Search Results (1,182)

Accurate and timely identification of cutter anomalies is crucial for ensuring the safety and efficiency of shield tunneling. To address the issues of poor timeliness and high costs associated with traditional periodic manual inspection methods, this study establishes a cutter anomaly identification model based on the BO-Light GBM algorithm, focusing on slightly weathered metamorphic rock formations. Six parameters closely related to the tunneling state were selected to construct the feature set, and one-class support vector machines (SVMs) were employed to remove anomalous samples. On this basis, a baseline Light GBM model with preset hyperparameters was developed, achieving a preliminary accuracy of 96.04%. Further hyperparameter tuning using Bayesian optimization boosted the overall accuracy of the final BO-Light GBM model to 99.40% while improving training efficiency by approximately 50% compared to exhaustive grid search. Interpretability analysis conducted via SHAP values revealed that chamber pressure, cutterhead rotation speed, total thrust, and cutterhead torque were the primary contributing features, with patterns consistent with actual tunneling conditions, confirming the accuracy of the model’s predictions. The research outcomes provide valuable theoretical guidance and technical support for similar engineering applications. Full article

Objective: Cardiovascular diseases have a high prevalence among the elderly, together with frailty syndrome, and both conditions negatively affect quality of life and limit patient autonomy. This study aimed to explore potential relationships between cardiovascular and metabolic parameters, renal function, and frailty domains to identify potential intervention targets. Methods: A cross-sectional study was conducted between January 2024 and April 2025 at the National Institute of Gerontology and Geriatrics “Ana Aslan”, including 359 patients aged over 40 years. Demographic, anthropometric, and clinical data were collected through interviews, medical records, and standardized assessments of frailty components (weakness, exhaustion, slow gait, balance impairment, reduced activity, cognitive decline, and weight loss), as well as cardiovascular diseases and comorbidities. Results: Most participants were aged 65–79 years. ROC curve identified triglycerides as a good indicator of both alcohol consumption (AUC = 0.631, p = 0.042) and smoking status (AUC = 0.676, p = 0.004), while HDL cholesterol showed an inverse association with smoking status (AUC = 0.356, p = 0.019). Reduced renal function was significantly associated with smoking status, balance, gait impairment, and reduced functional mobility. The Up and Go Test indicated a good discriminatory ability for renal function decline (AUC = 0.656, p < 0.001). Muscle strength, MMSE, and Tinetti scores showed inverse associations with renal function. Conclusions: Renal impairment appears to be a reliable indicator across multiple frailty domains, acting as an accelerator of frailty progression. Triglycerides reflect lifestyle-related factors, while the Up and Go Test may serve as a practical screening tool for renal dysfunction in frail older adults. These findings suggest the need to adapt traditional cardiovascular risk management to the frail geriatric population. Full article

The research problem concerning oils used for lubricating piston combustion engines is still very current and important. The proper selection of oil and its properties have a significant impact on engine reliability and durability, their efficiency, effective operating parameters, fuel consumption, environmental impact, and the proper operation of the turbocharger and exhaust system. The work concerned determining the effect of temperature and operating time on the dynamic viscosity of oils: mineral, semi-synthetic, and synthetic, used in compression-ignition engines (diesel engines). Dynamic viscosity tests were conducted for new oils, after a mileage of seven thousand kilometers, and after a mileage of fifteen thousand kilometers. The range of temperature measurement conditions used was from 0 to 50 °C and the shear transmission rate was 1000 s⁻¹. This range allows the oil to be preserved at low and medium temperatures, which are crucial for engine operation during start-up and short operating cycles. As the conducted studies showed, both temperature and operating time have a very large influence on the dynamic viscosity of oils. It was demonstrated that as the operating time of the oils in the engine increased, their dynamic viscosity decreased, and increasing the viscosity measurement temperature results in smaller absolute changes in it. Full article

Despite the groundbreaking impact of currently approved CAR T-cell therapies, substantial unmet clinical needs remain. This highlights the need for CAR T treatments that are easier to tune, combine, and program with logic rules, in oncology and autoimmunity. Modular CAR T cells use a two-part system: the CAR on the T cell binds an adaptor molecule (AM), and that adaptor binds the tumour-associated antigen (TAA). This design separates recognition of the target antigen and activation of the T cells, resulting in a cellular therapy concept with better control, flexibility, and safety compared to established direct-targeting CAR T-cell systems. The key advantage of the system is the adaptor molecule, often an antibody-based reagent, that targets the TAA. Adaptors can be swapped or combined without re-engineering the T cells, enabling straightforward multiplexing and logic-gated control. The CAR itself is designed to recognise the AM via a unique tag on the adaptor. Only when the CAR, AM, and antigen-positive target cell assemble correctly is T-cell effector function activated, leading to cancer cell lysis. This two-component system has several features that need to be considered when designing a modular CAR: First, the architecture of the CAR, i.e., how the binding domain and the backbone are designed, can influence tonic signalling and activation/exhaustion parameters. Second, the affinity of CAR–AM and AM–TAA will mostly define the engagement kinetics of the system. Third, the valency of the AM has an impact on exhaustion and non-specific activation of CAR T cells. And lastly, the architecture of the AM, especially the size, defines the pharmacokinetics and, consequently, the dosing scheme of the AM. The research conducted on direct-targeting CAR T cells have generated in-depth knowledge of the advantages and disadvantages of the technology in its current form, with remarkable clinical success in relapsed/refractory disease and long-term survival in otherwise difficult-to-treat patient populations. On the other hand, CAR T-cell therapy poses the risk of severe adverse events and antigen loss coupled with antigen-negative relapse which remains the main reason for failed therapies. Addressing these issues in the traditional setting of one CAR targeting one antigen will always be difficult due to the heterogeneous nature of most oncologic diseases, but the flexibility to change target antigens and the modulation of CAR T response by dosing the AM in a modular CAR system might be pivotal to mitigate these hurdles of direct CAR T cells. Since the first conception of modular CARs in 2012, there have been more than 30 constructs published, and some of those have been translated into phase I/II clinical trials with early signs of success, but whether these will progress into a late-stage clinical trial and gain regulatory approval remains to be seen. Full article

Liposomes started to be studied for drug delivery in 1970s, taking advantage of their ability to encapsulate hydrophilic and hydrophobic drugs using biodegradable and biocompatible molecules. Nowadays, they remain one of the most promising strategies for drug delivery not only in cancer treatment but also in gene therapies and vaccines. The design and development of liposomal systems have evolved significantly over the past decades, moving from conventional formulations to advanced, stimulus-responsive, and multifunctional nanocarriers. Analogous to the myth of the Trojan Horse, liposomes must mislead the host immune system to reach the interior of cancer cells in order to deliver the therapeutic payload. There are many barriers that liposomes have to overcome to circulate through the bloodstream and specifically target cancer cells without damaging other tissues. Crucial parameters such as lipid composition, particle size, zeta potential, and PEGylation have been systematically optimized to enhance pharmacokinetics and biodistribution and to improve delivery efficiency. Furthermore, conjugation with antibodies, peptides, or small molecules has enabled active targeting, while stimuli such as pH, temperature, and enzymatic activity have been exploited for controlled drug release within the tumor microenvironment. Such innovations have laid the groundwork for translating liposomal formulations from the bench to clinical applications. In this paper, we evaluate the physicochemical features of liposomal design that underpin their suitability and efficacy for anticancer drug delivery. We aimed to focus on two main aspects: conducting an exhaustive review of the physicochemical parameters of liposomal drugs that have already been approved by regulatory agencies, while maintaining a pedagogical approach when explaining the key design parameters for the optimal design of liposomes in oncology in detail. Full article

To address the limitations of traditional image encryption algorithms in key optimization and encryption quality assessment, in this paper we propose a framework for image encryption based on surrogate-assisted differential evolution. First, we construct a novel fitness function based on pixel correlation, which quantitatively evaluates and optimizes encryption quality by minimizing the pixel correlation coefficient. Second, we propose an adaptive hierarchical surrogate-assisted differential evolution algorithm (HSADE-IQUA), which combines global and local phases. In the global optimization phase, HSADE-IQUA significantly improves the convergence speed and solution quality in constrained optimization through adaptive parameter control. In the local optimization phase, the population size is dynamically adjusted using the exponential moving average (EMA), achieving a balance between exploration and exploitation. The performance of HSADE-IQUA has been validated on a commonly used expensive optimization benchmark suite, achieving excellent experimental results. Third, a Chen hyperchaotic-DNA coding fusion encryption framework optimized by HSADE-IQUA (HSADE-IQUA-DNA) was constructed and tested on standard computer vision images, labeled datasets, and remote sensing images, proving that HSADE-IQUA-DNA can significantly reduce pixel correlation, effectively resist exhaustive attacks, noise attacks, and shearing attacks, and accurately recover the original image. Compared with traditional chaotic image encryption, HSADE-IQUA-DNA not only has a bottleneck in parameter optimization but also alleviates the single-key issue, further improving encryption security. Full article

The objective of this study is to investigate the effects of Mn₂O₃ nanoparticle additives on the performance and emission characteristics of biodiesel fuels produced from vegetable- and waste-based oils. Biodiesel fuels were synthesized via the transesterification process, after which Mn₂O₃ nanoparticles were blended in different concentrations (50, 75, and 100 ppm). The prepared fuels were tested in a single-cylinder diesel engine operating under constant speed and variable load conditions. Engine performance parameters such as specific fuel consumption (SFC) and thermal efficiency, along with emission indicators including CO, HC, NOx, smoke opacity, and exhaust gas temperature, were systematically analyzed. Additionally, the experimental findings were modeled and validated using the machine learning-based linear regression method. The addition of Mn₂O₃ nanoparticles significantly improved combustion and emission performance. Among all samples, the COB10+ 100 ppm Mn₂O₃ fuel exhibited the best overall performance, achieving a 37.50% reduction in CO, 38.8% reduction in HC, and 33.84% reduction in smoke (soot) emissions compared to conventional diesel. This fuel also demonstrated an increase in thermal efficiency comparable to that of diesel. The improvement in thermal efficiency was attributed to enhanced the in-cylinder temperature, reduced ignition delay, and shorter combustion duration. Furthermore, the use of waste-derived vegetable oils contributed to lower production costs and a reduction in environmental impact. The linear regression model yielded an optimum prediction accuracy with a mean squared error of 5.86 × 10⁻⁶ for CO emission data. These findings indicate that Mn₂O₃ nanoparticles can effectively enhance the performance and sustainability of biodiesel fuels while maintaining economic and ecological advantages. Full article

Background: Immunological monitoring in multiple sclerosis (MS) patients treated with disease-modifying drugs may help predict infectious complications and guide treatment. The main objective of this study was to evaluate whether anti-CD20 treatments in MS patients induce immunodeficiency and whether certain immunological parameters can predict the risk of infection and response to treatment. Methods: This retrospective, observational, single-centre study included MS patients who started treatment with ocrelizumab or rituximab and received follow-up in the Neuroimmunology Unit of our centre between January 2017 and January 2023. The study was conducted in collaboration with the Immunology Department of this hospital. Results: Fifty-five patients were included, with a mean age of 47 years and a follow-up period of 24 months. Analyses of lymphocyte subpopulations (T, B, NK) and immunoglobulin levels (IgG, IgA, IgM) were performed before treatment and at 6-, 12- and 24-month follow-ups. In addition, we carried out an exhaustive study of B cells in the baseline analysis. Sixty-four percent of patients presented infections, mostly due to COVID-19. Three patients developed cryptogenic organising pneumonia. IgG hypogammaglobulinemia was the main risk factor for developing infections. Patients with infections had fewer mature memory B cells and a lower percentage of NK cells. Furthermore, a lower proportion of naïve and mature memory B cells was associated with inflammatory activity and disease progression, respectively. The absence of CD20 depletion during follow-up was associated with clinical worsening. Conclusions: Baseline immunophenotype and immunological monitoring can help predict the risk of infections and the efficacy of anti-CD20 therapy in MS patients. Full article

Driven by the urgent reduction in industrial energy consumption and nitrogen oxide (NO_x) emissions, numerical simulation becomes a significant tool to understand the internal working process and optimize the structure of the combustion chamber in coke oven. However, conventional numerical simulation is computationally expensive and impractical for real-time monitoring or multi-parameter optimization. To address this challenge, this study proposes a novel parameter fusion convolutional network (PFCN) to rapidly reconstruct the spatial temperature distribution in the combustion chamber of a coke oven. The key innovation of PFCN is its dual-stream encoding mechanism, which processes structural parameters (1 × 5 vector) and spatial coordinates (25 × 200 matrix) separately via dedicated encoders, followed by a cross-modal fusion to effectively integrate these heterogeneous inputs. Furthermore, a support vector machine (SVM) is coupled downstream of the PFCN to estimate the exhaust NO_x emissions based on the predicted physical information. This coupled PFCN–SVM framework allows universal applicability across different combustion chamber configurations. Based on this framework, parametric influence analysis and co-optimization of five key structural parameters are conducted for a three-stage air-supply coke oven. The results reveal that both the air staging ratio and staging height significantly affect combustion performance. Compared to the basecase, the optimized design simultaneously improves temperature homogeneity by 15.2% and reduces NO_x emissions by 8%, with negligible computational cost. This integrated data-driven approach demonstrates considerable potential for combustion chamber optimization, transient process predictions, multi-physics coupling analyses, and online control implementations. Full article

Despite the obvious shift in daily commuting towards electromobility, internal combustion engines (ICEs) still dominate the market, particularly in the transport sector. Their main drawback—cold-start emissions—has driven the development of active control strategies beyond passive exhaust optimizations. An electrically heated catalytic converter (EHC) helps the catalytic converter reach the light-off temperature more quickly through active control; however, it places additional demands on the already strained onboard electrical power distribution network. This paper presents a case study comparing two power supply and control configurations for managing the temperature of the EHC: (i) a smart-switch-based approach using bang-bang control, and (ii) a DC/DC converter with a proportional–integral–derivative (PID) controller. To define key target requirements for a dedicated DC/DC converter suitable for real-world conditions, measurement data such as temperature and electrical power demand were gathered through preliminary pollutant emissions tests performed in a laboratory environment using a programmable bench power supply. For the selected test procedure, engine cold-start emissions using various heater power supply scenarios were reduced by a factor of 6 for Total Hydrocarbons (THC) and by a factor of 5 for Carbon Monoxide (CO). Based on a comparative analysis of power supply parameters, a custom four-leg interleaved Buck converter was developed to meet the target power requirement and to specifically reduce voltage overstress caused by parasitic inductances in the onboard distribution network during rapid load current transients. The efficiency of the proposed DC/DC converter reached 95.8%. Unlike a bang-bang-controlled smart switch, the use of the DC/DC converter reduces both electrical and thermal stress on the vehicle’s cable harness. Full article

This study presents the development and validation of a low-cost, open-source sensor system for cyclists that automatically detects vehicle overtaking events while simultaneously monitoring air quality. The system integrates multiple ultrasonic sensors for autonomous overtaking detection and distance measurement with environmental sensors that record particulate matter, temperature, humidity, and GPS position. By combining these data streams, the system enables the analysis of correlations between traffic interactions and variations in particulate matter exposure under real-world cycling conditions. Test rides conducted in urban environments demonstrated that the system reliably identifies overtaking maneuvers and records corresponding environmental parameters. Elevated concentrations of particulate matter were observed during close vehicle passes and at traffic lights, highlighting moments of increased exposure to exhaust emissions. The automated detection mechanism eliminates the need for manual activation, ensuring complete and unbiased data collection. The modular design and energy-efficient operation of the system allow for flexible deployment in both mobile and stationary configurations. With its ability to objectively capture and relate safety and environmental data, the presented platform provides a foundation for large-scale field studies aimed at improving cyclist safety and understanding pollution exposure in urban traffic. Full article

An optical camera-based integrated sensing and communication (OC-ISAC) system model is proposed to address the intrinsic requirements of vehicular-to-everything (V2X) applications in complex outdoor environments. The model enables the coexistence and potential mutual enhancement of environmental sensing and data transmission within the visible light spectrum. It characterizes the OC-ISAC channel by modeling how light, either actively emitted for communication or passively reflected from the environment, originating from any voxel in three-dimensional space, propagates to the image sensor and contributes to the observed pixel values. This framework is leveraged to systematically analyze the impact of camera imaging parameters, particularly exposure time, on the joint performance of sensing and communication. To address the resulting trade-off, we develop an analytically tractable suboptimal algorithm that determines a near-optimal exposure time in closed form. Compared with the exhaustive numerical search for the global optimum, the suboptimal algorithm reduces computational complexity from $O\left(N\right)$ to $O\left(1\right)$, while introducing only a modest average normalized deviation of 5.71%. Both theoretical analysis and experimental results confirm that, in high-speed communication or mobile sensing scenarios, careful selection of exposure time and explicit compensation for the camera’s low-pass filtering effect in receiver design are essential to achieving optimal dual-functional performance. Full article

This article describes the environmentally friendly evolution of Formula 1 powertrains and fuels since the start of the season. It is the only study of its kind to encompass a comprehensive analysis of various solutions designed to make Formula 1 powertrains more environmentally friendly. Despite the difficulties in accessing descriptions of many of the advanced, unique proprietary technologies used in Formula 1, many of them have been analyzed to whatever extent was possible. A significant portion of the article was devoted to discussing the new regulations that will apply to powertrains between 2026 and 2030. In particular, it addresses the increased thermal efficiency of Formula 1 combustion engines, the introduction of sustainable synthetic fuels, and the further reduction in harmful exhaust emissions, including CO₂, from future Formula 1 powertrains. A wide, tabular comparison of the parameters, requirements, and operational characteristics of powertrains compliant with the regulations between 2014 and 2025 and between 2026 and 2030 was provided. Full article

Utilizing bio-based nano-fuel additives presents a promising path towards improved engine performance and reduced emissions. The response surface method is used in this investigation to predict and optimize the performance parameters and exhaust emissions of a single-cylinder diesel engine. The engine operates with B20 at 2900, 3100, and 3300 rpm and nanobiochar concentrations of 30, 60, and 90 ppm. The results showed a declining trend in all of the engine-out emissions when the nanobiochar additive was used. Based on the optimization results, an engine speed of 3108 rpm and a nanobiochar ratio of 90 ppm were found to be the optimal conditions within the defined range of the input parameters. At this point, the engine power, torque, BSFC, and emissions of NOx, CO, and UHC were measured at 5.78 kW, 17.96 Nm, and 309 g/kW·h, 104.9 ppm, 1.25 (%Vol.), and 104.9 ppm, respectively. These values represent significant improvements compared to the baseline B20 fuel. The modeling results showed that RSM could effectively predict engine performance and emissions when running on a green-based fuel like B20, with a 90 ppm nanobiochar additive. Full article

Heat exchangers are key components of advanced waste-heat recovery energy systems that operate on low-boiling working fluids. The efficiency and cost of power plants depend directly on their design characteristics. Increasing the heat-transfer surface area, on the one hand, reduces temperature differences and improves cycle efficiency, but on the other hand increases material consumption and equipment cost. For given fluid parameters and heat-exchanger duty, the required surface area is determined by the type of heat exchanger, the choice of device, the shape of the enhanced heating surface, and the methods of heat-transfer intensification. This paper provides a comprehensive analysis of the current state of heat exchangers for low-boiling working fluids and discusses their areas of application. A methodology has been developed for optimizing the main design characteristics of heat exchangers, including a search algorithm aimed at minimizing the total costs of equipment production and operation. Using this methodology, computational studies were carried out for advanced energy cycles with low-boiling working fluids (organic Rankine cycles, recompression supercritical CO₂ (s-CO₂) Brayton cycle). The relationships of weight, size, and cost parameters of heat exchangers for waste-heat recovery cycles using low-boiling fluids to exhaust-gas temperatures and external economic factors were obtained. Optimal channel geometric parameters and heat-exchanger design types were identified that ensure minimal material consumption and cost while delivering the required heat-transfer performance. Recommendations are formulated for selecting and designing heat exchangers for waste-heat recovery power plants using low-boiling working fluids, the implementation of which will improve their efficiency and reduce costs. Full article