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γ-TiAl alloy is a lightweight high-temperature structural material, featuring low density, excellent high-temperature strength, creep resistance, etc. It is a key material in the aerospace field. However, the essential defects of γ-TiAl alloys, such as poor room-temperature plasticity and low fracture toughness, have become the biggest obstacles to their practical application. Therefore, in this paper, the physical mechanism of modification of the mechanical properties and electronic structure of γ-TiAl alloys by doping with Sc, V, and Si was investigated by using the first-principles pseudopotential plane wave method. This paper specifically calculates the geometric structure, phonon spectrum, mechanical properties, electron density of states, Mulliken population analysis, and differential charge density of γ-TiAl alloys before and after doping. The results show that after doping, the structural parameters of γ-TiAl have changed significantly, and the doping models all have thermodynamic stability. The B, G, and E values of the doped system are, respectively, within the range of 94–112, 57–69, and 143–170 GPa, indicating that the material’s ability to resist compressive deformation is weakened. Moreover, the B/G values change from 1.5287 to 1.6350, 1.7279, and 1.6327, respectively, and a transformation from brittleness to plasticity occurs. However, it is still lower than the critical value of 1.75, indicating that the doped γ-TiAl alloy material retains its high-strength characteristics while also exhibiting a certain degree of toughness. The total elastic anisotropy index of the doped system increases, and the degree of anisotropy of mechanical behavior significantly increases. The total electron density of states diagram indicates that γ-TiAl alloys possess conductive properties. The covalent interactions between doped atoms and adjacent atoms have been weakened to varying degrees, which is manifested as a significant change in the charge distribution around each atom. The above results indicate that the doping of Sc, V, and Si can effectively tune the mechanical properties and electronic structure of γ-TiAl alloys. Full article

The study aimed to evaluate the influence of ink composition, a blend of blueberry and banana purée with hydrocolloids such as xanthan gum and carrageenan in concentrations ranging from 1 to 4%, on various physical properties. These parameters included dry matter, water activity, density, syneresis index, and rheological and textural attributes of fruit inks. Additionally, the stability of the inks post-printing and after 60 min was examined using image analysis method. Increased hydrocolloid additives from 1 to 4% caused the increase of the viscoelastic modulus G′ and G″, force and extrusion work values extrudability of inks. The stability and fidelity of the inks were enhanced, resulting in a notable reduction in syneresis during storage. The modulus of elasticity exceeded the modulus of viscosity for all ink formulations evaluated, thereby ensuring structural stability. Notably, the formulation comprising 4% xanthan gum and 4% carrageenan exhibited the highest values in both viscoelasticity and extrudability indices, indicating superior performance characteristics within the studied parameters. The shape of the printed objects remained comparable to the designed model over time. Considering the constraints associated with the use of carrageenan, it is possible to attain a comparable effect by utilising reduced concentrations of hydrocolloids. For instance, formulations incorporating 3% xanthan gum in tandem with either 3% carrageenan or 2% carrageenan can achieve similar functionalities. The 3D printing of fruit purées, including blueberries and bananas, represents a significant innovation in personalising food products in terms of consistency. This is particularly relevant for individuals with dysphagia, children, and the elderly. Full article

Biosurfactants (BS) are surface-active compounds synthesized by microorganisms with broad industrial applications. Although BS-producing strains are widely reported, little is known about their production by diazotrophic bacteria. This study investigated, for the first time, the BS produced by Bradyrhizobium sp. ESA 81, a diazotrophic bacterium isolated from the Brazilian semiarid region. The strain was cultivated in the mineral medium using sunflower oil and ammonium nitrate as carbon and nitrogen sources. The compound was chemically characterized using TLC, FAME, FTIR, and mass spectrometry (MALDI-TOF). The results revealed a mixture of glycolipids composed of trehalose linked to fatty acid chains ranging from C9 to C18. The BS exhibited a surface tension of 31.8 mN/m, a critical micelle concentration of 61.2 mg/L, and an interfacial tension of 22.1 mN/m. The BS also showed an emulsification index (EI24) of 55.0%. High stability was observed under extreme conditions of temperature (−20 to 121 °C), pH (2–12), NaCl (5–20%), and sucrose (1–5%). These findings indicate that the trehalolipid BS produced by Bradyrhizobium sp. ESA 81 is a stable and efficient surface-active agent, with promising potential for use in biotechnological and industrial processes. Full article

The construction sector, facing a persistent productivity gap compared to other industries, is hindered by fragmented value streams, inconsistent performance metrics, and the limited scalability of process improvements. We introduce a pioneering, four-tiered hierarchical productivity framework to respond to these challenges. This innovative approach integrates operational, tactical, strategic, and normative layers. At its core, the framework applies standardised, repeatable process steps—mapped using Value Stream Mapping (VSM)—to capture key indicators such as input efficiency, output effectiveness, and First-Time Quality (FTQ). These are then aggregated through takt time compliance, schedule reliability, and workload balance to evaluate trade synchronisation and flow stability. Higher-level metrics—flow efficiency, multi-resource utilisation, and ESG-linked performance—are integrated into an Overall Productivity Index (OPI). Building on a modular production model, the proposed framework supports real-time sensing, AI-driven monitoring, and intelligent process control, as demonstrated through an empirical case study of continuous process monitoring for Kelly drilling operations. This validation illustrates how sensor-equipped machinery and machine learning algorithms can automate data capture, map observed activities to standardised process steps, and detect productivity deviations in situ. This paper contributes to a multi-scalar measurement architecture that links micro-level execution with macro-level decision-making. It provides a foundation for real-time monitoring, performance-based coordination, and data-driven innovation. The framework is applicable across modular construction, digital twins, and platform-based delivery models, offering benefits beyond specialised foundation work to all construction trades. Grounded in over a century of productivity research, the approach demonstrates how emerging technologies can deliver measurable and scalable improvements. Framing productivity as an integrative, actionable metric enables sector-wide performance gains. The framework supports construction firms, technology providers, and policymakers in advancing robust, outcome-oriented innovation strategies. Full article

In embedding systems, protein–polysaccharide complexes can be utilized as wall materials to improve the bioavailability and activity of bioactive substances during delivery. This study used the antisolvent precipitation method to manufacture gliadin from highland barley distillers’ grains (HBDGG)–chitosan (Cs) nanoparticles. Using a variety of characterization techniques, the microstructure and interaction mechanism of HBDGG-Cs nanoparticles were examined, and their stability was assessed. In comparison to HBDGG, the results indicated that the addition of Cs enhanced the intensity of UV absorption and reduced the intensity of fluorescence. The content of α-helix dropped, while β-sheet, β-turn, and irregularly coiled content rose in the complexes. Hydrogen bonding, hydrophobic interactions, and electrostatic interactions were the primary forces that formed the nanoparticles. The contact force between HBDGG and Cs enhanced the stability of the nanoparticles. The particle size, polydispersity index (PDI), and zeta potential were 526.10 ± 11.78 nm, 0.20 ± 0.06, and 51.31 ± 0.66 mV, respectively, at a mass ratio of 1:1 between HBDGG and Cs. The nanoparticles exhibited good ionic, acid-base, and storage stability in addition to being widely distributed. This work offers a theoretical foundation for employing HBDGG-Cs nanoparticles to deliver bioactive components in food as well as a novel method for the comprehensive usage of HBDGG and Cs. Full article

(1) Background: This study aimed to compare alveolar bone preservation and early healing outcomes following a comprehensive laser-assisted post-extraction protocol compared to conventional extraction alone. In addition, the potential influence of serum vitamin D levels on bone regeneration was assessed. (2) Methods: Thirty tooth extractions were performed and randomized into two groups: a test group (G1, n =15) and a control group (G2, n = 15). G1 received a laser-assisted protocol using Er:YAG and Nd:YAG lasers for granulation tissue removal, socket disinfection, clot stabilization, de-epithelialization, and photobiomodulation (PBM) with the Genova handpiece (LightWalker, Fotona, Slovenia). G2 underwent standard mechanical extractions and socket debridement without laser. (3) Results: Procedures in G1 were on average 8.7 min longer, but patients in this group reported significantly lower postoperative pain during the first three days (p < 0.05). A statistically significant difference in alveolar height was observed at the distal lingual site (25.4 mm vs. 21.7 mm; p = 0.046), with other sites showing a trend toward significance. Cumulative bone preservation, measured by Bone Loss Index (BLI4), was significantly better in the laser group. Notably, a positive correlation was found between serum vitamin D levels and bone preservation: each 1 ng/mL increase in vitamin D corresponded to a 0.18 mm gain in alveolar height (p = 0.021). (4) Conclusions: The comprehensive laser-assisted post-extraction protocol reduced postoperative pain and improved alveolar bone preservation, particularly at the lingual distal site. Serum vitamin D levels positively correlated with healing outcomes, suggesting a potential synergistic role of systemic and local regenerative factors. Full article

Metal hydrides are emerging hydrogen-storage materials that have attracted much attention for their stability and practicality. The novel magnesium-based metal hydride Mg₂CsH₉ was investigated using the CALYPSO software (version 7.0). First-principles predictive methods were then employed to investigate the structural, mechanical, electronic, optical, and hydrogen-storage properties of Mg₂CsH₉ and its alkali metal substitution structure Mg₂RbH₉. The negative formation energy, compliance with the Born stability criterion, and absence of imaginary modes in the phonon spectrum collectively confirm the thermodynamic, mechanical, and dynamic stability of Mg₂XH₉ (X = Cs, Rb), fulfilling the basic criteria for practical hydrogen-storage applications. Mg₂RbH₉ is particularly outstanding in terms of its hydrogen-storage capacity, with a gravimetric capacity of 6.34 wt% and a volumetric capacity as high as 92.70 g H₂/L, surpassing many conventional materials. The pronounced anisotropic characteristics of both compounds further enhance their practicality and adaptability to complex working conditions. An analysis of Poisson’s ratio revealed that the chemical bonding in both compounds is predominantly ionic. The details of the band structures and density of states indicate that Mg₂CsH₉ and Mg₂RbH₉ are semiconductors. Their optical properties confirm them as being high-refractive-index materials. Full article

Accurate prediction of the Air Quality Index (AQI) is crucial for protecting public health; however, the inherent instability and high volatility of AQI present significant challenges. To address this, the present study introduces a novel hybrid deep learning model, KL-PV-CBGRU, which utilizes Kalman filtering to decompose AQI data into features and residuals, effectively mitigating volatility at the initial stage. For residual components that continue to exhibit substantial fluctuations, a secondary decomposition is conducted using variational mode decomposition (VMD), further optimized by the particle swarm optimization (PSO) algorithm to enhance stability. To overcome the limited predictive capabilities of single models, this hybrid framework integrates bidirectional gated recurrent units (BiGRU) with convolutional neural networks (CNNs) and convolutional attention modules, thereby improving prediction accuracy and feature fusion. Experimental results demonstrate the superior performance of KL-PV-CBGRU, achieving R² values of 0.993, 0.963, 0.935, and 0.940 and corresponding MAE values of 2.397, 8.668, 11.001, and 14.035 at 1 h, 8 h, 16 h, and 24 h intervals, respectively, in Shijiazhuang—surpassing all benchmark models. Ablation studies further confirm the critical roles of both the secondary decomposition process and the hybrid architecture in enhancing predictive accuracy. Additionally, comparative experiments conducted in Beijing validate the model’s strong transferability and consistent outperformance over competing models, highlighting its robust generalization capability. These findings underscore the potential of the KL-PV-CBGRU model as a powerful and reliable tool for air quality forecasting across varied urban settings. Full article

Diamond grinding is a key concrete pavement restoration technique for concrete pavements. Traffic degrades the serviceability of the concrete pavements, resulting in unsatisfactory skid levels and noise concerns. Diamond grinding is known to enhance longevity and performance by improving smoothness and friction. By removing flaws with a cutting head equipped with diamond blades, the procedure produces a “corduroy” texture that enhances braking and stability. Diamond grinding typically results in a 20–80% reduction in the International Roughness Index, significantly enhancing pavement smoothness. It also improves macrotexture and creates longitudinal drainage channels, which collectively increase skid resistance and lower the chance of hydroplaning. The paper aims to highlight the need for diamond grinding for concrete pavements, which, despite their longevity, have decreased serviceability from traffic. The review further explores emerging innovations and identifies the gaps in long-term performance tracking and life-cycle environmental assessment. This paper reviews the effectiveness of diamond grinding as a pavement rehabilitation technique, with emphasis on ride quality, surface friction, noise reduction, and durability. Field applications and evaluation metrics are discussed to assess their contribution to pavement performance. This review aims to support researchers, pavement engineers, and agencies by providing a comprehensive understanding of diamond grinding’s applications, performance metrics, and potential for sustainable pavement management. Full article

Microbial communities, as critical functional components of riverine ecosystems, play a pivotal role in biogeochemical cycles and water quality regulation. The South-to-North Water Diversion Middle Route Project (SNWD-MRP) is a major cross-basin water transfer initiative, and bacteria are essential for the stability of water quality in the project. This study employed environmental DNA (eDNA) metabarcoding targeting the 16S rRNA gene to investigate spatiotemporal variations in water quality and bacterial communities along the SNWD-MRP during summer and winter. Integrated analyses, including redundancy analysis (RDA), Mantel tests, and ecological network modeling, were applied to unravel the driving mechanisms of microbial succession. The water quality along the SNWD-MRP is generally classified as Grade I, with significant seasonal variations in water quality parameters and microbial community composition. In the summer, higher temperatures lead to an increased abundance of cyanobacteria. In contrast, during the winter, lower water temperatures and higher dissolved oxygen levels result in the dominance of Pseudomonas and Bacillota species. RDA identified the permanganate index as the primary driver of microbial composition across seasons, with total phosphorus and total nitrogen having a greater influence in winter. Mantel tests highlighted significant correlations between Cyanobacteria and total phosphorus during winter. Ecological network analysis revealed that the complexity and connectivity of the winter network increased, likely due to suitable nutrient levels rendering the microbial network more complex and stable. These findings underscore the synergistic effects of temperature and nutrient availability on microbial succession, providing actionable insights for optimizing water quality management and ecological stability in large-scale water diversion systems. Full article

The water source protection areas of the Yangtze-to-Huaihe Water Diversion Project (YHWDP) in Anhui Province serve as crucial ecological barriers to water quality protection. Quantifying their eco-environmental quality (EEQ) dynamics and driving mechanisms is critical for sustainable management. This paper calculated the Remote Sensing Ecological Index (RSEI) for the study area using Landsat satellite data (2015–2024). Temporal and spatial variation characteristics were analyzed using the Theil–Sen estimator, Mann–Kendall test, and coefficient of variation. Future trends were predicted using the Hurst exponent. Finally, the Geodetector model was applied to assess the impact of driving factors. EEQ exhibited a declining trend (p < 0.05), with significant intra-regional heterogeneity. Mean RSEI values ranked as follows: (1) Yangtze River–Huaihe River Connection < Yangtze River Water Northward Conveyance < Yangtze River–Chaohu Lake Water Diversion. (2) From 2015 to 2024, eco-environmental quality improved significantly, showing a spatial pattern of “south > north, east > west.” (3) Overall EEQ changes were characterized by slight to moderate fluctuations. Stability rankings: Yangtze River–Huaihe River Connection > Yangtze River–Chaohu Lake Water Diversion > Yangtze River Water Northward Conveyance. (4) Geodetector analysis identified precipitation, impervious area, and vegetation coverage as the primary factors influencing EEQ in the YHWDP’s water source protection areas. This study reveals ecological changes in the YHWDP region and validates the effectiveness of the comprehensive evaluation method. The findings provide actionable insights for ecological protection in large-scale water diversion projects. Full article

Chitosan is a natural biopolymer with intrinsic antimicrobial properties and strong metal ion chelating properties, making it an ideal matrix for the development of bioactive composites. In this study, silver and copper nanoparticles were synthesized using laser ablation in liquid (LAL) by the ablation of metallic targets into commercial chitosan (Cs) and chitosan produced from Hermetia illucens pupal exuviae (CsE) solutions, avoiding the use of chemical precursors or stabilizing agents. The nanocomposites obtained were characterized by UV–vis spectroscopy, TEM microscopy and FTIR spectroscopy in order to evaluate the size of the nanoparticles and the interactions between the polymer and metal nanoparticles. Antibacterial tests demonstrated the efficacy of Ag-based composites with a minimum inhibitory concentration (MIC) of 0.006 g/L, and Cu-based composites with a MIC of 0.003 g/L against both Escherichia coli and Micrococcus flavus. While the silver composites show antibacterial activity in both colloidal and film forms, the copper composites present antibacterial activity only in colloidal form. Swelling tests indicated that all films maintained a high water absorption capacity, with a swelling index over 200%, unaffected by nanoparticle integration. The results highlight the potential of LAL-synthesized metal–chitosan composites, particularly those based on insect chitosan, as sustainable and effective antimicrobial materials for biomedical and environmental applications. Full article

Algal blooms pose a serious threat not only to the lake ecosystem of Lake Bosten but also by negatively impacting its rapidly developing fisheries and tourism industries. This study focuses on Lake Bosten as the research area and utilizes multi-source remote sensing imagery from Landsat TM/ETM+/OLI and Sentinel-2 MSI. The Adjusted Floating Algae Index (AFAI) was employed to extract algal blooms in Lake Bosten from 2004 to 2023, analyze their spatiotemporal evolution characteristics and driving factors, and construct a Long Short Term Memory (LSTM) network model to predict the spatial distribution of algal-bloom frequency. The stability of the model was assessed through temporal segmentation of historical data combined with temporal cross-validation. The results indicate that (1) during the study period, algal blooms in Lake Bosten were predominantly of low-risk level, with low-risk bloom coverage accounting for over 8% in both 2004 and 2005. The intensity of algal blooms in summer and autumn was significantly higher than in spring. The coverage of medium- and high-risk blooms reached 2.74% in the summer of 2004 and 3.03% in the autumn of 2005, while remaining below 1% in spring. (2) High-frequency algal bloom areas were mainly located in the western and northwestern parts of the lake, and the central region experienced significantly more frequent blooms during 2004–2013 compared to 2014–2023, particularly in spring and summer. (3) The LSTM model achieved an R² of 0.86, indicating relatively stable performance. The prediction results suggest a continued low frequency of algal blooms in the future, reflecting certain achievements in sustainable water-resource management. (4) The interactions among meteorological factors exhibited significant influence on bloom formation, with the q values of temperature and precipitation interactions both exceeding 0.5, making them the most prominent meteorological driving factors. Monitoring of sewage discharge and analysis of agricultural and industrial expansion revealed that human activities have a more direct impact on the water quality of Lake Bosten. In addition, changes in lake area and water environment were mainly influenced by anthropogenic factors, ultimately making human activities the primary driving force behind the spatiotemporal variations of algal blooms. This study improved the timeliness of algal-bloom monitoring through the integration of multi-source remote sensing and successfully predicted the future spatial distribution of bloom frequency, providing a scientific basis and decision-making support for the sustainable management of water resources in Lake Bosten. Full article

Real-time and accurate monitoring of rice nitrogen status is essential for precision nitrogen management. Although unmanned aerial vehicle (UAV)-based spectral sensors have been widely used, existing estimation models that rely solely on crop phenotypes still suffer from limited accuracy and stability. In this study, the UAV vegetation indices (VIs), meteorological parameters (M) and fertilization (F) data were incorporated as input variables to establish rice N nutrition index (NNI) estimation models using three machine learning (ML) algorithms (adaptive boosting (AB), partial least squares (PLSR) and random forest (RF). The results showed that the models’ predictive accuracy ranked as follows based on input variable combinations: VI + M + F > VI + F > VI + M > VI. Among the three ML models, the RF algorithm demonstrated the best performance and achieved validation R² values ranging from 0.94 to 0.95 across all growth stages. Both meteorology and fertilization factors benefited the model, with their incorporation greatly improving model accuracy. This demonstrated the potential to enhance the diagnosis of seasonal rice nitrogen status and provide guidance for in-season site-specific N management through consumer-grade UAV imagery and machine learning. Full article

Background/Objectives: Messenger RNA (mRNA) modifications regulate key steps in gene expression, including splicing, translation, and stability. Despite over 300 known RNA modifications, the relatively small subset occurring in mRNA remains understudied compared with tRNA and rRNA. This review aims to systematically evaluate 15 known naturally occurring mRNA-specific modifications, rank them by publication frequency, and highlight emerging frontiers in epitranscriptomics, including discovering new naturally occurring mRNA modifications and environmental RNA (eRNA) epitranscriptomics. Methods: We conducted a structured literature review of PubMed-indexed publications to rank mRNA modifications by citation prevalence. Key modifications such as m⁶A, m⁵C, Ψ, and m¹A were analyzed in terms of enzymatic machinery (“writers,” “erasers,” and “readers”), molecular functions, and physiological relevance. We also reviewed technological advances, with a focus on nanopore sequencing for detection of RNA modifications in native and environmental contexts. Results: The modification m⁶A was identified as the most studied mRNA modification, followed by Ψ, m⁵C, and A-to-I editing (inosine). These modifications influence diverse mRNA processes, including translation efficiency, localization, and immune evasion. Cap-specific modifications such as Cap0, Cap1, and Cap2 were also described, highlighting their role in transcript stability and innate immune regulation. Advances in nanopore sequencing have enabled direct detection of RNA modifications and offer promise for eRNA (environmental RNA) surveys. The potential for nanopore sequencing of many other of the 335 known RNA modifications in the MODOMICS database using existing nanopore technologies is also discussed. Conclusions: mRNA modifications represent a critical, yet incompletely mapped, layer of gene regulation. Continued research—especially using nanopore and machine learning technologies—will help uncover their full biological significance. Exploration of eRNA and identifying new mRNA modifications will redefine our understanding of RNA biology. Full article