Miracle-dz/UniFAR-FTUs huggingface dataset:
Path Planning for Cellular-connected UAV Using Parabolic Equation-based Radio Wave Propagation Models

The high flexibility and mobility of cellular-connected unmanned aerial vehicles (UAVs) make them an important component of future wireless communication systems. However, planning their flight path is a challenging task, due to weak coverage regions in the sky and limited flight time. In this paper, we propose a novel approach that combines site-specific radio wave propagation models and deep reinforcement learning techniques to optimize the flight path for UAVs. Our approach aims to minimize the flying time while ensuring reliable communication connectivity with base stations. To that end, a parabolic equation method is employed to model radio wave propagation, while deep Q networks are applied to determine the optimal path. The proposed approach takes into account physical environmental factors and eliminates the need for time-consuming and labor-intensive measurement campaigns. We present numerical results for various random initial flying points, demonstrating the effectiveness of our approach in path planning for cellular-connected UAVs.
Effect of Total Dose Irradiation on Parasitic BJT in 130 nm PDSOI MOSFETs

In this work, the effects of total dose irradiation on the parasitic bipolar junction transistor (BTJ) in 130 nm PDSOI MOSFETs were investigated. The experimental results demonstrate that irradiation-induced oxide-trap charges can modify the E-B junction barrier, and thereby make the common-emitter gain β0 of the parasitic BJT in NMOS device increase, while decreasing it in a PMOS device. Additionally, irradiation-generated oxide-trap charges in shallow trench isolation (STI) elevate the surface electrostatic potential of the gate above the STI sidewall, thus providing an additional channel from the emitter to the collector. Moreover, these charges may generate parasitic reverse conductive paths at the STI/Si interface under high dose irradiation, thereby enhancing the leakage current in the front gate channel and diminishing the significance of the parasitic BJT. Under irradiation, the electric field intensity difference between two biases leads to higher β0 of the parasitic BJT in PG-biased devices than in ON-biased ones. Furthermore, the lifting effect of irradiation on β0 increases in wide or short channel irradiated devices, which can be explained using simulations and an emitter current crowding effect model.
Camillo Golgi’s Impact on Malaria Studies

Camillo Golgi was an esteemed Italian physician and biologist who made major advances in malaria research between the late 19th and early 20th centuries. His groundbreaking contributions in histology, especially through the development of the Golgi staining technique, revolutionized our understanding of cell structures—including Plasmodium parasites—through visualization. Golgi staining also allowed researchers to observe its complex life cycle while documenting it. His careful observations of malaria led to the identification and characterization of its various stages, both asexual forms within human red blood cells, as well as sexual forms carried by mosquito vectors. Golgi’s research highlighted the key role mosquitoes play in malaria transmission. He demonstrated the presence of Plasmodium sporozoites within the salivary glands of infected mosquitoes, providing insight into its life cycle and the dynamics of parasite transmission. His comprehensive approach contributed significantly to our understanding of malaria as a systemic illness, leading to subsequent research efforts within this field. The Golgi Protein complex is often located within the cis-Golgi of blood parasite life cycles and mosquito stages, indicating its possible role in optimizing asexual development during blood stages. Furthermore, its expression can be conditionally repressed or its gene can be inactivated to optimize this potential role in improving its functionality for optimizing sexual development during blood stages. Camillo Golgi remains one of the leading lights of malaria research today. His innovative staining techniques, detailed observations, and insightful interpretations have laid the groundwork for subsequent discoveries and advancements in malaria studies. By deciphering intricate parasite life cycle interactions with hosts, his work has provided invaluable insights into malaria biology, pathogenesis, and epidemiology.
Journal impact factor: a brief review.

At that time it did not occur to me that it would one day become the subject of widespread controversy. Like nuclear energy, the impact factor has become a mixed blessing. I expected that it would be used constructively while recognizing that in the wrong hands it might be abused. In the early 1960s Irving H. Sher and I created the journal impact factor to help select journals for the Science Citation Index (SCI). We knew that a core group of highly cited large journals needed to be covered in the SCI. However, we also recognized that small but important review journals would not be selected if we depended solely on simple publication or citation counts. 2
Korean Red Ginseng Prevents the Deterioration of Lung and Brain Function in Chronic PM2.5-Exposed Mice by Regulating Systemic Inflammation

This study was conducted to confirm the effects of Korean red ginseng on lung and brain dysfunction in a BALB/c mice model exposed to particulate matter (PM)2.5 for 12 weeks. Learning and cognitive abilities were assessed with Y-maze, passive avoidance, and Morris water maze tests. To evaluate the ameliorating effect of red ginseng extract (RGE), the antioxidant system and mitochondrial function were investigated. The administration of RGE protected lung and brain impairment by regulating the antioxidant system and mitochondrial functions damaged by PM2.5-induced toxicity. Moreover, RGE prevented pulmonary fibrosis by regulating the transforming growth factor beta 1 (TGF-β1) pathway. RGE attenuated PM2.5-induced pulmonary and cognitive dysfunction by regulating systemic inflammation and apoptosis via the nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB)/c-Jun N-terminal kinases (JNK) pathway. In conclusion, RGE might be a potential material that can regulate chronic PM2.5-induced lung and brain cognitive dysfunction.
The impacts of climate change on cattle water demand and supply in Khurutshe, Botswana

ion rates of groundwater and sustainability for the baseline period 241 Cattle water demand and supply by the year 2050 . 249 Cattle water demand by 2050 249 Contribution of surface water supply to water demand by 2050 252 The impacts of climate change on evaporation rates by 2050 . 254 Forage water content for cattle water supply by 2050 255 Abstract rates of groundwater and sustainability by 2050 257 Groundwater sustainability by 2050 259 Discussion of the findings.262 The importance of groundwater in Botswana and abstraction management options . 267 Groundwater abstraction management for cattle water demand 268 Controlling stocking rates 268 Tradable pumping permits 270 Summary . 271 Chapter 10 : Conclusions and Summaries . 273 Introduction.273 Rational for choosing this topic and contribution to knowledge . 273 Cattle population and water resource in Botswana . 275 Methodology . 276 Climate scenarios (rainfall and temperature) . 277 Rainfall patterns and pans water for cattle.278 Forage water content . 279 Model development and validity.279 Results and recommendations.280 Cattle water demand 280
SOBRE MARATIIALES (FILICOPSIDAE) TRIASICAS DE ARGENTINA Y AUSTRALIA. PARTE I. EL GENERO ASTEROTHECA

Esta primera parte de una revision de las Marattiales Triasicas de Argentina y Australia esta dedicada a Asterotheca, un genero cosmopolita y de amplia distribucion estratigrafica. Comprende: a) La redescripcion e ilustracion de algunas especies pobremente o mal conocidas. b) una breve discusion y/o mencion de las especies bien conocidas. c) un nuevo nombre, A. rigbyana, que se propone en cambio del mal usado y confuso A. fuchsi Solms, que queda como nombre dudoso. d) la descripcion de una especie diferente, no bautizada, del Triasico inferior de Tasmania, Australia. e) una breve discusion por la que A. denmeadi Walkom, es muy dudosamente aceptada como una Asterotheca. Para todas las especies se dan amplios datos geologicos y geograficos.
Reinforcement Learning-Based Connectivity Restoration in an Ocean Network of Fishing Vessels

An Offshore Communication Network (OCN) is a network of fishing vessels at sea aimed at providing wireless Internet access over the ocean. The connectivity of fishing vessels is essential to disseminate messages, monitor emergency management, and provide information services. The impact of extreme weather conditions on wireless signals, the inability to deploy additional infrastructure, the movements induced by sea waves, the expanded mobility freedom at sea, and the nonuniform density of nodes create connectivity holes in an OCN. This paper proposes a reinforcement learning-based connectivity restoration scheme for OCNs. During the planning phase, a node examines the history of its contacts with other nodes and estimates their mobility vector to determine the expected location. Mobile nodes discover the best spots to re-establish connectivity and the most appropriate path to reach these spots via a trialand-error strategy. In a reinforcement learning framework, we simulate actions to move toward the expected contact locations and learn the optimal movement directions without guessing the contact’s actual position. During the control phase, these learned policies are utilized to relocate isolated OCN nodes and restore high-quality connectivity. Simulation results show that our scheme improves the nodes’ connectivity probability.
Forages. Volume 1: An introduction to grassland agriculture.

Forages, Volume I, Seventh Edition is the most comprehensive text available for teachers of undergraduate Forages courses. This edition will provide students with a good balance of scientific principles, to aid in integrating the concepts they learn, and practical information on forage identification, plant characteristics, management, and utilization that can be used by forage management practitioners. Grassland ecosystems are extremely complex, including the plant/animal interface as well as the soil/climate/forage interface and the text must support understanding and integration of all of these considerations. The coverage of the science behind the plant characteristics and responses make the book applicable in many parts of the world, while other region-specific management information relates mainly to North America. This edition has been updated to address emerging areas of study, including the use of forage plants as bioenergy crops. The editors also address the renewed national interest in environmental issues such as water quality, global climate change and eutrophication in the Gulf. This edition also addresses the role of forages for wildlife habitat and food sources, another area of increased interest in recent years. These revisions respond to the generational change taking place among forage scientists and teachers in recent years.
THEORETICAL ASPECTS OF VIBRATIONAL SPECTROSCOPY OF CONDENSED SYSTEMS WITH IMPURITY PARTICLES

Some problems of vibrational spectroscopy of particles in condensed systems are considered in this work. One of the aspects of theoretical research is the study of the vibrational properties of individual particles in view of the nano-dimension of the molecules of the condensed system surrounding the particle. Using the apparatus of temperature, Green functions of the operators of polarization of condensed systems, two main mechanisms of influence on impurity particles from the medium, solvation and fluctuation, are distinguished. Theoretical results are obtained within the framework of these two mechanisms for calculating changes in the vibrational spectrum of individual particles. The theoretical results are used to analyze the experimental data on the vibrational spectra of the N2O molecule in polar solvents: methanol, ethyl alcohol, acetone, and 1,2-dichloroethane.
Detecting Improper Driving via Frozen CLIP

Road accidents have irrecoverable outcomes despite being predictable and preventable. Since one of the main reasons is human factor, which consists of driver drowsiness and distraction, researchers propose methods to detect improper driving using computer vision models. With the help of Contrastive Language-Image Pre-training (CLIP), image representations are learnt with a high generalization capacity under the supervision of natural language. The method adopts frozen CLIP model to extract features which are used to decode the drowsiness and distraction cues. Our method processes the video stream obtained from camera and effectively detects driver drowsiness and distraction. Experiments on NTHU Driver Drowsiness Detection and DMD Driver Monitoring datasets indicate that utilizing CLIP model and transformer decoder outperforms methods that use 2-dimensional CNN concatenated with LSTM or 3-dimensional CNNs.
The diurnal cycle observed by Meteosat-8 and simulated by a climate model

An analysis of the diurnal cycle in clouds and water vapour using observations from Meteosat-8 is presented. A combination of broadband and narrowband data from the GERB (Geostationary Earth Radiation Budget) and SEVIRI (Spinning Enhanced Visible and Infra-Red Imager) instruments provide information about daily variations in surface temperature, cloud and water vapour over Africa and surrounding regions. The high spatial and temporal resolution in the data allow a more detailed analysis than has previously been possible, particularly when considering topographic effects. Outgoing longwave radiation (OLR) data from GERB is analysed using empirical orthogonal functions (EOFs) and reveals interactions between solar heating, surface temperature and cloud evolution. These findings are used to help validate the new HiGEM (High resolution Global Environmental Modelling) high resolution climate model. To extend this work, narrowband radiances from SEVIRI are used to investigate the diurnal cycles in upper tropospheric cloud and humidity.
Amazing Reptile Fossils from the Marine Triassic of China

Known as the “true terrestrial tetrapod,” reptiles mainly live on land. However, some reptile groups made a secondary adaptation to their life in water. Generally called “marine reptiles,” they are mostly extinct, and their fossils are found in Mesozoic marine deposits around the world. Ichthyosaurs and Plesiosaurs are the most famous marine reptiles that lived in the “dinosaur age,” namely the Jurassic and Cretaceous. Many more “sea monsters” were ruling the ocean in the Triassic period, when dinosaurs had just arisen from its archosaur ancestors. I n t h e T r i a s s i c , m a r i n e reptiles were inhabitants of a nearshore environment and shal low epicontinental seas. So far their fossils are found in three major faunal provinces, i.e the Western Tethyan Province (nowaday Europe and the Middle East), the Eastern Tethyan Province (or by some authors, the Western Pacific Province, nowaday southern China) and the Eastern Pacific Province (North America). The collecting and the study of marine reptile fossils in Europe have a long history of nearly 200 years, while in China, the first specimen, named as Keichousaurus hui, was not published until 1956. By 1999, only a few genera and species had been known. All these specimens, mainly ichthyosaurs and nothosaurs, are incomplete and poorly preserved. Furthermore, some of these taxa were proved to be the nomen dubium (invalid name), such as Shingyisaurus, which is now referred to as Nothosaurus. sp. Great changes happened during the following decades. Since 1999, hundreds of complete skeletons have been excavated from Guizhou and Yunnan provinces, southwestern China with the discovery of at least 24 new genera. Among these new collections are some important reptile groups never reported before from the Eastern Tethyan Province, such as the placodont, the thalattosaur, and the protorosaur. Besides, some more amazing taxa came to light, including the first marine “thecodonts” and the most primitive turtle.
A Practical and Scalable Privacy-preserving Framework

ENCRYPT is an EU funded research initiative, working towards the development of a scalable, practical, adaptable privacy-preserving framework, allowing researchers and developers to process data stored in federated cross-border data spaces in a GDPR-compliant way. ENCRYPT proposes an intelligent and user-centric platform for the confidential processing of privacy-sensitive data via configurable, optimizable, and verifiable privacy-preserving techniques. Research and development activities leverage, improve, and complement technologies and cryptographic schemes that represent the current state-of-the-art in the field of data-in-use protection. Hence, ENCRYPT builds on top of cutting-edge technologies such as Fully Homomorphic Encryption, Secure Multi-Party Computation, Differential Privacy, Trusted Execution Environment, GPU acceleration, knowledge graphs, and AI-based recommendation systems, making them configurable in terms of security and, most importantly, performance. The ENCRYPT framework is being designed taking into consideration the needs and preferences of relevant actors and will be validated in realistic use cases provided by consortium partners in three sectors, namely healthcare (oncology domain), fintech, and cyber threat intelligence domain. This position paper provides an overview of ENCRYPT by presenting project objectives, use cases, and technology pillars.
Web-plugin to Detect Clickbait in News Articles using RNN and LSTM

'Clickbaits' are either false or misleading headlines that aim to attract users' attention and entice them to click on a link or a news article that isn't really interesting as the title makes it up to be. A clickbait title can degrade the user experience and spread disinformation or misinformation on the Internet. In this study, a novel approach for Clickbait detection in news articles using machine learning technique of Recurrent Neural Network and Long-Short Term Memory is proposed. The clickbait detection process takes place in two distinct steps, first to identify the headlines in a website using a web plug-in, these headlines are then fed into a machine learning model which can predict if the headline is a clickbait or not. This a binary classification problem where the model is trying to predict which of the two categories the input belongs to. The algorithm is put into action using a web plugin which is triggered when popular news websites are visited and fetch the headlines using DOM method, the headlines are then sent to the server for prediction upon which the headlines get marked either red or green which indicates the headlines to be either clickbait or not respectively which, this makes the user aware if a headlineis clickbait or not with just a glance. The result obtained by the above method is an improvement over the existing methods and the user interface provided by the web plugin removes any manual labor from the user side and offers an engaging yetsimplistic user experience.
Hydrogeochemical Characteristics and Groundwater Quality in Phreatic and Confined Aquifers of the Hebei Plain, China

This study aims to investigate hydrogeochemical characteristics and groundwater quality in the Hebei Plain and to discuss factors controlling the groundwater quality. A total of 54 groundwater samples were collected and analyzed for 31 hydrogeochemical parameters, and a fuzzy synthetic evaluation (FSE) method was used for assessing groundwater quality. Results show groundwater total hardness, total dissolved solids (TDS), and major ions excluding K+ in phreatic aquifers higher than that in confined aquifers. From the Piedmont plain to the littoral plain, phreatic aquifers towards the reducing environment, and the enhancement of water–rock interaction, ion exchange process, and evaporation probably resulted in the increase in groundwater TDS, major ions (excluding HCO3− and SO42−), B, and Mn concentrations. Moreover, phreatic groundwater chemistry was mainly controlled by rock weathering changing into evaporite dissolution and seawater intrusion from the Piedmont plain to the littoral plain, according to the Gibbs diagram. The proportion of drinkable groundwater in confined aquifers was 1.6 times that in phreatic aquifers. In phreatic aquifers, the proportion of drinkable groundwater in the Piedmont plain was as high as 68%, but none of the drinkable groundwater occurred in the central and littoral plains. Groundwater quality in phreatic aquifers was mainly controlled by five factors, including the water–rock interaction, the marine geogenic sources, the agricultural pollution, the acidification, and the reductive environment. By contrast, groundwater quality in confined aquifers was mainly controlled by three factors, including the water–rock interaction and redox processes, agricultural pollution, and the input of external water. Therefore, in the Hebei Plain, groundwater in confined aquifers is more suitable for drinking purposes than in phreatic aquifers. Additionally, phreatic groundwater in the Piedmont plain should be protected.
Fast Simulation Method with Reinforcement Learning for Automated Optimization of Electronic Systems

Design automation of electronic systems is challenging due to the growing design space, high performance tradeoffs, and rapid technological advances. To solve this problem, this paper presents an automated optimization framework that combines Fast Simulation with deep reinforcement learning for automatic circuit design. Fast Simulation can quickly and accurately evaluate circuit performance by neural networks. Deep reinforcement learning is used to find optimal parameters in the design space. Compared with existing reinforcement learning methods, the proposed method can automatically generate labels for the optimization results of the reinforcement learning agent by the simulator to retrain the neural network. To this end, the proposed optimization method performs better designs and reduces the required number of simulations.
Part-of-Speech (POS) Tagging for Standard Brunei Malay: A Probabilistic and Neural-Based Approach

—As online information increases over the years, text mining researchers developed Natural Language Processing tools to extract relevant and useful information from textual data such as online news articles. The Malay language is widely spoken, especially in the Southeast Asian region, but there is a lack of Natural Language Processing (NLP) tools such as Malay corpora and Part-of-Speech (POS) taggers. Existing NLP tools are mainly based on Standard Malay of Malaysia and Indonesian language, but there is none for the Bruneian Malay. We addressed this issue by designing a Standard Brunei Malay corpus consisting of over 114,000 lexical tokens, annotated using 17 Malay POS tagsets. Furthermore, we implemented two commonly used POS tagging techniques, Conditional Random Field (CRF) and Bi-directional Long Short-Term Memory (BLSTM), to develop Bruneian POS taggers and compared their performances. The results showed that both CRF and BLSTM models performed well in predicting POS tags on Bruneian texts. However, CRF models outperform BLSTM, where CRF using all features achieved an F-Measure of 92.06% on news articles and 90.71% of F-Measure on crime articles. Adding a batch normalization layer to the BLSTM model architecture increased the performance by 7.13%. To further improve the BLSTM models, we suggested increasing the training data and experimenting with different hyperparameter settings. The findings also indicated that modelling BLSTM with fastText has improved the POS prediction of Bruneian words.