Mobile Genetic Elements and Natural Gene Transfer in Rumen Microbial Ecosystem

As technology is going be modern different changes that occur in the nature become studied. Rumen microbes especially rumen bacteria play a vital role in utilization of ruminants feed. Without rumen microbe’s rumen can’t function correctly. So, when we feed our animals, we are also feeding rumen microbes which convert the plant fiber to VFA which used by ruminants as an energy source for maintenance and production. Through the process these microbes change in their genetic structure and function because of natural gene transfer which is mediated by movable genetic elements like bacteriophage, plasmid, transposons and other segments. In this review prokaryotes which include bacteria and archae have got large cover than eukaryotes because most significant changes in rumen are carried by those organisms. HGT is the way to perform this function because it enables bacteria to respond and adapt to their environment much more rapidly by acquiring large DNA sequence from other bacterium in a single transfer and mechanisms of bacterial gene transfer include transformation, transduction and conjugation.  So this event has an impact on the microbe themselves, for the rumen ecosystem, the animal and the environment where these changes are related. These changes include antibiotic resistance, better conversion of feeds and effects in methane and ammonia production. Keywords: Horizontal Gene Transfer, Prokaryotes, Rumen. Rumen Microbes DOI: 10.7176/ALST/78-03 Publication date: February 29th 202

Electric vehicles and low-voltage grid: impact of uncontrolled demand side response

The authors are looking at the impact of electric vehicles (EV) charging from low-voltage (LV) networks. Based on the data obtained from two different pilot projects: (i) Mini-E trial where EV users were incentivised to charge during the night; (ii) My Electric Avenue trial, where there were no similar incentives, authors want to quantify the impact of EV charging, presuming that the number of home-charging EV users will increase significantly in the near future. By assuming that the current load at individual household level is known or inferred, simulations are performed to estimate the future load. The authors look at different percentages of EV uptake and model clustered scenarios, where the social networking effect is imposed – users adopt an EV with a higher probability if their neighbour already has one. Simulations demonstrate that incentivising night-time charging can create large new peaks during the night, which could have negative effects on LV networks. On the other hand, simulations based on the data with no incentives shows that naturally occurring diversity in charging behaviour does not automatically result in comparable network stress at the same penetrations

Wearable Sensor Gait Analysis for Fall Detection Using Deep Learning Methods

World Health Organization (WHO) data show that around 684,000 people die from falls yearly, making it the second-highest mortality rate after traffic accidents [1]. Early detection of falls, followed by pneumatic protection, is one of the most effective means of ensuring the safety of the elderly. In light of the recent widespread adoption of wearable sensors, it has become increasingly critical that fall detection models are developed that can effectively process large and sequential sensor signal data. Several researchers have recently developed fall detection algorithms based on wearable sensor data. However, real-time fall detection remains challenging because of the wide range of gait variations in older. Choosing the appropriate sensor and placing it in the most suitable location are essential components of a robust real-time fall detection system. This dissertation implements various detection models to analyze and mitigate injuries due to falls in the senior community. It presents different methods for detecting falls in real-time using deep learning networks. Several sliding window segmentation techniques are developed and compared in the first study. As a next step, various methods are implemented and applied to prevent sampling imbalances caused by the real-world collection of fall data. A study is also conducted to determine whether accelerometers and gyroscopes can distinguish between falls and near-falls. According to the literature survey, machine learning algorithms produce varying degrees of accuracy when applied to various datasets. The algorithm’s performance depends on several factors, including the type and location of the sensors, the fall pattern, the dataset’s characteristics, and the methods used for preprocessing and sliding window segmentation. Other challenges associated with fall detection include the need for centralized datasets for comparing the results of different algorithms. This dissertation compares the performance of varying fall detection methods using deep learning algorithms across multiple data sets. Furthermore, deep learning has been explored in the second application of the ECG-based virtual pathology stethoscope detection system. A novel real-time virtual pathology stethoscope (VPS) detection method has been developed. Several deep-learning methods are evaluated for classifying the location of the stethoscope by taking advantage of subtle differences in the ECG signals. This study would significantly extend the simulation capabilities of standard patients by allowing medical students and trainees to perform realistic cardiac auscultation and hear cardiac auscultation in a clinical environment

Senior Subject Gait Analysis Using Self-supervised Method

There are many issues regarding the technology used for fall detection gait analysis in the geriatric center of senior patients. The fall detection system used should examine the privacy of the patients and the flexibility of the system. Several researchers have developed fall detection using wearable sensors due to their flexibility and nature of privacy. Most of those developed methods are supervised deep learning methods. However, data annotation is expensive because we use camera video recording and playback of each participant’s recorded video to label the data. Moreover, labelling using a camera recording limits the flexible and private nature of wearable sensor-based fall detection. This paper presents how to use unlabelled data to pre-train our models and use labelled data to fine-tune those pre-trained weights. We collected unlabelled and labelled data and applied self-supervised learning to detect falls. First, we performed pre-training on the unlabeled data using ResNet model. After that, fine-tune and train ResNet using the labelled dataset. The experiment in this study suggested that the best performance can be achieved by using pre-trained weights of unlabelled data from the accelerometer and gyroscope sensors. Furthermore, oversampling and modified loss functions are used to handle the dataset’s imbalance classes. With the ResNet pre-trained weights and re-training using the labelled data, the experiments achieved an F1-Score of 0.98

Analysis and clustering of residential customers energy behavioral demand using smart meter data

Clustering methods are increasingly being applied to residential smart meter data, providing a number of important opportunities for distribution network operators (DNOs) to manage and plan the low voltage networks. Clustering has a number of potential advantages for DNOs including, identifying suitable candidates for demand response and improving energy profile modelling. However, due to the high stochasticity and irregularity of household level demand, detailed analytics are required to define appropriate attributes to cluster. In this paper we present in-depth analysis of customer smart meter data to better understand peak demand and major sources of variability in their behaviour. We find four key time periods in which the data should be analysed and use this to form relevant attributes for our clustering. We present a finite mixture model based clustering where we discover 10 distinct behaviour groups describing customers based on their demand and their variability. Finally, using an existing bootstrapping technique we show that the clustering is reliable. To the authors knowledge this is the first time in the power systems literature that the sample robustness of the clustering has been tested

A general framework for quantifying the effects of land-use history on ecosystem dynamics

Land-use legacies are important for explaining present-day ecological patterns and processes. However, an overarching approach to quantify land-use history effects on ecosystem properties is lacking, mainly due to the scarcity of high-quality, complete and detailed data on past land use. We propose a general framework for quantifying the effects of land-use history on ecosystem properties, which is applicable (i) to different ecological processes in various ecosystem types and across trophic levels; and (ii) when historical data are incomplete or of variable quality. The conceptual foundation of our framework is that past land use affects current (and future) ecosystem properties through altering the past values of resources and conditions that are the driving variables of ecosystem responses. We describe and illustrate how Markov chains can be applied to derive past time series of driving variables, and how these time series can be used to improve our understanding of present-day ecosystem properties. We present our framework in a stepwise manner, elucidating its general nature. We illustrate its application through a case study on the importance of past light levels for the contemporary understorey composition of temperate deciduous forest. We found that the understorey shows legacies of past forest management: high past light availability lead to a low proportion of typical forest species in the understorey. Our framework can be a useful tool for quantifying the effect of past land use on ecological patterns and processes and enhancing our understanding of ecosystem dynamics by including legacy effects which have often been ignored

Toward Real-Time, Robust Wearable Sensor Fall Detection Using Deep Learning Methods: A Feasibility Study

Real-time fall detection using a wearable sensor remains a challenging problem due to high gait variability. Furthermore, finding the type of sensor to use and the optimal location of the sensors are also essential factors for real-time fall-detection systems. This work presents real-time fall-detection methods using deep learning models. Early detection of falls, followed by pneumatic protection, is one of the most effective means of ensuring the safety of the elderly. First, we developed and compared different data-segmentation techniques for sliding windows. Next, we implemented various techniques to balance the datasets because collecting fall datasets in the real-time setting has an imbalanced nature. Moreover, we designed a deep learning model that combines a convolution-based feature extractor and deep neural network blocks, the LSTM block, and the transformer encoder block, followed by a position-wise feedforward layer. We found that combining the input sequence with the convolution-learned features of different kernels tends to increase the performance of the fall-detection model. Last, we analyzed that the sensor signals collected by both accelerometer and gyroscope sensors can be leveraged to develop an effective classifier that can accurately detect falls, especially differentiating falls from near-falls. Furthermore, we also used data from sixteen different body parts and compared them to determine the better sensor position for fall-detection methods. We found that the shank is the optimal position for placing our sensors, with an F1 score of 0.97, and this could help other researchers collect high-quality fall datasets

Modelling the demand and uncertainty of low voltage networks and the effect of non-domestic consumers

The increasing use and spread of low carbon technologies are expected to cause new patterns in electric demand and set novel challenges to a distribution network operator (DNO). In this study, we build upon a recently introduced method, called 'buddying', which simulates low voltage (LV) networks of both residential and non-domestic (e.g. shops, offices, schools, hospitals, etc.) customers through optimisation (via a genetic algorithm) of demands based on limited monitored and customer data. The algorithm assigns a limited but diverse number of monitored households (the 'buddies') to the unmonitored customers on a network. We study and compare two algorithms, one where substation monitoring data is available and a second where no substation information is used. Despite the roll out of monitoring equipment at domestic properties and/or substations, less data is available for commercial customers. This study focuses on substations with commercial customers most of which have no monitored 'buddy', in which case a profile must be created. Due to the volatile nature of the low voltage networks, uncertainty bounds are crucial for operational purposes. We introduce and demonstrate two techniques for modelling the confidence bounds on the modelled LV networks. The first method uses probabilistic forecast methods based on substation monitoring; the second only uses a simple bootstrap of the sample of monitored customers but has the advantage of not requiring monitoring at the substation. These modelling tools, buddying and uncertainty bounds, can give further insight to a DNO to better plan and manage the network when limited information is available