| 1. |
- Alhalaweh, Amjad, et al.
(author)
-
Computational predictions of glass-forming ability and crystallization tendency of drug molecules
- 2014
-
In: Molecular Pharmaceutics. - : American Chemical Society (ACS). - 1543-8384 .- 1543-8392. ; 11:9, s. 3123-3132
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Journal article (peer-reviewed)abstract
- Amorphization is an attractive formulation technique for drugs suffering from poor aqueous solubility as a result of their high lattice energy. Computational models that can predict the material properties associated with amorphization, such as glass-forming ability (GFA) and crystallization behavior in the dry state, would be a time-saving, cost-effective, and material-sparing approach compared to traditional experimental procedures. This article presents predictive models of these properties developed using support vector machine (SVM) algorithm. The GFA and crystallization tendency were investigated by melt-quenching 131 drug molecules in situ using differential scanning calorimetry. The SVM algorithm was used to develop computational models based on calculated molecular descriptors. The analyses confirmed the previously suggested cutoff molecular weight (MW) of 300 for glass-formers, and also clarified the extent to which MW can be used to predict the GFA of compounds with MW < 300. The topological equivalent of Grav3_3D, which is related to molecular size and shape, was a better descriptor than MW for GFA; it was able to accurately predict 86% of the data set regardless of MW. The potential for crystallization was predicted using molecular descriptors reflecting Hückel pi atomic charges and the number of hydrogen bond acceptors. The models developed could be used in the early drug development stage to indicate whether amorphization would be a suitable formulation strategy for improving the dissolution and/or apparent solubility of poorly soluble compounds.
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| 2. |
- Alhalaweh, Amjad, et al.
(author)
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Data mining of solubility parameters for computational prediction of drug–excipient miscibility
- 2014
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In: Drug Development and Industrial Pharmacy. - : Informa UK Limited. - 0363-9045 .- 1520-5762. ; 40:7, s. 904-909
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Journal article (peer-reviewed)abstract
- Computational data mining is of interest in the pharmaceutical arena for the analysis of massive amounts of data and to assist in the management and utilization of the data. In this study, a data mining approach was used to predict the miscibility of a drug and several excipients, using Hansen solubility parameters (HSPs) as the data set. The K-means clustering algorithm was applied to predict the miscibility of indomethacin with a set of more than 30 compounds based on their partial solubility parameters [dispersion forces , polar forces and hydrogen bonding ]. The miscibility of the compounds was determined experimentally, using differential scanning calorimetry (DSC), in a separate study. The results of the K-means algorithm and DSC were compared to evaluate the K-means clustering prediction performance using the HSPs three-dimensional parameters, the two-dimensional parameters such as volume-dependent solubility and hydrogen bonding , and selected single (one-dimensional) parameters. Using HSPs, the prediction of miscibility by the K-means algorithm correlated well with the DSC results, with an overall accuracy of 94%. The prediction accuracy was the same (94%) when the two-dimensional parameters or the hydrogen-bonding (one-dimensional) parameter were used. The hydrogen-bonding parameter was thus a determining factor in predicting miscibility in such set of compounds, whereas the dispersive and polar parameters had only a weak correlation. The results show that data mining approach is a valuable tool for predicting drug–excipient miscibility because it is easy to use, is time and cost-effective, and is material sparing.
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| 3. |
- Alhalaweh, Amjad, et al.
(author)
-
Molecular Drivers of Crystallization Kinetics for Drugs in Supersaturated Aqueous Solutions
- 2019
-
In: Journal of Pharmaceutical Sciences. - : ELSEVIER SCIENCE INC. - 0022-3549 .- 1520-6017. ; 108:1, s. 252-259
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Journal article (peer-reviewed)abstract
- In this study, we explore molecular properties of importance in solution-mediated crystallization occurring in supersaturated aqueous drug solutions. Furthermore, we contrast the identified molecular properties with those of importance for crystallization occurring in the solid state. A literature data set of 54 structurally diverse compounds, for which crystallization kinetics from supersaturated aqueous solutions and in melt-quenched solids were reported, was used to identify molecular drivers for crystallization kinetics observed in solution and contrast these to those observed for solids. The compounds were divided into fast, moderate, and slow crystallizers, and in silico classification was developed using a molecular K-nearest neighbor model. The topological equivalent of Grav3 (related to molecular size and shape) was identified as the most important molecular descriptor for solution crystallization kinetics; the larger this descriptor, the slower the crystallization. Two electrotopological descriptors (the atom-type E-state index for -Caa groups and the sum of absolute values of pi Fukui(+) indices on C) were found to separate the moderate and slow crystallizers in the solution. The larger these descriptors, the slower the crystallization. With these 3 descriptors, the computational model correctly sorted the crystallization tendencies from solutions with an overall classification accuracy of 77% (test set).
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| 4. |
- Alhalaweh, Amjad, et al.
(author)
-
Physical stability of drugs after storage above and below the glass transition temperature : Relationship to glass-forming ability
- 2015
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In: International Journal of Pharmaceutics. - : Elsevier BV. - 0378-5173 .- 1873-3476. ; 495:1, s. 312-317
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Journal article (peer-reviewed)abstract
- Amorphous materials are inherently unstable and tend to crystallize upon storage. In this study, we investigated the extent to which the physical stability and inherent crystallization tendency of drugs are related to their glass-forming ability (GFA), the glass transition temperature (T-g) and thermodynamic factors. Differential scanning calorimetry was used to produce the amorphous state of 52 drugs [ 18 compounds crystallized upon heating (Class II) and 34 remained in the amorphous state (Class III)] and to perform in situ storage for the amorphous material for 12 h at temperatures 20 degrees C above or below the T-g. A computational model based on the support vector machine (SVM) algorithm was developed to predict the structure-property relationships. All drugs maintained their Class when stored at 20 degrees C below the T-g. Fourteen of the Class II compounds crystallized when stored above the T-g whereas all except one of the Class III compounds remained amorphous. These results were only related to the glass-forming ability and no relationship to e. g. thermodynamic factors was found. The experimental data were used for computational modeling and a classification model was developed that correctly predicted the physical stability above the T-g. The use of a large dataset revealed that molecular features related to aromaticity and pi-pi interactions reduce the inherent physical stability of amorphous drugs.
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| 5. |
- Alsyouf, Imad, 1965-, et al.
(author)
-
Soft computing applications in wind power systems: a review and analysis
- 2009
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In: European Offshore Wind 2009 Conference proceedings.
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Conference paper (other academic/artistic)abstract
- This paper reviews, analyses, discusses and summarises the recent research and development and trends in the applications of soft computing in the field of wind power systems. We show the usage and the influence of soft computing on the different aspects of wind power systems especially in the field of operation and maintenance. This work provides the state of the art in this area which will be a good guidance for future research work. The main results achieved from the study show that the soft computing techniques are adequate for solving the different challenges at the different phases of the life cycle processes of wind power systems. Using the various soft computing techniques with wind power systems proved to be useful for the wind energy business. Using these tools contribute by improving the robustness of the decisions at different phases of the system's life cycle. Soft computing can enhance the efficiency and effectiveness of the operation and maintenance of offshore wind power systems through improving the availability levels. Thus, providing secure, sustainable and competitive energy supply for the future.
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| 6. |
- Alzghoul, Ahmad, et al.
(author)
-
Addressing concept drift to improve system availability by updating one-class data-driven models
- 2015
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In: Evolving Systems. - : Springer. - 1868-6478 .- 1868-6486. ; 6:3, s. 187-198
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Journal article (peer-reviewed)abstract
- Data-driven models have been used to detect system faults, thereby increasing industrial system availability. The ability to search data streams while dealing with concept drift are challenges for data-driven models. The objective of this work is to demonstrate a general method to manage concept drift when using one-class data-driven models. The method has been used to develop an automatically retrained and updated polygon-based model. In this paper, the available industrial data allowed for use of one-class data-driven models, and the polygon-based model was selected because it has previously been successful. Possible scenarios that allow one-class data-driven models to be retrained or updated were identified. Based on the identified scenarios, a method to automatically update a polygon-based model online is proposed. The method has been tested and verified using data collected from a Bosch Rexroth Mellansel AB hydraulic drive system. Data representing relevant faults was inserted into the data set in close collaboration with engineers from the company. The results show that the developed polygon-based model method was able to address the concept drift issue and was able to significantly improve the classification accuracy compared to the static polygon-based model. Thereby, the model could significantly improve industrial system availability when applied in the relevant production process. This paper shows that the developed polygon-based model requires small memory space while its updating procedure is simple and fast. Finally, the identified scenarios may be helpful as input for supporting other one-class data-driven models to cope with concept drift, thus increasing the generalizability of the results.
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| 7. |
- Alzghoul, Ahmad, et al.
(author)
-
Comparing a knowledge-based and a data-driven method in querying data streams for system fault detection : A hydraulic drive system application
- 2014
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In: Computers in industry (Print). - : Elsevier. - 0166-3615 .- 1872-6194. ; 65:8, s. 1126-1135
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Journal article (peer-reviewed)abstract
- The field of fault detection and diagnosis has been the subject of considerable interest in industry. Fault detection may increase the availability of products, thereby improving their quality. Fault detection and diagnosis methods can be classified in three categories: data-driven, analytically based, and knowledge-based methods.In this work, we investigated the ability and the performance of applying two fault detection methods to query data streams produced from hydraulic drive systems. A knowledge-based method was compared to a data-driven method. A fault detection system based on a data stream management system (DSMS) was developed in order to test and compare the two methods using data from real hydraulic drive systems.The knowledge-based method was based on causal models (fault trees), and principal component analysis (PCA) was used to build the data-driven model. The performance of the methods in terms of accuracy and speed, was examined using normal and physically simulated fault data. The results show that both methods generate queries fast enough to query the data streams online, with a similar level of fault detection accuracy. The industrial applications of both methods include monitoring of individual industrial mechanical systems as well as fleets of such systems. One can conclude that both methods may be used to increase industrial system availability.
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| 8. |
- Alzghoul, Ahmad, et al.
(author)
-
Comparing a knowledge-based and a data-driven method in querying data streams for system fault detection: A hydraulic drive system application
- 2014
-
In: Computers in industry (Print). - : Elsevier BV. - 0166-3615 .- 1872-6194. ; 65:8, s. 1126-1135
-
Journal article (peer-reviewed)abstract
- The field of fault detection and diagnosis has been the subject of considerable interest in industry. Fault detection may increase the availability of products, thereby improving their quality. Fault detection and diagnosis methods can be classified in three categories: data-driven, analytically based, and knowledge-based methods. In this work, we investigated the ability and the performance of applying two fault detection methods to query data streams produced from hydraulic drive systems. A knowledge-based method was compared to a data-driven method. A fault detection system based on a data stream management system (DSMS) was developed in order to test and compare the two methods using data from real hydraulic drive systems. The knowledge-based method was based on causal models (fault trees), and principal component analysis (PCA) was used to build the data-driven model. The performance of the methods in terms of accuracy and speed, was examined using normal and physically simulated fault data. The results show that both methods generate queries fast enough to query the data streams online, with a similar level of fault detection accuracy. The industrial applications of both methods include monitoring of individual industrial mechanical systems as well as fleets of such systems. One can conclude that both methods may be used to increase industrial system availability
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| 9. |
- Alzghoul, Ahmad, et al.
(author)
-
Data stream forecasting for system fault prediction
- 2012
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In: Computers & industrial engineering. - : Elsevier. - 0360-8352 .- 1879-0550. ; 62:4, s. 972-978
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Journal article (peer-reviewed)abstract
- Competition among today’s industrial companies is very high. Therefore, system availability plays an important role and is a critical point for most companies. Detecting failures at an early stage or foreseeing them before they occur is crucial for machinery availability. Data analysis is the most common method for machine health condition monitoring. In this paper we propose a fault-detection system based on data stream prediction, data stream mining, and data stream management system (DSMS). Companies that are able to predict and avoid the occurrence of failures have an advantage over their competitors. The literature has shown that data prediction can also reduce the consumption of communication resources in distributed data stream processing. In this paper different data-stream-based linear regression prediction methods have been tested and compared within a newly developed fault detection system. Based on the fault detection system, three DSM algorithms outputs are compared to each other and to real data. The three applied and evaluated data stream mining algorithms were: Grid-based classifier, polygon-based method, and one-class support vector machines (OCSVM). The results showed that the linear regression method generally achieved good performance in predicting short-term data. (The best achieved performance was with a Mean Absolute Error (MAE) around 0.4, representing prediction accuracy of 87.5%). Not surprisingly, results showed that the classification accuracy was reduced when using the predicted data. However, the fault-detection system was able to attain an acceptable performance of around 89% classification accuracy when using predicted data.
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| 10. |
- Alzghoul, Ahmad, et al.
(author)
-
Data stream mining for increased functional product availability awareness
- 2011
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In: Functional Thinking for Value Creation. - Berlin, Heidelberg : Springer Berlin/Heidelberg. - 9783642196881 - 9783642196898 ; , s. 237-241
-
Conference paper (peer-reviewed)abstract
- Functional Products (FP) and Product Service Systems (PSS) may be seen as integrated systems comprising hardware and support services. For such offerings, availability is key. Little research has been done on integrating Data Stream Management Systems (DSMS) for monitoring (parts of) a FP to improve system availability. This paper introduces an approach for how data stream mining may be applied to monitor hardware being part of a Functional Product. The result shows that DSMS have the potential to significantly support continuous availability awareness of industrial systems, especially important when the supplier is to supply a function with certain availability.
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| 11. |
- Alzghoul, Ahmad, et al.
(author)
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Experimental and Computational Prediction of Glass Transition Temperature of Drugs
- 2014
-
In: JOURNAL OF CHEMICAL INFORMATION AND MODELING. - : American Chemical Society (ACS). - 1549-9596 .- 1549-960X. ; 54:12, s. 3396-3403
-
Journal article (peer-reviewed)abstract
- Glass transition temperature (T-g) is an important inherent property of an amorphous solid material which is usually determined experimentally. In this study, the relation between T-g and melting temperature (T-m) was evaluated using a data set of 71 structurally diverse druglike compounds. Further, in silico models for prediction of T-g were developed based on calculated molecular descriptors and linear (multilinear regression, partial least-squares, principal component regression) and nonlinear (neural network, support vector regression) modeling techniques. The models based on T-m predicted T-g with an RMSE of 19.5 K for the test set. Among the five computational models developed herein the support vector regression gave the best result with RMSE of 18.7 K for the test set using only four chemical descriptors. Hence, two different models that predict T-g of drug-like molecules with high accuracy were developed. If T-m is available, a simple linear regression can be used to predict T-g. However, the results also suggest that support vector regression and calculated molecular descriptors can predict T-g with equal accuracy, already before compound synthesis.
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| 12. |
- Alzghoul, Ahmad
(author)
-
Improving availability of industrial products through data stream mining
- 2011
-
Licentiate thesis (other academic/artistic)abstract
- Products of high quality are of great interest for industrial companies. The quality of a product can be considered in terms of production cost, operating cost, safety and product availability, for example. Product availability is a function of maintainability and reliability. Monitoring prevents unplanned stops, thus increasing product availability by decreasing needed maintenance. Through monitoring, failures can be detected and/or avoided. Detecting failures eliminates extra costs such as costs associated with machinery damage and dissatisfied customers, and time is saved since stops can be scheduled, instead of having unplanned stops. Product monitoring can be done through searching the data generated from sensors installed on products.Nowadays, the data can be collected at high rates as part of a data stream. Therefore, data stream management systems (DSMS) and data stream mining (DSM) are being used to control, manage and search the data stream. This work investigated how the availability of industrial products can be increased through the use of DSM and DSMS technologies.A review of the data stream mining algorithms and their applications in monitoring was conducted. Based on the review, a new data stream classification method, i.e. Grid-based classifier was proposed, tested and validated. Also, a fault detection system based on DSM and DSMS technologies was proposed. The proposed fault detection system was tested using data collected from Hägglunds Drives AB (HDAB) hydraulic motors. Thereafter, a data stream predictor was integrated into the proposed fault detection system to detect failures earlier, thus gaining more time for response actions. The modified fault detection system was tested and showed good performance. The results showed that the proposed fault detection system, which is based on DSM and DSMS technologies, achieved good performance (with classification accuracy around 95%) in detecting failures on time. Detecting failures on time prevents unplanned stops and may improve the maintainability of the industrial systems and, thus, their availability.
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| 13. |
- Alzghoul, Ahmad, et al.
(author)
-
Increasing availability of industrial systems through data stream mining
- 2011
-
In: Computers & industrial engineering. - : Elsevier. - 0360-8352 .- 1879-0550. ; 60:2, s. 195-205
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Journal article (peer-reviewed)abstract
- Improving industrial product reliability, maintainability and thus availability is a challenging task for many industrial companies. In industry, there is a growing need to process data in real time, since the generated data volume exceeds the available storage capacity. This paper consists of a review of data stream mining and data stream management systems aimed at improving product availability. Further, a newly developed and validated grid-based classifier method is presented and compared to one-class support vector machine (OCSVM) and a polygon-based classifier.The results showed that, using 10% of the total data set to train the algorithm, all three methods achieved good (>95% correct) overall classification accuracy. In addition, all three methods can be applied on both offline and online data.The speed of the resultant function from the OCSVM method was, not surprisingly, higher than the other two methods, but in industrial applications the OCSVMs' comparatively long time needed for training is a possible challenge. The main advantage of the grid-based classification method is that it allows for calculation of the probability (%) that a data point belongs to a specific class, and the method can be easily modified to be incremental.The high classification accuracy can be utilized to detect the failures at an early stage, thereby increasing the reliability and thus the availability of the product (since availability is a function of maintainability and reliability). In addition, the consequences of equipment failures in terms of time and cost can be mitigated.
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| 14. |
- Alzghoul, Ahmad
(author)
-
Mining data streams to increase industrial product availability
- 2013
-
Doctoral thesis (other academic/artistic)abstract
- Improving product quality is always of industrial interest. Product availability, a function of product maintainability and reliability, is an example of a measurement that can be used to evaluate product quality. Product availability and cost are two units which are especially important to manage in the context of the manufacturing industry, especially where industry is interested in selling or buying offers with increased service content. Industry in general uses different strategies for increasing equipment availability; these include: corrective (immediate or delayed) and preventive strategies. Preventive strategies may be further subdivided into scheduled and predictive (condition-based) maintenance strategies. In turn, predictive maintenance may also be subdivided into scheduled inspection and continuously monitored. The predictive approach can be achieved by early fault detection. Fault detection and diagnosis methods can be classified into three categories: data-driven, analytically based, and knowledge-based methods. In this thesis, the focus is mainly on fault detection and on data-driven models.Furthermore, industry is generating an ever-increasing amount of data, which may eventually become impractical to store and search, and when the data rate is increasing, eventually impossible to store. The ever-increasing amount of data has prompted both industry and researchers to find systems and tools which can control the data on the fly, as close to real-time as possible, without the need to store the data itself. Approaches and tools such as Data Stream Mining (DSM) and Data Stream Management Systems (DSMS) become important. For the work reported in this thesis, DSMS and DSM have been used to control, manage and search data streams, with the purpose of supporting increased availability of industrial products.Bosch Rexroth Mellansel AB (formerly Hägglunds Drives AB) has been the industrial partner company during the course of the work reported in this thesis. Related data collection concerning the functionality of the BRMAB hydraulic system has been performed in collaboration with other researchers in Computer Aided Design at Luleå University of Technology.The research reported in this thesis started with a review of data stream mining algorithms and their applications in monitoring. Based on the review, a data stream classification method, i.e. Grid-based classifier, was proposed, tested and validated (Paper A). Also, a fault detection system based on DSM and DSMS was proposed and tested, as reported in Paper A. Thereafter, a data stream predictor was integrated into the proposed fault detection system to detect failures earlier, thus demonstrating how data stream prediction can be used to gain more time for proactive response actions by industry (Paper B). Further development included an automatic update method which allows the proposed fault detection system to be able to overcome the problem of concept drift (Paper E). The proposed and modified fault detection systems were tested and verified using data collected in collaboration with Bosch Rexroth Mellansel AB (BRMAB). The requirements for the proposed fault detection system and how it can be used in product development and design of the support system were also discussed (Paper C). In addition, the performance of a knowledge-based method and a data- driven method for detecting failures in high-volume data streams from industrial equipment have been compared (Paper D). It was found that both methods were able to detect all faults without any false alert. Finally, the possible implications of using cloud services for supporting industrial availability are discussed in Paper F. Further discussions regarding the research process and the relations between the appended papers can be found in Chapter 2, Figure 4 and in Chapter 5, Figure 21.The results showed that the proposed and modified fault detection systems achieved good performance in detecting and predicting failures on time (see Paper A and Paper B). In Paper C, it is shown how data stream management systems may be used to increase product availability awareness. Also, both the data-driven method and the knowledgebased method were suitable for searching data streams (see Paper D). Paper E shows how the challenge of concept drift, i.e. the situation in which the statistical properties of a data stream change over time, was turned to an advantage, since the authors were able to develop a method to automatically update the safe operation limits of the one-class data-driven models.In general, detecting faults and failures on time prevents unplanned stops and may improve both maintainability and reliability of industrial systems and, thus, their availability (since availability is a function of maintainability and reliability). By the results, this thesis demonstrates how DSM and DSMS technologies can be used to increase product availability and thereby increase product quality in terms of availability.
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| 15. |
- Alzghoul, Ahmad, et al.
(author)
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Screening paper runnability in a web-offset pressroom by data mining
- 2009
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In: Proceedings of the 9th Industrial Conference on Advances in Data Mining. - Berlin : Springer Berlin/Heidelberg. - 9783642030666 ; , s. 161-175
-
Conference paper (peer-reviewed)abstract
- This paper is concerned with data mining techniques for identifying the main parameters of the printing press, the printing process and paper affecting the occurrence of paper web breaks in a pressroom.Two approaches are explored. The first one treats the problem as a task of data classification into “break” and “non break” classes. The procedures of classifier design and selection of relevant input variables are integrated into one process based on genetic search. The search process results in a set of input variables providing the lowest average loss incurred in taking decisions. The second approach, also based on genetic search, combines procedures of input variable selection and data mapping into a low dimensional space. The tests have shown that the web tension parameters are amongst the most important ones. It was also found that, provided the basic off-line paper parameters are in an acceptable range, the paper related parameters recorded online contain more information for predicting the occurrence of web breaks than the off-line ones. Using the selected set of parameters, on average, 93.7% of the test set data were classified correctly. The average classification accuracy of the break cases was equal to 76.7%.
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| 16. |
- Lindström, John, et al.
(author)
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Use of cloud services in functional products : availability implications
- 2014
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In: Product Services Systems and Value Creation. - : Elsevier. ; 16, s. 368-372
-
Conference paper (peer-reviewed)abstract
- The paper addresses the potential use of cloud services in Functional Products (FP) and its possible implications for availability. Further, how the implications for availability can be understood via modelling and simulation is addressed. The paper adds further specificity to literature by indicating the FP constituents for which cloud services are applicable and adequate.
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| 17. |
- Verikas, Antanas, et al.
(author)
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Screening web breaks in a pressroom by soft computing
- 2011
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In: Applied Soft Computing. - Amsterdam : Elsevier. - 1568-4946 .- 1872-9681. ; 11:3, s. 3114-3124
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Journal article (peer-reviewed)abstract
- The objective of this work is to identify the main parameters of the printing press, the printing process, and the paper affecting the occurrence of web breaks in a pressroom. Two approaches are explored. The first one treats the problem as a task of data classification into "break" and "non-break" classes. The procedures of classifier design and selection of relevant input variables are integrated into one process based on genetic search. The second approach, targeted for data visualization and also based on genetic search, combines procedures of input variable selection and data mapping into a two-dimensional space. The genetic search-based analysis has shown that the web tension parameters are amongst the most important ones. It was also found that the group of paper related parameters recorded online contain more information for predicting the occurrence of web breaks than the group of traditional parameters recorded off-line at a paper lab. Using the selected set of parameters, on average, 93.7% of the test set data were classified correctly. The average classification accuracy of web break cases was equal to 76.7%. (C) 2010 Elsevier B. V. All rights reserved.
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