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Advances in machine learning have enabled sufficiently accurate predictions of protein structure to be used in macromolecular structure determination with crystallography and cryo-electron microscopy data. The Phenix software suite has AlphaFold predictions integrated into an automated pipeline that can start with an amino acid sequence and data, and automatically perform model-building and refinement to return a protein model fitted into the data. Due to the steep technical requirements of running AlphaFold efficiently, we have implemented a Phenix-AlphaFold webservice that enables all Phenix users to run AlphaFold predictions remotely from the Phenix GUI starting with the official 1.21 release. This webservice will be improved based on how it is used by the research community and the future research directions for Phenix.

The application of machine learning to cryogenic electron microscopy (cryoEM) data analysis has added a valuable set of tools to the cryoEM data processing pipeline. As these tools become more accessible and widely available, the implications of their use should be assessed. We noticed that machine learning map modification tools can have differential effects on cryoEM densities. In this perspective, we evaluate these effects to show that machine learning tools generally improve densities for biomacromolecules while generating unpredictable results for ligands. This unpredictable behavior manifests both in quantitative metrics of map quality and in qualitative investigations of modified maps. The results presented here highlight the power and potential of machine learning tools in cryoEM, while also illustrating some of the risks of their unexamined use.

Copyright of Biology Teaching is the property of East China Normal University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Continue Education Research is the property of Harbin Normal University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Stochastic gradient descent method and its variants constitute the core optimization algorithms that achieve good convergence rates for solving machine learning problems. These rates are obtained especially when these algorithms are fine-tuned for the application at hand. Although this tuning process can require large computational costs, recent work has shown that these costs can be reduced by line search methods that iteratively adjust the step length. We propose an alternative approach to stochastic line search by using a new algorithm based on forward step model building. This model building step incorporates second-order information that allows adjusting not only the step length but also the search direction. Noting that deep learning model parameters come in groups (layers of tensors), our method builds its model and calculates a new step for each parameter group. This novel diagonalization approach makes the selected step lengths adaptive. We provide convergence rate analysis, and experimentally show that the proposed algorithm achieves faster convergence and better generalization in well-known test problems. More precisely, SMB requires less tuning, and shows comparable performance to other adaptive methods. [ABSTRACT FROM AUTHOR]

With the continuous expansion of network scale, the hierarchical and modular characteristics of network structure are becoming increasingly prominent. The traditional single-layer network research paradigm has certain limitations in characterizing the complex relationships between various network systems. Analyzing and constructing models for multi-layer networks has gradually become an important direction in complex network research. The author proposes a multi-layer network model for the Internet and the significance of constructing multi-layer network models in the field of the Internet, based on the characteristics of the multi-layer network structure presented in the application process of the Internet. By analyzing the characteristics of internet data, a multi-layer network model covering three types of networks, namely the internet infrastructure layer, business application layer, and user account layer, was designed. The experimental results show that the average shortest path lengths of the three networks, namely routing relationship network, web hyperchain network, and email address network, are 3.8, 2.9, and 1.7, respectively. Due to the existence of inter layer edges in multi-layer networks, nodes can reach each other across layers in addition to intra layer edges. Experimental results show that the average shortest path length of the three networks has been shortened to some extent, with values of 3.1, 2.7, and 1.6, respectively. On the basis of the single-layer network generation model, a layer correlation method for multi-layer networks was designed, and the model construction of multi-layer networks was achieved. [ABSTRACT FROM AUTHOR]

This study aimed to construct a risk prediction model for male patients diagnosed with type 2 diabetes mellitus and sarcopenia, and subsequently assess its effectiveness in predicting efficacy. The study subjects consisted of male patients diagnosed with type 2 diabetes who were admitted to the hospital between August 2023 and February 2024. The participants were categorized into two groups: the sarcopenic group (n = 92) and the non-sarcopenic group (n = 196). Patients’ clinical data, lifestyle habits, comorbidities, medical history, and laboratory test markers were collected and subjected to statistical analysis. The findings from both univariate and multivariate logistic regression analyses indicate that age and uric acid (UA) are associated with an increased risk of developing sarcopenia in male patients with type 2 diabetes mellitus. Conversely, Body Mass Index (BMI) and vitamin D are associated with a decreased risk of sarcopenia in men with type 2 diabetes. The probability model for predicting the risk of Sarcopenia in male patients with type 2 diabetes: P = 1/[1 + exp(4.227 − 2.029X1 − 1.165X2 + 0.752X3 + 0.216X4)]. Hosmer and Lemeshow’s goodness-of-fit test showed that χ2 = 7.993, p = 0.434. Receiver Operator Characteristic Curve (ROC) curve analysis showed that Area Under the curve (AUC) was 0.911, 95% Confidence Interval (CI) was 0.879–0.944 respectively. The probability model value was 0.88, which is greater than 0.5, as indicated by the analysis of the overall model quality result. A high clinical predictive value was demonstrated by the risk prediction model of type 2 diabetes mellitus with sarcopenia, which can be used to facilitate early intervention and prevention of the disease.

Approximate policy iteration (API) is a class of reinforcement learning (RL) algorithms that seek to solve the long-run discounted reward Markov decision process (MDP), via the policy iteration paradigm, without learning the transition model in the underlying Bellman equation. Unfortunately, these algorithms suffer from a defect known as chattering in which the solution (policy) delivered in each iteration of the algorithm oscillates between improved and worsened policies, leading to sub-optimal behavior. Two causes for this that have been traced to the crucial policy improvement step are: (i) the inaccuracies in the policy improvement function and (ii) the exploration/exploitation tradeoff integral to this step, which generates variability in performance. Both of these defects are amplified by simulation noise. Deep RL belongs to a newer class of algorithms in which the resolution of the learning process is refined via mechanisms such as experience replay and/or deep neural networks for improved performance. In this paper, a new deep learning approach is developed for API which employs a more accurate policy improvement function, via an enhanced resolution Bellman equation, thereby reducing chattering and eliminating the need for exploration in the policy improvement step. Versions of the new algorithm for both the long-run discounted MDP and semi-MDP are presented. Convergence properties of the new algorithm are studied mathematically, and a post-earthquake disaster response case study is employed to demonstrate numerically the algorithm's efficacy. [ABSTRACT FROM AUTHOR]

Introduction In recent years, the number of cardiac surgeries has been on the rise. Several predictive models have been developed to assess the risk of mortality for these patients. EuroSCORE II, a widely used model, demonstrates good discrimination and calibration for the European population, but its accuracy may vary in the Indonesian population. The purpose of this study was to develop a scoring model tailored to Indonesian population, which may have better accuracy in assessing in-hospital mortality risk among adult cardiac surgery patients. Method A retrospective study was conducted using medical records of adult cardiac surgery patients from four participating hospitals. Potential risk factors were included as variables and analyzed using bivariate and multivariable logistic regression with the L-backward method. Bootstrapping was applied to enhance the model’s validity. Receiver operating characteristic (ROC) curves were created for each model, and the area under the curve (AUC) was calculated to assess discrimination ability, while the Hosmer-Lemeshow test was used to evaluate calibration. Results We extracted data from 4,875 patients, with a mean age of 50.41 years, and most patients were men (63.1%). The majority of patients were in NYHA class I-II. The in-hospital mortality rate was 6.5%. From 62 potential variables, 13 variables were included in the final model. Our new model demonstrated strong discrimination and calibration (AUC 0.7564; Hosmer-Lemeshow p = 0.9510). Conclusion The newly developed scoring model exhibited good discrimination and calibration, making it a promising tool for predicting in-hospital mortality in adult cardiac surgery patients in Indonesia.

The current study aimed to build a model of the causal relationships between cognitive agility, psychological flow, self-differentiation, and academic passion among students of the Faculty of Education, Helwan University, and to verify the suitability of this model to the data of the research sample, on a sample of Helwan university students, and that sample consisted of (405) male and female students. of the students of the Faculty of Education, helwan university, who were selected from the students of the scientific and literary sections enrolled in the study groups (first, second, third, fourth), and their chronological ages ranged between (17.5: 22) years, with an average age of (19.207) years and a standard deviation of (1.0981). (164 males, 241 females) and the following tools were applied: scales of cognitive agility, psychological flow, self-differentiation, and academic passion prepared by the two researchers. Path analysis was also used to verify the validity of the hypotheses; The results of the current research have shown that all indicators of goodness of fit of the model are in the ideal range, and the value of the Chisquare (X2) is 1.684, which is a statistically non-significant value (P=0.194), which is a value greater than (0.05), and this indicates goodness of fit. causal model; The results also indicated that there are no statistically significant differences between the average scores of College of Education students on the psychological flow scale and its dimension (ability to face challenges, speed of response to required tasks) due to the gender variable (males, females), while there is a statistically significant difference at the level (0.01). between males and females in the dimension of decreased awareness of time while performing tasks, in favor of males. There are statistically significant differences at the level (0.001) between the average scores of college of education students on the psychological flow scale and its sub-dimensions due to the academic specialization variable in favor of students in scientific specializations. There are no statistically significant differences between The average scores of the College of Education students on the Psychological Flow Scale and its subdimensions are due to the bilateral interaction between gender (male, female) and academic specialization (scientific, literary). There are no statistically significant differences between the average scores of the College of Education students on the Self-Differentiation Scale and its sub-dimensions due to the gender variable. (males, females), there are statistically significant differences at significance levels (0.05, 0.01, 0.001) between the average scores of college of education students on the self-differentiation scale and its sub-dimensions due to the academic specialization variable in favor of students in scientific specializations. There are no statistically significant differences between the average scores of students The college of education on the self-differentiation Scale and its sub-dimensions are due to bilateral interaction. There are no statistically significant differences between the average scores of the college of education students on the academic passion scale and its sub-dimensions due to the gender variable (males, females). There are statistically significant differences at the (0.01) level between the average scores. college of education students on the academic passion scale and the dimension (compulsive passion) attributed to the academic specialization variable in favor of students in scientific specializations. There are no statistically significant differences between the average scores of College of Education students on the academic passion scale and its sub-dimensions (harmonious passion, compulsive passion) attributed to the bilateral interaction. [ABSTRACT FROM AUTHOR]

Oil peony is an important oil crop that is primarily sown by using artificial methods at present. Its seeder encounters the problems of low efficiency of seeding that significantly limits the highly efficient mechanized production of high-quality peony oil. In this study, Fengdan white oil peony seeds were used as the research object and repose angle as the response value to establish a discrete element model (DEM) and parameter calibration. The range of parameters of oil peony seeds was first obtained through an experiment, and their repose angle was obtained by an inclinometer. A three-dimensional DEM of oil peony seeds was then established. The Plackett–Burman (PB) test was utilized to screen the parameters that had a significant influence on the repose angle, and the steepest ascent (SA) test was applied to determine their optimum range of testing. Following this, based on Box–Behnken (BBD) test results, a second-order regression model between the important parameters and the repose angle was constructed. Finally, the absolute minimum difference between simulated and measured repose angles was utilized as the objective of optimization to obtain the following optimum combination of parameters: The values of the seed–steel collision recovery coefficient (CRC), seed–seed static friction coefficient (SFC), seed–steel SFC, and seed–seed rolling friction coefficient (RFC) were 0.704, 0.324, 0.335, and 0.045, respectively. This optimal combination of parameters was confirmed through simulations, and the error between simulated and measured repose angles was only 0.67%, indicating that the calibrated DEM of oil peony seeds was reliable. [ABSTRACT FROM AUTHOR]

Copyright of Experimental Technology & Management is the property of Experimental Technology & Management Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Experimental structure determination can be accelerated with artificial intelligence (AI)-based structure-prediction methods such as AlphaFold. Here, an automatic procedure requiring only sequence information and crystallographic data is presented that uses AlphaFold predictions to produce an electron-density map and a structural model. Iterating through cycles of structure prediction is a key element of this procedure: a predicted model rebuilt in one cycle is used as a template for prediction in the next cycle. This procedure was applied to X-ray data for 215 structures released by the Protein Data Bank in a recent six-month period. In 87% of cases our procedure yielded a model with at least 50% of Cα atoms matching those in the deposited models within 2 Å. Predictions from the iterative template-guided prediction procedure were more accurate than those obtained without templates. It is concluded that AlphaFold predictions obtained based on sequence information alone are usually accurate enough to solve the crystallographic phase problem with molecular replacement, and a general strategy for macromolecular structure determination that includes AI-based prediction both as a starting point and as a method of model optimization is suggested.

A quantitative method of main components (total sugar, reducing sugar, total acid, ammonia nitrogen, anthocyanins, procyanidins, total phenols, flavonoids and polysaccharides) in Lycium ruthenicum Murr was developed using Fourier transform near infrared spectroscopy (FT-NIR) combined with chemometric analysis. Firstly, 11 pretreatment methods were compared for the original spectra, and the optimal pretreatment method of each component was selected. Then, the model results established by partial least squares (PLS), interval partial least squares (iPLS) and synergistic interval partial least squares (siPLS) algorithms were compared, and finally siPLS was adopted for modeling. The results showed that the correlation coefficient of calibration (Rc) and prediction (RP) of total acids, ammonia nitrogen, anthocyanins, procyanidins, total phenols and flavonoids were all greater than 0.9818, and the relative analysis error (RPD) was more than 2.5, indicating an excellent model performance. The modeling effect of total sugar, reducing sugar, and polysaccharide was also good. The established calibration models could be used for actual detection. The predicted values of the verification samples did not significantly differ from the measured values, with a prediction error of only ±0.1%. Therefore, the prediction model had high reliability. The prediction model established in this study can enable nondestructive, rapid and accurate main components in dried and fresh fruits of Lycium ruthenicum Murr.

Bi-directional DC-AC converters are widely used in the field of electric vehicle-to-grid. However, the inductance of the grid-side interface filter is affected by the length of the grid connection and the power level, which presents nonlinear characteristics. This poses challenges for high-performance grid waveform control. In this paper, a modeling method for bi-directional DC-AC grid-connected converters based on type-II T-S fuzzy models is proposed, and the corresponding type-II T-S fuzzy control strategy is designed to address the parameter uncertainty and non-linearity issues. Simulation results show that type-II T-S fuzzy control offers superior control performance and better current waveform quality compared to type-I T-S fuzzy control under uncertainty parameter conditions. The effectiveness of the proposed strategy is further validated through a 1 kW prototype of a bi-directional DC-AC converter. [ABSTRACT FROM AUTHOR]

Copyright of Advanced Textile Technology is the property of Zhejiang Sci-Tech University Magazines and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Proteins are well known 'shapeshifters' which change conformation to function. In crystallography, multiple conformational states are often present within the crystal and the resulting electron‐density map. Yet, explicitly incorporating alternative states into models to disentangle multi‐conformer ensembles is challenging. We previously reported the tool FLEXR, which, within a few minutes, automatically separates conformational signal from noise and builds the corresponding, often missing, structural features into a multi‐conformer model. To make the method widely accessible for routine multi‐conformer building as part of the computational toolkit for macromolecular crystallography, we present a graphical user interface (GUI) for FLEXR, designed as a plugin for Coot 1. The GUI implementation seamlessly connects FLEXR models with the existing suite of validation and modeling tools available in Coot. We envision that FLEXR will aid crystallographers by increasing access to a multi‐conformer modeling method that will ultimately lead to a better representation of protein conformational heterogeneity in the Protein Data Bank. In turn, deeper insights into the protein conformational landscape may inform biology or provide new opportunities for ligand design. The code is open source and freely available on GitHub at https://github.com/TheFischerLab/FLEXR‐GUI. [ABSTRACT FROM AUTHOR]

In this study, three identically designed model buildings and their heating energy consumption was compared. Those pilot scale buildings are equal by size and were located on the same location. During the measuring campaign both external and internal temperatures were recorded, beside the energy consumption. The model buildings were labeled as A (constant baseline), B (baseline varying according to a time program), and C (unheated, blank). During the measurements, the thermal mass of the buildings was altered. The same amount of thermal mass was installed in all three model buildings during all measurements. According to our results under real weather conditions, the intermittent heating requires less energy than maintaining a constant temperature, and the energy saving is inversely proportional to the time constant at intermittent heating. Instead of specific heat mass, a thermal time constant was used to compare intermittent and constant heating. It was established that as the thermal constant of the model building increases, the energy savings between maintaining a variable base temperature and maintaining a constant base temperature decrease. The expected savings are between 4% and 7%. [ABSTRACT FROM AUTHOR]

Heat load prediction is crucial to the heat regulation of district heating systems (DHS). In heat load forecasting tasks, deep learning can frequently achieve more accurate model building. A deep learning algorithm, the temporal convolutional network (TCN), has been used for DHS heat load prediction. However, there are many hyperparameters for TCN. Manually tuning the TCN parameters cannot make the model have good performance. This study presents a hybrid method based on sand cat swarm optimization (SCSO) and TCN. The SCSO is used to optimize the hyperparameters (number of filters, filter size, dropout rate, and batch size) of TCN. To verify the effectiveness of SCSO-TCN, another two hybrid models, particle swarm optimization with TCN and the sparrow search algorithm with TCN, are established for comparison. The historical heat load data of three heat exchange stations in Tianjin is utilized for the testing experiments. The findings demonstrate that SCSO-TCN has higher predictive accuracy and better generalization ability than the PSO-TCN and SSA-TCN models. [ABSTRACT FROM AUTHOR]

Copyright of Fly Ash Comprehensive Utilization is the property of Hebei Fly Ash Comprehensive Utilization Magazine Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A quantitative method of main components (total sugar, reducing sugar, total acid, ammonia nitrogen, anthocyanins, procyanidins, total phenols, flavonoids and polysaccharides) in Lycium ruthenicum Murr was developed using Fourier transform near infrared spectroscopy (FT-NIR) combined with chemometric analysis. Firstly, 11 pretreatment methods were compared for the original spectra, and the optimal pretreatment method of each component was selected. Then, the model results established by partial least squares (PLS), interval partial least squares (iPLS) and synergistic interval partial least squares (siPLS) algorithms were compared, and finally siPLS was adopted for modeling. The results showed that the correlation coefficient of calibration (Rc) and prediction (RP) of total acids, ammonia nitrogen, anthocyanins, procyanidins, total phenols and flavonoids were all greater than 0.9818, and the relative analysis error (RPD) was more than 2.5, indicating an excellent model performance. The modeling effect of total sugar, reducing sugar, and polysaccharide was also good. The established calibration models could be used for actual detection. The predicted values of the verification samples did not significantly differ from the measured values, with a prediction error of only ±0.1%. Therefore, the prediction model had high reliability. The prediction model established in this study can enable nondestructive, rapid and accurate main components in dried and fresh fruits of Lycium ruthenicum Murr. [ABSTRACT FROM AUTHOR]

Landslide disasters occur frequently in Fujian Province, and early warning of landslide disasters on a regional scale is an important means of effective disaster prevention and mitigation. Due to the complex mechanism of landslide disasters, the traditional regional landslide early warning methods have such problems as insufficient accuracy. Deep learning mainly refers to the technology of feature extraction, abstraction, representation and learning by constructing the neural network model, which is a kind of machine learning. As a classical deep learning algorithm, convolutional neural network has more powerful classification and representation ability than traditional machine learning. Taking Fujian Province as the research area, this paper introduces the convolution neural network into the field of landslide disaster early warning and constructs a regional landslide early warning model of Fujian Province. The process is as follows: (1) The SMOTE optimization algorithm is used to optimize the sample database of landslide disasters in Fujian Province from 2010 to 2018, enlarging the number of positive samples and expanding the proportion of positive and negative samples from 1∶3.4 to 1∶2, and the total number of samples reaches 18040. (2) Construct a convolution neural network model structure, which includes an input layer, two convolution layers, two maximum pooling layers, a full connection layer and an output layer. (3) Use the convolution neural network to train the optimized samples (80% of the samples from 2010 to 2018 as the training set), and use the Bayesian optimization algorithm to optimize the model parameters to obtain the regional landslide early warning model of Fujian Province. (4) The model is tested with 20% of the samples from 2010 to 2018 as the test set, and the confusion matrix and ROC curve are used to test the model. The results show that the accuracy of the model ranges from 0.96 to 0.97, the AUC value is 0.977, indicating that the model accuracy and generalization ability are good. (5) The actual situation of the landslide disaster in the flood season of 2019 is taken as a positive sample, negative samples are collected through the method of time-space sampling, and the 2019 regional landslide sample verification set (603 samples) is constructed. The model is further verified by using the confusion matrix and ROC curve. The results show that the accuracy of the model ranges from 0.75 to 0.85, and the AUC value is 0.852. Although only the actual landslide samples in the flood season of 2019 is used for verification, good results is also achieved. In this paper, the convolution neural network algorithm is applied to the regional landslide early warning, which provides a new way to establish the regional landslide early warning model. The preliminary verification shows that the model is effective and will be further applied and verified in Fujian Province in the future.

Hyperspectral imaging technology has emerged as a prominent research area for quantitatively estimating soil nutrient content owing to its non-destructive, rapid, and convenient features. Our work collected the data from soil samples using the hyperspectrometer. Then, the data were processed. The competitive adaptive reweighted sampling (CARS) algorithm reduced the original 148 bands to 13, which accounted for 8.8% of the total bands. These selected bands possess a certain level of interpretability. Based on the modelling results, it can be concluded that the prediction model constructed by the least squares support vector machine (LSSVM) exhibited the highest accuracy. The coefficient determination, root mean square error, and ratio performance deviation were 0.8295, 2.95, and 2.42, respectively. These findings can provide theoretical support for the application of hyperspectral technology in detecting the content of the AHN in soil. Moreover, they can also serve as a reference for the rapid detection of other soil components.

Copyright of Biology Teaching is the property of East China Normal University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Canopy interception affects the effective rainfall for plant growth. Extensive studies of canopy interception have focused on trees, but few on crops, due to the longer canopy duration of trees. However, overlooking the canopy rainfall interception results in an overestimate of effective water for crop growth and development. It is still unclear how crop canopy interception is influenced. In this study we examined the effect of leaf area index (LAI) and rainfall characteristics on soybean canopy interception. The results showed that the LAI, rainfall intensity and rainfall duration were the most relevant factors affecting canopy interception. The relationship between canopy interception and LAI was expressed by a linear function, as well as the relationship between canopy interception and rainfall amount. We proposed a canopy interception model versus LAI and rainfall characteristics to simulated the water loss by canopy interception. The results indicated that canopy interception loss increased with bigger LAI and decreased with rainfall amount increasing, indicating that canopy interception can't be ignored in the crop production, especially with small LAI and high precipitation. [ABSTRACT FROM AUTHOR]

Density modification is a standard step to provide a route for routine structure solution by any experimental phasing method, with single‐wavelength or multi‐wavelength anomalous diffraction being the most popular methods, as well as to extend fragments or incomplete models into a full solution. The effect of density modification on the starting maps from either source is illustrated in the case of SHELXE. The different modes in which the program can run are reviewed; these include less well known uses such as reading external phase values and weights or phase distributions encoded in Hendrickson–Lattman coefficients. Typically in SHELXE, initial phases are calculated from experimental data, from a partial model or map, or from a combination of both sources. The initial phase set is improved and extended by density modification and, if the resolution of the data and the type of structure permits, polyalanine tracing. As a feature to systematically eliminate model bias from phases derived from predicted models, the trace can be set to exclude the area occupied by the starting model. The trace now includes an extension into the gamma position or hydrophobic and aromatic side chains if a sequence is provided, which is performed in every tracing cycle. Once a correlation coefficient of over 30% between the structure factors calculated from such a trace and the native data indicates that the structure has been solved, the sequence is docked in all model‐building cycles and side chains are fitted if the map supports it. The extensions to the tracing algorithm brought in to provide a complete model are discussed. The improvement in phasing performance is assessed using a set of tests. [ABSTRACT FROM AUTHOR]

Copyright of Hydrogeology & Engineering Geology / Shuiwendizhi Gongchengdizhi is the property of Hydrogeology & Engineering Geology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Natural Gas & Oil is the property of Editorial Department of Natural Gas & Oil and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Modern e-learning system is a representative service form in innovative service industry. This paper designs a personalized service domain system, optimizes various parameters and can be applied to different education quality evaluation, and proposes a decision tree recommendation algorithm. Information gain is carried out through many existing principles of improved decision tree algorithm, and the information gain of the algorithm determines the inheritance of information. The process of modern e-learning system is based on personalized teaching and humanized intelligent interaction. This paper theoretically analyzes the improvement performance of the existing e-learning system in teaching quality evaluation and shows a good classification effect. This model provides reference materials for the expansion of education and teaching and provides a feasible practical model for personalized teaching in online schools. The authors provide good educational conditions and environment for students and cultivate all-around talents for the society. [ABSTRACT FROM AUTHOR]

With the development of informatization, traditional industries have been seriously impacted by cross-border e-commerce (CBEC), and the financial management mode no longer meets the needs of CBEC users. As cross-border electronic trading platforms face enormous financial data challenges, they need financial supervision and effective network risk protection. Artificial intelligence (AI) can effectively improve the financial accounting ability and resource integration of platforms. However, at present, the underdeveloped financial management system of CBEC platforms and problems, such as the running records of platform funds have seriously hindered the development of the financial standardization of platforms because the financial management system affects managers' statistical analysis of financial data. Therefore, this paper analyzes the financial risks, the management problems and the causes of these problems of CBEC platforms and then uses AI to study the financial operation of CBEC platforms. Subsequently, this paper uses a machine learning (ML) algorithm to analyze the financial data clustering center and the security factor of the financial model. Finally, this paper proposes some corresponding strategies for financial model optimization and construction that can improve the information security and capital management of CBEC finance and promote the long-term development of CBEC platforms. The experimental results show that the classifier value and the safety factor of the financial model of CBEC platforms gradually increase under the ML algorithm. The mean value of the classifier value is approximately 1.14, and the mean value of the safety factor is approximately 1.37. Overall, the initial value of the classifier value of the platform financial model is 0.85, which increases to 1.41 on the seventh day, while the whole process increases by 0.56. The initial value of the safety factor of the platform financial model is 1.10, which increases to 1.64 on the seventh day, while the whole process increases by 0.54. The transaction information security and financial accounting accuracy of the CBEC platform financial model is better than that of the original financial model. The transaction information security is 10.4% higher than that of the original financial model, and the financial accounting accuracy is 10% higher than that of the original financial model. In other words, both AI and ML can promote the financial accounting standards and long-term development of CBEC platforms. [ABSTRACT FROM AUTHOR]

The problem of environmental water pollution is becoming increasingly important. Inland rivers and lakes form interconnected water networks with fragile water ecosystems, and urban water pollution problems occur frequently. Chemical oxygen demand (COD), dissolved oxygen (DO), total phosphorus (TP), total nitrogen (TN), and ammonia nitrogen (NH3-N) in inland rivers are important indicators to evaluate water health quality. Timely and accurate reflection of dynamic changes to the key indices of urban river health status are of vital practical significance to adjust water treatment policy and ensure the stability of the aquatic environment and people's health. This study used COD, DO, TP, TN and NH3-N as typical water quality parameters for a reservoir in Guangxi Province, China and established a set of standardized processes covering UAV hyperspectral sampling and ground spectral correction, spectral data preprocessing, and modeling. In combination with machine learning and statistical analysis, an inversion method for measuring urban inland water pollution from UAV hyperspectral imaging with different dynamic monitoring parameters was proposed. And we compared the different combinations of preprocessing algorithm-regression algorithm and dimensionality reduction algorithm to get a unified model for quantitative estimation of water quality parameter concentration. We evaluated the performance of the proposed model according to root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and coefficient of determination ( R 2 ). The experimental results showed that our model was superior to other algorithms in RMSE, MAE, MAPE, and R 2 . The MAPE of this model ranged from 0.01 to 0.12 and R 2 ranged from 0.84 to 0.98 in all water quality parameters. In general, this study provides an effective tool for decision-makers to investigate the source and physical mechanism of water pollution and establish a graded water quality evaluation model. [ABSTRACT FROM AUTHOR]

The application of machine learning to cryogenic electron microscopy (cryoEM) data analysis has added a valuable set of tools to the cryoEM data processing pipeline. As these tools become more accessible and widely available, the implications of their use should be assessed. We noticed that machine learning map modification tools can have differential effects on cryoEM densities. In this perspective, we evaluate these effects to show that machine learning tools generally improve densities for biomacromolecules while generating unpredictable results for ligands. This unpredictable behavior manifests both in quantitative metrics of map quality and in qualitative investigations of modified maps. The results presented here highlight the power and potential of machine learning tools in cryoEM, while also illustrating some of the risks of their unexamined use.

Oil peony is an important oil crop that is primarily sown by using artificial methods at present. Its seeder encounters the problems of low efficiency of seeding that significantly limits the highly efficient mechanized production of high-quality peony oil. In this study, Fengdan white oil peony seeds were used as the research object and repose angle as the response value to establish a discrete element model (DEM) and parameter calibration. The range of parameters of oil peony seeds was first obtained through an experiment, and their repose angle was obtained by an inclinometer. A three-dimensional DEM of oil peony seeds was then established. The Plackett–Burman (PB) test was utilized to screen the parameters that had a significant influence on the repose angle, and the steepest ascent (SA) test was applied to determine their optimum range of testing. Following this, based on Box–Behnken (BBD) test results, a second-order regression model between the important parameters and the repose angle was constructed. Finally, the absolute minimum difference between simulated and measured repose angles was utilized as the objective of optimization to obtain the following optimum combination of parameters: The values of the seed–steel collision recovery coefficient (CRC), seed–seed static friction coefficient (SFC), seed–steel SFC, and seed–seed rolling friction coefficient (RFC) were 0.704, 0.324, 0.335, and 0.045, respectively. This optimal combination of parameters was confirmed through simulations, and the error between simulated and measured repose angles was only 0.67%, indicating that the calibrated DEM of oil peony seeds was reliable.

[Purpose/Significance] The ancient Chinese agricultural books are the main carrier of traditional agricultural experience, and represent the productivity and the essence of agricultural history in China. The value of agricultural knowledge in them has not disappeared with the progress of the times, and still has practical guidance for the problems that arise in modern agriculture. However, the ancient Chinese agricultural books are written in ancient Chinese, which are obscure and without punctuation, making them difficult to use. Semantic retrieval is a retrieval method that automatically queries and extracts relevant information from information sources at the semantic level. It can accurately capture the true intention behind user problems and conduct searches based on it, and thereby it is capable of returning more accurate and the most consistent results to users. However, currently most relevant research only focuses on major languages, and there is insufficient research on sentence embedding in ancient Chinese prose. In order to fill the gap in the field and provide scholars with more convenient methods for retrieving ancient agricultural knowledge and tracing ancient agricultural knowledge, this study is based on comparative learning methods to construct a semantic retrieval model that can automatically return the most relevant ancient agricultural paragraph with input, using vernacular Chinese as the query. [Method/Process] SikuBERT, which is based on Siku Quanshu as the training corpus, is used as the basic model. Based on the method of comparative learning, the model is continued to be trained using the self-built ancient agricultural dataset, and a semantic retrieval model that can support the use of vernacular as a query and return the ancient agricultural paragraphs most similar to the query semantics is obtained. [Results/Conclusions] The Spearman coefficient of the ancient agricultural text semantic retrieval model can achieve 86.51% performance on the test set, which is a certain degree of improvement compared to the baseline model's 83.69% performance on the test set. The recall situation on the self built ancient agricultural literature retrieval test set has been improved to a certain extent compared to the baseline model, and the model can have good retrieval results on ancient agricultural literature. However, semantic retrieval models usually require relevant semantic similarity datasets or semantic matching datasets for training. Due to the lack of large-scale and pure ancient Chinese data in the field of ancient agricultural literature, and the high cost of constructing relevant datasets requiring personnel with high-standard relevant professional qualifications, this experiment used a self-built dataset for training, which is limited by the quantity and quality of ancient agricultural language corpus data. The current semantic retrieval model for ancient agricultural literature is still not as effective as expected. In the future, we will search for training methods suitable for small samples, such as transfer learning based on cross language pre-training models to improve the retrieval performance.

Determining the minimum supercooling temperature (Tsc) of unsaturated saline soils is the key scientific problem in cold‐region agricultural and engineering, and theoretical models with good predictability are urgently needed. This study establishes a framework for pore solution freezing nucleation in unsaturated saline soils, the chemical potential energy balance equation was established by determining phase transition driving force and potential energy barrier for the formation of stable crystal embryos. Finally, the physical model was constructed. By citing 46 sets of data from different researchers, the validity of the prediction results of this paper's model under different soil types and cooling rates is demonstrated, and the microscopic mechanism of supercooling occurring in the pore solution of positive permafrost is elucidated. The analysis found that electrolyte action restricts the free diffusion of molecules and promotes nucleation to occur, so that a maximum value of Tsc exists during the growth of salinity (in NaCl saline soils occurs at a solution concentration of 0.26 mol/L). Too fast cooling rate will lead to a lag in crystallization time and an increase in supercooling. The combined water content determines the Tsc when rc < 1 μm (pore size), and the effect of changes in water content and cooling rate on supercooling is more significant in small pores; the electrolyte concentration determines the Tsc when rc > 1 μm, Tsc‐max from concentration changes will be more significant in large pores. This study can provide theoretical reserves for controlling the freezing process of soils, studying the mechanism of freezing‐thawing hysteresis, and improving the related numerical calculation models. Plain Language Summary: Permafrost and seasonal permafrost cover more than 50% of land area in the world. The engineering problems caused by freezing‐thawing cycles seriously affect engineering safety and people's well‐being in many countries. Therefore, an in‐depth study of the soil freezing‐thawing cycle is necessary. The water in the soil pore space remains sometimes in liquid state when it is below the freezing temperature. This phenomenon is usually referred to as supercooling. There is still a lack of quantitative studies on the minimum temperature that can be reached by this phenomenon. In this paper, a model that can reasonably predict the minimum subcooling temperature of soil is developed based on thermodynamic methods. The model is verified to be of good accuracy by means of measured data from different researchers. Subsequently, the supercooling characteristics of the soil are discussed based on the model and the microscopic mechanism of the gradual development of freezing in the supercooling state is described. This study will help to recognize and control the soil freezing process in engineering and agricultural construction, and serve to protect engineering structures. Key Points: A physical model is proposed to quantify the supercooling degree of unsaturated saline soilsA maximum value of soil minimum supercooling temperature will occur with increasing salt concentrationToo fast cooling rate will lead to a lag in crystallization time and an increase in supercooling [ABSTRACT FROM AUTHOR]

Matrix-variate Gaussian-type or Wishart-type distributions in the real domain are widely used in the literature. When the exponential trace has an arbitrary power and when the factors involving a determinant and a trace enter into the model or a matrix-variate gamma-type or Wishart-type model with an exponential trace having an arbitrary power, they are extremely difficult to handle. One such model with factors involving a trace and a determinant and the exponential trace having an arbitrary power, in the real domain, is known in the literature as the Kotz model. No explicit evaluation of the normalizing constant in the Kotz model seems to be available. The normalizing constant that is widely used in the literature, is interpreted as the normalizing constant in the general model, and that is referred to as a Kotz model does not seem to be correct. One of the main contributions in this paper is the introduction of matrix-variate distributions in the real and complex domains belonging to the Gaussian-type, gamma-type, and type 1 and type 2 beta-types, or Mathai's pathway family, when the exponential trace has an arbitrary power and explicit evaluations of the normalizing constants therein. All of these models are believed to be new. Another new contribution is the logistic-based extensions of the models in the real and complex domains, with the exponential trace having an arbitrary exponent and connecting to extended zeta functions introduced by this author recently. The techniques and steps used at various stages in this paper will be highly useful for people working in multivariate statistical analysis, as well as for people applying such models in engineering problems, communication theory, quantum physics, and related areas, apart from statistical applications. [ABSTRACT FROM AUTHOR]

In certain problems in model building and Bayesian analysis, the results end up in forms connected with generalized zeta functions. This necessitates the introduction of an extended form of the generalized zeta function. Such an extended form of the zeta function is introduced in this paper. In model building situations and in various types of applications in physical, biological and social sciences and engineering, a basic model taken is the Gaussian model in the univariate, multivariate and matrix-variate situations. A real scalar variable logistic model behaves like a Gaussian model but with a thicker tail. Hence, for many of industrial applications, a logistic model is preferred to a Gaussian model. When we study the properties of a logistic model in the multivariate and matrix-variate cases, in the real and complex domains, invariably the problem ends up in the extended zeta function defined in this paper. Several such extended logistic models are considered. It is also found that certain Bayesian considerations also end up in the extended zeta function introduced in this paper. Several such Bayesian models in the multivariate and matrix-variate cases in the real and complex domains are discussed. It is stated in a recent paper that "Quantum Mechanics is just the Bayesian theory generalized to the complex Hilbert space". Hence, the models developed in this paper are expected to have applications in quantum mechanics, communication theory, physics, statistics and related areas. [ABSTRACT FROM AUTHOR]