bluuebunny/crossref_metadata_2025 huggingface dataset:
OMNI: Gas Chromatograph Captures Seven Common PET Radiotracer Analytes in under 5 Minutes
Authors: A’Keen Camry Vonyae’ , Mroz Jakub , Joseph Simon Kunta , Baquero Jairo , Cantorias Melchor V. , Carberry Patrick
Published: November 2023

A novel gas chromatography method was developed using automatic injections to identify and quantify the amount of residual solvents or analytes in samples of fluorine-18 and carbon-11 radiopharmaceuticals. This approach evaluates seven analytes in less than 5 versus 13 min of acquisition time. The method additionally includes a 3 min bakeout to aid in the removal and carry-over of higher-boiling impurities. Chromatographic parameters such as column temperature, hold time, column pressure, flow rate, and split ratios were adjusted and optimized to analyze radioactive drug samples containing analytes which include methanol, ethanol, acetone, acetonitrile, triethylamine, N,N-dimethylformamide, and dimethyl sulfoxide. The relative standard deviation for each solvent was determined to be no greater than 1.6%. The method limit of detection (LOD) and limit of quantification (LOQ) were between 0.053 and 0.163 and 0.000 (5.791 × 10−6) and 0.520 mg/mL, respectively. This GC technique, using flame ionization detection (FID), was validated and is currently employed for the routine quality control of all approved IND and RDRC PET radiopharmaceuticals at our center.
An EMR-Based Approach to Determine Frequency, Prescribing Pattern, and Characteristics of Patients Receiving Drugs with Pharmacogenomic Guidelines
Authors: MacKinnon George E. , Mills Megan , Stoddard Alexander , Urrutia Raul A. , Broeckel Ulrich
Published: November 2023

(1) Background: This retrospective analysis utilizing electronic medical record (EMR) data from a tertiary integrated health system sought to identify patients and prescribers who would benefit from pharmacogenomic (PGx) testing based on Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines. (2) Methods: EMR data from a clinical research data warehouse were analyzed from 845,518 patients that had an encounter between 2015 and 2019 at an academic medical center. Data were collected for 42 commercially available drugs with 52 evidence-based PGx guidelines from CPIC. Provider data were obtained through the EMR linked by specialty via national provider identification (NPI) number. (3) Results: A total of 845,518 patients had an encounter in the extraction period with 590,526 medication orders processed. A total of 335,849 (56.9%) patients had medication orders represented by CPIC drugs prescribed by 2803 providers, representing 239 distinct medications. (4) Conclusions: The results from this study show that over half of patients were prescribed a CPIC actionable medication from a variety of prescriber specialties. Understanding the magnitude of patients that may benefit from PGx testing, will enable the development of preemptive testing processes, physician support strategies, and pharmacist workflows to optimize outcomes should a PGx service be implemented.
Patients Are Using Dietary Supplement for the Treatment of Their Diseases without Consultation with Their Physicians and Pharmacists
Authors: Chiba Tsuyoshi
Published: November 2023

Inappropriate use of dietary supplements by patients might exacerbate their diseases. The aim of this study was to clarify the situation of dietary supplement use for disease treatment among patients. A cross-sectional online questionnaire survey was conducted between 18 and 30 November 2022. A preliminary survey revealed that the proportion of patients who used dietary supplements to treat their disease ranged from 7.7% (cancer) to 28.2% (menopausal disorder). In the actual survey, across all diseases, patients who used dietary supplements for treatment purposes were satisfied with their therapeutic effects. Information obtained via the internet was the most common reason given for starting to use supplements. The proportions of patients who used supplements concurrently with medicine ranged from 25.4% (liver disease) to 72.2% (mental disorder). Most users of supplements did not consult with their physicians or pharmacists about them. However, patients preferred face-to-face consultation with a physician or pharmacist when discussing the concomitant use of medicines. In conclusion, the role of pharmacists is important for the appropriate use of dietary supplements among patients, especially concomitant use with medicines. More awareness about dietary supplements is needed for healthcare professionals to consult with their patients.
Application-Aware Resource Allocation Based on Benefit–Cost Ratio in Computing Power Network with Heterogeneous Computing Resources
Authors: Wang Yahui , Li Yajie , Guo Jiaxing , Fan Yingbo , Chen Ling , Zhang Boxin , Wang Wei , Zhao Yongli , Zhang Jie
Published: November 2023

The computing power network (CPN) is expected to realize the efficient provisioning of heterogeneous computing power through the collaboration between cloud computing and edge computing. Heterogeneous computing resources consist of CPU, GPU, and other types of computing power. Different types of applications may have diverse requirements for heterogeneous computing resources, such as general applications, CPU-intensive applications, and GPU-intensive applications. Service providers are concerned about how to dynamically provide heterogeneous computing resources for different applications in a cost-effective manner, and how to deploy more applications as much as possible with limited resources. In this paper, the concept of the benefit–cost ratio (BCR) is proposed to quantify the usage efficiency of CPU and GPU in CPNs. An application-aware resource allocation (AARA) algorithm is designed for processing different types of applications. With massive simulations, we compare the performance of the AARA algorithm with a benchmark. In terms of blocking probability, resource utilization, and BCR, AARA achieves better performance than the benchmark. The simulation results indicate that more computing tasks can be accommodated by reducing 3.7% blocking probability through BCR-based resource allocation.
Analysis and Identification of Genes Associated with the Desiccation Sensitivity of Panax notoginseng Seeds
Authors: Wang Yanan , Wang Weiqing , Chi Xiulian , Cheng Meng , Wang Tielin , Zhan Xiaori , Bai Yunjun , Shen Chenjia , Li Xiaolin
Published: November 2023

Panax notoginseng (Burk.) F. H. Chen, a species of the genus Panax, radix has been traditionally used to deal with various hematological diseases and cardiovascular diseases since ancient times in East Asia. P. notoginseng produces recalcitrant seeds which are sensitive to desiccation and difficult to store for a long time. However, few data are available on the mechanism of the desiccation sensitivity of P. notoginseng seeds. To gain a comprehensive perspective of the genes associated with desiccation sensitivity, cDNA libraries from seeds under control and desiccation processes were prepared independently for Illumina sequencing. The data generated a total of 70,189,896 reads that were integrated and assembled into 55,097 unigenes with a mean length of 783 bp. In total, 12,025 differentially expressed genes (DEGs) were identified during the desiccation process. Among these DEGs, a number of central metabolism, hormonal network-, fatty acid-, and ascorbate-glutathione-related genes were included. Our data provide a comprehensive resource for identifying the genes associated with the desiccation sensitivity of P. notoginseng seeds.
An Overview of the Additive Manufacturing of Bast Fiber-Reinforced Composites and Envisaging Advancements Using the Patent Landscape
Authors: Balaji Devarajan , Arulmurugan Balasubramanian , Bhuvaneswari Venkateswaran
Published: November 2023

Natural fiber composites attract attention owing to their environmentally friendly attributes. Many techniques, including fiber treatment, coatings, and fiber orientations, are used to improve the strength of natural fiber-reinforced composites. Still, the strength needs to be improved as expected. At present, some automation in manufacturing is also supported. Recently, additive manufacturing (AM) of natural fiber-reinforced composites has attracted many researchers around the globe. In this work, researchers’ attention to various natural fibers that are 3D printed is articulated and consolidated, and the future scope of the additive manufacturing of natural fiber-reinforced composite is envisaged using the patent landscape. In addition, some of the advancements in additive manufacturing of natural fiber composites are also discussed with reference to the patents filed lately. This may be helpful for the researchers working on AM of natural fiber composites for taking their research into new orientations.
Influence of Type of Cross-Linking Agent on Structure and Transport Properties of Polydecylmethylsiloxane
Authors: Grushevenko Evgenia , Rokhmanka Tatiana , Sokolov Stepan , Basko Andrey , Borisov Ilya , Pochivalov Konstantin , Volkov Alexey
Published: November 2023

The development of membrane materials with high transport and separation properties for the removal of higher hydrocarbons from gas mixtures is an important and complex task. This work examines the effect of a cross-linking agent on the structure and transport properties of polydecylmethylsiloxane (C10), a material characterized by high selectivity towards C3+ hydrocarbons. C10 was cross-linked with various diene hydrocarbons, such as 1,7-octadiene (C10-OD), 1,9-decadiene (C10-DD), 1,11-dodecadiene (C10-DdD), and vinyl-terminated polysiloxanes, of different molecular weights: 500 g/mol (C10-Sil500) and 25,000 g/mol (C10-Sil25-OD). Using a number of characterization methods (IR-spectroscopy, WAXS, DSC, toluene sorption, and gas permeability), it was revealed that a change in the type and length of the cross-linking agent (at the same mole concentration of cross-linking agent) led to a significant change in the structure of the polymer material. The nature of cross-linking agent affected the arrangement of the decyl side-groups of the polymer, resulting in noticeable differences in the solubility, diffusivity, permeability, and selectivity of tested gases (N2, CH4, C2H6, and C4H10). For instance, an increase in the length of the hydrocarbon cross-linker was associated with a drop of n-butane permeability from 5510 (C10-OD) to 3000 Barrer (C10-DdD); however, the transition to a polysiloxane cross-linker led to an increase in corresponded permeability up to 8200 Barrer (C10-Sil25-OD). The n-butane/nitrogen selectivity was significantly higher for diene-type cross-linkers, and the maximum value was achieved for 1,7-octadiene (α(C4H10/N2) = 104).
Water Sorption in Hybrid Polyester/Glass/Jute Composites Processed via Compression Molding and Vacuum-Assisted Resin Transfer Molding
Authors: Aranha Rudá , Filho Mario A. Albuquerque , de Lima Santos Cícero , Fonseca Viviane M. , Rivera José L. V. , de Lima Antonio G. B. , de Amorim Wanderley F. , Carvalho Laura H.
Published: November 2023

The aim of this work is to analyze water sorption in hybrid polyester/glass fabric/jute fabric composites molded via compression and VARTM (Vacuum-Assisted Resin Transfer Molding). The laminates were produced with five different stacking sequences and subjected to water sorption testing at room temperature, 50 °C and 70 °C. This study consisted of two stages: experimental and theoretical stages. The composites had a fiber volume content ranging from 30% to 40%. Water absorption and diffusion coefficient in the hybrid composites were intermediate to those reinforced with a single type of fiber. There were no significant differences in these properties based on fiber arrangement once the composites reached saturation. Diffusion coefficient values were higher for specimens with jute fiber on at least one of the outer surfaces. Water sorption rates increased with higher immersion temperatures. The water sorption at saturation point was not affected by the manufacturing process. Among the hybrid composites, those with jute on the surfaces showed the highest diffusion coefficient, while those with glass on the surface had the lowest values. Higher diffusion coefficient values were observed at temperatures of 50 °C and 70 °C. The main influencing factors on the absorbed moisture content for composites are the presence and content of jute fibers in the system and the immersion temperature. The manufacturing process does not affect the water sorption at saturation point.
Liquid Crystal Polymer-Based Miniaturized Fully Implantable Deep Brain Stimulator
Authors: Ahn Seung-Hee , Koh Chin Su , Park Minkyung , Jun Sang Beom , Chang Jin Woo , Kim Sung June , Jung Hyun Ho , Jeong Joonsoo
Published: November 2023

A significant challenge in improving the deep brain stimulation (DBS) system is the miniaturization of the device, aiming to integrate both the stimulator and the electrode into a compact unit with a wireless charging capability to reduce invasiveness. We present a miniaturized, fully implantable, and battery-free DBS system designed for rats, using a liquid crystal polymer (LCP), a biocompatible and long-term reliable material. The system integrates the simulator circuit, the receiver coil, and a 20 mm long depth-type microelectrode array in a dome-shaped LCP package that is 13 mm in diameter and 5 mm in height. Wireless powering and control via an inductive link enable device miniaturization, allowing for full implantation and, thus, the free behavior of untethered animals. The eight-channel stimulation electrode array was microfabricated on an LCP substrate to form a multilayered system substrate, which was monolithically encapsulated by a domed LCP lid using a specialized spot-welding process. The device functionality was validated via an in vivo animal experiment using a neuropathic pain model in rats. This experiment demonstrated an increase in the mechanical withdrawal threshold of the rats with microelectrical stimulation delivered using the fully implanted device, highlighting the effectiveness of the system.
Advances in Hole Transport Materials for Layered Casting Solar Cells
Authors: Bui Vu Khac Hoang , Nguyen Thang Phan
Published: November 2023

Huge energy consumption and running out of fossil fuels has led to the advancement of renewable sources of power, including solar, wind, and tide. Among them, solar cells have been well developed with the significant achievement of silicon solar panels, which are popularly used as windows, rooftops, public lights, etc. In order to advance the application of solar cells, a flexible type is highly required, such as layered casting solar cells (LCSCs). Organic solar cells (OSCs), perovskite solar cells (PSCs), or dye-sensitive solar cells (DSSCs) are promising LCSCs for broadening the application of solar energy to many types of surfaces. LCSCs would be cost-effective, enable large-scale production, are highly efficient, and stable. Each layer of an LCSC is important for building the complete structure of a solar cell. Within the cell structure (active material, charge carrier transport layer, electrodes), hole transport layers (HTLs) play an important role in transporting holes to the anode. Recently, diverse HTLs from inorganic, organic, and organometallic materials have emerged to have a great impact on the stability, lifetime, and performance of OSC, PSC, or DSSC devices. This review summarizes the recent advances in the development of inorganic, organic, and organometallic HTLs for solar cells. Perspectives and challenges for HTL development and improvement are also highlighted.
The Characteristics of the Religiosity of Youth in Slovakia Today
Authors: Kondrla Peter , Ďurková Eva
Published: November 2023

This paper discusses the current characteristics of the religiosity of young people in Slovakia based on data that were obtained in a survey as part of the research project “Perspectives of the Development of Religiosity in Slovakia”, carried out in 2017–2020. The data presented in the article pertain to several areas related to the religious lives of young people. Firstly, data on general changes in the respondents’ religiosity (such as a weakening or a strengthening) are presented, followed by data on the causes that the respondents consider having influenced these changes. Secondly, since many of the respondents claimed factors related to receiving a lot of information as such causes, the article presents data to determine to what extent the respondents paid attention to religious media in Slovakia (newspaper, radio, television, and websites). Lastly, we present the data on the respondents’ attitudes towards the moral acceptance of some themes that are religiously controversial or politically divisive in Slovak society today. We then discuss the indications that emerge from the data on the religiosity of young people in Slovakia today, overall concluding that there is often an occurrence among Slovak youth of claiming an affiliation to an official church/confession/religious institution while developing and acting on individual religious opinions or beliefs.
Software-Defined Satellite Observation: A Fast Method Based on Virtual Resource Pools
Authors: Zhao Hang , Zhang Yamin , Jiang Qiangqiang , Wei Xiaofeng , Li Shizhong , Chen Bo
Published: November 2023

In recent years, the proliferation of remote sensing satellites has dramatically increased the demands of Earth observation and observing efficiency. Designing a promising satellite resource scheduling method is a pivotal way to meet the requirements of this scenario. However, with hundreds or more satellites involved, the existing optimization methods struggle to address the NP-hard resource scheduling problem effectively. In this paper, an approach named software-defined satellite observation (SDSO) is proposed. First, adopting the new design ideology, we define a unified specification based on a discrete spatial grid to describe the observation capability of all satellites. The observation resources are virtualized using the virtual resource pool technique and then stored in the database in advance, implementing on-demand acquisition for observation resources. Next, we designed a model of the remote sensing satellite resource scheduling problem based on a virtual resource pool and designed a solution method for searching information within the virtual resource pool. Finally, the experimental results show that the computational efficiency of the proposed SDSO methodology has a substantial advantage over the traditional methods. Meanwhile, with the growing number of satellites involved in scheduling, there is only a slight degradation in the execution performance of our method, while the time complexity of optimization-based approaches increases exponentially.
Effects of Temperature and Humidity on the Absorption Spectrum and Concentration of N2O Using an Open-Path Sensor System
Authors: Chen Jiahong , Zhao Yuefeng , Feng Zhihao , Zhang Nan , Wang Yanxuan , Shen Zhiqiang , Kang Zongmin , Li Qingsong
Published: November 2023

This paper examines the effects of temperature and humidity on the absorption spectrum and concentration of nitrous oxide (N2O) using a compact, portable open optical-path gas detection sensor system. We obtained the absorption coefficient and widened the linear function of the N2O absorption spectrum related to temperature by theoretical analysis and the high-resolution transmission molecular absorption database (HITRAN). Afterward, we conducted real-time monitoring of N2O in both campus and laboratory environments using lasers for a duration of 32 h and 6 h, respectively, and the results were compared and analyzed with the theoretical derivation. The results show that the concentration of N2O increased with increasing environmental temperature but decreased with increasing humidity. Furthermore, the variations in temperature and humidity significantly affected the peak values of the second-harmonic (2f) and first-harmonic (1f) signals. Finally, the temperature N2O concentration and humidity N2O curves were calibrated separately, and temperature changes were positively correlated with the N2O concentration, while humidity changes were negatively correlated with the N2O concentration. The experimental results indicate that the concentration of N2O and its absorption spectra are influenced by humidity and temperature, which has a significant reference value in the absorption and measurement of N2O in practical applications.
NO2 Concentration Estimation at Urban Ground Level by Integrating Sentinel 5P Data and ERA5 Using Machine Learning: The Milan (Italy) Case Study
Authors: Cedeno Jimenez Jesus Rodrigo , Brovelli Maria Antonia
Published: November 2023

The measurement of atmospheric NO2 pollution concentrations has become a critical topic due to its impact on human health. Ground sensors are the most popular method for measuring atmospheric pollution, but they can be expensive to purchase, install, and maintain. In contrast, satellite technology offers global coverage but typically provides concentration estimates at the tropospheric level, not at the ground level where most human activities take place. This work presents a model that can be used to estimate NO2 ground-level concentrations in metropolitan areas using Sentinel-5P satellite images and ERA5 meteorological data. The primary goal is to offer a cost-effective solution for Low- and Medium-Income Countries (LMICs) to assess air quality, thereby addressing the air quality measurement constraints. To validate the model’s accuracy, study points were selected in alignment with the Regional Agency for the Environment Protection (ARPA) NO2 sensor network in the Metropolitan City of Milan. The results showed that the RMSE of the model estimations was significantly lower than the standard deviation of the real measurements. This work fills the gaps in the literature by providing an accurate estimation model of NO2 in the Metropolitan City of Milan using both satellite data and ERA5 meteorological data. This work presents as an alternative to ground sensors by enabling more regions to assess their air quality effectively.
Trajectory Tracking Control of Transformer Inspection Robot Using Distributed Model Predictive Control
Authors: Wei Lai , Xiang Guofei , Ma Congjun , Jiang Xuejian , Dian Songyi
Published: November 2023

To overcome the difficulty in tracking the trajectory of an inspection robot inside a transformer, this paper proposes a distributed model predictive control method. First, the kinematics and dynamics models of a robot in transformer oil are established based on the Lagrange equation. Then, by using the nonlinear model predictive control method and following the distributed control theory, the motion of a robot in transformer oil is decoupled into five independent subsystems. Based on this, a distributed model predictive control (DMPC) method is then developed. Finally, the simulation results indicate that a robot motion control system based on DMPC achieves high tracking accuracy and robustness with reduced computing complexity, and it provides an effective solution for the motion control of robots in narrow environments.
Using Lower Limb Wearable Sensors to Identify Gait Modalities: A Machine-Learning-Based Approach
Authors: Hughes Liam David , Bencsik Martin , Bisele Maria , Barnett Cleveland Thomas
Published: November 2023

Real-world gait analysis can aid in clinical assessments and influence related interventions, free from the restrictions of a laboratory setting. Using individual accelerometers, we aimed to use a simple machine learning method to quantify the performance of the discrimination between three self-selected cyclical locomotion types using accelerometers placed at frequently referenced attachment locations. Thirty-five participants walked along a 10 m walkway at three different speeds. Triaxial accelerometers were attached to the sacrum, thighs and shanks. Slabs of magnitude, three-second-long accelerometer data were transformed into two-dimensional Fourier spectra. Principal component analysis was undertaken for data reduction and feature selection, followed by discriminant function analysis for classification. Accuracy was quantified by calculating scalar accounting for the distances between the three centroids and the scatter of each category’s cloud. The algorithm could successfully discriminate between gait modalities with 91% accuracy at the sacrum, 90% at the shanks and 87% at the thighs. Modalities were discriminated with high accuracy in all three sensor locations, where the most accurate location was the sacrum. Future research will focus on optimising the data processing of information from sensor locations that are advantageous for practical reasons, e.g., shank for prosthetic and orthotic devices.
Quantitative and Qualitative Analysis of Agricultural Fields Based on Aerial Multispectral Images Using Neural Networks
Authors: Strzępek Krzysztof , Salach Mateusz , Trybus Bartosz , Siwiec Karol , Pawłowicz Bartosz , Paszkiewicz Andrzej
Published: November 2023

This article presents an integrated system that uses the capabilities of unmanned aerial vehicles (UAVs) to perform a comprehensive crop analysis, combining qualitative and quantitative evaluations for efficient agricultural management. A convolutional neural network-based model, Detectron2, serves as the foundation for detecting and segmenting objects of interest in acquired aerial images. This model was trained on a dataset prepared using the COCO format, which features a variety of annotated objects. The system architecture comprises a frontend and a backend component. The frontend facilitates user interaction and annotation of objects on multispectral images. The backend involves image loading, project management, polygon handling, and multispectral image processing. For qualitative analysis, users can delineate regions of interest using polygons, which are then subjected to analysis using the Normalized Difference Vegetation Index (NDVI) or Optimized Soil Adjusted Vegetation Index (OSAVI). For quantitative analysis, the system deploys a pre-trained model capable of object detection, allowing for the counting and localization of specific objects, with a focus on young lettuce crops. The prediction quality of the model has been calculated using the AP (Average Precision) metric. The trained neural network exhibited robust performance in detecting objects, even within small images.
Application of Path Planning and Obstacle Avoidance for Riverbank Inspection
Authors: Jhang Jhong-Wei , Juang Jih-Gau
Published: November 2023

Most coastal trash comes from land. To prevent and control ocean pollution, it should be handled using sources that can maintain a clean ocean and improve the marine ecological environment. The proposed system can be used to inspect riverbanks and identify garbage on riverbanks. This waste can then be cleaned up before flowing into the sea. In this study, we utilized an unmanned aerial vehicle (UAV) to inspect riverbanks and applied path planning and obstacle avoidance to enhance the efficiency of UAV performance and ensure good adaptability in a complicated environment. Since most rivers in the middle and upper sections of the study area are rough and meandering, path planning was first addressed so that the drone could use the shortest path and less energy to perform the inspection task. Branches frequently protrude from the riverbank on both sides. Therefore, an instant obstacle avoidance algorithm was added to avoid various obstacles. Path planning was based on an Improved Particle Swarm Optimization (IPSO). A fuzzy system was added to the IPSO to adjust the parameters that could shorten the planned path. The Artificial Potential Field (APF) was applied for real-time dynamic obstacle avoidance. The proposed UAV system could be used to perform riverbank inspection successfully.
Synthesis and Crystal and Electronic Structures of the Zintl Phase Sr21Cd4Sb18
Authors: Ghosh Kowsik , Bobev Svilen
Published: November 2023

Reported herein are the synthesis and crystal chemistry analysis of the Zintl phase Sr21Cd4Sb18. Single crystals of this compound were grown using the Sn-flux method, and structural characterization was carried out using single-crystal X-ray diffraction. Crystal data: Monoclinic space group C2/m (No. 12, Z = 4); a = 18.2536(6) Å, b = 17.4018(5) Å, and c = 17.8979(6) Å, β = 92.024(1)°. The structure is based on edge- and corner-shared CdSb4 tetrahedra, which ultimately form octameric [Cd8Sb22] fragments, where two symmetry-equivalent subunits are connected via a homoatomic Sb–Sb interaction. The electronic band structure calculations contained herein reveal the emergence of a direct gap between the valence and the conduction bands.
Transportation Infrastructure and Digital Economy—Evidence from Chinese Cities
Authors: Shen Shuohua , Li Hongchang , Li Mingzhen
Published: November 2023

In this research, the influences of railways, roads, waterways, and civil aviation on the digital economy were analyzed using traffic, urban, and enterprise data in the integrated transport system. Regression was performed through the generalized spatial least square method (GS2SLS) in the empirical section to solve the endogeneity problem. It was verified that transportation infrastructure can promote the digital economy. While the development of railways, waterways, and roads is expected to rise by 1%, the digital economy will be increased by 0.0049, 0.0048, and 0.0031, respectively, and civil aviation’s effect is not significant. The robustness test results were still remarkable. From the industry level of cities, it was found that transportation infrastructure mainly promotes the development of the digital economy by upgrading the industrial structure. At the enterprise level, promoting entrepreneurship and facilitating the digital transformation of enterprises have become the main driving forces for the development of the digital economy, and strengthening labor flow is a vital promotion mechanism at the factor level of cities. In addition, a significant single-threshold effect is observed in promoting the digital economy by transportation infrastructure. In the cities that cross the threshold of the economic development level, the progress in the digital economy increases from 0.0027 and 0.0035 to 0.0059 and 0.0061 for 1% development of railways and roads; the promotion of the digital economy by transportation infrastructure is more evident in cities with a permanent-residents population of more than 3 million. Developing the digital economy and transport infrastructure is essential for economic recovery and sustainable development.
A Comparative Analysis of Outdoor Thermal Comfort Indicators Applied in China and Other Countries
Authors: Tao Zhiyi , Zhu Xiangdong , Xu Guoqiang , Zou Dezhi , Li Guo
Published: November 2023

Outdoor thermal comfort is an important criterion for evaluating the quality of outdoor activity environments and is also a significant indicator for assessing sustainable building design. Over the past century, more than 165 indoor and outdoor thermal comfort indexes have been developed to define human thermal comfort conditions under various circumstances and to quantify indoor and outdoor thermal environmental conditions. However, in the process of outdoor thermal comfort indicators becoming widely used worldwide, it remains a pressing research issue to compare the current state of application in China and other countries, identify the key areas of application for both sides, and outline the trends in outdoor thermal comfort index application. This study analyzed 346 articles on outdoor thermal comfort indicators. Employing bibliometric methods, we outline the general landscape of outdoor thermal comfort index applications in China and other countries. Additionally, we utilize comparative analysis to uncover similarities and differences in the research focus on outdoor thermal comfort. The research findings indicate the following: (1) Compared to China, other countries started outdoor thermal comfort index application research earlier. Their papers have higher average citation counts and engage in close academic collaborations. However, the quantity of published papers is fewer than in China. (2) The top five frequently used indexes in both China and other countries are PET (including mPET), UTCI, PMV, SET* (including OUT_SET*), and THI (including DI). China tends to use PET and UTCI more frequently than other countries. (3) The potential future directions for outdoor thermal comfort index applications in both China and other countries include: “monitoring and controlling regional outdoor thermal comfort at the temporal and spatial scales”, “multi-factors coupling effects on outdoor thermal comfort”, “human health assessment and prediction based on outdoor thermal comfort”, and “utilizing computational algorithms to calculate outdoor thermal comfort”. This study can serve as a reference for researchers and designers in the industry, contributing to the creation of sustainable outdoor environments.
Efficiency Optimization in Multi-Branch Converters through Dynamic Control
Authors: Pavlík Marek , Bereš Matej , Kováč Dobroslav , Vince Tibor , Kováčová Irena , Molnár Ján
Published: November 2023

As the global emphasis on solar energy intensifies, optimizing the efficiency of photovoltaic panels becomes crucial in meeting energy demands sustainably. Addressing this, our research delves deeply into advancing maximum power point tracking (MPPT), a pivotal component in perfecting the energy conversion process. Leveraging state-of-the-art mathematical modeling, in-depth simulations, and comprehensive experimental validation, we set out to markedly refine the performance of non-isolated multi-branch buck DC–DC converters. In this pursuit, we introduce an innovative algorithm meticulously designed to adjust the number of active branches. This adjustment is rooted in robust efficiency metrics, ensuring optimal power delivery even under dynamic and fluctuating conditions. We place a distinct emphasis on the transformative role of current in determining converter efficiency. Drawing from our findings, we advocate for an adaptive control strategy, precisely engineered to thrive in a spectrum of operational contexts. With this study, we not only present pivotal contributions to the domain of photovoltaic technology but also chart out clear expectations for future endeavors. Our hope is that these advancements serve as foundational steps, guiding the evolution of sustainable energy generation.
Organisational Interventions for Improving Mental Health of Project Management Practitioners during COVID-19 in Architecture, Engineering and Construction Sectors in Australia
Authors: Jin Xiaohua , Osei-Kyei Robert , Perera Srinath , Bawtree James , Tijani Bashir
Published: November 2023

The introduction of COVID-19-related psychosocial risks to the Australian architecture, engineering and construction (AEC) project organisation has triggered the development of innovative organisational interventions for mitigating the risks and promoting of positive mental health among project management (PM) practitioners. Therefore, the paper aims to explore the COVID-19-related organisational interventions for improving mental health in the AEC project organisation. Through a comprehensive literature review, 20 organisation interventions were retrieved, and an online expert forum was conducted with nine industry experts. The results of the expert opinion confirmed the 20 organisational interventions identified from the literature. Moreover, the organisational interventions were used to develop a questionnaire survey distributed among PM practitioners via convenient sampling. Mean score ranking analysis was used to analyses the survey responses from 58 participants. The research findings show that the identified organisational interventions are important, but are ranked differently by architecture, engineering and construction sectors based on their importance in promoting mental health.
Path Planning of an Electric Vehicle for Logistics Distribution Considering Carbon Emissions and Green Power Trading
Authors: Qiang Hao , Ou Rui , Hu Yanchun , Wu Zhenyu , Zhang Xiaohua
Published: November 2023

As environmental awareness continues to grow and government policies provide incentives, electric vehicles (EVs) are becoming more widely used in logistics distribution. Considering green power trading and carbon emissions, this paper addresses the green vehicle routing problem (GVRP) and constructs an electric vehicle path model with time windows to minimize the total cost. To solve the model, a hybrid adaptive genetic algorithm (HAGA) is proposed. An improved nearest-neighbor algorithm is adopted to improve the quality of the initial population, and the adaptive crossover and mutation operators are introduced to achieve the better solution. In addition, based on the Schneider case, HAGA is used to solve the models with and without considering green power trading separately, and the results show that considering green power trading can reduce the total cost by 3.22% and carbon emissions by 23.38 kg. Finally, the experimental simulations further prove that with the increase in case size, HAGA can effectively reduce total cost. And it is beneficial for the popularization of electric vehicles in logistics distribution.
A Prediction Hybrid Framework for Air Quality Integrated with W-BiLSTM(PSO)-GRU and XGBoost Methods
Authors: Chang Wenbing , Chen Xu , He Zhao , Zhou Shenghan
Published: November 2023

Air quality issues are critical to daily life and public health. However, air quality data are characterized by complexity and nonlinearity due to multiple factors. Coupled with the exponentially growing data volume, this provides both opportunities and challenges for utilizing deep learning techniques to reveal complex relationships in massive knowledge from multiple sources for correct air quality prediction. This paper proposes a prediction hybrid framework for air quality integrated with W-BiLSTM(PSO)-GRU and XGBoost methods. Exploiting the potential of wavelet decomposition and PSO parameter optimization, the prediction accuracy, stability and robustness was improved. The results indicate that the R2 values of PM2.5, PM10, SO2, CO, NO2, and O3 predictions exceeded 0.94, and the MAE and RMSE values were lower than 0.02 and 0.03, respectively. By integrating the state-of-the-art XGBoost algorithm, meteorological data from neighboring monitoring stations were taken into account to predict air quality trends, resulting in a wider range of forecasts. This strategic merger not only enhanced the prediction accuracy, but also effectively solved the problem of sudden interruption of monitoring. Rigorous analysis and careful experiments showed that the proposed method is effective and has high application value in air quality prediction, building a solid framework for informed decision-making and sustainable development policy formulation.
Ozone–Vacuum-Based Decontamination: Balancing Environmental Responsibility and Textile Waste
Authors: Bodoga Alexandra , Nistorac Andreea , Dragomir Alin , Ailenei Eugen Constantin , Seul Arina , Diaconu Mariana , Balan Catalin Dumitrel , Loghin Maria Carmen
Published: November 2023

This study explores the use of ozone decontamination as a sustainable approach for eradicating pathogens from various environments. Ozone, a highly reactive gas, demonstrates remarkable efficacy in eliminating bacteria, viruses, and fungi. Decontamination of textile materials using an innovative ozone treatment method conducted under vacuum conditions has been investigated. A hybrid apparatus comprising a vacuum and an ozone generator was employed for the decontamination process. Ozone decontamination offers environmental benefits by avoiding harmful by-products and minimising long-term environmental exposure. However, challenges include the need for proper equipment and training to ensure safety and effectiveness. This research underscores the promise of ozone decontamination as a powerful and eco-friendly method for pathogen eradication in textile materials with future developments in diverse settings.
Improving Interarea Mode Oscillation Damping in Multi-Machine Energy Systems through a Coordinated PSS and FACTS Controller Framework
Authors: Zamani Meysam , Shahgholian Ghazanfar , Fathollahi Arman , Mosavi Amir , Felde Imre
Published: November 2023

Power system stability is of paramount importance in the context of energy sustainability. The reliable and efficient operation of power systems is crucial for supporting modern societies, economies, and the growing demand for electricity while minimizing environmental impact and increasing sustainability. Due to the insufficient effect of power system stabilizers (PSSs) on damping the inter-area mode oscillations, Flexible AC Transmission System (FACTS) devices are utilized for damping this mode and stabilizing power systems. In the present study, a novel optimization framework considering different and variable weight coefficients based on eigenvalue locations is presented, and the parameters of PSS and variable impedance devices, including static Volt-Ampere Reactive (VAR) compensator (SVC) and Thyristor-Controlled Series Compensator (TCSC) (comprising amplifying gain factor and time constants of phase-compensating blocks), are optimized in a coordinated manner using the proposed optimization framework built based on genetic algorithm (GA). Moreover, in the suggested optimization framework, the locations of FACTS devices and control signals are considered optimization parameters. Numerical results for the IEEE 69-bus power system demonstrated an effective improvement in the damping of inter-area modes utilizing the offered approach.
Electric Vehicle Charging Station Power Supply Optimization with V2X Capabilities Based on Mixed-Integer Linear Programming
Authors: Šolić Antonio Josip , Jakus Damir , Vasilj Josip , Jolevski Danijel
Published: November 2023

The European Union is committed to both lowering greenhouse gas emissions and promoting the adoption of electric vehicles (EVs) on its roads. To achieve these goals, it is imperative to speed up the development of the charging infrastructure as well as to ensure the effective integration of the charging infrastructure into distribution networks. Given that EV charging costs significantly contribute to the total cost of owning an EV, it is important to hedge against rising electricity prices and ensure affordable charging for the end users. Connecting solar power plants and battery storage to the electric vehicle charging stations (EVCSs) serves as a measure of hedging against potential future electricity price increases but also as an option that can contribute to reducing impact on the distribution network loading. In addition to this, connecting EVCS through grid connections of existing consumers (office/residential buildings, shopping malls, etc.) can reduce grid connection costs for EVCS but also contribute to electricity cost reduction for both EVCS and existing end consumers. Additionally, by integrating advanced charging strategies like the vehicle-to-everything (V2X) approach, the overall charging costs can be reduced even further. This paper focuses on optimizing the power supply and operation of EVCS by considering strategic investments in grid connection, photovoltaic plants, and battery energy storage. The research explores the potential savings derived from reduced energy/charging costs, along with the reduction in peak power expenses for different power supply options. In addition to this, the research explores the effect of different EV charging strategies as well as EVCS grid connection on optimal investments and total system costs. The combined investment and energy management problem is focused on determining the optimal EVCS power supply and operation while minimizing total investment and operation expenditures over the project lifetime. The underlying optimization problems for different supply scenarios are cast as mixed-integer linear programming problems that can be solved efficiently. The results show the influence of different grid connection options and EV charging strategies on the joint operation and costs of EVCS and existing buildings.
Assessing the Environmental Performances of Nature-Based Solutions Implementation in Urban Environments through Visible and Near-Infrared Spectroscopy: A Combined Approach of Proximal and Remote Sensing for Monitoring and Evaluation
Authors: Bonifazi Giuseppe , Gasbarrone Riccardo , Serranti Silvia
Published: November 2023

The implementation of Nature-based Solutions (NbS) in urban environments is gaining momentum as a means to address environmental challenges and promote sustainable development. However, effective monitoring and evaluation are essential to assess the performance of NbS interventions and to guide decision-making. This research paper introduces a combined approach of proximal and remote sensing, based on visible and near-infrared spectroscopy, to monitor and evaluate NbS implementation in urban areas. The study focuses on the case of the UPPER (Urban Productive Parks for Sustainable Urban Regeneration) project and aims to establish urban Productive Parks as a novel NbS approach in the town of Latina (Italy). Field-based proximal sensing techniques (i.e., near-infrared spectroscopy, NIR) and satellite-based remote sensing data from the Sentinel-2 mission are employed. By integrating these techniques, the study enables comprehensive and multi-scale monitoring of vegetation health and assessment of vegetated areas. Various band ratio indices are calculated to assess vegetation coverage, water content, and urbanization. Temporal variations in these indices are analyzed to evaluate the effectiveness of NbS interventions and their impact on the urban environment. The combined approach of proximal and remote sensing demonstrates the potential for comprehensive and multi-scale monitoring of NbS in urban environments. The research findings contribute to the existing knowledge on NbS monitoring and evaluation, providing valuable insights for sustainable urban development and evidence-based decision-making.
Application of Diversity-Maintaining Adaptive Rafflesia Optimization Algorithm to Engineering Optimisation Problems
Authors: Pan Jeng-Shyang , Zhang Zhen , Chu Shu-Chuan , Lee Zne-Jung , Li Wei
Published: November 2023

The Diversity-Maintained Adaptive Rafflesia Optimization Algorithm represents an enhanced version of the original Rafflesia Optimization Algorithm. The latter draws inspiration from the unique characteristics displayed by the Rafflesia during its growth, simulating the entire lifecycle from blooming to seed dispersion. The incorporation of the Adaptive Weight Adjustment Strategy and the Diversity Maintenance Strategy assists the algorithm in averting premature convergence to local optima, subsequently bolstering its global search capabilities. When tested on the CEC2013 benchmark functions under a dimension of 30, the new algorithm was compared with ten optimization algorithms, including commonly used classical algorithms, such as PSO, DE, CSO, SCA, and the newly introduced ROA. Evaluation metrics included mean and variance, and the new algorithm outperformed on a majority of the test functions. Concurrently, the new algorithm was applied to six real-world engineering problems: tensile/compressive spring design, pressure vessel design, three-bar truss design, welded beam design, reducer design, and gear system design. In these comparative optimizations against other mainstream algorithms, the objective function’s mean value optimized by the new algorithm consistently surpassed that of other algorithms across all six engineering challenges. Such experimental outcomes validate the efficiency and reliability of the Diversity-Maintained Adaptive Rafflesia Optimization Algorithm in tackling optimization challenges. The Diversity- Maintained Adaptive Rafflesia Optimization Algorithm is capable of tuning the parameter values for the optimization of symmetry and asymmetry functions. As part of our future research endeavors, we aim to deploy this algorithm on an even broader array of diverse and distinct optimization problems, such as the arrangement of wireless sensor nodes, further solidifying its widespread applicability and efficacy.