Search Results (35,262)

The theory of integral inequalities has a wide range of applications in physics and numerical computation, and plays a fundamental role in mathematical analysis. The present study delves into the attractive domain of Hermite–Hadamard–Mercer (H-H-M)-type inequalities having a special emphasis on Wright’s general functions, referred to as Raina’s functions in the scientific literature. The main goal of our progressive study is to use Raina’s Fractional Integrals to derive two useful lemmas for second-differentiable functions. Using the derived lemmas, we proved a large number of fractional integral inequalities related to trapezoidal and midpoint-type inequalities where those that are twice differentiable in absolute values are convex. Some of these results also generalize findings from previous research. Next, we provide applications to error estimates for trapezoidal and midpoint quadrature formulas and to analytical evaluations involving modified Bessel functions of the first kind and q-digamma functions, and we show the validity of the proposed inequalities in numerical integration and analysis of special functions. Finally, the results are well-supported by numerous examples, including graphical representations and numerical tables, which collectively highlight their accuracy and computational significance. Full article

Cell motility is a process central to life and is undoubtedly influenced by mechanical and chemical signals. Even so, other stimuli are also involved in controlling cell migration in vivo and in vitro. Among these, electric fields have been shown to provide a powerful and programmable cue to manipulate cell migration. There is now a clear consensus that the electromigration of membrane components represents the first response to an external electric field, which subsequently activates downstream signals responsible for controlling cell migration. Here, we focus on a specific mode of electrotaxis: frictionless, amoeboid-like migration. We used the Finite Element Method to solve an active gel model coupled with a mathematical model of the electromigration of aquaporins and investigate the effect of electric fields on ameboid migration. We demonstrate that an electric field can polarize aquaporins in a cell and, consequently, that the electromigration of aquaporins can be exploited to regulate water flux across the cell membrane. Our findings indicate that controlling these fluxes allows modulation of cell migration velocity, thereby reducing the cell’s migratory capacity. Our work provides a mechanistic framework to further study the impact of electrotaxis and to add new insights into specific modes by which electric fields modify cell motility. Full article

Mulberry (Morus sp.) trees, traditionally cultivated for their leaves used in sericulture, have recently gained recognition for their adaptability and valuable ecosystem services. The biochemical composition of mulberry leaves varies both qualitatively and quantitatively, depending on genotype, environmental conditions, and cultivation practices. This study aimed to (1) identify differences in old local white (M. alba L.) and black mulberry (M. nigra L.) leaves, (2) perform a chemotype analysis of monitored local varieties, and (3) evaluate the influence of selected bioclimatic factors and pruning practices on the biochemical composition of leaves of white mulberry trees across Slovenian mesoregions. Black mulberry exhibited a higher phenolic content, particularly caffeoylquinic acid derivatives (16.05 mg/g dry weight (DW)), while white mulberry contained more quercetin glycosides (6.04 mg/g DW). Ward’s clustering identified three chemotypes, two of which had elevated protein and hydroxycinnamic acid levels, making them particularly suitable for silkworm feeding. Considering pruning practices of white mulberries, we determined significantly increased protein contents in yearly pruned trees (187.24 mg/g DW). Principal component analysis revealed interactions between bioclimatic, morphological, and biochemical factors, distinctly separating mulberries from the Sub-Mediterranean and Sub-Pannonian macroregions. White mulberries from Sub-Pannonian regions accumulated more caffeoylquinic acids in leaves under lower precipitation and total insolation, while those from Sub-Mediterranean regions exhibited higher kaempferol derivatives due to photo-thermal stress. These findings highlight the influence of climate and pruning on mulberry biochemical diversity and adaptation. Full article

With the development of electricity spot markets, the integrated renewable-storage plant (IRSP) has emerged as a crucial entity in real-time energy markets due to its flexible regulation capability. However, traditional methods face computational inefficiency in high-dimensional bidding scenarios caused by expansive decision spaces, limiting online generation of multi-segment optimal quotation curves. This paper proposes a policy migration-based optimization framework for high-dimensional IRSP bidding: First, a real-time market clearing model with IRSP participation and an operational constraint-integrated bidding model are established. Second, we rigorously prove the monotonic mapping relationship between the cleared output and the real-time locational marginal price (LMP) under the market clearing condition and establish mathematical foundations for migrating the self-dispatch policy to the quotation curve based on value function concavity theory. Finally, a generalized inverse construction method is proposed to decompose the high-dimensional quotation curve optimization into optimal power response subproblems within price parameter space, substantially reducing decision space dimensionality. The case study validates the framework effectiveness through performance evaluation of policy migration for a wind-dual energy storage plant, demonstrating that the proposed method achieves 90% of the ideal revenue with a 5% prediction error and enables reinforcement learning algorithms to increase their performance from 65.1% to 84.2% of the optimal revenue. The research provides theoretical support for resolving the “dimensionality–efficiency–revenue” dilemma in high-dimensional bidding and expands policy possibilities for IRSP participation in real-time markets. Full article

This article describes the implemented technological solution of utilizing waste heat by upgrading the potato protein drying line and using energy recuperation in the drying plant. In this article, the technological sequence of the potato starch and potato protein production plant was analyzed and the identification of possible solutions that lead to a reduction in energy demand was described. The method of analyzing the processing data is based on existing models describing the flow of mass and energy fluxes. The authors did not seek new mathematical descriptions of the physicochemical phenomena occurring during the drying processes, and only modification of the technological line based on the current state of knowledge in process engineering has been proposed. The full heat recovery of the production line was applied, and the exhaust air after drying and the heat from the decanter leachate after centrifugation of the coagulated potato protein, from two energy-coupled starch dryers, were used as the source of recovered heat energy. Temperature measurements were taken at key process nodes, and the energy effects were estimated after the process line upgrade. The solution proposed in the article fits with circular economy, bringing notable economic and environmental benefits consisting of utilizing waste heat from technological processes in the food industry. Full article

Accurate and efficient cryptocurrency price prediction is vital for investors in the volatile crypto market. This study comprehensively evaluates nine models—including baseline, zero-shot, and deep learning architectures—on 21 major cryptocurrencies using daily and hourly data. Our multi-dimensional evaluation assesses models based on prediction accuracy (MAE, RMSE, MAPE), speed, statistical significance (Diebold–Mariano test), and economic value (Sharpe Ratio). Our research found that the optimally fine-tuned TimeGPT model (without variables) demonstrated superior performance across both Daily and Hourly datasets, with its statistical leadership confirmed by the Diebold–Mariano test. Fine-tuned Chronos excelled in daily predictions, while TFT was a close second to TimeGPT for hourly forecasts. Crucially, zero-shot models like TimeGPT and Chronos were tens of times faster than traditional deep learning models, offering high accuracy with superior computational efficiency. A key finding from our economic analysis is that a model’s effectiveness is highly dependent on market characteristics. For instance, TimeGPT with variables showed exceptional profitability in the volatile ETH market, whereas the zero-shot Chronos model was the top performer for the cyclical BTC market. This also highlights that variables have asset-specific effects with TimeGPT: improving predictions for ICP, LTC, OP, and DOT, but hindering UNI, ATOM, BCH, and ARB. Recognizing that prior research has overemphasized prediction accuracy, this study provides a more holistic and practical standard for model evaluation by integrating speed, statistical significance, and economic value. Our findings collectively underscore TimeGPT’s immense potential as a leading solution for cryptocurrency forecasting, offering a top-tier balance of accuracy and efficiency. This multi-dimensional approach provides critical, theoretical, and practical guidance for investment decisions and risk management, proving especially valuable in real-time trading scenarios. Full article

This study aimed to optimize the urban public transportation system in Queretaro, Mexico, while meeting passenger demand by using Linear Programming (LP) and Goal Programming (GP) models to reduce redundant routes, minimize fuel consumption and CO₂ emissions, and balance costs with service coverage. Operational data from 316 drivers were collected on diesel consumption, working hours, and vehicle availability while incorporating twelve technical, labor, and regulatory constraints. The LP model reduced the number of routes from 148 to 124, achieving daily savings of 13,789 L of diesel, a reduction of 36,816 kg in CO₂ emissions, and an economic benefit of USD 17,071.90, equivalent to 13,253 tons of CO₂ avoided annually; these results demonstrate LP’s ability to deliver quantifiable improvements in efficiency and sustainability. The GP model integrated multiple and often conflicting objectives, such as maintaining a maximum fuel cost of USD 9312/day for 1944~buses distributed across five zones while ensuring a minimum coverage of 145 routes and 450,000~daily passengers, showing that it is possible to meet service targets with marginal cost overruns (USD 4118.66) when balancing both coverage and budget. The novelty of this paper lies in combining mathematical optimization models with real operational data and simultaneously reporting both economic and environmental impacts. This allows us to offer a replicable and highly interpretable tool with low computational cost for use in medium-sized cities seeking to align mobility planning with sustainability policies and operational efficiency. Full article

Developmental dyscalculia is a neurobiologically based learning disorder that impairs numerical processing and calculation abilities. Numerous studies underscore the critical importance of early detection to enable effective intervention, highlighting the need for individualized, structured, and adaptive approaches. Digital tools, particularly those based on serious games, appear to offer a promising level of personalization. This systematic review aims to evaluate the relevance of serious game-based digital solutions as tools for the detection and remediation of developmental dyscalculia in children aged 5 to 12 years. To provide readers with a comprehensive understanding of this field, the selected solutions were analyzed and classified according to the technologies employed (including emerging ones), their thematic focus, the mathematical abilities targeted, the configuration of experimental trials, and the outcomes reported. A systematic search was conducted across Scopus, Web of Knowledge, PubMed, Eric, PsycInfo, and IEEEXplore for studies published between 2000 and March 2025, yielding 7799 records. Additional studies were identified through reference screening. A total of 21 studies met the eligibility criteria. All procedures were registered in PROSPERO and conducted in accordance with PRISMA guidelines for systematic reviews. The methodological analysis of the included studies emphasized the importance of employing both control and experimental groups with adequate sample sizes to ensure robust evaluation. In terms of remediation, the findings highlight the value of pre- and post-intervention assessments and the implementation of individualized training sessions, ideally not exceeding 20 min in duration. The review revealed a greater prevalence of remediation-focused serious games compared to screening tools, with a growing trend toward the use of mobile technologies. However, the substantial methodological limitations observed across studies must be addressed to enable the rigorous evaluation of the potential of SGs to detect and support the improvement of difficulties associated with developmental dyscalculia. Moreover, despite the recognized importance of personalization and adaptability in effective interventions, relatively few studies incorporated machine learning algorithms to enable the development of fully adaptive systems. Full article

This study focuses on the use of almond shell ash (ASA) obtained from agricultural waste through the pyrolysis process in concrete production while, at the same time, presenting an environmentally sustainable design. For this purpose, ASA was obtained from the biomass energy facilities (BEF) for use in concrete mixes. A total of 25 concrete series were prepared, including 1 control series. In these series, 5%, 10%, 15% silica fume (SF), 5%, 10% metakaolin (MK), and 1%, 3%, 5%, and 7% ratios of ASA were chosen to be substituted by volume with cement. Fresh and hardened concrete tests were performed on the specimens. Experiments have shown that the use of ASA in concrete production improves concrete performance up to a certain extent. With the data obtained from the test results, performance evaluation was performed in the artificial neural network. Because of this evaluation, a mathematical model able to predict the concrete compressive strength with high accuracy was developed. To evaluate the effectiveness of the developed model, it was tested again on control specimens to confirm its accuracy and applicability. A life cycle assessment (LCA) was also performed. The aim is to make a new contribution to the literature and practical application with the method to be developed because of the study and to pioneer future studies in this field. Full article

During the running of urban railway trains, arcs of the pantograph–catenary (PC) system cause instantaneous high-temperature ablation of PC system materials, which severely impact the standard running of trains. Utilizing magnetohydrodynamics (MHD), a mathematical model of urban railway PC arcs is introduced in this article. The multiphysics finite element analysis platform COMSOL Multiphysics was used to solve and simulate the mathematical model of the PC arc. The simulation results were analyzed to explore the temperature dispersion law of the PC arc. Experimental measurements of arc duration and arc temperature were conducted, with the mathematical model’s accuracy validated through empirical comparisons. Based on the established mathematical model of the PC arc, the effects of PC gap and current intensity on the arc temperature were investigated. The results reveal that the PC arc’s temperature field follows a radially decaying dispersion, attaining maximum temperature in the center of the arc column. The surface temperature of the pantograph strip is higher than that of the contact wire. As the duration of the PC arc increases, the arc temperature gradually increases; the temperature of the PC arc diminishes with the increase in the PC gap. The PC current increases, and the arc zone temperature increases. The research conclusions of this article can provide a basis for mitigating the number of PC arcs and enhancing the quality of the PC current. Full article

Phase-retrieval problems are employed to tackle the challenge of recovering a complex signal from amplitude-only data. In phaseless spherical near-field antenna measurements, the task is to recover the complex coefficients describing the radiation behavior of the antenna under test (AUT) from amplitude near-field measurements. The coefficients refer, for example, to equivalent currents or spherical modes, and from these, the AUT’s far-field characteristic, which is usually of interest, can be obtained. In this article, the concept of a mask-based phase recovery is applied to spherical near-field antenna measurements. First, the theory of the mask approach is described with its mathematical definition. Then, several mask types based on random distributions, $\varphi$-rotations, or probes are introduced and discussed. Finally, the performances of the different masks are evaluated based on simulations with multiple AUTs and with Wirtinger flow as a phase-retrieval algorithm. The simulation results show that the mask approach can improve the reconstruction error depending on the number of masks, oversampling, and the type of mask. Full article

P-adic analysis, introduced by Kurt Hensel in the early 20th century, has developed into a fundamental area of mathematical research with broad applications in number theory, algebraic geometry, and mathematical physics. This study aims to examine the thematic evolution and scholarly impact of p-adic research through a comprehensive topic modeling and bibliometric analysis. Using classical bibliometric techniques (e.g., performance analysis, co-authorship, and co-citation networks) combined with Latent Dirichlet Allocation (LDA), we analyzed 7388 peer-reviewed documents published between 1965 and 2024. The computational workflow was conducted using R (version 4.4.1) and VOSviewer (version 1.6.20), which enabled the identification of 20 distinct research topics. These topics reveal both well-established and emerging areas, such as p-adic differential equations, harmonic analysis, and their connections to theoretical physics and cryptography. This study highlights key contributors, including Robert Coleman, Alain M. Robert, and Jean-Pierre Serre, whose work has shaped the development of the field. Temporal patterns observed in the topic distribution indicate dynamic shifts in research focus, while the interdisciplinary nature of recent contributions highlights the growing relevance of p-adic theory beyond pure mathematics. This analysis provides a data-driven overview of the intellectual structure of p-adic research, identifies underexplored areas, and suggests future directions for inquiry. The findings aim to support researchers in understanding historical trends, recognizing influential work, and identifying opportunities for further advancement and collaboration in the field. Full article

In a 1987 article of the last author dedicated to the memory of a pioneer of classical plasticity Aris Philips of Yale, the last author outlined three examples of self-organization during plastic deformation in metals: persistent slip bands (PSBs), shear bands (SBs) and Portevin Le Chatelier (PLC) bands. All three have been observed and analyzed experimentally for a long time, but there was no theory to capture their spatial characteristics and evolution in the process of deformation. By introducing the Laplacian of dislocation density and strain in the standard constitutive equations used for these phenomena, corresponding mathematical models and nonlinear partial differential equations (PDEs) for the governing variable were generated, the solution of which provided for the first time estimates for the wavelengths of the ladder structure of PSBs in Cu single crystals, the thickness of stationary SBs in metals and the spacing of traveling PLC bands in Al-Mg alloys. The present article builds upon the 1987 results of the aforementioned three examples of self-organization in plasticity within a unifying internal length gradient (ILG) framework and expands them in 2 major ways by: (i) introducing the effect of stochasticity and (ii) capturing statistical characteristics when PDEs are absent for the description of experimental observations. The discussion focuses on metallic systems, but the modeling approaches can be used for interpreting experimental observations in a variety of materials. Full article

Generative artificial intelligence (GenAI) is reshaping science, technology, engineering, and mathematics (STEM) education by offering new strategies to address persistent challenges in equity, access, and instructional capacity—particularly within Hispanic-Serving Institutions (HSIs). This review documents a faculty-led, interdisciplinary initiative at the University of La Verne (ULV), an HSI in Southern California, to explore GenAI’s integration across biology, chemistry, mathematics, and physics. Adopting an exploratory qualitative design, this study synthesizes faculty-authored vignettes with peer-reviewed literature to examine how GenAI is being piloted as a scaffold for inclusive pedagogy. Across disciplines, faculty-reported benefits such as simplifying complex content, enhancing multilingual comprehension, and expanding access to early-stage research and technical writing. At the same time, limitations—including factual inaccuracies, algorithmic bias, and student over-reliance—underscore the importance of embedding critical AI literacy and ethical reflection into instruction. The findings highlight equity-driven strategies that position GenAI as a complement, not a substitute, for disciplinary expertise and culturally responsive pedagogy. By documenting diverse, practice-based applications, this review provides a flexible framework for integrating GenAI ethically and inclusively into undergraduate STEM instruction. The insights extend beyond HSIs, offering actionable pathways for other minority-serving and resource-constrained institutions. Full article