Search

Thyroid cancer is a common endocrine carcinoma that occurs in the thyroid gland. Much effort has been invested in improving its diagnosis, and thyroidectomy remains the primary treatment method. A successful operation without unnecessary side injuries relies on an accurate preoperative diagnosis. Current human assessment of thyroid nodule malignancy is prone to errors and may not guarantee an accurate preoperative diagnosis. This study proposed a machine learning framework to predict thyroid nodule malignancy based on our collected novel clinical dataset. The ten-fold cross-validation, bootstrap analysis, and permutation predictor importance were applied to estimate and interpret the model performance under uncertainty. The comparison between model prediction and expert assessment shows the advantage of our framework over human judgment in predicting thyroid nodule malignancy. Our method is accurate, interpretable, and thus useable as additional evidence in the preoperative diagnosis of thyroid cancer. [ABSTRACT FROM AUTHOR]

A large number of materials and various criteria fashion material selection problems as complex multi-criteria decision-making (MCDM) problems. This paper proposes a new decision-making method called the simple ranking process (SRP) to solve complex material selection problems. The accuracy of the criteria weights has a direct impact on the outcomes of the new method. In contrast to current MCDM methods, the normalization step has been eliminated from the SRP method as a potential source of producing incorrect results. The application of the method is appropriate for situations with high levels of complexity in material selection because it only considers the ranks of alternatives in each criterion. The first scenario of vital-immaterial mediocre method (VIMM) is used as a tool to derive criteria weights based on expert assessment. The result of SRP is compared with a number of MCDM methods. In order to evaluate the findings of analytical comparison, a novel statistical measure known as compromise decision index (CDI) is proposed in this paper. CDI revealed that the MCDM methods' outputs for solving the material selection could not be theoretically proven and requires to be evaluated through practice. As a result, the dependency analysis-an additional innovative statistical measure is introduced to demonstrate the reliability of MCDM methods by assessing its dependency on criteria weights. The findings demonstrated that SRP is extremely reliant on criteria weights and its reliability rises with the number of criteria, making it a perfect tool for solving challenging MCDM problems. [ABSTRACT FROM AUTHOR]

Robots that can exhibit human-like behaviour, build social relationships with humans, and carry out social interaction that can be considered to have a social presence. Measuring the social presence of robots can provide an important basis for optimizing the design of robots' human-like behaviour, but until now, there has been no research and discussion on the presence of robots from the perspective of social interaction. Based on related studies, such as the theory of the presence of interpersonal interaction society, the mechanism of network social presence measurement and human–computer interaction, this study constructs a theoretical model of robot social presence, and develops corresponding measurement scales with five dimensions, namely, the presence, attention distribution, interactive expression and information understanding, perceived emotional interdependence, and interaction behaviour perception. The 5-dimensional robot social presence model was finalized, and a 17-question questionnaire scale was developed. The validation of the model and the development of the scale includes one expert assessment (involving three experts), one user interview (involving five interviewees), and two questionnaire surveys (involving 172 valid questionnaires and 494 valid questionnaires respectively). The final model shows good reliability of the measurement questionnaire, high inter-factor correlation, high model fit, high internal consistency of the dimensions, high reliability of the scale, and high convergent validity of all dimensions. This study provides a theoretical basis for the study of robot social presence and human–robot collaboration and provides a reference measurement tool for social robot-related development and research. [ABSTRACT FROM AUTHOR]

Invasive species can lead to community-level damage to the invaded ecosystem and extinction of native species. Most surveillance systems for the detection of invasive species are developed based on expert assessment, inherently coming with a level of uncertainty. In this research, info-gap decision theory (IGDT) is applied to model and manage such uncertainty. Surveillance of the Asian House Gecko, Hemidactylus frenatus Duméril and Bibron, 1836 on Barrow Island, is used as a case study. Our research provides a novel method for applying IGDT to determine the population threshold (K ) so that the decision can be robust to the deep uncertainty present in model parameters. We further robust-optimize surveillance costs rather than minimize surveillance costs. We demonstrate that increasing the population threshold for detection increases both robustness to the errors in the model parameter estimates, and opportuneness to lower surveillance costs than the accepted maximum budget. This paper provides guidance for decision makers to balance robustness and required surveillance expenditure. IGDT offers a novel method to model and manage the uncertainty prevalent in biodiversity conservation practices and modelling. The method outlined here can be used to design robust surveillance systems for invasive species in a wider context, and to better tackle uncertainty in protection of biodiversity and native species in a cost-effective manner. [ABSTRACT FROM AUTHOR]

This paper identifies nine factors affecting agricultural sustainable development by reading extensive literature and invites five experts to assess these factors using the pythagorean neutrosophic linguistic variable. Some of these factors can directly reduce the environmental footprint of agriculture, improve soil health, and promote biodiversity, while others can indirectly integrate to support the adoption of sustainable practices, further mitigating environmental degradation. Collectively, these factors reinforce ecological balance and play a critical role in advancing agricultural sustainable development. To unravel the complex relationships among these factors, a novel decision theory model is proposed, integrating pythagorean neutrosophic set (PNS) with Weighted Influence Nonlinear Specification System (WINGS) and Best-Worst Method (BWM). In addition, we not only categorized all factors into cause-and-effect factors, but also constructed a network relationship diagram based on them. The study shows that agricultural modernization (Y6) is the most important factor and land remediation (Y2) is the most influential factor. This integrated approach can more effectively address the common challenges of uncertainty and linguistic ambiguity in decision-making scenarios. Combining PNS with WINGS helps make the interactions and importance of factors more apparent, which is particularly suitable for analyzing key factors that promote agricultural sustainability. The incorporation of BWM further ensures the model's accuracy and objectivity. This method provides a more comprehensive and accurate reflection of decision-makers' opinions and judgments, improving decision-making efficiency, and can be widely applied not only in agriculture but also to other decision-making problems. [ABSTRACT FROM AUTHOR]

Traditional dental prosthetics require a significant amount of work, labor, and time. To simplify the process, a method to convert teeth scan images, scanned using an intraoral scanner, into 3D images for design was developed. Furthermore, several studies have used deep learning to automate dental prosthetic processes. Tooth images are required to train deep learning models, but they are difficult to use in research because they contain personal patient information. Therefore, we propose a method for generating virtual tooth images using image-to-image translation (pix2pix) and contextual reconstruction fill (CR-Fill). Various virtual images can be generated using pix2pix, and the images are used as training images for CR-Fill to compare the real image with the virtual image to ensure that the teeth are well-shaped and meaningful. The experimental results demonstrate that the images generated by the proposed method are similar to actual images. In addition, only using virtual images as training data did not perform well; however, using both real and virtual images as training data yielded nearly identical results to using only real images as training data. [ABSTRACT FROM AUTHOR]

Recent advancements in high-resolution imaging have significantly improved our understanding of microstructural changes in the skin and their relationship to the aging process. Line Field Confocal Optical Coherence Tomography (LC-OCT) provides detailed 3D insights into various skin layers, including the papillary dermis and its fibrous network. In this study, a deep learning model utilizing a 3D ResNet-18 network was trained to predict chronological age from LC-OCT images of 100 healthy Caucasian female volunteers, aged 20 to 70 years. The AI-based protocol focused on regions of interest delineated between the segmented dermal-epidermal junction and the superficial dermis, exploiting complex patterns within the collagen network for age prediction. The model achieved a mean absolute error of 4.2 years and exhibited a Pearson correlation coefficient of 0.937 with actual ages. Furthermore, there was a notable correlation (r = 0.87) between quantified clinical scoring, encompassing parameters such as firmness, elasticity, density, and wrinkle appearance, and the ages predicted by deep learning model. This strong correlation underscores how integrating emerging imaging technologies with deep learning can accelerate aging research and deepen our understanding of how alterations in skin microstructure are related to visible signs of aging. [ABSTRACT FROM AUTHOR]

Smartphone applications are one of the main delivery modalities in digital health. Many of these mHealth apps use gamification to engage users, improve user experience, and achieve better health outcomes. Yet, it remains unclear whether gamified approaches help to deliver effective, safe, and clinically beneficial products to users. This study examines the compliance of 69 gamified mHealth apps with the EU Medical Device Regulation and assesses the specific risks arising from the gamified nature of these apps. Of the identified apps, 32 (46.4%) were considered non-medical devices; seven (10.1%) were already cleared/approved by the regulatory authorities, and 31 (44.9%) apps were assessed as likely non-compliant or potentially non-compliant with regulatory requirements. These applications and one approved application were assessed as on the market without the required regulatory approvals. According to our analysis, a higher proportion of these apps would be classified as medical devices in the US. The level of risk posed by gamification remains ambiguous. While most apps showed only a weak link between the degree of gamification and potential risks, this link was stronger for those apps with a high degree of gamification or an immersive game experience. [ABSTRACT FROM AUTHOR]

This study aimed to examine the ability of adolescents to maintain breathing rhythm while swimming with and without goggles, in the context of pedagogical interventions for implementation of water competence skills, rather than simply teaching swimming technique (strokes). 25 females and 25 males, 12–13 years old, swam the front crawl both with goggles and without goggles. Distance covered and the ability to maintain breathing rhythm were evaluated by experts. For both girls and boys, the lack of goggles reduced the breath control. The boys in contrast to the girls, could "swim" (cover a distance) but did not have the "competence" to swim effectively/safely—with breathing rhythm—regardless of the goggle factor. Goggle-free swimming as an autonomous component of water competence is highly recommended in elementary swimming education. The following elements for pedagogical intervention in the area of water competence development are proposed: (1) the formatting of breath control on the basis of the student's preferred, simplest form of swimming (not strokes); (2) the a priori treatment of swimming goggles as an unnecessary teaching aid; (3) the gender differences in area of both adaptation in visual perception (the goggles factor) and motor control (breath control factor) should be considered. [ABSTRACT FROM AUTHOR]

Marine fisheries are increasingly impacted by climate change, affecting species distribution and productivity, and necessitating urgent adaptation efforts. Climate vulnerability assessments (CVA), integrating expert knowledge, are vital for identifying species that could thrive or suffer under changing environmental conditions. This study presents a first CVA for the Western Baltic Sea's fish community, a crucial fishing area for Denmark and Germany. Characterized by a unique mix of marine, brackish, and freshwater species, this coastal ecosystem faces significant changes due to the combined effects of overfishing, eutrophication and climate change. Our CVA involved a qualitative expert scoring of 22 fish species, assessing their sensitivity and exposure to climate change. Our study revealed a dichotomy in climate change vulnerability within the fish community of the Western Baltic Sea because traditional fishing targets cod and herring as well as other species with complex life histories are considered to face increased risks, whereas invasive or better adaptable species might thrive under changing conditions. Our findings hence demonstrate the complex interplay between life-history traits and climate change vulnerability in marine fish communities. Eventually, our study provides critical knowledge for the urgent development of tailored adaptation efforts addressing existing but especially future effects of climate change on fish and fisheries in the Western Baltic Sea, to navigate this endangered fisheries systems into a sustainable future. [ABSTRACT FROM AUTHOR]

With electronic healthcare systems undergoing rapid change, optimizing the crucial process of recording physician prescriptions is a task with major implications for patient care. The power of blockchain technology and the precision of the Raft consensus algorithm are combined in this article to create a revolutionary solution for this problem. In addition to addressing these issues, the proposed framework, by focusing on the challenges associated with physician prescriptions, is a breakthrough in a new era of security and dependability for the healthcare sector. The Raft algorithm is a cornerstone that improves the diagnostic decision-making process, increases confidence in patients, and sets a new standard for robust healthcare systems. In the proposed consensus algorithm, a weighted sum of two influencing factors including the physician acceptability and inter-physicians’ reliability is used for selecting the participating physicians. An investigation is conducted to see how well the Raft algorithm performs in overcoming prescription-related roadblocks that support a compelling argument for improved patient care. Apart from its technological benefits, the proposed approach seeks to revolutionize the healthcare system by fostering trust between patients and providers. Raft’s ability to communicate presents the proposed solution as an effective way to deal with healthcare issues and ensure security. [ABSTRACT FROM AUTHOR]

As mechanized open-pit coal mining intensifies, assessing and predicting slope stability has become increasingly important. To address the limitations of traditional mechanical calculations, numerical simulations, and physical experiments, this paper identifies the key factors impacting slope stability in open-pit mines and develops a multi-parameter sample data set. The study employs hyperparameters optimized using a Bayesian algorithm, introduces additional convolutional layers, and combines the Adam optimizer with dropout techniques to enhance the feature extraction and performance of one-dimensional convolutional neural networks (1D-CNN). This leads to a Bayesian-optimized one-dimensional convolutional neural network (B-1D MCNN) model for predicting slope stability.The study evaluates the classification performance and accuracy of various models for slope stability, including BP neural networks, genetic algorithm-optimized convolutional neural networks, 1D-CNN, and B-1D MCNN, using accuracy, precision, and F1-score as metrics. The analysis also examines the influence of factor indicators and training set length on the model's output to assess its generalization capabilities.The research findings suggest that: (1) the B-1D MCNN model for evaluating slope stability demonstrates the capability to accurately depict the nonlinear correlation between influencing factors and slope stability. (2) Compared with other models, the B-1D MCNN model has shown enhancements of 10.96% to 27.85%, 10.26% to 28.55%, and 8.98% to 25.05% in terms of Accuracy, F1-Score, and Precision, respectively. (3) As the length of the training dataset increases, the performance of the model improves accordingly. (4) The B-1D MCNN model shows a generalization power of 87.5%. [ABSTRACT FROM AUTHOR]

The fabricated metal product industries were identified as producers of variable and heterogeneous pollution. Workers in these manufacturing facilities are exposed to multiple pollutants present at variable concentrations. Specific known adverse health effects include bladder cancer associated with metalworking fluid exposure and lung cancer associated with electroplating processes. To reduce the incidence of these adverse effects, the main challenge is to identify the most hazardous pollutants within this complex exposure environment and evaluate the corresponding health potentials. In this study, exposure indices were formulated to assess multiple metal exposures with the ultimate goal of providing relevant information for exposure reduction and control measures. Fifteen plants, including metal mold manufacturing, metal casting, and surface treatment plants, were investigated in terms of total concentration, summation of corresponding ratio to threshold limit value (STLVr), hazard index (HI), and incremental cancer risk. The results revealed that emissions of aluminum, iron, and manganese were primarily found in the metal mold manufacturing/casting plants, while emissions of chromium, nickel, and zinc were found in surface treatment plants. STLVr and HI were more useful than the total concentration for identifying hazardous metals, which were chromium and nickel, and could specify the facilities that were in need of control measures. As for cancer risk, the metal mold manufacturing/casting plants had lower risk than the surface treatment plants, and the contributing metals for these two plant types were cobalt and chromium, respectively. This study established a useful procedure to evaluate health hazards and cancer risk. The resulting information is useful for prioritizing mitigation control of multiple metal exposures. [ABSTRACT FROM AUTHOR]

Green infrastructure networks enhance the protection and improvement of urban ecological environments, augment the efficiency and quality of ecosystem services, and furnish residents with healthier and more comfortable living conditions. Although previous research has investigated the construction or optimization methods of green infrastructure networks, these studies have been relatively isolated and lacking in case studies for mountainous cities. In the development of green infrastructure, mountainous cities must specifically consider the impact of terrain on network construction. Taking Fuzhou, a mountainous city in China, as an example, this study constructs and optimizes the green infrastructure network by employing morphological spatial pattern analysis, connectivity analysis, the Minimum Cumulative Resistance model, and circuit theory. These methodologies increase the connectivity of the Green Infrastructure within the study area, thereby promoting the health of the local ecosystem and creating conducive circumstances for the city's sustainable development. The findings reveal that: (1) Green infrastructure in Fuzhou takes up 5366.38 ha, constituting 21.76% of the study area, primarily situated in the northwest and south; (2) Fuzhou's Green Infrastructure network comprises 10 hubs and 17 corridors with a hub area of 1306.98 ha, predominantly distributed in the mountains encircling the city, including Meifeng Mountain, Gaogai Mountain, and Qingliang Mountain; (3) Based on optimization, the circuit centrality index categorizes hub importance into three protection levels, pinpointing nine crucial protected areas in the corridors and 680 areas requiring enhancement, including 68 areas for first-level improvement, 149 areas for second-level improvement, and 463 areas for third-level improvement. This research offers a methodological reference for constructing and optimizing green infrastructure networks in mountainous cities, providing both theoretical and practical foundations for optimizing green infrastructure networks in Fuzhou City. [ABSTRACT FROM AUTHOR]

Due to the rapid economic development of globalization and the intensification of economic and trade exchanges, cross-international and regional carbon emissions have become increasingly severe. Governments worldwide establish laws and regulations to protect their countries' environmental impact. Therefore, selecting robustness evaluation models and metrics is an urgent research topic. This article proves the reliability and scientific of the assessment data through literature coupling evaluation, multidisciplinary coupling mathematical model and international engineering case analysis. The innovation of this project's research lies in the comprehensive analysis of the complex coupling effects of various discrete data and uncertainty indicators on the research model across international projects and how to model and evaluate interactive effects accurately. This article provides scientific measurement standards and data support for governments worldwide to formulate carbon tariffs and carbon emission policies. Case analysis data shows that the carbon emission ratio of exporting and importing countries is 0.577:100; the carbon trading quota ratio is 32.50:100. [ABSTRACT FROM AUTHOR]

Over the last quarter century, increasing honey bee colony losses motivated standardized large-scale surveys of managed honey bees (Apis mellifera), particularly in Europe and the United States. Here we present the first large-scale standardized survey of colony losses of managed honey bees and stingless bees across Latin America. Overall, 1736 beekeepers and 165 meliponiculturists participated in the 2-year survey (2016–2017 and 2017–2018). On average, 30.4% of honey bee colonies and 39.6% of stingless bee colonies were lost per year across the region. Summer losses were higher than winter losses in stingless bees (30.9% and 22.2%, respectively) but not in honey bees (18.8% and 20.6%, respectively). Colony loss increased with operation size during the summer in both honey bees and stingless bees and decreased with operation size during the winter in stingless bees. Furthermore, losses differed significantly between countries and across years for both beekeepers and meliponiculturists. Overall, winter losses of honey bee colonies in Latin America (20.6%) position this region between Europe (12.5%) and the United States (40.4%). These results highlight the magnitude of bee colony losses occurring in the region and suggest difficulties in maintaining overall colony health and economic survival for beekeepers and meliponiculturists. [ABSTRACT FROM AUTHOR]

This study clinically implemented a ready-to-use quantitative perfusion (QP) cardiovascular magnetic resonance (QP CMR) workflow, encompassing a simplified dual-bolus gadolinium-based contrast agent (GBCA) administration scheme and fully automated QP image post-processing. Twenty-five patients with suspected obstructive coronary artery disease (CAD) underwent both adenosine stress perfusion CMR and an invasive coronary angiography or coronary computed tomography angiography. The dual-bolus protocol consisted of a pre-bolus (0.0075 mmol/kg GBCA at 0.5 mmol/ml concentration + 20 ml saline) and a main bolus (0.075 mmol/kg GBCA at 0.5 mmol/ml concentration + 20 ml saline) at an infusion rate of 3 ml/s. The arterial input function curves showed excellent quality. Stress MBF ≤ 1.84 ml/g/min accurately detected obstructive CAD (area under the curve 0.79; 95% Confidence Interval: 0.66 to 0.89). Combined visual assessment of color pixel QP maps and conventional perfusion images yielded a diagnostic accuracy of 84%, sensitivity of 70% and specificity of 93%. The proposed easy-to-use dual-bolus QP CMR workflow provides good image quality and holds promise for high accuracy in diagnosis of obstructive CAD. Implementation of this approach has the potential to serve as an alternative to current methods thus increasing the accessibility to offer high-quality QP CMR imaging by a wide range of CMR laboratories. [ABSTRACT FROM AUTHOR]

Human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) provide an excellent platform for potential clinical and research applications. Identifying abnormal Ca2+ transients is crucial for evaluating cardiomyocyte function that requires labor-intensive manual effort. Therefore, we develop an analytical pipeline for automatic assessment of Ca2+ transient abnormality, by employing advanced machine learning methods together with an Analytical Algorithm. First, we adapt an existing Analytical Algorithm to identify Ca2+ transient peaks and determine peak abnormality based on quantified peak characteristics. Second, we train a peak-level Support Vector Machine (SVM) classifier by using human-expert assessment of peak abnormality as outcome and profiled peak variables as predictive features. Third, we train another cell-level SVM classifier by using human-expert assessment of cell abnormality as outcome and quantified cell-level variables as predictive features. This cell-level SVM classifier can be used to assess additional Ca2+ transient signals. By applying this pipeline to our Ca2+ transient data, we trained a cell-level SVM classifier using 200 cells as training data, then tested its accuracy in an independent dataset of 54 cells. As a result, we obtained 88% training accuracy and 87% test accuracy. Further, we provide a free R package to implement our pipeline for high-throughput CM Ca2+ analysis. [ABSTRACT FROM AUTHOR]

The currently available distribution and range maps for the Great Grey Owl (GGOW; Strix nebulosa) are ambiguous, contradictory, imprecise, outdated, often hand-drawn and thus not quantified, not based on data or scientific. In this study, we present a proof of concept with a biological application for technical and biological workflow progress on latest global open access 'Big Data' sharing, Open-source methods of R and geographic information systems (OGIS and QGIS) assessed with six recent multi-evidence citizen-science sightings of the GGOW. This proposed workflow can be applied for quantified inference for any species-habitat model such as typically applied with species distribution models (SDMs). Using Random Forest—an ensemble-type model of Machine Learning following Leo Breiman's approach of inference from predictions—we present a Super SDM for GGOWs in Alaska running on Oracle Cloud Infrastructure (OCI). These Super SDMs were based on best publicly available data (410 occurrences + 1% new assessment sightings) and over 100 environmental GIS habitat predictors ('Big Data'). The compiled global open access data and the associated workflow overcome for the first time the limitations of traditionally used PC and laptops. It breaks new ground and has real-world implications for conservation and land management for GGOW, for Alaska, and for other species worldwide as a 'new' baseline. As this research field remains dynamic, Super SDMs can have limits, are not the ultimate and final statement on species-habitat associations yet, but they summarize all publicly available data and information on a topic in a quantified and testable fashion allowing fine-tuning and improvements as needed. At minimum, they allow for low-cost rapid assessment and a great leap forward to be more ecological and inclusive of all information at-hand. Using GGOWs, here we aim to correct the perception of this species towards a more inclusive, holistic, and scientifically correct assessment of this urban-adapted owl in the Anthropocene, rather than a mysterious wilderness-inhabiting species (aka 'Phantom of the North'). Such a Super SDM was never created for any bird species before and opens new perspectives for impact assessment policy and global sustainability. [ABSTRACT FROM AUTHOR]

This paper constructs a two-layer road data asset revenue allocation model based on a modified Shapley value approach. The first layer allocates revenue to three roles in the data value realization process: the original data collectors, the data processors, and the data product producers. It fully considers and appropriately adjusts the revenue allocation to each role based on data risk factors. The second layer determines the correction factors for different roles to distribute revenue among the participants within those roles. Finally, the revenue values of the participants within each role are synthesized to obtain a consolidated revenue distribution for each participant. Compared to the traditional Shapley value method, this model establishes a revenue allocation evaluation index system, uses entropy weighting and rough set theory to determine the weights, and adopts a fuzzy comprehensive evaluation and numerical analysis to assess the degree of contribution of participants. It fully accounts for differences in both the qualitative and quantitative contributions of participants, enabling a fairer and more reasonable distribution of revenues. This study provides new perspectives and methodologies for the benefit distribution mechanism in road data assets, which aid in promoting the market-based use of road data assets, and it serves as an important reference for the application of data assetization in the road transportation industry. [ABSTRACT FROM AUTHOR]

The interspecific transmission of pathogens can occur frequently in the environment. Among wild bees, the main spillover cases are caused by pathogens associated with Apis mellifera, whose colonies can act as reservoirs. Due to the limited availability of data in Italy, it is challenging to accurately assess the impact and implications of this phenomenon on the wild bee populations. In this study, a total of 3372 bees were sampled from 11 Italian regions within the BeeNet project, evaluating the prevalence and the abundance of the major honey bee pathogens (DWV, BQCV, ABPV, CBPV, KBV, Nosema ceranae, Ascosphaera apis, Crithidia mellificae, Lotmaria passim, Crithidia bombi). The 68.4% of samples were positive for at least one pathogen. DWV, BQCV, N. ceranae and CBPV showed the highest prevalence and abundance values, confirming them as the most prevalent pathogens spread in the environment. For these pathogens, Andrena, Bombus, Eucera and Seladonia showed the highest mean prevalence and abundance values. Generally, time trends showed a prevalence and abundance decrease from April to July. In order to predict the risk of infection among wild bees, statistical models were developed. A low influence of apiary density on pathogen occurrence was observed, while meteorological conditions and agricultural management showed a greater impact on pathogen persistence in the environment. Social and biological traits of wild bees also contributed to defining a higher risk of infection for bivoltine, communal, mining and oligolectic bees. Out of all the samples tested, 40.5% were co-infected with two or more pathogens. In some cases, individuals were simultaneously infected with up to five different pathogens. It is essential to increase knowledge about the transmission of pathogens among wild bees to understand dynamics, impact and effects on pollinator populations. Implementing concrete plans for the conservation of wild bee species is important to ensure the health of wild and human-managed bees within a One-Health perspective. [ABSTRACT FROM AUTHOR]

The evaluation of Physical Education Teaching Methods in Colleges and Universities faces two main challenges: an excess of evaluating elements and a lack of assessment framework. Hence, the research proposes the multi-feature fuzzy evaluation model based on artificial intelligence to streamline the evaluation process and provide an efficient framework for accessing teaching methods. The framework integrates natural/human language using fuzzy instructions considering three evaluation perspectives, including the management stage, instructors, and students and employs the enhanced cuckoo search optimization algorithm. After the teaching expert has determined each parameter's ratings, they are fed into the improved cuckoo search algorithm and solved using an unbiased function to assess the assessment's final result. It incorporates the students' mobility mechanism and movement vector deconstruction designed based on functional criteria. A system for evaluating the quality of instruction has been developed utilizing the proposed model with enhanced cuckoo search optimization. The results indicate that the proposed algorithm has achieved the highest scores across multiple assessment categories, average skill performances of 97.01%, learning progress of 87.36%, physical fitness of 93.49%, participation rate of 95.04%, student satisfaction of 95.49%, and physical education of 96.8% teaching efficiency. The usefulness of the proposed framework in enhancing physical education teaching methods has been demonstrated by comparing the results with traditional methods. It contributes to advancing pedagogical practices in the field. [ABSTRACT FROM AUTHOR]

To achieve the best management of the ecosystem and sustainable socioeconomic development, it is crucial to clarify the matching relationship between the supply and demand of ecosystem services (ESs). Four types of ESs were chosen for the Hexi region in this study: food supply, carbon sequestration, water yield, wind erosion control and sediment retention. We assessed the supply–demand and matching relationships of different ESs using the Integrated Valuation of Ecosystem Service and Tradeoffs (InVEST) model, the ESs supply–demand ratio and the four-quadrant model. Moreover, we also analyzed the supply–demand ratio of integrated ESs and their cold spots. The findings suggest that (1) from 2000 to 2020, the average supply of food supply, carbon sequestration, water yield, wind erosion control and sediment retention increased by 44.31 t/km2, 128.44 t/hm2, 14,545.94 m3/km2 and 0.14 kg/m2 respectively, which showed a spatial pattern of "high in the southeast and low in the northwest". The average demand for food supply and carbon sequestration increased by 1.33 t/km2 and 0.32 t/hm2 respectively, while the average demand for water yield and wind erosion control and sediment retention decreased by 2997.25 m3/km2 and 1.19 kg/km2 respectively. The spatial distribution is consistent with the layout of population density, production and residential areas, and fragile ecological areas. (2) The supply–demand ratio of food supply, carbon sequestration and water yield is greater than 0.095, which is in a state of oversupply, and the supply–demand ratio of wind erosion control and sediment retention is less than 0, which is in a state of shortage; all ESs are mainly in low-low spatial matching areas, mainly concentrated in the desert areas of the northwest in the Hexi region. (3) The supply–demand ratio of integrated ESs increased by 0.024, and the proportion of cold spots and sub-cold spots was more than 50% and concentrated in the northwest, while hot spots and sub-hot spots accounted for only about 16%, mainly distributed in the southern Qilian Mountains and some oasis areas. [ABSTRACT FROM AUTHOR]

In magnetic resonance imaging (MRI), the perception of substandard image quality may prompt repetition of the respective image acquisition protocol. Subsequently selecting the preferred high-quality image data from a series of acquisitions can be challenging. An automated workflow may facilitate and improve this selection. We therefore aimed to investigate the applicability of an automated image quality assessment for the prediction of the subjectively preferred image acquisition. Our analysis included data from 11,347 participants with whole-body MRI examinations performed as part of the ongoing prospective multi-center German National Cohort (NAKO) study. Trained radiologic technologists repeated any of the twelve examination protocols due to induced setup errors and/or subjectively unsatisfactory image quality and chose a preferred acquisition from the resultant series. Up to 11 quantitative image quality parameters were automatically derived from all acquisitions. Regularized regression and standard estimates of diagnostic accuracy were calculated. Controlling for setup variations in 2342 series of two or more acquisitions, technologists preferred the repetition over the initial acquisition in 1116 of 1396 series in which the initial setup was retained (79.9%, range across protocols: 73–100%). Image quality parameters then commonly showed statistically significant differences between chosen and discarded acquisitions. In regularized regression across all protocols, ‘structured noise maximum’ was the strongest predictor for the technologists’ choice, followed by ‘N/2 ghosting average’. Combinations of the automatically derived parameters provided an area under the ROC curve between 0.51 and 0.74 for the prediction of the technologists’ choice. It is concluded that automated image quality assessment can, despite considerable performance differences between protocols and anatomical regions, contribute substantially to identifying the subjective preference in a series of MRI acquisitions and thus provide effective decision support to readers. [ABSTRACT FROM AUTHOR]

One major issue in pharmaceutical supply chain management is the supply shortage, and determining the root causes of medicine shortages necessitates an in-depth investigation. The concept of risk management is proposed in this study to identify significant risk factors in the pharmaceutical supply chain. Fuzzy failure mode and effect analysis and data envelopment analysis were used to evaluate the risks of the pharmaceutical supply chain. Based on a case study on the Malaysian pharmaceutical supply chain, it reveals that the pharmacy node is the riskiest link. The unavailability of medicine due to unexpected demand, as well as the scarcity of specialty or substitute drugs, pose the most significant risk factors. These risks could be mitigated by digital technology. We propose an appropriate digital technology platform consisting of big data analytics and blockchain technologies to undertake these challenges of supply shortage. By addressing risk factors through the implementation of a digitalized supply chain, organizations can fortify their supply networks, fostering resilience and efficiency, and thereby playing a pivotal role in advancing the Pharma 4.0 era. [ABSTRACT FROM AUTHOR]

Statistics show that the inhabitants of Poland are producing increasingly more household waste. This article attempts to determine the current level of development of Poland in the field of waste management concerning other EU countries and partner countries; identify trends in the mass of generated, segregated, and mixed municipal waste; and obtain an idea of the attitude of the Polish population toward sorting waste at the source to bring the country to a higher level of waste management. The empirical base is statistical data published on the website of the EU Data Explorer and the Central Statistical Office. The ranking of countries was determined by the TOPSIS method using a synthetic indicator based on the selected diagnostic features. The significance of the obtained ranks was tested using the non-parametric Friedman test (p < 0.01). We established that Poland has been consistently ranked 16th-17th over the past 5 years. Unfortunately, thus far, no systematic approach has been found to raise citizens' awareness, which may be due to the lack of the necessary amount of data. Researchers recommend investigating the sensitivity of the relationship between the generation of alternative energy from waste and the authorities' action. [ABSTRACT FROM AUTHOR]

The clinical course of Lyme neuroborreliosis (LNB) is highly variable. Delayed diagnosis and treatment still remain actual challenges. Moreover, there is a lack of studies analyzing the factors associated with different LNB syndromes. We aimed to analyze clinical and epidemiological features of LNB in hospitalized adults in eastern Lithuania. A retrospective study was performed for patients presenting in the years 2010–2021. A total of 103 patients were included in the study, 100 with early, and three with late LNB. Patients with early LNB most often presented polyradiculitis [75/100, (75%)], which was also the most common initial neurological syndrome. Peripheral facial palsy was diagnosed in 53/100 (53%) patients, in 16/53 (30.2%) cases both facial nerves were affected. Encephalitis or myelitis was diagnosed in 14% of patients with LNB. A total of 76/103 (73.8%) patients were discharged with residual symptoms or signs. One patient presenting encephalomyelitis died because of bacterial complications. The absence of observed erythema migrans (EM) was the predictor of peripheral facial palsy, while female sex and EM untreated with antibiotics were predictors of isolated polyradiculitis. A fever of ≥ 38 ° °C and pleocytosis of ≥ 300 × 106/l were associated with the development of encephalitis or myelitis in patients with early LNB. [ABSTRACT FROM AUTHOR]

Managed honey bees have experienced high rates of colony loss recently, with pesticide exposure as a major cause. While pesticides can be lethal at high doses, lower doses can produce sublethal effects, which may substantially weaken colonies. Impaired learning performance is a behavioral sublethal effect, and is often present in bees exposed to insecticides. However, the effects of other pesticides (such as fungicides) on honey bee learning are understudied, as are the effects of pesticide formulations versus active ingredients. Here, we investigated the effects of acute exposure to the fungicide formulation Pristine (active ingredients: 25.2% boscalid, 12.8% pyraclostrobin) on honey bee olfactory learning performance in the proboscis extension reflex (PER) assay. We also exposed a subset of bees to only the active ingredients to test which formulation component(s) were driving the learning effects. We found that the formulation produced negative effects on memory, but this effect was not present in bees fed only boscalid and pyraclostrobin. This suggests that the trade secret "other ingredients" in the formulation mediated the learning effects, either through exerting their own toxic effects or by increasing the toxicities of the active ingredients. These results show that pesticide co-formulants should not be assumed inert and should instead be included when assessing pesticide risks. [ABSTRACT FROM AUTHOR]

Many Chinese wrinkle studies continue to use non-Chinese scales because few Chinese-based wrinkle scales have been developed. The study aims to develop a crow's feet grading scale for Chinese individuals. We enrolled 608 healthy Chinese subjects and measured data through the DermaTOP system. We chose exploratory factor analysis (EFA) to reduce the dimensions of the data. A three-factor structure was obtained by using EFA, and it explained a cumulative total of 89.551% of the variance. A computational formula was obtained by calculating the total factor tilt scores and taking the variance contribution rate of three factors as the weight. Based on the computational formula, a grading map was designed and tested. The model validation was conducted using both subjective assessments from the expert panel and objective results from the model calculations. The results showed that our grading scale model is stable. This study developed a Chinese crow's feet (CCF) grading scale, which included a parameter, a grading map, and literal descriptions. The CCF grading scale is a validated tool for evaluating the effects of cosmetics or specific therapies. More importantly, the CCF scale was developed based on objective data, which may inspire new ideas for wrinkle grading scale development in the future. [ABSTRACT FROM AUTHOR]

Managed bee species provide essential pollination services that contribute to food security worldwide. However, managed bees face a diverse array of threats and anticipating these, and potential opportunities to reduce risks, is essential for the sustainable management of pollination services. We conducted a horizon scanning exercise with 20 experts from across Europe to identify emerging threats and opportunities for managed bees in European agricultural systems. An initial 63 issues were identified, and this was shortlisted to 21 issues through the horizon scanning process. These ranged from local landscape-level management to geopolitical issues on a continental and global scale across seven broad themes—Pesticides & pollutants, Technology, Management practices, Predators & parasites, Environmental stressors, Crop modification, and Political & trade influences. While we conducted this horizon scan within a European context, the opportunities and threats identified will likely be relevant to other regions. A renewed research and policy focus, especially on the highest-ranking issues, is required to maximise the value of these opportunities and mitigate threats to maintain sustainable and healthy managed bee pollinators within agricultural systems. [ABSTRACT FROM AUTHOR]

This study conducted a comprehensive analysis of multiple supervised machine learning models, regressors and classifiers, to accurately predict diamond prices. Diamond pricing is a complex task due to the non-linear relationships between key features such as carat, cut, clarity, table, and depth. The analysis aimed to develop an accurate predictive model by utilizing both regression and classification approaches. To preprocess the data, the study employed various techniques. The work addressed outliers, standardized the predictors, performed median imputation of missing values, and resolved multicollinearity issues. Equal-width binning on the cut variable was performed to handle class imbalance. Correlation-based feature selection was utilized to eliminate highly correlated variables, ensuring that only relevant features were included in the models. Outliers were handled using the inter-quartile range method, and numerical features were normalized through standardization. Missing values in numerical features were imputed using the median, preserving the integrity of the dataset. Among the models evaluated, the RF regressor exhibited exceptional performance. It achieved the lowest root mean squared error (RMSE) of 523.50, indicating superior accuracy compared to the other models. The RF regressor also obtained a high R-squared ( R 2 ) score of 0.985, suggesting it explained a significant portion of the variance in diamond prices. Furthermore, the area under the curve with RF classifier for the test set was 1.00 (100 %) , indicating perfect classification performance. These results solidify the RF's position as the best-performing model in terms of accuracy and predictive power, both in regression and classification. The MLP regressor showed promising results with an RMSE of 563.74 and an R 2 score of 0.980, demonstrating its ability to capture the complex relationships in the data. Although it achieved slightly higher errors than the RF regressor, further analysis is needed to determine its suitability and potential advantages compared to the RF regressor. The XGBoost Regressor achieved an RMSE of 612.88 and an R 2 score of 0.972, indicating its effectiveness in predicting diamond prices but with slightly higher errors compared to the RF regressor. The Boosted Decision Tree Regressor had an RMSE of 711.31 and an R 2 score of 0.968, demonstrating its ability to capture some of the underlying patterns but with higher errors than the RF and XGBoost models. In contrast, the KNN regressor yielded a higher RMSE of 1346.65 and a lower R 2 score of 0.887, indicating its inferior performance in accurately predicting diamond prices compared to the other models. Similarly, the Linear Regression model performed similarly to the KNN regressor, with an RMSE of 1395.41 and an R 2 score of 0.876. The Support Vector Regression model showed the highest RMSE of 3044.49 and the lowest R 2 score of 0.421, indicating its limited effectiveness in capturing the complex relationships in the data. Overall, the study demonstrates that the RF outperforms the other models in terms of accuracy and predictive power, as evidenced by its lowest RMSE, highest R 2 score, and perfect classification performance. This highlights its suitability for accurately predicting diamond prices. The study not only provides an effective tool for the diamond industry but also emphasizes the importance of considering both regression and classification approaches in developing accurate predictive models. The findings contribute valuable insights for pricing strategies, market trends, and decision-making processes in the diamond industry and related fields. [ABSTRACT FROM AUTHOR]

Abstract A large number of materials and various criteria fashion material selection problems as complex multi-criteria decision-making (MCDM) problems. This paper proposes a new decision-making method called the simple ranking process (SRP) to solve complex material selection problems. The accuracy of the criteria weights has a direct impact on the outcomes of the new method. In contrast to current MCDM methods, the normalization step has been eliminated from the SRP method as a potential source of producing incorrect results. The application of the method is appropriate for situations with high levels of complexity in material selection because it only considers the ranks of alternatives in each criterion. The first scenario of vital-immaterial mediocre method (VIMM) is used as a tool to derive criteria weights based on expert assessment. The result of SRP is compared with a number of MCDM methods. In order to evaluate the findings of analytical comparison, a novel statistical measure known as compromise decision index (CDI) is proposed in this paper. CDI revealed that the MCDM methods’ outputs for solving the material selection could not be theoretically proven and requires to be evaluated through practice. As a result, the dependency analysis-an additional innovative statistical measure is introduced to demonstrate the reliability of MCDM methods by assessing its dependency on criteria weights. The findings demonstrated that SRP is extremely reliant on criteria weights and its reliability rises with the number of criteria, making it a perfect tool for solving challenging MCDM problems.

Peatland restoration is experiencing a global upsurge as a tool to protect and provide various ecosystem services. As the range of peatland types being restored diversifies, do previous findings present overly optimistic restoration expectations? In an eroding and restored upland peatland we assessed short-term (0–4 year) effects of restoration on ecohydrological functions. Restoration significantly reduced discharge from the site, transforming peat pans into pools. These retained surface water over half the time and were deeper during wet periods than before. In the surrounding haggs water tables stabilised, as drawdown during dry conditions reduced, increasing the saturated peat thickness. Despite these changes, there were no effects on photosynthesis, ecosystem respiration or dissolved organic carbon loads leaving the site. Soil respiration did not decrease as water tables rose, but methane emissions were higher from rewet pools. Restoration has had a dramatic effect on hydrology, however, consequent changes in other ecosystem functions were not measured in the 4 years after restoration. Whilst restoration is crucial in halting the expansion of degraded peatland areas, it is vital that practitioners and policymakers advocating for restoration are realistic about the expected outcomes and timescales over which these outcomes may manifest. [ABSTRACT FROM AUTHOR]

Based on the literature on the factors influencing port competitiveness, this paper evaluates the competitiveness of Chinese ports along the Belt and Road based on Porter's diamond model. The evaluation system includes four primary indicators, including port infrastructure, port operation scale, port hinterland economic level and port development potential, and 13 secondary indicators. Then, based on the entropy-TOPSIS method, an evaluation model of port competitiveness is constructed. Finally, 15 seaports along China's "Belt and Road" are used to evaluate competitiveness, and a comprehensive ranking of port competitiveness is obtained. The advantages and shortcomings of each port are pointed out according to the evaluation results to provide a reference for the long-term development of the competitiveness of ports along the "Belt and Road" in China. [ABSTRACT FROM AUTHOR]

The purpose of this study is to develop a method for synthesizing mathematical models of interconnected units of fuzzy chemical-technological systems (CTS) used for system modeling and optimization of their operating modes in a fuzzy environment. Since many CTSs in practice consist of many interconnected units, the development of their mathematical models combined into a single system of models, which allows systematic modeling and optimization of CTS parameters, is an urgent scientific and practical task. To develop a system of models of fuzzy described CTS, consisting of interconnected units, a system of methods is used that combines formal (experimental-statistical) and informal methods (methods of peer review, fuzzy set theory). A method for developing a system of mathematical models of CTS units under conditions of uncertainty due to the random and fuzzy nature of the available information is proposed. In the proposed method, mathematical models of various CTS units, depending on the nature of the initial and available information, are developed by various methods. Accordingly, various types of models are obtained, which are then combined into a single system of models, taking into account the interconnections of the system's units. These results make it possible to develop more adequate models and determine the optimal CTS operating modes in a fuzzy environment by using the experience, knowledge and intuition of the decision maker, subject matter experts. Based on the proposed method, models of coke chambers and the main rectification column are developed in the form of combined models, including statistical and fuzzy models. The results obtained on the example of delayed coking units can be exported to similar CTS in oil refining, petrochemicals and other industries. [ABSTRACT FROM AUTHOR]

Current advances in epilepsy treatment aim to personalize and responsively adjust treatment parameters to overcome patient heterogeneity in treatment efficiency. For tailoring treatment to the individual and the current brain state, tools are required that help to identify the patient- and time-point-specific parameters of epilepsy. Computational modeling has long proven its utility in gaining mechanistic insight. Recently, the technique has been introduced as a diagnostic tool to predict individual treatment outcomes. In this article, the Wendling model, an established computational model of epilepsy dynamics, is used to automatically classify epileptic brain states in intracranial EEG from patients (n = 4) and local field potential recordings from in vitro rat data (high-potassium model of epilepsy, n = 3). Five-second signal segments are classified to four types of brain state in epilepsy (interictal, preonset, onset, ictal) by comparing a vector of signal features for each data segment to four prototypical feature vectors obtained by Wendling model simulations. The classification result is validated against expert visual assessment. Model-driven brain state classification achieved a classification performance significantly above chance level (mean sensitivity 0.99 on model data, 0.77 on rat data, 0.56 on human data in a four-way classification task). Model-driven prototypes showed similarity with data-driven prototypes, which we obtained from real data for rats and humans. Our results indicate similar electrophysiological patterns of epileptic states in the human brain and the animal model that are well-reproduced by the computational model, and captured by a key set of signal features, enabling fully automated and unsupervised brain state classification in epilepsy. [ABSTRACT FROM AUTHOR]

Abstract Robots that can exhibit human-like behaviour, build social relationships with humans, and carry out social interaction that can be considered to have a social presence. Measuring the social presence of robots can provide an important basis for optimizing the design of robots’ human-like behaviour, but until now, there has been no research and discussion on the presence of robots from the perspective of social interaction. Based on related studies, such as the theory of the presence of interpersonal interaction society, the mechanism of network social presence measurement and human–computer interaction, this study constructs a theoretical model of robot social presence, and develops corresponding measurement scales with five dimensions, namely, the presence, attention distribution, interactive expression and information understanding, perceived emotional interdependence, and interaction behaviour perception. The 5-dimensional robot social presence model was finalized, and a 17-question questionnaire scale was developed. The validation of the model and the development of the scale includes one expert assessment (involving three experts), one user interview (involving five interviewees), and two questionnaire surveys (involving 172 valid questionnaires and 494 valid questionnaires respectively). The final model shows good reliability of the measurement questionnaire, high inter-factor correlation, high model fit, high internal consistency of the dimensions, high reliability of the scale, and high convergent validity of all dimensions. This study provides a theoretical basis for the study of robot social presence and human–robot collaboration and provides a reference measurement tool for social robot-related development and research.

Subsea permafrost carbon pools below the Arctic shelf seas are a major unknown in the global carbon cycle. We combine a numerical model of sedimentation and permafrost evolution with simplified carbon turnover to estimate accumulation and microbial decomposition of organic matter on the pan-Arctic shelf over the past four glacial cycles. We find that Arctic shelf permafrost is a globally important long-term carbon sink storing 2822 (1518–4982) Pg OC, double the amount stored in lowland permafrost. Although currently thawing, prior microbial decomposition and organic matter aging limit decomposition rates to less than 48 Tg OC/yr (25–85) constraining emissions due to thaw and suggesting that the large permafrost shelf carbon pool is largely insensitive to thaw. We identify an urgent need to reduce uncertainty in rates of microbial decomposition of organic matter in cold and saline subaquatic environments. Large emissions of methane more likely derive from older and deeper sources than from organic matter in thawing permafrost. [ABSTRACT FROM AUTHOR]

Developmental dysplasia of the hip (DDH) is a common cause of premature osteoarthritis. This osteoarthritis can be prevented if DDH is detected by ultrasound and treated in infancy, but universal DDH screening is generally not cost-effective due to the need for experts to perform the scans. The purpose of our study was to evaluate the feasibility of having non-expert primary care clinic staff perform DDH ultrasound using handheld ultrasound with artificial intelligence (AI) decision support. We performed an implementation study evaluating the FDA-cleared MEDO-Hip AI app interpreting cine-sweep images obtained from handheld Philips Lumify probe to detect DDH. Initial scans were done by nurses or family physicians in 3 primary care clinics, trained by video, powerpoint slides and brief in-person. When the AI app recommended follow-up (FU), we first performed internal FU by a sonographer using the AI app; cases still considered abnormal by AI were referred to pediatric orthopedic clinic for assessment. We performed 369 scans in 306 infants. Internal FU rates were initially 40% for nurses and 20% for physicians, declining steeply to 14% after ~ 60 cases/site: 4% technical failure, 8% normal at sonographer FU using AI, and 2% confirmed DDH. Of 6 infants referred to pediatric orthopedic clinic, all were treated for DDH (100% specificity); 4 had no risk factors and may not have otherwise been identified. Real-time AI decision support and a simplified portable ultrasound protocol enabled lightly trained primary care clinic staff to perform hip dysplasia screening with FU and case detection rates similar to costly formal ultrasound screening, where the US scan is performed by a sonographer and interpreted by a radiologist/orthopedic surgeon. This highlights the potential utility of AI-supported portable ultrasound in primary care. [ABSTRACT FROM AUTHOR]

Due to the frequent occurrence of numerous emergency events that have significantly damaged society and the economy, the need for emergency decision-making has been manifest recently. It assumes a controllable function when it is critical to limit property and personal catastrophes and lessen their negative consequences on the natural and social course of events. In emergency decision-making problems, the aggregation method is crucial, especially when there are more competing criteria. Based on these factors, we first introduced some basic concepts about SHFSS, and then we introduced some new aggregation operators such as the spherical hesitant fuzzy soft weighted average, spherical hesitant fuzzy soft ordered weighted average, spherical hesitant fuzzy weighted geometric aggregation, spherical hesitant fuzzy soft ordered weighted geometric aggregation, spherical hesitant fuzzy soft hybrid average, and spherical hesitant fuzzy soft hybrid geometric aggregation operator. The characteristics of these operators are also thoroughly covered. Also, an algorithm is developed within the spherical hesitant fuzzy soft environment. Furthermore, we extend our investigation to the Evaluation based on the Distance from Average Solution method in multiple attribute group decision-making with spherical hesitant fuzzy soft averaging operators. And a numerical illustration for "supply of emergency aid in post-flooding the situation" is given to show the accuracy of the mentioned work. Then a comparison between these operators and the EDAS method is also established in order to further highlight the superiority of the established work. [ABSTRACT FROM AUTHOR]

The mock-witness task is typically used to evaluate the fairness of lineups. However, the validity of this task has been questioned because there are substantial differences between the tasks for mock witnesses and eyewitnesses. Unlike eyewitnesses, mock witnesses must select a person from the lineup and are alerted to the fact that one lineup member might stand out from the others. It therefore seems desirable to base conclusions about lineup fairness directly on eyewitness data rather than on mock-witness data. To test the importance of direct measurements of biased suspect selection in eyewitness identification decisions, we assessed the fairness of lineups containing either morphed or non-morphed fillers using both mock witnesses and eyewitnesses. We used Tredoux's E and the proportion of suspect selections to measure lineup fairness from mock-witness choices and the two-high threshold eyewitness identification model to measure the biased selection of the suspects directly from eyewitness identification decisions. Results obtained in the mock-witness task and the model-based analysis of data obtained in the eyewitness task converged in showing that simultaneous lineups with morphed fillers were significantly more unfair than simultaneous lineups with non-morphed fillers. However, mock-witness and eyewitness data converged only when the eyewitness task mimicked the mock-witness task by including pre-lineup instructions that (1) discouraged eyewitnesses to reject the lineups and (2) alerted eyewitnesses that a photograph might stand out from the other photographs in the lineup. When a typical eyewitness task was created by removing these two features from the pre-lineup instructions, the morphed fillers no longer lead to unfair lineups. These findings highlight the differences in the cognitive processes of mock witnesses and eyewitnesses and they demonstrate the importance of measuring lineup fairness directly from eyewitness identification decisions rather than indirectly using the mock-witness task. [ABSTRACT FROM AUTHOR]

Abstract Thyroid cancer is a common endocrine carcinoma that occurs in the thyroid gland. Much effort has been invested in improving its diagnosis, and thyroidectomy remains the primary treatment method. A successful operation without unnecessary side injuries relies on an accurate preoperative diagnosis. Current human assessment of thyroid nodule malignancy is prone to errors and may not guarantee an accurate preoperative diagnosis. This study proposed a machine learning framework to predict thyroid nodule malignancy based on our collected novel clinical dataset. The ten-fold cross-validation, bootstrap analysis, and permutation predictor importance were applied to estimate and interpret the model performance under uncertainty. The comparison between model prediction and expert assessment shows the advantage of our framework over human judgment in predicting thyroid nodule malignancy. Our method is accurate, interpretable, and thus useable as additional evidence in the preoperative diagnosis of thyroid cancer.

We explored a new artificial intelligence-assisted method to assist junior ultrasonographers in improving the diagnostic performance of uterine fibroids and further compared it with senior ultrasonographers to confirm the effectiveness and feasibility of the artificial intelligence method. In this retrospective study, we collected a total of 3870 ultrasound images from 667 patients with a mean age of 42.45 years ± 6.23 [SD] for those who received a pathologically confirmed diagnosis of uterine fibroids and 570 women with a mean age of 39.24 years ± 5.32 [SD] without uterine lesions from Shunde Hospital of Southern Medical University between 2015 and 2020. The DCNN model was trained and developed on the training dataset (2706 images) and internal validation dataset (676 images). To evaluate the performance of the model on the external validation dataset (488 images), we assessed the diagnostic performance of the DCNN with ultrasonographers possessing different levels of seniority. The DCNN model aided the junior ultrasonographers (Averaged) in diagnosing uterine fibroids with higher accuracy (94.72% vs. 86.63%, P < 0.001), sensitivity (92.82% vs. 83.21%, P = 0.001), specificity (97.05% vs. 90.80%, P = 0.009), positive predictive value (97.45% vs. 91.68%, P = 0.007), and negative predictive value (91.73% vs. 81.61%, P = 0.001) than they achieved alone. Their ability was comparable to that of senior ultrasonographers (Averaged) in terms of accuracy (94.72% vs. 95.24%, P = 0.66), sensitivity (92.82% vs. 93.66%, P = 0.73), specificity (97.05% vs. 97.16%, P = 0.79), positive predictive value (97.45% vs. 97.57%, P = 0.77), and negative predictive value (91.73% vs. 92.63%, P = 0.75). The DCNN-assisted strategy can considerably improve the uterine fibroid diagnosis performance of junior ultrasonographers to make them more comparable to senior ultrasonographers. [ABSTRACT FROM AUTHOR]

Intraspecific social interactions in domestic cats are often categorised as affiliative or agonistic. However, public or professional assessment of encounters can have difficulty distinguishing rough-and-tumble play from true agonism. One possible issue is the potential occurrence of elements of both, play and agonism, within inter-cat play, for example when one cat wants to terminate a bout of play but the other seeks to continue the interaction, which subsequently may provoke more overt agonistic behaviour. To test this hypothesis, we conducted behavioural observations of 105 unique dyadic interactions of domestic cats (N = 210) captured on videos collected from owners and YouTube. We assessed cats for the frequency and duration of six behavioural elements. The dataset was reduced using PCA with a varimax rotation and factor scores were used to classify the population using hierarchical cluster analysis. To validate the identified clusters, the average scores of the constituent factors were compared and the data on interactions were labelled by four cat behaviour experts as "playful", "intermediate" or "agonistic". In addition, to evaluate properties of expert-labelled categories we used linear discriminant analysis followed by an ordinal regression. The results showed considerable convergent validity in factor distributions between clusters and expert-labelled groups: reciprocal wrestling was most closely associated with a group of playfully interacting cats, while vocalisation and chasing were associated with the agonistic group. The intermediate group, while having characteristics of both, was more closely related to the playful group than the agonistic group, with prolonged exchanges of interactive behaviours being a predominant feature. Thus, our findings support the suggestion of there being an intermediate category between mutual social play and agonism. This might escalate into a fully agonistic encounter, but does not necessarily reflect a break down in their social relationship but rather a short-term disagreement in social priorities. [ABSTRACT FROM AUTHOR]

In general, it is known that extreme climatic conditions such as El Niño and positive Indian Ocean Dipole (IOD+) cause prolonged drought in Indonesia's tropical peatlands so that groundwater levels (GWL) drop and peat is prone to fire. However, 27 years of GWL measurements in Central Kalimantan peat forests show the opposite condition, where the lowest GWL occurs several weeks before El Niño and after IOD+ reaches its peaks. We show that the dropped sea surface temperature anomaly induced by anomalously easterly winds along the southern Java-Sumatra occurs several weeks before the GWL drop to the lowest value. Local rainfall decreased, and GWL dropped sharply by 1.0 to 1.5 m, during the super El Niño events in 1997/98 and 2015, as well as remarkable events of IOD+ in 2019. It is suggested that the tropical peatland ecohydrological system (represented by the GWL), El Niño Southern Oscillation (ENSO), and IOD+ are teleconnected. Hence, monitoring GWL variability of peatland over the IMC is a possibility an alert for extreme climate events associated with El Niño and/or moderate IOD+. [ABSTRACT FROM AUTHOR]

The development of information measures associated with interval-valued intuitionistic fuzzy values (IVIFVs) has been an important research area over the past few decades. In the literature, the existing decision -making method using IVIFVs has some drawbacks, and the identification degree and information utilization suffer from a gap in the evaluation of alternatives. Therefore, the need for a reliable, useful, and comprehensive decision method is obvious. To obtain more accurate and reliable evaluation results, multiattribute group decision-making (MAGDM) problems, where the same attribute weights given by different decision-makers are different, are studied in this paper. First, the novel operational laws of IVIFVs and a new interval-valued intuitionistic fuzzy weighted arithmetic aggregation operator are defined to overcome the drawbacks of the IIFWA aggregation operator and avoid losing or distorting the original decision information in the process of aggregation. Second, the mean and variance of the possibility degrees of IVIFVs are defined based on the concept of a definite integral. Third, a novel MAGDM method based on the new aggregation operator and the mean and variance of the possibility degrees of IVIFVs is proposed to improve the identification of the evaluation results and ensure the effectiveness of the ranking order. Finally, the effectiveness and practicability of the proposed method are verified by an air combat training accuracy assessment example. This example can be used to assist decision-makers in evaluating air combat training hits in a timely and efficient manner, providing an objective, scientific basis for the realization and application of air combat training hit assessment and a new method and idea for MAGDM problems in an interval-valued intuitionistic fuzzy environment. [ABSTRACT FROM AUTHOR]

Radon is an established lung carcinogen concentrating in indoor environments with importance for many workers worldwide. However, a systematic assessment of radon levels faced by all workers, not just those with direct uranium or radon exposure, has not previously been completed. The objective of this study was to estimate the prevalence of workers exposed to radon, and the level of exposure (> 100–200 Bq/m3, 200–400 Bq/m3, 400–800 Bq/m3, and > 800 Bq/m3) in a highly exposed country (Canada). Exposures among underground workers were assessed using the CAREX Canada approach. Radon concentrations in indoor workplaces, obtained from two Canadian surveys, were modelled using lognormal distributions. Distributions were then applied to the susceptible indoor worker population to yield the number of exposed workers, by occupation, industry, province, and sex. In total, an estimated 603,000 out of Canada's 18,268,120 workers are exposed to radon in Canada. An estimated52% of exposed workers are women, even though they comprise only 48% of the labour force. The majority (68%) are exposed at a level of > 100–200 Bq/m3. Workers are primarily exposed in educational services, professional, scientific and technical services, and health care and social assistance, but workers in mining, quarrying, and oil and gas extraction have the largest number of exposed workers at high levels (> 800 Bq/m3). Overall, a significant number of workers are exposed to radon, many of whom are not adequately protected by existing guidelines. Radon surveys across multiple industries and occupations are needed to better characterize occupational exposure. These results can be used to identify exposed workers, and to support lung cancer prevention programs within these groups. [ABSTRACT FROM AUTHOR]

Failure mode and effects analysis (FMEA) has been widely used for potential risk modeling and management. Expert evaluation is used to model the risk priority number to determine the risk level of different failure modes. Dempster–Shafer (D–S) evidence theory is an effective method for uncertain information modeling and has been adopted to address the uncertainty in FMEA. How to deal with conflicting evidence from different experts is an open issue. At the same time, different professional backgrounds of experts may lead to different weights in modeling the evaluation. How to model the relative weight of an expert is an important problem. We propose an improved risk analysis method based on triangular fuzzy numbers, the negation of basic probability assignment (BPA) and the evidence distance in the frame of D–S evidence theory. First, we summarize and organize the expert's risk analysis results. Then, we model the expert's assessments based on the triangular fuzzy numbers as BPAs and calculate the negation of BPAs. Third, we model the weight of expert based on the evidence distance in the evidence theory. Finally, the Murphy's combination rule is used to fuse the risk assessment results of different experts and calculate the new risk priority number (RPN). At the end of this paper, we apply the proposed method to analyze seventeen failure modes of aircraft turbine blades. The experimental results verify the rationality and effectiveness of this method. [ABSTRACT FROM AUTHOR]

There is growing evidence that pesticides may be among the causes of worldwide bee declines, which has resulted in repeated calls for their increased scrutiny in regulatory assessments. One recurring concern is that the current frameworks may be biased towards assessing risks to the honey bee. This paradigm requires extrapolating toxicity information across bee species. Most research effort has therefore focused on quantifying differences in sensitivity across species. However, our understanding of how responses to pesticides may vary within a species is still very poor. Here we take the first steps towards filling this knowledge gap by comparing acute, lethal hazards in sexes and castes of the eusocial bee Bombus terrestris and in sexes of the solitary bee Osmia bicornis after oral and contact exposure to the pesticides sulfoxaflor, Amistar (azoxystrobin) and glyphosate. We show that sensitivity towards pesticides varies significantly both within and across species. Bee weight was a meaningful predictor of pesticide susceptibility. However, weight could not fully explain the observed differences, which suggests the existence of unexplored mechanisms regulating pesticide sensitivity across bee sexes and castes. Our data show that intra-specific responses are an overlooked yet important aspect of the risk assessment of pesticides in bees. [ABSTRACT FROM AUTHOR]