Supplier change, market competition and enterprise innovation

PurposeThe purpose of this paper is to study enterprise innovation in the perspective of external supplier relationship. On this purpose, this paper examines the impact of supplier change on enterprise innovation with the moderating role of market competition.Design/methodology/approachUsing 2012–2020 empirical data of Chinese listed manufacturing enterprises, this paper investigates the relationship among supplier change, market competition and enterprise innovation through a two-way interaction model.FindingsThe results show that supplier change has a negative impact on enterprise innovation. And market competition intensifies the negative relationship between supplier change and enterprise innovation. Additional analyses indicate that the main effect and the moderating effect are more significant when the enterprise is non-state-owned or has lower ownership concentration.Originality/valueThis paper studies enterprise innovation from the perspective of external stakeholders. It focuses on supplier relationship in a dynamic variation view, instead of the traditional static ones. Moreover, this paper explores the contingency effect of market competition and gives practical implications for managers to adjust innovation strategy flexibly.

Getting Students to (and Through) Advanced Math: Where Course Offerings and Content Are Not Adding Up. American Educator Panels. Research Report. RR-A827-10

This report uses Spring 2022 data from nationally representative surveys of principals and math teachers in kindergarten through grade 12 (K-12) to explore students' opportunities to prepare for and take advanced math. The authors found that small high schools, high schools in rural areas, and high schools that predominantly serve students from historically marginalized communities tend to offer fewer advanced math courses (e.g., precalculus, Advanced Placement math courses) and that uneven access to advanced math begins in middle school. K-12 teachers who work in schools that predominately serve students living in poverty are more likely to report skipping standards-aligned content and replacing the skipped content with concepts from previous grade levels. Also, more than half of K-12 math teachers said they need additional support for delivering high-quality math instruction, especially teachers who work in schools that serve predominantly high-poverty students. In the wake of the disproportionate impacts of the COVID-19 pandemic on students living in poverty and students of color, these results highlight a critical need for resources to support teachers and to increase student access to advanced courses. [For technical information about the surveys and analysis in this report, see "Learn Together Surveys. 2022 Technical Documentation and Survey Results. Research Report. RR-A827-9" (ED626092).]

Sellers' inflation, profits and conflict: why can large firms hike prices in an emergency?

The dominant view of inflation holds that it is macroeconomic in origin and must always be tackled with macroeconomic tightening. In contrast, we argue that the US COVID-19 inflation is predominantly a sellers' inflation that derives from microeconomic origins, namely the ability of firms with market power to hike prices. Such firms are price makers, but they only engage in price hikes if they expect their competitors to do the same. This requires an implicit agreement which can be coordinated by sector-wide cost shocks and supply bot-tlenecks. We review the long-standing literature on price-setting in concentrated markets and survey earnings calls and compile firm-level data to derive a three-stage heuristic of the inflationary process: (1) Rising prices in systemically significant upstream sectors due to commodity market dynamics or bottlenecks create windfall profits and provide an impulse for further price hikes. (2) To protect profit margins from rising costs, downstream sectors propagate, or in cases of temporary monopolies due to bottlenecks, amplify price pressures. (3) Labor responds by trying to fend off real wage declines in the conflict stage. We argue that such sellers' inflation generates a general price rise which may be transitory, but can also lead to self-sustaining inflationary spirals under certain conditions. Policy should aim to contain price hikes at the impulse stage to prevent inflation from the onset.

American Lessons: The COVID-19 Pandemic and the US Supreme Court

Ever since the outbreak of the COVID-19 Pandemic in America in March 2020, several US states imposed harsh measures to combat the pandemic. Such state measures have at times seriously violated human rights, such as freedom of religion or freedom of movement. This chapter attempts to look at how the US Supreme Court has responded to the pandemic and reviewed several state measures over the past couple of years through selected cases on freedom of religion and compulsory vaccinations. We particularly look at its views on the role of the judiciary during the crisis, the scrutiny applied on human rights violations, as well as whether changes in the Court's composition during the Trump Era have in fact influenced its judicial reasoning. Overall, has the COVID-19 pandemic had an impact on judicial review and the Court's role? If so, how?. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Prospects and Challenges of the Machine Learning and Data-Driven Methods for the Predictive Analysis of Power Systems: A Review

The use of machine learning and data-driven methods for predictive analysis of power systems offers the potential to accurately predict and manage the behavior of these systems by utilizing large volumes of data generated from various sources. These methods have gained significant attention in recent years due to their ability to handle large amounts of data and to make accurate predictions. The importance of these methods gained particular momentum with the recent transformation that the traditional power system underwent as they are morphing into the smart power grids of the future. The transition towards the smart grids that embed the high-renewables electricity systems is challenging, as the generation of electricity from renewable sources is intermittent and fluctuates with weather conditions. This transition is facilitated by the Internet of Energy (IoE) that refers to the integration of advanced digital technologies such as the Internet of Things (IoT), blockchain, and artificial intelligence (AI) into the electricity systems. It has been further enhanced by the digitalization caused by the COVID-19 pandemic that also affected the energy and power sector. Our review paper explores the prospects and challenges of using machine learning and data-driven methods in power systems and provides an overview of the ways in which the predictive analysis for constructing these systems can be applied in order to make them more efficient. The paper begins with the description of the power system and the role of the predictive analysis in power system operations. Next, the paper discusses the use of machine learning and data-driven methods for predictive analysis in power systems, including their benefits and limitations. In addition, the paper reviews the existing literature on this topic and highlights the various methods that have been used for predictive analysis of power systems. Furthermore, it identifies the challenges and opportunities associated with using these methods in power systems. The challenges of using these methods, such as data quality and availability, are also discussed. Finally, the review concludes with a discussion of recommendations for further research on the application of machine learning and data-driven methods for the predictive analysis in the future smart grid-driven power systems powered by the IoE.

Why China Cannot Decarbonise

[...]it is often argued—as by Yifei Li and Judith Shapiro, for example—that China's dictatorship should be an advantage in this context: ‘Given the limited time that remains to mitigate climate change and protect millions of species from extinction, we need to consider whether a green authoritarianism can show us the way' (Li and Shapiro 2020, quoted from the publisher's book description). Since CCP bosses do not have to contend with public hearings, environmental studies, recalcitrant legislatures, labour unions, a critical press, and so on, Xi should be able to force state-owned polluters to stop polluting or else, and ram through his promised transition to renewable energy (see Smith 2017, 2020c). Climate Action Tracker estimates that in 2021 China's emissions increased by 3.4 per cent to 14.1 gigatonnes of carbon dioxide equivalent (GtCO2e)—nearly triple those of the United States (4.9 GtCO2e) with a gross domestic product just three-fourths as large (CAT n.d.;EIA 2022). Since 2019, China's emissions have exceeded those of all developed countries combined and presently account for 33 per cent of total global emissions (Larsen et al. 2021;IEA 2021). In the first half of 2021, rebounding from the first wave of Covid-19, China's carbon dioxide emissions surged past pre-pandemic levels to reach an all-time high 20 per cent increase in the second quarter before dropping back in late 2021 and the first half of 2022 as the real estate collapse, Omicron lockdowns, and drought-induced hydropower reductions slashed economic growth to near zero in the summer (Hancock 2021;Myllyvirta 2022a;Riordan and Hook 2022). China promised to stop building coal-fired power plants abroad, but it is building more than 200 new coal-fired plants at home in a drive to boost economic growth, maintain jobs in coal-dependent regions, and ensure energy self-sufficiency—locking the country into coal reliance for many decades to come, derailing the transition to renewables, and dooming Xi's UN pledge to transition to a green and low-carbon mode of development (Xie 2020).

Interval Load Forecasting for Individual Households in the Presence of Electric Vehicle Charging

The transition to Electric Vehicles (EV) in place of traditional internal combustion engines is increasing societal demand for electricity. The ability to integrate the additional demand from EV charging into forecasting electricity demand is critical for maintaining the reliability of electricity generation and distribution. Load forecasting studies typically exclude households with home EV charging, focusing on offices, schools, and public charging stations. Moreover, they provide point forecasts which do not offer information about prediction uncertainty. Consequently, this paper proposes the Long Short-Term Memory Bayesian Neural Networks (LSTM-BNNs) for household load forecasting in presence of EV charging. The approach takes advantage of the LSTM model to capture the time dependencies and uses the dropout layer with Bayesian inference to generate prediction intervals. Results show that the proposed LSTM-BNNs achieve accuracy similar to point forecasts with the advantage of prediction intervals. Moreover, the impact of lockdowns related to the COVID-19 pandemic on the load forecasting model is examined, and the analysis shows that there is no major change in the model performance as, for the considered households, the randomness of the EV charging outweighs the change due to pandemic.

Real-time vibration monitoring and analysis of agricultural tractor drivers using an IoT-based system

Agricultural tractor drivers experience a high amplitude of vibration, especially during soil tillage operations. In the past, most research studied vibration exposure with more focus on the vertical (z) axis than on the fore-and-aft (x) and lateral (y) axes. This study examines how rotary soil tillage affects the vibration acceleration and frequency, and the power spectral densities (PSDs) at the seat pan and head along three translational axes in a real-field multiaxis vibration context. Moreover, this study aimed to identify the characteristics of the seat-to-head transmissibility (STHT) response to identifying the most salient resonant frequencies along the x-, y-, and z-axes. Nine (9) male tractor drivers operated the tractor with a mounted rotary tiller throughout the soil tillage process. In the event of a COVID-19 pandemic, and to respect social distancing, this study developed an Internet of Things (IoT) module with the potential to integrate with existing data loggers for online data transmission and to make the experimentation process more effective by removing potential sources of experimenter errors. The raw acceleration data retrieved at the seat pan and the head were utilized to obtain daily exposure (A(8)), PSDs, and STHT along the x-, y-, and z-axes. The vibration energy was found to be dominant along the z-axis than the x- and y-axes. A(8) response among tractor drivers exceeds the exposure action value explicitly stated by Directive 2002/44/EU. PSDs along the x-, y-, and z-axes depicted the low-frequency vibration induced by rotary soil tillage operation. The STHT response exhibited a higher degree of transmissibility along the y- and z-axes when compared with that along the x-axis. The frequency range of 4-7 Hz may plausibly be associated with cognitive impairment in tractor drivers during rotary soil tillage.

Molecular Diagnostics on Smartphone

During the COVID-19 pandemic, point-of-care genetic testing (POCT) devices were used for on-time and on-site detection of the virus, which helped to prevent and control the spread of the pandemic. Smartphones, which are widely used electronic devices with many functions, have the potential to be used as a molecular diagnostic platform for universal healthcare monitoring. Several integrated diagnostics platforms for the real-time and end-point detection of COVID-19 were developed using the functions of smartphones, such as the operating system, power, sound, camera, data storage, and display. These platforms use the 5V output power of smartphones, which can be amplified to power a micro-capillary electrophoresis system or a thin-film heater, and the CMOS camera of smartphones can capture the color change during a colorimetric loop-mediated isothermal amplification test and detect fluorescence signals. Smartphones can also be used with self-written web-based apps to enable automatic and remote pathogen analysis on POCT platforms. Our lab developed a handheld micro-capillary electrophoresis device for end-point detection of SARS-CoV-2, as well as an integrated smartphone-based genetic analyzer for the qualitative and quantitative colorimetric detection of foodborne pathogens with the help of a custom mobile app. © 2023 SPIE.

A Study on the Key Elements of Bangladesh's Vaccine Diplomacy During COVID-19

In May 2022, Bangladesh was ranked fifth in a Global Index comprising of 121 countries' performance of managing the impacts of COVID-19 pandemic. The Index provided Bangladesh with global recognition of its endeavours that aimed at lowering the number of confirmed COVID-19 cases since the mid-2020, in which the country's vaccine diplomacy played a greater role. In view of that the present study identified the key elements of Bangladesh's vaccine diplomacy in the context of the pandemic. The study employed the qualitative approach of the social science research, while the data were generated from both primary and secondary sources. The study found that Bangladesh pursued a proactive vaccine diplomacy with a combination of five key elements: identifying the critical areas of intervention, figuring out the volume of internal demand, counting on multiple sources of vaccines, generating diverse source of external funding and making the most use of ‘soft power' strategy. [ FROM AUTHOR] Copyright of Journal of Asian & African Studies (Sage Publications, Ltd.) is the property of Sage Publications, Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Claim Extraction and Dynamic Stance Detection in COVID-19 Tweets

The information ecosystem today is noisy, and rife with messages that contain a mix of objective claims and subjective remarks or reactions. Any automated system that intends to capture the social, cultural, or political zeitgeist, must be able to analyze the claims as well as the remarks. Due to the deluge of such messages on social media, and their tremendous power to shape our perceptions, there has never been a greater need to automate these analyses, which play a pivotal role in fact-checking, opinion mining, understanding opinion trends, and other such downstream tasks of social consequence. In this noisy ecosystem, not all claims are worth checking for veracity. Such a check-worthy claim, moreover, must be accurately distilled from subjective remarks surrounding it. Finally, and especially for understanding opinion trends, it is important to understand the stance of the remarks or reactions towards that specific claim. To this end, we introduce a COVID-19 Twitter dataset, and present a three-stage process to (i) determine whether a given Tweet is indeed check-worthy, and if so, (ii) which portion of the Tweet ought to be checked for veracity, and finally, (iii) determine the author's stance towards the claim in that Tweet, thus introducing the novel task of topic-agnostic stance detection. © 2023 ACM.

Marginal Cost of Energy Volatility in the Brazilian Interconnected Power System

From 2018 to 2022, on average, 70% of the Brazilian effective electric generation was produced by hydropower, 10% by wind power, and 20% by thermal power plants. Over the last five years, Brazil suffered from a series of severe droughts. As a result, hydropower generation was reduced, but demand growth was also declined as results of the COVID-19 pandemic and economic recession. From 2012 to 2022, the Brazilian reservoir system operated with, on average, only 40% of the active storage, but storage recovered to normal levels in the first three months of 2022. Despite large capacity of storage reservoirs, high volatility of the marginal cost of energy was observed in recent years. In this paper, we used two optimization models, NEWAVE and HIDROTERM for our study. These two models were previously developed for mid-range planning of the operation of the Brazilian interconnected power system. We used these two models to optimize the operation and compared the results with observed operational records for the period of 2018-2022. NEWAVE is a stochastic dual dynamic programming model which aggregates the system into four subsystems and 12 equivalent reservoirs. HIDROTERM is a nonlinear programming model that considers each of the 167 individual hydropower plants of the system. The main purposes of the comparison are to assess cooperation opportunities with the use of both models and better understand the impacts of increasing uncertainties, seasonality of inflows and winds, demand forecasts, decisions about storage in reservoirs, and thermal production on energy prices. © World Environmental and Water Resources Congress 2023.All rights reserved

Concern for Self-Health During the COVID-19 Pandemic in Canada: How to Tell an Intersectional Story Using Quantitative Data?

Rooted in Black feminism, intersectionality theory entered critical legal studies and travelled to public health and beyond. This chapter demonstrates one of many ways to apply intersectionality theory using a descriptive intercategorical approach to quantitative data. In so doing, I attempt to tell an intersectional story to make visible the intersectional inequalities for Canadians' concerns for self-health during the first wave of COVID-19 pandemic. For pedagogical purposes, I share a subset of Statistics Canada's COVID-19 Impacts Survey 2020 dataset of 239,143 participants and Stata code to encourage students to practice estimating intersectional outcomes and ask questions to explicate health inequalities. Although interrogating the systems of power is critical, this project does not statistically analyse but draws on the literature to discuss how interacting power structures might shape intersectional peoples' experiences. In addition, the analysed dataset is not representative of the Canadian population. Nonetheless, it might be helpful to showcase a case study on introductory-level quantitative intersectionality research. I hope, despite these limitations, this case study and the pedagogical tools will contribute to greater access to intersectionality research, generating a cadre of intersectionality data translators in public health. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

The Trump administration, the far-right and world politics

We contend that the Trump administration mainstreamed far-right politics through its foreign policy on China, the World Health Organization and its handling of the Covid-19 pandemic. Our Gramscian-Kautskyian theoretical perspective concentrates on elite power, class, and interconnections between advanced global capitalism and domestic inequality. We show that the administration amplified US far-right Sinophobia even as it deepened connections between US and Chinese corporate elites. Its foreign policy strategy attempted to appease transnational capitalist objectives through 'ultra-imperialism' and draw on far-right ideas to shore up its domestic support base. But the administration, much like previous ones, attempted to make China a subordinate 'responsible stakeholder' through integrating and pressuring it in the Liberal International Order. The Gramscian-Kautskyian approach highlights that Sino–US relations are a mix of security and economic competition and interdependency. Over all, we argue that the Trump administration was not such a threat to the establishment as commonly contended. [ FROM AUTHOR] Copyright of Globalizations is the property of Routledge and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Education in times of COVID-19. Socio-cognitive critical discourse analysis

This article summarizes a research whose general objective was to analyze the way in which the documentary corpus associated with the "Learn at home" strategy reproduces the relations of power, control, social-educational inequality and exclusion in its recipients. The units of analysis were organized in textual visualization matrices with double coding: one open, cross-coded and the other using NVivo v.12 software. Subsequently, the main lines of inquiry were categorized and an inductive categorical interpretation was carried out, relating the categories discourse and society with social knowledge as an interface. The findings indicate that the discursive structures analyzed reproduce power, control, inequality and exclusion, maintaining the status quo, prolonging educational social injustice and privileging symbolic elites;furthermore, the issuers resort to discursive strategies such as the principle of influence, values and praise to achieve the purposes of social domination. As for the research design, this was a qualitative documentary research, of discourse analysis type, in critical perspective from the socio-cognitive approach

A Review on Solar Energy Policy and Current Status: Top 5 Countries and Kazakhstan

The article describes the world's experience in developing the solar industry. It discusses the mechanisms of state support for developing renewable energy sources in the cases of five countries that are the most successful in this area—China, the United States, Japan, India, and Germany. Furthermore, it contains a brief review of state policy in producing electricity by renewable energy facilities in Kazakhstan. This paper uses statistical information from the International Renewable Energy Agency (IRENA), the International Energy Agency (IEA), British Petroleum (BP), and the Renewable Energy Network (REN21), and peer-reviewed sources. The research methodology includes analytical research and evaluation methods to examine the current state of solar energy policy, its motivators and incentives, as well as the prospects for its development in Kazakhstan and in the world. Research shows that solar energy has a huge development potential worldwide and is sure to take its place in gross electricity production. This paper focuses on the selected economic policies of the top five countries and Kazakhstan, in what may be considered a specific research limitation. Future research suggestions for the expansion of Renewable Energy (RE) in Kazakhstan could include analysing the impact of introducing dedicated policies and incentives for solar systems and exploring the benefits and challenges of implementing large RE zones with government–business collaboration.

Smart microfluidics: Synergy of machine learning and microfluidics in the development of medical diagnostics for chronic and emerging infectious diseases

The COVID-19 pandemic, emerging/re-emerging infections as well as other non-communicable chronic diseases, highlight the necessity of smart microfluidic point-of-care diagnostic (POC) devices and systems in developing nations as risk factors for infections, severe disease manifestations and poor clinical outcomes are highly represented in these countries. These POC devices are also becoming vital as analytical procedures executable outside of conventional laboratory settings are seen as the future of healthcare delivery. Microfluidics have grown into a revolutionary system to miniaturize chemical and biological experimentation, including disease detection and diagnosis utilizing muPads/paper-based microfluidic devices, polymer-based microfluidic devices and 3-dimensional printed microfluidic devices. Through the development of droplet digital PCR, single-cell RNA sequencing, and next-generation sequencing, microfluidics in their analogous forms have been the leading contributor to the technical advancements in medicine. Microfluidics and machine-learning-based algorithms complement each other with the possibility of scientific exploration, induced by the framework's robustness, as preliminary studies have documented significant achievements in biomedicine, such as sorting, microencapsulation, and automated detection. Despite these milestones and potential applications, the complexity of microfluidic system design, fabrication, and operation has prevented widespread adoption. As previous studies focused on microfluidic devices that can handle molecular diagnostic procedures, researchers must integrate these components with other microsystem processes like data acquisition, data processing, power supply, fluid control, and sample pretreatment to overcome the barriers to smart microfluidic commercialization.Copyright © 2023 by Begell House, Inc.

Right to nomination in Colombia, legal representatives, and termination of power in the general procedural code

This article covers the theoretical concept of the right to nomination in Colombia detailing its many exceptions where direct intervention is authorized both legally and constitutionally. After having explained the legal actions, various hypothesis, and controversies that, in turn, constitute exceptions, the General Code of Procedure's framework and the many vicissitudes of the physical appearance, along with proposals in such regard, are shown without forgetting, on the other hand, the statutes by which powers of attorney are to be produced during the current time (COVID-19). In this sense, after an adequate analysis, the theoretical reach, as well as the mechanism of action of the revocation of the power of attorney and its renunciation (causes for termination) are laid out. Among the latter, the death of the lawyer representing his or her client and two kinds of legal incompetence known as: «sudden incompetence» (fortuitous) and «original incompetence» (by nature), which do not constitute cause for procedural nullity. The methodology by which this article was written had the very intention of analyzing the statutes set forth before and after the 1991 Constitution, and before and after the doctrine (jurists) and case law;statutes in which comments, critics, and propositions are observed due to the existing "symbiosis” between Procedural Law and Positive Law. © 2022, Universidad Pontificia Bolivariana. All rights reserved.

Classic Augmentation based Classifier Generative Adversarial Network (CACGAN) for COVID-19 Diagnosis

Computer-aided diagnosis of COVID-19 from lung medical images has received increasing attention in previous clinical practice and research. However, developing such automatic model is usually challenging due to the requirement of a large amount of data and sufficient computer power. With only 317 training images, this paper presents a Classic Augmentation based Classifier Generative Adversarial Network (CACGAN) for data synthetising. In order to take into account, the feature extraction ability and lightness of the model for lung CT images, the CACGAN network is mainly constructed by convolution blocks. During the training process, each iteration will update the discriminator's network parameters twice and the generator's network parameters once. For the evaluation of CACGAN. This paper organized multiple comparison between each pair from CACGAN synthetic data, classic augmented data, and original data. In this paper, 7 classifiers are built, ranging from simple to complex, and are trained for the three sets of data respectively. To control the variable, the three sets of data use the exact same classifier structure and the exact same validation dataset. The result shows the CACGAN successfully learned how to synthesize new lung CT images with specific labels. © 2023 SPIE.

Vaccination Identification System using Raspberry Pi

The Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) causes Covid-19, an infectious illness. A methodology was created to track the vaccination history of people with the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) that causes Covid-19, an infectious illness. The system operates on a Raspberry Pi processor that is designed to authenticate the vaccination records of individuals. The Vaccination Identification System consists of various components connected to the Raspberry Pi Zero 2W microprocessor, Pi camera, an LCD display, LED indicators, a buzzer, a DC servo motor, and a PCB converter. The proposed system grants access to vaccinated individuals and denies access to those who are not vaccinated.