Photobiomodulation inhibits the expression of chondroitin sulfate proteoglycans after spinal cord injury via the Sox9 pathway

Both glial cells and glia scar greatly affect the development of spinal cord injury and have become hot spots in research on spinal cord injury treatment.The cellular deposition of dense extracellular matrix proteins such as chondroitin sulfate proteoglycans inside and around the glial scar is known to affect axonal growth and be a major obstacle to autogenous repair.These proteins are thus candidate targets for spinal cord injury therapy.Our previous studies demonstrated that 810 nm photo biomodulation inhibited the formation of chondroitin sulfate proteoglycans after spinal cord injury and greatly improved motor function in model animals.However,the specific mechanism and potential targets involved remain to be clarified.In this study,to investigate the therapeutic effect of photo biomodulation,we established a mouse model of spinal cord injury by T9 clamping and irradiated the injury site at a power density of 50 mW/cm~2 for 50 minutes once a day for 7 consecutive days.We found that photobiomodulation greatly restored motor function in mice and down regulated chondroitin sulfate proteoglycan expression in the injured spinal cord.Bioinformatics analysis revealed that photobiomodulation inhibited the expression of proteoglycan-related genes induced by spinal cord injury,and versican,a type of proteoglycan,was one of the most markedly changed molecules.Immunofluorescence staining showed that after spinal cord injury,versican was present in astrocytes in spinal cord tissue.The expression of versican in primary astrocytes cultured in vitro increased after inflammation induction,whereas photobiomodulation inhibited the expression of ve rsican.Furthermore,we found that the increased levels of p-Smad3,p-P38 and p-Erk in inflammatory astrocytes were reduced after photobiomodulation treatment and after delivery of inhibitors including FR 180204,(E)-SIS3,and SB 202190.This suggests that Sma d 3/Sox9 and MAP K/Sox9 pathways may be involved in the effects of photobiomodulation.In summary,our findings show that photobiomodulation modulates the expression of chondroitin sulfate proteoglycans,and versican is one of the key target molecules of photo biomodulation.MAPK/Sox9 and Smad3/Sox9 pathways may play a role in the effects of photo biomodulation on chondroitin sulfate proteoglycan accumulation after spinal cord injury.

Discrimination of polysorbate 20 by high-performance liquid chromatography-charged aerosol detection and characterization for components by expanding compound database and library

Analyzing polysorbate 20(PS20)composition and the impact of each component on stability and safety is crucial due to formulation variations and individual tolerance.The similar structures and polarities of PS20 components make accurate separation,identification,and quantification challenging.In this work,a high-resolution quantitative method was developed using single-dimensional high-performance liquid chromatography(HPLC)with charged aerosol detection(CAD)to separate 18 key components with multiple esters.The separated components were characterized by ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry(UHPLC-Q-TOF-MS)with an identical gradient as the HPLC-CAD analysis.The polysorbate compound database and library were expanded over 7-time compared to the commercial database.The method investigated differences in PS20 samples from various origins and grades for different dosage forms to evaluate the composition-process relationship.UHPLC-Q-TOF-MS identified 1329 to 1511 compounds in 4 batches of PS20 from different sources.The method observed the impact of 4 degradation conditions on peak components,identifying stable components and their tendencies to change.HPLC-CAD and UHPLC-Q-TOF-MS results provided insights into fingerprint differences,distinguishing quasi products.

Dynamic Offloading and Scheduling Strategy for Telematics Tasks Based on Latency Minimization

In current research on task offloading and resource scheduling in vehicular networks,vehicles are commonly assumed to maintain constant speed or relatively stationary states,and the impact of speed variations on task offloading is often overlooked.It is frequently assumed that vehicles can be accurately modeled during actual motion processes.However,in vehicular dynamic environments,both the tasks generated by the vehicles and the vehicles’surroundings are constantly changing,making it difficult to achieve real-time modeling for actual dynamic vehicular network scenarios.Taking into account the actual dynamic vehicular scenarios,this paper considers the real-time non-uniform movement of vehicles and proposes a vehicular task dynamic offloading and scheduling algorithm for single-task multi-vehicle vehicular network scenarios,attempting to solve the dynamic decision-making problem in task offloading process.The optimization objective is to minimize the average task completion time,which is formulated as a multi-constrained non-linear programming problem.Due to the mobility of vehicles,a constraint model is applied in the decision-making process to dynamically determine whether the communication range is sufficient for task offloading and transmission.Finally,the proposed vehicular task dynamic offloading and scheduling algorithm based on muti-agent deep deterministic policy gradient(MADDPG)is applied to solve the optimal solution of the optimization problem.Simulation results show that the algorithm proposed in this paper is able to achieve lower latency task computation offloading.Meanwhile,the average task completion time of the proposed algorithm in this paper can be improved by 7.6%compared to the performance of the MADDPG scheme and 51.1%compared to the performance of deep deterministic policy gradient(DDPG).

Systemic modulation of skeletal mineralization by magnesium implant promoting fracture healing: Radiological exploration enhanced with PCA-based machine learning in a rat femoral model

The clinical application of magnesium(Mg)and its alloys for bone fractures has been well supported by in vitro and in vivo trials.However,there were studies indicating negative effects of high dose Mg intake and sustained local release of Mg ions on bone metabolism or repair,which should not be ignored when developing Mg-based implants.Thus,it remains necessary to assess the biological effects of Mg implants in animal models relevant to clinical treatment modalities.The primary purpose of this study was to validate the beneficial effects of intramedullary Mg implants on the healing outcome of femoral fractures in a modified rat model.In addition,the mineralization parameters at multiple anatomical sites were evaluated,to investigate their association with healing outcome and potential clinical applications.Compared to the control group without Mg implantation,postoperative imaging at week 12 demonstrated better healing outcomes in the Mg group,with more stable unions in 3D analysis and high-mineralized bridging in 2D evaluation.The bone tissue mineral density(TMD)was higher in the Mg group at the non-operated femur and lumbar vertebra,while no differences between groups were identified regarding the bone tissue volume(TV),TMD and bone mineral content(BMC)in humerus.In the surgical femur,the Mg group presented higher TMD,but lower TV and BMC in the distal metaphyseal region,as well as reduced BMC at the osteotomy site.Principal component analysis(PCA)-based machine learning revealed that by selecting clinically relevant parameters,radiological markers could be constructed for differentiation of healing outcomes,with better performance than 2D scoring.The study provides insights and preclinical evidence for the rational investigation of bioactive materials,the identification of potential adverse effects,and the promotion of diagnostic capabilities for fracture healing.

Simultaneous purification of minor components in natural products using twin-column recycling chromatography with a step solvent gradient

The isolation of minor components from complex natural product matrices presents a significant challenge in the field of purification science due to their low concentrations and the presence of structurally similar compounds.This study introduces an optimized twin-column recycling chromatography method for the efficient and simultaneous purification of these elusive constituents.By introducing water at a small flowing rate between the twin columns,a step solvent gradient is created,by which the leading edge of concentration band would migrate at a slower rate than the trailing edge as it flowing from the upstream to downstream column.Hence,the band broadening is counterbalanced,resulting in an enrichment effect for those minor components in separation process.Herein,two target substances,which showed similar peak position in high performance liquid chromatography(HPLC)and did not exceed 1.8%in crude paclitaxel were selected as target compounds for separation.By using the twin-column recycling chromatography with a step solvent gradient,a successful purification was achieved in getting the two with the purity almost 100%.We suggest this method is suitable for the separation of most components in natural produces,which shows higher precision and recovery rate compared with the common lab-operated separation ways for natural products(thin-layer chromatography and prep-HPLC).

Data Component:An Innovative Framework for Information Value Metrics in the Digital Economy

The increasing dependence on data highlights the need for a detailed understanding of its behavior,encompassing the challenges involved in processing and evaluating it.However,current research lacks a comprehensive structure for measuring the worth of data elements,hindering effective navigation of the changing digital environment.This paper aims to fill this research gap by introducing the innovative concept of“data components.”It proposes a graphtheoretic representation model that presents a clear mathematical definition and demonstrates the superiority of data components over traditional processing methods.Additionally,the paper introduces an information measurement model that provides a way to calculate the information entropy of data components and establish their increased informational value.The paper also assesses the value of information,suggesting a pricing mechanism based on its significance.In conclusion,this paper establishes a robust framework for understanding and quantifying the value of implicit information in data,laying the groundwork for future research and practical applications.

Meter-Scale Thin-Walled Structure with Lattice Infill for Fuel Tank Supporting Component of Satellite:Multiscale Design and Experimental Verification

Lightweight thin-walled structures with lattice infill are widely desired in satellite for their high stiffness-to-weight ratio and superior buckling strength resulting fromthe sandwich effect.Such structures can be fabricated bymetallic additive manufacturing technique,such as selective laser melting(SLM).However,the maximum dimensions of actual structures are usually in a sub-meter scale,which results in restrictions on their appliance in aerospace and other fields.In this work,a meter-scale thin-walled structure with lattice infill is designed for the fuel tank supporting component of the satellite by integrating a self-supporting lattice into the thickness optimization of the thin-wall.The designed structure is fabricated by SLM of AlSi10Mg and cold metal transfer welding technique.Quasi-static mechanical tests and vibration tests are both conducted to verify the mechanical strength of the designed large-scale lattice thin-walled structure.The experimental results indicate that themeter-scale thin-walled structure with lattice infill could meet the dimension and lightweight requirements of most spacecrafts.

Comparison of debris flow susceptibility assessment methods:support vector machine,particle swarm optimization,and feature selection techniques

The selection of important factors in machine learning-based susceptibility assessments is crucial to obtain reliable susceptibility results.In this study,metaheuristic optimization and feature selection techniques were applied to identify the most important input parameters for mapping debris flow susceptibility in the southern mountain area of Chengde City in Hebei Province,China,by using machine learning algorithms.In total,133 historical debris flow records and 16 related factors were selected.The support vector machine(SVM)was first used as the base classifier,and then a hybrid model was introduced by a two-step process.First,the particle swarm optimization(PSO)algorithm was employed to select the SVM model hyperparameters.Second,two feature selection algorithms,namely principal component analysis(PCA)and PSO,were integrated into the PSO-based SVM model,which generated the PCA-PSO-SVM and FS-PSO-SVM models,respectively.Three statistical metrics(accuracy,recall,and specificity)and the area under the receiver operating characteristic curve(AUC)were employed to evaluate and validate the performance of the models.The results indicated that the feature selection-based models exhibited the best performance,followed by the PSO-based SVM and SVM models.Moreover,the performance of the FS-PSO-SVM model was better than that of the PCA-PSO-SVM model,showing the highest AUC,accuracy,recall,and specificity values in both the training and testing processes.It was found that the selection of optimal features is crucial to improving the reliability of debris flow susceptibility assessment results.Moreover,the PSO algorithm was found to be not only an effective tool for hyperparameter optimization,but also a useful feature selection algorithm to improve prediction accuracies of debris flow susceptibility by using machine learning algorithms.The high and very high debris flow susceptibility zone appropriately covers 38.01%of the study area,where debris flow may occur under intensive human activities and heavy rainfall events.

Cloud-Model-Based Feature Engineering to Analyze the Energy-Water Nexus of a Full-Scale Wastewater Treatment Plant

Wastewater treatment plants(WWTPs)are important and energy-intensive municipal infrastructures.High energy consumption and relatively low operating performance are major challenges from the perspective of carbon neutrality.However,water-energy nexus analysis and models for WWTPs have rarely been reported to date.In this study,a cloud-model-based energy consumption analysis(CMECA)of a WWTP was conducted to explore the relationship between influent and energy consumption by clustering its influent’s parameters.The principal component analysis(PCA)and K-means clustering were applied to classify the influent condition using water quality and volume data.The energy consumption of the WWTP is divided into five standard evaluation levels,and its cloud digital characteristics(CDCs)were extracted according to bilateral constraints and golden ratio methods.Our results showed that the energy consumption distribution gradually dispersed and deviated from the Gaussian distribution with decreased water concentration and quantity.The days with high energy efficiency were extracted via the clustering method from the influent category of excessive energy consumption,represented by a compact-type energy consumption distribution curve to identify the influent conditions that affect the steady distribution of energy consumption.The local WWTP has high energy consumption with 0.3613 kW·h·m^(-3)despite low influent concentration and volumes,across four consumption levels from low(I)to relatively high(IV),showing an unsatisfactory operation and management level.The average oxygenation capacity,internal reflux ratio,and external reflux ratio during high energy efficiency days recognized by further clustering were obtained(0.2924-0.3703 kg O_(2)·m^(-3),1.9576-2.4787,and 0.6603-0.8361,respectively),which could be used as a guide for the days with low energy efficiency.Consequently,this study offers a water-energy nexus analysis method to identify influent conditions with operational management anomalies and can be used as an empirical reference for the optimized operation of WWTPs.

PCA-LSTM:An Impulsive Ground-Shaking Identification Method Based on Combined Deep Learning

Near-fault impulsive ground-shaking is highly destructive to engineering structures,so its accurate identification ground-shaking is a top priority in the engineering field.However,due to the lack of a comprehensive consideration of the ground-shaking characteristics in traditional methods,the generalization and accuracy of the identification process are low.To address these problems,an impulsive ground-shaking identification method combined with deep learning named PCA-LSTM is proposed.Firstly,ground-shaking characteristics were analyzed and groundshaking the data was annotated using Baker’smethod.Secondly,the Principal Component Analysis(PCA)method was used to extract the most relevant features related to impulsive ground-shaking.Thirdly,a Long Short-Term Memory network(LSTM)was constructed,and the extracted features were used as the input for training.Finally,the identification results for the Artificial Neural Network(ANN),Convolutional Neural Network(CNN),LSTM,and PCA-LSTMmodels were compared and analyzed.The experimental results showed that the proposed method improved the accuracy of pulsed ground-shaking identification by>8.358%and identification speed by>26.168%,compared to other benchmark models ground-shaking.

Regulation effects of water and nitrogen on yield,water,and nitrogen use efficiency of wolfberry

Wolfberry(Lycium barbarum L.)is important for health care and ecological protection.However,it faces problems of low productivity and resource utilization during planting.Exploring reasonable models for water and nitrogen management is important for solving these problems.Based on field trials in 2021 and 2022,this study analyzed the effects of controlling soil water and nitrogen application levels on wolfberry height,stem diameter,crown width,yield,and water(WUE)and nitrogen use efficiency(NUE).The upper and lower limits of soil water were controlled by the percentage of soil water content to field water capacity(θ_(f)),and four water levels,i.e.,adequate irrigation(W0,75%-85%θ_(f)),mild water deficit(W1,65%-75%θ_(f)),moderate water deficit(W2,55%-65%θ_(f)),and severe water deficit(W3,45%-55%θ_(f))were used,and three nitrogen application levels,i.e.,no nitrogen(N0,0 kg/hm^(2)),low nitrogen(N1,150 kg/hm^(2)),medium nitrogen(N2,300 kg/hm^(2)),and high nitrogen(N3,450 kg/hm^(2))were implied.The results showed that irrigation and nitrogen application significantly affected plant height,stem diameter,and crown width of wolfberry at different growth stages(P<0.01),and their maximum values were observed in W1N2,W0N2,and W1N3 treatments.Dry weight per plant and yield of wolfberry first increased and then decreased with increasing nitrogen application under the same water treatment.Dry weight per hundred grains and dry weight percentage increased with increasing nitrogen application under W0 treatment.However,under other water treatments,the values first increased and then decreased with increasing nitrogen application.Yield and its component of wolfberry first increased and then decreased as water deficit increased under the same nitrogen treatment.Irrigation water use efficiency(IWUE,8.46 kg/(hm^(2)·mm)),WUE(6.83 kg/(hm^(2)·mm)),partial factor productivity of nitrogen(PFPN,2.56 kg/kg),and NUE(14.29 kg/kg)reached their highest values in W2N2,W1N2,W1N2,and W1N1 treatments.Results of principal component analysis(PCA)showed that yield,WUE,and NUE were better in W1N2 treatment,making it a suitable water and nitrogen management mode for the irrigation area of the Yellow River in the Gansu Province,China and similar planting areas.

A novel approach of jet polishing for interior surface of small-grooved components using three developed setups

It is a challenge to polish the interior surface of an additively manufactured component with complex structures and groove sizes less than 1 mm.Traditional polishing methods are disabled to polish the component,meanwhile keeping the structure intact.To overcome this challenge,small-grooved components made of aluminum alloy with sizes less than 1 mm were fabricated by a custom-made printer.A novel approach to multi-phase jet(MPJ)polishing is proposed,utilizing a self-developed polisher that incorporates solid,liquid,and gas phases.In contrast,abrasive air jet(AAJ)polishing is recommended,employing a customized polisher that combines solid and gas phases.After jet polishing,surface roughness(Sa)on the interior surface of grooves decreases from pristine 8.596μm to 0.701μm and 0.336μm via AAJ polishing and MPJ polishing,respectively,and Sa reduces 92%and 96%,correspondingly.Furthermore,a formula defining the relationship between linear energy density and unit defect volume has been developed.The optimized parameters in additive manufacturing are that linear energy density varies from 0.135 J mm^(-1)to 0.22 J mm^(-1).The unit area defect volume achieved via the optimized parameters decreases to 1/12 of that achieved via non-optimized ones.Computational fluid dynamics simulation results reveal that material is removed by shear stress,and the alumina abrasives experience multiple collisions with the defects on the heat pipe groove,resulting in uniform material removal.This is in good agreement with the experimental results.The novel proposed setups,approach,and findings provide new insights into manufacturing complex-structured components,polishing the small-grooved structure,and keeping it unbroken.

Dip2a regulates stress susceptibility in the basolateral amygdala

Dysregulation of neurotransmitter metabolism in the central nervous system contributes to mood disorders such as depression, anxiety, and post–traumatic stress disorder. Monoamines and amino acids are important types of neurotransmitters. Our previous results have shown that disco-interacting protein 2 homolog A(Dip2a) knockout mice exhibit brain development disorders and abnormal amino acid metabolism in serum. This suggests that DIP2A is involved in the metabolism of amino acid–associated neurotransmitters. Therefore, we performed targeted neurotransmitter metabolomics analysis and found that Dip2a deficiency caused abnormal metabolism of tryptophan and thyroxine in the basolateral amygdala and medial prefrontal cortex. In addition, acute restraint stress induced a decrease in 5-hydroxytryptamine in the basolateral amygdala. Additionally, Dip2a was abundantly expressed in excitatory neurons of the basolateral amygdala, and deletion of Dip2a in these neurons resulted in hopelessness-like behavior in the tail suspension test. Altogether, these findings demonstrate that DIP2A in the basolateral amygdala may be involved in the regulation of stress susceptibility. This provides critical evidence implicating a role of DIP2A in affective disorders.

Identifying Brand Consistency by Product Differentiation Using CNN

This paper presents a new method of using a convolutional neural network(CNN)in machine learning to identify brand consistency by product appearance variation.In Experiment 1,we collected fifty mouse devices from the past thirty-five years from a renowned company to build a dataset consisting of product pictures with pre-defined design features of their appearance and functions.Results show that it is a challenge to distinguish periods for the subtle evolution of themouse devices with such traditionalmethods as time series analysis and principal component analysis(PCA).In Experiment 2,we applied deep learning to predict the extent to which the product appearance variation ofmouse devices of various brands.The investigation collected 6,042 images ofmouse devices and divided theminto the Early Stage and the Late Stage.Results show the highest accuracy of 81.4%with the CNNmodel,and the evaluation score of brand style consistency is 0.36,implying that the brand consistency score converted by the CNN accuracy rate is not always perfect in the real world.The relationship between product appearance variation,brand style consistency,and evaluation score is beneficial for predicting new product styles and future product style roadmaps.In addition,the CNN heat maps highlight the critical areas of design features of different styles,providing alternative clues related to the blurred boundary.The study provides insights into practical problems for designers,manufacturers,and marketers in product design.It not only contributes to the scientific understanding of design development,but also provides industry professionals with practical tools and methods to improve the design process and maintain brand consistency.Designers can use these techniques to find features that influence brand style.Then,capture these features as innovative design elements and maintain core brand values.

π-Extended giant dimeric acceptor as a third component enables highly efficient ternary organic solar cells with efficiency over 19.2%

Ternary strategy with a suitable third component is a successful strategy to improve the photovoltaic performance of organic solar cells(OSCs).Very recently,Y-series based giant molecule acceptors or oligomerized acceptors have emerged as promising materials for achieving highly efficient and stable binary OSCs,while application as third component for ternary OSCs is limited.Here a novelπ-extended giant dimeric acceptor,GDF,is developed based on central Y series core fusion and rigid BDT as linker,and then incorporated into the state-of-the-art PM1:PC6 system to construct ternary OSCs.The GDF has a near planar backbone,resulting in increasedπ-conjugation,excellent crystallinity,and good electron transport capacity.When GDF is introduced into the PM1:PC6 system,it ensues in a cascade like the lowest unoccupied molecular orbitals(LUMO)energy level alignment,a complementary absorption band with PM1 and PC6,higher and balanced hole and electron mobility,slightly smaller domain size,and a higher exciton dissociation probability for PM1:PC6:GDF(1:1.1:0.1)blend film.As a consequence,the PM1:PC6:GDF(1:1.1:0.1)ternary OSC achieves a champion PCE of 19.22%,with a significantly higher open-circuit voltage and short-circuit current density,compared to 18.45%for the PM1:PC6(1:1.2)binary OSC.Our findings show that employing aπ-extended giant dimeric acceptor as a third component significantly improves the photovoltaic performance of ternary OSCs.

Research and Application of a Multi-Field Co-Simulation Data Extraction Method Based on Adaptive Infinitesimal Element

Regarding the spatial profile extraction method of a multi-field co-simulation dataset,different extraction directions,locations,and numbers of profileswill greatly affect the representativeness and integrity of data.In this study,a multi-field co-simulation data extractionmethod based on adaptive infinitesimal elements is proposed.Themultifield co-simulation dataset based on related infinitesimal elements is constructed,and the candidate directions of data profile extraction undergo dimension reduction by principal component analysis to determine the direction of data extraction.Based on the fireworks algorithm,the data profile with optimal representativeness is searched adaptively in different data extraction intervals to realize the adaptive calculation of data extraction micro-step length.The multi-field co-simulation data extraction process based on adaptive microelement is established and applied to the data extraction process of the multi-field co-simulation dataset of the sintering furnace.Compared with traditional data extraction methods for multi-field co-simulation,the approximate model constructed by the data extracted from the proposed method has higher construction efficiency.Meanwhile,the relative maximum absolute error,root mean square error,and coefficient of determination of the approximationmodel are better than those of the approximation model constructed by the data extracted from traditional methods,indicating higher accuracy,it is verified that the proposed method demonstrates sound adaptability and extraction efficiency.

Contrast Normalization Strategies in Brain Tumor Imaging:From Preprocessing to Classification

Cancer-related to the nervous system and brain tumors is a leading cause of mortality in various countries.Magnetic resonance imaging(MRI)and computed tomography(CT)are utilized to capture brain images.MRI plays a crucial role in the diagnosis of brain tumors and the examination of other brain disorders.Typically,manual assessment of MRI images by radiologists or experts is performed to identify brain tumors and abnormalities in the early stages for timely intervention.However,early diagnosis of brain tumors is intricate,necessitating the use of computerized methods.This research introduces an innovative approach for the automated segmentation of brain tumors and a framework for classifying different regions of brain tumors.The proposed methods consist of a pipeline with several stages:preprocessing of brain images with noise removal based on Wiener Filtering,enhancing the brain using Principal Component Analysis(PCA)to obtain well-enhanced images,and then segmenting the region of interest using the Fuzzy C-Means(FCM)clustering technique in the third step.The final step involves classification using the Support Vector Machine(SVM)classifier.The classifier is applied to various types of brain tumors,such as meningioma and pituitary tumors,utilizing the Contrast-Enhanced Magnetic Resonance Imaging(CE-MRI)database.The proposed method demonstrates significantly improved contrast and validates the effectiveness of the classification framework,achieving an average sensitivity of 0.974,specificity of 0.976,accuracy of 0.979,and a Dice Score(DSC)of 0.957.Additionally,this method exhibits a shorter processing time of 0.44 s compared to existing approaches.The performance of this method emphasizes its significance when compared to state-of-the-art methods in terms of sensitivity,specificity,accuracy,and DSC.To enhance the method further in the future,it is feasible to standardize the approach by incorporating a set of classifiers to increase the robustness of the brain classification method.

Using deep neural networks coupled with principal component analysis for ore production forecasting at open-pit mines

Ore production is usually affected by multiple influencing inputs at open-pit mines.Nevertheless,the complex nonlinear relationships between these inputs and ore production remain unclear.This becomes even more challenging when training data(e.g.truck haulage information and weather conditions)are massive.In machine learning(ML)algorithms,deep neural network(DNN)is a superior method for processing nonlinear and massive data by adjusting the amount of neurons and hidden layers.This study adopted DNN to forecast ore production using truck haulage information and weather conditions at open-pit mines as training data.Before the prediction models were built,principal component analysis(PCA)was employed to reduce the data dimensionality and eliminate the multicollinearity among highly correlated input variables.To verify the superiority of DNN,three ANNs containing only one hidden layer and six traditional ML models were established as benchmark models.The DNN model with multiple hidden layers performed better than the ANN models with a single hidden layer.The DNN model outperformed the extensively applied benchmark models in predicting ore production.This can provide engineers and researchers with an accurate method to forecast ore production,which helps make sound budgetary decisions and mine planning at open-pit mines.

A Hybrid Optimization Approach of Single Point Incremental Sheet Forming of AISI 316L Stainless Steel Using Grey Relation Analysis Coupled with Principal Component Analysiss

We investigated the parametric optimization on incremental sheet forming of stainless steel using Grey Relational Analysis(GRA) coupled with Principal Component Analysis(PCA). AISI 316L stainless steel sheets were used to develop double wall angle pyramid with aid of tungsten carbide tool. GRA coupled with PCA was used to plan the experiment conditions. Control factors such as Tool Diameter(TD), Step Depth(SD), Bottom Wall Angle(BWA), Feed Rate(FR) and Spindle Speed(SS) on Top Wall Angle(TWA) and Top Wall Angle Surface Roughness(TWASR) have been studied. Wall angle increases with increasing tool diameter due to large contact area between tool and workpiece. As the step depth, feed rate and spindle speed increase,TWASR decreases with increasing tool diameter. As the step depth increasing, the hydrostatic stress is raised causing severe cracks in the deformed surface. Hence it was concluded that the proposed hybrid method was suitable for optimizing the factors and response.

Investigation of the effect of hydrocarbons and monoesters in the gelators’composition on the properties of edible oleogel

Natural wax gelators have different compositions of compounds(hydrocarbons,wax esters,free fatty alcohols,and free fatty acids),which results in oleogels with varying properties.To maintain a consistent composition,the individual components can be added to the original wax gelator.The content of hydrocarbons and wax esters greatly affects the structuring process of liquid edible oils with waxes.The aim of this study was to evaluate the possibility of modifying the properties of beeswax as a gelling agent by adding hydrocarbons or monoesters to obtain oleogels with specific properties.Various tests were conducted to assess the changes in the oleogel properties,such as color,microstructure,oil-binding capacity,thermal and textural properties.The research results have shown that the addition of the studied fractions has led to a significant change in all properties of oleogels.The initial size of oleogel crystals(7.29±1.80μm)changed after adding fractions,varying from 5.28μm to 12.58μm with hydrocarbons and from 9.95μm to 30.41μm with wax esters.The addition of 30%–50% hydrocarbons decreased the ability of the oleogels to bind oil and made them less firm compared to samples with 10%-20% hydrocarbons.Adding 10%-20% monoesters increased the firmness of the oleogels,but this indicator decreased when their content was increased to 50%.The obtained data indicate that hydrocarbons and wax esters can be used for targeted correction of the gelling properties of beeswax.