Improve Resource Utilization Research Articles

Hardware-Assisted Low-Latency NPU Virtualization Method for Multi-Sensor AI Systems

Recently, AI systems such as autonomous driving and smart homes have become integral to daily life. Intelligent multi-sensors, once limited to single data types, now process complex text and image data, demanding faster and more accurate processing. While integrating NPUs and sensors has improved processing speed and accuracy, challenges like low resource utilization and long memory latency remain. This study proposes a method to reduce processing time and improve resource utilization by virtualizing NPUs to simultaneously handle multiple deep-learning models, leveraging a hardware scheduler and data prefetching techniques. Experiments with 30,000 SA resources showed that the hardware scheduler reduced memory cycles by over 10% across all models, with reductions of 30% for NCF and 70% for DLRM. The hardware scheduler effectively minimized memory latency and idle NPU resources in resource-constrained environments with frequent context switching. This approach is particularly valuable for real-time applications like autonomous driving, enabling smooth transitions between tasks such as object detection and route planning. It also enhances multitasking in smart homes by reducing latency when managing diverse data streams. The proposed system is well suited for resource-constrained environments that demand efficient multitasking and low-latency processing.

Fuel Cycle Performance of the Wielenga Innovation Static Salt Reactor

This work presents a detailed fuel cycle analysis of an innovative static molten salt fast reactor called Wielenga Innovation Static Salt Reactor (WISSR). The fuel salt is a mixture of 55 mol% NaCl and 45 mol% (U,TRU)Cl3, and the reactivity is controlled by moving a variable amount of liquid fuel pneumatically. The pneumatic transfer of fuel salt allows flexible fuel management. To flatten the power distribution, a simple out-in fuel management scheme was adopted by discharging burned fuel from the interior core region, moving fuel salts across the radial regions inward, and adding fresh fuel to the outermost region. The current design of WISSR features a burner design for efficiently burning transuranic nuclides (TRU) recovered from pressurized water reactor used nuclear fuels. Using a fresh fuel with 57.4 at.% TRU in heavy metals, an equilibrium cycle length of 102.5 days could be maintained with a refueling rate of 403 L/cycle. In an equilibrium cycle, the discharged fuel burnup could reach 19.6 at.%, with 25% of the charged TRU destroyed at a low TRU conversion ratio of 0.18. Detailed cycle-by-cycle analyses showed that major neutronics performance parameters converged to the equilibrium cycle values in about 30 cycles by employing enrichment zoning in the startup core in a way to approximate the equilibrium cycle core compositions. During the transition cycles, the prematurely discharged fuel salt was recycled into the core without chemical processing to improve resource utilization.

Integrating Stakeholder Priorities for the Optimization of Care of the Preterm Infant in a Safety-Net NICU.

This study aimed to engage clinical and community stakeholders to create a prioritization matrix of interventions to reduce neonatal brain injury and improve neurodevelopmental outcomes. We collaborated with our community partner to establish a Lived Experience Advisory Group (LEAG). Faculty performed a literature review to identify neonatal neuroprotective interventions; additional priorities from the LEAG were also included. Project leaders scored and presented intervention bundles for impact. Stakeholder priorities for value/feasibility were obtained via questionnaire. Mean values were incorporated into a prioritization matrix. Themes from discussions with LEAG members included the role of the neonatal intensive care unit (NICU) parent, the experience of trauma, shared decision-making, communication and trust with the health care team, and bias in medical care. Five interventions were placed in the highest priority quadrant of the prioritization matrix: thermoregulation, maternal antepartum steroids, delayed cord clamping, optimized oxygen delivery, and optimized glucose regulation. It is feasible to incorporate community and parent values into clinically oriented neuroprotective intervention bundles for preterm infants. This clinical-community collaboration is vital to ensure that our project objectives meet the needs and priorities of the population it is intended to serve. · Including input from parent advisory groups supports interventions that meet the needs of families.. · Collaboration between clinical and community stakeholders can improve neuroprotective and neurodevelopmental strategies.. · Incorporation of clinical interventions into a prioritization matrix improves resource utilization.. · Five perinatal interventions were ranked highly for impact and feasibility in the prioritization matrix..

Energy-Efficient Green Information Centric Networking for Future Wireless Communications

Energy-efficient green information-centric networking (EEGICN) is proposed in this paper for advancing future wireless communication networks by addressing the challenge of energy consumption. This model can adapt the power consumption of network nodes to optimized values according to the associated link utilization. The model aimed to reduce energy consumption and increase network performance and stability. The EEGICN model incorporates efficient routing mechanisms, content caching strategies, and energy-intelligent communication protocols to improve resource utilization across the network infrastructure. EEGICN minimizes large data transmission, reduces power consumption, and reduces network congestion. Popular resources are cached in key network locations, and proximity data is exchanged to achieve this. The model also includes dynamic power management algorithms that adapt to changing traffic demand and network conditions to provide consistent performance across a variety of operational scenarios. In comparison to current wireless network systems that employ various forms of cache, the evaluation findings demonstrated that EEGICN can increase network efficiency by dramatically lowering the number of hops and energy consumption. Future networks may find this application to be a quick and easy way to transmit content.

Meta-Learning Approaches for Context-Aware Decision Support in Smart Agriculture and Autonomous Systems

The increasing reliance on intelligent systems in agriculture and autonomous operations dictates the need for robust context-aware decision support systems (CADSS) to cope with heterogeneous and changing conditions. Conventional systems struggle with heterogeneous data and dynamic contexts, motivating the need for a meta-learning-driven solution. The paper proposes a CADSS-based meta-learning (CADSS-ML) framework to advance the adaptability and precision of the system in those domains. This proposed CADSS-ML framework develops hierarchical meta-learning using model-agnostic meta-learning (MAML) to allow fast task adaptation with ease and reptile algorithms for efficient optimization. Convolutional Neural Networks (CNN) have been used for feature extraction in spatial data, whereas transformer models have been used for temporal data. The Graph Neural Network (GNN) based module processes a sensor network topology, while an attention mechanism dynamically updates the weights on contextual variables. To perform decision-making, reinforcement learning with reward shaping guarantees adaptive and optimal control action outcomes. The proposed system also incorporates federated learning for data privacy across distributed sensor nodes. Experimental results confirm that improvements in prediction accuracy, decision reliability, and resource utilization are enhanced by 20%, 15%, and 12%, respectively, compared with baseline models. Some of the diverse applications of the proposed framework include precision farming, autonomous navigation, and smart irrigation.

Cloud data centers and networks: Applications and optimization techniques

As the volume and complexity of big data continue to escalate, optimizing the performance, scalability, and energy efficiency of big data applications within cloud data centers has become increasingly crucial. This journal presents a comprehensive survey of current optimization techniques, focusing on data placement, job scheduling, and network configurations tailored for cloud environments. We explore the impact of various data center topologies on the performance of big data frameworks like Hadoop, emphasizing the trade-offs between performance and energy efficiency. Advanced methodologies, including dynamic data placement strategies, locality-aware scheduling, and innovative reduce task placement techniques, are reviewed in depth. Additionally, we highlight the importance of network power effectiveness (NPE) and examine the role of optical and electronic switching technologies in enhancing data center efficiency. By synthesizing findings from recent studies, this paper provides valuable insights into the optimization of cloud data centers, offering recommendations for improving resource utilization and reducing job completion times while maintaining energy efficiency. The findings contribute to the ongoing efforts to scale and adapt cloud data infrastructures for the rapidly growing demands of big data applications.

Analysis of the characteristics and influencing factors of water ecological security in Beijing-Tianjin-Hebei region: A perspective of industrial agglomeration

Water ecological security (WES) is crucial for promotion of economic and social development characterized by high-quality, while exploring the characteristics of WES and its response mechanism to industrial agglomeration can help formulate and implement relevant policies. In this study, a WES evaluation index system tailored for the Beijing-Tianjin-Hebei (BTH) region was constructed based on the Pressure-State-Response (PSR) framework. Additionally, building on the assessment of the degree of industrial agglomeration within the BTH region, the research employs a spatial Durbin model to investigate the impact of industrial agglomeration on WES in the BTH region. The main results are as follows: (1) The level of WES in the BTH region has steadily increased from 2005 to 2021, consistent with the trend of industrial agglomeration in the study area. (2) Influenced by regional economic development and natural conditions, the WES in the BTH region exhibits significant regional disparities and spatial autocorrelation, with levels decreasing from the central area to the east, northwest, and south. (3) Industrial agglomeration enhances WES by improving resource utilization efficiency, reducing pollutant emissions, and providing regional governments with financial resources to improve public services and water ecological protection, leading to better water ecological quality. (4) The impact of different industries on WES varies. As the proportion of the primary industry in the BTH region is relatively small, the agglomeration of the secondary and tertiary industries is more effective in promoting improvements in WES. Moreover, the spatial spillover effects of industrial agglomeration in the BTH region are significant. To enhance the level of WES in the region, this study recommends strengthening water ecological protection, promoting industrial structure adjustment, and enhancing collaborative development.

Intelligent Manufacturing and Green Innovation—Evidence from China’s Listed Manufacturing Firms

The realization of intelligent and green manufacturing represents two core challenges currently faced by manufacturing enterprises. The “Intelligent Manufacturing Pilot Demonstration List” issued by the Ministry of Industry and Information Technology (MIIT) of the People’s Republic of China provides a sample of firms that have undergone stringent selection processes, demonstrating secure and controllable technological capabilities, with no intellectual property disputes. Using data from manufacturing firms listed on the Shanghai and Shenzhen stock exchanges from 2011 to 2020, we identify those that implemented intelligent manufacturing based on the aforementioned pilot list from 2015 to 2019 as the treatment group and the remaining firms as the control group to investigate whether intelligent manufacturing increases the number of green patent applications. The findings indicate that the implementation of intelligent manufacturing significantly increases green patent applications by 3.676% through three main pathways: reducing the level of financing constraints, improving resource utilization efficiency, and increasing R&D investment. Heterogeneity analysis reveals that state-owned enterprises exhibit a significantly stronger promotion effect on green innovation post-implementation of intelligent manufacturing compared to non-state-owned enterprises, with enterprises in the western region demonstrating the most pronounced enhancement in green innovation. Based on these findings, we propose corresponding recommendations from the perspectives of policy support and enterprise strategy.

Systematic Literature Review on Artificial Intelligence and Sustainable Practices in the Apparel Industry

This systematic literature review (SLR) investigates the role of artificial intelligence (AI) in promoting sustainable practices within the apparel industry, addressing the critical need for efficient and environmentally responsible solutions in a sector facing increasing sustainability pressures. The research methodology adheres to PRISMA guidelines, encompassing a comprehensive literature search across databases such as Scopus and Web of Science for articles published between 2010 and 2024. The review process involved rigorous filtering and screening, ultimately analyzing 31 relevant journal articles. Key findings reveal that AI applications significantly enhance operational efficiency and sustainability in apparel manufacturing and supply chains. Techniques such as machine learning for demand forecasting, genetic algorithms for supply chain optimization, and computer vision for quality control are instrumental in reducing waste and improving resource utilization. Despite these advancements, challenges in implementation and scalability persist, indicating areas where further investigation is necessary. The implications of this review underscore the potential of AI to transform the apparel industry by integrating sustainable practices into core operations. However, notable research gaps remain, particularly regarding the ethical implications of AI adoption, its impact on labour practices, and the need for interdisciplinary approaches that bridge technology with environmental sustainability. Future research directions should focus on developing innovative AI methodologies tailored to sustainability challenges, examining the socio-economic impacts of AI on labour within the apparel sector, and enhancing collaboration between academia and industry to foster practical applications of AI technologies. This SLR not only contributes to academic discourse but also serves as a valuable resource for practitioners seeking to implement sustainable practices through AI in the apparel industry.

Event-Driven Architecture: Harnessing Kafka and Spring Boot for Scalable, Real-Time Applications

This comprehensive article explores the transformative impact of Event-Driven Architecture (EDA) in modern financial services, focusing on its implementation using Apache Kafka and Spring Boot. The article examines how financial institutions have revolutionized their transaction processing capabilities through EDA adoption, achieving significant improvements in system performance, reliability, and customer experience. Through a detailed examination of real-world implementations, the analysis demonstrates how EDA has enabled banks to handle unprecedented transaction volumes while substantially reducing system latency and improving resource utilization. The integration of Kafka and Spring Boot has proven particularly effective, with major financial institutions leveraging these technologies to achieve superior performance metrics and system availability. The article further explores critical patterns, including event sourcing, CQRS, and saga patterns, alongside cloud-based best practices for monitoring, performance optimization, and security implementations. By examining various implementation strategies and their outcomes, this article provides a comprehensive framework for building scalable, resilient financial systems that meet the demanding requirements of modern banking operations while ensuring regulatory compliance and maintaining high security and reliability standards.

Physical education teaching scheduling technology based on chaotic genetic algorithm

In the modern educational environment, rational and efficient course scheduling is of great significance in ensuring teaching quality and improving resource utilization. The traditional sports scheduling methods are faced with the challenges of diversified demands and complex constraints. For this reason, the study proposes a new physical education course scheduling model after mathematically modeling the physical education scheduling model and improving the genetic environment based on chaotic genetic algorithm, and then proposes a new physical education course scheduling model. The experiment outcomes denote that the average computing time of the improved chaotic genetic algorithm is 28 s, and when the number of iterations is 175–200, the optimal number of individuals is 35 at most, and the value of the optimal fitness is 9.4. The simulation test outcomes denote that the new scheduling model can reasonably arrange the course in the 5th-6th section, which meets the needs of students. Meanwhile, when the amount of scheduling teachers is 4 and the amount of courses is 5, the average utilization rate of scheduling resources at this time is the highest 82.5%. Compared with the same type of scheduling model, the new model has the highest superiority of 90.7%, the highest stability of 90.1%, and the highest robustness of 91.6%. The proposed method can meet the diversified and dynamically changing teaching needs, and provides an effective optimization tool for physical education scheduling.

A multi-task genetic programming approach for online multi-objective container placement in heterogeneous cluster

Owing to the potential for fast deployment, containerization technology has been widely used in web applications based on microservice architecture. Online container placement aims to improve resource utilization and meet other service quality requirements of cloud data centers. Most current heuristic and hyper-heuristic methods for container placement rely on single allocation rules, which are inefficient in heterogeneous cluster scenarios. Moreover, some container placement tasks often have similar characteristics (e.g., resource request types and physical machine types), but traditional single-task optimization modeling cannot exploit potential common knowledge, resulting in repeated optimization during resource allocation. Therefore, a new multi-task genetic programming method is proposed to solve the online multi-objective container placement problem (MOCP-MTGP). This method considers selecting appropriate allocation rules according to the types of resource requests and cluster status. MOCP-MTGP can automatically generate multiple groups of allocation rules from historical workload patterns and different cluster states, and capture the similarities between all online tasks to guide the transfer of general knowledge during optimization. Comprehensive experiments show that the proposed algorithm can improve the resource utilization of clusters, reduce the number of physical machines, and effectively meet resource constraints and high availability requirements.

Orchestration Workflows in Distributed Systems: A Systematic Analysis of Efficiency Optimization and Service Coordination

This article presents a comprehensive analysis of orchestration workflows in modern software architectures, examining their role in optimizing service efficiency and coordination across distributed systems. Through a systematic investigation of implementation patterns and operational strategies, we demonstrate how orchestrated workflows enhance service integration, automate complex processes, and improve system reliability. The article employs a mixed-method approach, combining quantitative performance metrics from real-world implementations with qualitative analysis of architectural patterns across diverse enterprise environments. The findings reveal that organizations implementing robust orchestration workflows experienced a 47% reduction in service integration failures, 35% improvement in resource utilization, and 62% faster deployment cycles. The article also identifies critical success factors in orchestration implementation, including dynamic resource management, automated error handling, and comprehensive monitoring frameworks. Furthermore, The article proposes a novel framework for evaluating orchestration effectiveness in cloud-native environments, considering factors such as service mesh integration, container orchestration, and distributed tracing. This article contributes to the growing body of knowledge in service optimization by providing empirical evidence of orchestration benefits and offering practical guidelines for implementing scalable, resilient software systems through advanced workflow orchestration strategies.

Cloud-Native ETL: Integrating Databricks and Azure Data Factory for Scalable Data Processing in Enterprise Environments

This article examines the implementation of cloud-native ETL solutions leveraging Databricks and Azure Data Factory (ADF) for scalable data processing in enterprise environments. The article presents a comprehensive analysis of the architectural design, integration strategies, and performance optimization techniques for combining Databricks' powerful data transformation capabilities with ADF's robust workflow orchestration. Through a series of case studies and empirical evaluations, we demonstrate how this integrated approach addresses the challenges of big data processing, including scalability, flexibility, and cost-effectiveness. Our findings reveal significant improvements in processing efficiency and resource utilization, with observed reductions in ETL pipeline execution times by up to 40% and overall cloud infrastructure costs by 25%. The article also highlights best practices for data governance, security, and quality management within this framework. These insights provide valuable guidance for data engineers and IT professionals seeking to modernize their data processing infrastructure and harness the full potential of cloud-native ETL solutions.

Evaluating Scheduling Efficiency for Complex Scientific Workflows in Cloud Infrastructures

Scientific workflow scheduling in Cloud computing is critical for efficiently managing data-exhaustive and compute-rigorous applications. With the rising demand of distributed computing, the interest in feasible assignment planning calculations has developed primarily. Revised text: This paper presents a comprehensive survey of advanced scheduling techniques focusing on minimizing energy consumption and improving resource utilization. Various scheduling algorithms, including heuristic-based, meta-heuristic-based, and reinforcement learning-based methods, are analyzed and compared. Additionally, the paper addresses the challenges of scheduling workflows with complex dependencies, offering a novel multi-objective workflow scheduling algorithm using reinforcement learning. The algorithm outperforms current methods in terms of makespan and energy consumption. Finally, the paper highlights open research issues and future directions in scientific workflow scheduling for distributed computing. Through our experiments, we have achieved significant improvements in scheduling efficiency, demonstrated by various performance metrics illustrated in our graphs.

The Impact of Global Warming on the Rise in Heat-Related Illnesses in Emergency Medical Services.

Global warming is one of the critical problems affecting health, society, and the economy. High temperatures are linked to an increase in heat-related illnesses, which have significantly impacted the public health system, particularly emergency medical services (EMS). Analyzing the pattern of heat-related illness cases in EMS can improve resource utilization and preparedness within the public health system. A retrospective study was conducted on EMS data from Srinagarind Hospital, Thailand, covering the summer months (February to May) from 2020 to 2024. Patients with heat-related illnesses were identified in the EMS database using the 10th revision of the International Classification of Diseases (ICD-10) version 2019, specifically codes under "T67 Effects of Heat and Light", which include heat stroke, heat syncope, heat cramps, heat exhaustion, and heat fatigue. A total of 136 EMS operations from the hospital's database were analyzed. In the summer 2024 group, 95.7% (N=44) of the patients were male. The majority of EMS triage cases required resuscitation (P = 0.020). Outdoor activity was identified as a significant factor related to heat illness, with rates of 83.3%, 92.9%, 93.3%, 97.1%, and 93.5% over the five years of the study. The activation time was 1.30minutes for the summer of 2024 and 1.24minutes for the summer of 2023. Notably, the average scene time in the summer 2024 group was significantly longer at 25.2minutes, compared to 12.0minutes in the summer 2020 group (P<0.001). Outdoor activity was the most significant risk factor associated with increased heat-related illnesses. Other contributing factors included male gender, age between 20-40 years, scene temperatures above 35°C, and prolonged scene times exceeding 15minutes.

Outcomes of children transferred to a pediatric trauma center after blunt abdominal trauma: A 10-year experience

BackgroundMost injured children are initially seen at non-pediatric hospitals, then transferred to a pediatric trauma center for definitive care. Published outcomes of transferred children with blunt abdominal trauma (BAT) are sparse. Our objective is to describe this population and their disposition at a pediatric trauma center. MethodsThe study was performed at a level-1 pediatric trauma center (PTC) using data collected from electronic medical records and trauma registry. Patients 0-18 years with BAT transferred from outside facilities (OSF) between 2009 and 2019. Penetrating injuries were excluded. 923 patients were analyzed and grouped by whether computed tomography abdominal/pelvis (CTa/p) was obtained at each facility. Those with positive CTa/p at OSF were also compared to those with positive CTa/p results at our PTC. Descriptive statistics evaluated demographics, injury mechanism, Glasgow Coma Scale (GCS), Injury Severity Scale (ISS), disposition, and length of stay (LOS). ResultsMales had higher predominance of positive CTa/p at both OSF and PTC (p = 0.0012), with motor vehicle crash (MVC) being the most common injury mechanism (p = 0.0002). Patients with positive CTa/p at PTC (n = 156) were associated with statistically higher ISS, lower GCS, more dispositions to OR and ICU, and longer LOS (all p < 0.005). Of patients with negative CTa/p at OSF (n = 41), none received subsequent CTa/p upon arrival to PTC and only 2 were admitted in the setting of head trauma. Of the patients without CTa/p performed at either facility or negative CTa/p at PTC (n = 23), most were admitted for non-abdominal trauma. ConclusionCompared to those with positive CTa/p at OSF, children who had positive CTa/p at PTC were younger, had higher ISS scores, and longer LOS, suggesting they were more seriously injured. Children with BAT and negative CTa/p in absence of other injuries, may not require transfer to a PTC. Enhanced understanding of these patients may reduce unnecessary transfers, improving resource utilization.

Optimizing Security and Cost Efficiency in N-Level Cascaded Chaotic-Based Secure Communication System

In recent years, chaos-based secure communication systems have garnered significant attention for their unique attributes, including sensitivity to initial conditions and periodic orbit density. However, existing systems face challenges in balancing encryption strength with practical implementation, especially for multiple levels. This paper addresses this gap by introducing a novel N-level cascaded chaotic-based secure communication system for voice encryption, leveraging the four-dimensional unified hyperchaotic system. Performance evaluation is conducted using various security metrics, including Signal-to-Noise Ratio (SNR), Peak Signal-to-Noise Ratio (PSNR), Percent Residual Deviation (PRD), and correlation coefficient, as well as Field-Programmable Gate Array (FPGA) resource metrics. A new Value-Based Performance Metrics (VBPM) framework is also introduced, focusing on both security and efficiency. Simulation results reveal that the system achieves optimal performance at N = 4 levels, demonstrating significant improvements in both security and FPGA resource utilization compared to existing approaches. This research offers a scalable and cost-efficient solution for secure communication systems, with broader implications for real-time encryption in practical applications.

Waste Segregator: Waste Management Prototype

Smart Waste Dustbins utilize IoT, AI, and computer vision for efficient waste management. They feature multiple compartments, sensors, and automatic lid mechanisms for real-time waste sorting. Computer vision and AI algorithms accurately identify and segregate waste. Smart Waste Dustbins monitor fill levels and offer a user-friendly interface for public engagement. Sensors include ultrasonic, moisture, and metal sensors for proximity, moisture analysis, and metal item segregation. Smart Waste Dustbins revolutionize waste management, enhancing efficiency in segregation, recycling, and collection processes, leading to reduced environmental impact and improved resource utilization for sustainable communities.

Vanadium extraction from vanadinite and high carbon v-bearing stone coal by coupling reduction smelting and oxalic acid hydrothermal leaching

Vanadinite and high-carbon vanadium-bearing (V-bearing) stone coals are important vanadium sources widely used in vanadium extraction. The traditional sulfuric acid leaching method for extracting vanadium consumes a large amount of acid, wastes significant amounts of lead and carbon, and causes serious environmental pollution. This study discusses the integration of reduction smelting and oxalic acid hydrothermal leaching for processing vanadinite and high-carbon V-bearing stone coals. Specifically, high-carbon V-bearing stone coal is selectively added to vanadinite to produce coarse lead blocks and V-bearing slag during reduction smelting. Then, the oxalic acid hydrothermal leaching process is employed to extract vanadium from the V-bearing slag. This approach reduces the waste of lead and carbon resources and achieves co-extraction of vanadium from both V-bearing minerals. The results show that when the mass ratio of high-carbon V-bearing stone coal to vanadinite is 25%, and smelting is conducted at 1150°C for 15 min under argon protection, a separation efficiency of 90.67% for metallic lead and V-bearing slag is achieved, the grade of V2O5 in vanadium slag is 12.37%, and the recovery rate is 95.64% by ICP-OES analysis. Furthermore, the leaching rate of vanadium in the V-bearing slag can reach 85.34% via oxalic acid hydrothermal leaching under conditions of a leaching temperature of 100°C, a leaching duration of 30 min, an oxalic acid concentration of 25 wt%, a liquid-to-solid ratio of 10:1 mL/g, and a stirring speed of 500 rpm. The integration of reduction smelting and oxalic acid hydrothermal leaching offers benefits such as improved resource utilization, waste reduction, cost savings, and environmental enhancements. Thus, the findings of this research can serve as references for the vanadium extraction processes of related vanadium-containing minerals.