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The global supply chain has been severely impacted with the outbreak of COVID-19. The continuous supply of essential products in the post-COVID-19 world is a truly effective and strategic contest. The security and useability of inventory management are a main burden for industries along with the pressure from the government to fulfil the targets of net-zero economy in an uncertain circumstance. One of the most potential keys to these issues is an accurate demand forecasting process by blockchain technology. This article addresses a basic outline for blockchain-based supply chain (SC) and reveals how blockchain technology (BCT) can aid policymakers to cut carbon footprint during and postpandemic time in a fuzzy environment. This study fuzzifies all the carbon factors as intuitionistic triangular fuzzy numbers and uses a signed distance method to defuzzify the model. We consider that the retailer can embrace BCT to enhance demand forecasting. The planned scenario is modeled as an optimization problem to maximize the profit with low carbon emissions and suggest a solution method to solve it. A numerical example is also given to validate the model. We compare the optimal decisions of the SC with and without BCT. We discover that the pandemic and BCT have considerable influences on the optimal results. The study also shows that practitioners should exercise caution when developing operational strategies for maximizing profit with the least amount of carbon emissions during and postpandemic time.

The amount of carbon emission in the atmosphere continues to grow, and production, storage and transportation are major contributors that are repeatedly unnoticed when it comes to solving the problem. A carbon levy and cap EPQ model has been explored in this study for a controlled carbon emission in view of cleaner production under imperfect production processes where a fraction of the products is faulty. The model incorporates emissions from transportation, production, and storage activities, which are meant to be decreased by green investment. The manufacturer employs a rework approach to rectify the faulty products. Flexible rework allows the manufacturer to choose the rework rate, which can differ from the manufacturing rate, as well as the process of rework, which can be asynchronous or synchronous. A linear rework cost function of the rework rate is assumed. We sought to obtain the lowest cost with respect to optimal lot size, and backorder under the effect of controlling carbon emissions by applying the exponential form of green investment. Keeping these in mind, several mathematical models derived for various rework rate and rework process assumptions with the objective emphases on gaining a trade-off between cost and emissions, thereby finding the optimal production-distribution inventory strategy and an appropriate sustainable plan for handling both synchronous and asynchronous rework process. We show a constrained non-linear programme (NLP) and derive a calculus optimization technique to solve it. An iterative solution technique is used to arrive at the optimal decisions, which are shown analytically and numerically. The results validate the impacts of controlled carbon emissions on the production strategies. The findings mainly suggest that increasing the carbon levy reduces emissions. The firm has more options to decrease emissions caused by industrial activity with the help of green technologies. Although green technology has a higher upfront cost, the producer will reap the advantages of lower emissions.

Most of the consumers relied heavily on e-commerce for products and services for the past few years due to the recent COVID-19 pandemic. This kind of an unexpected behaviour among the consumer society has taken every industry by surprise so that many industries have begun operating online and offline businesses to ensure future competitiveness. Firms introducing online sales are deplorably facing many challenges in terms of logistics and delivery processes, such as short lead times, flexible delivery, capacity of warehouse, and the production process for controlling carbon emissions. Keeping these challenges in mind, a sustainable dual-channel vendor-buyer supply chain model has considered for a controllable emission under fuzzy demand and energy consumption. The model deals with limitation on warehouse floor-space area, and the warehouse divides into two stages such as one for satisfying online orders and the other for satisfying offline orders. The demand rate and energy consumptions are treated as the trapezoidal fuzzy number, and we use the signed distance method to defuzzify the fuzzy joint expected total cost. The objective focuses on obtaining a trade-off between cost and emissions, thereby determining the optimal production-distribution strategy and a proper sustainable plan for handling both online and offline orders. The aforesaid scenario is mathematically formulated in the form of constrained non-linear programme (NLP) and derive a Lagrangean multiplier method to solve it. An iterative solution algorithm is designed, and for better illustration of the developed theory, numerical analysis is carried out followed by a wide discussion on the sensitivity analysis for various parameters. Our results indicate that the optimal solutions of the sustainable fuzzy model slightly fluctuate from the solutions of the sustainable crisp model. According to results, considering the uncertainty in the system is a crucial factor to achieve the economic and environmental sustainability of the production sector. The research reveals that the practitioners should be careful in accounting flexibility in the input factors demand and energy to tackle the uncertainties that always fit the real situation.

The fruit fly Drosophila melanogaster has emerged as a key model organism in neuroscience, in large part due to the concentration of collaboratively generated molecular, genetic and digital resources available for it. Here we complement the approximately 140,000 neuron FlyWire whole-brain connectome1 with a systematic and hierarchical annotation of neuronal classes, cell types and developmental units (hemilineages). Of 8,453 annotated cell types, 3,643 were previously proposed in the partial hemibrain connectome2, and 4,581 are new types, mostly from brain regions outside the hemibrain subvolume. Although nearly all hemibrain neurons could be matched morphologically in FlyWire, about one-third of cell types proposed for the hemibrain could not be reliably reidentified. We therefore propose a new definition of cell type as groups of cells that are each quantitatively more similar to cells in a different brain than to any other cell in the same brain, and we validate this definition through joint analysis of FlyWire and hemibrain connectomes. Further analysis defined simple heuristics for the reliability of connections between brains, revealed broad stereotypy and occasional variability in neuron count and connectivity, and provided evidence for functional homeostasis in the mushroom body through adjustments of the absolute amount of excitatory input while maintaining the excitation/inhibition ratio. Our work defines a consensus cell type atlas for the fly brain and provides both an intellectual framework and open-source toolchain for brain-scale comparative connectomics.

Connections between neurons can be mapped by acquiring and analysing electron microscopic brain images. In recent years, this approach has been applied to chunks of brains to reconstruct local connectivity maps that are highly informative1-6, but nevertheless inadequate for understanding brain function more globally. Here we present a neuronal wiring diagram of a whole brain containing 5 × 107 chemical synapses7 between 139,255 neurons reconstructed from an adult female Drosophila melanogaster8,9. The resource also incorporates annotations of cell classes and types, nerves, hemilineages and predictions of neurotransmitter identities10-12. Data products are available for download, programmatic access and interactive browsing and have been made interoperable with other fly data resources. We derive a projectome-a map of projections between regions-from the connectome and report on tracing of synaptic pathways and the analysis of information flow from inputs (sensory and ascending neurons) to outputs (motor, endocrine and descending neurons) across both hemispheres and between the central brain and the optic lobes. Tracing from a subset of photoreceptors to descending motor pathways illustrates how structure can uncover putative circuit mechanisms underlying sensorimotor behaviours. The technologies and open ecosystem reported here set the stage for future large-scale connectome projects in other species.

A catalogue of neuronal cell types has often been called a 'parts list' of the brain1, and regarded as a prerequisite for understanding brain function2,3. In the optic lobe of Drosophila, rules of connectivity between cell types have already proven to be essential for understanding fly vision4,5. Here we analyse the fly connectome to complete the list of cell types intrinsic to the optic lobe, as well as the rules governing their connectivity. Most new cell types contain 10 to 100 cells, and integrate information over medium distances in the visual field. Some existing type families (Tm, Li, and LPi)6-10 at least double in number of types. A new serpentine medulla (Sm) interneuron family contains more types than any other. Three families of cross-neuropil types are revealed. The consistency of types is demonstrated by analysing the distances in high-dimensional feature space, and is further validated by algorithms that select small subsets of discriminative features. We use connectivity to hypothesize about the functional roles of cell types in motion, object and colour vision. Connectivity with 'boundary types' that straddle the optic lobe and central brain is also quantified. We showcase the advantages of connectomic cell typing: complete and unbiased sampling, a rich array of features based on connectivity and reduction of the connectome to a substantially simpler wiring diagram of cell types, with immediate relevance for brain function and development.

Abstract In the current global market, organizations use many promotional tools to increase their sales. One such tool is sales teams’ initiatives or promotional policies, i.e., free gifts, discounts, packaging, etc. This phenomenon motivates the retailer/or buyer to order a large inventory lot so as to take full benefit of promotional policies. In view of this the present paper considers a two-warehouse (owned and rented) inventory problem for a non-instantaneous deteriorating item with inflation and time value of money over a finite planning horizon. Here, demand depends on the sales team’s initiatives and shortages are partially backlogged at a rate dependent on the duration of waiting time up to the arrival of next lot. We design an algorithm to obtain the optimal replenishment strategies. Numerical analysis is also given to show the applicability of the proposed model in real-world two-warehouse inventory problems.