The Caching and Pricing Strategy for Information-Centric Networking with Advertisers’Participation

As users’access to the network has evolved into the acquisition of mass contents instead of IP addresses,the IP network architecture based on end-to-end communication cannot meet users’needs.Therefore,the Information-Centric Networking(ICN)came into being.From a technical point of view,ICN is a promising future network architecture.Researching and customizing a reasonable pricing mechanism plays a positive role in promoting the deployment of ICN.The current research on ICN pricing mechanism is focused on paid content.Therefore,we study an ICN pricing model for free content,which uses game theory based on Nash equilibrium to analysis.In this work,advertisers are considered,and an advertiser model is established to describe the economic interaction between advertisers and ICN entities.This solution can formulate the best pricing strategy for all ICN entities and maximize the benefits of each entity.Our extensive analysis and numerical results show that the proposed pricing framework is significantly better than existing solutions when it comes to free content.

A comparative study of data-driven battery capacity estimation based on partial charging curves

With its generality and practicality, the combination of partial charging curves and machine learning(ML) for battery capacity estimation has attracted widespread attention. However, a clear classification,fair comparison, and performance rationalization of these methods are lacking, due to the scattered existing studies. To address these issues, we develop 20 capacity estimation methods from three perspectives:charging sequence construction, input forms, and ML models. 22,582 charging curves are generated from 44 cells with different battery chemistry and operating conditions to validate the performance. Through comprehensive and unbiased comparison, the long short-term memory(LSTM) based neural network exhibits the best accuracy and robustness. Across all 6503 tested samples, the mean absolute percentage error(MAPE) for capacity estimation using LSTM is 0.61%, with a maximum error of only 3.94%. Even with the addition of 3 m V voltage noise or the extension of sampling intervals to 60 s, the average MAPE remains below 2%. Furthermore, the charging sequences are provided with physical explanations related to battery degradation to enhance confidence in their application. Recommendations for using other competitive methods are also presented. This work provides valuable insights and guidance for estimating battery capacity based on partial charging curves.

Wetting alternating with partial drying during grain filling increases lysine biosynthesis in inferior rice grain

Lysine content is a criterion of the nutritional quality of rice.Understanding the process of lysine biosynthesis in early-flowering superior grain(SG)and late-flowering inferior grain(IG)of rice would advance breeding and cultivation to improve nutritional quality.However,little information is available on differences in lysine anabolism between SG and IG and the underlying mechanism,and whether and how irrigation regimes affect lysine anabolism in these grains.A japonica rice cultivar was grown in the field and two irrigation regimes,continuous flooding(CF)and wetting alternating with partial drying(WAPD),were imposed from heading to the mature stage.Lysine content and activities of key enzymes of lysine biosynthesis,and levels of brassinosteroids(BRs)were lower in the IG than in the SG at the early grainfilling stage but higher at middle and late grain-filling stages.WAPD increased activities of these key enzymes,BR levels,and contents of lysine and total amino acids in IG,but not SG relative to CF.Application of 2,4-epibrassinolide to rice panicles in CF during early grain filling reproduced the effects of WAPD,but neither treatment altered the activities of enzymes responsible for lysine catabolism in either SG or IG.WAPD and elevated BR levels during grain filling increased lysine biosynthesis in IG.Improvement in lysine biosynthesis in rice should focus on IG.

A Mean-Field Game for a Forward-Backward Stochastic System With Partial Observation and Common Noise

This paper considers a linear-quadratic(LQ) meanfield game governed by a forward-backward stochastic system with partial observation and common noise,where a coupling structure enters state equations,cost functionals and observation equations.Firstly,to reduce the complexity of solving the meanfield game,a limiting control problem is introduced.By virtue of the decomposition approach,an admissible control set is proposed.Applying a filter technique and dimensional-expansion technique,a decentralized control strategy and a consistency condition system are derived,and the related solvability is also addressed.Secondly,we discuss an approximate Nash equilibrium property of the decentralized control strategy.Finally,we work out a financial problem with some numerical simulations.

Bayesian partial pooling to reduce uncertainty in overcoring rock stress estimation

The state of in situ stress is a crucial parameter in subsurface engineering,especially for critical projects like nuclear waste repository.As one of the two ISRM suggested methods,the overcoring(OC)method is widely used to estimate the full stress tensors in rocks by independent regression analysis of the data from each OC test.However,such customary independent analysis of individual OC tests,known as no pooling,is liable to yield unreliable test-specific stress estimates due to various uncertainty sources involved in the OC method.To address this problem,a practical and no-cost solution is considered by incorporating into OC data analysis additional information implied within adjacent OC tests,which are usually available in OC measurement campaigns.Hence,this paper presents a Bayesian partial pooling(hierarchical)model for combined analysis of adjacent OC tests.We performed five case studies using OC test data made at a nuclear waste repository research site of Sweden.The results demonstrate that partial pooling of adjacent OC tests indeed allows borrowing of information across adjacent tests,and yields improved stress tensor estimates with reduced uncertainties simultaneously for all individual tests than they are independently analysed as no pooling,particularly for those unreliable no pooling stress estimates.A further model comparison shows that the partial pooling model also gives better predictive performance,and thus confirms that the information borrowed across adjacent OC tests is relevant and effective.

Preoperative albumin-bilirubin score and liver resection percentage determine postoperative liver regeneration after partial hepatectomy

BACKGROUND The success of liver resection relies on the ability of the remnant liver to regenerate.Most of the knowledge regarding the pathophysiological basis of liver regeneration comes from rodent studies,and data on humans are scarce.Additionally,there is limited knowledge about the preoperative factors that influence postoperative regeneration.AIM To quantify postoperative remnant liver volume by the latest volumetric software and investigate perioperative factors that affect posthepatectomy liver regenera-tion.METHODS A total of 268 patients who received partial hepatectomy were enrolled.Patients were grouped into right hepatectomy/trisegmentectomy(RH/Tri),left hepa-tectomy(LH),segmentectomy(Seg),and subsegmentectomy/nonanatomical hepatectomy(Sub/Non)groups.The regeneration index(RI)and late rege-neration rate were defined as(postoperative liver volume)/[total functional liver volume(TFLV)]×100 and(RI at 6-months-RI at 3-months)/RI at 6-months,respectively.The lower 25th percentile of RI and the higher 25th percentile of late regeneration rate in each group were defined as“low regeneration”and“delayed regeneration”.“Restoration to the original size”was defined as regeneration of the liver volume by more than 90%of the TFLV at 12 months postsurgery.RESULTS The numbers of patients in the RH/Tri,LH,Seg,and Sub/Non groups were 41,53,99 and 75,respectively.The RI plateaued at 3 months in the LH,Seg,and Sub/Non groups,whereas the RI increased until 12 months in the RH/Tri group.According to our multivariate analysis,the preoperative albumin-bilirubin(ALBI)score was an independent factor for low regeneration at 3 months[odds ratio(OR)95%CI=2.80(1.17-6.69),P=0.02;per 1.0 up]and 12 months[OR=2.27(1.01-5.09),P=0.04;per 1.0 up].Multivariate analysis revealed that only liver resection percentage[OR=1.03(1.00-1.05),P=0.04]was associated with delayed regeneration.Furthermore,multivariate analysis demonstrated that the preoperative ALBI score[OR=2.63(1.00-1.05),P=0.02;per 1.0 up]and liver resection percentage[OR=1.02(1.00-1.05),P=0.04;per 1.0 up]were found to be independent risk factors associated with volume restoration failure.CONCLUSION Liver regeneration posthepatectomy was determined by the resection percentage and preoperative ALBI score.This knowledge helps surgeons decide the timing and type of rehepatectomy for recurrent cases.

THE INTERIOR TRANSMISSION EIGENVALUE PROBLEM FOR AN ANISOTROPIC MEDIUM BY A PARTIALLY COATED BOUNDARY

We consider the interior transmission eigenvalue problem corresponding to the scattering for an anisotropic medium of the scalar Helmholtz equation in the case where the boundary?Ωis split into two disjoint parts and possesses different transmission conditions.Using the variational method,we obtain the well posedness of the interior transmission problem,which plays an important role in the proof of the discreteness of eigenvalues.Then we achieve the existence of an infinite discrete set of transmission eigenvalues provided that n≡1,where a fourth order differential operator is applied.In the case of n■1,we show the discreteness of the transmission eigenvalues under restrictive assumptions by the analytic Fredholm theory and the T-coercive method.

A Cu-Pd alloy catalyst with partial phase separation for the electrochemical CO_(2) reduction reaction

Cu catalysts can convert CO_(2) through an electrochemical reduction reaction into a variety of useful carbon-based products.However,this capability provides an obstacle to increasing the selectivity for a single product.Herein,we report a simple fabrication method for a Cu-Pd alloy catalyst for use in a membrane electrode assembly(MEA)-based CO_(2) electrolyzer for the electrochemical CO_(2) reduction reaction(ECRR)with high selectivity for CO production.When the composition of the Cu-Pd alloy catalyst was fabricated at 6:4,the selectivity for CO increased and the production of multi-carbon compounds and hydrogen is suppressed.Introducing a Cu-Pd alloy catalyst with 6:4 ratio as the cathode of the MEAbased CO_(2) electrolyzer showed a CO faradaic efficiency of 92.8%at 2.4 V_(cell).We assumed that these results contributed from the crystal planes on the surface of the Cu-Pd alloy.The phases of the Cu-Pd alloy catalyst were partially separated through annealing to fabricate a catalyst with high selectivity for CO at low voltage and C_(2)H_4 at high voltage.The results of CO-stripping testing confirmed that when Cu partially separates from the lattice of the Cu-Pd alloy,the desorption of~*CO is suppressed,suggesting that C-C coupling reaction is favored.

Data-Driven Modeling of Partially Observed Biological Systems

We present a numerical approach for modeling unknown dynamical systems using partially observed data,with a focus on biological systems with(relatively)complex dynamical behavior.As an extension of the recently developed deep neural network(DNN)learning methods,our approach is particularly suitable for practical situations when(i)measurement data are available for only a subset of the state variables,and(ii)the system parameters cannot be observed or measured at all.We demonstrate that,with a properly designed DNN structure with memory terms,effective DNN models can be learned from such partially observed data containing hidden parameters.The learned DNN model serves as an accurate predictive tool for system analysis.Through a few representative biological problems,we demonstrate that such DNN models can capture qualitative dynamical behavior changes in the system,such as bifurcations,even when the parameters controlling such behavior changes are completely unknown throughout not only the model learning process but also the system prediction process.The learned DNN model effectively creates a“closed”model involving only the observables when such a closed-form model does not exist mathematically.

Adaptive detection of range-spread targets in homogeneous and partially homogeneous clutter plus subspace interference

Adaptive detection of range-spread targets is considered in the presence of subspace interference plus Gaussian clutter with unknown covariance matrix.The target signal and interference are supposed to lie in two linearly independent subspaces with deterministic but unknown coordinates.Relying on the two-step criteria,two adaptive detectors based on Gradient tests are proposed,in homogeneous and partially homogeneous clutter plus subspace interference,respectively.Both of the proposed detectors exhibit theoretically constant false alarm rate property against unknown clutter covariance matrix as well as the power level.Numerical results show that,the proposed detectors have better performance than their existing counterparts,especially for mismatches in the signal steering vectors.

Results Involving Partial Differential Equations and Their Solution by Certain Integral Transform

In this study,we aimto investigate certain triple integral transformand its application to a class of partial differentialequations.We discuss various properties of the new transformincluding inversion, linearity, existence, scaling andshifting, etc. Then,we derive several results enfolding partial derivatives and establish amulti-convolution theorem.Further, we apply the aforementioned transform to some classical functions and many types of partial differentialequations involving heat equations,wave equations, Laplace equations, and Poisson equations aswell.Moreover,wedraw some figures to illustrate 3-D contour plots for exact solutions of some selected examples involving differentvalues in their variables.

A Compact UHF Antenna Based on Hilbert Fractal Elements and a Serpentine Arrangement for Detecting Partial Discharg

Efforts to protect electric power systems from faults have commonly relied on the use of ultra-high frequency(UHF)antennas for detecting partial discharge(PD)as a common precursor to faults.However,the effectiveness of existing UHF antennas suffers from a number of challenges such as limited bandwidth,relatively large physical size,and low detection sensitivity.The present study addresses these issues by proposing a compact microstrip patch antenna with fixed dimensions of 100 mm×100 mm×1.6 mm.The results of computations yield an optimized antenna design consisting of 2nd-order Hilbert fractal units positioned within a four-layer serpentine arrangement with a fractal unit connection distance of 3.0 mm.Specifically,the optimized antenna design achieves a detection bandwidth for which the voltage standing wave ratio is less than 2 that is approximately 97.3%of the UHF frequency range(0.3–3 GHz).Finally,a prototype antenna is fabricated using standard printed circuit board technology,and the results of experiments demonstrate that the proposed antenna is capable of detecting PD signals at a distance of 8 m from the discharge source.

Probabilistic Global Maximum Power Point Tracking Algorithm for Continuously Varying Partial Shading Conditions on Autonomous PV Systems

A photovoltaic (PV) string with multiple modules with bypass diodes frequently deployed on a variety of autonomous PV systems may present multiple power peaks under uneven shading. For optimal solar harvesting, there is a need for a control schema to force the PV string to operate at global maximum power point (GMPP). While a lot of tracking methods have been proposed in the literature, they are usually complex and do not fully take advantage of the available characteristics of the PV array. This work highlights how the voltage at operating point and the forward voltage of the bypass diode are considered to design a global maximum power point tracking (GMPPT) algorithm with a very limited global search phase called Fast GMPPT. This algorithm successfully tracks GMPP between 94% and 98% of the time under a theoretical evaluation. It is then compared against Perturb and Observe, Deterministic Particle Swarm Optimization, and Grey Wolf Optimization under a sequence of irradiance steps as well as a power-over-voltage characteristics profile that mimics the electrical characteristics of a PV string under varying partial shading conditions. Overall, the simulation with the sequence of irradiance steps shows that while Fast GMPPT does not have the best convergence time, it has an excellent convergence rate as well as causes the least amount of power loss during the global search phase. Experimental test under varying partial shading conditions shows that while the GMPPT proposal is simple and lightweight, it is very performant under a wide range of dynamically varying partial shading conditions and boasts the best energy efficiency (94.74%) out of the 4 tested algorithms.

Will partial splenic embolization followed by splenectomy increase intraoperative bleeding?

BACKGROUND Partial splenic embolization(PSE)has been suggested as an alternative to splenectomy in the treatment of hypersplenism.However,some patients may experience recurrence of hypersplenism after PSE and require splenectomy.Currently,there is a lack of evidence-based medical support regarding whether preoperative PSE followed by splenectomy can reduce the incidence of complications.AIM To investigate the safety and therapeutic efficacy of preoperative PSE followed by splenectomy in patients with cirrhosis and hypersplenism.METHODS Between January 2010 and December 2021,321 consecutive patients with cirrhosis and hypersplenism underwent splenectomy at our department.Based on whether PSE was performed prior to splenectomy,the patients were divided into two groups:PSE group(n=40)and non-PSE group(n=281).Patient characteristics,postoperative complications,and follow-up data were compared between groups.Propensity score matching(PSM)was conducted,and univariable and multivariable analyses were used to establish a nomogram predictive model for intraoperative bleeding(IB).The receiver operating characteristic curve,Hosmer-Lemeshow goodness-of-fit test,and decision curve analysis(DCA)were employed to evaluate the differentiation,calibration,and clinical performance of the model.RESULTS After PSM,the non-PSE group showed significant reductions in hospital stay,intraoperative blood loss,and operation time(all P=0.00).Multivariate analysis revealed that spleen length,portal vein diameter,splenic vein diameter,and history of PSE were independent predictive factors for IB.A nomogram predictive model of IB was constructed,and DCA demonstrated the clinical utility of this model.Both groups exhibited similar results in terms of overall survival during the follow-up period.CONCLUSION Preoperative PSE followed by splenectomy may increase the incidence of IB and a nomogram-based prediction model can predict the occurrence of IB.

Two-stage extraction by partial grinding of impacted mandibular third molar in close proximity to the inferior alveolar nerve

BACKGROUND Extraction of impacted third molars often leads to severe complications caused by damage to the inferior alveolar nerve(IAN).AIM To proposes a method for the partial grinding of an impacted mandibular third molar(IMM3)near the IAN to prevent IAN injury during IMM3 extraction.METHODS Between January 1996 and March 2022,25 patients with IMM3 roots near the IAN were enrolled.The first stage of the operation consisted of grinding a major part of the IMM3 crown with a high-speed turbine dental drill to achieve sufficient space between the mandibular second molar and IMM3.After 6 months,when the root tips were observed to be away from the IAN on X-ray examination,the remaining part of the IMM3 was completely removed.RESULTS All IMM3s were extracted easily without symptoms of IAN injury after extraction.CONCLUSION Partial IMM3 grinding may be a good alternative treatment option to avoid IAN injury in high-risk cases.

Impact of pre- and peri-operative risk factors on length of stay and hospital readmission following minimally-invasive partial nephrectomy

Objective:We conducted an analysis of the American College of Surgeons National Surgical Quality Improvement Program database for minimally-invasive partial nephrectomy cases reported with the goal to identify pre-and peri-operative variables associated with length of stay(LOS)greater than 3 days and readmission within 30 days.Methods:Records from 2008 to 2018 for“laparoscopy,surgical;partial nephrectomy”for prolonged LOS and readmission cohorts were compiled.Univariate analysis with Chi-square,t-tests,and multivariable logistic regression analysis with odds ratios(ORs),p-values,and 95%confidence intervals assessed statistical associations.Results:Totally,20306 records for LOS greater than 3 days and 15854 for readmission within 30 days were available.Univariate and multivariable analysis exhibited similar results.For LOS greater than 3 days,undergoing non-elective surgery(OR=5.247),transfusion of greater than four units within 72 h prior to surgery(OR=5.072),pre-operative renal failure or dialysis(OR=2.941),and poor pre-operative functional status(OR=2.540)exhibited the strongest statistically significant associations.For hospital readmission within 30 days,loss in body weight greater than 10%in 6 months prior to surgery(OR=2.227)and bleeding disorders(OR=2.081)exhibited strongest statistically significant associations.Conclusion:Multiple pre-and peri-operative risk factors are independently associated with prolonged LOS and hospital readmission within 30 days of surgery using the American College of Surgeons National Surgical Quality Improvement Program data.Recognizing the risks factors that can potentially be improved prior to minimally-invasive partial nephrectomy is crucial to informing patient selection,optimization strategies,and patient education.

The Impact of the Inclusion of MgO Nano-Fillers in a Polyethylene Matrix on Dielectric Strength and Resistance to Partial Discharges

One commonly used strategy to enhance polymers specific properties such as the resistance to partial discharges erosion is the incorporation into the polymeric matrix of inorganic micro or nanoparticles. This study focused on the dielectric properties of Low-Density Polyethylene (LDPE) filled with nano-sized Magnesium Oxide (MgO) particles compounded by thermo-mechanical process and one of the purposes was to establish appropriate processing parameters in order to reach the desired dielectric properties. LDPE was used as a matrix and was reinforced by MgO particles having a nominal average size of 30 nm. The MgO nanoparticles were treated with a silane coupling agent (3-Glycidyloxypropyl Trimethoxysilane). The samples were initially prepared in a melt-mixing chamber with a MgO content of 1% wt. These pre-mixed samples were further treated by the means of thermo-mechanical mixing in a conical co-rotating twin-screw extruder in order to improve the dispersion and distribution of the MgO particles. In this report, both lifetime under a PD activity and AC dielectric strength of pure and nano-filled LDPE samples have been measured and compared. Nano-filled LDPE samples were found to exhibit an improve lifetime, without any detrimental impact on their short-term dielectric strength. This suggests that nano-filled LDPE may be for electric applications for which the dielectric materials may be exposed to partial discharge activities. This is significant result for the use of MgO-reinforced PE as an insulating material for HV cables since the resistance to PD is closely related to treeing resistance which is the main electrical degradation mechanism that leads to failure for shielded extruded power cables.

In-situ coating and surface partial protonation co-promoting performance of single-crystal nickel-rich cathode in all-solid-state batteries

The poor electrochemical performance of all-solid-state batteries(ASSBs),which is assemblied by Ni-rich cathode and poly(ethylene oxide)(PEO)-based electrolytes,can be attributed to unstable cathodic interface and poor crystal structure stability of Ni-rich cathode.Several coating strategies are previously employed to enhance the stability of the cathodic interface and crystal structure for Ni-rich cathode.However,these methods can hardly achieve simplicity and high efficiency simultaneously.In this work,polyacrylic acid(PAA)replaced traditional PVDF as a binder for cathode,which can achieve a uniform PAA-Li(LixPAA(0<x≤1))coating layer on the surface of single-crystal LiNi_(0.83)Co_(0.12)Mn_(0.05)O_(2)(SC-NCM83)due to H^(+)/Li^(+)exchange reaction during the initial charging-discharging process.The formation of PAA-Li coating layer on cathode can promote interfacial Li^(+)transport and enhance the stability of the cathodic interface.Furthermore,the partially-protonated surface of SC-NCM83 casued by H^(+)/Li^(+)exchange reaction can restrict Ni ions transport to enhance the crystal structure stability.The proposed SC-NCM83-PAA exhibits superior cycling performance with a retention of 92%compared with that(57.3%)of SC-NCM83-polyvinylidene difluoride(PVDF)after 200 cycles.This work provides a practical strategy to construct high-performance cathodes for ASSBs.

Analysis of Drug Medical Insurance Access Pricing Based on the Perspective of Binary Equilibrium

Objective To study the influencing factors in the process of national medical insurance negotiation and drug pricing from the dualistic equilibrium perspective,and to provide reference for the harmonious management of drug pricing in China.Methods Through the literature analysis and policy review,the pricing subject,pricing basis and price control system in the pricing process of medical-accessed medicines were analyzed from the perspective of binary equilibrium and harmonious management.Results and Conclusion It is found that four balances in the drug pricing process,two balances in pricing basis and three balances in price control system need to be considered,respectively.Drug pricing is the key content of national medical insurance access,which is also the hotspot of the policy in the pharmaceutical fields in recent years.Drug pricing not only reflects the value of drugs,but also reflects a lot of top-level designs of binary equilibriums in medical insurance policy.While the rational design of drug pricing requires the joint efforts of the government,pharmaceutical companies and relevant experts to comprehensively consider many equilibriums,so as to improve the relevant systems.

Maximizing Supermarket Profits:Data-Driven Strategies for Pricing,Sales,and Forecasting

The actual circumstances of daily life are crucial for the purchasing and pricing strategies of supermarkets.Developing strategies based on these circumstances can assist businesses in ensuring profits and fostering win-win cooperation.This paper explores methods to maximize profit through purchasing and sales strategies.Initially,the relevant data for various categories of vegetables is integrated.Through histograms,their sales patterns are directly understood,highlighting the most popular vegetables.Upon analyzing each vegetable category,it becomes evident that their sales data do not conform to normal distributions.Therefore,Spearman correlation coefficients are calculated,revealing strong correlations between certain categories,such as aquatic roots and edible fungi.A line chart depicting the top ten selling vegetables indicates a noticeable periodicity.Traditional fitting methods struggle to adequately model the sales of each vegetable category and their relationship with cost-plus pricing.To address this,additional factors such as holidays,weeks,and months are incorporated using techniques like random forest regression.This approach yields cost-plus pricing dependence curves that better capture the relationship,while effectively managing noise.Regarding sales volume prediction,the original data displays significant volatility,necessitating the handling of outliers using the threshold method.For missing data,linear interpolation is employed to mitigate the impact of continuous missing values on prediction accuracy.Subsequently,Adam-optimized long short-term memory(LSTM)networks are utilized to forecast incoming quantities for the next seven days.By extrapolating from normal sales volume,market capacity is estimated,allowing for additional sales through discount strategies.This framework has the potential to increase original income by 1.1 times.