A novel simulation-based analysis of a stochastic HIV model with the time delay using high order spectral collocation technique.

The economic impact of Human Immunodeficiency Virus (HIV) goes beyond individual levels and it has a significant influence on communities and nations worldwide. Studying the transmission patterns in HIV dynamics is crucial for understanding the tracking behavior and informing policymakers about the possible control of this viral infection. Various approaches have been adopted to explore how the virus interacts with the immune system. Models involving differential equations with delays have become prevalent across various scientific and technical domains over the past few decades. In this study, we present a novel mathematical model comprising a system of delay differential equations to describe the dynamics of intramural HIV infection. The model characterizes three distinct cell sub-populations and the HIV virus. By incorporating time delay between the viral entry into target cells and the subsequent production of new virions, our model provides a comprehensive understanding of the infection process. Our study focuses on investigating the stability of two crucial equilibrium states the infection-free and endemic equilibriums. To analyze the infection-free equilibrium, we utilize the LaSalle invariance principle. Further, we prove that if reproduction is less than unity, the disease free equilibrium is locally and globally asymptotically stable. To ensure numerical accuracy and preservation of essential properties from the continuous mathematical model, we use a spectral scheme having a higher-order accuracy. This scheme effectively captures the underlying dynamics and enables efficient numerical simulations.

Straw Addition Enhances Crop Yield, Soil Aggregation, and Soil Microorganisms in a 14-Year Wheat-Rice Rotation System in Central China.

Straw return utilizes waste resources to reduce the use of chemical fertilizers worldwide. However, information is still lacking on the relative impact of straw return on soil fertility, the nutrient composition of different soil aggregates, and soil microbial communities. Therefore, this study aimed to understand the effects of different management practices on the crop yield, soil fertility, and soil community composition in a 14-year wheat-rice rotation system. The treatments included a control (without fertilizer and straw addition), chemical fertilization (NPK), straw return without fertilizer (S), and straw addition with chemical fertilizer (NPKS). The results showed that NPKS improved the wheat and rice yield by 185.12% and 88.02%, respectively, compared to the CK treatment. Additionally, compared to the CK treatment, the N, P, and K contents of the wheat stem were increased by 39.02%, 125%, and 20.23% under the NPKS treatment. Compared to the CK treatment, SOM, TN, TP, AN, AP, AK, CEC, AFe, AMn, ACu, and AZn were increased by 49.12%, 32.62%, 35.06%, 22.89%, 129.36%, 48.34%, 13.40%, 133.95%, 58.98%, 18.26% and 33.33% under the NPKS treatment, respectively. Moreover, straw addition promoted the creation and stabilization of macro-aggregates in crop soils. The relative abundance of macro-aggregates (0.25-2 mm) increased from 37.49% to 52.97%. Straw addition was associated with a higher proportion of aromatic and carbonyl carbon groups in the soil, which, in turn, promoted the formation of macro-aggregates. Redundancy analysis showed that straw return significantly increased the microbial community diversity. These findings demonstrate that straw addition together with chemical fertilizer could increase the crop yield by improving soil fertility, soil aggregate stability, and the diversity of fungi.

The Role of Proton Pump Inhibitors in the Realm of Idiopathic Pulmonary Fibrosis and its Associated Comorbidities: A Systematic Review.

As the global incidence of idiopathic pulmonary fibrosis (IPF) is on the rise, there is a need for better diagnostic criteria, better treatment options, early and appropriate diagnosis, adequate care, and a multidisciplinary approach to the management of patients. This systematic review explores the role of proton pump inhibitors (PPIs) in IPF and answers the question, "Does proton pump inhibitor improve only the prognosis of gastroesophageal associated idiopathic pulmonary fibrosis or for other types of idiopathic pulmonary fibrosis too?" We used PubMed (PMC) and Google Scholar for data collection for this systematic review and followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for conducting this review. After in-depth literature screening and quality appraisal, 12 articles were selected for this systematic review. On the one hand, the efficacy of PPI therapy is supported by research such as the CAPACITY and ASCEND trials, a pilot randomized control trial (RCT) investigating the role of omeprazole in IPF and a bidirectional two-sample Mendelian randomization (MR) study, respectively. On the other hand, a systematic review and meta-analysis on antacid and antireflux surgery in IPF negate these results and show no statistical significance. Questions regarding the efficacy of PPI therapy must be dealt with in an adequately powered multicenter and double-blinded randomized control trial. The anti-inflammatory properties of antacids can serve as the cornerstone for future trials. In the following systematic review, antacid, antireflux therapy, omeprazole, and proton pump therapy are synonymous with stomach acid suppression therapy.

Investigation of ultra wide bandgap Flouro-perovskite materials RBeF3 (R[bond, double bond]K and Li) for smart window applications: A DFT study.

The human body is affected by ultraviolet radiation because it can penetrate and harm bodily cells. Although skin cancer and early aging are consequences of prolonged exposure to ultraviolet (UV) rays, sun rays signify immediate excessive exposure. In this context, some structural, optical, electrical, and mechanical properties of the beryllium-based cubic fluoro-perovskite RBeF3 (R[bond, double bond]K and Li) compounds are examined through the use of density functional theory (DFT) within generalized gradient approximation (GGA) using the Perdew-Burke-Ernzerhof (PBE) approximations (GGA-PBE). The compounds KBeF3 and LiBeF3 have space group 221-pm3m, and their lattice constants and volumes are (3.765, 3.566) Å and (53.380, 45.379) Å3, respectively, based on their structural properties. Computed results indicate that the compounds' bandgaps are 7.35 eV and 7.12 eV, respectively, with an indirect nature for KBeF3 and LiBeF3. The properties of the band structure indicate that both compounds are insulators. The bonding properties of these compounds, RBeF3, are a combination of covalent and ionic. Optical properties of the compounds are examined which reflect the light-matter interaction like reflectivity, conductivity, and absorption. These materials were likely very hard but brittle, based on a higher bulk modulus B from elastic features, the B/G ratio, Pugh's ratio, and Vickers hardness. The compound RBeF3, as determined by the findings, is used as a UV protection and reflection layer for car and room windows.

Influences of tobacco straw return with lime on microbial community structure of tobacco-planting soil and tobacco leaf quality.

Soil amendment is an important strategy for improving soil quality and crop yield. From 2014 to 2019, we conducted a study to investigate the effects of tobacco straw return with lime on soil nutrients, soil microbial community structure, tobacco leaf yield, and quality in southern Anhui, China. A field experiment was conducted with four treatments: straw removed (CK), straw return (St), straw return with dolomite (St + D), and straw return with lime (St + L). Results showed that after 5 years of application, the St + L significantly increased the soil pH by 16.9%, and the contents of soil alkaline nitrogen (N) and available potassium (K) by 17.2% and 23.0%, respectively, compared with the CK. Moreover, the St + L significantly increased tobacco leaf yield (24.0%) and the appearance (9.1%) and sensory (5.9%) quality of flue-cured tobacco leaves. The addition of soil conditioners (straw, dolomite, and lime) increased both the total reads and effective sequences of soil microorganisms. Bacterial diversity was more sensitive to changes in the external environment compared to soil fungi. The application of soil amendments (lime and straw) promoted the growth of beneficial microorganisms in the soil. Additionally, bacterial species had greater competition and limited availability of resources for survival compared to fungi. The results showed that soil microorganisms were significantly influenced by the presence of AK, AN, and pH contents. These findings can provide an effective method for improving the quality of flue-cured tobacco leaves and guiding the amelioration of acidic soil in regions where tobacco-rice rotation is practiced.

Comparison of Low-Dose Interleukin 2 Therapy in Conjunction With Standard Therapy in Patients With Systemic Lupus Erythematosus vs Rheumatoid Arthritis: A Systematic Review.

This systematic review aims to compare the efficacy and safety of a novel immunotherapy with low-dose interleukin 2 (IL2) across two of the most prevalent autoimmune diseases i.e. systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA). Contemporary therapeutic practices have not been able to achieve complete remission from these autoimmune disorders. In contrast, low-dose IL2 has shown promise in achieving this therapeutic goal via inducing self-tolerance in patients with autoimmune diseases; however, due to variable irregularities among autoimmune processes of variable diseases, the benefit of low-dose IL2 could not be determined among different autoimmune diseases. Therefore, we conducted a study to compare low-dose IL2 therapy effects on SLE and RA. We systematically screened four databases: PubMed, Medical Literature Analysis and Retrieval System Online (MEDLINE), PubMed Central (PMC), and Google Scholar. Inclusion and exclusion criteria were implemented. Quality appraisal of studies chosen for the review was done using the Cochrane Risk-of-Bias (RoB) assessment tool for randomized controlled trials, and the Newcastle-Ottawa Scale (NOS) and JBI critical appraisal tool for non-randomized clinical trials. Information was gathered from seven articles: three randomized controlled trials and four non-randomized clinical trials. Our review concluded that low-dose IL2 therapy in conjunction with respective standard therapies for SLE and RA has a higher efficacy and safety profile as compared to standard therapy alone and the therapeutic effects were comparable in both SLE and RA patients treated with low-dose IL2; however, this novel intervention does not seem to have a significant corrective effect on the biomarkers of RA as it does for SLE biomarkers.

Nitrogen Fertilization and Cultivar Interactions Determine Maize Yield and Grain Mineral Composition in Calcareous Soil under Semiarid Conditions.

Identifying the contributions of climate factors and fertilization to maize yield is significant for the assessment of climate change impacts on maize production under semiarid conditions. This experiment was conducted with an overall objective to find how N fertilization and cultivar interactions along with climatic conditions determine the mineral composition and maize yield responses of four divergent maize cultivars grown under eight different fertilization levels. The results showed that element contents were significantly affected by year (Y), cultivar (C), N fertilization, and N × C interaction. The element contents of grains were mainly influenced by N rate or N × C interactions. The results showed that maize yield was significantly affected by year (Y), genotype (G), N fertilization (N), and Y × G × N interaction. These results implied that the maize yield was significantly affected by changes in genotypes and environments. Overall, our findings are a result of the interactions of genetic, environmental, and agronomic management factors. Future studies could evaluate more extreme plant densities, N fertilizer levels, and environments to further enhance our understanding of management effects on the mineral composition and maize yield in calcareous soil.

Breaking It Down: A Systematic Review Unravelling the Impact of Attention Deficit Hyperactivity Disorder and Methylphenidate on Childhood Fractures.

Limb fractures are a common cause of pediatric hospital admissions and surgeries, with a significant prevalence in the United Kingdom across all injury categories. Among psychiatric conditions in children, attention deficit hyperactivity disorder (ADHD) stands out as frequently associated with fractures, particularly those involving extremities. ADHD, with diagnoses prevalent among a significant proportion of school-age children and adolescents, has witnessed a growing global incidence. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 checklist for our systematic literature search, using various databases and specific search terms related to ADHD and fractures. We considered articles from 2018 to 2023, focusing on English language papers with free full-text access. Our selection process used the PRISMA flowchart. We began with 1,890 articles and, after deduplication, title screening, abstract assessment, and quality evaluation included nine research papers in our review. Our primary focus was on examining fracture-related outcomes in individuals with ADHD compared to those without, considering medication status. These studies encompassed various designs, with a focus on the ADHD-fracture relationship and methylphenidate's (MPH) impact. Our study confirms that ADHD increases fracture risk and suggests that MPH may help mitigate this risk. Early ADHD detection is vital for nonpharmacological interventions. Orthopedic surgeons should proactively identify ADHD, while healthcare professionals should offer injury prevention guidance, particularly for at-risk groups.

Phase formation and dielectric properties of MgFe2O4 nanoparticles synthesized by hydrothermal technique.

In the recent development of energy storage devices, the scientific study has demonstrated a significant interest in the applications of the magnesium iron oxide (MgFe2O4) nanoparticles. In this work, we present synthesized novel MgFe2O4 nanoparticles at different molarities (0.1-0.5 M), via hydrothermal technique. An X-ray Diffractometer was used to study the phase analysis of the prepared samples at different molarities. A pure cubic phase of the MgFe2O4 is observed at molar concentrations of 0.3 M and 0.4 M. However, the mixed phases consisting of (MgFe2O4 + Î³-Fe2O3) were also observed at 0.1 M, 0.2 M, and 0.5 M. The pure cubic MgFe2O4 nanoparticles depict the large value of crystallite size, 19.5 nm, and the lowest dislocation density and strain. The vibrating Sample Magnetometer shows the ferromagnetic nature of the pure MgFe2O4 with a high saturation magnetization. The value of saturation magnetization surged from 36.88 emu/g to 55.2 emu/g at 0.4 M concentration. The dielectric response of the materials as a function of applied frequency was studied thoroughly by using an Impedance Analyzer. The highest value of dielectric constant and low tangent loss was also reported at 0.4 M. Cole-Cole plots are the affirmation of the contribution of both grains and grain boundaries in the charge mechanism. These distinctive features make the synthesized material an excellent choice for future spintronics and energy storage devices.

Investigating the synergistic effects of biochar, trans-zeatin riboside, and Azospirillum brasilense on soil improvement and enzymatic activity in water-stressed wheat.

BACKGROUND: Water stress is a major danger to crop yield, hence new approaches to strengthen plant resilience must be developed. To lessen the negative effects of water stress on wheat plants, present study was arranged to investigate the role of synergistic effects of biochar, trans-zeatin riboside (t-ZR), and Azospirillum brasilense on soil improvement and enzymatic activity in water-stressed wheat. RESULTS: In a three-replication experiment comprising of four treatments (T0: Control, T1: Drought stress (DS), T2: DS + t-ZR with biochar, T3: DS + A. brasilense with biochar), we observed notable improvements in soil quality and enzymatic activities in water-stressed wheat plants with the application of t-ZR and A. brasilense with biochar. In drought stress, Treatment having the application of A. brasilense with biochar performs best as compared to the other and significant increased the enzymatic activities such as peroxidase (7.36%), catalase (8.53%), superoxide dismutase (6.01%), polyphenol oxidase (14.14%), and amylase (16.36%) in wheat plants. Different enzymatic activities showed different trends of results. Soil organic C, dissolved organic C, dissolved organic N also enhanced 29.46%, 8.59%, 22.70% respectively with the application of A. brasilense with biochar under drought stress condition. CONCLUSIONS: The synergistic action of A. brasilense and biochar creates an effective microbiological environment that supports essential plant physiological processes during drought stress. This enhancement is attributed to improved soil fertility and increased organic matter content, highlighting the potential of these novel strategies in mitigating water stress effects and enhancing crop resilience.

A novel probabilistic q-rung orthopair linguistic neutrosophic information-based method for rating nanoparticles in various sectors.

The idea of probabilistic q-rung orthopair linguistic neutrosophic (P-QROLN) is one of the very few reliable tools in computational intelligence. This paper explores a significant breakthrough in nanotechnology, highlighting the introduction of nanoparticles with unique properties and applications that have transformed various industries. However, the complex nature of nanomaterials makes it challenging to select the most suitable nanoparticles for specific industrial needs. In this context, this research facilitate the evaluation of different nanoparticles in industrial applications. The proposed framework harnesses the power of neutrosophic logic to handle uncertainties and imprecise information inherent in nanoparticle selection. By integrating P-QROLN with AO, a comprehensive and flexible methodology is developed for assessing and ranking nanoparticles according to their suitability for specific industrial purposes. This research contributes to the advancement of nanoparticle selection techniques, offering industries a valuable tool for enhancing their product development processes and optimizing performance while minimizing risks. The effectiveness of the proposed framework are demonstrated through a real-world case study, highlighting its potential to revolutionize nanoparticle selection in HVAC (Heating, Ventilation, and Air Conditioning) industry. Finally, this study is crucial to enhance nanoparticle selection in industries, offering a sophisticated framework probabilistic q-rung orthopair linguistic neutrosophic quantification with an aggregation operator to meet the increasing demand for precise and informed decision-making.

Silicon and iron nanoparticles protect rice against lead (Pb) stress by improving oxidative tolerance and minimizing Pb uptake.

Lead (Pb) is toxic to the development and growth of rice plants. Nanoparticles (NPs) have been considered one of the efficient remediation techniques to mitigate Pb stress in plants. Therefore, a study was carried out to examine the underlying mechanism of iron (Fe) and silicon (Si) nanoparticle-induced Pb toxicity alleviation in rice seedlings. Si-NPs (2.5 mM) and Fe-NPs (25 mg L-1) were applied alone and in combination to rice plants grown without (control; no Pb stress) and with (100 µM) Pb concentration. Our results revealed that Pb toxicity severely affected all rice growth-related traits, such as inhibited root fresh weight (42%), shoot length (24%), and chlorophyll b contents (26%). Moreover, a substantial amount of Pb was translocated to the above-ground parts of plants, which caused a disturbance in the antioxidative enzyme activities. However, the synergetic use of Fe- and Si-NPs reduced the Pb contents in the upper part of plants by 27%. It reduced the lethal impact of Pb on roots and shoots growth parameters by increasing shoot length (40%), shoot fresh weight (48%), and roots fresh weight (31%). Both Si and Fe-NPs synergistic application significantly elevated superoxide dismutase (SOD), peroxidase (POD), catalase (CAT), and glutathione (GSH) concentrations by 114%, 186%, 135%, and 151%, respectively, compared to plants subjected to Pb stress alone. The toxicity of Pb resulted in several cellular abnormalities and altered the expression levels of metal transporters and antioxidant genes. We conclude that the synergistic application of Si and Fe-NPs can be deemed favorable, environmentally promising, and cost-effective for reducing Pb deadliness in rice crops and reclaiming Pb-polluted soils.

Ecological characteristics of sugar beet plant and rhizosphere soil in response to high boron stress: A study of the remediation potential.

High boron (B) stress degrades the soil environment and reduces plant productivity. Sugar beet has a high B demand and potential for remediation of B-toxic soils. However, the mechanism regarding the response of sugar beet plants and rhizosphere soil microbiome to high B stress is not clear. In the potted soil experiment, we set different soil effective B environments (0.5, 5, 10, 30, 50, and 100 mg kg-1) to study the growth status of sugar beets under different B concentrations, as well as the characteristics of soil enzyme activity and microbial community changes. The results showed that sugar beet growth was optimal at 5 mg kg-1 of B. Exceeding this concentration the tolerance index decreased. The injury threshold EC20 was reached at an available B concentration of 35.8 mg kg-1. Under the treatment of 100 mg kg-1, the B accumulation of sugar beet reached 0.22 mg plant-1, and the tolerance index was still higher than 60%, which had not yet reached the lethal concentration of sugar beet. The abundance of Acidobacteriota, Chloroflexi and Patescibacteria increased, which was beneficial to the resistance of sugar beet to high B stress. In summary, under high B stress sugar beet had strong tolerance, enhanced capacity for B uptake and enrichment, and changes in soil microbial community structure. This study provides a theoretical basis for clarifying the mechanism of sugar beet resistance to high B stress and soil remediation.

Numerical simulation of a fractional stochastic delay differential equations using spectral scheme: a comprehensive stability analysis.

The fractional stochastic delay differential equation (FSDDE) is a powerful mathematical tool for modeling complex systems that exhibit both fractional order dynamics and stochasticity with time delays. The purpose of this study is to explore the stability analysis of a system of FSDDEs. Our study emphasizes the interaction between fractional calculus, stochasticity, and time delays in understanding the stability of such systems. Analyzing the moments of the system's solutions, we investigate stochasticity's influence on FSDDS. The article provides practical insight into solving FSDDS efficiently using various numerical techniques. Additionally, this research focuses both on asymptotic as well as Lyapunov stability of FSDDS. The local stability conditions are clearly presented and also the effects of a fractional orders with delay on the stability properties are examine. Through a comprehensive test of a stability criteria, practical examples and numerical simulations we demonstrate the complexity and challenges concern with the analyzing FSDDEs.

Mathematical modeling and control of lung cancer with IL2 cytokine and anti-PD-L1 inhibitor effects for low immune individuals.

Mathematical formulations are crucial in understanding the dynamics of disease spread within a community. The aim of this work is to examine that the Lung Cancer detection and treatment by introducing IL2 and anti-PD-L1 inhibitor for low immune individuals. Mathematical model is developed with the created hypothesis to increase immune system by antibody cell's and Fractal-Fractional operator (FFO) is used to turn the model into a fractional order model. A newly developed system TCDIL2Z is examined both qualitatively and quantitatively in order to determine its stable position. The boundedness, positivity and uniqueness of the developed system are examined to ensure reliable bounded findings, which are essential properties of epidemic models. The global derivative is demonstrated to verify the positivity with linear growth and Lipschitz conditions are employed to identify the rate of effects in each sub-compartment. The system is investigated for global stability using Lyapunov first derivative functions to assess the overall impact of IL2 and anti-PD-L1 inhibitor for low immune individuals. Fractal fractional operator is used to derive reliable solution using Mittag-Leffler kernel. In fractal-fractional operators, fractal represents the dimensions of the spread of the disease and fractional represents the fractional ordered derivative operator. We use combine operators to see real behavior of spread as well as control of lung cancer with different dimensions and continuous monitoring. Simulations are conducted to observe the symptomatic and asymptomatic effects of Lung Cancer disease to verify the relationship of IL2, anti-PD-L1 inhibitor and immune system. Also identify the real situation of the control for lung cancer disease after detection and treatment by introducing IL2 cytokine and anti-PD-L1 inhibitor which helps to generate anti-cancer cells of the patients. Such type of investigation will be useful to investigate the spread of disease as well as helpful in developing control strategies from our justified outcomes.

Silica-Based Superhydrophobic and Superoleophilic Cotton Fabric with Enhanced Self-Cleaning Properties for Oil-Water Separation and Methylene Blue Degradation.

Superhydrophobic textiles with multifunctional characteristics are highly desired and have attracted tremendous research attention. This research employs a simple dip-coating method to obtain a fluorine-free silica-based superhydrophobic and superoleophilic cotton fabric. Pristine cotton fabric is coated with SiO2 nanoparticles and octadecylamine. SiO2 nanoparticles are anchored on the cotton fabric to increase surface roughness, and octadecyl amine lowers the surface energy, turning the hydrophilic cotton fabric into superhydrophobic. The designed cotton fabric exhibits a water contact angle of 159° and a sliding angle of 7°. The prepared cotton fabric is characterized by attenuated total reflectance-fourier transform infrared spectroscopy, X-ray diffraction, atomic force microscopy, scanning electron microscopy, and energy-dispersive X-ray spectroscopy. In addition, the coated fabric reveals excellent features, including mechanical and chemical stability, superhydrophobicity, superoleophilicity, and the self-cleaning ability. SiO2 nanoparticles and octadecylamine-coated cotton fabric demonstrate exceptional oil-water separation and wastewater remediation performance by degrading the methylene blue solution up to 89% under visible light. The oil-water separation ability is tested against five different oils with more than 90% separation efficiency. This strategy has the advantages of low-cost precursors, a simple and scalable coating method, enhanced superhydrophobicity and superoleophilicity, self-cleaning ability, efficient oil-water separation, and exceptional wastewater remediation performance.

Alleviation of cotton growth suppression caused by salinity through biochar is strongly linked to the microbial metabolic potential in saline-alkali soil.

Biochar is a typical soil organic amendment; however, there is limited understanding of its impact on the metabolic characteristics of microorganisms in saline-alkaline soil microenvironment, as well as the advantages and disadvantages of plant-microorganism interactions. To elucidate the mechanisms underlying the impact of saline-alkali stress on cotton, a 6-month pot experiment was conducted, involving the sowing of cotton seedlings in saline-alkali soil. Three different biochar application levels were established: 0 % (C0), 1 % (C1), and 2 % (C2). Results indicated that biochar addition improved the biomass of cotton plants, especially under C2 treatment; the dry weight of cotton bolls were 8.15 times that of C0. Biochar application led to a rise in the accumulation of photosynthetic pigments by 8.30-51.89 % and carbohydrates by 7.4-10.7 times, respectively. Moreover, peroxidase (POD) activity, the content of glutathione (GSH), and ascorbic acid (ASA) were elevated by 23.97 %, 118.39 %, and 48.30 % under C2 treatment, respectively. Biochar caused a reduction in Na+ uptake by 8.21-39.47 %, relative electrical conductivity (REC) of plants, and improved K+/Na+ and Ca2+/Na+ ratio indicating that biochar alleviated salinity-caused growth reduction. Additionally, the application of biochar enhanced the absorption intensity of polysaccharide fingerprints in cotton leaves and roots. Two-factor co-occurrence analysis indicated that the key differential metabolites connected to several metabolic pathways were L-phenylalanine, piperidine, L-tryptophan, and allysine. Interestingly, biochar altered the metabolic characteristics of saline-alkali soil, especially related to the biosynthesis and metabolism of amino acids and purine metabolism. In conclusion, this study demonstrates that biochar may be advantageous in saline soil microenvironment; it has a favorable impact on how plants and soil microbial metabolism interact.

q-Rung orthopair fuzzy dynamic aggregation operators with time sequence preference for dynamic decision-making.

The q-rung orthopair fuzzy set (q-ROPFS) is a kind of fuzzy framework that is capable of introducing significantly more fuzzy information than other fuzzy frameworks. The concept of combining information and aggregating it plays a significant part in the multi-criteria decision-making method. However, this new branch has recently attracted scholars from several domains. The goal of this study is to introduce some dynamic q-rung orthopair fuzzy aggregation operators (AOs) for solving multi-period decision-making issues in which all decision information is given by decision makers in the form of "q-rung orthopair fuzzy numbers" (q-ROPFNs) spanning diverse time periods. Einstein AOs are used to provide seamless information fusion, taking this advantage we proposed two new AOs namely, "dynamic q-rung orthopair fuzzy Einstein weighted averaging (DQROPFEWA) operator and dynamic q-rung orthopair fuzzy Einstein weighted geometric (DQROPFEWG) operator". Several attractive features of these AOs are addressed in depth. Additionally, we develop a method for addressing multi-period decision-making problems by using ideal solutions. To demonstrate the suggested approach's use, a numerical example is provided for calculating the impact of "coronavirus disease" 2019 (COVID-19) on everyday living. Finally, a comparison of the proposed and existing studies is performed to establish the efficacy of the proposed method. The given AOs and decision-making technique have broad use in real-world multi-stage decision analysis and dynamic decision analysis.

G-ACP: a machine learning approach to the prediction of therapeutic peptides for gastric cancer.

Conventional Gastrointestinal (GI) cancer treatments are quite expensive and have major hazards. Nowadays, a different strategy places more emphasis on creating tiny biologically active peptides that do not cause severe poisoning. Anticancer peptides (ACPs) are found through experimental screening, which is time-dependent and frequently fraught with difficulties. Gastric ACPs are emerging as a promising GI cancer treatment in the current day. It is crucial to identify novel gastric ACPs to have an improved knowledge of their functioning processes and treatment of gastric cancer. As a result of the post-genomic era's massive production of peptide sequences, rapid and effective ACPs using a computational method are essential. Several adaptive statistical techniques for distinguishing ACPs and non-ACPs have recently been developed. A variety of adapted statistically significant methods have been developed to differentiate between ACPs and non-ACPs. Despite significant progress, there is no specific model for the prediction of gastric ACPs because the specific model will predict a particular type of peptide more accurately and quickly. To overcome this, an initiative is taken for the creation of a reliable framework for the accurate identification of gastric ACPs. The current technique in particular contains four possible features along with one hybrid feature encoding mechanisms which are the target-class motif previously indicated by Amino Acid Composition, Dipeptide Composition, Tripeptide Composition (TPC), Pseudo Amino Acid Composition (PAAC), and their Hybrid. Machine Learning algorithms make high-performance and accurate prediction tools. Moreover, highly variable and ideal deep feature selection is done using an ANOVA-based F score for feature pruning. Experiments on a range of algorithms are carried out to identify the optimal operating strategy due to the diverse nature of learning. Following analysis of the empirical results, Naïve Bayes with TPC and Hybrid feature space outperforms other methods with 0.99 accuracy score on the testing dataset. To find the model generalization an external validation is carried out. In external datasets, the Extra Trees with PAAC features outperforms with the accuracy of 0.94. The comparison study shows that our suggested model will predict gastric ACPs more accurately and will be useful in drug development and gastric cancer. The predictive model can be freely accessed at https://github.com/humeraazad10/G-ACP.git.Communicated by Ramaswamy H. Sarma.

Modeling and analysis of hybrid-blood nanofluid flow in stenotic artery.

Current communication deals with the flow impact of blood inside cosine shape stenotic artery. The under consideration blood flow is treated as Newtonian fluid and flow is assumed to be two dimensional. The governing equation are modelled and solved by adopting similarity transformation under the stenosis assumptions. The important quantities like Prandtl number, flow parameter, blood flow rate and skin friction are attained to analyze the blood flow phenomena in stenosis. The variations of different parameters have been shown graphically. It is of interest to note that velocity increases due to change in flow parameter gamma and temperature of blood decreases by increasing nanoparticles volume fraction and Prandtl number. In the area of medicine, the most interesting nanotechnology approach is the nanoparticles applications in chemotherapy. This study provides further motivation to include more convincing consequences in the present model to represent the blood rheology.