Effect of Online Brand Community on Customer Behavior Exploration: Reconciling Mixed Findings via Regulatory Focus Theory

This study seeks to address the mixed findings of prior studies regarding the effect of online brand community on customer behavior. Based on the regulatory focus theory, we hypothesize that participation in a brand community tends to increase both visit and purchase frequencies of customers with promotion-focus; on the contrary, the same would typically decrease visit and purchase frequencies of customers with prevention-focus. By analyzing data from an online brand community using a “propensity-score matching” technique, we found a partial validation that attendance of the community led to increases in customer visit frequency for customers with both promotion-focus and prevention-focus. Further, our results show that customers with promotion-focus tend to purchase more; while customers with prevention-focus slightly decreased their purchase volume. Both theoretical and practical implications of our findings are discussed in the paper

Low-voltage ride through of multi-port power electronic transformer

A low-voltage ride-through (LVRT) control strategy for the multi-port power electronic transformer (PET) based on power co-regulation is proposed. During the sag and recovery of the grid-side voltage of the medium-voltage ac (MVac) port, the grid-connected active power of the low-voltage ac (LVac) port, rather than the power from external renewable energy sources (e.g., photovoltaic (PV)), is adjusted quickly to rebalance the power flowing across all ports, thereby preventing overcurrent and overvoltage. Moreover, a power-coordinate-frame-based LVRT mode classification is designed, and a total of six LVRT modes are classified to meet the LVRT requirements in all power configuration scenarios of the PET. In this way, the PET is endowed with the LVRT capability in both power-generation and power-consumption states, which is significantly different from traditional power generation systems such as PV or wind power. Furthermore, by optimizing the active power regulation path during LVRT transition, the overcurrent problem caused by the grid-voltage sag-depth detection delay is overcome. Finally, the effectiveness of the proposed control scheme is verified by experiments on a hardware-in-the-loop platform

On four species of the genus Mistaria Lehtinen, 1967 (Araneae, Agelenidae) from Kenya

In the current study, three species reported from Kenya are transferred from Agelena Walckenaer, 1805 to Mistaria Lehtinen, 1967, i.e. M. fagei (Caporiacco, 1949), comb. n., M. nairobii (Caporiacco, 1949), comb. n. and M. zorica (Strand, 1913), comb. n. One new species M. nyeupenyeusi G.M. Kioko & S. Li, sp. n. is described

Checklist of the spiders (Araneae) of Kenya

A checklist of 805 spider species and subspecies belonging to 57 families described and/or reported from Kenya up to 31 December 2018 is provided. Species distribution within Kenya is given according to counties and specific localities. A historical survey is provided and each record is presented in its original combination. The list is dominated by members of the families Salticidae and Linyphiidae (160 and 110 species, respectively). Eighteen families are represented by a single species. About 300 species are known exclusively from Kenya and 158 species are sub-endemics. Two hundred and forty two species are described from a single sex (159 females and 83 males) and 24 from juveniles. Nairobi County has the greatest number of records, five counties had a frequency of one, while nine counties had no collection records. There are two fossil spiders known from Kenya belonging to the family Oonopidae. One new combination is proposed: Hypsosinga holzapfelae (Lessert, 1936), comb. nov. (ex. Araneus Clerck, 1757).</p

QTL Detection for Kernel Size and Weight in Bread Wheat (Triticum aestivum L.) Using a High-Density SNP and SSR-Based Linkage Map

High-density genetic linkage maps are essential for precise mapping quantitative trait loci (QTL) in wheat (Triticum aestivum L.). In this study, a high-density genetic linkage map consisted of 6312 SNP and SSR markers was developed to identify QTL controlling kernel size and weight, based on a recombinant inbred line (RIL) population derived from the cross of Shixin828 and Kenong2007. Seventy-eight putative QTL for kernel length (KL), kernel width (KW), kernel diameter ratio (KDR), and thousand kernel weight (TKW) were detected over eight environments by inclusive composite interval mapping (ICIM). Of these, six stable QTL were identified in more than four environments, including two for KL (qKL-2D and qKL-6B.2), one for KW (qKW-2D.1), one for KDR (qKDR-2D.1) and two for TKW (qTKW-5A and qTKW-5B.2). Unconditional and multivariable conditional QTL mapping for TKW with respect to TKW component (TKWC) revealed that kernel dimensions played an important role in regulating the kernel weight. Seven QTL-rich genetic regions including seventeen QTL were found on chromosomes 1A (2), 2D, 3A, 4B and 5B (2) exhibiting pleiotropic effects. In particular, clusters on chromosomes 2D and 5B possessing significant QTL for kernel-related traits were highlighted. Markers tightly linked to these QTL or clusters will eventually facilitate further studies for fine mapping, candidate gene discovery and marker-assisted selection (MAS) in wheat breeding

The Bari Manifesto : An interoperability framework for essential biodiversity variables

Essential Biodiversity Variables (EBV) are fundamental variables that can be used for assessing biodiversity change over time, for determining adherence to biodiversity policy, for monitoring progress towards sustainable development goals, and for tracking biodiversity responses to disturbances and management interventions. Data from observations or models that provide measured or estimated EBV values, which we refer to as EBV data products, can help to capture the above processes and trends and can serve as a coherent framework for documenting trends in biodiversity. Using primary biodiversity records and other raw data as sources to produce EBV data products depends on cooperation and interoperability among multiple stakeholders, including those collecting and mobilising data for EBVs and those producing, publishing and preserving EBV data products. Here, we encapsulate ten principles for the current best practice in EBV-focused biodiversity informatics as 'The Bari Manifesto', serving as implementation guidelines for data and research infrastructure providers to support the emerging EBV operational framework based on trans-national and cross-infrastructure scientific workflows. The principles provide guidance on how to contribute towards the production of EBV data products that are globally oriented, while remaining appropriate to the producer's own mission, vision and goals. These ten principles cover: data management planning; data structure; metadata; services; data quality; workflows; provenance; ontologies/vocabularies; data preservation; and accessibility. For each principle, desired outcomes and goals have been formulated. Some specific actions related to fulfilling the Bari Manifesto principles are highlighted in the context of each of four groups of organizations contributing to enabling data interoperability - data standards bodies, research data infrastructures, the pertinent research communities, and funders. The Bari Manifesto provides a roadmap enabling support for routine generation of EBV data products, and increases the likelihood of success for a global EBV framework.Peer reviewe

Off-line evaluation of indoor positioning systems in different scenarios: the experiences from IPIN 2020 competition

Every year, for ten years now, the IPIN competition has aimed at evaluating real-world indoor localisation systems by testing them in a realistic environment, with realistic movement, using the EvAAL framework. The competition provided a unique overview of the state-of-the-art of systems, technologies, and methods for indoor positioning and navigation purposes. Through fair comparison of the performance achieved by each system, the competition was able to identify the most promising approaches and to pinpoint the most critical working conditions. In 2020, the competition included 5 diverse off-site off-site Tracks, each resembling real use cases and challenges for indoor positioning. The results in terms of participation and accuracy of the proposed systems have been encouraging. The best performing competitors obtained a third quartile of error of 1 m for the Smartphone Track and 0.5 m for the Foot-mounted IMU Track. While not running on physical systems, but only as algorithms, these results represent impressive achievements.Track 3 organizers were supported by the European Union’s Horizon 2020 Research and Innovation programme under the Marie Skłodowska Curie Grant 813278 (A-WEAR: A network for dynamic WEarable Applications with pRivacy constraints), MICROCEBUS (MICINN, ref. RTI2018-095168-B-C55, MCIU/AEI/FEDER UE), INSIGNIA (MICINN ref. PTQ2018-009981), and REPNIN+ (MICINN, ref. TEC2017-90808-REDT). We would like to thanks the UJI’s Library managers and employees for their support while collecting the required datasets for Track 3. Track 5 organizers were supported by JST-OPERA Program, Japan, under Grant JPMJOP1612. Track 7 organizers were supported by the Bavarian Ministry for Economic Affairs, Infrastructure, Transport and Technology through the Center for Analytics-Data-Applications (ADA-Center) within the framework of “BAYERN DIGITAL II. ” Team UMinho (Track 3) was supported by FCT—Fundação para a Ciência e Tecnologia within the R&D Units Project Scope under Grant UIDB/00319/2020, and the Ph.D. Fellowship under Grant PD/BD/137401/2018. Team YAI (Track 3) was supported by the Ministry of Science and Technology (MOST) of Taiwan under Grant MOST 109-2221-E-197-026. Team Indora (Track 3) was supported in part by the Slovak Grant Agency, Ministry of Education and Academy of Science, Slovakia, under Grant 1/0177/21, and in part by the Slovak Research and Development Agency under Contract APVV-15-0091. Team TJU (Track 3) was supported in part by the National Natural Science Foundation of China under Grant 61771338 and in part by the Tianjin Research Funding under Grant 18ZXRHSY00190. Team Next-Newbie Reckoners (Track 3) were supported by the Singapore Government through the Industry Alignment Fund—Industry Collaboration Projects Grant. This research was conducted at Singtel Cognitive and Artificial Intelligence Lab for Enterprises (SCALE@NTU), which is a collaboration between Singapore Telecommunications Limited (Singtel) and Nanyang Technological University (NTU). Team KawaguchiLab (Track 5) was supported by JSPS KAKENHI under Grant JP17H01762. Team WHU&AutoNavi (Track 6) was supported by the National Key Research and Development Program of China under Grant 2016YFB0502202. Team YAI (Tracks 6 and 7) was supported by the Ministry of Science and Technology (MOST) of Taiwan under Grant MOST 110-2634-F-155-001