EA Technology

Hydrogel brushes comprise cross-linked polymer chains that are attached at one end to a substrate. Their network architecture promotes electrochemical stability, which renders these materials suitable for electrochemical applications. However, the electrochemical properties of such hydrogel brushes have not yet been studied yet. In this work, we investigate the role of the cross-linker in the electrochemical behavior of negatively charged poly(3-sulfopropyl methacrylate) (PSPMA) hydrogel brushes. Using electrochemical impedance spectroscopy, we study the role of layer resistance in a poor solvent (0.1 M KOH) and the role of charge transfer in a good solvent (0.1 M KCl). In 0.1 M KOH, we observe that the polymer layer limits the ion diffusion to the electrode and that this phenomenon depends on the cross-linker content. There is an optimum in cross-linker content, for which the ion diffusion experiences the least resistance. We attribute this optimum to a variance in the flexible and rigid polymer phase. In 0.1 M KCl, we find that the solvated brush thickness is the parameter that determines the extent of the charge transfer resistance. The more cross-linked a hydrogel brush is, the lower the swelling ratio in a good solvent and thus the lower the charge transfer resistance becomes. With this knowledge, we can use the cross-linker content as a tool to minimize the layer resistance or tune the charge transfer resistance, which is useful for corrosion protection, electrochemical sensing, and ion gating applications.

<div class="section abstract"><div class="htmlview paragraph">Current studies about battery pack bottom strike usually focus on one test condition individually. To study the relation between quasi-static and dynamic crush in battery pack bottom strike, the paper combined quasi-static crush result and dynamic strike preset kinetic energy value with the same displacement damage on the battery pack bottom plate and cell. First, based on the finite element model of the battery pack, the quasi-static crush is applied. Several dynamic crush tests with different initial kinetic energy sets are also introduced. Then based on the same displacement damage, the pressure in quasi-static and kinetic energy in dynamic conditions are summarized. Fitting methods including polynomial regression, support vector regression (SVR), extreme learning machine (ELM), multilayer perceptron (MLP), Gaussian process regression (GPR), and K-nearest neighbor (KNN) regression are used to study the relation between the two different test load. The result shows that they have a strong relation. Compared with the case test of GPR and KNN, polynomial regression with a degree of 4 could be the best choice to predict the dynamic load value from quasi-static results globally for bottom plate and degree of 3 for cell.</div></div>

The acoustical-optical networks that are underwater have very dynamic, bandwidth-constrained, and time-sensitive conditions that require intelligent, secure and adaptive communications. In order to mitigate such issues, this paper presents Edge Intelligence-Assisted Federated Deep Learning Framework that will facilitate privacy-preserved, reliable, and energy-efficient data communication in hybrid underwater acoustic-optical space. The proposed architecture provides that every underwater node conducts local model training with measured acoustic-optical channel parameters and raw data are stored at the edge, therefore, guaranteeing excellent levels of privacy and preventing the risk of data exposure. It is a federated mechanism of aggregation of the global learning updates that are implemented on a distributed node, whereby the network is able to learn the best mode selection, power allocation, and routing policies without having to have a centralized collection of data. The trained federated model dynamically selects either between acoustic or optical links depending on turbidity, scattering, transmission distance, node energy and estimated packet success rate, where crosslayer adjustment is robust across the varying marine conditions. The simulation results with physics-based underwater channel models show that the framework proposed will give a 19 much higher throughput, lower latency by 24%, lower energy consumption by 18 percent, and increase the ability of the hybrid link selection by 35 percent, at full data privacy. In general, this paper offers a scalable, smart, and safe communication plan to next-generation Underwater Internet-of-Things (UIoT) systems to address applications including underwater environmental-monitoring, autonomous robotics, maritime surveillance, and ocean resource exploration.

The Panimbang River in Pandeglang, Indonesia, is widely used by residents for daily activities. This study analyzed water quality using physical parameters, including pH, temperature, dissolved oxygen (DO), Total Dissolved Solids (TDS), TSS (Total Suspended Solids), salinity, and alkalinity. Samples were taken around the estuary, which is surrounded by settlements. The results showed a pH of 4.50–6.49, below the standard of 6–9, indicating slightly acidic water. High temperatures, thought to be due to daytime sampling, affect DO. The average DO value was only 2.96 mg/L, well below the minimum threshold of 5 mg/L, indicating poor aeration. The average TDS is above 1000 ppm, indicating pollution from household waste and human activities. The average alkalinity of 384.976 mg/L CaCO₃ is within the safe range (30–500 mg/L), so it can stabilize pH. The water quality in the Panimbang River Estuary indicates environmental pressure from anthropogenic activities, resulting in slightly acidic water with low aeration and high solute content. However, adequate alkalinity still helps maintain the water's pH stability. This condition indicates the need for more intensive monitoring and waste management to protect estuarine ecosystems, which are vital to the community and the surrounding environment.

Endovascular repair of Stanford type A aortic dissection (TAAD) remains technically challenging because of the complex anatomy and dynamic biomechanics of the ascending aorta. This study aimed to evaluate the technical feasibility and safety of an innovative stent graft system in patients with acute ascending aortic syndromes. Ten patients were enrolled across three centres. Diagnoses comprised classic TAAD (n = 2), retrograde TAAD (n = 4), penetrating aortic ulcer (n = 1), and intramural haematoma (n = 3). All procedures were performed under general anaesthesia with percutaneous femoral access. One patient experienced fatal cardiac arrest during anaesthesia induction before device deployment and was excluded from analysis, as per protocol. The remaining nine patients underwent successful stent graft implantation. Primary outcomes included technical success and major adverse events at 1, 6, and 12 months. Among the nine patients, the stent graft system was successfully implanted intra-operatively in all cases. However, technical success at 30 days was achieved in eight patients, as one patient developed a type Ia endoleak on post-operative day seven. Two patients experienced new aortic dissections during follow up: one de novo and one stent graft induced new entry; only the latter underwent surgical intervention. At 12 months, no death, stroke, myocardial infarction, spinal cord injury, or stent graft migration was observed. The innovative stent graft system demonstrated technical feasibility and an acceptable short term safety profile for endovascular repair of acute ascending aortic syndromes. This anatomically responsive design may expand the therapeutic options for selected high risk patients. Larger, long term studies are needed to confirm these preliminary findings.

This paper in this context targets the issues of low flexibility and low capability to conflict effectively of the current multi-view BOM conversion algorithms when facing the multi-disciplinary and highly-complex nature of ship manufacturing, and so the knowledge graph-based hierarchical conversion algorithm of multi-view BOM is suggested. To begin with, it mathematically examines the structural variations and conversion needs of EBOM, PBOM and MBOM in the case of manufacturing a ship. On the foundation of this, a theoretical framework that will encompass data mapping, resource matching and conflict resolution is built, and an algorithm architecture, composed of three layers is designed, which are input, conversion, and output. A knowledge graph of BOM conversion is domain-specific that incorporates 5 entities of type core ones and 4 types of key relationship. By calling the rule base, the algorithm realizes BOM reconstruction, resolves three common conflicts, and achieves effective connection between various BOM views through specific processing strategies such as virtual part deletion, purchased part merging, and resource binding. Simulation tests on BOM data of different scales are conducted to verify the algorithm's performance. Results show that the algorithm's conversion time is directly proportional to the material scale, with an overall conversion accuracy of ≥94.5%, 100% data integrity, and excellent real-time performance. The algorithm is effective in resolving what has been a long-standing problem namely the disconnection between design and the manufacturing links when it comes to the traditional process of ship manufacturing, and it is also able to offer reliable technical support to the entire lifecycle of the digital collaboration between ship manufacturing.