Life Tables of Bactrocera cucurbitae (Coquillett) (Diptera: Tephritidae): with a Mathematical Invalidation for Applying the Jackknife Technique to the Net Reproductive Rate

Life table data for the melon fly, Bactrocera cucurbitae (Coquillett), reared on cucumber (Cucumis sativus L.) were collected under laboratory and simulated field conditions. Means and standard errors of life table parameters were estimated for two replicates using the jackknife technique. At 25ºC, the intrinsic rates of increase (_r_) found for the two replicates were 0.1354 and 0.1002 day-1, and the net reproductive rates (_R_~0~) were 206.3 and 66.0 offspring, respectively. When the cucumbers kept under simulated field conditions were covered with leaves, the _r_ and _R_~0~ for the two replicates were 0.0935 and 0.0909 day-1, 17.5 and 11.4 offspring, respectively. However, when similar cucumbers were left uncovered, the _r_ and _R_~0~ for the two replicates were 0.1043 and 0.0904 day-1, and 27.7 and 10.1 offspring, respectively. Our results revealed that considerable variability between replicates in both laboratory and field conditions is possible; this variability should be taken into consideration in data collection and application of life tables. Mathematical analysis has demonstrated that applying the jackknife technique results in unrealistic pseudo-_R_~0~ and overestimation of its variance. We suggest that the jackknife technique should not be used for the estimation of variability of _R_~0~

The impact of artificial intelligence on sustainable corporate brand:a netnography study of tesla

Abstract. The global market has become ever more turbulent due to digitalisation and digital transformation. Artificial Intelligence (AI) plays a central role in moving forward the advance of technology. AI has become an important research field in marketing while various companies have successfully implemented AI technologies to meet customers’ needs. However, the impacts of AI on brands have not been widely explored in both scientific and managerial aspects. Brands generate values for businesses by providing functional and non-functional benefits that can be contributed by implementing AI technologies. Mainly, developing sustainability is crucial to address stakeholders’ concerns for today’s brands. The sustainable corporate brand can be a solution to this market demand as its promise has sustainability as a core value. Through exploring this phenomenon, the thesis answers the research question: to what extent does AI contribute positive impacts on sustainable corporate brands in the electric autonomous vehicle (EAVs) sector? The EAVs industry, represented by the case company, Tesla, is chosen for conducting this research because it integrates the variants of electric vehicles that provide environmental benefits and the autonomous cars that use AI technologies. The study is performed using the qualitative research method of netnography. The data are collected from the publicly available information on Twitter and Youtube based on their relevance to the research question. One hundred sixty tweets and thirteen Youtube videos are extracted in textual form and analysed following the guidelines of thematic analysis and triangulated with multiple sources of data. The key results of the research suggest the unique characteristics of the three AI features, machine learning, natural language processing (NLP) and Big Data analytics, help create the normative emotions and efficacy in the mind of stakeholders. These norms of emotions and efficacy further motivate stakeholders’ normative actions that, in return, enhance the normative emotions and efficacy in a loop. Five elements represent the values AI technologies contribute to brand promise through creating a unique experience for the stakeholders that differentiate the brand from its competitors. The refreshed excitements and trust are brought by machine learning technologies. The fun and human characteristics and safety are brought by NLP technologies. Technology superiority is made possible through Big Data analytics. Four elements act for the values conveyed by AI technologies that enrich and expand the brand identity. NLP features can effectively enhance the connections between the focal brand and the other brand associations: the CEO, the affiliate brands and meaningful cultural references. The shared ownership of the brand is intensified through the co-creation of Big Data analytics. By contributing to brand promise and brand identity, AI implementation helps foster positive impacts in building an authentic, emotionally charged, and behaviourally based sustainable corporate brand

A perception and manipulation system for collecting rock samples

An important part of a planetary exploration mission is to collect and analyze surface samples. As part of the Carnegie Mellon University Ambler Project, researchers are investigating techniques for collecting samples using a robot arm and a range sensor. The aim of this work is to make the sample collection operation fully autonomous. Described here are the components of the experimental system, including a perception module that extracts objects of interest from range images and produces models of their shapes, and a manipulation module that enables the system to pick up the objects identified by the perception module. The system was tested on a small testbed using natural terrain

Measuring the delay time distribution of binary neutron stars. II. Using the redshift distribution from third-generation gravitational wave detectors network

We investigate the ability of current and third-generation gravitational wave (GW) detectors to determine the delay time distribution (DTD) of binary neutron stars (BNS) through a direct measurement of the BNS merger rate as a function of redshift. We assume that the DTD follows a power law distribution with a slope $\Gamma$ and a minimum merger time $t_{\rm min}$, and also allow the overall BNS formation efficiency per unit stellar mass to vary. By convolving the DTD and mass efficiency with the cosmic star formation history, and then with the GW detector capabilities, we explore two relevant regimes. First, for the current generation of GW detectors, which are only sensitive to the local universe, but can lead to precise redshift determinations via the identification of electromagnetic counterparts and host galaxies, we show that the DTD parameters are strongly degenerate with the unknown mass efficiency and therefore cannot be determined uniquely. Second, for third-generation detectors such as Einstein Telescope (ET) and Cosmic Explorer (CE), which will detect BNS mergers at cosmological distances, but with a redshift uncertainty inherent to GW-only detections ($\delta(z)/z\approx 0.1z$), we show that the DTD and mass efficiency can be well-constrained to better than 10\% with a year of observations. This long-term approach to determining the DTD through a direct mapping of the BNS merger redshift distribution will be supplemented by more near term studies of the DTD through the properties of BNS merger host galaxies at $z\approx 0$ (Safarzadeh & Berger 2019).Comment: 10 pages, Accepted to ApJ Letter

Adopting IoT Technology to Optimize Intelligent Water Management

Intelligent water management (IWM) has been used to study the supply and demand of tap water in Taiwan. This research aims to enhance existing and future water utility management. Leveraging the supervisory control and data acquisition (SCADA) technology that connects sensors to a distributive infrastructure, the system detects leaks, assesses quality, monitors discharge, and manages assets of water utility. In this paper, we propose a prototype of urban intelligent water system by installing an intelligent water meter. Three steps are undertaken to demonstrate the IWM: 1) choose the way of data transmission; 2) establish communication equipment and generate cloud database; and 3) apply big data analyses and value-added applications. By intelligently managing the water supply system, it generates benefits of saving water, saving energy and optimizing water resources dispatching

Different Renal Function Equations and Dosing of Direct Oral Anticoagulants in Atrial Fibrillation

BACKGROUND: Randomized trials of direct oral anticoagulants (DOACs) adopted the Cockcroft-Gault (CG) formula to calculate estimated glomerular filtration rate (eGFR) to determine the dosages of DOACs. OBJECTIVES: The authors aimed to investigate the agreements/disagreements of eGFRs calculated using different equations (CG, Modified Diet in Renal Disease [MDRD], and Chronic Kidney Disease Epidemiology Collaboration [CKD-EPI] formulas), and their impacts on the dosages of DOACs and clinical outcomes. METHODS: Medical data from a multicenter health care provider in Taiwan including 39,239 patients with atrial fibrillation were used. Among these patients, there were 11,185 and 2,323 patients treated with DOACs and warfarin, respectively. RESULTS: At the cutoff values of eGFR of 50 mL/min, the agreements were 78% between MDRD and CG and 81% between CKD-EPI and CG. The disagreements among the different equations were largely due to overestimations, especially for patients aged >75 years and with a body weight of <50 kg (58.8% for MDRD and 50.9% for CKD-EPI). Among patients receiving DOACs whose dosages were defined as “on label” based on MDRD or CKD-EPI, only those whose dosages were “truly on label” based on CG were associated with a lower risk of major bleeding (adjusted HR: 0.34; 95% CI: 0.26-0.45) compared to warfarin. CONCLUSIONS: The adoptions of MDRD or CKD-EPI rather than CG would result in inappropriate dosing of DOACs (mainly overdosing), which would attenuate the advantages of DOACs compared to warfarin. The CG equation should be used as the gold standard to calculate eGFRs and guide the DOAC dosages

Extranatural Inflation

We present a new model of inflation in which the inflaton is the extra component of a gauge field in a 5d theory compactified on a circle. The chief merit of this model is that the potential comes only from non-local effects so that its flatness is not spoiled by higher dimensional operators or quantum gravity corrections. The model predicts a red spectrum (n ~ 0.96) and a significant production of gravitational waves (r ~ 0.11). We also comment on the relevance of this idea to quintessence.Comment: 4 pages. Minor corrections and references added. Accepted for PR