Search Results (571)

This paper explores the optimal control issue for a linear–quadratic–Gaussian (LQG) system under the conditions of imperfect feedback and constraints related to energy harvesting. The system is equipped with various energy options, which allow it to gather energy for information transmission while also receiving imperfect feedback from an auxiliary filter that estimates packet loss. The primary goal of this study is to jointly design the energy selector and the controller to achieve an optimal balance between transmission costs and control performance. Initially, we separate the controller’s synthesis task from the energy selection task. The subproblem of optimal controller synthesis is characterized by a Riccati equation that takes continuous packet loss into account. Simultaneously, the energy selection task, influenced by imperfect feedback and constraints on energy costs, is reformulated as a Markov decision process (MDP) that operates with perfect acknowledgments through iterative updates of state information. Ultimately, the optimal energy selection policy that guarantees filtering performance is derived by solving a Bellman equation. The effectiveness of the proposed approach is confirmed through simulation results. Full article

The transition to a circular economy depends on the widespread adoption of sustainable products by consumers. However, the point-of-sale purchase decision is a complex process, influenced not only by ethical arguments but also by sensory cues. This study investigates how the aesthetics (visual design) and materiality (tactile sensation) of green products shape value perception and purchase intention. Using a qualitative methodology based on a focus group, the research directly compares consumer reactions to green products (e.g., a bamboo toothbrush) versus their conventional alternatives (e.g., plastic). Thematic analysis of the data reveals a fundamental dichotomy among consumers: while one segment associates high-tech aesthetics and perfect finishes with quality and hygiene, another segment values natural materials and their “imperfections” as signs of authenticity and responsibility. The results demonstrate that there is no single, universally accepted “sustainable aesthetic” and highlight the need for designers and marketers to align the visual and tactile language of products with the value system of the target consumer segment. The study provides a framework for understanding how design can act as either a barrier to or a catalyst for the adoption of sustainable products. Full article

Mutations may cause changes in the structure and function of immune-related proteins, thereby affecting the operation of the immune system. Deep mutational scanning combines saturation mutagenesis, functional selection, and high-throughput sequencing to evaluate the effects of mutations on a large scale and with high resolution. By systematically and comprehensively analyzing the impact of mutations on the functions of immune-related proteins, the immune response mechanism can be better understood. However, each stage in deep mutation scanning has its limits, and the approach remains constrained in several ways. These include data and selection biases that affect the robustness of effect estimates, insufficient library coverage and editability leading to uneven representation of sites and alleles, system-induced biased signals that deviate phenotypes from their true physiological state, and imperfect models and statistical processing that limit extrapolation capabilities. Therefore, this technology still needs further development. Herein, we summarize the principles and methods of deep mutational scanning and discuss its application in immunological research. The aim is to provide insights into the broader application prospects of deep mutational scanning technology in immunology. Full article

Emotion detection in Brazilian Portuguese is less studied than in English. We benchmarked a large language model (Mistral 24B), a language-specific transformer model (BERTimbau), and the lexicon-based EmoAtlas for classifying emotions in Brazilian Portuguese text, with a focus on eight emotions derived from Plutchik’s model. Evaluation covered four corpora: 4000 stock-market tweets, 1000 news headlines, 5000 GoEmotions Reddit comments translated by LLMs, and 2000 DeepSeek-generated headlines. While BERTimbau achieved the highest average scores (accuracy 0.876, precision 0.529, and recall 0.423), an overlap with Mistral (accuracy 0.831, precision 0.522, and recall 0.539) and notable performance variability suggest there is no single top performer; however, both transformer-based models outperformed the lexicon-based EmoAtlas (accuracy 0.797) but required up to 40 times more computational resources. We also introduce a novel “emotional fingerprinting” methodology using a synthetically generated dataset to probe emotional alignment, which revealed an imperfect overlap in the emotional representations of the models. While LLMs deliver higher overall scores, EmoAtlas offers superior interpretability and efficiency, making it a cost-effective alternative. This work delivers the first quantitative benchmark for interpretable emotion detection in Brazilian Portuguese, with open datasets and code to foster research in multilingual natural language processing. Full article

In the course of the 3D printing process, the occurrence of imperfect structures is attributable to the rapid cooling of molten polymer. In this study, gears were manufactured from PA6 using a dedicated 3D printer, and their performance was analyzed using finite element analysis (FEA), validated by wear tests. A subset of the gears was subjected to annealing heat treatments to investigate their influence on the behavior of the material. The novelty of this study lies in the correlation of the effects induced by heat treatment with the stress distribution, wear, and service life of 3D-printed gears. This provides useful information for optimizing polymer gears for engineering applications. This study’s novelty lies in highlighting the influence of heat treatments on wear behaviour and mechanical stress factors, offering new insights into the optimisation of 3D-printed polymer gears. Full article

SiCp/Al composites (Silicon Carbide Particle-Reinforced Aluminum Matrix Composites), due to their light weight, high strength, and superior wear resistance, are extensively utilized in aerospace and other sectors; nonetheless, they are susceptible to tool wear and surface imperfections during machining, which negatively impact overall machining performance. Supercritical carbon dioxide minimal quantity lubrication (SCCO₂-MQL) is an environmentally friendly and efficient lubrication method that significantly improves interfacial lubricity and thermal stability. Nonetheless, current lubrication models are predominantly constrained to gas–liquid two-phase scenarios, hindering the characterization of the three-phase lubrication mechanism influenced by the combined impacts of SCCO₂ phase transition and ultrasonic vibration. This study formulates a lubricant film thickness model that incorporates droplet atomization, capillary permeation, shear spreading, and three-phase modulation while introducing a pseudophase enhancement factor β_ps(p,T) to characterize the phase fluctuation effect of CO₂ in the critical region. Simulation analysis indicates that, with an ultrasonic vibration factor Af = 1200 μm·kHz, a lubricant flow rate Q_f = 16 mL/h, and a pressure gradient Δp_tot = 6.0 × 10⁵ Pa/m, the lubricant film thickness attains its optimal value, with Δp_tot having the most pronounced effect on the film thickness (normalized sensitivity S = 0.488). The model results align with the experimental trends, validating its accuracy and further elucidating the nonlinear regulation of the film-forming process by various parameters within the three-phase synergistic lubrication mechanism. This research offers theoretical backing for the enhancement of performance and the expansion of modeling in SCCO₂-MQL lubrication systems. Full article

The rise of virtual models in the digital age presents a new frontier for cosmetic advertising. Nevertheless, the comparative effectiveness of “idealized” versus “naturalistic” facial features in these models remains a topic of debate and an area of development. This study examines the impact of “idealized” and “naturalistic” facial features in virtual models on consumers’ cognitive and affective responses. Using eye-tracking and a structural equation model, we analyzed visual attention patterns and the roles of affective resonance, trustworthiness, likability, and expertise perception. The results indicate that non-homogeneous or defective naturalistic features increase visual attention and purchase intention, with consumers focusing on imperfections such as freckles. In contrast, idealized facial features mainly draw attention to areas such as the eyes and nose. Mediation analysis reveals that likability and affective resonance are primary influences on purchase intention, while expertise perception and trustworthiness are secondary. This experiment suggests that consumers prioritize socio-emotional connections over professional authority when evaluating naturalistic designs. Our findings provide a framework for virtual model design, helping brands balance aesthetics with psychological optimization, and offer insights into the interplay between visual stimuli and human cognitive and emotional processes in decision-making. Full article

This article presents a collection of schlieren visualizations captured using a custom-built, laboratory-based imaging system, designed to explore a wide range of flow and refractive phenomena. The experiments were conducted as a series of observational case studies, serving as educational bloc notes for students and researchers working in fluid mechanics, optics, and high-speed imaging. High-resolution images illustrate various phenomena including shockwave propagation from bursting balloons, vapor plume formation from volatile liquids, optical surface imperfections in transparent materials, and the dynamic collapse of soap bubbles. Each image is accompanied by brief experimental context and interpretation, highlighting the physical principles revealed through the schlieren technique. The resulting collection emphasizes the accessibility of flow visualization in a teaching laboratory, and its value in making invisible physical processes intuitively understandable. Full article

Personalized learning environments increasingly rely on learner modeling techniques that integrate both explicit and implicit data sources. This study introduces a hybrid profiling methodology that combines psychometric data from an extended Felder–Silverman Learning Style Model (FSLSM) questionnaire with behavioral analytics derived from Moodle Learning Management System interaction logs. A structured mapping process was employed to associate over 200 unique log event types with FSLSM cognitive dimensions, enabling dynamic, behavior-driven learner profiles. Experiments were conducted across three datasets: a university dataset from the International Hellenic University, a public dataset from Kaggle, and a combined dataset totaling over 7 million log entries. Deep learning models including a Sequential Neural Network, BiLSTM, and a pretrained MLSTM-FCN were trained to predict student performance across regression and classification tasks. Results indicate moderate predictive validity: binary classification achieved practical, albeit imperfect accuracy, while three-class and regression tasks performed close to baseline levels. These findings highlight both the potential and the current constraints of log-based learner modeling. The contribution of this work lies in providing a reproducible integration framework and pipeline that can be applied across datasets, offering a realistic foundation for further exploration of scalable, data-driven personalization. Full article

Gravity beneficiation is a key operation in mineral processing and waste recycling, enabling the production of concentrates with required quality. Among gravity separators, pulsating jigs remain widely applied due to their robustness and adaptability. This study evaluates the KOMAG laboratory jig for upgrading diverse feedstocks: hard coal with variable ash content, gravel aggregates with organic impurities, post-mining waste, and battery scrap. Tests were performed on a two-chamber jig with an air-pulsation system and advanced control. The results confirmed the feasibility of obtaining coal concentrates with 8%–10% ash at 59%–71% yield, complete removal of organic contaminants from aggregates with minimal losses, and recovery of combustible fractions from post-mining waste with favourable separation parameters (d₅₀ = 1.569 g/cm³, imperfection = 0.191). Beneficiation of shredded battery scrap achieved 74%–88% plastic removal and over 99% metallic recovery. Industrial implementations of KOMAG pulsating jigs validated these findings, showing high efficiency in coal, aggregate, and waste processing. This study demonstrates the versatility of pulsating jigging and its relevance in sustainable resource management, confirming that laboratory results can be effectively scaled to industrial practice. Full article

Cartilage tissue engineering aims to restore damaged cartilage using biomaterials, cells, and/or biological cues to support cell growth and tissue repair. Although in the past decades scientific advances have moved the field forward, their translation to a clinical setting is still hampered. One major hurdle to take is to reduce process variability to ensure a predictable biological outcome. Using enabling technologies such as bioprinting has shown the potential to improve process robustness. However, developing bioinks that balance printability with biological functionality remains a major challenge. This study presents the development and structure–property relationships of a novel gelatin-based hydrogel blend, GelMA/GelMA-AEMA, optimized for extrusion-based bioprinting (EBB) while maintaining the crucial biological properties of GelMA for tissue engineering applications. The novel GelMA/GelMA-AEMA blend demonstrated superior flowability and printability compared to GelMA, effectively addressing common 3D-printing defects such as filament shape inhomogeneity. A systematic rheological characterization revealed that the blend exhibits a softer, elastically dominated structure with improved compliance. The blend behaves as a yield-stress fluid with a strong shear-thinning degree, making it highly suitable for EBB. The superior flow properties of the blend are deemed to enhance bond slippage and stress-induced orientation of its more imperfect gel structure, resulting in greater macroscopic deformation and enhanced print fidelity. In addition, histological assessment of a 21-day in vitro study with iPSC-derived chondrocytes suggested that the blend is at least equally performant as GelMA in supporting matrix formation. Histological analysis shows similar matrix deposition profiles, whereas gene expression analysis and compression tests even have suggested superior characteristics for cartilage TE. This study emphasizes the central role of rheology in bioink development and provides foundations for future material development for EBB, with potential implications for cartilage tissue engineering. Full article

As IoT (internet of things) devices grow in prominence, safeguarding them from cyberattacks is becoming a pressing challenge. To bootstrap IoT security, device identification or authentication is crucial for establishing trusted connections among devices without prior trust. In this regard, radio frequency fingerprinting (RFF) is gaining attention because it is more efficient and requires fewer computational resources compared to resource-intensive cryptographic methods, such as digital signatures. RFF works by identifying unique manufacturing defects in the radio circuitry of IoT devices by analyzing over-the-air signals that embed these imperfections, allowing for the identification of the transmitting hardware. Recent studies on RFF often leverage advanced classification models, including classical machine learning techniques such as K-Nearest Neighbor (KNN) and Support Vector Machine (SVM), as well as modern deep learning architectures like Convolutional Neural Network (CNN). In particular, CNNs are well-suited as they use multidimensional mapping to detect and extract reliable fingerprints during the learning process. However, a significant limitation of these approaches is that they require large datasets and necessitate retraining when new devices not included in the initial training set are added. This retraining can cause service interruptions and is costly, especially in large-scale IoT networks. In this paper, we propose a novel solution to this problem: RFF using Siamese networks, which eliminates the need for retraining and allows for seamless authentication in IoT deployments. The proposed Siamese network is trained using in-phase and quadrature (I/Q) samples from 10 different Software-Defined Radios (SDRs). Additionally, we present a new algorithm, the Similarity-Based Embedding Classification (SBEC) for RFF. We present experimental results that demonstrate that the Siamese network effectively distinguishes between malicious and trusted devices with a remarkable 98% identification accuracy. Full article

Exposure of LIB materials to ambient conditions with some level of humidity, either accidentally owing to imperfect fabrication or cell damage, or deliberately due to battery opening operations for analytical or recycling purposes, is a rather common event. As far as humidity-induced damage is concerned, on the one hand the general chemistry is well known, but on the other hand, concrete structural details of these processes have received limited explicit attention. The present study contributes to this field with an investigation centered on the use of Raman spectroscopy for the assessment of structural modifications using common lithium iron phosphate (LFP) and nickel–cobalt–manganese/lithium–manganese oxide (NCM-LMO) cathodes. The impact of humidity has been followed through the observation of differences in Raman bands of pristine and humidity-exposed cathode materials. Vibrational spectroscopy has been complemented with morphological (SEM), chemical (EDS), and electrochemical analyses. We have thus pinpointed the characteristic morphological and compositional changes corresponding to corrosion and active material dissolution. Electrochemical tests with cathodes reassembled in coin cells allowed for the association of specific capacity losses with humidity damaging. Full article

This review systematically examines the evolution of maintenance strategies for complex systems, with a focus on the advancements in condition-based maintenance (CBM) decision-making methodologies. Traditional approaches, such as post-failure maintenance and time-based maintenance, are increasingly supplanted by CBM due to challenges like high costs or inefficiency in resource allocation. CBM leverages system reliability models in conjunction with component degradation data to dynamically establish maintenance thresholds, optimizing resource utilization while minimizing operational risks and repair costs. Research has expanded from single-component degradation systems to multi-component systems, leveraging degradation models and optimization algorithms to propose strategies addressing multi-level control limits, economic dependencies, and task constraints. Recent studies emphasize multi-component interactions, incorporating structural influences, imperfect repairs, and economic correlations into maintenance planning. Despite progress, challenges persist in modeling coupled degradation mechanisms and coordinating maintenance decisions for interdependent components. Future research directions should encompass adaptive learning strategies for dynamic degradation processes, such as those employed in intelligent agents for real-time environmental adaptation, and the incorporation of intelligent predictive technologies to enhance system performance and resource utilization. Full article

As the world’s largest developing and carbon-emitting country, China is accelerating its greenhouse gas (GHG) emission reduction process, and it is of vital importance in achieving the goals set out in the Paris Agreement. This paper examines the historical development and current operation of China’s carbon emissions trading market (CETM). The current progress of research on the implementation of carbon emissions trading policy (CETP) is described in four dimensions: environment, economy, innovation, and society. The results show that CETP generates clear environmental and social benefits but exhibits mixed economic and innovation effects. Furthermore, this paper analyses the challenges of China’s carbon market, including the green paradox, the low carbon price, the imperfections in cap setting and allocation of allowances, the small scope of coverage, and the weakness of the legal supervision system. Ultimately, this paper proposes recommendations for fostering China’s CETM with the anticipation of offering a comprehensive outlook for future research. Full article