Abstract: Efforts for carbon emission reduction have been identified as a major climate change mitigation target in the Paris Agreement. The 26th Conference of Parties to the United Nations Framework Convention on Climate Change highlighted the worldwide effort for cleaner and greener mining, urging the mineral industry to embrace greener operational emissions reduction technologies and strategies. Ghana pledged at the Paris Conference to reduce CO2 emissions by 15% and increase renewable energy share to 10% of the national energy mix. Here the fundamental question is how Ghana’s mining industry can contribute to CO2 emission reduction efforts. This paper investigates barriers to low-carbon emission policy implementation among mining companies in Ghana. The questionnaire survey was conducted among Ghana’s four large-scale gold mining companies. As the sample number of available companies is limited, we used a descriptive analysis to assess the responses. The results show that the companies expressed widely different ways to respond to the issues of cost, a government policy/regulations for carbon emission reduction, and the adoption/acquisition of low-carbon technologies. Those companies that are relatively less prepared for this endeavor emphasized the uncertainties of securing sufficient energy in case of using low-carbon technologies. They also expressed a need for more trained workers to handle low-carbon technologies. All companies agreed with a expected high cost of adopting low-carbon options. There was a communication gap with the relevant government ministry about low-carbon mining options where the government should be able to facilitate the involvement of othe low technology providers, such as solar PV installation.

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.

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.

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.

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.

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.

Productivity has been modest in construction due to many reasons, especially project-specific operations and fragmentation. The main aim of this research is to map the challenges towards industrialization in construction through a comparative study of the manufacturing industry. An inductive approach and a qualitative method are followed by applying semi-structured inter-views, and node-based thematic analysis. The key elements of industrialization in mature man-ufacturing companies are benchmarked. The findings indicate a specific order of steps for indus-trialization in construction. Data and information management with achieved systematization have been sources of productivity in manufacturing companies. However, the systematization starts from the offering, construction companies need to define their offering, that is the products, and define their respective data. Only accurate and good quality data on products enables the systematization of order delivery processes, which are the engine for a company’s success or failure. Continuous efficiency improvement is only possible with systematized processes and respective data related to the processes. The originality involves clarifying the industrialization of construction companies via products, respective data, and processes considering industrialized manufacturing companies.

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.

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

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.

The state of epidemic emergency in force in our country since March and the related requirement to implement various sanitary procedures constitute problems for the operation of all companies. However, the unique operation of underground mining companies increases their scale considerably. However, despite initial turbulent moments related to the occurrence of an unknown threat, it was possible to stabilise the situation in all such companies as a result of implementing various anti-threat actions. Following the initial introduction to basic properties of the SARS-CoV-2 coronavirus, the paper presents the results of the study (a case study) on actions taken in individual mining companies. There was an attempt to formulate the so-called good work practices recommended to be applied.

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.

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.

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 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.

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.

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.

Companies are increasingly focusing on sustainable business practices. Internal and external stakeholders’ expectations manifest in legal requirements, national and international standards, and market and customer expectations, among other things, must be considered. In addition to profit maximization as the usual target for corporate management, management must consider environmental sustainability aspects such as resource efficiency, greenhouse gas intensity, and a company's emissions behavior. In addition, social aspects relating to the company's employees, the immediate urban environment, the situation in the supply chain, and effects on the market environment must increasingly be considered. Specifically, companies are faced with the chal-lenge of dealing with conflicting objectives regarding the various aspects of sustainability and, if necessary, weighing them up against each other. These trade-offs must be made against the company's socio-economic and ecological environment, corporate strategy, and sustainability goals. This paper provides an overview of current approaches and research gaps on this topic through a literature review. It shows a lack of methods and frameworks to specifically deal with trade-offs and conflicts of goals.

In this data deposit, I describe a dataset that is the result of content mining 167,318 published articles for statistical test results. As a result of this content mining, 688,112 results from 50,845 articles were extracted. In order to provide a comprehensive set of data, the statistical results are supplemented with metadata from the article they originate from. The dataset is provided in a comma separated file (CSV) in long-format. For each of the 688,112 results, 20 variables are included, of which seven are article metadata and 13 pertain to the individual statistical results (e.g., reported and recalculated p-value). A five-pronged approach was taken to generate the dataset: (i) collect journal lists, (ii) spider journal pages for articles, (iii) download articles, (iv) add article metadata, and (v) mine articles for statistical results.

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.

Carbon Capture and Storage (CCS) stands as a pivotal technological stride toward a sustainable future, with the practice of injecting supercritical CO2 into subsurface formations having already been an established practice for enhanced oil recovery operations. The overarching objective of CCS is to protract the operational viability and sustainability of platforms and oilfields, thereby facilitating a seamless transition towards sustainable practices. This study introduces a comprehensive framework for optimizing well placements in CCS operations, employing a derivative-free method known as Bayesian optimization. The development plan is tailored for scenarios featuring aquifers devoid of flow boundaries, incorporating production wells tasked with controlling pressure buildup and injection wells dedicated to CO2 sequestration. Notably, the wells operate under group control, signifying predefined injection and production targets and constraints that must be adhered to throughout the project’s lifespan. As a result, the objective function remains invariant under specific permutations of the well locations. Our investigation delves into the efficacy of Bayesian optimization under the introduced permutation invariance. The results reveal that it demonstrates critical efficiency in handling the optimization task extremely fast. In essence, this study advocates for the efficacy of Bayesian optimization in the context of optimizing well placements for CCS operations, emphasizing its potential as a preferred methodology for enhancing sustainability in the energy sector.

The oil and gas industry in Nigeria, a vital contributor to the nation's economy, has long been associated with significant environmental challenges. Pollution, habitat destruction, and greenhouse gas emissions have raised global concerns about sustainability and corporate responsibility. This empirical study explores the relationship between environmental accounting practices and the financial performance of listed oil and gas companies in Nigeria. We investigated eight oil and gas companies that are publicly traded on the Nigerian Stock Exchange Market (NGX) as of January 17, 2022, from 2011 to 2022. The study employs Driscoll-Kraay standard errors and reveals that environmental accounting had significant effects on returns on assets, earnings per share, and liquidity ratio. The study revealed that the implementation of environmental accounting practices had diverse effects on the performance of oil and gas companies in Nigeria. The findings encourage policymakers and stakeholders in the sector to utilise the insights and design more effective regulations and incentives that promote environmental corporate responsibility. Also, valuable insights into the potential benefits and challenges associated with adopting environmental accounting practices, and influencing the decision-making processes of corporate stakeholders were provided to ensure sustainability in terms of improved financial performance of the oil and gas sector in Nigeria.

Sustainability is a characteristic manifested in the ability to maintain the necessary level of performance for construction companies when risk factors arise during the implementation of investment and construction projects. The risk factors that arise during the implementation of in-vestment and construction projects differ in nature, degree of influence, and other characteristics. Ignoring these factors and measures to manage them often leads to critical consequences in the form of disruptions in the timing of work. Risk is a combination of the probability and consequences of the occurrence of adverse events. The article considers the concept of risk as a potential possibility of occurrence of adverse situations and related consequences when exposed to these factors. Risk factors are characterized by surprise, discreteness of changes, the presence of threshold values, upon reaching which a transition to a different mode of operation is required. The article discusses the risk factors arising at the construction stage, their classification. A methodology has been developed that includes conducting a survey in the form of a questionnaire in order to collect information about risk factors that affect the implementation of investment and construction projects. Experts with experience in the construction sector were involved in the survey. The processing of the survey results made it possible to assess the significance of various risk factors in investment and construction projects. Thus, statistical and expert methods were used in the study. The results showed that financial, technical, legal, economic, managerial and natural factors have the greatest impact on investment and construction projects. It is recommended to pay special attention to the listed factors when developing measures aimed at preventing risks and their consequences. The methodology described in the study can be used by construction companies in strategic planning. The analysis of the stability of construction companies, depending on their use of various ways to counteract risk factors, allowed us to develop a number of practical recommendations to reduce the impact of the studied factors on achieving the goals of investment and construction projects.

Densely urbanized areas, with a low percentage of green vegetation, are highly exposed to Heat Waves (HW) which nowadays are increasing in terms of frequency and intensity also in the middle-latitude regions, due to ongoing Climate Change (CC). Their negative effects may combine with those of the UHI (Urban Heat Island), a local phenomenon where air temperatures in the compact built up cores of towns increase more than those in the surrounding rural areas, with significant impact on the quality of urban environment, on citizens health and energy consumption and transport, as it has occurred in the summer of 2003 on France and Italian central-northern areas. In this context this work aims at designing and developing a methodology based on aero-spatial remote sensing (EO) at medium-high resolution and most recent GIS techniques, for the extensive characterization of the urban fabric response to these climatic impacts related to the temperature within the general framework of supporting local and national strategies and policies of adaptation to CC. Due to its extension and variety of built-up typologies, the municipality of Rome was selected as test area for the methodology development and validation. First of all, we started by operating through photointerpretation of cartography at detailed scale (CTR 1: 5000) on a reference area consisting of a transect of about 5x20 km, extending from the downtown to the suburbs and including all the built-up classes of interest. The reference built-up vulnerability classes found inside the transect were then exploited as training areas to classify the entire territory of Rome municipality. To this end, the satellite EO HR (High Resolution) multispectral data, provided by the Landsat sensors were used within a on purpose developed "supervised" classification procedure, based on data mining and “object-classification” techniques. The classification results were then exploited for implementing a calibration method, based on a typical UHI temperature distribution, derived from MODIS satellite sensor LST (Land Surface Temperature) data of the summer 2003, to obtain an analytical expression of the vulnerability model, previously introduced on a semi-empirical basis.

The Upper Hunter Valley is a major coal mining area containing approximately 40% of the currently identified total coal reserves in New South Wales (NSW), Australia. Due to the ongoing increase of mining activities, PM10 (air-borne particle with an aerodynamic diameter less than 10 micrometres) pollution has become a major air quality concern in local communities. This paper summarises the spatial and temporal variability modes of PM10 pollution in the region, based on long-term multi-site monitoring data and the application of the rotated principal component analysis (RPCA) and wavelet analysis techniques. RPCA identified two distinct air quality clusters/subregions in the valley, one in the west/northwest, the other in the southeast. Wavelet analysis revealed the annual cycle to be most persistent temporal mode of PM10 variability in both subregions, with intermittent signals observed at time scales of around 120, 30~90 and under 30 days. How these variation modes are related to the effects of local PM10 emissions and the influence of meteorology at different time scales deserves further attention in future work. The findings will be used in air quality reporting and forecasting in NSW. The methodology and results can also be useful for air quality research in similar regions elsewhere.

As one of the typical deposits in Sichuan-Yunnan-Guizhou Pb-Zn metallogenic province, the Daliangzi Pb-Zn deposit has a close genetic relation with the structural system of “black/fracture zone” formed under the action of NWW-approx. EW strike-slip structures in the metallogenic province, but the mechanism of rock and ore-controlling action of the structural system remains unclear. Based on detailed analysis of tectonite-mineralized alteration lithofacies of R1 “black/fracture zone”, the tectonite-mineralized alteration lithofacies zones can be divided into 4 types in succession outward from the Pb-Zn mineralization center, i.e., ① brecciated and stockwork-like Pb-Zn mineralization-complex breccia facies zone, ② stockwork-like Pb-Zn mineralization-simple breccia and cataclasite facies zone, ③ veined pyrite-sulfide-dolomitic cataclasite facies zone, and ④ fine-veined calcite-black carbonized dolomite facies zone; Cl- and SO42--rich high-temperature oxidizing ore-bearing fluid mixes with reducing fluid in Zone ① while migration and precipitation occur in Zone ④. With the evolution of ore-forming fluid, the homogenization temperature decreases from Zone ① to Zone ④, the salinity increases from Zone ① to Zone ② and then decreases to Zones ③ and ④, and the fluid density has little change on the whole; the contents of Zn, Pb, Cu, Ga, Ge, Cd, Ag, and other metallogenic elements, the Zn/Pb ratio, and the CaO/MgO mole ratio decrease gradually from Zone ① to Zone ④; and REE fractionation, calcilization, silicification, and pyritization enhance gradually from Zone ① to Zone ④. This series of changes are the product of diapirism (cryptoexplosion) of strike-slip structures and “black/fracture zone”, among which the second-order structures (F5, F100, and other faults) derived from NWW-approx. EW striking dextral shear-tension faults F1 and F15 control the brecciated and stockwork-like Pb-Zn mineralized complex breccia facies zones and the stockwork-like Pb-Zn mineralized simple breccia and cataclasite facies zones. Therefore, this paper establishes the zoning mode of tectonite-mineralized alteration lithofacies of “black/fracture zone” and proposes that Zones ① and ② are important prospecting criteria. conclusions.

Hyperspectral imaging data holds great potential for the different stages of the mining life cycle in active and post-mining environments. However, the technology has yet to reach the stage of large-scale industrial implementation and acceptance. While hyperspectral satellite imagery can achieve high spectral resolution, signal-to-noise ratio (SNR) and global availability with break-through satellite systems like EnMAP, EMIT and PRISMA, limited spatial resolution poses chal-lenges for sectors like mining, which require decimetre to centimetre scale resolution for applica-tions such as reconciliation, ore/waste estimates, geotechnical assessments and environmental monitoring. Hyperspectral imaging from drones (referred to herein as Uncrewed Aerial Systems; UASs) offers high spatial resolution data relevant to the camp/ mine scale, with the capability for frequent, user-defined re-visit times. This has been made possible by the miniaturization of hy-perspectral imaging systems. Collection of data in the visible to near and shortwave infrared (VNIR-SWIR) wavelength regions enables the detection of different minerals and surface altera-tion patterns potentially revealing crucial information for exploration, extraction, re-mining, waste remediation, and rehabilitation. In this paper, we provide a review of relevant studies de-ploying hyperspectral imaging in or applicable to the mining sector, especially for the use of hy-perspectral VNIR-SWIR Uncrewed Aerial Systems. Where required, we draw on previous in-sights derived from satellite or ground-based systems. We also discuss UAS survey planning, and sampling considerations for validation and interpretation.

During the European unification process, they stated the need for drawing up a common energy strategic plan that will affects electricity production system in terms of infrastructure, production and generation. The EU strategic action plans included; reduction in emissions, an increase in renewable energies, energy efficiency. In this light therefore the energy efficiency was booked for researching. This article presents an in-depth study of the challenges in transforming a functioning PMO model to an EPMO model within four electricity companies from different countries within the EU and how those challenges can be address within companies implementing same methodology to bring about electricity production efficiency. Will these identified challenges make it impossible for the companies to achieve electricity production efficiency in the transformation from PMO to EPMO? PMO has been the major methodology used by these companies. To achieve production efficiency, the need for a heavyweight methodology was identified (EPMO) and implemented in four mega electricity producing companies with cross boarder distributions, one from France; Germany; Spain and UK

China is rich in coal resources under water bodies. However, for a long time, the safety prediction of high-intensity mining under water bodies is one of the problems encountered by the coal industry. It is of great significance to realize safe mining under water bodies, improve the recovery rate of coal resources and protect reservoir resources. Therefore, this article takes the No. 5 coal seam and No. 11 mining area of Wangwa Coal Mine as the research object, and integrates physical simulation, numerical simulation, theoretical analysis and other methods to study the development height of water-conducting fracture zones in fully mechanized top coal caving mining. Solid-liquid coupling physical simulation test reveals the failure characteristics of overlying strata in goaf and the seepage law of reservoir water under the influence of mining. By comparing the monitoring data of borehole leakage, the measured data obtained by borehole peeping with the height data of water-conducting fracture zone obtained by the traditional empirical formula of three-under standard, the error between the two is as high as -29.39 %. In this case, the variance correction coefficient is used to correct the empirical formula, and on this basis, in order to effectively protect the surface water dam and water body, the mining height of coal seam in the working face with limited height mining is inversely derived. The research results provide a basis for the safety prediction of high-intensity mining under the reservoir dam in the ecologically fragile areas of western China and provide a scientific guarantee for the formulation of safety measures under such conditions.

An analysis of workplace accidents in the mining sector has been done using the database from the Spanish administration between the period 2005-2015 and applying data mining techniques. Data has been processed by means of the software Weka. Two scenarios were chosen regarding the accidents database, surface and underground mining. The most important variables involved in occupation accidents and their association rules have been determined. These rules are formed by several predictor variables that cause an accident, defining its characteristics and context. This study exposes the 20 most important association rules of the sector, either surface or underground mining, based on statistical confidence levels of each rule obtained by Weka. The outcomes display the most typical immediate causes with the percentage of accident basis of each association rule. The most typical immediate cause is body movement with physical effort or overexertion and type of accident is physical effort or overexertion. On the other hand, the second most important immediate cause and type of accident change in both scenarios. Data mining techniques have been proved as a very powerful tool to find out the root of the accidents, apply corrective measures and verify their effectiveness, either for public or private companies.

The purpose is to verify trends of scientific production from 2010 to 2020, considering the best universities of the United States, China, the European Union (EU) and private companies. The top 30 universities in 2020 in China, the EU, and the US and private companies were selected from the SCImago institutions ranking (SIR). The positions in 2020, 2015, 2010 in SIR and three sub-indicators were analyzed by means of non-parametric statistics, taking into consideration the effect of time and group on rankings. American and European Union universities have lost positions to Chinese universities and even more to private companies, which have improved. In 2020, private companies have surpassed all other groups considering Innovation as sub-indicator. The loss of leadership of European and partly American universities mainly concerns research linked to the production of patents. This can lead to future risks of monopoly that may elude public control and cause a possible loss of importance of research not linked to innovation.

The paper presents the results of high precision control survey carried out in the underground mining of Oyu Tolgoi project using terrestrial laser scanning technology. The purpose of the measurements is to ensure the heading, total length and volume of the penetration and excavation works are being implementing in accordance with the planned and designed dimensions. We established a network of 18 reference points in the tunnel of the selected underground level which were repeatedly measured 4 times by two round readings of the Trimble S7 tacheometry and processed by using the Trimble Business Center software. All points except the one was within the permissible error. Based on these points the excavation length and volumes were determined using the Trimble SX12 laser scanner. The difference between the planned work quantity provided by the Oyu Tolgoi project and the actual values measured by the laser scanning was calculated using the comparative method. The TLS partial control measurements in the excavations made through the ore bodies revealed the length of 20285.72 m and volume of 441128.80 m3 for underground 7 mine levels while their project values were 16158.26 m length and 422837.50 m3 volume, respectively.

Indonesia is one of the most biodiverse countries in the world and a promising resource for novel natural compound producers. Actinomycetes produce about two-thirds of all clinically used antibiotics. Thus, exploiting Indonesia’s microbial diversity for actinomycetes may lead to the discovery of novel antibiotics. A total of 422 actinomycete strains were isolated from three different unique areas in Indonesia and tested for their antimicrobial activity. Nine potent bioactive strains were prioritized for further drug screening approaches. The nine strains were cultivated in different solid and liquid media and a combination of genome mining analysis and mass spectrometry (MS)-based molecular networking was employed to identify potential novel compounds. By correlating secondary metabolite gene cluster data with MS-based molecular networking results, we identified several gene cluster-encoded biosynthetic products from the nine strains, including naphthyridinomycin, amicetin, echinomycin, tirandamycin, antimycin, and desferrioxamine B. Besides, eight putative ion clusters and numerous gene clusters were detected that could not be associated with any known compound, indicating that the strains can produce novel secondary metabolites. Our results demonstrate that sampling of actinomycetes from unique and biodiversity-rich habitats, such as Indonesia, along with a combination of gene cluster networking and molecular networking approaches, accelerates natural product identification.

Considering the differences between the European and African continents concerning the management of the mining production sector, we decided to carry out this study with the main objective of demonstrating that, in Africa, mining can positively change the quality of life of the populations where it develops and, at the same time, it is possible to respect the environment, which is our main wealth. To achieve these objectives, it is necessary to present the mining activity of the continent, emphasizing both the negative aspects and its strong points. The most important thing is to make a good diagnosis of the situation, which will allow us to cure our "patient", that is, African mining production.

Digital transformation of companies is often referred to as a future track and a strategic path towards ensuring the survival and sustainability of companies. In this paper, we aim to outline this transformation process in Tunisian companies and identify its driving factors and finally explain its key success factors. To this end, we examine a sample of 70 companies operating in all economic sectors. The results indicate that such a digital strategy has a significant and a positive effect on the success of digital transformation of companies. Leadership has an effect at a low level of digital maturity. On the other hand, corporate culture does not have a significant effect on digital transformation. These results contribute to explaining this emerging phenomenon by focusing on the driving factors as well as the factors that contribute to its success.

This study tries to focusing on applying information communication technology for economic importance of Mid Oil Company (MDOC), which is among contemporary oil companies in Iraq. Since ICT forms a significant aspect of achieving organizational economic objectives, most of public and private bodies seek to utilize ICT tool and techniques to improve work efficiencies. The emerging growth in ICT has made it process and service automation possible in managing organizational activities. To determine this effect of ICT on organizations, this study formulated statistical research model at MDOC through using fifteen questionnaires, which were distributed on three axes. The outcome of the questionnaire analysis revealed that there is inadequate comprehensive infrastructure to suitably support ICT within the selected organization management. In addition, it revealed that there are no ideal standards to maintain and sustain internet networks. Based on these revelations, the study presents recommendation for the adoption of ICT with the aim of minimizing cost, enhancing communication and managing infrastructure. Also recommends the need to support IT department within an organization, with essential equipment, in terms of man and machine, for better efficiency to employ the key of ICT Impact in Iraqi governmental oil companies.

The Upper Hunter Valley is a major coal mining area in New South Wales (NSW), Australia. Due to the ongoing increase of mining activities, PM10 (air-borne particle with an aerodynamic diameter less than 10 micrometres) pollution has become a major air quality concern in local communities. The early part of this study identified two air quality subregions in the valley. This paper aims to provide a holistic summarisation of the relationships between elevated PM10 pollution in two subregions and the local- and synoptic-scale meteorological conditions for spring and summer, when PM10 pollution is relatively high. A catalogue of twelve synoptic types and a set of six local meteorological patterns have been quantitatively derived and linked to each other using the self-organising map (SOM) technique. The complex meteorology-air pollution relationships have been visualised/interpretated on the SOM planes for two representative locations. It was found that the influence of local meteorological patterns differed significantly for mean PM10 levels vs the occurrence of elevated pollution events and between air quality subregions. In contrast, synoptic types showed generally similar relationships with mean vs elevated PM10 pollution in the valley. Two local meteorological patterns, the hot-dry-northwesterly wind conditions and the hot-dry-calm conditions, were found most PM10 pollution conducive in the valley when combined with a set of synoptic counterparts. These synoptic types are featured with the influence of eastward migrating continental high pressure system and westerly troughs, or a ridge extending northwest towards coastal northern NSW or southern Queensland from the Tasman Sea. The method and results can be used in air quality research for other locations of NSW, or similar regions elsewhere.

One of the most important defining characteristics of groundwater quality is pH as it fundamentally controls the amount and chemical form of many organic and inorganic solutes in groundwater. Groundwater data are frequently characterized by a wide degree of variability of the factors which possibly influence pH distribution. For this reason, it is challenging to link the spatio-temporal dynamics of pH to a single environmental factor by the ordinary least squares regression technique of the conditional mean. In this study, quantile regression was used to estimate the response of pH to nine environmental factors (As, Cd, Fe, Mn, Pb, turbidity, electrical conductivity, total dissolved solids and nitrates). Results of 25%, 50%, 75% quantile regression and ordinary least squares (OLS) regression were compared. The standard regression of the conditional means (OLS) underestimated the rates of change of pH due to the selected factors in comparison with the regression quantiles. The effect of arsenic increased for sampling locations with higher pH values (higher quantiles) likewise the influence of Pb and Mn. However, the effects of Cd and Fe decreased for sampling locations in higher quantiles. It can be concluded that these detected heterogeneities would be missed if this study had focused exclusively on the conditional means of the pH values. Consequently, quantile regression provides a more comprehensive account of possible spatio-temporal relationships between environmental covariates in groundwater. This study is one of the first to apply this technique on groundwater systems in sub-Saharan Africa. The approach is useful and interesting and has broad application for other mining environments especially tropical low-income countries where climatic conditions can drive rapid cycling or transformations of pollutants. It is also pertinent to geopolitical contexts where regulatory; monitoring and management capacities are weak and where mining pollution of groundwater largely occur.

This study investigates the transition from surface to underground mining at the Sepon Gold mine. The stability of surface slopes is assessed prior to commencing underground operations. Stope mining is adopted based on ore body characteristics and geological features. Finite element numerical analysis, employing the Generalized Hoek-Brown criterion, evaluates slope stability for surface slopes and opening stopes. The pit design comprises a 70° slope angle, 20 m height, and 10-15 m safety berm, meeting stability requirements with a factor of safety of 2.46. Underground mining design includes a 62° ore body dip, 50 m thick crown pillar to prevent surface subsidence, and 5 m wide, 5 m high stopes. Sill pillars are left for support after each level excavation. Rock bolts are employed in specific stope areas where is necessary, with continuous monitoring for surface deformation. The study analyzes the influence of stope sizes on pit wall and pit bottom stability, identifying slope failures near the hanging wall close to the pit bottom during underground mining. A slight increase in the displacement zone is observed on the upper crest of the top footwall. Overall, the findings demonstrate successful stability in the transition from surface to underground mining at Sepon Gold mine.

Pakistan is under developing country and it has an unpredictable market nature of shareholder-investors observe the company’s performance. This research could help to companies in understanding financial behavior, attitude and investors’ satisfaction in stock trade. Financial behavior is comparatively new subject in Pakistan therefore; this study has examined the financial behavior and attitude of investors. The behavioral finance that has been attempted to understand the positive experiences influences investors’ financial behavior. This study has find out that investor satisfaction is strongest in influence of positive financial behavior of investor and trader in stock trading; positive experience and brokers suggestions are strengthens the investment decision of investors and increases behavior loyalty to prefer over competitor. The main purpose of research to determine the effect of financial behavior on investors’ attitude and behavioral loyalty and investors’ satisfaction to preference over competitor. The research framework links with experiences in stock trade for positive (negative) experiences, attitude and financial behavior is developed. The research framework is measured data from sample of Karachi and Karachi Stock Exchange; the data is analyzed in smart PLS based on PLS-SEM. This study focused on trading experience with company’s active investors and traders in banking industry in Pakistan. The future research could be research in other sectors with inter-related issue of investors and traders (brokers) in stock trade. This is the first study in this research area; this study will be determine the experiences with positive (negative) financial behavior, attitude, satisfaction and behavioral loyalty of investors and traders in stock trade. Therefore, adding in this area of study which will help understanding the investors and traders attitude, preference and financial behavior in financial market.

Nutraceuticals are combination of nutrients and pharmaceuticals and these are derived from various plants, microbes and animals too. The food products that are considered as nutraceuticals are categorised based on the availability in the market. It is a medical approach to improve health and remedy for illness. Nowadays there is an increase in shift towards use of nutraceuticals as its usage provides preventive therapies to various chronic diseases. Various natural nutraceuticals are based on extracts from Ginger, turmeric, garlic, amla, cinnamon, aloe vera etc. A wide variety of therapeutic values are provided by them such as anti-inflammatory, antibacterial, antifungal properties, allergy relief, antidiabetic and cardiovascular problems. India is in its infancy stage but have many emerging companies that are trying to meet the demand of growing population. Overall ‘Nutraceuticals’ has been known as a new era of well being, in which the food industry has become a main player.

Although various atmospheric corrosion simulation models have been developed that give good results, industrial environments are still challenging. This paper aims to investigate the effect industrial atmospheres in Bor on the mechanical properties of structural steel S235JR and to create a constitutive model and also laboratory experimental conditions that will give the best results as an accelerated corrosion test. Atmospheric corrosion tests lasted six months, using sites close to the Electrolytic Refining Plant and Sulphuric Acid Plant, as well as air quality monitoring stations at a remote location in the city of Bor. The laboratory experiments included a salt chamber test lasting 120 h and 240 h and immersing the specimen in the electrolyte from the Electrolytic Refining Plant for 30 days. The results were compared with the standard specimens stored in the laboratory. The X-ray diffraction (XRD) method was used to identify the composition of corrosion products. The scanning electron microscopy (SEM) analysis was applied to analyze the fracture surfaces of the specimens. The use of the Complete Gurson Model (CGM) described the tensile characteristics of the corroded specimens. The study has shown that an enhanced acceleration model and conditions could be developed using numerical analysis of the test results.

The rehabilitation of abandoned mining sites is an increasingly pressing issue in the context of sustainable development. Recent research has emphasized the need for a holistic approach to the abandoned mining sites and their environmental rehabilitation. Based on field analysis, environmental assessments, satellite imagery processing and geographic information operations, this paper pushes forward the existing knowledge by doing a comprehensive assessment of abandoned mining sites in the Romanian Carpathians and by proposing innovative and sustainable rehabilitation solutions. Our findings highlight that abandoned mining sites and their surrounding territories in the Romanian mountains have significant ecological imbalances and complex socio-economic issues. The findings also suggest that by adopting innovative, integrated, and sustainability-oriented approaches, territories affected by mining can be transformed into valuable and sustainable spaces to meet human needs. We conclude by presenting the importance of innovation in ecological reconstruction and spatial-functional reintegration of mining sites in mountain areas as a useful tool in making fair decisions, both in the context of implementing appropriate development policies as well as for resilience and environmental sustainability of mining-affected mountain areas.

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.

Buyer practices have changed as individuals are figuring out how to live with the new truth of COVID-19. Take-out and conveyance orders have expanded, and our customer has added new items to their menu because of new client inclinations. With every one of the continuous changes, the customer had numerous unanswered inquiries, for example, Smartbridge has broad involvement with café innovation development Café TECHNOLOGY CAPABILITIES :Are the most famous items as yet unchanged after COVID? :Which are the most sold item blends now? :What is the acknowledgment of new things? :What are clients purchasing alongside new things? :How have liquor deals changed? The customer previously had reports that followed item deals and operational measurements, notwithstanding, there was a need to get a more profound knowledge into item examination. The customer expected to recognize what items and introductions were being sold all the more frequently, measure the acknowledgment of new items, and figure out what items clients buy together to improve advertising efforts, advancements, and deals. he E-business industry is filling immensely in the Indian market. The modest 4G web bundles in India clearly gives a push to these ventures. Thus, as Covid19 first hit in Quite a while, individuals got terrified to go out from their homes in light of the fact that, in their mind, it's a dread of Covid. They even wonder whether or not to go out to purchase fundamental (FMCG) products. Frenzy purchasing additionally has seen and to stay away from this dread of COVID-19, individuals are offering inclinations to the E-Commerce destinations to purchase fundamental products and a few clients are new which joined to purchase fundamental merchandise during this Pandemic Lockdown period. Numerous clients are moving their purchasing conduct from disconnected retail locations to online stores. This paper examines the customer buying pattern during lockdown.

This research examines how rainfall and mining affect the slope damage resulting from the transition from open-pit mining to underground mining. Using unmanned aerial vehicle (UAV), the Huangniu slope of Dexing Copper Mine was fully characterized, and experiments were conducted on rock samples from appropriate sites. First, the mechanical properties of the samples were measured. Then, the parameters of the similarity simulation experiments were derived based on the similarity theory. Subsequently, the rainfall, rock slope, data acquisition and monitoring systems were designed. Finally, the rock mass failure with different slope angles was analyzed, and the deformation and damage patterns under the coupling effect were obtained. The results show that rainfall increases pore water pressure and moisture content. Rainfall and slope-slip water have more impact on the open-pit platform. The pore water pressure values on the upper rock mass rise faster than inside. In the open-pit mining stage, the rock mass shifts slightly to the upper left. In the room mining stage, vertical fractures and goaf sinking occur. The fractures above the mine room form a semi-ellipse. In the pillar mining stage, the overlying rock displacement is evident, and fractures persist. In the continuous pillar mining stage, the overlying rock collapses massively. The 65° slope model is the most damaged, while the 55° slope model is the least damaged. The results also suggest that the UAV guides sample selection.

Syanyonja village in the gold district of Busia, South East of the Republic of Uganda contain geologically epigenetic gold quartz vein deposits in carbonate-altered mafic metavolcanic rocks, deposited as quartz reefs in mineralized shear zones. In supracrustal rocks, alluvial gold is obtained from weathered auriferous quartz veins, which are of late orogenic granitic activity. The Syanyonja gold deposits have long been subjected to artisanal and small-scale gold mining (ASGM) by the locals for livelihood. This study determined the amount of mercury discharged with tailings into Namukombe stream, a major water body in Syanyonja village and investigated the impacts of ASGM on the mining population and the environment. Quantitation of mercury discharged with tailings was done by mass balance method. Field survey at the mining sites was done followed by administration of questionnaires to 50 stampeders in the village. The study indicates that about 8% of mercury mixed with auriferous materials are lost in tailings, accounting for an annual mercury release of about 1.757 kg into the environment. Socio-demographic results indicate that the majority of the mining population (64%) are male and ASGM have left health and environmental footprints, which directly or indirectly affects the population. The most common health problems among miners are malaria (36%) and abdominal pain (20%). The standard of living of the miners are evidently low, and most mines are characterized by school dropouts, prostitutes and thieves. Mining sites have deplorably poor hygiene, with evident burning of amalgams to recover gold. ASGM have been accompanied by wanton mowing down of vegetation, land degradation as well as mercuric pollution of water, air, land and aquatic ecosystems. It is suggested that the Ugandan government should re-enforce committees to follow up on ASGM activities, train artisans on sustainable gold mining using borax, magnets, sluice boxes as well as take up farming actively as an alternative.

Mercury has a strong environmental impact since both its organic and inorganic forms are toxic and it represents a pollutant of global concern. Liquid Hg is highly volatile and it can be released during natural and anthropogenic processes in the hydrosphere, biosphere and atmosphere. In this study the distribution of Gaseous Elemental Mercury (GEM) and the total and leached mercury concentrations on paints, plasters, roof tiles, concretes, metals, dust and wood structures were determined in the main buildings and structures of the former Hg-mining area of Abbadia San Salvatore (Siena, Central Italy). The mining complex (divided into 7 units) covers a surface of about 65 ha and contains mining structures and managers and workers buildings. In this work, nine surveys of GEM measurements were carried out from July 2011 to August 2015 for the buildings and structures located in the units 2, 3 and 6. Moreover, detailed measurements were performed in February, April, July, September and December 2016 in the edifices and mining structures of Unit 6. GEM concentrations showed a strong variability in terms of space and time mostly depending on the distance from the building hosting driers, furnaces and condensers and ambient temperature, respectively. In the Unit 2 surveys carried out in the hotter period (from June to September) showed GEM concentrations up to 27,500 ng m⁻³, while in the Unit 6 they were on average much higher and occasionally they saturated the GEM measurement device (>50,000 ng m⁻³). Concentrations of total (in mg kg⁻¹) and leached (in μg L⁻¹) mercury measured in different building materials (up to 46,580 mg kg⁻¹ and 4,470 mg L⁻¹ for total and leached mercury, respectively) showed for the same type of material highly variable values in dependence on the edifice or mining structure from which they were collected. The results obtained in this study are of relevant interest for the operational cleanings to be carried out during the reclamation activities.

Tailings generated by mining account for the largest proportion of global waste from industrial activities. Copper is relatively uncommon world-wide, with low concentrations in sediments and waters, yet is very elevated around mining sites. On the Keweenaw Peninsula of Michigan, USA, jutting out into Lake Superior, 140 mines extracted native copper from the Portage Lake Volcanic Series, part of an intercontinental rift system. Between 1901-1932, two mills at Gay (Mohawk, Wolverine) sluiced 22.7 million metric tonnes (MMT) of copper-rich tailings (stamp sands) into Grand (Big) Traverse Bay. Here we examine: 1) 3D coastal elevation and reef bathymetry, 2) particle dispersal, 3) copper concentrations and leaching, plus 4) shoreline toxicity. About 10 MMT of tailings created a migrating coastal beach that stretches 7 km from the Gay pile to the Traverse River Seawall. Another 10 MMT are moving underwater on the coastal shelf, threatening Buffalo Reef, an important lake trout and whitefish breeding ground. Aerial photos, ALS (plane) LiDAR, and relatively inexpensive UAS (drone) surveys characterize shoreline structure and aid initial remediation. Because sands (natural beach quartz, stamp sand basalt) are silicates of similar density, particle dispersal is similar and challenging. However, stamp sand beaches contrast greatly with natural sand beaches in both physical profile and chemistry. Dispersing particles retain copper, and release toxic concentrations. Leaching is elevated by exposure to high DOC and low pH waters. Field experiments, shoreline seining, and benthic sampling confirm serious impacts on aquatic biota, supporting tailings removal.

In western societies about one in six employees works in shifts. Shiftwork is associated with a number of poor somatic and psychological health outcomes, especially sleep issues. Higher rates of absenteeism and accidents in the workplace are possible consequences. Still, prevention programs and treatment options that are specifically tailored to shift workers’ needs are rare. We devised a 4-week online sleep intervention (n = 21) and compared treatment outcomes to our outpatient treatment for shift workers (n = 12). Measures included the WHO-5, ISI, and ESS scales as well as sleep diaries. Shift workers reported worse symptoms of insomnia than other participants. Results show significant average increases in sleep efficiency (+ 7%) and total sleep time (+ 25 min.), as well as significant improvements regarding insomnia symptoms and wellbeing in the online sample. Rates of improvement did not differ between the online and outpatient samples. Sleep disorders affect a relevant part of the working population, especially shift workers. Online approaches to treatment of these issues seem feasible and effective. Randomized controlled trials are needed.

The main purpose of this work was to tests of form and size of fatigue wear of the top layer of oil gear pump wheel (3PW-BPF-24). The object of tests was operated during a determined number of hours at strictly controlled operational and environmental parameters. A microscope of focal differentiation by Alicona InfiniteFocus G4 (with Focus-Variation optical measuring technology) was used for an identification, measurement and description of noticed superficial defects, which resulted from the fatigue process of the top layer and an earlier thermochemical treatment. The work was finished with an analysis of obtained results (based on the analysis of 3D scans) and conclusions concerning about optimization of the thermal treatment. Taking into consideration the presented results (suggestions) in the technology of improving strength properties of gear wheels (of this type oil pumps) may render it possible to obtain an appropriate fatigue endurance of the top layer, i.e. a minimization of observed superficial defects.

Insurers act as institutional investors and underwriters of risk, therefore improving their own environmental, social, and governance (ESG) performance is important for the transmission of ESG values to all other economic sectors. We analyze ESG scores of worldwide Property and Casualty (P&C) insurers during 2012-2022, and show that more sustainable insurers have high operating leverage, although their combined ratios and z-scores reveal that they are financially stable. Additional results for the US subsample illustrate that stocks issued by sustainable insurers deliver positive excess returns. Overall, these findings suggest that sustainable practices are associated with the ability of insurers to execute business and create value. This is important for insurance managers, investors, and policy makers, as insurers play a prominent role in promoting economic growth and stability.

Already more than 140 countries consider or have pledged to reach net-zero emission targets by 2050 or earlier and the share of global emissions falling into an emission pricing scheme has steeply increased over the past three years. Even where there are no direct implications for industry (yet), there is a series of subtle pressure points driving an increasing number of companies across the globe to work towards climate neutrality and pledging ambitious emission reduction goals. This article sheds light on the pressure points, the subtle triggers, the underlying considerations as well as the hoped-for benefits for industrial companies from achieving net-zero emissions. The observations and ideas presented in this paper are derived from quantitative data obtained via the Energy Efficiency Index of German Industry (EEI) and qualitative data. Not only societal, work force, supply chain and investor expectations play a large role, but also many strategic considerations which have the potential to make the company more resilient and profitable, particularly in time of crisis. Those companies that do not move towards decarbonisation, on the other hand, may face a costly late-mover disadvantage. This piece uncovers subtle interconnections, helping stakeholders from industry and beyond to grasp opportunities and challenges ahead.

Although the fusion energy sector is at a nascent stage, the private fusion energy market has grown. There are currently 38 private fusion energy companies around the world aiming to commercialise fusion energy in early 2030s and 2040s. Given the capability of fusion energy in transforming today’s energy paradigm and the global character of the market, it is important to analyse how these companies are interacting with international human rights standards. Therefore, this work investigates the involvement of the private fusion energy sector with two voluntary international initiatives in particular: the UN Global Compact and the UN Guiding Principles on Business and Human Rights (UNGP). This study attempts to answer two research questions: (i) Are private fusion energy companies participating in the UN Global Compact? (ii) How are private fusion energy companies publicly implementing the UNGP? Content analysis of secondary data collected from the UN Global Compact, Fusion Industry Association, ITER and companies’ official website as well as published reports is adopted. In summary, this work finds that private fusion energy companies are neither participants nor signatories of the UN Global Compact. Their observance of the UNGP is also very poor. This study contributes to the field by highlighting this gap which the private fusion energy companies need to consider and take measures in order to create a salutary human rights sector.

Prestressed concrete sleepers (or railroad ties) are principally designed in order to carry wheel loads from the rails to the ground of railway tracks. Their design takes into account static and dynamic loading conditions. In spite of the most common use of the prestressed concrete crossties in railway tracks, there have always been many demands from rail engineers to improve serviceability and functionality of concrete crossties. For example, signaling, fiber optic, equipment cables are often damaged either by ballast corners or by tamping machine. There has been a need to re-design concrete crosstie to cater cables internally so that they would not experience detrimental or harsh environments. Also, many concrete crossties need a retrofit for automatic train control device and similar signaling equipment. In contrast, the effects of holes and web openings on structural capacity of concrete crossties have not been thoroughly investigated. This paper accordingly highlights the effect of holes and web openings on the toughness and ductility of concrete crossties. The outcome of this research enables better decision making process for retrofiting prestressed concrete crossties with holes and web opening in practice.

Artisanal and small-scale gold mining (ASGM) has been an important source of income for communities in the Madre de Dios River Basin in Peru for hundreds of years. However, in recent decades, the scale of ASGM activities in the region has increased dramatically, and exposures to a variety of occupational and environmental hazards related to ASGM, including mercury, are becoming more widespread. The aims of our study were to: (1) examine patterns in the total hair mercury level of human participants in several communities in the region and compare these results to the 2.2 µg/g total hair mercury level equivalent to the World Health Organization (WHO) Expert Committee of Food Additives (JECFA)’s Provisional Tolerable Weekly Intake (PTWI); and (2), to measure the mercury levels of paco (Piaractus brachypomus) fish raised in local aquaculture ponds, in order to compare these levels to the EPA Fish Tissue Residue Criterion of 0.3 µg Hg/g fish (wet weight). We collected hair samples from 80 participants in four communities (one control and three where ASGM activities occurred) in the region, and collected 111 samples from fish raised in 24 local aquaculture farms. We then analyzed the samples for total mercury. Total mercury levels in hair were statistically significantly higher in the mining communities than in the control community, and increased with increasing distance away from the Madre de Dios headwaters (as the crow flies), did not differ by sex, and frequently exceeded the reference level. Regression analyses indicated that higher hair mercury levels were associated with residence in ASGM communities. The analysis of paco fish samples found no samples that exceeded the EPA tissue residue criterion. Collectively, these results align with other recent studies showing that ASGM activities are associated with elevated human mercury exposure. The fish farmed through the relatively new process of aquaculture in ASGM areas appeared to have little potential to contribute to human mercury exposure. More research is needed on human health risks associated with ASGM to discern occupational, residential, and nutritional exposure, especially through tracking temporal changes in mercury levels as fish ponds age, and assessing levels in different farmed fish species. Additionally, research is needed to definitively determine that elevated mercury levels in humans and fish result from the elemental mercury from mining, rather than from a different source, such as the mercury released from soil erosion during deforestation events from mining or other activities.

When there are multiple key strata in the overburden of deep coal seam and the surface subsid-ence coefficient after mining is small, it indicates that the overlying key strata fail to break com-pletely after mining. On this occasion, the stress concentration on the working face occurs easily, which in turn leads to the occurrence of dynamic disasters such as rock burst. This study adopted a comprehensive analysis method of field monitoring and numerical simulation to explore the in-fluence of key stratum on the evolution law of mining-induced stress in the working face. Dis-tributed optical fiber sensor (DOFS) and surface subsidence GNSS monitoring system were re-spectively arranged inside and at the mouth of the ground observation borehole. According to the monitoring results of strain obtained from DOFS, the height of broken stratum inside the overlying strata was obtained; according to the monitoring results of surface subsidence, the sur-face subsidence coefficient was proved to be less than 0.1, indicating that the high key stratum does not break completely, but enters a state of bending subsidence instead. In order to reveal the influence of key stratum on the mining-induced stress of working face, two 3DEC numerical models with and without key stratum were established for comparative analysis. As the numeri-cal simulation results show, when there are multiple key strata in the overburden, the stress in-fluence range and stress concentration coefficient of coal seam after mining are relatively large. The study revealed the working mechanism of rock burst accidents after large-scale mining and predicted the potential area of rock burst risk after the mining of the working face, which has been verified by field investigation. The research results are of great guiding significance for the revelation of the working mechanism of rock burst in deep mining condition and its prevention and control.

Clarifying the impact mechanism of digital transformation of enterprises on the M&A efficiency of listed companies can provide a factual basis for improving capital market regulatory policies. Taking the mergers and acquisitions of listed companies from 2007 to 2021 as a research sample, the influence mechanism of the digital transformation degree of companies on their M&A efficiency was studied. The research results show that the digital transformation of listed companies will improve their M&A efficiency. Digital transformation will reduce the degree of mispricing of stocks of M&A companies, curb conflicts between managers and agents of M&A companies, and improve their M&A efficiency. Further research finds that the promotion effect of digital transformation on M&A efficiency is more significant in non-state-owned companies, with a higher degree of financing constraint and high analyst attention. In the future, regulatory authorities should actively promote the digital transformation of listed companies, curb mispricing and management agency problems in the capital market with digital governance, and improve the efficiency of mergers and acquisitions in the capital market.

The crisis of mining-industrial activities in Western Europe since the middle of the 20th century caused the cessation of mining, triggering a structural crisis. It is necessary to look for alternatives, among which tourist activities based on mining heritage stand out. However, there are unattractive peripheral spaces in which new activities encounter obstacles to their development, facing the post-industrial and rural crises. The Riotinto Mining Basin (Huelva, Andalusia) represents an example of such spaces with an enormous cultural heritage. A central agent, the Río Tinto Foundation, has opted to enhance tourism value, while the mine has recently been reactivated. The objective of this research is to analyze the contribution of mining tourism to sustainable rural development. The methodology applied has been mixed based on conducting interviews and analyzing secondary data. The results are (a) achievement of the tourism value of the mining heritage; (b) difficulties for tourism to overcome its peripheral condition; (c) relative contribution of tourism to the improvement of the territorial image; (d) lack of coordination among stakeholders; (e) limited contribution of tourism to sustainable rural development, moving to a secondary economic role after the reopening of the mine.

The soil organic matter (SOM) content and stability in natural gypsum soils and soils from mining/industrial areas were compared, in terms of their physical, chemical and micromorphological properties, to determine whether the old brownfield soils were acting as carbon (C) sinks, counteracting increasing carbon dioxide concentrations in the atmosphere. The mining/industrial soils were 50–400 years old, so had been left sufficiently long for full self-restoration. Despite having very different origins, both natural and brownfield soils stored similar amounts of SOM, and had similar pH values, calcium carbonate contents and textures. They were also fully covered with similar grass plant communities. The mining/industrial soils exhibited similar microstructures to the gypsum soils and were often quite porous. The most striking difference between them and the natural gypsum soils was that the mining/industrial soils were strongly polluted with zinc, lead and cadmium. The 50-year-old brownfield soils differed from the others in terms of having a lower state of SOM transformation, with a much higher C/N ratio, lower aggregate water resistance index, lower dehydrogenase activity and greater areas of undecomposed or slightly decomposed plant residues. These parameters in the older (150–400 years) brownfield soils were similar to those of the natural soils.

The rapid technological development of society determined increased demand for safe potable water and food resources. Unfortunately, this progress causes complex environmental pollution, that is continuously challenging the scholars’ community. Therefore, it is important to chemically analyze the food for a better understanding of pollution-spreading mechanisms. Our study is focused on food analysis originating from Vatra Dornei City, which belongs to Suceava County in the Bukovina region of Romania. It represents a well-known Romanian spa and ski resort in the northern parts of the Oriental Carpathians Mountains. The mountain region owns a lot of mineral resources, mainly consisting of mineral and sparkling waters, uranium, manganese, copper, pyrite, chalcopyrite, polymetallic ores, baryte, gold and silver-bearing orebodies, etc. The present contribution aims to point out the results obtained from the analysis of soil, water and food samples collected from the local markets. The food samples consisted of lettuce, spinach, apples, pork (smoked) chicken meat (raw), milk and cheese. Last year the survey was conducted over six months. The results highlighted that the mining activities carried out during time caused environmental pollution with uranium and heavy metals due to the waste heaps’ weathering phenomena and tailing ponds’ presence.

Sensor fusion algorithms and models have been widely used in recent times. Although research evidence has informed the use of sensor fusion models in diverse applications, there is room for improvement, especially in home-based health monitoring applications which require less supervision and technical knowledge of users. The present work compares data mining-based fusion software packages such as RapidMiner Studio, Anaconda, Weka, and Orange, and proposes a data fusion framework suitable for in-home applications. 574 privacy-friendly (binary) images and 1,722 datasets gleaned from thermal and Radar sensing solutions respectively, were fused using the software packages on instances of homogeneous and heterogeneous data aggregation. Experimental results indicated that the proposed fusion framework achieved an average Classification Accuracy of 84.7% and 95.7% on homogeneous and heterogeneous datasets respectively, with the help of data mining and machine learning models such as Naïve Bayes, Decision Tree, Neural Network, Random Forest, Stochastic Gradient Descent, Support Vector Machine, K-Nearest Neighbours and CN2 induction. Further evaluation of the sensor data fusion framework based on cross validation of features indicated average values of 94.4% for Classification Accuracy, 95.7% Precision and 96.4% for Recall.

This paper presents an Artificial Intelligence approach to mining context and emotions related to olfactory cultural heritage narratives, in particular to fairy tales. We provide an overview of the role of smell and emotions in literature, as well as highlight the importance of olfactory experience and emotions from psychology and linguistic perspectives. We introduce a methodology for extracting smells and emotions from text, as well as demonstrate the context-based visualizations related to smells and emotions implemented in a novel Smell Tracker tool. The evaluation is performed using a collection of fairy tales from Grimm and Andersen. We find out that fairy tales often connect smell with emotional charge of situations. The experimental results show that we can detect smells and emotions with F1 score of 92.7 and 79.2, respectively.

Habitual entrepreneurs became an important group of entrepreneurs who make a large contribution to the process of wealth creation. Previous studies have indicated that habitual entrepreneurship is a widespread phenomenon. It highlights the need to focus on the habitual entrepreneurs to understand the dynamic aspect of entrepreneurship. The aim of the article is to present the results of preliminary research on the relationships of entrepreneurial behavior of novice entrepreneurs and habitual entrepreneurs, in particular their motivation and impact on the entrepreneurial process. Research were conducted on a sample of 373 small innovative enterprises in the fourth quarter of 2017. The scale of habitual entrepreneurs in the examined sample is 32.44% and is comparable with research carried out in other countries. Survey results reveal that habitual entrepreneurs have greater tendency to create opportunities (introduction new products or services) than novice entrepreneurs. Habitual entrepreneurs also have higher development rate (increase of turnover) and larger share in the international market than novice entrepreneurs. The article also contains indications for further research into the phenomenon of multiple entrepreneurship.

This paper provides a data set collected with an underground shaft mobile mapping system equipped with rotated Velodyne VLP-16, Velodyne Ultra Puck VLP-32c, Livox Tele-15, and IMU Xsens MTi-30. The ground truth data was acquired with a geodetic survey including 15 ground control points and 6 Faro Focus 3D Terrestrial Laser Scanner stations of a total 273,784,932 of 3D measurement points. The aim of the paper is to describe the data set of a mining shaft. Obtained mobile mapping data can be the basis for detailed analyzes of the technical condition of the shaft. This data set provides an end-user case study of realistic applications in mobile mapping technology. It provides ground truth data, mobile mapping data and software tools for manipulation and visualization. The project is released and maintained at https://michalpelka.github.io/mine-mapping-dataset/.

The purpose was to reflect on the sustainability of artisanal and informal mining in the Peruvian Andean zone by 2022. The type of research was basic, non-experimental design, ex post facto scope. The sample consisted of: statistics on crime prevention due to socio-environmental conflicts from the National Prosecutor's Office and the Ombudsman's Office, reports of interventions targeted at artisanal and informal miners. The procedure focused on gathering secondary information for a documentary review analysis that is based on proven and disseminated past facts. Artisanal and informal mining is strengthened in the Peruvian Andean zone by incidence and force: 1) the social license and agreements by community assembly, 2) the flexibility in compliance with the formalization, preventive and environmental protection laws, 3) the 50% increase in socio-environmental conflict due to mining activity, 4) a 25% progressive increase in the production costs of agricultural and livestock activities. The direct consequences on environmental sustainability are: 1) the delimited reconversion of the change in agricultural land use in a third of the communal territories in basin headwaters; 2) displacement of environmentally friendly economic activity by 20% in basin headwaters; 3) the impossibility of modification or intervention in water surfaces, bofedales by communal agreements.

Communication is indispensable for today's lifestyle, and thanks to technology, millions of people can communicate as quickly as possible. The effect of this breakthrough has transformed organizations to the degree that they generate billions of emails daily to facilitate their operations. There is implicit information behind this vast corpus of human-generated content that can be mined and used for their benefit. This paper tries to address the opportunity that email logs can bring to organizations and propose an approach to discover process models by combining supervised text classification and process mining. This framework consists of two main steps, text classification, and process mining. First, Emails will be classified with supervised machine learning, and to mine, the processes fuzzy Miner is used. To further investigate the application of this framework, we also applied this framework over a real-life dataset from a case study organization.

La minería es una industria intensiva en energía que requiere una fuente estable de electricidad. Con el aumento de la demanda de minerales y la disminución de las leyes, se espera que la demanda de energía aumente en un 36% para el 2035. Este aumento de la demanda depende del uso de combustibles fósiles ya que la electricidad producida y comprada en las empresas mineras se basan principalmente en el caso de los combustibles fósiles, los costos de los sistemas de almacenamiento de energía solar y eólica han caído a una escala sin precedentes, lo que alienta a las empresas mineras a probar estas tecnologías.Entre los factores que influyen en el uso de energía renovable, se encuentra el beneficio a la comunidad en la etapa de operación y cierre o post-cierre de un proyecto minero, el siguiente factor es el perfil de consumo eléctrico o demanda es importante para el cálculo de su almacenamiento o no, de las energías renovables a utilizar. Como tercer factor está el cálculo del consumo en los procesos. Los rangos de penetración determinarán si es posible su almacenamiento de energía, y si es posible utilizar control híbrido (solar - eólico), para ello es importante el uso de herramientas digitales como el programa Holmer, es una herramienta importante que técnica y económica.El gobierno debe tener una perspectiva que ayude en los temas regulatorios necesarios para impulsar proyectos de energía renovable con diferentes incentivos e intereses según el contexto minero.

This paper investigates the possible failure modes of the product development process in production companies that are active in the B2C markets with a focus on household products. Since these cases require short lead times and are difficult to differentiate, in many instances the end result will not be the desired one and could affect profitability for a season or for good. A model of these possibilities is created and an approach to plan contingencies for their solutioning is proposed in the article. The main guideline is to switch from failure probability determination to accepting failure as inevitable and using digital solutions to reinforce the development process in order to offset its impact. For this goal, an Industry 5.0 Abatement Factor (abbreviated IFAF) is introduced in the contingency planning approach, that factors in the evaluation the low cost of digital instruments and the proper mix of Technology, Humans and AI (abbreviated THAI). The new working procedure based on these concepts and their interlinkages is discussed based on specific examples.

A new approach based on input-output (IO) analysis has emerged to estimate the carbon footprints of companies and their products from cradle to gate. While they rely on the same principles as the GHG Protocol, they use a distributed iterative framework to improve the footprint estimations and reduce their uncertainty. While optimal estimations would result if all the world’s companies would enter such a system, this paper shows how such a distributed system could apply to the real world where many enterprises would stay out of the system. We show how it would increase the quality of the estimations with respect the GHG Protocol by integrating scope 1 and scope 2 data of the value chains in the footprint estimations and reducing the part of remaining scope 3 data. To help analyzing uncertainty, we show how to use the scope 1/2/3 decomposition to estimate the biases and the standard deviations of the computed production intensities. We illustrate the model on macroeconomic data for 44 sectors and two regions (Europe and Rest of World), using the Inter-Country Input-Output database (ICIO). Such a system would necessarily rely on Information and Communication Technology since the companies would be permanently interconnected in a large-scale meshed network, using an application protocol for data exchange.

As technology continues to play a central role in shaping global industries, the performance of technology firms on the NASDAQ has significant implications for broader economic trends. Investors, facing an increasingly complex landscape, require detailed insights to make informed decisions and navigate market volatility. Moreover, amid the ongoing digital transformation, understanding the operational dynamics of technology companies is critical for policymakers and regulatory bodies to create frameworks that foster innovation while ensuring market stability. In the current context, characterized by rapid technological advancements, market uncertainties, and evolving consumer demands, the need for a comprehensive study on the operational efficiency of NASDAQ-listed technology companies is more pertinent than ever. Objective of our study is to identify relationships and patterns among selected financial metrics to assess the operational efficiency of NASDAQ-listed technology companies between 2011 to 2023. Financial metrics used in the study include following variables namely revenues, cost of goods sold, operating expenses, research, and development expenses, selling and general administrative expenses, and operating income. Johansen's cointegration methodology is employed to unveil long-term relationships and equilibrium adjustments among the considered variables. Results of our study underscore the paramount importance of efficiently managing the cost of goods sold (COGS) for achieving superior operating performance among these leading technology firms. The findings point to a nuanced interplay among these operational metrics, highlighting the interconnectedness and sustained relationships among them. This suggests that the top technology companies exhibit a sophisticated understanding of the dynamics between these variables, allowing them to navigate the complexities of the industry with strategic finesse.

The transport sector is pivotal and indispensable in our daily existence, being the exclusive conduit for the intercontinental conveyance of commodities and individuals. Hence, comprehending the phenomenon of business failure within the transport industry assumes paramount significance in delineating an effective competitive policy for this sector. The primary objective of this research paper is to execute a comparative investigation between statistical models forecasting business failure and models founded on artificial intelligence, within the context of the transport sector. This analysis spans the temporal expanse from 2014 to 2021 and encompasses the nations of Portugal, Spain, France, and Italy, aiming to ascertain superior efficacy among these models. The dataset employed for this endeavor encompassed a final cohort of 4866 companies, comprising 2881 that endured as going concerns and 1985 that succumbed to business failure. The models developed for this study exhibited the capacity to accurately categorize a proportion of companies ranging from 71% to 73%. Nonetheless, upon comparative scrutiny of these outcomes with those derived from the statistical models dedicated to business failure prediction, it becomes evident that the latter demonstrate an enhanced predictive prowess, manifesting fewer errors in the classification of the scrutinized companies.

Developing prosperous and inclusive societies requires a reformulation of the business-society nexus toward sustainability. This means that all economically motivated behaviors of firms need to also consider their social and environmental impact, and all social and environmental policies need to also consider their impact on the economy. With the Companies Act 2013, the Indian government adopted a legislative approach to reconfigure the business-society nexus. Mandating what has been considered discretionary elicited an extensive academic debate. We employ Content Configuration Analysis on 70 local and international English-language book chapters, research articles, reports, reviews, and expert commentaries published between 2013 and 2019 to develop a typology of the advantages and disadvantages associated with the Companies Act 2013. We find that, among the large set of arguments for and against the Act, specific arguments extolling its advantages concurrently appear as disadvantages in other texts. This paradox is indicative of the difficulties of satisfying stakeholder expectations, as well as the complexities corporate responsibility programs face in India. By systematizing the opportunities and challenges associated with the Companies Act 2013, we reveal how, similar to China, context and culture influence India’s socioeconomic development trajectory beyond the conventional market economy canon.

The UK food industry faces significant challenges to remain sustainable. With major challenges such as Brexit on the horizon, companies can no longer rely on a low labour cost workforce to maintain low production costs and achieve economic sustainability. Smart Systems (SS) is being seen as an approach towards achieving significant improvements in both economic and environmental sustainability. However, there is little evidence to indicate whether UK food companies are prepared for the implementation of such systems. The purpose of this research is to explore the applicability of Smart Systems in UK food manufacturing companies and, to identify the key priority areas and improvement levers for the implementation of such systems. A triangulated primary research approach is adopted and includes a questionnaire, follow up interviews and visits to thirty-two food manufacturing companies in the UK. The questionnaire and interviews are guided by the development of a unique measuring instrument created by the authors that is focusses upon SS technologies and systems. This paper makes an original contribution in that it is one of few academic studies to explore the implementation of SS in the industry and, provides a new perspective on the key drivers and inhibitors around its implementation. Findings suggest that the current turbulence in the industry could be bringing food companies closer to the adoption of such systems, hence it is a good time to define and develop the optimum SS implementation strategy.

The purpose of this paper is to examine if there are links between interpersonal trust and competences, relations and cooperation in Polish telecom companies. It examines what factors affect trust in coworkers and managers in sustainable organizations. The paper surveys a sample of 175 employees of telecom companies in Poland by means of a questionnaire. The results indicate that the competences, relations and cooperation are related to interpersonal trust. Regression analysis showed that the competences and relations predict a significant variance in trust amongst coworkers. Additionally, cooperation contributes to prediction of trust in mangers. Given the importance of trust in sustainable organizations, a better comprehension of what factors are related to team confidence provides valuable information for stakeholders. The results provide valuable clues how to improve interpersonal trust in sustainable organizations.

The paper presents the findings of research on companies in the early stage of development based on the concept of ensuring their sustainability. The starting point is attributes shaping a sustainable model of start-ups. A solution has been proposed and examined that identifies the factors dynamising a business model, which give the company the ability to make fast changes in the configuration of the business model, and that applies stabilizing factors based on the concept of the Triple Bottom Line. The research problem is a response to the phenomena of the bankruptcy of many start-ups that cannot find a way to survive in the long term. The business models of companies in the early stage of development should be designed taking into account the possibility of their dynamic and iterative changes when the earlier assumptions do not work when implemented in practice. A cognitive gap has been observed as the relevant literature does not propose a systemic solution to the problem of instability of the startups viewed from the perspective of the business model concept. The research problem, therefore, is to identify potential new methods to ensure the sustainability of young companies trying to combine aspects dynamising changes in business models, taking into account economic, environmental and social aspects. The findings of research on companies in the early stage of development show that when the methods of increasing the flexibility of the business model are applied and attention is drawn to environmental and social activities and a strong emphasis is put on financial performance, reflecting the expectations of shareholders, the concept of ensuring the sustainability of young companies from the point of view of sustainable business model attributes emerges.

Logistics plays an important role in globalized companies and contributes to the development of foreign trade. A large number of external conditions, such as recession and inflation, affect logistics. Therefore, managers should find ways to improve operational performance, enabling them to increase efficiency while considering environmental sustainability due to the industry’s large scale of energy consumption. Based on data collected from the financial reports of top global logistics companies, this study uses a DEA model to calculate corporate efficiency by implementing a Grey forecasting approach to forecast future sustainability values. Consequently, the study addresses the problem of how to enhance operational performance while accounting for the impact of external conditions. This research can help logistics companies develop operation strategies in the future that will enhance their competitiveness vis-à-vis rivals in a time of global economic volatility.

The perceived poor performance of publicly traded companies on their sustainability commitments and the quality of sustainability reporting has prompted stakeholders to consider the economic, environmental, and social impacts of corporate activities. Economic activities have led to various threats in the form of climate change, pollution, greenhouse gas emissions, natural disasters, and other issues that have negatively impacted the environment and stakeholders. Companies are expected to report to stakeholders on their sustainability performance, but reality proves that present reporting falls below stakeholders’ expectations mainly due to its still voluntary nature. The present study aims to provide a literature review of the relationship between sustainability reporting and the role of companies governance, especially observing if climate change requirements and energy-needed changes are being accounted. Results highlight mixed evidence for the influence of board governance attributes, providing interesting insights for research advancement. The study has practical implications for businesses, regulators, governments, and other stakeholders in their policy deliberations and investment decisions. Further empirical studies are recommended to re-examine sustainability reporting using the variables identified as important factors and gaps in this study and other board characteristics to improve the generalizability of the results.

In software development, developers received bug reports that describe the software bug. Developers find the cause of bug through reviewing the code and reproducing the abnormal behavior that can be considered as tedious and time-consuming processes. The developers need an automated system that incorporates large domain knowledge and recommends a solution for those bugs to ease on developers rather than spending more manual efforts to fixing the bugs or waiting on Q&A websites for other users to reply to them. Stack Overflow is a popular question-answer site that is focusing on programming issues, thus we can benefit knowledge available in this rich platform. This paper, presents a survey covering the methods in the field of mining software repositories. We propose an architecture to build a recommender System using the learning to rank approach. Deep learning is used to construct a model that solve the problem of learning to rank using stack overflow data. Text mining techniques were invested to extract, evaluate and recommend the answers that have the best relevance with the solution of this bug report.

Typical modern information systems are required to process copious data. Conventional manual approaches can no longer effectively analyze such massive amounts of data, and thus humans resort to smart techniques and tools to complement human effort. Currently, network security events occur frequently, and generate abundant log and alert files. Processing such vast quantities of data particularly requires smart techniques. This study reviewed several crucial developments of existent data mining algorithms, including those that compile alerts generated by heterogeneous IDSs into scenarios and employ various HMMs to detect complex network attacks. Moreover, sequential pattern mining algorithms were examined to develop multi-step intrusion detection. These studies can focus on applying these algorithms in practical settings to effectively reduce the occurrence of false alerts. This article researched the application of data mining algorithms in network security. The academic community has recently generated numerous studies on this topic.

The use of the voice of the customer as a main input to guide decision making towards customer centricity strategies has become a necessity for companies. This research proposes a structured method of textual processing using the KDD (Knowledge Discovery Databases) methodology applied to the tweets of users of Colombian public sector companies, through the analysis of temporal sentiments and topic modeling to identify the areas in which actions should be taken to improve the perception of service. To fulfill such purpose, tweets from January to June 2022 are processed, followed by a temporal analysis of the evolution of the sentiment based on 3 enriched dictionaries; after, the LDA (Linear Discriminant Analysis) algorithm is implemented to find the areas with ailment for the user, in addition to propose a method to homologate the CIER (Comisión de Integración Energética Regional) survey. Finally, metrics are detailed to follow up the perception of the service. It is concluded that for the Acueducto the topic with the highest number of complaints is related to "Water truck request", for Enel "Servide Outages" and for Vanti: " Case solution and request information". Also, the homologation of 3 of the 5 pillars on which the CIER survey is based is presented.

This paper proposes a model to evaluate business projects to get into an incubator, allowing to rank them in order of selection priority. The model combines the Momentum method to build prospective scenarios and the Multiple Criteria Decision Making (MCDM) method TOPSIS-2N to rank the alternatives. Six business projects were evaluated to be incubated. The Momentum method made it possible for us to create an initial core of criteria for the evaluation of incubation projects. The TOPSIS-2N method supported the decision to choose the company to be incubated by ranking the alternatives in order of relevance. Our evaluation model has improved the existing models used by incubators. This model can be used and / or adapted by any incubator to evaluate the business projects to be incubated. The set of criteria for the evaluation of incubation projects is original and the use of prospective scenarios with a MCDM method to evaluate companies to be incubated does not exist in the literature.

This study examines the effect of market risk on the financial performance of 31 non-financial companies listed on the Casablanca Stock Exchange (CSE) over the period 2000-2016. We utilize three alternative variables to assess financial performance, namely return on assets, return on equity and profit margin. Next, we use the degree of financial leverage, the book-to-market ratio, and the gearing ratio as market risk variables. Besides, we employ the pooled OLS model, the fixed effects model, the random-effects model, the difference GMM and the system GMM models. The results show that market risk indicators have a negative and significant influence on the companies' financial performance. The elasticities are greater following the book-to-market ratio compared to the degree of financial leverage and the gearing ratio, respectively. In most cases, the firm size, the tangibility ratio, and the cash holdings ratio have a positive effect on financial performance, whereas the firms' age, the debt-to-income ratio, stock turnover, and leverage hurt the performance of these non-financial companies. Therefore, decision-makers and managers should mitigate market risk through appropriate strategies of risk management, such as derivatives and insurance techniques.

The paper proposes a traceability framework based on methodological approach for the deployment of IOTA-Based Distributed Ledger in the mining industry, as a first step of certification and labelling of the sustainable material production. The methodology is evaluated in real-world implementation in scope of the DIG_IT project. The implementation provides significant amount of heterogeneous data originating from different processes and sources during the operation in mines, such as vehicles operations, mining and raw material extraction, workers physical parameters, environmental parameters, sensors (field, biometric, assets), market (supply chains, commodity prices), weather data, etc. Most of these datasets, especially the data about the emission, needs integrity and the ability to be audited by the public community, government and the stakeholders. The methodology for establishing the transparency for the mining operations, the requirements, technical architecture, General Data Protection Regulation (GDPR) assessment, Distributed Ledger Technology (DLT) infrastructure, as well as end-to-end traceability, are proposed and evaluated with the actual deployment. The approach advocates end to end security for the increased traceability and data integrity based on Distributed Ledger Technology. Namely, W3C Decentralised Identities and Public Key Infrastructure (PKI) should be deployed from the data source to the cloud and anchored onto the Blockchain. The approach showed promising results with the regulations compliance made possible, making the data available and advertised publicly for the emission compliance.

Although a slightly radioactive element, thorium is considered very toxic because its various species, which reach the environment, can constitute an important problem for the health of the population. The present paper aims to expand the possibilities of using membrane processes in the removal, recovery and recycling of thorium from industrial residues reaching the municipal waste processing platforms. The paper includes a short introduction on the interest shown for this element, a weak radioactive metal, followed by highlighting of some common (domestic) uses. In a distinct but concise part, the bio-medical impact of thorium is presented. The classic technologies for obtaining thorium are concentrated in a single schema, and then the speciation of thorium is presented with an emphasis on the formation of hydroxo-complexes and complexes with common organic reagents. The determination of thorium has been highlighted both on the basis of its radioactivity, but especially through methods that call for extraction followed by an established electrochemical, spectral or chromatographic method. Membrane processes are presented based on the electrochemical potential difference, rapidly presenting barro-membrane processes, electrodialysis, liquid membranes and hybrid processes. A separate sub-chapter is devoted to proposals and recommendations for the use of membranes in order to achieve some progress in urban mining for the valorization of thorium.

This paper aims to assess whether the financial performance of Portuguese companies that publish sustainability reports (SR) differs from the financial performance of companies that do not publish SR. We use two methodological procedures. First, we conducted a univariate analysis to test the differences in the financial performance according to the disclosure of SR. Second, we conducted a multivariate analysis using a multiple linear regression that explains financial performance by the disclosure of SR, as well as control variables such as sector, size, leverage, growth, and liquidity. Findings indicate that financial performance of companies that disclose SR does not significantly differ from the financial performance of companies that do not disclose SR. The results are robust to both methodological procedures, as well as to the sample split by sectors.

The following article analyzes Italian companies with more than 10 employees that use online sales tools. The data used were acquired from the ISTAT-BES database. The article first presents a static analysis of the data aimed at framing the phenomenon in the context of Italian regional disparities. Subsequently, a clustering with k-Means algorithm is proposed by comparing the Silhouette coefficient and the Elbow method. The investigation of the innovative and technological determinants of the observed variable is carried out through the application of a panel econometric model. Finally, different machine learning algorithms for prediction are compared. The results are critically discussed with economic policy suggestions.

Rising raw material prices can have a significant impact on the financial performance of the food and beverage (F&B) business. The purpose of this study was to determine the impact of rising prices of staples onfinancial resilience F&B business in Indonesia. This study uses a quantitative approach with bivariate correlation analysis and secondary data from the financial reports of F&B companies listed on the Indonesia Stock Exchange for the 2020-2022 period. The results showed that the increase in the price of basic commodities had a significant positive impact onfinancial resilience F&B company. This can be seen from the decline in the profitability and liquidity of F&B companies as a result of the increase in the price of basic commodities. Therefore, F&B companies must pay attention to and manage the risk of rising prices of basic commodities to maintainfinancial resilience and sustainable business continuity.

The Sanaga River Valley is a deep-seated structure of about 220 km in width, bounded on its northern and southern sides by 025-0550 fault zones. Its internal structure has been very little described before this study. It is characterized by the presence of surface features where a thick stratum of shale and argillaceous sandstones have been documented. The use of electrical resistivity tomography for near-surface imaging and hydrogeological prospecting within shale succession is widespread. However, several methods of spatial modeling of resistivity contours are generally proposed for the interpretation of inverse resistivity model sections. To determine the most appropriate method for interpreting resistivity tomographies in the context of subsoil marked by significant heterogeneity, logarithmic contour intervals, and linear contour interval processing were implemented on data collected over five areas. The data consists of outcrops data, cuttings samples from groundwater boreholes, and resistivity sections ranging in length from 240 to 1000 m. Well-control of the inverse resistivity model sections derived from both processes shows that the linear contour intervals modeling gives a more realistic picture of the reservoir size of the wa-ter-bearing sand in both shale and argillaceous sandstone succession. The sedimentary architecture over Sanaga fault breccia is marked by vertical intrusion anomalies up to 80 m high. The shape of these anomalies, as well as the very low resistivity values that characterize them, suggests that they could be related to mud intrusion.

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.

Educational data-mining is an evolving discipline that focuses on the improvement of self-learning and adaptive methods. It is used for finding hidden patterns or intrinsic structures of educational data. In the arena of education, the heterogeneous data is involved and continuously growing in the paradigm of big-data. To extract meaningful information adaptively from big educational data, some specific data mining techniques are needed. This paper presents a clustering approach to partition students into different groups or clusters based on their learning behavior. Furthermore, personalized e-learning system architecture is also presented which detects and responds teaching contents according to the students’ learning capabilities. The primary objective includes the discovery of optimal settings, in which learners can improve their learning capabilities. Moreover, the administration can find essential hidden patterns to bring the effective reforms in the existing system. The clustering methods K-Means, K-Medoids, Density-based Spatial Clustering of Applications with Noise, Agglomerative Hierarchical Cluster Tree and Clustering by Fast Search and Finding of Density Peaks via Heat Diffusion (CFSFDP-HD) are analyzed using educational data mining. It is observed that more robust results can be achieved by the replacement of existing methods with CFSFDP-HD. The data mining techniques are equally effective to analyze the big data to make education systems vigorous.

Anthropogenic development can strongly influence natural river processes, leading to environmental changes that negatively affect important habitats and biodiversity, and consequently reduce economically important natural resources. This study investigated the effects of salinity intrusion on the habitat of the clam Corbicula japonica in the Seomjin River estuarine zone. We employed the Environmental Fluid Dynamics Code (EFDC) model, which incorporates topographic data and hydrological changes, to simulate salinity concentrations. Two salinity measurement facilities were installed in Seomjin River estuarine and operated to optimize the EFDC model. The results show that reduced flow rates due to intake have a negligible impact on the increased salinity. Maintaining optimal salinity (15–20 psu) during neap tides at the Seomjin River Bridge requires constant high flow rates, which poses significant challenges. Saltwater stratification is identified as the primary cause of pronounced salinity stratification, particularly during neap tides. Addressing this issue through river discharge and intake facility operation is challenging. Structural measures, including riverbed restoration and underwater barriers, are recommended to improve resistance to seawater intrusion. Future research should aim to develop scenarios to reduce salinity concentrations, quantify the reduction efficiency, and propose region-specific measures.

The aim of the research results presented in the article is to strengthen a better understanding of the processes related to the transformation of hard coal mining in Poland, in particular the preparation of employees and their representatives, as well as employers from transformed areas for the upcoming changes. In this perspective, anticipating future skills against the internal potential of entrepreneurs becomes more and more important. It is a key element that enables entrepreneurs to adapt to changes, create innovative solutions and remain competitive on the market.

Fully mechanized mining face production involves complex geological conditions; mining pressure; gas, water, fire and other mining environments. The working face needs a shearer, hydraulic support, a scraper conveyor and other equipment for coordination. The deep integration of intelligent technology with factors such as "humans, machines, the environment, and management" in the workplace is the core content of intelligent coal mines. This paper puts forward an advanced framework for coal mining robot technology systems, including single robots, robotized equipment, robot clusters and unmanned systems. The framework clarifies the common key technologies of coal mining robot research and development and cross-integration with new technologies such as 5G, the industrial internet, big data, artificial intelligence, and digital twins to improve the autonomous and intelligent application of coal mining robots. By establishing a scientific and complete standard system for coal mining robots, we aim to achieve customized research and development and standardized production of various types of robots. A specific analysis was conducted on the research progress of common key technologies such as explosion-proof design, mechanical system innovation, power drive, intelligent sensing, positioning and navigation, and underground communication of coal mining robots. The current research and application status of various types of coal mining robots in China are summarized. A new direction for future coal mine robot research and development is proposed. Moreover, robotic mining systems should be promoted to enhance the overall intelligence level and mining efficiency of mining equipment. To develop human‒machine environment-integrated robots to improve the autonomy and collaboration level of coal mining robots. The digital twinning of the entire mine robot system should be accelerated, the normalized operation level of coal mine robots should be improved, research on coal mining robots, support robots, and transportation robots should be carried out, intelligence should be achieved in fully mechanized mining faces, and equipment support for fewer and unmanned fully mechanized mining faces should be provided. The practical process of implementing coal mining robotization has been summarized, and the technical and engineering feasibility of the coal mining machine population has been verified.

Most of the research in data mining concentrated on finding the positive associations that exist in frequent patterns. Frequent patterns have no bearing on the time duration and, therefore, lose interestingness. The Regularity of an itemset is related to the time at which the transaction occurred and the time distance between the transactions. Frequent Regular item sets can be generated considering the Frequency and the Regularity of the occurrence of the Item Sets. The frequent and regular Item sets will be so huge that they cannot be handled in real time. The count of Frequent and regular item sets gets reduced drastically when the properties “closed” and “Maximality” are applied. Finding negative associations among such patterns are important in the field of medicine. Many drugs administered to cure some diseases have counter reactions leading to many other diseases. Negative associations among the mined patterns are important as they reveal significant contradictions frequently among medical drugs administered by medical practitioners to cure some diseases. This paper proposes a method that mines medical databases to find regular, frequent patterns applied with closed and maximal properties and find negative associations among those patterns. The proposed algorithm is accurate to 98% and performs 9% more efficiently than the other algorithms presented in the literature.

In this review, we will cover a general overview of the current panorama on lncRNAs with an actual or potential role as biological markers in cancer. We will discuss examples of multi-omics approaches that integrates information on somatic aberrations, gene expression and epigenomics, with the scope of providing a more comprehensive view of the functional impact of lncRNA profiles and how these paradigm can be exploited for the discovery and selection of lncRNAs with a functional role and their use as variables informing on progression and prognostic and help guiding the selection of therapeutic strategies for cancers. Finally, we propose a perspective for future evolution of the study of lncRNAs and the discovery of their functional and harnessing their potential to assist in clinical management of malignancies.

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.

Turmeric, Curcuma longa L., is a type of medicinal plant characterized by its perennial nature and rhizomatous growth. It is a member of the Zingiberaceae family and is distributed across the world’s tropical and subtropical climates, especially in South Asia. Its rhizomes are highly valued for food supplements, spices, flavoring agents, and yellow dye in South Asia since ancient times. It exhibits a diverse array of therapeutic qualities that encompass its ability to combat diabetes, reduce inflammation, act as an antioxidant, exhibit anticancer properties, and promote anti-aging effects. In this study, organic extracts of C. longa rhizomes were subjected to HPLC separation followed by mass spectrometry analysis. The Global Natural Product Social Molecular Networking (GNPS) approach was utilized for the first time in this ethnobotanically important species to conduct an in-depth analysis of its metabolomes based on their fragments. A total of 30 metabolites including 16 diarylheptanoids, 1 diarylpentanoid, 3 bisabolocurcumin ethers, 4 sesquiterpenoids, 4 cinnamic acid derivatives, and 2 fatty acid derivatives were identified. Among 16 diarylheptanoids identified in this study, five of them are reported for the first time in this species.