A lightweight network architecture for traffic sign recognition based on enhanced LeNet-5 network.

As an important part of the unmanned driving system, the detection and recognition of traffic sign need to have the characteristics of excellent recognition accuracy, fast execution speed and easy deployment. Researchers have applied the techniques of machine learning, deep learning and image processing to traffic sign recognition successfully. Considering the hardware conditions of the terminal equipment in the unmanned driving system, in this research work, the goal was to achieve a convolutional neural network (CNN) architecture that is lightweight and easily implemented for an embedded application and with excellent recognition accuracy and execution speed. As a classical CNN architecture, LeNet-5 network model was chosen to be improved, including image preprocessing, improving spatial pool convolutional neural network, optimizing neurons, optimizing activation function, etc. The test experiment of the improved network architecture was carried out on German Traffic Sign Recognition Benchmark (GTSRB) database. The experimental results show that the improved network architecture can obtain higher recognition accuracy in a short interference time, and the algorithm loss is significantly reduced with the progress of training. At the same time, compared with other lightweight network models, this network architecture gives a good recognition result, with a recognition accuracy of 97.53%. The network structure is simple, the algorithm complexity is low, and it is suitable for all kinds of terminal equipment, which can have a wider application in unmanned driving system.

Solvent-Free Enzymatic Synthesis of Ethyl 3-Oxobutyrate Derivative: Characterization and Optimization of Reaction Condition.

It was first found that porcine pancreatic lipase (PPL) could catalyze the Knoevenagel condensation of aromatic aldehydes and ethyl acetoacetate under solvent-free conditions in this paper. Under solvent-free conditions, the highest yield of PPL catalytic reaction was 99.38%, and the Z/E selectivity of the product was 3.93. In addition, the reaction conditions were optimized, and the factors affecting the product structure were studied.

Assessing the Efficacy of the ARMOR Tool-Based Deprescribing Intervention for Fall Risk Reduction in Older Patients Taking Fall Risk-Increasing Drugs (DeFRID Trial): Protocol for a Randomized Controlled Trial.

BACKGROUND: Falls in older patients can lead to serious health complications and increased health care costs. Fall risk-increasing drugs (FRIDs) are a group of drugs that may induce falls or increase the tendency to fall (ie, fall risk). Deprescribing is the process of withdrawal from an inappropriate medication, supervised by a health care professional, with the goal of managing polypharmacy and improving outcomes. OBJECTIVE: This study aims to assess the effectiveness of a deprescribing intervention based on the Assess, Review, Minimize, Optimize, and Reassess (ARMOR) tool in reducing the risk of falls in older patients and evaluate the cost-effectiveness of deprescribing FRIDs. METHODS: This is an open-label, parallel-group randomized controlled academic trial. Individuals aged 60-80 years who are currently taking 5 or more prescribed drugs, including at least 1 FRID, will be recruited. Demographic data, medical conditions, medication lists, orthostatic hypotension, and fall history details will be collected. Fall concern will be assessed using the Fall Efficacy Scale, and fall risk will be assessed by the Timed Up and Go test and Tinetti Performance-Oriented Mobility Assessment tool. In this study, all treating physicians will be randomized using a stratified randomization method based on seniority. Randomized physicians will do deprescribing with the ARMOR tool for patients on FRIDs. Participants will maintain diaries, and monthly phone follow-ups will be undertaken to monitor falls and adverse events. Physical assessments will be performed to evaluate fall risk every 3 months for a year. The rationality of prescription drugs will be evaluated using the World Health Organization's core indicators. RESULTS: The study received a grant from the Indian Council of Medical Research-Safe and Rational Use of Medicine in October 2023. The study is scheduled to commence in April 2024 and conclude by 2026. Efficacy will be measured by fall frequency and changes in fall risk scores. Cost-effectiveness analysis will also include the incremental cost-effectiveness ratio calculation. Adverse events related to deprescription will be recorded. CONCLUSIONS: This trial will provide essential insights into the efficacy of the ARMOR tool in reducing falls among the geriatric population who are taking FRIDs. Additionally, it will provide valuable information on the cost-effectiveness of deprescribing practices, offering significant implications for improving the well-being of older patients and optimizing health care resource allocation. The findings from this study will be pertinent for health care professionals, policy makers, and researchers focused on geriatric care and fall prevention strategies. TRIAL REGISTRATION: Clinical Trials Registry - India CTRI/2023/12/060516; https://ctri.nic.in/Clinicaltrials/pubview2.php. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/55638.

Combining immunotherapy and radiation therapy in gastrointestinal cancers: A review.

INTRODUCTION AND PURPOSE: With a significant global impact, treatment of gastrointestinal (GI) cancers still presents with challenges, despite current multimodality approaches in advanced stages. Clinical trials are expanding for checkpoint inhibition (ICI) combined with radiation therapy (RT). This review intends to offer a comprehensive image of the current data regarding the effectiveness of this association, and to reflect on possible directions to further optimize the results. RESULTS: Several early phase studies demonstrated encouraging potential. However, translating preclinical outcomes to clinical settings proves challenging, especially in immunologically "cold" environments. GI cancers exhibit heterogeneity, requiring tailored approaches based on disease stage and patient characteristics. Current results, though promising, lack the power of evidence to influence the general practice. CONCLUSIONS: Finding biomarkers for identifying or converting resistant cancers is essential for maximizing responses, moreover in this context strategic RT parameters need to be carefully considered. Our review emphasizes the significance of having a thorough grasp of how immunology, tumour biology, and treatment settings interact in order to propose novel research avenues and efficient GI cancer therapy.

Conservative strip tillage system in maize maintains high yield and mitigates GHG emissions but promotes N2O emissions.

Optimizing N application under straw-covered strip tillage is of great significance to the rational utilization of stover resources as well as ensure food and ecosystem security, and especially N2O emissions from agricultural systems. Quantifying N2O emissions and even the carbon footprint (CF) from agricultural systems is crucial for future protecting agricultural production systems. A two-year field experiment was conducted on black soil in Northeast China, which set up two tillage systems: strip tillage with straw returning (ST) and conventional tillage (control: CT) without straw and three nitrogen rates: 0, farmers' practice (Nfp 240 kg hm-2), and optimized nitrogen fertilizer (Nopt 180 kg hm-2). We examined the characteristics of N2O emissions and CF under the ST and CT systems. Among them, we indirectly calculated GHG emissions using the LCA method. Compared with CT, the ST system significantly reduces indirect GHG emissions, but did significantly increase direct cumulative N2O emissions by 20.7 %, most likely because the higher soil residual nitrate nitrogen content, WFPS, and soil temperature under ST was 13.0 %, 2 % and 5.7 % higher than that under CT. Nopt treatment markedly reduced cumulative N2O emissions by 36.0 %, CFarea, CFyield, and CFNPV by 22.4 %, 23.1 %, and 23.5 % in ST, respectively, compared to Nfp. The reduction in energy use of machinery in ST results in lower fuel consumption and thus generating less CF. What's more, the decrease of CFyield and CFNPV between nitrogen application treatments under ST was 5.2 % and 7.7 % higher than CT, respectively. ST system can effectively achieve higher grain yield and mitigate GHG emissions on black soil in Northeast China compared with CT, but attention should be paid to N2O emissions in the soil during the maize growth period. The sustainability of balancing GHG emissions, and economic and environmental benefits can be achieved by optimizing nitrogen fertilizer manage.

Optimal configuration method of demand-side flexible resources for enhancing renewable energy integration.

Demand-side flexible load resources, such as Electric Vehicles (EVs) and Air Conditioners (ACs), offer significant potential for enhancing flexibility in the power system, thereby promoting the full integration of renewable energy. To this end, this paper proposes an optimal allocation method for demand-side flexible resources to enhance renewable energy consumption. Firstly, the adjustable flexibility of these resources is modeled based on the generalized energy storage model. Secondly, we generate random scenarios for wind, solar, and load, considering variable correlations based on non-parametric probability predictions of random variables combined with Copula function sampling. Next, we establish the optimal allocation model for demand-side flexible resources, considering the simulated operation of these random scenarios. Finally, we optimize the demand-side resource transformation plan year by year based on the growth trend forecast results of renewable energy installed capacity in Jiangsu Province from 2025 to 2031.

[Prediction Spatial Distribution of Soil Organic Matter Based on Improved BP Neural Network with Optimized Sparrow Search Algorithm].

Soil organic matter is an important indicator of soil fertility, and it is necessary to improve the accuracy of regional organic matter spatial distribution prediction. In this study, we analyzed the organic matter content of 1 690 soil surface layers (0-20 cm) and collected data on the natural environment and human activities in the Weining Plain of the Yellow River Basin. The SOM spatial distribution prediction model was established with 1 348 points using classical statistics, deterministic interpolation, geostatistical interpolation, and machine learning, respectively, and 342 sample points data were used as the test set to test and analyze the prediction accuracy of different models. The results showed that the average SOM content of the surface soil of the Weining Plain was 14.34 g·kg-1, and the average soil organic matter variation across 1 690 sampling points was 34.81%, indicating a medium degree of variability. The results also revealed a spatial distribution trend, with low soil organic matter content in the northeast and southwest, high soil organic matter on the left and right banks of the Yellow River in the middle, and relatively high soil organic matter in the sloping terrain of the Weining Plain. The four types of methods in order of high to low prediction accuracy were the machine learning method, geostatistical interpolation method, deterministic interpolation method, and classical statistical method. Through comparison, the BP neural network that was improved based on the optimized sparrow search algorithm had the best prediction accuracy, and the optimized sparrow search algorithm had better convergence accuracy, avoided falling into local optimization, prevented data overfitting, and had better prediction ability. This optimization algorithm can improve the accuracy of SOM prediction and has good application prospects in soil attribute prediction.

Comparative study for coastal aquifer vulnerability assessment using deep learning and metaheuristic algorithms.

Coastal aquifer vulnerability assessment (CAVA) studies are essential for mitigating the effects of seawater intrusion (SWI) worldwide. In this research, the vulnerability of the coastal aquifer in the Lahijan region of northwest Iran was investigated. A vulnerability map (VM) was created applying hydrogeological parameters derived from the original GALDIT model (OGM). The significance of OGM parameters was assessed using the mean decrease accuracy (MDA) method, with the current state of SWI emerging as the most crucial factor for evaluating vulnerability. To optimize GALDIT weights, we introduced the biogeography-based optimization (BBO) and gray wolf optimization (GWO) techniques to obtain to hybrid OGM-BBO and OGM-GWO models, respectively. Despite considerable research focused on enhancing CAVA models, efforts to modify the weights and rates of OGM parameters by incorporating deep learning algorithms remain scarce. Hence, a convolutional neural network (CNN) algorithm was applied to produce the VM. The area under the receiver-operating characteristic curves for OGM-BBO, OGM-GWO, and VMCNN were 0.794, 0.835, and 0.982, respectively. According to the CNN-based VM, 41% of the aquifer displayed very high and high vulnerability to SWI, concentrated primarily along the coastline. Additionally, 32% of the aquifer exhibited very low and low vulnerability to SWI, predominantly in the southern and southwestern regions. The proposed model can be extended to evaluate the vulnerability of various coastal aquifers to SWI, thereby assisting land use planers and policymakers in identifying at-risk areas. Moreover, deep-learning-based approaches can help clarify the associations between aquifer vulnerability and contamination resulting from SWI.

[Ecological network optimization of Jiuquan City, Gansu, China based on complex network theory and circuit model]. / åŸºäºŽå¤æ‚ç½‘ç»œç†è®ºå’Œç”µè·¯æ¨¡åž‹çš„é ’æ³‰å¸‚ç”Ÿæ€ç½‘ç»œä¼˜åŒ–.

Building a scientific and reasonable ecological network is the key for optimizing the pattern of territorial development and protection, and is of great significance for ensuring regional ecological security and promoting the virtuous cycle of ecosystems. In previous studies, nodal attack method (destruction of ecological source area) was often used in the "robustness" evaluation of ecological networks. Actually, the ecological corridor is more fragile than the source area, and thus the nodal attack method is not reasonable. In this study, taking Jiuquan City as the research area, based on the circuit model to construct the ecological network, we carried out the topology optimization of ecological network by using three strategies (random edge increase, node degree and priority edge increase with low node intermedium number) in complex network theory. We compared and analyzed the "robustness" of ecological network before and after optimization by constructing edge attack strategy, and selected the best network optimization strategy. The results showed that 65 ecological source areas were identified in Jiuquan City, with a total area of 20275.15 km2, and that grassland accounted for 89.5% of the source area. We identified 179 ecological corridors with a total length of 6387.16 km, 158 ecological barrier points with a total area of 1385.5 km2. The unused land accounted for 92.2% of the total barrier points area. We identified 63 ecological pinch points, mainly concentrated in the source edge and corridor intersection. Among them, the spatial distribution of 11 barrier points and pinch points was consistent, which was the key area to be repaired in ecological network optimization. The three optimization strategies had significantly improved the stability of ecological network in Jiuquan City. The relative size of the maximum connected subgraph and the edge connected rate of the ecological network of the optimization strategy of adding edges according to degree were all the most stable under random attack mode and deliberate attack mode, which was the best optimization scheme for ecological network in Jiuquan City.

Research on Microgrid Optimal Dispatching Based on a Multi-Strategy Optimization of Slime Mould Algorithm.

In order to cope with the problems of energy shortage and environmental pollution, carbon emissions need to be reduced and so the structure of the power grid is constantly being optimized. Traditional centralized power networks are not as capable of controlling and distributing non-renewable energy as distributed power grids. Therefore, the optimal dispatch of microgrids faces increasing challenges. This paper proposes a multi-strategy fusion slime mould algorithm (MFSMA) to tackle the microgrid optimal dispatching problem. Traditional swarm intelligence algorithms suffer from slow convergence, low efficiency, and the risk of falling into local optima. The MFSMA employs reverse learning to enlarge the search space and avoid local optima to overcome these challenges. Furthermore, adaptive parameters ensure a thorough search during the algorithm iterations. The focus is on exploring the solution space in the early stages of the algorithm, while convergence is accelerated during the later stages to ensure efficiency and accuracy. The salp swarm algorithm's search mode is also incorporated to expedite convergence. MFSMA and other algorithms are compared on the benchmark functions, and the test showed that the effect of MFSMA is better. Simulation results demonstrate the superior performance of the MFSMA for function optimization, particularly in solving the 24 h microgrid optimal scheduling problem. This problem considers multiple energy sources such as wind turbines, photovoltaics, and energy storage. A microgrid model based on the MFSMA is established in this paper. Simulation of the proposed algorithm reveals its ability to enhance energy utilization efficiency, reduce total network costs, and minimize environmental pollution. The contributions of this paper are as follows: (1) A comprehensive microgrid dispatch model is proposed. (2) Environmental costs, operation and maintenance costs are taken into consideration. (3) Two modes of grid-tied operation and island operation are considered. (4) This paper uses a multi-strategy optimized slime mould algorithm to optimize scheduling, and the algorithm has excellent results.

Stress and strain analysis and parameter optimization of pipe truss tower connection of super-large tower crane based on FEM.

Clamping bushing structure is an internode connection mechanism designed for the standard section of tubular truss tower. In this paper, the clamping bushing structure of the connecting mechanism of super-large tower crane is taken as the research object, a three-dimensional model of clamping bushing structure is established and imported into ABAQUS, and its multi-body contact model is further constructed to study the contact and bearing relationship of the structure under multiple working conditions, and the accuracy of the calculation results of the model is verified by the experimental stress test under tensile working conditions. In addition, this study is based on the control variable method, and through the design of orthogonal test table, the influence degree of five variable parameters of clamping bushing on the bearing capacity of the structure is investigated. Finally, through the range analysis, the optimal horizontal combination of variables and parameters of clamping bushing structure is obtained, and the optimal matching relationship between the shape of the tower connecting mechanism and the bearing capacity is obtained. The results show that, compared with the original model, the stress concentration at the most dangerous section of the optimized joint and the bushing is obviously alleviated, in which the stress peaks of the upper and lower joints are kept below 500 MPa, and the stress peaks of the bushing groove are also reduced to between 573 and 722 MPa. Moreover, the designed and optimized lower joint can reduce the maximum equivalent plastic strain of the joint root circumference by 56.05% under the original maximum tensile condition, and the overall distribution trend of equivalent plastic strain is more uniform, and a more reliable structural design is obtained, which plays an important guiding role in the design, optimization and analysis of the connecting mechanism of the tower body of large tower crane.

Research on the impact of China's reform to delegate power, streamline administration, and optimize government services on the technology innovation efficiency of the pharmaceutical manufacturing industry.

Objective: The government has recently implemented reforms aimed at delegating power, streamlining administration, and optimizing government services. This reform has eliminated barriers that impede the growth of various industries, thereby unleashing innovative potential. Additionally, there have been several medical policies, including changes to medical insurance and centralized volume-based procurement. China's pharmaceutical market has undergone significant changes, leading to increased demands for innovation technology efficiency in pharmaceutical manufacturing. Methods: The three-stage BCC theory was employed to assess the effectiveness of technology innovation in the industry under this reform. Calculate precise comprehensive technical efficiency values, pure technical efficiency values, and scale efficiency values for technological innovation in the pharmaceutical industry across 30 provinces from 2018 to 2020, after removing environmental factors. Results: In 2020, Jiangsu and Shandong and nine other provinces reached the comprehensive technical efficiency frontier surface, joining Tianjin, Zhejiang, and Guangdong provinces. However, Gansu, Qinghai, Ningxia, and Xinjiang still need to catch up due to their smaller industrial scale and lack of technology. Discussion: To ensure the effectiveness of reforms, it is crucial to fully consider provincial differences. Articulating national and provincial policies is necessary to allow efficient provinces to continue and allocate resources toward less efficient provinces to improve overall efficiency.

Generation of a specific aptamer for accurate detection of sarafloxacin based on fluorescent/colorimetric/SERS triple-readout sensor.

Sarafloxacin (SAR), one of the most widely used fluoroquinolone antibiotics, is a serious threat to aquatic environments and human health due to its illegal abuse. Herein, we first screened an aptamer (SAR-1) that specifically binds to SAR using capture-SELEX technology. Based on molecular docking technology, SAR-1 was gradually truncated, and a short SAR-1a with better affinity and specificity was obtained. The optimal SAR-1a was further combined with a Pt nanoparticle (Pt NP)- decorated bimetallic Fe/Co-MOF to fabricate a multimode sensing platform for SAR determination. The Fe/Co-MOF@Pt NPs exhibited excellent peroxidase-like activity, which catalyzed the H2O2-mediated oxidation of 3,3',5,5'-tetramethylbenzidine (TMB), thereby enabling visual detection of SAR. Meanwhile, the generated oxTMB can also produce SERS responses and be used for the SERS detection of SAR. Moreover, the inherent fluorescence property of Fe/Co-MOF@Pt NPs enabled fluorescence detection of SAR. The designed triple-readout aptasensor showed good sensitivity for SAR detection with limits of detection of 0.125 ng/mL (fluorescent mode) and 0.05 ng/mL (colorimetric and SERS mode). The aptamer-based triple-mode sensing platform provided mutual verification of detection results in different output modes, effectively improving the assay accuracy and providing a promising tool for highly sensitive, selective, and accurate determination of SAR in daily life.

Patients' Satisfaction With Medication Delivery Pharmacy Services in a Tertiary Hospital in Asir, Saudi Arabia: A Cross-Sectional Study.

BACKGROUND: Pharmaceutical care plays a crucial role in optimizing medication administration and improving patient health outcomes. However, medication adherence remains a challenge, with a significant percentage of patients discontinuing their medications. Value-added services (VASs), such as medication delivery, have been introduced to enhance pharmacy services and medication adherence. OBJECTIVE: This study aims to evaluate satisfaction with a new medication delivery service at an Armed Forces Hospital outpatient pharmacy in Saudi Arabia and identify factors impacting utilization. METHODS: A cross-sectional survey assessed patient satisfaction with a new pharmacy delivery service between January 2023 and March 2023. The target population consisted of adult patients who had used the pharmacy delivery service for at least one month. The survey contained 23 Likert scale questions assessing satisfaction across three domains: delivery process/personnel, medication quality, and pharmacist adherence to best practices. RESULTS: A total of 110 patients responded to the survey, 383 invited patients; the mean age was 51.2 ± 15.3, and most were male 92 (83.6%), married 97 (88.2%), and living in urban areas 63 (57.3%). The overall satisfaction rate was 97 (88.1%), with 67 (60.9%) reporting satisfaction with the medication delivery service. On the delivery process/personnel items, over half strongly agreed that the delivery person called before arriving 59 (53.6%), medications were received on time 58 (51.8%), and the delivery person was polite 64 (58.2%). Most strongly agreed that the service helped with adherence 70 (63.6%) and saved travel costs 72 (65.5%) for medication-quality items. Most also strongly agreed that medications were properly packaged 65 (59.1%) and labeled 71 (64.5%). Regarding pharmacist practices, approximately 56 (50.9%) strongly agreed that the pharmacist provided education materials, inquired about adherence 49 (44.5%), and was respectful 55 (50%). Bivariate analyses found no significant associations between satisfaction and age, gender, residence, education, marital status, income, or disease (all p > 0.05). Satisfaction remained uniformly high across subgroups. CONCLUSION: The medication delivery service demonstrated excellent patient reception regardless of its characteristics. Overall satisfaction with these services was high. There was no association between sociodemographic characteristics and the level of satisfaction. Continued monitoring and refinement could maximize the quality of pharmaceutical care afforded through innovative models supporting medication adherence.

Vacuum cleaving of superconducting niobium tips to optimize noise filtering and with adjustable gap size for scanning tunneling microscopy.

Superconducting (SC) tips for scanning tunneling microscopy (STM) can enhance a wide range of surface science studies because they offer exquisite energy resolution, allow the study of Josephson tunneling, or provide spatial contrast based on the local interaction of the SC tip with the sample. The appeal of a SC tip is also practical. An SC gap can be used to characterize and optimize the noise of a low-temperature apparatus. Unlike typical samples, SC tips can be made with less ordered materials, such as from SC polycrystalline wires or by coating a normal metal tip with a superconductor. Those recipes either require additional laboratory infrastructure or are carried out in ambient conditions, leaving an oxidized tip behind. Here, we revisit the vacuum cleaving of an Nb wire to prepare fully gapped tips in an accessible one-step procedure. To show their utility, we measure the SC gap of Nb on Au(111) to determine the base temperature of our microscope and to optimize its RF filtering. The deliberate coating of the Nb tip with Au fully suppresses the SC gap and we show how sputtering with Ar+ ions can be used to gradually recover the gap, promising tunability for tailored SC gaps sizes. • Oxide free superconducting STM tips • RF filter optimization.

Forecasting the transportation energy demand with the help of optimization artificial neural network using an improved red fox optimizer (IRFO).

Transportation energy demand has a significant impact on worldwide energy consumption and greenhouse gas emissions. Accurate transportation energy demand predictions can help policymakers develop and implement successful energy policies and strategies. In this study, a novel approach to predict transportation energy demand using the Artificial Neural Network (ANN) based on the Improved Red Fox Optimizer (IRFO) has been suggested. The proposed method utilizes the ANN model to solve the complex nonlinear relationships between transportation energy demand and its effective parameters including Gross Domestic Product (GDP), population, and vehicle numbers. Also, the IRFO algorithm was utilized to modify the ANN model's parameters to improve the prediction accuracy. The experimental findings demonstrate the ANN-IRFO model performs better than the other method in terms of accuracy and effectiveness. It predicts the growth of GDP, population, and vehicles number by 5.5 %, 4.8 %, and 4.2 %, respectively. The findings demonstrate that the suggested method can provide accurate forecasts for transportation energy demand, which can help decision-makers to make informed decisions and policies regarding energy management and sustainability.

Optimizing cropping systems to close the gap between economic profitability and environmental health.

Supporting food security while maintaining ecosystem sustainability is one of the most important global challenges for humanity. Optimization of cropping systems is expected to promote the ecosystem services of agroecosystems. Yet, how and why cropping system influences the trade-offs between economic profitability and multiple ecosystem services remain poorly understood. We investigate the influence of six cropping systems on trade-offs between economic profitability and multiple ecosystem services after considering 36 agricultural ecosystem properties using field experiment data from 2020 to 2022. We show that designing cropping system is a critical tool to closing the gap between ecosystem sustainability and commercial profitability. Cropping system with three harvests within 2 yr had higher performance in overall ecosystem multiple services through enhancement of supporting, regulating, and economic performance without compromising provisioning compared with four other systems. These systems diminished the trade-off among multiple services, resulting in a 'win-win' situation for economics and multiple services. By contrast, the monoculture and double cropping systems lead to a strong trade-off between pairwise services including ecosystem health and profitability. Our work illustrates the substantial potential of rotation systems with three harvests within 2 yr in enforcing ecosystem services and closing the trade-offs among multiple agricultural ecosystem services.

Differences in Contents and Formation Methods of Clinical Questions in Chinese and Korean Clinical Practice Guidelines of Acupuncture-Moxibustion: Scoping Review.

OBJECTIVE: To analyze the differences in the needs of users and the value orientation of clinical practice guidelines (CPGs) by comparing the contents and formation methods of clinical questions in Chinese and Korean CPGs of acupuncture-moxibustion (Acup-Mox). METHODS: The full text of CPGs was systematically searched from the official websites of Chinese and Korean traditional medicine societies and Acup-Mox associations, with the topic "Acup-Mox for treating diseases" and the retrieval time up to September 28, 2022. Two researchers screened the CPGs independently, and extracted the guidelines' topics, content, quantity and formation methods of clinical questions. The quantitative data were collected by counting the frequency, and the qualitative data were classified and described by thematic analysis. RESULTS: A total of 29 guidelines were included in this study, including 20 Chinese guidelines (305 questions) and 9 Korean guidelines (223 questions). The differences lie in the aspects of content and diversity, and formation method. As for content and diversity, Chinese guidelines focused mainly on the questions related to treatment such as the operation of specific intervention (86, 28.2%), efficacy of intervention (78, 25.6%), and also involving questions in diagnosis, prevention, and prognosis. While the clinical questions in Korean guidelines were concentrated to efficacy of intervention (218, 97.8%). As for formation method, in Chinese guidelines, questions were usually collected directly from clinicians, and then determined and optimized by experts. In Korean guidelines, frequently used clinical Acup-Mox interventions would be screened first. Then the expert group would set up corresponding intervention control measures so as to form clinical questions related to treatment efficacy. CONCLUSIONS: The differences reflect the different needs of clinical practitioners, and the different aims or concepts in developing Acup-Mox guidelines between China and South Korea. Chinese guidelines emphasized promoting operation protocols and techniques of Acup-Mox for practical use, while Korean guidelines emphasized promoting the frequently used clinical intervention therapies. It is speculated that the guidelines from these two countries would play different roles in guiding clinical operation and supporting medical decision. In terms of formation methods of clinical questions, it is suggested to attach importance to optimizing process in formatting clinical questions to improve the clinical applicability of CPGs of Acup-Mox.

Optimization of preparation of NaA zeolite from fly ash for CO2 capture.

The green synthesis method of fly ash-based NaA zeolite was explored to reduce the synthesis cost and environmental hazards. For the prepared NaA samples, the effects of crystallization time, solid-liquid ratio, and Si/Al ratio were systematically studied. CO2 adsorption isotherm is used for adsorption model fitting analysis and adsorption selectivity determination. According to the experimental results, the optimized NaA zeolite synthesis conditions are as follows: the Si/Al ratio of NaA zeolite is 1.4, the solid-liquid ratio is 10, and the crystallization time is 6 h. The green synthesis method reported in this study can successfully prepare NaA zeolite and exhibit excellent CO2 adsorption performance, reaching 4.34 mmol/g, with high CO2 selective adsorption ability, reaching 89.2 for N2, 257.1 for O2, and 45.8 for CH4. The adsorbed CO2 can be released for further utilization, and NaA zeolite also has strong adsorption and regeneration performance, with a ten cycle adsorption capacity only decreasing by 1.17%. In addition, the use of cheap raw materials synthesis methods will promote the large-scale industry application of green synthesis technology in the future.

Data Science as a Core Competency in Undergraduate Medical Education in the Age of Artificial Intelligence in Health Care.

The increasingly sophisticated and rapidly evolving application of artificial intelligence in medicine is transforming how health care is delivered, highlighting a need for current and future physicians to develop basic competency in the data science that underlies this topic. Medical educators must consider how to incorporate central concepts in data science into their core curricula to train physicians of the future. Similar to how the advent of diagnostic imaging required the physician to understand, interpret, and explain the relevant results to patients, physicians of the future should be able to explain to patients the benefits and limitations of management plans guided by artificial intelligence. We outline major content domains and associated learning outcomes in data science applicable to medical student curricula, suggest ways to incorporate these themes into existing curricula, and note potential implementation barriers and solutions to optimize the integration of this content.