Miracle-dz/UniFAR-FTUs huggingface dataset:
Analytical Characterization and Optimum Detection of Nonlinear Multicarrier Schemes

It is widely recognized that multicarrier systems such as orthogonal frequency division multiplexing (OFDM) are suitable for severely time-dispersive channels. However, it is also recognized that multicarrier signals have high envelope fluctuations which make them especially sensitive to nonlinear distortion effects. In fact, it is almost unavoidable to have nonlinear distortion effects in the transmission chain. For this reason, it is essential to have a theoretical, accurate characterization of nonlinearly distorted signals not only to evaluate the corresponding impact of these distortion effects on the system’s performance, but also to develop mechanisms to combat them. One of the goals of this thesis is to address these challenges and involves a theoretical characterization of nonlinearly distorted multicarrier signals in a simple, accurate way. The other goal of this thesis is to study the optimum detection of nonlinearly distorted, multicarrier signals. Conventionally, nonlinear distortion is seen as a noise term that degrades the system’s performance, leading even to irreducible error floors. Even receivers that try to estimate and cancel it have a poor performance, comparatively to the performance associated to a linear transmission, even with perfect cancellation of nonlinear distortion effects. It is shown that the nonlinear distortion should not be considered as a noise term, but instead as something that contains useful information for detection purposes. The adequate receiver to take advantage of this information is the optimum receiver, since it makes a block-by-block detection, allowing us to exploit the nonlinear distortion which is spread along the signal’s band. Although the optimum receiver for nonlinear multicarrier schemes is too complex, due to its necessity to compare the received signal with all possible transmitted sequences, it is important to study its potential performance gains. In this thesis, it is shown that the optimum receiver outperforms the conventional detection, presenting gains not only relatively to conventional receivers that deal with nonlinear multicarrier signals, but also relatively to conventional receivers that deal with linear, multicarrier signals. We also present sub-optimum receivers which are able to approach the performance gains associated to the optimum detection and that can even outperform the conventional linear, multicarrier schemes.
Environmental Aspects of Textile Dyeing

European legislation relating to textile dyeing US legislation relating to textile dyeing Toxicology of textile dyes Environmentally responsible application of textile dyes Supercritical fluid textile dyeing technology Reducing pollution and waste minimisation in textile dyeing Decolourisation of textile dye effluent and re-use of spent dyebath Chemical treatment of textile dye effluent Biotechnological treatment of textile dye effluent.
Systemic inflammation and in hemodialysis patients are associated with down-regulation of Nrf2.

oxidative stress Abstract Background Oxidative stress and inﬂammation are common features and the main mediators of progression of chronic kidney disease (CKD) and its cardiovascular complications. Under normal conditions, oxidative stress activates the transcription factor, nuclear factor E2-related factor 2 (Nrf2), which is the master regulator of genes encoding antioxidant and detoxifying enzymes and related proteins. The available data on expression of Nrf2 and its key target gene products in CKD patients is limited. We therefore investigated this topic in a group of CKD patients on hemodialysis. p = 0.0001) compared to the healthy individuals. The NF- j B expression was inversely correlated with Nrf2 levels (r = - 0.54, p \ 0.01) in CKD patients. Plasma MDA and TNF- a levels were signiﬁcantly higher in CKD patients than in the healthy individuals. Up-regulation of NF j B in the CKD patients’ PBMC is coupled to down-regulation of Nrf2 and NQO1 expression. These observations are consistent with recent ﬁndings in CKD animals and point to the contribution of the impaired Nrf2 system in the pathogenesis of oxidative stress and inﬂammation in hemodialysis patients.
Redefining Regional French: Koineization and Dialect Levelling in Northern France

This study challenges the orthodox view that emergent regional varieties of French represent no more than an ephemeral dialect residue of little theoretical interest. It follows the life cycle of an obsolescent urban Picard variety, spoken in a mining town in the Pas-de-Calais, and attempts to unravel the complex reasons behind the survival of some local variants at the expense of others. Applying a sociolinguistic model developed by Peter Trudgill, it shows how the processes of levelling and simplification have driven change in a dialect contact situation, giving rise to a new, stable variety or koine. This is compared with other new urban varieties in Sweden and the UK, where different economic, social and demographic conditions have produced very different linguistic outcomes. The emergence of Regional French in the north, it is argued, may herald the start of a new diversification of French in Europe. This book will therefore interest both students of French and of language variation more generally.
The bushmeat trade, livelihood securities and alternative wildlife resources: A Case Study of Mankessim and its environs in the Mfantseman District (Ghana)

Extraction and use of fauna resources is a practice carried out in rural communities in most tropical regions and Africa in particular. In Ghana, forest fringe communities have depended on the extraction of bushmeat initially as a major source of protein and in recent times as an important livelihood activity which has become attractive to young people. Bushmeat has become an important trade commodity and the source of income for many rural household. However, studies have shown that bushmeat trading activities has resulted in the local extinction of large mammals due to the unsustainable way these fauna resources are being extracted.This study sought to ascertain the important contribution the trade in making towards peoples livelihoods and to discuss specific interventions in place to regulate the use of wildlife resources outside protected areas. A case study approach was adopted and Mankessim and Eyisam in the Mfantseman district in the Central region were chosen as the study area. The study population was bushmeat traders, hunters, chopbar operators and consumers of bushmeat. Both qualitative and quantitative techniques were used in collecting and analyzing data of the study. Purposive sampling was used to select five respondents from each of the categories for interviewing as well as key informants. Simple random sampling was used in selecting the consumers.The concept of livelihood and political ecology were the theoretical approaches adopted in explaining the findings of the research.The findings indicated that bushmeat trading activities involves a chain of actors composed of hunters, wholesalers and chopbar owners/retailers. This livelihood activity is a major contributor to household incomes of the actors of the trade. Skilled unemployed and under employed youth cease the opportunity of the high demand for bushmeat and the profitability of the trade to engage in bushmeat trading activities. Furthermore, the Wildlife Department recognizing the unsustainable extraction of fauna resources in unprotect areas has put in place rules and regulations to regulate the use of these resources.The study concludes among other things that, the creation of employment avenues for the youths in the district including the cultivation of wildlife as “crops” will reduce the number of young people entering the trade and also reduce the pressures on animals in the wild. Ineffective implementation of wildlife regulations could also result in unsustainable use of fauna resource.
Arkadia in transition : exploring late Bronze Age and early Iron Age human landscape

This research explores the region of Arkadia in the Late Bronze Age and Early Iron Age using an interpretative and phenomenologically inspired approach. It is region associated with many myths pointing to a continuing population throughout the period, yet beset with a problematic archaeological record. This has been the result of a number of factors ranging from the nature of the landscape to the history of research. However, the ability to locate sites of the Late Bronze Age and Early Iron Age within the landscape, allows insight into a region we had little hope of enlightening using more conventional approaches to the archaeological record. This theoretical and methodological stance is illustrated through an exploration of different aspects of the human experience such as religion, death and burial and the everyday. The ways in which these aspects can and usually are interpreted are considered, followed by a number of case studies, which are employed to explore how human actions were embedded within and informed by the very physicality of the landscape, and the differences apparent throughout time.
UPCommons Portal del coneixement obert de la UPC

In this work, we numerically consider a thermoelastic problem where the thermal law is modeled using the so-called Moore-Gibson-Thompson equation. This thermo-mechanical problem is written as a coupled system composed of a hyperbolic partial diﬀerential equation for a transformation of the displacement ﬁeld and a parabolic partial diﬀerential equation for a transformation of the temperature. Its variational formulation is written in terms of the derivatives of the above transformed functions, leading to a coupled linear system made of two ﬁrst-order variational equations. Then, a fully discrete algorithm is introduced and a discrete stability property is proved. A priori error estimates are also provided, from which the linear convergence is derived under suitable regularity conditions. Finally, some numerical results are shown, including the numerical convergence of the approximations, comparisons with the Lord-Shulman and type III Green-Naghdi theories and two-dimensional examples which demonstrate the behavior of the solution.
University of Birmingham Mathematical models of retinitis pigmentosa: The oxygen toxicity hypothesis

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Clinical Depression in Children and Adolescents With Sickle Cell Anaemia: In uencing Factors in a Resource-limited Setting

Osita Uchenna Ezenwosu (  osita.ezenwosu@unn.edu.ng ) University of Nigeria Faculty of Medical Sciences Barth Friday Chukwu University of Nigeria Faculty of Medical Sciences Ifeyinwa Lilian Ezenwosu University of Nigeria Teaching Hospital Ndubuisi Anyaele Uwaezuoke University of Nigeria Faculty of Medical Sciences Christopher Bismarck Eke University of Nigeria Faculty of Medical Sciences Maria Ifechukwu Udorah Annunciation Specialist Hospital, Emene Chinedu Arthur Idoko University of Nigeria Faculty of Medical Sciences Anthony Nnaemeka Ikefuna University of Nigeria Faculty of Medical Sciences Ifeoma Josephine Emodi University of Nigeria Faculty of Medical Sciences
Clasificación del régimen hidrológico natural y predicción de características hidroecológicas en el tercio norte de la Península Ibérica: = Classification of the natural flow regime and prediction of hydroecological characteristics in the Northern third of the Iberian Peninsula

El objetivo de esta tesis es avanzar en el conocimiento de tres aspectos criticos dentro del campo de la hidroecologia: la clasificacion hidrologica, la prediccion de caracteristicas hidrologicas y la valoracion de la alteracion hidrologica. En el Capitulo III se analizaron las implicaciones de clasificar redes fluviales utilizando diferentes procedimientos. Se han comparado clasificaciones basadas en series hidrologicas normalizadas y brutas aplicando dos aproximaciones de prediccion de clases. En el Capitulo 4 se han comparado 3 clasificaciones que utilizan un procedimiento inductivo pero que estan basadas en diferentes datos iniciales. La cuarta clasificacion utiliza un procedimiento basado en criterio de experto. El Capitulo 5 compara la capacidad de 5 tecnicas estadisticas y la aproximacion de regresion regional para predecir caracteristicas hidroecologicas a toda una red fluvial. En el Capitulo 6, se introduce y valora un procedimiento para evaluar la alteracion hidrologica que incluye 5 disenos alternativos en funcion de la disponibilidad de datos.
Two-Dose Severe Acute Respiratory Syndrome Coronavirus 2 Vaccine Effectiveness With Mixed Schedules and Extended Dosing Intervals: Test-Negative Design Studies From British Columbia and Quebec, Canada

Background. The Canadian coronavirus disease 2019 (COVID-19) immunization strategy deferred second doses and allowed mixed schedules. We compared 2-dose vaccine effectiveness (VE) by vaccine type (mRNA and/or ChAdOx1), interval between doses, and time since second dose in 2 of Canada’s larger provinces. Methods. Two-dose VE against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection or hospitalization among adults ≥ 18 years, including due to Alpha, Gamma, and Delta variants of concern (VOCs), was assessed ≥ 14 days postvaccination by test-negative design studies separately conducted in British Columbia and Quebec, Canada, between 30 May and 27 November (epi-weeks 22–47) 2021. Results. In both provinces, all homologous or heterologous mRNA and/or ChAdOx1 2-dose schedules were associated with ≥ 90% reduction in SARS-CoV-2 hospitalization risk for ≥ 7 months. With slight decline from a peak of . 90%, VE against infection was ≥ 80% for ≥ 6 months following homologous mRNA vaccination, lower by ≏ 10% when both doses were ChAdOx1 but comparably high following heterologous ChAdOx1 + mRNA receipt. Findings were similar by age group, sex, and VOC. VE was significantly higher with longer 7–8-week versus manufacturer-specified 3–4-week intervals between mRNA doses. Conclusions
IRANIAN JOURNAL OF VETERINARY SURGERY

PRF. Abstract ObjectiveDue to its specific position, the maxillary canine tooth is more exposed to the injuries, fractures and pulp necrosis. In these cases, the damaged teeth are usually pulled out. The root of this tooth is very large and sometimes extends over the premolar teeth. To extract it, a large amount of alveolar bone tissue should be harvested. DesignExperimental study AnimalsFive adult male dogs ProceduresIn this research, five dogs were selected and their maxillary canine teeth in right and left (10 teeth in total) were surgically removed. Then the right dental cavity was filled with insulin PRF and the left one filled with autograft cancellous bone chips. Three weeks after the surgery, the cavity was sampled for histopathological examination. Factors such as epithelialization rate, fibroplasia, fibrotic reaction and intensity of inflammation were studied. ResultsThe average score obtained from histological studies implies that epithelialization in PRF group is significantly higher than the other group, and there is no significant difference in other parameters. Conclusion and Clinical RelevanceSince PRF preparation is not painful and risky for the patient and does not require any special care after the surgery. Therefore, it is used preferably.
Preoperative versus postoperative chemoradiotherapy for locally advanced rectal cancer: Results of the German CAO/ARO/AIO-94 randomized phase III trial after a median follow-up of 11 years.

3516 Background: CAO/ARO/AIO-94 was published in 2004 with a median follow-up of 46 months (Sauer et al., N Engl J Med 2004). This trial established preoperative CRT as standard treatment for rectal cancer based on an improved local control rate at 5 years, however, no survival benefit could be shown. We here report results with a median follow-up of 134 months. METHODS We randomly assigned 823 patients with stage II or III rectal cancer to preoperative CRT (50.4 Gy) with 5-FU (1 g/msq/days 1-5, 29-33), surgery, and adjuvant 5-FU (500 mg/msq/days 1-5, 4 cycles), or the same schedule applied postoperatively. The study was designed to have 80% power to detect a difference of 10% in the 5-year overall survival as primary endpoint. Secondary endpoints included the cumulative incidence of local and distant relapses and disease-free survival. RESULTS Of 823 patients, 404 and 395 were randomized to preoperative and postoperative CRT, respectively; 24 were ineligible, and 38 requested a change in treatment group. Thus, 406 patients received preoperative CRT, 393 were treated in the postoperative arm. As of 12/2010, updated data for life and tumor status were available for 791 and 783 of 799 eligible patients, respectively. Overall survival at 10 years was 59.9 years (95% CI, 55.0-64.8%) in the preoperative arm, and 59.5% (95% CI, 54.6-64.4%) in the postoperative arm (p=0.86, log-rank test, according to intention to treat). The 10-year cumulative incidence of local relapse after macroscopically complete resection was 5.7% (95% CI, 3.2-8.2%) and 10.4% (95% CI, 7.1-13.4%) in the pre- and postoperative arms, respectively (p=0.009, log-rank test, according to actual treatment). No significant differences were detected for 10-year cumulative incidence of distant metastases (25.5% both, p=0.88) and DFS. CONCLUSIONS There is a persisting significant improvement of pre- vs. postoperative CRT on local control, however, no effect on overall survival. Integrating more effective systemic treatment into the combined modality treatment has been adopted in trial CAO/ARO/AIO-04 to possibly reduce distant metastases and improve survival.
TextBind: Multi-turn Interleaved Multimodal Instruction-following in the Wild

Large language models with instruction-following abilities have revolutionized the field of artificial intelligence. These models show exceptional generalizability to tackle various real-world tasks through their natural language interfaces. However, their performance heavily relies on high-quality exemplar data, which is often difficult to obtain. This challenge is further exacerbated when it comes to multimodal instruction following. We introduce TextBind, an almost annotation-free framework for empowering larger language models with the multi-turn interleaved multimodal instruction-following capabilities. Our approach requires only image-caption pairs and generates multi-turn multimodal instruction-response conversations from a language model. To accommodate interleaved image-text inputs and outputs, we devise MIM, a language model-centric architecture that seamlessly integrates image encoder and decoder models. We release our dataset, model, and demo to foster future research in the area of multimodal instruction following.
Multi-camera Bird's Eye View Perception for Autonomous Driving

Most automated driving systems comprise a diverse sensor set, including several cameras, Radars, and LiDARs, ensuring a complete 360\deg coverage in near and far regions. Unlike Radar and LiDAR, which measure directly in 3D, cameras capture a 2D perspective projection with inherent depth ambiguity. However, it is essential to produce perception outputs in 3D to enable the spatial reasoning of other agents and structures for optimal path planning. The 3D space is typically simplified to the BEV space by omitting the less relevant Z-coordinate, which corresponds to the height dimension.The most basic approach to achieving the desired BEV representation from a camera image is IPM, assuming a flat ground surface. Surround vision systems that are pretty common in new vehicles use the IPM principle to generate a BEV image and to show it on display to the driver. However, this approach is not suited for autonomous driving since there are severe distortions introduced by this too-simplistic transformation method. More recent approaches use deep neural networks to output directly in BEV space. These methods transform camera images into BEV space using geometric constraints implicitly or explicitly in the network. As CNN has more context information and a learnable transformation can be more flexible and adapt to image content, the deep learning-based methods set the new benchmark for BEV transformation and achieve state-of-the-art performance. First, this chapter discusses the contemporary trends of multi-camera-based DNN (deep neural network) models outputting object representations directly in the BEV space. Then, we discuss how this approach can extend to effective sensor fusion and coupling downstream tasks like situation analysis and prediction. Finally, we show challenges and open problems in BEV perception.
MMST-ViT: Climate Change-aware Crop Yield Prediction via Multi-Modal Spatial-Temporal Vision Transformer

Precise crop yield prediction provides valuable information for agricultural planning and decision-making processes. However, timely predicting crop yields remains challenging as crop growth is sensitive to growing season weather variation and climate change. In this work, we develop a deep learning-based solution, namely Multi-Modal Spatial-Temporal Vision Transformer (MMST-ViT), for predicting crop yields at the county level across the United States, by considering the effects of short-term meteorological variations during the growing season and the long-term climate change on crops. Specifically, our MMST-ViT consists of a Multi-Modal Transformer, a Spatial Transformer, and a Temporal Transformer. The Multi-Modal Transformer leverages both visual remote sensing data and short-term meteorological data for modeling the effect of growing season weather variations on crop growth. The Spatial Transformer learns the high-resolution spatial dependency among counties for accurate agricultural tracking. The Temporal Transformer captures the long-range temporal dependency for learning the impact of long-term climate change on crops. Meanwhile, we also devise a novel multi-modal contrastive learning technique to pre-train our model without extensive human supervision. Hence, our MMST-ViT captures the impacts of both short-term weather variations and long-term climate change on crops by leveraging both satellite images and meteorological data. We have conducted extensive experiments on over 200 counties in the United States, with the experimental results exhibiting that our MMST-ViT outperforms its counterparts under three performance metrics of interest. Our dataset and code are available at https://github.com/fudong03/MMST-ViT.
FedGKD: Unleashing the Power of Collaboration in Federated Graph Neural Networks

Federated training of Graph Neural Networks (GNN) has become popular in recent years due to its ability to perform graph-related tasks under data isolation scenarios while preserving data privacy. However, graph heterogeneity issues in federated GNN systems continue to pose challenges. Existing frameworks address the problem by representing local tasks using different statistics and relating them through a simple aggregation mechanism. However, these approaches suffer from limited efficiency from two aspects: low quality of task-relatedness quantification and inefficacy of exploiting the collaboration structure. To address these issues, we propose FedGKD, a novel federated GNN framework that utilizes a novel client-side graph dataset distillation method to extract task features that better describe task-relatedness, and introduces a novel server-side aggregation mechanism that is aware of the global collaboration structure. We conduct extensive experiments on six real-world datasets of different scales, demonstrating our framework's outperformance.