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Sustainability of flow chemistry and microreaction technology
No full textThis critical review provides an overview of the sustainability outcomes associated with flow chemistry, as a new concept in process chemistry, and its related technology and engineering field, microreaction technology. This study is broad in scope and covers cost and life cycle assessment methodologies applied to flow chemistry. The present review differs from past studies by providing a mechanistic viewpoint, i.e., starting by listing key levers (e.g. mass transfer), and then unravelling their impact on costs and life cycle, using reported flow chemistry studies as examples to underline the sustainability capability. This way, this review transitions from informing on how flow chemistry and microreactors can improve fundamental principles in chemical engineering and chemistry to their translation to improved sustainability. Gaps and opportunities in the reporting of microreactor/flow chemistry are identified. To put the reported achievements into perspective, a life cycle assessment (LCA) study is conducted that reports on the effects of the key levers, as identified in this study, for a flow chemistry reaction at the pilot scale, conducted at an industrial site (technology readiness level 4). This way, this study provides a quantitative forecast of what principally can be achieved when maximising the key levers, in terms of the individual LCA impact categories and their total average outcome
The impact of harvesting on the evolutionary dynamics of prey species in a prey-predator systems
No full textMatsuda and Abrams (Theor Popul Biol 45(1):76–91, 1994) initiated the exploration of self-extinction in species through evolution, focusing on the advantageous position of mutants near the extinction boundary in a prey-predator system with evolving foraging traits. Previous models lacked theoretical investigation into the long-term effects of harvesting. In our model, we introduce constant-effort prey and predator harvesting, along with individual logistic growth of predators. The model reveals two distinct evolutionary outcomes: (i) Evolutionary suicide, marked by a saddle-node bifurcation, where prey extinction results from the invasion of a lower forager mutant; and (ii) Evolutionary reversal, characterized by a subcritical Hopf bifurcation, leading to cyclic prey evolution. Employing an innovative approach based on Gröbner basis computation, we identify various bifurcation manifolds, including fold, transcritical, cusp, Hopf, and Bogdanov-Takens bifurcations. These contrasting scenarios emerge from variations in harvesting parameters while keeping other factors constant, rendering the model an intriguing subject of study
Thermoelectric response in zigzag chains: Impact of irradiation-induced conformational changes
No full textThis study explores the enhancement of thermoelectric response in a zigzag chain through irradiation with arbitrarily polarized light. The irradiation induces changes in hopping strengths, creating an asymmetric transmission profile around the Fermi energy, which is a crucial factor for achieving a higher figure of merit (FOM). Specific light configurations result in an FOM exceeding unity. We employ the Floquet-Bloch ansatz and minimal coupling scheme to model the irradiation effect, with transport properties evaluated using Green’s function technique within the Landauer-Büttiker formalism. The investigation covers electrical conductance, thermopower, and thermal conductance due to electrons and phonons. Our research deepens understanding and opens avenues for tailoring nanostructures to fine-tune thermoelectric properties, advancing highly efficient energy conversion devices exploiting irradiation effects
Toward Precision-Aware Safe Neural-Controlled Cyber-Physical Systems
No full textThe safety of neural network (NN) controllers is crucial, specifically in the context of safety-critical Cyber-Physical System (CPS) applications. Current safety verification focuses on the reachability analysis, considering the bounded errors from the noisy environments or inaccurate implementations. However, it assumes real-valued arithmetic and does not account for the fixed-point quantization often used in the embedded systems. Some recent efforts have focused on generating the sound quantized NN implementations in fixed-point, ensuring specific target error bounds, but they assume the safety of NNs is already proven. To bridge this gap, we introduce Nexus, a novel two-phase framework combining reachability analysis with sound NN quantization. Nexus provides an end-to-end solution that ensures CPS safety within bounded errors while generating mixed-precision fixed-point implementations for the NN controllers. Additionally, we optimize these implementations for the automated parallelization on the FPGAs using a commercial HLS compiler, reducing the machine cycles significantly
U-statistics Based Tests for Marginal Hazard Rate Orderings of Two Dependent Variables
No full textWe aim to compare marginal distributions of a bivariate random vector (X, Y) with reference to their hazard rates, (hF(t),hG(t)). In many applications, it is likely that the marginal hazard rates are ordered, e.g., hF(t)≤hG(t). We consider two U-statistics based tests for testing equality of the marginal hazards against the alternative that they are ordered. Further, we compare these tests with the existing W and S tests when X and Y are assumed to be independent. The two proposed statistics are also extended to cover situations when the pair (X, Y) is subjected to independent univariate censoring. We provide extensive simulation studies based on copulae where the power performance of these tests is assessed. The marginal distributions considered are Weibull, linear failure rate, Gompertz and three other families of distributions based on copulae. We apply the tests to two real data examples
Visualization analysis of educational data statistics based on big data mining
No full textThe visual analysis method of educational data statistics based on big data mining is studied to improve students’ academic performance. Introducing the Mahalanobis distance and covariance matrix into the Fuzzy C-Means (FCM) clustering algorithm improves the FCM clustering algorithm. Through the improvement of the FCM clustering algorithm, the education data is mined from the massive original education data. The mining results are analyzed statistically, and the statistical analysis chart of education data is drawn. By improving the force-guided layout algorithm, the mined educational data points are written into the elastic graph layout to realize the visual layout. The ECharts data visualization analysis component presents the visual layout results of education data points and the statistical analysis charts of education data. Experiments show that this method can effectively mine educational data and draw statistical analysis charts of educational data. Among them, learning analysis data occupy the highest proportion (15%), and privacy protection data occupy the lowest proportion (only 1%). The method can effectively lay out the educational data points and has a better visual effect. This method can effectively present the results of statistical analysis of educational data in visual form, in which learning analysis data is the most important
Determinants vs. Algebraic Branching Programs
No full textWe show that for every homogeneous polynomial of degree d, if it has determinantal complexity at most s, then it can be computed by a homogeneous algebraic branching program (ABP) of size at most O(d5s). Moreover, we show that for most homogeneous polynomials, the width of the resulting homogeneous ABP is just s-1 and the size is at most O(ds). Thus, for constant degree homogeneous polynomials, their determinantal complexity and ABP complexity are within a constant factor of each other and hence, a super-linear lower bound for ABPs for any constant degree polynomial implies a super-linear lower bound on determinantal complexity; this relates two open problems of great interest in algebraic complexity. As of now, super-linear lower bounds for ABPs are known only for polynomials of growing degree [9, 6], and for determinantal complexity the best lower bounds are larger than the number of variables only by a constant factor [10]. While determinantal complexity and ABP complexity are classically known to be polynomially equivalent [13], the standard transformation from the former to the latter incurs a polynomial blow up in size in the process, and thus, it was unclear if a super-linear lower bound for ABPs implies a super-linear lower bound on determinantal complexity. In particular, a size preserving transformation from determinantal complexity to ABPs does not appear to have been known prior to this work, even for constant degree polynomials
Efficient Grading of Prostate Cancer WSI with Deep Learning
No full textThe field of histopathology, which involves visual examination of tissue samples at a microscopic scale, is very important for the diagnosis of cancer. Although this task is currently performed by human experts, the design of computer vision-based systems to assist human experts is an interesting research area. This problem is ideal for the application of computer-based image analysis; especially, with the great success of convolutional neural networks (CNNs) in image segmentation and classification in the last decade. However, applying CNNs to this problem is challenging for a number of reasons, such as excessive high resolution (involving huge computational burden), variations in sample processing, and insufficient annotation. In this current work, we propose a CNN-based approach to tackle the problem of prostate cancer grading from Whole Slide Images (WSIs). We use a patch-based, multi-step training algorithm to address the challenges of large image size, tissue sample variations and partial annotation. Then we propose two novel classification strategies using an ensemble of CNN models to classify tissue slide images into different ISUP grades (1 – 5). We demonstrate the efficacy of our method on the publicly available large scale Prostate cANcer graDe Assessment (PANDA) Challenge dataset. The effectiveness of the technique is measured using Cohen’s quadratic kappa score. The results are shown to be highly accurate (kappa score of 0.88) and better than other leading state-of-the art methods
Enhancing MRI-Based Classification of Alzheimer\u27s Disease with Explainable 3D Hybrid Compact Convolutional Transformers
No full textAlzheimer\u27s disease (AD), characterized by progressive cognitive decline and memory loss, presents a formidable global health challenge, underscoring the critical importance of early and precise diagnosis for timely interventions and enhanced patient outcomes. While MRI scans provide valuable insights into brain structures, traditional analysis methods often struggle to discern intricate 3D patterns crucial for AD identification. Addressing this challenge, we introduce an alternative end-to-end deep learning model, the 3D Hybrid Compact Convolutional Transformers 3D (HCCT). By synergistically combining convolutional neural networks (CNNs) and vision transformers (ViTs), the 3D HCCT adeptly captures both local features and long-range relationships within 3D MRI scans. Extensive evaluations on prominent AD benchmark dataset, ADNI, demonstrate the 3D HCCT\u27s superior performance, surpassing state of the art CNN and transformer based methods in classification accuracy. Its robust generalization capability and interpretability marks a significant stride in AD classification from 3D MRI scans, promising more accurate and reliable diagnoses for improved patient care and superior clinical outcomes
Equivalence Testing: The Power of Bounded Adaptivity
No full textEquivalence testing, a fundamental problem in the field of distribution testing, seeks to infer if two unknown distributions on [n] are the same or far apart in the total variation distance. Conditional sampling has emerged as a powerful query model and has been investigated by theoreticians and practitioners alike, leading to the design of optimal algorithms albeit in a sequential setting (also referred to as adaptive tester). Given the profound impact of parallel computing over the past decades, there has been a strong desire to design algorithms that enable high parallelization. Despite significant algorithmic advancements over the last decade, parallelizable techniques (also termed non-adaptive testers) have Õ(log12 n) query complexity, a prohibitively large complexity to be of practical usage. Therefore, the primary challenge is whether it is possible to design algorithms that enable high parallelization while achieving efficient query complexity. Our work provides an affirmative answer to the aforementioned challenge: we present a highly parallelizable tester with a query complexity of Õ(log n), achieved through a single round of adaptivity, marking a significant stride towards harmonizing parallelizability and efficiency in equivalence testing