Development Of Technology Research Articles

Indonesian news article authorship attribution multilabel multiclass classification using IndoBERT

<p class="p1">Recent developments in technology have made it easier to produce digital con- tent, especially textual articles. But, it has a negative impact in the form of a rising public skepticism of digital data due to plagiarism. Indonesia, one of the world’s most populous countries, is not resistant to this problem. To resolve it, the authorship attribution (AA) task must be executed. However, there has been little investigation on AA for Indonesian articles. As a result, this research applies the AA task to an Indonesian digital news articles dataset. Continuing the previous research, dataset modification was carried out to increase data com- plexity by adding a new class, namely the author’s gender, and also by balancing the distribution of data versus labels to minimize potential overfitting, and model hyper-parameter configurations were carried out to enhance the results gained. This research successfully applied the IndoBERT model to the Indonesian AA task, yielding results in the form of precision = 0.92, recall = 0.90, and F1-score = 0.91. These results indicate that the Indonesian AA task has a lot of potential for development since it identifies writing patterns that may benefit the forensic field, detect plagiarism, and analyze Indonesian texts.</p>

Conservation agriculture measures as a strategy to create sustainable social and psychological changes in agricultural communities

The main aim of this study was to investigate the potential of encouraging intention to use conservation agriculture technologies to create sustainable social and psychological changes in agricultural communities. The study was carried out using a cross-sectional survey using 384 Iranian landowner farmers as the sample. The research instrument was a researcher-made questionnaire. Furthermore, its validity and reliability were evaluated using different quantitative and qualitative indices. The results revealed that the basic variables of Theory of Planned Behavior including attitude towards conservation agriculture (ACA), subjective norms of conservation agriculture (SNCA), and perceived behavioral control on conservation agriculture (PBCCA) positively and significantly affected the intention towards application of conservation agriculture technologies and practices (IACATP). Comparison of the original and extended versions of Theory of Planned Behavior showed that the inclusion of the variables perceived risk conventional agriculture (PRCA), knowledge about conservation agriculture (KCA), level of education, farm size, and income level in the original Theory of Planned Behavior could increase its explanatory power from 66.4% to 77.3%. This result was one of the most important contributions of the present study that can be considered a turning point for social change interventions in rural and agricultural communities. The study also led to new insights into the mechanisms of encouraging IACATP and social-psychological changes in agricultural communities. These innovative insights can be used by different users such as governmental policy-makers, technology developers, social change interventionists, environmental decision-makers, and researchers.

Techno-economic performance analysis of biomass-to-methanol with solid oxide electrolyzer for sustainable bio-methanol production

The analysis of the technical and economic performance of an integrated biomass to methanol and solid oxide electrolysis process (BtM-SOEC) is studied to find more sustainable process of bio-methanol production. The oil palm empty fruit branch (EFB) which is abundant in Thailand is used as biomass feedstock. Modeling of the BtM-SOEC is done using Aspen Plus. For technical aspects, the production rate of oxygen and hydrogen from the SOEC can be enhanced through an appropriate adjustment of the number of cells and cell temperature. The BtM-SOEC offers higher methanol yield and overall efficiency, while consumes less energy than the conventional biomass to methanol process (BtM). The maximum methanol production rate of 0.4995 kmol h-1 derived from BtM-SOEC is achieved at a number of cells of 325 cells and a cell temperature of 700 oC, at this condition the overall efficiency is 64.79 %. The economic assessment indicates that the conventional BtM and BtM-SOEC are still not economically feasible. However, the conventional BtM is more economically feasible than the BtM-SOEC. The methanol cost of BtM-SOEC can turn out to be economically feasible when renewable electricity cost and SOEC cost decrease substantially. The methanol cost of the BtM-SOEC (620 USD ton-1) can be competitive to that of the BtM (703 USD ton-1) when the cost of input renewable electricity decreases by 80%. Consequently, this research highlights the potential of BtM-SOEC from agricultural residues for sustainable bio-methanol production in the future market condition that the cost of renewable electricity tends to continuously decrease with the technology development and increased technology adoption and the carbon policy tends to be tightened to relieve global warming.

Recent Advancements in Bioelectronic Medicine: A Review.

Bioelectronic medicine is a multidisciplinary field that combines molecular medicine, neurology, engineering, and computer science to design devices for diagnosing and treating diseases. The advancements in bioelectronic medicine can improve the precision and personalization of illness treatment. Bioelectronic medicine can produce, suppress, and measure electrical activity in excitable tissue. Bioelectronic devices modify specific neural circuits using electrons rather than pharmaceuticals and uses of bioelectronic processes to regulate the biological processes underlining various diseases. This promotes the potential to address the underlying causes of illnesses, reduce adverse effects, and lower costs compared to conventional medication. The current review presents different important aspects of bioelectronic medicines with recent advancements. The area of bioelectronic medicine has a lot of potential for treating diseases, enabling non-invasive therapeutic intervention by regulating brain impulses. Bioelectronic medicine uses electricity to control biological processes, treat illnesses, or regain lost capability. These new classes of medicines are designed by the technological developments in the detection and regulation of electrical signaling methods in the nervous system. Peripheral nervous system regulates a wide range of processes in chronic diseases; it involves implanting small devices onto specific peripheral nerves, which read and regulate the brain signaling patterns to achieve therapeutic effects specific to the signal capacity of a particular organ. The potential for bioelectronic medicine field is vast, as it investigates for treatment of various diseases, including rheumatoid arthritis, diabetes, hypertension, paralysis, chronic illnesses, blindness, etc.

Environmental assessment of gasification and green hydrogen potential role in waste management decarbonization

Achieving sustainability involves implementing a circular economy model and decarbonizing the waste management sector. The development of innovative technologies that enable the reduction of emissions and resource recovery is one of the sector's greatest challenges. From this perspective, the waste-to-methanol (WtM) process represents a viable prospect, the potential environmental impacts of which have never been studied from a life-cycle perspective. A cradle-to-grave LCA analysis was conducted, studying WtM technology and its potential implementation of green hydrogen (WtM-GH) in the process, through a comparison with traditional waste-to-energy (WtE).The results showed that WtM-GH performs best for global impacts, with a global warming potential of 3.52·107 kgCO2eq instead of 1,04·108 kgCO2eq proper of the WtE. Positive effects take place also for the fossil resource scarcity. Looking for non-toxic regional/local impacts WtE represents the best scenario, with the best performance on water consumption (7.96·107 m3) and ionising radiation (2.82·107 kBq Co-60eq). Cumulative impact results, obtained through the normalization, found that WtM is the best scenario, thanks to the less toxic impact compared to the other two, especially for freshwater ecotoxicity (5.26·106 kg 1,4-DCB) and marine ecotoxicity (6.73·106 kg 1,4-DCB).WtM-GH allows the production of 1366 kg of methanol per ton of Refuse Derived Fuel, thanks to the conversion of the entire CO2 contained in the waste, enhancing the value of by-products and carbon credits on the market and making the process more economically sustainable.

In a search of the single-atom electrocatalysts for hydrogen production: the first sulfur-free mono- and diphenanthrenyl-terminated iron and cobalt(II) clathrochelates versus their thioanalogs

This paper reports on green chemistry approaches to the molecular design and synthesis of cheap, efficient and eco-friendly electrocatalysts of the hydrogen evolution reaction (HER). The title clathrochelates (including first those do not containing sulfur derivatives as “catalytic poisons”) were prepared using nucleophilic substitution of their chloroclathrochelate precursors and characterized by analytical, spectral and XRD methods. These complexes showed the HER 2H+/H2 electrocatalytic activity in the solutions. They form the Langmuir monolayers and possess a high physisorption on activated carbon (AC, up to 0.55mmol·g–1) and reduced graphene oxide (RGO, up to 0.33mmol·g–1). Contrary that on carbon paper (CP) is very low. Therefore, AC- or RGO-containing clathrochelate-immobilized components are suitable for preparation of hybrid CP-based cathodes, allowing to substantially increase a surface concentration of electrocatalytically active centers up to 0.5 μmol·m–2. Cyclic voltammetry data suggest that the electrochemically generated cobalt(I) complexes, as the catalytically active intermediates, are stable and most prospective candidates for electrocatalytic hydrogen production. Clathrochelate-based single-atom catalysts were prepared in accordance with basic principles of green chemistry. They are derivatives of abundant and cheap 3d-biometals and low-toxic α-dioximes and possess an extremely high atomic utilization efficiency matching the “economy of atoms” principle. Nowadays, the carbon-supported metallic platinum is used as HER catalyst. The reserves of this noble and expensive metal on the Earth are limited and its replacement by such cheap and abundant HER materials will accelerate the development and implementation of green hydrogen-producing technologies. The recommendations on chemical structures of optimal molecular electrocatalysts were evaluated.

Interactions between migration and immunity among oriental armyworm populations infected with the insect pathogenic fungus, Beauveria bassiana.

Migration and immunity are behavioral and physiological traits that protect organisms from environmental stressors or pathogen infection. Shifting from migration to residency has become more common in some wildlife populations owing to environmental changes. However, other biological shifts, such as interactions between migration and immunity among populations within a species are largely unexplored for many agricultural migratory pests. In the field, entomopathogenic fungi infection and transmission, particularly Beauveria bassiana, can cause reduced fitness and population declines across a broad range of insect species. Here, we investigated migration-immunity interactions between migrant and resident populations of the oriental armyworm, Mythimna separata, infected with B. bassiana (the sole fungus used in this work). We found that migratory M. separata exerted stronger pathogen resistance, faster development and lower pupal weight than residents. High-dose infections (5.0 × 105 and 5.0 × 106 conidia mL-1) led to seriously decreased reproductive capacity in migrants and residents. Low-dose infections (1.0 × 104 and 5.0 × 104 conidia mL-1) led to significantly increased host flight capacities. Consecutive flight tests showed that five flight nights inhibited the reproduction of paternal infected M. separata populations. The flights also led to far-reaching transgenerational impairment of larval development and immune defense among offspring populations. By contrast, two flight nights enhanced the reproductive capacities of both M. separata populations and did not exert negative transgenerational effects on offspring populations, which may facilitate migration. This study provides insights into interactions between migration and immunity among M. separata populations. These insights will guide development of future monitoring and management technologies of this pest. © 2024 Society of Chemical Industry.

Biodegradable microplastics induce profound changes in lettuce (Lactuca sativa) defense mechanisms and to some extent deteriorate growth traits

The development of agricultural technologies has intensified the use of plastic in this sector. Products of plastic degradation, such as microplastics (MPs), potentially threaten living organisms, biodiversity and agricultural ecosystem functioning. Thus, biodegradable plastic materials have been introduced to agriculture. However, the effects of biodegradable plastic substitutes on soil ecosystems are even less known than those of traditional ones. Here, we studied the effects of environmentally relevant concentrations of MPs prepared from a biodegradable plastic (a starch-polybutylene adipate terephthalate blend, PBAT-BD-MPs) on the growth and defense mechanisms of lettuce (Lactuca sativa) in CLIMECS system (CLImatic Manipulation of ECosystem Samples). PBAT-BD-MPs in the highest concentrations negatively affected some traits of growth, i.e., dry weight percentage, specific leaf area, and both C and N contents. We observed more profound changes in plant physiology and biochemistry, as PBAT-BD-MPs decreased chlorophyll content and triggered a concerted response of plant defense mechanisms against oxidative stress. In conclusion, exposure to PBAT-BD-MPs induced plant oxidative stress and activated plant defense mechanisms, leading to oxidative homeostasis that sustained plant growth and functioning. Our study highlights the need for in-depth understanding of the effect of bioplastics on plants.

UiO-67 metal-organic framework loaded on hardwood biochar for sustainable management of environmental boron contaminations

Boron contamination in water poses significant potential risks to human health and the environment, necessitating the development of efficient, cost-effective, and sustainable remediation technologies. This study introduces a novel composite material combining a zirconium-based metal-organic framework (UiO-67) and a low-cost carbonaceous material (hardwood biochar, BC) with synergetic efficiency to address boron-polluted waters. The UiO-67-biochar (UBC) composite exhibits effective surface chemistry and a remarkably high specific surface area of approximately 881.9m²/g, substantially increasing from the 19.7m²/g of biochar. Our experimental results demonstrate that UBC removed up to 88.5% of boron from 20 ppm polluted water, achieving levels compliant with the WHO standards. The composite also showed excellent reusability, maintaining 95% efficiency over multiple cycles without loss of crystallinity. Life cycle assessment and cost analysis indicate that an optimal MOF to biochar ratio of approximately 60wt% minimises CO2 emissions and costs while maximising the boron removal efficiency. The UiO-67-biochar composites proposed here offers a promising scalable solution for boron contamination and potentially other environmental pollutants, combining the high functionality of UiO-67 with the practical and economic advantages of biochar.

Improving performance of air quality monitoring: a qualitative data analysis

This research aims to improve performance of air quality monitoring and understand the latest relevant technological developments. Employing the Kitchenham systematic literature review (SLR) method, the study examines 436 journal articles and conference proceedings published from 2019 to 2023, sourced from the Web of Science (WoS) and Scopus databases. The analysis was carried out using Leximancer 5.0 and identified research five themes; i) air quality, ii) artificial intelligence (AI), iii) pollution, iv) middleware, and v) smart environment. The results showed that only 48 journals had strict inclusion and exclusion criteria include relevance to the research theme, methodological quality, and contribution to the research field. In addition, this research integrates AI and middleware, which has significantly contributed to improving air quality. These findings can become the basis for the development of air quality monitoring technology that is more sophisticated and responsive to environmental needs. This research contributes to further understanding air quality monitoring technology trends and designing solutions to improve overall air quality.

Physicochemical properties of acid sulphate soil profoundly influence the composition of rhizobacterial community of rice (Oryza sativa L.)

The soils of kari lands of Kuttanad, the ‘Rice bowl’ of Kerala, India are characterized as acid sulphate as they comprise of pyrite deposits. Productivity in these soils is at stake due to several constraints like high acidity and salinity, metal toxicity, nutrient unavailability, redox fluctuations, besides seasonal flooding. Sustainable management of acid sulphate soil is a critical priority to improve the rice output from these areas. Such soils would harbor unique innate microbial communities with definite abilities which could be exploited further for their sustainable amelioration. Development of inoculant technology with soil and crop specific beneficial microbial agents is expected to boost the production potential of kari soils. However, the soil parameters would impose a great influence on the rhizobacterial community development in these geologically distinct soils. We studied the rhizobacterial communities (at the family level), associated with rice grown in five acid sulphate (Purakkad, Vaikom, Ambalappuzha, Thakazhi and Kallara) as well as one non-acid sulphate (Muhamma) soil series of geographically unique Kuttanad region. We also examined the effects of soil physicochemical attributes on shaping the rhizosphere bacterial community assemblage. The soil physicochemical attributes were analyzed using standard procedures and correlations existing amongst them were also determined. A metagenomic approach was adopted to study the rhizobacterial communities (family level) and were correlated with soil parameters using canonical correspondence analysis (CCA). Compared to other acid sulphate regions, Thakazhi and Kallara soils indicated higher electrical conductivity, available nitrogen, potassium, organic carbon, aluminium as well as iron and lowest pH and available phosphorus. Intense significant relationships were exhibited amongst the acid sulphate properties and soil nutrient contents. The taxa summary after the Illumina MiSeq sequencing revealed the abundant rhizobacterial families in the soil samples as Anaerolineaceae, Ktedonobacteriaceae, Acidothermaceae, Acidimicrobiaceae, Clostridiaceae, Nocardioidaceae, Xanthobacteraceae, Methanobacteriaceae, Sphingomonadaceae and Peptostreptococcaceae. Acidothermaceae (14%) and Acidimicrobiaceae (12%) were found abundant exclusively in highly acid sulphate soil samples. Moreover, only a few shared taxa were observed between the soil samples, which denoted the uniqueness of each sample in terms of rhizobacterial communities. The shared taxa between highly acidic sampling areas include members of Acidothermaceae, Ktedonobacteraceae, Acidimicrobiaceae, Micrococcaceae, Stellaceae and Anaerolineaceae. CCA showed that pH, EC and Al content were the soil properties governing the bacterial assembly in the rhizosphere of actively tillering rice grown in acid sulphate soil followed by P and K. The data generated in the present study are considered to be of use in developing consortia of kari soil specific rice rhizosphere microbial agents which could be used as inoculants in future.

Grouting seepage characteristics in microfracture under triaxial stress conditions: Device design and experimental study

Water plugging by grouting in rock microfracture is a technical challenge in deep underground engineering. In this paper, a novel grouting test system was developed, which can simulate microfracture grouting tests under triaxial stress conditions. A specimen preparation method that can monitor the seepage and diffusion of microfine cement slurry in microfracture was also proposed. Microfracture grouting model tests were conducted on sandstone-like specimens to investigate the effects of confining pressure, roughness, and microfracture aperture on the seepage and diffusion characteristics of sandstone-like specimens. The test results show that microfine cement slurry can effectively seal the microfracture. However, when the microfracture aperture is small or the roughness is large, the slurry is easy to gather and deposit at the inlet, resulting in the phenomenon of "seepage filtration", which makes it difficult for the slurry to be injected into the microfracture. The pressure at the measuring point gradually decreases along the seepage and diffusion direction of the slurry, and the decreasing trend increases with the increase of microfracture roughness. The nonlinear relationship between the slurry volume flow rate and the hydraulic gradient can be described by Forchheimer's law. Moreover, both coefficients a and b in Forchheimer's equation show a gradual increase with increasing microfracture roughness. This study can provide a meaningful reference for the development of rock microfracture grouting technology in deep engineering.

Development of a hydroponic device using potassium Ion-Selective electrode and neural network technology

The rapid development of modern agricultural biotechnology and information technology has become a key driver of modern agricultural growth. The demand for ion detection is prevalent in all aspects of agriculture. This study details the research on an ion sensor agricultural system using potassium (K) ion-selective electrodes (ISEs). The main emphasis is on the application of artificial neural networks (ANNs) for driving ISE development of accuracy. ISEs based on solid contact with metal oxides, specifically manganese dioxide, are studied. Moreover, the design details of an ion signal acquisition device using the NI-9205 acquisition card and LabVIEW are presented. Finally, based on experimental results of nutrient solution samples, ANNs show better performance than the multiple linear regression method. The mean relative error of K-ISE detection was maintained within 4%. This study thoroughly discusses ISE technology to promote its adoption in agriculture.

Using Patient-Generated Health Data more efficient and effectively to facilitate the implementation of Value-Based Healthcare in the EU – Innovation Report

Healthcare services and products are rapidly changing due to the development of new technologies, offering relevant solutions to improve patient outcomes. Patient-Generated Health Data and knowledge-sharing across the European Union (EU) has a great potential of making healthcare provision more effective and efficient by putting the patient at the centre of the healthcare process. While such initiatives have been taken before, a uniting and overarching approach is still missing. The EU-funded IMPROVE project will develop an evidence-based and actual framework to effectively leverage the added value of people-centred integrated healthcare solutions, using predominantly PROMs, PPI, PREMs, and other Patient-Generated Health Data (PGHD). As a result, the project facilitates the effective and efficient implementation of Value-Based Healthcare across the EU by putting the patient central in the healthcare process.

A systematic review on internet addiction through various assessment tools among chinese college student’s context

With the rapid development of information technology, the internet has evolved into an essential tool in everyday life. The global proportion of internet users has consistently risen in recent years. Notably, China boasts substantial internet users, with students representing the largest demographic, especially college students. The purpose of this study is to explore the issue of internet addiction among Chinese college students through various assessment tools. This study conducted a systematic literature review with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. A total of 409 publications were screened, and 23 were deemed eligible for inclusion in the review. The result revealed that there is no gold standard for evaluating internet addiction. Five types of instruments have been identified, and reported prevalence rates vary due to distinct assessment instruments and thresholds, spanning from 6.81% to 54.86%. Furthermore, internet addiction is associated with four factors among Chinese college students. Therefore, this study furnishes epidemiological evidence to support the prevention of internet addiction and the enhancement of mental health among college students.

Design and implementation of a charge-sharing in-memory-computing macro with sparse feature for quantized neural network

With the rapid development of artificial intelligence technology, in-memory computing has become a research hotspot. In this article, we propose an in-memory computing (IMC) architecture that achieves high energy efficiency and performance. Our work is based on the working mechanism of charge sharing, enabling configurable multi-bit Multiply-Accumulate operations. This work employs a unique bit-cell structure to implement sparse strategies at the bit-level in IMC arrays and compensates for errors caused by non-ideal effects, thus achieving better energy efficiency and performance. A hardware-aware quantification method and a hardware simulation model based on Pytorch have been proposed to evaluate the hardware mapping and compare with other charge domain IMC works. The MNIST and CIFAR-10 datasets have been used to validate algorithm models and chip performance, achieving accuracy rates of 97.6% and 90.5%respectively. The IMC chip was fabricated with a 180 nm CMOS process. The measurement shows that the chip achieves an energy efficiency of 41.8 TOPS/W.

Carbonic anhydrase activity and metabolite variation of different microalgae species at alkaline pHs

A significant increase in atmospheric greenhouse gases over the last century has led to the development of several methods and technologies to remove carbon dioxide (CO2). Microorganisms produce carbonate minerals through the natural mineralization of CO2; however, the feasibility of this process remains in research. This work aimed to study the cultivation of different microalgae under alkaline pH to maintain their potential for carbon mitigation. According to the results, the highest carbonic anhydrase activity has been reached (4.64 mg/g) for Chlamydomonas reinhardtii at a pH of 10. C. reinhardtii, at a pH of 9.5, yielded the highest chlorophyll content (23.58 mg/g), while Spirulina at a pH of 8.5 produced the highest biomass (882.9 mg/L). Also, a positive correlation existed between pH and lipid content for C. reinhardtii. Spirulina, however, exhibits the opposite effect. According to a principal component analysis, there is an opposite relationship between pH and carbonic anhydrase (CA) activity for C. reinhardtii and filamentous-type cyanobacteria from Salda Lake. The following order of the suitability of the microalgae species for high carbon capture is determined by the Analytic hierarchy process method: Spirulina>C. reinhardtii > Chlorella vulgaris > Coccus-type cyanobacteria > Filamentous-type cyanobacteria from Salda Lake. Additionally, this study provided important results regarding the cyanobacteria species isolated from an alkaline lake, Lake Salda. This would contribute to future studies of carbon capture and carbon mitigation mechanisms.

Neem (Azadirachta indica A. Juss; Meliaceae) as the source for plant-based pesticides as an effective and sustainable biocontrol alternative

The neem tree has strengths such as effective and environmentally friendly active compounds, as well as the ability to improve the quality and productivity of plants organically. However, the use of botanical pesticides has several challenges, such as potentially lower effectiveness compared to chemical insecticides, high production costs, and requiring specific knowledge in formulation and application. Botanical pesticides are readily available tools for insect pest management and are eco-friendly. The research results show that a neem formula at a concentration of 20 ml.l-1 water can cause mortality of brown planthopper is 48.75 - 70%, while mortality in the synthetic insecticide (positive control) at a concentration of 2 ml.l-1 water (as recommended) resulted in 45% mortality. It is obvious that there is a need to develop biological control as alternatives for eco-friendly and sustainable for pests’ control in insect’s pest management strategies. This research study has both qualitative and quantitative approach, data collection strategies were desk review of information and will be based on empirical data and literature. For the desk study, relevant internet web pages were browsed to have an overview of the key concepts of the research issue, problem of pests and diseases control and benefit of neem tree as botanical pesticides. Nevertheless, the use of botanical pesticides can provide long-term benefits to the environment and human health and support more sustainable agriculture. There is a great opportunity for the development of more effective and affordable technology for botanical pesticide production for farmers, but there are also threats such as strong competition from synthetic pesticides. The objective this study aims to determine the potential of neem (Azadirachta indica A.Juss) as a plant producing botanical pesticides as an effective and sustainable biological control alternative.

Performance prediction of 304 L stainless steel based on machine learning

With the development of testing technology and data mining methodologies, machine learning (ML) has been widely applied for predicting the performance of materials in various systems including stainless steel with improved performance. To address the limitations of traditional experimental and statistical methods in predicting the thermal deformation of materials, a predictive machine learning model was developed based on the data obtained from thermal simulation compression tests. Specifically, the Arrhenius and the Modified Johnson-Cook (J-C) Constitutive Model were developed utilizing experimental data to initially predict the rheological behavior of 1.52% Cu-304L stainless steel. Then, a physical metallurgy (PM)-guided Back Propagation (BP) model was developed, and Genetic Algorithm (GA), Whale Optimization Algorithm (WOA), and Mind Evolutionary Algorithm (MEA) were incorporated into the model for optimization purposes, respectively. Finally, the results indicate that the PM-MEA-BP model exhibits superior predictive performance, accurately forecasting the texture evolution of 304L stainless steel with random crystal orientation under rolling deformation conditions. Additionally, the present work also clearly demonstrated that the model not only satisfies precision requirement but also has certain guiding significance for optimizing process parameters and forecasting the microstructure and properties of stainless steel.

A strategic insight into the market for carbon management capacity

This study presents the market for carbon management capacity via carbon capture, utilization, and storage technologies, identifying demand and supply forces, as well as clarifying the potential impact of market and non-market-based shocks on technology developers versus adopters. The paper addresses a prevailing gap in market analysis, introducing a microeconomic framework and unique dataset to identify key players, market forces, and policy incentives shaping the carbon capture, utilization, and storage landscape. The analysis equips industry stakeholders, policymakers, and investors with valuable insights regarding (1) leaders in the design, development, and manufacture of carbon capture, utilization, and storage technologies (supply), (2) early technology adopters (demand) who are at the forefront in the pursuit of sustainable carbon management, and (3) the need to use both market and non-market forces to achieve decarbonization across multiple sectors.