Output Quality Research Articles

Ecosystem service tradeoff and synergistic relationship in the Yellow River Delta High-Efficiency Eco-Economic Zone.

Exploring the tradeoff and synergy relationship among ecosystem services in the Yellow River Delta High-Efficiency Eco-Economic Zone is of great practical significance for regional ecosystem service function zoning and high-quality development. Using the InVEST model, spatial auto-correlation and trade-off synergism (ESTD) model, we analyzed the spatial and temporal variations of five ecosystem services (habitat quality, carbon storage, soil conservation, water conservation, and water purification), as well as their trade-off and synergistic relationships at the township scale from 2000 to 2020. The results showed that habitat quality, carbon storage, and nitrogen and phosphorus output decreased as a whole from 2000 to 2020, and soil conservation and water purification increased. Habitat quality showed a distribution pattern of high in the north and low in the south, and carbon sto-rage, nitrogen and phosphorus output, soil conservation and water purification showed a pattern of low in the north and high in the south. During the study period, synergistic relationships among the five ecosystem services were predominant in both time cross-section and time period, but there were still differences, with synergistic relationships mainly between carbon storage and other services in time cross-section, and between habitat quality and other ser-vices in time period. Our results can provide theoretical guidance and practical reference for the enhancement of ecosystem services and the zoning of ecosystem functions, as well as basic support for the optimization of spatial patterns of national territory.

Machine Translation and Natural Language Processing

Machine translation and natural language processing are powerful tools that allow for the automated translation of text from one language to another. Machine translation systems have evolved from rule-based systems to statistical and neural network-based approaches, with each approach having its own strengths and weaknesses. NLP, on the other hand, focuses on understanding the structure and meaning of natural language and includes tasks such as part-of-speech tagging, named entity recognition, and sentiment analysis. Thegoalofmachinetranslation,a subfield of natural language processing (NLP), is to create platform that translate text or speechautomatically.RBMT provides linguistic accuracy and control, but linguistic resources must be built and maintained meticulously. However, SMT makes use of large-scale parallel corpora to automatically learn translation patterns, making it adaptable to new language pairs and context-dependent translations.However, SMT may struggle with handling grammatical nuances and domain-specific vocabulary. Hybrid approaches combining the best of both RBMT andSMT and newer approaches like neural machine translation (NMT) are being explored to overcome the limitations and improve the quality of translation output.

Utilizing stable diffusion and fine-tuning models in advertising production and logo creation: An application of text-to-image technology

This article delves into the implementation of text-to-image technology, taking advantage of stable diffusion and fine-tuning models, in the realms of advertising production and logo design. The conventional methods of production often encounter difficulties concerning cost, time constraints, and the task of locating suitable imagery. The solution suggested herein offers a more efficient and cost-effective alternative, enabling the generation of superior images and logos. The applied methodology is built around stable diffusion techniques, which employ variational autoencoders alongside diffusion models, yielding images based on textual prompts. In addition, the process is further refined by the application of fine-tuning models and adaptation processes using a Low-Rank Adaptation approach, which enhances the image generation procedure significantly. The Stable Diffusion Web User Interface offers an intuitive platform for users to navigate through various modes and settings. This strategy not only simplifies the production processes, but also decreases resource requirements, while providing ample flexibility and versatility in terms of image and logo creation. Results clearly illustrate the efficacy of the technique in producing appealing advertisements and logos. However, it is important to note some practical considerations, such as the quality of the final output and limitations inherent in text generation. Despite these potential hurdles, the use of artificial intelligence-generated content presents vast potential for transforming the advertising sector and digital content creation as a whole.

Analysis of the Impact of Leadership, Organizational Culture, and Career Development on the Quality of Creative Industry Employees in Indonesia

This research investigates the impact of leadership styles, organizational culture, and career development on the quality of creative industry employees in Indonesia. Using a quantitative approach, a sample of 300 professionals from the creative sector was analyzed. The measurement model evaluation confirmed the reliability and validity of the constructs, while the structural model revealed significant positive relationships between leadership styles, organizational culture, and career development with the quality of creative industry employees. The results provide valuable insights for organizational strategies and practices within the Indonesian creative industry, emphasizing the importance of visionary leadership, innovative culture, and career development opportunities in enhancing the quality of creative outputs.

Radiation doses received in the UK breast screening programmes 2019-2023.

To report the latest UK mammography dose survey results and to compare radiation doses from digital breast tomosynthesis (DBT) and full-field digital mammography (FFDM) in UK breast screening. Anonymized exposure factors were collected for 111 152 screening cases and 5113 assessment cases from 405 x-ray sets across the United Kingdom using an online submission system linked to a national database of mammography quality control data. Output and beam quality measurements from each set were combined with exposure data to estimate mean glandular doses (MGD). FFDM doses increased by ∼10% compared to the 2016-2019 national survey but compressed breast thicknesses (CBT) remained similar. DBT doses were 34%-40% higher than FFDM overall and 34% higher than FFDM for breasts 50-60mm thick. We found a possible overestimation of PMMA breast equivalent thicknesses at low CBTs, but the evidence was not conclusive. Recent changes to the mix of x-ray models in use in UK breast screening have resulted in higher FFDM breast doses. DBT doses in the NHSBSP are on average higher than FFDM by ∼34%-40%. This is the first national study to report DBT and FFDM MGDs in UK breast screening.

Structures, Submodule Topologies and Control Strategies for Modular Multilevel Converter

Global energy demand is rapidly increasing, and the limitations and disadvantages of traditional energy sources are becoming more evident. Traditional energy sources are finite, and their sustainability is facing significant challenges. Compared to traditional multi-level converters, modular multi-level converters (MMC) adopt a modular design that divides the entire converter system into several independent sub-modules, each responsible for converting specific voltage levels. This article provides an overview of the theoretical aspects, topology structures, basic operation principles, and output characteristics of multi-level converters. This paper reviews the characteristics and application ranges of different topology structures, highlighting the differences in response speed, output quality, and control complexity. This paper also discusses the advantages and limitations of multi-level converters in various application scenarios. Explored the application of multi-level converters in wind power grid integration and electric vehicle charging systems, emphasizing their energy-saving and environmentally friendly features. In conclusion, this paper emphasizes the critical role of multi-level converters in power systems and future energy applications, stressing the importance of their reliability and promising future developments.

A quad DC source switched three-phase multilevel DC-link inverter topology

The concept of an isolated DC source cascaded multilevel inverter finds good solutions for generating quality output voltage for low-medium power applications. It shapes the output voltage from three levels into a number of steps closer to a sinusoidal shape using small DC sources or batteries. Several advantages have been sighted like lower voltage stress and bearing noise, and lesser THD. However, a common issue in the MLIs is the total components required which increase with the rise in voltage levels. This paper proposes a three-phase MLI design having several isolated quad voltage source modules including an H-Bridge inverter. The design suggested claims reduced switching components for current conduction paths showing improved output quality. The operational features of the suggested MLI have been analyzed using Matlab/Simulink software, furthermore, an experimental module is constructed for demonstrating the effectiveness of the simulated results.

Functional health prognosis approach for multistate manufacturing systems based on quality stochastic flow manufacturing network

Ensuring the predictability of the reliability of the final product is the functional goal of multistate manufacturing system health prognosis. However, the existing studies of basic reliability-oriented health state evaluation ignore the functional input–output characteristics of the dynamic operational manufacturing system. Therefore, a novel functional health prognosis approach considering functional fitness quality based on a quality stochastic flow manufacturing network (QSFMN) is proposed. First, to achieve the specified functional goal, the functional degradation mechanism is expounded from the system’s operational perspective, and based on this, the connotation of functional health considering product fitness quality is further provided. Second, from the perspective of functional fitness quality, through introducing a buffer to reflect dynamic production rhythm, a QSFMN model is proposed based operational quality data to expound the functional relationship between functional requirements, processing machines performance, and output quality. Third, a functional health prognosis method of multistate manufacturing systems based on QSFMN is proposed. Finally, a case study of subway flow receiver manufacturing systems is proposed to verify the effectiveness and advantage of the proposed approach.

Co-creation and critical factors for the development of an efficient public e-tourism system

This study identifies the factors that guide the adoption of a public e-tourism system resulting in value co-creation in the UAE. Integrating and comparing factors drawn from the third version of the Technology Acceptance Model (TAM3), the Technology-Task-Fit (TTF) theory, and push-to-use, an Analytic Hierarch Process (AHP) model was implemented with data collected using a structured questionnaire from purposively selected UAE e-tourism experts (N = 15) and analyzed using Microsoft Excel. The findings revealed that usefulness, convenience of use, and push-to-use were the most critical aspects for achieving an efficient public e-tourism system that allows for value co-creation in that order of ranking. The findings also suggest that computer self-efficiency is the most critical factor in effectively establishing an e-tourism system followed by government push-to-use. In conclusion, the findings demonstrate that usefulness and ease-of-use backed by computer self-efficiency, result demonstrability, and output quality are vital for the efficient adoption of a public e-tourism system resulting in value co-creation in the UAE.

Comparative transcriptomic analysis of the different developmental stages of ovary in the cuttlefish Sepia pharaonis

The cuttlefish, Sepia pharaonis, is characterized by rapid growth and strong disease resistance, making it an important commercially farmed cephalopod species in the southeastern coastal regions of China. However, in the reproductive process of S. pharaonis, there are challenges such as a low output of eggs, poor quality, and low survival rates of newly hatched juveniles. Therefore, there is an urgent need to study the molecular mechanisms underlying ovarian development in this species. In this study, we conducted the first transcriptomic analysis of the ovary at four developmental stages: the undeveloped stage, developing stage, nearly-ripe stage, and ripe stage, and compared the transcriptomics among these four stages using Illumina sequencing technology. The total numbers of clean reads of the four stages ranged from 40,890,772 to 52,055,714 reads. A total of 136,829 DEGs were obtained, GC base ratios of raw data were between 38.44 and 44.59%, and the number of uniquely mapped reads spanned from 88.08 to 95.90%. The Pearson correlation coefficient demonstrated a strong correlation among different samples within the same group, PCA and Anosim analysis also revealed that the grouping of these four stages was feasible, and each stage could be distinguished from the others. GO enrichment analysis demonstrated that ovarian follicle growth, sex differentiation, and transforming growth factor beta receptor, played a foreshadowing role at the early ovarian development stage, and the terms of small molecule metabolic process, peptide metabolic process, and catalytic activity were prominent at the mature stage. Meanwhile, KEGG analysis showed that the early ovarian development of S. pharaonis was mainly associated with the cell cycle, DNA replication, and carbon metabolism, while the mid-late ovarian development was involved with the signal transduction, endocrine system, and reproduction pathway. RT-qPCR further confirmed the consistent expression patterns of genes such as 17β-HSD, GH, VGS, NFR, and NYR in the ovaries of S. pharaonis, exhibiting elevated levels of expression during the maturation stage. Conversely, ER and OM exhibited high expression levels during the early stages of ovarian development. These transcriptomic data provide insights into the molecular mechanisms of S. pharaonis ovarian development. The findings of this study will contribute to improving the reproduction and development of cuttlefish and enriching the bioinformatics knowledge of cephalopods.

Industrial Chain Correlation and Total Factor Productivity

As a matter of fact, the total factor productivity, as a crucial metric for measuring the speed and quality of output in enterprises, plays a vital role in optimizing resource allocation and fostering the synergistic development of industries especially in recent years. With this in mind, this paper studies and investigates how the degree of industrial chain correlation affects the total factor productivity. By using the methods of benchmark regression analysis, robustness test, heterogeneity analysis and quantile regression, the empirical findings indicate that the impact of industrial chain connectivity on enterprise total factor productivity follows a "U"-shaped distribution and remains robust. Furthermore, its influence varies among enterprises with different ownership structures as well as production factor intensities according to the analysis. At the same time, the panel quantile regression reveals that the effect of industrial chain connectivity on enterprise total factor productivity differs across firms with varying levels of total factor productivity.

Self-filtering based on the fault ride-through technique using a robust model predictive control for wind turbine rotor current

This paper studies the possibility of connecting Wind Farms (WF) to the electric grid with the use of finite space model predictive command (FS-MPC) to manage wind farms to improve the quality of the current output from the doubly-fed induction generator (DFIG) with considering fault ride-through technique. This proposed system can generate active power and enhance the power factor. Furthermore, the reduction of harmonics resulting from the connection of non-linear loads to the electrical grid is achieved through the self-active filtering mechanism in DFIGs-WF, facilitated by the now algorithm proposed. FS-MPC technique has the ability to improve system characteristics and greatly reduce active power ripples. Therefore, MATLAB software is used to implement and verify the safety, performance, and effectiveness of this designed technique compared to the conventional strategy. The results obtained demonstrated the effectiveness of the proposed algorithm in handling the four operational modes (Maximum power point tracking, Delta, Fault, and Filtering). Additionally, the suggested technique exhibited flexibility, robustness, high accuracy, and fast dynamic response when compared to conventional strategies and some recently published scientific works. On the other hand, the THD value of the current was significantly reduced, obtaining at one test time the values 56.87% and 0.32% before and after filtering, respectively 27.50% and 0.26% at another time of testing, resulting in an estimated THD reduction percentage of 99.43% and 99.05%, respectively. These high percentages prove that the quality of the stream is excellent after applying the proposed strategy.

Capital tax incentives and firm innovation: evidence from accelerated depreciation in China

PurposeThis study aims to investigate the influence and impact mechanism of capital tax incentives on firm innovation.Design/methodology/approachThis study employs the difference-in-differences (DID) method, in conjunction with the exogenous impact of accelerated depreciation (AD) pilot policy. This study selects Chinese listed companies from 2010 to 2017 as the research sample.FindingsFirstly, AD exerts a substantial positive effect on the quantity and quality of the innovation output of firms, and the positive impact results primarily from heightened investment in fixed assets, particularly, machinery and equipment. Secondly, the influence of the policy is pronounced in non-state-owned enterprises, mature enterprises, less capital-intensive enterprises and non-high-tech industries, which all exhibit strong innovation incentives. Lastly, the tax incentive policy significantly stimulates firm innovation in the short term, but its long-term impact on innovation incentives lacks statistical significance.Originality/valueThis study highlights the significance of capital tax incentives in facilitating the innovation process in firms.

The Role of Education in Economic Advancement: A Case Study of Silicon Valley

Education is an important ingredient in the development of the human capital of any given country since it directly influences the quality of output of the workforce. With an educated workforce, efficiency is enhanced, as well as the level of productivity, due to the capacity of individual workers to be innovative. This paper seeks to analyze the impact of education in a country by analyzing the impact that education has on the level of economic growth, as well as its influence on a workforce’s capacity to absorb new technologies. Additionally, the paper also discusses how education is a crucial asset in reducing poverty and inequality in society by increasing sustainable household incomes as well as bringing about positive externalities. The paper shows from the case of Silicon Valley in the United States, education plays a great role in economic advancement of a nation or region. The paper shows that the Silicon Valley largely benefits from the presence of top higher education institutions such as Stanford University, which train a large number of technological innovation talents.

Attention guided multi-scale cluster refinement with extended field of view for amodal nuclei segmentation

Nuclei segmentation plays a crucial role in disease understanding and diagnosis. In whole slide images, cell nuclei often appear overlapping and densely packed with ambiguous boundaries due to the underlying 3D structure of histopathology samples. Instance segmentation via deep neural networks with object clustering is able to detect individual segments in crowded nuclei but suffers from a limited field of view, and does not support amodal segmentation. In this work, we introduce a dense feature pyramid network with a feature mixing module to increase the field of view of the segmentation model while keeping pixel-level details. We also improve the model output quality by adding a multi-scale self-attention guided refinement module that sequentially adjusts predictions as resolution increases. Finally, we enable clusters to share pixels by separating the instance clustering objective function from other pixel-related tasks, and introduce supervision to occluded areas to guide the learning process. For evaluation of amodal nuclear segmentation, we also update prior metrics used in common modal segmentation to allow the evaluation of overlapping masks and mitigate over-penalization issues via a novel unique matching algorithm. Our experiments demonstrate consistent performance across multiple datasets with significantly improved segmentation quality.

Digital transformation and innovation output of manufacturing companies-An analysis of the mediating role of internal and external transaction costs.

Digital transformation, based on digital technologies, has triggered economic growth in many industries and brought about production and service transformation in the manufacturing sector. As an important source of innovation output and a driving force for national economic development, it is of great significance to study the impact of digital transformation on innovation output in manufacturing companies. This study analyzes the effects of digital transformation on the quality, quantity, and overall innovation output of manufacturing companies from both the macro provincial-level digital transformation and micro enterprise-level digital transformation perspectives. Additionally, using data from manufacturing companies listed on the Shanghai and Shenzhen stock exchanges from 2012 to 2022, this study empirically tests the mechanism through which digital transformation affects innovation output from the perspectives of internal transaction costs and external transaction costs. The results show that digital transformation promotes overall improvement in innovation output of manufacturing companies and leads to improvements in both the quality and quantity of innovation output. Furthermore, the study finds that the effect of digital transformation on innovation output has a nonlinear characteristic under different levels of market competitiveness and market freedom. The mediation analysis reveals that the influence of digital transformation on innovation output can be attributed to the reduction of internal transaction costs and the enhancement of external transaction efficiency. In terms of digital policy formulation, it is necessary to coordinate the development of diverse and innovative digital infrastructure at the macro level with the micro-level ecosystems of enterprises, in order to reduce transaction costs within and outside innovative entities. Ultimately, it is essential for the government to foster a conducive free market environment that enhances transaction efficiency and timely regulates the excessive competition resulting from oligopolistic monopolies, thus maximizing the potential of digital transformation in promoting innovation output.

In situ three-dimensional reconstruction of laser powder bed fusion parts by light field camera

The assessment of output quality in the laser powder bed fusion process entails the analysis of the three-dimensional morphology of the manufactured parts, which plays a crucial role in quality control to prevent metallurgical defects. This study proposes a novel approach of three-dimensional reconstruction for laser powder bed fusion parts using a light field camera. Initially, a consistency method for high-precision camera calibration was explored to determine the virtual camera pixel size and coefficient of effective focal length of light field camera. Subsequently, the model based light field epipolar-plane image Unet is designed to obtain disparity information without any data augmentation techniques. By leveraging both binocular and light field optical paths, a robust mapping relationship of the disparity, depth, and visually coherent three-dimensional contour was established. Finally, the three-dimensional contours of laser powder bed fusion parts were reconstructed by the established mapping with a single exposure of the light field camera. The proposed method enables three-dimensional contour visualization of laser powder bed fusion parts from multi-view perspectives, which facilitates in situ monitoring of the spattering distance, three-dimensional spatial points and lines of the printed layers and contours of printed parts. With a calibration error of 50 μm and resolution of 180 μm for the light field camera (Lytro illum camera), the maximum relative and absolute errors between reconstructed three-dimensional spatial points were 4.33 % and 260 μm respectively when compared with theoretical dimension, and 0.5 % and 210 μm respectively when compared with manufactured dimension. The maximum root-mean-square error of reconstructed three-dimensional contour surfaces was found to be 1.55 mm. This study represents the first application of a light field camera for in situ three-dimensional reconstruction of laser powder bed fusion parts, which notably can be obtained from only a single round of calibration and exposure, enabling seamless monitoring of their surface morphology and facilitating online quality control. These advancements have vast potential for enhancing high-end additive manufacturing.

Numerical investigation on thermal performance of a solar greenhouse with synergetic energy release of short- and long-term PCM storage

To address the problem of low greenhouse temperature at late night in north China, a new strategy of synergetic release of short- and long-term PCM heat storage is proposed in this paper, i.e. supercooled salt hydrate units are activated to release seasonally stored latent heat after the organic PCM north wallboard with daily storage cannot provide the required indoor temperature. To achieve this strategy, a microclimate model considering multiple greenhouse elements, including solar radiation, crops, indoor air, water vapor, soil and PCMs, is developed and 3D computational fluid dynamic (CFD) simulations are employed to examine the thermal performances of this strategy under Beijing climate conditions. The results demonstrated that the new strategy promotes the greenhouse temperature suitable for tomato growth (15 °C) throughout the whole night in winter. The organic PCM (Tm = 25 °C) wallboard can reduce the daytime indoor temperature by 0.5–1.5 °C and increase the nighttime indoor temperature by 4–5.5 °C than without PCM wallboard on January 1st. But the greenhouse temperature with this single daily storage is still lower than the required 15 °C after 4:00 a.m. With consideration of the unit numbers and heating effects, triggering two supercooled sodium thiosulfate pentahydrate (STP) units at one time (rather than sodium acetate trihydrate (SAT) and calcium chloride hexahydrate (CCH)) is the reasonable choice for greenhouse heating under present conditions. The system can achieve synergetic energy release across seasons, days and nights, regulate the thermal environment in the greenhouse, and promote the quantity and quality of crop output.

A novel shipyard performance measurement approach through an integrated Value Engineering and Risk Assessment (VENRA) framework using a hybrid MCDM tool

As the ship manufacturer, including new building, repair, and conversion, the shipyard significantly impacts the ship’s quality and performance output. A well-planned ship design that will be built requires skilled shipbuilders who can fulfil quality, timeline, budget, safety, and environmental requirements from shipowners, rules, and regulations. Since diverse and multiple factors influence its efficiency and product output, evaluating the shipyard’s performance is critical for a more impactful and strategic advancement approach. This research aims to apply the novel integrated Value Engineering and Risk Assessment (VENRA) framework, which integrates five main elements: technical, business, external, personnel’s safety, and environment, for measuring shipyard performance. This paper demonstrates the VENRA business element in more detail and applies it to a shipyard case study. Integrated fuzzy Decision Making Trial and Evaluation Laboratory (DEMATEL) and Weighted Evaluation Technique (WET) are used to analyse the criteria’s interrelationship by determining the cause-effect group and weight ranking prioritisation. The objective grading system is developed to determine the shipyard’s performance score based on multi-resource qualitative and quantitative data. The shipyard’s case study demonstrates that ‘delivery time’ remains the most critical and influential aspect of the business elements’ performance. In addition, the top three most important factors, ‘delivery time’, ‘financial report condition’, and ‘ship manufacturing cost’, must be taken into account, as they directly influence the shipyard’s performance. Despite being a minor element, ‘innovation and human resources’ is the second most influential factor after ‘delivery time’. The case study’s results demonstrate that the framework can simultaneously identify cause-and-effect criteria groups while prioritising the most critical factors via methodologies.

The use of automation in the rendition of certain articles of the Saudi Commercial Law into English: a post-editing-based comparison of five machine translation systems.

Efforts to automate translation were made in the 1950s and 1960s, albeit with limited resources compared to current advanced standards. Machine translation is categorised under computational linguistics that examines employing computer software in the rendition of text from one language into another. The present paper seeks to compare five different machine translation systems for the sake of assessing the quality of their outputs in rendering certain articles of the Saudi Commercial Law into English through post-editing based on Human Translation Edit Rate. Each machine translation output is assessed against the same post-edited version, and the closest output to the post-edited version with regard to the use of the same lexicon and word order will achieve the lowest score. The lower the score of the machine translation output is, the higher quality it has. The paper then analyses the results of the Human Translation Edit Rate metric evaluation to ascertain as to whether or not high-quality machine translation outputs always produce acceptable Arabic-English legal translation. The present paper argues that the use of Human Translation Edit Rate metric is a useful tool for the sake of undertaking post-editing procedures as it is a combination of both human evaluation as well as automatic evaluation. It is also advantageous as it takes account of both the use of lexicon and word order. However, such metric cannot be sufficiently depended on as one term substitution, which will be counted according to this metric as a single error, may render the whole sentence invalid, particularly in legal translation. This paper offers a baseline for the quality assessment of machine translation output through post-editing based on Human Translation Edit Rate metric and how its results should be analysed within Arabic-English legal translation context, which may have implications for similar machine translation output quality assessment contexts.