Target Application Research Articles

Automatic real-time crack detection using lightweight deep learning models

Crack detection methods using deep learning models such as convolutional neural network (CNN) and the newly developed vision transformer (ViT) are expanding. However, there is still a lack of comparative evaluation of these models in real-time crack detection. In this paper, a total of 14 lightweight deep learning models, comprising seven CNN models, five ViT models and two hybrid models, are trained to build deep learning-based crack detection methods. Comprehensive experiments are conducted on the publicly available DeepCrack dataset, including accuracy, inference time, robustness and transfer learning experiments to compare the effectiveness and real-time performance of models. In terms of accuracy metrics and robustness performance, the ViT model using SegFormer segmentation method with MiT-B1 as backbone has the best performance, and in terms of the model inference time, the ViT models using TopFormer segmentation method demonstrate the fastest performance. If both the accuracy and inference time are considered, TopFormer with its small version of the backbone network has relatively better real-time performance, while the ViT model using SegFormer segmentation method with MiT-B0 as backbone and the CNN model using the fully convolutional network (FCN) segmentation method with HRNetV2-W18-Small as backbone have higher mean intersection over union (mIoU) values on computers and mobile devices, respectively. We also find that pre-training on a dataset that is more relevant to the target application scenario rather than on the widely used ImageNet gives better results for deep learning models. This study provides a reference for engineers to make choices about lightweight deep learning models.

Application of a large wall electric probe (LWEP) for plasma and ambient gas studies

Abstract The operation of a large wall electric probe (LWEP) is analyzed, where the probe surface area in contact with the plasma is only a few times smaller than the area of the plasma boundaries. Compared to a small standard Langmuir probe (SLP), the LWEP may have significantly greater resolution and sensitivity, but could substantially perturb the plasma and distort the measured properties. A target application for the LWEP is where the sensitivity of the measurement is critical, but the effects of the plasma perturbation is minimal. A specific case where a LWEP may outperform a SLP is the measurement of a nonlocal electron distribution function at energies of electron free diffusion, that is, at energies significantly exceeding thermal electron energies, in order to obtain information about the parameters of the plasma or ambient gas. Examination of the LWEP analysis process has revealed that, depending on the plasma volume and probe configurations, it may be necessary to measure either the first or second derivatives of the probe current with respect to the probe potential to derive the energetic part of the electron energy distribution function.

Boosting photoelectrochemical water splitting performance via nanostructured Ag-CuO thin films

Undoped and doped copper oxide (CuO) with silver (Ag) was prepared using the SILAR technique on the ITO substrate to generate green hydrogen from the water-splitting process. The (−111) and (111) were the most intense reflections of crystallographic planes observed in pure and Ag-doped CuO thin films. Ag doping within the crystal structure of pristine leads to increasing the crystallite size. FESEM images show homogeneity and uniform distribution for the pure and Ag-doped CuO thin films that confirm the particles have small sizes, give more features and are responsible for the target application. The doping process affected the energy band gap where it was 2.2 eV for the pure sample then descended to 1.9 eV as doping reached 5 % concentration of Ag element. The photoelectrochemical testing showed that the Ag 5 % content photoelectrode has a high value of photocurrent density approximately ∼ - 5 mA/cm2.

Targeting New Functions and Applications of Bacterial Two-Component Systems.

Two-component signal transduction systems (TCSs) are regulatory systems widely distributed in eubacteria, archaea, and a few eukaryotic organisms, but not in mammalian cells. A typical TCS consists of a histidine kinase and a response regulator protein. Functional and mechanistic studies on different TCSs have greatly advanced the understanding of cellular phosphotransfer signal transduction mechanisms. In this concept paper, we focus on the His-Asp phosphotransfer mechanism, the ATP synthesis function, antimicrobial drug design, cellular biosensors design, and protein allostery mechanisms based on recent TCS investigations to inspire new applications and future research perspectives.

Pivotal Role of Surface Terminations in MXene Thermodynamic Stability.

MXenes, i.e., two-dimensional transition metal carbides and nitrides, have been reported as promising materials for various applications, including energy storage, biomedicine, and electronics. The family of MXenes has proliferated, and the chemical space of synthesized MXenes has expanded to 13 transition metals and a dozen elements in surface terminations. The diverse chemistry of MXenes enables systematical tuning of MXene properties to meet the needs of target applications. However, synthesizing new MXene compositions largely relies on a trial-and-error approach. To overcome it, computational predictions of MXene compositions that are thermodynamically stable are crucial to rationalize experimental efforts. Here, we report a comprehensive computational screening for thermodynamically stable MXenes across 29 transition metals and 11 surface terminations. Density functional theory calculations are employed to compute the energy above the convex energy hull as a descriptor of thermodynamic stability. The results are analyzed to explore factors crucial for determining the thermodynamic stability of MXenes, by which the chemistry of surface terminations is found to play a crucial role. The insights on the chemistry of 998 MXene compositions predicted to be (meta)stable are given to systematically guide further research on MXene synthesis and application.

Tuning crosslinking of hybrid preceramic polymers in vat photopolymerization toward controlled ceramic yields

Control of preceramic polymer crosslinking for UV-curable processing is essential for fine 3D printing with high ceramic conversion for sustainable polymer-derived ceramics (PDC) engineering. While various factors influencing ceramic yield have been studied, the systematic exploration of the relationship between crosslinking and ceramic yield, especially when crosslinking increases volatile elements, remains open for further investigation. This study addresses this gap by utilizing vat photopolymerization (VP) additive manufacturing (AM) as a versatile platform for controlling preceramic crosslinking and ceramic yield. By rationally designing and tuning the photochemical crosslinking through digital light processing (DLP), we demonstrate that the ceramic yield can be enhanced from 64% to over 86%, even with added volatile elements. We reveal that the post-pyrolysis ceramic yield can be closely correlated with the pre-pyrolysis crosslinking of the preceramic network represented by its stiffness. This correlation thereby suggests a fast, energy-efficient, non-destructive methodology to predict and improve ceramic yield. Combined with these findings, our one-pot thiol-ene hybridization of polycarbosilane and polycarbosiloxane via DLP offers an exemplary method to generate hybrid preceramic polymers with tailored material properties toward target applications, and potentially even higher ceramic yields. This study thus contributes to achieving better resource- and energy-efficient preceramic polymer and PDC processing routes toward sustainable, advanced organic–inorganic materials manufacturing.

Papillary Intralymphatic Angioendothelioma of the Spleen: A Rare Lymphatic Tumour in the Context of a <I>PIK3CA </I>Mutation

Papillary intralymphatic angioendothelioma (PILA) is a locally aggressive, rarely metastasising lymphatic tumour that typically arises within the skin and soft tissues. We report a case of PILA arising within the spleen of a 6-year-old boy with megacephaly-capillary malformation (MCAP/MC-M) syndrome. MCAP/MC-M represents part of the phosphatidylinositol-4,5-biphosphate 3 kinase catalytic subunit alpha (<I>PIK3CA) </I>related overgrowth spectrum (PROS). This is the 6<sup>th</sup> reported case in the literature of a splenic PILA and the 2<sup>nd</sup> known to have occurred in the context of a confirmed <I>PIK3CA </I>mutation. In reporting this case we review prior reports of PILA occurring within the spleen and discuss the potential role of the <I>PIK3CA </I>mutation in the aetiology of the lesion, and note a theoretical application of mammalian target of rapamycin (mTOR) inhibitors as a possible therapeutic strategy.

Assessment of the Dose-Dependent Effect of Human Platelet Lysate on Wharton's Jelly-Derived Mesenchymal Stem/Stromal Cells Culture for Manufacturing Protocols.

Mesenchymal stem/stromal cells (MSCs)-based products have unique characteristics compared to other drugs because of their inherently variable effects depending on culture conditions and microenvironment. In some cases, cells can be produced individually, one batch at a time, for personalized therapy. Therefore, it is very important to optimize both culture conditions and medium composition under Good Manufacturing Practice (GMP) standards. MSCs properties have been exploited as potential cell therapies in regenerative medicine. The main mechanism of their protective and regenerative effect is based on their secretory activity. Simultaneously, their secretome is highly variable and sensitive to any change in environmental conditions. Depending on the type of damage and the target application, it is desirable to enhance the secretion of therapeutic factors. Changes in the modulation of environmental conditions can affect survival, migration ability, and both proliferative and clonogenic potentials. This study cultured Wharton's jelly-derived MSCs (WJ-MSCs) in media with varying concentrations of human platelet lysate (hPL). Two groups were created: one with low hPL concentration and another with a high hPL concentration. The effects of these different hPL concentrations were analyzed by assessing mesenchymal phenotype retention, secretory activity, clonogenic potential, proliferation, and migration capabilities. Additionally, the secretion levels of key therapeutic factors, such as Hepatocyte Growth Factor (HGF), Brain-Derived Neurotrophic Factor (BDNF), and Chemokine Ligand 2 (CCL-2), were measured. WJ-MSCs maintained their mesenchymal phenotype regardless of hPL concentration. However, a higher concentration of hPL promoted cell clonogenic potential, proliferation, migration, and increased secretion of therapeutic factors. Adjusting the hPL concentration in the culture medium modulates the response of WJ MSCs and enhances their therapeutic potential. Higher hPL concentration promotes increased secretory activity and improves the regenerative capacity of WJ-MSCs, suggesting a promising strategy to optimize MSC-based therapies.

Extension of paperboard modelling with Föppl–von Karman terms for improved stress state under suction pressure

A practical method for solving the bending stiffness of a thin orthotropic plate based on measured deflection and known loading was studied. The target application was paperboard manufacturing and related process control. Here, finite element (FE) method was used to create virtual paperboard and its deflection data. The accuracy of the linear Kirchhoff plate model used in the practical method was tested against the FE model with known load and material properties. It was found that in the case of significant deflections, the linear Kirchoff model was not accurate. The non-linear Föppl--von Kàrmàn model takes into account the occurring membrane stresses and extends on the Kirchhoff model, allowing for larger deflection. The non-linear Föppl--von Kàrmàn model was found to successfully describe the simulated situation and could be used to better solve for the paperboard's bending stiffness values over two axes.

Clinical Approach to STRIDE-II in Real-Life Settings: Analysis and Practical Recommendations.

We aimed to (1) analyze the applicability of the updated Selecting Therapeutic Targets in Inflammatory Bowel Disease (STRIDE-II) recommendations in real-world clinical practice, (2) identify barriers to their implementation, and (3) propose practical measures to overcome these obstacles. This qualitative study was based on a survey, a literature review, and expert opinions. Nine inflammatory bowel disease (IBD) experts identified 7 areas likely to be controversial or potential implementation barriers in daily clinical practice: endoscopy, histology, ultrasound, quality of life, biomarkers, symptom control, and patient-reported outcomes (PROs). Based on this, a survey was carried out among educational course participants. The experts discussed the literature review and survey results and proposed several statements and practical actions. A total of 55 gastroenterologists answered the survey. The reported difficulty level in reaching STRIDE-II treatment goals in clinical practice was high. Only 22% of participants performed clinical remission assessments using clinical indexes and PROs. Seventy percent of responders did not use fecal calprotectin cutoffs and considered changes from the previous levels instead. Mucosal healing as a long-term therapeutic goal was considered necessary to be individualized in specific patient subgroups (eg, elderly/fragile patients, multiple treatment failures, and last-line therapies). Other barriers, like the lack of access to imaging techniques or insufficient knowledge and skills among healthcare professionals, were detected. The experts suggested adding less stringent treatment goals and measurements, patient stratification, local adaptations, educational activities, and research. STRIDE-II recommendations face various implementation barriers needing careful evaluation in order to enhance their adoption in clinical practice, and ultimately improve outcomes in IBD patients.

Flexibility Assessment of Ternary Pumped Storage in Day-Ahead Optimization Scheduling of Power Systems

To explore the advantages in the flexibility of ternary pumped storage units (T-PSUs) compared with fixed-speed pumped storage units (FS-PSUs) and variable-speed pumped storage units (VS-PSUs) in the day-ahead optimal scheduling of power systems, this paper firstly establishes the mathematical model of T-PSUs which is suitable for the target application, and establishes a new hybrid pumped storage model combining both FS-PSUs and T-PSUs for the purpose of improving existing FS-PSUs. Secondly, a day-ahead optimal scheduling model used for a wind-thermal-pumped storage bundled transmission system with the goal of obtaining the lowest operating cost is established. Additionally, various indicators are proposed to comprehensively evaluate the flexibility of different pumped storage unit types on day-ahead optimal scheduling. Finally, a sensitivity analysis is conducted from three aspects: covering the wind power capacity, load rate, and pumped storage capacity. The results indicate that T-PSUs are superior to FS-PSUs and VS-PSUs in maintaining the smooth, economic, and robust operation of thermal power units, as well as in promoting wind power consumption. This provides a significant reference for evaluating the technical and economic benefits of the different types of pumped storage units in applications of future power grids.

Advances in nucleic acid aptamer-based detection of respiratory virus and bacteria: a mini review.

Respiratory pathogens infecting the human respiratory system are characterized by their diversity, high infectivity, rapid transmission, and acute onset. Traditional detection methods are time-consuming, have low sensitivity, and lack specificity, failing to meet the needs of rapid clinical diagnosis. Nucleic acid aptamers, as an emerging and innovative detection technology, offer novel solutions with high specificity, affinity, and broad target applicability, making them particularly promising for respiratory pathogen detection. This review highlights the progress in the research and application of nucleic acid aptamers for detecting respiratory pathogens, discussing their selection, application, potential in clinical diagnosis, and future development. Notably, these aptamers can significantly enhance the sensitivity and specificity of detection when combined with detection techniques such as fluorescence, colorimetry and electrochemistry. This review offers new insights into how aptamers can address the limitations of traditional diagnostic methods and advance clinical diagnostics. It also highlights key challenges and future research directions for the clinical application of nucleic acid aptamers.

Pythia: Non-random DNA repair allows predictable CRISPR/Cas9 integration and gene editing.

CRISPR-based genome engineering holds enormous promise for basic science and therapeutic applications. Integrating and editing DNA sequences is still challenging in many cellular contexts, largely due to insufficient control of the repair process. We find that repair at the genome-cargo interface is predictable by deep-learning models and adheres to sequence context specific rules. Based on in silico predictions, we devised a strategy of triplet base-pair repeat repair arms that correspond to microhomologies at double-strand breaks (trimologies), which facilitated integration of large cargo (>2 kb) and protected the targeted locus and transgene from excessive damage. Successful integrations occurred in >30 loci in human cells and in in vivo models. Germline transmissible transgene integration in Xenopus, and endogenous tagging of tubulin in adult mice brains demonstrated integration during early embryonic cleavage and in non-dividing differentiated cells. Further, optimal repair arms for single- or double nucleotide edits were predictable, and facilitated small edits in vitro and in vivo using oligonucleotide templates. We provide a design-tool (Pythia, pythia-editing.org) to optimize custom integration, tagging or editing strategies. Pythia will facilitate genomic integration and editing for experimental and therapeutic purposes for a wider range of target cell types and applications.

Sign Language Interpreting System Using Recursive Neural Networks

According to the World Health Organization (WHO), 5% of people around the world have hearing disabilities, which limits their capacity to communicate with others. Recently, scientists have proposed systems based on deep learning techniques to create a sign language-to-text translator, expecting this to help deaf people communicate; however, the performance of such systems is still low for practical scenarios. Furthermore, the proposed systems are language-oriented, which leads to particular problems related to the signs for each language. For this reason, to address this problem, in this paper, we propose a system based on a Recursive Neural Network (RNN) focused on Mexican Sign Language (MSL) that uses the spatial tracking of hands and facial expressions to predict the word that a person intends to communicate. To achieve this, we trained four RNN-based models using a dataset of 600 clips that were 30 s long; each word included 30 clips. We conducted two experiments; we tailored the first experiment to determine the most well-suited model for the target application and measure the accuracy of the resulting system in offline mode; in the second experiment, we measured the accuracy of the system in online mode. We assessed the system’s performance using the following metrics: the precision, recall, F1-score, and the number of errors during online scenarios, and the results computed indicate an accuracy of 0.93 in the offline mode and a higher performance for the online operating mode compared to previously proposed approaches. These results underscore the potential of the proposed scheme in scenarios such as teaching, learning, commercial transactions, and daily communications among deaf and non-deaf people.

Integration of surface swab with optical microscopy for detection and quantification of bacterial cells from stainless-steel surfaces.

Swab sampling is a common method for recovering microbes on various environmental surfaces. Its successful application for a specific target depends on the proper swab method and the following detection assay. Herein, we evaluated critical factors influencing surface swab sampling, aiming to achieve the optimal detection and quantification performance of optical detection for bacterial cells on stainless-steel surfaces. Our results showed the recovery rate of Salmonella enterica (SE1045) cells from the 10×10cm2 stainless-steel surface reached up to 92.71±2.19% when using ammonia bicarbonate-moistened polyurethane foam swabs for gentle collection, followed by ultrasound-assisted release in NH4HCO3 solution. Among the six different foam swabs, the Puritan™ Sterile Large Foam Swab contributed the lowest background noise and highest recovery efficiency when integrated with the optical detection assay. Notably, our method exhibited a strong linear relationship (r2=0.9983) between the detected cell numbers and the theoretical number of SE1045 cells seeded on surfaces in the range of 104-107 Colony Forming Units (CFU), with a limit of detection of 7.2×104 CFU 100cm-2. This integration was completed within 2 h, exhibiting the applicable potential in various settings.

RED-SEA Project: Towards a new-generation European interconnect

RED-SEA is a H2020 EuroHPC project, whose main objective is to prepare a new-generation European Interconnect, capable of powering the EU Exascale systems to come, through an economically viable and technologically efficient interconnect, leveraging European interconnect technology (BXI) associated with standard and mature technology (Ethernet), previous EU-funded initiatives, as well as open standards and compatible APIs.To achieve this objective, the RED-SEA project is being carried out around four key pillars: (i) network architecture and workload requirements-interconnects co-design – aiming at optimizing the fit with the other EuroHPC projects and with the EPI processors; (ii) development of a high-performance, low-latency, seamless bridge with Ethernet; (iii) efficient network resource management, including congestion and Quality-of-Service; and (iv) end-to-end functions implemented at the network edges.This paper presents key achievements and results at the midterm of the project for each key pillar in the way to reach the final project objective. In this regard we can highlight: (i) The definition of the network requirements and architecture as well as a list of benchmarks and applications; (ii) In addition to initially planned IPs progress, BXI3 architecture has evolved to support natively Ethernet at low level, resulting in reduced complexity, with advantages in terms of cost optimization, and power consumption; (iii) The congestion characterization of target applications and proposals to reduce this congestion by the optimization of collective communication primitives, injection throttling and adaptive routing; and (iv) the low-latency high-message rate endpoint functions and their connection with new open technologies.

Trait imputation enhances nonlinear genetic prediction for some traits.

The expansive collection of genetic and phenotypic data within biobanks offers an unprecedented opportunity for biomedical research. However, the frequent occurrence of missing phenotypes presents a significant barrier to fully leveraging this potential. In our target application, on one hand, we have only a small and complete dataset with both genotypes and phenotypes to build a genetic prediction model, commonly called a polygenic (risk) score (PGS or PRS); on the other hand, we have a large dataset of genotypes (e.g. from a biobank) without the phenotype of interest. Our goal is to leverage the large dataset of genotypes (but without the phenotype) and a separate genome-wide association studies summary dataset of the phenotype to impute the phenotypes, which are then used as an individual-level dataset, along with the small complete dataset, to build a nonlinear model as PGS. More specifically, we trained some nonlinear models to 7 imputed and observed phenotypes from the UK Biobank data. We then trained an ensemble model to integrate these models for each trait, resulting in higher R2 values in prediction than using only the small complete (observed) dataset. Additionally, for 2 of the 7 traits, we observed that the nonlinear model trained with the imputed traits had higher R2 than using the imputed traits directly as the PGS, while for the remaining 5 traits, no improvement was found. These findings demonstrate the potential of leveraging existing genetic data and accounting for nonlinear genetic relationships to improve prediction accuracy for some traits.

Requirements for upgrading trusted nodes to a repeater chain over 900 km of optical fiber

Abstract We perform a numerical study of the distribution of entanglement on a real-world fiber grid connecting the German cities of Bonn and Berlin. The connection is realized using a chain of processing-node quantum repeaters spanning roughly 900 kilometers. Their placement is constrained by the fiber grid we consider, resulting in asymmetric links. We investigate how minimal hardware requirements depend on the target application, as well as on the number of repeaters in the chain. We find that requirements for blind quantum computing are markedly different than those for quantum key distribution, with the required coherence time being around two and a half times larger for the former. Further, we observe a trade-off regarding how target secret-key rates are achieved when using different numbers of repeaters: comparatively low-quality entangled states generated at a high rate are preferred for higher numbers of repeaters, whereas comparatively high-quality states generated at a lower rate are favored for lower numbers of repeaters. To obtain our results we employ an extensive simulation framework implemented using NetSquid, a discrete-event simulator for quantum networks. These are combined with an optimization methodology based on genetic algorithms to determine minimal hardware requirements.

Validating, Implementing, and Monitoring Machine Learning Solutions in the Clinical Laboratory Safely and Effectively.

Machine learning solutions offer tremendous promise for improving clinical and laboratory operations in pathology. Proof-of-concept descriptions of these approaches have become commonplace in laboratory medicine literature, but only a scant few of these have been implemented within clinical laboratories, owing to the often substantial barriers in validating, implementing, and monitoring these applications in practice. This mini-review aims to highlight the key considerations in each of these steps. Effective and responsible applications of machine learning in clinical laboratories require robust validation prior to implementation. A comprehensive validation study involves a critical evaluation of study design, data engineering and interoperability, target label definition, metric selection, generalizability and applicability assessment, algorithmic fairness, and explainability. While the main text highlights these concepts in broad strokes, a supplementary code walk-through is also provided to facilitate a more practical understanding of these topics using a real-world classification task example, the detection of saline-contaminated chemistry panels.Following validation, the laboratorian's role is far from over. Implementing machine learning solutions requires an interdisciplinary effort across several roles in an organization. We highlight the key roles, responsibilities, and terminologies for successfully deploying a validated solution into a live production environment. Finally, the implemented solution must be routinely monitored for signs of performance degradation and updated if necessary. This mini-review aims to bridge the gap between theory and practice by highlighting key concepts in validation, implementation, and monitoring machine learning solutions effectively and responsibly in the clinical laboratory.

Experimental study on condensation-free radiant cooling panel with low-temperature for local cooling in vehicles - Part 1: Design process and feasibility evaluation with performance test

Although radiant cooling has significant advantages (high efficiency and thermal comfort), insufficient cooling capacity and condensation risk limit the practical application. In this study, a novel condensation-free radiant cooling panel (RCP) with high cooling capacity is proposed. The performance of proposed RCP was experimentally evaluated at different operating conditions. The transparent cover and surface-painting spray materials of the RCP were examined using Fourier-transform infrared spectroscopy to analyze their transmittance and absorptivity. The total cooling heat flux of the proposed RCP ranged between 2019.5 and 3824.5 W m–2, which are significantly higher than those of previous studies (70–1457 W m–2), owing to low surface temperatures. The radiant heat flux decreased by 13.5% when the distance between the cooling surface and the heater increased from 10 cm to 20 cm, and decreased by 52% when evaporating temperature increased from –40 °C to –20 °C. However, the heater and ambient temperatures showed insignificant effect. The results of this study report feasibility of the proposed RCP and parameters that influence performance. The target application of the proposed RCP is local cooling systems in vehicles to enhance thermal comfort, which is given in succeeding research, Part 2: Demonstration with passengers for thermal comfort analysis.