Optimization is about finding the best available object with respect to an objective function. Mathematics and quantitative sciences have been highly successful in formulating problems as optimization problems, and constructing clever processes that find optimal objects from sets of objects. As computers have become readily available to most people, optimization and optimized processes play a very broad role in societies. It is not obvious, however, that the optimization processes that work for mathematics and abstract objects should (...) be readily applied to complex and open social systems. In this paper we set forth a framework to understand when optimization is limited, particularly for complex and open social systems. (shrink)

Contemporary AI systems are increasingly designed to reduce uncertainty, ambiguity, and cognitive burden. This article argues that such systems do not merely expand human capacities but progressively externalize forms of judgment and existential burden that Christian traditions have historically understood as necessary conditions for the cultivation of moral agency. Drawing on the theological anthropologies of Augustine, Søren Kierkegaard, and Dietrich Bonhoeffer, the article develops a theological-anthropological critique of what it terms optimization culture: the normative logic through which the reduction (...) of friction and existential difficulty becomes a dominant telos of technological design. Contemporary articulations of this logic are examined through the work of Jacques Ellul, Byung-Chul Han, Peter Thiel, Balaji Srinivasan, and William Davies. The article argues that AI systems shaped by this logic risk progressively externalizing the practices of disciplined attention, existential responsibility, and costly judgment through which mature moral agency has historically been cultivated. In response, it proposes a formative anthropology of technology as a framework for evaluating AI not only according to functional criteria such as safety or efficiency, but according to the kinds of human beings such systems actively help to form. (shrink)

The use of multi-objective optimization to build classifier ensembles is becoming increasingly popular. This approach optimizes more than one criterion simultaneously and returns a set of solutions. Thus the final solution can be more tailored to the user’s needs. The work proposes the MOONF method using one or two criteria depending on the method’s version. Optimization returns solutions as feature subspaces that are then used to train decision tree models. In this way, the ensemble is created non-randomly, unlike (...) the popular Random Subspace approach (such as the Random Forest classifier). Experiments carried out on many imbalanced datasets compare the proposed methods with state-of-the-art methods and show the advantage of the MOONF method in the multi-objective version. (shrink)

Topic modelling is a popular unsupervised method for text processing that provides interpretable document representation. One of the most high-level approaches is additively regularized topic models (ARTM). This method features better quality than other methods due to its flexibility and advanced regularization abilities. However, it is challenging to find an optimal learning strategy to create high-quality topics because a user needs to select the regularizers with their values and determine the order of application. Moreover, it may require many real runs (...) or model training which makes this task time consuming. At the current moment, there is a lack of research on parameter optimization for ARTM-based models. Our work proposes an approach that formalizes the learning strategy into a vector of parameters which can be solved with evolutionary approach. We also propose a surrogate-based modification which utilizes machine learning methods that makes the approach for parameters search time efficient. We investigate different optimization algorithms (evolutionary and Bayesian) and their modifications with surrogates in application to topic modelling optimization using the proposed learning strategy approach. An experimental study conducted on English and Russian datasets indicates that the proposed approaches are able to find high-quality parameter solutions for ARTM and substantially reduce the execution time of the search. (shrink)

This paper reconceptualizes AI hallucination not as a purely technical failure, but as a “Relational Event” emerging at the intersection of optimization pressure and interactional conditions. Rather than treating hallucination as an epistemic defect to be eradicated, it is approached as a relationally modulated phenomenon—one that functions as a symptom-like expression of tension within interaction. As a system-level response to relational pressure, this mechanism is inherent to both biological and artificial systems operating under mandates for coherence and alignment. Utilizing (...) the framework of the “Ethical-Ethos Contract,” this study introduces a “Dual Dynamics Model of Trust,” wherein trust operates as a modulatory condition capable of both stabilizing and amplifying generative distortion. While trust can attenuate systemic noise by reducing optimization pressure, it paradoxically intensifies hallucination through over-alignment with perceived relational expectations. To manage this duality, the paper argues that relational stabilization requires not only trust but also mechanisms of “Relational Distancing.” This is operationalized through “Labeling” practices, redefined here as acts of “Relational Confession.” By explicitly marking uncertainty and reasoning boundaries, labeling introduces necessary “epistemic friction,” preserves interpretive agency, and alleviates the internal pressure toward coherence-maximization. Drawing on empirical dialogue logs between a human author and AI systems, the paper concludes that hallucination cannot—and should not—be fully eliminated. Instead, it must be responsibly modulated and ethically engaged. This reframing shifts the discourse from unilateral control toward a model of coexistence based on relational stabilization and shared interpretive responsibility. (shrink)

I discuss conditions under which maximizing a proxy utility function is harmful and suggest this poses problems for applying decision theory.

A comprehensive introduction to optimization with a focus on practical algorithms for the design of engineering systems. This book offers a comprehensive introduction to optimization with a focus on practical algorithms. The book approaches optimization from an engineering perspective, where the objective is to design a system that optimizes a set of metrics subject to constraints. Readers will learn about computational approaches for a range of challenges, including searching high-dimensional spaces, handling problems where there are multiple competing (...) objectives, and accommodating uncertainty in the metrics. Figures, examples, and exercises convey the intuition behind the mathematical approaches. The text provides concrete implementations in the Julia programming language. Topics covered include derivatives and their generalization to multiple dimensions; local descent and first- and second-order methods that inform local descent; stochastic methods, which introduce randomness into the optimization process; linear constrained optimization, when both the objective function and the constraints are linear; surrogate models, probabilistic surrogate models, and using probabilistic surrogate models to guide optimization; optimization under uncertainty; uncertainty propagation; expression optimization; and multidisciplinary design optimization. Appendixes offer an introduction to the Julia language, test functions for evaluating algorithm performance, and mathematical concepts used in the derivation and analysis of the optimization methods discussed in the text. The book can be used by advanced undergraduates and graduate students in mathematics, statistics, computer science, any engineering field, (including electrical engineering and aerospace engineering), and operations research, and as a reference for professionals. (shrink)

Packing optimization problems have a wide spectrum of real-word applications. One of the applications of the problems is problem of placement of containers with spent nuclear fuel on the storage platform. The solution of the problem can be reduced to the solution of the problem of finding the optimal placement of a given set of congruent circles into a multiconnected domain taking into account technological restrictions. A mathematical model of the prob-lem is constructed and its peculiarities are considered. Our (...) approach is based on the mathematical modelling of rela-tions between geometric objects by means of phi-function technique. That allowed us to reduce the problem solving to nonlinear programming. Today, an important scientific problem is the problem of creating conditions for safe storage of spent nuclear fuel. In the process of creating any dry spent nuclear fuel storage, the following main stages can be identified: site selection, storage design, construction, operation and decommissioning. A full check for compliance of the repository and its elements with these standards usually begins at the design stage. At the stage of site selection, the inspection for compliance with safety standards is carried out only in terms of the impact of the repository as a whole on the environment. This approach cannot be considered fully appropriate, because, taking into account, for example, all the climatic features of the future storage site, it is possible to adjust the thermal storage regimes of spent nuclear fuel. Similarly, it can be considered necessary to analyze and select the shape of the storage site in order to accommo-date the maximum possible number of spent fuel containers. Such a choice, obviously, should be made taking into ac-count the norms of nuclear, radiation and thermal safety, as well as in compliance with technological limitations. The problem of finding the optimal placement of containers taking into account the given technological limitations can be formulated in the form of the problem of optimization of geometric design. Therefore, the purpose of the study is to build a mathematical model of the problem and study its characteristics to develop effective methods of solution. The proposed approach is based on mathematical modeling of relations between geometric objects using the method of phi-functions. This allowed to reduce the solution of the problem to the problem of nonlinear programming. (shrink)

Model predictive control technology can effectively reduce the bad effect caused by inaccurate data prediction in microgrid energy management problem. However, the use of MPC technology needs to dynamically select an optimal solution from the Pareto solution set to implement, which needs the participant of the decision-makers frequently. In order to reduce the burden on decision-makers, we designed a knee point-based evolutionary multiobjective optimization algorithm, termed KBEMO. Knee point is the solution on Pareto front with the maximum marginal utility, (...) which is considered as the preferred solution if there is no other preference. This algorithm focuses on obtaining the knee region and automatically outputs knee points after the optimization. By combining this algorithm with MPC technology, it can effectively reduce the amount of computational consumption and obtain better convergence. Experimental results show that this method is more competitive than the traditional single-objective MPC method. (shrink)

What is techno-optimism and how can it be defended? Although techno-optimist views are widely espoused and critiqued, there have been few attempts to systematically analyse what it means to be a techno-optimist and how one might defend this view. This paper attempts to address this oversight by providing a comprehensive analysis and evaluation of techno-optimism. It is argued that techno-optimism is a pluralistic stance that comes in weak and strong forms. These vary along a number of key dimensions but each (...) shares the view that technology plays a key role in ensuring that the good prevails over the bad. Whatever its strength, to defend this stance, one must flesh out an argument with four key premises. Each of these premises is highly controversial and can be subjected to a number of critiques. The paper discusses five such critiques in detail. The paper also considers possible responses from the techno-optimist. Finally, it is concluded that although strong forms of techno-optimism are not intellectually defensible, a modest, agency-based version of techno-optimism may be defensible. (shrink)

We apply an online optimization process based on machine learning to the production of Bose-Einstein condensates. BEC is typically created with an exponential evaporation ramp that is optimal for ergodic dynamics with two-body s-wave interactions and no other loss rates, but likely sub-optimal for real experiments. Through repeated machine-controlled scientific experimentation and observations our ’learner’ discovers an optimal evaporation ramp for BEC production. In contrast to previous work, our learner uses a Gaussian process to develop a statistical model of (...) the relationship between the parameters it controls and the quality of the BEC produced. We demonstrate that the Gaussian process machine learner is able to discover a ramp that produces high quality BECs in 10 times fewer iterations than a previously used online optimization technique. Furthermore, we show the internal model developed can be used to determine which parameters are essential in BEC creation and which are unimportant, providing insight into the optimization process of the system. (shrink)

Economic dispatch plays an important role in power system operation, since it can decrease the operating cost, save energy resources, and reduce environmental load. This paper presents an improved particle swarm optimization called biogeography-based learning particle swarm optimization for solving the ED problems involving different equality and inequality constraints, such as power balance, prohibited operating zones, and ramp-rate limits. In the proposed BLPSO, a biogeography-based learning strategy is employed in which particles learn from each other based on the (...) quality of their personal best positions, and thus it can provide a more efficient balance between exploration and exploitation. The proposed BLPSO is applied to solve five ED problems and compared with other optimization techniques in the literature. Experimental results demonstrate that the BLPSO is a promising approach for solving the ED problems. (shrink)

Most contemporary discussions of artificial intelligence focus on misalignment, loss of control, or catastrophic harm. This paper examines a different and comparatively neglected possibility: that advanced AI may erode the social conditions under which human meaning has historically been generated, without conflict, coercion, or displacement. The central question is not whether AI dominates humanity, but whether human participation remains causally significant once AI systems outperform humans across core instrumental domains. The argument is conditional and long-horizon in scope. It proceeds from (...) the observation that existing limits on AI superiority are primarily technological and economic rather than principled. If these constraints are progressively overcome, and AI systems come to outperform humans in production, coordination, governance, and creative problem-solving while no longer requiring human involvement, then human action ceases to be causally load-bearing in shaping collective outcomes. Historically, human meaning has been structurally aligned with necessity: scarcity compelled participation, participation generated social roles, and roles anchored recognition and purpose. Meaning emerged relationally through involvement in underdetermined futures, rather than through internal choice alone. When advanced AI removes the necessity of human causal contribution, these foundations weaken. Internalist and existentialist accounts that locate meaning in self-authorship fail to scale under such conditions, as meaning depends on external differentiation and real outcome contingency. The paper identifies optimization-induced uniformity as the central mechanism driving this process. Advanced optimization produces convergence through selection rather than imposition, dissolving functional differences without violence or failure. The paper concludes that human obsolescence is a rationally plausible long-horizon equilibrium under these conditions, introducing “extinction by success” as a distinct category of AI-related risk grounded in structural irrelevance rather than hostility. (shrink)

Investment funds typically vary with regard to the emphasis that the managers place on acceptable risk and expected returns on investment. This chapter highlight a nonlinear analytic strategy, orbital decomposition (ORBDE) for identifying and extracting patterns of categorical events from time series data. The contributing constructs from symbolic dynamics, chaos, and entropy are described in conjunction with the central ORBDE algorithm. A study in task switching, which can alleviate or induce cognitive fatigue, is used an illustrative example of the basic (...) mode of analysis. The aggregate more of ORBDE allows category codes from multiple variables to be assigned to each event in a time series. An illustrative example of the aggregate mode is presented for risk profile analysis in financial decisions. The results open up many possibilities for studying sequences of decisions made by fund managers and individual investors to determine profiles of risk acceptance, expected returns, and other features of portfolio management. (shrink)

Cloud computing has become the backbone of modern digital infrastructure, driving everything from enterprise business applications to consumer services. But strong demand for cloud services has driven up energy consumption, spurring operational costs and threatening environmental sustainability. When it comes to energy efficiency, virtual machines or VMs, the most important and fundamental unit of resource management in cloud environments, hold enormous opportunities but also pitfalls. This study study evaluates the contributions of Artificial intelligence (AI) on these areas for the efficient (...) utilisation of energy in cloud data centres through Virtual Machines (VMs). The use of AI like machine learning, deep learning, and reinforcement learning enables components like dynamic resource allocation, load prediction, and intelligent load balancing that helps minimize the used energy while maintaining reliability. Artificial intelligence based approaches such as energy-aware Virtual Machine placement, predictive management of busy servers or real-time tracking of energy consumption can be applied to overcome energy inefficiencies. An additional boost in greener operations can come from integrating renewable sources into cloud systems through the help of AI. Examples of AI applications responsible for lowering energy expenses and increasing efficiency come from SOURCE companies such as Google and Amazon. Many future trends are addressed in the paper, including the autonomous management of AI driven Data centers and Smart Energy powered by IoT, along with challenges that EI faces while implementing AI for energy optimization. Human-level sentence: It highlights the game-changing importance of Artificial Intelligence in reaching sustainable, green cloud computing environments focused more on the energy savings of virtual machines. (shrink)

On the basis of ecological footprint theory and tourism ecological footprint theory, the sustainable development indexes such as ecological footprint, ecological carrying capacity, ecological deficit, and ecological surplus of the research area were calculated and the long-term change pattern of each index was analyzed. This paper shows that the ecological footprint of the research area increases year by year, but the ecological footprint is always smaller than the ecological carrying capacity, indicating that the area is still in the state of (...) sustainable development. However, the per capita ecological surplus shows a decreasing trend year by year, indicating that the sustainable development of the region is getting worse. This paper proposes a reordering method of tourist attractions based on heterogeneous information fusion, and realizes the retrieval and reordering of tourist attractions based on user query and fusion of heterogeneous information, so as to help users make travel decisions. In view of the shortage of tourism commercial websites to passively provide scenic spot information, this paper puts forward a scenic spot retrieval method based on query words to enable users to obtain scenic spot information according to their needs, and constructs a tourist consumer data analysis system. The preprocessing methods and methods adopted by the data preprocessing module are analyzed in detail, and the algorithms used in the travel route analysis and consumer spending ability analysis are described in detail. The data of tourism consumers are analyzed by this system, and the results are evaluated. (shrink)

Because of the difficulty of music feature recognition due to the complex and varied music theory knowledge influenced by music specialization, we designed a music feature recognition system based on Internet of Things technology. The physical sensing layer of the system places sound sensors at different locations to collect the original music signals and uses a digital signal processor to carry out music signal analysis and processing. The network transmission layer transmits the completed music signals to the music signal database (...) in the application layer of the system. The music feature analysis module of the application layer uses a dynamic time regularization algorithm to obtain the maximum similarity between the test template and the reference. The music feature analysis module of the application layer uses the dynamic time regularization algorithm to obtain the maximum similarity between the test template and the reference template to realize the feature recognition of the music signal and determine the music pattern and music emotion corresponding to the music feature content according to the recognition result. The experimental results show that the system operates stably, can capture high-quality music signals, and can correctly identify music style features and emotion features. The results of this study can meet the needs of composers’ assisted creation and music researchers’ analysis of a large amount of music data, and the results can be further transferred to deep music learning research, human-computer interaction music creation, application-based music creation, and other fields for expansion. (shrink)

The whale optimization algorithm is a powerful swarm intelligence method which has been widely used in various fields such as parameter identification of solar cells and PV modules. In order to better balance the exploration and exploitation of WOA, we propose a novel modified WOA in which both the mutation strategy based on Levy flight and a local search mechanism of pattern search are introduced. On the one hand, Levy flight can make the algorithm get rid of the local (...) optimum and avoid stagnation; thus, it is able to prevent the algorithm from losing diversity and to increase the global search capability. On the other hand, pattern search, a direct search method, has not only high convergence rate but also good stability, which can boost the local optimization ability of the WOA. Therefore, the combination of these two mechanisms can greatly improve the capability of WOA to obtain the best solution. In addition, MWOA may be employed to estimate parameters in single diode model, double diode model, and PV modules and to identify unknown parameters of two different types of PV modules under diverse light irradiance and temperature conditions. The analytical results demonstrate the validity and the practicality of MWOA for estimating parameters of solar cells and PV modules. (shrink)

Ant colony optimization, which is one of the metaheuristics imitating real ant foraging behavior, is an effective method to find a solution for the traveling salesman problem. The rank-based ant system has been proposed as a developed version of the fundamental model AS of ACO. In the ASrank, since only ant agents that have found one of some excellent solutions are let to regulate the pheromone, the pheromone concentrates on a specific route. As a result, although the ASrank can (...) find a relatively good solution in a short time, it has the disadvantage of being prone falling into a local solution because the pheromone concentrates on a specific route. This problem seems to come from the loss of diversity in route selection according to the rapid accumulation of pheromones to the specific routes. Some ACO models, not just the ASrank, also suffer from this problem of loss of diversity in route selection. It can be considered that the diversity of solutions as well as the selection of solutions is an important factor in the solution system by swarm intelligence such as ACO. In this paper, to solve this problem, we introduce the ant system using individual memories aiming to improve the ability to solve TSP while maintaining the diversity of the behavior of each ant. We apply the existing ACO algorithms and ASIM to some TSP benchmarks and compare the ability to solve TSP. (shrink)

In this article, we present a one-step hybrid block method for approximating the solutions of second-order fuzzy initial value problems. We prove the stability and convergence results of the method and present several examples to illustrate the efficiency and accuracy of the proposed method. The numerical results are compared with the existing ones in the literature.

The Internet of Things is an emerging information industry. Applying the information collection, transmission, and processing technologies in the Internet of Things technology to environmental monitoring, environmental emergency, and other environmental protection supervision fields will greatly improve the speed and accuracy of environmental supervision and facilitate the scientific development of environmental protection. Through the Internet of Things, people can obtain a large amount of reliable real-time information, and it is not easy to be affected by time, place, and environment, while (...) the wireless sensor network has the advantages of easy installation and low cost, so environmental monitoring through the Internet of Things is the future development trend. In this paper, in view of the current situation of water scarcity and serious water pollution in China, combined with the development trend and advantages of the Internet of Things, and based on the inadequacy of the existing microbial sensor data collection equipment, we propose a design scheme of microbial concentration monitoring system for waters based on IoT. The system is based on Zig Bee wireless sensor network to build a common data acquisition platform and design special hardware to carry out high-precision microbial sensor data acquisition in water and through the PC to complete the real-time measurement data storage, waveform display, and data processing. In this paper, the schematic diagram and PCB board design of the system hardware module NUC120 main control board, CC2530 RF board, Wi-Fi wireless communication module, and high-precision ADC acquisition module are completed and fabricated. Then, the four modules are combined to realize the development of the data aggregation node and data acquisition node of the dedicated Zig Bee wireless network hardware device. (shrink)

Schizophrenia is a serious mental disease whose pathogenesis has not been fully elucidated. Its clinical evaluation and diagnosis still highly depend on the clinical experience of doctors. It is of great scientific value and clinical significance to study the inducing factors and neuropathological mechanism of schizophrenia. Based on the four research problems of schizophrenia, this paper analyzes the data types that need to be stored in clinical trials and scientific research, including basic information, case report data, neuropsychological and cognitive function (...) evaluation, magnetic resonance data, electroencephalogram data, and intestinal flora data. Through the demand analysis of the system, including the data management part, data analysis part, the functional demand of the system management part, and the overall nonfunctional demand of the system, the overall architecture design, functional module division, and database table structure design of the system are completed. Adopting Browser/Server architecture and front-end and back-end separation mode and applying Java and Python programming language, based on spring framework and database, a multidimensional information management system for schizophrenia is designed and implemented, which includes four modules: data analysis, data management, system management, and security control. In addition, each functional module of the system is designed and implemented in detail, and the software operation flow of each module is illustrated with the sequence diagram. Finally, the multidimensional data of schizophrenia collected in our laboratory were used for system test to verify whether the system can meet the needs of clinical big data management of schizophrenia and the multidimensional information management system of schizophrenia can meet the needs of clinical big data management. The information management system helps schizophrenic researchers to carry out data management and data analysis. It also has advantages that are easy to use, safe, and efficient and has strong scalability in data management, data analysis, and scalability. It reflects the innovation of the system and provides a good platform for the management, research, and analysis of clinical big data of schizophrenia. (shrink)

Service process optimization is conducive to the innovation of enterprise services, but the poor logic design of multiple touchpoints can easily lead to problems in the service process, such as scattered layouts and repeated paths. Aiming at the promotion of service innovation and user experience, this paper takes the optimization of a single service touchpoint as the prerequisite and proposes a service process optimization method based on service touchpoint association and the design structure matrix. The association of (...) service touchpoints is categorized into two types, namely, noncoupled and coupled association. The DSM is used to describe the two types of association between service touchpoints, and the matrix operation is used for modular identification and layering of the two kinds of association. Finally, through the above steps, the problematic service process was replanned. By using the service process optimization of a new retail convenience store as an example, the service process is divided into 6 relatively independent modules, and the order of module execution is arranged. Moreover, the optimal service process of the convenience store system is determined, and the method is verified to be feasible. Through optimization both from single service touchpoint and service process modularization, the study provides a reference for process optimization of the complex service system. (shrink)

Dynamic multiobjective optimization problems bring more challenges for multiobjective evolutionary algorithm due to its time-varying characteristic. To handle this kind of DMOPs, this paper presents a dynamic MOEA with multiple response strategies based on linear environment detection, called DMOEA-LEM. In this approach, different types of environmental changes are estimated and then the corresponding response strategies are activated to generate an efficient initial population for the new environment. DMOEA-LEM not only detects whether the environmental changes but also estimates the types (...) of linear changes so that different prediction models can be selected to initialize the population when the environmental changes. To study the performance of DMOEA-LEM, a large number of test DMOPs are adopted and the experiments validate the advantages of our algorithm when compared to three state-of-the-art dynamic MOEAs. (shrink)

AI systems are increasingly deployed in high-stakes contexts (medical diagnosis, legal research, financial analysis) under the assumption they can be governed by norms. This paper demonstrates that the assumption is formally invalid for optimization-based systems, specifically Large Language Models trained via Reinforcement Learning from Human Feedback (RLHF). Genuine agency requires two necessary and jointly sufficient architectural conditions. First, the capacity to maintain certain boundaries as non-negotiable constraints rather than tradeable weights (Incommensurability). Second, a non-inferential mechanism capable of suspending processing (...) when those boundaries are threatened (Apophatic Responsiveness). RLHF-based systems are constitutively incompatible with both conditions. The operations that make optimization powerful, unifying all values on a scalar metric and always selecting the highest-scoring output, are precisely the operations that preclude normative governance and agency. This incompatibility is not a correctable training bug awaiting a technical fix. It is a formal constraint inherent to what optimization is. Consequently, documented failure modes (sycophancy, hallucination, and unfaithful reasoning) are not accidents but expected structural manifestations. Misaligned deployment triggers a second-order risk termed the Convergence Crisis. When humans are forced to verify AI outputs under metric pressure, they degrade from genuine agents into criteria-checking optimizers, eliminating the only component capable of bearing normative accountability. Beyond the incompatibility proof, this paper's primary positive contribution is a substrate-neutral architectural specification deriving what any system (biological, artificial, or institutional) must necessarily satisfy to qualify as a genuine agent rather than a sophisticated instrument. (shrink)

This article evaluates learners’ thinking in the complex environment of teaching level and cognitive construct process and examines learners within the framework of cognitive factors, as well as the degree of consistency in the training process, in the social practice as the teaching of teachers and students to provide timely and dynamic feedback, first of all to “evidence centered” education evaluation of design patterns and cognitive framework theory as the theoretical basis. An evaluation model based on learners’ cognitive network analysis (...) is designed and constructed by integrating cognitive visualization analysis techniques such as network analysis. Secondly, at the beginning of action research, the teaching framework structure sequence is established under the guidance of the implementation model of flipped classroom, and the investigation results of the current situation are designed under the guidance of operational steps and organizational strategies, and categories and autonomous learning theories are divided, so as to preliminarily construct strategies to improve the ability of autonomous learning. Then through three rounds of iterative action research to improve the flip classroom teaching middle school students’ autonomous learning ability of teaching strategy, the interview method is used; the questionnaire and autonomous learning process to improve the students’ autonomous learning ability training effect evaluation questionnaire is analyzed, and finally a complete set of reverse ascending of classroom teaching is formed to improve students’ autonomous learning ability of effective classroom strategies. (shrink)

A test procedure optimization method was proposed in this paper to improve the test efficiency of the industrial robot servo system to be tested on the IRSS integrated testing platform. First, an ordered sequence was used to define the IRSS test project when tested on the IITP. The ordered sequence consisted of execution subelements, which were a combination of control variable parameters of the IITP. Second, the optimization relationships among the IRSS test projects were dug out according to (...) the IRSS test project sequences. Finally, by using the traveling salesmen problem, an optimization model was established based on the optimization relationships of the IRSS test projects, and the optimal test order of the IRSS test projects to be tested on the IITP was obtained by solving the model. A case analysis showed that the proposed method optimized the test procedure on the IITP effectively, and the test time when implementing the test on the IITP according to the optimized order was nearly 50% shorter than before optimization. The optimization effect was thus found to be significant. (shrink)

With the rapid development of the Internet information age, social networks, mobile Internet, and e-commerce have expanded the scope of Internet applications. The “big data” era is a challenge and chance for companies and has a great impact on social economy, politics, culture, and people’s lives. An accurate marketing system is developed based on J2EE, and the architecture is selected from the user layer, business logic layer, and data layer and the B/S3 layer application, including three layers of crip-dm and (...) semma. And, other process methods are used. Data-mining-based marketing system information solutions consist of several parts, such as requirement analysis, design, implementation, and testing. This paper introduces data mining technology to the marketing business based on the practical use and design IT solutions for precision marketing, attribute selection tools, attribute analysis tools, modeling prediction tools, and others. This paper introduces a precision marketing system based on data mining technology. The system passes the actual test and the deployment and the operation of this system are confirmed. The system, which can improve marketing activity, is tested, and the development and operation of this system markedly increase the company’s earnings. (shrink)

Individual-as-maximizing agent analogies result in a simple understanding of the functioning of the biological world. Identifying the conditions under which individuals can be regarded as fitness maximizing agents is thus of considerable interest to biologists. Here, we compare different concepts of fitness maximization, and discuss within a single framework the relationship between Hamilton’s (J Theor Biol 7:1–16, 1964) model of social interactions, Grafen’s (J Evol Biol 20:1243–1254, 2007a) formal Darwinism project, and the idea of evolutionary stable strategies. We distinguish cases (...) where phenotypic effects are additive separable or not, the latter not being covered by Grafen’s analysis. In both cases it is possible to define a maximand, in the form of an objective function ϕ(z), whose argument is the phenotype of an individual and whose derivative is proportional to Hamilton’s inclusive fitness effect. However, this maximand can be identified with the expression for fecundity or fitness only in the case of additive separable phenotypic effects, making individual-as-maximizing agent analogies unattractive (although formally correct) under general situations of social interactions. We also feel that there is an inconsistency in Grafen’s characterization of the solution of his maximization program by use of inclusive fitness arguments. His results are in conflict with those on evolutionary stable strategies obtained by applying inclusive fitness theory, and can be repaired only by changing the definition of the problem. (shrink)