Search Results (11,487)

The increasing emphasis on appearance and well-being has underscored the significance of self-care. From an aesthetic perspective, this entails addressing the early onset of wrinkles and the initial signs of aging. In response, new techniques have been developed, supplementing existing methods, to mitigate the signs of aging. Aesthetic speech therapy has emerged in recent years as a non-invasive procedure to combat facial aging. The objective of this study is to evaluate its effects on the signs of facial aging in participants subjected to an experimental exercise protocol over a three-month period, focusing on orbicularis and zygomatic muscles, using both a digital evaluation analysis and a self-assessment questionnaire. A cohort of 21 female subjects, aged between 50 and 65, was instructed to perform a series of 4 targeted exercises for 15 min daily over a span of three months. The participants underwent monthly evaluations, each involving the collection of standardized photographic documentation and a three-dimensional facial scan. These scans were subsequently overlaid and analyzed by a colorimetric assay at the conclusion of the study period. Statistical tests were carried out by two-way ANOVA. Additionally, during the final evaluation (T3), the participants completed a questionnaire assessing their satisfaction with their self-image and the non-invasive aesthetic treatment they received. The statistical analysis of the overlays of the collected three-dimensional scans revealed a significant volumetric change around the orbicularis oris muscle. The difference between green and blue pixels was statistically significant (p < 0.05), as was the difference between blue and yellow pixels (p < 0.05). This change did not achieve statistical significance around the zygomatic muscles. The analysis of the participants’ questionnaire responses indicated an increasing level of satisfaction with their self-image at the end of the study compared to T0. Personal confidence increased by 20%, and participants reported a 53% improvement in satisfaction with their appearance in photographs. The observed volumetric changes may be attributed to modifications in the facial muscles targeted by the exercise protocol undertaken by the participants. However, further studies are warranted to delve deeper into this issue, considering the intricate process of facial aging and the complex three-dimensional structure of the face with its various components. Full article

Background/Objectives: Voice artificial intelligence (AI) technology is becoming increasingly common. Recent work indicates that middle-aged to older adults are less able to identify modern AI speech compared to younger adults, but the underlying causes are unclear. Methods: The current study with younger and middle-aged to older adults investigated factors that could explain the age-related reduction in AI speech identification. Experiment 1 investigated whether high-frequency information in speech—to which middle-aged to older adults often have less access due sensitivity loss at high frequencies—contributes to age-group differences. Experiment 2 investigated whether an age-related reduction in the ability to process prosodic information in speech predicts the reduction in AI speech identification. Results: Results for Experiment 1 show that middle-aged to older adults are less able to identify AI speech for both full-bandwidth speech and speech for which information above 4 kHz is removed, making the contribution of high-frequency hearing loss unlikely. Experiment 2 shows that the ability to identify AI speech is greater in individuals who also show a greater ability to identify emotions from prosodic speech information, after accounting for hearing function and self-rated experience with voice-AI systems. Conclusions: The current results suggest that the ability to identify AI speech is related to the accurate processing of prosodic information. Full article

CoCrFeNi high-entropy alloys represent a novel structural material with considerable application potential in a variety of fields, including aerospace, automobiles, ships, machining, energy, soft magnetic materials, and hydrogen storage materials. The present study investigates the impact of the Al element on the structure and properties of the alloy. The preparation of the Al_xCr_1−xCoFeNi (x = 0.1, 0.2, 0.3, 0.4, 0.5) powders involved the use of a variety of elemental metal powders as raw materials and a mechanical alloying process at 350 rpm for 40 h. The sintering of the alloy powders was subsequently conducted using spark plasma sintering at 1000 °C. The microstructure of the alloys was analyzed using XRD, SEM, and EDS, and the properties were analyzed using a universal testing machine, a hardness measurement, friction and wear measurement, and an electrochemical workstation. The study shows that when x = 0.1, the crystal structure of Al_0.1Cr_0.9CoFeNi consists of a double FCC phase and a trace amount of σ phase. As the aluminum content increases, part of the FCC phase begins to transform to BCC. When x = 0.2~0.5, the alloy consists of a double FCC phase and a BCC phase and a trace amount of a sigma phase. As the BCC phase in the alloy increases, the tensile strength of the alloy increases, the ability to deform plastically decreases, and the hardness increases. The highest ultimate tensile strength of 1163.14 MPa is exhibited by Al_0.5Cr_0.5CoFeNi, while the minimum elongation is 26.98% and the maximum hardness value is 412.6 HV. In the initial stage of friction measurement, the wear mechanism of Al_xCr_1−xCoFeNi is adhesive wear. However, as the test time progresses, an oxide layer begins to form on the alloy’s surface, leading to a gradual increase in the friction coefficient. At this stage, the wear mechanism becomes a combination of both adhesive and abrasive wear. Once the oxidation process and the wear process have reached a dynamic equilibrium, the friction coefficient stabilizes, and the wear mechanism transitions to a state of abrasive wear. The Al_0.1Cr_0.9CoFeNi alloy demonstrates the lowest friction coefficient and wear rate, exhibiting values of 0.513 and 0.020 × 10⁻³ mm³/Nm, respectively, while the Al_0.5Cr_0.5CoFeNi alloy demonstrates the highest performance, with a self-corrosion voltage of 0.202 V in a 3.5 wt.% NaCl solution. The experimental findings demonstrate that, in the presence of a decline in the Cr element within a high-entropy alloy, an augmentation in the Al element can facilitate the transition of the FCC phase to the BCC phase within the alloy, thereby enhancing its mechanical properties. However, in the Al_xCr_1−xCoFeNi HEAs, the presence of the Cr-rich and Cr-poor phases invariably results in selective corrosion in a neutral NaCl solution. The corrosion resistance of this alloy is weaker than that of a single-phase solid solution alloy with a similar chemical composition that only undergoes pitting corrosion. Full article

Photodynamic therapy (PDT) involves the use of photosensitizers (PSs) that, upon activation by specific wavelengths of light, generate reactive oxygen species (ROS), including singlet oxygen (¹O₂) and hydroxyl radicals (·OH), within the targeted tissue, typically tumor cells. The generated ROS induces cellular damage, disrupts cellular processes, and ultimately leads to apoptosis or necrosis of the tumor cells. However, the clinical application of PDT is significantly hindered by the limited tissue penetration ability of light. To address this limitation, laser-free self-luminescent photosensitive systems have emerged as potential solutions for achieving deep-tissue PDT and imaging. This review provides a comprehensive analysis of various laser-independent photosensitive systems, with a particular emphasis on those based on resonance energy transfer (RET), chemically induced electron exchange luminescence (CIEEL), and Cherenkov radiation energy transfer (CRET). The aim is to offer a theoretical framework for the development of novel photodynamic systems and to reassess the application potential of certain previously overlooked photosensitizers (PSs). Full article

Body image, a complex interplay of perceptions, thoughts, and feelings about one’s physical appearance, has been a subject of extensive research. It is a dynamic construct that evolves throughout a woman’s lifespan, influenced by a multitude of biological, psychological, and sociocultural factors. From adolescence, marked by the onset of puberty and societal pressures to conform to specific beauty standards, to adulthood and the physical changes associated with aging, women’s body image undergoes significant transformations. Aging is a universal process that affects all organs, including the female genitalia. The vaginal tract undergoes significant atrophy due to declining estrogen levels, particularly during and after menopause. Aesthetic gynecology offers a range of procedures to address both functional and aesthetic concerns related to aging genitalia. Aesthetic gynecology, a burgeoning field within women’s health, provides various procedures aimed at enhancing genital appearance and function. It also helps balance the hormonal and anatomical changes that every woman experiences over time. The goal is to strengthen each patient’s intimate well-being and self-esteem, enabling them to experience intimacy peacefully. While often driven by concerns about physical attractiveness and sexual satisfaction, the psychological implications of these procedures are complex and multifaceted. It is crucial to recognize the interplay between psychological factors and the decision to undergo these procedures. Collaboration between surgeons and mental health professionals can ensure that candidates are psychologically prepared and have realistic expectations. By adopting a patient-centered approach and conducting rigorous research, healthcare providers can ensure that aesthetic gynecology is used as a tool for empowerment rather than exploitation. This article explores the intricate relationship between psychological well-being and aesthetic gynecology, examining how these procedures can impact body image, self-esteem, and overall quality of life. Full article

This review analyses the evolution of digital creativity from 2000 to 2022, defining it as a dynamic process where digital technologies foster collaboration, inclusion, self-expression, and automation. Based on 29 studies from databases such as Dialnet, UOC, Scopus, the Web of Science, and PsyINFO, and utilising the PRISMA and SPIDER methodologies, the research provides a comprehensive overview of the field. Digital creativity is described as the generation of new and valuable ideas, products, or solutions through digital tools that combine cognitive and socio-emotional skills in collaborative environments. Intuition, understood as the ability to make quick and effective decisions based on pattern recognition and prior experiences, plays a crucial role in the creative process. The study highlights its impact on education, enabling students to explore self-expression and solve problems creatively, merging analogue and digital creativity. In the professional realm, it optimises innovative processes, promoting efficiency and collaboration. The integration of emerging technologies, such as programming and 3D animation, in educational curricula is emphasised to prepare students for future challenges. Additionally, interdisciplinary research is advocated to ensure that digital tools amplify creativity, addressing ethical issues such as intellectual property and the social implications of automated creativity. Finally, the current trends such as game-based learning, innovation driven by social networks, and artificial intelligence are examined, proposing directions for future research. Full article

The extraction of small water bodies in the Yellow River Basin has always been a key issue of concern in the fields of remote sensing technology application, water resource management, environmental science, and geographic information systems. Due to factors such as water bodies, human activities, and cloud cover, water body extraction becomes difficult. In addition, convolutional neural networks are prone to losing small water body feature information during the process of extracting local features, which can cause more imbalance between positive and negative samples of water bodies and non-water bodies. In response to these issues, this study focused on a specific research area—the middle and lower reaches of the Yellow River. We processed and analyzed high-resolution optical satellite images collected from the Yellow River Basin and other areas, with a particular emphasis on precise identification of small water bodies, and proposed a network structure, the SE-Attention-Residual-Unet (SE-ResUnet), for water extraction tasks.The main contributions of this article are threefold: (1) Introducing a channel attention mechanism with residual structure in the down-sampling process, and learning Unet’s skipping structure for multi-scale feature extraction and compensation, thereby enhancing the feature extraction ability of small water bodies, including rivers, lakes, and reservoirs. (2) Introducing a weighted-Dice (W-Dice) loss function to balance positive and negative samples and enhance the generalization of the model. (3) In comparative experiments on improving the Unet model with semantic segmentation networks such as Unet, PSPNet, Deeplabv3+ on a self-built dataset and remote sensing interpretation public dataset, excellent performance and results were achieved on the mIoU, OA, and F1-score metrics. On the self-built dataset, compared with Unet, the mIoU, OA, and F1-score improved by 0.38%, 0.12%, and 0.08%, respectively. On the publicly available dataset, for remote sensing interpretation of water extraction, the mIoU, OA, and F1-score improved by 0.63%, 0.26%, and 0.25%, respectively. The experimental results demonstrate that a strategy combining an attention mechanism and a weighted loss function has a significant effect on the effectiveness of the collaborative improvement of neural network models in water extraction tasks. Full article

Background: Serious games (SGs) are primarily aimed at promoting learning, skills training, and rehabilitation. Artificial intelligence, immersive technologies, the metaverse, and neurotechnologies promise the next revolution in gaming. Meta-skills are considered the “must-have” skills for thriving in the era of rapid change, complexity, and innovation. Μeta-skills can be defined as a set of higher-order skills that incorporate metacognitive, meta-emotional, and meta-motivational attributes, enabling one to be mindful, self-motivated, self-regulated, and flexible in different circumstances. Skillfulness, and more specifically meta-skills development, is recognized as a predictor of optimal performance along with mental and emotional wellness. Nevertheless, there is still limited knowledge about the effectiveness of integrating cutting-edge technologies in serious games, especially in the field of meta-skills training. Objectives: The current systematic review aims to collect and synthesize evidence concerning the effectiveness of advanced technologies in serious gaming for promoting meta-skills development. Methods: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology was employed to identify experimental studies conducted in the last 10 years. Four different databases were employed: Web of Science, PubMed, Scopus, and Google Scholar. Results: Forty-nine studies were selected. Promising outcomes were identified in AI-based SGs (i.e., gamified chatbots) as they provided realistic, adaptive, personalized, and interactive environments using natural language processing, player modeling, reinforcement learning, GPT-based models, data analytics, and assessment. Immersive technologies, including the metaverse, virtual reality, augmented reality, and mixed reality, provided realistic simulations, interactive environments, and sensory engagement, making training experiences more impactful. Non-invasive neurotechnologies were found to encourage players’ training by monitoring brain activity and adapting gameplay to players’ mental states. Healthy participants (n = 29 studies) as well as participants diagnosed with anxiety, neurodevelopmental disorders, and cognitive impairments exhibited improvements in a wide range of meta-skills, including self-regulation, cognitive control, attention regulation, meta-memory skills, flexibility, self-reflection, and self-evaluation. Players were more self-motivated with an increased feeling of self-confidence and self-efficacy. They had a more accurate self-perception. At the emotional level, improvements were observed in emotional regulation, empathy, and stress management skills. At the social level, social awareness was enhanced since they could more easily solve conflicts, communicate, and work in teams. Systematic training led to improvements in higher-order thinking skills, including critical thinking, problem-solving skills, reasoning, decision-making ability, and abstract thinking. Discussion: Special focus is given to the potential benefits, possible risks, and ethical concerns; future directions and implications are also discussed. The results of the current review may have implications for the design and implementation of innovative serious games for promoting skillfulness among populations with different training needs. Full article

This study addresses the task of ecologically assessing the consequences of natural fires. Statistical data are presented on the carbon dioxide emissions in millions of tons and analytical data on the locations of peat fires, as well as modern methods of detection and control of peat and forest fires, divided into groups. An analysis of the works of leading Russian and international scientists and research organizations engaged in the search for methods of peat fire forecasting is also presented. Our aim was to develop a more effective method of preventing peat soil ignition by changing its physical and moisture characteristics. To that end, peat samples were selected in the Tver region. The laboratory equipment and the methodology of our experimental studies are described in detail, in which we simulated the natural climatic conditions in the center of the Russian Federation. This study provides a mathematical description of the process of spontaneous ignition, which occurs according to the following steps: a heat flow heats the surface to the ignition temperature, creating a self-heating zone; eventually, a wave of ignition (smoldering) capable of self-propagation is formed. We experimentally determined the spontaneous thermal ignition conditions in our experimental studies of the fire hazards of selected peat samples, where the test material was loaded in a cylindrical container made of brass net with a 0.8 mm mesh, of the dimensions 30 × 30 mm. Thermocouple elements were placed inside the container, fixing the temperature of the surface and the center of the sample, where the smoldering or ignition zone of the test material formed. We analyzed the results of our experimental studies on peat samples’ self-heating chemical reaction, leading us to draw conclusions about the possibility of fires on peat soil depending on its physical and chemical characteristics. We also offer recommendations that will improve peat soils’ fire safety, permitting agricultural crop production without a peat fire risk. Full article

To achieve both safety and a lightweight design for rotary drilling rig masts, this study proposes an optimization method incorporating safety evaluation constraints. The method employs the limit state method to validate the mast structure and uses fuzzy comprehensive evaluation to quantify safety performance metrics. The optimization objective is to minimize the mast’s self-weight, with design variables defined as the geometric dimensions of key cross-sections, while imposing constraints on the strength, stiffness, stability, and safety scores. The safety score utilizes fuzzy comprehensive evaluation and a weighted aggregation method, considering indicators such as strength, stiffness, stability, and fatigue strength. An improved Salp Swarm Algorithm is utilized to execute the optimization process. Engineering case studies demonstrate that the optimized design reduces the mast’s self-weight by 6.5% under safety constraints. Compared to designs without safety constraints, the material usage increases slightly by 7.3%, but the safety performance improves by 14.74%. The findings indicate that integrating safety evaluation constraints into the optimization process not only enhances the structural safety of the mast but also achieves a favorable balance between safety and economic efficiency. This approach provides a valuable reference for the safety-focused design of rotary drilling rig masts. Full article

Understanding the influence of topography on wettability is essential for improving the modeling of superhydrophobic surfaces. Moreover, wetting predictions can foresee corrosion, biological contamination, self-cleaning properties, and all phenomena related to wetting. In this context, this research work reports the experimental corroboration of a novel theoretical model for stochastic surfaces that relates the static contact angle for the heterogeneous wetting of surfaces to the root mean square (RMS) slope of the surface structures, allowing wetting prediction through topography. For this study, hydrophobic and superhydrophobic alumina thin films with gradual roughness were constructed. The films were deposited on glass using the dip-coating technique, textured with boiling water, and functionalized to achieve low surface energy using Dynasylan F-8815. Surface wettability was characterized using the sessile drop technique, and the RMS slope of the alumina surfaces was quantified using the atomic force microscopy (AFM) technique. The model, presented here for the first time, fits the experimental data, allowing wetting prediction for hydrophobic and superhydrophobic surfaces considering static contact angles. As expected, topography plays a fundamental role in achieving superhydrophobicity. Therefore, defining a topographic criterion, as performed here, for obtaining superhydrophobic surfaces is highly relevant to reduce the production costs of these surfaces and also enable new production processes and designs. Full article

Vaccination represents a critical preventive strategy in the current global healthcare system, serving as an indispensable intervention against diverse pathogenic threats. Although conventional immunization relies predominantly on hypodermic needle-based administration, this method carries substantial limitations, including needle-associated fear, bloodborne pathogen transmission risks, occupational injuries among healthcare workers, waste management issues, and dependence on trained medical personnel. Microneedle technology has emerged as an innovative vaccine delivery system, offering convenient, effective, and minimally invasive administration. These microscale needle devices facilitate targeted antigen delivery to epidermal and dermal tissues, where abundant populations of antigen-presenting cells, specifically Langerhans and dermal dendritic cells, provide robust immunological responses. Multiple research groups have extensively investigated microneedle-based vaccination strategies. This transdermal delivery technique offers several advantages, notably circumventing cold-chain requirements and enabling self-administration. Numerous preclinical investigations and clinical trials have demonstrated the safety profile, immunogenicity, and patient acceptance of microneedle-mediated vaccine delivery across diverse immunization applications. This comprehensive review examines the fundamental aspects of microneedle-based immunization, including vaccination principles, transcutaneous immunization strategies, and microneedle-based transdermal delivery—including classifications, advantages, and barriers. Furthermore, this review addresses critical technical considerations, such as treatment efficacy, application methodologies, wear duration, dimensional optimization, manufacturing processes, regulatory frameworks, and sustainability considerations, followed by an analysis of the future perspective of this technology. Full article

The rapid development of machine learning and artificial intelligence approaches is finding ever wider application in various areas of life. This paper considers the problem of improving editorial and publishing processes, namely self-publishing, when designing book covers using machine learning and generative artificial intelligence (GAI) methods. When choosing a book, readers often have certain expectations regarding the design of the publication, including the color of the cover. These expectations can be called color preferences, and they can depend on the genre of the book, its target audience, and even personal associations. Cultural context can also influence color choice, as certain colors can symbolize different emotions or moods in different cultures. Cluster analysis of book cover images of the same genre allows us to identify color preferences inherent in the genre, which is proposed to be used when designing new covers. The capabilities of generative services for creating and improving cover designs are also investigated. An improved flow chart for using GAI in creating book covers in the process of self-publishing is proposed, which includes new stages, namely exploring, conditioning, and evolving. At these stages, the designer creates prompts for GAI and examines how they and GAI’s issuances correspond to the task. Conditioning allows for even more precise adjustment of prompts to features of each book, and the evolving stage also includes post-processing of results already received from GAI. Post-processing, in turn, can be performed both in generative services and by a designer. The experiment allowed us to use the machine-learning method to determine which colors are most often found in book cover layouts of one of the genres and to check whether these colors correspond to harmonious color palettes. In accordance with the proposed scheme of the design process using generative artificial intelligence, versions of book cover layouts of a given genre were obtained. Full article

Self-play methods have achieved remarkable success in two-player zero-sum games, attaining superhuman performance in many complex game domains. Parallelizing learners is a feasible approach to handle complex games. However, parallelizing learners often leads to the suboptimal exploitation of computational resources, resulting in inefficiencies. This paper introduces the Mixed Hierarchical Oracle (MHO), which is designed to enhance training efficiency and performance in complex two-player zero-sum games. MHO efficiently leverages interaction data among parallelized solvers during the Parallelized Oracle (PO) process, while employing Model Soups (MS) to consolidate fragmented computational resources and Hierarchical Exploration (HE) to balance exploration and exploitation. These carefully designed enhancements for parallelized systems significantly improve the training performance of self-play. Additionally, MiniStar is introduced as an open source environment focused on small-scale combat scenarios, developed to facilitate research in self-play algorithms. The MHO is evaluated on both the AlphaStar888 matrix game and MiniStar environment, and ablation studies further demonstrates its effectiveness in improving the agent’s decision-making capabilities. This work highlight the potential of the MHO to optimize compute resource utilization and improve performance in self-play methods. Full article

Self-excited systems rely on stable external stimuli to initiate and sustain oscillations via internal processes. However, these oscillations can compromise system stability and increase friction, limiting their practical applications. To overcome this issue, we propose the light-fueled stable self-rolling of a liquid crystal elastomer (LCE)-based wheel. A photothermal response model based on an LCE was used to analyze the temperature distribution within the LCE rods. The driving torque for self-rolling is generated by the contraction resulting from the LCE’s photothermal response, which displaces the wheel’s center of mass. We then derived the equilibrium equations and identified the critical conditions for achieving stable self-rolling motion. Through the interaction between the temperature field and driving torque, the wheel achieves continuous and stable self-rolling by absorbing thermal energy to counteract damping dissipation. Numerical simulations revealed that the stable self-rolling velocity is influenced by several key parameters, including heat flux, the contraction coefficient, gravitational acceleration, the initial damping torque, and the rolling damping coefficient. The proposed LCE-based wheel enhances system stability and significantly reduces frictional losses. These characteristics make it a promising candidate for applications in autonomous drive systems, micro-transportation devices, and photothermal energy conversion technologies. Full article