively. The transverse 3-point bending test has a lower FS and MOE compared to the longitudinal direction. The chemical constituents of Injibara, Mekaneselam, and Kombolcha have the highest to the lowest, respectively. 2-years old of bamboo fibres has the highest chemical constituent. The chemical constituents improved the flexural properties. Bamboo fibres in Ethiopia can be relevant for composite development, which has been applied in the area of requiring higher flexural properties.

Keywords: age, bamboo species, flexural properties, harvesting season, polypropylene

Procedia PDF Downloads 51

Authors: Utkrash Sarkar, Vineet Tiwari, Shailendra Singh

Abstract:

The development of social media has brought about a tremendous change in the marketing scenario for every industry. It has become a new hybrid element of the promotional mix in the marketing segment. This paper tries to show some light on the fact that in today’s scenario social media is a platform that everyone should take in consideration for any type of marketing campaign. In this paper, we have formulated a questionnaire, and through it, we have tried to gather information from the respondents that how social media is influencing their decision when they choose their travel destinations for tourism purpose, does it help in creating any awareness about places which they don’t have an idea? As a result, guiding the state government and providing them with a marketing strategy that how they can use social media in a better manner so that they could help increase their revenue and can make people aware about the places of the state which the target audience can plan to go for their next vacation.

Keywords: social media, marketing, information, decision making

Procedia PDF Downloads 183

Authors: Marcin Michalak

Abstract:

Due to the increasing efforts on saving our natural environment a change in the structure of energy resources can be observed - an increasing fraction of a renewable energy sources. In many countries traditional underground coal mining loses its significance but there are still countries, like Poland or Germany, in which the coal based technologies have the greatest fraction in a total energy production. This necessitates to make an effort to limit the costs and negative effects of underground coal mining. The longwall complex is as essential part of the underground coal mining. The safety and the effectiveness of the work is strongly dependent of the diagnostic state of powered roof supports. The building of a useful and reliable diagnostic system requires a lot of data. As the acquisition of a data of any possible operating conditions it is important to have a possibility to generate a demanded artificial working characteristics. In this paper a new approach of modelling a leg pressure in the single unit of powered roof support. The model is a result of the analysis of a typical working cycles.

Keywords: machine modelling, underground mining, coal mining, structure

Procedia PDF Downloads 368

Authors: M. O. Durojaye, J. T. Agee

Abstract:

This paper is on the one-dimensional, positive temperature coefficient (PTC) thermistor model with a hyperbolic tangent function approximation for the electrical conductivity. The method of asymptotic expansion was adopted to obtain the steady state solution and the unsteady-state response was obtained using the method of lines (MOL) which is a well-established numerical technique. The approach is to reduce the partial differential equation to a vector system of ordinary differential equations and solve numerically. Our analysis shows that the hyperbolic tangent approximation introduced is well suitable for the electrical conductivity. Numerical solutions obtained also exhibit correct physical characteristics of the thermistor and are in good agreement with the exact steady state solutions.

Keywords: electrical conductivity, hyperbolic tangent function, PTC thermistor, method of lines

Procedia PDF Downloads 322

Authors: David Rafetseder, Walter Bauer, Florian Poltschak, Wolfgang Amrhein

Abstract:

A flat double-stator linear induction machine (DSLIM) with a short squirrel-cage mover is designed for high thrust force at moderate speed < 5m/s. The performance and motor parameters are determined on the basis of a 2D time-transient simulation with the finite element (FE) software Maxwell 2015. Design guidelines and transformation rules for space vector theory of the LIM are presented. Resulting thrust calculated by flux and current vectors is compared with the FE results showing good coherence and reduced noise. The parameters of the equivalent circuit model are obtained.

Keywords: equivalent circuit model, finite element model, linear induction motor, space vector theory

Procedia PDF Downloads 566

Authors: Jameela Ali, Hamid A. Jalab, Loay E. George, Abdul Rahim Ahmad, Azizah Suliman, Karim Al-Jashamy

Abstract:

Red blood cells (RBC) are the most common types of blood cells and are the most intensively studied in cell biology. The lack of RBCs is a condition in which the amount of hemoglobin level is lower than normal and is referred to as “anemia”. Abnormalities in RBCs will affect the exchange of oxygen. This paper presents a comparative study for various techniques for classifying the RBCs as normal, or abnormal (anemic) using WEKA. WEKA is an open source consists of different machine learning algorithms for data mining applications. The algorithm tested are Radial Basis Function neural network, Support vector machine, and K-Nearest Neighbors algorithm. Two sets of combined features were utilized for classification of blood cells images. The first set, exclusively consist of geometrical features, was used to identify whether the tested blood cell has a spherical shape or non-spherical cells. While the second set, consist mainly of textural features was used to recognize the types of the spherical cells. We have provided an evaluation based on applying these classification methods to our RBCs image dataset which were obtained from Serdang Hospital-alaysia, and measuring the accuracy of test results. The best achieved classification rates are 97%, 98%, and 79% for Support vector machines, Radial Basis Function neural network, and K-Nearest Neighbors algorithm respectively.

Keywords: K-nearest neighbors algorithm, radial basis function neural network, red blood cells, support vector machine

Procedia PDF Downloads 409

Authors: Rishabh Ambavanekar

Abstract:

Expressive Aphasia (EA) is an oral disability, common among stroke victims, in which the Broca’s area of the brain is damaged, interfering with verbal communication abilities. EA currently has no technological solutions and its only current viable solutions are inefficient or only available to the affluent. This prompts the need for an affordable, innovative solution to facilitate recovery and assist in speech generation. This project proposes a novel concept: using a wearable low-cost electroencephalography (EEG) device-based brain-computer interface (BCI) to translate a user’s inner dialogue into words. A low-cost EEG device was developed and found to be 10 to 100 times less expensive than any current EEG device on the market. As part of the BCI, a machine learning (ML) model was developed and trained using the EEG data. Two stages of testing were conducted to analyze the effectiveness of the device: a proof-of-concept and a final solution test. The proof-of-concept test demonstrated an average accuracy of above 90% and the final solution test demonstrated an average accuracy of above 75%. These two successful tests were used as a basis to demonstrate the viability of BCI research in developing lower-cost verbal communication devices. Additionally, the device proved to not only enable users to verbally communicate but has the potential to also assist in accelerated recovery from the disorder.

Keywords: neurotechnology, brain-computer interface, neuroscience, human-machine interface, BCI, HMI, aphasia, verbal disability, stroke, low-cost, machine learning, ML, image recognition, EEG, signal analysis

Procedia PDF Downloads 119

Authors: Soumya Sajjan

Abstract:

Liver cancer is the most common cancer disease worldwide in men and women, and is one of the few cancers still on the rise. Liver disease is the 4th leading cause of death. According to new NHS (National Health Service) figures, deaths from liver diseases have reached record levels, rising by 25% in less than a decade; heavy drinking, obesity, and hepatitis are believed to be behind the rise. In this study, we focus on Development of Diagnostic Classifier for Ultrasound liver lesion. Ultrasound (US) Sonography is an easy-to-use and widely popular imaging modality because of its ability to visualize many human soft tissues/organs without any harmful effect. This paper will provide an overview of underlying concepts, along with algorithms for processing of liver ultrasound images Naturaly, Ultrasound liver lesion images are having more spackle noise. Developing classifier for ultrasound liver lesion image is a challenging task. We approach fully automatic machine learning system for developing this classifier. First, we segment the liver image by calculating the textural features from co-occurrence matrix and run length method. For classification, Support Vector Machine is used based on the risk bounds of statistical learning theory. The textural features for different features methods are given as input to the SVM individually. Performance analysis train and test datasets carried out separately using SVM Model. Whenever an ultrasonic liver lesion image is given to the SVM classifier system, the features are calculated, classified, as normal and diseased liver lesion. We hope the result will be helpful to the physician to identify the liver cancer in non-invasive method.

Keywords: segmentation, Support Vector Machine, ultrasound liver lesion, co-occurance Matrix

Procedia PDF Downloads 411

Authors: Sujay Kotwale, Ramasubba Reddy M.

Abstract:

Electrocardiogram (ECG) is a non-invasive technique used to study and analyze various heart diseases. Cardiac arrhythmia is a serious heart disease which leads to death of the patients, when left untreated. An early-time detection of cardiac arrhythmia would help the doctors to do proper treatment of the heart. In the past, various algorithms and machine learning (ML) models were used to early-time detection of cardiac arrhythmia, but few of them have achieved better results. In order to improve the performance, this paper implements principal component analysis (PCA) along with XGBoost model. The PCA was implemented to the raw ECG signals which suppress redundancy information and extracted significant features. The obtained significant ECG features were fed into XGBoost model and the performance of the model was evaluated. In order to valid the proposed technique, raw ECG signals obtained from standard MIT-BIH database were employed for the analysis. The result shows that the performance of proposed method is superior to the several state-of-the-arts techniques.

Keywords: cardiac arrhythmia, electrocardiogram, principal component analysis, XGBoost

Procedia PDF Downloads 119

Authors: Emily Wood, James K. Westphal, Itamar Lerner

Abstract:

Background: Polysomnography (PSG) recordings are regularly used in research and clinical settings to study sleep and sleep-related disorders. Typical PSG studies are conducted in professional laboratories and performed by qualified researchers. However, the number of sleep labs worldwide is disproportionate to the increasing number of individuals with sleep disorders like sleep apnea and insomnia. Consequently, there is a growing need to supply cheaper yet reliable means to measure sleep, preferably autonomously by subjects in their own home. Over the last decade, a variety of devices for self-monitoring of sleep became available in the market; however, very few have been directly validated against PSG to demonstrate their ability to perform reliable automatic sleep scoring. Two popular mobile EEG-based systems that have published validation results, the DREEM 3 headband and the Z-Machine, have never been directly compared one to the other by independent researchers. The current study aimed to compare the performance of DREEM 3 and the Z-Machine to help investigators and clinicians decide which of these devices may be more suitable for their studies. Methods: 26 participants have completed the study for credit or monetary compensation. Exclusion criteria included any history of sleep, neurological or psychiatric disorders. Eligible participants arrived at the lab in the afternoon and received the two devices. They then spent two consecutive nights monitoring their sleep at home. Participants were also asked to keep a sleep log, indicating the time they fell asleep, woke up, and the number of awakenings occurring during the night. Data from both devices, including detailed sleep hypnograms in 30-second epochs (differentiating Wake, combined N1/N2, N3; and Rapid Eye Movement sleep), were extracted and aligned upon retrieval. For analysis, the number of awakenings each night was defined as four or more consecutive wake epochs between sleep onset and termination. Total sleep time (TST) and the number of awakenings were compared to subjects’ sleep logs to measure consistency with the subjective reports. In addition, the sleep scores from each device were compared epoch-by-epoch to calculate the agreement between the two devices using Cohen’s Kappa. All analysis was performed using Matlab 2021b and SPSS 27. Results/Conclusion: Subjects consistently reported longer times spent asleep than the time reported by each device (M= 448 minutes for sleep logs compared to M= 406 and M= 345 minutes for the DREEM and Z-Machine, respectively; both ps<0.05). Linear correlations between the sleep log and each device were higher for the DREEM than the Z-Machine for both TST and the number of awakenings, and, likewise, the mean absolute bias between the sleep logs and each device was higher for the Z-Machine for both TST (p<0.001) and awakenings (p<0.04). There was some indication that these effects were stronger for the second night compared to the first night. Epoch-by-epoch comparisons showed that the main discrepancies between the devices were for detecting N2 and REM sleep, while N3 had a high agreement. Overall, the DREEM headband seems superior for reliably scoring sleep at home.

Keywords: DREEM, EEG, seep monitoring, Z-machine

Procedia PDF Downloads 107

Authors: Rania A. Aboalela, Javed I. Khan

Abstract:

This research presents a learning assessment theory of Cognitive Skill in Concept Space (CS2) to measure the assessed knowledge in terms of cognitive skill levels of the concepts. The cognitive skill levels refer to levels such as if a student has acquired the state at the level of understanding, or applying, or analyzing, etc. The theory is comprised of three constructions: Graph paradigm of a semantic/ ontological scheme, the concept states of the theory and the assessment analytics which is the process to estimate the sets of concept state at a certain skill level. Concept state means if a student has already learned, or is ready to learn, or is not ready to learn a certain skill level. The experiment is conducted to prove the validation of the theory CS2.

Keywords: cognitive skill levels, concept states, concept space, knowledge assessment theory

Procedia PDF Downloads 322

Authors: Maryam Khalilzadeh Poshtegal, Seyed Ahmad Mirbagheri, Mojtaba Noury

Abstract:

In this paper, by application of the input yearly data of rainfall, temperature and flow to the Urmia Lake, the simulation of water level fluctuation were applied by means of three models. According to the climate change investigation the fluctuation of lakes water level are of high interest. This study investigate data-driven models, support vector machines (SVM), SVM method which is a new regression procedure in water resources are applied to the yearly level data of Lake Urmia that is the biggest and the hyper saline lake in Iran. The evaluated lake levels are found to be in good correlation with the observed values. The results of SVM simulation show better accuracy and implementation. The mean square errors, mean absolute relative errors and determination coefficient statistics are used as comparison criteria.

Keywords: simulation, water level fluctuation, urmia lake, support vector machine

Procedia PDF Downloads 367

Authors: Rishabh Agrawal, S. C. Kaushik, T. S. Bhatti

Abstract:

In order to reduce energy consumption for sustainable development, continuous energy consumption tracking of building energy systems are essential. In this paper an assessment study has been done to identify the energy consumption & energy conservation potential for commercial buildings sector in Karnataka state, India. There are a total of 326 commercial buildings in the state of Karnataka who has qualified as designated consumers (i.e., having a Contract Demand ≥ 600 KVA), was consider for the study. It has estimated that the annual electricity sale to commercial sector is 3.62 Billion Units (BU) in alone Karnataka State, India, which is an account for 9.57 % of the total electricity sold. The commercial sector constitutes Government & private establishments, hospitals, hotels, restaurants, educational institutions, malls etc. Total 326 commercial buildings in the state accounting for annual energy consumption of 1295.72 Million Units (MU) which works out to about 35% of the sectoral consumption. The annual energy savings potential for 326 commercial buildings is assessed to be 0.25 BU.

Keywords: commercial buildings, connected load, energy conservation studies, energy savings, energy efficiency, energy conservation strategy, energy efficiency, thermal energy, HVAC system

Procedia PDF Downloads 580

Authors: Ebobo Urowoli Christiana

Abstract:

The Nigerian Prison Service statistics of 2007; 2009 revealed that though crime in the past was ascribed to men, but today there is a steady increase in the population of women involved in crime. This study focused on the investigation of female criminality in Lagos State: A case of Armed Robbery. Its major objective was to find out if there is an increase or decrease in female involvement in armed robbery and its growth rate. The major research question is 'Is there an increase in the perpetration of armed robbery by females in Lagos State?' the null hypotheses is 'There is no significant increase in the perpetration of armed robbery by females in Lagos State.' As a result, this study adopted the survey design, purposive sampling method and a sample size of 120 respondents. The rational choice theory was used to explain the reason for female involvement in armed robbery. Both primary and secondary data was generated for this study; the primary data was collected from the criminal records in Lagos State Police Command, Panti while the Quantitative data was collected using the questionnaire from 120 female detainees and inmates. The data collected was analyzed using the simple frequency tables and percentages and chi square was used to test for relationships. The study revealed a persistent rise in the prevalence of female armed robbery and recommended that youths should be equipped with educational/vocational skills in order to lead responsible lives.

Keywords: criminality, armed robbery, female, police commands, panti, nature

Procedia PDF Downloads 406

Authors: Nishaben Desai Dholakiya, Anirban Roy, Ranjan Dey

Abstract:

Understanding ocean thermodynamics has become increasingly critical as Earth's oceans serve as the primary planetary heat regulator, absorbing approximately 93% of excess heat energy from anthropogenic greenhouse gas emissions. This investigation presents a comprehensive analysis of Arabian Sea surface seawater thermodynamics, focusing specifically on heat capacity (Cp) and thermal expansion coefficient (α) - parameters fundamental to global heat distribution patterns. Through high-precision experimental measurements of ultrasonic velocity and density across varying temperature (293.15-318.15K) and salinity (0.5-35 ppt) conditions, it characterize critical thermophysical parameters including specific heat capacity, thermal expansion, and isobaric and isothermal compressibility coefficients in natural seawater systems. The study employs advanced machine learning frameworks - Random Forest, Gradient Booster, Stacked Ensemble Machine Learning (SEML), and AdaBoost - with SEML achieving exceptional accuracy (R² > 0.99) in heat capacity predictions. the findings reveal significant temperature-dependent molecular restructuring: enhanced thermal energy disrupts hydrogen-bonded networks and ion-water interactions, manifesting as decreased heat capacity with increasing temperature (negative ∂Cp/∂T). This mechanism creates a positive feedback loop where reduced heat absorption capacity potentially accelerates oceanic warming cycles. These quantitative insights into seawater thermodynamics provide crucial parametric inputs for climate models and evidence-based environmental policy formulation, particularly addressing the critical knowledge gap in thermal expansion behavior of seawater under varying temperature-salinity conditions.

Keywords: climate change, arabian sea, thermodynamics, machine learning

Procedia PDF Downloads 4

Authors: Alireza Rasekh, Peter Sergeant, Jan Vierendeels

Abstract:

This paper copes with the numerical simulation for convective heat transfer in the stator disk of an axial flux permanent magnet (AFPM) electrical machine. Overheating is one of the main issues in the design of AFMPs, which mainly occurs in the stator disk, so that it needs to be prevented. A rotor-stator configuration with 16 magnets at the periphery of the rotor is considered. Air is allowed to flow through openings in the rotor disk and channels being formed between the magnets and in the gap region between the magnets and the stator surface. The rotating channels between the magnets act as a driving force for the air flow. The significant non-dimensional parameters are the rotational Reynolds number, the gap size ratio, the magnet thickness ratio, and the magnet angle ratio. The goal is to find correlations for the Nusselt number on the stator disk according to these non-dimensional numbers. Therefore, CFD simulations have been performed with the multiple reference frame (MRF) technique to model the rotary motion of the rotor and the flow around and inside the machine. A minimization method is introduced by a pattern-search algorithm to find the appropriate values of the reference temperature. It is found that the correlations are fast, robust and is capable of predicting the stator heat transfer with a good accuracy. The results reveal that the magnet angle ratio diminishes the stator heat transfer, whereas the rotational Reynolds number and the magnet thickness ratio improve the convective heat transfer. On the other hand, there a certain gap size ratio at which the stator heat transfer reaches a maximum.

Keywords: AFPM, CFD, magnet parameters, stator heat transfer

Procedia PDF Downloads 250

Authors: Bilgehan Gultekin, Tuba Gultekin

Abstract:

Diplomatic public relations gives an ideal concept for recognition of palestine state in all over the europe. The first step of official recognition is approval of palestine state in international political organisations such as United Nations and Nato. So, diplomatic public relations provides a recognition process in communication scale. One of the aims of the study titled “Diplomatic Public Relations Techniques for Recognition of Palestine State in Europe” is to present some communication projects on diplomatic way. The study also aims at showing communication process at diplomatic level. The most important level of such kind of diplomacy is society based diplomacy. Moreover,The study provides a wider perspective that gives some creative diplomatic communication strategies for attracting society. To persuade the public for official recognition also is key element of this process. The study also finds new communication routes including persuasion techniques for society. All creative projects are supporting parts in original persuasive process of official recognition of Palestine.

Keywords: diplomatic public relations, diplomatic communication strategies, diplomatic communication, public relations

Procedia PDF Downloads 455

Authors: Aminu Mubarak Sadis

Abstract:

This study appraised the maintenance management practice in Federal University Dutse and Jigawa State Polytechnic Dutse, in Nigeria. The Physical Planning, Works and Maintenance Departments of the two Higher Institutions (Federal University Dutse and Jigawa State Polytechnic) are responsible for production and maintenance management of their physical assets. Over–enrollment problem has been a common feature in the higher institutions in Nigeria, Data were collected by the administered questionnaires and subsequent oral interview to authenticate the completed questionnaires. Random sampling techniques was used in selecting 150 respondents across the various institutions (Federal University Dutse and Jigawa State Polytechnic Dutse). Data collected was analyzed using Statistical Package for Social Science (SPSS) and t-test statistical techniques The conclusion was that maintenance management activities are yet to be given their appropriate attention on functions of the university and polytechnic which are crucial to improving teaching, learning and research. The unit responsible for maintenance and managing facilities should focus on their stated functions and effect changes were possible.

Keywords: appraisal, maintenance management, university, Polytechnic, practices

Procedia PDF Downloads 252

Authors: Alok Kumar Kamal, Vinod Kumar

Abstract:

The trench gate MOSFET has shown itself as the most appropriate power device for low to medium voltage power applications due to its lowest possible ON resistance among all power semiconductor devices. In this research work a double-epilayer PSGT structure using a thin layer of N+ polysilicon as gate material. The total ON-state resistance (RON) of UMOSFET can be reduced by optimizing the epilayer thickness. The optimized structure of Double-Epilayer exhibits a 25.8% reduction in the ON-state resistance at Vgs=5V and improving the switching characteristics by reducing the Reverse transfer capacitance (Cgd) by 7.4%.

Keywords: Miller-capacitance, double-Epilayer;switching characteristics, power trench MOSFET (U-MOSFET), on-state resistance, blocking voltage

Procedia PDF Downloads 72

Authors: Amal Ezzidani, Abdelghani Ben Tahar, Mohamed Hanini

Abstract:

A sequence of finite tandem queue is considered for this study. Each one has a single server, which operates under the egalitarian processor sharing discipline. External customers arrive at each queue according to a renewal input process and having a general service times distribution. Upon completing service, customers leave the current queue and enter to the next. Under mild assumptions, including critical data, we prove the existence and the uniqueness of the fluid solution. For asymptotic behavior, we provide necessary and sufficient conditions for the invariant state and the convergence to this invariant state. In the end, we establish the convergence of a correctly normalized state process to a fluid limit characterized by a system of algebraic and integral equations.

Keywords: fluid limit, fluid model, measure valued process, processor sharing, tandem queue

Procedia PDF Downloads 323

Authors: Xiuying Wang, Changliang Li, Bo Xu

Abstract:

A fixed-length feature vector is required for many machine learning algorithms in NLP field. Word embeddings have been very successful at learning lexical information. However, they cannot capture the compositional meaning of sentences, which prevents them from a deeper understanding of language. In this paper, we introduce a novel hierarchical tree long short-term memory (HTLSTM) model that learns vector representations for sentences of arbitrary syntactic type and length. We propose to split one sentence into three hierarchies: short phrase, long phrase and full sentence level. The HTLSTM model gives our algorithm the potential to fully consider the hierarchical information and long-term dependencies of language. We design the experiments on both English and Chinese corpus to evaluate our model on sentiment analysis task. And the results show that our model outperforms several existing state of the art approaches significantly.

Keywords: deep learning, hierarchical tree long short-term memory, sentence representation, sentiment analysis

Procedia PDF Downloads 349

Authors: Aysegul Sarac

Abstract:

The aim of this study is to analyze the impacts of integrating agile development principles and practices, in particular to reduce project lead time in a multi-project environment. We analyze Arçelik Washing Machine R&D Center in which multiple projects are conducted by shared resources. In the first part of the study, we illustrate the current waterfall model system by using a value stream map. We define all activities starting from the first idea of the project to the customer and measure process time and lead time of projects. In the second part of the study we estimate potential improvements and select a set of these improvements to integrate agile principles. We aim to develop a future state map and analyze the impacts of integrating lean principles on project lead time. The main contribution of this study is that we analyze and integrate agile product development principles in a real multi-project system.

Keywords: agile project management, multi project system, project lead time, product development

Procedia PDF Downloads 305