Miracle-dz/UniFAR-FTUs huggingface dataset:
Observation and modelling of wave nonlinearities and infragravity waves in the nearshore

Wave nonlinearities have long been recognised as being among the main drivers of sediment transport in the coastal zone. However, there are still significant errors in the prediction of this transport associated, partially due to inaccurate predictions of the velocity nonlinearities. The infragravity waves, which coexist with the short waves in the coastal zone, are long-period waves (with 20-200 s) associated to the short-wave groups. Their generation, propagation and dissipation mechanisms are already reasonably well understood, but their influence on sediment transport is still very poorly characterised. In order to (i) improve current predictions of velocity nonlinearities and (ii) investigate the role of infragravity waves in sediment transport, new experiments were carried out both in a fixed-bed wave flume and in a light-weight-sediment wave flume. The physical-modelling data set is used in combination with field data and numerical simulations for studying both subjects. Existing parameterizations of velocity nonlinearities account only for the influence of local wave parameters (e.g. wave height, wave length and water depth), which leads to considerable estimation errors, especially of the maximal values of nonlinearity. This work shows that the velocity nonlinearities depend also on non-local wave parameters: (i) offshore wave steepness, (ii) offshore spectral bandwidth and (iii) beach slope. A new parameterization is proposed, which reduces by about 50% the root-mean-square error relatively to former parameterizations. The experimental results in the light-weight-sediment wave flume demonstrate that wave conditions with the same short-wave energy, but different low-frequency modulation, shape different equilibrium beach profiles. The influence of the infragravity waves on the sediment transport is confirmed and depends on two different mechanisms: (i) advection of the short-wave suspended sediment by the infragravity-waves, which is dependent on the infragravity-wave height and phasing with the short-wave groups and (ii) modulation of short-wave nonlinearities by infragravity-wave motion, both directly and indirectly, through water-depth modulation. Changes in the beachface morphology induced by infragravity waves are connected to beach-profile changes in the surf and inner-shoaling zones, highlighting the existent link between the different zones of the cross-shore beach profile.
Job Satisfaction and Organizational Commitment among Employees in the Sultanate of Oman

This study investigated the nature of relationships of demographic factors (age and job tenure) and job satisfaction facets with organizational commitment. The study also sought to determine the impact of demographic factors and job satisfaction facets on organizational commitment. A sample consists of 128 employees from service industry selected randomly. Employees were given a Job Descriptive Index (JDI) questionnaire and the Organizational Commitment questionnaire (OCQ). Pearson’s product moment correlation coefficient and multiple regression analyses were used to analyze the data. The Results of the study show that the mean values of job satisfaction and organizational commitment are at moderate side. A moderate significant positive relationship was found among job satisfaction facets, demographic factors, and organizational commitment. Supervision, pay, overall job satisfaction, age, and job tenure were the significant predictors of organizational commitment.
Effects of the wind–mass balance constraint on ensemble forecasts in the hybrid‐4DEnVar

Ensemble forecast covariance plays an important role in the hybridized background error covariance framework to improve the resultant analysis quality. However, a localization of ensemble samples is needed to fully take advantage of flow‐dependent error modes. In this regard, it is an interesting and practical issue as to which variables, composing the ensemble perturbation, the localization should be applied to. This study examines this issue in a boreal‐summer month by comparing two experimental sets: (a) a set of model variables, and (b) another set of model wind variables and unbalanced dry mass (temperature and surface pressure) used for the control variables. In the comparison, it is shown that imposing the wind–mass balance on the ensemble perturbation improves the Antarctic lower‐stratospheric temperature and tropical mid‐/lower‐tropospheric humidity. The nonlinear wind–mass balance relationship smooths an evolution of humidity forecast forced by the resultant analysis increments.
Dynamics of Zika Virus Model with Nonlinear Incidence and Optimal Control Strategies

We formulate and analyze Zika virus transmission model with three nonlinear forces of infection from infected mosquito , asymptomatic and symptomatic humans. The sensitivity inde xes of the associated parameters of the model with respect to the basic reproduction number are calculated to identify interventi on strategies for prevention and control of Zika virus. Mult iple time-dependent optimal controls are considered. The analysis based on the u se of optimal control theory made popular by Pontryagin’s ma xi um principle is carried out, and the resulting optimality syst em is quantitatively simulated to investigate the impact of the controls on the dynamics of Zika virus. In addition, the effects of non-line arity of the forces of infection and other key parameters on t he disease transmission are illustrated.
Exploring the impacts of using the artificial intelligence voice-enabled chatbots on customers interactions in the United Arab Emirates

Over the past decade, chatbots have experienced a significant increase inpopularity, especially since the outbreak of COVID-19. In the United ArabEmirates, most businesses have accelerated their digital transformation andare relying on chatbots as a primary way to interact with customers. However,many of these chatbots lack a voice input option for customers. This studyinvestigates the benefits and challenges of incorporating artificial intelligence(AI) voice-enabled chatbots into United Arab Emirates (UAE) businesses andhow this impacts customer experience. Qualitative research techniques wereused to gather information, and the results demonstrate that implementing AIchatbots with voice input and sentiment analysis features can enhance thecustomer experience by making it more efficient and convenient.Additionally, the study found that AI chatbots can ultimately save businessestime and money, and while they may reduce the need for human agents, theywill not replace them entirely. Finally, an implementation framework andsuggestions are provided for businesses that are interested in adopting AIvoice-enabled chatbots for customer interactions.
SSRNet: A Lightweight Successive Spatial Rectified Network With Noncentral Positional Sampling Strategy for Hyperspectral Images Classification

Deep learning methods have been proved outperforming the traditional methods in the field of hyperspectral image classification (HSIC). However, in pursuit of higher accuracy, HSIC networks have become deeper and more complex, resulting in excessive parameters and computational cost. To deploy neural networks on small platforms such as mobile or embedded devices, many studies have focused on lightweight HSIC networks. Currently, these works are dominated by patch-based networks, which suffer from the low accuracy caused by lightweight scale and slow inference speed derived from structural deficiencies of such networks. It is worth noting that full convolutional networks (FCNs) are able to achieve fast inference, but they tend to consume massive memory. To this end, this article proposes a novel lightweight HSIC method, which consists of a successive spatial rectified network (SSRNet) and a noncentral positional sampling (NCPS) strategy. SSRNet is composed of a local channel attention-based spectral FCN and several separable atrous spatial pyramid modules (SASPMs). These shallow subnetworks are concatenated together to progressively optimize their outputs by successive spatial rectified learning. For decreasing memory access cost (MAC), SSRNet makes little patches as input to perform patch-wise pixel-to-pixel learning. After training, SSRNet is able to adapt to any size of hyperspectral images and complete fast inference of the full image directly. In particular, the NCPS sampling strategy enables all labeled pixels to equally traverse all spatial positions of each training patch through the positional shift sampling, which effectively alleviates the sparse problem of hyperspectral semantic labels. Experiments upon three public benchmark datasets indicate that SSRNet is comparable to the state-of-the-art methods in classification accuracy with less than 0.15 M parameters and only occupies less than 10-MB memory for single forward computation. Moreover, SSRNet behaves significantly superior to traditional patch-based networks in terms of the inference speed. The source codes can be available from the website of https://github.com/Pancakerr/HSIC-platform.
A review of the federal role in regional economic development

Globalization is forcing regions throughout the nation to find new competitive niches in rapidly changing markets. The resulting quest for new economic engines is different in every region, driven by a region’s distinct economic assets and the specific markets it can tap. At the same time, economic experts have discovered a whole new set of strategies that offer the greatest potential in helping regions compete in the global marketplace. These new strategies focus more on the region itself, namely, helping entrepreneurs and skilled workers innovate and seize new market opportunities—an approach strikingly at odds with past strategies that often aimed at recruiting industrial facilities to a region. ; Amid these tectonic shifts in how regions grow their economies, federal policy for economic development goes on largely unchanged. The federal development effort is carried out through nine federal departments and five independent agencies, forming a sort of Rube Goldberg policy apparatus cobbled together over the past 50 years or so. There is no unifying purpose to this legion of federal programs. New questions are being raised about this policy apparatus, however. With big deficits in prospect for the federal budget, every federal program is under new budget scrutiny. The Administration’s recent proposal to redesign economic development grant programs in the federal budget was perhaps the first effort to call attention to how the federal government shapes regional economic development. It will not be the last. ; This report frames in broad terms what the federal government’s future role could be in regional economic development. Three steps are essential in framing that role. The first step is to confirm what regional development policy is today. The second step is to identify what makes regional economies grow in the 21st century. The final step is to consider how federal policy might change to help regions grow in the future.
Energy Management of Fuel Cell Hybrid Vehicle Based on Partially Observable Markov Decision Process

This paper presents a nonmyopic energy management strategy (EMS) for controlling multiple energy flow in fuel cell hybrid vehicles. The control problem is solved by convex programing under a partially observable Markov decision process-based framework. We propose an average-reward approximator to estimate a long-term average cost instead of using a model to predict future power demand. Thus, the dependence between the system closed-loop performance and the model accuracy for predicting the future power demand is decoupled in the energy management design for fuel cell hybrid vehicles. The energy management scheme consists of a real-time self-learning system, an average-reward filter based on the Markov chain Monte Carlo sampling, and an action selector system through the rollout algorithm with a convex programing-based policy. The performance evaluation of the EMS is conducted via simulation studies using the data obtained from real-world driving experiments and its performance is compared with three benchmark schemes.
Constipation and colonic transit in Mexican patients

We read with interest the paper by Carmona et al. in relation to patients consulting for constipation in Mexico. This paper provides further information to a problem in Mexico that has a prevalence ranging from 7 to 19%. However, we must comment on two aspects. First, the Rome III Questionnaire (RIIIQ) was used to diagnose Chronic Constipation (CC) and IBS-C, and although the authors state that they previously translated and validated the questionnaire, there is no mention of the methodology used in that process. Sperber has extensively discussed this critical step and has recognized that the process of translating and adapting a questionnaire into a different language/culture is time and resource consuming, but it is important so that the instrument can be comprehensible, but at the same time maintain the meaning and intent of its original version. Thus, unless the process is carried out successfully, the results of a study using that questionnaire may not be valid. The RIIIQ has been translated into Spanish in a multinational effort in Latin America -including Mexicoand Spain, and is currently under the validation process but not yet available for research studies in our country. Secondly, it is interesting to compare their results with that of a small study that we reported several years ago. We included 18 patients consulting for constipation (83% females); 11 (61%) fulfilled Rome I criteria for IBS-C, 3 (17%) for CC and 4 (22%) had a mixture of IBS-C + CC. These figures are similar to those reported in Carmona’s study (IBS-C: 60%, CC: 25%). We measured the colonic transit by administering 24 radiopaque markers during 3 consecutive days (24/3) and abdominal X-rays were given in days 4 and 7. The 72-hour was used as the upper limit for a normal transit, finding that 41% of our patients had normal and 59% prolonged transit times. This is in contrast to the subgroup of 23 patients in Carmona’s study that underwent a similar methodology (24/3), from which 59% had prolonged transit times. Although it looks as if we had more patients with a normal transit (59 vs. 39%), these numbers are not different (X: p= 0.218). However, patients in Carmona’s study were selected as only those with a prolonged screening transit (24 markers/1 day) underwent the 24/3 evaluation. The fact that 39% (9/23 patients) of those with a prolonged 24/1 had a normal transit when undergoing the 24/3 methodology, is an unexplained inconsistency. This finding suggests that patients were under different conditions during both evaluations or that the screening methodology is not a reliable one, which needs to be elucidated. Finally, it is worth mentioning that in our study patients with prolonged transit reported lower scores on the physical component subscale of the SF-36 compared to those with normal transit. In contrast, the mental component was lower in those with both IBS-C + CC vs. IBS-C, suggesting that a prolonged colonic transit can impair quality of life.
Logarithmic Sobolev inequalities for Moebius measures on spheres

Abstract In this paper, using the method in [1], i.e., reduce Moebius measures μ x n {\mu_{x}^{n}} indexed by | x | < 1 {|x|<1} on spheres S n - 1 {S^{n-1}} ( n ≥ 3 {n\geq 3} ) to one-dimensional diffusions on [ 0 , π ] {[0,\pi]} , we obtain that the optimal Poincaré constant is not greater than 2 n - 2 {\frac{2}{n-2}} and the optimal logarithmic Sobolev constant denoted by C LS ⁢ ( μ x n ) {C_{\rm LS}(\mu_{x}^{n})} behaves like 1 n ⁢ log ⁡ ( 1 + 1 1 - | x | ) {\frac{1}{n}\log(1+\frac{1}{1{-}|x|})} . As a consequence, we claim that logarithmic Sobolev inequalities are strictly stronger than L 2 {L^{2}} -transportation-information inequalities.
Sorting, Prices, and Social Preferences

What impact do social preferences have in market-type settings where individuals can sort in response to relative prices? In four laboratory experiments, we show that sorting and prices strongly affect which social-preference types enter different economic settings and, as a result, the observed sharing behavior. Sorting allows us to distinguish between individuals who share because they obtain utility from sharing and those who share but obtain higher utility if not asked to share. We find that costless sorting significantly reduces the number of subjects who share, even when they have a strong motivation for sharing (positive reciprocity). Relative prices significantly alter the compositional effect of sorting: If the sharing environment is subsidized, we observe additional entry, but only by the least generous sharers who keep most of the subsidy for themselves. As a result, a small subsidy significantly reduces the average amount shared by those who enter. If, instead, the sharing environment is costly relative to the outside option, only individuals who obtain a high utility from sharing choose to participate. This yields an environment in which entry is low but significantly more is shared by those who enter. We discuss the implications for real-world giving situations with sorting. * We thank Jordi Brandts, Colin Camerer, Stefano DellaVigna, Hank Farber, Ernst Fehr, Glenn Harrison, Larry Katz, Botond Köszegi, Klaus Schmidt, Georg Weizsäcker, as well as participants at several seminars and conferences for helpful comments. We also thank Jason Dana, Scott Rick, Aniol Llorente and David Rodriguez for help in conducting the experiments and the Pittsburgh Experimental Economics Laboratory (PEEL), the Laboratori d'Economia Experimental (LEEX), and the Berkeley Experimental Social Science Laboratory (XLab) for access to their resources.
‘Hold the course(s)!’ – a qualitative interview study of the impact of pandemic-triggered contact restrictions on online instruction in community-based family medicine teaching

The COVID-19 pandemic has been identified as a catalyst for the digitalization of medical education. Less is known about the specific impact of the pandemic on decentralized, community-based education, such as in General Practitioner practices. The aim of this study is to understand the impact of the digital transformation process, triggered by the COVID-19 pandemic. As, family medicine courses involve both university-based and community-based teaching, this study focuses the mode and quality of instruction and instructors in family medicine teaching. A qualitative interview study was conducted. The participants (N = 12) of a multi-perspective Quality Circle in family medicine teaching were interviewed twice: first, in 2019, about digitalization in family medicine teaching in Tübingen, Germany, not yet aware of the global changes and local transformation that would take place shortly thereafter. Second, in a follow-up interview in 2020 about the transition process and digitalization following the impact of contact restrictions during the pandemic. Grounded Theory was used as a qualitative research approach to analyze the complex processes surrounding this transformation. By analyzing the interviews with various stakeholders of community and university-based teaching, a model for the digital transformation process of family medicine teaching at the University of Tübingen in response to an external stimulus (the pandemic) was developed. It involves six chronological steps: “The calm before the storm,” “The storm hits,” “All hands on deck,” “Adrift,” “Reset course,” and “The silver lining. ” This model seeks to understand the process of digital transformation and its impact on the teaching institution (medical faculty of the University of Tübingen, Institute for General Practice and Interprofessional Health Care) and instructors from an integrated perspective and thereby critically revisits prior concepts and opinions on the digitalization of medical teaching. Insights gained are presented as key messages.
Mesosphaerum suaveolens Essential Oil Attenuates Inflammatory Response and Oxidative Stress in LPS-Stimulated RAW 264.7 Macrophages by Regulating NF-κB Signaling Pathway

Mesosphaerum suaveolens (L.) Kuntze (Syn. Hyptis suaveolens (L.) Poit. ) is a wild essential-oil-bearing plant having multiple uses in traditional medicine, perfumery, food, agriculture, and pharmaceutical industries. The present paper is the first report on the in vitro anti-inflammatory effects of the leaf essential oil of M. suaveolens (MSLEO) and unravels its molecular mechanism in LPS-stimulated RAW 264.7 macrophage cells. GC-MS analysis of the essential oil (EO) isolated from the leaves by hydro-distillation led to the identification of 48 constituents, accounting for 90.55% of the total oil, and β-caryophyllene (16.17%), phyllocladene (11.85%), abietatriene (11.46%), and spathulenol (7.89%) were found to be the major components. MSLEO treatment had no effect on the viability of RAW 264.7 cells up to a concentration of 100 μg/mL, and the EO was responsible for a reduction in proinflammatory cytokines like IL-6, IL-1β, and TNF-α, a decrease in intracellular ROS production, and the restoration of oxidative damage by elevating the levels of endogenous antioxidative enzymes like CAT, SOD, GPx, and GSH. RT-qPCR analysis indicated that MSLEO reduced the mRNA expression levels of iNOS and COX-2 as compared to the LPS-induced group. In addition, a confocal microscopy analysis showed that MSLEO inhibited the translocation of NF-κB from the cytosol to the nucleus. The results of this experiment demonstrate that MSLEO possesses significant anti-inflammatory potential by preventing the activation of NF-κB, which, in turn, inhibits the downstream expression of other inflammatory mediators associated with the activation of the NF-κB pathway in LPS-induced RAW 264.7 cells. Thus, the leaf essential oil of M. suaveolens may prove to be a promising therapeutic agent for the treatment of inflammation, and targeting the NF-κB signaling pathway may be considered as an attractive approach for anti-inflammatory therapies.