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Samuel R Nyman,1 Wendy Ingram,2 Jeanette Sanders,2 Peter W Thomas,3 Sarah Thomas,3 Michael Vassallo,4 James Raftery,5 Iram Bibi,1 Yolanda Barrado-Martín1 1Department of Psychology and Ageing & Dementia Research Centre, Faculty of Science and Technology, Bournemouth University, Poole House, Talbot Campus, Poole, Dorset BH12 5BB, UK; 2Peninsula Clinical Trials Unit, Peninsula Medical School, University of Plymouth, Plymouth, Devon PL4 8AA, UK; 3Bournemouth University Clinical Research Unit, Faculty of Health and Social Sciences, Bournemouth University, Royal London House, Lansdowne Campus, Bournemouth, Dorset BH1 3LT, UK; 4Centre of Postgraduate Medical Research and Education, Faculty of Health and Social Sciences, Bournemouth University, Royal London House, Lansdowne Campus, Bournemouth, Dorset BH1 3LT, UK; 5Faculty of Medicine, University of Southampton, Highfield Campus, Southampton, SO17 1BJ, UKCorrespondence: Samuel R NymanDepartment of Medical Science and Public Health, Faculty of Health and Social Sciences, Bournemouth University, Poole House, Talbot Campus, Poole, Dorset BH12 5BB, UKTel +44 1202 968179Email snyman@bournemouth.ac.ukPurpose: To investigate the effect of Tai Chi exercise on postural balance among people with dementia (PWD) and the feasibility of a definitive trial on falls prevention.Patients and methods: Dyads, comprising community-dwelling PWD and their informal carer (N=85), were randomised to usual care (n=43) or usual care plus weekly Tai Chi classes and home practice for 20 weeks (n=42). The primary outcome was the timed up and go test. All outcomes for PWD and their carers were assessed six months post-baseline, except for falls, which were collected prospectively over the six-month follow-up period.Results: For PWD, there was no significant difference at follow-up on the timed up and go test (mean difference [MD] = 0.82, 95% confidence interval [CI] = −2.17, 3.81). At follow-up, PWD in the Tai Chi group had significantly higher quality of life (MD = 0.051, 95% CI = 0.002, 0.100, standardised effect size [ES] = 0.51) and a significantly lower rate of falls (rate ratio = 0.35, 95% CI =0.15, 0.81), which was no longer significant when an outlier was removed. Carers in the Tai Chi group at follow-up were significantly worse on the timed up and go test (MD = 1.83, 95% CI = 0.12, 3.53, ES = 0.61). The remaining secondary outcomes were not significant. No serious adverse events were related to participation in Tai Chi.Conclusion: With refinement, this Tai Chi intervention has potential to reduce the incidence of falls and improve quality of life among community-dwelling PWD [Trial registration: NCT02864056].Keywords: accidental falls, clinical trial, cognitive impairment, exercise, intervention

Determining the physical and chemical characteristics of municipal solid waste (MSW) aids in assessing the potential environmental impacts of any waste management initiative. The study aimed to determine the basic physical and chemical characteristics of urban MSW from two Pakistani megacities (Lahore and Rawalpindi). Physical, proximate, and ultimate micronutrient analyses, as well as an energy potential analysis, were performed on the sampled MSW. Physically, there was 65–70% biogenic content and 40–53% moisture content. Elemental contents were found to be carbon (38–48%) > oxygen (18–21%) > hydrogen (4–4.3%) > nitrogen (0.5–0.9%) > sulfur (0.4–0.8%). Carbohydrates are the most abundant (32–37%) micronutrients. The combustible fraction of waste has a high calorific value, an indication of its energy potential. The MSW generated by Rawalpindi (880 tons per day) and Lahore (6100 tons per day) was found to be suitable for energy production of 6213 and 40,897 MWh, respectively. The MSW of Rawalpindi and Lahore can generate 49 MW per day and 341 MW per day of electricity, respectively. MSW has the potential to be a valuable renewable energy source for Pakistan if appropriate policies and waste management technologies (such as thermochemical treatments and biochemical treatments) are implemented.

The digital revolution requires greater reliability from electric power systems. However, predicting the growth of electricity demand is challenging as there is still much uncertainty in terms of demographics, industry changes, and irregular consumption patterns. Machine learning has emerged as a powerful tool, particularly with the latest developments in deep learning. Such tools can predict electricity demand and, thus, contribute to better decision-making by energy managers. However, it is important to recognize that there are no efficient methods for forecasting peak demand growth. In addition, features that add complexity, such as climate change and economic growth, take time to model. Therefore, these new tools can be integrated with other proven tools that can be used to model specific system structures, such as system dynamics. This research proposes a unique framework to support decision-makers in dealing with daily activities while attentively tracking monthly peak demand. This approach integrates advances in machine learning and system dynamics. This integration has the potential to contribute to more precise forecasts, which can help to develop strategies that can deal with supply and demand variations. A real-world case study was used to comprehend the needs of the environment and the effects of COVID-19 on power systems; it also helps to demonstrate the use of leading-edge tools, such as convolutional neural networks (CNNs), to predict electricity demand. Three well-known CNN variants were studied: a multichannel CNN, CNN-LSTM, and a multi-head CNN. This study found that the multichannel CNN outperformed all the models, with an R2 of 0.92 and a MAPE value of 1.62% for predicting the month-ahead peak demand. The multichannel CNN consists of one main model that processes four input features as a separate channel, resulting in one feature map. Furthermore, a system dynamics model was introduced to model the energy sector’s dynamic behavior (i.e., residential, commercial, and government demands, etc.). The calibrated model reproduced the historical data curve fairly well between 2005 and 2017, with an R2 value of 0.94 and a MAPE value of 4.8%.

Biological toxicity testing plays an essential role in identifying the possible negative effects induced by substances such as organic pollutants or heavy metals. As an alternative to conventional methods of toxicity detection, paper-based analytical device (PAD) offers advantages in terms of convenience, quick results, environmental friendliness, and cost-effectiveness. However, detecting the toxicity of both organic pollutants and heavy metals is challenging for a PAD. Here, we show the evaluation of biotoxicity testing for chlorophenols (pentachlorophenol, 2,4-dichlorophenol, and 4-chlorophenol) and heavy metals (Cu2+, Zn2+, and Pb2+) by a resazurin-integrated PAD. The results were achieved by observing the colourimetric response of bacteria (Enterococcus faecalis and Escherichia coli) to resazurin reduction on the PAD. The toxicity responses of E. faecalis-PAD and E. coli-PAD to chlorophenols and heavy metals can be read within 10 min and 40 min, respectively. Compared to the traditional growth inhibition experiments for toxicity measuring which takes at least 3 h, the resazurin-integrated PAD can recognize toxicity differences between studied chlorophenols and between studied heavy metals within 40 min.

The present study was conducted in the Lahore district to analyze the impact of Farmer Field School (FFS) approach on the competency of the farmers. The population for the study was consisted of both the registered and non-registered farmers under the FFS approach in the public sector in Lahore district. A sample was drawn from the entire population of twenty four FFSs of Lahore district, by using simple random sampling technique. Out of twenty four, eight FFSs were selected randomly and from each FFS eight registered farmers was interviewed, thus the total sample size of registered farmers was sixty four. The non-registered farmers (non-member farmers) were also interviewed to make the research result more authenticated. Thus the number of non-registered farmers was also sixty four. Hence the total sample frame was 128 farmers. The data were collected with the help of a pre-tested and validated interview schedule especially designed for this purpose. The data were analyzed by using computer software Statistical Package for Social Sciences (SPSS). Analysis of the data shows that the farmers' competency in the conduction of agricultural activities land preparation and direct sowing were top in rank order in member and non-members' farmers. While on the other hand member farmers' competency in controlling harmful insects was at the bottom in rank order, however, non-members had lowest competency in conduction of Agro Ecosystem Analysis (AESA) activity. T-test clearly indicates that all aspects of competency in undertaking the agricultural activities had significant difference in favour of member farmers. It means member farmers had more competency in agriculture as compared to non-member farmers. [ABSTRACT FROM AUTHOR]

The dominant spider species of order Araneae include Oxyopes javanus (Family Oxyopidae), Araenidae spp. (Family Araenidae), Pardosa oakleyi and Hippasa partita (Family Lycosidae) at cotton-wheat zone were studied for their predatory potential against three aphid species of canola crop under laboratory experiment. For laboratory experimentation, three species of aphids viz. Lipaphis erysimi, Myzus persicae, Brevicoryne brassicae, and worms were used. The results showed that spider consumed all the preys with different proportions offered in multiple and single prey feeding experiments. Oxyopes javanus was ravenous predator followed by Aranidae spp. on foliage and Pardosa oakleyi and Hippasa partita on ground with female consumed high number of preys as compared to male. Field observation showed seasonal variation in aphid population which in turn affect the diet of predators. The present study suggest that dominant spider species consumed aphids when they are at peak which may help in containing aphid population and this results in successful pest management in canola crop. [ABSTRACT FROM AUTHOR]

IoT-based Smart Grids (SGs) are important to modern society. SGs can improve the profitability and reliability of the electric power system by incorporating renewable energies and highly developed communication technologies. The communication network plays an essential role in electrical networks, and trends favor implementing SGs with IoT devices. However, these IoT-based SGs are vulnerable to cyberattacks. This article presents our studies of malware that can attack IoT-based SGs. First, the article explains as a first step the conclusions of a literature survey on SGs complemented with a Delphi process with security experts to understand trends and malware with an emphasis on the IoT area. Next, the article discusses the behavior of the chosen malware using System Dynamics and calibration with stochastic optimization. Finally, conclusions are given, which identify research work to be carried out using more in-depth modeling with agent-based simulation and multiple resolution modeling (MRM). MRM can provide a platform to integrate with time, scale, and space specialized models of each system of the SGs to support the development of effective risk management schemes.

Failure in aerospace composites owing to low-velocity impact raises a significant maintenance concern because it can lead to invisible damage. For the aerospace industry, such defects pose a potential danger to the structural integrity of aircraft. This in turn jeopardises passenger safety and incurs high repair costs. Hence, it is important to efficiently monitor composite structures during the service life. In this study, the potential of low-frequency guided ultrasonic waves for health monitoring in laminated composite plates is investigated. This study focuses on the use of the first antisymmetric guided wave mode (A0). The first part of this study is to investigate the propagation of the A0 mode in three different undamaged composite plates experimentally. The dispersive and anisotropic behaviour are in agreement with the results of finite element simulations and semi-analytical analysis. The final part of this study presents the scattering of guided waves at the impact damage using a non-contact laser interferometer. Significant scattering activities were observed and the impact damage size can be estimated to be about 10 × 25 mm. In conclusion, these results demonstrate the potential of guided ultrasonic waves for the inspection of aerospace composite structures. [ABSTRACT FROM AUTHOR]

Cytochrome P4501A2 (CYP1A2), as a drug metabolising enzyme, has a great influence on the metabolism of many commonly used medicines with potential involvement in the bio-activation and detoxification of carcinogens. Therefore, in vivo measurement of CYP1A2 activity using a probe drug can be an important tool to investigate some critical factors that influence variability in drug pharmacokinetics. Caffeine is the only, in practice, accepted probe drug to measure the CYP1A2 phenotyping characteristics with caffeine clearance or the paraxanthine to caffeine ratio as the preferred metric. The purpose of present study was to determine the caffeine and its metabolite phenotypes in relation to cancer risk in healthy male volunteers of local population in Pakistan. The average value of metabolic ratio [(1,7-dimethylxanthine (17X) and caffeine 1,3,7-trimethylxanthine (137X)] was found to be 1.023 ± 0.31. In the present study, a Shim Pack CLC-ODS (150×4.6 mm, 5 m pore size) high-performance liquid chromatography (HPLC) column was used to quantify caffeine and paraxanthine (major metabolites of caffeine) concentrations in plasma. Retention time was 12.91 and 14.866 min for 17X and 137X, respectively. The linearity of calibration curve of 137X and 17X were covered 0 to 12 µg/ml (R2 = 0.994). A significant positive correlation was observed between metabolic ratio and cancer risk factors. All of the volunteers were found as fast metabolisers, consequently, CYP1A2 activity may relate to cancer incidence or high risk of cancerous diseases. [ABSTRACT FROM AUTHOR]

The binding behavior of anionic dyes, acid yellow 17 and acid blue 25, at an activated carbon–liquid interface has been investigated in organized media based on aqueous sodium dodecyl sulfate using differential electronic spectroscopy. The quantification of interactions occurring in the system was realized using a simple physical model, emphasizing the displacement of bound dyes from the carbon surface. The quantities of displaced dyes were comparable; however, acid blue 25 was relatively easily desorbed compared to acid yellow 17. These differences can be attributed to differences in the dye size and hydrophobicity. The model is valid for the premicellar region of the surfactant. The results throw light on some characteristics of solid–liquid interfaces.

Zwitterionic microvesicles formed by catanionic system, based on sodium dodecyl sulfate and hexadecyltrimethyl ammonium bromide, have been investigated for sustained urea release using UV–visible absorption spectroscopy. The change in variables such as temperature, sonication time and initial urea concentration was related to urea entrapment efficiency and release from microvesicles. Korsmeyer–Peppas model was applied to highlight release mechanism and kinetics. Both diffusion and erosion were responsible for urea release and rate constant varied with change in conditions. The quantification of association between urea and catanionic vesicles in terms of binding constant (Kbin) and binding free energy showed that urea binding was thermodynamically favored. Our results indicate that biocompatible pseudo-zwitterionic vesicles have enormous potential to act as sustained release system for nitrogenous fertilizers such as urea.

A smart grid is the future vision of power systems that will be enabled by artificial intelligence (AI), big data, and the Internet of things (IoT), where digitalization is at the core of the energy sector transformation. However, smart grids require that energy managers become more concerned about the reliability and security of power systems. Therefore, energy planners use various methods and technologies to support the sustainable expansion of power systems, such as electricity demand forecasting models, stochastic optimization, robust optimization, and simulation. Electricity forecasting plays a vital role in supporting the reliable transitioning of power systems. This paper deals with short-term load forecasting (STLF), which has become an active area of research over the last few years, with a handful of studies. STLF deals with predicting demand one hour to 24 h in advance. We extensively experimented with several methodologies from machine learning and a complex case study in Panama. Deep learning is a more advanced learning paradigm in the machine learning field that continues to have significant breakthroughs in domain areas such as electricity forecasting, object detection, speech recognition, etc. We identified that the main predictors of electricity demand in the short term: the previous week’s load, the previous day’s load, and temperature. We found that the deep learning regression model achieved the best performance, which yielded an R squared (R2) of 0.93 and a mean absolute percentage error (MAPE) of 2.9%, while the AdaBoost model obtained the worst performance with an R2 of 0.75 and MAPE of 5.70%.

Accuracy in national greenhouse gas (GHG) emissions estimation is a key element for outlining best strategies to reduce GHG emissions from various source sectors of the economy. In this study, an initial attempt has been made to estimate GHG emissions from waste sector in Rawal Town - an urban city of Rawalpindi district in Pakistan. Tier 1 approach of Revised 1996 Intergovernmental Panel on Climate Change (IPCC) Guidelines and the best available primary activity data collected for the study area were applied for the fiscal year June 2014-May 2015. Emissions of three GHGs - carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) under three waste management practices i.e. solid waste disposal sites, wastewater handling, and waste incineration were assessed. Out of the total 417.84 Gg CO2 equivalent emissions, the share of each gas (CH4, CO2 and N2O) in total emissions was found as 11.31, 0.53, and 0.58 Gg respectively. Solid waste disposal sites are identified as the largest source of CH4 emissions (10.73 Gg). To make the country's GHG emissions estimates more robust for waste sector, such type of local area based studies established on primary activity data could be advantageous for reducing uncertainties in national emission estimates.

Predicting the future peak demand growth becomes increasingly important as more consumer loads and electric vehicles (EVs) start connecting to the grid. Accurate forecasts will enable energy suppliers to meet demand more reliably. However, this is a challenging problem since the peak demand is very nonlinear. This study addresses the research question of how deep learning methods, such as convolutional neural networks (CNNs) and long-short term memory (LSTM) can provide better support to these areas. The goal is to build a suitable forecasting model that can accurately predict the peak demand. Several data from 2004 to 2019 was collected from Panama’s power system to validate this study. Input features such as residential consumption and monthly economic index were considered for predicting peak demand. First, we introduced three different CNN architectures which were multivariate CNN, multivariate CNN-LSTM and multihead CNN. These were then benchmarked against LSTM. We found that the CNNs outperformed LSTM, with the multivariate CNN being the best performing model. To validate our initial findings, we then evaluated the robustness of the models against Gaussian noise. We demonstrated that CNNs were far more superior than LSTM and can support spatial-temporal time series data.

Copyrights protection of digital data become inevitable in current world. Digital watermarks have been recently proposed as secured scheme for copyright protection, authentication, source tracking, and broadcast monitoring of video, audio, text data and digital images. In this paper a method to embed a watermark in region of non-interest (RONI) and a method for adaptive calculation of strength factor using neural network are proposed. The embedding and extraction processes are carried out in the transform domain by using Discrete Wavelet Transform (DWT). Finally, the algorithm robustness is tested against noise addition attacks and geometric distortion attacks. The results authenticate that the proposed watermarking algorithm does not degrade the quality of cover image as the watermark is inserted only in region of non-interest and is resistive to attacks.