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BEYOND SCREENS: A PSYCHO-THERAPEUTIC INTERVENTION FOR ADOLESCENTS SUFFERING FROM INTERNET GAMING ADDICTION
The Diagnostic and Statistical Manual of Mental Disorders (DSM 5) has now included Internet Gaming disorder in section III as the condition that warrants more clinical research. Internet or Online gaming has become one of the most popular sources of entertainment among children and adolescents, representing the fastest-growing segment leading to hazards as well. Thus, there is a requirement for a psycho-therapeutic intervention module to help in overall psychological well-being of the adolescents suffering from Internet Gaming Disorder (IGD). A total of 8 adolescents suffering from IGD were treated with scientifically prepared psycho-therapeutic module in total of 12 group sessions. The eclectic approach to treatment is proven to be effective for adolescents yielding significant improvement in the clinical condition and promotes wise engagement in gaming. (2023). All Rights Reserved. -
Review of Development and Characterisation of Shape Memory Polymer Composites Fabricated Using Additive Manufacturing Technology
Structures as well as components are generated by depositing filaments on one another via the technique of additive manufacturing. Among the various processes of printing, 4D printing combines the technology of 3D printing with the passage of time, resulting in additively generated parts that are responsive to stimuli from the outside via modifications of their form, volume, size, or mechanical qualities. Thus, the materials of shape memory are used in 4D printing and respond to environmental factors including temperature, pH, and humidity. Shape memory polymers (SMPs) are materials with a shape memory effect that are best suited for additive manufacturing. Contrarily, the method named fused filament fabrication (FFF) is employed most frequently among all additive manufacturing methods. In this regard, the objective of the present study is to evaluate all investigations that have been conducted on 4D-FFF materials mechanical properties. The study offers an unparalleled overview that highlights the possibilities of 4D FFF printing across multiple applications in engineering while keeping the end structures or components structural integrity in consideration. 2023 by the authors. -
Enhancement in air-cooling of lithium-ion battery packs using tapered airflow duct
Temperature uniformity and peak-temperature reduction of lithium-ion battery packs are critical for adequate battery performance, cycle life, and safety. In air-cooled battery packs that use conventional rectangular ducts for airflow, the insufficient cooling of cells near the duct outlet leads to temperature nonuniformity and a rise in peak temperature. This study proposes a simple method of using a converging, tapered airflow duct to attain temperature uniformity and reduce peak temperature in air-cooled lithium-ion battery packs. The conjugate forced convection heat transfer from the battery pack was investigated using computational fluid dynamics, and the computational model was validated using experimental results for a limiting case. The proposed converging taper provided to the airflow duct reduced the peak temperature rise and improved the temperature uniformity of the batteries. For the conventional duct, the boundary layer development and the increase in air temperature downstream resulted in hotspots on cells near the outlet. In contrast, for the proposed tapered duct, the flow velocity increased downstream, resulting in improved heat dissipation from the cells near the outlet. Furthermore, the study investigated the effects of taper angle, inlet velocity, and heat generation rate on the flow and thermal fields. Notably, with the increase in taper angle, owing to the increase in turbulent heat transfer near the exit, the location of peak temperature shifted from the exit region to the central region of the battery pack. The taper-induced improvement in cooling was evident over the entire range of inlet velocities and heat generation rates investigated in the study. The peak temperature rise and maximum temperature difference of the battery pack were reduced by up to 20% and 19%, respectively. The proposed method, being effective and simple, could find its application in the cooling arrangements for battery packs in electric vehicles. 2023 Y?ld?z Technical University. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/). All Rights Reserved. -
Possibilities for the Flow of Water and Blood through a Graphene Layer in a Geometry Analogous to Human Arterioles: An Observational Study
Atherosclerosis and aneurysm are two non-communicable diseases that affect the human arterial network. The arterioles undergo dimensional changes that prominently influence the flow of oxygen and nutrients to distal organs and organ systems. Several studies have emerged discussing the various possibilities for the circumstances surrounding the existence of these pathologies. In the present work, we analyze the flow of blood across the stenosis and the aneurysmic sac in contrast with the flow of water to explore alterations in the flow characteristics caused by introduction of the graphene layer. We investigate the blood flow past the graphene layer with varying porosity. The study is undertaken to replace usage of a stent along a blocked artery by inserting a thin layer of graphene along the flow channel in the post-pathological section of the geometry. To explain the flow, a 2D mathematical model is constructed, and the validity and exclusivity of the models solution are examined. When the artery wall is assumed to be inelastic, the computation of the mathematical system is evaluated using a finite element method (FEM) solver. We define a new parameter called critical porosity (Formula presented.) to explore the flow possibilities through the graphene layer. The findings indicate that the flow pattern was adversely affected by the graphene layer that was added to the flow field. The negative impact on the flow could be due to the position of the graphene layer placed. The (Formula presented.) values for the flow of blood across healthy arteriole, stenosed arteriole, and aneurysmic arteriole segments were (Formula presented.) and (Formula presented.) respectively. The critical porosity values were achieved with precision in terms of linear errors (Formula presented.), (Formula presented.), and (Formula presented.), respectively. The consequences of the present study disclose various possible ways to utilize graphene and its compounds in the medical and clinical arena, with a prior exploration of the chemical properties of the compound. The idea and the methodology applied for the present study are novel as there have been no previous research works available in this direction of the research field. 2023 by the authors. -
Fault Analysis and Compensation in a Five Level Multilevel DC-AC Converter
Existing Neutral clamped active (NCA) inverters have the property of high frequency common mode voltage, which can reduce the severity with less voltage gain. A newly designed five level (5L) NCA inverter can capable have achieved voltage step-up with a one stage inversion process. The proposed circuit common ground enhances DC link voltage usage while also mitigating common mode voltage with high frequency. The proposed topology is more compact and has less voltage stress than the conventional two stage topology. The proposed circuit contains merely seven power switches and two capacitors, whereas the conventional topology has ten switches and three capacitors, resulting in a more efficient layout. The proposed topology is developed in the simulink platform, and the simulation results are validated in a proto-type model with a power rating of 2000 W to validate its feasibility and performance with fault clearance capabilities. 2023, TUBITAK. All rights reserved. -
Forensic Investigation Approaches of DNA Analysis and Criminal Investigation
Deoxyribonucleic acid (DNA) has been a significant factor in the criminal justice system since it was first used in forensic investigations. The reference sample's DNA profile is typically compared to the DNA profile from the evidence sample from the crime scene criminal cases. Familial DNA analysis can identify a person and provide significant investigation leads even without a reference sample for comparison in a criminal investigation process. The potential source of a forensic biological sample is determined using several indirect database searching techniques. These DNA-based techniques include Mitochondrial DNA (mtDNA) analysis, investigative genetic genealogy (IGG), familial searching, and Y-STR database searching. This study examined these methods and compares them in terms of searching efficiency, database structures, searching methods, genotyping technologies, data security, data quality, and costs. It also raises several possible legal and privacy problems for scientists to consider further. The significance of familial DNA analysis, the procedures used for finding and identifying relatives using familial DNA, and its benefits in forensics are all covered in this paper. Additionally, future options for the appropriate application of this technology and social, legal, and ethical concerns related to familial DNA analysis have been considered. 2023 WITPress. All rights reserved. -
Fungi-Templated Silver Nanoparticle Composite: Synthesis, Characterization, and Its Applications
The self-assembly of nanoparticles on living bio-templates is a promising synthetic methodology adopted for synthesizing nano/microstructures with high efficiency. Therefore, the method of bio-templating offers various advantages in controlling the geometries of nano/microstructures, thereby increasing the efficiency of the synthesized material towards various functional applications. Herein, we utilized a filamentous fungus (Sclerotium rolfsii) as a soft bio-template to generate silver nanoparticle (AgNP) microtubules adhering to the fungal hyphae. The resulting composite combines the unique properties of silver nanoparticles with the biological activity of the fungi. The 3D fungal hyphaesilver nanoparticle (FH-AgNP) composite was characterized using SEM, elemental analysis, and the X-ray diffraction technique. Additionally, to highlight the functional application of the synthesized composite, dye degradation studies of methylene blue under visible light was effectuated, and a percentage degradation of 67.86% was obtained within 60 min, which highlights the potent catalytic activity of FH-AgNPs in dye degradation. Further, the antibacterial study of the composite was carried out against the bacterium Escherichia coli, and it was found that 200 ?g of the composite exhibited maximum antibacterial properties against Gram positive (Staphylococcus aureus) and Gram negative (Escherichia coli) bacteria. Overall, fungi-templated silver nanoparticle composites are a promising area of research due to their combination of biological activity and unique physical and chemical properties. 2023 by the authors. -
Siamese-Based Architecture for Cross-Lingual Plagiarism Detection in English-Hindi Language Pairs
The cross-lingual plagiarism detection (CLPD) is a challenging problem in natural language processing. Cross-lingual plagiarism is when a text is translated from any other language and used as it is without proper acknowledgment. Most of the existing methods provide good results for monolingual plagiarism detection, whereas the performances of existing methods for the CLPD are very limited. The reason for this is that it is difficult to represent the text from two different languages in a common semantic space. In this article, a novel Siamese architecture-based model is proposed to detect the cross-lingual plagiarism in English-Hindi language pairs. The proposed model combines the convolutional neural network (CNN) and bidirectional long short-term memory (Bi-LSTM) network to learn the semantic similarity among the cross-lingual sentences for the English-Hindi language pairs. In the proposed model, the CNN model learns the local context of words, whereas the Bi-LSTM model learns the global context of sentences in forward and backward directions. The performances of the proposed models are evaluated on the benchmark data set, that is, Microsoft paraphrase corpus, which is converted in the English-Hindi language pairs. The proposed model outperforms other models giving 67%, 72%, and 67% weighted average precision, recall, and F1-measure scores. The experimental results show the effectiveness of the proposed models over the baseline models because the proposed model is very efficient in representing the cross-lingual text very efficiently. Copyright 2023, Mary Ann Liebert, Inc., publishers 2023. -
Deep learning algorithms for intrusion detection systems in internet of things using CIC-IDS 2017 dataset
Due to technological advancements in recent years, the availability and usage of smart electronic gadgets have drastically increased. Adoption of these smart devices for a variety of applications in our day-to-day life has become a new normal. As these devices collect and store data, which is of prime importance, securing is a mandatory requirement by being vigilant against intruders. Many traditional techniques are prevailing for the same, but they may not be a good solution for the devices with resource constraints. The impact of artificial intelligence is not negligible in this concern. This study is an attempt to understand and analyze the performance of deep learning algorithms in intrusion detection. A comparative analysis of the performance of deep neural network, convolutional neural network, and long short-term memory using the CIC-IDS 2017 dataset. 2023 Institute of Advanced Engineering and Science. All rights reserved. -
Student Subjective Wellbeing amidst the Covid-19 Pandemic in Iran: Role of Loneliness, Resilience and Parental Involvement
The COVID-19 pandemic and lockdowns potentially severely impact adolescents mental well-being. This research aims to study students subjective well-being during the covid-19 pandemic in Iran and investigate the role of loneliness, resilience, and parental involvement. For this study, 629 students (female = 345) were recruited by purposive sampling. Students were assessed on the Students Subjective Well-Being, Loneliness Scale, Resilience Scale, and Parental Involvement. The results confirm our hypothesis that the relationship between parental involvement and students subjective well-being is mediated by loneliness. Furthermore, the results indicated a partial mediation of resilience in the relationship between parental involvement and students subjective well-being. This study theoretically contributes to a better understanding of the factors determining the impact of traumatic events such as a pandemic on adolescents mental health. The implications of this study indicate interventions that can be carried out to minimize the negative psychological consequences of the pandemic. 2022, The Author(s), under exclusive licence to Springer Nature B.V. -
Self-Powered Dynamic Glazing Based on Nematic Liquid Crystals and Organic Photovoltaic Layers for Smart Window Applications
Dynamic windows allow monitoring of in-door solar radiation and thus improve user comfort and energy efficiency in buildings and vehicles. Existing technologies are, however, hampered by limitations in switching speed, energy efficiency, user control, or production costs. Here, we introduce a new concept for self-powered switchable glazing that combines a nematic liquid crystal, as an electro-optic active layer, with an organic photovoltaic material. The latter aligns the liquid crystal molecules and generates, under illumination, an electric field that changes the molecular orientation and thereby the device transmittance in the visible and near-infrared region. Small-area devices can be switched from clear to dark in hundreds of milliseconds without an external power supply. The drop in transmittance can be adjusted using a variable resistor and is shown to be reversible and stable for more than 5 h. First solution-processed large-area (15 cm2) devices are presented, and prospects for smart window applications are discussed. 2023 American Chemical Society. -
Customized mask region based convolutional neural networks for un-uniformed shape text detection and text recognition
In image scene, text contains high-level of important information that helps to analyze and consider the particular environment. In this paper, we adapt image mask and original identification of the mask region based convolutional neural networks (R-CNN) to allow recognition at 3 levels such as sequence, holistic and pixel-level semantics. Particularly, pixel and holistic level semantics can be utilized to recognize the texts and define the text shapes, respectively. Precisely, in mask and detection, we segment and recognize both character and word instances. Furthermore, we implement text detection through the outcome of instance segmentation on 2-D feature-space. Also, to tackle and identify the text issues of smaller and blurry texts, we consider text recognition by attention-based of optical character recognition (OCR) model with the mask R-CNN at sequential level. The OCR module is used to estimate character sequence through feature maps of the word instances in sequence to sequence. Finally, we proposed a fine-grained learning technique that trains a more accurate and robust model by learning models from the annotated datasets at the word level. Our proposed approach is evaluated on popular benchmark dataset ICDAR 2013 and ICDAR 2015. 2023 Institute of Advanced Engineering and Science. All rights reserved. -
An Intelligent Decision Support System to Aid Profit Planning in Manufacturing Companies
In order to assure accuracy in profit planning and decision-making, this study uses an intelligent decision support system to investigate an appropriate approach for calculating the "Break-Even" point in multi-product segments while taking into account the implications for contribution margin, demand, and capacity. The research's methodology and findings may be used to propose new projects, grow businesses, and make decisions in processes that focus on many products. Data are used to illustrate the advanced level of break-even analysis and application, and a description of the convenient and system-generated method of computation is given. A mathematical approach has been used based on actual data to show how to determine the break-even point without sacrificing the influencing aspects such as contribution margin, capacity, product mix, and demand for each. The researchers have created a good system application-oriented platform to make it simple to calculate the break-even point, which will be crucial for decision-making and profit planning even with more than 500 SKU (Stock Keeping Unit). This research evaluated the data and created formulas for actual data structure-based analysis. The study's conclusions have a significant influence on those companies that need to determine the true break-even threshold. The challenge area of concern might be the applicability of this activity for other sectors and other countries as this research was centred on the plastic bag industry in Malaysia. Future research can also analyse other important factors like start-up and semi-variable costs as they are not included in the current study. The identified break-even threshold can still be used effectively given the current market demand and the product's capacity. 2023, Ismail Saritas. All rights reserved. -
An efficient classification of cirrhosis liver disease using hybrid convolutional neural network-capsule network
Liver cirrhosis is the diffuse and advanced phase of liver disease. Several morphological methods are used for imaging modalities. But, these modalities are biased and lack in higher detection accuracy. Hence, this work introduces automated cirrhosis liver disease classification using an optimized hybrid deep learning model. In this work, Magnetic Resonance Image (MRI) is considered for the process. Initially, an Extended Guided Filter (EGF) is used for eliminating the noise from input MRI images. Binomial thresholding is used to segment the tumor from the image. Then, Feature Extraction (FE) phase is carried out by Grey Level Co-occurrence Matrix (GLCM) and Gray level Run-length Matrix (GRLM). Finally, a hybrid of two Deep Learning (DL) algorithms Convolutional Neural Network and Capsule Network (HCNN-CN) are integrated to classify the Cirrhosis liver disease. Moreover, for fine tuning the parameters of the neural network, an optimization approach Adaptive Emperor Penguin Optimization (AEPO) is used. The proposed HCNN-CN-AEPO is compared over several approaches and depicted accuracy and sensitivity value of 0.993 and 0.986 on the real time dataset. The experimental results proved that the proposed HCNN-CN-AEPO can exactly diagnose the tumour. 2022 Elsevier Ltd -
Heteroatom engineered graphene-based electrochemical assay for the quantification of high-risk abused drug oxytocin in edibles and biological samples
The naive detection of scheduled H drug oxytocin is a vital requisite, owing to its deleterious impact on societal affluence prompted by unconstrained usage. Therefore, a reliable, cost-effective, and quick-to-respond analytic technique for this drug is in ample demand. In this work, we report electrochemical detection of oxytocin employing novel nitrogen, phosphorus co-doped coke-derived graphene (NPG) modified electrode. The electro-oxidation behavior of oxytocin was investigated on the NPG modified electrode by square wave stripping voltammetry (SWSV) in 0.1 M phosphate buffer of pH 7. The oxidation peak current was linear in two ranges, spanning from 0.1 nM to 10 nM and 15 nM to 95 nM. The limit of detection at the NPG electrode was calculated to be 40 pM. The practical application of developed sensor for the determination of oxytocin was examined in edible products and body fluids, hence signifying the possibility of having real-time surveillance over its misusage. 2022 Elsevier Ltd -
Evaluation of physicochemical and biological properties of SnO2 and Fe doped SnO2 nanoparticles
In recent decades, nanoparticle synthesis has been used for various physical and chemical methods. However, different toxic chemicals are used during this synthesis process to address these concerns, which has multiple effects on environmental toxicity and high cost. To avoid these problems, we need a cost-effective and environmentally friendly approach. In this study, green synthesis was used to make tin oxide (SnO2) and ferrous doped tin oxide (SFO) nanoparticles (NPs) from Morinda citrifolia leaf extracts. The X-ray diffraction patterns of SnO2 and SFO NPs reveal a tetragonal crystalline structure. From the FESEM image of synthesized SnO2 and SFO NPs, their spherical structure and chemical composition were identified by EDX spectrum. Through the DLS spectrum, the hydrodynamic size was observed at 66 and 61 nm for SnO2 and SFO NPs, respectively. In the FTIR spectrum, the OSnO stretching vibration peak arises at (606 & 509 cm?1 for SnO2 NPs) and (613 & 538 cm?1 for SFO NPs). Photoluminescence is used in materials to detect surface defects and impurity levels. The antibacterial activity of the SnO2, SFO NPs, and conventional antibiotics like amoxicillin NPs is effectively inhibited against S. aureus and E. coli bacterial strains. SFO NPs exhibit a higher antibacterial activity as compared to SnO2 and amoxicillin. The anticancer efficacy of increased SFO NPs compared to SnO2 NPs was tested against (MDA-MB-237) human breast cancer cells. These results suggest that Fe ions modified SnO2 NPs could be used in healthcare industrial applications to improve human health. 2022 Elsevier Ltd and Techna Group S.r.l. -
A facile one-step microwave synthesis of Pt deposited on N & P co-doped graphene intercalated carbon black - An efficient cathode electrocatalyst for PEM fuel cell
A facile, single step microwave assisted polyol route for simultaneously depositing platinum as well as co-doping graphene oxide, is herein proposed. However, low durability and full cell performance of Pt/NPG (platinum deposited on nitrogen phosphorous co-doped graphene) was observed due to restacking of graphene layers. This issue was addressed by intercalating CB into the graphene layers as spacers during the synthesis (in-situ addition of spacers - Pt/(NPG + S)). Moreover, to study the influence of spacers, external addition of spacers (ex-situ - Pt/(NPG) + S) were also examined. Results from our study indicate that in-situ addition of spacers- Pt/(NPG + S) enhanced the full cell performance (405 mW cm?2) and exhibited <40% ECSA loss (37.47%), thereby attaining DoE target. Thus, emerging as a durable cathode electrocatalyst (Pt/(NPG + S)) for PEM fuel cells. 2022 Hydrogen Energy Publications LLC -
Normalized Attention Neural Network with Adaptive Feature Recalibration for Detecting the Unusual Activities Using Video Surveillance Camera
Over the past few years, surveillance cameras have become common in many homes and businesses. Many businesses still employ a human monitor of their cameras, despite the fact that this individual is more probable to miss some anomalous occurrences in the video feeds owing to the inherent limitations of human perception. Numerous scholars have investigated surveillance data and offered several strategies for automatically identifying anomalous occurrences. Therefore, it is important to build a model for identifying unusual occurrences in the live stream from the security cameras. Recognizing potentially dangerous situations automatically so that appropriate action may be taken is crucial and can be of great assistance to law enforcement. In this research work, starting with an MRCNN for feature extraction and AFR for fine-tuning, this architecture has a number of key components (AFR). To increase the quality of the features extracted by the MRCNN, the AFR replicas the inter-dependencies among the features to enhance the quality of the low- and high-frequency features extracted. Then, a normalized attention network (NAN) is used to learn the relationships between channels, which used to identify the violence and speeds up the convergence process for training a perfect. Furthermore, the dataset took real-time security camera feeds from a variety of subjects and situations, as opposed to the hand-crafted datasets utilized in prior efforts. We also demonstrate the method's capability of assigning the correct category to each anomaly by classifying normal and abnormal occurrences. The method divided the information gathered into three primary groups: those in need of fire protection, those experiencing theft or violence, and everyone else. The study applied the proposed approach to the UCF-Crime dataset, where it outperformed other models on the same dataset. 2023 WITPress. All rights reserved. -
Enhanced electrical properties of CuO:CoO decorated with Sm2O3 nanostructure for high-performance supercapacitor
In the present investigation, we have synthesized samarium (Sm) nanoparticles (NPs) and anchored them onto the surface of CuO:CoO nanostructure (NS) by utilizing a simple chemical precipitation method. Nanostructures (NS) were characterized utilizing powdered X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), X-ray photoelectron spectroscopy (XPS), scanning electron spectroscopy (SEM), transmission electron spectroscopy (TEM), UVvisible spectroscopy (UVVis), and BrunauerEmmettTeller (BET) studies. Resulting Smx CuO: CoO (x = 1%, 5%, 10%, and 12%) NS were investigated for their anomalous electrical and supercapacitive behavior. NS energy storage performance was experimentally determined using cyclic voltammetry (CV), galvanostatic chargedischarge (GCD), and electrochemical impedance spectroscopy (EIS). Sm10%CuO:CoO exhibited better electrochemical response than other samples and showed a maximum specific capacitance of 283.6F/g at 0.25A/g in KOH electrolyte. However, contrary to our expectation, NS displayed rectifying nature in I-V, intercalative nature in C-V, and polaronic permittivity in all concentrations of Sm2O3 doping as compared with undoped CuO:CoO NS. The outstanding properties of Smx CuO:CoO NS are attributed to the synergy of high charge mobility of Sm NPs, leading to significant variation in dielectric permittivity, currentvoltage (I-V) response, capacitancevoltage (C-V) behavior, with the formation of Sm3+ ionic cluster. The clusters lead to a change in dipole moment creating a strong local electric field. Additionally, a CR2032 type symmetric supercapacitor cell was fabricated using Sm10%CuO:CoO, which exhibited a maximum specific capacitance of 67.4F/g at 0.1A/g. The cell was also subjected to 5000 GCD cycles where it retained 96.3% Coulombic efficiency. Graphical Abstract: [Figure not available: see fulltext.] 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. -
N-doped graphene quantum dots incorporated cobalt ferrite/graphitic carbon nitride ternary composite for electrochemical overall water splitting
Multicomponent electrocatalysts containing carbon supports play a crucial role in influencing the hydrogen and oxygen evolution reactions which enhance the total water splitting. Herein, we report a ternary composite with cobalt ferrite, graphitic carbon nitride, and N-doped graphene quantum dots prepared via hydrothermal technique. The purity of the samples is established by carrying out various characterization methods. The intrinsic characteristics of the obtained materials are investigated by employing electrocatalytic processes in an alkaline media toward hydrogen and oxygen evolution reactions. Cobalt ferrite/graphitic carbon nitride/N doped graphene quantum dots electrocatalyst demonstrates a very low overpotential towards hydrogen evolution reaction of 287 mV at a constant 10 mA cm?2 current density in 1.0 M KOH. Tafel slope and Rct values generated are 94 mV dec?1 and 0.86 cm2, respectively. Oxygen evolution reaction studies reveal an overpotential of 445 mV at 10 mA cm?2 with a Tafel slope of 69 mV dec?1. Finally, the cell potential needed for the cobalt ferrite/graphitic carbon nitride/N doped graphene quantum dots electrode to achieve 10 mA cm?2 in total water splitting is only 2.0 V while displaying long-term stability. 2022 Hydrogen Energy Publications LLC