Classical Ones Research Articles

Improved clinical data imputation via classical and quantum determinantal point processes

Imputing data is a critical issue for machine learning practitioners, including in the life sciences domain, where missing clinical data is a typical situation and the reliability of the imputation is of great importance. Currently, there is no canonical approach for imputation of clinical data and widely used algorithms introduce variance in the downstream classification. Here we propose novel imputation methods based on determinantal point processes (DPP) that enhance popular techniques such as the multivariate imputation by chained equations and MissForest. Their advantages are twofold: improving the quality of the imputed data demonstrated by increased accuracy of the downstream classification and providing deterministic and reliable imputations that remove the variance from the classification results. We experimentally demonstrate the advantages of our methods by performing extensive imputations on synthetic and real clinical data. We also perform quantum hardware experiments by applying the quantum circuits for DPP sampling since such quantum algorithms provide a computational advantage with respect to classical ones. We demonstrate competitive results with up to 10 qubits for small-scale imputation tasks on a state-of-the-art IBM quantum processor. Our classical and quantum methods improve the effectiveness and robustness of clinical data prediction modeling by providing better and more reliable data imputations. These improvements can add significant value in settings demanding high precision, such as in pharmaceutical drug trials where our approach can provide higher confidence in the predictions made.

Improved clinical data imputation via classical and quantum determinantal point processes

Imputing data is a critical issue for machine learning practitioners, including in the life sciences domain, where missing clinical data is a typical situation and the reliability of the imputation is of great importance. Currently, there is no canonical approach for imputation of clinical data and widely used algorithms introduce variance in the downstream classification. Here we propose novel imputation methods based on determinantal point processes (DPP) that enhance popular techniques such as the multivariate imputation by chained equations and MissForest. Their advantages are twofold: improving the quality of the imputed data demonstrated by increased accuracy of the downstream classification and providing deterministic and reliable imputations that remove the variance from the classification results. We experimentally demonstrate the advantages of our methods by performing extensive imputations on synthetic and real clinical data. We also perform quantum hardware experiments by applying the quantum circuits for DPP sampling since such quantum algorithms provide a computational advantage with respect to classical ones. We demonstrate competitive results with up to 10 qubits for small-scale imputation tasks on a state-of-the-art IBM quantum processor. Our classical and quantum methods improve the effectiveness and robustness of clinical data prediction modeling by providing better and more reliable data imputations. These improvements can add significant value in settings demanding high precision, such as in pharmaceutical drug trials where our approach can provide higher confidence in the predictions made.

Non-Hermitian CHSH* Game with a Single Trapped-Ion Qubit

Abstract The Clauser–Horne–Shimony–Holt (CHSH) game provides a captivating illustration of the advantages of quantum strategies over classical ones. In a recent study, a variant of the CHSH game leveraging a single qubit system, referred to as the CHSH* game, has been identified. We demonstrate that this mapping relationship between these two games remains effective even for a non-unitary gate. Here we delve into the breach of Tsirelson’s bound in a non-Hermitian system, predicting changes in the upper and lower bounds of the player’s winning probability when employing quantum strategies in a single dissipative qubit system. We experimentally explore the impact of the CHSH* game on the player’s winning probability in a single trapped-ion dissipative system, demonstrating a violation of Tsirelson’s bound under the influence of parity-time ( P T ) symmetry. These results contribute to a deeper understanding of the influence of non-Hermitian systems on quantum games and the behavior of quantum systems under P T symmetry, which is crucial for designing more robust and efficient quantum protocols.

Elastic and Elastoplastic Contact Mechanics of Concentrated Coated Contacts

Machines operate under increasingly harsher contact conditions, causing significant wear and contact fatigue. Sub-surface stresses are responsible for the premature contact fatigue of rolling element bearings, meshing gears, and cam–follower pairs. Surface protection measures include hard, wear-resistant coatings. Traditionally, contact integrity has been predicted using classical Hertzian contact mechanics. However, the theory is only applicable when the contact between a pair of ellipsoidal solids of revolution may be considered as a rigid indenter penetrating a semi-infinite elastic half-space. Many coatings act as thin bonded elastic layers that undergo considerably higher pressures than those predicted by the classical theory. Furthermore, inelastic deformation of bonded solids can cause plastic flow, work-hardening, and elastoplastic behaviour. This paper presents a comprehensive, integrated contact mechanics analysis that includes induced sub-surface stresses in concentrated counterformal finite line contacts for all the aforementioned cases. Generated pressures and deformation are predicted for hard coated surfaces, for which there is a dearth of relevant analysis. The contact characteristics, which are of particular practical significance, of many hard, wear-resistant advanced coatings are also studied. The paper clearly demonstrates the importance of using efficient semi-analytical, detailed holistic contact mechanics rather than the classical idealised methods or empirical numerical ones such as FEA. The novel approach presented for the finite line contact of thin-layered bonded solids has not hitherto been reported in the open literature.

Approaches to Developing Key Distribution Protocols Based on Quantum Key Distribution

Quantum protocols for generating shared secret keys represent an effective method of ensuring secure information transmission using quantum communication channels. However, they are limited in scalability due to the physical constraints of quantum channels, which necessitates the use of classical communication channels for exchanging information between participants. This leads to the necessity of developing security threat models and network protection, especially in the case of hybrid networks, where quantum channels are used alongside classical ones. The peculiarity of constructing quantum key distribution protocols is associated with the costliness of implementation and implies the inevitable use of classical communication channels for key distribution. This paper addresses the task of ensuring the security of communication between subscribers not directly connected by quantum channels, through intermediary nodes, and analyzes threats from internal and external adversaries. Special attention is paid to situations where both types of adversaries have information about the processes and protocols in the network. The paper examines widely used key distribution protocols employed in practice. It describes the main security requirements applied to key distribution protocols, and presents the results of an analysis of the compliance of the discussed protocols with these requirements

Neotropics as a Cradle for Adaptive Radiations.

Neotropical ecosystems are renowned for numerous examples of adaptive radiation in both plants and animals resulting in high levels of biodiversity and endemism. However, we still lack a comprehensive review of the abiotic and biotic factors that contribute to these adaptive radiations. To fill this gap, we delve into the geological history of the region, including the role of tectonic events such as the Andean uplift, the formation of the Isthmus of Panama, and the emergence of the Guiana and Brazilian Shields. We also explore the role of ecological opportunities created by the emergence of new habitats, as well as the role of key innovations, such as novel feeding strategies or reproductive mechanisms. We discuss different examples of adaptive radiation, including classic ones like Darwin's finches and Anolis lizards, and more recent ones like bromeliads and lupines. Finally, we propose new examples of adaptive radiations mediated by ecological interactions in their geological context. By doing so, we provide insights into the complex interplay of factors that contributed to the remarkable diversity of life in the Neotropics and highlight the importance of this region in understanding the origins of biodiversity.

HV-YOLOv8 by HDPconv: Better lightweight detectors for small object detection

Accurately identifying and localising small objects within images or videos is a critical challenge in the field of computer vision. It is mostly applied in scenarios that require high real-time performance, such as pedestrian detection and autonomous driving scenarios. These tiny targets generally include small objects at long distances, or objects appearing in low-resolution images, due to which it becomes exceptionally difficult to extract effective feature information. Since YOLOv8 with its large downsampling multiplier leads to deeper feature maps that make it difficult to detect tiny objects, we find that the use of residual structures in the convolution module can enhance the accuracy of small object detection. However, this undoubtedly increases the computational cost, so we lightened the convolution module to make it more suitable for practical applications and named it Halved Deep Pointwise Convolution (HDPConv). A cross-level partial module Variety of View Group Shuffle Cross Stage Partial Network (VOV-GSCSP) is also utilised, using a rational architecture as well as multi-scale information fusion, to ensure that the overall model is lightweight while obtaining rich gradient flows. On this basis, we propose a new network lightweight model HV-YOLOv8. In multiple sets of comparative experiments on two datasets (containing several state-of-the-art solutions as well as classical ones), we demonstrate the superiority of HV-YOLOv8, in particular, the accuracy is improved by 1.4% compared to YOLOv8, while the number of parameters and the amount of computation are drastically reduced.

Innovative Machine Learning Strategies for Early Detection and Prevention of Pregnancy Loss: The Vitamin D Connection and Gestational Health.

Early pregnancy loss (EPL) is a prevalent health concern with significant implications globally for gestational health. This research leverages machine learning to enhance the prediction of EPL and to differentiate between typical pregnancies and those at elevated risk during the initial trimester. We employed different machine learning methodologies, from conventional models to more advanced ones such as deep learning and multilayer perceptron models. Results from both classical and advanced machine learning models were evaluated using confusion matrices, cross-validation techniques, and analysis of feature significance to obtain correct decisions among algorithmic strategies on early pregnancy loss and the vitamin D serum connection in gestational health. The results demonstrated that machine learning is a powerful tool for accurately predicting EPL, with advanced models such as deep learning and multilayer perceptron outperforming classical ones. Linear discriminant analysis and quadratic discriminant analysis algorithms were shown to have 98 % accuracy in predicting pregnancy loss outcomes. Key determinants of EPL were identified, including levels of maternal serum vitamin D. In addition, prior pregnancy outcomes and maternal age are crucial factors in gestational health. This study's findings highlight the potential of machine learning in enhancing predictions related to EPL that can contribute to improved gestational health outcomes for mothers and infants.

Identifying quantum effects in seeded QED cascades via laser-driven residual gas in vacuum

The discrete and stochastic nature of the processes in the strong-field quantum electrodynamics (SF-QED) regime distinguishes them from classical ones. An important approach to identifying the SF-QED features is through the interaction of extremely intense lasers with plasma. Here, we investigate the seeded QED cascades driven by two counter-propagating laser pulses in the background of residual gases in a vacuum chamber via numerical simulations. We focus on the statistical distributions of positron yields from repeated simulations under various conditions. By increasing the gas density, the positron yields become more deterministic. Although the distribution stems from both the quantum stochastic effects and the fluctuations of the environment, the quantum stochastic effects can be identified via the width of the distribution and the exceptional yields, both of which are higher than the quantum-averaged results. The proposed method provides a statistical approach to identifying the quantum stochastic signatures in SFQED processes using high-power lasers and residual gases in the vacuum chamber.

Schizoaffective Disorder and Parkinson’s Disease: a case report

IntroductionWe present the case of a patient with schizoaffective disorder and Parkinson’s disease (PD), requiring treatment adjustment, with the use of high doses of quetiapine for the treatment of psychotic symptomatology.ObjectivesThe aim is to briefly review the treatment of dopaminergic psychosis in the elderly.Methods Patient aged 86 years, institutionalised, presenting severe episodes of behavioural alteration, high anxiety and delusions of harm, together with auditory and visual hallucinations. As relevant physical history, the patient has AHT, aortic insufficiency, and bladder cancer operated on in 2012. As psychiatric history of interest, the patient has been diagnosed since his 30s with schizoaffective disorder, Parkinson’s disease and moderate-severe cognitive impairment secondary to the previous two.As usual treatment, in addition to anticoagulation and antihypertensive therapy, the patient has been receiving L-dopa for his PD for years, antidepressant treatment with escitalopram 10mg, haloperidol 80 drops a day, divided into three doses, and lormetazepam 2mg as a hypnotic.In addition to the symptoms described above, the patient had episodes of confusional features, as well as marked stiffness in the cogwheel and significant gait disturbance, having suffered several falls without serious repercussions.Results Due to the comorbid neurological pathology, it was decided to progressively modify the treatment, withdrawing the benzodiazepine due to the risk of confusional disorder and replacing it with trazodone. Antipsychotic treatment was gradually replaced by extended-release quetiapine, reaching a maximum dose of 800mg. Likewise, escitalopram treatment is replaced by sertraline.With this adjustment, there was an improvement in the psychotic symptoms, as well as in the anxious symptoms. Episodes of distress are NOT observed, and the patient’s functionality improves, allowing him/her to participate in daily activities, both cognitive stimulation and physiotherapy.ConclusionsThe Spanish Society of Psychogeriatrics recommends that before using antipsychotics, it is advisable to first treat the underlying potentially treatable causes (pain, infections, toxic effects of drugs…), assess non-pharmacological interventions and always, if the use of antipsychotics is required, assess the risk-benefit ratio.In relation to the above, it is not surprising that in the elderly, the use of second-generation antipsychotics is recommended in the first place, as opposed to the classical ones. The latter are only recommended in emergency situations where an almost immediate effect is required.For dopaminergic psychosis, there are only controlled trials with clozapine. However, due to prescribing difficulties, aripiprazole or quetiapine is recommended in the first instance.Disclosure of InterestNone Declared

Drug repurposing as add-on treatment strategy for mania and bipolar depression: systematic synthesis and qualitative appraisal of the existing meta-analytic evidence

IntroductionIn the complex pathophysiology of bipolar disorder (BD), increasing evidence supports the involvement of neurobiological abnormalities beyond the classical ones, suggesting them as potential alternative therapeutic targets. Several drugs approved for different indications have thus been repurposed for the treatment of BD, all of them supported by a plausible biological rationale. Some recent reviews have provided an update on these possible additional treatment options for mania and bipolar depression, but no systematic synthesis and qualitative evaluation of meta-analytic findings has been made.ObjectivesTo provide a guidance on the available evidence on these treatments and their potential role in clinical practice, we conducted an umbrella review of meta-analyses of randomized placebo-controlled trials investigating drugs repurposed as add-on treatments for mania and bipolar depression.MethodsWe performed a systematic search and screening of the existing literature looking for the most up-to-date or comprehensive meta-analyses of randomized controlled trials (RCTs) on adults suffering from BD during an acute mood episode (mania or depression) which compared a repurposed drug and placebo as adjunctive treatments. We performed a critical appraisal according to “A MeaSurement Tool to Assess systematic Reviews” Version 2 (AMSTAR 2). We synthesized meta-analytic findings regarding efficacy, tolerability, and safety, also assessing the quality of evidence using the “Grading of Recommendations, Assessment, Development and Evaluations” (GRADE) approach.ResultsIn nine eligible meta-analyses investigating 12 drugs (four for mania and eight for bipolar depression) we observed a heterogeneous quality of reporting was according to AMSTAR 2.In mania, allopurinol (for symptoms reduction and remission at 4-8 weeks) and tamoxifen (for response and symptoms reduction at 4-6 weeks) showed higher efficacy than placebo, with evidence of low and very low quality, respectively.In bipolar depression, modafinil/armodafinil (for response, remission, and symptoms reduction at 6-8 weeks) and pramipexole (for response and symptoms reductionat 6 weeks) were superior to placebo, with low-quality evidence. Results on celecoxib and N-acetylcysteine were of low quality and limited to certain outcomes.ConclusionsOverall, the lack of evidence of high and moderate quality does not allow firm conclusions on the clinical utility of repurposed drugs as adjunctive treatments for mania and bipolar depression, limiting recommendations for their use in clinical practice. However, since some lines of evidence seem to hold some potential, and standard treatments for mania and bipolar depression remain not entirely satisfactory, the search for novel therapeutic targets and strategies for the management of BD warrants further research in the field.Disclosure of InterestNone Declared

Design of a Flexure-Based Flywheel for the Storage of Angular Momentum and Kinetic Energy

The flywheel is a widespread mechanical component used for the storage of kinetic energy and angular momentum. It typically consists of cylindrical inertia rotating about its axis on rolling bearings, which involves undesired friction, lubrication, and wear. This paper presents an alternative mechanism that is functionally equivalent to a classical flywheel while relying exclusively on limited-stroke flexure joints. This novel one-degree-of-freedom zero-force mechanism has no wear and requires no lubrication: it is thus compatible with extreme environments, such as vacuum, cryogenics, or ionizing radiation. The mechanism is composed of two coupled pivoting rigid bodies whose individual angular momenta vary during motion but whose sum is constant at all times when the pivoting rate is constant. The quantitative comparison of the flexure-based flywheel to classical ones based on a hollow cylinder as inertia shows that the former typically stores 6 times less angular momentum and kinetic energy for the same mass while typically occupying 10 times more volume. The freedom of design of the shape of the rigid bodies offers the possibility of modifying the ratio of the stored kinetic energy versus angular momentum, which is not possible with classical flywheels. For example, a flexure-based flywheel with rigid pivoting bodies in the shape of thin discs stores 100 times more kinetic energy than a classical flywheel with the same angular momentum. A proof-of-concept prototype was successfully built and characterized in terms of reaction moment generation, which validates the presented analytical model.

A Taxonomy for Learning with Perturbation and Algorithms

Weighting strategy prevails in machine learning. For example, a common approach in robust machine learning is to exert low weights on samples which are likely to be noisy or quite hard. This study summarizes another less-explored strategy, namely, perturbation. Various incarnations of perturbation have been utilized but it has not been explicitly revealed. Learning with perturbation is called perturbation learning and a systematic taxonomy is constructed for it in this study. In our taxonomy, learning with perturbation is divided on the basis of the perturbation targets, directions, inference manners, and granularity levels. Many existing learning algorithms including some classical ones can be understood with the constructed taxonomy. Alternatively, these algorithms share the same component, namely, perturbation in their procedures. Furthermore, a family of new learning algorithms can be obtained by varying existing learning algorithms with our taxonomy. Specifically, three concrete new learning algorithms are proposed for robust machine learning. Extensive experiments on image classification and text sentiment analysis verify the effectiveness of the three new algorithms. Learning with perturbation can also be used in other various learning scenarios, such as imbalanced learning, clustering, regression, and so on.

Abstract 2079: Tumor-derived fibrinogen promotes pancreatic ductal adenocarcinoma progression

Abstract Background: Hypercoagulability is a common clinical manifestation in patients with cancers, especially in pancreatic ductal adenocarcinoma (PDAC). Over 20% of PDAC patients with advanced-stage and worse prognosis undergo a venous thromboembolism (VTE) incidence. Fibrinogen (FG) may contribute to VTE due to its high expression in advanced PDAC tumors. Here, we set to determine the biological roles of tumor-derived FG in PDAC. In addition, our previous study showed that Von Willebrand Factor (VWF) A2 domain can effectively bind to FG and reduce platelet clot formation. Therefore, the functional roles of A2 on FG in PDAC were also studied. Methods: The correlation between survival rate and FG expression in PDAC was analyzed using RNAseq profiles of 149 PDAC patient samples from the TCGA database. PDAC subtyping of Baylor College of Medicine (BCM) PDAC patient-derived xenograft (PDX) cohort was performed using RNAseq data according to Moffitt’s criteria. The FG (three chains FGA, FGB, FGG) overexpression (OE) and knock-out (KO) PDAC cell lines were established. The gene expression and biological functions of FG were performed in PDX and cell lines using IHC and immunoblot assays. The FG effect on PDAC progression in vivo was studied in mouse models. Results: TCGA analysis showed that elevated tumor-derived FG gene expression is significantly correlated with worse overall survival and disease-free survival rates in PDAC patients. PDX lines RNAseq analysis clearly separated BCM PDAC PDXs into two subtypes: basal-like and classical PDACs. Basal-like PDXs showed significantly stronger FG staining than that in classical ones. Compared to the classical subtype, the basal-like subtype showed significantly upregulated genes that are associated with the epithelial-mesenchymal transition (EMT), IL6/JAK/STAT3, and TNFα signaling pathways. Accordingly, basal-like subtype PDAC exposed to IL6 or TNFα significantly increased FG and FG receptor expression with activated STAT3 and AKT pathways and elevated EMT markers, whereas classical subtype cells have minimal changes. In addition, we found that the FG-OE cells exhibit enhanced cell proliferation and migration properties whereas FG-KO cells reduced the effects. Consistent with in vitro data, FG-OE PDAC showed significantly higher tumor burdens than that of control PDAC in both orthotopic and subcutaneous PDAC mouse models. Furthermore, A2 protein treatment can effectively block the FG function and dramatically inactivate the FG-induced EMT process and AKT signaling pathway. Conclusions: Tumor-derived fibrinogen is upregulated in a basal-like subtype of PDAC cells and may contribute to tumor growth and metastasis by activating aberrant signaling pathways and promoting EMT. Our findings imply that elevated fibrinogen in PDAC could contribute to tumor progression and the A2 protein which target to fibrinogen may be a promising therapy for basal-like subtype PDAC. Citation Format: Dongliang Liu, Lisa Brubaker, Yichi Niu, Emily LaPlante, George Van Buren, Aleksandar Milosavljevic, Changyi Chen, Chenghang Zong, Miguel Cruz, Qizhi Yao. Tumor-derived fibrinogen promotes pancreatic ductal adenocarcinoma progression [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2079.

Quantum detection of new physics in top-quark pair production at the LHC

The recent observation of entanglement between top and anti-top quarks at the LHC opens the way to interpreting collider data with quantum information tools. In this work we investigate the relevance of quantum observables in searches of new physics. To this aim, we study spin correlations of \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$t\\overline{t }$$\\end{document} pairs originating from various intermediate resonances, and compare the discovery reach of quantum observables compared to classical ones. We find that they provide complementary information and, in several notable cases, also the additional leverage necessary to detect new effects.

ДИСПЕРГАЦИОННОЕ ПОЛУЧЕНИЕ ПИЩЕВЫХ ВИТАМИНОСОДЕРЖАЩИХ КОНЦЕНТРАТОВ

The objective of the study is to develop a technology for obtaining food vitamin-containing concentrates from natural raw materials using the high-frequency dispersion method. Tasks: to develop extraction conditions for obtaining a vitamin-containing concentrate based on sea buckthorn with maximum efficiency; to conduct an organoleptic and physicochemical analysis of the obtained sea buckthorn concentrate; to study the shelf life of the obtained concentrates; to determine the content of heavy metals in the sea buckthorn concentrate to confirm the environmental safety of the obtained product and the purity of the experiment. The objects of the study are technologies for producing food concentrates from natural plant materials. The developed technology was tested on the fruits of sea buckthorn (Hippophae rhamnoides L.), collected in the territory of the Republic of North Ossetia – Alania. The optimal extraction conditions for obtaining a vitamin-containing concentrate are presented, comparative results of the analysis of the vitamin composition of concentrates obtained using traditional technology and a concentrate prepared using the developed technology are given. Comparison of the results of the yield of products using the classical and proposed technologies for the production of sea buckthorn concentrates confirms the effectiveness of high-frequency dispersion in the process of obtaining food vitamin-containing concentrates. The proposed technology has an increased yield compared to classical ones by an average of 2 times. The developed technology for processing raw materials is a promising innovative approach to increasing the content of vitamins and preserving the beneficial properties of a natural product, since among the existing production technologies there is no method for extracting nutrients by dispersion using ultrasonic treatment in an organic medium. The shelf life of the concentrate was studied. To confirm the environmental safety of the product and the purity of the experiment, the content of heavy metals in the sea buckthorn concentrate was determined. According to the results of organoleptic analysis carried out in accordance with GOST ISO 6658-2016, the obtained sea buckthorn concentrate retains its main organoleptic characteristics, which increases its potential for use in the food industry. It is shown that the proposed technology allows solving the problem of import substitution and obtaining high-quality products using natural raw materials.

Qudit Concurrence and Monogamy

It is well known that entanglement is one of the distinctive aspects of the quantum world. The measurement and quantification of entanglement is one of the major problems in understanding and applying this resource. This research aims to investigate entanglement measures and their properties in high-dimensional systems. One such property is entanglement monogamy, viz. quantum correlations are not shareable, different from the classical ones. On the other hand, the study of entanglement depends on the definitions of its measures. Here we examine the applicability of concurrence, an entanglement measure known for its effectiveness in the analysis of qubit entanglement. While the entanglement of two-level systems or qubits is widely understood, its extension to higher dimensions remains challenging. Our research establishes equivalence between different concurrence definitions for bipartite qubit pure states and extends them to qudits. Subsequently, we investigate the monogamous relations of these measurements to explore their characteristics. This approach might enhance our comprehension of quantum traits in qudit systems and advance our understanding of entanglement in broader quantum contexts.

Some issues about the behaviour of external pre-stressed steel–concrete composite beams

A numerical experiment is performed to evaluate the behaviour of externally pre-stressed steel–concrete composite beams (EPSCCBs) up to their ultimate stages, considering second-order effects, tendon material and friction at tendon–deviator locations. The tendon force evolution with loading and slip at the steel–concrete interface is explored. A three-dimensional finite-element model is used to include all these aspects. The idea is to deconstruct the project of eight experimental EPSCCBs with different tendon profiles, considering the above-mentioned issues. The main findings indicate that the inclusion of second-order effects is essential for predicting the stress paths near the collapse loads and slip demands at the steel–concrete interface, while external tendons made of carbon fibre-reinforced polymer (CFRP) can be used as a substitute for classical steel ones, as they presented similar behaviours. The largest tendon force increments occurred for the CFRP tendons compared to other tendons made of glass, aramid and basalt fibre-reinforced polymers. Furthermore, it was found that a friction coefficient between 0.2 and 0.4 at the tendon–deviator interface better matches the available experimental tendon force evolution with loading. This finding seems to counteract the common assumption of using a null friction coefficient as suggested in other works.

Review of Experimental Activities and Recent Developments of Spouted Bed Reactors at Different Operational Scales

Recent research advances and technological developments of spouted bed reactors (SBRs) have been discussed in this work. SBR has aroused increasing interest since their invention in 1955 due to its flexibility in processing different feedstocks and the high process yields that can be achieved due to its characteristic fluid dynamics. However, even though highly satisfactory results have been obtained at the laboratory scale for different applications (i.e., drying or thermochemical reactions, among others), their full implementation at an industrial level is still scarce, mainly due to the challenges encountered for their scale-up. In this work, an initial short description of SBR and configurations is followed by a review of the main experimental activities that have been conducted at different scales in the period 2013–2023. Advanced solutions such as multi-unit reactors and the use of rectangular geometries instead of the classical cylindrical ones have arisen as potential areas for further study and development to achieve a reliable implementation of the spouted bed technology at an industrial scale.

On NN-torsion-free and NN-injective modules

In this paper, we introduce the concept of NN-torsion-free modules (respectively, NN-injective modules) as a generalization of nonnil-torsion-free modules (respectively, injective modules). We show that they behave in a way similar to the classical ones. As an application, we characterize ϕ-rings over which every NN-torsion-free module is NN-injective.