Sustainable purchasing planning in inbond logistics integrates ESG (environmental, social, and governance) criteria, establishing it as a distinct function within the supply chain. This study addresses four interrelated research questions: how purchasing quantities can be linked to suppliers’ and transportation modes’ social performance in tactical planning; how order allocation can prioritize low-emission transportation to advance the environmental and governance pillars of ESG; what integrated decision-making approach can simultaneously maximize sustainability performance, minimize cost, and mitigate sustainability risk; and through what mechanisms higher supplier social performance reduces total carbon emissions, and how the social-environmental-economic dimensions can be optimized under increasing carbon costs. To answer these questions, we develop a multi-period interval multi-objective programming model that accounts for demand uncertainty and non-linearity. The model explicitly links order allocation to suppliers’ social performance scores, encouraging social sustainability competition, and incorporates carbon trading and caps to manage carbon risks. Key findings show that higher carbon prices are internalized into procurement budgets; social sourcing and emissions reduction are interdependent-emphasizing social criteria can lower both cost and emissions by restricting volumes; and the emphasis on social performance is bounded by shortage constraints, providing clear thresholds for setting sustainability policies without compromising service levels. The model’s applicability is demonstrated through a numerical example. These findings highlight the critical role of suppliers’ ESG performance in managing demand and sustainability under uncertainty.
For radiative coolers that are not fully exposed to the sky, thermal radiation from parasitic hot objects, such as the ground surface under direct sunlight, poses a significant challenge and can severely impact performance. Here, we present a special configuration of reflectors for radiative coolers, suitable for building walls or other parts of buildings not fully oriented towards the sky. This configuration has the potential to reduce visible-light disturbance when a visibly transparent infrared-reflecting material, such as ITO, is used, while providing angular selectivity in the mid-infrared spectrum to reduce the impact of parasitic thermal radiation from objects below the horizon line and enhance radiative cooling to the cosmic background through the atmospheric transparency window. By using this method, the calculated net radiative exchange within the atmospheric window reaches up to 65.1 W/m2 under the idealized optical and environmental assumptions adopted in the model. This value represents an upper bound for the calculated net radiative exchange under these assumptions. Additionally, to minimize visual complications, we calculated and presented the limitations on the smallest size of the structure configuration. The calculations show that the reflectors size should be greater than seven times the maximum wavelength of the atmospheric window. Finally, experimental measurements conducted on a prototype of the proposed structure show good agreement between the calculations and theoretical predictions.
This paper proposes an information-driven framework for determining the number and placement of a limited number of phasor measurement units (PMUs) in distribution networks for machine learning (ML)-based fault location, with an a priori assessment of ML-task feasibility prior to model training. For fault classification tasks, the statistical separability of fault scenarios is quantified using the Mahalanobis distance under Gaussian measurement uncertainty, establishing a direct link to the pairwise Bayes error probability. For fault-location regression, the framework is extended using the Fisher information matrix (FIM), with the Cramér–Rao lower bound (CRLB) characterizing the attainable local estimation accuracy.The PMU placement problem is formulated as a max–min optimization of the pairwise Mahalanobis distance and addressed using a sequential strategy that progressively improves the least distinguishable fault-scenario regions. A stopping criterion based on the Bayes error bound determines when the prescribed upper bound on pairwise classification error is satisfied, providing an a priori assessment of classification feasibility without model training.After the classification criterion is satisfied, the placement objective transitions to fault-location regression, where additional PMUs are deployed to increase the Fisher information and improve the attainable local parameter-estimation accuracy as characterized by the CRLB. This defines a two-stage placement strategy, with the first stage addressing fault-type and faulted-cluster classification and the second addressing fault-location regression.The proposed framework is validated on a 20-kV distribution feeder using electromagnetic transient (EMT) simulations. A Random Forest (RF) model is used as an empirical benchmark, demonstrating consistency between the proposed information-theoretic metrics, based on Bayes error and CRLB, and the observed ML performance for both classification and regression tasks. A comparative evaluation against PMU placement based on maximum-observability further demonstrates that the proposed statistical distinguishability-based strategy can achieve comparable task-specific ML performance with fewer PMUs and a more favorable measurement structure for classification and fault-parameter estimation.
اثر زاویه پسگرایی و سرعت جریان بر مشخصههای آیرودینامیکی بالزن با بال انعطافپذیر در زوایای حمله مختلف با استفاده از آزمون تجربی در تونل باد مادون صوت، در این پژوهش مورد بررسی قرار گرفته است. آزمایشها در اعداد رینولدز 42000 تا 170000 که محدوده پرواز برای پرندگان طبیعی با ابعاد نزدیک به کبوتر است انجام شده است. مدل آزمون از دو بخش صلب (در ریشه) و انعطافپذیر (در نوک) تشکیل شده و حرکت نوسانی بالزدن در آن توسط سامانه الکترومکانیکی تامین شده است. نتایج نشان میدهد افزایش سرعت در بسامدهای بیبعد شده کمتر از 2/0، سبب کاهش نیروی برآی بال شده است. رفتار نمودار ضریب پیشران برحسب سرعت در زوایای حمله صفر تا 6 درجه کاملاً متفاوت از زوایای حمله بیش از 6 درجه است؛ به طوری که در زاویه حمله 3 درجه تغییرات بر حسب سرعت کمتر از 10 درصد بوده اما در زاویه حمله 18 درجه ضریب پیشران با افزایش سرعت از 5 به 20 متر برثانیه، 50 درصد کاهش داشته است. همچنین انعطافپذیری بال سبب شده است تا با افزایش سرعت اختلاف فشار دینامیکی جریان سبب تغییر شکل بال و در نهایت کاهش ضریب برآی بال شود. نیز ساختار جریان از زاویه حمله 9 درجه به بعد به گونهای است که توانایی تولید نیروی پیشران نداشته است.
This paper introduces a new sensitivity equation for parameter estimation and model updating, which is based on the autocorrelation function (ACF) of dynamic responses. The proposed method provides an exact and linear relationship between variations of structural parameters and variations of autocorrelation function data. The sensitivity equation is derived to formulate an objective function that is subsequently minimized using a least-squares approach. By iteratively minimizing the objective function, the structural parameters are updated to achieve an accurate finite element model. To evaluate the abilities of the proposed method, a three-dimensional jacket-type offshore platform subjected to harmonic loading is numerically simulated. Challenges such as modeling uncertainties, loading and measurement errors, added mass effects, and incomplete measurements are considered in this simulation. The results obtained from this method are compared with those of time-history-based methods. The comparative results reveal the high potential of the proposed method for practical applications with its enhanced sensitivity with respect to structural parameters and improved robustness against experimental uncertainties (up to 15%).
