Efficient Model Research Articles

A novel mouse model of calcific aortic valve stenosis.

Calcific aortic valve stenosis (CAVS) is one of the most challenging heart diseases in clinical with rapidly increasing prevalence. However, study of the mechanism and treatment of CAVS is hampered by the lack of suitable, robust and efficient models that develop hemodynamically significant stenosis and typical calcium deposition. Here, we aim to establish a mouse model to mimic the development and features of CAVS. The model was established via aortic valve wire injury (AVWI) combined with vitamin D subcutaneous injected in wild type C57/BL6 mice. Serial transthoracic echocardiography was applied to evaluate aortic jet peak velocity and mean gradient. Histopathological specimens were collected and examined in respect of valve thickening, calcium deposition, collagen accumulation, osteogenic differentiation and inflammation. Serial transthoracic echocardiography revealed that aortic jet peak velocity and mean gradient increased from 7 days post model establishment in a time dependent manner and tended to be stable at 28 days. Compared with the sham group, simple AVWI or the vitamin D group, the hybrid model group showed typical pathological features of CAVS, including hemodynamic alterations, increased aortic valve thickening, calcium deposition, collagen accumulation at 28 days. In addition, osteogenic differentiation, fibrosis and inflammation, which play critical roles in the development of CAVS, were observed in the hybrid model. We established a novel mouse model of CAVS that could be induced efficiently, robustly and economically, and without genetic intervention. It provides a fast track to explore the underlying mechanisms of CAVS and to identify more effective pharmacological targets.

Biomechanical analysis of combi-hole locking compression plate during fracture healing: A numerical study of screw configuration.

Locking compression plates (LCPs) have become a widely used option for treating femur bone fractures. However, the optimal screw configuration with combi-holes remains a subject of debate. The study aims to create a time-dependent finite element (FE) model to assess the impacts of different screw configurations on LCP fixation stiffness and healing efficiency across four healing stages during a complete fracture healing process. To simulate the healing process, we integrated a time-dependent callus formation mechanism into a FE model of the LCP with combi-holes. Three screw configuration parameters, namely working length, screw number, and screw position, were investigated. Increasing the working length negatively affected axial stiffness and healing efficiency (p < 0.001), while screw number or position had no significant impact (p > 0.01). The time-dependent model displayed a moderate correlation with the conventional time-independent model for axial stiffness and healing efficiency (ρ ≥ 0.733, p ≤ 0.025). The highest healing efficiency (95.2%) was observed in screw configuration C125 during the 4-8-week period. The results provide insights into managing fractures using LCPs with combi-holes over an extended duration. Under axial compressive loading conditions, the use of the C125 screw configuration can enhance callus formation during the 4-12-week period for transverse fractures. When employing the C12345 configuration, it becomes crucial to avoid overconstraint during the 4-8-week period.

Prediction of MicroRNA-Disease Potential Association Based on Sparse Learning and Multilayer Random Walks.

More and more studies have shown that microRNAs (miRNAs) play an indispensable role in the study of complex diseases in humans. Traditional biological experiments to detect miRNA-disease associations are expensive and time-consuming. Therefore, it is necessary to propose efficient and meaningful computational models to predict miRNA-disease associations. In this study, we aim to propose a miRNA-disease association prediction model based on sparse learning and multilayer random walks (SLMRWMDA). The miRNA-disease association matrix is decomposed and reconstructed by the sparse learning method to obtain richer association information, and at the same time, the initial probability matrix for the random walk with restart algorithm is obtained. The disease similarity network, miRNA similarity network, and miRNA-disease association network are used to construct heterogeneous networks, and the stable probability is obtained based on the topological structure features of diseases and miRNAs through a multilayer random walk algorithm to predict miRNA-disease potential association. The experimental results show that the prediction accuracy of this model is significantly improved compared with the previous related models. We evaluated the model using global leave-one-out cross-validation (global LOOCV) and fivefold cross-validation (5-fold CV). The area under the curve (AUC) value for the LOOCV is 0.9368. The mean AUC value for 5-fold CV is 0.9335 and the variance is 0.0004. In the case study, the results show that SLMRWMDA is effective in inferring the potential association of miRNA-disease.

Wheat Teacher: A One-Stage Anchor-Based Semi-Supervised Wheat Head Detector Utilizing Pseudo-Labeling and Consistency Regularization Methods

Wheat breeding heavily relies on the observation of various traits during the wheat growth process. Among all traits, wheat head density stands out as a particularly crucial characteristic. Despite the realization of high-throughput phenotypic data collection for wheat, the development of efficient and robust models for extracting traits from raw data remains a significant challenge. Numerous fully supervised target detection algorithms have been employed to address the wheat head detection problem. However, constrained by the exorbitant cost of dataset creation, especially the manual annotation cost, fully supervised target detection algorithms struggle to unleash their full potential. Semi-supervised training methods can leverage unlabeled data to enhance model performance, addressing the issue of insufficient labeled data. This paper introduces a one-stage anchor-based semi-supervised wheat head detector, named “Wheat Teacher”, which combines two semi-supervised methods, pseudo-labeling, and consistency regularization. Furthermore, two novel dynamic threshold components, Pseudo-label Dynamic Allocator and Loss Dynamic Threshold, are designed specifically for wheat head detection scenarios to allocate pseudo-labels and filter losses. We conducted detailed experiments on the largest wheat head public dataset, GWHD2021. Compared with various types of detectors, Wheat Teacher achieved a mAP0.5 of 92.8% with only 20% labeled data. This result surpassed the test outcomes of two fully supervised object detection models trained with 100% labeled data, and the difference with the other two fully supervised models trained with 100% labeled data was within 1%. Moreover, Wheat Teacher exhibits improvements of 2.1%, 3.6%, 5.1%, 37.7%, and 25.8% in mAP0.5 under different labeled data usage ratios of 20%, 10%, 5%, 2%, and 1%, respectively, validating the effectiveness of our semi-supervised approach. These experiments demonstrate the significant potential of Wheat Teacher in wheat head detection.

Agent-Guided Non-Local Network for Underwater Image Enhancement and Super-Resolution Using Multi-Color Space

The absorption and scattering of light in water usually result in the degradation of underwater image quality, such as color distortion and low contrast. Additionally, the performance of acquisition devices may limit the spatial resolution of underwater images, resulting in the loss of image details. Efficient modeling of long-range dependency is essential for understanding the global structure and local context of underwater images to enhance and restore details, which is a challenging task. In this paper, we propose an agent-guided non-local attention network using a multi-color space for underwater image enhancement and super-resolution. Specifically, local features with different receptive fields are first extracted simultaneously in the RGB, Lab, and HSI color spaces of underwater images. Then, the designed agent-guided non-local attention module with high expressiveness and lower computational complexity is utilized to model long-range dependency. Subsequently, the results from the multi-color space are adaptively fused with learned weights, and finally, the reconstruction block composed of deconvolution and the designed non-local attention module is used to output enhanced and super-resolution images. Experiments on multiple datasets demonstrated that our method significantly improves the visual perception of degraded underwater images and efficiently reconstructs missing details, and objective evaluations confirmed the superiority of our method over other state-of-the-art methods.

Assessment of Voice Disorders Using Machine Learning and Vocal Analysis of Voice Samples Recorded through Smartphones

Background: The integration of edge computing into smart healthcare systems requires the development of computationally efficient models and methodologies for monitoring and detecting patients’ healthcare statuses. In this context, mobile devices, such as smartphones, are increasingly employed for the purpose of aiding diagnosis, treatment, and monitoring. Notably, smartphones are widely pervasive and readily accessible to a significant portion of the population. These devices empower individuals to conveniently record and submit voice samples, thereby potentially facilitating the early detection of vocal irregularities or changes. This research focuses on the creation of diverse machine learning frameworks based on vocal samples captured by smartphones to distinguish between pathological and healthy voices. Methods: The investigation leverages the publicly available VOICED dataset, comprising 58 healthy voice samples and 150 samples from voices exhibiting pathological conditions, and machine learning techniques for the classification of healthy and diseased patients through the employment of Mel-frequency cepstral coefficients. Results: Through cross-validated two-class classification, the fine k-nearest neighbor exhibited the highest performance, achieving an accuracy rate of 98.3% in identifying healthy and pathological voices. Conclusions: This study holds promise for enabling smartphones to effectively identify vocal disorders, offering a multitude of advantages for both individuals and healthcare systems, encompassing heightened accessibility, early detection, and continuous monitoring.

Design rules applied to silver nanoparticles synthesis: A practical example of machine learning application.

The synthesis of silver nanoparticles with controlled physicochemical properties is essential for governing their intended functionalities and safety profiles. However, synthesis process involves multiple parameters that could influence the resulting properties. This challenge could be addressed with the development of predictive models that forecast endpoints based on key synthesis parameters. In this study, we manually extracted synthesis-related data from the literature and leveraged various machine learning algorithms. Data extraction included parameters such as reactant concentrations, experimental conditions, as well as physicochemical properties. The antibacterial efficiencies and toxicological profiles of the synthesized nanoparticles were also extracted. In a second step, based on data completeness, we employed regression algorithms to establish relationships between synthesis parameters and desired endpoints and to build predictive models. The models for core size and antibacterial efficiency were trained and validated using a cross-validation approach. Finally, the features’ impact was evaluated via Shapley values to provide insights into the contribution of features to the predictions. Factors such as synthesis duration, scale of synthesis and the choice of capping agents emerged as the most significant predictors. This study demonstrated the potential of machine learning to aid in the rational design of synthesis process and paves the way for the safe-by-design principles development by providing insights into the optimization of the synthesis process to achieve the desired properties. Finally, this study provides a valuable dataset compiled from literature sources with significant time and effort from multiple researchers. Access to such datasets notably aids computational advances in the field of nanotechnology.

Application of Neuro-Fuzzy to the elephant grass production process: A systematic bibliographic review

The transition to renewable energy sources can help combat climate change, as they emit fewer greenhouse gas emissions. Biomass is an important source of energy production, being composed of organic materials, such as residues from agricultural and forestry crops, among others, with emphasis on elephant grass. The application of Neuro-Fuzzy in production processes, especially laboratory ones, is of utmost importance, as it allows the creation of more accurate and efficient prediction and control models in the bioenergetic context. Given this context, the objective of this article is to identify how the state of knowledge is configured regarding the application of the use of Neuro-Fuzzy for the chemical quantification process of elephant grass cellulose. A Systematic Bibliographic Review was carried out to search and map published scientific data to identify numerous applications for the use of Neuro-Fuzzy, mainly in renewable energy. By carrying out the research using the Systemic Bibliographic Review, it was possible to identify several opportunities for the application of Neuro-Fuzzy in the chemical quantification of elephant grass. However, it was observed that this article presents a novelty on the application of the use of Neuro-Fuzzy for the process of chemical quantification of cellulose, in addition to the production of bioethanol from this biomass. Of the 22 documents analyzed in this research, 100% were articles in the form of applied research and literature review, demonstrating great relevance in this line of research, which is the application of Artificial Intelligence in field and laboratory production processes using elephant grass. as biomass to produce bioethanol.

Transfer of near infrared calibration for gasoline octane number based on screening consistent wavelengths combined with direct standardization algorithm

In order to share multivariate calibration models of gasoline research octane number between different near infrared spectrometers, a novel calibration transfer method, namely combination of screening consistent wavelengths and direct standardization (SWCSS-DS) was proposed. Firstly, screening wavelengths with consistent and stable signals (SWCSS) between instruments was used to select the wavelengths with best stability, and then direct standardization (DS) further corrected the systematic errors that still exist after the SWCSS was implemented. The spectra of 120 standard gasoline samples collected on two near infrared spectrometers of the same type were investigated in detail to verify the validity of the new algorithm. Compared results of other transfer methods such as SWCSS, Slope/Bias (S/B), direct standardisation (DS), and piecewise direct standardization (PDS), the root mean squared error for prediction (RMSEP) of SWCSS-DS algorithm for target samples was decreased from 5.75 to 0.295, and the Akaike information criterion value decreased from 1516 to 640, which were better than those of the SWCSS, S/B, DS and PDS algorithms. Therefore, the joint algorithm of SWCSS-DS has not only improved the universality of the master model, but also reduced the dimension of the spectral matrix and calibration equation, that would provide a more efficient model transfer strategy for the practical applications.

A comparative study of LightGBM, XGBoost, and GEP models in shear strength management of SFRC-SBWS

Steel fiber reinforced concrete (SFRC) was revealed to have superior mechanical properties compared to plain concrete, especially under shear loads. However, Due to a lack of prediction models for SFRC-Slender Beams without Stirrups (SFRC-SBWS) in the available literature, the application of SFRC-SBWS in reinforced concrete (RC) constructions is limited. This research aims to fill a literature gap by analyzing different Machine Learning (ML) algorithms, including Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Gene Expression Programming (GEP), to forecast the shear strength of SFRC-SBWS specimens. A database of 333 tested SFRC-SBWS specimens was collected and analyzed. The results revealed the LightGBM and XGBoost algorithms surpassed other studied algorithms with Coefficient of determination (R2) = 95.74% and 93.27%, Root Mean Square Error (RMSE) = 20.04 kN and 25.19 kN, respectively. A GEP-based closed-form model was suggested to forecast the shear strength, considering the most important factors, including beam depth effect, beam width, and longitudinal steel reinforcement ratio. The proposed GEP model has an R2 = 91.12% and RMSE = 101.44 kN, showing a slightly lower prediction accuracy. The effectiveness of the studied models was evaluated from various angles, and the results demonstrate the importance of managing new technological advancements to create reliable support methods. Engineers that use these approaches get a dependable tool for estimating shear resistance, which is crucial in guaranteeing the safety and lifespan of RC structures. The successful combination of classic engineering approaches and revolutionary ML strategies indicates the potential for improving accuracy and reducing bias in prediction-oriented investigations. As the engineering field supports technological improvements, combining robust and efficient prediction models, as described in this paper, can contribute considerably to resilient structure design and safety assurance. This leads to sustainable and robust management of structures with greater confidence in predictions.

A Novel Federated Learning Framework Based on Conditional Generative Adversarial Networks for Privacy Preserving in 6G

With the rapid development of 6G networks, data transmission speed has significantly increased, making data privacy protection issues even more crucial. The federated learning (FL) is a distributed machine learning framework with privacy protection and secure encryption technology, aimed at enabling dispersed participants to collaborate on model training without disclosing private data to other participants. Nonetheless, recent research indicates that the exchange of shared gradients may lead to information disclosure, and thus FL still needs to address privacy concerns. Additionally, FL relies on a large number of diverse training data to forge efficient models, but in reality, the training data available to clients are limited, and data imbalance issues lead to over fitting in existing federated learning models. To alleviate these issues, we introduce a Novel Federated Learning Framework based on Conditional Generative Adversarial Networks (NFL-CGAN). NFL-CGAN divides the local networks of each client into private and public modules. The private module contains an extractor and a discriminator to protect privacy by retaining them locally. Conversely, the public module is shared with the server to aggregate the shared knowledge of clients, thereby improving the performance of each client local network. Comprehensive experimental analyses demonstrate that NFL-CGAN surpasses traditional FL baseline methods in data classification, showcasing its superior efficacy. Moreover, privacy assessments also verified robust and reliable privacy protection capabilities of NFL-CGAN.

Controlling the incorporation of fluorinated amino acids in human cells and its structural impact.

Fluorinated aromatic amino acids (FAAs) are promising tools when studying protein structure and dynamics by NMR spectroscopy. The incorporation FAAs in mammalian expression systems has been introduced only recently. Here, we investigate the effects of FAAs incorporation in proteins expressed in human cells, focusing on the probability of incorporation and its consequences on the 19 F NMR spectra. By combining 19 F NMR, direct MS and x-ray crystallography, we demonstrate that the probability of FAA incorporation is only a function of the FAA concentration in the expression medium and is a pure stochastic phenomenon. In contrast with the MS data, the x-ray structures of carbonic anhydrase II reveal that while the 3D structure is not affected, certain positions lack fluorine, suggesting that crystallization selectively excludes protein molecules featuring subtle conformational modifications. This study offers a predictive model of the FAA incorporation efficiency and provides a framework for controlling protein fluorination in mammalian expression systems.

Wasserstein Distance- EEMD Enhanced Multi-Head Graph Attention Network for Rolling Bearing Fault Diagnosis Under Different Working Conditions

Traditional fault diagnosis models often overlook the interconnections between segments of vibration data, resulting in the loss of critical feature information. Therefore, an efficient fault diagnosis model tailored for rolling bearings is proposed in this paper. The 1D vibration signals are first preprocessed using ensemble empirical mode decomposition (EEMD) to generate multiple intrinsic mode functions (IMF) as individual nodes. The percentage distance between each node is calculated using the Wasserstein distance (WD) to capture the relationships between nodes and use it as the edge weights to construct a node graph. An improved multi-head graph attention network (MGAT) model is established to extract features and perform classification on the node graph. This MGAT model effectively utilizes the relationships between nodes and enhances the accuracy of fault diagnosis. The experimental results demonstrate that the proposed method achieves higher accuracy compared to similar models while requiring less processing time.

Real-time video surveillance on highways using combination of extended Kalman Filter and deep reinforcement learning

Highways, as one of the main arteries of transit and transportation in today's world, play a fundamental role in accelerating transportation, and for this reason, continuous monitoring of them is of great importance. Among these, monitoring compliance with transportation laws by vehicles is of utmost importance; for automation, efficient and vehicle-specific models can be used. In this article, a new method for video surveillance of highways is presented using an extended Kalman filter (EKF) and reinforcement learning models. There are three primary stages to the suggested approach. During the first stage, the extended Kalman filter (EKF) is used to identify and track multiple targets. Next, in the second stage, a convolutional neural network (CNN) processes each detected moving item to determine the kind of vehicle. During this stage, the CNN model's ideal configuration is ascertained using a new optimization approach that combines Particle Swarm Optimization (PSO) and reinforcement learning. After identifying the type of vehicle, in the third phase, the proposed method uses a separate CNN model for each target vehicle to assess its compliance with transportation safety principles. It should be mentioned that each vehicle's associated CNN model is configured during this phase using the suggested optimization methodology. Investigations have been conducted into the effectiveness of the suggested method in identifying violations of road safety laws as well as how well it performed in the two phases of vehicle type identification. According to the findings, the suggested approach can identify the kind of vehicle with 98.72% accuracy, which is at least 3.41% better than the approaches that were compared. On the other hand, this model can detect the violation of road safety laws for each vehicle with an average accuracy of 91.5%, which shows at least a 3.49% improvement compared to the other methods.

Efficient digital design of the nonlinear behavior of Hindmarsh–Rose neuron model in large-scale neural population

Spiking networks, as the third generation of neural networks, are of great interest today due to their low power consumption in cognitive processes. This important characteristic has caused the hardware implementation techniques of spiking networks in the form of neuromorphic systems attract a lot of attention. For the first time, the focus is on the digital implementation based on CORDIC approximation of the Hindmarsh–Rose (HR) neuron so that the hardware implementation cost is lower than previous studies. If the digital design of a neuron is done efficient, the possibility of implementing a population of neurons is provided for the feasibility of low-consumption implementation of high-level cognitive processes in hardware, which is considered in this paper through edge detector, noise removal and image magnification spiking networks based on the proposed CORDIC_HR model. While using less hardware resources, the proposed HR neuron model follows the behavior of the original neuron model in the time domain with much less error than previous study. Also, the complex nonlinear behavior of the original and the proposed model of HR neuron through the bifurcation diagram, phase space and nullcline space analysis under different system parameters was investigated and the good follow-up of the proposed model was confirmed from the original model. In addition to the fact that the individual behavior of the original and the proposed neurons is the same, the functional and behavioral performance of the randomly connected neuronal population of original and proposed neuron model is equal. In general, the main contribution of the paper is in presenting an efficient hardware model, which consumes less hardware resources, follows the behavior of the original model with high accuracy, and has an acceptable performance in image processing applications such as noise removal and edge detection.

Discovery of a proliferation essential gene signature and actin-like 6A as potential biomarkers for predicting prognosis and neoadjuvant chemotherapy response in triple-positive breast cancer.

Patients with triple-positive breast cancer (TPBC) have a higher risk of recurrence and lower survival rates than patients with other luminal breast cancers. However, there are few studies on the predictive biomarkers of prognosis and treatment responses in TPBC. Proliferation essential genes (PEGs) were acquired from clustered regularly interspaced short palindromic repeats-associated protein 9 (CRISPR-Cas9) technology, and cohorts of patients with TPBC were obtained from public databases and our cohort. To develop a TPBC-PEG signature, Cox regression and least absolute shrinkage and selection operator regression analyses were applied. Functional analyses were performed with gene set enrichment analysis. The relationship between candidate genes and neoadjuvant chemotherapy (NACT) sensitivity was explored via real-time quantitative polymerase chain reaction (RT-qPCR) and immunohistochemistry (IHC) on the basis of clinical samples. Among 900 TPBC-PEGs, 437 showed significant differential expression between TPBC and normal tissues. Three prognostic PEGs (actin-like 6A [ACTL6A], chaperonin containing TCP1 subunit 2 [CCT2], and threonyl-TRNA synthetase [TARS]) were identified and used to construct the PEG signature. Patients with high PEG signature scores exhibited a worse overall survival and lower sensitivity to NACT than patients with low PEG signature scores. RT-qPCR results indicated that ACTL6A and CCT2 expression were significantly upregulated in patients who lacked sensitivity to NACT. IHC results showed that the ACTL6A protein was highly expressed in patients with NACT resistance and nonpathological complete responses. This efficient PEG signature prognostic model can predict the outcomes of TPBC. Furthermore, ACTL6A expression level was associated with the response to NACT, and could serve as an important factor in predicting prognosis and drug sensitivity of patients with TPBC.

WIPE: A Write-Optimized Learned Index for Persistent Memory

Learned Index, which utilizes effective machine learning models to accelerate locating sorted data positions, has gained increasing attention in many big data scenarios. Using efficient learned models, the learned indexes build large nodes and flat structures, thereby greatly improving the performance. However, most of the state-of-the-art learned indexes are designed for DRAM, and there is hence an urgent need to enable high-performance learned indexes for emerging Non-Volatile Memory (NVM). In this article, we first evaluate and analyze the performance of the existing learned indexes on NVM. We discover that these learned indexes encounter severe write amplification and write performance degradation due to the requirements of maintaining large sorted/semi-sorted data nodes. To tackle the problems, we propose a novel three-tiered architecture of write-optimized persistent learned index, which is named WIPE , by adopting unsorted fine-granularity data nodes to achieve high write performance on NVM. Thereinto, we devise a new root node construction algorithm to accelerate searching numerous small data nodes. The algorithm ensures stable flat structure and high read performance in large-size datasets by introducing an intermediate layer (i.e., index nodes) and achieving accurate prediction of index node positions from the root node. Our extensive experiments on Intel DCPMM show that WIPE can improve write throughput and read throughput by up to 3.9× and 7×, respectively, compared to the state-of-the-art learned indexes. Also, WIPE can recover from a system crash in ∼ 18 ms. WIPE is free as an open-source software package. 1

Optimizing jet pump efficiency via drag reducing polymers and enhanced efficiency definitions

Liquid jet pumps are widely used in various industrial applications for fluid mixing, circulation, and transport. The efficiency and performance of liquid jet pumps play a crucial role in determining their overall effectiveness and economic viability. The performance of liquid jet pumps is primarily affected by parameters such as motive fluid pressure, nozzle design, and entrainment ratio. Liquid jet pumps exhibit a notable drawback in terms of comparatively lower efficiency when compared to alternative pump types. The reduced overall efficiency of liquid jet pumps stems primarily from energy dissipation incurred during the entrainment process. To address this obstacle, a water-water loop system was implemented in conjunction with a liquid jet pump, followed by the introduction of drag-reducing polymers (DRPs) into the suction flow of the liquid jet pump using a specific configuration. This configuration led to a significant reduction in drag within the liquid jet pump, raising its efficiency in some cases from 13.8% to 26.7% with a drag reduction of 46%, subsequently improving its overall performance. The resulting enhancement was evaluated using various efficiency models documented in the existing literature to comprehensively assess the overall performance of the liquid jet pump. A new interpretation of jet pump efficiency has been shared, along with a comparison of the various efficiencies.

PLATFORM EKONOMİSİ, SÜRDÜRÜLEBİLİRLİK, SİMBİYOZ, KAYNAK VERİMLİLİĞİ, YENİLENEBİLİR ENERJİ VE DÖNGÜSEL EKONOMİNİN KESİŞİMLERİ

At the center of global change is the rise of platform economy, sustainability, green economy, industrial symbiosis, circular economies, industry 4.0 paradigm, and resource efficiency models, which appear in an interrelated conceptual confusion of innovative paradigms. These emerging platforms and the competitive dynamics in the network society revolve around collecting and analyzing data from their interactions to achieve network effects, attracting users to a platform that can generate socio-economic value with environmental sensitivities. This study argues that the circular economy, platform economy, renewable energy and resource efficiency have intersections and close relations with digitalization. It is tried to identify the points of harmony and intersections in this field's great confusion of concepts. In addition, it is argued that the digitization accelerated by Industry 4.0, supports a more sustainable circular economy, platform economies, and therefore industrial symbiosis, and solutions are suggested for the perfect order by revealing the close linear inter-relationships. This study examines the impact of digitalization, circular economies, and sustainability on the development of more efficient processes. It aims to mimic the wasteless and flawless order found in the universe to reduce waste, transaction costs, and ensure the production of healthier products. Here, it is argued that the environmental and intelligent engineering of Industry 4.0 should be guided by a more sustainable, symbiotic, platform-based, digital, and circular operating way and should include theological dimensions besides the material production dimension.

Spatiotemporal patterns and driving factors of gross primary productivity over the Mongolian Plateau steppe in the past 20 years

The Eurasian steppe is the largest temperate grassland in the world. The grassland of the Mongolian Plateau (MP) represents an important part of the Eurasian steppe with high climatic sensitivity. Gross primary productivity (GPP) is a key indicator of the grassland's production, status and dynamic on the MP. In this study, we calibrated and evaluated the grassland-specific light use efficiency model (GRASS-LUE) against the observed GPP collected from nine eddy covariance flux sites on the MP, and compared the performance with other four GPP products (MOD17, VPM, GLASS and GOSIF). GRASS-LUE with higher R2 (0.91) and lower root mean square error (RMSE = 0.99 gC m−2 day−1) showed a better performance compared to the four GPP products in terms of model accuracy and dynamic consistency, especially in typical and desert steppe. The parameters of the GRASS-LUE are more suitable for water-limited grassland could be the reason for its outstanding performance in typical and desert steppe. Mean grassland GPP derived from GRASS-LUE was higher in the east and lower in the west of the MP. Grassland GPP was on average 205 gC m−2 over the MP between 2001 and 2020 with mean annual total GPP of 322 TgC yr−1. 30 % of the MP steppe showed a significant GPP increase. Growing season precipitation is the main factor affecting GPP of the MP steppe across regions. Anthropogenic factors (livestock density and population density) had greater effect on GPP than growing season temperature in pastoral counties in IM that take grazing as one of main industries. These findings can inform the status and trend of the productivity of MP steppe and help government and scientific research institutions to understand the drivers for spatial pattern of grassland GPP on the MP.