Search Results (955)

In this paper, we present the design and control of a novel aerial vehicle inspired by the biomechanics of a rabbit named “Flybbit”. Flybbit consists of two main components, namely a movable “Ears” part and a rigid “Body” part, forming a composite flying system with five controllable degrees of freedom (DOFs). The “Ears” part is equipped with two tiltable motors paired with optional-sized propellers, enabling additional thrust generation and flight stability maintenance, and the “Body” part incorporates four fixed motors, analogous to a rabbit’s limbs, to provide the primary propulsion. To fully exploit the actuation capability, we derive the system dynamics and introduce a dynamic control allocation method with an adaptive strategy to mitigate actuator saturation during complex combined maneuvers. Furthermore, we analyze the differential flatness property and develop a nonlinear inverse dynamics controller enhanced with hybrid external wrench estimation, enabling accurate trajectory tracking in five DOFs. Flybbit supports both manual operation via RC and autonomous flight via onboard computation. Comprehensive simulations and real-world experiments validate the proposed design and control framework. Full article

Wearable computing is evolving from a passive data collection paradigm into an active, precision-guided health orchestration system. This survey synthesizes developments across sensing modalities, wireless protocols, computational frameworks, and AI-driven analytics that collectively define the state of the art in mental and physical health monitoring. A narrative review methodology is used to map the landscape of hardware innovations—including microfluidic sweat sensing, smart textiles, and textile-embedded biosensing ecosystems—alongside advances in on-device AI acceleration, context-aware multimodal fusion, and privacy-preserving learning frameworks. The analysis highlights a shift toward multiplexed biochemical sensing for real-time metabolic profiling, neuromorphic and analog AI processors for ultra–low-power analytics, and closed-loop therapeutic systems capable of adapting interventions dynamically to both physiological and psychological states. These trends are examined in the context of emerging clinical and consumer use cases, with a focus on scalability, personalization, and data security. By grounding these insights in current research trajectories, this work positions wearable computing as a cornerstone of preventive, personalized, and participatory healthcare. Addressing identified technical and ethical challenges will be essential for the next generation of systems to become trusted, equitable, and clinically indispensable tools. Full article

The clinical use of 1,25-dihydroxycholecalciferol (1,25D3), the active form of vitamin D₃, is limited by its calcemic side effects and rapid metabolic degradation. To overcome these limitations, we designed novel vitamin D analogs with extended, rigidified, and branched side chains. Among them, PRI-1938, featuring a 5,6-trans triene system and 22,24-all-trans side-chain geometry, demonstrated markedly enhanced resistance to enzymatic catabolism. In vitro assays revealed that metabolic conversion of PRI-1938 by the nonselective cytochrome P450 3A4 (CYP3A4) enzyme was ca. 4-fold lower than that of the previously obtained PRI-1906 and over 9-fold lower than 1,25D3. All new analogs, including PRI-1927 and PRI-1937, exhibited significantly higher stability toward mitochondrial cytochrome P450 24A1 (CYP24A1), the vitamin D-selective catabolic enzyme, than that of 1,25D3. Molecular modeling and quantum mechanical calculations indicated that PRI-1938 adopts a highly stable conformation in the CYP24A1 active site, stabilized by four hydrogen bonds and multiple hydrophobic interactions. The spatially optimized interaction network reduces access to the catalytic heme, resulting in the lowest observed metabolic conversion. These findings highlight the critical role of the side-chain geometry in modulating metabolic stability and support the further development of PRI-1938 as a promising anticancer vitamin D analog. Full article

Industrial environments frequently rely on analog control instruments due to their reliability and robustness; however, automating the interpretation of these controls remains challenging due to variability in design, lighting conditions, and scale precision requirements. This research investigates the effectiveness of Vision–Language Models (VLMs) for automated interpretation of industrial controls through analysis of three distinct approaches: general-purpose VLMs, fine-tuned specialized models, and lightweight models optimized for edge computing. Each approach was evaluated using two prompting strategies, Holistic-Thought Protocol (HTP) and sequential Chain-of-Thought (CoT), across a representative dataset of continuous and discrete industrial controls. The results demonstrate that the fine-tuned Generative Pre-trained Transformer 4 omni (GPT-4o) significantly outperformed other approaches, achieving low Mean Absolute Error (MAE) for continuous controls and the highest accuracy and Matthews Correlation Coefficient (MCC) for discrete controls. Fine-tuned models demonstrated less sensitivity to prompt variations, enhancing their reliability. In contrast, although general-purpose VLMs showed acceptable zero-shot performance, edge-optimized models exhibited severe limitations. This work highlights the capability of fine-tuned VLMs for practical deployment in industrial scenarios, balancing precision, computational efficiency, and data annotation requirements. Full article

Cathelicidins are a family of antimicrobial peptides (AMPs) with broad-spectrum activity and immunomodulatory functions. Among them, the only human cathelicidin LL-37 has garnered significant interest due to its potent antimicrobial, antiviral, antifungal, antiparasitic, and antitumor properties. However, the clinical application of LL-37 is hindered by several limitations, including low proteolytic stability, cytotoxicity, and high production costs. To overcome these challenges, a wide range of design strategies have been employed to modify LL-37 and improve its therapeutic potential. LL-37-based analogs represent promising candidates for the development of next-generation antimicrobial and immunomodulatory therapies. Despite significant progress, further research is required to optimize peptide design, ensure cost-effective production, and validate long-term safety and efficacy. Advances in computational modeling, high-throughput screening, and nanotechnology will play an important role in the translation of modified cathelicidins into clinical practice. This review summarizes key strategies of chemical and structural modifications of LL-37 aimed at enhancing its functional properties. Particular attention is given to truncated and retro-analogs, which preserve or improve biological activity while exhibiting reduced toxicity and increased proteolytic resistance. Furthermore, we highlight the use of nanoscale delivery systems, which facilitate targeted delivery, prolong peptide half-life, and mitigate cytotoxic effects. Full article

Mine roof water inrush represents a prevalent hazard in mining operations, characterized by its concealed onset, abrupt occurrence, and high destructiveness. Since mine water inrush is controlled by multiple factors, rigorous risk assessment in hydrogeologically complex coal mines is critically important for operational safety. This study focuses on the roof water inrush hazard in coal seams of the Banji coal mine, China. The conventional water-conducting fracture zone height estimation formula was calibrated through comparative analysis of empirical models and analogous field measurements. Eight principal controlling factors were systematically selected, with subjective and objective weights assigned using AHP and EWM, respectively. Game theory was subsequently implemented to compute optimal combined weights. Based on this, the vulnerability index model and fuzzy comprehensive evaluation model were constructed to assess the roof water inrush risk in the coal seams. The risk in the study area was classified into five levels: safe zone, relatively safe zone, transition zone, relatively hazardous zone, and hazardous zone. A zoning map of water inrush risk was generated using Geographic Information System (GIS) technology. The results show that the safe zone is located in the western part of the study area, while the hazardous and relatively hazardous zones are situated in the eastern part. Among the two models, the fuzzy comprehensive evaluation model aligns more closely with actual engineering practices and demonstrates better predictive performance. It provides a reliable evaluation and prediction model for addressing roof water hazards in the Banji coal seam. Full article

To investigate the effects of clearance variations induced by back wear ring wear on internal flow and noise within centrifugal pumps at the design flow rate (Q_o = 25 m³/h), a combined Computational Fluid Dynamics (CFD) and Acoustic Finite Element Method (FEM) approach was employed. The SST-SAS turbulence model and Lighthill’s acoustic analogy, were applied to simulate the internal flow and acoustic fields, respectively, across four different clearance values. The impact laws of various back wear-ring clearances on flow-induced noise were analyzed. The results indicate that the head and efficiency of the centrifugal pump gradually decrease with the increase in the back wear-ring clearance. When the clearance reaches 1.05 mm, the head drops by 4.35% and the efficiency decreases by 14.86%. The radial force on the impeller decreases, while the axial force increases and its direction reverses by 180 degrees. The acoustic source strength at the rotor–stator interface, near the volute tongue, and at the outlet of the back wear ring increases with larger clearance; furthermore, high-sound-source regions expand around the balance holes and near the impeller suction side. The dominant SPL frequency for all clearance cases was the blade passing frequency (BPF). As clearance increases, the overall SPL curve shifts upwards; however, the variation gradient decreases noticeably when the clearance exceeds 0.75 mm. The overall internal SPL increases, with the total SPL under 1.05 mm being 1.8% higher than that under 0.15 mm. In total, the optimal back ring clearance is 0.45 mm, which achieves a 38% noise reduction while maintaining a 97.9% head capacity. Full article

Molecularly imprinted polymers (MIPs) have emerged as promising materials for selectively targeting biomolecules, including quorum sensing autoinducers that regulate bacterial communication and biofilm formation. In this study, both single-template and dual-template strategies were employed to design and synthesize MIPs capable of capturing autoinducer-2 analogs using (3R,4S)-tetrahydro-3,4-furandiol (T1) or (R/S) 2,2-dimethyl-1,3-dioxolane-4-methanol (T2) as the templates. This approach offers translational potential of a complementary or non-antibiotic strategy to conventional antimicrobial therapies in mitigating biofilm-associated infections. Computational modeling guided the rational selection of functional monomers, predicting favorable interaction energies (ΔE_C up to −135 kcal·mol⁻¹) and optimal hydrogen-bonding patterns to enhance template–polymer affinity. The synthesized MIPs were characterized using spectroscopic and microscopic techniques to confirm imprinting efficiency and structural integrity. The adsorption capacity measurements demonstrated higher adsorption capacity and selectivity of MIPs compared to non-imprinted polymers, with the highest selectivity equal to 3.36 for T1 and 3.14 for T2 on MIPs fabricated from methacrylic acid. Preliminary microbiological evaluations using Chromobacterium violaceum ATCC 12472 reveal that the MIPs prepared from 2-hydroxyethyl methacrylate effectively inhibited violacein production by up to 78.2% at 5.0 mg·mL⁻¹, consistent with quorum sensing interference. These findings highlight the feasibility of employing molecular imprinting to target autoinducer-2 analogs, introducing a novel synthetic strategy for disrupting bacterial communication. This further suggests that molecular imprinting can be leveraged to develop potent quorum-sensing inhibitors, an approach that offers translational potential as an alternative to conventional antimicrobial strategies to mitigate biofilm-associated infections. Full article

Topology optimization is increasingly employed to design fluid flow systems capable of achieving optimal performance under specific constraints. This study presents a density-based topology optimization approach specifically tailored for second-order reactive flows. The fluid-solid distribution within the domain is represented by continuous design variables expressed as an inverse permeability field. An adjoint method is used to efficiently compute gradients of the objective function, enabling the application of gradient-based algorithms to solve the optimization problem. The methodology is validated on a benchmark bend-pipe case, reproducing known optimal geometry. Subsequently, the method is applied to optimize a system involving second-order chemical reactions, aiming to maximize a desired reaction while limiting undesirable side reactions. Results demonstrate significant performance improvements, achieving gains in reaction efficiency ranging from 90.4% to 98.7% for the porous geometries and from 94.6% to 105.2% for real geometries. The optimization strategy successfully generates flow configurations analogous to those observed in modern gas turbines, highlighting the practical relevance and potential impact of the developed methodology. Full article

This article presents a new approach to the problem of transforming one quantum state into another. It is shown that an $r$-qubit superposition $|\mathit{x}⟩$ can be obtained from another $r$-qubit superposition $|\mathit{y}⟩$, by using only ${(2}^r-1)$ rotations, each presented by one controlled rotation gate. The quantum superpositions with real amplitudes are considered. The traditional two-stage approach $U_y^{-1}{U}_x:|\mathit{x}⟩\to |{0⟩}^{\oplus r}\to |\mathit{y}⟩$ requires twice as many rotations. Here, both transformations to the conventual basis state, $U_x: |\mathit{x}⟩\to |{0⟩}^{\oplus r}$ and $U_y: |\mathit{y}⟩\to |{0⟩}^{\oplus r}$, use ${(2}^r-1)$ rotations each on two binary planes, and many of these rotations require additional sets of CNOTs to be represented as 1- or 2-qubit-controlled gates. The proposed method is based on the concept of the discrete signal-induced heap transform (DsiHT) which is unitary and generated by a vector and a set of angular equations with given parameters. The quantum analog of this transform is described. The main characteristic of the DsiHT is the path of processing the data. It is shown that there exist such fast paths that allow for effective computing of the DsiHT, which leads to the simple quantum circuits for state preparation and transformation. Examples of such paths are given and quantum circuits for preparation and transformation of 2-, 3-, and 4-qubits are described in detail. CNOT gates are not used, but only controlled gates of elementary rotations around the $y$-axis. It is shown that the transformation and, in particular, only rotation gates with control qubits are required for initialization of 2-, 3-, and 4-qubits. The quantum circuits are simple and have a recursive form, which makes them easy to implement for arbitrary $r$-qubit superposition, with $r\ge 2.$ This approach significantly reduces the complexity of quantum state transformations, paving the way for more efficient quantum algorithms and practical implementations on near-term quantum devices. Full article

Motivated by questions arising at the intersection of information theory and geometry, we compare two dissimilarity measures between finite categorical distributions. One is the well-known Jensen–Shannon divergence, which is easy to compute and whose square root is a proper metric. The other is what we call the minmax divergence, which is harder to compute. Just like the Jensen–Shannon divergence, it arises naturally from the Kullback–Leibler divergence. The main contribution of this paper is a proof showing that the minmax divergence can be tightly approximated by the Jensen–Shannon divergence. The bounds suggest that the square root of the minmax divergence is a metric, and we prove that this is indeed true in the one-dimensional case. The general case remains open. Finally, we consider analogous questions in the context of another Bregman divergence and the corresponding Burbea–Rao (Jensen–Bregman) divergence. Full article

This paper introduces a new family of q-special polynomials, termed q-general Bell polynomials, and systematically explores their structural and analytical properties. We establish their generating functions, derive explicit series representations, and develop recurrence relations to characterize their combinatorial behavior. Additionally, we characterize their quasi-monomial properties and construct associated differential equations governing these polynomials. To demonstrate the versatility and applicability of this family, we investigate certain examples, including the q-Gould–Hopper–Bell and q-truncated exponential-Bell polynomials, deriving analogous results for each. Further, we employ computational tools in Mathematica to examine zero distributions and produce visualizations, offering numerical and graphical insights into polynomial behavior. Full article

The semantics of programming languages tend to be discussed with high levels of formality; much of the previous research—both philosophical and historical—has investigated them from this perspective. In this paper, I draw on the philosophical and cognitive theories of metaphor and use the early work of Adriaan van Wijngaarden as a historical case study to explore the conceptual and discursive surroundings of semantics. I investigate the relationships between the texts of semantics, the abstract entities they denote, and the metaphors, analogies, and illustrative language used to accompany or explain the same. This serves to further understanding of the historical developments of work in this area, the nature of programming languages and their semantics, and the importance of the communicative methods used in dissemination and education of computer science. Full article

Analog computing has re-emerged as a powerful tool for solving complex problems in various domains due to its energy efficiency and inherent parallelism. This paper summarizes recent advancements in analog computing, exploring discrete time and continuous time methods for solving combinatorial optimization problems, solving partial differential equations and systems of linear equations, accelerating machine learning (ML) inference, multi-beam beamforming, signal processing, quantum simulation, and statistical inference. We highlight CMOS implementations that leverage switched-capacitor, switched-current, and radio-frequency circuits, as well as non-CMOS implementations that leverage non-volatile memory, wave physics, and stochastic processes. These advancements demonstrate high-speed, energy-efficient computations for computational electromagnetics, finite-difference time-domain (FDTD) solvers, artificial intelligence (AI) inference engines, wireless systems, and related applications. Theoretical foundations, experimental validations, and potential future applications in high-performance computing and signal processing are also discussed. Full article

Background/Objectives: Understanding the stabilization mechanisms of amino acid conformations in different solvent environments is crucial for elucidating biomolecular interactions and crystallization processes. This study presents a comprehensive computational investigation of glycine, the simplest amino acid, in both its canonical and zwitterionic forms when interacting with dimethyl sulfoxide (DMSO) molecules. Methods: Using density functional theory (DFT) calculations at the B3LYP/6-311++G(d,p) level with empirical dispersion corrections, we examined the conformational landscape of glycine–DMSO clusters with one and two DMSO molecules, as well as implicit solvent calculations, and compared them with analogous water clusters. Results: Our results demonstrate that while a single water molecule is insufficient to stabilize the zwitterionic form of glycine, one DMSO molecule successfully stabilizes this form through specific interactions between the S=O and the methyl groups of DMSO and the NH₃⁺ and the oxoanion group of zwitterionic glycine, respectively. Topological analysis of the electron density using QTAIM and NCI methods reveals the nature of these interactions. When comparing the relative stability between canonical and zwitterionic forms, we found that two DMSO molecules significantly reduce the energy gap to approximately 12 kJ mol⁻¹, suggesting that increasing DMSO coordination could potentially invert this stability. Implicit solvent calculations indicate that in pure DMSO medium, the zwitterionic form becomes more stable below 150 K, while remaining less stable at room temperature, contrasting with aqueous environments where the zwitterionic form predominates. Conclusions: These findings provide valuable insights into DMSO’s unique role in biomolecular stabilization and have implications for protein crystallization protocols where DMSO is commonly used as a co-solvent. Full article