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Copyright of Linguistic Landscape: An International Journal (LL) is the property of John Benjamins Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the northwestern Himalayas, including Jammu and Kashmir (J&K), frequent landslides pose significant risks, necessitating proactive zoning to mitigate potential damage through effective land-use planning. Fourteen causative and two triggering factors, such as slope, aspect, curvature, relative relief (RR), terrain ruggedness index (TRI), geomorphon, dissection index (Di), lithology, structural tectonic, drainage density (Dd), stream power index (SPI), topographic wetness index (TWI), land use land cover (LULC), road density (Rd), earthquake density (Ed), and rainfall density (Rd), were selected based on terrain conditions to assess landslide susceptibility. Utilizing frequency ratio (FR) and information value (IV) approaches, a comprehensive landslide susceptibility mapping (LSM) study covered 54,922 km2, incorporating 6669 landslide instances. This dataset was split into 70% (4659 landslides) for modeling and 30% (2010 landslides) for validation. The landslide susceptibility map, classified into five categories (very low, low, moderate, high, and very high), delineates varying proportions of the study area. Using the FR approach, these zones cover 12.9% (7063 km2), 25.7% (14,101 km2), 25.6% (14,049 km2), 24.7% (13,586 km2), and 11.1% (6123 km2) of the area, respectively. Meanwhile, employing the IV approach, the coverage percentages are 5.7% (3119 km2), 11.0% (6063 km2), 20.1% (11,057 km2), 38.9% (21,373 km2), and 24.1% (13,310 km2). Validation using receiver operating characteristic curves revealed high correlations for both FR (AUC: 0.809) and IV (AUC: 0.778) models, indicating their effectiveness. The FR model, characterized by simplicity and higher accuracy, outperformed the IV model, offering valuable insights for local, regional, and governments in land-use planning, disaster prevention, and mitigation efforts. [ABSTRACT FROM AUTHOR]

Objective Machine learning has been widely applied in the fields of collapse, landslide and debris flow susceptibility analysis. The selection of nonhazard samples is a key issue in landslide susceptibility analysis. Traditional random sampling and manual labelling methods may involve randomness and subjectivity. Methods In view of the potential randomness and representativeness of noncollapse samples, this paper considered soil collapse susceptibility evaluation a positive-unlabelled (PU) learning problem and proposes a two-step convolutional neural network framework (ISpy-CNN) that combines an information value model and the Spy technique. First, 15 collapse-related factors were selected for modelling based on the geomorphological, geological, hydrological, and artificial environmental conditions of the study area. Low-information-value samples that were able to map the distribution structure of noncollapsing samples were screened by the information value model. Then, through the Spy technique and training the CNN model, negative samples with high confidence were identified from low-information-value samples that were classified as noncollapsed samples. Finally, based on the framework and traditional random sampling, we used support vector machine (SVM) and random forest (RF) models to compare and verify the reliability, prediction accuracy and data sensitivity of the proposed learning framework and other models. Results The results illustrate that the proposed ISpy-CNN method can improve the accuracy, F1 value, sensitivity and specificity on the validation set by 6.82%, 6.82%, 6.82%, 8.23%, respectively compared to random sampling and 2.86%, 2.89%, 2.86%, 2.31%, respectively compared to the traditional Spy technique. The prediction accuracy of step 2 in PU learning using the CNN model is higher than that of the RF and SVM models. The sample set screened by the ISpy-CNN framework exhibited greater stability, prediction accuracy and growth rate than those screened by the traditional Spy technique by adding the same number of training samples. Conclusion The ISpy-CNN framework proposed in this paper can better assist in the selection of nonhazard samples and real collapse spatial distribution maps, and the results of the framework are more consistent with the actual collapse distributions.

This study examines how price sensitivity parameters affect customer lifetime value in the luxury business. Quality, position, information, time, and customer lifetime were examined as price sensitivity factors followed by a conceptual model and research hypotheses were produced based on previous studies. Secondary analysis and in-depth interviews with industry specialists and customers. A representative sample of 232 A class consumers was included in a survey that was given to A-class customers in Egypt. A new level of price sensitivity known as ‘quality positioning value’ was found through the use of factor analysis. Multiple discriminant analysis was performed to validate the hypotheses and Cronbach’s alpha was utilised to assess the reliability of the data. This analysis sheds light on the effect of price sensitivity on customer lifetime value in the luxury industry.

The historical prevalence of banking crises and their profound impact on global economies underscores the imperative for policy makers to refine their crisis forecasting frameworks. Against this backdrop, the present study endeavors to predict potential banking crises in India by leveraging a spectrum of artificial intelligence and machine learning techniques (AI-ML). These techniques encompass logistic regression, random forest, naïve Bayes, gradient boosting, support vector machine, neural networks, K-nearest neighbors, and decision trees. Initially, a banking fragility index was constructed utilizing monthly banking data spanning 2002 to 2023, demarcating the periods of crisis and stability. Subsequently, an extensive array of early warning indicators (EWIs) encompassing asset prices, macroeconomic factors, external influences, and credit-related variables were employed to forecast crisis periods. Our findings reveal that AI-ML models exhibit reasonable accuracy in predicting banking crises. Moreover, advanced model performance metrics highlight neural networks and random forest models as particularly effective in crisis prediction, surpassing other methodologies. Notably, among the EWIs, variables related to credit, interest rates, and liquidity emerge as possessing relatively higher information value in discerning fragilities within the Indian banking system. Importantly, the methodological framework presented herein can be extrapolated for banking crisis prediction in other economies. [ABSTRACT FROM AUTHOR]

Landslide susceptibility zonation is a widely studied method for assessing the likelihood of landslides in specific areas. This study focuses on the Ranikhola watershed in the Sikkim Himalaya and utilizes the Frequency Ratio (FR) and Modified Information Value (MIV) methods to analyse landslide susceptibility. To enhance the susceptibility mapping a novel approach for the FR and MIV is introduced where the factor classes of higher importance were utilized. The study further evaluates a methodology that incorporates weighted ranking of landslide conditioning factor classes using FR and MIV indexes to generate landslide susceptibility maps (LSM). The landslide inventory comprises 124 landslides identified through satellite imagery from Q-GIS quick maps, ESRI base map, Google Earth, and Sentinel 2 A & B. Sixteen conditioning factors are considered, including elevation, slope angle, aspect, curvature, drainage characteristics, vegetation index, geology, soil type, rainfall, road density, and land use. The LSI and LSM are derived from these factors. The LSM created using traditional FR and MIV methods show that 9.55% and 5.96% of the watershed area fall within the High Susceptibility Zone (HSZ) and Very High Susceptibility Zone (VHSZ), respectively. However, the novel approach reveals that 11.54% and 10.29% of the study area fall within the HSZ and VHSZ. The weighted ranking method indicates that 16.22% of the Ranikhola watershed area is within the HSZ and VHSZ. The models are evaluated using the area under the receiver operating characteristic curve (AUC), with FR and MIV methods producing AUC values of 0.77 and 0.68, respectively. The new approach improves the AUC of the MIV method to 0.76, while the FR method remains relatively unchanged. The weighting method outperforms other FR and MIV methods, with an AUC of 0.90. Correlation analysis of the condition factors suggests that profile curvature, slope, stream power index, and topographic wetness index are the most influential factors, positively impacting each other and contributing to higher landslide susceptibility. The study emphasizes the importance of incorporating weighted ranking of landslide conditioning factor classes to create LSM, rather than relying on the total landslide susceptibility index (LSI) of factors. The findings provide valuable data for future large-scale investigations and efforts to enhance hazard preparedness in the Ranikhola watershed. [ABSTRACT FROM AUTHOR]

This study aimed to locate areas along the Debretabor-Alember route segment in northern Ethiopia that are susceptible to landslides. Geospatial tools, specifically frequency ratios (FR) and information values (IV), were used to develop landslide susceptibility maps (LSMs). A comprehensive on-site investigation and analysis of Google Earth imagery were conducted, resulting in the detection and analysis of 89 landslides, including current and historical events. The dataset used for validation comprised 78% of the previously documented landslides, whereas the remaining 22% was used for training. Several factors were considered in this study to determine landslide susceptibility, including "slope, aspect, curvature, elevation, lithology, distance from streams, land use and cover, precipitation, normalized difference vegetation index (NDVI)", and the FR and IV models. Based on the results obtained using the FR approach, specific areas exhibited different levels of susceptibility, ranging from very low to moderately high, medium, high, and very high. These areas covered a total of 18.4 km2 (19.9%), 18.9 km2 (20.5%), 19.7 km2 (20.3%), 17.7 km2 (20%), and 17.7 km2 (19%), respectively. The LSMs generated by the IV model indicated multiple susceptibility classes in the study area, varying from very low to very high. These maps revealed that 18.4 km2 (19.8%), 18.8 km2 (20%), 18.9 km2 (19.5%), 18.8 km2 (20.5%), and 18.3 km2 (19.8%) of the area fell into these susceptibility classes. The landslide density indicator method was employed to validate the LSMs. The FR and IV models demonstrated that a significant proportion of confirmed past and current landslide records (72.16% and 73.86%, respectively) occurred in regions with a high or very high susceptibility to landslides. Overall, the IV model, which utilized latent variable structural modeling (LSM) in the independent variable model, outperformed the fixed effects regression model (FR).

The study presents a novel approach designed to detect time-continuous states in time-series data, called the State-Detecting Algorithm (SDA). The SDA operates on unlabeled data and detects optimal change-points among intrinsic functional states in time-series data based on an ensemble of Ward's hierarchical clustering with time-connectivity constraint. The algorithm chooses the best number of states and optimal state boundaries, maximizing clustering quality metrics. We also introduce a series of methods to estimate the performance and confidence of the SDA when the ground truth annotation is unavailable. These include information value analysis, paired statistical tests, and predictive modeling analysis. The SDA was validated on EEG recordings of Guhyasamaja meditation practice with a strict staged protocol performed by three experienced Buddhist practitioners in an ecological setup. The SDA used neurophysiological descriptors as inputs, including PSD, power indices, coherence, and PLV. Post-hoc analysis of the obtained EEG states revealed significant differences compared to the baseline and neighboring states. The SDA was found to be stable with respect to state order organization and showed poor clustering quality metrics and no statistical significance between states when applied to randomly shuffled epochs (i.e., surrogate subject data used as controls). The SDA can be considered a general data-driven approach that detects hidden functional states associated with the mental processes evolving during meditation or other ongoing mental and cognitive processes. [ABSTRACT FROM AUTHOR]

Landslides are a natural threat that poses a severe risk to human life and the environment. In the Kumaon mountains region in Uttarakhand (India), Nainital is among the most vulnerable areas prone to landslides inflicting harm to livelihood and civilization due to frequent landslides. Developing a landslide susceptibility map (LSM) in this Nainital area will help alleviate the probability of landslide occurrence. GIS and statistical-based approaches like the certainty factor (CF), information value (IV), frequency ratio (FR) and logistic regression (LR) are used for the assessment of LSM. The landslide inventories were prepared using topography, satellite imagery, lithology, slope, aspect, curvature, soil, land use and land cover, geomorphology, drainage density and lineament density to construct the geodatabase of the elements affecting landslides. Furthermore, the receiver operating characteristic (ROC) curve was used to check the accuracy of the predicting model. The results for the area under the curves (AUCs) were 87.8% for logistic regression, 87.6% for certainty factor, 87.4% for information value and 84.8% for frequency ratio, which indicates satisfactory accuracy in landslide susceptibility mapping. The present study perfectly combines GIS and statistical approaches for mapping landslide susceptibility zonation. Regional land use planners and natural disaster management will benefit from the proposed framework for landslide susceptibility maps. [ABSTRACT FROM AUTHOR]

Landslide susceptibility zoning is necessary for landslide risk management. This study aims to conduct the landslide susceptibility evaluation based on a model coupled with information value (IV) and logistic regression (LR) for Badong County in Hubei Province, China. Through the screening of landslide predisposing factors based on correlation analysis, a spatial database including 11 landslide factors and 588 historical landslides was constructed in ArcGIS. The IV, LR and their coupled model were then developed. To validate the accuracy of the three models, the receiver operating characteristic curves (ROC) and the landslide density curves were correspondingly created. The results showed that the areas under the receiver operating characteristic curve (AUCs) of the three models were 0.758, 0.786 and 0.818, respectively. Moreover, the landslide density increased exponentially with the landslide susceptibility, but the coupled model exhibited a higher growth rate among the three models, indicating good performance of the proposed model in landslide susceptibility evaluation. The landslide susceptibility map generated by the coupled model demonstrated that the high and very high landslide susceptibility area mainly concentrated along rivers and roads. Furthermore, by counting the landslide numbers and analyzing the landslide susceptibility within each town in Badong County, it was discovered that Yanduhe, Xinling, Dongrangkou and Guandukou were the main landslide-prone areas. This research will contribute to landslide prevention and mitigation and serve as a reference for other areas. [ABSTRACT FROM AUTHOR]

The frequency of mass wasting events in Kashmir Himalaya has increased manifold leading to the degeneration of its paleoenvironment. The primary aim of the current study is to demonstrate a comparative evaluation of three distinct statistical models which include frequency ratio (FR), information value (Inv), and analytical hierarchy process (AHP), for developing landslide susceptible map (LSM) in and around Gool area of Ramban district in Jammu and Kashmir union territory, India. With total of 365 spotted landslides, 11 causative factors were evaluated for generating LSM which include slope, aspect, curvature, drainage density, relative relief, distance to fault, distance to road, stream power index, land use land cover, topographic wetness index, and lithology. Four of the eleven analyzed factors-slope, relative relief, drainage density, and distance to road play an influential role in landslide distribution. The sensitivity analysis of the receiver operator curve (ROC) is used to estimate the accuracy of the models. The ROC results revealed an accuracy of 0.80, 0.80, and 0.74 for Inv, FR, and AHP respectively. The accuracy and efficiency findings suggest that all models can provide a credible landslide susceptibility zonation for the region; however, the FR and Inv models are somewhat more accurate than AHP model. [ABSTRACT FROM AUTHOR]

This study aimed to locate areas along the Debretabor-Alember route segment in northern Ethiopia that are susceptible to landslides. Geospatial tools, specifically frequency ratios (FR) and information values (IV), were used to develop landslide susceptibility maps (LSMs). A comprehensive onsite investigation and analysis of Google Earth imagery were conducted, resulting in the detection and analysis of 89 landslides, including current and historical events. The dataset used for validation comprised 78% of the previously documented landslides, whereas the remaining 22% was used for training. Several factors were considered in this study to determine landslide susceptibility, including "slope, aspect, curvature, elevation, lithology, distance from streams, land use and cover, precipitation, normalized difference vegetation index (NDVI)", and the FR and IV models. Based on the results obtained using the FR approach, specific areas exhibited different levels of susceptibility, ranging from very low to moderately high, medium, high, and very high. These areas covered a total of 18.4 km² (19.9%), 18.9 km² (20.5%), 19.7 km² (20.3%), 17.7 km² (20%), and 17.7 km² (19%), respectively. The LSMs generated by the IV model indicated multiple susceptibility classes in the study area, varying from very low to very high. These maps revealed that 18.4 km² (19.8%), 18.8 km² (20%), 18.9 km² (19.5%), 18.8 km² (20.5%), and 18.3 km² (19.8%) of the area fell into these susceptibility classes. The landslide density indicator method was employed to validate the LSMs. The FR and IV models demonstrated that a significant proportion of confirmed past and current landslide records (72.16% and 73.86%, respectively) occurred in regions with a high or very high susceptibility to landslides. Overall, the IV model, which utilized latent variable structural modeling (LSM) in the independent variable model, outperformed the fixed effects regression model (FR). [ABSTRACT FROM AUTHOR]

In practice, selling goods through a powerful retailer such as Wal-Mart enables the supplier to access the retailer's ERP for accurate demand information (e.g., Wal-Mart's Retail Link). However, in the recent years, we observe the suppliers are suffering from longer and longer average account period when they contract with powerful retailers. Therefore, whether partnering with a powerful retailer at the cost of a longer account period becomes the supplier's strategic decision. In this paper, we formulate the supplier's tradeoffs among the information advantage, payment disadvantage, and channel competition when it makes retailing decisions. We study the supplier's two representative strategies: (1) relying on a small retailer that does not accumulate much information but can settle accounts immediately (referred to as Real-time Payment Retailing) or (2) relying on a powerful retailer that shares accurate demand information but incurs deferred payment (referred to as Deferred Payment Retailing). We built game-theoretical models and found that, interestingly, the supplier will prefer Deferred Payment Retailing when the supplier's cash opportunity cost is high. We identify three interactive forces, namely, the pricing power effect, the demand size effect, and the information value, to interpret the rationality of the supplier's preferences over Real-time and Deferred Payment Retailing strategies. [ABSTRACT FROM AUTHOR]

We conducted a questionnaire survey on heritage site managers to obtain the relationship between post disaster recovery and reconstruction strategies, protected area planning and value objects, and used the information model method to analyze and show the degree difference of this relationship. The research shows that i) cognitive correlation has been built between restoration strategies and all value objects, and the correlative identification complexities are different for different specific strategies. The specific strategy for “scenic spot restoration and industrial development” has the highest level of complexity of correlation identification, ii) the degrees of uncertainty for the value objects to be identified in the restoration strategies are different. The Tibetan villages are identified as the objects with the highest certainty and the lowest fluctuation, iii) the restoration strategies have responded the overall orders of the value object identification in the protected areas. The restoration strategy has more uncertainty and complexity fluctuation than identification of protected areas. Finally, the paper puts forward suggestions for sustainable restoration and development of the heritage sites, including strengthening the correlation between village evolution and heritage values, restoring the systematization of strategies, and planning the connection with overall value objects.

Despite the current relevance of social media influencers in brand communication strategies, questions remain about the factors that determine their influential power and how this power affects follower behaviour. This research examines the role of emotional attachment and perceived information value in the process of influence that can lead followers to manifest behavioural intentions toward the brands endorsed by influencers. The results show that both factors act as determinants of followers' perceived influence, which in turn predicts followers' positive word-of-mouth (WOM) about recommended brands and purchase intention. In fact, perceived influence plays a mediating role in these relationships. Positive WOM and purchase intention are also significantly related. The findings contribute to a deeper understanding of the nature and effects of the persuasive power of social media influencers. Key implications for researchers and practitioners are discussed. [ABSTRACT FROM AUTHOR]

Kenya is an important fulcrum of China's Belt and Road initiative in east Africa. However, due to its plateau rift terrain and aboriginal rain and drought season, geological disasters occur frequently in Kenya. The study used historical landslide data in Kenya as samples and selected several evaluation indexes, including elevation, slope, aspect, landform, plane curvature, soil type, annual average rainfall, stream power index, terrain witness index, and land use type. The landslide risk in Kenya was evaluated based on the information value model (IV), logistic regression model (LR), and extreme learning machine model (ELM), with the ELM model considering SIG, SIN, and HARDLIM functions as activation functions for discussion. The main findings are as follows: (1) The high-risk and above-grade areas of landslide disasters in Kenya are mainly concentrated in the plateau and plateau-rift transition zone in the southwest. (2) The ROC curve was used to evaluate the accuracy of the models, and the AUC values of the 0.977(IV), 0.965(LR), 0.859(ELM-SIG), 0.900(ELM-SIN), and 0.941(ELM-HARDLIM) models illustrate their validity. (3) Considering the PR curve results comprehensively, the recall rate and precision rate of the LR model are at a high level, marking it better than other models. (4) Nairobi, Central, Nyanza and Western provinces in Kenya account for a significant proportion of the high-risk and above-grade areas of landslide disasters.

To study the susceptibility of debris flow in Kangding City, the study area was divided into 421 watershed units. Spatial analysis tools in ArcGIS software and SPSS software were used to analyze the internal superposition of evaluation indicators and the correlation between evaluation indicators and debris flows disasters. By screening out the evaluation factors with a high degree of overlap and poor correlation, eight evaluation factors were selected for debris flow susceptibility assessment. These included watershed unit area, melton rate, form factor ratio, collapse and landslides density of catchment, average fractional vegetation cover of catchment, road density of catchment, average stream power index of catchment, and average rainfall during the multi-year flood season. The susceptibility of debris flow was quantitatively evaluated by combining the information value model and the entropy method. The weights of the evaluation indicators were quantitatively determined by the entropy method, and the evaluation factor weighted information quantity value was calculated. Based on this, the debris flow susceptibility in Kangding City was divided into four grades: extremely high, high, medium and low. The results of debris flow susceptibility assessment were tested using the frequency ratio model and the Receiver-Operating Characteristic (ROC) curve, with an AUC curve of 0.842, indicating high accuracy of the evaluation model.

The study presents a novel approach designed to detect time-continuous states in time-series data, called the State-Detecting Algorithm (SDA). The SDA operates on unlabeled data and detects optimal change-points among intrinsic functional states in time-series data based on an ensemble of Ward's hierarchical clustering with time-connectivity constraint. The algorithm chooses the best number of states and optimal state boundaries, maximizing clustering quality metrics. We also introduce a series of methods to estimate the performance and confidence of the SDA when the ground truth annotation is unavailable. These include information value analysis, paired statistical tests, and predictive modeling analysis. The SDA was validated on EEG recordings of Guhyasamaja meditation practice with a strict staged protocol performed by three experienced Buddhist practitioners in an ecological setup. The SDA used neurophysiological descriptors as inputs, including PSD, power indices, coherence, and PLV. Post-hoc analysis of the obtained EEG states revealed significant differences compared to the baseline and neighboring states. The SDA was found to be stable with respect to state order organization and showed poor clustering quality metrics and no statistical significance between states when applied to randomly shuffled epochs (i.e., surrogate subject data used as controls). The SDA can be considered a general data-driven approach that detects hidden functional states associated with the mental processes evolving during meditation or other ongoing mental and cognitive processes.

In a large ancient landslide, approximately 240,000 m3 of sediments were reactivated, posing a grave threat to the safety of iron ore stopes. To trace the deformation and evolution history of reactivated Landslide, we conducted geological surveys and combined real-time monitoring equipment to analyze the landslide data since 1986 and the deformation status of the reactivated Landslide. A multi-factor comprehensive landslide monitoring method and an Newton force early warning system (NFEWS) were established, focusing on underground stress, surface deformation information and landslide stability. Furthermore, we developed a four-level early warning grading standard, employing surface cracks and changes in underground stress thresholds as early warning indicators. This standard adds expert assessment to avoid false alarms and realize real-time dynamics of mining landslides during excavation and transportation. Through the case study and analysis of Nanfen open-pit mine, the NFEWS system offers valuable insights and solution for early warning of landslides in analogous open-pit mines. Finally, the evaluation index system of landslide hazard susceptibility was established by selecting the Newton force influence factor. A landslide susceptibility zoning map is constructed using the information value model. The rationality and accuracy are assessed from three perspectives: frequency ratio, landslide hazard point density, and receiver operating characteristic (ROC) curve. The improved Newton force landslide early warning system provides a good reference for the analysis and monitoring of the creep landslide evolution process. [ABSTRACT FROM AUTHOR]

Copyright of Modern Economic Science is the property of Modern Economic Science Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Under COVID-19 emergency decrees, countries imposed freedom-restricting measures that health experts endorsed to contain the disease. There has been debates about whether the pandemic has led to the backsliding of democratic standards and the promotion of illiberal and authoritarian practices. This study conducted a survey in Hong Kong, a non-democracy. At the height of the COVID-19 pandemic, the government postponed an election widely expected to be won by the opposition. This study explores whether health experts' opinions could affect public support for postponement of a regular election and government trustworthiness. It finds that neither health experts' affirmation nor negation increased support for the postponement, but rejecting the government mandate reduced government trustworthiness while affirming it did not. The negative opinions thus had asymmetric information value against affirmative opinions in a known-censored environment. This channel operates through democrats in Hong Kong, who are critical citizens of the regime. The strategy of silencing dissent would be cost-effective for preserving political trust while engaging experts to support the mandates appeared unhelpful. This study contributes to understanding the use of experts in influencing political trust within an authoritarian setting. [ABSTRACT FROM AUTHOR]

The use of high-quality information in a warehouse and the operational results achieved through it is not a novel research topic. Numerous studies have already shown that good information enhances competitiveness. The correlation clearly points out that decision-makers, when armed with good information, are capable of making good decisions. However, acquiring good, accurate information poses challenges. While members of an organization tend to favor communication channels from which the accuracy of received information can be verified over time, this is by no means a guarantee of the information's quality. This is because it consists of a multitude of non-reproducible, intuitive decisions. Modeling information as a warehouse resource is, therefore, a challenging task. During such studies, we continually encounter difficulties, as the verifiability of this in a certain - undefined - environment is simply not achievable. My goal is to create a model that can support decision-making in warehousing. [ABSTRACT FROM AUTHOR]

The Nyabarongo basin in Rwanda is subjected to hydrometeorological hazards, particularly floods, which are the most prevailing and devastating. Therefore, understanding flood-controlling factors is so pertinent for the development of scientifically driven flood prevention strategies. This study aimed at exploring a form of pixel-based information value model integrated with remote sensing techniques and geo-information system to assess the probability of flood incidence and geo-visualize prone areas at basin's scale. To do this, a flood inventory was initially generated using 226 past flooded locations, which were split into a 75:25 ratio for model training and validation, respectively. Fourteen flood-controlling factors were selected after a multicollinearity diagnosis. The results unveiled that more than half of the basin's surface area is covered by very high (8.6%) and high (21.5%) to medium (31.8%) probability of flood incidence. This dispersion was mainly influenced by rainfall, proximity to rivers, Land Use/Land Cover, elevation, and SPI which influence the basin's hydrological behavior. The evaluated accuracy of the applied model using the Area Under Curve of the Receiver Operating Characteristics (AUC–ROC) highlighted a commendable and accurate performance of 0.883 and 0.828 for success and prediction rate, respectively. The study's findings provide a scientifically driven reference for flood mitigation plans and act as a benchmark for decision-making and policy updates regarding flood risk management toward the Nyabarongo basin and other basins with similar characteristics nationally or regionally. [ABSTRACT FROM AUTHOR]

Data sharing to the multiple organizations are essential for analysis in many situations. The shared data contains the individual's private and sensitive information and results in privacy breach. To overcome the privacy challenges, privacy preserving data mining (PPDM) has progressed as a solution. This work addresses the problem of PPDM by proposing statistical transformation with intuitionistic fuzzy (STIF) algorithm for data perturbation. The STIF algorithm contains statistical methods weight of evidence, information value and intuitionistic fuzzy Gaussian membership function. The STIF algorithm is applied on three benchmark datasets adult income, bank marketing and lung cancer. The classifier models decision tree, random forest, extreme gradient boost and support vector machines are used for accuracy and performance analysis. The results show that the STIF algorithm achieves 99% of accuracy for adult income dataset and 100% accuracy for both bank marketing and lung cancer datasets. Further, the results highlights that the STIF algorithm outperforms in data perturbation capacity and privacy preserving capacity than the state-of-art algorithms without any information loss on both numerical and categorical data. [ABSTRACT FROM AUTHOR]

The article deals with the complex groups of archive documents, formed during the activities of the governor’s rule. A review of publications on the problems of source studies of the governor’s administration in the post reformation period is given. The author describes the type and quantity of documents, their preservation, departmental affiliation, metadata used for search, and information value. The author reveals lexical gaps in the composition of archive fonds, caused by incomplete preservation of documents. The issues of archival heuristics, i.e,. the location and ways of searching for documentary materials, were considered. The pool of documentary sources deposited in the Samara Regional Archive is characterized by systematicity; it allows to study the governor’s rule from different «angles»: staff composition, resource base, functional and informational connections, etc. An analysis of the administrative acts of the governor showed that the head of the region in his administrative function was, first of all, a «dispatcher» and controller of instructions (tasks, orders) of the central authorities. The documents issued by the governor on his own initiative made up a small (4-5%) part of the administrative documentation. The main (type-like) disadvantage of administrative documentation is depersonalization, i.e. small amount of information about the holders (subjects) of power. To minimize it, it is reasonable to use more actively the materials of governor’s correspondence, which is close to the documents of personal origin by information characteristics.

In this study, predictive models that characterize gold potential zones within the Josephine Prospecting Licence (PL) Area of Northwestern Ghana have been created by data-driven methods comprising frequency ratio and information value. These predictive models were evaluated using known locations of gold (Au) occurrence datasets and compared to each other. The mineral prospectivity models (MPMs) of gold occurrence areas within the Josephine PL Area were constructed by determining the spatial correlation between known locations of Au occurrences and eight mineralization related factors. The locations of these known Au occurrences, which characterize regions of anomalously high Au geochemical concentration and regions of previous or ongoing artisanal mining operations were identified by using geographic positioning systems (GPS). Eight mineralization related factors (geoscientific thematic layers) over the entire study area composed of analytic signal, lineament density, uranium-thorium ratio, uranium, potassium-thorium ratio, potassium, reduction-to-equator and geology were used to generate the MPMs. The predictive capacity of each of the MPMs generated was determined by employing the area under the receiver operating characteristics curve (AUC). The AUC score obtained for the predictive models produced based on the information value and the frequency ratio approaches were respectively 0.794 and 0.815. The AUC scores generated indicate that the MPMs produced are good predictive models (with an AUC greater than 0.7) and can therefore assist in narrowing down the highly prospective zones of mineral occurrences within the study area. However, the overall predictive potential of the frequency ratio approach was better than the model produced by the information value approach.

Copyright of Chinese Journal of Geological Hazard & Control is the property of China Institute of Geological Environmental Monitoring (CIGEM) Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper aims to outline the influences of ChatGPT on service individualization and service value co-creation in the travel industry. Data were collected from managers and experts of travel agencies in Turkey. Hypotheses were tested via the independent sample t-tests, enter regression analysis, and the bootstrapping approach. It is found that ChatGPT empowered service individualization and internalizing information, which in turn positively impacts service value co-creation. The study also finds that ChatGPT has a significant moderating impact on the relationship between internalizing information and service individualization as well as on the relationship between service individualization and service value co-creation. [ABSTRACT FROM AUTHOR]

Landslide is a major disaster threatening the safety and orderly production of an open-pit mine, so slope stability evaluation is of great significance to the support and monitoring arrangement. Landslide susceptibility mapping (LSM) was widely used in landslide prediction. The former research focused on the algorisms to improve its accuracy, which is relatively complete and left little room for further improvement. In this paper, new factors, including RQD and numerical simulation (NS), are selected to solve the limitation of traditional LSM on the integrity and stress state of the slope. The RQD value was obtained by machine learning and converted into rasters by the ordinary Kriging interpolation method. The slope stress was calculated by the finite difference method and converted into raster data using a program written by Fish language. Based on the information value (INV) method, gradient boosting decision tree (GDBT) was used as the main algorism to generate the LSM-NS. Finally, because LSM-NS contains landslides that have already occurred and those in high susceptibility due to its stress state, commonly used validation methods such as AUROC could no longer be used. Multiple validation methods were applied, such as stress monitoring and UAV tilt photography. The result indicates that the stress increases with crack generating in the high susceptibility area of LSM-NS, where traditional LSM could not predict. Therefore, the addition of RQD and NS could further improve the accuracy using existing algorism. LSM-NS is recommended as the more suitable model for landslide susceptibility assessment in a small area due to its excellent accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Shallow landslides pose serious threats to human existence and economic development, especially in the Himalayan areas. Landslide susceptibility mapping (LSM) is a proven way for minimizing the hazard and risk of landslides. Modeling as an essential step, various algorithms have been applied to LSM, but no consensus exists on which model is most suitable or best. In this study, information value (IV) and logistic regression (LR) were selected as representatives of the conventional algorithms, categorical boosting (CatBoost), and conventional neural networks (CNN) as the advanced algorithms, for LSM in Yadong County, and their performance was compared. To begin with, 496 historical landslide events were compiled into a landslide inventory map, followed by a list of 11 conditioning factors, forming a data set. Secondly, the data set was randomly divided into two parts, 80% of which was used for modeling and 20% for validation. Finally, the area under the curve (AUC) and statistical metrics were applied to validate and compare the performance of the models. The results showed that the CNN model performed the best (sensitivity = 79.38%, specificity = 91.00%, accuracy = 85.28%, and AUC = 0.908), while the LR model performed the worst (sensitivity = 79.38%, specificity = 76.00%, accuracy = 77.66%, and AUC = 0.838) and the CatBoost model performed better (sensitivity = 76.28%, specificity = 85.00%, accuracy = 80.81%, and AUC = 0.893). Moreover, the LSM constructed by the CNN model did a more reasonable prediction of the distribution of susceptible areas. As for feature selection, a more detailed analysis of conditioning factors was conducted, but the results were uncertain. The result analyzed by GI may be more reliable but fluctuates with the amount of data. The conclusion reveals that the accuracy of LSM can be further improved with the advancement of algorithms, by determining more representative features, which serve as a more effective guide for land use planning in the study area or other highlands where landslides are frequent. [ABSTRACT FROM AUTHOR]

Information value has been proposed and used as a probabilistic sensitivity measure, the idea being that uncertain parameters having higher information value are precisely those to which an optimal decision is more sensitive. In this paper, we study the notion of information density as a graphical complement to information value analysis, one that augments an information value calculation with associated directions of information gain. We formally examine mathematical details absent from its earlier presentation that guarantee information density exists and is well posed and describe its relationship to alternate measures of information value. We present its application in the context of a realistic case study and discuss the associated insights. [ABSTRACT FROM AUTHOR]

To study the susceptibility of debris flow in Kangding City, the study area was divided into 421 watershed units. Spatial analysis tools in ArcGIS software and SPSS software were used to analyze the internal superposition of evaluation indicators and the correlation between evaluation indicators and debris flows disasters. By screening out the evaluation factors with a high degree of overlap and poor correlation, eight evaluation factors were selected for debris flow susceptibility assessment. These included watershed unit area, melton rate, form factor ratio, collapse and landslides density of catchment, average fractional vegetation cover of catchment, road density of catchment, average stream power index of catchment, and average rainfall during the multi-year flood season. The susceptibility of debris flow was quantitatively evaluated by combining the information value model and the entropy method. The weights of the evaluation indicators were quantitatively determined by the entropy method, and the evaluation factor weighted information quantity value was calculated. Based on this, the debris flow susceptibility in Kangding City was divided into four grades: extremely high, high, medium and low. The results of debris flow susceptibility assessment were tested using the frequency ratio model and the Receiver-Operating Characteristic (ROC) curve, with an AUC curve of 0.842, indicating high accuracy of the evaluation model. [ABSTRACT FROM AUTHOR]

[Objective] The accurate and rapid landslide susceptibility zoning method were studied in order to provide a reference for regional safety monitoring, and provide a scientific basis for the government to control landslide disasters. [Methods] The study was conducted in the Guichi District of Chizhou City, Anhui Province. The coupled model of gradient boosting decision tree-logistic regression (GBDT-LR) and an information value (I) model was used to determine the evaluation of regional landslide susceptibility. The model learns from the original samples and combines them to generate new simulation samples in order to enhance the fitting ability of the model to evaluate landslide susceptibility. The Borderline-Smote algorithm was used to solve the problem of sample data asymmetry. The slope unit divided by r.slopeunits software was selected as the minimum evaluation unit, and a total of 10 evaluation factors were selected: slope gradient, slope aspect, terrain curvature, profile curvature, plane curvature, topographic wetness index (TWI), topographic relief, normalized difference vegetation index (NDVI), distance from fault, and distance from river. The landslide susceptibility model was evaluated from three aspects: frequency ratio, density of landslide disaster points and hidden danger points, and the receiver operating characteristic (ROC) curve. [Results] The experimental results showed that the frequency ratio of the coupled model I-GBDT-LR was 10%, 13%, and 7% greater than that of the I, LR, and I-LR models, respectively. The density of landslide disaster points and hidden danger points in the high risk area increased by about 9, 11, and 7, respectively, and the ROC accuracy increased by about 10%, 9%, and 5%, respectively. [Conclusion] The accuracy of the coupled model was higher than that of the single model, and the accuracy of the coupled model proposed was higher than that of the I-LR coupled model, which provides an effective and new evaluation method for landslide susceptibility evaluation.

There are many landslide disasters in southern Jiangxi, with a wide area and a small scale, and are characterized by mass and suddenness. More than 90% of landslides are caused by artificial slope cutting. In order to study the applicability of the susceptibility evaluation model for cutting slope landslides caused by cutting slopes in southern Jiangxi, taking Yinkeng Town, Yudu County, Ganzhou City as an example, based on the results of field geological surveys, and using GeoDetectors, the slope, the slope structure, rock formation, fault, road, and vegetation, were selected to carry out landslide susceptibility assessment by using the information value model (I), artificial neural network model (ANN), decision tree model (DT) and Logic regression model respectively. The results show that the AUC values obtained from information value model, artificial neural network model, decision tree model and logistic regression model are 0.800, 0.708, 0.672 and 0.586, respectively. The susceptibility results obtained by the information value model are in good agreement with the actual distribution of landslides in the study area. The specific value of the proportion of landslides in high-prone areas and medium-prone areas exceeds 80%. The information model is more suitable for the landslide susceptibility assessment under cutting slope in southern Jiangxi than the other three models. The assessment results provide a reference for the selection of the assessment model for the geohazard susceptibility in this region.

Generally, preclinical studies of medicines conducted in accordance with national and international regulatory recommendations allow minimising the risks of detecting serious adverse events in patients at the stage of clinical trials. Nevertheless, current analytical trends motivate the development of new prognostic approaches aimed at improving the reliability and accuracy of safety assessments.The aim of this study was to develop and test methodological approaches to comprehensive preclinical assessment of the key risk factors associated with the use of medicinal products in humans and to mathematical prediction of the corresponding benefits and risks.Materials and methods: the study combined information analysis and statistics; it used consolidated preclinical data on the protective properties of fabomotizole and national and international regulatory documents describing the principles and methods of evidence-based medicine, in particular, Bayesian statistics and prognostic research methods.Results: The article presents mathematical approaches developed to confirm the statistical reliability and prognostic significance of the results of preclinical assessment of the safety of medicines, based on Bayesian statistics, in particular, the concepts of weight of evidence (WoE), information value (IV), and normalised density (ND). Depending on the volume of the evaluated data, the WoE can be used to determine the weight of evidence of single variables, as well as entire groups of variables from individual tests or whole test panels, considered in the studies of general toxic effects, reproductive toxicity, genotoxicity and other studies characterising the condition of vital organs when evaluating the safety of medicines.Conclusions: The developed methodology allows evaluating the entire volume of information obtained in preclinical studies of medicinal products. The criteria in the form of WoE and IV in preclinical safety assessment of medicines can be used to estimate the benefits and risks of using medicines, including the products developed specifically for children and pregnant women.