Miracle-dz/UniFAR-FTUs huggingface dataset:
New BST–silica suspension coating material for dielectric thin films fabricated at low temperatures

Abstract A new Ba0·7Sr0·3TiO3 (BST)–silica suspension has been developed as a coating material for dielectric thin films. Using a spin on glass (SOG) wet process, a thin layer consisting of nanoparticles of BST and silica was formed on an Al/SiO2/Si wafer. The BST–silica suspension was made by mixing a BST nanoparticle dispersion with methyl siloxane oligomer. A silicon wafer was coated with the BST–silica suspension using a spin coater and heat treated. The sample was then coated with methyl siloxane oligomer and heat treated again, and finally, a top electrode was applied. The Al/SiO2/BST–SiO2/Al/SiO2/Si stack thus prepared is a metal insulator metal (MIM) capacitor. The electrical properties of the MIM capacitor were evaluated and its capacitance, dissipation factor and voltage coefficient of capacitance were determined to be 1054 pF mm−2, <0·1 and <100 ppm V−1 respectively.
On a general relationship between estimation, detection, and the Bhattacharyya coefficient (Corresp.)

A general and fundamental relationship between estimation, detection/pattern recognition, and the Bhattacharyya coefficient is established. Specifically, it is shown that a functional of the likelihood ratio necessary for Bayes optimal detection/pattern recognition is the Bayes-optimal mean-square estimate of the indicator variable \beta , where \beta = 1, 0 , for hypotheses H_{1}, H_{0} , respectively. The Bhattacharyya coefficient is then shown to be the expected value of the estimation error standard deviation.
A calcium-sensing receptor mutation causing hypocalcemia disrupts a transmembrane salt bridge to activate β-arrestin–biased signaling

A disease-associated mutation disrupts a calcium-sensing receptor structural motif and causes biased signaling through β-arrestin. GPCR signaling biased by a salt bridge The calcium-sensing receptor (CaSR) is a G protein–coupled receptor (GPCR) that plays an important role in extracellular calcium homeostasis by stimulating intracellular calcium signaling and mitogen-activated protein kinase (MAPK) pathways. Mutations in CASR that specifically affect either intracellular calcium or MAPK signaling have been associated with inherited forms of hypocalcemia. Gorvin et al. identified a CASR mutation that results in an Arg-to-Gly substitution at amino acid residue 680 (R680G) in CaSR in a family with hypocalcemia. Functional analysis of CaSRR680G in cultured cells revealed that this missense mutation did not affect intracellular calcium signaling but enhanced the ability of CaSR to stimulate MAPK signaling through a mechanism that depended on the scaffolding protein β-arrestin rather than on G proteins. Structural modeling and mutational analysis demonstrated that the substitution likely disrupted a salt bridge in CaSR. These findings identify a structural feature of CaSR that is important for controlling signaling bias. The calcium-sensing receptor (CaSR) is a G protein–coupled receptor (GPCR) that signals through Gq/11 and Gi/o to stimulate cytosolic calcium (Ca2+i) and mitogen-activated protein kinase (MAPK) signaling to control extracellular calcium homeostasis. Studies of loss- and gain-of-function CASR mutations, which cause familial hypocalciuric hypercalcemia type 1 (FHH1) and autosomal dominant hypocalcemia type 1 (ADH1), respectively, have revealed that the CaSR signals in a biased manner. Thus, some mutations associated with FHH1 lead to signaling predominantly through the MAPK pathway, whereas mutations associated with ADH1 preferentially enhance Ca2+i responses. We report a previously unidentified ADH1-associated R680G CaSR mutation, which led to the identification of a CaSR structural motif that mediates biased signaling. Expressing CaSRR680G in HEK 293 cells showed that this mutation increased MAPK signaling without altering Ca2+i responses. Moreover, this gain of function in MAPK activity occurred independently of Gq/11 and Gi/o and was mediated instead by a noncanonical pathway involving β-arrestin proteins. Homology modeling and mutagenesis studies showed that the R680G CaSR mutation selectively enhanced β-arrestin signaling by disrupting a salt bridge formed between Arg680 and Glu767, which are located in CaSR transmembrane domain 3 and extracellular loop 2, respectively. Thus, our results demonstrate CaSR signaling through β-arrestin and the importance of the Arg680-Glu767 salt bridge in mediating signaling bias.
A Retrospective Study of Patient Factors That Indicate Provider Nonadherence to an Institutional Clostridium difficile Treatment Guideline

Background: Clostridium difficile infection (CDI) is treated most often with metronidazole or vancomycin. Both have been effective in treatment of mild to moderate infection. In more severe cases, vancomycin may be more effective. Objectives: The primary objectives were to quantify the severity of CDI and to describe overall adherence to the institutional CDI guideline. Secondary objectives were to assess factors associated with adherence to the guidelines. Methods: Retrospective analysis of the electronic medical record was used to evaluate adherence to institutional guidelines. Data collected included demographics and other factors potentially contributing to adherence: Charlson comorbidity index, severity of infection, recurrence, intensive care unit (ICU) admission, infectious diseases (ID) consult, total duration and number of antibiotics, alternative therapies, and acid suppression. Descriptive statistics and bivariate analyses were used to describe and compare factors associated with guideline adherence; multivariate logistic regression assessed independent predictors of adherence. Results: A total of 387 patients met the inclusion criteria. CDI severity was 55.8% mild/moderate cases, 42.4% severe, 0.5% fulminant, and 1.3% prophylaxis. Overall, institutional guideline adherence was 51.9%. In bivariate analyses, 5 factors were associated with nonadherence to guidelines: older age, ICU admission, duration of antibiotics, mild/moderate and severe infection (all P < .05). In the logistic regression model, severe infection (P < .001) and longer duration of antibiotics (P < .05) were independently associated with guideline nonadherence. Conclusion: In this study, 42.4% of the patients met criteria for severe infection. Providers for patients with severe infection and longer duration of antibiotic therapy were less likely to adhere to the institutional guideline.
Acute Recanalization of Thrombo-Embolic Ischemic Stroke with pREset (ARTESp): the impact of occlusion time on clinical outcome of directly admitted and transferred patients

Objectives Acute Recanalization of Thrombo-Embolic Ischemic Stroke with pREset (ARTESp) is a prospective multicenter study assessing the efficacy and safety of the pREset stent retriever for the treatment of intracranial vessel occlusion. Determination of the effect of transfer status on clinical outcome was a secondary objective. Methods Efficacy was measured by recanalization success (Thrombolysis in Cerebral Infarction score ≥2b) and favorable clinical outcome at 90 days (modified Rankin Scale 0–2). Intracranial hemorrhage (ICH) and death at 90 days were safety measures. The outcome of directly admitted (DAP) and transferred (TP) patients was investigated using multivariable regression models. Results Four study centers included 100 patients (mean age 68.3 years, median National Institutes of Health Stroke Scale score 15). Recanalization success was achieved in 84.4% after a mean of 1.7 passes. ICH was detected in 14.0%, with 2.0% being symptomatic. At 90 days, 62.5% of the patients had a favorable outcome and 7.3% died. TP had longer occlusion times (289 vs 180 minutes, p<0.001) and a lower rate of favorable outcome (58.0% vs 78.4%, p=0.046) than DAP. Multivariable regression revealed occlusion time as the critical determinant (OR=0.963, 95% CI 0.931 to 0.997, p=0.032), whereas transfer status itself showed no significant association (OR=0.565, CI 0.133 to 2.393, p=0.438). Conclusions pREset proved to be safe and effective for the treatment of acute intracranial vessel occlusion. Increased occlusion time impaired clinical outcome in TP. Trial registration number NCT02437409; Results.
CiteSense: supporting sensemaking of research literature

Making sense of research literature is a complicated process that involves various information seeking and compre-hension tasks. The lack of support for sensemaking in existing systems presents important design challenges and opportunities. This research proposes the design of an integral environment to support literature search, selection, organization and comprehension. Our system prototype, CiteSense, offers lightweight interaction tools and a smooth transition among various information activities. This research deepens our understanding of the design of systems that support the sensemaking of research literature.
On the Connection Between Learning Two-Layers Neural Networks and Tensor Decomposition

We establish connections between the problem of learning a two-layers neural network with good generalization error and tensor decomposition. We consider a model with input $\boldsymbol x \in \mathbb R^d$, $r$ hidden units with weights $\{\boldsymbol w_i\}_{1\le i \le r}$ and output $y\in \mathbb R$, i.e., $y=\sum_{i=1}^r \sigma(\left \langle \boldsymbol x, \boldsymbol w_i\right \rangle)$, where $\sigma$ denotes the activation function. First, we show that, if we cannot learn the weights $\{\boldsymbol w_i\}_{1\le i \le r}$ accurately, then the neural network does not generalize well. More specifically, the generalization error is close to that of a trivial predictor with access only to the norm of the input. This result holds for any activation function, and it requires that the weights are roughly isotropic and the input distribution is Gaussian, which is a typical assumption in the theoretical literature. Then, we show that the problem of learning the weights $\{\boldsymbol w_i\}_{1\le i \le r}$ is at least as hard as the problem of tensor decomposition. This result holds for any input distribution and assumes that the activation function is a polynomial whose degree is related to the order of the tensor to be decomposed. By putting everything together, we prove that learning a two-layers neural network that generalizes well is at least as hard as tensor decomposition. It has been observed that neural network models with more parameters than training samples often generalize well, even if the problem is highly underdetermined. This means that the learning algorithm does not estimate the weights accurately and yet is able to yield a good generalization error. This paper shows that such a phenomenon cannot occur when the input distribution is Gaussian and the weights are roughly isotropic. We also provide numerical evidence supporting our theoretical findings.
Contact formation in the silver/aluminium thick film firing process – a phenomenological approach

In this paper the silver/aluminum thick film process is investigated. Aim of the work is a better understanding of the contact formation and the function aluminum fulfils. Depending on the local composition of the paste, that is the ratio of aluminum, silver and lead, different contact structures are found. These observations lead to a model explaining the contact formation for the silver/aluminum thick film process based on the model of the silver thick film process [2].
Joint transmission scheme for two multicast groups based on NOMA

Non-orthogonal Multiple Access(NOMA), as a key technology of spectral efficiency improvement in future 5G networks, has attracted a lot of attention. This paper proposes a joint transmission scheme for two multicast groups based on NOMA. Firstly, to guarantee rigorous channel difference to perform Serial Interference Cancellation(SIC), a Maximum and Minimum Group Method (MMGM) is proposed to divide each multicast group (MG) into three subgroups (the best, the middle and the worst subgroups), which makes the maximum and minimum of one MG's channel gains to be the other MG's two cutoff points. Secondly, according to the relationship between the two MGs' channel gains sets, two transmission modes are proposed. In different modes, BS respectively transmits to two different subgroup pairs (SPs) on the orthogonal frequency bands. In one mode, the one SP is consisted by a MG's best subgroup and the other MG's middle and worst subgroups. In the other mode, one MG's best and middle subgroups and the other MG's worst subgroup constitute the first SP. The second SP in each mode is made up of remaining subgroups. Finally, BS chooses the mode which can achieve higher spectral efficiency to complete data transmission with power domain superposition. Simulation results show that the scheme can improve spectral efficiency with higher average service ratio for users.
Synthesis, Structural Property, Photophysical Property, Photocatalytic Property of Novel ZnBiErO4 under Visible Light Irradiation

A novel photocatalyst ZnBiErO4 was firstly synthesized by solid-state reaction method and its structural and photocatalytic properties were analyzed by scanning electron microscopy, X-ray diffraction, X-ray photoelectron spectroscopy and UV-Vis diffuse reflectance. The results demonstrated that ZnBiErO4 crystallized with tetragonal crystal structure with space group I41/A. The lattice parameters for ZnBiErO4 were proved to be a = b = 10.255738 Å and c = 9.938888 Å. The band gap of ZnBiErO4 was estimated to be about 1.69 eV. Compared with nitrogen doped TiO2, ZnBiErO4 showed excellent photocatalytic activities for degrading methyl blue during visible light irradiation. The photocatalytic degradation of methyl blue with ZnBiErO4 or N-doped TiO2 as catalyst followed the first-order reaction kinetics. Moreover, the apparent first-order rate constant of ZnBiErO4 or N-doped TiO2 was 0.01607 min−1 or 0.00435 min−1. The reduction of total organic carbon, formation of inorganic products, such as SO42− and NO3− and the evolution of CO2 revealed the continuous mineralization of methyl blue during the photocatalytic process. ZnBiErO4 photocatalyst had great potential to purify textile industry wastewater.
Fault Sensitive Neutralization of Hardware Trojans Using Multi-level Triple Modular Redundancy Scheme

Hardware Trojans are malicious alterations in Integrated Circuits (ICs) that leaks confidential information or disables the entire IC. The detection of these Trojans is performed through logic or side channel based testing. Under sub-nm technologies this kind of detection will face more problems due to process variations. Therefore there is a need to address these vulnerabilities without relying on their detection. In this work we propose to neutralize the effect of Trojans by leveraging fault tolerant design methodologies like Triple Modular Redundancy (TMR) for security. Under this context a Fault can also be caused by a man made Trojan as compared to natural sources. To comprehensively address all these faults single TMR implementation is not helpful. In this work, a more efficient TMR approach to neutralize Trojans in the presence of natural faults is accomplished through multiple levels of TMR implemented after logic partitioning. We have diagnosed this approach for ISCAS'85 benchmark circuits of nodes within 900 and observed an improved Trojan Neutralization Rate (TNR). Our experiments show that the interplay of Trojan and natural faults actually reduces TNR. We have also shown that the Implementation of 3 levels of TMR considerably improves TNR.
Robust Adaptive Control for Fractional-order Financial Chaotic Systems with System Uncertainties and External Disturbances

In this paper, robust adaptive control for uncertain fractional-order financial chaotic systems with bounded unknown external disturbance is studied. By utilizing the fractional-order extension of the classical Lyapunov stability methods, an adaptive controller is presented for controlling fractional-order financial chaotic system. Quadratic Lyapunov function is employed in the stability analysis, and fractional-order adaptation laws are designed to update controller parameters online. The proposed controller can ensure that the system states converge to the origin asymptotically and all signals in the closed-loop system remain bounded. Finally, simulation results are presented to confirm our theoretical results. DOI: http://dx.doi. org/10.5755/j01.itc. 46.2. 13972
Brief cognitive-behavioural therapy for patients in the community with schizophrenia: randomised controlled trial in Beijing, China.

BackgroundBrief cognitive-behavioural therapy (CBT) is an emerging treatment for schizophrenia in community settings; however, further trials are needed, especially in non-Western countries.AimsTo test the effects of brief CBT for Chinese patients with schizophrenia in the community (trial registration: ChiCTR-TRC-13003709). MethodA total of 220 patients with schizophrenia from four districts of Beijing were randomly assigned to either brief CBT plus treatment as usual (TAU) or TAU alone. Patients were assessed at baseline, post-treatment and at 6- and 12-month follow-ups by raters masked to group allocation.ResultsAt the post-treatment assessment and the 12-month follow-up, patients who received brief CBT showed greater improvement in overall symptoms, general psychopathology, insight and social functioning. In total, 37.3% of those in the brief CBT plus TAU group experienced a clinically significant response, compared with only 19.1% of those in the TAU alone group (P = 0.003). ConclusionsBrief CBT has a positive effect on Chinese patients with schizophrenia in the community.
Optimal Scheduling for Electric Vehicle Charging With Discrete Charging Levels in Distribution Grid

To accommodate the increasing electric vehicle (EV) penetration in distribution grid, coordinated EV charging has been extensively studied in the literature. However, most of the existing works optimistically consider the EV charging rate as a continuous variable and implicitly ignore the capacity limitation in distribution transformers, which both have great impact on the efficiency and stability of practical grid operation. Towards a more realistic setting, this paper formulates the EV coordinated discrete charging problem as two successive binary programs. The first one is designed to achieve a desired aggregate load profile (e.g., valley-filling profile) at the distribution grid level while taking into account the capacity constraints of distribution transformers. Leveraging the properties of separable convex function and total unimodularity, the problem is transformed into an equivalent linear program, which can be solved efficiently and optimally. The second problem aims to minimize the total number of on-off switchings of all the EVs’ charging profiles while preserving the optimality of the former problem. We prove the second problem is NP-hard and propose a heuristic algorithm to approximately achieve our target in an iterative manner. Case studies confirm the validity of our proposed scheduling methods and indicate our algorithm’s potential for real-time implementations.
Chemical composition and biological activities of leaf and fruit essential oils from Eucalyptus camaldulensis

Abstract The chemical composition of the essential oils from the leaves and fruit of Eucalyptus camaldulensis grown in Mersin, Turkey was analyzed using gas chromatography (GC) and gas chromatography-mass spectrometry (GC-MS) techniques. The biological activities (antibacterial and antifungal) were examined using the agar well diffusion method. The main leaf oil constituents were p-cymene (42.1%), eucalyptol (1,8-cineole) (14.1%), α-pinene (12.7%) and α-terpinol (10.7%). The main constituents of the fruit oil were eucalyptol (1,8-cineole) (34.5%), p-cymene (30.0%), α-terpinol (15.1%) and α-pinene (9.0%). Our results showed that both types of oils are rich in terms of monoterpene hydrocarbons and oxygenated monoterpenes. The leaf and fruit essential oils of E. camaldulensis significantly inhibited the growth of Gram-positive (Staphylococcus aureus and Bacillus subtilis) and Gram-negative (Escherichia coli and Streptococcus sp.) bacteria (p<0.05). The oils also showed fungicidal activity against Candida tropicalis and C. globrata. Leaf essential oils showed more activity than fruit essential oils, probably due to the higher p-cymene concentration in leaves.
Reconfigurable Hybrid Beamforming for Dual-Polarized mmWave MIMO Channels: Stochastic Channel Modeling and Architectural Adaptation Methods

A reconfigurable hybrid beamforming (HBF) system is presented to dynamically exploit the channel diversities and antenna array directivity in millimeter wave (mmWave) channels. To develop an effective method for HBF architecture adaptation in wideband mmWave channels, the performance of an architecture is evaluated based on its average frequency-domain channel capacity. According to our analysis and simulations, the spatial covariance of the frequency-domain channels of using HBF can still be approximately modeled in a Kronecker form. Furthermore, beamforming directivity and channel spatial diversity have different advantages on capacity improvement in different transceiving conditions. Based on the results of channel modeling, system analysis, and simulations, a reconfigurable HBF architecture is thus proposed to enable flexible adjustments of the active subarrays, the antennas and polarizations of each subarray, and the active analog front-end and radio frequency chains linked to the active subarrays. A training procedure is meanwhile developed for the HBF system to learn proper architectures according to the system constraints, the channel statistics, the baseband precoder structure, and the channel capacity between the transmitter and the receiver. Compared with a fixed HBF system, such a reconfigurable system provides 3–15-dB power gains in channel capacity under various simulated channel conditions.
Towards an expressive and scalable Twitter profile hash for users recommendation

Microblogging websites such as Twitter produce tremendous amounts of data each second. Identifying people to follow is a heavy task that cannot be completely done by users. Consequently, real time recommendation systems require very efficient algorithm to quickly process this massive amount of data, so as to recommend users having similar interests. In this paper we present a tractable algorithm to build user profiles out of their tweets. We propose a scalable and extensible way of building content-based users profiles in real time. Scalability refers to the relative complexity of algorithms involved in building the users profiles with respect to state of the art solutions. Extensibility considers avoiding to recompute the model for newcomers. Our model is a graph of terms co-occurency, driven by the fact that users sharing similar interests will share similar terms. We show how this model can be encoded as a binary footprint, hence boosting comparison of profiles. We provide an empirical study to measure how the distance between users in the hash space differs from distance between users using standard Information Retrieval techniques. This experiment is based on a Twitter dataset we crawled, and represents 25K users and 1 million tweets. Our approach is driven by real time analysis requirements and is thus oriented on a trade-off between expressivity and efficiency. Experimental results shows that our approach outperforms vector space model by three orders of magnitude, with a precision of 58%.