Modern Slavery Characterisation through the Analysis of Energy Replenishment

The Brazilian economy was, until the end of the 19th Century, based on slave labour. However, in this first quarter of the 21st Century, the problem persists. These situations tend to be mistaken with “simple” violations of labour laws. This work aims to establish Occupational Health and Safety parameters, focusing on energy needs, to distinguish between the breach of labour legislation and modern rural slavery in the 21st Century in Brazil. In response to this challenge, bibliographical research was carried out on the feeding and energy replenishment conditions of Brazilian slaves in the 19th Century. Obtained data were compared with a sample where 392 cases of neo-slavery in Brazil are described. The energy spent and the energy supplied was calculated to identify the enslaved workers’ general feeding conditions in the two historical periods. The general conditions of food and water supply were analysed. It was possible to identify three comparable parameters: food quality, food quantity, and water supply. It was concluded that there is a parallelism of energy replenishment conditions between Brazilian slaves and neo-slaves of the 19th and 21st centuries, respectively, different from that of free workers. This difference can help authorities identify and punish instances of modern slavery.

An Efficient Far-Field Wireless Power Transfer via Field Intensity Shaping Techniques

Radiative (or far-field) energy replenishment for devices such as smartphones, laptops, robots, and small electric appliances paves the way to autonomous and continuous devices functioning, thus bypassing the need of operation interruptions, human maintenance activities, and replenishment by wired transformers. In this work, we investigate the feasibility of using a properly engineered antenna array able to deliver radiative power to devices in need of energy replenishment during their normal and unsupervised activity, whose locations are unknown. Both the case of single and multiple devices needing energy replenishment are addressed. A quantitative proof-of-concept study is carried out to validate the proposed approach. A 3D scenario is simulated to study the case of devices in need of energy replenishment within a standard office environment. Different antenna array configurations are investigated and the corresponding performances benchmarked against a standard installation of recharging antennas. Results confirm the outstanding capability of the proposed approach in terms of confinement and maximization of power transfer. Finally, in this framework, we also propose an efficient communication protocol that is able to manage multiple recharge demand given different operational rules.

A Joint Energy Replenishment and Data Collection Strategy in Heterogeneous Wireless Rechargeable Sensor Networks

In wireless rechargeable sensor networks, mobile vehicles (MVs) combining energy replenishment and data collection are studied extensively. To reduce data overflow, most recent work has utilized more vehicles to assist the MV to collect buffered data. However, the practical network environment and the limitations of the vehicle in the data collection are not considered. UAV-enabled data collection is immune to complex road environments in remote areas and has higher speed and less traveling cost, which can overcome the lack of the vehicle in data collection. In this paper, a novel framework joining the MV and UAV is proposed to prolong the network lifetime and reduce data overflow. The network lifetime is correlated with the charging order; therefore, we first propose a charging algorithm to find the optimal charging order. During the charging period of the MV, the charging time may be longer than the collecting time. An optimal selection strategy of neighboring clusters, which could send data to the MV, was found to reduce data overflow. Then, to further reduce data overflow, an algorithm is also proposed to schedule the UAV to assist the MV to collect buffered data. Finally, simulation results verified that the proposed algorithms can maximize network lifetime and minimize the data loss simultaneously.

Energy Replenishment Diversification Under Marine Spatial Planning Connects Big Data, Artificial Intelligence, Internet of Things for Coastal Community Sustainability

Background Controversial coal power plant restart and non-nuke policy were rejected by referendum in Taiwan on 24 Nov 2018. Outcomes affecting energy replenishment and undermining economic stability are criticized politicization because coal energy is detrimental but no nuke beneficial for the environment. These policies could be contradictory. Problem No autonomous energy puts the country into extreme risk and is controlled by foreigners. Developing energy faces a traditional tradeoff between economic development and environmental protection. Purpose The assumption an innovative energy diversification linking Big Data, artificial intelligence (AI), and the Internet of Things (IoT) is encouraged. The paper investigates upgrading liquified petroleum gas (LPG) to liquified natural gas (LNG) under marine spatial planning (MSP) for energy replenishment and creating an energy diversification framework through integrated coastal zone management (ICZM) linking Big Data, IoT, AI whether are supported. Results Online sampling investigation results, enhancing LPG to LNG at the ecologically biologically significant area (EBSA) under MSP was less encouraged but linking Big Data, IoT, AI innovation through ICZM was overwhelmingly supported and consistent with goalkeepers interview. Research finding gender, the environment, age revealed no significant difference from LNG through MSP and AI under ICZM. Males more support LNG energy replenishment and energy diversification. Energy deficiency, environmental protection, and economic development were interrelated. Conclusion The results LPG to LNG revealed less encouraging and implicates many coastal communities in favor of tangible stable electricity without blackout is important than the intangible slogan EBSA protection, or perhaps renewable energy is an alternative approach. An autonomous energy diversification framework through ICZM links Big Data, IoT, AI for sustainable coastal community development (SCCD) indicating no significant difference from gender, environment, age, and is consistent with the goalkeepers' interview results supporting energy diversification. The finding males more support LNG and energy diversification. Finally, the study suggests that legislative enforcement establishment designate expertise teamwork to coordinate administration, integrate programs of energy replenishment, creating an autonomous energy diversification framework for monitoring energy demand, supplies, replenishment, and connect Big Data, IoT, AI innovation for SCCD.