Branching Model Research Articles

A novel plant type, leaf disease and severity identification framework using CNN and transformer with multi-label method.

The growth of plants is threatened by numerous diseases. Accurate and timely identification of these diseases is crucial to prevent disease spreading. Many deep learning-based methods have been proposed for identifying leaf diseases. However, these methods often combine plant, leaf disease, and severity into one category or treat them separately, resulting in a large number of categories or complex network structures. Given this, this paper proposes a novel leaf disease identification network (LDI-NET) using a multi-label method. It is quite special because it can identify plant type, leaf disease and severity simultaneously using a single straightforward branch model without increasing the number of categories and avoiding extra branches. It consists of three modules, i.e., a feature tokenizer module, a token encoder module and a multi-label decoder module. The LDI-NET works as follows: Firstly, the feature tokenizer module is designed to enhance the capability of extracting local and long-range global contextual features by leveraging the strengths of convolutional neural networks and transformers. Secondly, the token encoder module is utilized to obtain context-rich tokens that can establish relationships among the plant, leaf disease and severity. Thirdly, the multi-label decoder module combined with a residual structure is utilized to fuse shallow and deep contextual features for better utilization of different-level features. This allows the identification of plant type, leaf disease, and severity simultaneously. Experiments show that the proposed LDI-NET outperforms the prevalent methods using the publicly available AI challenger 2018 dataset.

Modeling branch attributes and biomass for Catalpa bungei plantations under various fertilization regimes

The development and morphology of branches, a crucial step in producing high-quality large-diameter lumber, may be influenced by fertilization. The response of branch attributes to different fertilization regimes, however, is still poorly understood. The Catalpa bungei plantations, which had been growing for 6 years in northern China, were chosen to study how various fertilization measures affected branch attributes. The two fertilization techniques used were hole fertilization (HF) and water and fertilizer integration (WF), with no fertilization (CK) as a control. The quantity, density, morphology (e.g., diameter, length, and angle), and position (e.g., height and orientation) of branches, and organ biomass of 18 standard trees (total of 516 branches) were investigated. The results demonstrated a considerable increase in tree height, diameter at breast height (DBH), canopy ratio, branch quantity, and organ biomass following the addition of fertilizer. Both the maximum branch diameter and the number of branches rose with fertilization. Following fertilization, the number of branches rose by 16% (HF) and 28% (WF) compared to non-fertilized trees, while the maximum branch diameter increased by 3.5% (HF) and 17.3% (WF), respectively. WF led to an increase in the number of branches and largest branch diameter in comparison to CK and HF. The length, angle, and diameter of branches, however, were not affected significantly by different fertilization treatments. There were roughly equal amounts of branches in four orientations. The mixed-model analysis revealed that the number of branches was positively correlated with branch density and tree height. The branch diameter increased with the increase of branch length and angle. The branch length was negatively correlated with branch height and angle. The branch angle showed a larger angle at the bottom of the canopy. Tree height plus diameter at breast height combined, or just the diameter at breast height indicator alone, can both reliably predict the total biomass of trees. The branch models created in this research may offer some theoretical backing for understanding the crown dynamics of valuable tree species in northern China.

Branch architecture quantification of large-scale coniferous forest plots using UAV-LiDAR data

Branch architecture plays an important role in forest physical and ecological processes, greatly affecting forest spatial structure and the accumulation, production and distribution of organic carbon. Very few studies have quantified the branch architecture of large-scale forest plots, due to the lack of high-resolution large-scale three-dimensional data. The emergence of unmanned aerial vehicle-light detecting and ranging (UAV-LiDAR) provides great potential to overcome this limitation. However, due to insufficient quality of UAV-LiDAR data for branch reconstruction, existing algorithms remain tremendously challenging. We propose an effective branch reconstruction algorithm for UAV-LiDAR data to quantify branch architecture. First, individual branches are extracted using a region growth method that is guided by the branch growth direction and local smoothness constraint. Second, incorrect branches are eliminated based on three pieces of branch features, i.e., specific angles between branches at the same whorl, specific height differences between branches at different whorls, and the growth pattern of branches from the stem outward, reaching maximum distances at tips. Finally, branches are reconstructed using polynomial fitting, and branch architecture parameters are extracted based on branch models. The proposed algorithm simplifies branch reconstruction by skipping the separation of photosynthetic and non-photosynthetic components. It also adapts well to UAV-LiDAR point clouds, generating more realistic reconstructions. The algorithm successfully identified non-photosynthetic components in experiments involving 240 trees, including Scots pine, Norway spruce, and Radiata pine. The average overall accuracy for these three species was 0.88, 0.76, and 0.74, respectively. The proposed algorithm was tested for branch reconstruction using UAV-LiDAR data from a two-hectare Larix principis-rupprechtii plot. The accuracy of branch identification and parameter extraction accuracy were evaluated branch by branch based on the individual branches manually identified in terrestrial laser scanning data. Results showed the F-score of branch and stem identification was 0.58 and 1. The relative RMSE of branch length and angle was 36.87% and 18.3%, and ones of stem length and diameter were 1.46% and 6.16%. The proposed algorithm outperformed the well-known reconstruction algorithms, including TreeQSM, AdTree and Laplacian-based algorithms.

Fully connected-convolutional (FC-CNN) neural network based on hyperspectral images for rapid identification of P. ginseng growth years

P. ginseng is a precious traditional Chinese functional food, which is used for both medicinal and food purposes, and has various effects such as immunomodulation, anti-tumor and anti-oxidation. The growth year of P. ginseng has an important impact on its medicinal and economic values. Fast and nondestructive identification of the growth year of P. ginseng is crucial for its quality evaluation. In this paper, we propose a FC-CNN network that incorporates spectral and spatial features of hyperspectral images to characterize P. ginseng from different growth years. The importance ranking of the spectra was obtained using the random forest method for optimal band selection. Based on the hyperspectral reflectance data of P. ginseng after radiometric calibration and the images of the best five VNIR bands and five SWIR bands selected, the year-by-year identification of P. ginseng age and its identification experiments for food and medicinal purposes were conducted, and the FC-CNN network and its FCNN and CNN branch networks were tested and compared in terms of their effectiveness in the identification of P. ginseng growth years. It has been experimentally verified that the best year-by-year recognition was achieved by utilizing images from five visible and near-infrared important bands and all spectral curves, and the recognition accuracy of food and medicinal use reached 100%. The FC-CNN network is significantly better than its branching model in the effect of edible and medicinal identification. The results show that for P. ginseng growth year identification, VNIR images have much more useful information than SWIR images. Meanwhile, the FC-CNN network utilizing the spectral and spatial features of hyperspectral images is an effective method for the identification of P. ginseng growth year.

Blood flow and emboli transport patterns during venoarterial extracorporeal membrane oxygenation: A computational fluid dynamics study

ProblemDespite advances in Venoarterial Extracorporeal Membrane Oxygenation (VA-ECMO), a significant mortality rate persists due to complications. The non-physiological blood flow dynamics of VA-ECMO may lead to neurological complications and organ ischemia. Continuous retrograde high-flow oxygenated blood enters through a return cannula placed in the femoral artery which opposes the pulsatile deoxygenated blood ejected by the left ventricle (LV), which impacts upper body oxygenation and subsequent hyperoxemia. The complications underscore the critical need to comprehend the impact of VA-ECMO support level and return cannula size, as mortality remains a significant concern. AimThe aim of this study is to predict and provide insights into the complications associated with VA-ECMO using computational fluid dynamics (CFD) simulations. These complications will be assessed by characterising blood flow and emboli transport patterns through a comprehensive analysis of the influence of VA-ECMO support levels and arterial return cannula sizes. MethodsPatient-specific 3D aortic and major branch models, derived from a male patient's CT scan during VA-ECMO undergoing respiratory dysfunction, were analyzed using CFD. The investigation employed species transport and discrete particle tracking models to study ECMO blood (oxygenated) mixing with LV blood (deoxygenated) and to trace emboli transport patterns from potential sources (circuit, LV, and aorta wall). Two cannula sizes (15 Fr and 19 Fr) were tested alongside varying ECMO pump flow rates (50%, 70%, and 90% of the total cardiac output). ResultsCannula size did not significantly affect oxygen transport. At 90% VA-ECMO support, all arteries distal to the aortic arch achieved 100% oxygen saturation. As support level decreased, oxygen transport to the upper body also decreased to a minimum saturation of 73%. Emboli transport varied substantially between emboli origin and VAECMO support level, with the highest risk of cerebral emboli coming from the LV with a 15 Fr cannula at 90% support. ConclusionArterial return cannula sizing minimally impacted blood oxygen distribution; however, it did influence the distribution of emboli released from the circuit and aortic wall. Notably, it was the support level alone that significantly affected the mixing zone of VA-ECMO and cardiac blood, subsequently influencing the risk of embolization of the cardiogenic source and oxygenation levels across various arterial branches.

Modelling and numerical simulation of a concentrated mass-based branch vibration

To investigate the variation patterns of spectral characteristics of main branches in fruit trees due to uneven distribution of growth, pruning, and fruit quality, as well as to determine the optimal fruit vibration harvesting, a vibration differential model of tree branches was constructed using the concentrated mass method. A three-dimensional model of the fruit tree was built using finite element software. The spectral characteristics of each branch were obtained using the linear sweep frequency method. The method of changing the mass of each branch was employed to simulate the variation in branch mass. The frequency of occurrence, mean acceleration, and coefficient of variation of the inherent frequencies were used as evaluation indicators. The results revealed that when the vibration frequency of the fruit tree was at the maximum peak frequency in the tree trunk spectrum, the frequency of occurrence and mean acceleration were optimal. And the coefficient of variation of branch acceleration was less than 1, indicating a moderate dispersion. Additionally, reducing the branch mass resulted in an increase in the maximum peak frequency of the tree trunk spectrum. Therefore, the growth, pruning, and uneven distribution of the branches in fruit trees can alter the spectral characteristics. When harvesting fruit by vibration, selecting the maximum peak frequency in the trunk vibration spectrum as the excitation force frequency yields the best fruit detachment.

Multiple Branch Deep Neural Network and Feature Fusion Model for Underwater Image Enhancement

Underwater imaging faces formidable challenges due to light absorption, scattering, and restricted visibility, demanding sophisticated enhancement methods. This research presents a novel Multiple Branch Deep Neural Network and Feature Fusion (MB-DNN-FF) Model tailored specifically for Underwater Images Enhancement (UWIE), with a focus on its application to the UFO120 dataset. Driven by the limitations of existing techniques, our model harnesses the potential of Deep Learning (DL) and Feature Fusion (FF) to effectively address the intricate complexities present in underwater environments. The innovative architecture incorporates multiple branches, each strategically designed to tackle distinct challenges such as contrast degradation, limited visibility, and color distortion. Central to our model is feature fusion, a critical aspect that harmoniously integrates information from diverse branches, thereby enhancing overall image quality. The training and optimization processes are thoroughly detailed, encompassing unique strategies and loss functions fine-tuned for the nuances of UWIE, with emphasis on the characteristics of the UFO120 dataset. Experimental evaluations leverage the comprehensive UFO120 dataset, employing established performance metrics to quantitatively assess the efficacy of our model. Results exhibit substantial improvements over baseline models and state-of-the-art methods, showcasing the effectiveness and versatility of the proposed multiple branch model, particularly on the UFO120 dataset. The discussion interprets these findings in the context of underwater imaging challenges, highlighting the model's effectiveness and outlining potential applications. This research contributes a valuable asset to the area of UWIE, offering a nuanced and potent solution through the integration of multiple branches and feature fusion, validated on the UFO120 dataset. The conclusions underscore the significance of the MB-DNN-FF in advancing the state-of-the-art in UWIE, opening avenues for future research and practical applications in marine sciences, surveillance, and exploration.

Deep learning-assisted ultrasonic diagnosis of cervical lymph node metastasis of thyroid cancer: a retrospective study of 3059 patients.

This study aimed to develop a deep learning system to identify and differentiate the metastatic cervical lymph nodes (CLNs) of thyroid cancer. From January 2014 to December 2020, 3059 consecutive patients with suspected with metastatic CLNs of thyroid cancer were retrospectively enrolled in this study. All CLNs were confirmed by fine needle aspiration. The patients were randomly divided into the training (1228 benign and 1284 metastatic CLNs) and test (307 benign and 240 metastatic CLNs) groups. Grayscale ultrasonic images were used to develop and test the performance of the Y-Net deep learning model. We used the Y-Net network model to segment and differentiate the lymph nodes. The Dice coefficient was used to evaluate the segmentation efficiency. Sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) were used to evaluate the classification efficiency. In the test set, the median Dice coefficient was 0.832. The sensitivity, specificity, accuracy, PPV, and NPV were 57.25%, 87.08%, 72.03%, 81.87%, and 66.67%, respectively. We also used the Y-Net classified branch to evaluate the classification efficiency of the LNs ultrasonic images. The classification branch model had sensitivity, specificity, accuracy, PPV, and NPV of 84.78%, 80.23%, 82.45%, 79.35%, and 85.61%, respectively. For the original ultrasonic reports, the sensitivity, specificity, accuracy, PPV, and NPV were 95.14%, 34.3%, 64.66%, 59.02%, 87.71%, respectively. The Y-Net model yielded better accuracy than the original ultrasonic reports. The Y-Net model can be useful in assisting sonographers to improve the accuracy of the classification of ultrasound images of metastatic CLNs.

Deep neural network for investment decision planning on low-carbon transition in power grid

AbstractWith the urgency of mitigating global warming, the low-carbon transformation of power grid systems has emerged as a pivotal industry upgrade for sustainable development. We proposed a novel deep neural network-based approach for investment decision planning in the low-carbon transformation of power grids, which aimed to address multidimensional key indicators related to power grid transformation and provided reliable electricity industry layouts and investment plans for power system investment decisions. To achieve this, three targeted investment branch models were established, encompassing investment behavior, electricity production and consumption, and predictions of new capacity investment. These models effectively tackled challenges associated with power distribution, electricity price scheduling, power carbon quotas, and the feasibility of low-carbon power generation technologies. Subsequently, a global investment decision planning model was constructed, employing spatiotemporal neural networks and recurrent neural networks, which integrated the aforementioned branch models and incorporated existing low-carbon transformation data. A comparative analysis was conducted, examining the predicted results against actual values from three perspectives: power generation portfolio, grid economy, and overall investment decision plans. The results demonstrated the effectiveness of our method in accurately predicting future installed capacity of diverse low-carbon power generation technologies, sustainability indices, and investment returns. Notably, our method achieves an impressive forecasting accuracy of over 90% compared to actual values of investment decision planning over the past 4 years.

Determinants of Commercial Banks’ Credit in the Democratic Republic of Congo.

The bank’s loans to the economy in the Democratic Republic of Congo are among the lowest in the Sub-Saharan African countries. The present study investigates the specific reasons that explain that low level. The factors which explain the banks’ credit to the economy under investigation are the rate, the banking inclusion, the banks’ branches, and savings and deposits. To conduct this research, a quantitative approach is used to analyze secondary data collected from annual reports of the Central Bank of Congo and IMF from 2010 to 2019. To explain the credit to the economy, Eviews 9 software estimates the econometric model of factors rate, banking inclusion, banking branches, deposits and savings. As a result, the level of credit to the economy is significantly explained by the credit rate and savings. Credit lent by banks to the economy in the Democratic Republic of Congo is low because the credit rate is high, and banks do not gather enough savings and deposits. The Banks and the Government should adopt new strategies to increase savings in commercial banks.

Automated first-order tree branch modeling at plot-and individual-tree-levels from close-range sensing for silviculture and forest ecology

Automated first-order tree branch modeling at plot-and individual-tree-levels from close-range sensing for silviculture and forest ecology

Automated Lane Merging via Game Theory and Branch Model Predictive Control

Automated Lane Merging via Game Theory and Branch Model Predictive Control

Longtime behavior of the completely positively correlated Symbiotic Branching Model .

Longtime behavior of the completely positively correlated Symbiotic Branching Model .

Dutch Anaphoric Possessive Construction

The Dutch anaphoric possessive construction (APC), as exemplified by Tom zijn fiets ‘Tom his bike’, shows a peculiar mix of regularity and idiosyncracy. The article provides a theory-neutral description of its properties and quantitative information about its use in two treebanks, one of spoken Dutch (CGN) and one of written Dutch (Lassy Small). It argues that the APC has a right branching structure and models it in the framework of Constructional Head-driven Phrase Structure Grammar. The latter’s organization of constructions in terms of a finegrained hierarchy of phrase types is shown to provide the means to capture both what the APC has in common with other possessive constructions and what is idiosyncratic of it.

Construction and study of a three-dimensional visualization model of superficial temporal artery branches: With an explicatory case

The anatomical parameters of the superficial temporal artery branches were measured by a three-dimensional measurement method to provide anatomical reference for relevant clinical operations. Seventy original images were selected who had cranial CTA examination. The patients were aged 30-79 years, with an average of 60.0 years, including 32 females and 38 males. After reconstructing the superficial temporal artery by professional medical 3D reconstruction software, its anatomical parameters were measured. The length of the secondary branches of the frontal branch of the superficial temporal artery were 47.6±23.6mm and 37.3±21.6mm in males and females, respectively, with a statistically significant difference. The length of the secondary branches of the parietal branch of the superficial temporal artery were 39.6±20.4mm and 49.2±20.3mm in young and middle-aged people and older people respectively, which were statistically different. The remaining measures were not statistically different across gender and age groups. The frontal branch of the superficial temporal artery was divided into three types, and the parietal branch of the superficial temporal artery was divided into two types. The anatomical parameters of the superficial temporal artery branches can be accurately measured by means of 3D visualization, providing an anatomical reference for relevant clinical operations.

CLANET: a cross-linear attention network for semantic segmentation of urban scenes remote sensing images

ABSTRACT Semantic segmentation of high-resolution remote sensing images is important in land cover classification, road extraction, building extraction, water extraction, etc. However, high-resolution remote-sensing images have a lot of details. Due to the fixed receptive field of convolution blocks, it is impossible to model the correlation of global features. In addition, complex fusion methods cannot integrate spatial and global context information. In order to solve these problems, this paper proposes a cross-linear attention network (CLANet) to capture spatial and context information in images. The structure consists of a spatial branch and a context branch. The spatial branch is constructed by stacked convolution to better capture spatial information. The context branch models the global information based on the transformer deformation module. In addition, to effectively fuse spatial and context information, this paper also designs a feature fusion module (FFM), which uses a cross-linear attention mechanism for feature aggregation. Finally, this paper conducts many experiments on the ISPRS Vaihingen and the ISPRS Potsdam datasets. Among them, 82.28% of mIoU achieves on the ISPRS Vaihingen dataset. The experimental results show that CLANet has better performance and effect than the methods in recent years.

Experimental research of stress state and residual stresses of the hydropower pipeline branch model

The paper presents experimental research of branch model in laboratory conditions. The branch model is, in fact, a reduced real branch whose basic dimensions are interrelated on the basis of similarity theory which arise from the conditions of stress equality on the branch model and the real branch. Using numerical methods - finite element methods, measuring points on the branch model, were determined. At the defined measuring points, experimental measurements of stress were performed, namely using: strain gauges and recording with 3D cameras. The branch model was quasi-statically loaded with different values of internal pressure during the experiment. The values of the internal pressure reached such a level that the maximum stress at the measuring point with the highest measured stress reached values far above the yield strength of the branch model material. The most important results of experimental research on the branch model can be expressed through the following items:- Verification of the fact that in the field of elasticity, there is a linear relationship between stress and internal pressure.- Disclosing what happens when the stresses exceed the value of the yield strength of the branch material, whether there are plastic deformations of the branch model.- Defining the dependence of stress on internal pressure in the field of elasticity and the zone of residual stresses.- Coming to conclusions regarding the distribution of stresses and residual stresses on the branch model.

Zero-age horizontal branch models for −2.5 ≤ [Fe/H] ≤−0.5 and their implications for the apparent distance moduli of globular clusters

ABSTRACT Grids of zero-age horizontal branch (ZAHB) models are presented, along with a suitable interpolation code, for −2.5 ≤ [Fe/H] ≤ −0.5, in steps of 0.2 dex, assuming Y = 0.25 and 0.29, [O/Fe] = +0.4 and +0.6, and [m/Fe] = +0.4 for all of the other α-elements. The HB populations of 37 globular clusters (GCs) are fitted to these ZAHBs to derive their apparent distance moduli, (m − M)V. With few exceptions, the best estimates of their reddenings from dust maps are adopted. The distance moduli are constrained using the prediction that (MF606W − MF814W)0 colours of metal-poor, main-sequence stars at $M_{F606W} \mathrel {\rm{{\gt }\lower.5 ex\rm{\sim }}}5.0$ have very little sensitivity to [Fe/H]. Intrinsic (MF336W − MF606W)0 colours of blue HB stars, which provide valuable connections between GCs with exclusively blue HBs and other clusters of similar metallicity that also have red HB components, limit the uncertainties of relative (m − M)V values to within ±0.03–0.04 mag. The ZAHB-based distances agree quite well with the distances derived by Baumgardt & Vasiliev. Their implications for GC ages are briefly discussed. Stellar rotation and mass loss appear to be more important than helium abundance variations in explaining the colour–magnitude diagrams of second-parameter GCs (those with anomalously very blue HBs for their metallicities).

IL-4 Acts at an Early Fate-Determining Junction in Hematopoiesis to Induce NK Cell Subsets Expressing Endogenous CD16

Human NK cells are known to contain subsets of CD56 +CD16 + and CD56 +CD16 - cells. The former directly attack target cells by an antibody-dependent cellular cytotoxicity (ADCC) various organs in the body, whereas the latter are mainly found in lymph nodes and activate inflammatory and immune responses by secreting IFNγ. However, despite their importance in the biological defence systems, whether and how these subsets emerge is not yet fully understood. In particular, several recent studies have challenged the conventional linear differentiation model which positioned CD56 +CD16 - cells as progenitors of CD56 +CD16 + cells. However, during the stepwise fate specification from immature multipotent progenitors to NK cells, it is still unknown at which stage the final CD16-expressing capacity is determined and what stimuli are involved in this decision. Through cell fate tracking in human HPC-derived differentiation, present study has demonstrated that fate specification into distinct progenitor cells specific for the two subsets occurs at a stage before the cells have yet to express CD56. Furthermore, it showed that IL-4 has a positive effect, both in bona fide and PSC-derived hematopoiesis, on the fate decision to progenitor cells from which CD56 +CD16 + NK cells emerge. While IL-4 was previously known to act on NK cells after CD56 expression, the present results show that IL-4 is also involved in future fate decisions at a more earlier progenitor cell stage than previous reports. We first identified three CD34 -CD56 - NK progenitor cell fractions, which were clearly distinguished due to the intensity of CD117 expression (CD34 -CD56 -CD117 ++, CD34 -CD56 -CD 117 +, CD34 -CD56 -CD117 -) in the differentiation pathway from lineage-negative cord blood progenitors. Tracking the cell fate of each fraction showed that they all produced CD56 + NK cells, but that CD16-expressing cells emerged predominantly from the CD34 -CD56 -CD117 + and CD34 -CD56 -CD117 - fractions at a rate of 15-30%. In contrast, NK cells originating from the CD34 -CD56 -CD117 ++ fraction hardly expressed CD16. Based on expression profile comparisons, we next identified IL-4 as a factor involved in progenitor cell fate decisions. IL-4 stimulation during early stage differentiation increased the CD34 -CD56 -CD117 + and CD34 -CD56 -CD117 - fractions, and conversely reduced the CD34 -CD56 -CD117 ++ cells. As a result, not only was the emergence efficiency of CD16-expressing NK cells increased, but their cytotoxic potential was also enhanced. During this process, IL-4 enhanced EOMES expression, which was in common with previous reports on T cells. Finally, we investigated whether NK cells derived from human pluripotent stem cells (hPSCs) also develop along the same trajectory as bona fide hematopoietic cells. Using a refined system of our previously reported NK cell differentiation cultures, we found that, as in cord blood, many hPSC strains produced all of three progenitor cell fractions, and both CD16 + and CD16 - NK cells appeared at the end of culture. In contrast, some strains produced only CD16 - NK cells, which were nearly deficient in the CD34 -CD56 -CD117 + fraction on the way of differentiation. Interestingly, stimulation with IL-4 significantly reduced the proportion of CD34 -CD56 -CD117 ++ progenitor cells from those strains (52.8±14.4% to 10.4 ±4.7%), which instead also produced CD34 -CD56 -CD117 + progenitors (13±3.4% to 68.1 ±8.3%) from which around 15% of CD16 + NK cells were eventually generated (Figure). Our results support a branching model in human NK cell differentiation, no matter whether they are derived from cord blood or hPSCs. We found that IL-4 stimulation accelerates specification into the progenitors capable of producing CD16 + NK cells in the future at a much earlier stage than previously thought. Furthermore, we showed that IL-4 could correct strain-dependent differentiation propensities of hPSCs to stably induce functional NK cells with high ADCC activity.

Нова соціальна політика як шлях до відновлення людського капіталу

The article outlines the approaches and basic principles of forming a new social policy, which Ukraine should pursue in post-war reconstruction. It was determined that the primary goal of such policy should be a change in the paradigm of interaction between the government and society, which would ensure a transition to more pragmatic and effective relations between these parties, social compensation to alleviate social contradictions, as well as “depreciation” of conflicts. The authors emphasized that social protection should become less aimed at providing benefits and assistance to people experiencing poverty and more emphasis on supporting the self-help of Ukrainians. The focus is not only on delivering consumer goods but also on stimulating initiative and self-sufficiency skills. The article outlines the goals and tasks of the new social policy, which consist of restoring public trust in the state, comprehensive transformation of approaches to wages, taxes and social insurance, promotion of adequate employment, ensuring real efficiency of social expenditures, unification of efforts of society, business, and government. The new social policy’s principles should be determined: humanism and social justice, systematicity and comprehensiveness, preventive measures, targeting, adaptability, and multi-subjectivity. The authors suggested mechanisms for implementing the new social policy, among which a special place is taken by a social dialogue based on a four-way (employees, employers, state, local self-government bodies) and four-level (national, branch, territorial and local) model. Corporate social responsibility is defined as a means of deepening social dialogue and the need for its implementation in the form of a national policy model and its normative consolidation, as well as strengthening the joint efforts of business, the state, and civil society organizations to implement such a model, is emphasized.