Analysis and Design of Inpatient Daily Cense Information System in Bhayangkara Bengkulu Hospital

Background: Progressing technology in the world need to fast and accurate information in the hospital agencies as the basis for appropriate making decision. The inpatient daily census reporting of system Bhayangkara Hospital Bengkulu don't have utilized the Inpatient Daily census system electronically and still uses a manual system, so that the processing of report data is less than optimal. There are still a lot of inputting errors, inaccurate data, and inefficient time and energy. This study to aim design system information inpatient daily census reporting application at the Bhayangkara hospital to existing problems solving.Methods: The method used in designing and making this application is by utilizing software development methods, namely the waterfall method which includes identification, analysis, design or design, implementation and maintenance of the system.Results: The results this study is creation of an application to facilitys the processing of data into an inpatient daily census report that is needed and to overcome the problems that arise because of the report processing system manually. Design and Creation of Inpatient Daily Census Applications with Visual Basic 6.0 Programming at Bhayangkara Bengkulu Hospital have been made with the results of an analysis of existing systems and according to the method used, and the design of the forms that have been made in accordance with the manual form or home party needs sick and can simplify filling out forms and processing the data.Conclusions: At Bhayangkara Bengkulu Hospital still uses a manual inpatient daily census system, and not on time for reporting daily cencus patient data. The data structure contained in the ledger consists of patient identity, patient diagnosis, and others. There are three processes in the stage of analyzing the needs of the inpatient daily census system, namely the data input process, data processing and data output processes. ledger, patient data consisting of patient identity, doctor's name, patient diagnosis, treatment room, and treatment class. In designing the daily inpatient census system at Bhayangkara Bengkulu Hospit consists of patient data forms, incoming patients, outgoing patients, and patients moving. The implementation of the daily inpatient census system at the Bhayangkara Bengkulu Hospital has carried out socialization and discussions about the user interface design to officers or users of the electronic daily census system. And the maintenance of the daily inpatient census system is carried out in several stages (1) corrective, by correcting design and errors in the program, (2) adaptive, by modifying the system according to user needs, (3) perfective, namely processing census data computerized.

Expression Recognition Method Using Improved VGG16 Network Model in Robot Interaction

Aiming at the problems of poor representation ability and less feature data when traditional expression recognition methods are applied to intelligent applications, an expression recognition method based on improved VGG16 network is proposed. Firstly, the VGG16 network is improved by using large convolution kernel instead of small convolution kernel and reducing some fully connected layers to reduce the complexity and parameters of the model. Then, the high-dimensional abstract feature data output by the improved VGG16 is input into the convolution neural network (CNN) for training, so as to output the expression types with high accuracy. Finally, the expression recognition method combined with the improved VGG16 and CNN model is applied to the human-computer interaction of the NAO robot. The robot makes different interactive actions according to different expressions. The experimental results based on CK + dataset show that the improved VGG16 network has strong supervised learning ability. It can extract features well for different expression types, and its overall recognition accuracy is close to 90%. Through multiple tests, the interactive results show that the robot can stably recognize emotions and make corresponding action interactions.

Formation of service information in telemetry systems with data compression

In telemetry systems, using irreversible data compression, several message generation methods can be used. In the channel output packet, there may be several code words defining its composition. They can be combined and arranged in a strictly defined sequence. Such a data packet is a constant or variable length code combination, wherein the constant length of the packet is generated in the case of a predetermined and unchanged amount of information at the data output interval, and the variable is otherwise generated. The channel data packet can then be treated as a single whole: provide it with address information about the source of the message, information about the time interval at which the packet was formed, to bind significant samples to time, additional check symbols and codes to increase interference immunity of transmission, or to form a packet structure in the same way. Address, time and synchronization information in the literature is called overhead. The need to transmit overhead information reduces the efficiency of the transceiver systems. Therefore, the problem of reducing the volume of service information is extremely urgent.

Diagnosis of Atrial Fibrillation Using Machine Learning with Wearable Devices after Cardiac Surgery (Preprint)

BACKGROUND Some attempts have been made to detect atrial fibrillation with a wearable device equipped with photoelectric volumetric pulse wave technology, and it is expected to be applied under real clinical conditions. OBJECTIVE This study is the second part of a two-phase study aimed at developing a method for immediate detection of paroxysmal atrial fibrillation (AF) using a wearable device with built-in PPG. The objective of this study is to develop an algorithm to immediately diagnose atrial fibrillation by wearing an Apple Watch equipped with a photoplethysmography (PPG) sensor on patients undergoing cardiac surgery and using machine learning of the pulse data output from the device. METHODS A total of 80 subjects who underwent cardiac surgery at a single institution between June 2020 and March 2021 were monitored for postoperative atrial fibrillation using telemetry monitored ECG and Apple Watch. Atrial fibrillation was diagnosed by qualified physicians from telemetry-monitored ECGs and 12-lead ECGs; a diagnostic algorithm was developed using machine learning on pulse rate data output from the Apple Watch. RESULTS One of the 80 patients was excluded from the analysis due to redness of the Apple Watch wearer. 27 (34.2%) of the 79 patients developed AF, and 199 events of AF, including brief AF, were observed. 18 events of AF lasting longer than 1 hour were observed, and Cross-correlation analysis (CCF) showed that pulse rate measured by Apple Watch was strongly correlated (CCF 0.6-0.8) with 8 events and very strongly correlated (CCF >0.8) with 3 events. The diagnostic accuracy by machine learning was 0.7952 (sensitivity 0.6312, specificity 0.8605 at the point closest to the top-left) for the AUC of the ROC curve. CONCLUSIONS We were able to safely monitor pulse rate in patients after cardiac surgery by wearing an Apple Watch. Although the pulse rate from the PPG sensor does not follow the heart rate of the telemetry monitoring ECG in some parts, which may reduce the accuracy of the diagnosis of atrial fibrillation by machine learning, we have shown the possibility of clinical application of early detection of atrial fibrillation using only the pulse rate collected by the PPG sensor. CLINICALTRIAL The use of wristband type continuous pulse measurement device with artificial intelligence for early detection of paroxysmal atrial fibrillation Clinical Research Protocol No. jRCTs032200032 https://jrct.niph.go.jp/latest-detail/jRCTs032200032

MG2C: a user-friendly online tool for drawing genetic maps

Genetic map is a linear arrangement of the relative positions of sites in the chromosome or genome based on the recombination frequency between genetic markers. It is the important basis for genetic analysis. Several kinds of software have been designed for genetic mapping, but all these tools require users to write or edit code, making it time-costing and difficult for researchers without programming skills to handle with. Here, MG2C, a new online tool was designed, based on PERL and SVG languages.Users can get a standard genetic map, only by providing the location of genes (or quantitative trait loci) and the length of the chromosome, without writing additional code. The operation interface of MG2C contains three sections: data input, data output and parameters. There are 33 attribute parameters in MG2C, which are further divided into 8 modules. Values of the parameters can be changed according to the users’ requirements. The information submitted by users will be transformed into the genetic map in SVG file, which can be further modified by other image processing tools.MG2C is a user-friendly and time-saving online tool for drawing genetic maps, especially for those without programming skills. The tool has been running smoothly since 2015, and updated to version 2.1. It significantly lowers the technical barriers for the users, and provides great convenience for the researchers.

Targeting building energy efficiency using thermal infrared earth observation telescopes

Upgrading the energy performance of the UK’s entire building stock is the central pillar of any credible and cost-effective strategy to meeting net zero. This research aims to open up the revenue of using thermal infrared data from satellites to assist in processes on building energy performance improvement. High-resolution thermal infrared data output from space offers the potential for fast and effective monitoring provision that can cover large areas and targeted buildings or sites. We have interviewed a set of stakeholders from government, industry and community groups to build the specific use cases and find out detailed user requirements.

Feasibility of Low-Cost Particle Sensor Types in Long-Term Indoor Air Pollution Health Studies After Repeated Calibration Over a 2-Year Timeframe

Background Previous studies have explored using calibrated low-cost particulate matter (PM) sensors, but important research gaps remain regarding long-term performance and reliability. Objective Evaluate longitudinal performance of low-cost particle sensors by measuring sensor performance changes over 2 years of use. Methods 51 low-cost particle sensors (Airbeam 1 N=29; Airbeam 2 N=22) were calibrated four times over a 2-year timeframe between 2019-2021. Cigarette smoke-specific calibration curves for Airbeam 1 and 2 PM sensors were created by directly comparing simultaneous 1-min readings of a Thermo Scientific Personal DataRAM PDR-1500 unit with a 2.5 µm inlet. Results Inter-sensor variability in calibration coefficient was high, particularly in Airbeam 1 sensors at study initiation. Calibration coefficients for both sensor types trended downwards over time to <1 at final calibration timepoint [Airbeam 1 Mean (SD)= 0.87 (0.20); Airbeam 2 Mean (SD) = 0.96 (0.27)]. We lost more Airbeam 1 sensors (N=27, failure rate 48.2%) than Airbeam 2 (N=2, failure rate 16.7%) due to electronics, battery, or data output issues. Conclusions Evidence suggests degradation over time might depend more on particle sensor type, rather than individual usage. Repeated calibrations of low-cost particle sensors may increase confidence in reported PM levels in longitudinal indoor air pollution studies.

CITYGML APPLICATION DOMAIN EXTENSION FOR 3D STRATA REPRESENTATIONS IN THE SMARTKADASTER SYSTEM: TOWARDS BEYOND CADASTRE PURPOSE IN MALAYSIA

Recent advancements in 3D city modelling and emerging trends in implementing and realising Digital Twins motivate the Department of Survey and Mapping Malaysia (JUPEM) to develop and implement SmartKADASTER (SKiP) Phase 2. SmartKADASTER Phase I was a precursor to this system, and it primarily focused on applying two-dimensional (2D) spatial data for 3D spatial analysis. CityGML was used as the data model for various Levels of Detail (LoD) in this new initiative to represent city models across the Greater Kuala Lumpur region. SmartKADASTER however, lacks strata information. Therefore, to integrate strata information into the SKiP citymodel environment, an Application Domain Extension (ADE) for CityGML has been developed to convert existing Strata XML to StrataGML, a CityGML-compliant data output format. This paper describes the purpose of the SmartKADASTER initiative in Section 1. Section 2 explains additional context for the initiative as well as some backgrounds. Section 3 discusses the conversion workflow and ADE definitions, followed by a brief discussion of visualisation in Section 4 and a project summary in Section 5.

Principles and Practices of Plant-based Irrigation Management

Many consider tools for plant-based irrigation management methods to be the most precise way to manage irrigation in either a research or a commercial settings. Although many types of tools are available, they all measure some aspect of water movement along the soil–plant–atmosphere continuum. This article presents some of the more commonly used tools and the methods involved to properly employ them. In addition, recent literature is reviewed to provide context to the methods themselves and also to highlight each one’s specific advantages and disadvantages. Ultimately, there is no clear winner or “best” tool as all have disadvantages, either due to prohibitive cost, the amount of data output, the difficulty of data interpretation, lack of signal resolution, or lack of dynamic ability to provide decision support. Therefore, we conclude that the user should carefully weigh these varied advantages and disadvantages in the context of their production goals before deciding on a given tool for irrigation management.

Pengaruh Belajar Online di Era Pandemi Covid-19 terhadap Hasil Belajar IPS pada Siswa Kelas VIII di SMP Negeri 102 Jakarta

Penelitian ini bertujuan untuk mengetahui pengaruh belajar online terhadap hasil belajar IPS siswa kelas VIII di SMP Negeri 102 Jakarta. Pendekatan yang digunakan dalam penelitian ini adalah pendekatan kuantitatif. Sedangkan jenis dari penelitian ini adalah korelasional. Sampel yang digunakan pada penelitian berjumlah 68 siswa. Hasil penelitian menunjukkan bahwa variabel bebas (belajar online) berpengaruh negatif terhadap variabel terikat (hasil belajar). Pada analisis regresi sederhana, data output SPSS adalah thitung yang dihasilkan pada variabel belajar online adalah 0,542 dengan sig 0,590. Hasil analisis sig 0,590 lebih besar dari pada 0,05 dan thitung (0,542) lebih kecil dari Ttabel (1,670). Kemudian hasil tersebut menunjukkan bahwa Ha ditolak dan Ho diterima. Artinya selama pandemi Covid-19, sistem pembelajaran online tidak memberikan pengaruh yang signifikan terhadap hasil belajar IPS pada siswa kelas VIII SMP Negeri 102 Jakarta.