Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of climate change on food security

In addition to compromising food security, climate change-related extreme weather lowers urban residents' incomes and access to food. Unexpected effects of climate change on food availability are possible. This review paper aims to investigate how climate change affects the availability of food. Climate change can have a wide range of effects on agricultural systems, making it more challenging to sustainably produce enough food and inexpensive, nutrient-dense food. We must broaden our narrow focus from increasing yield and plant productivity to take into account the impact of climate change on the nutritional content of food crops in order to fully comprehend how climate change is affecting our world's food supply. Proactive adaptation can boost capacity to manage climate change by integrating climate change in long-term decision-making and eliminating disincentives to adjust behaviour in response to climate change.
Pspray: Timing Side-Channel based Linux Kernel Heap Exploitation Technique

The stealthiness of an attack is the most vital consideration for an attacker to reach their goals without being detected. Therefore, attackers put in a great deal of effort to increase the success rate of attacks in order not to expose information on the attacker and attack attempts resulting from failures. Exploitation of the kernel, which is a prime target for the attacker, usually takes advantage of heap-based vulnerabilities, and these exploits’ success rates fortunately remain low (e.g., 56.1% on average) due to the operating principle of the default Linux kernel heap allocator, SLUB. This paper presents P SPRAY , a timing side-channel attack-based exploitation technique that significantly increases the success probability of exploitation. According to our evaluation, with 10 real-world vulnerabilities, P SPRAY significantly improves the success rate of all those vulnerabilities (e.g., from 56.1% to 97.92% on average). To prevent this exploitation technique from being abused by the attacker, we further introduce a new defense mechanism to mitigate the threat of P SPRAY . After applying mitigation, the overall success rate of P SPRAY becomes similar to that from before using P SPRAY with negligible performance overhead (0.25%) and memory overhead (0.52%).
Classification of Chest X-ray Images using Convolutional Neural Nework

The current worldwide Covid-19 epidemic is linked to a respiratory lung infection caused by a novel corona virus disease (SARSCoV- 2), the evolution of which is still not known. More than 100,000 cases were confirmed worldwide using the current case definition of Covid-19 infection, based on pneumonia diagnosis, with a death rate ranging between 2% and 3%. Since the expanding sick population might not have simple access to current laboratory testing, new screening techniques are necessary. The Computed tomography of chest is an important technique for the former detection and treatment of Covid-19 pulmonary symptoms, even though its utility as a screening tool has not yetbeen established. Even though it lacked specificity, it exhibited excellent sensitivity. We demonstrate a neural network based on pneumonia and covid classification in Tensor Flow and Keras. The suggested method is based on the CNN uses images and the CNN model to categorize Covid-19 or pneumonia. It is anticipated that discoveries will become more successful. If the covid-19 or pneumonia classification algorithms and other feature extraction methods are added, the CNN approach will be successfully supported.
THE ε – ε PROPERTY AND THE BOUNDEDNESS OF ISOPERIMETRIC SETS WITH DIFFERENT MONOMIAL WEIGHTS

We consider a class of monomial weights x A = | x 1 | a 1 . | x N | a N in R N , where a i is a nonnegative real number for each i ∈ { 1 , . , N } , and we establish the ε − ε property and the boundedness of isoperimetric sets with diﬀerent monomial weights for the perimeter and volume. Moreover, we present cases of nonexistence of the isoperimetric inequality when it is not possible to associate the corresponding Sobolev inequality. Finally, for N = 2, we developed an original type of symmetrization, which we call star-shaped Steiner symmetrization
Impact of the assimilation of water vapour isotopologues on diabatic heating and precipitation in the tropics

The strong coupling between atmospheric circulation, moisture pathways and atmospheric diabatic heating is re-sponsible for most climate feedback mechanisms and controls the evolution of severe weather events. However, diabatic heating rates obtained from current meteorological reanalysis show signiﬁcant inconsistencies. Here, we theoretically assess with an Observation System Simulation Experiment (OSSE) the potential of the MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water (MUSICA) Infrared Atmospheric Sounding interferometer (IASI) 5 mid-tropospheric water isotopologue data for constraining uncertainties in meteorological analysis ﬁelds. For this purpose, we use the Isotope-incorporated General Spectral Model (IsoGSM) together with a Local Ensemble Transform Kalman Filter (LETKF) and assimilate synthetic MUSICA IASI water vapour isotopologue observations. We perform four ensemble simulations, three where synthetic IASI isotopologue and humidity data are assimilated and another one where no observational data at all are assimilated. By comparing the ensemble simulations with data assimilation with water isotopologues to the one 10 without any data assimilation we can, in contrast to the former study by Toride et al. (2021) where additionally conventional observations where considered, assess the direct impact of the IASI δ D and water vapour data on the meteorological variables, especially on the heating rates and vertical velocity. The assessment is performed for the tropics in the latitude range from 10 ◦ S to
From a Prediction Model to Meaningful Reports in School

Predictive analytics and prediction modeling has emerged as one of the largest applications of educational data mining methods, with particularly widespread application in dropout prediction. In this demo, we present reports from the BrightBytes Early Warning Solution, a widely-used platform providing information on which students are at risk of dropout and why. We show reports for teachers on individual students, the intervention management system they are integrated into, and more aggregated reports for school and district leaders. These reports and integration with intervention management are designed to help districts move from knowing which students are at risk to taking action to reduce student risk and improve outcomes.
An Outlook on The Contribution of Growing Number of Chinese Tourists in International Tourism

: The benefits of international travel extend well beyond the tourist sector. The retail, healthcare, and education sectors may all benefit from an increase in outbound tourism, which can help many nations' economy grow. The rise of Chinese outbound travel is a significant recent trend in the tourist sector. The rapid growth of Chinese outbound tourism has piqued the curiosity of academics as well as practitioners in the business. Many nations across the globe are now making it easier for Chinese people to get visas than they were in the past. In this paper, the contribution of number of Chinese tourists in international tourism is brought under review. Several past researches made over the contribution of Chinese outbound tourism in international tourism is reviewed. Based on this review, this paper gives some guidance for future empirical study on this important subject matter.
In vitro investigation of phytochemicals, Antioxidant and Hepatoprotective activity of Selaginella bryopteris

: The present study was aimed to evaluate phytoconstituents, total phenolic content, free radical scavenging activity and in vitro hepatoprotective effect of Selaginella bryopteris (SB) against acetaminophen (APAP) and carbon tetrachloride (CCl 4 ) induced hepatotoxicity on HepG2 cell lines. The preliminary phytochemicals screening of aqueous and ethanolic extract of SB confirmed the presence of phytochemical constituents and total phenolic contents. Both SB extracts exhibited free radical scavenging activities in dose dependent manner (100-500 μg/mL). Ethanolic extract at higher concentration (500 μg/mL) showed highest DPPH, ABTS and H 2 O 2 free radical scavenging activity followed by the SB aqueous extract. In vitro hepatoprotective activity was assessed by MTT assay confirmed the dose-dependent cellular protection (72%-88%) by SB extracts at three doses (25μg/mL, 50μg/mL and 100μg/mL) against CCl 4 and APAP induced toxicity in HepG2 cell line, the cell viability rate was found to be higher at 100μg/mL in SB ethanolic extract compared to the SB aqueous extract. No adverse effect was observed in the SB extract alone treated HepG2 cell line. The results indicated that Selaginella bryopteris extract has significant in vitro hepatoprotective activity against APAP and CCl 4 -inducedtoxicity which might be due to the presence of free radical scavenging phytoconstituents.
Bridging Software-Based and Hardware-Based Fault Injection Vulnerability Detection

—Software-based and hardware-based approaches have both been used to detect fault injection vulnerabilities. Software-based approaches can provide broad and rapid coverage, but may not correlate with genuine hardware vulnerabilities. Hardware-based approaches are indisputable in their results, but rely upon expensive expert knowledge and manual testing. This work bridges software-based and hardware-based fault injection vulnerability detection by contrasting results of both approaches. This demonstrates that: not all software-based vulnerabilities can be reproduced in hardware; prior conjectures on the fault model for EMP attacks may not be accurate; and that there is a coincidence between software-based and hardware-based approaches. Further, combining both approaches can yield a vastly more accurate and efﬁcient approach to detecting genuine fault injection vulnerabilities.
The control of surgical injuries utilizing rough neem oil in one year old ram: An effective report

Wound healing effect of crude neem ( A ZADIRACHTA INDICA Juss) oil, processed locally from the seed kernel was assessed on septic surgical wound in a one year old Uda ram. A wound approximately 7 cm in diameter created as a result of surgical drainage of abscess on the left caudal part of the elbow joint was flushed with the crude neem oil. A single application of the crude neem oil to the sutured wound resulted in a complete healing after seven days without systemic or topical application of antibiotics. Wound infection and dehiscence were not observed and the animal’s appetite improved significantly after the intervention and treatment. Thus, the study reveals crude neem oil may act as a cheaper, more convenient and effective wound healing oil in animals, especially, in the rural areas where veterinary services and/or orthodox chemically-based animal medicaments may not be readily available.
Airline Fare Prediction using Machine Learning

-Airline fare prediction plays a crucial role in enabling travelers to make informed decisions while planning their journeys. This study presents a data-driven approach utilizing the Random Forest algorithm to forecast airline fares accurately. By leveraging historical flight data, including various attributes such as departure and arrival locations, travel dates, airlines, and other relevant factors, we aim to develop a reliable model for fare prediction. The methodology employed involves several stages. Firstly, a comprehensive dataset containing a diverse range of flight records is collected, processed, and pre-processed. Feature engineering techniques are applied to extract meaningful insights from the data, ensuring the inclusion of relevant factors that impact fare fluctuations. Subsequently, the dataset is partitioned into training and testing sets to train the Random Forest model. The Random Forest algorithm is a powerful ensemble learning technique that combines multiple decision trees to make predictions. It provides robustness against overfitting and handles both numerical and categorical data effectively. Through an iterative process, the model is trained on the training set, and its performance is evaluated using various evaluation metrics such as mean absolute error and root mean squared error. To enhance the accuracy of the predictions, hyperparameter tuning is performed on the Random Forest model. This process involves systematically searching for the optimal values of model parameters using techniques such as grid search or random search. The best-performing model is then selected based on the evaluation results. Experimental results on real-world flight data demonstrate the efficacy of the proposed approach in predicting airline fares. The Random Forest model achieves high accuracy and generalizability, outperforming baseline models and showcasing its potential as a valuable tool for fare prediction. The insights derived from the model can assist both travelers and airline industry stakeholders in making informed decisions, optimizing pricing strategies, and improving overall customer satisfaction. In conclusion, this study presents an effective approach for airline fare prediction using the Random Forest algorithm. By harnessing the power of data and machine learning, it offers a practical solution to address the complex nature of fare dynamics in the aviation industry. The findings contribute to the advancement of fare prediction methodologies and hold significant implications for various stakeholders in the air travel ecosystem.
Least-squares support vector machine-based Kalman filtering for GNSS navigation with dynamic model real-time correction

The accuracy of Kalman filtering (KF) relies on the quality of the observations as well as the dynamic model. However, the dynamic model is usually assumed to be invariant, which is not realistic in real global navigation satellite system (GNSS) navigation applications due to unexpected vehicular motion. A new algorithm is proposed to enhance the KF by using the least-squares support vector machine (LSSVM) technique. The LSSVM-enhanced KF (LSSVM-KF) adaptively estimates the dynamic modelling bias from historical information, and then uses the bias estimate to compensate the dynamic model. The algorithm treats the dynamic model bias as a time-variant ambiguous function which is trained with the LSSVM. A k-fold cross-validation method is developed to tune the training parameters of the LSSVM. With the corrected dynamic model, the KF is implemented to estimate the navigation parameters. To integrate LSSVM with KF, the unscented transformation is introduced to numerically compute the covariance of the LSSVM training. To verify the algorithm, simulation, semi-simulation and real GNSS vehicular experiments were carried out. The results show that the LSSVM-KF can adequately adapt to time-variant dynamics and achieve a reliable and accurate GNSS navigation solution.
Modelling lymph flow in the lymphatic system: from 0D to 1D spatial resolution

In this study, we formulated a core mathematical model for describing the one-dimensional lymph flow in lymphatic vessels and branching network of lymphatic vessels. The 1D model was numerically implemented using the 1D haemodynamic modeling tools developed in T.M. Gamilov et al. and S. Simakov et al. [T.M. Gamilov et al. , Transl. Med. 6 (2013) 5–13 and S. Simakov et al. , Russian J. Numer. Anal. Math. Model. 28 (2013) 485–504]. The formulated model was calibrated using published data on lymph flow dynamics and other modelling studies of lymph flows. The comparison of 0D and 1D formulations of the lymph flow models is presented.
Estimation of Monthly Evapotranspiration using NOAA/AVHRR Satellite Images

: The purpose of this study is to estimate monthly evapotranspiration (ET) using normalized difference vegetation index (NDVI) obtained from NOAA/AVHRR data sets. Actual evapotranspiration was evaluated by the complementary relationship (Morton, 1978, Brutsaert and Stricker, 1979), and monthly NDVI was obtained by maximum value composite method from daily NDVI images in the Korean peninsula for the year 2001. The monthly actual ETs for each land cover were compared with the monthly NDVIs to determine relationships between actual ET and NDVI for each land cover category. There was a high correlation between monthly NDVI and monthly averaged actual ET. This study presents an alternative approach for land surface evapotranspiration based on remote sensing techniques.
Maxillary Sinus Floor Augmentation in Atrophic Alveolar Bone Using Osteotome Technique with a Crestal Approach as Indirect Method: A Case Report

: There are various sinus augmentation treatments, and factors influencing dental implant placement and sinus augmentation survival rates are still up for debate. In this case report, an indirect procedure using an osteotome technique and crestal approach was used. When there is more than 6 mm of remaining bone height and a rise of 3–4 mm is anticipated, the osteotome procedure may be advised. If the resorption is more advanced, a direct procedure involving a lateral antrostomy must be carried out.
A Wi-Fi Energy Model for Scalable Simulation

Wi-Fi devices are ubiquitous, thus they have been extensively studied to understand, for example, the impact of different channel conditions and network properties over network performance. However, improving network performance without considering energy consumption can lead to critical issues: battery depletion, higher costs, and increased latency. Existing works provide algorithms and techniques for more efficient use of energy for Wi-Fi communication, especially in the case of IoT networks, limited by battery capacity. But the ever-growing number of Wi-Fi devices along with the increase in traffic and heterogeneity of current networks make measuring the energy footprint of Wi-Fi communication particularly complex, especially at a large scale. Existing simulation models to study the energy consumption of Wi-Fi devices either suffer from scalability issues due to their fine granularity, or lack realism hindering their usage in practice. In this paper, we propose a power model tackling these scalability and accuracy issues through the use of a flow-based simulation model. By comparing the accuracy and performance of our model to state-of-the-art solution, we show that our approach achieves accurate energy predictions on large-scale and heterogeneous network infrastructures. Our flow-level model allows us to simulate the energy consumption of 800 nodes in a few seconds compared to more fine-grained simulators such as ns-3 that require more than 8 hours under the same scenario, with similar accuracy.
Erratum: Energy-momentum tensor from the Yang-Mills gradient flow (Progress of Theoretical and Experimental Physics (2013) 2013 (083B03))

The product of gauge fields generated by the Yang-Mills gradient flow for positive flow times does not exhibit the coincidence-point singularity and a local product is thus independent of the regularization. Such a local product can furthermore be expanded by renormalized local operators at zero flow time with finite coefficients that are governed by renormalization group equations. Using these facts, we derive a formula that relates the small flow-time behavior of certain gauge-invariant local products and the correctly-normalized conserved energy-momentum tensor in the Yang-Mills theory. Our formula provides a possible method to compute the correlation functions of a well-defined energy-momentum tensor by using lattice regularization and Monte Carlo simulation.
REMOTE AMBIENT AEROSOLS FOUND IN THE SOUTHERN GREAT PLAINS AND EASTERN NORTH ATLANTIC: A COMPREHENSIVE ANALYSIS OF OPTICAL BEHAVIOR AND AEROSOL-CLOUD INTERACTION

By Daniel Joseph Bonanno University of the Pacific 2022 The data presented in this thesis showcases the investigation of various aerosol types and the role they play in the hydrological system and global climate models. Long-range transport aerosols, including atmospheric soot, mineral dust, and ammonium sulfate, in addition to nearsite sources such as secondary organic aerosols (SOAs), sea spray, aliphatic hydrocarbons, and other organics, are prioritized in the analysis of two separate remote field studies. Each of these campaigns revealed the juxtaposition between dry summer months and wet winter months, and the connection between seasonal variability and sample collection. Two spectromicroscopic techniques were used to elucidate chemical composition, morphology and size of particles. Quantification of C, N, and O was performed using scanning transmission X-ray microscopy coupled with near edge X-ray fine structure (STXM/NEXAFS) and used to generate organic volume fractions and to provide mixing state parameters and diversity measurements. This was followed by utilizing scanning electron microscopy in the energy dispersive X-ray (SEM/EDX) mode on the same particle sets to characterize 11 heavier elements: Na, Mg, P, S, Cl, K, Ca, Mn, Fe, Ni, and Zn.
The efficacy of different strains of antifungal producing bacteria in controlling peacock spot fungus caused by Cycloconium oleaginum

We report the antifungal activity of eight different bacterial isolates ( Bacillus megaterium NB-3, Bacillus cereus NB-4, B. cereus NB -5, Bacillus subtilis NB-6, Corynebacterium xerosis NB- 2, Burkholderia mallei NB-8, B. subtilis (HNEB-1) and B. cereus NEB II voru ) against the olive leaf spot fungus ( Cycloconium oleaginum ). B. subtilis NB-6 (6.88 cm 2 ), B. megaterium NB-3 (7.13 cm 2 ), B. subtilis HNEB-1 (7.63 cm 2 ) and B. cereus NB-4(7.88 cm 2 ) were found to control significantly the growth of C. oleaginum , where the strains of C. xerosis NB-2 (12.75 cm 2 ), B. mallei NB-8 (15.63 cm 2 ) and B. cereus NB-5 (16.25 cm 2 ) controlled the fungal growth under the experimental conditions. B. cereus NEBII voru is the only tested bacterial strain that enhanced the fungal growth of C. oleaginum under our experimental conditions (46.25 cm 2 ).
Tracing Authenticity: A Blockchain and NFT-Based Approach to Product Verification

- Counterfeiting of products has been a persistent issue for decades. It is a growing concern, particularly in the luxury goods and high-tech electronics industries. [1] Traditional methods of product verification are often ineffective, as they can be easily replicated by counterfeiters[2] Although traditional approaches like RFID tagging have been implemented in the past to stop non real product, the recent advancements in technology have led to the development of more efficient and secure solutions. The article outlines a strategy for using blockchain technology and NFT to stop product fraud. The proposed system employs a decentralized network of nodes to authenticate product information, making it difficult for attackers to manipulate data. Blockchain technology provides a secure and tamper-resistant platform for tracking and verifying the authenticity of products [3] Non-Fungible Tokens (NFTs) offer a unique and verifiable digital identity that can be linked to physical products, providing an additional layer of authentication. [4] Our proposed method guarantees high levels of security and is easily scalable. The system is made to be affordable and simple to use, making it the perfect option for businesses. Several studies have demonstrated the potential of blockchain and NFTs for product verification in different contexts (Jiang et al., 2019; Liao et al., 2019). By providing a secure and transparent supply chain, blockchain and NFTs can enhance consumer trust and protect the reputation and profits of legitimate manufacturers and retailers. The results of our experimentation demonstrate the effectiveness of the proposed system in preventing counterfeit products. Overall, the system presents a reliable and practical solution for authenticating products and preventing counterfeiting.