Search results for: floating architectures

Authors: Ashlesha Khanapure, Harsh Kashyap, Abhinav Anand, Sanjana Habib, Anupama Bidargaddi

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Technologies that are developing quickly are being developed daily in a variety of disciplines, particularly the medical field. For the purpose of detecting bone fractures in X-ray pictures of different body segments, our work compares the ResNet-50 and MobileNetV3 architectures. It evaluates accuracy and computing efficiency with X-rays of the elbow, hand, and shoulder from the MURA dataset. Through training and validation, the models are evaluated on normal and fractured images. While ResNet-50 showcases superior accuracy in fracture identification, MobileNetV3 showcases superior speed and resource optimization. Despite ResNet-50’s accuracy, MobileNetV3’s swifter inference makes it a viable choice for real-time clinical applications, emphasizing the importance of balancing computational efficiency and accuracy in medical imaging. We created a graphical user interface (GUI) for MobileNet V3 model bone fracture detection. This research underscores MobileNetV3’s potential to streamline bone fracture diagnoses, potentially revolutionizing orthopedic medical procedures and enhancing patient care.

Keywords: CNN, MobileNet V3, ResNet-50, healthcare, MURA, X-ray, fracture detection

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Authors: Vinod Kumar Jatav, Dr. Vrijendra Singh

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WiMAX is an emerging wireless technology designed by WiMAX forum. WiMAX technology delivers broadband internet access with QoS, mobility and robust security. WiMAX is among the prominent mobile broadband wireless technology which laid the foundation for the next generation networks (NGN). The next-generation communication system for railway should facilitate high level network availability, fast mobility for high speed trains with reliability, high handover rate, the firmness of train operations, and high QoS. The system should also be capable to provide various railway services by transmitting big data efficiently. One of the most promising technologies for the next generation railway wireless communication is Mobile WiMAX. This paper analyses some of the network architectures for railway wireless communication and considers the elementary concepts to facilitate the users with broadband internet access on trains. The paper aims to recognize the suitability of Mobile WiMAX technology for the special requirements of broadband internet facilities and wireless telecommunication services of Railways.

Keywords: Broadband internet, IEEE 802.16e, mobile WiMAX, Railway wireless communication

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Authors: Ninomyslaw Jakubczyk, Anna Zwierzchowska, Adam Maszczyk

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Background: Central stabilisation training aims to improve neuromuscular coordination. It is used in the form of injury prevention and completing the swimmers' process. The aim of the study was to access the impact of this training on the results by disabled swimmers at 50 and 100 meters’ freestyle. Material/Method: 20 competitors with similar dysfunctions of the musculoskeletal system, randomly assigned to the experimental and control group, participated in the study. Each group consisted of 7 swimmers started in competitions from the standing starting position, and 3 started from the water. The study included a 4-week set of stabilization exercises, 4 times a week instead of pulling by legs. Exercises were held under specialist swimming conditions and involved controlled circuit muscle movements while maintaining a floating stable position in the water. Results: All groups improved their 'best times' besides swimmers started from standing position in the control group. There were no significant differences between intergroup and intra-group results, both at distance 50 and 100 meters’ freestyle. Conclusions: Better improvements in the experimental group were noted, but this effect cannot be attributed to 4-week stabilisation training. However, this investigation might suggest that this type of training could be beneficial for junior disabled swimmers.

Keywords: athletes, swimming, trunk exercises, youth

Procedia PDF Downloads 163

Authors: Güzi̇n Ti̇rkeş, Özge Teki̇n, Kerem Kurtuluş, Y. Yekta Yurtseven, Murat Baran

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A scanning electron microscope (SEM) is a type of electron microscope mainly used in nanoscience and nanotechnology areas. Automatic image recognition and classification are among the general areas of application concerning SEM. In line with these usages, the present paper proposes a deep learning algorithm that classifies SEM images into nine categories by means of an online application to simplify the process. The NFFA-EUROPE - 100% SEM data set, containing approximately 21,000 images, was used to train and test the algorithm at 80% and 20%, respectively. Validation was carried out using a separate data set obtained from the Middle East Technical University (METU) in Turkey. To increase the accuracy in the results, the Inception ResNet-V2 model was used in view of the Fine-Tuning approach. By using a confusion matrix, it was observed that the coated-surface category has a negative effect on the accuracy of the results since it contains other categories in the data set, thereby confusing the model when detecting category-specific patterns. For this reason, the coated-surface category was removed from the train data set, hence increasing accuracy by up to 96.5%.

Keywords: convolutional neural networks, deep learning, image classification, scanning electron microscope

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Authors: Mengqiu Deng, Xiao Peng, Yang Zhao

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The development of local energy systems requires the collective involvement of different actors from various levels of society. However, the stakeholder analysis of local energy systems still has been under-developed. This paper proposes an multi-agent system (MAS) framework to facilitate the development of stakeholder analysis of local energy systems. The framework takes into account the most influencing stakeholders, including prosumers/consumers, system operators, energy companies and government bodies. Different stakeholders are modeled based on agent architectures for example the belief-desire-intention (BDI) to better reflect their motivations and interests in participating in local energy systems. The agent models of different stakeholders are then integrated in one model of the whole energy system. An illustrative case study is provided to elaborate how to develop a quantitative agent model for different stakeholders, as well as to demonstrate the practicability of the proposed framework. The findings from the case study indicate that the suggested framework and agent model can serve as analytical instruments for enhancing the government’s policy-making process by offering a systematic view of stakeholder interconnections in local energy systems.

Keywords: multi-agent system, BDI agent, local energy systems, stakeholders

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Authors: Mohammad Borhani, Afshin Danehkar

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With the increase in population and the upward trend of energy demand, mankind has thought of using suppliers that guarantee a stable supply of energy, unlike fossil fuels, which, in addition to the widespread emission of greenhouse gases that one of the main factors in the destruction of the ozone layer and it will be finished in a short time in the not-so-distant future. In this regard, one of the sustainable ways of energy supply is the use of wind converters. That convert wind energy into electricity. For this reason, this research focused on wind turbines and their installation conditions. The main classification of wind turbines is based on the axis of rotation, which is divided into two groups: horizontal axis and vertical axis; each of these two types, with the advancement of technology in man-made environments such as cities, villages, airports, and other human environments can be installed and operated. The main difference between offshore and onshore wind turbines is their installation and foundation. Which are usually divided into five types; including of Monopile Wind Turbines, Jacket Wind Turbines, Tripile Wind Turbines, Gravity-Based Wind Turbines, and Floating Offshore Wind Turbines. For installation in a wind power plant requires an arrangement that produces electric power, the distance between the turbines is usually between 5 or 7 times the diameter of the rotor and if perpendicular to the wind direction be If they are 3 to 5 times the diameter of the rotor, they will be more efficient.

Keywords: wind farms, Savonius, Darrieus, offshore wind turbine, renewable energy

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Authors: Helmi Baazaoui, Mounir Zrigui

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This study addresses the challenge of hate speech detection in Tunisian Arabic text, a critical issue for online safety and moderation. Leveraging the strengths of the AraBERT model, we fine-tuned and evaluated its performance against the Bi-LSTM model across four distinct datasets: T-HSAB, TNHS, TUNIZI-Dataset, and a newly compiled dataset with diverse labels such as Offensive Language, Racism, and Religious Intolerance. Our experimental results demonstrate that AraBERT significantly outperforms Bi-LSTM in terms of Recall, Precision, F1-Score, and Accuracy across all datasets. The findings underline the robustness of AraBERT in capturing the nuanced features of Tunisian Arabic and its superior capability in classification tasks. This research not only advances the technology for hate speech detection but also provides practical implications for social media moderation and policy-making in Tunisia. Future work will focus on expanding the datasets and exploring more sophisticated architectures to further enhance detection accuracy, thus promoting safer online interactions.

Keywords: hate speech detection, Tunisian Arabic, AraBERT, Bi-LSTM, Gemini annotation tool, social media moderation

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Authors: Myoung Ah Kim, Dong Ho Sin, Chul Gyu Song

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Blood oxygen saturation system is a well-established, noninvasive photoplethysmographic method to monitor vital signs. Conventional blood oxygen saturation measurements for the two LED light source is the ambiguity of the oxygen saturation measurement principle and the measurement results greatly influenced and heat and motion artifact. A high accuracy in order to solve these problems blood oxygen saturation measuring method has been proposed using a broadband light source that can be easily understood by the algorithm. The measurement of blood oxygen saturation based on broad-band light source has advantage of simple testing facility and easy understanding. Broadband light source based on blood oxygen saturation measuring program proposed in this paper is a combination of LabVIEW and MATLAB. Using the wavelength range of 450 nm-750 nm using a floating light absorption of oxyhemoglobin and deoxyhemoglobin to measure the blood oxygen saturation. Hand movement is to fix the probe to the motor stage in order to prevent oxygen saturation measurement that affect the sample and probe kept constant interval. Experimental results show that the proposed method noticeably increases the accuracy and saves time compared with the conventional methods.

Keywords: oxygen saturation, broad-band light source, CCD, light reflectance theory

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Authors: Agastya Pratap Singh

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This review paper examines the emerging applications of generative adversarial networks (GANs) in the fields of image recognition and anomaly detection. With the rapid growth of digital image data, the need for efficient and accurate methodologies to identify and classify images has become increasingly critical. GANs, known for their ability to generate realistic data, have gained significant attention for their potential to enhance traditional image recognition systems and improve anomaly detection performance. The paper systematically analyzes various GAN architectures and their modifications tailored for image recognition tasks, highlighting their strengths and limitations. Additionally, it delves into the effectiveness of GANs in detecting anomalies in diverse datasets, including medical imaging, industrial inspection, and surveillance. The review also discusses the challenges faced in training GANs, such as mode collapse and stability issues, and presents recent advancements aimed at overcoming these obstacles.

Keywords: generative adversarial networks, image recognition, anomaly detection, synthetic data generation, deep learning, computer vision, unsupervised learning, pattern recognition, model evaluation, machine learning applications

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Authors: Pablo Chico De Guzman, Cesar Sanchez

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Microservice architectures are increasingly in distributed cloud applications due to the advantages on the software composition, development speed, release cycle frequency and the business logic time to market. On the other hand, these architectures also introduce some challenges on the testing and release phases of applications. Container technology solves some of these issues by providing reproducible environments, easy of software distribution and isolation of processes. However, there are other issues that remain unsolved in current container technology when dealing with multiple machines, such as networking for multi-host communication, service discovery, load balancing or data persistency (even though some of these challenges are already solved by traditional cloud vendors in a very mature and widespread manner). Container cluster management tools, such as Kubernetes, Mesos or Docker Swarm, attempt to solve these problems by introducing a new control layer where the unit of deployment is the container (or the pod — a set of strongly related containers that must be deployed on the same machine). These tools are complex to configure and manage and they do not follow a pure immutable infrastructure approach since servers are reused between deployments. Indeed, these tools introduce dependencies at execution time for solving networking or service discovery problems. If an error on the control layer occurs, which would affect running applications, specific expertise is required to perform ad-hoc troubleshooting. As a consequence, it is not surprising that container cluster support is becoming a source of revenue for consulting services. This paper presents i2kit, a deployment tool based on the immutable infrastructure pattern, where the virtual machine is the unit of deployment. The input for i2kit is a declarative definition of a set of microservices, where each microservice is defined as a pod of containers. Microservices are built into machine images using linuxkit —- a tool for creating minimal linux distributions specialized in running containers. These machine images are then deployed to one or more virtual machines, which are exposed through a cloud vendor load balancer. Finally, the load balancer endpoint is set into other microservices using an environment variable, providing service discovery. The toolkit i2kit reuses the best ideas from container technology to solve problems like reproducible environments, process isolation, and software distribution, and at the same time relies on mature, proven cloud vendor technology for networking, load balancing and persistency. The result is a more robust system with no learning curve for troubleshooting running applications. We have implemented an open source prototype that transforms i2kit definitions into AWS cloud formation templates, where each microservice AMI (Amazon Machine Image) is created on the fly using linuxkit. Even though container cluster management tools have more flexibility for resource allocation optimization, we defend that adding a new control layer implies more important disadvantages. Resource allocation is greatly improved by using linuxkit, which introduces a very small footprint (around 35MB). Also, the system is more secure since linuxkit installs the minimum set of dependencies to run containers. The toolkit i2kit is currently under development at the IMDEA Software Institute.

Keywords: container, deployment, immutable infrastructure, microservice

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Authors: Ionel D. Craiu, Mihai Nedelcu

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Thin-walled cylinders are often used by the offshore industry as columns of floating installations. Based on observed strains, the inverse Finite Element Method (iFEM) may rebuild the deformation of structures. Structural Health Monitoring uses this approach extensively. However, the number of in-situ strain gauges is what determines how accurate it is, and for shell structures with complicated deformation, this number can easily become too high for practical use. Any thin-walled beam member's complicated deformation can be modeled by the Generalized Beam Theory (GBT) as a linear combination of pre-specified cross-section deformation modes. GBT uses bar finite elements as opposed to shell finite elements. This paper proposes an iFEM/GBT formulation for the shape sensing of thin-walled cylinders based on these benefits. This method significantly reduces the number of strain gauges compared to using the traditional inverse-shell finite elements. Using numerical simulations, dent damage detection is achieved by comparing the strain distributions of the undamaged and damaged members. The effect of noise on strain measurements is also investigated.

Keywords: damage detection, generalized beam theory, inverse finite element method, shape sensing

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Authors: William Whiteley, Jens Gregor

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In recent developments of direct neural network based positron emission tomography (PET) reconstruction, two prominent architectures have emerged for converting measurement data into images: 1) networks that contain fully-connected layers; and 2) networks that primarily use a convolutional encoder-decoder architecture. In this paper, we present a multi-modal direct PET reconstruction method called MDPET, which is a hybrid approach that combines the advantages of both types of networks. MDPET processes raw data in the form of sinograms and histo-images in concert with attenuation maps to produce high quality multi-slice PET images (e.g., 8x440x440). MDPET is trained on a large whole-body patient data set and evaluated both quantitatively and qualitatively against target images reconstructed with the standard PET reconstruction benchmark of iterative ordered subsets expectation maximization. The results show that MDPET outperforms the best previously published direct neural network methods in measures of bias, signal-to-noise ratio, mean absolute error, and structural similarity.

Keywords: deep learning, image reconstruction, machine learning, neural network, positron emission tomography

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Authors: Ramiro Gustavo Ramirez Camacho, Waldir De Oliveira, Eraldo Cruz Dos Santos, Edna Raimunda Da Silva, Tania Marie Arispe Angulo, Carlos Eduardo Alves Da Costa, Tânia Cristina Alves Dos Reis

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It presents a preliminary design methodology of an axial fan based on the lift wing theory and the potential vortex hypothesis. The literature considers a study of acoustic and engineering expertise to model a fan with low noise. Axial fans with inadequate intake geometry, often suffer poor condition of the flow at the entrance, varying from velocity profiles spatially asymmetric to swirl floating with respect to time, this produces random forces acting on the blades. This produces broadband gust noise which in most cases triggers the tonal noise. The analysis of the axial flow fan will be conducted for the solution of the Navier-Stokes equations and models of turbulence in steady and transitory (RANS - URANS) 3-D, in order to find an efficient aerodynamic design, with low noise and suitable for industrial installation. Therefore, the process will require the use of computational optimization methods, aerodynamic design methodologies, and numerical methods as CFD- Computational Fluid Dynamics. The objective is the development of the methodology of the construction axial fan, provide of design the geometry of the blade, and evaluate aerodynamic performance

Keywords: Axial fan design, CFD, Preliminary Design, Optimization

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Authors: Barenten Suciu

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An electrical generator able to harness energy from the water waves and designed as a double-cone geared motor-generator (DCGMG), is proposed and theoretically investigated. Similar to a differential gear mechanism, used in the transmission system of the auto vehicle wheels, an angular speed differential is created between the cones rolling on two concentric circular rails. Water wave acting on the floating DCGMG produces and a gear-box amplifies the speed differential to gain sufficient torque for power generation. A model that allows computation of the speed differential, torque, and power of the DCGMG is suggested. Influence of various parameters, regarding the construction of the DCGMG, as well as the contact between the double-cone and rails, on the electro-mechanical output, is emphasized. Results obtained indicate that the generated electrical power can be increased by augmenting the mass of the double-cone, the span of the rails, the apex angle of the cones, the friction between cones and rails, the amplification factor of the gear-box, and the efficiency of the motor-generator. Such findings are useful to formulate a design methodology for the proposed wave-powered generator.

Keywords: amplification of angular speed differential, circular concentric rails, double-cone, wave-powered electrical generator

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Authors: Farouk A. N. Bouhadiba

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This work addresses the issue of the LMD(Licence, Master, Doctorat) in Algeria and the impact it has had on education quality in terms of educational affect and effect. It starts with a brief introduction to the financial means, the educational settings, and the social environment in place when the LMD was institutionalized in Algeria (2003-2004). Some factors for the success or failure of this top-down institutional endeavor are examined and analyzed. These include – among other factors - the teacher/student attitudes, apprehensions, and motivations on the one hand and the institutional euphoria for the LMD in Algeria on the other hand. Some issues at stake are discussed. More specifically, the professional versus the student affect on today’s attitudes, interests, and values is examined as a result of nearly two decades of LMD teaching and learning in Algerian universities. We shall then present some official curricula that, in terms of content, reflect the spirit, principles, and architectures of the LMD but which, in reality, are partially, if not fully, set aside when it comes to teaching practices, learning behaviors, motivation, and evaluation. The discussion on effect highlights attitudinal, developmental, and social markers that are indicative of the extent to which Education Quality in Algeria has been positively or negatively affected by the implementation of the LMD.

Keywords: LMD bachelor's masters doctorat, affects and effects, education quality, Algeria

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Authors: Agatha Padma Laksitaningtyas, Sumiyati Gunawan

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Mataram Irrigation Canal has 31,2 km length, is the main irrigation canal in Special Region Province of Yogyakarta, connecting Progo River on the west side and Opak River on the east side. It has an important role as the main water carrier distribution for various purposes such as agriculture, fishery, and plantation which should be free from sediment material. Bed Load Sediment is the basic sediment that will make the sediment process on the irrigation canal. Sediment process is a simultaneous event that can make deposition sediment at the base of irrigation canal and can make the height of elevation water change, it will affect the availability of water to be used for irrigation functions. To predict the amount of drowning sediments in the irrigation canal using two methods: Meyer-Peter and Muller’s Method which is an energy approach method and Einstein Method which is a probabilistic approach. Speed measurement using floating method and using current meters. The channel geometry is measured directly in the field. The basic sediment of the channel is taken in the field by taking three samples from three different points. The result of the research shows that by using the formula Meyer -Peter Muller get the result of 60,75799 kg/s, whereas with Einsten’s Method get result of 13,06461 kg/s. the results may serve as a reference for dredging the sediments on the channel so as not to disrupt the flow of water in irrigation canal.

Keywords: bed load, sediment, irrigation, Mataram canal

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Authors: S. Chandrasekaran, P. A. Kiran

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Increasing demand for large-sized Floating, Storage and Regasification Units (FSRUs) for oil and gas industries led to the development of novel geometric form of Buoyant Leg Storage and Regasification Platform (BLSRP). BLSRP consists of a circular deck supported by six buoyant legs placed symmetrically with respect to wave direction. Circular deck is connected to buoyant legs using hinged joints, which restrain transfer of rotational response from the legs to deck and vice-versa. Buoyant legs are connected to seabed using taut moored system with high initial pretension, enabling rigid body motion in vertical plane. Encountered environmental loads induce dynamic tether tension variations, which in turn affect stability of the platform. The present study investigates Mathieu stability of BLSRP under the postulated tether pullout cases by inducing additional tension in the tethers. From the numerical studies carried out, it is seen that postulated tether pullout on any one of the buoyant legs does not result in Mathieu type instability even under excessive tether tension. This is due to the presence of hinged joints, which are capable of dissipating the unbalanced loads to other legs. However, under tether pullout of consecutive buoyant legs, Mathieu-type instability is observed.

Keywords: offshore platforms, stability, postulated failure, dynamic tether tension

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Authors: Vramori Mitra, Bornali Sarma, Arun K. Sarma

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Recurrence is a ubiquitous feature of any real dynamical system. The states in phase space trajectory of a system have an inherent tendency to return to the same state or its close state after certain time laps. Recurrence quantification analysis technique, based on this fundamental feature of a dynamical system, detects evaluation of state under variation of control parameter of the system. The paper presents the investigation of nonlinear dynamical behavior of plasma floating potential fluctuations obtained by using a Langmuir probe in different magnetic field under the variation of discharge voltages. The main measures of recurrence quantification analysis are considered as determinism, linemax and entropy. The increment of the DET and linemax variables asserts that the predictability and periodicity of the system is increasing. The variable linemax indicates that the chaoticity is being diminished with the slump of magnetic field while increase of magnetic field enhancing the chaotic behavior. Fractal property of the plasma time series estimated by DFA technique (Detrended fluctuation analysis) reflects that long-range correlation of plasma fluctuations is decreasing while fractal dimension is increasing with the enhancement of magnetic field which corroborates the RQA analysis.

Keywords: detrended fluctuation analysis, chaos, phase space, recurrence

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Authors: Ramzi Rihane, Yassine Benayed

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Epilepsy is a chronic neurological disorder characterized by recurrent, unprovoked seizures resulting from abnormal electrical activity in the brain. Timely and accurate detection of these seizures is essential for improving patient care. In this study, we leverage the UK Bonn University open-source EEG dataset and employ advanced deep-learning techniques to automate the detection of epileptic seizures. By extracting key features from both time and frequency domains, as well as Spectrogram features, we enhance the performance of various deep learning models. Our investigation includes architectures such as Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), 1D Convolutional Neural Networks (1D-CNN), and hybrid CNN-LSTM and CNN-BiLSTM models. The models achieved impressive accuracies: LSTM (98.52%), Bi-LSTM (98.61%), CNN-LSTM (98.91%), CNN-BiLSTM (98.83%), and CNN (98.73%). Additionally, we utilized a data augmentation technique called SMOTE, which yielded the following results: CNN (97.36%), LSTM (97.01%), Bi-LSTM (97.23%), CNN-LSTM (97.45%), and CNN-BiLSTM (97.34%). These findings demonstrate the effectiveness of deep learning in capturing complex patterns in EEG signals, providing a reliable and scalable solution for real-time seizure detection in clinical environments.

Keywords: electroencephalogram, epileptic seizure, deep learning, LSTM, CNN, BI-LSTM, seizure detection

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Authors: Megha Gupta, Nupur Prakash

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Identification of plant diseases has been performed using machine learning and deep learning models on the datasets containing images of healthy and diseased plant leaves. The current study carries out an evaluation of some of the deep learning models based on convolutional neural network (CNN) architectures for identification of plant diseases. For this purpose, the publicly available New Plant Diseases Dataset, an augmented version of PlantVillage dataset, available on Kaggle platform, containing 87,900 images has been used. The dataset contained images of 26 diseases of 14 different plants and images of 12 healthy plants. The CNN models selected for the study presented in this paper are AlexNet, ZFNet, VGGNet (four models), GoogLeNet, and ResNet (three models). The selected models are trained using PyTorch, an open-source machine learning library, on Google Colaboratory. A comparative study has been carried out to analyze the high degree of accuracy achieved using these models. The highest test accuracy and F1-score of 99.59% and 0.996, respectively, were achieved by using GoogLeNet with Mini-batch momentum based gradient descent learning algorithm.

Keywords: comparative analysis, convolutional neural networks, deep learning, plant disease identification

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Authors: James Taylor

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This paper considers the political, geo-philosophical musings of Deleuze and Guattari on spatialisation, place and movement in relation to the religious nomad (wandering ascetics and reclusive forest monks) inhabiting the borderlands of Thailand. A nomadic science involves improvised ascetic practices between the molar lines striated by modern state apparatuses. The wandering ascetics, inhabiting a frontier political ecology, stand in contrast to the appropriating, sedentary metaphysics and sanctifying arborescence of statism and its corollary place-making, embedded in rootedness and territorialisation. It is argued that the religious nomads, residing on the endo-exteriorities of the state, came to represent a rhizomatic and politico-ontological threat to centre-nation and its apparatus of capture. The paper also theorises transitions and movement at the borderlands in the context of the state’s monastic reforms. These reforms, and its pervasive royal science, problematised the interstitial zones of the early ascetic wanderers in their radical cross-cutting networks and lines, moving within and across demarcated frontiers. Indeed, the ascetic wanderers and their allegorical war machine were seen as a source of wild, free-floating charisma and mystical power, eventually appropriated by the centre-nation in it’s becoming unitary and fixed.

Keywords: Deleuze and Guattari, religious nomad, centre-nation, borderlands, Buddhism

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Authors: Daniel Gonzales, Zev Winkelman, Trung Tran, Ricardo Sanchez, Dulani Woods, John Hollywood

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We have developed a distributed computing capability, Digital Forensics Compute Cluster (DFORC2) to speed up the ingestion and processing of digital evidence that is resident on computer hard drives. DFORC2 parallelizes evidence ingestion and file processing steps. It can be run on a standalone computer cluster or in the Amazon Web Services (AWS) cloud. When running in a virtualized computing environment, its cluster resources can be dynamically scaled up or down using Kubernetes. DFORC2 is an open source project that uses Autopsy, Apache Spark and Kafka, and other open source software packages. It extends the proven open source digital forensics capabilities of Autopsy to compute clusters and cloud architectures, so digital forensics tasks can be accomplished efficiently by a scalable array of cluster compute nodes. In this paper, we describe DFORC2 and compare it with a standalone version of Autopsy when both are used to process evidence from hard drives of different sizes.

Keywords: digital forensics, cloud computing, cyber security, spark, Kubernetes, Kafka

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Authors: Rewadee Ungpho

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The research on the musical culture of Sea Gypsies in Bulon archipelago, Satun Province, is considered as an anthropology research. Research objectives were to study the history and information culture and also to find the basis information for the restoration and preservation of the music culture of Sea Gypsies who live in Bulon archipelago. Findings of the research are as follows: 1) Musical characteristics of Sea Gypsies in Bulon archipelago is still traditional. It does not mix with any external musical influence such as musical instruments, language, and other musical characteristics. There are various kind of songs which can play a complete melody and rhythm, including a total of 8 songs as follows; Lagu-Ayam-Dide, Lagu-Sitipayong, Lagu-Bulong-pute, Lagu-Chemamat, Laguduwo, Lagu-Ma-I-nang, Lagu-Mana-Ikan. 2) The roles of culture/music in Bulon archipelago correlate with Urak Lawoi society. They use music in the ceremony of votive offering, in the floating ceremony held in Lipe Island and in various festivals. Therefore, music is a spiritual sacrifice and a spiritual instrument that conveys an Urak Lawoi, which makes the Urak Lawoi still unique and has a sense of ethnic identity. 3) The inheritance of Urak Lawoi music is still being made in a traditional way, as an oral tradition with no record. The teaching and learning must be one on one, and it required length of time to practice and accumulate the knowledge. Due to above mentioned reasons, a few people attend in the inheritance. Those who are interested may not be able to practice constantly. As a result, there is only a few, or even none, descendants left.

Keywords: sea gypsy, music, Bulon archipelago, ethnomusicology

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Authors: Duy M. P. Vo, Cornelia Sennewald, Gerald Hoffmann, Chokri Cherif

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The development of solutions to improve the resistance of buildings to short-term dynamic loads, particularly impact load, is driven by the urgent demand worldwide on securing human life and critical infrastructures. The research training group GRK 2250/1 aims to develop mineral-bonded composites that allow the fabrication of thin-layered strengthening layers providing available concrete members with enhanced impact resistance. This paper presents the development of 3D woven wire cellular structures that can be used as innovative reinforcement for targeted composites. 3D woven wire cellular structures are truss-like architectures that can be fabricated in an automatized process with a great customization possibility. The specific architecture allows this kind of structures to have good load bearing capability and forming behavior, which is of great potential to give strength against impact loading. An appropriate combination of topology and material enables an optimal use of thin-layered reinforcement in concrete constructions.

Keywords: 3D woven cellular structures, ductile behavior, energy absorption, fiber-based reinforced concrete, impact resistant

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Authors: M. Helen

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Chemical graph serves as a convenient model for any real or abstract chemical system. Dendrimers are novel three dimensional hyper branched globular nanopolymeric architectures. Drug delivery scientists are especially enthusiastic about possible utility of dendrimers as drug delivery tool. Dendrimers like poly L lysine (PLL), poly-propylene imine (PPI) and poly-amidoamine (PAMAM), etc., are used as gene carrier in drug delivery system because of their chemical characteristics. These characteristics of chemical compounds are analysed using topological indices (invariants under graph isomorphism) such as Wiener index, Zagreb index, etc., Prof. V. R. Kulli motivated by the application of Zagreb indices in finding the total π energy and derived Gourava indices which is an improved version over Zagreb indices. In this paper, we study the structure of PLL-Dendrimer that has the following applications: reduction in toxicity, colon delivery, and topical delivery. Also, we determine first and second Gourava indices, first and second hyper Gourava indices, product and sum connectivity Gourava indices for PLL-Dendrimer. Gourava Indices have found applications in Quantitative Structure-Property Relationship (QSPR)/ Quantitative Structure-Activity Relationship (QSAR) studies.

Keywords: connectivity Gourava indices, dendrimer, Gourava indices, hyper GouravaG indices

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Authors: Etika Sukma Adiyanti, H. N. Nurul Huda Putu Ekapraja, Gugun Gunawan

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Global warming causes climate change and sea level rise. This is a threat for coastal regions, especially for coastal settlements with activities that are influenced by this natural phenomenon. Consequences are damage of houses, humid house environment, sustainability of the houses, obstructed economic activities and domestic works, disruption of sanitation facilities, lack of electricity, failure of transport system, psychological issues and other. Icons Tambak Lorok as 'Fisherman Village' is not something familiar to residents of the city of Semarang. Especially for the housewife who every day have to buy the ingredients high in protein and omega fish auction which is adjacent to the main street market in the village of Tambak Lorok. However, there are major problems that are being experienced by this small neighborhood. In fact, this issue includes seven infrastructure that should spoil the fishermen in activity with marine life. With this research, we will investigate water urbanism and climate change resiliency in Semarang, specifically the traditional fisher community of Tambak Lorok. We intend to find out how the local people in the fisher settlement Tambak Lorok deal with water urbanism, proverty and living with floods. So, we have a good solution for this problem, Floating Stage. We think that Tambak Lorok needs a new design for the common future. With this, One Building at A Time for Tambak Lorok, will be a good solution.

Keywords: fisher community, environment, climate change, settlement

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Authors: Agastya Pratap Singh

Abstract:

Generative Adversarial Networks (GANs) have emerged as powerful tools in the fields of image recognition and anomaly detection due to their ability to model complex data distributions and generate realistic images. This systematic review explores recent advancements and applications of GANs in both image recognition and anomaly detection tasks. We discuss various GAN architectures, such as DCGAN, CycleGAN, and StyleGAN, which have been tailored to improve accuracy, robustness, and efficiency in visual data analysis. In image recognition, GANs have been used to enhance data augmentation, improve classification models, and generate high-quality synthetic images. In anomaly detection, GANs have proven effective in identifying rare and subtle abnormalities across various domains, including medical imaging, cybersecurity, and industrial inspection. The review also highlights the challenges and limitations associated with GAN-based methods, such as instability during training and mode collapse, and suggests future research directions to overcome these issues. Through this review, we aim to provide researchers with a comprehensive understanding of the capabilities and potential of GANs in transforming image recognition and anomaly detection practices.

Keywords: generative adversarial networks, image recognition, anomaly detection, DCGAN, CycleGAN, StyleGAN, data augmentation

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Authors: Rafiu O. Sanni, Abayomi O. Olajuyigbe, Nelson R. Osungbemiro, Rotimi F. Olaniyan

Abstract:

The Owena River lies within the drainage basins of the Oni, Siluko, and Ogbesse rivers. The river’s immediate surroundings are covered by dense forests, interspersed by plantations of cocoa, oil palm, kolanut, bananas, and other crops. The objectives were to identify the aquatic plants community, comprising the algae and aquatic macrophytes, observe their population dynamics in relation to the two seasons and identify their economic importance, especially to the neighbouring community. The study sites were determined using a stratified sampling method. Three strata were marked out for sampling namely strata I (upstream)–5 stations, strata II (reservoir) –2 stations, and strata III (outflow) 2 stations. These nine stations were tagged st1, st2, st3…st9. The aquatic macrophytes were collected using standard methods and identified at the University of Ibadan herbarium while the algal samples were collected using standard methods for microalgae. The periphytonic species were scraped from surfaces of rocks (perilithic), sucked with large syringe from mud (epipellic), scraped from suspended logs, washed from roots of aquatic angiosperms (epiphytic), as well as shaken from other particles such as suspended plant parts. Some were collected physically by scooping floating thallus of non-microscopic multicellular forms. The specimens were taken to the laboratory and observed under a microscope with mounted digital camera for photomicrography. Identification was done using Prescott.

Keywords: aquatic plants, aquatic macrophytes, algae, Owena river

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Authors: Rita Rostika, Junianto, Zulfiqar W. Ibrahim, Iskandar, Lantun P. Dewanti

Abstract:

The purpose of this research is to determine the influence of asparagus bean powder and its most effective administration dose to improve the gonad maturity of nilem carp (Osteochilus hasselti). The research is conducted in October-July 2017 located at Cirata Reservoir and Aquaculture Laboratory, Faculty of Fisheries and Marine Sciences, Padjadjaran University, Jatinangor. The research employs an experimental method using a Complete Random Design (RAL) with six treatments and three repetitions. The treatments include the addition of asparagus bean powder by 0% (Control), 4% per kg of feed, 5% per kg of feed, 6% per kg of feed, 7% per kg of feed, as well as the addition of vitamin E essential as the control. The results show that the addition of asparagus bean powder to the feed may influence the gonad maturity of nilem carp shown by its Gonado Somatic Index (GSI) parameter, fecundity, egg diameter and egg reaching its maturity phase or GVBD (Germinal Vesicle Breakdown). The best administration dose influencing nilem carp is the addition of asparagus bean powder by 7% per kg of feed with the average GSI of 15.02%, relative fecundity of 137 eggs/g of fish parent weight, egg diameter of 1,263 mm, and egg reaching its maturity phase (GVBD) of 78.15%.

Keywords: asparagus bean powder, nilem carp, gonad maturity, Cirata reservoir

Procedia PDF Downloads 169

Authors: Djallel Bouamama, Yasser R. Haddadi

Abstract:

Accurate brain tumor segmentation is crucial for diagnosis and treatment planning, yet it remains a challenging task due to the variability in tumor shapes and intensities. This paper introduces a distinct approach to brain tumor image segmentation by leveraging an advanced architecture known as Mamba-Unet. Building on the well-established U-Net framework, Mamba-Unet incorporates distinct design enhancements to improve segmentation performance. Our proposed method integrates a multi-scale attention mechanism and a hybrid loss function to effectively capture fine-grained details and contextual information in brain MRI scans. We demonstrate that Mamba-Unet significantly enhances segmentation accuracy compared to conventional U-Net models by utilizing a comprehensive dataset of annotated brain MRI scans. Quantitative evaluations reveal that Mamba-Unet surpasses traditional U-Net architectures and other contemporary segmentation models regarding Dice coefficient, sensitivity, and specificity. The improvements are attributed to the method's ability to manage class imbalance better and resolve complex tumor boundaries. This work advances the state-of-the-art in brain tumor segmentation and holds promise for improving clinical workflows and patient outcomes through more precise and reliable tumor detection.

Keywords: brain tumor classification, image segmentation, CNN, U-NET

Procedia PDF Downloads 47