Performance evaluation of endrills

This paper evaluates the performance of a relatively new type of drill called an endrill which is a cross between a drill and an endmill. Investigations into the effects of its cutting conditions on the drilling forces, surface finish, drill wear and hole oversize were carried out. It was found that endrills produced better quality holes than conventional twist drills, better surface finish and less oversize of the holes. Hence, with proper feed, speed and flow rate of the pressurized flushing coolant, a good finish of about R_a = 1 μm can be attained without reaming. Thus, the productivity of finished holes can be remarkably improved. Compared to twist drills, lower torque and thrust were observed which yielded improved tool life and reduced power consumption. No “walking phenomenon” was observed when this kind of drill was used and the amount of hole oversize was found to average about 0.7% of the drill diameter as compared to 1.6% when twist drills were used. Finally, general equations for the drill torque and thrust were derived from the experimental results.     

Design of artificial neuron controller for STATCOM in dSPACE environment

Reactive power compensation is an important issue in the control of electric power system. Reactive power from the source increases the transmission losses and reduces the power transmission capability of the transmission lines. Moreover, reactive power should not be transmitted through the transmission line to a longer distance. Hence Flexible AC Transmission Systems (FACTS) devices such as static compensator (STATCOM) unified power flow controller (UPFC) and static volt–ampere compensator (SVC) are used to alleviate these problems. In this paper, a voltage source converter (VSC) based STATCOM is developed with PI and Artificial Neural Network Controller (ANNC). The conventional PI controller has more tuning difficulties while the system parameter changes, whereas a trained neural network requires less computation time. The ANNC has the ability to generalize and can interpolate in between the training data. The ANNC designed was tested on a 75 V, ±3KVAR STATCOM in real time environment via state-of-the-art of digital signal processor advanced control engineering (dSPACE) DS1104 board and it was found that it was producing better results than the PI controller.     

Unsupervised context detection using wireless signals

The sensing context plays an important role in many pervasive and mobile computing applications. Continuing from previous work [D. Phung, B. Adams, S. Venkatesh, Computable social patterns from sparse sensor data, in: Proceedings of First International Workshop on Location Web, World Wide Web Conference (WWW), New York, NY, USA, 2008, ACM 69–72.], we present an unsupervised framework for extracting user context in indoor environments with existing wireless infrastructures. Our novel approach casts context detection into an incremental, unsupervised clustering setting. Using WiFi observations consisting of access point identification and signal strengths freely available in office or public spaces, we adapt a density-based clustering technique to recover basic forms of user contexts that include user motion state and significant places the user visits from time to time. High-level user context, termed rhythms, comprising sequences of significant places are derived from the above low-level context by employing probabilistic clustering techniques, latent Dirichlet allocation and its n-gram temporal extension. These user contexts can enable a wide range of context-ware application services. Experimental results with real data in comparison with existing methods are presented to validate the proposed approach. Our motion classification algorithm operates in real-time, and achieves a 10% improvement over an existing method; significant locations are detected with over 90% accuracy and near perfect cluster purity. Richer indoor context and meaningful rhythms, such as typical daily routines or meeting patterns, are also inferred automatically from collected raw WiFi signals.     

Bacteria-Responsive Self-Assembly of Antimicrobial Peptide Nanonets for Trap-and-Kill of Antibiotic-Resistant Strains

Bacterial trapping using nanonets is a ubiquitous immune defense mechanism against infectious microbes. These nanonets can entrap microbial cells, effectively arresting their dissemination and rendering them more vulnerable to locally secreted microbicides. Inspired by this evolutionarily conserved anti-infective strategy, a series of 15 to 16 residue-long synthetic β-hairpin peptides is herein constructed with the ability to self-assemble into nanonets in response to the presence of bacteria, enabling spatiotemporal control over microbial killing. Using amyloid-specific K114 assay and confocal microscopy, the membrane components lipoteichoic acid and lipopolysaccharide are shown to play a major role in determining the amyloid-nucleating capacity as triggered by Gram-positive and Gram-negative bacteria respectively. These nanonets displayed both trapping and killing functionalities, hence offering a direct improvement from the trap-only biomimetics in literature. By substituting a single turn residue of the non-amyloidogenic BTT1 peptide, the nanonet-forming BTT1-3A analog is produced with comparable antimicrobial potency. With the same sequence manipulation approach, BTT2-4A analog modified from BTT2 peptide showed improved antimicrobial potency against colistin-resistant clinical isolates. The peptide nanonets also demonstrated robust stability against proteolytic degradation, and promising in vivo efficacy and biosafety profile. Overall, these bacteria-responsive peptide nanonets are promising clinical anti-infective alternatives for circumventing antibiotic resistance.     

Fabricating Strong and Stiff Bioplastics from Whole Spirulina Cells

Since the 1950s, 8.3 billion tonnes (Bt) of virgin plastics have been produced, of which around 5 Bt have accumulated as waste in oceans and other natural environments, posing severe threats to entire ecosystems. The need for sustainable bio-based alternatives to traditional petroleum-derived plastics is evident. Bioplastics produced from unprocessed biological materials have thus far suffered from heterogeneous and non-cohesive morphologies, which lead to weak mechanical properties and lack of processability, hindering their industrial integration. Here, a fast, simple, and scalable process is presented to transform raw microalgae into a self-bonded, recyclable, and backyard-compostable bioplastic with attractive mechanical properties surpassing those of other biobased plastics such as thermoplastic starch. Upon hot-pressing, the abundant and photosynthetic algae spirulina forms cohesive bioplastics with flexural modulus and strength in the range 3–5 GPa and 25.5–57 MPa, respectively, depending on pre-processing conditions and the addition of nanofillers. The machinability of these bioplastics, along with self-extinguishing properties, make them promising candidates for consumer plastics. Mechanical recycling and fast biodegradation in soil are demonstrated as end-of-life options. Finally, the environmental impacts are discussed in terms of global warming potential, highlighting the benefits of using a carbon-negative feedstock such as spirulina to fabricate plastics.     

Sensitive and Reversible Detection of Methanol and Water Vapor by In Situ Electrochemically Grown CuBTC MOFs on Interdigitated Electrodes

The in situ electrochemical growth of Cu benzene‐1,3,5‐tricarboxylate (CuBTC) metal–organic frameworks, as an affinity layer, directly on custom‐fabricated Cu interdigitated electrodes (IDEs) is described, acting as a transducer. Crystalline 5–7 µm thick CuBTC layers are grown on IDEs consisting of 100 electrodes with a width and a gap of both 50 µm and a height of 6–8 µm. These capacitive sensors are exposed to methanol and water vapor at 30 °C. The affinities show to be completely reversible with higher affinity toward water compared to methanol. For exposure to 1000 ppm methanol, a fast response is observed with a capacitance change of 5.57 pF at equilibrium. The capacitance increases in time followed diffusion‐controlled kinetics (k = 2.9 mmol s^?0.5 g^?1_CuBTC). The observed capacitance change with methanol concentration follows a Langmuir adsorption isotherm, with a value for the equilibrium affinity K _e = 174.8 bar^?1. A volume fraction f _MeOH = 0.038 is occupied upon exposure to 1000 ppm of methanol. The thin CuBTC affinity layer on the Cu‐IDEs shows fast, reversible, and sensitive responses to methanol and water vapor, enabling quantitative detection in the range of 100–8000 ppm.     

Sustainability evaluation framework for building cooling systems: a comparative study of snow storage and conventional chiller systems

In Canada, the residential building sector consumes 17 % of the total energy and 15 % of the total greenhouse gas emissions. In particular, the energy demand for cooling in the residential sector is increasing due to the large occupancy floor area and high usage of air conditioning. Minimizing energy use and greenhouse gas emissions is one of the highest priority goals set for national energy management strategies in developed countries including Canada. In this study, a framework based on the life cycle assessment approach is developed to assess the environmental impacts of different building cooling systems, namely conventional snow storage system, watertight snow storage system, high-density snow storage system, and the conventional chiller cooling system. Moreover, all these systems have varying energy requirements and associated environmental impacts during different phases (extraction and construction, utilization, and end of life) of the life cycle of a building. A low-rise residential building in Kelowna (BC, Canada) has been selected for the pragmatic application of the proposed framework. The annual cooling energy demand for the building is estimated for different phases. Subsequently, the life cycle impact assessment has been carried out using SimaPro 8.1 software and TRACI 2.1 method. For sustainability evaluation of different cooling systems over their life cycle, multi-criteria decision analysis has been employed using the ‘Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE II).’ The results showed that the snow storage systems tend to reduce greenhouse gas emissions and associated environmental impacts more than the conventional system.     

Plating on acrylonitrile–butadiene–styrene (ABS) plastic: a review

ABS is an engineering plastic that has butadiene part uniformly distributed over the acrylonitrile-styrene matrix. It possesses excellent toughness, good dimensional stability, easy processing ability, chemical resistance, and cheapness. However, it suffers from inherent shortcomings in terms of mechanical strength and vulnerability to environmental conditions. Furthermore, it is non-conducting and easily fretted. Plating on ABS can serve to enhance the strength and structural integrity as well as to improve durability and thermal resistance resulting in metallic properties on the ABS material. ABS is described as the most suitable candidate for plating because it is possible to deposit an adherent metal coating on it by only the use of chemical pretreatment process and without the use of any mechanical abrasion. This article aims to review the history of ABS plastics, properties of ABS, processes and mechanisms of plating, and studies of plating on ABS involving mainly eco-friendly methods of plating by discussing the literature published in recent years. The details of electroplating of ABS carried out in the authors’ laboratory are also presented.