Search results for: data mining techniques

Authors: Jeongyeob Hong, Myeonghoon Park, Taeson Yoon

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After the advent of Achaeans which utilize different metabolism pathway and contain conspicuously different cellular structure, they have been recognized as possible materials for developing quality of human beings. Among diverse Achaeans, in this paper, we compared 16s RNA Sequences of four different species of Thermococcus: Achaeans genus specialized in sulfur-dealing metabolism. Four Species, Barophilus, Kodakarensis, Hydrothermalis, and Onnurineus, live near the hydrothermal vent that emits extreme amount of sulfur and heat. By comparing ribosomal sequences of aforementioned four species, we found similarities in their sequences and expressed protein, enabling us to expect that certain ribosomal sequence or proteins are vital for their survival. Apriori algorithms and Decision Tree were used. for comparison.

Keywords: Achaeans, Thermococcus, apriori algorithm, decision tree

Procedia PDF Downloads 285

Authors: Renata Kurpiewska-Korbut

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Making an assumption that the use and sharing of data generated in humanitarian action constitute a core function of humanitarian organizations, the paper analyzes the position of the largest Polish humanitarian non-governmental organizations in the humanitarian data ecosystem in Ukraine and their approach to non-personal and personal data management since February of 2022. Both expert interviews and document analysis of non-profit organizations providing a direct response in the Ukrainian crisis context, i.e., the Polish Humanitarian Action, Caritas, Polish Medical Mission, Polish Red Cross, and the Polish Center for International Aid and the applicability of theoretical perspective of contingency theory – with its central point that the context or specific set of conditions determining the way of behavior and the choice of methods of action – help to examine the significance of data complexity and adaptive approach to data management by relief organizations in the humanitarian supply chain network. The purpose of this study is to determine how the existence of well-established and accurate internal procedures and good practices of using and sharing data (including safeguards for sensitive data) by the surveyed organizations with comparable human and technological capabilities are implemented and adjusted to Ukrainian humanitarian settings and data infrastructure. The study also poses a fundamental question of whether this crisis experience will have a determining effect on their future performance. The obtained finding indicate that Polish humanitarian organizations in Ukraine, which have their own unique code of conduct and effective managerial data practices determined by contingencies, have limited influence on improving the situational awareness of other assistance providers in the data ecosystem despite their attempts to undertake interagency work in the area of data sharing.

Keywords: humanitarian data ecosystem, humanitarian data management, polish NGOs, Ukraine

Procedia PDF Downloads 90

Authors: Ramachandra C. G., Amarnath. M., Prashanth Pai M., Nagesh S. N.

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The machine's performance level drops down over a period of time due to the wear and tear of its components. The early detection of an emergent fault becomes very vital in order to obtain uninterrupted production in a plant. Maintenance is an activity that helps to keep the machine's performance at an anticipated level, thereby ensuring the availability of the machine to perform its intended function. At present, a number of modern maintenance techniques are available, such as preventive maintenance, predictive maintenance, condition-based maintenance, total productive maintenance, etc. Condition-based maintenance or condition monitoring is one such modern maintenance technique in which the machine's condition or health is checked by the measurement of certain parameters such as sound level, temperature, velocity, displacement, vibration, etc. It can recognize most of the factors restraining the usefulness and efficacy of the total manufacturing unit. This research work is conducted on a Batch Mill in a tire production unit located in the Southern Karnataka region. The health of the mill is assessed using amplitude of vibration as a parameter of measurement. Most commonly, the vibration level is assessed using various points on the machine bearing. The normal or standard level is fixed using reference materials such as manuals or catalogs supplied by the manufacturers and also by referring vibration standards. The Rio-Vibro meter is placed in different locations on the batch-off mill to record the vibration data. The data collected are analyzed to identify the malfunctioning components in the batch off the mill, and corrective measures are suggested.

Keywords: availability, displacement, vibration, rio-vibro, condition monitoring

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Authors: Polina Prokopovich

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Despite the well-established dependence of cartilage mechanical properties on the frequency of the applied load, most research in the field is carried out in either load-free or constant load conditions because of the complexity of the equipment required for the determination of time-dependent properties. These simpler analyses provide a limited representation of cartilage properties thus greatly reducing the impact of the information gathered hindering the understanding of the mechanisms involved in this tissue replacement, development and pathology. More complex techniques could represent better investigative methods, but their uptake in cartilage research is limited by the highly specialised training required and cost of the equipment. There is, therefore, a clear need for alternative experimental approaches to cartilage testing to be deployed in research and clinical settings using more user-friendly and financial accessible devices. Frequency dependent material properties can be determined through rheometry that is an easy to use requiring a relatively inexpensive device; we present how a commercial rheometer can be adapted to determine the viscoelastic properties of articular cartilage. Frequency-sweep tests were run at various applied normal loads on immature, mature and trypsinased (as model of osteoarthritis) cartilage samples to determine the dynamic shear moduli (G*, G′ G″) of the tissues. Moduli increased with increasing frequency and applied load; mature cartilage had generally the highest moduli and GAG depleted samples the lowest. Hydraulic permeability (KH) was estimated from the rheological data and decreased with applied load; GAG depleted cartilage exhibited higher hydraulic permeability than either immature or mature tissues. The rheometer-based methodology developed was validated by the close comparison of the rheometer-obtained cartilage characteristics (G*, G′, G″, KH) with results obtained with more complex testing techniques available in literature. Rheometry is relatively simpler and does not require highly capital intensive machinery and staff training is more accessible; thus the use of a rheometer would represent a cost-effective approach for the determination of frequency-dependent properties of cartilage for more comprehensive and impactful results for both healthcare professional and R&D.

Keywords: tissue, rheometer, biomaterial, cartilage

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Authors: Naman Singla, Ajay Pal Singh Chauhan

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As the demand for providing high bit rate and high bandwidth is increasing at a rapid rate so there is a need to see in this problem and finds a technology that provides high bit rate and also high bandwidth. One possible solution is by use of optical fiber. Optical fiber technology provides high bandwidth in THz. But the disadvantage of optical fiber is of high cost and not used everywhere because it is not possible to reach all the locations on the earth. Also high maintenance required for usage of optical fiber. It puts a lot of cost. Another technology which is almost similar to optical fiber is Free Space Optics (FSO) technology. FSO is the line of sight technology where modulated optical beam whether infrared or visible is used to transfer information from one point to another through the atmosphere which works as a channel. This paper concentrates on analyzing the performance of FSO in terms of bit error rate (BER) and quality factor (Q) using different modulation techniques like non return to zero on off keying (NRZ-OOK), differential phase shift keying (DPSK) and differential quadrature phase shift keying (DQPSK) using OptiSystem software. The findings of this paper show that FSO system based on DQPSK modulation technique performs better.

Keywords: attenuation, bit rate, free space optics, link length

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Authors: Spoorthi Srupad

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Environmental Impact Assessment (EIA) stands as a critical engineering application facilitated by Earth Resources and Environmental Remote Sensing. Employing advanced technologies, this process enables a systematic evaluation of potential environmental impacts arising from engineering projects. Remote sensing techniques, including satellite imagery and geographic information systems (GIS), play a pivotal role in providing comprehensive data for assessing changes in land cover, vegetation, water bodies, and air quality. This abstract delves into the significance of EIA in engineering, emphasizing its role in ensuring sustainable and environmentally responsible practices. The integration of remote sensing technologies enhances the accuracy and efficiency of impact assessments, contributing to informed decision-making and the mitigation of adverse environmental consequences associated with engineering endeavors.

Keywords: environmental impact assessment, engineering applications, sustainability, environmental monitoring, remote sensing, geographic information systems, environmental management

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Authors: Sam Ng, Lewis Leighton, Sam Atkinson, Carson Yan, Landan Hu, Leslie Cheung, Brian Yap, Kent Lung, Ketat Sarakune

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This paper introduces a method for verifying cryptocurrency reserves that balances the need for both transparency and data confidentiality. Our methodology employs cryptographic techniques, including Merkle Trees, Bulletproof, and zkSnark, to verify that total assets equal or exceed total liabilities, represented by customer funds. Importantly, this verification is achieved without disclosing sensitive information such as the total asset value, customer count, or cold wallet addresses. We delve into the construction and implementation of this methodology. While the system is robust and scalable, we also identify areas for potential enhancements to improve its efficiency and versatility. As the digital asset landscape continues to evolve, our approach provides a solid foundation for ensuring continued trust and security in digital asset platforms.

Keywords: cryptocurrency, crypto-currency, proof-of-reserve, por, zero-knowledge, ZKP

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Authors: Celal Dudak, Aslı Utku, Burak Yağlioğlu

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Uninterrupted and continuous satellite communication through the whole orbit time is becoming more indispensable every day. Data relay systems are developed and built for various high/low data rate information exchanges like TDRSS of USA and EDRSS of Europe. In these missions, a couple of task-dedicated communication satellites exist. In this regard, for Turkey a data relay system is attempted to be defined exchanging low data rate information (i.e. TTC) for Earth-observing LEO satellites appointing commercial GEO communication satellites all over the world. First, justification of this attempt is given, demonstrating duration enhancements in the link. Discussion of preference of RF communication is, also, given instead of laser communication. Then, preferred communication GEOs – including TURKSAT4A already belonging to Turkey- are given, together with the coverage enhancements through STK simulations and the corresponding link budget. Also, a block diagram of the communication system is given on the LEO satellite.

Keywords: communication, GEO satellite, data relay system, coverage

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Authors: Tanawat Hongthai, Dusit Thanapatay

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This paper presents the development of encrypted near field communication (NFC) data exchange format transmission in an NFC passive tag for the feasibility of implementing a genuine product authentication. We propose a research encryption and checking the genuine product into four major categories; concept, infrastructure, development and applications. This result shows the passive NFC-forum Type 2 tag can be configured to be compatible with the NFC data exchange format (NDEF), which can be automatically partially data updated when there is NFC field.

Keywords: near field communication, NFC data exchange format, checking the genuine product, encrypted NFC

Procedia PDF Downloads 275

Authors: A. Gürses, Z. Eroğlu, E. Şahin, K. Güneş, Ç. Doğar

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In recent years, polymer/clay nanocomposites have generated great interest in the polymer industry as a new type of composite material because of their superior properties, which includes high heat deflection temperature, gas barrier performance, dimensional stability, enhanced mechanical properties, optical clarity and flame retardancy when compared with the pure polymer or conventional composites. The investigation of change of the tensile properties of organoclay/linear low density polyethylene (LLDPE) nanocomposites with the use of Dioctyl terephthalate (DOTP) (as plasticizer) and calcite (as filler) has been aimed. The composites and organoclay synthesized were characterized using the techniques such as XRD, HRTEM and FTIR techniques. The spectroscopic results indicate that platelets of organoclay were well dispersed within the polymeric matrix. The tensile properties of the composites were compared considering the stress-strain curve drawn for each composite and pure polymer. It was observed that the composites prepared by adding the plasticizer at different ratios and a certain amount of calcite exhibited different tensile behaviors compared to pure polymer.

Keywords: linear low density polyethylene, nanocomposite, organoclay, plasticizer

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Authors: Yung Gi Wu

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With the growing of computer and network, digital data can be spread to anywhere in the world quickly. In addition, digital data can also be copied or tampered easily so that the security issue becomes an important topic in the protection of digital data. Digital watermark is a method to protect the ownership of digital data. Embedding the watermark will influence the quality certainly. In this paper, Vector Quantization (VQ) is used to embed the watermark into the image to fulfill the goal of data hiding. This kind of watermarking is invisible which means that the users will not conscious the existing of embedded watermark even though the embedded image has tiny difference compared to the original image. Meanwhile, VQ needs a lot of computation burden so that we adopt a fast VQ encoding scheme by partial distortion searching (PDS) and mean approximation scheme to speed up the data hiding process. The watermarks we hide to the image could be gray, bi-level and color images. Texts are also can be regarded as watermark to embed. In order to test the robustness of the system, we adopt Photoshop to fulfill sharpen, cropping and altering to check if the extracted watermark is still recognizable. Experimental results demonstrate that the proposed system can resist the above three kinds of tampering in general cases.

Keywords: data hiding, vector quantization, watermark, color image

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Authors: Ragi Poyyara, Vijaya Patnana, Mohammed Alam

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When acid is pumped into damaged reservoirs for damage removal/stimulation, distorted inflow of acid into the formation occurs caused by acid preferentially traveling into highly permeable regions over low permeable regions, or (in general) into the path of least resistance. This can lead to poor zonal coverage and hence warrants diversion to carry out an effective placement of acid. Diversion is desirably a reversible technique of temporarily reducing the permeability of high perm zones, thereby forcing the acid into lower perm zones. The uniqueness of each reservoir can pose several challenges to engineers attempting to devise optimum and effective diversion strategies. Diversion techniques include mechanical placement and/or chemical diversion of treatment fluids, further sub-classified into ball sealers, bridge plugs, packers, particulate diverters, viscous gels, crosslinked gels, relative permeability modifiers (RPMs), foams, and/or the use of placement techniques, such as coiled tubing (CT) and the maximum pressure difference and injection rate (MAPDIR) methodology. It is not always realized that the effectiveness of diverters greatly depends on reservoir properties, such as formation type, temperature, reservoir permeability, heterogeneity, and physical well characteristics (e.g., completion type, well deviation, length of treatment interval, multiple intervals, etc.). This paper reviews the mechanisms by which each variety of diverter functions and discusses the effect of various reservoir properties on the efficiency of diversion techniques. Guidelines are recommended to help enhance productivity from zones of interest by choosing the best methods of diversion while pumping an optimized amount of treatment fluid. The success of an overall acid treatment often depends on the effectiveness of the diverting agents.

Keywords: diversion, reservoir, zonal coverage, carbonate, sandstone

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Authors: Victor Breux, Jérôme Boutet, Alain Goret, Viviane Cattin

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Early detection of anomalies in data centers is important to reduce downtimes and the costs of periodic maintenance. However, there is little research on this topic and even fewer on the fusion of sensor data for the detection of abnormal events. The goal of this paper is to propose a method for anomaly detection in data centers by combining sensor data (temperature, humidity, power) and deep learning models. The model described in the paper uses one autoencoder per sensor to reconstruct the inputs. The auto-encoders contain Long-Short Term Memory (LSTM) layers and are trained using the normal samples of the relevant sensors selected by correlation analysis. The difference signal between the input and its reconstruction is then used to classify the samples using feature extraction and a random forest classifier. The data measured by the sensors of a data center between January 2019 and May 2020 are used to train the model, while the data between June 2020 and May 2021 are used to assess it. Performances of the model are assessed a posteriori through F1-score by comparing detected anomalies with the data center’s history. The proposed model outperforms the state-of-the-art reconstruction method, which uses only one autoencoder taking multivariate sequences and detects an anomaly with a threshold on the reconstruction error, with an F1-score of 83.60% compared to 24.16%.

Keywords: anomaly detection, autoencoder, data centers, deep learning

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Authors: Vildan Cırık, Emine Efe

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Complementary health approaches are growing in popularity worldwide and play a substantial role in health care. It is very important for paediatric nurses to have knowledge of practices affecting the medical conditions of patients and to communicate with them through integrative nursing care. The purpose of this study was to determine paediatric nurses’ knowledge and experiences of complementary health approaches (CHA) and their personal and professional attitudes to the use of complementary health approaches. This multicentre study was conducted with 1450 paediatric nurses in 18 hospitals in Turkey. Paediatric nurses included in the study were working in the following clinics: Paediatric Service, Paediatric Intensive Care, Paediatric Haematology/Oncology. Data collection focused on the paediatric nurses’ knowledge and experiences of CHA. A high proportion of our sample of paediatric nurses reported that they had used some form of CHA themselves; the most popular choices of CHA were prayer, massage, and vitamins techniques. Paediatric nurses reported positive experiences (drawing/music/art/dance therapies, prayer, herbs, thermal springs, massage, and reflexology) and negative experiences (herbs, thermal springs, prayer, and massage). This study may contribute to increased awareness of the potentially important role of paediatric nurses in the delivery of CHA. Paediatric nurses play important roles in helping patients to use complementary health approaches safely and accurately. Trainings on CHA should be organised, data collection forms including CHA should be created, and evidence-based studies should be focused towards improving the clinical practice of paediatric nurses.

Keywords: complementary health approaches, paediatric nurses, knowledge, experience, attitude, Turkey

Procedia PDF Downloads 192

Authors: Abdelhalem Shahen

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The world is now experiencing a digital revolution and greatly accelerated technological developments, in addition to the transition from the economy in its traditional form to the digital economy, which has resulted in the emergence of new tools that are appropriate to those developments, and from this, this paper attempts to explore the economic characteristics of the bitcoin currency that circulated recently. Due to the many advantages that distinguish it from money in its traditional forms, which have a range of economic effects. The study found that Bitcoin is among the technological innovations, which contain a set of characteristics that are worth studying, those that make it the focus of attention, such as the digital currency, the peer-to-peer property, Lower and Faster Transaction Costs, transparency, decentralized control, privacy, and Double-Spending, as well as security and Cryptographic, and finally mining.

Keywords: Digital Economics, Digital Currencies, Bitcoin, Features of Bitcoin

Procedia PDF Downloads 134

Authors: Charlotte Entwistle, Ryan Boyd

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Recent developments in information retrieval techniques and natural language processing have allowed for greater exploration of psychological and social processes. Linguistic analysis methods for understanding behaviour have provided useful insights within the field of mental health. One area within mental health that has received little attention though, is borderline personality disorder (BPD). BPD is a common mental health disorder characterised by instability of interpersonal relationships, self-image and affect. It also manifests through maladaptive behaviours, such as impulsivity and self-harm. Examination of language patterns associated with BPD could allow for a greater understanding of the disorder and its links to maladaptive thoughts and behaviours. Language analysis methods could also be used in a predictive way, such as by identifying indicators of BPD or predicting maladaptive thoughts, emotions and behaviours. Additionally, associations that are uncovered between language and maladaptive thoughts and behaviours could then be applied at a more general level. This study explores linguistic characteristics of BPD, and their links to maladaptive thoughts and behaviours, through the analysis of social media data. Data were collected from a large corpus of posts from the publicly available social media platform Reddit, namely, from the ‘r/BPD’ subreddit whereby people identify as having BPD. Data were collected using the Python Reddit API Wrapper and included all users which had posted within the BPD subreddit. All posts were manually inspected to ensure that they were not posted by someone who clearly did not have BPD, such as people posting about a loved one with BPD. These users were then tracked across all other subreddits of which they had posted in and data from these subreddits were also collected. Additionally, data were collected from a random control group of Reddit users. Disorder-relevant behaviours, such as self-harming or aggression-related behaviours, outlined within Reddit posts were coded to by expert raters. All posts and comments were aggregated by user and split by subreddit. Language data were then analysed using the Linguistic Inquiry and Word Count (LIWC) 2015 software. LIWC is a text analysis program that identifies and categorises words based on linguistic and paralinguistic dimensions, psychological constructs and personal concern categories. Statistical analyses of linguistic features could then be conducted. Findings revealed distinct linguistic features associated with BPD, based on Reddit posts, which differentiated these users from a control group. Language patterns were also found to be associated with the occurrence of maladaptive thoughts and behaviours. Thus, this study demonstrates that there are indeed linguistic markers of BPD present on social media. It also implies that language could be predictive of maladaptive thoughts and behaviours associated with BPD. These findings are of importance as they suggest potential for clinical interventions to be provided based on the language of people with BPD to try to reduce the likelihood of maladaptive thoughts and behaviours occurring. For example, by social media tracking or engaging people with BPD in expressive writing therapy. Overall, this study has provided a greater understanding of the disorder and how it manifests through language and behaviour.

Keywords: behaviour analysis, borderline personality disorder, natural language processing, social media data

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Authors: Pavel H. Llamocca, Victoria Lopez

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The main objective of our work is the comparative analysis of environmental data from Open Data bases, belonging to different governments. This means that you have to integrate data from various different sources. Nowadays, many governments have the intention of publishing thousands of data sets for people and organizations to use them. In this way, the quantity of applications based on Open Data is increasing. However each government has its own procedures to publish its data, and it causes a variety of formats of data sets because there are no international standards to specify the formats of the data sets from Open Data bases. Due to this variety of formats, we must build a data integration process that is able to put together all kind of formats. There are some software tools developed in order to give support to the integration process, e.g. Data Tamer, Data Wrangler. The problem with these tools is that they need data scientist interaction to take part in the integration process as a final step. In our case we don’t want to depend on a data scientist, because environmental data are usually similar and these processes can be automated by programming. The main idea of our tool is to build Hadoop procedures adapted to data sources per each government in order to achieve an automated integration. Our work focus in environment data like temperature, energy consumption, air quality, solar radiation, speeds of wind, etc. Since 2 years, the government of Madrid is publishing its Open Data bases relative to environment indicators in real time. In the same way, other governments have published Open Data sets relative to the environment (like Andalucia or Bilbao). But all of those data sets have different formats and our solution is able to integrate all of them, furthermore it allows the user to make and visualize some analysis over the real-time data. Once the integration task is done, all the data from any government has the same format and the analysis process can be initiated in a computational better way. So the tool presented in this work has two goals: 1. Integration process; and 2. Graphic and analytic interface. As a first approach, the integration process was developed using Java and Oracle and the graphic and analytic interface with Java (jsp). However, in order to open our software tool, as second approach, we also developed an implementation with R language as mature open source technology. R is a really powerful open source programming language that allows us to process and analyze a huge amount of data with high performance. There are also some R libraries for the building of a graphic interface like shiny. A performance comparison between both implementations was made and no significant differences were found. In addition, our work provides with an Official Real-Time Integrated Data Set about Environment Data in Spain to any developer in order that they can build their own applications.

Keywords: open data, R language, data integration, environmental data

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Authors: Vivek B. Ravindran, Aravind Surapaneni, Rebecca Traub, Sarvesh K. Soni, Andrew S. Ball

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Parasitic diseases have a devastating, long-term impact on human health and welfare. More than two billion people are infected with soil-transmitted helminths (STHs), including the roundworms (Ascaris), hookworms (Necator and Ancylostoma) and whipworm (Trichuris) with majority occurring in the tropical and subtropical regions of the world. Despite its low prevalence in developed countries, the removal of STHs from wastewater remains crucial to allow the safe use of sludge or recycled water in agriculture. Conventional methods such as incubation and optical microscopy are cumbersome; consequently, the results drastically vary from person-to-person observing the ova (eggs) under microscope. Although PCR-based methods are an alternative to conventional techniques, it lacks the ability to distinguish between viable and non-viable helminth ova. As a result, wastewater treatment industries are in major need for radically new and innovative tools to detect and quantify STHs eggs with precision, accuracy and being cost-effective. In our study, we focus on the following novel and innovative techniques: -Recombinase polymerase amplification and Surface enhanced Raman spectroscopy (RPA-SERS) based detection of helminth ova. -Use of metal nanoparticles and their relative nanozyme activity. -Colorimetric detection, differentiation and enumeration of genera of helminth ova using hydrolytic enzymes (chitinase and lipase). -Propidium monoazide (PMA)-qPCR to detect viable helminth ova. -Modified assay to recover and enumerate helminth eggs from fresh raw sewage. -Transcriptome analysis of ascaris ova in fresh raw sewage. The aforementioned techniques have the potential to replace current conventional and molecular methods thereby producing a standard protocol for the determination and enumeration of helminth ova in sewage sludge.

Keywords: colorimetry, helminth, PMA-QPCR, nanoparticles, RPA, viable

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Authors: B. Karabuluter, O. Karaduman

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Connected-Vehicles consists of intelligent vehicles, each of which can communicate with each other. Smart Cities are the most prominent application area of intelligent vehicles that can communicate with each other. The most important goal that is desired to be realized in Smart Cities planned for facilitating people's lives is to make transportation more comfortable and safe with intelligent/autonomous/driverless vehicles communicating with each other. In order to ensure these, the city must have communication infrastructure in the first place, and the vehicles must have the features to communicate with this infrastructure and with each other. In this context, intelligent transport studies to solve all transportation and traffic problems in classical cities continue to increase rapidly. In this study, current connected-vehicle applications developed for smart cities are considered in terms of communication techniques, vehicular networking, IoT, urban transportation implementations, intelligent traffic management, road safety, self driving. Taxonomies and assessments performed in the work show the trend of studies in inter-vehicle communication systems in smart cities and they are contributing to by ensuring that the requirements in this area are revealed.

Keywords: smart city, connected vehicles, infrastructures, VANET, wireless communication, intelligent traffic management

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Authors: Walid Ibrahim, Abdul Kasem, Sudeendra Doddi, Ilaria Giono, Tareq Sabagh, Muhammad Ammar, Nermin Osman

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Background: wire-guided localization (WL) is the most widely used method for the localization of non-palpable breast lesions. SAVI SCOUT occult lesion localization (SSL) is a new technique in breast-conservative surgery. SSL has the potential benefit of improving radiology workflow as well as accurate localization. Purpose: The purpose of this study is to compare the breast tissue specimen volume and weight and margin excision between WL and SSL. Materials and methods: A single institution retrospective analysis of 377 female patients who underwent wide local breast excision with SAVI SCOUT and or wire-guided technique between 2018 and 2021 in a UK University teaching hospital. Breast department. Breast tissue specimen volume and weight, and margin excision have been evaluated in the three groups of different localization. Results: Three hundred and seventy-seven patients were studied. Of these, 261 had wire localization, 88 had SCOUT and 28 had dual localization techniques. Tumor size ranged from 1 to 75mm (Median 20mm). The pathology specimen weight ranged from 1 to 466gm (Median 46.8) and the volume ranged from 1.305 to 1560cm³ (Median 106.32 cm³). SCOUT localization was associated with a significantly low specimen weight than wire or the dual technique localization (Median 41gm vs 47.3gm and 47gm, p = 0.029). SCOUT was not associated with better specimen volume with a borderline significance in comparison to wire and combined techniques (Median 108cm³ vs 105cm³ and 105cm³, p = 0.047). There was a significant correlation between tumor size and pathology specimen weight in the three groups. SCOUT showed a better >2mm safety margin in comparison to the other 2 techniques (p = 0.031). Conclusion: Preoperative SCOUT localization is associated with better specimen weight and better specimen margin. SCOUT did not show any benefits in terms of specimen volume which may be due to difficulty in getting the accurate specimen volume due to the irregularity of the soft tissue specimen.

Keywords: scout, wire, localization, breast

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Authors: Velimir "monty" Vesselinov, Trais Kliplhuis, Hope Jasperson

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Artificial intelligence (AI) is a powerful tool in the geothermal energy sector, aiding in both exploration and utilization. Identifying promising geothermal sites can be challenging due to limited surface indicators and the need for expensive drilling to confirm subsurface resources. Geothermal reservoirs can be located deep underground and exhibit complex geological structures, making traditional exploration methods time-consuming and imprecise. AI algorithms can analyze vast datasets of geological, geophysical, and remote sensing data, including satellite imagery, seismic surveys, geochemistry, geology, etc. Machine learning algorithms can identify subtle patterns and relationships within this data, potentially revealing hidden geothermal potential in areas previously overlooked. To address these challenges, a SIML (Science-Informed Machine Learning) technology has been developed. SIML methods are different from traditional ML techniques. In both cases, the ML models are trained to predict the spatial distribution of an output (e.g., pressure, temperature, heat flux) based on a series of inputs (e.g., permeability, porosity, etc.). The traditional ML (a) relies on deep and wide neural networks (NNs) based on simple algebraic mappings to represent complex processes. In contrast, the SIML neurons incorporate complex mappings (including constitutive relationships and physics/chemistry models). This results in ML models that have a physical meaning and satisfy physics laws and constraints. The prototype of the developed software, called GeoTGO, is accessible through the cloud. Our software prototype demonstrates how different data sources can be made available for processing, executed demonstrative SIML analyses, and presents the results in a table and graphic form.

Keywords: science-informed machine learning, artificial inteligence, exploration, utilization, hidden geothermal

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Authors: Mia Françoise

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This report aims to develop a strategy for assisting short-term investors to benefit from the current economic climate in South Africa by utilizing technical analysis techniques and predictive analytics. As part of this research, value investing and technical analysis principles will be combined to maximize returns for South African investors while optimizing volatility. As an emerging market, South Africa offers many opportunities for high growth in sectors where other developed countries cannot grow at the same rate. Investing in South African companies with significant growth potential can be extremely rewarding. Although the risk involved is more significant in countries with less developed markets and infrastructure, there is more room for growth in these countries. According to recent research, the offshore market is expected to outperform the local market over the long term; however, short-term investments in the local market will likely be more profitable, as the Johannesburg Stock Exchange is predicted to outperform the S&P500 over the short term. The instabilities in the economy contribute to increased market volatility, which can benefit investors if appropriately utilized. Price prediction and portfolio optimization comprise the two primary components of this methodology. As part of this process, statistics and other predictive modeling techniques will be used to predict the future performance of stocks listed on the Johannesburg Stock Exchange. Following predictive data analysis, Modern Portfolio Theory, based on Markowitz's Mean-Variance Theorem, will be applied to optimize the allocation of assets within an investment portfolio. By combining different assets within an investment portfolio, this optimization method produces a portfolio with an optimal ratio of expected risk to expected return. This methodology aims to provide a short-term investment with a stock portfolio that offers the best risk-to-return profile for stocks listed on the JSE by combining price prediction and portfolio optimization.

Keywords: financial stocks, optimized asset allocation, prediction modelling, South Africa

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Authors: Dona Shaini Abhilasha Nanayakkara, Kurugamage Jude Pravinda Gregory Perera

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E-commerce platforms have revolutionized the shopping experience, offering convenient ways for consumers to make purchases. To improve interactions with customers and optimize marketing strategies, it is essential for businesses to understand user behavior, preferences, and needs on these platforms. This paper focuses on recommending businesses to customize interactions with users based on their behavioral patterns, leveraging data-driven analysis and machine learning techniques. Businesses can improve engagement and boost the adoption of e-commerce platforms by aligning behavioral patterns with user goals of usability and satisfaction. We propose TheAnalyzer, a clustering-based system designed to enhance business productivity by analyzing user-profiles and improving human-computer interaction. The Analyzer seamlessly integrates with business applications, collecting relevant data points based on users' natural interactions without additional burdens such as questionnaires or surveys. It defines five key user analytics as features for its dataset, which are easily captured through users' interactions with e-commerce platforms. This research presents a study demonstrating the successful distinction of users into specific groups based on the five key analytics considered by TheAnalyzer. With the assistance of domain experts, customized business rules can be attached to each group, enabling The Analyzer to influence business applications and provide an enhanced personalized user experience. The outcomes are evaluated quantitatively and qualitatively, demonstrating that utilizing TheAnalyzer’s capabilities can optimize business outcomes, enhance customer satisfaction, and drive sustainable growth. The findings of this research contribute to the advancement of personalized interactions in e-commerce platforms. By leveraging user behavioral patterns and analyzing both new and existing users, businesses can effectively tailor their interactions to improve customer satisfaction, loyalty and ultimately drive sales.

Keywords: data clustering, data standardization, dimensionality reduction, human computer interaction, user profiling

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Authors: M. Govindarajan

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One of the major developments in machine learning in the past decade is the ensemble method, which finds highly accurate classifier by combining many moderately accurate component classifiers. In this research work, new ensemble classification methods are proposed with homogeneous ensemble classifier using bagging and heterogeneous ensemble classifier using arcing and their performances are analyzed in terms of accuracy. A Classifier ensemble is designed using Radial Basis Function (RBF) and Support Vector Machine (SVM) as base classifiers. The feasibility and the benefits of the proposed approaches are demonstrated by the means of standard datasets of intrusion detection. The main originality of the proposed approach is based on three main parts: preprocessing phase, classification phase, and combining phase. A wide range of comparative experiments is conducted for standard datasets of intrusion detection. The performance of the proposed homogeneous and heterogeneous ensemble classifiers are compared to the performance of other standard homogeneous and heterogeneous ensemble methods. The standard homogeneous ensemble methods include Error correcting output codes, Dagging and heterogeneous ensemble methods include majority voting, stacking. The proposed ensemble methods provide significant improvement of accuracy compared to individual classifiers and the proposed bagged RBF and SVM performs significantly better than ECOC and Dagging and the proposed hybrid RBF-SVM performs significantly better than voting and stacking. Also heterogeneous models exhibit better results than homogeneous models for standard datasets of intrusion detection. 

Keywords: data mining, ensemble, radial basis function, support vector machine, accuracy

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Authors: Vijay H. Ingole, Efthimia Lioliou

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Indirubin, a key bioactive component of traditional Chinese medicine, has gained increasing recognition for its potential in modern biomedical applications, particularly in pharmacology and therapeutics. The present work aimed to harness the potential by engineering an Escherichia coli strain capable of high-yield indirubin production. Through meticulous genetic engineering, we optimized the metabolic pathways in E. coli to enhance indirubin synthesis. Further, to explored the optimization of culture media and indirubin yield via batch and fed-batch fermentation techniques. By fine-tuning upstream process (USP) parameters, including nutrient composition, pH, temperature, and aeration, we established conditions that maximized both cell growth and indirubin production. Additionally, significant efforts were dedicated to refining downstream process (DSP) conditions for the extraction, purification, and quantification of indirubin. Utilizing advanced biochemical methods and analytical techniques such as UHPLC, we ensured the production of high purity indirubin. This approach not only improved the economic viability of indirubin bioproduction but also aligned with the principles of green production and sustainability.

Keywords: indirubin, bacterial strain, fermentation, HPLC

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Authors: Matthias Leitner, Gernot Pottlacher

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Experimental determination of critical point data like critical temperature, critical pressure, critical volume and critical compressibility of high-melting metals such as niobium is very rare due to the outstanding experimental difficulties in reaching the necessary extreme temperature and pressure regimes. Experimental techniques to achieve such extreme conditions could be diamond anvil devices, two stage gas guns or metal samples hit by explosively accelerated flyers. Electrical pulse-heating under increased pressures would be another choice. This technique heats thin wire samples of 0.5 mm diameter and 40 mm length from room temperature to melting and then further to the end of the stable phase, the spinodal line, within several microseconds. When crossing the spinodal line, the sample explodes and reaches the gaseous phase. In our laboratory, pulse-heating experiments can be performed under variation of the ambient pressure from 1 to 5000 bar and allow a direct determination of critical point data for low-melting, but not for high-melting metals. However, the critical point also can be estimated by extrapolating the liquid phase density according to theoretical models. A reasonable prerequisite for the extrapolation is the existence of data that cover as much as possible of the liquid phase and at the same time exhibit small uncertainties. Ohmic pulse-heating was therefore applied to determine thermal volume expansion, and from that density of niobium over the entire liquid phase. As a first step, experiments under ambient pressure were performed. The second step will be to perform experiments under high-pressure conditions. During the heating process, shadow images of the expanding sample wire were captured at a frame rate of 4 × 105 fps to monitor the radial expansion as a function of time. Simultaneously, the sample radiance was measured with a pyrometer operating at a mean effective wavelength of 652 nm. To increase the accuracy of temperature deduction, spectral emittance in the liquid phase is also taken into account. Due to the high heating rates of about 2 × 108 K/s, longitudinal expansion of the wire is inhibited which implies an increased radial expansion. As a consequence, measuring the temperature dependent radial expansion is sufficient to deduce density as a function of temperature. This is accomplished by evaluating the full widths at half maximum of the cup-shaped intensity profiles that are calculated from each shadow image of the expanding wire. Relating these diameters to the diameter obtained before the pulse-heating start, the temperature dependent volume expansion is calculated. With the help of the known room-temperature density, volume expansion is then converted into density data. The so-obtained liquid density behavior is compared to existing literature data and provides another independent source of experimental data. In this work, the newly determined off-critical liquid phase density was in a second step utilized as input data for the estimation of niobium’s critical point. The approach used, heuristically takes into account the crossover from mean field to Ising behavior, as well as the non-linearity of the phase diagram’s diameter.

Keywords: critical point data, density, liquid metals, niobium, ohmic pulse-heating, volume expansion

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Authors: Neha Kulshreshtha

Abstract:

This paper examines the patterns of 'Mouthing', a non-manual marker, and its distribution in Indian Sign Language (ISL). Linguistic research in Indian Sign Language is an emerging field where much is needed to be done. The little research which has happened focuses on the structure of ISL in terms of physical or manual markers, therefore a study of mouthing patterns would give an insight into the distribution of this particular non-manual marker. Data has been collected with the help of native ISL users through various techniques in which natural signs can be captured, for example, storytelling, informal conversations etc. The aim of the study is to find out the various situations where mouthing is used. Sometimes, the mouthing is not actually the articulation of the word as spoken in the local languages. The paper aims to find out whether the mouthing patterns in ISL are influenced by any local language or they are independent of any influence from the local language or both. Mouthing patterns have been studied in many sign languages and an investigation into ISL will reveal whether it falls in pattern with the other sign languages.

Keywords: Indian sign language, mouthing, non-manual marker, spoken language influence

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Authors: Trevor Toy, Josef Langerman

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Around a quarter of the world’s data is generated by financial with an estimated 708.5 billion global non-cash transactions reached between 2018 and. And with Open Banking still a rapidly developing concept within the financial industry, there is an opportunity to create a secure mechanism for connecting its stakeholders to openly, legitimately and consensually share the data required to enable it. Integration and data sharing of anonymised transactional data are still operated in silos and centralised between the large corporate entities in the ecosystem that have the resources to do so. Smaller fintechs generating data and businesses looking to consume data are largely excluded from the process. Therefore there is a growing demand for accessible transactional data for analytical purposes and also to support the rapid global adoption of Open Banking. The following research has provided a solution framework that aims to provide a secure decentralised marketplace for 1.) data providers to list their transactional data, 2.) data consumers to find and access that data, and 3.) data subjects (the individuals making the transactions that generate the data) to manage and sell the data that relates to themselves. The platform also provides an integrated system for downstream transactional-related data from merchants, enriching the data product available to build a comprehensive view of a data subject’s spending habits. A robust and sustainable data market can be developed by providing a more accessible mechanism for data producers to monetise their data investments and encouraging data subjects to share their data through the same financial incentives. At the centre of the platform is the market mechanism that connects the data providers and their data subjects to the data consumers. This core component of the platform is developed on a decentralised blockchain contract with a market layer that manages transaction, user, pricing, payment, tagging, contract, control, and lineage features that pertain to the user interactions on the platform. One of the platform’s key features is enabling the participation and management of personal data by the individuals from whom the data is being generated. This framework developed a proof-of-concept on the Etheruem blockchain base where an individual can securely manage access to their own personal data and that individual’s identifiable relationship to the card-based transaction data provided by financial institutions. This gives data consumers access to a complete view of transactional spending behaviour in correlation to key demographic information. This platform solution can ultimately support the growth, prosperity, and development of economies, businesses, communities, and individuals by providing accessible and relevant transactional data for big data analytics and open banking.

Keywords: big data markets, open banking, blockchain, personal data management

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Authors: Tatsanawalai Utarasakul, Ch. Hirannukhrao

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The purpose of this research was to investigate the appropriate guidelines for promoting manuscript publication of the academic staff in Faculty of Science and Technology, Suan Sunandha Rajabhat University (SciSSRU). Data were collected from 88 academic staff of SciSSRU. The qualitative approach and knowledge management were used to determine the guidelines for promoting manuscript publication. In addition, TUNA Model was applied in order to follow the process of knowledge management. Simplified techniques were presented and shared with academic staff in the Knowledge Management exhibition, brochure, and websites. The result of this study revealed that, the comparison of number of manuscript publication of academic staff between academic year 2012 and 2013 is rapidly increasing for 60 percentages.

Keywords: knowledge management, articles, publication, academic staff

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Authors: Samia Rabet, Rachida Chemini, Gerhard Schäfer, Farid Aiouache

Abstract:

The textile industry generates large quantities of wastewater, which poses significant environmental problems due to its complex composition and high levels of pollutants loaded principally with heavy metals, large amounts of COD, and dye. Separation treatment methods are often known for their effectiveness in removing contaminants whereas membrane separation techniques are a promising process for the treatment of textile effluent due to their versatility, efficiency, and low energy requirements. This study focuses on the modeling and simulation of membrane separation technologies with a cross-flow filtration process for textile effluent treatment. It aims to explore the application of mathematical models and computational simulations using ASPEN Plus Software in the prediction of a complex and real effluent separation. The results demonstrate the effectiveness of modeling and simulation techniques in predicting pollutant removal efficiencies with a global deviation percentage of 1.83% between experimental and simulated results; membrane fouling behavior, and overall process performance (hydraulic resistance, membrane porosity) were also estimated and indicating that the membrane losses 10% of its efficiency after 40 min of working.

Keywords: membrane separation, ultrafiltration, textile effluent, modeling, simulation

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