Big Amount Research Articles

Technical electrogenerating tools for road facilities: systematics and design specifics

BACKGROUND: Currently the Russian Federation Government has a strong focus to roads safety. This is due to the national project Safe High-Quality Roads. One of tools of pedestrian safety in an urban are road bumps. They are need for drivers speed slow.
 Contrarily world scientists create alternative sources of electrical energy. It led to tools type as technical electrogenerating devices.
 Whereas technical electrogenerating tools have big amount of developments by russian and world scientists. But in modern scientific literature there is no systematic concept about this tools type.
 AIMS: The paper purpose is knowledge summary about technical electrogenerating tools and develop their classification.
 METHODS: Energy of a moving car is accumulated and converted into electrical energy by devices type. Concept of technical electrogenerating tools is based on convertion of energy pulse of speed bump into electrical energy at the moment of car runover. This energy can be used for electricity supply of road infrastructure (crosswalk lighting, road sign ligtht, powering traffic lights).
 RESULTS: The basic classification feature of these tools is device type converting moving car energy into electrical energy: piezoelectric, hydraulic, electromechanical. In turn technical electrogenerating tools with electromechanical converter are classified by criteria: amount of motor, type of transmission mechanism, design of pressing platform and type of generator.
 CONCLUSIONS: The developed classification provides to systematize contributions of russian and world scientists designing devices of technical electrogenerating tools.

Identifying tower crane activities with data: the case of the concrete pouring

PurposeThe crane plays an essential role in modern construction sites as it supports numerous operations and activities on-site. Additionally, the crane produces a big amount of data that, if analyzed, could significantly affect productivity, progress monitoring and decision-making in construction projects. This paper aims to show the usability of crane data in tracking the progress of activities on-site.Design/methodology/approachThis paper presents a pattern-based recognition method to detect concrete pouring activities on any concrete-based construction sites. A case study is presented to assess the methodology with a real-life example.FindingsThe analysis of the data helped build a theoretical pattern for concrete pouring activities and detect the different phases and progress of these activities. Accordingly, the data become useable to track progress and identify problems in concrete pouring activities.Research limitations/implicationsThe paper presents an example for construction practitioners and researcher about a practical and easy way to analyze the big data that comes from cranes and how it is used in tracking projects' progress. The current study focuses only on concrete pouring activities; future studies can include other types of activities and can utilize the data with other building methods to improve construction productivity.Practical implicationsThe proposed approach is supposed to be simultaneously efficient in terms of concrete pouring detection as well as cost-effective. Construction practitioners could track concrete activities using an already-embedded monitoring device.Originality/valueWhile several studies in the literature targeted the optimization of crane operations and of mitigating hazards through automation and sensing, the opportunity of using cranes as progress trackers is yet to be fully exploited.

Behavior policy learning: Learning multi-stage tasks via solution sketches and model-based controllers.

Multi-stage tasks are a challenge for reinforcement learning methods, and require either specific task knowledge (e.g., task segmentation) or big amount of interaction times to be learned. In this paper, we propose Behavior Policy Learning (BPL) that effectively combines 1) only few solution sketches, that is demonstrations without the actions, but only the states, 2) model-based controllers, and 3) simulations to effectively solve multi-stage tasks without strong knowledge about the underlying task. Our main intuition is that solution sketches alone can provide strong data for learning a high-level trajectory by imitation, and model-based controllers can be used to follow this trajectory (we call it behavior) effectively. Finally, we utilize robotic simulations to further improve the policy and make it robust in a Sim2Real style. We evaluate our method in simulation with a robotic manipulator that has to perform two tasks with variations: 1) grasp a box and place it in a basket, and 2) re-place a book on a different level within a bookcase. We also validate the Sim2Real capabilities of our method by performing real-world experiments and realistic simulated experiments where the objects are tracked through an RGB-D camera for the first task.

Biogas improvement as renewable energy through conversion into methanol: A perspective of new catalysts based on nanomaterials and metal organic frameworks

In recent years, the high cost and availability of energy sources have boosted the implementation of strategies to obtain different types of renewable energy. Among them, methane contained in biogas from anaerobic digestion has gained special relevance, since it also permits the management of a big amount of organic waste and the capture and long-term storage of carbon. However, methane from biogas presents some problems as energy source: 1) it is a gas, so its storage is costly and complex, 2) it is not pure, being carbon dioxide the main by-product of anaerobic digestion (30%–50%), 3) it is explosive with oxygen under some conditions and 4) it has a high global warming potential (27–30 times that of carbon dioxide). Consequently, the conversion of biogas to methanol is as an attractive way to overcome these problems. This process implies the conversion of both methane and carbon dioxide into methanol in one oxidation and one reduction reaction, respectively. In this dual system, the use of effective and selective catalysts for both reactions is a critical issue. In this regard, nanomaterials embedded in metal organic frameworks have been recently tested for both reactions, with very satisfactory results when compared to traditional materials. In this review paper, the recent configurations of catalysts including nanoparticles as active catalysts and metal organic frameworks as support materials are reviewed and discussed. The main challenges for the future development of this technology are also highlighted, that is, its cost in environmental and economic terms for its development at commercial scale.

Text Classification of News Using Transformer-based Models for Portuguese

This work proposes the use of a fine-tuned Transformers-based Natural Language Processing (NLP) model called BERTimbau to generate the word embeddings from texts published in a Brazilian newspaper, to create a robust NLP model to classify news in Portuguese, a task that is costly for humans to perform for big amounts of data. To assess this approach, besides the generation of embeddings by the fine-tuned BERTimbau, a comparative analysis was conducted using the Word2Vec technique. The first step of the work was to rearrange news from nineteen to ten categories to reduce the existence of class imbalance in the corpus, using the K-means and TF-IDF techniques. In the Word2Vec step, the CBOW and Skip-gram architectures were applied. In BERTimbau and Word2Vec steps, the Doc2Vec method was used to represent each news as a unique embedding, generating a document embedding for each news. Metrics accuracy, weighted accuracy, precision, recall, F1-Score, AUC ROC and AUC PRC were applied to evaluate the results. It was noticed that the fine-tuned BERTimbau captured distinctions in the texts of the different categories, showing that the classification model based on this model has a superior performance than the other explored techniques.

Implementation of AI based Safety and Security System Integration for Smart City

Our Indian government has set a goal of creating 100 smart cities that will use smart technology such as smart grids, smart phones, and various monitoring devices to generate large amount of data. Traditionally, data centres have been in charge of these files. One of the most pressing issues in data centres is resource management. One efficient strategy to address this issue is to use the best method for handling data, and when we're talking about Smart Cities, which will create a big quantity of data, it's becoming increasingly important to manage this massive amount of data. It is also necessary to provide the better living standard for every citizen in the smart cities by providing good safety and security to them as well. As we know that every citizen is not smart enough to protect himself against the physical as well as cyber-crimes. In this paper, we have designed an advanced Artificial Intelligence (AI) based safety and security system for the human beings and their personal data in a smart city. The system architecture is designed with AI module with machine learning algorithms, IoT technology and sensors, smart drones, intelligent video surveillances, data analytics and cyber security modules. This system can efficiently protect the citizens and their personal data against the criminals with high speed and accuracy.

A Case Study on Credit Cards with Respect to India

When a country gets more and more developed it’s likely that it’s trade and commerce will also increase, increasing the number of transactions which will obviously range from small personal transactions to very big industrial transactions. In case of small amounts of transactions we can settle it with liquid cash , but for big amounts of transactions it is always difficult or rather impossible to deal with liquid cash and here comes the utility of dealing in negotiable instruments like cheques , promissory notes etc. In countries like India with a huge population we have a large number of transactions throughout the country every day and since we do not have proper developed infrastructure even today most of our transactions are done in cash. Dealings in cash always has some disadvantages of its own like maintaining a huge amount of currencies which is always a very costly affair for the country and then there are some persons who does not like to carry liquid cash as it may be a very troublesome matter according to their lifestyle. Here at this juncture a financial instrument known as credit card got inducted into the scenario which is mainly utilised for personal payment i.e. this instrument can only be utilised by some single individual to settle his or her own personal transactions. This instrument which is known as credit card has not only helped people in travelling without carrying liquid cash which was previously not possible but has also made possible to settle transactions with the help of various technological upgradations. Therefore with respect to India we see that with her huge market and more and more technological upgradation credit card is becoming an important mode of settling transactions. In this paper we will study the history and specific features of Credit Cards along with it’s utility in the modern world with a reference to modern India. This Study has been casual , exploratory and empirical in nature and the data needed for research work has been collected by using both the direct and indirect methods of data collection..

Determinants of the Penalty Waive by the Defaulters: The Case of Higher Education Loan Board (HESLB) in Tanzania

Higher education loan repayment has been one of the major challenges facing Higher Education Loan Board (HESLB). This study therefore, focused on exploring the factors for penalty waive on Higher Education Loan repayment. Also, the Ability to Pay Theory was the main source of inspiration for this paper. The study employed case study research design based on qualitative research approach. The purposive sampling technique was used to select 30 participants from HESLB. The collected data were analyzed by the use of thematic analysis. The study results revealed that the amount that was provided to the loanees was given in big amounts at once but in repayment, the beneficiaries were given some long time to pay in installment. Further, it was found that factors that motivated the establishment of the penalty waive for the default borrowers were complaints of the loan beneficiaries over charges, order from the president, motivation of the loan beneficiaries for voluntary compliance and creation of economic opportunity for the loan beneficiaries. Moreover, it was found that the trend of repayment had changed and increased following the penalty waive as the borrowers were repaying their loans in bulk and in time compared to the time before the penalty waive was in place. It was concluded that the appropriate way to make the loan beneficiaries to repay the loan is not to impose penalties and fines to them but to use the friendly way such as reminding them through phones to repay their loans. It was recommended that HESLB should create positive perception of the borrowers on the affordability of the loans so that they can repay quickly their loans.

Sentinel-2 high-resolution data for river discharge monitoring

River monitoring is an open issue due to many intrinsic problems of the ground monitoring network. Over the last few decades, the role of satellite sensors in river discharge estimation is significantly increased thanks to the strong growth in technologies and applications. Focusing on daily river discharge measurements, a non-linear regression model has been used to link the near-infrared (NIR) reflectance ratio between a dry and a wet pixel around the section of a river to the ground measurements of river discharge. The use of medium-resolution satellite data, such as those from MODIS sensors, enables to monitor high and low flows in medium-sized catchments (<100,000 km2), thanks to satellite frequent revisit time and wide spatial coverage. However, such sensors are not suitable to provide information for medium-narrow rivers (< 250 m wide), nor to study river features and patterns that are averaged within a single pixel. Here, we investigated the use of Sentinel-2 NIR reflectances to support the hypothesis that a higher spatial resolution, i.e. 10 m, is able to better identify the wet pixels, more related to the river dynamics, with obvious advantages for river discharge estimation compared to the medium resolution sensors (e.g., MODIS at 250 m). Moreover, it also allows both a finer distinction between vegetation, soil and water and the characterization of water turbidity in the river area. A new formulation enriched by the sediment component is proposed together with a first step toward an uncalibrated procedure to select the wet pixels. Google Earth Engine (GEE) platform has been employed for the data analysis, allowing to avoid the download of big amounts of data, fostering the reproducibility of the analysis in different locations. The accuracy of the river discharges derived from Sentinel-2 reflectances is evaluated against the in-situ observations from selected gauging stations along two Italian rivers, Po and Tiber. The results confirm the good performances obtained with high-resolution images over the Po River, with average Nash-Sutcliffe efficiency ranging between 0.39 and 0.56 for the different configurations adopted. Relatively worse results were obtained over the Tiber River where the Nash-Sutcliffe efficiency ranged between 0.2 and 0.61, due to an issue on the registration of Sentinel-2 images.

The Use of Tunnel Demolition Rocks to Produce Shotcrete for a Railway Infrastructure

The demolished rock materials of part of a 50 km long tunnel through the Alps, was characterized and used to produce shotcrete to secure the tunnel walls. The granulometric curves needed to match with reference curves. This was sometimes difficult because of the different mineralogy encountered. Then, the material was mixed to produce concrete with silica fume and steel fibres for the shotcrete. The fresh concrete properties were measured on site. Furthermore, the hardened state was controlled on site and in laboratory. The compression strength exhibited good values that correlated with the mixing proportion of the ingredients. The punch tests indicated relatively high values. They verified the safety of the worker, where material enrichment was present on the tunnel roofing parts. The steel fibre increased the ductility of the specimens. The porosity, the water permeability and the freeze / thaw resistance were also tested. The mixtures were continuously optimized by having the water / cement ratio and the superplasticizer dosage under control. All these adaptations allowed to reuse the big amount of tunnel demolition material. The concrete was produced in a mixing plant on site. This reduced the transportation and increased the environmental sustainability of such a long infrastructure.

Recent developments in the design of Hybrid Photovoltaic–Thermoelectric Generators: A Review

Photovoltaic cells do not convert sufficient amount of solar irradiation into electrical energy. A big amount of solar irradiation is unexploited in the form of thermal energy in case of solar photovoltaic energy conversion system. To make this conversion more efficient and effective, Hybrid photovoltaic and thermoelectric system (PV-TEG) can be used. In this system, there are two types of energy conversion involved. First is converting energy of solar irradiation into electricity by means of photovoltaic system and other is the conversion of waste heat of PV system into electricity via Thermoelectric Generator (TEG). Moreover, hybrid PV-TEG system is environmentally responsive and has no harmful impacts on environment. Hybrid PV-TEG system upturns the total dependability without degrading the quality of power produced. In this manuscript, summary of the prior works regarding progress of technological innovation in the hybrid PV-TEG structures is discussed. The scope for future work in utilizing this technology is also discussed.

Profiling hearing aid users through big data explainable artificial intelligence techniques.

Debilitating hearing loss (HL) affects ~6% of the human population. Only 20% of the people in need of a hearing assistive device will eventually seek and acquire one. The number of people that are satisfied with their Hearing Aids (HAids) and continue using them in the long term is even lower. Understanding the personal, behavioral, environmental, or other factors that correlate with the optimal HAid fitting and with users' experience of HAids is a significant step in improving patient satisfaction and quality of life, while reducing societal and financial burden. In SMART BEAR we are addressing this need by making use of the capacity of modern HAids to provide dynamic logging of their operation and by combining this information with a big amount of information about the medical, environmental, and social context of each HAid user. We are studying hearing rehabilitation through a 12-month continuous monitoring of HL patients, collecting data, such as participants' demographics, audiometric and medical data, their cognitive and mental status, their habits, and preferences, through a set of medical devices and wearables, as well as through face-to-face and remote clinical assessments and fitting/fine-tuning sessions. Descriptive, AI-based analysis and assessment of the relationships between heterogeneous data and HL-related parameters will help clinical researchers to better understand the overall health profiles of HL patients, and to identify patterns or relations that may be proven essential for future clinical trials. In addition, the future state and behavioral (e.g., HAids Satisfiability and HAids usage) of the patients will be predicted with time-dependent machine learning models to assist the clinical researchers to decide on the nature of the interventions. Explainable Artificial Intelligence (XAI) techniques will be leveraged to better understand the factors that play a significant role in the success of a hearing rehabilitation program, constructing patient profiles. This paper is a conceptual one aiming to describe the upcoming data collection process and proposed framework for providing a comprehensive profile for patients with HL in the context of EU-funded SMART BEAR project. Such patient profiles can be invaluable in HL treatment as they can help to identify the characteristics making patients more prone to drop out and stop using their HAids, using their HAids sufficiently long during the day, and being more satisfied by their HAids experience. They can also help decrease the number of needed remote sessions with their Audiologist for counseling, and/or HAids fine tuning, or the number of manual changes of HAids program (as indication of poor sound quality and bad adaptation of HAids configuration to patients' real needs and daily challenges), leading to reduced healthcare cost.

Identifying Rock Loads on TBM Shields During Standstills (Non-Advance-Periods)

Tunnel boring machine (TBM) operational data is mostly analysed with respect to data that was recorded during the advance of the TBM. Focusing on data that was recorded during standstills of a gripper TBM, we analyse rock loads that were passively recorded in the cylinders of a small roof support shield. These roof support cylinders are situated beneath the TBM’s shield – extending it against the rock mass during non-advance periods. Equipped with pressure sensors, they enable the unique opportunity of logging rock load variations throughout the tunnel. Hence due to the big amount of resulting data, techniques of unsupervised machine learning (i.e. cluster analysis) are used to automatically pre-process the TBM operational data. Furthermore, regression analysis is used to determine sections of the tunnel where rock loads are mainly occurring on the left or right side respectively. The data driven analysis shows that the main rock loads are occurring on the right side of the TBM which is in good accordance with observation from the construction site, as well as numerical models from literature. This paper contributes towards the understanding of rock load conditions in anisotropic rock masses recorded during the drive of a deep hard rock tunnel.

A Study of Performance and Comparison of NoSQL Databases: MongoDB, Cassandra, and Redis Using YCSB

Background/Objectives: Relational databases are a commonly utilized technology that allows for the storage, administration, and retrieval of various data schemas. However, for certain big databases, executing queries can become a time-consuming and inefficient procedure. Furthermore, storing enormous volumes of data necessitates servers with greater capacity and scalability. Relational databases have limits when it comes to dealing with scalability for big amounts of data. On the other hand, non-relational database systems, often known as NoSQL, were created to better fulfill the demands of key-value storing of enormous volumes of records. However, there are several NoSQL options, and the majority have not yet been extensively compared. The goal of this research is to examine different NoSQL databases and evaluate their performance in terms of typical data storage and retrieval. Methods: In this study, we use the YCSB tool to measure the performance of three NoSQL databases: MongoDB, Cassandra, and Redis. We test six different workloads with 100000, 250000, 500000, 750000, and 1000000 operations. Our test was designed with five different operations, i.e., 100000, 250000, 500000, 750000, and 1000000, with six different workloads to see which database is most suitable for applications which use a large amount of data to process. Findings: MongoDB is a superior performing NoSQL database among Cassandra and Redis. The numerous optimizations used by the designers of NoSQL solutions to improve performance, such as good cache memory operation, have a direct impact on the execution time. In all workloads except workload D, MongoDB has significantly reduced latency across all operation counts. Novelty: We also measure the average latency of different workload scenarios that include a mix of read, write, and update activities. Keywords: NoSQL; YCSB; big data; cloud computing; MongoDB; Cassandra; Redis

A Holistic Scalability Strategy for Time Series Databases Following Cascading Polyglot Persistence

Time series databases aim to handle big amounts of data in a fast way, both when introducing new data to the system, and when retrieving it later on. However, depending on the scenario in which these databases participate, reducing the number of requested resources becomes a further requirement. Following this goal, NagareDB and its Cascading Polyglot Persistence approach were born. They were not just intended to provide a fast time series solution, but also to find a great cost-efficiency balance. However, although they provided outstanding results, they lacked a natural way of scaling out in a cluster fashion. Consequently, monolithic approaches could extract the maximum value from the solution but distributed ones had to rely on general scalability approaches. In this research, we proposed a holistic approach specially tailored for databases following Cascading Polyglot Persistence to further maximize its inherent resource-saving goals. The proposed approach reduced the cluster size by 33%, in a setup with just three ingestion nodes and up to 50% in a setup with 10 ingestion nodes. Moreover, the evaluation shows that our scaling method is able to provide efficient cluster growth, offering scalability speedups greater than 85% in comparison to a theoretically 100% perfect scaling, while also ensuring data safety via data replication.

An Image Processing Approach for Detection of Prenatal Heart Disease.

Prenatal heart disease, generally known as cardiac problems (CHDs), is a group of ailments that damage the heartbeat and has recently now become top deaths worldwide. It connects a plethora of cardiovascular diseases risks to the urgent in need of accurate, trustworthy, and effective approaches for early recognition. Data preprocessing is a common method for evaluating big quantities of information in the medical business. To help clinicians forecast heart problems, investigators utilize a range of data mining algorithms to examine enormous volumes of intricate medical information. The system is predicated on classification models such as NB, KNN, DT, and RF algorithms, so it includes a variety of cardiac disease-related variables. It takes do with an entire dataset from the medical research database of patients with heart disease. The set has 300 instances and 75 attributes. Considering their relevance in establishing the usefulness of alternate approaches, only 15 of the 75 criteria are examined. The purpose of this research is to predict whether or not a person will develop cardiovascular disease. According to the statistics, naïve Bayes classifier has the highest overall accuracy.

SONOUNO WEB INTERFACE FOR SONIFICATION AND ANALYSIS OF ASTROPHYSICAL DATA

Nowadays visualization is the main method to display and analyze scientific information in astronomy, only one sense analyzing a big amount of datasets, even after 20 years of formal meeting to discuss the use and capabilities of sonification. Beyond the reluctance to use sonification, there is a growing need for new forms of data display either for research or for outreach. In this sense, and centred mainly on research, SonoUno start been developed. In this opportunity, the sonoUno web interface is presented, the aim of this contribution is to produce a tool for users who don't need large datasets, or are simply interested in testing the concepts of the SonoUno project, without going through all the trouble of installing a full featured SonoUno system. The results are promising, real data sets were opened, sonified and some mathematical functions applied.

An overview of Intrusion Detection within an Information System: The Improvment by Process Mining

Information Systems handle big amount of data within enterprises by offering the possibility to collect, treat, keep and make information avail- able. To realize these tasks, it is important to secure data from intrusions that can affect confidentiality, availability and integrity of information. Un- fortunately, with the time, technologies are more used and various types of attacks act on it to create intrusion or misuses within Information Systems. Research in intrusion detection field is still looking for solutions of such relevant problems. The purpose of this paper is to present an overview of existing intrusion detection techniques compared to a new issue based on process mining used for event logs analysis to detect abnormal events that occurs on the system. events are classified accordingly to security policy etablished with fuzzy logic to build a set of fuzzy rules, for the definition of normal and abnormal events and then reduce the high level of false alerts.

Development of an Arabic HQAS-based ASAG to consider an ignored knowledge in misspelled multiple words short answers

Many traditional question answering systems depend on an Automatic Short Answer Grading (ASAG) to evaluate misspelled multiple words short answers in an Arabic Language using common edit-based algorithms such as Hamming, Levenshtein, and Jaro_Winkler, but they ignore and hide a big amount of a significant knowledge of the student answer. In this paper, we have implemented a proposed edit-based Hierarchical question answering system (HQAS) using a traversing by Breadth-First Search (BFS) within an m-ary tree to consider the ignored significant knowledge due to the misspelling at the middle of the dual-ordered incomplete answer, the misspelling at middle and the end of the intra-ordered incomplete answer, and the misspelling due to switching in words of the intra-ordered complete answer. It can differentiate among the students based on their significant hidden knowledge and show a distribution of knowledge content on different depths of the topic to determine which of the topic depths the student has the most significant knowledge.

Entrepreneurship As the Primary Condition for Building Up The Mixed Economy

The entrepreneurship nature of Georgia is conditioned with Georgian economy`s transitional condition, also by its originalities. In particular, from one side: during of recent period the number, qualification of individual entrepreneurs was significantly increased, their activity covers more and more new fields, the market (entrepreneurial) infrastructure becomes wide, entrepreneurial mindset is being formed in the society; from another side: significant part of entrepreneurs didn`t possess proper special knowledge and experience and rational conducts under the market terms; besides, market infrastructure is still undeveloped and ineffective; state share is remained in national wealth and in the properties of joint-stock companies as well; because of lack of proper mechanism, efficiently reproduction of state share is impossible. Entrepreneurial ethics and culture is very low. Moreover, big amount of people dubiously stare at entrepreneurs and the results of their activity. Shortly, the origins of Georgian entrepreneurs` formation are unfavorable. Keywords: entrepreneurship, business, market, sale, management, competitiveness, customer.