Search Results (86)

Proactive cyber security has moved from being an innovative approach to an essential requirement for business success, especially considering the large number of new tools and technologies that organizations need to learn and use. In this paper, we discuss and demonstrate the possibility of automating important cyber security and immediate response steps with the use of dedicated artificial intelligence agents. We focus on local-first solutions that are able to keep sensitive data private and at the same time fit well with existing data protection policies and infrastructure. Using a minimalist AI agent addressing a local large language model, we experiment with skills aimed at running cyber security tools and processing their output in support of cyber incident response. Full article

Designing products that feel simple and intuitive while remaining efficient to manufacture and meaningful across cultures remains a key challenge in contemporary product development. Although minimalist design has achieved commercial success, most methodologies fail to systematically connect aesthetic intentions with engineering, usability, and production realities. This study proposes a four-phase iterative framework—Framing, Translation, Materialisation, and Experience that integrates human-centred design, simplicity heuristics—including ‘SHE’ (Shrink, Hide, Embody) tactics, quantitative aesthetic measurement, Design for X methods, and cross-cultural considerations to operationalise minimalist principles such as formal reduction, seriality, and industrial materiality. An empirical case study applied the framework to three competing automotive interior concepts through image-based evaluation by 113 respondents. Results provided preliminary evidence that a balanced hybrid approach consistently outperformed both traditional button-heavy and extreme single-screen minimalist designs across measures of usability, sense of order, trust, and user recommendation. Findings confirm that effective minimalism does not merely remove elements but strategically redistributes complexity into interface logic, production systems, and material quality. The framework offers designers a structured yet flexible path to create manufacturable, user-validated, and culturally sensitive minimalist products. Full article

Thrust vector control (TVC) technology significantly enhances the attitude control capability of aircraft. However, conventional multi-axis calibration is inherently cost-prohibitive and time-consuming, whereas static ground-bench calibration completely neglects the significant aerodynamic interference from external freestreams during actual flight. To resolve this operational bottleneck in engineering applications and calibration workflows, this paper proposes an agile, cost-effective hybrid modeling and process optimization methodology that seamlessly integrates low-cost ground experiments with high-fidelity numerical investigations. First, a customized one-axis force sensor test bench was developed to calibrate the baseline thrust vector performance under static ground conditions. Subsequently, computational fluid dynamics (CFD) simulations were conducted across the typical cruise speed range (30–60 m/s) of TVC aircraft to investigate the nonlinear interactions between the external freestream and the vectorized jet. The numerical results reveal that the interaction between the deflected jet exhaust and the external flow field significantly changes the flow structure and pressure distribution around the control surface. The momentum exchange, primarily governed by mass entrainment and cross-shear-layer mixing, broadens the control surface’s impact scope and enhances the injection effect, resulting in nonlinear variations in the control force under different operating conditions. Considering the influence of external flow and deflected jet, a dynamic control force model was established by integrating the ground static baseline model with simulation data. The fitting results demonstrate that this control model limits match error to within 3% across both experimental and numerical datasets, effectively suppressing loop uncertainties across the entire flight envelope while maintaining a minimalist sensing architecture. From a process engineering perspective, this “Hybrid Modeling”-driven approach substitutes expensive multi-degree-of-freedom testing hardware with an integrated simulation-experimental workflow, offering a highly scalable, simplified, and analyzable process method for TVC research and application. Full article

Deep learning has significantly advanced image super-resolution (SR), yet many state-of-the-art models remain too computationally expensive for resource-constrained devices. This paper demonstrates that a highly parameter-efficient design can achieve comparable performance to the very deep super-resolution network (VDSR) with a tiny fraction of parameters. Starting from the classic VDSR architecture (2016), we systematically evaluate three design choices: depthwise separable convolution (DSConv), Hybrid Attention Transformer (HAT), and a local residual connection (LR). HAT provides no performance gain—an honest negative result supported by controlled experiments (increased training, different reduction ratios, and standard convolution baseline). In contrast, LR alone yields a 0.20 dB improvement without introducing any extra parameters. Consequently, we discard HAT and propose DSConv+LR. Our model contains only 49,217 parameters—about 7.4% of VDSR—yet attains a peak signal-to-noise ratio (PSNR) of 35.21 dB on Set5 (×2), which is 99.7% of VDSR’s performance (35.33 dB). On additional benchmarks (Set14, BSD100, and Urban100), DSConv+LR maintains similar relative performance (within 0.12 dB of VDSR). Perceptual loss (AlexNet features, lower better) is 0.2556, slightly better than VDSR (0.2717). We acknowledge that modern lightweight networks such as cascaded residual attention network (CARN) and information multi-distillation network (IMDN) achieve 2–3 dB higher PSNR at the cost of 9–14× more parameters. This work advocates a minimalist approach while honestly reporting both its strengths and limitations. Full article

This paper presents a methodology for synthesizing static state feedback controllers utilizing a Fractional-Order Performance Index. Linear Quadratic Regulators are designed using integer-order integral weighting functions. In the proposed approach, fractional-order calculus is utilized to introduce an additional degree of freedom in controller synthesis, enabling enhanced shaping of the plant’s dynamic properties. The controller gains are obtained by solving a fractional Riccati-like equation, through which the temporal weighting properties inherent to fractional integration are embedded into a static feedback matrix. This formulation is a minimalist control structure suitable for implementation on resource-constrained hardware. The proposed method is validated via rapid control prototyping on an industrial NI PXIe platform and an analog third-order plant. Performance evaluation using Integral Absolute Error and Integral Absolute Control metrics demonstrates that the fractional order serves as a flexible tuning parameter, providing an alternative trade-off between settling time and control effort. Furthermore, frequency domain sensitivity analysis demonstrates the absence of resonant peaks and inherent attenuation of high-frequency measurement noise. As a result, the presented framework bridges fractional-order optimization techniques with industrial control platforms. Full article

Continuous monitoring of lower-limb kinematics in natural environments is essential for gait analysis and rehabilitation but remains challenging due to the limitations of optical systems and the inaccuracy of sparse inertial sensor methods. To address this, we propose a high-precision, minimalist wearable system utilizing only three inertial measurement units placed on the pelvis and shanks. In the data preprocessing stage, engineering modifications are made based on the traditional gradient descent algorithm to implement adaptive channel adjustment on the acceleration and magnetic data of a single IMU, aiming to alleviate the impact of motion acceleration and external magnetic interference on the temporal feature manifold. Subsequently, a pure Transformer neural network is utilized to capture long-range temporal dependencies, reconstructing full lower-limb kinematics without relying on rigid biomechanical assumptions. The model was optimized and deployed on an STM32N647 microcontroller to achieve real-time edge inference with a low latency of approximately 17 ms. Experimental results demonstrate that the proposed method achieves a mean absolute error of 2.41° for level walking, significantly outperforming traditional constrained Kalman filter approaches. Furthermore, it maintains high tracking robustness during complex nonlinear movements such as squatting and lunging. In conclusion, this edge-computing-enabled framework provides an accurate, comfortable, and real-time solution for unconstrained human motion capture in daily scenarios. Full article

This paper focuses on the early Qing monk-painter Hongren 弘仁, systematically exploring the pathways through which the Chan Buddhist “emptiness contemplation” is manifested in his landscape paintings. As a representative monk-painter, Hongren produced works that profoundly embody the Chan contemplation of emptiness, yielding a singular style defined by austere coldness, minimalist simplicity, and profound quietude. Transcending conventional stylistic descriptions in art history and essentialist philosophical deductions, this study adopts a comprehensive empirical approach that integrates poetry, calligraphy, painting, and seals (shi-shu-hua-yin 诗书画印). By adopting an interdisciplinary perspective of philosophy, religion, and art history, this study argues that Hongren’s landscapes are not merely subjective emotional expressions or aesthetic pursuits; rather, they constitute a visual extension and a spiritual externalization of his emptiness contemplation. Through a multi-layered analysis of his form, brushwork, composition, and artistic conception, combined with the mutual corroboration of poetic inscriptions on paintings and textual inscriptions on seals, this paper reveals how the Chan philosophy of “emptiness contemplation” is reflected within his artistic language. While Hongren’s style is the cumulative result of various factors such as the Ming-Qing dynasty transition, his personal life, the inheritance of painting techniques, and the regional culture of Mount Huang, this paper specifically takes Chan thought as its analytical starting point, focusing on its unique expression in his work. Hongren’s path of “Painting-Chan” (hua chan 画禅) not only infused early Qing painting with a sublime spiritual power but also provides a vital religious exegesis of the deep-seated Chinese tradition of “Technique Ascending to the Dao” (ji jin yu dao 技进于道). Full article

Terahertz (THz) fingerprint detection is central to identifying characteristic absorption fingerprints of biomolecules derived from their intrinsic rotational and vibrational modes. The development of guided-mode resonance (GMR) technology together with pixelated design offers a new approach to enhance the recognition capability of such fingerprint spectra. Here, a novel secondary grating metasurface based on cycloolefin polymer (COP) is proposed, which adopts an ultra-minimalist dual-pixel complementary architecture to excite high-quality (Q)-factor GMR. Its spectral resolution does not exceed 50 GHz, enabling precise capture of target molecular characteristic information and meeting the requirements of broadband fingerprint sensing. More importantly, the design regulates the dual-pixel grating units through parameter gradient optimization and incorporates a dual regulation mode of static pixel-targeted coverage and dynamic angle fine tuning. By adjusting geometric parameters and incident angles, broadband coverage from 1.15 THz to 2.20 THz is achieved, which can accurately match the multi-fingerprint detection requirements of glutamic acid (Glu) and glutamine (Gln). This metasurface sensor, integrating the advantages of pixelation and high-Q-factor GMR characteristics, provides an effective strategy for enhanced broadband THz fingerprint sensing and shows broad application potential in the field of biochemical trace detection. Full article

The progressive aging of the atrial fibrillation (AF) population, frequently characterized by high ischemic and bleeding risks, has led to a substantial increase in referrals for left atrial appendage occlusion (LAAO). The expansion of indications and the high procedural success rate of LAAO have further contributed to rising procedural volumes. However, this growth introduces important challenges: LAAO candidates are often elderly and frail, with increased anesthesia-related risks, and high-volume catheterization laboratories may face logistical constraints, particularly in centers without dedicated anesthesiology support. The current gold standard approach, transesophageal echocardiography (TEE) under general anesthesia (GA), ensures optimal imaging and procedural control but may increase procedural complexity and perioperative risks. In response, minimalist strategies are increasingly explored, targeting either the anesthetic protocol or the imaging modality. Conscious sedation (CS) protocols have been adopted to reduce anesthesia-related burden while maintaining TEE guidance. Alternatively, imaging-based strategies aim to replace TEE with less invasive modalities, including intracardiac echocardiography (ICE), transesophageal–intracardiac echocardiography (TE-ICE), and MicroTEE. Each approach presents specific advantages and limitations regarding safety, feasibility, operator expertise, and institutional resources. Taken together, these findings support a patient-centered approach to LAAO, whether traditional or minimalist, in which the choice of anesthetic strategy and echocardiographic guidance is driven by institutional resources, operator expertise, and individual patient characteristics rather than by expected differences in procedural or clinical efficacy. This review summarizes current evidence on minimalist LAAO pathways and discusses their role in achieving a tailored, resource-conscious procedural model. Full article

Handgrip strength (HGS) is widely used as a biomarker of muscle function and overall health in older adults. However, conventional analyses based on peak force values may overlook relevant temporal features of the HGS curve. This cross-sectional study proposes a novel methodological approach that examines the shape and variability of HGS(t) curves recorded from community-dwelling older adults. Functional principal component analysis (FPCA) was applied to assess the consistency of individual trials and the representativeness of mean curves. Statistical non-parametric mapping (SnPM) was then used to identify time regions showing significant differences between groups. Complementary analyses of discrete and derivative parameters, together with non-parametric comparisons based on the Hodges–Lehmann estimator and corresponding 95% confidence intervals, were conducted to quantify effect sizes. FPCA revealed high within-participant consistency, supporting the use of mean curves for group-level comparisons. SPM analyses indicated significant differences in the early force development phase. Importantly, this approach shows that sex differences are attributable to magnitude effects, with men generating higher forces and faster early rates of force development, and not to differences in the neuromuscular strategy of force production. Traditional discrete parameters partly captured these patterns but failed to reflect the full temporal dynamics. This methodological approach to the HGS curve may provide further insights into neuromuscular control mechanisms that cannot be truly captured by the minimalistic HGS discrete parameters. Full article

This study addresses a gap in the literature by explicitly linking responsive web design frameworks to concrete cybersecurity vulnerabilities, moving beyond traditional discussions of usability and device compatibility to incorporate security-by-design principles in contemporary frontend development. The research adopts a qualitative comparative approach and considers five widely used responsive design frameworks: Bootstrap, Tailwind CSS, Foundation, Pure CSS, and Skeleton. These frameworks were selected based on criteria such as maturity, adoption, and architectural diversity. Three research questions guide the analysis: the identification of cybersecurity risks associated with responsive design frameworks, the extent to which these risks vary across frameworks, and the mitigation strategies required to address them. The findings confirm that most critical vulnerabilities originate outside the frontend layer, reinforcing the separation between presentation and backend logic. However, the results demonstrate that frameworks significantly influence the security risk profile, particularly regarding cross-site scripting, dependency management, and configuration practices. Modern utility-first frameworks shift security concerns toward the build pipeline and toolchain, while minimalistic and abandoned frameworks introduce risks related to obsolescence and unpatched “forever-day” vulnerabilities. The study concludes that frontend security depends less on framework choice alone and more on governance, continuous maintenance, and the systematic adoption of secure development and DevSecOps practices. Full article

Membrane fouling remains a major obstacle to the performance of the reverse osmosis (RO) desalination processes. Artificial intelligence (AI) is now a promising approach for the reliable modeling of these complex systems. This study evaluates three modeling techniques—multiple linear regression (MLR), artificial neural networks (ANNs), and support vector regression (SVR)—for predicting transmembrane pressure (TMP) at the Boujdour desalination plant, based on five input parameters: temperature, turbidity, pH, conductivity, and feedflow. The analysis is based on an original dataset of 195 daily measurements, and due to the absence of timestamps, the study focuses on state-to-TMP prediction rather than chronological forecasting, with no temporal generalization claimed. Approximately 2000 augmented training samples generated using a conservative SMOGN approach were used for model development, while performance evaluation relied exclusively on 39 independent real test observations. Two modeling strategies were adopted: (i) a minimalist approach based on significant variables identified by an ordinary least squares (OLS) model (pH and conductivity), and (ii) a multivariate approach integrating all parameters to capture non-linear interactions. A rigorous validation framework was put in place to avoid information leakage and ensure the robustness and generalizability of the models. Performance was evaluated using R², RMSE, and MAE metrics, supplemented by robustness and significance analyses including bootstrap confidence intervals, paired statistical comparisons, and interpretability analyses based on permutation importance, partial dependence plots (PDPs), and individual conditional expectation (ICE) curves. The results indicate that the SVR model achieves the best average predictive accuracy among the tested models, albeit with moderate explanatory power. Full article

Background: Transcatheter aortic valve implantation (TAVI) is increasingly used across all risk groups, meaning more patients are living long-term with transcatheter bioprosthetic valves. These patients are often multimorbid and vulnerable to both thrombotic and bleeding complications. Optimal antithrombotic therapy remains uncertain due to differences in trial design, patient demographics, and procedural practices. Methods: We undertook a narrative review that included randomised controlled trials, observational studies, biomarker research, and guideline recommendations on post-TAVI antithrombotic therapy. We evaluated the available evidence for antiplatelet and anticoagulant strategies after TAVI, predictors of bleeding and thrombotic complications, to identify emerging approaches using biomarkers for personalised risk stratification. Results: Thrombotic events after TAVI are predominantly early and procedural in origin, while new-onset atrial fibrillation (AF) leads to substantial late risk. Subclinical leaflet thrombosis is frequent, but its clinical significance remains uncertain, as anticoagulation reduces CT-detected leaflet abnormalities without improving clinical outcomes. Early bleeding within the first 30 days remains a principal contributor to mortality, influenced by frailty, vascular access, comorbidity, and intensity of antithrombotic therapy. Randomised evidence consistently supports a minimalist, indication-driven regimen: single antiplatelet therapy for patients without an oral-anticoagulation (OAC) indication, and OAC monotherapy for those with AF. Routine OAC use in unselected patients carries no advantage and exposes them to harm. Biomarkers and machine-learning models show promise for future individualised risk assessment. Conclusions: Antithrombotic strategies post-TAVI should prioritise minimising bleeding while maintaining adequate thromboembolic protection. Single antiplatelet therapy for patients without an indication for OAC and OAC alone for those with AF offer the best balance of safety and efficacy. Ongoing trials may clarify the role of imaging-guided therapy and biomarker-based risk stratification and refine antithrombotic strategies. Full article

While advanced imaging techniques and ordered porous materials like MOFs have gained prominence, gas sorption remains the indispensable tool for characterizing the multiscale heterogeneity of industrially important disordered solids, such as catalysts and shales. This review examines recent developments in gas sorption methodologies specifically tailored for rigid, disordered porous media. We discuss experimental advances, including the choice of adsorbate and the utility of the overcondensation method for probing macroporosity and ensuring saturation. Furthermore, we critically evaluate theoretical approaches for determining pore size distributions (PSDs), contrasting classical methods with Density Functional Theory (DFT) and Grand Canonical Monte Carlo (GCMC) simulations. Special emphasis is placed on the impact of pore-to-pore cooperative effects, such as advanced condensation, cavitation, and pore-blocking, on the interpretation of sorption isotherms. We highlight how complementary techniques, including integrated mercury porosimetry, NMR, and computerized X-ray tomography (CXT), are essential for deconvolving these complex network effects and validating void space descriptors. We conclude that, while “brute force” molecular simulations on image-based reconstructions are progressing, “minimalist” pore network models, which incorporate cooperative mechanisms, currently offer the most empirically adequate approach. Ultimately, gas sorption remains unique in its ability to statistically characterize void spaces from Angstroms to millimeters in a single experiment. Full article

We envision a minimalist way to explain a number of astronomical facts associated with the unsolved missing mass problem by considering a new phenomenological paradigm. In this model, no new exotic particles need to be added, and the gravity is not modified; it is the perception that we have of a purely Newtonian (or purely Einsteinian) Universe, dubbed the Newton basis or Einstein basis (actually “viewed through a pinhole” which is “optically” distorted in some manner by a so-called magnifying effect). The $\kappa$ model is not a theory but rather an exploratory technique that assumes that the sizes of the astronomical objects (galaxies and galaxy clusters or fluctuations in the CMB) are not commensurable with respect to our usual standard measurement. To address this problem, we propose a rescaling of the lengths when these are larger than some critical values, say >100 pc - 1 kpc for the galaxies and ∼1 Mpc for the galaxy clusters. At the scale of the solar system or of a binary star system, the $\kappa$ effect is not suspected, and the undistorted Newtonian metric fully prevails. A key point of an ontological nature rising from the $\kappa$ model is the distinction which is made between the distances depending on how they are obtained: (1) distances deduced from luminosity measurements (i.e., the real distances as potentially measured in the Newton basis, which are currently used in the standard cosmological model) and (2) even though it is not technically possible to deduce them, the distances which would be deduced by trigonometry. Those “trigonometric” distances are, in our model, altered by the kappa effect, except in the solar environment where they are obviously accurate. In outer galaxies, the determination of distances (by parallax measurement) cannot be carried out, and it is difficult to validate or falsify the kappa model with this method. On the other hand, it is not the same within the Milky Way, for which we have valuable trigonometric data (from the Gaia satellite). Interestingly, it turns out that for this particular object, there is strong tension between the results of different works regarding the rotation curve of the galaxy. At the present time, when the dark matter concept seems to be more and more illusive, it is important to explore new ideas, even the seemingly incredibly odd ones, with an open mind. The approach taken here is, however, different from that adopted in previous papers. The analysis is first carried out in a space called the Newton basis with pure Newtonian gravity (the gravity is not modified) and in the absence of dark matter-type exotic particles. Then, the results (velocity fields) are transported into the leaves of a bundle (observer space) using a universal transformation associated with the average mass density expressed in the Newton basis. This approach will make it much easier to deal with situations where matter is not distributed centrosymmetrically around a center of maximum density. As examples, we can cite the interaction of two galaxies or the case of the collision between two galaxy clusters in the bullet cluster. These few examples are difficult to treat directly in the bundle, especially since we would include time-based monitoring (with an evolving $\kappa$ effect in the bundle). We will return to these questions later, as well as the concept of average mass density at a point. The relationship between this density and the coefficient $\kappa$ must also be precisely defined. Full article