Assessment of the Endorsers of e-Business Practices for Food Supply Chain Performance System

Globalisation and changing lifestyle of the people has escalated the demand for the more product customisation, taste preferences and awareness about the usage of quality food commodities. Recent developments in the field of information technology and its integration with the business practices has emerged as a new term named ‘e-business’ (EB). Increasing consumer base of the food supply chains (FSC), has escalated the demand of technological and operation advancements by mediating ‘EB’ activities. Such, practices become extensively crucial when the world is suffering from the pandemic of COVID-19, leading to distressing of FSC linkages causing frequent market closures. To tackle the same presented work, explores the various endorsers (EDR) of the ‘EB’ in FSC, which are contemplated by hybrid combination of multi-criteria decision making techniques. Outcomes of the present work aids managers to formulate the decision policies and develop a robust framework in the direction to cling the ‘EB’ practices with FSC.

Dynamic decision policy reconfiguration under outcome uncertainty

In uncertain or unstable environments, sometimes the best decision is to change your mind. To shed light on this flexibility, we evaluated how the underlying decision policy adapts when the most rewarding action changes. Human participants performed a dynamic two-armed bandit task that manipulated the certainty in relative reward (conflict) and the reliability of action-outcomes (volatility). Continuous estimates of conflict and volatility contributed to shifts in exploratory states by changing both the rate of evidence accumulation (drift rate) and the amount of evidence needed to make a decision (boundary height), respectively. At the trialwise level, following a switch in the optimal choice, the drift rate plummets and the boundary height weakly spikes, leading to a slow exploratory state. We find that the drift rate drives most of this response, with an unreliable contribution of boundary height across experiments. Surprisingly, we find no evidence that pupillary responses associated with decision policy changes. We conclude that humans show a stereotypical shift in their decision policies in response to environmental changes.

Identifying control ensembles for decision-making within the cortico-basal ganglia-thalamic circuit

During action selection, mammals exhibit a high degree of flexibility in adapting their decisions in response to environmental changes. Although the cortico-basal ganglia-thalamic (CBGT) network is implicated in this adaptation, it features a synaptic architecture comprising multiple feed-forward, reciprocal, and feedback pathways, complicating efforts to elucidate the roles of specific CBGT populations in the process of evidence accumulation during decision-making. In this paper we apply a strategic sampling approach, based on Latin hypercube sampling, to explore how CBGT network properties, including subpopulation firing rates and synaptic weights, map to parameters of a normative drift diffusion model (DDM) representing algorithmic aspects of information accumulation during decision-making. Through the application of canonical correlation analysis, we find that this relationship can be characterized in terms of three low-dimensional control ensembles impacting specific qualities of the emergent decision policy: responsiveness (associated with overall activity in corticothalamic and direct pathways), pliancy (associated largely with overall activity in components of the indirect pathway of the basal ganglia), and choice (associated with differences in direct and indirect pathways across action channels). These analyses provide key mechanistic predictions about the roles of specific CBGT network elements in shifting different aspects of decision policies.

Multi-Household Energy Management in a Smart Neighborhood in the Presence of Uncertainties and Electric Vehicles

The pathway toward the reduction of greenhouse gas emissions is dependent upon increasing Renewable Energy Sources (RESs), demand response, and electrification of public and private transportation. Energy management techniques are necessary to coordinate the operation in this complex scenario, and in recent years several works have appeared in the literature on this topic. This paper presents a study on multi-household energy management for Smart Neighborhoods integrating RESs and electric vehicles participating in Vehicle-to-Home (V2H) and Vehicle-to-Neighborhood (V2N) programs. The Smart Neighborhood comprises multiple households, a parking lot with public charging stations, and an aggregator that coordinates energy transactions using a Multi-Household Energy Manager (MH-EM). The MH-EM jointly maximizes the profits of the aggregator and the households by using the augmented ϵ-constraint approach. The generated Pareto optimal solutions allow for different decision policies to balance the aggregator’s and households’ profits, prioritizing one of them or the RES energy usage within the Smart Neighborhood. The experiments have been conducted over an entire year considering uncertainties related to the energy price, electric vehicles usage, energy production of RESs, and energy demand of the households. The results show that the MH-EM optimizes the Smart Neighborhood operation and that the solution that maximizes the RES energy usage provides the greatest benefits also in terms of peak-shaving and valley-filling capability of the energy demand.

Teachers Unions’ Leadership and Their Impact on Educational Reforms in Israel

This article examines the politics of the teacher unions in the education reforms in Israel. In particular, the impact of the Histadrut Hamorim (HH) and the Irgun Hamorim (IH) teacher unions in the three educational reforms that took place in the country, in the years 1968, 2008 and 2011 respectively, was analyzed. For this purpose, a systematic literature review on the topic was carried out. Firstly, the discourse about the teacher unions potential influence upon the education reform decision policies was studied. Secondly, the role of the teacher unions in the education policy reforms in Israel was examined from a historical perspective, with special reference to the two unions under consideration. In conclusion, our study revealed that the impact of the teacher unions upon the education policy formation relating to the reforms as suggested in this piece of work seemed to be concerned with the unions’ political and ideological attachment to the political party in power.

Advanced machine learning decision policies for diameter control of carbon nanotubes

The diameters of single-walled carbon nanotubes (SWCNTs) are directly related to their electronic properties, making diameter control highly desirable for a number of applications. Here we utilized a machine learning planner based on the Expected Improvement decision policy that mapped regions where growth was feasible vs. not feasible and further optimized synthesis conditions to selectively grow SWCNTs within a narrow diameter range. We maximized two ranges corresponding to Raman radial breathing mode frequencies around 265 and 225 cm−1 (SWCNT diameters around 0.92 and 1.06 nm, respectively), and our planner found optimal synthesis conditions within a hundred experiments. Extensive post-growth characterization showed high selectivity in the optimized growth experiments compared to the unoptimized growth experiments. Remarkably, our planner revealed significantly different synthesis conditions for maximizing the two diameter ranges in spite of their relative closeness. Our study shows the promise for machine learning-driven diameter optimization and paves the way towards chirality-controlled SWCNT growth.

Mobile Intercloud System for Edge Cloud Computing

Recent years have seen considerable interest in mobile cloud computing and edge cloud computing. This paper presents a mobile Intercloud system for supporting mobile cloud computing in general and edge cloud computing in particular. In essence, a mobile user with a mobile terminal can set up a virtual mobile terminal with applications and data in a central/home cloud. The virtual mobile terminal can facilitate task and computation offloading and other functions. Moreover, when a mobile terminal joins an edge cloud, the virtual mobile terminal (including required applications and data) can be migrated to enhance system efficiency and the user experience (e.g., shorter access delays). An experimental prototype has been developed for evaluating certain basic object transfer functions. To support the application transfer function, we formulate both finite- and infinite-horizon Markov decision models to determine decision policies (i.e., should an application be transferred to an edge cloud). The transfer decision depends on various factors, including transfer cost, duration associated with the edge cloud, usage probability, and usage cost in the central cloud and edge cloud. Based on the models, we obtain closed-form solutions for the decision policies, which can be expressed in meaningful formulas to provide useful insights for edge cloud computing in general. To evaluate the mobile Intercloud system for edge cloud computing, we conducted extensive evaluations, including experimental evaluation for testing the basic functions and protocols, analytical evaluation for studying the analytical models, and simulation evaluation for analyzing performance in a multiuser and multicloud environment in particular. The experimental, simulation, and analytical results provide useful insights into the design and development of the mobile Intercloud system for edge cloud computing as well as decision policies for application transfer.

Combining Process Tracing and Policy Capturing Techniques for Judgment Analysis in an Anti-Submarine Warfare Simulation

The Cognitive Shadow is a prototype decision support tool that can notify users when they deviate from their usual judgment pattern. Expert decision policies are learned automatically online while performing one’s task using a combination of machine learning algorithms. This study investigated whether combining this system with the use of a process tracing technique could improve its ability to model human decision policies. Participants played the role of anti-submarine warfare commanders and rated the likelihood of detecting a submarine in different ocean areas based on their environmental characteristics. In the process tracing condition, participants were asked to reveal only the information deemed necessary, and only that information was sent to the system for model training. In the control condition, all the available information was sent to the system with each decision. Results showed that process tracing data improved the model’s ability to predict human decisions compared to the control condition.

Overlapping influences shape motor activity during hasty sensorimotor decisions

Humans and other animals are able to adjust their speed-accuracy tradeoff (SAT) at will depending on the urge to act, favoring either cautious or hasty decision policies in different contexts. An emerging view is that SAT regulation relies on influences exerting broad changes on the motor system, tuning its activity up globally when hastiness is at premium. The present study aimed to test this hypothesis. Fifty subjects performed a task involving choices between left and right index fingers, in which incorrect choices led either to a high or to a low penalty in two contexts, inciting them to emphasize either cautious or hasty policies. We applied transcranial magnetic stimulation on multiple motor representations, eliciting motor evoked potentials (MEP) in nine finger and leg muscles. MEP amplitudes allowed us to probe activity changes in the corresponding finger and leg representations, while subjects were deliberating about which index to choose. Our data indicate that hastiness entails a broad amplification of motor activity, though this amplification was limited to the chosen side. On top of this effect, we identified a local suppression of motor activity, surrounding the chosen index representation. Hence, a decision policy favoring speed over accuracy appears to rely on overlapping processes producing a broad (but not global) amplification and a surround suppression of motor activity. The latter effect may help increasing the signal-to-noise ratio of the chosen representation, as supported by single-trial correlation analyses indicating a stronger differentiation of activity changes in finger representations in the hasty context.

Beyond Misperception–Two Types of Mental Model Errors in a Dynamic Decision Task

This article contributes to research on mental models and how they underpin decision policies. It proposes a framework for the joint use of mental models of dynamic systems and the theory of mental models initiated by Johnson–Laird and defines two types of errors: (1) misrepresentation of the system’s structure, and (2) failure to deploy relevant mental models of possibilities. We use a dynamic decision task based on Moxnes’ “reindeer experiment” to formulate three intuitive policies, their underlying mental models, and the reasoning, and evaluate the policies under varying initial conditions. Each of the policies generates problematic behaviors like dependance on initial conditions, underperformance because of flawed goal setting and oscillation due to leaving the delay in a feedback loop out of account. We identify errors of both types in the mental models and relate them to the behavioral problems. Limitations and questions for further research conclude the paper.