Browse Articles

Predictive value of complete blood cell count-based inflammatory markers for peritonitis risk in peritoneal dialysis patients: A multicenter cohort study

Background Peritonitis is a serious complication of peritoneal dialysis (PD). Inflammatory indices derived from routine complete blood count (CBC) parameters—including the pan-immune inflammatory value (PIV), systemic immune-inflammatory index (SII), platelet-to-lymphocyte ratio (PLR), neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), and platelet-to-monocyte ratio (PMR)—have shown prognostic value in various diseases. However, their comparative utility in predicting PD-associated peritonitis (PDAP) remains unclear. This multicenter cohort study aimed to evaluate and compare these indices to identify the best predictor of PDAP. Methods We retrospectively enrolled 2,036 PD patients from 10 centers. The associations between inflammatory markers (PIV, SII, PLR, NLR, MLR, PMR) and peritonitis risk were analyzed using restricted cubic splines. Optimal cut-offs were determined by ROC analysis. Survival differences were assessed using Kaplan–Meier curves and log-rank tests. Independent predictors were identified via multivariate Cox regression, with model discrimination evaluated by the C-index. Subgroup analyses were conducted by gender, age, body mass index (BMI), diabetes, albumin, PD vintage, and residual renal function. Results The median age was 51.0 years, 55.01% were male, and median dialysis vintage was 49.47 months. Diabetes prevalence was 21.02%. Over the follow-up, 147 patients (7.22%) developed peritonitis. Among the indices evaluated, PIV, SII, and PLR showed significant nonlinear associations with peritonitis risk (all P  < 0.05). Adjusted hazard ratios were 2.004 for PIV, 2.144 for SII, and 2.063 for PLR. Adjusted C-indices were 0.67 (PIV), 0.70 (SII), and 0.70 (PLR). No significant interactions were found in subgroup analyses. Conclusion Elevated PIV, SII, and PLR levels at PD initiation independently predict higher peritonitis risk. Although their discriminative ability is moderate, these routine, cost-effective indices may aid risk stratification and help identify patients needing closer monitoring or preventive interventions.

Microscopic pore characteristics governing macroscopic freeze thaw durability and service life prediction of ternary blended concrete

Preliminary study on comparative non-targeted metabolomics analysis sheds light on the chemical diversity of citrus fruit pulps

Citrus flavor and nutritional quality are closely tied to metabolite composition, yet comparative metabolic dissection of fruit pulp traits across citrus subspecies remains insufficient. Here, we applied non-targeted LC–MS/MS metabolomics to examine chemical diversity in the fruit pulp of three representative citrus varieties: Citrus reticulata ‘Hongju 418’, Citrus aurantium ‘Changshan-huyou,’ and Citrus junos ‘Hunan Xiangcheng.’ Through differential metabolite analysis, multivariate modeling, correlation network construction, and KEGG pathway enrichment, we characterized the extent and nature of metabolic divergence among these genotypes. PCA, PLS-DA, and OPLS-DA revealed distinct metabolic clusters, underscoring strong genotype-specific variation. More than 300 differentially expressed metabolites were identified, including flavonoid glycosides, organic acids, phenolic derivatives, and limonoids. Hongju 418 was enriched in flavonoid biosynthetic pathways, Xiangcheng accumulated higher concentrations of organic and amino acids, and Huyou displayed a unique hybrid profile marked by elevated fatty acid and purine metabolism. Correlation and KEGG analyses consolidated these observations, revealing coordinated pathway-level shifts that define subspecies-specific metabolic architectures. Collectively, this work deepens current understanding of citrus pulp chemotypes and provides a robust biochemical foundation for advances in citrus breeding, quality assessment, and functional product innovation.

Thermodynamic design and energy–exergy analysis of a water-cooled photovoltaic–thermal systems under tropical conditions

AgriOptNet: A hybrid optimization and lightweight deep learning framework for soil texture classification and crop recommendation based on nutrition

Agriculture is a central part of human subsistence, with classification of soil texture and nutrition-based crop recommendation being the central aspects of optimal agricultural practice. Nevertheless, traditional methods are subject to limitations of being less precise, computationally less optimal, and less versatile concerning varying soil and environmental conditions. Current deep learning models are frequently unable to compromise between performance and efficiency, whereas traditional optimization methods fail to handle high-dimensional agriculture data efficiently, resulting in suboptimal suggestions and poor real-time usage. To address these issues, this research presents AgriOptNet, a hybrid deep learning and optimization framework for intelligent soil texture classification and crop recommendation based on nutrition. AgriOptNet novelty is founded upon three integral constituents like Crop Recommendation through Entropy-Regularized Dynamic Deep Q-Learning with Adaptation to the Reward Function (MDQL-RA), optimally dynamic recommendations of crop inputs depending upon the health of soil, yield records, and surrounding environmental aspects and utilizing entropy regularization to accelerate exploration; Classification using a newly invented lightweight deep-learning model called SoilCropNet with a compound based on MobileNetV2, EfficientNetV2, and ShuffleNetV2 and provides precise, and computationally favourable classification along with squeeze-and-excitation as well as depth-wise separable convolutional enhanced properties; Feature selection through newly developed hybrid SailDragon Optimizer (SDO), combining Sailfish Optimization (SFOA) and Dragonfly-Based Optimization (DBOA), to obtain best-informing features for predictions without errors. The proposed AgriOptNet framework demonstrates superior performance with an accuracy of 99.87% and an F1-score of 98.75%, significantly outperforming existing techniques and ensuring high precision and efficiency for real-time precision agriculture applications.

Comparative analysis of deep learning models for garment segmentation in textile recycling

Abstract The textile industry is a key player in the global economy, but its expansion and resource consumption raise sustainability concerns. Addressing these issues is critical to fostering a more sustainable future. One key challenge in garment recycling is the removal of accessories and inserts before fibers can be recovered. This study presents a comparative analysis of deep learning models for garment segmentation as an enabling step for future automated textile recycling workflows. Accurate garment segmentation can support subsequent tasks such as garment classification, accessory localization, and selective removal, improving the consistency and efficiency of pre-processing before fiber recovery. Several state-of-the-art models, including You Only Look Once version 8 (YOLOv8), Inception Residual Network version 2 with U-Net (InceptionResNetV2-UNet), U2-Net, and Mask Region-based Convolutional Neural Network (Mask R-CNN), were evaluated to understand their strengths and weaknesses in handling garment complexity. The highest performance was demonstrated by the combination of the YOLOv8n detector plus U2-Net, with 99.56% accuracy, 99.11% F1-score, 98.23% Jaccard Index, 99.91% recall, and 98.33% precision, when tested on 347 laboratory images, showcasing robust segmentation capabilities under controlled conditions. The results suggest that this combination is well-suited for garment segmentation tasks and can provide a useful computer vision foundation for future accessory localization and automated removal modules. This comparison serves as a basis for future studies focused on improving segmentation robustness and integrating garment segmentation with downstream textile recycling processes.

Beyond citation-based metrics: Measuring interdisciplinarity via SBERT semantic embeddings and its heterogeneous effects on citation impact

Background Interdisciplinary research is a cornerstone of global science policy, yet decades of research have reached conflicting conclusions about its association with citation impact. This inconsistency stems primarily from traditional indicators, which rely on reference diversity rather than genuine semantic knowledge integration, and from small, discipline-specific samples that limit generalizability. Objective This study introduces a novel semantic interdisciplinarity measure based on Sentence-BERT (SBERT) embeddings, which directly captures cross-disciplinary knowledge integration at the textual level, and tests its heterogeneous relationship with citation impact across the full spectrum of scientific disciplines. Methods We analyzed 121,194 articles published 2015–2025 across all 19 root-level disciplines in OpenAlex. We validated the reliability of OpenAlex disciplinary classification using multi-dimensional semantic analyses, and compared our SBERT-based indicator with the Simpson Diversity Index and Rao–Stirling Index. We employed OLS and negative binomial regressions with discipline and year fixed effects (standard errors clustered at the discipline level), journal tier heterogeneity analysis, and domain-specific decomposition analyses. Results The semantic interdisciplinarity indicator shows moderate convergent validity with conventional citation-based metrics (r = 0.333–0.347, p < 0.001) and provides a small but meaningful increase in explanatory power beyond traditional indicators (Δ adjusted R² = 0.003), although its coefficient is marginally significant and negative (β = −1.5565, p = 0.085). Overall, semantic interdisciplinarity is positively associated with citation impact in baseline models, but this effect is primarily driven by cross-domain integration between epistemically distant domains, particularly in the natural sciences. The positive effect is consistent across all journal influence tiers, with the strongest effect observed in mid-tier journals, and presents stark heterogeneity across individual disciplines. Conclusion Boundary-spanning research bridging epistemically distant domains appears to deliver consistent citation rewards. Our findings address long-standing inconsistencies in the literature, and provide actionable insights for research evaluation and science policy.

Signal processing and machine learning analysis of IMU-based lower-limb kinematics in spinal sagittal imbalance

Characterisation of the bacterial and archaeal microbiota in processed colostrum collected from a spring-calving dairy herd

Colostrum feeding is critical for neonatal calf health, providing immunoglobulins (Ig) and other bioactive compounds that support immune function and early microbiome development. While the microbiota of fresh colostrum has been characterised, colostrum on commercial dairy farms is often refrigerated and reheated prior to feeding – practices that may alter its microbial composition. Therefore, the objective of this study was to characterise the prokaryotic community of refrigerated and reheated (processed) colostrum collected immediately before calf feeding. Twenty-one processed colostrum samples were collected from a single, primi- and multiparous Holstein-Friesian and Jersey, spring-calving dairy herd with no more than two donors contributing to each sample. Colostrum samples were refrigerated for no more than 24h and then re-heated in a 38°C water bath for 60 minutes. Colostrum samples were collected immediately prior to being fed to the calf. Microbial DNA was extracted and16S rRNA gene amplicon libraries were sequenced using the Illumina platform. Raw sequencing data were processed in R via the DADA2 pipeline, and an amplicon sequence variant (ASV) table was generated. Taxonomy was assigned using the SILVA database (v. 138.1) and data were subjected to α- and β-diversity analysis using Phyloseq, Microbiome and Vegan . Breed and parity had no effect (P ≥ 0.05) on α- and β-diversity. The mean Shannon index score (α-diversity) was 2.26 (SE 0.18), indicating unevenness and low levels of richness. Microbial community composition varied considerably between samples. Five archaeal ASV genus groups were identified, with Methanobrevibacter dominating this community(relative abundance (RA) of 85.19%). Four bacterial phyla were identified as the major contributors to the bacterial component of processed colostrum. Only 39 ASV genus groups were identified as having a RA > 0.05%. Processed colostrum was dominated by Pseudomonas (RA = 20.97%) and Acinetobacter (RA = 18.65%). These genera, along with 11 others, including Romboutsia, Flavobacterium. Lachnospiraceae NK3A20 group and Clostridium sensu stricto 1 were present across all samples and thus considered core bacteria. Overall, these findings indicate that refrigeration and reheating may significantly alter the natural colostrum microbiota, reduce diversity and increase heterogeneity of the community composition. Further research is needed to determine how these changes influence microbial seeding and calf health outcomes.

Whole-genome analysis of drug resistance and transmission patterns in Mycobacterium tuberculosis from Khyber Pakhtunkhwa, Pakistan

Abstract The emergence of drug-resistant Mycobacterium tuberculosis strains presents a significant challenge to tuberculosis (TB) control programmes worldwide, particularly in high-burden countries, such as Pakistan. This study characterises the genomic diversity and drug resistance mutation profiles of M. tuberculosis isolates from Khyber Pakhtunkhwa using whole-genome sequencing (WGS). A total of 35 clinical M. tuberculosis isolates underwent WGS. Sequence data were quality-filtered and mapped to the H37Rv reference genome, characterising variants, including single-nucleotide polymorphisms (SNPs). Lineage classification, drug resistance profiling, and identification of resistance mutations were performed using TB-Profiler software. SNP-based clustering was used to infer recent transmission events. All four major  M. tuberculosis  lineages (L1–L4) were detected; however, more than half of the isolates belonged to lineage L3 (CAS, n  = 19; 54.3%). Almost all isolates were drug-resistant (34/35), including pre-extensively drug-resistant (pre-XDR,  n  = 15; 42.9%) and multidrug-resistant (MDR,  n  = 14; 40.0%) strains. The most frequent resistance-associated mutations included  katG S315T (27/35) and  inhA c.-777C > T (4/35) for isoniazid resistance,  rpoB S450L (24/35) for rifampicin resistance,  embB M306I/V (18/35) for ethambutol resistance, and  gyrA D94G (8/35) and A90V (5/35) for fluoroquinolone resistance. Compensatory mutations were observed in  rpoC and  ahpC . Mutations associated with bedaquiline and clofazimine resistance ( mmpR5 c.140dupA, c.138_139dupTG) were detected in single isolates. Interestingly, one isolate exhibited a deletion encompassing  mmpR5, mmpL5 , and mmpS5 , potentially increasing bedaquiline susceptibility. Phylogenetic analysis using 6,635 genome-wide SNPs identified two transmission clusters with < 9 SNPs differences, suggesting ongoing local transmission. These results highlight the presence of drug-resistant  M. tuberculosis  strains in Khyber Pakhtunkhwa, encompassing both globally recognised and novel resistance-associated mutations. Our findings underscore the critical need to integrate WGS into TB surveillance and control programmes in Pakistan to guide targeted interventions and curb the spread of resistant strains.

Structural Design Strategies of Sustainable Wood‐Derived Solar‐Driven Interfacial Evaporator for Advancing Water Purification

ABSTRACT High energy consumption poses a significant limitation to conventional water purification technologies. Solar‐driven interfacial evaporation (SDIE) has emerged as a sustainable alternative. Wood, endowed with its inherent hierarchical porous structure, exceptional hydrophilicity, low thermal conductivity, and biocompatibility, stands out as an ideal substrate for SDIE. When used in SDIE, wood not only reduces embodied carbon by substituting energy‐intensive materials but also continues to store biogenic carbon throughout its service life, thereby lowering the device's overall carbon footprint. This review synthesizes research advances in wood‐based SDIE (W‐SDIE), focusing on structural design strategies for performance enhancement. Specifically, it elaborates on the advantages of wood's microstructure and chemical composition as a substrate; summarizes performance optimization approaches across three dimensions: heat management (light absorber design, insulating structure, and environmental energy harvesting), water management (water transport pathway, wettability regulation, and evaporation enthalpy reduction), and salt management (water‐salt transport regulation, salt‐oriented crystallization, and self‐cleaning mechanism); and outlines applications in desalination, wastewater treatment, photocatalytic degradation, and energy coupling. Ultimately, current challenges and potential solutions are discussed, providing insights for the future development of W‐SDIE.

Machine learning of electronic structure and atomistic properties from the external potential

Electronic structure calculations remain a major bottleneck in atomistic simulations and, not surprisingly, have attracted significant attention in machine learning (ML). Most existing approaches learn a direct map from molecular geometries, typically represented as graphs or encoded local environments, to molecular properties or use ML as a surrogate for electronic structure theory by targeting quantities, such as Fock or density matrices expressed in an atomic orbital (AO) basis. Inspired by the Hohenberg–Kohn theorem, in this work, we propose an operator-centric framework in which the external (nuclear) potential, expressed in an AO basis, serves as the model input. From this operator, we construct hierarchical, body-ordered representations of atomic configurations that closely mirror the principles underlying several popular atom-centered descriptors. At the same time, the matrix-valued nature of the external potential provides a natural connection to equivariant message-passing neural networks. In particular, we show that successive products of the external potential provide a scalable route to equivariant message passing and enable an efficient description of nonlocal effects. We demonstrate that this approach can be used to model molecular properties, such as energies and dipole moments, from the external potential or to learn effective operator-to-operator maps, including mappings to the Fock matrix from which multiple molecular observables can be simultaneously derived.

Jellyfish‐Inspired Hydrogels Enabling Synergistic Antifouling and Low‐Drift for Transformer‐Assisted Multimodal Marine Bioelectronics

ABSTRACT Hydrogels have emerged as a versatile materials platform for soft and biointegrated electronics. However, reliable hydrogel bioelectronics under seawater conditions is limited by biofouling at interfaces, high‐salinity swelling‐induced drift, and noise‐robust temporal decoding. Here, we present a hydration‐locked percolation strategy to engineer a jellyfish‐inspired hydrogel that combines synergistic antifouling with drift‐resistant conduction for AI‐assisted multimodal marine bioelectronics. The constructed hydration‐polyphenol network suppresses nonspecific adsorption and early biofilm evolution, achieving a synergistic antifouling system that repels and inactivates fouling organisms. Meanwhile, cross‐substrate wet anchoring and localized swelling suppression stabilize electron‐percolation pathways, thereby enabling long‐term stable high conductivity (22 S m − 1 ) and physiological signal acquisition with high signal‐to‐noise ratio in seawater. Notably, a tailored multimodal decoding framework integrating Transformer encoder with multilayer perceptron further enables accurate interpretation of complex physiological signals (98.5% accuracy) by capturing long‐range temporal dependencies and cross‐modal correlations. This integration of high‐performance bioinspired hydrogels with Transformer‐assisted decoding paves the way for long‐term, high‐fidelity marine bioelectronics.

Auger spectra of thiouracils: Three molecules and four edges

This study presents a comprehensive investigation of the Auger spectra of three thionated uracils using a combination of theory and experiment. Non-resonant Auger spectra measured at the K-edges of carbon, nitrogen, and oxygen, as well as at the L2,3 edge of sulfur, are reported. Even though the final decay states (valence two-hole states) are the same for all edges, the resulting Auger spectra are strikingly different, highlighting the local nature of the Auger decay. Theoretical modeling, which employed a series of approaches incorporating varying levels of correlation and different treatments of the metastable states, provides a clear interpretation of the experimental spectra. The analysis highlights the crucial role of electron correlation, evident in both the density of the decay states and the spectra constructed using channel-specific partial decay widths, as well as the importance of spin–orbit coupling in shaping the L-edge spectra of thiouracils.

Mechanistically Interpretable Artificial Intelligence for Designing Oxygen Electrocatalysts

ABSTRACT Oxygen reduction and evolution reactions (ORR and OER) are key electrochemical processes central to energy conversion and chemical transformation. However, the inherently complex, multi‐physics nature of ORR/OER—together with diverse operating environments—poses significant challenges to the rational design of electrocatalysts based on structure–property relationships. To overcome these challenges, we developed Two‐Stage Material Screening (TSMS), an AI‐driven framework that integrates density functional theory (DFT) computations, an active‐learning‐guided experimental feedback loop, and mechanistic interpretation to enable rapid discovery and systematic evaluation of promising electrocatalysts. Demonstrated in protonic solid oxide cells (P‐SOCs), TSMS screened 6,940,032 compositions and identified top‐performing candidates that were experimentally validated, achieving a peak power density of 2.68 W cm −2 in fuel cell mode and a current density of 3.51 A cm −2 at 1.3 V in electrolysis mode, with stable performance maintained over 500 h at 600°C. Our analysis revealed that electron affinity is strongly associated with thermodynamic stability, while d‐p hybridization and densification resistance emerge as the primary descriptors governing electrocatalytic activity. By combining predictive modeling with mechanistic understanding, TSMS establishes a versatile and broadly generalizable paradigm for accelerating materials discovery.

Quantum machine learning for predicting properties of van der Waals bilayers

Layering two-dimensional (2D) materials into van der Waals bilayers provides an effective method to achieve innovative quantum states and tunable electronic properties. Exploring the extensive configurational space resulting from various layer combinations, twist angles, and stacking patterns presents a significant computing challenge. As the volume of accessible datasets expands, training machine learning models on conventional hardware may ultimately become excessively expensive, prompting the advancement of quantum machine learning (QML) methodologies for materials discovery. This study presents a quantum-enhanced machine learning framework for predicting the bandgaps of van der Waals bilayers utilizing quantum simulators. We start with conventional feature selection to ascertain the most important descriptors affecting electronic properties. The properties are subsequently encoded into quantum states utilizing parameterized quantum circuits. We evaluated three quantum feature maps, namely ZFeatureMap, ZZFeatureMap, and PauliFeatureMap, and observed task-dependent performance. For the classification task, ZFeatureMap with two repetitions gave the strongest quantum-classifier performance, whereas for Quantum Support Vector Regressor (QSVR) bandgap regression, PauliFeatureMap with one repetition achieved the lowest average root-mean-square error (RMSE). These results indicate that feature-map choice should be optimized separately for classification and regression rather than being interpreted as the universal superiority of a single feature map. Using these feature-map comparisons, we employ a QSVR and a variational quantum regressor to predict the bandgaps of 1850 semiconducting vdW bilayers, illustrating that QML may attain significant predictive accuracy despite limited training data. Overall, the results demonstrate the feasibility of a simulator-based QML workflow for materials-property prediction. The quantum classifiers are competitive with the classical support vector classifier (SVC) baseline for zero-gap/non-zero-gap classification, whereas the classical Support Vector Regression (SVR) achieves the lowest RMSE in the regression task. Therefore, the present study should be interpreted as an exploratory benchmark of hybrid classical–quantum models for van der Waals (vdW) bilayers rather than as evidence of a definitive quantum performance advantage.

Element-specific dynamical decoupling and local structural ordering in liquid NiCoCr medium-entropy alloy

The Stokes–Einstein relation (SER) links diffusion and viscosity, but its applicability is challenged in systems with complex structure and chemical properties. Medium-entropy alloys (MEAs), with their inherent chemical complexity and propensity for diverse local ordering, offer a compelling platform to study such coupling phenomena in the liquid state. However, how element-specific structural preferences influence SER breakdown remains unexplored. Here, we investigate the microscopic mechanisms underlying SER breakdown in liquid equiatomic NiCoCr MEA using high-energy x-ray scattering and molecular dynamics simulations. A dynamic transition is observed near 1700 K (melting point 1682.9 K). While the self-diffusion coefficients of all elements retain Arrhenius behavior down to the deeply supercooled regime, the viscosity and structural relaxation times exhibit a clear cross-over between two Arrhenius regimes below this temperature, leading to the breakdown of the inverse scaling between diffusion and viscosity. This decoupling is primarily governed by the anomalous temperature dependence of the viscosity. Fractional SER analysis reveals element-specific decoupling, with Cr showing a fundamentally distinct departure from classical scaling compared to Ni and Co. Concurrently, non-Gaussian parameters reveal growing dynamic heterogeneity upon cooling. Structurally, short-range order strengthens with significant increases in icosahedral-like and mixed clusters, especially those centered on Cr, accompanied by an enhancement of local five-fold symmetry. The formation of these rigid, Cr-centered ordered domains amplifies local geometric constraints, which severely hinder cooperative atomic rearrangements, while leaving single-atom diffusion less affected. These results connect element-specific ordering to the viscosity-driven breakdown of SER in NiCoCr, providing a structural perspective on diffusion-viscosity decoupling in MEA liquids.

Electrowetting on thick substrates: A modified Young–Lippmann equation

Electrowetting is a versatile technique for controlling the apparent contact angle of drops on thin insulating films through an externally applied potential. In the light of slide electrification—the spontaneous charging of sliding drops on dielectric substrates—electrowetting has gained new importance. Drops can spontaneously attain kilovolt potentials and undergo spontaneous electrowetting. Often, drop sizes and substrate thicknesses are comparable, violating the assumptions underlying the Young–Lippmann and the plate capacitor equations. In this work, I numerically determine the capacitance of sessile drops on thick substrates and introduce an analytical approximation. I derive a modified Young–Lippmann equation that quantitatively describes electrowetting ranging from thin films to substrate thicknesses many times the drop size.

Coupled-cluster theory for positron binding in anions and polyatomic molecules

We present the positron coupled cluster singles and doubles (POS-CCSD) method to calculate positron binding energies in molecules. This framework treats electrons and positrons on an equal footing and includes up to simultaneous double-electron–single-positron excitations. We benchmark the approach by computing binding energies for atomic anions and several polar and non-polar polyatomic systems, comparing the results with independent theoretical studies and, where available, experimental data. The fully converged results for H− are in excellent agreement with quantum Monte Carlo and multi-reference configuration interaction results. Quantitative agreement with experiments is not reached in the present study due to the slow convergence of the binding energy with respect to the size of the orbital bases for the electrons and the positron. However, the POS-CCSD results underscore the critical role of electron correlation in the description of electron–positron systems required for a balanced description of these complex systems. In addition, we examine nuclear relaxation effects following positron attachment in LiH.

Local thermal probe in a model molecular chain: A dissipaton-based approach

We study a system consisting of an infinite one-dimensional model molecular chain locally coupled to a probe. Starting from the Hamiltonian of the chain–probe composite and the corresponding spectral densities, we evaluate the heat current between the probe and the chain. Inspired by the perspective of quantum dissipative dynamics, we adopt a dissipaton-based quantum approach that is nonperturbative and non-Markovian. The dissipaton algebra yields a set of hierarchically coupled equations of motion for the c-number dissipaton moments, with iterative cross-tier connections when higher-order chain–probe interactions are included. Numerical results reveal the effects of temperature, probe frequency, on-site energy modification, and higher-order couplings on heat transport.