Processing Capacity Research Articles

An Embodied Intelligence System for Coal Mine Safety Assessment Based on Multi-Level Large Language Models

Artificial intelligence (AI), particularly through advanced large language model (LLM) technologies, is reshaping coal mine safety assessment methods with its powerful cognitive capabilities. Given the dynamic, multi-source, and heterogeneous characteristics of data in typical mining scenarios, traditional manual assessment methods are limited in their information processing capacity and cost-effectiveness. This study addresses these challenges by proposing an embodied intelligent system for mine safety assessment based on multi-level large language models (LLMs) for multi-source sensor data. The system employs a multi-layer architecture implemented through multiple LLMs, enabling not only rapid and effective processing of multi-source sensor data but also enhanced environmental perception through physical interactions. By leveraging the tool invocation and reasoning capabilities of LLM in conjunction with a coal mine safety knowledge base, the system achieves logical inference, anomalous data detection, and potential safety risk prediction. Furthermore, its memory functionality ensures the learning and utilization of historical experiences, providing a solid foundation for continuous assessment processes. This study established a comprehensive experimental framework integrating numerical simulation, scenario simulation, and real-world testing to evaluate the system through embodied intelligence. Experimental results demonstrate that the system effectively processes sensor data and exhibits rapid, efficient safety assessment capabilities during embodied interactions, offering an innovative solution for coal mine safety.

Growing up in the modern world: how does artificial intelligence enhance firm growth?

PurposeThis paper examines the relationship between Artificial Intelligence (AI) technology development and firm growth. Specifically, it aims to explore how the availability of AI influences firm growth and whether larger firms benefit more from AI-driven technological advancements compared to smaller firms.Design/methodology/approachUsing a dataset from CRSP-Compustat covering public firms from 1975 to 2023, this study employs price per memory (PPM) as a proxy for AI technology accessibility to assess its impact on firm growth. The analysis focuses on three key growth metrics: total assets, tangible assets and market capitalization. By examining how data processing capacity influences these growth rates, the study compares the performance of large firms to small firms. A panel data regression is conducted, controlling for macroeconomic trends and industry-specific effects on firm growth. Additionally, the study investigates the heterogeneous impacts of AI technology accessibility across firms of different sizes.FindingsThe findings reveal that PPM, as a proxy for AI technology availability, significantly affects firm growth. Specifically, larger firms experience faster growth, especially in recent years, as AI technology becomes more accessible and cost-effective. These results suggest that large firms gain the most substantial benefits from AI advancements, further widening the growth gap between large and small firms.Originality/valueThis research extends prior studies on the impact of AI on firm growth by introducing PPM as a novel proxy for AI availability. It provides new insights into how AI technologies disproportionately benefit larger firms and offers important policy implications regarding firm financing and information regulation. This study also highlights areas for future empirical research on the role of AI in the financial industry.

Model of acceptance and use of social customer relationship management in micro and small enterprises

Objective of the study: The article aims to propose and test a Social Customer Relationship Management Acceptance and Usage Model that considers antecedent factors in micro and small enterprises. Methodology/approach: A survey analyzed through structural equation modeling was conducted with managers of micro and small Brazilian companies. Originality/Relevance: The use of social networks as a source of the process of monitoring, analysis and content generation for business strategy is recent and very relevant for the sustainability of micro and small businesses. Main results: Although some hypotheses were not validated, the study fills important gaps in empirical research on micro and small businesses. Replicating this study in different cultural contexts could offer further insights. Theoretical/methodological contributions: The positive results indicate that, despite some hypotheses being rejected, the study confirms the relevance of "Customer Information Processing Capacity" and its influence on the "Use of Social CRM," highlighting the importance of social interactions and engagement capacity for value co-creation in micro and small businesses. Socia /management contributions: Managers can leverage the relationship between the “Ability to Engage the Customer Via Social Media” and “Social Influence” to enhance their social media strategies and improve customer engagement.

Large-Area Clay Composite Membranes with Enhanced Permeability for Efficient Dye/Salt Separation

The escalating discharge of textile wastewater with plenty of dye and salt has resulted in serious environmental risks. Membranes assembled from two-dimensional (2D) nanomaterials with many tunable interlayer spacings are promising materials for dye/salt separation. However, the narrow layer spacing and tortuous interlayer transport channels of 2D-material-based membranes limit the processing capacity and the permeability of small salt ions for efficient dye/salt separation. In this work, a novel sepiolite/vermiculite membrane was fabricated using Meyer rod-coating and naturally occurring clay. The intercalation of sepiolite Nanofibers between vermiculite Nanosheets provides additional transport nanochannels and forms looser permeable networks, producing composite membranes with remarkably enhanced flux. As a result, the optimized membranes with 80% sepiolite exhibit remarkable flux as high as 78.12 LMH bar−1, outstanding dye rejection (Congo Red~98.26%), and excellent selectivity of dye/salt of 10.41. In addition, this novel all-clay composite membrane demonstrates stable separation performance under acidity, alkalinity and prolonged operation conditions. The large-scale sepiolite/vermiculite membranes made by the simple proposed method using low-cost materials provide new strategies for efficient and environmentally-friendly dye/salt separation.

Policy complexity suppresses dopamine responses.

Limits on information processing capacity impose limits on task performance. We show that male and female mice achieve performance on a perceptual decision task that is near-optimal given their capacity limits, as measured by policy complexity (the mutual information between states and actions). This behavioral profile could be achieved by reinforcement learning with a penalty on high complexity policies, realized through modulation of dopaminergic learning signals. In support of this hypothesis, we find that policy complexity suppresses midbrain dopamine responses to reward outcomes. Furthermore, neural and behavioral reward sensitivity were positively correlated across sessions. Our results suggest that policy compression shapes basic mechanisms of reinforcement learning in the brain.Significance statement Decision making relies on memory to store information about which actions to produce in which situations. This memory has limited capacity, which means that some information will be lost. The signatures of this information loss can be found in patterns of behavioral bias and randomness. However, relatively little is known about the neural mechanisms which ensure that actions achieve the highest possible reward given the limited capacity of decision memory. In this paper, we show that the neuromodulator dopamine is sensitive to the costs of memory, as predicted by a computational model of capacity-limited learning.

Forest value chain resilience from a local perspective in five European countries: analysis of predictors and co-drivers

Climate change-associated disturbances such as storms, wildfires, and pest outbreaks increasingly destabilize forest systems, threatening their ecological, economic, and social functions. These disruptions impact the forest value chain (FVC) by causing fluctuations in timber supply, from a quantity and quality perspective. This study employed the operational resilience framework (ORF) to assess FVC resilience in five European case studies (CZ, HR, DE, FIN, and ESP), focusing on timber supply as a key system variable. A resilience assessment was conducted using resilience thresholds, considering sustainability from both ecological and economic perspectives. Principal component analysis (PCA) identified three predictor groups that influenced FVC resilience: wood production (WP), harvesting systems (HS), and management and silviculture (MS). Findings revealed that regions with proactive management and sufficient processing capacities (CZ, HR, and ESP) maintained relative stability despite natural disturbances, while others (DE and FIN) experienced prolonged instability due to market-driven logging practices and limited adaptive measures. The study highlighted the frequent breaching of resilience thresholds, particularly during high-volume salvage logging following disturbances such as bark beetle outbreaks, windstorms, and wildfires. The results emphasized the importance of integrating adaptive and proactive strategies to mitigate these impacts. The ORF demonstrated potential for operationalizing FVC resilience and provided guidance for improving preparedness against future disturbances.

Adaptive Probabilistic Neural Network based Edge Data Center Authentication for Secure Load Balancing in Fog Computing

The growth of current technological advances has promising of fog computing, that increases the processing capacity of devices and provides fresh approaches for conventional applications in industry. Fog computing has become increasingly vital given the huge and sophisticated system of sensors to manage the information stream. Node authentication and secure load balancing are important in fog computing. The rationale behind developing this model is that the existing deep learning models are affected by the node authentication performance and also generate issues like high latency developed by the cloud-based architecture while processing huge amount of data, and node employment. Thus, the objective of the developed model is to implement an advanced deep learning method for better node authentication and an appropriate enhanced optimization scheme for performing optimal resource allocation. Additionally, secure load balancing is done at edge node centers in fog computing to allocate the workload for each node securely. Initially, the network initialization attributes in fog computing are provided to the authentication process. Here, the deep learning-based Edge Data Centre (EDC) authentication takes place with the help of the Adaptive Probabilistic Neural Network (A-PNN) model. The major intention of introducing an A-PNN method is to check whether the node is authenticated or not. If the EDC node is authenticated, then the A-PNN model suggests the particular EDC node for the resource allocation process, or else the node will be ignored or removed from the process. Here, the new hybrid approaches as Hybrid Heap with African Buffalo Optimization Algorithm (HH-ABO) are suggested for execute the process of optimization. Here, the parameters of the A-PNN model are tuned for maximizing accuracy, precision, and NPV and also minimizing the False Positive Rate (FPR) rate. Then the authenticated node is subjected to the load-balancing scheme. The allocation of allocation is carried out in the load balancing scheme. Here, the diverse constraints like resource allocation, energy consumption, cost, the execution time of the task, security by authentication, latency, and task completion time are considered for formulating the objective function using the HH-ABO algorithm. At last, it is performed well than the traditional methods using diverse parameters. The outcome demonstrate that the developed model achieved better results on load balancing at edge nodes in fog computing. The proposed model effectively resolves the load balancing issues and also it distributes workload for each node accurately. It effectively resolves the latency and deployment issues. In addition, the modern applications based on the proposed scheme will allow million of workloads from one node to another in a fast and reliable manner.

Motion-Based Visual Encoding Can Improve Performance on Perceptual Tasks with Dynamic Time Series.

Dynamic data visualizations can convey large amounts of information over time, such as using motion to depict changes in data values for multiple entities. Such dynamic displays put a demand on our visual processing capacities, yet our perception of motion is limited. Several techniques have been shown to improve the processing of dynamic displays. Staging the animation to sequentially show steps in a transition and tracing object movement by displaying trajectory histories can improve processing by reducing the cognitive load. In this paper, We examine the effectiveness of staging and tracing in dynamic displays. We showed participants animated line charts depicting the movements of lines and asked them to identify the line with the highest mean and variance. We manipulated the animation to display the lines with or without staging, tracing and history, and compared the results to a static chart as a control. Results showed that tracing and staging are preferred by participants, and improve their performance in mean and variance tasks respectively. They also preferred display time 3 times shorter when staging is used. Also, encoding animation speed with mean and variance in congruent tasks is associated with higher accuracy. These findings help inform real-world best practices for building dynamic displays.

Analysis of E-Commerce Management Model in the Context of Cloud Computing

With the rapid development of information technology, cloud computing, as a new generation of information technology, is profoundly changing the operation mode of e-commerce. Based on this, this paper analyzes the definition and characteristics of cloud computing and its positive impact on e-commerce management, revealing how cloud computing provides higher security, flexibility, data processing capacity, and economic benefits for e-commerce. In addition, this paper also discusses the new e-commerce management model based on “supply chain cloud” and “mobile cloud” to provide more theoretical support and practical guidance for the innovative development of enterprises.

Enhancing energy distribution through dynamic multi-hop for heterogeneous WSNs dedicated to IoT-enabled smart grids

This paper examines the impact of hetterogeneous wireless sensor networks (WSNs) on wireless communication systems, with a focus in Internet of Things (IoT) enabled smart grids. It introduces a novel approach for the fair distribution of energy and computational resources among sensor nodes (SNs), which is crucial for extending network lifespan, enhancing performance, and ensuring SG stability. The research highlights the role of initial energy and processing capacities of SNs. Although hierarchical clustering methods are effective in WSNs, finding the ideal routing protocol remains challenging due to extensive deployment. To address energy efficiency and network durability, the study proposes the integration of the heterogeneous dynamic multi-hop (HDM) approach with the low-energy adaptive clustering hierarchy (LEACH) protocol, specifically for IoT smart city applications. The HDM model combines multi-hop communication with dynamic hierarchical clustering, distinguishing between normal and advanced nodes based on energy levels to optimize cluster head selection probabilities. The methodology involves a comparative analysis between the HDM protocol and LEACH in a heterogeneous environment (H-LEACH) in terms of energy conservation, and network lifespan. Results demonstrate that the HDM protocol outperforms H-LEACH. Notably, HDM consumes half of the total energy over 4600 rounds, compared to H-LEACH’s 3000 rounds. These findings have important implications for deploying WSNs in smart grid applications, supporting sustainable and resilient urban IoT ecosystems.

СОСТОЯНИЕ И ПЕРСПЕКТИВЫ РАЗВИТИЯ ХМЕЛЕВОДСТВА В ЧУВАШСКОЙ РЕСПУБЛИКЕ

The brewing industry of the Russian Federation at the present stage is characterized by a high level of raw material dependence on supplies of raw materials, in particular hops, from foreign countries. The Chuvash Republic is a region that not only produces the largest volume of hops in the country, but also a territory where conditions are formed for the intensification of this important industry. Based on the study of the period of formation and development of the hop-growing industry in the region, the authors identified the reasons for the decline in hop production, most of which are directly related to the activities of brewing industry enterprises. The breakdown of production and economic ties between agricultural producers and processors has become the main reason for the crisis in the hop-growing industry of the Russian Federation. The state of the current potential for the development of hop growing in the Chuvash Republic is assessed by experts as quite stable. The region has preserved scientific and educational competencies that are implemented both in the field of selection and seed production, and in engineering and technological support; a machine-building cluster is being formed to localize the production of machines, units and equipment for hop growing. Currently, the first large investment project in hop growing in the country is being implemented in the Chuvash Republic, initiated by the Baltika brewing company. The authors believe that this investment model is key to the development of hop growing in the country, since along with the production of hops, related industries are developing, as well as new points of generation of production capacities for hop processing are being formed. The most important direction for realizing the potential for the development of hop growing is state support for the development of the industry, which covers all production and technological processes of hop cultivation and processing. Intensification of hop growing in the region is the most important task of the socio-economic development of the Chuvash Republic, as well as ensuring technological sovereignty in this industry.

Trauma and the default mode network: review and exploratory study

While PTSD continues to be researched in great depth, less attention has been given to the continuum of traumatic responses that resides outside this diagnosis. This investigation begins with a literature review examining the spectrum of responses through the lens of the default mode network (DMN). To build upon this literature, a systematic exploratory study was incorporated, examining DMN-related neuropsychological functioning of 27 participants (16 trauma-exposed, and 11 non-trauma-exposed), with a subset (15 participants) completing neuroimaging. This study revealed that in comparison to the control group, the trauma-exposed group had reductions in their capacity for self-referential processing, social cognition, autobiographical recall, prospection, and increased mind-wandering. While correlations were encountered between cognitive findings and brain volume, comparative volumetric findings between trauma-exposed and non-t rauma exposed were insignificant. This suggests that the conservation of DMN structural integrity may play a role in resilience, supporting the existing theory that reduced hippocampal volume may be a pre-existing vulnerability to PTSD rather than a consequence and that reductions in DMN related cognition are functionally mediated.

A Legal-Dogmatic View on Freedom of Expression in Social Networks

The freedom of the press and the right to free speech also apply in the digital sphere. Every citizen with Internet access has the ability to easily share and receive information and opinions with a global audience. The hope is that unfiltered free expression and diversity of opinion will lead to more democratic and pluralistic societies. This utopia, however, fails due to humans’ limited information processing capacity. No one is able to take note of all or even a fraction of the opinions and information presented on the Internet, process them and use them as a basis for their own opinions and actions. As a result, the Internet has been “platformized”. Platforms bring together various user groups and frequently create a closed ecosystem of various services and information for them. The purpose of this paper is to provide answers on questions, among other, what lies behind social networks function as information intermediaries and what rights social media users have against the deletion of their posts.

Ionomeric Nanofibers: A Versatile Platform for Advanced Functional Materials.

The one-dimensional nanomaterials known as nanofibers have remarkable qualities, such as large surface areas, adjustable porosity, and superior mechanical strength. Ionomers, types of polymers, have ionic functional groups that give them special properties, including high mechanical strength, water absorption capacity, and ionic conductivity. Integrating ionomers and nanofibers with diverse materials and advanced methodologies has been shown to improve the mechanical strength, processing capacity, and multifunctional attributes of ionomeric nanofibers. One-dimensional ionomeric nanomaterials offer a versatile platform for developing functional materials with ionic functionalities. This mini review critically examines recent progress in the development of ionomeric nanofibers, highlighting innovative fabrication techniques and their expanding applications across energy storage, environmental remediation, healthcare, advanced textiles, and electronics.

Analysis of competitive factors for exports of the Brazilian and Argentine Soybean Complex

The study aimed to investigate which factors explain the intensification in exports of soybean derivatives by Argentina, and of grains by Brazil. For this, the method of content analysis was used through semi-structured interviews. The results show that the soybean processing segment in Argentina has comparative advantages derived from the concentration of production, industrial plants with high processing capacities, facilitated logistics, tax differential, restricted domestic demand and the collection of royalties. While Brazil intensified grain exports, mainly due to Chinese demand, the imposition of trade barriers on derivatives, tax incentives and long distances.

Investigating Trajectories Linking Social Cognitive Capacity, Bias, and Social Isolation Using Computational Modeling.

Despite theoretical emphasis on loneliness affecting social information processing, empirical studies lack consensus. We previously adopted a clinical science framework to measure the association between social cognitive capacity and both objective and perceived social isolation in nonclinical participants. Our prior study found that while objective social isolation is linked to both social cognitive capacity and bias, loneliness is associated only with the latter. This study extended our previous model using a computational approach to capture implicit cognitive processes. We replicated and extended our earlier findings with a new sample of 271 participants, using neuropsychological tasks and a dot-probe paradigm analyzed via Drift Diffusion Model. We presented two complementary trajectories of how social cognitive bias may arise: the increased propensity to engage with salient social stimuli or a decreased information processing capacity dependent on the presence or absence of potential social threats. Furthermore, we found evidence that loneliness is associated with the time needed for perceptual processing of stimuli, both directly and indirectly, via social cognitive bias. Taken together, the complex and context-dependent nature of information processing biases observed in the current study suggests that complex and multifaceted interventions should be implemented to counter social information processing biases in lonely individuals.

Concurrency Conflict Modeling for Asynchronous Processing in Blockchain-Based Transactive Energy Systems

Blockchain is widely adopted for decentralized transaction management in systems like Transactive Energy (TE). Unfortunately, conventional blockchains with sequential models and restrictive participation rules do not meet energy sector requirements. High volumes of asynchronous-generated transactions impose severe concurrency challenges for blockchain. These difficulties worsen when participants process blocks concurrently, prompting branching and conflicting versions. This issue is often addressed by discarding blocks and reverting transactions, which is detrimental to TE systems. Preserving validated transactions is crucial to avoid disrupting physical asset exchanges and wasting computational resources. To address these challenges, this paper proposes a new model for identifying transaction discrepancies in conflict blocks while maintaining validated transactions. The model enables collaborative block building by integrating multiple blockchain views, eliminating competition, leader selection, and transaction reversals or discards. Block and transaction generation conflicts are addressed by establishing logical-temporal dependencies and leveraging pairwise interactions to detect them toward accelerating consensus. Hence, the model promotes concurrency to enhance transaction processing and avoid resource waste. Simulations indicate traditional models limit network potential to below 5% as blockchain height increases because of single contributions. Conversely, the proposed model uses multiple nodes’ views to achieve up to 90% of the network’s processing capacity.

KOnezumi-AID: Automation Software for Efficient Multiplex Gene Knockout Using Target-AID.

With the groundbreaking advancements in genome editing technologies, particularly CRISPR-Cas9, creating knockout mutants has become highly efficient. However, the CRISPR-Cas9 system introduces DNA double-strand breaks, increasing the risk of chromosomal rearrangements and posing a major obstacle to simultaneous multiple gene knockout. Base-editing systems, such as Target-AID, are safe alternatives for precise base modifications without requiring DNA double-strand breaks, serving as promising solutions for existing challenges. Nevertheless, the absence of adequate tools to support Target-AID-based gene knockout highlights the need for a comprehensive system to design guide RNAs (gRNAs) for the simultaneous knockout of multiple genes. Here, we aimed to develop KOnezumi-AID, a command-line tool for gRNA design for Target-AID-mediated genome editing. KOnezumi-AID facilitates gene knockout by inducing the premature termination codons or promoting exon skipping, thereby generating experiment-ready gRNA designs for mouse and human genomes. Additionally, KOnezumi-AID exhibits batch processing capacity, enabling rapid and precise gRNA design for large-scale genome editing, including CRISPR screening. In summary, KOnezumi-AID is an efficient and user-friendly tool for gRNA design, streamlining genome editing workflows and advancing gene knockout research.

К вопросу достижения рациональных норм потребления молочной продукции в Центральном федеральном округе

The article describes the production and intake of dairy products in the Central Federal District of the Russian Federation. The authors studied the local raw material base, processing infrastructure, and production volumes for the most popular types of dairy products from the perspectives of rational consumption standards and food security. A correlation analysis of the main performance indicators revealed the regional features and associated problems. The Central Federal District is experiencing active urbanization. As a result, the processing infrastructure has increased to meet the local needs while the raw material base lags behind the processing capacities. The current deficit of raw milk production is as high as 37%. Raw materials from neighboring regions may reduce the quality of final products. Milk production could be increased by raising the milk yield; however, this reserve has almost been exhausted, its mean value going by 3% over the average European level. The problem may be resolved by raising the dairy herd by 300,000 cows, preferably on dairy farms with milk yield of ≥8,000 kg, i.e., those with the best European indicators.

A short review of the sustainable utilization of coal gangue in environmental applications.

The production of coal gangue, a by-product of coal mining and washing, is rapidly increasing due to growing energy consumption. As the accumulated coal gangue has not been appropriately utilized, this has resulted in a squander of resources, waste disposal problems, and environmental pollution issues. However, coal gangue, a form of solid waste, exhibits various potential applications in the field of recycling. Currently, the processing capacity and utilization rate of coal gangue are insufficient to meet the demands of global low-carbon environmental protection. As such, this paper provides a detailed overview of the physical and chemical properties of coal gangue and its resource utilization methods in the environmental field, particularly in soil remediation, soil reclamation, the preparation of adsorbents for heavy metals, dyes, and CO2, and as catalysts for organic degradation and catalytic cracking. This review highlights the significance of coal gangue classification according to its properties for the comprehensive utilization of gangue. Additionally, we briefly introduce and discuss suggestions for future directions of research and development in this area. Nevertheless, with regard to the environmental impact of secondary pollution and the efficiency of coal gangue utilization, there remain numerous obstacles that hinder future research. This review is expected to provide significant insights into the development of coal gangue utilization strategies, potentially facilitating the efficient and practical large-scale management of solid waste.