Search Results (1,706)

Background: In late January 2025, a measles outbreak began in Gaines County, Texas, USA, and the outbreak extended to New Mexico. We used a variety of mathematical models to estimate the growth rate of the Texas–New Mexico measles outbreak of 2025. Methods: We used both empirical and mechanistic models based on differential equations to make the estimations that allow us to characterize this measles outbreak. Regarding empirical models, we used the exponential growth model to compute and estimate the growth rate, basic reproduction number, ${\mathcal{R}}_0$, and effective reproduction number ${\mathcal{R}}_t$. With regard to mechanistic models, we use the SIR and SEIR models to estimate the growth rate, basic reproduction number ${\mathcal{R}}_0$, and effective reproduction number ${\mathcal{R}}_t$. We used new weekly measles cases and also cumulative cases. Results: Using the exponential growth model, we estimated a basic reproduction number between 32 and 40. For the classical SIR model, we estimated that the basic reproduction number is approximately 30. Conclusion: We found that the current Texas–New Mexico measles outbreak of 2025 has a slightly higher growth rate and effective reproduction number ${\mathcal{R}}_t$ compared to several previous measles outbreaks around the world. Full article

Known attacks on the tropical implementation of Stickel protocol involve finding minimal covers for a certain covering problem, and this leads to an exponential growth in the worst case time required to recover the secret key as the used polynomial degree increases. The computational inefficiency of this attack is also observed in practice, unless the number of explored covers is limited, on the expense of the success rate of the attack. Consequently, it can be argued that Alice and Bob can still repel these attacks on tropical Stickel protocol by utilizing very high polynomial degrees, a feasible approach due to the efficiency of tropical operations. The same is true for the implementation of Stickel protocol over some other semirings with idempotent addition (such as the max–min or digital semiring). In this paper, we propose alternative methods to attack the Stickel protocols that avoid solving the covering problem. These methods involve framing the attacks as a mixed integer linear programming (MILP) problem or applying certain heuristic global optimization techniques. We also include a number of numerical experiments to analyze the success rate and the time required to execute the suggested attacks in practice. Full article

The Kronecker product has been commonly seen in various scientific fields to formulate higher-dimensional spaces from lower-dimensional ones. This paper presents a generalization of the Cannon–Kronecker product bases by introducing generalized Kronecker product bases of polynomials within an analytic framework. It investigates the convergence behavior of infinite series formed by these generalized products in various polycylindrical domains, including both open and closed configurations. The paper also delves into essential analytic properties such as order, type, and the ${\mathbb{T}}_{\rho }$-property to analyze the growth and structural characteristics of these bases. Moreover, the theoretical insights are applied to a range of classical special functions, notably Bernoulli, Euler, Gontcharoff, Bessel, and Chebyshev polynomials. Full article

Over the last century, improvements in computational power resulting from the exponential growth of microelectronics have been the driving force of outstanding global economic growth as well as of deep changes in society and ethical values. Manufacturing of silicon-based memory cells has, as a matter of fact, become an industry of strategic importance also from a geopolitical perspective. Despite such advancements, a lot of concern has recently aroused as physical limitations such as tunnel-effect phenomena, current leakage, and high power consumption are increasingly hindering further improvements in dynamic random-access memory. Spintronic technologies are promising alternatives to overcome such issues, being considered no longer merely an academic subject of interest, but increasingly becoming an industrial reality. In this review work, the history and the physical principles of spintronic devices are presented, focussing on new, groundbreaking materials. Concepts are exposed step by step and in an easy-to-understand manner, allowing even researchers who are not specialized in the fields of spintronics, microelectronics, and hardware engineering to understand the fundamentals and gain initial insight into the topic. Special attention is paid to half-metallic ferromagnets and Heusler alloys, which are considered among the most promising materials for the future of spintronics. Full article

Gamma-aminobutyric acid (GABA) is the main inhibitory neurotransmitter in the vertebrate central nervous system, known for its role in promoting sleep, reducing anxiety, regulating blood pressure, and modulating stress, cognition, and behavior. Microbial fermentation offers an effective method for GABA production, with certain lactic acid bacteria (LAB) strains recognized as efficient producers. This study assessed the GABA-producing potential of 31 LAB strains, including isolates from traditional Bulgarian foods and plants. The strains were cultivated in an MRS medium supplemented with 1% monosodium glutamate (MSG), and GABA production was quantified using HPLC after derivatization with dansyl chloride. Most strains produced between 200 and 300 mg/L of GABA. However, Levilactobacillus brevis NCTC 13768 showed much higher productivity, reaching 3830.7 mg/L. To further evaluate its capacity, L. brevis NCTC 13768 was cultivated for 168 h in MRS medium with and without MSG. Without MSG, GABA production peaked at 371.0 mg/L during the late exponential phase. In contrast with MSG, GABA levels steadily increased, reaching 3333.6 mg/L after 168 h. RT-qPCR analyses of the glutamic acid decarboxylase (GAD) system showed that the genes of glutamate decarboxylase (gadB), glutamate-GABA antiporter (gadC), and transcriptional regulator (gadR) are significantly overexpressed when the culture reaches the late stationary phase of growth (96 h after the beginning of cultivation). These results identify L. brevis NCTC 13768 as a high-yield GABA producer, with potential applications in the production of fermented functional foods and beverages. Full article

Grid point approaches for LEO mega-constellation coverage analysis become computationally prohibitive for constellations exceeding 10³ satellites due to exponential complexity growth. This paper presents the Mercator projection superposition (MPS) approach, which transforms coverage evaluation into a two-dimensional image-processing paradigm by projecting the satellite coverage onto Mercator maps. MPS decouples computational complexity from satellite count, enabling analysis of constellations exceeding 10⁴ satellites. Validation against grid point approaches shows ≤1.2% error with 60× speed improvement for 103-scale constellations without parallel computation. The method establishes that instantaneous coverage rates reliably approximate periodic rates within 0.04% precision for early-stage constellation design. Analysis of Starlink-based configurations identifies optimal principles governing mega-constellation coverage, with particular emphasis on configuration and orbital parameter relationships. These findings enable rapid design iteration and optimization for future mega-constellation development. Full article

This paper explores the mechanisms of energy transfer and failure zones in rock mass blasting. By combining theoretical derivation with numerical simulation, we examine the deformation, failure features, and source parameters of rock subjected to spherical charge blasting. Using the Mohr–Coulomb yield criterion, we classify the rock failure process into four zones: the cavity zone, fracture zone, radial fracture zone, and vibration zone. Additionally, we establish a dynamic partitioned model that considers explosion cavity expansion, compression wave propagation, and energy dissipation. Applying elastic failure conditions, we develop a calculation model for vibration parameters in each zone and use MATLAB programming to find numerical solutions for the radius of the failure zone, elastic potential energy, and the interface pressure over time. Verification with a granite underground blasting project in Qingdao shows the ratio of the spherical cavity radius to the charge radius is 1.49, and the crushing zone radius to the charge radius is 2.85. Theoretical results are consistent with the approximate method in magnitude and value, confirming the model’s reliability. The interface pressure sharply peaks and then decays exponentially. The growth of the fracture zone depends heavily on initial pressure, rock strength, and Poisson’s ratio. These findings support blasting engineering design and seismic effect assessment. Full article

This study analyzes, quantifies, and maps, from a bibliometric perspective, scientific research on microalgae energy production. It includes traditional simulation, machine learning, and hybrid approaches, covering 500 original articles from 2005 to 2024 in Scopus. We used Biblioshiny 4.1.2 software in RStudio 4.3.0 to categorize and evaluate the contributions of authors and journals. The studied field underwent an exponential growth in publications from 2004 to 2022, with an average annual increase of approximately 21%. Moreover, recent research focuses on photobioreactors, computational fluid dynamics, carbon dioxide capture, bio-oils, biodiesel, and hydrothermal liquefaction, increasingly integrating machine learning algorithms and hybrid methods. Since 2020, we have identified a clear trend toward combining modeling approaches to predict and improve energy efficiency, particularly for biodiesel, bio-derived hydrogen, and crude bio-oil produced via pyrolysis or hydrothermal liquefaction, which is often influenced by factors such as light, carbon dioxide, nutrients, and blending operations. Finally, recent advancements involve combining physical models with data to enable real-time optimization and control, supporting microalgae-based circular biorefining strategies. This review serves as a guide for future research in green energy materials and process modeling, inspiring colleagues to explore new ways for microalgae energy production and modeling. Full article

Climate change has been occurring due to global warming since the 1950s, causing impacts on natural and social systems, including health. This review article involves the One Health approach as a holistic approach that integrates environmental, human, and animal health, since there is a significant gap in knowledge about the impacts of climate change on health. The questions that guide this research are as follows: What is the state of the art in studies on climate change and One Health? What are the main topics addressed in studies on climate change and One Health at a global level? The main objective is to conduct a systematic review of studies on climate change and its relationship with One Health to assess the main topics studied, involving climate change and health at a global level, and identify the gaps and challenges of these studies. The review demonstrated the exponential growth of studies that relate climate change to One Health, especially in the last three decades, with more records of studies that address infectious diseases such as arboviruses. Furthermore, studies on climate and its impact on mental health were detected, causing depression, anxiety, post-traumatic stress disorder (PTSD), solastalgia, and eco-anxiety, especially in vulnerable populations such as indigenous communities, women, children, family farmers, and the elderly. The One Health approach was shown to be restricted to health-related issues. Thus, theoretical and experimental studies are still needed to assess the real impact of climate change on the various axes involving human health and its relationship with anthropogenic activities, environmental health, and animal health. Full article

Intestinal dysbiosis represents a critical determinant of clinical outcomes in patients undergoing allogeneic hematopoietic stem cell transplantation (allo-HSCT). Distinct microbiota patterns represent potential prognostic biomarkers and therapeutic targets. However, the exponential growth in microbiota research and analytical complexity has created significant interpretive challenges for clinicians. This review provides a synthesis of current literature examining microbiota fingerprints and their clinical implications. We analyzed key studies evaluating the clinical implications of intestinal microbiota fingerprints in allo-HSCT. Additionally, we examined current therapeutic strategies for microbiota modulation and approaches for translating research findings into clinical practice. We identified three major microbiota fingerprints: (1) decreased intestinal microbiota diversity, (2) reduced abundance of short-chain fatty acid-producing bacteria, and (3) Enterococcus domination. These fingerprints are associated with critical clinical outcomes including overall survival, Graft-versus-host disease, transplant-related mortality, and infection-related complications. While fecal microbiota transplantation and dietary interventions appear promising, current studies suffer from limited sample sizes and lack standardized protocols. Despite significant advances in microbiota research, biological, methodological, and logistical challenges continue to hinder its clinical translation. Understanding microbiota fingerprints represents a promising avenue for improving allo-HSCT outcomes. However, successful clinical implementation requires standardized methodologies, mechanistic studies, and multi-center collaborations to translate research into actionable clinical tools. Full article

With the rise of 6G, the exponential growth of data traffic, the proliferation of emerging applications, and the ubiquity of smart devices, the demand for spectral resources is unprecedented. Terahertz communication (100 GHz–3 THz) plays a key role in alleviating spectrum scarcity through ultra-broadband transmission. In this study, terahertz optical carrier-based systems are employed, where fiber-optic components are used to generate the optical signals, and the signal is transmitted via direct detection in the receiver side, without relying on fiber-optic transmission. In these systems, deep learning-based equalization effectively compensates for nonlinear distortions, while probability shaping (PS) enhances system capacity under modulation constraints. However, the probability distribution of signals processed by PS varies with amplitude, making it challenging to extract useful information from the minority class, which in turn limits the effectiveness of nonlinear equalization. Furthermore, in IM-DD systems, optical multipath interference (MPI) noise introduces signal-dependent amplitude jitter after direct detection, degrading system performance. To address these challenges, we propose a lightweight neural network equalizer assisted by the Synthetic Minority Oversampling Technique (SMOTE) and a clustering method. Applying SMOTE prior to the equalizer mitigates training difficulties arising from class imbalance, while the low-complexity clustering algorithm after the equalizer identifies edge jitter levels for decision-making. This joint approach compensates for both nonlinear distortion and jitter-related decision errors. Based on this algorithm, we conducted a 3.75 Gbaud W-band PAM4 wireless transmission experiment over 300 m at Fudan University’s Handan campus, achieving a bit error rate of 1.32 × 10⁻³, which corresponds to a 70.7% improvement over conventional schemes. Compared to traditional equalizers, the proposed new equalizer reduces algorithm complexity by 70.6% and training sequence length by 33%, while achieving the same performance. These advantages highlight its significant potential for future optical carrier-based wireless communication systems. Full article

Three fungal strains belonging to the genus Penicillium from different temperature classes (two Antarctic strains—psychrotolerant and mesophilic, and a temperate mesophilic) were used to investigate their metabolic cell response to cold stress. The exponential- and stationary-growth-phase fungal cultures were exposed to a transient temperature downshift from optimal to 6 and 15 °C, respectively. The activity of the enzymes hexokinase, glucose-6-phosphate dehydrogenase, and glyceraldehyde 3-phosphate dehydrogenase from the glycolytic pathway, and that of the enzymes isocitrate dehydrogenase, succinate dehydrogenase, and malate dehydrogenase from the TCA cycle were studied. In all experiments, the cold-induced oxidative stress increased the indicated enzymatic activities depending on the strain’s temperature characteristics, the degree of stress, and the growth phase. Furthermore, enzyme activity was lower in cells from stationary-phase cultures (older cells) compared to those from exponential-phase cultures (younger cells). The cellular response was more pronounced in mesophilic strains, regardless of the location of isolation. The cold-adapted Antarctic psychrotolerant strain exhibited enhanced tolerance to low-temperature stress compared to mesophilic strains. These findings emphasize the significance of temperature preferences and growth phase in the survival of fungi under conditions of cold-induced oxidative stress. New information could prove beneficial in forecasting the behaviour of fungal pathogens such as plant pathogens in agriculture and human pathogens in medicine. Full article

With the advancement of technologies such as 5G, digital twins, and edge computing, the Internet of Things (IoT) as a critical component of intelligent systems is profoundly driving the transformation of various industries toward digitalization and intelligence. However, the exponential growth of network connection nodes has expanded the attack exposure surface of IoT devices. The IoT devices with limited storage and computing resources struggle to cope with new types of attacks, and IoT devices lack mature authorization and authentication mechanisms. It is difficult for traditional data-sharing solutions to meet the security requirements of cloud-based shared data. Therefore, this paper proposes a blockchain-based multi-authority IoT data-sharing scheme with attribute-based searchable encryption for intelligent system (BM-ABSE), aiming to address the security, efficiency, and verifiability issues of data sharing in an IoT environment. Our scheme decentralizes management responsibilities through a multi-authority mechanism to avoid the risk of single-point failure. By utilizing the immutability and smart contract function of blockchain, this scheme can ensure data integrity and the reliability of search results. Meanwhile, some decryption computing tasks are outsourced to the cloud to reduce the computing burden on IoT devices. Our scheme meets the static security and IND-CKA security requirements of the standard model, as demonstrated by theoretical analysis, which effectively defends against the stealing or tampering of ciphertexts and keywords by attackers. Experimental simulation results indicate that the scheme has excellent computational efficiency on resource-constrained IoT devices, with core algorithm execution time maintained in milliseconds, and as the number of attributes increases, it has a controllable performance overhead. Full article

This study comprehensively evaluated the antimicrobial performance of copper ions against three bacterial species relevant to water systems: E. coli (ATCC 25922), P. aeruginosa (ATCC 27853), and S. epidermidis (ATCC 12228). Disinfection kinetics were determined at three copper concentrations (0.5, 1.5, and 3.3 mg/L) using the Gard model. E. coli exhibited the highest susceptibility, with inactivation rate constants of 0.63, 3.27, and 9.83, achieving complete inactivation at 3.3 mg/L. P. aeruginosa was the most resistant, showing values below 1.0 across all concentrations, while S. epidermidis displayed intermediate responses. Selected experiments further examined the influence of growth phase, temperature, and water chemistry. Exponential-phase cells were more sensitive than stationary-phase cultures, and higher temperatures (37 °C vs. 5 °C) significantly enhanced inactivation. Moderate bicarbonate (50 mg/L) improved bacterial removal by stabilizing soluble Cu²⁺ ions (2.60 lg reduction), whereas elevated calcium and magnesium (Ca²⁺ 100 mg/L, Mg²⁺ 50 mg/L) reduced effectiveness (≤2.10 lg reduction) through competitive interactions. In addition to culture-based methods, adenosine triphosphate (ATP) bioluminescence assays and flow cytometry (FCM) provided complementary insights, confirming early metabolic disruption and membrane damage prior to culturability loss in selected experiments. Full article

We investigate the solvability and stability properties of a class of nonlinear stochastic delay differential equations (SDDEs) driven by Wiener noise and incorporating discrete time delays. The equations are formulated within a Banach space of continuous, adapted sample paths. Under standard Lipschitz and linear growth conditions, we construct a solution operator and prove the existence and uniqueness of strong solutions using a fixed-point argument. Furthermore, we derive exponential mean-square stability via Lyapunov-type techniques and delay-dependent inequalities. This framework provides a unified and flexible approach to SDDE analysis that departs from traditional Hilbert space or semigroup-based methods. We explore a Banach space fixed-point approach to SDDEs with multiplicative noise and discrete delays, providing a novel functional-analytic framework for examining solvability and stability. Full article