Utility function based fair data scheduling algorithm for OFDM wireless network

A system model is formulated as the maximization of a total utility function to achieve fair downlink data scheduling in multiuser orthogonal frequency division multiplexing （OFDM） wireless networks. A dynamic subcarrier allocation algorithm （DSAA） is proposed, to optimize the system model. The subcarrier allocation decision is made by the proposed DSAA according to the maximum value of total utility function with respect to the queue mean waiting time. Simulation results demonstrate that compared to the conventional algorithms, the proposed algorithm has better delay performance and can provide fairness under different loads by using different utility functions.

Optimization of the Dressing Parameters in Cylindrical Grinding Based on a Generalized Utility Function

The existing studies, concerning the dressing process, focus on the major influence of the dressing conditions on the grinding response variables. However, the choice of the dressing conditions is often made, based on the experience of the qualified staff or using data from reference books. The optimal dressing parameters, which are only valid for the particular methods and dressing and grinding conditions, are also used. The paper presents a methodology for optimization of the dressing parameters in cylindrical grinding. The generalized utility function has been chosen as an optimization parameter. It is a complex indicator determining the economic, dynamic and manufacturing characteristics of the grinding process. The developed methodology is implemented for the dressing of aluminium oxide grinding wheels by using experimental diamond roller dressers with different grit sizes made of medium- and high-strength synthetic diamonds type AC32 and AC80. To solve the optimization problem, a model of the generalized utility function is created which reflects the complex impact of dressing parameters. The model is built based on the results from the conducted complex study and modeling of the grinding wheel lifetime, cutting ability, production rate and cutting forces during grinding. They are closely related to the dressing conditions （dressing speed ratio, radial in-feed of the diamond roller dresser and dress-out time）, the diamond roller dresser grit size/grinding wheel grit size ratio, the type of synthetic diamonds and the direction of dressing. Some dressing parameters are determined for which the generalized utility fimction has a maximum and which guarantee an optimum combination of the following： the lifetime and cutting ability of the abrasive wheels, the tangential cutting force magnitude and the production rate of the grinding process. The results obtained prove the possibility of control and optimization of grinding by selecting particular dressing parameters.

Optimal investment with transaction costs based on exponential utility function:a parabolic double obstacle problem

This paper concerns optimal investment problem with proportional transaction costs and finite time horizon based on exponential utility function. Using a partial differential equation approach, we reveal that the problem is equivalent to a parabolic double obstacle problem involving two free boundaries that correspond to the optimal buying and selling policies. Numerical examples are obtained by the binomial method.

Comprehensive Utility Function for Resource Allocation in Mobile Edge Computing

In mobile edge computing(MEC),one of the important challenges is how much resources of which mobile edge server(MES)should be allocated to which user equipment(UE).The existing resource allocation schemes only consider CPU as the requested resource and assume utility for MESs as either a random variable or dependent on the requested CPU only.This paper presents a novel comprehensive utility function for resource allocation in MEC.The utility function considers the heterogeneous nature of applications that a UE offloads to MES.The proposed utility function considers all important parameters,including CPU,RAM,hard disk space,required time,and distance,to calculate a more realistic utility value for MESs.Moreover,we improve upon some general algorithms,used for resource allocation in MEC and cloud computing,by considering our proposed utility function.We name the improved versions of these resource allocation schemes as comprehensive resource allocation schemes.The UE requests are modeled to represent the amount of resources requested by the UE as well as the time for which the UE has requested these resources.The utility function depends upon the UE requests and the distance between UEs and MES,and serves as a realistic means of comparison between different types of UE requests.Choosing(or selecting)an optimal MES with the optimal amount of resources to be allocated to each UE request is a challenging task.We show that MES resource allocation is sub-optimal if CPU is the only resource considered.By taking into account the other resources,i.e.,RAM,disk space,request time,and distance in the utility function,we demonstrate improvement in the resource allocation algorithms in terms of service rate,utility,and MES energy consumption.

Analysis of Optimal Portfolio with Different Utility Function

最佳的公事包的问题是那发现做贸易的策略令人满意最大的期望的用途功能题目到一些限制。在风险下面有最佳的做贸易策略中立概率措施(鞅措施) 如果并且仅当在市场上有没有套利机会。这份报纸主张最佳的财富和有不同用途的期望的用途的最佳的值工作。

Image Splicing Forgery Detection Using Feature-Based of Sonine Functions and Deep Features

The growing prevalence of fake images on the Internet and social media makes image integrity verification a crucial research topic.One of the most popular methods for manipulating digital images is image splicing,which involves copying a specific area from one image and pasting it into another.Attempts were made to mitigate the effects of image splicing,which continues to be a significant research challenge.This study proposes a new splicing detectionmodel,combining Sonine functions-derived convex-based features and deep features.Two stages make up the proposed method.The first step entails feature extraction,then classification using the“support vector machine”(SVM)to differentiate authentic and spliced images.The proposed Sonine functions-based feature extraction model reveals the spliced texture details by extracting some clues about the probability of image pixels.The proposed model achieved an accuracy of 98.93% when tested with the CASIA V2.0 dataset“Chinese Academy of Sciences,Institute of Automation”which is a publicly available dataset for forgery classification.The experimental results show that,for image splicing forgery detection,the proposed Sonine functions-derived convex-based features and deep features outperform state-of-the-art techniques in terms of accuracy,precision,and recall.Overall,the obtained detection accuracy attests to the benefit of using the Sonine functions alongside deep feature representations.Finding the regions or locations where image tampering has taken place is limited by the study.Future research will need to look into advanced image analysis techniques that can offer a higher degree of accuracy in identifying and localizing tampering regions.

Underwater Image Classification Based on EfficientnetB0 and Two-Hidden-Layer Random Vector Functional Link

The ocean plays an important role in maintaining the equilibrium of Earth’s ecology and providing humans access to a wealth of resources.To obtain a high-precision underwater image classification model,we propose a classification model that combines an EfficientnetB0 neural network and a two-hidden-layer random vector functional link network(EfficientnetB0-TRVFL).The features of underwater images were extracted using the EfficientnetB0 neural network pretrained via ImageNet,and a new fully connected layer was trained on the underwater image dataset using the transfer learning method.Transfer learning ensures the initial performance of the network and helps in the development of a high-precision classification model.Subsequently,a TRVFL was proposed to improve the classification property of the model.Net construction of the two hidden layers exhibited a high accuracy when the same hidden layer nodes were used.The parameters of the second hidden layer were obtained using a novel calculation method,which reduced the outcome error to improve the performance instability caused by the random generation of parameters of RVFL.Finally,the TRVFL classifier was used to classify features and obtain classification results.The proposed EfficientnetB0-TRVFL classification model achieved 87.28%,74.06%,and 99.59%accuracy on the MLC2008,MLC2009,and Fish-gres datasets,respectively.The best convolutional neural networks and existing methods were stacked up through box plots and Kolmogorov-Smirnov tests,respectively.The increases imply improved systematization properties in underwater image classification tasks.The image classification model offers important performance advantages and better stability compared with existing methods.

Pseudo-Semi-Overlap Functions-Based Fuzzy Rough Sets Applied to Image Edge Extraction

As an extension of overlap functions, pseudo-semi-overlap functions are a crucial class of aggregation functions. Therefore, (I, PSO)-fuzzy rough sets are introduced, utilizing pseudo-semi-overlap functions, and further extended for applications in image edge extraction. Firstly, a new clustering function, the pseudo-semi-overlap function, is introduced by eliminating the symmetry and right continuity present in the overlap function. The relaxed nature of this function enhances its applicability in image edge extraction. Secondly, the definitions of (I, PSO)-fuzzy rough sets are provided, using (I, PSO)-fuzzy rough sets, a pair of new fuzzy mathematical morphological operators (IPSOFMM operators) is proposed. Finally, by combining the fuzzy C-means algorithm and IPSOFMM operators, a novel image edge extraction algorithm (FCM-IPSO algorithm) is proposed and implemented. Compared to existing algorithms, the FCM-IPSO algorithm exhibits more image edges and a 73.81% decrease in the noise introduction rate. The outstanding performance of (I, PSO)-fuzzy rough sets in image edge extraction demonstrates their practical application value.

Brain mechanism of acupuncture for children with anisometropic amblyopia: a resting functional magnetic resonance imaging study based on voxel-mirrored homotopic connectivity

AIM:To explore the brain mechanism of acupuncture for children with anisometropic amblyopia using the voxelmirror homotopic connectivity(VMHC)analysis method of resting functional magnetic resonance imaging(rs-fMRI)technology based on clinical effectiveness.METHODS:Eighty children with anisometropic monocular amblyopia were randomly divided into two groups:control(40 cases,1 case of shedding)and acupuncture(40 cases,1 case of shedding)groups.The control group was treated with glasses,red flash,grating,and visual stimulations,with each procedure conducted for 5min per time.Based on routine treatment,the acupuncture group underwent acupuncture of“regulating qi and unblocking meridians to bright eyes”,Jingming(BL1),Cuanzhu(BL2),Guangming(GB37),Fengchi(GB20)acupoints were taken on both sides,with the needle kept for 30min each time.Both groups were treated once every other day,three times per week,for a total of 4wk.After the treatment,the overall curative effect of the two groups and the latency and amplitude changes of P100 wave of pattern visual-evoked potential were counted.At the same time,nine children with left eye amblyopia were randomly selected from the two groups and were scanned with rsfMRI before and after treatment.The differences in the brain regions between the two groups were compared and analyzed with VMHC.RESULTS:Chi-square test showed a notable difference in the total efficiency rate between the acupuncture(94.87%)and control groups(79.49%).Regarding the P100 wave latency and amplitude,the acupuncture group had significantly shorter latency and higher amplitude of P100 wave than the control group.Moreover,the VMHC values of the bilateral temporal lobe,superior temporal gyrus,and middle temporal gyrus were notably increased in the acupuncture group after treatment.CONCLUSION:Acupuncture combined with conventional treatment can significantly improve the corrected visual acuity and optic nerve conduction in children with anisometropic amblyopia.Compared with the conventional treatment,the regulation of acupuncture on the functional activities of the relevant brain areas in the anterior cerebellum may be an effective acupuncture mechanism for anisometropic amblyopia.

Vulnerable brain regions in adolescent major depressive disorder:A resting-state functional magnetic resonance imaging activation likelihood estimation meta-analysis

BACKGROUND Adolescent major depressive disorder(MDD)is a significant mental health concern that often leads to recurrent depression in adulthood.Resting-state functional magnetic resonance imaging(rs-fMRI)offers unique insights into the neural mechanisms underlying this condition.However,despite previous research,the specific vulnerable brain regions affected in adolescent MDD patients have not been fully elucidated.AIM To identify consistent vulnerable brain regions in adolescent MDD patients using rs-fMRI and activation likelihood estimation(ALE)meta-analysis.METHODS We performed a comprehensive literature search through July 12,2023,for studies investigating brain functional changes in adolescent MDD patients.We utilized regional homogeneity(ReHo),amplitude of low-frequency fluctuations(ALFF)and fractional ALFF(fALFF)analyses.We compared the regions of aberrant spontaneous neural activity in adolescents with MDD vs healthy controls(HCs)using ALE.RESULTS Ten studies(369 adolescent MDD patients and 313 HCs)were included.Combining the ReHo and ALFF/fALFF data,the results revealed that the activity in the right cuneus and left precuneus was lower in the adolescent MDD patients than in the HCs(voxel size:648 mm3,P<0.05),and no brain region exhibited increased activity.Based on the ALFF data,we found decreased activity in the right cuneus and left precuneus in adolescent MDD patients(voxel size:736 mm3,P<0.05),with no regions exhibiting increased activity.CONCLUSION Through ALE meta-analysis,we consistently identified the right cuneus and left precuneus as vulnerable brain regions in adolescent MDD patients,increasing our understanding of the neuropathology of affected adolescents.

Image Splicing Detection Using Generalized Whittaker Function Descriptor

Image forgery is a crucial part of the transmission of misinformation,which may be illegal in some jurisdictions.The powerful image editing software has made it nearly impossible to detect altered images with the naked eye.Images must be protected against attempts to manipulate them.Image authentication methods have gained popularity because of their use in multimedia and multimedia networking applications.Attempts were made to address the consequences of image forgeries by creating algorithms for identifying altered images.Because image tampering detection targets processing techniques such as object removal or addition,identifying altered images remains a major challenge in research.In this study,a novel image texture feature extraction model based on the generalized k-symbolWhittaker function(GKSWF)is proposed for better image forgery detection.The proposed method is divided into two stages.The first stage involves feature extraction using the proposed GKSWF model,followed by classification using the“support vector machine”(SVM)to distinguish between authentic and manipulated images.Each extracted feature from an input image is saved in the features database for use in image splicing detection.The proposed GKSWF as a feature extraction model is intended to extract clues of tampering texture details based on the probability of image pixel.When tested on publicly available image dataset“CASIA”v2.0(ChineseAcademy of Sciences,Institute of Automation),the proposed model had a 98.60%accuracy rate on the YCbCr(luminance(Y),chroma blue(Cb)and chroma red(Cr))color spaces in image block size of 8×8 pixels.The proposed image authentication model shows great accuracy with a relatively modest dimension feature size,supporting the benefit of utilizing the k-symbol Whittaker function in image authentication algorithms.

Structural and functional alterations in the brains of patients with anisometropic and strabismic amblyopia:a systematic review of magnetic resonance imaging studies

Amblyopia is the most common cause of vision loss in children and can persist into adulthood in the absence of effective intervention.Previous clinical and neuroimaging studies have suggested that the neural mechanisms underlying strabismic amblyopia and anisometropic amblyopia may be different.Therefore,we performed a systematic review of magnetic resonance imaging studies investigating brain alterations in patients with these two subtypes of amblyopia;this study is registered with PROSPERO(registration ID:CRD42022349191).We searched three online databases(PubMed,EMBASE,and Web of Science) from inception to April 1,2022;39 studies with 633 patients(324patients with anisometropic amblyo pia and 309 patients with strabismic amblyopia) and 580 healthy controls met the inclusion criteria(e.g.,case-control designed,pee r-reviewed articles) and were included in this review.These studies highlighted that both strabismic amblyopia and anisometropic amblyopia patients showed reduced activation and distorted topological cortical activated maps in the striate and extrastriate co rtices during tas k-based functional magnetic resonance imaging with spatial-frequency stimulus and retinotopic representations,respectively;these may have arisen from abnormal visual experiences.Compensations for amblyopia that are reflected in enhanced spontaneous brain function have been reported in the early visual cortices in the resting state,as well as reduced functional connectivity in the dorsal pathway and structural connections in the ventral pathway in both anisometro pic amblyopia and strabismic amblyopia patients.The shared dysfunction of anisometro pic amblyopia and strabismic amblyopia patients,relative to controls,is also chara cterized by reduced spontaneous brain activity in the oculomotor co rtex,mainly involving the frontal and parietal eye fields and the cerebellu m;this may underlie the neural mechanisms of fixation instability and anomalous saccades in amblyopia.With regards to specific alterations of the two forms of amblyo pia,anisometropic amblyo pia patients suffer more microstructural impairments in the precortical pathway than strabismic amblyopia patients,as reflected by diffusion tensor imaging,and more significant dysfunction and structural loss in the ventral pathway.Strabismic amblyopia patients experience more attenuation of activation in the extrastriate co rtex than in the striate cortex when compared to anisometropic amblyopia patients.Finally,brain structural magnetic resonance imaging alterations tend to be lateralized in the adult anisometropic amblyopia patients,and the patterns of brain alterations are more limited in amblyopic adults than in childre n.In conclusion,magnetic resonance imaging studies provide important insights into the brain alterations underlying the pathophysiology of amblyopia and demonstrate common and specific alte rations in anisometropic amblyo pia and strabismic amblyopia patients;these alterations may improve our understanding of the neural mechanisms underlying amblyopia.

Development and Validation of a Deep Learning Predictive Model Combining Clinical and Radiomic Features for Short-Term Postoperative Facial Nerve Function in Acoustic Neuroma Patients

Objective This study aims to construct and validate a predictable deep learning model associated with clinical data and multi-sequence magnetic resonance imaging(MRI)for short-term postoperative facial nerve function in patients with acoustic neuroma.Methods A total of 110 patients with acoustic neuroma who underwent surgery through the retrosigmoid sinus approach were included.Clinical data and raw features from four MRI sequences(T1-weighted,T2-weighted,T1-weighted contrast enhancement,and T2-weighted-Flair images)were analyzed.Spearman correlation analysis along with least absolute shrinkage and selection operator regression were used to screen combined clinical and radiomic features.Nomogram,machine learning,and convolutional neural network(CNN)models were constructed to predict the prognosis of facial nerve function on the seventh day after surgery.Receiver operating characteristic(ROC)curve and decision curve analysis(DCA)were used to evaluate model performance.A total of 1050 radiomic parameters were extracted,from which 13 radiomic and 3 clinical features were selected.Results The CNN model performed best among all prediction models in the test set with an area under the curve(AUC)of 0.89(95%CI,0.84–0.91).Conclusion CNN modeling that combines clinical and multi-sequence MRI radiomic features provides excellent performance for predicting short-term facial nerve function after surgery in patients with acoustic neuroma.As such,CNN modeling may serve as a potential decision-making tool for neurosurgery.

Existence of Arrow-Debreu Equilibrium with S-shaped Utility Function

This paper characterizes the optimal solution of subjective expected utility with S-shaped utility function, by using the prospect theory （PT）. We also prove the existence of Arrow-Debreu equilibrium.

Goal-Programming Model Based on the Utility Function of the Decision-maker

Based on the analysis of the problems in traditional GP model, this paper provides the model with the utility function of the decision-maker and compares this model with the one presented in reference article [1].

Functional recovery and muscle atrophy in pre-clinical models of peripheral nerve transection and gap-grafting in mice:effects of 4-aminopyridine

We recently demonstrated a repurposing beneficial effect of 4-aminopyridine(4-AP),a potassium channel blocker,on functional recove ry and muscle atrophy after sciatic nerve crush injury in rodents.However,this effect of 4-AP is unknown in nerve transection,gap,and grafting models.To evaluate and compare the functional recovery,nerve morphology,and muscle atrophy,we used a novel stepwise nerve transection with gluing(STG),as well as 7-mm irreparable nerve gap(G-7/0)and 7-mm isografting in 5-mm gap(G-5/7)models in the absence and presence of 4-AP treatment.Following surgery,sciatic functional index was determined wee kly to evaluate the direct in vivo global motor functional recovery.After 12 weeks,nerves were processed for whole-mount immunofluorescence imaging,and tibialis anterior muscles were harvested for wet weight and quantitative histomorphological analyses for muscle fiber crosssectional area and minimal Feret's diameter.Average post-injury sciatic functional index values in STG and G-5/7 models were significantly greater than those in the G-7/0 model.4-AP did not affect the sciatic functional index recovery in any model.Compared to STG,nerve imaging revealed more misdirected axons and distorted nerve architecture with isografting.While muscle weight,cross-sectional area,and minimal Feret's diameter were significantly smaller in G-7/0 model compared with STG and G-5/7,4-AP treatment significantly increased right TA muscle mass,cross-sectional area,and minimal Feret's diameter in G-7/0 model.These findings demonstrate that functional recovery and muscle atrophy after peripheral nerve injury are directly related to the intervening nerve gap,and 4-AP exerts diffe rential effects on functional recove ry and muscle atrophy.

Modified constraint-induced movement therapy enhances cortical plasticity in a rat model of traumatic brain injury:a resting-state functional MRI study

Modified constraint-induced movement therapy(mCIMT)has shown beneficial effects on motor function improvement after brain injury,but the exact mechanism remains unclear.In this study,amplitude of low frequency fluctuation(ALFF)metrics measured by resting-state functional magnetic resonance imaging was obtained to investigate the efficacy and mechanism of mCIMT in a control co rtical impact(CCI)rat model simulating traumatic brain injury.At 3 days after control co rtical impact model establishment,we found that the mean ALFF(mALFF)signals were decreased in the left motor cortex,somatosensory co rtex,insula cortex and the right motor co rtex,and were increased in the right corpus callosum.After 3 weeks of an 8-hour daily mClMT treatment,the mALFF values were significantly increased in the bilateral hemispheres compared with those at 3 days postoperatively.The mALFF signal valu es of left corpus callosum,left somatosensory cortex,right medial prefro ntal cortex,right motor co rtex,left postero dorsal hippocampus,left motor cortex,right corpus callosum,and right somatosensory cortex were increased in the mCIMT group compared with the control cortical impact group.Finally,we identified brain regions with significantly decreased mALFF valu es at 3 days postoperatively.Pearson correlation coefficients with the right forelimb sliding score indicated that the improvement in motor function of the affected upper limb was associated with an increase in mALFF values in these brain regions.Our findings suggest that functional co rtical plasticity changes after brain injury,and that mCIMT is an effective method to improve affected upper limb motor function by promoting bilateral hemispheric co rtical remodeling.mALFF values correlate with behavio ral changes and can potentially be used as biomarkers to assess dynamic cortical plasticity after traumatic brain injury.

Functional magnetic resonance imaging study of group independent components underpinning item responses to paranoid-depressive scale

BACKGROUND Our study expand upon a large body of evidence in the field of neuropsychiatric imaging with cognitive,affective and behavioral tasks,adapted for the functional magnetic resonance imaging(MRI)(fMRI)experimental environment.There is sufficient evidence that common networks underpin activations in task-based fMRI across different mental disorders.AIM To investigate whether there exist specific neural circuits which underpin differ-ential item responses to depressive,paranoid and neutral items(DN)in patients respectively with schizophrenia(SCZ)and major depressive disorder(MDD).METHODS 60 patients were recruited with SCZ and MDD.All patients have been scanned on 3T magnetic resonance tomography platform with functional MRI paradigm,comprised of block design,including blocks with items from diagnostic paranoid(DP),depression specific(DS)and DN from general interest scale.We performed a two-sample t-test between the two groups-SCZ patients and depressive patients.Our purpose was to observe different brain networks which were activated during a specific condition of the task,respectively DS,DP,DN.RESULTS Several significant results are demonstrated in the comparison between SCZ and depressive groups while performing this task.We identified one component that is task-related and independent of condition(shared between all three conditions),composed by regions within the temporal(right superior and middle temporal gyri),frontal(left middle and inferior frontal gyri)and limbic/salience system(right anterior insula).Another com-ponent is related to both diagnostic specific conditions(DS and DP)e.g.It is shared between DEP and SCZ,and includes frontal motor/language and parietal areas.One specific component is modulated preferentially by to the DP condition,and is related mainly to prefrontal regions,whereas other two components are significantly modulated with the DS condition and include clusters within the default mode network such as posterior cingulate and precuneus,several occipital areas,including lingual and fusiform gyrus,as well as parahippocampal gyrus.Finally,component 12 appeared to be unique for the neutral condition.In addition,there have been determined circuits across components,which are either common,or distinct in the preferential processing of the sub-scales of the task.CONCLUSION This study has delivers further evidence in support of the model of trans-disciplinary cross-validation in psychiatry.

Use of endolumenal functional lumen imaging probe in investigating paediatric gastrointestinal motility disorders

Investigating gastrointestinal(GI)motility disorders relies on diagnostic tools to assess muscular contractions,peristalsis propagation and the integrity and coordination of various sphincters.Manometries are the gold standard to study the GI motor function but it is increasingly acknowledged that manometries do not provide a complete picture in relation to sphincters competencies and muscle fibrosis.Endolumenal functional lumen imaging probe(EndoFLIP)an emerging technology,uses impedance planimetry to measure hollow organs cross sectional area,distensibility and compliance.It has been successfully used as a complementary tool in the assessment of the upper and lower oesophageal sphincters,oesophageal body,the pylorus and the anal canal.In this article,we aim to review the uses of EndoFLIP as a tool to investigate GI motility disorders with a special focus on paediatric practice.The majority of EndoFLIP studies were conducted in adult patients but the uptake of the technology in paediatrics is increasing.EndoFLIP can provide a useful complementary data to the existing GI motility investigation in both children and adults.