A new method for the rapid identification of external water types in rainwater pipeline networks using UV-Vis absorption spectroscopy.

Ultraviolet-visible (UV-Vis) absorption spectroscopy, due to its high sensitivity and capability for real-time online monitoring, is one of the most promising tools for the rapid identification of external water in rainwater pipe networks. However, difficulties in obtaining actual samples lead to insufficient real samples, and the complex composition of wastewater can affect the accurate traceability analysis of external water in rainwater pipe networks. In this study, a new method for identifying external water in rainwater pipe networks with a small number of samples is proposed. In this method, the Generative Adversarial Network (GAN) algorithm was initially used to generate spectral data from the absorption spectra of water samples; subsequently, the multiplicative scatter correction (MSC) algorithm was applied to process the UV-Vis absorption spectra of different types of water samples; following this, the Variational Mode Decomposition (VMD) algorithm was employed to decompose and recombine the spectra after MSC; and finally, the long short-term memory (LSTM) algorithm was used to establish the identification model between the recombined spectra and the water source types, and to determine the optimal number of decomposed spectra K. The research results show that when the number of decomposed spectra K is 5, the identification accuracy for different sources of domestic sewage, surface water, and industrial wastewater is the highest, with an overall accuracy of 98.81%. Additionally, the performance of this method was validated by mixed water samples (combinations of rainwater and domestic sewage, rainwater and surface water, and rainwater and industrial wastewater). The results indicate that the accuracy of the proposed method in identifying the source of external water in rainwater reaches 98.99%, with detection time within 10 s. Therefore, the proposed method can become a potential approach for rapid identification and traceability analysis of external water in rainwater pipe networks.

Cross Potential Selection: A Proposal for Optimizing Crossing Combinations in Recurrent Selection Using the Usefulness Criterion of Future Inbred Lines.

In plant breeding programs, rapid production of novel varieties is highly desirable. Genomic selection allows the selection of superior individuals based on genomic estimated breeding values. However, it is worth noting that superior individuals may not always be superior parents. The choice of the crossing pair significantly influences the genotypic value of the resulting progeny. This study has introduced a new crossing strategy, termed cross potential selection (CPS), designed to expedite the production of novel varieties of inbred crops. CPS integrates fast recurrent selection and usefulness criterion to generate novel varieties. It considers the segregation of each crossing pair and computes the expected genotypic values of the top-performing individuals, assuming that the progeny distribution of genotypic values follows a normal distribution. It does not consider genetic diversity and focuses only on producing a novel variety as soon as possible. We simulated a 30-year breeding program in two scenarios, low heritability (h2 = 0.3) and high heritability (h2 = 0.6), to compare CPS with two other selection strategies. CPS consistently demonstrated the highest genetic gains among the three strategies in early cycles. In the 3rd year of the breeding program with a high heritability (h2 = 0.6), CPS exhibited the highest genetic gains, 138 times that of 300 independent breeding simulations. Regarding long-term improvement, the other selection strategies outperformed CPS. Nevertheless, compared with the other two strategies, CPS achieved significant short-term genetic improvements. CPS is a suitable breeding strategy for the rapid production of varieties within limited time and cost.

Developing effective optimized machine learning approaches for settlement prediction of shallow foundation.

The precise assessment of shallow foundation settlement on cohesionless soils is a challenging geotechnical issue, primarily due to the significant uncertainties related to the factors influencing the settlement. This study aims to create an advanced hybrid machine learning methodology for accurately estimating shallow foundations' settlement (Sm). The initial contribution of the current research is developing and validating a robust hybrid optimization methodology based on an artificial electric field and single candidate optimizer (AEFSCO). This approach is thoroughly tested using various benchmark functions. AEFSCO will also be used to optimize three useful machine learning methods: long short-term memory (LSTM), support vector regression (SVR), and multilayer perceptron neural network (MLPNN) by adjusting their hyperparameters for predicting the settlement of shallow foundations. A database consisting of 189 individual case histories, conducted through various investigations, was used for training and testing the models. The database includes five input parameters and one output. These factors encompassed both the geometric characteristics of the foundation and the properties of the sandy soil. The results demonstrate that employing effective optimization strategies to adjust the ML models' hyperparameters can significantly improve the accuracy of predicted results. The AEFSCO has increased the coefficient of determination (R2) value of the MLPNN model by 9.3 %, the SVR model by 8 %, and the LSTM model by 22 %. Also, the LSTM-AEFSCO model is more accurate than the SVR-AEFSCO and MLPNN-AEFSCO models. This is shown by the fact that R2 went from 0.9494 to 0.9290 to 0.9903, which is an increase of 4.5 % and 6 %.

Validity of urinary catheter specimens for diagnosis of urinary tract infection in patients with short-term catheterization.

Current guidelines recommend urine culture after catheter replacement to diagnose catheter-associated urinary tract infections (CA-UTI) in patients with long-term catheters, but it's unclear if this applies to short-term catheterizations. We studied 52 patients with catheters for less than 28 days, showing symptoms of CA-UTI. We collected urine from the catheter port initially and from the new catheter within 2 hours of replacement. Positive culture rates were 36.5 % before and 28.8 % after replacement. Significant differences in urine culture results were observed in 32.7 % of cases postreplacement (P = .0184), increasing to 78.9 % after excluding negative pre-replacement cultures (P = 0.0003). Duration of catheterization didn't affect urine bacteriology changes post-replacement. This suggests that urine bacteriology often differs after catheter replacement in short-term catheterizations.

Pump-driven clinical infusions: laboratory comparison of pump types, fluid composition and flow rates on model drug delivery applying a new quantitative tool, the pharmacokinetic coefficient of short-term variation (PK-CV).

Critically ill or anesthetized patients commonly receive pump-driven intravenous infusions of potent, fast-acting, short half-life medications for managing hemodynamics. Stepwise dosing, e.g. over 3-5 min, adjusts physiologic responses. Flow rates range from < 0.1 to > 30 ml/h, depending on pump type (large volume, syringe) and drug concentration. Most drugs are formulated in aqueous solutions. Hydrophobic drugs are formulated as lipid emulsions. Do the physical and chemical properties of emulsions impact delivery compared to aqueous solutions? Does stepwise dose titration by the pump correlate with predicted plasma concentrations? Precise, gravimetric, flow rate measurement compared delivery of a 20% lipid emulsion (LE) and 0.9% saline (NS) using different pump types and flow rates. We measured stepwise delivery and then computed predicted plasma concentrations following stepwise dose titration. We measured the pharmacokinetic coefficient of short-term variation, (PK-CV), to assess pump performance. LE and NS had similar mean flow rates in stepwise rate increments and decrements between 0.5 and 32 ml/h and continuous flows 0.5 and 5 ml/h. Pharmacokinetic computation predictions suggest delayed achievement of intended plasma levels following dose titrations. Syringe pumps exhibited smaller variations in PK-CV than large volume pumps. Pump-driven deliveries of lipid emulsion and aqueous solution behave similarly. At low flow rates we observed large flow rate variability differences between pump types showing they may not be interchangeable. PK-CV analysis provides a quantitative tool to assess infusion pump performance. Drug plasma concentrations may lag behind intent of pump dose titration.

MFAM-AD: an anomaly detection model for multivariate time series using attention mechanism to fuse multi-scale features.

Multivariate time series anomaly detection has garnered significant attention in fields such as IT operations, finance, medicine, and industry. However, a key challenge lies in the fact that anomaly patterns often exhibit multi-scale temporal variations, which existing detection models often fail to capture effectively. This limitation significantly impacts detection accuracy. To address this issue, we propose the MFAM-AD model, which combines the strengths of convolutional neural networks (CNNs) and bi-directional long short-term memory (Bi-LSTM). The MFAM-AD model is designed to enhance anomaly detection accuracy by seamlessly integrating temporal dependencies and multi-scale spatial features. Specifically, it utilizes parallel convolutional layers to extract features across different scales, employing an attention mechanism for optimal feature fusion. Additionally, Bi-LSTM is leveraged to capture time-dependent information, reconstruct the time series and enable accurate anomaly detection based on reconstruction errors. In contrast to existing algorithms that struggle with inadequate feature fusion or are confined to single-scale feature analysis, MFAM-AD effectively addresses the unique challenges of multivariate time series anomaly detection. Experimental results on five publicly available datasets demonstrate the superiority of the proposed model. Specifically, on the datasets SMAP, MSL, and SMD1-1, our MFAM-AD model has the second-highest F1 score after the current state-of-the-art DCdetector model. On the datasets NIPS-TS-SWAN and NIPS-TS-GECCO, the F1 scores of MAFM-AD are 0.046 (6.2%) and 0.09 (21.3%) higher than those of DCdetector, respectively(the value ranges from 0 to 1). These findings validate the MFAMAD model's efficacy in multivariate time series anomaly detection, highlighting its potential in various real-world applications.

Key factors in epidemiological exposure and insights for environmental management: Evidence from meta-analysis.

In recent years, the precision of exposure assessment methods has been rapidly improved and more widely adopted in epidemiological studies. However, such methodological advancement has introduced additional heterogeneity among studies. The precision of exposure assessment has become a potential confounding factors in meta-analyses, whose impacts on effect calculation remain unclear. To explore, we conducted a meta-analysis to integrate the long- and short-term exposure effects of PM2.5, NO2, and O3 on all-cause, cardiovascular, and respiratory mortality in the Chinese population. Literature was identified through Web of Science, PubMed, Scopus, and China National Knowledge Infrastructure before August 28, 2023. Sub-group analyses were performed to quantify the impact of exposure assessment precisions and pollution levels on the estimated risk. Studies achieving merely city-level resolution and population exposure are classified as using traditional assessment methods, while those achieving sub-kilometer simulations and individual exposure are considered finer assessment methods. Using finer assessment methods, the RR (under 10 µg/m3 increment, with 95% confidence intervals) for long-term NO2 exposure to all-cause mortality was 1.13 (1.05-1.23), significantly higher (p-value = 0.01) than the traditional assessment result of 1.02 (1.00-1.03). Similar trends were observed for long-term PM2.5 and short-term NO2 exposure. A decrease in short-term PM2.5 levels led to an increase in the RR for all-cause and cardiovascular mortality, from 1.0035 (1.0016-1.0053) and 1.0051 (1.0021-1.0081) to 1.0055 (1.0035-1.0075) and 1.0086 (1.0061-1.0111), with weak between-group significance (p-value = 0.13 and 0.09), respectively. Based on the quantitative analysis and literature information, we summarized four key factors influencing exposure assessment precision under a conceptualized framework: pollution simulation resolution, subject granularity, micro-environment classification, and pollution levels. Our meta-analysis highlighted the urgency to improve pollution simulation resolution, and we provide insights for researchers, policy-makers and the public. By integrating the most up-to-date epidemiological research, our study has the potential to provide systematic evidence and motivation for environmental management.

GEPAF: A non-monotonic generalized activation function in neural network for improving prediction with diverse data distributions characteristics.

The world today has made prescriptive analytics that uses data-driven insights to guide future actions. The distribution of data, however, differs depending on the scenario, making it difficult to interpret and comprehend the data efficiently. Different neural network models are used to solve this, taking inspiration from the complex network architecture in the human brain. The activation function is crucial in introducing non-linearity to process data gradients effectively. Although popular activation functions such as ReLU, Sigmoid, Swish, and Tanh have advantages and disadvantages, they may struggle to adapt to diverse data characteristics. A generalized activation function named the Generalized Exponential Parametric Activation Function (GEPAF) is proposed to address this issue. This function consists of three parameters expressed: α, which stands for a differencing factor similar to the mean; σ, which stands for a variance to control distribution spread; and p, which is a power factor that improves flexibility; all these parameters are present in the exponent. When p=2, the activation function resembles a Gaussian function. Initially, this paper describes the mathematical derivation and validation of the properties of this function mathematically and graphically. After this, the GEPAF function is practically implemented in real-world supply chain datasets. One dataset features a small sample size but exhibits high variance, while the other shows significant variance with a moderate amount of data. An LSTM network processes the dataset for sales and profit prediction. The suggested function performs better than popular activation functions when a comparative analysis of the activation function is performed, showing at least 30% improvement in regression evaluation metrics and better loss decay characteristics.

Comparative study of ten machine learning algorithms for short-term forecasting in gas warning systems.

This research aims to explore more efficient machine learning (ML) algorithms with better performance for short-term forecasting. Up-to-date literature shows a lack of research on selecting practical ML algorithms for short-term forecasting in real-time industrial applications. This research uses a quantitative and qualitative mixed method combining two rounds of literature reviews, a case study, and a comparative analysis. Ten widely used ML algorithms are selected to conduct a comparative study of gas warning systems in a case study mine. We propose a new assessment visualization tool: a 2D space-based quadrant diagram can be used to visually map prediction error assessment and predictive performance assessment for tested algorithms. Overall, this visualization tool indicates that LR, RF, and SVM are more efficient ML algorithms with overall prediction performance for short-term forecasting. This research indicates ten tested algorithms can be visually mapped onto optimal (LR, RF, and SVM), efficient (ARIMA), suboptimal (BP-SOG, KNN, and Perceptron), and inefficient algorithms (RNN, BP_Resilient, and LSTM). The case study finds results that differ from previous studies regarding the ML efficiency of ARIMA, KNN, LR, LSTM, and SVM. This study finds different views on the prediction performance of a few paired algorithms compared with previous studies, including RF and LR, SVM and RF, KNN and ARIMA, KNN and SVM, RNN and ARIMA, and LSTM and SVM. This study also suggests that ARIMA, KNN, LR, and LSTM should be investigated further with additional prediction error assessments. Overall, no single algorithm can fit all applications. This study raises 20 valuable questions for further research.

Does wet-cupping therapy improve repeated sprint ability, perceived wellness, and rating of perceived exertion in young active males?

Wet-cupping therapy (WCT) is one of the oldest known medical techniques, used as a traditional and complementary therapy with a wide application all around the world for general health. Research on the effects of WCT on sports performance are sparse and inconsistent. Thus, we aimed to explore the effects of WCT on repeated sprint ability, wellness, and exertion in young active males. Forty-nine active adult males (age: [28 â€‹± â€‹5] years; body height [177 â€‹± â€‹8] cm; body mass: [79 â€‹± â€‹7] kg; body mass index: [25.4 â€‹± â€‹1.8] kg/m2) were selected for the study. The participants performed a running-based sprint test on two separate occasions (Control and Post-WCT). WCT was performed 24 â€‹h before the testing session. They completed the Hooper questionnaire to assess their well-being ( i.e. , sleep, stress, fatigue, and soreness) before each session. The rating of perceived exertion (RPE) was collected after each testing session. A higher maximum power (p â€‹< â€‹0.05, effect size [ES] â€‹= â€‹0.6), mean power (p â€‹< â€‹0.01, ES â€‹= â€‹0.5) and minimum power (p â€‹< â€‹0.01, ES â€‹= â€‹0.6) were recorded post-WCT as compared to Control session along with a better perceived sleep (p â€‹< â€‹0.01, ES â€‹= â€‹0.85). Perceived stress (p â€‹< â€‹0.01, ES â€‹= â€‹0.6) and RPE (p â€‹< â€‹0.001; ES â€‹= â€‹1.1) were lower during the post-WCT compared to the Control session. The present findings demonstrated that WCT moderately enhanced repeated sprint ability and had positive effects on perceived sleep, stress, and exertion. WCT may be an effective ergogenic aid to improve repeated sprint ability and general well-being in young adult males. Future large-scale multicentric clinical studies are paramount to confirm the results of our study.

K-Line Tilt May Influence the Short-Term Surgical Outcomes After Laminoplasty in Patients With Degenerative Cervical Myelopathy.

STUDY DESIGN: A retrospective study. OBJECTIVES: To explore the relationship between K-line tilt and short-term surgical outcomes following laminoplasty in patients with multilevel degenerative cervical myelopathy (DCM), and to evaluate the potential of K-line tilt as a reliable preoperative predictor. METHODS: A retrospective analysis was performed for 125 consecutive patients who underwent laminoplasty for multilevel DCM. The radiographic parameters utilized in this study encompassed T1 slope (T1S), C2-C7 lordosis (CL), C2-C7 sagittal vertical axis (cSVA), T1 slope minus C2-C7 lordosis (T1S-CL), C2-C7 range of motion (ROM), and K-line tilt. The neurological recovery was evaluated using the Japanese Orthopaedic Association (JOA) score. Pearson correlation coefficients were calculated to assess the relationship between K-line tilt and other classical cervical parameters. Logistic regression analysis was employed to examine the association between K-line tilt and surgical outcomes. RESULTS: Of the 125 patients, 89 were men. The mean age of the patients was 61.74 ± 11.31 years. The results indicated a correlation between the K-line tilt and the cSVA (r = 0.628, P < 0.001), T1S (r = 0.259, P = 0.004), and T1S-CL (r = 0.307, P < 0.001). The K-line tilt showed an association with the failure of the JOA recovery rate (RR) to reach the minimal clinically important difference (MCID) and the occurrence of postoperative kyphotic deformity. We identified cutoff values for the K-line tilt which predict the failure of the JOA RR to reach the MCID and postoperative kyphotic deformity as 10.13° and 9.93°, respectively. CONCLUSIONS: The K-line tilt is an independent preoperative risk factor associated with both the failure of the JOA RR to reach the MCID and the occurrence of postoperative kyphotic deformity in patients with multilevel DCM after laminoplasty.

Hybrid attention-based deep neural networks for short-term wind power forecasting using meteorological data in desert regions.

This study introduces an optimized hybrid deep learning approach that leverages meteorological data to improve short-term wind energy forecasting in desert regions. Over a year, various machine learning and deep learning models have been tested across different wind speed categories, with multiple performance metrics used for evaluation. Hyperparameter optimization for the LSTM and Conv-Dual Attention Long Short-Term Memory (Conv-DA-LSTM) architectures was performed. A comparison of the techniques indicates that the deep learning methods consistently outperform the classical techniques, with Conv-DA-LSTM yielding the best overall performance with a clear margin. This method obtained the lowest error rates (RMSE: 71.866) and the highest level of accuracy (R2: 0.93). The optimization clearly works for higher wind speeds, achieving a remarkable improvement of 22.9%. When we look at the monthly performance, all the months presented at least some level of consistent enhancement (RRMSE reductions from 1.6 to 10.2%). These findings highlight the potential of advanced deep learning techniques in enhancing wind energy forecasting accuracy, particularly in challenging desert environments. The hybrid method developed in this study presents a promising direction for improving renewable energy management. This allows for more efficient resource allocation and improves wind resource predictability.

Promoted production of Fe(IV)/Fe(V) intermediates in the calcium peroxide/ferrate(VI) process for low-damage removal of algal contaminants and membrane fouling control.

Ultrafiltration (UF) is widely employed for harmful algae rejection, whereas severe membrane fouling hampers its long-term operation. Herein, calcium peroxide (CaO2) and ferrate (Fe(VI)) were innovatively coupled for low-damage removal of algal contaminants and fouling control in the UF process. As a result, the terminal J/J0 increased from 0.13 to 0.66, with Rr and Rir respectively decreased by 96.74 % and 48.47 %. The cake layer filtration was significantly postponed, and pore blocking was reduced. The ζ-potential of algal foulants was weakened from -34.4 mV to -18.7 mV, and algal cells of 86.15 % were removed with flocs of 300 µm generated. The cell integrity was better remained in comparison to the Fe(VI) treatment, and Fe(IV)/Fe(V) was verified to be the dominant reactive species. The membrane fouling alleviation mechanisms could be attributed to the reduction of the fouling loads and the changes in the interfacial free energies. A membrane fouling prediction model was built based on a long short-term memory deep learning network, which predicted that the filtration volume at J/J0= 0.2 increased from 288 to 1400 mL. The results provide a new routine for controlling algal membrane fouling from the perspective of promoting the generation of Fe(IV)/Fe(V) intermediates.

The neurocognitive mechanism linking temperature and humidity with miners' working memory: an fNIRS study.

Background: In China's coal mines, employees work in environments reaching depths of 650 m, with temperatures around 40°C and humidity levels as high as 90%, adversely affecting their health, safety capabilities, and cognitive functions, especially working memory. This study aims to explore different temperature and humidity conditions' impact on neurocognitive mechanisms to enhance occupational health and safety. Methods: This study, conducted between June and August 2023, with 100 coalmine workers from the Hongliulin Mining Group, utilized functional near infrared spectroscopy (fNIRS) and short-term visual memory tasks to evaluate the effects of high temperatures and humidity on working memory by monitoring activity in the cerebral cortex. Behavioral data, and neurophysiological data were analyzed using Tukey's HSD for significant differences and multiple regression to explore the impact of temperature and humidity. The ß-values of Oxy-Hb for different regions of interest were calculated using General liner model (GLM), and the activation maps were plotted by NIRS_KIT. Results: High temperature and humidity (Condition IV) significantly depressed reaction times and working memory compared to other conditions, with temperature having a more pronounced impact than humidity on these cognitive measures (p < 0.05). Oxy-Hb concentration increased notably under Condition IV, emphasizing temperature's influence on brain oxygen levels. ROI analysis revealed varied brain activation patterns. The activation of ROI A and B (prefrontal cortex) increased with the increase of temperature and humidity, while ROI C (supplementary motor area) was less sensitive to temperature, indicating the complex influence of environmental factors on brain function. Conclusion: This study highlights the important effects of temperature and humidity on cognitive performance and brain function, highlighting the need to optimize the environment of miners' sites to improve productivity and safety.

Impact of Religious Affiliation on Clinical Outcomes in Liver Transplant Patients.

While the impact of spirituality as it relates to quality of life post-liver transplant (LT) has been studied, there are limited data showing how religious affiliation impacts objective measures such as survival. The aim of the study is to investigate whether LT recipients who identified as having a religious affiliation had better clinical outcomes when compared to LT recipients who did not. Religious affiliation is obtained as part of general demographic information for patients within our institution (options of "choose not to disclose" and "no religious affiliation" are available). Subjects in this retrospective cohort study which conformed with the Declarations of Helsinki and Istanbul were separated into cohorts: LT recipients who self-reported religious affiliation and LT recipients who did not. All LT recipients between March 2007 and September 2018 who had available information regarding their reported religion were included. Excluded patients included those who received a multi-organ transplant, underwent re-transplantation, received a partial liver graft, and identified as agnostic. Outcomes included 30-day readmission, death, and the composite outcome of re-transplantation/death. In an unadjusted analysis of 378 patients, there were no statistically significant differences between the two groups for 30-day readmission (OR=1.15, P=0.71), death (HR=0.63, P=0.19), or re-transplantation/death (HR=0.90, P=0.75). In multivariable analysis, adjusting for age at transplant and hospital admittance status when called for transplant, results were similar. We found no statistically significant difference in the outcomes measured between patients with and without self-reported religious affiliation. Further studies into the role of participation in religious activity and the impact of engagement with a religious community should be conducted in the future.

Short-term germinated legume flours as functional ingredients in food products.

Unlocking the potential of legumes through short-term germination offers an innovative approach to improving the functionality of the resultant flour. This review examines the multifaceted benefits of short-term germinated legume flour, emphasizing the enzymatic activities that breakdown complex legume compounds into simpler forms and reduce anti-nutritional factors. This process improves digestibility, nutrient bioavailability, and health-promoting properties. Furthermore, short-term germination enhances the techno-functional properties of legume flours without compromising their quality, avoiding excessive starch and protein degradation associated with prolonged germination. This review also explores the applications of short-term germinated legume flours in developing nutritious and healthy food products tailored to diverse dietary needs. Subsequent integration of these short-term germinated flours into food products provides a route for the development of cost-effective, nutritious, and sustainable options that can address malnutrition and enhance overall well-being.

Coping With Child Loss: Its Impact on the Mental Health of Chinese Parents.

Using data from the 2011-2018 China Health and Retirement Longitudinal Study, this research employs a two-way fixed effects model to investigate the impact of child loss on parental mental health. The findings indicate a significant decline in mental well-being among Chinese bereaved parents aged 45 to 65, as evidenced by elevated depression scores. Mechanism analysis reveals reduced emotional support from children and increased alcohol consumption, exacerbating mental health challenges. These effects persist regardless of the gender of the lost child and the gender of the parent, and such an adverse effect is found to exist for parents who lost biological children and those with a rural Hukou in China. Moreover, our study reveals that the pain of losing a child does not alleviate over time. These findings underscore the need for support for bereaved parents and call for societal and governmental attention to their challenges.

Exploratory study on short-term administration of oral fosravuconazole for tinea pedis.

We investigated the clinical efficacy of short-term, oral fosravuconazole (F-RVCZ) therapy for tinea pedis, commonly known as athlete's foot. F-RVCZ (equivalent to 100 mg ravuconazole) was administered orally once daily for 1 week for interdigital and vesicular tinea pedis and for 4 weeks for hyperkeratotic tinea pedis. Efficacy was evaluated based on mycological efficacy and clinical symptoms at Weeks 1, 4, and 8 for interdigital and vesicular tinea pedis and at Weeks 4, 8, and 12 for hyperkeratotic tinea pedis. Efficacy was confirmed at the end of treatment. Therapeutic efficacy increased over time from the end of treatment for both types of tinea pedis. All adverse drug reactions (ADRs) were within expectations and there were no cases of discontinuation due to ADRs or serious ADRs. Short-term oral F-RVCZ therapy is expected to be as effective or more effective than terbinafine and itraconazole, which have already been approved in Japan and may be a useful option for the treatment of tinea pedis.

Solution-Processed Polymer Memcapacitors with Stimulus-Controlled and Evolvable Synaptic Functionalities: From Short-Term Plasticity to Long-Term Plasticity to Metaplasticity.

In the vanguard of neuromorphic engineering, we develop a paradigm of biocompatible polymer memcapacitors using a seamless solution process, unleashing comprehensive synaptic capabilities depending on both the stimulation form and history. Like the human brain to learn and adapt, the memcapacitors exhibit analogue-type and evolvable capacitance shifts that mirror the complex flexibility of synaptic strengthening and weakening. With increasing frequency and intensity of the stimulation, the memcapacitors demonstrate an evolution from short-term plasticity (STP) to long-term plasticity (LTP), and even to metaplasticity (MP) at a higher level. A physical picture, featuring the stimulus-controlled spatiotemporal ion redistribution in the polymer, elaborates the origin of the memcapacitive prowess and resultant versatile synaptic plasticity. The distinctive MP behavior endows the memcapacitors with a dynamic learning rate (LR), which is utilized in an artificial neural network. The superiority of implementing a dynamic LR compared with conventional practices of using constant LR shines light on the potential of the memcapacitors to exploit organic neuromorphic computing hardware.

Research on precise lithium battery state of charge estimation method based on CALSE-LSTM model and pelican algorithm.

This paper presents an innovative fusion model called "CALSE-LSTM," which integrates Convolutional Neural Networks (CNNs), Long Short-Term Memory Networks (LSTMs), self-attention mechanisms, and squeeze-and-excitation attention mechanisms to optimize the estimation accuracy of the State of Charge (SoC). The model incorporates battery historical data as input and employs a dual-attention mechanism based on CNN-LSTM to extract diverse features from the input data, thereby enhancing the model's ability to learn hidden information. To further improve model performance, we fine-tune the model parameters using the Pelican algorithm. Experiments conducted under Urban Dynamometer Driving Schedule (UDDS) conditions show that the CALSE-LSTM model achieves a Root Mean Squared Error (RMSE) of only 1.73 % in lithium battery SoC estimation, significantly better than GRU, LSTM, and CNN-LSTM models, reducing errors by 31.9 %, 31.3 %, and 15 %, respectively. Ablation experiments further confirm the effectiveness of the dual-attention mechanism and its potential to improve SoC estimation performance. Additionally, we validate the learning efficiency of CALSE-LSTM by comparing model training time with the number of iterations. Finally, in the comparative experiment with the Kalman filtering method, the model in this paper significantly improved its performance by incorporating power consumption as an additional feature input. This further verifies the accuracy of CALSE-LSTM in estimating the State of Charge (SoC) of lithium batteries.