Search Results (4,323)

The escalating intensity and depth of coal mining operations have exacerbated underground strata pressure, resulting in fracture-induced air leakage channels that heighten risks of coal spontaneous combustion and gas explosions. These challenges necessitate enhanced flexibility and strength in cementitious materials used for roadway support. This study investigated the mechanical properties of fiber-reinforced polymer concrete (FRPC) for mining applications through response surface methodology (RSM) using Design Expert software. Three critical factors: styrene–acrylic emulsion content (5–15 wt.%), polypropylene fiber length (9–15 mm), and fiber content (0.7–1.1 kg/m³), were systematically investigated to establish factor-performance correlations via 3D response surfaces. This study experimentally investigated the effects of styrene–acrylic emulsion content, polypropylene fiber length, and fiber content on the mechanical properties of fiber-reinforced polymer concrete for mining roadway support. Response surface methodology was used as an empirical statistical tool to describe the response trends and factor interactions within the selected experimental range. The regression models developed in this study should therefore be interpreted as local empirical models rather than mechanics-based predictive equations. Using the flexural-to-compressive strength ratio as an index, the optimal formulation of FRPC was 10% emulsion, 12 mm fibers, and 1.1 kg/m³ fiber content. Microstructural characterization indicated that polypropylene fibers effectively inhibited crack propagation through bridging effects, while styrene–acrylic emulsion formed continuous film-like network structures on cement surfaces. Synergistically, both components enhanced matrix densification, achieving concurrent improvements in flexibility and strength. Field applications demonstrated that FRPC significantly reduced air leakage channels, decreased the risk of coal spontaneous combustion, and improved the safety of coal mine production. Full article

In our earlier methodology paper, we introduced a hierarchical framework combining graph compression, Diffusion Convolutional Recurrent Neural Networks (DCRNNs), and Multi-Agent Reinforcement Learning (MARL) to approximate Bellman’s optimality principle for real-time energy system control, validated using Palm Springs, California weather data. This extension paper addresses three critical advances. First, we provide a rigorous theoretical proof demonstrating that the DCRNN’s Markovian reduction of strongly path-dependent dynamics yields a bounded approximation error, with the error decaying exponentially in the mixing time of the underlying graph diffusion process. Second, we extend the framework to diverse weather regimes—from stable Mediterranean climates (San Diego) to highly variable marine west coast (Seattle), monsoon (Mumbai), and typhoon-prone regions (Hong Kong)—quantifying how weather-induced path dependence affects the required hidden state dimension and forecasting accuracy. We present comprehensive Leave-One-Out Cross-Validation (LOOCV) results across six cities, demonstrating consistent generalization with R² drops of less than 0.1% under out-of-sample testing. A controlled baseline comparison under matched training protocols shows that the graph-free GRU achieves comparable or higher R² on the one-step prediction task, which we attribute to the near-cumulative structure of the target and the small evaluation graph. We frame the DCRNN’s contribution around its theoretical guarantee and its potential advantage on larger graphs and longer horizons. We also characterize conditions under which the model expects to fail, specifically when weather stochasticity violates the geometric mixing assumption or when the effective temporal correlation length exceeds the GRU’s memory capacity. Third, we outline physics-informed enhancements that are proposed as future development: CFD-integrated loss functions, differentiable Model Predictive Control (MPC) heads, and a modular design enabling alternative turbine configurations. We also propose a standardized rooftop solar thermal deployment architecture with 200 m × 100 m, 100 m × 100 m, and 100 m × 50 m modules designed for data center footprints with pre-allocated HVAC space. We conclude with a stage-gated validation roadmap progressing from unit tests to hardware-in-the-loop simulation to full-scale FEED-site deployment. The completed contributions of this paper are the theorem, its empirical assumption verification, the multi-climate LOOCV study, the matched-protocol baseline comparison, and the sensor-failure robustness analysis. The remaining components are described as proposed extensions. Full article

This study combines a systematic review with a case study to address the following question: can Sentinel-2 data yield accurate Surface Soil Moisture (SSM) estimates? Among the 1158 papers identified through the review, 66 met the eligibility criteria based on the exclusive use of Sentinel-2 data to estimate SSM and on reference measurements acquired simultaneously. Analysis of the eligible papers reveals six interconnected critical issues, the foremost being the insufficient spatial and temporal density of point-based reference measurements and their limited availability. The case study is aimed at addressing these issues. Specifically, Theia SSM products—freely available at the plot scale with an accuracy of approximately 5 vol%—are used as reference measurements, enabling a three-year multi-temporal comparison with Sentinel-2 bands. The comparison across different land and vegetation cover types, including a burned area, shows that SSM retrieval accuracy from Sentinel-2 can be strongly modulated by vegetation status and soil moisture magnitude. Specifically, the maximum R² (Sentinel 2-bands against Theia SSM) increases by 0.44 (from 0.01 to 0.45) where NDVI is less than 0.35, and SSM is less than 15%. Moreover, extending the analysis to a multi-year time series improves R² relative to single-date results (i.e., by up to 0.43). Across eligible papers, different methodologies (21 machine-learning algorithms, 18 optical trapezoidal models, and 7 statistical methodologies), different image processing products (4 algorithms, 77 indices, principal component, and albedo), and all bands were used to identify the best methodology and/or optimal image processing products and/or optimal bands. The median R² for these outputs against the SSM reference data ranged from 0.44 (statistical methods) to 0.63 (optical trapezoidal models), indicating weak to moderate overall accuracy. Unlike other approaches, the optical trapezoidal models rely primarily on SWIR bands. Results show that SWIR bands yield moderate fit (R² up to 0.65) during the dry season, but negligible fit during the wet season. These values align with those of the case study, suggesting that the use of the Sentinel-2 imagery for SSM estimation is critical and requires well planned approaches. Findings reported in this paper provide concrete guidance on image selection, reference measurement design, and time-series length for researchers seeking reliable SSM estimation from Sentinel-2 data. Full article

In sudden disaster scenarios, the rapid strategy-driven task-chain construction of rescue task chains in UAV-assisted heterogeneous emergency rescue systems is critical for an effective emergency response. Such systems usually involve multiple types of rescue entities, such as reconnaissance UAVs, delivery UAVs, decision centers, and other emergency resources, which provide four-dimensional capabilities including reconnaissance, intelligence analysis, command and control, and rescue disposal. However, existing manual planning and heuristic optimization methods often suffer from low online decision efficiency in large-scale disaster scenarios and rarely examine how different task-chain construction strategies affect task-chain construction. To address this problem, this paper studies a strategy-aware rescue task-chain construction problem for UAV-assisted heterogeneous emergency rescue systems, in which heterogeneous rescue entities are mapped to reconnaissance, intelligence-analysis, command, and disposal nodes according to their four-dimensional capabilities under capability, distance, cost, availability, and strategy constraints. The problem is formulated as a constrained combinatorial optimization problem, and eight task-chain construction strategies are designed from three dimensions: command mode, information-sharing scope, and disposal mode. To solve this problem, a deep reinforcement learning framework integrating a pointer network and entropy-regularized advantage actor–critic (A2C) is proposed. The pointer network generates variable-length entity-selection sequences for task chains, while entropy-regularized advantage actor–critic (A2C) optimizes the selection policy using an entropy-regularized objective that considers success probability, execution cost, and resource utilization. Experiments across 72 scenarios show that the proposed method outperforms the genetic algorithm in terms of timeliness and overall multi-indicator performance, especially in large-scale rescue scenarios. The results further indicate that distributed command strategies achieve a more balanced and robust performance, providing practical guidance for strategy selection and resource scheduling in UAV-assisted heterogeneous emergency rescue. Specifically, compared with the genetic algorithm under the full-scale 72-scenario evaluation, the proposed method improves the cost-control indicator E2 by 51.2% and the topological indicators E4, E5, E6, and E7 by 86.8%, 346.5%, 375.7%, and 421.5%, respectively, all while reducing the online decision time from 36.5 s to 3.0 s (a 91.8% speedup). The genetic algorithm still outperforms the proposed method on the success-rate indicator E1 and entity-utilization indicator E3, which is discussed as a limitation of the proposed method in the discussion of applicability. Full article

Aluminum alloys are widely employed in the aerospace industry owing to their lightweight characteristics. Laser cladding offers an effective means to enhance their surface hardness and wear resistance; however, its application is constrained by several inherent challenges: the high thermal conductivity and low laser absorptivity of aluminum substrates, coupled with the property mismatch between the substrate and cladding powder, readily induce stress concentrations and crack formation in the clad layer. High-fidelity temperature field simulation is therefore essential for predicting residual stresses and microstructural evolution. Conventional simulation approaches rely primarily on calibrating heat source parameters to match the molten pool morphology, yet they overlook the dynamic evolution of thermophysical properties arising from compositional variations in the dilution zone—a critical factor that compromises predictive accuracy. To address this limitation, this study proposes a two-step correction strategy that establishes a linkage between composition-dependent thermophysical properties and heat source modeling. In the first step, the target dilution ratio is used to inversely determine the elemental composition within the dilution zone, enabling the recalibration of thermophysical parameters in the melt pool region. This measure reduced temperature simulation deviations near the melt pool from 41% to 17%. In the second step, a sensitivity analysis of the temperature field to heat source parameters reveals that laser absorptivity predominantly governs the overall thermal distribution, whereas shape parameters primarily influence the central melt pool temperature. Accordingly, a sequential optimization of absorptivity and shape parameters was implemented. Collectively, this two-step approach reduced the average deviation between measured and simulated temperatures from 18.7% to 9.63%, and diminished molten pool geometry errors—width, length, and depth—from 42%, 48%, and 43% to 9.6%, 19%, and 6.6%, respectively. The results demonstrate that the synergistic optimization of material properties and heat source models substantially enhances the accuracy of temperature field simulations, offering a more reliable theoretical foundation for predicting clad layer performance in laser cladding processes. Full article

At the microscale, the interplay between size effects and boundary constraints strongly influences the mechanical behavior of quasicrystal structures. To elucidate this interplay, we investigate the static and dynamic responses of one-dimensional (1D) hexagonal piezoelectric quasicrystal Timoshenko microbeams. Our model integrates the modified couple stress theory with a Winkler–Pasternak two-parameter elastic foundation. Under open-circuit conditions, the electric potential is generated solely by mechanical deformation through piezoelectric coupling. Using Hamilton’s principle, we establish a non-classical beam theory framework for piezoelectric quasicrystals that simultaneously incorporates size effects, piezoelectric coupling, and foundation constraints. A normalized system contact stiffness is introduced to quantify the coupling between size effects and the foundation, providing a possible reference for evaluating foundation parameters. Fourier series solutions reveal that increasing the material length scale parameter markedly enhances both bending stiffness and the free vibration frequencies, particularly at small scales. The elastic foundation further suppresses static deformation and elevates all natural frequencies. Notably, the phason field exhibits a non-classical, non-monotonic response under foundation constraints. Increasing the Pasternak shear stiffness not only reduces the overall phason deflection but also triggers a qualitative reversal of the size sensitivity. The apparent critical Pasternak stiffness required for this transition decreases systematically as the Winkler spring stiffness increases, indicating a synergistic effect between the two foundation parameters. Detailed scanning around the critical interval further reveals a transitional regime in which the phason deflection first rises and then declines, signifying a competitive handover between two driving mechanisms within a narrow stiffness band. Unlike existing size-dependent quasicrystal beam models, the present formulation simultaneously incorporates a two-parameter elastic foundation and piezoelectric coupling in a Timoshenko microbeam, and further reveals a non-monotonic phason response governed by a critical energy partition ratio. These findings provide theoretical insight into the complex multi-field coupling in quasicrystals, and the open-circuit formulation may be particularly relevant for high-frequency or electrically insulated configurations. Full article

Spontaneous imbibition is an important process in enhanced oil recovery, yet its accurate prediction in nanoscale pores remains a significant challenge due to the breakdown of continuum assumptions at the nanoscale. The classical Lucas-Washburn (L-W) equation, which suggests that imbibition length is proportional to the square root of time, fails to capture the complex early-time dynamics in nanopores. Therefore, in this work, coarse-grained molecular dynamics simulations were employed to systematically investigate two-phase imbibition across a range of pore sizes (1.85–7 nm), fluid types (C4–C20 alkanes), and wettability conditions. The results reveal a distinct transition from continuum-dominated to molecularly dominated flow as pore size decreases, with anomalous kinetics that originate from molecular-scale interactions in sub-2-nm pores. A refined imbibition model is proposed that explicitly incorporates an entrance resistance term in addition to the capillary and viscous forces. This new model successfully resolves the overestimated initial velocity by the L-W equation, revealing a dual-regime physics, including an early-time, almost linear regime influenced by entrance resistance, followed by the classical late-time L-W regime. The model demonstrates excellent agreement with molecular dynamics data for mesoscopic nanopores (r ≥ 3.5 nm), validating its physical basis. Although the proposed model is still not predictive due to the implicit inclusion of imbibition rate in the derivision, the findings provide a robust theoretical foundation for describing imbibition in nanopores and highlight the critical need for molecular-scale approaches in extreme nanoconfinement. Full article

Background/Objectives: Malnutrition is common in hospitalized older adults and is linked to functional decline, longer stay, readmission, and mortality. Nurses usually screen at admission, but evidence on nurse-led screening is scattered. This review mapped instruments, measurement properties, recognition and referral outcomes, and implementation barriers. Methods: A rapid scoping review followed the Joanna Briggs Institute methodology, PRISMA-ScR reporting, and the Population–Concept–Context framework. PubMed, Scopus, and Web of Science Core Collection were searched in June 2026 for studies published from 1 January 1999 to 31 July 2026; in July 2026, the evidence set underwent bibliographic and indexing verification only, with no search update. Two coauthors searched, screened, and charted the data; two others verified all decisions against full texts. Implementation findings were coded inductively without critical appraisal. Results: Of 1743 records, 1169 were screened and 18 included. Tools: MNA, MNA-SF, MST, MUST, NRS-2002, SNAQ. Standardized screening improved recognition; undernutrition identification rose from 38.6% to 73.1%. Nurse-administered MST reached 94% sensitivity and 89% specificity in a high-readmission-risk cohort, whereas nurse MNA-SF scoring showed ICC = 0.74 with only fair agreement (kappa = 0.37). Barriers included inconsistent scoring, limited nutrition knowledge, workload, and documentation gaps. Conclusions: Nurse-led screening is feasible and improves case finding, recognition, screening completion, and dietitian referral, although scoring errors and inter-rater disagreement persisted. No study measured complications, length of stay, readmission, functional decline, or mortality, so the evidence supports better identification, not better outcomes. Reported accuracy came from selected populations and single assessors. Future work should link screening fidelity to patient outcomes. Full article

Controlling the final microstructure of C-Mn and microalloyed C-Mn-Nb steels requires understanding how the prior austenite state and cooling path determine the transformation products, morphology, and mechanical response. In this work, a microstructure-informed prediction framework was developed to evaluate which transformation products can be predicted from experimentally quantified austenite descriptors within a defined industrial processing domain. A thermomechanical matrix of 80 specimens was combined with correlative light optical, scanning electron, and electron backscatter diffraction microscopy to quantify the prior austenite grain size, axial ratio, dislocation density, and cooling rate as the inputs, and the phase fractions, morphology descriptors, and hardness as the targets. The target-specific regression models from linear, kernel-based, tree-based, and gradient-boosting families were evaluated against a dummy regressor baseline using cross-validation. Reliable quantitative predictions were obtained for ferrite, pearlite, pearlite mean free path length, final size descriptor, and hardness, while the predictions for martensite, Widmanstätten ferrite, and individual bainitic subclasses remained limited by sparse occurrence and overlapping transformation windows. The framework is therefore proposed as a microstructure-informed process-window screening and experiment prioritization tool rather than a universal transformation model, with a closed-data transparency strategy enabling critical evaluation under industrial confidentiality constraints. Full article

Background: Inhaled nitric oxide (iNO) is a selective pulmonary vasodilator used as rescue therapy in acute respiratory distress syndrome (ARDS) for transient improvement in oxygenation. Although multiple studies demonstrate improvements in oxygenation parameters, iNO has not consistently shown a mortality benefit or reduced ventilation duration. Methods: A retrospective cohort study was conducted of mechanically ventilated adults with SARS-CoV-2 (COVID-19) admitted to a medical intensive care unit who received iNO. Demographic characteristics, comorbidities, illness severity scores, timing of iNO initiation, and clinical outcomes were analyzed, with comparisons made between survivors and non-survivors. Results: A total of 94 patients were included. The mean age was 52.0 years (Standard Deviation (SD) 14.1), 70.2% were male, and the mean body mass index was 36.5 kg/m² (SD 10.0). Hypertension (58.5%) and diabetes (36.2%) were the most common comorbidities. The mean Sequential Organ Failure Assessment (SOFA) score was 4.22 (SD 2.79), and the mean Acute Physiology and Chronic Health Evaluation II (APACHE II) score was 11.69 (SD 6.98). Overall, in-hospital mortality was 85.1% (80/94). The median time from intubation to iNO initiation was 7.6 days (mean 7.7 days). Survivors received iNO significantly earlier than non-survivors (mean 4.14 vs. 8.3 days after intubation, p = 0.024) and had a longer hospital length of stay (mean 37 vs. 23 days, p < 0.01). Duration of iNO therapy did not differ between groups (p = 0.12). Conclusions: In this cohort of critically ill patients with COVID-19 and severe respiratory failure, overall mortality was extremely high. Earlier initiation of inhaled nitric oxide was associated with improved survival, whereas duration of therapy was not. These findings suggest that the timing of iNO administration may be associated with clinical outcomes and warrant further prospective investigation to confirm this association. Full article

Silique development is a critical factor influencing the yield of rapeseed (B. napus). This study employed the CRISPR/Cas9-mediated BnMYB46 knockout mutant K46 to investigate the molecular mechanisms underlying its short-silique phenotype via integrated cytological, transcriptomic, untargeted metabolomic, and targeted hormone metabolomic analyses. The K46 mutant exhibited significantly reduced silique length, seed number per silique, and dry weight. Cytological observations revealed profound folding of epidermal cells and severe compression of parenchyma cells, consistent with impaired cell expansion. Multi-omics analysis identified plant hormone signal transduction as a core hub pathway disrupted at 20 DAF, and targeted metabolomics confirmed significant endogenous IAA and GA imbalances. qRT-PCR validation showed key hormone genes were significantly altered in K46, with decreases of 22.3-fold in IAA2 and 18.8-fold in GA3OX1 at 20 DAF, and a 165-fold increase in ABF4. These early hormonal disruptions drove downstream suppression of cell wall synthesis and carbohydrate metabolism pathways, leading to impaired cell expansion and compromised structural integrity. This study provides new insights into the hormonal regulatory network governing silique development and identifies potential targets for the molecular improvement of silique-related traits in rapeseed. Full article

Background/Objectives: The retina is a highly metabolically active tissue that depends on precise vascular regulation. Childhood represents a critical period of ocular growth, and retinal vascular characteristics may provide insights into the relationship between microvascular development, ocular biometry, and refractive development. However, retinal vascular parameters in children living at high altitude remain poorly characterized. Our goal was to characterize retinal vascular caliber and evaluate its associations with ocular biometric parameters, refractive status, and peripheral oxygen saturation among children living at high altitude. Methods: This cross-sectional baseline analysis included 1410 children from the Lhasa Childhood Eye Study. Retinal vascular parameters, including central retinal arteriolar equivalent (CRAE), central retinal venular equivalent (CRVE), and arteriolar-to-venular ratio (AVR), were measured from fundus photographs using computer-assisted IVAN software. Ocular biometric parameters, cycloplegic refraction, and SpO₂ were assessed. Multivariable linear regression and sensitivity analyses accounting for school clustering and ocular magnification were performed. Results: The mean age was 7.90 ± 0.49 years, and 47.6% were female. Girls had shorter axial length (AL) and larger CRAE and CRVE than boys. Longer AL was associated with smaller CRAE (B = −5.64, p < 0.001) and CRVE (B = −11.24, p < 0.001), while AVR was positively associated with AL (p = 0.03). Lower SpO₂ was independently associated with larger CRAE (B = −0.35, p = 0.042), but not CRVE. After AL-based relative ocular magnification correction, the associations between AL and CRAE or CRVE were no longer significant. Myopic children had smaller CRAE and CRVE and higher AVR than non-myopic children. Conclusions: Retinal vascular caliber was associated with ocular biometric characteristics, SpO₂, and refractive status in children living at high altitude. The attenuation of AL-related associations after magnification correction highlights the importance of accounting for ocular magnification in retinal vascular measurements. Full article

Accurate runoff forecasting serves as a fundamental basis for the scientific management of water resources and flood and drought risk mitigation. Owing to the nonlinearity, non-stationarity, and multi-scale temporal characteristics of runoff series, existing deep learning models still exhibit notable limitations in capturing long-term trends, responding to abrupt hydrological events, and ensuring model interpretability. The original Transformer relies on global self-attention, whose computational complexity increases quadratically with sequence length; it also has limited capacity to capture short-term local temporal dependencies such as rainfall–runoff relationships, and lacks prior constraints tailored to hydrological processes. To address these challenges, this study proposes a collaborative forecasting framework that integrates a gated convolutional Transformer (GCTrans) with an improved black kite algorithm (IBKA), enabling accurate, stable, and interpretable daily-scale runoff prediction. The GCTrans model consists of three customized modules: convolution-enhanced positional encoding (CEPE), which combines learnable positional encoding with local causal convolution to strengthen the temporal association of adjacent rainfall–runoff events, thereby providing a hydrologically meaningful positional reference for the attention mechanism; gated convolutional attention (GCA), which adopts a dual-path parallel architecture comprising global self-attention and local causal convolution to adaptively fuse long-term seasonal patterns with short-term storm-induced variations, thus capturing both baseflow evolution and flood peak responses; and a temporal gated output layer (TGOL), which performs adaptive feature weighting along the temporal dimension to selectively enhance the contribution of critical driving periods associated with extreme flood events, thereby improving the flood peak prediction accuracy. In addition, an improved black kite algorithm (IBKA) was developed by incorporating Tent chaotic initialization to enhance initial population diversity and introducing cosine adaptive inertia weights to dynamically balance global exploration and local exploitation, effectively alleviating premature convergence in high-dimensional hyperparameter spaces. Validation using data from the ME-Inland snowmelt-dominated watershed and the OR-Coastal storm-driven coastal watershed in the United States demonstrated that the GCTrans model consistently outperformed benchmark models including TCN, LSTM, Transformer, and Informer. After synergistic optimization with IBKA, both prediction accuracy and stability were further improved. SHAP-based interpretability analysis revealed that the model’s feature response patterns are statistically consistent with the rainfall–runoff generation mechanisms of the study basins: temperature-related drivers dominate in the inland watershed, while precipitation plays a dominant role in the coastal watershed, confirming the hydrological plausibility of the model’s decision-making logic. The integrated framework—encompassing model architecture, optimization algorithm, and interpretability—offers a valuable methodological reference for deep learning-based runoff forecasting in complex hydrological settings. Full article

Polyhydroxyalkanoates (PHAs) are a structurally diverse family of microbially synthesised, biodegradable polyesters that accumulate as intracellular carbon and energy reserves under conditions of nutrient imbalance. Their combination of adjustable mechanical performance with controlled hydrolytic and enzymatic degradation, and non-toxic degradation intermediates (ex. D-3-hydroxybutyrate) has made them a longstanding candidate biomaterial for human tissue engineering, drug delivery, and resorbable implants. Comparatively, their application in veterinary medicine remains an emerging and fragmented field, despite an arguably stronger practical case: veterinary practice faces acute pressure to replace non-degradable sutures, orthopaedic hardware, and single-use plastics with materials that avoid secondary retrieval surgery, that can be produced at low cost for large-scale animal use, and that align with growing regulatory and consumer demand for sustainable animal healthcare. This review consolidates current understanding of PHA biosynthesis, covering the core: phaA-phaB-phaC pathway, medium-chain-length variants, microbial producers, feedstock flexibility, and metabolic engineering strategies for yield improvement. It also examines material modification strategies, including blending, chemical grafting, surface functionalisation, electrospinning, and additive manufacturing, used to adapt PHAs for specific veterinary form factors. The clinical and preclinical evidence base is presented in detail across wound management, orthopaedic and soft-tissue regeneration, cardiovascular tissue engineering, drug delivery, and surgical devices, with attention to species-specific considerations in companion animals, horses, and food-producing ruminants. This review relies exclusively on peer-reviewed literature for its quantitative claims, while transparently noting where veterinary-specific data are lacking, extrapolated from rodent or human models, or in need of independent verification. Persistent barriers like production cost, batch-to-batch variability, absence of veterinary-specific regulatory pathways, and limited long-term in vivo safety data in large animals are analysed critically, alongside translational opportunities including waste-feedstock valorisation, hybrid PHA/ceramic and PHA/natural-polymer composites, and stimuli-responsive formulations. We conclude that PHAs are scientifically well positioned but institutionally under-validated for veterinary translation, and we outline a concrete research agenda to close this gap. Full article

The increasing environmental impact of supply-chain activities has intensified interest in carbon-regulation policies such as carbon taxes. This paper analyzes a two-echelon manufacturer–retailer system operating under decentralized decision making, where both parties independently optimize their decisions while facing potentially different carbon tax rates. We develop a production-inventory model that incorporates emissions-related costs, with the retailer determining the ordering cycle-length and the manufacturer selecting number of deliveries per production cycle. We derive analytical results that characterize the optimal decentralized decisions and identify conditions under which the structure of the solution changes. The analysis provides clear threshold conditions under which the optimal replenishment strategy simplifies to a lot-for-lot ordering pattern. We further find that raising the retailer’s carbon tax rate beyond a critical level does not induce additional emission reductions for either supply-chain member; rather, it only imposes a greater financial burden on the retailer. Extending the analysis to the manufacturer, we show that shipping decisions are governed by the interaction between emission-related and operational cost parameters. The results indicate that carbon taxes affect environmental performance only indirectly by triggering changes in discrete operational changes. Again, raising the manufacturer’s carbon tax rate may fail to generate additional emission reductions beyond certain threshold levels. A comprehensive numerical study illustrates the sensitivity of costs and emissions to key parameters, including tax rates and emission coefficients. The results highlight the role of shipment frequency and production rate as important operational levers for achieving both economic and environmental improvements. Overall, the findings provide insights into the effectiveness of carbon taxation and offer managerial and policy implications for designing sustainable supply-chain systems under decentralized control. Full article