Now showing items 1-20 of 9398

    • Gate-opening criterion for generating dam-break flow in non-rectangular wet bed channels

      Yang, S.; Wang, B.; Guo, Yakun; Zhang, J.; Chen, Y. (2020-11)
      A sudden dam failure is usually simulated by the rapid removal of a gate in laboratory tests and numerical simulations. The gate-opening time is often determined according to the Lauber and Hager instantaneous collapse criterion (referred to as Lauber-Hager criterion) established for a rectangular open channel with a dry bed. However, this criterion is not suitable for non-rectangular channels or initial wet-bed conditions. In this study, the effect of the gate-opening time on the wave evolution is investigated by using the large eddy simulation (LES) model. The instantaneous dam break, namely the dam break without a gate, is simulated for comparison. A gate-opening criterion for generating dam-break flow in non-rectangular wet bed channel is proposed in this study, which can be used as an extension of the Lauber-Hager criterion and provides a more comprehensive and reasonable estimate of the gate opening time.
    • Element failure probability of soil slope under consideration of random groundwater level

      Li, Z.; Chen, Y.; Guo, Yakun; Zhang, X.; Du, S. (2021)
      The instability of soil slopes is directly related to both the shear parameters of the soil material and the groundwater, which usually causes some uncertainty. In this study, a novel method, the element failure probability method (EFP), is proposed to analyse the failure of soil slopes. Based on the upper bound theory, finite element discretization, and the stochastic programming theory, an upper bound stochastic programming model is established by simultaneously considering the randomness of shear parameters and groundwater level to analyse the reliability of slopes. The model is then solved by using the Monte-Carlo method based on the random shear parameters and groundwater levels. Finally, a formula is derived for the element failure probability (EFP) based on the safety factors and velocity fields of the upper bound method. The probability of a slope failure can be calculated by using the safety factor, and the distribution of failure regions in space can be determined by using the location information of the element. The proposed method is validated by using a classic example. This study has theoretical value for further research attempting to advance the application of plastic limit analysis to analyse slope reliability.
    • The fluid mechanics of tensioned web roll coating

      Benkreira, Hadj; Shibata, Yusuke; Ito, K. (2021)
      Tensioned web-roll coating is widely used but has surprisingly received little research attention. Here, a new semi-empirical model that predicts film transfer from applicator roller to web is developed and tested against data collected from a pilot coating line. The film transfer is found to vary linearly with web to applicator speed ratio S. Flow stability investigations revealed three types of defects: rivulets, air entrainment due to dynamic wetting failure and cascade, occurring at different values of S and capillary number Ca. Rivulets occurred at Ca< 0.4 and S> 0.71-0.81, air entrainment at Ca>0.4 and S>0.71-0.83 and cascades at S>1.1 for Ca up to 6. Web speeds at which dynamic wetting failure occurred were, for the same Ca, comparatively higher than those that occur in dip coating. The data show that such hydrodynamic assistance is due to the coating bead being confined, more so with increasing web wrap angle β.
    • Turbulence structure and momentum exchange in compound channel flows with shore ice covered on the floodplains

      Wang, F.; Huai, W.; Guo, Yakun; Liu, M. (2021-04)
      Ice cover formed on a river surface is a common natural phenomenon during winter season in cold high latitude northern regions. For the ice-covered river with compound cross-section, the interaction of the turbulence caused by the ice cover and the channel bed bottom affects the transverse mass and momentum exchange between the main channel and floodplains. In this study, laboratory experiments are performed to investigate the turbulent flow of a compound channel with shore ice covered on the floodplains. Results show that the shore ice resistance restricts the development of the water flow and creates a relatively strong shear layer near the edge of the ice-covered floodplain. The mean streamwise velocity in the main channel and on the ice-covered floodplains shows an opposite variation pattern along with the longitudinal distance and finally reaches the longitudinal uniformity. The mixing layer bounded by the velocity inflection point consists of two layers that evolve downstream to their respective fully developed states. The velocity inflection point and strong transverse shear near the interface in the fully developed profile generate the Kelvin-Helmholtz instability and horizontal coherent vortices. These coherent vortices induce quasi-periodic velocity oscillations, while the dominant frequency of the vortical energy is determined through the power spectral analysis. Subsequently, quadrant analysis is used in ascertaining the mechanism for the lateral momentum exchange, which exhibits the governing contributions of sweeps and ejections within the vortex center. Finally, an eddy viscosity model is presented to investigate the transverse momentum exchange. The presented model is well validated through comparison with measurements, whereas the constants α and β appeared in the model need to be further investigated.
    • Machine Learning-based Feature Selection and Optimisation for Clinical Decision Support Systems. Optimal Data-driven Feature Selection Methods for Binary and Multi-class Classification Problems: Towards a Minimum Viable Solution for Predicting Early Diagnosis and Prognosis

      Neagu, Daniel; Campean, I. Felician; Parisi, Luca (University of BradfordFaculty of Engineering and Informatics, 2019)
      This critical synopsis of prior work by Luca Parisi is submitted in support of a PhD by Published Work. The work focuses on deriving accurate, reliable and explainable clinical decision support systems as minimum clinically viable solutions leveraging Machine Learning (ML) and evolutionary algorithms, for the first time, to facilitate early diagnostic predictions of Parkinson's Disease and hypothermia in hospitals, as well as prognostic predictions of optimal postoperative recovery area and of chronic hepatitis. Despite the various pathological aetiologies, the underlying capability of ML-based algorithms to serve as a minimum clinically viable solution for predicting early diagnosis and prognosis has been thoroughly demonstrated. Feature selection (FS) is a proven method for increasing the performance of ML-based classifiers for several applications. Although advances in ML, such as Deep Learning (DL), have denied the usefulness of any extrinsic FS by incorporating it in their architectures, e.g., convolutional filters in convolutional neural networks, DL algorithms often lack the required explainability to be understood and interpreted by clinicians within the context of the diagnostic and prognostic tasks of interest. Their relatively complicated architectures, the hardware required for running them and the limited explainability or interpretability of their architectures, the decision-making process – although as assistive tools - driven by the algorithms’ training and predictive outcomes have hindered their application in a clinical setting. Luca Parisi’s work fills this translational research gap by harnessing the explainability of using traditional ML- and evolutionary algorithms-based FS methods for improving the performance of ML-based algorithms and devise minimum viable solutions for diagnostic and prognostic purposes. The work submitted here involves independent research work, including collaborative studies with Marianne Lyne Manaog (MedIntellego®) and Narrendar RaviChandran (University of Auckland). In particular, conciliating his work as a Senior Artificial Intelligence Engineer and volunteering commitment as the President and Research Committee Leader of a student-led association named the “University of Auckland Rehabilitative Technologies Association”, Luca Parisi decided to embark on most research works included in this synopsis to add value to society via accurate, reliable and explainable, hence clinically viable applications of AI. The key findings of these studies are: (i) ML-based FS algorithms are sufficient for devising accurate, reliable and explainable ML-based classifiers for aiding prediction of early diagnosis for Parkinson’s Disease and chronic hepatitis; (ii) evolutionary algorithms-based optimisation is a preferred method for improving the accuracy and reliability of decision support systems aimed at aiding early diagnosis of hypothermia; (iii) evolutionary algorithms-based optimisation methods enable to devise optimised ML-based classifiers for improving postoperative discharge; (iv) whilst ML-based algorithms coupled with ML based FS methods are the minimum clinically viable solution for binary classification problems, ML-based classifiers leveraging evolutionary algorithms for FS yield more accurate and reliable predictions, as reducing the search space and overlapping regions for tackling multi-class classification problems more effectively, which involve a higher number of degrees of freedom. Collectively, these findings suggest that, despite advances in ML, state-of-the-art ML algorithms, coupled with ML-based or evolutionary algorithms for FS, are enough to devise accurate, reliable and explainable decision support systems for performing both an early diagnosis and a prediction of prognosis of various pathologies.
    • The Impact of Training in Person-Centred Dementia Care and Supervision on Burnout in Nursing Home Nurses: A Mixed Methods Study

      Oyebode, Jan R.; Downs, Murna G.; Smythe, Analisa (University of BradfordFaculty of Health Studies, 2018)
      Background: There is significant concern about nurse burnout in nursing homes. There has been little research to investigate whether training in person-centred care and supervision can reduce nursing home nurses’ burnout. Aims: To adapt training to be suitable for nursing home nurses and evaluate the impact of training and supervision on burnout and related outcomes. Study Design: Focus groups with nursing home nurses were used to inform adaptation of the training. Mixed methods were used to evaluate the impact of training and supervision employing a convergent parallel design, including a Randomised Controlled Trial with quantitative measures (primary outcome measure: the Maslach Burnout Inventory) to assess effectiveness and exploration of subjective experience using qualitative interviews. The findings of the RCT and qualitative interviews were then compared to determine the convergences and divergences. Findings: The training was adapted to include content on leadership and stress management. Hypotheses that the interventions would reduce burnout and impact on other quantitative outcomes were not supported. Qualitative interviews with nursing home nurses about training indicated that the nurses reported reduced burnout, enhanced self-efficacy, reduced isolation, better team working, more informed person centred dementia care and enhanced leadership. Nurses’ views on the impact of supervision included a range of benefits. There was convergence between quantitative measurement and subjective experience indicting significant levels of burnout, but divergence in terms of the impact of training in person-centred care and supervision. Conclusions: My study demonstrates that burnout is a significant issue for nursing home nurses in the UK. There was divergence in my findings in terms of the impact of training in person-centred care and supervision. The hypotheses about training and supervision having positive impact on burn-out were rejected. However, the qualitative findings suggest that nursing home nurses experienced positive benefits from the person-centred training and supervision, in particular on their sense of burnout, their approach to care and leadership skills. Recommendations are made regarding research, training and policy to address burnout in nursing home nurses.
    • Co-creating social licence for sharing health and care data

      Fylan, F.; Fylan, Beth (2021-05)
      Optimising the use of patient data has the potential to produce a transformational change in healthcare planning, treatment, condition prevention and understanding disease progression. Establishing how people's trust could be secured and a social licence to share data could be achieved is of paramount importance. The study took place across Yorkshire and the Humber, in the North of the England, using a sequential mixed methods approach comprising focus groups, surveys and co-design groups. Twelve focus groups explored people's response to how their health and social care data is, could, and should be used. A survey examined who should be able to see health and care records, acceptable uses of anonymous health and care records, and trust in different organisations. Case study cards addressed willingness for data to be used for different purposes. Co-creation workshops produced a set of guidelines for how data should be used. Focus group participants (n = 80) supported sharing health and care data for direct care and were surprised that this is not already happening. They discussed concerns about the currency and accuracy of their records and possible stigma associated with certain diagnoses, such as mental health conditions. They were less supportive of social care access to their records. They discussed three main concerns about their data being used for research or service planning: being identified; security limitations; and the potential rationing of care on the basis of information in their record such as their lifestyle choices. Survey respondents (n = 1031) agreed that their GP (98 %) and hospital doctors and nurses (93 %) should be able to see their health and care records. There was more limited support for pharmacists (37 %), care staff (36 %), social workers (24 %) and researchers (24 %). Respondents thought their health and social care records should be used to help plan services (88 %), to help people stay healthy (67 %), to help find cures for diseases (67 %), for research for the public good (58 %), but only 16 % for commercial research. Co-creation groups developed a set of principles for a social licence for data sharing based around good governance, effective processes, the type of organisation, and the ability to opt in and out. People support their data being shared for a range of purposes and co-designed a set of principles that would secure their trust and consent to data sharing.
    • Numerical behaviour of buried flexible pipes in geogrid-reinforced soil under cyclic loading

      Elshesheny, Ahmed; Mohamed, Mostafa H.A.; Nagy, N.M.; Sheehan, Therese (2020-06)
      Three-dimensional finite element models were executed and validated to investigate the performance of buried flexible high-density Polyethylene (HDPE) pipes, in unreinforced and multi-geogrid-reinforced sand beds, while varying pipe burial depth, number of geogrid-layers, and magnitude of applied cyclic loading. Geogrid-layers were simulated considering their geometrical thickness and apertures, where an elasto-plastic constitutive model represented its behaviour. Soil-geogrid load transfer mechanisms due to interlocked soil in-between the apertures of the geogrid-layer were modelled. In unreinforced and reinforced cases, pipe burial depth increase contributed to decreasing deformations of the footing and pipe, and the crown pressure until reaching an optimum value of pipe burial depth. On the contrary, the geogrid-layers strain increased with increasing pipe burial depth. A flexible slab was formed due to the inclusion of two-geogrid-layers, leading to an increase in the strain in the lower geogrid-layer, despite its lower deformation. Inclusion of more than two geogrid-layers formed a heavily reinforced system of higher stiffness, and consequently, strain distribution in the geogrid-layers varied, where the upper layer experienced the maximum strain. In heavily reinforced systems, increasing the amplitude of cyclic loading resulted in a strain redistribution process in the reinforced zone, where the second layer experienced the maximum strain.
    • Passive RFID Module with LSTM Recurrent Neural Network Activity Classification Algorithm for Ambient Assisted Living

      Oguntala, G.A.; Hu, Yim Fun; Alabdullah, Ali A.S.; Abd-Alhameed, Raed A.; Ali, Muhammad; Luong, D.K. (2021)
      IEEE Human activity recognition from sensor data is a critical research topic to achieve remote health monitoring and ambient assisted living (AAL). In AAL, sensors are integrated into conventional objects aimed to support targets capabilities through digital environments that are sensitive, responsive and adaptive to human activities. Emerging technological paradigms to support AAL within the home or community setting offers people the prospect of a more individually focused care and improved quality of living. In the present work, an ambient human activity classification framework that augments information from the received signal strength indicator (RSSI) of passive RFID tags to obtain detailed activity profiling is proposed. Key indices of position, orientation, mobility, and degree of activities which are critical to guide reliable clinical management decisions using 4 volunteers are employed to simulate the research objective. A two-layer, fully connected sequence long short-term memory recurrent neural network model (LSTM RNN) is employed. The LSTM RNN model extracts the feature of RSS from the sensor data and classifies the sampled activities using SoftMax. The performance of the LSTM model is evaluated for different data size and the hyper-parameters of the RNN are adjusted to optimal states, which results in an accuracy of 98.18%. The proposed framework suits well for smart health and smart homes which offers pervasive sensing environment for the elderly, persons with disability and chronic illness.
    • Polymers and boron neutron capture therapy(BNCT): a potent combination

      Pitto-Barry, Anaïs (Royal Society of Chemistry, 2021)
      Boron neutron capture therapy (BNCT) has a long history of unfulfilled promises for the treatment of aggressive cancers. In the last two decades, chemists, physicists, and clinical scientists have been coordinating their efforts to overcome practical and scientific challenges needed to unlock its full therapeutic potential. From a chemistry point of view, the two current small-molecule drugs used in the clinic were developed in the 1950s, however, they both lack some of the essential requirements for making BNCT a successful therapeutic modality. Novel strategies are currently used to design new drugs, more selective towards cancer cells and tumours, as well as able to deliver high boron contents to the target. In this context, macromolecules, including polymers, are promising tools to make BNCT an effective, accepted, and front-line therapy against cancer. In this review, we will provide a brief overview of BNCT, and its potential and challenges, and we will discuss the most promising strategies that have been developed so far.
    • A Comparison of Flare Forecasting Methods. IV. Evaluating Consecutive-day Forecasting Patterns

      Park, S.H.; Leka, K.D.; Kusano, K.; Andries, J.; Barnes, G.; Bingham, S.; Bloomfield, D.S.; McCloskey, A.E.; Delouille, V.; Falconer, D.; et al. (2020-02-19)
      A crucial challenge to successful flare prediction is forecasting periods that transition between "flare-quiet" and "flare-active." Building on earlier studies in this series in which we describe the methodology, details, and results of flare forecasting comparison efforts, we focus here on patterns of forecast outcomes (success and failure) over multiday periods. A novel analysis is developed to evaluate forecasting success in the context of catching the first event of flare-active periods and, conversely, correctly predicting declining flare activity. We demonstrate these evaluation methods graphically and quantitatively as they provide both quick comparative evaluations and options for detailed analysis. For the testing interval 2016-2017, we determine the relative frequency distribution of two-day dichotomous forecast outcomes for three different event histories (i.e., event/event, no-event/event, and event/no-event) and use it to highlight performance differences between forecasting methods. A trend is identified across all forecasting methods that a high/low forecast probability on day 1 remains high/low on day 2, even though flaring activity is transitioning. For M-class and larger flares, we find that explicitly including persistence or prior flare history in computing forecasts helps to improve overall forecast performance. It is also found that using magnetic/modern data leads to improvement in catching the first-event/first-no-event transitions. Finally, 15% of major (i.e., M-class or above) flare days over the testing interval were effectively missed due to a lack of observations from instruments away from the Earth-Sun line.
    • Supervised classification of bradykinesia in Parkinson’s disease from smartphone videos

      Williams, S.; Relton, S.D.; Fang, H.; Alty, J.; Qahwaji, Rami S.R.; Graham, C.D.; Wong, D.C. (2020-11)
      Background: Slowness of movement, known as bradykinesia, is the core clinical sign of Parkinson's and fundamental to its diagnosis. Clinicians commonly assess bradykinesia by making a visual judgement of the patient tapping finger and thumb together repetitively. However, inter-rater agreement of expert assessments has been shown to be only moderate, at best. Aim: We propose a low-cost, contactless system using smartphone videos to automatically determine the presence of bradykinesia. Methods: We collected 70 videos of finger-tap assessments in a clinical setting (40 Parkinson's hands, 30 control hands). Two clinical experts in Parkinson's, blinded to the diagnosis, evaluated the videos to give a grade of bradykinesia severity between 0 and 4 using the Unified Pakinson's Disease Rating Scale (UPDRS). We developed a computer vision approach that identifies regions related to hand motion and extracts clinically-relevant features. Dimensionality reduction was undertaken using principal component analysis before input to classification models (Naïve Bayes, Logistic Regression, Support Vector Machine) to predict no/slight bradykinesia (UPDRS = 0–1) or mild/moderate/severe bradykinesia (UPDRS = 2–4), and presence or absence of Parkinson's diagnosis. Results: A Support Vector Machine with radial basis function kernels predicted presence of mild/moderate/severe bradykinesia with an estimated test accuracy of 0.8. A Naïve Bayes model predicted the presence of Parkinson's disease with estimated test accuracy 0.67. Conclusion: The method described here presents an approach for predicting bradykinesia from videos of finger-tapping tests. The method is robust to lighting conditions and camera positioning. On a set of pilot data, accuracy of bradykinesia prediction is comparable to that recorded by blinded human experts.
    • The Automated Prediction of Solar Flares from SDO Images Using Deep Learning

      Abed, Ali K.; Qahwaji, Rami S.R.; Abed, A. (2021-04-15)
      In the last few years, there has been growing interest in near-real-time solar data processing, especially for space weather applications. This is due to space weather impacts on both space-borne and ground-based systems, and industries, which subsequently impacts our lives. In the current study, the deep learning approach is used to establish an automated hybrid computer system for a short-term forecast; it is achieved by using the complexity level of the sunspot group on SDO/HMI Intensitygram images. Furthermore, this suggested system can generate the forecast for solar flare occurrences within the following 24 h. The input data for the proposed system are SDO/HMI full-disk Intensitygram images and SDO/HMI full-disk magnetogram images. System outputs are the “Flare or Non-Flare” of daily flare occurrences (C, M, and X classes). This system integrates an image processing system to automatically detect sunspot groups on SDO/HMI Intensitygram images using active-region data extracted from SDO/HMI magnetogram images (presented by Colak and Qahwaji, 2008) and deep learning to generate these forecasts. Our deep learning-based system is designed to analyze sunspot groups on the solar disk to predict whether this sunspot group is capable of releasing a significant flare or not. Our system introduced in this work is called ASAP_Deep. The deep learning model used in our system is based on the integration of the Convolutional Neural Network (CNN) and Softmax classifier to extract special features from the sunspot group images detected from SDO/HMI (Intensitygram and magnetogram) images. Furthermore, a CNN training scheme based on the integration of a back-propagation algorithm and a mini-batch AdaGrad optimization method is suggested for weight updates and to modify learning rates, respectively. The images of the sunspot regions are cropped automatically by the imaging system and processed using deep learning rules to provide near real-time predictions. The major results of this study are as follows. Firstly, the ASAP_Deep system builds on the ASAP system introduced in Colak and Qahwaji (2009) but improves the system with an updated deep learning-based prediction capability. Secondly, we successfully apply CNN to the sunspot group image without any pre-processing or feature extraction. Thirdly, our system results are considerably better, especially for the false alarm ratio (FAR); this reduces the losses resulting from the protection measures applied by companies. Also, the proposed system achieves a relatively high scores for True Skill Statistics (TSS) and Heidke Skill Score (HSS).
    • Eccentric compression behaviour of concrete columns reinforced with steel-FRP composite bars

      Ge, W.; Chen, K.; Guan, Z.; Ashour, Ashraf F.; Lu, W.; Cao, D. (Elsevier, 2021)
      Eccentric compression behaviour of reinforced concrete (RC) columns reinforced by steel-FRP composite bars (SFCBs) was investigated through experimental work and theoretical analyses. The tension and compression test results show that SFCBs demonstrate a stable post-yield stiffness. The mechanical properties of the composite reinforcement have a significant influence on eccentric compression behaviour of the reinforced concrete columns, in terms of failure mode, crack width, deformation and bearing capacity. Formulae were also developed to discriminate failure mode and to determine moment magnification factor, bearing capacity and crack width of the columns studied, with the theoretical predictions being in a good agreement with the experimental results. In addition, parametric studies were conducted to evaluate the effects of mechanical properties of reinforcement, reinforcement ratio, eccentricity, slenderness ratio, types of reinforcement and concrete on the eccentric compression behaviour of RC columns. The results show that the compressive performance is significantly improved by using the high performance concrete, i.e. reactive powder concrete (RPC) and engineered cementious composites (ECC).
    • Creation of controlled polymer extrusion prediction methods in fused filament fabrication. An empirical model is presented for the prediction of geometric characteristics of polymer fused filament fabrication manufactured components

      Whiteside, Benjamin R.; Coates, Philip D.; Caton-Rose, Philip D.; Hebda, Michael J. (University of BradfordFaculty of Engineering and Informatics, 2019)
      This thesis presents a model for the procedures of manufacturing Fused Filament Fabrication (FFF) components by calculating required process parameters using empirical equations. Such an empirical model has been required within the FFF field of research for a considerable amount of time and will allow for an expansion in understanding of the fundamental mathematics of FFF. Data acquired through experimentation has allowed for a data set of geometric characteristics to be built up and used to validate the model presented. The research presented draws on previous literature in the fields of additive manufacturing, machine engineering, tool-path programming, polymer science and rheology. Combining these research fields has allowed for an understanding of the FFF process which has been presented in its simplest form allowing FFF users of all levels to incorporate the empirical model into their work whilst still allowing for the complexity of the process. Initial literature research showed that Polylactic Acid (PLA) is now in common use within the field of FFF and therefore was selected as the main working material for this project. The FFF technique, which combines extrusion and Computer Aided Manufacturing (CAM) techniques, has a relatively recent history with little understood about the fundamental mathematics governing the process. This project aims to rectify the apparent gap in understanding and create a basis upon which to build research for understanding complex FFF techniques and/or processes involving extruding polymer onto surfaces.
    • Integrated condition-based maintenance modelling and optimisation for offshore wind turbines

      Dao, Cuong D.; Kazemtabrizi, B.; Crabtree, C.J.; Tavner, P.J. (2021)
      Wind Energy published by John Wiley & Sons Ltd. Maintenance is essential in keeping wind energy assets operating efficiently. With the development of advanced condition monitoring, diagnostics and prognostics, condition-based maintenance has attracted much attention in the offshore wind industry in recent years. This paper models various maintenance activities and their impacts on the degradation and performance of offshore wind turbine components. An integrated maintenance strategy of corrective maintenance, imperfect time-based preventive maintenance and condition-based maintenance is proposed and compared with other traditional maintenance strategies. A maintenance simulation programme is developed to simulate the degradation and maintenance of offshore wind turbines and estimate their performance. A case study on a 10-MW offshore wind turbine (OWT) is presented to analyse the performance of different maintenance strategies. The simulation results reveal that the proposed strategy not only reduces the total maintenance cost but also improves the energy generation by reducing the total downtime and expected energy not supplied. Furthermore, the proposed maintenance strategy is optimised to find the best degradation threshold and balance the trade-off between the use of condition-based maintenance and other maintenance activities.
    • Protection of buried rigid pipes using geogrid-reinforced soil systems subjected to cyclic loading

      Elshesheny, Ahmed; Mohamed, Mostafa H.A.; Sheehan, Therese (2020-08)
      The performance of buried rigid pipes underneath geogrid-reinforced soil while applying incrementally increased cyclic loading was assessed using a fully instrumented laboratory rig. The influence of varying two parameters of practical importance was investigated; the pipe burial depth and the number of geogrid-layers. Measurements were taken for pipe deformation, footing settlement, strain in pipe and reinforcing layers, and pressure/soil stress on the pipe crown during various stages of cyclic loading. The research outcomes demonstrated a rapid increase in the rate of deformation of the pipe and the footing, and the rate of generated strain in the pipe and the geogrid-layers during the first 300 cycles. While applying further cycles, those rates were significantly decreased. Increasing the pipe burial depth and number of geogrid-layers resulted in reductions in the footing and the pipe deformations, the pressure on pipe crown, and the pipe strains. Redistribution of stresses, due to the inclusion of reinforcing layers, formed a confined zone surrounding the pipe providing it with additional lateral support. The pipe invert experienced a rebound, which was found to be dependent on pressure around the pipe and the degree of densification of the bedding layer. Data for strains measured in the geogrid-layers showed that despite the applied loading value and the pipe burial depth, the tensile strain in the lower geogrid-layer was usually higher than that measured in the upper layer.
    • Impact assessment of social media usage in B2B marketing: A review of the literature and a way forward

      Tiwary, N.K.; Kumar, R.K.; Sarraf, S.; Kumar, P.; Rana, Nripendra P. (2021)
      Although various critical elements, such as media publicity, word of mouth, legislation, and environmental factors, are not under the control of a company, they play a significant role in influencing its brand image. Uncertainty over how different social networking sites can support brands is one of the crucial reasons for the delayed acceptance of social media (SM) in business-to-business (B2B) transactions. SM possesses immense potential in relation to gathering customer data and assisting B2B marketers. Therefore, this study reviewed SM usage in the B2B context, based on 294 selected articles. The methodology included bibliometric analysis to identify the impact of SM usage in the B2B domain and content analysis to perform a thematic assessment. Our analysis found that many B2B firms cannot leverage SM’s potential to its fullest compared to business-to-customer (B2C) firms. However, SM can help B2B marketers build their brand presence and trust globally, ultimately helping them find potential customers and build relationships with global supply chain providers.
    • Descriptors for vitamin K3 (menadione): calculation of biological and physicochemical properties

      Liu, Xiangli; Abraham, M.H.; Acree, W.E. (2021-02)
      We have used literature values for the solubility of vitamin K3 in organic solvents to obtain Abraham descriptorsfor vitamin K3. Although these descriptors themselves are not exceptional in any way, when combined withequations that we have already set out, they lead to the prediction of important properties of vitamin K3.These include the vapor pressure and heat of sublimation (necessary for the analysis of data on the concentrationof vitamin K3 in ambient air), and the partitions air-water, air-blood, air-lung, air-fat, air-skin, water-lipid, water-membrane, water-skin, as well as permeation from water through skin. Values of the partitions into biologicalphases are all quite large by comparison to those for organic compounds in general.
    • Descriptors for Edaravone; Studies on its Structure, and Prediction of Properties

      Liu, Xiangli; Aghamohammadi, Amin; Afarinkia, Kamyar; Abraham, R.J.; Acree, W.E. Jr; Abraham, M.H. (2021-06-15)
      Literature solubilities and NMR and IR studies have been used to obtain properties or descriptors of edaravone. These show that edaravone has a significant hydrogen bond acidity so that it must exist in solution partly as the OH and NH forms, as found by Freyer et al. Descriptors have been assigned to the keto form which has a low hydrogen bond acidity, and which is the dominant form in nonpolar solvents. Physicochemical properties of the keto form can be been calculated such as solubilities in nonpolar solvents, partition coefficients from water to nonpolar solvents, and partition coefficients from air to biological phases.