Mixed Rule Research Articles

Dynamic monitoring of open-surface water bodies in the middle reaches of the Tarim River based on the GEE cloud platform

ABSTRACT This study innovatively utilizes the Google Earth Engine (GEE) cloud platform, combined with a multispectral index rule set, to address the challenges of extracting open-surface water bodies in the middle reaches of the Tarim River Basin (MROTR). To overcome the limitations of traditional index methods, particularly the reduced extraction accuracy caused by mixed pixels in arid environments, this research integrates indices such as MNDWI, NDVI, and EVI for automated water extraction, significantly enhancing the precision of water body delineation. By analyzing the spatiotemporal changes in open-surface water area (SWA) from 1990 to 2022, a notable increase in SWA was observed following the implementation of ecological gate-controlled water management, while a decreasing trend was identified within an 8-km range from the riverbanks. The results show a 93.7% accuracy in surface water identification and an SWA growth rate of 12.47 km2/year, with 96% of this growth attributed to seasonal water areas. Runoff loss decreased by 8.53% in the S1 and S2 regions but increased by 7.76% in the S3 and S4 regions. The mixed index rule method proved effective for large-scale water detection, offering new insights for managing arid region water resources.

Threat or prospect? Exploring the impact of digital entrepreneurs’ artificial intelligence perception and intention to adopt blockchain technology on the achievement of SDGs

This paper explores how digital entrepreneurs' intention toward blockchain technology adoption, perception of reduced costs, and knowledge of Artificial Intelligence impact achieving UN's Sustainable Development Goals (SDGs), drawing attention from various sectors. Present study applies explanatory sequential mixed method for data collection. Moreover, to work with the dual face patterned data, PLS-SEM is used to perform quantitative analysis of the data collected from 389 digital entrepreneurs who are chosen through purposive sampling and then content analysis is performed for the qualitative data according to the explanatory sequential mixed method's rule of thumb. The study's quantitative phase shows that factors such as perceived ease of use and usefulness of Industry 4.0 technologies, knowledge of artificial intelligence (KAI), and perception of reduced cost positively influence digital entrepreneurs' intention to adopt blockchain technology (BCT). Notably, KAI has the strongest impact. In the qualitative phase, it's found that digital entrepreneurs' KAI and willingness to adopt BCT strongly align with achieving several UN Sustainable Development Goals (SDGs), suggesting BCT adoption's potential for sustainable outcomes. The outcomes of this study set a new benchmark in the domain of SDGs achievement with careful integration to Industry 4.0, AI and BCT. This study results undoubtedly instigate the digital entrepreneurs to adopt BCT in doing their start-up and convince the policymakers to set regulatory landscape with convenient environment for the utilization of BCT which then ultimately accelerates the achievement of SDGs.

Monetary policy rule under rare events: With implications by digital finance development

This article studies the macroeconomic impacts of rare events and the choice of monetary policy rule in an estimated DSGE model using Chinese data. We quantify the characteristics of adverse macroeconomic effects on both the supply and demand side and explore the suitable monetary policy response in the context of digital finance development. The results show that the early stage of severe negative macroeconomic impacts amplified by financial frictions requires quantity-based tools to restore macroeconomic stability. After the impediment of financial frictions and the early impact of rare events are mitigated, price-based tools are more effective than quantity-based tools in achieving a rapid recovery. Therefore, a mixed monetary policy rule shows the advantage of flexibility and results in the highest welfare. In addition, digital finance development reduces financial frictions and stabilizes money demand, thus enhancing the relative importance of price-based tools.

Microstructural evolution and properties of Ti/Al clad plate fabricated by vacuum rolling and heat treatment

Ti/Al clad plate is a highly promising material due to its low cost, lightweight nature, high strength, and excellent corrosion resistance. However, the deformation coordination between Ti and Al is very poor, and both metals easily are oxidated, resulting in a weak interfacial bonding. In this study, Ti/Al clad plate was fabricated by vacuum rolling, obtaining a clear interface without oxidation and compound. The as-rolled Ti/Al clad plate had a shear strength of 76 MPa with the fracture being at the Al substrate due to the solution of Mg2Si precipitates in the Al substrate during rolling. As a result, the solution and aging is necessary to enhance interfacial bonding. There were fine and dispersed Mg2Si precipitates in the as-treated Al substrate, and an (Al, Si)3Ti layer with ∼300 nm thickness appeared at the interface. Besides, the interfacial shear strength was up to 140 MPa, and the fracture still was at the Al side. The as-treated clad plate had an elongation of 33.5% which exceeded that of 25.8% calculated by the mixed rule. Obviously, the thin (Al, Si)3Ti layer is helpful to improve interfacial shear strength and plasticity of the Ti/Al clad plate.

Mechanical Properties of Internally Hierarchical Multiphase Lattices Inspired by Precipitation Strengthening Mechanisms.

In metal metallurgy, precipitation strengthening is widely used to increase material strength by utilizing the impediment effect of the second-phase particles on dislocation movements. Inspired by this mechanism, in this paper, novel multiphase heterogeneous lattice materials are developed with enhanced mechanical properties utilizing a similar hindering effect of second-phase lattice cells on the shear band propagation. For this purpose, biphase and triphase lattice samples are fabricated using high-speed multi jet fusion (MJF) and digital light processing (DLP) additive manufacturing techniques, and a parametric study is carried out to investigate their mechanical properties. Different from the conventional random distribution, the second-phase and third-phase cells in this work are continuously distributed along the regular pattern of a larger-scale lattice to form internal hierarchical lattice structures. The results show that the triphase lattices possess balanced mechanical properties. Interestingly, this indicates that introducing a relatively weak phase also has the potential to improve the stiffness and plateau stress, which is distinct from the common mixed rule. This work is aimed at providing new references for the heterogeneous lattice design with outstanding mechanical properties through material microstructure inspiration.

Rule reductions of decision formal context based on mixed information

Formal concept analysis is an effective tool in knowledge discovery. Nonetheless, it ignores the negative information, especially in rule extraction. And some applications require to mix positive and negative information for management and representation explicitly. This paper discusses an emerging method for rule reduction in formal contexts with mixed information by presenting a mixed decision rule based on a mixed concept lattice. Based on the mixed concept model, we discuss the relationships between mixed concept, formal concept and three-way concept, respectively. In this paper, to fully consider the mixed information, we construct a mixed decision rule. Moreover, to reduce the redundance of rule, a novel approach for weak-basis from the viewpoint of granular computing is designed to selection the core mixed decision rules and necessary mixed decision rules. Finally, the comparison of mixed decision rules and three-way decision rules is perfectly discussed, and case study is presented for the difference.

On the impact of low interest rates on common withdrawal rules in old age

Ensuring a desired standard of living in retirement has been strongly challenged by increasing life expectancy, and simultaneously by the current and possibly long-lasting low interest environment. In contrast to literature in this field which claims annuitization of wealth being a vital part of retirement planning, many people manage their retirement savings and withdrawal policy during the retirement period independently. To this end, several easily applicable self-managed withdrawal rules are commonly recommended by financial advisors. We cast doubt on the viability of these self-managed withdrawal rules, particularly in an environment with increasing life expectancies and low interest rates. Further, we show that a mixed rule which combines the fixed percentage and the remaining lifetime rule can significantly improve retirees' welfare in an expected utility framework compared to other simple self-managed withdrawal rules. The results provide important insights for revising common recommendations by financial advisors, designing retirement products, and regulation.

Extraction of Aquaculture Pond Region in Coastal Waters of Southeast China Based on Spectral Features and Spatial Convolution

To control the negative effects resulting from the disorderly development of aquaculture ponds and promote the development of the aquaculture industry, rapid and accurate identification and extraction techniques are essential. An aquaculture pond is a special net-like water body divided by complex roads and dikes. Simple spectral features or spatial texture features are not sufficient to accurately extract it, and the mixed feature rule set is more demanding on computer performance. Supported by the GEE platform, and using the Landsat satellite data set and corresponding DEM combined with field survey data, we constructed a decision-making model for the extraction of aquaculture ponds in the coastal waters, and applied this method to the coastal waters of Southeast China. This method combined the image spectral information, spatial features, and morphological operations. The results showed that the total accuracy of this method was 93%, and the Kappa coefficient was 0.86. The overlapping proportions of results between the automated extraction and visual interpretation for test areas were all more than 90%, and the average was 92.5%, which reflected the high precision and reliability of this extraction method. Furthermore, in 2020, the total area of coastal aquaculture ponds in the study area was 6348.51 km2, which was distributed primarily in the cities of Guangdong and Jiangsu. Kernel density analysis suggested that aquaculture ponds in Guangdong and Jiangsu had the highest degree of concentration, which means that they face higher regulatory pressure in the management of aquaculture ponds than other provinces. Therefore, this method can be used to extract aquaculture ponds in coastal waters of the world, and holds great significance to promote the orderly management and scientific development of fishery aquaculture.

Reinforcement learning explains various conditional cooperation

Recent studies show that different update rules are invariant regarding the evolutionary outcomes for a well-mixed population or homogeneous network. In this paper, we investigate how the Q-learning algorithm, one of the reinforcement learning methods, affects the evolutionary outcomes in square lattice. Especially, we consider the mixed strategy update rule, among which some agents adopt Q-learning method to update their strategies, the proportion of these agents (these agents are denoted as Artificial Intelligence (AI)) is controlled by a simple parameter ρ. The rest of other agents, the proportion is denoted by 1 − ρ, adopt the Fermi function to update their strategies. Through extensive numerical simulations, we found that the mixed strategy-update rule can facilitate cooperation compared with the pure Fermi- function-based update rule. Besides, if the proportion of AI is moderate, cooperators among the whole population exhibit conditional behavior and moody conditional behavior. However, if the whole population adopts the pure Fermi-function-based strategy update rule or the pure Q-learning-based strategy update rule, then cooperators among the whole population exhibit the hump-shaped conditional behavior. Our results provide a new insight to understand the evolution of cooperation from AI's view.

Toward a Theory of Heteronomy

AbstractIt is thought that political order in premodern Europe was characterized by overlapping and crosscutting forms of authority. Scholars have called this heteronomy, arguing that it preceded and may follow the modern sovereign state system and that it has characterized various historical locales around the world. We maintain that this conception has been under-examined, and we identity three different forms of heteronomy that existed historically. These include: (1) interstitial heteronomy, where polities with limited capacity in low-density regions experience zones of informal mixed rule on the frontier; (2) functional heteronomy, where states in thickly populated systems develop complex patterns of functional differentiation; and (3) personalistic heteronomy, where power that is invested in individuals rather than territorially defined polities can produce patterns of dual vassalage. We develop a theory of heteronomy based on the density of the system (low, high) and the nature of political relations (territorial, personal), and using the resulting two-dimensional map we explore the form of heteronomy that existed and may exist in different systems across time and space. We conclude that when scholars envisage heteronomy in the modern system, it is mostly functional, and not interstitial or personalistic heteronomy, which they have in mind.

A quadrature rule of Lobatto-Gaussian for numerical integration of analytic functions

<p style='text-indent:20px;'>A novel quadrature rule is formed combining Lobatto six point transformed rule and Gauss-Legendre five point transformed rule each having precision nine. The mixed rule so formed is of precision eleven. Through asymptotic error estimation the novelty of the quadrature rule is justified. Some test integrals have been evaluated using the mixed rule and its constituents both in non-adaptive and adaptive modes. The results are found to be quite encouraging for the mixed rule which is in conformation with the theoretical prediction.</p>

Real-time Automated Guided vehicles scheduling with Markov Decision Process and Double Q-Learning algorithm

As an outcome of Industry 4.0, Automated Guided Vehicles (AGVs) are becoming increasingly popular as a mode of mobility on flexible shop floors for material handling. The efficiency of such systems is determined by a variety of parameters, including collision and deadlock-free operation. In multi-AGV systems, it is challenging to plan the shortest path for each AGV without conflict or collision. However, significant hurdles remain on the real-time scheduling of AGVs due to the high dynamics, difficulty, and uncertainty of the shop floor environment. This paper proposes a Deep Reinforcement Learning (DRL) technique based AGV real-time scheduling strategy with a set of rules to overcome these obstacles. AGV real-time scheduling is first defined as a Markov Decision Process (MDP) with the representation of the state, action, reward and optimal mixed rule policy. Following that, a novel Deep Reinforcement Double Q-Learning is used to schedule different AGVs to different states. Ultimately, the suggested technique exhibited superiority over the previously developed real-time Q-learning AGV scheduling technique.

Effect of reputation-based heterogeneous investment on cooperation in spatial public goods game

Cooperation that emerges in the situation of multi-person social dilemma has attracted intensive interest and been analyzed under the widely used public goods game (PGG) model within the framework of evolutionary game theory. Different from the original version of spatial PGG that investments are even for all the groups, in this work, a heterogeneous investment model is present by the real observation that an individual would make a rigorous judgment on to what extent they will cooperate based on the reputation condition among the group. In the model, the investors’ investment amounts to different groups are non-uniform and dynamically modified. Once all of the remaining members’ reputation values inside the group reach the corresponding threshold, the investor will completely invest one unit to the group without hesitation; otherwise, the incomplete amounts involving the basic part and the extra part will instead of the complete one. Considering that different reputation evaluation criteria exist in reality, two different threshold situations including the subjective and objective reputation threshold are considered. Simulation results show that the heterogeneous investments based on the two situations are efficient for the promotion of cooperation as well as the improvement of the whole system's average payoffs. Moreover, compared with the simple subjective threshold or the fixed objective threshold, the cooperation level can be enhanced in the mixed rule, in which cooperators can utilize the maximum value among the two kinds of reputation threshold as the judgment basis for their investment decisions. The current work is conducive to better understand the effect of reputation-based heterogeneity investment on the persistence and emergence of cooperation in real-world systems.

A unified model of anisotropy, thermoelasticity, inelasticity, phase transition and reaction for high-pressure ramp-loaded RDX single crystal

A unified thermomechanical consistent model integrating multiple mechanisms, including material anisotropy, nonlinear thermoelasticity, inelasticity, phase transition , and chemical reaction, is developed for cyclotrimethylene trinitramine (RDX) crystal under high-pressure ramp-loading. (i) The nonlinear thermoelasticity of material is described by pressure-temperature-dependent elasticity tensor and a free-energy based complete equation of state. (ii) The cracking-mediated brittle damage on eleven cleavage planes and dislocation-mediated ductile plasticity on thirteen slip systems are considered in inelastic deformation. (iii) The phase transition kinetics is dependent on a thermodynamic consistent driving force. (iv) The chemical reaction is described by a reactive flow model integrating equations of state for solid reactants and gaseous products, Arrhenius-type reaction rate model and mixed rule. The proposed model is utilized to simulate the mechanical-thermal-chemical responses of (100) and (010) oriented ramp-loaded RDX crystals at pressure up to ~33GPa. The simulated results are well in agreement with the experimental data and reveal a four-wave structure (elastic-inelastic-phase transition-reaction wave) hidden behind the pressure wave profiles. Crack and dislocation activities on (110) and (1-10) crystal planes play a dominant role in macroscopic inelasticity of (100) oriented RDX. Compared to (100) oriented RDX, the (010) oriented RDX exhibits a higher α→γ phase transition criterion and a faster chemical reaction, which is associated with the different overall heating rate and thermomechanical state.

Toward Refined Nash Equilibria for the SET K-COVER Problem via a Memorial Mixed-Response Algorithm

Area coverage and network lifetime are two contradictory issues to the architecture development of a wireless sensor network (WSN). A satisfactory balance could be achieved by deploying abundant sensor nodes randomly and dividing them into <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> exclusive cover sets. Toward self-organized partition with higher efficiency, we address the problem from the perspective of networked potential games and propose a memorial mixed-response algorithm (MMRA), which is implemented in a distributed and synchronous manner. Being viewed as a game player, each sensor node first updates its memory using a temporary action, which is generated by following a mixed response rule. After this, the coordination evolves into the next iteration by each player randomly drawing an action from its memory with equal probabilities. We prove that our algorithm converges with probability 1 to a convention of Nash equilibria, with the worst approximation ratio strictly larger than 0.5. Moreover, it is also found that a tradeoff between solution efficiency and computation time could be achieved via the adjustment of the amount of randomness introduced via the memory length <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> as well as the probability <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p_{m}$ </tex-math></inline-formula> , where better partition results are more likely to be generated using a larger <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> and smaller <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p_{m}$ </tex-math></inline-formula> . Comparisons with existing distributed methods demonstrate the superiority of our method in terms of solution refinement as well as convergence speed.

Numerical Solution for Real Double Definite Integrals

The double integral is numerically evaluated in this paper using higher precision quadrature rules. With the combination of Newtonian and Gaussian rules of precision three each, a mixed quadrature rule of precision five is obtained. Three test problems are used to numerically validate the rule. The approximations are compared to analytical solutions, and error bounds are calculated.

Micro Hierarchical Structure and Mechanical Property of Sparrow Hawk (Accipiter nisus) Feather Shaf

In this study, the real 3D model of the feather shaft that is composed of medulla and cortex is characterized by X-ray computer tomography, and the structural features are quantitatively analyzed. Compression and tensile tests are conducted to evaluate the mechanical performance of the feather shaft and cortex at different regions. The analysis of the 3D model shows that the medulla accounts for ∼70% of the shaft volume and exhibits a closed-cell foam-like structure, with a porosity of 59%. The cells in the medulla show dodecahedron and decahedron morphology and have an equivalent diameter of ∼30 μm. In axial compression, the presence of medulla enhances the shaft stability. Especially, the combined effect of the medulla and cortex increases the buckling strength of the middle and distal shaft by 77% and 141%, respectively, compared to the calculated value of the shaft using linear mixed rule. The tensile properties of the cortex along the shaft axis are anisotropic because of the different fiber structures. As the fiber orientation gradually becomes uniform in the axial direction, the Young’s modulus and tensile strength of the cortex on the dorsal gradually increase from calamus to the distal shaft, and the fracture mode changes from tortuous fracture to V-shaped fracture. The cortex on the lateral shows the opposite trend, that is the distal shaft becomes weaker due to fiber tangles.

Deep reinforcement learning based AGVs real-time scheduling with mixed rule for flexible shop floor in industry 4.0

Driven by the recent advances in industry 4.0 and industrial artificial intelligence, Automated Guided Vehicles (AGVs) has been widely used in flexible shop floor for material handling. However, great challenges aroused by the high dynamics, complexity, and uncertainty of the shop floor environment still exists on AGVs real-time scheduling. To address these challenges, an adaptive deep reinforcement learning (DRL) based AGVs real-time scheduling approach with mixed rule is proposed to the flexible shop floor to minimize the makespan and delay ratio. Firstly, the problem of AGVs real-time scheduling is formulated as a Markov Decision Process (MDP) in which state representation, action representation, reward function, and optimal mixed rule policy, are described in detail. Then a novel deep q-network (DQN) method is further developed to achieve the optimal mixed rule policy with which the suitable dispatching rules and AGVs can be selected to execute the scheduling towards various states. Finally, the case study based on a real-world flexible shop floor is illustrated and the results validate the feasibility and effectiveness of the proposed approach.

Numerical treatment of the generalized Love integral equation

In this paper, the generalized Love integral equation has been considered. In order to approximate the solution, a Nyström method based on a mixed quadrature rule has been proposed. Such a rule is a combination of a product and a “dilation” quadrature formula. The stability and convergence of the described numerical procedure have been discussed in suitable weighted spaces and the efficiency of the method is shown by some numerical tests.

Beyond Data Ownership

Data ownership proposals are widely misunderstood, aim at the wrong goal, and would be self-defeating if implemented. This Article, first, shows that data ownership proposals do not argue for the bundle of ownership rights that exists over property at common law. Instead, these proposals focus on transferring rights over personal information solely through consent. Second, this Article shows the flaws of a property approach to personal information. Such an approach magnifies well-known problems of consent in privacy law: asymmetric information, asymmetric bargaining power, and leaving out inferred data. It also creates a fatal problem: a moral hazard problem where corporations lack incentives to reduce privacy harm. That moral hazard problem makes data ownership self-defeating. Recognizing these deficiencies entails abandoning the idea that property over personal data can achieve meaningful protection. This Article, third, develops proposals for privacy law reform amidst a national discussion on how to formulate federal and state privacy statutes. These involve implementing a combination of what the Calabresi-Melamed framework calls property and liability rules. This mixed rule system is essential because property rules alone fail to protect data subjects from the risks of future uses and abuses of their personal information. The Article implements this reform proposal with two recommendations. First, it proposes bolstering private rights of action for privacy harm irrespective of whether such harm was coupled with a statutory breach. Second, it proposes reinforcing ongoing use restrictions over personal data by strengthening the purpose limitation principle, a key and underutilized ongoing use restriction in American law.