Miracle-dz/UniFAR-FTUs huggingface dataset:
A new fuzzy linear regression algorithm based on the simulation of fuzzy samples and an application on popularity prediction of Covid-19 related videos

Abstract A new approach to regression modeling for crisp input-fuzzy output is introduced. The procedure starts with sample generation of symmetrical triangular fuzzy outputs and applying robust linear regression (RLR) a substantial number of times to crisp data. Then, the centers of the coefficients are determined as the mean of upper and lower values. Similarly, the spreads are assumed as the half-length of the resulting intervals. Concurrently, outliers are labeled during the RLR. The total absolute difference between left and right endpoints as a distance between two fuzzy numbers is considered as an error measure. Finally, at the control phase, the estimated spreads are narrowed via bisection. Successively at the correction phase, spreads are widened with respect to outliers, and the constraints, and whether getting a better sum of errors. Numerical examples and comparison studies are given to clarify the proposed method. Furthermore, given the profound effects of the worldwide pandemic, the topic of popularity prediction in YouTube videos related to Covid-19 is chosen as an application.
Comparison between different populations of Nile Pufferfish (Tetraodon lineatus) in Lake Nasser, Aswan, Egypt using Mitochondrial CO1 gene

The study about Nile Pufferfish (Tetraodon lineatus) and its growth conditions in Lake Nasser, Aswan, Egypt which is still at a very early stage. Recently, Pufferfish might be considered nutritious food due to its content of good amount of oil, protein, minerals. A total of 30 Nile Pufferfish samples were collected using commercial nets from Lake Nasser (15 fish samples from Khor El Ramla as a North Lake and 15 samples from Tushka East as a South Lake). The total lengths of North and south Lake samples ranged between 16 – 39 and 16 - 28cm with a mean of 27.1 and 21.3 cm, respectively. While, the total body weight of North and south Lake samples ranged between 136 – 1636 and 129-608 g with a mean of 680.8 and 265.7 g, respectively. In addition, the results indicate that there are different groups from T. lineatus of the two locations based on the partial sequence of CO1 gene. The nucleotide frequencies of CO1 gene of North Lake samples were 25.80%, 31.45%, 24.75% and 18.00% for T, C, A and G, respectively. While, the Nucleotide frequencies of South Lake samples were 25.96%, 31.62%, 24.31% and 18.11% for T, C, A and G, respectively. The habitat and location of fish has significant effects on the weight and length of Pufferfish but the origin has the same father. Segregation was happened in the deep water which leads to more haplotypes in the same area. Therefore, proper utilization of this species towards sustainable management, nutritional composition, and biosecurity issues will lead to achieving a sustainable blue economy.
Deep Collaborative Intelligence-Driven Traffic Forecasting in Green Internet of Vehicles

Accompanied with the development of green wireless communication, the green Internet of Vehicles (GIoV) has been a latent solution for future transportation. Among them, intelligent traffic forecasting for key nodes in GIoV is a significant research topic. Much research had been devoted to this issue, and graph learning-based approaches seemed to be a promising solution. However, existing research works concentrated more on graph-structured features in GIoV yet neglected global reliability. To deal with such issue, this work combines both deep embedding and graph embedding together and proposes a deep collaborative intelligence-driven traffic forecasting model in GIoV. By establishing more reliable feature spaces for traffic flow prediction, forecasting efficiency is expected to be promoted. Specifically, deep embedding is utilized to generate more abstract representation for basic features of road networks, and graph embedding is employed to update feature representation for different timestamps. Their collaboration contributes to considerable reliability. In addition, experiments are also conducted on a real-world dataset, and the results indicate that forecasting deviation receives about 15%-25% reduction.
The implications of home office adaptation during the COVID-19 pandemic: an experimental analysis on the Quality of Work Life

The COVID-19 pandemic brought uncertainties to the social, economic and political scenario, leading to extraordinary challenges to firms. This paper sheds light on how the home office adaptation affects the Quality of Work Life (QWL). We conducted an experiment with 322 participants to understand the mechanism by which tests the effect of home office adaptation on the QWL. Our results point out that companies that adapted better to home office positively impact the participants' perception of QWL. We expect to contribute to the organizational theory and human resources field by providing empirical data about the adaptation to the home office and expectation of QWL. Furthermore, we provide evidence to practitioners who may get advantage of our experimental strategy to suit their firms better to this new scenario.
Contrastive Learning-based Robust Object Detection under Smoky Conditions

Object detection is to effectively find out interested targets in images and then accurately determine their categories and positions. Recently many excellent methods have been developed to provide powerful detection capability. However, their performance may degrade significantly under severe weather such as smoky conditions. In this paper, we propose a contrastive learning-based robust object detection algorithm for smoke images. The proposed object detector consists of two modules: contrastive learning module and object bounding box prediction module. The first module learns representation vectors by maximizing agreement between different augmented views of the same smoke image. These representations are then sent to the second module to yield the bounding box for each object. In addition, we also propose a novel affine data augmentation method. Extensive experiments have been conducted on A2I2-Haze dataset which is the first real haze dataset with in-situ smoke measurement aligned to aerial and ground imagery. This dataset is also the only dataset used in the 5th UG2+ challenges of CVPR 2022 for both training and testing. Compared with state-of-the-art methods, evaluation results show the superiority of our proposed object detector.
An Exact Algorithm for Coalition Structure Generation and Complete Set Partitioning

Solving the Coalition Structure Generation problem is a major challenge in cooperative game theory. It involves partitioning the set of agents into subsets (or coalitions) such that the total reward is maximized. We study this problem in Characteristic Function Games, i.e., scenarios where every possible subset of agents is a potential coalition, and the outcome (or value) of every such subset is represented as a single, numerical value on which non-members have no influence. In this setting, the coalition structure generation problem becomes identical to the Complete Set Partitioning problem, where every subset of elements has a cost, and the goal is to find a partition in which the sum of subset costs is minimal. This is also identical to the Winner Determination problem in combinatorial auctions when a bid is placed on every possible bundle of goods, and the goal is to find a partition that maximizes the profit of the auctioneer. To date, there are two state-of-the-art, exact algorithms for solving this problem: (1) a dynamicprogramming algorithm called DP (Yeh, 1986; Rothkopf et al., 1995) and (2) a tree-search algorithm called IP (Rahwan et al., 2009). Each of these two algorithms has it relative strengths and weaknesses compared to the other. More specifically, in terms of worst-case performance, DP is significantly better as it runs in O(3) time (given n elements), while IP runs in O(n) time. However, when tested against popular value distributions, IP was shown to be often faster than DP (by several orders of magnitude in some cases). Furthermore, IP has the advantage of being anytime, meaning that its solution quality improves gradually over time, allowing it to return a valid solution at any moment during its execution. DP, on the other hand, is not an anytime algorithm; it can only return a solution once it has completed its execution. The contribution of this article is twofold. Firstly, we show that some of DP’s operations and memory requirements are redundant. Building upon this, we develop ODP—an optimal version of DP that avoids all redundancies. Secondly, we develop a hybrid algorithm, called ODP-IP, that has the best features of its constituent parts, ODP and IP. Specifically, it is an anytime algorithm just like IP, and runs in O(3) just like ODP. Better still, when tested against a wide variety of value distributions, ODP-IP is empirically shown to significantly outperform both ODP and IP for all distributions.
Optical galaxies within $8000\,\ {\rm km} \ {\rm s}^{-1}$ – IV. The peculiar velocity field

The density field of optically selected galaxies within 8000 km s −1 is used to predict the peculiar velocities of D n −σ and IRTF galaxies employing linear gravitational instability theory. To obtain acceptable fits to the observed peculiar velocities in the CMB frame, it is necessary to fit two parameters: β o ≡ Ω 0.6 b o −1 (where b o is a biasing parameter for optical galaxies), which scales the predictions of the optical density field within 8000 km s −1 ; and a residual bulk motion due to sources outside the volume. The fits yield β o =0.50±0.06 (1 σ random errors) for both the IRTF and the D n −σ samples, and a residual bulk motion of 405±45 km s −1 towards l=292°, b=7° for the IRTF field galaxies
A hybrid approach for improving the flexibility of production scheduling in flat steel industry

Nowadays the steel market is becoming ever more competitive for European steelworks, especially as far as ﬂat steel products are concerned. As such competition determines the price products, proﬁt can be increased only by lowering production and commercial costs. Production yield can be signiﬁcantly increased through an appropriate scheduling of the semi-manufactured products among the available sub-processes, to ensure that customers’ orders are timely completed, resources are optimally exploited, and delays are minimized. Therefore, an ever-increasing attention is paid toward production optimization through efﬁcient scheduling strategies in the scientiﬁc and industrial communities. This paper proposes a hybrid approach to improve the ﬂexibility of production scheduling in steelworks producing ﬂat steel products. Such approach combines three methods holding different scopes and modelling different aspects: an auction-based multi-agent system is applied to face production uncertainties, multi-objective mixed-integer linear programming is used for global optimal scheduling of resources under steady conditions, while a continuous ﬂow model copes with long-term production scheduling. According to the obtained simulation results, the integration and combination of these three approaches allow scheduling production in a ﬂexible way by providing the capability to adapt to different production conditions.
Comparative study on the use of hypertonic saline vs. isotonic saline nasal irrigation following endoscopic sinus surgery hipertonicznego

: Introduction: Various nasal saline solutions have been widely used following endoscopic sinus surgery. Irrigation solutions minimize synechia formation, improve mucosal healing and enhance the removal of infective debris and crusts. Aim: This study aims to compare the efficacy of hypertonic and isotonic saline solutions in nasal irrigation following, endoscopic sinus surgery. Material and methods: Forty patients who underwent endoscopic sinus surgery were randomly and evenly sorted into: group “A”, which received hypertonic saline nasal irrigation, and group “B”, which received isotonic nasal irrigation The outcomes were assessed for mucociliary clearance (MCT), Lund-Kennedy endoscopic score for crusts and mucosal edema, while nasal obstruction, nasal discharge and facial pain were analyzed by SNOT22, whereby their severity was assessed by means of visual analog score. The patients were followed up for 3 weeks. Results: There was a significant difference in improvement in both groups. Group “A” MCT value on day 7 was P = 0.023, day 14 P = 0.002, and day 21 P = 0.009: for mucosal edema P = 0.009, P = 0.001, P = 0.025; and, for crusts P = 0.015, P = 0.010, P = 0.011: and, for nasal obstruction P = 0.006, P = 0.0001, P = 0.0001 respectively; and for nasal discharge and facial pain P = 0.010, P = 0.047, and P = 0.0062, P = 0.028 for days 14, and 21, respectively. Conclusion: Better results in MCT, nasal mucosal edema, crusts, nasal obstruction, discharge, and facial pain/pressure were achieved with the usage of hypertonic saline nasal irrigation.
Spatially resolved spectroscopy across stellar surfaces

Context. High-precision stellar analyses require hydrodynamic 3D modeling. Testing such models is feasible by retrieving spectral line shapes across stellar disks, using differential spectroscopy during exoplanet transits. Observations were presented in Papers I, II, and III, while Paper IV explored synthetic data at hyper-high spectral resolution for different classes of stars, identifying characteristic patterns for Fe I and Fe II lines. Aims. Anticipating future observations, the observability of patterns among photospheric lines of different strength, excitation potential and ionization level are examined from synthetic spectra, as observed at ordinary spectral resolutions and at different levels of noise. Time variability in 3D atmospheres induces changes in spectral-line parameters, some of which are correlated. An adequate calibration could identify proxies for the jitter in apparent radial velocity to enable adjustments to actual stellar radial motion. Methods. We used spectral-line patterns identified in synthetic spectra at hyper-high resolution in Paper IV from 3D models spanning Teff = 3964–6726 K (spectral types ~K8 V–F3 V) to simulate practically observable signals at different stellar disk positions at various lower spectral resolutions, down to λ/Δλ = 75 000. We also examined the center-to-limb temporal variability. Results. Recovery of spatially resolved line profiles with fitted widths and depths is shown for various noise levels, with gradual degradation at successively lower spectral resolutions. Signals during exoplanet transit are simulated. In addition to Rossiter-McLaughlin type signatures in apparent radial velocity, analogous effects are shown for line depths and widths. In a solar model, temporal variability in line profiles and apparent radial velocity shows correlations between jittering in apparent radial velocity and fluctuations in line depth. Conclusions. Spatially resolved spectroscopy using exoplanet transits is feasible for main-sequence stars. Overall line parameters of width, depth and wavelength position can be retrieved already with moderate efforts, but a very good signal-to-noise ratio is required to reveal the more subtle signatures between subgroups of spectral lines, where finer details of atmospheric structure are encoded. Fluctuations in line depth correlate with those in wavelength, and because both can be measured from the ground, searches for low-mass exoplanets should explore these to adjust apparent radial velocities to actual stellar motion.
Gut microbiota composition impacts host gene expression by changing chromatin accessibility

Variation in gut microbiome is associated with wellness and disease in humans, yet the molecular mechanisms by which this variation aﬀects the host are not well understood. A likely mechanism is through changing gene regulation in interfacing host epithelial cells. Here, we treated colonic epithelial cells with live microbiota from ﬁve healthy individuals and quantiﬁed induced changes in transcriptional regulation and chromatin accessibility in host cells. We identiﬁed over 5,000 host genes that change expression, including 588 distinct associations between speciﬁc taxa and host genes. The taxa with the strongest inﬂuence on gene expression alter the response of genes associated with complex traits. Further, using ATAC-seq, we show that these changes in gene expression are likely the result of changes in host chromatin accessibility induced by exposure to gut microbiota. We then created a manipulated microbial community with titrated doses of Collinsella , demonstrating that both natural and controlled microbiome composition leads to distinct, and predictable, gene expression proﬁles in the host. Together, our results suggest that speciﬁc microbes play an important role in regulating expression of individual host genes involved in human complex traits. Our work also supports the hypothesis that one of the mechanisms by which the microbiome regulates host gene expression is through changes in chromatin accessibility in host cells. Finally, the ability to ﬁne tune the expression of host genes by manipulating the microbiome suggests future therapeutic routes for human wellness. Our results suggest that speciﬁc microbes in the microbiome may be important in regulating host gene expression in the gut, and that microbes can induce changes to a large number of genes. Furthermore, our data support the hypothesis that changes in chromatin accessibility in host cells is one of the mechanisms by which the microbiome induces changes in expression of host genes that are involved in complex traits, including those that have already been associated with microbiome composition. Finally, our work suggests a molecular mechanism by which supplementing the microbiome can inﬂuence human health through changes in host cellular response. We have shown that by manipulating microbiome composition by supplementing a single microbe, we can inﬂuence the host cell regulatory response in a predictable way. This work and future research will help to determine which microbes may be most beneﬁcial as interventional therapy to improve one’s health.
Structure of the human lipid-sensitive cation channel TRPC3

The TRPC channels are crucially involved in store-operated calcium entry and calcium homeostasis, and they are thus implicated in human diseases such as neurodegenerative disease, cardiac hypertrophy, and spinocerebellar ataxia. We present structure of the full-length human TRPC3, a lipid-gated TRPC member, in a lipid-occupied, closed state at 3.3 Angstrom. TRPC3 has an acorn-like shape with four elbow-like membrane reentrant helices prior to the first transmembrane helix. The TRP helix is perpendicular to, and thus disengaged from, the pore-lining S6, suggesting a different gating mechanism. The third transmembrane helix S3 is remarkably long, resulting in a windmill-like transmembrane domain, and constituting an extracellular domain that may serve as a sensor of external stimuli. We identified two lipid binding sites, one being sandwiched between the pre-S1 elbow and the S4-S5 linker, and the other being close to the ion-conducting pore, where the conserved LWF motif of the TRPC family is located.
Cross section and method uncertainties: the application of sensitivity analysis to study their relationship in radiation transport benchmark problems

Sensitivity analysis is applied to the study of an air transport benchmark calculation to quantify and distinguish between cross-section and method uncertainties. The boundary detector response was converged with respect to spatial and angular mesh size, P/sub l/ expansion of the scattering kernel, and the number and location of energy grid boundaries. The uncertainty in the detector response due to uncertainties in nuclear data is 17.0 percent (one standard deviation, not including uncertainties in energy and angular distribution) based upon the ENDF/B-IV ''error files'' including correlations in energy and reaction type. Differences of approximately 6 percent can be attributed exclusively to differences in processing multigroup transfer matrices. 6 tables, 9 figures (auth)
Examining the Impact of User Reviews On Mobile Applications Examining the Impact of User Reviews On Mobile Applications Development Development

User reviews were often collected to enlighten mobile applications (apps) developers on areas for improvement and novel features. However, users might not always possess the required technical expertise to make commercially feasible suggestions. The value of user reviews also varied due to their unmanageable volume and content irrelevance. In our study, over 40,000 user reviews with 50 apps would be analyzed using Python coding and regression analysis to examine the impacts of innovation and improvement led by users on apps performance in terms of revenues and user ratings. The developers’ lead time in responding to user reviews would be included as a moderator to investigate whether apps performance would be enhanced if developers respond faster. Our study should represent one of the first few attempts in offering empirical confirmation of the value of co-creation of apps with users.
A Comparison of Importance Weights for Multiattribute Utility Analysis Derived from Holistic, Indifference, Direct Subjective and Rank Order Judgments.

Abstract : Research done in the 1960's and early 1970's suggested that although statistical weights and subjective weights show some correspondence in regression-like situations, subjective weights tend to be too flat by comparison; statistical weights usually show that some attributes are quite important, while others are hardly important at all. More recent discussions of this literature, however, have pointed out a number of methodological problems with much of the early research, and have reached a more optimistic conclusion with respect to subjective weights. Several experiments support the more recent interpretation. The present study compared weight estimation procedures for additive, riskless, four-attribute value functions with linear single-attribute values. Self-explicated (subjective) weights were assessed from direct subjective and rank order estimates of attribute importance; observer-derived weights were determined both from indifference judgments (axiomatic approach) and from holistic evaluations (statistical approach) of alternatives. Assessed weights were compared to a true weight vector used to generate feedback during pre-assessment learning trials (constructed with zero inter-attribute correlations). Although self-explicated weights tended to be flatter than observer-derived weights, resulting composites correlated equally well with true composites. Only slight differences were found in ordinal correspondence between true and assessed weights.
DIGITAL NUDGING TO PROMOTE ENERGY CONSERVATION DIGITAL NUDGING TO PROMOTE ENERGY CONSERVATION BEHAVIOR – FRAMING AND DEFAULT RULES IN A SMART HOME BEHAVIOR – FRAMING AND DEFAULT RULES IN A SMART HOME

Increasingly, new energy-efficient technologies connected to smart home arise and bear great potential of influencing user's decisions. Thereby, behavioral interventions like digital nudging are promising to influence behavior. While nudging has been investigated in several contexts to promote sustainable behavior, little is known about its effectiveness in digital choice environments promoting daily energy conservation behavior, especially through mobile applications. As private households account for a large share of total energy consumption, which needs to be reduced to counteract climate change, we conducted an online survey to test the nudging elements framing and default rules, as well as their combination. We surveyed 231 participants and found a large effect of framing and an even larger effect for the combination. This paper contributes by exploring these digital nudges, which received little attention in prior research, and by providing insights on the design of smart home applications to reduce energy consumption.