The changes in functional groups during in situ co-pyrolysis of tar-rich coal with wheat straw were systematically examined using synchrotron diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) coupled with thermogravimetric analysis (TGA). Dynamic changes in C=C, C-O, and C-O-C groups were monitored and assessed across 50–500 °C, complemented by thermogravimetric analysis to assess synergistic effects. It revealed that co-pyrolysis significantly alters the thermal cracking pathways of oxygenated structures, reducing the overall onset temperature by approximately 150 °C. Specifically, instead of maintaining thermal stability, co-pyrolysis promoted early structural aromatization and advanced the C=O decomposition onset by 50 °C compared to coal, achieving a remarkable functional group cleavage rate of 47%. Additionally, the C=C formation temperature was advanced by 150 °C. Furthermore, co-pyrolysis effectively suppressed the secondary structural transformations observed in biomass by limiting the relative accumulation of C–O–C structures to merely a 5% increase, compared to a 52% surge in wheat straw. Interestingly, while DRIFTS confirms facilitated localized bond cleavage and deoxygenation, TGA reveals a macroscopic negative synergy regarding overall weight loss. These findings provide profound insights into the complex radical interactions during co-conversion, offering a crucial theoretical basis for optimizing coal–biomass co-pyrolysis technologies.

Mineral scale deposition remains a major flow-assurance constraint in oil and gas operations, especially in water-flooding and produced-water reinjection, where mixing between incompatible brines promotes super-saturation and precipitation of poorly soluble salts. This work introduces a novel extension of traditional methods used for modeling chemical inhibition and the predictive evaluation of oilfield scale-inhibitor molecules. A systematically optimized Two-Dimensional Quantitative Structure–Activity Relationship Model based on the k-Nearest Neighbors algorithm 2D-QSAR-KNN model was developed to quantitatively link molecular constitution of phosphonate inhibitors, brine chemistry, and operating factors with inhibition efficiency IE %. The optimized model achieved strong accuracy and generalization R2train = 0.9182, R2test = 0.9306, and R2global = 0.9208 with low prediction errors RMSEtrain = 4.7888%, RMSEtest = 4.5485%, and RMSEglobal = 4.7421%. Median absolute errors remained minimal for the train set = 0.80%, and test set = 1.63%, and model stability was confirmed by high correlation with experimental IE % r = 0.94 and R2train/R2test ≈ 0.99, showing no sign of overfitting. Additionally, an inverse-2D-QSAR framework was applied to identify the optimal molecular descriptor profile expected to maximize inhibitory performance within normalized bounds, providing rational rules for next-generation inhibitor design. The findings highlight the practical value of QSAR-inspired AI modeling to accelerate molecule screening and dosage exploration prior to laboratory validation, supporting more cost-effective, interpretable, and environmentally aware sulfate-scale inhibition strategies under high-salinity reservoir conditions.

The journal retracts the article entitled “A Fuzzy Multicriteria Decision-Making (MCDM) Model for Sustainable Supplier Evaluation and Selection Based on Triple Bottom Line Approaches in the Garment Industry” [...]

As semiconductor devices continue to scale down and integrate more densely, the atomic-level planarization of metal interconnects and dielectric layers is critical. Consequently, the development of chemical mechanical planarization (CMP) materials must address both high polishing performance and environmental sustainability. In this study, γ-Fe2O3@SiO2 core–shell abrasive particles were designed to overcome the performance and recyclability limitations of conventional SiO2 abrasives. The γ-Fe2O3 core enables an efficient magnetic separation from spent slurry, while the tunable SiO2 shell enhances the dispersion stability and modulates the polishing characteristics. When applied to the CMP of tungsten (W) thin films, the optimized γ-Fe2O3@SiO2 abrasives achieved a higher removal rate and lower surface roughness than commercial SiO2-based slurries. Notably, the abrasives maintained a high performance even after 10 reuse cycles through simple magnetic recovery. This demonstrates a highly efficient and sustainable design strategy for next-generation CMP materials.

Gas analyzers in post-combustion CO2 capture plants are accurate but slow and sequential, yielding sparse, non-synchronous concentration records across absorber stages. We address this missing-data problem by reconstructing continuous CO2 profiles with Moving Horizon Estimation (MHE) constrained by a mechanistic absorber model and available measurements; these MHE reconstructions are used as supervisory labels to train an end-to-end Stacked Denoising Autoencoder–Gated Recurrent Unit (SDAE-GRU) model. At run time, we deploy a hybrid soft sensor using the SDAE-GRU together with the mechanistic model and fuse their open-loop predictions via covariance-weighted blending with Gaspari-Cohn localization. We validate this approach on a pilot-scale MEA absorber using data from seven pilot runs conducted at distinct operating conditions, using datasets 1–5 for training/tuning and 6–7 for blind validation. On the blind validation runs, the hybrid estimator achieves a MAPE of 3.79% for stage-wise CO2 predictions (averaged over all stages and time samples), outperforming both constituents evaluated standalone: 7.86% for the GRU-only soft sensor and 6.79% for the mechanistic model. Because MHE is used only offline to generate labels and to estimate model-error covariances, the deployed estimator is lightweight and suitable for online monitoring.

Two types of basalt fiber-reinforced polymer (BFRP) anchor cables—a Tension-concentrated anchor cable (TCAC) and a Pressure-dispersed anchor cable (PDAC)—were developed through structural modification of the rod body and implemented for reinforcing fractured rock masses on highway tunnel slopes in western Henan Province, China. The feasibility of replacing conventional steel rods with BFRP bars and the corresponding anchorage mechanisms were investigated. The experimental results indicate that the axial force distribution differs markedly between the two anchors. The TCAC exhibits a decreasing axial force with depth, forming a concave distribution under low load and a convex distribution under high load, with the force approaching zero beyond 100 cm. In contrast, the PDAC displays a relatively uniform axial force that sharply decreases near the bearing plate, and, under increasing load, the axial force at the anchorage end tends to rise; Both anchors demonstrate single-peak interfacial shear stress distributions. For the TCAC, the peak progressively shifts toward deeper regions with increasing load, whereas the peak of the PDAC consistently appears near the bearing plate, with only its magnitude increasing. Stability analysis using GEO5 software reveals that the slope safety factor increases from 1.32 (without anchors) to 1.36 (with anchors), thus satisfying the design requirements. The results reveal the different anchoring mechanisms of tension-concentrated anchor cables and pressure-dispersed anchor cables, providing practical guidance for their selection and application in slope stabilization engineering.

High-pressure water injection (HPWI) can rapidly replenish the formation energy of low-permeability reservoirs, but it may trigger multi-scale fractures, leading to premature water breakthrough between injection and production wells. To identify the main causes and regulate the mainstream line (i.e., the preferential flow path with the highest streamline density/flow rate), a two-zone and five-point numerical model was developed. This model couples the static damage zone (dominated by micro-fractures) and the fracture development zone (dominated by macro-fractures). Through sensitivity analysis, the ways in which micro-fracture damage and macro-fracture geometry control the evolution of seepage patterns and the risk of water breakthrough were quantified. The results show that in the representative scenarios of this paper, micro-fracture damage is mainly associated with an increased risk of water breakthrough by forming equivalent weakening zones and enhancing the directional extension trend of main fractures. The scale of macro-fractures has the strongest correlation with the water breakthrough response. When the fracture scale increases to a certain proportion close to the well spacing, the seepage mode changes from “fracture + matrix cooperation” to “main-fracture-dominated short-circuit channel”. Based on this, a design and verification of a combined control scheme of “chemical profile control + cyclic water injection” was proposed and carried out in well groups with high water cut and strong channeling. Simulations show that this combination helps to weaken the flow conductivity of preferential channels and improve the uniformity of the flow field. This paper can provide technical support for the prevention, control, and early warning of water breakthrough and the regulation of main flow lines in the high-pressure water injection development of similar low-permeability reservoirs.

Ultrasound has emerged as a versatile and promising tool to enhance and speed up traditional processing operations used by the food industry or to be used as an alternative food-processing method. This review provides an overview of the fundamental principles of sonication and its diverse applications in food processing. The core concepts of acoustic cavitation and the influence of power on processing outcomes are discussed in detail. The design and operation of different ultrasound systems, including direct-contact probe and indirect-contact bath systems, and their respective advantages were reviewed. Furthermore, a wide array of applications were explored, namely extraction, homogenization, degassing and deodorizing, pasteurization and vegetable blanching, drying and dehydration, freezing and thawing, brining and hydration, and cutting, highlighting how ultrasound waves can enhance process efficiency and improve product quality. The review also provides a critical analysis of the challenges and limitations associated with scaling up the technology for industrial use, including potential impacts on food quality, safety considerations, and economic viability. Finally, future perspectives and potential areas for further research are outlined to encourage the broader adoption of this technology in the food sector.

To address the critical need for accurate human thermal comfort prediction in winter heating environments, this study established a comprehensive thermal comfort dataset containing 2089 valid samples through experiments. On this basis, thermal comfort prediction models were constructed using three multi-class machine learning algorithms: Support Vector Classification, K-Nearest Neighbors, and Random Forest. The predictive performance of 63 different feature combinations was systematically evaluated. The results indicate that the feature subset comprising indoor air temperature, forehead temperature, cheek temperature, dorsal hand temperature, heart rate, and systolic blood pressure yields the optimal prediction performance. Among the evaluated models, the Random Forest model demonstrated superior overall performance, achieving an accuracy exceeding 90% and an AUC ranging from 96% to 99%, significantly outperforming the SVC and KNN models. Compared with the traditional Predicted Mean Vote (PMV) model, the machine learning models developed in this study showed a substantial improvement in prediction accuracy under identical conditions; notably, the Random Forest model improved accuracy by approximately 40% over the PMV model. Based on these findings, a smart heating system framework integrating environmental sensors, wearable devices, and intelligent control valves is proposed, providing a theoretical basis and technical approach for realizing personalized and energy-efficient heating control.

In response to growing concerns over global warming and energy sustainability, transitioning from fossil-fuel-based heating systems to renewable alternatives is essential. This study evaluates the economic and environmental performance of geothermal heat pumps for building heating and compares it with conventional coal-fired boilers, natural-gas boilers, and diesel furnaces. Using the heating degree-day (HDD) method, heating energy demand was analyzed for four U.S. cities—Anchorage (AK), San Francisco (CA), Salt Lake City (UT), and Las Vegas (NV)—representing diverse climatic zones. The analysis integrates thermodynamic and economic parameters, including the coefficient of performance (COP = 2–5) and annual fuel-utilization efficiency (AFUE = 80–97%), to evaluate heating-system performance and operational cost across different climatic regions. Sensitivity analysis with ±10% variations in fuel and electricity prices and system efficiencies demonstrates that geothermal heating remains the most stable and emission-efficient option under all scenarios. Results indicate that geothermal systems, despite higher reported initial investment, achieve lower operational and emissions-related costs and offer a robust and sustainable solution for decarbonizing building-heating systems. For example, the estimated seasonal geothermal heating cost is $370.59 in Anchorage compared with $646.48 for coal heating and $3375.65 for diesel systems. Furthermore, policy evaluation indicates that federal and state incentives, such as investment tax credit under the Inflation Reduction Act and rebate programs, can reduce installation costs by 25–40%, improving economic feasibility, particularly in colder regions. The analysis focuses exclusively on energy and emissions-related costs and does not explicitly model capital investment or levelized cost metrics.