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Purpose: The paper empirically compares employee motivation and its impact on performance in Ghanaian Mining Companies, where in measuring performance, the job satisfaction model is used. Design/methodology/approach: The study employed exploratory research design in gathering data from four large-scale Gold mining companies in Ghana with regards to their policies and structures in the effectiveness of motivational tools and strategies used by these companies. Findings: The study observed that, due to the risk factors associated with the mining industry, management has to ensure that employees are well motivated to curb the rate at which employees embark on industrial unrest which affect performance, and employees are to comply with health and safety rules because the industry contribute hugely to the Gross Domestic Product (GDP) of the country. Research limitations/implications: Limitation to the present study include the researcher’s inability to contact other mining companies. However, the study suggests possibilities for future research including contacting other mining companies, expanding the sample size, managers ensuring that the safety and health needs of staff are addressed particularly those exposed to toxic and harmful chemicals. Originality/value: A lot of studies have been done on mining companies in the past. This paper fills a gap perceived that employees in this sector are highly motivated in spite of the challenges being faced by them, and knowing more about what keeps employees moving is still of national interest.

Different from single coal seam mining, the stress evolution in the end mining stage of close‐distance coal seams is extremely complex. The unreasonable position and support of the lower retracement channel will cause serious deformation and damage to the surrounding rock. Taking the Yanzishan coal mine as the engineering background, the deformation and failure characteristics of the retracement channel under different overlay environments and the key influencing factors of position design were discussed by numerical simulation, theoretical analysis, and field investigation. The results show that the coupling superposition of upper coal pillar high stress and mining dynamic pressure will form a dangerous area with severe ground pressure behavior. The retracement channel should be preferentially designed in zone A (overlying mining roadways), followed by zone B (overlying end‐mining coal pillar), and finally, zone C (overlying section coal pillar). In addition, the rational horizontal distance between the lower retracement channel and the upper end‐mining coal pillar should make the channel in a good stress environment. The safety distance between the retracement channel and the nearest main roadway (end‐mining coal pillar width) should be greater than the severe range of advance abutment pressure. Finally, the design principle and control strategy for the lower retracement channel is proposed. The feasibility and rationality of the study are verified by industrial applications. Considering Yanzishan coal mine as the engineering background, the position of the lower retracement channel can be divided into three: zone A, zone B, and zone C, which, in turn, are located below mining roadways, end‐mining coal pillar, and section coal pillar, respectively. Numerical simulation, theoretical analysis, and field investigation were used to discuss the deformation and failure characteristics of the retracement channel, the load transfer law of residual coal pillar, and the range of advanced abutment pressure. Finally, the design principle and control strategy for the lower retracement channel is proposed, and the field tests were carried out in the N0381 working face.

This study aims to dynamically mine the demands of hotel consumers. A total of 378,270 online reviews in the cities of Beijing, Chengdu, and Guangzhou in China were crawled using Python. Natural language processing (e.g., opinion mining and the BERT model) and an improved Kano model (containing One-dimensional, Attractive, Indifferent, and Must-be) were utilised to analyse online hotel reviews. The results indicate that the hotel attributes that consumers care about (e.g., Clean, Breakfast, and Front Desk) are dynamically fluctuating, and the attention and satisfaction of corresponding attributes will also change. This study classified consumer demand into eight types across cities and found that it changes over time. In addition, we also found that hotel attributes, satisfaction and attention, and consumer demands vary among different cities. Existing studies of capturing consumer demand are usually time-consuming and static, and the results are subjective. This study compared and analysed the consumer demands of hotels in different cities via a dynamic perspective, and used hybrid methods to improve the granularity of the analysis, expanding the general applicability of the Kano model. Hotel managers can refer to the results of this article to allocate resources for improvement and create competitive hotel services.

Bacterial growth dynamics, typically represented by growth curves, are fundamental yet complex features of living populations. Traditional analyses focusing on specific parameters often overlook the full temporal patterns of growth. Here, we systematically investigated how genomic and environmental factors shape bacterial growth dynamics by analyzing 870 growth curves from five Escherichia coli strains with varying genome sizes cultured in 29 chemically defined media. Using dynamic time warping, clustering, and gradient boosting decision trees, we found that environmental components, especially glucose, primarily determine overall growth curve patterns, while genome size governs detailed growth parameters such as lag time, growth rate, and carrying capacity. Notably, finer clustering revealed increased genomic influence and decreased environmental impact, suggesting a hierarchical interaction where the environment modulates broad growth behavior and the genome fine-tunes specific growth responses. These findings provide insights into the coordinated roles of genome and environment in bacterial population dynamics, advancing our understanding of microbial growth regulation.

Efficiently detecting outliers or anomalies is an important problem in many areas of science, medicine and information technology. Applications range from data cleaning to clinical diagnosis, from detecting anomalous defects in materials to fraud and intrusion detection. Over the past decade, researchers in data mining and statistics have addressed the problem of outlier detection using both parametric and non-parametric approaches in a centralized setting. However, there are still several challenges that must be addressed. First, most approaches to date have focused on detecting outliers in a continuous attribute space. However, almost all real-world data sets contain a mixture of categorical and continuous attributes. Categorical attributes are typically ignored or incorrectly modeled by existing approaches, resulting in a significant loss of information. Second, there have not been any general-purpose distributed outlier detection algorithms. Most distributed detection algorithms are designed with a specific domain (e.g. sensor networks) in mind. Third, the data sets being analyzed may be streaming or otherwise dynamic in nature. Such data sets are prone to concept drift, and models of the data must be dynamic as well. To address these challenges, we present a tunable algorithm for distributed outlier detection in dynamic mixed-attribute data sets.

The High Average Utility Itemset Mining (HAUIM) technique, a variation of High Utility Itemset Mining (HUIM), uses the average utility of the itemsets. Historically, most HAUIM algorithms were designed for static databases. However, practical applications like market basket analysis and business decision-making necessitate regular updates of the database with new transactions. As a result, researchers have developed incremental HAUIM (iHAUIM) algorithms to identify HAUIs in a dynamically updated database. Contrary to conventional methods that begin from scratch, the iHAUIM algorithm facilitates incremental changes and outputs, thereby reducing the cost of discovery. This paper provides a comprehensive review of the state-of-the-art iHAUIM algorithms, analyzing their unique characteristics and advantages. First, we explain the concept of iHAUIM, providing formulas and real-world examples for a more in-depth understanding. Subsequently, we categorize and discuss the key technologies used by varying types of iHAUIM algorithms, encompassing Apriori-based, Tree-based, and Utility-list-based techniques. Moreover, we conduct a critical analysis of each mining method's advantages and disadvantages. In conclusion, we explore potential future directions, research opportunities, and various extensions of the iHAUIM algorithm.

Knowledge discovery and information extraction of large and complex datasets has attracted great attention in wide-ranging areas from statistics and biology to medicine. Tools from machine learning, data mining, and neurocomputing have been extensively explored and utilized to accomplish such compelling data analytics tasks. However, for time-series data presenting active dynamic characteristics, many of the state-of-the-art techniques may not perform well in capturing the inherited temporal structures in these data. In this paper, integrating the Koopman operator and linear dynamical systems theory with support vector machines, we develop a novel dynamic data mining framework to construct low-dimensional linear models that approximate the nonlinear flow of high-dimensional time-series data generated by unknown nonlinear dynamical systems. This framework then immediately enables pattern recognition, e.g., classification, of complex time-series data to distinguish their dynamic behaviors by using the trajectories generated by the reduced linear systems. Moreover, we demonstrate the applicability and efficiency of this framework through the problems of time-series classification in bioinformatics and healthcare, including cognitive classification and seizure detection with fMRI and EEG data, respectively. The developed Koopman dynamic learning framework then lays a solid foundation for effective dynamic data mining and promises a mathematically justified method for extracting the dynamics and significant temporal structures of nonlinear dynamical systems.

With the increase of mining depth, the problem of large deformation of surrounding rock in mining roadway becomes more serious under high stress and strong mining conditions. In order to prevent significant deformation and other strong dynamic pressures in the mining roadway of an extra-thick coal seam, a novel method based on the integration of deep and shallow combined roof cutting (DSC-RC) technology and high prestress constant resistance (HPCR) support technology is proposed. The DSC-RC technology encompasses two distinct techniques: deep pre-splitting blasting and shallow dense drilling. The deep pre-splitting blasting technology actively controls the collapse location of the overlying rock structure, thereby reducing the cantilever length of the goaf roof and the load of the overlying rock transmitted to the roadway sides. This results in the optimization of the roadway stress environment. The shallow dense drilling technology can cause the expansion and connection of cracks between the holes, forming a shallow cutting line that breaks the rock layer. The two roof cutting technologies are mutually reinforcing, hastening the collapse of the roof, and exploiting the support effect of the roof caving gangue after crushing and expansion, thereby reducing the pressure on the front of the working face and the solid coal sides. Concurrently, HPCR support technology uses CRLD anchor cables to ensure the stability of the roadway roof during blasting. The comprehensive evaluation value of surrounding rock stress is used to analyze the effect of roof cutting and pressure relief. Through numerical simulation, the influence of roof cutting parameters on the roadway is analyzed. The stress evolution law of surrounding rock under different roof cutting parameters is obtained, and the advantages of roof cutting are verified. The surrounding rock stress in the original stress peak area is obviously reduced after roof cutting, and the stress peak position is obviously far away from the roadway. The field data verified the effectiveness of the new method. The pressure of hydraulic support in the working face, the pressure of advanced hydraulic single support in the roadway, and the deformation of surrounding rock were significantly reduced. The results of the research may provide a new approach to the control of the surrounding rock in extra-thick coal seams under the influence of mining.

In public health emergencies, rapid perception and analysis of public demands are essential prerequisites for effective crisis communication. Public demands serve as the most instinctive response to the current state of a public health crisis. Therefore, the government must promptly grasp and leverage public demands information to enhance the effectiveness and efficiency of health emergency management, that is planned to better deal with the outbreak and meet the medical demands of the public. This study employs dynamic topic mining and knowledge graph construction to analyze public demands, presenting a spatial-temporal evolution analysis method for emergencies based on EBU models. EBU models are three large language models, including ERNIE, BERTopic, and UIE. The data analysis of Shanghai's city closure and control during the COVID-19 epidemic has verified that this method can simplify the labeling and training process, and can use massive social media data to quickly, comprehensively, and accurately analyze public demands from both time and space dimensions. From the visual analysis, geographic information on public demands can be quickly obtained and areas with serious problems can be located. The classification of geographical information can help guide the formulation and implementation of government policies at different levels, and provide a basis for health emergency material dispatch. This study extends the scope and depth of research on health emergency management, enriching subject categories and research methods in the context of public health emergencies. The use of social media data underscores its potential as a valuable tool for analyzing public demands. The method can provide rapid decision supports for decision-making for public services such as government departments, centers for disease control, medical emergency centers and transport authorities.

This study investigates the dynamic performance of a road sign equipped with a multi-material electronic signboard subjected to mining-induced seismic tremors. The key innovative aspect lies in providing new insights into the dynamic performance of multi-material electronic signboards under high-energy mining tremors, enhancing their safety assessment in mining areas. Experimental modal analysis and finite element analysis were conducted, and the numerical model of the sign was calibrated by adjusting ground stiffness to align experimental and computational data. The fundamental natural frequencies and their corresponding mode shapes were identified as 2.75 Hz, 3.09 Hz, 8.46 Hz, and 13.50 Hz. Numerical results were validated using MAC methods, demonstrating strong agreement with experimental values and confirming the accuracy of the numerical predictions. Damping ratios of 3.79% and 3.71% for the first and second modes, respectively, were measured via hammer tests. To evaluate the sign’s dynamic performance under high-energy mining-induced tremors, two events were applied as kinematic excitation of the structure. These tremors, recorded in different mining regions, exhibited significant variations in peak ground acceleration (PGA) and dominant frequency range. A key finding was that frequency matching between the dominant frequencies of the tremor and the natural frequencies of the sign had a greater impact on the sign’s dynamic response than PGA. The Szombierki tremor, with dominant frequencies of 1.6–4.8 Hz, induced significantly higher stress and displacement compared to the Moskorzyn tremor (5–10 Hz) despite the latter having twice the PGA. These results highlight that a road sign structure can exhibit widely varying dynamic behaviors depending on the seismic characteristics of the mining zone. Therefore, a comprehensive assessment of mining-induced tremors in relation to the seismicity of specific areas is crucial for understanding their potential impact on such structures. The dynamic performance assessment also revealed that the electronic multi-material signboard did not undergo plastic deformation, confirming it as a safe material solution for use in mining areas.