Knowledge Of Processes Research Articles

Geometric morphometric analysis of beak shape of Columbimorphae (Columbas, Van, Mardin and Dönek).

Selective breeding over centuries has led to the emergence of numerous pigeon breeds from the single species Columba livia, showcasing unparalleled phenotypic diversity. In Eastern Turkey, Van pigeons originate, while Tumbler pigeon varieties thrive countrywide, reflecting local traditions. The avian beak, vital for survival, offers insights into domestication and traits influenced by natural selection. Geometric morphometrics, a shape analysis method, allows for a comprehensive examination of beak shapes among domestic pigeons, providing a nuanced understanding of their complexity. Understanding beak diversity in pigeons, especially those found in Turkey, enhances our knowledge of avian evolution and adaptation processes. The study utilized 48 skulls from 4 pigeon breeds, including wild rock pigeons and domestic pigeons of Tumbler, Mardin and Van breeds, all free from pathological lesions and adults. Geometric morphometric analyses of beak shape were conducted using dorsal and lateral photographs, with landmarks placed using tpsDig software. MorphoJ software facilitated procrustes analysis and principal component analysis (PCA) to assess morphological variability and differentiate pigeon breeds based on shape patterns, with significance set at p < 0.05. PCA revealed significant shape variations among pigeon breeds, with 47 principal components identified for lateral beak views and 36 principal components for dorsal views. Canonical variates analysis further distinguished morphological patterns among breeds, indicating distinct shape variations in both dorsal and lateral views, elucidating the unique characteristics of each breed's beak morphology. Our results demonstrate statistically significant differences in pigeon beak shape, particularly in the lateral view, confirming the importance of these variations (p < 0.05) and suggesting that such differences occur less than 5% of the time under the null hypothesis.

Derecho y neurociencia. La toma de decisión de los jueces

The text explores the relationship between neuroscience and law, focusing on how brain processes affect judicial decisions. Brain systems 1 and 2 are distinguished, as they operate intuitively and deliberately re-spectively, and their implications in perception, memory, cognitive biases, heuristics and decision making in the judicial field are examined. In ad-dition, the role of emotions, stress and empathy in judicial impartiality is analyzed, as well as the importance of non-verbal communication in the assessment of testimonial evidence. It is highlighted how personal biases and attitudes also influence decisions and it is suggested that awareness and training can mitigate these biases. In summary, knowledge of brain processes can improve the quality of judi-cial decisions by reducing the influence of cognitive biases and improving impartiality.

Physical and optical properties of cirrus and subvisible cirrus clouds over Arabian sea and Bay of Bengal region

Cirrus clouds play a crucial role in regulating the Earth’s radiation budget, and this paper aims to contribute to a deeper understanding of their optical and geometrical properties over the Arabian Sea (AS) and Bay of Bengal (BoB) regions using CALIPSO data. Comprehensive statistics are derived, encompassing mean values of cirrus cloud top, base altitude, geometrical thickness, cloud optical depth, and temperature. Over the AS region, the mean values are 15.10 ± 1.50 km, 12.63 ± 1.75 km, 2.52 ± 1.37 km, 0.4 ± 0.58, and -62.30 ± 10.6 (°C), respectively. For the BoB region, the corresponding values are 15.43 ± 1.51 km, 12.72 ± 1.74 km, 2.71 ± 1.46 km, 0.49 ± 0.67, and -64.35 ± 10.7 (°C). A larger spread in optical depth and a higher frequency of occurrence for both cirrus and subvisible cirrus (SVC) clouds were observed over BoB compared to AS. Additionally, the study delves into SVC cloud characteristics, emphasizing their thinness and higher base altitudes compared to cirrus clouds. This comprehensive investigation contributes valuable insights into the distinctive properties of cirrus and SVC clouds in these regions, enhancing our knowledge of atmospheric processes and their implications for climate modelling and predictions.

Stakeholders Reactions to Rebranding of Madiun City: Case Study in Indonesia

The Indonesian people have called Madiun City “Kota Brem,” “Kota Pecel,” and “Kota Sepur” since the Dutch era. In line with the Mayor’s leadership policy, the city branding of Madiun was shifted from “Madiun Kota Gadis” to “Madiun Kota Karismatik” and finally to “Madiun Kota Pendekar”. This study examines stakeholders’ reactions to the rebranding of Madiun City. This study uses qualitative research paradigms and case study methods. The primary data sources were obtained through extensive interviews and non-participant observations. Secondary data came from documentation. Data analysis uses Miles and Huberman’s interactive model approach. The study found that the stakeholders are apathetic when responding to the city rebranding due to their low participation and lack of knowledge in the decision-making process. Differences of interest between the government and stakeholders result in a low response.

A Māori data governance assessment of the NZ COVID Tracer app

PurposeDuring the COVID-19 pandemic, the NZ COVID Tracer App (NZCTA) was released as a digital intervention to support contact tracing processes in Aotearoa New Zealand. This paper examines whether NZCTA met the data governance requirements of the Indigenous Māori people. Māori are an interesting case study as they have unique Treaty and data sovereignty rights, and a higher risk of COVID-related mortality.MethodsThe NZCTA was assessed against 24 criteria drawn from the Māori Data Governance Model. The assessment drew on documentary sources and the authors’ knowledge of NZCTA and contact tracing process. Each criteria was assessed as ‘met’, ‘partially met’ or ‘not met’.ResultsOur retrospective assessment showed a mixed performance against the Māori Data Governance Model, with NZCTA only fulfilling seven of the 24 model criteria and failing to meet nine.ConclusionThere is significant room for improvement in future digital health interventions for Māori. Much work remains to be done in the Aotearoa public sector to uphold Māori data sovereignty and address systemic barriers to genuine partnership with Māori.

Effect of vegetation cover and soil moisture on water repellency persistence of drained peat soils

AbstractPeatlands play an essential role in the environment and their conservation requires knowledge of water-related processes. Their hydrophobicity is an important factor influencing the flow of water in soils. Drainage of fens and adapting them for agricultural production have contributed to the degradation of peat soils. The main goal of this study was to evaluate the effect of vegetation cover (meadows, alder swamp and birch forest) of drained peat soils on the persistence of their water repellency as a function of their moisture content. Soil samples (Rheic Fibric Histosol and Rheic Murshic Hemic Histosol) were collected from four sites in the Biebrza Wetlands (Poland). The sites include extensive meadows, intensive meadows, alder swamp forest, and secondary birch forest. The study used the Water Drop Penetration Time (WDPT) to assess the persistence of soil water repellency during the drying of soil samples in laboratory conditions. The study proposed a three-straight lines model to describe the relationship between WDPT and soil moisture content. Results revealed that the peat soils in intensive meadows showed the highest WDPT, followed by the secondary birch and alder swamp forest. and then by semi-natural meadows, which has the lowest WDPT.

Effectiveness of Crop Farmers' Use of Indigenous Knowledge on Land Degradation Control Practices in Imo State, Nigeria

This study analyzed the effectiveness of the crop farmers' use of indigenous knowledge on land degradation control practices in Imo State, Nigeria. A multistage sampling procedure was used to select 180 crop farmers for the study. Data were collected using a structured questionnaire and analyzed using mean, percentages, and multiple regression analysis. Crop farmers perceived zero tillage to help in conserving soil texture (x̄=3.52), replanting of deforested areas enables them to maintain vegetative cover (x̄=3.45), use of organic manure to improve soil's organic matter contents (x̄=3.42) and controlling both water and wind erosions by making ridges (x̄=3.30) and rainwater harvesting (x̄=3.10) as highly effective in controlling soil degradation. Age, marital status, educational level, household size, monthly income, membership of association, extension contact and farm size) They significantly influenced their perceived effectiveness of using indigenous knowledge on land degradation control. The major constraints faced by crop farmers were inadequate funds to practice land degradation control practices (99.44%) and limited availability of farmland (98.31%), poor extension contact (86.44%), poor knowledge and education of land degradation control processes (86.44%), poor government support (81.92%) and poor access to information (75.14%). The study concludes that crop farmers experienced soil degradation and were practicing indigenous land degradation, although some factors constrained them. Therefore, crop farmers should come together to transform their indigenous knowledge and control practices, thereby discussing and looking for the best way to tackle their peculiar land degradation challenges.

FUSION3D: Multimodal data fusion for 3D shape reconstruction: A soft-sensing approach

Traditional high-fidelity imaging techniques, such as X-ray computer tomography (CT), excel in capturing intricate shape details through high-resolution two-dimensional (2D) images. However, the extensive cost or impracticality of obtaining X-ray images poses a significant challenge in various applications (e.g., dense metal components in manufacturing). To overcome this limitation, we propose a soft sensing approach, named FUSION3D, aimed at reconstructing three-dimensional (3D) shapes from 2D sliced images and heterogenous process inputs using a novel model based on multi-modal process knowledge. The developed FUSION3D method is built upon a deep learning framework that utilizes continuous normalizing flow to model the complex shape distribution of 3D objects. We propose a principled probabilistic framework to regularize 3D point clouds by modeling them as a distribution of distributions. Specifically, we learn a two-level hierarchy of distributions where the first level is the distribution of shapes, and the second level is the distribution of points given a shape. Our generative regularization model learns each level of the distribution with a continuous normalizing flow. The invertibility of normalizing flows enables the computation of the likelihood during training and allows us to train our model in the variational inference framework. Our methodology is validated through a case study in additive manufacturing, demonstrating its effectiveness as a soft sensing approach for accurate 3D shape reconstruction without relying on high-fidelity imaging techniques like CT during model deployment.

Diseminasi Proses Sertifikasi Halal pada Perajin Kemplang Desa Meranjat Ilir Kecamatan Indralaya Selatan

Meranjat Ilir Village is located in the South Indralaya District, Ogan Ilir Regency, South Sumatra Province, with one of the leading products, kemplang. The kemplang business is the main livelihood for the community, as well as an income for craftswomen in Meranjat Ilir Village. One important factor in maintaining product quality, increasing selling value in marketing, and business sustainability is halal certification. The awareness of small and medium entrepreneurs to carry out halal certification is very low. Therefore, an activity is needed to socialize the halal certification process. This community service activity aimed to provide knowledge and understanding, as well as socialize with the community of kemplang crafters in Meranjat Ilir Village regarding the halal certification process for food products, especially kemplang products. The method used was the lecturing method which was carried out through the presentation and explanation of material accompanied by discussion and question-answer. The target audience in this activity was the community of kemplang crafters in Meranjat Ilir Village. The results showed that kemplang crafters in Meranjat Ilir Village gained knowledge and understanding of the halal certification process. There was an increase in understanding of the target audience by 28.39% after the presentation of the material.

Using Enquiry Action Learning to help students understand their decision-making

ABSTRACT An Enquiry Action Learning sequence was undertaken by MSc social work students at a Scottish University as part of a decision-making module, to assist them to work together in gathering information to present to a case conference. Drawing on the taught sessions on ethics, legislation, policy and human rights, the students plan their research, explore community resources and prepare a case report. Through this research, the students gained understanding of multi-agency roles and perspectives and built their knowledge throughout the activity. This paper analyses what the students learnt from Enquiry Action Learning, with particular emphasis on how it will impact on their decision-making practice in the future, and evaluates the EAL activity using the Revised Bloom’s Taxonomy (RBT). The benefits of Enquiry Action Learning are multiple, and the findings show that the students developed group work skills and acquired knowledge of multi-agency decision-making processes in practice. They learned how to evidence their decision-making, applying theoretical knowledge to practice situations. They were able to relate and contextualize their classroom-based learning in practice situations, developing a deeper understanding of the social work role.

Joint mining of fluid knowledge and multi-sensor data for gas–water two-phase flow status monitoring and evolution analysis

Gas-water two-phase flow process widely exists in petroleum, chemical and other industrial processes, which is intricate, manifested by dynamic evolution of fluid structures and real-time fluctuation of flow parameters. Detailed description and real-time monitoring of flow status are of great significance to safe operation of industry processes. In this paper, a monitoring framework jointly mining the fluid knowledge and latent property behind multi-sensor data is proposed to address the issues of uncertainty and sample scarcity in transitional flow statuses, which enables real-time monitoring on structural evolution and parameter change of flow process. Firstly, the shared iterative algorithm with multi-task weight discovery is proposed to establish the relative semantic description system to characterize flow process knowledge by mining the relationship between multi-sensor data and different fluid semantic properties, which is helpful for interpretable monitoring of flow status structure and parameter information. Secondly, latent properties mined from multi-sensor data by sparse exponential discriminant analysis contribute to flow characterization completeness and discrimination. Thirdly, the process knowledge and latent information of flow status is transformed into different degrees of describable semantic and latent properties, which obtain relative representations of flow status in the joint description space realized by rank support vector machine. The results demonstrate that the method enables precise monitoring and detailed description of typical and uncertain flow statuses in the absence of transitional flow status modeling samples.

Utilizing organizational ambidexterity to implement long-term technical change in fast-paced manufacturing settings

PurposeIn this paper, we focus on how and when organizations adopt different types of ambidexterity to facilitate projects that operate with fundamentally different time scales compared with the dominant functions of the organization.Design/methodology/approachUsing a comparative case study design, four case studies were conducted of long-term projects in two similar manufacturing plants within the same organization.FindingsWe found organizations first use structural and sequential ambidexterity in change efforts, during which new process knowledge is developed. When structural and sequential ambidexterity are not viable, change agents use this developed knowledge to support contextual ambidexterity. This contextual ambidexterity allows change agents to move between distinct time conceptions of event time and clock time.Research limitations/implicationsOne of the limitations of this study was that it only focused on two plants within one organization in order to control for variation. Future studies should look at a wider range of companies, technologies and industries.Practical implicationsWhile structurally and temporally decoupling change efforts help with differentiation of new technological change, there are limitations with these efforts. It is important to build an organization’s contextual ambidexterity as well as organizational supports to facilitate switching between clock time and event time.Originality/valueThis paper helps explain how and when organizations use different types of ambidexterity in resolving temporal conflicts when implementing longer-term technological change in fast-paced manufacturing settings.

GreenPLM: the concept of sharing community knowledge for new green product development and process planning

GreenPLM: the concept of sharing community knowledge for new green product development and process planning

Experimental analysis on the progressive failure mechanism of soft rock subjected to tunnel excavation

The failure of the soft rock can seriously threaten the construction and operation of tunnels, which may suffer from larger deformation and instability. The knowledge of the failure process and mechanism of soft rock can help to optimize the tunnel design and construction. In the present paper, a large-scale physical model test with the use of both contact and non-contact measurement methods was conducted to investigate the progressive failure mechanism of soft rock. The results show that a notable pattern of progressive failure was observed in the soft rock, whereby failure initiates in the proximal regions and propagates to the distal regions within the surrounding rock. The occurrence of the failure intensifies the deformation of the soft rock, and the tunnel suffers from the cross-section flattening finally. The effectiveness of the non-contact measurement method was accessed by the comparison of the strain measurements between embedded sensors and digital photogrammetry analysis. Subsequently, the radius of the failure zone determined by the stress distribution and mechanical analysis is between two and three times the radius of the tunnel. The results of model tests were then verified by numerical analysis. Eventually, the failure modes were evaluated by local relative deformation. The shear failure takes place in the tunnel inverted arch which suffers from large shear stress, while compression failure appears on the tunnel sidewall, and the tension failure occurs at the top of the tunnel arch.

A Study on the Design of Knee Exoskeleton Rehabilitation Based on the RFPBS Model.

The gait rehabilitation knee exoskeleton is an advanced rehabilitative assistive device designed to help patients with knee joint dysfunction regain normal gait through training and activity support. This paper introduces a design framework based on the process knowledge representation method to optimize the design and control efficiency of the knee exoskeleton. This framework integrates knowledge of design objects and processes, specifically including requirements, functions, principle work areas, and the representation and multi-dimensional dynamic mapping of the Behavior-Structure (RFPBS) matrix, achieving multi-dimensional dynamic mapping of the knee exoskeleton. This method incorporates biomechanical and physiological knowledge from the rehabilitation process to more effectively simulate and support gait movements during rehabilitation. Research results indicate that the knee rehabilitation exoskeleton design, based on the RFPBS process knowledge representation model, accomplishes multi-dimensional dynamic mapping, providing a scientific basis and effective support for the rehabilitation of patients with knee joint dysfunction.

Turbulence generation in the solar corona through nonlinear interaction between 3D kinetic Alfvén wave and magnetic islands

This study investigates the interaction between kinetic Alfvén waves (KAWs) and magnetic islands in solar corona region. KAWs and magnetic islands are believed to be one of the key players in the heating of the coronal plasma and particle acceleration. We have used the dynamical model for KAWs to simulate its propagation through magnetic islands. The equation is solved numerically by using the finite difference and pseudospectral technique for spatial and temporal variations. The simulation results have been studied and signatures of turbulent structures evolving with time have been observed. Turbulence plays a considerable role in tapping out magnetic energy to thermal energy. The plot of the energy spectrum as a function of wavenumber exhibits a power law behavior in the inertial range, characterized with an exponent around −5/3 . Beyond this range, the spectrum becomes steeper. In order to gain a more thorough knowledge of the physical processes that underlie the development of localized structures and to estimate the current sheet scale sizes, a semi-analytically model is also performed. The results indicate that the size of localized structures are comparable to ion gyro-radius, and different intensities of KAW further modify this length scale formed at different times. The purpose of the study is to acquire an understanding of the KAW propagation in magnetic islands and the way it contributes to the formation of turbulence and energy release in the solar corona. A comparative aspect of the non-linear interaction of 3D KAW with magnetic island and null point is also presented which shows that non-linear interaction of 3D KAW with null point, leads to rapid disorganization of the magnetic field and the creation of localized structures.

Ensemble of HMMs for Sequence Prediction on Multivariate Biomedical Data

Background: Biomedical data are usually collections of longitudinal data assessed at certain points in time. Clinical observations assess the presences and severity of symptoms, which are the basis for the description and modeling of disease progression. Deciphering potential underlying unknowns from the distinct observation would substantially improve the understanding of pathological cascades. Hidden Markov Models (HMMs) have been successfully applied to the processing of possibly noisy continuous signals. We apply ensembles of HMMs to categorically distributed multivariate time series data, leaving space for expert domain knowledge in the prediction process. Methods: We use an ensemble of HMMs to predict the loss of free walking ability as one major clinical deterioration in the most common autosomal dominantly inherited ataxia disorder worldwide. Results: We present a prediction pipeline that processes data paired with a configuration file, enabling us to train, validate and query an ensemble of HMMs. In particular, we provide a theoretical and practical framework for multivariate time-series inference based on HMMs that includes constructing multiple HMMs, each to predict a particular observable variable. Our analysis is conducted on pseudo-data, but also on biomedical data based on Spinocerebellar ataxia type 3 disease. Conclusions: We find that the model shows promising results for the data we tested. The strength of this approach is that HMMs are well understood, probabilistic and interpretable models, setting it apart from most Deep Learning approaches. We publish all code and evaluation pseudo-data in an open-source repository.

Oxidation of molten zirconium-containing droplet in water

During a severe accident in light water reactors, the molten reactor core (corium) falls into a water pool in the form of a jet. Complex interactions may occur between the melt and coolant known as molten fuel–coolant interactions (FCI), including energetic coolant evaporation and metallic melt (e.g., Zr and Fe) oxidation. This may further lead to steam and hydrogen explosions, which are both substantial safety risks for nuclear power plants.The heat of reaction and hydrogen production during oxidation can influence the progress and severity of the accidents. For example, the reaction heat may prolong the liquid state of corium, potentially leading to high-intensity explosions, whereas the generated hydrogen can create a combustible atmosphere, increasing the risk of hydrogen explosion. Therefore, this study evaluates the hydrogen production and oxidation degree of molten metallic droplets falling into a water pool to improve the FCI models for the risk evaluation of severe accident safety. The MISTEE-OX facility at KTH, which has been primarily built to study steam explosions is modified to investigate oxidation during FCI and provide experimental data on the oxidation behaviour of metallic droplets (Zr/Fe) quenched in a subcooled water pool. The dynamics of the falling droplets and generated bubbles are recorded using a high-speed camera, and the total volume of the bubbles is measured using a graduated cylinder. This study presents preliminary experimental results of the oxidation between Zr/Fe droplets and water, as well as recent improvements in measurement methods and facility upgrades. Our research findings are useful to enhance the knowledge of the oxidation process in FCI phenomena and validate the related mechanistic models in FCI codes.

An in-silico approach towards multivariate acceptable ranges in biopharmaceutical manufacturing

Multivariate interactions between process parameters can heavily impact product quality and process performance in biopharmaceutical manufacturing processes. Thus, multivariate interactions should be identified and appropriately controlled. This article describes an in-silico approach to establish multivariate acceptable ranges; these ranges help to illustrate the combined impact of multiple input variables on product quality and process performance. Additionally, this article includes a case study for a monoclonal antibody polishing application.Proven acceptable ranges are set by changing only one input parameter at a time while keeping all others constant to understand the impact of process variability on product quality or process performance, but the impact of synergistic variables are not evaluated. Within multivariate acceptable ranges, any combination of input parameters of a unit operation yields the desired product quality and process performance. The layered approach applied in this article is based on risk assessment and statistical models to leverage prior knowledge and existing data. The risk assessment is specific for a manufacturing facility but is applicable to multiple products manufactured in the same facility. No additional wet-lab experiments are required for building the statistical models when development and process characterization are executed using a design of experiments approach, compared to a univariate evaluation of data. The established multivariate acceptable range justifies revised normal operating ranges to ensure process control. Further, the determination of multivariate acceptable ranges adds to overall process knowledge, ultimately supporting the implementation of a more effective control strategy.

Symmetric spatiotemporal learning network with sparse meter graph for short-term energy-consumption prediction in manufacturing systems

Short-term energy-consumption prediction is the basis of anomaly detection, real-time scheduling, and energy-saving control in manufacturing systems. Most existing methods focus on single-node energy-consumption prediction and suffer from difficult parameter collection and modelling. Although several methods have been presented for multinode energy-consumption prediction, their prediction performance needs to be improved owing to a lack of appropriate knowledge guidance and learning networks for complex spatiotemporal relationships. This study presents a symmetric spatiotemporal learning network (SSTLN) with a sparse meter graph (SMG) (SSTLN-SMG) that aims to predict multiple nodes based on energy-consumption time series and general process knowledge. The SMG expresses process knowledge by abstracting production nodes, material flows, and energy usage, and provides initial guidance for the SSTLN to extract spatial features. SSTLN, a symmetrical stack of graph convolutional networks (GCN) and gated linear units (GLU), is devised to achieve a trade-off not only between spatial and temporal feature extraction but also between detail capture and noise suppression. Extensive experiments were performed using datasets from an aluminium profile plant. The experimental results demonstrate that the proposed method allows multinode energy-consumption prediction with less prediction error than state-of-the-art methods, methods with deformed meter graphs, and methods with deformed learning networks.