Miracle-dz/UniFAR-FTUs huggingface dataset:
Batch Fabrication of Pneumatic Valve Arrays by Combining MEMS With Printed Circuit Board Technology

This paper introduces a novel approach that combines Printed Circuit Board (PCB or PC board) Technology with MEMS technology to batch-fabricate large-area arrays of released mechanical structures that are of macro-scale size. As an initial demonstration of this approach. we have batch-fabricated a passively-addressed array of 256 electrostatically-actuated valves and air jets, intended for use in a “Smart” application. Preliminary experiments have been performed showing that these valves can switch a substantial air flow multiple times per second. The electrostatic voltage required to maintain valve closure was found to be many times higher than predicted analytically through modeling of the geometries, flow rates, and forces involved, indicating a need for further analysis and experimentation to better understand the physical effects that come into play at this macroscopic scale. Using PCB technology as a means for extending the MEMS design and fabrication methodologies has the potential to enable batch fabrication of macro-scale arrays of sensors, actuators, conduits, and other elements of a complete system.
A simple simulation approach to risk and cost analysis, with applications to swordfish and cod fisheries

Monte Carlo simula­ tion is used to quantify the uncer­ tainty in the results of sequential population analysis and related sta­ tistics. Probability density functions describe the measured or perceived uncertainty in the inputs to the assessment model. Monte Carlo sim­ ulation is then used to examine the variability in the resulting parameter estimates (stock sizes and fishing mortalities), derived statistics (e.g., F0.1), and in the management regu­ lations necessary to achieve various management objectives. We show how relative frequency histograms of the simulation results can be used to describe the risk of not meeting a given managementgoal as a function of the catch quota selected. We also show how to compute the expected cost, in terms of potential yield fore­ gone, associated with picking a con­ servative quota. This enables one to balance risks and costs or to allow risk to vary within proscribed limits while keeping the catch quota stable. We illustrate the use of the Monte Carlo approach with examples from two fisheries: North Atlantic sword­ fish and northern cod.
Non-equilibrium steady states of entangled polymer mixtures under shear flow

By solving the full equations of an extended two-fluid model in two dimensions, we give the first numerical study revealing non-equilibrium steady states in sheared entangled polymer mixtures. This research provides answers for some fundamental questions in sheared binary mixtures of entangled polymers. Our results reveal that non-equilibrium steady states with finite domain size do exist, and apparent scaling exponents L ∥ ~ γ · − 1 . 05 and L ⊥ ~ γ · − 1 are found over six decades of shear rate. Since the wall effects get involved in our simulations, the dependence of average domain size on system size cannot be strictly eliminated. In addition, as an obvious influence of viscoelasticity, the polymer viscosity η p appears to induce linear translation of the fitted lines. Through two-dimensional numerical simulations, we show the detailed dynamic evolution of microstructure in binary polymer mixtures with asymmetric composition under shear flow. It is found that the phase patterns are significantly different from symmetric fluids studied previously. Finally, we also identify the importance of wall effects and confirm the irreplaceable role of inertia for a non-equilibrium steady state.
U-Net based Semantic Segmentation of Kidney and Kidney Tumours of CT Images

Semantic segmentation of kidney tumours in medical image data is an important step for diagnosis as well as in planning and monitoring of treatments. Morphological heterogeneity of kidneys and tumours in medical image data is a major challenge for automatic segmentation methods, therefore segmentations are typically performed manually by radiologists. In this paper, we use a state-of-the-art segmentation method based on the deep learning U-Net architecture to propose a segmentation algorithm for automatic semantic segmentation of kidneys and kidney tumours of 2D CT images. Therefore, we particularly focus on transfer learning of UNet architectures and provide an experimental evaluation of different hyperparameters for data augmentation, various loss functions, U-Net encoders with varying complexity as well as different transfer learning strategies to increase the segmentation accuracy. We have used the results of the evaluation to fix the hyperparameters of our final segmentation algorithm, which has achieved a high segmentation accuracy for kidney pixels and a lower segmentation accuracy for tumor pixels.
Interactions between food and nutrition security and the socio-economic and environmental dimensions of sustainability in small-scale farms: evidence from a simultaneous confirmatory factor analysis in Poland

ABSTRACT Given the unique role of small farms in providing their own food and nutrition security, the classic three-dimensional approach to sustainability evaluation is not exhaustive. In addition to the socio-economic and environmental dimensions, the question of sustainable development should be extended to the dimension of food and nutrition security. But yet the relations among the different sustainability dimensions at the farm level are not obvious. Consequently, the objective of this paper is to assess the interactions between food and nutrition security and the socio-economic and environmental dimensions of sustainability in small-scale farms. The study is based on surveys conducted in 2018 on a sample of 710 small-scale farms in Poland. By applying structural equation modeling with simultaneous confirmatory factor analysis, the study found a positive relationship between the socio-economic and food and nutrition security dimensions of sustainability, but there was a lack of synergy between the environmental component and the other dimensions. GRAPHICAL ABSTRACT
Self-Affirmation Reduces Uncertainty Aversion for Potential Gains.

Decisions about one's health are often accompanied by uncertain outcomes, which may be either positively or negatively valenced. The presence of this uncertainty, which can range along a continuum from risk to ambiguity (i.e., decisions in which the outcome probabilities are known or unknown), can be perceived as threatening, and individuals tend to be averse to uncertain outcomes, and will attempt to avoid uncertainty when possible. We proposed that one way to reduce uncertainty aversion could be to provide opportunities to affirm one's core values, or "self-affirmation. " Prior research has suggested that self-affirmation promotes health behaviour by providing a buffer against potential threats to the self. However, the degree to which self-affirmation affects decision-making is still unclear. Across two studies, we tested the effects of a self-affirmation manipulation on risk (Study 1) and ambiguity (Study 2) preferences for both potential gains and losses. In both studies, we found that, compared to the non-affirmed group, affirmed individuals were more accepting of uncertainty when the decision involved potential gains, but not for potential losses. Further, for risky decisions, the increased acceptance of uncertainty came at the expense of making choices consistent with expected value, such that self-affirmed individuals made more disadvantageous choices than non-affirmed individuals. Our results suggest both benefits and costs of self-affirmation in the context of risky choice, an important finding given the many applications of self-affirmation in behavioral decision making contexts.
High Performance RF MEMS Metal-Contact Switches and Capacitive Switches

This dissertation presents designs, fabrication processes and measurements of a series of high performance RF MEMS switches.Chapter 2 presents a miniature RF MEMS metal contact switch based on a tethered-cantilever structure. The miniature size and the use of tethers result in an excellent biaxial residual stress and stress gradient tolerance. The switch is built using thin metal process with a large biaxial stress and a high stress gradient (50 MPa and -105 MPa/um), and works well under these conditions. In the up-state, the switch capacitance is 9.4 fF and results in an isolation of 20 dB at 20 GHz. In the down-state, the switch resistance is 3.6 ohm for a gold-gold contact under 30 V actuation voltage. The switch is compatible with CMOS back-end processing. With its miniature size, the switch could be placed in arrays to achieve lower contact resistance and higher power handling.Chapter 3 presents a multi-contact mN-force RF MEMS metal-contact switchwith a pull-down voltage (Vp) of 45 V-50 V and an operation voltage of 60V-65V. The switch gets a contact force of s 2.0 mN under 65 V actuation voltage and a release force of s 1.2 mN (simulated). The switch gets an on-state resistance of s 1.8 with Ru-Au contact and an off-state capacitance of 13.5 fF, which results in a figure of merit of 24 fs. In the temperature stability measurement, the switch shows a change of 4V in pull-down voltage and a change of 2V in release voltage from 25 C to 125 C. In the high power handling measurement, the switch demonstrates a reliability of > 10 million cold switching cycles with 5 W RF power.Chapter 4 first presents a high capacitance ratio (Cr) capacitive switch with continuous tuning capability after pull-down. The measured up-state capacitance is 74 fF. The pull-down voltage of the switch is 30V -32V and there is an 8.4% linear tuning range from 33V to 40V actuation voltage. The measured down-state capacitance is 1296 fF under 40V actuation voltage, resulting in a Cr of 17.5. Next, a back-to-back switch using the high Cr switch is designed to improve IP2 without extra power supply. The back-to-back switch shows an up-state capacitance of 31fF, a Cr of 19.7 and a 6.8% continuous tuning range from 34V to 40V. The back-to-back switch shows a 14 dB higher OIP2 than the single switch does.
Adaptive and Risk-Aware Target Tracking for Robot Teams With Heterogeneous Sensors

We consider a scenario where a team of robots with heterogeneous sensors must track a set of targets or hazards which may induce sensory failures on the robots. In particular, the likelihood of failures depends on the proximity between the targets and the robots. We propose a control framework that explicitly addresses the competing objectives of tracking quality maximization and sensor preservation (which impacts the future quality of the generated target estimates). Our framework consists of a risk-aware component—which accounts for the risk of suffering sensor failures, and an adaptive component—which balances the aforementioned competing objectives while accounting for failures that have already occurred. Based on a measure of the observability of the tracking process, our framework can generate aggressive as well as safer robot configurations to track the targets. Crucially, the heterogeneous sensing capabilities of the robots are explicitly considered at each step, allowing for a more expressive risk vs. tracking quality trade-off. Real robot experiments with induced sensor failures demonstrate the efficacy of the proposed approach.
Can prohibitions of non‐audit services and an expanded auditor liability improve audit quality?

Law entrusts auditors to conduct statutory audits. They fulfil a societal role in providing an opinion on the truth and fairness of the financial statements of audited entities and reducing the risk of misstatement. The purpose of an audit is to enhance the credibility of financial reports prepared by management (Watts & Zimmerman, 1986). Thereby, audits contribute to financial stability, trust and market confidence in the economy by protecting investors from agency risk, which in turn reduces the cost of capital for companies (European Commission, 2010). To fulfil this function, auditors need to provide an adequate service quality. According to the generally accepted definition of DeAngelo (1981), audit quality is the market-assessed joint probability that an auditor will discover material misstatements (auditor competence) and report them (auditor independence). This definition stresses that providing a high factual audit quality is insufficient but that users must also perceive audit quality as appropriate. Accounting scandals like Carillion, a UK construction and facility services company, or Wirecard, a German fintech company, raise public suspicion of auditing failures and result in regulatory initiatives, which seek to improve audit quality. In the UK, the regulatory response mainly focused on expanding the prohibition of non-audit services (NAS) by audit firms (Department for Business, Energy, & Industrial Strategy, 2021). The provision of NAS to a public-interest entity (PIE) audit client is now limited to services regarding legally required reports and audit-related services (FRC, 2019). Moreover, the Big 4 audit firms have to operationally separate their audit and NAS practices by 30 June 2024 (FRC, 2020). The latter should ensure a focus on audit quality and protect auditors from influences from the NAS practice. The UK regulator even considered an audit-only firm approach, which would have resulted in a spin-off of UK audit firms' consulting arms (Marriage, 2018). The German legislator, who originally made use of the European Union (EU) Member State option to allow certain tax and valuation services from the blacklist of prohibited NAS, just recently reversed this decision in response to the Wirecard scandal. More importantly, Germany significantly increased the existing liability caps in case of negligent misconduct. In audits of PIEs, auditor liability is now unlimited in cases of gross negligence. The EU also considers eliminating or setting more appropriate liability caps (Council of the European Union, 2021).
Radix ranunculus temate saponins sensitizes ovarian cancer to Taxol via upregulation of miR-let-7b

A common cause of treatment failure in ovarian cancer is acquired drug resistance. Therefore, effective novel drugs against chemoresistance need to be developed. MicroRNAs (miRNAs or miRs) serve key regulatory roles in tumorigenesis and chemoresistance. The objective of the present study was to explore the role of miR-let-7b in ovarian cancer chemoresistance, and to develop novel strategy for the treatment of drug-resistant ovarian cancer. For this purpose, reverse transcription-quantitative PCR was performed to evaluate the expression level of miR-let-7b in fresh ovarian cancer tissues and cell lines. miR-let-7b mimic was transfected into ovarian cancer cell lines. Functional experiments, cell apoptosis and cell viability assays were carried out to identify the tumor-suppressor function of miR-let-7b. The treatment effect of Radix ranunculus temate saponins (RRTS), one of the primary constituents extracted from the traditional Chinese medicine radix Ranunculi ternati, was identified in vitro and in vivo. The results revealed that miR-let-7b was downregulated significantly in chemoresistant ovarian cancer patients. miR-let-7b overexpression suppressed cell growth and invasion and enhanced sensitivity to Taxol of ovarian cancer cells. Furthermore, miR-let-7b levels in ovarian cancer tissue were inversely associated with collagen type III α1 chain (COL3A1) levels. COL3A1, a non-fibrillar collagen associated with chemoresistance, was targeted by miR-let-7b. RRTS showed cytotoxic effects on ovarian cancer cells through inducing miR-let-7b expression and decreasing COL3A1 expression. In addition, RRTS sensitized ovarian cancer to Taxol both in vitro and in vivo. In conclusion, the present results revealed synergistic cytotoxicity of RRTS and Taxol on against ovarian cancer cells via upregulating expression of miR-let-7b. Combination of Taxol and RRTS may be a novel treatment strategy for patients with TR ovarian cancer.
Intervenções educativas para atividade física em adultos brasileiros: revisão sistemática

OBJETIVO: Sumarizar as principais evidências de intervenções educativas delineadas para o aumento dos níveis de atividade física (AF) em adultos brasileiros. MÉTODOS: Revisão sistemática de estudos de intervenção conduzidos no Brasil, que implementaram componentes educativos com a finalidade de promover o aumento dos níveis de AF em populações de adultos (18 a 65 anos). Em outubro de 2020, buscas sistemáticas foram conduzidas em seis bases de dados e nas listas de referências dos artigos avaliados. RESULTADOS: Dos 2.511 artigos iniciais, nove compuseram a síntese. Foram observadas amostras com características específicas (como vulnerabilidade social, inatividade física e sobrepeso ou obesidade), com maior número de mulheres. Cinco intervenções (55,6%) ocorreram nos cenários da atenção primária à saúde (APS) do Sistema Único de Saúde (SUS). Apenas em quatro estudos (44,4%) houve descrição dos referenciais pedagógicos estruturantes das abordagens educativas, dentro os quais o aconselhamento se configurou como a estratégia mais utilizada, como aquelas realizadas por meio de encontros presenciais, visitas domiciliares, palestras e chamadas telefônicas (n = 8; 88,9%). Resultados positivos foram observados em três distintos indicadores: aumento do volume semanal de AF (n = 4); aumento do índice de AF no lazer (n = 1); e aumento da proporção de mulheres classificadas como “muito ativas/ativas” (n = 1). Visto as especificidades amostrais, o domínio “seleção dos participantes” apresentou elevado número de intervenções com alto risco de viés. CONCLUSÕES: As abordagens educativas produziram alguns efeitos positivos em distintos indicadores de AF, destacando-se o aconselhamento como principal estratégia utilizada e as abordagens que envolveram outras temáticas de saúde, como nutrição e estresse. Contudo, frente aos diversos determinantes da AF no Brasil, é importante que futuras intervenções sejam conduzidas em variadas localizações do país, de forma que avaliem, de maneira mais ampla, seus processos de implementação e articulação com os distintos profissionais que atuam na APS.
Mean line aerodynamic design of an axial compressor using a novel design approach based on reinforcement learning

This paper develops a novel design approach based on reinforcement learning, which can independently complete the mean line aerodynamic design process of the axial compressor. The approach combines Deep Deterministic Policy Gradient (DDPG) algorithm with mean line aerodynamic predicting program HARIKA to acquire the design experiences of the axial compressor. DDPG combines basic reinforcement learning algorithm with artificial neural networks to get continuous observation and give corresponding actions. After the specific modification of the DDPG, multi-objective optimization can be integrated into the design process. Under the guidance of this approach, the design and optimization processes of a 9-stage high-pressure axial compressor were completed without expert experiences. At the design point, the isentropic efficiency was 88.5% and the surge margin was 25%, which meets the requirement of the compressor’s efficiency and stability. And there was an increase of 13.4% and 22%, respectively, compared to the initial design. Moreover, through the analysis of the design results, the distributions of aerodynamic parameters conform to expert experiences. To verify the approach, traditional optimization methods, multi-island genetic optimization algorithm (GA), and multi-objective particle swarm optimization algorithm (MOPSO) were used to solve the same optimization problem. The DDPG optimized efficiency was 0.2% lower than the traditional optimization method, and the pressure ratio at the work point was, respectively, 0.6% and 2% higher than that of the MOPSO and GA, which proved the effectiveness of the new design approach. Furthermore, after training, this approach can give design results immediately near the specific design requirements, which is different from the traditional optimization methods. The new approach saved 93% evaluation steps compared to the GA in the −3% design point and finished the design process in 8 steps in the +3% design point, where GA failed to complete.
Global estimation of clear-sky shortwave aerosol direct radiative effects based on CALIPSO observations

ABSTRACT The total and individual aerosol direct radiative effects (ADREs) were estimated for clear-sky conditions using Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) observations and radiative transfer model. In this study, a parametric sensitivity analysis was performed for the Santa Barbara DISORT Atmospheric Radiative Transfer model using a global sensitivity analysis method. Uncertainties in the ADREs due to aerosol optical properties and surface albedo errors were evaluated. The single-scattering albedo and asymmetry factor for marine, dust, pollution, and smoke aerosols required for the model calculations were obtained from the classified AErosol RObotic NETwork observations. The estimated average global ADREs and errors at the top-of-atmosphere (TOA) and the surface were −2.36 ± 0.54 and −4.78 ± 2.2 Wm−2, respectively. In regions with higher dust, pollution, and smoke aerosol loading, the ADREs exhibited significant seasonal variability. In the Sahara and Arabian deserts, during the June-July-August season with higher dust aerosol loading, the seasonal average ADREs in the region were −3.23 and −21.37 Wm−2 at the TOA and surface, respectively. In the Indian region, during the March–April–May season with higher pollution aerosol loading, the ADREs were −10.33 and −28.04 Wm−2 at the TOA and surface, respectively. In Southern Africa, the smoke aerosol with a single-scattering albedo of 0.87 caused negative radiative effects at the TOA, and during the September-October-November season with higher smoke aerosol loading, the seasonal average ADREs were −2.34 and −6.36 Wm−2 at the TOA and surface, respectively.
MSAFF-Net: Multiscale Attention Feature Fusion Networks for Single Image Dehazing and Beyond

Single image dehazing is a critical problem in computer vision. However, most recently proposed learning-based dehazing methods achieve unsatisfactory quality with dehazed images due to inaccurate parametric estimation. The size of these models is also large to be applied with mobile devices’ limited resources. Last, most models are tailored to image dehazing, achieving poor migration. Thus, we propose a compact multiscale attention feature fusion network with a model size of 2 MB called MSAFF-Net to perform end-to-end single image dehazing. In the proposed model, we design a simple and powerful feature extraction module to extract complex features from hazy images. We use a channel attention module and a multiscale spatial attention module to consider the regions with haze-relevant features. To our knowledge, this study is the first to directly apply the attention mechanism rather than to embed it into certain modules for single image dehazing. We compare MSAFF-Net with other approaches on the NTIRE18, RESIDE, and Middlebury Stereo datasets. We show that MSAFF-Net achieves comparable or better performance than other models. We also extend MSAFF-Net to single image deraining, and various experiments demonstrate its effectiveness. Results suggest that MSAFF-Net can directly restore clear images using channels with the most useful haze- or rain-relevant features and spatial locations.