With the overnight growth in Working from Home (WFH) owing to the pandemic, organisations and their employees have had to adapt work-related processes and practices quickly with a huge reliance upon technology. Everyday activities such as social interactions with colleagues must therefore be reconsidered. Existing literature emphasises that social interactions, typically conducted in the traditional workplace, are a fundamental feature of social life and shape employees' experience of work. This experience is completely removed for many employees due to the pandemic and, presently, there is a lack of knowledge on how individuals maintain social interactions with colleagues via technology when working from home. Given that a lack of social interaction can lead to social isolation and other negative repercussions, this study aims to contribute to the existing body of literature on remote working by highlighting employees' experiences and practices around social interaction with colleagues. This study takes an interpretivist and qualitative approach utilising the diary-keeping technique to collect data from twenty-nine individuals who had started to work from home on a full-time basis as a result of the pandemic. The study explores how participants conduct social interactions using different technology platforms and how such interactions are embedded in their working lives. The findings highlight the difficulty in maintaining social interactions via technology such as the absence of cues and emotional intelligence, as well as highlighting numerous other factors such as job uncertainty, increased workloads and heavy usage of technology that affect their work lives. The study also highlights that despite the negative experiences relating to working from home, some participants are apprehensive about returning to work in the traditional office place where social interactions may actually be perceived as a distraction. The main contribution of our study is to highlight that a variety of perceptions and feelings of how work has changed via an increased use of digital media while working from home exists and that organisations need to be aware of these differences so that they can be managed in a contextualised manner, thus increasing both the efficiency and effectiveness of working from home.

Eutrophication in fresh water has become a critical challenge worldwide and chlorophyll-a content is a key water quality parameter that indicates the extent of eutrophication and algae concentration in a body of water. In this paper, a forecasting model for the high accuracy prediction of chlorophyll-a content is proposed to enable aquafarm managers to take remediation actions against the occurrence of toxic algal blooms in the aquaculture industry. The proposed model combines the ensemble empirical mode decomposition (EEMD) technique and a deep learning (DL) long short-term memory (LSTM) neural network (NN). With this hybrid approach, the time-series data are firstly decomposed with the aid of the EEMD algorithm into manifold intrinsic mode functions (IMFs). Secondly, a multi-attribute selection process is employed to select the group of IMFs with strong correlations with the measured real chlorophyll-a dataset and integrate them as inputs for the DL LSTM NN. The model is built on water quality sensor data collected from the Loch Duart salmon aquafarm in Scotland. The performance of the proposed novel hybrid predictive model is validated by comparing the results against the dataset. To measure the overall accuracy of the proposed novel hybrid predictive model, the Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) were used.

Providing an accurate prediction of water quality parameters for improved water quality management is a topical issue in the aquaculture industry. Conventional prediction methods have shown different challenges like a poor generalization, poor prediction accuracy, and high time complexity. Aiming at these challenges, a novel hybrid prediction model with ensemble empirical mode decomposition (EEMD) and deep learning (DL) long-short term memory (LSTM) neural network is proposed in this paper. In this innovative hybrid EEMD-DL-LSTM model, firstly, the integrity of the datasets is enhanced by applying moving average filtering and linear interpolation techniques of water quality parameter datasets pre-treatment. Secondly, the measured real sensor water quality parameters dataset is decomposed with the aid of the EEMD algorithm into disparate IMFs and a corresponding residual item. Thirdly, a multi-feature selection process is applied to make a careful selection of a strongly correlated group of IMFs with the measured real water quality parameter datasets and integrate them as inputs to the DL-LSTM neural network. The presented model is built on water quality sensor data collected from an Abalone farm in South Africa. The performance of the novel hybrid prediction model is validated by comparing the results against the real datasets. To measure the overall accuracy of the novel hybrid prediction model, different statistical indices, namely the Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE), are used.

This study aimed to systematically review total daily sedentary time in South Asian adults. Seven electronic databases were searched, identifying relevant articles published in peer-reviewed journals between March 1990 and March 2021. The study was designed in accordance with PRISMA guidelines. Prospective or cross-sectional design studies reporting total daily sedentary time in South Asian adults (aged ≥18 years), reported in English, were included. Study quality and risk of bias were assessed, and the weighted mean total daily sedentary time was calculated. Fourteen full texts were included in this systematic review from studies that were conducted in Bangladesh, India, Norway, Singapore, and the United Kingdom. Pooled sedentary time across all studies was 424 ± 8 min/day. Sedentary time was measured using self-report questionnaires in seven studies, with a weighted mean daily sedentary time of 416 ± 19 min/day. Eight studies used accelerometers and inclinometers with a weighted mean sedentary time of 527 ± 11 min/day. South Asian adults spend a large proportion of their time being sedentary, especially when recorded using objective measures (~9 h/day). These findings suggest that South Asians are an important target population for public health efforts to reduced sedentary time, and researchers and practitioners should seek to standardise and carefully consider the tools used when measuring sedentary time in this population.

Currently, global warming has been a serious issue, which is closely related to anthropogenic emission of Greenhouse Gas (GHG) in the atmosphere, particularly Carbon Dioxide (CO2). To help achieve carbon neutrality by decreasing CO2 emissions, Oxy-Fuel Combustion (OFC) technology is becoming a hot topic in recent years. However, few findings have been reported about the implementation of OFC in dual-injection Spark Ignition (SI) engines. This work numerically explores the effects of Water Injection (WI) strategies on OFC characteristics in a practical dual-injection engine, including GDI (only using GDI), P50-G50 (50% PFI and 50% GDI) and PFI (only using PFI). The findings will help build a conceptual and theoretical foundation for the implementation of OFC technology in dual-injection SI engines, as well as exploring a solution to increase engine efficiency. The results show that compared to Conventional Air Combustion (CAC), there is a significant increase in BSFC under OFC. Ignition delay (θF) is significantly prolonged, and the spark timing is obviously advanced. Combustion duration (θC) of PFI is a bit shorter than that of GDI and P50-G50. There is a small benefit to BSFC under a low water-fuel mass ratio (Rwf). However, with the further increase of Rwf from 0.2 to 0.9, there is an increment of 4.29%, 3.6% and 3.77% in BSFC for GDI, P50-G50 and PFI, respectively. As WI timing (tWI) postpones to around −30 °CA under the conditions of Rwf ≥ 0.8, BSFC has a sharp decrease of more than 6 g/kWh, and this decline is more evident under GDI injection strategy. The variation of maximum cylinder pressure (Pmax) and combustion phasing is less affected by WI temperature (TWI) compared to the effects of Rwf or tWI. BSFC just has a small decline with the increase of TWI from 298 K to 368 K regardless of the injection strategy. Consequently, appropriate WI strategies are beneficial to OFC combustion in a dual-injection SI engine, but the benefit in fuel economy is limited.

Background: Community-based physical activity programmes typically evolve to respond to local conditions and feedback from stakeholders. Process evaluations are essential for capturing how programmes are implemented, yet often fail to capture delivery evolution over time, meaning missed opportunities for capturing lessons learnt. Methods: This research paper reports on a staged approach to a process evaluation undertaken within a community-based UK 12-month physical activity programme that aimed to capture change and adaptation to programme implementation. Twenty-five one-to-one interviews, and twelve focus groups took place over the three years of programme delivery. Participants included programme participants, management, and service deliverers. Results: Programme adaptations that were captured through the ongoing process evaluation included changes to the design of promotional material, programme delivery content, ongoing training in behaviour change and the addition of regular participant community events. We address how these strands evolved over programme delivery, and how the process evaluation was able to capture them. Conclusion: The pragmatic evaluation approach enabled changes in response to the local context, as well as improvements in the programme to be captured in a timely manner, allowing the delivery to be responsive and the evaluation flexible.

Integrated care aims to improve population health. Obesity and mental health are major health issues worldwide. The complexity of the multifactorial drivers of these public health problems has led to the adoption of a whole system approach. This review aims to highlight factors that influence the planning, implementation and evaluation of whole system integrated care for these conditions. Using the framework of Arksey and O' Malley, we will perform a comprehensive search in the following databases: MEDLINE, CINAHL, PsychINFO, PubMed, British Nursing Database, Web of Science, Health Systems Evidence, Cochrane Library and University of York Centre for Reviews and Dissemination. Further hand-search of reference lists and the grey literature will be conducted. The search will be restricted to articles published from 2000 to 2020. The review is expected to be completed by August 2021. Full texts of the potential studies will be screened for the inclusion criteria. Quality of studies will be appraised. Narrative synthesis will be completed using data extracted from the included studies. A favourable ethics opinion for this study was obtained from the Institute for Health Research Ethics Committee of the University of Bedfordshire (IHREC937). This review expects to identify information relating to factors that facilitate or hinder whole system integrated care for obesity and mental health. The finding from this review will be widely disseminated to stakeholders to inform implementation of whole system integrated care initiatives.

Increased intracellular heat shock protein-72 (Hsp72) and heat shock protein-90α (Hsp90α) have been implicated as important components of acquired thermotolerance, providing cytoprotection during stress. This experiment determined the physiological responses characterising increases in Hsp72 and Hsp90α mRNA on the first and tenth day of 90-min heat acclimation (in 40.2 °C, 41.0 % relative humidity (RH)) or equivalent normothermic training (in 20 °C, 29 % RH). Pearson’s product-moment correlation and stepwise multiple regression were performed to determine relationships between physiological [e.g. (Trec, sweat rate (SR) and heart rate (HR)] and training variables (exercise duration, exercise intensity, work done), and the leukocyte Hsp72 and Hsp90α mRNA responses via reverse transcription quantitative polymerase chain reaction (RT-QPCR) (n = 15). Significant (p < 0.05) correlations existed between increased Hsp72 and Hsp90α mRNA (r = 0.879). Increased core temperature was the most important criteria for gene transcription with ΔTrec (r = 0.714), SR (r = 0.709), Trecfinal45 (r = 0.682), area under the curve where Trec ≥ 38.5 °C (AUC38.5 °C; r = 0.678), peak Trec (r = 0.661), duration Trec ≥ 38.5 °C (r = 0.650) and ΔHR (r = 0.511) each demonstrating a significant (p < 0.05) correlation with the increase in Hsp72 mRNA. The Trec AUC38.5 °C (r = 0.729), ΔTrec (r = 0.691), peak Trec (r = 0.680), Trecfinal45 (r = 0.678), SR (r = 0.660), duration Trec ≥ 38.5 °C (r = 0.629), the rate of change in Trec (r = 0.600) and ΔHR (r = 0.531) were the strongest correlate with the increase in Hsp90α mRNA. Multiple regression improved the model for Hsp90α mRNA only, when Trec AUC38.5 °C and SR were combined. Training variables showed insignificant (p > 0.05) weak (r < 0.300) relationships with Hsp72 and Hsp90α mRNA. Hsp72 and Hsp90α mRNA correlates were comparable on the first and tenth day. When transcription of the related Hsp72 and Hsp90α mRNA is important, protocols should rapidly induce large, prolonged changes in core temperature.

Pretest guidelines typically stipulate that no exercise should be performed 48 h prior to a maximal incremental exercise (Formula presented.) test. However, no study has specifically investigated if this timescale alters key outcome variables associated with (Formula presented.). Twenty apparently healthy males split into two equal groups performed (Formula presented.) during three visits (visits 1–(Formula presented.) EXP1, 2–(Formula presented.) EXP2 and 4–(Formula presented.) EXP3). The experimental group only, performed muscle-damaging exercise during visit 3. From (Formula presented.) EXP2 to (Formula presented.) EXP3 average time to exhaustion (TTE) decreased by 45 s (9%) (p < 0.01), maximum blood lactate decreased by 1.2 mmol/L (11%) (p = 0.03), and perceived readiness decreased by 8 mm (18%) (p = 0.01). There were no changes in any (Formula presented.) variables in the control group (p ≥ 0.37). Performing (Formula presented.) 48 h following muscle-damaging exercise impairs specific, but not all, physiological outcome variables.