Precious Energy Research Articles

REALIGNING INDONESIAN WASTE-TO-ENERGY POLICIES WITH SDG 11: ANALYSIS OF LEGAL COMPLIANCE AND ITS IMPLICATIONS

To date, Indonesia is still grappling with waste management issues, which is a significant obstacle to realizing Sustainable Development Goals (SDG) 11. One of the ways to deal with this issue is through the development of the waste-to-energy (WtE) system, which can be utilized to transform waste into precious energy resources. However, this system must also be supported with robust legal compliance to ensure that its development is not solely focused on generating profits through energy production but also meets its noble purpose of improving sustainability as conceptualized within SDG 11. This study utilizes the normative legal research method, along with a statutory approach. Analysis of this study finds that the current Indonesian legal framework is insufficient in providing a robust mechanism of compliance to ensure that the waste-to-energy system can contribute to the broader waste-management issues and their ramifications, specifically due to the lack of recognition of the unique intricacies of the development of WtE systems, along with the lack of normative implementation of SDG 11. The paper proposes a model of normative construction to cover these issues as a way of ensuring growth and environmental sustainability in Indonesia, particularly regarding waste management.

Solar Lights for Highways with Zone-Wise Speed Control System for Vehicle

The challenge of providing adequate lighting along highways, particularly at critical points such as sharp turns and speed breakers, poses significant safety concerns. Traditional energy sources often fail to meet the demands of continuous lighting, especially in remote areas. To tackle this challenge, we present an automatic lighting system aimed at selectively activating lights during nighttime while also reducing vehicle speed near or at danger zones. Entire system is designed as automatic, human involvement is not required; lights are controlled automatically by sensing the vehicles. Whenever the vehicle enters into the danger zone, lights will be energized and they remain in ON state until the vehicle leaves that area. The system will be activated automatically during dark. In this method precious energy acquired from the solar panel can be saved. During the day time the battery will be charged and this stored energy is utilized in the nights[10]. It is a novel approach to vehicle navigation and safety implementation, and also aimed at automatically sensing the areas/zones like Schools, Hospital Accident, etc and automatically reducing the speed of the vehicle for safety purposes.

Research on the Heterogeneous Autonomous Underwater Vehicle Cluster Scheduling Problem Based on Underwater Docking Chambers

The onboard energy supply of Autonomous Underwater Vehicles (AUVs) is one of the main limiting factors for their development. The existing methods of deploying and retrieving AUVs from mother ships consume a significant amount of energy during submerging and surfacing, resulting in a small percentage of actual working time. Underwater docking chambers provide support to AUVs underwater, saving their precious energy and addressing this issue. When an AUV cluster is assigned multiple tasks, scheduling the cluster becomes essential, and task allocation and path planning are among the core problems in AUV cluster scheduling research. In this paper, based on the underwater docking chamber, an Improved Genetic Local Search Algorithm with Prior Knowledge (IGLSAPK) is proposed to simultaneously solve the task allocation and path planning problems. Under constraints such as onboard energy supply, AUV quantity, and AUV type, the algorithm groups AUVs, assigns tasks, and plans paths to accomplish tasks at different locations, aiming to achieve overall efficiency. The algorithm first generates an initial population using prior knowledge to improve its search efficiency. It then combines an improved local search algorithm to efficiently solve large-scale, complex, and highly coupled problems. The algorithm has been evaluated through simulation experiments and comparative experiments, and the results demonstrate that the proposed algorithm outperforms other algorithms in terms of speed and optimality. The algorithm presented in this paper addresses the grouping, task allocation, and path planning problems in heterogeneous AUV clusters. Its practical significance lies in its ability to handle tasks executed by a heterogeneous AUV group, making it more practical compared to previous algorithms.

Efficient offloading in disaster-affected areas using unmanned aerial vehicle-assisted mobile edge computing: A gravitational search algorithm-based approach

The collection and processing of real-time data from a disaster-affected area is challenging. Unmanned aerial vehicles (UAVs) can efficiently gather the data and then transfer it to the edge servers (ESs) to timely initiate the rescue process. Consideration of energy and delay in a UAV-assisted edge network is very important, as both the UAVs and the smart mobile devices (SMDs) in the network have energy constraints and low processing capacity. Offloading is a promising technique to preserve the precious energy of the SMDs. In this research, gravitational search algorithm (GSA)-based offloading is presented for UAV-assisted mobile edge computing (MEC)-enabled disaster-affected areas. The problem is first mathematically formulated and shown to be computationally hard. Efficient encoding of agents (solution vectors) is given for the offloading problem. Fitness function is designed by considering the energy, delay, and load balancing of the ESs. The proposed GSA is executed by considering multiple disaster scenarios, and its performance is compared with other evolutionary algorithms (EAs) like the genetic algorithm (GA), particle swarm optimization (PSO), and fireworks algorithm (FWA). It has been observed that the GSA outperforms the other EAs in almost all the considered experiment scenarios. GSA claims a 30%–40% improvement for delay, 3%–5% for energy consumption, and more than 40% for load balancing. Statistical and convergence analyses are also conducted. The convergence of the GSA is found to be faster than that of the other EAs.

A Fog Computing Framework for Intrusion Detection of Energy-Based Attacks on UAV-Assisted Smart Farming

Precision agriculture and smart farming have received significant attention due to the advancements made in remote sensing technology to support agricultural efficiency. In large-scale agriculture, the role of unmanned aerial vehicles (UAVs) has increased in remote monitoring and collecting farm data at regular intervals. However, due to an open environment, UAVs can be hacked to malfunction and report false data. Due to limited battery life and flight times requiring frequent recharging, a compromised UAV wastes precious energy when performing unnecessary functions. Furthermore, it impacts other UAVs competing for charging times at the station, thus disrupting the entire data collection mechanism. In this paper, a fog computing-based smart farming framework is proposed that utilizes UAVs to gather data from IoT sensors deployed in farms and offloads it at fog sites deployed at the network edge. The framework adopts the concept of a charging token, where upon completing a trip, UAVs receive tokens from the fog node. These tokens can later be redeemed to charge the UAVs for their subsequent trips. An intrusion detection system is deployed at the fog nodes that utilize machine learning models to classify UAV behavior as malicious or benign. In the case of malicious classification, the fog node reduces the tokens, resulting in the UAV not being able to charge fully for the duration of the trip. Thus, such UAVs are automatically eliminated from the UAV pool. The results show a 99.7% accuracy in detecting intrusions. Moreover, due to token-based elimination, the system is able to conserve energy. The evaluation of CPU and memory usage benchmarks indicates that the system is capable of efficiently collecting smart-farm data, even in the presence of attacks.

Evaluation of 5G Edge and Cloud Computing for Data Processing in Visual Referencing of Mobile Robot Manipulators

To fit the requirements of growing flexibility in production processes, intralogistics is increasingly automated, for example, by Mobile Robot Manipulators, which can transport material and perform assembly tasks. However, these transport systems have limited battery capacity and recharging involves occasional downtime. New approaches, such as Edge Computing, offer transport systems to offload intensive computational processes, e.g., for path planning or AI-based image analysis, and thus saving precious energy resources. However, since offloading such processes onto a server is tied to high security and transmission requirements, using 5G for communication might be an attractive solution. Therefore, an exemplary referencing use case of a Mobile Robot Manipulator is presented and the 5G based communication with two cloud servers and one edge server is examined. Our experimental results show that with a mean transmission time of 540 milliseconds Edge Computing facilitates both faster and more stable data transmission compared to Cloud Computing with a mean transmission time of 1,973 milliseconds. This has a significant impact on industrial processes whose efficiency and productivity depend on the quality of data transfer.

Vehicle Selection and Resource Optimization for Federated Learning in Vehicular Edge Computing

As a distributed deep learning paradigm, federated learning (FL) provides a powerful tool for the accurate and efficient processing of on-board data in vehicular edge computing (VEC). However, FL involves the training and transmission of model parameters, which consumes the vehicles’ precious energy resources and takes up much time. It is a departure from many applications with severe real-time requirements in VEC. And the capabilities and data quality of each vehicle are distinct that will affect the performance of training the model. Therefore, it is crucial to select the appropriate vehicles to participate in learning tasks and optimize resource allocation under learning time and energy consumption constraints. In this paper, taking the vehicle position and velocity into consideration, we formulate a min-max optimization problem to jointly optimize the on-board computation capability, transmission power, and local model accuracy to achieve the minimum cost in the worst case of FL. Specifically, we propose a greedy algorithm to select vehicles with higher image quality dynamically, and it keeps the system’s overall cost to a minimum in FL. The formulated optimization problem is a nonlinear programming problem, so we decompose it into two subproblems. For the resource allocation problem, we use the Lagrangian dual problem and the subgradient projection method to approximate the optimal value iteratively. For the local model accuracy problem, we develop an adaptive harmony algorithm for heuristic search. The simulation results show that our proposed algorithms have well convergence and effectiveness and achieve a tradeoff between cost and fairness.

Shipping the sunshine? A critical research agenda on the global hydrogen transition

Large amounts of low-carbon hydrogen imports from the Global South are needed for the hydrogen transition in Germany and the EU. In an unequal global system, it is far from certain that the promised financial, technological and socio-economic benefits will materialise and outweigh the costs for the Global South countries who supply the precious energy resource to northern economies. To strengthen social science perspectives in hydrogen research, we sketch the contours of a critical research agenda on the global hydrogen transition.

Review of Anomaly Detection and Removal Techniques from Autonomous Car Data

Artificial Intelligence and the development of computers has brought a huge change in our daily lives. Simple tasks that required humans to use precious time and energy are now being done by computers. One aspect of this is using AI in self-driving cars. By introducing such technologies in the world, it can be said that travelling has become a whole new experience. However, Artificial Intelligence cannot be considered perfect. It has its own faults and these faults can lead to undesired circumstances such as car accidents. One of the major faults in Artificial Intelligence is the presence of anomalies in data. Anomalies are values that are either too extreme to be considered, or are just not needed. Detecting these anomalies is a major task and requires algorithms that efficiently extract them. This paper discusses a few of these techniques and further highlights which ones can be used in autonomous self-driving cars.

A Refined Prediction Parameter for Molecular Alignability in Stretched Polymers and a New Light-Harvesting Material for AlGaAs Photovoltaics.

Light-harvesting concentrators have a high potential to make highly efficient but precious energy converters, such as multijunction photovoltaics, more affordable for everyday applications. They collect sunlight, including diffusively scattered light, on large areas and redirect it to much smaller areas of the highly efficiency solar cells. Among the best current concepts are pools of randomly oriented light-collecting donor molecules that transfer all excitons to few aligned acceptors reemitting the light in the direction of the photovoltaics. So far, this system has only been realized for the 350–550 nm wavelength range, suitable for AlGaInP photovoltaics. This was achieved by using acceptor molecules that aligned during mechanical stretching of polymers together with donors, that stay random in that very same material and procedure. However, until recently, very little was known about the factors that are responsible for the alignability of molecules in stretched polymers and therefore it was difficult to find suitable donors and acceptors, as well as for other spectral ranges. Recently, a structural parameter was introduced with a high predictivity for the alignability of molecules that contain rigid band-like structures or linear aromatic π-systems. However, for light concentrators in more red spectral ranges, molecular systems often contain larger and extended, planar-like π-systems for which the previously reported parameter is not directly applicable. Here, we present a refined prediction parameter also suitable for larger plane-like structures. The new parameter depends on the number of in-plane atoms divided by out-of-plane atoms as determined by computational geometry optimization and additionally the planar aspect ratio for molecules that contain only in-plane atoms. With the help of this parameter, we found a new system that can efficiently collect and redirect light for the second 500–700 nm AlGaAs layer of current world-record multijunction photovoltaics. Similarly, as the previously reported system for the blue-green layer, it has also overall absorption and re-directioning quantum efficiencies close to 80–100%. Both layers, together, already cover about 75% of the energy in the solar spectrum.

EETSP: Energy‐efficient two‐stage routing protocol for wireless sensor network‐assisted Internet of Things

SummaryWireless sensor network (WSN) is a key technology that can support Industrial Revolution 4.0. The sensor nodes (SNs) are the important component of WSN. Recharge or repair of batteries of SNs is impossible, and thus, energy is the most valuable resource of WSN. Over the years, many methods have been proposed to conserve the precious energy resource of WSN. The most important concern in WSN is how much of the energy of the SNs can be saved, which can contribute to the better network life of WSN. WSN‐assisted IoT output is reduced when a wide region of the network is deployed. Energy‐efficient routing is one of the ways to reduce power consumption in WSNs. Therefore, a novel energy‐efficient two‐stage routing protocol (EETSP) has been proposed to reduce the energy used in WSN and extend the stability period as well as the network lifetime of WSN. Through this protocol, the number of packets communicated to the base station (BS) is significantly improved, and the cluster heads (CHs) and secondary cluster heads (SCHs) reduce the energy utilization in the network. SCH works as a backup CH. EETSP consists of two stages. The selection of CH and SCH is made in the primary stage, based on the input parameters. Intercluster routing and intracluster routing are performed in the second stage. The improvements of EETSP are examined against LEACH and Z‐SEP, and obtained results indicate that the EETSP is better in terms of stability period, throughput and network lifetime.

Smarter Grid in the 5G Era: A Framework Integrating Power Internet of Things With a Cyber Physical System

As the energy infrastructure of smart cities, smart grid upgrades traditional power grid systems with state-of-the-art information and communication technologies. In particular, as the full deployment of the Internet of Things in the power grid (a.k.a. power Internet of Things or PIoT), the newly introduced information flow together with inherent energy flow makes it more efficient for power generation, transmission, distribution, and consumption. To further exploit the precious energy and the latest 5G technologies, this article boosts to add a value flow in the smart grid, mainly including the value created by innovative services and market mechanisms and the value added by the information flow. Specifically, by integrating PIoT with cyber-physical systems, this article sketches a conceptual framework of the cyber-physical power system (CPPS). The CPPS carries out holistic perception and ubiquitous connection of distributed energy sources and electrical facilities and builds up a smarter power grid with global information interaction, intelligent decision-making, and real-time agile control. Finally, for illustration purposes, we conduct a case study regarding an intelligent home management system.

Homeodomain Transcription Factors: Integral Modulators of Human Decidualization

Abstract Ineffective embryo implantation accounts for a significant percentage of female infertility, and often renders IVF procedures unsuccessful. Decidualization, the dramatic uterine morphological response to ovarian hormone exposure, is a prerequisite for embryo implantation. Despite its significance in reproduction, the genetic framework of decidualization was not systematically studied until our recent development of a suitable high-throughput screening tool, immortalized human endometrial stromal cells (hESCs) that carry the yellow fluorescent protein gene under the control of the progesterone-sensitive prolactin promoter (PRL-Y cells). We recently used PRL-Y cells to perform a genome-wide siRNA functional screen and results revealed that 36 members of the homeodomain-containing family of transcription factors (HDTFs) are modulators of human decidualization. To determine which HDTFs are transcriptionally sensitive to ovarian hormone exposure, RT-PCR was performed on wildtype hESCs for the 36 HDTF hits over a 72-hour time course of E2/P4/cAMP exposure. Twenty HDTF hits (55%) were both detectable by PCR and showed variable expression in response to ovarian hormone treatment. Interestingly, all of these homeodomain factors, with a few distinct exceptions, exhibited decreased transcriptional expression in response to ovarian hormone treatment. This suggests that precious energy is used to transcribe these factors during the pre-decidualized phase, and that they may be required to maintain homeostasis during times of low hormone exposure. Because siRNA is not fully efficient, in order to confirm which HDTFs are required for normal decidualization, we generated a doxycyline-inducible Cas9-expressing hESC clone in order to subsequently generate individual knockout hESC lines for each HDTF hit. Cas9 expression was turned on 5 days prior to crRNA and trRNA transfection targeting the first exon of each HDTF. Five days after transfection, the cells were treated for 72 hours with ovarian hormone induction medium before RNA was isolated for gene expression analysis. As a pool of cells prior to any cloning (which likely includes knockout and wildtype cells in different ratios) the results indicate that several HDTFs are required for proper decidualization. The reporter transcripts of PRL and EREG are significantly abrogated or entirely undetectable in certain knockout lines. Interestingly these include some original siRNA HDTF hits whose expression is undetectably low by PCR. Sequencing validation will be necessary to confirm that knocking out such low levels of these transcripts genuinely has the robust effect on the human decidualization reaction that we are witnessing in these results. Together these findings comprise significant initial steps in characterizing the intricate upstream roles of HDTFs in human decidualization and female fertility.

A Bioinspired Elastic Hydrogel for Solar-Driven Water Purification.

The global demand for clean and safe water will continue to grow well into the 21st century. Moving forward, the lack of access to clean water, which threatens human health and strains precious energy resources, will worsen as the climate changes. Therefore, future innovations that produce potable water from contaminated sources must be sustainable. Inspired by nature, a solar absorber gel (SAG) is developed to purify water from contaminated sources using only natural sunlight. The SAG is composed of an elastic thermoresponsive poly(N-isopropylacrylamide) (PNIPAm) hydrogel, a photothermal polydopamine (PDA) layer, and a sodium alginate (SA) network. Production of the SAG is facile; all processing is aqueous-based and occurs at room temperature. Remarkably, the SAG can purify water from various harmful reservoirs containing small molecules, oils, metals, and pathogens, using only sunlight. The SAG relies on solar energy to drive a hydrophilic/hydrophobic phase transformation at the lower critical solution temperature. Since the purification mechanism does not require water evaporation, an energy-intensive process, the passive solar water-purification rate is the highest reported. This discovery can be transformative in the sustainable production of clean water to improve the quality of human life.

To grow or not to grow ….

To grow or not to grow ….

Integrated Power Anomaly Defense: Towards Oversubscription-Safe Data Centers

Energy storage devices (e.g., batteries) are critical components for high-availability data center infrastructure today. Without resilient energy management of these devices, existing power-hungry data centers are largely unguarded targets for cyber criminals. Particularly for some of today's scale-out data centers, power infrastructure oversubscription unavoidably taxes the data center's backup energy resources (i.e., UPS), leaving very little room for dealing with power emergency. As a result, an attacker could manipulate the computing system to generate peak power demand and disrupt power-constrained server racks. This article aims at protecting data centers from malicious loads that seek to drain precious energy backup, overload server racks and compromise workload performance. We term such load as Elusive Power Peak (EPP) and demonstrate its basic three-phase attacking model. To defend against EPP, we propose IPAD, a remediation solution build on integrated software and hardware mechanisms. IPAD not only increases the attacking cost considerably by hiding vulnerable server racks from visible power peaks, but also strengthens the last line of defense against hidden power spikes with fine-grained power control strategy. We show that IPAD can effectively raise the bar of power-related attack, with reasonable design overhead.

Hyperparameter Optimization Using Sustainable Proof of Work in Blockchain

Hyperparameters are pivotal for machine learning models. The success of efficient calibration, often surpasses the results obtained by devising new approaches. Traditionally, human intervention is required to tune the models, however, this obtuse outlook restricts the proficiency and competence. Automating this crucial characteristic of learning sustainably, proffers a significant boost in performance and cost optimization. Blockchain technology has revolutionized industries utilizing its Proof-of-Work algorithms for consensus. This complicated solution generates a lot of useless computations across the nodes attached to the network and thus, fritters away a huge amount of precious energy. In this paper, we propose to exploit these inane computations for training deep learning models instead of calculating purposeless hash values, thus, suggesting a new consensus schema. This work distinguishes itself from other related works by capitalizing on the parallel processing prospects it generates for hyperparameter tuning of complex deep learning models. We address this aspect through the framework of Bayesian optimization which is an effective methodology for the global optimization of functions with expensive evaluations. We call our work, Proof of Deep Learning with Hyperparameter Optimization (PoDLwHO).

Non-fragile estimation for discrete-time T--S fuzzy systems with event-triggered protocol

This paper investigates the non-fragile state estimation problem for a class of discrete-time T-S fuzzy systems with time-delays and multiple missing measurements under event-triggered mechanism. First of all, the plant is subject to the time-varying delays and the stochastic disturbances. Next, a random white sequence, the element of which obeys a general probabilistic distribution defined on $[0,1]$, is utilized to formulate the occurrence of the missing measurements. Also, an event generator function is employed to regulate the transmission of data to save the precious energy. Then, a non-fragile state estimator is constructed to reflect the randomly occurring gain variations in the implementing process. By means of the Lyapunov-Krasovskii functional, the desired sufficient conditions are obtained such that the Takagi-Sugeno (T-S) fuzzy estimation error system is exponentially ultimately bounded in the mean square. And then the upper bound is minimized via the robust optimization technique and the estimator gain matrices can be calculated. Finally, a simulation example is utilized to demonstrate the effectiveness of the state estimation scheme proposed in this paper.

A 0.8-V, 1.54-pJ/940-MHz Dual-Mode Logic-Based 16×16-b Booth Multiplier in 16-nm FinFET

The dual-mode logic (DML) defines runtime adapted digital architectures that switch to either improved performance or lower energy consumption as a function of the actual computational workload. This flexibility is demonstrated for the first time by silicon measurements on a $16\times 16$ -b Booth multiplier fabricated as a part of an ultralow-power digital signal processing (DSP) architecture for 16-nm FinFET technology. When running in the full-speed mode, the DML multiplier can achieve a performance boost of 19.5% as compared to the equivalent standard CMOS design. The same circuit saves precious energy (−27%, on average) when the energy-efficient mode is enabled, while occupying 13% less silicon area.

IOT based solar energy prophecy using RNN architecture

It is the 21st century and scientists say that by the end of this century, resources will be replenished and the only way the future generations can access energy is through renewable resources— those which are inexhaustible. One such source is sunlight, which has a guaranteed stay in the long run. The energy thus given is termed as solar energy. In the present paper it is tried to solve the issue of limited resources and their adverse effects. Since the power generated from solar energy systems is highly variable, due to its dependence on meteorological conditions, an efficient method of usage of this fluctuating but precious energy source has to come in picture. This requires the scope of reliable forecast information as the development of predictive control algorithms for efficient energy management and monitoring for residential grid connected photovoltaic systems. The paper has given an overview of different applications and models for solar irradiance and photovoltaic power prediction, including time series models based on live measured data from rooftop solar power plant located at 17.5203° N, 78.3674° E. For experimentation, data collected over four years from the solar power plant was used in order to the train machine and understand the characteristics of the solar power plant and gives the predicted energy as the result. The use of Deep Learning is done where LSTM is used for the training and keras and tensorflow are used for obtaining the result. The mean square error thus obtained is 0.015.