Generalized quantum measurement

We overcome one of Bell's objections to `quantum measurement' by generalizing the definition to include systems outside the laboratory. According to this definition a {\sl generalized quantum measurement} takes place when the value of a classical variable is influenced significantly by an earlier state of a quantum system. A generalized quantum measurement can then take place in equilibrium systems, provided the classical motion is chaotic. This paper deals with this classical aspect of quantum measurement, assuming that the Heisenberg cut between the quantum dynamics and the classical dynamics is made at a very small scale. For simplicity, a gas with collisions is modelled by an `Arnold gas'.Comment: 11 pages, LaTeX, no figures, title change

Factors Associated with Depression among University Students in Malaysia: A Cross-sectional Study

Depression is a recurrent mental health problem among younger demographics, and university students are particularly susceptible owing to stress, workload and independent living, amongst other factors.  This study explores the prevalence of depression and the factors influencing depression among university students in Malaysia. This cross-sectional study involved 1,023 university students (response rate 90.4%). Depression was assessed using the Centre for Epidemiological Studies Short Depression Scale (CESD -10). Binary logistic regression was used to determine predictors of depression based on sociodemographic, physiological, lifestyle, and health characteristics. Approximately 30% of respondents experienced depression, and 4.4% of this category suffered severe depression. This study demonstrates that instances of depression were 2.52 times higher (95% CI: 1.71-3.71) in second year students compared to first year students, and 1.63 times higher (95% CI: 1.08-2.45) in students staying outside campus compared to students staying inside campus. Students from poor, not well-off, and quite well-off family background had 15.26 (95% CI: 2.77-84.88), 4.85 (95% CI: 1.01-23.34) and 5.62 times (95% CI: 1.16-27.25) higher chance for depression than wealthier students, respectively. Students with mild, moderate, and severe sleeping problems were 2.50 times (95% CI: 1.61-3.88), 3.34 times (95% CI: 2.18-5.11), and 3.66 times (95% CI: 1. 93 -6. 94) more likely to be depressed than those without sleeping problem, respectively. Students with post-traumatic stress disorder (PTSD) were 1.42 times higher (95% CI: 1.07-2.56) to suffer from depression. This study concludes that higher education institutions need to pay special attention to the mental health of those students especially those in their second year, living off campus, from lower economic backgrounds, with sleeping problem, or suffering PTSD

WristTrack? A Mobile Healthcare Surveillance System for Wrist Recovery Exercises

Physiotherapy is an important component for injury recovery. Progressive exercises can help in facilitating recovery when executed correctly, but it can cause damaging effects when done wrongly. However, not all patients are able to make it to the hospital every time due to reasons such as post-surgery immobility. This study introduces a self-served physiotherapy system for wrist exercises (i.e., WristTrack), allowing patients to perform wrist conditioning at their own time and place of convenience. The system integrates wearable devices with mobile and web platform, to capture, visualize and provide useful metrics for both doctors and patients to make steady progression in their recovery process

Recycling of Pretreated Polyolefin-Based Ocean-Bound Plastic Waste by Incorporating Clay and Rubber

Plastic waste found in oceans has become a major concern because of its impact on marine organisms and human health. There is significant global interest in recycling these materials, but their reclamation, sorting, cleaning, and reprocessing, along with the degradation that occurs in the natural environment, all make it difficult to achieve high quality recycled resins from ocean plastic waste. To mitigate these limitations, various additives including clay and rubber were explored. In this study, we compounded different types of ocean-bound (o-HDPE and o-PP) and virgin polymers (v-LDPE and v-PS) with various additives including a functionalized clay, styrene-multi-block-copolymer (SMB), and ethylene-propylene-based rubber (EPR). Physical observation showed that all blends containing PS were brittle due to the weak interfaces between the polyolefin regions and the PS domains within the polymer blend matrix. Blends containing clay showed rough surfaces and brittleness because of the non-uniform distribution of clay particles in the polymer matrix. To evaluate the properties and compatibility of the blends, characterizations using differential scanning calorimetry (DSC), scanning electron microscopy (SEM), and small-amplitude oscillatory shear (SAOS) rheology were carried out. The polymer blend (v-LDPE, o-HDPE, o-PP) containing EPR showed improved elasticity. Incorporating additives such as rubber could improve the mechanical properties of polymer blends for recycling purposes

Spatiotemporal maintenance of flora in the Himalaya biodiversity hotspot:Current knowledge and future perspectives

Mountain ecosystems support a significant one‐third of all terrestrial biodiversity, but our understanding of the spatiotemporal maintenance of this high biodiversity remains poor, or at best controversial. The Himalaya hosts a complex mountain ecosystem with high topographic and climatic heterogeneity and harbors one of the world's richest floras. The high species endemism, together with increasing anthropogenic threats, has qualified the Himalaya as one of the most significant global biodiversity hotspots. The topographic and climatic complexity of the Himalaya makes it an ideal natural laboratory for studying the mechanisms of floral exchange, diversification, and spatiotemporal distributions. Here, we review literature pertaining to the Himalaya in order to generate a concise synthesis of the origin, distribution, and climate change responses of the Himalayan flora. We found that the Himalaya supports a rich biodiversity and that the Hengduan Mountains supplied the majority of the Himalayan floral elements, which subsequently diversified from the late Miocene onward, to create today's relatively high endemicity in the Himalaya. Further, we uncover links between this Miocene diversification and the joint effect of geological and climatic upheavals in the Himalaya. There is marked variance regarding species dispersal, elevational gradients, and impact of climate change among plant species in the Himalaya, and our review highlights some of the general trends and recent advances on these aspects. Finally, we provide some recommendations for conservation planning and future research. Our work could be useful in guiding future research in this important ecosystem and will also provide new insights into the maintenance mechanisms underpinning other mountain systems

Recognizing recurrent neural networks (rRNN): Bayesian inference for recurrent neural networks

Recurrent neural networks (RNNs) are widely used in computational neuroscience and machine learning applications. In an RNN, each neuron computes its output as a nonlinear function of its integrated input. While the importance of RNNs, especially as models of brain processing, is undisputed, it is also widely acknowledged that the computations in standard RNN models may be an over-simplification of what real neuronal networks compute. Here, we suggest that the RNN approach may be made both neurobiologically more plausible and computationally more powerful by its fusion with Bayesian inference techniques for nonlinear dynamical systems. In this scheme, we use an RNN as a generative model of dynamic input caused by the environment, e.g. of speech or kinematics. Given this generative RNN model, we derive Bayesian update equations that can decode its output. Critically, these updates define a 'recognizing RNN' (rRNN), in which neurons compute and exchange prediction and prediction error messages. The rRNN has several desirable features that a conventional RNN does not have, for example, fast decoding of dynamic stimuli and robustness to initial conditions and noise. Furthermore, it implements a predictive coding scheme for dynamic inputs. We suggest that the Bayesian inversion of recurrent neural networks may be useful both as a model of brain function and as a machine learning tool. We illustrate the use of the rRNN by an application to the online decoding (i.e. recognition) of human kinematics