Miracle-dz/UniFAR-FTUs huggingface dataset:
SGLT2 Inhibitors vs. GLP-1 Agonists to Treat the Heart, the Kidneys and the Brain

Sodium glucose cotransporter 2 (SGLT2) inhibitors and glucagon-like-peptide-1 receptor (GLP-1-R) agonists are novel therapeutic agents used for the management of type 2 diabetes mellitus (T2DM). Recently, large-scale randomized clinical trials have been conducted to assess the cardiovascular safety of these medications. The findings of these trials have revealed that both SGLT2 inhibitors and GLP-1-R agonists exhibit favorable cardioprotective effects, including reduction in cardiovascular and all-cause mortality, a decreased risk of chronic kidney disease progression, a decrease in hospitalization for heart failure (HF), an effect shown by SGLT2 inhibitors, and stroke prevention, an effect shown by GLP-1-R agonists. Based on the results from above studies, the European and American Diabetes Associations have issued new recommendations strongly endorsing the use of SGLT2 inhibitors and GLP-1-R agonists in combination with metformin for patients with T2DM who have additional cardiovascular (CV) comorbidities or risk factors. The primary aim of this combined therapy is to prevent CV events. Although both medication groups offer beneficial effects, they demonstrate slightly different profiles. SGLT2 inhibitors have exhibited better effects regarding a reduced incidence of HF, whereas GLP-1-R agonists have shown a reduced risk of CV events, particularly stroke. Moreover, recent European Society of Cardiology as well as American College of Cardiology and American Heart Association guidelines of HF treatment stressed the importance of SGLT2 inhibitor administration in patients with HF regardless of T2DM. In this context, we present and discuss the outcomes of the most recent trials investigating the impact of SGLT2 inhibitors and GLP-1-R agonists on renal and cardiovascular outcomes in patients, both with and without T2DM. Additionally, we explore the synergistic effects of combining SGLT2 inhibitors and GLP-1-R agonists in patients with cardiovascular disease.
Analysis and Optimal Control of a Fractional Order SEIR Epidemic Model With General Incidence and Vaccination

In this article, we present an analysis and optimal control investigation of a fractional order SEIR epidemic model with General Incidence and Vaccination. We utilize fractional calculus to account for memory effects and non-local interactions in the disease transmission process, enhancing the model’s ability to capture real-world complexities. The utilization of fractional derivatives plays a crucial role in accounting for long-term memory in the system, allowing us to better understand the disease dynamics. Our analysis focuses on investigating the existence, uniqueness, and stability of equilibrium points while considering the impact of vaccination on the disease dynamics. Additionally, we develop an optimal control strategy to minimize the number of infected individuals over a given time horizon by optimizing the vaccination rate. Numerical simulations are performed to validate the theoretical results and demonstrate the effectiveness of the proposed optimal control strategy in mitigating the spread of the epidemic. The findings of this study contribute to a better understanding of the dynamics of fractional order SEIR epidemic models and provide insights into the design of efficient control measures for infectious diseases. The ability to accurately capture memory effects and non-local interactions through fractional derivatives opens up new possibilities for developing more robust and effective intervention strategies in public health settings.
Application of an Integrated Granular and Suspended Sludge Methane Reactor for a Two-Stage Anaerobic Digestion System to Deal with Biodegradable Municipal Solid Waste

This study aims to investigate the performance of a two-stage anaerobic digestion system using a hybrid methane reactor to deal with biodegradable municipal solid waste. The reactor allowed both suspended sludge and granular sludge to work together. The feedstock was fermented in one continuous stirred tank at different pH conditions for 5 d. Furthermore, the liquid hydrolysate was diluted and pumped into a methane reactor with different organic loading rates. In the fermentative reactor, raising the pH condition from 4.5 to 6.5 caused a sharp increase in volatile fatty acids concentration, mainly due to the increase in acetate and propionate. The efficiency of the methane reactor was proven by the results of hydrodynamic analysis and biogas production. The relationship between biogas production and operating parameters in this reactor was modeled using a quadratic multivariate regression model. Overall, by maintaining the fermentative reactor at a pH of 6.0–6.5, the methane reactor was able to achieve an organic loading rate of 7.6 g-TS.L−1·d−1 with outstanding biogas quality and yield. In terms of microbiology, the most dominant phyla in the reactor included Firmicutes, Bacteroidetes, Proteobacteria, Euryarchaeota, Synergistetes, and Chloroflexi. Among them, the species with the highest relative abundance in granular sludge was Firmicutes, while that in suspended sludge was Bacteroidetes.
Synthesis of 1,2,3-triazoles using Grignard reactions through the protection of azides

An efficient method to prepare organomagnesium intermediates having a protected azido group is reported. Protection of azido groups with di-(tert-butyl)(4-(dimethylamino)phenylphosphine (amphos) and following iodine–magnesium exchange realized the preparation of organomagnesium intermediates, which served in the synthesis of diverse azides by transformation with various electrophiles followed by deprotection with elemental sulfur. Furthermore, click reactions of azides with alkynes enabled synthesizing a wide variety of 1,2,3-triazoles.
Peningkatan Deteksi Kecelakaan di Jalan Raya Menggunakan Real-ESRGAN pada Citra CCTV Persimpangan Jalan

Kegagalan sistem deteksi kecelakaan pada kamera CCTV dapat berpengaruh pada peningkatan angka kematian di jalan raya. Penggunaan metode CNN dalam pembangunan sistem deteksi kecelakaan CCTV sudah banyak dilakukan sebelumnya. Namun, masalah umum yang sering ditemui yaitu lensa yang kotor dan zoom varifocal yang tidak fokus secara otomatis membuat kualitas citra CCTV yang dihasilkan mengalami penurunan, sehingga berpengaruh kepada performa sistem. Pada penelitian ini dilakukan pembangunan model untuk mendeteksi kecelakaan pada citra CCTV menggunakan pre-trained model MobileNetV2 yang dilakukan optimasi dengan melakukan upscaling pada dataset menggunakan model Real-ESRGAN untuk menghasilkan performa yang lebih optimal. Penelitian ini menggunakan dataset citra CCTV yang berjumlah 989 dan terdiri dari 2 jenis kelas prediksi diantaranya accident dan non-accident. Hasil penelitian diperoleh model MobileNetV2 berhasil menghasilkan validation accuracy 97%, testing accuracy 94% dan average inference time 3.33 detik pada skenario uji GT. Selama proses uji, didapatkan bahwa model kurang optimal jika mengidentifikasi data baru dengan kondisi objek yang berkerumunan. Selain itu, berdasarkan skenario uji X2,X4,X8 didapatkan bahwa kualitas citra yang dihitung berdasarkan nilai PSNR dan SSIM sangat berpengaruh pada performa klasifikasi seperti Akurasi, precision, recall, dan AUC score.
Generalized Eulerian Numbers

We generalize the Eulerian numbers to sets of numbers Eμ(k,l), (μ=0,1,2,···) where the Eulerian numbers appear as the special case μ=1. This can be used for the evaluation of generalizations Eμ(k,Z) of the Geometric series G0(k;Z)=G1(0;Z) by splitting an essential part (1-Z)-(μK+1) where the numbers Eμ(k,l) are then the coefficients of the remainder polynomial. This can be extended for non-integer parameter k to the approximative evaluation of generalized Geometric series. The recurrence relations and for the Generalized Eulerian numbers E1(k,l) are derived. The Eulerian numbers are related to the Stirling numbers of second kind S(k,l) and we give proofs for the explicit relations of Eulerian to Stirling numbers of second kind in both directions. We discuss some ordering relations for differentiation and multiplication operators which play a role in our derivations and collect this in Appendices.
Investigating the Acute Metabolic Effects of the N-Methyl Carbamate Insecticide, Methomyl, on Mouse Liver

Many pesticides have been identified as endocrine and metabolism-disrupting chemicals with hepatotoxic effects. However, data are limited for insecticides in the n-methyl carbamate class, including methomyl. Here, we investigate the liver and systemic metabolic effects of methomyl in a mouse model. We hypothesize that methomyl exposure will disrupt xenobiotic and intermediary metabolism and promote hepatic steatosis in mice. Male C57BL/6 mice were exposed daily to 0–5 mg/kg methomyl for 18 days. Mice were fed water and regular chow diet ad libitum. Metabolic phenotyping was performed, and tissue samples were collected. Effects were generally greatest at the highest methomyl dose, which induced Cyp1a2. Methomyl decreased whole body weight while the liver:body weight and testes:body weight ratios were increased. Hepatic steatosis increased while plasma LDL decreased. Fasting blood glucose and the glucose tolerance test area under the curve decreased along with hepatic glycogen stores. Methomyl, however, did not increase liver oxidative stress or injury. Collectively, these data demonstrate that methomyl disrupts hepatic xenobiotic and intermediary metabolism while increasing the testes:body weight ratio, suggesting that it may be an endocrine disrupting chemical. Besides methomyl’s known action in cholinesterase inhibition, it may be involved in aryl hydrocarbon receptor activation. The potential impact of n-methyl carbamate insecticides on metabolic health and diseases, including toxicant-associated steatotic liver disease (TASLD), warrants further investigation.
A Dijkstra-Based Approach to Fuelbreak Planning

One of the most effective methods of preventing large-scale wildfires is creating fuelbreaks, buffer zones whose purpose is to stop or delay the spread of the fire, providing firefighters an opportunity to control the fire. Fuelbreaks are already applied in several countries and have proven their effectiveness. However, creating fuelbreaks involves deforestation, so the length of the fuelbreaks should be minimized as much as possible. In this paper, we propose the implementation of a greedy Dijkstra-based fuelbreak planning algorithm which identifies locations in which fuelbreaks could significantly reduce the risk of large wildfires, at a relatively low deforestation cost. We demonstrate the stages and output of the algorithm both on artificial forests and on actual forests in Israel. We discuss the factors which determine the cost effectiveness of fuelbreaks from a tree-economy perspective and demonstrate how fuelbreaks’ effectiveness increases as large wildfires become more frequent.
A Survey About Patents, Invention and Commercialization Processes in Kazakhstan

This article is about the importance of intellectual property in the innovation process. It focuses on the results obtained in the survey with owners of patents in Kazakhstan. The questions were related to the processes of invention and commercialization. From the mainstream of responses, we identified those that are crucial in the process of invention and those that can affect the process of commercialization in the future. Namely, we were interested in the variables which are significant in the process of invention and how they affect commercialization. The result of the post-hoc test identified the data which differ within groups. It was found that eight variables were different in the combination of groups: the number of authors, the type of resource R&D, the number of patents, and the evaluation of the invention, expressed in terms of money. This test revealed that these variables change from group to group. Moreover, it allowed us to look deeper into the inventive process and identify the direction of these changes.
Relationship between Motor Corticospinal System, Endogenous Pain Modulation Mechanisms and Clinical Symptoms in Patients with Knee Osteoarthritis: New Perspectives on an Old Disease

Knee osteoarthritis (OA) is a painful condition characterized by joint and bone changes. A growing number of studies suggest that these changes only partially explain the pain experienced by individuals with OA. The purpose of the current study was to evaluate if corticospinal and bulbospinal projection measurements were interrelated in patients with knee OA, and to explore the relationship between these neurophysiological measures and temporal summation (excitatory mechanisms of pain) on one hand, and clinical symptoms on the other. Twenty-eight (28) patients with knee OA were recruited. Corticospinal projections were measured using transcranial magnetic stimulation, while bulbospinal projections were evaluated with a conditioned pain modulation (CPM) protocol using a counter-irritation paradigm. Validated questionnaires were used to document clinical and psychological manifestations. All participants suffered from moderate to severe pain. There was a positive association between corticospinal excitability and the effectiveness of the CPM (rs = 0.67, p = 0.01, n = 13). There was also a positive relationship between pain intensity and corticospinal excitability (rs = 0.45, p = 0.03, n = 23), and between pain intensity and temporal summation (rs = 0.58, p = 0.01, n = 18). The results of this study highlight some of the central nervous system changes that could be involved in knee OA and underline the importance of interindividual variability to better understand and explain the semiology and pathophysiology of knee OA.
Asymmetric detrended fluctuation analysis reveals asymmetry in the RR intervals time series

In this paper we apply the Asymmetric Detrended Fluctuation Analysis to the RR intervals time series. The mathematical background of the ADFA method is discussed in the context of heart rate variability and heart rate asymmetry. We calculate the α and α ADFA scaling exponents for 100 RR intervals time series recorded in a group of healthy volunteers (20-40 years of age) with the use of the local ADFA. It is found that on average α < α , and that locally α dominates most of the time over α both results are highly statistically significant.
Presenting a New Method to Classify Alerts Received from Intrusion Detection Systems

With the growth of the internet networks today, security of data exchange is considered as an important task. Therefore, the use of security tools is increasing day by day. Intrusion detection systems are among these tools. They are only able to labela message received from a network as‘alert’,but they are unable to describe system status. Some methods have been developed to solve the above problem through correlating the alerts received from intrusion detection systems. By correlating the interrelated alerts, the methods would be able to describe system status. One of the steps of correlation methods of alerts is to classify them. System status can be described better when classification is performed efficiently. Here, we present a method for classifying alerts.
Effect of clay minerals supplementation on nutrient digestibility, blood parameters, and performance of buffalo calves

This study was conducted to investigate the effect of adding clay minerals such as bentonite and choline on the digestion of nutrients, blood parameters, and growth performance of buffalo calves. Twelve clinically healthy male buffalo calves aged approximately 11–12 months with average body weight (336.50 ± 20.55 kg) were randomly distributed into three different groups (four animals each). The 1 st group was a control or basal diet without supplements, while the 2 nd (T1) and 3 rd (T2) groups were fed the same basal diet with a supplement of 1% bentonite and choline, respectively. The results revealed that the average daily gain and total dry matter intake (DMI) did not affected by dietary supplementation. However, the feed conversion ratio was improved (P< 0.05) in the clay minerals groups in comparison to the control group. The treatment groups were higher (P<0.05) in serum glucose, and calcium and lower (P<0.05) in triglycerides, and urea-N concentrations than the control. Digestibility of crude protein (CP), crude fiber (CF), and either extract (EE) was increased (P<0.05) with the supplementation of clay minerals. Also, the supplement bentonite increased (P<0.05) dry matter (DM), organic matter (OM), and nitrogen-free extract (NFE) digestibility as compared with other groups. The feeding value in terms of starch value (SV) and digestible crude protein (DCP) was improved (P<0.05) with clay minerals groups. It can be concluded that clay minerals such as bentonite and choline are considered one of the effective ways to improve the average daily gain, blood biochemical parameters, nutrient digestibility, and feed conversion ratio of buffalos calves while the carcass characteristics were not significantly affected.
Gastric Intestinal Metaplasia: Challenges and the Opportunity for Precision Prevention

Simple Summary Gastric adenocarcinoma is the fifth most common cancer worldwide and the fourth most lethal. It is often asymptomatic at an early stage, when survival rates are highest (>90%) with minimally invasive endoscopic intervention or surgery. Gastric intestinal metaplasia (GIM) is a persistent, premalignant lesion in the stomach, arising in the context of chronic inflammation, which predisposes one to gastric cancer. It is considered a pivotal stage along a continuum to gastric cancer and has a median latency period of ~6 years before progression to cancer typically occurs, offering a window of opportunity for intervention. However, only a small proportion (0.25–2.5%) with GIM ultimately progress to cancer; therefore, approaches to surveillance vary widely around the world. We summarise the current evidence supporting best clinical practice in the diagnosis, assessment and management of GIM, and the opportunities to achieve precision in predictions in the coming decades. Abstract GIM is a persistent, premalignant lesion whereby gastric mucosa is replaced by metaplastic mucosa resembling intestinal tissue, arising in the setting of chronic inflammation, particularly in the context of Helicobacter pylori. While the overall rates of progression to gastric adenocarcinoma are low, estimated at from 0.25 to 2.5%, there are features that confer a much higher risk and warrant follow-up. In this review, we collate and summarise the current knowledge regarding the pathogenesis of GIM, and the clinical, endoscopic and histologic risk factors for cancer. We examine the current state-of-practice with regard to the diagnosis and management of GIM, which varies widely in the published guidelines and in practice. We consider the emerging evidence in population studies, artificial intelligence and molecular markers, which will guide future models of care. The ultimate goal is to increase the detection of early gastric dysplasia/neoplasia that can be cured while avoiding unnecessary surveillance in very low-risk individuals.
Determination of mean square deviation of surface roughness in a planar gradient optical waveguide

Determining the mean square deviation σ of surface roughness and the imaginary part of permittivity n''of a planar gradient optical waveguides n(y)= n3+Δn'(y)+ in''(y) = n'(y)+ in''(y), using the integral waveguide scattering method. For experiment two samples are prepared. The first sample using exchange with silver ions and obtained a parabolic profile, subsequently the method of solid-state diffusion of lead oxide in the glass substrate provides a waveguide with a Gaussian distribution profile permittivity. In both cases, laser radiation is used with a wavelength of 0.6328 microns.
Conversational AI and Vaccine Communication: Systematic Review of the Evidence

Background Since the mid-2010s, use of conversational artificial intelligence (AI; chatbots) in health care has expanded significantly, especially in the context of increased burdens on health systems and restrictions on in-person consultations with health care providers during the COVID-19 pandemic. One emerging use for conversational AI is to capture evolving questions and communicate information about vaccines and vaccination. Objective The objective of this systematic review was to examine documented uses and evidence on the effectiveness of conversational AI for vaccine communication. Methods This systematic review was conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. PubMed, Web of Science, PsycINFO, MEDLINE, Scopus, CINAHL Complete, Cochrane Library, Embase, Epistemonikos, Global Health, Global Index Medicus, Academic Search Complete, and the University of London library database were searched for papers on the use of conversational AI for vaccine communication. The inclusion criteria were studies that included (1) documented instances of conversational AI being used for the purpose of vaccine communication and (2) evaluation data on the impact and effectiveness of the intervention. Results After duplicates were removed, the review identified 496 unique records, which were then screened by title and abstract, of which 38 were identified for full-text review. Seven fit the inclusion criteria and were assessed and summarized in the findings of this review. Overall, vaccine chatbots deployed to date have been relatively simple in their design and have mainly been used to provide factual information to users in response to their questions about vaccines. Additionally, chatbots have been used for vaccination scheduling, appointment reminders, debunking misinformation, and, in some cases, for vaccine counseling and persuasion. Available evidence suggests that chatbots can have a positive effect on vaccine attitudes; however, studies were typically exploratory in nature, and some lacked a control group or had very small sample sizes. Conclusions The review found evidence of potential benefits from conversational AI for vaccine communication. Factors that may contribute to the effectiveness of vaccine chatbots include their ability to provide credible and personalized information in real time, the familiarity and accessibility of the chatbot platform, and the extent to which interactions with the chatbot feel “natural” to users. However, evaluations have focused on the short-term, direct effects of chatbots on their users. The potential longer-term and societal impacts of conversational AI have yet to be analyzed. In addition, existing studies do not adequately address how ethics apply in the field of conversational AI around vaccines. In a context where further digitalization of vaccine communication can be anticipated, additional high-quality research will be required across all these areas.