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International audience; In this paper, the feasibility of satellite remote sensing in detecting and predicting locations of buried objects in the archaeological site of Saruq Al-Hadid, United Arab Emirates (UAE) was investigated. Satellite-borne synthetic aperture radar (SAR) is proposed as the main technology for this initial investigation. In fact, SAR is the only satellite-based technology able to detect buried artefacts from space, and it is expected that fine-resolution images of ALOS/PALSAR-2 (L-band SAR) would be able to detect large features (>1 m) that might be buried in the subsurface (<2 m) under optimum conditions, i.e., dry and bare soil. SAR data were complemented with very high-resolution Worldview-3 multispectral images (0.31 m panchromatic, 1.24 m VNIR) to obtain a visual assessment of the study area and its land cover features. An integrated approach, featuring the application of advanced image processing techniques and geospatial analysis using machine learning, was adopted to characterise the site while automating the process and investigating its applicability. Results from SAR feature extraction and geospatial analyses showed detection of the areas on the site that were already under excavation and predicted new, hitherto unexplored archaeological areas. The validation of these results was performed using previous archaeological works as well as geological and geomorphological field surveys. The modelling and prediction accuracies are expected to improve with the insertion of a neural network and backpropagation algorithms based on the performed cluster groups following more recent field surveys. The validated results can provide guidance for future on-site archaeological work. The pilot process developed in this work can therefore be applied to similar arid environments for the detection of archaeological features and guidance of on-site investigations

AbstractEssential oils (EOs) are increasingly used as biopesticides due to their insecticidal potential. This study addresses their non-target effects on a biological control agent: the egg parasitoidTrichogramma evanescens. In particular, we tested whether EOs affected parasitoid fitness either directly, by decreasing pre-imaginal survival, or indirectly, by disrupting parasitoids’ orientation abilities. The effect of Anise, Fennel, Sweet orange, Basil, Coriander, Oregano, Peppermint, Mugwort, Rosemary and Thyme EOs were studied on five strains ofT. evanescens. Specific experimental setups were developed, and data obtained from image analysis were interpreted with phenomenological models fitted with Bayesian inference. Results highlight the fumigant toxicity of EOs on parasitoid development. Anise, Fennel, Basil, Coriander, Oregano, Peppermint and Thyme EOs are particularly toxic and drastically reduce the emergence rate ofT. evanescens. Most EOs also affect parasitoid behavior: (i) Basil, Coriander, Oregano, Peppermint, Mugwort and Thyme EOs are highly repellent for naive female parasitoids; (ii) Anise and Fennel EOs can have repellent or attractive effects depending on strains; and (iii) Sweet orange, Oregano and Rosemary EOs have no detectable impact on orientation behavior. This study shows that EOs fumigation have non-target effects on egg parasitoids. This highlights the need to cautiously precise the deployment framework of biopesticides in an agroecological perspective.

AbstractNew results from fieldwork carried out on Failaka island testify to relative sea‐level fluctuations during Late Pleistocene and Holocene. This study is based on analysing, sampling and dating geomorphological sea‐level indicators, including beach ridges or beachrocks. The results have been compared and harmonised with recalibrated data from pre‐existing literature to reconstruct the relative sea‐level local chronology. The vertical displacement identified may have induced coastline mobility and significant environmental changes and impacted the human occupation in Failaka and along the coastal areas of Kuwait. Eventually, the results highlight the impact of large‐scale crustal deformations on regional relative sea‐level variations.

International audience; Some users try to post false reviews to promote or to devalue other's products and services. This action is known as deceptive opinions spam, where spammers try to gain or to profit from posting untruthful reviews. Therefore, we conducted this work to develop and to implement new semantic features to improve the Arabic deception detection. These features were inspired from the study of discourse parse and the rhetoric relations in Arabic. Looking to the importance of the phrase unit in the Arabic language and the grammatical studies, we have analyzed and selected the most used unit markers and relations to calculate the proposed features. These last were used basically to represent the reviews texts in the classification phase. Thus, the most accurate classification technique used in this area which has been proven by several previous works is the Support Vector Machine classifier (SVM). But there is always a lack concerning the Arabic annotated resources specially for deception detection area as it is considered new research area. Therefore, we used the semi supervised SVM to overcome this problem by using the unlabeled data.

To identify how blockchain technology affects current and future research, we carried out a bibliometric overview of the journal articles written on the blockchain. We aimed to answer some of the questions to visualize the trend of the publications regarding the advancement of blockchain utilization in fields relating to finance, economics, and social science. We used the Scopus database for the literature research, which resulted in 506 papers by 1278 authors from 79 countries. Our study showed that from 2008 till 2021, publishing about blockchain was more significant in conference papers than articles by a factor of 2. Our study also showed the importance of citation regarding published academic articles, the link to the number of publications, the authors, the universities, the affiliated organizations, and the countries of the publications. The use of authoring and citation analysis give valuable insights. On the topic of blockchain, we identified Financial Innovation as the most impactful journal on the topic, the National Natural Science Foundation of China as the leading funding sponsor on blockchain research, the USA as the highest publication production country, and Hong Kong as the highest country in the average citation per document produced. We finally identified the 20 most cited articles on the blockchain topics. Unlike other brief bibliometric studies, our study's investigation and findings could become a first stage of learning for those interested in carrying out a bibliometric study. In addition, our study could become the starting point for any future research on any blockchain projects.

Women are underrepresented in business, academic, and political decision-making bodies across the world. To investigate the causal effect of gender representation on multilateral negotiations, we experimentally manipulate the composition of triads in a majoritarian, divide-the-dollar game. We document a robust gender gap in earnings driven largely by the exclusion of women from alliances rather than differential shares within alliances. Experiments with different subject pools show that distinct bargaining dynamics can underlie the same inequitable outcomes; gender-biased outcomes can be caused by outright discrimination, but they can also be driven by more complex dynamics related to differences in bargaining strategies. Although replacing the male with a female majority all but eliminates the gap in one pool, it has minimal effect in the other. These findings show that there is no “one-size-fits-all” solution to the gender gap we uncovered and highlight the importance of studying bargaining dynamics in detail. This paper was accepted by Marie-Claire Villeval, behavioral economics and decision analysis. Funding: A. Baranski recognizes financial support from Tamkeen [under the New York University Abu Dhabi Research Institute; Grant CG005]. J. Tremewan acknowledges funding from the Agence Nationale de la Recherche [Grant ANR-21-CE03-0018 (Project ENDURA)]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.01800 .

Citizenship by investment (CBI) programs have recently garnered significant academic and media attention. Turkey introduced such a program in 2017 that offers citizenship in exchange for investment in residential property. Eventually, thousands of foreigners, mainly from the Middle East and Asia have purchased houses, particularly in Istanbul. Foreigners' share in total houses sold in Istanbul almost sextupled and exceeded 10 percent of total sales. This study estimates the short-run impact of relatively wealthy foreigners on the residential property prices in Istanbul to buy a Turkish passport. It finds that the Turkish CBI program positively impacts house prices by two percent in the districts, which are likely to be favored most by immigrant investors.

Part 3: Human-Centered AI; International audience; Personas are often created based on user interviews. Yet, researchers rarely make their interview questions publicly available or justify how they were chosen. We manually extract 276 interview questions and categorize them into 10 themes, making this list publicly available for researchers and practitioners. We also demonstrate an approach of using natural language processing to assist in selecting persona interview questions for a given use case.