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Research on the impact of seawater intrusion on nitrogen (N) cycling in coastal estuarine ecosystems is crucial; however, there is still a lack of relevant research conducted underin-situfield conditions. The effects of elevated salinity on N cycling processes and microbiomes were examinedin situseawater intrusion experiments conducted from 2019 to 2021 in the Nakdong River Estuary (South Korea), where an estuarine dam regulates tidal hydrodynamics. After the opening of the Nakdong Estuary Dam (seawater intrusion event), the density difference between seawater and freshwater resulted in varying degrees of seawater trapping at topographically deep stations. Bottom-water oxygen conditions had been altered in normoxia, hypoxia, and weak hypoxia due to the different degrees of seawater trapping in 2019, 2020, and 2021, respectively. Denitrification mostly dominated the nitrate (NO₃^-) reduction process, except in 2020 after seawater intrusion. However, denitrification rates decreased because of reduced coupled nitrification after seawater intrusion due to the dissolved oxygen limitation in 2020. Dissimilatory nitrate reduction to ammonium (DNRA) rates immediately increased after seawater intrusion in 2020, replacing denitrification as the dominant pathway in the NO₃^-reduction process. The enhanced DNRA rate was mainly due to the abundant organic matter associated with seawater invasion and more reducing environment (maybe sulfide enhancement effects) under high seawater-trapping conditions. Denitrification increased in 2021 after seawater intrusion during weak hypoxia; however, DNRA did not change. Small seawater intrusion in 2019 caused no seawater trapping and overall normoxic condition, though a slight shift from denitrification to DNRA was observed. Metagenomic analysis revealed a decrease in overall denitrification-associated genes in response to seawater intrusion in 2019 and 2020, while DNRA-associated gene abundance increased. In 2021 after seawater intrusion, microbial gene abundance associated with denitrification increased, while that of DNRA did not change significantly. These changes in gene abundance align mostly with alterations in nitrogen transformation rates. In summary, ecological change effects in N cycling after the dam opening (N retention or release, that is, eutrophication deterioration or mitigation) depend on the degree of seawater intrusion and the underlying freshwater conditions, which constitute the extent of seawater-trapping.

 

Recent advances in Artificial Intelligence (AI) have brought society closer to the long-held dream of creating machines to help with both common and complex tasks and functions. From recommending movies to detecting disease in its earliest stages, AI has become an aspect of daily life many people accept without scrutiny. Despite its functionality and promise, AI has inherent security risks that users should understand and programmers must be trained to address. The ICE (integrity, confidentiality, and equity) cybersecurity labs developed by a team of cybersecurity researchers addresses these vulnerabilities to AI models through a series of hands-on, inquiry-based labs. Through experimenting with and manipulating data models, students can experience firsthand how adversarial samples and bias can degrade the integrity, confidentiality, and equity of deep learning neural networks, as well as implement security measures to mitigate these vulnerabilities. This article addresses the pedagogical approach underpinning the ICE labs, and discusses both sample activities and technological considerations for teachers who want to implement these labs with their students. 

Recent advances in Artificial Intelligence (AI) have brought society closer to the long-held dream of creating machines to help with both common and complex tasks and functions. From recommending movies to detecting disease in its earliest stages, AI has become an aspect of daily life many people accept without scrutiny. Despite its functionality and promise, AI has inherent security risks that users should understand and programmers must be trained to address. The ICE (integrity, confidentiality, and equity) cybersecurity labs developed by a team of cybersecurity researchers addresses these vulnerabilities to AI models through a series of hands-on, inquiry-based labs. Through experimenting with and manipulating data models, students can experience firsthand how adversarial samples and bias can degrade the integrity, confidentiality, and equity of deep learning neural networks, as well as implement security measures to mitigate these vulnerabilities. This article addresses the pedagogical approach underpinning the ICE labs, and discusses both sample activities and technological considerations for teachers who want to implement these labs with their students. 

Recent advances in Artificial Intelligence (AI) have brought society closer to the long-held dream of creating machines to help with both common and complex tasks and functions. From recommending movies to detecting disease in its earliest stages, AI has become an aspect of daily life many people accept without scrutiny. Despite its functionality and promise, AI has inherent security risks that users should understand and programmers must be trained to address. The ICE (integrity, confidentiality, and equity) cybersecurity labs developed by a team of cybersecurity researchers addresses these vulnerabilities to AI models through a series of hands-on, inquiry-based labs. Through experimenting with and manipulating data models, students can experience firsthand how adversarial samples and bias can degrade the integrity, confidentiality, and equity of deep learning neural networks, as well as implement security measures to mitigate these vulnerabilities. This article addresses the pedagogical approach underpinning the ICE labs, and discusses both sample activities and technological considerations for teachers who want to implement these labs with their students.

 

Poly(vinylidene fluoride) (PVDF)‐based polymers demonstrate great potential for applications in flexible and wearable electronics but show low piezoelectric coefficients (e.g., −d₃₃< 30 pC N⁻¹). The effective improvement for the piezoelectricity of PVDF is achieved by manipulating its semicrystalline structures. However, there is still a debate about which component is the primary contributor to piezoelectricity. Therefore, current methods to improve the piezoelectricity of PVDF can be classified into modulations of the amorphous phase, the crystalline region, and the crystalline–amorphous interface. Here, the basic principles and measurements of piezoelectric coefficients for soft polymers are first discussed. Then, three different categories of structural modulations are reviewed. In each category, the physical understanding and strategies to improve the piezoelectric performance of PVDF are discussed. In particular, the crucial role of the oriented amorphous fraction at the crystalline–amorphous interface in determining the piezoelectricity of PVDF is emphasized. At last, the future development of high performance piezoelectric polymers is outlooked.