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Microlearning, as an informal learning approach, has gained increasing popularity in both the training industry and professional studies disciplines over the last several years. However, despite the rising interest in microlearning, how to design microlearning activities and what microcontent is reasonable for microlearning activities have rarely been addressed. In this study, we designed a 6-week micro-reading activity. During the process, 30 students majoring in Education Technology participated in the activity. They were randomly divided into an electronic book reading (e-reading) group and a paper book reading (p-reading) group, and were required to read the same book within 6 weeks. Formative and summative evaluations were performed to assess the effectiveness of the micro-reading activity. Descriptive statistics and bar and line chart were applied to reflect the results of formative evaluation; One-way ANOVA analysis was used to process the data of summative evaluations. Students' average reading time, reading pace, and weekly reading notes were recorded and analyzed as the formative evaluation. The results indicated that all students achieved the reading objective, and they kept relatively high engagement in the micro-reading activity. According to the results of the summative evaluation, both groups of students' average scores were relatively high. However, compared to the p-reading group, the average score of the e-reading group was higher, and some significant differences were also found in the average scores of the two groups. Therefore, the 6-week micro-reading activity was beneficial for students' reading effectiveness, and the e-reading type was more effective for promoting students' reading effectiveness. In the future, it would be worthwhile for designers to consider how to design the duration of activities in a reasonable way and to compare how new electronic reading platforms compare with traditional print sources.

Massive open online courses (MOOCs; online courses delivered over the Internet) enable distance learning without time and place constraints. MOOCs are popular; however, active participation level among students who take MOOCs is generally lower than that among students who take in-person courses. Students who take MOOCs often lack guidance, and the courses often fail to provide personalized learning materials. Artificial intelligence (AI) has been applied to manage increasing amounts of learning data in learners' online activity records. Driven by the trend in big data, AI technology has drawn increasing attention in various fields. AI-based recommendation systems (RSs) are powerful tools for improving resource acquisition through supply customization, and they can provide personalized learning materials as study guides. In this study, a personalized learning path for MOOCs based on long short-term memory (LSTM) was proposed to meet students' personal needs for learning. According to students' video-watching behaviors, we proposed an MOOC material RS that identifies students with similar learning behaviors through clustering and then uses the clustering results and the learning paths of each group of students to construct an LSTM model to recommend learning paths. The system's learning path recommendations can effectively improve the online participation of learners, and students who received recommendations progressed from the slow-progress group to the medium-progress or fast-progress group. In addition, the learning attitude questionnaire results indicated that the proposed system not only motivated learners to continue learning and achieve high learning capacity but also supported their study planning according to their individual learning needs.

Pair programming (PP) can help improve students' computational thinking (CT), but the trajectory of CT skills and the differences between high-scoring and low-scoring students in PP are unknown and need further exploration. In this study, a total of 32 fifth graders worked on Scratch tasks in 16 pairs. The group discourse of three learning topics (comprising 9 projects) was collected. After the audio files were transcribed, 1,303 conversations were obtained. They were analyzed via Epistemic Network Analysis (ENA) Webkit, which can reveal the trajectory of students' CT development via analyzing codes of discourse related to CT in PP. Three Scratch learning topics were assessed based on the Dr. Scratch platform to acquire the level of students' CT and to determine the low- and high-scoring groups. Results indicated that CT concepts and CT practices were always closely related in PP and CT practices, and CT perspectives could be gradually and closely related after a long period of CT training. A significant difference between the two groups' CT structures was found. The high-scoring group had more fragments of CT practice and connecting of CT perspectives, while the low-scoring group showed more fragments of CT concepts and expressing of CT perspectives. This research provides insights into cultivating primary school students' CT using Scratch in the context of PP. The findings can provide suggestions for instructors to design instructional interventions to facilitate students' CT skills via PP learning. Instructors can improve CT skills by guiding students to constantly ask questions, and specifying the role swap between driver and navigator in PP. Besides, instructors could give more consideration to the development of CT perspectives, and especially the ability to question.

Generative artificial intelligence has been regarded as a transformative tool. While responsible and ethical applications could bring opportunities to education, their misuse could pose demanding challenges. It is necessary to clarify the technological affordances and challenges in a normative way to lay the foundation for future development. This study addressed the dearth of literature by performing a systematic review, aiming to (1) explore the utility and availability from the technological affordances perspective; (2) summarize the current challenges in risks prevention; and (3) propose possible directions for future research and practice. A total of 27 academic articles published in core journals between 2020 and 2023 were analyzed, and the inductive grounded approach was used to categorize the coding schemes. The findings revealed four technological affordances: accessibility, personalization, automation, and interactivity; and five challenges: academic integrity risk, response errors and bias, over-dependence risk, the widening digital divide, and privacy and security. We propose future directions, encourage educational organizations to formulate guidelines for the ethical use of AI in education, call on educators to embrace future trends in AI education instead of shunning its use, and guide students to treat it as a thought aid and reference, rather than relying on it entirely.

Abstract Background Stevens–Johnson syndrome-toxic epidermal necrolysis (SJS-TNE) overlap is a rare skin disorder characterized by erythema, blisters, extensive exfoliation, epidermal detachment, the involvement of multiple mucosae, and positive Nikolsky’s sign. SJS-TEN has a high mortality rate. Our case involves a rare occurrence of drug-induced Stevens–Johnson syndrome-toxic epidermal necrolysis overlap with a delayed onset in the setting of quetiapine and famotidine therapy. Case presentation An 82-year-old Taiwanese female was admitted to our hospital for decreased urine output, generalized edema, and multiple skin blisters and bedsores. With further spread of the lesions, multiple ruptured bullae with shallow erosions on the face, trunk, and limbs and mucosal involvement affected 20% of the total body surface area. Nikolsky’s sign was positive. A diagnosis of Steven–Johnson syndrome was highly suspected. One month prior, she had started famotidine and quetiapine. Intravenous methylprednisolone treatment was initiated, which ameliorated the skin lesions after 3 days. However, new lesions developed after only 1 day of methylprednisolone tapering. The patient died 12 days after admission. Conclusion Stevens–Johnson syndrome-toxic epidermal necrolysis is a rare skin disorder. Although it is mainly acute and has a high mortality rate, delayed onset can still occur. Quetiapine and famotidine are generally safe and effective for treating geriatric and gastrointestinal problems, but rare drug hypersensitivity reactions can lead to debilitating consequences. Therefore, increased clinical awareness and the initiation of supportive care are imperative. Optimal management guidelines are still lacking, and confirmation of developed guidelines through randomized controlled trials is needed. Collaboration for better management strategies is warranted.

Massive open online courses (MOOCs; online courses delivered over the Internet) enable distance learning without time and place constraints. MOOCs are popular; however, active participation level among students who take MOOCs is generally lower than that among students who take in-person courses. Students who take MOOCs often lack guidance, and the courses often fail to provide personalized learning materials. Artificial intelligence (AI) has been applied to manage increasing amounts of learning data in learners’ online activity records. Driven by the trend in big data, AI technology has drawn increasing attention in various fields. AI-based recommendation systems (RSs) are powerful tools for improving resource acquisition through supply customization, and they can provide personalized learning materials as study guides. In this study, a personalized learning path for MOOCs based on long short-term memory (LSTM) was proposed to meet students’ personal needs for learning. According to students’ video-watching behaviors, we proposed an MOOC material RS that identifies students with similar learning behaviors through clustering and then uses the clustering results and the learning paths of each group of students to construct an LSTM model to recommend learning paths. The system’s learning path recommendations can effectively improve the online participation of learners, and students who received recommendations progressed from the slow-progress group to the medium-progress or fast-progress group. In addition, the learning attitude questionnaire results indicated that the proposed system not only motivated learners to continue learning and achieve high learning capacity but also supported their study planning according to their individual learning needs.

Abstract Background Diabetic foot ulcer (DFU) is one of the main chronic complications caused by diabetes, leading to amputation in severe cases. Bacterial infection affects the wound healing in DFU. Methods DFU patients who met the criteria were selected, and the clinical data were recorded in detail. The pus exudate from the patient’s foot wound and venous blood were collected for biochemical analysis. The distribution of bacterial flora in pus exudates of patients was analyzed by 16S rRNA sequencing, and the correlation between DFU and pathogenic variables, pyroptosis and immunity was analyzed by statistical analysis. Then, the effects of key bacteria on the inflammation, proliferation, apoptosis, and pyroptosis of polymorphonuclear leukocytes were investigated by ELISA, CCK-8, flow cytometry, RT-qPCR and western blot. Results Clinical data analysis showed that Wagner score was positively correlated with the level of inflammatory factors, and there was high CD3+, CD4+, and low CD8+ levels in DFU patients with high Wagner score. Through alpha, beta diversity analysis and species composition analysis, Corynebacterium accounted for a large proportion in DFU. Logistics regression model and Person correlation analysis demonstrated that mixed bacterial infections could aggravate foot ulcer, and the number of bacteria was closely related to inflammatory factors PCT, PRT, immune cells CD8+, and pyroptosis-related proteins GSDMD and NLRP3. Through in vitro experiments, Corynebacterium inhibited cell proliferation, promoted inflammation (TNF-α, PCT, CRP), apoptosis and pyroptosis (IL-1β, LDH, IL-18, GSDMD, NLRP3, and caspase-3). Conclusion Mixed bacterial infections exacerbate DFU progression with a high predominance of Corynebacterium, and Corynebacterium promotes inflammation, apoptosis and pyroptosis to inhibit DFU healing.

Abstract A cost‐effective chemical prelithiation solution, which consists of Li+, polyaromatic hydrocarbon (PAH), and solvent, is developed for a model hard carbon (HC) electrode. Naphthalene and methyl‐substituted naphthalene PAHs, namely 2‐methylnaphthalene and 1‐methylnaphthalene, are first compared. Grafting an electron‐donating methyl group onto the benzene ring can decrease electron affinity and thus reduce the redox potential, which is validated by density functional theory calculations. Ethylene glycol dimethyl ether (G1), diethylene glycol dimethyl ether, and triethylene glycol dimethyl ether solvents are then compared. The G1 solution has the highest conductivity and least steric hindrance, and thus the 1‐methylnaphthalene/G1 solution shows superior prelithiation capability. In addition, the effects of the interaction time between Li+ and 1‐methylnaphthalene in G1 solvent on the electrochemical properties of a prelithiated HC electrode are investigated. Nuclear magnetic resonance data confirm that 10‐h aging is needed to achieve a stable solution coordination state and thus optimal prelithiation efficacy. It is also found that appropriate prelithiation creates a more Li+‐conducing and robust solid‐electrolyte interphase, improving the rate capability and cycling stability of the HC electrode.

Abstract Background Optimal medical therapy (OMT) for uncomplicated type B aortic dissection (uTBAD) provides excellent short-term outcomes during follow up; however, its long-term therapeutic effectiveness is unsatisfactory. This study evaluated the predictive value of systemic immune-inflammation index (SII) for adverse events among patients with acute uTBAD undergoing OMT. Methods We performed a retrospective analysis of a prospectively maintained database between 2013 and 2020. The primary end point in this study was composite outcomes including aortic intervention, all-cause mortality, retrograde type A aortic dissection (rTAAD) and aortic diameter growth > 5 mm. The patients were divided into high and low SII groups according to the optimal cut-off value of SII as determined using the receiver operating characteristic curve. Cox proportional hazards models were constructed to estimate the hazards ratios and identify the predictors of composite outcomes. Results A total of 124 patients with acute uTBAD who underwent OMT were enrolled. One patient died during hospitalisation. At the end of a mean follow-up duration of 51 ± 23 months, 53 (43.1%) patients experienced composite outcomes, 15 patients (12.2%) died, 31 (25.2%) underwent aortic intervention, 21 (17.1%) exhibited diameter growth of > 5 mm, and 2 developed rTAAD. The patients were divided into low SII group (n = 78, 62.9%) and high SII group (n = 46, 37.1%) as per the optimal cut-off SII value of 1449. The incidence of composite outcomes in high SII group was significantly higher than that in low SII (28 [60.9%] vs. 26[33.3%], p 1449 at the time of diagnosis is an independent predictor of OMT failure.

The digital skills divide has been raised as a serious issue during the COVID-19. However, few studies explored the predictive influence of personality traits on college students' digital skills in online learning. To address this gap, this study took the second-level digital divide as the focus to conduct a two-round survey of college students over nearly 2 years based on cross-sectional and longitudinal methods to explore whether there is a digital skill divide or personality trait differences between urban and rural college students while learning online, and whether college students' personality traits can predict their digital skills. The results confirmed the rural and urban college students' digital skills divide. There were significant differences in all dimensions of their digital skills except for mobile skills. In addition, the digital skills divide of these college students persisted for nearly 2 years. Specifically, this study further confirmed that there were significant differences in the urban and rural college students' extraversion, neuroticism, and agreeableness, but there was no significant difference in their openness and conscientiousness. Additionally, there was no change in the urban and rural college students' personality trait differences in nearly 2 years. Personality traits could positively predict college students' digital skills. This study provides evidence for bridging the second-level digital divide of the rural and urban college students from the perspective of personality traits.

This study presents a streamlined fabrication process for lithium-ion battery (LIB) electrodes, involving the dispersion of carbon nanotubes (CNT), silicon (Si), and graphene oxide (GO) in an aqueous solution, followed by vacuum filtration to produce self-standing composite electrodes. Two reduction routes are employed to form reduced graphene oxide (rGO). The chemically reduced CNT/Si/rGO-5 %-Chem anode exhibits superior mechanical resilience compared to thermally reduced counterparts, which suffer from reduced strength and structural integrity. Chemical reduction also enhances electrochemical performance, increasing the initial capacity of the non-reduced CNT/Si/GO-5 % composite anode from 1,461 to 2,342 mAh g−1, with improved long-term cycling performance. Electrochemical impedance spectroscopy shows lower pre-cycle charge transfer resistance (148 Ω) and superior solid electrolyte interphase (SEI) resistance (43 Ω) for chemically reduced anodes compared to thermally reduced ones. After cycling, the chemically reduced composite anode exhibits reduced electrolyte resistance and charge transfer resistance, indicating stable electrochemical reactions. The composite structure undergoes adaptive rearrangements during cycling, optimizing active material utilization. In summary, CNTs accommodate silicon swelling, while chemically reduced rGO promotes stable SEI formation, highlighting the benefits of chemical reduction in enhancing mechanical durability and electrochemical performance, making the self-standing CNT/Si/rGO composite film a promising LIB anode.

The digital skills divide has been raised as a serious issue during the COVID-19. However, few studies explored the predictive influence of personality traits on college students’ digital skills in online learning. To address this gap, this study took the second-level digital divide as the focus to conduct a two-round survey of college students over nearly 2 years based on cross-sectional and longitudinal methods to explore whether there is a digital skill divide or personality trait differences between urban and rural college students while learning online, and whether college students’ personality traits can predict their digital skills. The results confirmed the rural and urban college students’ digital skills divide. There were significant differences in all dimensions of their digital skills except for mobile skills. In addition, the digital skills divide of these college students persisted for nearly 2 years. Specifically, this study further confirmed that there were significant differences in the urban and rural college students’ extraversion, neuroticism, and agreeableness, but there was no significant difference in their openness and conscientiousness. Additionally, there was no change in the urban and rural college students’ personality trait differences in nearly 2 years. Personality traits could positively predict college students’ digital skills. This study provides evidence for bridging the second-level digital divide of the rural and urban college students from the perspective of personality traits.

Backgrounds: Hematocrit is found an independent risk factor for acute kidney injury (AKI) in certain patients, but this effect in patients with acute myocardial infarction (AMI) is unclear. We aim to identify the relationship between hematocrit and AKI in patients with AMI. Methods: The patient data for the discovery and validation cohorts were extracted from the electronic Intensive Care Unit database and the Medical Information Mart for Intensive Care III database, respectively, to identify the relationship between hematocrit and AKI. With normal hematocrit as the reference, patients were divided into five groups based on the initial hematocrit value. The primary outcome was AKI during hospitalization. A multivariable logistic regression and a marginal effect analysis were used to evaluate the relationship between hematocrit and AKI. Results: In this study, a total of 9692 patients diagnosed with AMI were included, with 7712 patients in the discovery cohort and 1980 patients in the validation cohort. In the discovery cohort, hematocrit in 30–33%, 27–30% or

Distribution of lightning plays a key role in lightning protection of transmission lines. The design of wind deviation under lightning overvoltage of the transmission tower is an empirical parameter in the transmission line design code. Meteorological data from Hong Kong are investigated to analyze the joint distribution of lightning and wind speed. It has been uncovered that daily cloud-to-ground (CG) flashes follow the Burr distribution, which is highly skewed toward a few days with notable lightning. The lightning and wind follow a Gumbel copula joint distribution. According to empirical and theoretical distribution, there are 239 days with more than 1,000 CG lightning flashes per day, and approximately 20% of these days have a maximum wind speed of approximately or over 15 m/s. In 5 days with the number of CG lightning flashes above 30,000, 3 days have a maximum wind speed of over 15 m/s and the other 2 days have a maximum wind speed of over 10 m/s, which suggests that the severe convection with a squall line contributes much to the likelihood of the days with high wind and lightning storms.

The usage of adhesively bonded joints, such as single-lap and double-lap joints, is increasing rapidly in aerospace composite structures as a popular alternative to bolts and rivets. Compared to the conventional joining methods such as fastening and riveting, adhesive-bonding technology better prevents damage to composite structures due to the smooth configuration and the mitigation of stress concentration around holes. In this work, the built-in progressive-damage-modeling techniques in Abaqus, including the cohesive zone model (CZM) and the virtual crack closure technique (VCCT), are used to predict the strength and progressive failure of composite single-lap joints subjected to tensile loading. Modeling of an adhesive layer by using a zero/non-zero-thickness cohesive element, cohesive surface, and VCCT is investigated, as is the effect of brittle and ductile adhesives. Two-dimensional finite-element models with different damage-modeling strategies are performed in this study. The failure-load predictions are compared with the experimental results obtained from the literature. For the ductile adhesive, the predicted failure loads using a zero/non-zero-thickness cohesive elements and a cohesive surface are all shown to be in good agreement with the experiments. However, the VCCT technique predicts higher failure loads. For a brittle adhesive, on the other hand, the predictions by zero/non-zero-thickness cohesive elements and cohesive surfaces reveal notable deviations compared to the experimental results. In contrast to the ductile adhesive, the VCCT technique is revealed to be accurate in predicting the brittle adhesive.

Particles larger than 10 nm from engine exhaust are gaining global concerns. In light of this, to investigate how EGR affects gasoline vehicle SPN10 (solid particles larger than 10 nm) emissions, seven gasoline vehicles (hybrid or conventional) were studied experimentally. The results revealed that EGR vehicles risk failing the current limit (6 * 1011 #/km) more than those without EGR if the cut-off size was tightened from 23 nm to 10 nm. More specifically, during the WLTC test, EGR increased the SPN10 emission factors by 2 ∼ 3 times depending on vehicle powertrains (conventional or hybrid). Notably, SPN10 emissions increased significantly when EGR was actively engaged but showed a decrease when the EGR rate remained constant. EGR and the enriched fuel–air mixture are the critical reasons for the increased SPN10.

Abstract Background Emergent endotracheal intubation (ETI) is a serious complication after Oesophagectomy. It is still unclear that perioperative risk factors and prognosis of these patients with ETI. Methods Between January 2015 and December 2018, 21 patients who received ETI after esophagectomy were enrolled (ETI group) at the department of thoracic surgery, Fujian Union hospital, China. Each study subject matched one patient who underwent the same surgery in the current era were included (control group). Patient characteristics and perioperative factors were collected. Results Patients with ETI were older than those without ETI (p = 0.022). The patients with history of smoking in ETI group were significantly more than those in control group (p = 0.013). The stay-time of postanesthesia care unit (PACU) in ETI group was significantly longer than that in control group (p = 0.001). The incidence of anastomotic leak or electrolyte disorder in ETI group was also higher than that in control group (p = 0.014; p = 0.002). Logistic regression analysis indicated history of smoke (HR 6.43, 95%CI 1.39–29.76, p = 0.017) and longer stay time of PACU (HR 1.04, 95%CI 1.01–1.83, p = 0.020) both were independently associated with higher risks of ETI. The 3-year overall survival (OS) rates were 47.6% in patients with ETI and 85.7% in patients without ETI (HR 4.72, 95%CI 1.31-17.00, p = 0.018). COX regression analysis indicated ETI was an independent risk factor affecting the OS. Conclusion The study indicated that history of smoking and longer stay-time in PACU both were independently associated with higher risks of ETI; and ETI was an independent risk factor affecting the OS of patients after esophagectomy. Trial registration This trial was retrospectively registered with the registration number of ChiCTR2000038549.

As of December 2021, the cryptocurrency market had a market value of over US$270 billion, and over 5,700 types of cryptocurrencies were circulating among 23,000 online exchanges. Reinforcement learning (RL) has been used to identify the optimal trading strategy. However, most RL-based optimal trading strategies adopted in the cryptocurrency market focus on trading one type of cryptocurrency, whereas most traders in the cryptocurrency market often trade multiple cryptocurrencies. Therefore, the present study proposes a method based on deep Q-learning for identifying the optimal trading strategy for multiple cryptocurrencies. The proposed method uses the same training data to train multiple agents repeatedly so that each agent has accumulated learning experiences to improve its prediction of the future market trend and to determine the optimal action. The empirical results obtained with the proposed method are described in the following text. For Ethereum, VeChain, and Ripple, which were considered to have an uptrend, a horizontal trend, and a downtrend, respectively, the annualized rates of return were 725.48%, −14.95%, and − 3.70%, respectively. Regardless of the cryptocurrency market trend, a higher annualized rate of return was achieved when using the proposed method than when using the buy-and-hold strategy.

Abstract Based on incredibly increasing applications in modern optoelectronic devices, the demand for securing a superior conductive transparent electrode (TCE) candidate becomes significant and urgent. However, boosting both transmittance and conductance simultaneously is an intrinsic limitation. In this work, we present silver nanoscale plasmonic wires (Ag NPWs) to function as TCEs in the visible light region by lowering their corresponding plasma frequencies. By carefully designing geometric dimensions of the Ag NPWs, we also optimize the performance for red, green, and blue colors, respectively. The demonstrated figure of merits for RGB colors appeared respectively 443.29, 459.46, and 133.78 in simulation and 302.75, 344.11, and 348.02 in experiments. Evidently, our Ag NPWs offer much greater FoMs beyond conventional TCEs that are most frequently comprised of indium tin oxide and show further advantages of flexibility and less Moire effect for the applications of flexible and high-resolution optoelectronic devices.

Abstract Background Rhomboid intercostal block (RIB) is a new regional anesthesia technique that provides postoperative analgesia for breast surgery and thoracoscopic surgery. The published papers are not yet fully integrated and do not adequately address the impact and safety of the RIB on postoperative pain. Methods The PubMed, Web of Science and Embase were searched from 2016 to 2021 for all available randomized controlled trials (RCTs) that evaluated the analgesic efficacy and safety of RIB after thoracic surgery and breast surgery. Random and fixed-effects meta-analytical models were used where indicated, and between-study heterogeneity was assessed. The primary outcome was Postoperative Numerical Rating Scale (NRS) scores of patients at rest recorded 0–1, 6–8, 24 h after surgery. The secondary outcomes included rate of postoperative nausea and vomiting (PONV), postoperative fentanyl consumption and presence of complications of the block. Results From 81 records identified, four studies met our inclusion criteria, including 216 patients (RIB:108 patients; no block: 108 patients). In the primary outcome, RIB group showed significantly lower postoperative NRS at rest at first 0–1 h and 6–8 h (weighted mean difference [WMD] = -1.55; 95% confidence internal [CI] = -2.92 to -0.19; p

Abstract Conventional vapor–liquid–solid mechanism of nanowire growth opens up new opportunities of fabricating nanowires with controllable morphologies and aspect ratios. However, gaseous precursors have disadvantages of high material and processing cost, high toxicity, and limited scalability. By contrast, synthesizing nanowires via solid–liquid–solid mechanism could be a facile alternative since the low cost and nontoxic solid precursor is adopted in the process. In this study, the cooling control is found to be very critical for the solid–liquid–solid nanowire growth. Without a sufficient negative vertical temperature gradient, the nucleation and continuous growth of silicon nanowires could not occur. High volume gas flow cooling, fluctuating the heating temperature, decreasing the cooling rate, and applying a heat sink are all efficacious to promote silicon nanowire formation. In addition to the nanowires formed under high gas flow cooling on the silicon wafer sputtered with a nickel thin film, the solid–liquid–solid mechanism‐derived silicon nanowire growth can also be economically achieved by adopting a solution‐based coating of a nickel precursor onto the silicon substrate paired with a programmed slow cooling condition without using any gas, which could be transferred to other eutectic systems for cost‐effective nanomaterial fabrication.

Background During counter movement jumps, adding weight in the eccentric phase and then suddenly releasing this weight during the concentric phase, known as accentuated eccentric loading (AEL), has been suggested to immediately improve jumping performance. The level of evidence for the positive effects of AEL remains weak, with conflicting evidence over the effectiveness in enhancing performance. Therefore, we proposed to theoretically explore the influence of implementing AEL during constrained vertical jumping using computer modelling and simulation and examined whether the proposed mechanism of enhanced power, increased elastic energy storage and return, could enhance work and power. Methods We used a simplified model, consisting of a ball-shaped body (head, arm, and trunk), two lower limb segments (thigh and shank), and four muscles, to simulate the mechanisms of AEL. We adjusted the key activation parameters of the muscles to influence the performance outcome of the model. Numerical optimization was applied to search the optimal solution for the model. We implemented AEL and non-AEL conditions in the model to compare the simulated data between conditions. Results Our model predicted that the optimal jumping performance was achieved when the model utilized the whole joint range. However, there was no difference in jumping performance in AEL and non-AEL conditions because the model began its push-off at the similar state (posture, fiber length, fiber velocity, fiber force, tendon length, and the same activation level). Therefore, the optimal solution predicted by the model was primarily driven by intrinsic muscle dynamics (force-length-velocity relationship), and this coupled with the similar model state at the start of the push-off, resulting in similar push-off performance across all conditions. There was also no evidence of additional tendon-loading effect in AEL conditions compared to non-AEL condition. Discussion Our simplified simulations did not show improved jump performance with AEL, contrasting with experimental studies. The reduced model demonstrates that increased energy storage from the additional mass alone is not sufficient to induce increased performance and that other factors like differences in activation strategies or movement paths are more likely to contribute to enhanced performance.

CeCu mixed oxides surface modified by Ti were prepared by self-assembly method. CeCuTi1 (molar ratio of Ce:Cu:Ti = 1:1:1) exhibited enhanced sulfur resistance during benzene catalytic combustion reaction due to the newly formed Ti-O-Cu/Ce structures with strong-acid sites or incremental Lewis-Brønsted acid complex at 300 °C. H2O accelerated the SO2 poisoning because hydroxy groups promote the formation of sulfates. The decreased reducibility and enhanced acidity of sulfated CeCuTi inhibited the activity but improved SO2 resistance, respectively. Meanwhile, Ti weakened the CuO-CeO2 synergistic effects, reducing the activity of fresh CeCuTi. Effects of Ti on activity and sulfur resistance were offered systemically.

To understand students’ learning behaviors, this study uses machine learning technologies to analyze the data of interactive learning environments, and then predicts students’ learning outcomes. This study adopted a variety of machine learning classification methods, quizzes, and programming system logs, found that students’ learning characteristics were correlated with their learning performance when they encountered similar programming practice. In this study, we used random forest (RF), support vector machine (SVM), logistic regression (LR), and neural network (NN) algorithms to predict whether students would submit on time for the course. Among them, the NN algorithm showed the best prediction results. Education-related data can be predicted by machine learning techniques, and different machine learning models with different hyperparameters can be used to obtain better results.