Search Results (523)

Background/Objectives. This manuscript presents an overview of advances in oncological radiotherapy as an effective treatment method for cancerous tumors, focusing on mechanisms of action within metabolite–antimetabolite systems. The urgency of this topic is underscored by the fact that cancer remains one of the leading causes of death worldwide: as of 2022, approximately 20 million new cases were diagnosed globally, accounting for about 0.25% of the total population. Given prognostic models predicting a steady increase in cancer incidence to 35 million cases by 2050, there is an urgent need for the latest developments in physics, chemistry, molecular biology, pharmacy, and strict adherence to oncological vigilance. The purpose of this work is to demonstrate the relationship between the nature and mechanisms of past diagnostic and therapeutic oncology approaches, their current improvements, and future prospects. Particular emphasis is placed on isotope technologies in the production of therapeutic nuclides, focusing on the mechanisms of formation of simple and complex theranostic compounds and their classification according to target specificity. Methods. The methodology involved searching, selecting, and analyzing information from PubMed, Scopus, and Web of Science databases, as well as from available official online sources over the past 20 years. The search was structured around the structure–mechanism–effect relationship of active pharmaceutical ingredients (APIs). The manuscript, including graphic materials, was prepared using a narrative synthesis method. Results. The results present a sequential analysis of materials related to isotope technology, particularly nucleus stability and instability. An explanation of theranostic principles enabled a detailed description of the action mechanisms of radiopharmaceuticals on various receptors within the metabolite–antimetabolite system using specific drug models. Attention is also given to radioactive nanotheranostics, exemplified by the mechanisms of action of radioactive nanoparticles such as Tc-99m, AuNPs, wwAgNPs, FeNPs, and others. Conclusions. Radiotheranostics, which combines the diagnostic properties of unstable nuclei with therapeutic effects, serves as an effective adjunctive and/or independent method for treating cancer patients. Despite the emergence of resistance to both chemotherapy and radiotherapy, existing nuclide resources provide protection against subsequent tumor metastasis. However, given the unfavorable cancer incidence prognosis over the next 25 years, the development of “preventive” drugs is recommended. Progress in this area will be facilitated by modern medical knowledge and a deeper understanding of ligand–receptor interactions to trigger apoptosis in rapidly proliferating cells. Full article

The Standard Model (SM) fails to explain many problems (neutrino masses, dark matter, and matter–antimatter asymmetry, among others) that may be resolved with new particles beyond the SM. No observation of such new particles may be explained either by their exceptionally high mass or by considerably small coupling to SM particles. The latter case implies relatively long lifetimes. Such long-lived particles (LLPs) then to have signatures different from those of SM particles. Searches in the “central region” are covered by the LHC general purpose experiments. The forward small angle region far from the interaction point (IP) is unexplored. Such particles are expected to have the energy as large as E = $\mathcal{O}$(1 TeV) and Lorentz time dilation factor $\gamma =E/m\approx {10}^2$–${10}^3$ (with m the particle mass) hence long enough decay distances. A new class of specialized LHC detectors dedicated to LLP searches has been proposed for the forward regions. Among these experiments, FASER is already operational, and FACET is under consideration at a location 100 m from the LHC IP5 (the CMS detector intersection). However, some features of FACET require a specially enlarged beam pipe, which cannot be implemented for LHC Run 4. In this study, we explore a simplified version of the proposed detector PREFACE compatible with the standard LHC beam pipe in the HL-LHC Run 4. Realistic Geant4 simulations are performed and the background is evaluated. An initial analysis of the physics potential with the PREFACE geometry indicates that several significant channels could be accessible with sensitivities comparable to FACET and other LLP searches. Full article

Background: Energy drinks (EDs) are non-alcoholic, functional beverages sold worldwide in more than 165 countries. These products are very popular and often consumed by children, teenagers, and young adults to improve physical performance, reduce drowsiness, and improve memory and concentration with increased intellectual effort. However, their consumption is associated with an increased risk of various health consequences. Objectives: The purpose of this non-systematic review was to discuss the components of EDs and their effects, summarize the AEs reported in the literature associated with the consumption of EDs, and briefly characterize the possible ED-related drug interactions. Methods: Scientific evidence was extracted by searching the databases PubMed and Google Scholar. In addition, the reference lists of the retrieved papers were reviewed and cross-referenced to reveal additional relevant scientific evidence. Results: The most common ingredients in EDs are caffeine, taurine, glucuronolactone, B vitamins, the vitamin-like compound inositol, and sweeteners (sugar, fructose, glucose–fructose syrup or artificial sweeteners). Although it is difficult to conclusively prove a cause-and-effect relationship between the consumption of EDs and the observed pathophysiological abnormalities, most scientific evidence (mostly clinical case reports) indicates that both occasional and especially chronic use of EDs is associated with the occurrence of numerous adverse effects (AEs). Among these, the best documented AEs are those on the cardiovascular system. It should also be noted that the components of EDs (primarily caffeine) may have drug interactions; therefore, EDs may be an important factor influencing the safety of pharmacotherapy in patients consuming EDs. Conclusions: Consuming energy drinks lead to various health problems and may interfere with pharmacotherapy due to the potential development of drug interactions. Due to the widespread availability of EDs, their suggestive advertising aimed at the youngest customers, and ambiguous regulations, new legislative policies are required to limit the widespread consumption of such products and their negative health effects. Full article

Background: The management of end-stage renal disease (ESRD) is undergoing a paradigm shift, with increasing emphasis on kidney transplantation as a preferred treatment modality for elderly patients (≥65 years), who constitute a substantial portion of new ESRD cases. Transplantation offers markedly superior survival and quality of life (QoL) advantages compared to dialysis for this demographic. Nevertheless, key determinants such as frailty, physical functionality, and cognitive function have emerged as critical predictors of post-transplant success. Despite their relevance, standardized methodologies for evaluating these parameters in transplantation candidacy remain absent. This systematic review examines the influence of frailty, physical functionality, and cognitive function on outcomes in elderly kidney transplant recipients. Methods: Adhering to PRISMA guidelines, a rigorous literature search was conducted across PubMed, CINAHL, Embase, PsycINFO, and the Web of Science for studies published up to October 31, 2024. Relevant studies focused on elderly transplant candidates and examined correlations between frailty, physical functionality, or cognitive function and post-transplant outcomes. The Newcastle–Ottawa Scale was employed to evaluate studies quality. Results: Seven studies met the inclusion criteria. Five explored physical functionality, demonstrating that better pre-transplant physical performance predicts enhanced survival. Two studies addressed frailty, utilizing the Fried frailty phenotype, and linked frailty to elevated mortality and diminished QoL recovery. Notably, no studies explored cognitive function in elderly kidney transplant candidates or recipients and its association with post-transplant outcomes, exposing a salient gap in the literature. The included studies’ varied methodologies, reliance on single time-point assessments, and exclusive focus on kidney transplant recipients restrict both comparability among studies and the generalizability of findings to the broader end-stage renal disease (ESRD) population. Conclusions: These findings underscore the profound impact of physical functionality and frailty on transplant outcomes in the growing elderly kidney transplant population, illuminating the necessity for standardized assessment protocols and targeted pre-transplant interventions. The critical gap in cognitive function research underscores a vital direction for future investigation. This research received no external funding. This review is registered with PROSPERO under registration ID CRD42025645838. Full article

The Adaptive Skip-gram (AdaGram) algorithm extends traditional word embeddings by learning multiple vector representations per word, enabling the capture of contextual meanings and polysemy. Originally implemented in Julia, AdaGram has seen limited adoption due to ecosystem fragmentation and the comparative scarcity of Julia’s machine learning tooling compared to Python’s mature frameworks. In this work, we present a Python-based reimplementation of AdaGram that facilitates broader integration with modern machine learning tools. Our implementation expands the model’s applicability beyond natural language, enabling the analysis of scientific notation—particularly chemical and physical formulas encoded in LaTeX. We detail the algorithmic foundations, preprocessing pipeline, and hyperparameter configurations needed for interdisciplinary corpora. Evaluations on real-world texts and LaTeX-encoded formulas demonstrate AdaGram’s effectiveness in unsupervised word sense disambiguation. Comparative analyses highlight the importance of corpus design and parameter tuning. This implementation opens new applications in formula-aware literature search engines, ambiguity reduction in automated scientific summarization, and cross-disciplinary concept alignment. Full article

Polymers characterize a different and important class of materials through various industries, all with unique functional properties and structural attributes. Conventional models of polymer classification depend greatly on labor-intensive methods liable to human error and subjectivity. Hence, a continually growing requirement for new polymers with greater properties is a deep understanding and exploration of the chemical space. Hence, data-driven methods for polymers are developing and able to deal with unique challenges originating from the outstanding physical and chemical range of polymers at smaller and larger scales. Recently, Deep Learning (DL) models have considerably transformed material science by allowing for the automatic study and classification of composite polymers. In this paper, a novel optimization algorithm with a DL-Based Neural Networks for Data-Driven Polymer Classification (OADLNN-DDPC) model is proposed. The main intention of the OADLNN-DDPC model is to improve the classification model for data-driven polymers using state-of-the-art optimization algorithms. The data normalization stage is initially executed via Z-score normalization to convert input data into a beneficial format. In addition, the proposed OADLNN-DDPC model implements the bald eagle search (BES) model for feature selection to detect and retain the most appropriate features. For the polymer classification process, the bidirectional gated recurrent unit (BiGRU) technique is employed. Lastly, the zebra optimizer algorithm (ZOA) is implemented for the tuning process. Extensive experiments conducted on a polymers dataset with 19,500 records and 2048 features demonstrated that OADLNN-DDPC achieves an accuracy of 98.58%, outperforming existing models, such as LSTM (83.37%), PLS-DA (88.18%), and K-NN (98.36%). The simulation process of the OADLNN-DDPC model is performed under the polymer classification dataset. The experimental analysis specified that the OADLNN-DDPC model demonstrated improvement over another existing model. Full article

Laser cladding, a cutting-edge surface modification technique for metals, offers a novel approach to enhancing the wear and corrosion resistance of substrates due to its rapid heating and cooling capabilities, precise control over coating thickness and dilution rates, and non-contact processing characteristics. However, disparities in the physical properties between the coating material and the substrate, coupled with the improper utilization of process parameters, can lead to coating defects, thereby compromising the quality of the coating. This paper examines the effects of material systems and process parameters on laser cladding composite coatings and shows that cracking is mainly caused by thermal and residual stresses. This article summarizes the methods for crack improvement and prevention in five aspects: the selection of processes in the preparation stage, the application of auxiliary fields in the cladding process, heat treatment technology, the use of auxiliary software, and the search for new processes and new structural materials. Finally, the future development trends of laser cladding technology are presented. Full article

This article proposes a model to estimate the optimal cutting speed and depth of cut used in the machining process by chip removal during the turning operation, considering the structural integrity of the workpiece to be machined. The structural integrity model is proposed considering the rounding of the cutting tool nose as a measure of roughness requested in the workpiece, the electrical power capacity delivered by the machine tool motor as a load-limiting factor for the process, the geometry of the desired workpiece, and the physical machining parameters given by cutting tool manufacturers. Based on these criteria, an estimation algorithm is proposed that integrates these parameters and executes the search for the optimal cutting depth and cutting speed, meeting the structural integrity criterion in accordance with the minimum machining time criterion in the turning process, establishing a balance between process reliability and minimization of machining time. The proposed model is innovative since it presents a new methodology to determine the depth of cut and calculate the machining speed under the criterion of preserving the structural integrity of the piece to be machined to the maximum. This means that the depth of cut and spindle speed estimated under the proposed methodology guarantee that during the machining process, the workpiece will not suffer structural damage from the cutting forces involved in the machining process, minimizing the effects of loading in areas of stress concentration, thereby contributing to highlighting and involving the concept of process reliability. This model provides a new theoretical method for technologists involved in the calculation of the machining process, offering them a theoretical basis for their proposals for depth of cut and cutting speed. Full article

It has previously been shown that complex-valued electromagnetic fields can substantially increase the symmetry of Maxwell’s Equations (MEs). They are consistent with known experimental findings in classical electrodynamics and result in some interesting predictions. For example, complex MEs predict the existence of magnetic monopoles that would have escaped detection in past experimental searches for them. This paper extends the basic complex-valued MEs for use inside matter. The increased symmetry of the extended MEs is demonstrated by an electromagnetic duality transformation analogous to that of the standard MEs and a fundamentally new type of duality transform. A derived wave equation unexpectedly shows that the imaginary-valued portion of waves inside of matter propagates without attenuation or reduced speed. Demonstrating the existence of the imaginary-valued field components predicted by this theory could have substantial implications for understanding physical and biological phenomena. To illustrate this, ways in which imaginary-valued field components would contribute to existing electromagnetic field theories of consciousness are described. The ability of complex-valued fields to account for disparate phenomena (failure of past experimental searches to find magnetic monopoles; several poorly understood features of subjective time and memory) increases the probability of their existence. Full article

Urban development within an urban agglomeration is unbalanced; the coordinated development of urban agglomerations is the core task of urban development. There are now many mechanisms and methods to promote the coordinated development of urban agglomerations; however, there is a lack of research on promoting the coordinated development of urban agglomerations from the perspective of highway transportation volume regulation. According to the physical characteristics of highway transportation networks, the logical characteristics of urban regional connectivity, and the connection characteristics of complex networks, a two-layer complex network model was designed. The objective function and constraint conditions for urban agglomeration transportation volume regulation were proposed, and the optimal solution of the highway transportation volume regulation was solved. Due to the many variables and constraints, a hierarchical solution method was adopted. A probability search iteration algorithm was proposed innovatively to solve multivariable, many-to-many allocation problems. The algorithm is universal and can be applied to solving similar problems. Taking provincial urban agglomerations as an example, the process of solving the regulation model and realizing the method was explained. The transportation volume regulation methods and strategies proposed in this study realize the best combination of macro control and micro control, static and dynamic control, coordinated development, and collaborative transportation. It is an innovative exploration and study of highway transportation volume allocation and collaborative transportation in urban agglomerations and opens up a new direction for research on the coordinated development of urban agglomerations. The coordinated development of urban agglomerations provides a guarantee for the sustainable development of urban agglomerations. Therefore, this study is also of great significance for promoting the sustainable development of urban agglomerations. Full article

The isotopic shift of the bound-electron g factor in highly charged ions (HCI) provides a sensitive probe for testing physics beyond the Standard Model, particularly through interactions mediated by a hypothetical scalar boson. In this study, we analyze the sensitivity of this method within the Higgs portal framework, focusing on the uncertainties introduced by quantum electrodynamics corrections, including finite nuclear size, nuclear recoil, and nuclear polarization effects. All calculations are performed for the ground-state $1s$ configuration of hydrogen-like HCI, where theoretical predictions are most accurate. Using selected isotope pairs (e.g., ${\mathrm{He}}^{4/6}$, ${\mathrm{Ne}}^{20/22}$, ${\mathrm{Ca}}^{40/48}$, ${\mathrm{Sn}}^{120/132}$, ${\mathrm{Th}}^{230/232}$), we demonstrate that the dominant source of uncertainty arises from finite nuclear size corrections, which currently limit the precision of new physics searches. Our results indicate that the sensitivity of this method decreases with increasing atomic number. These findings highlight the necessity of improved nuclear radius measurements and the development of alternative approaches, such as the special differences method, to enable virtually the detection of fifth-force interactions. Full article

Physically based realistic direct volume rendering (DVR) is a critical area of research in scientific data visualization. The prevailing realistic DVR methods are primarily rooted in outdated theories of participating media rendering and often lack comprehensive analyses of their applicability to realistic DVR scenarios. As a result, the fidelity of material representation in the rendered output is frequently limited. To address these challenges, we present a novel multi-material radiative transfer model (MM-RTM) designed for realistic DVR, grounded in recent advancements in light transport theories. Additionally, we standardize various transfer function techniques and propose five distinct forms of transfer functions along with proxy volumes. This comprehensive approach enables our DVR framework to accommodate a wide range of complex transfer function techniques, which we illustrate through several visualizations. Furthermore, to enhance sampling efficiency, we develop a new multi-hierarchical volumetric acceleration method that supports multi-level searches and volume traversal. Our volumetric accelerator also facilitates real-time structural updates when applying complex transfer functions in DVR. Our MM-RTM, the unified representation of complex transfer functions, and the acceleration structure for real-time updates are complementary components that collectively establish a comprehensive framework for realistic multi-material DVR. Evaluation from a user study indicates that the rendering results produced by our method demonstrate the most realistic effects among various publicly available state-of-the-art techniques. Full article

Concrete and mortar production consumes significant natural resources, leading to environmental concerns and sustainability challenges. Sustainable alternatives, such as industrial byproducts, have been explored to replace clinkers and aggregates. Basalt rock powder (BRP) is a promising option due to its physical and chemical properties, including its better particle size distribution and compatibility with cementitious composites, and studies have highlighted its pozzolanic activity and its potential to improve mechanical properties (compressive strength, flexural strength, and durability). Reusing rock dust as a raw material could transform it into a mineral byproduct, benefiting the new material and reducing waste volumes. This article presents a systematic literature review on the use of BRP in construction materials, conducted using the Scopus, ScienceDirect, PubMed, and Web of Science databases and following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) procedures. The search resulted in 787 articles (up to December 2024) and, after the screening process, 17 met the inclusion criteria. From the selected articles, information regarding the utilization of this waste product; its influence on mechanical properties, pozzolanic activity, and durability; and the sustainability associated with its use was compiled. The risk of bias was low as the search was comprehensive, all the papers were peer-reviewed, and all authors reviewed the papers independently. In conclusion, the studies demonstrate the potential of using BRP as a component of cementitious materials, indicating it as a possible innovative solution to the current challenges in the construction industry. Full article

Neurotechnology is a rapidly emerging field with vast potential within healthcare, but also has inherent concerns. There is, therefore, a need to ensure the responsible and ethical development and regulation of these technologies. This horizon scan aimed to provide an overview of neurotechnologies in development and those approved by the FDA as of June 2024 for a range of conditions relating to mental health, healthy ageing, and physical disability. Searches of clinical trials, conferences, journals, and news were performed in March 2024. Relevant technologies were identified through a process of screening, data extraction and synthesis. A total of 81 unique neurotechnologies were identified, with 23 relating to mental health, 31 to healthy ageing, and 42 to physical disability. A total of 79% percent did not yet have FDA approval and 77.4% were at earlier stages of development (pilot/feasibility studies), with 22.6% at pivotal or post-market stages. Digital elements were common features of the technologies, including software, apps, and connectivity to other devices; however, there were only three technologies with an identifiable AI component. A complex regulatory landscape and unique ethical and safety concerns associated with neurotechnology could pose challenges to innovators, though the emerging nature of the field also provides an opportunity to pre-emptively address potential issues. Full article

Robots and 3D scanning systems are essential in modern industrial production, enhancing quality control, reducing costs, and improving production efficiency. Such systems align with Industry 4.0 trends, incorporating the Internet of Things (IoT), Big Data, Cyber–Physical Systems, and Artificial Intelligence to drive innovation. This paper focuses on the physical adaptation of old or out-of-use articulated robot arms for new tasks such as manipulation with a handheld 3D scanner, with the goal of automated quality control. The adaptation was carried out using a methodology that features the application of several techniques such as 3D digitization (photogrammetry), reverse engineering and 3D modeling (SolidWorks), the CAD search engine (3Dfindit), and 3D printing (fused deposition modeling—FDM). Reconstructed 3D models were used to design connecting elements, such as gripper jaws. The final results show that it is possible to create a connecting element utilizing this approach with very little expenditure of resources and time. Full article