Miracle-dz/UniFAR-FTUs huggingface dataset:
Detecting Adverse Drug Reactions from User-Generated Twitter Data: A Case Study

Adverse Drug Reactions (ADRs) are defined as unwanted drug effects that cause induced mortality and morbidity in health-care. Health-related subjects can be discussed throughout the broad span of social media conversations. Plethora of information available in social media and health-related forums, as well as the rich expression of public opinion, has recently piqued the public health community’s interest in using these sources for pharmacovigilance. We investigate the role of sentiment analysis characteristics in detecting ADR mentions based on user generated dataset obtained from Twitter online streaming API. Our proposed model uses BERT-CNN model with final layer of Support vector machine (SVM) to classify the ADRs mentions. In our study, we extracted tweets from tweeter using Tweepy API and performed data pre-processing, data annotation and data augmentation to create a strong corpus. For data augmentation, we used Marian MT model for to increase the number of tweets with the help of back translation. We passed this corpus to BERT-Base model to get word embeddings and then used CNN model to get important features from data. To get the better efficiency, we used SVM which classifies a tweet. The evaluation study reveals that our proposed model achieved 92% accuracy and 78% F1score. Data augmentation and BERT pre-trained model are the main keys of our proposed model which help us to achieve better result than other machine learning models.
Broad-band-enhanced and minimal hysteresis perovskite solar cells with interfacial coating of biogenic plasmonic light trapping silver nanoparticles

ABSTRACT Perovskite solar cells (PSCs) have evolved into a viable potential alternative energy source due to their cost-effectiveness and simple fabrication techniques. Despite the immense research interest attracted by PSCs, their rooftop application remains constrained by their low efficiency and hysteretic nature. To address these issues, in this paper, the introduction of silver metallic nanoparticles (AgNPs) with plasmonic effect was achieved by an experimental approach to stimulate the generation and transport of charge carriers, which subsequently results to minimal hysteresis and broadband-enhanced plasmonic devices. Due to the far-field light scattering effect, AgNPs improve the light-trapping of the devices. As a result, electrical characteristics and light absorption are significantly enhanced, leading to broadband increase in photovoltaic performance. With the introduction of one coating cycle of AgNPs, the highest power conversion efficiency (PCE) of the perovskite solar cells increases from a reference value of 4.543 to 10.856%. The fill factor (FF) of such a device was 0.650, the current density (J sc) was 18.998 mA/cm2, and voltage (V oc) was 0.879 V. This device shows an improvement of ~139.0% in PCE, ~61.7% in FF, ~43.7% in J sc and ~2.8% in V oc over the pristine device that lacks AgNPs. The findings of this study contribute to further insight and understanding of plasmonic PSCs by offering useful references and recommendations for PSCs with noble metal NPs incorporation.
Global Horizontal Irradiance in West Africa: Evaluation of the WRF-Solar Model in Convective Permitting Mode with Ground Measurements

The number of solar power plants has increased in West Africa in recent years. Reliable reanalysis data and short-term forecasting of solar irradiance from numerical weather prediction models could provide an economic advantage for the planning and operation of solar power plants, especially in data-poor regions such as West Africa. This study presents a detailed assessment of different shortwave (SW) radiation schemes from the Weather Research and Forecasting option Solar (WRF-Solar) model with appropriate configurations for different atmospheric conditions in Ghana and the southern part of Burkina Faso. We applied a two one-way nested domain (D1=15 km and D2=3 km) to investigate four different SW schemes namely, CAM, Dudhia, RRTMG, Goddard and RRTMG without aerosol and with aerosol inputs (RRTMG_AERO). The simulation results were validated using hourly measurements from different automatic weather stations established in the study region in recent years. The results show that the RRTMG_AERO_D01 generally outperforms the other SW radiation schemes to simulate GHI under all-sky condition (RMSE=235 W/m2 (19%); MAE=172 W/m2 (14%)) and also under cloudy skies. Moreover, RRTMG_AERO_D01 shows the best performance on a seasonal scale. Both the RRTMG_AERO and Dudhia experiments indicate a good performance under clear skies. However, the sensitivity study of different SW radiation schemes in the WRF-Solar model suggests that RRTMG_AERO gives better results. Therefore, it is recommended to use it for solar irradiance forecasts over Ghana and the southern part of Burkina Faso.
Comprehensive Review of Different Pendulum Structures in Engineering Applications

For years, the pendulum system has been the most popular experimental setup for education and research in robotics and control theory. This paper examines the different kinematic and dynamic structures of pendulum systems, which are frequently used in literature, considering their usage in various engineering fields. Each pendulum system resembles a real physical system and provides a deeper understanding of the behavior of the system. Although each pendulum has its own advantages and disadvantages according to its structure and dynamics, these contain differences depending on the application area and usage method. This paper aims to review and simplify the understanding of the various pendulum systems used in research and applications, presenting a summary of the general properties of pendulum systems to the researcher.
Features Extraction and Concatenation of Transfer Learning Models for Early Diagnosis of Diabetic Retinopathy

Diabetic retinopathy is of interest because it is the cause of blindness in the working population and because its incidence is also increasing sharply in developing countries. Early detection of this disease is essential for a good prognosis. However, the lack of ophthalmologists and the cost of diagnosis with an increasing number of patients with diabetic retinopathy pose many problems. Rapid detection of this disease can indeed be helpful for ophthalmologists. This study deals with the development of an automatic system to classify and predict the diagnosis of diabetic retinopathy through retinal images. The system adopted is based on the technique of convolutional neural networks using a concatenation for all the feature maps of extraction of three models of transfer learning techniques such as ResNet50, InceptionV3, and EfficientNetB5 to generate a detection and prediction model. Following that, a classifier was given these features in order to classify diabetic retinopathy. The dataset used in this research is the publicly available Kaggle dataset (APTOS2019) of retina images with different preprocessing steps. The results obtained are promising, where the best achieved was 97.24% accuracy and 0.07% loss. It can be noted that the developed system has good performance compared to those published in the literature.
Recent Advances and Current Management for Desmoid Tumor Associated with Familial Adenomatous Polyposis

For nearly half a century, desmoid tumor (DT) has been considered a major complication that occurs in approximately 10%-25% of familial adenomatous polyposis (FAP) patients. It is also the leading cause of death in patients undergoing colectomy. We believe that the mortality rate is improving due to the understanding of the natural history of DT and recent advances in medical treatment. The risk factors of DT development include trauma, having a distal germline APC variant, having a family history of DTs, and estrogens. In the era of minimally invasive surgery, several reports demonstrated no significant difference in both surgical approach (laparoscopic vs. open) and surgical procedure (ileal pouch-anal anastomosis vs. ileorectal anastomosis). Regarding the treatment of FAP-associated DT, rapidly proliferating and life-threatening intra-abdominal DT accounts for approximately 10% of FAP-associated DT; however, it has been shown that it can be controlled by identifying and introducing cytotoxic chemotherapy. Moreover, tyrosine kinase inhibitors and γ-secretases, which are used to treat sporadic DT, which is more frequent than FAP-associated DT, are expected to be effective. In the future, such treatment is expected to further reduce the mortality rate from DT associated with FAP. In addition to the conventional staging of intra-abdominal DT, the classification proposed in Japan was recently thought to be useful for the treatment strategy of FAP-associated DTs. In this review, we summarize the recent advances and current management for the FAP-associated DT, including recent data from Japan.
Improved Thermal Conductivity Function for Unsaturated Soil with Physics-based Parameters

This paper proposes a new relationship between thermal conductivity and degree of saturation of unsaturated soils, referred to as the thermal conductivity function (TCF). The new sigmoidal relationship between thermal conductivity and degree of saturation was developed so that all parameters have a physical meaning. After calibration of the model using data from the literature and comparison with other available TCFs, the parameters of the new TCF were related to those of the soil-water retention curves for the soils investigated in the calibration process. The linkage between the parameters of the TCF and SWRC indicates that the parameters reflect the point of air entry and pore size distribution of the soil, as well as the maximum and minimum thermal conductivity values encountered at saturated and dry conditions, respectively.
Machine-Aided Bridge Deck Crack Condition State Assessment Using Artificial Intelligence

The Federal Highway Administration (FHWA) mandates biannual bridge inspections to assess the condition of all bridges in the United States. These inspections are recorded in the National Bridge Inventory (NBI) and the respective state’s databases to manage, study, and analyze the data. As FHWA specifications become more complex, inspections require more training and field time. Recently, element-level inspections were added, assigning a condition state to each minor element in the bridge. To address this new requirement, a machine-aided bridge inspection method was developed using artificial intelligence (AI) to assist inspectors. The proposed method focuses on the condition state assessment of cracking in reinforced concrete bridge deck elements. The deep learning-based workflow integrated with image classification and semantic segmentation methods is utilized to extract information from images and evaluate the condition state of cracks according to FHWA specifications. The new workflow uses a deep neural network to extract information required by the bridge inspection manual, enabling the determination of the condition state of cracks in the deck. The results of experimentation demonstrate the effectiveness of this workflow for this application. The method also balances the costs and risks associated with increasing levels of AI involvement, enabling inspectors to better manage their resources. This AI-based method can be implemented by asset owners, such as Departments of Transportation, to better serve communities.
Prototype Design of E-BMX 2000 Watt Electric Motorcycle

Limited supplies of fossil fuels such as gasoline, diesel and LPG resulting in vehicles that use internal combustion would be abandoned and replaced by electric vehicles. There have been many coverages that have reviewed the advantages of electric vehicles, where the advantages of electric vehicles include simple mechanical components, relatively low pollution, low noise levels, and could become a solution for future vehicles for an environmentally friendly life. This study aims to design and build a motorcycle prototype, a work which includes the design and manufacture of a BMX type motorcycle frame powered by a 2000 watt BLDC motor power, and testing the performance of the prototype using dynotest and compared to conventional motorcycles to compare the performance characteristics of both motorcycle. The frame is designed using Solidworks, then manufactured by cutting, bending, drilling, welding, and painting process. Mechanical components include frames that use mild steel pipes with diameter of 1.5 inches and thickness of 1.2 mm. Other mechanical components use components available on the market, such as headsets, front forks, disc brakes, hydraulic caliper brakes, and wheel assemblies. The electrical components used are 48V 2000-watt QS Motor, 60V 15 Ah Lithium battery, Yuyangking 80A controller and electric gas throttle with voltage indicator. The results of the dynotest show that the maximum torque reaches 204 NM (roller) which is achieved at 976 rpm. The maximum speed reaches 69.7 Km/hour. The range of the single-charged battery could cover a distance of 30 km. The characteristic of 2000 Watt E-BMX is very powerful at low revs and stable at high revs, shown by achieving maximum torque at low RPM. To save battery life, it is necessary to limit the remaining voltage, which is done at 55 Volts, so that the battery do not completely run out. It could be concluded that electric motorcycle developed in this study is suitable for use in urban areas (paved tracks or light off-road) with determined mileage and the path travelled, and battery management is needed so the battery does not run out in the middle of a trip.
On an ODE-PDE coupling model of the mitochondrial swelling process

Mitochondrial swelling has huge impact to multicellular organisms since it triggers apoptosis, the programmed cell death. In this paper we present a new mathematical model of this phenomenon. As a novelty it includes spatial effects, which are of great importance for the in vivo process. Our model considers three mitochondrial subpopulations varying in the degree of swelling. The evolution of these groups is dependent on the present calcium concentration and is described by a system of ODEs, whereas the calcium propagation is modeled by a reaction-diffusion equation taking into account spatial effects. We analyze the derived model with respect to existence and long-time behavior of solutions and obtain a complete mathematical classification of the swelling process.
Non-linear oscillations of a plate in a flow of gas

Nous demontrons l'existence et l'unicite des solutions faibles d'un probleme de vibration non lineaire d'une plaque maintenue dans un ecoulement gazeux, et nous etudions les proprietes de ces solutions. La dynamique de la plaque est decrite par une modification des equations de von Karman qui tient compte des moments d'inertie angulaires des elements de la plaque. Nous utilisons la linearisation de la theorie des flots derivant d'un potentiel pour decrire l'effet de l'ecoulement gazeux. Notre methode permet de traiter aussi bien le cas infrasonique que le cas supersonique.
Transcriptomic Comparison of Human Peripartum and Dilated Cardiomyopathy Identifies Differences in Key Disease Pathways

Peripartum cardiomyopathy (PPCM) is a rare form of acute onset heart failure that presents in otherwise healthy pregnant women around the time of delivery. While most of these women respond to early intervention, about 20% progress to end-stage heart failure that symptomatically resembles dilated cardiomyopathy (DCM). In this study, we examined two independent RNAseq datasets from the left ventricle of end-stage PPCM patients and compared gene expression profiles to female DCM and non-failing donors. Differential gene expression, enrichment analysis and cellular deconvolution were performed to identify key processes in disease pathology. PPCM and DCM display similar enrichment in metabolic pathways and extracellular matrix remodeling suggesting these are similar processes across end-stage systolic heart failure. Genes involved in golgi vesicles biogenesis and budding were enriched in PPCM left ventricles compared to healthy donors but were not found in DCM. Furthermore, changes in immune cell populations are evident in PPCM but to a lesser extent compared to DCM, where the latter is associated with pronounced pro-inflammatory and cytotoxic T cell activity. This study reveals several pathways that are common to end-stage heart failure but also identifies potential targets of disease that may be unique to PPCM and DCM.
Natural Compounds: Co-Delivery Strategies with Chemotherapeutic Agents or Nucleic Acids Using Lipid-Based Nanocarriers

Cancer is one of the leading causes of death, and latest predictions indicate that cancer- related deaths will increase over the next few decades. Despite significant advances in conventional therapies, treatments remain far from ideal due to limitations such as lack of selectivity, non-specific distribution, and multidrug resistance. Current research is focusing on the development of several strategies to improve the efficiency of chemotherapeutic agents and, as a result, overcome the challenges associated with conventional therapies. In this regard, combined therapy with natural compounds and other therapeutic agents, such as chemotherapeutics or nucleic acids, has recently emerged as a new strategy for tackling the drawbacks of conventional therapies. Taking this strategy into consideration, the co-delivery of the above-mentioned agents in lipid-based nanocarriers provides some advantages by improving the potential of the therapeutic agents carried. In this review, we present an analysis of the synergistic anticancer outcomes resulting from the combination of natural compounds and chemotherapeutics or nucleic acids. We also emphasize the importance of these co-delivery strategies when reducing multidrug resistance and adverse toxic effects. Furthermore, the review delves into the challenges and opportunities surrounding the application of these co-delivery strategies towards tangible clinical translation for cancer treatment.
Mesenchymal Stem Cell Behavior under Microgravity: From Stress Response to a Premature Senescence

Mesenchymal stem cells are undifferentiated cells able to acquire different phenotypes under specific stimuli. Wharton’s jelly is a tissue in the umbilical cord that contains mesenchymal stromal cells (MSCs) with a high plasticity and differentiation potential. Their regeneration capability is compromised by cell damage and aging. The main cause of cell damage is oxidative stress coming from an imbalance between oxidant and antioxidant species. Microgravity represents a stressing condition able to induce ROS production, ultimately leading to different subcellular compartment damages. Here, we analyzed molecular programs of stemness (Oct-4; SOX2; Nanog), cell senescence, p19, p21 (WAF1/CIP1), p53, and stress response in WJ-MSCs exposed to microgravity. From our results, we can infer that a simulated microgravity environment is able to influence WJ-MSC behavior by modulating the expression of stress and stemness-related genes, cell proliferation regulators, and both proapoptotic and antiapoptotic genes. Our results suggest a cellular adaptation addressed to survival occurring during the first hours of simulated microgravity, followed by a loss of stemness and proliferation capability, probably related to the appearance of a molecular program of senescence.
Reconfigurable Wide Band Antenna with Gain Enhancement using Metamaterial Superstrate for Ku Band Applications

A high gain reconfigurable frequency antenna with hexagonal patch and superstrate metamaterial is designed for Ku band applications from 14 GHz to 18 GHz. Using PIN diodes, the proposed antenna has four states, working on wideband to narrow bands. 6-unit cells are designed on FR4 dielectric to act as superstrate to enhance the antenna gain up to 6 dB spaced from an optimum high from the designed main antenna. This antenna has 15mmxl0mm as size on FR4 PCB with 1.6 mm thick, which is compact, simple and more efficient.
A model to measure the performance of evolutionary algorithms applied to solve the root identification problem

The existence of a model for representing the performance of metaheuristics applied to solve problems with high computational requirements is paramount to determine the solution quality given a certain avaliable run-time and vice versa. In this work, a statistical model is proposed to describe the performance of evolutionary algorithms applied to solve the Root Identification Problem. Given an unknown problem size, a parameter setting and a performance model are estimated for two well-known evolutionary algorithms, Population-Based Incremental Learning (PBIL) and Cross generational elitist selection Heterogeneus recombination and Cataclismic mutation (CHC). The performance model is validated over a benchmark corresponding to huge search spaces.
Antifungal activity in vitro using kraft-pulping crude black liquor and hydrothermal treatment black liquor against wood-decaying fungi

Abstract We investigated antifungal properties of two kraft-pulping liquors as potential environmentally friendly wood preservatives targeting in deterioration by white rot (WRF) and brown rot fungi (BRF). The in vitro antifungal activity of crude black liquor (BL) and the product from a hydrothermal gasification (BLWSupc) process were investigated. Both products showed high bioactivity, with BL completely inhibiting the activity of WRF and BRF; 80% was entirely suppressed at low BL concentrations (5–10% vol/vol). Using the BLWSupc at 2.55% wt/wt, we achieved the same inhibitory effect as BL (15% vol/vol) for the two fungi. These antifungal effects can be explained by the composition of liquors that act as antioxidants, promoting enzymatic inactivation and inhibiting the growth of WRF and BRF. BLWSupc exhibited radical quenching activity against DPPH of 43.9%, and the BL, a phenolic content about 20 times higher (23.635 mg/L) than BLWSupc (2.093 mg/L), suggesting that antifungal activity of BL and BLW is related to their chemical composition. An opportunity demonstrated by this study is the reuse of BLWSupc as a protective treatment within a sustainable chain.
S-SNHF: sentiment based social neural hybrid filtering

Deep learning has yielded success in many research fields. In the last few years, deep learning techniques have been applied in recommender systems to solve cold start and data sparsity problems. However, only a few attempts have been made in social-based recommender systems. In this study, we address this issue and propose a novel recommendation model called Sentiment based Social Neural Hybrid Filtering (S-SNHF). This model combines collaborative and content-based filtering with social information using a deep neural architecture based on Generalized Matrix Factorization (GMF) and Hybrid Multilayer Perceptron (HybMLP). Furthermore, for achieving higher recommendation reliability, the hybrid sentiment analysis model is integrated to analyse users’ opinions and infer their preferences. The results of the empirical study performed with three popular datasets show the contribution of both, social information and sentiment analysis on the recommendation performance and that our approach achieves signiﬁcantly better recommendation accuracy, compared with state-of-the-art recommendation methods.