Drug law enforcement : a review of the evaluation literature

This article describes the results of a systematic review of drug law enforcement evaluations. The authors describe the search procedures and document the results in five main categories: international/national interventions (e.g., interdiction and drug seizure), reactive/ directed interventions (e.g., crackdowns, raids, buy-busts, saturation patrol, etc.), proactive/ partnership interventions (e.g., third-party policing, problem-oriented policing, community policing, drug nuisance abatement, etc.), individualized interventions (e.g., arrest referral and diversion), or interventions that used a combination of reactive/directed and proactive/ partnership strategies. Results indicate that proactive interventions involving partnerships between the police and third parties and/or community entities appear to be more effective at reducing both drug and nondrug problems in drug problem places than are reactive/ directed approaches. But the general quality of research in drug law enforcement is poor, the range of interventions that have been evaluated is limited, and more high-quality research is needed across a greater variety of drug interventions.

Speech recognition in adverse environments using lip information

The performance of automatic speech recognition systems deteriorates in the presence of noise. One known solution is to incorporate video information with an existing acoustic speech recognition system. We investigate the performance of the individual acoustic and visual sub-systems and then examine different ways in which the integration of the two systems may be performed. The system is to be implemented in real time on a Texas Instruments' TMS320C80 DSP.

The use of speech and lip modalities for robust speaker verification under adverse conditions

Investigates the use of lip information, in conjunction with speech information, for robust speaker verification in the presence of background noise. We have previously shown (Int. Conf. on Acoustics, Speech and Signal Proc., vol. 6, pp. 3693-3696, May 1998) that features extracted from a speaker's moving lips hold speaker dependencies which are complementary with speech features. We demonstrate that the fusion of lip and speech information allows for a highly robust speaker verification system which outperforms either subsystem individually. We present a new technique for determining the weighting to be applied to each modality so as to optimize the performance of the fused system. Given a correct weighting, lip information is shown to be highly effective for reducing the false acceptance and false rejection error rates in the presence of background noise

Parametric differences between a real-world distributed denial-of-service attack and a flash event

Distributed Denial-of-Service (DDoS) attacks continue to be one of the most pernicious threats to the delivery of services over the Internet. Not only are DDoS attacks present in many guises, they are also continuously evolving as new vulnerabilities are exploited. Hence accurate detection of these attacks still remains a challenging problem and a necessity for ensuring high-end network security. An intrinsic challenge in addressing this problem is to effectively distinguish these Denial-of-Service attacks from similar looking Flash Events (FEs) created by legitimate clients. A considerable overlap between the general characteristics of FEs and DDoS attacks makes it difficult to precisely separate these two classes of Internet activity. In this paper we propose parameters which can be used to explicitly distinguish FEs from DDoS attacks and analyse two real-world publicly available datasets to validate our proposal. Our analysis shows that even though FEs appear very similar to DDoS attacks, there are several subtle dissimilarities which can be exploited to separate these two classes of events.

Street-level drug law enforcement : a meta-analytical review

Our paper presents the results of a meta-analytical review of street level drug law enforcement. We conducted a series of meta-analyses to compare and contrast the effectiveness of four types of drug law enforcement approaches, including community-wide policing, problem-oriented/ partnership approaches that were geographically focused, hotspots policing and standard, unfocused law enforcement efforts. We examined the relative impact of these different crime control tactics on streetlevel drug problems as well as associated problems such as property crime, disorder and violent crime. The results of the meta-analyses, together with examination of forest plots, reveal that problem-oriented policing and geographically-focused interventions involving cooperative partnerships between police and third parties tend to be more effective at controlling drug problems than community-wide policing efforts that are unfocused and spread out across a community. But geographically focused and community-wide drug law enforcement interventions that leverage partnerships are more effective at dealing with drug problems than traditional, law enforcement-only interventions. Our results suggest that the key to successful drug law enforcement lies in the capacity of the police to forge productive partnerships with third parties rather than simply increasing police presence or intervention (e.g., arrests) at drug hotspots.

Dynamic texture reconstruction from sparse codes for unusual event detection in crowded scenes

Unusual event detection in crowded scenes remains challenging because of the diversity of events and noise. In this paper, we present a novel approach for unusual event detection via sparse reconstruction of dynamic textures over an overcomplete basis set, with the dynamic texture described by local binary patterns from three orthogonal planes (LBPTOP). The overcomplete basis set is learnt from the training data where only the normal items observed. In the detection process, given a new observation, we compute the sparse coefficients using the Dantzig Selector algorithm which was proposed in the literature of compressed sensing. Then the reconstruction errors are computed, based on which we detect the abnormal items. Our application can be used to detect both local and global abnormal events. We evaluate our algorithm on UCSD Abnormality Datasets for local anomaly detection, which is shown to outperform current state-of-the-art approaches, and we also get promising results for rapid escape detection using the PETS2009 dataset.

Mussel-inspired porous SiO2 scaffolds with improved mineralization and cytocompatibility for drug delivery and bone tissue engineering

Porous SiO2 scaffolds with mesopore structure (named as MS scaffolds) have been proposed as suitable for bone tissue engineering due to their excellent drug-delivery ability; however, the mineralization and cytocompatibility of MS scaffolds are far from optimal for bone tissue engineering, and it is also unclear how the delivery of drugs from MS scaffolds affects osteoblastic cells. The aims of the present study were to improve the mineralization and cytocompatibility of MS scaffolds by coating mussel-inspired polydopamine on the pore walls of scaffolds. The effects of polydopamine modification on MS scaffolds was investigated with respect to apatite mineralization and the attachment, proliferation and differentiation of bone marrow stromal cells (BMSCs), as was the release profile of the drug dexamethasone (DEX). Our results show that polydopamine can readily coat the pore walls of MS scaffolds and that polydopamine-modified MS scaffolds have a significantly improved apatite-mineralization ability as well as better attachment and proliferation of BMSCs in the scaffolds, compared to controls. Polydopamine modification did not alter the release profile of DEX from MS scaffolds but the sustained delivery of DEX significantly improved alkaline phosphatase (ALP) activity of BMSCs in the scaffolds. These results suggest that polydopamine modification is a viable option to enhance the bioactivity of bone tissue engineering scaffolds and, further, that DEX-loaded polydopamine MS scaffolds have potential uses as a release system to enhance the osteogenic properties of bone tissue engineering applications.

Under the carpet : the politics and trauma of patient harm

Few studies have investigated iatrogenic outcomes from the viewpoint of patient experience. To address this anomaly, the broad aim of this research is to explore the lived experience of patient harm. Patient harm is defined as major harm to the patient, either psychosocial or physical in nature, resulting from any aspect of health care. Utilising the method of Consensual Qualitative Research (CQR), in-depth interviews are conducted with twenty-four volunteer research participants who self-report having been severely harmed by an invasive medical procedure. A standardised measure of emotional distress, the Impact of Event Scale (IES), is additionally employed for purposes of triangulation. Thematic analysis of transcript data indicate numerous findings including: (i) difficulties regarding patients‘ prior understanding of risks involved with their medical procedure; (ii) the problematic response of the health system post-procedure; (iii) multiple adverse effects upon life functioning; (iv) limited recourse options for patients; and (v) the approach desired in terms of how patient harm should be systemically handled. In addition, IES results indicate a clinically significant level of distress in the sample as a whole. To discuss findings, a cross-disciplinary approach is adopted that draws upon sociology, medicine, medical anthropology, psychology, philosophy, history, ethics, law, and political theory. Furthermore, an overall explanatory framework is proposed in terms of the master themes of power and trauma. In terms of the theme of power, a postmodernist analysis explores the politics of patient harm, particularly the dynamics surrounding the politics of knowledge (e.g., notions of subjective versus objective knowledge, informed consent, and open disclosure). This analysis suggests that patient care is not the prime function of the health system, which appears more focussed upon serving the interests of those in the upper levels of its hierarchy. In terms of the master theme of trauma, current understandings of posttraumatic stress disorder (PTSD) are critiqued, and based on data from this research as well as the international literature, a new model of trauma is proposed. This model is based upon the principle of homeostasis observed in biology, whereby within every cell or organism a state of equilibrium is sought and maintained. The proposed model identifies several bio-psychosocial markers of trauma across its three main phases. These trauma markers include: (i) a profound sense of loss; (ii) a lack of perceived control; (iii) passive trauma processing responses; (iv) an identity crisis; (v) a quest to fully understand the trauma event; (vi) a need for social validation of the traumatic experience; and (vii) posttraumatic adaption with the possibility of positive change. To further explore the master themes of power and trauma, a natural group interview is carried out at a meeting of a patient support group for arachnoiditis. Observations at this meeting and members‘ stories in general support the homeostatic model of trauma, particularly the quest to find answers in the face of distressing experience, as well as the need for social recognition of that experience. In addition, the sociopolitical response to arachnoiditis highlights how public domains of knowledge are largely constructed and controlled by vested interests. Implications of the data overall are discussed in terms of a cultural revolution being needed in health care to position core values around a prime focus upon patients as human beings.

Unusual Event Detection in Crowded Scenes Using Bag of LBPs in spatio-temporal patches

Modelling events in densely crowded environments remains challenging, due to the diversity of events and the noise in the scene. We propose a novel approach for anomalous event detection in crowded scenes using dynamic textures described by the Local Binary Patterns from Three Orthogonal Planes (LBP-TOP) descriptor. The scene is divided into spatio-temporal patches where LBP-TOP based dynamic textures are extracted. We apply hierarchical Bayesian models to detect the patches containing unusual events. Our method is an unsupervised approach, and it does not rely on object tracking or background subtraction. We show that our approach outperforms existing state of the art algorithms for anomalous event detection in UCSD dataset.

Planning volunteer responses to low-volume mass gatherings: do event characteristics predict patient workload?

INTRODUCTION: Workforce planning for first aid and medical coverage of mass gatherings is hampered by limited research. In particular, the characteristics and likely presentation patterns of low-volume mass gatherings of between several hundred to several thousand people are poorly described in the existing literature. OBJECTIVES: This study was conducted to: 1. Describe key patient and event characteristics of medical presentations at a series of mass gatherings, including events smaller than those previously described in the literature; 2. Determine whether event type and event size affect the mean number of patients presenting for treatment per event, and specifically, whether the 1:2,000 deployment rule used by St John Ambulance Australia is appropriate; and 3. Identify factors that are predictive of injury at mass gatherings. METHODS: A retrospective, observational, case-series design was used to examine all cases treated by two Divisions of St John Ambulance (Queensland) in the greater metropolitan Brisbane region over a three-year period (01 January 2002-31 December 2004). Data were obtained from routinely collected patient treatment forms completed by St John officers at the time of treatment. Event-related data (e.g., weather, event size) were obtained from event forms designed for this study. Outcome measures include: total and average number of patient presentations for each event; event type; and event size category. Descriptive analyses were conducted using chi-square tests, and mean presentations per event and event type were investigated using Kruskal-Wallis tests. Logistic regression analyses were used to identify variables independently associated with injury presentation (compared with non-injury presentations). RESULTS: Over the three-year study period, St John Ambulance officers treated 705 patients over 156 separate events. The mean number of patients who presented with any medical condition at small events (less than or equal to 2,000 attendees) did not differ significantly from that of large (>2,000 attendees) events (4.44 vs. 4.67, F = 0.72, df = 1, 154, p = 0.79). Logistic regression analyses indicated that presentation with an injury compared with non-injury was independently associated with male gender, winter season, and sporting events, even after adjusting for relevant variables. CONCLUSIONS: In this study of low-volume mass gatherings, a similar number of patients sought medical treatment at small (<2,000 patrons) and large (>2,000 patrons) events. This demonstrates that for low-volume mass gatherings, planning based solely on anticipated event size may be flawed, and could lead to inappropriate levels of first-aid coverage. This study also highlights the importance of considering other factors, such as event type and patient characteristics, when determining appropriate first-aid resourcing for low-volume events. Additionally, identification of factors predictive of injury presentations at mass gatherings has the potential to significantly enhance the ability of event coordinators to plan effective prevention strategies and response capability for these events.