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Abstract This work initiates a concept of reduced reverse degree based $$\mathcal {RR_D\,M}$$ RR D M -Polynomial for a graph, and differential and integral operators by using this $$\mathcal {RR_D\,M}$$ RR D M -Polynomial. In this study twelve reduced reverse degree-based topological descriptors are formulated using the $$\mathcal {RR_D\,M}$$ RR D M -Polynomial. The topological descriptors, denoted as $$\mathbb{T}\mathbb{D}$$ T D ’s, are numerical invariants that offer significant insights into the molecular topology of a molecular graph. These descriptors are essential for conducting QSPR investigations and accurately estimating physicochemical attributes. The structural and algebraic characteristics of the graphene and graphdiyne are studied to apply this methodology. The study involves the analysis and estimation of Reduced reverse degree-based topological descriptors and physicochemical features of graphene derivatives using best-fit quadratic regression models. This work opens up new directions for scientists and researchers to pursue, taking them into new fields of study.

Skin cancer poses a significant risk to the healthcare system worldwide and is projected to increase substantially over the next two decades, particularly if not detected in its early stages. The primary aim of this study is to construct a quantitative structure-property relationship (QSPR) by correlating calculated entropies with topological indices and specific physical-chemical properties of pharmaceuticals, in order to enhance their usefulness. The bicubic regression model is constructed through degree-based topological entropies to perform the QSPR analysis for the prediction of physiochemical properties like polar surface area, complexity, molar refractivity, boiling point, and polarity of skin cancer drugs. It is also examined that degree-based entropies provide best-fit models for skin cancer physiochemical properties. We inspected five physio-chemical properties of these anticancer drugs and found this methodology suitable for predicting best-fit approximations regarding R2 value. It is expected that this approach will be very favourable to examining problems related to mathematical chemistry.

Gold is generally considered a noble metal since it is inherently inert in its bulk state. However, gold demonstrates reactivity when it is in its ionic state. The inherent inertness of bulk gold has resulted in its widespread recognition as a vital raw material in various biomedical processes. The applications of these technologies include drug delivery microchips, dental prostheses, reconstructive surgery, culinary additives, and cardiovascular stents. Gold can also exist in molecules or ions, particularly gold ions, which facilitates the production of gold nanomaterials. In this paper, we have computed differential and integral operators by using the M-Polynomial of gold crystals and by utilizing this polynomial, we have also computed eleven topological indices like 1st Zagreb, 2nd Zagreb, Hyper, Sigma, Second Modified, General Randic, General Reciprocal Randic, 3rd Redefined Zagreb, Symmetric Division Degree, Harmonic, Inverse Sum indices for the structure of Gold crystal.

The spinel ferrites are famous due to their unique electrical and magnetic performance. The current inspection is about the electrical and magnetic features of Holmium (Ho) substituted Co-Zn-ferrites (Co0.6Zn0.4HoxFe2−xO4; where x = 0.0 and 0.1). The Single-phase cubic configuration of prepared ferrites was perceived from X-ray diffraction data. The substituted sample has one order larger direct current (dc) resistivity as compared to the un-substituted sample. The substitution of Holmium decreased dielectric loss significantly. The dielectric study of samples was completed within the frequency range of 0.1–20 MHz. The comparison of dielectric parameters for different temperature values was measured. The magnetic characterization was done within the applied field of 2 kOe. The evaluated properties of samples like larger dc resistivity and less dielectric loss showed that the materials can be utilized in high-frequency applications.

Abstract The assessment of the duration of pharmacological prescriptions is an important phase in pharmacoepidemiologic studies aiming to investigate persistence, effectiveness or safety of treatments. The Sessa Empirical Estimator (SEE) is a new data-driven method which uses k-means algorithm for computing the duration of pharmacological prescriptions in secondary data sources when this information is missing or incomplete. The SEE was used to compute durations of exposure to pharmacological treatments where simulated and real-world data were used to assess its properties comparing the exposure status extrapolated with the method with the “true” exposure status available in the simulated and real-world data. Finally, the SEE was also compared to a Researcher-Defined Duration (RDD) method. When using simulated data, the SEE showed accuracy of 96% and sensitivity of 96%, while when using real-world data, the method showed sensitivity ranging from 78.0 (nortriptyline) to 95.1% (propafenone). When compared to the RDD, the method had a lower median sensitivity of 2.29% (interquartile range 1.21–4.11%). The SEE showed good properties and may represent a promising tool to assess exposure status when information on treatment duration is not available.

Entropy is a thermodynamic function used in chemistry to determine the disorder and irregularities of molecules in a specific system or process. It does this by calculating the possible configurations for each molecule. It is applicable to numerous issues in biology, inorganic and organic chemistry, and other relevant fields. Metal–organic frameworks (MOFs) are a family of molecules that have piqued the curiosity of scientists in recent years. They are extensively researched due to their prospective applications and the increasing amount of information about them. Scientists are constantly discovering novel MOFs, which results in an increasing number of representations every year. Furthermore, new applications for MOFs continue to arise, illustrating the materials’ adaptability. This article investigates the characterisation of the metal–organic framework of iron(III) tetra-p-tolyl porphyrin (FeTPyP) and CoBHT (CO) lattice. By constructing these structures with degree-based indices such as the K-Banhatti, redefined Zagreb, and the atom-bond sum connectivity indices, we also employ the information function to compute entropies.

In this paper, we prove commutativity of prime near rings by using the notion of β-derivations. Let M be a prime near ring. If there exist p,q ϵ M and two sided nonzero β-derivation f on M, where β:M→M is a homomorphism, satisfying the following conditions: f([s,t])=s^p [β(s),β(t)]s^q ∀ s,t ϵ M f([s,t])=-s^p [β(s),β(t)]s^q ∀ s,t ϵ M

In this paper, we prove commutativity of prime near rings by using the notion of β-derivations. Let M be a prime near ring. If there exist 𝑢1 , 𝑣1 𝜖 𝑀 and two sided generalized β-derivation G associated with the nonzero two sided β-derivation 𝑔 on M, where 𝛽: 𝑀 → 𝑀 is a homomorphism, satisfying the following conditions: i. 𝐺([𝑝1 , 𝑞1 ]) = 𝑝1 𝑢1[𝛽(𝑝1 ),𝛽(𝑞1 )]𝑝1 𝑣1 ∀ 𝑝1 , 𝑞1 𝜖 𝑀 ii. 𝐺([𝑝1 , 𝑞1 ]) = 𝑝1 𝑢1[𝛽(𝑝1 ),𝛽(𝑞1 )]𝑝1 𝑣1 ∀ 𝑝1 , 𝑞1 𝜖 𝑀 Then M is a commutative ring

In this paper, we prove commutativity of prime near rings by using the notion of β-derivations. Let M be a zero symmetric prime near ring. If there exist p ≥ 0, q ≥ 0 and a nonzero two sided β-derivation d on M, where β : M → M is a homomorphism, such that d satisfy one of the following conditions: [β(s),d(t)] = sp(β(s)oβ(t))sq ∀ s, t ∈ M [β(s),d(t)] = −sp(β(s)oβ(t))sq ∀ s, t ∈ M [d(s),β(t)] = tp(β(s)oβ(t))tq ∀ s, t ∈ M [d(s),β(t)] = −tp(β(s)oβ(t)tq ∀ s, t ∈ M

Sol-gel procedure was taken into consideration to produce ferrite series with chemical formula MnYbyFe2-yO4 with y = 0.00 to 0.10 (step = 0.025). Structural, electrical and magnetic aspects of ytterbium (Yb) switched Mn-ferrites were the main focus. Every sample exhibited cubic phase spinel assembly. Exchange of ytterbium with iron in the system produced enlargement in coercivity while abridgment in remanence and saturation magnetization. Involvement of Yb ion in the ferrite assembly led to reduction of both real and imaginary constituents of permittivity. The ytterbium introduction in the lattice enhanced the value of impedance. The examination of hysteresis loop was done under the range of -2K to +2KOe. Temperature dependent resistivity, activation energy and drift mobility were carried out by two probe method. The characterization outcomes recommended that the compounds are appropriate for microwave absorption usages.

Due to significant advancements being made in the field of drug design, the use of topological descriptors remains the primary approach. When combined with QSPR models, descriptors illustrate a molecule’s chemical properties numerically. Numbers relating to chemical composition topological indices are structures that link chemical composition to physical characteristics. This research concentrates on the analysis of curvilinear regression models and degree-based topological descriptors for thirteen skin cancer drugs. The physicochemical characteristics of the skin cancer drugs are examined while regression models are built for computed index values. An analysis is performed for several significant results based on the acquired data.

Entropy is a thermodynamic function in chemistry that reflects the randomness and disorder of molecules in a particular system or process based on the number of alternative configurations accessible to them. Distance-based entropy is used to solve a variety of difficulties in biology, chemical graph theory, organic and inorganic chemistry, and other fields. In this article, the characterization of the crystal structure of niobium oxide and a metal–organic framework is investigated. We also use the information function to compute entropies by building these structures with degree-based indices including the K-Banhatti indices, the first redefined Zagreb index, the second redefined Zagreb index, the third redefined Zagreb index, and the atom-bond sum connectivity index.

Aim: To summarize the evidence on the performance of artificial intelligence vs. traditional pharmacoepidemiological techniques.Methods: Ovid MEDLINE (01/1950 to 05/2019) was searched to identify observational studies, meta-analyses, and clinical trials using artificial intelligence techniques having a drug as the exposure or the outcome of the study. Only studies with an available full text in the English language were evaluated.Results: In all, 72 original articles and five reviews were identified via Ovid MEDLINE of which 19 (26.4%) compared the performance of artificial intelligence techniques with traditional pharmacoepidemiological methods. In total, 44 comparisons have been performed in articles that aimed at 1) predicting the needed dosage given the patient’s characteristics (31.8%), 2) predicting the clinical response following a pharmacological treatment (29.5%), 3) predicting the occurrence/severity of adverse drug reactions (20.5%), 4) predicting the propensity score (9.1%), 5) identifying subpopulation more at risk of drug inefficacy (4.5%), 6) predicting drug consumption (2.3%), and 7) predicting drug-induced lengths of stay in hospital (2.3%). In 22 out of 44 (50.0%) comparisons, artificial intelligence performed better than traditional pharmacoepidemiological techniques. Random forest (seven out of 11 comparisons; 63.6%) and artificial neural network (six out of 10 comparisons; 60.0%) were the techniques that in most of the comparisons outperformed traditional pharmacoepidemiological methods.Conclusion: Only a small fraction of articles compared the performance of artificial intelligence techniques with traditional pharmacoepidemiological methods and not all artificial intelligence techniques have been compared in a Pharmacoepidemiological setting. However, in 50% of comparisons, artificial intelligence performed better than pharmacoepidemiological techniques.

In this paper, we initiate the concept of α-multiplier on almost distributive lattices. We prove some useful results by using the notion of α-multiplier and generalize the idea of multiplier on almost distributive lattices.

AimTo perform a systematic review on the application of artificial intelligence (AI) based knowledge discovery techniques in pharmacoepidemiology.Study Eligibility CriteriaClinical trials, meta-analyses, narrative/systematic review, and observational studies using (or mentioning articles using) artificial intelligence techniques were eligible. Articles without a full text available in the English language were excluded.Data SourcesArticles recorded from 1950/01/01 to 2019/05/06 in Ovid MEDLINE were screened.ParticipantsStudies including humans (real or simulated) exposed to a drug.ResultsIn total, 72 original articles and 5 reviews were identified via Ovid MEDLINE. Twenty different knowledge discovery methods were identified, mainly from the area of machine learning (66/72; 91.7%). Classification/regression (44/72; 61.1%), classification/regression + model optimization (13/72; 18.0%), and classification/regression + features selection (12/72; 16.7%) were the three most frequent tasks in reviewed literature that machine learning methods has been applied to solve. The top three used techniques were artificial neural networks, random forest, and support vector machines models.ConclusionsThe use of knowledge discovery techniques of artificial intelligence techniques has increased exponentially over the years covering numerous sub-topics of pharmacoepidemiology.Systematic Review RegistrationSystematic review registration number in PROSPERO: CRD42019136552.

As compared to conventional physical and chemical techniques, the biological method (green synthesis) is an economical, nontoxic, fast, eco-friendly and cost-effective technique that utilizes plant extract to generate highly stable and biocompatible nanoparticles. The existing study focuses on the synthesis of zinc sulphide nanoparticles (ZnS NPs) using P. persica leaf extract which is used as a reducing agent and their associated properties. The prepared samples were examined in terms of structural, optical, morphological and compositional properties. The SEM images showed the agglomerated spherical particles while XRD spectra confirmed the hexagonal (wurtzite) phase of synthesized ZnS samples with average crystallite sizes found to vary from 12.45 to 14.36 nm. The optical absorption spectra displayed the absorption peaks at 331 nm (4.26 eV) and 339 nm (4.10 eV). This blue shift in the absorption band suggests the formation of ZnS NPs compared with bulk ZnS (∼ 3. 6 8 eV) material. The FTIR peaks from 612– to 630 cm − 1 and 860– to 1022 cm − 1 are because of the symmetric bending vibrations of the Zn–S bands. Electrical measurements showed that the resistivity is found to be decreasing with increasing temperature, signifying the semiconducting behavior of the synthesized ZnS NPs. The findings of this study suggest the feasible use of P. persica leaves extract for the production of ZnS NPs which could be utilized as multifunctional material in medical applications, biosensors, photocatalytic and electrical applications. [ABSTRACT FROM AUTHOR]