Hasil untuk "Special industries and trades"

Long Yang, Xiao Yuan, Chen Chun et al.

The objective of this study was to explore the protective effect of astragaloside IV on a model of isoproterenol-induced (ISO) hyper-trophic injury in rat cardiomyocytes H9c2 (cell line derived from embryonic BD1X rat heart tissue). A cell hypertrophy injury model was established (H9c2 cells treated with 100 μmol L–1 ISO). The cells were divided into normal control, a model group, and an astragalo-side IV group at several concentrations. Astragaloside IV was pre-administered for 2 hours, followed by ISO treatment for 24 hours. Cell viability, cell surface area, apoptosis rate, lactate dehydrogenase (LDH) activity, reactive oxygen species (ROS), superoxide dismutase (SOD), the mRNA levels of Bcl-2, Bax, p62, and LC3, the protein expressions of Sirt1, p62, caspase-3, beclin, and p53 and the LC3II/LC3I ratio were detected. Astragaloside IV significantly alleviated ISO-induced hypertrophy injury in H9c2 cells, reduced cell surface area and LDH release, decreased apoptosis rate and intracellular ROS levels, increased SOD levels, upregulated the expressions of autophagy-related mRNA and proteins, and downregulated the expressions of apoptosis-related mRNA and proteins. Astragaloside IV can effectively inhibit ISO-induced hypertrophy and apoptosis in H9c2 cells, and its mechanism may be related to promoting auto-phagy and reducing oxidative stress.

E. Tianlong, Kai Qin, Zhao Liu et al.

Abstract Ultra-high-voltage (UHV) autotransformers are widely employed in long-distance power transmission systems. Their operation involves complex energy conversion and coupling mechanisms, including high-intensity magnetic induction energy and strong induced currents. From the perspective of power systems and automation control, it is essential to construct a comprehensive equivalent control circuit for UHV autotransformers, integrating the analysis of induced current and magnetic flux density into the domain of analog electronics. Numerical analysis has become a core approach for investigating the external thermal physical characteristics of transformer power and various thermal management strategies. In this paper, the Message Passing Interface (MPI) and Portable, Extensible Toolkit for Scientific Computation (PETSc) parallel computing framework is adopted to compute and analyze the electro-thermal coupling in a UHV autotransformer. The dielectric loss of transformer components is thoroughly examined. A linear numerical simulation method for evaluating dielectric loss is assessed through parallel computation and validated via the design of a three-dimensional coupling model for leakage flux and core temperature rise. The dielectric loss calculation is applied to the transformer. Magnetostriction measurements under rated output power and various current and voltage conditions reveal the correlation between the coupled data and the thermal topology. The MPI-PETSc framework significantly enhances the computational efficiency of three-dimensional electro-thermal coupling problems in UHV autotransformers through distributed computing and efficient numerical solving, making it suitable for large-scale, high-precision engineering simulations.

Jing Zhang, Pei Li, Junfeng Wan et al.

Proper management of nitrogen-containing pig manure is crucial to realize its benefits of supporting plants-grow as fertilizer while minimizing its impact on the environment and climate change. Dry collection, rinsing and water submerging are manure cleaning techniques adopted in different types of pig farms and in different regions. As the first step of manure management, manure cleaning technique affects manure generation and nitrogen flow in the subsequent treatment and utilization processes. This short communication is to discuss different manure cleaning techniques and their impacts on nitrogen flow through pig manure management processes. Reducing nitrogen losses should focus on solid manure treatment such as composting when manure is dry collected. More diversified pathways of nitrogen losses are possible when manure is cleaned using water submerging technique. It is thus needed to develop proper and specific nitrogen management strategies and technologies, taking into account the manure cleaning technique adopted in pig farms.

Tarequl Islam

The printing and labeling industries are struggling to meet the need for more complex and dynamic design requirements coming from the customers. It is now crucial to implement technological advancements to manage workflow, productivity, process optimization, and continual improvement. There has never been a time when the imagery and embellishments of apparel has been more commercially viable as it is now. Images and text are fused directly to fabric by heat transfer printing and labeling. For screen development which is required for heat transfer label mass production, many industries are still using the conventional method of screen development process. A CTS (computer-to-screen) innovates the printing and labeling industries by enhancing workflow, lowering consumable consumptions and chemical usage, speeding up setup, guaranteeing flawless design, and raising the print quality of the producing screens. The study's objective is to assess how CTS machines are used and how they affect existing heat transfer screen development processes in one of Bangladesh's leading printing and labeling companies. The study's primary goal is to highlight and analyze how the use of CTS machines reduces material and operational costs by optimizing the process. Costs for CapEx and OpEx are computed and compared for using CTS technology before and after adoption. Savings data such as material, consumable, and operating cost savings versus depreciation and machine payback period analysis were taken into consideration. It is clear from this study that CTS machines in the printing and labeling industries can guarantee profitability on top of Capital Expenditures.

Haijun Li, Tianqi Shi, Zifu Shang et al.

Machine translation (MT) has become indispensable for cross-border communication in globalized industries like e-commerce, finance, and legal services, with recent advancements in large language models (LLMs) significantly enhancing translation quality. However, applying general-purpose MT models to industrial scenarios reveals critical limitations due to domain-specific terminology, cultural nuances, and stylistic conventions absent in generic benchmarks. Existing evaluation frameworks inadequately assess performance in specialized contexts, creating a gap between academic benchmarks and real-world efficacy. To address this, we propose a three-level translation capability framework: (1) Basic Linguistic Competence, (2) Domain-Specific Proficiency, and (3) Cultural Adaptation, emphasizing the need for holistic evaluation across these dimensions. We introduce TransBench, a benchmark tailored for industrial MT, initially targeting international e-commerce with 17,000 professionally translated sentences spanning 4 main scenarios and 33 language pairs. TransBench integrates traditional metrics (BLEU, TER) with Marco-MOS, a domain-specific evaluation model, and provides guidelines for reproducible benchmark construction. Our contributions include: (1) a structured framework for industrial MT evaluation, (2) the first publicly available benchmark for e-commerce translation, (3) novel metrics probing multi-level translation quality, and (4) open-sourced evaluation tools. This work bridges the evaluation gap, enabling researchers and practitioners to systematically assess and enhance MT systems for industry-specific needs.

Meng Li, Yu Zhang, Leshan Li

The Industry 4.0 refers to a industrial ecology which will merge the information system, physical system and service system into an integrate platform. Since now the industrial designers either conceive the physical part of products, or design the User Interfaces of computer systems, the new industrial ecology will give them a chance to redefine their roles in R&D work-flow. In this paper we discussed the required qualities of industrial designer in the new era, according to an investigation among Chinese enterprises. Additionally, how to promote these qualities though educational program.

Thomas G. Kelly, Mohammad D. Soorati, Klaus-Peter Zauner et al.

Swarm robotics is a study of simple robots that exhibit complex behaviour only by interacting locally with other robots and their environment. The control in swarm robotics is mainly distributed whereas centralised control is widely used in other fields of robotics. Centralised and decentralised control strategies both pose a unique set of benefits and drawbacks for the control of multi-robot systems. While decentralised systems are more scalable and resilient, they are less efficient compared to the centralised systems and they lead to excessive data transmissions to the human operators causing cognitive overload. We examine the trade-offs of each of these approaches in a human-swarm system to perform an environmental monitoring task and propose a flexible hybrid approach, which combines elements of hierarchical and decentralised systems. We find that a flexible hybrid system can outperform a centralised system (in our environmental monitoring task by 19.2%) while reducing the number of messages sent to a human operator (here by 23.1%). We conclude that establishing centralisation for a system is not always optimal for performance and that utilising aspects of centralised and decentralised systems can keep the swarm from hindering its performance.

Surya N Reddy, Vaibhav Kurrey, Mayank Nagar et al.

Proper use of personal protective equipment (PPE) can save the lives of industry workers and it is a widely used application of computer vision in the large manufacturing industries. However, most of the applications deployed generate a lot of false alarms (violations) because they tend to generalize the requirements of PPE across the industry and tasks. The key to resolving this issue is to understand the action being performed by the worker and customize the inference for the specific PPE requirements of that action. In this paper, we propose a system that employs activity recognition models to first understand the action being performed and then use object detection techniques to check for violations. This leads to a 23% improvement in the F1-score compared to the PPE-based approach on our test dataset of 109 videos.

Ashok Urlana, Charaka Vinayak Kumar, Ajeet Kumar Singh et al.

Large language models (LLMs) have become the secret ingredient driving numerous industrial applications, showcasing their remarkable versatility across a diverse spectrum of tasks. From natural language processing and sentiment analysis to content generation and personalized recommendations, their unparalleled adaptability has facilitated widespread adoption across industries. This transformative shift driven by LLMs underscores the need to explore the underlying associated challenges and avenues for enhancement in their utilization. In this paper, our objective is to unravel and evaluate the obstacles and opportunities inherent in leveraging LLMs within an industrial context. To this end, we conduct a survey involving a group of industry practitioners, develop four research questions derived from the insights gathered, and examine 68 industry papers to address these questions and derive meaningful conclusions. We maintain the Github repository with the most recent papers in the field.

Pietro Iob, Mauro Schiavo, Angelo Cenedese

This paper presents a study on transforming a traditional human-operated vehicle into a fully autonomous device. By leveraging previous research and state-of-the-art technologies, the study addresses autonomy, safety, and operational efficiency in industrial environments. Motivated by the demand for automation in hazardous and complex industries, the autonomous system integrates sensors, actuators, advanced control algorithms, and communication systems to enhance safety, streamline processes, and improve productivity. The paper covers system requirements, hardware architecture, software framework and preliminary results. This research offers insights into designing and implementing autonomous capabilities in human-operated vehicles, with implications for improving safety and efficiency in various industrial sectors.

Safoura Zadhossein, Yousef Abbaspour-Gilandeh, Mohammad Kaveh et al.

Drying is one of the common techniques for preserving agri-food product quality. However, for each product, the appropriate drying parameters should be identified to optimize drying quality and energy consumption. The present work aims to explore the performance of a hot air dryer (HAD) to dry cantaloupe (Cucurbita maxima) slices at three temperatures (50, 60, and 70 °C). The effects of drying temperature/duration on drying kinetics, energy, and exergy parameters of cantaloupe slices were investigated. The obtained data indicated a decrease in drying time and specific energy consumption (SEC) with temperature. On the other hand, the effective moisture diffusivity (Deff), energy utilization (EU), energy utilization ratio (EUR), exergy loss, exergy efficiency, exergetic improvement potential (EIP) and sustainability index (SI) increased with temperature. SEC, Deff, EU, EUR, exergy loss, exergy efficiency, EIP, and SI were in the range of 85.48–139.77 MJ/kg, 2.91 × 10−12–6.18 × 10−12 m2/s, 0.0207–0.0925 kJ/s, 0.1951- 0.8703, 0.0088–0.0447 kJ/s, 0.2839–0.9239, 0.0047–0.0117 kJ/s and 3.0880–3.8540, respectively. Moreover, adaptive neuro-fuzzy inference systems (ANFISs) and artificial neural networks (ANNs) were used as two state-of-the-art intelligent algorithms to predict the drying dynamics of cantaloupe slices in HAD and the performance of both methods was found to be reliable (R2 > 0.97). Indeed, ANFIS provided better performance for predicting energy utilization, energy utilization ratio, and exergy loss with R2 values of 0.9919, 0.9961, and 0.9939, respectively. On the other hand, ANN outperformed ANFIS in predicting exergy efficiency and moisture ratio by achieving an R2 value of 0.9999 for both parameters. The authors believe the outcomes of the present study can be used as a framework for choosing efficient drying parameters for drying cantaloupe or similar fruits in HAD systems.

Hui Yang, Stella Hadjiantoni, Yunfei Long et al.

The ability to automatically identify industry sector coverage in articles on legal developments, or any kind of news articles for that matter, can bring plentiful of benefits both to the readers and the content creators themselves. By having articles tagged based on industry coverage, readers from all around the world would be able to get to legal news that are specific to their region and professional industry. Simultaneously, writers would benefit from understanding which industries potentially lack coverage or which industries readers are currently mostly interested in and thus, they would focus their writing efforts towards more inclusive and relevant legal news coverage. In this paper, a Machine Learning-powered industry analysis approach which combined Natural Language Processing (NLP) with Statistical and Machine Learning (ML) techniques was investigated. A dataset consisting of over 1,700 annotated legal articles was created for the identification of six industry sectors. Text and legal based features were extracted from the text. Both traditional ML methods (e.g. gradient boosting machine algorithms, and decision-tree based algorithms) and deep neural network (e.g. transformer models) were applied for performance comparison of predictive models. The system achieved promising results with area under the receiver operating characteristic curve scores above 0.90 and F-scores above 0.81 with respect to the six industry sectors. The experimental results show that the suggested automated industry analysis which employs ML techniques allows the processing of large collections of text data in an easy, efficient, and scalable way. Traditional ML methods perform better than deep neural networks when only a small and domain-specific training data is available for the study.

Ed Felten, Manav Raj, Robert Seamans

Recent dramatic increases in AI language modeling capabilities has led to many questions about the effect of these technologies on the economy. In this paper we present a methodology to systematically assess the extent to which occupations, industries and geographies are exposed to advances in AI language modeling capabilities. We find that the top occupations exposed to language modeling include telemarketers and a variety of post-secondary teachers such as English language and literature, foreign language and literature, and history teachers. We find the top industries exposed to advances in language modeling are legal services and securities, commodities, and investments. We also find a positive correlation between wages and exposure to AI language modeling.

Christoph Nölle, Petri Kannisto

In the pursue for sustainability in process industry, digital twins necessitate the communication and storage of timeseries data about Industrial Internet of Things (IIoT). Regarding timeseries, this paper first presents a set of requirements specific to process industries. Then, it surveys how existing IIoT technologies meet the requirements. The technologies include the API specifications Asset Administration Shell (AAS), Digital Twin Definition Language (DTDL), NGSI-LD and Open Platform Communications Unified Architecture (OPC UA) as well as six commercial platforms. All the technologies leave significant gaps regarding the requirements, which means that tailor-made extensions are necessary.

Bin Hu, Jiawei Wu, Peng Li et al.

The front-row shading reduction coefficient is a key parameter used to calculate the system efficiency of a photovoltaic (PV) power station. Based on the Hay anisotropic sky scattering model, the variation rule of solar radiation intensity on the surface of the PV array during the shaded period is simulated, combined with the voltage–current characteristics of the PV modules, and the shadow occlusion operating mode of the PV array is modeled. A method for calculating the loss coefficient of front shadow occlusion based on the division of the PV cell string unit and Hay anisotropic sky scattering model is proposed. This algorithm can accurately evaluate the degree of influence of the PV array layout, wiring mode, array spacing, PV module specifications, and solar radiation on PV power station system efficiency. It provides a basis for optimizing the PV array layout, reducing system loss, and improving PV system efficiency.

Maria C. G. Hart, Sarah Eckhoff, Michael H. Breitner

Abstract With rising electricity demand through digitization and innovation, the urgency of climate change mitigation, and the recent geopolitical crisis, stakeholders in developing countries face the complex task to build reliable, affordable, and low-emission energy systems. Information inaccessibility, data unavailability, and scarce local expertise are major challenges for planning and transitioning to decentralized solutions. Motivated by the calls for more solution-oriented research regarding sustainability, we design, develop, and evaluate the web-based decision support system NESSI4D web+ that is tailored to the needs and capabilities of various stakeholders in developing countries. NESSI4D web+ is open access and considers location-specific circumstances to facilitate multi-energy planning. Its applicability is demonstrated with a case study of a representative rural village in southern Madagascar and evaluated through seven interviews with experts and stakeholders. We show that NESSI4D web+ can support the achievement of the United Nations Sustainable Development Goals and enable the very prerequisite of digitization: reliable electrification.

Patrícia Alves Ramiro

Este artigo apresenta considerações teórico-metodológicas sobre pesquisa referente à reconversão social de famílias da condição de moradores em terras de usina, tanto trabalhadores agrícolas quanto operários rurais, para a de assentados de reforma agrária. Trata-se de objeto em construção que, baseado na situação empírica da falência da usina Santa Maria, na região do Brejo paraibano, aponta para a questão da reconstrução dos mundos subjetivos, imposta pela nova situação vivida, através do conceito de “desenraizamento” e dos meios de sua superação, centrais na obra de Pierre Bourdieu. elocation-id: e2230208 Recebido: 10.jul.2022 • Aceito: 19.out.2022 • Publicado: 7.dez.2022 Artigo original / Revisão por pares duplo-cego / Acesso aberto

Isnaini Rahmadi, Sugiyono Sugiyono, Nugraha Edhi Suyatma

Ketupat is an Indonesian indigenous food made from rice which is wrapped by young coconut leaves and cooked into the boiling water. Storage temperatures might affect the physicochemical and microbiological characteristics of ketupat. The objective of this research was to observe the effect of 2 rice varieties with different amylose content and the percentage of ketupat filling on the change of physicochemical and microbiological characteristics of ketupat during storage at cold temperature (T= 5 oC; RH= 19,2 %). Ketupat was produced by using two rice varieties, namely IR 64 and Pandan Wangi. The percentage of ketupat filling resulted in higher ketupat hardness during storage. In addition, the hardness of ketupat increased during storage. The percentages of ketupat filling did not affect pH and aw of the ketupat during storage; however, the higher the percentages of ketupat filling resulted in the lower water content. The water content and pH did not change during storage. Ketupat stored at cold temperature was able to maintain the microbiological quality until the 9th day.

Nkwetta Ajong Aquilas, Johannes Tabi Atemnkeng

The global increasing trend in total greenhouse gas emissions in recent decades has triggered the need for climate-related development financing. This study analyzes the effects of climate-related development mitigation finance and renewable energy consumption on greenhouse gas emissions in the Congo Basin. Using panel data from 2002 to 2020, panel regression estimates empirically reveal (1) a minimal significant increase in greenhouse gas emissions with respect to an increase in climate-related development mitigation finance (2) An increase in climate-related mitigation finance significantly promotes the consumption of renewable energy (3) An increase in renewable energy consumption reduces greenhouse gas emissions (4) An increase in renewable energy consumption reduces the effect of climate-related mitigation finance on greenhouse emissions. This study suggest the continuous flow of climate-related development mitigation finance from donor countries and bodies to developing countries on a more regular basis and the putting in place of a mechanism that tracks climate funds to ensure that they are effectively used in generating renewable energies such as solar, hydroelectric, biomass, wind and geothermal energies.

Ayan Chatterjee, Bestoun S. Ahmed, Erik Hallin et al.

Today, machine learning (ML) is widely used in industry to provide the core functionality of production systems. However, it is practically always used in production systems as part of a larger end-to-end software system that is made up of several other components in addition to the ML model. Due to production demand and time constraints, automated software engineering practices are highly applicable. The increased use of automated ML software engineering practices in industries such as manufacturing and utilities requires an automated Quality Assurance (QA) approach as an integral part of ML software. Here, QA helps reduce risk by offering an objective perspective on the software task. Although conventional software engineering has automated tools for QA data analysis for data-driven ML, the use of QA practices for ML in operation (MLOps) is lacking. This paper examines the QA challenges that arise in industrial MLOps and conceptualizes modular strategies to deal with data integrity and Data Quality (DQ). The paper is accompanied by real industrial use-cases from industrial partners. The paper also presents several challenges that may serve as a basis for future studies.