Model Results Research Articles

Semantic segmentation of coastal aerial/satellite images using deep learning techniques: An application to coastline detection

A new CNN based approach supported by semantic segmentation, was proposed. This approach is frequently used to carry out regional-scale studies. The core of our method revolves around a CNN model, based on the famous U-Net architecture. Its purpose is to identify different classes of pixels on satellite images and later to automatically detect the coastline. The recently launched Coast Train dataset was used to train the CNN model. Traditional coastline detection was improved (“water/land” segmentation) by means of two new aspects the use of the Sobel-edge loss function and the segmentation of the satellite images into several categories like built-up areas, vegetation and land besides beach/sand and water classes. The approach used ensures a more precise coastline extraction, distinguishing water pixels from all other categories. Our model adeptly identifies features, such as cliff vegetation or coastal roads, that some models might overlook. In this way, coastline localization and its drawing for regional scale study, have minor uncertainties. The performance of the CNN-based method, achieving 85% accuracy and 80% IoU (Intersection over Union) in the segmentation process. The ability of the model to extract the coastline was validated on a Sicilian case study, notably the San Leone beach (Agrigento). The model's results align closely with the ground truth, moreover, its reliability was further confirmed when it was tested on other Sicilian coastal regions.Beyond robustness, the model offers a promising avenue for enhanced coastal analysis potentially applicable to coastal planning and management.

Improving carbon flux estimation in tea plantation ecosystems: A machine learning ensemble approach

Tea plant (Camellia sinensis) is a major global crop consumed as a drink after water. Quantifying carbon flux, specifically the net ecosystem exchange (NEE), in tea plantations is essential for determining carbon sequestration and ecosystem carbon balance. The Eddy covariance (EC) system is widely used for continuous monitoring of carbon flux but high costs associated with installation and maintenance limit its widespread adoption. In addition, EC flux data is often discarded due to malfunction of instruments caused by adverse weather conditions. Therefore, additional approaches for estimating NEE are necessary to overcome these challenges and ensure accurate NEE measurement. For this purpose, three standalone tree-based machine learning (ML) models were used for NEE estimation using EC flux data collected from tea ecosystem located in subtropical region (Danyang county of Zhenjiang city) of China. To address the accuracy limitations inherent in standalone ML models, the ensemble mechanism based on voting regressor method was proposed. In addition, k-fold cross-validation based on early stopping process was also used to enhance the performance of standalone ML models. Based on visual plots (scatter diagram, heatMap, Taylor diagram) and performing indices (root-mean-square error (RMSE), determination coefficient (R2), mean absolute error (MAE), mean absolute percentage error (MAPE), Nash–Sutcliffe efficiency (NSE), correlation coefficient (r), Kling Gupta Efficiency (KGE) and index of agreement (d)), the findings indicated that non-linear ensemble-generalized regression neural network (NLE-GRNN) significantly improved standalone ML model's results. In current study, the highest NSE, r and d in case of standalone ML model (DT) achieved 0.49, 0.73 and 0.75 respectively while our proposed NLE-GRNN model improved 48 % in NSE value (NSE = 0.97), 25 % in r value (r = 0.98) and 24 % in d value (d = 0.99). Likewise, NLE-GRNN significantly reduce errors (MAE, MAPE and RMSE) and provides NEE estimate closet to the observed value. The impact of climatic variables on NEE using shapley additive explanations (SHAP) analysis revealed that Rg (solar radiation) and Tair (air temperature) were the prime factors controlling NEE variation in the tea ecosystem. Considering the high accuracy and stability of the studied ML models, it is recommended to apply developed ensemble ML model (NLE-GRNN) for significant improvement of NEE estimate in the tea biomes or other ecosystems.

A novel mass integration cogeneration system: Working fluid selection model and multi-criteria decision analysis

As the globe faces major difficulties such as climate change, environmental pollution, and limited energy resources, improving energy efficiency and decreasing the environmental effects of energy systems have become research priorities. A novel mass integration cogeneration (MICHP) system is proposed in this paper. In addition, a mathematical model relating the basic physical properties of the working fluid to the system's performance has been developed. R1233zd(E), R1224yd(Z), R601a, R245fa, and acetone have been chosen to be applied to the MICHP to confirm the model's accuracy. By utilizing the technique for order preference by similarity to an ideal solution, the entropy weight method, and energy, exergy, economic, and environmental analyses of the MICHP, the general efficiency of the working fluid is further explored. The results show that the intermediate heat exchanger is the largest exergy destruction equipment in the system. R601a has the highest return on investment of 0.53, and the annual operating cost is found to be proportional to the latent heat of the working fluid. R1233zd(E) has the lowest environmental impact, with a total equivalent warming impact of −4.89 × 106 kgCO2eq. The multi-criteria decision analysis of the working fluid agrees with the order of the proposed mathematical model's results, proving the model's accuracy. Consequently, the mathematical model can provide a helpful technique guide for working fluid selection in cogeneration systems.

The influence of social identity on attitudes toward wildlife.

Wildlife conservation depends on supportive social as well as biophysical conditions. Social identities such as hunter and nonhunter are often associated with different attitudes toward wildlife. However, it is unknown whether dynamics within and among these identity groups explain how attitudes form and why they differ. To investigate how social identities help shape wildlife-related attitudes and the implications for wildlife policy and conservation, we built a structural equation model with survey data from Montana (USA) residents (n=1758) that tested how social identities affect the relationship between experiences with grizzly bears (Ursus arctos horribilis) and attitudes toward the species. Model results (r2=0.51) demonstrated that the hunter identity magnified the negative effect of vicarious property damage on attitudes toward grizzly bears (β=-0.381, 95% confidence interval [CI]: -0.584 to -0.178, p<0.001), which in turn strongly influenced acceptance (β=-0.571, 95% CI: -0.611 to -0.531, p<0.001). Our findings suggested that hunters' attitudes toward grizzly bears likely become more negative primarily because of in-group social interactions about negative experiences, and similar group dynamics may lead nonhunters to disregard the negative experiences that out-group members have with grizzly bears. Given the profound influence of social identity on human cognitions and behaviors in myriad contexts, the patterns we observed are likely important in a variety of wildlife conservation situations. To foster positive conservation outcomes and minimize polarization, management strategies should account for these identity-driven perceptions while prioritizing conflict prevention and promoting positive wildlife narratives within and among identity groups. This study illustrates the utility of social identity theory for explaining and influencing human-wildlife interactions.

A Visual Environment for Data Driven Protein Modeling and Validation.

In structural biology, validation and verification of new atomic models are crucial and necessary steps which limit the production of reliable molecular models for publications and databases. An atomic model is the result of meticulous modeling and matching and is evaluated using a variety of metrics that provide clues to improve and refine the model so it fits our understanding of molecules and physical constraints. In cryo electron microscopy (cryo-EM) the validation is also part of an iterative modeling process in which there is a need to judge the quality of the model during the creation phase. A shortcoming is that the process and results of the validation are rarely communicated using visual metaphors. This work presents a visual framework for molecular validation. The framework was developed in close collaboration with domain experts in a participatory design process. Its core is a novel visual representation based on 2D heatmaps that shows all available validation metrics in a linear fashion, presenting a global overview of the atomic model and provide domain experts with interactive analysis tools. Additional information stemming from the underlying data, such as a variety of local quality measures, is used to guide the user's attention toward regions of higher relevance. Linked with the heatmap is a three-dimensional molecular visualization providing the spatial context of the structures and chosen metrics. Additional views of statistical properties of the structure are included in the visual framework. We demonstrate the utility of the framework and its visual guidance with examples from cryo-EM.

Effect of unusual texture on tension-compression asymmetry for Mg-RE alloy by viscoplastic self-consistent modeling

The tension-compression asymmetry presents notable challenges for the application of magnesium alloys in many fields. In this study, the solid-solution treated Mg-8.5Gd-4.5Y-0.8Zn-0.4Zr alloy's tension-compression asymmetry was examined using optical microscope (OM), x-ray diffraction (XRD), viscoplastic self-consistent (VPSC) modeling, and electron backscatter diffraction (EBSD). The VPSC hardening parameters were significantly adjusted based on the Schmid factor of deformation modes in rare earth magnesium (Mg-RE) alloy, which came from the EBSD data. Excellent agreement was found between the modified VPSC model's calculation results, especially the stress-strain curves and pole figures. The alloy exhibited good strength with a negligible tension-compression asymmetry and an impressive 0.98 ratio of compressive yield strength to tensile yield strength (CYS/TYS). The main cause could be attributed to the unusual texture of (11-20) <0001> in alloy, which eliminated the imbalance in tension and compression deformation by having a negative effect on the activation of {10-12} twinning in tensile and a positive effect in compressive deformation. The activation level of {10-12} twinning was 0.37 and 0.40 calculated by VPSC model, in the plastic deformation of tension and compression, respectively; in the tensile and compression samples, the EBSD data indicated that approximately 31.9% and 31.1% (area proportion) of the grains were deformed with twins, respectively. Both tension and compression deformation showed the {10-12} twinning in the early stage of deformation, which transformed to {11-22} twinning in the later stage. The considerable activation of pyramidal <c+a> during the later stages of deformation endowed the alloy with good ductility.

Cyclic loading experiments and numerical analysis of laterally loaded piles constructed in marine soil

This study aims to thoroughly analyze the lateral loads that impact tubular steel piles, which are extensively employed in the construction of coastal structures. The ASTM D 3966 standard was followed for conducting field tests on test piles installed at the Mersin International Port. The “time-displacement” and “load-displacement” curves were obtained from the cyclic loading test of the laterally loaded piles at the construction site. The finite elements models of the tests conducted on the construction site with the same parameters was built to perform numerical analysis. At the end of the analysis, it was determined that the numerical model's results were highly consistent with those obtained from the field test. After verifying the field experiments with numerical models, a parametric study was conducted. Parametric studies were conducted to compare the lateral displacements of tubular steel piles with variations in pile diameter, wall thickness, and load application height effect. The relationship between pile head displacements and these parameters appears to be almost linear. It is noteworthy that a change of approximately 10 percent in these parameters shows a correlation with changes of up to 3 percent in deformations.

Restoration of load capacity and stiffness of continuous steel–concrete composite beams having web openings using externally applied FRP strips

AbstractThe aim of this study is to explore the applicability of externally bonded fiber‐reinforced polymer (FRP) composites to enhance the structural performance of steel–concrete composite beams with web openings in terms of load capacity and stiffness. In order to achieve this aim, the ABAQUS software was used to create a three‐dimensional (3D) non‐linear finite element model (FEM) to simulate the behavior of FRP‐strengthened continuous composite beams with web openings exposed to monotonic loadings. After ascertaining the accuracy of the proposed model's results in successfully predicting failure patterns and load capacities of the experimentally tested specimens available in the literature, the suggested model was used to create a parametric study. The parametric study focused on the impacts of the opening location, opening shapes, and opening area on the failure pattern, load carrying capacity, and stiffness of continuous steel–concrete composite beams. Additionally, strengthening the web openings using different configurations and lengths of FRP strips with and without bolts was investigated. Results showed that the presence of web openings in location 2 exhibited the lowest load capacity of all investigated beams (20.80%–42.50% lower than the control composite beam). Moreover, the continuous composite beams with a circular opening were the best case and gave a higher failure load as compared to the rectangular opening at all locations. Additionally, all the simulated FRP‐strengthened composite beams in the third group demonstrated significant values of load capacities and stiffness among all the analyzed specimens.

Decoding contextual influences on auditory perception from primary auditory cortex.

Perception can be highly dependent on stimulus context, but whether and how sensory areas encode the context remains uncertain. We used an ambiguous auditory stimulus - a tritone pair - to investigate the neural activity associated with a preceding contextual stimulus that strongly influenced the tritone pair's perception: either as an ascending or a descending step in pitch. We recorded single-unit responses from a population of auditory cortical cells in awake ferrets listening to the tritone pairs preceded by the contextual stimulus. We find that the responses adapt locally to the contextual stimulus, consistent with human MEG recordings from the auditory cortex under the same conditions. Decoding the population responses demonstrates that cells responding to pitch-class-changes are able to predict well the context-sensitive percept of the tritone pairs. Conversely, decoding the individual pitch-class representations and taking their distance in the circular Shepard tone space predicts the opposite of the percept. The various percepts can be readily captured and explained by a neural model of cortical activity based on populations of adapting, pitch-class and pitch-class-direction cells, aligned with the neurophysiological responses. Together, these decoding and model results suggest that contextual influences on perception may well be already encoded at the level of the primary sensory cortices, reflecting basic neural response properties commonly found in these areas.

Unreliability tracing of power systems with reservoir hydropower based on a temporal recursive model

AbstractPower system unreliability tracing model allocates the system's reliability index to individual components, identifying potential weaknesses. This study expands its scope by considering the impact of storage resources. Unreliable factors leading to load shedding are categorized into two groups: objective factors inherent to the component and insufficient storage resources. The latter requires a retrospective analysis of other components that caused unreliability previously. When allocating responsibility for load shedding at a certain time, it begins by allocating it among components based on differences between fixed expected output and actual supply. Expected output insufficiency is considered as the unreliable factor. This insufficiency due to insufficient storage resources is then decomposed into segments, each caused by excessive output in earlier instances of the same component. The expected output excess is attributed to the expected output insufficiency of other components in previous times, for which responsibility has been allocated to each component. Consequently, the expected output insufficiency at a particular time can be traced back based on a temporal recursive model, with the load shedding further allocated to components before that time. Case studies based on several systems demonstrate that the proposed model's allocation results are reasonable and more accurate than the traditional model.

Activation energy and angular momentum in electromagnetic radiation micropolar nanofluids: Focus on microbial eukaryotic species

Microbial eukaryotic species play a crucial role in biomedical research, aiding in the study of bacterial impurities, microorganism pathogenesis, and development of tactics to combat antimicrobial struggle. These species represent a complex and diverse aspect of bacteriological life, influencing ecosystems, human health, and technological progress. This study aims to create a comprehensive computational model to analyze the magnetohydrodynamics of a non-Newtonian micropolar nanofluid over an exponentially stretchable surface. It incorporates the Buongiorno model, which considers thermophoretic diffusion and Brownian motion effects to study the thermal behavior of micropolar nanofluids. It is also investigated how thermal radiation affects the current model. The classical Navier's stokes equations of motions of the current model are transform into a system of ordinary differential equation by employing similarity approach. The classical Navier-Stokes equations of motion are transformed into ordinary differential equations using similarity transformations, and MATLAB is used to solve them numerically. Here we considered three different cases, (i)-injection fw>0, (ii)-impermeable wall fw=0, (iii)-suction fw<0 which are illustrated visually. The consequence of various flow parameters effects on velocity, temperature, concentration, motile microorganism, and angular profiles are illustrated graphically. The high concordance between the model's results and the available data validates its originality. Present results indicate that while a larger porosity and Hartman number decrease velocity, increasing the mass and thermal convective factors raises the velocity profile. Raising the thermophoretic and magnetic parameters causes the temperature profile to climb; however, raising the temperature exponent and Prandtl number causes it to fall. The behavior of micropolar nanofluids is better understood thanks to this research, which could have applications in a variety of industries including manufacturing, energy, electronics, and healthcare. The novelty of the current model is verified using data that has already been published, and a great agreement is observed. When the values of the parameter K lies between the ranges 0.2<K< 0.3, and 0.3<K< 0.4, percentage increment noted in heat transfer enhancement is about 2.3 % and 6.3 % Until now, no such investigation has been performed to examine the implications of three-dimensional bio-convection micropolar based Casson fluid flow under the activation energy and suction/injection. From the outcomes, it was established that nanofluids are more fruitful for heat transfer enhancement.

Development of Mathematics Teaching Models According to the Theory of Knowledge Creation to Promote the Mathematical Competence of Mathayom 6 Student

Background and Aims: The development of human thought is greatly aided by mathematics, which fosters creativity, and logical and systematic thought, benefits life, and raises the standard of living. (1) the first goal of this research is to create a mathematical teaching model for Mathayom 6 students that is based on the theory of knowledge creation. And (2) Examine the effects of utilizing a theory of knowledge creation-based mathematics teaching model to enhance Mathayom 6 students' mathematical proficiency. Methodology: Three research steps make up this study. (1) Phase 1: Basic information, current conditions, and conditions for teaching mathematics are studied, along with concepts, theories, and research about models of teaching mathematics based on knowledge creation theory to enhance secondary school students' mathematical competency. Year 6 by speaking with five education specialists. (2) Phase 2: Creating a teaching model for mathematics based on the theory of knowledge creation to help students in Mathematical Competency 6 and (3) Phase 3: Testing out a model of teaching mathematics based on the theory of knowledge creation to improve Mathayom 6 students' performance in the subject. Thirty Mathayom 6 students from Thesaban 2 School in Nangrong Municipality, Nangrong District, Buriram Province participated in the model's testing as a sample group during the second semester of the academic year 2022; (1) A mathematics teaching model based on the theory of knowledge creation to support Mathayom 6 students' mathematical competency is one of the instruments used to gather data. (2) Learning Satisfaction Scale; (3) Mathematics Competency Test; and (2) Achievement Test, the t-test (Dependent), mean, percentage, and standard deviation are among the statistics used in data analysis. Results: (1) A mathematical competency-promoting teaching model for Mathayom 6 students, based on the theory of knowledge creation. There are six parts. These include the model's goals, instructional strategies, social systems, response theories, and support networks. Four steps make up the teaching process: (1.1) Step of operation (Operation: O) (1.2) Step two: Action (Acting: A) (1.3) Step three: Knowledge creation (Creation: C), additionally, (1.4) Recapitulating the idea (Idea: C) and a high degree of appropriateness was indicated by the model's evaluation results, which had a mean of 4.47 and a standard deviation of 0.41. (2) The application of the theory of knowledge creation to the mathematics teaching model revealed that at a statistical significance of.05., students' academic achievement is higher than it was before they started studying. When Mathayom 6 students use the mathematics teaching model, their performance in mathematics exceeds the 70% threshold. Additionally, a high degree of student satisfaction has been observed with the mathematics teaching model. Conclusion: To emphasize mathematical competency, the study presents a comprehensive teaching model for Mathayom 6 students that is based on the theory of knowledge creation. The effectiveness of the model is demonstrated by the evaluation results, which show a significant improvement in academic achievement and student satisfaction, both of which exceed the performance threshold of 70%.

UNVEILING THE IMPACT OF GEOPOLITICAL RISKS ON TURKISH ECONOMY AND FISCAL DYNAMICS

This research aims to examine the impact of geopolitical risks on Türkiye's trade openness, household consumption, investment, government debt, and budget deficit for the quarters 2006:I–2022:IV. There are no empirical studies in the literature that examine the relationship between geopolitical risks and Turkish fiscal policy. The vector autoregressive (VAR) approach is utilized to examine the relationships among the variables. Geopolitical risks contribute to a very serious increase in inflation in Türkiye. The increase in geopolitical risks negatively affects consumption and investment and increases budget deficits and government debt. Additionally, Türkiye's trade volume has also decreased as a result of rising geopolitical risks, according to the VAR model's results. These findings demonstrate how vulnerable the Turkish economy is to geopolitical risks. Succinctly, Türkiye's public finances and economy are negatively impacted by geopolitical risks according to the analysis's findings. As a matter of fact, taking precautionary fiscal policy measures against geopolitical risks is important in eliminating the fiscal and economic losses that may arise. In this respect, this study has the potential to make a new contribution to the fiscal policy literature and to guide fiscal policy makers.

Wave height distribution for plunging breakers induced by air bubbles

This study highlights the wave height distributions based on plunging breaking waves and air bubble effects in the surf zone. Wave height distributions predicted previously by different models have been reformulated in terms of air bubble effects. Some of the most commonly applied wave height distributions are judged using laboratory observations in a custom-built wave flume. Results have revealed a notable deviation from the Rayleigh distribution, with the proposed model demonstrating a closer match to experimental data, particularly for larger wave heights. Probability densities of larger wave heights are reduced significantly by entrained air bubbles, leading to measured wave heights below Rayleigh distribution predictions. In addition, the different wave height parameters derived from the proposed model exhibited good agreement with laboratory data compared to the Rayleigh distribution. Furthermore, theoretical analysis shows the scale parameter's dependence on the decay coefficient, aligning well with measurements and simplifying the proposed distribution to a one-parameter model. Again, based on the error analysis, the proposed model's results show good performance compared to others.

Enhancing depression detection: A multimodal approach with text extension and content fusion

AbstractBackgroundWith ubiquitous social media platforms, people express their thoughts and emotions, making social media data valuable for studying and detecting depression symptoms.ObjectiveFirst, we detect depression by leveraging textual, visual, and auxiliary features from the Weibo social media platform. Second, we aim to comprehend the reasons behind the model's results, particularly in medicine, where trust is crucial.MethodsTo address challenges such as varying text lengths and abundant social media data, we employ a text extension technique to standardize text length, enhancing model robustness and semantic feature learning accuracy. We utilize tree‐long short‐term memory and bidirectional gate recurrent unit models to capture long‐term and short‐term dependencies in text data, respectively. To extract emotional features from images, the integration of optical character recognition (OCR) technology with an emotion lexicon is employed, addressing the limitations of OCR technology in accuracy when dealing with complex or blurred text. In addition, auxiliary features based on social behaviour are introduced. These modalities’ output features are fed into an attention fusion network for effective depression indicators.ResultsExtensive experiments validate our methodology, showing a precision of 0.987 and recall rate of 0.97 in depression detection tasks.ConclusionsBy leveraging text, images, and auxiliary features from Weibo, we develop text picture sentiment auxiliary (TPSA), a novel depression detection model. we ascertained that the emotional features extracted from images and text play a pivotal role in depression detection, providing valuable insights for the detection and assessment of the psychological disorder.

Modeling the impacts of extreme climate scenarios on soil acidity (pH and exchangeable aluminum) in Abbay River Basin, Ethiopia

The Abbay River Basin faces the looming threat of extreme climate events, including prolonged droughts and erratic rainfall patterns, which can significantly affect soil health and fertility. This study aimed to explore the influence of extreme climate conditions on soil pH and exchangeable aluminum, aiming to promote sustainable agricultural practices in Ethiopia. The Africa Soil Information Service (ASIS) provided datasets on soil pH and exchangeable aluminum. The European Copernicus Climate Change Data Store was used to download historical and future datasets of extreme climatic indices from 1980 to 2010 and 2015–2050, respectively. The Coupled Model Intercomparison Project Phase 6 model ensemble was used to predict future climate impacts under three shared socioeconomic scenarios: SSP1-2.6, SSP2-4.3, and SSP5-8.5. Data extraction, quality control, and clustering were conducted before analysis, and the model was validated for its accuracy and reliability in predicting soil parameter changes. An artificial neural network model was utilized to predict the effects of extreme climate indices on soil pH and exchangeable aluminum concentrations. The model was designed to accurately and reliably predict changes in soil parameters. This study compared the changes in soil pH and aluminum concentrations using paired t tests. The model's diagnostic results indicated a significant impact of extreme climate scenarios on soil pH and exchangeable aluminum. Extreme climate factors such as heavy precipitation and cooler night time temperatures significantly contribute to soil acidification and an increase in aluminum concentration. Under the SSP1-2.6 and SSP2-4.5 emission scenarios, soil pH levels are expected to increase by 8.38 % and 3.79 %, respectively. These changes in soil pH are expected to have significant impacts on the exchangeable aluminum content in the soil, with increases of 37 % and 5.38 %, respectively, under the same emission scenarios. However, the SSP5.8 scenario predicted a 45 % increase in exchangeable aluminum and a 9.36 % decrease in soil pH. Therefore, this study significantly enhances our understanding of the influence of climate change on soil health. The development of strategies to mitigate climate change impacts on agriculture in the region must consider the effects of extreme climate indices.

Smartphone-based machine learning model for real-time assessment of medical kidney biopsy

BackgroundKidney biopsy is the gold-standard for diagnosing medical renal diseases, but the accuracy of the diagnosis greatly depends on the quality of the biopsy specimen, particularly the amount of renal cortex obtained. Inadequate biopsies, characterized by insufficient cortex or predominant medulla, can lead to inconclusive or incorrect diagnoses, and repeat biopsy. Unfortunately, there has been a concerning increase in the rate of inadequate kidney biopsies, and not all medical centers have access to trained professionals who can assess biopsy adequacy in real time. In response to this challenge, we aimed to develop a machine learning model capable of assessing the percentage cortex of each biopsy pass using smartphone images of the kidney biopsy tissue at the time of biopsy. Methods747 kidney biopsy cores and corresponding smartphone macro images were collected from five unused deceased donor kidneys. Each core was imaged, formalin-fixed, sectioned, and stained with Periodic acid–Schiff (PAS) to determine cortex percentage. The fresh unfixed core images were captured using the macro camera on an iPhone 13 Pro. Two experienced renal pathologists independently reviewed the PAS-stained sections to determine the cortex percentage. For the purpose of this study, the biopsies with less than 30% cortex were labeled as inadequate, while those with 30% or more cortex were classified as adequate. The dataset was divided into training (n=643), validation (n=30), and test (n=74) sets. Preprocessing steps involved converting High-Efficiency Image Container iPhone format images to JPEG, normalization, and renal tissue segmentation using a U-Net deep learning model. Subsequently, a classification deep learning model was trained on the renal tissue region of interest and corresponding class label. ResultsThe deep learning model achieved an accuracy of 85% on the training data. On the independent test dataset, the model exhibited an accuracy of 81%. For inadequate samples in the test dataset, the model showed a sensitivity of 71%, suggesting its capability to identify cases with inadequate cortical representation. The area under the receiver-operating curve (AUC-ROC) on the test dataset was 0.80. ConclusionWe successfully developed and tested a machine learning model for classifying smartphone images of kidney biopsies as either adequate or inadequate, based on the amount of cortex determined by expert renal pathologists. The model's promising results suggest its potential as a smartphone application to assist real-time assessment of kidney biopsy tissue, particularly in settings with limited access to trained personnel. Further refinements and validations are warranted to optimize the model's performance.

Synthesis of Optimal Control of Spacecraft Angular Momentum for Spatial Turn Taking into Account Energy Costs Using Quaternions

Background: In this paper, we propose solving the specific original problem of control synthesis of spacecraft attitude. Optimization of the control program is made with the use of a new criterion of quality that combines energy costs and duration of reorientation under restrictions on control (the presence of a time factor limits the duration of slew maneuver). Methods: The construction of optimal control for angular momentum change is based on the quaternion method and L.S. Pontryagin maximum principle. An analytical solution to the problem was obtained on the base of a differential equation relating the orientation quaternion and angular momentum of a spacecraft. Results: Key properties of the optimal solution are formulated in analytical form; the features of optimal motion are studied in detail. The control law is formulated in the form of explicit dependence between control and phase variables. In a case when the controlling torque is limited by the given restriction (at the beginning and end of a turn), analytical formulas have been written for the duration of braking and acceleration. Main relations which determine optimal values of parameters of the algorithm for control of angular momentum are given. Examples and results of mathematical modeling of spacecraft motion formed by optimal control were given. This data in addition to the theoretical descriptions illustrates the process of reorientation in evident form and demonstrates the practical feasibility of a designed method for control of angular momentum during spatial turn. Conclusion: The designed optimal algorithm of control of spacecraft motion improves the efficiency of spacecraft attitude system, and originates more economical performance of spacecraft during flight on orbit.

Temporal Association Rule Mining: Race-Based Patterns of Treatment-Adverse Events in Breast Cancer Patients Using SEER-Medicare Dataset.

Disparities in the screening, treatment, and survival of African American (AA) patients with breast cancer extend to adverse events experienced with systemic therapy. However, data are limited and difficult to obtain. We addressed this challenge by applying temporal association rule (TAR) mining using the SEER-Medicare dataset for differences in the association of specific adverse events (AEs) and treatments (TRs) for breast cancer between AA and White women. We considered two categories of cancer care providers and settings: practitioners providing care in the outpatient units of hospitals and institutions and private practitioners providing care in their offices. We considered women enrolled in the Medicare fee-for-service option at age 65 who qualified by age and not disability, who were diagnosed with breast cancer with attributed patient factors of age and race, marital status, comorbidities, prior malignancies, prior therapy, disease factors of stage, grade, and ER/PR and Her2 status and laterality. We included 141 HCPCS drug J codes for chemotherapy, biotherapy, and hormone therapy drugs, which we consolidated into 46 mechanistic categories and generated AE data. We consolidated AEs from ICD9 codes into 18 categories associated with breast cancer therapy. We applied TAR mining to determine associations between the 46 TR and 18 AE categories in the context of the patient categories outlined. We applied the spark.mllib implementation of the FPGrowth algorithm, a parallel version called PFP. We considered differences of at least one unit of lift as significant between groups. The model's results demonstrated a high overlap between the model's identified TR-AEs associated set and the actual set. Our results demonstrate that specific TR/AE associations are highly dependent on race, stage, and venue of care administration. Our data demonstrate the usefulness of this approach in identifying differences in the associations between TRs and AEs in different populations and serve as a reference for predicting the likelihood of AEs in different patient populations treated for breast cancer. Our novel approach using unsupervised learning enables the discovery of association rules while paying special attention to temporal information, resulting in greater predictive and descriptive power as a patient's health and life status change over time.

Feasibility of integrated numerical and Adaptive Neuro-Fuzzy Inference System models in predicting the thermophysical properties of unsteady coupled micropolar–Casson hybrid nanofluids

This article aims to probe postulated phenomena using a paired micropolar and Casson hybrid fluid over a rotating disk. The intension to design a numerical technique (Runge–Kutta fourth-order along with shooting technique) integrated Adaptive Neuro-Fuzzy Inference System (ANFIS) envisioned with a thermal and exponentially space-dependent heat source, nonlinear thermal radiation and entropy production is developed in this phase of work. To ensure that, a nonlinear partial differential equation set of equations has been transformed into an ordinary differential equation by using the proper self-similarity variables. The model's research results, with a few notable outliers, are mostly consistent with those from prior research that was merged into the dataset used to train the ANFIS model. With the impact of active factors, the results are esthetically exhibited for numerous profiles. This displays that with the rise in magnetic field and radiation, the velocity and temperature profiles increase sharply, resulting in a contradiction phenomenon with the decreasing electric field inputs. Also, tilting of vortex viscosity, spin gradient viscosity and microinertia density on the various microrotation components displays inclination. Moreover, ANFIS training was exploited to analyze the approximate solutions for specific scenarios, and the developed ANFIS was evaluated against a testing dataset to emphasize its performance. Due to their longer render, the nanoparticles exploited here are deemed suitable for use in bone implants, iodinated agents for blood imaging and red blood cell stimulation. Thus, the results of this study may be applied to therapeutic anemia therapies.