Miracle-dz/UniFAR-FTUs huggingface dataset:
Complex Fuzzy Assessment of Green Flight Activity Investments for Sustainable Aviation Industry

The aviation industry harms the environment mainly via the creation of carbon emissions. Hence, action needs to be taken to ensure the environmental sustainability of the aviation industry such as the recycling of waste products, effective waste management and the introduction of energy efficiency measures. However, at the same time, the implementation of improvements to remediate such problems leads to the creation of additional costs for aviation companies. Companies thus need to conduct comprehensive priority analyses regarding the optimum strategy for the sustainability of the aviation industry. However, there is a very limited number of studies in the literature that focused on which approach should be prioritized. Accordingly, this study aimed at the assessment of the viability of investing in so-called green flight measures in the aviation industry, for which a completely original decision-making model was created. Firstly, the various strategic priorities were weighted and the impact-relation directions between them illustrated aimed at the identification of potential influences by means of a multi stepwise weight assessment ratio analysis (M-SWARA) methodology that incorporates bipolar q-rung orthopair fuzzy sets (q-ROFSs) and golden cut. Secondly, the various flight activities are ranked, and the potential impacts of these activities determined in terms of the strategic priorities of a sustainable aviation industry employing q-ROF as the elimination and choice translating reality (ELECTRE) technique. All the calculations were also computed with intuitionistic fuzzy sets (IFSs) and Pythagorean fuzzy sets (PFSs) aimed at verifying the validity of the findings. The analysis concluded that while energy efficiency comprises the most important factor in terms of strategic priority investment for the circular economy-based aviation industry, emergency response makes up the most crucial activity in the industry. Operational efficiency must be prioritized to decrease the amount of fuel consumed, in connection with which flight routes should be planned according to current weather conditions, which would serve to shorten flight times and, thus, help to increase energy efficiency. Such an approach would make a positive contribution to minimizing carbon emissions aimed at ensuring the sustainability of the aviation industry.
Water From the Lake of Memory: The Regulatory Model of Nostalgia

We organize the literature on triggers and functions of nostalgia by advancing a regulatory model in which the emotion serves as a homeostatic corrective (i.e., a process that establishes and maintains a relatively stable psychological equilibrium) that countervails the negative effects of psychological perturbations and adverse environmental conditions. We illustrate complementary approaches to testing this model as it applies to transient, or state-level, nostalgia and show how the model can be generalized to different levels of analysis, including chronic, or trait-level, nostalgia and collective nostalgia. We then formulate a proposal for future research inspired by recent developments in causal mediation analysis and conclude with a discussion of the model’s potential boundary conditions.
Catalytic ozonation of phenol by magnetic Mn0.7Ce0.3Ox/CNT@Fe3C

A high-efficient and stable catalyst is highly desired to catalyze ozone for refractory organic pollutants removal. In this work, Mn-Ce bimetallic oxide loaded CNT@Fe3C (Mn0.7Ce0.3Ox/CNT@Fe3C) was synthesized with Mn0.7Ce0. 3Ox as the active component and magnetic CNT@Fe3C as the support. The catalytic performance of Mn0.7Ce0. 3Ox/CNT@Fe3C towards catalytic ozonation was evaluated. The TOC removal efficiency of phenol degradation after 45 min of reaction was 98%, which was 1.5 times and 1.8 times that of monometallic CeO2/CNT@Fe3C (65%) and MnxOy/CNT@Fe3C (54%), respectively. The synthesized-Mn0.7Ce0. 3Ox/CNT@Fe3C possessed good reusability during five successive cycles and remained efficient over a wide range of pH 4.2–8.3. The results of EPR measurements and quenching experiments demonstrated that hydroxyl radicals (·OH) were the dominant reactive oxidation species (ROS) for phenol mineralization in the Mn0.7Ce0. 3Ox/CNT@Fe3C/O3 system. Moreover, the magnetic Mn0.7Ce0. 3Ox/CNT@Fe3C is easily recovered and reused.
Fast generation of cubic graphs

In this paper an efficient algorithm to generate regular graphs with small vertex valency is presented. The running times of a program based on this algorithm and designed to generate cubic graphs are below two natural benchmarks: (a) If N(n) denotes the number of pairwise non-isomorphic cubic graphs with n vertices and T(n) the time needed for generating the list of all these graphs, then T(n)/N(n) decreases gradually for the observed values of n. This suggests that T(n)/N(n) might be uniformly bounded for all n, ignoring the time to write the outputs, but we are unable to prove this and in fact are not confident about it. (b) For programs that generate lists of non-isomorphic objects, but cannot a priori make sure to avoid the generation of isomorphic copies, the time needed to check a randomly ordered list of these objects for being non-isomorphic is a natural benchmark. Since for large lists (n ≥ 22, girth 3) existing graph isomorphism programs take longer to canonically label all of the N(n) graphs than our algorithm takes to generate them, our algorithm is probably faster than any method which does one or more isomorphism test for every graph. © 1996 John Wiley & Sons, Inc.
Fast Tube Model Predictive Control for Driverless Cars Using Linear Data-Driven Models

Model predictive control (MPC) has been widely applied to different aspects of autonomous driving, typically using nonlinear physically derived models for prediction. However, feedback control systems inherently correct for model errors, and thus in many applications it is sufficient to use a linear time-invariant (LTI) model for control design, especially when using robust control methods. This philosophy of approach appears to have been neglected in current driverless car research and is the research gap that we aim to address here. Namely, instead of deriving meticulous nonlinear physical models of vehicle dynamics and solving a correspondingly complex optimal control problem (OCP), we identify a low-order data-driven LTI model and handle its uncertainty via robust linear MPC methods. We develop a two-step control scheme for driverless cars based on tube MPC (TMPC), which introduces structural robustness, ensuring constraint compliance despite modeling error in the data-driven prediction model. Furthermore, we use fast optimization methods designed to exploit the special structure of the linear MPC problem. We evaluate the proposed control scheme using a vehicle model identified from real-world data and simulations in IPGCarmaker, where the model of the vehicle under control is inherently nonlinear and uses detailed 3-D physics. Our results show that an LTI model can be effectively used for the task of lane-keeping, that TMPC can prevent lane departure and possible collisions due to model uncertainty, and that linear models allow for several algorithmic improvements that can decrease computation time by an order of magnitude compared with naive MPC implementations.
Positronium formation in positron-helium scattering

The positronium-formation cross sections in positron-helium scattering have been calculated with the use of a distorted-wave polarized-orbital method from the threshold to 100 eV. The results with and without the matrix elements involving the distorted target wave functions are found to differ appreciably. The results of the first Born approximation are not expected to be correct even at the incident-positron energy 100 eV. The measured values at 20 eV are found to be less than (1/2) of the present predicted values. The sharp rise of the formation cross section within the ore-gap region as observed by Charlton et al. has also been noticed by us. The minimum in the differential cross section has been found at all energies as in the case of hydrogen atom.
Constraining the form of supertags with the strong connectivity hypothesis

Supertag-based linguistic theories specify the extension of elementary structures through the application of linguistic principles (e.g. condition on elementary tree minimality). Recent evidence from psycholinguistic experiments supports the idea that syntactic relations built during processing are based on a fully connected structure. In particular, existing experimental data can be accounted for by assuming a strong connectivity hypothesis, i.e. that the supertag of each word is inserted into a single fully connected syntactic structure. By assuming the strong connectivity hypothesis and the extended projection principle, we have devised a dynamic version of Tree Adjoining Grammar (called DVTAG). DVTAG supertags exhibit an asymmetrical behavior with respect to their possibilities for combination with other supertags on the left and the right, respectively, and one DVTAG supertag can license more than one semantic unit. After introducing the formalism, we describe the extraction of a wide-coverage DVTAG from parsed corpora, and give a detailed description of the form of the DVTAG supertags in comparison with LTAG.
The Experiences of Hispanic and African American Student-Fathers on College Campuses

Hispanic and African American student-fathers are an invisible population in the student support literature on college campuses. In this qualitative study, we examined the lived experiences of 17 student-fathers attending Hispanic Serving Institutions and Historically Black Colleges and Universities. Six themes emerged from our data: family support, uncertainty in parenting practices, co-parenting relationships, responsibility as a father, negative stereotypes, and fatherhood as motivation. Our findings have implications for institutions of higher education.
Family Caregivers of People with Dementia Associate with Poor Health-Related Quality of Life: A Nationwide Population-Based Study

Despite the growing awareness of poor health-related quality of life (HRQoL) in family caregivers of people with dementia (PWD), their relationship has rarely been explored with population-based samples. The current cross-sectional study aimed to determine the detrimental impact of informal dementia caregiving on HRQoL by using nationally representative population-based samples from the Korean Community Health Survey. Demographics, socioeconomic, and physical and mental health-related characteristics as well as HRQoL measured by the Korean version of the European Quality of Life Questionnaire Five Dimension (EQ-5D) were compared between 9563 family caregivers of PWD and 186,165 noncaregivers. Caregivers had lower index scores and higher frequency of some/extreme problems in all five dimensions of the EQ-5D compared with noncaregivers. Logistic regression adjusting for potential confounding factors found that caregivers had a higher frequency of poor HRQoL (lowest quartile of EQ-5D index) than noncaregivers (adjusted odds ratio [95% confidence interval] = 1.46 [1.39–1.53]). Compared to noncaregivers, caregivers had a higher frequency of some/extreme problems in each dimension of the EQ-5D: mobility (1.30 [1.21–1.40]), self-care (1.62 [1.46–1.80]), usual activity (1.39 [1.29–1.51]), pain/discomfort (1.37 [1.31–1.45]), and anxiety/depression (1.51 [1.42–1.61]). A one-to-one propensity score matching analysis confirmed that poor HRQoL was more frequently found in caregivers compared to noncaregivers (1.38 [1.29–1.48]). Our results indicated that family caregivers of PWD are significantly associated with overall poor HRQoL, underscoring the detrimental impact of informal dementia caregiving on HRQoL. Given the high frequency of poor HRQoL in dementia caregivers and the important recognition of its serious consequences on physical and mental health, clinicians should take into consideration efficient interventions to improve health and HRQoL for family caregivers of PWD.
Power system excitation and governor design using optimal control theory

Abstract Integrated excitation and/or governor control systems are the result of using novel approaches for the selection of stabilizing signals in electric power systems. Such controllers have usually been derived on the basis of a linearized system model, an attractive practical feature of these being that a linear, time-invariant optimal controller may be derived. The power system is non-linear, however, and it is for this non-linear system that an optimal controller is ideally required. Previous work in the area is shown to be deficient in the light of this requirement and subsequently time-invariant integrated controllers suitable for use with the non-linear system are developed. These controllers incorporate model following with integral action and are shown to effect improved control of the nonlinear system over a wide range of operating conditions. The manner in which the proposed control system design procedure may permit the integrated control of large power systems is also of considerable pract.
Multivariate Splines with Nondegenerate Partitions

Any set of hyperplanes partitions $E^n $ into a set of polyhedra. A multivariate spline of degree n is a polynomial of total degree n on each polyhedron with all partial derivatives of order $n - 1$ being continuous everywhere. An especially simple canonical form is presented for splines with respect to nondegenerate (if a set of hyperplanes has nonempty intersection then the corresponding set of normal vectors is linearly independent) partitions. Use of the canonical form, for fitting data, involves linear regression for fixed partitions and nonlinear regression for varying partitions. The canonical form gives rise to an ill-conditioned linear regression problem. However, some preliminary numerical experience in low dimensions indicates that the ill-conditioning is overcome with the use of singular value decomposition.
ReDCIM: Reconfigurable Digital Computing- In -Memory Processor With Unified FP/INT Pipeline for Cloud AI Acceleration

Cloud AI acceleration has drawn great attention in recent years, as big models are becoming a popular trend in deep learning. Cloud AI runs high-efficiency inference, high-accuracy inference and training, in demand of flexible floating-point (FP)/integer (INT) multiply–accumulation (MAC) support. Many computing-in-memory (CIM) processors have been proposed for efficient AI acceleration. They usually rely on analog CIM techniques that are only suitable for high-efficiency neural network (NN) inference with low-precision INT MAC support. Since cloud AI demands high efficiency, high accuracy, and high flexibility simultaneously, we propose an innovative architecture reconfigurable digital CIM (ReDCIM) that meets all three requirements. We design the first CIM-based cloud AI processor, ReDCIM, which constructs a unified FP/INT pipeline architecture based on exponent pre-alignment and reconfigurable in-memory accumulation. Bitwise in-memory Booth multiplication is proposed to reduce computation on CIM. The fabricated ReDCIM chip achieves a state-of-the-art energy efficiency of 29.2 TFLOPS/W at BF16 and 36.5 TOPS/W at INT8.
Acoustic scattering by penetrable homogeneous objects

The scattered field is found when a time harmonic acoustic wave is incident upon a finite object characterized by a density and wavenumber different from that of the surrounding medium. The interface is assumed to be either Lyapunoff or piecewise Lyapunoff. The problem is cast as a pair of coupled surface integral equations for the total field and its normal derivative on the interface. The Neumann series obtained by straightforward iteration is proven to be convergent for ranges of density and wavenumber, and specific bounds on these ranges are given. For small enough wavenumbers, the series converges for all values of the interior density. The iteration appears simpler than the usual Born approximation, which involves volume as well as surface integrals. The method is illustrated in the case of a spherical interface.
Enhanced Nasal Deposition and Anti-Coronavirus Effect of Favipiravir-Loaded Mucoadhesive Chitosan–Alginate Nanoparticles

Favipiravir (FVR) is a repurposed antiviral drug for treating mild to moderate cases of the novel coronavirus disease 2019 (COVID-19). However, its poor solubility and permeability limit its clinical efficacy. To overcome its physicochemical and pharmacokinetic limitations, we statistically designed a mucoadhesive chitosan–alginate nanoparticles (MCS-ALG-NPs) as a new carrier for FVR using response surface methodology, which provided suitable characteristics for transmucosal delivery. The use of mucoadhesive polymers for intranasal administration promotes the residence time and contact of FVR in the mucus membrane. The optimized FVR-MCS-ALG-NPs demonstrated superior mucoadhesion, higher permeation and deposition in the nasal mucosa, and a significant increase in the inhibition of viral replication over 35-fold compared with free FVR. The overall results suggest that MCS-ALG-NPs could be used as an effective mucoadhesive carrier to enhance the activity of FVR against COVID-19.
Entanglement Characteristic Time from Complex Moduli via i-Rheo GT

Tassieri et al. have introduced a novel rheological tool called “i-Rheo GT” that allows the evaluation of the frequency-dependent materials’ linear viscoelastic properties from a direct Fourier transform of the time-dependent relaxation modulus G(t), without artifacts. They adopted i-Rheo GT to exploit the information embedded in G(t) derived from molecular dynamics simulations of atomistic and quasi-atomistic models, and they estimated the polymers’ entanglement characteristic time (τe) from the crossover point of the moduli at intermediate times, which had never been possible before because of the poor fitting performance, at short time scales, of the commonly used generalized Maxwell models. Here, we highlight that the values of τe reported by Tassieri et al. are significantly different (i.e., an order of magnitude smaller) from those reported in the literature, obtained from either experiments or molecular dynamics simulations of different observables. In this work, we demonstrate that consistent values of τe can be achieved if the initial values of G(t), i.e., those governed by the bond-oscillation dynamics, are discarded. These findings have been corroborated by adopting i-Rheo GT to Fourier transform the outcomes of three different molecular dynamics simulations based on the following three models: a dissipative particle dynamics model, a Kremer–Grest model, and an atomistic polyethylene model. Moreover, we have investigated the variations of τe as function of (i) the ‘cadence’ at which G(t) is evaluated, (ii) the spring constant of the atomic bone, and (iii) the initial value of the shear relaxation modulus G(O). The ensemble of these results confirms the effectiveness of i-Rheo GT and provide new insights into the interpretation of molecular dynamics simulations for a better understanding of polymer dynamics.
Influence of habitat quality and diversity on two populations of Eurasian curlew (Numenius arquata) with contrasting dynamics in Western France

Like most shorebirds in Europe, breeding populations of Eurasian curlew (Numenius arquata) are suffering from habitat loss and degradation mainly caused by changes in agricultural practices. In Deux-Sèvres (France), the number of pairs has gradually declined since the early 2000s in the main, historical breeding site, while a new breeding site has appeared recently 80 kilometres further north with increasing number of pairs through the survey period. Many wheat fields and rare dry grasslands are found in the north, whereas the landscape in the south is mainly composed of tillage plots, hay meadows, and pastures. This study aims to highlight differences in food availability and quality between the two areas. Sample series of ground-dwelling and vegetation-dwelling invertebrates were carried out during three key stages of the species breeding cycle with pitfall traps and sweep nets. Dry grasslands in the north were found to be the most favourable habitat in terms of prey availability for adults and for chicks during the brood-rearing period. Moreover, hay meadows and pastures in the south seemed to be resource-abundant feeding habitats. Therefore, the habitats of the northern site seem to offer a greater abundance of invertebrates and thus a potentially larger food resource than the southern one. It follows that the northern site likely offers better breeding conditions, especially for the growth of chicks. An increase in the area of dry grasslands in the north and the establishment of adapted agricultural management in the south would be favourable for the conservation of local curlew populations.
The Activity of Polyhomoarginine against Acanthamoeba castellanii

Simple Summary Acanthamoeba is free-living amoeba known to cause severe vision threatening eye infection. In most of the cases treating this infection is difficult due to the Acanthamoeba’s ability to switch between an active feeding stage (trophozoite) and inactive dormant stage (cyst). Arginine rich peptides are highly positively charged and can kill the microorganisms by acting on their negatively charged surface. This study assessed the anti-amoebic activity of polyhomoarginines of different lengths against Acanthamoeba castellanii trophozoites and cysts. Polyhomoarginine showed excellent anti-amoebic activity against both the stages of Acanthamoeba castellanii. Abstract Arginine-rich peptides can have broad-spectrum anti-bacterial and anti-fungal activities. Polyhomoarginine consists of highly cationic residues which can act on the negatively charged microbial cell membranes. Acanthamoeba is a free-living protozoan known to cause a rare corneal infection which is difficult to diagnose and treat. This study evaluated the activity of the polyhomoarginines against Acanthamoeba castellanii. Acanthamoeba amoebicidal, amoebistatic, encystation and excystment assays were performed using protocols described in the literature. The activity of polyhomoarginines (PHAs) of different lengths (10 to 400 residues) was measured against the trophozoites and cysts of Acanthamoeba castellanii ATCC30868 in concentrations ranging from 0.93 μM to 15 μM. Data were represented as mean ± SE and analysed using one-way ANOVA. Overall, PHAs demonstrated good anti-acanthamoeba activity against both trophozoites and cysts. PHA 30 reduced the number of viable trophozoites by 99%, inhibited the formation of cysts by 96% and the emergence of trophozoites from cysts by 67% at 3.75 μM. PHA 10 was similarly active, but at a slightly higher concentration of 15 μM, reducing the numbers of viable trophozoites by 98%, inhibiting cyst formation by 84% and preventing the emergence of trophozoites from cysts by 99%. At their greatest anti-amoeba concentrations, PHA 10 gave only 8% haemolysis at 15 μM while PHA 30 gave <40 % haemolysis at 3.75 μM. Polyhomoarginine 10 showed excellent anti-amoebic activity against both forms of Acanthamoeba castellanii and was non-toxic at its most active concentrations. This implies that polyhomoarginines can be developed into a potential therapeutic agent for Acanthamoeba keratitis. However, there is a need to carry out further pre-clinical and then in vivo experiments in the AK animal model.