bluuebunny/crossref_metadata_2025 huggingface dataset:
Feasibility Study on the Applicability of Intelligent Well Completion
Authors: Sleptsov Alexander , Medvedeva Lyudmila , Marinina Oksana , Savenok Olga
Published: July 2024

The relevance of assessing the applicability of intelligent wells using autonomous inflow control devices lies in the active development of the relevant sector of the oil and gas industry and the limited understanding of the economic efficiency of intelligent wells. The use of autonomous inflow control devices allows for a change in the composition of flow to the well, thus contributing to delaying the breakthrough of undesirable formation fluids, but at the same time, such an effect affects the dynamics of formation fluid production, which undoubtedly has a huge impact on the economic effect of the project. The analysis of scientific publications on the topic of “intelligent well completion” as a downhole production monitoring and remote production control system has shown that the vast majority of researchers pay attention to the evaluation of technological efficiency, ignoring the economic aspects of the proposed solutions. This study considered the dependence of the economic effect on the geological reservoir and technological well characteristics for variant 1—intelligent horizontal well (HW) completion using autonomous inflow control devices and variant 2—conventional horizontal well completion using the open hole. Calculations of production levels and dynamics in the two variants were performed on a created sector hydrodynamic model of a horizontal well operating in the depletion mode. The analysis of the obtained results allowed us to determine the applicability criteria of the proposed configuration of formation and well characteristics at the object of study, as well as to establish general dependencies of the net discounted income of an intelligent well. As a result of this study, it was determined that the economic efficiency of intelligent well completion with the use of autonomous inflow control devices relative to conventional well completion increases with decreasing permeability and drawdown pressure on the reservoir and reaches maximum values at the object of study at the thickness of the oil-saturated part of the reservoir about 5–6 m and the location of the wellbore in it at 35–40% of the thickness of the oil-saturated part below the gas–oil contact (GOC). This article covers the research gap in evaluating the economic efficiency of intelligent HW completion using AICD relative to conventional HW completion in oil rims.
Combined Deep-Fill and Histogram Equalization Algorithm for Full-Borehole Electrical Logging Image Restoration
Authors: Wang Junhua , Hou Zhenxue , Zhang Zhiqiang , Wang Meng , Cheng Haoran
Published: July 2024

Electrical borehole imaging tools cannot achieve full-borehole images due to their structure limitation. Gaps always occur between pads, and it is necessary to fill in the gaps for subsequent interpretation. In this paper, an improved model for borehole image restoration and enhancement is established by combining a “Deep-Fill” image repair algorithm based on generative adversarial networks (GANs) with histogram equalization principles. Firstly, resistivity data is converted into images, and the anomalous areas are manually repaired. Then, the manually repaired images undergo iterative training using the “Deep-Fill” model. Finally, the repaired images are further enhanced through histogram equalization principles. Results show the overall restoration quality of the model surpasses that of the original GAN-based restoration model, particularly in terms of texture coherence at junctions. This approach not only enhances the quality of repaired images but also improves the interpretability of geological features of the electrical imaging logs.
Educational Approaches for Integrating Advanced Environmental Remediation Technologies into Environmental Engineering: The ‘Four Styles’ Model
Authors: Liu Shan , Zhang Jiaquan , Tao Min , Tang Ping , Zhan Changlin , Guo Jianlin , Li Yanni , Liu Xianli
Published: July 2024

The current talent training system for the environmental engineering major (EEM) at local colleges faces significant challenges, including undefined training objectives, an incomplete curriculum, inconsistent practical teaching platforms, and homogeneous teaching teams. To address these issues, this study introduces the ‘Four Styles’ cultivation system implemented at the EEM of Hubei Polytechnic University. This system integrates advanced environmental remediation technologies into environmental engineering education through the development of a ‘1 + multiple’ curriculum, the establishment of ‘cloud + field’ practical platforms, and the formation of a diverse ‘1 + 2’ teaching team. The effectiveness of this system was evaluated using self-assessment scores from graduates and employer satisfaction ratings. Results showed that graduates rated their application ability with an average score of 3.96 ± 0.11, with the highest scores in work ethics (4.14), lifelong self-learning (4.11), and teamwork (4.09). Employer satisfaction with graduates’ abilities averaged 81.6 ± 2.33%, with the highest ratings for work ethics (86.0%), teamwork (85.5%), and lifelong self-learning (84.7%) Despite these successes, areas for improvement were identified, including better training in analyzing engineering problems (3.79) and mastering modern tools (3.79). These findings suggest that the ‘Four Styles’ cultivation system effectively enhances the practical skills of EEM students while identifying areas for future curriculum development.
Cube: An Open-Source Software for Clock Offset Estimation and Precise Point Positioning with Ambiguity Resolution
Authors: Liu Shuai , Yuan Yunbin , Guo Xiaosong , Wang Kezhi , Xiao Gongwei
Published: July 2024

Precise point positioning (PPP) is a prevalent, high-precision spatial absolution positioning method, and its performance can be enhanced by ambiguity resolution (AR). To fulfill the growing need for high-precision positioning, we developed an open-source GNSS data processing package based on the decoupled clock model called Cube, which integrates decoupled clock offset estimation and precise point positioning with ambiguity resolution (PPP-AR). Cube is a secondary development based on RTKLIB. Besides the decoupled clock model, Cube can also estimate legacy clocks for the International GNSS Service (IGS), as well as clocks with satellite code bias extraction, and perform PPP-AR using the integer-recovered clock model. In this work, we designed satellite clock estimation and PPP-AR experiments with one week of GPS data to validate Cube’s performance. Results show that the software can produce high-precision satellite clock products and positioning results that are adequate for daily scientific study. With Cube, researchers do not need to rely on public PPP-AR products, and they can estimate decoupled clock products and implement PPP-AR anytime.
A Goaf-Locating Method Based on the D-InSAR Technique and Stratified Okada Dislocation Model
Authors: Zhang Kewei , Wang Yunjia , Du Sen , Zhao Feng , Wang Teng , Zhang Nianbin , Zhou Dawei , Diao Xinpeng
Published: July 2024

Illegal coal mining is prevalent worldwide, leading to extensive ground subsidence and land collapse. It is crucial to define the location and spatial dimensions of these areas for the efficient prevention of the induced hazards. Conventional methods for goaf locating using the InSAR technique are mostly based on the probability integral model (PIM). However, The PIM requires detailed mining information to preset model parameters and does not account for the layered structure of the coal overburden, making it challenging to detect underground goaves in cases of illegal mining. In response, a novel method based on the InSAR technique and the Stratified Optimal Okada Dislocation Model, named S-ODM, is proposed for locating goaves with basic geological information. Firstly, the S-ODM employs a numerical model to establish a nonlinear function between the goaf parameters and InSAR-derived ground deformation. Then, in order to mitigate the influence of nearby mining activities, the goaf azimuth angle is estimated using the textures and trends of the InSAR-derived deformation time series. Finally, the goaf’s dimensions and location are estimated by the genetic algorithm–particle swarm optimization (GA-PSO). The effectiveness of the proposed method is validated using both simulation and real data, demonstrating average relative errors of 6.29% and 7.37%, respectively. Compared with the PIM and ODM, the proposed S-ODM shows improvements of 19.48% and 52.46% in geometric parameters. Additionally, the errors introduced by GA-PSO and the influence of ground deformation monitoring errors are discussed in this study.
Random Forest—Based Identification of Factors Influencing Ground Deformation Due to Mining Seismicity
Authors: Owczarz Karolina , Blachowski Jan
Published: July 2024

The goal of this study was to develop a model describing the relationship between the ground-displacement-caused tremors induced by underground mining, and mining and geological factors using the Random Forest Regression machine learning method. The Rudna mine (Poland) was selected as the research area, which is one of the largest deep copper ore mines in the world. The SAR Interferometry methods, Differential Interferometric Synthetic Aperture Radar (DInSAR) and Small Baseline Subset (SBAS), were used in the first case to detect line-of-sight (LOS) displacements, and in the second case to detect cumulative LOS displacements caused by mining tremors. The best-prediction LOS displacement model was characterized by R2 = 0.93 and RMSE = 5 mm, which proved the high effectiveness and a high degree of explanation of the variation of the dependent variable. The identified statistically significant driving variables included duration of exploitation, the area of the exploitation field, energy, goaf area, and the average depth of field exploitation. The results of the research indicate the great potential of the proposed solutions due to the availability of data (found in the resources of each mine), and the effectiveness of the methods used.
A Novel Approach for As-Built BIM Updating Using Inertial Measurement Unit and Mobile Laser Scanner
Authors: Yang Yuchen , Chen Yung-Tsang , Hancock Craig , Hamm Nicholas A. S. , Zhang Zhiang
Published: July 2024

Building Information Modeling (BIM) has recently been widely applied in the Architecture, Engineering, and Construction Industry (AEC). BIM graphical information can provide a more intuitive display of the building and its contents. However, during the Operation and Maintenance (O&M) stage of the building lifecycle, changes may occur in the building’s contents and cause inaccuracies in the BIM model, which could lead to inappropriate decisions. This study aims to address this issue by proposing a novel approach to creating 3D point clouds for updating as-built BIM models. The proposed approach is based on Pedestrian Dead Reckoning (PDR) for an Inertial Measurement Unit (IMU) integrated with a Mobile Laser Scanner (MLS) to create room-based 3D point clouds. Unlike conventional methods previously undertaken where a Terrestrial Laser Scanner (TLS) is used, the proposed approach utilizes low-cost MLS in combination with IMU to replace the TLS for indoor scanning. The approach eliminates the process of selecting scanning points and leveling of the TLS, enabling a more efficient and cost-effective creation of the point clouds. Scanning of three buildings with varying sizes and shapes was conducted. The results indicated that the proposed approach created room-based 3D point clouds with centimeter-level accuracy; it also proved to be more efficient than the TLS in updating the BIM models.
Selective Cellular Uptake and Druggability Efficacy through Functionalized Chitosan-Conjugated Polyamidoamine (PAMAM) Dendrimers
Authors: Hu Ye , Chen Jian , Hu Wenyan
Published: July 2024

Nanotechnology has ushered in significant advancements in drug design, revolutionizing the prevention, diagnosis, and treatment of various diseases. The strategic utilization of nanotechnology to enhance drug loading, delivery, and release has garnered increasing attention, leveraging the enhanced physical and chemical properties offered by these systems. Polyamidoamine (PAMAM) dendrimers have been pivotal in drug delivery, yet there is room for further enhancement. In this study, we conjugated PAMAM dendrimers with chitosan (CS) to augment cellular internalization in tumor cells. Specifically, doxorubicin (DOX) was initially loaded into PAMAM dendrimers to form DOX-loaded PAMAM (DOX@PAMAM) complexes via intermolecular forces. Subsequently, CS was linked onto the DOX-loaded PAMAM dendrimers to yield CS-conjugated PAMAM loaded with DOX (DOX@CS@PAMAM) through glutaraldehyde crosslinking via the Schiff base reaction. The resultant DOX@CS@PAMAM complexes were comprehensively characterized using Fourier-transform infrared (FTIR) spectroscopy, transmission electron microscopy (TEM), and dynamic light scattering (DLS). Notably, while the drug release profile of DOX@CS@PAMAM in acidic environments was inferior to that of DOX@PAMAM, DOX@CS@PAMAM demonstrated effective acid-responsive drug release, with a cumulative release of 70% within 25 h attributed to the imine linkage. Most importantly, DOX@CS@PAMAM exhibited significant selective cellular internalization rates and antitumor efficacy compared to DOX@PAMAM, as validated through cell viability assays, fluorescence imaging, and flow cytometry analysis. In summary, DOX@CS@PAMAM demonstrated superior antitumor effects compared to unconjugated PAMAM dendrimers, thereby broadening the scope of dendrimer-based nanomedicines with enhanced therapeutic efficacy and promising applications in cancer therapy.
Adoption of Deep-Learning Models for Managing Threat in API Calls with Transparency Obligation Practice for Overall Resilience
Authors: Basheer Nihala , Islam Shareeful , Alwaheidi Mohammed K. S. , Papastergiou Spyridon
Published: July 2024

System-to-system communication via Application Programming Interfaces (APIs) plays a pivotal role in the seamless interaction among software applications and systems for efficient and automated service delivery. APIs facilitate the exchange of data and functionalities across diverse platforms, enhancing operational efficiency and user experience. However, this also introduces potential vulnerabilities that attackers can exploit to compromise system security, highlighting the importance of identifying and mitigating associated security risks. By examining the weaknesses inherent in these APIs using security open-intelligence catalogues like CWE and CAPEC and implementing controls from NIST SP 800-53, organizations can significantly enhance their security posture, safeguarding their data and systems against potential threats. However, this task is challenging due to evolving threats and vulnerabilities. Additionally, it is challenging to analyse threats given the large volume of traffic generated from API calls. This work contributes to tackling this challenge and makes a novel contribution to managing threats within system-to-system communication through API calls. It introduces an integrated architecture that combines deep-learning models, i.e., ANN and MLP, for effective threat detection from large API call datasets. The identified threats are analysed to determine suitable mitigations for improving overall resilience. Furthermore, this work introduces transparency obligation practices for the entire AI life cycle, from dataset preprocessing to model performance evaluation, including data and methodological transparency and SHapley Additive exPlanations (SHAP) analysis, so that AI models are understandable by all user groups. The proposed methodology was validated through an experiment using the Windows PE Malware API dataset, achieving an average detection accuracy of 88%. The outcomes from the experiments are summarized to provide a list of key features, such as FindResourceExA and NtClose, which are linked with potential weaknesses and related threats, in order to identify accurate control actions to manage the threats.
Close-Range Coordination to Enhance Constant Distance Spacing Policies in Oversaturated Traffic Systems
Authors: Massow Kay , Pfeifer Niko , Ketzler Fabian , Radusch Ilja
Published: July 2024

In the pursuit of string stability within CACC (cooperative adaptive cruise control) platoons, prevalent research has favored constant time gap (CTG) spacing policies; namely, vehicle interspacing increases linearly with the speed. Although constant distance gap (CDG) spacing policies have greater potential to enhance traffic capacity, they suffer from notable limitations regarding string stability and diminished safety margins at high velocities. In our previous work, we proposed applying CDG in specific scenarios, such as starting platoons at signalized intersections, where traffic throughput is critical and safety requirements can be met due to relatively low speeds. We demonstrated the substantial potential of CDG to increase the capacity of signalized intersections under oversaturated conditions. However, our study also revealed potential performance drops of CDG in dense traffic networks. To address these issues, we propose close-range coordination between vehicles to (1) limit platoon length, (2) create gaps for merging, and (3) avoid entering intersections when there is a high likelihood of stopping within the intersection area. In this paper, we extend our previous work by implementing these three measures. We successfully evaluate their positive impact on CDG’s performance in entire traffic systems through large-scale traffic simulations involving several thousand vehicles, thereby affirming our earlier hypothesis
In Situ Pipe Prover Volume Measurement Method
Authors: Hu Jiacheng , Zhou Weikang , Chen Aijun , Cai Jiale , Yu Jing , Cui Zhengzhiyong , Li Dongsheng
Published: July 2024

To improve the accuracy of in situ measurement of the standard volumes of pipe provers and to shorten the traceability chain, a new method of in situ pipe prover volume measurement was developed alongside a supporting measurement device. This method is based on the geometric dimension approach, which measures the inner diameter and length of a pipe prover to calculate its volume. For inner diameter measurement, a three-probe inner-diameter algorithm model was established. This model was calibrated using a standard ring gauge of Φ313 mm, with the parameters calculated through fitting. Another standard ring gauge of Φ320 mm was used to verify the inner diameters determined by the algorithmic model. A laser interferometer was employed for the segmented measurement of the pipe prover length. The comprehensive measurement system was then used for in situ measurement of the standard pipe prover. The newly developed system achieved an expanded uncertainty of 0.012% (k = 2) in volume measurement, with the deviation between the measured and nominal pipe prover volumes being merely 0.007%. These results demonstrate that the proposed in situ measurement method offers ultra-high-precision measurement capabilities.
Development of an Integrated System of sEMG Signal Acquisition, Processing, and Analysis with AI Techniques
Authors: Laganà Filippo , Pratticò Danilo , Angiulli Giovanni , Oliva Giuseppe , Pullano Salvatore A. , Versaci Mario , La Foresta Fabio
Published: July 2024

The development of robust circuit structures remains a pivotal milestone in electronic device research. This article proposes an integrated hardware–software system designed for the acquisition, processing, and analysis of surface electromyographic (sEMG) signals. The system analyzes sEMG signals to understand muscle function and neuromuscular control, employing convolutional neural networks (CNNs) for pattern recognition. The electrical signals analyzed on healthy and unhealthy subjects are acquired using a meticulously developed integrated circuit system featuring biopotential acquisition electrodes. The signals captured in the database are extracted, classified, and interpreted by the application of CNNs with the aim of identifying patterns indicative of neuromuscular problems. By leveraging advanced learning techniques, the proposed method addresses the non-stationary nature of sEMG recordings and mitigates cross-talk effects commonly observed in electrical interference patterns captured by surface sensors. The integration of an AI algorithm with the signal acquisition device enhances the qualitative outcomes by eliminating redundant information. CNNs reveals their effectiveness in accurately deciphering complex data patterns from sEMG signals, identifying subjects with neuromuscular problems with high precision. This paper contributes to the landscape of biomedical research, advocating for the integration of advanced computational techniques to unravel complex physiological phenomena and enhance the utility of sEMG signal analysis.
A Review on the Application of Machine Learning in Gamma Spectroscopy: Challenges and Opportunities
Authors: Zehtabvar Mehrnaz , Taghandiki Kazem , Madani Nahid , Sardari Dariush , Bashiri Bashir
Published: July 2024

Gamma spectroscopy is an important analytical technique across various fields. Gamma spectroscopy uses the energy spectra of emitted gamma rays to examine the type and quantity of isotopes that exist in samples. Like any other analytical technique, traditional gamma spectroscopy encounters challenges that in some cases make the analysis uncertain. Machine Learning (ML) algorithms have been proposed as an approach for enhancing the precision and robustness of gamma spectroscopy. The current study introduces the basics behind ML and illustrates how they are applied in gamma spectroscopy using case studies. Major findings discussed herein demonstrate the developmental capability that ML has in improving gamma spectroscopy. Radioisotope identification, optimizing detector performance, and simplifying environmental monitoring processes have been the main areas in which ML algorithms have been deployed for improvement. These include the ability to predict and provide real-time spectrometry, among others, even though these opportunities come with their shortfalls such as the necessity for huge training datasets. This review explains that integrating ML into gamma spectroscopy marks a major change from current analytic techniques, with possible further developments in radiation detection and environmental science. It serves as very useful material for those studying or practicing AI and gamma spectroscopy and want to know more about it or need guidance on what is happening so far regarding AI integrated into gamma spectroscopy analysis.
A Review of Resilience Assessment and Recovery Strategies of Urban Rail Transit Networks
Authors: Hu Junhong , Yang Mingshu , Zhen Yunzhu
Published: July 2024

Rail transit is an important means of ensuring the sustainable development of urban transportation, but disturbance events caused by natural disasters, human factors, and other influences can lead to disruptions in rail transit operations. To cope with the impact of disturbance events on urban rail transit networks, and to explore the changes in rail transit network performance and recovery strategies under the influence of disturbance events from a resilience perspective, this paper overviews the existing research on resilience assessment and recovery strategies for urban rail transit networks. Firstly, the characteristics of the urban rail transit network and the model construction method are analyzed. Secondly, on the basis of combing the connotation development of system resilience, urban rail transit network resilience is defined, while the existing resilience metrics and assessment indexes are classified and summarized. Finally, the failure scenarios and recovery strategies of urban rail transportation network are deeply studied and discussed. The research results show that urban rail transit network resilience has been widely concerned by scholars, and certain results have been achieved in three aspects of resilience connotation, resilience assessment and recovery strategy. Nevertheless, further research is needed on these aspects. We propose future research directions that involve exploring modeling methods aligned with actual network topologies, developing unified indexes for resilience assessment and focusing on resilience assessment and recovery strategies under uncertain disturbance events. The research results can provide an important reference for the resilient operation and sustainable construction of urban rail transit.
Climate Neutrality in German Family Firms: Current Trends and Developments
Authors: Göhler Georg , Nugel Simon , Klingler Anna-Lena
Published: July 2024

The decarbonization of the economy is one of the most pressing challenges for governments in Germany and around the world to address the climate crisis. To achieve the German goal of climate neutrality by 2050, it will be crucial to find the appropriate policies to promote the process of decarbonization. An area that has received little attention thus far is the role of family firms in this process. This paper examines the status quo of German family firms in the process of setting and implementing climate targets, based on a survey of 600 German family and non-family firms. Our results show how differently the targets, efforts, motivations, and challenges are perceived by these firms. We find that small family firms lag behind non-family firms in setting targets, while larger family firms are currently catching up. We investigate the influence of motivational factors and challenges on the setting of climate protection targets by using a binary logistic regression analysis. Results indicate that family firms are less likely to set targets, while company size and intrinsic motivation positively influence this. Economic challenges, while being significant, do not hinder the establishment of climate targets.
Meaningful Youth Engagement in Sustainability Processes in Japan and Finland: A Comparative Assessment
Authors: Fukuda Miki , Zusman Eric
Published: July 2024

In many countries, young people are justifiably viewed as possessing the energy and ideas required to advance the sustainability agenda. However, the degree to which youth can influence that agenda depends on how meaningfully they engage in Sustainable Development Goals (SDGs) and related processes. Further, the extent to which they can meaningfully engage may vary across countries. Though studies have shed light on the core elements of meaningful youth engagement, those insights have not been used to compare how these elements vary across countries. The paper aims to fill this knowledge gap with a preliminary assessment of youth engagement in sustainability processes in Finland and Japan. The approach used herein draws upon relevant literature to construct an analytical framework comprised of four key elements underpinning meaningful youth engagement: (1) aims and justifications; (2) power-sharing; (3) transparency and accountability; and (4) support. The study then employs text mining, institutional analysis, and key informant interviews to offer a preliminary assessment of how meaningfully youth have engaged in sustainability processes in Finland and Japan. The assessment reveals that youth engagement mechanisms in Finland more clearly explicate aims and justifications, balance power dynamics, and enhance transparency and accountability than in Japan. Both countries could do more to offer support to young people. The article also suggests that additional research is needed on three areas: (1) the effect of underlying socio-cultural differences on youth engagement; (2) the relationship between youth engagement and the performance of SDGs 4, 7, and 13; and (3) the role of education as an upstream enabler of engagement.
The US Economy as a Network: A Comparison across Economic and Environmental Metrics
Authors: Hawkins Jason , Karki Sagun
Published: July 2024

Environmental-economic analysis is an evolving field that seeks to situate the human economy within environmental systems through its consumption of environmental resources and cycling of resources and waste products back into the environment. Environmental accounting has seen increased focus in recent years as national and regional governments look to better track environmental flows to aid in policy development and evaluation. This study outlines a conceptual environmental-economic framework founded on network science principles. An empirical study operationalizes portions of the framework and highlights the need for further research in this area to develop new data sources and analytic methods. We demonstrate a spatial mismatch between the location of water-intensive industries and the natural location of water resources (i.e., lakes, rivers, and precipitation), which climate change is likely to exacerbate. We use eigenvector centrality to measure differences in the US economy according to economic trade flow and five associated environmental flow accounts (land use, water consumption, energy use, mineral metal use, and greenhouse gas production). Population normalization helps to identify low-population counties that play a central role in the environmental-economic system as a function of their natural resources.
Detection of Growth Stages of Chilli Plants in a Hydroponic Grower Using Machine Vision and YOLOv8 Deep Learning Algorithms
Authors: Schneider Florian , Swiatek Jonas , Jelali Mohieddine
Published: July 2024

Vertical indoor farming (VIF) with hydroponics offers a promising perspective for sustainable food production. Intelligent control of VIF system components plays a key role in reducing operating costs and increasing crop yields. Modern machine vision (MV) systems use deep learning (DL) in combination with camera systems for various tasks in agriculture, such as disease and nutrient deficiency detection, and flower and fruit identification and classification for pollination and harvesting. This study presents the applicability of MV technology with DL modelling to detect the growth stages of chilli plants using YOLOv8 networks. The influence of different bird’s-eye view and side view datasets and different YOLOv8 architectures was analysed. To generate the image data for training and testing the YOLO models, chilli plants were grown in a hydroponic environment and imaged throughout their life cycle using four camera systems. The growth stages were divided into growing, flowering, and fruiting classes. All the trained YOLOv8 models showed reliable identification of growth stages with high accuracy. The results indicate that models trained with data from both views show better generalisation. YOLO’s middle architecture achieved the best performance.
Cultural Heritage Tourism and Sustainability: A Bibliometric Analysis
Authors: Geçikli Recep Murat , Turan Orhan , Lachytová Lenka , Dağlı Erkan , Kasalak Murad Alpaslan , Uğur Sinem Burcu , Guven Yigit
Published: July 2024

Cultural heritage tourism is a very important issue for the cultural transfer and sustainability of tourism. In parallel, cultural heritage tourism in the context of sustainability has become a popular field that has attracted the attention of researchers in recent years. Therefore, this study aims to analyze international publications on the relationship between cultural heritage tourism and sustainability, identifying trends in development and future research opportunities. Based on this purpose, 657 related studies have been found in the Web of Science database and analyzed in the Bibliometrix R package to map and systematically review the literature. By focusing specifically on the relationship between cultural heritage and sustainability, this study fills a gap in the existing literature, which often handles these issues separately. In addition, the research results contain valuable information that can shape future research agendas.
Feature Distributions of Technologies
Authors: Zhu Jiannan , Deng Chao , Pan Jiaofeng , Gu Fu , Guo Jianfeng
Published: July 2024

In this study, we propose a big data-based method for characterizing the feature distributions of multiple technologies within a specific domain. Traditional approaches, such as Gartner’s hype cycle or S-curve model, portray the developmental trajectory of individual technologies. However, these approaches are insufficient to encapsulate the aggregate characteristic distribution of multiple technologies within a specific domain. Thus, this study proposes an innovative method in terms of four proposed features, namely versatility, significance, commerciality, and disruptiveness, to characterize the technologies within a given domain. The research methodology involves that the features of technologies are quantitively portrayed using the representative keywords and volumes of returned search results from Google and Google Scholar in two-dimensional analytical spaces of technique and application. We demonstrate the applicability of this method using 452 technologies in the domain of intelligent robotics. The results of our assessment indicate that the versatility values are normally distributed, while the values of significance, commerciality, and disruptiveness follow power-law distributions, in which few technologies possess higher feature values. We also show that significant technologies are more likely to be commercialized or cause potential disruption, as such technologies have higher scores in these features. Further, we validly prove the robustness of our approach by comparing historical trends with the literature and characterizing technologies in reduced analytical spaces. Our method can be widely applied in analyzing feature distributions of technologies in different domains, and it can potentially be exploited in decisions like investment, trade, and science policy.
Technology in Forensic Sciences: Innovation and Precision
Authors: Chango Xavier , Flor-Unda Omar , Gil-Jiménez Pedro , Gómez-Moreno Hilario
Published: July 2024

The advancement of technology and its developments have provided the forensic sciences with many cutting-edge tools, devices, and applications, allowing forensics a better and more accurate understanding of the crime scene, a better and optimal acquisition of data and information, and faster processing, allowing more reliable conclusions to be obtained and substantially improving the scientific investigation of crime. This article describes the technological advances, their impacts, and the challenges faced by forensic specialists in using and implementing these technologies as tools to strengthen their field and laboratory investigations. The systematic review of the scientific literature used the PRISMA® methodology, analyzing documents from databases such as SCOPUS, Web of Science, Taylor & Francis, PubMed, and ProQuest. Studies were selected using a Cohen Kappa coefficient of 0.463. In total, 63 reference articles were selected. The impact of technology on investigations by forensic science experts presents great benefits, such as a greater possibility of digitizing the crime scene, allowing remote analysis through extended reality technologies, improvements in the accuracy and identification of biometric characteristics, portable equipment for on-site analysis, and Internet of things devices that use artificial intelligence and machine learning techniques. These alternatives improve forensic investigations without diminishing the investigator’s prominence and responsibility in the resolution of cases.
Per- and Polyfluoroalkyl Substances (PFASs) and Their Potential Effects on Female Reproductive Diseases
Authors: Yi Yuqing , Feng Yang , Shi Yuechen , Xiao Jiaming , Liu Ming , Wang Ke
Published: July 2024

Per- and polyfluoroalkyl substances (PFASs) are a class of anthropogenic organic compounds widely present in the natural and human living environments. These emerging persistent pollutants can enter the human body through multiple channels, posing risks to human health. In particular, exposure to PFASs in women may cause a series of reproductive health hazards and infertility. Based on a review of the existing literature, this study preliminarily summarizes the effects of PFAS exposure on the occurrence and development of female reproductive endocrine diseases, such as polycystic ovary syndrome (PCOS), endometriosis, primary ovarian insufficiency (POI), and diminished ovarian reserve (DOR). Furthermore, we outline the relevant mechanisms through which PFASs interfere with the physiological function of the female ovary and finally highlight the role played by nutrients in reducing the reproductive health hazards caused by PFASs. It is worth noting that the physiological mechanisms of PFASs in the above diseases are still unclear. Therefore, it is necessary to further study the molecular mechanisms of PFASs in female reproductive diseases and the role of nutrients in this process.
Microplastic Particles and Fibers in Seasonal Ice of the Northern Baltic Sea
Authors: Reineccius Janika , Heck Mareike , Waniek Joanna J.
Published: July 2024

Microplastic pollution is a pervasive issue, with remarkably high concentrations observed even in the most remote locations such as Arctic sea ice and snow. The reason for such large microplastic abundances in sea ice is still speculative and applies mainly to saline or freshwater conditions. In this study, we investigated seasonal ice core samples collected in March 2021 from the northern Baltic Sea (Gulf of Bothnia) for their microplastic distributions. The Baltic Sea is characterized by low salinity and can be ice-covered for up to six months annually. Microplastics were analyzed in the melted ice samples using an adsorption technique and Raman microscopy to identify their abundances, colors, shapes, and sizes to calculate their masses. Due to the strong dynamic of the ice layer and the repeated melting and freezing processes during the ice formation, no discernible trends in microplastic abundances, masses, or polymer types were observed throughout the ice core length. The average microplastic abundance (±SD) in the Baltic Sea ice was determined to be 22.3 ± 8.6 N L−1, with 64.9% of the particles exhibiting a particulate shape and 35.1% having a fibrous shape. The most prevalent polymer type was polyethylene terephthalate (PET), accounting for 44.4% of all polymers. This is likely due to the high proportion of PET fibers (93.8%). The majority of particle-shaped microplastics were identified as polyethylene (PE; 37.2%), followed by PET (17.2%), polyvinyl chloride (PVC; 15.9%), and polypropylene (PP; 15.9%). No correlations were found between microplastic concentrations and proximity to land, cities, industries, or rivers, except for PP mass concentrations and particle sizes, which correlated with distances to industries in Luleå, Sweden.
Awareness, Intention to Use Pre-Exposure Prophylaxis, and Factors Associated with Awareness among Men Who Have Sex with Men in the Republic of Korea
Authors: Jang Sarang , Sohn Aeree
Published: July 2024

This study assessed pre-exposure prophylaxis (PrEP) awareness, knowledge, and attitudes in a sample of 1389 men who have sex with men (MSM). Using a self-report survey, we measured PrEP knowledge mean scores, attitudes toward PrEP, and awareness rates to identify the key influences on PrEP uptake. PrEP awareness among participants was 66.3%, with 33.7% having never heard of PrEP. Approximately 4.5% had previous experience with PrEP but were not current users, while only 2.7% reported that they were currently taking it. Logistic regression analysis revealed that the significant predictors of PrEP awareness were educational attainment, marital status, prior HIV testing knowledge, and attitudes toward PrEP. Several demographic factors were not significant predictors of intention to use PrEP; however, using the internet to meet partners, number of partners, HIV testing, positive attitudes, and reduced perceived barriers played a critical role in shaping PrEP use intentions. These findings highlight the critical role of targeted educational strategies and tailored public health messaging in enhancing the awareness and intention to use PrEP among MSM.
Longitudinal Monitoring of the Effects of Anti-Adenoviral Treatment Regimens in a Permissive In Vivo Model
Authors: Tollefson Ann E. , Cline-Smith Anna , Spencer Jacqueline F. , Ying Baoling , Reyna Dawn M. , Lipka Elke , James Scott H. , Toth Karoly
Published: July 2024

Adenovirus infections of immunocompromised patients can cause life-threatening disseminated disease. While there are presently no drugs specifically approved to treat these infections, there are several compounds that showed efficacy against adenovirus in preclinical studies. For any such compound, low toxicity is an essential requirement. As cumulative drug effects can accentuate pathology, especially in patients with other morbidities, it is important to limit antiviral exposure to what is absolutely necessary. This is achievable by monitoring the virus burden of the patients and administering antivirals to suppress virus replication to a non-pathogenic level. We modeled such a system using Syrian hamsters infected with a replication-competent adenovirus vector, in which luciferase expression is coupled to virus replication. We found that virus replication could be followed in vivo in the same animal by repeated measurement of luciferase expression. To test the utility of an interrupted treatment regimen, we used NPP-669 and valganciclovir, two antiviral compounds with high and moderate anti-adenoviral efficacy, respectively. We found that short-term treatment of adenovirus-infected hamsters at times of peak virus replication can prevent virus-associated pathology. Thus, we believe that this animal model can be used to model different treatment regimens for anti-adenoviral compounds.
A Novel Mastadenovirus from Nyctalus noctula Which Represents a Distinct Evolutionary Branch of Viruses from Bats in Europe
Authors: Speranskaya Anna S. , Dorokhin Alexander V. , Korneenko Elena V. , Chudinov Ivan K. , Samoilov Andrei E. , Kruskop Sergei V.
Published: July 2024

Bats are natural hosts of a wide variety of viruses, including adenoviruses. European bats are known to carry mastadenoviruses categorized as species B (widespread in European Vespertilionidae bats) and whose taxonomy has not been clarified. We examined fecal samples from Vespertilionidae bats (five species) captured in central Russia and found that 2/12 (16%) were positive for mastadenoviruses. The partial genome of the mastadenovirus was assembled from Pipistrellus nathusii, representing the bat adenovirus species B. The complete genome (37,915 nt) of a novel mastadenovirus was assembled from Nyctalus noctula and named BatAdV/MOW15-Nn19/Quixote. Comparative studies showed significant divergence of the Quixote genome sequence from European bat mastadenoviruses, while the only known virus showing low similarity was the isolate WA3301 from an Australian bat, and together they formed a subclade that separated from other BatAdVs. Phylogenetic and comparative analysis of the protein-coding genes provided evidence that Quixote is related to a novel species within the genus Mastadenovirus, provisionally named “K” (as the next available letter for the species). Phylogenetic analyses revealed that some earlier viruses from Western European bats, for which only partial DNA polymerase genes are known, are most likely members of the tentatively named species “K”. Thus, at least two species of mastadenovirus are circulating in bats throughout Europe, from western to eastern areas.
Comparison of Post-Vaccination Response (Humoral and Cellular) to BNT162b2 in Clinical Cases, Kidney and Pancreas Transplant Recipient with Immunocompetent Subjects over Almost Two Years of Parallel Monitoring
Authors: Walory Jaroslaw , Ksiazek Iza , Wegrzynska Karolina , Baraniak Anna
Published: July 2024

Background: Vaccination is one of the most effective medical interventions to prevent infectious diseases. The introduction of vaccines against coronavirus acute respiratory syndrome 2 (SARS-CoV-2) was aimed at preventing severe illness and death due to coronavirus disease 2019 (COVID-19). Solid organ transplant recipients (SOTRs) are at high risk of infection with SARS-CoV-2 and serious effects associated with COVID-19, mainly due to the use of immunosuppressive therapies, which further cause suboptimal response to COVID-19 vaccination. Aim of the study: We aimed to compare post-vaccination response to BNT162b2 in kidney–pancreas transplant recipient, specifically in immunocompetent individuals, over two years of simultaneous monitoring. Methods: To determine the humoral response, the levels of the IgG and IgA anti-S1 antibodies were measured. To assess the cellular response to SARS-CoV-2, the released IFN-γ-S1 was determinate. Results and Conclusion: After primary vaccination, compared to immunocompetent subjects, SOTR showed lower seroconversion for both antibody classes. Only the additional dose produced antibodies at the level reached by the control group after the baseline vaccination. During the monitored period, SOTR did not achieve a positive cellular response in contrast to immunocompetent individuals, so in order to obtain longer protection, including immune memory, the adoption of booster doses of the vaccine should be considered.
A Pseudovirus Nanoparticle Displaying the Vaccinia Virus L1 Protein Elicited High Neutralizing Antibody Titers and Provided Complete Protection to Mice against Mortality Caused by a Vaccinia Virus Challenge
Authors: Huang Pengwei , Xia Ming , Vago Frank S. , Jiang Wen , Tan Ming
Published: July 2024

The recent worldwide incidence of mpox infection and concerns about future emerging variants of mpox viruses highlight the need for the development of a new generation of mpox vaccines. To achieve this goal, we utilized our norovirus S nanoparticle vaccine platform to produce and evaluate two pseudovirus nanoparticles (PVNPs), S-L1 and S-J1. These PVNPs displayed the L1 neutralizing antigen target of the vaccinia virus and a yet-untested J1 antigen of the mpox virus, respectively, with the aim of creating an effective nanoparticle-based mpox vaccine. Each self-assembled PVNP consists of an inner shell resembling the interior layer of the norovirus capsid and multiple L1 or J1 antigens on the surface. The PVNPs improved the antibody responses toward the displayed L1 or J1 antigens in mice, resulting in significantly greater L1/J1-specific IgG and IgA titers than those elicited by the corresponding free L1 or J1 antigens. After immunization with the S-L1 PVNPs, the mouse sera exhibited high neutralizing antibody titers against the vaccinia virus, and the S-L1 PVNPs provided mice with 100% protection against mortality caused by vaccinia virus challenge. In contrast, the S-J1 PVNPs induced low neutralizing antibody titers and conferred mice weak protective immunity. These data confirm that the L1 protein is an excellent vaccine target and that the readily available S-L1 PVNPs are a promising mpox vaccine candidate worthy of further development.
Comparison of Eye Axial Length Measurements Taken Using Partial Coherence Interferometry and OCT Biometry
Authors: Rizzieri Nicola , Facchin Alessio
Published: July 2024

This study evaluates the inter-device measurement properties of partial coherence interferometry (PCI) and spectral domain optical coherence tomography (SD-OCT) in measuring axial length, particularly for myopia management. We recruited 82 eyes from 41 adult participants with a mean age of 31.0 ± 17.6 years and a mean spherical equivalent of −2.20 ± 2.28 D. Axial length was measured using SD-OCT and PCI for both the right and left eyes. Agreement between the two measurements was assessed using Bland–Altman analysis, and graphs and values were compared with linear mixed models. The results show a near-to-zero and non-significant bias between measurements. The 95% limits of agreement showed a value of 0.06 mm. Both devices can accurately measure the axial length. OCT biometry performed with SD-OCT can be successfully interchanged with partial coherence interferometry, but they should be cautiously interchanged when performing longitudinal comparisons.