bluuebunny/crossref_metadata_2025 huggingface dataset:
Percutaneous Microwave Ablation of Hepatocellular Carcinoma with “Double Fusion” Technique: Technical Note and Single-Center Preliminary Experience
Authors: Biondetti Pierpaolo , Ascenti Velio , Shehab Anas , Ierardi Anna , Carriero Serena , Lanza Carolina , Angileri Salvatore , Guzzardi Giuseppe , Carrafiello Gianpaolo
Published: July 2023

Percutaneous image-guided thermal ablation is included in most society guidelines for treatment of hepatocellular carcinoma (HCC). The results of this treatment in terms of efficacy depend on the ability to precisely place the device into the target tumor. Ultrasound (US) is a commonly used imaging guidance modality for its real-time feedback. However, an accurate device deployment remains challenging in some clinical scenarios, including cases of tumors that are undetectable or not clearly visible by US. To overcome this problem, fusion imaging techniques have been developed, which combine images from different modalities. The most widely known technique combines pre-procedural contrast-enhanced computed tomography (CT) or magnetic resonance imaging (MRI) with real-time US scans. Cone beam CT (CBCT) is a technology that can provide intra-procedural cross-sectional images, which can be registered to images from other modalities, including preprocedural CT/MR scans. The aim of our study is to report the preliminary experience on percutaneous microwave ablation (MWA) of patients with HCC that were treated using the “double fusion“ technique, which combines the use of US fusion imaging and CBCT fusion imaging. We describe the technical details, feasibility, safety and short-term efficacy of this technique in a small series of eight patients with 11 HCCs.
Neurological and Psychiatric Manifestations of Long COVID-19 and Their [18F]FDG PET Findings: A Review
Authors: Hameed Rizwanullah , Bahadur Anuradha Rosario , Singh Shashi Bhushan , Sher Juwairah , Todua Maia , Moradi Leah (Mahsa) , Bastakoti Sanjiv , Arslan Maeen , Ajmal Hanfa , Lee Gha Young , Ayubcha Cyrus , Werner Thomas J. , Alavi Abass , Revheim Mona-Elisabeth
Published: July 2023

For more than two years, lingering sequalae of COVID-19 have been extensively investigated. Approximately 10% of individuals infected by COVID-19 have been found to experience long-term symptoms termed “long COVID-19”. The neurological and psychiatric manifestations of long COVID-19 are of particular concern. While pathogenesis remains unclear, emerging imaging studies have begun to better elucidate certain pathological manifestation. Of specific interest is imaging with [18F]FDG PET which directly reflects cellular glycolysis often linked to metabolic and inflammatory processes. Seeking to understand the molecular basis of neurological features of long COVID-19, this review encompasses the most recent [18F]FDG PET literature in this area.
Creating Inclusive Schools for Autistic Students: A Scoping Review on Elements Contributing to Strengths-Based Approaches
Authors: White Jia , McGarry Sarah , Falkmer Marita , Scott Melissa , Williams P. John , Black Melissa H.
Published: July 2023

Strengths-based approaches leveraging the strengths and interests of autistic students are increasingly recognised as important to meeting their school-related needs. A scoping review exploring elements contributing to strengths-based approaches for autistic students in schools was undertaken. Eighteen articles were identified, with results conceptualised according to the Bioecological Model of Development. One personal (strengths and interests), six microsystem (specialised instructions, curriculum integration, curriculum differentiation, common interests with peers, reciprocal roles and adult involvement), three mesosystem (matching resources and activities, real-life learning experiences and benefiting all students), and three exosystem (cost-effective and timesaving, collaboration with colleagues and parents and teachers’ attitude and knowledge) elements were identified. Findings highlight the interrelatedness of the elements contributing to strengths-based approaches for autistic students, which can be used to aid in the development of more inclusive school environments.
A Two-Path Multibehavior Model of User Interaction
Authors: Qu Mingyue , Wang Nan , Li Jinbao
Published: July 2023

Personalized recommendation is an important part of e-commerce platforms. In recommendation systems, a neural network is used to enhance collaborative filtering to accurately capture user preferences, so as to obtain better recommendation performance. Traditional recommendation methods focus on the results of a single user behavior, ignoring the modeling of multiple interaction behaviors of users, such as click, add to cart and purchase. Although many studies have also focused on multibehavior modeling, two important challenges remain: (1) Since the multiple behaviors of the time-evolving trends of context information are ignored, it is still a challenge to identify the multimodal relationships of behaviors; (2) surveillance signals are still sparse. In order to solve these problem, this paper proposes a two-path multibehavior model of user interaction (TP_MB). First, a two-path learning strategy is introduced to maximize the multiple-interaction information of users and items learned by the two paths, which effectively enhances the robustness of the model. Second, a multibehavior dependent encoder is designed. Contextual information is obtained through behavior dependencies in the interaction of different users. In addition, three contrastive learning methods are designed, which not only obtain additional auxiliary supervision signals, but also alleviate the problem of sparse supervision signals. Extensive experiments on two real datasets demonstrate that our method outperforms state-of-the-art multibehavior recommendation methods.
Logging In-Operation Battery Data from Android Devices: A Possible Path to Sourcing Battery Operation Data
Authors: Schimpe Michael
Published: July 2023

Operating Lithium-ion batteries requires a deep understanding of their performance. Laboratory experiments are conducted mostly under controlled ambient conditions and repetitive loads, whereas real-world operations feature a large spectrum of operating conditions. This leads to a large gap, which in-operation battery field data aims to close. Literature states the necessity for large datasets for field data studies; however, these are not available today. In this article, an overview of the possibilities of sourcing battery field data from Android devices is presented. Today, there are two billion Android devices in active use, and most of them are equipped with a lithium-ion battery and are also connected to the internet. An Android device study featuring multiple devices is conducted, evaluating signal quality and differences. Exemplary data analysis with a focus on state estimation algorithms is then tested on the sourced data.
Research on the Detection Method of Coal Mine Roadway Bolt Mesh Based on Improved YOLOv7
Authors: Sun Siya , Ma Hongwei , Wang Keda , Wang Chuanwei , Wang Zhanhui , Yuan Haining
Published: July 2023

Aiming at the environment of low illumination, high dust, and heavy water fog in coal mine driving face and the problems of occlusion, coincidence, and irregularity of bolt mesh laid on coal wall, a YOLOv7 bolt mesh-detection algorithm combining the image enhancement and convolutional block attention module is proposed. First, the image brightness is enhanced by a hyperbolic mapping transform-based image enhancement algorithm, and the image is defogged by a dark channel-based image defogging algorithm. Second, by introducing a convolutional block attention model in the YOLOv7 detection network, the significance of bolt mesh targets in the image is improved, and its feature expression ability in the detection network is enhanced. Meanwhile, the original activation function ReLU in the convolutional layer Conv of the YOLOv7 network is replaced by LeakyReLU so that the activation function has stronger nonlinear expression capability, which enhances the feature extraction performance of the network and thus improves the detection accuracy. Finally, the training and testing samples were prepared using the actual video of the drilling and bolting operation, and the proposed algorithm is compared with five classical target detection algorithms. The experimental results show that the proposed algorithm can be better applied to the low illumination, high dust environment, and irregular shape on the detection accuracy of coal mine roadway bolt mesh, and the average detection accuracy of the image can reach 95.4% with an average detection time of 0.0392 s.
Consumer Subjective Impressions in Virtual Reality Environments: The Role of the Visualization Technique in Product Evaluation
Authors: Palacios-Ibáñez Almudena , Felip-Miralles Francisco , Galán Julia , García-García Carlos , Contero Manuel
Published: July 2023

The availability and affordability of consumer virtual reality (VR) devices have fueled their adoption during the product design process. High fidelity virtual prototypes can be created more quickly and are more cost-effective than using traditional methods, but certain product features are still difficult to evaluate, resulting in perceptual differences when a product is assessed using different visualization techniques. In this paper, we report two case studies in which a group of participants evaluated different designs of a product typology (i.e., a watering can) as presented in VR, VR with passive haptics (VRPH) and in a real setting (R) for the first case study, and VR and R for the second case study. The semantic differential technique was used for product evaluation, and an inferential statistical method using aligned rank transform (ART) proceedings was applied to determine perceptual differences between groups. Our results showed that product characteristics assessed by touch are the most susceptible to being affected by the environment, while the user background can have an effect in some product features.
Supervised Single Channel Speech Enhancement Method Using UNET
Authors: Hossain Md. Nahid , Basir Samiul , Hosen Md. Shakhawat , Asaduzzaman A.O.M. , Islam Md. Mojahidul , Hossain Mohammad Alamgir , Islam Md Shohidul
Published: July 2023

This paper proposes an innovative single-channel supervised speech enhancement (SE) method based on UNET, a convolutional neural network (CNN) architecture that expands on a few changes in the basic CNN architecture. In the training phase, short-time Fourier transform (STFT) is exploited on the noisy time domain signal to build a noisy time-frequency domain signal which is called a complex noisy matrix. We take the real and imaginary parts of the complex noisy matrix and concatenate both of them to form the noisy concatenated matrix. We apply UNET to the noisy concatenated matrix for extracting speech components and train the CNN model. In the testing phase, the same procedure is applied to the noisy time-domain signal as in the training phase in order to construct another noisy concatenated matrix that can be tested using a pre-trained or saved model in order to construct an enhanced concatenated matrix. Finally, from the enhanced concatenated matrix, we separate both the imaginary and real parts to form an enhanced complex matrix. Magnitude and phase are then extracted from the newly created enhanced complex matrix. By using that magnitude and phase, the inverse STFT (ISTFT) can generate the enhanced speech signal. Utilizing the IEEE databases and various types of noise, including stationary and non-stationary noise, the proposed method is evaluated. Comparing the exploratory results of the proposed algorithm to the other five methods of STFT, sparse non-negative matrix factorization (SNMF), dual-tree complex wavelet transform (DTCWT)-SNMF, DTCWT-STFT-SNMF, STFT-convolutional denoising auto encoder (CDAE) and casual multi-head attention mechanism (CMAM) for speech enhancement, we determine that the proposed algorithm generally improves speech quality and intelligibility at all considered signal-to-noise ratios (SNRs). The suggested approach performs better than the other five competing algorithms in every evaluation metric.
Data Aging Matters: Federated Learning-Based Consumption Prediction in Smart Homes via Age-Based Model Weighting
Authors: Skianis Konstantinos , Giannopoulos Anastasios , Gkonis Panagiotis , Trakadas Panagiotis
Published: July 2023

Smart homes, powered mostly by Internet of Things (IoT) devices, have become very popular nowadays due to their ability to provide a holistic approach towards effective energy management. This is made feasible via the deployment of multiple sensors, which enables predicting energy consumption via machine learning approaches. In this work, we propose FedTime, a novel federated learning approach for predicting smart home consumption which takes into consideration the age of the time series datasets of each client. The proposed method is based on federated averaging but aggregates local models trained on each smart home device to produce a global prediction model via a novel weighting scheme. Each local model contributes more to the global model when the local data are more recent, or penalized when the data are older upon testing for a specific residence (client). The approach was evaluated on a real-world dataset of smart home energy consumption and compared with other machine learning models. The results demonstrate that the proposed method performs similarly or better than other models in terms of prediction error; FedTime achieved a lower mean absolute error of 0.25 compared to FedAvg. The contributions of this work present a novel federated learning approach that takes into consideration the age of the datasets that belong to the clients, experimenting with a publicly available dataset on grid import consumption prediction, while comparing with centralized and decentralized baselines, without the need for data centralization, which is a privacy concern for many households.
Multi-Time Interval Dynamic Optimization Model of New Energy Output Based on Multi-Energy Storage Coordination
Authors: Wang Qiwei , Cheng Songqing , Ma Shaohua , Chen Zhe
Published: July 2023

In response to the problem of mismatch between new energy output and multi-energy load requirement in multi-energy power systems, this article proposes a dynamic optimization model for new energy output in multiple time intervals based on multi-energy storage coordination. First, considering the energy conversion characteristics of multi-energy storage, the dynamic optimization method of new energy output based on the discrete division of subinterval of scheduling time is studied. Then, considering the cost of adjusting various resources comprehensively, the optimization objective of new energy output is studied, and a model-solving method based on a directed graph topology distributed algorithm is proposed. Finally, simulation verification was conducted, and the simulation results showed that the method proposed in this paper can effectively suppress the new energy fluctuation and reduce peak-shaving costs.
Ontology Development for Creating Identical Software Environments to Improve Learning Outcomes in Higher Education Institutions
Authors: Stolic Predrag , Milosevic Danijela , Stevic Zoran , Radovanovic Ilija
Published: July 2023

Students engage in remote learning within a diverse computer environment. While virtual machines can address the challenges posed by heterogeneity, there remain unresolved issues, particularly related to the complexity of software management. An imperative is to discover an automated solution that facilitates the creation of consistent software environments for educational purposes. This paper introduces ontology engineering principles as a means to tackle the complexities associated with software management. A suitable ontology is developed using OWL syntax, integrating knowledge pertaining to the required software within a specific academic domain. The practical applicability of this knowledge is enabled through the implementation of dedicated SPARQL queries within a Python program. The effectiveness of the automated solution in achieving identical software environments is verified through testing, conducted in both controlled laboratory settings and by students themselves, thus simulating authentic teaching scenarios. The solution not only adheres to the principles of reusability but can also be adapted or integrated into existing ontologies. Furthermore, it presents an opportunity to create automated and self-adjusting virtual machines, offering significant potential for educational and other domains.
MRI Image Fusion Based on Sparse Representation with Measurement of Patch-Based Multiple Salient Features
Authors: Hu Qiu , Cai Weiming , Xu Shuwen , Hu Shaohai
Published: July 2023

Multimodal medical image fusion is a fundamental, but challenging, problem in the fields of brain science research and brain disease diagnosis, as it is challenging for sparse representation (SR)-based fusion to characterize activity levels with a single measurement and not lose effective information. In this study, the Kronecker-criterion-based SR framework was applied for medical image fusion with a patch-based activity level, integrating salient features of multiple domains. Inspired by the formation process of vision systems, the spatial saliency was characterized by textural contrast (TC), composed of luminance and orientation contrasts, to promote the participation of more highlighted textural information in the fusion process. As a substitute for the conventional l1-norm-based sparse saliency, the sum of sparse salient features (SSSF) was used as a metric for promoting the participation of more significant coefficients in the composition of the activity level measurement. The designed activity level measurement was verified to be more conducive to maintaining the integrity and sharpness of detailed information. Various experiments on multiple groups of clinical medical images verified the effectiveness of the proposed fusion method in terms of both visual quality and objective assessment. Furthermore, this study will be helpful for the further detection and segmentation of medical images.
Design of Flexible Multi-Band Miniature Antenna Based on Minkowski Fractal Structure and Folding Technique for Miniature Wireless Transmission System
Authors: Liu Kechen , Sun Dapeng , Su Tao , Zheng Xu , Li Chaobo
Published: July 2023

In light of the predicament concerning the small gain and narrow frequency range of miniature antennas, this paper employs the implementation of a fractal repeating array structure and a double-layer folding antenna structure. Through these measures, the miniature antenna is endowed with a high gain and an expansive frequency range, all within its diminutive size. The paper presents an exquisite and high-gain flexible multi-band antenna, utilizing a dielectric substrate composed of the flexible material polyimide, with a thickness of merely 0.1 mm. The implementation of this flexible material bestows a feathery mass of merely 4 mg upon the antenna, enabling it to seamlessly conform to various shapes. This makes it particularly well-suited for employment within miniature wireless transmission systems and compact mobile communication devices. In an endeavor to enhance impedance matching and radiation characteristics, the Minkowski fractal structure is ingeniously incorporated as the repeating array element. This repeating array structure assumes a pivotal role and, when combined with the double-layer folding antenna structure, achieves the objective of miniaturization. Remarkably, the antenna’s dimensions measure a mere 0.04 λ0 × 0.026 λ0 (λ0 @ 2.4 GHz). The proposed antenna boasts a remarkably diminutive volume of merely 5 × 3 × 0.1 mm3, with the measured and simulated results exhibiting a striking concurrence. Both sets of results demonstrate resonance across multiple frequencies, namely, 2.4 GHz, 5.2 GHz and 5.8 GHz. Furthermore, within the effective frequency range, the antenna attains a maximum gain of 1.65 dBi and 4.37 dBi, respectively.
Robustness of Double-Layer Group-Dependent Combat Network with Cascading Failure
Authors: Yu Jintao , Xiao Bing , Cui Yuzhu
Published: July 2023

The networked combat system-of-system (CSOS) is the trend of combat development with the innovation of technology. To achieve the combat effectiveness, studying the ability of CSOS to cope with external interference is of great importance. Here we report a modeling method of CSOS from the perspective of complex networks and explore the robustness of the combat network based on this. Firstly, a more realistic double-layer heterogeneous dependent combat network model is established. Then, the conditional group dependency situation is considered to design failure rules for dependent failure, and the coupling relation between the double-layer subnets is analyzed for overload failure. Based on this, the initial load and capacity of the node are defined, respectively, as well as the load redistribution strategy and the status judgment rules for the cascading failure model. Simulation experiments are carried out by changing the attack modes and different parameters, and the results show that the robustness of the combat network can be effectively improved by improving the tolerance limit of one-way dependency of the functional net, the node capacity of the functional subnet, and the tolerance of the overload state. The conclusions of this paper can provide a useful reference for network structure optimization and network security protection in the military field.
A Homotopy-Based Approach to Solve the Power Flow Problem in Islanded Microgrid with Droop-Controlled Distributed Generation Units
Authors: Lima-Silva Alisson , Freitas Francisco Damasceno , Fernandes Luis Filomeno de Jesus
Published: July 2023

This paper proposes a homotopy-based approach to solve the power flow problem (PFP) in islanded microgrid networks with droop-controlled distributed generation (DG) units. The technique is based on modifying an “easy” problem solution that evolves with the computation of intermediate results to the PFP solution of interest. These intermediate results require the solution of nonlinear equations through Newton–Raphson (NR) method. In favor of convergence, the intermediate solutions are close to each other, strengthening the convergence qualities of the technique for the solution of interest. The DG units are modeled with operational power limits and three types of droop-control strategies, while the loads are both magnitude voltage- and frequency-dependent. To evaluate the method performance, simulations are performed considering the proposed and classical NR methods, both departing from a flat start estimation. Tests are carried out in three test systems. Different load and DG unit scenarios are implemented for a 6-, 38-, and 69-bus test system. A base case is studied for all systems, while for the two larger models, a loading factor is used to simulate the load augmenting up to the maximum value. The results demonstrated that for the largest-size model system, only the homotopy-based approach could solve the PFP for stringent requirements such as the diversification of the load profile and hard loading operation point.
Review and Evaluation of Reinforcement Learning Frameworks on Smart Grid Applications
Authors: Vamvakas Dimitrios , Michailidis Panagiotis , Korkas Christos , Kosmatopoulos Elias
Published: July 2023

With the rise in electricity, gas and oil prices and the persistently high levels of carbon emissions, there is an increasing demand for effective energy management in energy systems, including electrical grids. Recent literature exhibits large potential for optimizing the behavior of such systems towards energy performance, reducing peak loads and exploiting environmentally friendly ways for energy production. However, the primary challenge relies on the optimization of such systems, which introduces significant complexities since they present quite dynamic behavior. Such cyberphysical frameworks usually integrate multiple interconnected components such as power plants, transmission lines, distribution networks and various types of energy-storage systems, while the behavior of these components is affected by various external factors such as user individual requirements, weather conditions, energy demand and market prices. Consequently, traditional optimal control approaches—such as Rule-Based Control (RBC)—prove inadequate to deal with the diverse dynamics which define the behavior of such complicated frameworks. Moreover, even sophisticated techniques—such as Model Predictive Control (MPC)—showcase model-related limitations that hinder the applicability of an optimal control scheme. To this end, AI model-free techniques such as Reinforcement Learning (RL) offer a fruitful potential for embedding efficient optimal control in cases of energy systems. Recent studies present promising results in various fields of engineering, indicating that RL frameworks may prove the key element for delivering efficient optimal control in smart buildings, electric vehicle charging and smart grid applications. The current paper provides a comprehensive review of RL implementations in energy systems frameworks—such as Renewable Energy Sources (RESs), Building Energy-Management Systems (BEMSs) and Electric Vehicle Charging Stations (EVCSs)—illustrating the benefits and the opportunities of such approaches. The work examines more than 80 highly cited papers focusing on recent RL research applications—between 2015 and 2023—and analyzes the model-free RL potential as regards the energy systems’ control optimization in the future.
Hydrogen as Short-Term Flexibility and Seasonal Storage in a Sector-Coupled Electricity Market
Authors: Loschan Christoph , Schwabeneder Daniel , Maldet Matthias , Lettner Georg , Auer Hans
Published: July 2023

The rapid expansion of renewable energies has the potential to decarbonize the electricity supply. This is more challenging in difficult-to-electrify sectors. The use of hydrogen provides a massive potential for this issue. However, expanding hydrogen production increases electricity demand while providing additional flexibility to the electricity market. This paper mainly aims to analyze the economic effects of this sector coupling between the European electricity and national hydrogen markets. The developed energy market model jointly considers both markets to reach an overall welfare optimum. A novel modeling approach allows the interaction of these markets without the need for several iterative optimization runs. This allows for a detailed analysis of various market participants’ changes in consumer and producer surpluses. The optimization is conducted in 13 connected Central European countries to account for various power plant fleets, generation mixes, and electricity prices. Results show an overall welfare increase of EUR 4 to 28 billion in 2030 and an EUR 5 to 158 billion increase in 2040. However, there is a surplus shift from consumers to producers. The consumer surplus is reduced by up to EUR 44 billion in 2030 and EUR 60 billion while producers benefit to achieve the overall welfare benefits. The reduction of consumer surplus changes if significant price peaks occur. Fuel cell applications can avoid these price peaks, resulting in a surplus shift from thermal power plants to consumers. Hence, consumer surplus can increase by up to EUR 146 billion in the respective 2040 scenarios. Pink hydrogen accounts for a sizable portion of total hydrogen production, up to 58 percent in 2030 and up to 30 percent in 2040. As a result, nuclear power plants that are nearly entirely allocated in France stand to benefit greatly from this sector coupling. Additional efforts could be made to address the link between hydrogen and natural gas prices. Furthermore, the potential for cross-border hydrogen trade and the implementation of national legal and regulatory frameworks could be assessed.
A Modern Strategy for the Selection of Efficacious Solar Panels at Industrial Level under an Interval-Valued Picture Fuzzy Set Hybrid Environment
Authors: Razzaque Asima , Masmali Ibtisam , Hassan Rimsha , Shuaib Umer , Razaq Abdul , Alhamzi Ghaliah , Noor Saima
Published: July 2023

Solar panels hold a significant amount of importance at an industrial level because they contribute to environmental sustainability by reducing carbon emissions, contribute to cost savings through reduced electricity bills, provide energy independence and reliability, and serve as a long-term investment with attractive returns. One of the objectives of this research article is to address the limitations of the current score function utilized in multi-criteria decision-making (MCDM) problems within an interval-valued picture fuzzy (IVPF) environment and to present an enhanced score function. Moreover, IVPF hybrid arithmetic operators and IVPF hybrid geometric operators are introduced in this article. These operators are further thoroughly examined to identify their key characteristics. By conducting a case study, an algorithm is formulated to select the most suitable solar panel to maximize energy availability at the industrial level in the framework of the newly proposed IVPF hybrid operators. In addition, a comprehensive comparative study is conducted to demonstrate the significance and validity of recently proposed novel techniques compared with existing methods.
Passive Flow Control Application Using Single and Double Vortex Generator on S809 Wind Turbine Airfoil
Authors: Özden Mustafa , Genç Mustafa Serdar , Koca Kemal
Published: July 2023

The current study is aimed at investigating the influences of vortex generator (VG) applications mounted to the suction and pressure surfaces of the S809 wind turbine airfoil at low Reynolds number flow conditions. Both single and double VG applications were investigated to provide technological advancement in wind turbine blades by optimizing their exact positions on the surface of the airfoil. The results of the smoke-wire experiment for the uncontrolled case reveal that a laminar separation bubble formed near the trailing edge of the suction surface, and it was moved towards the leading edge as expected when the angle of attack was increased, resulting in bubble burst and leading-edge flow separation at α = 12°. The u/U∞, laminar kinetic energy and total fluctuation energy contours obtained from the numerical study clearly show that both the single and double VG applications produced small eddies, and those eddies in the double VG case led the flow to be reattached at the trailing edge of the suction surface and to gain more momentum by energizing. This situation was clearly supported by the results of aerodynamic force; the double VG application caused the lift coefficient to increase, resulting in an enhancement of the aerodynamic performance. A novel finding is that the VG at the pressure surface caused the flow at the wake region to gain more energy and momentum, resulting in a reattached and steadier flow condition.
A Three-Day Forest-Bathing Retreat Enhances Positive Affect, Vitality, Optimism, and Gratitude: An Option for Green-Care Tourism in Italy?
Authors: Guardini Beatrice , Secco Laura , Moè Angelica , Pazzaglia Francesca , De Mas Giovanna , Vegetti Matteo , Perrone Rocco , Tilman Ann , Renzi Manuela , Rapisarda Stefania
Published: July 2023

Forest-bathing experiences can be seen as guided recreational activities led by non-clinical trained practitioners in the context of green, slow, and mindful tourism. Notwithstanding its growing practice, there is a lack of research on the psychological benefits which can help support nature-based tourism destination managers in unlocking the potential of this emerging tourism demand. This study will fill in this gap by testing the hypothesis that a three-day forest-bathing retreat, which fits with the most common type of holidays in Europe, will enhance positive affect, vitality, optimism, and gratitude as indicators of hedonic and eudaimonic well-being. Forty-four adults were involved in the residential program and filled-in self-report questionnaires before and after the experience to assess the well-being dimensions considered. Results showed a significant increase for all the assessed variables. We conclude that forest bathing as a mindful tourism practice carried out in natural settings dominated by forests can favor hedonic and eudaimonic well-being, thus becoming a potential source of attractiveness for nature-based touristic destinations, but more transdisciplinary efforts are needed to exploit this potential. In particular, research gaps still exist in Europe on cause–effect relations between forest features and psychological benefits, how forests could be managed to guarantee these benefits to the advantage of a tourism destination, and how tourism and forest management sectors could collaborate in this direction.
Responses and Post-Recovery of Physiological Traits after Drought–Heatwave Combined Event in 12 Urban Woody Species
Authors: Wang Yongkang , Xing Chen , Gu Yilin , Zhou Yang , Song Jinyan , Zhou Ziyi , Song Jia , Gao Jun
Published: July 2023

The frequency and intensity of droughts combined with heatwave events have increased under climate change, increasing destruction in urban areas and leading to severe impacts on urban plants. These impacts remain poorly understood at the species level. Here, we investigate the effects of a drought–heatwave event on 12 urban woody species using in situ records of the dynamic changes in physiological traits in the field before, during and after the drought–heatwave event to assess resistance and resilience to hot drought. We found the following: (1) Hydraulic and photosynthesis traits showed an instantaneous decline during the hot drought event in the 12 species, with severe drought-induced xylem embolism in hydraulic systems and a high percentage loss of hydraulic conductivity (PLC). (2) The two conifer species were less resistant to hot droughts than broadleaves but capable of post-stress recovery, suggesting that conifers showed better resilience and that broadleaves showed better resistance under hot drought stress. (3) The evergreen species showed strong resistance, while three deciduous species showed strong resilience to hot drought stress. (4) The three shrubs may be more vulnerable to hot droughts than trees, as they showed lower resistance and were not able to recover the current year’s growth.
Dynamic Modelling to Describe the Effect of Plant Extracts and Customised Starter Culture on Staphylococcus aureus Survival in Goat’s Raw Milk Soft Cheese
Authors: Silva Beatriz Nunes , Coelho-Fernandes Sara , Teixeira José António , Cadavez Vasco , Gonzales-Barron Ursula
Published: July 2023

This study characterises the effect of a customised starter culture (CSC) and plant extracts (lemon balm, sage, and spearmint) on Staphylococcus aureus (SA) and lactic acid bacteria (LAB) kinetics in goat’s raw milk soft cheeses. Raw milk cheeses were produced with and without the CSC and plant extracts, and analysed for pH, SA, and LAB counts throughout ripening. The pH change over maturation was described by an empirical decay function. To assess the effect of each bio-preservative on SA, dynamic Bigelow-type models were adjusted, while their effect on LAB was evaluated by classical Huang models and dynamic Huang–Cardinal models. The models showed that the bio-preservatives decreased the time necessary for a one-log reduction but generally affected the cheese pH drop and SA decay rates (logDref = 0.621–1.190 days; controls: 0.796–0.996 days). Spearmint and sage extracts affected the LAB specific growth rate (0.503 and 1.749 ln CFU/g day−1; corresponding controls: 1.421 and 0.806 ln CFU/g day−1), while lemon balm showed no impact (p > 0.05). The Huang–Cardinal models uncovered different optimum specific growth rates of indigenous LAB (1.560–1.705 ln CFU/g day−1) and LAB of cheeses with CSC (0.979–1.198 ln CFU/g day−1). The models produced validate the potential of the tested bio-preservatives to reduce SA, while identifying the impact of such strategies on the fermentation process.
Preparation of Complementary Food for Infants and Young Children with Beef Liver: Process Optimization and Storage Quality
Authors: Shen Ruheng , Yang Dawei , Zhang Li , Yu Qunli , Ma Xiaotong , Ma Guoyuan , Guo Zhaobin , Chen Cheng
Published: July 2023

In this study, fuzzy mathematics and response surface modeling were applied to optimize the preparation process of beef liver paste and characterize the proximate composition, sensory and physicochemical qualities, and in vitro simulated digestive properties while refrigerated at 0–4 °C (0, 3, 7, 15, 30, 45, and 60 days). The results showed that the optimal preparation process was 4.8% potato starch, 99.4% water, 10.2% olive oil, and a 3:2 ratio of chicken breast and beef liver. The beef liver paste prepared contained essential amino acids for infants and children, with a protein content of 10.29 g/100 g. During storage, the pH of the beef liver paste decreased significantly (p < 0.05) on day 7, texture and rheological properties decreased significantly after 30 days, a* values increased, L* and b* values gradually decreased, and TVB-N and TBARS values increased significantly (p < 0.05) on day 7 but were below the limit values during the storage period (TVB-N value ≤ 15 mg/100 g, TBARS value ≤ 1 mg/Kg). In vitro simulated digestion tests showed better digestibility and digestive characteristics in the first 15 days. The results of this study provide a reference for the development of beef liver products for infant and child supplementation.
Solitary and Periodic Wave Solutions of the Space-Time Fractional Extended Kawahara Equation
Authors: Varol Dilek
Published: July 2023

The extended Kawahara (Gardner Kawahara) equation is the improved form of the Korteweg–de Vries (KdV) equation, which is one of the most significant nonlinear evolution equations in mathematical physics. In that research, the analytical solutions of the conformable fractional extended Kawahara equation were acquired by utilizing the Jacobi elliptic function expansion method. The given expansion method was applied to different fractional forms of the extended Kawahara equation, such as the fraction that occurs in time, space, or both time and space by suitably changing the variables. In addition, various types of fractional problems are exhibited to expose the realistic application of the given method, and some of the obtained solutions were illustrated in two- or three-dimensional graphics as proof of the visualization.
Pluripotency-Associated microRNAs in Early Vertebrate Embryos and Stem Cells
Authors: Maraghechi Pouneh , Aponte Maria Teresa Salinas , Ecker András , Lázár Bence , Tóth Roland , Szabadi Nikolett Tokodyné , Gócza Elen
Published: July 2023

MicroRNAs (miRNAs), small non-coding RNA molecules, regulate a wide range of critical biological processes, such as proliferation, cell cycle progression, differentiation, survival, and apoptosis, in many cell types. The regulatory functions of miRNAs in embryogenesis and stem cell properties have been extensively investigated since the early years of miRNA discovery. In this review, we will compare and discuss the impact of stem-cell-specific miRNA clusters on the maintenance and regulation of early embryonic development, pluripotency, and self-renewal of embryonic stem cells, particularly in vertebrates.
Variations in NSP1 of Porcine Reproductive and Respiratory Syndrome Virus Isolated in China from 1996 to 2022
Authors: Zhang Zhiqing , Zhang Hang , Luo Qin , Zheng Yajie , Kong Weili , Huang Liangzong , Zhao Mengmeng
Published: July 2023

Since its successful isolation in China in 1995, the porcine reproductive and respiratory syndrome virus (PRRSV) has been mutating into highly pathogenic strains by constantly changing pathogenicity and genetic makeup. In this study, we investigated the prevalence and genetic variation of nonstructural protein 1 (NSP1) in PRRSV-2, the main strain prevalent in China. After formulating hypotheses regarding the biology of the NSP1 protein, the nucleotide and amino acid similarity of NSP1 were analyzed and compared in 193 PRRSV-2 strains. The results showed that NSP1 has a stable hydrophobic protein with a molecular weight of 43,060.76 Da. Although NSP1 lacked signal peptides, it could regulate host cell signaling. Furthermore, NSP1 of different strains had high nucleotide (79.6–100%) and amino acid similarity (78.6–100%). In the amino acid sequence comparison of 15 representative strains of PRRSV-2, multiple amino acid substitution sites were found in NSP1. Phylogenetic tree analysis showed that lineages 1 and 8 had different evolutionary branches with long genetic distances. This study lays the foundation for an in-depth understanding of the nature and genetic variation of NSP1 and the development of a safe and effective vaccine in the future.
Geodynamic ‘Hotspots’ in a Periglacial Landscape: Natural Hazards and Impacts on Productive Activities in Chilean Fjordlands, Northern Patagonia
Authors: Soto María-Victoria , Arriagada-González Joselyn , Molina-Benavides Martina , Cabello Misael , Contreras-Alonso Miguel , Ibarra Ignacio , Guevara Gabriela , Sepúlveda Sergio A. , Maerker Michael
Published: July 2023

In this paper, we study natural hazards and their potential impacts on productive activities in the Comau Fjord in Chilean Northern Patagonia. We carried out hazard mapping to identify areas with evidence of geomorphological activity on slopes in terms of landslides, river/tsunami flooding, and glacial retreat. The assessment of different geomorphic processes was carried out by both detailed fieldwork and analysis of satellite remote sensing and aerial photography information. We identified terrain units that are subject to multi-hazards overlapping different spatially distributed hazard maps. This information was overlaid with spatial data of economic activities in the area in order to establish the impacts of the natural hazards on the local salmon and mussel farming infrastructure (risk). The results suggest significant exposure levels for these productive activities and potential damage due to the occurrence of natural hazards. The extension of a major highway (CH-7 Austral Highway) on the east coast of the Comau Fjord will be a new incentive for economic development in the area. However, the highway construction sites show a high level of exposure to natural hazards, especially floodings and landslides. Our study highlights that the geohazard potential might have a high negative impact on future productive activity in the fjord as well as on the new highway infrastructure.
Knowledge, Practice and Self-Reported Confidence Level of Croatian Dentists in the Use of Local Anesthesia: A Cross-Sectional Study
Authors: Tadin Antonija , Aleric Klaudia , Jerkovic Daniel , Gavic Lidia
Published: July 2023

Objectives: To provide safe and effective local anesthesia, dentists must have knowledge of neuroanatomy, anesthesia agents, techniques, equipment, and proper use of local anesthetics. This study aims to explore the knowledge, practices, and confidence regarding local anesthetics and anesthetic techniques in dentistry. Material and Methods: The online cross-sectional questionnaire was conducted via social media, and yielded 441 responses from across the country (69.8% women and 30.2% men; 70.7% general dentists; and 29.3 specialists). The data collected included sociodemographic characteristics, knowledge, and practices pertaining to the usage of local anesthesia. The questionnaire also documents their self-assessed confidence level in applying different local anesthetic techniques and experiences with adverse reactions. The obtained data were processed by description and using a generalized linear model for regression. Results: The respondents had a median knowledge score of 6 out of a possible 14 points regarding local anesthetics in dental medicine, and their median self-confidence level in the successful application of various techniques of local anesthesia was 54 out of a maximum of 85 points. The results showed that a higher knowledge level was associated with the female gender (OR 1.83, CI 1.13–2.98, p = 0.014) and specialization in oral surgery (OR 7.04, CI 1.71–29.07, p = 0.007). In contrast, a lack of confidence in using various local anesthetic techniques was also associated with the female gender (OR 0.63, CI 0.41–0.99, p = 0.047) and specialization in orthodontics (OR 0.16, CI 0.03–0.88, p = 0.035). Of the respondents, 81.4% (n = 371) experienced a local complication, and 42.2% (n = 186) experienced a systemic complication during local anesthesia. The complications experienced cannot be associated with a lack of knowledge or self-confidence (p > 0.05). The majority of respondents (364 of the 441 total—82.5%) expressed interest in receiving further education on the topic of local anesthesia. Conclusions: The research results show that the dentists involved in the study have poor knowledge of local anesthetics and moderate self-reported confidence levels in using various local anesthetic techniques. Moreover, dentists’ self-confidence in applying different techniques of local anesthesia is not related to their knowledge. Therefore, it would be necessary for dentists to undergo a continuing dental education program that enables them to enhance their skills and knowledge in local anesthesia.
Unmet Supportive Care Needs after Non-Small Cell Lung Cancer Resection at a Tertiary Hospital in Seoul, South Korea
Authors: Park Junhee , Jung Wonyoung , Lee Genehee , Kang Danbee , Shim Young Mog , Kim Hong Kwan , Jeong Ansuk , Cho Juhee , Shin Dong Wook
Published: July 2023

The aim of this study is to identify the prevalence and predictors of unmet needs of non-small cell lung cancer (NSCLC) patients undergoing surgical resection in Seoul, South Korea. A total of 949 patients who completed survey questionnaires that included the Cancer Survivors’ Unmet Needs Korean version (CaSUN-K), fear of cancer recurrence (FCR) inventory-short form, and European Organization for Research and Treatment of Cancer Quality of Life Core Questionnaire (EORTC QLQ-C30) were recruited from January to October 2020. Multivariable logistic regression was used to determine the potential correlation of significant unmet needs, defined as any moderate or strong need, for each domain of CaSUN-K. Of the 949 participants, the mean age was 63.4 ± 8.8 years old, and 529 (55.7%) were male. Overall, 91.8% of participants reported one or more unmet need. The highest domains of moderate-to-strong unmet needs were existential survivorship (59.1%), comprehensive cancer care (51.2%), and information (49.7%). High FCR and poor emotional function were associated with moderate-to-strong unmet needs across all domains of CaSUN-K. NSCLC survivors with a recent diagnosis had more frequent disease-related unmet needs. Interventions to reduce the unmet needs of NSCLC patients should focus on relieving FCR and improving emotional functioning. Furthermore, emphasis should be placed on decreasing disease-related needs, particularly for early survivors of lung cancer during the re-entry phase.