Maximal Associated Regression: A nonlinear extension to Least Angle Regression

This paper proposes Maximal Associated Regression (MAR), a novel algorithm that performs forward stage-wise regression by applying nonlinear transformations to fit predictor covariates. For each predictor, MAR selects between a linear or additive fit as determined by the dataset. The proposed algorithm is an adaptation of Least Angle Regression (LARS) and retains its efficiency in building sparse models. Constrained penalized splines are used to generate smooth nonlinear transformations for the additive fits. A monotonically constrained extension of MAR (MARm) is also introduced in this paper to fit isotonic regression problems. The proposed algorithms are validated on both synthetic and real datasets. The performances of MAR and MARm are compared against LARS, Generalized Linear Models (GLM), and Generalized Additive Models (GAM) under the Gaussian assumption with a unity link function. Results indicate that MAR-type algorithms achieve a superior subset selection accuracy, generating sparser models that generalize well to new data. MAR is also able to generate models for sample deficient datasets. Thus, MAR is proposed as a valuable tool for subset selection and data exploration, especially when a priori knowledge of the dataset is unavailable

An inventory of human light exposure behaviour

Light exposure is an essential driver of health and well-being, and individual behaviours during rest and activity modulate physiologically relevant aspects of light exposure. Further understanding the behaviours that influence individual photic exposure patterns may provide insight into the volitional contributions to the physiological effects of light and guide behavioural points of intervention. Here, we present a novel, self-reported and psychometrically validated inventory to capture light exposure-related behaviour, the Light Exposure Behaviour Assessment (LEBA). An expert panel prepared the initial 48-item pool spanning different light exposure-related behaviours. Responses, consisting of rating the frequency of engaging in the per-item behaviour on a five-point Likert-type scale, were collected in an online survey yielding responses from a geographically unconstrained sample (690 completed responses, 74 countries, 28 time zones). The exploratory factor analysis (EFA) on an initial subsample (n = 428) rendered a five-factor solution with 25 items (wearing blue light filters, spending time outdoors, using a phone and smartwatch in bed, using light before bedtime, using light in the morning and during daytime). In a confirmatory factor analysis (CFA) performed on an independent subset of participants (n = 262), we removed two additional items to attain the best fit for the five-factor solution (CFI = 0.95, TLI = 0.95, RMSEA = 0.06). The internal consistency reliability coefficient for the total instrument yielded McDonald’s Omega = 0.68. Measurement model invariance analysis between native and non-native English speakers showed our model attained the highest level of invariance (residual invariance CFI = 0.95, TLI = 0.95, RMSEA = 0.05). Lastly, a short form of the LEBA (n = 18 items) was developed using Item Response Theory on the complete sample (n = 690). The psychometric properties of the LEBA indicate the usability for measuring light exposure-related behaviours. The instrument may offer a scalable solution to characterise behaviours that influence individual photic exposure patterns in remote samples. The LEBA inventory is available under the open-access CC-BY license. Instrument webpage: https://leba-instrument.org/ GitHub repository containing this manuscript: https://github.com/leba-instrument/leba-manuscript

Aging and Urban Mobility in Bandar Sunway: A Holistic Approach

Human longevity is constantly changing the demographic outlook of the world’s population and older people are projected to double in the next 30 years from 11% to 22% of the world’s population. Malaysia is no exception and, like most western and developing nations, the number of Malaysians aged 60 years and above has been gradually rising from the 1970s onwards and is currently estimated to represent 10% of its population. This has created an urgent need to develop age-friendly cities, so that older individuals living in urban areas can have an improved life. It is important that the aging population continues to lead healthy and productive lives as far as possible. In this project, which is a work in progress, we surveyed a suburban community, aged 50 years and above, residing in Bandar Sunway and its vicinity in the state of Selangor Darul Ehsan, Malaysia. The aim was to assess their health and perceptions on mobility through targeted questionnaires, in-depth interviews and focus groups and identify the factors associated with healthy aging in a holistic manner. The overall goal is to promote a healthy mind in a healthy body despite the advancing years. In the preliminary phase we surveyed 73 participants aged between 52 – 85 years and compared responses and clinical parameters for individuals below (N = 36) and ≥ 65 years (N = 37) in age. Based on their Body Mass Index (BMI), the participants were generally healthy with a normal BMI (45%) or slightly overweight (41%) with a higher BMI and blood lipid levels. There were no significant differences in the cognitive assessments between the two age groups (p = 0.945). A majority (70%) of the participants were satisfied with their lives in Bandar Sunway, but some reported several health related issues and chronic diseases. However, this was not a factor that hindered their quality of life. Older adults in Bandar Sunway still preferred driving their own vehicles instead of taking public transports. This was due to several shortfalls in the transportation systems: pricing, schedules of transport, safety, and cleanliness. Preliminary results have identified several aspects of public transportation in urban areas that can be improved to better serve the aging community. In doing so, we anticipate the findings and recommendations will be applicable to a much wider community in Malaysia and other parts of the world. The project is aligned with the theme of improving health and well-being and will provide a model for understanding and dealing with aging in the local community

Optimised spectral effects of programmable LED arrays (PLA)s on bioelectricity generation from algal-biophotovoltaic devices.

Funder: Higher Institution Centre of Excellence (HICoE) Fund, Ministry of Education: Air ocean and land interaction; Grant(s): IOES-2014FThe biophotovoltaic cell (BPV) is deemed to be a potent green energy device as it demonstrates the generation of renewable energy from microalgae; however, inadequate electron generation from microalgae is a significant impediment for functional employment of these cells. The photosynthetic process is not only affected by the temperature, CO2 concentration and light intensity but also the spectrum of light. Thus, a detailed understanding of the influences of light spectrum is essential. Accordingly, we developed spectrally optimized light using programmable LED arrays (PLA)s to study the effect on algae growth and bioelectricity generation. Chlorella is a green microalga and contains chlorophyll-a (chl-a), which is the major light harvesting pigment that absorbs light in the blue and red spectrum. In this study, Chlorella is grown under a PLA which can optimally simulate the absorption spectrum of the pigments in Chlorella. This experiment investigated the growth, photosynthetic performance and bioelectricity generation of Chlorella when exposed to an optimally-tuned light spectrum. The algal BPV performed better under PLA with a peak power output of 0.581 mW m-2 for immobilized BPV device on day 8, which is an increase of 188% compared to operation under a conventional white LED light source. The photosynthetic performance, as measured using pulse amplitude modulation (PAM) fluorometry, showed that the optimized spectrum from the PLA gave an increase of 72% in the rETRmax value (190.5 μmol electrons m-2 s-1), compared with the conventional white light source. Highest algal biomass (1100 mg L-1) was achieved in the immobilized system on day eight, which translates to a carbon fixation of 550 mg carbon L-1. When artificial light is used for the BPV system, it should be optimized with the light spectrum and intensity best suited to the absorption capability of the pigments in the cells. Optimum artificial light source with algal BPV device can be integrated into a power management system for low power application (eg. environment sensor for indoor agriculture system)

Optimized coloured light for enhancing colour discrimination in blurred and cloudy visions

International audienc