Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of stormwater runoff quantity integral management via sponge city construction: A pilot case study of Jinan

ABSTRACT The development of urbanization results in a marked increase in impervious areas, destroying the natural geological features and hydrological properties of the urban developed area. This may cause high risk of flooding, runoff pollution, and water shortages. An integral method is proposed to realize the multi-goals management of stormwater runoff, which called sponge city construction, and taken a pilot area of sponge city construction as an example to evaluate the effect of sponge city construction on stormwater quantity control via InfoWorks Integrated Catchment Model (ICM) model. The results showed that the annual total rainfall volume capture ratio is 77.4%, the drainage pipeline capacity was improved and flooding risk was decreased effectively. The high-risk flooding area was reduced about 44.76% of 50-year return period rainfall event with rainfall duration of 24 h. So, sponge city construction provides a novel method for the stormwater integral management.
Koala methylomes reveal divergent and conserved DNA methylation signatures of X chromosome regulation

X chromosome inactivation (XCI) mediated by differential DNA methylation between sexes is an iconic example of epigenetic regulation. Although XCI is shared between eutherians and marsupials, the role of DNA methylation in marsupial XCI remains contested. Here, we examine genome-wide signatures of DNA methylation across fives tissues from a male and female koala (Phascolarctos cinereus), and present the first whole-genome, multi-tissue marsupial ‘methylome atlas’. Using these novel data, we elucidate divergent versus common features of representative marsupial and eutherian DNA methylation. First, tissue-specific differential DNA methylation in koalas primarily occurs in gene bodies. Second, females show significant global reduction (hypomethylation) of X chromosome DNA methylation compared to males. We show that this pattern is also observed in eutherians. Third, on average, promoter DNA methylation shows little difference between male and female koala X chromosomes, a pattern distinct from that of eutherians. Fourth, the sex-specific DNA methylation landscape upstream of Rsx, the primary lncRNA associated with marsupial XCI, is consistent with the epigenetic regulation of female-specific (and presumably inactive X chromosome-specific) expression. Finally, we use the prominent female X chromosome hypomethylation and classify 98 previously unplaced scaffolds as X-linked, contributing an additional 14.6 Mb (21.5%) to genomic data annotated as the koala X chromosome. Our work demonstrates evolutionarily divergent pathways leading to functionally conserved patterns of XCI in two deep branches of mammals.
Unsteady Reynolds-averaged Navier–Stokes investigation of free surface wave impact on tidal turbine wake

Tidal current is a promising renewable energy source. Previous studies have investigated the influence of surface waves on tidal turbines in many aspects. However, the turbine wake development in a surface wave environment, which is crucial for power extraction in a turbine array, remains elusive. In this study, we focus on the wake evolution behind a single turbine and its interaction with surface waves. A numerical solver is developed to study the effects of surface waves on an industrial-size turbine. A case without surface wave and two cases with waves and different rotor depths are investigated. We obtain three-dimensional flow field descriptions near the free surface, around the rotor, and in the near- and far-wake. In a comparative analysis, the time-averaged and instantaneous flow fields are examined for various flow characteristics, including momentum restoration, power output, free surface elevation and vorticity dynamics. A model reduction technique is employed to identify the coherent flow structures and investigate the spatial and temporal characteristics of the wave–wake interactions. The results indicate the effect of surface waves in augmenting wake restoration and reveal the interactions between the surface waves and the wake structure, through a series of dynamic processes and the Kelvin–Helmholtz instability.
Autocrine and paracrine interferon signalling as ‘ring vaccination’ and ‘contact tracing’ strategies to suppress virus infection in a host

The innate immune response, particularly the interferon response, represents a first line of defence against viral infections. The interferon molecules produced from infected cells act through autocrine and paracrine signalling to turn host cells into an antiviral state. Although the molecular mechanisms of IFN signalling have been well characterized, how the interferon response collectively contribute to the regulation of host cells to stop or suppress viral infection during early infection remain unclear. Here, we use mathematical models to delineate the roles of the autocrine and the paracrine signalling, and show that their impacts on viral spread are dependent on how infection proceeds. In particular, we found that when infection is well-mixed, the paracrine signalling is not as effective; by contrast, when infection spreads in a spatial manner, a likely scenario during initial infection in tissue, the paracrine signalling can impede the spread of infection by decreasing the number of susceptible cells close to the site of infection. Furthermore, we argue that the interferon response can be seen as a parallel to population-level epidemic prevention strategies such as ‘contact tracing’ or ‘ring vaccination’. Thus, our results here may have implications for the outbreak control at the population scale more broadly.
The cost of quantum locality

It has been more than 20 years since Deutsch and Hayden demonstrated that quantum systems can be completely described locally—notwithstanding Bell’s theorem. More recently, Raymond-Robichaud proposed two other approaches to the same conclusion. In this paper, all these means of describing quantum systems locally are proved formally equivalent. The cost of such descriptions is then quantified by the dimensionality of their underlining space. The number of degrees of freedom of a single qubit’s local description is shown to grow exponentially with the total number of qubits considered as a global system. This apparently unreasonable cost to describe such a small system in a large Universe is nonetheless shown to be expected. Finally, structures that supplement the universal wave function are investigated.
Privacy Care

The emergence of ubiquitous computing (UbiComp) environments has increased the risk of undesired access to individuals’ physical space or their information, anytime and anywhere, raising potentially serious privacy concerns. Individuals lack awareness and control of the vulnerabilities in everyday contexts and need support and care in regulating disclosures to their physical and digital selves. Existing GUI-based solutions, however, often feel physically interruptive, socially disruptive, time-consuming and cumbersome. To address such challenges, we investigate the user interaction experience and discuss the need for more tangible and embodied interactions for effective and seamless natural privacy management in everyday UbiComp settings. We propose the Privacy Care interaction framework, which is rooted in the literature of privacy management and tangible computing. Keeping users at the center, Awareness and Control are established as the core parts of our framework. This is supported with three interrelated interaction tenets: Direct, Ready-to-Hand, and Contextual. Direct refers to intuitiveness through metaphor usage. Ready-to-Hand supports granularity, non-intrusiveness, and ad hoc management, through periphery-to-center style attention transitions. Contextual supports customization through modularity and configurability. Together, they aim to provide experience of an embodied privacy care with varied interactions that are calming and yet actively empowering. The framework provides designers of such care with a basis to refer to, to generate effective tangible tools for privacy management in everyday settings. Through five semi-structured focus groups, we explore the privacy challenges faced by a sample set of 15 older adults (aged 60+) across their cyber-physical-social spaces. The results show conformity to our framework, demonstrating the relevance of the facets of the framework to the design of privacy management tools in everyday UbiComp contexts.
Fault-based Built-in Self-test and Evaluation of Phase Locked Loops

With the increasing pressure to obtain near-zero defect rates for the automotive industry, there is a need to explore built-in self-test and other non-traditional test techniques for embedded mixed-signal components, such as PLLs, DC-DC converters, and data converters. This article presents a very low-cost built-in self-test technique for PLLs specifically designed for fault detection. The methodology relies on exciting the PLL loop in one location via a pseudo-random signal with noise characteristics and observing the response from another location in the loop via all digital circuitry, thereby inducing low area and performance overhead. The BIST circuit along with a PLL under test is designed in 65 nm technology. Fault simulations performed at the transistor and system-level show that the majority of non-catastrophic faults that result in parametric failures can be detected with the proposed approach.
Approximate Learning and Fault-Tolerant Mapping for Energy-Efficient Neuromorphic Systems

Brain-inspired deep neural networks such as Convolutional Neural Network (CNN) have shown great potential in solving difficult cognitive problems such as object recognition and classification. However, such architectures have high computational energy demand and sensitivity to variation effects, making them inapplicable for energy-constrained embedded learning platforms. To address this issue, we propose a learning and mapping approach that utilizes approximate computing during early design phases for a layer-wise pruning and fault tolerant weight mapping scheme of reliable and energy-efficient CNNs. In the proposed approach, approximate CNN is prepared first by layer-wise pruning of approximable neurons, which have high error tolerance margins using a two-level approximate learning methodology. Then, the pruned network is retrained to improve its accuracy by fine-tuning the weight values. Finally, a fault-tolerant layer-wise neural weight mapping scheme is adopted to aggressively reduce memory operating voltage when loading the weights of error resilient layers for energy-efficiency. Thus, the combination of approximate learning and fault tolerance aware memory operating voltage downscaling techniques enable us to implement robust and energy-efficient approximate inference engine for CNN applications. Simulation results show that the proposed fault tolerant and approximate learning approach can improve the energy-efficiency of CNN inference engines by more than 50% with less than 5% reduction in classification accuracy. Additionally, more than 26% energy-saving is achieved by using the proposed layer-wise mapping-based cache memory operating voltage down-scaling.
Probabilistic Text Entry—Case Study 3

The reading and writing of text is an activity that has long dominated our interac­ tions with computers. Early user interfaces often displayed only text and relied on input obtained via a desktop keyboard. Actions on the keyboard were deterministic; if you hit a particular key, the action linked to that key was immediately triggered. Today, most of our desktop keyboard interactions retain this predictability with a few exceptions such as the automatic spelling correction of some word processors. However, using a desktop keyboard may not always be desirable or practical. For example, we may be away from our desk and need to enter text on a mobile device. We may want to enter text in new computing environments such as virtual reality (VR), augmented reality (AR), or in environments embedded with sensors and dis­ plays (e.g., autonomous cars). This chapter looks at the techniques and challenges associated with moving away from our trusty desktop keyboard to input methods that are inherently uncertain.
Intelligent Mediator-based Enhanced Smart Contract for Privacy Protection

With the development of ICT technology, users are actively participated in content creation and sharing. Service providers have provided more diverse and extensive information services. With the recent evolution to a customized service, the demand for the use of personal information is increasing. However, it affects the efficiency and convenience of service provision, as social problems and security threats such as personal information leakage, human trafficking, and misuse increase. In this article, we proposed an intelligent mediator-based enhanced smart contract for privacy protection. The proposed approach performs the tasks, which are mediation transactions, authorization, and transaction record management, with blockchain-based personal information management. Then it is possible to prevent misuse of personal information and to support rational decision-making on the transparency of information and the use of personal information by autonomously performing personal information management.
A Convenient Synthesis of Fluorobenzaldehydes by KF/Ph4PBr/18-Crown-6 Reagent System

4-Fluorobenzaldehyde was easily synthesized from 4-chlorobenzaldehyde by potassium fluoride in the presence of tetraphenylphosphonium halide plus 18-crown-6 or poly(ethylene glycol) dimethyl ether. Several fluorobenzaldehyde derivatives were similarly prepared from the corresponding chloro-derivatives in good yields.
An Overview of Telehealth in Total Joint Arthroplasty

With the increase in technological advances over the years, telehealth services in orthopedic surgery have gained in popularity, yet adoption among surgeons has been slow. With the onset of the COVID-19 pandemic, however, orthopedic surgery practices nationwide have accelerated adaptation to telemedicine. Telehealth can be effectively applied to total joint arthroplasty, with the ability to perform preoperative consultations, postoperative follow-up, and telerehabilitation in a virtual, remote manner with similar outcomes to in-person visits. New technologies that have emerged, such as virtual goniometers, wearable sensors, and app-based patient questionnaires, have improved clinicians’ ability to conduct telehealth visits. Benefits of using telehealth include high patient satisfaction, cost-savings, increased access to care, and more efficiency. Notably, some challenges still exist, including widespread accessibility and adaptation of new technologies, inability to conduct an in-person orthopedic physical examination, and regulatory barriers, such as insurance reimbursement, increased medicolegal risk, and privacy and confidentiality concerns. Despite these hurdles, telehealth is here to stay and can be successfully incorporated in any total joint arthroplasty practice with the appropriate adjustments.
Mathematical Simulation of the Temperature Dependence of Time Temperature Integrator (TTI) and Meat Qualities

The temperature dependence of time temperature integrator (TTI) was investigated in terms of the Arrhenius activation energy (Ea) to determine TTI requirements to accurately predict meat quality during storage. Mathematical simulation was conducted using a numerical analysis. First, using Euler's method and MS Excel VBA, the TTI color change was kinetically modeled and numerically calculated under several storage conditions. From the TTI color variable profiles calculated from the storage time-temperature profiles, , which is a constant temperature representing the whole temperature profiles, was calculated. Upon predicting Pseudomonas spp. concentrations (one of the meat qualities) from , it was found that if be true, then Pseudomonas concentrations were calculated to be constant with the same TTI color values, regardless of time-temperature profiles, whereas if then Pseudomonas concentrations varied even with the same TTI color values. This indicates that each TTI color value represents its own fixed degree of meat quality, only if .
Device Performance Correlated with Structural Properties of Vertically Aligned Nanorod Arrays in Polymer/ZnO Solar Cells

Polymer-based solar cells consisting of ZnO nanorods and poly(1-methoxy-4-(2-ethylhexyloxy)-p-phenylenevinylene) (MEH-PPV) are investigated by current−voltage characterization and intensity modulated photovoltage spectroscopy (IMVS). The high quality ZnO nanorod arrays were prepared by electrodeposition, in which the length (Ln) and the concentration of deep level defects of ZnO nanorods were controlled by deposition time (Td). Results show that increasing Td leads to ZnO nanorods with linearly increased Ln but differently increased defect concentration for the MEH-PPV/ZnO solar cells, providing a peak device power conversion efficiency of 0.34% at AM 1.5 illumination (100 mW/cm2) for Td = 10 min. The electron lifetimes in MEH-PPV/ZnO nanorod devices at open circuit were studied by means of IMVS for the first time, and the influences of Ln and defect concentration on the charge recombination kinetics and device performance were revealed. It is found that, in the MEH-PPV/ZnO devices with high quality ZnO n.
Perineural Invasion Correlates With Common Pathological Variables and Clinical Outcomes of Patients With Squamous Cell Carcinoma of the Vulva Treated With Primary Radical Surgery and Inguinal-femoral Lymphadenectomy

Background/Aim: The aims of the study were: i) to assess the incidence of perineural invasion (PNI) in squamous cell carcinoma of the vulva and ii) to correlate PNI with common pathological prognostic variables and clinical outcome of patients. Patients and Methods: The hospital records of 64 patients with vulvar squamous cell carcinoma who underwent primary radical surgery were reviewed. Results: PNI was significantly related to stage (p=0.038), size (p=0.038), lymph-vascular space involvement (p=0.013) and nodal status (p=0.038), but not to patient age, tumor grade and stromal invasion. Five-year disease-free survival was 30.0% in patients with PNI and 53.1% in those without PNI (p=0.018), and the corresponding 5-year overall survival was 50.0% and 77.1% (p=0.031), respectively. Conclusion: PNI was associated with common pathological prognostic variables and with a poorer clinical outcome in patients with vulvar squamous cell carcinoma.
Influence of Adsorbed Species on the Reconstruction of 4H−SiC(0001) Surfaces

Surface reconstructions of unterminated 4H−SiC(0001) surfaces have been investigated theoretically using the first principal density functional theory. A (2 × 1) reconstruction was found for the Si(0001) surface, whereas the C(0001) surface retained its initial (1 × 1) structure. The downward relaxation was, on the other hand, much larger for the C surface than for the Si surface. The effects of adsorption of C2H2 (or Si) on the two surfaces were also studied. The adsorbates were then observed to bond strongly to the surfaces of Si(0001) and C(0001), respectively. They also influenced the surfaces in the direction of bulk parameters.
Advancing care for acute gastrointestinal bleeding using artificial intelligence

The future of gastrointestinal bleeding will include the integration of machine learning algorithms to enhance clinician risk assessment and decision making. Machine learning algorithms have shown promise in outperforming existing clinical risk scores for both upper and lower gastrointestinal bleeding but have not been validated in any prospective clinical trials. The adoption of electronic health records provides an exciting opportunity to deploy risk prediction tools in real time and also to expand the data available to train predictive models. Machine learning algorithms can be used to identify patients with acute gastrointestinal bleeding using data extracted from the electronic health record. This can lead to an automated process to find patients with symptoms of acute gastrointestinal bleeding so that risk prediction tools can be then triggered to consistently provide decision support to the physician. Neural network models can be used to provide continuous risk predictions for patients who are at higher risk, which can be used to guide triage of patients to appropriate levels of care. Finally, the future will likely include neural network‐based analysis of endoscopic stigmata of bleeding to help guide best practices for hemostasis during the endoscopic procedure. Machine learning will enhance the delivery of care at every level for patients with acute gastrointestinal bleeding through identifying very low risk patients for outpatient management, triaging high risk patients for higher levels of care, and guiding optimal intervention during endoscopy.
Metabolic surgery: A clinical update

Metabolic and bariatric surgery has grown beyond ‘experimental’ weight‐loss surgery. As techniques have advanced over the last few decades, so has the growing body of research and evidence, proving that both weight‐loss and metabolic health improvement are induced. Metabolic surgery has become the more appropriate term for weight‐loss surgery because of the altered gastrointestinal anatomy and subsequent beneficial metabolic effects. Although the tool of metabolic surgery has been well refined, a large portion of the global population does not have adequate access to it. This clinical update aims to (a) inform healthcare providers from all disciplines about the myriad of benefits of metabolic surgery and (b) equip them with the necessary knowledge to bridge the gap between patients in need of metabolic treatment and the therapies in metabolic surgery available to them.
Tailor: Targeting heavy tails in flow cytometry data with fast, interpretable mixture modeling

Automated clustering workflows are increasingly used for the analysis of high parameter flow cytometry data. This trend calls for algorithms which are able to quickly process tens of millions of data points, to compare results across subjects or time points, and to provide easily actionable interpretations of the results. To this end, we created Tailor, a model‐based clustering algorithm specialized for flow cytometry data. Our approach leverages a phenotype‐aware binning scheme to provide a coarse model of the data, which is then refined using a multivariate Gaussian mixture model. We benchmark Tailor using a simulation study and two flow cytometry data sets, and show that the results are robust to moderate departures from normality and inter‐sample variation. Moreover, Tailor provides automated, non‐overlapping annotations of its clusters, which facilitates interpretation of results and downstream analysis. Tailor is released as an R package, and the source code is publicly available at www.github. com/matei-ionita/Tailor.
Extraction de relations sémantiques entre noms et verbes au-delà des liens morphologiques

Nous étudions les relations de proximité sémantique entre les noms et les verbes à partir de données calculées sur un corpus de 200 millions de mots par un programme d’analyse distributionnelle automatique. Nous exposons les résultats d’une méthode d’extraction de couples Nom/Verbe, qui combine un indice de proximité distributionnelle et un indice de cooccurrence : un couple est extrait si le nom et le verbe apparaissent avec les mêmes arguments sur l’ensemble du corpus, d’une part, et s’ils apparaissent au moins une fois dans un même paragraphe munis du même argument, d’autre part. L’article élabore une typologie des 1441 couples extraits et démontre l’intérêt de prendre en compte les couples non liés morphologiquement, qui constituent 70 % des données.