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1. Combining simulations and experiments for the molecular engineering of multifunctional collagen mimetic peptide-based materials

   https://doi.org/10.1039/D0SM01562H  

   Hilderbrand, Amber M.; Taylor, Phillip A.; Stanzione, Francesca; LaRue, Mark; Guo, Chen; Jayaraman, Arthi; Kloxin, April M. (February 2021, Soft Matter)

   null (Ed.)

   Assembling peptides allow the creation of structurally complex materials, where amino acid selection influences resulting properties. We present a synergistic approach of experiments and simulations for examining the influence of natural and non-natural amino acid substitutions via incorporation of charged residues and a reactive handle on the thermal stability and assembly of multifunctional collagen mimetic peptides (CMPs). Experimentally, we observed inclusion of charged residues significantly decreased the melting temperature of CMP triple helices with further destabilization upon inclusion of the reactive handle. Atomistic simulations of a single CMP triple helix in explicit water showed increased residue-level and helical structural fluctuations caused by the inclusion of the reactive handle; however, these atomistic simulations cannot be used to predict changes in CMP melting transition. Coarse-grained (CG) simulations of CMPs at experimentally relevant solution conditions, showed, qualitatively, the same trends as experiments in CMP melting transition temperature with CMP design. These simulations show that when charged residues are included electrostatic repulsions significantly destabilize the CMP triple helix and that an additional inclusion of a reactive handle does not significantly change the melting transition. Based on findings from both experiments and simulations, the sequence design was refined for increased CMP triple helix thermal stability, and the reactive handle was utilized for the incorporation of the assembled CMPs within covalently crosslinked hydrogels. Overall, a unique approach was established for predicting stability of CMP triple helices for various sequences prior to synthesis, providing molecular insights for sequence design towards the creation of bulk nanostructured soft biomaterials. 

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2. High-temperature 2D ferromagnetism in conjugated microporous porphyrin-type polymers

   https://doi.org/10.1039/D0CP02312D  

   Pimachev, Artem; Nielsen, Robert D.; Dahnovsky, Yuri (July 2020, Physical Chemistry Chemical Physics)

   null (Ed.)

   The need for magnetic 2D materials that are stable to the enviroment and have high Curie temperatures is very important for various electronic and spintronic applications. We have found that two-dimensional porphyrin-type aza-conjugated microporous polymer crystals are such a material (Fe-aza-CMPs). Fe-aza-CMPs are stable to CO, CO 2 , and O 2 atmospheres and show unusual adsorption, electronic, and magnetic properties. Indeed, they are semiconductors with small energy band gaps ranging from 0.27 eV to 0.626 eV. CO, CO 2 , and O 2 molecules can be attached in three different ways where single, double, or triple molecules are bound to iron atoms in Fe-aza-CMPs. For different attachment configurations we find that for CO and CO 2 a uniform distribution of the molecules is most energetically favorable while for O 2 molecules aggregation is most energetically preferable. The magnetic moments decrease from 4 to 2 to 0 for singly, doubly, triply occupied configurations for all gasses respectively. The most interesting magnetic properties are found for O 2 molecules attached to the Fe-aza-CMP. For a single attachment configuration we find that an antiferromagnetic state is favorable. When two O 2 molecules are attached, the calculations show the highest exchange integral with a value of J = 1071 μeV. This value has been verified by two independent methods where in the first method J is calculated by the energy difference between ferromagnetic and anitferromagnetic configurations. The second method is based on the frozen magnon approach where the magnon dispersion curve has been fitted by the Ising model. For the second method J has been estimated at J = 1100 μeV in excellent agreement with the first method. 

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3. “Okay, whatever”: An Evaluation of Cookie Consent Interfaces

   https://doi.org/10.1145/3491102.3501985  

   Habib, Hana; Li, Megan; Young, Ellie; Cranor, Lorrie (April 2022, CHI '22: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems)

   Many websites have added cookie consent interfaces to meet regulatory consent requirements. While prior work has demonstrated that they often use dark patterns — design techniques that lead users to less privacy-protective options — other usability aspects of these interfaces have been less explored. This study contributes a comprehensive, two-stage usability assessment of cookie consent interfaces. We first inspected 191 consent interfaces against five dark pattern heuristics and identified design choices that may impact usability. We then conducted a 1,109-participant online between-subjects experiment exploring the usability impact of seven design parameters. Participants were exposed to one of 12 consent interface variants during a shopping task on a prototype e-commerce website and answered a survey about their experience. Our findings suggest that a fully-blocking consent interface with in-line cookie options accompanied by a persistent button enabling users to later change their consent decision best meets several design objectives. 

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4. Two Worlds Apart! Closing the Gap between Regulating EU Consent and User Studies

   Bielova, Nataliia; Santos, Cristiana; Gray, Colin M. (May 2024, Harvard Journal of Law & Technology)

   The EU ePrivacy Directive requires consent before using cookies or other tracking technologies, while the EU General Data Protection Regulation (“GDPR”) sets high-level and principle-based requirements for such consent to be valid. However, the translation of such requirements into concrete design interfaces for consent banners is far from straightforward. This situation has given rise to the use of manipulative tactics in user experience (“UX”), commonly known as dark patterns, which influence users’ decision-making and may violate the GDPR requirements for valid consent. To address this problem, EU regulators aim to interpret GDPR requirements and to limit the design space of consent banners within their guidelines. Academic researchers from various disciplines address the same problem by performing user studies to evaluate the impact of design and dark patterns on users’ decision making. Regrettably, the guidelines and user studies rarely impact each other. In this Essay, we collected and analyzed seventeen official guidelines issued by EU regulators and the EU Data Protection Board (“EDPB”), as well as eleven consent-focused empirical user studies which we thoroughly studied from a User Interface (“UI”) design perspective. We identified numerous gaps between consent banner designs recommended by regulators and those evaluated in user studies. By doing so, we contribute to both the regulatory discourse and future user studies. We pinpoint EU regulatory inconsistencies and provide actionable recommendations for regulators. For academic scholars, we synthesize insights on design elements discussed by regulators requiring further user study evaluations. Finally, we recommend that EDPB and EU regulators, alongside usability, Human-Computer Interaction (“HCI”), and design researchers, engage in transdisciplinary dialogue in order to close the gap between EU guidelines and user studies. 

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5. CMP-PIM: an energy-efficient comparator-based processing-in-memory neural network accelerator

   https://doi.org/10.1145/3195970.3196009  

   Angizi, Shaahin; He, Zhezhi; Rakin, Adnan Siraj; Fan, Deliang (June 2018, 55th Annual Design Automation Conference)

   In this paper, an energy-efficient and high-speed comparator-based processing-in-memory accelerator (CMP-PIM) is proposed to efficiently execute a novel hardware-oriented comparator-based deep neural network called CMPNET. Inspired by local binary pattern feature extraction method combined with depthwise separable convolution, we first modify the existing Convolutional Neural Network (CNN) algorithm by replacing the computationally-intensive multiplications in convolution layers with more efficient and less complex comparison and addition. Then, we propose a CMP-PIM that employs parallel computational memory sub-array as a fundamental processing unit based on SOT-MRAM. We compare CMP-PIM accelerator performance on different data-sets with recent CNN accelerator designs. With the close inference accuracy on SVHN data-set, CMP-PIM can get ∼ 94× and 3× better energy efficiency compared to CNN and Local Binary CNN (LBCNN), respectively. Besides, it achieves 4.3× speed-up compared to CNN-baseline with identical network configuration. 

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