Machine Learning-Based Power Consumption Prediction for Unmanned Aerial Vehicles in Dynamic Environments

Unmanned aerial vehicles are becoming integrated into a wide range of modern IoT and CPS environments for various industrial, military, and entertainment applications. With growing estimations for this market in the future, the problem of energy consumption and its prediction is becoming increasingly important for optimal battery-saving, as well as the safety of the application and thus protection of surrounding persons near the drone flight. This paper presents a machine learning-based approach for the prediction of the power consumption of unmanned aerial vehicles at certain times of the flight. Instead of predicting the power consumption in prescribed environments with complex, time-consuming measurement techniques, our approach is fast, easy to implement, and predicts real-world power consumption in five classes, with a balanced accuracy of 66.7 percent

Automated Defect Detection of Screws in the Manufacturing Industry Using Convolutional Neural Networks

Defect detection in industrial production processes is an important and necessary part of quality control. Many defects can occur during the manufacturing process, causing high manufacturing costs. Thus the inspection of screws, which represent an indispensable element of many mechanical components, is a critical process. To reduce manufacturing costs and increase efficiency, a reliable method for inspection is Deep Learning. It can help simplify the process of quality control and increase the velocity and volume of detected defects in screws. This approach uses a CNN model to classify non-defective and defective screws with different types of defects. Instead of manual quality control methods, which can be easily biased, our CNN approach is accurate, cost-efficient, and fast, with an accuracy of over 97 percent. With this approach corresponding to industrial production processes, different defects in screws and non-defective screws can be classified from images according to a real-world industrial inspection scenario

Subject-Independent Detection of Yes/No Decisions Using EEG Recordings During Motor Imagery Tasks: A Novel Machine-Learning Approach with Fine-Graded EEG Spectrum

The classification of sensorimotor rhythms in electroencephalography signals can enable paralyzed individuals, for example, to make yes/no decisions. In practice, these approaches are hard to implement due to the variability of electroencephalography signals between and within subjects. Therefore, we report a novel and fast machine learning model, meeting the need for efficiency and reliability as well as low calibration and training time. Our model extracts finely graded frequency bands from motor imagery electroencephalography data by using power spectral density and training a random forest algorithm for classification. The goal was to create a non-invasive generalizable method by training the algorithm with subject-independent EEG data. We evaluate our approach using one of the currently largest publicly available electroencephalography datasets. With a balanced accuracy of 73.94%, our novel algorithm outperforms other state-of-the-art non-subject-dependent algorithms

A new dominant peroxiredoxin allele identified by whole-genome re-sequencing of random mutagenized yeast causes oxidant-resistance and premature aging

The combination of functional genomics with next generation sequencing facilitates new experimental strategies for addressing complex biological phenomena. Here, we report the identification of a gain-of-function allele of peroxiredoxin (thioredoxin peroxidase, Tsa1p) via whole-genome re-sequencing of a dominantSaccharomyces cerevisiae mutant obtained by chemical mutagenesis. Yeast strain K6001, a screening system for lifespan phenotypes, was treated with ethyl methanesulfonate (EMS). We isolated an oxidative stress-resistant mutant (B7) which transmitted this phenotype in a background-independent, monogenic and dominant way. By massive parallel pyrosequencing, we generated an 38.8 fold whole-genome coverage of the strains, which differed in 12,482 positions from the reference (S288c) genome. Via a subtraction strategy, we could narrow this number to 13 total and 4 missense nucleotide variations that were specific for the mutant. Via expression in wild type backgrounds, we show that one of these mutations, exchanging a residue in the peroxiredoxin Tsa1p, was responsible for the mutant phenotype causing background-independent dominant oxidative stress-resistance. These effects were not provoked by altered Tsa1p levels, nor could they be simulated by deletion, haploinsufficiency or over-expression of the wild-type allele. Furthermore, via both a mother-enrichment technique and a micromanipulation assay, we found a robust premature aging phenotype of this oxidant-resistant strain. Thus, TSA1-B7 encodes for a novel dominant form of peroxiredoxin, and establishes a new connection between oxidative stress and aging. In addition, this study shows that the re-sequencing of entire genomes is becoming a promising alternative for the identification of functional alleles in approaches of classic molecular genetics

Slow Growth and Increased Spontaneous Mutation Frequency in Respiratory Deficient afo1- Yeast Suppressed by a Dominant Mutation in ATP3

A yeast deletion mutation in the nuclear-encoded gene, AFO1, which codes for a mitochondrial ribosomal protein, led to slow growth on glucose, the inability to grow on glycerol or ethanol, and loss of mitochondrial DNA and respiration. We noticed that afo1- yeast readily obtains secondary mutations that suppress aspects of this phenotype, including its growth defect. We characterized and identified a dominant missense suppressor mutation in the ATP3 gene. Comparing isogenic slowly growing rho-zero and rapidly growing suppressed afo1- strains under carefully controlled fermentation conditions showed that energy charge was not significantly different between strains and was not causal for the observed growth properties. Surprisingly, in a wild-type background, the dominant suppressor allele of ATP3 still allowed respiratory growth but increased the petite frequency. Similarly, a slow-growing respiratory deficient afo1- strain displayed an about twofold increase in spontaneous frequency of point mutations (comparable to the rho-zero strain) while the suppressed strain showed mutation frequency comparable to the repiratory-competent WT strain. We conclude, that phenotypes that result from afo1- are mostly explained by rapidly emerging mutations that compensate for the slow growth that typically follows respiratory deficiency

Guidelines and Recommendations on Yeast Cell Death Nomenclature

Elucidating the biology of yeast in its full complexity has major implications for science, medicine and industry. One of the most critical processes determining yeast life and physiology is cellular demise. However, the investigation of yeast cell death is a relatively young field, and a widely accepted set of concepts and terms is still missing. Here, we propose unified criteria for the definition of accidental, regulated, and programmed forms of cell death in yeast based on a series of morphological and biochemical criteria. Specifically, we provide consensus guidelines on the differential definition of terms including apoptosis, regulated necrosis, and autophagic cell death, as we refer to additional cell death routines that are relevant for the biology of (at least some species of) yeast. As this area of investigation advances rapidly, changes and extensions to this set of recommendations will be implemented in the years to come. Nonetheless, we strongly encourage the authors, reviewers and editors of scientific articles to adopt these collective standards in order to establish an accurate framework for yeast cell death research and, ultimately, to accelerate the progress of this vibrant field of research

Guidelines and recommendations on yeast cell death nomenclature

Elucidating the biology of yeast in its full complexity has major implications for science, medicine and industry. One of the most critical processes determining yeast life and physiology is cel-lular demise. However, the investigation of yeast cell death is a relatively young field, and a widely accepted set of concepts and terms is still missing. Here, we propose unified criteria for the defi-nition of accidental, regulated, and programmed forms of cell death in yeast based on a series of morphological and biochemical criteria. Specifically, we provide consensus guidelines on the differ-ential definition of terms including apoptosis, regulated necrosis, and autophagic cell death, as we refer to additional cell death rou-tines that are relevant for the biology of (at least some species of) yeast. As this area of investigation advances rapidly, changes and extensions to this set of recommendations will be implemented in the years to come. Nonetheless, we strongly encourage the au-thors, reviewers and editors of scientific articles to adopt these collective standards in order to establish an accurate framework for yeast cell death research and, ultimately, to accelerate the pro-gress of this vibrant field of research

Detection of Excessive Daytime Sleepiness in Resting-State EEG Recordings: A Novel Machine Learning Approach Using Specific EEG Sub-Bands and Channels

Excessive Daytime Sleepiness (EDS) is a threat to the safety of the patient as well as others and can lead to higher rates of mortality. While current tests for EDS are time-consuming, expensive, and inaccurate, modern data-driven approaches can significantly improve diagnosis and treatment quality. Using an improved machine learning (ML) method in combination with the unfolding of electroencephalogram (EEG) bandwidths in a fine-graded equidistant 99-point spectrum, we successfully developed and evaluated a fast classification model to correctly diagnose EDS. Our model achieved a good level of accuracy of 87.00 percent on completely unseen resting-state EEG data. Our model is one of the first to detect long term EDS instead of short-term drowsiness from EEG data and significantly extends related work on the detection of drowsiness by considering the local occurrence of differences in spectral power