Electronic health records (EHRs) are becoming a vital source of data for healthcare quality improvement, research, and operations. However, much of the most valuable information contained in EHRs remains buried in unstructured text. The field of clinical text mining has advanced rapidly in recent years, transitioning from rule-based approaches to machine learning and, more recently, deep learning. With new methods come new challenges, however, especially for those new to the field. This review provides an overview of clinical text mining for those who are encountering it for the first time (e.g. physician researchers, operational analytics teams, machine learning scientists from other domains). While not a comprehensive survey, it describes the state of the art, with a particular focus on new tasks and methods developed over the past few years. It also identifies key barriers between these remarkable technical advances and the practical realities of implementation at health systems and in industry.

Sentiment analysis plays a crucial role in understanding customer feedback, guiding product development, and informing business decisions. This paper introduces term sentiment entropy (TSE), a novel weighting method that leverages the distribution of sentiment labels associated with words to enhance text classification accuracy. TSE complements traditional TF-IDF techniques by capturing the nuances of sentiment variation across different contexts. Experiments across diverse public datasets demonstrate TSE's potential to improve sentiment analysis performance, especially in capturing subtle sentiment shifts and adapting to specific domains. While computational cost and label quality pose challenges, TSE offers a promising avenue for refining sentiment analysis and opening new research frontiers.

This study presents a unique model for assessing the dependability of continuous parts of combined systems in open-pit mining through the application of fuzzy logic. Continuous sub-systems as part of the combined system of coal exploitation in surface mines have the basic function of ensuring safe operation, high capacity with high reliability and low costs. These subsystems are usually part of the thermal power plant's coal supply system and ensure stable fuel supply. The model integrates various independent partial indicators of dependability into an expert system specifically designed for evaluating these systems. It deconstructs the complex parameter of system dependability into distinct partial indicators: reliability, maintainability, and logistical support. These indicators are then integrated using fuzzy composition (max-min composition). Historical data from 2018 to 2023 is utilized alongside the fuzzy model to provide a retrospective analysis of system dependability, serving to validate the model's effectiveness. What sets this model apart from conventional approaches is its consideration of practical dependability indicators, thereby obviating the need for extensive long-term monitoring and data collection to portray the system's status accurately over time. This model serves as a valuable tool for assisting decision-makers in open-pit mining operations, facilitating planning, exploitation control, and the selection of maintenance strategies to ensure consistent production and cost reduction. Designed for quick assessment, the model relies on expert judgments and assessments to determine system dependability efficiently.

This research investigates the impact of Al and Ti elements on the formation of self-generated ordered phase in high-entropy alloys (HEA), which have been receiving widespread concentration in present times thanks to their remarkable multifunctional properties and wide range of component selection. By adjusting the dual elements of Al and Ti, the study explores the regulation of quantity and distribution of the in-situ self-generated ordered phases in the alloy. The results of the annealing heat treatment process demonstrated that, through accession Al and Ti elements, the Cr-Fe-Ni medium-entropy alloy transfer from BCC phase to BCC+FCC phase and the precipitation of new phases such as B2, L21, and σ. This led to a synergistic impact of second phase strengthening, and also of solid solution strengthening, resulting in improved compressive properties of HEA.

It is imperative to conduct experimental studies on the seepage behavior of gas-bearing coal under cyclic dynamic loading conditions. This paper focuses on the evolution of coal permeability under the combined effects of dynamic loading, static loading, and gas adsorption. The principal conclusions are as follows. (1) As the frequency and amplitude of dynamic loading increase, the development of pore and fissure structures within the coal body becomes increasingly pronounced during dynamic loading cycles, resulting in a gradual rise in permeability. Notably, as the coal approaches its yielding stage, the permeability can increase by up to 47%. (2) The permeability curve is divided into four regions: the compaction reduction zone, the oscillation zone, the gradual recovery zone, and the abrupt increase zone of failure. Ultimately, in the failure phase, the permeability surges dramatically, potentially reaching 4 to 5 times the initial permeability. (3) When the static load stage is constant, the rate of change in permeability of the coal under dynamic loading decreases with increasing adsorption amount. When the adsorption amount is constant, the rate of change in permeability of the coal under dynamic loading increases with the increase in static load stress stage, with the maximum increase reaching 75.2%. It can be concluded from the rate of change in permeability and the dynamic loading sensitivity coefficient that the permeability of the coal is highly sensitive to cyclic dynamic loading, with increased sensitivity associated with larger static load stages and decreased sensitivity with greater adsorption amounts.

This study presents an advanced Simulated Shearer Machine Cutting Experiment System enhanced with digital twin technology. Central to this system is a simulated shearer drum, designed based on similarity theory to mirror the operational dynamics of actual mining cutters accurately. The setup incorporates a modified machining center equipped with sophisticated sensors that monitor various parameters such as cutting states, forces, torque, vibration, temperature and sound. These sensors are crucial for precisely simulating the shearer cutting actions. The integration of digital twin technology is pivotal, featuring a real-time data management layer, a dynamic simulation mechanism model layer, and an application service layer that facilitates virtual experiments and algorithm refinement. This multifaceted approach allows for in-depth analysis of simulated coal cutting, utilizing sensor data to comprehensively evaluate the shearer's performance. The study also includes tests on simulated coal samples. The system effectively conducts experiments and captures cutting condition signals via the sensors. Through time-domain analysis of these signals, gathered while cutting materials of varying strengths, it is determined that the cutting force signal characteristics are particularly distinct. By isolating the cutting force signal as a key feature, the system can effectively distinguish between different cutting modes. This capability provides a robust experimental basis for coal-rock identification research, offering significant insights into the nuances of shearer operation.

Developing and utilising of underground space is an vital direction for urban growth. Underground commercial streets, as a significant component of underground space accommodating extensive human social activities, and consequently necessitate the creation of human-scale spaces. In the evolution of urban design development towards greater refinement, applying architectural theories and excessively subjective designs has resulted in a deficiency of human-centered design and a disordered spatial environment. This study merges environmental psychology and architectural theory to determine the appropriate length of spatial scale. Two experiments focusing on spatial perception evaluation were conducted using a virtual experimental platform that featured varying dimensions of spatial scale combinations. These quantified combinations were correlated with perception evaluation, and regression analysis was employed to identify appropriate scale ranges, which were superimposed on the range of length selection. Finally, the optimal length and scale combination for underground commercial street spaces were established, providing a reference for the human-centered design of these environments.

Clarifying the distribution of temperature and pressure in the wellbore and cavern during hydrogen injection and extraction is crucial for quantitatively assessing cavern stability and wellbore integrity. This paper establishes an integrated flow and heat transfer model for the cavern and wellbore during hydrogen injection and production, analyzing the variations in temperature and pressure in both the wellbore and the cavern. The specific conclusions are as follows. (1) The temperature and pressure parameters of hydrogen and natural gas within the chamber and wellbore were compared. Under identical injection and production conditions, the temperature of hydrogen in the chamber was 10°C higher than that of natural gas, and 16°C higher in the wellbore. The pressure of hydrogen in the chamber was 2.9 MPa greater than that of natural gas, and 2.6 MPa higher in the wellbore. (2) A comparative analysis was conducted on the impact of surrounding rock’s horizontal and numerical distance on temperature during hydrogen and natural gas injection processes. As the distance from the cavity increases, from 5 to 15 meters, the temperature fluctuation in the surrounding rock diminishes progressively, with the temperature effect in the hydrogen storage chamber extending at least 10 meters. (3) The influence of rock thermal conductivity parameters on temperature during the processes of hydrogen injection and natural gas extraction is compared. The better the thermal conductivity, the deeper the thermal effects penetrate the rock layers, with the specific heat capacity having the most significant impact.

The working environment of mining electric locomotives is wet and muddy coal mine 1 roadway. There may be idling or slipping between the wheels and rails of mining electric locomotives 2 due to low friction between the wheel and rail and insufficient utilization of creep rate. Therefore, it 3 is necessary to control the creep rate within a reasonable range. In this paper, the autonomous control 4 algorithm for mining electric locomotives based on improved ε-greedy is theoretically proven to be 5 convergent and effective firstly. Secondly, after analyzing the contact state between the wheel and rail 6 under wet and slippery road conditions, it was concluded that the value of creep rate is an important 7 factor affecting the autonomous driving of mining electric locomotives. Therefore, the autonomous 8 control method for mining electric locomotives based on creep control is proposed in this paper. 9 Finally, the effectiveness of the proposed method was verified through simulation. The problem of 10 wheel slipping and idling caused by insufficient friction of mining electric locomotives in coal mining 11 environments is effectively suppressed. Autonomous operation of vehicles with optimal driving 12 efficiency can be achieved through quantitative control and utilization of the creep rate between 13 wheels and rails

In an effort to enhance the efficiency and safety of open-pit mines, this study explores the optimization of end slope road parameters and slope structures, specifically focusing on unmanned driving lanes.A significant aspect of the study is the development of a truck trajectory offset model, which considers the different reaction times between automated sensors and human drivers in adapting to environmental changes. To test these concepts, the study uses numerical simulations to confirm the stability of the proposed end slope designs. Using Victory West Mine No. 1 as a case study, the research determines the optimized width for unmanned driving lanes and the maximum angle for safe steepening of end slopes. The findings indicate that the optimized unmanned lane width for NTE240 mining dump trucks is 1743 mm, allowing for a 2-degree increase in the slope angle at the south end slope. This optimization leads to a steep mining stripping volume of 3.2735 million m³ and a coal output of 2.49628 million tons, maintaining a stripping ratio of 1.31m³/t. These results demonstrate that the unmanned driving road width optimization not only ensures slope safety but also significantly boosts the economic benefits of steep mining in open-pit mines.

The degree of ore rock fragmentation of mine blasting is an important index for evaluating the effect of engineering blasting. In order to study the effect of millisecond delayed blasting on the internal fragmentation of the ore rock, LS-DYNA numerical simulation software was used to obtain the damage characteristics of the ore rock in the blasting area using the RHT model. The Mohr-Coulomb strength damage criterion was applied to analyze the internal stress state of the ore rock, and the effects of 25ms, 50ms and 75ms delay time conditions on ore rock fragmentation were studied. The results show that, within a certain range of delay time, the degree of rock fragmentation increases with the increase of delay time. The stress state in the ore rock due to blasting is strengthened with the increase of delay time in the direction of the blast hole, which shows that the stress concentration is strengthened from the excavation face to the interior of the ore rock. The damage and the degree of fragmentation show a characteristic relationship of 25ms＜50ms＜75ms, which is verified by carrying out blasting experiments on site.

The growth rate in academic and scientific publications increased very fast. Researchers must choose a good representative and significant literature in publications, which has become challenging worldwide. Usually, the paper citation number indicates this paper's potential influence and importance. However, this standard metric of citation numbers cannot be used as a measurement to judge the popularity and significance of the recently published papers. To address this challenge, this paper presents an effective prediction method called FutureCite to predict the future citation level of research articles. FutureCite combines machine learning with text and graph mining techniques, leveraging their abilities in classification, datasets in-depth analysis, and feature extraction. FutureCite aims to predict future citation levels of research articles applying a multilabel classification approach. FutureCite can extract significant semantic features and capture the interconnection relationships found in scientific articles during feature extraction using textual content, citation networks, and metadata as feature resources. Our goal is to contribute to the advancement of substantial effective approaches impacting the citation counts in scientific publications by enhancing the precision of future citations. We conducted several experiments using a comprehensive publication dataset to evaluate our method and determine the impact of using a variety of machine learning algorithms. FutureCite demonstrated its robustness and efficiency and showed promising results based on different evaluation metrics. Using the FutureCite model has significant implications in improving the researchers' ability to determine targeted literature for their research and better understand the potential impact of research publications.

Multimodal remote sensing data classification can enhance the model’s ability to distinguish land features through multimodal data fusion. In this context, how to help models understand the relationship between multimodal data and target tasks is the focus of researchers. Inspired by human feedback learning mechanisms, causal reasoning mechanisms, and knowledge induction mechanisms, this paper integrates causal learning, reinforcement learning, and meta learning into a unified remote sensing data classification framework and proposed the causal meta-reinforcement learning (CMRL). First, based on feedback learning mechanisms, we have overcome the limitations of traditional implicit optimization of fusion features and customized a reinforcement learning environment for multimodal remote sensing data classification tasks. Through feedback interactive learning between agents and the environment, we help them understand the complex relationships between multimodal data and labels, thereby achieving full mining of multimodal complementary information. Second, based on the causal inference mechanism we designed causal distribution prediction action, classification reward, and causal intervention reward, capturing pure causal factors in multimodal data and cutting off false statistical associations between non-causal factors and class labels. Finally, based on the knowledge induction mechanism, we designed a bi-layer optimization mechanism based on meta-learning. By constructing a meta training task and meta validation task simulation model in the generalization scenario of unseen data, we helped the model induce cross-task shared knowledge, thereby improving its generalization ability for unseen multimodal data. The experimental results on multiple sets of multimodal datasets show that the proposed method achieved state-of-the-art performance in multimodal remote sensing data classification tasks.

To further enhance the intelligence level of coal mining faces and achieve autonomous derivation, learning, and optimization of shearer navigation cutting, this paper proposes the methods of shearer digital twin navigation cutting motion planning based on the concept of shearer autonomous navigation cutting technology and intelligent coal mining face digital twins. The study includes the digital twin theory and the construction method of the shearer digital twin navigation cutting motion planning system based on this theory. Based on the digital twin theory, a shearer digital twin navigation cutting motion planning system was constructed. This system supports the service functions of shearer cutting digital twin, dynamic navigation map digital twin, reinforcement learning environment construction, and motion planning through the physical perception layer, comprehensive data layer, and digital-model fusion analysis layer. Finally, by comparing the effects of the DQN-NAF and DDPG deep reinforcement learning algorithms in the shearer motion planning task within the constructed digital twin environment, the results show that the DQN-NAF algorithm demonstrates better performance and stability in solving the shearer digital twin motion planning task.

Ensuring precise electric locomotive stopping is pivotal for achieving intelligent and unmanned auxiliary transportation systems. Currently, human drivers' expertise plays a central role in the stopping process, posing challenges for automation and cargo location accuracy, particularly in coal mining. This paper focuses on achieving accurate electric locomotive stopping under diverse conditions by utilizing permanent magnet synchronous motor-driven locomotives. This technique introduces a novel stopping control method that combines a fuzzy Proportional-Integral-Derivative (F-PID) controller and a vector control model for permanent magnet synchronous motors (PMSM). After this, we design the F-PID using the PMSM technique based on the new fuzzy rules for each sub-system. In the end, extensive simulations and real-world experiments are conducted on an electric locomotive stopping test bed, which demonstrate the effectiveness of the proposed control method. From the simulation results, it confirming that the ability to achieve precise stopping under various working conditions.

Smart City has emerged as the mainstream paradigm for urban governance innovation, sustainable development, and strategy upgrades, which is drawing attention from scholars worldwide. However, current frameworks for Smart City assessment remain incomplete and simplistic. In is paper, 30 national or provincial capitals in China were selected and we designed a tri-dimensional SPI model—Social, Physical, and Information Space—for smart city spatial development assessment. Utilizing methods such as entropy weighting, coupled coordination degree models, and the Dagum Gini coefficient, this study assesses the spatial development and coupled coordination of 30 cities from 2011 to 2019. Finally, by means of BP neural networks, the study examines the contribution of each indicator to the spatial coupled coordination. The results indicated that with a narrowing disparity in development speeds among different regions, the spatial coupled coordination development level of smart capitals in China has steadily increased, presenting a pattern of staggered distribution. Moreover, the IS subsystem plays the most significant role in coupled coordination. The significance of this research lies in its tri-dimensional spatial perspective of the spatial development and coupled coordination differences of the Smart City, providing evidence-based support for the regional layout and optimization in China.

For highly time-constrained very short-term investors, reading and extracting valuable information from financial news poses significant challenges. The wide range of topics covered in these news articles further compounds the difficulties for investors. The diverse content adds complexity and uncertainty to the text, making it arduous for very short-term investors to swiftly and accurately extract valuable insights. Variations in authors, media sources, and cultural backgrounds also introduce additional complexities. Hence, performing a bull-bear semantic analysis of financial news using text mining technologies can alleviate the volume, time, and energy pressures on very short-term investors while enhancing the efficiency and accuracy of their investment decisions. This study proposes labeling bull-bear words from a futures corpus detection method that extracts valuable information from financial news, allowing investors to understand market trends quickly. Generative AI models are trained to provide real-time bull-bear advice, aiding investors in adapting to market changes and devising effective trading strategies. Experimental results show the effectiveness of various models, with Random Forest and SVMs achieving an impressive 80% accuracy rate. MLP and Deep learning models also perform well. By leveraging these models, the study reduces the time spent reading financial articles, enabling faster decision-making and increasing the likelihood of investment success. Future research can explore the application of this method in other domains and enhance model design for improved predictive capabilities and practicality.

As RPG has high sales and profits, lots of developers have supplied various RPG to market but it changed to mass production type with sensational advertising, low quality and excessive charging and similar contents which affects game market and users’ game play experience. The author of this paper studied ways to improve mobile RPG by collecting and analyzing users’ reviews using crawling on Google Play Store. The author of this paper used topic modeling that uses text mining technique and LDA (Latent Dirichlet Allocation) to extract meaningful information from collected big data and visualized it. Inferring users’ reviews, figuring out opinions objectively and seeking ways to improve games are helpful in improving mobile RPG that can be played continuously.

Topological Data Analysis (TDA) refers to a collection of methods that find the structure of shapes in data. Although recently, TDA methods have been used in many areas of data mining, it has not been widely applied to text mining tasks. In most text processing algorithms, the order in which different entities appear or co-appear is being lost. Assuming these lost orders are informative features of the data, TDA may play a significant role in the resulted gap on text processing state of the art. Once provided, the topology of different entities through a textural document may reveal some additive information regarding the document that is not reflected in any other features from traditional text processing methods. In this paper, we introduce a novel approach that hires TDA in text processing in order to capture and use the topology of different same-type entities in textural documents. First, we will show how to extract some topological signatures in the text using persistent homology-i.e., a TDA tool that captures topological signature of data cloud. Then we will show how to utilize these signatures for text classification.

Statistics modules are included in most university degrees, independent of the degree area. They have an important role in preparing students for the real world. The overall goal of this paper is to identify the common statistical issues students face during the transition from secondary education to tertiary education. Drawing upon data from the University College Dublin (UCD) Math Support Center (MSC) and Universidad Rey Juan Carlos (URJC), our approach aims to optimize learning experiences while advancing sustainability objectives. In this paper, we conducted a categorization of statistical concepts taught in university (based on URJC). Using this categorization, UCD MSC tutor comments were categorized and validated, and subsequently descriptive analyses and text mining were used on the UCD MSC comments to achieve a deeper understanding of the statistical issues. Building on this study, we plan to develop a suite of evidence-based sustainable educational applications and games to support undergraduate students internationally in first-and second-year statistical modules. Our findings on students’ main statistical issues contribute to the cultivation of lasting statistical literacy among students. This research advances the sustainability agenda within statistical education.

Subway construction is often in a complex natural and human-machine operating environment, and that complicated setting leads to subway construction more prone to safety accidents, which can cause substantial casualties and monetary losses. Thus, it is necessary to investigate the safety risks of subway construction. The existing literature on the identification and assessment of subway construction safety risks(SCSR) is susceptible to the influence of subjective factors. Moreover, although existing studies have explored the interrelationships between different risks, these studies usually analyze the interrelationships of single risks, lack the study of risk chain transfer relationships, and fail to find out the key path of risk transfer. Therefore, this paper innovatively combines text mining, association rules and complex networks to deep mine subway construction safety incident reports and explore risk transfer process. Firstly, it uses text mining technology to identify subway construction safety risk; Then, association rules are introduced to explore the causal relationships among safety risk; Finally, the key safety risk and important transfer paths of subway construction safety accidents (SCSA) are obtained based on the complex network model. Research results show that (a) improper safety management, unimplemented safety subject responsibilities, violation of operation rules, non-perfect safety responsibilities system and insufficient safety education and training are the key safety risk in SCSA; (b) two shorter key risk transfer paths in the subway construction safety network can be obtained: insufficient safety education and training→lower safety awareness→violation of operation rules→safety accidents; insufficient safety checks or hidden trouble investigations→violation of operation rules→safety accidents; (c) in the process of risk transfer, the risk can be controlled by controlling the key nodes or cutting off the transfer path. The results of the study provide new ideas and methods for SCSR identification and influence element mining, which help safety managers propose accurate subway construction safety risk control measures.

Due to the continued deepening of today’s mining projects, there is a need to develop new or improved rockbolts that are strong and yielding and have high energy absorption when exposed to dynamic loading. Impact tests can provide useful information about the dynamic response of rockbolts but most laboratory tests involve high costs in preparation and result validation. Numerical modeling is an alternative that, in addition to complementing laboratory results, can be used to represent the process of deformation and energy absorption of support elements. In this paper the implementation and results obtained from a finite difference (FDM) numerical model are presented. The model functions as a simulation tool to illuminate all the elements that conform the large-scale (1:1) impact test and their behavior and influence on the dynamic response of a threadbar bolt (22 mm nominal diameter). The model was calibrated using published results and based on these results as well as parametric analysis, the response of each component (steel tube, grout, and bolt) could be identified and its behavior in terms of absorbed energy and displacement could be observed. Results show the model can provide important preliminary information to make design decisions about support elements design.

The technology for determining the point’s coordinates on the earth’s surface using the global navigation satellite system (GNSS) is becoming the norm along with ground-based methods. In this case, determining coordinates does not cause any particular difficulties. However, to identify normal heights using this technology with a given accuracy, special research is required. The fact is that satellite determinations of geodetic heights (h) over an ellipsoid surface differ from ground-based measurements of normal height (HN) over a quasi-geoid surface by a certain value called quasi-geoid height or height anomaly (ζ). In relation to determining heights of a certain territory, the concept of geoid height (N) is usually operated when dealing with a geoid model. In this work, geodetic and normal heights are determined for 5 control points in three different regions in Lebanon, where measurements are carried out using GNSS technology and geometric levelling. The obtained quasi-geoid heights are compared with geoid heights derived from the global Earth model EGM2008. The results obtained showed that, in the absence of gravimetric data, the combination of global Earth model data, geometric levelling for selected areas and satellite determinations allows the creation of a high accurate altitude network for mountainous areas.

The escalation of mining activities in the karst regions of Guizhou Province has heightened the occurrence of water-inrush incidents in deep coal mines. This study focused on water-inrush phenomena within the Xinhua mining area of Jinsha County, Guizhou Province, aiming to investigate the sources of these incidents. The findings indicated that the overlying limestone of the Changxing Formation in the coal seam served as a vulnerable aquifer under certain conditions, leading to water inrushes. The analysis of the spatiotemporal distribution patterns of water-inrush incidents at the working face indicated that previous mining operations damaged the shallow Changxing Formation limestone, resulting in the accumulation of goaf water and the formation of numerous mining-induced fractures. These fractures served as rapid conduits for water inrushes from both atmospheric precipitation and underground sources at the deep working face. The examination of surface water and mine water quality demonstrated that both exhibited similar characteristics, predominantly featuring bicarbonate, sulfate, and sodium compositions. Investigation into the relationship between mine water inflow and atmospheric precipitation established that atmospheric precipitation influenced the mine water supply cycle, with a replenishment period of ~10 months during the operational phase of the Jinyuan Coal Mine and about one month post-closure. The fractures induced by mining activities within the Changxing Formation limestone facilitated water flow, with atmospheric precipitation serving as the primary water source for the mine. This study offered a valuable scientific foundation for addressing water-related damage resulting from atmospheric precipitation in mines susceptible to water inrushes under analogous hydrogeological conditions.

Beliefs that maintain patriarchal principles may affect individuals’ willingness to avoid conflicts in their intimate partner relationships and eventually are likely to affect chances of intimate partner economic violence. Nonetheless, research has yet to fully explore these associations. This study used a sample of 321 adults in the ultra-Orthodox Jewish community—a patriarchal and traditional culture—to examine associations in beliefs supporting patriarchal principles at the micro (gendered domestic roles), meso, and macro ( institutional power of men and inherent inferiority of women) levels; conflict avoidance and economic violence; and sex differences in these factors. Descriptive statistics were used to explore gender differences in research variables, and path analysis examined the correlations between research variables for men and women. Beliefs in patriarchal ideologies were moderate at all levels, although slightly higher among men. Men tended to avoid conflict with their intimate partner significantly more than women did. Beliefs in support of patriarchal ideologies predicted conflict avoidance, especially among women. Finally, in opposition to prior research findings, this study revealed nonsignificant sex differences in the prevalence of economic violence victimization. These findings, however, do not suggest that economic violence victimization is not rooted in sex issues. We discuss the findings and the meanings assigned to conflict avoidance by men and women while considering gender disparities of power and control. We suggest that the contribution of men’s tendency to avoid conflict likely moderated their chances to perpetrate economic violence.

Direct and indirect effects after mine operations cease must ideally be subject to perpetual monitoring routines in order to detect possible risks or avoid adverse effects on the surrounding ecosystems at an early stage. In this contribution, mining subsidence lakes created inside the nature reserve Kirchheller Heide and Hilsfeld Forest are subjected to analysis for a long-term monitoring scheme. For this purpose, we employ high-resolution unmanned aerial system (UAS)-based multispectral and thermal mapping tools to provide a fast, non-invasive and multitemporal environmental monitoring method. Specifically, we propose to monitor vegetation evolution through multispectral analysis, biotypes identification using machine learning algorithms, and water surface extent detection, together with their thermal behavior. The aim of this contribution is to present the proposed workflow and first results to establish a baseline for future analyses and subsequent surveys for longterm multi-temporal monitoring

Households and businesses are generating unprecedented levels of electrical and electronic wastes (e-waste), fueled by modernisation and rapid obsolescence. While the challenges imposed by e-waste are similar everywhere in the world, disparities in progress to deal with it exists; with developing nations such as South Africa lagging. The increase in e-waste generation increases the need to formulate strategies to manage it. This paper presents an overview of e-waste management on a global and South African scenarios with a specific case for Cathode Ray Tube (CRT) waste management practices in South Africa. CRTs present the biggest problem for recyclers and policy makers because they contain hazardous elements such as lead and antimony. Common disposal practices have been either landfilling or incineration. The research into the South African practices with regards to CRT waste management showed that there is still more to be done to effectively manage this waste stream. This is despite clear waste regulatory frameworks in the country. However, recent developments have placed e-waste as a priority waste stream, which should lead to intensified efforts in dealing with it. Overall, these efforts should aim to maximise diversion from landfilling and to create value-addition opportunities, leading to social and environmental benefits.

In previous papers we have derived equations of motion with radiation terms and proved the existence-uniqueness of periodic solution of the 3-body problem of classical electrodynamics. The number of equations of motion – 12 is more than the number of unknown functions – 9. It turns out that 9 equations are independent and the rest 3 ones are consequences of them. These three equations have a solution and they give the expressions for the energy of the moving particles. The main goal of the present paper is to estimate the energy of rounding about the nuclei particles and compare it with the values given by quantum mechanics.

Text mining is a technological trend often highlighted in the continuous exchange of information through interconnected media. Its applicability goes beyond private organizations, as the public sector also requires it to treat textual information regarding services offered. Within this scenario, public security emerges as a prominent user of text mining that seeks to ensure the construction of data and knowledgebases to support decision-making about law enforcement actions to ensure citizen welfare. The primary objectives of this article are: (i) to develop a survey to identify text mining applications, techniques, opportunities, and challenges in public security, and (ii) to outline research directions concerning these topics and provide insights so that interested researchers can develop new studies. The literature was searched within four databases: Scopus, IEEE Xplore, ACM Digital Library, and Web of Science. A filtering process was applied to extract the works most aligned with the target theme, resulting in the selection of 194 of the most relevant works for a literature review. There were identified nineteen key applications of text mining related to public security and the most recurrent techniques and technologies reported between 2014 to 2021, supporting outlining three axes for future directions: one with possible expansion of objectives for new research; another on changes and adaptations in scopes for the methodological context; and the last one on expansions and changes in application scenarios based on the literature.

The Double U-pipe ground heat exchanger, recognized for its straightforward process, cost-effectiveness, high heat exchange efficiency, and low thermal resistance, stands as the predominant ground heat exchanger type in today's shallow geothermal energy development and utilization. Recent years have seen significant research into the heat transfer influencing factors and heat exchange performance of Double U-pipe ground heat ex-changers via experimental testing methods. Yet, studies integrating numerical simulation with in-situ testing have been less frequent. Drawing on the cylindrical heat source model theory and the outcomes of regional in-situ thermal response tests, this paper develops a Double U-pipe ground heat transfer model by establishing physical, mathematical, and heat transfer geometric models. It assesses the impact of varying inlet temperatures, flow rates, and initial ground temperatures on heat exchange efficiency under heating conditions. The results validate the precision of the double U-pipe ground heat exchanger model based on in-situ testing. They show that enhancing the temperature differential between the inlet and initial temperatures, elevating the initial ground temperature, and moderately increasing the flow rate can boost the system's heat exchange efficiency.

This paper presents a particular tunneling method or new Austrian tunneling method (NATM) which plays an important role in reducing the subsidence of the Earth surface and the damage to the structures in urban areas. It has a wide range of applications in shallow tunneling projects all over the world. In this study, numerical modeling of third-line Metro tunnel in Tehran is under discussion which is designed and stabilized by NATM. The foregoing tunnel is excavated manually with a one-meter advancing step. In this project, the constructors used a lattice girder and sprayed concrete with 31 cm thickness as the initial lining. A suitable numerical software for this modeling is Plaxis 3D tunnel, which allows performing high-resolution finite element modeling (FEM) of the studied object. The performance of this method is investigated and compared with other NATM methods. As a result of numerical modeling, we give a value of 30.01 mm earth subsidence in the most damaged area of the settlement, in comparing with that of earth surface monitoring, which confirmed it with a dramatically low difference. Moreover, this tunnel was drilled and excavated with various methods, among which the less settlement has obtained by the proposed method. The results are promising to continue the tunneling with this method to expand the subway line in the city.

This study addresses the importance of conducting mass movement susceptibility mapping and hazard assessment using quantitative techniques, including machine learning, in the Northern Lima Commonwealth (NLC). Previous exploration of the topographic variables revealed high correlation and multicollinearity among some of them, which led to dimensionality reduction through principal component analysis (PCA). Six susceptibility models were generated using weights of evidence, logistic regression, multilayer perceptron, support vector machine, random forest, and naive Bayes methods to produce quantitative susceptibility maps and assess the hazard associated with two scenarios: the first being an El Niño phenomenon and the second being an earthquake exceeding 8.8Mw. The main findings indicate that machine learning models exhibit excellent predictive performance for the presence and absence of mass movement events, as all models surpassed an AUC value of >0.9, with the random forest model standing out. In terms of hazard levels, in the event of an El Niño phenomenon or an earthquake exceeding 8.8Mw, approximately 40% of the NLC area would be exposed to the highest hazard levels. It also highlights the importance of integrating methodologies to reduce model uncertainty, such as comprehensive analysis of variables supported by high quality data and organized workflows. The findings of this research are expected to serve as a supportive tool for land managers in formulating effective disaster prevention and risk reduction strategies.

Composite fiber materials are superior materials due to their high strength and light weight. Composites reflect the properties of their constituents, which is proportional to the volume fraction of each phase. There are different fiber reinforcement types and each affects it’s flexural, tensile and compression strength. When selecting a composite for a specific application, the forces excreted on the composite must be known in order to determine the reinforcement type. Unidirectional fiber reinforcement will allow very strong load resistance but only in one direction where as a random orientated fiber reinforcement can resist less load but can maintain this quota in all directions. These materials are said to be anisotropic. Certain composite fibers, taking into consideration there weights, are physically stronger than conventional metals. This research deals with the analysis of three composite materials with different reinforcement types, volume fraction and phase content. It was found that material A (glass epoxy) was the strongest in the longitudinal direction with a flexural strength of 534 MPa in the longitudinal and 420 MPa in the transverse direction. The flexural stresses of material B (glass silicone) and material C (glass polyester) where both found in the 120 to 135 MPa range. Differences were due to their differences in matrix composition and reinforcement type.

Hydraulic fracturing has enabled production from unconventional reservoirs in the U.S., but production rates often decline sharply, limiting recovery factors to under 10%. This study optimizes the CO2 huff-n-puff process for multistage-fractured horizontal wells in Wolfcamp A formation in the Delaware Basin. The potential for enhanced oil recovery and CO2 sequestration simultaneously was addressed using a coupled geomechanics-reservoir simulation. Geomechanical properties were derived from a 1D mechanical earth model and integrated into reservoir simulation to replicate hydraulic fracture geometries. The fracture model was validated using a robust production history matching. Fluid phase behavior analysis refined the equation of state, and 1D slim tube simulations determined a minimum miscibility pressure of 4300 psi for CO2 injection. After the primary production phase, various CO2 injection rates were tested from 1 to 25 MMSCFD/well, resulting in incremental oil recovery ranging from 6.3% to 69.3%. Different injection, soaking, and production cycles were analyzed to determine the ideal operating condition. The optimal scenario improved cumulative oil recovery by 68.8% while keeping the highest CO2 storage efficiency. The simulation approach proposed by this study provides a systematic workflow for evaluating and optimizing CO2 huff-n-puff in hydraulically fractured wells, enhancing the recovery factor of unconventional reservoirs.

This article examines the impact of climate change on the hydrometeorological state of some lakes and reservoirs in the Akmola and North Kazakhstan regions. Time series with values of various climate parameters for the period 1986-2023 are used for the climate analysis; the reliability of the results was assessed using the Fisher criteria. Hydrometric indicators were studied using the ArcGIG program. Using GIS to spatially analyze water climate data, 8-channel satellite images from 1995 to 2023 were used. During the study, it was found that the area of the main lakes has undergone changes.

This paper introduces LUNARMINERS framework and applies it for lunar water-ice extraction in Shackleton Crater at the lunar South Pole. The LUNARMINERS combines artificial intelligence swarm robotics with biomimetic swarm behavior. In the proposed framework, the foraging behavior of ants, the light attraction behavior of fireflies, and the migratory flight strategy of wild geese have been comprehensively analyzed and applied to robotic swarms for lunar mining. This research takes into consideration important aspects of the lunar environment, such as permanently shadowed regions (PSRs) and communication limitations in lunar craters, by simulating them in Robot Operating System (ROS). The LUNARMINERS framework has advantages over other methods in that it allows for the survey of an area of 0.025 km2 per day while achieving 100% water-ice deposition effectiveness. Studies show that by applying the LUNARMINER framework, the exploration of the entire Shackleton Crater can be completed in 3.75 Earth years via 15 robots. Simulations of the implementation of the proposed LUNARMINERS framework have demonstrated reliable high-levels of extraction efficiency, with the framework producing 12.9 metric tons of water-ice per day, which potentially can support up to 1,990 people per day based on astronaut’s daily water requirements from NASA's Human Integrated Design Handbook (HIDH) and thus, establishing a sustainable benchmark for lunar habitation. The results from this study show that the advances in the proposed LUNARMINERS framework can contribute to future lunar exploration and ISRU, setting new standards for sustainable off-Earth mining.

Throughout the Covid19 period, families were forced to stay indoors, adapting to online school-ing, remote work, and virtual social engagements, inevitably altering the dynamics within households. There is a notable increase in mental health challenges in terms of anxiety and de-pression in children and adolescents. This study intends to explore the psychosocial effects of the Covid-19 pandemic in Italian families adopting self- and proxy-report questionnaires on anxiety, anger, and health-related quality of life. The results showed that approximately 20% obtained a clinical anxiety score and only 10% obtained a clinical anger score. There was found a difference in the perception of the quality of life reported by the child and that perceived by the parent. The stepwise regression model showed that total anxiety scores were predicted by sex, quality of life scores from the parents' self-report version and total anger score. Another stepwise regression model identified physiological and social anxiety as the best predictors that impact the quality of life. Parental well-being actively influences the well-being of children, so it is fundamental to implement preventive programmes and promote child well-being providing parents the most adequate support possible.

The Marun-Keu complex plays a significant role in understanding the geological evolution of the Ural orogen; however, it remains poorly understood. This study aims to provide insights into the complex's age, protolith composition, rock formation conditions, and its position in the region's history. The zircons from the host granitic gneisses are characterized by magmatic cores with an age of 470 Ma and metamorphic rims with an age of approximately 370 Ma. We suggest that the metamorphic rims were formed during eclogite metamorphism and that the metagranitoids hosting the eclogites experienced eclogite metamorphism simultaneously with the basic and ultrabasic rocks that are common in this area. Heterogeneous zircons were also isolated from the selvage of a pegmatite vein, in which four domains are distinguished, two to three of which can be identified within single grains, as follows: 1) igneous cores with an age of approximately 470 Ma and the geochemical characteristics of zircon crystallized in basic rocks; 2) zircons recrystallized during eclogite metamorphism with geochemical characteristics intermediate between those of the magmatic cores and true eclogitic zircon; 3) pegmatitic zircon, exhibiting the most sharply differentiated REE spectra of all four domains, characterized by a prominent positive Ce anomaly and a weakly expressed negative Eu anomaly; and 4) eclogitic zircon, observed in the form of veins and rims, superimposed in relation to the other three domains. The age of the latter three domains is within the error range and is estimated to be approximately 370 Ma. This indicates that the processes of eclogite metamorphism and the formation of pegmatites occurred at approximately the same time in the studied area.

Objective: To examine the dose-response relationship between average daily temperature and sunlight hours with daily steps across a 35-week period in school-aged children, and to evaluate whether there were differences by sex. Methods: We conducted a follow-up study involving 655 children (50.8% girls, mean age 10.45 ± 0.95) from six public primary schools in Cuenca, Spain. We measured daily steps using Xiaomi Mi Band 3 Smart Bracelets from October 2022 to June 2023 (over 35 weeks). We collected daily average temperature from the local weather station in Cuenca and sunlight hours during the same period. We used ANCOVA models and LOESS regression to examine the dose-response relationship between average daily temperature and daily hours of sunlight with daily steps. Additionally, we performed a multiple linear regression model. Results: Our findings revealed significant variations in daily steps across the 35 weeks. The relationship between environmental factors and daily steps was non-linear, in both girls and boys. The optimal values for higher activity levels were an average temperature of 14°C and 13 hours of sunlight. Furthermore, a 1 ºC increase in temperature was associated with an increase of 74 ± 130 steps/day, while an increase of one hour of sunlight was associated with an increase of 315 ± 237 steps/day. However, sunlight hours may act as a moderating factor. Conclusion: Our study showed a non-linear association between t average daily temperature and sunlight hours with daily steps over a 35-week period. Additional strategies may be necessary to encourage increased physical activity during periods of extreme temperatures or sunlight exposure.

The growing adoption of electric propulsion systems in Maritime Autonomous Surface Ships (MASS) necessitates advancements in shipboard electronics for safe, efficient, and reliable operation. These advancements are crucial for tasks like real-time sensor data processing, control algorithms for autonomous navigation, and robust decision-making capabilities. This study investigates research trends in MASS using bibliographic analysis to inform policy and future research directions in this evolving field. We analyze 3,363 MASS-related articles from the Web of Science database, employing co-occurrence word analysis and latent Dirichlet allocation (LDA) topic modeling. Findings reveal a rapidly growing field dominated by image recognition research. Keywords like "datum," "image," and "detection" suggest a focus on collecting and analyzing marine data, particularly with deep learning for synthetic aperture radar imagery. LDA confirms this, with "Image analysis and classification research" as the leading topic. The study also identifies national and organizational leaders in MASS research. However, research on Arctic routes lags behind other areas. This work provides valuable insights for policymakers and researchers, promoting a deeper understanding of MASS and informing future policy and research agendas for the integration of electric propulsion systems in the maritime industry.

This study investigates water consumption in two areas with limited water resources – the Salar de Atacama and Salar de Atacama-Vertiente Pacifico basins in Chile's Antofagasta Region – with the aim of developing strategies that incorporate social and environmental aspects into water management. A qualitative approach was employed that involved a focus group with four water management representatives and surveys of the general population (156 responses). Additionally, the current state of water rights in the basins was examined and the feasibility of the proposed strategies was assessed. The findings reveal that the mining industry's development approach is viewed negatively, mainly due to inadequate community engagement, confidential consumption data, and limited government oversight. Adopting a comprehensive water stewardship approach that considers social and environmental factors would help mitigate the adverse effects of mining on communities. Therefore, improved management and transparent collaboration among stakeholders are essential for responsible water resource use in mining.

Education plays a key role in human societies and the development of nations. Providing social justice in a country creates educational justice, and educational justice grants equal opportunities for growth and prosperity to the members of the society. In reality, there are differences between urban and rural educational facilities, as well as between girls and boys. Deprived and rural areas are full of losses, from economic problems to intellectual and cultural blockages. Failure to enhance these areas leads to further decline in these environments, and one of its consequences in education is the dropout of students. Girls are especially exposed to this problem due to gender discrimination, traditional and religious views, and living conditions. The purpose of this research was to obtain in-depth information about the experiences and reactions of girls in deprived areas and to identify its factors. This case study was conducted in the Khuzestan province of Iran using the grounded theory, and the results of the interviews were categorized after coding and its paradigm model was presented. In the end, solutions were presented to reduce the dropout rate of girls in deprived areas and prevent this problem.

In order to comprehensively evaluate the employee satisfaction of mine occupational health and safety management system, an analytic method based on fuzzy analytic hierarchy process and 2- tuple linguistic model was established. Firstly, based on the establishment of 5 first- grade indicators and 20 second- grade ones, the weight of each indicator is calculated and validated by method of FAHP and root mean square model. Secondly, a path based on the time-ordered Weighted Averaging Operator (T-OWA) model is constructed. Finally, the model is validated by empirical analysis. The results demonstrate that the employee satisfaction of the mine occupational health and safety management system is of the “general” rank. The method including the comprehensive evaluation of employee satisfaction and the quantitative analysis of language evaluation information ensures the authenticity of the language evaluation information.

Many anti-tumor effects of group-specific component-derived macrophage-activating factors (GcMAFs) have been reported; however, the specific mechanisms remain unclear. Controlling tumor-associated macrophages (TAMs) in the tumor microenvironment is essential for cancer treatment. This study assessed the role of GcMAF in macrophage activation, elucidated the mechanisms by which it exerts its anti-tumor effects, and determined its effects on TAMs in the tumor microenvironment. GcMAF-stimulated RAW264.7 macrophages and EMT6 breast tumor cells were co-cultured in a 0.4-µm pore cell culture insert, and gene and protein expression and cell viability were evaluated. DNA microarray analysis of GcMAF-stimulated RAW264.7 cells was conducted. The induction of M2 RAW264.7 cells by interleukin (IL)-4 and IL-13 was analyzed. GcMAF stimulation increased the tumor necrosis factor-α and inducible nitric oxide synthase mRNA and protein levels in RAW264.7 cells but decreased the viability of co-cultured EMT6 cells. These results were confirmed in the M2 RAW264M2.7 cells, suggesting that GcMAF exerts anti-tumor effects by inducing the differentiation of macrophages into the M1 type and reprogramming M2 macrophages to the M1 type. The anti-tumor activity of GcMAF via M2-to-M1 macrophage reprogramming could aid in developing novel cancer immunotherapies.

The structure and physicochemical properties of polyvinyl alcohol (PVA) and PVA/glycerol films have been investigated by Fourier-transform infrared spectroscopy (FT-IR), X-ray Diffraction (XRD), thermogravimetry/differential thermal analysis (TG/DTA), and advanced scanning probe microscopy (SPM). In the pure PVA films, SPM allowed us to observe ribbon-shaped domains with a different frictional and elastic contrast, which apparently originated from a correlated growth or assembly of PVA crystalline nuclei located within individual PVA clusters. The incorporation of 1.0 wt.% glycerol led to modification in shape of those domains from ribbon-like in pure PVA to rounded in PVA/glycerol 1.0 wt.% films; changes in the relative intensities of the XRD peaks and a decrease of the amorphous halo in the XRD pattern were also detected, while the DTA peak corresponding to the melting point remained at almost the same temperature. For higher glycerol content, FT-IR revealed additional glycerol-characteristic peaks presumably related to the formation of glycerol aggregates, and XRD, FT-IR, and DTA all indicated a reduction in crystallinity. For more than 2.0 wt.% glycerol, the plasticization of the films complicated the acquisition of SPM images without tip-induced surface modification. Our study contributes to the understanding of crystallinity in PVA and how it is altered by a plasticizer such as glycerol.

The paper provides and comments on the results of studies of the effect of sandstone-based abrasives and quartz sand alone on the wear of martensitic surfaces of wear-resistant steels. The wear process was examined on a ring-on-ring test rig seeking to determine the mass decrement parameter which characterised wear. The tests were conducted for three sandstone varieties: Carboniferous, Permian, and Cretaceous, and they made it possible to determine that the most intense process of deterioration of wear-resistant steels took place in the presence of quartz sand grain, while less intense wear was observed in the case of sandstone-based abrasives. The mass decrement values established in the presence of the sandstones in question did not differ significantly between individual sandstone varieties. Based on a surface damage analysis, the basic damage mechanism was found to be micro-scratching, however, with regard to the sandstones examined, it was also determined that individual grains could be pressed into surface irregularities, as well as that films of soft hematite cement developed in the Permian sandstone and that inclusions of carbonaceous matter were formed in the Carboniferous sandstone. With reference to the wear process observations, a wear model was described for the surface of the steels examined in the presence of sandstone-based abrasives.

Open-vocabulary learning has recently gained prominence as a means to enable image segmentation for arbitrary categories based on textual descriptions. This advancement has extended the applicability of segmentation systems to a broader range of generally purpose scenarios. However, current methods often revolve around specialized architectures and parameters tailored to specific segmentation tasks, resulting in a fragmented landscape of segmentation models. In response to these challenges, we introduce OVAMTSeg, a versatile framework designed for Open-Vocabulary and Multitask Image Segmentation. OVAMTSeg harnesses adaptive prompt learning to empower the model to capture category-sensitive concepts, enhancing its robustness across diverse multi-task and scenario contexts. Text prompts are employed to effectively capture semantic and contextual features of the text, while cross-attention and cross-modal interactions enable the fusion of image and text features. Furthermore, a transformer-based decoder is incorporated for dense prediction. Extensive experimental results underscore the effectiveness of OVAMTSeg, showcasing its state-of-the-art performance and superior generalization capabilities across three segmentation tasks. Notable achievements include a 47.5 mIoU in referring expression segmentation, 51.6 mIoU on Pascal-VOC with four unseen classes, 46.6 mIoU on Pascal-Context in zero-shot segmentation, 65.9 mIoU on Pascal-5i, and 35.7 mIoU on COCO-20i datasets for one-shot segmentation.

This systematic review explores the cross-cultural validation of neuropsychological assessments and their clinical applications in cognitive behavioral therapy (CBT), focusing on culturally adapted CBT (CA-CBT) across diverse populations and settings. Methods: Following the PRISMA guidelines, a comprehensive search was conducted in multiple academic databases, including PubMed, PsycINFO, Scopus, and Web of Science. Keywords related to cognitive-behavioral therapy, cultural adaptation, and specific populations were used. Inclusion criteria encompassed randomized controlled trials (RCTs) and pilot studies that assessed CA-CBT for various mental health conditions. Results: The review included studies involving Chinese Americans, Latino caregivers, Syrian refugees, Jordanian children, Malaysian Muslims, Afghan refugees, Iraqi women, Japanese children and adolescents, and Tanzanian and Kenyan children. CA-CBT demonstrated significant effectiveness in reducing symptoms of depression, anxiety, PTSD, and psychosis. For instance, Research has shown that CA-CBT is more effective than standard CBT in reducing depressive symptoms among Chinese Americans and significantly lowering PTSD symptoms in Syrian refugee women. The method has been well-received and feasible in diverse populations, such as Jordanian children and Afghan refugees. Long-term benefits are promising, with sustained improvements reported in various studies. Additionally, digital and remote delivery methods have demonstrated potential for expanding the accessibility of CA-CBT. Conclusions: CA-CBT is a valuable and effective intervention for diverse cultural populations, significantly improving mental health outcomes. However, future research must address limitations such as small sample sizes, short follow-up periods, and variability in assessment tools. Studies should include larger and more diverse sample sizes, longer follow-up periods, rigorous control groups, and comprehensive outcome measures to further validate and enhance the application of CA-CBT across different cultural contexts.

This text mining study delves into the multifaceted contributions of the energy sector to the Sus-tainable Development Goals (SDGs). By analyzing a wide range of academic literature, we un-cover key themes, trends, and challenges shaping the intersection of energy and sustainability. The findings reveal that the energy sector plays a pivotal role in achieving SDGs such as affordable and clean energy (SDG 7) and climate action (SDG 13). Critical issues encompass governance, policy frameworks, and technological innovations. This research underscores the need for inter-disciplinary collaboration and holistic approaches in addressing complex energy-related sus-tainability challenges. The insights derived here provide guidance to policymakers, researchers, and stakeholders seeking to harness the energy sector's potential for a more sustainable and eq-uitable future.

Ultramafic rocks become promising candidates for carbon sequestration by enhanced carbon dioxide (CO2) mineralization strategies due to their highly CO2-reactive mineral composition and its abundant availability. This study reports a mineralogy and microtextures of a representative ultramafic rock from the Ma-Hin Creek in northern Thailand and observes evidence of CO2 mineralization occurring through the interaction between CO2 and the rock with the existence of water under ambient conditions. After sample collection, rock description was determined by optical petrographic analysis. The rock petrography reveals a cumulated wehrlite comprising over 50% olivine and minor amounts of clinopyroxene, plagioclase, and chromian spinel. Approximately 25% of the wehrlite has altered to serpentine and chlorite. A series of CO2 batch experiments were conducted on six different rock sizes at a temperature of 40°C and pressure of 1 atm over five consecutive days. The post-experimental products were dried, weighed, and geochemically analyzed to detect changes in mineral species. Experimental results showed that product weight and the presence of calcite increased with reducing grain size. Additionally, the modal mineralogy of the wehrlite theoretically suggests a potential CO2 uptake of up to 53%, which is higher than the average uptake values of mafic rocks. These findings support the suitable rock investigation approach and the preliminary assessment of carbon mineralization potential, contributing to enhanced rock weathering techniques for CO2 removal that could be adopted by mining and rock supplier industries.

This study aims to explain individual engagement in sustainable waste management behaviors (SWMBs) based on the application of protection motivation theory (PMT). SWMBs include waste avoidance, green purchasing behavior, reuse and recycle, and waste disposal behaviors. This study applies PMT to explore how individuals’ SWMBs are influenced by their perceived threats caused by environmental contamination from waste disposal and their perceived coping capability. The Bangkok metropolitan area was selected as a case study because it has faced serious waste management problems caused by massively increasing amounts of solid waste during the last ten years. Questionnaire surveys were conducted with 193 public and private officers residing in the city of Bangkok. The one-way analysis of variance (ANOVA) was performed to justify the effect of individual threat appraisal and coping appraisal on the engagement in SWMBs. The results demonstrated that respondents’ self-efficacy highly influenced all types of SWMBs. The perceived probability of being impacted from pollutants influenced all of the SWMBs except green purchasing behaviors. Response efficacy did not influence all SWMBs; however, the perceived severity of adverse consequences caused by pollutants highly influenced reuse and recycle behaviors. It could be suggested that PMT is well suited for investigating low-cost and simple SWMBs. It could also be suggested that different communication campaigns should be established to enhance citizens’ engagement in each type of SWMB.

Population-based data on hematologic malignancies (HM) are rare in Greece regarding adults (≥18 years old). The aim of this study was the assessment of the burden of these malignant neoplasms (MN) for adults in Crete. The study assessed temporal trends of incidence and mortality, depicted their spatial distribution and extracted net-survival curves of patients. Population-data on these MN were obtained from the database of the Cancer Registry of Crete, for a 22 year time period (1/1/1992-31/12/2013). All HM were classified into four main groups of diseases: (1) Hodgkin’s lymphoma (HL); (2) Non-Hodgkin’s lymphomas (NHLs); (3) Multiple myeloma (MM); and (4) All chronic and acute leukemias. Annual, gender-specific, age standardized incidence and mortality rates were estimated (ASIR/100.000/year; ASMR/100.000/year). A total of 3,380 newly diagnosed cases were included in the final analyses; All ASIRs/100,000 increased significantly throughout the study period presenting differences in their geographical distribution of rates among municipalities of Crete. ASIRs for NHLs, HL, MM, leukemias and for all 4 MN were 13.1, 2.6, 4.7, 11.7 and 32.1, respectively, in 2013. Five-year net-survival for patients diagnosed with NHLs, HL, MM and leukemias were: 82%, 85%, 52% and 56%, respectively. This first research article studying a long period of time for adults in Greece generates hypotheses on the existence of risk factors that may partly explain this variation of rates.

Children on farms face high risks of work and non-work-related fatalities, with tractors being a significant contributor. This study examines children's involvement in fatal tractor-related accidents within agriculture in Iceland in 1950-2024, explores adult reflections on childhood tractor-driving experiences, and analyses Members of Parliament´s arguments against setting a minimum age for off-road tractor driving. Data rests on fatal tractor-related accidents using newspaper archives and supplementary sources, narrative interviews with former summer children who stayed at farms in childhood, and parliamentary debates on tractor-related legislation. Over half of the 81 registered accidents involved children, primarily boys, with 75% occurring between 1958 and 1988 when no minimum age for off-road tractor driving existed. Children's fatality incidence rate was more than four times higher than adults. Arguments against minimum age requirements for off-road driving included the need for child labour, children's superior driving skills, and a denial that children were more often victims than adults. Since 1988, no child has died when driving a tractor. A human-centred approach focusing on working conditions, driver capacity, and adherence to safety procedures and legal frameworks is needed to prevent future accidents.

In the last few decades, we have witnessed an unprecedented transformation in the way information is created, disseminated, and consumed. The advent of the digital age has revolutionized not only our access to information but also the very fabric of our social, political, and cultural lives. Digital media, a term encompassing everything from online news portals and social media platforms to streaming services and mobile applications, now forms an integral part of the daily lives of billions of individuals worldwide.

The conventional well placement was done manually by using a numerical reservoir simulator, and it required a lengthy trial-and-error process. It required great experience and expertise to manipulate the variables and uncertainties that lie on the reservoir to determine the best placement of the well. In addition, the traditional gradient-based methods such as Line-search and Trust-region were not viable in terms of maximum results obtained. Gradient-based methods were too dependent on the surface gradient of the solution and may only converge to local optima instead of global optima. Complex reservoirs have rough surfaces with high uncertainties, which hinders the traditional gradient-based method from converging to global optima. Thus, genetic algorithms were utilized to automate the manual trial-and-error process and to overcome the limitations of the traditional gradient-based method. The objectives of this study were to analyse the effect of different initial well placement distributions, the number of random solution sizes, and the crossover rate on cumulative oil production. A synthetic reservoir model built using CMG Builder was used as the testing platform for the optimization problems. Well-placement parameterization and optimization set-up were carried out using the CMG CMOST optimization tool. The integration of CMG IMEX and CMOST optimized cumulative oil production based on the objectives of the study. The results obtained showed that the higher number of random solutions used resulted in higher cumulative oil production, with more generations needed to reach the optimum solution. It can be concluded that the larger number of random solutions used increased the probability of reaching the optimum solution, but will take more generations.

The planned closure of potash mines, achieved through the injection of highly mineralized brines into the worked-out area, is a complex and not fully studied process. A critical concern arises when brines obstruct the aerodynamic connections between the flooded mine's airspace and the atmosphere, potentially leading to the formation of closed cavities where explosive gases can accumulate. To address this hazard, it is imperative to develop systems capable of extracting the gas-air mixture from the unflooded domed portions of the worked-out area. This is the focus of this study, in which we analyze gas-dynamic processes within the potassium mine worked-out area during planned flooding. Two distinct scenarios are examined: the first involves controlled flooding with saturated brines, while the second contemplates flooding resulting from a hypothetical breakthrough of supra-salt strata, leading to the ingress of groundwater into the worked-out area. A novel mathematical model is introduced to predict the evolution of gas-air mixture parameters in the unflooded dome segment of the worked-out area. Utilizing this model, we assess the effectiveness of proposed measures designed to eliminate explosive gases from the worked-out area. Specifically, a pipeline system is proposed for the removal of gases, followed by their dilution below the maximum permissible concentrations in the effluent jet. The findings from this study contribute valuable insights into ensuring the safe and efficient closure of potash mines, shedding light on potential risks and effective mitigation strategies for gas-related hazards during planned flooding.

Despite the increasing prevalence of pet ownership in Chinese societies, standardized tools to assess grief from pet loss remain lacking. Research predominantly focuses on Western populations, creating a gap in understanding pet bereavement in Chinese cultural settings. This study aimed to adapt and validate the Pet Bereavement Questionnaire (PBQ-C) for a Chinese context to create a culturally appropriate assessment tool. A total of 245 participants with companion animal loss experiences were recruited through the university of the research team. They were invited to complete an online survey including the Chinese PBQ, the Depression subscale of the Depression Anxiety Stress Scales (DASS-21), and the Inventory of Complicated Grief (ICG). Both Exploratory Factor Analysis and Confirmatory Factor Analysis were conducted to examine the psychometric properties of the PBQ-C and the findings supported a three-factor structure—grief, anger, and guilt—aligned with the original PBQ, with three items were reassigned to different factors. Despite these adjustments, the PBQ-C showed strong internal consistency, split-half reliability, and concurrent validity. The validated PBQ-C provides a culturally sensitive tool for assessing pet bereavement in Chinese society that can promote research and counselling support for this under-researched and under-recognized type of loss of human-animal relationships.

The nematode Caenorhabditis elegans, with its highly sensitive olfactory system, has emerged as a promising tool for testing chemotaxis. In the field of cancer diagnostics, there is a growing interest in the development of non-invasive screening methods for the detection of volatile organic compounds in a patient's urine. The objective of this study was to contribute to the existing body of knowledge by evaluating the ability of a Caenorhabditis elegans-based chemotaxis assay to discriminate between urine samples from healthy individuals and patients diagnosed with breast or colon cancer. Following synchronization of the developmental stages of C. elegans, nematodes were exposed to the urine of cancer patients and healthy individuals. Subsequently, chemotactic indices were calculated for each urine sample. Our results demonstrated a statistically significant difference in the chemotactic response of C. elegans to urine samples from cancer patients compared to healthy volunteers (p < 0.001). Moreover, the test exhibited promising diagnostic utility, with a sensitivity of 98%, a specificity of 62%, and a detection rate of 78%. Our findings expand on previous observations, confirming the remarkable sensitivity of C. elegans hermaphrodites to discriminating cancer-related volatile organic compounds in urine samples.

This study investigates the effect of perceived discrimination on the mental health of Afghan refugees, and secondly, tests the distress moderating effects of pre-migration traumatic experiences and post-resettlement adjustment factors. In a cross-sectional design, 259 Afghans completed surveys assessing perceived discrimination and a number of other factors using scales developed through inductive techniques. Multivariate analyses consisted of a series of hierarchical regressions testing the effect of perceived discrimination on distress, followed by a sequential analysis of moderator variables. Perceived discrimination was significantly associated with higher distress, and this relationship was stronger among those with a strong intra-ethnic identity, high civic engagement, and high pre-resettlement traumatic experiences. Discrimination is a significant source of stress for Afghan refugees, which may exacerbate stresses associated with other post-migration stressors. Future research is needed to tailor interventions that can help mitigate the stress associated with discrimination among this highly vulnerable group.

The concept of economic self-reliance, widely known by Thai people as the philosophy of sufficiency economy, has been widely promoted in rural Thai societies. By practicing this philosophy, it is expected that the citizens’ quality of life and local environments could be sustainably improved. This study aims to explore the contribution of the community practices of the sufficiency economy philosophy to rural villagers’ quality of life improvement, and to investigate potential factors that determine the trust of villagers in the philosophy. With the purpose to propose strategies which could enhance trust and promote villagers’ practices of the philosophy, the study investigated influences of three relevant factors on trust towards the philosophy. Those factors included factors related to cognitive-based trust, factors related to emotional-based trust, and factors related to demographic characteristics. Questionnaire surveys and in-depth interviews with community leaders and local villagers were conducted in the Ban Jamrung community, in Thailand’s Rayong province. The results of the statistical analysis revealed that the residents who applied the sufficiency economy philosophy in their daily lives experienced a relatively better quality of life. Additionally, it was found that trust in the philosophy could be predicted more by rational factors than by emotional factors. These findings could be utilized to develop strategies to maintain and enhance the trust of the people in the philosophy of sufficiency economy.

Seaports are critical hubs in global trade, facing increasing pressure to accelerate green transi-tions while navigating evolving economic and environmental landscapes. This research analyses the intricate relationship between port organizational ecosystem resilience and the successful implementation of green transitions, highlighting the challenges and opportunities in moving towards a more sustainable future. The research employed a combined focus group interview and participatory observation methodology to gather insights from experts. Analysis revealed that while technological advancements are a primary driver for greening port operations, a lack of managerial capacity is a significant impediment to the successful integration of these innova-tions. It was identified several challenges, including outdated infrastructure, cybersecurity threats, and the need for ongoing investments in new technologies, along with the need to foster a sustainable behaviour culture. Experts emphasized the importance of developing managerial competencies and continuous learning. The research also highlights the role of strong stake-holder partnerships, including collaboration with government agencies, industry associations, and environmental organizations. This research provides valuable insights for policymakers, port managers, and other stakeholders seeking to navigate the challenges for ensuring a sus-tainable future for the maritime sport organizational ecosystems and maritime industry.

CO2 geological sequestration in coal seams can achieve the dual objectives of CO2 emission reduction and enhanced coalbed methane production, making it a highly promising carbon capture and storage technology. However, the injection of CO2 into coal reservoirs in the form of supercritical fluid (ScCO2) leads to complex physicochemical reactions with the coal seam, altering the properties of the coal reservoir and impacting the effectiveness of CO2 sequestration and methane production enhancement.In this paper, theoretical calculations based on ReaxFF-MD were conducted to study the interaction mechanism between ScCO2 and the macromolecular structure of both low-rank and high-rank coal, to address the limitations of experimental methods. The reaction of ScCO2 with low-rank coal and high-rank coal exhibits significant differences. At swelling stage, the low-rank coal experiences a decrease in aromatic structure and aliphatic structure, high-rank coal shows an increase in aromatic structure and a decrease in aliphatic structure, while swelling phenomenon is more pronounced in high-rank coal. At dissolution stage, low-rank coal is initially decomposed into two secondary molecular fragments, and then recombine to form new molecular structure, the aromatic structure increased and the aliphatic structure decreased. In contrast, high-rank coal occurs stretches-breakage-movement-reconnection, a reduction in aromatic structure and an increase in aliphatic structure.The primary reasons for these variations lie in distinct molecular structure compositions and the properties of ScCO2, leading to different reaction pathways of functional group and aromatic structure. The reaction pathways of functional groups and aromatic structures in coal can be summarized as follows: the breakage of the O-H bond in hydroxyl groups, the breakage of the C-OH bond in carboxyl groups, transformation of aliphatic structures into smaller hydrocarbon compounds or formation of long-chain alkenes, and various pathways involving broken, rearrangement, recombination of aromatic structures. In low-rank coal, there is a higher abundance of oxygen-containing functional groups and aliphatic structures. The breakage of O-H and C-OH chemical bonds results in the formation of free radical ions, while some aliphatic structures detach to produce hydrocarbons. Additionally, some of these aliphatic structures combine with carbonyl groups and free radical ions to generate new aromatic structures. Conversely, in high-rank coal, the lower content of oxygen-containing functional groups and aliphatic structures, along with stronger intramolecular forces, results in fewer chemical bond breakages and makes it less conducive to the formation of new aromatic structures.These results elucidate the specific deformations of different chemical groups, offering a molecular-level understanding of the interaction between CO2 and coal.

(1) Background: The CEO message of hospital homepage contain various contents such as the hospital's future vision, promises with customers, upgraded services and public activities. The CEO’s message of the homepage includes non-financial information as well as financial information of corporates. Also, it provides useful information for not only company's goals and vision but also firm performance and strategies for the future. This study aims to investigate associations between CEO’s message of hospitals homepages and financial status. We used the balanced scorecard frame to analyze what content on the hospital's homepage is related to the hospital's various financial ratios. (2) Methods: We adopt a text mining method to extract significantly repeated keywords from the CEO’s message of hospital website. And we classify these keywords by a balanced scorecard frame. To examine the relationship between keywords of CEO’s message of the hospital homepage and hospital’s financial ratio, T-test is conducted for the difference in the TF-IDF (Term Frequency is Divided by Inverse Document Frequency) mean of the home page contents and its relationship with the views of the balanced scorecard framework. (3) Results: According to empirical results on 65 samples collected from local hospitals, there are some significant relationship between the qualitative content of the hospital's homepage and the quantitative financial ratio that indicates profitability, activity, leverage, liquidity, and transfer to essential business fund (EBF) income. (4) Conclusions: The introduction section of a homepage is most accessible to customers, containing the aims and ideals of hospitals and reflecting their values and visions [1]. In addition, in view of financial status, they can either emphasize financial strength or focus on other areas to mask weakness of financial information. This study reminds us of the importance of hospital website’s disclosure, and it can be inferred from the financial status of the hospital. It also highlights the need for harmonization between quantitative data, financial statements, and qualitative data, CEO’s messages. (5) Implications: To our best knowledge, this paper is the first research attempting to investigate the relation between text of hospital homepage and financial ratio of hospital through text mining technique and balanced scorecard frame. Hospitals take a crucial part in a country’s welfare and healthcare backbone industry. Nevertheless, in many countries, hospital organization sectors tend to remain a source of critical fiscal deficits due to its ineffective and sloppy management. We expect that the result of this paper can provide hospital managers to useful information.

Previous studies separately addressed the impact of urban expansion on vegetation cover and thermal conditions, as well as its effect on moisture and drought indicators, using geospatial techniques. Some studies, such as those by Malah et al. (2021) and Niu and Li (2020), focused on assessing urban environmental quality using an environmental quality index without considering urban expansion. Meanwhile, other studies, like those conducted by Indrawati et al. (2020), primarily focused on evaluating urban environ-ments using an integrated environmental index consisting of four indicators (greenness, moisture, drought, and cumulative clustering). The study by Zhu et al. (2019) applied the same integrated environmental index (IEI) and, by comparing it with temperature, re-vealed a negative correlation illustrating the effects of the environmental index on land surface temperature (LST). These two studies could be utilized to construct an index assessing the environmental condition in Riyadh. This is the case in the present study, where the addition of a temperature indicator due to the hot desert nature of the study area created the Urban Environmental Thermal Index to evaluate the urban en-vironment and provide results suitable to this region. It is expected that the present study will align with the findings of previous relevant studies, showing a negative im-pact of urban expansion on urban environmental components.

The correct selection of the value of p is a complex and iterative procedure that requires experience in the interpretation of the obtained interpolated maps. Inverse Distance Weighting is a method applied to the porosities of the K and L hydrocarbon reservoirs, discovered in the Neogene (Lower Pontian) subsurface sandstones in the northern Croatia (Pannonian Basin System). They represent small and large data samples. Also a standard statistical analysis of the data was made, followed by the qualitative-quantitative analysis of the maps, based on the selection of different values of the power distance exponent (p-value) for the K and L reservoirs maps. According to the qualitative analysis, for a small data set, p-value could be set on 1 or 2 giving the most optimal result, while for a large data set, a p value of 3 and 4 could be applied. By quantitative analysis, for the case of small data p=2 is recommended, resulting in a root mean square error value of 0.03458, a mean absolute error of 0.02013 and a median absolute deviation of 0.00546. Oppositely, a p-value of 3 and 4 is selected as appropriate for a large data set, with root mean square errors of 0.02435 and 0.02437, mean square errors of 0.01582 and 0.01509 and median absolute deviations 0.00896 and 0.00444. Eventually for a small data set, it is recommended to use a p-value of 2, and for a large data set, a p-value of 3 or 4.

Early childhood may be a stressful period for families with a significant impact on parents’ mental health and the child’s healthy development. The goal of the SKKIPPI cohort study was to assess the occurrence and determinants of psychosocial stress and mental health disorders in parents as well as in their offspring in early childhood in three German regions. Based on random samples from three residents' registration offices, parents with infants up to 12 months of age at the time of sampling, were invited to participate in the study. A screening online questionnaire was newly developed, available in four languages, to assess common psy-chosocial stressors and mental health problems of parents with small children. The study enrolled 4,984 mothers and 962 fathers. The most common potential psychosocial stressors at baseline were professional problems (mothers 22%, fathers 33%), lack of social sup-port (20%, 14%), and severe, negative experiences in childhood (22%, 16%). Obses-sive-compulsive thoughts (21%, 16%), depressive (9%, 9%) and anxiety symptoms (11%, 7%) were the most frequently reported mental health problems by both parents. Regulatory prob-lems of the child were reported by between 1.5% and 5.1% of parents. The SKKIPPI study showed that a substantial proportion of parents are burdened by psychoso-cial problems and suffer from mental health problems in the first years after birth. Early pre-ventive and low-threshold support measures should be available in the health and social care system. The newly developed questionnaire can be used to screen in the relevant populations in order to avoid disturbances in the mother-child attachment.

Grand challenges represent wicked problems that affect many people globally. To overcome grand challenges and sustain our existence as human beings, issues have become increasingly central, and in recent years have led to an increase in research and literature on grand challenges affecting both international business areas and multinational enterprises. The aim of this study is to assess the status of literature in all areas connected to multinational corporations and grand challenges, which are global issues with significant implications. From 2013 to 2023, the WoS database was used to investigate eight search terms from literature, and bibliometric and thematic analyses were carried out based on the principles of systematic literature analysis. The most focused areas of the research are performance, multinational enterprises, foreign direct investment, management, firms’ innovation, knowledge, international business, and impact. Asian, African, European, and other countries may emerge together in different clusters depending on whether the major challenges they are struggling with are similar or identical. the terms of grand challenges and innovation (open and social), technology transfer, R&amp;D internalization, digital transformation or technology and artificial intelligence in the literature of multinational enterprises indicates that these themes are used as a tool to solve grand challenges.

The emergence of the Covid-19 pandemic has created both negative and positive changes, including implementing the circular economy across the globe. This Systematic Review follows the PRISMA statement and employs the Text Mining (Voyant Tools) technique to visualize and analyze the impacts of the Covid-19 on three aspects of the circular economy: economic, social, and environmental. The research employs Latent Dirichlet Allocation (LDA) to identify five major topics: (1) Shortage of medical equipment but high medical waste during Covid-19 due to the high demand in healthcare; (2) The long term negative impacts of lockdown on economic and social activities because of Covid-19 pandemic; (3) The reports on impacts of Covid-19 pandemic on the manufacturing globally, and their coping strategies and new opportunities; (4) The impacts of international restriction on the tourism, trade, shipping, and aviation industry, causing billion-dollar losses; (5) The reduction of pollution with health environment improvements with example cases from China and EU. The research identifies current literature gaps in the circular economy and Covid- 19 topics and encourages the application of text mining tools into researching to stimulate the research process and assist in communicating with the public.

The paper provides a discussion on the results of studies of the effect exerted by combined degradation factors typical of four types of wear: abrasion, impact-abrasion, tribocorrosion, and impact-abrasion-corrosion, conducted for chain wheels made of Ni-Cu alloyed Austempered Ductile Iron. The studies consisted in determining the content of retained austenite in the structure of the cast irons in question, establishing the measures of wear following wear testing, and identifying basic surface degradation mechanisms observed in the chain wheels tested following multifactorial wear processes. The chain wheels made of ADI were found to have sustained the greatest damage under the impact-abrasion-corrosion (three-factor) wear scenario, while the wear was least advanced in the abrasion (one-factor) wear case. Another observation derived from the studies is that the combined effect of dynamic forces, corrosion, and quartz sand-based abrasives causes increased surface degradation in the cast iron grades taken into consideration compared to processes characterised by a reduced number of degradation factors (i.e. one- or two-factor wear processes). Additional hardness tests and XRD analyses have revealed a distinctive effect attributable to combined degradation factors on the surface hardness increase value and implied that bench testing was followed by phase transition.

In the former mining district of La Carolina (southern Spain), waste materials were accumulated without first waterproofing the substrate. Additionally, these mining dams were abandoned without any type of restoration, conditions that present a significant risk of contaminating the surrounding soils and waters. Prior to carry out the necessary works for isolating these residues, morphological characterization of the contact with the base substrate is needed. For that purpose, in recent years, different indirect study techniques have been used, among which electrical to-mography stands out. In this work, a less common geophysical technique, magnetometry, both in its magnetic and gra-diometric modalities, is used as an alternative method to analyse the geometry and structure of a mining dam. The results obtained with this methodology are compared with the results provided by electrical techniques, as well as with direct data from mechanical surveys and aerial photo-graphs prior to waste deposition. The magnetic anomaly curves obtained with geometric models generated with the Mag2dc program were also analysed. This comparative study shows the use-fulness of magnetic prospecting in characterizing the base geometry of these mining structures and therefore the interest in its application to environmental characterization studies.

This paper propose an original approach of achieving a Cymatics based visual perception of image-extracted text. In this context, an effective approach for automated text detection and recognition for the natural scene images is proposed. The incoming image is firstly enhanced by employing CLAHE and DWT. Afterwards, the text regions of the enhanced image are detected by employing the MSER feature detector. The non-text MSERs are removed by employing the geometrical and contour based filters. The remaining MSERs are grouped into words or phrases by finding out similarities between them. The text recognition is performed by employing an OCR function. The extracted text is sequentially analysed on character by character basis. Each character is converted into a methodical acoustic excitation. Finally, these excitations are converted into the systematic visual perceptions by using the phenomenon of Cymatics. The system functionality is tested with an experimental setup. For the case of studied natural scenes, the suggested approach achieves 80% precision in text localization and 53% precision in end-to-end text recognition. The devised system principle is novel and can be employed in various applications like visual art, encryption, education, integration of impaired people, etc.

1) Background: Several studies have shown the benefit of drawing when learning anatomy. Virtual Reality (VR) also appears to be useful to increase the understanding of the location of anatomical structures. This study aims to describe how students in an Operating Room Nursing (OR nursing) master study program perceive the benefit of active learning methods, including drawing and the use of VR. 2) Methods: We combined these two methods in a class of 29 students. 38 half-finished drawings of anatomical structures were made. The students were to complete these during lectures with guidance from the teacher. The students also attended a two-hour VR session where they were to find various anatomical structures. A paper questionnaire was applied to evaluate the teaching program. Data were analyzed statistically. 3) Results: The students highly appreciated these learning methods. They reported that drawing made them work more actively during lectures and increased their learning outcome. They also perceived that the use of VR technology increased their understanding of the location of anatomical structures. 4) Conclusion: The combination of drawing and VR technology may be of benefit to OR Nursing students when learning anatomy by both increasing student activity and learning outcomes.

The concept of a Social License to Operate (SLO) has become increasingly important in the sustainability literature in recent years. Having its origins in the business discourse of the mining industry with respect to limiting opposition to mining projects, the notion of a social contract above and beyond legal requirements has since become applied across a number of different industries. Despite the concepts adoption confusion exists over the practices and outcomes of SLO, and particularly the nature of engagement. Given this situation it is surprising that not more attention to the role of marketing, and social marketing in particular, in operationalizing the concept. The paper discusses the potential of social marketing to contribute to SLO. Economic, political and social relations are complex in SLO and exchange is intricate in such relational environments. A community-based social marketing orientation is proposed as a means to improve exchange relations and enhance engagement. Seven models of SLO related social marketing models are discussed with community-based social marketing and adaptive co-marketing models being regarded as the most positive for the achievement of an SLO. Potential barriers to adoption of these approaches are noted.

In this paper, the ratio of gross agricultural production (GAP) and agricultural carbon emission is defined as agricultural carbon emission efficiency (ACEE). Considering rural labor transfer (RLT) increases the agricultural machinery intensity (AMI), the two are substitutes for each other and may interact with agricultural carbon emission efficiency (ACEE), we constructed a Simultaneous Equations Model (SEM) of ACEE, RLT and AMI, and analyzed the interaction mechanism of these three variables by using the Three-Stage Least Squares (3SLS) method to solve the endogeneity of the model. The following conclusions are drawn. First, RLT and AMI significantly promote the improvement of ACEE, while the improvement of ACEE and AMI further promotes RLT. Secondly, the causal relationship and influence mechanism of ACEE, RLT and AMI are interactive and multi-directional. An increase in AMI promotes ACEE, but an increase in ACEE inhibits an increase in AMI, suggesting that the marginal rate of substitution of machinery for labor is decreasing. Finally, China has significant regional heterogeneity. Labor transfer and machinery are only direct factors in increasing ACEE, while factors such as economic base, technology level and farmers' attitudes provide environmental support for the effective improvement of ACEE.

With the development of deep learning, several Graph Neural Networks (GNN)-based approaches have been utilized for text classification. However, GNNs encounter challenges in capturing contextual text information within a document sequence. To address this, a novel text classification model RB-GAT is proposed by combining RoBERTa-BiGRU embedding and a multi-head Graph ATtention Network (GAT). First, the pre-trained RoBERTa model is exploited to learn word and text embeddings in different contexts. Second, the Bidirectional Gated Recurrent Unit (BiGRU) is employed to capture long-term dependencies and bidirectional sentence information from the text context. Next, the multi-head graph attention network is applied to analyze this information, which serves as a node feature for the document. Finally, the classification results are generated through a Softmax layer. Experimental results on three benchmark datasets demonstrate that our method can achieve an accuracy of 71.48%, 98.45%, and 80.32% on Ohsumed, R8, and MR, which is superior to the existing nine text classification approaches.

In this paper it is examined how, if at all, logical laws can be normative for human reasoning, wherein the notion of normativity is analysed with respect to approaches to logic given in works of Aristotle, Descartes, Kant, Frege and Wittgenstein. During the ancient and medieval period, logic was being considered in terms of discourse and dialogical practice, but since Descartes and especially Kant it has been treated as a system of laws with which the process of individual human reasoning has been compared. Therefore, normativity can be investigated in private sphere (for thinking and reasoning) and in public sphere (for dialogic practices in a community). Wittgenstein discussed both aspects of normativity: in Tractatus, a focus is on laws of logic that are primarily normative for the state of affairs in the world, while in Philosophical Investigations an emphasis is on a social aspect of normativity (which is closer to Aristotle’s view), which is derived from adopted rules that have been applied in a certain community. Taken that way, logic is certainly normative in the public sphere, but the more difficult issue is whether logic is normative for thinking, regarding to the difference between the logical laws and those of thought.

Taking due account of users' perspectives is crucial for improving the quality of healthcare services. This study aimed to analyse the representations and evaluation criteria that users of a public health agency express towards care and treatment services and to explore whether and how the content and meaning of their communications varied according to pre-pandemic, pandemic or post-pandemic periods. 501 communications (73.4% complaints, 16% appreciations, 10.6% enquiries) sent to the public relations office of an Italian health agency were collected. An automatic content analysis procedure (ACASM) was applied to the textual corpus. Four main thematic cores were identified concerning the request for care and respect, the value of the doctor-patient relationship and the difficulties in contacting services and accessing care. Two main latent dimensions of meaning were identified, which capture the dialectic between the demand for relationships and the demand for access to care, and between attention to the relational competence of health workers and attention to the needs and rights of users. Communications collected during the pre-pandemic and post-pandemic periods mainly concern the difficulty of access to care; those collected during the pandemic period mainly concern the doctor-patient relationship. Interpersonal aspects and timely access to care appear to be crucial in users' assessment of the quality of care.

In an era where the proliferation of large language models blurs the lines between human and machine-generated content, discerning text authenticity is paramount. This study investigates transformer-based language models—BERT, RoBERTa, and DistilBERT—in distinguishing human-written from machine-generated text. By leveraging a comprehensive corpus, including human-written text from sources such as Wikipedia, WikiHow, various news articles in different languages, and texts generated by OpenAI's GPT-2, we conduct rigorous comparative experiments. Our findings highlight the superior effectiveness of ensemble learning models over single classifiers in this critical task. This research underscores the versatility and efficacy of transformer-based methodologies for a wide range of natural language processing applications, significantly advancing text authenticity detection systems. The results demonstrate competitive performance, with the transformer-based method achieving an F-score score of 0.83 with RoBERTa-large (monolingual) and 0.70 with DistilBERT-base-uncased (multilingual).

The article focuses on mining of non-energy mineral resources with minimum environmental impacts. It issues from research results of a project Competence Centre for Effective and Ecological Mining of Mineral Resources implemented at the Faculty of Mining and Geology at VŠB-Technical University of Ostrava, Czech Geological Survey, a company Watrad ltd., a state enterprise Diamo, a company RPS Ostrava plc and a company Sedlecký kaolin plc. The paper starts with a partial analysis of the existing legal norms related to mining and processing of mineral resources. Next, it analyses mineral resource mining options free of negative environmental impacts. The fundamental tool to assess potential environmental impacts of mining is the implementation of Environmental Impact Assessment (EIA) process for a given mineral resource. In the Czech Republic environmental impact assessment is carried out by course of Act 100/2001 Coll. Its important amendment is Act 39/2015 Coll. claiming, inter alia, that the environmental impact assessment is rigidly connected with other permits and procedures, such as the zoning process and building construction permits. The article describes the environmental impacts of mining of non-energy mineral resources, including the following factors: appropriation of land, impacts on surface water, ground water and soil, noise, influence on the landscape character, and air pollution. The paper also includes a case study summarizing information on the environmental factors that may play a role in potential underground mining of graphite in the deposit Český Krumlov - Městský Vrch and the deposit Lazec - Křenov.

The philosophy of testing statistical hypothesis is a natural consequence and functional extension of mathematical analysis of Probability. Along with the concept of recurrence when applied to random sequences and functions, it leads to the analysis of a priori and posterior which implies testing statistical hypothesis. Testing statistical hypothesis also involves algebraic, functional and dimensional considerations, which are found in the works of Laplace. Aspects of mathematical analysis such as universality of solutions, Laws of Large Numbers, Entropy, Information, and various functional dependencies are the main factors explained in the five properties that lead to implication of testing statistical hypothesis. Various interesting examples with modern scientific significance from genetics, astrophysics, and other areas give methodological access to answers of different problems and phenomena which are involved in the logic of testing statistical hypothesis.

This article describes the POTENTIALS project, which aimed to develop business models for the reuse of former industrial and mining sites in different regions. The project focused on the creation of eco-industrial parks as a result to enable sustainable energy production and reduce waste and pollution. A key aspect was the Territorial Impact Assessment (TIA), a new and complex policy assessment tool. Therefore, the TEQUILA (Territorial Efficiency Quality and Identity Layer Assessment) approach is used to describe the TIA and emphasizes its application in assessing the impact of EU policies and projects on territorial cohesion. It underlines the need for a differentiated understanding of the regional characteristics and potential impacts of policies; and stresses the importance of regionalized impact models for the assessment. Furthermore, the process and results of applying the TEQUILA methodology to assess the territorial impact of different scenarios of business models within the POTENTIALS project is given.

Despite the importance of influenza vaccination for healthcare workers (HCWs), their uptake remains challenging. This study aimed to measure influenza vaccine hesitancy among HCWs in Nakhon Phanom province, Thailand. A representative cross-sectional survey was conducted at six hospitals during August – September 2020 using a self-administered questionnaire. HCWs delaying getting influenza vaccines, accepting but unsure, or refusing with doubts were categorized as hesitant. Those accepting without any doubts were classified as non-hesitant. Vaccine hesitancy determinants were identified by multivariable logistic regression analysis. A total of 338 participants (97%) completed the questionnaires; most were female nurses working at district hospitals without pre-existing chronic medical conditions. Nearly 60% of participants (197/338) exhibited influenza vaccine hesitancy, and significant factors included age above 50 years adjusted odds ratio [aOR] 3.2, 95% CI 1.3-8.5), fair knowledge of influenza/vaccination (aOR 0.4, 95% CI 0.2-0.8), and negative influence of other HCW (High level– aOR 2.3, 95% CI 1.1-4.8; Moderate level- aOR 2.1, 95% CI 1.1-4.4). Imparting updated information to the HCW combined with positive guidance from influential peers may help reduce this hesitancy.

Developing sustainability competences is an important endeavor for education, as competence development can be complex and requires appropriate approaches. In the case of preparing future educators, this becomes even more important, because of their multiplier role. The current study presents how a project, based on living-lab methodology, helped preservice preschool teachers, attending a course on sustainability issues, develop sustainability-specific professional action competence. Students collaborated with an organization and a school and were trained to deliver to 6-year-old pupils, activities concerning the protection of sea turtles. The process included reflection, based on a given template. After the intervention, the preservice preschool teachers reflected upon their role and professional performance and discussed how they, benefited from this project. They further analyzed through a group interview, how the Living Lab methodology raised their professional confidence and self-efficacy, and helped them develop content knowledge, pedagogical content knowledge, and a sense of purpose and motivation for teaching about sustainability. Outcomes indicate that participants felt confident and efficacious to teach about sustainability, and developed their content knowledge, pedagogy content knowledge, motivation, and volition due to a strong sense of purpose and responsibility. The overall process facilitated the development of critical thinking, systemic thinking, reflection, and collaboration..

The article presents a multiphase numerical computational fluid dynamics (CFD) for simulating hydrothermal liquefaction of sewage sludge in a continuous plug-flow reactor. The discrete particle method (DPM) was used to analyze the solid particle interaction in liquid-solid high shear flows to investigated coupling computational fluid dynamics (CFD) and discrete phase model (DPM) method. Increasing solid particles interaction were observed with increasing liquid velocity. The study examined the influence of parameters such as flow rate, temperature and residence time on the efficiency of bio-oil production. Furthermore, the trajectories of the solid particles are affected significantly by the ash flow and the solid particle size, thus the trajectories of particles resembles the streamlines. A parametric study is performed to investigate the effect of slurry flow rate, temperature and external heat transfer coefficient on the biocrude oil productions performance. This simulation data will be used in the future to design and scale up a large-scale HTL reactor.

The authors argue that despite significant numbers of foreign workers present in the Russian labor market Russia remains unattractive for highly skilled foreign workers. The economic crisis the country has faced since 2014 has resulted in a further outflow of foreign nationals from OECD countries. So Russia has to look for employees among newcomers from the former Soviet states, the majority of whom come from Central Asia. Russian politics with regard to immigration is characterized by the collision between “geopolitical” and domestic policy rationales. On one hand, seeking to maximize its influence in the post-Soviet space Russia provides preferential conditions of employment for citizens of partner countries in the framework of the Eurasian Economic Union. On the other hand, the Kremlin is afraid to liberalize its approach to immigration and the integration of immigrants through fear of losing popularity among the electorate. The second substantial feature of Russian immigration politics is an extreme level of securitization of migration issues. The government considers these issues primarily through a policing lens. One cannot claim that the Russian state does not take any steps to liberalize “migratory regulation”; however, the effect of these measures is being vastly reduced by new restrictions. In addition, liberally designed laws do not bring the expected results due to the corrupt practices that pervade the whole of migration regulation.

Healthcare professionals constantly face situations that reflect ecosystems’ degradation. These can negatively affect their mental health. Research suggests that hope and resilience can play an important role in this scenario, since they are related to/predict mental health in highly heterogeneous samples (considering geography, age, profession, health, etc.). In this context, the aims of the present study are: to characterize and explore the relationship between hope, resilient coping and mental health of Portuguese healthcare professionals. Using Google Forms, 276 healthcare professionals answered to the GHQ-28, the (adult) Trait Hope Scale, and the Brief Resilient Coping Scale (cross-sectional study). The minimum and maximum possible scores were reached, with the exception of the maximum score of GHQ-28-Total. Regarding Hope, 19.6% scored below the midpoint (M=43.46, SD=11.97); 29.3% revealed low resilience (M=14.93, SD=4.05); and the average of 4 of the 5 Mental Health scores (exception: Severe Depression) indicates the probability of a psychiatric case. Hope correlated with Social Dysfunction and GHQ-28-Total; resilient coping proved to be a (weak) predictor of 4 of the 5 GHQ-28 indicators (exception: Severe depression). The results support the need to promote the sample's mental health, hope and resilient coping. They also suggest that stimulating resilient coping may contribute to improving healthcare professionals’ mental health.

Technological equipment in quarries that extract and deliver aggregates for different uses operates in a predetermined flow depending on the type of rocks exploited and the dimensional characteristics imposed on the final products. In this context, the interruptions in operation required to replace high-wear parts (such as the teeth of excavators and bucket loaders) must be limited as much as possible through technological solutions to increase their service life. The evolution of the wear of the teeth of the quarry equipment that come into direct contact with the rocks was concretely established in the production process, in parallel with the wear values obtained by simulating the wear phenomenon in laboratory conditions, in order to validate the data collected during the operation of the machines. Preventive-repetitive maintenance, within the activities of reconditioning the worn surfaces of the teeth, through the charging process by manual electric welding with covered electrodes, was applied directly to the machine, which led to the shortening of the interruptions in operation necessary to replace these spare parts.

Background: Annual influenza epidemics are a serious public health problem, causing particularly severe morbidity and mortality in high-risk groups. The study aimed to understand Poles' attitudes and beliefs towards influenza vaccinations in the flu season of 2022–2023, analyze preferences and unmet needs regarding influenza vaccinations, and examine the perception and acceptance of the influenza vaccine among individuals at risk of flu complications. Methods: A cross-sectional survey-based study was carried out on a sample of 1446 respondents. The Google Forms questionnaire was created and disseminated electronically using social media and e-mail. The pre-test was performed at the beginning of the project. Results: The majority of respondents (71%) could identify "high-risk groups" recommended for influenza vaccination. Furthermore, it was observed that individuals aged 21 to 59 demonstrated a higher level of knowledge in this regard compared to those below 20 and above 65 years old, and individuals with higher and medium education levels were much better at identifying risk groups for influenza virus infection. The majority of respondents (52.01%) reported receiving influenza vaccination at some point in the past, with 32.12% receiving it in the 2022–2023 flu season and 41.09% in the 2021–2022 season. The majority of respondents declaring acceptance of the vaccine for the 2022-2023 season were in the high-risk group. Only 17.28% of respondents declared receiving both influenza and SARS-CoV-2 vaccines in the 2022-2023 season, with the vast majority being respondents from the "high-risk group" (p

This review focuses on the emerging evidence for the association between estimated cardiorespiratory fitness (eCRF) and metabolic risk factors. Given the challenges associated with directly measuring cardiorespiratory fitness (CRF) in large populations, eCRF presents a practical alternative for predicting metabolic health risks. A literature search identified six relevant cohort studies from 2021 to 2024 that investigated the association of eCRF with hypertension, hyperglycemia, dyslipidemia, and obesity. The review findings highlight a consistent inverse relationship between higher eCRF and lower incidence of metabolic risks in line with CRF cohort studies. This review highlights the importance of low eCRF as a primordial indicator for metabolic risks and underscores the need for broader application. Future research directions should include exploring eCRF's predictive accuracy across diverse populations and health outcomes and testing its real-world applicability in healthcare and public health settings.

This study focused on the stories of women planters in the implementation of Taranuman Project in the Third District of Albay. Particularly, this explores and documents the experiences, challenges, and successes of women planters involved. By understanding their stories, this undertaking shed light on the motivations and aspirations of women planters, the barriers they have experienced, and their strategies to achieve success. The researchers used descriptive-qualitative research and made use of in-depth interviews and focus group discussions (FGDs). From the collection of the respondents’ narratives, three (3) recurring themes emerged on the motivation and aspirations of women planters in Taranuman Project; Taranuman para sa Pagkasararo (Social cohesion, belonging and unity), Taranuman para sa Pangkabuhayan (Economic empowerment), and Taranuman para sa Karapatang Pampulitika (Political rights, engagement and participation). This study also revealed major challenges in its implementation including infertility of the soil, the distant location of the lands to be cultivated for the project, and the insufficiency in the supply of tools and equipment. However, despite the emerging problems, strategies to counter the difficulties were also formulated. Even so, the researchers strongly recommend that implementers should expand the Taranuman Project in all of the district’s municipalities, enhance women planters' financial and communication skills through training, maintain close partnerships with agencies for project support, encourage beneficiary communication, and develop comprehensive strategies to overcome ongoing challenges and ease beneficiaries’ burdens.

Background: The receptor activator of the nuclear factor-kB (RANK)/RANK ligand (RANKL)/osteoprotegerin (OPG) pathway is a determining pathway in the balance between bone formation and resorption; disruptions in this complex can affect bone metabolism. Methods: This study analyzed the changes in RANKL, OPG and 25(OH)D levels, the RANKL/OPG ratio, and other bone turnover markers (BTM) from diagnosis to complete remission in children with acute lym-phoblastic leukemia (ALL). This is a prospective observational cohort study. This study was carried out in the Instituto Mexicano del Seguro Social in Mexico City. Thirty-three patients (4-17 years) with newly diagnosed B-cell ALL were included. The patients were treated with the HP09 chemotherapy protocol. Children who had previously been treated with corticosteroids were excluded. A pe-ripheral blood sample at diagnosis and remission was collected to determine the 25(OH)D and BTM concentrations. Results: Increased RANKL (p=0.001) and osteocalcin (p

Under the new pattern of “double carbon” development, good ESG performance is the best way to promote the sustainable development of enterprises, and the ESG investment strategies are directly affected by the strategic vision of managers. Based on the upper echelons theory and stakeholder theory, this paper takes Chinese A-share listed companies from 2011 to 2022 as samples to empirically analyze the impact of managerial myopia on corporate ESG performance. The results show that managerial myopia significantly inhibits corporate ESG performance, mainly by inhibiting corporate green investment and green innovation sustainability. Furthermore, for state-owned enterprises (SOE), heavy polluting enterprises (HPE) and non-high-tech enterprises, the inhibitory effect of manager myopia on ESG performance is stronger. When the enterprise is in the growth stage, the above inhibition effect is more severe. For external governance, the greater the analyst attention and public environmental attention are, the more conducive they are to alleviating the restraining effect of managerial myopia on enterprise ESG performance. Therefore, effectively improving the time cognition level of managers and strengthening external supervision have become important measures for comprehensively optimizing the ESG performance.

To examine the effect of synchronous dance movements on social bonding and perceived closeness between generations, 168 young (20-45 years) and older (65-90 years) participants were randomly assigned to six dyad conditions. These included dancing synchronously or asynchronously with an in-age-group or an out-age-group unfamiliar partner for 11 minutes. Participants completed social bonding and groups’ closeness questionnaires. To assess variation across individuals’ and dyads’ measurements, a generalized estimating equations modeling analysis was conducted. In line with the first two hypotheses, synchronized dance increased social bonding, and young adults showed enhanced perception of closeness between generations. Hypothesis 3 that synchronous dance with out-age-group members would foster greater perceived closeness compared to in-age-group members was not confirmed. Surprisingly, results indicated that asynchronous movements with the in-age-group led to a higher degree of closeness between generations than asynchronous movements with the out-age-group. The study offers new directions for practitioners in welfare, nursing, and therapy fields, particularly for engaging young adults in intergenerational dance interventions.

In 2020, students of Pakistan had to adapt to the online environment for the very first time. This study aims to analyze the perceptions of medical, dental, and allied health students about online education in Pakistan. A descriptive, cross-sectional study was done to assess the level of acceptance of undergraduate students. A pre-validated questionnaire regarding demographics, past-experience of e-learning, advantages disadvantages of e-learning, and general perception of students towards e-learning was distributed. Descriptive statistics were computed for demographics, Mann-Witney-U test was used to compare the differences of perceptions between pre-clinical year and clinical years students. Kruskal-Wallis test was applied to compare the results of three specialties of students. Chi-square was used to compare overall category-wise positive and negative responses of students. 1200 students participated in the study. The major advantage identified by all students was the ‘comfortable environment’ in which they studied online. The major disadvantage selected by preclinical year students was ‘anxiety due to social isolation’ and that chosen by clinical year students was ‘lack of patient interaction’. Overall, 72% of students had a negative perception of e-learning. Student-teacher training, student counselling sessions, and innovative techniques need to be introduced to enhance student engagement and reduce pandemic stress.

The stepwise hydrogenation of the C=C bond and C=O group of acrolein on Au₃ and Au₅ model systems is investigated using the density functional theory(DFT) PW91 functional. Our results show that the C=C hydrogenation is more favorable than that of C=O bond on Au₃ with the barriers of the rate-determining step being 0.35 and 0.62 eV respectively. On the other hand, the C=O reduction is preferred over the hydrogenation of the C=C bond on Au₅. The corresponding barriers of the rate-determining steps are 0.45 and 0.54 eV, respectively. This demonstrated that the second hydrogenation step controls the reaction on both Au₃ and Au₅ for C=O and C=C hydrogenation and the C=O hydrogenation on Au₅ is preferred over the hydrogenation of the C=C bond, which is helpful to address the reactivity of small size-selected supported gold clusters.

In this study, we provided an empirical testability of the FF3FM against the co-moment-augmented FF3FM using the Newey–West GMM methods to account for potential endogeneity. The results showed that value risk is not significantly priced, and systematic skewness and systematic kurtosis play more important roles in explaining asset return than market and size factors. We discovered that there is much to gain in moving from the incumbent FF3FM to the co-moment-augmented FF3FM. By extending this study to Nigeria and using out-of-sample data that span across the recent COVID-19 period, the study has contributed to existing knowledge significantly. The study concludes that the co-moment-augmented FF3FM is superior to the incumbent FF3FM in pricing assets, and the anomalies associated with the return-size risk factor relationship are reduced by accounting for systematic skewness and systematic kurtosis in the FF3FM. Therefore, we recommend that investors should take a long position in size-hedged portfolios and hold systematic skewness and kurtosis as constant, and vice visa.

Wastewater genomic surveillance is an efficient and economic approach to track infectious diseases and can help detect unusual epidemiological events. The current study uses this approach for the first time in Morocco to monitor the epidemiology of SARS-CoV-2 and Influenza A, B and RSV virus infections during the third wave of COVID-19 caused by the Omicron variant. The virome was concentrated from wastewater collected from two sewer sheds of two cities, Agadir and Inezgane, using the PEG/NaCl method. The viral copy number was determined using qRT-PCR. All 26 samples in both cities were tested positive for SARS-CoV-2 at various viral loads. For Influenza A virus, two samples were tested positive in Inezgane. The estimated SARS-CoV-2 viral RNA copy numbers observed were then used to estimate the number of infected individuals using the SEIR model. The number of viral copies of SARS-CoV-2 for Agadir and Inezgane varied from 82932 ± 15479 to 507865 ± 58759 copies/L and 61244 ± 8552 to 497506 ± 18061 copies/L, respectively. The estimated number of cases correlates positively with the number of reported cases. Next Generation Sequencing showed that samples contain the following two variants: BA.1 and BA.2 that have been detected in clinical samples. This study demonstrates the effectiveness and feasibility of wastewater genomic surveillance in monitoring pathogens such as SARS-CoV-2 in Morocco.

In this study, the determination of static E50 and υ parameters of rock materials was investigated using P-S wave velocities and Shore hardness (SH), non-destructive measurement methods. We used multiple linear regression (MLR), multiple non-linear regression (MNLR) and artificial neural network (ANN) models to estimate and determine these parameters. When the models defined by MLR, MNLR, and ANN were compared to the R2 values, ANN models that estimate E50 and υ parameters of rock materials using non-destructive methods (Vp, Vs, Vp/Vs, d, and SH) could be determined with higher accuracy than MLR and MNLR models. The study’s originality is based on the fact that ores such as galena, chromite, and barite have been studied for the first time on rock mechanics, and in this respect, it offers an innovative perspective. In addition, the use of all non-destructive measurement methods, Vp, Vs, and Shore hardness tests, also increases the originality of the study.

The existing segmentation-based scene text detection methods mostly need complicated post-processing, and the post-processing operation is separated from the training process, which greatly reduces the detection performance. The previous method, DBNet successfully simplified post-processing and integrated the post-processing into a segmentation network. However, the training process of the model took a long time for 1200 epochs and the sensitivity to texts of various scales was lacking, leading to some text instances being missed. Considering the above two problems, we design the text detection Network with Binarization of Hyperbolic Tangent(HTBNet). First of all, we propose Binarization of Hyperbolic Tangent (HTB), optimized along with which, the segmentation network can expedite the initial convergent speed by reducing the amount of epochs from 1200 to 600. Because features of different channels in the same scale feature map focus on the information of different regions in the image, to better represent the important features of all objects in the image, we devise the Multi-Scale Channel Attention(MSCA). Meanwhile considering that multi-scale objects in the image cannot be simultaneously detected, we propose a novel module named Fused Module with Channel and Spatial(FMCS), which can fuse the multi-scale feature maps from channel and spatial dimension. Finally we adopt cross entropy as the loss function, which measures the difference between predicted values and ground truths. The experimental results show that HTBNet compared with lightweight models has achieved competitive performance and speed on Total-Text(F-measure:86.0%, FPS:30) and MSRA-TD500 (F-measure:87.5%, FPS:30).

Views of aging include peoples’ assessment of their own aging process and their subjective age. Positive aging views relate to good psychological well-being which predicts better physical and mental health. While these were substantially studied, the moderating roles of self-aging attitudes and psychological well-being in the subjective age-age discrimination connection have been much less explored. The current study used a convenience sample of 568 participants (mean = 66.21y, SD = 11.95, age range 50-95), 55.8% women, 67.1% employed. In line with the hypotheses, young subjective age and psychological well-being were connected to less age discrimination in the workplace and higher psychological well-being mitigated the subjective age-age discrimination at work connection. When the perception of old age as a period of loss was added to the model, adults who perceived old age as a period of loss and reported lower levels of psychological well-being, demonstrated the strongest relationship between an increase in subjective age and an increase in age-related discrimination at work. The findings emphasize the importance of the psychological well-being of older employees as a resource for improving their attitudes to their last years at work.