In the last decade,space solar power satellites(SSPSs)have been conceived to support net-zero carbon emissions and have attracted considerable attention.Electric energy is transmitted to the ground via a microwave pow...In the last decade,space solar power satellites(SSPSs)have been conceived to support net-zero carbon emissions and have attracted considerable attention.Electric energy is transmitted to the ground via a microwave power beam,a technology known as microwave power transmission(MPT).Due to the vast transmission distance of tens of thousands of kilometers,the power transmitting antenna array must span up to 1 kilometer in diameter.At the same time,the size of the rectifying array on the ground should extend over a few kilometers.This makes the MPT system of SSPSs significantly larger than the existing aerospace engineering system.To design and operate a rational MPT system,comprehensive optimization is required.Taking the space MPT system engineering into consideration,a novel multi-objective optimization function is proposed and further analyzed.The multi-objective optimization problem is modeled mathematically.Beam collection efficiency(BCE)is the primary factor,followed by the thermal management capability.Some tapers,designed to solve the conflict between BCE and the thermal problem,are reviewed.In addition to these two factors,rectenna design complexity is included as a functional factor in the optimization objective.Weight coefficients are assigned to these factors to prioritize them.Radiating planar arrays with different aperture illumination fields are studied,and their performances are compared using the multi-objective optimization function.Transmitting array size,rectifying array size,transmission distance,and transmitted power remaine constant in various cases,ensuring fair comparisons.The analysis results show that the proposed optimization function is effective in optimizing and selecting the MPT system architecture.It is also noted that the multi-objective optimization function can be expanded to include other factors in the future.展开更多
The multi-objective optimization of backfill effect based on response surface methodology and desirability function(RSM-DF)was conducted.Firstly,the test results show that the uniaxial compressive strength(UCS)increas...The multi-objective optimization of backfill effect based on response surface methodology and desirability function(RSM-DF)was conducted.Firstly,the test results show that the uniaxial compressive strength(UCS)increases with cement sand ratio(CSR),slurry concentration(SC),and curing age(CA),while flow resistance(FR)increases with SC and backfill flow rate(BFR),and decreases with CSR.Then the regression models of UCS and FR as response values were established through RSM.Multi-factor interaction found that CSR-CA impacted UCS most,while SC-BFR impacted FR most.By introducing the desirability function,the optimal backfill parameters were obtained based on RSM-DF(CSR is 1:6.25,SC is 69%,CA is 11.5 d,and BFR is 90 m^(3)/h),showing close results of Design Expert and high reliability for optimization.For a copper mine in China,RSM-DF optimization will reduce cement consumption by 4758 t per year,increase tailings consumption by about 6700 t,and reduce CO_(2)emission by about 4758 t.Thus,RSM-DF provides a new approach for backfill parameters optimization,which has important theoretical and practical values.展开更多
In terms of tandem cold mill productivity and product quality, a multi-objective optimization model of rolling schedule based on cost fimction was proposed to determine the stand reductions, inter-stand tensions and r...In terms of tandem cold mill productivity and product quality, a multi-objective optimization model of rolling schedule based on cost fimction was proposed to determine the stand reductions, inter-stand tensions and rolling speeds for a specified product. The proposed schedule optimization model consists of several single cost fi.mctions, which take rolling force, motor power, inter-stand tension and stand reduction into consideration. The cost function, which can evaluate how far the rolling parameters are from the ideal values, was minimized using the Nelder-Mead simplex method. The proposed rolling schedule optimization method has been applied successfully to the 5-stand tandem cold mill in Tangsteel, and the results from a case study show that the proposed method is superior to those based on empirical formulae.展开更多
The advent of Network Function Virtualization(NFV)and Service Function Chains(SFCs)unleashes the power of dynamic creation of network services using Virtual Network Functions(VNFs).This is of great interest to network...The advent of Network Function Virtualization(NFV)and Service Function Chains(SFCs)unleashes the power of dynamic creation of network services using Virtual Network Functions(VNFs).This is of great interest to network operators since poor service quality and resource wastage can potentially hurt their revenue in the long term.However,the study shows with a set of test-bed experiments that packet loss at certain positions(i.e.,different VNFs)in an SFC can cause various degrees of resource wastage and performance degradation because of repeated upstream processing and transmission of retransmitted packets.To overcome this challenge,this study focuses on resource scheduling and deployment of SFCs while considering packet loss positions.This study developed a novel SFC packet dropping cost model and formulated an SFC scheduling problem that aims to minimize overall packet dropping cost as a Mixed-Integer Linear Programming(MILP)and proved that it is NP-hard.In this study,Palos is proposed as an efficient scheme in exploiting the functional characteristics of VNFs and their positions in SFCs for scheduling resources and deployment to optimize packet dropping cost.Extensive experiment results show that Palos can achieve up to 42.73%improvement on packet dropping cost and up to 33.03%reduction on average SFC latency when compared with two other state-of-the-art schemes.展开更多
The current existing problem of deep learning framework for the detection and segmentation of electrical equipment is dominantly related to low precision.Because of the reliable,safe and easy-to-operate technology pro...The current existing problem of deep learning framework for the detection and segmentation of electrical equipment is dominantly related to low precision.Because of the reliable,safe and easy-to-operate technology provided by deep learning-based video surveillance for unmanned inspection of electrical equipment,this paper uses the bottleneck attention module(BAM)attention mechanism to improve the Solov2 model and proposes a new electrical equipment segmentation mode.Firstly,the BAM attention mechanism is integrated into the feature extraction network to adaptively learn the correlation between feature channels,thereby improving the expression ability of the feature map;secondly,the weighted sum of CrossEntropy Loss and Dice loss is designed as the mask loss to improve the segmentation accuracy and robustness of the model;finally,the non-maximal suppression(NMS)algorithm to better handle the overlap problem in instance segmentation.Experimental results show that the proposed method achieves an average segmentation accuracy of mAP of 80.4% on three types of electrical equipment datasets,including transformers,insulators and voltage transformers,which improve the detection accuracy by more than 5.7% compared with the original Solov2 model.The segmentation model proposed can provide a focusing technical means for the intelligent management of power systems.展开更多
In shale gas mining,the inter-fracture interference effect will significantly occur if the actual well deviates from the planned trajectory.To reduce production loss,operators want to get back on the planned trajector...In shale gas mining,the inter-fracture interference effect will significantly occur if the actual well deviates from the planned trajectory.To reduce production loss,operators want to get back on the planned trajectory economically and safely.Based on this,a multi-objective optimization model of deviationcorrection trajectory is established considering the production loss evaluation.Firstly,the functional relationship between the production envelope and the fracturing depth is constructed,and the production loss is obtained by combining the calculation method of volume flow.Based on the proposed“double-arc”trajectory design method,the production loss of the fracture on the deviation-correction trajectory is obtained.Finally,combined with the well profile energy evaluation,a new optimization model of deviation-correction trajectory is established.The results demonstrate that after optimizing the fracturing depth,the production loss of the deviation-correction trajectory is reduced by 13.2%.The maximum curvature value results in a trajectory with a minimum production loss yet a maximum well profile energy.The proposed model reduces the well profile energy by 15.6%compared with the existing model.It is proved that the proposed model can reduce the probability of drilling accidents and achieve high gas production in the later mining stage.This study fully considers various factors affecting horizontal wells in the fracturing area,which can provide theoretical guidance for the design of deviationcorrection trajectory.展开更多
BACKGROUND Understanding the impact of early sensory deficits on brain development is essential for understanding developmental processes and developing potential interventions.While previous studies have looked into ...BACKGROUND Understanding the impact of early sensory deficits on brain development is essential for understanding developmental processes and developing potential interventions.While previous studies have looked into the impact of prenatal experiences on language development,there is a lack of research on how these experiences affect early language and brain function development in individuals with sensorineural hearing loss(SNHL).AIM To investigate SNHL effects on early brain development and connectivity in 4-month-olds vs healthy newborns and controls.METHODS The research involved analyzing the functional brain networks of 65 infants,categorized into three groups:28 healthy newborns,224-month-old participants with SNHL,and 15 age-matched healthy participants.The resting-state functional connectivity was measured and compared between the groups using functional near-infrared spectroscopy and graph theory to assess the brain network properties.RESULTS Significant differences were found in resting-state functional connectivity between participants with SNHL and age-matched controls,indicating a developmental lag in brain connectivity for those with SNHL.Surprisingly,SNHL participants showed better connectivity development compared to healthy newborns,with connectivity strengths of 0.13±0.04 for SNHL,0.16±0.08 for controls,and 0.098±0.04 for newborns.Graph theory analysis revealed enhanced global brain network properties for the SNHL group,suggesting higher communication efficiency at 4 months.No significant differences were noted in network properties between 4-month-old SNHL participants and neonates.A unique pattern of central hubs was observed in the SNHL group,with 2 hubs in the left hemisphere compared to 6 in controls.CONCLUSION 4-month-old infants with SNHL have a distinct brain network pattern with efficient long-distance information transmission but less effective local communication compared to age-matched controls.展开更多
In this paper, the isogeometric analysis (IGA) is employed to develop an acoustic radiation model for a double plate-acoustic cavity coupling system, with a focus on analyzing the sound transmission loss (STL). The fu...In this paper, the isogeometric analysis (IGA) is employed to develop an acoustic radiation model for a double plate-acoustic cavity coupling system, with a focus on analyzing the sound transmission loss (STL). The functionally graded (FG) plate exhibits a different material properties in-plane, and the power-law rule is adopted as the governing principle for material mixing. To validate the harmonic response and demonstrate the accuracy and convergence of the isogeometric modeling, ANASYS is utilized to compare with numerical examples. A plane wave serves as the acoustic excitation, and the Rayleigh integral is applied to discretize the radiated plate. The STL results are compared with the literature, confirming the reliability of the coupling system. Finally, the investigation is conducted to study impact of cavity depth and power-law parameter on the STL.展开更多
In the Internet era,recommendation systems play a crucial role in helping users find relevant information from large datasets.Class imbalance is known to severely affect data quality,and therefore reduce the performan...In the Internet era,recommendation systems play a crucial role in helping users find relevant information from large datasets.Class imbalance is known to severely affect data quality,and therefore reduce the performance of recommendation systems.Due to the imbalance,machine learning algorithms tend to classify inputs into the positive(majority)class every time to achieve high prediction accuracy.Imbalance can be categorized such as by features and classes,but most studies consider only class imbalance.In this paper,we propose a recommendation system that can integrate multiple networks to adapt to a large number of imbalanced features and can deal with highly skewed and imbalanced datasets through a loss function.We propose a loss aware feature attention mechanism(LAFAM)to solve the issue of feature imbalance.The network incorporates an attention mechanism and uses multiple sub-networks to classify and learn features.For better results,the network can learn the weights of sub-networks and assign higher weights to important features.We propose suppression loss to address class imbalance,which favors negative loss by penalizing positive loss,and pays more attention to sample points near the decision boundary.Experiments on two large-scale datasets verify that the performance of the proposed system is greatly improved compared to baseline methods.展开更多
Maxillary bone loss impairs essential functions (chewing, swallowing, speech) and gives patients a very unaesthetic appearance due to the removal of facial support tissues, leading to serious psychological consequence...Maxillary bone loss impairs essential functions (chewing, swallowing, speech) and gives patients a very unaesthetic appearance due to the removal of facial support tissues, leading to serious psychological consequences. Treatment is multidisciplinary and requires a resective surgery if the cause is tumor-related, or an additive surgery if the cause is traumatic. This article aims to show the role of making a prosthesis to restore function (chewing, swallowing, speech) and aesthetics following maxillary bone loss. We will eighter present a clinical case involving a right maxillary tumor that was surgically resected followed by radiotherapy, and subsequently rehabilitated with a maxillofacial prosthesis in the consultation and dental treatment center of the university hospital center of Casablanca.展开更多
To better meet the needs of crop growth and achieve energy savings and efficiency enhancements,constructing a reliable environmental model to optimize greenhouse decision parameters is an important problem to be solve...To better meet the needs of crop growth and achieve energy savings and efficiency enhancements,constructing a reliable environmental model to optimize greenhouse decision parameters is an important problem to be solved.In this work,a radial-basis function(RBF)neural network was used to mine the potential changes of a greenhouse environment,a temperature error model was established,a multi-objective optimization function of energy consumption was constructed and the corresponding decision parameters were optimized by using a non-dominated sorting genetic algorithm with an elite strategy(NSGA-Ⅱ).The simulation results showed that RBF could clarify the nonlinear relationship among the greenhouse environment variables and decision parameters and the greenhouse temperature.The NSGA-Ⅱ could well search for the Pareto solution for the objective functions.The experimental results showed that after 40 min of combined control of sunshades and sprays,the temperature was reduced from 31℃to 25℃,and the power consumption was 0.5 MJ.Compared with tire three days of July 24,July 25 and July 26,2017,the energy consumption of the controlled production greenhouse was reduced by 37.5%,9.1%and 28.5%,respectively.展开更多
In this paper, we present an algorithm to solve the inequality constrained multi-objective programming (MP) by using a penalty function with objective parameters and constraint penalty parameter. First, the penalty fu...In this paper, we present an algorithm to solve the inequality constrained multi-objective programming (MP) by using a penalty function with objective parameters and constraint penalty parameter. First, the penalty function with objective parameters and constraint penalty parameter for MP and the corresponding unconstraint penalty optimization problem (UPOP) is defined. Under some conditions, a Pareto efficient solution (or a weakly-efficient solution) to UPOP is proved to be a Pareto efficient solution (or a weakly-efficient solution) to MP. The penalty function is proved to be exact under a stable condition. Then, we design an algorithm to solve MP and prove its convergence. Finally, numerical examples show that the algorithm may help decision makers to find a satisfactory solution to MP.展开更多
This paper deals with the optimality conditions and dual theory of multi-objective programming problems involving generalized convexity. New classes of generalized type-I functions are introduced for arcwise connected...This paper deals with the optimality conditions and dual theory of multi-objective programming problems involving generalized convexity. New classes of generalized type-I functions are introduced for arcwise connected functions, and examples are given to show the existence of these functions. By utilizing the new concepts, several sufficient optimality conditions and Mond-Weir type duality results are proposed for non-differentiable multi-objective programming problem.展开更多
The cochlea is one of the most complex organs in the human body,exhibiting a complex interplay of characteristics in acoustic,mechanical,electrical,and biological functions.Functional cochlea models are an essential p...The cochlea is one of the most complex organs in the human body,exhibiting a complex interplay of characteristics in acoustic,mechanical,electrical,and biological functions.Functional cochlea models are an essential platform for studying hearing mechanics and are crucial for developing next-generation auditory prostheses and artificial hearing systems for sensorineural hearing restoration.Recent advances in additive manufacturing,organ-on-a-chip models,drug delivery platforms,and artificial intelligence have provided valuable insights into how to manufacture artificial cochlea models that more accurately replicate the complex anatomy and physiology of the inner ear.This paper reviews recent advancements in the applications of advanced manufacturing techniques in reproducing the physical,biological,and intelligent functions of the cochlea.It also outlines the current challenges to developing mechanically,electrically,and anatomically accurate functional models of the inner ear.Finally,this review identifies the major requirements and outlook for impactful research in this field going forward.Through interdisciplinary collaboration and innovation,these functional cochlea models are poised to drive significant advancements in hearing treatments,and ultimately enhance the quality of life for individuals with hearing loss.展开更多
This study proposes a new component of the composite loss function minimised during training of the Super-Resolution(SR)algorithms—the normalised structural similarity index loss LSSIMN,which has the potential to imp...This study proposes a new component of the composite loss function minimised during training of the Super-Resolution(SR)algorithms—the normalised structural similarity index loss LSSIMN,which has the potential to improve the natural appearance of reconstructed images.Deep learning-based super-resolution(SR)algorithms reconstruct high-resolution images from low-resolution inputs,offering a practical means to enhance image quality without requiring superior imaging hardware,which is particularly important in medical applications where diagnostic accuracy is critical.Although recent SR methods employing convolutional and generative adversarial networks achieve high pixel fidelity,visual artefacts may persist,making the design of the loss function during training essential for ensuring reliable and naturalistic image reconstruction.Our research shows on two models—SR and Invertible Rescaling Neural Network(IRN)—trained on multiple benchmark datasets that the function LSSIMN significantly contributes to the visual quality,preserving the structural fidelity on the reference datasets.The quantitative analysis of results while incorporating LSSIMN shows that including this loss function component has a mean 2.88%impact on the improvement of the final structural similarity of the reconstructed images in the validation set,in comparison to leaving it out and 0.218%in comparison when this component is non-normalised.展开更多
The application of organic fertilizers has become an increasingly popular practice in maize production to reduce thegaseous nitrogen(N) loss and soil degradation caused by inorganic fertilizers. Organic fertilizer pla...The application of organic fertilizers has become an increasingly popular practice in maize production to reduce thegaseous nitrogen(N) loss and soil degradation caused by inorganic fertilizers. Organic fertilizer plays a key rolein improving soil quality and stabilizing maize yields, but few studies have compared different substitution rates. Afield study was carried out in 2021 and 2022, based on a long-term trial initiated in 2016, which included five organicfertilizer N substitution rates with equal inputs of 200 kg N ha^(–1): 0% organic fertilizer(T1, 100% inorganic fertilizer),50.0% organic+50.0% inorganic fertilizer(T2), 37.5% organic+62.5% inorganic fertilizer(T3), 25.0% organic+75.0%inorganic fertilizer(T4), and 12.5% organic+87.5% inorganic fertilizer(T5), as well as a no fertilizer control(T6). Theresults of the two years showed that T3 and T1 had the highest grain yield and biomass, respectively, and there wasno significant difference between T1 and T3. Compared with T1, the 12.5, 25.0, 37.5, and 50.0% substitution rates in T5, T4, T3, and T2 significantly reduced total nitrogen losses(NH_(3), N_(2)O) by 8.3, 16.1, 18.7, and 27.0%, respectively.Nitrogen use efficiency(NUE) was higher in T5, T3, and T1, and there were no significant differences among them.Organic fertilizer substitution directly reduced NH_(3)volatilization and N_(2)O emission from farmland by lowering theammonium nitrogen and alkali-dissolved N contents and by increasing soil moisture. These substitution treatmentsreduced N_(2)O emissions indirectly by regulating the abundances of AOB and nirK-harboring genes by promotingsoil moisture. Specifically, the 37.5% organic fertilizer substitution reduces NH_(3)volatilization and N_(2)O emission from farmland by reducing the ammonium nitrogen and alkali-dissolved N contents and increasing moisture, which negatively regulate the abundance of AOB and nir K-harboring genes to reduce N_(2)O emissions indirectly in rainfed maize fields on the Loess Plateau of China.展开更多
Currently,challenges such as small object size and occlusion lead to a lack of accuracy and robustness in small object detection.Since small objects occupy only a few pixels in an image,the extracted features are limi...Currently,challenges such as small object size and occlusion lead to a lack of accuracy and robustness in small object detection.Since small objects occupy only a few pixels in an image,the extracted features are limited,and mainstream downsampling convolution operations further exacerbate feature loss.Additionally,due to the occlusionprone nature of small objects and their higher sensitivity to localization deviations,conventional Intersection over Union(IoU)loss functions struggle to achieve stable convergence.To address these limitations,LR-Net is proposed for small object detection.Specifically,the proposed Lossless Feature Fusion(LFF)method transfers spatial features into the channel domain while leveraging a hybrid attentionmechanism to focus on critical features,mitigating feature loss caused by downsampling.Furthermore,RSIoU is proposed to enhance the convergence performance of IoU-based losses for small objects.RSIoU corrects the inherent convergence direction issues in SIoU and proposes a penalty term as a Dynamic Focusing Mechanism parameter,enabling it to dynamically emphasize the loss contribution of small object samples.Ultimately,RSIoU significantly improves the convergence performance of the loss function for small objects,particularly under occlusion scenarios.Experiments demonstrate that LR-Net achieves significant improvements across variousmetrics onmultiple datasets compared with YOLOv8n,achieving a 3.7% increase in mean Average Precision(AP)on the VisDrone2019 dataset,along with improvements of 3.3% on the AI-TOD dataset and 1.2% on the COCO dataset.展开更多
Traditional meteorological downscaling methods face limitations due to the complex distribution of meteorological variables,which can lead to unstable forecasting results,especially in extreme scenarios.To overcome th...Traditional meteorological downscaling methods face limitations due to the complex distribution of meteorological variables,which can lead to unstable forecasting results,especially in extreme scenarios.To overcome this issue,we propose a convolutional graph neural network(CGNN)model,which we enhance with multilayer feature fusion and a squeeze-and-excitation block.Additionally,we introduce a spatially balanced mean squared error(SBMSE)loss function to address the imbalanced distribution and spatial variability of meteorological variables.The CGNN is capable of extracting essential spatial features and aggregating them from a global perspective,thereby improving the accuracy of prediction and enhancing the model's generalization ability.Based on the experimental results,CGNN has certain advantages in terms of bias distribution,exhibiting a smaller variance.When it comes to precipitation,both UNet and AE also demonstrate relatively small biases.As for temperature,AE and CNNdense perform outstandingly during the winter.The time correlation coefficients show an improvement of at least 10%at daily and monthly scales for both temperature and precipitation.Furthermore,the SBMSE loss function displays an advantage over existing loss functions in predicting the98th percentile and identifying areas where extreme events occur.However,the SBMSE tends to overestimate the distribution of extreme precipitation,which may be due to the theoretical assumptions about the posterior distribution of data that partially limit the effectiveness of the loss function.In future work,we will further optimize the SBMSE to improve prediction accuracy.展开更多
Dear Editor,Sleep deprivation and loss can have detrimental effects on brain function.Among common patterns of sleep loss are delayed sleep onset(early night sleep loss,EL)and premature awakening(late night sleep loss...Dear Editor,Sleep deprivation and loss can have detrimental effects on brain function.Among common patterns of sleep loss are delayed sleep onset(early night sleep loss,EL)and premature awakening(late night sleep loss,LL).Here,we investigated the distinct impacts of EL and LL on resting-state brain activity.A total of 100 healthy students from several universities in Beijing were recruited and randomly assigned to one of three groups:EL,LL,or full sleep(FS).Restingstate functional magnetic resonance imaging(rs-fMRI)scans were conducted following the sleep manipulations.Compared to the FS group,the LL group showed abnormal low-frequency fluctuation(fALFF)in the prefrontal cortex and insula.展开更多
AIM: To explore the effects and mechanism of action of antidepressant mirtazapine in functional dyspepsia(FD) patients with weight loss.METHODS: Sixty depressive FD patients with weight loss were randomly divided into...AIM: To explore the effects and mechanism of action of antidepressant mirtazapine in functional dyspepsia(FD) patients with weight loss.METHODS: Sixty depressive FD patients with weight loss were randomly divided into a mirtazapine group(MG), a paroxetine group(PG) or a conventional therapy group(CG) for an 8-wk clinical trial. Adverse effects and treatment response were recorded. The Nepean Dyspepsia Index-symptom(NDSI) checklist and the 17-item Hamilton Rating Scale of Depression(HAMD-17) were used to evaluate dyspepsia and depressive symptoms, respectively. The body composition analyzer was used to measure body weight and fat. Serum hormone levels were measured by ELISA.RESULTS:(1) After 2 wk of treatment, NDSI scores were significantly lower for the MG than for the PG and CG;(2) After 4 or 8 wk of treatment, HAMD-17 scores were significantly lower for the MG and PG than for the CG;(3) After 8 wk of treatment, patients in the MG experienced a weight gain of 3.58 ± 1.57 kg, which was significantly higher than that observed for patients in the PG and CG. Body fat increased by 2.77 ± 0.14kg, the body fat ratio rose by 4%, and the visceral fat area increased by 7.56 ± 2.25 cm2; and(4) For the MG, serum hormone levels of ghrelin, neuropeptide Y(NPY), motilin(MTL) and gastrin(GAS) were significantly upregulated; in contrast, those of leptin, 5-hydroxytryptamine(5-HT) and cholecystokinin(CCK) were significantly downregulated. CONCLUSION: Mirtazapine not only alleviates symptoms associated with dyspepsia and depression linked to FD in patients with weight loss but also significantly increases body weight(mainly the visceral fat in body fat). The likely mechanism of mirtazapine action is regulation of brain-gut or gastrointestinal hormone levels.展开更多
文摘In the last decade,space solar power satellites(SSPSs)have been conceived to support net-zero carbon emissions and have attracted considerable attention.Electric energy is transmitted to the ground via a microwave power beam,a technology known as microwave power transmission(MPT).Due to the vast transmission distance of tens of thousands of kilometers,the power transmitting antenna array must span up to 1 kilometer in diameter.At the same time,the size of the rectifying array on the ground should extend over a few kilometers.This makes the MPT system of SSPSs significantly larger than the existing aerospace engineering system.To design and operate a rational MPT system,comprehensive optimization is required.Taking the space MPT system engineering into consideration,a novel multi-objective optimization function is proposed and further analyzed.The multi-objective optimization problem is modeled mathematically.Beam collection efficiency(BCE)is the primary factor,followed by the thermal management capability.Some tapers,designed to solve the conflict between BCE and the thermal problem,are reviewed.In addition to these two factors,rectenna design complexity is included as a functional factor in the optimization objective.Weight coefficients are assigned to these factors to prioritize them.Radiating planar arrays with different aperture illumination fields are studied,and their performances are compared using the multi-objective optimization function.Transmitting array size,rectifying array size,transmission distance,and transmitted power remaine constant in various cases,ensuring fair comparisons.The analysis results show that the proposed optimization function is effective in optimizing and selecting the MPT system architecture.It is also noted that the multi-objective optimization function can be expanded to include other factors in the future.
基金Funded by the Deep Underground National Science&Technology Major Project gram of China(No.2024ZD1003704)the National Natural Science Foundation of China(Nos.51834001 and 52374111)。
文摘The multi-objective optimization of backfill effect based on response surface methodology and desirability function(RSM-DF)was conducted.Firstly,the test results show that the uniaxial compressive strength(UCS)increases with cement sand ratio(CSR),slurry concentration(SC),and curing age(CA),while flow resistance(FR)increases with SC and backfill flow rate(BFR),and decreases with CSR.Then the regression models of UCS and FR as response values were established through RSM.Multi-factor interaction found that CSR-CA impacted UCS most,while SC-BFR impacted FR most.By introducing the desirability function,the optimal backfill parameters were obtained based on RSM-DF(CSR is 1:6.25,SC is 69%,CA is 11.5 d,and BFR is 90 m^(3)/h),showing close results of Design Expert and high reliability for optimization.For a copper mine in China,RSM-DF optimization will reduce cement consumption by 4758 t per year,increase tailings consumption by about 6700 t,and reduce CO_(2)emission by about 4758 t.Thus,RSM-DF provides a new approach for backfill parameters optimization,which has important theoretical and practical values.
基金Project(51074051)supported by the National Natural Science Foundation of ChinaProject(N110307001)supported by the Fundamental Research Funds for the Central Universities,China
文摘In terms of tandem cold mill productivity and product quality, a multi-objective optimization model of rolling schedule based on cost fimction was proposed to determine the stand reductions, inter-stand tensions and rolling speeds for a specified product. The proposed schedule optimization model consists of several single cost fi.mctions, which take rolling force, motor power, inter-stand tension and stand reduction into consideration. The cost function, which can evaluate how far the rolling parameters are from the ideal values, was minimized using the Nelder-Mead simplex method. The proposed rolling schedule optimization method has been applied successfully to the 5-stand tandem cold mill in Tangsteel, and the results from a case study show that the proposed method is superior to those based on empirical formulae.
基金supported by the National Natural Science Foundation of China(NSFC)No.62172189 and 61772235the Natural Science Foundation of Guangdong Province No.2020A1515010771+1 种基金the Science and Technology Program of Guangzhou No.202002030372the UK Engineering and Physical Sciences Research Council(EPSRC)grants EP/P004407/2 and EP/P004024/1,and Innovate UK grant 106199-47198.
文摘The advent of Network Function Virtualization(NFV)and Service Function Chains(SFCs)unleashes the power of dynamic creation of network services using Virtual Network Functions(VNFs).This is of great interest to network operators since poor service quality and resource wastage can potentially hurt their revenue in the long term.However,the study shows with a set of test-bed experiments that packet loss at certain positions(i.e.,different VNFs)in an SFC can cause various degrees of resource wastage and performance degradation because of repeated upstream processing and transmission of retransmitted packets.To overcome this challenge,this study focuses on resource scheduling and deployment of SFCs while considering packet loss positions.This study developed a novel SFC packet dropping cost model and formulated an SFC scheduling problem that aims to minimize overall packet dropping cost as a Mixed-Integer Linear Programming(MILP)and proved that it is NP-hard.In this study,Palos is proposed as an efficient scheme in exploiting the functional characteristics of VNFs and their positions in SFCs for scheduling resources and deployment to optimize packet dropping cost.Extensive experiment results show that Palos can achieve up to 42.73%improvement on packet dropping cost and up to 33.03%reduction on average SFC latency when compared with two other state-of-the-art schemes.
基金Jilin Science and Technology Development Plan Project(No.20200403075SF)Doctoral Research Start-Up Fund of Northeast Electric Power University(No.BSJXM-2018202).
文摘The current existing problem of deep learning framework for the detection and segmentation of electrical equipment is dominantly related to low precision.Because of the reliable,safe and easy-to-operate technology provided by deep learning-based video surveillance for unmanned inspection of electrical equipment,this paper uses the bottleneck attention module(BAM)attention mechanism to improve the Solov2 model and proposes a new electrical equipment segmentation mode.Firstly,the BAM attention mechanism is integrated into the feature extraction network to adaptively learn the correlation between feature channels,thereby improving the expression ability of the feature map;secondly,the weighted sum of CrossEntropy Loss and Dice loss is designed as the mask loss to improve the segmentation accuracy and robustness of the model;finally,the non-maximal suppression(NMS)algorithm to better handle the overlap problem in instance segmentation.Experimental results show that the proposed method achieves an average segmentation accuracy of mAP of 80.4% on three types of electrical equipment datasets,including transformers,insulators and voltage transformers,which improve the detection accuracy by more than 5.7% compared with the original Solov2 model.The segmentation model proposed can provide a focusing technical means for the intelligent management of power systems.
基金financial support from the Natural Science Foundation of China(No.42002307)Fundamental Research Funds for the Central Universities(No.2652019070)National Key Research and Development Program of China(No.2018YFC0603405)
文摘In shale gas mining,the inter-fracture interference effect will significantly occur if the actual well deviates from the planned trajectory.To reduce production loss,operators want to get back on the planned trajectory economically and safely.Based on this,a multi-objective optimization model of deviationcorrection trajectory is established considering the production loss evaluation.Firstly,the functional relationship between the production envelope and the fracturing depth is constructed,and the production loss is obtained by combining the calculation method of volume flow.Based on the proposed“double-arc”trajectory design method,the production loss of the fracture on the deviation-correction trajectory is obtained.Finally,combined with the well profile energy evaluation,a new optimization model of deviation-correction trajectory is established.The results demonstrate that after optimizing the fracturing depth,the production loss of the deviation-correction trajectory is reduced by 13.2%.The maximum curvature value results in a trajectory with a minimum production loss yet a maximum well profile energy.The proposed model reduces the well profile energy by 15.6%compared with the existing model.It is proved that the proposed model can reduce the probability of drilling accidents and achieve high gas production in the later mining stage.This study fully considers various factors affecting horizontal wells in the fracturing area,which can provide theoretical guidance for the design of deviationcorrection trajectory.
基金Supported by the National Social Science Foundation,No.18BY0911.
文摘BACKGROUND Understanding the impact of early sensory deficits on brain development is essential for understanding developmental processes and developing potential interventions.While previous studies have looked into the impact of prenatal experiences on language development,there is a lack of research on how these experiences affect early language and brain function development in individuals with sensorineural hearing loss(SNHL).AIM To investigate SNHL effects on early brain development and connectivity in 4-month-olds vs healthy newborns and controls.METHODS The research involved analyzing the functional brain networks of 65 infants,categorized into three groups:28 healthy newborns,224-month-old participants with SNHL,and 15 age-matched healthy participants.The resting-state functional connectivity was measured and compared between the groups using functional near-infrared spectroscopy and graph theory to assess the brain network properties.RESULTS Significant differences were found in resting-state functional connectivity between participants with SNHL and age-matched controls,indicating a developmental lag in brain connectivity for those with SNHL.Surprisingly,SNHL participants showed better connectivity development compared to healthy newborns,with connectivity strengths of 0.13±0.04 for SNHL,0.16±0.08 for controls,and 0.098±0.04 for newborns.Graph theory analysis revealed enhanced global brain network properties for the SNHL group,suggesting higher communication efficiency at 4 months.No significant differences were noted in network properties between 4-month-old SNHL participants and neonates.A unique pattern of central hubs was observed in the SNHL group,with 2 hubs in the left hemisphere compared to 6 in controls.CONCLUSION 4-month-old infants with SNHL have a distinct brain network pattern with efficient long-distance information transmission but less effective local communication compared to age-matched controls.
文摘In this paper, the isogeometric analysis (IGA) is employed to develop an acoustic radiation model for a double plate-acoustic cavity coupling system, with a focus on analyzing the sound transmission loss (STL). The functionally graded (FG) plate exhibits a different material properties in-plane, and the power-law rule is adopted as the governing principle for material mixing. To validate the harmonic response and demonstrate the accuracy and convergence of the isogeometric modeling, ANASYS is utilized to compare with numerical examples. A plane wave serves as the acoustic excitation, and the Rayleigh integral is applied to discretize the radiated plate. The STL results are compared with the literature, confirming the reliability of the coupling system. Finally, the investigation is conducted to study impact of cavity depth and power-law parameter on the STL.
基金supported by the National Key Research and Development Program of China(Grant numbers:2021YFF0901705,2021YFF0901700)the State Key Laboratory of Media Convergence and Communication,Communication University of China+1 种基金the Fundamental Research Funds for the Central Universitiesthe High-Quality and Cutting-Edge Disciplines Construction Project for Universities in Beijing(Internet Information,Communication University of China).
文摘In the Internet era,recommendation systems play a crucial role in helping users find relevant information from large datasets.Class imbalance is known to severely affect data quality,and therefore reduce the performance of recommendation systems.Due to the imbalance,machine learning algorithms tend to classify inputs into the positive(majority)class every time to achieve high prediction accuracy.Imbalance can be categorized such as by features and classes,but most studies consider only class imbalance.In this paper,we propose a recommendation system that can integrate multiple networks to adapt to a large number of imbalanced features and can deal with highly skewed and imbalanced datasets through a loss function.We propose a loss aware feature attention mechanism(LAFAM)to solve the issue of feature imbalance.The network incorporates an attention mechanism and uses multiple sub-networks to classify and learn features.For better results,the network can learn the weights of sub-networks and assign higher weights to important features.We propose suppression loss to address class imbalance,which favors negative loss by penalizing positive loss,and pays more attention to sample points near the decision boundary.Experiments on two large-scale datasets verify that the performance of the proposed system is greatly improved compared to baseline methods.
文摘Maxillary bone loss impairs essential functions (chewing, swallowing, speech) and gives patients a very unaesthetic appearance due to the removal of facial support tissues, leading to serious psychological consequences. Treatment is multidisciplinary and requires a resective surgery if the cause is tumor-related, or an additive surgery if the cause is traumatic. This article aims to show the role of making a prosthesis to restore function (chewing, swallowing, speech) and aesthetics following maxillary bone loss. We will eighter present a clinical case involving a right maxillary tumor that was surgically resected followed by radiotherapy, and subsequently rehabilitated with a maxillofacial prosthesis in the consultation and dental treatment center of the university hospital center of Casablanca.
基金Supported by the National"Thirteenth Five-year Plan"National Key Program(2016YFD0701301)the Heilongjiang Provincial Achievement Transformation Fund Project(NB08B-011)。
文摘To better meet the needs of crop growth and achieve energy savings and efficiency enhancements,constructing a reliable environmental model to optimize greenhouse decision parameters is an important problem to be solved.In this work,a radial-basis function(RBF)neural network was used to mine the potential changes of a greenhouse environment,a temperature error model was established,a multi-objective optimization function of energy consumption was constructed and the corresponding decision parameters were optimized by using a non-dominated sorting genetic algorithm with an elite strategy(NSGA-Ⅱ).The simulation results showed that RBF could clarify the nonlinear relationship among the greenhouse environment variables and decision parameters and the greenhouse temperature.The NSGA-Ⅱ could well search for the Pareto solution for the objective functions.The experimental results showed that after 40 min of combined control of sunshades and sprays,the temperature was reduced from 31℃to 25℃,and the power consumption was 0.5 MJ.Compared with tire three days of July 24,July 25 and July 26,2017,the energy consumption of the controlled production greenhouse was reduced by 37.5%,9.1%and 28.5%,respectively.
文摘In this paper, we present an algorithm to solve the inequality constrained multi-objective programming (MP) by using a penalty function with objective parameters and constraint penalty parameter. First, the penalty function with objective parameters and constraint penalty parameter for MP and the corresponding unconstraint penalty optimization problem (UPOP) is defined. Under some conditions, a Pareto efficient solution (or a weakly-efficient solution) to UPOP is proved to be a Pareto efficient solution (or a weakly-efficient solution) to MP. The penalty function is proved to be exact under a stable condition. Then, we design an algorithm to solve MP and prove its convergence. Finally, numerical examples show that the algorithm may help decision makers to find a satisfactory solution to MP.
文摘This paper deals with the optimality conditions and dual theory of multi-objective programming problems involving generalized convexity. New classes of generalized type-I functions are introduced for arcwise connected functions, and examples are given to show the existence of these functions. By utilizing the new concepts, several sufficient optimality conditions and Mond-Weir type duality results are proposed for non-differentiable multi-objective programming problem.
基金support from the UCL GRS/ORS scholarshipUCL Fellowship Incubator Award+9 种基金supported by the NIHR Cambridge Biomedical Research Centre(NIHR203312)funded by the Royal National Institute for Deaf People(RNID,G100138)funded by the Rosetrees Trust Enterprise Fellowship(EF2020100099)RNID Flexigrant(F112)Wellcome Trust Developing Concept Fund(RG93172/BANCE/40181)by the Evelyn Trustfunded by the Woolf Fisher Trust,New Zealandthe Cambridge Commonwealth,European,&International Trustby Trinity CollegeUniversity of Cambridge。
文摘The cochlea is one of the most complex organs in the human body,exhibiting a complex interplay of characteristics in acoustic,mechanical,electrical,and biological functions.Functional cochlea models are an essential platform for studying hearing mechanics and are crucial for developing next-generation auditory prostheses and artificial hearing systems for sensorineural hearing restoration.Recent advances in additive manufacturing,organ-on-a-chip models,drug delivery platforms,and artificial intelligence have provided valuable insights into how to manufacture artificial cochlea models that more accurately replicate the complex anatomy and physiology of the inner ear.This paper reviews recent advancements in the applications of advanced manufacturing techniques in reproducing the physical,biological,and intelligent functions of the cochlea.It also outlines the current challenges to developing mechanically,electrically,and anatomically accurate functional models of the inner ear.Finally,this review identifies the major requirements and outlook for impactful research in this field going forward.Through interdisciplinary collaboration and innovation,these functional cochlea models are poised to drive significant advancements in hearing treatments,and ultimately enhance the quality of life for individuals with hearing loss.
基金support from the following institutional grant.Internal Grant Agency of the Faculty of Economics and Management,Czech University of Life Sciences Prague,grant no.2023A0004(https://iga.pef.czu.cz/,accessed on 6 June 2025).
文摘This study proposes a new component of the composite loss function minimised during training of the Super-Resolution(SR)algorithms—the normalised structural similarity index loss LSSIMN,which has the potential to improve the natural appearance of reconstructed images.Deep learning-based super-resolution(SR)algorithms reconstruct high-resolution images from low-resolution inputs,offering a practical means to enhance image quality without requiring superior imaging hardware,which is particularly important in medical applications where diagnostic accuracy is critical.Although recent SR methods employing convolutional and generative adversarial networks achieve high pixel fidelity,visual artefacts may persist,making the design of the loss function during training essential for ensuring reliable and naturalistic image reconstruction.Our research shows on two models—SR and Invertible Rescaling Neural Network(IRN)—trained on multiple benchmark datasets that the function LSSIMN significantly contributes to the visual quality,preserving the structural fidelity on the reference datasets.The quantitative analysis of results while incorporating LSSIMN shows that including this loss function component has a mean 2.88%impact on the improvement of the final structural similarity of the reconstructed images in the validation set,in comparison to leaving it out and 0.218%in comparison when this component is non-normalised.
基金supported by the State Key Laboratory of Arid Land Crop Science, Gansu Agricultural University,China (GSCS-2022-Z02)the National Key R&D Program of China (2022YFD1900300)+2 种基金the National Natural Science Foundation of China (32260549)the Innovation Group of Basic Research in Gansu Province, China (25JRRA807)the Major Special Research Projects in Gansu Province, China (22ZD6NA009)。
文摘The application of organic fertilizers has become an increasingly popular practice in maize production to reduce thegaseous nitrogen(N) loss and soil degradation caused by inorganic fertilizers. Organic fertilizer plays a key rolein improving soil quality and stabilizing maize yields, but few studies have compared different substitution rates. Afield study was carried out in 2021 and 2022, based on a long-term trial initiated in 2016, which included five organicfertilizer N substitution rates with equal inputs of 200 kg N ha^(–1): 0% organic fertilizer(T1, 100% inorganic fertilizer),50.0% organic+50.0% inorganic fertilizer(T2), 37.5% organic+62.5% inorganic fertilizer(T3), 25.0% organic+75.0%inorganic fertilizer(T4), and 12.5% organic+87.5% inorganic fertilizer(T5), as well as a no fertilizer control(T6). Theresults of the two years showed that T3 and T1 had the highest grain yield and biomass, respectively, and there wasno significant difference between T1 and T3. Compared with T1, the 12.5, 25.0, 37.5, and 50.0% substitution rates in T5, T4, T3, and T2 significantly reduced total nitrogen losses(NH_(3), N_(2)O) by 8.3, 16.1, 18.7, and 27.0%, respectively.Nitrogen use efficiency(NUE) was higher in T5, T3, and T1, and there were no significant differences among them.Organic fertilizer substitution directly reduced NH_(3)volatilization and N_(2)O emission from farmland by lowering theammonium nitrogen and alkali-dissolved N contents and by increasing soil moisture. These substitution treatmentsreduced N_(2)O emissions indirectly by regulating the abundances of AOB and nirK-harboring genes by promotingsoil moisture. Specifically, the 37.5% organic fertilizer substitution reduces NH_(3)volatilization and N_(2)O emission from farmland by reducing the ammonium nitrogen and alkali-dissolved N contents and increasing moisture, which negatively regulate the abundance of AOB and nir K-harboring genes to reduce N_(2)O emissions indirectly in rainfed maize fields on the Loess Plateau of China.
基金supported by Chongqing Municipal Commission of Housing and Urban-Rural Development(Grant No.CKZ2024-87)China Chongqing Municipal Science and Technology Bureau(Grant No.2024TIAD-CYKJCXX0121).
文摘Currently,challenges such as small object size and occlusion lead to a lack of accuracy and robustness in small object detection.Since small objects occupy only a few pixels in an image,the extracted features are limited,and mainstream downsampling convolution operations further exacerbate feature loss.Additionally,due to the occlusionprone nature of small objects and their higher sensitivity to localization deviations,conventional Intersection over Union(IoU)loss functions struggle to achieve stable convergence.To address these limitations,LR-Net is proposed for small object detection.Specifically,the proposed Lossless Feature Fusion(LFF)method transfers spatial features into the channel domain while leveraging a hybrid attentionmechanism to focus on critical features,mitigating feature loss caused by downsampling.Furthermore,RSIoU is proposed to enhance the convergence performance of IoU-based losses for small objects.RSIoU corrects the inherent convergence direction issues in SIoU and proposes a penalty term as a Dynamic Focusing Mechanism parameter,enabling it to dynamically emphasize the loss contribution of small object samples.Ultimately,RSIoU significantly improves the convergence performance of the loss function for small objects,particularly under occlusion scenarios.Experiments demonstrate that LR-Net achieves significant improvements across variousmetrics onmultiple datasets compared with YOLOv8n,achieving a 3.7% increase in mean Average Precision(AP)on the VisDrone2019 dataset,along with improvements of 3.3% on the AI-TOD dataset and 1.2% on the COCO dataset.
基金partially funded by the National Natural Science Foundation of China(U2142205)the Guangdong Major Project of Basic and Applied Basic Research(2020B0301030004)+1 种基金the Special Fund for Forecasters of China Meteorological Administration(CMAYBY2020-094)the Graduate Student Research and Innovation Program of Central South University(2023ZZTS0347)。
文摘Traditional meteorological downscaling methods face limitations due to the complex distribution of meteorological variables,which can lead to unstable forecasting results,especially in extreme scenarios.To overcome this issue,we propose a convolutional graph neural network(CGNN)model,which we enhance with multilayer feature fusion and a squeeze-and-excitation block.Additionally,we introduce a spatially balanced mean squared error(SBMSE)loss function to address the imbalanced distribution and spatial variability of meteorological variables.The CGNN is capable of extracting essential spatial features and aggregating them from a global perspective,thereby improving the accuracy of prediction and enhancing the model's generalization ability.Based on the experimental results,CGNN has certain advantages in terms of bias distribution,exhibiting a smaller variance.When it comes to precipitation,both UNet and AE also demonstrate relatively small biases.As for temperature,AE and CNNdense perform outstandingly during the winter.The time correlation coefficients show an improvement of at least 10%at daily and monthly scales for both temperature and precipitation.Furthermore,the SBMSE loss function displays an advantage over existing loss functions in predicting the98th percentile and identifying areas where extreme events occur.However,the SBMSE tends to overestimate the distribution of extreme precipitation,which may be due to the theoretical assumptions about the posterior distribution of data that partially limit the effectiveness of the loss function.In future work,we will further optimize the SBMSE to improve prediction accuracy.
基金supported by the STI2030-Major Projects(2021ZD0202100,2021ZD0200801,and 2021ZD0201900)the National Natural Science Foundation of China(82130040,82288101).
文摘Dear Editor,Sleep deprivation and loss can have detrimental effects on brain function.Among common patterns of sleep loss are delayed sleep onset(early night sleep loss,EL)and premature awakening(late night sleep loss,LL).Here,we investigated the distinct impacts of EL and LL on resting-state brain activity.A total of 100 healthy students from several universities in Beijing were recruited and randomly assigned to one of three groups:EL,LL,or full sleep(FS).Restingstate functional magnetic resonance imaging(rs-fMRI)scans were conducted following the sleep manipulations.Compared to the FS group,the LL group showed abnormal low-frequency fluctuation(fALFF)in the prefrontal cortex and insula.
文摘AIM: To explore the effects and mechanism of action of antidepressant mirtazapine in functional dyspepsia(FD) patients with weight loss.METHODS: Sixty depressive FD patients with weight loss were randomly divided into a mirtazapine group(MG), a paroxetine group(PG) or a conventional therapy group(CG) for an 8-wk clinical trial. Adverse effects and treatment response were recorded. The Nepean Dyspepsia Index-symptom(NDSI) checklist and the 17-item Hamilton Rating Scale of Depression(HAMD-17) were used to evaluate dyspepsia and depressive symptoms, respectively. The body composition analyzer was used to measure body weight and fat. Serum hormone levels were measured by ELISA.RESULTS:(1) After 2 wk of treatment, NDSI scores were significantly lower for the MG than for the PG and CG;(2) After 4 or 8 wk of treatment, HAMD-17 scores were significantly lower for the MG and PG than for the CG;(3) After 8 wk of treatment, patients in the MG experienced a weight gain of 3.58 ± 1.57 kg, which was significantly higher than that observed for patients in the PG and CG. Body fat increased by 2.77 ± 0.14kg, the body fat ratio rose by 4%, and the visceral fat area increased by 7.56 ± 2.25 cm2; and(4) For the MG, serum hormone levels of ghrelin, neuropeptide Y(NPY), motilin(MTL) and gastrin(GAS) were significantly upregulated; in contrast, those of leptin, 5-hydroxytryptamine(5-HT) and cholecystokinin(CCK) were significantly downregulated. CONCLUSION: Mirtazapine not only alleviates symptoms associated with dyspepsia and depression linked to FD in patients with weight loss but also significantly increases body weight(mainly the visceral fat in body fat). The likely mechanism of mirtazapine action is regulation of brain-gut or gastrointestinal hormone levels.
