Mathematical Modeling of HIV Investigating the Effect of Inconsistent Treatment

HIV is a retrovirus that infects and impairs the cells and functions of the immune system. It has caused a great challenge to global public health systems and leads to Acquired Immunodeficiency Syndrome (AIDS), if not attended to in good time. Antiretroviral therapy is used for managing the virus in a patient’s lifetime. Some of the symptoms of the disease include lean body mass and many opportunistic infections. This study has developed a SIAT mathematical model to investigate the impact of inconsistency in treatment of the disease. The arising non-linear differential equations have been obtained and analyzed. The DFE and its stability have been obtained and the study found that it is locally asymptotically stable when the basic reproduction number is less than unity. The endemic equilibrium has been obtained and found to be globally asymptotically stable when the basic reproduction number is greater than unity. Numerical solutions have been obtained and analyzed to give the trends in the spread dynamics. The inconsistency in treatment uptake has been analyzed through the numerical solutions. The study found that when the treatment rate of those infected increases, it leads to an increase in treatment population, which slows down the spread of HIV and vice versa. An increase in the rate of treatment of those with AIDS leads to a decrease in the AIDS population, the reverse happens when this rate decreases. The study recommends that the community involvement in advocating for consistent treatment of HIV to curb the spread of the disease.

Fast assignment reduction in inconsistent incomplete decision systems

This paper focuses on fast algorithm for computing the assignment reduct in inconsistent incomplete decision systems. It is quite inconvenient to judge the assignment reduct directly ac-cording to its definition. We propose the judgment theorem for the assignment reduct in the inconsistent incomplete decision system, which greatly simplifies judging this type reduct. On such basis, we derive a novel attribute significance measure and construct the fast assignment reduction algorithm （F-ARA）, intended for com-puting the assignment reduct in inconsistent incomplete decision systems. Final y, we make a comparison between F-ARA and the discernibility matrix-based method by experiments on 13 Univer-sity of California at Irvine （UCI） datasets, and the experimental results prove that F-ARA is efficient and feasible.

PCR ALGORITHM FOR PARALLEL COMPUTING MINIMUM-NORM LEAST-SQUARES SOLUTION OF INCONSISTENT LINEAR EQUATIONS

This paper presents a new highly parallel algorithm for computing the minimum-norm least-squares solution of inconsistent linear equations Ax = b(A∈Rm×n,b∈R (A)). By this algorithm the solution x = A + b is obtained in T = n(log2m + log2(n - r + 1) + 5) + log2m + 1 steps with P=mn processors when m × 2(n - 1) and with P = 2n(n - 1) processors otherwise.

Exposing Image Forgery with Inconsistent Reflection Line Midpoint

In recent years,there has been a backlash of sorts and the authenticity of images has been routinely questioned.Seeing is no longer believing.There is an urgent need for robust image forensic techniques to expose photo forgery.This paper proposed a novel and effective technique to expose image forgery using inconsistent reflection.More specifically,a new technique was presented to calculate reflection line midpoint,the definition of midpoint ratio was given,and three standards were proposed and employed to detect image forgery.Accuracy and effectiveness of the proposed technique were evaluated using a data set consisting of 200 authentic and forged images.Experimental results indicate that the proposed method can detect image forgery with very high success rate.

A Dialogue System for Coherent Reasoning with Inconsistent Knowledge Bases

Traditionally, the AI community assumes that a knowledge base must be consistent. Despite that, there are many applications where, due to the existence of rules with exceptions, inconsistent knowledge must be considered. One way of restoring consistency is to withdraw conflicting rules;however, this will destroy part of the knowledge. Indeed, a better alternative would be to give precedence to exceptions. This paper proposes a dialogue system for coherent reasoning with inconsistent knowledge, which resolves conflicts by using precedence relations of three kinds: explicit precedence relation, which is synthesized from precedence rules;implicit precedence relation, which is synthesized from defeasible rules;mixed precedence relation, which is synthesized by combining explicit and implicit precedence relations.

Some Mathematical Properties of the Dynamically Inconsistent Bellman Equation： A Note on the Two-Sided Altruism Dynamics

This article describes some dynamic aspects on dynastic utility incorporating two-sided altruism with an OLG setting. The special case is analyzed where the weights of two-sided altruism are dynamically inconsistent. The Bellman equation for two-sided altruism proves to be reduced to one-sided dynamic problem, but the effective discount factor is different only in the current generation. It is shown that a contraction mapping result of value function cannot be achieved in general, and that there can locally exist an infinite number of self-consistent policy functions of the class C＂ with distinct steady states （indeterminacy of self-consistent, differentiable policy functions）.

Reasoning with Inconsistent Ontologies

Reasoning with inconsistent ontologies involves using an inconsistency reasoner to get meaningful answers from inconsistent ontologies. This paper introduces an improved inconsistency reasoner, which selects consistent subsets using minimal inconsistent sets and a resolution method, to improve the run-time performance of the reasoning processing. A minimal inconsistent set contains a minimal explanation for the inconsistency of a given ontology. Thus, it can replace the consistency checking operation, which is executed frequently in existing approaches. When selecting subsets of the inconsistent ontology, formulas which can be directly or indirectly resolved with the negation of the query formula are selected because only those formulas affect the consequences of the reasoner. Therefore, the complexity of the reasoning processing is significantly reduced. Tests show that the run-time performance of the inconsistency reasoner is significantly improved.

Reducing Inconsistent Rules Based on Irregular Decision Table

In this paper, we study the problem of rule extraction from data sets using the rough set method. For inconsistent rules due to improper selection of split-points during discretization, and/or to lack of information, we propose two methods to remove their inconsistency based on irregular decision tables. By using these methods, inconsistent rules are eliminated as far as possible, without affecting the remaining consistent rules. Experimental test indicates that use of the new method leads to an improvement in the mean accuracy of the extracted rules.

Combating inconsistent evaluation of intra-tumor immune status by a novel transcriptomic signature in hepatocellular carcinoma

Dear Editor,Harnessing the power of the immune system via immune checkpoint inhibition has revolutionized the treatment paradigm of many malignancies,including hepatocellular carcinoma(HCC).1 Despite encouraging efficacy seen with the immune checkpoint blockade(ICB)agents in a subset of patients with HCC,however,there remain a large number of HCC patients experience ICB resistance and failed to derive durable benefit from these agents.

Learning multi-tasks with inconsistent labels by using auxiliary big task

Multi-task learning is to improve the performance of the model by transferring and exploiting common knowledge among tasks.Existing MTL works mainly focus on the scenario where label sets among multiple tasks(MTs)are usually the same,thus they can be utilized for learning across the tasks.However,the real world has more general scenarios in which each task has only a small number of training samples and their label sets are just partially overlapped or even not.Learning such MTs is more challenging because of less correlation information available among these tasks.For this,we propose a framework to learn these tasks by jointly leveraging both abundant information from a learnt auxiliary big task with sufficiently many classes to cover those of all these tasks and the information shared among those partiallyoverlapped tasks.In our implementation of using the same neural network architecture of the learnt auxiliary task to learn individual tasks,the key idea is to utilize available label information to adaptively prune the hidden layer neurons of the auxiliary network to construct corresponding network for each task,while accompanying a joint learning across individual tasks.Extensive experimental results demonstrate that our proposed method is significantly competitive compared to state-of-the-art methods.

Truth Discovery on Inconsistent Relational Data

In this era of big data, data are often collected from multiple sources that have different reliabilities, and there is inevitable conflict with respect to the various information obtained when it relates to the the same object.One important task is to identify the most trustworthy value out of all the conflicting claims, and this is known as truth discovery. Existing truth discovery methods simultaneously identify the most trustworthy information and source reliability degrees and are based on the idea that more reliable sources often provide more trustworthy information,and vice versa. However, there are often semantic constrains defined upon relational database, which can be violated by a single data source. To remove violations, an important task is to repair data to satisfy the constrains,and this is known as data cleaning. The two problems above may coexist, but considering them together can provide some benefits, and to the authors knowledge, this has not yet been the focus of any research. In this paper, therefore, a schema-decomposing based method is proposed to simultaneously discover the truth and to clean the data, with the aim of improving accuracy. Experimental results using real world data sets of notebooks and mobile phones, as well as simulated data sets, demonstrate the effectiveness and efficiency of our proposed method.

Time-Inconsistent Stochastic LQ Problem with Regime Switching

This paper investigates a time-inconsistent stochastic linear-quadratic problem with regime switching that is characterized via a finite-state Markov chain.Open-loop equilibrium control is studied in this paper whose existence is characterized via Markov-chain-modulated forward-backward stochastic difference equations and generalized Riccati-like equations with jumps.

Integrate inconsistent and heterogeneous data based on user feedback

Purpose–Data integration is to combine data residing at different sources and to provide the users with a unified interface of these data.An important issue on data integration is the existence of conflicts among the different data sources.Data sources may conflict with each other at data level,which is defined as data inconsistency.The purpose of this paper is to aim at this problem and propose a solution for data inconsistency in data integration.Design/methodology/approach–A relational data model extended with data source quality criteria is first defined.Then based on the proposed data model,a data inconsistency solution strategy is provided.To accomplish the strategy,fuzzy multi-attribute decision-making(MADM)approach based on data source quality criteria is applied to obtain the results.Finally,users feedbacks strategies are proposed to optimize the result of fuzzy MADM approach as the final data inconsistent solution.Findings–To evaluate the proposed method,the data obtained from the sensors are extracted.Some experiments are designed and performed to explain the effectiveness of the proposed strategy.The results substantiate that the solution has a better performance than the other methods on correctness,time cost and stability indicators.Practical implications–Since the inconsistent data collected from the sensors are pervasive,the proposed method can solve this problem and correct the wrong choice to some extent.Originality/value–In this paper,for the first time the authors study the effect of users feedbacks on integration results aiming at the inconsistent data.

Two Items of Inconsistent Surface Grammatical Patterns in Present-Day English

表层语法格式有一些矛盾现象,常使语言教师感到难以对学生解释清楚。如能考察一下与这些格式有关的词汇的不同的历史发展速度,问题常可迎刃而解。存在于这种表层的矛盾现象下面的深层语法原则,往往是一贯的,并无矛盾。本文通过英语中的两个例子来说明表层语法与深层语法之间的这种关系,一个是动词过去时的构成,一个是“是-非”问句的构成。

Interaction between Surface Acoustic Wave and Quantum Hall Effects

Surface acoustic wave(SAW)is a powerful technique for investigating quantum phases appearing in twodimensional electron systems.The electrons respond to the piezoelectric field of SAW through screening,attenuating its amplitude,and shifting its velocity,which is described by the relaxation model.In this work,we systematically study this interaction using orders of magnitude lower SAW amplitude than those in previous studies.At high magnetic fields,when electrons form highly correlated states such as the quantum Hall effect,we observe an anomalously large attenuation of SAW,while the acoustic speed remains considerably high,inconsistent with the conventional relaxation model.This anomaly exists only when the SAW power is sufficiently low.

Mathematical Modeling of the Co-Infection Dynamics of HIV and Tuberculosis Incorporating Inconsistency in HIV Treatment

A non-linear HIV-TB co-infection has been formulated and analyzed. The positivity and invariant region has been established. The disease free equilibrium and its stability has been determined. The local stability was determined and found to be stable under given conditions. The basic reproduction number was obtained and according to findings, co-infection diminishes when this number is less than unity, and persists when the number is greater than unity. The global stability of the endemic equilibrium was calculated. The impact of HIV on TB was established as well as the impact of TB on HIV. Numerical solution was also done and the findings indicate that when the rate of HIV treatment increases the latent TB increases while the co-infected population decreases. When the rate of HIV treatment decreases the latent TB population decreases and the co-infected population increases. Encouraging communities to prioritize the consistent treatment of HIV infected individuals must be emphasized in order to reduce the scourge of HIV-TB co-infection.

Recycling a Scarce Resource

Caleb Munyao,a 48-year-old middle-scale farmer from Kenya’s eastern region,understands the importance of water better than most.On his 13-hectare farm,where he grows maize,yellow beans,and mangoes,every drop of water is precious.Kenya,like many countries in Africa,faces significant challenges in managing its water resources.The country’s water scarcity issues are exacerbated by climate change,rapid urbanisation,and inconsistent rainfall patterns.

RESTlogic: Detecting Logic Vulnerabilities in Cloud REST APIs

logical testing model and resource lifecycle information,generate test cases and complete parameters,and alleviate inconsistency issues through parameter inference.Once again,we propose a method of analyzing test results using joint state codes and call stack information,which compensates for the shortcomings of traditional analysis methods.We will apply our method to testing REST services,including OpenStack,an open source cloud operating platform for experimental evaluation.We have found a series of inconsistencies,known vulnerabilities,and new unknown logical defects.

Agricultural Investment Project Decisions Based on an Interactive Preference Disaggregation Model Considering Inconsistency

Agricultural investment project selection is a complex multi-criteria decision-making problem,as agricultural projects are easily influenced by various risk factors,and the evaluation information provided by decisionmakers usually involves uncertainty and inconsistency.Existing literature primarily employed direct preference elicitation methods to address such issues,necessitating a great cognitive effort on the part of decision-makers during evaluation,specifically,determining the weights of criteria.In this study,we propose an indirect preference elicitation method,known as a preference disaggregation method,to learn decision-maker preference models fromdecision examples.To enhance evaluation ease,decision-makers merely need to compare pairs of alternatives with which they are familiar,also known as reference alternatives.Probabilistic linguistic preference relations are employed to account for the presence of incomplete and uncertain information in such pairwise comparisons.To address the inconsistency among a group of decision-makers,we develop a pair of 0-1mixed integer programming models that consider both the semantics of linguistic terms and the belief degrees of decision-makers.Finally,we conduct a case study and comparative analysis.Results reveal the effectiveness of the proposed model in solving agricultural investment project selection problems with uncertain and inconsistent decision information.