bluuebunny/crossref_metadata_2025 huggingface dataset:
Psychosocial Factors Affecting Wellbeing and Sources of Support of Young Adult Cancer Survivors: A Scoping Review
Authors: Timko Olson Erica R. , Olson Anthony , Driscoll Megan , Bliss Donna Z.
Published: December 2024

Background/Objectives: To identify and analyze what is known about the psychosocial factors affecting the wellbeing and sources of support of young adult (YA) cancer survivors. Methods: The search strategy included Neoplasms, young adults, psycho* or emotional well* or mental health. The OVID Medline and CINAHL databases were searched. Included were cancer survivors (YA) ages 18–39 at the time of the study. The studies included qualitative and quantitative designs, written in English, and published between January 2016 and October 2024. The results were recorded according to PRISMA-ScR guidelines. Results: Thirteen studies with 4992 participants found psychosocial factors to be the most important influence on life satisfaction with social support the most decisive factor. This expands the results of previous reviews by including a variety of study designs and data collection tools to provide a comprehensive understanding of the YA experience. Psychosocial concerns affecting wellbeing led to social isolation, low connectedness with family and friends, and significant distress. Consistent with previous reviews, the greatest challenges to wellbeing were psychosocial needs, which included seeking and delivering information that is easy to understand but detailed, which can decrease frustration and anger, and needs to be readily available and accessible. Unlike older adult cancer survivors, YA survivors are more likely to have reduced psychosocial functioning compared to their peers and suffer from higher distress than their adult peers and non-YA cancer survivors with anxiety as the most reported symptom. Conclusions: Interventions need to be developed that lessen the impact of a cancer diagnosis and cancer treatments. The specific needs of YAs must be further researched and evaluated to determine specific interventions and the support needed during this crucial stage of cancer survivorship. Future research must also increase the focus on the racial and ethnic diversity of participants as well as prioritizing underserved populations and the impact of the COVID-19 pandemic.
Strategic Switching from Conventional Urea to Nano-Urea for Sustaining the Rice–Wheat Cropping System
Authors: Kumar Ashwani , Sheoran Parvender , Devi Sunita , Kumar Naresh , Malik Kapil , Rani Manu , Kumar Arvind , Dhansu Pooja , Kaushik Shruti , Bhardwaj Ajay Kumar , Mann Anita , Yadav Rajender Kumar
Published: December 2024

In the face of declining crop yields, inefficient fertilizer usage, nutrient depletion, and limited water availability, the efficiency of conventional NPK fertilizers is a critical issue in India. The hypothesis of this study posits that nano-nitrogen could enhance growth and photosynthetic efficiency in crop plants compared to conventional fertilizers. For this, a randomized block design (RBD) field experiment was conducted with six treatments: no nitrogen (T1), 100% N through urea (T2), and varying levels of N replacement with nano-nitrogen (33%: T3; 50%: T4; 66%: T5; and 100%: T6). Morphological and physiological traits and yield attributes were measured at physiological maturity, and yield attributes were measured at harvest. Results showed that 33% nitrogen replacement with nano-nitrogen (T3) outperformed conventional urea (T2) in physiological traits and achieved higher grain yields (3789 kg/ha for rice and 4206 kg/ha for wheat) compared to T2 (3737 kg/ha for rice and 4183 kg/ha for wheat with 100% urea). Although T4 and T5 showed statistically similar yields, they were lower than T2 and T3 for rice, while 50%, 66%, and 100% replacements reduced wheat yield by 2.49%, 8.39%, and 41.26%, respectively, compared to T2. Key enzymes of N metabolism decreased with higher nano-nitrogen substitution. Maximum nitrogen availability was observed in T2 and T3. This study concludes that nano-nitrogen is an effective strategy to enhance growth, balancing productivity and environmental sustainability.
One-Off Irrigation Combined Subsoiling and Nitrogen Management Enhances Wheat Grain Yield by Optimizing Physiological Characteristics in Leaves in Dryland Regions
Authors: Huang Ming , Zhang Shuai , Yang Mengqi , Sun Yuhao , Xie Qinglei , Zhao Cuiping , Ren Kaiming , Zhao Kainan , Jia Yulin , Zhang Jun , Wu Shanwei , Li Chunxia , Wang Hezheng , Fu Guozhan , Shaaban Muhammad , Wu Jinzhi , Li Youjun
Published: December 2024

Irrigation practice, tillage method, and nitrogen (N) management are the three most important agronomic measures for wheat (Triticum aestivum L.) production, but the combined effects on grain yield and wheat physiological characteristics are still poorly understood. We conducted a three-year split–split field experiment at the junction of the Loess Plateau and Huang-Huai-Hai Plain in China. The two irrigation practices (I0: non-irrigation and I1: one-off irrigation), three tillage methods (RT: rotary tillage, PT: plowing, and ST: subsoiling), and four N managements (N0, N120, N180, and N240) were assigned to the main plots, subplots, and sub-subplots, respectively. Irrigation practice, tillage method, N management, and most of their two-factor and three-factor interactions could significantly affect grain yield and the physiological characteristics of the leaves of winter wheat. One-off irrigation increased the grain yield by 46.9% by optimizing the activities of superoxide dismutase (SOD), peroxidase (POD) and catalase (CAT), the contents of proline (Pro) and soluble sugar (SS), and the net photosynthesis rate (Pn) in leaves during most growth stages of wheat. The improvement of grain yield and physiological characteristics under one-off irrigation was considerably affected by the tillage method and N management, and the effectiveness of one-off irrigation was improved under subsoiling and N180 or N240. One-off irrigation combining subsoiling and N180 had no significant difference relative to one-off irrigation combining subsoiling and N240, while it significantly increased grain yield by 47.1% over the three years, as well as increasing the activities of SOD, POD, and CAT, and Pn in wheat leaves by 23.2%, 41.2%, 26.1%, and 53.0%, respectively, and decreasing the contents of malondialdehyde (MDA), Pro, and SS by 29.2%, 65.4%, and 18.2% compared to non-irrigation rotary tillage combined with N240 across the two years and three stages. The wheat grain yield was significantly associated with the physiological characteristics in flag leaves, and the coefficient was greatest for POD activity, followed by SOD activity and Pn. Therefore, one-off irrigation combining subsoiling and N180 is an optimal strategy for the high-yield production of wheat in dryland regions where the one-off irrigation is assured.
Unraveling the Molecular Mechanisms of Blueberry Root Drought Tolerance Through Yeast Functional Screening and Metabolomic Profiling
Authors: Fan Xinyu , Lin Beijia , Yin Yahong , Zong Yu , Li Yongqiang , Zhu Youyin , Guo Weidong
Published: December 2024

Blueberry plants are among the most important fruit-bearing shrubs, but they have shallow, hairless roots that are not conducive to water and nutrient uptake, especially under drought conditions. Therefore, the mechanism underlying blueberry root drought tolerance should be clarified. Hence, we established a yeast expression library comprising blueberry genes associated with root responses to drought stress. High-throughput sequencing technology enabled the identification of 1475 genes potentially related to drought tolerance. A subsequent KEGG enrichment analysis revealed 77 key genes associated with six pathways: carbon and energy metabolism, biosynthesis of secondary metabolites, nucleotide and amino acid metabolism, genetic information processing, signal transduction, and material transport and catabolism. Metabolomic profiling of drought-tolerant yeast strains under drought conditions detected 1749 differentially abundant metabolites (DAMs), including several up-regulated metabolites (organic acids, amino acids and derivatives, alkaloids, and phenylpropanoids). An integrative analysis indicated that genes encoding several enzymes, including GALM, PK, PGLS, and PIP5K, modulate key carbon metabolism-related metabolites, including D-glucose 6-phosphate and β-D-fructose 6-phosphate. Additionally, genes encoding FDPS and CCR were implicated in terpenoid and phenylalanine biosynthesis, which affected metabolite contents (e.g., farnesylcysteine and tyrosine). Furthermore, genes for GST and GLT1, along with eight DAMs, including L-γ-glutamylcysteine and L-ornithine, contributed to amino acid metabolism, while genes encoding NDPK and APRT were linked to purine metabolism, thereby affecting certain metabolites (e.g., inosine and 3′,5′-cyclic GMP). Overall, the yeast functional screening system used in this study effectively identified genes and metabolites influencing blueberry root drought tolerance, offering new insights into the associated molecular mechanisms.
Connecting Dynamics and Thermodynamics in Polymer–Resin Cured Systems
Authors: Miccio Luis A. , Sill Clemens , Wehlack Carsten , Schwartz Gustavo A.
Published: December 2024

This work connects the calorimetric responses of different rubber–resin blends with varying resin contents with their alpha relaxation dynamics. We used differential scanning calorimetry and broadband dielectric spectroscopy to characterize the calorimetric and dielectric responses of styrene–butadiene, polybutadiene, and polyisoprene with different resin contents. To model the results, we used the Gordon–Taylor equation combined with an extension of the Adam–Gibbs approach. Thus, we propose a simple and effective model that allows us to estimate the blend dynamics from the temperature dependence of the relaxation times of the pure components and the calorimetric measurement of the glass transition temperature of only one blend composition. By estimating an effective interaction parameter from calorimetry, we achieved accurate alpha relaxation dynamics predictions for different resin concentrations. Our highly predictive approach provides a realistic description of the expected dynamics. This study offers valuable insights into the dynamic properties of polymer compounds, paving the way for the fast and effective development of advanced and more sustainable materials.
Modification and Use of Naturally Renewable Cardanol-Type Prepolymers in One-Component Silyl-Terminated Prepolymer Sealant Systems
Authors: Berzins Ritvars , Meri Remo Merijs , Zicans Janis , Abele Agnese , Sytar Volodymyr , Kabat Oleh , Klymenko Anton , Mitina Nataliia
Published: December 2024

The current research is devoted to integrating naturally renewable cardanol derivatives into one-component silyl-terminated-polyether-based prepolymer systems to improve climatic resistance and obtain materials with versatile mechanical properties that could be significant to various sectors of the economy. Various cardanol-type products are used in industries that require high climatic resistance, and thus combining cardanol with commercially available silyl-terminated polyether prepolymers would improve its material climatic resistance, maintaining its market and application value as well as improving material sustainability. The results obtained in this work show that depending on how the cardanol prepolymer Ultra Lite 513 is modified, it is possible to increase the elasticity (670%) or tensile strength (104%) of the material as well as significantly increase the climatic resistance of the material, thus improving the quality and sustainability of the adhesive compared to existing silyl-terminated-prepolymer-based adhesives on the market.
Remote Sensing Image Interpretation for Coastal Zones: A Review
Authors: Sun Shuting , Xue Qingqing , Xing Xinying , Zhao Huihui , Zhang Fang
Published: December 2024

Coastal zones, where land meets ocean, are home to a large portion of the global population and play a crucial role in human survival and development. These regions are shaped by complex geological processes and influenced by both natural and anthropogenic factors, making effective management essential for addressing population growth, environmental degradation, and resource sustainability. However, the inherent complexity of coastal zones complicates their study, and traditional in situ methods are often inefficient. Remote sensing technologies have significantly advanced coastal zone research, with different sensors providing diverse perspectives. These sensors are typically used for classification tasks (e.g., coastline extraction, coastal classification) and retrieval tasks (e.g., aquatic color, wetland monitoring). Recent improvements in resolution and the advent of deep learning have led to notable progress in classification, while platforms like Google Earth Engine (GEE) have enabled the development of high-quality, global-scale products. This paper provides a comprehensive overview of coastal zone interpretation, discussing platforms, sensors, spectral characteristics, and key challenges while proposing potential solutions for future research and management.
An Overview of Within-Season Agricultural Monitoring from Remotely Sensed Data
Authors: Cao Ruyin
Published: December 2024

Remote sensing data have been widely used to monitor various agricultural activities, such as crop distribution mapping, crop phenology extraction, farmland soil moisture monitoring, crop diseases prevention, and crop ideotype breeding [...]
A Low-Cost 3D Mapping System for Indoor Scenes Based on 2D LiDAR and Monocular Cameras
Authors: Li Xiaojun , Li Xinrui , Hu Guiting , Niu Qi , Xu Luping
Published: December 2024

The cost of indoor mapping methods based on three-dimensional (3D) LiDAR can be relatively high, and they lack environmental color information, thereby limiting their application scenarios. This study presents an innovative, low-cost, omnidirectional 3D color LiDAR mapping system for indoor environments. The system consists of two two-dimensional (2D) LiDARs, six monocular cameras, and a servo motor. The point clouds are fused with imagery using a pixel-spatial dual-constrained depth gradient adaptive regularization (PS-DGAR) algorithm to produce dense 3D color point clouds. During fusion, the point cloud is reconstructed inversely based on the predicted pixel depth values, compensating for areas of sparse spatial features. For indoor scene reconstruction, a globally consistent alignment algorithm based on particle filter and iterative closest point (PF-ICP) is proposed, which incorporates adjacent frame registration and global pose optimization to reduce mapping errors. Experimental results demonstrate that the proposed density enhancement method achieves an average error of 1.5 cm, significantly improving the density and geometric integrity of sparse point clouds. The registration algorithm achieves a root mean square error (RMSE) of 0.0217 and a runtime of less than 4 s, both of which outperform traditional iterative closest point (ICP) variants. Furthermore, the proposed low-cost omnidirectional 3D color LiDAR mapping system demonstrates superior measurement accuracy in indoor environments.
FlightScope: An Experimental Comparative Review of Aircraft Detection Algorithms in Satellite Imagery
Authors: El Ghazouali Safouane , Gucciardi Arnaud , Venturini Francesca , Venturi Nicola , Rueegsegger Michael , Michelucci Umberto
Published: December 2024

Object detection in remotely sensed satellite pictures is fundamental in many fields such as biophysical and environmental monitoring. While deep learning algorithms are constantly evolving, they have been mostly implemented and tested on popular ground-taken photos. This paper critically evaluates and compares a suite of advanced object detection algorithms customized for the task of identifying aircraft within satellite imagery. The goal is to enable researchers to choose efficiently from algorithms that are trainable and usable in real time on a deep learning infrastructure with moderate requirements. Using the large HRPlanesV2 dataset, together with rigorous validation with the GDIT dataset, this research encompasses an array of methodologies including YOLO versions 5, 8, and 10, Faster RCNN, CenterNet, RetinaNet, RTMDet, DETR, and grounding DINO, all trained from scratch. This exhaustive training and validation study reveals YOLOv5 as the pre-eminent model for the specific case of identifying airplanes from remote sensing data, showcasing high precision and adaptability across diverse imaging conditions. This research highlights the nuanced performance landscapes of these algorithms, with YOLOv5 emerging as a robust solution for aerial object detection, underlining its importance through superior mean average precision, recall, and intersection over union scores. The findings described here underscore the fundamental role of algorithm selection aligned with the specific demands of satellite imagery analysis and extend a comprehensive framework to evaluate model efficacy. This aims to foster exploration and innovation in the realm of remote sensing object detection, paving the way for improved satellite imagery applications.
Validity Analysis of Monocular Human Pose Estimation Models Interfaced with a Mobile Application for Assessing Upper Limb Range of Motion
Authors: Moreira Rayele , Teixeira Silmar , Fialho Renan , Miranda Aline , Lima Lucas Daniel Batista , Carvalho Maria Beatriz , Alves Ana Beatriz , Bastos Victor Hugo Vale , Teles Ariel Soares
Published: December 2024

Human Pose Estimation (HPE) is a computer vision application that utilizes deep learning techniques to precisely locate Key Joint Points (KJPs), enabling the accurate description of a person’s pose. HPE models can be extended to facilitate Range of Motion (ROM) assessment by leveraging patient photographs. This study aims to evaluate and compare the performance of HPE models for assessing upper limbs ROM. A physiotherapist evaluated the degrees of ROM in shoulders (flexion, extension, and abduction) and elbows (flexion and extension) for fifty-two participants using both Universal Goniometer (UG) and five HPE models. Participants were instructed to repeat each movement three times to obtain measurements with the UG, then positioned while photos were captured using the NLMeasurer mobile application. The paired t-test, bias, and error measures were employed to evaluate the difference and agreement between measurement methods. Results indicated that the MoveNet Thunder INT16 model exhibited superior performance. Root Mean Square Errors obtained through this model were <10° in 8 of 10 analyzed movements. HPE models demonstrated better performance in shoulder flexion and abduction movements while exhibiting unsatisfactory performance in elbow flexion. Challenges such as image perspective distortion, environmental lighting conditions, images in monocular view, and complications in the pose may influence the models’ performance. Nevertheless, HPE models show promise in identifying KJPs and facilitating ROM measurements, potentially enhancing convenience and efficiency in assessments. However, their current accuracy for this application is unsatisfactory, highlighting the need for caution when considering automated upper limb ROM measurement with them. The implementation of these models in clinical practice does not diminish the crucial role of examiners in carefully inspecting images and making adjustments to ensure measurement reliability.
Advancements in Smart Wearable Mobility Aids for Visual Impairments: A Bibliometric Narrative Review
Authors: Zhang Xiaochen , Huang Xiaoyu , Ding Yiran , Long Liumei , Li Wujing , Xu Xing
Published: December 2024

Research into new solutions for wearable assistive devices for the visually impaired is an important area of assistive technology (AT). This plays a crucial role in improving the functionality and independence of the visually impaired, helping them to participate fully in their daily lives and in various community activities. This study presents a bibliometric analysis of the literature published over the last decade on wearable assistive devices for the visually impaired, retrieved from the Web of Science Core Collection (WoSCC) using CiteSpace, to provide an overview of the current state of research, trends, and hotspots in the field. The narrative focuses on prominent innovations in recent years related to wearable assistive devices for the visually impaired based on sensory substitution technology, describing the latest achievements in haptic and auditory feedback devices, the application of smart materials, and the growing concern about the conflicting interests of individuals and societal needs. It also summarises the current opportunities and challenges facing the field and discusses the following insights and trends: (1) optimization of the transmission of haptic and auditory information while multitasking; (2) advance research on smart materials and foster cross-disciplinary collaboration among experts; and (3) balance the interests of individuals and society. Given the two essential directions, the low-cost, stand-alone pursuit of efficiency and the high-cost pursuit of high-quality services that are closely integrated with accessible infrastructure, the latest advances will gradually allow more freedom for ambient assisted living by using robotics and automated machines, while using sensor and human–machine interaction as bridges to promote the synchronization of machine intelligence and human cognition.
Two Novel Semi-Quantum Secure Direct Communication Protocols in IoT
Authors: Tian Yuan , Zhang Nanyijia , Li Jian
Published: December 2024

As Internet of Things (IoT) technology continues to advance, there is a growing awareness of IoT security within the industry. Quantum communication technology can potentially significantly improve the communication security of IoT devices. Based on semi-quantum cryptography and utilizing single photons, this paper introduces two semi-quantum secure direct communication (SQSDC) protocols for use in smart door locks. Protocol 1 is more efficient, and the efficiency analysis shows that the communication efficiency is as high as 28.57%. Security analysis demonstrates the asymptotic security of the protocols, effectively resisting intercept–measure–resend attacks and entangle–measure attacks from potential eavesdroppers. The extended SQSDC protocol (protocol 2) builds upon protocol 1 by enabling a single qubit to transmit two bits of information, resulting in a double efficiency outcome.
Design and Test of a Calibration System for Avalanche Photodiodes Used in X-Ray Compton Polarimeters for Space
Authors: Alimenti Andrea , Cologgi Fabrizio , Fabiani Sergio , Torokhtii Kostiantyn , Silva Enrico , Del Monte Ettore , Baffo Ilaria , Bonomo Sergio , Brienza Daniele , Campana Riccardo , Centrone Mauro , De Iulis Giulia , Costa Enrico , Cucinella Giovanni , Curatolo Andrea , De Angelis Nicolas , De Cesare Giovanni , Del Re Andrea , Di Cosimo Sergio , Di Filippo Simone , Di Marco Alessandro , Di Persio Giuseppe , Donnarumma Immacolata , Fanelli Pierluigi , Kumar Abhay , Leonetti Paolo , Locarini Alfredo , Loffredo Pasqualino , Lombardi Giovanni , Minervini Gabriele , Modenini Dario , Muleri Fabio , Natalucci Silvia , Negri Andrea , Perelli Massimo , Rossi Monia , Rubini Alda , Scalise Emanuele , Soffitta Paolo , Terracciano Andrea , Tortora Paolo , Zaccagnino Emanuele , Zambardi Alessandro
Published: December 2024

The development and calibration of a measurement system designed for assessing the performance of the avalanche photodiodes (APDs) used in the Compton scattering polarimeter of the CUSP project is discussed in this work. The designed system is able to characterize the APD gain GAPD and energy resolution across a wide range of temperatures T (from −20 °C to +60 °C) and bias voltages Vbias (from 260 V to 410 V). The primary goal was to experimentally determine the GAPD dependence on the T and Vbias in order to establish a strategy for stabilizing GAPD by compensating for T fluctuations, acting on Vbias. The results demonstrate the system capability to accurately characterize APD behavior and develop feedback mechanisms to ensure its stable operation. This work provides a robust framework for calibrating APDs for space environments. It is essential for the successful implementation of spaceborne polarimeters such as the Compton scattering polarimeter foreseen aboard the CUbeSat Solar Polarimeter (CUSP) mission under development to perform solar flare X-ray polarimetry.
Using Heart Rate and Behaviors to Predict Effective Intervention Strategies for Children on the Autism Spectrum: Validation of a Technology-Based Intervention
Authors: Emezie Amarachi , Kamel Rima , Dunphy Morgan , Young Amanda , Nuske Heather J.
Published: December 2024

Many children on the autism spectrum engage in challenging behaviors, like aggression, due to difficulties communicating and regulating their stress. Identifying effective intervention strategies is often subjective and time-consuming. Utilizing unobservable internal physiological data to predict strategy effectiveness may help simplify this process for teachers and parents. This study examined whether heart rate data can predict strategy effectiveness. Teachers and coders from the research team recorded behavioral and heart rate data over three months for each participating student on the autism spectrum using the KeepCalm app, a platform that provides in-the-moment strategy suggestions based on heart rate and past behavioral data, across 226 instances of strategy interventions. A binary logistic regression was performed to assess whether heart rate reduction, time to return to heart rate baseline, and documented skills and challenging behaviors predicted strategy effectiveness. Results suggested that heart rate reduction may be a significant predictor, and supported the existing practice of using behavioral patterns as proxies for strategy effectiveness. Additional analyses indicate proactive strategies are more effective and are associated with greater reduction in heart rate, relative to reactive strategies. Further exploration of how internal physiological data can complement observable behaviors in assessing intervention strategy effectiveness is warranted given the novelty of our findings.
Cloud–Edge–End Collaborative Federated Learning: Enhancing Model Accuracy and Privacy in Non-IID Environments
Authors: Li Ling , Zhu Lidong , Li Weibang
Published: December 2024

Cloud–edge–end computing architecture is crucial for large-scale edge data processing and analysis. However, the diversity of terminal nodes and task complexity in this architecture often result in non-independent and identically distributed (non-IID) data, making it challenging to balance data heterogeneity and privacy protection. To address this, we propose a privacy-preserving federated learning method based on cloud–edge–end collaboration. Our method fully considers the three-tier architecture of cloud–edge–end systems and the non-IID nature of terminal node data. It enhances model accuracy while protecting the privacy of terminal node data. The proposed method groups terminal nodes based on the similarity of their data distributions and constructs edge subnetworks for training in collaboration with edge nodes, thereby mitigating the negative impact of non-IID data. Furthermore, we enhance WGAN-GP with attention mechanism to generate balanced synthetic data while preserving key patterns from original datasets, reducing the adverse effects of non-IID data on global model accuracy while preserving data privacy. In addition, we introduce data resampling and loss function weighting strategies to mitigate model bias caused by imbalanced data distribution. Experimental results on real-world datasets demonstrate that our proposed method significantly outperforms existing approaches in terms of model accuracy, F1-score, and other metrics.
A Lyapunov Optimization-Based Approach to Autonomous Vehicle Local Path Planning
Authors: Arjmandzadeh Ziba , Abbasi Mohammad Hossein , Wang Hanchen , Zhang Jiangfeng , Xu Bin
Published: December 2024

Autonomous vehicles (AVs) offer significant potential to improve safety, reduce emissions, and increase comfort, drawing substantial attention from both research and industry. A critical challenge in achieving SAE Level 5 autonomy, full automation, is path planning. Ongoing efforts in academia and industry are focused on optimizing AV path planning, reducing computational complexity, and enhancing safety. This paper presents a novel approach using Lyapunov Optimization (LO) for local path planning in AVs. The proposed LO model is benchmarked against two conventional methods: model predictive control and a sampling-based approach. Additionally, an AV prototype was developed and tested in Norman, Oklahoma, where it collected data to evaluate the performance of the three control algorithms used in this study. To minimize costs and increase real-world applicability, a vision-only solution was employed for object detection and the generation of bird’s-eye-view coordinate data. Each control algorithm, i.e., Lyapunov Optimization (LO) and the two baseline methods, were independently used to generate safe and smooth paths for the AV based on the collected data. The approaches were then compared in terms of path smoothness, safety, and computation time. Notably, the proposed LO strategy demonstrated at least a 20 times reduction in computation time compared to the baseline methods.
MR Elastography Using the Gravitational Transducer
Authors: Darwish Omar Isam , Koch Vitali , Vogl Thomas J. , Wolf Marcos , Schregel Katharina , Purushotham Arnie , Vilgrain Valérie , Paradis Valérie , Neji Radhouene , Sinkus Ralph
Published: December 2024

MR elastography is a non-invasive imaging technique that provides quantitative maps of tissue biomechanical properties, i.e., elasticity and viscosity. Currently, hepatic MR elastography is deployed in the clinic to assess liver fibrosis in MAFLD patients. In addition, research has demonstrated MR elastography’s ability to non-invasively assess chronic liver disease and to characterize breast cancer lesions and brain tumors. MR elastography requires efficient mechanical wave generation and penetration, motion-sensitized MRI sequences, and MR elastography inversion algorithms to retrieve the biomechanical properties of the tissue. MR elastography promises to enable non-invasive and versatile assessment of tissue, leading to better diagnosis and staging of several clinical conditions.
Arrhythmia Detection by Data Fusion of ECG Scalograms and Phasograms
Authors: Scarpiniti Michele
Published: December 2024

The automatic detection of arrhythmia is of primary importance due to the huge number of victims caused worldwide by cardiovascular diseases. To this aim, several deep learning approaches have been recently proposed to automatically classify heartbeats in a small number of classes. Most of these approaches use convolutional neural networks (CNNs), exploiting some bi-dimensional representation of the ECG signal, such as spectrograms, scalograms, or similar. However, by adopting such representations, state-of-the-art approaches usually rely on the magnitude information alone, while the important phase information is often neglected. Motivated by these considerations, the focus of this paper is aimed at investigating the effect of fusing the magnitude and phase of the continuous wavelet transform (CWT), known as the scalogram and phasogram, respectively. Scalograms and phasograms are fused in a simple CNN-based architecture by using several fusion strategies, which fuse the information in the input layer, some intermediate layers, or in the output layer. Numerical results evaluated on the PhysioNet MIT-BIH Arrhythmia database show the effectiveness of the proposed ideas. Although a simple architecture is used, their competitiveness is high compared to other state-of-the-art approaches, by obtaining an overall accuracy of about 98.5% and sensitivity and specificity of 98.5% and 95.6%, respectively.
Digital Twin-Assisted Lightpath Provisioning and Nonlinear Mitigation in C+L+S Multiband Optical Networks
Authors: Ghasrizadeh Sadegh , Khare Prasunika , Costa Nelson , Ruiz Marc , Napoli Antonio , Pedro Joao , Velasco Luis
Published: December 2024

Multiband (MB) optical transmission targets increasing the capacity of operators’ optical transport networks. However, nonlinear impairments (NLI) affect each optical channel in the C+L+S bands differently, and, therefore, the routing and spectrum assignment (RSA) problem needs to be complemented with fast and accurate tools to consider the quality of transmission (QoT) within the provisioning process. This paper proposes a digital twin-assisted approach for lightpath provisioning to provide a complete solution for the RSA problem that ensures the required QoT in MB optical networks. The OCATA time domain digital twin is proposed, not only to estimate the QoT of a selected path but also to support the QoT-based channel assignment process. OCATA is based on a Deep Neural Network (DNN) to model the propagation of the optical signal. However, because of the different impacts of nonlinear noise on each channel and the large number of channels that need to be considered in C+L+S MB scenarios, OCATA needs to be adapted to make it scalable, while keeping its high accuracy and fast QoT estimation characteristics. In consequence, a complete methodology is proposed in this work that limits the number of channels being modeled to just a few. Moreover, OCATA-MB helps to mitigate NLI noise by programming the receiver at the provisioning time and thus with very little complexity compared to its equivalent implemented during the operation. NLI noise mitigation can be applied in the case when a lightpath cannot be provisioned because none of the available channels can provide the required QoT, making it an advantageous tool for reducing connection blocking. Exhaustive simulation results demonstrate the remarkable accuracy of OCATA-MB in estimating the QoT for any channel. Interestingly, by utilizing the proposed OCATA-MB-assisted lightpath provisioning approach, a reduction of the blocking ratio exceeding 50% when compared to traditional approaches is shown when NLI noise mitigation is not applied. If NLI mitigation is implemented, an additional over 50% blocking reduction is achieved.
Correction: Bennett, A.W.; Loose, S.M. Benchmarking Economic Sustainability: What Factors Explain Heterogeneity Between Wine Businesses? Sustainability 2023, 15, 16686
Authors: Bennett Anthony William , Loose Simone Mueller
Published: December 2024

The authors would like to make the following corrections to the published paper [...]
Bibliometric Analysis of Global Publications on Management, Trends, Energy, and the Innovation Impact of Green Hydrogen Production
Authors: Rodríguez-Aburto César , Poma-García José , Montaño-Pisfil Jorge , Morcillo-Valdivia Pablo , Oyanguren-Ramirez Fernando , Santos-Mejia Cesar , Rodriguez-Flores Ruben , Virú-Vasquez Paul , Pilco-Nuñez Alex
Published: December 2024

The aim of this bibliometric analysis was to evaluate the evolution of scientific research in hydrogen, focusing on green hydrogen production, storage, and utilization. Articles from prominent databases were analyzed, revealing a strong emphasis on sustainable hydrogen technologies through keywords like “hydrogen production”, “green hydrogen”, and “solar power generation”. Mature research areas include production methods and electrolysis, while emerging topics such as energy efficiency and policy are gaining traction. The most-cited papers, from Energy Conversion and Management to the International Journal of Hydrogen Energy, cover techno-economic assessments and case studies on deploying hydrogen technologies. Key findings highlight the variability of the Levelized Cost of Hydrogen (LCOH) across technologies and regions. Deep learning applications, including Fast Fourier Transform-based forecasting and explainable AI models, are transforming production optimization, while Life Cycle Assessment (LCA) emphasizes renewable energy’s role in reducing carbon intensity and resource consumption. Diverse strategies, such as fiscal incentives for wind energy and use of urban waste, underline the importance of local solutions. This analysis reflects the rapid growth of hydrogen research, driven by international collaboration and innovations in sustainable production, storage, and optimization. It is hoped that this paper will help to shed more light on the current and future understanding of green hydrogen.
Assessing the Environmental Impacts of the Valorization of Creole-Antillean Avocado via an Extractive-Based Biorefinery in the Montes de María Region
Authors: Valdez-Valdes Stefany A. , Tejeda-Benitez Lesly P. , González-Delgado Ángel D.
Published: December 2024

In recent years, the environmental evaluation of biorefineries has become critical for ensuring sustainable practices in bio-based production systems. This study focuses on the application of the Waste Reduction (WAR) Algorithm to assess the environmental impacts of an Extractive-based Creole-Antillean Avocado Biorefinery located in Northern Colombia, aimed at producing bio-oil, chlorophyll, and biopesticide from avocado pulp, peel, and seed, respectively. The environmental impacts were evaluated using the WAR algorithm, which quantifies the potential environmental impacts (PEI) of different process streams. The following four scenarios were developed: (1) considering only waste, (2) including waste and products, (3) including waste and energy sources, and (4) incorporating waste, products, and energy consumption. This study analyzed global impacts focusing on atmospheric and toxicological categories, with a detailed assessment of the most critical scenario. The results indicated that Scenario 4 had the highest PEI, particularly in the atmospheric and toxicological categories, driven by emissions of volatile organic compounds (VOCs), greenhouse gases (GHGs), and the presence of heavy metals. However, the avocado biorefinery process demonstrated a net reduction in overall environmental impacts, with negative PEI generation rates across all scenarios, suggesting that the biorefinery transforms high-impact substances into products with lower global impact potential. Energy consumption emerged as a significant contributor to environmental impacts, particularly in acidification potential (AP) and Atmospheric Toxicity Potential (ATP). Using natural gas as an energy source had a relatively lower environmental impact compared to coal and liquid fuels, emphasizing the need to optimize energy use in biorefinery design to improve environmental performance.
Improving the Methods of Solid Domestic Waste Disposal to Reduce Its Human Impact on the Environment
Authors: Baibatyrova Bekzat , Tileuberdi Ayaulym , Begentayev Meiram , Kuldeyev Erzhan , Nyrlybayev Ruslan , Altybayev Zhaksylyk , Sarsenbayev Bakhytzhan , Abduova Aisulu , Sauganova Gaukhar
Published: December 2024

Kazakhstan is embarking on a fresh approach to managing waste, aiming to recycle and repurpose solid household waste while integrating innovative, eco-friendly technologies for its treatment and disposal. The main goal is to reduce the amount of waste sent to landfills and instead convert it into more stable forms, lessening its harmful impact on the environment. This effort requires a comprehensive strategy, especially as existing landfills still need to be managed and rehabilitated when they reach capacity. It is essential to use municipal solid waste as a resource for producing various goods, which not only helps in landfill management but also aligns with principles of sustainability. This approach aligns with circular economic principles and helps to reduce the technological impact of landfills on the surrounding environment. The main types of waste in the Aktas landfill were identified as a result of this research: it is glass, of which only 3% is recycled. In our results, the setup allowed for the extraction of valuable components from the fine fraction, which can then be recycled. This enriches the organic material ready for composting (organo-mineral raw material mixture) with food waste.
Impact of Building Orientation on Energy Performance of Residential Buildings in Various Cities Across Afghanistan
Authors: Karimi Mustafa , Chikamoto Tomoyuki , Lee Myonghyang , Tanaka Teppei
Published: December 2024

Building or solar orientation, a key architectural design parameter, significantly influences energy consumption in buildings. Optimizing building orientation to harness passive solar benefits is a fundamental and cost-effective measure in designing energy-efficient buildings. However, the optimal orientation varies based on geographical location, climatic conditions, and building type. Notably, Afghanistan’s building sector currently lacks tailored energy efficiency regulations. Therefore, this study investigates the impact of building orientation on the energy performance of residential buildings across nine cities in Afghanistan, each characterized by distinct climatic conditions and geographic locations, employing BEoptTM energy simulation software. The findings reveal diverse optimal orientations, dividing the country into three distinct climatic zones: subarctic (optimal orientation: south-southeast), continental (optimal orientation: south), and hot-arid (optimal orientation: north). The optimal orientations in these regions yield potential energy savings ranging from 25.6% to 48.9% compared to the least efficient orientations. These insights are critical for establishing location-specific building regulations in Afghanistan, promoting energy-efficient design, and addressing the country’s current trend of unsustainable development.
Multi-Objective Optimization of Manufacturing Process Using Artificial Neural Networks
Authors: Marcineková Katarína , Janáková Sujová Andrea
Published: December 2024

This paper focuses on the optimization of a critical operation in the furniture manufacturing process, identifying it as a key priority for improvement by applying Systems Theory. The primary objective of this study is to develop a mathematical model for optimizing the detected key process by employing artificial neural networks (ANNs) which mirror adaptive management principles. Three input and three output parameters significantly impacting the effectiveness of this key process have been systematically identified and experimentally measured. It was necessary to perform multi-objective optimization (MOO), which consisted in achieving the minimum values of cost and process time and the maximum value of the quality index through the optimal setting of the input parameters (cutting speed, feed rate, and volume of removed material). The application of ANNs in MOO in this research study is a novelty in this field. The results obtained through application of the ANN method reveal the optimal values of the examined parameters, which represent the best combination of input technical variables leading to the best results in output economic parameters. This multi-objective optimizing solution facilitates enhanced process efficiency. By integrating Systems Theory, Complexity Theory, and adaptive management, this research advances sustainable process improvements by minimizing resource use, reducing waste, and enhancing overall system efficiency.
Historical Evolution, Logical Deconstruction and Future Trends in Marine Ecological Compensation Policies in China
Authors: Huang Weichao , Su Shipeng , Gong Jing , Lin Shenglong
Published: December 2024

The marine ecological compensation policy has become an important tool for China to protect the ocean and promote the construction of ecological civilization. However, problems such as a fragmented policy system structure and inefficient implementation still plague the sustainable development of marine ecological compensation policy. In the context of the high-quality development of the marine economy in the future, it is necessary to evaluate the current status of the construction of China’s marine ecological compensation policy system and explore the plans to improve the marine ecological compensation policy in depth. This paper takes the marine ecological compensation policies issued from 1982 to 2023 as the analysis sample, uses semantic network analysis to analyze the evolution of China’s marine ecological compensation policy and divides the evolution into four stages based on the focuses of the policies in different periods. This paper uses a policy element model to analyze the scope, direction and dynamic mechanism of the change in the marine ecological compensation policy. The study found that the policy elements at the abstract level remained stable for a long time, and the policy elements at the procedural level and operational level are heterogeneous at different stages; China’s marine ecological compensation has gradually developed in the direction of improved quality, land and sea coordination, full-process supervision and integrated management, and is affected by the macro-environment, institutional demand and supply and cost–benefit coordination and balance. The marine ecological compensation policies should start with the top-level design of the supplementary system, improve the supporting tools and coordinate multiple synergies to promote the improvement of the marine ecological compensation policy system.
Desempenho financeiro de unidades de beneficiamento de leite e derivados, numa abordagem das margens e custos por produto: um estudo de caso
Authors:   , Silva Jefre Milione da
Published: December 2024

A competição entre unidades de beneficiamento de leite e derivados se apresenta cada vez mais acirrada. As menores acabam por enfrentar dificuldades ainda maiores e constituem um caso típico de empresas que adotam uma abordagem clássica de custos conjuntos na sua gestão. Uma análise pouco produtiva de dados pode levar a incertezas para a compreensão de custos e margens. O objetivo do presente estudo foi o de desenvolver e descrever um processo de geração de indicadores com o intuito de orientar tomadas de decisão. A abordagem foi discutida em duas frentes: (1) o desenvolvimento e descrição de uma abordagem de análise para obtenção de indicadores sobre custos e margens unitárias por produto, em termos dos produtos produzidos num cenário real vigente; e (2) análise do desempenho financeiro, com base em proposição de alternativas para melhoria de desempenho econômico-financeiro de uma unidade de beneficiamento de leite e derivados: cenário planejado versus o cenário vigente. Realizou-se um estudo de caso com coleta de dados para estimativas de custos e margens mensais e unitárias por produto. A abordagem de análise proposta tem seu foco na simplicidade, de fácil domínio e para ser operado no chão de fábrica em tempo real. A ferramenta é composta por um conjunto de planilhas em Excel, com as entradas dos dados primários, geração e comparação de indicadores, na forma de tabelas com detalhamentos e resumo dos resultados, para planejamento em tempo real. A expectativa é de que uma gestão mais apropriada do processo produtivo é um fator decisivo para o sucesso do desempenho financeiro de unidades de beneficiamento de leite e derivados.