Tech Industry Research Articles

Sustainable and green removal of arsenic from crude tin via vacuum gasification–directional condensation

In the realm of high–tech industries, Sn has emerged as an indispensable strategic metal. However, the pyrometallurgical refining process of crude Sn is marred by the production of a substantial amount of hazardous solid waste, notably As–Al slag, posing a direct threat to the ecological safety of the production areas. This study delineates a novel process for the removal of As from crude Sn, employing vacuum gasification–directional condensation, corroborated by theoretical calculations and laboratory experimental research. Theoretically, at a system pressure ranging from 1–10 Pa, the initial volatilization temperatures of Sn and As exhibit significant variance. However, the formation of intermetallic compounds can induce Sn volatilization. Experimentally, the condensation temperature for As was predominantly within 331.7–484.7 K. During the vacuum gasification–directional condensation process, the presence of Sn4As3 intermetallic compounds impeded As volatilization. And under optimal conditions, the As content in the Sn product was recorded at 0.0053 %. Inspired from the calculations, a multistage vacuum distillation method was successfully proposed and achieved to remove As from crude tin. Typically, the total material recovery rate is 99.96 %, and the direct rate of Sn is 93.02 %. The total power consumption and emission of this process are better than the corresponding parameters of other treatment processes, which is a promising solution for the cleaning and effective removal of As.

A Human-Centric Approach towards Equity and Inclusion in AI Education

Artificial Intelligence (AI) has become pervasive in modern lives, with AI generative tools driving further transformation. However, a notable issue persists: the underrepresentation of females and individuals from ethnic and racial minorities in the tech industry. Despite generally positive attitudes toward technology among young students, this enthusiasm often does not extend to aspirations for careers in the field. To address this disparity, many schools in the United States are now offering computer science and AI courses at the high school level. Nevertheless, students from underrepresented groups often feel disconnected from these subjects, leading to low enrollment rates. Research underscores that students' career aspirations are solidified between the ages of 10-14 yrs, highlighting the importance of engaging them with computer science and computing skills during this formative period. Leveraging the Bourdieusian concept of social capital, this paper proposes educational interventions tailored for elementary schools. By nurturing students' technical social capital, these interventions aim to foster an inclusive ecosystem from an early age, when aspirations are taking shape. Ultimately, the goal is to enhance the accessibility of computer science education and related skills, empowering young students from underrepresented groups to pursue higher studies and careers in computer science and AI fields.

Anytime-valid off-policy Inference for Contextual Bandits

Contextual bandit algorithms are ubiquitous tools for active sequential experimentation in healthcare and the tech industry. They involve online learning algorithms that adaptively learn policies over time to map observed contexts X t to actions A t in an attempt to maximize stochastic rewards R t . This adaptivity raises interesting but hard statistical inference questions, especially counterfactual ones: for example, it is often of interest to estimate the properties of a hypothetical policy that is different from the logging policy that was used to collect the data—a problem known as “off-policy evaluation” (OPE). Using modern martingale techniques, we present a comprehensive framework for OPE inference that relaxes unnecessary conditions made in some past works (such as performing inference at prespecified sample sizes, uniformly bounded importance weights, constant logging policies, and constant policy values, among others), significantly improving on them both theoretically and empirically. Importantly, our methods can be employed while the original experiment is still running (that is, not necessarily post hoc), when the logging policy may be itself changing (due to learning), and even if the context distributions are a highly dependent time series (such as if they are drifting over time). More concretely, we derive confidence sequences for various functionals of interest in OPE. These include doubly robust ones for time-varying off-policy mean reward values, but also confidence bands for the entire cumulative distribution function of the off-policy reward distribution. All of our methods (a) are valid at arbitrary stopping times; (b) only make nonparametric assumptions; (c) do not require importance weights to be uniformly bounded, and if they are, we do not need to know these bounds; and (d) adapt to the empirical variance of our estimators. In summary, our methods enable anytime-valid off-policy inference using adaptively collected contextual bandit data.

Silicon Valley Bank Collapse: Causes & Consequences

This paper discusses the concept of bank bankruptcy and highlights the recent failure of Silicon Valley Bank (SVB) as a significant case similar to the Lehman Brothers' collapse in 2008. Lehman's failure was attributed to the U.S. housing market collapse and internal risk management issues. SVB's failure, occurring in 2023, resulted from both internal factors (improper decisions, high-risk strategies, and poor financial management) and external factors (economic recession, Federal Reserve interest rate hikes, regulatory challenges, and changes in the tech industry). The bankruptcy of SVB has far-reaching economic impacts on depositors, borrowers, entrepreneurs, investors, and the banking industry. It triggers a crisis of confidence, liquidity pressure, and systemic risks. The Federal Reserve implements emergency measures to address the fallout, reflecting on regulatory approaches to mitigate threats to the banking system. The comparison with Lehman Brothers underscores similarities in improper development strategies, asset structures, risk management, and regulatory oversight, leading to systemic risks and undermining financial market confidence.

Enhanced Machine Learning for Cloud Computing Electricity Cost Prediction

Cloud computing is becoming popular in the tech industry as it offers convenient computing without the need to buy physical hardware. Instead, companies provide these services using their own computers and servers powered by electricity. However, running these data centers consumes a lot of energy, and with rising electricity prices, minimizing energy usage has become a big challenge. One way to tackle this is by efficiently managing data storage and scheduling tasks. In this article, we suggest using an advanced machine learning model called Extreme Gradient Boosting (XGBoost) to predict electricity prices and optimize data storage, helping to reduce energy costs in data centers

Facing up to Problem Gambling: Tracing the Emergence of Facial Recognition Technology as a means of Enforcing Voluntary Self-Exclusion

AbstractComputer technology has long been touted as a means of increasing the effectiveness of voluntary self-exclusion schemes – especially in terms of relieving gaming venue staff of the task of manually identifying and verifying the status of new customers. This paper reports on the government-led implementation of facial recognition technology as part of an automated self-exclusion program in the city of Adelaide in South Australia—one of the first jurisdiction-wide enforcements of this controversial technology in small venue gambling. Drawing on stakeholder interviews, site visits and documentary analysis over a two year period, the paper contrasts initial claims that facial recognition offered a straightforward and benign improvement to the efficiency of the city’s long-running self-excluded gambler program, with subsequent concerns that the new technology was associated with heightened inconsistencies, inefficiencies and uncertainties. As such, the paper contends that regardless of the enthusiasms of government, tech industry and gaming lobby, facial recognition does not offer a ready ‘technical fix’ to problem gambling. The South Australian case illustrates how this technology does not appear to better address the core issues underpinning problem gambling, and/or substantially improve conditions for problem gamblers to refrain from gambling. As such, it is concluded that the gambling sector needs to pay close attention to the practical outcomes arising from initial cases such as this, and resist industry pressures for the wider replication of this technology in other jurisdictions.

Salvage technoscience: Conserving and extracting the value of the Amazon Rainforest

Very few forests take up as much space in contemporary Western imagination as the Amazon. It is the archetypical mythological forest that many people want to save and even more people want to exploit. Over the years, it has been the object of multiple campaigns to ‘save the rainforest’ from resource extraction both within South America and beyond, within scientific communities as well as broader publics. This article addresses a concern with how to extract the resources of the Amazon Forest in ways that are more sustainable than past and contemporary logging, mining and farming. This concern is promoted by Brazilian scientists who have allied themselves with well-intended tech industry initiatives and small-scale entrepreneurs. We refer to this combination of conservation with extraction as salvage technoscience, an approach currently dominating the struggle for the resources of the Amazon by attempting to reconcile the opposing views of the forest as an economic asset and as an object to be preserved. Our contribution thus demonstrates the interwoven nature of capitalist extraction and environmental conservation under the auspices of technoscientific progress.

Artificial Intelligence, Big Data, and Cloud Infrastructures: Policy Recommendations for Enhancing Women's Participation in the Tech-Driven Economy

This study investigates the underrepresentation of women in Artificial Intelligence (AI), Big Data, and Cloud Infrastructures, exploring the barriers and challenges they face and assessing the effectiveness of current policies and initiatives to promote gender diversity within the tech industry. Employing quantitative research methods, the study used a survey distributed to 572 female professionals in tech-related roles across various industries, achieving a 67.9% response rate. Multiple regression analysis was utilized to test four main hypotheses concerning barriers to entry and advancement, the inclusivity of educational programs, the impact of diverse teams on innovation and performance, and the effectiveness of gender-inclusive policies. Key findings indicate that the type of organization and specific tech sectors significantly influence the barriers experienced by women. Notably, gender diversity within teams correlates strongly with improved innovation and performance. However, educational and training programs often fail to be sufficiently inclusive, underscoring the need for programs better tailored to women's needs in tech fields. Moreover, the study confirms that implementing gender-inclusive policies substantially increases women's participation in tech roles, especially when these policies are applied long-term. Based on the findings, recommendations are made for adopting comprehensive, inclusive practices at organizational and educational levels, promoting diversity in team composition and leadership, committing long-term to effective policy implementation, and developing supportive networks through mentorship and sponsorship programs. These measures are aimed at reducing gender disparities and enhancing the integration of women into the high-tech economy. The study underscores the critical role that strategic policy-making and organizational change play in fostering an inclusive tech environment that not only addresses gender disparities but also enhances overall industry innovation and performance.

Impact of Artificial Intelligence in Education Sector

Abstract: Building intelligent computers that can carry out tasks that traditionally require human intelligence is the goal of artificial intelligence (AI), a broad field of computer science. While there are many different approaches to AI, it is an interdisciplinary discipline, and recent developments in machine learning and deep learning in particular are causing a paradigm change in almost every area of the tech industry. Machines equipped with artificial intelligence are able to mimic or even outperform human brain functions. And as generative AI tools like ChatGPT and Google's Bard proliferate and selfdriving car technology advances, AI is quickly becoming a part of daily life and a field that businesses in every sector are investing in.

Disparities in Correlation Between Total Revenue and EPS: The Influence of R&D and Regulatory Environments in Tech and Pharmaceutical Industries

In the realm of financial analysis, discerning the correlation between total revenue and Earnings Per Share (EPS) serves as a pivotal indicator of a corporations financial stability and potential for growth. This research navigates through the divergent pathways of the tech and pharmaceutical industries, elucidating a marked correlation in the former and a distinctly subdued one in the latter. By immersing into an incisive analysis that encompasses industry-specific attributes, market dynamics, and financial protocols, this paper not only shines a light on the disparities in the revenue-EPS correlation across these industries but also posits that the divergences can be substantially attributed to the disparate R&D investment strategies and regulatory environments that inherently characterize them. The encapsulated findings offer a refined lens through which the financial undercurrents of pivotal industries can be examined, providing foundational insights for investors and policy-makers alike.

Layoffs and corporate performance: evidence based on the US tech industry

Layoffs and corporate performance: evidence based on the US tech industry

Deep Time and Microtime: Anthropocene Temporalities and Silicon Valley’s Longtermist Scope

Living in Anthropocene times entails living in relation to two seemingly separate temporalities – the microtime of digital operations and the deep time of geological upheaval. Though divergent, these temporalities are united by their unavailability to perception; microtime proceeds too fast to perceive directly, while deep time is too vast to apprehend. Taking these temporalities as a point of departure, this paper develops three arguments. First, it asserts that the temporalities of deep time and microtime increasingly impact contemporary existence, complicating familiar categorizations of temporal experience. Second, it argues that these ostensibly separate temporalities are ontologically connected through the operations of the tech industry, which is constructing a microtemporal system that extracts the planet’s deep time resources to delimit the future both materially and cognitively. Third, it suggests that Silicon Valley legitimizes these processes by funding the philosophy of longtermism, which appeals to distant timescales to marginalize injustices in the present.

Field-building and the epistemic culture of AI safety

The emerging field of “AI safety” has attracted public attention and large infusions of capital to support its implied promise: the ability to deploy advanced artificial intelligence (AI) while reducing its gravest risks. Ideas from effective altruism, longtermism, and the study of existential risk are foundational to this new field. In this paper, we contend that overlapping communities interested in these ideas have merged into what we refer to as the broader “AI safety epistemic community,” which is sustained through its mutually reinforcing community-building and knowledge production practices. We support this assertion through an analysis of four core sites in this community’s epistemic culture: 1) online community-building through Web forums and career advising; 2) AI forecasting; 3) AI safety research; and 4) prize competitions. The dispersal of this epistemic community’s members throughout the tech industry, academia, and policy organizations ensures their continued input into global discourse about AI. Understanding the epistemic culture that fuses their moral convictions and knowledge claims is crucial to evaluating these claims, which are gaining influence in critical, rapidly changing debates about the harms of AI and how to mitigate them.

Clean and sustainable elimination of arsenic–aluminum slag from smelted crude tin via vacuum volatilization – Fractional condensation

In high–tech industries, Sn is an indispensable strategic metal. However, the pyrometallurgical refining process of crude Sn is marred by the production of a substantial amount of hazardous solid waste, primarily As–Al slag, which poses a direct threat to the ecological safety of production areas. To address this problem, this study developed a novel process for that eliminates As–Al slag from a source of crude Sn by employing a vacuum volatilization – fractional condensation process, which was validated by theoretical calculations and laboratory experiments. Theoretically, at system pressures in the 1–10 Pa, the initial volatilization temperatures of Sn, Sb, and As vary significantly. However, the formation of intermetallic compounds can induce the volatilization of Sn. Experimentally, the condensation temperature for Sb was predominantly between 603 K and 810 K, and that for As was between 331.7 K and 484.7 K, which are significantly different ranges. Under optimal experimental conditions, the separation rate of Sb in the Sn–Sb alloy was 93.92%, and the resultant Sn product contained 0.0143% Sb. During the volatilization of the Sn–As alloy, the presence of Sn4As3 compounds impeded the volatilization of As; hoewver, the As content in the Sn product was 0.0053%. In the directional condensation of the As–Sb alloy, the condensation temperatures for As and Sb were similar to those of the pure metals, yielding As and Sb products with purities of 99.99% and 99.95%, respectively. Industrial production trials treating HACT via a continuous vacuum separation – intermittent vacuum separation process achieved direct recovery rates of 72.99% for Sn and 71.15% for Sb, with a total energy consumption as low as 1086 kWh/t. Similarly, the treatment of LACT via a multi–stage continuous vacuum separation process yielded residues that satisfied production standards at a treatment cost of 1187 RMB/t. This entire process requiresno additives, and substantially mitigates the generation of As–Al slag at the source.

Imaginaries of democratization and the value of open environmental data: Analysis of Microsoft's planetary computer

The proliferation of environmentally oriented programs within the tech industry, and the industry's coinciding efforts toward data and technology democratization, generate concerns about the status of environmental data within digital economy. While the accumulation of digital personal data has been a cornerstone of domination of the data analytics industry, many believe environmental data to be a source of “untapped potential.” The potential of environmental data, the argument goes, would benefit equally the digital economy, environmental sciences, and academic data and artificial intelligence experts. This article analyzes the proliferation of the rhetoric about open environmental data by focusing on Microsoft's Planetary Computer cloud computing program and computer vision experts who curate and use biodiversity data stored on Microsoft's servers. Through an analytical framework of sociotechnical imaginaries, the article draws connections between visions of future for environmental knowledge production and governance promoted by Microsoft and the work of computer vision experts intending to benefit from the potential of environmental data as machine learning training sets while at the same time helping environmental sciences. Although environmental data on the Planetary Computer is democratized, it nonetheless becomes a valued asset to data economy, but often with unintended consequences, such as enabling citizen science biodiversity data to be used by state surveillance apparatus. The article challenges the view that data's democratization is unproblematically serving environmental sciences by examining the consequences of imaginaries of democratization emerging from the data industry leaders and processes of nonmonetary valuation of environmental data by experts who curate these datasets.

What Does the World's Biggest Tech Event Tell Us? Key Pointers from CES 2024

Getting to know what's in the making by tech industry leaders is something transformative to anyone's awareness in the 21 century. It's like our grandparents having to read journal every morning. What would people at that time consider the person who doesn't read the journal at all? You name it. For tech it's the same; knowing how the technological landscape is shaping helps you prepare for the future by being emotionally ready. For software engineers, it's even critical than ordinary citizens. This is because it's more than emotional readiness. It is career and professional readiness as well. But how you will get to know the key technology industry leaders are working on? One of the key venues is the Consumer Electronics Show (CES). It is an annual giant event that brings in tech leaders in the industry to talk about their latest innovations. This paper brings to you key observations from CES 2024, held in January. From those observations you can extrapolate key future points about the tech ahead of us.

Analysis of the Influence of High - Tech industry Agglomeration on Sulfur Dioxide Emissions in Yangtze River Delta

This article mainly studies the impact of high-tech industry agglomeration on sulfur dioxide emissions in the Yangtze River Delta region. By establishing an index system, it is found that the development level of high-tech industry in different regions of the Yangtze River Delta is obviously different, among which Anhui province has the lowest level , and Shanghai and Jiangsu province have the highest level . In further empirical analysis, this paper uses panel data regression analysis, found that the high-tech industry agglomeration level of sulfur dioxide emissions have influences on inverted "U" type, namely with the improvement of agglomeration level, sulfur dioxide emission levels fall after rise, and in recent years, the population is not the key factors influencing the sulfur dioxide emission levels in Yangtze river delta, The secondary industry is still an important factor affecting sulfur dioxide emissions.

도시의 산업구조 변화에 따른 도시디자인 전략 연구

A city that takes on collapse of regional core industry would reset vision of city, seek new growth engine industry, and establish urban planning to rebuild regional economy and recover city's vitality. In 90s Malmö city of Sweden encountered a financial catastrophe resulted by bankruptcy of shipbuilding industry which had supported Malmö's Economy. Malmö city set ups Eco City as future vision of city, and drives related projects and policy to achieve goals. As result, high tech industry companies flow in and population of city grows and Malmö is chosen as the best lively city in Europe. The Study aims to suggest strategy and methods of urban design for reference of Korea city where meet similar crises through analyzing success of Malmö. Even Malmö selects high-tech industry as new engine for city, urban design of Malmö focuses on life quality of citizen, and invests projects to visualize city vision. The city establishes detaild design guideline to realize ‘eco-city’, and develop urban design to practice and experience eco-life such as green area, bike path, and energy recycle. Malmö gives lessons including cooperative setting of city vision, urban design centered on residence, linkage with vision and design, and importance of landmark.

Exploring the Influence of Organizational Culture, Organisational Structure and Pay Structure on Career Progression for Women in the Tech Industry

Women's career progression in the tech industry is stagnant, which causes concern about the opportunities for women in this sector. This study explores the complex relationship between organisational culture, structure, and pay in shaping the progression of women's careers. The proposed conceptual framework illustrates the significant factors in promoting inclusive environments to facilitate women's professional development. The glass ceiling theory explains the systemic organisational obstacles that prevent women from advancing inside organisations. The results emphasise that organisations and policymakers need to tackle these barriers to promoting gender equality and establishing conditions that support women's success and economic empowerment.

The domino effect of silicon valley Bank's bankruptcy and the role of FED's monetary policy

This paper examines the spillover effects of bankruptcy by important tech industry banks—Silicon Valley Bank (SVB), Silvergate Bank, and Signature Bank—on the top 10 institutions in the MSCI Bank Index and the role that monetary policy by the US Federal Reserve (the Fed) played in this contagion, using Dynamic Conditional Correlation-Exponentional Generalized Autoregressive (DCC-EGARCH) and time-varying Granger-causality models. Our findings show that the dynamic conditional correlations among the banks were higher during the period of the SVB crisis, implying the presence of financial contagion from the bank's bankruptcy due to uncertainty triggered by its collapse. Financial contagion emerges between SVB and the top 10 banks, and the degree of contagion rises during the crisis period. Moreover, the Fed's monetary policy plays a significant role in contagion due to bank failures. The deepening of financial contagion followed the Fed's increases in the federal funds rate to combat inflation.