bluuebunny/crossref_metadata_2025 huggingface dataset:
The Influence of Physical Characteristics of Wet Wipe Fabrics on the Microbial Biomass Accumulation
Authors: Ziklo Noa , Yuli Idit , Bibi Maayan , Salama Paul
Published: June 2024

The physicochemical properties of nonwoven wet wipe fabrics have a strong influence on the ability of microorganisms to attach and multiply, until a biofilm is formed. Cellulose-based fabrics, being biodegradable, represent a major contamination risk. In addition, having a hydrophilic nature, they provide a good platform for microorganisms attachment. To optimize biodegradable wet wipes antimicrobial quality, it is crucial to assess the impact of physicochemical properties, e.g., density, pore size, fiber diameter, contact angle and surface charge. Here, we investigated the physical characteristics of commonly used nonwoven fabrics from both synthetic (Polyethylene terephthalate, PET) and natural components (wood pulp and viscose), to evaluate their effect on microbial contamination. We found that the hydrophobicity of the fabric had varying influence on attachment, depending on the microbial strain. However, the geometry, as well as the fabric pore size greatly affected attachment regardless of the microbial strain, in which a larger pore size resulted in lower accumulation of microbial biomass. Our study gives insight into the characteristics of wet wipes that can affect the preservation efficacy and microbial contamination risk, in one of the biggest segments in the personal care industry.
Palynological Record of the Aalenian–Bajocian Cooling Event from the Santanghu Basin, Northwest China
Authors: Yang Bing , Zhang Xinzhi , Li Weitong , Sun Siyuan , Yi Jinjun
Published: June 2024

The Aalenian–Bajocian (early Middle Jurassic) cooling event (ABCE) was a significant global climate disturbance during the Jurassic. Our analysis of sporopollen fossils from 18 mudstone and silty mudstone samples, collected from the lacustrine-terrestrial succession Xishanyao Formation in the Santanghu Basin, Northwest China, revealed a total of 191 species belonging to 53 genera. We identified an assemblage, the Cyathidites–Deltoidospora–Osmundacidites–Cycadopites assemblage, which dates to the Aalenian–Bajocian (early Middle Jurassic). This assemblage can be further divided into three subassemblages in stratigraphic order: the Cyathidites–Osmundacidites–Cycadopites subassemblage, the Cyathidites–Cycadopites–Psophosphaera subassemblage, and the Cyathidites–Deltoidospora–Osmundacidites subassemblage. We applied the Sporomorph EcoGroup (SEG) model to interpret the paleoclimate features. The sporopollen fossils indicate that the Santanghu Basin underwent a shift in vegetation types, from ground cover vegetation as the dominant form to canopy trees and then back to ground cover vegetation as the primary vegetation during the Aalenian–Bajocian. The SEG model analysis demonstrates that the CCP subassemblage is characterized by a low lowland SEG/upland SEG ratio, low wetter/drier ratio within the lowland SEG, and a low warmer/cooler ratio within the lowland SEG. These characteristics reflect the vegetation’s response to the ABCE in the Santanghu Basin.
Can Machine Learning Assist in Diagnosis of Primary Immune Thrombocytopenia? A Feasibility Study
Authors: Miah Haroon , Kollias Dimitrios , Pedone Giacinto Luca , Provan Drew , Chen Frederick
Published: June 2024

Primary Immune Thrombocytopenia (ITP) is a rare autoimmune disease characterised by the immune-mediated destruction of peripheral blood platelets in patients leading to low platelet counts and bleeding. The diagnosis and effective management of ITP are challenging because there is no established test to confirm the disease and no biomarker with which one can predict the response to treatment and outcome. In this work, we conduct a feasibility study to check if machine learning can be applied effectively for the diagnosis of ITP using routine blood tests and demographic data in a non-acute outpatient setting. Various ML models, including Logistic Regression, Support Vector Machine, k-Nearest Neighbor, Decision Tree and Random Forest, were applied to data from the UK Adult ITP Registry and a general haematology clinic. Two different approaches were investigated: a demographic-unaware and a demographic-aware one. We conduct extensive experiments to evaluate the predictive performance of these models and approaches, as well as their bias. The results revealed that Decision Tree and Random Forest models were both superior and fair, achieving nearly perfect predictive and fairness scores, with platelet count identified as the most significant variable. Models not provided with demographic information performed better in terms of predictive accuracy but showed lower fairness scores, illustrating a trade-off between predictive performance and fairness.
Correction: Chatzimichail, T.; Hatjimihail, A.T. A Software Tool for Estimating Uncertainty of Bayesian Posterior Probability for Disease. Diagnostics 2024, 14, 402
Authors: Chatzimichail Theodora , Hatjimihail Aristides T.
Published: June 2024

There was an error in the original publication [...]
Persisting Hypercalcemia and Hyperparathyroidism after Kidney Transplantation Have a Negative Impact on Graft and Patient Survival
Authors: Egli Hannes , Burla Naomi , Breuer Eva , Baron Camilla , Hübel Kerstin , de Rougemont Olivier , Seeger Harald , Vetter Diana
Published: June 2024

Hyperparathyroidism (HPT) with hypercalcemia, often deemed irreversible and detrimental to graft survival post-kidney transplantation (KT), prompts pre-transplant parathyroidectomy in hypercalcemic patients. In this retrospective analysis of 1212 kidney transplant recipients (KTRs) between 2006 and 2019, the incidence and effect of persistent HPT and hypercalcemia on graft and patient survival, and risk factors for persistence were analyzed until 60 months of follow up (FU). At KT, 5.7% (n = 69) had no HPT, 32.7% (n = 396) had HPT without hypercalcemia and 37.0% (n = 448) had HPT with hypercalcemia. At 2 years FU, 26.4% (n = 320) of patients had no HPT and 6% (n = 73) had HPT with hypercalcemia. Dialysis and dialysis duration were linked to HPT development, while dialysis, KT waiting time and donor type correlated with persisting hypercalcemia after KT. KTRs with normalized PTH and recovered hypercalcemia had improved death-censored graft survival (p < 0.001) and overall patient survival (p < 0.001). HPT with hypercalcemia is frequent at time of KT with normalization of PTH and calcium in a substantial proportion of patients after a KT. These findings question the routine pre-KT parathyroidectomy for suspected parathyroid autonomy. Persisting HPT, especially with hypercalcemia, adversely affects graft and patient survival, suggesting the need for more aggressive treatment of HPT, especially in cases of persisting hypercalcemia.
An Intelligent Mechanism to Detect Multi-Factor Skin Cancer
Authors: Abdullah Abdullah , Siddique Ansar , Shaukat Kamran , Jan Tony
Published: June 2024

Deep learning utilizing convolutional neural networks (CNNs) stands out among the state-of-the-art procedures in PC-supported medical findings. The method proposed in this paper consists of two key stages. In the first stage, the proposed deep sequential CNN model preprocesses images to isolate regions of interest from skin lesions and extracts features, capturing the relevant patterns and detecting multiple lesions. The second stage incorporates a web tool to increase the visualization of the model by promising patient health diagnoses. The proposed model was thoroughly trained, validated, and tested utilizing a database related to the HAM 10,000 dataset. The model accomplished an accuracy of 96.25% in classifying skin lesions, exhibiting significant areas of strength. The results achieved with the proposed model validated by evaluation methods and user feedback indicate substantial improvement over the current state-of-the-art methods for skin lesion classification (malignant/benign). In comparison to other models, sequential CNN surpasses CNN transfer learning (87.9%), VGG 19 (86%), ResNet-50 + VGG-16 (94.14%), Inception v3 (90%), Vision Transformers (RGB images) (92.14%), and the Entropy-NDOELM method (95.7%). The findings demonstrate the potential of deep learning, convolutional neural networks, and sequential CNN in disease detection and classification, eventually revolutionizing melanoma detection and, thus, upgrading patient consideration.
Femicide Circumstances and Harmfulness: Case Report and Focusing Review
Authors: Stassi Chiara , La Mantia Marco , Lo Re Giuseppe Francesco , Martines Valentina , Zerbo Stefania , Albano Giuseppe Davide , Malta Ginevra , Argo Antonina
Published: June 2024

(1) Background: Femicide is an increasing phenomenon consisting of the murder of a woman for gender-related reasons. Despite the enactment of new laws aimed at controlling the phenomenon by toughening the penalties and introducing aggravating circumstances, there is an increasing trend that testifies to the persistence of a flaw in the actual measures. (2) Case Presentation. We report the case of the murder of a 32-year-old woman—perpetrated by an ex-husband who refused to accept the end of the marriage—the analysis of which allowed us to frame the case as femicide. (3) Discussion. Despite global awareness of this phenomenon, the identification of risk factors to predict and prevent femicide is of utmost importance. This can be achieved by a multidisciplinary approach involving police officers, legal professionals, hospitals, governmental and nongovernmental organizations, and medico-legal departments aimed at promoting standardized methodologies. (4) Conclusions. We evaluate the contribution of forensic investigations to the identification of key elements that can help frame the murder of a woman as a femicide. Considering the devastating consequences for children who witness this kind of violence within the domestic setting, the planning of more impactful preventive actions is, thus, mandatory to minimize effects on public health.
CBCT in Dental Implantology: A Key Tool for Preventing Peri-Implantitis and Enhancing Patient Outcomes
Authors: Hussaini Souheil , Glogauer Michael , Sheikh Zeeshan , Al-Waeli Haider
Published: June 2024

(1) Introduction: Trust is a cornerstone of the patient–physician relationships. Unforeseen complications in the health care system could jeopardize patients’ trust in their physicians. (2) Aim: This article presents a quantitative figure regarding foreseeing the necessity of a three-dimensional quantitative visualization of bone structure and concurrently preparing for an ancillary procedure by a dentist to successfully perform the surgery that could minimize unforeseen complications; (3) Materials and method: This retrospective study has been derived based on an analysis of 1134 patients who had received 4800 dental implants from January 2001 to August 2020, out of which 200 cases were randomly selected for this study. Each procedure during implant treatment was categorized as OPG (Orthopantomography) or OPG with CBCT as per all the procedures which included and were coded as follows, 1: Surgery & Restoration, 2: GBR (Guided Bone Regeneration), 3: GTR (Guided Tissue Regeneration), 4: Block Bone Graft, 5: Spreading, 6: Splitting, 7: Internal Sinus, 8: External Sinus, 9: PRF (Platelet Rich Fibrin). Any of the 200 cases in which implant placement could not have been performed for reasons related to a lack of CBCT were selected for this study. The surgery was aborted halfway through without implant placement in these cases due to a lack of bone quantity and/or lack of primary stability. These cases were registered for re-evaluation and statistical analysis; (4) Results: 7% of the cases that used OPG alone led the surgeon to unexpectedly abort in the middle of the surgery without implant placement. All (100%) of the patients who had CBCT during treatment planning were able to receive implants during the surgery. None of the patients left the surgery without receiving implants if CBCT was used (0%); (5) Discussion: Radiographic image quality is defined as the amount of information within the image that allows the radiologist to make a diagnostic decision with a particular level of certainty (Martin et al., 1999) and hence the importance of CBCT. The unexpected 7% of devastating situations for patients who started surgery but did not have implant placement led to [A] aborting the surgery, [B] procedural difficulties requiring an alternative treatment plan, [C] a negative impact on the patient’s behavior, and [D] wanting to change doctor due to a lack of trust; (6) Conclusion: This study indicates that in implant dentistry patients’ mistrust could be avoided by 7% if CBCT is obtained. It also shows the significance of cone-beam computed tomography as an adjunct to panoramic radiography during the diagnosis and treatment planning phase. The use of panoramic radiography alone can lead to a 7% likelihood of misdiagnosis. A lack of CBCT during treatment planning negatively affects the outcome of surgical procedures.
Experimental Identification of the Translational Dynamics of a Novel Two-Layer Octocopter
Authors: Elhesasy Mohamed , Khader Rashed , Dief Tarek N. , Kamra Mohamed M. , Okasha Mohamed , Alnuaimi Saeed K.
Published: June 2024

This paper proposes a systematic approach for identifying the translational dynamics of a novel two-layer octocopter. Initially, we derive the non-linear theoretical dynamic model of the conventional octocopter using the Newton–Euler formulation, aimed at obtaining a simplified model suitable for tuning PID gains necessary for controller implementation. Following this, a controller is designed and tested in the Matlab/Simulink environment to ensure stable flight performance of the octocopter. Subsequently, the novel octocopter prototype is developed, fabricated, and assembled, followed by a series of outdoor flight tests conducted under various environmental conditions to collect data representing the flight characteristics of the two-layer vehicle in different scenarios. Based on the data recorded during flights, we identify the transfer functions of the translational dynamics of the modified vehicle using the prediction error method (PEM). The empirical model is then validated through different flight tests. The results presented in this study exhibit a high level of agreement and demonstrate the efficacy of the proposed approach to predict the octocopter’s position based only on motor inputs and initial states of the system. Despite the inherent non-linearity, significant aerodynamic interactions, and strongly coupled nature of the system, our findings highlight the robustness and reliability of the proposed approach, which can be used to identify the model of any type of multi-rotor or fixed-wing UAV, specifically when you have a challenging design.
Adaptive Joint Carrier and DOA Estimations of FHSS Signals Based on Knowledge-Enhanced Compressed Measurements and Deep Learning
Authors: Jiang Yinghai , Liu Feng
Published: June 2024

As one of the most widely used spread spectrum techniques, the frequency-hopping spread spectrum (FHSS) has been widely adopted in both civilian and military secure communications. In this technique, the carrier frequency of the signal hops pseudo-randomly over a large range, compared to the baseband. To capture an FHSS signal, conventional non-cooperative receivers without knowledge of the carrier have to operate at a high sampling rate covering the entire FHSS hopping range, according to the Nyquist sampling theorem. In this paper, we propose an adaptive compressed method for joint carrier and direction of arrival (DOA) estimations of FHSS signals, enabling subsequent non-cooperative processing. The compressed measurement kernels (i.e., non-zero entries in the sensing matrix) have been adaptively designed based on the posterior knowledge of the signal and task-specific information optimization. Moreover, a deep neural network has been designed to ensure the efficiency of the measurement kernel design process. Finally, the signal carrier and DOA are estimated based on the measurement data. Through simulations, the performance of the adaptively designed measurement kernels is proved to be improved over the random measurement kernels. In addition, the proposed method is shown to outperform the compressed methods in the literature.
A Dynamic Entropy Approach Reveals Reduced Functional Network Connectivity Trajectory Complexity in Schizophrenia
Authors: Blair David Sutherland , Miller Robyn L. , Calhoun Vince D.
Published: June 2024

Over the past decade and a half, dynamic functional imaging has revealed low-dimensional brain connectivity measures, identified potential common human spatial connectivity states, tracked the transition patterns of these states, and demonstrated meaningful transition alterations in disorders and over the course of development. Recently, researchers have begun to analyze these data from the perspective of dynamic systems and information theory in the hopes of understanding how these dynamics support less easily quantified processes, such as information processing, cortical hierarchy, and consciousness. Little attention has been paid to the effects of psychiatric disease on these measures, however. We begin to rectify this by examining the complexity of subject trajectories in state space through the lens of information theory. Specifically, we identify a basis for the dynamic functional connectivity state space and track subject trajectories through this space over the course of the scan. The dynamic complexity of these trajectories is assessed along each dimension of the proposed basis space. Using these estimates, we demonstrate that schizophrenia patients display substantially simpler trajectories than demographically matched healthy controls and that this drop in complexity concentrates along specific dimensions. We also demonstrate that entropy generation in at least one of these dimensions is linked to cognitive performance. Overall, the results suggest great value in applying dynamic systems theory to problems of neuroimaging and reveal a substantial drop in the complexity of schizophrenia patients’ brain function.
EFE-CNA Net: An Approach for Effective Image Deblurring Using an Edge-Sensitive Focusing Encoder
Authors: Zheng Fengbo , Zhang Xiu , Jiang Lifen , Liang Gongbo
Published: June 2024

Deep learning-based image deblurring techniques have made great advancements, improving both processing speed and deblurring efficacy. However, existing methods still face challenges when dealing with complex blur types and the semantic understanding of images. The segment anything model (SAM), a versatile deep learning model that accurately and efficiently segments objects in images, facilitates various tasks in computer vision. This article leverages SAM’s proficiency in capturing object edges and enhancing image content comprehension to improve image deblurring. We introduce the edge-sensitive focusing encoder (EFE) module, which utilizes masks generated by the SAM framework and re-weights the masked portion following SAM segmentation by detecting its features and high-frequency information. The EFE module uses the masks to locate the position of the blur in an image while identifying the intensity of the blur, allowing the model to focus more accurately on specific features. Masks with greater high-frequency information are assigned higher weights, prompting the network to prioritize them during processing. Based on the EFE module, we develop a deblurring network called the edge-sensitive focusing encoder-based convolution–normalization and attention network (EFE-CNA Net), which utilizes the EFE module to enhance the deblurring process, employs an image-mask decoder to merge features from both the image and the mask from the EFE module, and incorporates the CNA Net as its base network. This design enables the model to focus on distinct features at various locations, enhancing its learning process through the guidance provided by the EFE module and the blurred images. Testing results on the RealBlur and REDS datasets demonstrate the effectiveness of the EFE-CNA Net, achieving peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) metrics of 28.77, 0.902 (RealBlur-J), 36.40, 0.956 (RealBlur-R), 31.45, and 0.919 (REDS).
Detection of Track Bed Defects Based on Fibre Optic Sensor Signals and an Improved Hidden Markov Model
Authors: Li Wenya , He Lang , Li Zhengying , Wan Yuan
Published: June 2024

Railway track bed defects affect the normal operation of trains and pose great safety risks. In order to detect such issues early, we developed a railway track bed defect detection method which uses optical fibre sensors and an improved HMM (hidden Markov model) to detect the signals collected by a DAS (distributed acoustic sensing) system. First, by analysing the physical process of train operation and determining the number of hidden states, a waveform segmentation method based on average amplitude was used to solve the problem of unequal signal lengths. Second, an adaptive power spectrum energy ratio calculation method was employed to extract track fault features, a set of which was constructed by combining various quantity features. Then, normal and abnormal models were trained according to the sensor measurement area. Finally, the probability of detecting the signal with each model was compared to determine whether the signal was abnormal. Experiments were conducted to compare the applicability of the waveform segmentation method and the feature extraction method. The results show that the HMM based on both waveform segmentation and track bed defect feature sets had the highest recognition rate, the lowest number of false detection areas, and a greater impact on the signal in the early development stage of track bed defects. The proposed method, therefore, has strong recognition ability, which makes it suitable for track bed defect detection.
Inkjet-Printed Reflectarray Antenna Integrating Feed and Aperture on a Flexible Substrate Using Origami Techniques
Authors: Lin Yi-Xin , Ko Kuan-Yu , Lai Fei-Peng , Chen Yen-Sheng
Published: June 2024

This paper presents an innovative method for fabricating reflectarray antennas using inkjet printing technology on flexible substrates, markedly enhancing integration and manufacturability compared to traditional PCB methods. The technique employs inkjet printing to deposit conductive inks directly onto a flexible polyethylene naphthalate (PEN) substrate, seamlessly integrating feed and reflectarray components without complex assembly processes. This streamlined approach not only reduces manufacturing complexity and costs but also improves mechanical flexibility, making it ideal for applications requiring deployable antennas. The design process includes an origami-inspired folding of the substrate to achieve the desired three-dimensional antenna structures, optimizing the focal length to dimension ratio (F/D) to ensure maximum efficiency and performance. The feed and the reflectarray geometry are optimized for an F/D of 0.6, which achieves high gain and aperture efficiency, demonstrated through detailed simulations and measurements. For normal incidence, the configuration achieves a peak gain of 9.3 dBi and 48% radiation efficiency at 10 GHz; for oblique incidence, it achieves 7.3 dBi and 40% efficiency. The study underscores the significant potential of inkjet-printed antennas in terms of cost-efficiency, precision, and versatility, paving the way for new advancements in antenna technology with a substantial impact on future communication systems.
Transforming Color: A Novel Image Colorization Method
Authors: Shafiq Hamza , Lee Bumshik
Published: June 2024

This paper introduces a novel method for image colorization that utilizes a color transformer and generative adversarial networks (GANs) to address the challenge of generating visually appealing colorized images. Conventional approaches often struggle with capturing long-range dependencies and producing realistic colorizations. The proposed method integrates a transformer architecture to capture global information and a GAN framework to improve visual quality. In this study, a color encoder that utilizes a random normal distribution to generate color features is applied. These features are then integrated with grayscale image features to enhance the overall representation of the images. Our method demonstrates superior performance compared with existing approaches by utilizing the capacity of the transformer, which can capture long-range dependencies and generate a realistic colorization of the GAN. Experimental results show that the proposed network significantly outperforms other state-of-the-art colorization techniques, highlighting its potential for image colorization. This research opens new possibilities for precise and visually compelling image colorization in domains such as digital restoration and historical image analysis.
Optimizing Biomass Supply Chains to Power Plants under Ecological and Social Restrictions: Case Study from Poland
Authors: Banaś Jan , Utnik-Banaś Katarzyna , Zięba Stanisław
Published: June 2024

The growing demand for social and regulatory forest ecosystem services can significantly modify the availability and cost of biomass for energy purposes. This article presents a model for optimizing biomass supply chains using a linear programming framework integrated with a geographic information system (GIS). Based on a given type of biomass resource, its calorific value, price, distance from the power plant, and transportation costs, the model identifies the optimal source of biomass, allowing it to cover the demand for the required total energy value with the lowest possible costs. The case study includes the Połaniec power plant in southeastern Poland and potential sources of forest biomass and agricultural straw within 100 km of the plant. The impact of constraints on the availability and cost of biomass was analyzed in the following scenarios: (1) all forest and agriculture biomass is available, (2) forest area in Natura 2000 network is excluded, and (3) firewood and forests with dominated ecological and social function are excluded. Unit costs of biomass varied depending on biomass availability and energy demands. The lowest unit costs of biomass (3.19 EUR/MJ) were for energy demand at the level of 1 TJ yearly for all kinds of biomass and the highest (4.91 EUR/MJ) for ecological and social constraints and energy demand 4 TJ. As energy demand increased, unit costs increased, and the ability to meet this demand with just one type of biomass decreased. The energy biomass sector can utilize the model to benefit both biomass producers and their final buyers.
Kerr Electro-Optic Effect-Based Methodology for Measuring and Analyzing Electric Field Distribution in Oil-Immersed Capacitors
Authors: Xing Zhaoliang , Ge Hao , Zeng Fanqiu , Guo Shaowei , Gao Chunjia
Published: June 2024

In the current design and verification processes of insulation structures for high-voltage oil-immersed capacitors, there is a heavy reliance on electric field simulation calculations using idealized models that lack empirical validation of spatial electric fields. This study employs the Kerr electro-optic effect to establish a non-contact optical remote sensing system for measuring the spatial electric field distribution in the insulating liquid dielectric (benzyltoluene) between the capacitor’s element and the case under various temperatures and main insulation distances. The findings reveal that the measured spatial electric field stress can be up to 15% higher than the simulated values. The electric field stress measured in the Y1 direction (up toward the capacitor top) is comparable to that measured in the Y2 direction (down toward the capacitor end). Furthermore, when varying the main insulation distance, the electric field stress consistently shows a negative correlation with increasing measurement distance. Specifically, at a main insulation distance of 1.5 mm, the electric field stress is 1.81 times that at 5.5 mm. As the temperature rises, the spatial electric field stress increases gradually, and the electric field distribution becomes more uneven at higher temperatures. At 80 °C, the field stress is approximately 1.57 times that at 20 °C, with the measured field stress at 80 °C being 19% higher than the simulated value. Finally, this paper undertakes a comprehensive theoretical analysis and experimental validation to elucidate the discrepancies between simulated and measured spatial electric fields. Leveraging these insights, it proposes advanced optimization strategies for the insulation structures of capacitor elements. The outcomes of this study furnish substantial technical and theoretical support, significantly enhancing the design, verification, and optimization processes for insulation in oil-immersed capacitors.
A Water–Energy–Carbon–Economy Framework to Assess Resources and Environment Sustainability: A Case Study of the Yangtze River Economic Belt, China
Authors: Zhu Hua , Zhang Qing , You Hailin
Published: June 2024

Developing a comprehensive research framework that integrates the water–energy–carbon (WEC) system with economic development is crucial to fostering sustainable development. However, common evaluation indicators for sustainable development fail to cover the most up-to-date climate objectives and policies comprehensively and also lack a decoupling analysis between various subsystems and economic development. By incorporating the Tapio model and the coupling coordination degree model (CCDM), we introduce a novel water–energy–carbon–economy (WECE) framework to evaluate the sustainability of regional resources and the environment. Taking the Yangtze River Economic Belt (YREB) as an example, we have constructed a comprehensive water–energy–carbon (WEC) indicator system that aligns with China’s sustainable development objectives and its most recent carbon emission reduction strategies. Employing the indicator system, we conducted an assessment of the sustainable development within the YREB from 2010 to 2019. The results reveal that the YREB has yet to achieve full decoupling between water use, energy consumption, carbon emissions, and economic development, with a prevailing trend towards weak decoupling (WD). The WEC system within the YREB exhibited coordination from 2010 to 2019. Notably, only the WEC system in Sichuan attained good coordination in 2019, indicating the imperative for more extensive initiatives in resource and environmental development to realize sustainable objectives. Finally, we delve into the driving mechanism of the coupling coordination degree (CCD) of the WEC system. Our findings suggest that, from the perspective of system collaborative management, the integrated approach of the WEC system offers superior benefits compared to individual management components. Consequently, it is imperative to bolster collaboration and institute a comprehensive set of policies to ensure sustainable development within the region.
Optimization of the Joint Operation of an Electricity–Heat–Hydrogen–Gas Multi-Energy System Containing Hybrid Energy Storage and Power-to-Gas–Combined Heat and Power
Authors: Yang Jun , Zeng Linjun , He Kangjie , Gong Yongguo , Zhang Zhenhua , Chen Kun
Published: June 2024

With the continuous development of hydrogen storage systems, power-to-gas (P2G) and combined heat and power (CHP), the coupling between electricity–heat–hydrogen–gas has been promoted and energy conversion equipment has been transformed from an independent operation with low energy utilization efficiency to a joint operation with high efficiency. This study proposes a low-carbon optimization strategy for a multi-energy coupled IES containing hydrogen energy storage operating jointly with a two-stage P2G adjustable thermoelectric ratio CHP. Firstly, the hydrogen energy storage system is analyzed to enhance the wind power consumption ability of the system by dynamically absorbing and releasing energy at the right time through electricity–hydrogen coupling. Then, the two-stage P2G operation process is refined and combined with the CHP operation with an adjustable thermoelectric ratio to further improve the low-carbon and economic performance of the system. Finally, multiple scenarios are set up, and the comparative analysis shows that the addition of a hydrogen storage system can increase the wind power consumption capacity of the system by 4.6%; considering the adjustable thermoelectric ratio CHP and the two-stage P2G, the system emissions reduction can be 5.97% and 23.07%, respectively, and the total cost of operation can be reduced by 7.5% and 14.5%, respectively.
Solar Radiation Forecasting: A Systematic Meta-Review of Current Methods and Emerging Trends
Authors: Chodakowska Ewa , Nazarko Joanicjusz , Nazarko Łukasz , Rabayah Hesham S.
Published: June 2024

Effective solar forecasting has become a critical topic in the scholarly literature in recent years due to the rapid growth of photovoltaic energy production worldwide and the inherent variability of this source of energy. The need to optimise energy systems, ensure power continuity, and balance energy supply and demand is driving the continuous development of forecasting methods and approaches based on meteorological data or photovoltaic plant characteristics. This article presents the results of a meta-review of the solar forecasting literature, including the current state of knowledge and methodological discussion. It presents a comprehensive set of forecasting methods, evaluates current classifications, and proposes a new synthetic typology. The article emphasises the increasing role of artificial intelligence (AI) and machine learning (ML) techniques in improving forecast accuracy, alongside traditional statistical and physical models. It explores the challenges of hybrid and ensemble models, which combine multiple forecasting approaches to enhance performance. The paper addresses emerging trends in solar forecasting research, such as the integration of big data and advanced computational tools. Additionally, from a methodological perspective, the article outlines a rigorous approach to the meta-review research procedure, addresses the scientific challenges associated with conducting bibliometric research, and highlights best practices and principles. The article’s relevance consists of providing up-to-date knowledge on solar forecasting, along with insights on emerging trends, future research directions, and anticipating implications for theory and practice.
Comparative Analysis of Machine Learning Techniques in Predicting Wind Power Generation: A Case Study of 2018–2021 Data from Guatemala
Authors: Carrera Berny , Kim Kwanho
Published: June 2024

The accurate forecasting of wind power has become a crucial task in renewable energy due to its inherent variability and uncertainty. This study addresses the challenge of predicting wind power generation without meteorological data by utilizing machine learning (ML) techniques on data from 2018 to 2021 from three wind farms in Guatemala. Various machine learning models, including Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BiLSTM), Bagging, and Extreme Gradient Boosting (XGBoost), were evaluated to determine their effectiveness. The performance of these models was assessed using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) metrics. Time series cross-validation was employed to validate the models, with GRU, LSTM, and BiLSTM showing the lowest RMSE and MAE. Furthermore, the Diebold–Mariano (DM) test and Bayesian model comparison were used for pairwise comparisons, confirming the robustness and accuracy of the top-performing models. The results highlight the superior accuracy and robustness of advanced neural network architectures in capturing the complex temporal dependencies in wind power data, making them the most reliable models for precise forecasting. These findings provide critical insights for enhancing grid management and operational planning in the renewable energy sector.
Pb Pollution Stress in Alnus cremastogyne Monitored by Antioxidant Enzymes
Authors: Zhao Jiaheng , Hu Hongling , Gao Shun , Chen Gang , Zhang Chenghao , Deng Wen , Li Chuang
Published: June 2024

Lead (Pb) is a common toxic heavy metal element that can be absorbed by plant roots and enter the food chain, damaging human health. Alnus cremastogyne has a wide native range, is fast growing, has a wide range of timber uses, and has rhizomatous roots that can improve planted soils. In this study, we evaluated whether Alnus cremastogyne has the potential to remediate Pb-contaminated soils through a 6-month pot experiment in soils with different Pb concentrations (0, 50, 100, 200, and 400 mg/kg). Our results indicate that Alnus cremastogyne plant height, basal diameter, and organ biomass decreased, H2O2 and MDA content increased, and the activities of antioxidant enzymes and osmotic regulators increased and then decreased with increasing lead concentrations. The Pb bioconcentration factor of Alnus cremastogyne was less than 1 at all Pb concentrations, and Pb accumulated mainly in the root system. This indicates that Alnus cremastogyne is not a Pb-enriched plant and does not have outstanding Pb transport capacity. The growth of Alnus cremastogyne was not significantly affected at low Pb concentrations, and its plant height, basal diameter, and biomass were significantly suppressed under high Pb stress. Therefore, Alnus cremastogyne is not a suitable species for the remediation of lead-contaminated soils but can be used as a silvicultural species in environments with low lead levels.
Wood Density and Carbon Concentration Jointly Drive Wood Carbon Density of Five Rosaceae Tree Species
Authors: Guo Pingping , Zhao Xiping , Wang Xingchang , Feng Qi , Li Xinjing , Tan Yangyang
Published: June 2024

Wood can store carbon and help mitigate global climate change. Carbon density (CD), the basis for measuring and analyzing C storage, is the product of wood density (WD) and C concentration, which are dependent on wood structure, cellulose concentration (CC), hemicellulose concentration (HC), and lignin concentration (LC). However, little attention has been paid to the C concentration of cellulose, hemicellulose, and lignin, which are fundamental factors in C storage and affect the credibility of accurate CD estimates. In order to disentangle the CD drives, WD, C concentration, CC, HC, and LC of the branch, stem, and root were quantified for five Rosaceae species from temperate forests in Northeastern China. The species were Sorbus alnifolia (Sieb.et Zucc.) K. Koch, Pyrus ussuriensis Maxim., Malus baccata (L.) Borkh., Crataegus pinnatifida var. major N. E. Brown, and Padus racemosa (Linn.) Gilib. The WD, CC, HC, and LC differed among species and tree organs, with the highest variability for the HC. The structural carbon concentration (SCC) was lower than the organic carbon concentration (OCC) and even the Intergovernmental Panel on Climate Change (IPCC) default value of 45%, with a maximum deviation of 2.6%. CD differed dramatically among species and tree organs. Based on SCC calculations, the highest CD was found in Sorbus alnifolia root (0.27 × 106 g/m3), while the lowest was found in Padus racemosa branch (0.22 × 106 g/m3). The results suggest that when estimating CD accurately at species level, it is important to consider not only WD but also structural carbohydrates and lignin concentration, providing important information on C fluxes and long-term C sequestration for forests. The study findings provide valuable insights into CD variations among tree species and organs and are valuable for forest management and policy development to improve carbon sequestration.
Inhibitor Tolerance Capacity of Pichia kudriavzevii NBRC1279 and NBRC1664
Authors: Akita Hironaga , Matsushika Akinori
Published: June 2024

The thermotolerant yeast Pichia kudriavzevii (previously known as Issatchenkia orientalis), can produce ethanol from a variety of carbon sources and grows at around 45 °C. Thus, this yeast is considered a useful biocatalyst for producing ethanol from lignocellulose through simultaneous saccharification and fermentation (SSF). SSF has several advantages, such as a simplified manufacturing process, ease of operation and reduced energy input. Using P. kudriavzevii NBRC1279 and NBRC1664, we previously succeeded in producing ethanol through SSF; however, the extent to which inhibitors by-produced from lignocellulose hydrolysis affect the growth and ethanol productivity of the two strains remains to be investigated. In this study, to better understand the inhibitor tolerance capacity of the two strains, spot assay, growth experiment, real-time quantitative PCR (RT-qPCR) analysis and multiple sequence alignment analysis were carried out. When P. kudriavzevii NBRC1279 and NBRC1664, as well as Saccharomyces cerevisiae BY4742 as a control, were cultured on SCD plates containing 17% ethanol, 42 mM furfural, 56 mM 5-hydroxymethylfurfural (HMF) or 10 mM vanillin, only P. kudriavzevii NBRC1664 was able to grow under all conditions. Moreover, the inhibitor tolerance capacity of P. kudriavzevii NBRC1664 was greater than those of other strains using SCD medium containing the same concentrations of various inhibitors. When an RT-qPCR analysis of seven gene sequences from aldehyde dehydrogenase and the aldehyde dehydrogenase family protein (ADHF) was performed using P. kudriavzevii NBRC1664 cultivated in the presence of 56 mM HMF, ADHF1 and ADHF2 were up-regulated in the early logarithmic growth phase. Moreover, a multiple sequence alignment of the amino acid sequences of ADHF1, ADHF2 and the known ADH suggested that ADHF1 and ADHF2 may catalyze the reversible NAD+-dependent oxidation of HMF. Our data may be useful for future studies on the metabolic engineering of more useful strains for ethanol production from lignocellulose.
Correlation between Microorganisms and Volatile Compounds during Spontaneous Fermentation of Sour Bamboo Shoots
Authors: Zhang Xinyuan , Wang Qiong , Xie Yuanhong , Zhang Hongxing , Jin Junhua , Xiong Yong , Pang Xiaona , Vriesekoop Frank
Published: June 2024

Chinese sour bamboo shoot is a traditionally, spontaneous fermented food that is particularly popular due to its complex and distinctive flavor. The volatile compounds of sour bamboo shoot originate mainly from the raw materials and the microbial fermentation. To reveal the correlation between microorganisms and flavor, third-generation sequencing and Gas Chromatography-Ion Mobility Spectrometry were applied to analyze the dynamics of microbial communities at the species level and volatile compounds during sour bamboo shoot fermentation. The abundance of Lactobacillus acetotolerans and Lactobacillus fermentum increased during the fermentation, while Lactobacillus amyloliquefaciens decreased at first but then began to rise. At the end of fermentation, Lactobacillus amyloliquefaciens and Lactobacillus acetotolerans became the predominant species. A total of sixty-seven volatile compounds, which included twenty-three esters, nineteen alcohols, eight ketones, six aldehydes, six aromatic hydrocarbons, four acids and one ether, were identified. These compounds constituted the primary flavor of sour bamboo, which created a complex flavor of sour bamboo shoot. Among them, the contents of acetic acid, propionic acid, and isoamyl alcohol gradually increased during the fermentation process, and they became the main volatile compounds. Furthermore, the correlation between microorganisms and volatile compounds was investigated through two-way Orthogonal Partial Least Squares (O2PLS), which revealed a positive correlation between Lactobacillus amylolyticus and ethyl propanoate. Additionally, the abundance of Lactobacillus acetotolerans and Lactobacillus fermentum was found to be positively correlated with 2-heptenal. These findings provide a theoretical basis for understanding the formation mechanism of sour bamboo shoot flavor and the standardized production of high-quality sour bamboo shoots.
Transforming Network Management: Intent-Based Flexible Control Empowered by Efficient Flow-Centric Visibility
Authors: Risdianto Aris Cahyadi , Usman Muhammad , Rathore Muhammad Ahmad
Published: June 2024

The Internet architecture has recently shifted towards a framework characterized by multiple interconnected cloud sites, all linked via an L3 IP network. With this shift, managing networking controls among multiple cloud sites is becoming a significant operational challenge. In particular, ensuring effective networking control necessitates a deeper understanding of flow-level dynamics for comprehensively monitoring interconnection statuses across multiple sites. In this paper, we first propose an IO Visor-enabled tracing solution for Linux-based boxes to efficiently enable the comprehensive collection of network packet flows across interconnected sites. Next, we apply IP prefix-based flow-level analysis at a centralized location to support the intent-based networking control application. This flow-level analysis involves generating policy-based specific action (i.e., redirect) via SDN controllers for specific source IP prefixes, which are causing unknown or potentially vulnerable flows. Furthermore, we employ an open-source ONOS SDN controller to tackle the challenge of managing the hybrid SDN-IP interconnections. By leveraging intent-based networking control, we effectively apply ONOS intents based on IP routing information and generated a set of forwarding action. We evaluate our proposed solution in an experimental SDN-cloud testbed, demonstrating its effectiveness in real-world scenarios. Overall, through the seamless integration of these monitoring and control approaches, we manage to enhance the adaptability and security of the interconnected cloud sites of the testbed.
Influence of Nanoparticles and PVA Fibers on Concrete and Mortar on Microstructural and Durability Properties
Authors: Sridhar Radhika , Aosai Pakjira , Imjai Thanongsak , Setkit Monthian , Shirkol Anoop , Laory Irwanda
Published: June 2024

Nanoparticles are one of the effective methodologies implemented in concrete technology. The main objective of this research is to study the influence of nano alumina with different percentage variations ranging from 1% to 3% along with the incorporation of PVA fibers. From the mechanical properties test, the optimum dosage was determined to further study the durability behavior. This research work also investigates the hybridization of two nanoparticles such as nano silica (NS) and nano alumina (NA). The results show that the increasing quantity of NA reduces the compressive strength of the mortar due to agglomeration (cluster of particles), which results in a greater molecular attraction force. From the test results, it is concluded that the optimum dosage has been attained with an addition of 2% NA with 0.3% PVA. The compression strength test results at 14 days and 28 days reveal that the addition of NA tends the mineral admixture to react at early ages in the hydration process, which produces a new chemical compound to fill the pores. The rapid chloride penetration (RCPT) test results at 28 days significantly improved with the incorporation of nanoparticles due to their effective size and chemical reaction towards the other compounds. The test results from the hybridization of nanoparticles showed that the compressive strength was significantly enhanced compared to that of the control mortar and mortar with NA. They are effective up to certain limits beyond that addition, and the workability was reduced. Amongst all mixtures, the maximum compression strength has been attained for the mix with the addition of NA 0.5% and NS 2.5% comparatively. The microstructural properties of mortar were also studied through scanning electron microscope (SEM) analysis. The results showed that the incorporation of nanoparticles in the mortar matrix turns homogeneous with fewer pores and greater amount of hydration compounds; thereby, pore refinement has improved the hydration compounds remarkably.