Time Stamp Research Articles

Association of Primary Care Visit Length With Potentially Inappropriate Prescribing

Time is a valuable resource in primary care, and physicians and patients consistently raise concerns about inadequate time during visits. However, there is little evidence on whether shorter visits translate into lower-quality care. To investigate variations in primary care visit length and quantify the association between visit length and potentially inappropriate prescribing decisions by primary care physicians. This cross-sectional study used data from electronic health record systems in primary care offices across the US to analyze adult primary care visits occurring in calendar year 2017. Analysis was conducted from March 2022 through January 2023. Regression analyses quantified the association between patient visit characteristics and visit length (measured using time stamp data) and the association between visit length and potentially inappropriate prescribing decisions, including inappropriate antibiotic prescriptions for upper respiratory tract infections, coprescribing of opioids and benzodiazepines for painful conditions, and prescriptions that were potentially inappropriate for older adults (based on the Beers criteria). All rates were estimated using physician fixed effects and were adjusted for patient and visit characteristics. This study included 8 119 161 primary care visits by 4 360 445 patients (56.6% women) with 8091 primary care physicians; 7.7% of patients were Hispanic, 10.4% were non-Hispanic Black, 68.2% were non-Hispanic White, 5.5% were other race and ethnicity, and 8.3% had missing race and ethnicity. Longer visits were more complex (ie, more diagnoses recorded and/or more chronic conditions coded). After controlling for scheduled visit duration and measures of visit complexity, younger, publicly insured, Hispanic, and non-Hispanic Black patients had shorter visits. For each additional minute of visit length, the likelihood that a visit resulted in an inappropriate antibiotic prescription changed by -0.11 percentage points (95% CI, -0.14 to -0.09 percentage points) and the likelihood of opioid and benzodiazepine coprescribing changed by -0.01 percentage points (95% CI, -0.01 to -0.009 percentage points). Visit length had a positive association with potentially inappropriate prescribing among older adults (0.004 percentage points; 95% CI, 0.003-0.006 percentage points). In this cross-sectional study, shorter visit length was associated with a higher likelihood of inappropriate antibiotic prescribing for patients with upper respiratory tract infections and coprescribing of opioids and benzodiazepines for patients with painful conditions. These findings suggest opportunities for additional research and operational improvements to visit scheduling and quality of prescribing decisions in primary care.

Exploring relationships between physician stress, burnout, and diagnostic elements in clinician notes.

To understand the relationship between stressful work environments and patient care by assessing work conditions, burnout, and elements of the diagnostic process. Notes and transcripts of audiotaped encounters were assessed for verbal and written documentation related to psychosocial data, differential diagnosis, acknowledgement of uncertainty, and other diagnosis-relevant contextual elements using 5-point Likert scales in seven primary care physicians (PCPs) and 28 patients in urgent care settings. Encounter time spent vs time needed (time pressure) was collected from time stamps and clinician surveys. Study physicians completed surveys on stress, burnout, and work conditions using the Mini-Z survey. Physicians with high stress or burnout were less likely to record psychosocial information in transcripts and notes (psychosocial information noted in 0% of encounters in 4 high stress/burned-out physicians), whereas low stress physicians (n=3) recorded psychosocial information consistently in 67% of encounters. Burned-out physicians discussed a differential diagnosis in only 31% of encounters (low counts concentrated in two physicians) vs. in 73% of non-burned-out doctors' encounters. Burned-out and non-burned-out doctors spent comparable amounts of time with patients (about 25min). Key diagnostic elements were seen less often in encounter transcripts and notes in burned-out urgent care physicians.

Notarial practice of will declaration in civil transactions in the countries of the European Union

The aim of the article was to consider the peculiarities of the notarial practice of declaring will in civil transactions in the countries of the European Union EU, in view of the further adaptation of positive practices in Ukraine. The main methodological tools used in the research were the methods of observation and comparison. The conducted research showed that the European Regulation regulating the matter established the legal basis for the use of electronic trust services in notarial practice. The use of qualified electronic signatures and seals, electronic time stamps and authentication in this area gives confidence in a higher level of document security. The use of electronic ID and electronic trust services also simplifies time-consuming formalities in notarial practice. It was found that integrated video conferencing systems, business process workflows and electronic legal signature systems are becoming mandatory components of the digitization of notarial practice. Gaya, a European program for electronic identification, can be an example for the implementation of conditions for the current notarial practice of declaration of will in civil transactions in Ukraine.

Information dissemination dynamics through Vehicle-to-Vehicle communication built upon traffic flow dynamics over roadway networks

This research is dedicated to developing a discrete mathematical simulation framework to track information dissemination dynamics via Vehicle-to-Vehicle (V2V) communication factoring traffic flow dynamics, traffic intersection operation settings, and traffic intersection geometrical design over a road traffic network. Specifically, we develop information network flow models (INFMs), including IFNM-a and IFNM-r, respectively for tracking information wavefront spreading dynamics at arterial intersections and at highway-ramp intersections. Next, by integrating IFNMs with the information and traffic coupled cell transmission model (IT-CTM) model developed by Du et al. [16] for capturing the information front propagation dynamics on a road segment, we establish a discrete mathematical simulation framework (IFNM-CTM) to track the information front spreading dynamics over a road network at discrete time stamps. Furthermore, by combining the IFNM-CTM framework and the deep search algorithms, this study tracks the information coverage dynamics and investigates its correlation to traffic congestion evolution over a traffic network at discrete time stamps. Our experiments built upon Sioux Falls city network indicate that the IFNM-CTM is able to track the information front spreading, including location and coverage, accurately with the mean absolute error (MAE) less than 6% and 5%, respectively. More importantly, our studies found a strong correlation existing between information front spreading dynamics and traffic congestion evolution over the network. Specifically, a mild congestion condition (i.e., LOS C and D) provides the best traffic condition to sustain information spreading as compared to sparse (i.e., LOS A or B) and heavily congested (i.e., LOS E or F) traffic conditions since neither of them can sustain stable and constant wireless communication due to the limited transmission range of DSRC and interference issues.

A high sensitivity Cherenkov detector for prompt gamma timing and time imaging

We recently proposed a new approach for the real-time monitoring of particle therapy treatments with the goal of achieving high sensitivities on the particle range measurement already at limited counting statistics. This method extends the Prompt Gamma (PG) timing technique to obtain the PG vertex distribution from the exclusive measurement of particle Time-Of-Flight (TOF). It was previously shown, through Monte Carlo simulation, that an original data reconstruction algorithm (Prompt Gamma Time Imaging) allows to combine the response of multiple detectors placed around the target. The sensitivity of this technique depends on both the system time resolution and the beam intensity. At reduced intensities (Single Proton Regime—SPR), a millimetric proton range sensitivity can be achieved, provided the overall PG plus proton TOF can be measured with a 235 ps (FWHM) time resolution. At nominal beam intensities, a sensitivity of a few mm can still be obtained by increasing the number of incident protons included in the monitoring procedure. In this work we focus on the experimental feasibility of PGTI in SPR through the development of a multi-channel, Cherenkov-based PG detector with a targeted time resolution of 235 ps (FWHM): the TOF Imaging ARrAy (TIARA). Since PG emission is a rare phenomenon, TIARA design is led by the concomitant optimisation of its detection efficiency and Signal to Noise Ratio (SNR). The PG module that we developed is composed of a small PbF_{2} crystal coupled to a silicon photoMultiplier to provide the time stamp of the PG. This module is currently read in time coincidence with a diamond-based beam monitor placed upstream the target/patient to measure the proton time of arrival. TIARA will be eventually composed of 30 identical modules uniformly arranged around the target. The absence of a collimation system and the use of Cherenkov radiators are both crucial to increase the detection efficiency and the SNR, respectively. A first prototype of the TIARA block detector was tested with 63 MeV protons delivered from a cyclotron: a time resolution of 276 ps (FWHM) was obtained, resulting in a proton range sensitivity of 4 mm at 2sigma with the acquisition of only 600 PGs. A second prototype was also evaluated with 148 MeV protons delivered from a synchro-cyclotron obtaining a time resolution below 167 ps (FWHM) for the gamma detector. Moreover, using two identical PG modules, it was shown that a uniform sensitivity on the PG profiles would be achievable by combining the response of gamma detectors uniformly distributed around the target. This work provides the experimental proof-of-concept for the development of a high sensitivity detector that can be used to monitor particle therapy treatments and potentially act in real-time if the irradiation does not comply to treatment plan.

VEHICLE BLACK BOX IMPLEMENTATION FOR INTERNET OF VEHICLES BASED LONG RANGE TECHNOLOGY

The vehicle Black Box system records variations in vehicles in order to minimize and analyze accidents; or records traffic rules violations automatically and reports them to the traffic authority system. In many countries (like Iraq) traffic violation recording implemented manually causing false traffic fine recordings, even when the vehicle is not at the same location at traffic fine recording, so it is necessary to develop such systems that confirm the availability of the vehicle in that location during violation occurrence. In this paper, a Vehicle Black Box system based 433MHz long-range wireless modulation technology developed for safe driving issues by utilizing gas and flames sensors, and for false traffic violation recording issues by proofing the availability of the vehicle at traffic violation location by utilizing a GPS module. The system-based Internet of Vehicles model transmits the acquired data, where two nodes representing vehicles used with one base station form Vehicle-to-Infrastructure communication mode. The system was tested in real-time and the sensed data was transmitted and stored in a database for future analyzing purposes where the acquired GPS data was used as proof of the vehicle’s correct location with date and time stamps. Two vehicle data where captured and stored indicating their correct location with date and time stamps and stored in a database at the base station. The measured Received Signal Strength indicator and Signal-to-Noise ratio values are stored for analyzing purposes. The performance of the system in such Internet of Vehicle environment is discussed according to the readings.

Secure Revocation Features in eKYC - Privacy Protection in Central Bank Digital Currency

Central bank digital currencies require the implementation of eKYC to verify whether a trading customer is eligible online. When an organization issues an ID proof of a customer for eKYC, that proof is usually achieved in practice by a hierarchy of issuers. However, the customer wants to disclose only part of the issuer's chain and documents to the trading partner due to privacy concerns. In this research, delegatable anonymous credential (DAC) and zero-knowledge range proof (ZKRP) allow customers to arbitrarily change parts of the delegation chain and message body to range proofs expressed in inequalities. That way, customers can protect the privacy they need with their own control. Zero-knowledge proof is applied to prove the inequality between two time stamps by the time stamp server (signature presentation, public key revocation, or non-revocation) without disclosing the signature content and stamped time. It makes it possible to prove that the registration information of the national ID card is valid or invalid while keeping the user's personal information anonymous. This research aims to contribute to the realization of a sustainable financial system based on self-sovereign identity management with privacy-enhanced PKI.

Detection of Moving Objects in Earth Observation Satellite Images

Moving objects have characteristic signatures in multi-spectral images made by Earth observation satellites that use push broom scanning. While the general concept is applicable to all satellites of this type, each satellite design has its own unique imaging system and requires unique methods to analyze the characteristic signatures. We assess the feasibility of detecting moving objects and measuring their velocities in one particular archive of satellite images made by Planet Labs Corporation with their constellation of SuperDove satellites. Planet Labs data presents a particular challenge in that the images in the archive are mosaics of individual exposures and therefore do not have unique time stamps. We explain how the timing information can be restored indirectly. Our results indicate that the movement of common transportation vehicles, airplanes, cars, and boats, can be detected and measured.

SanMove: next location recommendation via self-attention network

PurposeThis paper aims to address the following issues: (1) most existing methods are based on recurrent network, which is time-consuming to train long sequences due to not allowing for full parallelism; (2) personalized preference generally are not considered reasonably; (3) existing methods rarely systematically studied how to efficiently utilize various auxiliary information (e.g. user ID and time stamp) in trajectory data and the spatiotemporal relations among nonconsecutive locations.Design/methodology/approachThe authors propose a novel self-attention network–based model named SanMove to predict the next location via capturing the long- and short-term mobility patterns of users. Specifically, SanMove uses a self-attention module to capture each user's long-term preference, which can represent her personalized location preference. Meanwhile, the authors use a spatial-temporal guided noninvasive self-attention (STNOVA) module to exploit auxiliary information in the trajectory data to learn the user's short-term preference.FindingsThe authors evaluate SanMove on two real-world datasets. The experimental results demonstrate that SanMove is not only faster than the state-of-the-art recurrent neural network (RNN) based predict model but also outperforms the baselines for next location prediction.Originality/valueThe authors propose a self-attention-based sequential model named SanMove to predict the user's trajectory, which comprised long-term and short-term preference learning modules. SanMove allows full parallel processing of trajectories to improve processing efficiency. They propose an STNOVA module to capture the sequential transitions of current trajectories. Moreover, the self-attention module is used to process historical trajectory sequences in order to capture the personalized location preference of each user. The authors conduct extensive experiments on two check-in datasets. The experimental results demonstrate that the model has a fast training speed and excellent performance compared with the existing RNN-based methods for next location prediction.

Design and performance of a 16-channel coarse-fine TDC prototype ASIC

This paper presents a prototype of a 16-channel Time-to-Digital Converter (TDC) based on the coarse-fine architecture for a small animal Position Emission Tomography (PET) system, which achieves both a large measurement range and high precision. The coarse time stamp is provided by two 12-bit counters, and the duplication of hardware is intended to avoid metastability when the hit signal edge is close to the edge of the counter clock. The fine time stamp is realized with a tunable delay line, which is adjusted by a 32-stage Delay-Locked Loop (DLL) and provides submultiples of the counter clock period. The device has been designed and fabricated in a 180 nm CMOS process. With a 200 MHz system clock, the full-scale measurement range is 20.48μs and the fine bin size is 156 ps. The test results show that the differential nonlinearity (DNL) is between −0.2 to ＋0.2 LSB, and the and integral nonlinearity (INL) is within the range of −0.5 to 0.2 LSB for all 16 channels. The one-shot time precision is better than 60 ps RMS. Power consumption of each channel is less than 8 mW.

Exploiting the Sensitivity of Dual-Frequency Smartphones and GNSS Geodetic Receivers for Jammer Localization

Smartphones now dominate the Global Navigation Satellite System (GNSS) devices capable of collecting raw data. However, they also offer valuable research opportunities in intentional jamming, which has become a serious threat to the GNSS. Smartphones have the potential to locate jammers, but their robustness and sensitivity range need to be investigated first. In this study, the response of smartphones with dual-frequency, multi-constellation reception capability, namely, a Xiaomi Mi8, a Xiaomi 11T, a Samsung Galaxy S20, and a Huawei P40, to various single- and multi-frequency jammers is investigated. The two-day jamming experiments were conducted in a remote area with minimal impact on users, using these smartphones and two Leica GS18 and two Leica GS15 geodetic receivers, which were placed statically at the side of a road and in a line, approximately 10 m apart. A vehicle with jammers installed passed them several times at a constant speed. In one scenario, a person carrying the jammer was constantly tracked using a tacheometer to determine the exact distance to the receivers for each time stamp. The aim was, first, to determine the effects of the various jammers on the smartphones’ positioning capabilities and to compare their response in terms of the speed and quality of repositioning with professional geodetic receivers. Second, a method was developed to determine the position of the interference source by varying the signal loss threshold and the recovery time on the smartphone and the decaying carrier-to-noise ratio (CNR). The results indicate that GNSS observations from smartphones have an advantage over geodetic receivers in terms of localizing jammers because they do not lose the signal near the source of the jamming, but they are characterized by sudden drops in the CNR.

A modified asynchronous sequential Kalman track fusion based on sound propagation time

This paper presents a sequential Kalman asynchronous track fusion algorithm based on the effective sound velocity method proposed to deal with the problem of random asynchrony of multi-node measurement information in the distributed underwater multi-target detection system due to the propagation effect of the sound channel. This algorithm updates the time stamp of target information reported by each sensor by using the effective sound velocity method, so as to obtain the actual time when the target state information appears in the underwater acoustic channel. Then, according to the actual asynchronous situation of the multi-sensor, it uses the sequential filter algorithm to fuse the asynchronous sensors. The simulation results show that the algorithm can improve the positioning accuracy of the original algorithm, has a strong adaptability to sensor target loss and accuracy loss, and has a certain application value.

Contextual and environmental factors that influence health: A within-subjects field experiment protocol.

Despite the growing research on environment-physical activity (PA) relationships, field experimental studies are limited. Such studies offer opportunities to focus on real-world environmental exposure and related PA and health outcomes, allowing researchers to better isolate the causal effect of exposures/interventions. Focusing on the street/pedestrian environment as a routine setting for people's daily activities, this research aims to develop and test a field experiment protocol that integrates instantaneous assessments of the environment, PA, and health outcomes. The protocol involves the use of state-of-the-art environmental monitoring and biosensing techniques and focuses on physically active road users (pedestrians and bicyclists) who are more directly exposed to their surrounding environment than others such as drivers. An interdisciplinary research team first identified the target measurement domains for the health outcomes (e.g., stress, thermal comfort, PA) and the street-level environmental exposures (e.g., land use, greenery, infrastructure conditions, air quality, weather) guided by the previous literature which was primarily observational. Portable or wearable measurement instruments (e.g., GPS, accelerometer, biosensor, mini camera, smartphone app, weather station, air quality sensor) were identified, pilot tested, and selected for the identified measures. We ensured that these measures are readily linkable using the time stamp and include eye-level exposures as they impact the users' experiences more directly yet missing in most prior studies relying on secondary, aerial-level measures. A 50-min experimental route was then determined to include typical everyday environments in park and mixed-use settings and to engage participants in three common modes of transportation (walking, bicycling, and driving). Finally, a detailed staff protocol was developed, pilot-tested, and used in a 36-participant within-subject field experiment in College Station, TX. The experiment was successfully executed, showing its potential to support future field experiments that can provide more accurate real-time, real-environment, and multi-dimensional information. Our study demonstrates the feasibility of capturing the multifold health benefits/harms related to walking and bicycling in varying urban environments by combining field experiments with environmental, behavioral, and physiological sensing. Our study protocol and reflections can be helpful for a broad spectrum of research addressing the complex and multi-level pathways between the environment, behavior, and health outcomes.

Long Day’s Journey Into Night

Long Day’s Journey Into Night

Power supply arm protection scheme of high-speed railway based on wide-area current differential

When fault occurs on cross-coupling autotransformer (AT) power supply traction network, the up-line and down-line feeder circuit breakers in the traction substation trip at the same time without selectivity, which leads to an extended power failure. Based on equivalent circuit and Kirchhoff’s current law, the feeder current characteristic in the substation, AT station and sectioning post when T–R fault, F–R fault, and T–F fault occur are analyzed and their expressions are obtained. When the traction power supply system is equipped with wide-area protection measurement and control system, the feeder protection device in each station collects the feeder currents in other two stations through the wide-area protection channel and a wide-area current differential protection scheme based on the feeder current characteristic is proposed. When a short-circuit fault occurs in the power supply arm, all the feeder protection devices in each station receive the feeder currents with time stamp in other two stations. After data synchronous processing and logic judgment, the fault line of the power supply arm can be identified and isolated quickly. The simulation result based on MATLAB/Simulink shows that the power supply arm protection scheme based on wide-area current differential has good fault discrimination ability under different fault positions, transition resistances, and fault types. The verification of measured data shows that the novel protection scheme will not be affected by the special working conditions of the electrical multiple unit (EMU), and reliability, selectivity, and rapidity of relay protection are all improved.

Research on carbon flow traceability system for distribution network based on blockchain and power flow calculation

With the proposal of the two-carbon goal, energy conservation and emission reduction will become the focus of China’s energy system in the future for a long time to come. The establishment of a complete and efficient carbon traceability system will play an important role in promoting carbon emission reduction in the power system. Based on blockchain, this paper uses the consensus mechanism, time stamp, decentralization features, smart contract and other functions of blockchain, combined with the power flow calculation and the characteristics related to carbon emission and active power of the generator set, to obtain the corresponding carbon emission intensity of the generator set and carbon flow rate. It realizes the calculation and tracing of carbon emission flow in power distribution network and ensures the reliability of carbon traceability results, high efficiency of information transmission and transparency of traceability process. Firstly, based on the characteristics of the master-slave multi-chain structure in the consortium chain, In this paper, high-voltage substation nodes are the main chain nodes, and carbon flow tracing and calculation are carried out for the associated low-voltage substations, and the information of high-voltage or low-voltage substation nodes is guaranteed to be tamper-free through the hash anchoring method. The master-slave multi-chain model adopted in this paper is that the main chain adopts EA-DPoS (Evaluation and Agent-DPoS) algorithm, the slave chain adopts improved PBFT algorithm, and the comprehensive evaluation and reward and punishment mechanism are introduced to complete the consensus. Secondly, considering the security requirements of the power system data and the fact that some nodes of the distribution network do not have powerful computing resources comparable to those of the power grid company or major nodes, this paper encrypts and decrypts relevant data in the main chain node by combining the smart contract of blockchain. Meanwhile, cloud service providers with computing resources are responsible for generator power distribution combined with power flow calculation and carbon emission intensity calculation of the generator set. The power grid company adopts the cloud computing framework based on the double check mechanism to calculate the carbon flow rate while verifying the correct calculation results of the cloud service provider, and finally realizes the safe and accurate tracing of the carbon flow of the distribution network.

Learning From Sparse Demonstrations

In this article, we develop the method of continuous Pontryagin differentiable programming (Continuous PDP), which enables a robot to learn an objective function from a few sparsely demonstrated keyframes. The keyframes, labeled with some time stamps, are the desired task-space outputs, which a robot is expected to follow sequentially. The time stamps of the keyframes can be different from the time of the robot's actual execution. The method jointly finds an objective function and a time-warping function such that the robot's resulting trajectory sequentially follows the keyframes with minimal discrepancy loss. The Continuous PDP minimizes the discrepancy loss using projected gradient descent by efficiently solving the gradient of the robot trajectory with respect to the unknown parameters. The method is first evaluated on a simulated robot arm and then applied to a 6-DoF quadrotor to learn an objective function for motion planning in unmodeled environments. The results show the efficiency of the method, its ability to handle time misalignment between keyframes and robot execution, and the generalization of objective learning into unseen motion conditions.

Abstract 11: Identifiable Patterns Of Noninvasive Cerebral Oximetry During Endovascular Therapy For Acute Ischemic Stroke Are Associated With Neurological Deterioration

Background: Cerebral near-infrared spectroscopy (NIRS) is a useful tool to monitor real-time cerebral oxygenation levels. However, the mechanisms through which varying oxygenation levels during endovascular thrombectomy (EVT) affect functional outcomes remain to be determined. In this study, we categorized NIRS trends into descriptive “fingerprints” and examined the relationship between these fingerprints and neurological worsening. Methods: We enrolled patients that presented to Yale New Haven Hospital with large vessel occlusion acute ischemic stroke and underwent EVT. NIRS was implemented into the standard operating procedure. Time stamps of defined events (medication administration, recanalization, etc.) were synchronized with corresponding NIRS values. We inspected NIRS curves from arrival to angio-suite to time of recanalization. Neurological deterioration was defined as an increase of 4 points or more on the National Institutes of Health Stroke Scale (NIHSS). Results: Forty-eight patients (mean age 72 ± 13, mean NIHSS 14) were analyzed. Five “fingerprints” were observed in the affected hemisphere: sustained decreases, downward rSO2 peaks, no change, upward rSO2 peaks, and sustained increases (Fig. 1), which were assigned nominal values of -2, -1, 0, 1, and 2, respectively. After adjusting for age and admission NIHSS, sustained decreases and downward rSO2 peaks were independently associated with neurological deterioration (P = 0.0076, Fig. 2). Conclusions: Identifiable NIRS “fingerprints” of downward rSO2 peaks and sustained decreases in the affected hemisphere during EVT are associated with neurological deterioration. Further distillation of identifiable intraprocedural NIRS trends in real time could provide guidance for anesthesia and hemodynamic management during EVT to optimize patient outcomes after stroke.

Reaching Submillisecond Accuracy in Stellar Occultations and Artificial Satellite Tracking

In recent years, there appeared a need for astronomical observations timed with submillisecond accuracy. These include, e.g., timing stellar occultations by small, subkilometer, or fast near-Earth asteroids and tracking artificial satellites in low-Earth orbit using optical sensors. Precise astrometry of fast-moving satellites and accurate timing of stellar occultations have parallel needs, requiring a reliable time source and good knowledge of camera delays. Thus, there is a need for an external device that would enable equipment and camera testing to check if they reach the required accuracy in time. We designed, constructed, and thoroughly tested a New EXposure Timing Analyser (NEXTA), a Global Navigation Satellite System–based precise timer allowing us to reach an accuracy of 0.1 ms, which is an order of magnitude better than in previously available tools. The device is a simple strip of blinking diodes to be imaged with a camera and compare the imaged time with the internal camera time stamp. Our tests spanned a range of scientific cameras widely used for stellar occultations and ground-based satellite tracking. The results revealed high reliability of both NEXTA and most of the tested cameras but also pointed out that practically all cameras had internal time biases of various levels. NEXTA can serve the community, being easily reproducible with inexpensive components. We provide all the necessary schemes and usage instructions.

Timestamp Feature Variation based Weather Prediction Using Multi-Perception Neural Classification for Successive Crop Recommendation in Big Data Analysis

The recent generation has a lot of information for analysing growth in future prediction. Especially India is an extensive agricultural resource for the world's expansive economic growth. But in extensive data analysis, a problem for the recommendation of the seasonal crop is tedious because of improper feature analysis due to varying periods in weather conditions. So time variation-based big data analysis is essential for research improvement. To resolve this problem, we propose a Timestamp feature variation-based weather prediction using multi-perception neural classification (TFV-MPNC) for successive crop recommendation in big data analysis. Initially, the pre-processing was carried out to prepare the redundant noise dataset for fast prediction. Initially, the Preprocessing ensures the Contemporary Forecasting rate (CFR) for predicting the previous deficiency rate. Based on that Time stamp feature analysis (TSFA). The Dense region harvest rate (DRHR) was evaluated, and features were decision using Fuzzy intensive decision Function (FIDF), selected the scaled features and trained with multi-perception neural classification (MPNN). The proposed system produces higher forecasting by prediction features as well supportive to the weather dependences related to higher classification rate in precision, and recall has the best classification result.