Artificial Intervention Research Articles

Oriented Fermentation of Food Waste towards High-Value Products: A Review

Food waste has a great potential for resource recovery due to its huge yield and high organic content. Oriented fermentation is a promising method with strong application prospects due to high efficiency, strong robustness, and high-value products. Different fermentation types lead to different products, which can be shifted by adjusting fermentation conditions such as inoculum, pH, oxidation-reduction potential (ORP), organic loading rate (OLR), and nutrients. Compared with other types, lactic acid fermentation has the lowest reliance on artificial intervention. Lactic acid and volatile fatty acids are the common products, and high yield and high purity are the main targets of food waste fermentation. In addition to operational parameters, reactors and processes should be paid more attention to for industrial application. Currently, continuously stirred tank reactors and one-stage processes are used principally for scale-up continuous fermentation of food waste. Electro-fermentation and iron-based or carbon-based additives can improve food waste fermentation, but their mechanisms and application need further investigation. After fermentation, the recovery of target products is a key problem due to the lack of green and economic methods. Precipitation, distillation, extraction, adsorption, and membrane separation can be considered, but the recovery step is still the most expensive in the entire treatment chain. It is expected to develop more efficient fermentation processes and recovery strategies based on food waste composition and market demand.

Controlling parameter estimation precision by designing the spin environment

The artificial intervention of quantum environment is a basic task of quantum information engineering. In this paper, we investigate the dynamical behaviors of a single system coupled to a spin environment by a z–x-type interaction. We focus on studying how to enhance the precision of parameter by the pre-designed factors of the environment. The numerical simulation results show that the initial state of the environment obviously affects the dynamics of coherence of the central spin system. We find that the environment should be prepared in a block entangled state with Bell state. This option is beneficial to increasing the quantum Fisher information. Further, if the environment is dominated by higher-frequencies particles, the decoherence in noisy environment can be effectively suppressed and the precision of parameter estimation can be enhanced.

Succession of phytoplankton functional groups and driving variables in a young canyon reservoir

Reservoir eutrophication is a global problem that needs to be urgently solved, and exploring phytoplankton dynamics in young reservoirs is extremely important for artificial intervention during the process of eutrophication. However, the succession of phytoplankton communities and their responses to environmental variables in young canyon reservoirs remain obscure. Therefore, phytoplankton functional group data were collected from a young canyon reservoir for 2 years, and the succession pattern and its response to environmental variables were simultaneously evaluated. The results revealed significant spatial differences and rapid interannual changes in some environmental variables (water temperature, suspended solids) in the young canyon reservoir. Non-metric multidimensional scaling showed that phytoplankton functional group structure differed obviously among the zones of the reservoir, while significant interannual succession (from the Lo and Y groups to the X2 group) occurred in the lacustrine zone. Additionally, redundancy analysis demonstrated that the structure of phytoplankton functional groups varied significantly between the spatially different environments. Finally, the succession of phytoplankton functional groups in the lacustrine zone was mainly driven by increases in nutrients and water temperature. The rules underlying and dominant mechanism driving short-term succession in the young canyon reservoir revealed in this study may provide a deeper understanding of the intervention mechanisms that could be implemented to alleviate reservoir eutrophication.

Are all natural monopolies in Ukraine equally “natural”?

Are all natural monopolies in Ukraine equally “natural”?

A novel ResNet-based model structure and its applications in machine health monitoring

Machine health monitoring has become increasingly important in modern manufacturers because of its ability to reduce downtime of the machine and cut down the production cost. Enormous signals acquired from machinery are capable of reflecting current working conditions by in-depth analysis with various data-driven methods. Hand-crafted feature extraction and representation from the traditional methods are essential but daunting tasks, and these methods may not be suitable for these massive data. Compared with traditional methods, deep learning ones are able to extract the best feature combination during model training without any artificial intervention, which makes it easier, more efficient, and more effective to monitor machine health, but the training cost and training time hamper its application. The short-time Fourier transform is adopted as the data preprocessing method to cut down the training cost and boost the training procedure. Inspired by the great achievements of ResNet, the new optimized model based on ResNet has been proposed with layer-by-layer dimension reduction of the feature maps. The proposed model is also able to avoid information loss in the conventional pooling layer. All the potential candidate model blocks are introduced and compared, and the best one is selected as the final one. Repeated model block layers are adapted for the best feature combinations, followed by a two-layer full connection layer for the final targets. The proposed method is validated by conducting experiments on bearing fault diagnosis and tool wear prediction dataset. The final results show that the proposed model achieves the best accuracy rate in the classification task and the lowest root mean squared error in the prediction task.

'Innate Nature' and 'Complete Nature': The Catholic Natural Family Planning Program and the Competition of Natural Methods in Mid-1970s Korea.

This article reviews the competition of two natural family planning methods in the mid-1970s when the Catholic Natural Family Planning program was underway in Korea. The Catholic Church, emphasizing the natural law, has recommended Natural Family Planning (NFP), a method of regulating childbirth by abstinence during the fertile period, since the mid-twentieth century. However, a group of gynecologists working at St. Mary's Hospital, a Catholic general hospital in Korea, questioned the utility of NFP. As an alternative, they proposed the method of Ovulation Regulation (OR), which regulates the menstrual cycle by inducing ovulation with steroids agents. This seemed to be no different than contraception with oral contraceptives disapproved of by the Catholic Church, but many doctors who advocated OR thought that this could be a new 'natural' family planning method to replace NFP. What is noteworthy here is the fact that not only NFP advocates, but also OR advocates attempted to justify their methods based on the authority of the 'nature.' In the debate over natural family planning methods, nature's legitimacy was given premise, not the object of doubt. Rather, the issue was the definition of nature. First, 'nature' in NFP signifies 'innate nature,' which excludes human intervention. According to this point of view, OR with steroids agents could not be natural. On the contrary, a group of doctors who advocated OR considered nature 'primal completeness.' If the natural order of the menstrual cycle could be restored, the artificial intervention of the administration of steroids was not a problem. Thus, both groups defended their arguments by redefining nature, rather than raising an issue of nature itself. The competition between 'innate nature' and 'complete nature,' a proxy war between NFP and OR, resulted in the victory of the former as the meaning of nature became fixed. Advocates of NFP pointed out that OR inhibits other physiological functions in the process of inducing ovulation, suggesting that the idea of 'complete nature' could never be achieved. The meaning of nature could no longer be controversial. Since the intervention was unnatural, nature meant innateness, the absence of intervention. Accordingly, the Catholic Bishops of Korea approved the Billings Method, a kind of the NFP, as the official family planning method, and gynecologists at St. Mary's Hospital of Korea also focused on the development and supplementation of the Billings Method. In short, the debate over the methods of natural family planning in mid1970s Korea was a clash of 'innate nature' and 'complete nature.' As a result, this confirmed the limitations of medical practice and reconfirmed the power of magisterium, the church's authority over medical practice.

End-to-End Speech Emotion Recognition With Gender Information

Many works have focused on speech emotion recognition algorithms. However, most rely on the proper selection of speech acoustic features. In this paper, we propose a novel emotion recognition algorithm that does not rely on any speech acoustic features and combines speaker gender information. We aim to benefit from the rich information from speech raw data, without any artificial intervention. In general, speech emotion recognition systems require manual selection of appropriate traditional acoustic features as classifier input for emotion recognition. Utilizing deep learning algorithms, and the network automatically select important information from raw speech signal for the classification layer to accomplish emotion recognition. It can prevent the omission of emotion information that cannot be direct mathematically modeled as a speech acoustic characteristic. We also add speaker gender information to the proposed algorithm to further improve recognition accuracy. The proposed algorithm combines a Residual Convolutional Neural Network (R-CNN) and a gender information block. The raw speech data is sent to these two blocks simultaneously. The R-CNN network obtains the necessary emotional information from the speech data and classifies the emotional category. The proposed algorithm is evaluated on three public databases with different language systems. Experimental results show that the proposed algorithm has 5.6%, 7.3%, and 1.5%, respectively accuracy improvements in Mandarin, English, and German compared with existing highest-accuracy algorithms. In order to verify the generalization of the proposed algorithm, we use FAU and eNTERFACE databases, in these two independent databases, the proposed algorithm can also achieve 85.8% and 71.1% accuracy, respectively.

Microscopic Characteristic and Chemical Composition Analysis of Three Medicinal Plants and Surface Frosts.

The accumulation of chemical constituents of some medicinal plants, such as Paeonia ostii T. Hong et J. X. Zhang, Houpoëa officinalis (Rehder and E. H. Wilson) N. H. Xia and C. Y. Wu. and Atractylodes lancea (Thunb.) DC, can precipitate on the surface and form frosts after natural or artificial intervention. The characteristics of these three medicinal plants and their frosts were analyzed by light microscope, polarizing microscope, stereomicroscope, and metalloscope. The results of ordinary Raman of P. ostii and H. officinalis showed that the frosts of P. ostii matched paeonol, while that of H. officinalis matched magnolol and honokiol. In P. ostii and its frost, 19 peaks were identified by UPLC-Q/TOF-MS, and the main component was paeonol. Eleven components were identified in H. officinalis and its frosts, and the main components were magnolol and honokiol. A. lancea and its frosts were analyzed by gas chromatography-mass spectrometry (GC-MS), 21 were identified, and its main components were hinesol and β-eudesmol. These three medicinal plants accumulate compounds and precipitate frosts on the surface. The results show that the components of the frosts provide a basis for quality evaluation and research on similar medicinal plants, and reveals the scientific connotation of “taking the medicinal materials’ precipitated frosts as the best” of P. ostii, H. officinalis, and A. lancea, to some extent.

Dynamics of echocardiography parameters after circular PADN procedure in patients with mitral valve defects, atrial fibrillation, and high pulmonary hypertension

Aim. To analyze the dynamics of echocardiographic parameters after surgical treatment of severe pulmonary hypertension in patients with mitral valve disease and atrial fibrillation.Material and Methods. Data of surgical treatment in 202 patients with mitral valve disease complicated by severe pulmonary hypertension with blood pressure more than 40 mm Hg and atrial fibrillation were analyzed. Surgical intervention in these patients consisted in surgical correction of mitral dysfunction with artificial prosthetic valve or valve-preserving intervention (group 1, n = 62). In patients of group 2 (n = 89), correction of mitral valve defect was also performed as well as the Maze IV procedure for concomitant atrial fibrillation using bipolar radiofrequency ablator AtriCure. Patients of group 3 (n = 51) underwent integrated surgery, which consisted in the elimination of mitral valve defect, surgical correction of atrial fibrillation by Maze IV procedure, as well as circular radiofrequency denervation of the trunk and mouth of the pulmonary arteries (pulmonary artery denervation (PADN)).Conclusions. The circular PADN procedure was effective and safe, significantly reduced the level of pulmonary hypertension in the postoperative period (p = 0.018), and promoted reverse remodeling of the heart cavities. Integrated surgical correction in patients with mitral dysfunction, atrial fibrillation, and severe pulmonary hypertension may significantly reduce the phenomenon of heart failure (p = 0.023). Further analysis of the effectiveness of radiofrequency denervation of pulmonary arteries with the study of a larger number of patients, analysis of long-term results, as well as determining the possibility of this technique in patients with non-valvular forms of pulmonary hypertension are required.

On-unit CT measures as tools for artificial intelligence to identify random vs. systematic anatomical changes in radiotherapy patients

Background: Although head and neck (H&N) cancer survival is steadily increasing, the close proximity of tumor volumes to organs at risk (OARs) makes radiotherapy planning and delivery challenging for these patients. Changes in patient anatomy (i.e. weight-loss, tumor shrinkage) over 7 weeks of daily radiotherapy may result in increased dosages of radiation to OARs relative to the original treatment plan, consequently hindering post-treatment quality of life. Artificial intelligence-based approaches can improve prediction and monitoring of these effects through identification of systematic changes.
 Objective: To collect and perform an analysis of on-unit CT measurements as surrogate measures of dose changes. Correlations among CT measures may indicate random vs. systematic changes in dose deposition (i.e. dosimetry) and further improve artificial intelligence-based approaches that determine which patients benefit most from treatment re-planning.
 Methods: 250 H&N cancer patients treated with curative chemo-radiotherapy were retrospectively analyzed. Five CT measures including face and neck diameter, chin and shoulder position, and head shift were calculated motivated by current literature. Dosimetric changes were calculated for OARs (pharyngeal constrictor, brainstem, parotid and submandibular glands) and tumour volumes. Conventional correlation analysis and hierarchical clustering were performed to assess group-wise correlations. K-medoid clustering and principal components analysis were conducted to infer groupings of the patients as random or systematic.
 Results: There is a positive correlation between increased dosages to central-axis anatomical structures (spinal cord, pharyngeal constrictor, submandibular glands) and systematic weight-loss effects (change in BMI and weight loss through the face and neck). In line with current literature, clustering indicated that 30.4% of the cohort exhibited systematic anatomical changes, potentially correctable by re-planning. MANOVA confirmed that the systematic anatomical changes corresponded to the spinal cord and brain stem (p<0.005), and Mann-Whitney U tests confirmed that the measures could identify systematic dose increases to the pharyngeal constrictor (p<0.05). Further statistical analyses will be conducted.
 Conclusions: On-unit CT measures appear to be able to distinguish random and systematic dosimetric effects, correlated with changes in dose as expected. These measures can be utilized to improve artificial intelligence-based patient monitoring and intervention techniques.

Genetic Circuit-Assisted Smart Microbial Engineering.

Rapid advances in DNA synthesis, genetic manipulation, and biosensors have greatly improved the ability to engineer microorganisms with complex functions. By accurately integrating quality biosensors and complex genetic circuits, recently emerged smart microorganisms have enabled exciting opportunities for dissecting complex signaling networks and making responses without artificial intervention. However, because of the lack of design principles, developing such smart microorganisms remains challenging. In this review, we propose the concept of smart microbial engineering (SME) and describe the general features of basic SME, including the circuit architecture, components, and design process. We also summarize the latest SME achievements, remaining challenges, and potential solutions in this growing field.

Responses of tree growth to artificial intervention on micro-topography in degraded woodland on hillslope

We explored the effects of microtopography changes from artificial intervention of soil and water conservation (contour reverse-slope terrace, CRT) on tree growth in a degraded woodland on hillslope in water resource area of Songhua dam in Kunming City. The differences of the diameter at breast height (DBH), tree height, new branch increment, and leaf area index (LAI) of dominant tree species in different plots were compared through dynamic monitoring. Then, the causes of differences were analyzed by combining the soil water availability. The results showed that the maximum and mean values of DBH and height of Pinus yunnanensis (dominant species) in contour reverse-slope terrace plot were larger than those in control plot (CK), and the proportion of small-sized and low-dwarf trees in degraded forest community was higher, which indicated that the growth potential of new and seriously degraded forest was stimulated. The annual variation rate of the ave-rage new branch length and diameter of P. yunnanensis in different plots reached 72.4% and 39.1%, respectively. The changes of new branch growth of P. yunnanensis, LAI of plots, and the new branch growth rate were greater in CRT than those in CK. Soil water content was significantly correlated with both the new branch growth and LAI. The ratio of available water in plot (64.2%) was higher than that in CK (54.7%). During the rainy season (from May to September), the available water in CRT existed longer, which was conducive to tree growth and the improvement of plant community structure in degraded woodland.

From natural driving to artificial intervention: Changes of the Yellow River estuary and delta development

The estuarine delta of the Yellow River is a region of strong land-ocean-human interactions that undergoes a unique evolutionary process. In this paper, we combined historical data and remote sensing images to better understand the evolution of the Yellow River delta. Changes in the Yellow River estuarine flow path from natural swing to artificial diversion were first studied to understand the rules and mechanism of the delta formation. The complex geomorphic structural system of the Yellow River delta is constituted by contemporary and modern deltas and their sub-deltas. Remote sensing images and measured data indicate that variations in runoff and sediment load in the Yellow River estuary will affect the deltaic erosion and siltation. The natural and artificial factors that cause runoff and sediment changes include climate change, soil erosion control in the middle reaches, building reservoirs along the Yellow River, which are used as water supply for local cities and farmland irrigation, as well as the water and sediment regulation experiment in the Xiaolangdi reservoir. Furthermore, artificial intervention of diversion channels has contributed to the maintained stability of the Yellow River estuary environment, so as to have regional development and prosperity of the Yellow River delta.

The resilience research of the plant and soil optimization design base on the habitat-site design in semiarid green space.

As the northwest region's advancement in urbanization has improved, the contradiction between urbanization and the fragile ecological environment has been gradually resolved. Facing the constraint of regional fragile ecological pressures, northwestern cities must create new ecological processes by using resilient designs. It is necessary to promote an organic ease construction mode, which is proper for the ecological matrix space in ecological construction of the northwestern landscape. In this research, which aims to solve problems and conflicts between the plant landscape design and site habitat conditions, we used the experimental method for plant landscape construction and soil optimization based on the previous theory and foundation of habitat-site design. We established the research framework as "natural plant community investigation experimental base simulation database construction." First, based on natural plant community research, the ecological prototype of the near natural plant community was quantized and evaluated, and the database was established; second, the plant community fabric was constructed, and the soil optimization technique was tested by using a "habitat island," the purpose of which was to create a stably structured, low-maintenance, drought-adaptive, water-resistant, and beautiful plant community model to screen the suitable soil medium ratio and mulching type for plant growth. Finally, following the "artificial intervention, natural formation" design idea, the practice of habitat unit construction and established database of the plant community structure model was carried out to provide theoretical and technical support for the plant landscape construction and soil optimization of habitat construction in the northwestern green space.

Brain Image Recognition Algorithm and High Performance Computing of Internet of Medical Things Based on Convolutional Neural Network

Due to the wide variety of medical images and the complexity of the human body structure, the characteristics of manual extraction of medical images are difficult, the adaptive ability is poor, and the classification effect needs to be improved. Aiming at the shortcomings of traditional medical image recognition methods, this paper proposes an adaptive convolutional neural network model CNN-BN-PReLU based on the convolutional neural network method. The model first performs batch normalization (BN) processing on the input of each feature map of each layer of network, and then adaptively adjusts the parameters by using Parametric Rectified Linear Unit (PReLU) to compare the BN algorithm. Based on the performance before and after the activation function, an adaptive convolutional neural network model is constructed. The experimental results show that the model can abstract the image features without artificial intervention, speed up the network convergence speed and shorten the training time, and significantly improve the image recognition rate and reduce the misdiagnosis rate and missed diagnosis rate of the disease.

Artificial Interference of Stress Field in a Near‐Fracture Zone by Water Injection in a Preexisting Crack

The in situ stress has an important influence on fracture propagation and fault stability in deep formation. However, the development of oil and gas resources can only be determined according to the existing state of in situ stress in most cases. It is passive acceptance of existing in situ stress. Unfortunately, in some cases, the in situ stress conditions are not conducive to resource development. If the in situ stress can be interfered in some ways, the stress can be adjusted to a more favorable state. In order to explore the method of artificial interference, this paper established the calculation method of the in situ stress around the cracks based on fracture mechanics at first and obtained the redistribution law of the in situ stress. Based on the obtained redistribution law, attempts were made to interfere with the surrounding in situ stress by water injection in the preexisting crack. On this basis, the artificial stress intervention was applied. The results show that artificial interference of stress can effectively be achieved by water injection in the fracture. And changing the fluid pressure in the crack is the most effective way. By stress artificial intervention, critical pressure for water channelling in fractured reservoirs, directional propagation of cracks in hydraulic fracturing, and stress adjustment on the structural plane were applied. This study provides guidance for artificial stress intervention in the exploitation of the underground resource.

Formation, distribution and resource potential of the "sweet areas (sections)" of continental shale oil in China

Abstract Shale oil refers to oil stored in organic-rich shale. It is an important part of unconventional oil and provides a large amount of resources. Based on the systematic study of the geological characteristics of shale oil in China's major continental basins, the geological connotation of the “sweet area (section)” in shale oil is proposed in this paper, and it refers to areas that can be preferentially explored and developed in oil-bearing shale formations under current economic and technical conditions. The formation conditions and distribution characteristics of continental shale oil in China are analyzed with a focus on the geological properties of the Triassic Yanchang Formation in the Ordos Basin, the Cretaceous Qingshankou Formation in the Songliao Basin and the Permian Lucaogou Formation of the Jimusar Sag in the Junggar Basin. It is found that the “sweet areas” of shale oil with medium-high maturation are mainly located in thick shales with a Ro greater than 0.9%, and the “sweet sections” are mainly located in the middle and lower parts of the shale formation. The geological resources of continental shale oil in the major continental basins in China are estimated to be approximately 3700 × 108 tons. The in situ conversion process (ICP) may be a key technology for the effective development of medium-to low-mature shale oil. The scale, maximum burial depth and present depth of the organic-rich shale are proposed as the three key parameters for the optimization of pilot sites by taking the Triassic Yanchang Formation in the Ordos Basin as an example. Using ICP, it is expected that the recoverable resources of China's continental shale oil will be approximately (400–500) × 108 tons. The terminology of man-made reservoir is proposed in this paper. In an man-made reservoir, the overall industrial output of the sweet areas (sections) is formed by artificial intervention and the construction of an underground fracture network system with man-made permeability. Continental shale oil may be another revolutionary source of unconventional oil and is expected to be first successfully produced in China.

An Automatic Transfusion Set for Accelerating Inoculation Process of Agarwood Artificial Inducer

The formation of agarwood resin can be accelerated by natural or artificial intervention of Aquilaria tree. Many planters developed varies inoculant or inducer in order to accelerate the formation of agarwood. Aquilaria trees require more than 20 years to form natural agarwood formation naturally, thus it becomes impractical especially for commercial purposes. On the other hand, the production of varies booster commonly unequipped with standard inoculating apparatus. The existing agarwood-inducing techniques require longer time to complete the inoculation process for each tree. Therefore, this study aims to develop a novel automated transfusion set for facilitating the inoculation process of Aquilaria tree. An automatic transfusion is equipped with magnetic sensor to monitor the inoculants volume. The automated transfusion set operated by 12V water pump that controlled by Arduino controller to give a constant pressure during inoculation process. The infusion of inoculants also controlled by Arduino controller with aids of magnetic sensor. Hence, the total volume of inoculants being infused inside the Aquilaria tree can be monitored. The result for the automated transfusion is indeed reduces the time of inoculation process which is roughly 70 ml in 2 hours compared to the conventional techniques (whole tree inoculation and syringes inoculation) by 2 hours for 50 ml. Thus, this invention is potentially benefitting the planters to reduce time and laborious particularly in inoculating liquid inducers or inoculants.

Simulation of Solidification Process of Metallic Gallium and Its Application in Preparing 99.99999% Pure Gallium

Computer simulations were performed on the spontaneous solidification and artificial intervention solidification processes of liquid gallium (Ga) by using molecular dynamics. The formation and evolution mechanism of the microstructure of liquid Ga under different solidification conditions were systematically studied using a combination of microstructural methods including the pair distribution function, bond-type index, and cluster-type index. The simulation results revealed that spontaneous homogeneous nucleation started from icosahedrons and defective icosahedrons. Artificial intervention can effectively control the thermostatic crystallization processes of liquid Ga, favoring crystal formation with large grains. The simulation results explain important experimental phenomena and the solidification theory of Ga smelting by the crystallization method from the microscopical point of view. We tested experimentally the process conditions for high-purity Ga smelting by the crystallization method as obtained from our simulations, resulting in successful preparation of metallic Ga with purity of 99.99999%.

A level set image segmentation method based on a cloud model as the priori contour

A novel image segmentation method combining a cloud model and a level set (CM-LS) is proposed in this article. At present, the cloud model can only obtain the rough segmentation result of an image, but the level set method is sensitive to the initial contour. The core idea of this method is to use the rough segmentation result of cloud model as the initial contour of the level set and then obtain the final result by the contour evolution. In this method, the cloud model is used to decompose the boundary of the image, which reduces the occurrence probability and occurrence degree of the instability problem caused by artificial intervention; at the same time, the convergence of the level set function is accelerated, and the initializing operation of the level set function that uses the cloud model algorithm can also effectively reduce the noise sensitivity of the function itself. Compared with the conventional level set method, the proposed method is general and accurate. The experimental data set in this article includes natural images of the Berkeley database, medical images and synthetic noise images. The experimental results show that the method is effective.