Search Results (2,117)

The objective of this study is to evaluate the efficiency of surgical instruments’ manual cleaning versus automated cleaning in an ultrasonic cleaner for the removal of biofilms on surgical forceps contaminated with Staphylococcus epidermidis and Pseudomonas aeruginosa. Subsequently, the residual microbial load was quantified through microbiological culture, aiming to evaluate the effectiveness of biofilm removal under different reprocessing conditions. Cleaning is an essential step in the processing of surgical instruments to ensure the effective removal of dirt and microorganisms. Through adhesion, microorganisms can attach to surfaces and form biofilms, organized structures surrounded by an extracellular matrix consisting of various components, which favor metabolic exchanges, adaptation, resistance, and bacterial dispersion. These biofilms increase the pathogenic potential of microorganisms, contributing to the occurrence of Healthcare-Associated Infections, and to avoid these, it is essential that preventive measures aimed at microbial reduction are adopted. Automated cleaning proved more effective than manual cleaning, and the combined approach achieved the greatest microbial reduction, though persistent contamination was still observed. The ability of adhesion and biofilm formation on the surfaces of surgical instruments is regarded as a challenge for complete microbial removal. These findings enhance the need for more rigorous reprocessing protocols and complementary strategies to ensure greater safety in the use of reusable instruments in clinical practice. Full article

This paper estimates how corporate innovation investment responds to climate policy uncertainty using panel data with 3197 listed firms from 2010 to 2022 in China. The findings show that climate policy uncertainty positively contributes to corporate innovation investment, and this result continues to hold after controlling for endogeneity and conducting a series of robustness tests. Furthermore, we find that stringent government environmental regulation serves as a potential mechanism, compelling firms to adopt cleaner production and increase their investment in innovation. Additionally, this positive relationship is stronger for firms with higher government subsidies and disappears for firms with a higher allocation of fixed assets. We also find that firms with fewer connections to the government are more sensitive to climate policy uncertainty and they tend to increase their investment in innovation to mitigate the uncertainty. Furthermore, when firms invest more in innovation during periods of high policy uncertainty, their long-term performance and firm value are likely to improve. This study sheds light on the importance and influence of climate policy uncertainty on corporate innovation investment in China. Full article

Deep learning has emerged as an effective tool for automatic waste classification, supporting cleaner cities and more sustainable recycling systems. Because environmental protection is central to the United Nations Sustainable Development Goals (SDGs), improving the sorting and processing of everyday waste is a practical step toward this broader objective. In many real-world settings, however, waste is still sorted manually, which is slow, labor-intensive, and prone to human error. Although convolutional neural networks (CNNs) can automate this task with high accuracy, many state-of-the-art models remain too large and computationally demanding for low-cost edge devices intended for deployment in homes, schools, and small recycling facilities. In this work, we investigate lightweight waste-classification models suitable for TinyML deployment while preserving competitive accuracy. We first benchmark multiple CNN architectures to establish a strong baseline, then apply complementary compression strategies including quantization, pruning, singular value decomposition (SVD) low-rank approximation, and knowledge distillation. In addition, we evaluate an RL-guided multi-teacher selection benchmark that adaptively chooses one teacher per minibatch during distillation to improve student training stability, achieving up to 85% accuracy with only 0.496 M parameters (FP32 ≈ 1.89 MB; INT8 ≈ 0.47 MB). Across all experiments, the best accuracy–size trade-off is obtained by combining knowledge distillation with post-training quantization, reducing the model footprint from approximately 16 MB to 281 KB while maintaining 82% accuracy. The resulting model is feasible for deployment on mobile applications and resource-constrained embedded devices based on model size and TensorFlow Lite Micro compatibility. Full article

Hybridization in vehicle powertrains extends beyond the aggregate system level and can target individual components to enhance engine performance. While prior studies have highlighted the performance benefits of electrified turbochargers, this work focuses on mitigating engine-out emissions for a medium- to heavy-duty diesel engine with an electrified airpath. Unlike conventional engines and actuators, the alternative engine architecture with an electrified airpath provided superior airpath control. This is critical for fuel-led diesel engines, where the initial combustion cycles during the tip-in phase of a transient operate at a rich equivalence ratio. In this work, a 3.2 L two-cylinder opposed piston two-stroke (OP2S) engine equipped with an Electrically Assisted Turbocharger (EAT) and an electrically operated EGR pump was experimentally tested in a Hardware in the Loop (HIL) setup under transient conditions. Actuator positions were varied to identify strategies that mitigate soot and NO_x without compromising transient response. The experiments are discussed case-wise, where the effects of each airpath actuator, including fuel rate shaping, are analyzed, showing to what extent each strategy mitigates emissions. At the end, an optimized case is presented to the readers for their perusal. The electrified airpath, along with fuel rate shaping, demonstrated cumulative soot reduction up to 92% and NO_x emissions by 77% for a transient load step between 3 and 13 bar BMEP at a mid-engine speed of 1250 rpm. Full article

Energy management of multi-carrier energy hubs (MCEHs) is a challenging task, particularly when fuel cell electric vehicle (FCEV) stations are included, due to the stochastic nature of FCEV demand, system loads, and integrated renewable energy resources (RERs) such as wind turbines (WTs) and photovoltaic (PV) systems. This paper aims to optimize the energy management of an MCEH-based microgrid to simultaneously minimize total operating costs and emissions. To this end, a novel enhanced quadratic interpolation optimization (EQIO) algorithm is proposed. The proposed EQIO algorithm incorporates two key improvements: a best-to-mean quasi-oppositional-based learning (BMQOBL) strategy and an evaluation mutation (EM) strategy. The performance of EQIO is evaluated using the CEC 2022 benchmark functions, and the obtained results are compared with those of other optimization techniques. Three case studies are investigated: (i) energy management of the MCEH microgrid without RERs, (ii) sustainable operation (with RERs), and (iii) sustainable operation with RERs combined with the application of demand-side response (DSR). Moreover, the proposed framework explicitly supports long-term sustainability goals by enhancing renewable energy utilization, reducing the carbon footprint, and promoting cleaner transportation through efficient integration of FCEV infrastructure. The results demonstrate that integrating RERs reduces operating costs and emissions by 51.47% and 59.69%, respectively, compared to the case without RERs. Furthermore, the combined application of RERs and DSR achieves cost and emission reductions of 55.26% and 53.93%, respectively, compared to the case without RERs. Full article

Liquefied petroleum gas (LPG) is a widely available alternative fuel, easily stored in liquid form, capable of displacing diesel fuel in compression-ignition engines. Bio-LPG extends this pathway because it is a renewable drop-in form of LPG; its distinguishing advantage is not a different in-cylinder combustion chemistry, but a lower life-cycle greenhouse-gas intensity that depends on feedstock and production route. This review, therefore, combines a systematic synthesis of CI-engine LPG combustion evidence with a Bio-LPG transition perspective. A PRISMA-guided search of major databases (2000–2025) yielded 47 studies with matched diesel baseline. Evidence was categorized by LPG utilization pathway, distinguishing between fumigation, gaseous port injection, and in-cylinder LPG direct injection (gaseous or liquid), alongside engine class, pilot fuel fraction, and key operating parameters (injection timing/quantity, intake conditioning, exhaust gas recirculation (EGR), and boost). Data were normalized as percentage deviations relative to diesel and synthesized across standardized load bins (25/50/75/100%). Among studies reporting nitrogen oxides (NO_x), 20 of 37 showed net reductions, while results in 12 studies were load-dependent; particulate matter (PM), smoke, and soot indicators decreased in 17 of 27 cases. While intake-path strategies generally reduced NO_x and smoke, they often increased CO and HC emissions at low loads. The limited emerging liquid-phase direct-injection evidence shows the closest diesel-like efficiency response, although the evidence base remains limited. Overall, the engine-level findings identify the most promising LPG/Bio-LPG deployment pathways, while the specific additional climate benefit of Bio-LPG lies in its lower well-to-wheel greenhouse-gas intensity. Full article

DOC coupled with SCR represents a key technological approach for reducing gaseous pollutant emissions from diesel engines. Based on engine bench testing using a light-duty diesel engine as a prototype, this study investigates the impact of DOC coupled with SCR at different catalyst loadings on diesel engine emission characteristics. Results indicate that higher DOC loadings lead to greater exhaust backpressure losses, with a maximum pressure difference reaching 4.3 kPa. The temperature difference across the DOC was minimally affected by catalyst loading. Higher DOC loading enhanced catalytic activity toward CO and THC. At medium-to-low loads, this effect was pronounced, while at high loads, the influence of catalyst loading diminished. Higher DOC loading enhances NO oxidation capacity. Under external characteristic conditions, elevated engine exhaust temperatures maximize post-DOC NO₂ formation, increasing post-DOC NO₂ production by over 100%. These findings provide useful guidance for optimizing diesel aftertreatment systems to achieve a better balance between pollutant reduction, energy consumption, and environmental sustainability, thereby supporting the sustainable development of cleaner diesel engine technologies. Full article

This article builds on the argument that design for complex interactive systems should shift from creating linear transactional interactions toward organizing relational complexity. Grounded in Karen Barad’s agential realism, we argue that a designer’s role can benefit from not predefining interactions but from curating the material-discursive conditions under which meaningful relations can emerge. To explore the empirical and temporal dimensions of this practice, we conducted an exploratory workshop setting the conditions for emergent gameplay dynamics and discussions on agential realist anticipation. Participants utilized a custom-designed game and built their own physical controllers to anticipate and adapt to shifting gameplay conditions. Our results demonstrate how alterations in relational constraints, rather than explicit pre-programmed goals, drove the emergence of non-predefined gameplay rhythms. The findings provide empirical grounding for an agential realist understanding of anticipation, showing that an interactive system’s identity lies in its unfolding processual patterns rather than a static final state. Based on these findings, we propose three design principles for further exploration: Design for Relational Emergence, Design for Re-membering, and Design for Emergent Patterns. Consequently, we conclude by outlining a conceptual approach for non-linear computational architectures, drawing on principles from Enactive AI and reservoir computing. Full article

Unpolluted water, both freshwater and saltwater, is essential for achieving several United Nations Sustainable Development Goals, particularly SDGs 6, 3, 2, 14, and 15. This study maps emerging water-quality monitoring technologies at intermediate technological readiness levels (TRLs 4–5) and their potential patent markets (TRL 9). A total of 40,469 patent families were retrieved from the Espacenet worldwide database using IPC G01N33/18 and used to analyze sensing parameters. A subset of 2146 water-pollution-related patents was analyzed in detail. The analysis covered sensing parameters, temporal trends, compound annual growth rates (CAGR), legal status, geographic distribution of patent origins and markets, and the technological landscape, including application domains and niche clusters. The results show pronounced exponential growth in patent filings since 2014 and a high share of active documents, indicating sustained global investment. Innovation leadership is concentrated in China, South Korea, India, the United States, and Japan, with export-oriented patents largely held by transnational corporations, while African participation remains limited. Technological trends prioritize multiparameter environmental and biological sensing, addressing pH, temperature, turbidity, dissolved oxygen, nutrients, heavy metals, polycyclic aromatic hydrocarbons (PAHs), and oxidation–reduction potential. Emerging solutions integrate autonomous platforms, remote sensing, Internet-of-Things architectures, and machine-learning-based analytics. Persistent bottlenecks include sensor robustness in harsh aquatic environments and the reliable discrimination between background variability and early pollution signals. Strengthening low-cost and scalable deployment remains essential to ensure water quality, support environmental sustainability, and minimize risks. Full article

Schistosomiasis is a major neglected tropical disease in sub-Saharan Africa. This study compared the epidemiology of urinary schistosomiasis among children living in two distinct ecosystems in Mauritania: the Senegal River Basin (Trarza region) and the arid oasis areas (Adrar and Tagant regions). A cross-sectional study was conducted between February 2023 and February 2024 involving 856 children across 14 sites. Urine samples were collected from school-aged children and subjected to macroscopic and microscopic examinations. A questionnaire was administered to each child to determine sociodemographic factors. Environmental and geographical factors were documented in the localities. The prevalence rate of urinary schistosomiasis among children in the Senegal River Valley was 32.4%. In the oases zone, prevalences were 6.43% and 3.35% in Tagant and Adrar, respectively. Macroscopic hematuria was 29.1%, 6.04%, and 4.18% in Trarza, Adrar, and Tagant, respectively. The intensities of infection were 48.9, 6.43, and 40 eggs/10 mL in the Trarza, Adrar, and Tagant regions, respectively. Based on sex, prevalence was higher among boys in the Trarza and Tagant regions, while in Adrar, it was higher among girls. Prevalence among children using polluted water sources with dense vegetation in the department of Tékane, in the Trarza region, was significantly higher (35.7%) than among those using cleaner water sources (21%). Urinary schistosomiasis remains highly prevalent among children living along the Senegal River, while considerably lower transmission was observed in oasis settings. These findings highlight the strong influence of environmental and water-related factors on transmission dynamics and underscore the need for targeted, context-specific control strategies. Full article

Maritime transport is essential for global trade, yet ship emissions remain a major source of air pollution in coastal and port areas, with potential impacts on local air quality and human health. Cruise ships are particularly relevant in urban ports because, beyond propulsion, they require a continuous onboard energy supply for hotel services while berthed. This study develops a bottom-up emission inventory for cruise ship calls at Istanbul Galataport during the 2024 season, estimating CO₂ as a greenhouse gas (GHG) and NO_x, SO_x, and particulate matter (PM) as air-quality pollutants generated during manoeuvring and hotelling phases. Ship technical characteristics (engine type, installed main and auxiliary power, engine speed class, and year of build) were obtained from the IHS database, while port call activity data were provided by the terminal operator. Emission factors were primarily based on the IMO Third Greenhouse Gas Study and complemented with established literature sources to address missing vessel information and ensure methodological consistency. Results indicate that hotelling dominates total emissions, reflecting the high auxiliary power demand during berths. Results show that total annual emissions from 164 cruise ship calls amount to approximately 31,360 t·y⁻¹ of CO₂, 370 t·y⁻¹ of NO_x, 350 t·y⁻¹ of SO_x, and 44 t·y⁻¹ of PM. Hotelling operations account for the dominant share of emissions, contributing more than 90% of total CO₂ and the majority of NO_x and SO_x emissions, due to sustained auxiliary engine demand during berth stays. These findings confirm that cruise ship activity represents a significant localized emission source in densely populated port environments and provide a quantitative baseline for evaluating mitigation measures such as shore power, cleaner fuels, and operational strategies aimed at reducing at-berth emissions. Full article

The removal efficiency of wet scrubbers is governed by complex nonlinear interactions among operating parameters such as liquid level, airflow velocity, and dust concentration, making accurate real-time prediction challenging, which in turn leads to operational instability, increased energy consumption, and excessive emissions. To address this bottleneck, we first introduce multi-gene genetic programming (MGGP) to develop interpretable models quantifying multi-parameter coupling and predicting removal efficiency for PM₁, PM_2.5, PM₁₀, and TSP. Key input variables, including liquid level height, inlet airflow velocity, system pressure, and inlet dust concentration, were identified via correlation analysis. Explicit mathematical models were derived. Global sensitivity analysis using the elementary effect test (EET) identified inlet airflow velocity as most influential. Uncertainty quantification via quantile regression (QR) confirmed the model’s reliability with narrow prediction intervals and high coverage probabilities. MGGP offers a favorable balance of accuracy, generalization, and interpretability compared to extreme gradient boosting (XGBoost) and multiple nonlinear regression (MNR). Its explicit form quantifies parameter interactions, enabling efficient on-site monitoring with low computational cost. This study provides an interpretable prediction tool for intelligent wet scrubber operation, supporting cleaner production and refined control in complex industrial processes. Full article

Comamonas testosteroni is an aerobic, Gram-negative bacterium belonging to the class of β-proteobacteria that is naturally present in soils, wastewater and sludge. It has recently gained popularity for its ability to act as a biocatalyst for the degradation of microplastics and other complex organics. Microplastics are globally considered as ubiquitous pollutants due to the increased use of polymers (plastics) which break down over time. In the urban water cycle, the drinking water treatment plants and the wastewater treatment plants are the first and last barriers to microplastics pollution, respectively. While conventional water and wastewater treatment has seen continuous technological improvements in producing cleaner effluents, industry technology adoption for the targeted removal of microplastics has been minimal. Therefore, the treatment of microplastics in soils and wastewater is of growing interest, and understanding C. testosteroni may provide insight into biological treatment and degradation of these pollutants. This review provides a summary of (1) favorable microbiological and environmental properties of C. testosteroni that lend themselves to bioremediation; (2) evidence of the bacterium’s ability to degrade microplastics, steroids, and organic pollutants; (3) implementation potential in the wastewater treatment process train; and (4) challenges and limitations in its application for microplastics biodegradation. Overall, while treatment applications of C. testosteroni through inoculation of media such as soil and wastewater are mentioned, further research into C. testosteroni concentrations found typically at wastewater treatment facilities would be beneficial. Full article

Natural fibre hybrid composites have gained attention as cleaner alternatives to synthetic glass fibre systems due to their renewable feedstocks and inherent density advantage. However, moisture ingress degrades fibre–matrix integrity and mechanical performance, making durability a critical design constraint. This study systematically investigates the water absorption kinetics and post-immersion mechanical property retention in ramie–flax/epoxy hybrid composites across four fibre loadings (10–40 wt.%), with the ramie-to-flax weight ratio fixed at 1:1 in all formulations. Tensile, flexural, and impact properties were evaluated under dry and saturated conditions; Fickian diffusion kinetics were analysed to quantify moisture transport parameters; and fracture surfaces were examined by SEM. A density-based material efficiency analysis quantified the lightweighting benefit relative to equivalent synthetic glass/epoxy composites. Water absorption increased monotonically with fibre content; all formulations reached equilibrium after approximately 120 h. The 30 wt.% composite achieved dry-state tensile, flexural, and impact strengths of ca.49 MPa, ca.58 MPa, and 2.82 kJ/m² respectively, retaining ca.78%, ca.69%, and ca.82% after full saturation, superior to all other loadings. These results establish 30 wt.% as the optimal fibre loading for moisture-exposed semi-structural applications, supporting the adoption of ramie–flax composites within a cleaner manufacturing framework. Full article

This systematic review evaluates recent scientific and technological advances in water quality monitoring and pollution alarms for ports, based on records retrieved from seven databases following the PRISMA protocol. A total of 414 documents were screened, resulting in 141 articles (TRL 3) and 56 patents (TRL 4–5). Bibliometric, patentometric, and thematic analyses were conducted using Bibliometrix and ORBIT^®. Results show sustained growth in both academic and technological outputs, with a patent Compound Annual Growth Rate (CAGR) of 32%, compared with 13% for scientific publications, indicating accelerated translation from research to innovation. The conversion rate from scientific research to patenting increased from 14% (2010–2015) to 47% (2020–2023). Analysis of patent legal status reveals that 52% of patent families remain valid (48% granted; 4% pending), while 33% are lapsed, 13% revoked, and 2% expired, reflecting the dynamic and emerging character of the field. Technological ownership is highly concentrated, with China accounting for nearly all active patents, whereas scientific production is more geographically distributed. Thematic analysis identifies four main scientific clusters: environmental monitoring, chemical pollutants, seashore hazards, and eutrophication. The main technological domains of the patents are analysis of biological materials, control, and environmental technologies. Emerging areas of focus at TRL 3 and TRL 4–5 include microplastics, climate-change impacts, aquaculture risks, real-time sensing, IoT-enabled platforms, machine-learning analytics, autonomous monitoring systems, and bioindicator-based early-warning tools. This review provides a quantitative roadmap to support sustainable port operations, coastal ecosystem protection, and progress toward multiple synergistic United Nations Sustainable Development Goals (SDGs). Full article