Quantized Decoders that Maximize Mutual Information for Polar Codes

In this paper,we innovatively associate the mutual information with the frame error rate(FER)performance and propose novel quantized decoders for polar codes.Based on the optimal quantizer of binary-input discrete memoryless channels(BDMCs),the proposed decoders quantize the virtual subchannels of polar codes to maximize mutual information(MMI)between source bits and quantized symbols.The nested structure of polar codes ensures that the MMI quantization can be implemented stage by stage.Simulation results show that the proposed MMI decoders with 4 quantization bits outperform the existing nonuniform quantized decoders that minimize mean-squared error(MMSE)with 4 quantization bits,and yield even better performance than uniform MMI quantized decoders with 5 quantization bits.Furthermore,the proposed 5-bit quantized MMI decoders approach the floating-point decoders with negligible performance loss.

HARDY'S INEQUALITIES WITH MAXIMIZERS FOR W^(1,p) FUNCTIONS ON BOUNDED STAR DOMAINS

With the help of a radially invariant vector field, we derive inequalities of the Hardy kind, with no boundary terms, for W^(1,p) functions on bounded star domains. Our results are not obtainable from the classical inequalities for W_0^(1,p) functions. Unlike in W_0^(1,p),our inequalities admit maximizers that we describe explicitly.

On Utilization of K-Means for Determination of <i>q</i>-Parameter for Tsallis-Entropy-Maximized-FCM

In this paper, we consider a fuzzy c-means (FCM) clustering algorithm combined with the deterministic annealing method and the Tsallis entropy maximization. The Tsallis entropy is a q-parameter extension of the Shannon entropy. By maximizing the Tsallis entropy within the framework of FCM, membership functions similar to statistical mechanical distribution functions can be derived. One of the major considerations when using this method is how to determine appropriate q values and the highest annealing temperature, Thigh?, for a given data set. Accordingly, in this paper, a method for determining these values simultaneously without introducing any additional parameters is presented. In our approach, the membership function is approximated by a series of expansion methods and the K-means clustering algorithm is utilized as a preprocessing step to estimate a radius of each data distribution. The results of experiments indicate that the proposed method is effective and both q and Thigh can be determined automatically and algebraically from a given data set.

Study of Optimal Frequency to the Repairable System Due to Failure Finding Interval to Maximize Availability System

This research article is based on a study of optimal frequency to the repairable system due to the failure finding interval to maximize as well as minimize the availability of some components devices. We studied together maintenance and corrective actions that carried out item of failure and periodic failure finding designed to check whether a system is still working. The model is proved as well as useful application in detecting the problem related to finding failure tasks of different scheme devices by maximization. The model formulated and the numerical application to the relevant mathematical model have been discussed to demonstrate the article quality. Therefore based on probability analytic development, the optimal maintenance policy is then obtained as solution of an optimization problem in which the maintenance cost rate is the objective function and the risk of corrective maintenance is the constraint function. Finally, the solution to the optimal device in the considered development model has been well adjusted due to derivation to the experimental observation rather than theory which will be taken into consideration in the next applied practical design research related and the system device provided that, the proactive device agreed with using the exponential distribution to the survive distribution function which can not be considered as valid.

Tuning the metal filling fraction in metal-insulator-metal ultra-broadband perfect absorbers to maximize the absorption bandwidth

In this paper, we propose a methodology to maximize the absorption bandwidth of a metal-insulator-metal(MIM) based absorber. The proposed structure is made of a Cr-Al_2O_3-Cr multilayer design. At the initial step,the optimum MIM planar design is fabricated and optically characterized. The results show absorption above 0.9 from 400 nm to 850 nm. Afterward, the transfer matrix method is used to find the optimal condition for the perfect light absorption in an ultra-broadband frequency range. This modeling approach predicts that changing the filling fraction of the top Cr layer can extend light absorption toward longer wavelengths. We experimentally proved that the use of proper top Cr thickness and annealing temperature leads to a nearly perfect light absorption from 400 nm to 1150 nm, which is much broader than that of a planar design. Therefore, while keeping the overall process lithography-free, the absorption functionality of the design can be significantly improved. The results presented here can serve as a beacon for future performance-enhanced multilayer designs where a simple fabrication step can boost the overall device response without changing its overall thickness and fabrication simplicity.

Stimuli-responsive gel-micelles with flexible modulation of drug release for maximized antitumor efficacy

Engineered stimuli-responsive drug delivery devices hold vast promise in biological applications for disease treatment due to their maximized therapeutic efficacy In this study a novel, stably cross-linked, and pH-sensitive biodegradable gel-micelle was constructed with amphiphilic conjugates of trimethylene dipiperidine- methacrylic anhydride-hyaluronic acid-stearylamine （TMDP-MA-HA-SA, TMHS） to improve tumor-targeting with flexible intracellular delivery of paditaxel （PTX）. The cross-linked methacrylate bonds significantly improved the biostability of TMHS gel-micelle （~ 200 nm） over the non-cross-linked under physiological conditions, while hyaluronic acid plays an important role in active tumor targetability. The gradual degradation of cross-linked hyaluronic acid shell was triggered by the concentrated hyaluronidase. Meanwhile, under acidic conditions （pH 〈 6.5）, the tertiary amines of pH-sensitive TMDP moieties were protonated and thereby solubilized the gel-micellar core-portions. The resultant pH-triggered inner-core spaces rapidly prompted PTX release in the presence of multiple cytosolic enzymes that mainly degraded the remaining hydrophobic stearylamine core. During the in vitro cytotoxicity assay, PTX-loaded TMHS gel-micelles （ct~） revealed anticancer efficacy against human hepatocellular carcinoma HepG2 cells with ICs0 of 1.42 gg/mL （PTX concentration）, significantly lower than other groups. In parallel, the in vivo anti-tumor efficacy of CLTMHSptx gel-micelles against BALB/c xenograft tumor animal model demonstrated the greater tumor growth inhibition capacity of 72.06%, compared to other treatment groups at a safe concentration. Consequently, the cross-linked and stimuli-responsive CLTMHSFrx gel-micelles hold a great potential for flexible modulation of intracellular delivery of hydrophobic anticancer drugs with maximized antitumor efficacy.

Anticonvulsant potential of rosuvastatin in combination with carbamazepine and valproate in animal models of epilepsy

BACKGROUND Epilepsy impacts millions of people,with many not responding to existing treatments.Some evidence links neuroinflammatory processes to epilepsy.Statins exhibit anti-inflammatory and neuroprotective properties,potentially offering antiepileptic effects.AIM To evaluate the anticonvulsant effects of rosuvastatin in animal models of epilepsy.METHODS Ninety-six albino mice were divided into 16 groups.In the maximal electroshock seizure(MES)model,eight groups received intraperitoneal vehicle,carbama-zepine,rosuvastatin,or a combination.Outcomes measured included seizure protection[tonic hind limb extension(THLE)],duration of THLE,seizure duration,and mortality.In the pentylenetetrazol(PTZ)model,eight groups were pretreated with vehicle,valproate,rosuvastatin,or a combination,with outcomes measured as seizure latency,seizure duration,and mortality.RESULTS In the MES model,rosuvastatin exhibited protection against THLE in a small percentage of mice.Rosuvastatin shortens the duration of THLE in a dose-dependent manner.However,none of these were statistically significant com-pared to the control group.The combination of rosuvastatin 10 mg/kg with carbamazepine 4 mg/kg resulted in a significant reduction in seizure duration compared to the control group,better than carbamazepine alone at 4 mg/kg and 6 mg/kg.In the PTZ model,rosuvastatin alone showed no significant effects on latency,duration of seizure,or mortality.However,rosuvastatin 10 mg/kg combined with valproate 100 mg/kg significantly delayed the onset of seizures,seizure duration and mortality percentage,better than valproate alone at 100 mg/kg.CONCLUSION Rosuvastatin enhanced the anticonvulsant effects of carbamazepine and valproate.Further studies are required to explore the antiepileptic potential of rosuvastatin at various doses,durations,dosage forms,routes and models.

A NECESSARY AND SUFFICIENT CONDITION FOR A POINT TO BE A GLOBAL MAXIMIZER OF ACONSTRAINED OPTIMIZATION

It is difficult to judge whether a given point is a global maximizer of an unconstrained optimization problem. This paper deals with this problem by considering global information via integral and gives a necessary and sufficient condition judging whether a given point is a global maximizer of an unconstrained optimization problem. An algorithm is offered under such a condition and finally two test problems are verified via the offered algorithm.

Designer self-assemble peptides maximize the therapeutic benefits of neural stem cell transplantation for Alzheimer's Disease via enhancing neuron differentiation and paracrine action

With the support by the National Natural Science Foundation of China,Dr.Cui Guohong’s laboratory at the Department of Neurology,Shanghai No.9People’s Hospital,School of Medicine,Shanghai Jiaotong University,reported a designer self-assemble peptide to maximize the therapeutic benefits of neural stem cell transplantation for Alzheimer’s Disease(AD),which was published in Mol Neurobiol(Jan 14。

花椒病虫害绿色防控技术研究进展

花椒(Zanthoxylum bungeanum Maxim.)经济价值较高,在中国广泛种植,但其生长过程中易遭受病虫危害,科学的防治技术措施对花椒的绿色发展至关重要。从生态调控技术、生物防治技术、理化诱控技术、科学用药技术4个方面对花椒病虫害绿色防控技术进行综述,以期为生产上花椒病虫害绿色防控提供参考。

花椒精油成分分析及其对四种植物病原真菌的抗菌作用

采用索氏提取法制备花椒(Zanthoxylum bungeanum Maxim.)精油,利用菌丝生长速率法评价花椒精油对尖镰刀菌(Fusarium oxysporum Schltdl)、禾谷镰孢菌(Fusarium graminearum Schwabe)、盘长孢状刺盘孢(Colletotrichum gloeosporioides Penz.&Sacc.)和茄链格孢菌(Alternaria solani)菌丝生长的抑制活性,采用气相色谱-质谱联用仪(GC-MS)探究花椒精油的物质组成。结果表明,花椒精油对禾谷镰孢菌、尖镰刀菌、盘长孢状刺盘孢和茄链格孢菌均表现出一定的抗菌活性,半数效应浓度(EC50)分别为1.552、2.243、6.064、7.078 mg/mL。GC-MS分析显示,花椒精油中的主要成分为萜类化合物,包括乙酸芳樟酯、芳樟醇、D-柠檬烯和桉叶油醇等。

二分图中高效计算top-n maximalα-biclique的方法研究

针对当二分图中一类节点的数量固定时,如何搜索另一类型节点数量排序为前n的maximalα-biclique的问题,提出了一种基础搜索算法和一种基于共同邻居概念的改进搜索算法。在使用(1,α)-core剪枝方法加快搜索的算法基础上,基于共同邻居搜索算法使用共同邻居的概念对算法进行了改进,该算法只遍历节点的二跳邻居,并利用节点顺序和最小阈值提高搜索效率。实验结果表明,两种算法都可以有效且高效地搜索节点数量排名为前n的maximalα-biclique。与基础搜索算法相比,基于共同邻居搜索算法的搜索效率提升了80%,在实际应用场景中更具优势。

Revisiting the stretch-induced force deficit:A systematic review with multilevel meta-analysis of acute effects

Background:When recommending avoidance of static stretching prior to athletic performance,authors and practitioners commonly refer to available systematic reviews.However,effect sizes(ES)in previous reviews were extracted in major part from studies lacking control conditions and/or prepost testing designs.Also,currently available reviews conducted calculations without accounting for multiple study outcomes,with ES:0.03 to 0.10,which would commonly be classified as trivial.Methods:Since new meta-analytical software and controlled research articles have appeared since 2013,we revisited the available literatures and performed a multilevel meta-analysis using robust variance estimation of controlled prepost trials to provide updated evidence.Furthermore,previous research described reduced electromyography activity—also attributable to fatiguing training routines—as being responsible for decreased subsequent performance.The second part of this study opposed stretching and alternative interventions sufficient to induce general fatigue to examine whether static stretching induces higher performance losses compared to other exercise routines.Results:Including 83 studies with more than 400 ES from 2012 participants,our results indicate a significant,small ES for a static stretch-induced maximal strength loss(ES=0.21,p=0.003),with high magnitude ES(ES=0.84,p=0.004)for stretching durations≥60 s per bout when compared to passive controls.When opposed to active controls,the maximal strength loss ranges between ES:0.17 to0.28,p<0.001 and 0.040 with mostly no to small heterogeneity.However,stretching did not negatively influence athletic performance in general(when compared to both passive and active controls);in fact,a positive effect on subsequent jumping performance(ES=0.15,p=0.006)was found in adults.Conclusion:Regarding strength testing of isolated muscles(e.g.,leg extensions or calf raises),our results confirm previous findings.Nevertheless,since no(or even positive)effects could be found for athletic performance,our results do not support previous recommendations to exclude static stretching from warm-up routines prior to,for example,jumping or sprinting.

VARIATIONAL ANALYSIS FOR THE MAXIMAL TIME FUNCTION IN NORMED SPACES

For a general normed vector space,a special optimal value function called a maximal time function is considered.This covers the farthest distance function as a special case,and has a close relationship with the smallest enclosing ball problem.Some properties of the maximal time function are proven,including the convexity,the lower semicontinuity,and the exact characterizations of its subdifferential formulas.

GLOBAL BOUND ON THE GRADIENT OF SOLUTIONS TO p-LAPLACE TYPE EQUATIONS WITH MIXED DATA

In this paper,the study of gradient regularity for solutions of a class of elliptic problems of p-Laplace type is offered.In particular,we prove a global result concerning Lorentz-Morrey regularity of the non-homogeneous boundary data problem:-div((s^(2)+|▽u|^(2)p-2/2)▽u)=-div(|f|^(p-2)f)+g inΩ,u=h in■Ω,with the(sub-elliptic)degeneracy condition s∈[0,1]and with mixed data f∈L^(p)(Q;R^(n)),g∈Lp/(p-1)(Ω;R^(n))for p∈(1,n).This problem naturally arises in various applications such as dynamics of non-Newtonian fluid theory,electro-rheology,radiation of heat,plastic moulding and many others.Building on the idea of level-set inequality on fractional maximal distribution functions,it enables us to carry out a global regularity result of the solution via fractional maximal operators.Due to the significance of M_(α)and its relation with Riesz potential,estimates via fractional maximal functions allow us to bound oscillations not only for solution but also its fractional derivatives of orderα.Our approach therefore has its own interest.

A Bitcoin Address Multi-Classification Mechanism Based on Bipartite Graph-Based Maximization Consensus

Bitcoin is widely used as the most classic electronic currency for various electronic services such as exchanges,gambling,marketplaces,and also scams such as high-yield investment projects.Identifying the services operated by a Bitcoin address can help determine the risk level of that address and build an alert model accordingly.Feature engineering can also be used to flesh out labeled addresses and to analyze the current state of Bitcoin in a small way.In this paper,we address the problem of identifying multiple classes of Bitcoin services,and for the poor classification of individual addresses that do not have significant features,we propose a Bitcoin address identification scheme based on joint multi-model prediction using the mapping relationship between addresses and entities.The innovation of the method is to(1)Extract as many valuable features as possible when an address is given to facilitate the multi-class service identification task.(2)Unlike the general supervised model approach,this paper proposes a joint prediction scheme for multiple learners based on address-entity mapping relationships.Specifically,after obtaining the overall features,the address classification and entity clustering tasks are performed separately,and the results are subjected to graph-basedmaximization consensus.The final result ismade to baseline the individual address classification results while satisfying the constraint of having similarly behaving entities as far as possible.By testing and evaluating over 26,000 Bitcoin addresses,our feature extraction method captures more useful features.In addition,the combined multi-learner model obtained results that exceeded the baseline classifier reaching an accuracy of 77.4%.

A free monoid containing all prefix codes

In this paper,we exhibit a free monoid containing all prefix codes in connection with the sets of i-th powers of primitive words for all i≥2.This extends two results given by Shyr and Tsai in 1998 at the same time.

Continuity of truncated Hardy-Littlewood maximal function

This paper focuses on the continuity of the truncated Hardy-Littlewood maximal function.We first show that the truncated Hardy-Littlewood maximal function is lower semi-continuous.Then by investigating the behavior of the truncated Hardy-Littlewood maximal function when the truncated parameterγchanges,we obtain an equivalent condition of the continuity of the truncated Hardy-Littlewood maximal function.

Community detection on elite mathematicians’collaboration network

Purpose:This study focuses on understanding the collaboration relationships among mathematicians,particularly those esteemed as elites,to reveal the structures of their communities and evaluate their impact on the field of mathematics.Design/methodology/approach:Two community detection algorithms,namely Greedy Modularity Maximization and Infomap,are utilized to examine collaboration patterns among mathematicians.We conduct a comparative analysis of mathematicians’centrality,emphasizing the influence of award-winning individuals in connecting network roles such as Betweenness,Closeness,and Harmonic centrality.Additionally,we investigate the distribution of elite mathematicians across communities and their relationships within different mathematical sub-fields.Findings:The study identifies the substantial influence exerted by award-winning mathematicians in connecting network roles.The elite distribution across the network is uneven,with a concentration within specific communities rather than being evenly dispersed.Secondly,the research identifies a positive correlation between distinct mathematical sub-fields and the communities,indicating collaborative tendencies among scientists engaged in related domains.Lastly,the study suggests that reduced research diversity within a community might lead to a higher concentration of elite scientists within that specific community.Research limitations:The study’s limitations include its narrow focus on mathematicians,which may limit the applicability of the findings to broader scientific fields.Issues with manually collected data affect the reliability of conclusions about collaborative networks.Practical implications:This study offers valuable insights into how elite mathematicians collaborate and how knowledge is disseminated within mathematical circles.Understanding these collaborative behaviors could aid in fostering better collaboration strategies among mathematicians and institutions,potentially enhancing scientific progress in mathematics.Originality/value:The study adds value to understanding collaborative dynamics within the realm of mathematics,offering a unique angle for further exploration and research.

A grouping strategy for reinforcement learning-based collective yawcontrol of wind farms

Reinforcement learning(RL)algorithms are expected to become the next generation of wind farm control methods.However,as wind farms continue to grow in size,the computational complexity of collective wind farm control will exponentially increase with the growth of action and state spaces,limiting its potential in practical applications.In this Letter,we employ a RL-based wind farm control approach with multi-agent deep deterministic policy gradient to optimize the yaw manoeuvre of grouped wind turbines in wind farms.To reduce the computational complexity,the turbines in the wind farm are grouped according to the strength of the wake interaction.Meanwhile,to improve the control efficiency,each subgroup is treated as a whole and controlled by a single agent.Optimized results show that the proposed method can not only increase the power production of the wind farm but also significantly improve the control efficiency.