Transformation Vector Research Articles

System of performance indicators of the ESG–concept: state and prospects for assessment

In the world community, the concept of sustainable development has firmly established itself as a priority trend of globalization through the transformation of key development vectors into the format of the ESG–concept, within which the performance of enterprises is expressed by indicators characterizing care for the environment, personnel, transparency and efficiency of social activities. The main emphasis is on the formation of anti-corruption activities, ensuring the transparency of enterprise reporting, information openness, the use of modern management tools within the framework of the Industry 5.0 concept, eliminating gender, social and other conflicts, comprehensively ensuring a good image of the organization. In these conditions, the creation of international ratings of compliance of enterprise policies with the ESG–agenda is being updated. At the same time, there is a lack of a unified methodology for assessing the performance indicators of the ESG–concept, in the form of a unified reporting form for enterprises. The purpose of the study is to develop ESG–monitoring based on systematization of indicators in priority areas of activity and calculation of the integral ESG–indicator. The scientific novelty lies in the development of an ESG–monitoring model, which is a decomposition diagram of the organization of ESG–monitoring of enterprises in IDF0 notation as part of the implementation of the state strategy for transplantation of sustainable development goals, including the formation of a list of indicators, the development of a unified methodology for their calculation, the development of a unified form of statistical reporting, and monitoring with current frequency. The proposed toolkit is a promising mechanism for government regulation, by ranking and comparing enterprises according to the level of compliance with the ESG–concept, to create an accurate investment impulse in the field of transplantation of sustainable development goals and ensuring the homeostasis of economic systems in conditions of uncertainty and instability of the external environment.

Agrobacterium-Mediated Transformation and Targeted Mutagenesis Using SpCas12f in Rice.

Clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated proteins (Cas) (CRISPR-Cas) is an adaptive prokaryote immune system against foreign DNA/RNA that is now applied widely to genome editing. A miniature Cas, CRISPR-Cas12f, is one-half to one-third the size of the CRISPR-Cas9 that is commonly used in genome editing experiments in many organisms, including higher plants. The compactness of CRISPR-Cas12f is expected to be advantageous in terms of vector construction and transformation frequency. Moreover, CRISPR-Cas12f can be useful for virus vector-mediated genome editing because the size of the transgene is the major restriction in the use of virus vectors. Here, we describe our protocol for targeted mutagenesis using Cas12f derived from Syntrophomonas palmitatica (SpCas12f) via Agrobacterium-mediated transformation in rice. We also summarize some approaches to improve the frequency of targeted mutagenesis using SpCas12f.

Improved NaS Battery State of Charge and State of Health Estimation: A Novel Integration of Temporal Fusion Transformer, Isolation Forest, and Support Vector Regression

Improved NaS Battery State of Charge and State of Health Estimation: A Novel Integration of Temporal Fusion Transformer, Isolation Forest, and Support Vector Regression

Optimizing Precipitation Forecasting and Agricultural Water Resource Allocation Using the Gaussian-Stacked-LSTM Model

Our study investigates the use of machine learning models for daily precipitation prediction using data from 56 meteorological stations in Jilin Province, China. We evaluate Stacked Long Short-Term Memory (LSTM), Transformer, and Support Vector Regression (SVR) models, with Stacked-LSTM showing the best performance in terms of accuracy and stability, as measured by the Root Mean Square Error (RMSE). To improve robustness, Gaussian noise was introduced, particularly enhancing predictions for zero-precipitation days. Key predictors identified through variable attribution analysis include temperature, dew point, prior precipitation, and air pressure. Additionally, we demonstrate the practical benefits of precipitation forecasts in optimizing water resource allocation. A prediction-based strategy outperforms equal distribution in managing resources efficiently, as shown in a case study using 2022 Beidahu data. Overall, our research advances precipitation forecasting through deep learning and offers valuable insights for water resource management.

Enhancing Agrobacterium-mediated soybean transformation efficiency with an auxiliary solution

Soybean is a crucial source of oil, protein, and biofuel, necessitating efficient transformation systems for advancing research. Agrobacterium-mediated transformation is currently the primary method used in the soybean transformation industry and scientific research. However, the low efficiency and genotype dependency of this technology leave significant room for improvement. This study aimed to enhance soybean transformation efficiency by generating and validating three reporter vectors (ZsGreen, TdTomato, and Ruby) and using Agrobacterium Auxiliary Solution (AAS) containing Silwet L-77 and hormone mixtures. Our findings demonstrate that AAS significantly improves hairy root transformation rates. Specifically, this combination increased total root and cotyledon transformation efficiencies compared to the control. We also found that larger vectors like Ruby reduced transformation efficiency compared to smaller markers like GFP and RFP. Furthermore, AAS slightly reduced the co-transformation rate of two separate vectors compared to single vector transformations. Additionally, AAS enhanced soybean hypocotyl transformation rates across various varieties, consistently increasing positive root and explant efficiencies. Notably, transformation rates varied significantly between varieties, with Forrest differing from Williams 82 and Dongnong 50. This research highlights the importance of auxiliary agents and vector size in optimizing soybean transformation, providing insights for future advancements in genetic modification and biotechnology.

Surface display and application of cadmium-binding protein CADR on the cell wall of Chlamydomonas reinhardtii

Algae has been proven to have the potential to be efficient biosorbents in the detection and remediation of heavy metal pollution such as cadmium in the environment. This study aims to enhance the cadmium adsorption capacity of Chlamydomonas reinhardtii by expressing the cadmium-binding protein CADR on the cell wall by the surface display technology. Firstly, the golden gate technique was employed to construct the transformation vector PET-X-CADR, which anchored CADR to the cell wall with the cell wall protein GP1. The high-throughput screening with the fluorescence signal of the fusion tag YFP resulted in three engineered algal strains with high expression of CADR on the cell wall. Physiological experiments demonstrated that the CADR displayed on the cell wall did not affect the growth of the engineered algal strains exposed to cadmium with the concentration below 200 μmol/L. In the presence of 200 μmol/L cadmium, the growth rates of CADR-engineered algal strains were three times as that of the wild-type, indicating stronger tolerance of the CADR-engineered algae to cadmium. The protein lyase GLE released during the mating of Chlamydomonas was used to isolate the cell walls of wild-type and engineered strains, the cadmium content of which was compared. The results showed that the cell wall of the engineered strain exhibited an increase of 33% in cadmium adsorption capacity. This study gives insights into the application of algae in the management of cadmium pollution in the environment, especially in the recycling of heavy metals from the environment.

The transcription factor TaNF-YB4 overexpression in wheat increases plant vigor and yield

Addressing food security is a priority in developing countries. This study aimed to improve wheat yield by overexpressing the TaNF-YB4 transcription factor, which is involved in carbon assimilation and stress tolerance. An expression cassette for TaNF-YB4 was developed in a modified wheat transformation vector (pSB219) and examined through transient expression in Nicotiana tabacum, followed by Agrobacterium-mediated transformation of wheat variety FSD-2008. T0 transgenic plants were propagated to obtain T3 generation PCR-positive plants. Transgene expression was assessed in PCR-verified T2 plants using RT-PCR and qRT-PCR at six weeks post-germination. qRT-PCR analysis using the ΔΔCT method indicated higher TaNF-YB4 expression in transgenic lines than in the wild-type control plants. Improved agronomic and phenotypic traits were observed with a 6–36 % increase in 1000-grain weight in the selected transgenic lines. Root architecture assessments demonstrated enhanced root length, surface area, and projected area in transgenic lines compared with wild-type plants. Additionally, notable variances in total chlorophyll, protein, and sugar content levels were observed between the transgenic lines and control plants, demonstrating statistical significance with a p-value ≤ 0.05. This study indicates that low-level constitutive expression of TaNF-YB4 can enhance wheat yield, presenting a viable strategy for improving wheat productivity.

Efficient Predictive Control for Dual Three-Phase PMSM Drive With Vector Transformation

Efficient Predictive Control for Dual Three-Phase PMSM Drive With Vector Transformation

Time Evolution of Orbital Angular Momentum Modes for Deep-Routing Multiplexing Channels

Optical orbital angular momentum (OAM) mode multiplexing has emerged as a promising technique for boosting communication capacity. However, most existing studies have concentrated on channel (de)multiplexing, overlooking the critical aspect of channel routing. This challenge involves the reallocation of multiplexed OAM modes across both spatial and temporal domains—a vital step for developing versatile communication networks. To address this gap, we introduce a novel approach based on the time evolution of OAM modes, utilizing the orthogonal conversion and diffractive modulation capabilities of unitary transformations. This approach facilitates high-dimensional orthogonal transformations of OAM mode vectors, altering both the propagation direction and the spatial location. Using Fresnel diffraction matrices as unitary operators, it manipulates the spatial locations of light beams during transmission, breaking the propagation invariance and enabling temporal evolution. As a demonstration, we have experimentally implemented the deep routing of four OAM modes within two distinct time sequences. Achieving an average diffraction efficiency above 78.31%, we have successfully deep-routed 4.69 Tbit·s−1 quadrature phase-shift keying (QPSK) signals carried by four multiplexed OAM channels, with a bit error rate below 10–6. These results underscore the efficacy of our routing strategy and its promising prospects for practical applications.

Cholesky Decomposition in Spin-Free Dirac-Coulomb Coupled-Cluster Calculations.

We present an implementation for the use of Cholesky decomposition (CD) of two-electron integrals within the spin-free Dirac-Coulomb (SFDC) scheme that enables to perform high-accuracy coupled-cluster (CC) calculations at costs almost comparable to those of their nonrelativistic counterparts. While for nonrelativistic CC calculations, atomic-orbital (AO)-based algorithms, due to their significantly reduced disk-space requirements, are the key to efficient large-scale computations, such algorithms are less advantageous in the SFDC case due to their increased computational cost in that case. Here, molecular-orbital (MO)-based algorithms exploiting the CD of the two-electron integrals allow us to reduce disk-space requirements and lead to computational cost in the CC step that is more or less the same as in the nonrelativistic case. The only remaining overhead in a CD-SFDC-CC calculation is due to the need to compute additional two-electron integrals, the somewhat higher cost of the Hartree-Fock calculation in the SFDC case, and additional cost in the transformation of the Cholesky vectors from the AO to the MO representation. However, these additional costs typically amount to less than 5-15% of the total wall time and are thus acceptable. We illustrate the efficiency of our CD scheme for SFDC-CC calculations on a series of illustrative calculations for the X(CO)4 molecules with X = Ni, Pd, Pt.

A High-Throughput Screening Approach for Evaluating the Binding Affinity of Environmental Pollutants to Nuclear Receptors.

Increasing levels of compounds have been detected in the environment, causing widespread pollution and posing risks to human health. However, despite their high environmental occurrence, there is very limited information regarding their toxicological effects. It is urgent to develop high-throughput screening (HTS) methods to guide toxicological studies. In this study, a receptor-ligand binding assay using an HTS system was developed to determine the binding potency of environmental pollutants on nuclear receptors. The test is conducted using a microplate reader (i.e., a 96-well plate containing various chemicals) by measuring the fluorescence polarization (FP) of a specific fluorescent probe. This assay consists of four parts: the construction and transformation of recombinant vectors, the expression and purification of the receptor protein (ligand-binding domain), receptor-probe binding, and competitive binding of chemicals with the receptor. The binding potency of two environmental pollutants, perfluorooctanesulfonic acid (PFOS) and triphenyl phosphate (TPHP), with peroxisome proliferator-activated receptor gamma (PPARγ) was determined to illustrate the assay procedure. Finally, the advantages and disadvantages of this method and its potential applications were also discussed.

The Technical and Economic Aspects of Integrating Energy Sectors for Climate Neutrality

With the development of an energy sector based on renewable primary sources, structural changes are emerging for the entire national energy system. Initially, it was estimated that energy generation based on fossil fuels would decrease until its disappearance. However, the evolution of CO2 capture capacity leads to a possible coexistence for a certain period with the renewable energy sector. The paper develops this concept of the coexistence of the two systems, with the positioning of green hydrogen not only within the renewable energy sector but also as a transformation vector for carbon dioxide captured in the form of synthetic fuels, such as CH4 and CH3OH. The authors conducted pilot-scale research on CO2 capture with green H2, both for pure (captured) CO2 and for CO2 found in combustion gases. The positive results led to the respective recommendation. The research conducted by the authors meets the strict requirements of the current energy phase, with the authors considering that wind and solar energy alone are not sufficient to meet current energy demand. The paper also analyzes the economic aspects related to price differences for energy produced in the two sectors, as well as their interconnection. The technical aspect, as well as the economic aspect, of storage through various other solutions besides hydrogen has been highlighted. The development of the renewable energy sector and its demarcation from the fossil fuel energy sector, even with the transcendent vector represented by green hydrogen, leads to the deepening of dispersion aspects between the electricity sector and the thermal energy sector, a less commonly mentioned aspect in current works, but of great importance. The purpose of this paper is to highlight energy challenges during the current transition period towards climate neutrality, along with solutions proposed by the authors to be implemented in this phase. The current stage of combustion of the CH4−H2 mixture imposes requirements for the capture of the resulting CO2.

Linear Time-Periodic theory-based novel stability analysis method for voltage-source converter under unbalanced grid conditions

Small-signal stability analysis is essential for the safe operation and parameter design of power electronic-dominated grids. Unbalanced conditions introduce more frequency-coupling terms, which are unneglectable in the stability assessment. This paper proposes a novel Linear Time-Periodic (LTP) theory-based stability analysis method and focuses on grid-connected voltage-source converters (VSCs) without improved unbalancing controls. Firstly, the analysis of fundamental solutions of the LTP system reveals that each LTP mode is characterized by a unique damping factor and multiple oscillation frequencies, defined by LTP eigenvalues and related transformation vectors, i.e., (λi,Vi(t)). Then, a method for calculating (λi,Vi(t)) is proposed using the characteristic matrix of the Harmonic State-Space (HSS) model. An iterative sorting method based on the time-domain interpretation of HSS eigenvalues/eigenvectors is proposed to determine accurate (λi,Vi(t)) in the case of the minimum truncation order. Finally, generalized definitions and calculation methods of the widely used indicators for modal analysis are presented to assess system stability and guide the parameter design. Numerical and simulation results verify the proposed stability analysis method and indicate its advantages compared to the existing Floquet and HSS methods.

Anomalous behavior detection based on optimized graph embedding representation in social networks

Anomalous behaviors in social networks can lead to privacy leaks and the spread of false information. In this paper, we propose an anomalous behavior detection method based on optimized graph embedding representation. Specifically, the user behavior logs are first extracted into a social network user behavior temporal knowledge graph, based on which the graph embedding representation method is used to transform the network topology and temporal information in the user behavior knowledge graph into structural embedding vectors and temporal information embedding vectors, and the hybrid attention mechanism is used to merge the two types of vectors to obtain the final entity embedding to complete the prediction and complementation of the temporal knowledge graph of user behavior. We use graph neural networks, which use the temporal information of user behaviors as a time constraint and capture both user behavioral and semantic information. It converts the two parts of information into vectors for concatenation and linear transformation to obtain a comprehensive representation vector of the whole subgraph, as well as joint deep learning models to evaluate abnormal behavior. Finally, we perform experiments on the Yelp dataset to validate that our method achieves a 9.56% improvement in the F1-score.

A Grid-Forming Converter with MVDC Supply and Integrated Step-Down Transformer: Modeling, Control Perspectives, and Control Hardware-in-the-Loop (C-HIL) Verification

A grid-forming voltage source converter with an integrated step-down transformer could be a promising solution for supplying low-voltage alternating current loads from a medium-voltage direct current supply. However, it may require a control system that gathers feedback signals from both the primary and secondary sides of the transformer, which in turn complicates the derivation of a standard form linear model. The absence of such a model complicates control tuning, as well as the assessment of dynamics and stability of the converter system. The objective of this paper is to address this gap in knowledge. For the case study, a conventional H-bridge converter with a step-down transformer and an αβ-frame dual-loop grid-forming controller is considered. Initially, comprehensive guidelines on deriving a standard form linear model for this converter system are presented. Then, the impact of controlling the VSC in a dq frame and the changes in the transformer vector group on the small-signal model of the VSC are analyzed. The aspects of control tuning are also discussed in detail, and the model’s accuracy and efficacy are validated both theoretically and through control hardware-in-the-loop (C-HIL) tests using a Typhoon HIL setup.

A Rapid Method for Screening Pathogen-Associated Molecular Pattern-Triggered Immunity-Intensifying Microbes.

PAMP-triggered immunity (PTI) is the first layer of plant defense response that occurs on the plant plasma membrane. Recently, the application of a rhizobacterium, Bacillus amyloliquefaciens strain PMB05, has been demonstrated to enhance flg22Pst- or harpin-triggered PTI response such as callose deposition. This PTI intensification by PMB05 further contributes to plant disease resistance to different bacterial diseases. Under the demand for rapid and large-scale screening, it has become critical to establish a non-staining technology to identify microbial strains that can enhance PTI responses. Firstly, we confirmed that the expression of the GSL5 gene, which is required for callose synthesis, can be enhanced by PMB05 during PTI activation triggered by flg22 or PopW (a harpin from Ralstonia solanacearum). The promoter region of the GSL5 gene was further cloned and fused to the coding sequence of gfp. The constructed fragments were used to generate transgenic Arabidopsis plants through a plant transformation vector. The transgenic lines of AtGSL5-GFP were obtained. The analysis was performed by infiltrating flg22Pst or PopW in one homozygous line, and the results exhibited that the green fluorescent signals were observed until after 8 h. In addition, the PopW-induced fluorescent signal was significantly enhanced in the co-treatment with PMB05 at 4 h after inoculation. Furthermore, by using AtGSL5-GFP to analyze 13 Bacillus spp. strains, the regulation of PopW-induced fluorescent signal was observed. And, the regulation of these fluorescent signals was similar to that performed by callose staining. More importantly, the Bacillus strains that enhance PopW-induced fluorescent signals would be more effective in reducing the occurrence of bacterial wilt. Taken together, the technique by using AtGSL5-GFP would be a promising platform to screen plant immunity-intensifying microbes to control bacterial wilt.

Mathematical modeling of insulating coating of thermal conductivity including body’s own radiation and non-local spatial effects

Abstract In this paper thermal conductivity analysis of insulating coating is discussed. The general partial differential thermal conductivity equation is used with consideration to nonlocal boundary effects. We relying on the Eringen’s approach involves heat-flow vector transformation using nonlocal influence function and weight coefficients. Also presented model includes own surface radiation of the coating.

Mode transformation and dark spot formation of cylindrical vector beams by thin dielectric film

An experimental approach is demonstrated for the mode transformation of cylindrical vector (CV) beams in the far field. This approach is based on the cost-effective fabrication of a thin dielectric film that precisely transforms the single-ring-shaped mode of CV beams into the double-ring-shaped mode with a switchable intensity ratio among the rings. Further, this film also increases the dark core size of the fundamental mode more than two times in a controlled manner, resulting in a dark spot space around the focus in the far field. In contrast to earlier research, our method does not employ tight focusing conditions to generate a dark spot with vector beams, which will be an advantage to the optical trapping of the low-refractive-index particles at more considerable distances from imaging systems.

Renewable energy in India: key trends, drivers and prospects

The article examines the features and trends in the development of renewable energy in India in the context of the modern transformation of the global energy economy. The dynamics of growth of installed capacity in the country’s energy sector by type of generation sources has been analyzed, and stable trends in changes in the structure of primary energy consumption have been identified. The main factors for the successful development of key areas of renewable energy have been identified. Particular attention is paid to the analysis of innovative schemes and instruments of public-private partnership in the development of renewable energy. The main government documents that determine the vector and pace of transformation of the Indian energy sector taking into account the global climate agenda are considered. It is concluded that with undeniable successes in the development of renewable energy and favorable conditions for its further growth, in the foreseeable future in India, traditional energy will not lose its significant importance due to the role that it and the infrastructure created for it play in ensuring economic growth and well-being population, sustainable energy supply and energy security of the country in the context of increasing turbulence in the global energy market.

A Fast Survival Support Vector Regression Approach to Large Scale Credit Scoring via Safe Screening.

Survival models have found wider and wider applications in credit scoring recently due to their ability to estimate the dynamics of risk over time. In this research, we propose a Buckley-James safe sample screening support vector regression (BJS4VR) algorithm to model large-scale survival data by combing the Buckley-James transformation and support vector regression. Different from previous support vector regression survival models, censored samples here are imputed using a censoring unbiased Buckley-James estimator. Safe sample screening is then applied to discard samples that guaranteed to be non-active at the final optimal solution from the original data to improve efficiency. Experimental results on the large-scale real lending club loan data have shown that the proposed BJS4VR model outperforms existing popular survival models such as RSFM, CoxRidge and CoxBoost in terms of both prediction accuracy and time efficiency. Important variables highly correlated with credit risk are also identified with the proposed method.