Miracle-dz/UniFAR-FTUs huggingface dataset:
Economic Development and Environmental Sustainability in the GCC Countries: New Insights Based on the Economic Complexity

The economic development and environmental sustainability nexus have long been a fiercely debated issue. Researchers have widely acknowledged the environmental Kuznets curve (EKC) hypothesis when evaluating this relationship. Recently, an emerging strand of research examined the EKC through the lens of the Economic Complexity Index (ECoI) as a broader measure of economic development. However, empirical evidence of the index’s environmental impact is still limited. Despite its growing prominence, no prior research has been conducted in the Gulf Cooperation Council (GCC) using the ECoI, particularly in the EKC context. Furthermore, research comparing the ECoI differentiated impacts on Ecological Footprint and Carbon Dioxide (CO2) emissions is largely lacking. Extending on this line of research, our investigation intends to ascertain the influence of ECoI, income, globalization as well as non-renewable energy consumption on two dominant environmental pressure metrics: CO2 emissions and ecological footprint per capita (EFpc) within the EKC hypothesis context in six GCC countries during 1995–2018. To this end, Pedroni’s cointegration approach was conducted to examine the long-term association between variables; cointegration coefficients were analyzed using Dynamic and Fully modified OLS. Our investigation indicates the emergence of an inverted U-shaped link between ECoI and environmental sustainability in the GCC region for both CO2 emissions and EFpc. Furthermore, according to the individual country analysis, our findings demonstrate that the EKC hypothesis is sensitive to both the environmental degradation indicator used and the country analyzed; such that the quadratic link incorporating ECoI is confirmed for Saudi Arabia, Bahrain, United Arab Emirates, and Kuwait when EFpc is employed. In comparison, it holds for Kuwait, Oman, and Qatar when CO2 emissions are used. Moreover, the findings show that income per capita and non-renewables consumption significantly harm environmental sustainability, however, in terms of EFpc only. In contrast, through its three sub-dimensions, globalization contributes to the environmental burden by increasing both EFpc and CO2 emissions. These conclusions emphasize the economic complexity’s dominant role in mitigating environmental pollution in GCC beyond a certain threshold. Finally, the paper reaches a concise set of implications. Among the foremost, the GCC nations could enhance their environmental sustainability by diversifying their energy sources and increasing reliance on renewable sources, encouraging investment in carbon-reduction technologies, converting their economy from energy-intensive to technology-intensive, as well as imposing strict environmental laws to enable globalization to improve environmental quality.
Lymphocyte Subsets and Pulmonary Nodules to Predict the Progression of Sarcoidosis

The search for biological markers, which allow a relatively accurate assessment of the individual course of pulmonary sarcoidosis at the time of diagnosis, remains one of the research priorities in this field of pulmonary medicine. The aim of our study was to investigate possible prognostic factors for pulmonary sarcoidosis with a special focus on cellular immune inflammation markers. A 2-year follow-up of the study population after the initial prospective and simultaneous analysis of lymphocyte activation markers expression in the blood, as well as bronchoalveolar lavage fluid (BALF) and lung biopsy tissue of patients with newly diagnosed pulmonary sarcoidosis, was performed. We found that some blood and BAL fluid immunological markers and lung computed tomography (CT) patterns have been associated with a different course of sarcoidosis. We revealed five markers that had a significant negative association with the course of sarcoidosis (worsening pulmonary function tests and/or the chest CT changes)—blood CD4+CD31+ and CD4+CD44+ T lymphocytes, BALF CD8+CD31+ and CD8+CD103+ T lymphocytes and a number of lung nodules on chest CT at the time of the diagnosis. Cut-off values, sensitivity, specificity and odds ratio for predictors of sarcoidosis progression were calculated. These markers may be reasonable predictors of sarcoidosis progression.
System Budgeting to System Realisation - A 22nm FDSOI 5G mmWave Front-End Module (FEM) Perspective

A comprehensive top-down system design methodology is presented and supported with a design of a Front-End Module (FEM) for 5G mobile applications targeting 24GHz-29GHz. While adopting package and PCB floor-planning and thermal challenges early in the design, a link budget analysis of a FEM in a system simulator followed by an implementation in GlobalFoundries' 22nm FDSOI process is reported with a focus on novel architectures to address system constraints. The FEM shows an excellent correlation between simulations and measurements and is further characterized post silicon by applying actual 5G signals in a real-time measurement mimicked simulation environment. A unified environment for co-designing and analysing the IC and package system is also described.
On the utility of fast transforms in parity-mixed PDEs: Part II. Application to conned rotating convection

To demonstrate the utility of the parity ltering methods described Vasil, Brummell, & Julien (2007, J. Comp. Phys., ? , ? ), we introduce a numerical code designed to solve for Rayleigh-B enard convection in a conned rotating box. That is, using a straightforward pseudospectral framework, we incorporate techniques for eciently computing parity-mixed Coriolis accelerations in a time-dependent numerical solver. The goals of the presented numerical code are to provide a tool to investigate aspects of conned rotating convection experiments with a simple model and to illustrate the application of parity ltering.
EMPIRICAL EVIDENCE ON ROLE OF SELF-EFFICACY UPON COMMITMENT OF TEACHERS IN HIGHER EDUCATION INSTITUTIONS

Obviously, the self-efficacy and commitment of instructors in advanced education institutions over the Sothern District Universities of KP have not been discussed nor measured. Of the few reviews concerning this subject, none has focused on individual factors like age, carnal orientation, workload, experience, instruction etc., also the impact they may have on the dedication of HEI's instructors. The inspiration driving current review is to evaluate how much age, sex, workload, experience, education, and self-efficacy influence the commitment of HEI's instructors in the Sothern District Universities of KP. This study has used quantitative way of data collection. This research study has population which consists of all the male and female teachers working in the “Gomal University, D.I. Khan, Khushal Khan Khattak University Karakand University of Science & Technology Bannu, in Khyber Pakhtunkhwa, Pakistan. Sample of 162 was selected as number of questionnaires received responded. The results of this study demonstrated that there was a significant association between job commitment and self-efficacy. There is affirmative association among teacher’s commitment and self-efficacy; it means that when self-efficacy increase, the organizational commitment will also increase. The outcomes of study may help in recruitment process by giving the required standard attributes for selection of new employees, such as behavior, attitudes etc. The study may help the future research by inspiring them to evaluate their current environment and situations, thus questioning the status quo. From this study it is understood that self-efficacy is important and it increases the level of commitment of employee to the job and organization.
Graph Convolution Based Efficient Re-Ranking for Visual Retrieval

Visual retrieval tasks such as image retrieval and person re-identification (Re-ID) aim at effectively and thoroughly searching images with similar content or the same identity. After obtaining retrieved examples, re-ranking is a widely adopted post-processing step to reorder and improve the initial retrieval results by making use of the contextual information from semantically neighboring samples. Prevailing re-ranking approaches update distance metrics and mostly rely on inefficient crosscheck set comparison operations while computing expanded neighbors based distances. In this work, we present an efficient re-ranking method which refines initial retrieval results by updating features. Specifically, we reformulate re-ranking based on Graph Convolution Networks (GCN) and propose a novel Graph Convolution based Re-ranking (GCR) for visual retrieval tasks via feature propagation. To accelerate computation for large-scale retrieval, a decentralized and synchronous feature propagation algorithm which supports parallel or distributed computing is introduced. In particular, the plain GCR is extended for cross-camera retrieval and an improved feature propagation formulation is presented to leverage affinity relationships across different cameras. It is also extended for video-based retrieval, and Graph Convolution based Re-ranking for Video (GCRV) is proposed by mathematically deriving a novel profile vector generation method for the tracklet. Without bells and whistles, the proposed approaches achieve state-of-the-art performances on seven benchmark datasets from three different tasks, i.e., image retrieval, person Re-ID and video-based person Re-ID.
The Correct Hot Carrier Degradation Model

A model for the apparent negative activation energy that is observed when measuring hot carrier degradation has alluded the reliability physics community for several decades. The phenomenon is observed from changes in threshold voltage and mobility degradation and has been distinct from negative bias temperature instability (NBTI) in that the effect consistently increases with lower temperature. This effect is so consistent that an actual negative activation energy can be measured and has been reported from -0.1 to -0.4eV. We propose a model that considers the energetics of HCI that allows the mechanism to be modeled consistently for Silicon and GaN.
Chimera states in spatiotemporal systems: Theory and Applications

In this paper, we propose a retrospective and summary on recent studies of chimera states. Chimera states demonstrate striking inhomogeneous spatiotemporal patterns emerging in homogeneous systems through unexpected spontaneous symmetry breaking, where the consequent spatiotemporal patterns are composed of both coherence and incoherence domains, respectively characterized by the synchronized and desynchronized motions of oscillators. Since the discovery of chimera states by Kuramoto and others, this striking collective behavior has attracted a great deal of research interest in the community of physics and related interdisciplinary fields from both theoretical and experimental viewpoints. In recent works exploring chimera states, rich phenomena such as the spiral wave chimera, multiple cluster chimera, amplitude chimera were observed from various types of model systems. Theoretical framework by means of self-consistency approach and Ott–Antonsen approach were proposed for further understanding to this sym.
Thrombectomy for Anterior Circulation Stroke in a Witnessed Late Time Window Versus Early Time Window

Introduction Thrombectomy is the standard treatment for anterior circulation stroke due to large vessel occlusions in a late time window (6 to 24 hours) for patients selected based on perfusion imaging. Most patients treated in late time window studies presented as unwitnessed or wake-up strokes. Whether patients presenting with unwitnessed stroke have an actual time window greater than 6 hours is unclear. The aim of this study was to assess the outcomes of thrombectomy in the treatment of patients presenting with anterior circulation large vessel stroke in an actual late time window of more than 6 hours. Methods This single-center registry of thrombectomy in the treatment of stroke caused by anterior circulation large vessel occlusions (LVOs) included 430 patients treated between 2011 and 2019. Patients were divided into 2 groups: an early time window (≤ 6 hours) group and a late time window group (> 6 hours). Results Outcomes of the early and the late time window groups, respectively, were recanalization of 86.8% vs 82.7% (P = .29), symptomatic intracranial hemorrhage of 8.2% vs 5.7% (P = .40), good clinical outcome of 45.4% vs 41.3% (P = .46), and mortality of 20.2% vs 25% (P = .30) at 3 months. Conclusions Thrombectomy for anterior circulation large vessel occlusions after 6 hours of symptoms onset seems to be as safe and effective as the standard thrombectomy within 6 hours from symptoms onset, even without perfusion analysis. Randomized trials are needed to confirm these findings.
On the Galois Theory of Differential Fields

1. Summary. In a preceding paper [7] there was presented a Galois theory, for a certaini kind of differenitial field extension called strongly normal. The Galois group of a strongly normal extension is enidowed with a structure very much like that of a group variety, as studied by Weil [14]. In the present paper the study of such Galois groups is renewed for the purpose of clarifyinig their conniection with group varieties on the onie hand and with strongly normal extensiolis on the other. Consider a differential field S of characteristic 0 with algebraically closed field of constants W, and a strongly normal extension A of 5; suppose given a universal extension 5* of X, and denote the field of constants of 5* by AV. For reasons of convenience we use the term "Galois group of & over 7" for the group of all (ipso facto strong) isomorphisms of & over 5, and not for its subgroup conisisting of all automorphisms of & over J. The field R*, being algebraically closed anid of finite transeendence degree over A, may be used as a uniiversal domain for algebraic geometry (Weil [13], ch. I, ? 1); it is the group varieties of this algebraic geometry, defilned over E and slightly generalized to permit group varieties which are reducible (i. e. which have more thani one comiponent), whieh we consider. By an "algebraic group" we miean either a Galois group or a group variety as above. Chapter I is primarily a study of " rational " homomorphisms of algebraic groups into algebraic groups; these seenm to be the homomorphismls appropriate to the consideratioin of algebraic groups. Ratioinal homomorphisms of group varities inlto group varieties, without restrictioin to fields of eharacteristic 0, were considered by Weil [14] (who omitted the adjective "rational"). Specializing the coneept of rational homomorplhism we obtaini that of birational isomorphisin. In Chapter II it is showni that every Galois group is birationally isomorphic to a group variety, and conversely that every irreducible group variety is birationallv isomorphic to a Galois group; this
Comparative study of Deep Learning Models for Covid 19 Diagnosis

The Covid 19 pandemic that started a couple of years ago has had a devastating effect on mankind across the globe. The disease had no known treatment. Early detection and prevention was very important to curtail the effects of the Pandemic. In this work two deep learning models the RestNet and the models are proposed for diagnosing Corona from chest X-rays and CT scans. The models were trained with publicly available data sets of covid and non covid images. It has been found that Inception V3 performs better than ResNet for chest x-rays and RestNet performs better for CT Scans. The performance of the RestNet is found to be similar for both the chest x-rays and CT scans datasets.
The Impact of Zooplankton Calcifiers on the Marine Carbon Cycle

Shelled pteropods and planktic foraminifers are calcifying zooplankton that contribute to the biological carbon pump via the sinking of their calcareous shells. However, their importance for regional and global plankton biomass and carbon fluxes is not well understood. Here, we modeled global annual patterns of pteropod and foraminifer total carbon (TC) biomass and total inorganic carbon (TIC) export fluxes over the top 200 m using five species distribution models (SDMs). An extended version of the MARine Ecosystem DATa (MAREDAT) of zooplankton abundance observations was used to estimate the biomass of both plankton groups. We found hotspots of mean annual pteropod biomass in the high Northern latitudes and the global upwelling systems, and in the high latitudes of both hemispheres and the tropics for foraminifers. This largely agrees with previously observed distributions. For both groups, temperature is the strongest environmental correlate, followed by chlorophyll‐a. We found mean annual standing stocks of 52 Tg TC (48 to 57 Tg TC) and 0.9 Tg TC (0.6 to 1.1 Tg TC) for pteropods and foraminifers, respectively. This translates to mean annual TIC fluxes of 14 Tg TIC yr−1 (9 to 22 Tg TIC yr−1) for pteropod shells and 11 Tg TIC yr−1 (3 to 27 Tg TIC yr−1) for foraminifer tests. These results are similar to previous estimates for foraminifers, but approximately a factor of ten lower for pteropods. Pteropods contribute 0.2%–3.2% and foraminifers 0.1%–3.8% to global surface carbonate fluxes. Including global coccolithophore fluxes, this leaves 40%–60% of the global carbonate fluxes unaccounted for. Our figures are likely lower‐bound estimates due to sampling data characteristics and uncertainty associated with organism growth rates.
Using successive difference replication for estimating variances

Fay and Train (1995) present a method called successive difference replication that can be used to estimate the variance of an estimated total from a systematic random sample from an ordered list. The estimator uses the general form of a replication variance estimator, where the replicate factors are constructed such that the estimator mimics the successive difference estimator. In the paper, we establish the conditions for successive difference replication to be equivalent to successive difference estimator. We also discuss the impact of using a subset of the replicates as an estimator instead of the full set of replicates that is needed for the successive difference replication estimator to be equivalent to the successive difference estimator. Fay and Train (1995) present a method called successive difference replication (SDR) that can be used to estimate the variance of an estimated total from a systematic random sample from an ordered list. The estimator uses the general form of a replication variance estimator, where the replicate factors are constructed such that it mimics the successive difference (SD) estimator. We add to the methodology the idea of a connected loop, which helps us understand how row assignments work. We also provide a method for using SDR with a Hadamard matrix that is smaller than the sample size. The method also helps us understand the consequences of using a reduced set of replicates with SDR. The paper begins by reviewing systematic random sampling from an ordered list. Next, we review the SD estimator and how it is suited for variance estimation of systematic random samples. The main section of the paper presents two theorems that provide conditions for the SDR estimator to be equivalent to the SD estimator. The paper ends with empirical examples that examine alternative row assignments and the suitability of using of a reduced set of replicates. 1.1 Review of Systematic Sampling For the remainder of our discussion sys will be used as shorthand for systematic random sampling from an ordered list. We abbreviate sys this way because systematic sampling from an unordered or randomly ordered list, can be shown to be equivalent to simple random sampling (Madow and Madow 1944). For our discussion, we focus solely on equal probability selection and selecting sample in only one dimension. Other names for sys include "linear systematic sampling" (Murthy and Rao 1988) and "1-in-a sampling" (Gregoire and Valentine 2008). The sample design sys is easy to implement and can be very efficient compared with simple random sample without replacement sample design (srswor). To implement sys, we first sort the universe by a variable that is known for every unit in the universe. With a defined sampling interval k > 0, we randomly generate r from a uniform distribution on the interval (0, r ). The
COVID-19 Incidence and Disease Course Among Patients at an Allergy Department

Background Since the coronavirus pandemic in 2020, there is not much reported about the disease course of COVID-19 in patients with allergic diseases. Objectives The aim of this study was to investigate the cumulative incidence and severity of COVID-19 among patients from the allergy department compared with the general Dutch population and people from their household. Design We conducted a comparative longitudinal cohort study. Methods In this study patients of the allergy department were included with their household members as a control group. Data from the beginning of the pandemic were systematically obtained through questionnaires by telephonic interviews and retrieved from electronic patient files between October 15, 2020 and January 29, 2021. Main outcomes were confirmed SARS-CoV-2 infection, disease duration, hospitalization, intensive care admission, and mortality. Questions regarding applied social distancing measures were inventoried as well. Results Three hundred and eighty nine patients (median age 39.1 (18.7-84.7) years, 69.9% female) and 441 household members (median age 42.0 (18.0-91.5), 44.1% female) were included. The cumulative COVID-19 incidence in patients was higher compared with the general population (10.5% vs 5.6%, P < .001). In total, 41 (10.5%) patients attending the allergy clinic compared to 38 (8.6%) household members were infected with SARS-CoV-2 (P = .407). Median disease duration was 11.0 (0.0-61.0) days in patients compared to 10.5(1.0-232.0) days in household members (P = .996). Conclusion The cumulative COVID-19 incidence in patients from the allergy cohort was higher compared with the general Dutch population, but similar compared with household members. There was no difference in symptoms, disease duration, or hospitalization rate between the allergy cohort and their household members.
Bi-LSTM-LDA— A Topic Modelling Technique to Identify the Relevant Law for Indian Legal Cases

AI had already changed the way of approaching the problem in many industries. Now it is changing the way of practicing the law. Already many models were created to predict the outcome of a case given the facts of the case by analysing the prior cases. Before any model starts predicting the outcome of the case, it is most important to identify the section code since punishment or judgment of a case depends on the law or article under which a specific case comes under. As far as Supreme Court of India is concerned, the section code plays the major role. Hence this paper proposes Bi-LSTM-LDA model that identifies the relevant law given only the facts of the case. The proposed model is ensemble of Bi-LSTM and LDA (Latent Dirichlet Allocation) algorithms that assigns section code given the facts of the case. The proposed model is compared with existing neural topic modelling models and the results shows Bi-LSTM-LDA outperforms than others with an accuracy of 96%.