Sampling Strategy Research Articles

Impact of Effort Reward Imbalance and Fatigue on General Self Efficacy of Nurses Working in Tertiary Care Hospitals

The study aims to investigate the impact of effort-reward imbalance and fatigue on the self-efficacy of nurses working in tertiary care hospitals. A purposive sampling strategy was used to recruit participants from both the public and private sector hospitals. The sample consisted of (N=91) participants with a mean age of 27 years. Effort Reward Imbalance (ERI-16 item condensed version) scale, along with the Visual Analog Fatigue Scale and General Self-Efficacy tool was used to measure the relationship among these variables. Results showed that there was a significant relationship between fatigue and self-efficacy of nurses. However, age, effort-reward imbalance, and fatigue were the significant predictors of self-efficacy. Married nurses reflected satisfactory levels of self-efficacy as compared to unmarried nurses. Considering the limited resources for data collection, it is advised to consider a collaboration between hospitals and the research committee. Further, there is a need to conduct a longitudinal study followed by interviews to explore personnel reflection among participants.

Relationship between Social Media Addiction and Urgency in Young Adults

This study intended to inspect the relationship of urgency (positive and negative) and social media addiction (SMA) in young adults. The study also explored the gender differences on SMA and urgency. Two of the proposed hypotheses were put into testing, i.e., i) There is a relationship between SMA and urgency among young adults, and ii) There are gender differences in SMA and urgency. This research used a correlational research design and convenience sampling strategy. A sample of 300 university students was collected that included 150 males and 150 females age ranging between 18-25 years (M= 19.03, SD= 3.29). The data was collected in person through a self-constructed demographic questionnaire, Social Networking Addiction Scale (SNAS), and Urgency, Premeditation, Perseverance, Sensation Seeking, and Positive Urgency (UPPS-P) Impulsive Behavior Scale. The data was analyzed through SPSS software and Pearson correlation along with independent sample t-test were employed to test the hypotheses. The findings revealed that there is a positive relationship between urgency and SMA among young adults, however, no significant gender differences were found in urgency, social media addiction, and their subscales. This study urges young adults to limit social media use to prevent psychological dependence that can harm both productivity and mental well-being. It highlights the tendency of individuals to turn to social media as an escape when overwhelmed by emotional states, often sharing more than they would in a more balanced state of mind. This emotional oversharing can lead to unintended consequences, impacting personal and professional lives.

Mineral Signatures in Oued K’sob River (Algeria): Highlighting Geological Variations and Potential Anthropogenic Impacts

This study examines the mineral composition of sediment samples collected from five distinct zones along the Oued K’sob River in Bordj Bou Arreridj, Algeria using scanning electron microscopy (SEM). The analysis revealed intriguing patterns, the Zone 5 emerged as a distinct outlier, exhibiting significantly lower calcium (29.32% vs. 53.25% in Zone 1) and iron (14.94% vs. 6.31% in Zone 1) content compared to other zones, while displaying a higher silicon content (28.82% vs. 25.27% in Zone 1). This suggests a unique geological origin or specific weathering processes shaping the mineral fingerprint of Zone 5. Interestingly, Zones 1, 2, and 3 displayed a relative consistency in silicon content (around 25%, with pairwise comparisons exceeding p > 0.05), suggesting potential similarities in mineral sources or enrichment mechanisms within their watersheds. However, Zone 4 deviated slightly, showing a higher calcium content (64.18% vs. 29.32% in Zone 5) and lower silicon content (17.99% vs. 28.82% in Zone 5), indicating potential differences in its geological makeup or weathering processes. Furthermore, all zones exhibited trace amounts of elements like lead (Pb) and cadmium (Cd) (ranging from 0.76% to 2.16% for Pb and 0.000% to 0.114% for Cd) (Please recheck these values They are very high). This warrants further investigation to understand their potential anthropogenic origins and impact on the river ecosystem. Further investigations incorporating detailed geological surveys, isotopic analyses, and expanded sampling strategies are recommended to gain a more comprehensive understanding of the complex factors shaping the sediment chemistry within the Oued K’sob River system.

Praising Heroes with Superior Morality in Hausa Songs: A Treatise of Courage

The aim of this article is to examine the role played by Gobir’s courage in protecting the city-state and the significance of superior morality in dousing problems that courage might cause and its importance to spectator(s) as portrayed by the lyrics of the song, Bahagon Gulbi. The song features some qualities of war and is a court song, which consists of courage and manliness and the immemorial character of Gobirawa time. Using the purposive sampling strategy, thirteen stanzas (13) were selected from the song while examining courage and superior morality. The findings revealed that the lyrical structures of the song motivated Gobirawa’s confidence, courage and wisdom of their heroes accordingly. In other words, the singer’s superior morality guides his courage to benefit from wisdom in praising heroes to go to war and defend the city-state and not be afraid of nothing but believe in Allah because one may die even at home. Akwara’s sermon to Gobirawa has faith in dying while protecting the city-state as a noble death and respecting superior authority as superior morality. With this belief in mind, he encourages his master to pay homage to Sarkin Musulmi Muhammadu Tambari (1924-1930) for peace and harmony to reign significantly between the two states. Succinctly, the song celebrates, encourages and appreciates Gobirawa to reinvent while learning from the past.

The Effect of Net Profit Margin (NPM) and Total Assets Turnover (TATO) Return on Assets (ROA) at PT Telekomunikasi Indonesia (Persero) Tbk for the Period of 2019-2023

Finding a correlation between ROA and the independent Factors nett profit margin and total asset turnover is the main objective of this study. The research population consists of twenty quarterly financial reports that span the years 2019–2023, sourced from PT Telekomunikasi Indonesia (Persero) Tbk. The study employs quantitative approaches using data extracted from the company's financial records and a sample strategy based on purposeful sampling. To analyse the data, statisticians employ tools like coefficient of determination, hypothesis testing, multiple linear regression, and correlation. Net Profit Margin (NPM) and Total Assets Turnover (TATO) have a partial and simultaneous effect on Return on Assets (ROA) at the 0.05 level of significance

Death Anxiety and Depression among Nurses: The Mediating Role of Empathy

Background: The COVID-19 pandemic significantly affected life all over the world. Death anxiety has a significant impact on the mental health of individuals, especially in the health care professionals. The fear and apprehension surrounding mortality has significantly affected nurses, leading to various mental health challenges, including depression. While the awareness of death can impose substantial psychological strain, it can also foster positive traits essential for patient care, such as empathy. Objective: The study identifies the relationship between death anxiety, depression and empathy. It also explores the mediating role of empathy in the relationship between death anxiety and depression among nurses. Method: A cross-sectional research design and a purposive sampling strategy were used to collect data from nurses (N=200) working at different government and private hospitals in Lahore, Pakistan, during the COVID-19 pandemic. The participants' ages ranged from 23 to 52. The Siddiqui-Shah Depression Scale, Urdu-translated versions of the Death Anxiety Scale, and Jefferson Scale of Empathy were administered to nurses. Results: The results reveal a positive relation between death anxiety with depression (r= .43**) and empathy (r = .31**). Death anxiety and empathy were found to be significant predictors of depression. Furthermore, empathy was found to be a significant mediator between death anxiety and depression. Conclusion: Based on the findings, clinical psychologists may devise various interventions/management plans to manage death anxiety and depression among nurses.

Temporal and spatial variations in the effect-based ecotoxicological assessment of streams

BackgroundWater bodies are affected by chemical contamination, including micropollutants, which is not fully captured by conventional chemical monitoring methods. The inclusion of integrative, effect-based in vivo and in vitro methods in standardized assessment procedures offers the possibility of bridging discrepancies between chemical and biological assessments and has already been proposed in several studies. However, there is a need to develop a comparable ecotoxicological assessment system for surface waters as for chemical and ecological status. This study aims to contribute to this discourse by investigating the temporal and spatial variation of ecotoxicological effects by assessing water grab samples of 15 different sites in central Germany over the course of 1 year using different in vitro assays.ResultsThe level of measured estrogenicity and anti-estrogenicity varied between the four measurement campaigns, while baseline toxicity, dioxin-like effects and mutagenicity showed relatively constant detectable effects over the study period. The impact of conventionally treated wastewater appeared to be one of the strongest influencing stressors, as direct comparisons of ecotoxicity upstream and downstream of wastewater treatment plant dischargers showed a significant increase for most of the conducted bioassays. Comparison of the measured estrogenicity with proposed threshold values showed effects within ecotoxicologically relevant ranges.ConclusionsBioassays record ecotoxicological effects on the basis of specific modes of action, allowing whole groups of substances to be identified as pollutants. Recording ecotoxicological status in this way is a useful complement to water assessment tools and can contribute to successful water management. Although most of the assays in this study were very consistent in detecting strong anthropogenic influences, possible temporal variations of individual assays should be taken into account when planning sampling strategies to improve the comparability of results.

Optimization of sampling design for soil total organic carbon assessment in the precision agriculture framework: Impact of different variogram models and potentiality of ground penetrating radar (GPR) covariate information

Assessing soil organic carbon (SOC) at the field scale is crucial for efficient environmental and agronomic management, especially within the precision agriculture framework. This enables the implementation of crop and soil management strategies to enhance soil quality, increase carbon sequestration, and improve crop yields. However, the process of sampling and assessing SOC is resource-intensive, demanding both in time and labour. This aspect is particularly relevant in agronomic research, where the need to assess the spatial correlation structure of the investigated variables requires the collection of large datasets with georeferenced data and often measurements need to be repeated over time as in the case long-term field experiments − LTE. Methods for minimizing information loss while reducing the sampling scheme, such as spatial simulated annealing (SSA), are particularly valuable in the scope of SOC assessment, especially when faced with budget and time/labour constraints. Within the structure of the SSA method, two critical components can be identified: i) the inclusion of highly informative covariates for the primary variable (SOC); ii) the selection of the most appropriate variogram model for spatial variability assessment. Covariates strongly correlated with SOC, such as those obtained from ground penetrating radar (GPR) that can be collected at a higher spatial density compared to SOC data, along with a well-performing model, can significantly enhance the efficiency of the sampling scheme reduction process. We conducted a study using data from an agronomic field experiment, which included 71 georeferenced sampling locations and, through an iterative downsizing process utilizing SSA, we progressively reduced the number of sampling points by removing 10, 15, and 20 observations. The sampling scheme was refined according to two distinct variogram models: the spherical and Gaussian-Matérn models, both for the primary variable (SOC) and the covariate GPR variable. This process allowed us to identify the optimal variogram model, which has a key role in maximizing the reduction of redundant points while preserving those with valuable information, and to assess the role of the covariate variable both in improving the optimization of the sampling scheme for SOC and in replacing the primary variable to optimize the sampling scheme. Finally, to assess the impact of sampling scheme reduction, a validation process was performed by estimating the dropped points by means of the remaining points using ordinary kriging and regression kriging and analysing the accuracy of such estimation. Our analysis demonstrated that it was possible to reduce the original sampling scheme by approximately 20%, equivalent to eliminating 15 sampling points out of 71, without compromising its predictive capability.

Neural network-based reconstruction of steady-state temperature systems with unknown material composition

Effective thermal management is crucial for the performance and longevity of devices such as computer chips and batteries. A fundamental challenge in this field is accurately determining temperature distributions, which are often limited by incomplete observations. We introduce a neural-network-based approach to overcome these challenges. Specifically, a physics-informed fully convolutional auto-encoder coupled with a preceding spatial propagator network module is used to reconstruct complete temperature fields from partial observations, without requiring additional information about the spatial distribution of materials with varying thermal properties. This paper details the development and validation of our model, demonstrating its effectiveness in virtual sensing setups for both simulated and real-world thermal systems. The reconstruction capabilities for different sampling strategies are investigated. The most challenging of which is the “grid-edge” sampling where only discrete temperature sensors on the periphery of the systems are taken as input, amounting to a sampling ratio as low as 3.12%. The presented method is able to reconstruct the full temperature field with a relative average error of 1.1%. The NN-based approach outperforms the standard Kriging method in predicting the maximum temperature in a system, and proves more robust when the available measurements contain little information.

Drug-target interaction prediction with collaborative contrastive learning and adaptive self-paced sampling strategy

BackgroundDrug-target interaction (DTI) prediction plays a pivotal role in drug discovery and drug repositioning, enabling the identification of potential drug candidates. However, most previous approaches often do not fully utilize the complementary relationships among multiple biological networks, which limits their ability to learn more consistent representations. Additionally, the selection strategy of negative samples significantly affects the performance of contrastive learning methods.ResultsIn this study, we propose CCL-ASPS, a novel deep learning model that incorporates Collaborative Contrastive Learning (CCL) and Adaptive Self-Paced Sampling strategy (ASPS) for drug-target interaction prediction. CCL-ASPS leverages multiple networks to learn the fused embeddings of drugs and targets, ensuring their consistent representations from individual networks. Furthermore, ASPS dynamically selects more informative negative sample pairs for contrastive learning. Experiment results on the established dataset demonstrate that CCL-ASPS achieves significant improvements compared to current state-of-the-art methods. Moreover, ablation experiments confirm the contributions of the proposed CCL and ASPS strategies.ConclusionsBy integrating Collaborative Contrastive Learning and Adaptive Self-Paced Sampling, the proposed CCL-ASPS effectively addresses the limitations of previous methods. This study demonstrates that CCL-ASPS achieves notable improvements in DTI predictive performance compared to current state-of-the-art approaches. The case study and cold start experiments further illustrate the capability of CCL-ASPS to effectively predict previously unknown DTI, potentially facilitating the identification of new drug-target interactions.

A unique interplay of access and selection shapes peritoneal metastasis evolution in colorectal cancer.

Whether metastasis in humans can be accomplished by most primary tumor cells or requires the evolution of a specialized trait remains an open question. To evaluate whether metastases are founded by non-random subsets of primary tumor lineages requires extensive, difficult-to-implement sampling. We have realized an unusually dense multi-region sampling scheme in a cohort of 26 colorectal cancer patients with peritoneal metastases, reconstructing the evolutionary history of on average 28.8 tissue samples per patient with a microsatellite-based fingerprinting assay. To assess metastatic randomness, we evaluate inter- and intra-metastatic heterogeneity relative to the primary tumor and find that peritoneal metastases are more heterogeneous than liver metastases but less diverse than locoregional metastases. Metachronous peritoneal metastases exposed to systemic chemotherapy show significantly higher inter-lesion diversity than synchronous, untreated metastases. Projection of peritoneal metastasis origins onto a spatial map of the primary tumor reveals that they often originate at the deep-invading edge, in contrast to liver and lymph node metastases which exhibit no such preference. Furthermore, peritoneal metastases typically do not share a common subclonal origin with distant metastases in more remote organs. Synthesizing these insights into an evolutionary portrait of peritoneal metastases, we conclude that the peritoneal-metastatic process imposes milder selective pressures onto disseminating cancer cells than the liver-metastatic process. Peritoneal metastases' unique evolutionary features have potential implications for staging and treatment.

Maximum subspace transferability discriminant analysis: A new cross-domain similarity measure for wind-turbine fault transfer diagnosis

In the field of fault transfer diagnosis, many approaches only focus on the distribution alignment and knowledge transfer between the source domain and target domain. However, most of these approaches ignore the precondition of whether this transfer task is transferable. Current mainstream transferability discrimination methods heavily depend on expert knowledge and are extremely vulnerable to the noise interference and variations in feature scale. This limits their applicability due to the intelligent requirements and complex industrial environment. To address the challenges mentioned previously, this paper introduces a novel cross-domain similarity measure called maximum subspace transferability discriminant analysis (MSTDA) with zero-label prior knowledge. MSTDA is comprised of a maximum subspace representation and a similarity measurement criterion. During the phase of maximum subspace representation, a new kernel-induced Hilbert space is designed to map the low-dimensional original samples into the high-dimensional space to maximize the separability of different faults and then solve the separable intrinsic fault features. Following that, a novel similarity measurement criterion that is resistant to variations in feature scale is developed. This criterion is based on the orthogonal bases of intrinsic feature subspaces. The mini-batch sampling strategy is used to ensure the timeliness of MSTDA. Finally, the experimental results on three cases, particularly in the actual wind turbine dataset, confirm that the proposed MSTDA outperforms other well-known similarity measure methods in terms of transferability evaluation. The related code can be downloaded from https://qinyi-team.github.io/2024/09/Maximum-subspace-transferability-discriminant-analysis.

Evaluating sampling techniques for quantifying Asiatic garden beetle (Maladera formosae) (Coleoptera: Scarabaeidae) infestations in commercial mint.

The Asiatic garden beetle, Maladera formosae Brenske (AGB), has become a significant pest of commercial mint fields in northern Indiana. Larval feeding on mint roots can cause stunted growth and plant death when densities are high. Sampling approaches that provide reliable estimates of larval densities in mint have not been established, leaving farmers without the knowledge necessary to implement integrated pest management (IPM) strategies. To address this knowledge gap, we evaluated strategies for estimating AGB larval densities and plant performance in commercial mint systems. We used 2 sampling methods to collect larval density and plant performance data from 3 mint fields and conducted simulations to optimize sampling intensity (accuracy and precision) and sampling scheme (random vs. systematic) using these data. Additionally, we examined the sensitivity and efficiency of each sampling method. Compared to the cup-cutter method, the quadrat method provided the most accurate and precise estimates of larval density and plant performance, with ≤ 7 samples required per 0.2 ha. Quadrat excavation was also more sensitive, increasing the probability of detecting AGB larvae within a 32 m2 plot by 76.7%, and requiring significantly less time to survey an equivalent volume of soil for AGB larvae. When the quadrat method was employed, random sampling schemes provided below-ground biomass estimates that were significantly closer to the true mean of the sampling area. The results of this research will facilitate the development of IPM decision-making tools for farmers and support future research for AGB and other soil insect pests affecting mint production.

Survey of a class of iterative row-action methods: The Kaczmarz method

AbstractThe Kaczmarz algorithm is an iterative method that solves linear systems of equations. It stands out among iterative algorithms when dealing with large systems for two reasons. First, at each iteration, the Kaczmarz algorithm uses a single equation, resulting in minimal computational work per iteration. Second, solving the entire system may only require the use of a small subset of the equations. These characteristics have attracted significant attention to the Kaczmarz algorithm. Researchers have observed that randomly choosing equations can improve the convergence rate of the algorithm. This insight led to the development of the Randomized Kaczmarz algorithm and, subsequently, several other variations emerged. In this paper, we extensively analyze the native Kaczmarz algorithm and many of its variations using large-scale systems as benchmarks. Through our investigation, we have verified that, for consistent systems, various row sampling schemes can outperform both the original and Randomized Kaczmarz method. Specifically, sampling without replacement and using quasirandom numbers are the fastest techniques. However, for inconsistent systems, the Conjugate Gradient method for Least-Squares problems overcomes all variations of the Kaczmarz method for these types of systems.

Automated High-Throughput Biological Sex Identification from Archeological Human Dental Enamel Using Targeted Proteomics.

Biological sex is key information for archeological and forensic studies, which can be determined by proteomics. However, the lack of a standardized approach for fast and accurate sex identification currently limits the reach of proteomics applications. Here, we introduce a streamlined mass spectrometry (MS)-based workflow for the determination of biological sex using human dental enamel. Our approach builds on a minimally invasive sampling strategy by acid etching, a rapid online liquid chromatography (LC) gradient coupled to a high-resolution parallel reaction monitoring (PRM) assay allowing for a throughput of 200 samples per day (SPD) with high quantitative performance enabling confident identification of both males and females. Additionally, we developed a streamlined data analysis pipeline and integrated it into a Shiny interface for ease of use. The method was first developed and optimized using modern teeth and then validated in an independent set of deciduous teeth of known sex. Finally, the assay was successfully applied to archeological material, enabling the analysis of over 300 individuals. We demonstrate unprecedented performance and scalability, speeding up MS analysis by 10-fold compared to conventional proteomics-based sex identification methods. This work paves the way for large-scale archeological or forensic studies enabling the investigation of entire populations rather than focusing on individual high-profile specimens. Data are available via ProteomeXchange with the identifier PXD049326.

DCE-MRI of the liver with sub-second temporal resolution using GRASP-Pro with navi-stack-of-stars sampling.

Respiratory motion-induced image blurring and artifacts can compromise image quality in dynamic contrast-enhanced MRI (DCE-MRI) of the liver. Despite remarkable advances in respiratory motion detection and compensation in past years, these techniques have not yet seen widespread clinical adoption. The accuracy of image-based motion detection can be especially compromised in the presence of contrast enhancement and/or in situations involving deep and/or irregular breathing patterns. This work proposes a framework that combines GRASP-Pro (Golden-angle RAdial Sparse Parallel MRI with imProved performance) MRI with a new radial sampling scheme called navi-stack-of-stars for free-breathing DCE-MRI of the liver without the need for explicit respiratory motion compensation. A prototype 3D golden-angle radial sequence with a navi-stack-of-stars sampling scheme that intermittently acquires a 2D navigator was implemented. Free-breathing DCE-MRI of the liver was conducted in 24 subjects at 3T including 17 volunteers and 7 patients. GRASP-Pro reconstruction was performed with a temporal resolution of 0.34-0.45 s per volume, whereas standard GRASP reconstruction was performed with a temporal resolution of 15 s per volume. Motion compensation was not performed in all image reconstruction tasks. Liver images in different contrast phases from both GRASP and GRASP-Pro reconstructions were visually scored by two experienced abdominal radiologists for comparison. The nonparametric paired two-tailed Wilcoxon signed-rank test was used to compare image quality scores, and the Cohen's kappa coefficient was calculated to evaluate the inter-reader agreement. GRASP-Pro MRI with sub-second temporal resolution consistently received significantly higher image quality scores (P < 0.05) than standard GRASP MRI throughout all contrast enhancement phases and across all assessment categories. There was a substantial inter-reader agreement for all assessment categories (ranging from 0.67 to 0.89). The proposed technique using GRASP-Pro reconstruction with navi-stack-of-stars sampling holds great promise for free-breathing DCE-MRI of the liver without respiratory motion compensation.

Using removable sensors in structural health monitoring: A Bayesian methodology for attachment-to-attachment uncertainty quantification

Structural responses are vulnerable to perturbations when subjected to variable environmental and operational conditions, thereby contributing to the slow uptake of Structural Health Monitoring (SHM) beyond academic research. More importantly, SHM systems using removable sensors face additional sensing variability due to the inherent uncertainty in sensor reattachment compared to permanently attached sensors. In this study, a novel probabilistic approach is developed for output-only damage detection utilizing ultrasonic guided waves. Experimental uncertainties and sensor attachment factors are simultaneously considered for the first time in a Bayesian context. The uncertainty introduced by reattached sensors is incorporated into the likelihood function in a comprehensive way. Bayesian model class selection using an adaptive sampling scheme is adopted to evaluate the evidence of discrete damage classes. A reference case to an aluminum plate with progressive notches is utilized to demonstrate the significant effects of attachment uncertainty on guided wave signals. The optimal data normalization model for guided wave processing under different attachment states is evaluated using the Mahalanobis distance. The notch length is identified accurately along with the uncertainty, even though the latent adhesive parameters cannot be estimated directly.

Phylogeography and species distribution modeling unveil unnoticed Pliocene diversity: the case of a montane Iberian bush-cricket, Antaxius spinibrachius (Orthoptera: Tettigoniidae)

Abstract The geographic distribution and systematics of many groups of insects are still understudied across large geographic areas of the Iberian Peninsula. This lack of knowledge and the fact that many species have a complex evolutionary history due to the existence of “refugia within refugia” have hindered the taxonomic description of the true biodiversity in the Iberian Peninsula. Here, we discuss the evolutionary history of a flightless Iberian bush-cricket Antaxius spinibrachius (Fischer, 1853) using 1 nuclear and 2 mitochondrial markers. We applied species distribution modeling to design sampling strategies in climatically favorable areas and to detect missing isolated populations or unknown lineages. Following the species distribution modeled using all of the presence records available, we discovered 4 new isolated populations in Sierra Madrona, Serra de São Mamede, Sierra de Montánchez, and Sierra de la Demanda. Phylogenetic analyses recovered 2 major Pliocene lineages with a north–south geographic speciation pattern in the Iberian Peninsula. This north–south vicariant event split the common ancestor of these species on either side of the Tagus Valley, probably associated with the Pliocene climate change and the reorganization of paleobasins of the western Iberian Peninsula during the Miocene–Pliocene. We described the southern Iberian Plateau lineage of Antaxius as a new species of bush-cricket based on phylogenetic and morphological evidence, Antaxius oretanus sp. nov. We assessed the conservation status of the new species as “VU B2ab(iii,v)” under the IUCN criteria.

Unsupervised machine learning with different sampling strategies and topographic factors for distinguishing between landslide source and runout areas to improve landslide inventory production

This study derived 12 topographical and hydrological factors related to landslides from a 10-m digital elevation model. Three unsupervised machine learning algorithms were employed to distinguish between the features of landslide sources and runout areas for Typhoon Morakot. Two sampling strategies were designed in this study. The first strategy involved creating a data set by pairing landslide sources and runout areas on the basis of the size of polygonal areas recorded in the inventory and then exploring the effectiveness of feature separation with k-means++ as the primary method and EM (expectation maximization) and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) as supplementary methods. Because of practical challenges associated with the inability to determine whether remote sensing results correspond to landslide sources or runout areas, the first sampling strategy was used to test the feasibility of the adopted algorithms. In the second sampling strategy, the effectiveness of feature separation was tested by dividing polygon samples into several intervals on the basis of their sizes, thereby verifying the practicability of the adopted algorithms in real-world operations. This study verified that combining unsupervised machine learning algorithms with topographic factors facilitates the effective separation of landslide sources and runout areas, thereby enhancing the quality of landslide inventories and reducing the cost of landslide inventory creation. Moreover, it indicated that meter-scale topographic data yield classification results similar to those obtained from manually created landslide inventories based on centimeter-scale stereo aerial photos. The proposed method effectively balances the cost and quality of landslide inventories.

Does economic globalization promote civil peace in developing countries?

This study questions the widely supported claim that economic globalization benefits developing countries by enhancing their prospects for civil peace. Drawing on ideas from across the social sciences, this study questions the nature of causation between economic globalization and civil peace and how it should be conceptualized and modeled. Instead of assuming a straightforward and static relationship whereby economic globalization directly increases the likelihood of civil peace, a range of alternative hypotheses are considered, including those about reverse and reciprocal causation and about dynamic and path-dependent effects. Using dynamic panel regression and vector autoregression, the study empirically examines how trade openness and foreign direct investment affect civil violence in a sample of all countries classified by the United Nations as developing. The results find positive links between economic globalization and civil peace, as much of the literature predicts, but show that this relationship mainly arises from complex patterns of causation rather than from the pacifying effects of economic globalization. These results are robust to various model specifications and sampling strategies. The study ends by considering whether economic globalization may indirectly promote civil peace by fostering long-term economic development.