Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploration of Solubilization Strategies: Enhancing Bioavailability for Low Solubility Drugs

This review explores various strategies aimed at improving the solubilization of low-solubility drugs, including formulation design, nanoparticle technologies, prodrug strategies, and particle size reduction methods. Water solubility plays a crucial role in shaping bioavailability, formulation strategies, and therapeutic efficacy. Nanotechnology, particularly in nanomedicines, is a promising avenue to tackle solubility challenges, but faces barriers like production costs, formulation reproducibility, and varying pharmacokinetics. Despite these challenges, the burgeoning landscape of innovative drug delivery technologies offers advantages, particularly for formulation scientists. Understanding molecular properties is crucial for resolving these challenges, with solid dispersions and lipid-based delivery techniques emerging as sought-after solutions. Commercializing these advancements requires a leap in technology and infrastructure, making it essential to streamline the process and identify optimal approaches. Pioneering methodologies, such as Fagerholm's predictive model for human oral bioavailability based on chemical structure, demonstrate promising predictive accuracy. The integration of artificial intelligence and innovative solubility enhancement technologies is pivotal in transforming drug delivery, tackling solubility concerns, and streamlining research and development expenses.
Smart Car Using IoT and Image Processing

Smart and automated cars are the future of the automobile industry. With the developing technology, automation is introduced in every sector due to its advantages like improved accuracy and precision over manual handling. Due to this very same reason automation is been introduced in cars as well. Nowadays many car manufacturing companies are automating many of the mechanisms in their vehicles to provide the best in-segment features and to give the customers an effortless experience of driving their vehicle. Also, as nowadays automation is introduced in these newly manufactured vehicles it has improved the safety and security of drivers. Within this manuscript, a system is been envisioned that is an excellent example of how automation in cars can make them smart-car and solve many issues faced by drivers while driving. The setup is integrated with a range of impressive mechanisms devised through the amalgamation of diverse hardware and software elements. The configuration comprises an automatic wiper mechanism, parking alarm mechanism, lane detection mechanism, and also weather parameter sensing mechanism. All these mechanisms are contributing in some or another way to reducing the stress on the driver while driving the car The automated rain wiper system detects rainfall and automatically activates the rain wiper. The system is developed to mitigate driving distractions and permit drivers to concentrate on their primary task of driving. Parking alarm system assists drivers while parking their car and helps avoid any unusual circumstances. The weather parameters sensing mechanism senses the weather parameters around the car so that accordingly the driver can adjust A/C settings and also helps automate the driving. A Lane detection system is very useful nowadays so that the driver can keep an eye on the lane while driving, also it can assist in automated driving.
Exploring Cognitive Sustainability Concerns in Public Responses to Extreme Weather Events: An NLP Analysis of Twitter Data

The United States has a long history of experiencing extreme weather events. Hurricanes are among the most devastating natural disasters that have significant economic and physical impacts on the country. By applying Natural Language Processing (NLP) to Twitter data for sentiment analysis, emotion detection, and topic modelling, this study provides a more thorough understanding of public response and concerns during five study cases of hurricanes that hit the United States: Harvey, Irma, Maria, Ida, and Ian. The findings on sentiment analysis revealed that 64.75% of the tweets were classified as Negative and 35.25% as Positive. For emotion detection, the predominant emotion was anger, with 39.91%. These results were centred around the main public concerns shown by the topic modelling: hurricane management, donation and support, and disaster impacts. Our future work will focus on understanding people’s responses to extreme weather events through the evolving concept of Cognitive Sustainability.
Fake News Detection via Sentiment Neutralization

Everybody knows how fake news or disinformation can have big impact on deciding our daily lives. As people rely on social networks rather than reliable news channels to receive information, we no longer have enough protection mechanisms to help us judge between genuine and fake news. In this work, we propose a sentiment-based analysis for fake news detection. The approach leverages a few of the most recent acclaimed techniques, namely, Large Language Models and Artificial Intelligent Generated Content for sentiment analysis and the subsequent fake news detection. We have ChatGPT to help us rewrite a text for its neutralized version. By comparing the two, before and after the neutralization, we figure out their difference, such as different frequencies of sentiment word usage which can help us for fake news detection. Also, we observe an imbalanced improvement between the fake news and genuine ones. Fake news rather than genuine news in general contains more emotional words, or negative sentiment words to be specific. Taking advantage of such observation can indeed help to further improve the detection result. After all, the evaluation confirms the aforementioned statements and shows the superiority of the proposed method to other state-of-the-art methods, in datasets that own different languages.
Doppler Velocity De-Aliasing Based on Lag-1 Cross-Correlation for Dual-Polarization Weather Radar

Alternate transmission and alternate reception (ATAR) mode, as a crucial polarization mode, enables a single channel to be utilized in dual-polarization weather radars, which reduces the weight, power, and cost of the radar. The ATAR mode has a broad application based on high-altitude platforms with limited load and power. The interval between co-polarization signals is twice the pulse repetition time (PRT) in the ATAR mode. Thus, the Nyquist velocity interval is half of the one existing in the traditional radars. This hampers the application of the ATAR mode in severe weather observations. In this letter, a velocity de-aliasing algorithm is proposed, where lag-1 cross-correlations between adjacent radials are utilized to calculate the velocity estimates. This extends the Nyquist velocity interval of the ATAR model twofold compared to that of the traditional algorithm, which uses lag-1 autocorrelations between adjacent radials. In addition, the constraints of the proposed algorithm are analyzed. The proposed algorithm is validated via X-band PWR data, and the results show that compared to the true value, the biases of the de-aliased velocities in the ATAR mode are within ±1 m/s for 96.9% of the range bins. This method is also suitable for use with the alternate transmission and simultaneous reception (ATSR) mode.
Intracranial electrophysiology of spectrally degraded speech in the human cortex

Introduction Cochlear implants (CIs) are the treatment of choice for severe to profound hearing loss. Variability in CI outcomes remains despite advances in technology and is attributed in part to differences in cortical processing. Studying these differences in CI users is technically challenging. Spectrally degraded stimuli presented to normal-hearing individuals approximate input to the central auditory system in CI users. This study used intracranial electroencephalography (iEEG) to investigate cortical processing of spectrally degraded speech. Methods Participants were adult neurosurgical epilepsy patients. Stimuli were utterances /aba/ and /ada/, spectrally degraded using a noise vocoder (1–4 bands) or presented without vocoding. The stimuli were presented in a two-alternative forced choice task. Cortical activity was recorded using depth and subdural iEEG electrodes. Electrode coverage included auditory core in posteromedial Heschl’s gyrus (HGPM), superior temporal gyrus (STG), ventral and dorsal auditory-related areas, and prefrontal and sensorimotor cortex. Analysis focused on high gamma (70–150 Hz) power augmentation and alpha (8–14 Hz) suppression. Results Chance task performance occurred with 1–2 spectral bands and was near-ceiling for clear stimuli. Performance was variable with 3–4 bands, permitting identification of good and poor performers. There was no relationship between task performance and participants demographic, audiometric, neuropsychological, or clinical profiles. Several response patterns were identified based on magnitude and differences between stimulus conditions. HGPM responded strongly to all stimuli. A preference for clear speech emerged within non-core auditory cortex. Good performers typically had strong responses to all stimuli along the dorsal stream, including posterior STG, supramarginal, and precentral gyrus; a minority of sites in STG and supramarginal gyrus had a preference for vocoded stimuli. In poor performers, responses were typically restricted to clear speech. Alpha suppression was more pronounced in good performers. In contrast, poor performers exhibited a greater involvement of posterior middle temporal gyrus when listening to clear speech. Discussion Responses to noise-vocoded speech provide insights into potential factors underlying CI outcome variability. The results emphasize differences in the balance of neural processing along the dorsal and ventral stream between good and poor performers, identify specific cortical regions that may have diagnostic and prognostic utility, and suggest potential targets for neuromodulation-based CI rehabilitation strategies.
The Impact of Digitalization on Family Firms’ Performance: The Moderating Role of Family Goals

In this article, we examine the performance effects of business information technology (IT) alignment in the context of family firms. Using a unique survey-based dataset of 954 European family firms, this study provides empirical support for the prediction that business-IT alignment is associated with family firms’ performance. However, this result is weaker in family firms that prioritize family goals.
AGILE IT SERVICE MANAGEMENT DESIGN OF FINTECHCO DIGITALIZATION BASED ON COBIT 2019 DEVOPS FOCUS AREA

The Indonesian Financial Technology (Fintech) industry plays a pivotal role in driving digital technology advancements and propelling the nation's growth. The rapid development of Fintech presents a unique challenge for established financial companies, necessitating effective government regulation through a Sandbox approach. FintechCo, operating under the watchful eye of State-Owned Enterprises and the Financial Service Authority, must strike a delicate balance between their agile services and security risks. Hence, FintechCo necessitates an examination of optimal agile services through the utilization of five stages derived from the Design Science Research approach. This need arises to adhere to the Good Corporate Governance standards established by State-Owned Enterprises (SOE) and the Financial Services Authority (FSA). The cutting-edge COBIT 2019 DevOps Focus Area framework is employed for analysis. The research identifies three highest priority Governance and Management Objectives (GMOs): Managed Security Services (DSS05), Managed Problem (DSS03), and Managed Solution Identification and Build (BAI03). These GMOs are thoroughly evaluated, and comprehensive recommendations are provided based on their seven key components. There is an estimated 17% increase in the average capability score, from 2.82 to 3.30, across all three GMOs. The contribution of this study is twofold: to serve as a valuable knowledge resource for agile IT services in digitalization within organizations and to offer practical implications for FintechCo to enhance their digitalization. Furthermore, this research sets a precedent for the Fintech industry, signifying a benchmark for excellence and innovation.
Sustainable Development in GCC (Do Logistics Indicators Matter?)

Objective: Although logistics practices could contribute a lot to countries’ economic and ecological factors, there is still a need to know which logistics practices could contribute to specific ecological domains. Previous studies in many cases have limited their investigation either to a specific environmental indicator, such as CO2, or to a specific region. This study opts to close the gaps of previous studies by investigating the impacts of logistics aspects on the environmental variables in Gulf countries and expanding the focus of the tested environmental domain to include energy consumption as an additional variable to CO2. Methods: The study adopted a panel data approach, which uses both cross-sectional and time series approaches together. The panel approach includes more data coverage, reduces the variables’ internal correlations, and provides a more efficient model with high freedom degrees. The logistics performance data has been derived from LPI, which was published by the World Bank in 2007. At the time of writing this paper, the index covers data from 2007 to 2023. Although LPI data are available from 2007 to 2023, the CO2 and energy consumption were only available until 2020. Results: The results indicated that different logistics indicators have different impacts on ecological issues. The results revealed that only infrastructure and customs logistics indicators have significant effects on CO2 emission and energy consumption in the GCC. Conclusions: This study recommends that decision-makers should consider the environmental consequences of engaging in logistics practices. Although the improvements in logistics indicators will lead to facilitating the movements of goods internally and externally, caution should be taken with regard to harm it may cause to the environment.
A critique on English homophones and homographs

This paper critiques the treatment of English homographs and homophones in linguistic literatures. The study is motivated by the fact that the available data are questionable on the subject, thus calling for re-analysis. Methodologically, the study is a critical analysis of data obtained through a documentary process in secondary reading. The study employs the Semantic Theory and the Referential Theory of Meaning as the basis of data analysis. These theories explain lexemes in terms of how they are articulated and what they refer to. The findings of the analysis reveal that most of the linguistic literatures offer contestable and confusing definitions of homographs/homophones. For instance ‘affect’ and ‘effect’, just like’ profit’ and ‘prophet’, are not homophones though they are regarded as so in some available literature. Also, ‘conduct’ (N) and ‘conduct’ (V) are regarded as homographs but in reality they are not. Therefore, homophones are words with the same pronunciation but different spellings and meanings while homographs are words that are spelt the same but pronounced differently and have different meanings as the case of 'live' (verb) /lɪv/ and ‘live’ (adjective) /laɪv/.
Annual emission of carbon dioxide from soils of the southern taiga zone of Russia

Data on the dynamics of CO 2 emission from soddy weakly podzolic and gray forest soils of the southern taiga zone have been obtained in the course of year-round field observations for 26 months. A modified method of determining the CO 2 efflux from soils in the winter period is suggested. It is shown than annual dynamics of the CO 2 emission from soils depends on particular weather conditions. The curves of emission may have either one peak or several peaks (a serrate type of the curve) in the summer period. In the winter period (from November to April), the rate of soil respiration does not exceed 100 mg C/(m 2 h), but it is always higher than 0 mg C/m 2 per h. In the summer period (from May to October), the rate of soil respiration is 3.5 to 4.5 times higher than in the winter period. Annual efflux of CO 2 from the soils of the southern taiga zone varies from 2.65 to 11.5 t C/ha depending on the weather conditions of the year and the particular types of soils and phytocenoses. The contribution of winter respiration to annual respiration constitutes 21-27% in the soils under natural cenoses and 12-14% in the soils under agrocenoses. The most significant influence on the CO 2 emission is exerted by the soil temperature. In particular, the respiration intensity of soddy-podzolic soils is very sensitive to temperature fluctuations.
Three brazilian Sand-opisthobranchia

Entre os Opistobrânquios, os Acochlidiacea ocupam posicao especial, porque possuem saco visceral saliente sem concha; sao, em parte, unisexuais, e ocorrem tambem na agua doce. O colar nervoso prefaringeo aproxima-os aos Tectibranchia. Odhner (1952) estabelece 3 familias dos Acochlidiacea. Duas destas, as Hedylopsidae e as Microhedylidae, distinguem-se respectivamente pela separacao completa (Fig. 18) e apenas posterior (Fig. 1, 2, 24, 34) entre o pe e o corpo restante.
Malicious Traffic Detection Method Based on Improved Sparse Self-Encoder

As the Internet becomes increasingly vital, a plethora of malicious activities permeate through its traffic. However, traditional machine learning-based detection methods generally utilize raw feature data or undergo some feature selection, which cannot adapt to the diversity of features in complex data. Therefore, this paper proposes a malicious traffic detection method based on an improved sparse auto-encoder. It processes data using a K-sparse auto-encoder and incorporates a feature evaluation coefficient based on information theory into the neuron selection process, applying it to malicious traffic detection. Experimental results demonstrate that the enhanced model achieves improvements of 4.6%, 0.8%, and 9.3% in precision, recall, and false positive rate, respectively, on the NSL-KDD dataset and improvements of 3.6%, 10.5%, and 0.2% on the Trojan traffic dataset.
Effect of Zwitterionic Additives on Solvation and Transport of Sodium and Potassium Cations in (Ethylene Oxide)10: A Molecular Dynamics Simulation Study

Sodium- (Na+) and potassium- (K+) ion batteries are cost-effective alternatives to lithium-ion (Li+) batteries due to the abundant sodium and potassium resources. Solid polymer electrolytes (SPEs) are essential for safer and more efficient Na+ and K+ batteries because they often exhibit low ionic conductivity at room temperature. While zwitterionic (ZW) materials enhance Li+ battery conductivity, their potential for Na+ and K+ transport in batteries remains unexplored. In this study, we investigated the effect of three ZW molecules (ChoPO4, i.e., 2-methacryloyloxyethyl phosphorylcholine, ImSO3, i.e., sulfobetaine ethylimidazole, and ImCO2, i.e., carboxybetaine ethylimidazole) on the dissociation of Na+ and K+ coordination with ethylene oxide (EO) chains in EO-based electrolytes through molecular dynamics simulations. Our results showed that ChoPO4 possessed the highest cation–EO10 dissociation ability, while ImSO3 exhibited the lowest. Such dissociation ability correlated with the cation–ZW molecule coordination strength: ChoPO4 and ImSO3 showed the strongest and the weakest coordination with cations. However, the cation–ZW molecule coordination could slow the cationic diffusion. The competition of these effects resulted in accelerating or decelerating cationic diffusion. Our simulated results showed that ImCO2 enhanced Na+ diffusion by 20%, while ChoPO4 and ImSO3 led to a 10% reduction. For K+, ChoPO4 reduced its diffusion by 40%, while ImCO2 and ImSO3 caused a similar decrease of 15%. These findings suggest that the ZW structure and the cationic size play an important role in the ionic dissociation effect of ZW materials.
On-Street Parking Prediction: A Comparative Study

Urban computing techniques harnessing digital mobility traces can assume a pivotal role in comprehending travel patterns and behavioral dynamics within an urban context. These methodologies yield invaluable insights for transportation planners, enabling them to make informed decisions and augment the overall efficiency of urban transportation systems. The goal of this research is to analyze the parking phenomenon in urban areas and develop predictive models for parking-related indicators. Specifically, we introduce two quantitative metrics: the Average Parking Time and the Average Number of Simultaneously Parked Vehicles, aimed at characterizing parking activities from temporal and volumetric perspectives. A large collection of raw GPS traces from a sample of private cars in the metropolitan areas of Rome was used to investigate and model the spatiotemporal dynamics of parking demand and saturation on public streets. Our investigation entailed the application of a diverse array of Machine Learning and Deep Learning techniques, encompassing statistical models, Graph Convolutional Networks (GCNs), and Convolutional Neural Networks (CNNs), for the prediction of these indicator values. In our experiments, we found that the 3D-CLoST model excelled in accurately predicting parking indicators compared to other techniques. Interestingly, we also observed that statistical models were able to achieve performance levels that were similar to those of more complex models.
Contrast-induced acute kidney injury; possible ameliorative effect of sodium-glucose co-transporter 2 inhibitors

Contrast-associated acute renal injury is a common complication that occurs after the administration of contrast media for diagnostic or therapeutic purposes. It is characterized by a sudden decline in kidney function within 48-72 hours after the administration of contrast media. The incidence of contrast-associated acute renal injury varies depending on the patient’s risk factors and the type and amount of contrast media used. Sodium-glucose co-transporter 2 (SGLT2) inhibitors are a group of antidiabetic drugs that inhibit the reabsorption of glucose in the nephrons, running to raise urinary glucose excretion and improve glycemic control. Recent studies have shown that SGLT2 inhibitors may have a protective effect against contrast-associated acute renal injury. Preventive measures for contrast-associated acute renal injury include hydration, minimizing contrast volume and rate of administration, and avoiding contrast in high-risk patients. The administration of SGLT2 inhibitors may also be a useful preventive measure in patients at risk for contrast-associated acute renal injury.