Search Results (1,284)

Precision medicine in prostate cancer (PCa) is increasingly best understood as a continuum linking risk-adapted detection, multimodal diagnosis and phenotyping, and implementation-ready decision pathways. Contemporary clinical guidelines emphasize structured diagnostic strategies, appropriate use of advanced imaging, and selective deployment of biomarkers when results can alter management. Upstream risk enrichment using polygenic risk scores and multivariable prediction models may improve the yield of clinically significant disease while mitigating harms related to overdiagnosis. At the point of suspicion, magnetic resonance imaging-first pathways and reflex biomarker testing provide practical tools to reduce unnecessary biopsy while maintaining safeguards for the detection of clinically important disease. Beyond diagnosis, prostate-specific membrane antigen positron emission tomography refines disease-state phenotyping in initial staging, biochemical recurrence, and limited-burden presentations, while standardized acquisition and reporting improve reproducibility and multidisciplinary communication. Germline and tumor-based molecular profiling should be operationalized as a longitudinal care process with clear consent, turnaround targets, and test-to-action rules that define what each result enables at specific decision nodes. Finally, longitudinal monitoring approaches, including liquid biopsy and artificial intelligence-enabled pathology, are evolving rapidly and require transparent reporting and rigorous risk-of-bias appraisal before broad clinical adoption. This narrative review synthesizes key evidence across the precision continuum and outlines a decision-node-based, test-to-action framework for maximizing clinical benefit, maintaining quality, and ensuring equitable access. Full article

Air pollution is primarily caused by human activities such as industrial emissions, road traffic, waste incineration, and fossil fuel power plants. Pollution refers to the presence of harmful substances in the air, such as nitrogen dioxide (NO₂), sulfur dioxide (SO₂), ozone (O₃), carbon monoxide (CO), and other environmental pollutants. Some pollutants pose health risks even at low doses. Given the critical importance of air quality, monitoring air pollution has become an urgent and essential subject. Air quality monitoring relies on accurate data, so changeable environments and sensor issues make using interval diagnostic techniques for addressing uncertainty in systems interesting. In this article, we focus on three key aspects to achieve precise and efficient results: (1) the use of an accurate fault detection method that accounts for data uncertainty while maintaining model symmetry, (2) the implementation of a reliable detection index invariant to symmetric sensor behaviors, and (3) the combination of both to improve fault localization accuracy. This paper presented a fault detection and localization framework designed for uncertain and nonlinear monitoring environments. A novel fault-sensitive detection index was developed and integrated into an elimination-based localization strategy within a reduced-rank interval kernel PCA (RR-IKPCA) model. By exploiting information contained in modified residual subspaces and explicitly accounting for measurement uncertainty, the proposed approach enhances fault sensitivity while preserving robust localization capability, as validated on the AIRLOR air quality monitoring network. Full article

The abrupt failure of shear-deficient RC beams may lead to harmful consequences under dynamic loading. The use of Carbon Fiber Reinforced Polymers (CFRP) aims to convert this brittle fracture into a ductile one. However, the complexity of the multiple damage mechanisms makes it difficult to assess their condition using conventional testing methods. In this study, the damage evolution of a shear-critical reference beam and its CFRP-strengthened counterpart was monitored using the acoustic emission (AE) technique. After correcting attenuated AE amplitudes, damage analysis was performed using the Shannon entropy approach based on true source amplitudes. The entropy analysis performed with these corrected data clearly revealed the shear failure in the reference beam through abrupt drops in entropy, indicating damage homogenization. In contrast, the entropy remaining high and dynamically varying over a much longer deflection range in the CFRP-strengthened beam demonstrated that CFRP distributes damage over a wider region and that different damage mechanisms, such as debonding and fiber breakage, in addition to concrete cracking, were simultaneously active. Full article

One of the most critical challenges in maritime transport decarbonization, as part of the EU greenhouse gas (GHG) neutrality strategy, is the reduction in GHG and harmful emissions from the energy systems of existing vessels. Furthermore, the potential for implementing decarbonization technologies in operating vessels remains significantly more limited compared to newly constructed ships. Selecting appropriate decarbonization measures requires a comprehensive evaluation of technological feasibility, economic viability, and environmental performance, in accordance with the regulatory frameworks established by the IMO and the EU. A major limitation in such decision-making processes is ensuring the representativeness and reliability of expert judgments. In order to improve the reliability of results by expanding and structuring the information base, this study proposes and implements a method based on the integration of SWOT analysis with multi-criteria decision-making (MCDM) methods. The objective of this study was to examine the methodological aspects of testing the integrated application of comprehensive analysis and ranking methods for decarbonization technologies as applied to a prototype oil tanker. Based on the SWOT analysis method, technological solutions that are available for practical application were identified for the medium-term decarbonization period considered in the study, up to 2030–2035. Subsequent rating based on several applied multi-criteria (MCDM) analysis methods (TOPSIS, COPRAS, SAW) allowed us to examine the range, stability and sensitivity of the obtained solutions in relation to the methods themselves and scenarios with variations in the weighting factors of the evaluation criteria. The complete match of the ratings obtained using the TOPSIS and COPRAS methods confirms the stability of the multi-criteria decision-making process (priority-compromise order): CCS, kite, air lubrication, Flettner rotor. The performed sensitivity analysis showed that the technology rankings remain relatively stable when the weighting factor for the CO₂ reduction criterion varies within a range of approximately ±10%, while larger deviations result in an increasing difference between all three MCDM methods. For the TOPSIS method, the change limits for the critical values of the threshold indicators were ±20%, the COPRAS method showed intermediate results, and changing the weighting coefficients within a ±20% range did not alter the selection of the best technology. The results obtained allow for a positive assessment of the effectiveness of the proposed integrated methodology when applied as an alternative in the initial stage of ranking decarbonization methods for in-service ships. Full article

Logistics, as a vital component of economic growth, relies on fossil fuel burning, which accelerates carbon emissions into the atmosphere and harms the environment. Logistics, encompassing transportation, warehousing, and supply chain operations, is among the fastest-growing sources of carbon emissions globally, contributing significantly to GHG emissions. Climate change causes forced migration, extinctions, natural disasters, and health problems that disrupt the ecosystem’s dynamics. This work aims to critically examine the current palliative measures to limit the negative impact on global climate change while also methodically examining various aspects of the human world affected by the growing rate of carbon emissions globally, as the world turns to low-carbon economics as a powerful and inventive way to mitigate the climate crisis from carbon emissions. Under themes such as climate impacts, ecological disruption, socioeconomic ramifications, health implications, and mitigation techniques, a broad range of integrated publications focused on logistics and climate-related concerns were examined. The final section of the document emphasises the significance of zero emissions and outlines the regulations set by the Intergovernmental Panel on Climate Change (IPCC). It also makes a strong case for investing in sustainable and cutting-edge technologies in order to quickly achieve favourable global climate conditions. Full article

The primary factor in the formation of polycyclic aromatic hydrocarbons (PAHs) in diesel engines, which pose environmental and health risks, is the chemical composition of the diesel fuel. Higher-carbon alcohols have emerged as promising oxygenated blending components for compression ignition engines due to their potential to improve combustion and reduce harmful emissions. However, limited data exist regarding their impact on PAH formation and toxicity characteristics. This study investigates the effects of 15% (v/v) n-propanol, n-butanol, and n-pentanol blends with petroleum diesel (D) and waste cooking oil biodiesel (B) on total PAH emissions, PAH dispersion, and toxicity in a diesel engine under steady-state conditions. Total PAH concentrations and individual species distributions were quantified, and toxicity was evaluated using toxicity equivalency factor (TEF) methodology. Results indicate that the addition of higher alcohols significantly reduces total PAH emissions compared to the respective base fuels. A marked decrease in high-molecular-weight (4–6 ring) PAH compounds was observed, suggesting suppression of heavy PAH formation pathways. Toxicity-weighted PAH emissions also decreased with alcohol blending. Furthermore, total PAH concentrations for all tested blends remained below the Occupational Safety and Health Administration (OSHA) permissible exposure limit (PEL = 0.2 mg/m³) under the examined operating conditions. These findings demonstrate that 15% higher alcohol blends are effective in mitigating PAH emissions without adverse environmental health implications. Full article

This study examines the impact of Japan’s energy policy on sustainable economic growth from 2005 to 2025. The analysis involved adding carbon externalities to a DSGE model to evaluate the effects of a carbon tax, a renewable energy subsidy, and energy-efficiency improvements. All policies are uniformly assessed within a unified, dynamic, micro-founded macroeconomic model that links energy use and emissions. According to the empirical findings, the carbon tax is the most effective policy for reducing emissions (6.8%). Nonetheless, this improvement incurs economic costs, as output and welfare fall by 0.85% and 0.35%, respectively. Renewable energy subsidies have no unbalanced effect. They reduce emissions by 3.2%. However, they support output by 0.42% and welfare by 0.28%. It occurs through substitution and investment effects. The most prominent outcomes of energy-efficient design are 1.2% increase in output, 0.75% increase in welfare, and 2.5% decrease in emissions. It also indicates the degree of decoupling. The carbon price is vital to achieving decarbonization; however, policies that support improved energy efficiency and clean energy could be accompanied by differential pricing. Findings indicate that a coordinated policy mix would be most effective for Japan in meeting emissions targets without harming growth or improving welfare. Full article

Water hyacinth (Pontederia crassipes Mart.) is native to the Amazon basin. It has spread to freshwater ecosystems in over 80 countries in tropical, subtropical, and warm temperate regions. Due to its invasive nature, water hyacinth is listed among the world’s 100 worst invasive alien species. Infestations of water hyacinth affect the abiotic components of these ecosystems, including water evaporation, flow, and quality; oxygen and nitrogen levels; sunlight transmission; and greenhouse gases. These changes reduce the abundance and diversity of primary producers in the food web, including phytoplankton and aquatic plants. Consequently, these alterations affect consumers in the food web, including zooplankton, invertebrates, fish, and birds. A negative correlation has often been observed between water hyacinth infestations and the abundance and diversity of these organisms, particularly native species. However, the abundance of some introduced species among these consumers has increased due to water hyacinth infestations. These changes alter the structure and function of natural ecosystems compared to what they were before infestations occurred. Infestations also negatively impact daily human activities and livelihoods, harming local communities and increasing disease transmission. Global warming and the eutrophication of freshwater ecosystems allow water hyacinth to spread into additional non-native areas in high latitudes, thereby increasing the threat it poses. Water hyacinth also contributes to global warming by increasing methane emissions. Over the past century, management strategies have shifted toward restoring the structure and function of ecosystems by progressively integrating various sectors. The infestation of water hyacinth is a complicated, site-specific process influenced by time, climate, existing biotic and abiotic factors, and ecosystem resilience. Therefore, long-term monitoring of environmental outcomes is essential for developing sustainable, site-specific strategies. Robust evaluation systems are necessary to track the efficacy of interventions and to understand the broader ecological ramifications of management strategies. Water hyacinth is still sold in some local markets for ornamental purposes. Raising public awareness of its invasive characteristics is necessary. Full article

The growing demand for concrete poses a significant environmental challenge, but alkali-activated high-performance concrete (AA-HPC) offers a more sustainable alternative by potentially reducing carbon emissions and ecological harm. This study explores the latest developments in machine learning (ML) applications aimed at predicting the compressive strength of AA-HPC, with a focus on minimizing experimental expenses, construction duration, and environmental impact. Among nine evaluated ML models, the combination of extreme gradient boosting (XGBoost) with the African vultures optimization algorithm (AVOA) emerged as the most effective. AVOA proved highly efficient in optimizing model parameters, achieving the lowest root mean square error (RMSE) during hyperparameter tuning. On the training dataset, XGB-AVOA reached an R² of 0.994 and an RMSE of 2.368, while on the testing dataset, it maintained superior performance with an R² of 0.975 and an RMSE of 5.664. These findings highlight AVOA’s strength in fine-tuning XGBoost compared to alternative optimizers such as grey wolf optimizer (GWO), whale optimization algorithm (WOA), social spider optimization (SSO), and gorilla troops optimizer (GTO). To support practical implementation, a graphical user interface (GUI) has also been developed, allowing researchers to efficiently utilize the XGB-AVOA model for accurate, cost-effective, and time-saving predictions in laboratory environments. Full article

Neuroinflammation is increasingly recognized as a key modulator of therapeutic response and adverse events in Alzheimer’s disease (AD), especially during anti-amyloid-β (Aβ) monoclonal antibody (Aβ-mAb) treatment. We applied longitudinal translocator protein (TSPO) positron emission tomography (PET) to evaluate TSPO-associated neuroinflammatory responses to chronic Aβ-mAb therapy and their modulation by the peroxisome proliferator-activated receptor γ (PPARγ) agonist pioglitazone. App^NL-G-F knock-in mice underwent TSPO-PET and Aβ-PET imaging at 5, 7.5, and 10 months of age across four treatment arms: placebo, Aβ-mAb, pioglitazone, and combination therapy. TSPO-PET detected early and progressive neuroinflammatory responses to Aβ-mAb that appeared lower with pioglitazone co-treatment. Both mono- and combination therapy were associated with altered temporal and spatial dynamics of the TSPO-PET signal. In addition, we applied a previously validated microglia desynchronization index based on TSPO-PET connectivity, which captured individual variation in regional TSPO-PET organization and correlated with cognitive performance. Together, TSPO-PET and its regional synchronicity can quantify longitudinal, region-specific treatment effects, which may help differentiate harmful from adaptive neuroinflammatory responses. These findings highlight the potential of TSPO-PET as a stratification biomarker to optimize therapeutic interventions. TSPO-PET therefore enables in vivo tracking of treatment-associated neuroinflammatory responses during anti-Aβ immunotherapy and provides a non-invasive framework for evaluating combination strategies targeting amyloid pathology and immune regulation in AD. Full article

The uncontrolled discharge of synthetic azo dyes such as methyl orange (MO) into water bodies has become a major environmental concern because of their strong chemical stability, limited biodegradability, and harmful effects on aquatic ecosystems. In this study, MgNiFe layered double hydroxides (LDHs) were synthesized through a co-precipitation route using a molar ratio of (Mg + Ni)/Fe equal to 3, and their adsorption ability toward MO in aqueous media was investigated. The prepared materials were characterized by X-ray diffraction (XRD), scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy (SEM–EDX), Fourier-transform infrared spectroscopy (FTIR), and inductively coupled plasma atomic emission spectroscopy (ICP-AES). The characterization results revealed the successful formation of a hydrotalcite-like layered structure with good crystallinity, a relatively uniform distribution of metallic species, and the incorporation of carbonate anions within the interlayer galleries. In addition, the adsorption performance was evaluated by studying the effects of several operational factors, namely adsorbent dosage, initial pH, and contact time. To better understand the interaction between these parameters and identify the optimum operating conditions, a Box–Behnken response surface design was applied. The results indicate solution pH is the most influential parameter in the adsorption process. Under optimized conditions, a maximum removal efficiency of 86.86% was obtained, corresponding to an adsorption capacity of approximately ~86.86 mg·g⁻¹ (based on 100 mL solution volume). The enhanced adsorption performance may be attributed to the combined effect of the multivalent metal cations (Mg²⁺, Ni²⁺, and Fe³⁺), likely increases the surface positive charge density of the LDH and promotes interactions with anionic dye molecules. These interactions are suggested to involve electrostatic attraction and possible surface adsorption processes. However, in the absence of post-adsorption characterization, the exact adsorption mechanism remains hypothetical. Overall, the results demonstrate the promising potential of MgNiFe LDHs as efficient adsorbent materials for the treatment of dye-contaminated wastewater. Full article

The increasing emission of harmful gases into the atmosphere represents a major environmental challenge, driving the need for efficient air purification materials. Poly(methyl methacrylate) (PMMA) has emerged as a promising candidate due to its favorable physicochemical properties and adsorption potential. In this study, the interactions between PMMA and selected sulfur-containing pollutants (CH₃SH, COS, CS₂, H₂S, and SO₂) were systematically investigated using a multiscale computational approach. Initial structural exploration was performed using extended tight-binding (xTB) methods, followed by refinement at the density functional theory (DFT) level, while molecular dynamics (MD) simulations were employed to capture the dynamic behavior of the systems. The results suggest that all investigated gases exhibit attractive interactions with PMMA, with interaction strength strongly dependent on molecular polarity and electronic structure. Among the studied systems, SO₂ shows the strongest binding, while CS₂ exhibits the weakest interaction. Energy decomposition based on symmetry-adapted perturbation theory (SAPT) and electronic structure analyses suggest that electrostatic and donor–acceptor interactions play a dominant role for strongly interacting systems, whereas weaker interactions are primarily governed by dispersion forces. Full article

Biogas from municipal solid waste is a promising pathway for renewable energy production while mitigating environmental pollution and public health risks. In this study, biogas emissions from a sanitary landfill in Maringá, southern Brazil, were evaluated using three models (IPCC, LandGEM, and CETESB tool) to estimate methane generation and energy recovery potential. Experimental analysis revealed methane concentrations from 51.10 ± 8.89% to 57.06 ± 1.19% across collection drains, indicating favorable conditions for energy utilization. Methane generation was estimated under different scenarios, reaching up to 1.30 × 10⁴ tonnes of CH₄, with peak production projected over 25–26 years depending on the model. Beyond energetic relevance, controlled biogas recovery can substantially reduce methane emissions, a key precursor of tropospheric ozone, and limit hazardous trace gas release, improving air quality and reducing population exposure to harmful pollutants. These findings are particularly relevant in developing countries, where insufficient waste management infrastructure leads to uncontrolled emissions, posing elevated environmental and health risks. This study supports integrating landfill biogas recovery into waste management and climate strategies, contributing to Sustainable Development Goals related to clean energy (SDG 7), climate action (SDG 13), and health (SDG 3), demonstrating it as a scalable solution for sustainable urban development. Full article

Replacing traditional hydrocarbon fuel in aircraft turbine engines with hydrogen fuel contributes, in line with current trends, to reducing harmful carbon dioxide emissions and enabling increased flight altitude. Given the high research costs of full-scale turbine engines, research on miniature turbojet engines, due to their availability and relatively low modification costs, can play a significant role in better understanding and developing concepts for adapting existing hydrocarbon-based fuel systems to hydrogen fuel. This article presents the results of a comprehensive numerical analysis of the hydrogen combustion process—illustrating changes in its location and structure—for multiple variants of design changes to the combustion chamber of the miniature GTM-140 turbojet engine, primarily involving appropriate shaping of airflows through the holes in the glow tube and the location of the hydrogen injection point. Based on this analysis, a modernized combustion chamber geometry was proposed, which should ensure a stable hydrogen combustion process that is safe for the thermal resistance of the structural material—and structurally comparable to the baseline Jet-A1 hydrocarbon fuel combustion process. The obtained results can give ground for the construction and experimental testing of a hydrogen-powered turbine engine. Full article

The catalytic modification of combustion processes using nanoparticle additives has emerged as a promising strategy to improve fuel oxidation and reduce pollutant formation in compression ignition (CI) engines. In this study, the catalytic effects of nano-sized boron oxide (B₂O₃) on biodiesel combustion were systematically investigated. Jojoba oil, a non-edible and drought-resistant feedstock, was transesterified to produce second-generation biodiesel and blended with diesel fuel. Among the tested blends, J10 (10% biodiesel and 90% diesel) was selected as the base fuel blend due to its favorable combustion and emission characteristics. To explore catalytic enhancement mechanisms, B₂O₃ nanoparticles were introduced at concentrations of 25, 50, and 75 ppm. The high surface area and oxygen buffering capacity of B₂O₃ nanoparticles are expected to enhance oxidation reactions and promote radical formation during combustion. This catalytic effect contributes to improved combustion efficiency, as evidenced by a significant reduction in incomplete combustion products. Compared with diesel fuel (D100), HC emissions were reduced by up to 53.34%, while CO emissions decreased by 24.42–41.98% depending on the operating conditions and fuel blends. In addition, a noticeable improvement in combustion quality was reflected in the brake thermal efficiency (BTE), where variations of up to 11.61% were observed across different fuel blends. Response Surface Methodology (RSM) was employed to quantify the interaction between nanoparticle concentration and engine load and to identify optimal catalytic operating conditions. The optimal parameters were determined as 12.14 ppm B₂O₃ and 1.36 kW load, yielding a desirability of 0.7128. Under these conditions, the engine achieved a BSFC of 458.83 g/kWh and BTE of 22.01%, with emissions reduced to 0.041% CO, 14.29 ppm HC, and 346.44 ppm NO_x. The results demonstrate that nano B₂O₃ functions as a combustion catalyst by enhancing oxidation pathways and improving fuel-air interaction, thereby increasing combustion efficiency and reducing harmful emissions. Full article