Shift In Industry Research Articles

Producing 100+ MPa Field Concrete in Developing Countries: Requirements and Challenges

Over the past three decades, there has been a paradigm shift in the concrete industry in which high strength and high performance concrete became more widely in use. However, producing ultra-high strength concrete surpassing 100 MPa compressive strength in the field remains a challenging task. This is primarily due to the various factors involved in such concrete and its sensitivity to many of these factors. This study aims at producing field concrete surpassing 100 MPa compressive strength using readily available materials worldwide. The study also addresses the requirements and challenges of 100+ MPa concrete in the field in order to possess similar properties to conjugate mixtures produced in the laboratory having same mix proportions. Concrete mixtures were prepared with different water-to-cement ratios and incorporated variety of chemical and mineral admixtures. Tests included fresh concrete, self-consolidation as well as hardened concrete properties in order to determine the properties of the concrete produced. The impact of other vital factors such as mixing process, ambient temperature, curing process and pumping are addressed taking field conditions into consideration. Several field visits were conducted to monitor field concrete that was produced using the designated mixtures. The study herein revealed that reaching 100 plus MPa concrete is doable using variety of readily-available constituents and mix proportion. However, the study pinpoints the importance of other crucial factors and field practices. Recommendations are provided to concrete users and practitioners to exercise better quality control and ensure high rate of success in producing ultra-high strength concrete in the field.

Analyzing the Financial Innovation Frontier: Risk-Return Profiles of Emerging Fintech Leaders

This paper delves into the evolving landscape of financial technology (fintech) companies, highlighting the substantial role they play in modern finance. By examining leading fintech firms such as Square, PayPal, and Robinhood, among others, we employ the Fama-French Three Factor Model to investigate their risk-return dynamics over recent years. We explore how these companies, known for pioneering accessible financial services and products, influence investment behaviors and the broader market. The study aims to understand the incremental risks and returns attributed to size and value factors in the context of these innovators in the financial sector. Results from the model offer insights into the relationship between company size, value characteristics, and expected returns, providing a nuanced understanding of the investment landscape in the fintech domain. The findings are significant for investors, regulators, and policymakers as they navigate the financial ecosystem reshaped by technological advancements and industry shifts.

Effect of Oxygen-Enriched Air on Flame Temperature and Emission Characteristics During Hot Producer Gas Combustion in a Modified Cross-draft Gasifier-Burner

ABSTRACT Converting biomass into energy is increasingly recognized as a vital strategy for achieving sustainable energy solutions and mitigating climate change. As global industries shift toward greener alternatives, the use of biomass, such as wood scraps, offers significant potential to reduce net greenhouse gas emissions compared to fossil fuels. Gasification provides a cleaner alternative to traditional combustion methods, particularly for applications requiring high-temperature heat sources, such as ceramics, metal processing, and cement production. This study investigates the optimization of flame temperature during the combustion of synthetic gas produced from a modified cross-draft gasifier and burner by adjusting oxygen-enriched secondary air concentrations, ranging from 21% to 36% by volume. Combustion using 36% oxygen-enriched air achieved a flame temperature of 1,358.3°C, reflecting a 20% increase compared to combustion with standard air (21% oxygen). Further reducing excess oxygen in the exhaust gases led to an additional 4% increase in flame temperature, reaching 1,416.0°C. However, this reduction also resulted in a marked rise in NOx emissions, increasing from 119 ppm to 424 ppm, due to the higher burner temperatures. These findings underscore the importance of balancing oxygen enrichment to optimize combustion performance while managing pollutant emissions, offering a sustainable alternative to fossil fuels for emission-intensive industries.

The Transformative Role of Mobile Applications in Digital Banking: A Comprehensive Analysis of Customer Engagement and Service Evolution

The digital transformation of banking services through mobile applications represents a pivotal shift in the financial services industry, fundamentally changing how customers interact with their financial institutions. This comprehensive article research examines the multifaceted role of mobile applications in driving digital banking transformation, with particular emphasis on security implementations, customer engagement strategies, and the impact of automation and personalization on service delivery. The article analyzes the critical aspects of mobile banking infrastructure, including multi-layered security protocols, regulatory compliance frameworks, and technological integration strategies that have shaped the modern banking landscape. Special attention is given to the accelerated digital adoption during the COVID-19 pandemic, which served as a catalyst for unprecedented changes in banking behavior and technological advancement. Through analysis of industry data and expert insights, this research demonstrates how mobile banking applications have evolved from simple transaction tools into sophisticated platforms that integrate advanced security protocols, artificial intelligence, personalized analytics, and seamless customer engagement features. The article reveals significant improvements in customer satisfaction, operational efficiency, and service accessibility, with mobile banking users showing an increase during the pandemic period and an increase in customer engagement for institutions implementing advanced analytics and personalization strategies. This article contributes to the understanding of mobile banking's evolutionary trajectory and provides valuable insights for financial institutions seeking to enhance their digital banking capabilities in an increasingly competitive landscape.

Decarbonizing the Food System with Microbes and Carbon-Neutral Feedstocks.

Harnessing CO2 and CO2-derived C1-C2 compounds for microbial food production can mitigate greenhouse gas emissions and boost sustainability within the food sector. These innovative technologies support carbon neutrality by generating nutrient-rich edible microbial biomass and biocompounds using autotrophic and heterotrophic microbes. However, qualifying microbial food viability and future impacts in the food sector remains challenging due to their diversity, technical complexity, socioeconomic forces, and incipient markets. This review provides an overview of microbial food systems and then delves into the technical interplay among feedstocks, microbes, carbon fixation platforms, bioreactor operations, and downstream processes. The review further explores developing markets for microbial food products, the industrial landscape, economic drivers, and emerging trends in next-generation food products. The analysis suggests a transformative shift in the food industry is underway, yet significant challenges persist, such as securing cost-effective feedstocks, improving downstream processing efficiency, and gaining consumer acceptance. These challenges require innovative solutions and collaborative efforts to ensure the future commercial success of microbial foods-doing so will create myriad opportunities to transform and decarbonize our food system.

Smart Vehicles, Smarter Defenses: AI's Role in SDV Cybersecurity

Software-Defined Vehicles (SDVs) represent a paradigm shift in the automotive industry, offering unprecedented flexibility, connectivity, and intelligence. This paper explores SDVs, elaborating on their architecture, benefits, intersection with technologies like smart cities and autonomous vehicles, cybersecurity challenges, and digital forensics methodologies for investigating cyber incidents. Finally, it examines the WP.29 regulation framework and its enforcement across nations, accompanied by detailed visualization charts.

(Invited) Study on the Effect of Additive in High Nickel NMC – Graphite Libs for HF- Scavenging and Stabilization of Cei Layer

Lithium ion batteries have been marked by steady progress over the past few decades owing to their high volumetric and gravimetric energy storage densities. The advent of LIBs resulted in the consistent shift of automotive industry towards hybrid EV's and BEV's. The Nissan Leaf, introduced in December 2010, became the first modern all-electric, zero tailpipe emission five door family hatchback to be produced for the mass market from a major manufacturer. Nissan has made constant efforts in improving the existing LIBs.With the advent of new high nickel cathode materials there has been an improvement in the energy density of LIBs and the ability to internalize the material supply chain. However, these cathode materials induce additional challenges due to instability of cathode electrolyte interface and dissolution of transition metals in the systems.[1-4]In the current work, electrolyte additive has been explored for stabilizing the cathode surface by stabilizing the cathode electrolyte interface in NMC811 and Graphite system in carbonate electrolyte system. The cycling data was analyzed with different capacity vs voltage and/current to identify the effect of additive and cycling degradation on the reaction kinetics and potentials. A methodology involving Distribution of Relaxation time (DRT) analysis, equivalent circuit design and electrochemical impedance fitting was developed to identify the different electrochemical components of the cell and their contribution towards the increase in internal resistance. The analysis was conducted during formation cycles and at different checkpoints for long cycling scenarios. The contribution of cathode and anode resistances were deconvoluted for the mixed impedance signal. Furthermore, XPS and TEM analysis were used to verify different features of the cathode and anode wherever applicable and feasible, to study the effect of the additive. Cui, Z., & Manthiram, A. (2023). Thermal Stability and Outgassing Behaviors of High‐nickel Cathodes in Lithium‐ion Batteries. Angewandte Chemie International Edition, 62(43), e202307243.Yu Wu, Xiang Liu, Li Wang, Xuning Feng, Dongsheng Ren, Yan Li, Xinyu Rui, Yan Wang, Xuebing Han, Gui-Liang Xu, Hewu Wang, Languang Lu, Xiangming He, Khalil Amine, Minggao Ouyang, Development of cathode-electrolyte-interphase for safer lithium batteries, Energy Storage Materials, Volume 37, 2021, Pages 77-86, ISSN 2405-8297.Hou, J., Lu, L., Wang, L. et al. Thermal runaway of Lithium-ion batteries employing LiN(SO2F)2-based concentrated electrolytes. Nat Commun 11, 5100 (2020).Yu Wang, Xuning Feng, Yong Peng, Fukui Zhang, Dongsheng Ren, Xiang Liu, Languang Lu, Yoshiaki Nitta, Li Wang, Minggao Ouyang, Reductive gas manipulation at early self-heating stage enables controllable battery thermal failure, Joule, Volume 6, Issue 12, 2022, Pages 2810-2820,ISSN 2542-4351.

Understanding Electrolyte Stability and Instability in Lithium-Ion Batteries with Nickel-Rich Cathodes

Developing electrolyte solutions that are stable at the electrode-electrolyte interfaces is critically important to enable high performance lithium-ion batteries. The discovery in the early 1990s that a mixture of ethylene carbonate (EC) and alkyl carbonate(s) could passivate graphite led to a step change in the cell energy density,1,2 and modern batteries still retain this skeleton electrolyte formulation and graphite anode combination. However, as the battery industry shifts to nickel-rich layered oxide cathodes for lower cost and higher energy density cells,3,4 an incompatibility between EC-containing electrolytes and the cathode is starting to be revealed. This is driving research efforts to, firstly, understand the chemical reactions at the nickel-rich cathode-electrolyte interface5-9 and, secondly, explore new EC-free electrolyte solutions.10-13 Here we examine the dominant degradation phenomena in nickel-rich NMC/graphite full cells with EC-containing and EC-free electrolytes. We combine a range of methods including cell cycling, operando pressure measurements, OEMS, XPS, XRF, and solution NMR to understand the behaviour of the two electrolyte systems throughout the full cell. The results of these studies will be discussed in conjunction with their implications for further development of compatible electrolytes for advanced lithium-ion battery chemistries.

Magnetically Guided Diagnosis of Current Flow Patterns inside Fault-Simulated Li-Ion Batteries

As the energy industry shifts towards cleaner and more electrified solutions, there's a growing demand for improved batteries, especially for electric vehicles (EVs) and stationary energy storage systems (ESSs). Lithium-ion batteries (LIBs) have been dominant due to their advantages such as high energy/power density, long lifespan, and fast charging capabilities. Despite advancements in cell design making them more efficient and compact, current LIBs face new challenges, particularly in improving reliability and addressing safety concerns. Recent instances of battery recalls due to explosions and fires have underscored the need to address potential failures stemming from internal faults in large-format cell designs and manufacturing, or from "latent" defects arising from misuse or aging under extreme conditions. Therefore, understanding failure scenarios and developing diagnostic tools are crucial for facilitating smoother troubleshooting for battery manufacturers and ensuring the development of safer battery technologies.Advanced imaging methods, both postmortem and in situ, have been developed to reveal the underlying causes of battery failures at the material level. These techniques help identify physical and chemical defects in active materials, degradation of electrolytes, and structural damage in inactive components like separators and current collectors. However, connecting these material-level defects with overall cell failure is difficult due to their random occurrence within complex cell structures. Additionally, many microscale imaging tools designed for customized cells are impractical for commercial cells, making it nearly impossible to detect small errors in large-scale cells during postmortem analysis. Therefore, global imaging techniques are more effective for identifying abnormalities and examining local faults. One powerful tool for this purpose is cell-penetrating X-ray computed tomography, which provides 3D images of the cell's internal structures, although it is time-consuming. However, this method may struggle to detect invisible errors that can lead to abnormal current flow in areas where latent defects exist. Thermal imaging is another useful tool for detecting abnormal heat generation in batteries caused by current flow, but without information on current direction, determining the type and extent of defects and cell degradation may be limited.This research introduces a real-time, noninvasive method for imaging magnetic fields (MFI) generated by current flow within Li-ion pouch cells. This technique provides fast scanning speeds (~100 mm/min), high spatial resolution (0.161 mm2), and a simple setup without the need for magnetic field shielding. Initially, we verified the correlation between magnetic fields and current by conducting MFI studies on 1D straight wires and 2D sheets carrying current. Subsequently, we identified an appropriate pouch cell geometry for analyzing current flow patterns using MFI, cross-referencing with current distribution maps generated by COMSOL simulations. To illustrate the effectiveness of current pattern analysis in identifying failure modes, we analyzed MFI data from fault-simulated batteries (FSBs) deliberately manufactured with faults like lead-tab contact failure, electrode misalignment, and stacking process folds. By using MFI destructive interference with a countercurrent-carrying cell-shaped 2D sheet below, we could selectively enhance areas of failure where abnormal current flows occur. This MFI-guided visualization of current distribution patterns in FSBs provides a nondestructive, immediate diagnostic approach to identify fault types that may not be discernible through electrochemical analysis alone.

Unveiling consumer satisfaction and its driving factors of EVs in China using an explainable artificial intelligence approach

As China’s electric vehicle (EV) industry shifts from policy-driven to market-oriented development, understanding post-purchase satisfaction and its driving factors becomes imperative. This study compiled objective product attributes and consumer online reviews for 1321 EV models from China’s largest automotive website, Autohome, covering the period between 2014 and 2022. By employing data mining and sentiment analysis (SA) techniques, this research extracted consumers’ overall satisfaction with EVs and identified the subjective product attributes that garnered the most attention in consumer online comments. Utilizing a machine learning (ML)—SHapley Additive exPlanations (SHAP) framework, the research pinpointed the most impactful objective and subjective product attributes on consumer satisfaction and ranked their impact intensity both statically and dynamically. The findings reveal that Chinese consumers are generally satisfied or very satisfied with their EVs. From a static perspective, distinctive objective product attributes of EVs, such as total motor power and range, play a crucial role in influencing consumer satisfaction. In terms of subjective product attributes, aspects like space, design, handling, and comfort are the most captivating to consumers and significantly shape their satisfaction. However, the dynamic analysis indicates that range anxiety persists, despite gradually increasing consumer satisfaction as the EV market matures. Additionally, price remains a crucial factor, particularly following the widespread implementation of subsidy withdrawal policies, making it the most sensitive factor for EV consumers. This study represents the first application of an explainable artificial intelligence framework to quantify the marginal impacts of various automotive product attributes on consumer satisfaction.

Engaged Amazigh Poetry in Meteor Airlines' "Agdal": Reviving Tradition while Addressing Global Environmental Issues

This paper analyzes Meteor Airlines' 2024 album Agdal, which revitalizes traditional Amazigh poetry through the emerging genre of Amazigh Rock. The study aims to explore how Agdal integrates the cultural heritage of pre-Saharan Amazigh oasis communities into global environmental discussions, with an emphasis on preserving nomadic and pastoral traditions. Using a qualitative analysis of the album’s lyrics, music style, and its reception, this research examines how the album marks a shift in Morocco's cultural industry from commercialized music production to efforts centered on cultural preservation and archiving. Findings suggest that Agdal, by merging traditional Amazigh poetry with contemporary rock elements, engages transnational audiences and contributes to an ongoing global dialogue on ecological and cultural sustainability. The study concludes that contemporary Amazigh music plays a crucial role in preserving historical memory, fostering cultural resilience, and raising environmental awareness, illustrating the impact of local cultural production on wider global issues

The Synergy Between Industry 5.0 and Circular Economy for Sustainable Performance in the Chinese Manufacturing Industry

The industrial shift from Industry 4.0 to Industry 5.0 has transformed organizational thinking, moving the focus from purely technological implementation to a more human-centered approach. The current study has focused on the Industry 5.0 technological capabilities to bring into circular economy practices aligned with sustainable development goals, aiming to enhance sustainable performance. Moreover, the resource-based theory has grounded the development of the comprehensive framework on Industry 5.0 technological capabilities (artificial intelligence capabilities, big data analytical capabilities, Internet of Things capabilities, machine learning capabilities, and blockchain technology capabilities) and circular economy practices (eco-design, management system, and investment recovery) to achieve sustainable performance (environmental performance, social performance, and economic performance). Data have been collected from 179 respondents from the Chinese manufacturing industry. Additionally, data have been analyzed using the structural equation modeling technique. The results showed that Industry 5.0 technological capabilities directly affect sustainable performance. Moreover, circular economy practices played a dual, moderating, and mediating role between Industry 5.0 technological capabilities and sustainable performance. The current study has contributed to filling a gap in the literature on Industry 5.0 capabilities, especially in the circular economy and sustainable performance perspective. The practical contribution recommended is that if organizations focused on their Industry 5.0 technological capabilities, it would boost circular economy practices and sustainable performance to achieve sustainable development goals.

Exploring advanced techniques in process automation and control: A generic framework for oil and gas industry applications

This paper explores advanced techniques in process automation and control, offering a generic framework tailored for applications within the oil and gas industry. The framework addresses the growing need for operational efficiency, safety, and sustainability in an industry characterized by complex, high-risk processes. The adoption of cutting-edge technologies such as Artificial Intelligence (AI), Machine Learning (ML), and Industrial Internet of Things (IIoT) has revolutionized process control systems, enabling real-time monitoring, predictive maintenance, and optimization of production assets. Advanced Process Control (APC) techniques, including model predictive control (MPC), multivariable control systems, and data-driven analytics, play a pivotal role in enhancing production, minimizing downtime, and reducing operational costs. This generic framework leverages the integration of AI and big data analytics for optimizing control strategies, ensuring accurate decision-making and improving operational responsiveness to changing conditions. Additionally, the paper examines how automated control systems mitigate risks associated with human error, while enhancing safety protocols by providing early warnings and real-time insights into equipment performance. Automation also supports the industry's shift towards more sustainable practices by improving energy efficiency, minimizing environmental impact, and enabling remote monitoring in hazardous locations. Through case studies and examples, the paper illustrates the successful implementation of these techniques in upstream, midstream, and downstream operations, highlighting the significant role of digital transformation in driving innovation. The proposed framework can be adapted to diverse oil and gas settings, serving as a foundation for developing scalable, flexible, and sustainable automation solutions. By outlining the key components and benefits of advanced process automation and control, this study contributes to the ongoing discourse on how technological advancements can reshape operational strategies in the oil and gas sector. Keywords: Process Automation, Control Systems, Oil And Gas Industry, Artificial Intelligence (AI), Machine Learning (ML), Industrial Internet of Things (IIoT), Advanced Process Control (APC), Model Predictive Control (MPC), Predictive Maintenance, Digital Transformation, Sustainability.

Two Pandemics and Workplace Shifts: Generational Considerations: A Multi-Factor Comparison of How Pandemics Impact Young Workers: Insights for Management and Employees©

This paper will compare several business-related factors. There has been relatively little academic research conducted to compare and contrast a breadth of workplace issues and dimensions during the early twentieth century’s Spanish Flu epidemic and the early twenty-first century’s COVID-19 pandemic in the United States. This manuscript analyzes several variables and adds to the literature by examining significant factors that impacted workers and their workplaces during these two significant healthcare incidents. The major factors discussed include the labor force members during both of these pandemics, the demographics of those workforces, the workplace safety, the labor economic data during these two pandemics, the wages & how they fluctuated, the labor unions & evolving perspectives towards unionization, and the dominant industry shifts. Several of the findings regarding Millennials and Generation Z workers are supported by this author’s current research of university students who were employed during the COVID-19 pandemic. The insights provided in this manuscript are instrumental for business managers and human resource management professionals, higher education, and for those who provide corporate training.

Review of the opportunities and challenges to accelerate mass‐scale application of smart grids with large‐language models

AbstractSmart grids represent a paradigm shift in the electricity industry, moving from traditional one‐way systems to more dynamic, interconnected networks. These grids are characterised by their intelligent automation, robust structure, and enhanced interaction with customers, backed by comprehensive monitoring and data analytics. The key of this transformation is the integration of data‐driven methods into smart grids. Compared to previous big data solutions, large language models (LLMs), with their advanced generalisation abilities and multi‐modal competencies, are crucial in effectively managing and integrating diverse data sources. They address challenges such as data inconsistency, inadequate quality, and heterogeneity, thereby enhancing the operational efficiency and reliability of smart grids. Furthermore, at the system level, LLMs improve human–system interactions, making smart grids more user‐friendly and intuitive. Last but not the least, the structure of LLMs performs inherent advantages in bolstering system security and privacy, alongside in resolving issues related to system compatibility and integration. The paper reviews the data‐empowered smart grids and for the first time finds and proposes opportunities and future directions for adopting LLMs to accelerate the mass‐scale application of Smart Grids.

Patient-reported disability progression outcomes among patients with multiple sclerosis: Results of an outcomes-based agreement.

Outcomes-based agreements (OBAs) are agreements between payers and manufacturers in which payment for medications is tied to patient outcomes. These contracts aim to measure the value of prescription medications on predefined clinical indicators in real-world patient populations. OBAs are gaining traction in the United States as the health care industry shifts from volume-based to value-based care. Multiple sclerosis (MS) is an appealing therapeutic area for OBAs because of its prevalence, high cost of medications, and multiple effective therapeutic options. To describe findings from an OBA that was prospectively conducted in a large regional health system for patients with MS taking interferon β-1a or dimethyl fumarate. In this prospective real-world analysis, commercial or health insurance exchange members were included based on the parameters of the OBA. Disability progression was assessed using a patient-reported outcome, patient-determined disease steps (PDDS). In the OBA, members aged 18 years or older with an MS diagnosis were included in the contract. A baseline score was collected for eligible members, with follow-up scores occurring between a 90-day and 180-day postbaseline score. If a follow-up score was greater than the baseline score, a subsequent PDDS score was collected between 90-days and 120-days to determine if the PDDS score remained elevated, indicating that the member had disability progression. During the contract period, 410 patients were eligible for PDDS collection, with 241 and 169 patients in the dimethyl fumarate and interferon β-1a cohorts, respectively. There were 162 patients who were lost to follow-up, and 64 patients who were ineligible per contract parameters. Of the remaining 184 eligible patients (107 on dimethyl fumarate and 77 on interferon β-1a), 21 (11%) patients had confirmed disability progression (6 on dimethyl fumarate [5.6%] and 15 on interferon β-1a [19.5%]). Our findings suggest that meaningful patient-reported outcomes, such as disability progression, can be operationalized in an innovative OBA.

Sensing and Perception in Robotic Weeding: Innovations and Limitations for Digital Agriculture

The challenges and drawbacks of manual weeding and herbicide usage, such as inefficiency, high costs, time-consuming tasks, and environmental pollution, have led to a shift in the agricultural industry toward digital agriculture. The utilization of advanced robotic technologies in the process of weeding serves as prominent and symbolic proof of innovations under the umbrella of digital agriculture. Typically, robotic weeding consists of three primary phases: sensing, thinking, and acting. Among these stages, sensing has considerable significance, which has resulted in the development of sophisticated sensing technology. The present study specifically examines a variety of image-based sensing systems, such as RGB, NIR, spectral, and thermal cameras. Furthermore, it discusses non-imaging systems, including lasers, seed mapping, LIDAR, ToF, and ultrasonic systems. Regarding the benefits, we can highlight the reduced expenses and zero water and soil pollution. As for the obstacles, we can point out the significant initial investment, limited precision, unfavorable environmental circumstances, as well as the scarcity of professionals and subject knowledge. This study intends to address the advantages and challenges associated with each of these sensing technologies. Moreover, the technical remarks and solutions explored in this investigation provide a straightforward framework for future studies by both scholars and administrators in the context of robotic weeding.

Optimizing cooling strategies in the Czochralski process for large-diameter silicon ingots

As the semiconductor industry shifts towards larger wafer sizes, such as 300 mm and 450 mm, controlling defects is crucial to ensure device performance and yield. Conversely, a high cooling rate is essential for achieving higher production rates. Therefore, finding the optimal cooling strategy is critical to maintaining high production rates while ensuring high-quality wafer production. This paper employs a simulation model to investigate the impact of various cooling strategies on point defect formation during the CZ process for 450 mm diameter silicon ingots. Using the 3D energy equation coupled with the Navier-Stokes equation and moving mesh theory, the transient CZ process is simulated, incorporating defect evaluation equations. Beside original CZ puller configuration, two cooling strategies are examined: one with a small gap and long cooling jacket (Case II) and another with a large gap and short cooling jacket (Case III), compared against a baseline setup (Case I). The simulations reveal that Case II, while enhancing the crystallization rate, increases non-uniformity. Conversely, Case III produces a flatter solid-liquid interface and lower defect concentrations, achieving a maximum Cv-Ci of 0.4 × 1014 cm−3, compared to 1.1 × 1014 cm−3 and -2.5 × 1014 cm−3 in Case I and II. These findings suggest that adjusting cooling strategies can significantly impact the quality and uniformity of large- diameter silicon ingots.

Energy transition in the oil and gas sector: Business models for a sustainable future

The global energy landscape is undergoing a significant transformation as industries shift towards more sustainable practices, driven by environmental regulations, societal pressure, and technological advancements. The oil and gas sector, traditionally dependent on fossil fuels, is at the forefront of this energy transition. This review explores the evolving business models that facilitate the industry's transition to a low-carbon economy, focusing on strategies for achieving sustainability while maintaining profitability. Key approaches include diversifying energy portfolios by investing in renewable energy sources such as wind, solar, and hydrogen, as well as enhancing operational efficiency through digital technologies like artificial intelligence and the Internet of Things (IoT). The transition is also marked by an increased focus on carbon capture, utilization, and storage (CCUS) technologies, which mitigate the environmental impact of traditional oil and gas operations. Business models are being reshaped to incorporate circular economy principles, where waste and emissions are minimized through innovation and resource optimization. Furthermore, partnerships between oil and gas companies and renewable energy firms are emerging as a critical pathway for mutual growth and sustainability. This energy transition also involves rethinking the workforce and reskilling employees for emerging sectors, emphasizing the need for adaptability in business models. Regulatory frameworks play a pivotal role in shaping these models, as policies increasingly favor green investments and low-carbon initiatives. As oil and gas companies diversify their portfolios, they are creating new revenue streams that align with global decarbonization goals while ensuring financial resilience. This study highlights how these innovative business models are setting the foundation for a sustainable future in the oil and gas sector, ensuring that the industry remains relevant in a rapidly changing energy ecosystem. Keywords: Energy Transition, Oil and Gas, Business Models, Sustainability, Renewable Energy, Carbon Capture, Circular Economy, Digital Transformation, Decarbonization, Low-Carbon Economy.

Predicting noise and vibration in electric brake systems at mass production stage using q-axis current

This study explores the challenges of noise, vibration, and harshness (NVH) in electric brake systems within the context of the automotive industry's shift towards environmentally friendly technologies. The electrification of vehicles and their braking systems leads to weight reduction and enhanced braking efficiency. However, these advancements also yield new NVH challenges. The intricate motor-driven mechanisms of electric brake systems are prone to cogging torque, magnetic imbalance, and radial electromagnetic forces, which degrade the user experience and necessitate service interventions. Therefore, this study presents an approach to predicting noise and vibration levels using q-axis current measurements obtained during the initial actuation of electric brake systems in the mass production stage. This study aims to establish a predictive model that can identify potential NVH problems early, enhance the quality and reliability of electric brake systems, and improve user satisfaction. This method enhances sustainable vehicle technologies, improving the overall user experience. The study delves into the intricate relationship between electrical currents and NVH phenomena by integrating electromagnetic analysis with mechanical dynamics. Therefore, this paves the way for the development of advanced diagnostic tools in electric vehicle technology.