Formation and properties of new Ni-based amorphous alloys with critical casting thickness up to 5 mm

New Ni-based bulk metallic glasses were synthesized in NixCua−xTiyZrb−yAl10 (a~b~45 at.%) system, based on a ternary alloy, Ni45Ti20Zr35. The additions of Al and Cu greatly increase the glass-forming ability (GFA). The best GFA is located around Ni40Cu5Ti16.5Zr28.5Al10, from which fully amorphous samples of up to 5 mm thickness were successfully fabricated by an injection mold casting method. These alloys exhibit high glass-transition temperatures Tg ~ 760 to 780 K, and relatively wide undercooled-liquid regions ΔT (defined by the difference between Tg and the first crystallization temperature Tx1 upon heating) ~ 40–50 K. Mechanical tests on these alloys show quite high Vicker's Hardness ~ 780 to 862 kg/mm^2, Young's modulus ~ 111 to 134 GPa, shear modulus ~ 40 to 50 GPa and high fracture strength ~ 2.3 to 2.4 GPa. The effect of small Si-addition and a discrepancy between GFA and ΔT are also reported. The exceptional GFA and the all-metallic compositions give these new alloys excellent promise for both scientific and engineering applications

Bulk metallic glass formation in binary Cu-rich alloy series – Cu100−xZrx (x=34, 36, 38.2, 40 at.%) and mechanical properties of bulk Cu64Zr36 glass

The compositional dependence of a glass-forming ability (GFA) was systematically studied in a binary alloy series Cu100−xZrx (x=34, 36, 38.2, 40 at.%) by the copper mold casting method. Our results show the critical casting thickness jumps from below 0.5 mm to above 2 mm when x changes from 34 to 36 while further increase in x reduces the critical casting thickness. The best glass former Cu64Zr36 does not correspond to either the largest undercooled liquid region (ΔT=Tx1−Tg, where Tg is the glass transition temperature, and Tx1 is the onset temperature of the first crystallization event upon heating) or the highest reduced glass transition temperature (Trg=Tg/Tl, where Tl is the liquidus temperature). Properties of bulk amorphous Cu64Zr36 were measured, yielding a Tg ~ 787 K, Trg ~ 0.64, ΔT ~ 46 K, Hv (Vicker's Hardness) ~ 742 kg/mm^2, Young's Modulus ~ 92.3 GPa, compressive fracture strength ~ 2 GPa and compressive strain before failure ~ 2.2%

Examining Employee Social Media Deviance: A Psychological Contract Breach Perspective

With the prevalence of social media, employees’ deviant behaviors on social media can go viral and result in unpredictable negative outcomes beyond the workplace. This paper investigates the relationship between abusive supervision and employee social media deviance from the theoretical perspective of psychological contract breach (PCB), and examine the moderating role of social media controls. Building on prior studies of abusive supervision and employee workplace deviance, this paper argues that abusive supervision plays a crucial motivational role in triggering employee social media deviance. Our results demonstrate that employees who experience abusive supervision are more likely to perceive PCB, and thus engage in social media deviance. User awareness of social media policy and informal sanctions can weaken the positive relationship between employee perceived PCB and social media deviance

Anger or Fear? Effects of Discrete Emotions on Deviant Security Behavior

Deterrence theory has received considerable attention in recent years. However, scholars have begun to call for research beyond the deterrence approach on security behaviors, and argue that the theory of emotion should not be omitted from information systems security decision making [15, 81]. In this research, we examine and distinguish effects of anger and fear on perceived costs of sanctions and deviant security behavior. A research model is developed based on deterrence theory and cognitive appraisal theory of emotion. We propose to design a scenario of introducing a new security monitoring system, to analyze the interplays of anger, fear, perceived certainty, perceived severity of sanctions and deviant security behavior. The results will have important implications for comprehensively understanding employees’ deviant security behavior

DeepfakeArt Challenge: A Benchmark Dataset for Generative AI Art Forgery and Data Poisoning Detection

The tremendous recent advances in generative artificial intelligence techniques have led to significant successes and promise in a wide range of different applications ranging from conversational agents and textual content generation to voice and visual synthesis. Amid the rise in generative AI and its increasing widespread adoption, there has been significant growing concern over the use of generative AI for malicious purposes. In the realm of visual content synthesis using generative AI, key areas of significant concern has been image forgery (e.g., generation of images containing or derived from copyright content), and data poisoning (i.e., generation of adversarially contaminated images). Motivated to address these key concerns to encourage responsible generative AI, we introduce the DeepfakeArt Challenge, a large-scale challenge benchmark dataset designed specifically to aid in the building of machine learning algorithms for generative AI art forgery and data poisoning detection. Comprising of over 32,000 records across a variety of generative forgery and data poisoning techniques, each entry consists of a pair of images that are either forgeries / adversarially contaminated or not. Each of the generated images in the DeepfakeArt Challenge benchmark dataset has been quality checked in a comprehensive manner. The DeepfakeArt Challenge is a core part of GenAI4Good, a global open source initiative for accelerating machine learning for promoting responsible creation and deployment of generative AI for good

Huron-to-Erie Water Quality Data Platform

To address the challenges of environmental degradation, creation of a sustainable urban environment, and increased public engagement and awareness, a mass-oriented, user-friendly and cloud-based data platform has been developed and deployed to provide integrative water quality data in one of the most critical urban corridors of the Laurentian Great Lakes system. In this paper, we describe the data platform developed for the watershed and connecting channels between Lake Huron and Lake Erie, including the St. Clair River, Lake St. Chair, and the Detroit River. This data platform greatly facilitates the access of data across data providers and agencies. Several example applications are provided of platform use for temporal and spatial characterization of intake water source quality and urban beach health through consideration of Escherichia coli, Dissolved Oxygen, pH, and blue-green algae detections along the Huron-to-Erie corridor. Although data collection for each of these parameters was designed for unique purposes and supported through varied agencies, this paper shows the collective advantages of applying the data beyond the original scope of collection

IMBER – Research for marine sustainability: Synthesis and the way forward

The Integrated Marine Biogeochemistry and Ecosystem Research (IMBER) project aims at developing a comprehensive understanding of and accurate predictive capacity of ocean responses to accelerating global change and the consequent effects on the Earth system and human society. Understanding the changing ecology and biogeochemistry of marine ecosystems and their sensitivity and resilience to multiple drivers, pressures and stressors is critical to developing responses that will help reduce the vulnerability of marine-dependent human communities. This overview of the IMBER project provides a synthesis of project achievements and highlights the value of collaborative, interdisciplinary, integrated research approaches as developed and implemented through IMBER regional programs, working groups, project-wide activities, national contributions, and external partnerships. A perspective is provided on the way forward for the next 10 years of the IMBER project as the global environmental change research landscape evolves and as new areas of marine research emerge. IMBER science aims to foster collaborative, interdisciplinary and integrated research that addresses key ocean and social science issues and provides the understanding needed to propose innovative societal responses to changing marine systems

Machine learning and word sense disambiguation in the biomedical domain: design and evaluation issues

BACKGROUND: Word sense disambiguation (WSD) is critical in the biomedical domain for improving the precision of natural language processing (NLP), text mining, and information retrieval systems because ambiguous words negatively impact accurate access to literature containing biomolecular entities, such as genes, proteins, cells, diseases, and other important entities. Automated techniques have been developed that address the WSD problem for a number of text processing situations, but the problem is still a challenging one. Supervised WSD machine learning (ML) methods have been applied in the biomedical domain and have shown promising results, but the results typically incorporate a number of confounding factors, and it is problematic to truly understand the effectiveness and generalizability of the methods because these factors interact with each other and affect the final results. Thus, there is a need to explicitly address the factors and to systematically quantify their effects on performance. RESULTS: Experiments were designed to measure the effect of "sample size" (i.e. size of the datasets), "sense distribution" (i.e. the distribution of the different meanings of the ambiguous word) and "degree of difficulty" (i.e. the measure of the distances between the meanings of the senses of an ambiguous word) on the performance of WSD classifiers. Support Vector Machine (SVM) classifiers were applied to an automatically generated data set containing four ambiguous biomedical abbreviations: BPD, BSA, PCA, and RSV, which were chosen because of varying degrees of differences in their respective senses. Results showed that: 1) increasing the sample size generally reduced the error rate, but this was limited mainly to well-separated senses (i.e. cases where the distances between the senses were large); in difficult cases an unusually large increase in sample size was needed to increase performance slightly, which was impractical, 2) the sense distribution did not have an effect on performance when the senses were separable, 3) when there was a majority sense of over 90%, the WSD classifier was not better than use of the simple majority sense, 4) error rates were proportional to the similarity of senses, and 5) there was no statistical difference between results when using a 5-fold or 10-fold cross-validation method. Other issues that impact performance are also enumerated. CONCLUSION: Several different independent aspects affect performance when using ML techniques for WSD. We found that combining them into one single result obscures understanding of the underlying methods. Although we studied only four abbreviations, we utilized a well-established statistical method that guarantees the results are likely to be generalizable for abbreviations with similar characteristics. The results of our experiments show that in order to understand the performance of these ML methods it is critical that papers report on the baseline performance, the distribution and sample size of the senses in the datasets, and the standard deviation or confidence intervals. In addition, papers should also characterize the difficulty of the WSD task, the WSD situations addressed and not addressed, as well as the ML methods and features used. This should lead to an improved understanding of the generalizablility and the limitations of the methodology

A new clustering method for detecting rare senses of abbreviations in clinical notes

AbstractAbbreviations are widely used in clinical documents and they are often ambiguous. Building a list of possible senses (also called sense inventory) for each ambiguous abbreviation is the first step to automatically identify correct meanings of abbreviations in given contexts. Clustering based methods have been used to detect senses of abbreviations from a clinical corpus [1]. However, rare senses remain challenging and existing algorithms are not good enough to detect them. In this study, we developed a new two-phase clustering algorithm called Tight Clustering for Rare Senses (TCRS) and applied it to sense generation of abbreviations in clinical text. Using manually annotated sense inventories from a set of 13 ambiguous clinical abbreviations, we evaluated and compared TCRS with the existing Expectation Maximization (EM) clustering algorithm for sense generation, at two different levels of annotation cost (10 vs. 20 instances for each abbreviation). Our results showed that the TCRS-based method could detect 85% senses on average; while the EM-based method found only 75% senses, when similar annotation effort (about 20 instances) was used. Further analysis demonstrated that the improvement by the TCRS method was mainly from additionally detected rare senses, thus indicating its usefulness for building more complete sense inventories of clinical abbreviations