Anti-Predator Vigilance as an Indicator of the Costs and Benefits of Supplemental Feeding in Newly Released ‘Alalā (Corvus hawaiiensis)

Although supplemental feeding is commonly used as a conservation strategy during animal translocations, it comes with a number of pros and cons which can be hard to quantify. Providing additional food resources may lead to improved physical health, survivorship, and reproduction. However, offering predictable food sources could make individuals more conspicuous to predators and less aware of their surroundings, disrupting their natural predator-prey dynamic. Decisions such as release cohort size and supplemental feeder design could influence the balance of these costs and benefits, depending on how animals behave in the face of predation risk and static food sources. Additionally, animals released to the wild from long term human care must balance foraging and predation risk while adjusting to a novel environment. To help conservation managers make informed decisions in light of these potential costs, we studied the behavior of a cohort of 11 conservation-bred ‘alalā (Corvus hawaiiensis) at supplemental feeding stations after release into the wild. Vigilance, foraging behavior and social group size was quantified via 1,320 trail camera videos of ‘alalā over the span of 12 months. We found that vigilance increased over time since release, suggesting that ‘alalā learn and adjust to their novel surroundings. Both vigilance and eating decreased with group size, indicating that although conspecifics may share the burden of scanning for threats, they also increase competition for food. We also found that the design of the feeder may have limited birds' abilities to express anti-predator behavior since less vigilance was observed in individuals that manipulated the feeder. Yet, birds may have been able to offset these costs since they increasingly scrounged for food scraps next to the feeder as time progressed. We discuss how changes to behavior over time, social interactions, and feeder design should all be considered when planning supplemental feeding as part of wildlife translocations.

Biomimetic Leadership for 21st Century Companies

Biomimicry is a scientific discipline that aims to model the behavior or properties of biological systems so as to adapt them to other scientific areas. Recently, this approach has been adopted in order to develop an organizational model called “Organizational Biomimicry”. It proposes a systemic approach, a worldview that places the organization and the people related to it as an integral part of nature, and an R&D system based on continuous learning from nature. The effective management of this business model depends on leaders who can make dynamic decisions, generate commitment to the views of the company, define specific goals, actively learn on multiple levels and tackle conflicts. This type of leadership may actually be being exercised in business practice; however, no leadership style inspired by biomimicry has been theorized to date. Thus, the aim of this research was to present a biomimetic leadership model that considers nature as a model, measure and mentor. To this end, we proposed, firstly, a definition of a biomimetic leader from the point of view of the characteristics of biomimetic organizations. Then, we determined the characteristics of this leadership type. Secondly, we conducted a review of the main leadership styles analyzed in the recent literature about management; then, for each leadership type, we extracted the characteristics that will adapt to the biomimetic leadership model. From this process, we obtained the traits of a biomimetic leader. This characterization (definition plus characteristics) was subjected to an expert panel, which determined its validity.

AI-based soft-sensor for shelf life prediction of ‘Kesar’ mango

This paper presents prediction of shelf-life of ‘Kesar’ cultivar of mangoes stored under specified conditions based on their respiration rate and ripeness levels. A deep-CNN was fine-tuned on 1524 image data of mangoes stored under different conditions to classify the ripeness levels of mangoes as ‘unripe’, ‘early-ripe’, ‘partially-ripe’ and ‘ideally-ripe’. CO2 respiration rate (RRCO2) was further calculated using principle of enzyme kinetics to establish a correlation between RRCO2 and ripeness levels. A Support Vector Regression model was employed to predict the shelf life and ripeness levels of mangoes under different storage conditions, thereby creating an AI based soft-sensor. The developed methodology can be used for other climacteric fruits besides mangoes. This solution can be used by producers and distributors for post-harvest handling of climacteric fruits like mango. It will also aid retailers in taking dynamic decisions with respect to pricing, logistics and storage conditions to be maintained to get the desired ripening rate, thus, contributing to reduction of wastage of fruits and subsequent economic losses.Article highlights Variation in CO2 respiration rate of ‘Kesar’ mangoes over different maturity stages were observed under different supply chain scenarios simulated in lab environment AI models were developed based on respiration rate and ripeness levels for prediction of shelf life of mangoes under different supply chain scenarios. These models once deployed helps all stake holders in fruit supply chain to take dynamic decisions such as repricing, recycling and repurposing based on the predicted shelf life thus minimizing wastage and maximizing profit.

Optimal Policy for Dynamic Assortment Planning Under Multinomial Logit Models

We study the dynamic assortment planning problem, where for each arriving customer, the seller offers an assortment of substitutable products and the customer makes the purchase among offered products according to an uncapacitated multinomial logit (MNL) model. Because all the utility parameters of the MNL model are unknown, the seller needs to simultaneously learn customers’ choice behavior and make dynamic decisions on assortments based on the current knowledge. The goal of the seller is to maximize the expected revenue, or, equivalently, to minimize the expected regret. Although dynamic assortment planning problem has received an increasing attention in revenue management, most existing policies require the estimation of mean utility for each product and the final regret usually involves the number of products [Formula: see text]. The optimal regret of the dynamic assortment planning problem under the most basic and popular choice model—the MNL model—is still open. By carefully analyzing a revenue potential function, we develop a trisection-based policy combined with adaptive confidence bound construction, which achieves an item-independent regret bound of [Formula: see text], where [Formula: see text] is the length of selling horizon. We further establish the matching lower bound result to show the optimality of our policy. There are two major advantages of the proposed policy. First, the regret of all our policies has no dependence on [Formula: see text]. Second, our policies are almost assumption-free: there is no assumption on mean utility nor any “separability” condition on the expected revenues for different assortments. We also extend our trisection search algorithm to capacitated MNL models and obtain the optimal regret [Formula: see text] (up to logrithmic factors) without any assumption on the mean utility parameters of items.

Long-term stress and trait anxiety alter brain network balance in dynamic decisions during working memory

Long-term stress has a profound impact on the human brain and cognition, and trait anxiety influences stress-induced adaptive and maladaptive effects. However, the neurocognitive mechanisms underlying long-term stress and trait anxiety interactions remain elusive. Here we investigated how long-term stress and trait anxiety interact to affect dynamic decisions during working-memory (WM) by altering functional brain network balance. In comparison to controls, male participants under long-term stress experienced higher psychological distress and exhibited faster evidence accumulation but had a lower decision-threshold during WM. This corresponded with hyper-activation in the anterior insula, less WM-related deactivation in the default-mode network, and stronger default-mode network decoupling with the frontoparietal network. Critically, high trait anxiety under long-term stress led to slower evidence accumulation through higher WM-related frontoparietal activity, and increased decoupling between the default-mode and frontoparietal networks. Our findings provide neurocognitive evidence for long-term stress and trait anxiety interactions on executive functions with (mal)adaptive changes.

Green product diffusion: The impacts of asymmetric retailers' strategic product decisions

With the continuous deterioration of the environment and the improvement of consumer green awareness, more and more producers began to launch green products. For example, many automobile companies began to produce new energy vehicles. However, whether a new product can be successfully introduced to the market depends not only on the product's quality improvement, but also on its sales channels. In this paper, we model a supply chain composed of a manufacturer and two asymmetric retailers to analyze how the retailers' strategic decisions affect the introduction of a newer green product. Backward induction is adopted to survey the dynamic decisions of the supply chain members. Given the leading retailer's product choice, the follower-up retailer's product choices and decision optimums are defined by specific thresholds of consumer green valuation and production costs. Results show that the follower-up retailer would make completely different responses within a same threshold range when the leading retailer takes different product decisions. In other words, even if the leading retailer chooses green new products, the follower will not necessarily imitate the choice of green products, and it could be more advantageous to choose the old generation products (for price competition). Furthermore, results show that green product introduction does not necessarily bring Pareto improvement to both the two retailers. Finally, we derive the specific intervals in which green products can be successfully introduced into the market.Our modelling work and results provide instructive managerial insights on green product introduction in a retailer led supply chain.

Fuzzy Decision-Based Efficient Task Offloading Management Scheme in Multi-Tier MEC-Enabled Networks

Multi-access edge computing (MEC) is a new leading technology for meeting the demands of key performance indicators (KPIs) in 5G networks. However, in a rapidly changing dynamic environment, it is hard to find the optimal target server for processing offloaded tasks because we do not know the end users’ demands in advance. Therefore, quality of service (QoS) deteriorates because of increasing task failures and long execution latency from congestion. To reduce latency and avoid task failures from resource-constrained edge servers, vertical offloading between mobile devices with local-edge collaboration or with local edge-remote cloud collaboration have been proposed in previous studies. However, they ignored the nearby edge server in the same tier that has excess computing resources. Therefore, this paper introduces a fuzzy decision-based cloud-MEC collaborative task offloading management system called FTOM, which takes advantage of powerful remote cloud-computing capabilities and utilizes neighboring edge servers. The main objective of the FTOM scheme is to select the optimal target node for task offloading based on server capacity, latency sensitivity, and the network’s condition. Our proposed scheme can make dynamic decisions where local or nearby MEC servers are preferred for offloading delay-sensitive tasks, and delay-tolerant high resource-demand tasks are offloaded to a remote cloud server. Simulation results affirm that our proposed FTOM scheme significantly improves the rate of successfully executing offloaded tasks by approximately 68.5%, and reduces task completion time by 66.6%, when compared with a local edge offloading (LEO) scheme. The improved and reduced rates are 32.4% and 61.5%, respectively, when compared with a two-tier edge orchestration-based offloading (TTEO) scheme. They are 8.9% and 47.9%, respectively, when compared with a fuzzy orchestration-based load balancing (FOLB) scheme, approximately 3.2% and 49.8%, respectively, when compared with a fuzzy workload orchestration-based task offloading (WOTO) scheme, and approximately 38.6%% and 55%, respectively, when compared with a fuzzy edge-orchestration based collaborative task offloading (FCTO) scheme.

Dynamic Optimization and Heuristics Based Online Coverage Path Planning in 3D Environment for UAVs

Path planning is one of the most important issues in the robotics field, being applied in many domains ranging from aerospace technology and military tasks to manufacturing and agriculture. Path planning is a branch of autonomous navigation. In autonomous navigation, dynamic decisions about the path have to be taken while the robot moves towards its goal. Among the navigation area, an important class of problems is Coverage Path Planning (CPP). The CPP technique is associated with determining a collision-free path that passes through all viewpoints in a specific area. This paper presents a method to perform CPP in 3D environment for Unmanned Aerial Vehicles (UAVs) applications, namely 3D dynamic for CPP applications (3DD-CPP). The proposed method can be deployed in an unknown environment through a combination of linear optimization and heuristics. A model to estimate cost matrices accounting for UAV power usage is proposed and evaluated for a few different flight speeds. As linear optimization methods can be computationally demanding to be used on-board a UAV, this work also proposes a distributed execution of the algorithm through fog-edge computing. Results showed that 3DD-CPP had a good performance in both local execution and fog-edge for different simulated scenarios. The proposed heuristic is capable of re-optimization, enabling execution in environments with local knowledge of the environments.