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The objective of this study is to understand how humidity and temperature affect the degradation behavior of polydioxanone (PDO). After changing the degradation environments, we examined the weight loss, pH value, thermal properties, mechanical properties, morphology, and rheological properties of the PDO samples. The specimens were kept in a wet or dry condition for 12 weeks. During degradation, weight retention, pH values, tensile strength, and viscosity decreased, whereas the melting temperature ([T.sub.m]) and degree of crystallinity ([D.sub.c]) increased. Compared with dry samples, wet PDO samples exhibited a higher hydrolysis rate. Also, higher temperatures led to more hydrolysis. Humidity was found to decrease the molecular weight of the samples and increase the degradation rate. KEYWORDS degradation, disk, injection-molding, polydioxanone, rheology, 1 | INTRODUCTION Recently, interest in biodegradable polymers are rising due to the necessity of developing environmentally-friendly polymers. [1] The major advantage of biodegradable polymers is that the products can [...]

The growing need for lithium-ion batteries, fueled by the widespread use of electric vehicles (EVs) and portable electronic devices, requires high energy density and safety. The cathode material Li1-x(NiyCozMn1-y-z)O2 (NCM) shows promise, but attaining high efficiency necessitates optimization of both composition and manufacturing methods. Polycrystalline LiNiCoMnO2 powders were synthesized and assessed in this investigation using a polyvinyl alcohol (PVA) solution method. The study examined different synthesis conditions, such as the PVA to metal ions ratio and the molecular weight of PVA, to assess their influence on powder characteristics. Electrochemical analysis indicated that cathode materials synthesized with a relatively high quantity of PVA with a molecular weight of 98,000 exhibited the highest discharge capacity of 170.34 mAh/g and a high lithium-ion diffusion coefficient of 1.19 × 10−9 cm2/s. Moreover, decreasing the PVA content, irrespective of its molecular weight, led to the production of powders with reduced surface areas and increased pore sizes. The adjustments of PVA during synthesis resulted in pre-sintering observed during the synthesis process, which had an impact on the long-term stability of batteries. The electrodes produced from the synthesized powders had a positive impact on the insertion and extraction of Li+ ions, thereby improving the electrochemical performance of the batteries. This study reveals that cathode materials synthesized with a high quantity of PVA with a molecular weight of 98,000 exhibited the highest discharge capacity of 170.34 mAh/g and a high lithium-ion diffusion coefficient of 1.19 × 10−9 cm2/s. The findings underscore the significance of optimizing methods for synthesizing PVA-based materials to enhance the electrochemical properties of NCM cathode materials, contributing to the advancement of lithium-ion battery technology. The findings underscore the significance of optimizing methods for synthesizing PVA-based materials and their influence on the electrochemical properties of NCM cathode materials. This contributes to the continuous progress in lithium-ion battery technology. [ABSTRACT FROM AUTHOR]

Worldwide, significant efforts are made to identify energy sources that can help achieve carbon neutrality and promote sustainable development. The development of a catalyst that combines durability and high performance is essential for the commercialization of proton‐exchange membrane fuel cells (PEMFCs). In a fuel cell, carbon corrosion occurs during startup and shutdown due to improper local flooding caused by inadequate water management. In this study, a Pt‐based catalyst is designed with excellent durability and high activity. Introducing a metal oxide layer modified with Pt/multi‐walled carbon nanotubes reduces the direct contact between carbon and the fuel cell environment. This helps prevent carbon corrosion and inhibits the separation, aggregation, and growth of Pt nanoparticles. Moreover, the catalyst exhibits enhanced oxygen reduction activity due to the electronic effect of the metal oxide layer that is coated on it. In this study, by implementing a carbon erosion acceleration protocol, excellent catalytic properties during a load‐cycling experiment consisting of 5,000 cycles are reported. The practical application of the developed catalyst in PEMFCs offers an effective approach to developing Pt‐group metal catalysts with exceptional activity. [ABSTRACT FROM AUTHOR]

Cathode active materials and conductive additives for thermal batteries operating at high temperatures have attracted research interest, with a particular focus on compounds offering high thermal stability. Recently, FeF3 has been proposed as a candidate for high-voltage cathode materials; however, its commercialization is hindered by its low conductivity. In this study, conductive additives, such as Ni-coated carbon composites (multi-walled carbon nanotubes (MWCNTs) and carbon black (CB)), were utilized to enhance the thermal stability and conductivity of FeF3. The incorporation of metal–carbon conductive additives in the FeF3 composite increased the thermal stability by more than 10 wt.% and ensured high capacity upon conductivity enhancement. The FeF3@Ni/MWCB 15 wt.% composite containing 30 wt.% Ni exhibited a discharge capacity of ∼86% of the theoretical capacity of 712 mAh/g. The use of Ni-coated carbon-based conductive additives will allow the application of FeF3 as an effective high-temperature cathode material for thermal batteries. [ABSTRACT FROM AUTHOR]

Abstract With the rapid growth of big data, extracting meaningful knowledge from data is crucial for machine learning. The existing Swarm Learning data collaboration models face challenges such as data security, model security, high communication overhead, and model performance optimization. To address this, we propose the Swarm Mutual Learning (SML). Firstly, we introduce an Adaptive Mutual Distillation Algorithm that dynamically controls the learning intensity based on distillation weights and strength, enhancing the efficiency of knowledge extraction and transfer during mutual distillation. Secondly, we design a Global Parameter Aggregation Algorithm based on homomorphic encryption, coupled with a Dynamic Gradient Decomposition Algorithm using singular value decomposition. This allows the model to aggregate parameters in ciphertext, significantly reducing communication overhead during uploads and downloads. Finally, we validate the proposed methods on real datasets, demonstrating their effectiveness and efficiency in model updates. On the MNIST dataset and CIFAR-10 dataset, the local model accuracies reached 95.02% and 55.26%, respectively, surpassing those of the comparative models. Furthermore, while ensuring the security of the aggregation process, we significantly reduced the communication overhead for uploading and downloading.

The anterior cruciate ligament (ACL) and posterior cruciate ligament (PCL) are important structures to maintain knee stability. The present study aimed to further enrich understandings of the morphology of the cruciate ligaments and explore the relationship between the diameter of ACL and PCL.

Sugar beets account for 30% of global sugar production each year, and their byproducts are an important source of bioethanol and animal feed. Sugar beet is an important cash crop in Inner Mongolia, China. To achieve high yields and sugar content, it is essential to supply nitrogen fertilizer in accordance with the growth characteristics of sugar beet, thereby enhancing the efficiency of nitrogen fertilizer utilization. A two-year experiment was carried out in the experimental field of the Inner Mongolia Academy of Agricultural & Animal Husbandry Sciences. The impact of varying ratios of nitrogen-based fertilizer to topdressing on nitrate nitrogen and ammonium nitrogen levels in the 20–60 cm soil layer, as well as the activities of protease, urease, catalase, and sucrose in the 20–40 cm soil layer were investigated during the rapid leaf growth period and root and sugar growth period. Results indicated that different ratios of nitrogen-based fertilizer to topdressing significantly influenced the levels of nitrate nitrogen and ammonium nitrogen, and the activities of protease and urease in the 0–20 cm soil layer, with these effects diminishing as soil depth increased. The activities of catalase and sucrose were minimally impacted. Nitrogen was applied at 150 kg/ha during the growth period of sugar beet, according to the growth characteristics of sugar beet to maximize nitrogen utilization efficiency. Topdressing was completed with irrigation at the rapid growth stage. The nitrogen-based fertilizer to topdressing ratio of 6:4 resulted in optimal crop yield and sugar yield of sugar beet under shallow drip irrigation. Additionally, the activities of protease and urease in different soil treatments were significantly different, and the activities of protease and urease in the 0–40 cm soil layer were identified as useful soil physiological indicators for nitrogen utilization in sugar beet.

In this work, the effect of Sn doping on the formation of an orthorhombic κ-Ga2O3 was investigated through experiments and first-principles calculations. The κ-Ga2O3 films were grown on sapphire and GaN on-sapphire by a mist chemical vapor deposition. For κ-Ga2O3 on sapphire grown at 650 °C, the β-phase emerged upon the strain relaxation of the κ-phase. Doping with Sn resulted in the thicker κ-phase with the thinner β-phase, suggesting the Sn-induced delay of strain relaxation. The phase stabilization can be attributed to the compressive strain induced by Sn substituting smaller Ga. This compressive strain also led to the thinner intermediate layers underneath the κ-phase. κ-Ga2O3 on GaN grown at 670 °C, on the other hand, exhibited neither the intermediate layers nor the β-phase layer on top. In comparison with the undoped κ-Ga2O3 on GaN, the Sn-doped sample showed a more abrupt interface with less severe rotational-domain formation. Our first-principles calculations based on density functional theory suggested that Sn+ on a pentahedral Ga site has the lowest formation energy under the oxygen-rich condition and maintains relatively low formation energy even under the oxygen-poor condition. Given the pentahedron coordination is unique to the κ-phase, this could have also contributed to the phase stabilization. Understanding the effect of Sn could provide ways to improve the structural quality of κ-Ga2O3, which is a promising material for next-generation electronics. Please check and confirm the edit made in article title.ConfirmedPlease confirm if the author names are presented accurately and in the correct sequence (given name, middle name/initial, family name). Author 1 Given name: [Ha Young] Last name [Kang] Author 4 Given name: [Tae Hoon] Last name [Lee], Author 1 Given name: [Roy Byung Kyu] Last name [Chung]. Also, kindly confirm the details in the metadata are correct.ConfirmedPlease confirm if the author names are presented accurately and in the correct sequence (given name, middle name/initial, family name). Author 1 Given name: [specify authors given name] Last name [specify authors last name]. Also, kindly confirm the details in the metadata are correct.Confirmed [ABSTRACT FROM AUTHOR]

Background and purpose: Despite the superior dose conformity of proton therapy, the dose distribution is sensitive to daily anatomical changes, which can affect treatment accuracy. This study evaluated the dose recalculation accuracy of two synthetic computed tomography (sCT) generation algorithms in a commercial treatment planning system. Materials and methods: The evaluation was conducted for head-and-neck, thorax-and-abdomen, and pelvis sites treated with proton therapy. Thirty patients with two cone-beam computed tomography (CBCT) scans each were selected. The sCT images were generated from CBCT scans using two algorithms, Corrected CBCT (corrCBCT) and Virtual CT (vCT). Dose recalculations were performed based on these images for comparison with “ground truth” deformed CTs. Results: The choice of algorithm influenced dose recalculation accuracy, particularly in high dose regions. For head-and-neck cases, the corrCBCT method showed closer agreement with the “ground truth”, while for thorax-and-abdomen and pelvis cases, the vCT algorithm yielded better results (mean percentage dose discrepancy of 0.6 %, 1.3 % and 0.5 % for the three sites, respectively, in the high dose region). Head-and-neck and pelvis cases exhibited excellent agreement in high dose regions (2 %/2 mm gamma passing rate >98 %), while thorax-and-abdomen cases exhibited the largest differences, suggesting caution in sCT algorithm usage for this site. Significant systematic differences were observed in the clinical target volume and organ-at-risk doses in head-and-neck and pelvis cases, highlighting the importance of using the correct algorithm. Conclusions: This study provided treatment site-specific recommendations for sCT algorithm selection in proton therapy. The findings offered insights for proton beam centers implementing adaptive radiotherapy workflows.

Parrondo's paradox refers to the counterintuitive phenomenon whereby two losing strategies, when alternated in a certain manner, can result in a winning outcome. Understanding the optimal sequence in Parrondo's games is of significant importance for maximizing profits in various contexts. However, the current predefined sequences may not adapt well to changing environments, limiting their potential for achieving the best performance. We posit that the optimal strategy that determines which game to play should be learnable through experience. In this Letter, we propose an efficient and robust approach that leverages Q learning to adaptively learn the optimal sequence in Parrondo's games. Through extensive simulations of coin-tossing games, we demonstrate that the learned switching strategy in Parrondo's games outperforms other predefined sequences in terms of profit. Furthermore, the experimental results show that our proposed method can be easily adjusted to adapt to different cases of capital-dependent games and history-dependent games.

This paper studies how the GeoGebra 3D Calculator with augmented reality (AR) serves as a tool for visualizing 3D graphs from functions of two variables in a mid-sized multivariable calculus class. We assessed this AR tool’s usability as a content delivery system. In our study, 39 students in the AR group received instructions on using the GeoGebra 3D Calculator with AR tool, while another 28 students in the control group worked exclusively with PowerPoint slides. Statistical testing reveals that the GeoGebra 3D Calculator with AR proves more effective than PowerPoint slides in terms of usability for teaching and learning multivariable calculus. However, the students’ attitudes and engagement do not show significant differences between the AR and control groups. This preliminary study highlights the crucial role of innovative educational technologies like the GeoGebra 3D Calculator with AR in enhancing the visualization of 3D graphs and other multivariable calculus topics. It suggests an advancement in the evolution of digital tools in education. The increased usability observed in the AR group indicates a promising direction for future educational strategies, especially in fields that require strong spatial visualization skills.

In this study, we investigate the attitudes of students towards an immersive educational instruction method used to augment in-person biology laboratories. Specifically, we employ the Oculus Quest 2 in a mid-sized biology classroom setting to conduct a virtual heart dissection instructional class using Virtual Reality (VR). We engaged with 23 students from a Singaporean secondary school to gather insights into their experiences and perspectives. The focus of this study is twofold: firstly, to assess the potential of VR technology, particularly the Oculus Quest 2, as a tool for enhancing learning in biology, and secondly, to evaluate the feasibility of replacing traditional in-person laboratory settings with VR-based learning environments. By examining the students’ experiences, this research sheds light on the effectiveness of VR technology in enhancing academic engagement and understanding of this specific content taught in a standardized biology syllabus. Our findings reveal that Oculus Quest 2 with VR can significantly stimulate student interest in this topic. The study provides insights into how VR as part of educational technology can transform the traditional learning experience, offering a more interactive and engaging approach to education in biology. These preliminary results have important implications for the future of educational methodologies in science and technology subjects.

Solving mathematical equations faster and more efficiently has been a Holy Grail for centuries for scientists and engineers across all disciplines. While electronic digital circuits have revolutionized equation solving in recent decades, it has become apparent that performance gains from brute-force approaches of compute-solvers are quickly saturating over time. Instead, paradigms that leverage the universes’ natural tendency to minimize a system’s free energy, such as annealers or Ising Machines, are being sought after due to favorable complexity scaling. Here, we introduce a programmable analog solver leveraging the formal mathematical equivalence between Maxwell’s equations and photonic circuitry. It features a mesh network of nanophotonic beams to find solutions to partial differential equations. As an example, we designed, fabricated, and demonstrated a novel application-specific photonic integrated circuit comprised of electro-optically reconfigurable nodes and experimentally validated 90 % accuracy with respect to a commercial solver. Finally, we tested this photonic integrated chip performance by simulating thermal diffusion on a spacecraft’s heat shield during re-entry to a planet’s atmosphere. The programmable light-circuitry presented herein offers a facile route for solving complex problems and thus will have profound potential applications across many scientific and engineering fields.

The enormous energy carried by discharged water poses a serious threat to the Piano Key Weir (PKW) and its downstream hydraulic structures. However, previous research on energy dissipation in PKWs has mainly focused downstream effects, and the research methods have been largely limited to physical model experiments. To deeply investigate the discharge capacity and hydraulic characteristics of PKW, this study established a PKW model with universally applicable geometric parameters. By combining physical model experiments and numerical simulations, the flow pattern of the PKW, the discharge at the overflow edges, and the variation in the energy dissipation were revealed for different water heads. The results showed that the discharge of the side wall constitutes the majority of the total discharge at low water heads, resulting in a relatively high overall discharge efficiency. As the water head increases, the proportion of discharge from the inlet and outlet keys increases, while the proportion from the side wall decreases. This change results in less discharge from the side wall and a consequent reduction in the overall discharge efficiency. The PKW exhibits superior energy dissipation efficiency under low water heads. However, this efficiency exhibits an inverse relationship with an increasing water head. The overall energy dissipation efficiency can reach 40% to 70%. Additionally, the collision of the water flows inside the outlet chamber and the mixing of the overflow jet play a primary role in energy dissipation. The findings of this study have significant implications for hydraulic engineering construction and PKW operational safety.