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Journal articles on the topic 'Intelligenza, L''

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Published: 4 June 2021

Last updated: 4 February 2022

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1

Lenzi, Leonardo. "Emozioni intelligenti, intelligenza emozionata: strategie per coltivare se stessi." CHILD DEVELOPMENT & DISABILITIES - SAGGI, no. 2 (January 2011): 77–85. http://dx.doi.org/10.3280/cdd2010-002005.

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Č sempre piů diffusa la consapevolezza che l'attitudine della cura affondi le sue radici nell'essere di chi si prende cura, e non possa basarsi solo sulle competenze che informano il suo fare: per prendersi cura occorre "essere etici". L'etica č qualcosa che ha a che fare con la ragione, con l'intelletto. Se le passioni si conformano alla ragione, esse rendono un'azione ancor piů virtuosa. Ma se le emozioni devono essere intelligenti, l'intelletto puň essere emozionato? Il cuore umano č capace di emozionarsi per tre dimensioni caratteristiche dell'intelletto: la conoscenza, l'etica e l'apertura alla bellezza, a cui, se emozionate, corrispondono altrettante attivitŕ: la realizzazione, la compassione, la contemplazione. L'Autore esamina queste tre dimensioni, concludendo infine che la cura di sé, troppo spesso indirizzata al benessere, altrettanto spesso trascura l'Essere: č quindi necessaria una pedagogia specifica che ci renda capaci di coltivare l'"essere etico" che č in ognuno di noi.

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Wu, Tong. "Design and Application of Intelligent House Robot." Learning & Education 9, no. 3 (December 29, 2020): 49. http://dx.doi.org/10.18282/l-e.v9i3.1572.

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As a family-oriented hexapod bionic intelligent house robot, it takes life safety monitoring, intelligent house control, air quality detection, family security, automatic inspection and obstacle avoidance as its main functions. Combined with core technologies including Face and Radio Frequency Identification, Electromagnetic Resonance and Induction, LAM, Intelligent Navigation and Cloud Robot, the robot is established a unique triangular gait in the form of hexapod, which can maintain the function and action stability in various environments, thus become a family housekeeper with multiful functions and practical intelligence.

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Fontaine, Marion. "L'?il intelligent." Sociétés & Représentations 12, no. 2 (2001): 307. http://dx.doi.org/10.3917/sr.012.0307.

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4

Gallup, Gordon G. "Are we intelligent enough to ask intelligent questions about animal intelligence? Review ofAnimal Intelligence, edited by L. Weiskrantz. Oxford, Clarendon Press, 1985, 223 pp, $49.00." American Journal of Primatology 18, no. 1 (1989): 67–69. http://dx.doi.org/10.1002/ajp.1350180109.

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Hui, Gong. "Artificial Intelligence and the Future of Labour Demand." Learning & Education 9, no. 2 (November 10, 2020): 61. http://dx.doi.org/10.18282/l-e.v9i2.1400.

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Artificial intelligence (AI) is set to influence every aspect of our lives. As a technology platform, AI can automate tasks previously performed by labour or create new tasks and activities in which humans can be productively employed. Recent technological change has been biased towards automation, with insufficient focus on creating new tasks where labour can be productively employed. The consequences of this choice have been stagnating labour demand, declining labour share in national income, rising inequality and lowering productivity growth. The current tendency is to develop AI in the direction of further automation, with better economic and social outcomes.

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Wang, Hongming. "Computer Intelligent Test Paper System Based on Genetic Algorithm." Learning & Education 9, no. 3 (December 29, 2020): 134. http://dx.doi.org/10.18282/l-e.v9i3.1599.

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The rapid development of the Internet has brought tremendous changes to people’s lives. Through the network function, the online examination is gradually accepted by various educational and teaching institutions. Currently, online exams have become the main method of teaching evaluation. In order to solve the problem of intelligent test paper more effectively, this paper proposes a mufti-threaded intelligent test paper strategy based on genetic algorithm, and designs the computer system structure in the standard test question bank. Convergence simulation and experimental results show that the algorithm is better than simple particle swarm optimization algorithm, simple genetic algorithm and its improved algorithm. Established a mathematical model and objective function for test paper composition, and proposed an intelligent test paper composition strategy based on genetic algorithm. The investigator used overall coding, crossover and mutation operations to improve the global optimization capability and convergence speed. It overcomes the phenomenon of premature and improves the accuracy and speed of convergence. It has the advantages of strong optimization ability and good stability.

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7

Rushton, J. Philippe. "Creativity, intelligence, and psychoticism." Personality and Individual Differences 11, no. 12 (January 1990): 1291–98. http://dx.doi.org/10.1016/0191-8869(90)90156-l.

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MacCallum, K. J. "Does intelligent CAD exist?" Artificial Intelligence in Engineering 5, no. 2 (April 1990): 55–64. http://dx.doi.org/10.1016/0954-1810(90)90002-l.

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9

Forest, David. "L’« intelligence collective » : anatomie d’un poncif." Questions de communication, no. 15 (July 1, 2009): 51–65. http://dx.doi.org/10.4000/questionsdecommunication.447.

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10

Littover, Mati. "Artificial intelligence applications in manufacturing." Engineering Applications of Artificial Intelligence 6, no. 5 (October 1993): 485–88. http://dx.doi.org/10.1016/0952-1976(93)90011-l.

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Yang, Andrea. "United States Intelligence: An encyclopedia." Government Publications Review 18, no. 3 (May 1991): 287–88. http://dx.doi.org/10.1016/0277-9390(91)90008-l.

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12

Weiss, Volkmar. "Major genes of general intelligence." Personality and Individual Differences 13, no. 10 (October 1992): 1115–34. http://dx.doi.org/10.1016/0191-8869(92)90026-l.

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13

Neubauer, Aljoscha C. "Selective reaction times and intelligence." Intelligence 14, no. 1 (January 1990): 79–96. http://dx.doi.org/10.1016/0160-2896(90)90015-l.

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Sharratt, Brian. "Intelligent interfaces: The IMPACT survey." Artificial Intelligence in Engineering 6, no. 1 (January 1991): 5–16. http://dx.doi.org/10.1016/0954-1810(91)90010-l.

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15

Rudas, Imre J. "Intelligent Engineering Systems." Journal of Advanced Computational Intelligence and Intelligent Informatics 2, no. 3 (June 20, 1998): 69–71. http://dx.doi.org/10.20965/jaciii.1998.p0069.

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Building intelligent systems has been one of the great challenges since the early days of human culture. From the second half of the 18th century, two revolutionary changes played the key role in technical development, hence in creating engineering and intelligent engineering systems. The industrial revolution was made possible through technical advances, and muscle power was replaced by machine power. The information revolution of our time, in turn, canbe characterized as the replacement of brain power by machine intelligence. The technique used to build engineering systems and replace muscle power can be termed "Hard Automation"1) and deals with industrial processes that are fixed and repetitive in nature. In hard automation, the system configuration and the operations are fixed and cannot be changed without considerable down-time and cost. It can be used, however, particularly in applications calling for fast, accurate operation, when manufacturing large batches of the same product. The "intelligent" area of automation is "Soft Automation," which involves the flexible, intelligent operation of an automated process. In flexible automation, the task is programmable and a work cell must be reconfigured quickly to accommodate a product change. It is particularly suitable for plant environments in which a variety of products is manufactured in small batches. Processes in flexible automation may have unexpected or previously unknown conditions, and would require a certain degree of "machine" intelligence to handle them.The term machine intelligence has been changing with time and is machinespecific, so intelligence in this context still remains more or less a mysterious phenomenon. Following Prof. Lotfi A. Zadeh,2) we consider a system intelligent if it has a high machine intelligence quotient (MIQ). As Prof. Zadeh stated, "MIQ is a measure of intelligence of man-made systems," and can be characterized by its well defined dimensions, such as planning, decision making, problem solving, learning reasoning, natural language understanding, speech recognition, handwriting recognition, pattern recognition, diagnostics, and execution of high level instructions.Engineering practice often involves complex systems having multiple variable and multiple parameter models, sometimes with nonlinear coupling. The conventional approaches for understanding and predicting the behavior of such systems based on analytical techniques can prove to be inadequate, even at the initial stages of setting up an appropriate mathematical model. The computational environment used in such an analytical approach is sometimes too categoric and inflexible in order to cope with the intricacy and complexity of real-world industrial systems. It turns out that, in dealing with such systems, one must face a high degree of uncertainty and tolerate great imprecision. Trying to increase precision can be very costly.In the face of the difficulties above, Prof. Zadeh proposes a different approach for Machine Intelligence. He separates Hard Computing techniques based Artificial Intelligence from Soft Computing techniques based Computational Intelligence.•Hard computing is oriented toward the analysis and design of physical processes and systems, and is characterized by precision, formality, and categorization. It is based on binary logic, crisp systems, numerical analysis, probability theory, differential equations, functional analysis, mathematical programming approximation theory, and crisp software.•Soft computing is oriented toward the analysis and design of intelligent systems. It is based on fuzzy logic, artificial neural networks, and probabilistic reasoning, including genetic algorithms, chaos theory, and parts of machine learning, and is characterized by approximation and dispositionality.In hard computing, imprecision and uncertainty are undesirable properties. In soft computing, the tolerance for imprecision and uncertainty is exploited to achieve an acceptable solution at low cost, tractability, and a high MIQ. Prof. Zadeh argues that soft rather than hard computing should be viewed as the foundation of real machine intelligence. A center has been established - the Berkeley Initiative for Soft Computing (BISC) - and he directs it at the University of California, Berkeley. BISC devotes its activities to this concept.3) Soft computing, as he explains2),•is a consortium of methodologies providing a foundation for the conception and design of intelligent systems,•is aimed at formalizing of the remarkable human ability to make rational decision in an uncertain, imprecise environment.The guiding principle of soft computing, given by Prof. Zadeh2) is: Exploit the tolerance for imprecision, uncertainty, and partial truth to achieve tractability, robustness, low solution cost, and better rapport with reality.Fuzzy logic is mainly concerned with imprecision and approximate reasoning, neurocomputing mainly with learning and curve fitting, genetic computation mainly with searching and optimization and probabilistic reasoning mainly with uncertainty and propagation of belief. The constituents of soft computing are complementary rather than competitive. Experience gained over the past decade indicates that it can be more effective to use them combined, rather than exclusively.Based on this approach, machine intelligence, including artificial intelligence and computational intelligence (soft computing techniques) is one pillar of Intelligent Engineering Systems. Hundreds of new results in this area are published in journals and international conference proceedings. One such conference, organized in Budapest, Hungary, on September 15-17, 1997, was titled'IEEE International Conference on Intelligent Engineering Systems 1997' (INES'97), sponsored by the IEEE Industrial Electronics Society, IEEE Hungary Section, Bá{a}nki Doná{a}t Polytechnic, Hungary, National Committee for Technological Development, Hungary, and in technical cooperation with the IEEE Robotics & Automation Society. It had around 100 participants from 29 countries. This special issue features papers selected from those papers presented during the conference. It should be pointed out that these papers are revised and expanded versions of those presented.The first paper discusses an intelligent control system of an automated guided vehicle used in container terminals. Container terminals, as the center of cargo transportation, play a key role in everyday cargo handling. Learning control has been applied to maintaining the vehicle's course and enabling it to stop at a designatedlocation. Speed control uses conventional control. System performance system was evaluated by simulation, and performance tests slated for a test vehicle.The second paper presents a real-time camera-based system designed for gaze tracking focused on human-computer communication. The objective was to equip computer systems with a tool that provides visual information about the user. The system detects the user's presence, then locates and tracks the face, nose and both eyes. Detection is enabled by combining image processing techniques and pattern recognition.The third paper discusses the application of soft computing techniques to solve modeling and control problems in system engineering. After the design of classical PID and fuzzy PID controllers for nonlinear systems with an approximately known dynamic model, the neural control of a SCARA robot is considered. Fuzzy control is discussed for a special class of MIMO nonlinear systems and the method of Wang generalized for such systems.The next paper describes fuzzy and neural network algorithms for word frequency prediction in document filtering. The two techniques presented are compared and an alternative neural network algoritm discussed.The fifth paper highlights the theory of common-sense knowledge in representation and reasoning. A connectionist model is proposed for common-sense knowledge representation and reasoning, and experimental results using this method presented.The next paper introduces an expert consulting system that employs software agents to manage distributed knowledge sources. These individual software agents solve users' problems either by themselves or thorough mutual cooperation.The last paper presents a methodology for creating and applying a generic manufacturing process model for mechanical parts. Based on the product model and other up-to-date approaches, the proposed model involves all possible manufacturing process variants for a cluster of manufacturing tasks. The application involves a four-level model structure and Petri net representation of manufacturing process entities. Creation and evaluation of model entities and representation of the knowledge built in the shape and manufacturing process models are emphasised. The proposed process model is applied in manufacturing process planning and production scheduling.References:1) C. W. De Silva, "Automation Intelligence," Engineering Application of Artificial Intelligence, 7-5, 471-477, (1994).2) L. A. Zadeh, "Fuzzy Logic, Neural Networks and Soft Computing," NATO Advanced Studies Institute on Soft Computing and Its Application, Antalya, Turkey, (1996).3) L. A. Zadeh, "Berkeley Initiative_in Soft Computing," IEEE Industrial Electronics Society Newsletter. 41-3, 8-10, (1994).

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Billatos, Samir B., and Pai-Chung Tseng. "Knowledge-based optimization for intelligent machining." Journal of Manufacturing Systems 10, no. 6 (January 1991): 464–75. http://dx.doi.org/10.1016/0278-6125(91)90004-l.

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17

LABBÉ, Yves. "Foi et intelligence dans l' 'unique argument'." Revue Philosophique de Louvain 88, no. 3 (August 1, 1990): 345–68. http://dx.doi.org/10.2143/rpl.88.3.556103.

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18

Jing, Shumei. "The Writing of Women’s Awakening Consciousness from the Perspective of Gender Criticism—Take the TV Series “The Little Nyonya” as an Example." Learning & Education 9, no. 3 (December 29, 2020): 145. http://dx.doi.org/10.18282/l-e.v9i3.1601.

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Women’s liberation has always been the focus of gender criticism. Television “The Little Nyonya” from the feminist perspective, with hybrid living space in the form of Baba culture as the background, shaped the survival during the Anti-Japanese War in under cover of male supremacy, especially in the feudal etiquette, social circles to days of Tianlan, Juxiang, and Yueniang has represented the typical female characters, depicting the intelligence and qualities of Nyonya, shows them from swallowing the defense, active resistance to victory break free of this growth process into confusion, explore, growth, struggle and sacrifice. As a hymn reflecting women’s strength, courage, independence and confidence, “The Little Nyonya” has certain enlightening significance for the audience, especially the female audience, to think about their own life choices and pursuits.

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19

Hartwig, Susanne Smith, Gary L. Sapp, and Gypsy Abbott Clayton. "Comparison of the Stanford-Binet Intelligence Scale: Form L-M and the Stanford-Binet Intelligence Scale Fourth Edition." Psychological Reports 60, no. 3_part_2 (June 1987): 1215–18. http://dx.doi.org/10.1177/0033294187060003-243.1.

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Composite standard age scores and the four Area standard age scores of the Standard-Binet Fourth Edition (SBIV) were each compared to the Stanford-Binet Form L-M IQs of 30 11-yr.-old public school children. The Pearson correlation of the total scores was .72, while the correlations of the SBIV Area scores and Form L-M IQs ranged from .81 (Verbal Reasoning vs L-M), .72 (Quantitative Reasoning vs L-M), .47 (Short-term Memory vs L-M) to .40 (Abstract/Visual Reasoning vs L-M). Classification by IQ suggested that both tests tend to categorize individuals similarly, and no sex biases were obtained.

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20

Uma, G., B. E. Prasad, and O. Nalini Kumari. "Distributed intelligent systems: issues, perspectives and approaches." Knowledge-Based Systems 6, no. 2 (June 1993): 77–86. http://dx.doi.org/10.1016/0950-7051(93)90022-l.

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21

HE, MINGFENG, QIU-HUI PAN, and ZHIRUI WANG. "A CELLULAR AUTOMATA MODEL OF LEARNING BASED ON BIT-STRING WITH INTELLIGENCE." International Journal of Modern Physics C 17, no. 05 (May 2006): 677–83. http://dx.doi.org/10.1142/s0129183106008947.

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A cellular automata model of learning in a close ecosystem on the basis of Penna model with intelligence is set up. We use the Penna model with intelligence to describe the individual. The individuals are set in a lattice of L × L and the rules of learning is expressed by cellular automata. Then, we present the results of our simulations and discuss the evolution of intelligence and knowledge respectively. From the results we find that the intelligence for the living individuals is more than that for the dead individuals. It can be concluded the effect of natural selection on the evolution of intelligence.

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Shen, Quanwei, and Yuzhong Wang. "An Exploratory Study on the Matching of Learning Style and Teaching Mode of College Students under the Trend of Personalized Learning." Learning & Education 9, no. 3 (December 29, 2020): 53. http://dx.doi.org/10.18282/l-e.v9i3.1573.

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At present, artificial intelligence, learning analysis,machine learning and otherinformation technologies are transforming education in an all-round way. The deep integration of information technology and education has given birth to a large number of new things, such as Micro-Teaching Assistant APP, Rain Classroom APP, Blue Ink Cloud Class APP, Super Star Learning APP, smart classroom and virtual reality. The “classsystem” and “batch system” teaching mode, which emerged in industrialsociety and lasted for nearly 400 years, have been challenged unprecedeningly. Starting from the general trend of individualized learning, this paper discusses the conflict between the mass teaching mode of “class system” and individualized learning, as well as the dilemma of matching learning style and teaching mode, and puts forward four coping strategies based on this, so as to provide reference for the policy making of education administration department and contribute to the development of education and teaching.

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23

Rudas, Imre J. "Intelligent Engineering Systems." Journal of Advanced Computational Intelligence and Intelligent Informatics 4, no. 4 (July 20, 2000): 237–39. http://dx.doi.org/10.20965/jaciii.2000.p0237.

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The "information revolution" of our time affects our entire generation. While a vision of the "Information Society," with its financial, legal, business, privacy, and other aspects has emerged in the past few years, the "traditional scene" of information technology, that is, industrial automation, maintained its significance as a field of unceasing development. Since the old-fashioned concept of "Hard Automation" applicable only to industrial processes of fixed, repetitive nature and manufacturing large batches of the same product1)was thrust to the background by keen market competition, the key element of this development remained the improvement of "Machine Intelligence". In spite of the fact that L. A. Zadeh already introduced the concept of "Machine Intelligence Quotient" in 1996 to measure machine intelligence2) , this term remained more or less of a mysterious meaning best explicable on the basis of practical needs. The weak point of hard automation is that the system configuration and operations are fixed and cannot be changed without incurring considerable cost and downtime. Mainly it can be used in applications that call for fast and accurate operation in large batch production. Whenever a variety of products must be manufactured in small batches and consequently the work-cells of a production line should be quickly reconfigured to accommodate a change in products, hard automation becomes inefficient and fails due to economic reasons. In these cases, new, more flexible way of automation, so-called "Soft Automation," are expedient and suitable. The most important "ingredient" of soft automation is its adaptive ability for efficiently coping with changing, unexpected or previously unknown conditions, and working with a high degree of uncertainty and imprecision since in practice increasing precision can be very costly. This adaptation must be realized without or within limited human interference: this is one essential component of machine intelligence. Another important factor is that engineering practice often must deal with complex systems of multiple variable and multiple parameter models almost always with strong nonlinear coupling. Conventional analysis-based approaches for describing and predicting the behavior of such systems in many cases are doomed to failure from the outset, even in the phase of the construction of a more or less appropriate mathematical model. These approaches normally are too categorical in the sense that in the name of "modeling accuracy," they try to describe all structural details of the real physical system to be modeled. This significantly increases the intricacy of the model and may result in huge computational burden without considerably improving precision. The best paradigm exemplifying this situation may be the classic perturbation theory: the less significant the achievable correction is, the more work must be invested for obtaining it. Another important component of machine intelligence is a kind of "structural uniformity" giving room and possibility to model arbitrary particular details a priori not specified and unknown. This idea is similar to that of the ready-to-wear industry, whose products can later be slightly modified in contrast to the custom-tailors' made-to-measure creations aiming at maximum accuracy from the beginning. Machines carry out these later corrections automatically. This "learning ability" is another key element of machine intelligence. To realize the above philosophy in a mathematically correct way, L. A. Zadeh separated Hard Computing from Soft Computing. This revelation immediately resulted in distinguishing between two essential complementary branches of machine intelligence: Hard Computing based Artificial Intelligence and Soft Computing based Computational Intelligence. In the last decades, it became generally known that fuzzy logic, artificial neural networks, and probabilistic reasoning based Soft Computing is a fruitful orientation in designing intelligent systems. Moreover, it became generally accepted that soft computing rather than hard computing should be viewed as the foundation of real machine intelligence via exploiting the tolerance for imprecision, uncertainty and partial truth to achieve tractability, robustness, low solution cost and better rapport with reality. Further research in the past decade confirmed the view that typical components of present soft computing such as fuzzy logic, neurocomputing, evolutionary computation and probabilistic reasoning are complementary and best results can be obtained by their combined application. These complementary branches of Machine Intelligence, Artificial Intelligence and Computational Intelligence, serve as the basis of Intelligent Engineering Systems. The huge number of scientific results published in journals and conference proceedings worldwide substantiates this statement. Three years ago, a new series of conferences in this direction was initiated and launched with the support of several organizations including the IEEE Industrial Electronics Society and IEEE Hungary Section in technical cooperation with IEEE Robotics & Automation Society. The first event of the series hosted by Bdnki Dondt Polytechnic, Budapest, Hungary, was called "19997 IEEE International Conference on Intelligent Engineering Systems " (INES'97). The Technical University of Vienna, Austria hosted the next event of the series in 1998, followed by INES'99 held by the Technical University of Kosice, Slovakia. The present special issue consists of the extended and revised version of the most interesting papers selected out of the presentations of this conference. The papers exemplify recent development trends of intelligent engineering systems. The first paper pertains to the wider class of neural network applications. It is an interesting report of applying a special Adaptive Resonance Theory network for identifying objects in multispectral images. It is called "Extended Gaussian ARTMAP". The authors conclude that this network is especially advantageous for classification of large, low dimensional data sets. The second paper's subject belongs to the realm of fuzzy systems. It reports successful application of fundamental similarity relations in diagnostic systems. As an example failure detection of rolling-mill transmission is considered. The next paper represents the AI-branch of machine intelligence. The paper is a report on an EU-funded project focusing on the storage of knowledge in a corporate organizational memory used for storing and retrieving knowledge chunks for it. The flexible structure of the system makes it possible to adopt it to different SMEs via using company-specific conceptual terms rather than traditional keywords. The fourth selected paper's contribution is to the field of knowledge discovery. For this purpose in the first step, cluster analysis is done. The method is found to be helpful whenever little or no information on the characteristics of a given data set is available. The next paper approaches scheduling problems by the application of the multiagent system. It is concluded that due to the great number of interactions between components, MAS seems to be well suited for manufacturing scheduling problems. The sixth selected paper's topic is emerging intelligent technologies in computer-aided engineering. It discusses key issues of CAD/CAM technology of our days. The conclusion is that further development of CAD/CAM methods probably will serve companies on the competitive edge. The seventh paper of the selection is a report on seeking a special tradeoff between classical analytical modeling and traditional soft computing. It nonconventionally integrates uniform structures obtained from Lagrangian Classical Mechanics with other simple elements of machine intelligence such as saturated sigmoid transition functions borrowed from neural nets, and fuzzy rules with classical PID/ST, and a simplified version of regression analysis. It is concluded that these different components can successfully cooperate in adaptive robot control. The last paper focuses on the complexity problem of fuzzy and neural network approaches. A fuzzy rule base, be it generated from expert operators or by some learning or identification schemes, may contain redundant, weakly contributing, or outright inconsistent components. Moreover, in pursuit of good approximation, one may be tempted to overly assign the number of antecedent sets, thereby resulting in large fuzzy rule bases and much problems in computation time and storage space. Engineers using neural networks have to face the same complexity problem with the number of neurons and layers. A fuzzy rule base and neural network design, hence, have two important objectives. One is to achieve a good approximation. The other is to reduce the complexity. The main difficulty is that these two objectives are contradictory. A formal approach to extracting the more pertinent elements of a given rule set or neurons is, hence, highly desirable. The last paper is an attempt in this direction. References 1)C. W. De Silva. Automation Intelligence. Engineering Application of Artificial Intelligence. Vol. 7. No. 5. 471-477 (1994). 2)L. A. Zadeh. Fuzzy Logic, Neural Networks and Soft Computing. NATO Advanced Studies Institute on Soft Computing and Its Application. Antalya, Turkey. (1996). 3)L. A. Zadeh. Berkeley Initiative in Soft Computing. IEEE Industrial Electronics Society Newsletter. 41, (3), 8-10 (1994).

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Lin, Youling, Larry M. Austin, and James R. Burns. "An intelligent algorithm for mixed-integer programming models." Computers & Operations Research 19, no. 6 (August 1992): 461–68. http://dx.doi.org/10.1016/0305-0548(92)90001-l.

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Parisi, Alfio V., and Graham D. Allen. "A fitness analysis system with an intelligent interface." Computers in Biology and Medicine 22, no. 6 (November 1992): 437–41. http://dx.doi.org/10.1016/0010-4825(92)90042-l.

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Shi, Fu-Gui. "L-fuzzy interiors and L-fuzzy closures." Fuzzy Sets and Systems 160, no. 9 (May 2009): 1218–32. http://dx.doi.org/10.1016/j.fss.2008.09.002.

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Yla-Jarkko, K. H., C. Codemard, J. Singleton, P. W. Turner, I. Godfrey, S. U. Alam, J. Nilsson, J. K. Sahu, and A. B. Grudinin. "Low-noise intelligent cladding-pumped L-band EDFA." IEEE Photonics Technology Letters 15, no. 7 (July 2003): 909–11. http://dx.doi.org/10.1109/lpt.2003.813433.

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Huygelier, Hanne, Ruth Van der Hallen, Johan Wagemans, Lee de-Wit, and Rebecca Chamberlain. "The Leuven Embedded Figures Test (L-EFT): measuring perception, intelligence or executive function?" PeerJ 6 (March 26, 2018): e4524. http://dx.doi.org/10.7717/peerj.4524.

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Performance on the Embedded Figures Test (EFT) has been interpreted as a reflection of local/global perceptual style, weak central coherence and/or field independence, as well as a measure of intelligence and executive function. The variable ways in which EFT findings have been interpreted demonstrate that the construct validity of this measure is unclear. In order to address this lack of clarity, we investigated to what extent performance on a new Embedded Figures Test (L-EFT) correlated with measures of intelligence, executive functions and estimates of local/global perceptual styles. In addition, we compared L-EFT performance to the original group EFT to directly contrast both tasks. Taken together, our results indicate that performance on the L-EFT does not correlate strongly with estimates of local/global perceptual style, intelligence or executive functions. Additionally, the results show that performance on the L-EFT is similarly associated with memory span and fluid intelligence as the group EFT. These results suggest that the L-EFT does not reflect a general perceptual or cognitive style/ability. These results further emphasize that empirical data on the construct validity of a task do not always align with the face validity of a task.

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Muratet, G., and P. Bourseau. "Artificial intelligence for process engineering — state of the art." Computers & Chemical Engineering 17 (1993): S381—S388. http://dx.doi.org/10.1016/0098-1354(93)80255-l.

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Marijuan, Pedro C. "Intelligence as adaptive behavior: An experiment in computational neuroethology." Biosystems 24, no. 4 (1991): 321–23. http://dx.doi.org/10.1016/0303-2647(91)90051-l.

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Yao, Wei, and Fu-Gui Shi. "A note on specialization L-preorder of L-topological spaces, L-fuzzifying topological spaces, and L-fuzzy topological spaces." Fuzzy Sets and Systems 159, no. 19 (October 2008): 2586–95. http://dx.doi.org/10.1016/j.fss.2008.03.023.

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Connors, Timothy. "Putting the “L” into Intelligence-Led Policing: How Police Leaders Can Leverage Intelligence Capability." International Journal of Intelligence and CounterIntelligence 22, no. 2 (March 10, 2009): 237–45. http://dx.doi.org/10.1080/08850600802698218.

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Čimoka, Dace, and Alexander Šostak. "L-fuzzy syntopogenous structures, Part I: Fundamentals and application to L-fuzzy topologies, L-fuzzy proximities and L-fuzzy uniformities." Fuzzy Sets and Systems 232 (December 2013): 74–97. http://dx.doi.org/10.1016/j.fss.2013.04.009.

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Aglan, Alya. "Un réseau français de l'«intelligence service»: «Jade-Fitzroy»." Revue d’histoire moderne et contemporaine 40, no. 2 (1993): 289–302. http://dx.doi.org/10.3406/rhmc.1993.2490.

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Dewsbury, Donald A. "Celebrating E. L. Thorndike a century after Animal Intelligence." American Psychologist 53, no. 10 (1998): 1121–24. http://dx.doi.org/10.1037/0003-066x.53.10.1121.

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Huang, Xiaokun, Qingguo Li, and Qimei Xiao. "The L -ordered semigroups based on L -partial orders." Fuzzy Sets and Systems 339 (May 2018): 31–50. http://dx.doi.org/10.1016/j.fss.2017.10.003.

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Shi, Fu-Gui. "L-metric on the space of L-fuzzy numbers." Fuzzy Sets and Systems 399 (November 2020): 95–109. http://dx.doi.org/10.1016/j.fss.2020.03.015.

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Yue, Zhang. "Prime L-fuzzy ideals and primary L-fuzzy ideals." Fuzzy Sets and Systems 27, no. 3 (September 1988): 345–50. http://dx.doi.org/10.1016/0165-0114(88)90060-7.

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Saar, Virpi, Sari Levänen, and Erkki Komulainen. "Cognitive Profiles of Finnish Preschool Children With Expressive and Receptive Language Impairment." Journal of Speech, Language, and Hearing Research 61, no. 2 (February 15, 2018): 386–97. http://dx.doi.org/10.1044/2017_jslhr-l-16-0365.

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Purpose The aim of this study was to compare the verbal and nonverbal cognitive profiles of children with specific language impairment (SLI) with problems predominantly in expressive (SLI-E) or receptive (SLI-R) language skills. These diagnostic subgroups have not been compared before in psychological studies. Method Participants were preschool-age Finnish-speaking children with SLI diagnosed by a multidisciplinary team. Cognitive profile differences between the diagnostic subgroups and the relationship between verbal and nonverbal reasoning skills were evaluated. Results Performance was worse for the SLI-R subgroup than for the SLI-E subgroup not only in verbal reasoning and short-term memory but also in nonverbal reasoning, and several nonverbal subtests correlated significantly with the composite verbal index. However, weaknesses and strengths in the cognitive profiles of the subgroups were parallel. Conclusions Poor verbal comprehension and reasoning skills seem to be associated with lower nonverbal performance in children with SLI. Performance index (Performance Intelligence Quotient) may not always represent the intact nonverbal capacity assumed in SLI diagnostics, and a broader assessment is recommended when a child fails any of the compulsory Performance Intelligence Quotient subtests. Differences between the SLI subgroups appear quantitative rather than qualitative, in line with the new Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM V) classification (American Psychiatric Association, 2013).

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Schoenthaler, S. J., S. P. Amos, H. J. Eysenck, E. Peritz, and J. Yudkin. "Controlled trial of vitamin-mineral supplementation: Effects of intelligence and performance." Personality and Individual Differences 12, no. 4 (January 1991): 351–62. http://dx.doi.org/10.1016/0191-8869(91)90287-l.

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Ghanim, M. H., and H. M. Hasan. "L-closure spaces." Fuzzy Sets and Systems 33, no. 3 (December 1989): 383–91. http://dx.doi.org/10.1016/0165-0114(89)90126-7.

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Chen, Liming, Matthew Wallhead, Heping Zhu, and Amy Fulcher. "Control of Insects and Diseases with Intelligent Variable-rate Sprayers in Ornamental Nurseries1." Journal of Environmental Horticulture 37, no. 3 (September 1, 2019): 90–100. http://dx.doi.org/10.24266/0738-2898-37.3.90.

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Abstract Intelligent spray technology can reduce pesticide use and safeguard the environment; however, its ability to effectively control insects and disease must be validated before its adoption by growers. Comparative tests for two different laser-guided variable-rate intelligent sprayers and the same sprayers with conventional constant-rate mode were conducted to control pests at two ornamental nurseries in two growing seasons in Ohio. Crabapple [Malus ‘Sutyzam’ (Sugar Tyme®), M. sargentii], apple (Malus pumila), maple [Acer ×freemanii ‘Jeffersred' (Autumn Blaze®), A. rubrum ‘Franksred' (Red Sunset®) and A. rubrum], birch (Betula nigra and Betula populifolia ‘Whitespire'), London planetree (Platanus ×acerifolia ‘Bloodgood') and dogwood (Cornus florida) were used as the test plants. Intelligent spray technology reduced pesticide use by 56.1% and 51.8% on average at the two nurseries, respectively. Compared to conventional air-assisted sprayers, severity of scab on apple trees and powdery mildew in dogwood was reduced on intelligent spray-treated plants at one nursery, and there were equal or fewer leafhoppers in maple trees and aphids in birch trees when sprayed using intelligent spray technology at both nurseries. These results suggest that intelligent, variable-rate sprayers achieve equivalent or greater insect and disease control in ornamental tree nurseries compared to conventional, constant-rate sprayers. Index words: aphid, apple scab, environmental protection, leafhopper, precision spray, powdery mildew, sustainable. Species used in this study: apple (Malus pumila Mill), birches (Betula nigra L, Betula populifolia Marsh. ‘Whitespire'), crabapples [Malus ‘Sutyzam' (Sugar Tyme®), M. sargentii Rehder], dogwood (Cornus florida L.), maples [Acer ×freemanii E. Murray ‘Jeffersred' (Autumn Blaze®), A. rubrum L. ‘Franksred'(Red Sunset®) and A. rubrum L.], London planetree [Platanus ×acerifolia (Ait.) Willd. ‘Bloodgood'].

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Ghareeb, A. "L-fuzzy semi-preopen operator in L-fuzzy topological spaces." Neural Computing and Applications 21, S1 (May 19, 2011): 87–92. http://dx.doi.org/10.1007/s00521-011-0642-2.

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Martinek, Pavel. "Completely lattice L-ordered sets with and without L-equality." Fuzzy Sets and Systems 166, no. 1 (March 2011): 44–55. http://dx.doi.org/10.1016/j.fss.2010.11.003.

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Metaj-Macula, Albulena. "The Relationship between Emotional Intelligence and Perceived Social Support." Journal of Educational and Social Research 7, no. 1 (January 26, 2017): 168–72. http://dx.doi.org/10.5901/jesr.2017.v7n1p168.

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Abstract The studty aims at examining and understanding the relation between Emotional Intelligence and Perceived social support in a sample of 525 students of the University of Prishtina. Great number of studies in the field of Emotional Intelligence, have shown that that this new construct of Intelligence, (EI), operates within the social context, therefore the examination of socially relevant variables is crucial for personal and social context. It has been hypothesised that Emotional Intelligence and it’s dimensions correlate positively with the Perceived Social Support and the relation between these two variables is a great predictor of positive interactions, interpersonal relationships and is very relevant for the educational context as well. The study is based on the ability and competency based model of the Emotional Intelligence construct. Emotional Intelligence Scale (Schutte, N. S., Malouff, J. M., Hall, L. E., Haggerty, D. J., Cooper, J. T., Golden, C. J., & Dornheim, L. (1998) and the Multidimensional Scale of Perceived Social Support (Zimet, Dahlem, Zimet & Farely, 1998) were administered to gather data in order to test the hypotheisis. The preliminary findings support theoretical and empirical perspective, and appear to be promising by emphasizing the Emotional Intelligence consutruct as an added value for the educational context, in specific for youth interactions and their wellbeing.

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Li, Sanjiang, and Maokang Luo. "A note on stratified L-real line and unit L-interval." Fuzzy Sets and Systems 147, no. 2 (October 2004): 327–32. http://dx.doi.org/10.1016/j.fss.2004.01.001.

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Zhang, Hua-Peng, and Jin-Xuan Fang. "L-topological vector spaces and families of L-fuzzy pseudo-norms." Fuzzy Sets and Systems 182, no. 1 (November 2011): 13–20. http://dx.doi.org/10.1016/j.fss.2010.04.013.

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Gabriel, Richard P., Tim Finin, and Ron Sun. "David L Waltz, in Memoriam." AI Magazine 33, no. 4 (December 21, 2012): 19. http://dx.doi.org/10.1609/aimag.v33i4.2440.

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Dave Waltz began his career of creativity and collaboration after completing his dissertation in 1972 at the MIT AI Lab. That dissertation created the field of constraint propagation by showing that constraints and a rich but simple descriptive system were sufficient to recover 3-dimensional information from a 2-dimensional projection. Besides an education, Dave picked up a passion for the high-energy atmosphere that propelled the MIT AI Lab to prominence—an atmosphere that he spent the rest of his life recreating

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Blum, Eleni. "Leichtbau mit intelligent wachsenden Gitterstrukturen." VDI-Z 161, no. 06 (2019): 58–59. http://dx.doi.org/10.37544/0042-1766-2019-06-58.

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Der Leichtbau leistet zum Beispiel in der Automobilbranche bereits einen vielversprechenden Beitrag zur Reduzierung von Emissionen und Materialaufwand. „Digitales Material“ wie periodische und/oder stochastische Gitterstrukturen revolutioniert diese Technologie. Dazu zählen Strukturen, die mithilfe von Software generiert und in das Bauteilvolumen integriert werden. Additiv gefertigt mittels „Laser-Powder Bed Fusion“ (L-PBF), sorgen sie für einen robusten Aufbau von Komponenten bei gleichzeitiger Reduktion von Gewicht.

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Zeddini, Besma, Madhi Zargayouna, Moncef Temani, and Adnan Yassine. "De l´intelligence collective pour le transport à la demande." Techniques et sciences informatiques 30, no. 3 (March 28, 2011): 339–60. http://dx.doi.org/10.3166/tsi.30.339-360.

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