Resonance tube igniter

Reasonance induced in stoichiometric mixtures of gaseous hydrogen-oxygen produces temperatures /over 1100 deg F/ high enough to cause ignition. Resonance tube phenomenon occurs when high pressure gas is forced through sonic or supersonic nozzle into short cavity. Various applications for the phenomenon are discussed

Managing Queuing Problems Through Online Booking System

Queuing is one of the important issues to the service industry due to its impact towards the operations capabilities and customer satisfaction of the organization. The determination of how long a customer should wait for a product or service has long been a major concern for service management specialists who bear the trade-off between minimizing operation costs incurred in optimizing the configuration of a queue system, as well as, minimizing the cost of queuing of the customers. As the current economies progressively changing into a service dominated one, it is essential to thoroughly understand the know-how to effectively deal with queuing lines to improve customer satisfaction of service. Fast food restaurants are popular among price-sensitive youths and working adults who value the conducive environment and convenient services. McDonald’s chains of restaurants promote their sales by offering package meals which are perceived to be inexpensive. These promotional meals attract good response, resulting in occasional long queues and inconvenient queuing times. However, customers are willing to queue and pay to get food. Restaurants should avoid losing their customers due to a long wait on the line. It is because people today demand not only for quality food but also for speed. Fast food restaurant players explore on the approaches to optimize the efficiency of restaurant management. One important area that defines how well and efficient a fast food restaurant delivers its product and services to customers is by their implementation of the queue management practices at the restaurant and the level of customers satisfaction. A study is conducted to monitor the distribution of queuing time, queue length, customer arrival and departure patterns at a McDonald’s restaurant located in Tampines, Singapore. Thus, the purpose of this study is to propose an online system that will aid in managing queue during the service and hence, to optimize the queuing time. There were few methods involved in order to achieve the objectives, including conducting observation, interview, time study and develop the online booking system. Through this system, it can help to manage queue and improving the customer satisfaction

Method of Creating Ultra-Fine Particles of Materials Using a High-Pressure Mill

A method for creating ultra-fine particles of material using a high-pressure mill is described. The method includes placing a material in a first chamber and subjecting the material to a high-pressure fluid jet to divide it into particles. These particles are then transferred to a second chamber in which they are subjected to cavitation to further divide the particles into relatively smaller particles. These relatively smaller particles are then transferred to a third chamber, in which the particles collide with a collider to still further divide them into ultra-fine particles of the material. The mill of the present invention includes a first chamber having an high-pressure liquid jet nozzle, first and second slurry nozzles, a second cavitation chamber and a third chamber which houses a collider. In one embodiment, the slurry nozzle has an inner surface and sharp edges that project slightly out from the inner surface. Sensors may be located throughout the mill to collect data on the comminution process and to use the data to control the resultant particle size. The product size of the ultra-fine particles made according to the mill of the present invention are preferably less than 15 microns. Further, the particles produced using the mill of the present invention are formed as flakes or platelets which have been broken along nature planes in the material

The Desktop Muon Detector: A simple, physics-motivated machine- and electronics-shop project for university students

This paper describes an undergraduate-level physics project that incorporates various aspects of machine- and electronics-shop technical development. The desktop muon detector is a self-contained apparatus that employs plastic scintillator as a detection medium and a silicon photomultiplier for light collection. These detectors can be used in conjunction with the provided software to make interesting physics measurements. The total cost of each counter is approximately $100.Comment: 29 pages, 14 figure

The Zipf law for random texts with unequal probabilities of occurrence of letters and the Pascal pyramid

We model the generation of words with independent unequal probabilities of occurrence of letters. We prove that the probability $p(r)$ of occurrence of words of rank $r$ has a power asymptotics. As distinct from the paper published earlier by B. Conrad and M. Mitzenmacher, we give a brief proof by elementary methods and obtain an explicit formula for the exponent of the power law.Comment: 4 page

Dynamic Analysis of Executables to Detect and Characterize Malware

It is needed to ensure the integrity of systems that process sensitive information and control many aspects of everyday life. We examine the use of machine learning algorithms to detect malware using the system calls generated by executables-alleviating attempts at obfuscation as the behavior is monitored rather than the bytes of an executable. We examine several machine learning techniques for detecting malware including random forests, deep learning techniques, and liquid state machines. The experiments examine the effects of concept drift on each algorithm to understand how well the algorithms generalize to novel malware samples by testing them on data that was collected after the training data. The results suggest that each of the examined machine learning algorithms is a viable solution to detect malware-achieving between 90% and 95% class-averaged accuracy (CAA). In real-world scenarios, the performance evaluation on an operational network may not match the performance achieved in training. Namely, the CAA may be about the same, but the values for precision and recall over the malware can change significantly. We structure experiments to highlight these caveats and offer insights into expected performance in operational environments. In addition, we use the induced models to gain a better understanding about what differentiates the malware samples from the goodware, which can further be used as a forensics tool to understand what the malware (or goodware) was doing to provide directions for investigation and remediation.Comment: 9 pages, 6 Tables, 4 Figure

Service-based survey of dystonia in Munich

We performed a service-based epidemiological study of dystonia in Munich, Germany. Due to favourable referral and treatment patterns in the Munich area, we could provide confident data from dystonia patients seeking botulinum toxin treatment. A total of 230 patients were ascertained, of whom 188 had primary dystonia. Point prevalence ratios were estimated to be 10.1 (95% confidence interval 8.4-11.9) per 100,000 for focal and 0.3 (0.0-0.6) for generalised primary dystonia. The most common focal primary dystonias were cervical dystonia with 5.4 (4.2-6.7) and essential blepharospasm with 3.1 (2.1-4.1) per 100,000 followed by laryngeal dystonia (spasmodic dysphonia) with 1.0 (0.4-1.5) per 100,000. Copyright (C) 2002 S. Karger AG, Base

Tracking Cyber Adversaries with Adaptive Indicators of Compromise

A forensics investigation after a breach often uncovers network and host indicators of compromise (IOCs) that can be deployed to sensors to allow early detection of the adversary in the future. Over time, the adversary will change tactics, techniques, and procedures (TTPs), which will also change the data generated. If the IOCs are not kept up-to-date with the adversary's new TTPs, the adversary will no longer be detected once all of the IOCs become invalid. Tracking the Known (TTK) is the problem of keeping IOCs, in this case regular expressions (regexes), up-to-date with a dynamic adversary. Our framework solves the TTK problem in an automated, cyclic fashion to bracket a previously discovered adversary. This tracking is accomplished through a data-driven approach of self-adapting a given model based on its own detection capabilities. In our initial experiments, we found that the true positive rate (TPR) of the adaptive solution degrades much less significantly over time than the naive solution, suggesting that self-updating the model allows the continued detection of positives (i.e., adversaries). The cost for this performance is in the false positive rate (FPR), which increases over time for the adaptive solution, but remains constant for the naive solution. However, the difference in overall detection performance, as measured by the area under the curve (AUC), between the two methods is negligible. This result suggests that self-updating the model over time should be done in practice to continue to detect known, evolving adversaries.Comment: This was presented at the 4th Annual Conf. on Computational Science & Computational Intelligence (CSCI'17) held Dec 14-16, 2017 in Las Vegas, Nevada, US