Wide Application Research Articles

3D cell culture of nanofiber hydrogel promotes the growth and osteogenesis of bone marrow mesenchymal stem cells (BMSC)

Hydrogels, as a type of polymer network material with high water content, find wide application in various tissue repair procedures. In addition, a three-dimension (3D) cell culture platform similar to natural extracellular matrix can be constructed in vitro by modifying natural polymer chains. Therefore, this study based on natural polymer hydrogel, the introduction of inorganic nanoparticles POSS to prepare GelMA-POSS nanohydrogel. The physical and chemical properties of the hydrogel were evaluated through SEM, swelling test and degradation analysis. Subsequently, the GelMA-POSS nano-hydrogel was used as matrix material to culture rBMSCs in vitro. CCK-8 and flow cytometry were used to detect the proliferation ability and apoptosis rate of rBMSCs. The expression of osteogenic markers in rBMSCs was detected by qPCR, Western Blotting and ELISA assay, and the osteogenic differentiation of rBMSCs was detected by ALP staining and alizarin red staining. The results indicate that compared with pure gelatin (Gel) hydrogel, GelMA-POSS nanohydrogel has slower degradation rate and better water retention capacity. Furthermore, in vitro cell culture experiments showed that GelMA-POSS nano-hydrogel had good biocompatibility, and could promote the proliferation of rBMSCs and increase the expression level of osteogenic markers in cells, and induce osteogenic differentiation of rBMSCs. GelMA-POSS nano-hydrogel based on POSS significantly improves the physical and chemical properties of Gel hydrogel, and has good biocompatibility and the ability to induce osteogenic differentiation of rBMSCs, Thus, it holds great promise as a potential material for repairing bone defects.

Exploring the combined influence of primary and secondary vortex flows on heat transfer enhancement and friction factor in a dimpled configuration twisted tape with double pipe heat exchanger using SiO2 nano fluid

The double-pipe heat exchanger has a wider application in food industries, chemical industries, and a wide variety of fields where heat transmission is necessary. This investigation focuses on optimizing heat transfer rates through active, passive, and combined approaches. Within the realm of passive methods, this study specifically examines the use of twisted tape additions in pipe flow. In this study, different factors like swirl flow, primary and secondary vortex flows caused by tape twist, and the presence of a V-cut on the tape are studied. These factors include heat transfer rate, friction factor, and the standard for judging the performance of a V-cut on the tape insert. Experiments have been conducted on a double-pipe heat exchanger with nanofluid silicon dioxide as the working medium. This study encompasses a Reynolds number range of 6000–14,000. Notably, investigations reveal that the introduction of a v-cut on the tape engenders a secondary vortex flow. The enhancement in the Nusselt number at the higher Reynolds number for the primary vortex was observed ineffective as the depth of cut increases. The friction factor is increased by 6.37 times for the e/c ratio, which equals 0.17 (tooth to the depth of the v cut) in comparison to smooth pipe, while the increase in heat transfer rate for the same is 87.73% for Re = 6000.

Novel self-setting cements based on tricalcium silicate/(β-tricalcium phosphate/monocalcium phosphate anhydrous)/hydroxypropyl methylcellulose: From hydration mechanism to biological evaluations

Despite the clinical success of tricalcium silicate (TCS)-based materials in endodontics, the inferior handling characteristic, poor anti-washout property and slow setting kinetics hindered their wider applications. To solve these problems, an injectable fast-setting TCS/β-tricalcium phosphate/monocalcium phosphate anhydrous (β-TCP/MCPA) cement was developed for the first time by incorporation of hydroxypropyl methylcellulose (HPMC) and β-TCP/MCPA. The physical-chemical characterization (setting time, anti-washout property, injectability, compressive strength, apatite mineralization and sealing property) of TCS/(β-TCP/MCPA) were conducted. Its hydration mechanism was also investigated. Furthermore, the cytocompatibility and osteogenic/odontogenic differentiation of stem cells isolated from human exfoliated deciduous teeth (SHED) treated with TCS/β-TCP/MCPA were studied. The results showed that HPMC could provide TCS with good anti-washout ability and injectability but slow hydration process. However, β-TCP/MCPA effectively enhanced anti-washout characteristics and reduced setting time due to faster hydration kinetics. TCS/(β-TCP/MCPA) obtained around 90 % of injection rate and high compressive strength whereas excessive additions of β-TCP/MCPA compromised its injectability and compressive strength. TCS/(β-TCP/MCPA) can induce apatite deposition and form a tight marginal sealing at the dentin-cement interface. Additionally, TCS/(β-TCP/MCPA) showed good biocompatibility and promoted osteo/odontogenic differentiation of SHED. In general, our results indicated that TCS/(β-TCP/MCPA) may be particularly promising as an injectable bioactive cements for endodontic treatment.

Epidemiology of Disorders of the Gut-Brain Interaction: An Appraisal of the Rome IV Criteria and Beyond.

Disorders of the gut-brain interaction (DGBIs) are presently classified into mutually exclusive anatomical area-related symptom-based categories according to the Rome IV criteria. The pathophysiology of visceral nociception, which contributes to the wide range of symptoms of DGBIs, involves complex psychobiological processes arising from the bidirectional interactions of multiple systems at the gut and brain levels, which affect symptom expression and illness behaviors. The attitude toward an illness and expression of pain and bowel habit vary across cultures with variable interpretation based on sociocultural beliefs, which may not tally with the medical definitions. Thus, psychological factors impact DGBI definitions, their severity and health care utilization. Due to the poor localization and multisegment referral of visceral pain, the anatomical site of pain may not correspond to the affected segment, and there may be a variable degree of overlap among symptoms. The somewhat restrictively defined Rome IV criteria assume one-to-one correlation of symptoms with underlying pathophysiology and ignore overlapping DGBIs, nonstandardized symptom categories, and change or shift in category over time. The microorganic nature of DGBIs resulting from systemic, metabolic or motility disorders, gut dysbiosis and inflammation are not addressed in the Rome IV criteria. Although there is a multidimensional clinical profile that does address these factors, it is not followed rigorously in practice. Threshold changes for diagnostic criteria or addition/deletion of symptoms leads to wide variation among different DGBI criteria resulting in uncertain comparability of results. Although the Rome IV criteria are excellent for research studies and therapeutic trials in homogenous populations, further improvement is needed for their wider applicability in clinical practice.

High-performance grinding of ceramic matrix composites

Ceramic matrix composites (CMCs) are highly promising materials for the next generation of aero-engines. However, machining of CMCs suffers from low efficiency and poor surface finish, which presents an obstacle to their wider application. To overcome these problems, this study investigates high-efficiency deep grinding of CMCs, focusing on the effects of grinding depth. The results show that both the surface roughness and the depth of subsurface damage (SSD) are insensitive to grinding depth. The material removal rate can be increased sixfold by increasing the grinding depth, while the surface roughness and SSD depth increase by only about 10%. Moreover, it is found that the behavior of material removal is strongly dependent on grinding depth. As the grinding depth is increased, fibers are removed in smaller sizes, with the fiber length in chips being reduced by about 34%. However, too large a grinding depth will result in blockage by chip powder, which leads to a dramatic increase in the ratio of tangential to normal grinding forces. This study demonstrates that increasing the depth of cut is an effective approach to improve the machining efficiency of CMCs, while maintaining a good surface finish. It provides the basis for the further development of high-performance grinding methods for CMCs, which should facilitate their wider application.

Superconducting in-memory computing architecture coupling with memristor synapses for binarized neural networks

In-memory computing electronic components offer a promising non-von Neumann strategy to develop energy-efficient and high-speed hardware systems for artificial intelligence (AI). However, the implementation of conventional electronic hardware demands a huge computational and power budget, thereby limiting their wider application. In this work, we propose a novel superconducting in-memory computing architecture by coupling the memristor device. Leveraging the phase transition of the superconductor induced by external applied Joule power, we can modulate the state of the bottom superconductor based on memristor resistive states and applied voltages, enabling the execution of in-memory computing operations. We then successfully implement vector-matrix multiplication of input and output signals within the designed array, facilitating its integration into AI systems. Constructing a binarized neural network with superconductor-memristor arrays achieves a high level of accuracy, approximately 97%, in handwritten number classification. Through an evaluation of power consumption in our proposed architecture, we find a remarkable ∼48 400× advantage in power efficiency compared to typical memristor systems. This marks the inaugural demonstration of a superconducting in-memory computing architecture through memristor coupling, offering a promising hardware platform for various AI systems with superior energy efficiency and computing capacity.

Deep learning‐based aggregate analysis to identify cut‐off points for decision‐making in pancreatic cancer detection

AbstractThis study addresses the problem of detecting pancreatic cancer by classifying computed tomography (CT) images into cancerous and non‐cancerous classes using the proposed deep learning‐based aggregate analysis framework. The application of deep learning, as a branch of machine learning and artificial intelligence, to specific medical challenges can lead to the early detection of diseases, thus accelerating the process towards timely and effective intervention. The concept of classification is to reasonably select an optimal cut‐off point, which is used as a threshold for evaluating the model results. The choice of this point is key to ensure efficient evaluation of the classification results, which directly affects the diagnostic accuracy. A significant aspect of this research is the incorporation of private CT images from Vilnius University Hospital Santaros Klinikos, combined with publicly available data sets. To investigate the capabilities of the deep learning‐based framework and to maximize pancreatic cancer diagnostic performance, experimental studies were carried out combining data from different sources. Classification accuracy metrics such as the Youden index, (0, 1)‐criterion, Matthew's correlation coefficient, the F1 score, LR+, LR−, balanced accuracy, and g‐mean were used to find the optimal cut‐off point in order to balance sensitivity and specificity. By carefully analyzing and comparing the obtained results, we aim to develop a reliable system that will not only improve the accuracy of pancreatic cancer detection but also have wider application in the early diagnosis of other malignancies.

Ultra-small gold nanoparticles embedded cyclodextrin metal-organic framework composite membrane to achieve antibacterial and humidity-responsive functions

Cyclodextrin metal-organic framework (CD-MOF) is an edible and porous material that can serve as a template for synthesizing small-sized metal nanoparticles. However, its highly hydrophilic nature has limited its wider application. Herein, ultra-small gold nanoparticles (U-AuNPs) were loaded into CD-MOF to produce a composite material Au@CD-MOF. The CD-MOF was utilized as a template to control the size of the AuNPs. The synthesized Au@CD-MOF was easily dispersible in aqueous medium and its released U-AuNPs exhibited effective water dispersion stability within 120 days. Additionally, compared to gold nanoparticles prepared using traditional methods (T-AuNPs), the U-AuNPs exhibited superior antibacterial properties. Furthermore, hydrophilic Au@CD-MOF was incorporated into a hydrophobic polydimethylsiloxane (PDMS) matrix (Au@CD-MOF/PDMS) to achieve a humidity-responsive antibacterial function. The composite membrane exhibited remarkable responsiveness to humidity, showing almost no release of U-AuNPs at 0 % humidity. However, it exhibited approximately 89 % release within 1 h, and complete release of U-AuNPs was observed within 4 h under 100 % humidity. These findings highlight the successful preparation of a humidity-responsive antibacterial composite membrane, which has great potential applications in various scenarios, particularly in the field of antibacterial food packaging.

Sustainable Approaches in Green Synthesis of Silica Nanoparticles Using Extracts of Chlorella and Its Application.

Silica nanoparticles, also known as SiO2 nanoparticles, have wider applications in biomedical, building, water treatment, agriculture, and food industries. It is used as an anticaking agent in the food industry, used to remove heavy metals from water, and used in cement-based materials. SiO2 nanoparticles synthesized by physical and chemical methods require high energy and use of toxic chemicals which is quite expensive, have a greater impact causing health-related issues, and have environmental side effects. Hence, there is a need to synthesize nanoparticles in an eco-friendly way. The biological or green synthesis method uses microbes, such as bacteria, fungi, algae, and plants for synthesizing nanoparticles. Algae contain natural biochemicals that act as reducing agents. These biomolecules are non-toxic as they are naturally occurring compounds and can be used to fabricate nanoparticles by avoiding the use of toxic chemicals in an eco-friendly method. In this study, silica nanoparticles were synthesized by green synthesis methods using microalgae extract. Further, the green synthesized silica nanoparticles were characterized using ultra violet-visible (UV-VIS) spectroscopy, Fourier transform-infrared (FT-IR) spectroscopy, X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), and energy-dispersive X-ray analysis (EDAX). The antimicrobial activity of the silica nanoparticles against E. coli was studied. This study revealed that the nanoparticles can be synthesized using green synthesis methods with low cost, less toxic chemicals, eco-friendly, and have antimicrobial activity against E. coli.

Development and initial validation of the cannabis-related psychosis risk literacy scale (CPRL): a multinational psychometric study.

Public education efforts to address and reduce potential harms from cannabis use in Arab countries are either slow or inexistent, and do not follow the steadily increasing trends of cannabis use in Arab youth. Several decades of research on substance use, it can be suggested that being aware of, and knowing about, psychosis risk related to cannabis can at least limit the consumption of the substance. Motivated by a lack of measures specifically designed to measure literacy about cannabis-related psychosis risk in younger populations, and based on an extensive literature review, we aimed to create and validate a new self-report scale to assess the construct, the Cannabis-related Psychosis Risk Literacy Scale (CPRL), in the Arabic language. A cross-sectional study was carried-out during the period from September 2022 to June 2023, enrolling 1855 university students (mean age of 23.26 ± 4.96, 75.6% females) from three Arab countries (Egypt, Kuwait and Tunisia). Starting from an initial pool of 20 items, both Exploratory Factor Analysis and Confirmatory Factor Analysis suggested that the remaining 8 items loaded into a single factor. The scale demonstrated good internal consistency, with both McDonald omega and Cronbach's alpha values exceeding 0.7 (omega = 0.85 / alpha = 0.85). The CPRL showed measurement invariance across gender and country at the configural, metric, and scalar levels. Concurrent validity of the CPRL was established by correlations with less favourable attitudes towards cannabis (r = -.14; p <.001). In addition, higher literacy levels were found in students who never used cannabis compared to lifetime users (4.18 ± 1.55 vs. 3.44 ± 1.20, t(1853) = 8.152, p <.001). The newly developed CPRL scale offers a valid and reliable instrument for assessing and better understanding literacy about cannabis-related psychosis risk among Arabic-speaking young adults. We believe that this new scale is suitable as a screening tool of literacy, as an instrument for measuring the effect of public education interventions aimed at promoting cannabis-related psychosis risk literacy among young people, and as a research tool to facilitate future studies on the topic with a wider application.

Control of mobil robots in fuzzy environment

In this work, the application of fuzzy hardware control to the dynamic movement of an autonomous two-wheeled mobile robot under uncertainty is considered the most relevant topic of the day. There are also adverse reactions. These led to an imprecise mathematical model for the two-wheeled robotic test rig. Therefore, the goal of developing a control system for a two-wheeled robot with a fuzzy logic control algorithm was set. The development of fuzzy logic control systems according to modern applications is very important to create intelligent robots that can be used in wider applications. In this work, a fuzzy control model for a non-linear two-wheeled robot system is designed, followed by a real-time implementation of the developed control in a robot device. Finally, the experimental results of the system were analyzed and discussed, and the closed-loop response of the fuzzy logic two-wheeled robot system with PID control was tested. The fuzzy control has a better response than the PID controller. A similar effect was observed for the normalized speed of the motors, as less energy was required to balance and stabilize the two-wheeled robot with fuzzy control. One of the main advantages of the method proposed in the article is to enable the autonomous two-wheeled mobile robot to operate without collision with obstacles and with high dynamic accuracy in a low-obstacle environment. As a result of the simulation, the effectiveness and reliability of the change avoidance behavior in uncertain environments and the speed control of the two-wheeled mobile robot platform of the proposed fuzzy control strategy are realized.

Small-strain folding of semi-rigid elastomer derives high-performance 3D-printable soft origami actuators

Elastomers with hyperelastic deformation bring prosperity to soft robotics, especially in constituting fluidic actuators, largely due to the merit of large deformation and airtightness. However, the large (typically 0.5–1.5 strain) in-plane stretching of such materials concurrent to motion generation inevitably causes energy loss, hinders force output and accuracy. Particularly, the high nonlinearity of the low-durometer (typically 10A to 30A Shore) hyperelastic elastomers makes the modeling and control of actuators a well-known challenge. In this work, we proposed an alternative approach of using semi-rigid elastomer of significantly larger durometer (70 A–100 A) to create the typical fluidic soft actuator with axial translation, by utilizing small-strain folding to generate motion. Deformation constraints and property programming are combined into a single-piece body, enabling easy fabrication by Selective Laser Sintering 3D-printing and post-treatment for origami patterned structure. Systematic analyses on the principles, modeling and design are presented. The long lifespan (over 1 million cycles), superior output linearity, high energy efficiency (more than 60% increase), and drastically improved force output (more than 98% increase) were validated experimentally, showing high potentials in enabling high-performance soft actuators that are easy to design, fabricate and drive, strong to use, and accurate to control, towards even wider applications.

NMR metabolomic modeling of age and lifespan: A multicohort analysis.

Metabolomic age models have been proposed for the study of biological aging, however, they have not been widely validated. We aimed to assess the performance of newly developed and existing nuclear magnetic resonance spectroscopy (NMR) metabolomic age models for prediction of chronological age (CA), mortality, and age-related disease. Ninety-eight metabolic variables were measured in blood from nine UK and Finnish cohort studies (N ≈31,000 individuals, age range 24-86 years). We used nonlinear and penalized regression to model CA and time to all-cause mortality. We examined associations of four new and two previously published metabolomic age models, with aging risk factors and phenotypes. Within the UK Biobank (N ≈102,000), we tested prediction of CA, incident disease (cardiovascular disease (CVD), type-2 diabetes mellitus, cancer, dementia, and chronic obstructive pulmonary disease), and all-cause mortality. Seven-fold cross-validated Pearson's r between metabolomic age models and CA ranged between 0.47 and 0.65 in the training cohort set (mean absolute error: 8-9 years). Metabolomic age models, adjusted for CA, were associated with C-reactive protein, and inversely associated with glomerular filtration rate. Positively associated risk factors included obesity, diabetes, smoking, and physical inactivity. In UK Biobank, correlations of metabolomic age with CA were modest (r = 0.29-0.33), yet all metabolomic model scores predicted mortality (hazard ratios of 1.01 to 1.06/metabolomic age year) and CVD, after adjustment for CA. While metabolomic age models were only moderately associated with CA in an independent population, they provided additional prediction of morbidity and mortality over CA itself, suggesting their wider applicability.

Assessing the risk and effect of climate change with two-layer fuzzy logic-SWARA: A comparative practice in Turkiye

Many factors such as population growth, development of industry/technology, and increase in production-consumption disrupt the ecological balance and cause climate change, which is a global problem. Determining the criteria that cause climate change is very important in finding effective solutions to the problem. In the study, the criteria were determined, weighted with a new method, Step-wise Weight Assessment Ratio Analysis (SWARA), and ranked according to their priorities with two-layer fuzzy logic model. The Fuzzy SWARA method allows the evaluation process, which becomes complicated due to the difficulties and factors experienced in decision-making, to be carried out more effectively and realistically. The risk and effect of climate change in Turkiye were evaluated regionally. However, the developed model also has a wide application area. Research findings revealed that the highest risk/effect of climate change have the Marmara and Central Anatolia regions. The lowest risk region is the Eastern Anatolia. Air pollution, population growth and deforestation have the highest weights. Important suggestions have presented especially for priority criteria. In this way, the factors that should be prioritized in climate change environmental problem solutions have been revealed and will make it easier for researchers and managers to provide more effective management.

Comprehensive Assessment and Analysis of the Current Global Aerosol Optical Depth Products

Aerosol Optical Depth (AOD) is one of the most important optical properties of aerosols that may affect the energy budgets of our Earth–atmosphere system significantly. Currently, while regional and even global AOD knowledge has been given by various satellites or models, these products are still fraught with uncertainties. In this study, one sophisticated satellite-derived AOD product from MODIS (MODerate resolution Imaging Spectral-radiometer) and two state-of-the-art model-based AOD products from CAMS (Copernicus Atmosphere Monitoring Service) and MERRA-2 (Modern-Era Retrospective analysis for Research and Application Version 2), based on AERONET measurements from 2000–2022, analyzed the spatial distribution characteristics of global AOD. Then using the Mann-Kendall (MK) trend test, the AOD changing trends revealed by the three products were also computed and analyzed. The accuracies of these products and the reliabilities of changing trends derived are discussed and concluded finally. Our study demonstrates that MODIS products have wider applicability, matching best with AERONET globally, while CAMS and MERRA-2 products are only reliable in North America, South America, and Europe. Through comparative analysis of the AOD trends, we found that MODIS, CAMS, and MERRA-2 AOD consistently exhibited decreasing trends in eastern Asia, Europe, and eastern North America. On the other hand, different products showed increasing trends in regions like West Asia, South Asia, and South Africa, suggesting their limited reliability. The reliability assessment shows that 41.45% of the areas have consistent trends among the three products, with approximately 3.2% showing significant and consistent results. When using site trend validation, the proportions of sites with consistent trends are highest at 64.56% and 46.84% respectively. The regions with the best reliability of global trend changes are mainly distributed in North America, Europe, Australia, eastern Asia, and Central South America. This study provides new insights for validating aerosol changes using remote sensing and has the potential to enhance future monitoring and evaluation methods of aerosol products.

Nonlinear fatigue life prediction model based on isodamage curve considering two‐block loading

AbstractA non‐linear life prediction model considering the loading history is proposed based on isodamage curve. The stress‐controlled low‐high block loading of low cycle fatigue (LCF) at high temperature, with various previous cycles, was performed on a nickel‐based superalloy. The damage evolvement of the prior loading was revealed using scanning electron microscopy under the low‐high block loading, especially the failure mechanism of coaxing effect. In addition, load history correlation factor was introduced to describe the influence of prior cycle. Based on the Ni‐based superalloy, the life prediction model of two‐step loading, including the low‐high block (LH) and high‐low block loading (HL), was proposed, agreeing well with the experimental results for different metals. Compared with other life prediction models, the proposed model demonstrated the higher prediction accuracy and wider applicability.

Improving mechanical properties of hemihydrate phosphogypsum via alkali-activated mineral admixtures

The low strength of hemihydrate phosphogypsum (HPG) limits its wider applications in construction, and the use of alkali-activated mineral admixtures to improve HPG's mechanical properties has enormous environmental benefits. In this paper, slag and fly ash were added to HPG, which was then activated by NaOH. The effects of alkali activation on the mechanical properties of HPG were investigated, and the hydration mechanism of modified and enhanced HPG was investigated by X-ray diffractometer (XRD), Thermogravimetric analysis (TGA), Fourier-transform infrared (FTIR), and Low-field nuclear magnetic resonance (LNMR). The results showed that alkali-activated mineral admixtures can improve the compressive strength of HPG up to 40 MPa with the optimal mix ratio: HPG:(GGBS + FA) = 60:40 by weight, where the ratio of GGBS to FA is 4:1 by weight, and NaOH accounts for 1% by mass of the binder. When NaOH was added at 1 wt% of binder, it could effectively activate slag and fly ash to enhance the mechanical properties of HPG. With the increase of the dosage of NaOH, settings of HPG were prolonged and its strength was reduced. The hydration products such as ettringite (AFt) and C–S–H gel generated by NaOH-activated slag and fly ash can effectively fill the pores of HPG paste and thus improve its mechanical properties. When hydrated for 28d, the decrease in strength due to the increase in NaOH dosage can be attributed to the decrease in AFt generated from hydration. The findings of this study pave the theoretical and technical foundations for greater and wider utilization of phosphogypsum for a sustainable future.

Sensing at the Nanoscale Using Nitrogen-Vacancy Centers in Diamond: A Model for a Quantum Pressure Sensor.

The sensing of stress under harsh environmental conditions with high resolution has critical importance for a range of applications including earth's subsurface scanning, geological CO2 storage monitoring, and mineral and resource recovery. Using a first-principles density functional theory (DFT) approach combined with the theoretical modelling of the low-energy Hamiltonian, here, we investigate a novel approach to detect unprecedented levels of pressure by taking advantage of the solid-state electronic spin of nitrogen-vacancy (NV) centers in diamond. We computationally explore the effect of strain on the defect band edges and band gaps by varying the lattice parameters of a diamond supercell hosting a single NV center. A low-energy Hamiltonian is developed that includes the effect of stress on the energy level of a ±1 spin manifold at the ground state. By quantifying the energy level shift and split, we predict pressure sensing of up to 0.3 MPa/Hz using the experimentally measured spin dephasing time. We show the superiority of the quantum sensing approach over traditional optical sensing techniques by discussing our results from DFT and theoretical modelling for the frequency shift per unit pressure. Importantly, we propose a quantum manometer that could be useful to measure earth's subsurface vibrations as well as for pressure detection and monitoring in high-temperature superconductivity studies and in material sciences. Our results open avenues for the development of a sensing technology with high sensitivity and resolution under extreme pressure limits that potentially has a wider applicability than the existing pressure sensing technologies.

Hybrid impulses for almost sure quasi-synchronization of stochastic complex networks: an indefinite Lyapunov function method

. This article studies quasi-synchronization of stochastic complex networks under hybrid impulses. With parameter mismatches, different from the previous quasi-synchronization results in the sense of mean square on stochastic complex networks, almost sure quasi-synchronization is investigated, in which noises have a positive effect on the synchronization. By utilizing the Lyapunov method, stochastic analysis theory, average impulsive interval, and average impulsive gain, we formulate the almost sure quasi-synchronization criteria when the average impulsive interval z 0 satisfies z 0 < + ∞ and z 0 = + ∞ , respectively, where the synchronizing impulses and the desynchronizing impulses are simultaneously considered. It is worth noticing that in our criteria of quasi-synchronization, the mutual restraints among the average impulsive interval, average impulsive gain, and noise intensity are given. Moreover, for the criteria in this article, the piecewise continuous scalar functions cannot only replace the constant coefficient of the upper bound estimation for the diffusion operator of a Lyapunov function in available literature but also even be unbounded, which has wider applications than some existing works. Our corollaries regarding complete synchronization and analyses of the synchronization for stochastic complex networks with aperiodic intermittent noises provide sufficient conditions in closed form. Finally, the theoretical results are applied to a coupled chaotic Lorentz system, and several numerical examples are presented to demonstrate the advantages of the theoretical results.

Machine Learning with High-Cardinality Categorical Features in Actuarial Applications

AbstractHigh-cardinality categorical features are pervasive in actuarial data (e.g., occupation in commercial property insurance). Standard categorical encoding methods like one-hot encoding are inadequate in these settings.In this work, we present a novel Generalised Linear Mixed Model Neural Network (“GLMMNet”) approach to the modelling of high-cardinality categorical features. The GLMMNet integrates a generalised linear mixed model in a deep learning framework, offering the predictive power of neural networks and the transparency of random effects estimates, the latter of which cannot be obtained from the entity embedding models. Further, its flexibility to deal with any distribution in the exponential dispersion (ED) family makes it widely applicable to many actuarial contexts and beyond. In order to facilitate the application of GLMMNet to large datasets, we use variational inference to estimate its parameters—both traditional mean field and versions utilising textual information underlying the high-cardinality categorical features.We illustrate and compare the GLMMNet against existing approaches in a range of simulation experiments as well as in a real-life insurance case study. A notable feature for both our simulation experiment and the real-life case study is a comparatively low signal-to-noise ratio, which is a feature common in actuarial applications. We find that the GLMMNet often outperforms or at least performs comparably with an entity-embedded neural network in these settings, while providing the additional benefit of transparency, which is particularly valuable in practical applications.Importantly, while our model was motivated by actuarial applications, it can have wider applicability. The GLMMNet would suit any applications that involve high-cardinality categorical variables and where the response cannot be sufficiently modelled by a Gaussian distribution, especially where the inherent noisiness of the data is relatively high.