Process Output Research Articles

Assessing English Translation of Historical Intertextuality in Maḥfūẓ's Cairo Trilogy

By skillfully integrating a wide range of scholarly and intercultural references into his novels, the Egyptian novelist Najīb Maḥfūẓ has differentiated himself as an author of exceptional prowess. To convey his intended meanings, he has made extensive use of historical allusions, including (past figures and events). In the present study, we attempt to assess the translation of these types of intertextuality utilized in Maḥfūẓ's Cairo trilogy via adopting the criteria of "accuracy, clarity and naturalness" proposed by Larson in 1984. This study follows a qualitative descriptive approach to analysis. We aim to discover the tactics utilized in translating historical intertextuality into English and to identify the challenges faced by the subject translators and the reasons behind them. The current study hypothesizes that irrespective of what method the translators use to translate historical intertextuality, the translator's historical and cultural background would seriously influence the output of the translation process. The study concludes that intertextuality is a crucial device for all language users since no speech ever starts in a vacuum, and it is via intertextuality that our old legacy is continually refreshed and viewed from different points of view. It further concludes that intertextual expressions that are more culturally specific are the most challenging and he translator's familiarity with the previous texts of SL plays a significant role in the process of deciphering the meaning of intertextual texts.

Integrating supervised and unsupervised learning approaches to unveil critical process inputs

This study introduces a machine learning framework tailored to large-scale industrial processes characterized by a plethora of numerical and categorical inputs. The framework aims to (i) discern critical parameters that influence the output and (ii) generate accurate out-of-sample qualitative and quantitative predictions of production outcomes. Specifically, we address the pivotal question of the significance of each input in shaping the process outcome, using an industrial Chemical Vapor Deposition (CVD) process as an example. The initial objective involves merging subject matter expertise and clustering techniques exclusively on the process output, here, coating thickness measurements at various positions in the reactor. This approach identifies groups of production runs that share similar qualitative characteristics, such as film mean thickness and standard deviation. In particular, the differences of the outcomes represented by the different clusters can be attributed to differences in specific inputs, indicating that these inputs are potentially critical to the production outcome. Shapley value analysis corroborates the formed hypotheses. Leveraging this insight, we subsequently implement supervised classification and regression methods using the identified critical process inputs. The proposed methodology proves to be valuable in scenarios with a multitude of inputs and insufficient data for the direct application of deep learning techniques, providing meaningful insights into the underlying processes.

The making of the ‘Old Town Sakon Nakhon creative district’ in Thailand: A process evaluation study of the creative placemaking agenda through festivalisation

This article looks at the creative district development process of a rural area in Thailand and sheds light on the transformative potential of utilising arts- and culture-based activities like festivals as a rural community development and regeneration strategy towards its creative placemaking agenda. Using Old Town District, Sakon Nakhon as a case study, this research investigates the application and implementation of the creative district concept outside of the usual urban centres and as a specific output of the development process. It utilises the logic model framework in evaluating the process and is further supported by interviews, focus group, observations, and document analysis in examining the various contexts surrounding its development and in analysing festivalisation as its main operational approach. Key findings highlight Thailand's political context and the selective community engagement as major barriers to the process. It also underscores the lack of authenticity and the quasi-coerced participation of the residents in the curation of the festival, reducing the locals' sense of place and belongingness instead of enriching it. This paper concludes that there is a shortfall in theory and implementation on the creative district development process of the Old Town District.

Risk-benefit comparative analysis of different control methods for reservoirs under the water to electricity mode

Abstract For some small hydropower stations that are not under the control of power dispatching department, there are many ways to implement the reservoir operation scheme formulated under the premise of determining power by water. This paper respectively selects the output, flow and water level process as the control conditions, explores the risks and benefits of reservoir operation with different control variables, and selects the optimal operation scheme through comprehensive comparative analysis. Simulation results of a hydropower station in the Han River Basin show that when the output is taken as the control condition, the waste water is small, the power generation benefit is high, and the risk of the water level outside boundary is within the acceptable range. Considering the risk and benefit of reservoir operation and taking output as the control condition, it has significant advantages over water level and flow.

Architecture and anatomy of executive processes: evidence from verbal fluency and Trail Making Test in 2009 stroke patients.

The few voxel-wise lesion-symptom mapping (VLSM) studies aimed at identifying the anatomy of executive function are limited by the absence of a model and by small populations. Using Trail Making Test (TMT) and verbal fluency and a model of their architectures, our objective was to identify the key structures underlying two major executive processes, set-shifting and strategic word search. We applied a validated VLSM analysis to harmonized cognitive and imaging data from 2009 ischemic stroke patients as a part of the Meta VCI Map consortium. All contrast analyses used an adjusted threshold with 2000 Freedman-Lane permutations (p ≤ 0.05). The TMT parts A and B were associated with structures involved in visual-spatial processing, the motor system, the frontal lobes, and their subcortical connections. Set-shifting depended on the left dorsomedial frontal region. Both semantic and phonemic fluency tests depended on verbal output abilities and processing speed with similar slopes in different languages. The strategic search process depended on Broca's area, F2 and related tracts, temporal and deep regions. Lastly, the lesion map of set-shifting did not overlap with those of strategic word search processes. Our results identify the anatomical substrates of two main executive processes, revealing that they represent only a specific subpart of previously reported structures. Finally, our results indicate that executive functions depend on several specific, anatomically separable executive processes mainly operating in various parts of the frontal lobes.

Biomanufacturing Harvest Optimization With Small Data

In biopharmaceutical manufacturing, fermentation processes play a critical role in productivity and profit. A fermentation process uses living cells with complex biological mechanisms, leading to high variability in the process outputs, namely, the protein and impurity levels. By building on the biological mechanisms of protein and impurity growth, we introduce a stochastic model to characterize the accumulation of the protein and impurity levels in the fermentation process. However, a common challenge in the industry is the availability of only a very limited amount of data, especially in the development and early stages of production. This adds an additional layer of uncertainty, referred to as model risk, due to the difficulty of estimating the model parameters with limited data. In this article, we study the harvesting decision for a fermentation process (i.e., when to stop the fermentation and collect the production reward) under model risk. We adopt a Bayesian approach to update the unknown parameters of the growth-rate distributions, and use the resulting posterior distributions to characterize the impact of model risk on fermentation output variability. The harvesting problem is formulated as a Markov decision process model with knowledge states that summarize the posterior distributions and hence incorporate the model risk in decision-making. Our case studies at MSD Animal Health demonstrate that the proposed model and solution approach improve the harvesting decisions in real life by achieving substantially higher average output from a fermentation batch along with lower batch-to-batch variability.

Historical evolution of olive oil production processes focusing on the role of water, the contribution of energy sources, and the by-product management: The case-study of Crete, Greece

There are numerous studies dealing with olive oil management from ancient civilizations to the mid last century, but they are limited on the historical value of information. At the same time, much knowledge is widely available and accessible on the contemporaneous production of olive oil, the necessary inputs (water and energy) and outputs (by-products) of the production process. The present study aims to shed light on olive oil extraction management from antiquity to present and to bridge the gap between archaeological and modern agricultural, engineering, and environmental disciplines. For the purposes of this study, Crete, Greece, a well-known and traditional olive oil producing region is investigated. This study is dedicated to unveil practices concerning: (a) the processing of the olives, (b) the various energy aspects per era, (c) the role of water and energy at each stage of the extraction process, and (d) management of by-products per era. The main findings support that: (a) the evolution of the extraction processes was relatively slow and remained almost the same from Minoan times until the middle of the 20th century, (b) the importance of water has been demonstrated from the beginning in the efficient extraction of the maximum amount of olive oil, (c) wastewater was first reported during the Hellenistic-Roman period due to the increased quantities produced, (d) by-product management was only considered in the previous century for environmental purposes, (e) olive oil production has been a human-based process for centuries and was greatly increased by the introduction of animals, and (f) olive oil production was further increased with the utilization of mechanical and electrical energy. It can be therefore clearly concluded that past practices have both similarities and differences with the present ones, which in turn have been optimized in terms of energy sources, water uses, olive mill equipment, and environmental considerations, to result in maximum olive oil production with minimum environmental impacts. Based on this work, important lessons can be drawn that show the historical evolution of extraction and management practices.

A novel prompting method for few-shot NER via LLMs

In various natural language processing tasks, significant strides have been made by Large Language Models (LLMs). Researchers leverage prompt method to conduct LLMs in accomplishing specific tasks under few-shot conditions. However, the prevalent use of LLMs’ prompt methods mainly focuses on guiding generative tasks, and employing existing prompts may result in poor performance in Named Entity Recognition (NER) tasks. To tackle this challenge, we propose a novel prompting method for few-shot NER. By enhancing existing prompt methods, we devise a standardized prompts tailored for the utilization of LLMs in NER tasks. Specifically, we structure the prompts into three components: task definition, few-shot demonstration, and output format. The task definition conducts LLMs in performing NER tasks, few-shot demonstration assists LLMs in understanding NER task objectives through specific output demonstration, and output format restricts LLMs’ output to prevent the generation of unnecessary results. The content of these components has been specifically tailored for NER tasks. Moreover, for the few-shot demonstration within the prompts, we propose a selection strategy that utilizes feedback from LLMs’ outputs to identify more suitable few-shot demonstration as prompts. Additionally, to enhance entity recognition performance, we enrich the prompts by summarizing error examples from the output process of LLMs and integrating them as additional prompts.

Unravelling the relationship between language anxiety and foreign language speech fluency in a monologue production

ABSTRACT This study investigated the interplay between language anxiety of advanced foreign language learners and their speech fluency measured with temporal indices. Language anxiety levels of 59 university level students of English were identified with the Input, Processing and Output Anxiety Scale (the IPOAS; MacIntyre & Gardner, 1994. The effects of induced anxiety on three stages of cognitive processing in computerized vocabulary learning.’ Studies in Second Language Acquisition 16 (1): 1–17. doi:10.1017/S0272263100012560) and the Post-Session Survey on Anxiety. The former informed about the levels of anxiety experienced at three stages of linguistic processing, hence, input anxiety, internal processing anxiety, and output anxiety. The latter was applied to identify language anxiety levels exhibited during a monologue production, reported as task-specific language anxiety. Speech samples from the performance were analyzed for fluency breakdown (the number of filled and silent pauses), speed of speech (articulation rate), and composite measures (speech rate). The findings revealed that the advanced foreign language learners with higher levels of internal processing anxiety and output anxiety used filled pauses more frequently. Furthermore, the levels of output anxiety and task-specific language anxiety were inversely related to speech and articulation rate. The study also showed statistically significant differences in the production of filled pauses and speech rate between advanced foreign language learners with low and high levels of language anxiety as measured by the IPOAS.

Stimuli‐Responsive Prototissues via DNA‐Mediated Self‐Assembly of Polymer Giant Unilamellar Vesicles

AbstractGaining insight into the complex functions of tissues, which involve communicating cell types, by utilizing materials that mimic the properties of real tissue, is an important step in developing advanced biomedical applications. However, building 3D networks of interconnected protocells capable of chemical information processing and collective output remains a challenge. Herein, the construction of a prototissue based on the DNA‐mediated assembly of polymeric giant unilamellar vesicles (pGUVs) are presented with differential sensitivity, forming a multicompartment communicating system. One set of pGUVs hosts microgels as artificial Mg2+ storage organelles, which can be triggered to release their Mg2+ by pH changes in the environment. The downstream linked set of protocells contains a Mg2+ sensitive dye that responds to the Mg2+ signal. The density of complementary DNA strands on the surface of the respective pGUVs determines not only the size of the pGUV ensemble but also modulates sensitivity toward magnesium. Moreover, Mg2+ signaling to downstream protocells loaded with monomeric actin induces the in situ formation of an artificial cytoskeleton. Overall, through the clustering of protocells hosting distinct artificial organelles with controlled architecture, such unique prototissues that mimic intratissue communication generate new prospects in using advanced functional materials for multi‐step catalysis and biomedicine.

A novel prediction method for low wind output processes under very few samples based on improved W‐DCGAN

AbstractThe threat of long‐term low wind output processes (LWOP) on the supply ability of the power system is escalating with the increasing integration of wind power. Accurate prediction of LWOP is crucial for maintaining the stable operation of the power system. However, the occurrence probability of LWOP is low and the available samples are lacking, limiting the high‐accuracy predictive modeling of LWOP. Therefore, a novel prediction method for LWOP under very few samples based on improved Wasserstein deep convolutional generative adversarial networks (W‐DCGAN) is proposed here. Firstly, a multi‐dimensional identification method is proposed to accurately identify historical LWOP. Then, an LWOP sample generation model based on improved W‐DCGAN is established. The model integrates a long short‐term memory layer into the deconvolutional layer of the generator to enhance the temporal characteristics of generated samples. Finally, three prediction algorithms are used to construct LWOP prediction models based on both generated and actual samples, respectively. The wind power operation data from a province in China is taken as an example to verify the effectiveness of the proposed method. The results show that the prediction accuracy of LWOP can be improved by 14.36%–55.85%.

Investigation of Effects of Spreading Parameters on Powder Bed Quality in Additive Manufacturing

Powder bed-based additive manufacturing processes such as laser powder bed fusion, binder jetting, and electron beam melting are commonly utilized in various critical areas such as medical, aviation, and energy. Common to all these operations, the powders are first spread onto the built platform in a layer-by-layer fashion and selectively fused or bound with a suitable method. The quality of the process depends on several process parameters, including how the powders are spread onto the built platform. Powder spreading is an important step in these operations and can affect various process outputs. In this study, the powder spreading is numerically investigated using the discrete element method to determine the effects of the layer thickness, rotation, and transition velocities selected as parameters with a powder spreader roller. The relationship between powder spreading parameters and powder volume packing fraction was investigated using the 'Hertz-Mindlin and Johnson-Kendall-Robert’s (JKR) contact model considering the interactions between particle-particle and particle-built plate, has been added to the numerical model. A Design of Experiment combined with analysis of variance (ANOVA) was utilized to gain a broader understanding on the relationship between process parameters and green density and dynamic angle of repose.

Locus coeruleus activation 'resets' hippocampal event representations and separates adjacent memories.

Memories reflect the ebb and flow of experiences, capturing unique and meaningful events from our lives. Using a combination of functional magnetic resonance imaging (fMRI), neuromelanin imaging, and pupillometry, we show that arousal and locus coeruleus (LC) activation transform otherwise continuous experiences into distinct episodic memories. As sequences unfold, encountering a context shift, or event boundary, triggers arousal and LC processes that predict later memory separation. Boundaries furthermore promote temporal pattern separation within left hippocampal dentate gyrus, which correlates with heightened LC responses to those same transition points. We also find that a neurochemical index of prolonged LC activation correlates with diminished arousal responses at boundaries, suggesting a connection between elevated LC output and impaired event processing. These findings align with the idea that arousal processes initiate a neural and memory 'reset' in response to significant changes, constructing the very episodes that define everyday memory.

A simultaneous energy self-sufficient desalination and energy output process based on a novel membrane stack design

Electrodialysis (ED) uses electric energy to conduct the desalination, while bipolar membrane reverse electrodialysis (BMRED) converts the chemical energy containing in acids and alkalis into electric energy. Here, a novel membrane stack (BMRED-ED) based on BMRED and ED was designed to realize the simultaneous energy self-sufficient desalination and energy output process. The stack was studied in two different operation modes systematically. Results of the open-loop operation mode demonstrate that the maximum power density (Pd,max) can obtain a highest value of 4.53 W·m−2, and the maximal average apparent permeability (α) between the open circuit voltage (OCV) and theoretical electromotive force (EMF) is 92.23 %. Results of the closed-loop operation mode indicate that the final desalination ratio (ξdel) can attain as high as 89.57 %, and the corresponding energy utilization ratios of the desalination (ηdes) and the energy output (ηout) are 0.90 % and 14.00 %, respectively. Moreover, the effect of desalinated salt solution concentrations and discharging current densities on the closed-loop operation performance are much more remarkable than that of cation species in the alkali and salt solutions. This work provides a feasible and competitive strategy for simultaneous energy self-sufficient desalination and energy output.

Quantitative assessment methods and qualitative evaluation system for the complementary characteristics of multi-energy

Assessing and analyzing the complementary characteristics of renewable energy (RE) is crucial for designing, operating, and optimizing multi-energy complementary systems (MECSs). However, unified and precise quantitative descriptions of the complementary and stability characteristics among various energy outputs in MECSs have lacked attention and research. Here, this study innovatively proposed a mathematical model for the multi-energy complementarity index (MECI), which considers the complementarity rates of multiple energy outputs during zero and non-zero output periods, and a mathematical model for the multi-energy volatility index (MEVI), which accounts for fluctuation thresholds and the overall volatility of output processes. An evaluation system for multi-energy complementarity characteristics qualitative analysis has been established. The natural output processes of RE at three MECSs in China were applied in the case calculations and verification. Results show that the hydropower rated discharge (Qrating) has a significant negative correlation with MECI, with the MECI decreasing by an average of 0.0046 for every 5 m³/s increase in Qrating. The relationship between the Qrating and MEVI shows an overall negative correlation with local fluctuations. Notably, The MECI of the BeiPan River MECSs exhibits significant seasonal characteristics, with the MEVI in summer (0.378) and autumn (0.395) higher than those in spring (0.132) and winter (0.160), closely related to the natural seasonal variations of the three energy sources: water, wind, and solar. We believe that the study can assist in evaluating and making decisions on the multi-energy complementarity characteristics of RE bases in the future, making a significant contribution to achieving dual carbon goals.

Principal-Multiagents problem under equivalent changes of measure: General study and an existence result

We study a general contracting problem between the principal and a finite set of competitive agents, who perform equivalent changes of measure by controlling the drift of the output process and the compensator of its associated jump measure. In this setting, we generalize the dynamic programming approach developed by Cvitanić et al. (2018) and we also relax their assumptions. We prove that the problem of the principal can be reformulated as a standard stochastic control problem in which she controls the continuation utility (or certainty equivalent) processes of the agents. Our assumptions and conditions on the admissible contracts are minimal to make our approach work. We review part of the literature and give examples on how they are usually satisfied. We also present a smoothness result for the value function of a risk–neutral principal when the agents have exponential utility functions. This leads, under some additional assumptions, to the existence of an optimal contract.

Identifying interventions that improve medication safety & rational use of medicines in India.

Background & objectives Medication-related harm is known to be the cause for about 1/10th of hospitalizations. Some estimates from India show that about 90 per cent of medicines consumed are inessential or irrational and contribute towards high out-of-pocket expenditure on health. In this context, the Indian Council of Medical Research in 2022 constituted a National Task Force (NTF) to explore possible solutions that could improve safe and rational use of medicines (SRUMs). The objective of this study was to identify research ideas in the field of SRUM through a survey of relevant stakeholders, and further to prioritize the research ideas using a pre-identified set of criteria. Methods The responses from the identified stakeholders were assessed using the Child Health and Nutrition Research Initiative method, which is an established research priority-setting methodology. First, the NTF asked for two to six research ideas from relevant Indian and global stakeholders on solutions to improve SRUM. The ideas were checked for duplicates, re-phrased where necessary and classified into various sub-themes. Subsequently, the research ideas were scored by Indian experts with relevant technical expertise using a pre-defined set of five criteria: innovativeness, effectiveness, translational value, answerability and applicability. Each research idea received from a stakeholder was assigned a score under each of the five criteria. The overall research priority score was calculated as a mean of all five criteria-specific scores and converted into a percentage. Results The final output of the prioritization process was a list of research ideas or questions, ranked by their scores. Total 209 unique ideas were received from 190 respondents, which were scored by 27 experts. The top three research topics on medication safety focused on cost-effective strategies for improving antimicrobial stewardship, safe use of poly-pharmacy in geriatric patients and drug take-back policy interventions. Regarding the rational use of medicine, the top three topics included testing mobile application-based antimicrobial stewardship interventions, development of diagnostics for antimicrobial resistance, and behavioural interventions. Interpretation & conclusions Several priority ideas found in this study also align with those of global priority, e.g., safe disposal practices and enhanced pharmacovigilance, rational use of medicines. Patient engagement, which underlines many of the top scoring ideas found in this study, is also inclined with the top research priorities reported by the WHO priority exercise on research into the safe use of medicines. However, to the best of our knowledge, this is the first such work from a low- and middle- income country on medication safety and rational use of medicines. The findings of this research priority-setting exercise can help to guide research for the development of policy-relevant and novel interventions to improve SRUM in India.

Hydrogen Production from Natural Gas Using Hot Blast Furnace Slag: Techno-economic Analysis and CFD Modeling

A process for thermal decomposition of methane to hydrogen and solid carbon is presented and examined. It utilizes the high-temperature heat from the slag by-product of blast furnace ironmaking to drive a thermal decomposition reaction, making it a waste-heat-to-hydrogen technology. This is accomplished via dry granulation of molten slag that feeds a fluidized bed reactor to effect methane–slag contact. First, the proposed process and the heat and mass balances are presented. It is found that it could produce an amount of hydrogen that is equivalent to about 20% of the reductant, depending on the iron-to-slag ratio. Then, a techno-economic analysis investigates the capital and operating costs of the process, compares the hydrogen production cost to that of other processes, and examines cost sensitivity to the prices of process inputs and outputs. This analysis suggests that the process would be suitable for on-site hydrogen production and use within a plant. In addition, using the hot slag to drive the methane decomposition would reduce hydrogen production cost by 15% compared to combusting a portion of the natural gas itself. Finally, a computational fluid dynamics (CFD) modeling study of the fluidized bed reactor examines the thermal decomposition of methane and its dependence on reaction kinetics as well as reactor design and operation. The bed operated in the bubbling regime at an average temperature between 1020 and 1060 °C and resulted in as high as 82% conversion of the methane to hydrogen, with additional optimization still possible.Graphical

Assessment of a large language model based digital intelligent assistant in assembly manufacturing

The use of Digital Intelligent Assistants (DIAs) in manufacturing aims to enhance performance and reduce cognitive workload. By leveraging the advanced capabilities of Large Language Models (LLMs), the research aims to understand the impact of DIAs on assembly processes, emphasizing human-centric design and operational efficiency. The study is novel in considering the three primary objectives: evaluating the technical robustness of DIAs, assessing their effect on operators' cognitive workload and user experience, and determining the overall performance improvement of the assembly process. Methodologically, the research employs a laboratory experiment, incorporating a controlled setting to meticulously assess the DIA's performance. The experiment used a between-subjects design comparing a group of participants using the DIA against a control group relying on traditional manual methods across a series of assembly tasks. Findings reveal a significant enhancement in the operators' experience, a reduction in cognitive load, and an improvement in the quality of process outputs when the DIA is employed. The article contributes to the study of the DIA's potential and AI integration in manufacturing, offering insights into the design, development, and evaluation of DIAs in industrial settings.

An approach to modelling defocusing and keyhole reflectivity in keyhole laser processes

Keyhole laser-based processes have been adopted widely due to the high-quality welds and cuts that they produce and the accurately controlled dimensions of the end part. Fast-running simulation tools are required in order to estimate the dependence of process outputs, efficiency, and stability on the process variables. This work presents a fast-running simulation tool for keyhole laser-based processes with inert or no assisting gas that takes into account the effect of laser beam defocusing and the alteration of the effective reflectivity of the material due to the keyhole cavity. The model is built upgrading a successful model for the thermal phenomena based on finite differences and the enthalpy method. The presented model is compared to experimental results for the laser cutting process with both CO2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$_2$$\\end{document} and fiber laser sources and inert assisting gas. The model accurately predicts cutting depth and kerf, heat-affected zone width, and the dependence of the cutting depth on the position of the focal plane. The model manages to achieve reduced computational time enabling its utilization in process digital twins.