Cognitive Modeling and its Applications in Industry:

With the rapid development of artificial intelligence technology and educational information, cognitive computing has become a new direction of the development of The Times. At the same time, problem solving ability as the advanced wisdom skills of students' learning results, is the comprehensive embodiment of students' ability. The study of cognitive computing in the process of human problem solving will provide a path for computer combined with artificial intelligence technology to simulate peers to realize human assistance, understanding and decision-making, and also provide guidance and supervisor for personalized teaching in the process of education. This study is based on the SOAR model, from the perspective of learners cognitive problem solving process simulation to get the educational cognitive model, and specific problem solving as an example, analysis of the cognitive behavior process of the subject and the model of the internal elements of the correlation, so as to verify the accuracy of the model overall.

Bridging the gap between theory and practice of approximate Bayesian inference

Many artificial intelligence (AI) theoretical models are inspired by human/natural/social intelligence mechanisms. Three main schools of artificial intelligence have been formed, that is, symbolism, connectionism and behaviorism. Cognitive computing is one of the key fields of AI. It is a critical task for AI researchers to develop advanced cognitive computing models. Cognitive computing is the third and the most transformational phase in computing’s evolution, after the Tabulating Era and Programming Era. Inspired by human’s granularity thinking based problem solving mechanism and the cognition law of “global precedence”, a data-driven granular cognitive computing model (DGCC) is proposed in this paper. It integrates two contradictory mechanisms, namely, human’s cognition mechanism of “global precedence” which is a cognition process of “from coarser to finer” and the information processing mechanism of machine learning systems which is “from finer to coarser”. According to DGCC, deep learning is taken as a combination of symbolism and connectionism, and named hierarchical structuralism in this paper.

Wild mushrooms have become an integral part of human diet owing to their pleasant organoleptic, nutraceutical and medicinal significance. Six wild mushrooms occurring in the foot-hills of the Western Ghats are endowed with phenolics, tannins, phytic acid, flavonoids, vitamins and pigments. They showed meagre trypsin inhibition activity and two mushrooms (Lentinus polychrous and Termitomyces schimperi) were devoid of haemagglutinin activity. The GC-MS analysis revealed terpenes, alcohols, fatty acids, amines, sterols and other components. The T. schimperi consist of a highest number of bioactive components (33) followed by A. hemibapha (31), Boletus edulis (30), Hygrocybe alwisii (25), L. polychrous (20) and L. squarrosulus (17). Many compounds in these mushrooms are valuable in cancer prevention, health-promotion, pharmacological significance (e.g. analgesic, sedatives, hallucinogen, antiangiogenic and antioxidant), food industry (emulsifiers, flavours, enzyme inhibition, fragrance, precursors and detoxicants), cosmetics (antiseptic, anti-microbial and anti-viral), other environmental and industrial applications (anti-fouling, nematicide, lubricant and perfumes). Many components of the wild mushrooms assessed are valuable in food industries, cosmetics production, environmental and industrial applications. Qualitative and quantitative profile of bioactive principles of wild mushrooms are dependent on the geographic location, forest type, soil condition, substrate availability and influence of wild fauna. There is immense scope to study the precise pharmaceutical compounds present in the ethnically-valued wild mushrooms and their retention, employing appropriate methods of processing.

Amylases are widely distributed and are one of the most studied enzymes. Such enzymes hydrolyze the starch molecules into polymers composed of glucose units. Amylases have potential application in a wide number of industrial processes such as food, fermentation and pharmaceutical industries. Amylases can be obtained from plants, animals and microorganisms. However, enzymes from fungal and bacterial sources have dominated applications in industrial sectors. The microbial source of amylase is preferred to other sources because of its plasticity and vast availability. The production of α-amylase is essential for conversion of starches into oligosaccharides. Starch is an important constituent of the human diet and is a major storage product of many economically important crops such as wheat, rice, maize, tapioca, and potato. The properties of each α-amylase such as thermostability, pH profile, pH stability, and Ca-independency are important in the development of fermentation process. This review focuses on the isolation, substrates of α-amylases, production of bacterial and fungal α-amylases, properties of α-amylases, and the use of these enzymes in industrial applications.

What counts as a philosophical issue in computational cognitive science? This chapter briefly reviews possible answers before focusing on a specific subset of philosophical issues. These surround challenges that have been raised by philosophers regarding the scope of computational models of cognition. The arguments suggest that there are aspects of human cognition that may, for various reasons, resist explanation or description in terms of computation. The primary targets of these “no go” arguments have been semantic content, phenomenal consciousness, and central reasoning. This chapter reviews the arguments and considers possible replies. It concludes by highlighting the differences between the arguments, their limitations, and how they might contribute to the wider project of estimating the value of ongoing research programs in computational cognitive science.

The lack of conceptual analysis within cognitive science results in multiple models of the same phenomena. However, these models incorporate assumptions that contradict basic structural features of the domain they are describing. This is particularly true about the domain of mathematical cognition. In this paper we argue that foundational theoretic aspects of psychological models for language and arithmetic should be clarified before postulating such models. We propose a means to clarify these foundational concepts by analyzing the distinctions between metric and linguistic compositionality, which we use to assess current models of mathematical cognition. Our proposal is consistent with the scientific methodology that determines that careful conceptual analysis should precede theoretical descriptions of data. Scientific theories must not only be true, but also coherent and systematic. Part of their being coherent and systematic rests on the conceptual clarity of the most fundamental terms that these theories explain. This means that scientific theories are not only determined by experimentation and quantification, but also by conceptual analysis. Actually, it is conceptual clarity that distinguishes a set of random facts from a systematic scientific explanation. Cognitive psychology has produced an impressive corpus of data. However, few attempts are made to clarify the meaning of the concepts that are being used in interpreting these data, whether the use of concepts in different theories is consistent and how these concepts relate to one another. One of the central theoretical issues in psychology is the nature of measurement in psychological experiments and its relation to cognitive capacities. Some of the most influential psychologists have addressed this issue directly by defining mapping relations between mental content and units of measurement. For instance, Stevens (1951) proposed different scales (ex: nominal, ordinal, interval, ratio) and distinguished them in terms of their formal properties. A fascinating aspect of the \\server05\productn\T\THE\27-1\THE107.txt unknown Seq: 2 4-JAN-08 8:01 54 Journal of Theoretical and Philosophical Psy. Vol. 27, No. 1, 2007 topic of measurement in psychology is that certain cognitive computations appear to preserve metric and scale structure, while others remain qualitative and are not metrically structured. Recently, mathematical, temporal and spatial cognition gained interest in different areas of psychology, and it is certainly a fundamental concern in cognitive science. However, the models that have been proposed to account for mathematical cognition and the representation of quantities reveal serious conceptual confusion, a situation that could be alleviated by revisiting the fundamental assumptions of the theory of measurement. For instance, these models differ with respect to their approach to the acquisition of numerical concepts and the representational structure underlying discrete quantities. According to one model, the content of discrete quantities is determined by linguistic representations in the course of psycholinguistic development (Carey, 2004). However, other models favor a visual-attentional mechanism that automatically picks out the discrete quantities provided that there is a limit to the size of the perceived set, independently of linguistic representations (Xu & Spelke, 2000). These two models assume that the representation of small and large quantities has different formats and are processed differently in the mind. This assumption has specific predictions concerning the psychophysical properties of discrete quantity representations and it is another dimension in which these models of mathematical cognition differ. Another model, which differs from the visual-attentional and psycholinguistic accounts, assumes a common metric underlying the numerical representations of all sizes. This model assumes that all numbers are represented in the same format (on the same mental continuum) and differs from the previous models with respect to the development of mathematical cognition. For instance, according to the ‘common metric’ account, the innate structure of magnitude based representations (Gallistel and Gelman, 1992) allows cognitive agents to represent the numerical attributes of the physical world with no functional dependence on their linguistic or visual-perceptual capacities. As a consequence of the linguistic independence postulated by this account, the ‘common metric’ model generalizes to mathematical cognition processes in human and non-human animals. But even within the models that propose a common metric, there are divergences concerning the type of mapping between the physical and mental quantities. Some theorists (Dehaene, 2001) assume a logarithmic mapping with constant variability, while others (Gallistel & Gelman, 1992) assume a linear mapping with scalar variability. These views also have different predictions about the psychophysical properties of the discrete quantities. In this paper, we evaluated the assumptions of these models by critically assessing their implications with respect to the theory of measure\\server05\productn\T\THE\27-1\THE107.txt unknown Seq: 3 4-JAN-08 8:01

Causal inference is one of the central capabilities of the natural intelligence that plays a crucial role in thought, perception, reasoning, and problem solving. This paper presents a set of cognitive models for causation analyses and causal inferences. The taxonomy and mathematical models of causations are created. The framework and properties of causal inferences are elaborated. Methodologies for uncertain causal inferences are discussed. The formalization of causal inference methodologies enables machines to mimic complex human reasoning mechanisms in cognitive informatics, cognitive computing, and computational intelligence.

Artificial Intelligence in the American Healthcare Industry: Looking Forward to 2030

The promising development of hydrogen and fuel cell technologies has garnered increased attention in recent years, assuming a significant role in industrial applications and the decarbonisation of the shipping industry. Given that the shipping industry generates considerable greenhouse gas emissions, it is crucial and imperative to implement integrated solutions based on clean energy sources, thereby meeting the proposed climate objectives. This study presents the standard hydrogen production, storage, and transport methods and analysis technologies that use hydrogen fuel cells in marine and industrial applications. Technologies based on hydrogen fuel cells and hybrid systems will have an increased perspective of application in industry and maritime transport under the conditions of optimising technological models, developing the hydrogen industrial chain, and updating standards and regulations in the field. However, there are still many shortcomings. The paper’s main contribution is analysing the hydrogen industrial chain, presenting the progress and obstacles associated with the technologies used in industrial and marine applications based on hydrogen energy.

Recent findings and future directions of grafted gum karaya polysaccharides and their various applications: A review

Both human brain and computer (electronic brain) could process data and do some cognition and computation tasks. Is the cognition of human brain equal to the computation of computer? It is obviously not. In this talk, the relationships of brain cognition models and intelligence computation models are summarized into four different types, that is, human brain cognition inspired intelligence computation (BCIIC), intelligence computation without human brain cognition (ICOBC), intelligence computation assisted human brain cognition (ICABC), and the integration of human brain cognition and intelligence computation (BC&IC). There are three paradigms in traditional artificial intelligence (AI) studies, that is, symbolism AI, connectionism AI, and behaviorism AI. The physical symbol system hypothesis is used in the symbolism AI. Human brain cognition is taken as a kind of symbolic processing, and the processes of human thinking are computed by symbol in the symbolism AI [1,2]. The connectionism AI relies on the bionics to simulate human brain. In the connectionism AI, neuron is taken as the basic unite of human thinking, and the intelligence is taken as the result of interconnected neurons competition and collaboration [3,4]. In the behaviorism AI, intelligence depends on the perception and behavior, “Perception-action” model is used, and intelligence may not require knowledge, knowledge representation and knowledge reasoning [5]. The symbolism AI and connectionism AI are two different types of human brain cognition inspired intelligence computation, while the behaviorism AI is a representative case of intelligence computation without human brain cognition. Usually, AI researchers get some inspiration from human brain cognition in their studies. On the other way, intelligence computation could also assist human brain cognition studies. The bidirectional cognitive computing model (BCC) is such a case. It studies the bidirectional transformations between the intension and extension of a concept. It is used to simulate some human brain cognition tasks such as learning and recognition [6,7]. Cognitive computing is one of the core fields of artificial intelligence [8,9]. Data-driven granular cognitive computing (DGCC) is an example of the integration of human brain cognition and intelligence computation [10,11]. It takes data as a special kind of knowledge expressed in the lowest granularity level of a multiple granularity space. It integrates two contradictory mechanisms, namely, the human’s cognition mechanism of ‘‘global precedence’’ which is a cognition process of ‘‘from coarser to finer’’ and the information processing mechanism of machine learning systems which is ‘‘from finer to coarser’’, in a multiple granularity space. It is data-driven cognitive computing model. The integration of human brain cognition and intelligence computation would be an important research topic of artificial intelligence. Some scientific research issues of the integration of human brain cognition and intelligence computation are discussed based on DGCC.

Banana fibers are cost-effective, abundantly available and environmentally sustainable, but they often have some drawbacks, such as hydrophilicity and performance variations. The experimental approach is intended to be used in this work to examine the effects of cold glow discharge argon plasma modification on the surface of banana fibers. The emphasis is on examining the mechanisms that underlie the morphological and chemical changes observed in banana fibers, as well as identifying the mechanisms that enhance the fiber–matrix interface and, as a consequence, the mechanical properties of the laminates. Significant improvements were observed for epoxy matrix laminates reinforced with banana fibers when the surface of the banana fibers was modified by plasma of argon gas (Ar) gas with varying power densities of 80,120 W for 30 min, which changed the surface chemical characteristics. The FTIR and XRD investigations revealed that plasma treatment imparted active functional groups onto the surface of the banana fiber, establishing chemical interactions with the epoxy resin. As a result, banana fibers and modified BFREC composites can be used to manufacture interior panels, headliners, seatbacks, dashboards, automobile industry, and industrial applications in order to nurture self-sustaining biodegradable natural resources.

The debate about relational resources and their influences has followed an internal focus on organizations, and in the literature this has been shown to have a high influence on the ways of the resource-based view. This article aims to analyze the development of operational competencies following the focus of the relational view from the interaction of relational resources shared in supply chains. The research was guided by the strategy of multiple case studies in the steel industry, automotive and industrial applications, pulp production, and engineering for the energy industry. The results of the analysis of the four cases show that deeper relationships in the form of partnerships enable a process of investing in specific assets, knowledge accumulation, learning exchange, and a combination of complementary strategic resources that develop relational operational competencies. And, these competencies that represent the company's ability to promote a skill set to use resources efficiently and to provide a barrier to imitation are further developed by the information and knowledge constructs.

Tri-n-butyl phosphate (TBP) is an excellent industrial solvent for metal extraction in the nuclear chemical industry. It is also being used as a major constituent of hydraulic fluids in the aircraft industry and other industrial applications. Risk assessment studies demand limiting of airborne concentration of TBP during normal as well as accidental exposure to humans, as the toxicity index for TBP is quite high. Published data on TBP vapor pressure has been analyzed, and an empirical equation is reported for the temperature range of 273.15 to 562.15 K. The performance of the reported equation is compared with the correlations available in the literature.