Search Results (19,168)

Transanal Irrigation (TAI) and Colon Hydrotherapy (CHT) represent emerging therapeutic options that may complement first-line interventions or serve as rescue treatments for chronic constipation and fecal incontinence. Their clinical utility depends on patient characteristics, specific therapeutic goals, device features, and probe type, as well as the procedural setting. This review presents the various pathophysiological contexts in which these techniques can be applied, analyzing their specific characteristics and potential pros and cons. Moreover, these interventions are also considered within a Psycho-Neuro-Endocrino-Immunological (PNEI) framework, given the potential influence of intestinal function and microbiota modulation on the bidirectional communication pathways linking the enteric nervous system, neuroendocrine regulation, immune activity, and global patient well-being. Since there is not yet enough scientific data on this topic, future research should prioritize randomized controlled trials comparing these techniques with other standard treatments (e.g., laxatives or dietary fiber) in defined patient populations. Longitudinal studies will also be essential to clarify long-term safety, potential effects on microbiota, and both risks and benefits. Standardization of technical procedures also remains a critical need, especially regarding professional competencies, operating parameters (e.g., instilled volumes and pressure ranges), and reproducible protocols. Moreover, future investigations should incorporate objective outcome measures, as colonic transit time, stool form and frequency, indices of inflammation or intestinal wall integrity, and changes to microbiome composition. In conclusion, TAI and CHT have the potential to serve as important interventions for the treatment and prevention of chronic constipation and intestinal dysbiosis, as well as their broader systemic correlates, in the setting of bio-integrated medicine. Full article

Background: Extended platelet-rich fibrin (e-PRF) membranes are a novel 100% autologous biomaterial with a longer resorption time (4–6 months) than traditional solid-PRF membranes (two weeks). In part 1 of this 2-part publication series, four clinical variations for using these novel e-PRF membranes for socket preservation were introduced. In this randomized clinical trial (RCT), all four iterations of e-PRF membranes were compared to traditional collagen membranes in alveolar ridge preservation for hard and soft tissue dimensional changes and early wound healing outcomes. Methods: A single-center RCT was conducted, including 55 patients requiring the extraction of a single tooth with planned implant placement. All sockets were grafted with a “sticky bone” (bone allograft mixed with PRF) and secured with either a collagen membrane (control) or e-PRF membranes utilizing the four variations present in Part 1 (both formed extra-orally or intra-orally, each with or without an overlying solid PRF membrane). The time of fabrication and application of each e-PRF iteration was recorded. Cone beam computed tomography was utilized to evaluate horizontal and vertical ridge dimensions at baseline and 3 months post-operatively, and soft tissue thickness was also measured at both time intervals utilizing an endodontic reamer. Early wound healing was recorded at 2 weeks, utilizing the Landry, Turnbull, and Howley Index by three blinded clinicians. Results: The results demonstrated that, at 3 months, the e-PRF membranes fabricated utilizing all 4 treatment variations demonstrated equal improvements in horizontal and vertical ridge dimensions and soft tissue thickness when compared to collagen membranes. Additionally, the membrane (p = 0.029) and membrane w/solid (p = 0.021) groups demonstrated statistically significant superior early wound healing compared to the collagen membrane group. Notably, the Bio-Filler groups demonstrated statistically significant reduction in fabrication/application time compared to the membrane groups. Conclusions: Within the limitations of this RCT, all e-PRF iterations performed comparably to collagen membranes in maintaining both hard and soft tissue ridge dimensions when combined with sticky bone, while also significantly improving soft tissue wound healing. Future RCTs with alternative grafting materials, direct wound-margin assessment, and evaluation of patient-reported outcomes are necessary to clarify the advantages of each membrane type. Full article

The article presents an approach to synthesizing artificial intelligence agents (AI agents), in particular, control and decision support systems for process operators in various industries. Such a system contains an identifier in the feedback loop that generates digital predictive associative search models of the Just-in-Time Learning (JITL) type. It is demonstrated that the system can simultaneously solve (outside the control loop) two additional tasks: online operator pre-training and mutual adaptation of the operator and the system based on real-world production data. Solving the latter task is crucial for teaching the operator and the system collaborative handling of abnormal situations. AI agents improve control efficiency through self-learning, personalized operator support, and intelligent interface. Stabilization of process variables and minimization of deviations from optimal conditions make it possible to operate process plants close to constraints with sustainable product qualities. Along with higher yield of target product(s), this reduces equipment wear and tear, utilities consumption and associated harmful emissions. This is the key merit of Model Predictive Control (MPC) systems, which justify their application. JITL-type models proposed in the article are more precise than conventional ones used in MPC; therefore, they enable the operation even closer to process constraints. Altogether, this further improves the reliability of production systems and contributes to their sustainable development. Full article

In this work, we first establish a new connection between adjoint Bernoulli’s polynomials and gamma function as a new sequence of linear positive operators denoted by ${\mathfrak{S}}_{r,\varsigma ,\lambda }(.;.)$. Further, convergence results for these sequences of operators, i.e., ${\mathfrak{S}}_{r,\varsigma ,\lambda }(.;.)$ are derived in various functional spaces with the aid of the Korovkin theorem, the Voronovskaja-type theorem, the first order of the modulus of continuity, the second order of the modulus of continuity, Peetre’s K-functional, the Lipschitz condition, etc. In the last section, we focus our research on the bivariate extension of these sequences of operators; their uniform rate of approximation and order of approximation are investigated in different functional spaces. Full article

Developing electrocatalysts that are efficient and durable for the oxygen evolution reaction (OER) is essential for improving the energy efficiency of alkaline water splitting. Spinel-type transition-metal oxides have emerged as promising non-noble alternatives; however, their catalytic performance is often limited by sluggish charge transport and insufficient utilization of active sites. Herein, we present a systematic comparative study of electrodeposited Co₃O₄ (CO-300) and Cu-substituted Co₂CuO₄ (CCO-300) nanosheet films directly grown on Ni foam. Structural, morphological, and spectroscopic analyses reveal that Cu²⁺ integration into Co-oxide spinel lattice modifies the local electronic environment and produces a more open and interconnected nanosheet architecture, thereby enhancing conductivity and increasing the density of accessible redox-active sites. As a result, the optimized CCO-300 exhibits superior catalytic performance at higher current densities, along with a smaller Tafel slope (44 mV dec^–1), a larger electrochemically active surface area (ECSA), and reduced charge-transfer resistance compared to CCO-300, indicating accelerated reaction kinetics and improved electron-ion transport. Furthermore, the multistep chronopotentiometry measurements and long-term stability tests over 100 h at current densities of 10 and 250 mA cm^–2 highlight the excellent operational stability of the CCO-300 catalyst. Full article

Within the scope of the study, the parameters of friction pendulum-type (FPS) isolators used or planned to be used in different projects were evaluated specifically for the project and its location. The evaluations were conducted within a performance-based seismic design framework using displacement, re-centering, and force-based operability criteria, as implemented through the Türkiye Building Earthquake Code (TBDY) 2018. The friction coefficient and radius of curvature were evaluated, along with the lower and upper limit specifications determined according to TBDY 2018. The planned control points were the period of the isolator system, the isolator re-centering control, and the ratio of the base shear force to the structure weight. Within the scope of the study, isolator groups with different axial load values and different spectra were evaluated. A dataset was prepared by using the parameters obtained from the re-centering, period, and shear force analyses to determine the conditions in which the isolator continued to operate and those in which conditions prevented its operation. Machine learning models were developed to identify FPS isolator configurations that do not satisfy the code-based operability criteria, based on isolator properties, spectral acceleration coefficients corresponding to different earthquake levels, mean dead and live loads, and the number of isolators. The resulting Bagging model predicted an isolator’s operability with a high degree of accuracy, reaching 96%. Full article

As global airspace operations grow increasingly complex, the risk of near-mid-air collisions (NMACs) poses a persistent and critical challenge to aviation safety. Traditional collision-avoidance systems, while effective in many scenarios, are limited by rule-based logic and reliance on transponder data, particularly in environments featuring diverse aircraft types, unmanned aerial systems (UAS), and evolving urban air mobility platforms. This paper introduces a novel, integrative machine learning framework designed to analyze NMAC incidents using the rich, contextual information contained within the NASA Aviation Safety Reporting System (ASRS) database. The methodology is structured around three pillars: (1) natural language processing (NLP) techniques are applied to extract latent topics and semantic features from pilot and crew incident narratives; (2) cluster analysis is conducted on both textual and structured incident features to empirically define distinct typologies of NMAC events; and (3) supervised machine learning models are developed to predict pilot decision outcomes (evasive action vs. no action) based on integrated data sources. The analysis reveals seven operationally coherent topics that reflect communication demands, pattern geometry, visibility challenges, airspace transitions, and advisory-driven interactions. A four-cluster solution further distinguishes incident contexts ranging from tower-directed approaches to general aviation pattern and cruise operations. The Random Forest model produces the strongest predictive performance, with topic-based indicators, miss distance, altitude, and operating rule emerging as influential features. The results show that narrative semantics provide measurable signals of coordination load and acquisition difficulty, and that integrating text with structured variables enhances the prediction of maneuvering decisions in NMAC situations. These findings highlight opportunities to strengthen radio practice, manage pattern spacing, improve mixed equipage awareness, and refine alerting in short-range airport area encounters. Full article

The global shift toward decarbonization and the rise of the digital economy are compelling manufacturing firms to undergo a complex twin transformation across their structures, operations, and value chains. Business model innovation (BMI), especially in digital servitization (DSBMI), emerges as a crucial catalyst in facilitating this change. However, there is a lack of systematic exploration of how DSBMI influences corporate decarbonization (CD). To fill this knowledge gap, a comprehensive qualitative meta-analysis of 27 case studies was conducted, identifying multiple DSBMI practices for CD employed by industrial firms. These practices can be summarized into three main types: efficiency DSBMI, novelty DSBMI, and convergent DSBMI. A system has at least two of these, while all three may coexist. Based on dynamic capabilities theory, this study also introduces six roles for the three types of DSBMI practices, which interact to help firms sense opportunities, seize them through BMI, and transform their operations and ecosystems—collectively enabling decarbonization through internal optimization (efficiency DSBMI), downstream innovation (novelty DSBMI), and value chain-wide cooperation (convergent DSBMI). The findings offer a comprehensive theoretical framework that guides companies to achieve economic benefits while advancing their CD goals through multi-level BMI strategies. Finally, the study discusses its limitations and proposes directions for future research. Full article

In photovoltaic (PV) power generation, the intermittency and uncertainty caused by meteorological factors pose challenges to grid operations. Accurate PV power prediction is crucial for optimizing power dispatching and balancing supply and demand. This paper proposes a PV power prediction model based on Density Peak Clustering Algorithm (DPCA)–Crested Porcupine Optimizer (CPO)–Random Forest (RF)–Gated Recurrent Unit (GRU)–Kolmogorov–Arnold Network (KAN). First, the DPCA is used to accurately classify weather conditions according to meteorological data such as solar radiation, temperature, and humidity. Then, the CPO algorithm is established to optimize the factor screening characteristic variables of the RF. Subsequently, a hybrid GRU model with a KAN layer is introduced for short-term PV power prediction. The Shapley Additive Explanation (SHAP) method values evaluating feature importance and the impact of causal features. Compared with other contrast models, the DPCA-CPO-RF-KAN-GRU model demonstrates better error reduction capabilities under three weather types, with an average fitting accuracy R² reaching 97%. SHAP analysis indicates that the combined average SHAP value of total solar radiation and direct solar radiation contributes more than 70%. Finally, the Kernel Density Estimation (KDE) is utilized to verify that the KAN-GRU model has high robustness in interval prediction, providing strong technical support for ensuring the stability of the power grid and precise decision-making in the electricity market. Full article

As China’s urbanization rate continues to rise, the scale of high-rise residences also grows, emerging as one of the main sources of building energy consumption and carbon emissions. It is therefore crucial to conduct energy-efficient design tailored to local climate and resource endowments during the schematic design phase. At the same time, consideration should also be given to its impact on economic efficiency and environmental comfort, so as to achieve synergistic optimization of energy, carbon emissions, and economic and environmental performance. This paper focuses on typical high-rise residences in three cities across China’s northwestern region, each with distinct climatic conditions and solar energy resources. The optimization objectives include building energy consumption intensity (BEI), useful daylight illuminance (UDI), life cycle carbon emissions (LCCO2), and life cycle cost (LCC). The optimization variables include 13 design parameters: building orientation, window–wall ratio, horizontal overhang sun visor length, bedroom width and depth, insulation layer thickness of the non-transparent building envelope, and window type. First, a parametric model of a high-rise residence was created on the Rhino–Grasshopper platform. Through LHS sample extraction, performance simulation, and calculation, a sample dataset was generated that included objective values and design parameter values. Secondly, an SVM prediction model was constructed based on the sample data, which was used as the fitness function of MOPSO to construct a multi-objective optimization model for high-rise residences in different cities. Through iterative operations, the Pareto optimal solution set was obtained, followed by an analysis of the optimization potential of objective performances and the sensitivity of design parameters across different cities. Furthermore, the TOPSIS multi-attribute decision-making method was adopted to screen optimal design patterns for high-rise residences that meet different requirements. After verifying the objective balance of the comprehensive optimal design patterns, the influence of climate differences on objective values and design parameter values was explored, and parametric models of the final design schemes were generated. The results indicate that differences in climatic conditions and solar energy resources can affect the optimal objective values and design variable settings for typical high-rise residences. This paper proposes a building optimization design framework that integrates parametric design, machine learning, and multi-objective optimization, and that explores the impact of climate differences on optimization results, providing a reference for determining design parameters for climate-adaptive high-rise residences. Full article

As core infrastructure of power transmission networks, power towers require high-precision 3D models, which are critical for intelligent inspection and digital twin applications of power transmission lines. Traditional reconstruction methods, such as LiDAR scanning and oblique photogrammetry, suffer from issues including high operational risks, low modeling efficiency, and loss of fine details. To address these limitations, this paper proposes a 3D Gaussian Splatting (3DGS)-based method for power tower 3D reconstruction to enhance reconstruction efficiency and detail preservation capability. First, a multi-view data acquisition scheme combining “unmanned aerial vehicle + oblique photogrammetry” was designed to capture RGB images acquired by Unmanned Aerial Vehicle (UAV) platforms, which are used as the primary input for 3D reconstruction. Second, a sparse point cloud was generated via Structure from Motion. Finally, based on 3DGS, Gaussian model initialization, differentiable rendering, and adaptive density control were performed to produce high-precision 3D models of power towers. Taking two typical power tower types as experimental subjects, comparisons were made with the oblique photogrammetry + ContextCapture method. Experimental results demonstrate that 3DGS not only achieves high model completeness (with the reconstructed model nearly indistinguishable from the original images) but also excels in preserving fine details such as angle steels and cables. Additionally, the final modeling time is reduced by over 70% compared to traditional oblique photogrammetry. 3DGS enables efficient and high-precision reconstruction of power tower 3D models, providing a reliable technical foundation for digital twin applications in power transmission lines. By significantly improving reconstruction efficiency and reducing operational costs, the proposed method supports sustainable power infrastructure inspection, asset lifecycle management, and energy-efficient digital twin applications. Full article

In modern power systems, the implementation of load-frequency control (LFC) must reconcile continuous-time plant dynamics with discrete-time digital controllers operating under coarsely sampled communications. This paper develops a sampled-data $H_{\infty }$ framework for PI-type secondary LFC that explicitly accounts for aperiodic sampling and reduced inertia due to high wind penetration. Using a two-sided looped Lyapunov functional and free-matrix inequalities, sampling-interval-dependent linear matrix inequalities (LMIs) are derived for stability, $H_{\infty }$ performance and an exponential decay rate (EDR). The synthesis returns PI gains and the admissible maximum sampling period (MASP) via simple bisection. Numerical examples based on one-area, two-area, and three-area power systems demonstrate that the proposed stability conditions allow larger admissible sampling periods compared with existing approaches, while preserving satisfactory dynamic behaviour under different operating scenarios. Full article

Pseudomonas aeruginosa is a key indicator of hygienic and operational deficiencies in swimming pools, particularly in tourist facilities with high and variable user loads. This study reports the results of a four-year regulatory surveillance program (2016–2019) assessing P. aeruginosa contamination in tourist swimming pools in Andalusia, Spain. The program involved 14 hotels and 58 unique installations. A total of 2053 water samples collected from different installation types (outdoor and indoor pools, whirlpools, and cold-plunge pools) were analyzed using standardized ISO methods within the framework of Spanish legislation, and prevalence comparisons were based on proportion tests. The overall prevalence of P. aeruginosa was 5.1%, with marked differences among installation types, reflecting both variation in contamination rates and unequal sampling intensity. Whirlpools consistently showed the highest contamination rates, whereas indoor pools and cold-plunge pools exhibited lower prevalence. No significant differences were observed between chlorine- and bromine-treated pools, and contaminated samples were detected across the full range of disinfectant concentrations, including values within regulatory limits. Temporal analysis revealed that apparent seasonal peaks were installation-dependent rather than reflecting a uniform seasonal trend. Winter detections were confined to indoor pools and whirlpools, which remain operational year-round, while outdoor pools and cold-plunge pools were underrepresented during the low season due to reduced sampling. A marked increase in prevalence was observed in 2019, driven mainly by summer months and high-risk installations; however, this rise was not directly associated with tourist volume and does not support causal inference. These findings highlight the importance of installation-specific and operational factors in shaping P. aeruginosa contamination patterns. The study underscores the need for targeted surveillance strategies focusing on high-risk installations and for cautious interpretation of seasonal patterns in datasets derived from routine regulatory monitoring. Full article

This paper presents the concepts of a vehicle pressure regulation ventilation system based on the results of absolute pressure measurements in land transport vehicles: passenger cars, buses and trains. Despite the fact that absolute pressure affects human well-being and health, this parameter is often overlooked in studies assessing thermal comfort. Absolute pressure measurements were taken during normal passenger transport operation. The studies were conducted for various terrain types: lowlands, highlands, and mountains. Absolute pressure fluctuations in land transport depended primarily on altitude, with the largest atmospheric pressure differences recorded in mountains and the smallest in lowlands. A pressure change of 8 hPa within a 24 h period constitutes an unfavorable mechanical stimulus for the human body and causes changes in the excitability of the nervous system. In all measurement series, absolute pressure fluctuations exceeded 8 hPa. Based on the results of absolute pressure measurements and altitude, a simplified model for predicting absolute pressure in transport vehicles was developed. To reduce absolute pressure fluctuations inside passenger land vehicle cabins, a ventilation scheme regulating pressure inside land vehicle cabins was proposed. Full article

Sliding bearings–rotor systems are widely present in rotating machinery structures. The dynamic behavior triggered by friction and rub-impact faults is a key factor restricting the safe and stable operation of a rotor system. Existing studies mainly focus on analyzing dynamic characteristics but rarely explore the degree of friction and rub-impact in the system. This paper takes the sliding bearing–double-disk rotor system with friction and rub-impact as the research model, and defines the concept of the rubbing ratio. It analyzes the influence of relevant structural parameters on the system. The results reveal that the system exhibits rich nonlinear dynamics. Specifically, increasing either the rotor–stator clearance or the lubricant viscosity can drive the system into a broader regime of chaotic motion, while simultaneously reducing the extent of the rub-impact contact region. As the stator stiffness increases from 10⁷ N/m to 9 × 10⁷ N/m, the number of chaotic windows in the bifurcation diagram increases from one to three, while the maximum rubbing force rises by approximately 58% and the rubbing ratio increases from 50% to 56%. The phenomenon of coexisting attractors in the system is also revealed and analyzed. The above research results help to reveal the motion laws of this type of rotor system and have certain guiding significance for parameter matching and optimization design of the system dynamics. Full article