Decoding the gene-disease associations in type 2 diabetes: A curated dataset for text mining-based classification.

Type 2 Diabetes (T2D) exerts a substantial impact on mortality rates. According to 2023 statistics, more than half a billion individuals are experiencing the effects of T2D, making it one of the top 10 leading contributors to worldwide deaths. Multiple factors contribute to the onset of T2D, such as obesity, poor diet and lifestyle, the mutation in specific genes and many more. Among the various factors that contribute to the development of T2D, genetics is a pivotal aspect. Due to the significant influence of genes in the initiation and advancement of various phases of T2D, our focus lies on exploring the association between T2D and genes. In the present article, we have curated Standard disease gene association data which contains evidence or reference sentences which contain this disease gene association information, which is further classified into 4 classes: Yes, No, Ambiguous and X each pertaining to Positive, Negative, Ambiguous and Not related disease-gene associations respectively. For the purpose of this work, we downloaded T2D related abstracts from PubMed using EDirect and further pre-processed this abstract data to extract Reference Sentences Data. This data was later double-fold manually validated to compile this disease gene association data. The data produced in this article serves as reference data for the training text mining-based biological literature classifiers. Classifiers will further be used to predict classes of published literature, not just for T2D, but can also be expanded beyond to encompass a wide range of disease and their complications. The compilation of positively linked genes derived from these predictions can then be utilized for in-depth system-level analysis of T2D.

Meta-omics assisted microbial gene and strain resources mining in contaminant environment.

Human activities have led to the release of various environmental pollutants, triggering ecological challenges. In situ, microbial communities in these contaminated environments are usually assumed to possess the potential capacity of pollutant degradation. However, the majority of genes and microorganisms in these environments remain uncharacterized and uncultured. The advent of meta-omics provided culture-independent solutions for exploring the functional genes and microorganisms within complex microbial communities. In this review, we highlight the applications and methodologies of meta-omics in uncovering of genes and microbes from contaminated environments. These findings may assist in future bioremediation research.

Influence of land use change on habitat quality: a case study of coal mining subsidence areas.

Revealing the spatiotemporal evolution characteristics and key driving processes behind the habitat quality is of great significance for the scientific management of production, living, and ecological spaces in resource-based cities, as well as for the efficient allocation of resources. Focusing on the largest coal-mining subsidence area in Jiangsu Province of China, this study examines the spatiotemporal evolution of land use intensity, morphology, and functionality across different time periods. It evaluates the habitat quality characteristics of the Pan'an Lake area by utilizing the InVEST model, spatial autocorrelation, and hotspot analysis techniques. Subsequently, by employing the GTWR model, it quantifies the influence of key factors, unveiling the spatially varying characteristics of their impact on habitat quality. The findings reveal a notable surge in construction activity within the Pan'an Lake area, indicative of pronounced human intervention. Concurrently, habitat degradation intensifies, alongside an expanding spatial heterogeneity in degradation levels. The worst habitat quality occurs during the periods of coal mining and large-scale urban construction. The escalation in land use intensity emerges as the primary catalyst for habitat quality decline in the Pan'an Lake area, with other factors exhibiting spatial variability in their effects and intensities across different stages.

Boundary sample-based class-weighted semi-supervised learning for malignant tumor classification of medical imaging.

Medical image classification plays a pivotal role within the field of medicine. Existing models predominantly rely on supervised learning methods, which necessitate large volumes of labeled data for effective training. However, acquiring and annotating medical image data is both an expensive and time-consuming endeavor. In contrast, semi-supervised learning methods offer a promising approach by harnessing limited labeled data alongside abundant unlabeled data to enhance the performance of medical image classification. Nonetheless, current methods often encounter confirmation bias due to noise inherent in self-generated pseudo-labels and the presence of boundary samples from different classes. To overcome these challenges, this study introduces a novel framework known as boundary sample-based class-weighted semi-supervised learning (BSCSSL) for medical image classification. Our method aims to alleviate the impact of intra- and inter-class boundary samples derived from unlabeled data. Specifically, we address reliable confidential data and inter-class boundary samples separately through the utilization of an inter-class boundary sample mining module. Additionally, we implement an intra-class boundary sample weighting mechanism to extract class-aware features specific to intra-class boundary samples. Rather than discarding such intra-class boundary samples outright, our approach acknowledges their intrinsic value despite the difficulty associated with accurate classification, as they contribute significantly to model prediction. Experimental results on widely recognized medical image datasets demonstrate the superiority of our proposed BSCSSL method over existing semi-supervised learning approaches. By enhancing the accuracy and robustness of medical image classification, our BSCSSL approach yields considerable implications for advancing medical diagnosis and future research endeavors.

Vegetation communities and soil properties along the restoration process of the Jinqianghe mine site in the Qilian Mountains, China.

The study explores the impact of mine grassland restoration on plant communities and soil properties in alpine grasslands, a subject of significant interest due to the observed relationship between grassland changes, plant communities, and soil properties. While prior research has mainly focused on the consequences of grassland degradation on plant diversity and soil characteristics, the specific effects of varying restoration degrees in alpine mining grasslands at the regional scale remain poorly understood. To address this knowledge gap, we established 15 sampling plots (0.5m×0.5m) across five different restoration degrees within alpine mining grasslands in the Qilian Mountains, China. Our objective was to assess the variations in plant diversity and soil properties along these restoration gradients. We conducted comprehensive analyses, encompassing soil properties [soil water content (SWC), available nitrogen (AN), total phosphorus (TP), nitrate nitrogen (NO3-N), ammonium nitrogen (NH4-N), total nitrogen (TN), available phosphorus (AP), soil organic carbon (SOC), nitrate nitrogen, soil pH, and electrical conductivity (EC)], plant characteristics (height, density, frequency, coverage, and aboveground biomass), and plant diversity indices (Simpson, Shannon-Wiener, Margalef, Dominance, and Evenness indexes). Our findings included the identification and collection of 18 plant species from 11 families and 16 genera across the five restoration degrees: Very Low Restoration Degree (VLRD), Low Restoration Degree (LRD), Moderate Restoration Degree (MRD), High Restoration Degree (HRD), and Natural Grassland (NGL). Notably, species like Carex duriuscula, Cyperus rotundus, and Polygonum viviparum showed signs of recovery. Principal component analysis and Pearson correlation analysis revealed that soil pH, SWC, SOC, NO3-N, and AN were the primary environmental factors influencing plant communities. Specifically, soil pH and EC decreased as restoration levels increased, while SWC, AN, TP, NH4-N, TN, AP, SOC, and NO3-N exhibited a gradual increase with greater restoration efforts. Furthermore, the HRD plant community demonstrated similarities to the NGL, indicating the most effective natural recovery. In conclusion, our study provides valuable insights into the responses of plant community characteristics, plant diversity, and soil properties across varying restoration degrees to environmental factors. It also elucidates the characteristics of plant communities along recovery gradients in alpine grasslands.

Artificial intelligence and sustainable development during the pandemic: An overview of the scientific debates.

The current work aims to analyze the main themes related to artificial intelligence (AI) and sustainable development during the pandemic period. This study provides an overview of the specialized literature related to AI and sustainability from the beginning of the pandemic through 2023. The present paper analyses scientific literature emphasizing both artificial intelligence's positive and negative impacts on sustainable development objectives (SDGs). To conduct the research, we employed bibliometric analysis and text-mining techniques to identify the major themes in the literature indexed in the Web of Science and Scopus databases. Firstly, we used descriptive measures to identify the authors' impact, the article production by country, the main keywords used, and other descriptive data. We further used data reduction methods based on co-word analysis (such as multiple correspondence analysis) on authors' keywords to show patterns in the themes explored in the literature. Bibliometric analysis was supplemented by text mining using Latent Dirichlet allocation (LDA) and structural topic modeling on abstracts to provide a comprehensive view of scientific debates on AI and sustainable development. Our research has identified various themes in the literature related to AI and sustainable development. These themes include social sustainability, health-related issues, AI technologies for energy efficiency, sustainability in industry and innovation, IoT technologies for smart and sustainable cities, urban planning, technologies for education and knowledge production, and the impact of technologies on SDGs. We also found that there is a significant positivity bias in the literature when discussing the impact of AI on sustainable development. Despite acknowledging certain risks, the literature tends to focus on the potential benefits of AI across various sectors. In addition, the analysis shows a growing emphasis on energy efficiency, which is facilitated by the use of AI technologies. Our study contributes to a better understanding of current scholarly discussion trends and emerging scientific avenues regarding AI and sustainable development. It also highlights the areas where research is needed and the implications for practitioners and policymakers.

Endocrine system-related adverse events associated with PD-1/PD-L1 inhibitors: data mining from the FDA adverse event reporting system.

Background: Various immune checkpoint inhibitors, such as programmed cell death protein-1 (PD-1) and its ligand (PD-L1), have been approved for use, but they have side effects on the endocrine glands. Methods: Adverse event reports related to PD-1/PD-L1 inhibitors from the FDA Adverse Event Reporting System (FAERS) from the first quarter of 2019 to the first quarter of 2023 were extracted, and the reported Odds ratio methods (ROR method) and comprehensive standard methods (MHRA methods) were used for data mining and analysis. Results: A total of 5,322 reports (accounts for 6.68% of the total reports)of AEs in endocrine system were collected, including 1852 of pabolizumab (34.80%), 2,326 of navuliumab (43.71%), 54 of cimipriliumab (1.01%), 800 of atilizumab (15.03%), 222 of duvariumab (4.17%) and 68 of averumab (1.28%). Endocrine system-related AEs were mainly present in men (excluding those treated with pembrolizumab) aged ≥65 years. The ratio of AEs components in the endocrine system for the six drugs was approximately 3-8%. The main endocrine glands involved in AEs were the thyroid (pembrolizumab), pituitary and adrenal (nivolumab), adrenal (cemiplimab, atezolizumab, and avelumab), and thyroid (durvalumab). Most patients experienced AEs between 30 and 365 (mean, 117) days,the median time was 61d. AEs resulted in prolonged hospitalization in >40% and death in >10% of cases after administration of pembrolizumab, nivolumab, or durvalumab. Conclusion: Men aged ≥65 years should be concerned about endocrine-related AEs. There was a lengthy interval between the use of PD-1/PD-L1 inhibitors and endocrine system-related AEs, but the outcome was serious. Special attention should be given to endocrine system-related AEs when using pembrolizumab, nivolumab, or durvalumab.

The cultural representations and symbolizations emerging from Italian psychologists working in multidisciplinary assisted reproduction teams: A linguistic analysis with the emotional text mining.

This study aims to explore the perspectives of Italian psychologists who work in assisted reproductive treatment (ART) centres regarding their roles within multidisciplinary teams. Twenty-eight psychologists were interviewed, recorded and their transcribed text was analysed using emotional text mining. The analysis revealed four clusters representing the psychologists' cultural symbolizations of their works: 'Clinical Practice with the patient', 'Placing Psychology within the Treatment', 'Psychologist's Loneliness' and; 'Collusion with Medicine'. The symbolic representations emerging clearly highlighted a lack of integration of psychology within the medical field. Psychologists expressed emotional and practical difficulties in trying to integrate their role, including a desire to provide psychological assistance, feelings of loneliness and concerns about jeopardizing their professional opportunities, which are intertwined with the medical field. Present findings underscore the importance of integrating psychology within ART centres and multidisciplinary teams and of establishing operational guidelines for psychologists. These steps are crucial for reaching integration of psychologists within the medical setting.

Chronic neck and low back pain from personal experiences: a written narrative approach.

Background: Chronic neck and low back pain are very common and have detrimental effects for people and society. In this study, we explore the experiences of individuals with neck and/or back pain using a written narrative methodology. Materials & methods: A total of 92 individuals explained their pain experience using written narratives. Narratives were analyzed through thematic analysis and text data mining. Results: Participants wrote about their experience in terms of pain characteristics, diagnosis process, pain consequences, coping strategies, pain triggers, well-being and future expectations. Text data mining allowed us to identify concurrent networks that were basically related with pain characteristics, management and triggers. Conclusion: Written narratives are useful to understand individuals' experiences from their point of view.

Future groundwater potential mapping using machine learning algorithms and climate change scenarios in Bangladesh.

The aim of the study was to estimate future groundwater potential zones based on machine learning algorithms and climate change scenarios. Fourteen parameters (i.e., curvature, drainage density, slope, roughness, rainfall, temperature, relative humidity, lineament density, land use and land cover, general soil types, geology, geomorphology, topographic position index (TPI), topographic wetness index (TWI)) were used in developing machine learning algorithms. Three machine learning algorithms (i.e., artificial neural network (ANN), logistic model tree (LMT), and logistic regression (LR)) were applied to identify groundwater potential zones. The best-fit model was selected based on the ROC curve. Representative concentration pathways (RCP) of 2.5, 4.5, 6.0, and 8.5 climate scenarios of precipitation were used for modeling future climate change. Finally, future groundwater potential zones were identified for 2025, 2030, 2035, and 2040 based on the best machine learning model and future RCP models. According to findings, ANN shows better accuracy than the other two models (AUC: 0.875). The ANN model predicted that 23.10 percent of the land was in very high groundwater potential zones, whereas 33.50 percent was in extremely high groundwater potential zones. The study forecasts precipitation values under different climate change scenarios (RCP2.6, RCP4.5, RCP6, and RCP8.5) for 2025, 2030, 2035, and 2040 using an ANN model and shows spatial distribution maps for each scenario. Finally, sixteen scenarios were generated for future groundwater potential zones. Government officials may utilize the study's results to inform evidence-based choices on water management and planning at the national level.

Post-marketing risk analysis of bendamustine: a real-world approach based on the FAERS database.

Objective: Bendamustine was approved for treating chronic lymphocytic leukemia and indolent B-cell non-Hodgkin lymphoma. Despite its therapeutic benefits, the long-term safety of bendamustine in a large population remains inadequately understood. This study evaluates the adverse events (AEs) associated with bendamustine, using a real-world pharmacovigilance database to support its clinical application. Methods: We conducted a post-marketing risk analysis to assess the association between bendamustine and its AEs. Data were extracted from the US FDA's Adverse Event Reporting System (FAERS), covering the period from January 2017 to September 2023. The characteristics of bendamustine-associated AEs and the onset time were further analyzed. Statistical analysis was performed using MYSQL 8.0, Navicat Premium 15, Microsoft EXCEL 2016, and Minitab 21.0. Results: 9,461,874 reports were collected from the FAERS database, 9,131 identified bendamustine as the "primary suspected" drug. We identified 331 significant disproportionality preferred terms (PTs). Common AEs included pyrexia, neutropenia, infusion site reaction, progressive multifocal leukoencephalopathy (PML), injection site vasculitis, and pneumonia-all documented on bendamustine's label. Notably, 16 unexpected and significant AEs were discovered, including hypogammaglobulinemia, which is concerning due to its potential to increase infection susceptibility following bendamustine treatment. Other significant findings were anaphylactic reactions, PML, and cutaneous malignancies, suggesting updates to the drug's label may be necessary. Physicians should monitor for neurological and skin changes in patients and discontinue treatment if PML is suspected. Moreover, the median onset time for bendamustine-associated AEs was 13 days, with an interquartile range [IQR] of 0-59 days, predominantly occurring on the first day post-initiation. The ß of bendamustine-related AEs suggested risk reduction over time. Conclusion: Our study uncovered some potential pharmacovigilance signals for bendamustine, providing important insights for its safe and effective clinical use.

A real-world pharmacovigilance study of drug-induced QT interval prolongation: analysis of spontaneous reports submitted to FAERS.

Purpose: To identify the most commonly reported drugs associated with QT interval prolongation in the FDA Adverse Event Reporting System (FAERS) and evaluate their risk for QT interval prolongation. Methods: We employed the preferred term (PT) "electrocardiogram QT prolonged" from the Medical Dictionary for Regulatory Activities (MedDRA) 26.0 to identify adverse drug events (ADEs) of QT interval prolongation in the FAERS database from the period 2004-2022. Reporting odds ratio (ROR) was performed to quantify the signals of ADEs. Results: We listed the top 40 drugs that caused QT interval prolongation. Among them, the 3 drugs with the highest number of cases were quetiapine (1,151 cases, ROR = 7.62), olanzapine (754 cases, ROR = 7.92), and citalopram (720 cases, ROR = 13.63). The two most frequently reported first-level Anatomical Therapeutic Chemical (ATC) groups were the drugs for the nervous system (n = 19, 47.50%) and antiinfectives for systemic use (n = 7, 17.50%). Patients with missing gender (n = 3,482, 23.68%) aside, there were more females (7,536, 51.24%) than males (5,158, 35.07%) were involved. 3,720 patients (25.29%) suffered serious clinical outcomes resulting in deaths or life-threatening conditions. Overall, most drugs that caused QT interval prolongation had early failure types according to the assessment of the Weibull's shape parameter (WSP) analysis. Conclusions: Our study offered a list of drugs that frequently caused QT interval prolongation based on the FAERS system, along with a description of some risk profiles for QT interval prolongation brought on by these drugs. When prescribing these drugs in clinical practice, we should closely monitor the occurrence of ADE for QT interval prolongation.

Factors affecting the adoption of circular economy in mining companies of developing economies - A Namibian stakeholder perspective.

The factors influencing the adoption and implementation of CE in developing countries are not yet fully examined. By focusing on the Namibian mining sector, this study highlights the perspectives of local stakeholders on CE adoption in a developing country. The mine managers recognized that waste is problematic and that CE practices are beneficial for mining companies and Namibia at large. Our findings also indicated that stronger academic institutions providing CE training and helping develop CE solutions, public awareness campaigns, financial support for CE practices, cooperation among industry stakeholders, and clear CE policy would all help drive the implementation of CE.

Assessing the impact of mining on cyclic and linear methylsiloxane distribution in Tibetan soils: Source contribution and transport pattern.

The pervasive presence of methylsiloxanes (MSs), comprising linear and cyclic congeners, in the environment poses significant ecological risks, yet the understanding of their transport mechanisms and deposition patterns remains limited. This study analyzed the concentrations of 12 linear-MSs (L3-L14) and 7 cyclic-MSs (D3-D9) in 29 surface soil samples collected across varying altitudes (3726 to 4863 m) near the Jiama mining sector in Tibet, aiming to investigate the distribution and transport dynamics of MSs from the emission source. The distribution of total MS concentration (ranging from 50.1 to 593 ng/g) showed a remarkable correlation with proximity to the mining site, suggesting the emergent source of mining activities for the MSs in the remote environment of the Tibetan Plateau. Employing the innovative model of robust absolute principal component scores-robust geographically weighted regression (RAPCS-RGWR), the analysis predicted that the mining operations contributing 57.1 % of the total soil MSs, would significantly surpass contributions from traffic emissions (14.7 %), residential activities (13.2 %), and the environmental factor of total organic matter content (14.9 %). The Boltzmann equation effectively modeled the distribution pattern of soil MSs, highlighting atmospheric transport and gravitational settling as key distribution mechanisms. However, linear-MSs exhibited longer transport distances than cyclic-MSs and were more profoundly affected by prevailing wind directions, suggesting their differential environmental behaviors and risks. Our study underscored that the mining sector possibly emerged as a significant source of Tibetan MSs, and provided insights into the transport and fate of MSs in remote, high-altitude environments. The findings emphasize the need for targeted pollution control strategies to mitigate the environmental footprint of mining activities in Tibet and similar regions.

An ensemble-based strategy for robust predictive volcanic rock typing efficiency on a global-scale: A novel workflow driven by big data analytics.

Laboratory measurements, paleontological data, and well-logs are often used to conduct mineralogical and chemical analyses to classify rock samples. Employing digital intelligence techniques may enhance the accuracy of classification predictions while simultaneously speeding up the whole classification process. We aim to develop a comprehensive approach for categorizing igneous rock types based on their global geochemical characteristics. Our strategy integrates advanced clustering, classification, data mining, and statistical methods employing worldwide geochemical data set of ~25,000 points from 15 igneous rock types. In this pioneering study, we employed hierarchical clustering, linear projection analysis, and multidimensional scaling to determine the frequency distribution and oxide content of igneous rock types globally. The study included eight classifiers: Logistic Regression (LR), Gradient Boosting (GB), Random Forest (RF), K-nearest Neighbors (KNN), Support Vector Machine (SVM), Artificial Neural Network (ANN), and two ensemble-based classifier models, EN-1 and EN-2. EN-1 consisted of LR, GB, and RF aggregates, whereas EN-2 comprised the predictions of all ML models used in our study. The accuracy of EN-2 was 99.2 %, EN-1 achieved 98 %, while ANN yielded 98.2 %. EN-2 provided the best performance with highest initial curve for longest time on the receiver operating characteristic (ROC) curve. Based on the ranking features, SiO2 was deemed most important followed by K2O and Na2O. Our findings indicate that the use of ensemble models enhances the accuracy and reliability of predictions by effectively capturing diverse patterns and correlations within the data. Consequently, this leads to more precise results in rock typing globally.

"Hg distribution and accumulation in soil and vegetation in areas impacted by artisanal gold mining in the Southern Amazonian region of Madre de Dios, Peru."

Artisanal and small-scale gold mining (ASGM) is the primary global source of anthropogenic mercury (Hg) emissions. It has impacted the Amazon rainforest in the Peruvian region of Madre de Dios. However, few studies have investigated Hg's distribution in terrestrial ecosystems in this region. We studied Hg's distribution and its predictors in soil and native plant species from artisanal mining sites. Total Hg concentrations were determined in soil samples collected at different depths (0-5 cm and 5-30 cm) and plant samples (roots, shoots, leaves) from 19 native plant species collected in different land cover categories: L1 naked soil (L1), gravel piles (L2), natural regeneration (L3), reforestation (L4), and primary forest (L5) in the mining sites. Hg levels in air were also studied using passive air samplers. The highest Hg concentrations in soil (average 0.276 and 0.210 mg kg-1 dw.) were found in the intact primary forest (L5) at 0-5 cm depth and in the plant rooting zones at 5-30 cm depth, respectively. Moreover, the highest Hg levels in plants (average 0.64 mg kg-1 dw) were found in foliage of intact primary forest (L5). The results suggest that the forest in these sites receives Hg from the atmosphere through leaf deposition and that Hg accumulates in the soil surrounding the roots. The Hg levels found in the plant leaves of the primary forest are the highest ever recorded in this region, exceeding values found in forests impacted by Hg pollution worldwide and raising concerns about the extent of the ASGM impact in this ecosystem. Correlations between Hg concentrations in soil, bioaccumulation in plant roots, and soil physical-chemical characteristics were determined. Linear regression models showed that the soil organic matter content (SOM), pH, and electrical conductivity (EC) predict the Hg distribution and accumulation in soil and bioaccumulation in root plants.

Sustainable strategy for rehabilitating phosphate mining sites and valorisation of phosphate industry by-products and sludge using pistachio tree (Pistacia atlantica), false pepper (Schinus molle), and eucalyptus (Eucalyptus globulus) trees.

The development of anthroposols has been proposed as a new environmentally friendly approach to ensuring the successful revegetation of phosphate mining sites. The phosphate industry's by-products, including phosphogypsum (PG), phosphate sludge (PS), and sewage sludge (SS), can be valuable resources in restoring the ecological balance of mined soil areas. The aim of this study was to safely and sustainably restore the ecological integrity of the phosphate mining site through the evaluation of nutrients and heavy metals dynamics in soil and plant tissues of three tree species and treated by-products containing 65 % PG, 30 % PS, and 5 % SS. The tree species used were Pistacia atlantica, Schinus molle, and Eucalyptus globulus. The experimental layout was a randomised complete block design with six replicates and three treatments. Growth diameter, height, nutrient uptakes and heavy metal dynamic were evaluated from the rhizosphere soils and plant tissues over two years. Hierarchical head maps of correlations between the measured growth parameters, soil and nutrient uptakes of the tree species were analysed using a phylogenetic generalised linear mixed model. S. molle and E. globulus had higher average diameter and height than P. atlantica plants. P. atlantica and S. molle showed greater nitrogen, phosphorus, potassium, calcium, and magnesium concentrations than E. globulus trees. Tree growth parameters were closely linked to soil nutrient bioavailability. The heavy metal accumulation ratio was higher in the E. globulus and S. molle leaves than in stems. Using by-products could be valorised for rehabilitating mine sites together with E. globulus and S. molle species.

Equivalent sentiment measures for cross-language analysis of corporate communications.

This paper presents a technique for sentiment measurement in many languages. The method allows researchers to efficiently analyze corporate documents, management reports, and financial statements using python. When the texts are written in many languages, the method extracts equivalent cross-linguistic sentiment features that can be used for statistical analysis or machine learning. We use Open Multilingual WordNet, a large lexicon organizing words into semantic groups, as the knowledge base about word equivalence in more than 200 languages. We experiment with a parallel English-French corpus and find that our senitment measures across the two languages are comparable. The method produces a consistent classification of positive and negative texts in two languages, and sentiment measure values correlate. The paper provides a detailed account of the method and python code, So that it can be applied to other languages, text mining, quantitative communication studies, and management research.•Method to create equivalent sentiment measures in multiple languages•Based on established lexicons and WordNet•Validated for English and French.

A threat to the natural World Heritage site rarely happens alone.

This study contributes an empirical investigation of the likelihood that different external threats to a UNESCO Natural World Heritage Site occur in combination with each other when site characteristics and location are controlled for. For the purpose of the analysis, the World Heritage database and the UNESCO State of Conservation Reports are used and the nine most frequently appearing external threats are identified. These databases include 6852 site-year observations and 3316 threats over the period 1979-2023. The most commonly identified external threats are illegal activities, with eleven percent of all observations and mining with six percent. Transport infrastructure, tourism and visitor pressure are also common threats. Estimation results based on the multivariate Probit (equation system) model demonstrate that there are strong positive correlations between many pairs of the nine external threats. Most apparent are the links between illegal activities and loss of identity, social cohesion, changes in local population and community, water infrastructure (dams) and farming, as well as illegal activities and land conversion. There are also clear links between tourism and infrastructure. This emphasises that the various threats seldom appear in isolation from each other. Results also highlight that the threats have different drivers. Among the determinants, site characteristics and location are the most important ones. The likelihood of threats is highest for Natural Heritage Sites covered by forests or those in marine and coastal areas, Africa as well as the Arab region. It is also possible to identify a general increase in threats over time, although with a diminishing rate of growth towards the period 2015-2019. Contrary to this development and the general downturn in threats during the Covid-19 pandemic period of 2020-2023, pressure from tourism continues to grow. Methodologically, the results emphasize the need for multivariate Probit models when research goes beyond analyses of descriptive statistics and single equation approaches.

Experimental investigation on dynamic deformation characteristics of the overlying strata in backfilling strip mining at different time scales.

The dynamic subsidence disaster caused by underground mining of coal resources is a complex spatiotemporal process, which is a common disaster in mining areas. The backfilling strip mining technology is a green and sustainable coal mining method, which has been commonly used to reduce the subsidence disaster of the overlying strata and protect surface buildings. The transient deformation is the main reason of surface buildings damage; therefore, in this study, the similar material model was used to research dynamic deformation characteristics of the overlying strata in backfilling strip mining at different time scales, and the optical image method was employed to monitor and obtain the movement data of the overlying strata automatically. The data analysis shows that there is a time-scale effect in mining subsidence. The deformation of the overlying strata increases instantaneously at a certain time under the monitoring of small time scale, and this phenomenon gradually disappears as time scales increase. According to the subsidence velocity of small time scale, the subsidence state of the overlying strata can be further divided into the abrupt subsidence state and the gentle subsidence state. This is really significant for promoting the development of the backfilling strip mining technology and preventing the damage of surface buildings.