Home Energy Management System Using NILM and Low-Cost HAN

Home energy management systems(HEMs) are used to provide comfortable life for consumers as well as to save energy. An essential component of HEMs is a home area network(HAN) that is used to remotely control the electric devices at homes and buildings. Although HAN prices have dropped in recent years but they are still expensive enough to prohibit a mass scale deployments. In this paper, a very low cost alternative to the expensive HANs is presented. We have applied a combination of non-intrusive load monitoring(NILM) and very low cost one-way HAN to develop a HEM. By using NILM and machine learning algorithms we find the status of devices and their energy consumption from a central meter and communicate with devices through the one-way HAN. The evaluations show that the proposed machine learning algorithm for NILM achieves up to 99% accuracy in certain cases. On the other hand our radio frequency(RF)-based one-way HAN achieves a range of 80 feet in all settings.     

Home Energy Management System Using NILM and Low-Cost HAN

Home energy management systems (HEMs) are used to provide comfortable life for consumers as well as to save energy. An essential component of HEMs is a home area network (HAN) that is used to remotely control the electric devices at homes and buildings. Although HAN prices have dropped in recent years but they are still expensive enough to prohibit a mass scale deployment. In this paper, a very low cost alternative to the expensive HANs is presented. We have applied a combination of non-intrusive load monitoring (NILM) and very low cost one-way HAN to develop a HEM. By using NILM and machine learning algorithms we find the status of devices and their energy consumption from a central meter and communicate with devices through the one-way HAN. The evaluations show that the proposed machine learning algorithm for NILM achieves up to 99% accuracy in certain cases. On the other hand our radio frequency (RF)-based one-way HAN achieves a range of 80 feet in all settings.     

In-Vitro Catalytic and Antibacterial Potential of Green Synthesized CuO Nanoparticles against Prevalent Multiple Drug Resistant Bovine Mastitogen Staphylococcus aureus

Nanoparticles prepared from bio-reduction agents are of keen interest to researchers around the globe due to their ability to mitigate the harmful effects of chemicals. In this regard, the present study aims to synthesize copper oxide nanoparticles (CuO NPs) by utilizing root extracts of ginger and garlic as reducing agents, followed by the characterization and evaluation of their antimicrobial properties against multiple drug resistant (MDR) S. aureus. In this study, UV-vis spectroscopy revealed a reduced degree of absorption with an increase in the extract amount present in CuO. The maximum absorbance for doped NPs was recorded around 250 nm accompanying redshift. X-ray diffraction analysis revealed the monoclinic crystal phase of the particles. The fabricated NPs exhibited spherical shapes with dense agglomeration when examined with FE-SEM and TEM. The crystallite size measured by using XRD was found to be within a range of 23.38–46.64 nm for ginger-doped CuO and 26–56 nm for garlic-doped CuO. Green synthesized NPs of ginger demonstrated higher bactericidal tendencies against MDR S. aureus. At minimum and maximum concentrations of ginger-doped CuO NPs, substantial inhibition areas for MDR S. aureus were (2.05–3.80 mm) and (3.15–5.65 mm), and they were measured as (1.1–3.55 mm) and (1.25–4.45 mm) for garlic-doped NPs. Conventionally available CuO and crude aqueous extract (CAE) of ginger and garlic roots reduced MB in 12, 21, and 38 min, respectively, in comparison with an efficient (100%) reduction of dye in 1 min and 15 s for ginger and garlic doped CuO NPs.     

Titration of Apparent In-Cellula Affinities of Protein-Protein Interactions**

A genetic assay permits simultaneous quantification of two interacting proteins and their bound fraction at the single-cell level using flow cytometry. Apparent in-cellula affinities of protein-protein interactions can be extracted from the acquired data through a titration-like analysis. The applicability of this approach is demonstrated on a diverse set of interactions with proteins from different families and organisms and with in-vitro dissociation constants ranging from picomolar to micromolar.     

Offshore Software Maintenance Outsourcing Process Model Validation: A Case Study Approach

The successful execution and management of Offshore Software Maintenance Outsourcing (OSMO) can be very beneficial for OSMO vendors and the OSMO client. Although a lot of research on software outsourcing is going on, most of the existing literature on offshore outsourcing deals with the outsourcing of software development only. Several frameworks have been developed focusing on guiding software system managers concerning offshore software outsourcing. However, none of these studies delivered comprehensive guidelines for managing the whole process of OSMO. There is a considerable lack of research working on managing OSMO from a vendor’s perspective. Therefore, to find the best practices for managing an OSMO process, it is necessary to further investigate such complex and multifaceted phenomena from the vendor’s perspective. This study validated the preliminary OSMO process model via a case study research approach. The results showed that the OSMO process model is applicable in an industrial setting with few changes. The industrial data collected during the case study enabled this paper to extend the preliminary OSMO process model. The refined version of the OSMO process model has four major phases including (i) Project Assessment, (ii) SLA (iii) Execution, and (iv) Risk.     

Photoactive and Intrinsically Fuel Sensing Metal–Organic Framework Motors for Tailoring Collective Behaviors of Active-Passive Colloids

Microorganisms display nonequilibrium predator–prey behaviors, such as chasing–escaping and schooling via chemotactic interactions. Even though artificial systems have revealed such biomimetic behaviors, switching between them by control over chemotactic interactions is rare. Here, a spindle-like iron-based metal–organic framework (MOF) colloidal motor which self-propels in glucose and H₂O₂, triggered by UV light is reported. These motors display intrinsic UV light-triggered fuel-dependent chemotactic interactions, which are used to tailor the collective dynamics of active-passive colloidal mixtures. In particular, the mixtures of active MOF motors with passive colloids exhibit distinctive “chasing–escaping” or “schooling” behaviors, depending on glucose or hydrogen peroxide being used as the fuel. The transition in the collective behaviors is attributed to an alteration in the sign of ionic diffusiophoretic interactions, resulting from a change in the ionic clouds produced. This study offers a new strategy on tuning the communication between active and passive colloids, which holds substantial potentials for fundamental research in active matter and practical applications in cargo delivery, chemical sensing, and particle segregation.     

A sustainable bio-adsorbent for thermal energy storage for space heating applications

Thermal energy storage is an emerging technology that allows the storage of heat when it is available, which can be used later. One of the available technologies for thermal energy storage is the adsorption of moisture from air by adsorbents. Several adsorbents have been studied in the literature for this application, but there is a need for a sustainable adsorbent that can be eco-friendly, cost effective, and available for scale-up for commercialization of the technology. The current paper focused on the synthesis of a flax shives-based composite (equal weight percent of flax shives and salt hydrates) prepared by the impregnation method and its application in thermal energy storage. The composite showed durability, stability, and reasonable energy storage density with a very low cost per unit of energy. The structural characterization of the hybrid was performed by scanning electron microscopy (SEM)/energy-dispersive X-ray spectroscopy (EDX). The thermal energy storage density, as well as the charging/discharging characteristics were measured using a laboratory-scale thermal energy storage apparatus. The flax/CaCl₂/LiCl hybrid showed reasonable energy storage density at 74 kWh/m³ for 50% inlet relative humidity after regeneration at 120°C. Although the energy storage density was not high, the flax/CaCl₂ composite was found to be the most cost-effective material, as it showed the lowest cost per energy stored at 0.98 CAD/kWh at 50% relative humidity (RH) after regeneration at 120°C.