Now showing items 21-40 of 9413

    • Computational intelligence for safety assurance of cooperative systems of systems

      Kabir, Sohag; Papadopoulos, Y. (2020-12)
      Cooperative Systems of Systems (CSoS) including Autonomous systems (AS), such as autonomous cars and related smart traffic infrastructures form a new technological frontier for their enormous economic and societal potentials in various domains. CSoS are often safety-critical systems, therefore, they are expected to have a high level of dependability. Due to the open and adaptive nature of the CSoS, the conventional methods used to provide safety assurance for traditional systems cannot be applied directly to these systems. Potential configurations and scenarios during the evolving operation are infinite and cannot be exhaustively analysed to provide guarantees a priori. This paper presents a novel framework for dynamic safety assurance of CSoS, which integrates design time models and runtime techniques to provide continuous assurance for a CSoS and its systems during operation.
    • 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, George 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.
    • Descriptors for adamantane and some of its derivatives

      Abraham, M.H.; Acree, W.E. Jr; Liu, Xiangli (2021-03-01)
      Literature data on solubilities of adamantane in organic solvents have been used to obtain properties, or descriptors, of adamantane. There is much less data on substituted adamantanes but we have been able to obtain descriptors for some 40 substituted adamantanes. These descriptors can then be used to estimate a wide range of physicochemical, environmental and other properties of the adamantanes. For the first time, the water-solvent partition coefficient and the gas-solvent partition coefficient into a large range of solvents, can be estimated, the latter being equivalent to Henry's Law constants. A variety of other important properties can also be estimated. These include vapor pressures, enthalpies of vaporization and sublimation, partitions from air and from blood into biological tissues, and skin permeability from water. The descriptors themselves are not exceptional. Adamantane itself has a rather low dipolarity, zero hydrogen bond acidity and a very low hydrogen bond basicity, in common with other multicyclic aliphatic compounds. These lead to adamantane being a very hydrophobic compound, as is evident from our estimated water-octanol partition coefficient.
    • Process simulation and evaluation of ethane recovery process using Aspen-HYSYS

      Rezakazemi, M.; Rahmanian, Nejat; Jamil, Hassan; Shirazian, S. (2018-01)
      In this work, the process of ethane recovery plant was simulated for the purpose of Front End Engineering Design. The main objective is to carry out a series of simulation using Aspen HYSYS to compare recovery of ethane from Joule Thomson (JT) Valve, Turbo-Expander and Twister Technology. Twister technology offers high efficiency, more ethane recovery and lower temperature than JT valve and turbo-expander process. It lies somewhere between isenthalpic and isentropic process due to its mechanical configuration. Three processes were compared in terms of recovery of ethane. To conduct the simulations, a real gas plant composition and design data were utilized to perform the study for comparison among chosen technologies which are available for ethane recovery. The same parameters were used for the comparisons. Effect of operating conditions including pressure, temperature, and flow rate as well as carbon dioxide on the recovery of ethane was examined.
    • Investigation about profitability improvement for synthesis of benzyl acetate in different types of batch distillation columns

      Aqar, D.Y.; Rahmanian, Nejat; Mujtaba, Iqbal M. (2018-08-01)
      In this work, for the first time, the synthesis of benzyl acetate via the esterification of acetic acid and benzyl alcohol is investigated in the reactive distillation system using a middle vessel (MVD), inverted (IBD), and conventional batch reactive distillation columns. The measurement of the performance of these column schemes is determined in terms of profitability through minimization of the batch time for a defined separation task. The control variables (reboil ratio for MVD, IBD columns) and (reflux ratio in case of CBD column) are considered as piecewise constants over batch time. The optimization results obviously indicate that the CBD system is a more attractive process in terms of batch time reduction, and maximum achievable yearly profit as compared to the MVD, and IBD operations.
    • An experimental investigation on seeded granulation of detergent powders

      Rahmanian, Nejat; Halmi, M.H.; Choy, D.; Patel, R.; Yusup, S.; Mujtaba, Iqbal M. (2016-08-20)
      Granulation is commonly used as an enlargement process of particles produce granules with desirable characteristics and functionality. Granulation process transforms fine powders into free-flowing, dust-free granules with the presence of liquid binder at certain operating conditions. The main focus of this research is on seeded granulation of detergent powders, a new phenomenon of granulation in which a layer of fine powders surround the coarse particle. This is already proven for calcium carbonate (Rahmanian et al., 2011). Here, detergent granules were produced in a 5 L high shear Cyclomix granulator using different fine/coarse powder ratio (1/3, 1, 3) and different binder ratio of 10 %, 20 % and 30 %. The granules were then characterized for their particle size distribution, strength and structure. It was found that a high percentage (70 wt. %) of granules in the desired size range between 125 - 1,000 µm were produced using the powder ratio of 1/3 and a binder content of 10 %. Low mean crushing strength (3.0 N) with a narrow distribution was obtained using this condition. Structure characterization of the detergent granules produced in the granulator shows that consistent seeded granule structures are produced under the optimum process and formulation conditions of 1/3 powder ratio with 10 % binder.
    • Effect of various packing structure on gas absorption for enhanced CO2 capture

      Rahmanian, Nejat; Rehan, M.; Sumani, A.; Nizami, A.S. (2018-08-01)
      The increasing concentration of carbon dioxide (CO2) in the atmosphere is a primary global environmental concern due to its detrimental impacts on climate change. A significant reduction in CO2 generation together with its capture and storage is an imperative need of the time. CO2 can be captured from power plants and other industries through various methods such as absorption, adsorption, membranes, physical and biological separation techniques. The most widely used systems are solvent based CO2 absorption method. The aim of this study was to analyze the effect of various random and structured packing materials in absorption column on CO2 removing efficiency. Aspen plus was used to develop the CO2 capture model for different packing materials with Monoethanolamine (MEA) solvent in order to optimize the system. It was found that the lowest re-boiler duty of 3,444 kJ/KgCO2 yield the highest rich CO2 loading of 0.475 (mole CO2/mole MEA) by using the BX type of structured packing having the highest surface area. The surface area of the different packing materials were inversely proportional to the temperature profiles along the column. Furthermore, the packing materials with higher surface areas yielded higher CO2 loading profiles and vice versa. The findings of this study and recommendation would help further research on optimization of solvent-based CO2 capturing technologies.