Miracle-dz/UniFAR-FTUs huggingface dataset:
Microstructure and nanoindentation studies of M23C6 carbides in Fe-18Cr-8Ni steel after long-term ageing at high temperature

ABSTRACT Understanding the M23C6 carbides precipitation sequence aims to determine a correct heat treatment design in order to optimise the mechanical properties of the Fe-18Cr-8Ni steel. In this paper, the ageing influence at 800°C on the mechanical properties and microstructure of this steel has been studied. The aged material was examined with nanoindentation, macrohardness, scanning electron microscopy, light microscopy, and X-ray diffraction techniques. The hardness test results showed a continuous softening of the Fe-18Cr-8Ni steel when increasing the treatment time up to 6000 minutes. The nanoindentation tests revealed a decrease in the nanohardness of the austenite and M23C6 carbides as the ageing time increased. Before the ageing process, the solution treatment produced completely austenitic steel. After 30 and 60 minutes of ageing, the M23C6 carbides were located at the grain boundaries. The precipitation of the M23C6 carbides within the grains was observed from 300 to 6000 minutes of ageing. The coarsening of the M23C6 carbides occurred at high holding times.
Effects of maintenance, traffic and climate condition on International Roughness Index of flexible pavement

ABSTRACT This paper focuses on developing the predictive models for International Roughness Index (IRI) of flexible pavement using several soft computing systems. About 564 sections with 4229 observations of the flexible pavement from the database of the long-term pavement performance (LTPP) program were used for developing and validating the prediction models. Equivalent single axle load (ESAL), structural number (SN), thickness, resilient modulus (MR) of the base layer, age, annual truck volume trend, total annual precipitation, freezing index, and maintenance treatments were the predictive attributes used in models. Various statistical criteria were used for evaluating the performance of the developed models, comprehensively. The results showed that the models developed in the current study are fairly promising tools for the prediction of IRI and capable of representing the complex relationship between the IRI and the predictive attributes selected. The models created through the Gradient Boosting Method (GBM) and Random Forest (RF), which were markedly outperformed the results of other methods, produced a correlation coefficient (R) greater than 0.90 and mean absolute error lower than 0.2. Furthermore, the SN was found to be the most important parameter for the IRI according to the variable importance score of the best performing prediction model.
The influence of varying positive affect in approach-motivation intensity on creative idea generation and creative idea evaluation: an fNIRS study

Abstract The aim of this study was to explain previous inconsistent results regarding the effects of positive affect on creative cognition based on the motivational dimensional model of affect theory and provide the underlying neural correlates of the effects of different approach-motivation intensities of positive affect on creative processes (creative idea generation and creative idea evaluation) using the functional near-infrared spectroscopy (fNIRS) technique. Sixty participants were randomly assigned to three groups (high-approach-motivated positive affect (HAM), low-approach-motivated positive affect (LAM) and affectively neutral state (NS)) to complete corresponding tasks in creative idea generation and creative idea evaluation stages. In the creative idea generation stage, the results indicated that the LAM group achieved a higher performance in flexibility and originality than the NS group. However, compared with the NS group, the LAM group exhibited a lower sensitivity in the creative idea evaluation stage. The fNIRS technique provides an ideal approach with high ecological validity for exploring the issue of affect and creativity. In current study, fNIRS results showed that the HAM group exhibited significantly more activation in the bilateral frontal lobe than the LAM group and NS group in the creative idea generation stage. This result could reflect individuals attempting to overcome a narrowed attentional scope state through the activation of bilateral frontal cortical resources in the creative process. In the creative idea evaluation stage, compared with the NS group, the LAM group showed significant deactivation in the right dorsolateral prefrontal cortex, which is a brain area associated with executive, controlled processing. Moreover, the approach-motivation intensity altered the connectivity between the left prefrontal cortex and left superior temporal gyrus in the creative idea generation stage. Thus, the influences of positive affect on creative cognitive processes (both creative performance and hemodynamic responses) were modulated by the approach-motivation intensity in the creative idea generation and creative idea evaluation stages.
Design of Nanotechnological Carriers for Ocular Delivery of Mangiferin: Preformulation Study

(1) Background: Mangiferin (MGN) is a natural compound, showing anti-inflammatory and antioxidant activities for the potential treatment of eye diseases. The poor physicochemical features of MGN (low solubility and high instability) justify its nanoencapsulation into nanostructured lipid carriers (NLC) to improve its ocular bioavailability. (2) Methods: Firstly, MGN-NLC were prepared by the high shear homogenization coupled with the ultrasound (HSH−US) method. Finally, unloaded and MGN-loaded NLC were analyzed in terms of ocular tolerance. (3) Results: MGN-NLC showed good technological parameters suitable for ocular administration (particle size below 200 nm). The ORAC assay was performed to quantify the antioxidant activity of MGN, showing that the antioxidant activity of MGN-NLC (6494 ± 186 μM TE/g) was higher than that of the free compound (3521 ± 271 μM TE/g). This confirmed that the encapsulation of the drug was able to preserve and increase its activity. In ovo studies (HET-CAM) revealed that the formulation can be considered nonirritant. (4) Conclusions: Therefore, NLC systems are a promising approach for the ocular delivery of MGN.
The Effect of Regularly Dosed Acetaminophen vs No Acetaminophen on Renal Function in Plasmodium knowlesi Malaria (PACKNOW): A Randomized, Controlled Trial

Abstract Background Acetaminophen inhibits cell-free hemoglobin-induced lipid peroxidation and improves renal function in severe falciparum malaria but has not been evaluated in other infections with prominent hemolysis, including Plasmodium knowlesi malaria. Methods PACKNOW was an open-label, randomized, controlled trial of acetaminophen (500 mg or 1000 mg every 6 hours for 72 hours) vs no acetaminophen in Malaysian patients aged ≥5 years with knowlesi malaria of any severity. The primary end point was change in creatinine at 72 hours. Secondary end points included longitudinal changes in creatinine in patients with severe malaria or acute kidney injury (AKI), stratified by hemolysis. Results During 2016–2018, 396 patients (aged 12–96 years) were randomized to acetaminophen (n = 199) or no acetaminophen (n = 197). Overall, creatinine fell by a mean (standard deviation) 14.9% (18.1) in the acetaminophen arm vs 14.6% (16.0) in the control arm (P = .81). In severe disease, creatinine fell by 31.0% (26.5) in the acetaminophen arm vs 20.4% (21.5) in the control arm (P = .12), and in those with hemolysis by 35.8% (26.7) and 19% (16.6), respectively (P = .07). No difference was seen overall in patients with AKI; however, in those with AKI and hemolysis, creatinine fell by 34.5% (20.7) in the acetaminophen arm vs 25.9% (15.8) in the control arm (P = .041). Mixed-effects modeling demonstrated a benefit of acetaminophen at 72 hours (P = .041) and 1 week (P = .002) in patients with severe malaria and with AKI and hemolysis (P = .027 and P = .002, respectively). Conclusions Acetaminophen did not improve creatinine among the entire cohort but may improve renal function in patients with severe knowlesi malaria and in those with AKI and hemolysis. Clinical Trials Registration NCT03056391.
Analysis and Design of Radio Frequency Integrated Circuits for Breast Cancer Radar Imaging in CMOS Technology

Breast cancer is by far the most incident tumor among female population. Early stage prevention is a key factor in delivering long term survival of breast cancer patients. X-ray mammography is the most commonly used diagnostic technique to detect non-palpable tumors. However, 10-30% of tumors are missed by mammography and ionizing radiations together with breast compression do not lead to comfort in patient treatment. In this context, ultrawideband microwave radar technology is an attractive alternative. It relies on the dielectric contrast of normal and malignant tissues at microwave frequencies to detect and locate tumors inside the breast. This work presents the analysis and design of radio frequency integrated circuits for breast cancer imaging in CMOS technology. The first part of the thesis concerns the system analysis. A behavioral model of two different transceiver architectures for UWB breast cancer imaging employing a SFCW radar system are presented. A mathematical model of the direct conversion and super heterodyne architectures together with a numerical breast phantom are developed. FDTD simulations data are used to on the behavioral model to investigate the limits of both architectures from a circuit-level point of view. Insight is given into I/Q phase inaccuracies and their impact on the quality of the final reconstructed images. The result is that the simplicity of the direct conversion architecture makes the receiver more robust toward the critical impairments for this application. The second part of the thesis is dedicated to the circuit design. The main achievement is a 65nm CMOS 2-16GHz stepped frequency radar transceiver for medical imaging. The RX features 36dB conversion gain, >29dBm compression point, 7dB noise figure, and 30Hz 1/f noise corner. The TX outputs 14dBm with >40dBc harmonic rejection and <109dBc/Hz phase noise at 1MHz offset. Overall power dissipation is 204mW from 1.2V supply. The radar achieves 3mm resolution within the body, and 107dB dynamic range, a performance enabling the use for breast cancer diagnostic imaging. To further assess the capabilities of the proposed radar, a physical breast phantom was synthesized and two targets mimicking two tumors were buried inside the breast. The targets are clearly identified and correctly located, effectively proving the performance of the designed radar as a possible tool for breast cancer detection.
Association between Pet Ownership and Mental Health and Well-Being of Canadians Assessed in a Cross-Sectional Study during the COVID-19 Pandemic

The objective of this cross-sectional study was to assess the association between pet ownership and quality of life (QoL), loneliness, anxiety, stress, overall health, and mental health of Canadians during the COVID-19 pandemic using a One Health perspective. An online bilingual survey was completed by 1500 Canadians in April–May 2021. Socio-demographics, health, QoL, stress and anxiety, loneliness, social support, pet ownership, and attitude towards pets data were collected. Crude and adjusted associations between pet ownership and mental health and well-being indicators were estimated. The 1500 participants were from all provinces and territories, half were women; half of the participants were pet owners by design. The crude association estimates showed that pet owners had poorer QoL, overall health, and mental health than non-pet owners, and were lonelier, more stressed, and more anxious than non-pet owners. Adjusted estimates showed that these associations disappeared with the inclusion of the confounders (socio-economic, demographic, health, and pet-related variables). Our results suggest that there was no association between pet ownership and the mental health and well-being indicators measured in the present study.
MVFLS: Multi-participant Vertical Federated Learning based on Secret Sharing

Vertical Federated Learning (VFL) is a distributed machine learning method that combines all features from different collaborative train models for Federated Learning (FL) clients with data involving different feature spaces. Currently, VFL mainly uses homomorphic encryption (HE) to preserve privacy. However, HE incurs high computation and communication costs among FL participants, which increase exponentially with the number of participants. Thus, existing VFL methods are unsuitable for multi-party scenarios. Moreover, in VFL scenarios, it is difﬁcult to establish an FL server that can be trusted by all participants. To solve these problems, we propose an efﬁcient Multi-participant Vertical Federated Learning approach based on Secret Sharing (MVFLS). On the one hand, it uses secret sharing instead of homomorphic encryption, which effectively reduces the computational cost. On the other hand, beneﬁting from secret sharing, it enables VFL to be performed in multi-participant scenarios while eliminating the dependence on having an FL server. This can further reduce the risk of data leakage on the server side and effectively protect the security of participants. Experimental results on real-world and synthetic data sets show that MVFLS can signiﬁcantly reduce computational cost and improve model accuracy compared with stat-of-the-art federated learning methods.
Cellular Base Station Imaging for UAV Detection

As the use of unmanned aerial vehicles (UAVs) is greatly increasing, there is an emerging threat of using UAVs in infrastructure/cyber-attacks and data-eavesdropping. From the safety and security perspective, it is a timely need to build an air surveillance system that enables a seamless detection function for low-and-middle altitude flying targets. However, it is unrealistic to widely deploy classical radar stations due to the astronomical cost. Rethinking the role of cellular mobile communication networks, we desire to add a “vision-like” capability to the widely deployed outdoor cellular base stations (BSs) to realize joint imaging and communication (JIAC) simultaneously through sharing the existing cellular communication infrastructure and spectrum. In this work, it is for the first time to systematically study and demonstrate the concept of cellular base station imaging for UAV detection, which allows a cellular BS to work like an inverse synthetic-aperture radar (ISAR) besides communication. Firstly, we provide the JIAC transmission signalling and systematic operation mechanism. Secondly, the feasibility of JIAC is investigated and analysed to support the idea of cellular base station imaging. Finally, numerical simulation evaluates the imaging performance of three typical types of cellular BSs operating at 900 MHz, 3.5 GHz and 28 GHz, respectively, which implies that cellular BS imaging works for UAV detection! Furthermore, the radar imaging function, as a new by-product, requires only a very little change to the current orthogonal frequency-division multiplexing (OFDM) communication signalling and has nearly no influence on the current communication operation and performance.
Safe Garbage Collection = Regions + Intensional Type Analysis

We present a technique to implement type-safe garbage collectors by combining existing type systems used for compiling type-safe languages. We adapt the type systems used in region inference [16] and intensional type analysis [8] to construct a safe stop-and-copy garbage collector for higher-order polymorphic languages. Rather than using region inference as the primary method of storage management, we show how it can be used to implement a garbage collector which is provably safe. We also introduce a new region calculus with non-nested object life-times which is signiﬁcantly simpler than previous calculi. Our approach also formalizes more of the interface be-tween garbage collectors and code generators. The efﬁ-ciency of our safe collectors are algorithmically competitive with unsafe collectors.
SparseCore: stream ISA and processor specialization for sparse computation

Computation on sparse data is becoming increasingly important for many applications. Recent sparse computation accelerators are designed for specific algorithm/application, making them inflexible with software optimizations. This paper proposes SparseCore, the first general-purpose processor extension for sparse computation that can flexibly accelerate complex code patterns and fast-evolving algorithms. We extend the instruction set architecture (ISA) to make stream or sparse vector first-class citizens, and develop efficient architectural components to support the stream ISA. The novel ISA extension intrinsically operates on streams, realizing both efficient data movement and computation. The simulation results show that SparseCore achieves significant speedups for sparse tensor computation and graph pattern computation.
Secure and Robust Federated Learning for Predictive Maintenance in Optical Networks

Machine learning (ML) has recently emerged as a powerful tool to enhance the proactive optical network maintenance and thereby improves network reliability and reduces un-planned downtime and maintenance costs. However, it is challenging to develop an accurate and reliable ML model for solving predictive maintenance tasks (e.g., anomaly detection, fault diagnosis, remaining useful prediction etc) mainly due to the unavailability of a sufficient amount of training data since the device failure does not occur often in optical networks. Federated learning (FL) is a promising candidate to tackle the aforementioned challenge by enabling the development of a global ML model using datasets owned by many vendors without revealing their business-confidential data. While FL greatly enhances the data privacy, it is vulnerable to various model inversion and poisoning attacks. In this paper, we propose a robust collaborative learning framework for predictive maintenance in a cross-vendor setting, whereby the defensive mechanisms to protect against the aforementioned attacks are implemented. The multi-party computation (MPC)-based secure aggregation is adopted to defend against the model inversion attacks whereas a trained autoencoder based anomaly detection model is used to recognize the model poisoning attacks launched by compromised vendors. The proposed framework is applied to the semiconductor laser degradation prediction use case. We conduct experiments on semiconductor laser reliability data obtained from different laser manufacturers under various attack scenarios to evaluate the attack defense and detection capabilities of the proposed approach. Our experiments confirm that a global ML model can be accurately built with sensitive datasets in federated learning even when a subset of vendors is compromised.
To What Extent Does Frailty Influence the Risk of Developing Urolithiasis?

Urolithiasis has become more prevalent in recent years, given the rapid rise of the global geriatric population. Although factors such as ethnicity, dietary and fluid intake, co-morbidity status and age have been associated with increased incidence of urolithiasis, the links between frailty status and risks of developing urolithiasis are not yet known. In this commentary, we will explore the scale and significance of this relationship based on emerging evidence. We will review the plausible factors on how a more severe frailty status may be significantly associated with greater risks of developing urolithiasis. We will also discuss the strategies that may help to lower the incidence of urolithiasis in older and frail individuals. We hope our article will bring greater awareness on this issue and motivate further research initiatives evaluating the relationship between frailty and urolithiasis, as well as holistic prevention strategies to lower the risks of developing urolithiasis within this vulnerable population.
Becoming a Smart City: A Textual Analysis of the US Smart City Finalists

—The term “smart city” is widely used, but there is no consensus on the deﬁnition. Many citizens and stakeholders are unsure about what a smart city means in their community and how it affects cost and privacy. This paper describes how city planners and companies envision a smart city using data from the 2015 Smart City Challenge. We use text analysis techniques to investigate the technology and themes necessary for creating a smart city using surveys, document similarity, cluster analysis, and topic modeling from the seven ﬁnalists from the 2015 Smart City Challenge Applicants. With this investigation, we ﬁnd that smart city requests include various technologies, and the goal of smart cities is to enhance and connect the communities to improve the lives of its’ citizens. On average, aspiring smart cities requested 12 new or improved technologies. We also ﬁnd that two of the seven studied smart city applications center privacy in their proposals. The analysis within gives governments and citizens a common interpretation of a smart city.
STRIKING THE RIGHT NOTE WHEN THINGS GET TOUGH:

Purpose: With the ever-increasing connectivity through the use of the Internet and social media for the exchange between consumers and brands, brand crises are increasingly becoming virtually inevitable. To minimize reputational damage when confronted with a crisis, brands utilize various crisis communication efforts to maintain positive customer evaluations. Through the rapid dissemination of news and press releases, online newspaper portals pose an important platform for a brands’ crisis communication to win public sympathy and support. This study aims to examine the effect of communication strategy and message framing within crises of different degrees of severity on customer’s crisis evaluations. Methodology: To provide an overview on how a brand’s crisis communication must be conceptualized to obtain positive customer responses, a 2 (emotional versus rational message framing) x 2 (accommodative versus defensive crisis communication strategy) x 2 (low versus high crisis severity) between-subjects design has been made use of. This study reached 217 German participants that were randomly shown one of eight manipulated conditions of a fictitious brand's press release about a production malfunction in an online newspaper. It was thereby interrogated how the use of crisis communication strategy and message framing within crises of different severities influences consumers’ perceived response sincerity, brand trust, brand attitude, and purchase intention Findings: For this study, a multivariate analysis of variance has been performed. This studies’ results have shown several positive effects of an accommodative communication strategy and an emotional message frame on consumers’ crisis evaluations. It has thereby also become clear that when involved in a low as opposed to a high severity brand crisis, consumer evaluations are less strongly negatively influenced. Additionally, findings indicate that during a low severity crisis, a defensive communication strategy, and during a high severity crisis an accommodative strategy leads to more positive consumer evaluations. Additionally, a defensive crisis communication strategy paired with an emotional message frame and an accommodative strategy utilized with a rational frame enables brand communicators to generate more positive customer evaluations. Implications: This study contributes to the field of research by confirming several hypotheses, but also serves to add to the existing knowledge by investigating the interaction effects between the studies variables. Providing recommendations to communicators contributes to improving brand crisis communication approaches to effectively obtain positive customer evaluations.