Differential Approach Research Articles

Natural Hydrogels Support Kidney Organoid Generation and Promote in vitro Angiogenesis.

To date strategies aiming to modulate cell to extracellular matrix (ECM) interactions during organoid derivation remain largely unexplored. Here renal decellularized extracellular matrix (dECM) hydrogels are fabricated from porcine and human renal cortex as biomaterials to enrich cell-to-ECM crosstalk during the onset of kidney organoid differentiation from human pluripotent stem cells (hPSCs). Renal dECM-derived hydrogels are used in combination with hPSC-derived renal progenitor cells to define new approaches for 2D and 3D kidney organoid differentiation, demonstrating that in the presence of these biomaterials the resulting kidney organoids exhibit renal differentiation features, and the formation of an endogenous vascular component. Based on these observations, a new method to produce kidney organoids with vascular-like structures is achieved through the assembly of hPSC-derived endothelial-like organoids with kidney organoids in 3D. Major readouts of kidney differentiation and renal cell morphology are assessed exploiting these culture platforms as new models of nephrogenesis. Overall, this work shows that exploiting cell-to-ECM interactions during the onset of kidney differentiation from hPSCs facilitates and optimizes current approaches for kidney organoid derivation thereby increasing the utility of these unique culture cell platforms for personalized medicine. This article is protected by copyright. All rights reserved.

Urbanization and regional climate change-linked warming of Indian cities

AbstractCities are vulnerable to the compounding effects of both climate change and urbanization. Here we show that urbanization alone has led to an overall 60% enhancement in warming in Indian cities, with eastern Tier-II cities leading the way. Such a difference in the urban contribution to warming over cities across India calls for a differential approach to combat urban warming effectively.

Design, simulation and energy dissipation evaluating of a new lead shear damper

This study presents a novel design for a lead shear damper (LSD) that features lead blocks encased in laminated steel and rubber plates, intersected by X-shaped steel sticks. It combines the excellent energy dissipation and fatigue resistance of lead with the uniform yielding capabilities of X-shaped steel sticks along their height. Cyclic loading testing and fatigue tests are conducted in order to determine the mechanical properties of the LSD. The test results reveal that the LSD has a variable yielding force, and exhibits stiffness hardening when the damper's deformation direction deviates from the origin. However, when the deformation direction returns to the origin, there is no stiffness hardening observed, and the resistant force remains constant. Furthermore, this LSD can work effectively despite the failure of energy dissipation parts, which improves hysteretic energy dissipation under large deformation. In order to replicate the hysteretic behaviour, a uniaxial constitutive model is formulated and its parameters are determined by the utilization of the differential evolution approach, based on test data. The observed strong concurrence between the experimental and simulated hysteresis curves serves as compelling evidence for the efficacy of the differential evolution method. Meanwhile, a three-story building from the third-generation benchmark problem is employed as the testbed structure to examine the energy dissipation capacity of the developed dampers. Nonlinear seismic analysis on the benchmark model equipped with the proposed dampers is also conducted, and the results are compared to those fitted with conventional dampers, such as mild dampers and friction dampers. The findings of this study indicate that the performance of the LSD in reducing structural displacements is superior to that of mild dampers and friction dampers.

Quantifying Structural Shading and Reflection Effects on Single Axis Tracked Bifacial Photovoltaic System Performance

Structural elements of bifacial photovoltaic (PV) systems, such as module frames, module supports, and torque tubes, affect module rear irradiance profiles through both shading and reflection. Standard bifacial energy yield models represent racking effects as a flat rear irradiance de‐rate factor; however, this neglects 1) the impact of racking reflection, and 2) the variation of racking effects over time. In this work, a differential irradiance approach is presented to quantify the effect of racking shading and reflection on bifacial rear irradiance. Using this method, racking effects on system energy yield, irradiance mismatch losses, and bifacial gain are calculated. This is applied to a single‐axis‐tracked 2‐in‐portrait bifacial PV system in Livermore, California with 74.5% reflective racking using bifacial_radiance ray tracing software. Racking reflection counteracts the irradiance reduction and mismatch losses caused by racking shading. Racking reflection reduces average rear irradiance shading by up to 19.2% per timestamp and 9.1% per year compared to absorptive racking. Racking effects vary diurnally, seasonally, and with respect to albedo. In a single day, the impact of racking on rear irradiance varies up to 14.6%. Applying this method will improve energy yield modeling accuracy of bifacial PV systems.

Poor Decision Making and Sociability Impairment Following Central Serotonin Reduction in Inducible TPH2-Knockdown Rats.

Serotonin is an essential neuromodulator for mental health and animals' socio-cognitive abilities. However, we previously found that a constitutive depletion of central serotonin did not impair rat cognitive abilities in stand-alone tests. Here, we investigated how a mild and acute decrease in brain serotonin would affect rats' cognitive abilities. Using a novel rat model of inducible serotonin depletion via the genetic knockdown of tryptophan hydroxylase 2 (TPH2), we achieved a 20% decrease in serotonin levels in the hypothalamus after three weeks of non-invasive oral doxycycline administration. Decision making, cognitive flexibility, and social recognition memory were tested in low-serotonin (Tph2-kd) and control rats. Our results showed that the Tph2-kd rats were more prone to choose disadvantageously in the long term (poor decision making) in the Rat Gambling Task and that only the low-serotonin poor decision makers were more sensitive to probabilistic discounting and had poorer social recognition memory than other low-serotonin and control individuals. Flexibility was unaffected by the acute brain serotonin reduction. Poor social recognition memory was the most central characteristic of the behavioral network of low-serotonin poor decision makers, suggesting a key role of social recognition in the expression of their profile. The acute decrease in brain serotonin appeared to specifically amplify the cognitive impairments of the subgroup of individuals also identified as poor decision makers in the population. This study highlights the great opportunity the Tph2-kd rat model offers to study inter-individual susceptibilities to develop cognitive impairment following mild variations of brain serotonin in otherwise healthy individuals. These transgenic and differential approaches together could be critical for the identification of translational markers and vulnerabilities in the development of mental disorders.

Neural-Integrated Meshfree (NIM) Method: A differentiable programming-based hybrid solver for computational mechanics

While deep learning and data-driven modeling approaches based on deep neural networks (DNNs) have recently attracted increasing attention for solving partial differential equations, their practical applications to real-world scientific and engineering problems remain limited due to the relatively low accuracy and high computational costs. In this study, we present a differentiable programming-based hybrid meshfree approach within the field of computational mechanics. This approach seamlessly integrates neural network-based deep learning architectures with physics-based meshfree discretization techniques, thus referred to as the neural-integrated meshfree (NIM) method. To effectively approximate the solution, NIM employs a hybrid scheme called NeuroPU approximation, which combines continuous DNN representations with partition of unity (PU) basis functions associated with the underlying spatial discretization. This neural-numerical hybridization not only enhances the solution representation through functional space decomposition but also reduces both the size of DNN model and the need of automatic differentiation for spatial gradient computations, leading to a significant improvement in training efficiency. Under the NIM framework, we propose two truly meshfree solvers: the strong form-based NIM (S-NIM) and the local variational form-based NIM (V-NIM). In the S-NIM solver, the strong-form governing equation is directly considered in the loss function, while the V-NIM solver employs a local Petrov–Galerkin approach that allows the construction of variational residuals based on arbitrary overlapping subdomains. This ensures both the satisfaction of underlying physics and the preservation of the meshfree property. We perform extensive numerical experiments on both stationary and transient benchmark problems to assess the effectiveness of the proposed NIM methods in terms of accuracy, scalability, generalizability, and convergence properties. Moreover, comparative analysis with other physics-informed machine learning methods demonstrates that NIM, especially V-NIM, significantly enhances both accuracy and efficiency in end-to-end predictive capabilities.

Nonlinear optimal control for robotic exoskeletons with electropneumatic actuators

Nonlinear optimal control for robotic exoskeletons with electropneumatic actuators

Discriminative diagnosis of ovarian endometriosis cysts and benign mucinous cystadenomas based on the ConvNeXt algorithm

PurposeThe objective of this study was to develop a deep learning model, using the ConvNeXt algorithm, that can effectively differentiate between ovarian endometriosis cysts (OEC) and benign mucinous cystadenomas (MC) by analyzing ultrasound images. The performance of the model in the diagnostic differentiation of these two conditions was also evaluated. MethodsA retrospective analysis was conducted on OEC and MC patients who had sought medical attention at the Fourth Affiliated Hospital of Harbin Medical University between August 2018 and May 2023. The diagnosis was established based on postoperative pathology or the characteristics of aspirated fluid guided by ultrasound, serving as the gold standard. Ultrasound images were collected and subjected to screening and preprocessing procedures. The data set was randomly divided into training, validation, and testing sets in a ratio of 5:3:2. Transfer learning was utilized to determine the initial weights of the ConvNeXt deep learning algorithm, which were further adjusted by retraining the algorithm using the training and validation ultrasound images to establish a new deep learning model. The weights that yielded the highest accuracy were selected to evaluate the diagnostic performance of the model using the validation set. Receiver operating characteristic (ROC) curves were generated, and the area under the curve (AUC) was calculated. Additionally, sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, negative likelihood ratio, and odds ratio were calculated. Decision curve analysis (DCA) curves were plotted. Results: The study included 786 ultrasound images from 184 patients diagnosed with either OEC or MC. The deep learning model achieved an AUC of 0.90 (95 % CI: 0.85–0.95) in accurately distinguishing between the two conditions, with a sensitivity of 90 % (95 % CI: 84 %-95 %), specificity of 90 % (95 % CI: 77 %-97 %), a positive predictive value of 96 % (95 % CI: 91 %-99 %), a negative predictive value of 77 % (95 % CI: 63 %-88 %), a positive likelihood ratio of 9.27 (95 % CI: 3.65–23.56), and a negative likelihood ratio of 0.11 (95 % CI: 0.06–0.19). The DCA curve demonstrated the practical clinical utility of the model. ConclusionsThe deep learning model developed using the ConvNeXt algorithm exhibits high accuracy (90 %) in distinguishing between OEC and MC. This model demonstrates excellent diagnostic performance and clinical utility, providing a novel approach for the clinical differentiation of these two conditions.

Differential Operator Approach to ıquantum Groups and Their Oscillator Representations

Differential Operator Approach to ıquantum Groups and Their Oscillator Representations

SAGAbg. I. A Near-unity Mass-loading Factor in Low-mass Galaxies via Their Low-redshift Evolution in Stellar Mass, Oxygen Abundance, and Star Formation Rate

Measuring the relation between star formation and galactic winds is observationally difficult. In this work we make an indirect measurement of the mass-loading factor (the ratio between the mass outflow rate and star formation rate) in low-mass galaxies using a differential approach to modeling the low-redshift evolution of the star-forming main sequence and mass–metallicity relation. We use Satellites Around Galactic Analogs (SAGA) background galaxies, i.e., spectra observed by the SAGA Survey that are not associated with the main SAGA host galaxies, to construct a sample of 11,925 spectroscopically confirmed low-mass galaxies from 0.01 ≲ z ≤ 0.21 and measure auroral line metallicities for 120 galaxies. The crux of the method is to use the lowest-redshift galaxies as the boundary condition of our model, and to infer a mass-loading factor for the sample by comparing the expected evolution of the low-redshift reference sample in stellar mass, gas-phase metallicity, and star formation rate against the observed properties of the sample at higher redshift. We infer a mass-loading factor of ηm=0.92−0.74+1.76 , which is in line with direct measurements of the mass-loading factor from the literature despite the drastically different sets of assumptions needed for each approach. While our estimate of the mass-loading factor is in good agreement with recent galaxy simulations that focus on resolving the dynamics of the interstellar medium, it is smaller by over an order of magnitude than the mass-loading factor produced by many contemporary cosmological simulations.

Testing a Hyperspectral, Bio‐Optical Approach to Identification of Phytoplankton Community Composition in the Chesapeake Bay Estuary

AbstractThe multi‐to hyperspectral evolution of satellite ocean color sensors is anticipated to enable satellite‐based identification of phytoplankton biodiversity, a key factor in aquatic ecosystem functioning and upper ocean biogeochemistry. In this work the bio‐optical Phytoplankton Detection with Optics (PHYDOTax) approach for deriving taxonomic (class‐level) phytoplankton community composition (PCC, e.g. diatoms, dinoflagellates) from hyperspectral information is evaluated in the Chesapeake Bay estuary on the U.S. East Coast. PHYDOTax is among relatively few optical‐based PCC differentiation approaches available for optically complex waters, but it has not yet been evaluated beyond the California coastal regime where it was developed. Study goals include: (a) testing the approach in a turbid estuary including novel incorporation of colored dissolved organic matter (CDOM) and non‐algal particles (NAP), and (b) performance assessment with both synthetic mixture and field data sets. Algorithm skill was robust on synthetic mixtures. Using field data, cryptophyte and/or cyanophyte phytoplankton groups were predicted, but diatom and dinoflagellate detection was not conclusive. For one field data set, small but significant improvements were observed in predicted PCC groups when tested with incorporation of CDOM and NAP into the algorithm, but not for the second field data set. Sensitivity to three hyperspectral‐relevant spectral resolutions (1, 5, 10 nm) was low for all field and synthetic data. PHYDOTax can identify some phytoplankton groups in the estuary using hyperspectral, field‐collected measurements, but validation‐quality data with broad temporospatial coverage are needed to determine whether the approach is robust enough for science applications.

Differential Solvent DEEP-STD NMR and MD Simulations Enable the Determinants of the Molecular Recognition of Heparin Oligosaccharides by Antithrombin to Be Disentangled.

The interaction of heparin with antithrombin (AT) involves a specific sequence corresponding to the pentasaccharide GlcNAc/NS6S-GlcA-GlcNS3S6S-IdoA2S-GlcNS6S (AGA*IA). Recent studies have revealed that two AGA*IA-containing hexasaccharides, which differ in the sulfation degree of the iduronic acid unit, exhibit similar binding to AT, albeit with different affinities. However, the lack of experimental data concerning the molecular contacts between these ligands and the amino acids within the protein-binding site prevents a detailed description of the complexes. Differential epitope mapping (DEEP)-STD NMR, in combination with MD simulations, enables the experimental observation and comparison of two heparin pentasaccharides interacting with AT, revealing slightly different bound orientations and distinct affinities of both glycans for AT. We demonstrate the effectiveness of the differential solvent DEEP-STD NMR approach in determining the presence of polar residues in the recognition sites of glycosaminoglycan-binding proteins.

Affective disorders in patients with epilepsy: primary process or consequence of paroxysmal activity?

The high prevalence of primary affective disorders in patients with epilepsy is beyond doubt. However, patients in this group are often identified as suffering from mood disorders that do not fulfil the criteria for primary mood disorders, such as recurrent depressive disorder and bipolar affective disorder. The aim of this paper is to analyze the classification and clinical picture of affective disorders in patients with epilepsy and differential diagnostic approaches to identify primary affective disorders and secondary affective disorders in epilepsy. A set of diagnostic signs for the differential assessment of affective disorders in patients with epilepsy is presented. Research of affective pathology in patients with epilepsy and the development of strategies for its successful treatment are still needed.

Delay-sensitive task offloading and efficient resource allocation in intelligent edge–cloud environments: A discretized differential evolution-based approach

The number of smart wireless devices (WDs) has enormously increased over the last few years due to the advancement of 5G/B5G networks. The advanced applications of such smart WDs, e.g., augmented reality, virtual reality, online gaming, etc., demand excessive resources. Although the WDs are equipped with limited resources, the evolution of edge computing and offloading techniques enables the WDs to offload their resource-intensive tasks to the nearby edge node. These edge nodes might experience higher loads and delays when WDs generate a huge number of tasks. Moreover, the wireless channel bandwidth and transmission data rate of the wireless channels are also limited. Therefore, optimizing the use of available bandwidth as a valuable resource and reducing latency emerge as crucial objectives while offloading tasks. In this paper, a delay-aware resource-constrained offloading problem for edge–cloud systems is mathematically formulated as a 0–1 integer linear programming, and it is shown to be NP-complete. Then, a delay-aware resource-constrained offloading algorithm based on a discretized differential evolution (DARC-DE) is designed. The objectives of the DARC-DE are to maximize the utilization of the resources as bandwidth and minimize the delay. The vectors are efficiently encoded along with the decoding technique. The fitness function is designed by considering execution, offloading, queuing, transmission delay, and bandwidth utilization. The DARC-DE is shown to be executed in polynomial time. To evaluate DARC-DE, extensive simulation is performed in two different scenarios with varying numbers of tasks and edges. Simulation results demonstrate that the proposed DARC-DE can minimize total delay by 15% to 40% in comparison to particle swarm optimization, genetic algorithm, and bees algorithm, respectively. Simulation results also indicate a significant improvement in bandwidth utilization. Taguchi method and alternative average convergence rate are conducted. The statistical tests—analysis of variance, post-hoc, and Friedman tests—are also performed.

A generalized differential quadrature approach to the modelling of heat transfer in non-similar flow with nonlinear convection

This study aims to investigate heat transfer and entropy production in the non-similar flow of an incompressible fluid, with nonlinear convection and viscous dissipation. To obtain precise solutions, the Sparrow-Quack-Boerner local non-similarity method is implemented. The non-similarity arises from the nonlinear convection term present in the momentum equation. The non-similarity arises from the nonlinear convection term present in the momentum equation. Consequently, the inclusion of non-similarity terms into the energy equation is achieved through the interconnection of momentum and energy equations. Entropy generation is explored by employing the second law of thermodynamics. Numerical results from several truncation levels are presented in tabular form, and equations for both first and second-level truncations are generated. The one- and two-equation models are solved by implementing the Generalized Differential Quadrature Method (GDQM). Comparison with a second level of truncation reveals significantly greater inaccuracies in numerical results obtained from the first level. This discrepancy arose due to the omission of non-similar terms in the primary governing equations at the first truncation stage. The validity and accuracy of the derived numerical solutions are further demonstrated by the application of the GDQM and the midpoint method with Richardson extrapolation. Additionally, a plot of the numerical data produced from the second level of truncations is presented together with a discussion of the various physical factors. Increasing the mixed convection parameter accelerates fluid velocity, also when the viscous dissipation parameter increases, temperature and the Bejan number increase. The Prandtl number and the thickness of the thermal boundary layer are observed to be inversely related. Moreover, the quantity of entropy generated at the stretching surface and inside the boundary layer rises in proportion to the increases in the Prandtl and Eckert numbers.

A differential privacy location protect approach with intelligence data collection paradigm for MCS

Location information is often required for task allocation in mobile crowdsensing. However, directly uploading location information can raise concerns about privacy leakage among users. To address this issue and protect users privacy when uploading location data to the server, this paper proposes a novel method based on differential privacy for preserving location information. The main idea is to utilize Hilbert mapping to transform the two-dimensional location coordinate of the user into a one-dimensional Hilbert index. Then, Laplace noise is added to the index to introduce a perturbed index, ensuring the protection of location privacy. To enhance the usability of the perturbed index, this paper treats the Laplace noise as a random variable and employs mathematical integration to measure the distance between users and tasks. Leveraging the characteristic of the Hilbert index, wherein a small change in the indexes can result in a large distance between corresponding coordinates, the combination of differential privacy and Hilbert mapping offers enhanced effectiveness compared to conventional differential privacy location preservation schemes. Finally, the proposed scheme is evaluated using the real dataset, and the experiments validate its efficacy.

Robust asset-liability management games for n players under multivariate stochastic covariance models

This paper investigates a non-zero-sum stochastic differential game among n competitive CARA asset-liability managers, who are concerned about the potential model ambiguity and aim to seek the robust investment strategies. The ambiguity-averse managers are subject to uncontrollable and idiosyncratic random liabilities driven by generalized drifted Brownian motions and have access to an incomplete financial market consisting of a risk-free asset, a market index and a stock under a multivariate stochastic covariance model. The market dynamics permit not only stochastic correlation between the risky assets but also path-dependent and time-varying risk premium and volatility, depending on two affine-diffusion factor processes. The objective of each manager is to maximize the expected exponential utility of his terminal surplus relative to the average among his competitors under the worst-case scenario of the alternative measures. We manage to solve this robust non-Markovian stochastic differential game by using a backward stochastic differential equation approach. Explicit expressions for the robust Nash equilibrium investment policies, the density generator processes under the well-defined worst-case probability measures and the corresponding value functions are derived. Conditions for the admissibility of the robust equilibrium strategies are provided. Finally, we perform some numerical examples to illustrate the influence of model parameters on the equilibrium investment strategies and draw some economic interpretations from these results.

Fiscal policy and inequality in middle- and high-income countries: redistributive effects of tax and spending shocks

Abstract Motivated by the sharp increases in public spending following the global financial crisis, we employ the GMM Panel VAR approach at annual frequency between 2004 and 2014 to investigate the dynamic response of alternative income distribution variables to shocks imposed on tax revenues and three key components of social expenditures: social protection, health, and education. We confirm the potential of fiscal policy to reduce income inequality in the medium to longer run, but point to the differential approaches to pursue such a goal in middle- versus high-income countries. We find that the particular expenditure component under consideration matters in terms of the dynamic effect on inequality and on different parts of the income distribution, as well as in terms of the implied time profile. In middle-income countries, positive education spending shocks are the most effective in achieving better distributional outcomes over a medium run of several years. By contrast, in high-income countries, positive health spending and tax shocks have a more pronounced favorable dynamic distributional effect.

Differential approach to preoperative preparations for diffuse purulent peritonitis in children

BACKGROUND: Diffuse purulent peritonitis is the most severe complication of abdominal surgical pathology accompanied by the development of serious metabolic disorders, and its elimination is a preoperative task.
 AIM: This study aimed to compare traditional preoperative preparations using a new scheme that provides a differential approach considering the stage of peritonitis, degree of endotoxicosis, child’s age, and succinate administration.
 MATERIALS AND METHODS: An analysis of the results of treatment of patients aged 1–14 years (average age 7.9 ± 3.9 years) with diffuse purulent peritonitis of various etiologies who were treated from 2006 to 2022 in the children’s surgical department of the V.D. Seredavin Samara Regional Hospital, Samara. The patients were divided into two groups. The main group included 237 patients who underwent preoperative preparation according to the proposed scheme (depending on age, peritonitis stage, and degree of endotoxemia) lasting at least 4–6 h. In addition, for intracorporeal detoxification, the antihypoxant meglumine sodium succinate was used. The control group comprised 102 children who received traditional therapy according to generally accepted approaches and national guidelines for pediatric surgery lasting 2–3 h. Changes in hemodynamic parameters, electrolytes, hematocrit, platelets, and blood gases were comprehensively examined.
 RESULTS: After preoperative preparation, a statistically significant improvement in hemodynamic parameters was observed in the main group compared with that in the control group; the indicators were close to normal values. The dynamics of hematological parameters indicated more pronounced hemodilution in patients of the main group of all ages, which proves a significant improvement in the rheological properties of blood. Electrolyte and blood gas indicators did not reach the indicators of healthy children in any groups; however, in the main group, they were closer to normal. Complications of varying severity in the main group occurred in 21 children (8.8% ± 1.4%; p 0.001), whereas in the control group, they were diagnosed in 47 patients (46.0% ± 4.6%). A statistically significant reduction in the length of stay of patients in the intensive care unit after surgery was noted in 80% of children in the main group.
 CONCLUSIONS: The proposed scheme of preoperative preparation based on a differential approach using the antihypoxant meglumine sodium demonstrated greater effectiveness than traditional treatment. The composition, volume, and duration of preoperative preparation are dependent on the state of homeostasis and the age of the patient.

Unveiling gender differences: a semantic differential approach to studying mindsets in language learning

ABSTRACT Studying language learning beliefs plays an important role in language learning research. The aim of our study was to reconstruct the system of meanings that underlie the gender differences in the beliefs about people involved in foreign language learning. In our study, we used the semantic differential to study beliefs about people involved in foreign language learning. The sample for the study consisted of 86 female and 76 male volunteer participants. Results show that the semantic spaces of beliefs about people involved in foreign language learning have the same dimension but significant differences in the content: the semantic space for males is based on the factors Trustworthiness, Openness to communication and new knowledge and Activity; for females – Evaluation, Diligence and Interpersonal communication. Significant shifts in beliefs about people involved in foreign language learning between females and males are in the factors Activity and Openness to communication and, to a much lesser extent, High emotionality – Emotional stability. According to the results of the study, we concluded that females had a more positive attitude towards people involved in foreign language learning. Additionally, it was concluded that females were more likely to have a growth language learning mindset.