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Acetic acid (AA), an important commodity chemical, is produced via methanol carbonylation, emitting one ton of CO₂ per ton of product. As a sustainable alternative, we report the electrochemical oxidation of bioethanol to selectively produce AA using a novel Pdsingle bondSn alloy catalyst with nanodendritic morphology supported on nickel foam (PdSn@NF). The catalyst was synthesized via electrodeposition, and the presence of ammonium chloride in the deposition bath was found to critically affect the Pd-to-Sn ratio and, consequently, the catalyst performance. The vital role of catalyst structure, surface composition, and morphology on the activity and selectivity of PdSn@NF towards the EOR was revealed by X-ray diffractometry, emission spectroscopy, and electron microscopy. Specifically, the nanodendritic morphology of the PdSn@NF resulted in the formation of highly active undercoordinated sites, while in situ Raman spectroscopy suggested that Sn helps mitigate CO poisoning – likely a result of a lowered d-band center. Due to the strong synergy between the structural and electronic properties of PdSn@NF, ~100 % faradaic efficiency (FE) to AA at 400 mA cm−2 was achieved with lab-grade ethanol (LGE) in an H-type cell. In continuous flow operation, the FE declined due to product accumulation on active sites; however, this was mitigated by employing current pulses to remove surface-bound products. An optimized pulsing protocol restored ~100 % FE of AA for LGE and achieved ~94 % FE with bioethanol at 400 mA cm−2 despite the presence of fermentation impurities. This study underscores the promise of PdSn@NF as a highly selective and industrially relevant electrocatalyst for sustainable AA production. 

Abstract Multi-year marine heatwaves (MHWs) in the Gulf of Alaska (GOA) are major climate events with lasting ecological and economic effects. Though often seen as local Pacific phenomena, our study shows their persistence depends on trans-basin interactions between the North Pacific and North Atlantic. Using observational data and climate model experiments, we find that prolonged MHWs occur as sequential warming episodes triggered by atmospheric wave trains crossing ocean basins. These wave trains alter surface heat flux, initiating MHWs in the GOA and changing North Atlantic sea surface temperatures (SSTs). In turn, Atlantic SST anomalies reinforce wave activity, fueling subsequent MHW episodes in a feedback loop. This mechanism appears in historical events (1949–52, 1962–65, 2013–16, and 2018–22), highlighting MHWs as a trans-basin phenomenon. Our findings link GOA MHWs to trans-basin atmospheric wave dynamics and identify North Atlantic SSTs as a potential predictor of their duration. 

Abstract The Sc2.0 global consortium to design and construct a synthetic genome based on theSaccharomyces cerevisiaegenome commenced in 2006, comprising 16 synthetic chromosomes and a new-to-nature tRNA neochromosome. In this paper we describe assembly and debugging of the 902,994-bp syntheticSaccharomyces cerevisiaechromosomesynXVIof the Sc2.0 project. Application of the CRISPR D-BUGS protocol identified defective loci, which were modified to improve sporulation and recover wild-type like growth when grown on glycerol as a sole carbon source when grown at 37˚C. LoxPsym sites inserted downstream of dubious open reading frames impacted the 5’ UTR of genes required for optimal growth and were identified as a systematic cause of defective growth. Based on lessons learned from analysis of Sc2.0 defects andsynXVI, anin-silicoredesign of thesynXVIchromosome was performed, which can be used as a blueprint for future synthetic yeast genome designs. Thein-silicoredesign ofsynXVIincludes reduced PCR tag frequency, modified chunk and megachunk termini, and adjustments to allocation of loxPsym sites and TAA stop codons to dubious ORFs. This redesign provides a roadmap into applications of Sc2.0 strategies in non-yeast organisms. 

In recent years, there has been a notable increase in the prevalence of malicious websites, leading to a majority of cyber-attacks and data breaches. Malicious websites often incorporate JavaScript code to execute attacks on web browsers. Despite existing methodologies documented in the literature, the analysis and detection of malicious JavaScript pose significant challenges due to the dynamic nature of JavaScript and the use of advanced evasion techniques. These challenges motivate the need for an innovative and efficient approach to comprehensively analyze the code to identify its malicious intent. In this paper, we introduce a monitoring approach for analyzing JavaScript code, which can capture all of the code’s features at runtime. Our method leverages the security reference monitor technique to mediate JavaScript security-sensitive executions, including function calls and property accesses. Therefore, the proposed method can capture behaviors at runtime regardless of how the code is written, even with recent advanced evasion techniques like WebAssembly diversification. We have implemented our approach as a JavaScript dynamic analysis framework called JSMBox in a Chromium-based browser extension. Our experiments demonstrated that JSMBox is capable of effectively countering sophisticated evasion techniques found in modern malicious JavaScript code, including WebAssembly diversification. We have also evaluated the framework’s ability to classify malicious behaviors based on a large-scale raw dataset comprising about 20,000 malicious and benign webpages. Our developed tool automatically launches the browser to execute these webpages, records JavaScript code execution events, and captures their execution frequency as extracted features. We have tested the extracted dataset with various machine-learning models, yielding promising experimental results that confirm the effectiveness of our approach and achieve a high accuracy rate. 

The large demand of mobile devices creates significant concerns about the quality of mobile applications (apps). Developers need to guarantee the quality of mobile apps before it is released to the market. There have been many approaches using different strategies to test the GUI of mobile apps. However, they still need improvement due to their limited effectiveness. In this article, we propose DinoDroid, an approach based on deep Q-networks to automate testing of Android apps. DinoDroid learns a behavior model from a set of existing apps and the learned model can be used to explore and generate tests for new apps. DinoDroid is able to capture the fine-grained details of GUI events (e.g., the content of GUI widgets) and use them as features that are fed into deep neural network, which acts as the agent to guide app exploration. DinoDroid automatically adapts the learned model during the exploration without the need of any modeling strategies or pre-defined rules. We conduct experiments on 64 open-source Android apps. The results showed that DinoDroid outperforms existing Android testing tools in terms of code coverage and bug detection.