A Review on the Stingless Beehive Conditions and Parameters Monitoring using IoT and Machine Learning

One of the stingless bee types named Heterotrigona Itama are widespread in the tropics and subtropics especially in Malaysia. Due to its excellent nutritional content, stingless bee honey has gained favour in recent years. According to some studies, stingless bee honey has been used to cure eye infections, open wounds, diabetes, hypertension, and a variety of other diseases. Additionally, this stingless bee is non-venomous and smaller in size than common bees. Nevertheless, beekeepers may encounter a number of obstacles that may result in colony failure and under-production. These problems can be attributed to a variety of factors such as surrounding temperature, surrounding humidity and predators. Numerous stingless bee colonies and other bee species lost in 2006 due to Colony Collapse Disorder as a result of this problem. Therefore, this article will review previous research on optimizing stingless beehive conditions via the use of the Internet of Things (IoT) and machine learning to minimise this issue. To begin, a review of existing research on the characteristics of stingless bees, particularly the Heterotrigona Itama species, has been conducted to understand the natural habitat of Heterotrigona Itama. Following that, the articles on colony division was reviewed in order to transition the colony from the conventional hive to the artificial hive which also reviewed its design from the past article to simplify the sensors installation, IoT monitoring system and honey harvesting. Then, the prior article on sensors and IoT deployment was examined to monitor and analysis the data online without disturbing the colony activity inside the beehives. Finally, the article on the application of machine learning with the beehive dataset was reviewed the most precise and accurate machine learning method to predict the existence of bee activity in the hives and the future condition of beehive.

Anomalous Cognition in the Context of Time: Does the Viewer Describe a Probabilistic Future?

In this process-oriented study, we examined the influence of the time dimension on Psi effects in two experimental conditions (present vs. future). For data collection, selected viewers with experience in the remote viewing method gathered information about targets that were distant in space (the present) and time (the future). The present condition was composed of binary truth statements consisting of two possible options related to current world knowledge. The future condition consisted of two options that were not yet determined at the time of viewing, but depended on the outcome of future mixed martial arts fights. According to the associative remote viewing (ARV) method, the binary outcomes of the present and future options were each associated with a photo, which had to be described by the viewers. An independent judge analyzed the viewers’ qualitative reports through binary correspondence ratings amounting to a hit (1) or no hit (0) per trial. Independently of the time condition, a Psi effect could be observed. The hit rates of the judge (0.88 and 0.62 for the present and future, respectively) were significantly higher than the expected value (0.5) under the null hypothesis (present: p < 0.001, ESP = 0.73; future: p = 0.027, ESF = 0.22; binomial distribution). In addition, the hit rates in the two time conditions differed significantly from each other (χ2 = 9.01; df = 1, p < 0.003). The results confirm the hypothesis that Psi is not completely independent of the time dimension and that the hit rate is influenced by a priori target probabilities. With regard to the Informational Psi (IΨ) theory, we will discuss the implications of a probabilistic future for the understanding of Psi effects.

Early Detection of COVID-19 Patient’s Survavibility Based On The Image Of Lung X-Ray Image Using Deep Neural Networks

SARS-CoV-2 causes an infection called COVID-19, which is caused by a new coronavirus. One of the symptomps that dangerous to the patients is developing pneumonia in their lungs. To detect pneumonia symptoms, one of the newest methods is using CNN (Convolution Neural Networks). The problem is when able to detect pneumonia, the patient's survivability, which knowing this will be helpful to decide the priority for each patient, is still in question. The CNN used in this research to classify the patient’s future condition, but met some major problems that the dataset is very few and unbalance. The image augmentation was used to multiply the dataset, and class weight was applied to prevent miscalculation on minority class. 6 CNN architectures used to find the best model. The result VGG19 architecture has the best overall accuracy, in training, it has 80% accuracy, 89% accuracy invalidation, and 82% f1 score accuracy on classifying the testing dataset means the best model if looking for accuracy on prediction, but this cost a prediction time that longest compared to other CNN architectures. MobileNet is the fastest, but it cost much worse on prediction accuracy, only 55%. The ResNet50 model has balanced prediction accuracy/time, it got 77% f1 accuracy, and also 8.49 seconds of prediction time, 9 seconds less than VGG19.

Intelligent Condition Monitoring Techniques for Early Fault Diagnosis of Rotating Machines – A Review

Condition-based maintenance is always an important strategy of maintenance to prolong the effective life of rotating machines as they run on high speeds with a variety of loads in some cases under severe conditions. If the monitoring of the current condition is not done accurately then rotating machines such as turbines, engine, bearing, shafts, gearbox, motors, and compressors leads to catastrophic failures with some serious consequences on the rate of production, safety, loss of manpower and sudden increase in repairing cost. Condition-based maintenance is also be called predictive type maintenance is a far superior technique as compared to preventive maintenance and run-to-break maintenance. In predictive maintenance current status of the machine while in operation, being monitored carefully and based on a brief analysis of the future condition of the machine predicted. This paper going to review the various intelligent condition monitoring techniques developed or used by various researchers to monitor the health of rotating machines and able to predict the faults at the earliest time.

THE FUTURE OF THE LANGUAGES OF THE FINNO-UGRAIAN PEOPLES OF THE RUSSIAN FEDERATION

This article attempts to predict the future condition and development of the languages of the Finno-Ugric peoples. The problem of language forecasting is not a priority area of linguistic studies. Some researchers are skeptical about the very possibility of predicting the development of a language as unforeseen extra-linguistic factors can affect it and accelerate decelerate its development. The Russian history saw a lot of such factors: the language building during the post-revolutionary years, repressions of the 1930s, struggle against “nationalism” and “panfinism”, liquidation of national schools, policy of building a “new historical community - the so-called “Soviet people” with Russian as a single language, various educational reforms, etc. In the Russian Federation, the Finno-Ugric peoples mostly reside in the five Finno-Ugric republics of Karelia, Komi, Mari El, Mordovia and Udmurtia and in the two autonomous districts of Khanty-Mansi (Ugra) and Yamalo-Nenets. The Finno-Ugric peoples living in the latter districts are small nations and will not be discussed in this article. There are also numerous Finno-Ugric diasporas in Bashkortostan, Tatarstan and some other Russian regions. All of them differ by the following two demographic criteria: the ratio of Russians to the titular population and the number of state languages in the republics in question and the existence of laws governing those languages. This study, which is based on papers written by various linguistic scholars from both the Finno-Ugric republics and foreign countries, statistics and population census results, allows us to conclude that the process of giving the national languages the status of a state language had virtually no impact on the change in the language situation, nor did it slow down the language shift towards Russian - rather, it accelerated that shift. Such alarming situation with national languages should encourage linguistic scientists and authorities to pay special attention to the problems of planning, forecasting and preserving those languages.

AI Driven Planetary Bot for Future Vision

These days focus is more on technologies like Artificial Intelligence, Machine Learning and IoT. There is lots of platforms available for IOT implementation. ESP8266 chip is among them Here the implementation is about prediction of different aspects of weather data that can be used in many ways like predicting the future condition of different region of earth or predicting future condition of different planets and their different regions. To implement this system, we need different sensors like pressure sensor humidity sensor, temperature sensor and a light intensity sensor i.e DHT11 is utilize for temperature and humidity data together and LDR. Is for light intensity. The data which is sensed by different sensors are than uploaded to Thingspeak which is an API for cloud server by the help of NodeMCU and then converted to csv format. The data can be used for monitoring the real time values too. Machine Learning Environment can be setup by the help of a CNN model. Training of model can be done by recorded values of sensor data. After recording data from sensors to NodeMCU like temperature, pressure, humidity and light intensity and after these values are sent to python environment that is Jupyter notebook for further analysis. Here the data which is used is real time data to predict the particular value and test the model.

Does Temporal Distance Influence Abstraction? A Large Pre-Registered Experiment

Construal level theory has been extraordinarily generative both within and beyond social psychology, yet the individual effects that form the empirical foundation of the theory have yet to be carefully probed and precisely estimated using large samples and preregistered analysis plans. In a highly powered and preregistered study, we tested the effect of temporal distance on abstraction, using one of the most common operationalizations of temporal distance (thinking about a future point in time that is one day vs. one year from today) and one of the most common operationalizations of abstraction (preference for more abstract vs. concrete action representations, as assessed by the Behavioral Identification Form). Participants preferred significantly more abstract action representations in the distant (vs. near) future condition, with an effect size of d = .276, 95% CI [.097, .455]. We discuss implications, future directions, and constraints on the generality of these results.

Future tense can reduce present bias in infrastructure design

Design professionals (N = 261) were randomly assigned to either a future or present-framed project description before recommending design attributes for an infrastructure project. The future condition led designers to propose a significantly longer infrastructure design life, useful life to the community, and acceptable return on financial investment. The findings suggest that using future framing when soliciting sustainable design expertise can be a straightforward and inexpensive way to lessen present bias.

Rule-based modelling for adaptive complex infrastructure planning

&lt;p&gt;Dealing with uncertainty in infrastructure planning is a challenge. Planning decisions need to be made in face of unknown future condition, and, in the meantime, it is essential that they are flexible enough to be adapted as new information unfolds. This indeed is important for multi-sector decision making where the complexity of the interconnected system and the uncertainty thereof hinders the modelling and analysis. Multistage stochastic optimisation provides a mechanism to incorporate these two attributes into planning decisions. However, its expensive computation as well as the appropriateness of its sequential decisions beyond the first few stages reduce its implementability. We introduce `Decision Rule' as a way to approximate the multistage problem, where the decisions at each stage are functions of the system complexity and the future uncertainty. We introduce a family of linear, polynomial, conditional if-then based rules and show how they approximate the multistage stochastic problem. We investigate their implications for urban water demand and supply network planning problem. Further we discuss some state-of-the-art and emerging tools for increasing the accuracy of the rules.&lt;/p&gt;

A hybrid predictive methodology for head checks in railway infrastructure

This paper presents a hybrid method to assess the rail health with the focus on a specific type of rail surface defect called head check. The proposed method uses physics-based and data-driven models in order to model defect initiation and defect evolution on a rail for a given rail traffic tonnage. Ultrasonic (US) and Eddy Current (EC) defect detection measurements are used to provide Infrastructure Managers (IMs) with insight in the current rail condition. The defect initiation results obtained from the first part of the hybrid method which consists of the physics-based model is successfully validated with the EC measurements. Furthermore, the US and EC measurements are utilized to derive a data-driven model for defect evolution. Finally, a set of robust and predictive Key Performance Indicators (KPIs) are proposed to quantify the future condition of the rail based on different characteristics of rail health resulting from the defect initiation and defect evolution analysis.