Innovative Components Research Articles

Experimental study on the impact of hybrid GFRP composites with graphene oxide and silicon carbide fillers on mechanical and wear properties

Recently, there has been a rapid increase in the demand for manufacturing reliable and innovative components. FRP composites are ideal for various engineering applications due to their low cost, lightweight, high strength-to-weight ratio, mechanical properties, and heat resistance. This research focuses on fabricating GFRP composites with graphene oxide (GO) and silicon carbide (SiC) fillers using the hand-layup method. Mechanical tests were performed using UTM, and wear testing was conducted through fretting wear analysis. The outcomes reveal that the optimal mechanical characteristics, including tensile strength, flexural strength, and short beam strength (SBS), were 242.73 MPa, 317.13 MPa, and 40.05 MPa, respectively, at a 0.5 wt.% GO filler concentration. The laminate composite achieved a maximum tensile and flexural modulus of 8.03 GPa and 13.59 GPa correspondingly with the incorporation of 0.5% GO micro-fillers to the epoxy matrix with respect to the NE composite, due to improved interfacial bonding between the matrix and filler. Additionally, the best wear resistance in the laminate composite was observed with a hybrid micro-filler combination of 1% SiC and 1% GO introduced into the polymer matrix, leading to minimal material removal from the GFRP laminate composite. The worn surfaces of the composites were analyzed using (FE-SEM) to explore potential wear mechanisms. It was revealed that during the initial phases of wear, the participation of the fillers plays a substantial role in the GFRP composite. The introduction of hybrid fillers into composite renders it an encouraging material for industrial purposes requiring exalted strength and exceptional wear resistance.

Enhanced multi-modal abdominal image registration via structural awareness and region-specific optimization

Multi-modal abdominal image registration is a critical step in achieving accurate diagnosis and treatment. However, the large field of view, complex organ structures, and significant anatomical variability in the abdominal region pose substantial challenges to precise multi-modal registration. To address these challenges, we propose a novel framework based on structural awareness and region-specific optimization, which progressively enhances registration accuracy through a hierarchical strategy. The core innovation lies in introducing the Modality Independent Structural Awareness (MISA) loss, which comprises two key components: first, it ensures structural consistency across modalities by deeply understanding the critical anatomical structures; second, it employs a region-specific weighting strategy to prioritize key anatomical regions, such as organ boundaries, thereby achieving high-precision, region-specific constraints in multi-modal registration. Experimental results demonstrate that our method outperforms state-of-the-art approaches on two public multi-modal abdominal datasets, particularly in accurately capturing complex anatomical structures and maintaining tissue consistency. Further analysis confirms that the MISA loss and its two innovative components are pivotal in enhancing multi-modal abdominal image registration accuracy.

Unsupervised prototype self-calibration based on hybrid attention contrastive learning for enhanced few-shot action recognition

The collection and annotation of large-scale video data pose significant challenges, prompting the exploration of few-shot models to recognize unseen actions with limited training samples. However, most existing models rely on complex temporal alignment strategies, neglecting the necessity of constructing accurate class prototypes in action recognition. To address this limitation, we propose an unsupervised prototype self-calibration based on hybrid attention contrastive learning (UPSHC), aiming to refine prototypes through various strategies. Our network comprises three innovative components: a hybrid attention network (HAN), a dual adaptive contrastive learning mechanism (DACL), and an unsupervised prototype self-calibration mechanism (UPSC). The HAN integrates self-attention and cross-attention mechanisms to highlight important spatiotemporal regions, reducing intra-class variations and enhancing inter-class variations. The DACL mechanism constructs prototype-centered and query-centered contrastive losses to deeply explore the similarities between prototypes and samples, improving the model’s sensitivity to subtle inter-class differences. Additionally, DACL employs adaptive margins to dynamically adjust the distances between positive and negative samples, mitigating the unreliability of fixed margins. The UPSC module utilizes unlabeled query samples in an unsupervised manner to optimize target prototypes without requiring additional training. Experimental results on three benchmark datasets demonstrate that UPSHC achieves state-of-the-art performance, highlighting the superiority of our prototype enhancement approach in few-shot action recognition.

Prospects of digital scientific publishing on blockchain: The concept of DAP

Background Traditional publishing models, open access and major publishers, cannot adequately address the key challenges of academic publishing today: Speed of peer review, recognition of work and incentive mechanisms, transparency and thrust of the system. Methods To address these challenges, the authors propose Democratisation of Academic Publishing (DAP) platform, which is based on the novel HashNET DLT platform. The DAP introduces several innovative components: tracking the activities of all participants in the peer review process using blockchain and smart contracts, the introduction of the Scholarly Wallet for holding reputation (non-fungible) and reward (fungible) tokens, the use of the Scholarly Wallet as the main interface to the DAP platform, the Virtual Editor that enables automatic discovery of the research area and invitation of reviewers, and finally the global database of evaluated reviewers, ranked by the quality of their previous work. Results The DAP platform is in the development phase, with the design and functionalities of all modules defined. An exception is the central component of DAP, the Scholarly Wallet module, whose first prototype has already been created, tested and published. The implementation of DAP is planned for the next phase of the HorizonEurope TruBlo project and other research initiatives. The DAP platform will be connected to the publishing ecosystem: 1) as a backend system (distributed blockchain database) for existing publishing platforms and 2) as a standalone publishing platform with its own API interface. Conclusions The authors believe that DAP has the potential to significantly improve academic peer review and knowledge dissemination. It is expected that the use of blockchain technology, the fast HashNET consensus platform and tokens for reward (fungible) and reputation/ranking (non-fungible) will lead to a more efficient and transparent way of rewarding all participants in the peer review process and ultimately advance scientific research.

Standard Essential Patents and Abuse of Dominance: Implications for India's Telecom Sector

The rapid advancement of technology has positioned Standard Essential Patents (SEPs) as a critical component of innovation in the telecommunications sector, particularly in India. SEPs are patents that cover inventions essential to industry standards, facilitating interoperability and ensuring effective communication between devices. However, the significant market power held by SEP holders raises important concerns regarding the potential abuse of dominance, especially when these entities impose excessive licensing fees or engage in discriminatory practices. This paper examines the intersection of SEPs and competition law in India, focusing on the legal challenges faced by the Competition Commission of India (CCI) in regulating SEP holders under the Competition Act, 2002. By analyzing the regulatory framework and its implications for market dynamics, this study highlights the necessity for a balanced approach that protects both intellectual property rights and competitive practices. The findings suggest that while existing legal mechanisms aim to address abuses of dominance, there is a pressing need for clearer guidelines and stronger enforcement to ensure that SEPs foster innovation without stifling competition in India's rapidly evolving telecom landscape.

The Development Instructional Model Based on Steam Education by TP-SMART MODEL to Enhance Technological Innovation and Creativity Skills of Secondary Student Mathayom 6

The Development Instructional Model Based on Steam Education by TP-SMART MODEL To Enhance Technological Innovation and Creativity Skills of Secondary student Mathayom 6, divided into 4 steps. Step 1: Study the current and expected conditions of student learning and innovation skills development. 2) Develop the model 3) Study the results of use 4) Evaluate the results. Sample group used in the study Mathayom 6/4 students at That Phanom School, Semester 1, academic year 2023, 1 classroom, 26 people. By means of Cluster Random Sampling, statistics used include mean (X), standard deviation (S.D.), essential needs index (PNI Modified), and t-test (Match paired t-test) The results of the study found that 1) The results from the synthesis of learning skills and innovation components consisted of 3 components: Including (1) communication and cooperation (2) critical thinking and problem-solving and (3) Creative thinking and innovation Elements with the highest demand index values are creativity and innovation PNIModified=.294. 2) The model development is consistent with values between 0.80-1.00 and the overall average is 0.933. The confirmation results found that the overall components were appropriate at the highest level (X=4.258, S.D.=0.668). 3) Results of the comparative analysis of scores before and after using the format. The difference is statistically significant at .05. 4) Overall evaluation of the use of the model The average is at a high level (X=4.29, S.D.=0.65).

Toward a Context and Feasibility Driven Entrepreneurial Education: Evidence from Entrepreneurial Intention Determinants of Malaysian Students

Abstract Irrespective of support or opportunities, evidence suggests that most higher education business students in emerging markets are still primarily guided reactively (out of personal need or temporarily) into for-profit entrepreneurship rather than through personal incentive raising doubts on the impact of University entrepreneurial push strategies and programmes. We address the lack of meso level quantitative studies and propose a new student-related context sensitive conceptual model for student entrepreneurial intention in emerging markets exploring the extent to which a context-based and feasibility-oriented conceptual model is required. We propose the components Risk and Innovation, Autonomy, National norms and number of languages spoken all affect entrepreneurial intention. Furthermore, we suggest that education has a moderating effect overall and more so on the risk and innovation component. Our theoretical framework was partially supported as we show compelling evidence that risk and innovation, autonomy, and languages spoken have a significant effect on student entrepreneurial intention. The results indicate that conceptual frameworks differ by context and entrepreneurship education in emerging markets is contingent on specific human capital of students but can be stimulated through improving cognitive institutional structures and curricula focusing on context and attitudes towards risk taking.

Successful Precipitation Downscaling Through an Innovative Transformer-Based Model

In this research, we introduce a novel method leveraging the Transformer architecture to generate high-fidelity precipitation model outputs. This technique emulates the statistical characteristics of high-resolution datasets while substantially lowering computational expenses. The core concept involves utilizing a blend of coarse and fine-grained simulated precipitation data, encompassing diverse spatial resolutions and geospatial distributions, to instruct Transformer in the transformation process. We have crafted an innovative ST-Transformer encoder component that dynamically concentrates on various regions, allocating heightened focus to critical spatial zones or sectors. The module is capable of studying dependencies between different locations in the input sequence and modeling at different scales, which allows it to fully capture spatiotemporal correlations in meteorological element data, which is also not available in other downscaling methods. This tailored module is instrumental in enhancing the model’s ability to generate outcomes that are not only more realistic but also more consistent with physical laws. It adeptly mirrors the temporal and spatial distribution in precipitation data and adeptly represents extreme weather events, such as heavy and enduring storms. The efficacy and superiority of our proposed approach are substantiated through a comparative analysis with several cutting-edge forecasting techniques. This evaluation is conducted on two distinct datasets, each derived from simulations run by regional climate models over a period of 4 months. The datasets vary in their spatial resolutions, with one featuring a 50 km resolution and the other a 12 km resolution, both sourced from the Weather Research and Forecasting (WRF) Model.

Digital Government Construction and Provincial Green Innovation Efficiency: Empirical Analysis Based on Institutional Environment in China

Green innovation provides powerful incentives to achieve sustained social progress. However, the available research examines the financial drivers of green innovation, overlooking the impact of digital government development and the institutional environment. The integration of digital government construction with the institutional environment, and the coupling of the two with green innovation, will paint a picture of the future that promotes sustainable social progress and the modernization of governance. This research utilizes data from 31 provinces in China from 2018 to 2022 to study the impact of digital government construction and the institutional environment on the provincial green innovation efficiency. An empirical analysis is conducted on the basis of analyzing the spatiotemporal evolution and pattern of digital government construction, the institutional environment and the provincial green innovation efficiency. Firstly, digital government construction emphasizes data openness and sharing, and data become a key link between those inside and outside the government. The digital platform becomes an important carrier connecting the government and multiple subjects in collaborative innovation to continuously shape a new digital governance ecology. The netting of digital ecology is conducive to the institutional environment, serving to break the path dependence and create a more open, inclusive and synergistic institutional environment. Based on this, we consider that digital government construction positively affects the institutional environment, and this is verified. Secondly, a good government–market relationship, mature market development, a large market service scale, a complete property rights system and a fair legal system brought about by the improved institutional environment provide macro-external environmental support for enhanced innovation dynamics. Based on this, it is proposed that the institutional environment positively affects the provincial green innovation efficiency. Meanwhile, building on embeddedness theory, the industrial embeddedness of the institutional environment for green innovation highlights the scattered distribution of innovation components. Geographical embeddedness stresses indigenous resource distribution grounded in space vicinity and clustering. The better the institutional environment, the greater the forces of disempowerment at the industrial tier and the easier it is for resources to flow out. This may potentially have a detrimental role in improving the local green innovation efficiency. In view of this, it is proposed that the institutional environment negatively affects the provincial green innovation efficiency, and this is verified. Thirdly, digital government construction, as an important aspect of constructing a digital governance system and implementing the strategy of a strong network state, can effectively release the multiplier effect of digital technology in ecological environment governance and green innovation, continuously enhancing the provincial green innovation efficiency. In view of this, it is proposed that digital government construction positively affects the provincial green innovation efficiency, and this is verified. When the institutional environment is used as a mediating variable, digital government construction will have a certain non-linear impact in terms of provincial green innovation efficiency improvement. Building on the evidence-based analysis results, it is found that the institutional environment plays a competitive mediating role. This study integrates digital government construction, the institutional environment and the provincial green innovation efficiency under a unified analytical structure, offering theoretical inspiration and operational directions to enhance the provincial green innovation efficiency.

Designing mouthwash formulations with innovative molecular components to control initial dental erosion in vivo.

This study aimed to examine and compare the efficacy of mouthwashes containing different proteins and peptide on the prevention of enamel erosion in vivo, as well as to evaluate the participants' satisfaction with the formulations. Twelve participants were selected and underwent five cross-over mouthwash phases: Water (control); 0.1mg/mL CaneCPI-5; 0.5mg/mL MaquiCPI-3; 0.1mg/mL CsinCPI-2; and 0.037mg/mL Stn15pSpS. After prophylaxis, the participants rinsed (1min), followed by the acquired enamel pellicle (AEP) formation (2h). An erosive challenge was made (biopsy, citric acid 1%, 15s) on the buccal surface of the central maxillary incisors. The Relative Surface Reflection Intensity (%SRI) was assessed and analyzed by ANOVA/Tukey's tests. The calcium release in acid was measured by the Arsenazo method and verified by Kruskal-Wallis/Dunn's tests. The Spearman's correlation was used between analyses. A questionnaire evaluated the satisfaction of participants. For both analyses, the results showed that mouthwashes containing the proteins or peptide were significantly more effective in preventing enamel erosion compared to deionized water, with no significant differences among the active ingredients (p < 0.05). Also, there was a significant negative correlation between %SRI and calcium released (r=-0.5754). The questionnaire revealed that the volunteers were satisfied with the taste of the products. In addition, the experimental procedures were well tolerable, and no side effects were reported. All mouthwashes containing proteins or peptide were acceptable and effective in protecting enamel against initial dental erosion in vivo. This study highlights the potential of these pioneer organic components for the development of mouthwashes designed for people with risk of erosive tooth wear.

Climate Change Mitigation in the Dairy Sector: Uncovering Heterogeneity Through Eco‐Efficiency Clubs

ABSTRACTCombining climate change goals with economic targets is crucial for the dairy sector, which is a significant contributor to agricultural greenhouse gas (GHG) emissions worldwide. In this paper, we assess economic and climate change implications of dairy production with panel data of Irish dairy farms from 2013 to 2021. We calculate transient, persistent, and overall eco‐efficiency with respect to GHG emissions by way of stochastic frontier analysis. The innovative component of our analysis is the application of club convergence analysis to explore heterogeneity in transient eco‐efficiency development over time. This offers a nuanced understanding of farm‐level responses to climate challenges. We then explore correlations between convergence clubs and GHG mitigation technology usage. Despite high overall eco‐efficiency, our results uncover significant variability across farms, with some showing improvement and others decline in eco‐efficiency over time. We also find that changes in milk yield and specialization are positively related to better eco‐efficiency development, while GHG mitigation technologies do not play an important role. These insights suggest that the average high eco‐efficiency masks considerable underlying heterogeneity, presenting opportunities for tailored policy interventions. Our study contributes to the literature by highlighting the complex dynamics of eco‐efficiency in the dairy sector and underscoring the need for policy frameworks that account for farm‐level diversity.

Advanced Beamforming and Multi‐Access Edge Computing: Empowering Ultra‐Reliable and Low‐Latency Applications in 6G Networks

ABSTRACT6G technology is expected to transform communications by allowing the Internet of Everything, representing a huge advance in 2030. While B5G has not yet been established, various nations are actively working on 5G, but certain research groups are presently devoting their attention to the creation of 6G technologies. The upcoming 6G networks promise higher quality of service (QoS) features, including virtual reality and holographic communications. Multi‐Access Edge Computing (MEC) and, in particular, the offloading idea are key components of 6G innovation that enable resource‐intensive application design. If the wireless link used for computational offloading is inefficient, MEC's true potential can be impeded. Intelligent beamforming and MEC have garnered attention recently. Systematically optimizing the wireless communication environment, these developments improve connectivity between user equipment (UE) and base station (BS). By increasing the electrical charge of the reflectable signal, intelligent beamforming increases the range and efficacy of Back Communication. Particularly, this study assesses how well the MEC structure performs in urban microcellular settings when intelligent beamforming is used in communications. It has been demonstrated that the use of Intelligent Reflecting Surfaces (IRS) greatly reduces spectrum and energy usage. This study's results are implemented in Python software. Our suggested strategy shows better latency compared to other optimization methods and shorter task completion times than traditional methods. When compared to conventional techniques, the suggested MEC‐enabled network showcasing lower latency (4.9 ms) and efficient network congestion management and task completion time (30 ms) demonstrates a significant performance. These results highlight how intelligent beamforming and MEC have a lot of potential to shape the architecture of 6G networks in the future.

Design thinking: from the scientist engineer to the philosopher

This article examines design thinking as a novel project paradigm aligned with the polyparadigmatic nature of contemporary philosophical knowledge. It explores design thinking as an innovative approach that rejects the absolutization of a single methodology and analyzes the contradictions of technical creativity within the design thinking context. The phenomenon of design thinking is considered based on complexity theory, transhumanism, transversality of modern scientific knowledge, and the principles of transformation and technologization of modern society. The article traces the formation of design thinking in the context of post-non-classical science. It also analyzes engineering as technical creativity based on two figures: the engineer-scientist and the engineer-artist / designer. The study notes that technical creativity can unfold in different dimensions, such as ignoring potential consequences to achieve goals or focusing on interaction with the world and the communicative community. Technical creativity is seen as an innovative design-thinking component and a factor transforming the human doer from a scientist-engineer to a philosopher-engineer.

Publishing podcasts: an analysis of the innovation component of the Ukrainian market

The article is devoted to identifying the podcast activity of Ukrainian publishers as an important innovation component of their marketing activities. The objective of the article is to study the level of podcast activity of Ukrainian publishing houses and to analyze the thematic diversity of domestic publishing podcasts as an aspect of the innovative component of the publishing business. The 130 Ukrainian publishing houses were analyzed to determine their podcasting activity by formal and content features. It has been found that only 8 publishers create their own podcasts (mostly 1-2 podcasts from the publishing house; a total of 14 podcasts, 163 episodes). The methodological basis of the study is content analysis, methods of generalization, comparison, analysis, and synthesis. The study found that publishers are the most active: Laboratory (podcasts such as “Laboratory of Senses” that includes 22 episodes; “Laboratory of Nonfiction” - 26 episodes); Astrolabe (podcast of Radio “Astrolabe” with 32 episodes); Dukh i Litera (podcast “Laboratory: 1000” including 4 episodes; “Dukh i Litera: Podcast” – 20 episodes). The overwhelming majority of the analyzed episodes in all podcasts (58%) by category are devoted to discussing books published by publishing houses, in particular in the form of conversations with translators, publicists, and editors; 27% of them are episodes that do not deal with book publishing. Literary and literary-related topics are devoted to 8% of podcasts; authors’ creative paths, genres, etc. are covered in 7% of podcasts.

Development of rapid methods to monitor nutritional standards in sustainable healthy public catering

Abstract Background The WHO and FAO in the Second International Conference on Nutrition stress the role of nutrition standards for public facilities catering services (CS) and in the Sustainable Healthy Diet guiding principles (SHD) underline synergies among foods, nutrients present in dietary patterns and their bioavailability, degree of food processing, and subsequent health impact. In Italy almost one in 10 people eat a meal in CS every day. In accordance with SHD, the Italian Ministry of Health (MH) in the national guidelines for CS indicate activating procedures for the conservation of nutritional qualities (NQ) of micronutrients and bioactive substances. The central purchasing body of the Region of Friuli Venezia Giulia have applied these criteria, introducing the process of critical control points of nutrient analysis (NACCP) in the Public Procurement for the school catering of 69 municipalities and for all 18 regional hospital with more than 3 million meals per year served. The aim of the project was to develop rapid and cheap methods for monitoring the NQ of meals by applying the NACCP process. Methods 150 samples of pasta, milk and also vegetables, meat, fish with different degrees of processing, were taken from the CS and analysed both with chemical techniques and with optical rapid techniques (1300 analyses). Results The samples were subjected to conventional analyses to determine the oxidation state and the content of characterizing molecules such as polyphenols, histamine, and total compounds of Maillard. The same samples were then analyzed by color measurements. The results showed a good correlation between the two analytical systems, in particular the color measurements and the Maillard compounds and the color measurements and the oxidation state. Conclusions Project findings can be applied in the NACCP process to rapidly cost-effectively monitor the nutritional quality of CS meals, and adhere to WHO and FAO the SHD guiding principles and MH guidelines for CS. Key messages • The development of rapid methods of monitoring the nutritional standards of public catering can implement sustainable food systems thereby promoting the healthy diets recommended by the FAO and WHO. • Rapid methods of monitoring nutritional standards represent an innovative component in improving the culture of food and nutritional security in public catering.

A non-linear integer-valued autoregressive model with zero-inflated data series

A new non-linear stationary process for time series of counts is introduced. The process is composed of the survival and innovation component. The survival component is based on the generalized zero-modified geometric thinning operator, where the innovation process figures in the survival component as well. A few probability distributions for the innovation process have been discussed, in order to adjust the model for observed series with the excess number of zeros. The conditional maximum likelihood and the conditional least squares methods are investigated for the estimation of the model parameters. The practical aspect of the model is presented on some real-life data sets, where we observe data with inflation as well as deflation of zeroes so we can notice how the model can be adjusted with the proper parameter selection.

Sustainable development of the Republic of Bashkortostan of the Russian Federation: current state and prospects

The issues and prospects of sustainable development of the Republic of Bashkortostan with a focus on innovation aspects have been considered in order to develop recommendations to ensure the long-term progress of the region. The research methodology is based on the analysis and comparison of economic, social, and environmental factors of sustainable development, assessment of the role of innovation in each sphere, study of successes and experience of other constituent entities of the Russian Federation and foreign countries, as well as on the proposals for creating regional centers for assessing and monitoring sustainable development that in their activities will be focused on comprehensive research in regional socio-economic systems, considering the innovation component. The main spheres and components of ensuring sustainable development of the region have been defined and presented. The main study results are: identification of key aspects of sustainable development of the Republic of Bashkortostan; identification of issues and prospects in developing the economy and social and environmental spheres; analysis of the role of innovation in ensuring sustainable development; recommendations development for creating regional centers for assessing and monitoring sustainable development. Sustainable development of the territory is one of the main strategic objectives of the constituent entity. In order to achieve it, it is necessary to continue the work, as well as to increase attention to innovations, education and management systems. The centers creation can contribute to the system analysis and assessment of sustainable development processes, as well as the development of recommendations and strategies to improve the economic, social, and environmental condition of the territory.

Artificial Intelligence and Employee Well-Being: Balancing Technological Progressions with Human-Centric Workplace Strategies, a Research Agenda

Artificial intelligence (AI) enabled technologies are now corporate organisations' top priorities due to the availability of large data and the advent of the Internet of Things during the past ten years. AI is becoming a crucial component of business model innovation, process transformation, disruption, and gaining a competitive edge for companies adopting digital and data-centric cultures. This study investigates the implications of smart technology, artificial intelligence, robotics, and algorithms (STARA) on the future of work, with a particular emphasis on employee well-being and workplace dynamics. As futurists project that by 2025, 52% of all work functions will be automated, replacing one-third of current jobs, the rapid advancement of STARA creates both opportunities and risks. While automation has the potential to produce 133 million new jobs, it also threatens to eliminate 75 million employments, raising employee anxieties about job security and future roles. Despite the rising volume of studies on smart automation, there is still a major vacuum in our understanding of its implications on employees' mental health, well-being, and the entire workplace. This study investigates STARA's dual influence: while technology reduces physical strain and automates tedious jobs, it also raises new difficulties such as job displacement concerns and shifts in worker dynamics. The study emphasizes the need of human resource professionals to develop methods that strike a balance between technological integration and employee assistance. Key areas of focus include providing reskilling opportunities, adopting mental health initiatives, and encouraging open conversation regarding AI's expanding role in the workforce. By addressing these concerns, organisations may build a more resilient workforce that is better suited to the fourth industrial revolution. The study intends to contribute to a better understanding of how organisations may safeguard and improve employee well-being in the face of fast technological change, ensuring that STARA integration encourages innovation while simultaneously supporting a healthy and engaged workforce

Decarbonizing European Industry: A Novel Technology to Heat Supply Using Waste and Renewable Energy

This study examines the potential for the smart integration of waste and renewable energy sources to supply industrial heat at temperatures between 150 °C and 250 °C, aiming to decarbonize heat demand in European industry. This work is part of a European project (SUSHEAT) which focuses on developing a novel technology that integrates several innovative components: a Stirling cycle high-temperature heat pump (HTHP), a bio-inspired phase change material (PCM) thermal energy storage (TES) system, and a control and integration twin (CIT) system based on smart decision-making algorithms. The objective is to develop highly efficient industrial heat upgrading systems for industrial applications using renewable energy sources and waste heat recovery. To achieve this, the specific heat requirements of different European industries were analyzed. The findings indicate that industrial sectors such as food and beverages, plastics, desalination, textiles, ceramics, pulp and paper, wood products, canned food, agricultural products, mining, and chemicals, typically require process heat at temperatures below 250 °C under conditions well within the range of the SUSHEAT system. Moreover, two case studies, namely the Pelagia and Mandrekas companies, were conducted to validate the effectiveness of the system. An analysis of the annual European heat demand by sector and temperature demonstrated that the theoretical potential heat demand that could be met by the SUSHEAT system is 134.92 TWh annually. Furthermore, an environmental impact assessment estimated an annual significant reduction of 19.40 million tonnes of CO2 emissions. These findings underscore the significant potential of the SUSHEAT system to contribute to the decarbonization of European industry by efficiently meeting heat demand and substantially reducing carbon emissions.

MG-Net: A fetal brain tissue segmentation method based on multiscale feature fusion and graph convolution attention mechanisms

Background and Objective:Fetal brain tissue segmentation provides foundational support for comprehensively understanding the neurodevelopment of normal and congenital disease-affected fetuses. Manual labeling is very time-consuming, and automated segmentation methods can greatly improve the efficiency of doctors. At the same time, fetal brain tissue undergoes various changes throughout the pregnancy, leading to a continuous change in tissue contrast, which greatly increases the difficulty of training segmentation methods. This study aims to develop an automated segmentation model that can efficiently and accurately segment fetal brain tissue, improving the workflow for medical professionals. Methods:We propose a novel deep learning-based segmentation model that incorporates three innovative components: Firstly, a new Dual Dilated Attention Block (DDAB) is proposed in the encoder part to enhance the feature extraction of local spatial and structural contextual information. Secondly, a Multi-scale Deformable Transformer (MSDT) is integrated into the bottleneck to improve the feature extraction of global information on local spatial and structural contextual information. Thirdly, we use a novel block based on Graph Convolution Attention (GCAB) in the decoder, which effectively enhances the features at the decoder.The code is available at https://github.com/unicoco7/MG-Net/. Results:We trained and tested on the FeTA 2021 and FeTA 2022 datasets, and evaluated using seven popular metrics, including Dice, IoU, MAE, BoundaryF, PRE, SEN, and SPE. Compared to the current state-of-the-art 3D segmentation models such as nnFormer, SwinUNETR, and 3DUX-net, our proposed method has surpassed all of them in metrics like Dice, IoU, and MAE. Specifically, on the FeTA 2021 dataset, our model achieved a Dice of 0.8666, an IoU of 0.7646, and an MAE of 0.0027; on the FeTA 2022 dataset, it achieved a Dice of 0.8552, an IoU of 0.7470, and an MAE of 0.0005. Conclusion:In this paper, we propose a model for three-dimensional fetal brain tissue segmentation based on multi-scale feature fusion and graph convolution attention mechanism, and conduct experimental evaluation on the FeTA 2021 and FeTA 2022 datasets. Understanding the boundaries of fetal brain tissue is crucial for doctors’ diagnosis, so the proposed model is expected to improve the speed and accuracy of doctors’ diagnoses.