Transformants Research Articles

A Combined Computational Fluid Dynamics and Thermal–Hydraulic Modeling Approach for Improving the Thermal Performance of Corrugated Tank Transformers: A Comparative Study

In this study, we present a complete analysis of the thermal behavior of an oil-filled distribution transformer using two different approaches: numerically, by applying computational fluid dynamics (CFD) simulations; and analytically, by using thermal–hydraulic modelling (THM). The THM method becomes challenging when the corrugated transformer tank wall structures are complex, and therefore CFD simulations are required to provide in-depth details of the winding oil thermal–hydraulic behavior and to generate parameters for improving the THM calculation. The numerical and analytical thermal performance results were then compared with heat-run measurements of a case study transformer and the accuracy of both approaches on different thermal performance parameters was validated. This study has certain reference value for improving the thermal performance and operation efficiency of distribution transformers, and eventually aims to provide engineers with an effective tool to design the most efficient and reliable distribution transformers with corrugated tank walls for various applications.

Efficient photochemical conversion of naproxen by butanedione: Role of energy transfer

Photochemical active species generated from photosensitizers, e.g., dissolved organic matter (DOM), play vital roles in the transformation of micropollutants in water. Here, butanedione (BD), a redox-active moiety in DOM and widely found in nature, was employed to photo-transform naproxen (NPX) with peracetic acid (PAA) and H2O2 as contrasts. The results obtained showed that the BD exhibited more applicable on NPX degradation. It works in the lake or river water under UV and solar irradiation, and its NPX degradation efficiency was 10–30 times faster than that of PAA and H2O2. The reason for the efficient transformation of pollutants is that the BD system was proved to be a non-free radical dominated mechanism. The quantum yield of BD (Ф254 nm) was calculated to be 0.064, which indicates that photophysical process is the dominant mode of BD conversion. By adding trapping agents, direct energy transfer from 3BD* to NPX (in anoxic environment) or dissolved oxygen (in aerobic environment) was proved to play a major role (> 91 %). Additionally, the BD process reduces the toxicity of NPX and promotes microbial growth after irradiation. Overall, this study significantly deepened the understanding of the transformation between BD and micropollutants, and provided a potential BD-based process for micropollutants removal under solar irradiation.

Numerical inverse transformation of double sided Laplace transform with parameter optimization

There are efficient numerical inverse Laplace transformation (NILT) procedures for double sided Laplace transforms (DSLT), which are based on some hyperparameters. If those hyperparameters are set properly, the computed inverse is accurate, while using an incorrect hyperparameter results in a completely incorrect inverse.To the best of the authors knowledge, there is no NILT procedure for DSLT, which automatically sets the hyperparameters based on the DSLT and the point where the inverse needs to be computed. In this paper we propose a procedure which automatically optimizes the hyperparameters of the NILT method and this way it eliminates the unforeseen dependency of the computed inverse on the inherent hyperparameters.

Towards carbon neutrality: The role of innovation and resources efficiency in China's economic transformation

This study investigates the necessary paradigm shift in redefining productivity. It achieves this by examining the complex interplay between technological innovation, efficiency in natural resource utilization, and the pursuit of green economic recovery as a path towards carbon neutrality. The research employs the Autoregressive Distributed Lag (ARDL) model to analyze the dynamics across these domains, focusing specifically on China for the period 2010–2022. Through comprehensive analysis, the study reveals significant results that illuminate potential pathways for achieving sustainable development. The findings highlight the crucial role of promoting technological innovation and optimizing natural resource use efficiency in driving green economic recovery and mitigating carbon emissions. These results emphasize the importance for policymakers to prioritize investments in innovation, resource efficiency, and green initiatives. This prioritization facilitate the transition towards carbon neutrality and foster long-term environmental sustainability and economic resilience.

Exploiting graded triangular gratings for optimal nano-focusing: A novel approach to enhance SERS efficiency

Plasmonic graded nano-gratings enable rainbow trapping of multiple resonant modes over a wide wavelength spectrum, useful for multi-channel Surface Enhanced Raman Spectroscopy (SERS) of molecular species. However, rectangular nano-gratings have limitations in achieving efficient rainbow trapping and localizing a wide spectrum of plasmonic modes due to their stepwise geometry, which induces high dissipation of surface plasmon polaritons into the substrate. An alternative platform of graded triangular nano-gratings enables increased localization and more efficient adiabatic transformation between neighboring grooves. Varying groove angles, depths, and periods in the tapered geometry allow for smooth adjustment of the surface plasmon polariton propagation constant, reducing losses and maximizing nano-focusing inside the groove tips. To overcome the limitation of low aspect ratio in wet-etching silicon, we employed a multi-step process of reactive ion etching of a SiO2 barrier layer to generate aperture width, followed by anisotropic wet-etching. The resulting graded triangular nano-gratings showed excellent SERS enhancement along three laser wavelength excitations. The enhancement factors of 638 and 785 nm wavelengths are 8.5 × 109 and 9 × 108, respectively, for the detection of 1 µM Rhodamine 6G. In addition, graded triangular nano-gratings show similar enhancement factors for other species, specifically the lipid DPEE-PEG, at the 532 nm laser excitation wavelength with an excellent SERS enhancement factor of 1.5 × 109. Owing to the ability of the graded triangular gratings to elicit pronounced SERS responses across three distinct laser excitations, they unequivocally qualify as “rainbow trapping” structures. Wider apertures, lower ohmic losses, and the ability to tune the groove angle beyond conventional etching methods bode well for graded triangular gratings as a superior platform for miniature sensors.

Can digital transformation affect coal utilization efficiency in China? Evidence from spatial econometric analyses

The swift development of digital technology in China, coupled with the escalating significance of coal resource utilization in the industrial sector, necessitates an investigation into the role of digital transformation in fostering the sustainable development of coal resources. We utilize the Spatial Dubin Model (SDM) to analyze data from 30 Chinese provinces between 2011 and 2019, exploring the relationship between digital transformation and coal utilization efficiency. Additionally, the research investigates spatial spillover effects and the moderating effect mechanism at play. Key findings reveal that: Firstly, a noteworthy positive correlation exists between digital transformation and coal utilization efficiency. Secondly, digital transformation yields significant positive impacts on proximate cities. Lastly, analysis of moderating effects indicates that digital development policies can enhance the positive association between digital transformation and coal utilization efficiency. These findings furnish novel insights and avenues for regions seeking to propel sustainable development in coal resource utilization.

On the Substrate Scope of Dimethylsulfonium Propionate Lyases toward Dimethylsulfoxonium Propionate Derivatives

AbstractThe six dimethylsulfonium propionate (DMSP) lyases DddQ, DddW, DddP, DddY, DddK and DddL catalyze the elimination of dimethyl sulfide from DMSP and can also cleave the marine metabolite dimethylsulfoxonium propionate (DMSOP) to DMSO and acrylate. In this study the potency of all six enzymes for the conversion of four DMSOP analogs with longer alkyl chains that were synthesized in three steps from 3‐mercaptopropionic acid was investigated. For this purpose, the pH dependency of the enzyme reactions was determined, showing optimal conditions at pH 8 for all enzymes but DddP, for which an optimum of pH 6 was found. Efficient transformations were observed for DddQ, DddW, DddY and DddK, for which the Michaelis‐Menten kinetics were determined for all four substrate analogs. HPLC analysis of the dialkylsulfoxides obtained with the most efficient enzyme DddW revealed that these compounds were obtained with a low to moderate enantiomeric excess (up to 25 % ee), demonstrating that this enzyme has a preference for one of the enantiomers of the DMSOP analogs.

A novel perspective on the role of long non-coding RNAs in regulating polyphenols biosynthesis in methyl jasmonate-treated Siraitia grosvenorii suspension cells

Siraitia grosvenorii (S. grosvenorii) is considered a promising economic crop due to its various bioactive compounds. Polyphenols, one of the main bioactive compounds in S. grosvenorii suspension cells, are highly beneficial for human health. Therefore, elucidating their biosynthetic regulatory mechanisms is crucial for the efficient production of these bioactive compounds. Previous studies on the regulation of plant bioactive components have more often focused on the role of protein-coding genes but overlooked non-coding genes. In this study, we provide a novel perspective on the role of long non-coding RNAs (lncRNAs) in regulating polyphenols biosynthesis in methyl jasmonate (125 µM)-treated S. grosvenorii cells. Firstly, metabolomics analysis revealed 70 differential metabolites mainly composed of polyphenols (phenolic acids and flavonoids) in the cells. The intracellular polyphenols content (maximum 41.12 mg g−1) increased synchronously with the upregulation of polyphenols biosynthetic enzyme activity. Secondly, transcriptomics analysis indicated 496 differentially-expressed lncRNAs and 3595 differentially-expressed mRNAs in the cells. Besides, 29 putative regulatory pairs between differentially-expressed lncRNAs and their target mRNAs were identified, which might affect plant signal transduction, polyphenols and their precursors biosynthesis, and related transport pathways, ultimately mediating the accumulation of polyphenols in the cells. Finally, using an efficient transient transformation strategy established in this study, an antisense-lncRNA of phenylalanine ammonia-lyase (lnc-PAL) was preliminarily proven to be a negative regulatory factor on polyphenols biosynthesis in S. grosvenorii. Therefore, our findings expanded the knowledge of lncRNAs in polyphenols biosynthesis, providing a theoretical basis for the efficient production of these compounds in S. grosvenorii cells.

Repurpose the antimicrobial peptide Buforin II for plasmid transformation into Escherichia coli

Antimicrobial peptides Buforin II, derived from histone H2A, demonstrates strong cell-penetrating activity without cell lysis and strong affinity for internal cellular nucleic acids, making it a potential candidate for macromolecule delivery into bacteria. Herein, we designed a peptide that is the fusion of Buforin II and a polycation tail (KH)6 and assessed its efficiency in delivering plasmid (pGEX-RG-(TAG)5, 7,142 bp) into Escherichia coli OmniMAX. The peptide and plasmid were incubated at 25°C to form the complexes at various peptide concentrations from 5 to 50 µg/mL. After that, the complexes were incubated with the E. coli competent cells at 25°C. In comparison with the transformation efficiency and normalized transformation efficiency of conventional heat-shock method, a 1.28 and 7.83 times higher transformation efficiency, correspondingly, was achieved by using novel peptide-based delivery system at peptide concentration of 5 µg/mL. The cell viability of over 90% was displayed at peptide concentration of 5 µg/mL. This study can lead to the development of a transformation approach under mild conditions and an ideal tool for gene delivery.

Research on the Transformation Efficiency of Scientific and Technological Achievements of Minis-try-affiliated Universities Based on Network DEA

Previous studies on the transformation of scientific and technological achievements in universities were based on the black box model, and the results obtained were not comprehensive enough. This paper divides the transformation of scientific and technological achievements of universities into the knowledge research and development stage and the value transformation stage. The two-stage network DEA model is used to study the transformation efficiency of scientific and technological achievements of 55 subordinate universities based on the data from 2009 to 2017.The research in this paper finds that the overall efficiency of the transformation of scientific and technological achievements in Chinese universities is relatively stable, and the efficiency of the knowledge research and development stage is relatively high, but the efficiency of the value realization stage is seriously low, resulting in a low level of overall efficiency."985" engineering colleges and "double first-class" colleges have higher input and output efficiency, and cluster analysis is used to classify and summarize the technological transformation of each college. Finally, corresponding development suggestions are put forward.

Simulated sunlight/periodate-triggered formation of toxic halogenated bisphenols in highly saline water.

Periodate (PI)-based oxidation using the activators, such as metal ions and light irradiation, has emerged as a feasible treatment strategy for the effective remediation of contaminated water and wastewater. Given the pervasive nature of PI residues and solar exposure during application, the role of solar light in remediating the challenging highly saline water matrices needs to be elucidated. In this study, bisphenol A (BPA) was selected as the targeted micropollutant, which can be efficiently eliminated by the simulated sunlight (SSL)/PI system in the presence of high-level Cl- (up to 846.0 mM) at pH 7.0. The presence of different background constituents of water, such as halides, nitrate, and dissolved organic matter, had no effect, or even accelerated BPA abatement. Particularly, the ubiquitous Br- or I- appreciably enhanced the BPA transformation efficiency, which may be ascribed to the generation of high-selective reactive HOBr or HOI. The in silico predictions suggested that the transformation products generated by halide-mediated SSL/PI systems via halogen substitutions showed greater persistence, bioaccumulation, and aquatic toxicity than BPA itself. These findings highlighted a widespread phenomenon during PI-based oxidative treatment of highly saline water, which needs special attention under solar light illumination.

AdaFormer: Efficient Transformer with Adaptive Token Sparsification for Image Super-resolution

Efficient transformer-based models have made remarkable progress in image super-resolution (SR). Most of these works mainly design elaborate structures to accelerate the inference of the transformer, where all feature tokens are propagated equally. However, they ignore the underlying characteristic of image content, i.e., various image regions have distinct restoration difficulties, especially for large images (2K-8K), failing to achieve adaptive inference. In this work, we propose an adaptive token sparsification transformer (AdaFormer) to speed up the model inference for image SR. Specifically, a texture-relevant sparse attention block with parallel global and local branches is introduced, aiming to integrate informative tokens from the global view instead of only in fixed local windows. Then, an early-exit strategy is designed to progressively halt tokens according to the token importance. To estimate the plausibility of each token, we adopt a lightweight confidence estimator, which is constrained by an uncertainty-guided loss to obtain a binary halting mask about the tokens. Experiments on large images have illustrated that our proposal reduces nearly 90% latency against SwinIR on Test8K, while maintaining a comparable performance.

Proxyformer: Nyström-Based Linear Transformer with Trainable Proxy Tokens

Transformer-based models have demonstrated remarkable performance in various domains, including natural language processing, image processing and generative modeling. The most significant contributor to the successful performance of Transformer models is the self-attention mechanism, which allows for a comprehensive understanding of the interactions between tokens in the input sequence. However, there is a well-known scalability issue, the quadratic dependency (i.e. O(n^2)) of self-attention operations on the input sequence length n, making the handling of lengthy sequences challenging. To address this limitation, there has been a surge of research on efficient transformers, aiming to alleviate the quadratic dependency on the input sequence length. Among these, the Nyströmformer, which utilizes the Nyström method to decompose the attention matrix, achieves superior performance in both accuracy and throughput. However, its landmark selection exhibits redundancy, and the model incurs computational overhead when calculating the pseudo-inverse matrix. We propose a novel Nyström method-based transformer, called Proxyformer. Unlike the traditional approach of selecting landmarks from input tokens, the Proxyformer utilizes trainable neural memory, called proxy tokens, for landmarks. By integrating contrastive learning, input injection, and a specialized dropout for the decomposed matrix, Proxyformer achieves top-tier performance for long sequence tasks in the Long Range Arena benchmark.

A Twist for Graph Classification: Optimizing Causal Information Flow in Graph Neural Networks

Graph neural networks (GNNs) have achieved state-of-the-art results on many graph representation learning tasks by exploiting statistical correlations. However, numerous observations have shown that such correlations may not reflect the true causal mechanisms underlying the data and thus may hamper the ability of the model to generalize beyond the observed distribution. To address this problem, we propose an Information-based Causal Learning (ICL) framework that combines information theory and causality to analyze and improve graph representation learning to transform information relevance to causal dependence. Specifically, we first introduce a multi-objective mutual information optimization objective derived from information-theoretic analysis and causal learning principles to simultaneously extract invariant and interpretable causal information and reduce reliance on non-causal information in correlations. To optimize this multi-objective objective, we enable a causal disentanglement layer that effectively decouples the causal and non-causal information in the graph representations. Moreover, due to the intractability of mutual information estimation, we derive variational bounds that enable us to transform the above objective into a tractable loss function. To balance the multiple information objectives and avoid optimization conflicts, we leverage multi-objective gradient descent to achieve a stable and efficient transformation from informational correlation to causal dependency. Our approach provides important insights into modulating the information flow in GNNs to enhance their reliability and generalization. Extensive experiments demonstrate that our approach significantly improves the robustness and interpretability of GNNs across different distribution shifts. Visual analysis demonstrates how our method converts informative dependencies in representations into causal dependencies.

The Inhibitor: ReLU and Addition-Based Attention for Efficient Transformers (Student Abstract)

To enhance the computational efficiency of quantized Transformers, we replace the dot-product and Softmax-based attention with an alternative mechanism involving addition and ReLU activation only. This side-steps the expansion to double precision often required by matrix multiplication and avoids costly Softmax evaluations but maintains much of the core functionality of conventional dot-product attention. It can enable more efficient execution and support larger quantized Transformer models on resource-constrained hardware or alternative arithmetic systems like homomorphic encryption. Training experiments on four common benchmark tasks show test set prediction scores comparable to those of conventional Transformers with dot-product attention. Our scaling experiments also suggest significant computational savings, both in plaintext and under encryption. In particular, we believe that the ReLU and addition-based attention mechanism introduced in this paper may enable privacy-preserving AI applications operating under homomorphic encryption by avoiding the costly multiplication of encrypted variables.

Understanding the Role of the Projector in Knowledge Distillation

In this paper we revisit the efficacy of knowledge distillation as a function matching and metric learning problem. In doing so we verify three important design decisions, namely the normalisation, soft maximum function, and projection layers as key ingredients. We theoretically show that the projector implicitly encodes information on past examples, enabling relational gradients for the student. We then show that the normalisation of representations is tightly coupled with the training dynamics of this projector, which can have a large impact on the students performance. Finally, we show that a simple soft maximum function can be used to address any significant capacity gap problems. Experimental results on various benchmark datasets demonstrate that using these insights can lead to superior or comparable performance to state-of-the-art knowledge distillation techniques, despite being much more computationally efficient. In particular, we obtain these results across image classification (CIFAR100 and ImageNet), object detection (COCO2017), and on more difficult distillation objectives, such as training data efficient transformers, whereby we attain a 77.2% top-1 accuracy with DeiT-Ti on ImageNet. Code and models are publicly available.

Parageobacillus thermoglucosidasius Strain Engineering Using a Theophylline Responsive RiboCas for Controlled Gene Expression.

The relentless increase in atmospheric greenhouse gas concentrations as a consequence of the exploitation of fossil resources compels the adoption of sustainable routes to chemical and fuel manufacture based on biological fermentation processes. The use of thermophilic chassis in such processes is an attractive proposition; however, their effective exploitation will require improved genome editing tools. In the case of the industrially relevant chassis Parageobacillus thermoglucosidasius, CRISPR/Cas9-based gene editing has been demonstrated. The constitutive promoter used, however, accentuates the deleterious nature of Cas9, causing decreased transformation and low editing efficiencies, together with an increased likelihood of off-target effects or alternative mutations. Here, we rectify this issue by controlling the expression of Cas9 through the use of a synthetic riboswitch that is dependent on the nonmetabolized, nontoxic, and cheap inducer, theophylline. We demonstrate that the riboswitches are dose-dependent, allowing for controlled expression of the target gene. Through their use, we were then able to address the deleterious nature of Cas9 and produce an inducible system, RiboCas93. The benefits of RiboCas93 were demonstrated by increased transformation efficiency of the editing vectors, improved efficiency in mutant generation (100%), and a reduction of Cas9 toxicity, as indicated by a reduction in the number of single nucleotide polymorphisms (SNPs) observed. This new system provides a quick and efficient way to produce mutants in P. thermoglucosidasius.

Metal-Free Click-Chemistry: A Powerful Tool for Fabricating Hydrogels for Biomedical Applications.

Increasing interest in the utilization of hydrogels in various areas of biomedical sciences ranging from biosensing and drug delivery to tissue engineering has necessitated the synthesis of these materials using efficient and benign chemical transformations. In this regard, the advent of "click" chemistry revolutionized the design of hydrogels and a range of efficient reactions was utilized to obtain hydrogels with increased control over their physicochemical properties. The ability to apply the "click" chemistry paradigm to both synthetic and natural polymers as hydrogel precursors further expanded the utility of this chemistry in network formation. In particular, the ability to integrate clickable handles at predetermined locations in polymeric components enables the formation of well-defined networks. Although, in the early years of "click" chemistry, the copper-catalyzed azide-alkyne cycloaddition was widely employed, recent years have focused on the use of metal-free "click" transformations, since residual metal impurities may interfere with or compromise the biological function of such materials. Furthermore, many of the non-metal-catalyzed "click" transformations enable the fabrication of injectable hydrogels, as well as the fabrication of microstructured gels using spatial and temporal control. This review article summarizes the recent advances in the fabrication of hydrogels using various metal-free "click" reactions and highlights the applications of thus obtained materials. One could envision that the use of these versatile metal-free "click" reactions would continue to revolutionize the design of functional hydrogels geared to address unmet needs in biomedical sciences.

Impact of weak nonlocal nonlinearity on modulational instability and chirp-free soliton trains in parabolic law optical fiber

This study considers the generation and evolution of chirp-free soliton trains in a focusing cubic-quintic nonlinear optical fiber media (by utilizing experimental parameters), under the influence of weak nonlocal nonlinearity. Analysis of modulational instability (MI) reveals that the MI gain exist only in the anomalous dispersion regime, with the quintic nonlinear term increasing the maximum gain and sideband frequencies. The nonlocal nonlinearity parameter generally suppresses the impact of MI, because the maximum gain is greatly reduced with increase in nonlocality. By using the subsidiary ordinary differential equation method, stationary periodic soliton trains are obtained; with the intensity of the optical beam greatly reduced and frequency increased as nonlocality is gradually stepped up. However, an increase in nonlocality instead reduces the frequency of the traveling optical signals; derived via an efficient transformation method. Our results strongly suggest that nonlocal nonlinearity has potential applications in the control of laser beams; as nonlinear periodic optical signals can be easily transformed to very weak quasi-plane waves.

Utilizing Joukowsky transformation in the design of optical fiber for elliptical-to-circular mode conversion.

This study addresses the challenge of enhancing coupling efficiency between optical fibers and elliptical Gaussian beams emitted by semiconductor lasers, particularly in fiber communication systems. We introduce a method for fiber design utilizing the Joukowsky transformation to facilitate efficient mode transformation from elliptical to circular, thereby augmenting the coupling efficiency with both single-mode and multimode fibers. Theoretical analysis and numerical simulations demonstrate that the fiber with a structurally transitional core maintains high-efficiency mode transformation across various lengths, and its structure has been optimized accordingly. Additionally, our investigation reveals the designed fiber's ability to preserve polarization states, which could have significant implications in precision optical applications. The proposed design offers an approach to improving performance in optical communication systems, especially in wavelength-division multiplexing (WDM) transmission systems and fiber lasers.