Miracle-dz/UniFAR-FTUs huggingface dataset:
Mid- to late-Holocene paleoenvironmental changes inferred from organic geochemical proxies in Lake Tangra Yumco, Central Tibetan Plateau

Investigations of the paleoclimate and paleoenvironmental changes in the Tibetan Plateau in recent years have led to many significant achievements. However, high-resolution lake records from the mid- to late-Holocene have not been determined for the Central Tibetan Plateau, which is in the transition zone between the Indian summer monsoon (ISM) system and the westerlies. In this study, we analyzed the distribution and compound-specific carbon isotope compositions (δ13C) of sedimentary n-alkanes and the bulk sediment total organic matter (TOC), total nitrogen (TN), and C/N ratios to reconstruct the paleoenvironmental changes over the past 6 cal. ka BP using a 376-cm sediment core from the southern basin of Tangra Yumco, Central Tibetan Plateau. Our results indicated that clear stepwise decreases in lake productivity and lake level occurred during the mid- to late-Holocene based on decreases in δ13C values of mid-chain n-alkanes and Paq (an n-alkane-based proxy). Additionally, the variations in the TOC, TN, and C/N ratios revealed generally increased inputs of allochthonous organic matter over the past 6 cal. ka BP. When our findings at Tangra Yumco were compared with records from the Central Tibetan Plateau and adjacent regions, we found generally consistent patterns of lake environmental changes over the past 6 cal. ka BP, suggesting that the climate of the Central Tibetan Plateau was primarily influenced by the ISM system at the millennial time scale. Therefore, our results depict the evolution of the ISM over the past 6 cal. ka BP in the Central Tibetan Plateau, which corresponds to summer solar insolation and the migrations of the Intertropical Convergence Zone (ITCZ). We also propose that the climate of the North Atlantics influenced the climate of the Central Tibetan Plateau through some indirect climatic mechanisms.
Climate change impacts on ectomycorrhizal fungi associated with Australian eucalypts

xviii Chapter 1. General Introduction 1 1.1 Scope and aims 1 1.2 Climate science 3 1.3 Ecological responses 4 1.4 Carbon cycling 6 1.5 Australian eucalypts 8 1.6 Mycorrhizal fungi 9 1.7 Effects of climate change parameters on ECM fungi, eucalypts and mycorrhizal symbioses 22 Chapter 2. Establishment of a collection of Australian Pisolithus sp. isolates.
The Primary Healthcare Providers’ Attitudes towards Artificial Intelligence and Information Communication Technologies Implementation into Pancreatic Cancer Risk Prediction, Prevention and Early Diagnosis

The detection of Pancreatic cancer at the early stages of its development is still extremely difficult. This relatively rare malignant disease is notorious for its great mortality rate and extremely low patient life expectancy. One of the main reasons for this is the delayed diagnosis when the treatment of invasive and non-invasive procedures is ineffective. A lot of factors have been recognized as risky for the development of pancreatic cancer. The earliest possible detection of these factors by the patient could ease disease prevention, as part of the risk factors could be modified or mitigated. Capabilities of Information Communication Technologies (ICT) and Artificial Intelligence (AI) have the potential to support and enhance cancer prediction, prevention, and/or patient monitoring and management. The role of primary healthcare providers in disease screening and early detection is well-known. The objective of this study is to analyze the primary healthcare providers’ attitudes toward the utilization of ICT and AI in pancreatic cancer prediction, prevention, and early diagnosis. A survey based on a Likert 5-scale has been performed among 57 primary healthcare medical specialists in the context of the iHELP EU-funded project. The outcomes demonstrate that most of the medical specialists support the implementation of the ICT and AI technologies but are not fully confident about their capabilities and are efficient in detecting, extracting, and correlating significant medical data towards the development of reliable risk predictors and the provision of personalized recommendations for prevention and intervention. Finally, the AI tools that are developed in the context of the project are assessed and evaluated towards changing the expressed beliefs.
Fast Flexible Neighbor-Joining Using Multicomputing

In this study, we tackle the challenge of reconstructing the evolutionary history of a group of species, a crucial problem in bioinformatics. Phylogenetic trees visually represent relationships among organisms. While the Neighbor-Joining method (NJ) is effective, it faces limitations with larger datasets, whereas the Unweighted Pair Group Method with Arithmetic Mean (UPGMA) is more efficient for such sets. However, the quality of the UPGMA tree may be compromised due to its assumptions about uniform evolutionary rates. In this work, we introduce a parallel implementation strategy using multi-threaded computing. This approach combines accuracy comparable to Neighbor-Joining with the time efficiency of UPGMA. Experimental tests on synthetic datasets ranging from 1k to 32k OTUs show good performance, accuracy, and scalability of the proposed solution when compared to Biotite, a popular tool employing a parallel approach for UPGMA and NJ. For large dataset (16k-32k) we achieved speedup up to 6 using a 24-core CPU. Morover, with appropriated setup our implementation is up to 447 times faster than NJ and up to 133 times faster than UPGMA with satisfactory quality.
DouRN: Improving DouZero by Residual Neural Networks

Deep reinforcement learning has made significant progress in games with imperfect information, but its performance in the card game Doudizhu (Chinese Poker/Fight the Landlord) remains unsatisfactory. Doudizhu is different from conventional games as it involves three players and combines elements of cooperation and confrontation, resulting in a large state and action space. In 2021, a Doudizhu program called DouZero [8] surpassed previous models without prior knowledge by utilizing traditional Monte Carlo methods and multilayer perceptrons. Building on this work, our study incorporates residual networks into the model, explores different architectural designs, and conducts multi-role testing. Our findings demonstrate that this model significantly improves the winning rate within the same training time. Additionally, we introduce a call scoring system to assist the agent in deciding whether to become a landlord. With these enhancements, our model consistently outperforms the existing version of DouZero and even experienced human players.11The source code is available at https://github.com/Yingchaol/Douzero_Resnet. git.
Types of Surgical Repair and Outcome in Patients with Tetralogy of Fallot: Experience from A Single Center in Saudi Arabia

Background: Congenital heart disease is the most prevalent form of primary congenital disability in newborns and is the leading cause of death in children with congenital malformations. It occurs in approximately 0.8% of live births. With advances in both palliative and corrective surgery, the number of children with congenital heart disease surviving to adulthood has increased dramatically . Tetralogy of Fallot (TOF) is one of the conotruncal family of heart lesions in which the primary defect is an anterior deviation of the infundibular septum (the muscular septum that separates the aortic and pulmonary outflows). Objective: To revise patients with tetralogy of Fallot (TOF) underwent palliative or total corrective surgical repair in Madinah, Saudi Arabia. Methods: retrospective study was approved from the institutional review board of Madinah cardiac center and abandoned the need for patient consent. Patient Demographic data and procedure details were retrieved from the hospital information system (HIS). From 2014 till end of 2017, 72 patients had the diagnosis of TOF (ICD 10 code, Q21.3). Twelve patients were excluded from them. Thus, 60 patients were left as the study cohort. Fifty four patients had total repair from the start (Group I), 3 patients had initial palliative procedure (group II) and 3 patients had both initial palliative procedure then later total repair. All patients from 1 day to less than 14 years are included. All data were analyzed using GraphPad prism version 5.00 for windows. Results: 61.67% of our patients were males, and 38.33% were females with the ratio of 1.6 (Male: Female). Six (10%) cases were subjected to a palliative correction surgery (group I) and 57 (95%) cases were subjected to total repair surgery (group II). 11 (18.3%) cases had post-operative complications, 2 (18.2%) cases from group I and 9 (81.8%) cases from group II. Regarding to the type of surgery there was no statistically significant difference between gender and pre and post-operation stay. Also no significance with length of stay, re-intervention, mortality and complications. While there was a significant correlation between age, weight, RPA and LPA. Conclusion: In our cohort of TOF patients the incidence of the disease was higher among males than females. The continuous follow up was highly recommended for TOF patients. The outcomes of surgical treatment including palliative, total and intervention correction surgeries was a complete recovery of 28.33% of studied patients, and the remaining (71.67%) had post-surgical complications.
Variations of Growth Parameters in Some Aromatic Rice Verities under Salt Stress

A pot experiment with six aman rice varieties viz. BRRI dhan34, BRRI dhan38, Binadhan-9, Binadhan-13, Kalijira and Rajbhogh was conducted at the experimental shed of the Department of Agronomy, Sher-e Bangla Agricultural University, Dhaka during the period of June- December, 2014 to investigate the effect of salinity on the growth, physiology and yield of some aromatic rice under different salt stress conditions. The experiment was carried out with five salt stress treatments viz. control (without salt), S25 (25 mM NaCl), S50 (50 mM NaCl), S75 (75 mM NaCl) and S100 (100 mM NaCl). Salt stress significantly reduced the plant height, tiller hill -1 and leaves hill -1 of these varieties at all growth duration. Among the varieties Binadhan-9 and BRRI dhan38 performed better under saline condition. On the other hand, local variety Kalijira did not perform well under saline condition. Bangladesh Agron. J. 2016, 19(2): 1-10
A Cost-Reward Optimization Approach to Bank Loan Approvals

Financial institutions often grapple with revenue losses caused by delinquent customers defaulting on loans or customer rejections due to risk aversion. This paper explores machine learning and optimization methods to address this challenge. The approach involves two key steps which are firstly, to employ a range of machine learning methods to select the best algorithm for predicting customer delinquency, and secondly, to use these results to optimize interest rates, thereby reducing delinquency and risk. This optimization offers both new and existing customers an opportunity to access optimized interest rates, enabling financial institutions to increase revenues while managing risk and delinquency effectively. The models achieved high accuracy, with the top-performing algorithm achieving a F1-score of 0.88 and an ROC of 0.986. Consequently, optimized interest rates led to a 37% reduction in risk and a corresponding increase in returns for a specific customer cohort. In conclusion, this research demonstrates that implementing the proposed method allows financial institutions to profit from approved loans without being overly conservative.
Synthesis of Bio Sponge for Diabetic Foot Ulcer

A very dangerous consequence of diabetes is diabetic foot ulcer (DFU). Patients with a DFU have skin that is more prone to open sores and more challenging to heal than healthy individuals. Without early treatment, the condition will worsen and frequently result in amputation or even death. A few available ways to treat Diabetic Foot Ulcers are wound dressings using Hydrogels, Pressure releasing treatments like foot braces, Compression wraps, shoe Inserts, Surgeries and Vascular reconstruction. The majority of existing therapeutic approaches are unable to produce effective wound healing. The major downside of these therapies is that they are inexpensive; nevertheless, hydrogel dressings can induce peri-wound maceration or hyper granulation, as well as adhering to the wound and causing stress to the fragile skin. In this manuscript, an effort is made by the authors to develop an all-natural composite bio sponge that could heal the DFU using sodium alginate (SA). The research community has widely expressed the qualities of such similar bio sponges which include biocompatibility and non-toxicity in using biopolymer like sodium alginate in various biomedical applications as it has a good track record of healing DFUs. These bio sponges can be modified by using different materials with wound-healing-friendly characteristics. Alginate is utilized to produce various wound dressing materials, including alginate, hydrocolloid, hydrogel, foam, wafers, film dressings and nano fibers. Alginate wound dressings can minimize bacterial infections at the wound site by maintaining a physiologically moist environment, and absorb extra wound fluid. The proportion of various polymers combined with alginate, the type of cross linker used, the length of cross linking, the type of excipients used, the incorporation of copper nanoparticles, and the use of antibacterial agents are all factors that influence the therapeutic efficacy of these wound dressings.
Enhancing Employee Promotion Prediction with a Novel Hybrid Model Integrating Convolutional Neural Networks and Random Forest

In the ever-evolving landscape of human resources, the critical task of identifying employees ready for promotion remains a complex challenge. To address this issue, we propose a novel hybrid model that seamlessly integrates Convolutional Neural Networks (CNNs) with Random Forest. Through a two-step process, we initially train the CNN comprising Conv1D and Dense layers. Subsequently, we harness the extracted features from the Nth layer, merging them with the original dataset. These augmented features are then input into the Random Forest algorithm. This innovative approach has yielded remarkable results, achieving an astounding accuracy rate of 99%. This surpasses the performance of both standalone Random Forest and CNN models, as well as various other machine learning methods. The presented model not only enhances the prediction accuracy for employee promotions but also offers a powerful tool for HR managers seeking to make informed and data-driven decisions in workforce advancement, ultimately contributing to more effective and efficient talent management.
Research on Fine-Grained Image Classification with Residual Network and Feature Fusion

With the development of deep learning, deep residual networks have been emerging. Deep residual networks are widely used in image classification by virtue of the advantages of mitigating the gradient vanishing problem and solving the degradation of the depth performance of traditional deep convolutional increasing networks. Fine-grained image classification is a subcategory of image classification that classifies subcategories of the same category. In this paper, we first use Mixup, a data augmentation approach, to increase the diversity of training samples and thus improve the generalization ability of the model, then we propose to solve the challenges posed by the fine-grained classification problem with a deep residual network, ResNet50, and finally, the vectors that have been processed through the last three layers of ResNet50 are spliced in order to achieve multilayer feature fusion. Comparison with the traditional method on the CUB_200_2011 dataset shows that our method can achieve better classification results.
Geochronology and phase equilibria modelling of ultra‐high temperature sapphirine + quartz‐bearing granulite at Usilampatti, Madurai Block, Southern India

Sapphirine‐bearing granulite from Usilampatti in the Madurai block of southern India preserves a variety of mineral textures and reactions that help in reconstructing a three‐stage metamorphic evolution. Corroded biotite, sillimanite and quartz inclusions within garnet represent relics from the prograde history. Peak metamorphic conditions were attained with the development of sapphirine + quartz in textural equilibrium (Stage 1). This was followed by nearly isothermal decompression, leading to the formation of sapphirine + cordierite at Stage 2. Subsequent retrograde hydration (Stage 3) is only locally evident. Using the Perple_X software and the model system NCKFMASH, the peak P‐T conditions were estimated from core compositions, and the retrograde evolution was deduced from rim or symplectite compositions of different minerals as computed by isopleths of XMg garnet, XCa garnet, XMg orthopyroxene, XMg sapphirine and XMg biotite. The P‐T conditions for Stage 1 thus obtained, and supported by thermodynamic modelling using the winTWQ programme, is approximately 9 kbar and 940°C. Stage 2 conditions were constrained as 6.7 kbar and 900°C. Dating of zircon and monazite in the sapphirine‐bearing granulite and associated gneisses by the U‐Pb method using LA‐ICP‐MS indicates metamorphic overprint of zircon (lower intercept ages of discordant data arrays) at 546 ± 8 and 547 ± 11 Ma and metamorphic growth of monazite between 542 ± 3 and 551 ± 2 Ma. Upper intercept ages for zircon point to zircon growth at approximately 2514 ± 66 Ma. Although it remains unclear whether the metamorphic age data refer to Stage 1 or Stage 2 or, most likely, a continuum between both, they clearly document a late Ediacaran age for ultra‐high temperature (UHT) metamorphism in the area, which, based on the obtained P‐T path, was most likely the result of crustal thickening followed by uplift and erosion. Thus, it is concluded that the sapphirine‐bearing granulites formed in response to Pan‐African orogeny that led to the collision of the western and eastern Madurai domains, whereas initial zircon growth probably took place during late Neoarchaean arc magmatism that formed much of the western domain. Copyright © 2017 John Wiley & Sons, Ltd.
Enhancing Fraud Detection in Transactive Energy Markets: Investigating the Impact of Data Augmentation on the Performance of Machine Learning and Deep Learning Techniques

The rise of e-banking has resulted in an increase in fraudulent transactions within Energy Markets, emphasizing the critical need for robust fraud detection systems in financial institutions. A major challenge in fraud detection is the prevalent class imbalance issue found in the datasets used for analysis. Previous research has employed diverse methodologies to develop effective classification models. This study investigates the impact of data augmentation techniques on the performance of both traditional machine learning methods and deep learning methods, aiming to enhance fraud detection capabilities. To accomplish this objective, widely used machine learning techniques, including decision trees, support vector machines, and random forests, are combined with two deep neural network models. Through an extensive series of experiments, the findings demonstrate a significant improvement in the performance of the random forest model when data augmentation is applied. This improvement is measured using the F1-Measure metric and surpasses the performance of the other compared methods. Additionally, the results indicate that, overall, the inclusion of data augmentation enhances model performance in terms of recall but may have an impact on precision. Notably, the performance of deep learning methods is negatively affected by the application of data augmentation. The research contributes to the development of more accurate fraud detection systems in energy markets, benefiting financial institutions and energy market operators in combating fraudulent activities and strengthening transaction security and reliability.
Study of Structures on Face Recognition with Optimal Transport for Small Data Resources

Face recognition in real-world scenarios poses challenges due to diverse image qualities and varying noise levels. To improve the Deep Joint Distribution Optimal Transportation (DeepJDOT) model's performance, we propose a two-step preprocessing strategy involving facial detection and cropping. Our goal is to achieve highly precise face recognition in the presence of different noise environments. We curate a dataset of 18 Hollywood celebrities with diverse appearances for evaluation. To enhance the DeepJDOT model, we adopt a sophisticated neural network architecture, enabling it to capture intricate facial features. Leveraging optimal transport, the model aligns class representations between domains and adapts to various noise scenarios. Through rigorous evaluation, our enhanced DeepJDOT model demonstrates superior face recognition performance, setting a new standard for robustness in real-world scenarios.
Optimal RIMC-based DLC strategy for time-delayed higher-order integrating and unstable processes with a case study on inverted pendulum

This study explores a double loop control (DLC) mechanism enhanced by a relocated internal model control (RIMC) approach, tailored for complex industrial processes characterized by integrating or unstable dynamics and inherent dead-time. The inner loop stabilization is managed through a proportional-derivative (PD) controller, which is designed adhering to the Routh stability criteria. For the outer loop, the RIMC concept is employed to construct a servo controller. Optimizing the controller settings is achieved using the equilibrium optimizer, a metaheuristic technique. This paper presents an evaluation of the stability and robustness of the newly developed RIMC-enhanced DLC system. Compared to existing methods, this approach demonstrates a significant enhancement in performance metrics. The effectiveness of this strategy is further illustrated through a case study involving an inverted pendulum, where the system efficiently managed disturbance pulses.
A Pro-Active Defense Framework for IoT Systems

As the number of interconnected IoT devices continues to skyrocket, so does the attack surface for malicious actors. loT devices are often resource-constrained and deployed in diverse and challenging environments (e.g., critical infras-tructures), making them susceptible to various vulnerabilities and attacks. Additionally, the attacks are often multi-step, which complicates the detection and mitigation efforts. Without proactive measures, these vulnerabilities can be exploited to devastating effects. In this paper, we propose a proactive defense framework for IoT systems security. We provide a high-level overview of the various components, encompassing training, detection, and attack management. Our approach begins with the design and implementation of IoTEDef, an innovative network intrusion detection system that employs per-step detectors based on the cyber kill chain and self-evolves to enhance the accuracy of the early step detectors. We present experimental results demonstrating significant enhancements in detector adversarial robustness following multiple rounds of evolution. We then briefly describe IoTPredictor, which utilizes real-time alerts generated by IoTEDef to predict potential attack next-steps, and IoTAtM, a system that leverages these predictions to take optimal actions for containing attacks using reinforcement learning. We also present IoTDSCreator, a tool designed to generate realistic datasets of multi-step attacks, serving as a valuable training data source for those components. We conclude by outlining future research.