Nursing Standards and Nursing Process

The automatic detection of divergences from a desired process behavior is a common research topic in the business process management community. An established technique to analyze processes is called conformance checking. Given a definition of a process in form of a process model, conformance checking can be used to test whether the executions of a process contained in a so-called event log data structure are conforming with the process as it was defined. The result is a comparison of the execution traces and their respective correct execution, according to the process model. This technique provides insights into where the divergence has occurred and how the execution must be altered to conform to the process model. However, a problem is that it requires a process model to be available. Process models in the correct format are not always available. Contrary to conformance checking, process anomaly detection aims to find anomalous executions without relying on a predefined process model. A process anomaly detection algorithm derives the process logic from the event log itself and exploits the patterns found within the event log to distinguish normal from anomalous process executions. Though process anomaly detection provides the benefit of not relying on a process model, it typically does not provide the level of detail that conformance checking does. A process execution can either be normal or it can be anomalous. This dissertation proposes process anomaly correction, a novel approach that combines the benefits of conformance checking and process anomaly detection. Given only an event log, process anomaly correction detects anomalous executions, clearly indicates where the anomaly has occurred during the execution and suggests possible corrective measures. The solution presented in this work is based on a new concept to the field of process anomaly detection: Process learning. In process learning, the task of understanding the process based on the example data is transformed into a learning problem in which a neural network is trained to predict the very next activity in a running process execution. The resulting machine learning model thus represents an approximation of the real process that created the data. This cumulative dissertation consists of five contributions to the field of business process management that demonstrate how, starting from a process anomaly detection, process anomaly correction is achieved in a series of four steps. (1) Process learning is employed to generate an approximated model of the process logic. (2) The limitation of only distinguishing between normal and anomalous process executions is overcome by employing the process learning model which processes the process executions on a finer level of detail than existing approaches. (3) The necessity of providing manual threshold settings, as it is typical for process anomaly detection algorithms, is replaced by an automatic parameterization utilizing the process learning model. (4) The predictive capabilities of the process learning model are exploited to generate possible corrections of detected anomalies. The resulting process anomaly correction approach can be employed in scenarios where classic conformance checking would be infeasible, due to the restriction of relying on a process model. Furthermore, it can be employed alongside classical conformance checking, for it incorporates more information coming from the event log than classical conformance checking (such as employees executing a process step, in which country the process is executed, etc.), and thus provides a new perspective for the process analyst.

Business process modelling is an important part of system design. When designing or redesigning a business process, stakeholders specify, negotiate, and agree on business requirements to be satisfied, including non-functional requirements that concern the quality of the business process. This thesis addresses the question of how to specify and compute the quality of a business process, given the model that stakeholders use. The motivation for this thesis is the increasing importance of the quality of business processes. Knowing the quality of specific business processes enables stakeholders to judge if these processes need improvement. Knowing the quality of the constructs of those processes (viz., events, inputs, activities, and outputs) and the way they are structured enables a more detailed analysis of their shortcomings and provides a basis for the design of improvements. The research challenge of this thesis is grounded in the assumption that: “Organisations need an appropriate means to effectively compute achievement of their goals and objectives by their business processes.” Given this challenge, the main research question on which this thesis focuses is: “Can the quality of a business process be computed quantitatively at different levels of granularity?” The research objective is: “To develop frameworks, factors, and metrics for computing non-functional requirements (quality) of business processes quantitatively at different levels of granularity.” The outcomes of this thesis are: 1) BPIMM, a language-independent business process integrating meta-model, based on the concepts of seven mainstream business process modelling languages: BPMN, EPC, RAD, UML AD, SADT, IDEF0, and IDEF3. 2) BPC-QC (Business Process Concept - Quality Computation), an approach to quality computation at the lowest level of granularity of a business process. The approach consists of: i. BPC-QEF (Business Process Concept - Quality Evaluation Framework), a language-independent generic framework and algorithm to compute the quality of the constructs of a business process: event, input, activity, and output. ii. A set of business process quality dimensions and factors. The following quality dimensions are distinguished: performance, efficiency, reliability, recoverability, permissibility, and availability. Each dimension categorises different quality aspects in terms of factors. A non-exhaustive set of sixteen quantitative factors is provided. iii. Quality metrics for each of the quality factors, to facilitate a quantitative computation of the quality of a specific construct of a business process. 3) BP-QC (Business Process - Quality Computation), an approach to compute the quality at the highest level of granularity of a business process. The approach consists of: i. BP-CQCF (Business Process - Compositional Quality Computation Framework), a language-independent generic framework and algorithm to compute the quality of a business process as a whole, given the quality of its constructs. ii. A set of generic business process modelling patterns to decompose a business process into more succinct parts, namely: sequential, parallel with synchronisation, exclusive, inclusive, simple loop, and complex loop. iii. A set of over one hundred computational formulae. For each combination of modelling pattern and a quality factor, there is a formula to compute the quality. 4) AAV (Approach to Application and Validation), an evaluation plan to evaluate BPIMM, BPC-QC and BP-QC in practice, together with expert stakeholders. The plan consists of the units of measure, a measurement model, and a case study procedure. To evaluate the applicability of the contributions of this thesis to real world business needs, four case studies have been conducted in different environments: a Dutch educational institution, a global financial institution, an international financial service provider, and a Dutch research project on crisis management. Each of these case studies concerns a different, single business process. This thesis shows that: 1) A quality computation approach can be adopted independent of a business process modelling language. 2) Quantitative quality factors can be introduced specifically for the constructs of a business process. 3) Quantitative metrics and computational formulae can be developed for specific quality factors, allowing the computation of different aspects of the quality of a business process quantitatively at different levels of granularity. 4) An evaluation plan can be developed to evaluate the applicability of the contributions of this thesis (viz., BPIMM, BPC-QC, and BP-QC). The contributions of this thesis are designed to be beneficial to the areas of business and management, requirements engineering, software engineering, and business process modelling. In the areas of requirements engineering and software engineering, these contributions are intended to help practitioners to consider non-functional requirements at the earliest stage. In the area of business process modelling, information systems, service computing, and cloud computing, the contributions can be used for quality-driven modelling, design, and redesign. To conclude, knowing the quality value of a business process at different levels of granularity provides a basis for its improvement.

Companies increasingly adopt process management systems (PrMS), which offer promising perspectives for a more flexible and efficient process execution. However, for many process-aware application systems (e.g., ERP or CRM systems), the underlying process logic is still hard-coded. As a consequence, these business applications are both complex to design and costly to maintain; i.e., they require long development cycles, and even simple process changes might result in costly code adaptions and high efforts for testing. One reason for this situation stems from the fact that contemporary PrMS were primarily designed for the support of highly structured, repetitive business processes. By contrast, many processes found in practice are rather unstructured or semi-structured, i.e., they are knowledge-intensive and driven by user decisions. In addition, the business functions to be integrated with these processes usually cannot be straight-jacketed into activities. For all these reasons, the activity-centered paradigm of contemporary PrMS is by far too inflexible for realizing more advanced business applications. This deficiency mainly stems from the unsatisfactory integration of processes and data in existing PrMS. Despite emerging approaches, which target at a tighter integration of process and data, a unified and comprehensive understanding of the relationships between them is still missing. This thesis first analyzes real processes not adequately supported by existing PrMS and elaborates their characteristic properties. As a major insight it became clear that in many application scenarios comprehensive process support requires both object- and process-awareness. This means, business processes and business data must not be treated independently from each other. Instead, business processes must comply with the underlying data structure. In particular, in accordance to the given data model comprising object types and object relations, the modeling, execution and monitoring of processes must be based on two levels of granularity: object behavior and object interactions. Further, the individual processes, coordinating the behavior of single object instances, must be coordinated with the ones of related object instances. Opposed to these well-defined process support granularity levels, activities must be executable at different levels of granularity. In particular, while a particular user may only want to work on a particular object instance, another one may want to process a number of related object instances in one go. Furthermore, process execution must be accomplished in a data-driven manner; i.e., the progress of a process instance mainly depends on available business objects and on value changes of their attributes. Finally, authorized users must be able to access and manage process-related objects at any point in time. Based on the properties identified, this thesis elaborates major requirements for enabling object-awareness in processes management systems. The major contribution of the thesis is the PHILharmonicFlows framework, which addresses these requirements and enables object-aware process management in a comprehensive manner. In particular, the framework not only provides a new process modeling approach, but also establishes a well-defined operational semantics enabling the automatic and dynamic generation of end-user components for object- and process-aware business applications at run-time (e.g., overview tables and user forms). Overall, a holistic framework integrating data, processes and users offers promising perspectives in order to overcome the numerous limitations of contemporary PrMS. This thesis considers research in this area as fundamental maturation of process management technology.

Business processes and information systems mutually affect each other in non-trivial ways. Frequently, the business process design and the information system design are not well aligned. This means that business processes are designed without taking the information system impact into account, and vice versa. Missing alignment at design time often results in quality problems at runtime, such as large response times of information systems, large process execution times, overloaded information systems or interrupted processes. Aligning business process quality and information system quality at design time requires to
\nsolve the following problems (P). Business process quality and information system quality have to be characterized. P1: In contrast to information system quality, which is specified in the ISO/IEC 9126 standard, for example, there is no common and comprehensive understanding of business process quality. P2: Beyond that, current business process
\nmodeling notations do not aim to represent quality aspects. The impact of a business process on the quality of an information system, and vice versa, is unknown at design
\ntime. P3: The mutual impact between business processes and information systems must be predicted at design time.
\nIn this thesis, the Business Process Quality Reference-Model (BPQRM), a quality model for business processes, is introduced. The model allows for a comprehensive characterization of business process quality (P1). The BPQRM is applied successfully in a case study to
\nidentify potential for process quality improvement in practice. Based on the BPQRM an existing process modeling notation is extended by model elements to represent quality
\naspects (P2). Simulation is a powerful means to predict the impact of a business process on the quality of an information system, and vice versa, at design time. This thesis proposes two simulation approaches to predict the mutual impact between business processes and information systems, in terms of performance (P3). The approach Business
\nIT Impact Simulation (BIIS) defines interfaces between the business process simulation and the information system simulation. Performance-relevant information is exchanged
\nvia the interfaces between both simulations. Using business process simulation and information system simulation in isolation, workload burstiness is not adequately reflected.
\nThis is especially true for occasional, volatile peak loads. Workload burstiness can significantly affect the performance of business processes and information systems. The approach Integrated Business IT Impact Simulation (IntBIIS) for the integration of business processes and information systems in a single simulation allows for reflecting workload burstiness correctly. The simulation approaches support the comparison of design alternatives and the verification of a certain design against requirements. A case study confirms the feasibility in practice and the acceptance from practitioners’ point of
\nview.

Existing solutions for analysis and optimization of manufacturing processes, such as online analysis processing or statistical calculations, have shortcomings that limit continuous process improvements. In particular, they lack means of storing and integrating the results of analysis. This makes the valuable information that can be used for process optimizations used only once and then disposed. The goal of the Advanced Manufacturing Analytics (AdMA) research project is to design an integrated platform for data-driven analysis and optimization of manufacturing processes using analytical techniques, especially data mining, in order to carry out continuous improvement of production. The achievement of this goal is based on the integration of the data related to the manufacturing processes, especially from Manufacturing Execution Systems (MES), with the other operating data, e.g. from Enterprise Resource Planning (ERP) systems. This work is based on AdMA platform described in [1] and Deep Business Process Optimization platform described in [2]. It is focused on the conceptual development of the Process Insight Repository, which is a part of the AdMA platform. The Process Insight Repository is aimed at storing the manufacturing process related data and the insights associated with it. Being part of the AdMA platform, the Process Insight Repository is oriented on storing the insights retrieved by application of data mining techniques to the data of manufacturing processes, so that the newly extracted knowledge can be stored along with the process data itself. Chapter 2 describes the conceptual schema of the Process Insight Repository. The conceptual schema defines what data must be stored in the Process Insight Repository and how different parts of this data are interconnected. Chapter 3 provides a review of technologies that can be used for the implementation of the Process Insight Repository. This includes technologies for storing manufacturing process data, free form knowledge and data mining related data. Chapter 4 describes the details of the prototype implementation of the Process Insight Repository. The result of this work is the created conceptual schema of the Process Insight Repository and a prototype implementation as a proof of concept.

The 3DP process has been modified to directly fabricate high green density parts using
\nsubmicron powder. The slurry-based 3DP process deposits the powder bed by spraying a
\ndispersed slurry of the component material onto a piston. Alumina, silicon nitride, and
\nlead zirconate titanate components with green densities as high as 67% have been
\nfabricated by the slurry-based 3DP process. Solution phase binder systems have proven
\nto be successful for the new process. Substantially improved surface finish over the
\nconventional dry powder-based 3DP process has been demonstrated. Layer heights less
\nthan 50 Jlm can be prepared with this process. Thus, the stepped surface topography
\ncommonly observed in solid free form parts is substantially reduced.

A chemical absorption process is the most commercially used process for CO2 separation in flue gases. However, the process needs a huge amount of energy to strip CO2 in a stripper. To overcome this problem, a new gas separation process using chemical absorption method based on self-heat recuperation technology is proposed in this study for energy saving. In this process, the regeneration energy of absorbent is provided from the exhausted heat of absorber and stripper by using a compressor and heat exchangers. The process is divided into two modules (absorber and stripper), in which the internal heat circulation is maximized. Then, the process is reconstructed by combining these modules. We evaluated the amount of energy consumption of the process as compared with the conventional gas separation process for CO2 by using a commercial process simulator (PRO/II). From the simulation results, energy consumption of the proposed process decreased to one-third at that of conventional heat recovery process. Thus, the proposed process based on self-heat recuperation technology is a very promising process for energy saving of gas separation.

The world is steadily moving toward greener alternatives of established chemical production processes. Process modeling and simulation play a vital role in conceiving, designing and optimizing these greener processes. In many cases, these simulation tools are limited to a few project design stages, thus limiting their true potential. This thesis presents the benefits of process modeling and simulation at different levels of process development. The work focuses on ammonia production, which sustains global agricultural and mining activities. Ammonia production has traditionally been based on large-scale plants. The thrust toward large-scale production in order to gain economic advantages has overshadowed the benefits that could be derived from small-scale production plants. Additionally, the ammonia industry is a major consumer of global fossil fuels, and in the process of providing ammonia, also burdens the planet with greenhouse gases. In order to ensure sustainable ammonia production, this thesis highlights the techno-economic advantages that result from small-scale ammonia plants based on biomass feedstock. The use of biomass as a potential feedstock can help neutralize the carbon footprint of ammonia production. To establish the feasibility of such a process, this thesis presents the application of process simulations at different modeling levels.<br> <br> A Computational Fluid Dynamics (CFD) model was used to understand the flow patterns inside a dual fluidized bed gasifier. This elevated the understanding of the hydrodynamics of the gasifier freeboard, which is neglected by the conventional two-phase methodology. The CFD simulation was utilized to perform a Residence Time Distribution (RTD) analysis of the reactor. Four different tracer approaches namely the frozen velocity approach, the snapshot approach, the data sampling approach and the transient approach, were compared. The RTD analysis formed the basis of a steady-state compartment model that was developed in ASPEN Plus simulation software. The ASPEN Plus gasifier model decoupled the pyrolysis, gasification, and combustion sections of the gasifier to affect a better comprehension of the process and the results. The model predicated satisfactory results upon validation. Additionally, the model could also be used to predict the output for different biomass feedstocks.<br> <br> ASPEN Plus is used to simulate the biomass gasification process as well as downstream gas conditioning that leads to ammonia production. Apart from major constituent gases, the model accounted for tar production in the gasifier process as well as for its subsequent removal. Three different configurations of the biomass-to-ammonia process, namely, Autothermal Reforming (ATR), Steam Methane Reforming (SMR), and CO₂ Reforming (CR) have been modeled and compared. The configurations differ on the basis of the reforming of the methane and tars, which are present in the syngas. The output of the ASPEN Plus simulation is fed into an MS Excel-based framework, which performs Life Cycle Costing (LCC) and Life Cycle Assessment (LCA) for the simulated flowsheet. Finally, these outputs are utilized by an MS Excel-/VB-based Multi-Objective Optimization (MOO) framework in order to optimize both LCC and LCA for changing process parameters.<br> <br> Three biomass feedstocks, namely, wood, straw pellets, and bagasse, which are available in Australia, India, and Brazil, respectively, have been compared in order to understand the effect of biomass composition, the supply chain, and the national economic and environmental scenario on the viability of the biomass-to-ammonia process. The MOO results that were derived for changing flowsheet configurations as well as for different biomass feedstocks that were grown at different locations worldwide are expected to highlight a more rigorous analysis of the biomass-to-ammonia process. Such an analysis will provide decision makers with consistent and comparable data to objectively judge the viability of the proposed process. This analysis distinguishes this thesis from past studies that focused on ammonia production from biomass, and presented their LCC and LCA results for a single process configuration and for a single feedstock. The results predicted that different biomass feedstocks, process configurations, and geographical regions have their own advantages. The results provide a benchmark for comparison of other biomass-to-ammonia processes on a techno-enviro-economic scale. The methodology can be employed as an example in the development of different sustainable chemical production processes.

Glacier derived from accumulated snow and characterized by movement and certain size to some extent,is one of the most sensitive indicators to climate.Ice formation processes include the process,mechanism and results of snow to ice transformation.Ice formation process can indicate the growth conditions,formation processes,physical characteristics of glacier,and so on;spatial variations of ice formation process can also reflect glacier change,and can indicate climate change.First study of ice formation process on glacier in China was performed in 1962,when Xie Zichu et al.,studied the ice formation process on Glacier No.1 at the Urumqi river head,Tianshan mountain,and subsequently scholars studied the ice formation process on glacier in Qilian mountain and so on.With the development of studying on ice formation process,the conception of ice formation process with China's features comes into being.This paper reviewed the development process of ice formation zones and its further development in China and the study results of ice formation process on glaciers in China after the 1960s,and these studies mainly focused on Qilian mountain,Tianshan mountain,Altay mountain,West Kunlun mountain,Himalaya mountain,South-East Tibetan and Hengduan mountains;and the prospects of the development and significance of ice formation process studies,the limitation and deficiency of previous studies and the watchful aspects in future studies all have been put forward at the end of the paper.

The community's conventional salt production business is one of the economic drivers of the community. In addition to being able to provide a lot of jobs for people in Indonesia's coastal areas, the people's salt production business is also seen as a way to reduce poverty. Pidie Regency, which is included in the Province of Aceh, is one area that has a wide coastline and can be utilized as a salt farmer business. Pidie is an area that has a salt production business with a total production of 449.05 tons/month. The problem faced in the traditional salt processing business in Pidie District is the unknown level of risk that exists in each salt production process. The purpose of this study is to determine the level of risk in the traditional salt production process using failure mode and effect analysis (FMEA) and probability impact matrix (PIM) in the Saho Adab people's salt business group in Keude Ie Leubeu Village, Kembang Tanjong District, Pidie District. FMEA is a form of qualitative analysis that assesses the severity , occurrence and detection on each activity and then given a value on a predetermined scale. Whereas risk assessment using PIM only assesses the severity and occurrence. Based on the analysis of failure and effect modes, the salt production process has a high level of risk, namely the packaging process with an RPN value (risk priority number) 75, the salt water storage process with an RPN value of 48, the drying process with an RPN value of 45 and the level of the soil process with an RPN 45. While the results of the matrix impact probability obtained by the salt production process that has a high level of risk, namely the process of storing salt with a value of 5, the packaging process with a value of 4, the process of leveling sand with a value of 3,5 and the process of salt water filtration with sand filtration media containing NaCl with a value of 3,5.

The effects of fluid flow and surface stabilisation on sonoluminescence and sonochemistry.

Objective To explore the clinical value of application research in business process reengineering in transferring of inpatients for check-up. Methods We reorganized transferring of inpatients for check-up and service tache by introducing the Systematic Redesign method from the therory of Business Process Reengineering. The reengineered process was adopted and applied in the transferring processes. We randomly selected patients for check-up(126 cases for electrocardiogram,130 cases for Xrayand 128 cases for CT examination)and investigated the time cost for check-up and patients satisfaction rate. The above results were compared with those patients(112 cases for electrocardiogram,112 cases for Xrayand 120 cases for CT examination)before use of reengineered process. Results The time cost for the above mentioned check-up was shorter and patients satisfaction rate were alleviated compared with those before use of reengineered process(U=14.55,12.26,13.57;x2=17.37,14.65,10.75,P＜0.01).Conclusion Application of business process reengineering in the transferring process of inpatients for check-up proved pivotal important in that it could shorten time cost for transferring process and improve patients satisfaction rate.It possessed realistic significance for increasing benefit and competitions for hospitals. Key words: Business process reengineering; Inpatients; Check-up; Transferring; Process

Quality monitoring in infrastructural design-build projects : the analysis of an audit-based monitoring system

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In Peer-to-Peer (P2P) overlay networks, a group of multiple peer processes are required to cooperate to make a global decision. Each process takes a value and exchanges values with the other processes until the agreement condition is satisfied. We define existentially and preferentially precedent relations which show what values a process can take after taking a value and which value a process prefers to another value, respectively. Based on the precedent relations, each process takes the most preferable value in the values which are changeable from current value, at each round. We discuss a coordination protocol among multiple values in a type of heterogeneous system, where some pair of processes has different precedent relations on the same domain. A process learns the precedent relations of another process through exchanging values. A process takes a value that other processes can take by using the knowledge on the other processes.