An Overview of Recent Strategies in Pathogen Sensing

Pathogenic bacteria are one of the major concerns in food industries and water treatment facilities because of their rapid growth and deleterious effects on human health. The development of fast and accurate detection and identification systems for bacterial strains has long been an important issue to researchers. Although confirmative for the identification of bacteria, conventional methods require time-consuming process involving either the test of characteristic metabolites or cellular reproductive cycles. In this paper, we review recent sensing strategies based on micro- and nano-fabrication technology. These technologies allow for a great improvement of detection limit, therefore, reduce the time required for sample preparation. The paper will be focused on newly developed nano- and micro-scaled biosensors, novel sensing modalities utilizing microfluidic lab-on-a-chip, and array technology for the detection of pathogenic bacteria

Illegal Immigration: A Continuing Issue for the 1980s

[Excerpt] Illegal immigration is not simply a matter of mounting numbers of individuals occupying American turf. Illegal immigrants compete for employment and income opportunities with citizen workers, usually low-wage-earning minorities, women, and youth. Equally disturbing is the creation and institutionalization of a permanent subclass of rightless persons within American society that unauthorized residence here fosters. What follows is a discussion of the issue of illegal immigration, its causes, its social and economic ramifications and the need for a multifaceted, comprehensive policy. It is only through such a complete policy that we can hope to control the problem

Determination of azo dyes using Smartphone Digital Image

The development of an economical and simple colorimetric system based on a smartphone camera and image processing was included in this study.  This method was applied to determine three azo dyes namely: Methyl Blue (M.B), Methyl Red (M.R) and Methyl Orange (M.O) using the smartphone's camera as a detector. The results of the radar diagrams were giving a good agreement with the results of the calibration curves which were built using data of RGB for each dye. For establishing the accuracy and precision of this method, a classical method (spectrophotometric) was used for validation. This advancement in smartphone-based detection and identification systems will revolutionize environmental monitoring, ensuring rapid and effective diagnosis of contaminants for individuals and communities. Streamlining the Digital image colorimetry DIC process on smartphones is essential to public health and safety while promoting more conscious and sustainable practices worldwide

Emerging Technologies in the Area of Food Sciences

In the modern era, world has experienced tremendous boost in the field of food science and technology, realising its impact on the economic growth and people’s standard of living. India is using its newer technology for food processing in the field of science and technology. We are among the world’s top nations in the number of scientific publications and patents in food technology. The government has made considerable investment and is encouraging public-private partnership to achieve self-reliance in different agricultural sectors. It has a strong presence in the field of biotechnology, particularly related to agriculture technology, including pre- and post-harvest management, processing technology etc.

Prolegomenon to a media theory of machine learning

This paper, outlines a groundwork for a media theory of machine learning by introducing two new concepts, compute-computing and compute-computed, and a framework for their interaction. Compute-computing (computing as generative) is here understood as the “active” learning component of a system, whereas compute-computed (computing as generated) is understood as the “passive”, coded, imprinted or inscribed aspect of a system. I introduce these two concepts to help us to think through the specificity of algorithmic systems that are more than just the operative, sequential or parallel systems of computational processing to which we have become accustomed. Indeed, in the case of machine learning systems, these systems have the capacity to be self-positing in the sense of generating models and data structures that internalise certain pattern characteristics of data, without the requirement that they are translated into formal data structures by a human programmer. That is, they are able to capture the abstract form of data input into the system, identify key characteristics, frames or patterns, and store this for comparison and classification of other data streams or objects

Novelty detection based condition monitoring scheme applied to electromechanical systems

This study is focused on the current challenges dealing with electromechanical system monitoring applied in industrial frameworks, that is, the presence of unknown events and the limitation to the nominal healthy condition as starting knowledge. Thus, an industrial machinery condition monitoring methodology based on novelty detection and classification is proposed in this study. The methodology is divided in three main stages. First, a dedicated feature calculation and reduction over each available physical magnitude. Second, an ensemble structure of novelty detection models based on one-class support vector machines to identify not previously considered events. Third, a diagnosis model supported by a feature fusion scheme in order to reach high fault classification capabilities. The effectiveness of the fault detection and identification methodology has been compared with classical single model approach, and verified by experimental results obtained from an electromechanical machine. © 2018 IEEE.Postprint (author's final draft

Lesson learned from the recovery of an orphan source inside a maritime cargo: analysis of the nuclear instrumentations used, and measures realized during the operations

In this paper, the authors analyze the case study of the recovery of an orphan source of 60Co inside a maritime cargo full of metal wastes in the Italian Harbor of Genova carried out by the Italian Fire Fighters. Orphan radioactive sources or Radiological Dispersal Devices are a critical security issue in large geographical areas, and they result in a safety concern for people who may become accidentally exposed to ionizing radiation. The abandonment of orphan sources can usually be related to three factors: human errors, cost reasons (in order to avoid the payment of disposal procedures), or malevolent purposes (like the production of dirty bombs). The present data concern the nuclear safety measures implemented during the recovery event and the pool of procedures carried out in order to reduce the risks for the involved harbor operators. Following data collection and analysis, an important lesson about the management of such events and scenarios can be learned

Signal fingerprinting and machine learning framework for UAV detection and identification.

Advancement in technology has led to creative and innovative inventions. One such invention includes unmanned aerial vehicles (UAVs). UAVs (also known as drones) are now an intrinsic part of our society because their application is becoming ubiquitous in every industry ranging from transportation and logistics to environmental monitoring among others. With the numerous benign applications of UAVs, their emergence has added a new dimension to privacy and security issues. There are little or no strict regulations on the people that can purchase or own a UAV. For this reason, nefarious actors can take advantage of these aircraft to intrude into restricted or private areas. A UAV detection and identification system is one of the ways of detecting and identifying the presence of a UAV in an area. UAV detection and identification systems employ different sensing techniques such as radio frequency (RF) signals, video, sounds, and thermal imaging for detecting an intruding UAV. Because of the passive nature (stealth) of RF sensing techniques, the ability to exploit RF sensing for identification of UAV flight mode (i.e., flying, hovering, videoing, etc.), and the capability to detect a UAV at beyond visual line-of-sight (BVLOS) or marginal line-of-sight makes RF sensing techniques promising for UAV detection and identification. More so, there is constant communication between a UAV and its ground station (i.e., flight controller). The RF signals emitting from a UAV or UAV flight controller can be exploited for UAV detection and identification. Hence, in this work, an RF-based UAV detection and identification system is proposed and investigated. In RF signal fingerprinting research, the transient and steady state of the RF signals can be used to extract a unique signature. The first part of this work is to use two different wavelet analytic transforms (i.e., continuous wavelet transform and wavelet scattering transform) to investigate and analyze the characteristics or impacts of using either state for UAV detection and identification. Coefficient-based and image-based signatures are proposed for each of the wavelet analysis transforms to detect and identify a UAV. One of the challenges of using RF sensing is that a UAV\u27s communication links operate at the industrial, scientific, and medical (ISM) band. Several devices such as Bluetooth and WiFi operate at the ISM band as well, so discriminating UAVs from other ISM devices is not a trivial task. A semi-supervised anomaly detection approach is explored and proposed in this research to differentiate UAVs from Bluetooth and WiFi devices. Both time-frequency analytical approaches and unsupervised deep neural network techniques (i.e., denoising autoencoder) are used differently for feature extraction. Finally, a hierarchical classification framework for UAV identification is proposed for the identification of the type of unmanned aerial system signal (UAV or UAV controller signal), the UAV model, and the operational mode of the UAV. This is a shift from a flat classification approach. The hierarchical learning approach provides a level-by-level classification that can be useful for identifying an intruding UAV. The proposed frameworks described here can be extended to the detection of rogue RF devices in an environment