Bournemouth University
  • Bournemouth, Dorset, United Kingdom
Recent publications
With the growing concern about climate change, businesses have been under increasing pressure from regulatory agencies and customers to implement proactive environmental practices such as eco-innovation. While environmental pressures have been extensively discussed in the literature as drivers of eco-innovation, empirical evidence on the influence of these pressures on eco-innovation behaviour remains inconsistent. Therefore, the current study essentially aims to investigate the direct effect of institutional pressures, namely, regulation, eco-friendly product demand, and competitive pressure on eco-innovation, coupled with the indirect effect of these pressures by mediating internal drivers of eco-innovation including green absorptive capacity and strategically environmental orientation among manufacturing SMEs in Egypt. Based on a sample of 176 managers and owners of these enterprises, a cross-sectional survey is conducted to collect data related to research constructs. The results of data analysis using Smart-PLS show that all external pressures are not directly associated with eco-innovation. Of the six indirect hypothesized effects, only four indirect effects are supported. The results illustrate that green absorptive capacity mediates the relationship between institutional pressure (eco-friendly product demand and competitive pressure) and eco-innovation. The results also show that strategically environmental orientation mediates the relationship between institutional pressure (regulation and competitive pressure) and eco-innovation. This study provides an in-depth understanding of firms’ responses to institutional pressures as well as the notable implications for SMEs managers, policymakers and future researchers.
Addressing high and stagnant physical inactivity rates remains a pervasive challenge for researchers, and a priority for health organisations, governments, and physical activity practitioners. Leaders are a prevailing feature of numerous physical activity contexts and can fundamentally influence people’s physical activity behaviours and experiences. In line with this, fitness companies and organisations commonly claim that the leaders of their classes, groups, or sessions will motivate, inspire, and ensure exercisers achieve their goals. We argue, however, that there is insufficient evidence regarding how leaders can best facilitate positive behaviours among, and outcomes for, group members to be confident that these claims are translating into strong physical activity leadership on the ground. In this article, we therefore call for research that equips leaders with greater knowledge and practical guidelines for how to maximise their effectiveness. To facilitate such research, we provide an overview of research that has examined the most effective ways for physical activity leaders to promote health-enhancing behaviours (e.g. greater participation) and positive experiences that may lead to such behaviours (e.g. greater exercise enjoyment) among those they lead. Then, with the shortcomings of this extant research in mind, we outline four broad recommendations for future research: (a) conduct research in novel and varied contexts, (b) focus on insufficiently active populations, (c) utilise qualitative methods, and (d) focus on translation and implementation. Such research would, we believe, generate knowledge that enables physical activity leaders to capitalise on their potential to be powerful agents of behaviour change.
Background and objective Chronic low back pain is pervasive, societally impactful, and current treatments only provide moderate relief. Exploring whether therapeutic elements, either unrecognised or perceived as implicit within clinical encounters, are acknowledged and deliberately targeted may improve treatment efficacy. Contextual factors (specifically, patient’s and practitioner’s beliefs/characteristics; patient-practitioner relationships; the therapeutic setting/environment; and treatment characteristics) could be important, but there is limited evidence regarding their influence. This research aims to review the impact of interventions modifying contextual factors during conservative care on patient’s pain and physical functioning. Databases and data treatment Four electronic databases (Medline, CINAHL, PsycINFO and AMED) were searched from 2009 until 15th February 2022, using tailored search strategies, and resulted in 3476 unique citations. After initial screening, 170 full-text records were potentially eligible and assessed against the inclusion–exclusion criteria. Thereafter, studies were assessed for methodological quality using a modified Downs and Black scale, data extracted, and synthesised using a narrative approach. Results Twenty-one primary studies ( N = 3075 participants), were included in this review. Eight studies reported significant improvements in pain intensity, and seven in physical functioning, in favour of the contextual factor intervention(s). Notable contextual factors included: addressing maladaptive illness beliefs; verbal suggestions to influence symptom change expectations; visual or physical cues to suggest pain-relieving treatment properties; and positive communication such as empathy to enhance the therapeutic alliance. Conclusion This review identified influential contextual factors which may augment conservative chronic low back pain care. The heterogeneity of interventions suggests modifying more than one contextual factor may be more impactful on patients’ clinical outcomes, although these findings require judicious interpretation.
Attempts to better understand the relationship between training and competition load and injury in football are essential for helping to understand adaptation to training programmes, assessing fatigue and recovery, and minimising the risk of injury and illness. To this end, technological advancements have enabled the collection of multiple points of data for use in analysis and injury prediction. The full breadth of available data has, however, only recently begun to be explored using suitable statistical methods. Advances in automatic and interactive data analysis with the help of machine learning are now being used to better establish the intricacies of the player load and injury relationship. In this article, we examine this recent research, describing the analyses and algorithms used, reporting the key findings, and comparing model fit. To date, the vast array of variables used in analysis as proxy indicators of player load, alongside differences in approach to key aspects of data treatment—such as response to data imbalance, model fitting, and a lack of multi-season data—limit a systematic evaluation of findings and the drawing of a unified conclusion. If, however, the limitations of current studies can be addressed, machine learning has much to offer the field and could in future provide solutions to the training load and injury paradox through enhanced and systematic analysis of athlete data.
Body area sensing systems specifically designed for motion capture need to consider the wearer’s comfort and wearability criteria. In this paper, after studying body models and their approximation by link-segment models, the kinematics and inverse kinematics problems for determining motion are explored. Different sensor technologies and related motion capture systems are then discussed within the context of wearability and portability challenges of the systems. For such systems, the weight and size of the system need to be kept small and the system should not interfere with the user’s movements. The requirements will be considered in terms of portability: portable motion capture systems should be less sensitive in accurate positioning of sensors and have more battery lifetime or less power consumption for their wider adoption as an assisted rehabilitation platform. Therefore, a proposed signal processing technique is validated in a controlled setting to address the challenges. By reducing sampling frequency, the power consumption will be reduced but there would be more variability in data whereas by utilising an adaptive filtering approach the variation can be compensated for. It is shown how by using the technique it is possible to reduce the energy consumption; therefore, the potential to decrease the battery size leading to a less bulky on-body sensing system with more comfort to the wearer.
The associations among readers’ cognitive skills (general cognitive ability, reading skills, and attentional functioning), task demands (easy versus difficult questions), and process measures (total fixation time on relevant and irrelevant paragraphs) was investigated to explain task-oriented reading accuracy and efficiency (number of scores in a given time unit). Structural equation modeling was applied to a large dataset collected with sixth-grade students, which included samples of dysfluent readers and those with attention difficulties. The results are in line with previous findings regarding the dominant role of general cognitive ability in the accuracy of task-oriented reading. However, efficiency in task-oriented reading was mostly explained by the shorter viewing times of both paragraph types (i.e., relevant and irrelevant), which were modestly explained by general cognitive ability and reading fluency. These findings suggest that high efficiency in task orientation is obtained by relying on a selective reading strategy when reading both irrelevant and relevant paragraphs. The selective reading strategy seems to be specifically learned, and this potentially applies to most students, even those with low cognitive abilities.
The Sustainable Development Goals (SDGs) are a call to action for governments, companies and communities to rebalance the relationship between the economy, the environment and society. Although companies represent a vital partner in achieving the SDGs, the discussion about the involvement of Small and Medium Enterprises (SMEs) in such goals is scarce. Drawing upon the ‘powercube’ approach, this research investigates what sustainable development means to SMEs, how they view the SDGs and why they engage – or do not engage – with such goals. Sixteen face-to-face interviews were conducted within rural and urban locations in the UK. The results show that although SMEs are interested in sustainable development, power dynamics impede their understanding and implementation of SDGs guidelines. This research offers to SME managers actionable insights on SDGs' implementation strategies and it provides a research agenda on how institutions and stakeholders can facilitate SMEs adoption of SDGs.
The present paper highlights the growing relevance of the Circular Economy (CE), its adoption by Small- and Medium-sized Enterprises (SMEs), and the relationship between the drivers of CE. Using a case study of CE adoption by Tamil Nadu state in India, we analyse the interactions between the drivers and examine the challenges and benefits of CE adoption. Using Total Interpretive Structural Modelling (TISM) this paper identifies 10 main drivers relevant for SMEs in Tamil Nadu based on literature and discussions with 78 industrial-academic experts, comments on the driving, dependent and linking elements that impact the uptake and adoption of CE. The modelling results confirm that three drivers, namely urbanisation, funding availability and resource consumption, are relevant and support the successful adoption of CE. The paper is among the first that uses the TISM technique to establish a contextual linkage between CE drivers and relationship between the different drivers.
The urbanization of Mesopotamia in the 4th millennium BCE led to unprecedented social, economic, and political changes. Tell Brak, located in the Syrian Khabur basin, is one of the best-known early urban sites from this period. Surveys suggest that urban growth at Tell Brak resulted from peripheral expansion driven by the migration of several distinct groups; however, it is not known whether these groups remained recognizably distinct within the newly formed urban center. In the current study, the impact of early urbanization on social organization was explored using non-metric dental data from skeletons excavated from the main site at Tell Brak (n = 111) and its satellite mound Tell Majnuna (n = 179). The Arizona State University Dental Anthropology System (ASUDAS) was employed to examine biodistance between population subsets from the period of early urbanization in the Late Chalcolithic (LC) and the Early Bronze Age (EBA). The results demonstrate differences in dental morphology among the LC groups indicating segmentation within the early urban population at Tell Brak. Patterns of social organization associated with urbanization have thus framed the socio-cultural landscape of even the earliest cities, and bioarchaeological data can be a useful tool for understanding both ancient and modern urbanization.
Many studies have attempted to identify the perceptual underpinnings of developmental prosopagnosia (DP). The majority have focused on whether holistic and configural processing mechanisms are impaired in DP. However, previous work suggests that there is substantial heterogeneity in holistic and configural processing within the DP population; further, there is disagreement as to whether any deficits are face-specific or reflect a broader perceptual deficit. This study used a data-driven approach to examine whether there are systematic patterns of variability in DP that reflect different underpinning perceptual deficits. A group of individuals with DP (N = 37) completed a cognitive battery measuring holistic/configural and featural processing in faces and non-face objects. A two-stage cluster analysis on data from the Cambridge Face Perception Test identified two subgroups of DPs. Across several tasks, the first subgroup (N = 21) showed typical patterns of holistic/configural processing (measured via inversion effects); the second (N = 16) was characterised by reduced or abolished inversion effects compared to age-matched control participants (N = 91). The subgroups did not differ on tasks measuring upright face matching, object matching, non-face holistic processing, or composite effects. These findings indicate two separable pathways to face recognition impairment, one characterised by impaired configural processing and the other potentially by impaired featural processing. Comparisons to control participants provide some preliminary evidence that the deficit in featural processing may extend to some non-face stimuli. Our results demonstrate the utility of examining both the variability between and consistency across individuals with DP as a means of illuminating our understanding of face recognition in typical and atypical populations.
The COVID-19 pandemic has altered our routines, our conversations, the specific social contexts in which we hear or use certain words, and potentially, the representation of the words related to the disease and its consequences. Here we investigated whether the effects of the pandemic have changed the representation of the affective features of COVID-19-related words. To this aim, we collected new ratings of valence (from unpleasant to pleasant) and arousal (from calm to activated) dimensions for COVID-19-related words (e.g., hospital) and COVID-19-unrelated words (e.g., whale). Subsequently, we compared these scores with those from databases that reported ratings for the same pool of words before the pandemic. Our results showed significant changes in arousal for COVID-19-related words but not unrelated words, thus revealing that the pandemic social context modified their affective representation. These findings support the flexibility of emotional representations and the malleability and dynamicity of the mental lexicon as a function of contextual factors.
The Routing Protocol for Low power and Lossy networks (RPL) utilises the Objective Function (OF) to form a Destination Oriented Directed Acyclic Graph (DODAG) to reach the destination by selecting the best path. Many works in literature have explored this domain concerning the Internet of Things (IoT) applications. Although, the application of RPL protocol from IoT to the Internet of Vehicles (IoV) in the smart city still presents a big test. Since this gap has not been much traversed, it motivated us to present our findings on this research gap. This paper has realised the transition of RPL protocol from IoT to IoV for the first time. The network performance has been analysed using RPL in a static and mobile environment based on three configurations: Quality of Service (QoS) parameters, network scalability and mobility models. Also, a comprehensive analysis of the RPL performance in both environments has been bestowed in our paper. Finally, we have summarised our inputs and stated potential future directions for researchers. The experiments have been performed using Contiki OS/Cooja Simulator, BonnMotion tool and Wireshark. Simulation results have shown that Self-similar Least Action Walk (SLAW) has outperformed Random Way-Point (RWP) and Nomadic mobility model. High value of Packet Delivery Ratio (PDR) is achieved in mobile/dynamic environment than static. These findings can be directly applied to IoV and IoT applications using RPL protocol like Traffic Monitoring System (TMS), smart corridors, Electronic Toll Collection (ETC), etc. in smart city. Moreover, this article will help the researchers in gaining a better insight of RPL protocol in static and mobile environments for future works.
Evidence exploring the relationship between corporate social responsibility (CSR) disclosure and corporate financial performance (CFP) is consistently inconsistent, if not outright contradictory. We assert that much of this confusion is due to a failure to integrate both firm internal performance and the external environment into theoretical and empirical analyses of the effect of CSR disclosure on firm efficiency. This paper attempts to bring these two facets together in an examination of banking sector efficiency in a situation where the entire external environment is in flux, namely transition. Using a database of 319 banks from 21 transition countries, and using dynamic panel and quantile regressions, we provide empirical evidence that banks in transition countries saw benefits in firm performance only when CSR activities were layered on top of a strategy which already was profitable. Indeed, once profitability was achieved, only then did CSR disclosure begin to confer a competitive edge in developing firm resources. However, the external environment continues to exert an influence, and even where banks met profitability goals, predatory institutions can still make engaging in CSR a detriment to competitive advantage.
The study of heterostructured materials (HSMs) answered one of the most pressing questions in the metallurgical field: “is it possible to greatly increase both the strength and the strain hardening, to avoid the “inevitable” loss of ductility?”. From the synergy between the deformation modes of zones with greatly different flow stress, low stacking fault energy (SFE) alloys can reduce their typical trade-off between strength and ductility. Stainless steel (SS) is a low-SFE material, which is widely applied for structural, biomedical, biosafety, food-processing, and daily applications. The possibility to combine its corrosion resistance and biocompatibility with the outstanding mechanical behaviour of HSMs can convert SS into a promising option for low-cost and high-effective advanced material. This paper reviews all the microstructural aspects of HS SS obtained by different processing methods and their correlation with crystallographic texture and properties such as mechanical, corrosion, biological, and magnetic characteristics. The critical comparison between experimental and modelling findings is also presented in terms of the deformation mechanisms, microstructural and texture features. Thus, the processing-microstructure-properties relationship in HS SS is the focus of this publication. The multi-disciplinary perspectives of HS SS are also discussed. This review paper will serve as a reference for understanding and designing new multi-functional HS SSs.
This study assessed the influence of environmental factors, air travel, and epoch estimation method on locomotor demands of international men's rugby sevens match-play. Eighteen men's rugby sevens players wore 10 Hz Global Positioning Systems (STATsport) during 52 international matches over nine global tournaments (418 observations). Whole-match average speed was recorded, whilst average speed and relative high-speed distance (>5.0 m·s-1) were quantified using FIXED and ROLL methods over 60-420 s epochs (60 s increments) to establish worst-case scenario demands. Linear mixed models compared FIXED versus ROLL estimation methods and assessed whether temperature, humidity, travel duration, number of time-zones crossed, and travel direction were associated with locomotor responses. Temperature and humidity were positively associated with overall and worst-case scenario average speed (effect estimates; b: 0.18 to 0.54), whilst worst-case scenario high-speed distance at 300 s was also related to temperature (b: 0.19). Easterly air travel compromised overall and 180 and 300 s worst-case scenario average speed (b: -8.31 to-7.39), alongside high-speed distance over 300 s (b: -4.54). For worst-case scenario average speed and high-speed distance, FIXED underestimated ROLL at all epoch lengths (∼9.9 to 18.4%, p≤0.001). This study indicated that international rugby sevens match-play locomotor responses were greater as air temperature increased but reduced following eastward air travel. Underestimation of demands in FIXED vs ROLL over 60-420 s epochs was confirmed. Such climatic and travel influences warrant the adoption of strategies targeted at maximising performance and safety according to the tournament conditions. Knowing the most demanding periods of match-play facilitates training specificity.
Background: While the blood pressure (BP)-lowering effect of renal denervation (RDN) has been established, long-term durability is a key prerequisite for a broader clinical implementation. Aims: Our aims were to assess the long-term durability of the office BP (OBP)-lowering efficacy, antihypertensive medication (AHM) use, and safety of ultrasound RDN (uRDN). Methods: Four weeks after withdrawal of AHM, patients with untreated daytime ambulatory BP ≥135/85 mmHg and <170/105 mmHg were randomised to uRDN (n=74) or sham (n=72) in the RADIANCE-HTN SOLO trial. Initiation of AHM was encouraged for home BP >135/85 mmHg following primary endpoint ascertainment at 2 months. Patients and physicians were unblinded at 6 months. Results: Fifty-one of 74 patients (age: 53.9±11 years; 67% men) originally randomised to uRDN completed the 36-month follow-up. Initial screening OBP upon study entry was 145/92±14/10 mmHg on a mean of 1.2 AHM (range: 0-2.0). Baseline OBP after AHM washout was 154/99±13/8 mmHg. At 36 months, patients were on an average of 1.3 AHM (range: 0-3.0) with 8 patients on no AHM. OBP decreased by 18/11±15/9 mmHg from baseline to 36 months (p<0.001 for both). Overall, OBP control (<140/90 mmHg) improved from 29.4% at screening to 45.1% at 36 months (p=0.059). For patients uncontrolled at screening (n=36), systolic OBP decreased by 10.8 mmHg (p<0.001) at 36 months on similar AHM (p=0.158). Conclusions: The safety and effectiveness of uRDN was durable to 36 months, with reduced OBP and improved OBP control despite a similar starting medication burden. No new uRDN-related long-term safety concerns were identified.
The advent of deep learning has brought in disruptive techniques with unprecedented accuracy rates in so many fields and scenarios. Tasks such as the detection of regions of interest and semantic features out of images and video sequences are quite effectively tackled because of the availability of publicly available and adequately annotated datasets. This paper describes a use case scenario with a deep learning models’ stack being used for crowd behaviour analysis. It consists of two main modules preceded by a pre-processing step. The first deep learning module relies on the integration of YOLOv5 and DeepSORT to detect and track down pedestrians from CCTV cameras’ video sequences. The second module ingests each pedestrian’s spatial coordinates, velocity, and trajectories to cluster groups of people using the Coherent Neighbor Invariance technique. The method envisages the acquisition of video sequences from cameras overlooking pedestrian areas, such as public parks or squares, in order to check out any possible unusualness in crowd behaviour. Due to its design, the system first checks whether some anomalies are underway at the microscale level. Secondly, It returns clusters of people at the mesoscale level depending on velocity and trajectories. This work is part of the physical behaviour detection module developed for the S4AllCities H2020 project.
Counter-terrorism and its preventive and response actions are crucial factors in security planning and protection of mass events, soft targets and critical infrastructures in urban environments. This paper presents a comprehensive Decision Support System developed under the umbrella of the S4AllCitites project, that can be integrated with legacy systems deployed in the Smart Cities. The system includes urban pedestrian and vehicular evacuation, considering ad-hoc predictive models of the evolution of incendiary and mass shooting attacks in conjunction with a probabilistic model for threat assessment in case of improvised explosive devices. The main objective of the system is to provide decision support to public or private security operators in the planning and real time phases in the prevention or intervention against a possible attack, providing information on evacuation strategies, the probability or expected impact of terrorist threats and the state of the traffic network in normal or unusual conditions allowing the emergency to be managed throughout its evolution.
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12,178 members
Edwin Roland Van Teijlingen
  • Centre for Midwifery, Maternal & Perinatal Health
Taha Rassem
  • Faculty of Science and Engineering
Nirmal Aryal
  • Faculty of Health and Social Science
Fred Charles
  • Department of Creative Technology
Bournemouth, Dorset, United Kingdom
Head of institution
Katie Adie