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Keeping Healthcare Workers Safe During a Pandemic: Evaluating Doffing Area Design for Safer Removal of Personal Protective Equipment
No full textObjective This study proposes a user-centered methodology to quantify the design affordances of doffing spaces, focusing on the safety and efficiency of healthcare workers (HCWs).Background Doffing personal protective equipment (PPE) poses a significant challenge for healthcare workers (HCWs) due to the high risk of self-contamination. The physical design of the doffing area plays an important role in ensuring safety during this process. However, there currently are no established spatial metrics for assessing the design of doffing spaces.Methods Four doffing areas in two Biocontainment Units (BCUs) were evaluated using Functional Scenario (FS) analysis method. FSs, representing the spatial needs of key users (HCW and Trained Observer-TO) were developed based on observations, literature, and discussions with staff. For each FS, we defined quantifiable metrics for visualizing the user's needs and evaluating doffing area design performance.Results We defined 11 FSs (seven for HCWs and four for TOs) and 19 associated spatial metrics. FSs for the HCW focused on the prevention of self- and cross-contamination, as well as facilitating visibility, efficiency, and situational and process awareness. The FSs for the TO center on preventing self-contamination, promoting visibility and process awareness, and safe waste management.Conclusions The FS approach allowed for the quantification of doffing area affordances and evaluation of how they impact HCW performance, emphasizing design aspects that enhance safety and efficiency. The presented metrics and study findings are expected to inform the future design of spaces where doffing occurs and provide new guidance for improved doffing safety.
Indo-Gem: An activatable theranostic prodrug, a "Turn-On" fluorescent probe, and a targetable imaging agent in the zebrafish gallbladder system
No full textTheranostics are crucial in both cancer diagnosis and targeted drug delivery, as they enable the simultaneous detection and treatment of disease within a single molecular or conjugated platform. However, theranostics is still facing challenges like real-time tracking for "where" prodrugs are activated in vivo, which poses a significant problem for molecule development. Organ-specific action to inspire the creation of innovative disease-curing technologies that are both accurate and efficient is still an underestimated task. We herein showcase the novel serendipitous gallbladder-targeting indole-based prodrug Indo-Gem for precise imaging-guided cancer therapy. Indo-Gem was prepared by an indole-malononitrile (dye) moiety, attached to the selected parent drug Gemcitabine via a disulfide cleavable linker. Indo-Gem is triggered by DTT with 27-fold fluorescent enhancement at pH 7.4, registering within 21 min. Unlike the most ignored adverse effects in prodrug activation, this indole-based dye targeting in a specific organ region of a zebrafish model via a combination of imaging ability and drug release. This is the first case that employs an indole-based moiety in prodrug design without any use of targeting ligands; the profound success of Indo-Gem and the well-defined mechanism suggest that the indole might serve as a scaffold to create novel therapeutic prodrugs with improved drug potency in future drug development.
Artificial Noise-Aided Max-Min Fairness Secrecy Precoding With Partial Wiretap Channel Knowledge
No full textWe propose an artificial noise (AN)-aided max-min fairness (MMF) secure precoding algorithm. The algorithm jointly optimizes precoders and AN covariance matrix without alternation for a multiuser multiple-input multiple-output system with multiple colluding eavesdroppers. The formulated AN-aided MMF problem involves several challenges such as non-convexity, non-smoothness, coupled optimization variables, and partial wiretap channel state information at a transmitter (CSIT). To address the challenges, we first consider the perfect wiretap CSIT and approximate the reformulated problem as a smooth problem. Then, we derive the first-order optimality condition and interpret it as a nonlinear eigenvalue problem. Finally, we adopt a power iteration method to solve the condition. To extend the algorithm to the partial wiretap CSIT scenario, we reformulate the problem with an average wiretap rate by deriving an approximate lower bound of the rate. This enables us to exploit the partial channel knowledge. Subsequently, we propose the algorithm for the partial wiretap CSIT. Simulations validate the proposed methods.
CeO2/NiO heterostructures for extremly-selective acetone detection: The critical role of the Ce3+/Ce4+ ratio in NiO nanodomes
No full textExhaled breath analysis has emerged as a promising and non-invasive approach for diagnosing disease by detecting specific biomarkers. Acetone in exhaled breath can be used as a biomarker in diabetic patients, promoting the development of highly sensitive and selective acetone gas sensors. In this study, CeO2-decorated NiO nanodomes (NDs) were developed to enhance the response and selectivity for acetone detection. The optimal response was achieved using the NDs decorated with 5 nm thick CeO2, at 400 degrees C, affording about a 15.5-fold increase in the selective acetone response (Rgas/Rair - 1 = 25.4, 50 ppm). The enhanced gas response led to a ppb-level theoretical detection limit for the CeO2-decorated NiO NDs, even under humid conditions. This improvement is attributed to the formation of a p-n junction, as well as the increased contents of oxygen defects in sensing layers, driven by the higher Ce3+/Ce4+ ratio. These oxygen defects facilitate greater adsorption of oxygen ions on the CeO2 and NiO surface, thereby enhancing the gas sensing performance. These results demonstrate the potential of CeO2-decorated NiO NDs for highly selective acetone detection, paving the way for non-invasive breath-based diagnostics.
Scalar spin chirality Nernst effect
No full textThe scalar spin chirality, which characterizes the fundamental unit of noncoplanar spin structures, plays an important role in rich chiral physics of magnetic materials. In particular, the intensive research efforts over the past two decades have demonstrated that the scalar spin chirality is the source of various novel Hall transports in solid-state systems, offering a primary route to bring about chiral phenomena in condensed matter physics. However, in all of the previous studies, the scalar spin chirality has been given as a static quantity, serving only a passive role in the transport properties of materials. It remains an open question whether or not the scalar spin chirality itself can exhibit a Hall-type transport. In this work, we show that the answer is yes: The scalar spin chirality is Hall transported in Kagome ferromagnets and antiferromagnets under an external bias, engendering a phenomenon which we dub the scalar spin chirality Nernst effect. Notably, this effect is present even in the absence of any spin-orbit coupling. The analytical theory for the scalar spin chirality Nernst effect is corroborated by atomistic spin simulations. Our findings call for the need to lift the conventional assumption that the scalar spin chirality is a static quantity in order to discover the active roles of the scalar spin chirality in transport properties.
Metabolic Engineering of Corynebacterium glutamicum for the Vitamin B12 Independent Production of 3-Hydroxypropionic Acid
No full textSerrated Leaf-Like N-Doped Copper Sulfide Enabling Bifunctional Oxygen Reduction/Evolution via Dual-Mode Cathode Reactions for High Energy Density and Cycle Stability in Zinc-Air Batteries
No full textZinc-air batteries (ZABs) are promising electrochemical energy storages, but inefficient oxygen reduction reaction (ORR) during discharging and oxygen evolution reaction (OER) during charging at their cathodes impede achieving high energy density and stable cycling. We report a serrated leaf-like nitrogen-doped copper sulfide (N-CuS) cathode with conductive N 2p-S 3p hybridized orbitals, oxygen-transporting mesopores, and about fivefold larger surface area than Cu. A ZAB with the N-CuS cathode exhibits a 788 mAh g(-1) capacity (96% of theoretical) and a hitherto highest energy density of 916.0 Wh kg(-1), surpassing one with the state-of-the-art Pt/C+RuO2 cathode (712.43 mAh g(-1) and 874 Wh kg(-1)). Density functional theory calculations elucidate that O=O bond dissociation is 0.97 eV more favorable on N-CuS than CuS. Subsequently, protonation of surface-adsorbed *O to *OH occurs via dissociate (0.55 V), non-spit associate (1.05 V), and split associate (1.05 V) pathways, with *OH then desorbing as OH-. Under anaerobic conditions, copper oxide transitions from CuO to Cu2O (1.05 V) and eventually to Cu (0.75 V) releasing oxygen to sustain ORR. Additionally, a ZAB with the N-CuS cathode achieves about threefold longer cyclability than one with the Pt/C+IrO2 cathode, and about six-fold longer cyclability than one with the Pt/C+RuO2 cathode.
Reducing Preheating Requirements for 1-N-Scale Thruster with Ammonium Dinitramide-H2O2 Monopropellant
No full textU-Know-DiffPAN: An Uncertainty-aware Knowledge Distillation Diffusion Framework with Details Enhancement for PAN-Sharpening
No full textConventional methods for PAN-sharpening often struggle to restore fine details due to limitations in leveraging high-frequency information. Moreover, diffusion-based approaches lack sufficient conditioning to fully utilize Panchromatic (PAN) images and low-resolution multispectral (LRMS) inputs effectively. To address these challenges, we propose an uncertainty-aware knowledge distillation diffusion framework with details enhancement for PAN-sharpening, called U-Know-DiffPAN. The U-KnowDiffPAN incorporates uncertainty-aware knowledge distillation for effective transfer of feature details from our teacher model to a student one. The teacher model in our U-Know-DiffPAN captures frequency details through freqeuncy selective attention, facilitating accurate reverse process learning. By conditioning the encoder on compact vector representations of PAN and LRMS and the decoder on Wavelet transforms, we enable rich frequency utilization. So, the high-capacity teacher model distills frequency-rich features into a lightweight student model aided by an uncertainty map. From this, the teacher model can guide the student model to focus on difficult image regions for PANsharpening via the usage of the uncertainty map. Extensive experiments on diverse datasets demonstrate the robustness and superior performance of our U-Know-DiffPAN over very recent state-of-the-art PAN-sharpening methods. The project page is available at https://kaist-viclab.github.io/UKnow-DiffPAN-site/