bluuebunny/crossref_metadata_2025 huggingface dataset:
Protein hydrolysates enhance recovery from drought stress in tomato plants: phenomic and metabolomic insights
Authors: Leporino Marzia , Rouphael Youssef , Bonini Paolo , Colla Giuseppe , Cardarelli Mariateresa
Published: March 2024

Introduction

High-throughput phenotyping technologies together with metabolomics analysis can speed up the development of highly efficient and effective biostimulants for enhancing crop tolerance to drought stress. The aim of this study was to examine the morphophysiological and metabolic changes in tomato plants foliarly treated with two protein hydrolysates obtained by enzymatic hydrolysis of vegetal proteins from Malvaceae (PH1) or Fabaceae (PH2) in comparison with a control treatment, as well as to investigate the mechanisms involved in the enhancement of plant resistance to repeated drought stress cycles.

Methods

A phenotyping device was used for daily monitoring morphophysiological traits while untargeted metabolomics analysis was carried out in leaves of the best performing treatment based on phenotypic results.

Results

PH1 treatment was the most effective in enhancing plant resistance to water stress due to the better recovery of digital biomass and 3D leaf area after each water stress event while PH2 was effective in mitigating water stress only during the recovery period after the first drought stress event. Metabolomics data indicated that PH1 modified primary metabolism by increasing the concentration of dipeptides and fatty acids in comparison with untreated control, as well as secondary metabolism by regulating several compounds like phenols. In contrast, hormones and compounds involved in detoxification or signal molecules against reactive oxygen species were downregulated in comparison with untreated control.

Conclusion

The above findings demonstrated the advantages of a combined phenomics-metabolomics approach for elucidating the relationship between metabolic and morphophysiological changes associated with a biostimulant-mediated increase of crop resistance to repeated water stress events.
Current knowledge of human Mpox viral infection among healthcare workers in Cameroon calls for capacity-strengthening for pandemic preparedness
Authors: Nka Alex Durand , Bouba Yagai , Fokam Joseph , Ka'e Aude Christelle , Gabisa Jeremiah Efakika , Mandeng Nadia , Mfonkou Delors Jacques Toumansie , Ambe Chenwi Collins , Mballa Mpouel Marie-Laure , Djikeussi Tatiana , Tchounga Boris Kevin , Ayuk Ngwese Derrick Tambe , Njume Debimeh , Mbala Nomo Sonia Emmanuelle , Ngoufack Jagni Semengue Ezechiel , Tiotsia Tsapi Armand , Fokou Bernadette Bomgning , Simo Kamdem Ingrid Koster , Tommo Tchouaket Michel Carlos , Takou Désiré , Pabo Willy , Sosso Samuel Martin , Tandi Erick , Esso Linda , Etoundi Mballa Georges Alain , Zoung-Kanyi Bissek Anne-Cecile , Gregory Edie Halle-Ekane , Ndembi Nicaise , Colizzi Vittorio , Perno Carlo-Federico , Ndjolo Alexis
Published: March 2024

Introduction

An increased incidence of human Monkeypox (Mpox) cases was recently observed worldwide, including in Cameroon. To ensure efficient preparedness and interventions in the health system, we sought to assess the knowledge of Mpox's transmission, prevention, and response among healthcare workers (HCWs) in Cameroon.

Methods

A cross-sectional online survey was conducted among HCWs in Cameroon using 21-item questions adapted from the United States Centers for Disease Control and Prevention (US-CDC) standard questionnaire on Mpox. The overall knowledge of Mpox was assessed by cumulative score and categorized as excellent (≥80%, 17/21) or good (≥70%, ≥15/21) knowledge. The regression analysis was used to identify the predictors of Mpox knowledge.

Results

The survey enrolled 377 participants, but only responses from 342 participants were analyzed. Overall, 50.6% were female participants, and 59.6% aged 30 years or younger. The majority of the participants were medical doctors (50.3%); most worked in central-level hospitals (25.1%) and had 1–5 years of experience (70.7%). A total of up to 92.7% were aware of Mpox, with social media (58.7%) and radio/television (49.2%) as the main sources. The mean knowledge score was 14.0 ± 3.0 (4 to 20), with only 12.9% having excellent knowledge (≥80%) and 42.1% having good knowledge of Mpox. Younger age (26–30 years old) was associated with good knowledge, while workplace type was associated with excellent knowledge of Mpox (aOR [95% CI]: 4.01 [1.43–11.24]). Knowledge of treatment/management of Mpox was generally poor across the different professional categories.

Conclusion

Knowledge of Mpox among HCWs is substandard across different professionals. Thus, for optimal preparedness and immediate interventions for Mpox and similar emerging pathogens, capacity-strengthening programs should be organized for HCWs while encouraging scientific literature and organizational social media websites.
A multiscale inflammatory map: linking individual stress to societal dysfunction
Authors: Vodovotz Yoram , Arciero Julia , Verschure Paul F. M. J. , Katz David L.
Published: March 2024

As populations worldwide show increasing levels of stress, understanding emerging links among stress, inflammation, cognition, and behavior is vital to human and planetary health. We hypothesize that inflammation is a multiscale driver connecting stressors that affect individuals to large-scale societal dysfunction and, ultimately, to planetary-scale environmental impacts. We propose a “central inflammation map” hypothesis to explain how the brain regulates inflammation and how inflammation impairs cognition, emotion, and action. According to our hypothesis, these interdependent inflammatory and neural processes, and the inter-individual transmission of environmental, infectious, and behavioral stressors—amplified via high-throughput digital global communications—can culminate in a multiscale, runaway, feed-forward process that could detrimentally affect human decision-making and behavior at scale, ultimately impairing the ability to address these same stressors. This perspective could provide non-intuitive explanations for behaviors and relationships among cells, organisms, and communities of organisms, potentially including population-level responses to stressors as diverse as global climate change, conflicts, and the COVID-19 pandemic. To illustrate our hypothesis and elucidate its mechanistic underpinnings, we present a mathematical model applicable to the individual and societal levels to test the links among stress, inflammation, control, and healing, including the implications of transmission, intervention (e.g., via lifestyle modification or medication), and resilience. Future research is needed to validate the model’s assumptions and conclusions against empirical benchmarks and to expand the factors/variables employed. Our model illustrates the need for multilayered, multiscale stress mitigation interventions, including lifestyle measures, precision therapeutics, and human ecosystem design. Our analysis shows the need for a coordinated, interdisciplinary, international research effort to understand the multiscale nature of stress. Doing so would inform the creation of interventions that improve individuals’ lives; enhance communities’ resilience to stress; and mitigate the adverse effects of stress on the world.
Wild boar (Sus scrofa) carcasses as an attraction for scavengers and a potential source for soil contamination with the African swine fever virus
Authors: Tummeleht Lea , Häkkä Susanna Suvi Siviä , Jürison Margret , Vilem Annika , Nurmoja Imbi , Viltrop Arvo
Published: March 2024

The wild boar (Sus scrofa) is a social animal species native to Eurasia. During the last decade, the wild boar population in Estonia has been severely affected by the African swine fever virus (ASFV), which has also affected domestic pig farming. The potential transmission routes of ASFV remain unclear and are currently under intensive investigation. This pilot study aimed to clarify the frequency and characteristics of contacts between living wild boars and the carcasses of their conspecifics, which could play a role in the transmission of ASFV. Wild animals' contact and scavenging behavior on wild boar carcasses were studied using trail cameras in an experimental setting on Hiiumaa, Western Estonia. Four legally hunted carcasses were used in the present study. This study aimed to determine whether intraspecies scavenging occurs in wild boars. The persistence of ASFV DNA in soil contaminated with infected wild boar carcasses was investigated separately. Among the 17 identified wildlife species that visited wild boar carcasses, the common raven (Corvus corax) was the most frequent one (37.26%), followed by raccoon dogs (Nyctereutes procyonoides; 4.25%), carcass conspecific/wild boars (3.16%), and red foxes (Vulpes vulpes; 2.14%). Regarding the direct contact with the carcass, the same species ranking was detected: common raven (74.95%), raccoon dogs (9.94%), wild boars (4.21%), and red foxes (4.21%). No clear signs of cannibalism were noted among the wild boars, although brief physical contact with the carcasses was evident. The persistence of ASFV DNA in soil contaminated by infected wild boar carcasses was investigated separately. This study revealed that ASFV DNA from infected carcasses could be detected in forest soil for prolonged periods, even after removing the carcasses. Hence, the carcasses of infected wild boars may play an important role in spreading the African swine fever virus in wild boar populations; thus, prompt removal and disinfection of the soil could be considered necessary to limit the spread of the infection.
Customization of the ASR System for ATC Speech with Improved Fusion
Authors: Fan Jiahao , Pan Weijun
Published: March 2024

In recent years, automatic speech recognition (ASR) technology has improved significantly. However, the training process for an ASR model is complex, involving large amounts of data and a large number of algorithms. The task of training a new model for air traffic control (ATC) is considerable, as it may require many researchers for its maintenance and upgrading. In this paper, we developed an improved fusion method that can adapt the language model (LM) in ASR to the domain of air traffic control. Instead of using vocabulary in traditional fusion, this method uses the ATC instructions to improve the LM. The perplexity shows that the LM of the improved fusion is much better than that of the use of vocabulary. With vocabulary fusion, the CER in the ATC corpus decreases from 0.3493 to 0.2876. The improved fusion reduces the CER of the ATC corpora from 0.3493 to 0.2761. Although there is only a difference of less than 2% between the two fusions, the perplexity shows that the LM of the improved fusion is much better.
A Decision Support System for Sustainable Agriculture and Food Loss Reduction under Uncertain Agricultural Policy Frameworks
Authors: Barons Martine J. , Walsh Lael E. , Salakpi Edward E. , Nichols Linda
Published: March 2024

The EU Green Deal requires the reduction in pesticides and fertilisers in food crop production, whilst the sustainable development goals require reductions in food loss and food waste. In a complex and interacting system like the food system, these goals are difficult to coordinate. Here, we show an approach using Bayesian network modelling for decision support. Bayesian networks are important tools for modelling complex systems which may develop emergent behaviour and for providing quantitative comparisons for different candidate policies, approaches or interventions under the Integrating Decision Support System paradigm. Using lettuce as an exemplar crop, we demonstrate that expected food loss changes under different agricultural input reduction and integrated pest management combinations can be quantified to aid decision making for growers.
New Eldercare Robot with Path-Planning and Fall-Detection Capabilities
Authors: Elwaly Ahmad , Abdellatif A. , El-Shaer Y.
Published: March 2024

The rapid growth of the elderly population has led to an increased demand for effective and personalized eldercare solutions. In this paper, the design and development of an eldercare robot is presented. This robot is specifically tailored to meet the two specific challenges faced by the elderly. The first is the continuous indoor tracking of the elder, while the second is the fall detection. A comprehensive overview of the hardware and software components, as well as the control architecture of the robot is presented. The hardware design of the robot incorporates a range of features, including a perception system comprising a 2D Lidar, IMU, and camera for environment mapping, localization, and fall detection. The software stack of the robot is explained as consisting of layers for perception, mapping, and localization. The robot is tested experimentally to validate its path planning capability by using Hector SLAM and the RRT* technique. Experimental path planning has shown a positioning accuracy of 93.8% on average. Elderly fall detection is achieved by using the YOLOv7 algorithm at a percentage of 96%. Experimental results have been discussed and evaluated.
Fault Detection and VSC-HVDC Network Dynamics Analysis for the Faults in Its Host AC Networks
Authors: Rana Kiran , Kishor Nand , Negi Richa , Biswal Monalisa
Published: March 2024

High-voltage direct current (HVDC) transmission is preferred over high-voltage alternating current (HVAC) for long power lines for asynchronous power grid interconnection and high-level renewable energy integration. The control and protection functions associated with HVDC systems help with fast and secure clearance of faults. The control and protection challenges in the embedded HVDC network are of great concern for the stable and secure operation of an HVDC network. The DC fault current may reach an extremely high level in a rather short period because of the low impedance in a DC system, which is dangerous for converters, and disturbances in the AC network directly influence the performance of the HVDC system. Sometimes, faults on the AC side may lead to disconnection or failure of the DC link, causing reliability problems as well as huge economic losses. AC and DC protection solutions are being developed for HVDC systems to enhance their sustainability and reliability. As such, AC and DC faults should be detected and cleared at a faster rate. Therefore, in this article, the feasibility of the synchro-squeezed transform (SST) is analyzed for detection purposes. For more accurate and faster detection, the signal is first decomposed using the empirical mode decomposition (EMD) technique, and then the SST is applied. A discrete Teager energy (DTE) spectrum is obtained with the processed signal, which works as the detection index. The algorithm shows low sampling frequency requirements, with higher efficiency and reliability for the purpose. PSCAD/EMTDC version 4.6 software and MATLAB 2022a software is used for the modeling and simulation.
Application of Unsteady Fluid Flow Simulation in the Process of Regulating an Industrial Hydraulic Network
Authors: Fil’o Milan , Brestovič Tomáš , Lázár Marián , Jasminská Natália , Dobáková Romana , Kender Štefan
Published: March 2024

In this article, an analytical solution to a hydraulic network with a wide range of pipe lengths (up to 10 km) is proposed. The Finite-Difference Time-Domain (FDTD) method was applied with the aim of creating a regulation model for controlling both the flow rate of water from one of the two sources and the discharge pressure in the system. The system inertia requires an understanding of boundary conditions in the operation of pipeline networks, which must be known in order to regulate the required parameters with only minor deviations. The proposed model was compared to experimental data, while the mean absolute deviations in the individual system branches ranged from 1 to 5.19%. The created regulation model was subsequently tested by applying linear, sine and stochastic changes in the output load, while the ability to control the discharge pressure and the selected water flow rate was analysed. The effect of coefficient ε, which multiplies the effect of the difference between the measured and the predicted value of the discharge pressure on the boundary conditions of the discharge pressure in the system, was analysed in this paper. With the use of the proposed unsteady simulation of the fluid flow in the hydraulic system arranged in parallel and in series, the maximum deviation of the regulated pressure was 1.2% and the maximum deviation of the regulated flow rate was 5.3%.
Effects of Buffer and Capping Layers on Thermal Stability of CoFeB/MgO Frames at Various Temperatures
Authors: Kang Byeongwoo , Hwang Young Hyun , Kim Yong Jin , Lee Jong Seong , Song Seo Hyun , Lee Seungwon , Lee Jisung , Lee OukJae , Park Seung-Young , Ju Byeong-Kwon
Published: March 2024

The utilization of CoFeB thin films in spintronic devices has attracted significant attention due to their exceptional magnetic properties, which include high saturation magnetization and spin polarization. However, the effect of ambient temperature on the magnetic properties of CoFeB/MgO frames, particularly those with different buffer and capping layers, remains unexplored. Therefore, in this study, the magnetostatic and dynamic properties of CoFeB/MgO frames were investigated at various temperatures. Using vibrating sample magnetometry and ferromagnetic resonance spectroscopy, changes in key parameters such as saturation magnetization, the Gilbert damping constant, magnetic anisotropy field, in-plane uniaxial magnetic anisotropy energy, and thermal stability factor were investigated. Furthermore, the thermal stabilities of CoFeB/MgO frames with Ta buffer and capping layers were compared with those of CoFeB/MgO frames with W buffer and capping layers by examining the changes in the key parameters at various temperatures. These results reveal that the thermal stability of the latter surpassed that of the former. This study provides significant insights for the development of thermally robust spintronic devices capable of operating above room temperature.
Development of the AI Pipeline for Corneal Opacity Detection
Authors: Yoshitsugu Kenji , Shimizu Eisuke , Nishimura Hiroki , Khemlani Rohan , Nakayama Shintaro , Takemura Tadamasa
Published: March 2024

Ophthalmological services face global inadequacies, especially in low- and middle-income countries, which are marked by a shortage of practitioners and equipment. This study employed a portable slit lamp microscope with video capabilities and cloud storage for more equitable global diagnostic resource distribution. To enhance accessibility and quality of care, this study targets corneal opacity, which is a global cause of blindness. This study has two purposes. The first is to detect corneal opacity from videos in which the anterior segment of the eye is captured. The other is to develop an AI pipeline to detect corneal opacities. First, we extracted image frames from videos and processed them using a convolutional neural network (CNN) model. Second, we manually annotated the images to extract only the corneal margins, adjusted the contrast with CLAHE, and processed them using the CNN model. Finally, we performed semantic segmentation of the cornea using annotated data. The results showed an accuracy of 0.8 for image frames and 0.96 for corneal margins. Dice and IoU achieved a score of 0.94 for semantic segmentation of the corneal margins. Although corneal opacity detection from video frames seemed challenging in the early stages of this study, manual annotation, corneal extraction, and CLAHE contrast adjustment significantly improved accuracy. The incorporation of manual annotation into the AI pipeline, through semantic segmentation, facilitated high accuracy in detecting corneal opacity.
Correction: Pius et al. Effects of Aging on Osteosynthesis at Bone–Implant Interfaces. Biomolecules 2024, 14, 52
Authors: Pius Alexa K. , Toya Masakazu , Gao Qi , Lee Max L. , Ergul Yasemin Sude , Chow Simon Kwoon-Ho , Goodman Stuart Barry
Published: March 2024

Max L. Lee was not included as an author in the original publication [...]
A New Macro-Element for Predicting the Behavior of Masonry Structures under In-Plane Cyclic Loading
Authors: Decret Damien , Malecot Yann , Sieffert Yannick , Vieux-Champagne Florent , Daudeville Laurent
Published: March 2024

A new macro model for the finite element modeling of unreinforced masonry (URM) exhibiting in-plane nonlinear cyclic behavior is proposed. The ultimate objective is to predict the seismic response of multi-story URM buildings. The macro model enables the modeling of URM shear walls with a limited number of degrees of freedom (DOF) at low computation times. The macro model consists of a deformable elastic frame supported by diagonal struts with nonlinear behavior aiming to capture all dissipative phenomena occurring during seismic events. The nonlinear constitutive behavior of diagonal struts is inspired by models documented in the literature, ensuring a robust foundation for the proposed approach. This paper first provides a comprehensive review of the principal models currently available for URM analysis. It then articulates the rationale behind the development of this new numerical model, aiming to address the limitations encountered in existing methodologies and to offer a simple and fast tool for predicting the seismic behavior of URM buildings. Afterward, the new model is presented and tested with the simulations of two experimental campaigns performed on different URM walls. The comparison between experimental and numerical results shows that with a limited number of DOF and parameters, it is possible to obtain a prediction of the experimental results with satisfying accuracy.
Progressive Irreversible Proprioceptive Piezo2 Channelopathy-Induced Lost Forced Peripheral Oscillatory Synchronization to the Hippocampal Oscillator May Explain the Onset of Amyotrophic Lateral Sclerosis Pathomechanism
Authors: Sonkodi Balázs
Published: March 2024

Amyotrophic lateral sclerosis (ALS) is a mysterious lethal multisystem neurodegenerative disease that gradually leads to the progressive loss of motor neurons. A recent non-contact dying-back injury mechanism theory for ALS proposed that the primary damage is an acquired irreversible intrafusal proprioceptive terminal Piezo2 channelopathy with underlying genetic and environmental risk factors. Underpinning this is the theory that excessively prolonged proprioceptive mechanotransduction under allostasis may induce dysfunctionality in mitochondria, leading to Piezo2 channelopathy. This microinjury is suggested to provide one gateway from physiology to pathophysiology. The chronic, but not irreversible, form of this Piezo2 channelopathy is implicated in many diseases with unknown etiology. Dry eye disease is one of them where replenishing synthetic proteoglycans promote nerve regeneration. Syndecans, especially syndecan-3, are proposed as the first critical link in this hierarchical ordered depletory pathomechanism as proton-collecting/distributing antennas; hence, they may play a role in ALS pathomechanism onset. Even more importantly, the shedding or charge-altering variants of Syndecan-3 may contribute to the Piezo2 channelopathy-induced disruption of the Piezo2-initiated proton-based ultrafast long-range signaling through VGLUT1 and VGLUT2. Thus, these alterations may not only cause disruption to ultrafast signaling to the hippocampus in conscious proprioception, but could disrupt the ultrafast proprioceptive signaling feedback to the motoneurons. Correspondingly, an inert Piezo2-initiated proton-based ultrafast signaled proprioceptive skeletal system is coming to light that is suggested to be progressively lost in ALS. In addition, the lost functional link of the MyoD family of inhibitor proteins, as auxiliary subunits of Piezo2, may not only contribute to the theorized acquired Piezo2 channelopathy, but may explain how these microinjured ion channels evolve to be principal transcription activators.
Diagnostic and Therapeutic Challenges of Oligosymptomatic Vesicovaginal Fistula in the Complex Case of Endometriosis
Authors: Strojny Agnieszka A. , Baran Arkadiusz , Wiejak Katarzyna , Scholz Anna , Maksym Radosław B.
Published: March 2024

Endometriosis is a complex condition causing surgical challenges, sometimes leading to urogynecological complications, the diagnosis and treatment of which are not always obvious. We present a case of a 46-year-old woman with a history of severe endometriosis and adenomyosis who developed an oligosymptomatic vesicovaginal fistula (VVF) as a complication of surgery. The patient’s medical history included multiple surgeries for endometriosis, a cesarean section, and a laparoscopic hysterectomy. After the excision of the full-thickness infiltration of the urinary bladder, she experienced postoperative bowel obstruction treated by laparotomy. Subsequent urinary complications of bladder healing were eventually recognized as oligosymptomatic VVF. Symptoms of VVFs may vary, making a diagnosis challenging, especially when the lesion is narrow. Imaging techniques such as cystoscopy and cystography are helpful for diagnosis. The treatment options for VVFs range from surgical repair to conservative methods, like bladder catheterization, hormonal therapy, and platelet-rich plasma (PRP) injections, depending on the lesions’ size and location. In this case, the patient’s VVF was treated with PRP injections, a low-invasive method in urogynecology. PRP, known for its pleiotropic role, is increasingly used in medicine, including gynecology. The patient’s fistula closed after 6 weeks from the PRP session, highlighting the potential of this conservative treatment modality.
Robotic Platforms for Therapeutic Flexible Endoscopy: A Literature Review
Authors: Tada Naoya , Sumiyama Kazuki
Published: March 2024

Flexible endoscopy, initially developed for diagnosis and tissue sampling, has been adapted for therapeutic interventions, leading to the emergence of natural orifice transluminal endoscopic surgery (NOTES) in the 2000s. The need for a triangulation function to enhance the intuitiveness and safety of NOTES has prompted the development of dual-arm, flexible endoscopic robotic platforms. Although the global interest in NOTES has decreased in the last decade, no-scar surgery concepts are still being applied to other complex endoluminal interventions, such as endoscopic submucosal dissection (ESD) and endoscopic full-thickness resection (EFTR), with ongoing research and development. The application of robotics in flexible endoscopy may facilitate the standardization of these procedures and expedite their global spread. Various robotic platforms have been developed and tested in the preclinical and clinical settings to demonstrate their efficacy and safety. In this article, we review the publications on technology and elucidate their advantages and existing challenges.
But We Do Not Know Anything, We Were Born in This Predicament: Experiences of Learners Facing Xenophobia in South Africa
Authors: Dube Bekithemba , Setlalentoa Wendy
Published: March 2024

In this article, we discuss the experiences of learners who face xenophobia in South Africa. While extensive research has been conducted on xenophobia, few studies have specifically examined its impact on school-going children, whose presence at school is not by design in South Africa. We explore their lived experiences within the curriculum, thus exposing various trajectories that hinder effective teaching and learning. To theorise our findings, we tap into Whitehouse and Lanman’s notion of social cohesion. For data collection, we used a participatory action research approach. Through a series of interviews and group discussions, we engaged with a diverse group of 13 participants, which consisted of 10 migrant learners and 3 teachers. The study found that xenophobia is a significant social pathology in South Africa which found its way into the classroom walls, thereby affecting the performance of migrant learners. It affects the victim’s identity and has profound consequences for the perpetrators. Ultimately, the effects of xenophobia contribute to a cycle of school violence. We assert the imperative of addressing the distressing impact of xenophobia on children within classrooms. Based on our findings, we argue that initial teacher education programmes are key in fostering a non-violent society through promoting cohesion and cultural responsiveness.
Ballistic Performance of Quasi-One-Dimensional Hafnium Disulfide Field-Effect Transistors
Authors: Matić Mislav , Poljak Mirko
Published: March 2024

Hafnium disulfide (HfS2) monolayer is one of the most promising two-dimensional (2D) materials for future nanoscale electronic devices, and patterning it into quasi-one-dimensional HfS2 nanoribbons (HfS2NRs) enables multi-channel architectures for field-effect transistors (FETs). Electronic, transport and ballistic device characteristics are studied for sub-7 nm-wide and ~15 nm-long zigzag HfS2NR FETs using non-equilibrium Green’s functions (NEGF) formalism with density functional theory (DFT) and maximally localized Wannier functions (MLWFs). We provide an in-depth analysis of quantum confinement effects on ON-state performance. We show that bandgap and hole transport mass are immune to downscaling effects, while the ON-state performance is boosted by up to 53% but only in n-type devices. Finally, we demonstrate that HfS2NR FETs can fulfill the industry requirements for future technology nodes, which makes them a promising solution for FET architectures based on multiple nanosheets or nanowires.
Optimizing CNN Hardware Acceleration with Configurable Vector Units and Feature Layout Strategies
Authors: He Jinzhong , Zhang Ming , Xu Jian , Yu Lina , Li Weijun
Published: March 2024

Convolutional neural network (CNN) hardware acceleration is critical to improve the performance and facilitate the deployment of CNNs in edge applications. Due to its efficiency and simplicity, channel group parallelism has become a popular method for CNN hardware acceleration. However, when processing data involving small channels, there will be a mismatch between feature data and computing units, resulting in a low utilization of the computing units. When processing the middle layer of the convolutional neural network, the mismatch between the feature-usage order and the feature-loading order leads to a low input feature cache hit rate. To address these challenges, this paper proposes an innovative method inspired by data reordering technology, aiming to achieve CNN hardware acceleration that reuses the same multiplier resources. This method focuses on transforming the hardware acceleration process into feature organization, feature block scheduling and allocation, and feature calculation subtasks to ensure the efficient mapping of continuous loading and the calculation of feature data. Specifically, this paper introduces a convolutional algorithm mapping strategy and a configurable vector operation unit to enhance multiplier utilization for different feature map sizes and channel numbers. In addition, an off-chip address mapping and on-chip cache management mechanism is proposed to effectively improve the feature access efficiency and on-chip feature cache hit rate. Furthermore, a configurable feature block scheduling policy is proposed to strike a balance between weight reuse and feature writeback pressure. Experimental results demonstrate the effectiveness of this method. When using 512 multipliers and accelerating VGG16 at 100 MHz, the actual computing performance reaches 102.3 giga operations per second (GOPS). Compared with other CNN hardware acceleration methods, the average computing array utilization is as high as 99.88% and the computing density is higher.
IoT-Based Intrusion Detection System Using New Hybrid Deep Learning Algorithm
Authors: Yaras Sami , Dener Murat
Published: March 2024

The most significant threat that networks established in IoT may encounter is cyber attacks. The most commonly encountered attacks among these threats are DDoS attacks. After attacks, the communication traffic of the network can be disrupted, and the energy of sensor nodes can quickly deplete. Therefore, the detection of occurring attacks is of great importance. Considering numerous sensor nodes in the established network, analyzing the network traffic data through traditional methods can become impossible. Analyzing this network traffic in a big data environment is necessary. This study aims to analyze the obtained network traffic dataset in a big data environment and detect attacks in the network using a deep learning algorithm. This study is conducted using PySpark with Apache Spark in the Google Colaboratory (Colab) environment. Keras and Scikit-Learn libraries are utilized in the study. ‘CICIoT2023’ and ‘TON_IoT’ datasets are used for training and testing the model. The features in the datasets are reduced using the correlation method, ensuring the inclusion of significant features in the tests. A hybrid deep learning algorithm is designed using one-dimensional CNN and LSTM. The developed method was compared with ten machine learning and deep learning algorithms. The model’s performance was evaluated using accuracy, precision, recall, and F1 parameters. Following the study, an accuracy rate of 99.995% for binary classification and 99.96% for multiclassification is achieved in the ‘CICIoT2023’ dataset. In the ‘TON_IoT’ dataset, a binary classification success rate of 98.75% is reached.
Enhancing Security and Trust in Internet of Things through Meshtastic Protocol Utilising Low-Range Technology
Authors: Messina Fabrizio , Santoro Corrado , Santoro Federico Fausto
Published: March 2024

The rapid proliferation of Internet of Things (IoT) devices has raised significant concerns regarding the trustworthiness of IoT devices, which is becoming a crucial aspect of our daily lives. In this paper, we deal with this important aspect by taking into account Meshtastic, a dynamic mesh networking protocol that offers robustness and adaptability, important characteristics for the dynamic and heterogeneous IoT environment. LoRaWAN (Low-Range Wide Area Network), a low-power, long-range wireless communication standard, introduces energy efficiency and extends the reach of IoT networks, enabling secure communication over extended distances. To improve the trustworthiness of IoT devices, we present an integrated approach that leverages the strengths of Meshstastic’s dynamic mesh networking capabilities and LoRa’s low-power, long-range communication, along with the integration of a reputation model specifically designed for IoT. We evaluated the performance of the proposed solution through several simulations and real-world experiments. The results show that the devices’ measured values of trust reflect the real behaviour of the devices. These findings underscore the viability and applicability of the Meshtastic protocol utilising LoRa technology as a pivotal step towards establishing resilient and trustworthy IoT infrastructures in the face of evolving security challenges.
Linear-Extended-State-Observer-Based Adaptive RISE Control for the Wrist Joints of Manipulators with Electro-Hydraulic Servo Systems
Authors: Mi Junjie , Deng Wenxiang , Yao Jianyong , Liang Xianglong
Published: March 2024

Manipulators are multi-rigid-body systems composed of multiple moving joints. During movement, the Coriolis force, centrifugal force, and gravity of the system undergo significant changes. The last three degrees of freedom (DOFs) of the wrist joint of a manipulator control the end attitude. Improving the command tracking accuracy of the wrist joint is a key challenge in controlling the end attitude of manipulators. In this study, a dynamics model of the mechanical arm–wrist joint is established based on the Lagrange method. An adaptive continuous robust integral of the sign of the error (ARISE) controller is designed using the reverse step method. Additionally, a linear extended state observer (LESO) is employed to estimate the time-varying interference existing in the system and compensate for it in the designed control rate. The stability of the Lyapunov function and the boundedness of the observer are proven. The proposed control method for the wrist joint is compared with other controllers on an experimental platform of multi-DOF hydraulic manipulators. The results demonstrate that the proposed method improves the control performance of hydraulic manipulators. The application of this method offers a new strategy and idea for achieving high-performance tracking control in hydraulic manipulators.
Dynamic Modeling and Control of Supercritical Carbon Dioxide Power Cycle for Gas Turbine Waste Heat Recovery
Authors: Ma Bowen , Zhang Fan , Lee Kwang Y. , Hu Hemin , Wang Tao , Zhang Bing
Published: March 2024

The gas turbine is a crucial piece of equipment in the energy and power industry. The exhaust gas has a sufficiently high temperature to be recovered for energy cascade use. The supercritical carbon dioxide (S-CO2) Brayton cycle is an advanced power system that offers benefits in terms of efficiency, volume, and flexibility. It may be utilized for waste heat recovery (WHR) in gas turbines. This study involved the design of a 5 MW S-CO2 recompression cycle specifically for the purpose of operational control. The dynamic models for the printed circuit heat exchangers, compressors, and turbines were developed. The stability and dynamic behavior of the components were validated. The suggested control strategies entail utilizing the cooling water controller to maintain the compressor inlet temperature above the critical temperature of CO2 (304.13 K). Additionally, the circulating mass flow rate is regulated to modify the output power, while the exhaust gas flow rate is controlled to ensure that the turbine inlet temperature remains within safe limits. The simulations compare the performance of PI controllers tuned using the SIMC rule and ADRC controllers tuned using the bandwidth method. The findings demonstrated that both controllers are capable of adjusting operating conditions and effectively suppressing fluctuations in the exhaust gas. The ADRC controllers exhibit a superior control performance, resulting in a 55% reduction in settling time under the load-tracking scenario.
Synthesis of Guanidine and Its Deposition on Bacterial Cellulose as Green Heterogeneous Catalyst for Transesterification to Methyl Esters
Authors: Răducanu Cristian Eugen , Dobre Tănase , Mihăiescu Dan Eduard , Moroşan Alina , Jidveian Roxana , Cioroiu Tîrpan Doinița Roxana , Vasiliu Alexandru Dan , Gogoaşă Cristina Ionela , Pârvulescu Oana Cristina , Trică Bogdan
Published: March 2024

Green catalysts used in the transesterification reaction of biodiesel must have biodegradability and non-toxicity as their main characteristics, being thus friendly to the environment, since they perform in processes in which the content of CO2, which is increasing from year to year, should be reduced. As a consequence, their manufacture can be extremely rigorous. This work presents the two-step construction, synthesis, and deposition of such a green heterogeneous catalyst and its testing in the catalysis of the transesterification of triglycerides with methanol, resulting in methyl esters. A CSTR-type reactor was used to perform transesterification, and the biodiesel yields obtained had values in the range of 91.7–95.7%, using 2, 3, and 4 g/g catalyst to oil, under conditions like those for obtaining commercial biodiesel in homogeneous catalysis, i.e., a 65 °C process temperature and a 4:1, 5:1 or 6:1 methanol-to-oil molar ratio.
An Improved AC-Link Voltage Matching Control for the Multiport Modular Multilevel DC Transformer in MVDC Applications
Authors: Chen Yong , Yu Songtao , Wang Yizhen , Yang Ruixiong , Cheng Xu
Published: March 2024

In this paper, an improved AC-link voltage matching control (IVM) strategy is proposed for the multiport modular multilevel DC transformer (M3DCT), which comprises a single-phase modular multilevel converter (MMC) and a series of cascaded H-bridge units. The objective of the proposed IVM strategy is to address the AC-link voltage mismatch phenomenon. Distinct from existing control methods, such as various phase-shifting control methods and the conventional AC-link voltage matching control strategy, the proposed IVM strategy orchestrates the operation of the M3DCT in an innovative fashion. It allows the sum of inserted submodule (SM) numbers in the upper and lower arms to be flexible, no longer confined to a specific SM number per arm. Consequently, the AC-link voltage of the M3DCT is maintained proximate to the matched operating condition, regardless of the degree of mismatch in the DC side voltage of the M3DCT. This enables the enhancement of the M3DCT’s overall operational performance, particularly under conditions of light loads within medium-voltage DC (MVDC) distribution systems. The correctness and effectiveness of the proposed control strategy and the corresponding analysis are substantiated through simulation results.
CO2 Storage Site Analysis, Screening, and Resource Estimation for Cenozoic Offshore Reservoirs in the Central Gulf of Mexico
Authors: Hu Xitong , Bhattacherjee Rupom , Botchway Kodjo , Pashin Jack C. , Chakraborty Goutam , Bikkina Prem
Published: March 2024

The storage potential of hydrocarbon reservoirs in the central Gulf of Mexico (GOM) makes future development of CO2 storage projects in those areas promising for secure, large-scale, and long-term storage purposes. Focusing on the producing and depleted hydrocarbon fields in the continental slope of the central GOM, this paper analyzed, assessed, and screened the producing sands and evaluated their CO2 storage potential. A live interactive CO2 storage site screening system was built in the SAS® Viya system with a broad range of screening criteria combined from published studies. This offers the users a real-time assessment of the storage sites and enables them to adjust the filters and visualize the results to determine the most suitable filter range. The CO2 storage resources of the sands were estimated using a volumetric equation and the correlation developed by the National Energy Technology Laboratory (NETL). The results of this study indicate that 1.05 gigatons of CO2 storage resources are available in the developed reservoirs at the upper slope area of the central GOM. The Mississippi Canyon and Green Canyon protraction areas contain the fields with the largest storage resources.
Hydrogen Safety Challenges: A Comprehensive Review on Production, Storage, Transport, Utilization, and CFD-Based Consequence and Risk Assessment
Authors: Calabrese Marcella , Portarapillo Maria , Di Nardo Alessandra , Venezia Virginia , Turco Maria , Luciani Giuseppina , Di Benedetto Almerinda
Published: March 2024

This review examines the central role of hydrogen, particularly green hydrogen from renewable sources, in the global search for energy solutions that are sustainable and safe by design. Using the hydrogen square, safety measures across the hydrogen value chain—production, storage, transport, and utilisation—are discussed, thereby highlighting the need for a balanced approach to ensure a sustainable and efficient hydrogen economy. The review also underlines the challenges in safety assessments, points to past incidents, and argues for a comprehensive risk assessment that uses empirical modelling, simulation-based computational fluid dynamics (CFDs) for hydrogen dispersion, and quantitative risk assessments. It also highlights the activities carried out by our research group SaRAH (Safety, Risk Analysis, and Hydrogen) relative to a more rigorous risk assessment of hydrogen-related systems through the use of a combined approach of CFD simulations and the appropriate risk assessment tools. Our research activities are currently focused on underground hydrogen storage and hydrogen transport as hythane.
A Review of Artificial Intelligence Methods in Predicting Thermophysical Properties of Nanofluids for Heat Transfer Applications
Authors: Basu Ankan , Saha Aritra , Banerjee Sumanta , Roy Prokash C. , Kundu Balaram
Published: March 2024

This present review explores the application of artificial intelligence (AI) methods in analysing the prediction of thermophysical properties of nanofluids. Nanofluids, colloidal solutions comprising nanoparticles dispersed in various base fluids, have received significant attention for their enhanced thermal properties and broad application in industries ranging from electronics cooling to renewable energy systems. In particular, nanofluids’ complexity and non-linear behaviour necessitate advanced predictive models in heat transfer applications. The AI techniques, which include genetic algorithms (GAs) and machine learning (ML) methods, have emerged as powerful tools to address these challenges and offer novel alternatives to traditional mathematical and physical models. Artificial Neural Networks (ANNs) and other AI algorithms are highlighted for their capacity to process large datasets and identify intricate patterns, thereby proving effective in predicting nanofluid thermophysical properties (e.g., thermal conductivity and specific heat capacity). This review paper presents a comprehensive overview of various published studies devoted to the thermal behaviour of nanofluids, where AI methods (like ANNs, support vector regression (SVR), and genetic algorithms) are employed to enhance the accuracy of predictions of their thermophysical properties. The reviewed works conclusively demonstrate the superiority of AI models over the classical approaches, emphasizing the role of AI in advancing research for nanofluids used in heat transfer applications.
Phosphorus Removal Rate and Efficiency in an Electrochemical Sequencing Reactor for the Treatment of Wastewater with Low Organic Carbon Content
Authors: Mielcarek Artur , Bryszewski Kamil Łukasz , Rodziewicz Joanna , Kłobukowska Karolina , Janczukowicz Wojciech
Published: March 2024

Energy is essential for the operation of wastewater treatment systems. Simultaneously, it can be a factor facilitating the electrochemical purification processes. Previous studies have shown that under specific conditions, there is no technological justification for using bio-electrochemical reactors designed for the simultaneous removal of both phosphorus and nitrogen compounds. This is because similar dephosphatation process effects can be achieved in an electrochemical reactor. Additionally, in a bio-electrochemical reactor, a portion of the organic substrate introduced for biological treatment is lost due to the electrocoagulation process. The aim of the research was to determine the influence of low direct current densities (0.4–2.0 A/m2) on the rate and efficiency of phosphorus and other compound removal in a sequencing electrochemical reactor treating real wastewater from a greenhouse with low organic compound content. In the conducted studies, an increase in electric current density resulted in an increase in the removal rates of phosphorus from 26.45 to 34.79 mg/L·h, nitrogen from 2.07 to 6.58 mg/L·h, and organic compounds from 0.44 to 1.50 mg/L·h. This corresponded to maximum removal efficiencies of 88.6 ± 2.5% for phosphorus, 7.4 ± 2.5% for nitrogen, and 51.1 ± 8.3% for organic compounds. As a result of electrocoagulation, sludge rich in phosphorus was obtained, ranging from 347 ± 38 mg/L (18.1% P) to 665 ± 36 mg/L (11.7% P). The obtained results can be utilized in the future for the development of two-stage systems for wastewater treatment with a low content of organic compounds, aiming at the removal of phosphorus and nitrogen.