Output Space Entropy Search Framework for Multi-Objective Bayesian Optimization

We consider the problem of black-box multi-objective optimization (MOO) using expensive function evaluations (also referred to as experiments), where the goal is to approximate the true Pareto set of solutions by minimizing the total resource cost of experiments. For example, in hardware design optimization, we need to find the designs that trade-off performance, energy, and area overhead using expensive computational simulations. The key challenge is to select the sequence of experiments to uncover high-quality solutions using minimal resources. In this paper, we propose a general framework for solving MOO problems based on the principle of output space entropy (OSE) search: select the experiment that maximizes the information gained per unit resource cost about the true Pareto front. We appropriately instantiate the principle of OSE search to derive efficient algorithms for the following four MOO problem settings: 1) The most basic single-fidelity setting, where experiments are expensive and accurate; 2) Handling black-box constraints which cannot be evaluated without performing experiments; 3) The discrete multi-fidelity setting, where experiments can vary in the amount of resources consumed and their evaluation accuracy; and 4) The continuous-fidelity setting, where continuous function approximations result in a huge space of experiments. Experiments on diverse synthetic and real-world benchmarks show that our OSE search based algorithms improve over state-of-the-art methods in terms of both computational-efficiency and accuracy of MOO solutions.

Results of Parallel-Machine Scheduling Model with Maintenance Activity considering Time-Dependent Deterioration, Delivery Times, and Resource Allocation

This paper investigates parallel-machine scheduling models with maintenance activity, delivery times, time-dependent deterioration, and resource allocation. We consider two forms of the problem: the first is to minimize the sum of total completion times, total machine loads, the total absolute deviation of job completion times, and the total resource allocation; the second is to minimize the sum of total waiting times, total machine loads, the total absolute deviation of job waiting times, and the total resource allocation. The problems are proved to be solvable in polynomial time.

QoS-Aware Resource Allocation and Femtocell Selection for 5G Heterogeneous Networks

5G is not a simple cellular technology; it's a real revolution to improve the connection speed that assures Quality of Service (QoS) requirements and user satisfaction in a heterogeneous environment. 5G network is considered as a Het- erogeneous Networks (HetNets) able to support a multitude of speci c use cases (such as Smart Metering and Videoconferencing) and new services, where per- formance requirements will be extremely polarized. In this context, several key issues for 5G communications should be addressed to satisfy QoS provisioning. Radio resource allocation is considered as an important 5G key issue for Inter- net of Things (IoT) communications. In this paper, we propose a QoS-aware resource allocation and femtocell selection for 5G HetNets named QoS-RAS. Our proposed approach maximizes the total resource utilization of the network and it ensures a balanced load by selecting the suitable femtocell for each user and allocating the available resources with an adequate manner. Our work gives the best scenarios that aim to enhance system model performance in terms of resource utilization ratio, dropped request probability, total average throughput and fairness index.

Reward influences the allocation but not the availability of resources in visual working memory

Visual working memory possesses strict capacity constraints which place limits on the availability of resources for encoding and maintaining information. Studies have shown that prospective rewards improve performance on visual working memory tasks, but it remains unclear whether rewards increase total resource availability or rather influence the allocation of resources without affecting availability. Participants performed a continuous report visual working memory task with oriented grating stimuli. On each trial, participants were presented with a priority cue, which signaled the item most likely to be probed, and a reward cue, which signaled the magnitude of a performance-contingent reward. We showed that rewards decreased recall error for cued items and increased recall error for non-cued items. We further demonstrated that rewards produced a tradeoff in the probability of successfully encoding a cued versus a non-cued item rather than a tradeoff in recall precision or the probability of binding errors. Lastly, we showed that rewards only affected resource allocation when participants were given the opportunity to engage proactive control prior to encoding. These findings indicate that rewards influence the flexible allocation of resources during selection and encoding in visual working memory, but do not augment total capacity.

An Enzymatic Method for Harvesting Functional Melanosomes After Keratin Extraction – Maximizing Resource Recovery from Human Hair

Hair contains about 80% keratins and 1–3% melanin packaged in melanosomes. Both of which are high-value and functional raw materials that have potential applications in wide ranging fields. While keratin extraction has been widely refined, efficient methods of melanosome extraction are limited. The extraction of melanosomes requires complete removal of keratin, thus combined keratin extraction and melanosome isolation is logical. Herein, a successive process to harvest melanosomes after keratin extraction from human hair waste was developed. The yield of melanosome was about 1.3% of total hair mass. The structure of harvested melanosomes is well preserved based on surface morphology and interior ultrastructural observations using electron microscopy. The chemical structure, UV-filtering ability, and thermal stability of the melanosomes are examined to demonstrate preservation of native functions. Our strategy of combining melanosome isolation with keratin extraction is shown to be effective and significantly improves the total resource recovery efficiency from human hair waste.

Parallel-Machine Scheduling with DeJong’s Learning Effect, Delivery Times, Rate-Modifying Activity, and Resource Allocation

We investigate parallel-machine scheduling with past-sequence-dependent (p-s-d) delivery times, DeJong’s learning effect, rate-modifying activity, and resource allocation. Each machine has a rate-modifying activity. We consider two versions of the problem to minimize the sum of the total completion times, the total absolute deviation of job completion times, and the total resource allocation and the sum of the total waiting times, the total absolute deviation of job waiting times, and the total resource allocation, respectively. The problems under our present model can be solved in polynomial time.

Nonlinear sociodynamics of competitive sociotypes of molecular and cosmic human

As one of the most important factors determining the nature of the dynamic behavior of a social system, the article considers the competitive relationship of two alternative sociotypes, conventionally designated as molecular human and cosmic human. The molecular sociotype is understood as the personality of the average consumer, whose behavioral determinants are largely determined by the selfish gene. The cosmic human is a person who has realized the dependence on the selfish gene. Representatives of the cosmic sociotype consider the products of their own mind, their contribution to culture, as a more significant function than gene replication. This explains the different values ​​of the coefficients of reproduction of the total resource of the system by molecular human and cosmic human. Three possible scenarios for the evolution of a social system have been identified for different values ​​of the coefficients of reproduction of the total resource of the system by both sociotypes with a constant share of the population. In this case, the aggregate resource is understood as the entire intellectual, cultural and economic product produced by the social system during a certain conditional cycle of reproduction. The dynamics of a social system with a variable value of the share of a productive comic sociotype is considered in the work on the basis of a nonlinear two-dimensional model. The mathematical model demonstrates complex nonlinear quasicyclic behavior.

Fusion breeding as an approach to sustainable energy

This article examines an approach for sustainable energy called fusion breeding. This is the use of 14 meV fusion neutrons to breed fuel for thermal nuclear reactors. Currently thermal nuclear reactors use for fuel, only the isotope of uranium, 235U, which is 0.7% of the total resource. In order for nuclear power to be sustainable, it is necessary to breed nuclear fuel (233U or 239Pu) from fertile material (238U or 232Th). This resource could supply tens of terawatts for thousands of years. By any reasonable criterion, it both sustainable and carbon free. While most efforts at breeding envision fission reactors of one type or another, fusion is also a possible approach to breeding. Not only that, fusion has many advantages as a route for breeding that fission simply does not have. This article makes the case for fusion breeding.

Sustainable Total Resource Management in Thailand Healthcare Industry under Uncertain Situations

This study aims to validate the sustainable total resource management measures and provide a hierarchical structure to manage the medical resources for the healthcare industry in Thailand. Prior studies have failed to deal with the valid attributes and to present a theoretical hierarchical structure. The Thailand healthcare industry faces medical resource depletion in the current pandemic outbreak. To address these gaps, this study proposes a fuzzy Delphi method to screen out the less important attributes in order to enhance the validity of measures. Fuzzy interpretive structural modelling transfers the complex interrelationships into a hierarchical structure and provides the direction for practical improvement. The result shows that green human resources practices, collaboration in supply chain networks, analysis and knowledge management, and technology innovation are all important aspects of the hierarchical structure for practical improvement. The linkage criteria are (1) green ability, (2) green motivation, (3) operational efficiency, (4) environmental regulation, and (5) energy conservation. The theoretical and managerial implications are subsequently discussed.