bluuebunny/crossref_metadata_2025 huggingface dataset:
Microservices-Based Resource Provisioning for Multi-User Cloud VR in Edge Networks
Authors: Choi Ho-Jin , Komuro Nobuyoshi , Kim Won-Suk
Published: August 2024

Cloud virtual reality (VR) is attracting attention in terms of its lightweight head-mounted display (HMD), providing telepresence and mobility. However, it is still in the research stages due to motion-to-photon (MTP) latency, the need for high-speed network infrastructure, and large-scale traffic processing problems. These problems are expected to be partially solved through edge computing, but the limited computing resource capacity of the infrastructure presents new challenges. In particular, in order to efficiently provide multi-user content such as remote meetings on edge devices, resource provisioning is needed that considers the application’s traffic patterns and computing resource requirements at the same time. In this study, we present a microservice architecture (MSA)-based application to provide multi-user cloud VR in edge computing and propose a scheme for planning an efficient service deployment considering the characteristics of each service. The proposed scheme not only guarantees the MTP latency threshold for all users but also aims to reduce networking and computing resource waste. The proposed scheme was evaluated by simulating various scenarios, and the results were compared to several studies. It was confirmed that the proposed scheme represents better performance metrics than the comparison schemes in most cases from the perspectives of networking, computing, and MTP latency.
Electromagnetic Signal Analysis for Electrical Fault Diagnosis in Synchronous Generators
Authors: Park Junki , Harmony Peter Nkwocha , Moon Hyoungjun , Baek Jeihoon
Published: August 2024

This study investigates the enhancement of fault diagnostics in synchronous generators by incorporating electromagnetic signal analysis with conventional diagnostic methodologies. The critical role of synchronous generators in maintaining power system stability and efficiency in industrial and power plant environments is underscored. Finite element modeling (FEM) is employed to simulate various fault conditions, such as stator and rotor winding faults. This study proposes a method that integrates stator current and stray magnetic flux analysis to identify five distinct types of short-circuit faults in stator and rotor windings, thereby enhancing the diagnostic capabilities for electrical faults in synchronous generators. This approach successfully identifies these electrical faults using non-invasive methods, offering a cost-effective solution that enhances fault detection. These findings are based on simulation results and serve as a preliminary stage for further validation through experimental studies. This integration is crucial for the development of efficient diagnostic systems that are capable of adapting to complex fault patterns, reducing human intervention, and streamlining maintenance operations, thus improving the reliability of synchronous generators globally.
TLDM: An Enhanced Traffic Light Detection Model Based on YOLOv5
Authors: Song Jun , Hu Tong , Gong Zhengwei , Zhang Youcheng , Cui Mengchao
Published: August 2024

Traffic light detection and recognition are crucial for enhancing the security of unmanned systems. This study proposes a YOLOv5-based traffic light-detection algorithm to tackle the challenges posed by small targets and complex urban backgrounds. Initially, the Mosaic-9 method is employed to enhance the training dataset, thereby boosting the network’s ability to generalize and adapt to real-world scenarios. Furthermore, the Squeeze-and-Excitation (SE) attention mechanism is incorporated to improve the network. Moreover, the YOLOv5 algorithm’s loss function is optimized by substituting it with Efficient Intersection over Union loss (EIoU_loss), which addresses issues like missed detection and false alarms. Experimental results demonstrate that the model trained with this enhanced network achieves an mAP (mean average precision) of 99.4% on a custom dataset, which is 6.3% higher than that of the original YOLOv5, while maintaining a detection speed of 74 f/s. Therefore, this algorithm offers higher detection accuracy and effectively meets real-time operational requirements. The proposed method has a strong application potential, and can be widely used in the field of automatic driving, assisted driving, etc. Its application is not only of great significance in improving the accuracy and speed of traffic sign detection, but also can provide technical support for the development of intelligent transportation systems.
EffiMultiOrthoBearNet: An Efficient Lightweight Architecture for Bearing Fault Diagnosis
Authors: Yang Wenyin , Wu Zepeng , Ma Li , Guo Linjiu , Chang Yumin
Published: August 2024

Amidst the advent of Industry 4.0 and the rapid advancements in smart manufacturing, the imperative for developing resource-efficient condition monitoring and fault prediction technologies tailored for industrial equipment in resource-limited settings has become increasingly evident. This study puts forward EffiMultiOrthoBearNet, an innovative, lightweight, deep learning model specifically designed for the accurate identification and classification of bearing faults. Central to EffiMultiOrthoBearNet’s architecture is the integration of multi-scale convolutional layers and orthogonal attention mechanisms—key innovations that significantly enhance the model’s performance. Leveraging advanced feature extraction capabilities, EffiMultiOrthoBearNet meticulously processes Continuous Wavelet Transform (CWT) images from the CWRU dataset, ensuring the precise delineation of essential bearing signal traits through its multi-scale and attention-enhanced mechanisms. Optimized for supreme operational efficiency in resource-deprived environments, EffiMultiOrthoBearNet achieves unmatched classification accuracy—up to 100% under ideal circumstances and consistently above 90% amidst significant noise and operational complexities. Demonstrating remarkable adaptability and efficiency, EffiMultiOrthoBearNet provides a pioneering and practical fault diagnosis solution for industrial machinery across a wide range of application scenarios, even under stringent resource limitations.
Spatial Information Entropy-Assisted Integrated Sensing and Communication for Integrated Satellite-Terrestrial Networks
Authors: Wang Xue , Lin Xiaojing , Jia Min
Published: August 2024

To better meet communication needs, 6G proposes Integrated Satellite-Terrestrial Networks. Integrated Sensing and Communication (ISAC) is one of the key technologies of Integrated Satellite-Terrestrial Networks, which can reduce the energy consumption of the system, improve communication efficiency, and increase the utilization rate of spectrum resources. In the existing technology, the Modulated Wideband Converter (MWC) system can provide support for the miniaturization and intelligence of wireless device sensing and communication systems. Therefore, the MWC system can be used as a preliminary application of ISAC technology. However, the reconstruction effect of the conventional MWC system under the influence of noise is not stable. Therefore, we propose a signal processing optimization scheme for the MWC system based on spatial information entropy. First, the subsequent reconstruction algorithm is considered to require the dynamic and flexible processing of the sampled signals to reduce the influence of noise. Second, for the shortcomings of the original Orthogonal Matching Pursuit (OMP) algorithm, the concept of the genetic algorithm is used to optimize the algorithm by constructing the feature factor through spatial information gain and spatial information features. According to the simulation results, compared with the traditional MWC system, the scheme proposed in this paper is improved in all indicators.
Adaptive QSMO-Based Sensorless Drive for IPM Motor with NN-Based Transient Position Error Compensation
Authors: Sun Linfeng , Guo Jiawei , Jiang Xiongwen , Kawaguchi Takahiro , Hashimoto Seiji , Jiang Wei
Published: August 2024

In commercial electrical equipment, the popular sensorless drive scheme for the interior permanent magnet synchronous motor, based on the quasi-sliding mode observer (QSMO) and phase-locked loop (PLL), still faces challenges such as position errors and limited applicability across a wide speed range. To address these problems, this paper analyzes the frequency domain model of the QSMO. A QSMO-based parameter adaptation method is proposed to adjust the boundary layer and widen the speed operating range, considering the QSMO bandwidth. A QSMO-based phase lag compensation method is proposed to mitigate steady-state position errors, considering the QSMO phase lag. Then, the PLL model is analyzed to select the estimated speed difference for transient position error compensation. Specifically, a transient position error compensator based on a feedback time delay neural network (FB-TDNN) is proposed. Based on the back propagation learning algorithm, the specific structure and optimal parameters of the FB-TDNN are determined during the offline training process. The proposed parameter adaptation method and two position error compensation methods were validated through simulations in simulated wide-speed operation scenarios, including sudden speed changes. Overall, the proposed scheme fully mitigates steady-state position errors, substantially mitigates transient position errors, and exhibits good stability across a wide speed range.
Operational Tests for Delay-Tolerant Network between the Moon and Earth Using the Korea Pathfinder Lunar Orbiter in Lunar Orbit
Authors: Kim Inkyu , Han Sang Ik , Har Dongsoo
Published: August 2024

The Korea Pathfinder Lunar Orbiter (KPLO) was launched on 5 August 2022, equipped on the SpaceX Falcon 9 launch vehicle. At present, the KPLO is effectively carrying out its scientific mission in lunar orbit. The KPLO serves as a cornerstone for the development and validation of Korean space science and deep space technology. Among its payloads is the DTNPL, enabling the first-ever test of delay-tolerant network (DTN) technology for satellites in lunar orbit. DTN technology represents a significant advancement in space communication, offering stable communication capabilities characterized by high delay tolerance, reliability, and asymmetric communication speeds—a necessity for existing satellite and space communication systems to evolve. In this paper, we briefly give an overview of the Korea Lunar Exploration Program (KLEP) and present scientific data gathered through the KPLO mission. Specifically, we focus on the operational tests for DTN-ION conducted for message and file transfer, as well as real-time video streaming, during the initial operations of the KPLO. Lastly, this study offers insights and lessons learned from KPLO DTNPL operations, with the goal of providing valuable guidance for future advancements in space communication.
Illumination and Shadows in Head Rotation: Experiments with Denoising Diffusion Models
Authors: Asperti Andrea , Colasuonno Gabriele , Guerra Antonio
Published: August 2024

Accurately modeling the effects of illumination and shadows during head rotation is critical in computer vision for enhancing image realism and reducing artifacts. This study delves into the latent space of denoising diffusion models to identify compelling trajectories that can express continuous head rotation under varying lighting conditions. A key contribution of our work is the generation of additional labels from the CelebA dataset, categorizing images into three groups based on prevalent illumination direction: left, center, and right. These labels play a crucial role in our approach, enabling more precise manipulations and improved handling of lighting variations. Leveraging a recent embedding technique for Denoising Diffusion Implicit Models (DDIM), our method achieves noteworthy manipulations, encompassing a wide rotation angle of ±30°. while preserving individual distinct characteristics even under challenging illumination conditions. Our methodology involves computing trajectories that approximate clouds of latent representations of dataset samples with different yaw rotations through linear regression. Specific trajectories are obtained by analyzing subsets of data that share significant attributes with the source image, including light direction. Notably, our approach does not require any specific training of the generative model for the task of rotation; we merely compute and follow specific trajectories in the latent space of a pre-trained face generation model. This article showcases the potential of our approach and its current limitations through a qualitative discussion of notable examples. This study contributes to the ongoing advancements in representation learning and the semantic investigation of the latent space of generative models.
Medical Image Classification with a Hybrid SSM Model Based on CNN and Transformer
Authors: Hu Can , Cao Ning , Zhou Han , Guo Bin
Published: August 2024

Medical image classification, a pivotal task for diagnostic accuracy, poses unique challenges due to the intricate and variable nature of medical images compared to their natural counterparts. While Convolutional Neural Networks (CNNs) and Transformers are prevalent in this domain, each architecture has its drawbacks. CNNs, despite their strength in local feature extraction, fall short in capturing global context, whereas Transformers excel at global information but can overlook fine-grained details. The integration of CNNs and Transformers in a hybrid model aims to bridge this gap by enabling simultaneous local and global feature extraction. However, this approach remains constrained in its capacity to model long-range dependencies, thereby hindering the efficient extraction of distant features. To address these issues, we introduce the MambaConvT model, which employs a state-space approach. It begins by locally processing input features through multi-core convolution, enhancing the extraction of deep, discriminative local details. Next, depth-separable convolution with a 2D selective scanning module (SS2D) is employed to maintain a global receptive field and establish long-distance connections, capturing the fine-grained features. The model then combines hybrid features for comprehensive feature extraction, followed by global feature modeling to emphasize on global detail information and optimize feature representation. This paper conducts thorough performance experiments on different algorithms across four publicly available datasets and two private datasets. The results demonstrate that MambaConvT outperforms the latest classification algorithms in terms of accuracy, precision, recall, F1 score, and AUC value ratings, achieving superior performance in the precise classification of medical images.
A Taxonomy of Low-Power Techniques in Wearable Medical Devices for Healthcare Applications
Authors: Tesema Workineh , Jimma Worku , Khan Muhammad Iqbal , Stiens Johan , da Silva Bruno
Published: August 2024

Chronic diseases are the most prevalent and non-communicable health crisis globally. Most chronic disease patients require continuous physiological monitoring, using wearable technology for timely treatment, precise illness detection, and preventive healthcare. Nonetheless, efficient power management is required for such resource-constrained wearable devices. This work aims to analyze low-power techniques (LPTs) in wearable medical devices using a data-driven approach and identify novel approaches promising higher power savings. Through an intensive literature analysis, we identify the most relevant LPTs for minimizing power consumption in wearable devices for physiological monitoring while recognizing the barriers to adopting these techniques. As a result, a novel taxonomy based on the common characteristics of the LPTs is proposed, along with strategies for the combination of LPTs. Through our analysis, we propose possible enhancements in using LPTs and suggest mechanisms for the medical device industry to facilitate their adoption. Overall, our proposed strategies guide the use of LPTs on wearable medical devices toward continuous physiological monitoring.
Accuracy and Uncertainty of Position Detection of Moving Objects by Resonator Arrays
Authors: Simonazzi Mattia , Sandrolini Leonardo , Mariscotti Andrea
Published: August 2024

Arrays of magnetically coupled resonators have recently been proposed as position sensors for both static and dynamic applications. Despite their promising characteristics, an accurate evaluation of their functioning in non-ideal conditions is fundamental and necessary to adapt them to industrial contexts. This article analyses the accuracy of the position measurement in resonator arrays under system parameter variations. Appropriate parameters for the evaluation of the sensor performance have been discussed and analysed considering non-ideal system components by exploiting the Monte Carlo method.
The Design and Real-Time Optimization of an EtherCAT Master for Multi-Axis Motion Control
Authors: Zhang Jianjun , Xia Manjiang , Li Han , Li Shasha , Shi Juan
Published: August 2024

To address the issues of low bandwidth, weak real-time performance, and poor synchronization in traditional fieldbuses for multi-axis motion control, a solution for the implementation of an EtherCAT master based on the IgH EtherCAT Master open-source software framework and an embedded hardware platform is proposed. On a hardware platform centered around the AM64x Sitara processor, a Linux real-time operating system based on the Xenomai real-time kernel is constructed, and the IgH master framework is ported to realize a high-performance EtherCAT master. The configuration process of the EtherCAT bus is detailed, a master application program is developed, and methods for the real-time performance optimization of the master—such as exclusive CPU usage by the master process and the optimization of the network card driver—are proposed. Finally, experiments are conducted on a six-axis servo control platform, with the packet analysis of the periodic EtherCAT data frames sent by the master. The experimental results show that the optimized master, under a high-speed communication cycle of 500 microseconds, maintains maximum jitter within 20 microseconds and average jitter within 1 microsecond, meeting the requirements for high-precision multi-axis motion control.
IntiGIS-Local: A Geospatial Approach to Assessing Rural Electrification Alternatives for Sustainable Socio-Economic Development in Isolated Communities—A Case Study of Guasasa, Cuba
Authors: Domínguez Javier , Bellini Carlo , Arribas Luis , Amador Julio , Torres-Pérez Mirelys , Martín Ana M.
Published: August 2024

Rural electrification is a crucial step for the socio-economic development of isolated communities. Decentralized power generation, typically more favorable for renewable energies, requires an accurate analysis of the different electrification options, whose convenience depends on multiple factors. The application of Geographical Information Systems (GISs) to energy planning allows the assessment at a local level, considering the variability and demand distribution of spatial resources. This work introduces IntiGIS-local, a GIS-based model implemented in the ArcGIS environment, designed to calculate the levelized energy cost (LEC) for different electrification options. The model allows the comparison between three power generation alternatives: solar system, diesel generator set and solar–diesel hybrid system. Configurations are adjustable through input variables, with a special focus on the confrontation between individual systems and microgrids. The objective is to provide an adequate groundwork for developing a decision-making tool to assess diverse rural electrification options in future studies. The model IntiGIS-local is tested in the case study of the Guasasa community (Cuba).
Design of Inductive Power Transfer Charging System with Weak Coupling Coefficient
Authors: Chen Yuhang , Yang Shichun , Zheng Mengchao , Yan Xiaoyu
Published: August 2024

Inductive power transfer (IPT) technology is used in various applications owing to its safety features, robust environmental adaptability, and convenience. In some special applications, the charging pads are required to be as compact as possible to accommodate practical spatial requirements, and even size requirements dictate that the diameter of the charging pad matches the air gap. However, such requirements bring about a decrease in the transmission efficiency, power, and tolerance to misalignment of the system. In this paper, by comparing a double-sided inductor–capacitor–capacitor (LCC), double-sided inductor–capacitor–inductor (LCL), series–series (SS), and inductor–capacitor–capacitor–series (LCC-S) compensation topologies in IPT systems, we identified a double-sided LCC compensation topology that is suitable for weak coupling coefficients. Furthermore, this study modeled and simulated the typical parameters of coreless coils in circular power pads, such as the number of coil layers, turns, wire diameter, and wire spacing, to enhance the mutual inductance of the magnetic coupler during misalignment and long-distance transmission. A wireless charging system with 640 W output power was built, and the experimental results show that a maximum dc-dc efficiency of over 86% is achieved across a 200 mm air gap when the circular power pad with a diameter of 200 mm is well aligned. The experimental results show that using a suitable compensation topology and optimizing the charging pad parameters enables efficient IPT system operation when the coupling coefficient is 0.02.
Eco-Innovation Method for Sustainable Development of Energy-Producing Products Considering Quality and Life Cycle Assessment (QLCA)
Authors: Siwiec Dominika , Pacana Andrzej
Published: August 2024

The sustainability of products remains a challenge, mainly due to the lack of consistent approaches for simultaneously taking into account the key criteria of the concept in the process. This research aims to develop an eco-innovative QLCA method to create new product solutions that integrate quality (customer satisfaction) and environmental impact assessment throughout the product life cycle. The QLCA method includes: (i) product prototyping according to quality and environmental criteria; (ii) prospective assessment of the quality of prototypes, taking into account customer requirements; (iii) prospective life cycle assessment of product prototypes using a cradle-to-grave approach in accordance with ISO 14040; and (iv) setting the direction of product development while taking into account the fulfilment of customer expectations and the need to care for the environment throughout the product life cycle. Owing to the lack of previous research in this area, as well as the popularity of photovoltaic (PV) panels in reducing greenhouse gases, an illustration was obtained and test of the method was carried out on the example of silicon photovoltaic panel modules (Crystalline Si PV Module). In accordance with the adopted assumptions, the results of the QLCA method test showed that the modelled PV prototypes will, in most cases, be satisfactory for customers, but they still require improvement actions to reduce carbon dioxide (CO2) emissions throughout their life cycle. These activities should be consistent so as to achieve quality that satisfies customers. The QLCA method can be used by designers, managers, and decision-makers at the early stages of design, but also during the product maturity phase for its sustainable development.
On the Influence of Engine Compression Ratio on Diesel Engine Performance and Emission Fueled with Biodiesel Extracted from Waste Cooking Oil
Authors: Alotaibi Jasem Ghanem , Alajmi Ayedh Eid , Alsaeed Talal , Al-Lwayzy Saddam H. , Yousif Belal F.
Published: August 2024

Despite the extensive research on biodiesels, further investigation is warranted on the impact of compression ratios on emissions and engine performance. This study addresses this gap by evaluating the effects of increasing the engine’s compression ratio on engine performance metrics—brake-specific fuel consumption (BSFC), power, torque, and exhaust gas temperature—and emissions—unburnt hydrocarbons (HCs), carbon dioxide (CO2), carbon monoxide (CO), nitrogen oxides (NOx), and oxygen (O2)—when fueled with a 20% blend of waste cooking oil biodiesel (WCB20) and petroleum diesel (PD) under various operating conditions. The viscosity of the prepared fuels was measured at 25 °C and 40 °C. Experiments were conducted on a single-cylinder diesel engine under wide-open throttle conditions at three different speeds (1400 rpm, 2000 rpm, and 2600 rpm) and two compression ratios (16:1 and 18:1). The results revealed that at a lower compression ratio, both WCB20 and petroleum diesel exhibited reduced BSFC compared to higher compression ratios. However, increasing the compression ratio from 16:1 to 18:1 significantly decreased HC emissions but increased CO2 and NOx emissions. Engine power increased with engine speed for both fuels and compression ratios, with WCB20 initially producing less power than diesel but surpassing it at higher compression ratios. WCB20 demonstrated improved combustion quality with lower unburnt hydrocarbons and carbon monoxide emissions due to its higher oxygen content, promoting complete combustion. This study provides critical insights into optimizing engine performance and emission characteristics by manipulating compression ratios and utilizing biodiesel blends, paving the way for more efficient and environmentally friendly diesel engine operations.
Applying a Current Sharing Method Based on Partial Energy Processing to Multiphase LLC Resonant Converters
Authors: Lee Yue-Lin , Chen Han-Hsiang , Hwu Kuo-Ing
Published: August 2024

In this paper, partial energy processing is applied to the current sharing technique for multiphase LLC resonant converters. The proposed circuit consists of an LLC resonant converter and a flyback converter, where the flyback converter is only used for partial energy processing. The input voltage of the LLC resonant converter is fine-tuned by the flyback converter to solve the problem of a voltage gain difference between the two phases of the LLC resonant converter caused by the error of the resonant tank components, which prevents the output current from being nonequalized. Since the compensation power is much smaller than the output power, and only one phase will be during circuit operation, the impact on the overall efficiency is minimal. Due to the low dependence between the LLC resonant converter and the flyback converter, they are operated at different switching frequencies. In addition, due to the low dependence between each phase, the circuit can be expanded using odd and even phases.
A Comprehensive Review on the Hydrogen–Natural Gas–Diesel Tri-Fuel Engine Exhaust Emissions
Authors: Muhssen Hassan Sadah , Zöldy Máté , Bereczky Ákos
Published: August 2024

Natural gas (NG) is favored for transportation due to its availability and lower CO2 emissions than fossil fuels, despite drawbacks like poor lean combustion ability and slow burning. According to a few recent studies, using hydrogen (H2) alongside NG and diesel in Tri-fuel mode addresses these drawbacks while enhancing efficiency and reducing emissions, making it a promising option for diesel engines. Due to the importance and novelty of this, the continuation of ongoing research, and insufficient literature studies on HNG–diesel engine emissions that are considered helpful to researchers, this research has been conducted. This review summarizes the recent research on the HNG–diesel Tri-fuel engines utilizing hydrogen-enriched natural gas (HNG). The research methodology involved summarizing the effect of engine design, operating conditions, fuel mixing ratios and supplying techniques on the CO, CO2, NOx and HC emissions separately. Previous studies show that using natural gas with diesel increases CO and HC emissions while decreasing NOx and CO2 compared to pure diesel. However, using hydrogen with diesel reduces CO, CO2, and HC emissions but increases NOx. On the other hand, HNG–diesel fuel mode effectively mitigates the disadvantages of using these fuels separately, resulting in decreased emissions of CO, CO2, HC, and NOx. The inclusion of hydrogen improves combustion efficiency, reduces ignition delay, and enhances heat release and in-cylinder pressure. Additionally, operational parameters such as engine power, speed, load, air–fuel ratio, compression ratio, and injection parameters directly affect emissions in HNG–diesel Tri-fuel engines. Overall, the Tri-fuel approach offers promising emissions benefits compared to using natural gas or hydrogen separately as dual-fuels.
Biological Pollution of Indoor Air, Its Assessment and Control Methods
Authors: Stocka Natalia , Butarewicz Andrzej , Stocki Marcin , Borowik Piotr , Oszako Tomasz
Published: August 2024

The aim of the entry was to write a substantial contribution that analyses and compares the biological pollution of indoor air, the possibilities of its assessment and the control methods. In addition, the aim of our entry was to review journals covering both commercial and residential buildings. By analysing the above topics from the existing articles, one can have the impression that air pollution is one of the most important problems that need to be solved in the modern world. Adequate air quality is important for maintaining human health, affects the health of ecosystems, including animals, and determines crop production. With the development of civilisation, the quality of air in the atmosphere and indoors is constantly deteriorating. Indoor air pollution can be divided into physical (e.g., noise, inadequate lighting, ionising radiation), chemical (e.g., tobacco smoke, household products) and microbiological (bacteria, viruses, fungi and products of their metabolism) factors. Each of these factors can have a negative impact on a person’s health or cause premature death. The entry deals with indoor air pollution, focussing on biological pollutants. It compares different methods available and describes the method of sampling to analyse indoor air pollution and ways to reduce it.
Adult-Onset Case of Female Idiopathic Hypogonadotropic Hypogonadism and Ataxia: Genetic Background
Authors: Chiarello Paola , Seminara Giuseppe , Bossio Sabrina , Rocca Valentina , Colao Emma , Iuliano Rodolfo , Aversa Antonio
Published: August 2024

Adult-onset cases of idiopathic hypogonadotropic hypogonadism (IHH) are characterized by partial or normal puberty development until adolescence and by the impairment of the hypothalamic–pituitary–gonadal (HPG) axis in adulthood. WDR11 and DCC genes are known to be involved in axonal development, particularly of hypothalamic GnRH neurons, and ciliogenesis. We report a female case of adult-onset hypogonadism and cerebellar ataxia, in which we identified two gene mutations. A panel of 48 genes was set up to search for variants in the causative genes of CHH. The variants found were analyzed following the American College of Medical Genetics and Genomics (ACMG) criteria to define their pathogenicity. We identified a missense heterozygous variant in the WDR11 gene NM_018117.12:c.2306T>G (p.Met769Arg) and a mutation in a second gene DCC resulting in amino acid substitutions NM_005215.4:c.3533C>T (p.Ser1178Phe). These variants were classified as being of uncertain clinical significance. We assume that there is a link between the variants found and the impairment of the gonadotrophic and neurological phenotype of the patient. Therefore, we propose the genetic test to identify the best therapeutic approach to identify infertility in female patients with IHH; we believe it is necessary to test WDR11 and DCC genes in larger populations with the same condition to introduce it in future protocols of assessment.
NOx Abatement by a TiO2-Based Coating under Real-Life Conditions and Laboratory-Scale Durability Assessment
Authors: Hot Julie , Fériot Clément , Lenard Emilie , Ringot Erick
Published: August 2024

In urban environments, various pollutants generated by road traffic, human, and industrial activities degrade outdoor and indoor air quality. Among these pollutants, nitrogen oxides (NOx) are subject to air quality regulations designed to protect human health and the environment. It is therefore crucial to keep their concentration as low as possible. Advanced oxidation processes are a practical choice for the degradation of NOx; among them, heterogeneous photocatalysis has proven to be a viable route. However, while the efficiency of this process has been widely demonstrated on a laboratory scale, it is still the subject of debate for real-life applications. The purpose of this study was to present a new field experiment on the application of a photocatalytic coating to outdoor walls. Air quality monitoring stations were used to evaluate the NOx concentration reduction instead of the chemiluminescent analyzer, in order to increase the number of sampling points. Statistical analysis was carried out to interpret the results. Density probability functions were plotted and showed a positive impact of the coating, leading to lower NOx concentrations. This work was completed by a laboratory-scale assessment of the coating’s durability using abrasion, QUV, and immersion/drying tests. The air depollution capacity of the chosen coating was significantly reduced after QUV testing.
Factors Driving Unexpected Drought-Induced Nothofagus dombeyi Mortality in a Valdivian Temperate Rainforest, Argentina
Authors: Suarez María Laura , Sasal Yamila , Facciano Loreta
Published: August 2024

Understanding the drivers of drought-induced tree mortality remains a significant scientific challenge. Here, we investigated an unexpected mortality event of Nothofagus dombeyi (Mirb.) Oerst. following the 2014–15 drought in a Valdivian rainforest, Argentina. Our focus was on long-term growth trend differences between vital and dead trees, and how the mixing of species in tree neighbourhoods drives tree growth during drought. The inter-annual variation of basal area increments of vital and 2014–15-dead N. dombeyi trees showed a similar pattern through the 1930–2015 period, while the climate–growth relationships indicated that precipitation during the growing season promoted growth in both vitality classes, regardless of whether they were in the wettest location. For the period 1990–2015, both vitality classes showed similar estimated growth regardless of competition level, whereas species mingling in the neighbourhood significantly affected the dead tree growth. Network analysis revealed that drought performance covaried positively with a neighbourhood dominated by species functionally different from the focal species only in vital trees. These findings suggest a nuanced response of N. dombeyi to drought, shaped by multifaceted interactions at both the individual tree and neighbourhood levels. This research underscores that species-specific relationships under different mixtures imply different tree responses within a stand, and add complexity to understanding drought response at the individual level.
Understanding Tree Mortality Patterns: A Comprehensive Review of Remote Sensing and Meteorological Ground-Based Studies
Authors: Eliades Filippos , Sarris Dimitrios , Bachofer Felix , Michaelides Silas , Hadjimitsis Diofantos
Published: August 2024

Land degradation, desertification and tree mortality related to global climate change have been in the spotlight of remote sensing research in recent decades since extreme climatic events could affect the composition, structure, and biogeography of forests. However, the complexity of tree mortality processes requires a holistic approach. Herein, we present the first global assessment and a historical perspective of forest tree mortality by reviewing both remote sensing and meteorological ground-based studies. We compiled 254 papers on tree mortality that make use of remotely sensed products, meteorological ground-based monitoring, and climatic drivers, focusing on their spatial and temporal patterns and the methods applied while highlighting research gaps. Our core results indicate that international publications on tree mortality are on the increase, with the main hotspots being North America (39%) and Europe (26%). Wetness indicators appear as the barometer in explaining tree mortality at a local scale, while vegetation indicators derived from multispectral optical sensors are promising for large-scale assessments. We observed that almost all of the studies we reviewed were based on less than 25 years of data and were at the local scale. Longer timeframes and regional scale investigations that will include multiple tree species analysis could have a significant impact on future research.
Virtual Reality in the Classroom: Transforming the Teaching of Electrical Circuits in the Digital Age
Authors: Albarracin-Acero Diego Alejandro , Romero-Toledo Fidel Alfonso , Saavedra-Bautista Claudia Esperanza , Ariza-Echeverri Edwan Anderson
Published: August 2024

In response to the digital transformation in education, this study explores the efficacy of virtual reality (VR) video games in teaching direct current electrical circuits at a public university in Colombia. Using a mixed-method action research approach, this study aimed to design, implement, and evaluate a VR-based educational strategy to enhance undergraduate learning experiences. The methodology integrated VR into the curriculum, facilitating a comparison of this innovative approach with traditional teaching methods. The results indicate that the VR strategy significantly improved students’ comprehension of electrical circuits and increased engagement, demonstrating the utility of immersive technologies in educational settings. Challenges such as the need for technological integration and curriculum adaptation were also identified. This study concludes that VR video games can effectively augment electrical engineering education, offering a model for incorporating advanced digital tools into higher education curricula. This approach aligns with ongoing trends in digital transformation, suggesting significant potential for broad applications across various educational contexts.
Investigating FWI Moisture Codes in Relation to Satellite-Derived Soil Moisture Data across Varied Resolutions
Authors: Atalay Hatice , Sunar Ayse Filiz , Dervisoglu Adalet
Published: August 2024

In the Mediterranean region, particularly in Antalya, southern Türkiye, rising forest fire risks due to climate change threaten ecosystems, property, and lives. Reduced soil moisture during the growing season is a key factor increasing fire risk by stressing plants and lowering fuel moisture content. This study assessed soil moisture and fuel moisture content (FMC) in ten fires (2019–2021) affecting over 50 hectares. The Fire Weather Index (FWI) and its components (FFMC, DMC, DC) were calculated using data from the General Directorate of Meteorology, EFFIS (8 km), and ERA5 (≈28 km) satellite sources. Relationships between FMCs, satellite-based soil moisture datasets (SMAP, SMOS), and land surface temperature (LST) data (MODIS, Landsat 8) were analyzed. Strong correlations were found between FWI codes and satellite soil moisture, particularly with SMAP. Positive correlations were observed between LST and FWIs, while negative correlations were evident with soil moisture. Statistical models integrating in situ soil moisture and EFFIS FWI (R: −0.86, −0.84, −0.83 for FFMC, DMC, DC) predicted soil moisture levels during extended fire events effectively, with model accuracy assessed through RMSE (0.60–3.64%). The SMAP surface (0–5 cm) dataset yielded a lower RMSE of 0.60–2.08%, aligning with its higher correlation. This study underlines the critical role of soil moisture in comprehensive fire risk assessments and highlights the necessity of incorporating modeled soil moisture data in fire management strategies, particularly in regions lacking comprehensive in situ monitoring.
Effects of Dietary Glutamate on the Growth Performance and Antioxidant Capacity of Juvenile Chinese Mitten Crab (Eriocheir sinensis)
Authors: Zheng Jiajun , He Yisong , Shi Mengyu , Jia Li , Xu Yang , Tan Yue , Qi Changle , Ye Jinyun
Published: August 2024

In order to explore the effects of glutamate on the growth performance, antioxidant capacity and protein metabolism of juvenile Chinese mitten crab, 0%, 1% and 2% glutamate were supplemented to low protein (30%) and normal protein (35%) diets, respectively. There were 5 parallel tanks in each treatment, and the feeding duration was 8 weeks. The results showed that dietary glutamate did not significantly affect the weight gain of Chinese mitten crab. Diets supplemented with 2% glutamate significantly decreased the crude protein of crabs. The T-AOC of crabs fed the 30% protein diets was significantly lower than crabs fed the 35% protein diets. At 30% protein level, the superoxide dismutase (SOD) activity significantly increased with the increase in glutamate content. Dietary glutamate significantly down-regulated the relative expressions of mTOR, PI3K, S6K1 and 4EBP at 35% protein level. In conclusion, dietary glutamate cannot significantly increase the growth of Chinese mitten crab, but it can improve the antioxidant capacity in Chinese mitten crab under low protein conditions.
Early Detection of Food Safety and Spoilage Incidents Based on Live Microbiome Profiling and PMA-qPCR Monitoring of Indicators
Authors: Cohen Hakmon May , Buhnik-Rosenblau Keren , Hanani Hila , Korach-Rechtman Hila , Mor Dagan , Etkin Erez , Kashi Yechezkel
Published: August 2024

The early detection of spoilage microorganisms and food pathogens is of paramount importance in food production systems. We propose a novel strategy for the early detection of food production defects, harnessing the product microbiome. We hypothesize that by establishing microbiome datasets of proper and defective batches, indicator bacteria signaling production errors can be identified and targeted for rapid quantification as part of routine practice. Using the production process of pastrami as a model, we characterized its live microbiome profiles throughout the production stages and in the final product, using propidium monoazide treatment followed by 16S rDNA sequencing. Pastrami demonstrated product-specific and consistent microbiome profiles predominated by Serratia and Vibrionimonas, with distinct microbial signatures across the production stages. Based on the established microbiome dataset, we were able to detect shifts in the microbiome profile of a defective batch produced under lactate deficiency. The most substantial changes were observed as increased relative abundances of Vibrio and Lactobacillus, which were subsequently defined as potential lactate-deficiency indicators. PMA-qPCR efficiently detected increased levels of these species, thus proving useful in rapidly pinpointing the production defect. This approach offers the possibility of the in-house detection of defective production events with same-day results, promoting safer food production systems.
Gastrodin Alleviates DSS-Induced Colitis in Mice through Strengthening Intestinal Barrier and Modulating Gut Microbiota
Authors: Li Jiahui , Jia Jinhui , Teng Yue , Xie Chunyuan , Li Chunwei , Zhu Beiwei , Xia Xiaodong
Published: August 2024

Inflammatory bowel diseases (IBDs) are commonly associated with dysfunctional intestinal barriers and disturbed gut microbiota. Gastrodin, a major bioactive ingredient of Gastrodia elata Blume, has been shown to exhibit anti-oxidation and anti-inflammation properties and could mitigate non-alcoholic fatty liver disease, but its role in modulating IBD remains elusive. The aim of this study was to investigate the impact of gastrodin on DSS-induced colitis in mice and explore its potential mechanisms. Gastrodin supplementation alleviated clinical symptoms such as weight loss, a shortened colon, and a high disease activity index. Meanwhile, gastrodin strengthened the intestinal barrier by increasing the 0expression of tight junction proteins and mucin. Furthermore, Gastrodin significantly reduced pro-inflammatory cytokine secretion in mice by downregulating the NF-κB and MAPK pathways. Gut microbiota analysis showed that gastrodin improved the DSS-disrupted microbiota of mice. These findings demonstrate that gastrodin could attenuate DSS-induced colitis by enhancing the intestinal barrier and modulating the gut microbiota, providing support for the development of a gastrodin-based strategy to prevent or combat IBD.
Substance Delivery across the Blood-Brain Barrier or the Blood-Retinal Barrier Using Organic Cation Transporter Novel Type 2 (OCTN2)
Authors: Tashima Toshihiko
Published: August 2024

The membrane impermeability of a drug poses a significant challenge in drug research and development, preventing effective drug delivery to the target site. Specifically, the blood-brain barrier (BBB) presents a formidable obstacle to the delivery of drugs targeting the central nervous system (CNS) into the brain, whereas the blood-retinal barrier (BRB) presents a tremendous obstacle to the delivery of drugs targeting the ocular diseases into the eyes. The development of drugs for Alzheimer’s or Parkinson’s disease targeting the CNS and for diabetic retinopathy and age-related macular degeneration targeting the eyes remains an unmet medical need for patients. Transporters play a crucial physiological role in maintaining homeostasis in metabolic organs. Various types of solute carrier (SLC) transporters are expressed in the capillary endothelial cells of the BBB, facilitating the delivery of nutrients from the blood flow to the brain. Therefore, carrier-mediated transport across the BBB can be achieved using SLC transporters present in capillary endothelial cells. It is well-known that CNS drugs typically incorporate N-containing groups, indicating that cation transporters facilitate their transport into the brain. In fact, carrier-mediated transport across the BBB can be accomplished using glucose transporter type 1 (Glut1) as a glucose transporter, L-type amino acid transporter 1 (LAT1) as a large neutral amino acid transporter, and H+/cation antiporter as a cation transporter. Surprisingly, although organic cation transporter novel type 2 (OCTN2) is expressed in the capillary endothelial cells, there has been limited investigation into OCTN2-mediated substance delivery into the brain across the BBB. Furthermore, it is suggested that OCTN2 is expressed at the BRB. In this prospective review, I present the advantages and possibilities of substance delivery into the brain across the BBB or into the eyes across the BRB, mediated by OCTN2 via carrier-mediated transport or receptor-mediated transcytosis.
Customizable Hydrogel Coating of ECM-Based Microtissues for Improved Cell Retention and Tissue Integrity
Authors: Elgin Shani , Silberman Eric , Shapira Assaf , Dvir Tal
Published: August 2024

Overcoming the oxygen diffusion limit of approximately 200 µm remains one of the most significant and intractable challenges to be overcome in tissue engineering. The fabrication of hydrogel microtissues and their assembly into larger structures may provide a solution, though these constructs are not without their own drawbacks; namely, these hydrogels are rapidly degraded in vivo, and cells delivered via microtissues are quickly expelled from the area of action. Here, we report the development of an easily customized protocol for creating a protective, biocompatible hydrogel barrier around microtissues. We show that calcium carbonate nanoparticles embedded within an ECM-based microtissue diffuse outwards and, when then exposed to a solution of alginate, can be used to generate a coated layer around the tissue. We further show that this technique can be fine-tuned by adjusting numerous parameters, granting us full control over the thickness of the hydrogel coating layer. The microtissues’ protective hydrogel functioned as hypothesized in both in vitro and in vivo testing by preventing the cells inside the tissue from escaping and protecting the microdroplets against external degradation. This technology may provide microtissues with customized properties for use as sources of regenerative therapies.