Electron beam induced conductivity effect of polymer resists and charging induced electron beam deflection simulation in electron beam lithography

The electron beam induced conductivity (EBIC) effect of polymer materials was investigated and three electron beam resists, PBS, EBR900, and ZEP7000, were used as target materials in this research. An external bias method was applied for the direct measurements of EBIC current and the EBIC conductivity was then derived. The change of carrier drift velocity and the carrier concentration under field is believed to be the reason for the conductivity increase and the approach of the asymptotic values in the initial state EBIC characteristics. The temperature effect on EBIC was illustrated by EBR900 and a simple single trap level analysis gave good agreement in explanation of initial state conductivity dependence on temperature changeThe dependence of initial state EBIC values on dose rates and bias was also determined and an empirical from of EBIC-bias relationship was established for the electron beam deflection modeling. The long term EBIC characteristics of resists were investigated under different dose rates. The variation of EBIC versus time was explained by a transient theory that considers rates of carrier generation, carrier trapping, carrier detrapping, and carrier recombination contribution. The trapping model analysis on steady state EBIC results revealed that PBS has a uniform distribution of traps, while EBR900 and ZEP7000 fall in the same category that has exponential distribution of traps. The steady state EBIC gain was also determined for the three resists and the onset of the plasma region was observed for EBR900 and ZEP7000. Monte Carlo simulation on electron beam range advised that only the positive charging layer needed to be considered for charging simulation and the image method was applied for the construction of the surface potential calculation. A simulation model for the calculation of electron beam deflection owing to resist charging was developed in the second part of this research. Charging patterns of circles and squares of different dimensions were considered. Both SIMION and MATHEMATICA were used as simulation tools and the limitation of SIMION was illustrated and discussed. The model programmed in MATHEMATICA simulated electron beam deflection during e-beam writing. With the combination of the empirical EBIC-bias relationship of EBR900, the model simulated surface potential decay after irradiation and the results showed good agreement with literature values. The extent of deflection per surface potential was shown to increase with pattern dimension size under the same secondary electron emission yield, while the maximum deflection per surface potential per unit area decreased as the pattern dimension decreased

An Efficient Semi-Streaming PTAS for Tournament Feedback Arc Set with Few Passes

We present the first semi-streaming polynomial-time approximation scheme (PTAS) for the minimum feedback arc set problem on directed tournaments in a small number of passes. Namely, we obtain a (1 + ?)-approximation in time O (poly(n) 2^{poly(1/?)}), with p passes, in n^{1+1/p} ? poly((log n)/?) space. The only previous algorithm with this pass/space trade-off gave a 3-approximation (SODA, 2020), and other polynomial-time algorithms which achieved a (1+?)-approximation did so with quadratic memory or with a linear number of passes. We also present a new time/space trade-off for 1-pass algorithms that solve the tournament feedback arc set problem. This problem has several applications in machine learning such as creating linear classifiers and doing Bayesian inference. We also provide several additional algorithms and lower bounds for related streaming problems on directed graphs, which is a largely unexplored territory

Information Variability Impacts in Auctions

A wide variety of auction models exhibit close relationships between the winner's expected profit and the expected difference between the highest and second-highest order statistics of bidders' information, and between expected revenue and the second-highest order statistic of bidders' expected asset values. We use stochastic orderings to see when greater environmental variability of bidders' information enhances expected profit and expected revenue

Using Analytical Information for Digital Business Transformation through DataOps: A Review and Conceptual Framework

Organisations are increasingly practising business analytics to generate actionable insights that can guide their digital business transformation. Transforming business digitally using business analytics is an ongoing process that requires an integrated and disciplined approach to leveraging analytics and promoting collaboration. An emerging business analytics practice, Data Operations (DataOps), provides a disciplined approach for organisations to collaborate using analytical information for digital business transformation. We propose a conceptual framework by reviewing the literature on business analytics, DataOps and organisational information processing theory (OIPT). This conceptual framework explains how organisations can employ DataOps as an integrated and disciplined approach for developing the analytical information processing capability and facilitating boundary-spanning activities required for digital business transformation. This research (a) extends current knowledge on digital transformation by linking it with business analytics from the perspective of OIPT and boundary-spanning activities, and (b) presents DataOps as a novel approach for using analytical information for digital business transformation

Leveraging Data and Analytics for Digital Business Transformation through DataOps: An Information Processing Perspective

Digital business transformation has become increasingly important for organisations. Since transforming business digitally is an ongoing process, it requires an integrated and disciplined approach. Data Operations (DataOps), emerging in practice, can provide organisations with such an approach to leverage data and analytics for digital business transformation. This paper proposes a framework that integrates digital business transformation, data analytics, and DataOps through the lens of information processing theory (IPT). The details of this framework explain how organisations can employ DataOps as an integrated and disciplined approach to understand their analytical information needs and develop the analytical information processing capability required for digital business transformation. DataOps-enabled digital business transformation, in turn, improves organisational performance by improving operational efficiency and creating new business models. This research extends current knowledge on digital transformation by bringing in DataOps and analytics through IPT and thereby provides organisations with a novel approach for their digital business transformations

Removal of Arsenic (III) from groundwater applying a reusable Mg-Fe-Cl layered double hydroxide

BACKGROUND: Layered double hydroxide compounds (LDHs) have been applied for the removal of oxyanions including arsenate (As(V)). However, the aim of this present research is to develop a LDH to treat arsenite (As(III)). Both batch and column sorption studies were conducted to assess the effect of LDH dosage, contact time, solution pH and initial As(III) concentrations on the As(III) removal performance. The potential re-use of this sorbent was also investigated.<p></p> RESULTS: For 2 g L−1 of Mg-Fe-Cl LDH, As(III) in test solution can be reduced from 400 µg L−1 to <10 µg L−1 after a contact time of 2 h. High As(III) concentration in Bangladesh groundwater can be reduced to meet the national drinking water standards (<50 µg L−1). The maximum adsorption capacity of As(III) by Mg-Fe-Cl LDH is 14.6 mg g−1-LDH. Further, reusability of this sorbent was at least 20 cycles of regeneration with effective As(III) removal between 93.0 and 98.5%. Moreover, As(III) removal was unaffected by the solution pH but affected by the co-existing competing anions and concentration of As(III). Finally, the main mechanism of As(III) removal by Mg-Fe-Cl LDH was suggested to be chemical sorption together with anion and ligand exchange with interlayer Cl− and OH− ions.<p></p> CONCLUSION: High efficiency of sorption of As(III) by the developed Mg-Fe-Cl LDH was demonstrated in this study which is generally not the case for most other sorbent materials. Pilot-scale trials are needed to explore the suitability of full application of the developed Mg-Fe-Cl LDH for the removal of As(III).<p></p&gt

Managing and Making Sense of Data to Drive Digital Transformation: A Case Study

We explore how organizations manage and make sense of data collaboratively to drive digital transformation. We present the results of an in-depth case study of a financial organization that used Data Operations (DataOps) - a collaborative data management practice to transform its digital-first offering initiative and thereby redefining its value proposition. Drawing on sensemaking theory, we develop a process model that explains how organizations use DataOps to perceive cues through data democratization, extract plausible and comprehensive insights from data through data storytelling to make interpretations, and leverage data products to take actions that drive data-driven digital transformation. Our findings have implications for data-driven digital transformation as we show how DataOps constitutes a new class of data management practices that enable collaboration between data managers and data consumers and allow organizations to make evidence-based decisions to drive their digital transformation

Li-Fraumeni Syndrome: Adopting a Diagnosis with an Unknown Family History

Introduction: Li-Fraumeni Syndrome (LFS) is a rare, autosomal dominant disease that is typically screenedfor and identified in patients with a known history of cancer and family history ofcancer.Presentation of Case: Ms. DA is an adopted 39-year-old South American female with a past medical history of iron deficiency anemia, celiac disease, and polio. She presented with ductal carcinoma in-situ with spindle cell atypia at age 35 and leiomyosarcoma of the ankle at age 36. Genetic counseling was offered due to her age and cancer types, with subsequent genetic testing revealing a TP53-R337H mutation, which was diagnostic of LFS. Given her high risk status, extensive cancer screenings at common LFS body sites were recommended, but she was lost to follow-up. Four years later, she returned after discovering a new right breast mass on self-examination. She was referred to a new breast surgeon, who was able to establish consistent and close follow-up while providing counseling and education regarding lifetime risk and cancer trajectory of classical LFS compared to the Brazilian LFS-subtype.Conclusion: The importance of having a high level of suspicion for patients with anunknown family history is crucial as seen in this case. Without a biological familyhistory, clinicians rely on their judgement to decide when further work-up is warranted.Advancements in medicine and genetic testing have increased the ability to accuratelydiagnose genetic diseases to help patients make life-saving decisions about theirhealth