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In the pursuit of scalable and energy-efficient neuromorphic devices, recent research has unveiled a novel category of spiking oscillators, termed thermal neuristors. These devices function via thermal interactions among neighboring vanadium dioxide resistive memories, emulating biological neuronal behavior. Here, we show that the collective dynamical behavior of networks of these neurons showcases a rich phase structure, tunable by adjusting the thermal coupling and input voltage. Notably, we identify phases exhibiting long-range order that, however, does not arise from criticality, but rather from the time non-local response of the system. In addition, we show that these thermal neuristor arrays achieve high accuracy in image recognition and time series prediction through reservoir computing, without leveraging long-range order. Our findings highlight a crucial aspect of neuromorphic computing with possible implications on the functioning of the brain: criticality may not be necessary for the efficient performance of neuromorphic systems in certain computational tasks.

CMOS-RRAM integration holds great promise for low energy and high throughput neuromorphic computing. However, most RRAM technologies relying on filamentary switching suffer from variations and noise, leading to computational accuracy loss, increased energy consumption, and overhead by expensive program and verify schemes. We developed a filament-free, bulk switching RRAM technology to address these challenges. We systematically engineered a trilayer metal-oxide stack and investigated the switching characteristics of RRAM with varying thicknesses and oxygen vacancy distributions to achieve reliable bulk switching without any filament formation. We demonstrated bulk switching at megaohm regime with high current nonlinearity, up to 100 levels without compliance current. We developed a neuromorphic compute-in-memory platform and showcased edge computing by implementing a spiking neural network for an autonomous navigation/racing task. Our work addresses challenges posed by existing RRAM technologies and paves the way for neuromorphic computing at the edge under strict size, weight, and power constraints.

Abstract In this study, thermoplastic starch (TPS) biofilms were developed using starch isolated from the seeds of Melicoccus bijugatus (huaya) and reinforced with bentonite clays at concentrations of 1%, 3%, and 5% by weight. Novelty of this research lies in utilizing a non-conventional starch source and enhancing properties of TPS through clay reinforcement. FTIR analysis verified bentonite’s nature of clays, while SEM analysis provided insights into morphology and agglomeration behavior. Key findings include a notable increase in biofilm thickness and elastic modulus with higher clay content. Specifically, tensile strength of biofilms improved from 2.5 MPa for pure TPS to 5.0 MPa with 5% clay reinforcement. The elastic modulus increased from 25 MPa (TPS) to 60 MPa (5% clay). Thermal stability also showed enhancement, with initial degradation temperature increasing from 110 °C for pure TPS to 130 °C for TPS with 5% clay. Water vapor permeability (WVP) tests demonstrated a decrease in WVP values from 4.11 × 10−10 g m−1 s−1 Pa−1 for pure TPS to 2.09 × 10−10 g m−1 s−1·Pa−1 for TPS with 5% clay, indicating a significant barrier effect due to clay dispersion. These results suggest that biofilms based on huaya starch and reinforced with bentonite clay have considerable potential for sustainable food packaging applications, offering enhanced mechanical and barrier properties.

Abstract In the pursuit of scalable and energy-efficient neuromorphic devices, recent research has unveiled a novel category of spiking oscillators, termed “thermal neuristors.” These devices function via thermal interactions among neighboring vanadium dioxide resistive memories, emulating biological neuronal behavior. Here, we show that the collective dynamical behavior of networks of these neurons showcases a rich phase structure, tunable by adjusting the thermal coupling and input voltage. Notably, we identify phases exhibiting long-range order that, however, does not arise from criticality, but rather from the time non-local response of the system. In addition, we show that these thermal neuristor arrays achieve high accuracy in image recognition and time series prediction through reservoir computing, without leveraging long-range order. Our findings highlight a crucial aspect of neuromorphic computing with possible implications on the functioning of the brain: criticality may not be necessary for the efficient performance of neuromorphic systems in certain computational tasks.

Abstract Background Medical school learning environment (MSLE) has a holistic impact on students’ psychosomatic health, academic achievements, and personal development. Students in different grades perceive MSLE in different ways. Thus, it is essential to investigate the specific role of student’s grade in the perception of MSLE. Methods Using the Johns Hopkins Learning Environment Scale (JHLES) as a quantification instrument for the perception level of MSLE, 10,901 medical students in 12 universities in China were categorized into low or high JHLES group according to their questionnaires. We investigated the relationship between student’s grade and JHLES category by univariate analysis employing Pearson Chi-square test and Welch’s ANOVA. Then multivariable logistic regression analysis confirmed the predictive efficacy of student’s grade. A nomogram concerning the prediction of low JHLES score probability in medical students was also constructed. Results A significant difference between two JHLES categories among students in different grades was observed (p

This work reports that synchronization of Mott material-based nanoscale coupled spiking oscillators can be drastically different from that in conventional harmonic oscillators. We investigated the synchronization of spiking nanooscillators mediated by thermal interactions due to the close physical proximity of the devices. Controlling the driving voltage enables in-phase 1:1 and 2:1 integer synchronization modes between neighboring oscillators. Transition between these two integer modes occurs through an unusual stochastic synchronization regime instead of the loss of spiking coherence. In the stochastic synchronization regime, random length spiking sequences belonging to the 1:1 and 2:1 integer modes are intermixed. The occurrence of this stochasticity is an important factor that must be taken into account in the design of large-scale spiking networks for hardware-level implementation of novel computational paradigms such as neuromorphic and stochastic computing.

Abstract In the construction of tunnels under existing stations, it is necessary to control their settlement. When there is a pile foundation in the existing station, the pile cutting has a significant impact on the settlement of the existing station. To determine the influence of existing piles on the settlement of subway stations, a reasonable pile-cutting time is proposed. Based on the Chengdu Metro Line 9 underpassing the existing Line 1 hatchery station, the settlement law of the tunnel underpassing the existing Line 1 station is analysed via a numerical simulation. Furthermore, the deformation and stress characteristics of the existing piles, pipe roofs, and tunnel linings and the supporting effect on the existing station are discussed. It is concluded that the cutting of existing piles causes a change in the tunnel bearing system, thus resulting in a certain deformation of the station. The influence of different pile cutting times on the settlement of the existing station is then analysed, and it is clarified that the tunnel support stiffness is significantly enhanced after the construction of the secondary lining. At this time, the settlement of the existing pile station is significantly reduced. Finally, through a field investigation, the effect of surface grouting, pipe shed, and multilayer lining on the settlement control of the existing station while the existing pile foundation exists is determined. This research can provide a reference for the settlement control and foundation underpinning of existing stations at ultra-small distances in underground excavation tunnels.

Abstract Background Fostering empathy has been continuously emphasized in the global medical education. Empathy is crucial to enhance patient-physician relationships, and is associated with medical students’ academic and clinical performance. However, empathy level of medical students in China and related influencing factors are not clear. Methods This was a cross-sectional study among medical students in 11 universities. We used the Jefferson Scale of Empathy Student-version of Chinese version to measure empathy level of medical students. Factors associated with empathy were identified by the univariate and multivariate logistic regression analyses. Based on the variables identified above, the nomogram was established to predict high empathy probability of medical students. Receiver operating characteristic curve, calibration plot and decision curve analysis were used to evaluate the discrimination, calibration and educational utility of the model. Results We received 10,901 samples, but a total of 10,576 samples could be used for further analysis (effective response rate of 97.02%). The mean empathy score of undergraduate medical students was 67.38 (standard deviation = 9.39). Six variables including gender, university category, only child or not, self-perception doctor-patient relationship in hospitals, interest of medicine, Kolb learning style showed statistical significance with empathy of medical students (P

Bitterness is a key factor that affects the consumption of quinoa products, even if they are nutritious. In this study, a non-targeted metabolomics approach based on UHPLC-Orbitrap-MS was applied to comprehensively profile the characteristic metabolites of twenty-two quinoas. A total of twenty key metabolites were identified correlated with bitterness, among which, fifteen were triterpenoid saponins. In addition, these metabolites bind to the active site of the human bitter taste receptor and are the main compounds that produce the bitter taste of quinoa. Our results contribute to a deeper understanding of the origin of quinoa bitterness and provide directions for optimizing its flavor to improve market acceptance.

Abstract CMOS-RRAM integration holds great promise for low energy and high throughput neuromorphic computing. However, most RRAM technologies relying on filamentary switching suffer from variations and noise, leading to computational accuracy loss, increased energy consumption, and overhead by expensive program and verify schemes. We developed a filament-free, bulk switching RRAM technology to address these challenges. We systematically engineered a trilayer metal-oxide stack and investigated the switching characteristics of RRAM with varying thicknesses and oxygen vacancy distributions to achieve reliable bulk switching without any filament formation. We demonstrated bulk switching at megaohm regime with high current nonlinearity, up to 100 levels without compliance current. We developed a neuromorphic compute-in-memory platform and showcased edge computing by implementing a spiking neural network for an autonomous navigation/racing task. Our work addresses challenges posed by existing RRAM technologies and paves the way for neuromorphic computing at the edge under strict size, weight, and power constraints.

Abstract The geometry of 4-string contact interaction of closed string field theory is characterized using machine learning. We obtain Strebel quadratic differentials on 4-punctured spheres as a neural network by performing unsupervised learning with a custom-built loss function. This allows us to solve for local coordinates and compute their associated mapping radii numerically. We also train a neural network distinguishing vertex from Feynman region. As a check, 4-tachyon contact term in the tachyon potential is computed and a good agreement with the results in the literature is observed. We argue that our algorithm is manifestly independent of number of punctures and scaling it to characterize the geometry of n-string contact interaction is feasible.

The hardness of ‘Rio Red’ grapefruits treated postharvest with carboxymethyl chitosan (CMCS), Cryptococcus laurentii or C. laurentii induced by CMCS was measured during storage. The results showed that compared with the control group (treatment with sterile water), CMCS-C. laurentii treatment significantly maintained the firmness of grapefruits during storage (P < 0.05). Moreover, fruit mass loss percentage and the polysaccharide content and polysaccharide hydrolase activity of the cell wall were measured and cell wall microstructure was observed during storage for 75 days. The results indicated that CMCS-C. laurentii treatment was effective in maintaining the contents of sodium carbonate soluble pectin (SCSP) and 24% KOH-soluble fraction (24KSF) and reducing the contents of water-soluble pectin (WSP), ionic-soluble pectin (ISP) and 4% KOH-soluble fraction (4KSF) in the fruit. Compared with the control group, the activities of the cell wall-degrading enzymes pectin methylesterase (PE), polygalacturonase (PG), cellulase (CX), α-L-arabinofuranosidase (α-L-Af) and β-galactosidase (β-Gal) were significantly inhibited by CMCS-C. laurentii treatment. In the middle and later stages of storage, the cell wall structure of the fruit treated with CMCS-C. laurentii was more stable and the intracellular structure was relatively intact. In conclusion, CMCS-C. laurentii treatment can slow down the degradation of organelles and cell walls in postharvest grapefruits, maintain cell integrity better and delay fruit softening.

BackgroundStudy engagement is regarded important to medical students’ physical and mental wellbeing. However, the relationship between learning environment of medical schools and the study engagement of medical students was still unclear. This study was aimed to ascertain the positive effect of learning environment in study engagement.MethodsWe collected 10,901 valid questionnaires from 12 medical universities in China, and UWES-S was utilized to assess the study engagement levels. Then Pearson Chi-Square test and Welch’s ANOVA test were conducted to find the relationship between study engagement and learning environment, and subgroup analysis was used to eradicate possible influence of confounding factors. After that, a multivariate analysis was performed to prove learning environment was an independent factor, and we constructed a nomogram as a predictive model.ResultsWith Pearson Chi-Square test (p

Abstract We compute the gravitational action of a free massive Majorana fermion coupled to two-dimensional gravity on compact Riemann surfaces of arbitrary genus. The structure is similar to the case of the massive scalar. The small-mass expansion of the gravitational yields the Liouville action at zeroth order, and we can identify the Mabuchi action at first order. While the massive Majorana action is a conformal deformation of the massless Majorana CFT, we find an action different from the one given by the David-Distler-Kawai (DDK) ansatz.

Abstract Background Studies exploring influencing factors of emotional engagement among medical students are scarce. Thus, we aimed to identify influencing factors of medical students’ emotional engagement. Methods We carried out a multi-center cross-sectional study among 10,901 medical students from 11 universities in China. The Chinese version of Utrecht Work Engagement Scale-Student version (UWES-S) was used to evaluate emotional engagement level of medical students. The predictors related to engagement level were determined by the logistic regression analysis. Furthermore, we constructed a nomogram to predict emotional engagement level of medical students. Results A total of 10,576 sample were included in this study. The mean emotional engagement score was 74.61(± 16.21). In the multivariate logistic regression model, we found that males showed higher engagement level compared with females [odds ratio (OR) (95% confidence interval (CI)): 1.263 (1.147, 1.392), P

Witten's open string field theory with a generalized version of stubs is reformulated as a cubic string field theory using an auxiliary string field. The gauge symmetries and equations of motion as well as the associative algebra of the resulting theory are investigated. Integrating out either the original or auxiliary field is shown to recover the conventional cubic theory. Our analysis demonstrates that deformations due to the stubs can be described as a homotopy transfer purely in the context of strong deformation retract. We also discuss to what extent the vertex regions resulting from stubs provide a model for the elementary interactions of closed string field theory.

Aim: The position of the body during surgery may affect the patient’s body functions, especially the hemodynamic parameters. We aimed to comparatively analyze the effects of lithotomy and prone position on respiratory mechanics, arterial oxygenation, and hemodynamic parameters in patients who underwent percutaneous nephrolithotomy (PNL). Materials and methods: The study included 40 patients aged 16-63 years who underwent kidney stone surgery. The patients had no history of diabetes or cardiopulmonary disease and had an American Society of Anesthesiology (ASA) score of I–II. The pH, partial arterial oxygen pressure, partial arterial carbon dioxide pressure, HCO3, arterial oxygen saturation, end-tidal carbon dioxide (EtCO2), alveolar oxygen partial pressure, dead space volume/tidal volume ratio, P(A-a)O2, peak inspiratory pressure (PIP), inspiratory plateau airway pressure (PPlt), systolic arterial pressure, diastolic arterial pressure, mean arterial pressure, and heart rate (HR) values were assessed simultaneously throughout the surgery and comparatively analyzed both for lithotomy and prone positions. Results: There was a significant difference between lithotomy and prone positions with regard to pH and HCO3 values, which are among the arterial blood gas parameters measured at 20 minutes (p