Model Assessment Research Articles

Histological and morphometrical evaluation of the urethral wall after bioresorbable stent implantation in male New Zealand White Rabbits: A preliminary study.

The aim of the study was the histological and morphometrical evaluation of the urethral wall at three time points after bioresorbable stent implantation in male New Zealand White Rabbits. The research was performed on 26 male New Zealand White rabbits aged 3-4 months and weighing 2.1-3.0 kg. Two models of bioresorbable sodium alginate-based stents were developed and implanted into the urethral lumen for one (T1), three (T3), and six weeks (T6). Sections of 5 µm thickness were cut from the urethra at intervals of 2 mm. The sliced sections were stained with hematoxylin-eosin (H&E), Van Gieson's (VG), Von Kossa, and Movat-Russell modified pentachrome (MOVAT) staining methods. The study provided valuable information for future models of urethral stents. The first model of the stent failed to fit the requirements due to inadequate mechanical properties. It curled up on itself losing the ability to adhere to the animals' urethra and was bioresorbed three weeks after implantation. The more rigid no. 2 stent was effective in widening the urethral lumen but did not biodegrade during the experiment. A comprehensive assessment of the second model's properties of biosorption and biointegration requires an extended observation of at least 12 months for an in depth morphological analysis. Stent migration is not likely to be caused solely by the mechanical properties of the urethra or urinary flow but mainly by muscle contraction of the organ wall.

Arsenic exposure and measures of glucose tolerance in Bangladeshi adults: A cross-sectional study.

Arsenic has been associated with diabetes and impaired glucose tolerance in many studies, although some reports have shown null findings. We conducted a cross-sectional study among 300 adults in Bangladesh. Participants were randomly selected from a roster of 1800 people who previously participated in studies of arsenic and skin lesions. We measured fasting glucose and insulin levels. We assessed drinking water arsenic concentration using graphite furnace atomic absorption spectrophotometry (GF-AAS) and toenail arsenic concentration using inductively coupled mass spectrometry (ICP-MS). We ran covariant-adjusted, linear regression and spline models to examine associations of arsenic concentrations with the homeostatic model assessment of insulin resistance (HOMA-IR), a marker of insulin resistance, and HOMA of beta-cell function (HOMA-β), a marker of beta-cell function. Among 285 participants with complete data, the median (IQR) arsenic concentration was 4.0 (6.9) μg/g in toenails and 39.0 (188.5) μg/L in drinking water. Arsenic concentrations were not associated with insulin resistance or beta-cell function. HOMA-IR was 0.67% lower and HOMA-β was 0.28% lower per µg/g increment in toenail arsenic, but these effect estimates were small, and confidence intervals crossed the null value. Although arsenic exposure has been associated with diabetes, we found no evidence of an adverse effect on insulin resistance or beta-cell function.

ASsociation Between Lipopolysaccharides (LPS) and homeostatic model assessment of insulin resistance (HOMA-IR) in type 2 diabetes mellitus (T2DM)

ASsociation Between Lipopolysaccharides (LPS) and homeostatic model assessment of insulin resistance (HOMA-IR) in type 2 diabetes mellitus (T2DM)

Comparative Assessment of Machine Learning Models for Predicting Glucose Intolerance Risk

Comparative Assessment of Machine Learning Models for Predicting Glucose Intolerance Risk

Optimizing Manipulative Skills Development: The Odyssey with Score Model Assessment

Optimizing Manipulative Skills Development: The Odyssey with Score Model Assessment

Penerimaan Aplikasi Loklok Menggunakan Metode Technology Acceptance Model (TAM) oleh Masyarakat Jakarta

Nowadays, the development of communication and information technology has brought major chages in various aspect of life, one of them is in entertainment aspects. For example video streaming technology. Video streaming technology has been part of modern society. Loklok is of video streaming technology application which is popular in Indonesia. The purpose of this research is to analyze the acceptance video streaming technology on Loklok application and find out whether it can be generally acceptance by the people of Jakarta. This research was done for loklok application users. Loklok is an application or platform that provides video streaming services with many varieties of genres such Korean dramas, western films and animations. The conceptual model of technology acceptance assessment is based on the Technology Acceptance Model (TAM), which is a research model that analyzes the factors that influence the acceptance of computer technology. The statistical analysis used is the partial least squares structural equation model. This research uses quantitative methods with data collection techniques in the form of filling out online questionnaires through Google Forms with the number of respondents determined by the Slovin formula and sampling techniques using simple random sampling. This study uses PLS-SEM data analys with SmartPLS tools with a sample size of 100. The results of this study indicate that the variables Perceived Usefulness -> Attitude of use, Perceived ease of use -> Attitude of use, Attitude of use - > Behavioral Intention Of Use and Behavioral Intention Of Use -> Actually System Use are positive which means that the LokLok application is well received by the Jakarta Community.

A Probabilistic Statistical Risk Assessment Method for Soil Erosion Using Remote Sensing Data: A Case Study of the Dali River Basin

Soil erosion risk assessment enables the identification of areas requiring priority treatment and avoids wasting human and material resources. The factor scoring method used in existing studies has high subjectivity, and the method of expressing erosion risk according to the soil erosion intensity ignores the random nature of the occurrence of erosion; therefore, neither method accurately reflects the risk of soil erosion. In order to address this issue, this study proposes a soil erosion risk assessment method that integrates the outcome and the probability of occurrence of soil erosion by means of a probabilistic statistical model. Subsequently, experimental research is conducted in the Dali River Basin. On the basis of long time-series data, using mathematical statistics as a tool and drawing on the empirical frequency formula, the probabilistic statistical risk assessment model is combined with the Modified Universal Soil Loss Equation (RUSLE) model to account for the probability of regional soil erosion at different intensity levels in the long time-series, which is combined with the intensity of erosion to carry out soil erosion risk assessment. The results of our study show the following: (1) The central and southwestern regions of the Dali River Basin (DRB) present medium and high levels of soil erosion risk, with the proportion of low-risk areas increasing annually, accounting for 78.97% of the DRB in 2020, while extremely high-risk areas account for only 0.40% of the DRB. (2) The major components impacting soil erosion risk in the DRB, as revealed by the geodetector, are the normalized difference vegetation index (NDVI) and slope, where the interaction between the two dominated the spatial variation in soil erosion risk. (3) Comparing the soil erosion risk and its status in the coming years, the proposed assessment method based on the occurrence probability can reveal the future soil erosion risk better than the traditional assessment method.

Life-Cycle Cost Assessment of the Base-Isolated-Reinforced Concrete Structure

Seismic isolation structures have excellent seismic mitigation effects, but implementing isolation measures may increase the initial cost of the structure. This paper presents a method to calculate the seismic loss and assess the life-cycle cost of isolated structures, which is convenient for owners to make decisions. The life-cycle cost assessment model is divided into two parts: initial cost assessment and seismic loss assessment over life-cycle period. Among them, the assessment method of the isolation layer is proposed, including the cost assessment of isolation bearings and dampers. The assessment method of seismic loss considers direct loss, indirect loss, casualties and integrating casualties into a unified assessment system. Direct loss includes building loss, indirect loss includes rental loss, relocation loss, income loss, personal property loss and business inventory loss. The seismic loss model is associated with seismic damage probability, which is obtained through seismic vulnerability and seismic probability risk assessment. The 8-story base-isolated-reinforced concrete structure illustrates the feasibility of the life-cycle-cost assessment model. The example shows that the initial cost of the isolated structure exceeds that of the nonisolated structure, however, the rental loss, income loss, personal property loss and casualties are much lower than those of nonisolated structures. The income loss of the isolated structure is the maximum proportion in life-cycle cost, and the casualty loss is the minimum proportion in the seismic loss, due to its excellent seismic mitigation effects.

Dynamic land cover and ecosystem service changes in global coastal deltas under future climate scenarios

Coastal deltas, housing 4.5% of the global population, face profound shifts due to climate change and intensified human activities. This study utilizes the Patch-generating Land Use Simulation (PLUS) for detailed land cover dynamics and the Integrated Valuation of Ecosystem Services and Trade-Offs (InVEST) models for ecosystem services assessment, focusing on 46 global coastal deltas under future scenarios (SSP126, SSP245, SSP585). Our findings reveal a 145% increase in urban areas from 1995 to 2015, with expected continuations in urban sprawl. Grassland and barren areas are projected to diminish by up to 15% and 5%, respectively, with urban areas potentially expanding by up to 39% across scenarios. Significant decreases in surface water yield under SSP245 and SSP585 could challenge water sustainability. Moreover, a universal decline in carbon storage necessitates enhanced carbon sequestration strategies. Soil retention is anticipated to decline, leading to increased erosion risks, while habitat quality varies, improving in 29 deltas under SSP126 but worsening in 33 under SSP245 and SSP585. These insights underscore the urgency of strategic, adaptive land use planning to bolster ecosystem services, directly supporting the United Nations' Decade of Ocean Science for Sustainable Development and advancing global sustainable delta management.

Automotive Consumer Loans Risk Assessment Predictive Modeling Using Generative Adversarial Network and Stacked Autoencoder Neural Networks

In this study, we propose a predictive model for automotive consumer loans risk assessment, leveraging Generative Adversarial Networks (GANs) and Stacked Autoencoder Neural Networks (SAEs). We address issues of high dimensionality and sparsity inherent in big data environments and tackle the class imbalance problem using GANs. Feature selection is effectively carried out using SAEs. Experimental results prove the superiority of our approach over traditional neural networks and our model without GANs or SAEs. The proposed model shows significant potential for application in personal credit risk assessment within automotive finance and beyond. Future work is aimed at extending and improving our model and applying it to other domains.

Performance assessment of data-driven models for sky luminance prediction in Indian tropical climate

The precise prediction of sky luminance distribution is inevitable for design of efficient daylighting systems. The lack of measured sky luminance data in developing countries like India makes the current standard design sky models redundant. Most of the designer use Perez sky model for prediction of sky luminance distribution using solar insolation conditions for such locations. Data-driven models using machine learning can be a possible solution for prediction of sky luminance distribution in such locations. The present study compares the prediction accuracy of such data-driven models using four different machine learning techniques and compares the results Perez sky model. It becomes clear from the results that the data-driven model does not perform well when the location is different from the training dataset location, and the Perez sky model gives better results for such cases.

Assessing bicycle safety risks using emerging mobile sensing data

The surge in global electric bicycle ownership has exerted immense pressure on bicycle infrastructure. Theoretically, there’s a need to reassess the risk factors associated with multiple bike lane users. Based on this, there’s a practical need to re-evaluate the safety and quality of outdated infrastructure. This paper aims to reconsider risk factors related to bicycle infrastructure safety in the context of electric bicycles sharing lanes with traditional bicycles. Moreover, many countries lack precise spatial data concerning bicycle infrastructure. This study introduces a mobile sensing method based on bicycles, aiming to acquire daytime and nighttime bike lane datasets in a cost-effective, efficient, and large-scale manner. A computer vision-based bicycle risk factor assessment model was established, and the distribution of bicycle safety risk factors was visually analyzed. Research data was collected from a representative 59.5-kilometer bicycle lane area in Beijing. The results confirm the significant impact of the surge in electric bicycles, with electric bike users accounting for 72.1% of cyclists, 32.3% wearing helmets, and 8.4% riding against traffic. During the day, the highest-ranking risk factors include the type of bicycle lanes (half lacking dedicated lanes or being shared), roadside parking, and subpar road conditions. At night, insufficient street lighting are notable concerns. The research methodology is easily replicable and can be extended to new multi-user coexistence cycling environments or countries without bicycle spatial data, offering insights for bicycle safety policies and road design.

Temperature overshoot responses to ambitious forestation in an Earth System Model

Despite the increasing relevance of temperature overshoot and the rather ambitious country pledges on Afforestation/Reforestation globally, the mitigation potential and the Earth system responses to large-scale non-idealized Afforestation/Reforestation patterns under a high overshoot scenario remain elusive. Here, we develop an ambitious Afforestation/Reforestation scenario by harnessing 1259 Integrated Assessment Model scenarios, restoration potential maps, and biodiversity constraints, reaching 595 Mha by 2060 and 935 Mha by 2100. We then force the Max Planck Institute’s Earth System Model with this scenario which yields a reduction of peak temperature by 0.08 oC, end-of-century temperature by 0.2 oC, and overshoot duration by 13 years. Afforestation/Reforestation in the range of country pledges globally could thus constitute a useful mitigation tool in overshoot scenarios in addition to fossil fuel emission reductions, but socio-ecological implications need to be scrutinized to avoid severe side effects.

Geosite assessment as the first step for the development of canyoning activities in North Montenegro

Abstract The objective of the study is to investigate the current state of the canyons in the northern part of Montenegro, their potential for sustainable development, which could be achieved through canyoning as an attractive tourist attraction, and to assess the area’s geotourism potential. The canyons encompassed in this research are the Nevidio Canyon, the Tara Canyon, the Grlja Canyon, and the canyon of the river Ibar. The research was carried out using the modified Geosite Assessment Model, which includes tourists’ opinions on the significance of indicators in the assessment process. The obtained results show that all four canyons possess significant scientific, educational, and aesthetic values for the development of canyoning tourism. Since surveyed tourists pointed out not only the advantages but disadvantages of these geosites, these findings should be further used for the valorization and geoconservation of the geosite and provide a more favorable deal for the arrival of as many tourists as possible. In the future, investigated geosites should be improved with tourist content and activities, such as interpretive boards for tourists, expert guides, organized visits, tourist infrastructure, and promotion. Successful activities in these critical areas could contribute to sustainable geotourism being one of the key economically beneficial activities of the local population that will develop local communities through the arrival of a larger number of domestic and foreign tourists.

Susceptibility assessment of multi-hazards using random forest—back propagation neural network coupling model: a Hangzhou city case study

As the demand for regional geological disaster risk assessments in large cities continues to rise, our study selected Hangzhou, one of China’s megacities, as a model to evaluate the susceptibility to two major geological hazards in the region: ground collapse and ground subsidence. Given that susceptibility assessments for such disasters mainly rely on knowledge-driven models, and data-driven models have significant potential for application, we proposed a high-accuracy Random Forest—Back Propagation Neural Network Coupling Model. By using nine evaluation factors selected based on field surveys and expert recommendations, along with disaster data, the model's predictive results indicate a 3–40% improvement in model performance metrics such as AUC, accuracy, precision, recall, and F1-score, compared to single models and traditional SVM and logistic regression models. Ultimately, using the predictive results of this model, we created susceptibility maps for individual disasters and developed a muti-hazards susceptibility map by employing the expert weight discrimination method and the overlay evaluation method. Furthermore, we discussed the feature importance in the prediction process. Our study validated the feasibility of using advanced machine learning models for urban geological disaster assessment, providing a replicable template for other cities.

SolBeePopecotox: A Population Model for Pesticide Risk Assessments of Solitary Bees.

In agricultural landscapes, solitary bees occur in a large diversity of species and are important for crop and wildflower pollination. They are distinguished from honey bees and bumble bees by their solitary lifestyle as well as different nesting strategies, phenologies, and floral preferences. Their ecological traits and presence in agricultural landscapes imply potential exposure to pesticides and suggest a need to conduct ecological risk assessments for solitary bees. However, assessing risks to the large diversity of managed and wild bees across landscapes and regions poses a formidable challenge. Population models provide tools to estimate potential population-level effects of pesticide exposures, can support field study design and interpretation, and can be applied to expand study data to untested conditions. We present a population model for solitary bees, SolBeePopecotox, developed for use in the context of ecological risk assessments. The trait-based model extends a previous version with the explicit representation of exposures to pesticides from relevant routes. Effects are implemented in the model using a simplified toxicokinetic-toxicodynamic model, BeeGUTS (GUTS = generalized unified threshold model for survival), adapted specifically for bees. We evaluated the model with data from semifield studies conducted with the red mason bee, Osmia bicornis, in which bees were foraging in tunnels over control and insecticide-treated oilseed rape fields. We extended the simulations to capture hypothetical semifield studies with two soil-nesting species, Nomia melanderi and Eucera pruinosa, which are difficult to test in empirical studies. The model provides a versatile tool for higher-tier risk assessments, for instance, to estimate effects of potential exposures, expanding available study data to untested species, environmental conditions, or exposure scenarios. Environ Toxicol Chem 2024;00:1-17. © 2024 SETAC.

Distinctive aerosol–cloud–precipitation interactions in marine boundary layer clouds from the ACE-ENA and SOCRATES aircraft field campaigns

Abstract. The aerosol–cloud–precipitation interactions within the cloud-topped marine boundary layer (MBL) are examined using aircraft in situ measurements from Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) and Southern Ocean Clouds, Radiation, Aerosol Transport Experimental Study (SOCRATES) field campaigns. SOCRATES clouds exhibit a larger number concentration and smaller cloud droplet effective radius (148.3 cm−3 and 8.0 µm) compared to ACE-ENA summertime (89.4 cm−3 and 9.0 µm) and wintertime clouds (70.6 cm−3 and 9.8 µm). The ACE-ENA clouds, especially during the winter, feature stronger drizzle formation via droplet growth through enhanced collision–coalescence that is attributed to a relatively cleaner environment and deeper cloud layer. Furthermore, the aerosol–cloud interaction (ACI) indices from the two aircraft field campaigns exhibit distinct sensitivities, indicating different cloud microphysical responses to aerosols. The ACE-ENA winter season features relatively fewer aerosols, which are more likely activated into cloud droplets under the conditions of sufficient water vapor availability and strong turbulence. The enriched aerosol loading during ACE-ENA summer and SOCRATES generally leads to smaller cloud droplets competing for the limited water vapor and exhibiting a stronger ACI. Notably, the precipitation susceptibilities are stronger during the ACE-ENA than during the SOCRATES campaigns. The in-cloud drizzle behavior significantly alters sub-cloud cloud condensation nuclei (CCN) budgets through the coalescence-scavenging effect and, in turn, impacts the ACI assessments. The results of this study can enhance understanding and aid in future model simulation and assessment of the aerosol–cloud interaction.

Geochemical Modeling and Quality Assessment of the Surface Waters from the Mouhoun River System, Burkina Faso: Implications of Water–Rock Interactions and Anthropogenic Activities

Major ion and heavy metal geochemistry was used to investigate the factors that control the Mouhoun River water quality in western Burkina Faso. Major cation concentrations was in the following decreasing order: Ca2+> Na+> K+> Mg2+, reflecting silicate weathering. However, relatively high HCO3- and SO42- concentrations could be related to dissolution of calcite and sulfide minerals, respectively. The average heavy metal concentrations in the riverine system were in the following order: FeT>Ag>Mn>Zn>Pb>Cu>Ni>AsT>Cr>Hg>Co>Cd with FeT and Ag exceeding the World Health Organization permissible limits. Concentrations of all heavy metals, except Cr, were higher in the Mouhoun system compared to the average world river concentrations. Adsorption and speciation models showed that Zn, Ag, Cd, Co, Cr and Ni are likely to remain in their free ionic forms, and thus posing serious threats to aquatic life and human health. Ag-Hg-Ni-Cd association around Poura and Tenado on the principal component plot suggested that the widespread artisanal gold mining in these areas may have contributed to these metals’ loading. In contrast, NH4+, Pb, Cu, Cr, AsT and Zn were clustered around a densely populated and industrialized Kou watershed, pointing to agricultural and industrial sources of these pollutants. Furthermore, the Kou River had the highest metal index followed by Tenado, Samendeni, Poura and Boromo. Hence, the chemistry of the Mouhoun River and its tributary appears to be mainly controlled by population density and various anthropogenic activities taking place in their drainage basins. The findings could provide avenues for sound and sustainable water resource management in a water scarce semi-arid environment.

Political Cleavages and Changing Exposure to Global Warming

Why do some countries pass laws to reduce emissions that cause climate change while others do not? We theorize that climate change-related disasters cause politicians to view global warming as more proximate, but whether they have incentives to enact mitigation laws depends on their country’s geographic vulnerability to future damages. We use a spatial integrated assessment model to measure global warming’s local economic effects, which allows us to predict how political leaders respond to disasters based on their vulnerability. An analysis of mitigation laws from 1990–2020 in 155 countries shows that only governments in locations facing the greatest future climate damage react to disasters by passing mitigation policies. Distinct from the historical North-South divide, our findings highlight a growing geographic cleavage in national responses to climate change.

Comparative Effects of Moderate Intensity Continuous Training and High Intensity Interval Training on Serum Levels of Myonectin in Rats

Background: Myonectin (CTRP15), a recently identified myokine, plays a crucial role in regulating glucose metabolism. Objectives: This study aimed to compare the effects of high-intensity interval training (HIIT) and moderate-intensity continuous training (MICT) on serum myonectin levels, using matched training volumes for both interventions. Methods: Twenty-four male Wistar rats, aged 18 to 20 weeks, were randomly assigned to three groups of eight: Control, MICT, and HIIT. The training groups participated in HIIT and MICT programs (three days per week) on a treadmill for eight weeks. Measurements included serum glucose, insulin, myonectin, and the homeostasis model assessment of insulin resistance (HOMA-IR). Additionally, the weight of the Flexor hallucis longus (FHL), gastrocnemius muscles, and epididymal and retroperitoneal fat pads was recorded. Results: Both HIIT and MICT significantly reduced weight gain and fat mass (epididymal and retroperitoneal) compared to the control group (P < 0.05). Eight weeks of HIIT significantly decreased serum insulin levels and HOMA-IR compared to both the control and MICT groups (P < 0.05). No significant differences in serum myonectin levels were observed between the groups. Conclusions: This study suggests that HIIT is more effective than MICT in improving metabolic status in rats, though it does not significantly affect serum myonectin levels.