The packaging industry has long considered pallets to be rigid structures. However, in a unit load, the weight of the product produces compressive forces that are distributed across the pallet causing the top deckboards to deflect. Corrugated paperboard boxes are highly susceptible to changing support conditions; therefore, the deckboard deflection directly impacts the vertical compression strength of the box. Therefore, the objective of this study is to evaluate the effect of pallet deckboard stiffness on the vertical compression strength and deflection of corrugated paperboard boxes. Additional treatments included gaps between the deckboards and location of the box relative to the pallet stringers.

Corrugated boxes are ubiquitous in shipping and warehousing logistics. In physical distribution, corrugated boxes are often shipped in a unit load form where the interaction between the components determines the effectiveness and safety of the overall system. When lower stiffness pallets are used to support the corrugated boxes, the compression strength of boxes is reduced due to the uneven support conditions caused by the deforming top deckboards of the pallet. In this study, a modification of the principle of beam on elastic foundation was used to predict the effect of pallet deck stiffness on the performance of a corrugated box. In the model, the corrugated box acts as the elastic foundation, and the deckboard is represented as the beam.Pallet deck stiffness, pallet connection stiffness, and package stiffness are required model inputs.The resulting model was capable of predicting the normalized distribution of forces along the boxes 0 length sidewall but was not capable of predicting the compression strength of the box at failure.

Real-time anomaly detection system (ADS) and anomaly classification system (ACS) techniques are becoming a crucial need for future power electronic dominated grid (PEDG). Artificial intelligence techniques such as recurrent neural networks, specifically long short-term memory (LSTM) provide a promising solution to detect anomalies in power grids. The main challenge is the implementation of these methods for real-time detection and classification for preventing catastrophic failure in PEDG. This article is addressing the challenge for detection and classification of anomalies in real-time in PEDG. The proposed approach is based on integration of model predictive control (MPC) and LSTM for realizing real-time ADS and ACS. The LSTM detection network can utilize the same time-series input data as the MPC, allowing for anomaly classification and correction. The proposed integrated LSTM-MPC approach has features of power electronics internal failure detection and corrective actions, which is an important aspect in future PEDG to differentiate inverters internal failures versus anomalies. Such internal failures include open circuit fault that needs to be detected and classified from a potential cyber-attack, allowing resilient operation of PEDG. The proposed integrated LSTM-MPC scheme for real-time ADS and ACS scheme is tested on a realistic 14-bus system dominated with inverters forming PEDG.

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The effects of repeated dry-cleanings and launderings with and without fabric softeners on the thickness, air permeability, dimensional stability, and thermal insulative properties of two blanket fabrics were evaluated in this study. One of the two softeners used in laundering was added to the rinse, and the other was added to the dryer. Changes in the thermal-transmittance properties of the test fabrics were determined with a guarded hot/cold plate. After repeated dry-cleanings and launderings, the blanket fabrics exhibited a significant reduction in thickness. Fabric dimensions were altered sifinificantly during the first few washings. Laundering and dry-cleaning also significantly changed the air permeability of the blanket fabrics: the permeability increased for one blanket fabric and decreased for the other. There was no significant change, however, in the thermal conductivity of the blanket fabrics after repeated launderings with or without fabric softeners or after repeated dry-cleanings. Thus, the thermal insulative properties of the blanket fabrics were not affected by repeated cleanings or by the use of fabric softeners during the washing or drying cycle of laundering. _ .le