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Membrane electrode assemblies enable CO2 electrolysis at industrially relevant rates, yet their operational stability is often limited by formation of solid precipitates in the cathode pores, triggered by cation crossover from the anolyte due to imperfect ion exclusion by anion exchange membranes. Here we show that anolyte concentration affects the degree of cation movement through the membranes, and this substantially influences the behaviors of copper catalysts in catholyte-free CO2 electrolysers. Systematic variation of the anolyte (KOH or KHCO3) ionic strength produced a distinct switch in selectivity between either predominantly CO or C2+ products (mainly C2H4) which closely correlated with the quantity of alkali metal cation (K+) crossover, suggesting cations play a key role in C-C coupling reaction pathways even in cells without discrete liquid catholytes. Operando X-ray absorption and quasi in situ X-ray photoelectron spectroscopy revealed that the Cu surface speciation showed a strong dependence on the anolyte concentration, wherein dilute anolytes resulted in a mixture of Cu+ and Cu0 surface species, while concentrated anolytes led to exclusively Cu0 under similar testing conditions. These results show that even in catholyte-free cells, cation effects (including unintentional ones) significantly influence reaction pathways, important to consider in future development of catalysts and devices.

Accurate occupancy is crucial for planning for sustainable buildings. Using massive, passively-collected mobile phone data, we introduce a novel framework to estimate building occupancy at unprecedented scale. We show that, at urban-scale, occupancy differs widely from current estimates based on building types. For commercial buildings, we find typical occupancy rates are 5 times lower than current assumptions imply, while for residential buildings occupancy rates vary widely by neighborhood. Our mobile phone based occupancy estimates are integrated with a state-of-the-art urban building energy model to understand their impact on energy use predictions. Depending on the assumed relationship between occupancy and internal building loads, we find energy consumption which differs by +1% to −15% for residential buildings and by −4% to −21% for commercial buildings, compared to standard methods. This highlights a need for new occupancy-to-load models which can be applied at urban-scale to the diverse set of city building types. Building retrofits offer enormous potential for energy reduction and must be designed with occupancy in mind. Here, the authors developed a method for estimating building occupancy at urban scale using mobile phone traces and they find that energy saving estimates differ by +1 to −15% for residential buildings and by −4 to −21% for commercial buildings.

Flow transitions are an important fluid-dynamic phenomena for many reasons, including the direct effect on the aerodynamic forces acting on the body. In the present study, two-dimensional (2-D) and three-dimensional (3-D) wake transitions of a NACA0012 airfoil are studied for angles of attack in the range $0^\\circ \\leq \\alpha \\leq 20^\\circ$ and Reynolds numbers $500 \\leq {\\textit {Re}} \\leq 5000$. The study uses water-channel experiments and 2-D and 3-D numerical simulations based on the nodal spectral-element method, level-set function-based immersed-interface method and Floquet stability analysis. The different wake states are categorised based on the time-instantaneous wake structure, non-dimensional frequency and aerodynamic force coefficients. The wake states and transition boundaries are summarised in a wake regime map. The critical angle of attack and Reynolds number for the supercritical Hopf bifurcation (i.e. steady to periodic wake transition) varies as $\\alpha _1 {\\sim} {\\textit {Re}}^{-0.65}$, while the critical angle of attack for the onset of three dimensionality varies as $\\alpha _{3D} {\\sim} {\\textit {Re}}^{-0.5}$. Over the entire Reynolds number range, the transition to 3-D flow occurs through a mode C (subharmonic) transition. Beyond this initial transition, further instabilities of the 2-D periodic base flow arise and are investigated. For instance, at $ {\\textit {Re}}=2000$ and $\\alpha _{3D,2}=11.0^\\circ$, mode C coexists together with modes related to modes A and QP seen in a stationary circular cylinder wake. In contrast, at $ {\\textit {Re}}=5000$ and $\\alpha _{3D,2}=8.0^\\circ$, the dominant mode C coexists with mode QP. Three-dimensional simulations well beyond critical angles indicate that 2-D vortex-street transitions are approximately maintained in the fully saturated 3-D wakes in a spanwise-averaged sense.

Sturge-Weber Syndrome (SWS) is a congenital neurovascular disorder caused by a somatic mosaic mutation in the R183Q GNAQ gene and characterized by capillary-venous malformations of the brain, skin, and eyes. Clinical manifestations include facial port-wine birthmark, glaucoma, seizures, headache or migraine, hemiparesis, stroke or stroke-like episodes, developmental delay, behavioral problems, and hormonal deficiencies. SWS requires careful monitoring, management, and early identification to improve outcome and prevent neurological deterioration. Over the last 25 years, biomarkers have been developed to improve early diagnosis and prognosis and allow for the monitoring of clinical status and treatment response. Importantly, advancements in biomarker research may enable presymptomatic treatment for infants with SWS. This review summarizes current, ongoing, and potential future SWS biomarker studies. These biomarkers, in combination with clinical data, offer a rich source of data for rare disease research leveraging machine learning in future research.

In order to exploit plants as environmental biosensors, previous researches have been focused on the electrical signal response of the plants to different environmental stimuli. One of the important outcomes of those researches has been the extraction of meaningful features from the electrical signals and the use of such features for the classification of the stimuli which affected the plants. The classification results are dependent on the classifier algorithm used, features extracted and the quality of data. This paper presents an innovative way of extracting features from raw plant electrical signal response to classify the external stimuli which caused the plant to produce such a signal. A curve fitting approach in extracting features from the raw signal for classification of the applied stimuli has been adopted in this work, thereby evaluating whether the shape of the raw signal is dependent on the stimuli applied. Four types of curve fitting models—Polynomial, Gaussian, Fourier and Exponential, have been explored. The fitting accuracy (i.e., fitting of curve to the actual raw signal) depicted through R-squared values has allowed exploration of which curve fitting model performs best. The coefficients of the curve fit models were then used as features. Thereafter, using simple classification algorithms such as Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA) etc. within the curve fit coefficient space, we have verified that within the available data, above 90% classification accuracy can be achieved. The successful hypothesis taken in this work will allow further research in implementing plants as environmental biosensors.

We study the parameterized complexity of a variant of the classic video game Snake that models real-world problems of motion planning. Given a snake-like robot with an initial position and a final position in an environment (modeled by a graph), our objective is to determine whether the robot can reach the final position from the initial position without intersecting itself. Naturally, this problem models a wide-variety of scenarios, ranging from the transportation of linked wagons towed by a locomotor at an airport or a supermarket to the movement of a group of agents that travel in an “ant-like” fashion and the construction of trains in amusement parks. Unfortunately, already on grid graphs, this problem is PSPACE-complete. Nevertheless, we prove that even on general graphs, the problem is solvable in FPT time with respect to the size of the snake. In particular, this shows that the problem is fixed-parameter tractable (FPT). Towards this, we show how to employ color-coding to sparsify the configuration graph of the problem to reduce its size significantly. We believe that our approach will find other applications in motion planning. Additionally, we show that the problem is unlikely to admit a polynomial kernel even on grid graphs, but it admits a treewidth-reduction procedure. To the best of our knowledge, the study of the parameterized complexity of motion planning problems (where the intermediate configurations of the motion are of importance) has so far been largely overlooked. Thus, our work is pioneering in this regard.

Sleep problems are commonly reported in Rett syndrome (RTT); however the electroencephalographic (EEG) biomarkers underlying sleep dysfunction are poorly understood. The aim of this study was to analyze the temporal evolution of quantitative EEG (qEEG) biomarkers in overnight EEGs recorded from girls (2-9 yrs. old) diagnosed with RTT using a non-traditional automated protocol. In this study, EEG spectral analysis identified high delta power cycles representing slow wave sleep (SWS) in 8-9h overnight sleep EEGs from the frontal, central and occipital leads (AP axis), comparing age-matched girls with and without RTT. Automated algorithms quantitated the area under the curve (AUC) within identified SWS cycles for each spectral frequency wave form. Both age-matched RTT and control EEGs showed similar increasing trends for recorded delta wave power in the EEG leads along the antero-posterior (AP). RTT EEGs had significantly fewer numbers of SWS sleep cycles; therefore, the overall time spent in SWS was also significantly lower in RTT. In contrast, the AUC for delta power within each SWS cycle was significantly heightened in RTT and remained heightened over consecutive cycles unlike control EEGs that showed an overnight decrement of delta power in consecutive cycles. Gamma wave power associated with these SWS cycles was similar to controls. However, the negative correlation of gamma power with age (r = -.59; p<0.01) detected in controls (2-5 yrs. vs. 6-9 yrs.) was lost in RTT. Poor % SWS (i.e., time spent in SWS overnight) in RTT was also driven by the younger age-group. Incidence of seizures in RTT was associated with significantly lower number of SWS cycles. Therefore, qEEG biomarkers of SWS in RTT evolved temporally and correlated significantly with clinical severity.

We studied the structural transformations and atomic rearrangements in strontium titanate (SrTiO3) via nanosecond pulsed laser irradiation-induced melting and ultrafast quenching. Using scanning transmission electron microscopy, we determine that the laser-irradiated surface in single-crystalline SrTiO3 transforms into an amorphous phase with an interposing disordered crystalline region between amorphous and ordered phases. The formation of disordered phase is attributed to the rapid recrystallization of SrTiO3 from the melt phase constrained by an epitaxial relation with the pristine region, which eases up on the surface, leading to amorphous phase formation. With electron energy-loss spectroscopic analysis, we confirm the transformation of Ti+4 to Ti+3 due to oxygen vacancy formation as a result of laser irradiation. In the disordered region, the maximum transformation of Ti+4 is observed to be 16.2 ± 0.2%, whereas it is observed to be 20.2 ± 0.2% in the amorphous region. Finally, we deduce that the degree of the disorder increases from atomically disordered to amorphous transition in SrTiO3 under laser-irradiation. The signatures of short-range ordering remain similar, leading to a comparable fingerprint of electronic structure. With these results, this study addresses the gap in understanding the atomic and electronic structure modified by pulsed laser irradiation and functionalizing pristine SrTiO3 for electronic, magnetic, and optical applications.

Background: Diagnosing lung diseases accurately is crucial for proper treatment. Convolutional neural networks (CNNs) have advanced medical image processing, but challenges remain in their accurate explainability and reliability. This study combines U-Net with attention and Vision Transformers (ViTs) to enhance lung disease segmentation and classification. We hypothesize that Attention U-Net will enhance segmentation accuracy and that ViTs will improve classification performance. The explainability methodologies will shed light on model decision-making processes, aiding in clinical acceptance. Methodology: A comparative approach was used to evaluate deep learning models for segmenting and classifying lung illnesses using chest X-rays. The Attention U-Net model is used for segmentation, and architectures consisting of four CNNs and four ViTs were investigated for classification. Methods like Gradient-weighted Class Activation Mapping plus plus (Grad-CAM++) and Layer-wise Relevance Propagation (LRP) provide explainability by identifying crucial areas influencing model decisions. Results: The results support the conclusion that ViTs are outstanding in identifying lung disorders. Attention U-Net obtained a Dice Coefficient of 98.54% and a Jaccard Index of 97.12%. ViTs outperformed CNNs in classification tasks by 9.26%, reaching an accuracy of 98.52% with MobileViT. An 8.3% increase in accuracy was seen while moving from raw data classification to segmented image classification. Techniques like Grad-CAM++ and LRP provided insights into the decision-making processes of the models. Conclusions: This study highlights the benefits of integrating Attention U-Net and ViTs for analyzing lung diseases, demonstrating their importance in clinical settings. Emphasizing explainability clarifies deep learning processes, enhancing confidence in AI solutions and perhaps enhancing clinical acceptance for improved healthcare results.

Currently, preclinical testing of therapies for hepatoblastoma (HB) is limited to subcutaneous and intrasplenic xenograft models that do not recapitulate the hepatic tumors seen in patients. We hypothesized that injection of HB cell lines into the livers of mice would result in liver tumors that resemble their clinical counterparts. HepG2 and Huh-6 HB cell lines were injected, and tumor growth was monitored with bioluminescence imaging (BLI) and magnetic resonance imaging (MRI). Levels of human α-fetoprotein (AFP) were monitored in the serum of animals. Immunohistochemical and gene expression analyses were also completed on xenograft tumor samples. BLI signal indicative of tumor growth was seen in 55% of HepG2- and Huh-6-injected animals after a period of four to seven weeks. Increased AFP levels correlated with tumor growth. MRI showed large intrahepatic tumors with active neovascularization. HepG2 and Huh-6 xenografts showed expression of β-catenin, AFP, and Glypican-3 (GPC3). HepG2 samples displayed a consistent gene expression profile most similar to human HB tumors. Intrahepatic injection of HB cell lines leads to liver tumors in mice with growth patterns and biologic, histologic, and genetic features similar to human HB tumors. This orthotopic xenograft mouse model will enable clinically relevant testing of novel agents for HB.