Pull-out strength of steel roof and wall cladding systems

When steel roof and wall cladding systems are subjected to wind uplift/suction forces, local pull-through/dimpling failures or pull-out failures occur prematurely at their screwed connections. During extreme wind events such as storms and hurricanes, these localized failures then lead to severe damage to buildings and their contents. An investigation was therefore carried out to study the failure that occurs when the screw fastener pulls out of the steel battens, purlins, or girts. Both two-span cladding tests and small-scale tests were conducted using a range of commonly used screw fasteners and steel battens, purlins, and girts. Experimental results showed that the current design formula may not be suitable unless a reduced capacity factor of 0.4 is used. Therefore, an improved design formula has been developed for pull-out failures in steel cladding systems. The formula takes into account thickness and ultimate tensile strength of steel, along with thread diameter and the pitch of screw fasteners, in order to model the pull-out behavior more accurately. This paper presents the details of this experimental investigation and its results.

Cyclic pull-out strength of steel roof and wall cladding systems

When crest-fixed thin steel roof cladding systems are subjected to wind uplift, local pull-through or pull-out failures occur prematurely at their screwed connections. During high wind events such as storms and cyclones these localised failures then lead to severe damage to buildings and their contents. In recent times, the use of thin steel battens/purlins has increased considerably. This has made the pull-out failures more critical in the design of steel cladding systems. Recent research has developed a design formula for the static pull-out strength of steel cladding systems. However, the effects of fluctuating wind uplift loading that occurs during high wind events are not known. Therefore a series of constant amplitude cyclic tests has been undertaken on connections between steel battens made of different thicknesses and steel grades, and screw fasteners with varying diameter and pitch. This paper presents the details of these cyclic tests and the results.

Intrusion detection in distributed systems, an approach based on taint marking

This paper presents a new framework for distributed intrusion detection based on taint marking. Our system tracks information flows between applications of multiple hosts gathered in groups (i.e., sets of hosts sharing the same distributed information flow policy) by attaching taint labels to system objects such as files, sockets, Inter Process Communication (IPC) abstractions, and memory mappings. Labels are carried over the network by tainting network packets. A distributed information flow policy is defined for each group at the host level by labeling information and defining how users and applications can legally access, alter or transfer information towards other trusted or untrusted hosts. As opposed to existing approaches, where information is most often represented by two security levels (low/high, public/private, etc.), our model identifies each piece of information within a distributed system, and defines their legal interaction in a fine-grained manner. Hosts store and exchange security labels in a peer to peer fashion, and there is no central monitor. Our IDS is implemented in the Linux kernel as a Linux Security Module (LSM) and runs standard software on commodity hardware with no required modification. The only trusted code is our modified operating system kernel. We finally present a scenario of intrusion in a web service running on multiple hosts, and show how our distributed IDS is able to report security violations at each host level.

Creeping insulation - Hong Kong green wall

Hong Kong is a densely populated city suffering badly from the urban heat island effect. Green wall offers a means of ameliorating the situation but there are doubts over its suitability in Hong Kong’s unique environment. In this paper, we look at the potential for green walls in Hong Kong first by summarising some of the Chinese green walling systems and associated vegetation in use, then by an introduction to three existing green walls in Hong Kong, and finally through a small experiment aimed at identifying the likely main effects of green walled housing. The results indicate that green walling in Hong Kong is likely to provide enhanced internal house environment in terms of warm weather temperature reduction, stabilisation and damping, with direct energy savings in air-conditioning and indirect district benefits of reduced heat island effect and carbon emissions. The green walling insulation properties also suggest the possibility of warmer homes in winter and/or energy savings in mechanical heating provision.

Creeping insulation - Hong Kong green wall

Hong Kong is a densely populated city suffering badly from the urban heat island effect. Green wall offers a means of ameliorating the situation but there are doubts over its suitability in Hong Kong’s unique environment. In this paper, we look at the potential for green walls in Hong Kong first by summarizing some of the Chinese green walling systems and associated vegetation in use, then by an introduction to three existing green walls in Hong Kong, and finally through a small experiment aimed at identifying the likely main effects of green walled housing. The results indicate that green walling in Hong Kong is likely to provide enhanced internal house environment in terms of warm weather temperature reduction, stabilization and damping, with direct energy savings in air-conditioning and indirect district benefits of reduced heat island effect and carbon emissions. The green walling insulation properties also suggest the possibility of warmer homes in winter and/or energy savings in mechanical heating provision.

Real-time video event detection in crowded scenes using MPEG derived features : a multiple instance learning approach

This paper presents an investigation into event detection in crowded scenes, where the event of interest co-occurs with other activities and only binary labels at the clip level are available. The proposed approach incorporates a fast feature descriptor from the MPEG domain, and a novel multiple instance learning (MIL) algorithm using sparse approximation and random sensing. MPEG motion vectors are used to build particle trajectories that represent the motion of objects in uniform video clips, and the MPEG DCT coefficients are used to compute a foreground map to remove background particles. Trajectories are transformed into the Fourier domain, and the Fourier representations are quantized into visual words using the K-Means algorithm. The proposed MIL algorithm models the scene as a linear combination of independent events, where each event is a distribution of visual words. Experimental results show that the proposed approaches achieve promising results for event detection compared to the state-of-the-art.

Advances in skin cancer early detection and diagnosis

OBJECTIVES: To provide an overview of 1) traditional methods of skin cancer early detection, 2) current technologies for skin cancer detection, and 3) evolving practice models of early detection. DATA SOURCES: Peer-reviewed databased articles and reviews, scholarly texts, and Web-based resources. CONCLUSION: Early detection of skin cancer through established methods or newer technologies is critical for reducing both skin cancer mortality and the overall skin cancer burden. IMPLICATIONS FOR NURSING PRACTICE: A basic knowledge of recommended skin examination guidelines and risk factors for skin cancer, traditional methods to further examine lesions that are suspicious for skin cancer and evolving detection technologies can guide patient education and skin inspection decisions.

Skin cancer detection by one click - are we any closer?

To the editor...

The detection of incipient faults in small multi-cylinder diesel engines using multiple acoustic emission sensors

This thesis investigates condition monitoring (CM) of diesel engines using acoustic emission (AE) techniques. The AE signals recorded from a small size diesel engine are mixtures of multiple sources from multiple cylinders. Thus, it is difficult to interpret the information conveyed in the signals for CM purposes. This thesis develops a series of practical signal processing techniques to overcome this problem. Various experimental studies conducted to assess the CM capabilities of AE analysis for diesel engines. A series of modified signal processing techniques were proposed. These techniques showed promising results of capability for CM of multiple cylinders diesel engine using multiple AE sensors.

Enabling aircraft emergency landings using active visual site detection

The ability to automate forced landings in an emergency such as engine failure is an essential ability to improve the safety of Unmanned Aerial Vehicles operating in General Aviation airspace. By using active vision to detect safe landing zones below the aircraft, the reliability and safety of such systems is vastly improved by gathering up-to-the-minute information about the ground environment. This paper presents the Site Detection System, a methodology utilising a downward facing camera to analyse the ground environment in both 2D and 3D, detect safe landing sites and characterise them according to size, shape, slope and nearby obstacles. A methodology is presented showing the fusion of landing site detection from 2D imagery with a coarse Digital Elevation Map and dense 3D reconstructions using INS-aided Structure-from-Motion to improve accuracy. Results are presented from an experimental flight showing the precision/recall of landing sites in comparison to a hand-classified ground truth, and improved performance with the integration of 3D analysis from visual Structure-from-Motion.

An evaluation of different features and learning models for anomalous event detection

The huge amount of CCTV footage available makes it very burdensome to process these videos manually through human operators. This has made automated processing of video footage through computer vision technologies necessary. During the past several years, there has been a large effort to detect abnormal activities through computer vision techniques. Typically, the problem is formulated as a novelty detection task where the system is trained on normal data and is required to detect events which do not fit the learned ‘normal’ model. There is no precise and exact definition for an abnormal activity; it is dependent on the context of the scene. Hence there is a requirement for different feature sets to detect different kinds of abnormal activities. In this work we evaluate the performance of different state of the art features to detect the presence of the abnormal objects in the scene. These include optical flow vectors to detect motion related anomalies, textures of optical flow and image textures to detect the presence of abnormal objects. These extracted features in different combinations are modeled using different state of the art models such as Gaussian mixture model(GMM) and Semi- 2D Hidden Markov model(HMM) to analyse the performances. Further we apply perspective normalization to the extracted features to compensate for perspective distortion due to the distance between the camera and objects of consideration. The proposed approach is evaluated using the publicly available UCSD datasets and we demonstrate improved performance compared to other state of the art methods.

Complete genomic sequence of a Rubus yellow net virus isolate and detection of genome-wide pararetrovirus-derived small RNAs

Rubus yellow net virus (RYNV) was cloned and sequenced from a red raspberry (Rubus idaeus L.) plant exhibiting symptoms of mosaic and mottling in the leaves. Its genomic sequence indicates that it is a distinct member of the genus Badnavirus, with 7932. bp and seven ORFs, the first three corresponding in size and location to the ORFs found in the type member Commelina yellow mottle virus. Bioinformatic analysis of the genomic sequence detected several features including nucleic acid binding motifs, multiple zinc finger-like sequences and domains associated with cellular signaling. Subsequent sequencing of the small RNAs (sRNAs) from RYNV-infected R. idaeus leaf tissue was used to determine any RYNV sequences targeted by RNA silencing and identified abundant virus-derived small RNAs (vsRNAs). The majority of the vsRNAs were 22-nt in length. We observed a highly uneven genome-wide distribution of vsRNAs with strong clustering to small defined regions distributed over both strands of the RYNV genome. Together, our data show that sequences of the aphid-transmitted pararetrovirus RYNV are targeted in red raspberry by the interfering RNA pathway, a predominant antiviral defense mechanism in plants. © 2013.

HMM relative entropy rate concepts for vision-based aircraft manoeuvre detection

Machine vision is emerging as a viable sensing approach for mid-air collision avoidance (particularly for small to medium aircraft such as unmanned aerial vehicles). In this paper, using relative entropy rate concepts, we propose and investigate a new change detection approach that uses hidden Markov model filters to sequentially detect aircraft manoeuvres from morphologically processed image sequences. Experiments using simulated and airborne image sequences illustrate the performance of our proposed algorithm in comparison to other sequential change detection approaches applied to this application.

Practical Modbus flooding attack and detection

The Modicon Communication Bus (Modbus) protocol is one of the most commonly used protocols in industrial control systems. Modbus was not designed to provide security. This paper confirms that the Modbus protocol is vulnerable to flooding attacks. These attacks involve injection of commands that result in disrupting the normal operation of the control system. This paper describes a set of experiments that shows that an anomaly-based change detection algorithm and signature-based Snort threshold module are capable of detecting Modbus flooding attacks. In comparing these intrusion detection techniques, we find that the signature-based detection requires a carefully selected threshold value, and that the anomaly-based change detection algorithm may have a short delay before detecting the attacks depending on the parameters used. In addition, we also generate a network traffic dataset of flooding attacks on the Modbus control system protocol.