Miracle-dz/UniFAR-FTUs huggingface dataset:
Channel Coding for Multimedia Transmission on High-Speed Flying Devices

Communication systems for high-speed flying devices, such as drones and missiles, have performances with error-floor caused by the Doppler effect, which causes inter-carrier interference (ICI) and destroys real-time data transmission. Channel coding cannot reduce error-floors, but channel coding may still achieve performance with turbo-cliff. This paper proposes a broadband communication system for high-speed flying devices using soft 4 quadrature amplitude modulation (4-QAM) modulations with the optimal threshold for practical implementation assuming that the maximum/minimum log-likelihood ratio (LLR) values of ±709. We use orthogonal frequency division multiplexing (OFDM) with low-density parity-check (LDPC) codes as the channel coding scheme and minimum mean squared error (MMSE) equalization. To reduce the computational complexity and to keep the data rate high, we use only a single pilot for the channel estimation. Computer-based simulations for several high speeds are performed to evaluate the performance of the proposed high-speed flying devices system. The bit error rate (BER) performance is evaluated based on LLR under additive white Gaussian noise (AWGN) and multipath Rayleigh fading channels. The results confirmed that the proposed system with the optimal threshold can avoid unstable jumping error with better turbo-cliff and lower error-floor. The maximum speed the system can achieve for BER of 10−2 is 400 km/h. The results of this paper are expected to contribute significantly to the development of communication systems on flying devices.
Evaluating the Effects of Aloe vera Gel on Cerebellum Istomorphometrical Changes in Diabetic Male Rats

Background: Diabetes mellitus can lead to histomorphometrical changes in the brain. Recent studies have shown that Aloe vera gel has antioxidant and neuroprotective effects, which is independent of glucose-lowering effects. Objectives: The present study aimed to investigate the effects of A. vera gel on histomorphometrical changes of cerebellum following streptozotocin (STZ)-induced diabetic male rats. Methods: A total of 25 male Wistar rats were randomly allocated into five groups as follows: (1) the control group received normal saline; (2) A. vera gel group; (3) diabetic group (normal saline); (4) treatment group diabetic rats, which received A. vera; and (5) diabetic rats which received insulin. A single dose of STZ [60 mg/kg; intraperitoneal (IP)] was used for the induction of diabetes in rats. All the treatments were administered daily for eight weeks. Subsequently, histomorphometrical changes were evaluated in the cerebellum of the rats. Results: The results showed that the number of granular and purkinje cells reduced in the cerebellum granulosa region, while the number of glial cells increased in the molecular region of the cerebellum in diabetic rats compared to the control group (P < 0.05). These changes were improved in treated rats by insulin or A. vera. Also, the thickness of molecular, purkinje, granular, and white matter layers at the apex of lobules and depth of sulcus in the diabetic group had a significant reduction compared to other groups (P < 0.001). Conclusions: Our results confirmed that improvement of the cerebellar tissue changes in diabetic rats following the use of A. vera gel is comparable to insulin. However, more investigations are required to determine the protective effects of A. vera gel against diabetes-induced cerebellum histomorphometrical changes.
A Robust Optimization Approach for Optimal Power Flow Solutions Using Rao Algorithms

This paper offers three easy-to-use metaphor-less optimization algorithms proposed by Rao to solve the optimal power flow (OPF) problem. Rao algorithms are parameter-less optimization algorithms. As a result, algorithm-specific parameter tuning is not required at all. This quality makes these algorithms simple to use and able to solve various kinds of complex constrained optimization and engineering problems. In this paper, the main aim to solve the OPF problem is to find the optimal values of the control variables in a given electrical network for fuel cost minimization, real power losses minimization, emission cost minimization, voltage profile improvement, and voltage stability enhancement, while all the operating constraints are satisfied. To demonstrate the efficacy of Rao algorithms, these algorithms have been employed in three standard IEEE test systems (30-bus, 57-bus, and 118-bus) to solve the OPF problem. The OPF results of Rao algorithms and the results provided by other swarm intelligence (SI)/evolutionary computing (EC)-based algorithms published in recent literature have been compared. Based on the outcomes, Rao algorithms are found to be robust and superior to their competitors.
Estimation of DAS microseismic source mechanisms using unsupervised deep learning

Distributed acoustic sensing (DAS) is an increasingly prevalent technology for seismic acquisition, especially in reservoir monitoring settings. Recently, it has attracted interest for microseismic monitoring during hydraulic fracturing and as a complement to broadband seismometers for measuring teleseismic waves generated by earthquakes. A key component of these data is the source mechanism information encoded in the direct arrivals and how to best utilize this data to make inferences about the source mechanism, given DAS measurements of the direct arrivals is an open question. DAS is a relatively new technology, providing very large datasets of different physical aspects of the waveﬁeld than geophones or seismometers. Consequently, conventional moment tensor inversion is challenging to transfer directly to DAS data. Instead, we turn our attention to deep learning algorithms for estimating these source mechanisms. Viewing our seismic data as containing diagnostic features of the source mechanism, we reduce the data to only those features most pertinent to moment tensor inversion by training a convolutional auto-encoder. The extracted features are then analyzed using clustering and generative adversarial networks (GAN). Clustering based on source mechanism is observed, conﬁrming the extracted features contain important source mechanism information. Furthermore, we develop a trained GAN that provides an accurate mapping from feature space to moment tensor estimate, which shows promise when applied to a ﬁeld DAS-microseismic dataset collected during hydraulic fracturing. Data modeled with the predicted source mechanism shows a strong correlation to the ﬁeld data event.
Identifying features of health misinformation on social media sites: an exploratory analysis

PurposeThe purpose of this paper is to explore the features of health misinformation on social media sites (SMSs). The primary goal of the study is to investigate the salient features of health misinformation and to develop a tool of features to help users and social media companies identify health misinformation.Design/methodology/approachEmpirical data include 1,168 pieces of health information that were collected from WeChat, a dominant SMS in China, and the obtained data were analyzed through a process of open coding, axial coding and selective coding. Then chi-square test and analysis of variance (ANOVA) were adopted to identify salient features of health misinformation.FindingsThe findings show that the features of health misinformation on SMSs involve surface features, semantic features and source features, and there are significant differences in the features of health misinformation between different topics. In addition, the list of features was developed to identify health misinformation on SMSs.Practical implicationsThis study raises awareness of the key features of health misinformation on SMSs. It develops a list of features to help users distinguish health misinformation as well as help social media companies filter health misinformation.Originality/valueTheoretically, this study contributes to the academic discourse on health misinformation on SMSs by exploring the features of health misinformation. Methodologically, the paper serves to enrich the literature around health misinformation and SMSs that have hitherto mostly drawn data from health websites.
Value Function Guided Subgroup Identification via Gradient Tree Boosting: A Framework to Handle Multiple Outcomes for Optimal Treatment Recommendation

Abstract In randomized clinical trials, there has been an increasing interest in identifying subgroups with heterogeneous responses to study treatment based on baseline patient characteristics. Even though the benefit risk assessment of any patient population or subgroups is almost always a multi-facet consideration, the statistical literature of subgroup identification has largely been limited to a single clinical outcome. In the article, we propose a nonparametric method that searches for subgroup membership scores by maximizing a value function that directly reflects the subgroup-treatment interaction effect considering clinical priorities of multiple outcomes based on win difference. A gradient tree boosting algorithm is proposed to search for the individual subgroup membership scores. We conduct simulation studies to evaluate the performance of the proposed method and an application to a colon cancer adjuvant clinical trial is performed for illustration.
Exploration on the Mechanism of the Impact of Green Supply Chain Management on Enterprise Sustainable Development Performance

Green supply chain management (GSCM) is an important management means for enterprises to achieve sustainable development and green transformation. However, the intrinsic mechanism by which GSCM impacts the sustainable development performance (SDP) of enterprises remains a theoretical “black box”. Based on the Porter’s hypothesis, this study constructs a moderated mediation model from the theoretical perspective of green innovation and signal transmission to explore this intrinsic mechanism. Then, this study analyzes data from China’s Shanghai and Shenzhen A-share listed companies from 2015 to 2020 to conduct an empirical analysis to test the model. The results show that GSCM has a significantly positive effect on SDP. The mediating effect of green innovation and the moderating effect of green subsidies are significant. The results remain valid after a series of endogeneity and robustness tests.
Take a Breath: Respiratory Sounds Improve Recollection in Synthetic Speech

This study revisits Whalen et al. (1995, JASA) by evaluating English speaking participants in a perception experiment to determine if their recollection is affected by including breath noises in sentences generated by a speech synthesis system. Whalen found an improvement in recollection for sentences that were preceded by a breath noise compared to sentences without one. While Whalen and colleagues used formant synthesis to render the English sentences, we use a modern concatenative synthesis system. The present study uses inhalations of three different lengths: 0 ms (no breath noise), 300 ms (short breath noise), and 600 ms (long breath noise). Our results are consistent with Whalen and colleagues for the 600 ms condition, but not for the 300 ms condition, indicating that not all inhalations improved recollection. The present study also found a signiﬁcant effect for sentence length, illustrating that shorter sentences have higher accuracy for recollection than longer sentences. Overall, the present study indicates that respiratory sounds are important to the recollection of synthesized speech and that re-searchers should focus on longer and more complex types of speech, such as paragraphs or dialogues, for future studies.
SAR Target Classification Using the Multikernel-Size Feature Fusion-Based Convolutional Neural Network

It is well-known that the convolutional neural network (CNN) is an effective method for synthetic aperture radar (SAR) target classification. In the convolutional layer of CNN, convolutional kernels of different sizes can extract different feature information of the target. The small-size kernel can extract the local texture feature information, and the large-size kernel can extract the global contour feature information. Traditional CNN methods usually use fixed-size kernels for convolution, and they generally lose part of the target’s feature information, resulting in the inaccurate classification of the SAR targets. This article proposes a novel CNN model based on multikernel-size feature fusion (MKSFF-CNN) for SAR target classification. MKSFF-CNN designs a convolutional methodology with a multichannel parallel topology, it uses convolutional kernels of different sizes to extract the multikernel-size deep features of the SAR target, and then, these features are fused in an optimal way to acquire the lowest loss. Moreover, MKSFF-CNN concatenates the fused features extracted by the convolutional layers of different dimensions to achieve the finest classification. MKSFF-CNN greatly elevates the feature representation completeness of the SAR targets so that more useful feature information can be exploited for SAR target classification. Undoubtedly, MKSFF-CNN can achieve a better classification performance compared with traditional CNN models with a fixed kernel size. The superiority of MKSFF-CNN is validated on the moving and stationary target acquisition and recognition (MSTAR) dataset with the detailed objective and subjective evaluation.
Discrete Pseudo-Fractional Fourier Transform and Its Fast Algorithm

In this article, we introduce a new discrete fractional transform for data sequences whose size is a composite number. The main kernels of the introduced transform are small-size discrete fractional Fourier transforms. Since the introduced transformation is not, in the generally known sense, a classical discrete fractional transform, we call it discrete pseudo-fractional Fourier transform. We also provide a generalization of this new transform, which depends on many fractional parameters. A fast algorithm for computing the introduced transform is developed and described.
Study of the second-generation of CdTe and CIGS thin film PV modules under natural sunlight conditions

There is a significant amount of research in the literature concerning the performance of solar panels operating during outdoor exposure, but the full topic is not yet exhausted. One of the reasons is that the photovoltaic cell technology is constantly evolving. In this paper, a comparison of two types of CdTe and CIGS modules operated with a nominal power of 80 W and 140 W, respectively is studied. The module tests were performed under external conditions during autumn, winter, spring, and summer from October 2019 to July 2020 in the temperate climate of Miękinia, South Poland. The photovoltaic panels were connected to the electric grid via microinverters. During the tests, the temperature of the panels was monitored. To determine the influence of solar radiation on the energy conversion efficiency of photovoltaic panels, a pyranometer installed in the plane of the panels was used. Based on the monitoring of the atmospheric conditions and the measurement of instantaneous power, the efficiency of the modules is determined.
Co-Simulation Analysis for Performance Prediction of Synchronous Reluctance Drives

To improve the design of electric drives and to better predict the system performance, numerical simulation has been widely employed. Whereas in the majority of the approaches, the machines and the power electronics are designed and simulated separately, to improve the fidelity, a co-simulation should be performed. This paper presents a complete coupled co-simulation model of synchronous reluctance machine (SynRel) drive, which includes the finite element model of the SynRel, the power electronics inverter, the control system, and application examples. The model of SynRel is based on a finite element model (FEM) using Simcenter MagNet. The power electronics inverter is built using PLECS Blockset, and the drive control model is built in Simulink environment, which allows for coupling between MagNet and PLECS. The proposed simulation model provides high accuracy thanks to the complete FEA-based model fed by actual inverter voltage. The comparison of the simulation results with experimental measurements shows good correspondence.
Materialising Care across Borders: Sent Things and Family Ties between Sweden and Ukraine

This article is an exploration of transnational family links and how they are materialised. Based on interviews with Ukrainian migrants living in Sweden, we discuss different dimensions of the everyday practices of sending things back and forth between family members. We find that what these packages embody and represent are more complex than tropes of economic need, obligation and responsibility allow for. Of course, in many senses they do reveal stories of highly gendered practices of care and duty, and economic divides between Sweden and Ukraine. We find, however, that they are also stories of mutuality, love, fun and shifting post-Soviet subjectivities. This article then both underlines the enduring importance of physical things in maintaining close family connections across distance and reminds us that these material connections are not fixed but instead are mutable circulations, shaping and shaped by generational change and lifecourse experiences.
Ranking of different of investment risk in high-tech projects using TOPSIS method in fuzzy environment based on linguistic variables

Examining the trend of the global economy shows that global trade is moving towards high-tech products. Given that these products generate very high added value, countries that can produce and export these products will have high growth in the industrial sector. The importance of investing in advanced technologies for economic and social growth and development is so great that it is mentioned as one of the strong levers to achieve development. It should be noted that the policy of developing advanced technologies requires consideration of various performance aspects, risks and future risks in the investment phase. Risk related to high-tech investment projects has a meaning other than financial concepts only. In recent years, researchers have focused on identifying, analyzing, and prioritizing risk. There are two important components in measuring investment risk in high-tech industries, which include identifying the characteristics and criteria for measuring system risk and how to measure them. This study tries to evaluate and rank the investment risks in advanced industries using fuzzy TOPSIS technique based on verbal variables.
Impacts of Pore Network‐Scale Wettability Heterogeneity on Immiscible Fluid Displacement: A Micromodel Study

The mixed‐wet nature of reservoir formations imposes a wide range of rock wettability from strong resident‐fluid wetting to strong invading‐fluid wetting. The characteristics of two‐phase flow in porous media composed of mixed‐wetting surfaces remain poorly understood. In this study, we investigated the displacement of resident ethylene glycol (EG) by hexane in two mixed‐wet micromodels of identical 2.5‐D geometry heterogeneity, with uniformly or heterogeneously distributed patches strongly wetting to hexane. These patches are mixed among pores with unaltered EG‐wetting surfaces. Along with control tests in the originally EG‐wet micromodel, we show the classic fingering and transitions in flow regimes at logCa (capillary number) from −7.2 to −3.9. Moreover, pore‐scale distributions of wettability and their spatial correlation influence displacement efficiency. In the two mixed‐wet micromodels, we found (a) an increase of steady‐state hexane saturation at the end of experiments by up to 0.12 in the capillary fingering regime and a decrease of at most by 0.06 in the viscous fingering regime, compared to the EG‐wet micromodel, and (b) dispersed and fragmented hexane distribution after displacement. Brine drainage during supercritical CO2 (scCO2) injections in these micromodels occurs with lower wettability contrasts, and under similar viscosity ratios and interfacial tensions resulted in higher displacement efficiency relative to displacement of EG by hexane. While mixed‐wettability can enhance displacement efficiency compared to uniform wettability, the dynamics of immiscible fluids in strong mixed‐wet reservoirs are expected to be less pronounced in contributing to the efficiency of geological CO2 sequestration, oil recovery, and remediation of hydrocarbon‐contaminated aquifers.
4’-demethylnobiletin-rich fermented Citrus reticulata (ponkan) attenuated the disturbance in clock gene expression and locomotor activity rhythms caused by high-fat diet feeding

ABSTRACT Demethylnobiletins are nobiletin metabolites, with 4′-demethylnobiletin being the most abundant in the demethylated compounds upon feeding with nobiletin. Previous studies have repeatedly shown that demethylnobiletin has higher physiological activity than nobiletin; however, no studies have yet suggested that 4′-demethylnobiletin could affect biological rhythms. Therefore, in the present study, we analyzed the effects of fermented Citrus reticulata, containing a large amount of 4′-demethylnobiletin, on the locomotor activity level, locomotor activity rhythms under normal light-dark and constant-dark conditions, and peripheral clock rhythms. We demonstrated that fermented Citrus reticulata not only modified the changes in the locomotor activity rhythms caused by high-fat diet feeding in mice but also inhibited the phase shift of the peripheral clock rhythms caused by high-fat diet feeding. Taken together, the results of this study suggest that fermented Citrus reticulata could potentially be a new functional food that could help to maintain normal biological rhythms.
Conspiracy beliefs and attitudes toward COVID-19 vaccinations

During the coronavirus pandemic, this study aimed to investigate the impact of conspiracy beliefs on Finnish attitudes toward vaccinations in general and COVID-19 vaccinations in particular. This study was a conceptual replication in Finland of a study by Pivetti et al. (2021). Some 529 Finnish participants responded to a self-report questionnaire during the partial lockdown in Finland in spring 2020. The hypothesized relationships between variables of interest were integrated in a serial multiple mediation model via structural equation modelling. Results showed that endorsing general conspiracy beliefs directly predicted (1) general attitudes toward vaccines and (2) COVID-19 conspiracy beliefs, and indirectly predicted (3) attitudes toward COVID-19 vaccines via the serial mediation of COVID-19 conspiracy beliefs and general attitudes toward vaccines. As for the antecedents of beliefs in conspiracy theories, political orientation and moral purity predicted beliefs in COVID-19 conspiracy theories. Trust in science was inversely related to general conspiracy beliefs. As for the consequences of conspiracy beliefs, COVID-19 conspiracy beliefs directly predicted support for governmental restrictions (negatively) and the perception of informational contamination (positively).
The Effect of Catalyst Calcination Temperature on Catalytic Decomposition of HFC-134a over γ-Al2O3

This paper explores the thermal and catalytic pyrolysis of HFC-134a over γ-Al2O3 calcined at temperatures of 550 °C (A550), 650 °C (A650), 750 °C (A750), and 850 °C (A850). The physicochemical properties of catalysts were studied through thermogravimetric analysis (TGA), Brunauer–Emmett–Teller equation for nitrogen physisorption analysis (BET), X-ray diffraction (XRD), and temperature-programmed desorption of ammonia (NH3-TPD). The non-catalytic pyrolysis of HFC-134a showed less than 15% decomposition of HFC-134a. Catalysts increased the decomposition as A650 revealed the highest decomposition efficiency by decomposing more than 95% HFC-134a for 8 h followed by A750, A850, and A550. The larger surface area and pore volume paired with a low amount of strong acidic sites were considered as the main contributors to the comparatively longer catalytic activity of A650.