Payame Noor University

The stability problem in the sense of Ulam has recently been introduced and studied in the context of locally convex cones, as demonstrated in [29, 30]. In this work, we investigate the stability of 3D Cauchy–Jensen type operators within the framework of locally convex cones. We establish new stability results that provide a deeper understanding of the behavior of these operators under perturbations. Our findings contribute to the theoretical advancement of Hyers–Ulam stability in locally convex cones and highlight the unique properties of 3D Cauchy–Jensen type operators in these settings. This study enriches the mathematical foundation of stability theory and offers fresh insights into the interplay between some operators and locally convex structures.

The Internet of Things (IoT) has accelerated the connectivity between physical objects and the Internet. It has become common to integrate IoT devices into our lifestyles, considering the fact that they make traditional devices to be more intelligent and self‐sufficient. The usage of 5G‐enabled IoT can be one such improvement, as it integrates multiple devices and allows for effective interaction and data sharing. However, with the growing extreme increase in the number of devices being connected, resource utilization efficiency has emerged as one major challenge. Comparing the existing resource management strategies with the current environment brought by even more complex IoT, the former have consistently failed, leading to the wastage of too much energy. Resource allocation and efficient utilization in IoTs encompass processing power, bandwidth, and energy for the appropriate and effective functioning of devices and networks. The conventional designs are inherently inefficient in that they cannot match with the pace and nature of IoT data structures, hence making it difficult to achieve any meaningful performance, and resources are also wasted in the process; thus, there exists the necessity for energy‐efficient approaches that are adaptable to dynamic workloads. In consideration of the aforementioned factors, this paper proposes an entirely new approach employing a Kohonen neural network to address the issue of resource allocation with a focus on energy efficiency. The first of these steps is the collection of data obtained from IoT devices and the processing of this data in order to detect the important features; the second step is the usage of the algorithm to produce a resource map indicating the spatial distribution of resources, and the final step is the real‐time modification of the resource map by incoming data to promote appropriate resource allocation. The analysis shows that when using the method provided, energy, costs, and delays in the implementation of the process have improved.

Silibinin (C25H22O10), a notable bioactive flavonolignans, is recognized for its anticancer properties. However, due to its poor water solubility, the objective of this study was to design and synthesize nanocarriers to enhance the solubility of silibinin for effective delivery to AGS gastric cancer cells. This study details the synthesis of PEG400‐OA nanoparticles for silibinin delivery to AGS cells. Various physicochemical techniques, including FT‐IR, TGA, EDX, FE‐SEM, and TEM, were employed to characterize the silibinin‐loaded nanoparticles (SLNs), confirming particle size, elemental composition, thermal stability, and paramagnetic properties. The anticancer effects of the SLNs were assessed using MTT assay, scratch test, and Q‐RT‐PCR. The SLNs exhibited particle sizes ranging from 45 to 60 nm, with thermal stability below 110°C. TEM images suggested a micelles/liposomes structure due to the low polydispersity and spherical shape of the particles. EDX analysis revealed the presence of C, O, N, and P, confirming the incorporation of phospholipids (micelle/liposome) within the SLNs. The IC50 of SLNs in AGS cells was determined to be 28.21 μg/mL. Antimigration effects of SLNs's were demonstrated through the downregulation of miR‐181a and upregulation of its potential targets (TGFB, SMAD3, and β‐catenin genes), as well as the upregulation of miR‐34a and downregulation of its potential target (E‐Cadherin antimigration gene). The findings suggest that nanoparticles serve as effective nanocarriers for the targeted delivery of silibinin to cancer cells. Silibinin‐loaded micelles/liposomes nanoparticles (SLNs) appear to inhibit cancer cell proliferation and migration by modulating the expressionof miRNAs and their target mRNAs.

A theoretical scheme is proposed to generate a strongly entangled photon pair by a $\Lambda$-configured three-level atom interacting with a two-mode quantized cavity field through two strong classical fields resonating with the corresponding atomic transitions. The initial state of the two-mode cavity field is considered to be the tensor product of an arbitrary Fock state of the first mode and the vacuum and one-photon states of the second mode, while that of the three-level atom is prepared in its upper excited state. Then, the time-evolution operator in the interaction picture is used to derive two classes of continuous variable entangled displaced number squeezed states (EDNSSs) in terms of the even and odd states of the second mode, respectively. The quadrature squeezing of the EDNSSs for the first mode in the initial vacuum state is considered concerning the coherent and squeezed fields. The sub-Poissonian statistics and the photon anti-bunching effect corresponding to both the coherent and squeezed fields are investigated in terms of the excited states of the first mode, as well as the coherent and squeezed fields with the same amplitudes. Furthermore, the cross-correlation between the two bosonic modes and their two-qubit entanglement are studied; and for example, it is shown that the destructive interference (anti-correlation) corresponds to an increase in entanglement. We highlight the role of the excited states of the first mode in controlling the nonclassical properties of the even and odd EDNSSs, compared to those of the entangled coherent-squeezed states (ECSSs).

There are several models for soft regression analysis in the literature, but relatively few are based on quantiles, and these models are limited to the linear case. As quantile-based regression models offer a series of benefits (like robustness and handling of asymmetric distributions) but have not been considered in the nonlinear case, we present the first soft nonlinear quantile-based regression model in this paper. Considering nonlinearity instead of limiting to linearity in the modeling brings numerous advantages such as a higher flexibility, more accurate predictions, a better model fit and an improved explainability/interpretability of the model. In particular, we embed fuzzy quantiles into nonlinear regression analysis with crisp predictor variables and fuzzy responses. We propose a new method for parameter estimation by implementing a three-stage technique on the basis of the center and the spreads. In the framework of this procedure, we utilize kernel-fitting, a least quantile loss function, least absolute errors, and generalized cross-validation criteria to estimate the model parameters. We perform comprehensive comparative analysis with other soft nonlinear regression models that have demonstrated superiority in previous studies. The results reveal that the proposed nonlinear quantile-based regression technique leads to better outcomes compared to the competitors.

On‐chip resistors are susceptible to temperature variations, affecting the performance of linear voltage‐to‐current (VI) conversion and vice versa. This paper introduces an approach to implement resistive networks that are highly immune to temperature variations across a wide range by combining complementary‐to‐absolute‐temperature (CTAT) and proportional‐to‐absolute‐temperature (PTAT) resistors existing in standard CMOS technology. The proposed resistive networks, aiming for linear VI conversion in voltage and current references (VCRs), yield ultra‐low temperature coefficient (TC). Optimization is carried out using a multi‐objective heuristic algorithm to find the optimal placement, TC and sizes of the elements within the final configuration. Post‐layout simulation results in a standard 0.18‐μm CMOS process demonstrate the possibility of implementing sub‐3 ppm/°C resistors across −40 ~ 120°C temperature range, improving the prior art by more than 5×. A modern VCR configuration is implemented based on the proposed methodology, and simulation results verify the effectiveness of the modified approach in improving the accuracy of VI conversion.

A comprehensive investigation is conducted in the present study to analyze the non-covalent interactions displayed by the methyl salicylate complex when exposed to various solvents. The density functional theory (DFT) method is utilized to explore the impact of cation-π interaction on the strength and characteristics of the intramolecular hydrogen bond (IMHB). The findings display an augmentation in the strength of cation-π interaction within the gas phase compared to the solution. The analyses of atoms in molecules (AIM) and the natural bond orbital (NBO) are employed to provide further information on the nature of the studied interactions. According to the findings, the HB present in the considered complex falls into the medium HBs category. In addition, our investigation indicates that the cation-π interaction reinforces the IMHB in diverse solvents, but the reverse is true for the gas phase. Finally, an evaluation of the electronic properties, stability, and reactivity of the complex is performed by investigating frontier molecular orbitals, such as energy gap, chemical hardness, and electronic chemical potential. The results of this study that are ubiquitous in biological systems may be useful for the design and synthesis of a variety of supramolecular complexes with the desired properties.

Over the past decades, the unique properties and diverse uses of ionic liquids (ILs) have garnered significant interest from scholars and industry professionals worldwide. The Web of Science search results indicate that several thousand research articles associated with ionic liquids (ILs) are reported annually, demonstrating their importance. ILs are liquefied organic salts made up of organic cations and inorganic or organic anions, remaining liquid at room temperature or below 100 °C. As a result, numerous chemical structures of cation and anion pairs allow researchers to synthesize a limitless variety of salt structures tailored for specific physicochemical properties and applications. While some ILs are toxic and have significant cross-solubility in water, raising concerns about their potential ecosystem and environmental fate, having information on physicochemical properties, or at least an initial estimation, seems necessary. Predictive modeling provides a rational assessment strategy for physicochemical property prediction of ILs established especially on the information of the structures of the ions building them. Also, it allows for the prediction of their properties before synthesis. Computational approaches, commonly recognized as in-silico techniques, can minimize the time, cost, and energy required to perform experimental measurements for a broad range of ILs. The modeling approaches are regressed from empirical property data and can predict the properties of novel ILs that are not utilized in model training. In addition, mathematical models are used in the computer-aided molecular design (CAMD) of ILs with desirable properties. Various approaches have been suggested for developing predictive models for the physicochemical properties of ILs. These methods are grouped into regression (linear or nonlinear) approaches and theoretical (predictive).

In materials informatics, combining cheminformatics with machine learning is a powerful way to accelerate novel materials design. This chapter provides an easy-to-understand introduction to machine learning techniques designed for predicting outcomes in materials informatics. Beginning with foundational principles such as supervised and unsupervised learning, the chapter explains important algorithms for predictive tasks, such as regression, classification, and clustering. Focusing on real-world use, the chapter covers how to evaluate models, choose the best features, and handle unique data challenges in materials datasets. By explaining machine learning clearly in materials informatics, this chapter helps readers use computational tools effectively to create innovative materials.

This study proposes a novel network architecture called SYnergistic CLosed‐loop Supply Chain Network Design (SYCLSCND), which incorporates antifragility, sustainability, and agility while considering environmental needs, risk, and robustness. Robust Stochastic Optimization (RSO) and weighted value at risk (WVaR) are recommended for coping with risk and robustness. For the first time, this model includes the expected value and WVaR of cost as an objective function. By including Blockchain Technology (BCT), sustainability (including renewable energy and hybrid vehicles for transportation items), agility (paying attention to demand fulfillment limits), and antifragility (flexible capacity), this research enhances the model. The case study is in the automotive industry. As seen in sensitivity analysis, a main model is 3.78% less than without synergistic. Finally, this study examines the impact of varying demand levels, conservatism coefficient, access level functions, and resiliency scores on cost and time computation. Decreasing demand levels make the use of certain technologies impractical and economically unfavorable. Increasing the conservatism coefficient increases cost and time computation. Different access level functions determine the model's risk‐seeking or risk‐averse nature. Increasing the resiliency score initially does not affect cost but opens new facilities and increases the cost when it reaches 41%. Increasing the scale of the problem exponentially increases cost and time computation.

Quantum tunnelling plays a role in some physical phenomena, such as electron emission from metals in the presence of an external electric field, alpha radiation from the nucleus, etc. The phenomenon of neutron emission from the surface of palladium hydride metal when palladium and platinum electrodes are electrolysed in water or heavy-water electrolyte, has been observed, which has not been given a convincing reason so far. In this article, we investigate the probability of quantum tunnelling of proton or deuteron for pass-through of the Coulomb barrier of palladium nucleus. This entry of proton or deuteron in the nucleus causes the neutron to exit from the nucleus. According to the Empire software results, just $^{105}$Pd can produce neutrons by the quantum tunnelling phenomenon. The calculation results for this neutron production rate per unit of time agree with its experimental results.

Oxidative synthesis of pyrazoles from aromatic alcohols and phenyl hydrazine has been achieved by using trans-5-hydroperoxy-3,5-dimethyl-1,2-dioxolan-3-yl ethaneperoxate and KOH. The reaction proceeds via oxidative coupling of primary and secondary alcohols with phenylhydrazines by singlet molecular oxygen produced from trans-5-hydroperoxy-3,5-dimethyl-1,2-dioxolane-3-yl ethaneperoxate at room temperature, and broad range of derivatives were obtained in good to excellent yields. Advantageous features of the present methodology include mild reaction conditions, transition metal-free, good functional-group tolerance, use of readily available primary and secondary alcohols and so on. Oxidative synthesis of pyrazoles from aromatic alcohols and phenyl hydrazine has been achieved by using trans-5-hydroperoxy-3,5-dimethyl-1,2-dioxolan-3-yl ethaneperoxate/KOH. The reaction proceeds via oxidative coupling of primary and secondary alcohols with phenylhydrazines by singlet oxygen produced from trans-5-hydroperoxy-3,5-dimethyl-1,2-dioxolane-3-yl ethaneperoxate and a range of derivatives were obtained in good to excellent yields

This study investigates the effect of embedding silver nanoparticles (Ag) in the mesoporous TiO₂ layer on the performance of dye-sensitized solar cells (DSSCs) using natural dyes. Ag nanoparticles were incorporated into the TiO₂ matrix using different configurations, and their impact on photovoltaic performance was systematically analyzed. The results indicate that the most homogeneous distribution of Ag, achieved through co-deposition with TiO₂, led to a 94% improvement in efficiency compared to DSSCs without Ag. Structural and optical analyses confirmed that Ag nanoparticles enhance light absorption via plasmonic effects and facilitate charge transport by reducing electron–hole recombination. Among the Ag-doped DSSCs, the highest efficiency of 1.28% was achieved in the sample where Ag and TiO₂ were co-deposited within the mesoporous layer, demonstrating the effectiveness of this approach. This study highlights the critical role of Ag nanoparticles in improving DSSC performance and provides a cost-effective strategy for optimizing next-generation solar cells. Graphical Abstract

Medical studies have shown that vitamin D deficiency is strongly associated with several metabolic disorders, including diabetes, cardiovascular diseases, and cancer. It is crucial to regularly check the concentration of vitamin D in the blood serum. Traditional methods for detecting 25-hydroxyvitamin D3 [25(OH)D3] as a marker of vitamin D status are expensive and time-consuming, and require a skilled workforce and specialized laboratory. This study developed a simple and cost-effective fluorescence system for 25-hydroxyvitamin D3 determination. The fluorescent APTA-nanobiosensors were fabricated using cadmium telluride quantum dots modified with thioglycolic acid (CdTe-TGA QDs) and functionalized with thiol-25(OH)D3-aptamer through ligand exchange. The thiol-25(OH)D3-aptamer interacted directly with CdTe-TGA QDs, increasing fluorescence intensity. However, it decreased when the target molecules of 25-hydroxyvitamin D3 were introduced. The structural and morphological characteristics of APTA-nanobiosensors were confirmed by UV–visible spectroscopy, Fourier-transform infrared spectroscopy (FT-IR), x-ray photoelectron spectroscopy (XPS), field emission scanning electron microscopy (FESEM), energy dispersive x-ray spectroscopy (EDX), transmission electron microscopy (TEM), and dynamic light scattering (DLS). According to the typical Stern–Volmer equation, the relationship between fluorescent quenching and target concentration was linear with a detection limit of 1.35 × 10–8 M, a quantification limit of 4.50 × 10–8 M, and a relative standard deviation of 1.75%. The optimized APTA-nanobiosensor demonstrated high specificity toward the target and stability over 28 days. Furthermore, it detected 25-hydroxyvitamin D3 in human serum and urine with a recovery rate of 96.00–101.14%. The results indicate that the APTA-nanobiosensors could be valuable in developing robust sensing technology for low-concentrated analytes. Graphical Abstract

Introduction/objective This study aimed to evaluate the impact of virtual reality-based cognitive behavioral group therapy (VR-CBGT) on reducing anxiety and depression in patients with Parkinson’s disease. Methods A randomized clinical trial with a pretest and posttest design was conducted involving 90 Parkinson’s patients, from Roozbeh Hospital, Tehran in 2023. Participants were randomly assigned to an experimental group receiving 12 sessions of VR-CBGT over 3 months or a control group with no psychological intervention. Both groups completed the Beck Depression Inventory and Beck Anxiety Inventory before and after the intervention. Data were analyzed using multivariate analysis of covariance in SPSS-25. Results The experimental group showed significant reductions in depression and anxiety compared to the control group. VR-CBGT explained 15% of the variance in depression ( F = 6.148, P < 0.0005) and 31% in anxiety ( F = 17.800, P < 0.0005). Conclusion VR-CBGT effectively alleviates depression and anxiety in Parkinson’s patients, offering a promising therapeutic approach. Integrating VR into CBT enhances engagement and real-life skill application, supporting its use as a complementary intervention.

Lari is an endangered Iranian language spoken in south of Iran and some Persian Gulf Countries such as United Arab Emirates (UAE). Lari children learn standard Persian in Iran when they commence primary school at the age of seven, so they face problems when communicating in Persian. For those native speakers of Lari who live in the UAE, the story is different as they use other languages (e.g., Arabic, Hindi, or English) as the lingua franca in business while Lari as their mother tongue at home. Thus, in addition to their mother tongue, the children’s proficiency in Persian is affected by the lingua franca. This paper aims to examine the linguistic impact of Lari (as the mother tongue) and other lingua franca on the Persian variety which is spoken by Lari speakers. To do so, a total number of 20 Lari-speaking participants (10 Iran-residing students and 10 UAE-residing students) were selected via convenience sampling, and interviewed using a researcher-made questionnaire and some linguistic prompts. Findings showed that Lari has posed negative effects on Iran-residing students’ performance in speaking Persian as their mother tongue interferes with the way they communicate in the second language. The reasons for these can be attributed to different phonological systems of Lari and Persian (using phoneme /k/ for /ɣ/) and differences in syntactic structures of the languages (using Lari anāx kerda for the Persian xaste kardan ‘to irritate’). On the other hand, Lari speakers in the UAE have been affected by both lingua franca (loanwords such as Arabic qassāla for Persian māšin lebās-šuyi ‘washing machine’ or plural forms such as Arabic kotob for Persian ketāb-hā ‘books’) and their mother tongue, when speaking Persian. Therefore, their proficiency in Persian is affected by the interference of, both border, and vernacular languages. Some recommendations and a sample of tasks are provided to improve speaking fluency in Lari speakers.

In this paper, we establish the initial Taylor-Maclaurin coefficients for normalized analytic functions $f\,$ in the open unit disk. We also assume that $f\,$ and its inverse g=f\,^{-1} satisfy the following conditions for $-\pi /2<\phi <\pi /2$, where $\psi$ is a univalent function whose range is symmetric with respect to the real axis, and $\tau$ and $\nu$ are non-zero real numbers. We also examine other classes of related functions and establish connections with previously known results.