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We propose herein a motion detection artificial vision model which uses analog electronic circuits. The proposed model is comprised of four layers. The first layer is a differentiation circuit of the large capacitor and resistance (CR) coefficient, and the second layer is a differentiation circuit of the small CR coefficient. Thus, the speed of the movement object is detected. The third layer is a difference circuit for detecting the movement direction, and the fourth layer is a multiple circuit for detecting pure motion output. The model was shown to be capable of detecting a movement object in the image. Moreover, the proposed model can be used to detect two or more objects, which is advantageous for detection in an environment in which several objects are moving in multiple directions simultaneously. From a technological viewpoint, the proposed model facilitates clarification of the mechanism of the biomedical vision system, which should enable design and simulation by an analog electric circuit for detecting the movement and speed of objects.

Examines the tenth published year of the ITCRR. Runs the whole gamut of textile innovation, research and testing, some of which investigates hitherto untouched aspects. Subjects discussed include cotton fabric processing, asbestos substitutes, textile adjuncts to cardiovascular surgery, wet textile processes, hand evaluation, nanotechnology, thermoplastic composites, robotic ironing, protective clothing (agricultural and industrial), ecological aspects of fibre properties – to name but a few! There would appear to be no limit to the future potential for textile applications.

This work shows the results of a systematic literature review on biomedical text mining. The purpose of this study is to identify the different text mining approaches used in different application areas of the biomedical domain, the common tools used and the challenges of biomedical text mining as compared to generic text mining algorithms. This study will be of value to biomedical researchers by allowing them to correlate text mining approaches to specific biomedical application areas. Implications for future research are also discussed.

The review was conducted following the principles of the Kitchenham method. A number of research questions were first formulated, followed by the definition of the search strategy. The papers were then selected based on a list of assessment criteria. Each of the papers were analyzed and information relevant to the research questions were extracted.

It was found that researchers have mostly harnessed data sources such as electronic health records, biomedical literature, social media and health-related forums. The most common text mining technique was natural language processing using tools such as MetaMap and Unstructured Information Management Architecture, alongside the use of medical terminologies such as Unified Medical Language System. The main application area was the detection of adverse drug events. Challenges identified included the need to deal with huge amounts of text, the heterogeneity of the different data sources, the duality of meaning of words in biomedical text and the amount of noise introduced mainly from social media and health-related forums.

To the best of the authors’ knowledge, other reviews in this area have focused on either specific techniques, specific application areas or specific data sources. The results of this review will help researchers to correlate most relevant and recent advances in text mining approaches to specific biomedical application areas by providing an up-to-date and holistic view of work done in this research area. The use of emerging text mining techniques has great potential to spur the development of innovative applications, thus considerably impacting on the advancement of biomedical research.

This paper presents a comprehensive pallet-picking approach for forklift robots, comprising a pallet identification and localization algorithm (PILA) to detect and locate the pallet and a vehicle alignment algorithm (VAA) to align the vehicle fork arms with the targeted pallet.

Opposing vision-based methods or point cloud data strategies, we utilize a low-cost RGB-D camera, and thus PILA exploits both RGB and depth data to quickly and precisely recognize and localize the pallet. The developed method guarantees a high identification rate from RGB images and more precise 3D localization information than a depth camera. Additionally, a deep neural network (DNN) method is applied to detect and locate the pallet in the RGB images. Specifically, the point cloud data is correlated with the labeled region of interest (RoI) in the RGB images, and the pallet's front-face plane is extracted from the point cloud. Furthermore, PILA introduces a universal geometrical rule to identify the pallet's center as a “T-shape” without depending on specific pallet types. Finally, VAA is proposed to implement the vehicle approaching and pallet picking operations as a “proof-of-concept” to test PILA’s performance.

Experimentally, the orientation angle and centric location of the two kinds of pallets are investigated without any artificial marking. The results show that the pallet could be located with a three-dimensional localization accuracy of 1 cm and an angle resolution of 0.4 degrees at a distance of 3 m with the vehicle control algorithm.

PILA’s performance is limited by the current depth camera’s range (< = 3 m), and this is expected to be improved by using a better depth measurement device in the future.

The results demonstrate that the pallets can be located with an accuracy of 1cm along the x, y, and z directions and affording an angular resolution of 0.4 degrees at a distance of 3m in 700ms.

In the USA, there has recently been an unprecedented convergence of complementary/alternative medicine (CAM) with mainstream biomedical care. This confluence may lead to a deeply rooted philosophical conflict. This qualitative study works to identify factors that health-care leaders can use, which will build a pathway to greater integrative practice between medical doctors and CAM practitioners – from parallel existence to partnership – by examining the tensions between biomedical medicine and naturopathic medicine. The purpose of this study is to offer short-term suggestions for partnership and long-term recommendations for better understanding.

An original qualitative study using semi-structured with CAM practitioners and biomedical practitioners.

Areas of conflict that are preventing synergy are identified and a pathway for health-care leaders to follow to create greater integration and partnerships is suggested.

This is a qualitative and exploratory study that has significant limitations on generalizability.

This study suggest steps that both types of health-care practitioners can take to increase their success at working together on an individual level, a group level, an organizational level and on an industry-wide basis, as well as provide a specific pathway to create greater integrative practice for health-care leaders.

The results indicate that stronger partnerships between different types of medical practitioners increase patient choice, patient satisfaction and outcomes.

Increasing interested in CAM modalities is driving more contact between CAM practitioners and biomedical practitioners. This contact is best established in partnership between practitioners rather than in parallel. This original research outlines the sources of conflict and provides recommendations for encouraging greater synergy.

To examine how clinicians navigate providing treatment to Borderline Personality Disorder (BPD) in the context of the DSM 5, deinstitutionalization, and the biomedical model.

We conducted 39 interviews with mental health providers in the United States in a two-year period preceding and following the release of the DSM 5. Using Constructivist Grounded Theory, we analyzed the data for themes that emerged.

Clinicians faced pressures from insurance companies, the DSM categories, and their professional training to focus on biomedical treatments. These treatments, which emphasized pharmaceuticals and short courses of care, were ill-suited to BPD, which has a strong evidence base recommending long-term therapeutic interventions. We term this contradiction a “biomedical mismatch” and use Gidden’s concept of structuration to better understand how clinicians navigate the system of care. Providers ranged in their responses to the mismatch: some championed biomedicine, others were complicit, and a final group behaved as activists, challenging the paradigm. The sum of the strategies had downstream effects which included crisis reinstitutionalization and a discourse of untreatability. Ultimately, we discuss how social factors such as gender bias, stigma, and trauma are insufficiently represented in the biomedical model of care for BPD.

BPD fits poorly within the biomedical underpinnings of the current system. Accordingly, it illuminates the structuration of health care and where the rules of care break down. More precisely, deinstitutionalization was designed to remove patients from long courses of inpatient care. Many patients with BPD have failed to experience this outcome, with some patients now cycling through long courses of short-term crisis reinstitutionalization instead of having effective outpatient care over long periods. This unintended consequence of deinstitutionalization calls for a more biopsychosocial response to BPD.