Discovering hidden patterns:Association rules for cardiovascular diseases in type 2 diabetes mellitus

BACKGROUND It is increasingly common to find patients affected by a combination of type 2 diabetes mellitus(T2DM)and coronary artery disease(CAD),and studies are able to correlate their relationships with available biological and clinical evidence.The aim of the current study was to apply association rule mining(ARM)to discover whether there are consistent patterns of clinical features relevant to these diseases.ARM leverages clinical and laboratory data to the meaningful patterns for diabetic CAD by harnessing the power help of data-driven algorithms to optimise the decision-making in patient care.AIM To reinforce the evidence of the T2DM-CAD interplay and demonstrate the ability of ARM to provide new insights into multivariate pattern discovery.METHODS This cross-sectional study was conducted at the Department of Biochemistry in a specialized tertiary care centre in Delhi,involving a total of 300 consented subjects categorized into three groups:CAD with diabetes,CAD without diabetes,and healthy controls,with 100 subjects in each group.The participants were enrolled from the Cardiology IPD&OPD for the sample collection.The study employed ARM technique to extract the meaningful patterns and relationships from the clinical data with its original value.RESULTS The clinical dataset comprised 35 attributes from enrolled subjects.The analysis produced rules with a maximum branching factor of 4 and a rule length of 5,necessitating a 1%probability increase for enhancement.Prominent patterns emerged,highlighting strong links between health indicators and diabetes likelihood,particularly elevated HbA1C and random blood sugar levels.The ARM technique identified individuals with a random blood sugar level>175 and HbA1C>6.6 are likely in the“CAD-with-diabetes”group,offering valuable insights into health indicators and influencing factors on disease outcomes.CONCLUSION The application of this method holds promise for healthcare practitioners to offer valuable insights for enhancing patient treatment targeting specific subtypes of CAD with diabetes.Implying artificial intelligence techniques with medical data,we have shown the potential for personalized healthcare and the development of user-friendly applications aimed at improving cardiovascular health outcomes for this high-risk population to optimise the decision-making in patient care.

Semantic Consistency and Correctness Verification of Digital Traffic Rules

The consensus of the automotive industry and traffic management authorities is that autonomous vehicles must follow the same traffic laws as human drivers.Using formal or digital methods,natural language traffic rules can be translated into machine language and used by autonomous vehicles.In this paper,a translation flow is designed.Beyond the translation,a deeper examination is required,because the semantics of natural languages are rich and complex,and frequently contain hidden assumptions.The issue of how to ensure that digital rules are accurate and consistent with the original intent of the traffic rules they represent is both significant and unresolved.In response,we propose a method of formal verification that combines equivalence verification with model checking.Reasonable and reassuring digital traffic rules can be obtained by utilizing the proposed traffic rule digitization flow and verification method.In addition,we offer a number of simulation applications that employ digital traffic rules to assess vehicle violations.The experimental findings indicate that our digital rules utilizing metric temporal logic(MTL)can be easily incorporated into simulation platforms and autonomous driving systems(ADS).

Density Clustering Algorithm Based on KD-Tree and Voting Rules

Traditional clustering algorithms often struggle to produce satisfactory results when dealing with datasets withuneven density. Additionally, they incur substantial computational costs when applied to high-dimensional datadue to calculating similarity matrices. To alleviate these issues, we employ the KD-Tree to partition the dataset andcompute the K-nearest neighbors (KNN) density for each point, thereby avoiding the computation of similaritymatrices. Moreover, we apply the rules of voting elections, treating each data point as a voter and casting a votefor the point with the highest density among its KNN. By utilizing the vote counts of each point, we develop thestrategy for classifying noise points and potential cluster centers, allowing the algorithm to identify clusters withuneven density and complex shapes. Additionally, we define the concept of “adhesive points” between two clustersto merge adjacent clusters that have similar densities. This process helps us identify the optimal number of clustersautomatically. Experimental results indicate that our algorithm not only improves the efficiency of clustering butalso increases its accuracy.

Improved STNModels and Heuristic Rules for Cooperative Scheduling in Automated Container Terminals

Improving the cooperative scheduling efficiency of equipment is the key for automated container terminals to copewith the development trend of large-scale ships. In order to improve the solution efficiency of the existing spacetimenetwork (STN) model for the cooperative scheduling problem of yard cranes (YCs) and automated guidedvehicles (AGVs) and extend its application scenarios, two improved STN models are proposed. The flow balanceconstraints in the original model are decomposed, and the trajectory constraints of YCs and AGVs are added toacquire the model STN_A. The coupling constraint in STN_A is updated, and buffer constraints are added toSTN_A so that themodel STN_B is built.As the size of the problem increases, the solution speed of CPLEX becomesthe bottleneck. So a heuristic method containing three groups of heuristic rules is designed to obtain a near-optimalsolution quickly. Experimental results showthat the computation time of STN_A is shortened by 49.47% on averageand the gap is reduced by 1.69% on average compared with the original model. The gap between the solution ofthe heuristic rules and the solution of CPLEX is less than 3.50%, and the solution time of the heuristic rules is onaverage 99.85% less than the solution time of CPLEX. Compared with STN_A, the computation time for solvingSTN_B increases by 58.93% on average.

Learning Vector Quantization-Based Fuzzy Rules Oversampling Method

Imbalanced datasets are common in practical applications,and oversampling methods using fuzzy rules have been shown to enhance the classification performance of imbalanced data by taking into account the relationship between data attributes.However,the creation of fuzzy rules typically depends on expert knowledge,which may not fully leverage the label information in training data and may be subjective.To address this issue,a novel fuzzy rule oversampling approach is developed based on the learning vector quantization(LVQ)algorithm.In this method,the label information of the training data is utilized to determine the antecedent part of If-Then fuzzy rules by dynamically dividing attribute intervals using LVQ.Subsequently,fuzzy rules are generated and adjusted to calculate rule weights.The number of new samples to be synthesized for each rule is then computed,and samples from the minority class are synthesized based on the newly generated fuzzy rules.This results in the establishment of a fuzzy rule oversampling method based on LVQ.To evaluate the effectiveness of this method,comparative experiments are conducted on 12 publicly available imbalance datasets with five other sampling techniques in combination with the support function machine.The experimental results demonstrate that the proposed method can significantly enhance the classification algorithm across seven performance indicators,including a boost of 2.15%to 12.34%in Accuracy,6.11%to 27.06%in G-mean,and 4.69%to 18.78%in AUC.These show that the proposed method is capable of more efficiently improving the classification performance of imbalanced data.

Principles of Shiology——Revealing the Basic Rules of Human Shiance

The objective principles of shiology are mainly reflected in three fields as food acquisition, eaters' health and shiance order. Most of the objective principles in the field of food acquisition have been revealed by agronomy and foodstuff science. This research mainly focuses on 10 principles in the field of eaters' health and shiance order and in addition, there are also five lemmas that extend from the above principles. The 10 principles are the core theory of the shiology knowledge system, which play an important role in the objective principles revealed by human beings and constitute one of the basic principles of human civilization. Compared with the scientific principles of mathematics, physics, chemistry and economics, the principles of shiology have three characteristics as popularity, practicability and survivability. The principles of shiology in the field of eaters' health are all around us, and everyone can understand and master them. Using the principles of shiology can improve the healthy life span of 8 billion people. The principles of shiology in the field of shiance order is an important tool of social governance, which can reduce human social conflicts, reduce social involution, improve overall efficiency of social operation, and maintain the sustainable development of human beings.

Emoti-Shing: Detecting Vishing Attacks by Learning Emotion Dynamics through Hidden Markov Models

This study examines vishing, a form of social engineering scam using voice communication to deceive individuals into revealing sensitive information or losing money. With the rise of smartphone usage, people are more susceptible to vishing attacks. The proposed Emoti-Shing model analyzes potential victims’ emotions using Hidden Markov Models to track vishing scams by examining the emotional content of phone call audio conversations. This approach aims to detect vishing scams using biological features of humans, specifically emotions, which cannot be easily masked or spoofed. Experimental results on 30 generated emotions indicate the potential for increased vishing scam detection through this approach.

Deciphering the Hidden Secrets between the Early Skin Wrinkling & the Metabolic (X) Syndrome with the Possible Reversal of This Process at the Molecular Level

The aging process is a group of degenerative changes that physiologically occur in most of the people in the elderly. This affects one or more of the human body systems. The treatment of diseases related to the aging process has a huge impact on the economy of all nations. Aging of the skin comes on the top and despite that, the results of the already present lines of treatment are not always satisfactory. This acts as a stimulus for us to dig deeper to discover the root causes of the premature aging of the skin. This was simply caused by the accumulation of repeated minute damage to the internal structure skin. In other words, if the degree of minute damage is more than the capacity of the skin to repair, the repeated micro-damage is presented in the long run as a skin wrinkling. Moreover, the skin acts as a mirror that reflects the internal structures of the human body. Thus, the more degenerative changes in the human body systems, the more the skin could become wrinkled. Our strategy to prevent or at least slow down the aging process of the skin depends on 2 main steps;the 1st is to reduce the micro-damage as can as possible, and the 2nd is to enhance the capacity of tissue regeneration to be able to reverse the already present damaged skin. As the 2 processes are synchronized with each other, this strategy would be considered the ideal for prevention of skin wrinkling especially premature ones. This not only reverses premature skin wrinkling but also protects it from future wrinklings. This review sharply pointed out the role of the functional collagen of the dermal layer of the skin in the prevention of skin wrinklings. Therefore, it would be the target to study how collagen works in the complex machinery of the dermal layer of the skin. This concept deeply believes that the recovery of dermal collagen has a much better effect than simply ingesting collagen or receiving a topical collagen booster. .

Verification and Application Evaluation of Intelligent Audit Rules for The UN9000 Urine Analysis System

Objective:To apply and verify the application of intelligent audit rules for urine analysis by Cui et al.Method:A total of 1139 urine samples of hospitalized patients in Tai’an Central Hospital from September 2021 to November 2021 were randomly selected,and all samples were manually microscopic examined after the detection of the UN9000 urine analysis line.The intelligent audit rules(including the microscopic review rules and manual verification rules)were validated based on the manual microscopic examination and manual audit,and the rules were adjusted to apply to our laboratory.The laboratory turnaround time(TAT)before and after the application of intelligent audit rules was compared.Result:The microscopic review rate of intelligent rules was 25.63%(292/1139),the true positive rate,false positive rate,true negative rate,and false negative rate were 27.66%(315/1139),6.49%(74/1139),62.34%(710/1139)and 3.51%(40/1139),respectively.The approval consistency rate of manual verification rules was 84.92%(727/856),the approval inconsistency rate was 0%(0/856),the interception consistency rate was 12.61%(108/856),and the interception inconsistency rate was 0%(0/856).Conclusion:The intelligence audit rules for urine analysis by Cui et al.have good clinical applicability in our laboratory.

Review of Arbitral Award of Network Loan and Analysis of Construction of Implementation Review Rules

The network arbitration cases arising from the network lending disputes are pouring into the courts in large numbers.It is reported that the network arbitration system of some arbitration institutions even“can accept more than 10,000 cases every day,”while online lending is booming,it has also caused a lot of contradictions and disputes,and traditional dispute resolution methods have failed to effectively respond to the need for efficient and convenient resolution of online lending disputes.This paper tries to study the arbitral award of online loans and proposes the construction of implementation review rules.

Fresnel Equations Derived Using a Non-Local Hidden-Variable Particle Theory

Problem: The Fresnel equations describe the proportions of reflected and transmitted light from a surface, and are conventionally derived from wave theory continuum mechanics. Particle-based derivations of the Fresnel equations appear not to exist. Approach: The objective of this work was to derive the basic optical laws from first principles from a particle basis. The particle model used was the Cordus theory, a type of non-local hidden-variable (NLHV) theory that predicts specific substructures to the photon and other particles. Findings: The theory explains the origin of the orthogonal electrostatic and magnetic fields, and re-derives the refraction and reflection laws including Snell’s law and critical angle, and the Fresnel equations for s and p-polarisation. These formulations are identical to those produced by electromagnetic wave theory. Contribution: The work provides a comprehensive derivation and physical explanation of the basic optical laws, which appears not to have previously been shown from a particle basis. Implications: The primary implications are for suggesting routes for the theoretical advancement of fundamental physics. The Cordus NLHV particle theory explains optical phenomena, yet it also explains other physical phenomena including some otherwise only accessible through quantum mechanics (such as the electron spin g-factor) and general relativity (including the Lorentz and relativistic Doppler). It also provides solutions for phenomena of unknown causation, such as asymmetrical baryogenesis, unification of the interactions, and reasons for nuclide stability/instability. Consequently, the implication is that NLHV theories have the potential to represent a deeper physics that may underpin and unify quantum mechanics, general relativity, and wave theory.

Exploring the rules of cropland elevation uplift in China

Cropland elevation uplift(CLEU) has recently become a new challenge for agricultural modernization,food security,and sustainable cropland use in China.Uncovering the rules of CLEU is of great theoretical and practical significance for China’s sustainable agricultural development and rural revitalization strategy.However,existing studies lack in-depth disclosure of multi-scale CLEU evolution rules,making it difficult to support the formulation of specific cropland protection policies.We analyzed the spatio-temporal evolution and multiscale CLEU in China from 1980 to 2020 using the Lorenz curve,gravity center model,hotspot analysis,and cropland elevation spectrum.The results indicated that the center of gravity of cropland moved to the northeast from 1980 to 2000 and then shifted to the northwest.The spatial distribution of cropland became increasingly imbalanced from 1980 to 2000.The change hotspots clustered in the northwest and the northeast,whereas cold-spots were mainly in southeastern China.The average elevation of cropland increased by 17.38 m,and the elevation uplift rule in different regions differed evidently across scales.From 1980 to 2000,all provinces except Xinjiang,Inner Mongolia,Gansu,and Yunnan exhibited CLEU,with Qinghai,Tibet,Beijing,and Guangdong showing the most noticeable uplifting.The CLEU can alleviate the shortage of cropland to some extent.However,without a planning constraint,the CLEU will lead to the increase of ecological risk and food security risk.

Switchable hidden spin polarization and negative Poisson's ratio in two-dimensional antiferroelectric wurtzite crystals

Two-dimensional(2D)antiferroelectric materials have raised great research interest over the last decade.Here,we reveal a type of 2D antiferroelectric(AFE)crystal where the AFE polarization direction can be switched by a certain degree in the 2D plane.Such 2D functional materials are realized by stacking the exfoliated wurtzite(wz)monolayers with“self-healable”nature,which host strongly coupled ferroelasticity/antiferroelectricity and benign stability.The AFE candidates,i.e.,Zn X and Cd X(X=S,Se,Te),are all semiconductors with direct bandgap atΓpoint,which harbors switchable antiferroelectricity and ferroelasticity with low transition barriers,hidden spin polarization,as well as giant in-plane negative Poisson's ratio(NPR),enabling the co-tunability of hidden spin characteristics and auxetic magnitudes via AFE switching.The 2D AFE wz crystals provide a platform to probe the interplay of 2D antiferroelectricity,ferroelasticity,NPR,and spin effects,shedding new light on the rich physics and device design in wz semiconductors.

Rules Mining-Based Gene Expression Programming for the Multi-Skill Resource Constrained Project Scheduling Problem

Themulti-skill resource-constrained project scheduling problem(MS-RCPSP)is a significantmanagement science problem that extends from the resource-constrained project scheduling problem(RCPSP)and is integrated with a real project and production environment.To solve MS-RCPSP,it is an efficient method to use dispatching rules combined with a parallel scheduling mechanism to generate a scheduling scheme.This paper proposes an improved gene expression programming(IGEP)approach to explore newly dispatching rules that can broadly solve MS-RCPSP.A new backward traversal decoding mechanism,and several neighborhood operators are applied in IGEP.The backward traversal decoding mechanism dramatically reduces the space complexity in the decoding process,and improves the algorithm’s performance.Several neighborhood operators improve the exploration of the potential search space.The experiment takes the intelligent multi-objective project scheduling environment(iMOPSE)benchmark dataset as the training set and testing set of IGEP.Ten newly dispatching rules are discovered and extracted by IGEP,and eight out of ten are superior to other typical dispatching rules.

Existence of hidden attractors in nonlinear hydro-turbine governing systems and its stability analysis

This work studies the stability and hidden dynamics of the nonlinear hydro-turbine governing system with an output limiting link,and propose a new six-dimensional system,which exhibits some hidden attractors.The parameter switching algorithm is used to numerically study the dynamic behaviors of the system.Moreover,it is investigated that for some parameters the system with a stable equilibrium point can generate strange hidden attractors.A self-excited attractor with the change of its parameters is also recognized.In addition,numerical simulations are carried out to analyze the dynamic behaviors of the proposed system by using the Lyapunov exponent spectra,Lyapunov dimensions,bifurcation diagrams,phase space orbits,and basins of attraction.Consequently,the findings in this work show that the basins of hidden attractors are tiny for which the standard computational procedure for localization is unavailable.These simulation results are conducive to better understanding of hidden chaotic attractors in higher-dimensional dynamical systems,and are also of great significance in revealing chaotic oscillations such as uncontrolled speed adjustment in the operation of hydropower station due to small changes of initial values.

Attribute Reduction for Information Systems via Strength of Rules and Similarity Matrix

An information system is a type of knowledge representation,and attribute reduction is crucial in big data,machine learning,data mining,and intelligent systems.There are several ways for solving attribute reduction problems,but they all require a common categorization.The selection of features in most scientific studies is a challenge for the researcher.When working with huge datasets,selecting all available attributes is not an option because it frequently complicates the study and decreases performance.On the other side,neglecting some attributes might jeopardize data accuracy.In this case,rough set theory provides a useful approach for identifying superfluous attributes that may be ignored without sacrificing any significant information;nonetheless,investigating all available combinations of attributes will result in some problems.Furthermore,because attribute reduction is primarily a mathematical issue,technical progress in reduction is dependent on the advancement of mathematical models.Because the focus of this study is on the mathematical side of attribute reduction,we propose some methods to make a reduction for information systems according to classical rough set theory,the strength of rules and similarity matrix,we applied our proposed methods to several examples and calculate the reduction for each case.These methods expand the options of attribute reductions for researchers.

Reliability and validity of the hidden hunger assessment scale in China-revised for high school students

Objective:Hidden hunger remains a severe public health problem that affects millions of people worldwide.In China,challenges related to dietary imbalance and hidden hunger persist.Micronutrient inadequacy deserves more attention among adolescents,given its vital role in their growth and development;however,this problem appears to have been largely ignored.High school students,in particular,are often at a high risk of hidden hunger but have limited assessment tools available.Therefore,this study aims to revise the hidden hunger assessment scale for high school students(HHAS-HSS)in China and assess its reliability and validity.Methods:Based on a literature review,expert consultation,pre-experiment,and formal survey,a hidden hunger assessment scale was revised for high school students.The formal survey involved 9336 high school students in 11 of the 16 cities in Anhui Province,China,and 9038 valid questionnaires were collected and included in the analysis.The item analysis,internal consistency reliability,test-retest reliability,content validity,exploratory factor analysis,and confirmatory factor analysis of the HHAS-HSS were examined.Results:The HHAS-HSS included a total of 4 dimensions and 12 items:"vegetables and food diversity"(three items),"fruits and dairy products"(three items),"micronutrient-dense foods"(four items),and"health condition and eating habits"(two items).The results showed a Cronbach's alpha of 0.758,a split-half reliability of 0.829,and a test-retest reliability of o.793,indicating good internal consistency.Using the Bartlett's test and Kaiser-Meyer-Olkin test(KMO)to test the exploratory factor analysis presented a four-factor model of the HHAS-HSS,the KMO0 value was 0.820(P<0.001),which indicated the possibility for factor confirmatory factor analysis.Using the maximum variance rotation method,four factors were obtained,and the cumulative variance explained rate was 57.974%.Confirmatory factor analysis also supported the division of the scale into four dimensions,and the fitting indices were x^(2)=1417.656,x^(2)/df=29.534,goodness-of-fit index=0.974,adjusted goodnessof-fit index=0.958,parsimonious goodness-of-fit index=0.600,normed fit index=0.938,incremental fit index=0.940,Tucker-Lewis index=0.917,comparative fit index=0.939,and root mean square error of approximation=0.056.Except for x^(2)/df,all the indices reached the fitting standard,and the above results showed that the construct validity of the scale reached an acceptable level.Conclusions:The HHAS-HSS has good validity and reliability for Chinese high school students.It is a convenient self-report measure of hidden hunger risk.

Proposing Adjustments to the Dry Season Inter-Reservoir Operation Rules of the Red River System to Improve Water Use Efficiency of the Reservoirs

The Red-Thai Binh River system is an important water resource to the Northern Delta, serving the development of agriculture, people’s livelihood and other economic sectors through its upstream reservoirs and a system of water abstraction works along the rivers. However, due to the impact of climate change and pressure from socio-economic development, the operation of the reservoir system according to Decision No. 740/QD-TTg was issued on June 17, 2019 by the Prime Minister of Government promulgating the Red-Thai Binh River system inter-reservoir operation rules (Operation rules 740) has some shortcomings that need adjustments for higher water use efficiency, meeting downstream water demand and power generation benefits. Through the results of water balance calculation and analysis of economic benefits from water use scenarios, this research proposed adjustment to the inter-reservoir operation during dry season in the Red River system. The result showed that an average water level of 1.0 - 1.7 m should be maintained at Hanoi during the increased release period.

An Intelligent Medical Expert System Using Temporal Fuzzy Rules and Neural Classifier

As per World Health Organization report which was released in the year of 2019,Diabetes claimed the lives of approximately 1.5 million individuals globally in 2019 and around 450 million people are affected by diabetes all over the world.Hence it is inferred that diabetes is rampant across the world with the majority of the world population being affected by it.Among the diabetics,it can be observed that a large number of people had failed to identify their disease in the initial stage itself and hence the disease level moved from Type-1 to Type-2.To avoid this situation,we propose a new fuzzy logic based neural classifier for early detection of diabetes.A set of new neuro-fuzzy rules is introduced with time constraints that are applied for thefirst level classification.These levels are further refined by using the Fuzzy Cognitive Maps(FCM)with time intervals for making thefinal decision over the classification process.The main objective of this proposed model is to detect the diabetes level based on the time.Also,the set of neuro-fuzzy rules are used for selecting the most contributing values over the decision-making process in diabetes prediction.The proposed model proved its efficiency in performance after experiments conducted not only from the repository but also by using the standard diabetic detection models that are available in the market.

Application of"Six Thinking Hats"in the Investigation and Improvement of Hidden Dangers in Food Safety

With the development of economy,people's living standards are constantly improving,and the requirements for food safety are getting higher and higher.The Food Safety Law stipulates that enterprises should implement the main responsibility of food safety,and the investigation and improvement of food safety hazards plays an important role in improving the food safety management level of enterprises and reducing food safety risks.This paper combines the innovative thinking mode of six thinking hats with food safety,discusses the application mode of six thinking hats in food safety investigation and improvement,and hopes to improve food safety level through this way.