Binary Representation Research Articles

Effect of dodecyl dimethyl benzyl ammonium chloride on dyeing kinetics and thermodynamics of spandex fibers dyed with acid dye

Spandex fiber has been widely used in clothing. Adding a small amount of spandex to textiles can greatly improve the elasticity, wear resistance, and other mechanical properties. But it is difficult to color it into a deep hue with acid dyes. The main purpose of this paper is to investigate how the cationic dyeing auxiliary dodecyl dimethyl benzyl ammonium chloride (DDBAC) can effectively improve the dyeability and dyeing depth of spandex fibers, and its influence on dyeing kinetics and thermodynamics of spandex fiber. The results showed that adding DDBAC could increase the potential of spandex zeta. Kinetically, when spandex adsorbed acid dyes, adding DDBAC could decrease the dyeing rate constant, and improve the dyeing equilibrium concentration and half-dyeing time. Thermodynamically, without DDBAC, the adsorption isotherm of spandex to Acid Red 414 conforms to the Langmuir model. The adsorption isotherm of spandex to Acid Red 414 after DDBAC is added is not entirely obedient to the Langmuir partition type but is close to the Langmuir + Nernst binary adsorption model. Meanwhile, the new binary saturation adsorption value S increased with the increase of temperature and DDBAC dosage and tended to occur more in the Nernst-type partitioning mechanism.

An efficient Radix-4 butterfly structure based on the complex binary number system and distributed arithmetic

Complex number arithmetic is pivotal in various applications, requiring the selection of an efficient multiplier for high-performance computations. Fast Fourier transform (FFT)-based multipliers are widely employed for computing complex number products, but their reliance on using dedicated multipliers and treating the real and imaginary parts as two entities significantly add to the cost and complexity of the system. Distributed arithmetic (DA) is a technique that replaces complex multiplications with a bit-level shift and addition mechanism. The complex binary number system (CBNS) utilizes binary arithmetic, which treats the real and imaginary parts as a single entity, which can simplify complex number arithmetic and computations. This paper introduces an approach integrating the CBNS with DA in a Radix-4 decimation in time FFT 8-bit and 16-bit butterfly structure. The proposed design significantly reduces arithmetic computations and eliminates dedicated multipliers, demonstrating a reduction in power consumption, area size, and lookup tables, as well as increasing overall clock performance compared to the conventional FFT architecture on Artix-7, Kintex-7, and Virtex-7 field-programmable gate array chips.

Specifying Precision in Visual-orthographic Prediction Error Representations for a Better Understanding of Efficient Reading.

Efficient visual word recognition presumably relies on orthographic prediction error (oPE) representations. On the basis of a transparent neurocognitive computational model rooted in the principles of the predictive coding framework, we postulated that readers optimize their percept by removing redundant visual signals, allowing them to focus on the informative aspects of the sensory input (i.e., the oPE). Here, we explore alternative oPE implementations, testing whether increased precision by assuming all-or-nothing signaling and more realistic word lexicons results in adequate representations underlying efficient word recognition. We used behavioral and electrophysiological data (i.e., EEG) for model evaluation. More precise oPE representations (i.e., implementing a binary signaling and a frequency-sorted lexicon with the 500 most common five-letter words) explained variance in behavioral responses and electrophysiological data 300 msec after stimulus onset best. The original less-precise oPE representation still best explains early brain activation. This pattern suggests a dynamic adaption of represented visual-orthographic information, where initial graded prediction errors convert into binary representations, allowing accurate retrieval of word meaning. These results offer a neuro-cognitive plausible account of efficient word recognition, emphasizing visual-orthographic information in the form of prediction error representations central to the transition from perceptual processing to the access of word meaning.

Predicting Drug Resistance in Mycobacterium tuberculosis: A Machine Learning Approach to Genomic Mutation Analysis

Background/Objectives: Tuberculosis (TB) is one of the leading causes of death by infectious disease in the world, caused by the bacterium Mycobacterium tuberculosis (M. tuberculosis). First- and second-line drugs are used to treat active tuberculosis. However, drug resistance presents a critical challenge in the global fight against tuberculosis. Materials and Methods: Drug resistance diagnosis is typically performed through drug susceptibility testing, either via culture-based methods or molecular rapid tests. In recent years, studies have utilized whole-genome sequencing of M. tuberculosis isolates combined with machine learning techniques to predict drug resistance. In this study, we evaluated four machine learning classification models: Extreme Gradient Boosting Classifier (XGBC), Logistic Gradient Boosting Classifier (LGBC), Gradient Boosting Classifier (GBC), and an Artificial Neural Network (ANN). These models were trained using a Variant Call Format (VCF) file preprocessed by the CRyPTIC consortium. We employed three datasets: the original dataset, a dataset reduced through principal component analysis, and a dataset that prioritized the most important features identified by the XGBC model. Results: The four models were utilized on the principal component analysis dataset, while the XGBC model was additionally implemented with an arbitrarily reduced dataset focusing on the most significant mutations identified during model training. All models were trained and tested across these datasets, and their performances were compared. The XGBC model trained on the original dataset outperformed the others, achieving sensitivity values of 0.97, 0.90, and 0.94, specificity values of 0.97, 0.99, and 0.96, and F1-scores of 0.93, 0.94, and 0.92 for ethambutol, isoniazid, and rifampicin, respectively. Discussion: This study highlights the effectiveness of a binary representation of mutations (indicating their presence or absence) as a robust approach for training XGBC models that accurately classify resistance and susceptibility to ethambutol, isoniazid, and rifampicin in M. tuberculosis. Conclusion: The XGBC model trained on the original dataset demonstrated superior performance compared to the other models, indicating its potential for improving drug resistance prediction in TB. This approach could be further explored for clinical applications in the fight against drug-resistant tuberculosis.

Quantitative Coding and Complexity Theory of Continuous Data: Part I: Motivation, Definition, Consequences

When encoding real numbers as (necessarily infinite) bit-strings, the naïve binary/decimal expansion is well-known [ doi:10.1112/plms/s2-43.6.544 ] computably “ un reasonable”, rendering, for example, tripling qualitatively discontinuous on Cantor’s sequence space. Encoding reals as sequences of (finite integer numerators and denominators, in binary, of) rational approximations does make common operations qualitatively computable, yet admits no bounds on their computational complexity/quantitative continuity. Dyadic approximations, on the other hand, are known polynomially, and signed binary expansions even linearly, “reasonable” in a rigorous sense recalled in the introduction of this work. But how to distinguish between un/suitable encodings of spaces common in Calculus beyond the reals, such as Banach or Sobolev? With respect to qualitative computability/continuity on topological spaces, the technical condition of admissibility had been identified [ doi:10.1016/0304-3975(85)90208-7 ] for an encoding over Cantor space (historically called a representation ) to be “reasonable” [ doi:10.1007/978-3-030-59234-9_9 ] . Roughly speaking, admissibility requires the representation to be (i) continuous, and to be (ii) maximal with respect to continuous reduction. Admissible representations exist for a large class of spaces. And for (precisely) these does the Kreitz–Weihrauch—sometimes aka Main —Theorem of Computable Analysis hold, which characterizes continuity of functions by continuity of mappings translating codes, so-called realizers . We refine qualitative computability/continuity on topological spaces to quantitative continuity/complexity on metric spaces by proposing a notion, and investigating the properties, of polynomially/linearly admissible representations. Roughly speaking, these are (i) close to “optimally” continuous, namely linearly/polynomially relative to the space’s entropy, and they are (ii) maximal with respect to relative linear/polynomial quantitatively continuous reductions defined in the main text. Quantitatively admissible representations are closed under composition over generalized ground spaces beyond Cantor’s. Such representations exhibit a quantitative strengthening of the qualitative Main Theorem , namely now characterizing quantitative continuity of functions by quantitative continuity of realizers. A large class of compact metric spaces is shown to admit polynomially admissible representations over compact ultra metric spaces, and some even a generalization of the linearly admissible signed binary encoding. Quantitative admissibility thus provides the desired criterion for complexity-theoretically “reasonable” encodings.

The Use of Hierarchical Temporal Memory and Temporal Sequence Encoder for Online Anomaly Detection in Industrial Cyber-Physical Systems

This study introduces a novel, practical approach for designing a hierarchical online anomaly detection system for industrial cyber-physical systems. The proposed method utilizes the Hierarchical Temporal Memory (HTM) unsupervised learning algorithm, which requires data to be encoded as sparse binary distributed representations (SDRs). A new SDR encoding method termed the temporal sequence encoder (TSSE) is presented to convert system outputs into SDRs. This approach enables HTM to retain high memory capacity and robust performance when processing data streams of slowly varying physical measurements, typical of many industrial processes. The effectiveness of the proposed system is demonstrated on the Secure Water Treatment (SWaT) dataset, which comprises data collected from a fully operational, scaled-down water treatment plant. The system achieves a recall of 0.906, a precision of 0.935, and an F1 score of 0.92 on SWaT. Compared to previous methods, our approach achieves state-of-the-art recall (~5.3% improvement), along with competitive precision and F1 score, by learning in an online manner without the need for expensive dataset collection, labeling, or retraining phases. These findings suggest that the proposed online anomaly detection method can be effectively applied to a broad range of water treatment and large-scale industrial cyber-physical systems.

4-bit Absolute-value Detector Based on Multisim

In many circuit systems, it is necessary to compare the amplitude of the signal, so it is necessary to design an absolute value comparator to compare the input values. This paper introduces a four-bit binary absolute value comparator, which can realize the size comparison of four-bit binary signed numbers. This paper introduces some basic working principles of the absolute value comparator through the truth table. The absolute value is obtained by using the adder to invert the input four-digit number and add 1. The three-bit comparator is realized by connecting the three comparators, and the four-bit absolute value circuit is connected with the three-bit comparator to realize the four-bit signed absolute value comparator. The final design results are displayed using the simulation software Multisim, and it can be observed that the circuit works in a normal way. Finally, this circuit is simple and easy to implement, and has good delay and energy consumption. In the future, it can be composed of existing components such as half adders and comparators, which can reduce the design cost and design difficulty to a certain extent.

Enhanced Image Retrieval Using Multiscale Deep Feature Fusion in Supervised Hashing.

In recent years, deep-network-based hashing has gained prominence in image retrieval for its ability to generate compact and efficient binary representations. However, most existing methods predominantly focus on high-level semantic features extracted from the final layers of networks, often neglecting structural details that are crucial for capturing spatial relationships within images. Achieving a balance between preserving structural information and maximizing retrieval accuracy is the key to effective image hashing and retrieval. To address this challenge, we introduce Multiscale Deep Feature Fusion for Supervised Hashing (MDFF-SH), a novel approach that integrates multiscale feature fusion into the hashing process. The hallmark of MDFF-SH lies in its ability to combine low-level structural features with high-level semantic context, synthesizing robust and compact hash codes. By leveraging multiscale features from multiple convolutional layers, MDFF-SH ensures the preservation of fine-grained image details while maintaining global semantic integrity, achieving a harmonious balance that enhances retrieval precision and recall. Our approach demonstrated a superior performance on benchmark datasets, achieving significant gains in the Mean Average Precision (MAP) compared with the state-of-the-art methods: 9.5% on CIFAR-10, 5% on NUS-WIDE, and 11.5% on MS-COCO. These results highlight the effectiveness of MDFF-SH in bridging structural and semantic information, setting a new standard for high-precision image retrieval through multiscale feature fusion.

APPLICATION OF GLOBAL SENSITIVITY ANALYSIS FOR IDENTIFICATION OF PROBABILISTIC DESIGN SPACES

The design space (DS) is defined as the combination of materials and process conditions that provides assurance of quality for a pharmaceutical product. A model-based approach to identify a probability-based DS requires costly simulations across the entire process parameter space (certain) and the uncertain model parameter space and material properties space. We demonstrate that application of global sensitivity analysis (GSA) can significantly reduce model complexity and reduce computational time for identifying and quantifying a probabilistic DS by screening out nonimportant uncertain parameters. The novelty of this approach is that the use of an indicator function, which takes only binary values as a model function, allows application of a straightforward GSA based on Sobol' sensitivity indices and avoids using more costly Monte Carlo filtering or GSA for constrained problems. We consider a chemical reaction example to illustrate how this formulation results in a model reduction and a significant decrease of model runs.

Binary Arithmetic Optimization Algorithm Using a New Transfer Function for Fusion Modeling

Organizations use fusion data modeling to integrate multiple data sources and build precise representations that achieve better organizational clarity. One recent method that has proven effective in many benchmark tests is the arithmetic optimization algorithm (AOA). AOA applies basic distribution behavior to arithmetic operations such as multiplication, division, addition, and subtraction. This paper focuses on the innovative application of AOA in addressing the feature selection problem. The binary version of this algorithm (BAOA) is introduced to solve problems of binary nature. The main part of this version is the transfer function that converts a continuous search space into a discrete search space. Therefore, a new Fountain-shaped transfer function is proposed to enhance global exploration and local exploitation in the BAOA algorithm. The performance of the proposed Fountain-shaped transfer function has been compared with V-shaped and S-shaped transfer functions. Based on ten public datasets, the performance of the proposed transfer function is validated. The Experimental results show the superiority of the proposed Fountain-shaped transfer function not only in getting high classification accuracy with few selected features but also requires inexpensive computational costs.

АНАЛІЗ ІМОВІРНИХ ВИТОКІВ ПОМИЛОК В ОБЧИСЛЕННІ МАТЕМАТИЧНИХ МОДЕЛЕЙ БОЙОВИХ ДІЙ У КОМП’ЮТЕРНИХ СИСТЕМАХ ІМІТАЦІЙНОГО МОДЕЛЮВАННЯ

Currently, there are many situations when practically conducting a scientific experiment or recreating this or that situation in order to make an informed decision is impossible or requires significant financial costs. In such cases, computer simulation systems always come to the rescue, which have been used for a long time to make decisions affecting human lives, planning military operations, directions for the development of the types of the Armed Forces and industries, and even the prospects of state formation. But specialists in the relevant fields, based on the results of computer calculations with the representation of numbers in binary floating-point code, do not take into account the peculiarities of the accuracy of calculations in a computer with binary numbers and the influence of binary arithmetic on the results of simulation modeling. As a rule, the mathematical results of computer calculations are considered to be reliable, although they may contain an error that depends on the hardware of the electronic computing machine, namely on the bit rate of the processor bus. This is due to the IEEE 754 technical standard for floating-point arithmetic. In the article, the authors analyzed the mathematical models used in combat simulation systems and approaches to their solution. The representation of binary floating-point numbers in computer systems of different bit sizes has been detailed, and possible sources of errors when numbers are represented in binary code have been determined, as well as mathematical calculations. This approach allows you to take into account possible error limits at the stage of entering data into computer systems for calculation, as well as directly when performing mathematical calculations in electronic computing systems.

Stochastic Computing Architectures: Modeling, Optimization, and Applications

With the rapid development of artificial intelligence (AI), the design and implementation of very large-scale integrated circuits (VLSI) based on traditional binary computation are facing challenges of high complexity, computational power, and high power consumption. The development of Moore’s law has reached the limit of physical technology, and there is an urgent need to explore new computing architectures to make up for the shortcomings of traditional binary computing. To address the existing problems, Stochastic Computing (SC) is an unconventional stochastic sequence that converts binary numbers into a coded stream of digital pulses. It has a remarkable symmetry with binary computation. It uses logic gate circuits in the probabilistic domain to implement complex arithmetic operations at the expense of computational accuracy and time. It has low power and logic resource consumption and a small circuit area. This paper analyzes the basic concepts and development history of SC and neural networks (NNs), summarizes the development progress of SC with NN at home and abroad, and discusses the development trend of SC and the future challenges and prospects of NN. Through systematic summarization, this paper provides new learning ideas and research directions for developing AI chips.

New prognostic index for neoadjuvant chemotherapy outcome in patients with advanced high-grade serous ovarian cancer

BackgroundA validated prognostic index for the outcome of patients with advanced high-grade serous ovarian cancer (HGSOC) undergoing neoadjuvant chemotherapy (NACT) remains elusive. To address this need, we developed an ovarian neoadjuvant chemotherapy prognostic index (ONCPI) to improve predictive accuracy.MethodsWe encompassed an analysis of the clinicopathological characteristics of patients with advanced HGSOC who were administered platinum-based NACT. Blood inflammatory composite markers were calculated and converted into binary values using optimal cutoffs. Omental hematoxylin and eosin (H&E) stained slides were selected for the assessment of chemotherapy response score (CRS), which served as a measure of NACT efficacy. Logistic regression analysis and Cox proportional hazards regression model were utilized to construct a prognostic index.ResultsMultivariate logistic analysis showed that both CRS and neutrophil-to-lymphocyte ratio (NLR) independently influenced the response to platinum-based chemotherapy. Meanwhile, Kaplan–Meier and Cox regression analysis revealed that CRS score was significantly correlated with progression-free survival (PFS) and overall survival (OS), and patients with high NLR showed poor OS. We further developed an ovarian neoadjuvant chemotherapy prognostic index (ONCPI) based on the CRS and NLR. The area under the curve (AUC) value of ONCPI was 0.771 (P < 0.001, 95% CI: 0.656–0.887) for the prediction of platinum resistance. This AUC value surpasses that of the individual NLR and CRS, which were 0.670 (P = 0.018, 95% CI: 0.547–0.793) and 0.714 (P = 0.003, 95% CI: 0.590–0.839), respectively. Moreover, survival analysis suggested that patients with ONCPI of 0 and 1 were significantly associated with improved PFS and OS.ConclusionsThe ONCPI emerges as a significant prognostic marker for predicting NACT outcome in advanced HGSOC patients and holds promise for integration into clinical practice and risk-stratified trial design.

Gwforge: a user-friendly package to generate gravitational-wave mock data

Abstract Next-generation gravitational-wave detectors, with their improved sensitivity and wider frequency bandwidth, will be capable of observing almost every compact binary coalescence signal from epochs before the first stars began to form, increasing the number of detectable binaries to hundreds of thousands annually. This will enable us to observe compact objects through cosmic time, probe extreme matter phenomena, do precision cosmology, study gravity in strong field dynamical regimes and potentially allow observation of fundamental physics beyond the standard model. However, the richer data sets produced by these detectors will pose new computational, physical and astrophysical challenges, necessitating the development of novel algorithms and data analysis strategies. To aid in these efforts, this paper introduces gwforge, a user-friendly, lightweight Python package, to generate mock data for next-generation detectors. gwforge allows users to seamlessly simulate data while abstracting away technical complexities, enabling more efficient testing and development of analysis pipelines. Additionally, the package’s data generation process is optimized using high-throughput systems like HTCondor, significantly speeding up the simulation of large populations of gravitational-wave events. We demonstrate the package’s capabilities through data simulation examples and highlight a few potential applications: performance loss due to foreground noise, bright-siren cosmology and impact of waveform systematics on binary parameter estimation.

Search anything: segmentation-based similarity search via region prompts

AbstractSearch Anything is presented, a novel approach to perform similarity search in images. In contrast to other approaches to image similarity search, Search Anything enables users to utilize point, box, and text prompts to search for similar regions in a set of images. The region selected by a prompt is automatically segmented, and a binary feature vector is extracted. This feature vector is then used as a query for an image region index, and the images that contain the corresponding regions are returned. Search Anything is trained in a self-supervised manner on mask features extracted by the FastSAM foundation model and semantic features for masked image regions extracted by the CLIP foundation model to learn binary hash code representations for image regions. By coupling these two foundation models, images can be indexed and searched at a more fine-grained level than finding only entire similar images. Experiments on several datasets from different domains in a zero-shot setting demonstrate the benefits of Search Anything as a versatile region-based similarity search approach for images. The efficacy of the approach is further supported by qualitative results. Ablation studies are performed to evaluate how the proposed combination of semantic features and segmentation features together with masking improves the performance of Search Anything over the baseline using CLIP features alone. For large regions, relative improvements of up to 9.87% in mean average precision are achieved. Furthermore, considering context is beneficial for searching small image regions; a context of 3 times an object’s bounding box gives the best results. Finally, we measure computation time and determine storage requirements.

Teaching Schoolchildren to Solve Problems on the Topic "Number Systems"

The author has been teaching programming to Gomel schoolchildren for more than four decades. For the last twenty-five years, training has been focused primarily on preparing for programming competitions from school to international competitions (IOI). Since 1997, 15 students of the author have won a total of 26 medals at IOI 1997 - 2023. The article contains a description of the teaching methods and tools used by the author to teach the topic “Number Systems”. An essential technical basis for training is the instrumental distance learning system DL.GSU.BY, was created and developed under the leadership of the author from 1999 to the present time. In this article, the following questions are sequentially considered (with the solution of the corresponding original problems from the American and Russian Internet Olympiads): binary number system, binary counting, converting numbers from decimal to binary, and vice versa. Hexadecimal and octal number systems. Conversion of numbers from one number system to another.

Interpolation of compound semiconductor alloy parameters from those of their constituents

Several methods have been proposed for interpolation of the value of physical parameters of quaternary alloys from those of their constituent ternary and binary sub-alloys. These expressions agree when non-linear bowing terms are not required; they differ in how the bowing terms of the bounding ternaries should be utilized. Common interpolation expressions for quaternaries can be generalized into two groups: (1) those that use a linear interpolation of the nearest ternary parameter values and (2) those that interpolate over binary values with a bowing term derived from the bounding ternaries. The second group of methods is equivalent to a polynomial expansion over the alloy’s interpolation space. For compound semiconductor alloys, the geometry of the composition space is the direct sum of the group-III and group-V mixture sub-spaces. The mixture sub-spaces are best described using barycentric coordinates on a regular simplex. A general polynomial expansion of the value of an alloy parameter using barycentric coordinates for the group-III and group-V simplex spaces is described along with an algorithm to generate interpolation expressions for alloys with arbitrary numbers of elements, including quinary and senary alloys. It is shown that a polynomial expansion produces values in closer agreement with the direct gap of quaternaries lattice-matched to common substrates than do approaches using an interpolation of the ternary values, despite a prominent recommendation to the contrary. Finally, a quaternary correction term is described that improves the predicted direct bandgap energies of GaInAsSb for compositions near those lattice matched to InP, InAs, and GaSb.

The interacting double white dwarf population with LISA: Stochastic foreground and resolved sources

Aims. We investigate the impact of tidal torques and mass transfer on the population of double white dwarfs that will be observed with LISA. Methods. Our Galactic distribution of double white dwarfs is based on the combination of a cosmological simulation and a binary population synthesis model. We used a semi-analytical model to evolve double white dwarf binaries considering ten different hypotheses for the efficiency of tidal coupling and three hypotheses for the birth spins of white dwarfs. We then estimated the stochastic foreground and the population of resolvable binaries for LISA for these different combinations. Results. Our predicted double white dwarf binary distribution can differ substantially from the distribution expected if only gravitational waves (GWs) are considered. If white dwarfs spin slowly, then we predict an excess of systems around a few due to binaries that outspiral after the onset of mass transfer. This excess of systems leads to differences in the confusion noise, which are most pronounced for strong tidal coupling. In that case, we find a significantly higher number of resolvable binaries than in the GW-only scenario. If instead white dwarfs spin rapidly and tidal coupling is weak, then we find no excess around a few mHz, and the confusion noise due to double white dwarfs is very low. In that scenario, we also predict a subpopulation of outspiralling binaries below 0.1 mHz. Using the Fisher matrix approximation, we estimate the uncertainty on the GW-frequency derivative of resolvable systems. We find that, even for non-accreting systems, the mismodelling error due to neglecting effects other than GWs is larger than the statistical uncertainty, and thus this neglect would lead to biased estimates for mass and distance. Conclusions. Our results suggest that the population of double white dwarfs is likely to be different from what is expected in the standard picture, which highlights the need for flexible tools in LISA data analysis. Because our semi-analytical model hinges upon a simplistic approach to determining the stability of mass accretion, it will be important to deepen our comprehension of stability in mass-transferring double white dwarf binaries.

ABOUT THE USE OF DIGITAL HYDRAULIC VALVES IN INDUSTRY

The article discusses digital hydraulic valves, which are an innovative alternative to traditional proportional analog valves. Digital hydraulics utilize a combination of simple, reliable, and inexpensive ON/OFF type valves, offering high precision in flow and pressure control through programmable logic controllers. The main advantage of such systems is the reduction in energy consumption, as they eliminate the need for constant pump operation and have no internal leakage. The article highlights the economic benefits of digital valves, including lower initial investments due to their lower cost compared to proportional counterparts. Importantly, if one valve fails, the system remains operational, as the failure of individual components does not critically affect overall performance. Special attention is given to coding schemes and control methods, particularly binary coding and pulse number modulation. Binary coding allows for significantly improved regulation accuracy with a minimal number of valves and ensures system fault tolerance. The use of parallel-connected valves, which offer a wide range of states, allows precise control of flows and pressures without the need for switching after the desired positions are set. Additionally, digital hydraulic systems provide fast response times and precise control, making them effective for various industrial applications. Further research in this area could lead to the development of new technological solutions and broader implementation in the industry. The article also covers the technical aspects of such systems, particularly pulse-width modulation, the most common approach to controlling two-way valves. PWM achieves high regulation accuracy through frequency modulation, although low switching frequencies may cause pressure pulsations, which must be compensated through specialized system design or damping devices. In conclusion, digital hydraulic valves represent a promising solution for increasing the efficiency and reliability of hydraulic systems in modern industries, with the potential for significant reductions in energy consumption and operational costs.

Research on ESG performance and OFDI willingness: Based on the fusion of financing constraints and productivity threshold

This paper constructs a theoretical model on how ESG performance affects enterprise's Outward Foreign Direct Investment (OFDI) intention from the perspective of integrating financing constraints and productivity thresholds, and uses data from Chinese A-share listed companies for empirical testing. The findings indicate a positive correlation between ESG performance and the willingness of corporations to OFDI. Furthermore, the heterogeneity analysis reveals that the impact of ESG performance on OFDI is more significant for enterprises with a high degree of digital transformation, containing foreign equity, operating in industries. The study concludes that better ESG performance bolsters corporate OFDI willingness through two distinct mechanisms. These mechanisms include alleviating financing constraints and lowering the productivity threshold. Additionally, better ESG performance not only increases the scale of OFDI but also fosters the binary marginal expansion of OFDI. This paper offers several solutions for enterprises seeking to expand their global reach, focusing on leveraging their ESG performance. It also provides policy recommendations for government departments seeking to optimize the ESG rating system and OFDI regulatory service system.