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The aim of the Early Career Leadership Academy (ECLA; established in 2017) is to improve the retention rates of women; people of color; lesbian, gay, bisexual, transgender, and queer (LGBTQ+); and people with disabilities in the atmospheric sciences and related fields by cultivating leadership, providing mentorship, and fostering a strong community of supportive peers and collaborators. Since 2017, five classes have completed the program, with the class composition spanning professional sectors and personal identities. Approximately 70% of ECLA participants were identified as women, people of color, LGBTQ+, and/or people with disabilities. Nearly half of all graduates have filled leadership positions within the American Meteorological Society (AMS), with a majority reporting that their first leadership position was not held until after completion of ECLA. A benefit of leadership training that spans across sectors includes the realization that despite different day-to-day responsibilities, many of the challenges faced by early career professionals related to extreme weather, climate change, and diversity and inclusion are similar across workplaces. ECLA is organized by AMS volunteers who created a program based on adaptive leadership principles. Given the diversity of professional backgrounds and lived experiences, the programmatic design of ECLA is also built upon each class's professional development needs. Many of the topics are fundamental to leading effective, diverse teams, yet are rarely covered in traditional academic preparation. Recommendations and a list of actionable strategies are provided to help ensure that early career professionals will continue to be agents of positive change in the weather, water, and climate enterprise and beyond. SIGNIFICANCE STATEMENT: An early career leadership training program was created in 2017 to help increase retention rates of women, people of color, LGBTQ+, and people with disabilities in the atmospheric sciences and related fields. Since then, more than 100 participants have completed the AMS Early Career Leadership Academy and filled many leadership positions within the AMS. We share lessons that we learned during the creation and evolution of the program, along with recommendations for where future efforts should be concentrated to build a more diverse, equitable, and inclusive future that furthers scientific excellence and innovation. KEYWORDS: Community; Societal Impacts; Planning; Education; History, 1. Introduction The summer of 2023 marked the culmination of the fifth program of the Early Career Leadership Academy (ECLA), an American Meteorological Society (AMS) program that was born from [...]

The Department of Justice's March 21 antitrust lawsuit against Apple has drawn praise (Feiner 2024) and sharp criticism (Abbott 2024; Rinehart 2024). (1) One of us (Shughart 2024) blasted the [...]

Just before dawn on August 2, 1990, Iraqi dictator Saddam Hussein deployed units of the Republic Guard Forces Command across the Kuwaiti border in a brutally swift assault that quickly [...]

Herbert G. Bohnert (1918–84) was a student and lifelong friend of Carnap. He wrote a doctoral thesis on Ramsey sentences and, after wavering between academia and the computer industry, eventually taught at Michigan State University. Bohnert defended Carnapian positions throughout his career and was especially productive in the 1970s. Unfortunately, Carnap's philosophy was deemed hopelessly out of date during this period, and partly for this reason, Bohnert is almost completely forgotten today. This fate is undeserved. In this article, I reconstruct his career and philosophical development by drawing on publications, as well as diary entries and private correspondence. I then argue that Carnap scholarship would benefit from closer attention to Bohnert's writings. I support this claim by means of a case study from the philosophy of mathematics: Bohnert's account of a conversation about set theory with Carnap in 1968 sheds new light on the contested question of whether Carnap held mathematics to be analytic in natural language. [ABSTRACT FROM AUTHOR]

Imagine the dilemma of a single mother working as a state accountant, who, amid the rising cost of living, finds herself reaching out to numerous agencies for support, only to [...]

In 1988, Xerox CEO David Kearns co-authored a book titled Winning the Brain Race: A Bold Plan to Make Our Schools Competitive. Three years later, Kearns became deputy secretary of [...]

The advancement of the computer and information industry has led to the emergence of new demands for multivariate time series anomaly detection (MTSAD) models, namely, the necessity for unsupervised anomaly detection that is both efficient and accurate. However, long-term time series data typically encompass a multitude of intricate temporal pattern variations and noise. Consequently, accurately capturing anomalous patterns within such data and establishing precise and rapid anomaly detection models pose challenging problems. In this paper, we propose a decomposition GAN-based transformer for anomaly detection (DGTAD) in multivariate time series data. Specifically, DGTAD integrates a time series decomposition structure into the original transformer model, further decomposing the extracted global features into deep trend information and seasonal information. On this basis, we improve the attention mechanism, which uses decomposed time-dependent features to change the traditional focus of the transformer, enabling the model to reconstruct anomalies of different types in a targeted manner. This makes it difficult for anomalous data to adapt to these changes, thereby amplifying the anomalous features. Finally, by combining the GAN structure and using multiple generators from different perspectives, we alleviate the mode collapse issue, thereby enhancing the model's generalizability. DGTAD has been validated on nine benchmark datasets, demonstrating significant performance improvements and thus proving its effectiveness in unsupervised anomaly detection. [ABSTRACT FROM AUTHOR]

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The US National Institute of Standards and Technology (NIST) on Tuesday published its timetables for moving government agencies off current types of encryption onto what they hope will be quantum-resistant [...]

For modern businesses regardless of industry or size, the monetary impact of a data breach is substantial. https://www.ibm.com/reports/data-breach discovered that, from March 2023 to February 2024, the average cost of [...]

The US leads the world in quantum computing research, but it will need more government investment to maintain its lead over China, an assessment by the Information Technology and Innovation [...]

It is commonly assumed that data volume and network energy consumption are directly proportional, a notion perpetuated by numerous studies and media coverage. This paper challenges this assumption, offering a comprehensive examination of network operations to explain why the relationship between energy consumption and data volume is nonlinear. The power model approach is explored as an alternative methodology for calculating network energy consumption providing a more reliable representation of network energy use. The power model demonstrates that simple energy intensity calculations, expressed as kilowatt hours per gigabyte of data, are insufficient for accurately estimating real‐world network energy consumption. [ABSTRACT FROM AUTHOR]

This systematic review focused on understanding the impact of big data on marketing productivity, following the guidelines of systematic literature reviews and using the PICOC (problem/population, intervention, comparison, results, context) method. 50 high-impact articles were selected in Scopus, prioritizing those in the areas of engineering, computer science and business, and published between 2020 and 2023. These articles, selected for their relevance and contribution to the study objectives, showed that the big data offers notable benefits in the marketing industry. The ability to customize marketing strategies to individual customer needs, improved optimization, and a better understanding of customer behaviors and preferences were key aspects. These findings highlight how big data can boost productivity in marketing, strengthening customer relationships and increasing loyalty by improving understanding and adaptation to the specific demands and preferences of each customer. This deeper, more personalized approach to consumers represents a significant shift in the effectiveness and efficiency of marketing strategies in the current era. [ABSTRACT FROM AUTHOR]

Hidden champions are SMEs with strong global competitiveness in specialized fields but low profile. China's hidden champions show less internationalization compared to their global peers, despite niche market dominance. Consequently, an exploration of the internationalization trajectories and determinants for China's hidden champions is essential. This study utilizes fsQCA to investigate the internationalization strategies of 91 hidden champions, from China, using the "Efficiency-Structure-Environment" analytical framework. Five principal causal pathways for international operations are illustrated, categorized into three main driving models: "Efficiency-Environment,""Efficiency-Structure," and "System Interaction," with the first model being the most dominant. The study further classifies hidden champions by ownership, finding that both state-owned and private enterprises predominantly follow the "Efficiency-Structure" driving model. Additionally, when classified by certification types, all categories are found to align with the "System Integration" model. Notably, the international path for cultivation hidden champions is also well-explained by the "Efficiency Dominance" model. Lastly, the study delineates industry-specific models: general equipment manufacturers employ the "Efficiency-Structure" model, special equipment manufacturers use the "Structure-Environment" model, and the computer industry follows the "System Integration" model. Plain language summary: The international operations of China's hidden champions This research focuses on China's hidden champions, which are small and competitive companies in specific fields that are not widely known. Despite their success in niche markets, these companies have not expanded internationally as their global counterparts. To understand why, the study examines the international strategies of 91 hidden champions using a method called fsQCA. The findings reveal five main paths that lead to internationalization, driven primarily by factors such as efficiency, the business environment, and organizational structure. The study also explores how ownership type and certification play a role in these strategies. Notably, it highlights different approaches for fostering the international growth of hidden champions. The research concludes by identifying industry-specific patterns, showing how companies in general equipment manufacturing, special equipment manufacturing, and the computer industry adopt distinct driving models for their international operations. [ABSTRACT FROM AUTHOR]

Recently, cloud computing has affected a large part of the computer industry, including software companies and internet service providers. It has proven efficient in managing tasks for applications. Despite its popularity, cloud computing does not meet the requirements of applications because it faces many limitations, such as high latency and bandwidth bottlenecks. These limitations will significantly affect applications sensitive to delays. To meet this challenge, fog computing is introduced as an extension to cloud computing. It improves quality of service (QoS) for applications that suffer from latency by keeping resources and services close to the end-user. How to efficiently and fairly allocate the available resources, e.g., CPU, bandwidth, and memory, between different requested tasks is a complex challenge. The main goal of this paper is to study the concepts of fog computing, architecture, environment, and metrics that affect resource allocation in fog computing. It also summarizes the classification of modern resource allocation approaches based on QoS metrics (2017-2023). On the other hand, highlighting the pros and cons of these studies as well as future research directions to develop different approaches. [ABSTRACT FROM AUTHOR]

Sustainability and resilience in addressing construction's environmental, social, and economic challenges rely on interoperability. A model-centred approach using standardised information structures like industry foundation classes (IFC) is essential for data sharing in architecture, engineering, construction, and facility management. Achieving complete interoperability across domains requires further research. This review paper focuses on IFC schema, highlighting upcoming developments like IFC 5 and "IFC x", with a core emphasis on modularisation to enhance domain interoperability, improved links between building information modelling (BIM) and geographic information systems (GIS), along with IoT integration into BIM, cloud-based collaboration, and support for other advanced technologies such as augmented reality (AR), virtual reality (VR), artificial intelligence (AI), and digital twins. Through a critical examination of the IFC and an outlook towards its future enhancements, the research has the potential to offer valuable insights into shaping the trajectory of future advancements within the AEC and facility management sectors. The study's discoveries could aid in establishing standardised data exchange protocols in these industries, promoting uniformity across projects, facilitating smoother communication, and mitigating errors and inefficiencies. Anticipating enhancements in the IFC could catalyse innovation, fostering the adoption of emerging technologies and methodologies. Consequently, this could drive the creation of more sophisticated tools and procedures, ultimately enhancing project outcomes and operational effectiveness. [ABSTRACT FROM AUTHOR]

Increasingly, anecdote and research have suggested a high rate of perfectionism among adolescents. The authors of the most common measure used in perfectionism research, the Frost Multidimensional Perfectionism Scale, have proposed that perfectionism comprises six subcomponents, however this has been the subject of debate in regard to adolescent populations. Our study examined the psychometric properties of the Frost Multidimensional Perfectionism Scale in a sample of 930 New Zealand adolescents. Broadly consistent with our hypotheses, and previous research, the results showed that Frost and colleagues' original six-factor structure did not provide a good fit to the adolescent sample. Instead, exploratory factor analyses reveals a four-factor solution reflecting 'Concerns and Doubts', 'Parental Perceptions', 'Personal Standards' and 'Organisation'. Further analyses provides support for two second-order factors representing Positive and Negative perfectionism. Overall, despite differing from Frost and colleagues' original six-factor conceptualisation, the four first-order components conform to the two hypothesized second-order components. Keywords: Perfectionism, Adolescence, Mental Health, Factor Analysis, INTRODUCTION For at least two decades, there has been growing concern over the apparent increase in perfection amongst today's adolescents (e.g., Curran & Hill, 2019), with some researchers suggesting that [...]

In recent years, the digitization of cultural heritage has been favored by significant advancements in specific technologies, such as photogrammetry and three-dimensional scanning. The digital representations of artifacts, paintings, books, and collections, as well as buildings or archaeological sites, has led to the transfer of cultural organizations to the digital space. On the other hand, the rapid development of immersive technologies and the Internet of Things is expected to decisively shape virtual cultural heritage in the coming years. However, this digital transition should expand its impact on most of the population. This article aims to cover the lack of structured methodology in the design and development of inclusive virtual spaces in cultural heritage. This research introduces a holistic framework that is mainly based on the disciplines of virtual museology. The proposed methodology takes into account the advancements in extended reality and the creative industry of computer games. The multisensory approach would lead to advanced immersive experiences, while the multilayered approach of cultural heritage content would enhance accessibility in inclusive virtual spaces. Moreover, this holistic framework could provide evidence from the virtual worlds that could be applied to real cultural heritage organizations. [ABSTRACT FROM AUTHOR]

Data-driven garment animation is a current topic of interest in the computer graphics industry. Existing approaches generally establish the mapping between a single human pose or a temporal pose sequence, and garment deformation, but it is difficult to quickly generate diverse clothed human animations. We address this problem with a method to automatically synthesize dressed human animations with temporal consistency from a specified human motion label. At the heart of our method is a two-stage strategy. Specifically, we first learn a latent space encoding the sequence-level distribution of human motions utilizing a transformer-based conditional variational autoencoder (Transformer-CVAE). Then a garment simulator synthesizes dynamic garment shapes using a transformer encoder–decoder architecture. Since the learned latent space comes from varied human motions, our method can generate a variety of styles of motions given a specific motion label. By means of a novel beginning of sequence (BOS) learning strategy and a self-supervised refinement procedure, our garment simulator is capable of efficiently synthesizing garment deformation sequences corresponding to the generated human motions while maintaining temporal and spatial consistency. We verify our ideas experimentally. This is the first generative model that directly dresses human animation. [ABSTRACT FROM AUTHOR]

The article discusses the concept of Industry 4.0, which focuses on automation, data exchange, and decentralization in manufacturing systems. It highlights the importance of intelligent diagnostics and artificial intelligence, specifically machine learning and computer vision, in this new era of manufacturing. The article explores the applications of these technologies in industrial processes, emphasizing their benefits in terms of automation, quality control, and productivity. The collection of articles in the text covers various fields, including additive manufacturing, robotics, pavement maintenance, text-to-image synthesis, and digital meter reading. Each study showcases the potential of machine learning, deep learning, and artificial neural networks in improving efficiency, accuracy, and safety in these industries. [Extracted from the article]

In light of the rise of generative AI and recent debates about the socio‐political implications of large‐language models, chatbots, and the like, this paper analyzes the E.U.'s Artificial Intelligence Act (AIA), the world's first comprehensive attempt by a government body to address and mitigate the potentially negative impacts of AI technologies. The paper critically analyzes the AIA from a business and computer ethics point of view—a perspective currently lacking in the academic (e.g., GBOE‐related) literature. It evaluates, in particular, the AIA's strengths and weaknesses and proposes reform measures that could help to strengthen the AIA. Among the AIA's strengths are its legally binding character, extra‐territoriality, ability to address data quality and discrimination risks, and institutional innovations such as the AI Board and publicly accessible logs and database for AI systems. Among its main weaknesses are its lack of effective enforcement, oversight, and control, absence of procedural rights and remedy mechanisms, inadequate worker protection, institutional ambiguities, insufficient funding and staffing, and inadequate consideration of sustainability issues. Reform suggestions include establishing independent conformity assessment procedures, strengthening democratic accountability and judicial oversight, introducing redress and complaint mechanisms, ensuring the participation and inclusion of workers, guaranteeing political independence of the AI Board, providing enhanced funding and staffing of market surveillance authorities, and mandating "green AI." [ABSTRACT FROM AUTHOR]

Researchers warn of a new critical vulnerability in Apache OFBiz, an open-source https://www.cio.com/article/272362/what-is-erp-key-features-of-top-enterprise-resource-planning-systems.html system and framework. The flaw potentially allows for remote code execution (RCE) without authentication and was patched [...]

Background Long-term care facilities (LTCFs) are hotspots for pathogen transmission. Infection control interventions are essential, but the high density and heterogeneity of interindividual contacts within LTCF may hinder their efficacy. Here, we explore how the patient-staff contact structure may inform effective intervention implementation. Methods and findings Using an individual-based model (IBM), we reproduced methicillin-resistant Staphylococcus aureus colonisation transmission dynamics over a detailed contact network recorded within a French LTCF of 327 patients and 263 staff over 3 months. Simulated baseline cumulative colonisation incidence was 21 patients (prediction interval: 11, 31) and 35 staff (prediction interval: 19, 54). We examined the potential impact of 3 types of interventions against transmission (reallocation reducing the number of unique contacts per staff, reinforced contact precautions, and hypothetical vaccination protecting against acquisition), targeted towards specific populations. All 3 interventions were effective when applied to all nurses or healthcare assistants (median reduction in MRSA colonisation incidence up to 35%), but the benefit did not exceed 8% when targeting any other single staff category. We identified 'supercontactor' individuals with most contacts ('frequency-based,' overrepresented among nurses, porters, and rehabilitation staff) or with the longest cumulative time spent in contact ('duration-based,' overrepresented among healthcare assistants and patients in elderly care or persistent vegetative state (PVS)). Targeting supercontactors enhanced interventions against pathogen spread in the LTCF. With contact precautions, targeting frequency-based staff supercontactors led to the highest incidence reduction (20%, 95% CI: 19, 21). Vaccinating a mix of frequency- and duration-based staff supercontactors led to a higher reduction (23%, 95% CI: 22, 24) than all other approaches. Although based on data from a single LTCF, when varying epidemiological parameters to extend to other pathogens, our results suggest that targeting supercontactors is always the most effective strategy, indicating this approach could be applied to prevent transmission of other nosocomial pathogens. Conclusions By characterising the contact structure in hospital settings and identifying the categories of staff and patients more likely to be supercontactors, with either more or longer contacts than others, interventions against nosocomial spread could be more effective. We find that the most efficient implementation strategy depends on the intervention (reallocation, contact precautions, vaccination) and target population (staff, patients, supercontactors). Importantly, both staff and patients may be supercontactors, highlighting the importance of including patients in measures to prevent pathogen transmission in LTCF., Author(s): Quentin J. Leclerc 1,2,3,*, Audrey Duval 1,2,3, Didier Guillemot 1,2,4, Lulla Opatowski 1,2, Laura Temime 3,5 Introduction Healthcare-associated infections (HAIs) are a major threat worldwide, with more than 4 [...]

Phishing campaigns that spoof well-known global brands such as Disney, IBM, Nike, Best Buy, and Coca-Cola to trick recipients into clicking on malicious emails should be a thing of the [...]

Security researchers warn that some PC and server manufacturers are using insecure cryptographic keys as the root of trust for Secure Boot, an important security feature in modern computers that [...]

How to Power the Next Generation of Human Services Modular system modernization with integrated data, advanced analytics, and detailed, readily available reporting is transformational to carrying out high-quality and cost-effective [...]

Quantum technologies involving sensing, networking, and computing are beginning to transform areas as diverse as health, geology, astrophysics, materials science, and finance. Building on decades of work in theoretical and [...]

Copyright of Društvena Istraživanja is the property of Institute of Social Sciences Ivo Pilar 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 abstract 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 abstract. (Copyright applies to all Abstracts.)

The paper examines traditional knowledge about the role of financial and physical resources in entrepreneurship. Based on Bourdieu's theory, we search for an answer to the question of which forms of economic capital are used by Croatian entrepreneurs in the computer programming industry and what their value in the context of accessing other forms of capital is. The study is based on a qualitative methodological approach. In-depth interview technique accompanied by unstructured observations was used in data collection. In addition to primary data, research includes the use of qualitative and quantitative secondary data. Research suggests that in the computer programming industry, economic capital is just one tool in the conversion game of entrepreneurial capital. Moreover, the interviewed entrepreneurs attach the least importance to it in business, favoring the value of intangible resources such as specialized knowledge, business connections and personal acquaintances. The company's start-up phase is characterized by the entrepreneur's reliance on personal savings, which is replaced by financing based on retained earnings in the later stages of business. Other forms of growth financing, such as bank loans and recapitalization of external investors, are used by a minority of larger companies, with a good base of symbolic capital. The results on capital conversions indicate relatively easy conversion of economic capital into cultural capital and symbolic capital, and less frequent use of economic capital to create social capital. Fresh insights into the entrepreneurs' perceptions provided by the study expand existing knowledge about entrepreneurship within the computer programming industry, suggesting that it is an industry with huge potential for young talents without a personal financial base that is commonly considered a precondition for entering entrepreneurship. [ABSTRACT FROM AUTHOR]

The paper deals with the impact of microtransactions on the development of new business models in the field of computer games, caused by various monetized offerings within computer games, as well as other platforms such as gaming consoles and smartphones. The paper also examines the reasons for the emergence of business models based on microtransactions in the video game industry and the infl uence of the industry’s transition towards a consumer-oriented model. The central hypothesis of the paper is that microtransactions have a strong impact on the development of business models in the entire video game industry, and that over time, the value of products monetized in this way increases, despite consumers’ efforts to prevent this development direction of the industry. The paper conducted research on a population of consumers of video game content, mainly students of the Faculty of Organization and Informatics at the University of Zagreb, as well as respondents from other EU countries. The conclusion is that the number of microtransactions and the number of business models based on microtransactions will continue to grow in the future, but there are ways to combat those forms of microtransactions that have negative effects on consumers, especially minors, and, in some situations, even illegal. [ABSTRACT FROM AUTHOR]

Ruike Renee Zhao has faith in the robots. She has faith in the possibilities they offer and the future they promise. She has faith in their engineering and their mechanics. [...]

This study proposes a conceptualization of a prototype And a possibility to converge classical database and fully quantum database. This study mostly identifies the gap between this classical and quantum database and proposes a prototype that can be implemented in future products. It is a way that can be used in future industrial product development on hybrid quantum computers. The existing concept used to consider oracle as a black box in this study opens up the possibility for the quantum industry to develop the QASAM module so that we can create a fully quantum database instead of using a classical database as BlackBox. As the Toffoli gate is basically an effective NAND gate it is possible to run any algorithm theoretically in quantum computers. So we will propose a logical design for memory management for the quantum database, security enhancement model, Quantum Recovery Manager & automatic storage management model, and more for the quantum database which will ensure the quantum advantages. In this study, we will also explain the Quantum Vector Database as well as the possibility of improvement in duality quantum computing. It opens up a new scope, possibilities, and research areas in a new approach for quantum databases and duality quantum computing. CCS Concepts: • Computer systems organization? Quantum computing; • Software and its engineering? Software infrastructure; • General and reference? Reference works; General literature; General literature; Reference works; Performance; • Information systems? DBMS engine architectures; Main memory engines; Key-value stores; Database utilities and tools; • Hardware? Quantum technologies;. [ABSTRACT FROM AUTHOR]

The study consists of an unreliable multiserver retrial queue with feedback where clients may resist to join the orbit and the service provider is erratic. In this model we have considered c service providers at a time and if all the c servers are busy, the incoming clients wait in the retrial orbit. The model is unfolded using Matrix Geometric Method (MGM) to obtain the rate matrix and stationary probabilities. The system with retrials is modelled and the performance measures and reliability indices are procured. These derived quotients are then visualised and validated with the help of tables and graphs. Further, the cost analysis of the model is carried out and the optimal cost for the system is obtained using Particle Swarm Optimization (PSO) algorithm for matrix method. The optimal repair and retrial rates are obtained using PSO technique which helps the computer and manufacturing industry to work towards better decision policies. [ABSTRACT FROM AUTHOR]

THE PAPER PROVIDES AN OVERVIEW OF THE BUSINESS LANGUAGE DEVELOPMENT AND ACQUISITION. SIGNIFICANT ENGLISH TERMS OF BUSINESS ARE DEALT WITH IN THE PAPER. WE HAVE SELECTED THEM TAKING INTO CONSIDERATION THE CURRICULUM OF THE SCHOOL OF COMPUTER SCIENCE FOR BUSINESS MANAGEMENT, WHICH ENCOMPASSES BUSINESS SUBJECTS THAT EQUIP LEARNERS WITH HARD AND SOFT SKILLS DEMANDED BY EMPLOYERS NOWADAYS. EMPHASIS IS PLACED UPON THE LINGUISTIC INFLUENCES THAT NEED TO BE INCLUDED IN THE INSTRUCTION OF ENGLISH FOR SPECIFIC PURPOSES (ESP), FOR INSTANCE FOREIGN TERMS THAT ARE USED IN CERTAIN BUSINESS AREAS AND UPON THE LEXICAL-SEMANTIC FIELDS WITHIN THE BUSINESS VOCABULARY. THESE LEXICAL-SEMANTIC FIELDS SHOW THE INTERACTION BETWEEN THE COMMON LANGUAGE OF BUSINESS AND THE SPECIALIZED LANGUAGE IN FUNCTIONAL AREAS OF BUSINESS. THEREFORE, THE ESP TEACHER SHOULD INCORPORATE THESE LEXICALFIELDS IN LESSONS BECAUSE THEY ARE EEFECTIVE TOOLS IN CREATING EXERCISES THAT ACCELERATE THE ACQUISITION OF SPECIALIZED TERMS. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to examine the effects of relationship digitization benefits on reseller profit. The authors hypothesized two pathways by which the digitization benefits improve reseller profitability: direct and indirect. The direct pathway is via enhancing the reseller's digital resources and capabilities and the indirect pathway is by improving the relationship with a focal manufacturer by reducing inequity and enhancing interorganizational trust. Design/methodology/approach: The authors use the survey methodology to attempt to understand the interorganizational dynamics of digitization. The authors survey more than 200 resellers in the computer and computer components industry. Findings: Overall, the pattern of results generally supports the framework for direct and indirect effects of digitization benefits on the bottom line of the reseller, as well as the idea of examining the benefits structure components of benefit magnitude and benefit asymmetry. Sales benefit magnitude improves reseller profit directly, as enhanced digital resources result in a more efficient and effective reseller salesforce. The indirect effects flow through to profitability via inequity and trust. Originality/value: The authors are not aware of any prior research that uses the asymmetry and magnitude framework to approach the digitization phenomenon. [ABSTRACT FROM AUTHOR]

This case study situates an early text-to-speech computer developed for blind persons, the Kurzweil Reading Machine (KRM), within a broader history of speech synthesis technologies. Though typically no more than a footnote in the technical history of speech synthesis, the KRM was still a powerful symbol of innovation that reveals how disability can be used as a pretext for funding technology development. I argue that various boosters held the KRM up as a symbol of technological solutionism that promised to fully enroll blind people into the US political economy. However, the success of the KRM as a symbol belies its technical flaws, the federal subsidies needed to bring it to fruition, and the structural barriers to its use that were elided by its Utopian promise. KEYWORDS: text to speech, speech synthesis, reading machines, assistive technologies, IN EARLY 1976 WALTER CRONKITE DEFERRED HIS USUAL sign-off on the CBS Evening News to a new text-tospeech computer system, the Kurzweil Reading Machine (KRM), named for its twenty-seven-year-old inventor, [...]

This article investigates how computing discourses, including user guides, news articles, and advertisements, urged personal computer users in the 1970s and 1980s to preventively care for their devices. Through hygiene recommendations related to eating, drinking, and dusting, these discourses warned that computers' 'health' depended upon humans. Importantly, they interpreted care as individual responsibility by putting the onus on users to behave properly. Within this frame, such texts described repairs as unfortunate medical interventions resulting from neglect. The piece argues that computing discourses have historically defined 'care' and 'repair' in opposition, as acts of doting prevention and undesirable intervention, respectively. KEYWORDS: care, maintenance, prevention, repair, computer history, A 1984 MACWORLD ARTICLE ASKED ITS READER, 'What on earth do teeth and the Macintosh have in common?' The article explained that 'with the Mac, you're not up against cavities, [...]

In terms of utility and technical quality, open-source software solutions have become a common option for many libraries. As barriers to adoption have been reduced and systems such as FOLIO appear poised to change the landscape of LIS technology, it is worth examining how the use of open source can support the normative core values of librarianship and to outline a strategy for critical engagement with the technology that is beneficial to patrons and libraries. Such a strategy will require further codification, institutionalization, and investigation of open source at many levels., INTRODUCTION Open-source software has continued to gain popularity among libraries in the past decade. It has moved from the periphery to become a major competitor with some of the most [...]

The financial system, a complex network, operates primarily through the exchange of capital, where the role of information is critical. This study utilizes the transfer entropy method to examine the strength and direction of information flow among different capital flow time series and investigate the community structure within the transfer networks. Moreover, the spreading dynamics of the capital flow transfer networks are observed, and the importance and traveling time of each node are explored. The results imply a dominant role for the food and drink industry within the Chinese market, with increased attention towards the computer industry starting in 2014. The community structure of the capital flow transfer networks significantly differs from those constructed from stock prices, with the main sector predominantly encompassing industry leaders favored by primary funds with robust capital flow connections. The average traveling time from sectors such as food and drink, coal, and utilities to other sectors is the shortest, and the dynamic flow between these sectors displays a significant role. These findings highlight that comprehension of information flow and community structure within the financial system can offer valuable insights into market dynamics and help to identify key sectors and companies. [ABSTRACT FROM AUTHOR]

Node-graph-based procedural materials are vital to 3D content creation within the computer graphics industry. Leveraging the expressive representation of procedural materials, artists can effortlessly generate diverse appearances by altering the graph structure or node parameters. However, manually reproducing a specific appearance is a challenging task that demands extensive domain knowledge and labor. Previous research has sought to automate this process by converting artist-created material graphs into differentiable programs and optimizing node parameters against a photographed material appearance using gradient descent. These methods involve implementing differentiable filter nodes [Shi et al. 2020] and training differentiable neural proxies for generator nodes to optimize continuous and discrete node parameters [Hu et al. 2022a] jointly. Nevertheless, Neural Proxies exhibits critical limitations, such as long training times, inaccuracies, fixed resolutions, and confined parameter ranges, which hinder their scalability towards the broad spectrum of production-grade material graphs. These constraints fundamentally stem from the absence of faithful and efficient implementations of generic noise and pattern generator nodes, both differentiable and non-differentiable. Such deficiency prevents the direct optimization of continuous and discrete generator node parameters without relying on surrogate models. We present Diffmat v2, an improved differentiable procedural material library, along with a fully-automated, end-to-end procedural material capture framework that combines gradient-based optimization and gradient-free parameter search to match existing production-grade procedural materials against user-taken flash photos. Diffmat v2 expands the range of differentiable material graph nodes in Diffmat [Shi et al. 2020] by adding generic noise/pattern generator nodes and user-customizable per-pixel filter nodes. This allows for the complete translation and optimization of procedural materials across various categories without the need for external proprietary tools or pre-cached noise patterns. Consequently, our method can capture a considerably broader array of materials, encompassing those with highly regular or stochastic geometries. We demonstrate that our end-to-end approach yields a closer match to the target than MATch [Shi et al. 2020] and Neural Proxies [Hu et al. 2022a] when starting from initially unmatched continuous and discrete parameters. [ABSTRACT FROM AUTHOR]

Industrial defect detection is a hot topic in the field of computer vision and industry. Industrial defects are diverse and complex, and well‐known machine learning based methods can often not effectively extract features of industrial defects and achieve good detection results. To address the above problems, this paper introduces a deep learning model for industrial defect detection. First, a two‐branch decoupled head, which can facilitate model training through separating the prediction of category and regression is designed. Also, two inverted bottleneck structures are designed to enhance the ability of the model to extract features. Moreover, an attention‐enhanced feature fusion (AEFF) module is designed and integrated into the neck network to achieve effective feature fusion. Extensive experiments are conducted on three public datasets, namely the DeepPCB dataset, NEU‐DET dataset, and NRSD‐MN dataset. The obtained results demonstrate that the proposed model achieves competitive results compared to the state‐of‐the‐art methods. The proposed model achieves mAP@0.5:0.95, 71.78%, 36.04%, and 48.69% on the PCB dataset, NEU‐DET dataset, and the NRSD‐MN dataset, respectively. [ABSTRACT FROM AUTHOR]

In the context of corporate activities on an unprecedented scale, the economies of the nations of the modern world have to cope with a number of challenges, some of which frequently develop into issues. Any crisis that needs to be resolved requires careful planning, detailed implementation, and a substantial investment of human and financial resources. On the one hand, a crisis hampers business processes, and on the other hand, it may well turn into an opportunity for quicker business development. The present paper aims at analyzing the process of introduction of an online service - as one of the areas of business development - on the example of business entities carrying out their activities in the Republic of Ajara A.R. The research has been targeted at small and medium businesses of HoReCa sector only. The study has been carried out using an interdisciplinary methodology, namely in-depth interviews with business entities, as well as a survey of business entities in respect to particular components of the research. It was revealed that the business sectors that have developed rapidly in recent years in terms of crises and restrictions caused by the pandemic are the following: • online trade; • delivery services; • online educational platforms; • Computer and communication business; The analysis showed that: • The rapid development of online services of the majority of business entities was significantly caused by the Covid-19 pandemic; • Most business entities would not have considered implementing of these services if not the pandemic restrictions; • Most businesses think the crisis caused by the pandemic andforce majeure to be the reason for the rapid development of their business. [ABSTRACT FROM AUTHOR]

Managing the continuous and fast-growing volume of information, the progress in the Internet-of-Things, and the evolution from digitalization to networking are huge technological chores. Si-based integrated chips face increasing demands as they strive to meet these challenges. However, there is growing recognition that information processing and computing based on molecules performing logic operations may play a decisive role in shaping the future of the computer industry. Molecular logic gates are molecular counterparts of electronic devices that, instead of exclusively by electrical signals, can be stimulated by diverse chemical or physical input signals that produce optical outputs according to a well-defined logical transfer function. Several materials have been applied for molecular logic, however, the Ln3+-based ones appear to be a commendable choice, as they can respond to both chemical and physical stimuli, presenting unique photophysical properties that make them quite popular for photonics applications. Here we critically review illustrative molecular logic systems based on Ln3+ ions and discuss their potential for integration in future molecular photonic–electronic hybrid logic computing systems. [ABSTRACT FROM AUTHOR]

This study examines the role of target venture CEO overconfidence in the acquisitions initiated by established firms. We postulate that the more confident a target venture CEO, the greater the difficulty for an acquirer to negotiate and integrate with the target, reducing the likelihood of a completed acquisition. We further contend that this proposed main effect may vary depending on certain contextual factors. Results based on a sample of acquisitions within the U.S. computer industry suggest that target venture CEO overconfidence reduces the likelihood of acquisition completion and that this effect is contingent on crucial boundary conditions. Plain English Summary: Many acquirers consider target CEO characteristics when deciding whether to buy a venture. Analyzing a sample of acquisitions within the U.S. computer industry, we find that the extent to which target CEOs have excessive self-confidence reduces the likelihood of acquisition completion. This is because CEOs with excessive self-confidence can be direct and aggressive when negotiating with acquirers. This effect is stronger both when ventures have more technological resources and exhibit higher growth, and also when acquisition exits within an industry are more prevalent. Buyers are advised to be more flexible and prepared when interacting with highly confident entrepreneurs. [ABSTRACT FROM AUTHOR]

In this work, we present a boundary oriented graph embedding (BOGE) approach for the graph neural network to assist in rapid design and digital prototyping. The cantilever beam problem has been solved as an example to validate its potential of providing physical field results and optimized designs using only 10 ms. Providing shortcuts for both boundary elements and local neighbor elements, the BOGE approach can embed unstructured mesh elements into the graph and performs an efficient regression on large-scale triangular-mesh-based finite element analysis (FEA) results, which cannot be realized by other machine-learning-based surrogate methods. It has the potential to serve as a surrogate model for other boundary value problems. Focusing on the cantilever beam problem, the BOGE approach with 3-layer DeepGCN model achieves the regression with mean square error (MSE) of0.011706 (2.41% mean absolute percentage error) for stress field prediction and 0.002 735 MSE (with 1.58% elements having error larger than 0.01) for topological optimization. The overall concept of the BOGE approach paves the way for a general and efficient deep-learning-based FEA simulator that will benefit both industry and Computer Aided Design (CAD) design-related areas. [ABSTRACT FROM AUTHOR]

The demand for state government health and human services (H/HS), such as child care, cash, food, and medical benefits continues to grow due to the COVID-19 pandemic, rising inflation, and [...]