Real-time Situation Research Articles

Urgent Need for Real Time Web Mapping GIS Applications to Dengue Surveillance for Epidemic Control and Management in India

Dengue epidemics has been increased both horizontal and vertical structure across the country since 2006, and hence, urgent need for GIS based dengue surveillance for epidemic control and management in the nation. The dengue epidemic cases and the mortality caused by dengue has become essentially public health important in India, and the situation has become worse since 2006. Dengue cases were clinically confirmed with thousands of cases and hundreds of deaths annually. The report of dengue epidemics is becoming daily news nowadays and it was reported from 24 states and Union Territories in India. The spatial and seasonal occurrence of dengue epidemics were reported across the country during the period of April to November annually, however, the vulnerability of epidemics was reported between the periods of mid-July to mid-November of every year. The geo-environmental and climate variables are fuelling to create a conducive environment for profusion of dengue vector mosquitoes Aedes species (Ae. aegypti or Ae. Albopictus). The information relevant to the geographical site specification of dengue vectors breeding habitats, vector abundance, vector density, etc., could be collected using global positioning systems (GPS), and these information are mapped and overlay on the thematic layers of climate variables (Temperature, relative humidity, saturation deficiency and Rainfall) under the geographical information systems (GIS) software platform for spatial analysis (cluster analysis, nearest neighborhood analysis, fussy analysis, probability of maximum and minimum likelihood analysis etc.,) for spatial risk prediction. The Google based internet GIS mapping could be used to embedded web mapping real time situation and updating the information with online data base connectivity (ODBC) within the GIS spatial engine for providing the real time information relevant to the dengue epidemics, and hence, the appropriate precaution measures could be taken to control dengue epidemics in the nation early in advance.

Research on Education and Teaching of Artificial Intelligence Enabling Intelligent Construction

This study is committed to in-depth analysis of the practical application of artificial intelligence in intelligent construction teaching and its empowerment effect. An efficient and intelligent teaching platform is built, which can intelligently recommend relevant learning resources according to students’ learning progress and feedback, and track students’ learning situation in real time. This personalized learning style not only improves the learning efficiency of students, but also enables them to better adapt and master the knowledge and skills in the field of intelligent construction. The introduction of artificial intelligence technology has innovated the method of intelligent construction teaching. Based on the learning analysis technology of big data, we can conduct in-depth analysis of students’ learning behaviors and achievements, so as to grasp students’ learning status and needs more accurately. These achievements not only show the great potential of artificial intelligence technology in the field of education, but also provide new ideas and methods for the reform and innovation of intelligent construction teaching.

Critical care resources in surge response: towards real-time situation awareness

ABSTRACT In large-scale emergencies and disasters, real-time situation awareness around health resources can be lifesaving. Understanding the situation at hand, the pressing needs and the actions needed often using information systems that require manual data entry. Relying on humans to enter data manually during surge response is both error-prone and an impediment to the critical requirement of real-time situation awareness. This paper explores the potential of Ultra-High Frequency Radio Frequency Identification (UHF RFID) technology to automate the process of manual data entry followed in providing situation awareness related to critical care resources during surge response. Using design science, an RFID-based prototype was developed to identify and track intensive care resources in real-time. The system was tested in a simulated hospital ward environment to understand deployment challenges and a dashboard was developed to reflect real-time data from multiple sources simultaneously. When properly deployed, UHF RFID can be a viable approach to automating critical care reporting during surge response. Automating routine processes can enhance data quality and minimise workload on health professionals leading to better patient care and evidence based decision-making.

InfoGCN++: Learning Representation by Predicting the Future for Online Skeleton-based Action Recognition.

Skeleton-based action recognition has made significant advancements recently, with models like InfoGCN showcasing remarkable accuracy. However, these models exhibit a key limitation: they necessitate complete action observation prior to classification, which constrains their applicability in realtime situations such as surveillance and robotic systems. To overcome this barrier, we introduce InfoGCN++, an innovative extension of InfoGCN, explicitly developed for online skeletonbased action recognition. InfoGCN++ augments the abilities of the original InfoGCN model by allowing real-time categorization of action types, independent of the observation sequence's length. It transcends conventional approaches by learning from current and anticipated future movements, thereby creating a more thorough representation of the entire sequence. Our approach to prediction is managed as an extrapolation issue, grounded on observed actions. To enable this, InfoGCN++ incorporates Neural Ordinary Differential Equations, a concept that lets it effectively model the continuous evolution of hidden states. Following rigorous evaluations on three skeleton-based action recognition benchmarks, InfoGCN++ demonstrates exceptional performance in online action recognition. It consistently equals or exceeds existing techniques, highlighting its significant potential to reshape the landscape of real-time action recognition applications. Consequently, this work represents a major leap forward from InfoGCN, pushing the limits of what's possible in online, skeleton-based action recognition.

Fast Charging Guidance and Pricing Strategy Considering Different Types of Electric Vehicle Users’ Willingness to Charge

As the penetration rate of electric vehicles (EVs) increases, how to reasonably distribute the ensuing large charging load to various charging stations is an issue that cannot be ignored. This problem can be solved by developing a suitable charging guidance strategy, the development of which needs to be based on the establishment of a realistic EV charging behaviour model and charging station queuing system. Thus, in this paper, a guidance and pricing strategy for fast charging that considers different types of EV users’ willingness to charge is proposed. Firstly, the EVs are divided into two categories: private cars and online ride-hailing cars. These categories are then used to construct charging behaviour models. Based on this, a charging decision model for EV users is constructed. At the same time, a first-come-first-served (FCFS) charging station queuing system is constructed to model the real-time charging situation in the charging station in a more practical way. Finally, a dynamic tariff updating model is used to obtain the optimal time-of-use tariff for each charging station, and then the tariffs are used to guide the fast-charging demand. By comparing the spatial and temporal distribution of charging demand loads at charging stations under different scenarios and considering whether the tariffs at each charging station play a guiding role, it is verified that the proposed strategy effectively optimises the balanced distribution of EV charging loads and alleviates the congestion at charging stations.

In situ Single-Pass Perfused Rabbit Intestinal Study sample Analysis of Ractopamine by UPLC

In recent years, the prediction of oral drug absorption in humans has been a challenge for researchers and many techniques for permeability studies have been developed for several purposes, including biowaiver processes. The Single-Pass Intestinal Perfusion (SPIP) method performed in rats can provide permeability results closest to in vivo condition. he use of SPIP in rats and calculations for absorption prediction in humans may indicate the transport mechanisms and/or pre-systemic metabolism involved on permeation processes of drugs, since this model is the closest to in vivo conditions. Ractopamine hydrochloride is a commercial beta-adrenergic agonist commonly used as a dietary supplement in cattle production for improved feed efficiency and growth promotion. Currently, regulatory target tissues (as approved in the New Animal Drug Application with Food and Drug Administration) for ractopamine residue testing are muscle and liver. The aim of the study is to develop and validate an UPLC assay method to determine whether detectable and quantifiable levels of perfused ractopamine in small intestine of cattle or not. The separation is carried on Acquity UPLC BEH C18 (150mm x 2.1mm, 1.7 µm) reversed phase column using acetonitrile and 0.01M ammonium formate as the mobile phase in gradient mode at flow rate of 0.5mL/min. The precision, accuracy, extraction recovery, matrix effect, and stability meet the requirements of the guiding principles. In order to verify the sensitivity and selectivity of the method in a real-time situation, the developed method is successfully applied to a perfusion study in small intestine of rabbit and the samples were analyzed by Empower software.

Multi-Chambered Core/Shell Supraparticles for Real-Time, Full-Time Diagnosis and Treatment Integration of Tumors.

To a certain extent, theranostic nanoplatforms promote tumor treatment efficiency. However, timely monitoring of the critical stages and signal sustainability of the entire process is challenging. In this study, multi-chambered core/shell magnetic nanoparticles (MC-MNPs) as drug and imaging agent multi-loaded nanocarriers with a synergistic release function are reported. Supraparticles with stable chambers are formed by the supercooling self-assembly of several core/shell magnetic nanoparticles composed of amphiphilic copolymers as the core and hydrophilic magnetic iron oxide nanoparticles as the shell. Desalinized doxorubicin and coumarin 6 are stored in different cavities of nanocarriers, and chitosan is used as an outer encapsulation layer. Based on their construction properties, MC-MNPs can exhibit gradient-degraded and steady-released controllability in the tumor environment. Furthermore, real-time accumulation situations and full-time diagnostic signals of nanocarriers are thoroughly demonstrated using fluorescence imaging and T2-weighted magnetic resonance imaging before and after magnetic hyperthermia in targeted tumors under an alternating magnetic field. Thus, MC-MNPs as theranostic nanocarriers exhibit great potential for the timely monitoring and full-time guidance of tumor treatment.

A victim risk identification model for nature-induced urban disaster emergency response.

In recent years, nature-induced urban disasters in high-density modern cities in China have raised great concerns. The delayed and imprecise understanding of the real-time post-disaster situation made it difficult for the decision-makers to find a suitable emergency rescue plan. To this end, this study aims to facilitate the real-time performance and accuracy of on-site victim risk identification. In this article, we propose a victim identification model based on the You Only Look Once v7-W6 (YOLOv7-W6) algorithm. This model defines the "fall-down" pose as a key feature in identifying urgent victims from the perspective of disaster medicine rescue. The results demonstrate that this model performs superior accuracy (mAP@0.5, 0.960) and inference speed (5.1ms) on the established disaster victim database compared to other state-of-the-art object detection algorithms. Finally, a case study is illustrated to show the practical utilization of this model in a real disaster rescue scenario. This study proposes an intelligent on-site victim risk identification approach, contributing significantly to government emergency decision-making and response.

An Adaptive Missing Data Restoration Method for UAV Confrontation Based on Deep Regression Model

Completing missions with autonomous decision-making unmanned aerial vehicles (UAV) is a development direction for future battlefields. UAV make decisions based on battlefield situation information collected by sensors and can quickly and accurately perform complex tasks such as path planning, cooperative reconnaissance, cooperative pursuit and attacks. Obtaining real-time situation information of enemy is the basis for realizing autonomous decision-making of the UAV. However, in practice, due to internal sensor failure or interference of enemy, the acquired situation information is prone to be missing, which affects the training and decision-making of autonomous UAV. In this paper, an adaptive missing situation data restoration method for UAV confrontation is proposed. The UAV confrontation situation data are acquired through JSBSim, an open-source UAV simulation platform. By fusing temporal convolutional network and long short-term memory sequences, we establish a deep regression method for missing data restoration and introduce an adaptive mechanism to reduce the training time of the restoration model in response to dynamic changes in the enemy’s strategy during UAV confrontation. In addition, we evaluate the reliability of the proposed method by comparing with different baseline models under different degrees of data missing conditions. The performance of our method is quantified by five metrics. The performance of our proposed method is better than the other benchmark algorithms. The experimental results show that the proposed method can solve the missing data restoration problem and provide reliable situation data while effectively reducing the training time of the restoration model.

Proposed Supercluster-Based UMBBFS Routing Protocol for Emergency Message Dissemination in Edge-RSU for 5G VANET

Vehicular ad hoc networks (VANETs) can bolster road safety through the proactive dissemination of emergency messages (EMs) among vehicles, effectively reducing the occurrence of traffic-related accidents. It is difficult to transmit EMs quickly and reliably due to the high-speed mobility of VANET and the attenuation of the wireless signal. However, poor network design and high vehicle mobility are the two most difficult problems that affect VANET’s network performance. The real-time traffic situation and network dependability will also be significantly impacted by route selection and message delivery. Many of the current works have undergone studies focused on forwarder selection and message transmission to address these problems. However, these earlier approaches, while effective in forwarder selection and routing, have overlooked the critical aspects of communication overhead and excessive energy consumption, resulting in transmission delays. To address the prevailing challenges, the proposed solutions use edge computing to process and analyze data locally from surrounding cars and infrastructure. EDGE-RSUs are positioned by the side of the road. In intelligent transportation systems, this lowers latency and enhances real-time decision-making by employing proficient forwarder selection techniques and optimizing the dissemination of EMs. In the context of 5G-enabled VANET, this paper introduces a novel routing protocol, namely, the supercluster-based urban multi-hop broadcast and best forwarder selection protocol (UMB-BFS). The improved twin delay deep deterministic policy gradient (IT3DPG) method is used to select the target region for emergency message distribution after route selection. Clustering is conducted using modified density peak clustering (MDPC). Improved firefly optimization (IFO) is used for optimal path selection. In this way, all emergency messages are quickly disseminated to multiple directions and also manage the traffic in VANET. Finally, we plotted graphs for the following metrics: throughput (3.9 kbps), end-to-end delay (70), coverage (90%), packet delivery ratio (98%), packet received (12.75 k), and transmission delay (57 ms). Our approach’s performance is examined using numerical analysis, demonstrating that it performs better than the current methodologies across all measures.

A kinetic modelling approach to explore mechanism of Cr(VI) detoxification by a novel strain Pseudochrobactrum saccharolyticum NBRI-CRB 13 using response surface methodology.

A novel Pseudochrobactrum saccharolyticum strain NBRI-CRB 13, isolated from tannery sludge, was studied to grow up to 500 mgL-1 of Cr(VI) and showed Cr(VI) detoxification by reducing > 90% of Cr(VI) at different concentrations 25, 50 and 100 mgL-1. Kinetic studies showed that first-order models were fitted (R2 = 0.998) to the time-dependent Cr(VI) reduction with degradation rate constant (k) (1.03-0.429h-1). Cr(VI) detoxification was primarily related to the extracellular fraction of microbial cells, which showed a maximum extracellular reductase enzyme activity led to 94.6% reduction of Cr(VI). Moreover, the strain showed maximum extracellular polymeric substances (EPS) production at 100 mgL-1 Cr(VI), which is presumably the reason for Cr(VI) removal as EPS serves as the metal binding site for Cr(VI) ions. Further, an optimization study using Box-Behnken design was conducted considering parameters viz., pH, temperature, and initial concentration of Cr(VI). The maximum percent reduction of Cr(VI) was obtained at pH 6.5, temperature 30°C with 62.5 mgL-1Cr(VI) concentration. Further, the Cr(VI) reduction and adsorption ability of strain P. saccharolyticum NBRI-CRB13 were confirmed by SEM-EDS, FTIR, and XRD analyses. FTIR analysis confirmed the presence of functional groups (-OH, -COOH, -PO4) on bacterial cell walls, which were more likely to interact with positively charged chromium ions. The study elucidated the reduction of Cr(VI) by the novel bacterium within 24h using the response surface methodology approach and advocated its application in real-time situations.

Efficient multi-objective rolling strategy of photovoltaic/hydrogen system via short-term photovoltaic power forecasting

Green hydrogen serves as an effective energy carrier for achieving sustainable development goals. However, the high production cost and intermittent renewable energy output cause obstacles to hydrogen production. Various electrolyzer control methods have been proposed to promote electrolyzers' efficiency. Traditional electrolyzer control methods may lead to significant differences in electrolyzer wear among large-scale hydrogen production systems. Hence, this paper proposes a multi-objective rolling strategy based on photovoltaic power forecasting to mitigate electrolyzer switch fluctuations, enhance hydrogen production efficiency, and better manage the electrolyzers. Initially, the trend of photovoltaic output is calculated based on the predicted values of the photovoltaic output from the particle swarm algorithm (PSO)-backpropagation (BP) model, and during the power ramp-up phase, some electrolyzers are activated in advance to expedite the cold start process and enhance the hydrogen production. Simultaneously, the electrolyzers' power distribution and operational status are adjusted in conjunction with the real-time power situation. Finally, a multi-objective rolling strategy is proposed to balance the operational indicators of the electrolyzers. The results indicate that the proposed method yields 4,952,642 Nm3 hydrogens over six months, which has increased by 5.37% and 9.00% compared to the simple start-stop and rotation strategy, respectively. Moreover, switch action times are reduced by 27.68% and 43.22%, respectively. These results demonstrate the effectiveness of the proposed method in significantly enhancing hydrogen production and improving the economic feasibility of photovoltaic/hydrogen systems.

Mitigating Market Volatility Using Machine Learning Techniques

This paper uses advanced machine learning algorithms to control market fluctuations and improve the accuracy of the financial forecast decision-making tool. The application of techniques is then simulated with models and real-time situations to evaluate the technique's effectiveness on market forecasting. By analyzing the data, we show that using machine learning as the approach to build models that are more accurate than the existing ones reduces risks connected with flapping markets. Some of the difficulties observed are described, and the results suggest possible solutions. In conclusion, our results indicate that these techniques are essential for enhancing financial stability in volatile markets.

Improving Biology Learning Through Augmented Reality Technology in Indonesia: A Review

Biology is always considered difficult either to teach or to learn. As a field of study that learning about living subjects, biology has very broad and abstract concepts. Many students find the abstract concepts of biology are hard to understand. Some students even feel that studying biology is a dull experience. This type of perception in students subsequently led to low academic achievement. Hence, an innovative and attractive learning approach is needed for teaching biology. Augmented Reality (AR) technology has a great possibility to improve biology learning experience due to its ability to concretize the abstract concepts in biology by providing natural interaction between virtual and real-time situations simultaneously. In the last decade, numerous research of implementing AR in education including biology learning have been studied globally and nationally. In order to evaluate the effectiveness of AR in improving biology learning especially for students in Indonesia, this literature review research is conducted. The reviewed articles are retrieved through Google Scholar database with the assigned criteria published between 2020 to 2024, AR implemented, biology learning, student perception, and/or achievement in biological studies. The analysis shows that implementing AR in biology learning increases students’ interest, learning motivation, involvement, collaboration, independent learning, knowledge retention, and achievement. However, the conflicted finding is reported regarding the influence of AR on students’ critical thinking ability. Technical problems related to downloading and distributing AR applications are the main challenge that has been reported when using AR in biology learning.

Performance Evaluation of Ekantakuna Intersection

Majority of the intersections in Kathmandu valley are unsignalized and hence traffic management becomes one of the emerging issues for traffic police and traffic designers. The number of vehicles in Kathmandu Valley is increasing at the rate of 12% annually (Department of Transport Management, 2018). The manual traffic management is very tedious and hectic nowadays. The consequence leads to traffic congestion and sometimes crashes too. Hence, the performance evaluation of such unsignalized intersection is essential and considering the real time situations, scenario analysis should be done. The research was carried out in Ekantakuna Intersection. The data's were collected in Ekantakuna Intersection through video-graphic surveying during weekdays: Monday to Friday both morning and evening during peak hours (9 to 11 AM in morning and 4 to 6 PM in evening).The model was developed in PTV VISSIM ( Micro-simulation software ) and thus the developed model was calibrated and validated using traffic volume. The relationship between field volume (actual volume) and VISSIM volume (Simulated Volume) was found with the help of R2 Value (Coefficient of Determination) which is found to be 0.9993 which indicates that variance of VISSIM output explains 99.9 percentage of variance in field data's. Pedestrian movement was not given priority and hence pedestrian simulation is not considered. Total three scenario analysis were performed in base case scenario. Among them the best scenario analysis was found to be scenario 1 which indicates the restriction of vehicles from service lanes to main lanes and allowing the movement of vehicles from Balkhu(service) to Satdobato(service) and Jawalakhel lanes, and Satdobato(service) to Balkhu(service ) and Bhaisepati lane.

Virtual robots based on multi task scheduling algorithms in visual graphic design and digital art applications

With the application of virtual robots and computer entertainment interaction, the design process of digital art has undergone new development. In the process of art design, AI image technology provides users with more interactive experiences. This article investigates the application of virtual robots based on multi task scheduling algorithms in visual graphic design and digital art. This article proposes a new approach that uses a multi task scheduling algorithm to unify graphic design and image edge detection tasks. Graphic design involves creating and editing graphic elements, and image edge detection extracts useful features by analyzing edge information in the image. Use multi task scheduling algorithms to schedule and optimize tasks, in order to allocate computing resources reasonably and maximize the overall efficiency of tasks. The results show that the multitask scheduling algorithm dynamically determines the execution order and time allocation of tasks based on their priority and resource availability. It can adjust task scheduling according to real-time situations to adapt to different computing environments and task requirements. Through comparative experiments, it has been proven that the method based on multi task scheduling algorithm achieves better results in graphic design and image edge detection tasks, and can significantly improve computational efficiency.

The Real-Time Image Sequences-Based Stress Assessment Vision System for Mental Health

Early detection and prevention of stress is crucial because stress affects our vital signs like heart rate, blood pressure, skin temperature, respiratory rate, and heart rate variability. There are different ways to determine stress using different devices, such as the electrocardiogram (ECG), electrodermal activity (EDA), the electroencephalogram (EEG), photoplethysmography (PPG), or a questionnaire-based method of stress assessment. In this study, we proposed a camera-based real-time stress detection system using remote photoplethysmography (rPPG). We trained different machine learning models using three datasets: the SWELL dataset, the PPG sensor dataset, and the last ECG and EEG-based stress dataset. The models with the highest predictive accuracy were used to classify stress based on HR and HRV features obtained from the face using a camera. HR and HRV estimations from the face were validated on the PURE public dataset and the custom dataset. In this study, it was observed that the random forest algorithm performs significantly better than other models, achieving an impressive 99% predictive accuracy in the SWELL dataset. In the second dataset, the logistic regression technique shows the best result, achieving an accuracy rate of 84.24%. In the last dataset, the ensemble model achieved an accuracy rate of 67%. We also checked the proposed algorithm in the process of public speaking to estimate stress in a real-time situation.

A novel unmanned aerial vehicle driven real-time situation awareness for fire accidents in chemical tank farms

A large number of flammable hazardous materials are stored in chemical tank farms, where fire-induced domino accidents can be easily triggered. In this study, a novel real-time fire situation awareness (FSA) approach based on UAV is proposed to capture spatio-temporal evolution characteristics and predict development trends of fire accidents. Firstly, fire images are acquired by UAV, and the key parameters of fire are extracted in real time based on YOLOv8 network. Then, the thermal radiation and impact on surrounding equipment are predicted by combining LSTM network, solid flame model and improved probit model. The proposed method is verified by small-scale tank fire experiments, which demonstrate its superiority in terms of physical consistency and prediction accuracy. The results show that the mean absolute percentage error (MAPE) of fire parameter extraction is not higher than 5.43%, the MAPE of thermal radiation prediction is not higher than 25%, and the dynamic time to failure (dttf) for the model tank at different location is predicted. This work has the potential to provide a novel solution for real-time assessment of fire size and trend prediction to support firefighting, emergency rescue and decision making in fire accident scenarios.

Investigation on the joint travel behavior in bike sharing systems during the COVID-19 pandemic: Insights from New York City

As the COVID-19 pandemic worsened, many people saw bikes as one of the safest means of transportation in the hard-hit cities. All the bike sharing utilization patterns during the pandemic are worthy of careful attention. However, there is still a lack of comprehensive understanding of niche but notable cycling behaviors, such as multi-person round trip (MPRT), defined as two or more cyclists intentionally riding together then returning bikes to the original docking station. This study extends the relevant literature by firstly proposing a MPRT identification framework based on individual bike sharing trip records, with consideration of interpersonal relationships between co-travelers, as well as the specificity of round trips against one-way trips. Taking New York City as a case study, this study examines the changes over space and time of MPRT frequencies from 2019 (i.e. pre-pandemic period) to 2020 (i.e. pandemic period), and the reasons for it. Notably, special consideration of the aforementioned analysis is paid to the influence of the real-time situation of COVID-19 in terms of cases, deaths, hospitalizations, and tests. Results reveal that (1) the MPRT frequencies obey a long tail distribution, both prior to and during the COVID-19 outbreak; (2) the group size, temporal patterns and co-traveler community are profoundly affected by the COVID-19 outbreak; (3) four indicators related to COVID-19 show different influences on co-travelers over time; (4) bike sharing availability and personal economic situation are closely related with MPRT frequencies. These findings can help develop more targeted strategies for improving the operation of a bike sharing system to meet the possible diversified demands of cyclists during the future pandemics.

Customer Feedback Analysis Using Facial Emotion Recognition

Our lives are being significantly impacted by the rapid development of wireless technology and mobile gadgets on this day. The digital economy demands that services be developed almost instantly while also paying close attention to client feedback. It becomes difficult to manage and analyze the information gathered about products from customers. Also, everyone is not intended to provide clear feedback whether the product was satisfactory or not. It is a very difficult and time- consuming task to analyze the data collected manually. To proceed with the problem and through much research we came across a solution, an emotion recognition system which can overcome this situation in real time. Keywords:CNN, Machine Learning, NaiveBayes.