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How corporate decision makers respond to firm performance shortfalls has key implications for strategic change in corporate resource allocations. Performance feedback theory assumes problem-solving decision makers faced with performance below aspirations pursue problemistic search and greater strategic change to improve performance. Research on self-enhancement, however, assumes decision makers respond by engaging in lower levels of search and change to portray a favorable self-image. We contribute to advancing this dialogue by investigating contextual features of decision making that prompt self-enhancing behaviors. Specifically, we examine the moderating effects of product and geographic diversification, which reflect indicators of task complexity, and bright spots (defined as the greatest increases in percentage sales of product and geographic segments within a diversified firm’s respective portfolios) on the linkage between performance below aspirations and strategic change. We argue that diversification and bright spots create greater opportunities for corporate decision makers to make self-enhancing interpretations, thereby weakening the relationship between performance below aspirations and strategic change. Empirical tests on Standard & Poor’s 1,500 firms from 1998 to 2006 largely supported the hypotheses.

The bright spot phenomenon during the gas explosion was because of the soot particles of high heat radiation characteristics generated during the explosion process. The formation mechanism of soot and precursor polycyclic aromatic hydrocarbons (PAHs) of the methane explosion was numerically simulated using CHEMKIN-PRO. The methane explosion soot of the CH4–air premixed gas explosion experiments with volume concentrations of 8% was collected, and the pore size distribution and surface structure of the soot were analyzed by low-pressure nitrogen gas adsorption (LP-N2GA), scanning electron microscopy (SEM), and transmission electron microscopy (TEM). The results show that C2 and C3 play an important role in the formation of PAHs in the early stage of the explosion reaction. The LP-N2GA isotherms demonstrate that the pore type of the soot particles is mostly wedge-shaped, which was verified with SEM observations. The SEM analysis showed that the methane explosion soot is composed of a large number of spherical soot aggregates with diameters between 4 and 50 μm and the pores at the particle surface are well developed, some of the particles exhibit a melt sintering feature. Soot aggregates collide with each other with a chain-branched structure, and the diameters of the majority of the particles are of 100 nm according to TEM images. In addition, graphite-like lattice stripes can be clearly seen inside the particles when magnified to 8 nm. This work will provide the basis for further analysis of soot formation in the gas explosion process.

Isaiah Speight had taken three chemistry courses in high school. So when he got to his first college chemistry class he didn’t think he needed to take it seriously. “I got slammed in my first exam,...

What a year it has been! When it is all over, few people will regard 2020 as a bright spot in their memories. We are all tired, many of us are sick, and we are further from each other – literally a...

Positive deviance approaches, which have been used to identify and study high performers (bright spots) and translate their successes to poorer performers, offer great potential for chronic disease management. However, there are few examples of applying positive deviance approaches across different geographic contexts. Building on prior research that developed a new measure for appropriate diabetes preventive care (DMPrevCare) and identified priority counties for this strategy, we introduce a geospatial approach for identifying bright spot counties and case matching them to priority counties that need improvement. We used the Local Moran's I tool to identify DMPrevCare spatial outliers, which are counties with larger percentages of Medicare beneficiaries receiving appropriate diabetes preventive care (DMPrevCare) surrounded by counties with smaller percentages of Medicare beneficiaries receiving DMPrevCare. We define these spatial outliers as bright spots. The Robert Wood Johnson Foundation County Health Rankings Peer Counties tool was used to link bright spot counties to previously identified priority counties. We identified 25 bright spot counties throughout the southern and mountain western United States. Bright spot counties were linked to 45 priority counties, resulting in 23 peer (bright/priority) county groups. A geospatial approach was shown to be effective in identifying peer counties across the United States that had either poor or strong metrics related to DMPrevCare, but were otherwise similar in terms of demographics and socioeconomic characteristics. We describe a framework for the next steps in the positive deviance process, which identifies potential factors in bright spot counties that positively impact diabetes care and how they may be applied to their peer priority counties.

Bright spots, strong indicators of the existence of hydrocarbon accumulations, have been primarily used by geophysicists in oil and gas exploration. Recently, machine-learning algorithms, adopted to automate bright spot detection, have mainly relied on feature extraction and shallow classification workflows to achieve an 85.4% F1 score at best, on 2-D seismic data. Deep neural networks have proved their effectiveness in image classification applications, outperforming humans in some instances, but have not been applied to bright spot detection yet. However, their data-hungry nature poses a challenge in domains suffering from expensive data acquisition, such as seismic data analysis problems; they generally require millions of training samples before achieving good performance. In this article, we implement SeisNet, a convolutional neural network with a “butterfly” architecture that overcame the limited data challenge by implementing data augmentation and inductive transfer-learning techniques. Moreover, we adopt a novel formulation that allows us to detect bright spots and estimate their volume. Our approach was tested against different pretraining and transfer-learning methods and was shown to outperform other approaches in the literature by achieving a 95.6% F1 score on bright spot detection. Our model accurately predicted the volume of the bright spot with an average absolute error that is not more than 0.04% of the total volume of the seismic image. This article is an important step in establishing pretrained networks for other seismic applications such as earthquake prediction; our domain-specific pretrained network, proven to outperform state-of-the-art pretrained networks on bright spot detection, may be used to jump-start the training of deep models on other seismic problems.

In seismic exploration, matching pursuit (MP), which decomposes wavelets based on best signal matching, can not only extract accurate frequency information, but also can pick up the strong amplitudes of possible bright spots adaptively according to the threshold or iteration control. RGB (Red-Green-Blue) blending technique of spectral bands can make full use of the information of all frequency bands, which can reflect the general frequency changes of seismic data reducing the interpretation ambiguity. Therefore, in order to improve the precision of interpretation based on bright spot technique, the authors take both amplitude and frequency into account and propose a new approach for hydrocarbon identification by combining both methods. First, we choose the decomposed wavelets of strong amplitudes related to hydrocarbons in MP algorithm to predict the possible bright spots, so as to remove the effect of unrelated reflections in the bright spots interpretation. Then, RGB blending technique is used to the low-, mid-, and high-frequency spectral bands of the bright spot prediction section to assist for the hydrocarbon identification. Finally, the validity of the method is verified by both the model and field data tests. Results demonstrate that the proposed method can improve the precision of hydrocarbon identification, and reduce the interpretation ambiguity of the reservoirs.

Alam El Bueib Formation is one of the most prolific oil-producing reservoirs in the Matruh Basin, Khalda Concession. The Jade oil field is located in the Matruh Basin in the northern part of the Western Desert, Egypt. The primary goal of this project is to learn more about the Alam El Bueib (AEB) Formation’s hydrocarbon presence and entrapment conditions. The analysis of 3D seismic and well log data can help attain these goals. The 3D seismic data interpretation resulted in the identification of a number of significant continuous reflectors that dominated the study region and controlled the major and minor faults. The identified reflectors comprise the following reflectors arranged in ascending order (young to old) as follows: top Alamein Dolomite, AEB-1, AEB-3G, AEB-4, AEB-6, and Masajid and Khatatba formations respectively. The identified reflectors have been correlated using well log data to age the Khatatba and Masajid of the Upper Jurassic. The other reflectors of AEB-1, AEB-3G, AEB-4, and AEB-6 have an age of Lower Cretaceous. The petrophysical characteristics show Alam El Bueib-3G and Alam El Bueib-6 members are mainly composed of sandstone. These members have a good porosity value range from 12.5 to 16%. The hydrocarbon saturation reaches up to 80% and the movable hydrocarbon volume reaches 15%, whereas the water saturation values range between 4 and 22% with a shale volume ranging from 8 to 14%. A master fault with NE-SW trend has been identified splitting the investigated area. On the downthrown side of the master fault, the fault blocks between the normal faults create half grabens. Direct hydrocarbon indicators (DHI) were discovered in seismic data in the form of a bright spot representing the gas-water contact (GWC), which represents evidence of hydrocarbon accumulations in the AEB-3G member at 10,280 ft and the GWC at 12,225 ft in the AEB-6 member.

We present observations (with NAO Rozhen and AS Vidojevica telescopes) of the AM Canum Venaticorum (AM CVn) type binary star CR Bootis (CR Boo) in the UBV bands. The data were obtained in two nights in July 2019, when the V band brightness was in the range of 16.1-17.0 mag. In both nights, a variability for a period of $25 (\pm 1)$ min and amplitude of about 0.2 magnitudes was visible. These brightness variations are most likely indications of "humps". During our observational time, they appear for a period similar to the CR Boo orbital period. A possible reason of their origin is the phase rotation of the bright spot, placed in the contact point of the infalling matter and the outer disc edge. We estimated some of the parameters of the binary system, on the base of the observational data., 8 pages, 2 figures

Side-scan sonars have seen wide deployment in underwater imaging, but the substantially lower visual quality of sonar images leads to a few questions, such as bright spots-like small target with a few pixels, the difficult distinguish of bright spot and speckle noise. In order to solve this problem, we propose a two-stage detection method without obtaining sample information in advance. In the first stage, we fit across-track signal to obtain the peak points of the signal profile, which indicates the position where the target or shadow may be located at this across-track signal. In the second stage, the disordered peak points are clustered using a tree-growing clustering method. A cluster represents the place where the target or shadow may appear in the side-scan sonars image. The target is then detected by matching lights and shadows with possible locations. Experimental results verified that the proposed method is efficient in small target detection from lower visual quality images.

Research question: Pull effects in attention from smaller leagues towards bigger leagues have been under discussion. Nevertheless, causal empirical evidence on such attention interception is non-existent, and the suspension of sport leagues during the COVID-19 lockdown provides a perfect context for examination. The purpose of this study is to test whether the fan interest towards smaller leagues suffers from the presence of bigger leagues. Research methods: While COVID-19 suspended top professional football worldwide, the Belarus league remained active. We used daily follower statistics (Facebook, Twitter, Instagram, Youtube) three month before, during, and three months after the lockdown. Since we have these statistics for Belarus clubs as well as for 847 lockdown-affected football clubs (48 first division leagues, N max= 142,139), we use a difference-in-differences approach to estimate the effect of the shock caused by the lockdown. Results and findings: Results indicate an increase in social media attention (throughout all social media channels) for Belarus clubs after the worldwide lockdown. A decrease was then observed after the revitalization of worldwide professional football leagues. The growth rates slowed down to an almost before COVID-19 level after the restart of other leagues. Implications: First, keeping a league open during a pandemic boosts consumer/fan attention, which may lead league organizers and club managers to maintain a league open longer than advised by national health authorities. Second, since bigger leagues seem to pull of attention from smaller leagues, they could compensate the smaller leagues (financially or non-financial) for the loss of fan interest.

In-situ measurements of the spectral information on the lunar surface are of significance to study the geological evolution of the Moon. China’s Chang’E-4 (CE-4) Yutu-2 rover has conducted several in-situ spectral explorations on the Moon. The visible and near-infrared imaging spectrometer (VNIS) onboard the rover has acquired a series of in-situ spectra of the regolith at the landing site. In general, the mineralogical research of the lunar surface relies on the accuracy of the in-situ data. However, the spectral measurements of the Yutu-2 rover may be affected by shadows and stray illumination. In this study, we analyzed 106 CE-4 VNIS spectra acquired in the first 24 lunar days of the mission and noted that six of these spectra were affected by the shadows of the rover. Therefore, a method was established to correct the effects of the rover shadow on the spectral measurements. After shadow correction, the FeO content in the affected area is corrected to 14.46 wt.%, which was similar to the result calculated in the normal regolith. Furthermore, according to the visible images, certain areas of the explored sites were noted to be unusually bright. Considering the reflectance, geometric information, and shining patterns of the multi-layer insulation (MLI), we examined the influence of the specular reflection of the MLI on the bright spot regionsd, and found that the five sets of data were likely not affected by the specular reflection of the MLI. The results indicated that the complex illumination considerably influences the in situ spectral data. This study can provide a basis to analyze the VNIS scientific data and help enhance the accuracy of interpretation of the composition at CE-4 landing sites.

Since the start of ALMA observatory operation, new and important chemistry of infrared cold core was revealed. Molecular transitions at millimeter range are being used to identify and to characterize these sources. We have investigated the 231 GHz ALMA archive observations of the infrared dark cloud region C9, focusing on the brighter source that we called as IRDC-C9 Main. We report the existence of two sub-structures on the continuum map of this source: a compact bright spot with high chemistry diversity that we labelled as core, and a weaker and extended one, that we labelled as tail. In the core, we have identified lines of the molecules OCS(19-18), $^{13}$CS(5-4) and CH$_{3}$CH$_{2}$CN, several lines of CH$_{3}$CHO and the k-ladder emission of $^{13}$CH$_{3}$CN.We report two different temperature regions: while the rotation diagram of CH$_{3}$CHO indicates a temperature of 25 K, the rotation diagram of $^{13}$CH$_{3}$CN indicates a warmer phase at temperature of $\sim450$K. In the tail, only the OCS(19-18) and $^{13}$CS(5-4) lines were detected. We used the $Nautilus$ and the \textsc{Radex} codes to estimate the column densities and the abundances. The existence of hot gas in the core of IRDC-C9 Main suggests the presence of a protostar, which is not present in the tail., Comment: 13 pages, 11 figures. Accept in MNRAS