Real-time Basis Research Articles

Analysis and Management of Electricity and Water Through IOT and Hadoop

This paper focuses on electricity and water issue prevails to give real time solution for the problems. The approach is such that real time data of the electricity and water is fetched through sensors and using IoT technology passes on to the main server, where the data is analyzed using Hadoop technology of big data to process the data to provide solutions like user based segmentation, area based segmentation, time of use tariff, electricity theft, water leakage on real time basis.

New Methodology and Instrumental System for Floating Structures Monitoring and Analysis

Floating structures are widely used in Marine Civil Engineering constructions, offshore and port operations. Safe operation of those structures requires sensing, monitoring and control systems able to deal not only with the physical parameters that affect to the system behaviour, but also with others parameters related to human interaction, since they are frequently manually operated. Any failure in these operations has a very high economic impact, so detailed previous studies and simulations are needed. In this paper, it has been developed a new methodology and an instrumental system for floating structures response analysis, scalable for field and laboratory. This new approach conjugates control, monitoring and wireless communication systems in a real time basis, offering the possibility to register and simulate all the parameters involved in operations with floating structures, including human interaction. The system developed is modular. An actual implementation of the system and positive results of the tests conducted are shown.

Monitoring of severe weather events using RGB scheme of INSAT-3D satellite

In this study, real-time analysis of products and information dissemination (RAPID), a web-based quick visualisation and analysis tool for INSAT satellite data on a real-time basis has been introduced for identification of pre-monsoon severe weather events. The tool introduces the next generation weather data access and advanced visualisation. The combination of channels using red–green–blue (RGB) composites of INSAT-3D satellite and its physical significant value contents are presented. The solar reflectance and brightness temperatures (BTs) are the major components of the RGB composite. The solar reflectance component of the shortwave thermal infrared (IR) (\(1.6~\upmu \hbox {m}\)), visible (\(0.5~\upmu \hbox {m}\)) and thermal IR channels (\(10.8~\upmu \hbox {m}\)) representing the cloud microstructure is known as Day Microphysics (DMP) RGB and the BT differences between 10.8, 12.0 and \(3.9~\upmu \hbox {m}\) is known as Night Microphysics (NMP) RGB. The threshold technique has been developed separately for both the RGB products of the year 2015–2016 and 2016–2017 of March–June, prior to the event (1–3 hr) for the detection of the thunderstorms. A validation analysis was conducted using the Forecast Demonstration Project of Storm Bulletins for pre-monsoon weather systems prepared by the India Meteorological Department and RADAR observations, demonstrating that this approach is extremely useful in recognising the area of convection prior to the occurrence of the events by the RGB thresholds. The validation of these thresholds has been carried out for March–June 2017. Both the RGBs i.e., DMP and NMP have a reasonable agreement with the ground-based observations and RADAR data. This threshold technique yields a very good probability of thunderstorm detection more than 94% and 93% with acceptable false alarm conditions, less than 3% and 5% for DMP and NMP, respectively. Furthermore, the limitations of these RGB products are additionally highlighted, and the future extent of refinement of these products in perspective of a rapid scan strategy is proposed. The threshold techniques are found to be useful for nowcasting application and are being used operationally using the RAPID tool.

Integrating Business Intelligence and Analytics in Managing Public Sector Performance: An Empirical Study

Business intelligence and analytics (BIA) is emerging as a critical area to boost organizational performance. Nowadays, data is not only important and valuable to the organization but recognized as necessary to spike the organization performance and success. As a result, many organizations spend a considerable amount of investment toward obtaining faster accurate information on a real-time basis. The previous study revealed that even though many organizations use business intelligence technologies for obtaining information, yet they still lack analytics implementation. Therefore, this study aims to discover the integrated implementation factors of business intelligence and analytics in managing organizational performance, particularly for organizations of the public sector. In achieving this, a depth literature review was carried out to identify the influential factors in the implementation of business intelligence, business analytics, and performance management. The subject matter experts in Business Intelligence (BI), Business Analytics (BA) and Organisational Performance Management (OPM) were invited to participate in this empirical study, which was conducted in Malaysia. The study was carried out through interviewing experts, in order to identify the essential factors for business intelligence and data analytics implementation. Twenty essential factors and sixty-four sub-factors were identified and analyzed to construct the integrated factors in BIA and OPM implementation. The result of the study revealed four integrated factors of the BIA and OPM implementation, such as skill, documentation, visualization, and work culture. Finance, data management, software, strategic planning, and decision-making are other factors integrated with BI, BA, and OPM respectively. Finally, this study illustrates the integrated factors in a visual form.

The Future of Employee Engagement: Real-Time Monitoring and Digital Tools for Engaging a Workforce

At no other time have organizations had so much opportunity to measure and evaluate the effectiveness and efficiency of their workforce. While not all companies have yet to embrace the tools and technology available to them, leading companies have been able to capitalize on new technologies that track productivity, sales, customer satisfaction, work flows, quality, and workplace interactions on a frequent, sometimes real time basis. Additionally, the tools to synthesize and analyze this data has also rapidly advanced in the last few years with the more mainstream availability of statistical modeling, machine learning technology, and artificial intelligence applications. Yet, when it comes to measuring and tracking employee engagement, most companies still evaluate engagement on an annual, or longer, basis using traditional survey techniques. While these practices have provided a wealth of insight into the dimensions and impact of engagement, it is time to rethink how we are measuring engagement and, more importantly, how the same digital tools can be applied towards improving the productivity, retention, and satisfaction of the workforce. We propose a series of research topics to address this modern trend of the impact of technological advances on both the measurement of engagement and the application of human resource management (HRM) practices to improve engagement.

Human wrist motion classification using decision tree and principle component analysis

Abstract This study investigates and acts as a trial clinical outcome for human motion and behaviour analysis in consensus of health related quality of life in Malaysia. Both methods were developed in MATLAB platform by managing and analyzing motion data in graphical user interface (GUI) form. By attaching the wearable sensors to each segments of interest, the collected data can be reconstructed in real time basis. Artificial intelligence technique is proposed to classify the subjects’ motions. Data reconstruction technique can be used to regenerate the motion of subjects’ limbs movement in 2D representation. Corresponding tests are performed to validate the accuracy of the application. The application is potentially to be used to access the quality of human motion in hospitals, clinics and human motion research. An experiment was set up in a laboratory environment for data collecting. Subjects were asked to perform three shape drawings using left and right hands. The instruments demonstrate adequate internal consistency of optimum average accuracy of 52 % for decision tree and 80 % for principle component analysis. Results were presented in tabular, image and graphical forms. The expected output of the study can be used to estimate joint angles in predicting Parkinson Disease for early stage symptoms, to track the moving behaviour in clinical gait analysis, to analyse limb movement in detecting resting tremor, to trace wrist motion in regenerating children handwriting difficulty, and to measure joint motion in evaluating patient rehabilitation progress.

Smart grid resources optimisation using service oriented middleware

In this paper, we propose a new Service Oriented Architecture (SOA) for Smart Grid Resource Optimisation Middleware (SGROM) which allows Smart Grid Constituencies (SGC) such as Power Generators (PG), Power Transporters (PT), Power Distributors (PD), and Power Consumers (PC) to optimise their pay-offs using the smart grid. The proposed resource optimisation management middleware aims to support power consumers with sustainable energy from power distributors, transparently at the best price on a real-time basis (variable pricing). It will also decide automatically for the power distributors the best power generators and connecting transporter lines based on the current power generation and transportation costs, the demand of the distributor, and the maximum power supply from the generator. The SGROM smart engine is modelled as a Mixed-Integer Linear Program (MILP) which aims at optimising the profits and resource utilisation over the whole smart grid. The experimental results show that substantial cost improvement can be achieved when using SGROM, as compared to the traditional grid system.

Smart grid resources optimisation using service oriented middleware

In this paper, we propose a new Service Oriented Architecture (SOA) for Smart Grid Resource Optimisation Middleware (SGROM) which allows Smart Grid Constituencies (SGC) such as Power Generators (PG), Power Transporters (PT), Power Distributors (PD), and Power Consumers (PC) to optimise their pay-offs using the smart grid. The proposed resource optimisation management middleware aims to support power consumers with sustainable energy from power distributors, transparently at the best price on a real-time basis (variable pricing). It will also decide automatically for the power distributors the best power generators and connecting transporter lines based on the current power generation and transportation costs, the demand of the distributor, and the maximum power supply from the generator. The SGROM smart engine is modelled as a Mixed-Integer Linear Program (MILP) which aims at optimising the profits and resource utilisation over the whole smart grid. The experimental results show that substantial cost improvement can be achieved when using SGROM, as compared to the traditional grid system.

Accuracy of transrectal strain elastography in detection of prostate cancer

Background: Elastography has emerged as a boon in aiding diagnosis of various neoplastic conditions. Strain elastography helps in differentiating hard lesions from the normal tissue on a real-time basis and targeting biopsies of the same described by other authors in various conditions. We assess a series of cases for the detection of prostate cancer using strain elastography of prostate. Aims: The aim of this study was to assess the accuracy of transrectal strain elastography in diagnosis of prostate cancer. Materials and Methods: This is an observational cross-sectional, prospective study. Transrectal strain elastography was performed using a C-10 3 v endocavity probe with elastography software and was compared against biopsy results on 25 adult male patients with raised prostate-specific antigen levels. Statistical significance of qualitative data across two study groups was tested using the Chi-square test or Fisher's exact test. The entire data were analyzed using SPSS version 16.0, Inc., Chicago, software for Microsoft Windows. Results: Ten (40%) out of 25 patients demonstrated carcinoma prostate, 14 patients had benign prostatic hyperplasia, and 1 had prostatic abscess. Transrectal real-time elastography scores in patients with carcinoma patient were higher than those of benign conditions, i.e., 3 and 4 scores with accuracy of 92%, sensitivity of 85.7%, and specificity of 94.4%. Conclusions: The overall accuracy of strain elastography was 92%, which enhanced the diagnostic yield in prostate carcinoma. Real-time strain elastography is a highly sensitive and specific technique for diagnosing prostatic carcinoma and guiding the prostate biopsy.

Design and Implementation of a Customable Automatic Vehicle Location System in Ambulances and Emergency Vehicle Systems.

Background:Automatic vehicle location (AVL) refers to a system that calculates the geographical location of any vehicle, i.e., latitude and longitude. Vehicle location information about one or more moving vehicles can be stored in the internal memory and accessed when vehicles are available (offline tracking). It is also possible to get location information on a real-time basis (online tracking). The real-time tracking systems designed to date may incorporate three devices: global positioning system (GPS), geographic information system, and cellular communication platforms that may be either a general packet radio service (GPRS) or any private and local radiofrequency network.Methods:The GPS-based navigation system has been designed so as to allow for user-friendly real-time tracking applications for any emergency vehicles like ambulances. First, GPS coordinates are obtained from the SIM908 module and sent via to a server transmission control protocol/internet protocol. Server codes, which are written in C#, load Google map to show real-time location.Results:We designed online tracking AVL hardware in the two simple and advanced versions. The latter enables both the ambulance driver and the data center to monitor path real-time besides enabling the vehicle driver to receive and make calls and send or receive messages. The former only sends latitude and longitude to the data server continuously, and the path travelled by vehicle is displayed.Conclusion:SIM908 integrates GSM, GPRS, and GPS in one package. It can be a proper choice for real-time economic tracking systems despite its low accuracy in finding geolocations.

Dynamic methodology for risk assessment in industrial processes by using quality control charts

Although several Quantitative Risk Assessment (QRA) methodologies have often proven effective in industry they generally lack for the ability of update on a real-time basis offering a static perspective of industrial risks. In recent years this situation has been treated by the development of the Dynamic Risk Assessment (DRA) methodologies updating the analysis and frequency of the accident precursor events by means of the application of the Bayesian inference method. Additional dynamic methodologies have emerged but in order to obtain an analysis in parallel with the evolution of the process, it is necessary to incorporate the characteristics of prevention, simultaneity and immediacy that allow corrective actions to be carried out sufficiently in advance because the industrial process is showing a risk situation outside control limits. In this work a new methodology based also on the dynamic risk assessment approach and on the application of control charts together with the use of the Monte Carlo Markov Chain (MCMC) methods are used to monitor the causes of accident with a concept of Statistical Risk Control (SRC).

Presence: An Integrated Mobile Solution for Truancy Detection using RFID and Cloud-based Notification Services

This study aims to use mobile and cloud-based notification services to detect truancy in students and notify the concerned party in a real-time basis. Attendance logs are captured by radio-frequency identification (RFID) scanners at the entrance and exit gates of the campus. Students’ identification cards are embedded with a chip that uses radio frequency to communicate data to a reader. The mobile application pulls data from the database through a web service to reflect whether or not the student gets inside the campus. The presence of the students in their respective classes is further checked by the faculty through a user interface. Parents and sponsors can check, at real-time, the presence of the students in the campus as well as in their classes. To handle fresh new information in a timely fashion, the system implements cloud-based notification services called Google Cloud Messaging for Android (GCM). Parents get immediate notification about the students’ missed classes, violations, and class suspensions.

Estimation of Consistency of Production of the Pertussis Component of DTP Vaccine in Terms of Immunogenic Activity and Specific Safety Using Shewhart Charts

WHO experts attribute the resurgence of whooping cough to the wide use of acellular pertussis vaccines (aPs) as components of combination products. In this regard, WHO encourages countries that have not yet switched to the use of aPs to continue to use whole-cell pertussis vaccines (wPs) for primary vaccination. The experience of using pertussis vaccines has shown that companies do not always produce highly efficacious products. The use of statistical methods of samples quality control helps to ensure consistency of the technological process, which results in the production of more homogeneous products, and rules out the possibility of producing low-quality products. This paper presents the results of retrospective evaluation of the consistency of the wP (as a pertussis component of the DTP vaccine) production using Shewhart control charts. It was shown that at some points in time during the analyzed period from January 2017 until March 2018 the technological process of the company lacked proper statistical control. This increased the risk of producing non-uniform and defective products. In order to improve the quality and consistency of pertussis component batches, the company’s quality control and quality assurance services should make extensive use of Shewhart charts on a real-time basis.

The Blockchain Ecosystem

The dynamics of blockchain, its ongoing development, and its systemic vulnerabilities are rooted in its ecosystem – the community of producers, suppliers, customers, stakeholders, and competitors. This paper provides a high-level description of the framework of the blockchain ecosystem, beginning with blockchain development, and drilling down to a level of detail about blockchain use cases and stakeholders. To provide strategic perspective, this description takes place at the highest possible level, bearing in mind, of course, that many smaller ecosystems comprise blockchain. In order to elucidate the components of the blockchain ecosystem, this paper provides examples of companies currently operating in each area. However, this paper does not endorse or criticize any particular business, practice, or model. Notably, however, blockchain companies do not always easily fit into one area of the ecosystem – they may operate in more than one area, evolve in capabilities and transition between areas over time, or even cease to exist. This illustrative map can serve many purposes. Using this map, policymakers can obtain insight into how they can incentivize a strong blockchain ecosystem with reduced vulnerabilities. Traditional companies can use this map to gain insight into how the blockchain ecosystem will come to interact with the ecosystem of their own traditional businesses. Investors and business analysts can use this map to gain insight into which companies will hold the leadership roles in the blockchain ecosystem over time. And of course, entrepreneurs can identify potentially profitable opportunities to participate in and enhance the blockchain ecosystem. Importantly, the blockchain ecosystem is a living map, exhibiting constant change, and it will therefore require updating on a regular, real-time basis. What is provided in this paper, then, is simply a snapshot in time of the blockchain ecosystem as it currently exists. The intention is to continue to study the ever-evolving blockchain ecosystem, obtain insight, and subsequently improve and refine the map.

Selective Laser Sintering of Ag Conductive Inks with a Real-Time Non-uniform Rational Basis Spline Curve Interpolator

A method is proposed for the fabrication of Ag patterns on plastic substrates by printing Ag conductive inks, followed by selective laser sintering (SLS). The printing and sintering processes were realized by a motion control system with a real-time non-uniform rational basis spline (NURBS) curve interpolator. The proposed approach achieves a constant laser scanning speed along the curve path. Ag patterns fabricated by a NURBS curve interpolator and compared to those produced using a linear interpolator. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License .

Guest Editorial: Special Issue on AI Powered Network Management: Data-Driven Approaches Under Resource Constraints

In recent years, the explosive development of mobile communications and networking, together with the wave of Internet of Things (IoT), has led to super-complex systems, which are difficult to model and manage. At the same time, such systems are generating a large amount of data on a real-time basis, from both the user and network sides. How to utilize such data to relieve the dependence on restrictive, sometimes even unrealistic, system models are the key leading to more efficient and effective future networks, especially when under various resource constraints as in IoT systems. Fortunately, recent advancements in artificial intelligence (AI), empowered by modern machine learning algorithms, have demonstrated remarkable success in a variety of fields and are stimulating numerous data-driven approaches as well as applications. Combining the availability of big data in complex IoT communication networks and the recent advancements in AI, it now comes the time to renovate how we resolve network management issues to more efficiently and effectively fulfill the dynamic demands of network subscribers, especially in the presence of stringent network resource constraints. With the fuel (IoT data) and the engine (AI), data-driven network management will enable us to dynamically and adaptively meet the spatio-temporal network demands in the most resource-aware and resource-smart manner.

Remote Surveillance Technologies: Realizing the Aim of Right Patient, Right Data, Right Time.

The convergence of multiple recent developments in health care information technology and monitoring devices has made possible the creation of remote patient surveillance systems that increase the timeliness and quality of patient care. More convenient, less invasive monitoring devices, including patches, wearables, and biosensors, now allow for continuous physiological data to be gleaned from patients in a variety of care settings across the perioperative experience. These data can be bound into a single data repository, creating so-called data lakes. The high volume and diversity of data in these repositories must be processed into standard formats that can be queried in real time. These data can then be used by sophisticated prediction algorithms currently under development, enabling the early recognition of patterns of clinical deterioration otherwise undetectable to humans. Improved predictions can reduce alarm fatigue. In addition, data are now automatically queriable on a real-time basis such that they can be fed back to clinicians in a time frame that allows for meaningful intervention. These advancements are key components of successful remote surveillance systems. Anesthesiologists have the opportunity to be at the forefront of remote surveillance in the care they provide in the operating room, postanesthesia care unit, and intensive care unit, while also expanding their scope to include high-risk preoperative and postoperative patients on the general care wards. These systems hold the promise of enabling anesthesiologists to detect and intervene upon changes in the clinical status of the patient before adverse events have occurred. Importantly, however, significant barriers still exist to the effective deployment of these technologies and their study in impacting patient outcomes. Studies demonstrating the impact of remote surveillance on patient outcomes are limited. Critical to the impact of the technology are strategies of implementation, including who should receive and respond to alerts and how they should respond. Moreover, the lack of cost-effectiveness data and the uncertainty of whether clinical activities surrounding these technologies will be financially reimbursed remain significant challenges to future scale and sustainability. This narrative review will discuss the evolving technical components of remote surveillance systems, the clinical use cases relevant to the anesthesiologist’s practice, the existing evidence for their impact on patients, the barriers that exist to their effective implementation and study, and important considerations regarding sustainability and cost-effectiveness.

NaYF4@Yb,Ho,Au/GO-nanohybrid materials for SERS applications-Pb(II) detection and prediction.

Nowadays, nano hybrid materials (NHMs) with latent applications have been employed in different fields, particularly for sensor applications. Among NHMs, GO based - upconversion NHMs system is an emerging area for the rapid detection of different hazardous materials either as an aptamer based or free-labeled sensing techniques. For the detection of Pb(II) in water, NaYF4@Yb,Ho,Au/GO-NaYF4@Yb,Ho,Au NHMs system was developed. The synthesized NHMs were characterized by XRD, FT-IR, SEM with EDS, TEM and Raman characterization techniques for observation and confirmation. NaYF4@Yb,Ho,Au/GO-NaYF4@Yb,Ho,Au NHMs fabricated sensors were observed to detect and quantify on real-time basis Pb(II) via surface-enhanced-Raman spectroscopy within the dynamic linear range of 98-99%, with detection limits of 1.16 × 10-9 g/mL and 1.15 × 10-8 g/mL obtained respectively for NaYF4@Yb,Ho,Au and GO- NaYF4@Yb,Ho,Au NHMs. The relative standard deviation (RSD) value achieved for both were less than ∼10%, indicative of reproducibility in the quantification results for Pb(II) traces in water when combined with genetic algorithm partial least square (GA-PLS). Results suggest that the GO-NHMs better enhanced the SERS Pb (II) vis-à-vis NaYF4@Yb,Ho,Au. The GO-wrapped NHMs exhibited a further better SERS performance because the heterostructure of GO-NHMs could be potentially useful for SERS-based immunoassay due to the much easier charge transfer between graphene and the metal ions and molecules so the homogeneity of the SERS probe was improved simultaneously (chemical enhancement) by GO-NHMs.

Real-time CO2 uptake/emission measurements as a tool for early indication of toxicity in Lemna-tests

This paper presents an application of continuous monitoring of the emission and uptake rate of CO2 in Lemna toxicity test. On a real-time basis, the CO2 concentration data were collected by the Arduino platform-based respiratory activity measuring system (ResTox) and reported as CO2 concentration dynamic curves. The results of CO2 measurements demonstrated that tested metals (Co, Cu, Hg, and Cd), as well as herbicides (nicosulfuron, diquat, and tembotrione), stimulated the CO2 exchange rates at low doses, while at high doses CO2 exchange rates were inhibited. The addition of higher concentrations of clopyralid stimulated photosynthetic activity and caused a higher increase in respiration rates indicating its mode of action as auxin mimic herbicide. The results obtained underline the necessity of considering other biological endpoints like continuous measurements of gas exchange from the very beginning of exposure to toxicants. Simultaneous measurements of real-time CO2 concentrations, as the primary effect of toxicant mode of action, and processes that are supported by carbon flux, as the secondary effect or endpoint, are needed to relate actual and substrate-induced or inhibited respiration and photosynthesis to those processes. Therefore, continuous measurements of CO2 exchange rates can be implemented for the initial screening of potential toxicity to give valuable information that is needed for further examination of toxicity mechanisms and risk assessment.

Monitoring the Enzymatic Oxidation of Xenobiotics by Hydrogen Peroxide through Oxidation–Reduction Potential Measurements

In this work, three xenobiotics (orange II, phenol, and bisphenol A) were oxidized by hydrogen peroxide in the presence of a horseradish peroxidase (HRP) using a fed-batch system. During the experiments, the oxidation–reduction potential (ORP) of the reaction mixture was measured continuously. Results demonstrate that ORP values only increased when both substrates of the enzyme (hydrogen peroxide and the target compound) were present in the reaction mixture. For all of the tested pollutants, the continuous addition of hydrogen peroxide caused an increase in ORP values. When the reducing substrate was depleted, the addition of an excess of hydrogen peroxide caused a decrease of ORP values. The time at which ORP reached a maximum represented the end of the oxidation process. This maximum could be easily detected by means of the derivative of ORP as a function of time. To extend the application of the developed technique, the enzymatic oxidation of a binary mixture of BPA and OII was also followed using ORP measurements. Results were similar to those observed with only one reducing substrate. This work demonstrates that ORP measurements can be useful to maximize hydrogen peroxide efficiency through the controlled addition of the oxidant during the oxidation of OII, phenol, and BPA catalyzed by HRP. This approach allows a minimization of time and process costs since the reaction end-point can be easily detected on a real-time basis.