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Business Process mapping (BP mapping) is important for a company to identify their activities. Previous research suggests several approaches for process identification and BP mapping, which would be easier if the company had already implemented a computer-based information system. The research presented in this paper has the purpose of providing an alternative method for BP mapping especially for the company that does not implement the computer-based information system. A proposed method is using job description documents that the company had to identify elements needed to perform BP mapping which are actor, process, document, and flow of documents. A Natural Language Process (NLP) which is text mining method is used for mining job documents to identify those elements that exist in each job position. To illustrate the applicability of the proposed method, samples of job descriptions of 15 companies are taken. It shows that the proposed method can be applied.

Purpose The purpose of this paper is twofold: to identify and explore the reasons why organisations decide to use process mapping software (PMS) facilities in support of business process management (BPM); and to determine the objectives set by senior management for its introduction, and understand extent to which organisations achieve expected benefits. Design/methodology/approach This paper uses an exploratory research design and investigates the elements of organisations’ objectives, implementation and evaluation of using PMS. The research data were collected through semi-structured interviews with business managers responsible for the implementation of PMS. The respondent organisations were selected from a range of industries who were using the same software. Findings The results of the research show that organisations do set objectives for using PMS, relevant to a wide range of business, operational and strategic objectives, dependant on the needs of the organisation. Additionally, the results show that some gain further advantages post-implementation, based on their PMS experience. Regarding explicit evaluation of their investment, organisations attempt this to a very limited extent; whilst recognising a broad a range of “softer” benefits. Research limitations/implications This exploratory research has been conducted on a small range of organisations, all using the same software, therefore the results cannot be clearly generalizable. The research suggests organisations are making effective decisions regarding adopting PMS, further research on the evaluating its benefits could support better decision-making in the future. Practical implications The practical implications of this research are for decision-makers in organisations recognising and understanding the strategic/operational benefits that could be achieved by implementing a software system for BPM. Originality/value Whilst the use of process mapping of organisation’s operations is widespread the benefits achieved by organisations are only partially understood. Knowledge of the strategic impact of BPM is limited, as reported by numerous researchers. This research attempts to explore the context of organisations using such software, and point towards further approaches to its investigation.

We review the classical definition of the Mathematical Superposition Principle and the Measurable Superposition Effect, relevant to Young’s double-slit (YDS) experiment and the two-beam Mach–Zehnder Interferometer (MZI) and contrasts them with the prevailing interpretations by Quantum Mechanics. YDS is known to be “at the heart of understanding Quantum Mechanics” and MZI is being used to demonstrate quantum entanglement, a step forward to build quantum computers. We would demonstrate that classical interpretations are the correct ones for these two superposition effects. We recognize that cosmic universe is entangled by gravity and electromagnetic fields generated by innumerable stars of all the galaxies. Yet, we have succeeded in developing approximate mathematical theories, which we validate reproducibly by constructing local instruments for local and causal validity. Data in our instruments is generated as some physical transformation in a chosen “known entity” (detector) that exchanges energy with the “unknown entity” (detectee) under study. The energy exchange must always be guided by one of the natural forces of interaction, compatible between the detector and the detectee. This is known as the locality principle. Thus, all data-generating interactions represent locally entangled (interacting) phenomena. This is true irrespective of the expansive quantum-mathematical definition of the word “entanglement”. All useful engineering data is generated in an instrument as some physical transformation in a detector after it interacts and exchanges energy with one or more superposed signals stimulating it. The proper superposition equation, modeling such interaction processes, must incorporate the characteristic interaction parameter of the detector that would multiply all the superposed signals. Then the mathematically allowed normalization procedure of the unmeasurable summed amplitudes becomes constrained against ad hoc normalization. We recommend: (i) We need to underscore the explicit incorporation of the old fashion “Interaction Process Mapping Thinking” and bring back the engineering reality in all of Physics discourse. (ii) Experimental optical physicists should take active roles in the development of fundamental physics.

Lignin depolymerization has been studied for decades to produce carbon-neutral chemicals/biofuels and biopolymers. Among different chemical reaction pathways, catalytic hydrogenolysis favors reactions under relatively mild conditions, while its yield of bio-oil and high-value aromatic products is relatively high. In this study, the influence of reaction parameters on lignin hydrogenolysis are discussed by chemical process parameter mapping and modeled using three different machine learning algorithms based upon literature experimental data. The best R2 scores for solid residue and aromatic yield were 0.92 and 0.88 for xgboost, respectively. The parameter importance was examined, and it was observed that lignin-to-solvent ratio and average pore size have a larger impact on lignin hydrogenolysis results. Finally, the optimal conditions of lignin hydrogenolysis were predicted by chemical process parameter mapping using the best-fit machine learning model, which indicates that further process improvements can potentially generate higher yields in industrial applications.

Additive manufacturing (AM) is a new paradigm for the design and production of high-performance components for aerospace, medical, energy, and automotive applications. This review will exclusively cover directed energy deposition (DED)-AM, with a focus on the deposition of powder-feed based metal and alloy systems. This paper provides a comprehensive review on the classification of DED systems, process variables, process physics, modelling efforts, common defects, mechanical properties of DED parts, and quality control methods. To provide a practical framework to print different materials using DED, a process map using the linear heat input and powder feed rate as variables is constructed. Based on the process map, three different areas that are not optimized for DED are identified. These areas correspond to the formation of a lack of fusion, keyholing, and mixed mode porosity in the printed parts. In the final part of the paper, emerging applications of DED from repairing damaged parts to bulk combinatorial alloys design are discussed. This paper concludes with recommendations for future research in order to transform the technology from “form” to “function,” which can provide significant potential benefits to different industries.

Addressing the uncertainty and variability in the quality of 3D printed metals can further the wide spread use of this technology. Process mapping for new alloys is crucial for determining optimal process parameters that consistently produce acceptable printing quality. Process mapping is typically performed by conventional methods and is used for the design of experiments and ex situ characterization of printed parts. On the other hand, in situ approaches are limited because their observable features are limited and they require complex high-cost setups to obtain temperature measurements to boost accuracy. Our method relaxes these limitations by incorporating the temporal features of molten metal dynamics during laser-metal interactions using video vision transformers and high-speed imaging. Our approach can be used in existing commercial machines and can provide in situ process maps for efficient defect and variability quantification. The generalizability of the approach is demonstrated by performing cross-dataset evaluations on alloys with different compositions and intrinsic thermofluid properties. Process development for 3D printing of new metal alloys can be time-consuming and variability in the printing outcome makes it even more challenging. Here, authors demonstrate an in-situ method using high-speed imaging and deep learning to accelerate the process design for a more consistent quality.

PurposeThe significance of business processes has fostered a close collaboration between academia and industry. Moreover, the business landscape has witnessed continuous transformation, closely intertwined with technological advancements. Our main goal is to offer researchers and process analysts insights into the latest developments concerning artificial intelligence (AI) and machine learning (ML) to optimize their processes in an organization and identify research gaps and future directions in the field.Design/methodology/approachIn this study, we perform a systematic review of academic literature to investigate the integration of AI/ML in business process management (BPM). We categorize the literature according to the BPM life-cycle and employ bibliometric and objective-oriented methodology to analyze related papers.FindingsIn business process management and process map, AI/ML has made significant improvements using operational data on process metrics. These developments involve two distinct stages: (1) process enhancement, which emphasizes analyzing process information and adding descriptions to process models and (2) process improvement, which focuses on redesigning processes based on insights derived from analysis.Research limitations/implicationsWhile this review paper serves to provide an overview of different approaches for addressing process-related challenges, it does not delve deeply into the intricacies of fine-grained technical details of each method. This work focuses on recent papers conducted between 2010 and 2024.Originality/valueThis work addresses a significant gap by employing a pioneering approach to introduce challenges in BPM alongside AI/ML techniques and integrated tools. Hence, it offers comprehensive guidelines that elucidate the alignment between ML methods and solutions to current challenges across the BPM life-cycle, including process enhancement and process improvement. Additionally, by detailing various aspects of the life-cycle phases and highlighting ML technique characteristics, this research demonstrates potential approaches for future exploration, thereby enhancing applicability for both process analysts and researchers in this context.

Commercial iterative reconstruction techniques help to reduce the radiation dose of computed tomography (CT), but altered image appearance and artefacts can limit their adoptability and potential use. Deep learning has been investigated for low-dose CT (LDCT). Here, we design a modularized neural network for LDCT and compare it with commercial iterative reconstruction methods from three leading CT vendors. Although popular networks are trained for an end-to-end mapping, our network performs an end-to-process mapping so that intermediate denoised images are obtained with associated noise reduction directions towards a final denoised image. The learned workflow allows radiologists in the loop to optimize the denoising depth in a task-specific fashion. Our network was trained with the Mayo LDCT Dataset and tested on separate chest and abdominal CT exams from Massachusetts General Hospital. The best deep learning reconstructions were systematically compared to the best iterative reconstructions in a double-blinded reader study. This study confirms that our deep learning approach performs either favourably or comparably in terms of noise suppression and structural fidelity, and is much faster than commercial iterative reconstruction algorithms. Reducing the radiation dose for medical CT scans can provide a less invasive imaging method, but requires a method for reconstructing an image up to the image quality from a full-dose scan. In this article, Wang and colleagues show that the deep learning approach, combined with the feedback from radiologists, produces higher quality reconstructions than or similar to that using the current commercial methods.

We study the first-passage dynamics of a non-Markovian stochastic process with time-averaged feedback, which we model as a one-dimensional Ornstein–Uhlenbeck process wherein the particle drift is modified by the empirical mean of its trajectory. This process maps onto a class of self-interacting diffusions. Using weak-noise large deviation theory, we calculate the leading order asymptotics of the time-dependent distribution of the particle position, derive the most probable paths that reach the specified position at a given time and quantify their likelihood via the action functional. We compute the feedback-modified Kramers rate and its inverse, which approximates the mean first-passage time, and show that the feedback accelerates dynamics by storing finite-time fluctuations, thereby lowering the effective energy barrier and shifting the optimal first-passage time from infinite to finite. Although we identify alternative mechanisms, such as slingshot and ballistic trajectories, we find that they remain sub-optimal and hence do not accelerate the dynamics. These results show how memory feedback reshapes rare event statistics, thereby offering a mechanism to potentially control first-passage dynamics.

Purpose Laser hot wire additive manufacturing (LHWAM) is a newer technology within the space of large-scale directed energy deposition (DED) additive manufacturing (AM) processes. This study aims to map known AM flaw types such as lack of fusion and keyholing, as well as a dripping flaw unique to hot wire processes, across process parameter space using a small number of single-track experiments. Design/methodology/approach A semianalytical model was calibrated using a small initial set of experimental data. Lack of fusion and keyholing flaws were mapped across process space using existing models. The dripping flaw was modeled via analytical methods calibrated with experimental data, and then mapped across processing space. Further experimental data beyond the small initial set was used to evaluate the accuracy of the process maps developed. A website and executable were deployed to users of the process for convenient rapid process parameter selection. Findings With the process maps generated during this work, users can easily and rapidly generate desirable parameter sets for a range of conditions, enabling the intelligent utilization of the entire stable processing regime. Practical implications The methodology developed can be applied to other LHWAM machines or DED processes to rapidly and inexpensively generate a systematic understanding of processing space for build planning. Originality/value LHWAM shows advantages over other large-scale DED processes, but a systematic physically informed study of the key flaw regions across process space had not been conducted, limiting more widespread use of the process and creating a gap that this study fills.