Sustainable production and waste management policies for COVID-19 medical equipment under uncertainty: A case study analysis.

Unfortunately, an abrupt corona-virus disease (COVID-19) outbreak brought a drastic change in human lives. Almost every sector of human-beings and their related activities are severely infected and affected by this COVID-19 pandemic. As days are passing, the impact of the COVID-19 epidemic is going to be more severe. The fundamental needs for personal protective equipment (PPEs) are rising drastically all over the world. In India, many non-pharmaceutical companies or organizations such as automobile companies are engaged in producing the PPEs at a very marginal rate. Thus this paper proposes a modeling and optimization framework for sustainable production and waste management (SPWM) decision-making model for COVID-19 medical equipment under uncertainty. To quantify the uncertainties among parameter values, we have taken advantage of the intuitionistic fuzzy set theory. A robust ranking function is presented to obtain a crisp version of it. Furthermore, a novel interactive intuitionistic fuzzy programming approach is developed to solve the proposed SPWM model. An ample opportunity to generate the desired solution sets are also depicted. The performance analysis based on multiple criteria such as savings from baseline, co-efficient of variations, and desirability degrees is also introduced. Practical managerial implications are also discussed based on the significant findings after applying to the real case study data-set. Finally, conclusive remarks and the future research direction are also addressed on behalf of the current contributing study.

Erratum: Arshad, N., et al. Super Hydrophilic Activated Carbon Decorated Nanopolymer Foam for Scalable, Energy Efficient Photothermal Steam Generation, as an Effective Desalination System. Nanomaterials 2020, 10, 2510.

The authors wish to make the following corrections to this paper [...].

Super Hydrophilic Activated Carbon Decorated Nanopolymer Foam for Scalable, Energy Efficient Photothermal Steam Generation, as an Effective Desalination System.

Clean water scarcity is still an intense, prolonged global issue that needs to be resolved urgently. The solar steam generation has shown great potential with a high energy conversion efficiency for clean water production from seawater and wastewater. However, the high evaporation rate of water cannot be preserved due to the inevitable fouling of solar absorbers. Herein, a self-floatable and super hydrophilic solar-driven steam generator composed of activated carbon coated melamine foam (ACM). The deposited ACM photothermal layer exhibits outstanding solar absorption (92%) and an efficient evaporation rate of 1.27 kg m-2 h-1, along with excellent photothermal conversion efficiency (80%) as compared to commercially available primitive solar stills. The open porous assembly of melamine foam equipped with 80% flexibility (0.8 MPa) enabled smooth water transport and sustain heat accumulation within the matrix. The thermal insulation of ACM is 10 times greater than pure water. Moreover, open porous assembly of designed solar-powered steam generator rejects salt ions as well as volatile organic compounds efficiently. The low-cost and facile fabrication of photothermal based water production presents a potential solution to single step drinking water supply from various resources of the sea, the lakes and mixtures of emulsified oil and industrial wastewater.

Low-cost green recyclable biomaterial for energy-dependent electrical switching and intact biofilm with antibacterial properties.

A highly cost-effective recycled biomaterial extracted from lime peel has been made biocompatible and has been coated on a commercial fluorine-doped tin oxide (FTO) substrate of glass using the spin coating method. Structural, morphologic, electronic, and antibacterial measurements were thoroughly characterized as a green biomaterial thin film using X-rays (XRD), PL, FTIR, Raman, SEM, HRTEM, AFM, I-V, and antibacterial diffusion techniques. The comprehensive analysis of structures of recyclable waste in the form of lime peel extract (LPE) as thin films showed the crystalline cellulose structure that corresponds to the lattice fringe (0.342 nm) exposed by HRTEM. The K+1 interstitial active sites or vacancies in LPE/FTO thin films are confirmed by the PL spectra that show important evidence about conduction mechanisms, and hence facilitates Ag+1 ion migration from the top to the bottom electrode. The AFM investigations revealed the minor surface roughness (169.61 nm) of the LPE/FTO film, which controls the current leakage that is associated with surface defects. The designed memory cell (Ag/LPE/FTO) exhibits stable, reproducible electrical switching under low operational voltage and is equipped with excellent retention over 5 × 103 s. Furthermore, owing to presence of flavonoids and their superior antioxidant nature, lime peel extract powder shows tremendous antimicrobial activity against gram-positive and Gram-negative bacterial strains.

Xbee-Based WSN Architecture for Monitoring of Banana Ripening Process Using Knowledge-Level Artificial Intelligent Technique.

Real-time monitoring of fruit ripeness in storage and during logistics allows traders to minimize the chances of financial losses and maximize the quality of the fruit during storage through accurate prediction of the present condition of fruits. In Pakistan, banana production faces different difficulties from production, post-harvest management, and trade marketing due to atmosphere and mismanagement in storage containers. In recent research development, Wireless Sensor Networks (WSNs) are progressively under investigation in the field of fruit ripening due to their remote monitoring capability. Focused on fruit ripening monitoring, this paper demonstrates an Xbee-based wireless sensor nodes network. The role of the network architecture of the Xbee sensor node and sink end-node is discussed in detail regarding their ability to monitor the condition of all the required diagnosis parameters and stages of banana ripening. Furthermore, different features are extracted using the gas sensor, which is based on diverse values. These features are utilized for training in the Artificial Neural Network (ANN) through the Back Propagation (BP) algorithm for further data validation. The experimental results demonstrate that the projected WSN architecture can identify the banana condition in the storage area. The proposed Neural Network (NN) architectural design works well with selecting the feature data sets. It seems that the experimental and simulation outcomes and accuracy in banana ripening condition monitoring in the given feature vectors is attained and acceptable, through the classification performance, to make a better decision for effective monitoring of current fruit condition.