Periodic Data Collection Research Articles

Prescribing pattern of antibiotics in ENT outpatient department in a tertiary care teaching hospital

Background: Antibiotics are the most commonly used and misused of all drugs. Patterns of antibiotic resistance widely follows local patterns of antibiotic prescribing and usage. Periodic data collection and analysis of antibiotic prescriptions at regional level are essential to understand and combat antibiotic resistance. Objective of the study was to study the prescribing pattern of antibiotics in outpatient department of Otorhinolaryngology in a tertiary care teaching hospital. Methods: A prospective observational study of prescribing pattern of antibiotics was conducted in the department of Otorhinolaryngology. Data of antibiotic utilization in outpatient department was collected through duplicate copies of the prescriptions. The data obtained was analyzed and the conclusions were drawn using descriptive analysis.Results: A total of 606 prescriptions were collected and analyzed during the study. Average number of antibiotics prescribed per prescription was 1.24 most of the antibiotics were prescribed for a duration of 1 to 5 days. Commonly prescribed antibiotic class was penicillin (48.1%) followed by quinolone (18.3%). Most of the antibiotics were prescribed by their brand names (98.8%).Conclusions: The present study highlights the wide use of extended spectrum antibiotics, along with brand names for prescribing. Interventions for prescribing of generic drugs from the essential drug list needs to be encouraged among physicians.

Tectona grandis canopy cover predicted by remote sensing

The phytosanitary status of Tectona grandis plantations are monitored conventionally with periodic data collection in the field, which is often costly and has low efficiency. The objective of this research was to develop a methodology to predict the canopy cover of T. grandis plantations using multispectral images of the Sentinel-2 (S2) satellite and photographic imagery. The study was carried out in a T. grandis plantation of seminal origin, in Caceres, Mato Grosso state, Brazil. Hemispherical photographic (HP) images of the plant canopy were obtained with a digital camera coupled to a “fisheye” lens fixed at 1.3 m high at two dates in the rainy and the dry season. Cloudless and no shadow images of the S2 satellite bands were concurrently obtained with the field images. Multivariate permutative analysis of variance (PERMANOVA) and partial least squares regression (PLSR) were used to predict canopy cover percentage. The accuracy of the predicted T. grandis canopy cover (%) by the PLSR model approach was 77.8 ± 0.09%. The results indicate that a PLS model calibrated with 28 HP sample images can accurately estimate the percentage canopy cover for a continuous area of T. grandis plantations and facilitate mapping of canopy heterogeneity to monitor threats of diseases, mortality, fires, pests and other disturbances.

Energy-efficient chain-based data gathering applied to communicating concrete

Wireless Sensor Networks are very convenient to monitor structures or even materials, as in McBIM project (Materials communicating with the Building Information Modeling). This project aims to develop the concept of “communicating concretes,” which are concrete elements embedding wireless sensor networks, for applications dedicated to Structure Health Monitoring in the construction industry. Due to applicative constraints, the topology of the wireless sensor network follows a chain-based structure. Node batteries cannot be replaced or easily recharged, it is crucial to evaluate the energy consumed by each node during the monitoring process. This area has been extensively studied leading to different energy models to evaluate energy consumption for chain-based structures. However, no simple, practical, and analytical network energy models have yet been proposed. Energy evaluation models of periodic data collection for chain-based structures are proposed. These models are compared and evaluated with an Arduino XBee–based platform. Experimental results show the mean prediction error of our models is 5%. Realizing aggregation at nodes significantly reduces energy consumption and avoids hot-spot problem with homogeneous consumptions along the chain. Models give an approximate lifetime of the wireless sensor network and communicating concretes services. They can also be used online by nodes for a self-assessment of their energy consumptions.

On the trade-off between timeliness and accuracy for low voltage distribution system grid monitoring utilizing smart meter data

Due to limited bandwidth and high delays in access to Smart Meter measurements, it is not possible in most cases to access measurements from the complete set of smart meters in a low-voltage grid area for distribution grid monitoring. Distribution system state estimation can be performed based on measurements of voltage and active and reactive power from a subset of selected smart meters. Increasing the number of selected smart meters will, on the one hand, increase the accuracy of distribution system state estimation, while on the other hand, it will degrade timeliness of the monitoring data. This paper proposes to utilize part of the idle time of the legacy periodic smart meter data collection for access to measurements from the subset of selected smart meters for distribution system state estimation. It subsequently proposes a methodology on how to quantitatively analyze this trade-off. The methodology is applied to an example LV grid area with 20 customers using a weighted least square state estimation with support of pseudo-measurements obtained during the regular smart meter collection cycle.

Oral pediatric pathologies: Incidence and demography – An institutional study in Delhi, India

Objective: Development and growth is at its most dynamic phase before adolescence. The increased awareness of early diagnosis having a better prognosis has led to the identification of many oral pathologies in a pediatric population. While many profiles of oral biopsies from children are available, the role of regional and geographic variations could be ascertained through periodic evaluation and data collection. The main aim of this retrospective study was to assess the distribution, frequency, and type of pediatric cases that are seen in a dental setting catering to predominantly lower socioeconomic strata of population in a region of Delhi, India. Materials and Methods: Archives of biopsies submitted to the department of oral and maxillofacial pathology were taken from the year 2012 to 2018 and all cases under the age of 13 or below were included in the study. A total of 851 archived cases were retrieved, of which 60 fulfilled our criteria for case selection. The available data were categorized into seven groups according to (1) age (0–4, 5–8, and 9–13 years), (2) sex, (3) site (area affected and intra-/extraosseous), (4) inflammatory/reactive, (5) cystic (odontogenic {inflammatory/developmental}/ non-odontogenic), (6) neoplastic ([a] odontogenic/non-odontogenic and [b] benign/malignant), and (7) others (infections). Results: The analysis showed that most of the lesions were within the 9–13 years age group (61.66%) with male gender predominance, M:F ratio being 1.6:1. The lesions were mostly extraosseous (n = 34) with mandible being commonly afflicted (36.6%). Among the pathologic cases, the lesions were mostly non-odontogenic with the mucocele appearing as the most common reactive lesion. The incidence of radicular cyst (n = 5) was found to be higher among the odontogenic cystic lesions (n = 12). One (rhabdomyosarcoma) out of 10 neoplastic lesions was malignant Benign:Malignant ratio (9:1). While ameloblastoma was seen as the common benign odontogenic tumor, the ossifying fibroma was predominant among the non-odontogenic group. Tuberculosis followed by osteomyelitis was seen to be prevalent under the category of infections. Conclusion: This study helps us to observe the common lesions or conditions afflicting children in this part of India and their association with age, sex, and site. It was found that a higher incidence of reactive lesion is present in this age group, while the neoplastic lesions are predominantly benign similar to other studies.

A Periodic Multinode Charging and Data Collection Scheme With Optimal Traveling Path in WRSNs

To deal with the problem of energy shortage in wireless sensor networks, wireless power transfer is a promising technology. Using a mobile device (MD) to charge multiple sensor nodes simultaneously is a more efficient way to meet the requirements from a large-scale network. In this article, a periodic multinode charging and data collection scheme based on MD is proposed to maintain a perpetual network operation, in which the network is divided into multiple cells, and the MD periodically traverses each cell to charge and collect data for the sensor nodes with the objective of maximizing the amount of data collected by the unit energy of the MD. To realize a multinode charging, an anchor optimization strategy to optimize the position of anchor point in the cell is proposed. Then, in order to solve the path planning problem of MD, a discrete fireworks algorithm (DFWA) based on population entropy (PE-FWA) is proposed. Compared with MD scheduling algorithm and DFWA, experimental results show that PE-FWA has better performance.

Advertisement Tax Policy in Malang Regency(Study on the Implementation of Regional Regulation of Malang Regency)

This research is entitled Advertising Tax in Malang Regency (Study on the of Malang Regency Regulation Number 1 of 2019 concerning Amendments to the Regional Regulation of Malang Regency Number 8 of 2010 concerning Regional Taxes [1]) which is the result of interview and literature research to answer questions: How to implement the policy implementation in the advertisement tax service. There are two problems that will be examined in this research, namely: 1. What is the form of the advertisement tax service implementation (tax potential data collection, supervision and collection strategies)? 2. What are the driving factors and constraints both internally and externally in policy implementation. To answer this problem, this researcher uses qualitative research methods. The study used several data collection techniques, namely observation, interviews and documentation. So the researcher will go directly to the field and conduct research by means of observation and in-depth interviews with several related informants. The data obtained were then presented descriptively and analyzed using descriptive analytic techniques. From the results of this study, the aim of this research is to provide an understanding of the community regarding Implementation of the advertisement tax service policy in the Regional Regulation of Malang Regency Number 1 of 2019 in Malang Regency. Based on the existing data, the author tries to describe / describe systematically. 1. Potential data collection is realized with npwpd services and periodic field data collection. Supervision is carried out to provide control and inspection. The collection strategy has been running according to good policy and governance. 2. The factors driving and constraining the implementation of policies, the driving factors are the improvement of facilities, the competence of officers and the support of regional banks. While the obstacle factors are the number of types of taxes, the area of ​​Malang Regency and the low level of compliance.

Energy consumption optimization for self‐powered IoT networks with non‐orthogonal multiple access

SummaryEnergy harvesting (EH) has been considered as one of the promising technologies to power Internet of Things (IoT) devices in self‐powered IoT networks. By adopting a typical harvest‐then‐transmit mode, IoT devices with the EH technology first harvest energy by using wireless power transfer (WPT) and then carry out wireless information transmission (WIT), which leads to the coordination between WPT and WIT. In this paper, we consider optimizing energy consumption of periodical data collection in a self‐powered IoT network with non‐orthogonal multiple access (NOMA). Particularly, we take into account time allocation for the WPT and WIT stages, node deployment, and constraints for data transmission. Moreover, to thoroughly explore the impact of different multiple access methods, we theoretically analyse and compare the performance achieved by employing NOMA, frequency division multiple access (FDMA), and time division multiple access (TDMA) in the considered IoT network. To validate the performance of the proposed method, we conduct extensive simulations and show that the NOMA outperforms the FDMA and TDMA in terms of energy consumption and transmission power.

Multi-Temporal Agricultural Land-Cover Mapping Using Single-Year and Multi-Year Models Based on Landsat Imagery and IACS Data

The spatial distribution and location of crops are necessary information for agricultural planning. The free availability of optical satellites such as Landsat offers an opportunity to obtain this key information. Crop type mapping using satellite data is challenged by its reliance on ground truth data. The Integrated Administration and Control System (IACS) data, submitted by farmers in Europe for subsidy payments, provide a solution to the issue of periodic field data collection. The present study tested the performance of the IACS data in the development of a generalized predictive crop type model, which is independent of the calibration year. Using the IACS polygons as objects, the mean spectral information based on four different vegetation indices and six Landsat bands were extracted for each crop type and used as predictors in a random forest model. Two modelling methods called single-year (SY) and multiple-year (MY) calibration were tested to find out their performance in the prediction of grassland, maize, summer, and winter crops. The independent validation of SY and MY resulted in a mean overall accuracy of 71.5% and 77.3%, respectively. The field-based approach of calibration used in this study dealt with the ‘salt and pepper’ effects of the pixel-based approach.

Challenges, Designs, and Performances of a Distributed Algorithm for Minimum-Latency of Data-Aggregation in Multi-Channel WSNs

In wireless sensor networks (WSNs), the sensed data by sensors need to be gathered, so that one very important application is periodical data collection. There is much effort which aimed at the data collection scheduling algorithm development to minimize the latency. Most of previous works investigating the minimum latency of data collection issue have an ideal assumption that the network is a centralized system , in which the entire network is completely synchronized with full knowledge of components. In addition, most of existing works often assume that any (or no) data in the network are allowed to be aggregated into one packet and the network models are often treated as tree structures. However, in practical, WSNs are more likely to be distributed systems , since each sensor’s knowledge is disjointed to each other, and a fixed number of data are allowed to be aggregated into one packet. This is a formidable motivation for us to investigate the problem of minimum latency for the data aggregation without data collision in the distributed WSNs when the sensors are considered to be assigned the channels and the data are compressed with a flexible aggregation ratio, termed the minimum-latency collision-avoidance multiple-data-aggregation scheduling with multi-channel (MLCAMDAS-MC) problem. A new distributed algorithm, termed the distributed collision-avoidance scheduling (DCAS) algorithm, is proposed to address the MLCAMDAS-MC. Finally, we provide the theoretical analyses of DCAS and conduct extensive simulations to demonstrate the performance of DCAS.

A Polling-Based Transmission Scheme Using a Network Traffic Uniformity Metric for Industrial IoT Applications.

The Industrial Internet of Things (IIoT) applications are required to provide precise measurement functions as feedback for controlling devices. The applications traditionally use polling-based communication protocols. However, in polling-based communication over current industrial wireless network protocols such as ISA100.11a, WirelessHART have difficulty in realizing both scheduled periodic data collection at high success ratio and unpredictable on-demand communications with short latency. In this paper, a polling-based transmission scheme using a network traffic uniformity metric is proposed for IIoT applications. In the proposed scheme, a center node controls the transmission timing of all polling-based communication in accordance with a schedule that is determined by a Genetic Algorithm. Communication of both periodic and unpredictable on-demand data collection are uniformly assigned to solve the above difficulties in the schedule. Simulation results show that network traffic is generated uniformly and a center node can collect periodic data from nodes at high success ratio. The average success probability of periodical data collection is and the lowest probability is .

Network Under Limited Mobile Devices: A New Technique for Mobile Charging Scheduling With Multiple Sinks

Recently, many studies have investigated scheduling mobile devices to recharge and collect data from sensors in wireless rechargeable sensor networks (WRSNs) such that the network lifetime is prolonged. In reality, because mobile devices are more powerful and expensive than sensors, the cost of the mobile devices often consumes a high portion of the budget. Due to a limited budget, the number of mobile devices is often limited. Some sensors in WRSNs may not be operated without time limits due to the limited number of mobile devices. Therefore, sensors in a sensing field must be weighted by their importance, that is, the more important an area covered by a sensor, the higher the weight of the sensor. Therefore, in this paper, the problem of scheduling limited mobile devices for energy replenishment and data collection in WRSNs with multiple sinks such that the total weight of the recharged sensors that can be operated without time limits is maximized, termed the periodic energy replenishment and data collection with limited mobile devices (PERDCLMD) problem, is studied. In addition, the greedy scheduling algorithm (GSA) is proposed for the PERDCLMD problem accordingly. Simulation results show that the GSA provides a good performance in terms of the total weight of the recharged sensors.

Faculty Equity, Diversity, Culture and Climate Change in Academic Medicine: A Longitudinal Study

There is a national call for academic medicine to use evidence-based initiatives to improve its culture and climate. The authors report data-driven policy and programmatic interventions that were associated with increased faculty diversity, equity, respectful behavior and improved faculty climate, at UC San Diego Health Sciences. MethodsBased on demographic and survey data, interventions were designed to improve the climate between 2005 and 2015. Interventions included routine measuring and dissemination of demographic data, changes and dissemination of policy and procedures, and new and improved faculty development programming. Impact was measured using demographic data over time, salary equity studies, and school-wide climate surveys in 2005, 2011, and 2015. Specific outcomes included measures of diversity, salary equity, behavior, and climate. ResultsOver the ten-year period, the proportion of women increased from 16% to 23% of tenure/tenure-track faculty and 31%–40% of all faculty. Underrepresented minority faculty increased from less than 1%–7% of tenure/tenure-track faculty and from 5% to 8% of all faculty. While women continued to be paid less than men, the adjusted difference dropped from 23% to 12%. Reports of inappropriate behavior by faculty decreased significantly, while satisfaction and knowledge about institutional mentoring and resources improved. ConclusionMultiple interventions including new faculty development programs, changes in policy, and measuring demographics/climate supported diverse faculty recruitment, enhanced a culture of respect and improved faculty morale. Cultural changes in policy, periodic faculty data collection with dissemination, and increased faculty development, improve the climate in academic medicine.

Duplicate suppression for efficient floating car data collection in heterogeneous LTE-DSRC vehicular networks

Collecting data from a large number of agents scattered over a region of interest is becoming an increasingly appealing paradigm to feed big data archives that lay the ground for a vast array of applications. Vehicular Floating Car Data (FCD) collection is a major representative of this paradigm. Massive data collection from floating vehicles is the key to Intelligent Transportation Systems. We address the design and performance evaluation of a data collection protocol for the use case of periodic data collection. We target robustness, optimizing the amount of data and the value of the collection period, keeping in mind the goals of autonomous node operation and minimal coordination effort. From a system point of view, we believe that best solutions should jointly exploit the Long Term Evolution (LTE) cellular access network and the Dedicated Short-Range Communication (DSRC) based Vehicular Ad Hoc Network (VANET). Through a detailed comparative analysis, we show that such a hybrid approach offers superior performance, especially as for offloading the cellular radio access. A lightweight signaling procedure is designed, based on the DSRC VANET, which is able to avoid most of the duplicated data records, even if a distributed operation approach is pursued. The impact of the proposed protocol on the VANET load is evaluated and proved to be quite small, so that it does not interfere with other VANET-specific messages.

Traffic Aware Data Collection MAC Protocol for Wireless Sensor Networks

In this paper, we propose a new medium access control (MAC) protocol for wireless sensor networks for periodic data collection. The proposed MAC scheme is specifically designed for wireless sensor networks which have periodic traffic with different sampling rates. In our protocol design, sink determines the time schedule for all other nodes in the network. We discuss the design of traffic aware MAC protocol and provide a comparison with the similar MAC protocols through simulation. Under different traffic generation rate, our protocol outperforms the previous one in terms of energy consumption and packet delay. Simulation results show that our protocol demonstrates up to 35% better performance than that of most recent protocol which is proposed for this kind of application, in respect of energy consumption. Comparative analysis and simulation results show that our design considerably gives a good compromise between energy efficiency and packet latency.

Adaptive Data Collection protocol for Extending Lifetime of Periodic Sensor Networks

In Periodic Sensor Network (PSN), the periodic energy efficient collection of a large amount of sensed data by the sensor nodes is considered as one of the main challenges in this type of network. An adaptive sampling approach to periodic data collection is needed for energy optimization due to the limited lifetime nature of the sensor batteries. In this paper, we propose a protocol, called an Adaptive Data Collection protocol (ADaC), which collects periodically sensor readings and prolong the lifetime of a Periodic Sensor Network (PSN). The lifetime of ADaC protocol is divided into cycles. Each cycle is composed of two stages.First, data collection. Second, sampling rate adaptation based on the similarity between periods of one cycle using Euclidean distance measure to adapt its rate of sampling according to the dynamic modification of the monitored environment. ADaC allows each sensor to adapt its sampling rate in accordance with the monitored environment conditions. We conduct extensive simulation experiments on real sensor data by applying OMNeT++ network simulator to explain the effectiveness of the ADaC protocol in comparison with a method without using adaptive sampling.

SMAC: A Soft MAC to Reduce Control Overhead and Latency in CDMA-Based AMI Networks

The use of state-of-the-art 3G cellular CDMA technologies in a utility owned AMI network results in a large amount of control traffic relative to data traffic, increases the average packet delay and hence are not an appropriate choice for smart grid distribution applications. Like the CDG, we consider a utility owned cellular like CDMA network for smart grid distribution applications and classify the distribution smart grid data as scheduled data and random data. Also, we propose SMAC protocol, which changes its mode of operation based on the type of the data being collected to reduce the data collection latency and control overhead when compared to 3G cellular CDMA2000 MAC. The reduction in the data collection latency and control overhead aids in increasing the number of smart meters served by a base station within the periodic data collection interval, which further reduces the number of base stations needed by a utility or reduces the bandwidth needed to collect data from all the smart meters. The reduction in the number of base stations and/or the reduction in the data transmission bandwidth reduces the CAPital EXpenditure (CAPEX) and OPerational EXpenditure (OPEX) of the AMI network. The proposed SMAC protocol is analyzed using markov chain, analytical expressions for average throughput and average packet delay are derived, and simulation results are also provided to verify the analysis.

Decreasing Resource Utilization Using Standardized Clinical Assessment and Management Plans (SCAMPs).

Standardized clinical assessment and management plans (SCAMPs) are a novel quality improvement initiative shown to improve patient care, diminish practice variation, and reduce resource utilization. Unlike clinical practice guidelines, a SCAMP is a flexible algorithm that undergoes iterative updates based on periodic data collection and review. We recently implemented a SCAMP for the closed treatment of pediatric torus fractures. The purpose of this study is to analyze the effect of SCAMP implementation on resource utilization, practice variability, cost of care, and outcomes. This study was a retrospective review of prospectively collected data on 273 patients with pediatric torus fractures. The pre-SCAMP cohort included 116 subjects from 2008 to 2010. The SCAMP cohort included 157 subjects from 2011 to 2013. The pre-SCAMP cohort was treated according to the judgment of attending fellowship-trained pediatric orthopaedic surgeons. The SCAMP cohort was treated with a standardized algorithm including radiographs and splint application at initial presentation, with a single follow-up at 3 weeks. Patient demographics were analyzed to verify comparability between cohorts. Follow-up data including clinic visits, x-rays and practice variability was recorded. Costing analysis was conducted using time-derived activity-based costing methodology. Outcomes were compared using Poisson regression analysis. Incident rate ratios (IRR) with 95% confidence limits were estimated. No differences in clinical results were observed between the pre-SCAMP and SCAMP cohorts, and all patients demonstrated return to baseline activity at final follow-up. Patient demographics were comparable across cohorts. The SCAMP cohort had a 48% reduction in clinic visits [IRR, 0.52; 95% confidence interval (CI), 0.44-0.60; P<0.001], 60% reduction in x-rays (IRR, 0.40; CI, 0.33-0.47; P<0.001), and a 23% reduction in x-rays per clinic visit (IRR, 0.77; 95% CI, 0.65-0.91; P<0.001). Furthermore, SCAMP implementation resulted in a 49% reduction in the overall cost of care. SCAMPs provide a novel alternative to CPGs to implement cost effective changes in Orthopaedic practice. For pediatric torus fractures, SCAMP implementation resulted in decreased practice variability, resource utilization, and overall cost of care while maintaining clinical outcomes. Level 3.

Adaptive data collection approach based on sets similarity function for saving energy in periodic sensor networks

Disaster monitoring becomes a requirement for collecting and analysing data in order to offer a better disaster management situation. Periodic sensor networks PSNs are usually used in disaster monitoring and are characterised by the acquisition of sensor data from remote sensor nodes before being forwarded to the sink in a periodic basis. The major challenges in PSN are energy saving and collected data reduction in order to increase the sensor network lifetime and to ensure a long-time monitoring for disasters. In this paper, we propose an adaptive sampling approach for energy-efficient periodic data collection in sensor networks. Our proposed approach provides each sensor node the ability to identify redundancy between collected data over time, by using similarity functions, and allowing for sampling adaptive rate. Experiments on real sensors data show that our approach can be effectively used to conserve energy in the sensor network and to increase its lifetime, while still keeping a high quality of the collected data.

Adaptive data aggregation with probabilistic routing in wireless sensor networks

Periodical extraction of raw sensor readings is one of the most representative and comprehensive applications in Wireless sensor networks. In order to reduce the data redundancy and the communication load, in-network data aggregation is usually applied to merge the packets during the routing process. Aggregation protocols with deterministic routing pre-construct the stationary structure to perform data aggregation. However, the overhead of construction and maintenance always outweighs the benefits of data aggregation under dynamic scenarios. This paper proposes an Adaptive Data Aggregation protocol with Probabilistic Routing for the periodical data collection events. The main idea is to encourage the nodes to use an optimal routing structure for data aggregation with certain probability. The optimal routing structure is defined as a Multi-Objective Steiner Tree, which can be explored and exploited by the routing scheme based on the Ant Colony Optimization and Genetic Algorithm hybrid approach. The probabilistic routing decision ensures the adaptability for some topology transformations. Moreover, by using the prediction model based on the sliding window for future arriving packets, the adaptive timing policy can reduce the transmission delay and can enhance the aggregation probability. Therefore, the packet transmission converges from both spatial and temporal aspects for the data aggregation. Finally, the theoretical analysis and the simulation results validate the feasibility and the high efficiency of the novel protocol when compared with other existing approaches.