Anti-jamming Wireless Sensor Network Model

A wireless sensor network (WSN) of a tree-like topology is considered, which performs measurements and transmits their results to the consumer. Under the interference influence, the WSN nodes transmitters low power makes the transmitted information vulnerable, which leads to significant data loss. To reduce the data loss during transmission, a noise-immune WSN model is proposed. Such a WSN, having detected a stable connection absence between a pair of nodes, transfers the interaction between these nodes to a radio channel free from interference influence. For this, the model, in addition to forming a network and transferring application data, provides for checking the communication availability based on the keep-alive mechanism and restoring the network with a possible channel change. A feature point of the proposed approach is the ability to restore network connectivity when exposed to interference of significant power and duration, which makes it impossible to exchange service messages on the channel selected for the interaction of nodes. To support the model, work algorithms and data structures have been developed, indicators have been formalized to assess an anti-jamming system work quality.

Peningkatan Pengetahuan KESGILUT pada Anak Berkebutuhan Khusus melalui Aplikasi Teledentistry

The parents of special needs children (SNC) problem in West Sumatra is the poor oral health of SNC, due to a lack of oral and dental health maintenance knowledge. The teledentistry application “SpecialSmile” is the solution offered. The aim is to improve oral health knowledge remotely. The method is carried out by preparing educational content in scientific articles or audiovisuals form. Program evaluation was collected by filling out a questionnaire containing 10 questions about oral health knowledge, before and after using this application. Data analysis was done descriptively. The results obtained from 49 users, before using the application only 22.95% of parents had good knowledge and after using the application there was an increase of 80.33% of parents who had good knowledge regarding oral health maintenance among SNC. This program concludes that remote education using the SpecialSmile application can increase the knowledge of ABK parents about maintaining SNC's oral health

Penerapan Metode Double Exponential Smoothing dan Moving Average pada Peramalan Permintaan Produk Gasket Cap di PT. Nesinak Industries

PT. Nesinak Industries is a company which focuses on the manufacturing process of an electronic component as well as automotive components (vehicle). In business activities, such as production, a strategy is required to survive in competition. Planning and forecasting are a strategy that can be implemented to accomplish these goals. In this study, the data used are previous sealing application data from January 2019 to March 2021. The objective of this study is to forecast product demand over the next period in order to be able to respond to customer demand. Data processing in this study utilize the Brown exponential double smoothing method and the moving average is then determined with the lowest MAPE (Mean Absolute Percentage Error) value to be used for the company’s product demand prediction calculations. The value of taken from Brown's exponential dual smoothing method is the value of with the two lowest error values from 0.1 to 0.9, whose value with the least error value is = 0.8 and = 0.9. In terms of the moving average method, the researchers tested a period of three months and a period of four months. In the MAPE calculation, the results of exponential double smoothing = 0.8 of 26.92 %, exponential double smoothing = 0.9 of 26.22 %, moving average n = 3 of 32.46%, and moving average n = 4 of 34.77%.

High-Brightness Image Enhancement Algorithm

In this paper, we introduce a tone mapping algorithm for processing high-brightness video images. This method can maximally recover the information of high-brightness areas and preserve detailed information. Along with benchmark data, real-life and practical application data were taken to test the proposed method. The experimental objects were license plates. We reconstructed the image in the RGB channel, and gamma correction was carried out. After that, local linear adjustment was completed through a tone mapping window to restore the detailed information of the high-brightness region. The experimental results showed that our algorithm could clearly restore the details of high-brightness local areas. The processed image conformed to the visual effect observed by human eyes but with higher definition. Compared with other algorithms, the proposed algorithm has advantages in terms of both subjective and objective evaluation. It can fully satisfy the needs in various practical applications.

Medical students’ psychological and behavioral responses to the COVID-19 pandemic: A descriptive phenomenological study

On February 19, 2020, the Iranian government officially confirmed the first deaths due to COVID-19 and within a week, all universities were closed. The purpose of this study is to explore Iranian medical students' psychological and behavioral responses to the COVID-19 pandemic. This descriptive phenomenological study was conducted on 52 medical students. Data were collected using a purposive sampling method by means of synchronous virtual focus group discussions which were conducted using the WhatsApp messaging application. Data were analyzed using the MAXQDA software version 2020. Data analysis resulted in the emergence of three categories consisting of psychological responses to the pandemic and the behavioral and psychological responses to the quarantine. Most of the extracted themes are related to students’ psychological reactions to the pandemic. During the quarantine period, students suffered from uncertainty, experienced boredom, worried about delay in their graduation, and were concerned about losing employment opportunities. Medical students must be prepared for crisis situations like the present pandemic. We recommend that online courses and training programs be developed with the aim of offsetting the negative effects of university closure on students’ education and skill training.

Relationship between School Culture and Work Motivation in Vocational High Schools

The research is based on the results of the author's observations showing that the teacher's motivation is not optimal. Therefore, many factors affect work motivation, one of which is organizational culture. The purpose of the study was to determine: 1) work motivation, 2) school culture and 3) the relationship between organizational culture and work motivation. The hypothesis of the research is "there is a significant relationship between school culture and work motivation". This type of research is correlational. The population consists of all teachers who are in SMK totaling 63 people and also the sample of this study. The research instrument is a Likert Scale model with five alternative answers that have been tested for validity and reliability using the SPSS application. Data analysis using the Product Moment correlation formula. The results showed that 1) school culture was in the good category with a score of 87.6%, 2) work motivation was in the very high category with a score of 90.2%, and 3) there was a significant relationship between school culture and teacher work motivation of 0.450 which belongs to the moderately correlated

The Autonomy-validity Dilemma in Mechanical Prediction Procedures: The Quest for a Compromise

A robust finding in psychological research is that combining information with a mechanical rule results in more valid predictions than combining information holistically in the mind. Nevertheless, information is typically combined holistically in practice, resulting in suboptimal predictions and decisions. Earlier research showed that decision makers are more likely to use mechanical prediction procedures when they retain autonomy in the decision-making process. However, it remains largely unknown how different autonomy-enhancing features affect predictive validity. Therefore, in two pre-registered studies (total N = 342), we investigated if and how prediction procedures can be designed such that they satisfy decision-makers’ autonomy needs and acceptance without reducing predictive validity. Based on archival application data from a university admission procedure, participants predicted applicants’ first-year GPA and chance of dropout. The results of Bayesian analyses showed that participants preferred prediction procedures in which they retained autonomy by choosing consistent predictor weights of a mechanical rule or by holistically adjusting the predictions of an optimal regression model. In general, these prediction procedures resulted in slightly higher predictive validity compared to fully holistic prediction. Providing participants with predictor validity information slightly increased predictive validity when participants could choose predictor weights, but not when making holistic predictions or adjusting optimal model predictions. Giving decision makers a role in designing mechanical rules through choosing weights based on explicit predictive validity information could help promote the implementation and validity of mechanical prediction in practice.

FDA Should Re-evaluate mRNA Vaccine Risks and Consider Previously Neglected Evidence

After examining both the substantive evidence and approval process used by FDA’s in approving the mRAN vaccines, I showed that due to routine suppression of critical discoveries by leading medical publishers and limitations in FDA’s approval process, FDA could consider only flawed and self-serving findings in context of flawed medical theories in favor of the approval. As reflected in the brief for FDA advisory committee, FDA did not consider fatal flaws in clinical trials, the symptom-based research methods, interference affects of many factors on vaccine benefits/risks, and potential long-term side effects. While off-target expression of mRNA vaccines were known evils in mRNA vaccines, FDA did not consider. It did not address three critical problems responsible for erratic expression: coating variations among mRAN molecules, differences in local hydrodynamic properties in each person, and the massive differences in influencing variables among different persons. By relying on cherry-picked, self-servicing, and deeply flawed application data and flawed contextual knowledge promoted by leading medical publishers, FDA would not see plainly predictable acute personal injuries and expected latent side effects on some people and missed obvious dangers to fetuses, babies, and people with diminished vital functional capacities. Given the fact that the vaccines are imposed on the population, defectiveness of the vaccines can cause the worst catastrophes, FDA must change its review process to overcome biases and suppression practiced by vaccines sponsors and leading medical journals. A workable process must include proactively seeking and considering any relevant information from any source and conducting expanded analysis without being constrained by flawed research models. By using an improved review process, FDA could not have found that mRNA vaccines are effective and safe. I urge FDA to re-valuate mRNA use licenses for the sake of billions of human beings.

A Combined Anomaly and Trend Detection System for Industrial Robot Gear Condition Monitoring

Conditions monitoring of industrial robot gears has the potential to increase the productivity of highly automated production systems. The huge amount of health indicators needed to monitor multiple gears of multiple robots requires an automated system for anomaly and trend detection. In this publication, such a system is presented and suitable anomaly detection and trend detection methods for the system are selected based on synthetic and real world industrial application data. A statistical test, namely the Cox-Stuart test, appears to be the most suitable approach for trend detection and the local outlier factor algorithm or the long short-term neural network performs best for anomaly detection in the application of industrial robot gear condition monitoring in the presented experiments.

Sleep and Alcohol Use Patterns During Federal Holidays in the United States.

We conducted a retrospective observational study using remote wearable and mobile application data to identify US public holidays associated with significant changes in sleep behaviors, including sleep duration, bedtime and waketime, and the consistency of sleep timing, as well as changes in the point prevalence of alcohol use. These metrics were collected and analyzed from objective, high resolution sleep-wake data and survey responses of 24,250 US subscribers to the wrist-worn biometric device platform, WHOOP (Boston, Massachusetts, USA), who were active users during May 1, 2020 through May 1, 2021. Compared to baseline, statistically significant differences in sleep and alcohol measures were found on the US public holidays and their eves. For example, New Year's Eve corresponded with a sleep consistency decrease of 13.8% (+/- 0.3), a sleep onset of 88.9 minutes (+/- 3.2) later, a sleep offset of 78.1 minutes (+/- 3.1), and more than twice as many participants reported alcohol consumption (138.0% +/- 6.7) compared to baseline. The majority of US public holidays and holiday eves were associated with sample-level increases in sleep duration, decreases in sleep consistency, later sleep onset and offset, and increases in the prevalence of alcohol consumption.