A ARTE A PARTIR DO SEU LUGAR: O TRABALHO IN SITU DE DANIEL BUREN E OS ESPA�OS EXPOSITIVOS NOS NOS 19701 / Art from its place: Daniel Buren?s in situ work and exhibition spaces in the 1970?s

Pela an�lise de algumas instala��es realizadas pelo artista franc�s Daniel Buren (1938)�durante a d�cada de 1970, procura-se evidenciar a import�ncia dos locais especializados de exposi��o da arte para a constru��o do sentido da hist�ria da arte contempor�nea. Apesquisa ora apresentada se organiza em torno dos escritos de Daniel Buren e na documenta��o fotogr�fica produzida na ocasi�o de cada uma das interven��es analisadas, centrando-se em tr�s pontos principais: na an�lise da situa��o dos museus de arte europeus que ent�o se abriam para a arte contempor�nea; na atua��o comercial e pr�tica das galerias de vanguarda nos Estados-Unidos e, finalmente, no papel exercido no campo art�stico pelos ?novos museus? que, ao final da d�cada de 1970, se consolidam como espa�os importantes para a anima��o da vida cultural no hemisf�rio Norte.Palavras-chave:Trabalho in situ. Museu. Galeria. Novos museus. D�cada de 1970.�AbstractThrough the analysis of some installations carried out by the French artist Daniel Buren (1938) during the 1970s, we seek to highlight the importance of specialized art exhibition sites for the construction of the meaning of the history of contemporary art. The� research presented here is organized around the writings of Daniel Buren and the photographic documentation produced during each of the analyzed interventions, focusing on three main points: the analysis of the situation of European art museums that were then opening up to the contemporary art; in the commercial and practical performance of avant-garde galleries in the United States and, finally, in the role played in the artistic field by the ?new museums? which, at the end of the 1970s, were consolidated as important spaces for the animation of cultural life in the North hemisphere.Keywords:Work in situ. Museum. Gallery. New museums. 1970s.

Criteria for evaluating lithium–air batteries in academia to correctly predict their practical performance in industry

The criteria for evaluating lithium–air batteries in laboratory-based experiments are proposed for accurately predicting the performance of practical cells in industry.

Robust Learning of Consumer Preferences

When companies develop new products, there are often competing designs from which to choose to take to market. How to decide? Traditional methods, such as focus groups, do not scale to the modern marketplace in which tastes evolve rapidly. In “Robust Learning of Consumer Preferences,” Feng, Caldentey, and Ryan develop a data-driven approach to deciding which design to produce by displaying a sequence of subsets of possible designs to potential customers. Their framework finds solutions that are robust to any model of consumer choice within a broad family that includes common choice models studied in the literature as special cases. Previous research focuses on algorithms whose performances are tied to a given choice model. Their algorithm is shown to be asymptotically optimal in a worst-case sense. The promising practical performance of the algorithm is demonstrated through a comprehensive numerical study using real data.

Development of an experiment-split method for benchmarking the generalization of a PTM site predictor: Lysine methylome as an example

Many computational classifiers have been developed to predict different types of post-translational modification sites. Their performances are measured using cross-validation or independent test, in which experimental data from different sources are mixed and randomly split into training and test sets. However, the self-reported performances of most classifiers based on this measure are generally higher than their performances in the application of new experimental data. It suggests that the cross-validation method overestimates the generalization ability of a classifier. Here, we proposed a generalization estimate method, dubbed experiment-split test, where the experimental sources for the training set are different from those for the test set that simulate the data derived from a new experiment. We took the prediction of lysine methylome (Kme) as an example and developed a deep learning-based Kme site predictor (called DeepKme) with outstanding performance. We assessed the experiment-split test by comparing it with the cross-validation method. We found that the performance measured using the experiment-split test is lower than that measured in terms of cross-validation. As the test data of the experiment-split method were derived from an independent experimental source, this method could reflect the generalization of the predictor. Therefore, we believe that the experiment-split method can be applied to benchmark the practical performance of a given PTM model. DeepKme is free accessible via https://github.com/guoyangzou/DeepKme.

Spectral efficiency analysis of two-user Downlink PD-NOMA with LTE modulation and coding schemes

Non-orthogonal multiple access (NOMA) is a promising user multiplexing technique for future wireless networks that allows increasing their spectral efficiency (SE). Power-Domain NOMA (PD-NOMA) is one of the most perspective techniques in the NOMA group. It makes it possible to perform the transmission of information symbols of several users within the same time-frequency resource segment (RS) without a spreading code. Many research works show the high efficiency of PD-NOMA compared to the orthogonal multiple access (OMA). However, these results are obtained analytically using Shannon’s formula and not taking into account the real performance of existing modulation and coding schemes (MCS). The issue is that it is impossible to obtain the achievable practical performance of PD-NOMA systems in this way. We obtain the SE in RS of a PD-NOMA system with Long Term Evolution (LTE) MCS’s and compare it with OMA. As a result, we conclude that PD-NOMA gains the system SE when the multiplexed user’s signal-to-noise ratio (SNR) outreaches the threshold of the highest performing MCS provided for the transmission by a MCS table.

IMPACT OF JUSTICE IN EMPLOYEE PERFORMANCE AND MODERATING ROLE OF ARTIFICIAL INTELLIGENCE: A CONCEPTUAL STUDY

This study investigates the impact of justice’s dimensions: distributive justice, procedural justice, interactional justice on performance appraisal satisfaction, and artificial intelligence utilisation to improve employee performance in Penang multinational companies (MNCs). They are grounded with Organizational Justice Theory as the theoretical framework to examine the relationship between Artificial Intelligence utilisation in enhancing justice to improve employee performance. In conducting this study, the required data will be collected through a closed-ended structured questionnaire in Penang, Malaysia. The questionnaire was adopted and adapted from many related studies. The data collection approach for this research is based on internet-based technologies such as online platforms. This paper proposes ways of managing employee performance by boosting performance appraisal satisfaction based on a practical performance appraisal that is relevant and unbiased. The paper will provide novelty into justice in performance appraisal through artificial intelligence that is pivotal to the employee performance optimisation in Penang multinational companies. The paper will help organisations recognise the significance of enhancing the performance appraisal as a practical strategic approach to integrate human resource activities with business policies and provide a better understanding of the impact of justice to the performance appraisal in the Penang multinational companies (MNCs).

3D Octave and 2D Vanilla Mixed Convolutional Neural Network for Hyperspectral Image Classification with Limited Samples

Owing to the outstanding feature extraction capability, convolutional neural networks (CNNs) have been widely applied in hyperspectral image (HSI) classification problems and have achieved an impressive performance. However, it is well known that 2D convolution suffers from the absent consideration of spectral information, while 3D convolution requires a huge amount of computational cost. In addition, the cost of labeling and the limitation of computing resources make it urgent to improve the generalization performance of the model with scarcely labeled samples. To relieve these issues, we design an end-to-end 3D octave and 2D vanilla mixed CNN, namely Oct-MCNN-HS, based on the typical 3D-2D mixed CNN (MCNN). It is worth mentioning that two feature fusion operations are deliberately constructed to climb the top of the discriminative features and practical performance. That is, 2D vanilla convolution merges the feature maps generated by 3D octave convolutions along the channel direction, and homology shifting aggregates the information of the pixels locating at the same spatial position. Extensive experiments are conducted on four publicly available HSI datasets to evaluate the effectiveness and robustness of our model, and the results verify the superiority of Oct-MCNN-HS both in efficacy and efficiency.

Relationship of critical speed derived from a 10-minute submaximal treadmill test to 5-km and 10-km running performances

It has been shown that the critical speed (CS) predicted from a perceptually self-regulated 10-min submaximal treadmill test (T10) is reliable and closely matches the CS estimated from conventional methods. To assess the relationship between the T10 and 5-km and 10-km running performances, 36 recreational runners (mean SD: age: 32.2 ± 6.2 years, height: 173.2 ± 7.3 cm, weight: 70.9 ± 8.8 kg, V̇O2max: 53.3 ± 6.1 mL.kg-1.min-1) performed a ramp incremental test and two T10 tests (the first as a familiarization trial). Results showed that the T10 CS (3.9 ± 0.44 m.s-1) was significantly correlated with runners’ last 6 months best performances in 5-km (20.3 ± 2.7 min; r = -0.90) and 10-km (42.7 ± 5.7 min; r = -0.91), the V̇O2max (r = 0.75), the speed associated with the gas exchange threshold (vGET: 3.38 ± 0.36 m.s-1; r = 0.76), the speed associated with the second ventilatory threshold (vVT2: 4.15 ± 0.49 m.s-1; r = 0.84), and the speed associated with the V̇O2max (vV̇O2max: 4.78 ± 0.54 m.s-1; r = 0.87). Moreover, 79% and 83% of the variance in 5-km and 10-km performances could be explained solely by the CS predicted from the T10. Results evidenced the strong relationship and practical performance relevance of the T10 CS test. Novelty: • Critical speed derived from a 10-min submaximal treadmill test (T10) is significantly correlated with 5-km and 10-km running performances • The T10 critical speed test may represent a useful tool for assessing running performance capabilities

Examining the Effectiveness of Using Adaptive AI-Enabled e-Learning during the Pandemic of COVID-19

This study aimed to identify the effect of using adaptive AI-enabled e-learning on developing digital content creative design skills among postgraduate students. The research tools included an achievement test and an observation checklist for rating the practical performance. Research results concluded that, regardless of learning styles, the proposed adaptive e-learning environment had a positive effect on developing both cognitive achievement and practical performance of digital content creative design skills. The results also indicated that there is a significant difference at the 0.01 level between the mean scores of the first experimental group’s students using the global learning style-based adaptive e-learning environment and the second experimental group’s students using the sequential learning style-based adaptive AI-enabled e-learning environment in the achievement test and observation checklist after measurement of digital content creative design skills in favor of the second experimental group’s students. The study provided a number of suggestions and recommendations for making the utmost use of various design layouts of adaptive AI-enabled e-learning environments in developing different cognitive and performance aspects of learning as well as taking full advantage of digital content creative design skills mastery in producing a plethora of advanced electronic educational applications in the foreseeable future.