Actual Process Research Articles

Actualization of Meaning and Motives For Entering The Sufism Order On Max Weber Perpective

This paper interprets the actualization of the core beliefs and values meaning of the Tijaniyya Sufi Order by Kiai Djauhari, Kiai Tidjani and Kiai Ahmad Fauzi, the muqaddam of the Tijaniyya Order as well as the leaders and caregivers of a pesantren. This is a phenomenological study, conducted at the Al-Amien Prenduan Islamic boarding school. Observations, interviews and documentation were conducted to collect data. Max Weber's theory of social action were employed to analyze the data. This study concludes that the actualization was performed through being the role models as well as internalizing the practice of the Tijaniyya in the pesantren traditions. This actualization process stimulates the emergence of five motives of talqin Tijani (entering the Tijaniyya Order). They are traditional motives, affectual motives, instrumentally rational motives, value rational motives, and intuitive motives.

Building Three-dimensional Virtual Historic Fashion Museum Exhibitions: From the Perspective of Technology Affordances

Museums are currently undertaking the task of digital transformation. Transformational technology generates revitalization of the curatorship, cultural heritage presentation, management model, and traditional roles of museums. The creation of three-dimensional (3D) virtual museum exhibitions is important to fulfill the revolutionary operational requirements and projections of recent museums through digital transformation. The objectives of this project were to (1) create realistic 3D virtual museum exhibitions accessible from the web, focusing on historic fashions, and (2) provide an analysis of the process and resources for creating 3D virtual museum exhibitions featuring object-focused exhibits. As a theoretical background of this project, the theory of technology affordances and the affordance actualization process were applied in the creation of 3D virtual museum exhibitions. The results of this project provide cultural heritage museums and institutions with useful precedents for the development of 3D virtual historic fashion museum exhibitions.

Digitalized opportunity space and managerial archetypes: An opportunity-centric perspective on digital innovation and transformation

As proliferating digital literature ignores how digital opportunities are generated in the first place, digital innovation and transformation can be ontologically ungrounded. To fill this gap, following a realist perspective of entrepreneurship, this article examines digital opportunities as profit propensities afforded by a digitalized environment, while treating digital innovation and transformation as the actualization process. To elaborate, first, we coin Digitalized Opportunity Space (DOS) to describe the digitalized environment as a critical intersection of four spaces - resource, utility, digital, and institutional- that muster conditions without which digital opportunities cannot be actualized. Second, we develop four managerial archetypes - resource orchestrators, utility integrators, digital evangelists, and institutional realists- who tend to recognize digital opportunities based on their organizational location and thereby endorse different digital innovations. Third, to facilitate a systemic and balanced approach to digital transformation, we recommend group-level heuristics - the generic density perspective, the ecosystem view of the firm, multi-dimensional network effects, and disciplined imagination- which restore totality from partiality. Taken together, the DOS framework (of opportunity generation), the managerial archetypes (of opportunity recognition), and the group-level heuristics (of opportunity actualization) constitute the opportunity-centric (vs technology-centric or organization-centric) perspective on digital innovation and transformation, thereby materializing the connection between digital literature and entrepreneurship.

An Interpretive and Contextual Approach to Interior Design Education

The teaching and learning of interior design processes are collaborative exercises situated in experience in the phenomenological sense. Researchers interested in evolving interior design philosophies need to understand the underlying values inherent in existing theories and the contradictions that occur when these theories oppose actual interior design processes as they are taught and explored in the studio environment. This paper is organised along three streams. First, the rationale situates the dichotomies that currently exist in design theory production and pedagogy. Second, the study methodology and data analysis are described. Finally, consideration is given to how design pedagogies could be restructured in light of these findings and how theory and practice can be viewed as symbiotic parts of a whole rather than as theoretical opposites.

Elimination of Random Mixed Noise in ECG using Convolutional Denoising Autoencoder with Transformer Encoder.

Electrocardiogram (ECG) signals frequently encounter diverse types of noise, such as baseline wander (BW), electrode motion (EM) artifacts, muscle artifact (MA), and others. These noises often occur in combination during the actual data acquisition process, resulting in erroneous or perplexing interpretations for cardiologists. To suppress random mixed noise (RMN) in ECG with less distortion, we propose a Transformer-based Convolutional Denoising AutoEncoder model (TCDAE) in this study. The encoder of TCDAE is composed of three stacked gated convolutional layers and a Transformer encoder block with a point-wise multi-head self-attention module. To obtain minimal distortion in both time and frequency domains, we also propose a frequency weighted Huber loss function in training phase to better approximate the original signals. The TCDAE model is trained and tested on the QT Database (QTDB) and MIT-BIH Noise Stress Test Database (NSTDB), with the training data and testing data coming from different records. All the metrics perform the most robust in overall noise and separate noise intervals for RMN removal compared with the baseline methods. We also conduct generalization tests on the Icentia11k database where the TCDAE outperforms the state-of-the-art models, with a 55% reduction of the false positives in R peak detection after denoising. The TCDAE model approximates the short-term and long-term characteristics of ECG signals and has higher stability even under extreme RMN corruption. The memory consumption and inference speed of TCDAE are also feasible for its deployment in clinical applications.

Magnetically driven winds from accretion disks in post-asymptotic giant branch binaries

Context. Jets are commonly detected in post-asymptotic giant branch (post-AGB) binaries and originate from an accretion process onto the companion of the post-AGB primary. These jets are revealed by high-resolution spectral time series. Aims. This paper is part of a series. In this work, we move away from our previous parametric modelling and include a self-similar wind model that allows the physical properties of post-AGB binaries to be characterised. This model describes magnetically driven jets from a thin accretion disk threaded by a large-scale, near equipartition vertical field. Methods. We expanded our methodology in order to simulate the high-resolution dynamic spectra coming from the obscuration of the primary by the jets launched by the companion. We present the framework to exploit the self-similar jet models for post-AGB binaries. We performed a parameter study to investigate the impact of different parameters (inclination, accretion rate, inner and outer launching radius) on the synthetic spectra. Results. We successfully included the physical jet models into our framework. The synthetic spectra have a very similar orbital phase coverage and absorption strengths as the observational data. The magnetohydrodynamic (MHD) jet models provide a good representation of the actual jet creation process in these evolved binaries. Challenges remain, however, as the needed high-accretion rate would induce accretion disks that are too hot in comparison to the data. Moreover, the rotational signature of the models is not detected in the observations. In future research, we will explore models with a higher disk ejection efficiency and even lower magnetisation in order to solve some of the remaining discrepancies between the observed and synthetic dynamic spectra.

Investigation of soybean oil epoxidation process with phase transfer catalyst: Risk of thermal runaway

This paper explores an efficient and eco-friendly epoxidation process using the phase transfer catalyst ([(C18H37)2(CH3)2N]3{PO4[W(O)(O2)2]4}), which offers more advantages over the use of carboxylic and inorganic acids as catalysts in the Prileschajew epoxidation process. Consequently, a study of the process's thermal hazards is imperative. The paper conducts a comprehensive analysis of the process, employing a combination of calorimetric techniques. The critical runaway temperature, stabilization temperature, and required heat dissipation rate to prevent thermal runaway reactions were calculated using the Semenov model. On-line Fourier transform infrared spectroscopy and reaction calorimetry were used to relate the reaction mechanism and exothermic behavior of the actual production process, and a reliable model was developed for the calculation of reaction enthalpy. The findings indicate that the thermal risk depends on the rate of double bond epoxidation, which provides offering valuable insights for safe industrial-scale ESO production.

Features of using ozone for cleaning marine pollution

The subject of the research is issues related to the field of ship power plants and their elements, as well as the operation of water transport and its environmental safety. Studying the processes of ballast water purification and disinfection, as well as oil-containing water that are formed during the ships operation, shows the possibility of increasing the efficiency of these processes. It is proposed to consider these processes, starting from the stage of supplying the ozone-air mixture to the flow of treated water, without limiting ourselves to the study of the actual process of oxidation by ozone of a contaminated, including bacteriologically hazardous, substance. This approach makes it possible to establish the limiting stages of the contaminated water purification and disinfection process, which includes the stage of ozone dissolution in water during its transition from the ozone-air mixture, as well as the stage of subsequent diffusion of ozone dissolved in water into the reaction contact zone. It has been shown that the kinetic characteristics of the limiting stages depend on the conditions of their implementation, which can reduce the efficiency of ozone use due to the limited time of its existence. A technological proposal that makes it possible to increase the efficiency of ozone use and the reliability of ensuring the requirements for the quality of cleaning and disinfection of marine pollution has been developed. Technological proposals provide for the preliminary saturation with ozone of a specially allocated water stream for this purpose, the supply of this stream to the concentration zone of the oxidized and disinfected substance. A device in which the developed technological fragment, including a pump supplying a water flow to a contact column, an ozone generator, a device for supplying an ozone-saturated water flow to a filter, is implemented is also proposed.

Attitude toward human papillomavirus self-sampling and associated factors among Thai women undergoing colposcopy.

To compare attitudes toward self-sampling for human papillomavirus (HPV) testing before and after specimen collection in women undergoing colposcopy. The factors associated with the pre-sampling attitude were also studied. This prospective study enrolled women with abnormal cervical cytology and/or positive high-risk HPV who attended colposcopy clinics at 10 cancer centers in Thailand between October 2021 and May 2022. Prior to colposcopy, the attitudes of the women toward self-sampling were surveyed through a questionnaire. Written and verbal instructions for self-sampling were provided before the process and subsequent colposcopy. The attitudes toward self-sampling were reassessed after the actual self-sampling. Factors associated with the attitudes were analyzed. A total of 499 women were included in this study. The mean age was 39.28±11.36 years. A total of 85.3% were premenopause, and 98.8% had sexual experience. With the full score of 45, the attitude score after self-sampling was significantly higher than the attitude score before self-sampling (39.69±5.16 vs. 37.76±5.71; P<0.001). On univariate analysis, the factors associated with attitude before HPV self-sampling were age, menopausal status, sexual activity, education level, income, knowledge regarding HPV, and prior high-grade squamous intraepithelial lesion histology. The remaining significant factor on multivariate analysis was sexual activity within the past year (B=0.105, 95% confidence interval, 0.014-2.870; P=0.048). Attitudes toward self-sampling improved after the actual self-sampling process, as evidenced by higher attitude scores. Sexual activity was the only independent factor related to the attitude before self-sampling.

A chimp algorithm based on the foraging strategy of manta rays and its application.

To address the issue of poor performance in the chimp optimization (ChOA) algorithm, a new algorithm called the manta ray-based chimpa optimization algorithm (MChOA) was developed. Introducing the Latin hypercube method to construct the initial population so that the individuals of the initial population are evenly distributed in the solution space, increasing the diversity of the initial population. Introducing nonlinear convergence factors based on positive cut functions to changing the convergence of algorithms, the early survey capabilities and later development capabilities of the algorithm are balanced. The manta ray foraging strategy is introduced at the position update to make up for the defect that the algorithm is prone to local optimization, which effectively improves the optimization performance of the algorithm. To evaluate the performance of the proposed algorithm, 27 well-known test reference functions were selected for experimentation, which showed significant advantages compared to other algorithms. Finally, in order to further verify the algorithm's applicability in actual production processes, it was applied to solve scheduling problems in three flexible workshop scenarios and an aviation engine job shop scheduling in an enterprise. This confirmed its efficacy in addressing complex real-world problems.

A novel performance degradation assessment method for rotating machinery based on the fault information and the dynamic simulation

The performance degradation assessment (PDA) of key components such as gears and rolling bearings is the core technology of prognostics and health management for rotating machinery. Most existing PDA methods may have two deficiencies: (1) the assessment indicator constructed does not consider capturing fault characteristics directly; (2) feature selection is generally based on the measured data of different fault levels, which is difficult to obtain in actual processes; moreover, the selection results lack universality and are difficult to extend to other equipment. To address these issues, this paper proposes a novel PDA method based on fault information and dynamic simulation. First, anomaly detection is performed using four well-known indicators in combination with Mahalanobis distance. Secondly, fault identification is performed using envelope spectrum analysis on anomaly signals to determine the fault type, e.g. gear fault or outer race fault. Thirdly, based on the fault type information, the candidate feature set including fault-domain indicators is selected based on the established dynamic simulation signals to obtain a preliminary assessment vector for the first stage. The stability of the fault domain indicators which capture fault characteristics directly is tested through actual measured normal data. It is used as the second stage of selecting to obtain the assessment vector. Finally, the PDA indicator is calculated based on the assessment vector and Mahalanobis distance. Four experiment case studies demonstrate the proposed PDA method can effectively isolate faults with different defect sizes as well as track the whole performance degradation. The above analysis indicates that the proposed PDA method is expected to be used for the actual rotating machinery.

Design and Development of an Integrated Virtual-Reality Training Simulation Sand Table for Rail Systems

With the rapid development of the economy, it is imperative to improve the quality of training for operational and managerial talents in the railway industry. To address issues such as efficiency, safety, and cost in railway industry practical training, it is crucial to establish a more comprehensive and efficient high-value virtual–real integration simulation experimental training resource. Therefore, this paper takes the actual work process as the core driving force, utilizes advanced information technology and intelligent devices, and is based on the comprehensive training platform for automatic control of physical trains combined with unmanned aerial vehicle (UAV) equipment. It achieves the integration of hardware and software construction to design and develop a comprehensive simulation training sand table system that incorporates functions such as training, demonstration, testing, and experiments. This system builds an integrated platform for training simulation functions, capable of simulating a railway centralized traffic control system, enabling railway dispatching simulation, driving simulation, and inspection simulation experiences. Additionally, it designs experimental processes at three levels: cognition, practical operation, and enhancement, tailored to the needs of talent development. The rail transportation training simulation sand table effectively reduces training costs and enhances the practical ability training quality of railway operation management personnel, meeting the requirements for talent development in the railway and related industries under new circumstances.

Enzyme-Assisted Circular Additive Manufacturing as an Enabling Technology for a Circular Bioeconomy—A Conceptual Review

Additive manufacturing (AM) is a decisive element in the sustainable transformation of technologies. And yet its inherent potential has not been fully utilized. In particular, the use of biological materials represents a comparatively new dimension that is still in the early stages of deployment. In order to be considered sustainable and contribute to the circular economy, various challenges need to be overcome. Here, the literature focusing on sustainable, circular approaches is reviewed. It appears that existing processes are not yet capable of being used as circular economy technologies as they are neither able to process residual and waste materials, nor are the produced products easily biodegradable. Enzymatic approaches, however, appear promising. Based on this, a novel concept called enzyme-assisted circular additive manufacturing was developed. Various process combinations using enzymes along the process chain, starting with the preparation of side streams, through the functionalization of biopolymers to the actual printing process and post-processing, are outlined. Future aspects are discussed, stressing the necessity for AM processes to minimize or avoid the use of chemicals such as solvents or binding agents, the need to save energy through lower process temperatures and thereby reduce CO2 consumption, and the necessity for complete biodegradability of the materials used.

Streptomycetes as Microbial Cell Factories for the Biotechnological Production of Melanin.

Melanins are complex, polymeric pigments with interesting properties like UV-light absorbance ability, metal ion chelation capacity, antimicrobial action, redox behaviors, and scavenging properties. Based on these characteristics, melanins might be applied in different industrial fields like food packaging, environmental bioremediation, and bioelectronic fields. The actual melanin manufacturing process is not environmentally friendly as it is based on extraction and purification from cuttlefish. Synthetic melanin is available on the market, but it is more expensive than animal-sourced pigment and it requires long chemical procedures. The biotechnological production of microbial melanin, instead, might be a valid alternative. Streptomycetes synthesize melanins as pigments and as extracellular products. In this review, the melanin biotechnological production processes by different Streptomyces strains have been revised according to papers in the literature. The different fermentation strategies to increase melanin production such as the optimization of growth conditions and medium composition or the use of raw sources as growth substrates are here described. Diverse downstream purification processes are also reported as well as all the different analytical methods used to characterize the melanin produced by Streptomyces strains before its application in different fields.

Extrinsic calibration for large FOV based on inverse depth parameterized bundle adjustment

To overcome the reliance on high-precision calibration plates in camera calibration, this paper proposes an extrinsic calibration method suitable for three-dimensional digital image correlation (3D-DIC) in large field of view (FOV). The method combines image feature algorithms with DIC techniques to extract matching point pairs (MPPs) from the left and right images of binocular cameras. These MPPs are then homogenized within the FOV. Next, initial values of the extrinsic parameters are solved based on epipolar constraint theory. Finally, the calibration parameters are nonlinearly optimized using the bundle adjustment method. To achieve stable and reliable numerical optimization in large FOV applications, the calibration control points (CCPs)’ spatial coordinates are represented using inverse depth parameterization. In scenarios where there might be a lack of sufficient CCPs, speckle patterns are artificially introduced to supplement the scene features. However, there is a lack of reliable experimental basis on how to add CCPs within the FOV. Therefore, through simulations, the factors affecting calibration accuracy are analyzed to guide the actual calibration process. The proposed method’s reliability and accuracy in large FOV 3D measurement are verified through experiments.

Biodirected Screening and Preparation of Larimichthys crocea Angiotensin-I-Converting Enzyme-Inhibitory Peptides by a Combined In Vitro and In Silico Approach.

Food-derived angiotensin-I-converting enzyme (ACE)-inhibitory peptides have gained attention for their potent and safe treatment of hypertensive disorders. However, there are some limitations of conventional methods for preparing ACE-inhibitory peptides. In this study, in silico hydrolysis, the quantitative structure-activity relationship (QSAR) model, LC-MS/MS, inhibition kinetics, and molecular docking were used to investigate the stability, hydrolyzability, in vitro activity, and inhibition mechanism of bioactive peptides during the actual hydrolysis process. Six novel ACE-inhibitory peptides were screened from the Larimichthys crocea protein (LCP) and had low IC50 values (from 0.63 ± 0.09 µM to 10.26 ± 0.21 µM), which were close to the results of the QSAR model. After in vitro gastrointestinal simulated digestion activity of IPYADFK, FYEPFM and NWPWMK were found to remain almost unchanged, whereas LYDHLGK, INEMLDTK, and IHFGTTGK were affected by gastrointestinal digestion. Meanwhile, the inhibition kinetics and molecular docking results were consistent in that ACE-inhibitory peptides of different inhibition forms could effectively bind to the active or non-central active centers of ACE through hydrogen bonding. Our proposed method has better reproducibility, accuracy, and higher directivity than previous methods. This study can provide new approaches for the deep processing, identification, and preparation of Larimichthys crocea.

Structured discriminative Gaussian graph learning for multimode process monitoring

AbstractAiming at the actual industrial process background that different modes share the same system configurations and control structure, this article proposes a novel structured discriminant Gaussian graph learning for multimode process monitoring. The proposed method considers not only the sparsity of graph model but also the measurement of data variation based on a mismatched graph and the common node support between different graphical structures. The objective function involves two sets of regularization terms: the trace terms for mismatched measurements and the ‐norm imposed on the union of decomposed graph matrices. Due to the introduced mismatched trace terms, the cost of matching the data points and graph models that have inconsistent class labels can be expanded, which brings more discrimination for the graph‐based mode identification. While the common structure extracted by the ‐norm forces the estimated graph models to have structural similarities, thus alleviating the negative influence caused by graph discrimination. Once a relatively accurate and discriminative reference graph model is obtained, the downstream test graph learning and analysis can be conducted online by employing the moving window techniques. By comparing the matched and mismatched graph‐based measurements, the process mode can be identified correctly and stably. To grasp the abnormal process changes, the ‐norm for the row sparsity is again applied to the graph difference matrices, the sensitive monitoring statistics and the fault isolation results can be obtained effectively. All the optimization problems in this paper can be solved using the alternating direction multiplier (ADMM) algorithm. The effectiveness of our proposed approach is illustrated by the application to a real blast furnace iron‐making production process.

Heart rate detection method based on Ballistocardiogram signal of wearable device：Algorithm development and validation

BackgroundHeart rate, as the four vital signs of human body, is a basic indicator to measure a person's health status. Traditional electrocardiography (ECG) measurement, which is routinely monitored, requires subjects to wear lead electrodes frequently, which undoubtedly places great restrictions on participants' activities during the normal test. At present, the boom of wearable devices has created hope for non-invasive, simple operation and low-cost daily heart rate monitoring, among them, Ballistocardiogram signal (BCG) is an effective heart rate measurement method, but in the actual acquisition process, the robustness of non-invasive vital sign collection is limited. Therefore, it is necessary to develop a method to improve the robustness of heart rate monitoring. ObjectiveTherefore, in view of the problem that the accuracy of untethered monitoring heart rate is not high, we propose a method aimed at detecting the heartbeat cycle based on BCG to accurately obtain the beat-to-beat heart rate in the sleep state. MethodsIn this study, we implement an innovative J-wave detection algorithm based on BCG signals. By collecting BCG signals recorded by 28 healthy subjects in different sleeping positions, after preprocessing, the data feature set is formed according to the clustering of morphological features in the heartbeat interval. Finally, a J-wave recognition model is constructed based on bi-directional long short-term memory (BiLSTM), and then the number of J-waves in the input sequence is counted to realize real-time detection of heartbeat. The performance of the proposed heartbeat detection scheme is cross-verified, and the proposed method is compared with the previous wearable device algorithm. ResultsThe accuracy of J wave recognition in BCG signal is 99.67%, and the deviation rate of heart rate detection is only 0.27%, which has higher accuracy than previous wearable device algorithms. To assess consistency between method results and heart rates obtained by the ECG, seven subjects are compared using Bland-Altman plots, which show no significant difference between BCG and ECG results for heartbeat cycles. ConclusionsCompared with other studies, the proposed method is more accurate in J-wave recognition, which improves the accuracy and generalization ability of BCG-based continuous heartbeat cycle extraction, and provides preliminary support for wearable-based untethered daily monitoring.

MicroPSCal: A MicroStation package for storage calculation of pumped storage power station during planning and designing

Pumped storage power plants can effectively guarantee the healthy development of energy and promote energy transformation and green development. The calculation accuracy is directly related to the flood control safety of the reservoir. The current storage calculation method of storage capacity is inefficient and complicated resulting in deviations between calculated values and actual storage capacity. The paper is devoted to the problem of efficiency and quality of capacity calculation in the planning and design stage of pumped storage power plants. A toolkit MicroPSCal is developed based on MicroStation software to simulate and calculate the corresponding storage capacity of different elevations and draw the storage capacity curve, which can be applied in the actual planning and design process of pumped storage power stations. The results of the calculation showed that this method can make full use of terrain accuracy to achieve high-precision calculation of reservoir capacity of different elevations and automatically draw high-quality storage curves. Compared with traditional storage capacity calculation methods, calculation time was reduced from 18 hours to 45 minutes. This method has obvious advantages and provides a new technical idea for storage capacity calculation during the planning and design of pumped storage power plants.

A correction method for radial distortion and nonlinear response of infrared cameras.

The key feature of non-contact temperature measurement provided by infrared (IR) cameras underpins their versatility. However, the accuracy of temperature measurements with IR cameras depends on imaging quality due to their non-contact nature, such as the lens, body temperature, and measurement environment. This paper addresses the correction of radial distortion and nonlinear response issues in IR cameras. To address radial distortion, we have designed a passive checkerboard calibration board specifically for infrared cameras. This board is used to calibrate the IR camera and derive the necessary camera parameters. Subsequently, these parameters are applied during the actual measurement process to rectify radial distortion effectively. Building on the radial distortion correction method mentioned above, we propose a multi-point segmented calibration approach that considers different temperature ranges and imaging regions. This method alleviates the issue of reduced temperature measurement accuracy due to variations in camera responses by computing gain and offset coefficient matrices for each temperature range. Experimental results demonstrate the effectiveness of the calibration board in correcting radial distortion in IR cameras, with a mean reprojection error of less than 0.16 pixels. Regarding the nonlinear response problem, the introduced method significantly reduces the relative error in temperature measurement. In the verification phase, spanning from 100 to 500 °C, the average relative error in temperature measurement decreases by 0.49% from 1.61% before and after correction, which highlights a substantial improvement in temperature measurement accuracy. This work gives a useful reference to improve the imaging quality and temperature measurement accuracy using infrared cameras.