Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of first order systems on manifolds without boundary: A spectral theoretic approach

In this thesis we study first order systems of partial differential equations on manifolds without boundary. The thesis is, in part, based upon my publications. The importance of analysing first order systems can be seen directly from the special case, namely, the study of Dirac-type operators. In the first part, I assume my manifolds to be 4-dimensional and consider the simplest non-trivial first order linear differential operators on such manifolds. I give a systematic way of extracting the geometric content encoded within these operators. More importantly, a new concept called covariant subprincipal symbol is defined and further employed in the spectral analysis developed in the next chapter. In the second part, by applying the hyperbolic equation method and using Fourier Tauberian theorems, I establish the relationship between the Weyl coefficients of an elliptic self-adjoint first order differential operator and the residues of the corresponding eta function, which can be easily generalised to the pseudo-differential case. The special case of this relationship is examined explicitly and it is combined with the analysis of the first part. The third part of the thesis involves a detailed analysis of the massless Dirac operator, whereas the massive case is also investigated with the help of the abstract adjugation operation on operators. In contrast to the asymptotics of large eigenvalues, there has not been a systematic and robust way of analysing the asymptotics of small eigenvalues. The last part of the thesis gives a rigorous perturbation analysis of the massless Dirac operator on a topological 3-sphere, which complements the results obtained in the second part. In particular, explicit asymptotic formulae for small eigenvalues are derived for general perturbations of the standard metric. These asymptotic formulae are tested on generalised Berger spheres for which we have explicit expressions for eigenvalues.
Subselection of seasonal ensemble precipitation predictions for East Africa

The Greater Horn of Africa (GHA) is highly vulnerable to climate and weather hazards such as drought, heat waves, and floods. There is a need for accurate seasonal forecasts to prepare for risks (such as crop failure and reduced grazing opportunities) and take advantage of favorable conditions (rains arrive on time and where they are needed) when they arise. As such, information at finer spatial scales than current state‐of‐the‐art global prediction models can provide is needed. Dynamical downscaling is one method employed to obtain information at finer scales. However, providers of seasonal forecasts over the GHA are hampered by limited computational resources and time constraints that restrict the number of global model ensemble members that can be downscaled. Some ensemble subselection criteria must be employed. Currently, providers take an uninformed (or random) approach. Specifically, forecasters simply take the first ensemble member of the global model seasonal forecast ensemble. However, recent work, focused on decadal prediction, has shown that subselecting global model ensemble members in an informed way, that is, according to their ability to reproduce key features of the climate system, results in improved predictions. This emerges from the fact that the climate system is likely more predictable than our models would have us believe. Seeing an opportunity for improvement, we apply the same thinking to the seasonal context and assess several procedures for subselecting ensemble members from seasonal predictions with exchangeable members. Such informed subselections have the potential to take advantage of information in an ensemble of global simulations that might be missed by random selection. Three subselection methods are investigated, with a focus on seasonal predictions for rainfall over GHA. We demonstrate that informed subselection leads to systematically higher skill than random subselection. We find that (1) for small subsample sizes, such as would be chosen for dynamical downscaling and/or downstream impact modeling, informed subselection nearly always outperforms random subselection, (2) subselecting based on well‐known teleconnections benefits those seasons in which such pathways are active, such as OND and JJAS, and (3) k$$ k $$ ‐means subselection outperforms random selection for small ensemble sizes throughout all seasons, including the notoriously difficult to predict MAM season. These techniques require only input that is available at the time of the forecast release and are easy to apply operationally.
Geometric Parameterisation in Finite Element Models of Femoroacetabular Impingement

Abnormal bony morphology is a factor implicated in hip joint soft tissue damage and an increased lifetime risk of osteoarthritis. One geometric feature causing impingement and thus resulting in such damage is a bony lump on the femoral neck, known as a cam deformity. A three-dimensional geometric parameterisation system was developed to capture key variations in the femur and acetabulum of subjects with clinically diagnosed cam deformity. Novel quantitative measures of the size and position of cams were taken and used to assess differences in morphological deformities between males and females. The precision of the measures was sufficient to identify differences between subjects that could not be seen using two-dimensional imaging; cams were found to be more superiorly located in males than in females. As well as providing a means to distinguish between subjects more clearly, the geometric hip parameterisation facilitated flexible and rapid automated generation of a range of hip geometries including cams. These were used to develop finite element models. Patient-specific parametric finite element models of hips under impingement conditions were verified with comparison to their patient-specific segmentation-based equivalents. The parameterisation system was then used to generate further models to investigate the effects of bone morphology on tissue strains. This demonstrated that a combination of cam location and extent affect impingement severity, highlighting the importance of reporting the full three-dimensional geometry used for parametric models.
Exact bright and dark solitary wave solutions of the generalized higher-order nonlinear Schrödinger equation describing the propagation of ultra-short pulse in optical fiber

Considering linear and nonlinear optical effects like group velocity dispersion, higher-order dispersion, Kerr nonlinearity, self-steepening, stimulated Raman scattering we obtain a higher-order nonlinear Schrödinger equation describing the propagation of ultra-short pulse in optical fiber. We construct exact bright and dark solitary wave solutions of the generalized obtained equation, obeying to some constraint relations between coefficient’s equation via the Bogning-Djeumen Tchaho-Kofané method (BDKm). The generalized higher-order nonlinear Schrödinger equation is obtained by affecting coefficients ni(i = 0, 1, ., 5) to different terms of non modified equation. New solutions are obtained, and the term or higher-order dispersion can be considered as the new selector of solitary wave-type propagating in the higher-order nonlinear optical fiber.
Adaptive neural tracking control for nonstrict‐feedback nonlinear systems with unknown backlash‐like hysteresis and unknown control directions

In this paper, a neural network–based adaptive tracking control problem is investigated for a class of nonstrict‐feedback nonlinear systems in the presence of unknown backlash‐like hysteresis nonlinearity, unmodeled dynamics, and unknown control directions. A state feedback controller is developed for the considered system by applying the adaptive backstepping technique and neural networks. The design difficulties exhibited in this paper due to unmodeled dynamics, unknown control directions, and nonstrict‐feedback form are handled by resorting to a dynamic signal, a Nussbaum function, and the variable separation approach, respectively. It is shown that the designed adaptive controller can guarantee that all the signals remain bounded and that the desired signal can be tracked with a small domain of the origin. A numerical example and an example of a real plant for a one‐link manipulator are provided to show the feasibility of the newly designed controller scheme.
Big data and partial least‐squares prediction

We give a commentary on the challenges of big data for Statistics. We then narrow our discussion to one of those challenges: dimension reduction. This leads to consideration of one particular dimension reduction method—partial least‐squares (PLS) regression—for prediction in big high‐dimensional regressions where the sample size and the number of predictors are both large. We show that in some regression contexts single‐component PLS predictions converge at the usual root‐n rate as (n,p)→∞ regardless of the relationship between the sample size n and number of predictors p. Asymptotically, PLS predictions then behave as regression predictions in the usual context where p is fixed and n→∞. These results support the conjecture that PLS regression can be an effective method for prediction in big high‐dimensional regressions. The Canadian Journal of Statistics 46: 62–78; 2018 © 2017 Statistical Society of Canada
Overview of the NOAA/ESRL Federated Aerosol Network

To estimate global aerosol radiative forcing, measurements of aerosol optical properties are made by the National Oceanic and Atmospheric Administration (NOAA) Earth System Research Laboratory (ESRL)’s Global Monitoring Division (GMD) and their collaborators at 30 monitoring locations around the world. Many of the sites are located in regions influenced by specific aerosol types (Asian and Saharan desert dust, Asian pollution, biomass burning, etc.). This network of monitoring stations is a shared endeavor of NOAA and many collaborating organizations, including the World Meteorological Organization (WMO)’s Global Atmosphere Watch (GAW) program, the U.S. Department of Energy (DOE), several U.S. and foreign universities, and foreign science organizations. The result is a long-term cooperative program making atmospheric measurements that are directly comparable with those from all the other network stations and with shared data access. The protocols and software developed to support the program facilitate participation in GAW’s atmospheric observation strategy, and the sites in the NOAA/ESRL network make up a substantial subset of the GAW aerosol observations. This paper describes the history of the NOAA/ESRL Federated Aerosol Network, details about measurements and operations, and some recent findings from the network measurements.
Enhanced Distributed Resource Selection and Power Control for High Frequency NR V2X Sidelink

The 3GPP has standardized the 5G New Radio (NR) sidelink (SL) technology that enables direct device-to-device communications. SL will be key in realizing several high-speed, low-latency vehicle-to-everything (V2X) applications, such as automated driving. To meet these applications’ high data rate requirements, operating SL at mmWave and sub-THz frequencies will be essential. However, the current SL design primarily caters to sub-6 GHz frequencies and does not consider the directionality of transmissions at high frequencies. For Mode 2 of SL, where SL UEs perform sensing-based autonomous resource selection, this results in hidden node interference due to the transmit (Tx) UE’s inability to sense transmissions not aligned with the primary direction of communication. We propose paired transmission and sensing of sidelink control information (SCI), whereby SL Tx UEs transmit and receive SCI in an additional paired direction directly opposite to the primary direction. This helps eliminate hidden node interference while reducing the number of exposed nodes. Simulations in NR V2X highway deployments show that the paired scheme improves the average packet reception ratio (PRR) by 27% over the state-of-the-art at the highest traffic loads. We also propose enhanced transmit power control strategies to minimize interference between concurrent SL transmissions. This enables better resource reuse and further improves performance, with our combined solution achieving at least 95% average PRR in all scenarios. Finally, we present a stochastic geometry-based analytical model for a single-lane highway V2X network and validate it against simulation results. Our model provides insights into the reliability and capacity of high-frequency SL networks.
Event‐triggered control for linear systems with actuator saturation and disturbances

This study considers the event-triggered control problem for linear systems with actuator saturation and external disturbances. First, owing to the impact of the switching event-triggered mechanism, a new method is derived to design the controller and the event-triggered parameters such that the estimation of the domain of attractions is maximised. Compared with the existing results, a larger minimum inter-event interval is presented by combining with a convex analysis approach in stability analysis. Then, this method is extended to the system with disturbances to both guarantee the stability and reject disturbances. Finally, simulation examples are given to demonstrate the superiority of the proposed method.
Study of Interference Immunity of MMSE, ZF, and ML Demodulation Algorithms in MIMO Systems Under High-Order Modulation

This report analyzes the error performance of MIMO technology in wireless networks when using high-level modulation. Different reception methods, such as Minimum Mean Squared Error Detection (MMSE), Zero Forcing (ZF), and Maximum Likelihood (ML) are considered. Curves of error performance for modulation methods QAM-16, QAM-256, QAM-1024, and QAM-4096 are obtained using a 2×2 antenna configuration. It is shown that at high modulation levels, the error performance of MMSE and ZF methods is practically the same, while ML provides significant energy gain compared to these methods. Additionally, it is shown that the gain of the ML method compared to linear MMSE and ZF methods decreases with increasing modulation order.
Differentiation characteristics of karst vegetation resilience and its response to climate and ecological restoration projects

In light of the recent pressure from global warming, extreme drought events, and deleterious human activity, the strength and long‐term change trends of vegetation in karst regions—in terms of their resistance to external disturbances—have not been studied systematically. Therefore, herein, we quantified the vegetation resilience and its nonlinear change trends in south China karst under different environmental gradients by measuring the lag‐1 autocorrelation to time‐series Normalized Difference Vegetation Index (1990–2018), clarifying the driving forces of vegetation resilience changes. It was shown that the vegetation resilience change in south China karst was not monotonous. In the first stage (pre‐2002), precipitation and warming promoted the increase of regional vegetation resilience (slope = −0.045, p < 0.0001). In the second stage (during 2002–2010), the increasing trend of vegetation resilience was not obvious and vegetation resilience was difficult to keep up with vegetation productivity, indicating the time‐lagged effect of ecological restoration projects to vegetation resilience. In the third stage (post‐2010), due to the continuous advancement of ecological restoration projects, vegetation resilience increased significantly and had the largest amplitude (slope = −0.128, p < 0.0001). Simultaneously, under different environmental gradients, vegetation resilience showed significant differentiation characteristics. In comparison to non‐karst regions, increases in the vegetation resilience were more obvious in karst regions especially in the post‐2010. With increases in the soil depth, the vegetation resilience exhibited an increasing trend, indicating its dependence on soil. At slopes >25°, the vegetation resilience increased most obviously and the resilience of meadows was the largest, which can be the preferred vegetation type for ecological restoration projects. This research provides another perspective to understand karst vegetation ecosystem and the results will facilitate the protection of karst ecosystems.
A Narrative Review of Osteoid Osteomas and an Audit on the Practice of CT-Guided Radiofrequency Ablation in the Northern Irish Population: A First in the Literature

Introduction Osteoid osteoma is a benign condition of the bone, usually affecting young males. This retrospective study explores the demographics of osteoid osteomas in the Northern Irish population. It also aims to audit the practice of CT-guided radiofrequency ablation of osteoid osteomas at a major orthopaedic centre in Belfast, Northern Ireland, and to investigate the possible causes of treatment failure. Methods Forty-seven osteoid osteoma patients, diagnosed based on clinico-radiologic features and treated with CT-guided radiofrequency ablation, were found eligible for inclusion and analysis. We collected data from electronic health records (March 2011 to May 2022) and reviewed the radiological images and associated reports. Information about demographics, clinical indices, operative technique, clinical outcomes, biopsy results, and follow-up were also gathered. Data were then analysed using IBM SPSS Statistics for Mac, version 28.0. 1.1 (14) (IBM Corp., Armonk, NY). Results The average age of patients was 19.3 years, with a male-to-female predilection of 2.1:1. The proximal and mid-tibial shafts were the most frequently involved sites. On average, patients had symptoms for 15.6 months, while the mean treatment delay period was 6.9 months. Primary clinical success was observed in 37 patients (78.7%), while ten patients had a clinical failure. Two out of the 10 patients with treatment failure underwent subsequent successful ablations, raising the secondary clinical success rate to (83.0%). Chi-Square association tests found no correlation between primary treatment outcomes and other qualitative variables (gender, bone type, lesion location, and Kayser classification). Moreover, binary logistic regression tests found no predictability of age and treatment delay on treatment outcomes. The overall observed complication rate was 4%, with only one significant side effect reported (third-degree skin burn). Conclusion We concluded that the demographics of osteoid osteomas in the Northern Irish population are comparable to what is previously established in the literature. Furthermore, we reasoned that CT-guided radiofrequency ablation is an efficient, safe, and effective minimally invasive technique in the management of osteoid osteomas.
An assessment of strategies used on resources mobilization: A case of the Université Chrétienne Bilingue Du Congo (UCBC)

Many people in institutions or organizations have excellent visions and initiatives but most ofthem don’t see their visions come true because of either lack of resources or mismanagement ofthe mobilized resources. This study focuses on an assessment of strategies used on resourcesmobilization in the Université Chrétienne Bilingue du Congo (UCBC). In particular, the studysought to find out whether community participation, local fundraising, management practicesand marketing strategies affect resource mobilization at UCBC. The study adopted a quantitativeapproach targeting a population of 509 subjects. Stratified and purposive sampling techniqueswere used to get a sample size of 95 subjects. Data was collected using structured questionnairewith Likert scale measurement. The collected data was analyzed using confirmatory factoranalysis method. The results found out that management practices (p-value 0.0018), marketingstrategies (p-value 0.0430), local fundraising (p-value 0.0849) and community participation (pvalue 0.0019) were positive and significantly related to resource mobilization. The study recommends that UCBC considers the significant variables as they affect resource mobilization performance.
Prognostic Significance of PD-L1 Expression in Gastric Cancer Patients with Peritoneal Metastasis

Background: Recently, many studies have explored the relationship between the expression of programmed death ligand 1 (PD-L1) and prognosis in gastric cancer, but there is still controversy. Additionally, few studies have specifically investigated the expression of PD-L1 in patients with peritoneal metastasis. Methods: Immunohistochemistry was used to analyze the expression of PD-L1 in gastric cancer patients with peritoneal metastasis. The combined positive score (CPS) was calculated to evaluate the expression of PD-L1, and the clinicopathological data were analyzed to explore prognostic significance. Results: In total, 147 gastric cancer patients with peritoneal metastasis were enrolled. The negative PD-L1 expression was defined as a CPS < 1, and high PD-L1 expression was defined as a CPS ≥ 10. PD-L1 expression with CPS ≥ 1 and CPS-negative was detected in 67 (45.58%) and 80 (54.42%) patients, respectively. High PD-L1 expression at PD-L1 CPS ≥ 10 was detected in 21(14.29%) patients. The median overall survival (OS) was 18.53 months in the CPS < 10 group and 27.00 months in the CPS ≥ 10 group; the OS difference between the two groups was significant (p = 0.015). Multivariate analysis demonstrated that a poor Eastern Cooperative Oncology Group performance score (ECOG PS) (p = 0.002) and severe peritoneal metastasis (p = 0.033) were significantly associated with poor survival, while palliative chemotherapy (p = 0.002) and high PD-L1 expression (p = 0.008) were independent and significantly favorable prognostic factors. Conclusions: Our study demonstrated that PD-L1 expression was widely presented in gastric cancer patients with peritoneal metastasis, while a CPS no less than 10 predicted better prognosis.
Estimating the Aboveground Biomass of Various Forest Types with High Heterogeneity at the Provincial Scale Based on Multi-Source Data

It is important to improve the accuracy of models estimating aboveground biomass (AGB) in large areas with complex geography and high forest heterogeneity. In this study, k-nearest neighbors (k-NN), gradient boosting machine (GBM), random forest (RF), quantile random forest (QRF), regularized random forest (RRF), and Bayesian regularization neural network (BRNN) machine learning algorithms were constructed to estimate the AGB of four forest types based on environmental factors and the variables selected by the Boruta algorithm in Yunnan Province and using integrated Landsat 8 OLI and Sentinel 2A images. The results showed that (1) DEM was the most important variable for estimating the AGB of coniferous forests, evergreen broadleaved forests, deciduous broadleaved forests, and mixed forests; while the vegetation index was the most important variable for estimating deciduous broadleaved forests, the climatic factors had a higher variable importance for estimating coniferous and mixed forests, and texture features and vegetation index had a higher variable importance for estimating evergreen broadleaved forests. (2) In terms of specific model performance for the four forest types, RRF was the best model both in estimating the AGB of coniferous forests and mixed forests; the R2 and RMSE for coniferous forests were 0.63 and 43.23 Mg ha−1, respectively, and the R2 and RMSE for mixed forests were 0.56 and 47.79 Mg ha−1, respectively. BRNN performed the best in estimating the AGB of evergreen broadleaved forests; the R2 was 0.53 and the RMSE was 68.16 Mg ha−1. QRF was the best in estimating the AGB of deciduous broadleaved forests, with R2 of 0.43 and RMSE of 45.09 Mg ha−1. (3) RRF was the best model for the four forest types according to the mean values, with R2 and RMSE of 0.503 and 52.335 Mg ha−1, respectively. In conclusion, different variables and suitable models should be considered when estimating the AGB of different forest types. This study could provide a reference for the estimation of forest AGB based on remote sensing in complex terrain areas with a high degree of forest heterogeneity.