Class Of Processes Research Articles

Universal bound on the relaxation rates for quantum Markovian dynamics

Abstract Relaxation rates provide important characteristics both for classical and quantum processes. Essentially they control how fast the system thermalizes, equilibrates, {decoheres, and/or dissipates}. Moreover, very often they are directly accessible to be measured in the laboratory and hence they define key physical properties of the system. Experimentally measured relaxation rates can be used to test validity of a particular theoretical model. %In this note 
Here we analyze a fundamental question: {\em does quantum mechanics provide any nontrivial constraint for relaxation rates?} We prove the conjecture formulated a few years ago that any quantum channel implies that a maximal rate is bounded from above by the sum of all the relaxation rates divided by the dimension of the Hilbert space. It should be stressed that this constraint is universal (it is valid for all quantum systems with finite number of energy levels) and it is tight (cannot be improved). In addition, the constraint plays an analogous role to the seminal Bell inequalities and the well known Leggett-Garg inequalities (sometimes called temporal Bell inequalities). Violations of Bell inequalities rule out local hidden variable models, and violations of Leggett-Garg inequalities rule out macrorealism.
Similarly, violations of the relaxation bound rule out Markovian (meaning CP-divisible) evolution.

Recurrences Reveal Shared Causal Drivers of Complex Time Series

Unmeasured causal forces influence diverse experimental time series, such as the transcription factors that regulate genes or the descending neurons that steer motor circuits. Combining the theory of skew-product dynamical systems with topological data analysis, we show that simultaneous recurrence events across multiple time series reveal the structure of their shared unobserved driving signal. We introduce a physics-based unsupervised learning algorithm that reconstructs causal drivers by iteratively building a recurrence graph with glasslike structure. As the amount of data increases, a percolation transition on this graph leads to weak ergodicity breaking for random walks—revealing the shared driver’s dynamics, even from strongly corrupted measurements. We relate reconstruction accuracy to the rate of information transfer from a chaotic driver to the response systems, and we find that effective reconstruction proceeds through gradual approximation of the driver’s dynamical attractor. Through extensive benchmarks against classical signal processing and machine learning techniques, we demonstrate our method’s ability to extract causal drivers from diverse experimental datasets spanning ecology, genomics, fluid dynamics, and physiology. Published by the American Physical Society 2025

The production and characterization of PBT/PBS fiber dyeable with dispersed dye

The work aimed to study the color properties of poly(butylene terephthalate) (PBT) modified with various contents of poly(butylene succinate) (PBS). The PBT/PBS fibers were successfully prepared using a melt-spinning process. The PEO-g-MA, 2 wt%, was used as a coupling agent. A disperse dye: blue dye was applied as a dyeing material for PBT/PBS fibers. The fibers were dyed at the temperature of 98 °C (using the classic process for PBT fibers). The morphology, mechanical properties, thermal properties, crystal structure, and dyeability were also investigated. The fiber dyeing results are explored in CIELAB (position data of color coordinates L*, a*, b*) color systems. The results show the relationship between the dyeability of PBT/PBS fibers and the contents of PBS in the polymer matrix. The SEM image of the fiber of PBT100 and PBT:PBS blend after dyeing shows that the color dyeing on the fiber increased with PBS contents, supporting the result from spectroscopy.

Tailoring the Reprocessability of Thiol-Ene Networks through Ring Size Effects.

Recycling thermosetting materials presents itself as a major challenge in achieving sustainable material use. Dynamic covalent cross-linking of polymers has emerged as a viable solution that can combine the structural integrity of thermosetting materials with the (re-)processability of thermoplastics. Thioether linkages between polymer chains are quite common, and their use dates back to the vulcanization of rubbers. While it is known that thioether bonds can be triggered to exchange through transalkylation reactions, this process is usually slow, as thioether moieties not only have to be activated by an alkylating agent, but the activated thioether also has to associate with a second thioether moiety in a classical SN2-type process. Here, we present the rational design of dynamic polymer networks based on simple dithiol-based monomers and a fatty acid derived triene. Two neighboring thioethers can undergo a much faster bond exchange reaction, and we found that the exchange dynamics can be further tuned over almost three orders of magnitude by tailoring the distance between two thioether functionalities. This resulted in thioether-cross-linked materials that could be processed by extrusion, a continuous reprocessing technique that was previously not accessible for this class of cross-linked materials, while still exhibiting appealing creep-resistance below 70 °C.

Bioleaching of Metal-Polluted Mine Tailings: A Comparative Approach Between Ex Situ Slurry-Phase Stirred Reactors Versus In Situ Electrokinetic Percolation

This work compares two technologies for the remediation of metal-polluted mine tailings based on lab-scale bioleaching experiments performed in (a) conventional agitated slurry-phase reactors and (b) in situ electrokinetic percolation. While ex situ bioleaching in agitated reactors has been widely studied, only a few previous works have studied the in situ option that couples bioleaching and electrokinetics. Real mine tailings from an abandoned sphalerite mine in southern Spain were used. The leaching medium was externally generated in a bioreactor using an autochthonous acidophilic culture and then added to tailings in batch experiments. This medium enabled metal leaching from mine tailings without the stringent operating conditions required by a classic bioleaching process. Metal removal efficiencies and kinetic rate constants after 15 d of treatments were calculated. Additionally, advantages or disadvantages between the two methods were discussed. The results for the innovative EK-percolation method showed rates and efficiencies that were comparable to, and in some cases better than, those achieved with conventional stirred slurry systems.

Analysis of the Distribution of Non-Metallic Inclusions and Its Impact on the Fatigue Strength Parameters of Carbon Steel Melted in an Electric Furnace.

Steels are currently the most commonly used industrial construction materials. The use of steels depends on their properties, including their fatigue strength. Despite the fact that many works have been devoted to fatigue strength studies, there is still a lack of research discussing the fatigue strength of low-carbon steels. This deficiency is also visible when analyzing the influence of impurities on the fatigue properties of these steels. In most cases, the literature of material fatigue tests includes results obtained for materials produced on the laboratory scale, and it is difficult to directly translate these results to the industrial scale, on which steels for industrial applications are produced. This paper presents studies on the influence of non-metallic inclusions on the fatigue strength coefficient. The analyzed steel contained an average of 0.23% C, 1.23% Mn, and 0.0025 B. It was melted in 140-ton production furnaces, and after being tapped into a ladle, it was desulphurized and refined with argon. A classic plastic working process was used to produce steel samples. Based on the analysis of the test results, it was mainly found that the fatigue resistance coefficient k decreased with the increase in impurities spacing, and with a large share of smaller non-metallic inclusions, a higher fatigue resistance coefficient was noted, which may indicate that small non-metallic inclusions with an oval shape do not reduce the fatigue life of steel, regardless of its microstructure.

Long short-term-memory-based depth of anesthesia index computation for offline and real-time clinical application in pigs

We here present a deep-learning approach for computing depth of anesthesia (DoA) for pigs undergoing general anesthesia with propofol, integrated into a novel general anesthesia specialized MatLab-based graphical user interface (GAM-GUI) toolbox. This toolbox permits the collection of EEG signals from a BIOPAC MP160 device in real-time. They are analyzed using classical signal processing algorithms combined with pharmacokinetic and pharmacodynamic (PK/PD) predictions of anesthetic concentrations and their effects on DoA and the prediction of DoA using a novel deep learning-based algorithm. Integrating the DoA estimation algorithm into a supporting toolbox allows for the clinical validation of the prediction and its immediate application in veterinary practice. This novel, artificial-intelligence-driven, user-defined, open-access software tool offers a valuable resource for both researchers and clinicians in conducting EEG analysis in real-time and offline settings in pigs and, potentially, other animal species. Its open-source nature differentiates it from proprietary platforms like Sedline and BIS, providing greater flexibility and accessibility.

Diels-Alder reaction in hydrogel synthesis: Mechanisms and functional aspects.

The Diels-Alder reaction, a classical (4+2) cycloaddition process, holds significant standing within the realms of organic synthesis and polymer chemistry, frequently employed in areas such as pharmaceutical production and material science. Recently, hydrogels constructed via Diels-Alder reactions have garnered considerable attention from researchers. This review aims to summarize the advancements in utilizing the Diels-Alder reaction for hydrogel synthesis, exploring its impact on structural design, functionalization, and application domains. Initially, the fundamental principles of the Diels-Alder reaction are introduced alongside an examination of its benefits and characteristics in hydrogel fabrication. Subsequently, applications of Diels-Alder-generated hydrogels in biomedicine, smart responsive materials, drug delivery systems, among other fields, are comprehensively reviewed. Challenges and limitations encountered during hydrogel synthesis using this reaction are also discussed. Finally, prospective research directions and future prospects of Diels-Alder reactions in hydrogel synthesis are contemplated.

Particle swarm optimization with local search for height-map surface reconstruction from point clouds in reverse engineering

AbstractSurface reconstruction is a classical process in industrial engineering and manufacturing, particularly in reverse engineering, where the goal is to obtain a digital model from a physical object. For that purpose, the real object is typically scanned or digitized and the resulting point cloud is then fitted to mathematical surfaces through numerical optimization. The choice of the approximating functions is crucial for the accuracy of the process. Real-world objects such as manufactured workpieces often require complex nonlinear approximating functions, which are not well suited for standard numerical methods. In a previous paper presented at the ISMSI 2023 conference, we addressed this issue by using manually selected approximation functions via optimization through the cuckoo search algorithm with Lévy flights. Building upon that work, this paper presents an enhanced and extended method for surface reconstruction by using height-map surfaces obtained through a combination of exponential, polynomial and logarithmic functions. A feasible approach for this goal is to consider continuous bivariate distribution functions, which ensures consistent models along with good mathematical properties for the output shapes, such as smoothness and integrability. However, this approach leads to a difficult multivariate, constrained, multimodal continuous nonlinear optimization problem. To tackle this issue, we apply particle swarm optimization, a popular swarm intelligence technique for continuous optimization. The method is hybridized with a local search procedure for further improvement of the solutions and applied to a benchmark of 15 illustrative examples of point clouds fitted to different surface models. The performance of the method is analyzed through several computational experiments. The numerical and graphical results show that the method is able to recover the shape of the point clouds accurately and automatically. Furthermore, our approach outperforms other alternative methods significantly in terms of the numerical errors. We conclude that our method can be successfully applied to the reconstruction of manufactured workpieces in real industrial settings.

Image Processing Technique for Enhanced Combustion Efficiency of Wood Pellets

The combustion efficiency of wood pellets is partly affected by their average length. The ISO 17829 standard defines the methodology for assessing the average length of sample pellets, but the method does not always lead to representative data. Furthermore, a standard analysis is time-consuming as it requires manual measurement of the pellets using a caliper. This paper, whilst evaluating the effect of pellet length on combustion efficiency, proposes a pending-patented dimensional image processing method (DIP) for assessing pellet length. DIP allows the dimensional data of grouped and stacked pellets to be obtained by exploiting the shadows produced by pellets when exposed to a light source, assuming that different-sized pellets produce different shadows. Thus, the proposed method allows for the extraction of dimensional information from non-distinct objects, overcoming the reliance of classical image processing methods on object distance for effective segmentation. Combustion tests, carried out using pellets varying only in length, confirmed the influence of length on combustion efficiency. Shorter pellets, compared to longer ones, significantly reduced CO emissions by up to 94% (mg/MJ). However, they exhibited a higher fuel mass consumption rate (kg/h), with an increase of up to 22.8% compared to the longest sample. In addition, longer pellets produced fewer but larger shadows than shorter ones. Further studies are needed to correlate the number and size of shadows with samples’ average length so that DIP could be implemented in stoves and programmed to communicate with the control unit and automatically optimize the setting in order to improve combustion efficiency.

Development of a Python Software for the Cardiovascular System Segmentation such as Arteries and Left Ventricle

J. Festas, L.C. Sousa, S.S. Siusa, S.I.S. PintoCardiovascular diseases (CVDs) are the leading cause of death globally, accounting for approximately 17.9 million deaths annually, with coronary artery disease (CAD) as a major contributor. CAD results from the build-up of atherosclerotic plaques, reducing myocardial perfusion and leading to conditions like angina and myocardial infarctions, severely affecting the left ventricle’s function. Advanced diagnostic tools are being developed to enhance the segmentation of cardiovascular structures, improving diagnostic accuracy and treatment outcomes.Accurate segmentation is crucial for analyzing coronary behavior via Computed Tomography Angiography (CTA), a non-invasive alternative to coronary angiography, and for planning surgical interventions. Furthermore, the models retrieved after segmentation can be enlarged simulating hyperemia for further hemodynamic simulations. Similarly, segmenting the left ventricle in dynamic CT scans is vital for assessing cardiac health through measurements like ventricular volume and ejection fraction.However, manual segmentation is time-consuming and suffers from variability, which can lead to significant diagnostic errors [1]. As such, there is a pressing need for more automated tools that enhance accuracy while reducing manual labor.Most common advancements are in the realms of deep learning. These have propelled automated medical image segmentation, achieving results with high efficiency and reliability [2]; however, these require extensive data sets and high variability. In scenarios where such datasets are limited or exhibit low variance, these models risk developing biases or failing to achieve accurate results in cases that are very different then the norm of the training dataset.To mitigate these risks, we are developing a Python-based semi-automatic algorithm that relies on existing open-source libraries, enhancing them with custom developments tailored to our specific needs, while relying on technician input to enhance classical image processing, ensuring adaptability to varied clinical conditions. We are testing this tool with patient data, with results that are very promising: segmentation similarity results of around 85%. We plan to release it as open-source software, encouraging a community-driven approach for continuous improvement and adaptation to clinical needs.The initial tests allows to believe this tool can be a robust and accurate software that can be used in hospital or for investigation purposes. The semi-automatic nature allows for user input and post processing tailoring of the segmentation.[1] Tavakoli, V. et al. (2013) A survey of shaped-based registration and segmentation techniques for cardiac images. Computer Vision and Image Understanding 117, no. 9: 966-989.[2] Litjens, G. et al. (2017) A survey on deep learning in medical image analysis. Medical Image Analysis 42: 60-88.

A Multi-Architecture Approach for Offensive Language Identification Combining Classical Natural Language Processing and BERT-Variant Models

Offensive content is a complex and multifaceted form of harmful material that targets individuals or groups. In recent years, offensive language (OL) has become increasingly harmful, as it incites violence and intolerance. The automatic identification of OL on social networks is essential to curtail the spread of harmful content. We address this problem by developing an architecture to effectively respond to and mitigate the impact of offensive content on society. In this paper, we use the Davidson dataset containing 24,783 samples of tweets and proposed three different architectures for detecting OL on social media platforms. Our proposed approach involves concatenation of features (TF-IDF, Word2Vec, sentiments, and FKRA/FRE) and a baseline machine learning model for the classification. We explore the effectiveness of different dimensions of GloVe embeddings in conjunction with deep learning models for classifying OL. We also propose an architecture that utilizes advanced transformer models such as BERT, ALBERT, and ELECTRA for pre-processing and encoding, with 1D CNN and neural network layers serving as the classification components. We achieve the highest precision, recall, and F1 score, i.e., 0.89, 0.90, and 0.90, respectively, for both the “bert encased preprocess/1 + small bert/L4H512A8/1 + neural network layers” model and the “bert encased preprocess/1 + electra small/2 + cnn” architecture.

A Survey on Interference Mitigation for Wireless Body Area Networks

Wireless body area networks (WBANs) are noteworthy, dependable, and most advantageous development in many health directed applications. WBANs in health system connect tiny sensors to invasive or non-invasive medical equipment for patient examinations utilizing low power wireless technology. When WBANs are utilized in crowded areas or in conjunction with other wireless sensor networks, communication interference can arise. This can lead to unstable signal integrity, which can impair system performance. Thus, interference mitigation needs to be taken into account when designing. In this paper we survey interference mitigation methods used in WBANs, classify them, and finally point to some future research directions in this area. In particular, we start by reviewing sample papers that tackle the interference management problem through classical signal processing and shaping methods. Then we review some works on cooperative communications approaches to encounter this problem. After that we consider approaches that are not centralized through the use of game theoretic formulations. We close our discussion by surveying model free algorithm through learning based approaches.

Metasurface polarization optics: From classical to quantum

Metasurface polarization optics, manipulating polarization using metasurfaces composed of subwavelength anisotropic nanostructure array, has enabled a lot of innovative integrated strategies for versatile and on-demand polarization generation, modulation, and detection. Compared with conventional bulky optical elements for polarization control, metasurface polarization optics provides a feasible platform in a subwavelength scale to build ultra-compact and multifunctional polarization devices, greatly shrinking the size of the whole polarized optical system and network. Here, we review the recent progresses of metasurface polarization optics in both classical and quantum regimes, including uniform and spatially varying polarization-manipulating devices. Basic polarization optical elements such as meta-waveplate, meta-polarizer, and resonant meta-devices with polarization singularities provide compact means to generate and modulate uniform polarization beams. Spatial-varying polarization manipulation by employing the pixelation feature of metasurfaces, leading to advanced diffraction and imaging functionalities, such as vectorial holography, classic and quantum polarization imaging, quantum polarization entanglement, quantum interference, and modulation. Substituting conventional polarization optics, metasurface approaches pave the way for on-chip classic or quantum information processing, flourishing advanced applications in displaying, communication, imaging, and computing.

Enhancing Distributed Neural Network Training Through Node-Based Communications.

The amount of data needed to effectively train modern deep neural architectures has grown significantly, leading to increased computational requirements. These intensive computations are tackled by the combination of last generation computing resources, such as accelerators, or classic processing units. Nevertheless, gradient communication remains as the major bottleneck, hindering the efficiency notwithstanding the improvements in runtimes obtained through data parallelism strategies. Data parallelism involves all processes in a global exchange of potentially high amount of data, which may impede the achievement of the desired speedup and the elimination of noticeable delays or bottlenecks. As a result, communication latency issues pose a significant challenge that profoundly impacts the performance on distributed platforms. This research presents node-based optimization steps to significantly reduce the gradient exchange between model replicas whilst ensuring model convergence. The proposal serves as a versatile communication scheme, suitable for integration into a wide range of general-purpose deep neural network (DNN) algorithms. The optimization takes into consideration the specific location of each replica within the platform. To demonstrate the effectiveness, different neural network approaches and datasets with disjoint properties are used. In addition, multiple types of applications are considered to demonstrate the robustness and versatility of our proposal. The experimental results show a global training time reduction whilst slightly improving accuracy. Code: https://github.com/mhaut/eDNNcomm.

Feature Extraction and Classification of Retinal Images Using Sobel Segmentation and Linear SVC

Eye diseases such as Diabetic Retinopathy, Cataract, and Glaucoma are significant causes of visual impairment and blindness worldwide. Early detection and accurate diagnosis are crucial for effective treatment and management of these conditions. This study aimed to develop a machine learning model for the automated classification of retinal images into four categories: Normal, Diabetic Retinopathy, Cataract, and Glaucoma. The dataset, sourced from Kaggle, comprised approximately 1000 images per class, which were pre-processed using Sobel segmentation to enhance relevant features. Hu Moments were employed for feature extraction due to their invariance to scale, rotation, and translation. The classification was performed using a Linear Support Vector Classifier (SVC), and the model's performance was evaluated through 5-fold cross-validation. The average performance metrics were 44.34% for accuracy, 48.26% for precision, 44.34% for recall, and 41.76% for F1-score. These results indicate that while Sobel segmentation and Hu Moments effectively highlight and capture essential features of retinal images, the Linear SVC classifier's performance is moderate, suggesting the need for more advanced classifiers. The study's findings contribute to the ongoing research in automated eye disease diagnosis by demonstrating the strengths and limitations of classical image processing and machine learning techniques. Future research should focus on exploring more sophisticated models, such as convolutional neural networks, and addressing dataset imbalances to enhance classification accuracy and reliability. This study underscores the potential for automated diagnostic tools in clinical settings but also highlights the necessity for further optimization to achieve practical applicability.

Efficient Cryogenic Nonlinear Conversion Processes in Periodically‐Poled Thin‐Film Lithium Niobate Waveguides

AbstractPeriodically poled thin‐film lithium niobate (TFLN) waveguides, which enable efficient quadratic nonlinear processes, serve as crucial foundation for classical and quantum signal processing. To expand their application scope, the first investigation of nonlinear conversion processes in periodically poled TFLN waveguides at cryogenic conditions (7 K) is provided. Through systematic experimental characterization, it is found that the periodically poled TFLN waveguide retains its high conversion efficiency at both cryogenic and room temperatures for both classical second‐harmonic generation and quantum photon‐pair generation processes. Particularly, the photon‐pair source at cryogenic conditions shows high brightness (≈8 MHz µW−1) and broad bandwidth (>100 THz). These results demonstrate the significant potential of TFLN wavelength conversion devices for cryogenic applications and foster future scalable quantum photonic systems.

Shortcuts to adiabaticity in harmonic traps: A quantum-classical analog.

We present a technique for efficiently transitioning a quantum system from an initial to a final stationary state in less time than is required by an adiabatic (quasistatic) process. Our approach makes use of Nelson's stochastic quantization, which represents the quantum system as a classical Brownian process. Thanks to this mathematical analogy, known protocols for classical overdamped systems can be translated into quantum protocols. In particular, one can use classical methods to find optimal quantum protocols that minimize both the time duration and some other cost function to be freely specified. We have applied this method to the time-dependent harmonic oscillator and tested it on two different cost functions: (i) the cumulative energy of the system over time and (ii) the dynamical phase of the wave function. In the latter case, it is possible to construct protocols that are "adiabatically optimal," i.e., they minimize their distance from an adiabatic process for a given duration.

Extracting full information from OCT scans-signs of early age-related macular degeneration within inner retinal layers by local neighbourhood statistics. Part II: Results.

Associations between the occurrence of early age related macular degeneration (AMD) and alterations in retinal layer thicknesses have been reported, based on classical processing of optical coherence tomography (OCT) data by noise removal and subsequent image segmentation. However, speckle noise within OCT data itself bears a substantial part of the total information. For this reason, we designed an omics-type approach for full exploitation of OCT data, which was able to identify signs of early AMD throughout the retina as a whole. A nested case-control study was designed with 200 early AMD cases and 200 healthy controls. For each participant, within a randomly selected OCT scan and a randomly selected column therein, manual grading was performed for 26 retinal feature positions. At every position, a total of 3792 descriptors were computed, based on nonlinear transformations of OCT data, first-order neighbourhood statistics and Haralick features. Equivalence and differences between cases and controls were tested for each descriptor at every graded position. Results of multiple testing were expressed in terms of false and true discovery rates controlled by the Benjamini-Yekutieli procedure. In terms of the amount and disparity of true discoveries, overall non-equivalence was found for early AMD and healthy groups. Strong difference signals were observed at the internal limiting membrane and two central retinal positions, particularly for descriptors emphasising speckle noise. Between the retinae of healthy controls and early AMD patients, significant differences were observed at the level of local neighbourhood statistics within OCT data. Thus, independent evidence was obtained for AMD affecting not only the outer retinal layers but the retina as a whole, even in the early stages of the disease. Within OCT data, both cartoon and speckle bear essential parts of the total information. We pursued a constructive, completely documented, traceable and repeatable approach without invoking artificial intelligence methods.

Extracting full information from OCT scans-signs of early age-related macular degeneration within inner retinal layers by local neighbourhood statistics. Part I: Methodology.

Associations between the occurrence of early age-related macular degeneration (AMD) and alterations in retinal layer thicknesses have been reported based on classical processing of optical coherence tomography (OCT) data by noise removal and subsequent image segmentation. However, speckle noise within OCT data itself bears a substantial part of the total information. For this reason, an omics-type approach was designed for full exploitation of OCT data, which was able to identify signs of early AMD throughout the retina as a whole. A nested case-control study was designed with 200 early AMD cases and 200 healthy controls. For every participant, within a randomly selected OCT scan and a randomly selected column therein, manual grading was performed for 26 retinal feature positions. At each position, a total of 3792 descriptors were computed, based on nonlinear transformations of OCT data, first-order neighbourhood statistics and Haralick features. Equivalence and differences between cases and controls were tested for every descriptor at each graded position. Results of multiple testing were expressed in terms of false and true discovery rates controlled by the Benjamini-Yekutieli procedure. In terms of the amount and disparity of true discoveries, overall non-equivalence of early AMD and healthy groups was found. Strong difference signals were observed at the internal limiting membrane and two central retinal positions, particularly for descriptors emphasising speckle noise. Between retinae of healthy controls and early AMD patients, significant differences were observed at the level of local neighbourhood statistics within the OCT data. Thus, independent evidence was obtained for AMD affecting not only the outer retinal layers but also the retina as a whole, even in the early stages of the disease. Within OCT data, both cartoons and speckle bear essential parts of total information. A constructive, completely documented, traceable and repeatable approach was pursued without invoking artificial intelligence methods.