Search Results (125)

While the split diesel bracketing ammonia (D/N/D) injection mode has shown high potential for performance enhancement in ammonia/diesel dual-fuel engines, its key operational parameters require systematic investigation. This study conducts a numerical investigation to systematically evaluate the D/N/D injection parameters. The effects of the start of diesel pre-injection (SODI-pre), start of diesel main injection (SODI-main), and diesel split ratio (DSR) on combustion and emission characteristics were systematically evaluated. Results indicate that at an SODI-pre of −15 °CA ATDC and an SODI-main of −5 °CA ATDC with a DSR of 15% minimizes equivalent indicated specific fuel consumption (EISFC: 156.7 g/kWh), NOx (8.6 g/kWh), and CO₂ (192.5 g/kWh) but leads to relatively high knock index (KI: 1.15 MPa/°CA) and incomplete combustion emissions (CO, soot, unburned NH₃, and HC). Retarding SODI-main to −1 °CA ATDC and raising the DSR to 30% substantially reduces the incomplete combustion emissions, with unburned NH₃ and HC reaching 0.009 and 0.003 g/kWh, respectively, while offering a favorable overall trade-off among EISFC (157.9 g/kWh), KI (1.19 MPa/°CA), CO (0.067 g/kWh), soot (0.083 g/kWh), NO (9.7 g/kWh), and CO₂ (194.0 g/kWh). Consequently, the selected injection strategy provides a favorable trade-off between thermal performance and multi-pollutant control. Full article

Marine bacteria represent a vast and largely untapped resource for biotechnological innovation, offering solutions to global challenges in health, sustainability, and environmental conservation. The ocean’s unique conditions have driven marine bacteria to evolve diverse metabolic capabilities, resulting in the production of bioactive compounds, enzymes, and other metabolites with wide-ranging applications. Recent advances in high-throughput sequencing, metagenomics, and analytical chemistry have unlocked new opportunities for leveraging these microorganisms in fields as varied as medicine, agriculture, and bioremediation. This review highlights the role of marine bacteria in the One Health framework, showcasing their contributions to antimicrobial discovery, nutraceutical development, pathogen biocontrol, and environmental cleanup, including microplastic degradation. This review also examines emerging methodologies such as microbiome mining and advanced culturing techniques, which hold the key to realizing the full potential of marine bacteria in a sustainable bioeconomy. By bridging fundamental research with applied sciences, marine biotechnology promises to deliver transformative impacts on human, animal, and environmental health. Full article

With the rapid advancement of artificial intelligence technologies in the era of big data, time series forecasting has become indispensable in critical fields such as environmental monitoring and financial market analysis. However, the existing forecasting models often encounter performance limitations when extracting high-dimensional features and generally cannot dynamically focus on critical information within long-term sequences. To address these challenges, this study proposes a multi-head attention fusion model (MAFM) designed to enhance the predictive accuracy and modelling capability for high-dimensional and nonlinear data across diverse application scenarios. Experiments were conducted on two heterogeneous datasets from the environmental and financial domains. After the key hyperparameters of the MAFM were optimized through an orthogonal experimental design, the model achieved coefficients of determination exceeding 0.90 on both datasets. Furthermore, the results of four comparative experiments demonstrate that the MAFM consistently outperforms traditional machine learning models, including support vector regression and extreme gradient boosting, as well as state-of-the-art deep learning models such as long short-term memory, temporal convolutional networks, and transformers. Compared with the best-performing baseline model on each sub-dataset, the MAFM reduced the mean squared error by 44.4%, 8.3%, 29.4%, and 65.5%, respectively, highlighting its superior predictive performance and strong generalization capability. In summary, the proposed MAFM provides an efficient, robust, and interpretable solution for time series forecasting tasks across multiple domains. Its outstanding performance demonstrates significant potential for practical applications in environmental monitoring, financial forecasting, and other real-world scenarios. Full article

Driven by the increasing energy density of battery energy storage systems, immersion cooling (IC) has emerged as a promising approach for mitigating thermal runaway (TR) hazards. In this study, overcharge-induced TR tests were conducted on commercial 314 Ah lithium iron phosphate batteries in an accelerating rate calorimeter to compare their thermal, pressure, mass loss, and gas venting responses under air cooling (AC) and static ester-based immersion cooling. For the two cells tested, internal short circuit onset occurred at 1150 s under AC and 1377 s under IC, while TR was triggered at 1232 and 1404 s, respectively. The peak surface temperature decreased from 422.4 °C under AC to 302.4 °C under IC, and the maximum surface temperature difference was reduced by approximately 31%. The maximum chamber pressure rise rate decreased from 3.12 to 1.68 kPa/s, although a higher late-stage cumulative pressure was observed under IC within the sealed ARC chamber. Battery mass loss decreased from 1014.2 g (18.27%) under AC to 845.2 g (15.24%) under IC. In addition, the CO₂ fraction in the post-cooling gas mixture increased from 30.4% to 38.4%, while the H₂ fraction decreased from 43.6% to 36.9%. Based on the modified Le Chatelier calculation, the estimated lower explosive limit increased from 6.16% to 7.18%, suggesting lower composition-based ignitability under the adopted assumptions. Overall, the tested static ester-based immersion cooling configuration delayed TR evolution, reduced peak thermal response and mass loss, and moderated the transient pressure rise under the present experimental conditions. These findings provide experimental reference data for the thermal-safety design of large-capacity battery energy storage systems. Full article

Quantitative source apportionment of groundwater contamination is essential for sustainable water resource management, yet the performance of receptor models in complex hydrogeological settings remains debated. This study employed Positive Matrix Factorization (PMF) and PCA-APCS-MLR (Principal Component Analysis–Absolute Principal Component Score–Multiple Linear Regression) models to analyze 16 hydrochemical parameters from 460 groundwater samples (collected at 339 sites), delineating pollution sources and characterizing the groundwater chemistry in the southern recharge zone of Poyang Lake, China’s largest freshwater lake. Both models consistently identified five primary pollution sources: mixed anthropogenic activities (contributing 13.6% and 8.6%, respectively), natural geological processes (28.9% and 45.6%), sewage discharge (23.5% and 24.4%), industrial effluents (13.3% and 12.1%), and agricultural practices (20.6% and 9.3%). Notably, heightened contamination was observed near industrial parks and urban centers through two models. The integrated analysis revealed that anthropogenic activities—particularly sewage discharge, agricultural practices, and industrial effluents—are the dominant drivers of groundwater quality deterioration. These human-induced inputs account for the vast majority of the pollution load (reaching up to ~71%), fundamentally altering the natural hydrochemical regime. Notably, elevated Mn²⁺ and NH₄⁺-N concentrations are intricately linked to a combination of industrial effluents and legacy domestic sewage, which exacerbate the mobilization of natural background elements within the aquifer. These findings provide critical mechanistic insights into the complex interplay between human activities and groundwater hydrochemistry, demonstrating how dual-receptor modeling can unravel overlapping natural and anthropogenic inputs. Ultimately, this study offers a scientific basis for targeted pollution control and the sustainable management of global freshwater lake recharge zones. Full article

TiO₂-based heterostructures have attracted considerable attention in photocatalytic pollutant degradation owing to their enhanced photoresponse and improved charge separation. The phase structure of TiO₂ strongly affects its band structure and interfacial charge-transfer behavior, making phase structure control critical for optimizing photocatalytic performance. However, due to the small difference in free energy among TiO₂ phase structure and the strong dependence of TiO₂ nucleation and growth on the local reaction environment, it remains challenging to precisely control the phase structure of TiO₂ in the TiO₂-based heterostructure nanomaterials. Herein, we achieved the phase engineering of TiO₂/MXene heterostructure nanomaterials through a solvent-regulation strategy. Specifically, by regulating the acetonitrile/water ratio in the hydrothermal solvent, TiO₂ with distinct phase structures was in situ grown on hydrothermally treated MXene nanosheets, resulting in two representative TiO₂/MXene heterostructure nanosheets: anatase TiO₂/MXene and rutile TiO₂/MXene. Acetonitrile likely acted as a surface-adsorbing agent during TiO₂ formation, stabilizing the anatase phase and promoting the preferential formation of anatase TiO₂. Benefiting from the optimized heterostructure, TiO₂/MXene heterostructure nanosheets promoted the generation of singlet oxygen (¹O₂), leading to enhanced photocatalytic degradation. Full article

Student mental health and academic stress are increasingly addressed through digital monitoring, yet evidence for personalized physiological thresholds based on exhaled VOCs, their in-the-wild feasibility, and their trigger–experience correspondence in everyday student life remains limited. This study examines whether exhaled breath signals can support personalized, real-world stress monitoring in university students using a two-stage design that moves from laboratory calibration to daily life validation. A total of 24 university students took part in the laboratory phase (Study 1; N = 24). Under two stress tasks, a social-conflict video task and a Stroop task, we derived an individualized breath-trigger threshold (θ_i) for each participant. We then invited 21 of them to join a three-day field deployment (Study 2; N = 21). Each participant’s θ_i from Study 1 was used directly as the trigger threshold for daily monitoring in order to test the association between trigger events and subjectively noticeable emotional deviations and to assess preliminary trigger–experience correspondence in daily life. The results show that 78.6% of paired trigger–EMA records were rated as subjectively salient, with 93.9% of these rated at medium-to-high intensity. These events occurred most frequently during study/work activities (60.6%), in dorm/home settings (57.6%), and when participants were alone (63.6%), suggesting that the triggers captured personally meaningful emotional episodes embedded in routine academic life rather than random physiological fluctuations. Overall, this study presents a portable breath-based emotion sampling device for student academic contexts and a reproducible protocol that combines laboratory thresholding with daily life validation. The findings provide preliminary and exploratory indications of the feasibility and within-person transferability of VOC-based emotion detection in students, and offer methodological support for future digital emotion monitoring and intervention design based on breath signals. Full article

As wireless networks evolve from 5G toward 6G, the complexity of the electromagnetic environment increases sharply. Spectrum usage expands significantly into millimetre-wave (mmWave) and terahertz (THz) high-frequency bands. Network node density and mobility increase markedly. Moreover, communication-sensing-computation functions are deeply integrated. Accurate, real-time, full-band Electromagnetic Spectrum Maps (ESMs) have become a core infrastructure for 6G spectrum situational awareness, Dynamic Spectrum Access (DSA), interference coordination, and Integrated Sensing and Communication (ISAC). However, while a growing body of recent work extends radio mapping to multi-band and temporal domains, the predominant focus of existing Radio Map research remains the two-dimensional spatial power distribution at a single fixed frequency—essentially a degenerate special case of ESM after the frequency and time dimensions are collapsed—and no existing survey unifies 3D spatial construction, time-varying prediction, and full 6G system integration under a shared 4D formalism. This paper focuses on the three core research dimensions of ESMs, i.e., 3D spatial ESM construction, dynamic time-varying ESM modelling and prediction, and ESM integration with 6G systems. Under a unified four-dimensional ESM framework (space × frequency × time × power), we clarify the hierarchical relationships among ESM/SEM/REM/Radio Map/Channel Knowledge Maps (CKMs). Then, we systematically review 3D ESM construction, dynamic ESM modelling and prediction, and the integration of ESM with CKM/Digital Twin Networks (DTNs)/ISAC. Finally, we identify five, core open problems that constrain the development of the field to provide a systematic reference for 6G intelligent spectrum management research. Full article

Electromagnetic signal waveform recognition (ESWR) constitutes a fundamental enabling technology for modern spectrum management, cognitive radio, and electronic warfare applications. Among various ESWR subtasks, automatic modulation recognition (AMR) has attracted the most intensive research efforts and serves as the primary focus of this survey. Over the past decade, deep learning (DL) has fundamentally transformed ESWR by replacing hand-crafted feature engineering with data-driven end-to-end learning paradigms. However, the rapid proliferation of DL-based approaches has resulted in a fragmented research landscape. This paper addresses this gap by proposing a unified system-component framework that decomposes any DL-ESWR system into four foundational modules: (i) dataset construction and data augmentation, (ii) signal representation and preprocessing, (iii) core network architecture, and (iv) training and optimization strategy. Through this systematic lens, we provide a comprehensive review that catalogs the state of the art across recent publications and precisely attributes each innovation to specific modules within our framework. Furthermore, we identify eight core challenges confronting the practical deployment of DL-ESWR systems and systematically analyze how targeted modular innovations address each challenge. A critical analysis of prevalent benchmark datasets reveals significant limitations in channel diversity, modulation coverage, and ecological validity. Finally, we outline seven promising future research directions, including foundation models for wireless signals, physics-informed neural networks, and waveform recognition for emerging communication paradigms, such as semantic communications and integrated sensing and communication (ISAC). This survey aims to provide researchers and practitioners with a structured roadmap for understanding, evaluating, and advancing the field of AI-enabled electromagnetic signal waveform recognition. Full article

Under the constraints of limited maintenance budgets, research on pavement performance prediction is of considerable practical significance for improving the efficiency of maintenance decision-making, optimizing fund allocation, and ensuring highway serviceability. This study was conducted in conjunction with pavement maintenance projects on ordinary national and provincial highways in Shanxi Province, China. Representative routes were selected based on the natural zoning and road network scale of Linfen City. A comprehensive factor system influencing pavement service performance was established, encompassing pavement characteristics, traffic attributes, climatic and environmental conditions, and maintenance management levels. By employing Particle Swarm Optimization (PSO) to tune the hyperparameters of a Long Short-Term Memory (LSTM) network, a PSO-LSTM prediction model incorporating a sliding window mechanism was constructed for the Pavement Condition Index (PCI) and Ride Quality Index (RQI). The model achieves coefficients of determination (R²) of 0.845 and 0.869 for PCI and RQI, respectively, enabling dynamic prediction of pavement service performance and thereby providing scientific support and a data-driven basis for the formulation of pavement maintenance strategies. Full article

Fine migration is widely recognized as a primary cause of production decline in unconsolidated sandstone reservoirs. Migrated fines may accumulate within pore throats and obstruct flow channels, or they may be transported into the wellbore with the produced fluids, leading to operational issues such as wellbore plugging, pump sticking, and equipment abrasion. Despite extensive studies on fine migration, the role of particle wettability has received limited attention. In this study, the mineralogical composition of formation particles was first characterized using X-ray diffraction (XRD) and quantitative clay analysis. Surface modification experiments were then conducted to investigate the effect of hexadecylamine (HDA) on particle wettability and to determine the optimal reaction conditions. Surface characterization techniques were employed to elucidate the modification mechanism. Subsequently, sand-packed tube displacement experiments were performed to evaluate the influence of wettability alteration on fine migration behavior. The underlying mechanisms were further interpreted through interfacial thermodynamic analysis. Two potential field application schemes are proposed to facilitate practical implementation in oilfield operations. The results indicate that the water contact angle of formation particles increased from 0° to 150° when treated with 0.8 wt% HDA for 24 h. Surface characterization confirms that HDA molecules were physically adsorbed onto the particle surfaces. Displacement experiments demonstrate that the permeability reduction rate decreases significantly with increasing particle hydrophobicity. Thermodynamic analysis suggests that the work of adhesion on the modified particle surface was reduced by 93.3%, thereby weakening fluid–particle interfacial coupling and suppressing fine mobilization. This study provides a wettability-based approach for mitigating permeability damage caused by fine migration in unconsolidated sandstone reservoirs. Full article

Immersion cooling has been widely investigated in battery thermal management due to its high cooling efficiency; however, the influence of coolant properties on the thermal behavior and temperature uniformity of large-capacity energy storage battery modules remains unclear. In this study, a three-dimensional numerical model is developed to investigate the thermal performance of an immersion-cooled battery module consisting of 52 prismatic cells. The cooling performance of silicone oil (SO), synthetic hydrocarbon (SH), and two synthetic esters (SE) with different viscosities is systematically compared under various discharge rates and volumetric flow rates. The battery thermal model was validated through single-cell experiments under natural air convection conditions. The research results indicate that at a 0.5C discharge rate, the 30 cSt SE achieves a reduction in maximum battery pack temperature of 6.3% and 7.0% compared to SO and SH, respectively. Furthermore, the maximum temperature difference is significantly reduced by 22.9% and 25.4% under the same conditions. Due to differences in the inherent properties and flow heat transfer characteristics of the coolant, at a volumetric flow rate of 12 L/min, the 30 cSt SE resulted in a 15.8% reduction in module temperature difference compared to the 20 cSt SE. To further evaluate the internal thermal balance of the battery module, two thermal uniformity indicators were introduced to quantify the consistency of the highest temperature of individual cells and the internal temperature difference. Considering both the temperature performance and thermal uniformity at the module level, from a heat dissipation performance perspective, the 30 cSt SE demonstrates significant potential for thermal management of large-scale prismatic battery packs. Full article

Rice seed vigor is one of the critical factors determining rice yield and quality. Identifying substances related to seed vigor and rapidly assessing seed vigor by non-destructive methods are of great significance for increasing rice production. This study employed near-infrared diffuse reflectance spectroscopy (NIR-DRS) and transmission spectroscopy (NIR-TS) to evaluate the vigor of naturally aged rice seeds. The NIR-DRS failed to establish a reliable relationship between spectral data and seed vigor, proving ineffective in distinguishing seed vigor. After enhancing the spectral differences between viable and non-viable seeds, the NIR-TS successfully identified high-vigor and non-viable seeds, with a partial least squares discriminant analysis (PLS-DA) model achieving accuracy and germination rates of 84.52% and 88.57% on the test set, respectively. Furthermore, three algorithms, including interval partial least squares (iPLS), genetic algorithm (GA), and competitive adaptive reweighted sampling (CARS), were applied to extract characteristic spectral wavelengths associated with seed vigor. Among these, the CARS algorithm performed the best, identifying 38 characteristic wavelengths. Wavelength analysis indicated that rice seed vigor is primarily influenced by molecules such as starch, protein, moisture, and lipids. Using the characteristic wavelengths selected by the CARS algorithm, a PLS-DA prediction model for rice seed vigor was constructed, achieving high accuracy and germination rates of 90.47% and 95.38% on the test set, respectively. This study demonstrates that NIR-TS outperforms NIR-DRS in assessing rice seed vigor. Moreover, wavelength selection techniques can effectively identify characteristic spectral features related to seed vigor and significantly enhance the prediction accuracy of the model. Full article

Urban stray cats are in close contact with humans and are important potential vectors for zoonotic diseases. However, comparative studies in the gut microbiota of cats living in different environments remain limited. Here, we conducted a comparative analysis of the gut microbiota between stray and domestic cats using 16S rRNA gene sequencing of fecal samples from 14 stray and 11 domestic cats in Hefei, China. Domestic cats harbored significantly higher alpha diversity (Sobs index, Padj. = 0.039; Shannon index, Padj. = 0.024) and more complex microbial co-occurrence networks than stray cats. Beta diversity analysis confirmed distinct community structures between the groups. Linear discriminant analysis identified 12 taxa enriched in stray cats, including Escherichia-Shigella and other potential opportunistic pathogens. Functional prediction indicated that the gut microbiota of domestic cats was enriched in genes related to DNA repair and cellular structure maintenance, whereas that of stray cats showed higher abundance of functions associated with secondary metabolism, defense mechanisms, and stress response. Phenotypic prediction further revealed increased proportions of stress-tolerant and facultatively anaerobic bacteria in stray cats. These findings demonstrate that lifestyle and environmental exposure shape the feline gut microbiota, with stray cats exhibiting a less diverse, more stress-adapted, and potentially pathogen-enriched microbial profile. This study provides insights into the ecological adaptation of gut microbiota and highlights implications for zoonotic risk and One-Health-oriented management of urban cat populations. Full article