Miracle-dz/UniFAR-FTUs huggingface dataset:
The archaeology of Early Islamic Trans-Saharan trading towns in West Africa: a comparative view and progressive methodology from the entrepot of Essouk-Tadmekka.

This thesis reassesses the Early Islamic Trans-Saharan Trade primarily via archaeological data from the northern Sahel of West Africa. Specifically, it focuses on the trading entrepots which developed on the southern fringes of the Sahara during the Early Islamic period. This research was motivated by the recognition that historical sources, while offering useful insights into Trans-Saharan commerce, are unable to answer certain key questions due to inherent limitations in source material. Additionally, although previous archaeology at the entrepot sites has provided useful data, its analytical application has been limited. This is largely because most relevant archaeological work was done 30 or more years ago and was technologically or methodologically limited in aspects of artifactual analysis and dating. It was therefore decided that our understanding of entrepot archaeology would be best served by a new field project. Accordingly, excavation and surface collection was undertaken at the previously untested entrepot site of Essouk-Tadmekka in northern Mali. The results from Essouk provide a new perspective on the Early Islamic Trans-Saharan Trade process and make the following important contributions to research on Trans-Saharan commerce: 1) new evidence that large-scale trade was earlier than traditionally believed (c. 750-950AD) as well as fresh data on the nature of the 10th/early 11th century 'trade boom' 2) greater insight into the changing socio-economic history of the entrepot system, demonstrating specifically that the Almoravid era (c. 1050-1140AD) inaugurated a profound shift in the organization of trade, and also showing how there was a final alteration and disruption of the 'Essouk' system by the arrival of new Berber groups (c. 1300-1400AD) 3) better understanding of the movement of Trans-Saharan commodities and their archaeological correlates, including new evidence relevant to the gold trade (coin moulds) and new ways of interpreting the flow of Trans-Saharan Trade (e.g. via in-depth analyses of notionally 'local' ceramic traditions).
Detecting Vehicle Numbers Using Google Lens-Based ESP32CAM to Read Number Characters

plates continues to increase. This research aimed to detect vehicle license plates using ESP32CAM and utilize photo text reading using Google Lens, which can be used online to retrieve numeric characters. The method approach was to connect Wifi connectivity to the ESP32CAM, which had been programmed to detect vehicle plates. Vehicle plates that have been detected and recognized were inputted into Google Lens to capture the resulting text from the ESP32CAM camera recording. The results of this study were that for 70 seconds, ten plate samples were obtained, which were 100% perfect in reading license plates on Google Lens, namely six plates and two plates read 90%, one plate read 60%, and one plate read 0%. The research conclusions obtained were ten samples, six samples with perfect readings, and one error sample because of the white plate color. Thus, the main objective was to obtain the results of the vehicle plate detection and retrieve the text from the recording results
THE EFFECT OF CONTRAST ENHANCEMENT ON EPIPHYTE SEGMENTATION USING GENERATIVE NETWORK

Abstract. The performance of the deep learning-based image segmentation is highly dependent on two major factors as follows: 1) The organization and structure of the architecture used to train the model and 2) The quality of input data used to train the model. The input image quality and the variety of training samples are highly influencing the features derived by the deep learning filters for segmentation. This study focus on the effect of image quality of a natural dataset of epiphytes captured using Unmanned Aerial Vehicles (UAV), while segmenting the epiphytes from other background vegetation. The dataset used in this work is highly challenging in terms of pixel overlap between target and background to be segmented, the occupancy of target in the image and shadows from nearby vegetation. The proposed study used four different contrast enhancement techniques to improve the image quality of low contrast images from the epiphyte dataset. The enhanced dataset with four different methods were used to train five different segmentation models. The segmentation performances of four different models are reported using structural similarity index (SSIM) and intersection over union (IoU) score. The study shows that the epiphyte segmentation performance is highly influenced by the input image quality and recommendations are given based on four different techniques for experts to work with segmentation with natural datasets like epiphytes. The study also reported that the occupancy of the target epiphyte and vegetation highly influence the performance of the segmentation model.
Risk Factors for Diabetic Retinopathy in Chinese Patients with Different Diabetes Duration: Association of C-Peptide and BUN/Cr Ratio with Type 2 Diabetic Retinopathy

Background and Aim Controlling the risk factors was the most effective strategy to prevent diabetic retinopathy (DR). This study aimed to recognize the risk factors of DR, and explores whether the effect of those factors is modified by diabetes mellitus (DM) duration. Methods A total of 1058 DM patients with information about DR assessment were included. DR was measured by a complete ophthalmic examination and was classified as having one or more distinct microaneurysms in the eyes. Data from the lab and clinical factors were gathered. Multivariate logistic analysis was used to examine the risk factors, and the best-fitting model was selected by a backward stepwise based on A1C. Results In the current study, 274 (25.9%) patients developed DR. In the entire subjects, baseline age, the level of C-peptide, and urinary creatinine were all presented as protective effects of DR, whose odds ratios (ORs) and 95% confidence intervals (CIs) were 0.79 (0.62, 0.99), 0.75 (0.61, 0.91), and 0.70 (0.52, 0.93), respectively. Conversely, systolic pressure (SBP), urinary albumin, and BUN/Cr ratio were the important risk factors for DR with ORs (95% CIs) 1.21 (1.01, 1.46), 1.55 (1.30, 1.84), and 1.33 (1.11, 1.59), respectively. In stratification analysis, females with higher SBP would be more likely to develop DR in the short-duration group, while C-peptide and urinary creatinine showed protective effects in the long-duration group. BUN/Cr ratio all presented as a risk factor, with ORs 1.38 (p = 0.041) and 1.33 (p = 0.014) in short- and long-duration groups, respectively. Conclusion Although renal functions presented a significant association with DR in all DM patients, the risk factors of DR varied widely in different disease-duration subjects. Target strategies to prevent DR should be put forward individually, considering the patient’s DM duration. Improving the BUN/Cr ratio may be beneficial to delaying DR.
Author Name Disambiguation Based on Heterogeneous Graph

Since multiple people share the same name in the real world, this will cause performance degradation to academic search systems and lead to misattribution of publications. The author name disambiguation algorithm has not yet to be well solved. In this paper, we propose a disambiguation method that combines heterogeneous graph-based and improved label propagation, first we construct a publication heterogeneous graph network, then graph neural networks is applied to aggregate the nodes representation and relation types, finally combined with the improved label propagation algorithm to realize clustering. The task of author name disambiguation is completed to improve the retrieval performance. Experimental results on two public datasets show that our method was improved by 2.8% and 4.9% over the suboptimal method, respectively. Our method can effectively reduce the number of publications returning the wrong author and improve the performance of the academic retrieval system.
Practicing self-hypnosis to reduce chronic pain: A qualitative exploratory study of HYlaDO

Background: Nearly a quarter of Canada’s population suffers from chronic pain, a long-lasting medical condition marked by physical pain and psychological suffering. Opioids are the primary treatment for pain management in this condition; yet, this approach involves several undesirable side effects. In contrast to this established approach, non-pharmacological interventions, such as medical hypnosis, represent an efficient alternative for pain management in the context of chronic pain. HYlaDO is a self-hypnosis program designed to improve pain management for people with chronic pain. Purpose: This research aimed to evaluate the HYlaDO program based on the proof-of-concept level of the ORBIT model and investigated participants’ subjective experience. Research design: Qualitative study. Study sample: Seventeen participants with chronic pain took part in this study. Data collection: We conducted individual semi-structured interviews with patients who had participated in HYlaDO to identify the three targets of desired change: pain, anxiety and autonomy in self-hypnosis practice. Results: Thematic analysis revealed that the practice of hetero-hypnosis and self-hypnosis decreased (i) pain and (ii) anxiety. Also, it (iii) indicated the development of an independent and beneficial self-hypnosis practice by having integrated the techniques taught. Conclusion: These results confirm that the established targets were reached and support further development, implementation and scaling up of this program. Consequently, we believe it is justified to move to the next step of program development.
For Someone, You Are the Whole World: Host-Specificity of Salmonella enterica

Salmonella enterica is a bacterial pathogen known to cause gastrointestinal infections in diverse hosts, including humans and animals. Despite extensive knowledge of virulence mechanisms, understanding the factors driving host specificity remains limited. In this study, we performed a comprehensive pangenome-wide analysis of S. enterica to identify potential loci determining preference towards certain hosts. We used a dataset of high-quality genome assemblies grouped into 300 reference clusters with a special focus on four host groups: humans, pigs, cattle, and birds. The reconstructed pangenome was shown to be open and enriched with the accessory component implying high genetic diversity. Notably, phylogenetic inferences did not correspond to the distribution of affected hosts, as large compact phylogenetic groups were absent. By performing a pangenome-wide association study, we identified potential host specificity determinants. These included multiple genes encoding proteins involved in distinct infection stages, e.g., secretion systems, surface structures, transporters, transcription regulators, etc. We also identified antibiotic resistance loci in host-adapted strains. Functional annotation corroborated the results obtained with significant enrichments related to stress response, antibiotic resistance, ion transport, and surface or extracellular localization. We suggested categorizing the revealed specificity factors into three main groups: pathogenesis, resistance to antibiotics, and propagation of mobile genetic elements (MGEs).
Photoinduced Contact Evolution and Junction Rearrangement in Two‐Dimensional van der Waals Heterostructure

Devices based on 2DMs van der Waals (vdW) heterostructures always compose of multiple contacts. Due to the instability of nanoscale 2DMs and interfaces, these contacts can be affected by the operation‐induced photo or thermal effect. They can trigger the evolution of junctions and rearrange the junctions across a device, which are detrimental for applications. Herein, vdW heterostructure of indium selenide (InSe) and black phosphorus (BP) on Au electrodes are investigated to reveal the contact evolution and its relation to device performance. During operation, light irradiation changes the I–V characteristics from symmetry to strong rectification. Photocurrent mapping and Kelvin‐probe force microscopy (KPFM) reveal triple junctions in this heterostructure, i.e., Au‐InSe junction, InSe homojunction, and InSe‐BP heterojunction. The variation of I–V characteristics of vdW heterostructure is ascribed to the evolution of Au‐InSe junction from quasi‐ohmic junction with a near‐zero work function difference (Δφ) to a strong Schottky junction (Δφ = ≈0.27 eV). The stabilized device demonstrates distinguished time‐domain response at individual junctions and overall device, indicating the evolution of contacts and the consequent opposite junction directions degrade the overall device performance. This research emphasizes the importance of dealing with heterogeneous contacts and junction directions in designing vdW heterostructure photodetectors.
Application of Neural Network Tools in Process Mining

Dominant current technologies in process mining use schema induction approaches based on graph and au- tomaton methods. The paper investigates the application of neural network approaches in schema induction focusing on three alternative architectures: MLP, CNN and LSTM networks. The proposed neural network models can be used to discover XOR, loop and parallel execution templates. In the case of loop detection, the performed test analyses show the dominance of CNN approach where the string is represented with a two- dimensional similarity matrix. The usability of the proposed approach is demonstrated with test examples.
Synthesis and magnetic structure of the layered manganese oxide selenide Sr 2 MnO 2 Ag 1 . 5 Se 2

The synthesis of a high-purity sample of the layered oxide selenide Sr2MnO2Ag1.5Se2 is reported. At ambient temperature it crystallises in the space group I4/mmm with two formula units in the unit cell and lattice parameters a=4.08771(1) Å, c=19.13087(8) Å. The compound displays mixed-valent manganese in a formal oxidation state close to +2.5 and powder neutron diffraction measurements reveal that below the Néel temperature of 63(1) K this results in an antiferromagnetic structure which may be described as A-type, modelled in the magnetic space group PI4/mnc (128.410 in the Belov, Neronova and Smirnova (BNS) scheme) in which localised Mn moments of 3.99(2) μB are arranged in ferromagnetic layers which are coupled antiferromagnetically. In contrast to the isostructural compound Sr2MnO2Cu1.5S2, Sr2MnO2Ag1.5Se2 does not display long range ordering of coinage metal ions and vacancies, nor may significant amounts of the coinage metal readily be deintercalated using soft chemical methods.
Rainfall Variability and Labor Allocation in Uzbekistan: The Role of Women's Empowerment

IZA DP No. 16421 SEPTEMBER 2023 Rainfall Variability and Labor Allocation in Uzbekistan: The Role of Women’s Empowerment* Employing novel household survey data, this paper examines how rainfall variability and mean temperature affect individual labor supply in Uzbekistan, a highly traditional lower-middle-income country in Central Asia. The findings suggest that rainfall variability induces the reallocation of labor supply: (i) out of agriculture to unemployment, (ii) from unemployment to business activities and irregular remunerated activities, and (iii) from being out of labor force to unemployment. These effects differ in rural and urban areas and by gender. In addition, active women’s involvement in the labor market and household decision-making mediates the impact of climate variability on employment choices, especially in rural areas. This implies that traditional gender roles may make households in developing countries more vulnerable to adverse consequences of climate change, while women’s empowerment may smooth such consequences. JEL Classification: J16, J21, J43, P28, Q54
Prevalence of the Activities of Daily Living Disability among Seniors in China: A Meta-Analysis

: This meta-analysis aimed to systematically assess the prevalence of Activities of Daily Living (ADL) disability among elderly in China. Two reviewers conducted a meta-analysis using articles available in nine databases. Methods: The Newcastle–Ottawa Scale (NOS) was used to assess the quality of the studies. The random-effects model was used to estimate the prevalence of ADL disability. The source of heterogeneity among subgroups was determined
Clinical evaluation on automatic segmentation results of convolutional neural networks in rectal cancer radiotherapy

Purpose Image segmentation can be time-consuming and lacks consistency between different oncologists, which is essential in conformal radiotherapy techniques. We aimed to evaluate automatic delineation results generated by convolutional neural networks (CNNs) from geometry and dosimetry perspectives and explore the reliability of these segmentation tools in rectal cancer. Methods Forty-seven rectal cancer cases treated from February 2018 to April 2019 were randomly collected retrospectively in our cancer center. The oncologists delineated regions of interest (ROIs) on planning CT images as the ground truth, including clinical target volume (CTV), bladder, small intestine, and femoral heads. The corresponding automatic segmentation results were generated by DeepLabv3+ and ResUNet, and we also used Atlas-Based Autosegmentation (ABAS) software for comparison. The geometry evaluation was carried out using the volumetric Dice similarity coefficient (DSC) and surface DSC, and critical dose parameters were assessed based on replanning optimized by clinically approved or automatically generated CTVs and organs at risk (OARs), i.e., the Planref and Plantest. Pearson test was used to explore the correlation between geometric metrics and dose parameters. Results In geometric evaluation, DeepLabv3+ performed better in DCS metrics for the CTV (volumetric DSC, mean = 0.96, P< 0.01; surface DSC, mean = 0.78, P< 0.01) and small intestine (volumetric DSC, mean = 0.91, P< 0.01; surface DSC, mean = 0.62, P< 0.01), ResUNet had advantages in volumetric DSC of the bladder (mean = 0.97, P< 0.05). For critical dose parameters analysis between Planref and Plantest, there was a significant difference for target volumes (P< 0.01), and no significant difference was found for the ResUNet-generated small intestine (P > 0.05). For the correlation test, a negative correlation was found between DSC metrics (volumetric, surface DSC) and dosimetric parameters (δD95, δD95, HI, CI) for target volumes (P< 0.05), and no significant correlation was found for most tests of OARs (P > 0.05). Conclusions CNNs show remarkable repeatability and time-saving in automatic segmentation, and their accuracy also has a certain potential in clinical practice. Meanwhile, clinical aspects, such as dose distribution, may need to be considered when comparing the performance of auto-segmentation methods.
An application of contingent choice modelling to assess environmental management options in the shrimp-mangrove system in the Indian Sundarbans

The Indian Sundarbans, in West Bengal, is inhabited by small-scale farmers and traditional paddy-cum-prawn cultivators; however, this unique region is also ideal for large-scale commercial shrimp aquaculture. Recent policy initiatives may facilitate expansion of commercial shrimp aquaculture in India, potentially setting the stage for conflict between different stakeholders in the Indian Sundarbans. This research project presents policymakers with an ex ante analysis of four alternative development scenarios, based on a more participatory approach to the decision making process. Using a contingent choice modelling methodology to quantify the preferences of local stakeholders for economic, social, and environmental attributes, policymakers and local stakeholders can measure and compare the preferences of local stakeholders for alternative management options. Local stakeholders, including shrimp fry collectors, shrimp farmers, and agricultural farmers, prefer a sustainable approach to development in the region, especially with respect to the management of mangrove forests, and access to alternative income generating opportunities.
Analyse du probleme de la determination du modele de vitesse pour l'imagerie sismique

L'exploration des ressources petrolieres necessite l'obtention d'images du sous-sol. Generalement, ces images sont obtenues a partir de donnees sismiques. Les donnees sismiques, enregistrees en surface, sont traitees pour traduire le champ d'ondes en termes d'images profondeur. Ce traitement, appele migration profondeur, implique la determination d'un modele de vitesse. Seule la precision avec laquelle on construit le modele de vitesse influe sur la precision de l'image profondeur. Il est possible d'accelerer et d'ameliorer la determination du modele de vitesse en considerant les points suivants: un nombre de parametres pour le modele de vitesse au moins egal a 200 pour un milieu de type marmousi; l'utilisation de la pression effective et de la composition de la roche comme donnees geologiques dans le processus de determination du modele de vitesse. En effet, ces deux parametres geologiques sont tres correles aux vitesses des ondes sismiques. De plus, il est possible de transformer en termes de vitesses ces deux parametres; l'utilisation du critere c#e#n#n#o pour determiner efficacement le modele de vitesse; l'utilisation des panneaux de coherence comme critere de validite du modele de vitesse retenu
Predicting stable employment trajectories among young people with disabilities

ABSTRACT Research aiming to explain disabled people’s inequalities in the labour market has primarily focused on transitional factors between school and work, wage gaps, or socioeconomic background characteristics as explanations for (no-)entry in the labour market. There is a lack of longitudinal studies that map how disabled people fare in the labour market over time. Therefore, the objective of this paper is to identify, describe and predict stable employment paths of long-term disabled people. Our study employs detailed longitudinal data with total coverage of the Norwegian population – we focus on 11 birth cohorts (1973–83) of disabled individuals and we follow their employment trajectories between the ages 20 and 34. To describe employment trajectories and create a typology of longitudinal labour market attachments, we employ sequence analysis and subsequently linear probability models to analyse the association between the disability’s severity, gender, educational enrolment, early-work experience and employment trajectories. We identify four main types of trajectories: permanently work-disabled, stable employment, early marginalisation, and unstable employment. Our findings indicate that men are more likely than women to have stable employment trajectories. Starting higher education, as well as parental higher education, is linked with the likelihood of stable employment.