Large Set Research Articles

New advances for quantum‐inspired optimization

AbstractAdvances in quantum computing with applications in combinatorial optimization have evolved at an increasing rate in recent years. The quadratic unconstrained binary optimization (QUBO) model is at the center of these developments and has become recognized as an effective alternative method for representing a wide variety of combinatorial optimization problems. Additional momentum has resulted from the arrival of quantum computers and their ability to solve the Ising spin glass problem, another form of the QUBO model. This paper highlights advances in solving QUBO models and extensions to more general polynomial unconstrained binary optimization (PUBO) models as important alternatives to traditional approaches. Computational experience is provided that compares the performance of unique quantum‐inspired metaheuristic solvers—the Next Generation Quantum (NGQ) solver for QUBO models and the NGQ‐PUBO solver for PUBO models—with the performance of CPLEX and the Dwave quantum advantage solver. Extensive results, including experiments with a set of large set partitioning problems representing the largest QUBO models reported in the literature to date, along with maximum diversity and max cut problem sets, disclose that our solvers outperform both CPLEX and Dwave by a wide margin in terms of both computational time and solution quality.

High-Precision Spectroscopy of ^{20}O Benchmarking AbInitio Calculations in Light Nuclei.

The excited states of unstable ^{20}O were investigated via γ-ray spectroscopy following the ^{19}O(d,p)^{20}O reaction at 8 AMeV. By exploiting the Doppler shift attenuation method, the lifetimes of the 2_{2}^{+} and 3_{1}^{+} states were firmly established. From the γ-ray branching and E2/M1 mixing ratios for transitions deexciting the 2_{2}^{+} and 3_{1}^{+} states, the B(E2) and B(M1) were determined. Various chiral effective field theory Hamiltonians, describing the nuclear properties beyond ground states, along with a standard USDB interaction, were compared with the experimentally obtained data. Such a comparison for a large set of γ-ray transition probabilities with the valence space in medium similarity renormalization group abinitio calculations was performed for the first time in a nucleus far from stability. It was shown that the abinitio approaches using chiral effective field theory forces are challenged by detailed high-precision spectroscopic properties of nuclei. The reduced transition probabilities were found to be a very constraining test of the performance of the abinitio models.

Transcriptome Profiling of Oncorhynchus mykiss Infected with Low or Highly Pathogenic Viral Hemorrhagic Septicemia Virus (VHSV).

The rainbow trout (Oncorhynchus mykiss) is the most important produced species in freshwater within the European Union, usually reared in intensive farming systems. This species is highly susceptible to viral hemorrhagic septicemia (VHS), a severe systemic disease widespread globally throughout the world. Viral hemorrhagic septicemia virus (VHSV) is the etiological agent and, recently, three classes of VHSV virulence (high, moderate, and low) have been proposed based on the mortality rates, which are strictly dependent on the viral strain. The molecular mechanisms that regulate VHSV virulence and the stimulated gene responses in the host during infection are not completely unveiled. While some preliminary transcriptomic studies have been reported in other fish species, to date there are no publications on rainbow trout. Herein, we report the first time-course RNA sequencing analysis on rainbow trout juveniles experimentally infected with high and low VHSV pathogenic Italian strains. Transcriptome analysis was performed on head kidney samples collected at different time points (1, 2, and 5 days post infection). A large set of notable genes were found to be differentially expressed (DEGs) in all the challenged groups (e.s. trim63a, acod1, cox-2, skia, hipk1, cx35.4, ins, mtnr1a, tlr3, tlr7, mda5, lgp2). Moreover, the number of DEGs progressively increased especially during time with a greater amount found in the group infected with the high VHSV virulent strain. The gene ontology (GO) enrichment analysis highlighted that functions related to inflammation were modulated in rainbow trout during the first days of VHSV infection, regardless of the pathogenicity of the strain. While some functions showed slight differences in enrichments between the two infected groups, others appeared more exclusively modulated in the group challenged with the highly pathogenic strain.

Pericardial Effusion Predicts Clinical Outcomes in Patients with COVID-19: A Nationwide Multicenter Study

Rationale and objectivesThe prognostic role of pericardial effusion (PE) in Covid 19 is unclear. The aim of the present study was to estimate the prognostic role of pericardial effusion in patients with Covid 19 in a large multicentre setting. Materials and methodsThis retrospective study is a part of the German multicenter project RACOON (Radiological Cooperative Network of the Covid 19 pandemic). The acquired sample comprises 1197 patients, 363 (30.3%) women and 834 (69.7%) men. In every case, chest computed tomography was analyzed for PE. Data about 30-day mortality, need for mechanical ventilation and need for intensive care unit admission were collected. Data were evaluated by means of descriptive statistics. Group differences were calculated with Mann-Whitney test and Fisher exact test. Uni-and multivariable regression analyses were performed. ResultsOverall, 46.4% of the patients were admitted to intensive care unit (ICU), mechanical lung ventilation was performed in 26.6%, and 30-day mortality was 24%.PE was identified in 159 patients (13.3%). The presence of PE was associated with 30-day mortality: HR = 1.54, CI95% (1.05; 2.23), p=0.02 (univariable analysis), and HR = 1.60, CI95% (1.03; 2.48), p=0.03 (multivariable analysis). Furthermore, density of PE was associated with the need for intubation (OR = 1.02, CI95% (1.003; 1.05), p=0.03) and the need for ICU admission (OR = 1.03, CI95% (1.005; 1.05), p=0.01) in univariable regression analysis. The presence of PE was associated with 30-day mortality in male patients, HR=1.56, CI95%(1.01-2.43), p=0.04 (multivariable analysis). In female patients, none of PE values predicted clinical outcomes. ConclusionThe prevalence of PE in Covid 19 is 13.3%. PE is an independent predictor of 30-day mortality in male patients with Covid 19. In female patients, PE plays no predictive role.

A critical assessment of the design displacements of friction pendulum systems

The equivalent linear static procedure suggested in seismic codes for determining the maximum displacements of seismic isolation systems is evaluated comprehensively for friction pendulum isolators with respect to the benchmark nonlinear response history analysis procedure for a set of radii of curvature and coefficients of friction. A large set of selected and scaled near field ground motions recorded on stiff and soft soils are employed in comparative evaluations. It has been observed that equivalent linear static procedure significantly overestimates the maximum isolator displacements for all practical combinations of radius of curvature and friction coefficient. Overestimation is larger for ground motions on soft soils. Alternatively, a practical description of inelastic displacement spectra is introduced where the hazard is defined in terms of the 1-second spectral acceleration accounting for the seismicity and soil class of the site, and friction pendulum isolation systems are characterized by their radius of curvature and nominal friction coefficient. Employing the proposed design spectra in design and production may lead to significant reductions of the isolator sizes. Moreover, realistic estimation of maximum isolator displacements also leads to realistic estimation of isolator stiffness, as well as the internal design forces of both the isolation system and the superstructure through response spectrum analysis under design spectrum.

A large-scale empirical study on mobile performance: energy, run-time and memory

Software performance concerns have been attracting research interest at an increasing rate, especially regarding energy performance in non-wired computing devices. In the context of mobile devices, several research works have been devoted to assessing the performance of software and its underlying code. One important contribution of such research efforts is sets of programming guidelines aiming at identifying efficient and inefficient programming practices, and consequently to steer software developers to write performance-friendly code.Despite recent efforts in this direction, it is still almost unfeasible to obtain universal and up-to-date knowledge regarding software and respective source code performance. Namely regarding energy performance, where there has been growing interest in optimizing software energy consumption due to the power restrictions of such devices. There are still many difficulties reported by the community in measuring performance, namely in large-scale validation and replication. The Android ecosystem is a particular example, where the great fragmentation of the platform, the constant evolution of the hardware, the software platform, the development libraries themselves, and the fact that most of the platform tools are integrated into the IDE’s GUI, makes it extremely difficult to perform performance studies based on large sets of data/applications. In this paper, we analyze the execution of a diversified corpus of applications of significant magnitude. We analyze the source-code performance of 1322 versions of 215 different Android applications, dynamically executed with over than 27900 tested scenarios, using state-of-the-art black-box testing frameworks with different combinations of GUI inputs. Our empirical analysis allowed to observe that semantic program changes such as adding functionality and repairing bugfixes are the changes more associated with relevant impact on energy performance. Furthermore, we also demonstrate that several coding practices previously identified as energy-greedy do not replicate such behavior in our execution context and can have distinct impacts across several performance indicators: runtime, memory and energy consumption. Some of these practices include some performance issues reported by the Android Lint and Android SDK APIs. We also provide evidence that the evaluated performance indicators have little to no correlation with the performance issues’ priority detected by Android Lint. Finally, our results allowed us to demonstrate that there are significant differences in terms of performance between the most used libraries suited for implementing common programming tasks, such as HTTP communication, JSON manipulation, image loading/rendering, among others, providing a set of recommendations to select the most efficient library for each performance indicator. Based on the conclusions drawn and in the extension of the developed work, we also synthesized a set of guidelines that can be used by practitioners to replicate energy studies and build more efficient mobile software.

Analyzing larger sample sets with rapid methods: Incomplete-block designs with free-sorting and free-linking tasks

As rapid, holistic methods for similarity and description—such as sorting and projective mapping—have grown in popularity, a limiting factor is the number of samples that can be presented to subjects: more than 25 food samples decreases the quality and stability of results. While incomplete-block designs could address this, their use has not been developed for these holistic methods. In this paper we present an empirical investigation into the use of incomplete-block designs with free sorting and the newer free linking. We compare these two methods because while their results are comparable, the cognitive tasks are different, and thus their suitability for incomplete-block designs may differ. We evaluated the effects of incomplete-block designs in two studies. In Study 1, 20 subjects evaluated 6/10 chocolate bars by free linking in an incomplete-block design, with each subject completing 2 blocks; results were compared to a complete-block evaluation of the 10 bars by free sorting and free linking. In Study 2, a total of 90 subjects evaluated 62 terms from a chocolate flavor-wheel in 3 conditions (between subjects): free sorting with complete blocks (N = 30, all 62 terms) and free sorting (N = 30) or free linking (N = 30) with 3 incomplete blocks of 16/62 terms. We introduce a novel method to evaluate stability for the incomplete-block designs that we call “pairwise simulation.” From Study 1, we find that pairwise simulation provides adequate stability estimates and that, with sufficient pairwise cooccurrences, free linking with incomplete blocks produces results that are comparable to free sorting or linking with complete blocks. From Study 2, we demonstrate that free linking with incomplete blocks can produce high quality results from a large sample set, maintaining the increased discrimination capacity that marks free linking in general, and that with incomplete blocks, free linking is likely to be more stable than free sorting. This research demonstrates that incomplete-block designs can be used with free linking, and also provides a new, effective method through pairwise simulation for evaluating stability with incomplete-block designs, which cannot be resampled using standard bootstrapping approaches.

Strained AlGaInAs on InP: Bandgap dependence on composition—Model benchmark and optimization

Influence of chemical composition on the bandgap of AlGaInAs deposited on InP is often calculated using models for unstrained composition and then corrected for strain-induced bandgap energy changes using deformation potentials. This method relies on up to 25 coefficients, many of which are burdened with large uncertainty. In this paper, a large set of experimental data is used to verify the accuracy of existing approaches and to search for optimal deformation potentials. It is shown that the main source of inaccuracy is not the deformation potentials, but the unstrained bandgap formulas. Additionally, a novel model is proposed, yielding the highest accuracy on our dataset. For the first time, composition determination of a quaternary alloy on InP is reported using inductively coupled plasma-optical emission spectrometry, which is used as a benchmark for modeling.

Computer vision systems and deep learning for the recognition of athlete's movement: A review article

The process of detecting people in videos and then tracking their movement is one of the very important topics. The process of tracking people and studying their behaviour could result in a large set of information that can help researchers in studying reactions. The techniques of detection and tracking the movement of people are used in the sports field, where the athlete's movement is studied and analyzed within the game. Based on the information obtained from the process of tracking the athlete's movement, it is possible to improve the playing performance as well as avoid injuries and choose the best playing strategy. In some games, the accuracy of athlete's performance is a measure of the points given to the athlete's like gymnastics. This study reviews a set of articles that relied on computer vision as well as deep learning in the process of distinguishing and analysing the athlete's movement. The articles are confined to the years from 2015 to 2022, dealing with different indoor and outdoor sports. Certainly, the study of indoor games is better because the influence of weather conditions is less than that of outdoor ones. Reviewing the articles demonstrates that relying on computer vision systems is more effective than relying on human rulers, as humans are more prone to error. As for relying on the deep learning techniques for detecting the object, the results are very positive, due to the correct detection of the object. The results of the analysis of this object will be more accurate.

Green finance: Evidence from large portfolios and networks during financial crises and recessions

AbstractIn this article, we study the relevance of green finance from a portfolio and a network perspective. The estimates are derived from a regularized graphical model, which allows us to deal with two important issues. First, we refer to the curse of dimensionality, as we focus on a relatively large set of companies. Second, we explicitly take into account the heavy‐tailed distributions of financial time series, which reflect the impact of crises and recessions. Focusing on a time interval spanning across well‐known tail events, from the US subprime crisis to the recent outbreak of the COVID‐19 pandemic, we show that the selected green stocks offer a relevant contribution to the minimization of the overall portfolio risk. Moreover, they outperform the gray assets in terms of risk, profitability, and risk‐adjusted return in a statistically significant way. These findings are consistent with the estimates obtained from the network analysis. Indeed, the gray stocks exhibit a greater connection within the dynamic networks and, then, are more exposed to the risk of a greater propagation of negative spillover effects during stressed periods. Interestingly, the relevance of the green stocks increases when moving from the standard Gaussian to the leptokurtic setting. The policy implications suggested by these results induce policymakers to undertake synergistic interventions with private finance aimed at supporting a green economy and environmentally responsible companies.

Predictive Modeling of Light–Matter Interaction in One Dimension: A Dynamic Deep Learning Approach

The mathematical modeling and the associated numerical simulation of the light–matter interaction (LMI) process are well-known to be quite complicated, particularly for media where several electronic transitions take place under electromagnetic excitation. As a result, numerical simulations of typical LMI processes usually require a high computational cost due to the involvement of a large number of coupled differential equations modeling electron and photon behavior. In this paper, we model the general LMI process involving an electromagnetic interaction medium and optical (light) excitation in one dimension (1D) via the use of a dynamic deep learning algorithm where the neural network coefficients can precisely adapt themselves based on the past values of the coefficients of adjacent layers even under the availability of very limited data. Due to the high computational cost of LMI simulations, simulation data are usually only available for short durations. Our aim here is to implement an adaptive deep learning-based model of the LMI process in 1D based on available temporal data so that the electromagnetic features of LMI simulations can be quickly decrypted by the evolving network coefficients, facilitating self-learning. This enables accurate prediction and acceleration of LMI simulations that can run for much longer durations via the reduction in the cost of computation through the elimination of the requirement for the simultaneous computation and discretization of a large set of coupled differential equations at each simulation step. Our analyses show that the LMI process can be efficiently decrypted using dynamic deep learning with less than 1% relative error (RE), enabling the extension of LMI simulations using simple artificial neural networks.

Varieties of wealth: toward a comparative sociology of wealth inequality

Abstract Research on wealth inequality and wealth concentration has made much progress in establishing statistics on the distribution of private wealth, its historical development, and its components in a large set of countries. Yet, this research is less concerned with country-specific contexts that have an impact on the practical relevance of private wealth and its distribution. This article develops a framework for comparative research that is interested in this question. Starting from a typology that distinguishes six “capacities of wealth,” it asserts that private assets can be more important, more powerful, or more tolerable, depending on the institutional regulation of the political economy, for example, limitations on private property rights, welfare state redistribution, the electoral system, and cultural patterns of wealth legitimation. Using the USA and Germany as exemplary cases, I show that such differences exist and are politically relevant and that investigating the practical significance of private wealth in different political economies would open up new research trajectories in wealth research.

Elastic and inelastic acceleration amplification of nonstructural components characterized using recorded building responses

This paper presents a comprehensive study of the acceleration (or force) amplification behavior of building nonstructural components characterized using a large set of recorded building responses available in the Center for Engineering Strong Motion Data (CESMD) repository. The elastic acceleration amplification demands of nonstructural components are analyzed statistically and subsequently compared with the corresponding inelastic demands characterized by the constant-ductility floor response spectra. Different simplified hysteretic rules are adopted for evaluating the inelastic acceleration responses of nonstructural components. The numerical study demonstrates that the elastic acceleration amplification characteristics of the nonstructural components are highly dependent on their natural periods relative to the specific vibration modes of the supporting structures as well as their attachment locations relative to the building height. The presence of component ductility effectively reduces their seismic acceleration demands, particularly in period ranges tuned with their supporting structures. Furthermore, the hysteretic behavior of nonstructural components may moderately modify their acceleration amplification effects. Findings from this study substantiate current seismic design provisions of nonstructural components with evidence derived from building floor responses recorded during real earthquakes.

Measurement of non-equilibrium characteristics of thermoelectric materials

The study of material properties relies on accurate and reliable testing instruments. Sensitively capturing changes in material performance is key to understanding the mechanisms of material properties. The Seebeck coefficient and resistivity testing instrument are essential for research on many functional materials. However, the current instrument is limited in its ability to capture sensitive changes due to testing principles. Here, we prepared three different types of non-equilibrium Zn4Sb3. We found that the existing static testing instrument has problems with testing interruption or inaccuracy when the sample undergoes chemical reactions, rapid atomic diffusion, or phase transitions. The cause of interruption is that the temperature control setting cannot meet the accuracy requirements of the testing instrument. The instrument's large temperature difference setting will cause the determination of the phase transition temperature to become inaccurate. To address the limitations, we have developed a dynamic testing instrument and tested the Seebeck coefficient and resistivity of Zn4Sb3 and Ni in the range of 300 K–800 K. By ensuring the accuracy of each module of the instrument, optimizing the alternating temperature rise time, and improving the data acquisition calculation method, we have achieved accurate capture of sensitive performance changes in the non-equilibrium sample. Comparatively, dynamic testing reduces the measurement time by approximately 300 % compared to static testing. The standard deviation of measured Seebeck coefficient and resistivity is less than ±4 %. This study demonstrates that dynamic testing of the Seebeck coefficient and resistivity is an effective strategy for measuring non-equilibrium functional materials.

Search-Based Software Testing Driven by Automatically Generated and Manually Defined Fitness Functions

Search-based software testing (SBST) typically relies on fitness functions to guide the search exploration toward software failures. There are two main techniques to define fitness functions: (a) automated fitness function computation from the specification of the system requirements, and (b) manual fitness function design. Both techniques have advantages. The former uses information from the system requirements to guide the search toward portions of the input domain more likely to contain failures. The latter uses the engineers’ domain knowledge. We propose ATheNA , a novel SBST framework that combines fitness functions automatically generated from requirements specifications and those manually defined by engineers. We design and implement ATheNA-S , an instance of ATheNA that targets Simulink ® models. We evaluate ATheNA-S by considering a large set of models from different domains. Our results show that ATheNA-S generates more failure-revealing test cases than existing baseline tools and that the difference between the runtime performance of ATheNA-S and the baseline tools is not statistically significant. We also assess whether ATheNA-S could generate failure-revealing test cases when applied to two representative case studies: one from the automotive domain and one from the medical domain. Our results show that ATheNA-S successfully revealed a requirement violation in our case studies.

A Systematic Review on Cardioprotection Strategies to Prevent Breast Cancer Therapy Cardiotoxicity

Background: Breast cancer is the most prevalent malignancy among women on a global scale, affecting an average of 2.1 million individuals annually. In addition, several studies have shown that it is often treated using chemotherapy drugs and targeted therapy, but these treatment methods have been reported to have side effects on the cardiovascular system. This indicates that there is a need to develop new and effective strategies to prevent the associated side effects. Therefore, this systematic review aims to obtain cardioprotection strategies to prevent cardiotoxicity in breast cancer patients receiving chemotherapy or targeted therapy. Methods: Data collection was carried out using the online database PubMed with keywords ((cardioprotection) AND (breast cancer therapy) AND (cardiotoxic) OR (Cardiac dysfunction.)) From the initial search, a total of 150 studies were obtained, while articles that did not meet the eligibility criteria were excluded. Furthermore, the articles were reviewed using full-text reading, leading to the inclusion of nineteen in this systematic review. Results: A total of eleven studies evaluated the effect of cardio-protective drugs on Anthracycline (ANT), and nine assessed Trastuzumab. The results showed that eight studies used Beta-blocker (BB) as cardio-protective strategies, while one, one, three, two, one, one, and one utilized Angiotensin receptor blockers (ARB), ARB + Beta-blocker, ACE-inhibitor (ACE-I) + Beta-blocker, ACE-I/ARB/BB, spironolactone, statin, and Dexrazoxane (DZR), respectively. In large reports, the risk of CTRCD was reduced by the use of a combination of RAAS inhibitors and Beta-blockers. Furthermore, spironolactone, statins, and DZR mitigated the decrease in LVEF compared to the control group in small studies. Conclusions: Based on the results, chemotherapy decreased left ventricular ejection fraction (LVEF) and induced heart failure. Furthermore, the review showed that a combination of reninangiotensin-aldosterone system (RAAS) inhibitors and Beta-blockers reduced CTRCD. Future studies in larger settings were needed to investigate the efficacy of other strategies, such as statins, Spironolactone, exercise, and DZR.

Automated Test Suite Generation for Software Product Lines Based on Quality-Diversity Optimization

A Software Product Line (SPL) is a set of software products that are built from a variability model. Real-world SPLs typically involve a vast number of valid products, making it impossible to individually test each of them. This arises the need for automated test suite generation, which was previously modeled as either a single-objective or a multi-objective optimization problem considering only objective functions. This article provides a completely different mathematical model by exploiting the benefits of Quality-Diversity (QD) optimization that is composed of not only an objective function (e.g., t -wise coverage or test suite diversity) but also a user-defined behavior space (e.g., the space with test suite size as its dimension). We argue that the new model is more suitable and generic than the two alternatives because it provides at a time a large set of diverse (measured in the behavior space) and high-performing solutions that can ease the decision-making process. We apply MAP-Elites, one of the most popular QD algorithms, to solve the model. The results of the evaluation, on both realistic and artificial SPLs, are promising, with MAP-Elites significantly and substantially outperforming both single- and multi-objective approaches, and also several state-of-the-art SPL testing tools. In summary, this article provides a new and promising perspective on the test suite generation for SPLs.

Stability of the Fe3O4 (√2 x √2)R 45° Surface in 0.1 M NaOH Investigated by High-Energy Surface X-Ray Diffraction

Transition metal oxides, especially Fe, Co, and Ni oxides, are of great interest as electrocatalysts for the oxygen evolution reaction (OER) in alkaline solution. However, detailed data on the atomic-scale oxide-electrolyte interface structure of these systems are rare. We present studies of a model oxide electrocatalyst, -reconstructed Fe3O4(001). This oxide surface has been extensively investigated under ultrahigh vacuum (UHV) conditions, leading to a detailed understanding of its atomic structure [2]. In previous in situ surface X-ray diffraction studies, we demonstrated that the reconstructed surface can survive transfer into electrochemical environment and determined the stability of the reconstruction as a function of potential [2]. Specifically, we showed that the ()R45° reconstructed Fe3O4(001) surface can be transferred into 0.1 M NaOH at 0.2 V vs. Ag/AgCl while maintaining surface structure and that the reconstruction is irreversibly lifted at sufficiently negative potentials.Here, we present more detailed in situ studies of this system, using the novel technique of High-Energy Surface X-Ray Diffraction (HESXRD) [3,4]. This technique enabled us to measure rapidly a huge reciprocal space volume, which allows to obtain very large sets of crystal truncation rods (CTRs) that can be analysed by surface crystallographic methods. These data provide detailed insights into the temporal stability of the surface at fixed potentials as well as potential-dependent changes in surface structure.In the experiments the Fe3O4(001) single crystal samples were prepared by sputtering and annealing in UHV, followed by sample transfer into the hanging-meniscus cell employed for the HESXRD studies. A new experimental procedure allowed to reduce the time of sample transfer and establishment of potential control to below one minute.The in situ HESXRD measurements were performed at beamline P21.2 of PETRA III at DESY. 7 symmetrically independent CTRs and 2 ()R45° fractional order rods could be measured. These data enabled detailed comparison of the oxide-electrolyte interface with the surface structure under UHV conditions [1]. The potential-dependent data indicate clear structural changes when applying more positive or more negative potentials than 0.2 V vs. Ag/AgCl, as illustrated in part A and B of Fig. 1. However, all surface structures were stable at constant potential over several hours, as indicated in part C of Fig. 1. The quality of the obtained results allows to determine quantitative analysis of the interface structure. First results of this analysis will be presented.We gratefully acknowledge financial support by the German ministry of Science and Education (BMBF) via projects 05K19FK3 and 05K22FK1.

Nearest-neighbour parametrization of DNA single, double and triple mismatches at low sodium concentration

DNA mismatches, that is, base pairs different from the canonical AT and CG, are involved in numerous biological processes and can be a problem for technological applications such as PCR amplification. The nearest-neighbour (NN) model is the standard approach for predicting melting temperatures and is used in methods of secondary structure predictions and modelling of hybridization kinetics. However, despite its biological and technological importance, existing NN parameters that include DNA mismatches are incomplete, and those available were obtained from a limited set of melting temperature at high sodium concentration. To our knowledge, there is currently no NN set of parameters for up to three mismatches covering all configurations at low sodium concentrations. Here, we are applying the NN model to a large set of 4096 published melting temperatures, covering all combinations of single, double and triple mismatches. Dealing with such a large set of temperature is challenging in several ways, bringing new methodological problems. Here, optimizing a large number of 252 independent parameters has required the development of a new method where we readjust the seed parameters using the definition of the Gibbs free energy. The new parameters predict the training set within 1.1 °C and the validation set to 2.7 °C.

(Invited) Hybrid Bonding from Hype to Reality: How Will Hybrid Bonding Dtw Pave the Way to Large Set of Applications?

Packaging and 3D integration have grown in importance as one of the key technology enablers to compensate for the slowing of two-dimensional scaling. Today, advanced 3D SoC are calling for fine interconnect where microbumps are hitting manufacturing limitation at 10µm pitch and beyond. Hybrid bonding technology is a good candidate for high interconnect density and connects dies in packages using tiny copper-to-copper connections, as opposed to bumps. This technology is already mature for wafer to wafer hybrid bonding and is expected to open new opportunities for die to wafer hybrid bonding, providing the same downscaling in pitch as well as additional flexibility and heterogeneity. This derived technology is of great interest for many applications such as chiplet, memory stacking, photonics with imagers, displays, optic transceivers and RF. However, there are still some hurdles to overcome to get this technology in full production. CEA-Leti has been involved in hybrid bonding process development for more than 20 years with industrial partners in wafer to wafer and today in die to wafer. This presentation will discuss about: Limitations of the technology: specific process challenges, interconnect alignment, design and architecture,...What needs to be developed to pass the hurdle: known good die, throughput & yield,...Key achievements / performances and roadmap at CEA-Leti on this promising technology