bluuebunny/crossref_metadata_2025 huggingface dataset:
Culinary Medicine or Culinary Nutrition? Defining Terms for Use in Education and Practice
Authors: Croxford Sharon , Stirling Emma , MacLaren Julia , McWhorter John , Frederick Lynn , Thomas Olivia
Published: February 2024

Examination of how terms such as culinary nutrition, culinary nutrition science, culinary medicine, culinary nutrition professional, culinary nutrition intervention, culinary nutrition activity, and culinary nutrition competency are used in practice, and the creation of consensus definitions will promote the consistent use of these terms across work areas and disciplines. Thirty leading practitioners, academics, and researchers in the fields of food and nutrition across Australia, the United States, Canada, United Kingdom, Europe, and Asia were approached by investigators via email to submit definitions of key terms using a Qualtrics survey link. Further participants were reached through snowball recruitment. Initial emails were sent in October and November 2021 with subsequent reminders between November 2021 and March 2022. Two researchers undertook content analysis of the text answers for each of the terms and generated definitions for discussion and consensus. Thirty-seven participants commenced the survey and twenty-three submitted one or more definitions. Agreed definitions fell into two categories: practice concepts and practitioners. Further discussion amongst investigators led to the creation of a visual map to demonstrate the interrelationship of terms. Culinary nutrition science underpins, and interprofessional collaboration characterizes practice in this area, however, further work is needed to define competencies and model best practice.
Molecular Characterization Analysis and Adaptive Responses of Spodoptera frugiperda (Lepidoptera: Noctuidae) to Nutritional and Enzymatic Variabilities in Various Maize Cultivars
Authors: Zhang Qiangyan , Zhang Yanlei , Zhang Kexin , Liu Huiping , Gou Yuping , Li Chunchun , Haq Inzamam Ul , Quandahor Peter , Liu Changzhong
Published: February 2024

The fall armyworm, Spodoptera frugiperda Smith (Lepidoptera: Noctuidae), a common agricultural pest known for its extensive migration and wide host ranges, causes considerable harm to maize (Zea mays L.). In this study, we utilized two molecular marker genes, COI and Tpi, to compare the genetic characteristics of the collected original samples. Additionally, through an interactive study between S. frugiperda larvae and six maize varieties aiming to understand the insect’s adaptability and resistance mechanisms, our analysis revealed that both the COI and Tpi genes identified S. frugiperda as the corn strain. Further examination of the larvae showed significant differences in nutritional indices, digestive, and detoxification enzyme activities. Special maize varieties were found to offer higher efficiency in nutrient conversion and assimilation compared with common varieties. This study revealed adaptations in S. frugiperda’s digestive and detoxification processes in response to the different maize varieties. For instance, larvae reared on common maize exhibited elevated amylase and lipase activities. Interestingly, detoxification enzyme activities exhibited different patterns of variation in different maize varieties. The Pearson correlation analysis between nutritional indices, enzyme activities, and the nutritional content and secondary metabolites of maize leaves provided deeper insights into the pest’s adaptability. The results highlighted significant relationships between specific nutritional components in maize and the physiological responses of S. frugiperda. Overall, our findings contribute substantially to the understanding of S. frugiperda’s host plant adaptability, offering critical insights for the development of sustainable pest management strategies.
Influence of Mining Sequence of Branch on Stope Pressure Behaviour on Continuous Mining and Continuous Backfilling
Authors: Lin Hai , Yang Yang , Chen Cheng , Ding Chenxi
Published: February 2024

Instability in coal pillars and filling bodies is a common occurrence during the mining process of continuous mining and continuous backfilling (CMCB). In view of this, combining numerical simulation, similarity simulation, and on-site testing approaches, backfill mining models were established in Flac3d5.01 software, similarity model test bench, and “two-stage”, “three-stage”, and “four-stage” mining sequences were conducted; the stress characteristics of coal pillar-filling body and the displacement evolution law of surrounding rock have been compared under three typical mining sequences. The results show that compared to two-stage mining sequence, three-stage and four-stage mining sequences provide sufficient time for the solidification of the filling body. The coal pillar exhibits better stability in the early stage of mining, but the stress concentration phenomenon is more significant in the later stage of mining. The stress concentration coefficient is the highest when the width of the coal pillar is 10 m. The integrity of the overburden is intact in different mining sequences, with only a small amount of separation and longitudinal cracks. Increasing the number of mining stages significantly reduces the roof subsidence, with the maximum roof subsidence in the three- and four-stage mining sequences being only 62.0% and 33.9% of that in the two-stage mining sequence. “Two stages”, “three stages”, and “four stages” of mining sequences are implemented in response to the requirements of weak and thick coal seam mining in Haoyuan Coal Mine and gangue disposal in Chahasu Coal Mine. Good engineering applications are achieved, enabling the realisation of safe, green, and efficient coal mining.
Co-Injection of Foam and Particles: An Approach for Bottom Water Control in Fractured-Vuggy Reservoirs
Authors: Wang Jianhai , Feng Yibo , Cao Aiqing , Zhang Jingyu , Chen Danqi
Published: February 2024

Fractured-vuggy carbonate reservoirs are tectonically complex; their reservoirs are dominated by holes and fractures, which are extremely nonhomogeneous and are difficultly exploited. Conventional water injection can lead to water flooding, and the recovery effect is poor. This paper takes the injection of foam and solid particles to control bottom water as the research direction. Firstly, the rheological properties of foam were studied under different foam qualities and the presence of particles. The ability of foam to carry particles was tested. By designing a microcosmic model of a fractured-vuggy reservoir, we investigated the remaining oil types and the distribution caused by bottom water. Additionally, we analyzed the mechanisms of remaining oil mobilization and bottom water plugging during foam flooding and foam–particle co-injection. The experimental results showed that foam was a typical power-law fluid. Foam with a quality of 80% had good stability and apparent viscosity. During foam flooding, foam floated at the top of the dissolution cavities, effectively driving attic oil. Additionally, the gas cap is released when the foam collapses, which can provide pressure energy to supplement the energy of the reservoir. Collaborative injection of foam and solid particles into the reservoir possessed several advantages. On one hand, it inherited the benefits of foam flooding. On the other hand, the foam transported particles deep into the reservoir. Under the influence of gravity, particles settled and accumulated in the fractures or cavities, forming bridge plugs at the connection points, effectively controlling bottom water channeling. The co-injection of foam and solid particles holds significant potential for applications.
Exploring Semantic Prompts in the Segment Anything Model for Domain Adaptation
Authors: Wang Ziquan , Zhang Yongsheng , Zhang Zhenchao , Jiang Zhipeng , Yu Ying , Li Li , Li Lei
Published: February 2024

Robust segmentation in adverse weather conditions is crucial for autonomous driving. However, these scenes struggle with recognition and make annotations expensive, resulting in poor performance. As a result, the Segment Anything Model (SAM) was recently proposed to finely segment the spatial structure of scenes and to provide powerful prior spatial information, thus showing great promise in resolving these problems. However, SAM cannot be applied directly for different geographic scales and non-semantic outputs. To address these issues, we propose SAM-EDA, which integrates SAM into an unsupervised domain adaptation mean-teacher segmentation framework. In this method, we use a “teacher-assistant” model to provide semantic pseudo-labels, which will fill in the holes in the fine spatial structure given by SAM and generate pseudo-labels close to the ground truth, which then guide the student model for learning. Here, the “teacher-assistant” model helps to distill knowledge. During testing, only the student model is used, thus greatly improving efficiency. We tested SAM-EDA on mainstream segmentation benchmarks in adverse weather conditions and obtained a more-robust segmentation model.
High Spatial Resolution Leaf Area Index Estimation for Woodland in Saihanba Forestry Center, China
Authors: Wang Changjing , Zhou Hongmin , Zhang Guodong , Duan Jianguo , Lin Moxiao
Published: February 2024

Owing to advancements in satellite remote sensing technology, the acquisition of global land surface parameters, notably, the leaf area index (LAI), has become increasingly accessible. The Sentinel-2 (S2) satellite plays an important role in the monitoring of ecological environments and resource management. The prevalent use of the 20 m spatial resolution band in S2-based inversion models imposes significant limitations on the applicability of S2 data in applications requiring finer spatial resolution. Furthermore, although a substantial body of research on LAI retrieval using S2 data concentrates on agricultural landscapes, studies dedicated to forest ecosystems, although increasing, remain relatively less prevalent. This study aims to establish a viable methodology for retrieving 10 m resolution LAI data in forested regions. The empirical model of the soil adjusted vegetation index (SAVI), the backpack neural network based on simulated annealing (SA-BP) algorithm, and the variational heteroscedastic Gaussian process regression (VHGPR) model are established in this experiment based on the LAI data measured and the corresponding 10 m spatial resolution S2 satellite surface reflectance data in the Saihanba Forestry Center (SFC). The LAI retrieval performance of the three models is then validated using field data, and the error sources of the best performing VHGPR models (R2 of 0.8696 and RMSE of 0.5078) are further analyzed. Moreover, the VHGPR model stands out for its capacity to quantify the uncertainty in LAI estimation, presenting a notable advantage in assessing the significance of input data, eliminating redundant bands, and being well suited for uncertainty estimation. This feature is particularly valuable in generating accurate LAI products, especially in regions characterized by diverse forest compositions.
The Dunes of Belvedere–San Marco of Aquileia: Integrating High-Resolution Digital Terrain Models and Multispectral Images with Ground-Penetrating Radar Survey to Map the Largest System of Continental Dunes of Northern Italy
Authors: Vanzani Federica , Fontana Alessandro , Ronchi Livio , Boaga Jacopo , Chiarini Veronica , Hajdas Irka
Published: February 2024

The interpretation of high-resolution remote-sensed data (i.e., LiDAR-derived DTMs, aerial photos and satellite images), compared with ground-penetrating radar surveys, historical cartography, geomorphological surveys and stratigraphic data, allowed us to map a large system of dunes near the Grado-Marano Lagoon (NE Italy) and reconstruct its evolution. Remote sensing investigations allowed us to recognize, map and interpret the sandy reliefs as a field of continental aeolian landforms extending for over 15 km2 and consisting of parabolic dunes elongated in the WSW direction. Radar soundings, together with the description of stratigraphic sections and cores, documented the internal clinostratification of the dunes, supporting their aeolian origin. Radiocarbon dating documents that the dunes formed 22 ka ago, at the end of the Last Glacial Maximum, and probably evolved until the first part of the Late Glacial, when vegetation was scarce. The landforms were fed by the sands blown from a paleochannel of Isonzo River flowing eastward of the dune’s field and blown by Bora. This is a very strong katabatic wind, still characterizing the area, but that was likely much stronger during last glaciation, when it was probably sustained by a stronger wind pattern in Central Europe.
Light-Pollution-Monitoring Method for Selected Environmental and Social Elements
Authors: Górniak-Zimroz Justyna , Romańczukiewicz Kinga , Sitarska Magdalena , Szrek Aleksandra
Published: February 2024

Light pollution significantly interferes with animal and human life and should, therefore, be included in the factors that threaten ecosystems. The main aim of this research is to develop a methodology for monitoring environmental and social elements subjected to light pollution in anthropogenic areas. This research is based on yearly and monthly photographs acquired from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-Orbiting Partnership (Suomi NPP) satellite; land cover data from the CORINE Land Cover (CLC) program; and environmental data from the European Environment Agency (EEA) and the World Database on Protected Areas (WDPA). The processing of input data for further analyses, the testing of the methodology and the interpretation of the final results were performed in GIS-type software (ArcGIS Pro). Light pollution in the investigated area was analyzed with the use of maps generated for the years 2014 and 2019. The environmental and social elements were spatially identified in five light pollution classes. The research results demonstrate that the proposed methodology allows for the identification of environmental and social elements that emit light, as well as those that are subjected to light pollution. The methodology used in this work allows us to observe changes resulting from light pollution (decreasing or increasing the intensity). Owing to the use of publicly available data, the methodology can be applied to light pollution monitoring as part of spatial planning in anthropogenic areas. The proposed methodology makes it possible to cover the area exposed to light pollution and to observe (almost online) the environmental and social changes resulting from reductions in light emitted by anthropogenic areas.
Dynamic Screening Strategy Based on Feature Graphs for UAV Object and Group Re-Identification
Authors: Zhang Guoqing , Liu Tianqi , Ye Zhonglin
Published: February 2024

In contemporary times, owing to the swift advancement of Unmanned Aerial Vehicles (UAVs), there is enormous potential for the use of UAVs to ensure public safety. Most research on capturing images by UAVs mainly focuses on object detection and tracking tasks, but few studies have focused on the UAV object re-identification task. In addition, in the real-world scenarios, objects frequently get together in groups. Therefore, re-identifying UAV objects and groups poses a significant challenge. In this paper, a novel dynamic screening strategy based on feature graphs framework is proposed for UAV object and group re-identification. Specifically, the graph-based feature matching module presented aims to enhance the transmission of group contextual information by using adjacent feature nodes. Additionally, a dynamic screening strategy designed attempts to prune the feature nodes that are not identified as the same group to reduce the impact of noise (other group members but not belonging to this group). Extensive experiments have been conducted on the Road Group, DukeMTMC Group and CUHK-SYSU-Group datasets to validate our framework, revealing superior performance compared to most methods. The Rank-1 on CUHK-SYSU-Group, Road Group and DukeMTMC Group datasets reaches 71.8%, 86.4% and 57.8%, respectively. Meanwhile, our method performance is explored on the UAV datasets of PRAI-1581 and Aerial Image, the infrared datasets of SYSU-MM01 and CM-Group and the NIR dataset of RBG-NIR Scene dataset; the unexpected findings demonstrate the robustness and wide applicability of our method.
A 19-Bit Small Absolute Matrix Encoder
Authors: Geng Liming , Cao Guohua , Shang Chunmin , Ding Hongchang
Published: February 2024

With the application of encoders in artificial intelligence and aerospace, the demand for the miniaturization and high measurement accuracy of encoders is increasing. To solve this problem, a new absolute matrix encoder is proposed in this paper, which can realize 19-bit matrix coding by engraving two circles of matrix code, and has the advantages of fewer circles of code disk engraving and higher measurement accuracy. This article mainly focuses on the design of a new matrix code disk, encoding and decoding methods, decoding circuit design, Matlab simulation analysis, and experimental error analysis. The experimental results show that the encoder designed in this paper achieves ultra-small volume Φ30 mm × 20 mm, and the angle measurement accuracy is 2.57”.
Advancing Digital Twin-Based Collision Avoidance: A Comprehensive Analysis of Communication Networks for Safety-Critical Applications in Industry 4.0
Authors: Moldovan Christian , Ulrich Silas , Köster Volker , Tiemann Janis , Lewandowski Andreas
Published: February 2024

This study presents a theoretical framework for defining the performance level of wireless safety functions within industrial environments. While acknowledging the simplifications inherent in our approach—primarily based on packet loss rates as a measure of system performance—the study underscores the dynamic challenges posed by real-world warehouses. Through an in situ measurement study of a forklift truck safety system, we validate the proposed method and emphasize the need for a more nuanced examination of wireless communication in complex settings. The study advocates for an expanded theoretical framework that considers fluctuations in warehouse dynamics, accounting for their impact on packet loss rates and, consequently, the precision of performance-level assessments. Furthermore, the research highlights the complexity introduced by wireless system characteristics not addressed in the simplified model, urging future investigations to incorporate these factors for a comprehensive understanding of wireless safety systems. The absence of specific criteria for wireless systems within existing standards emphasizes the necessity for a specialized framework in addressing safety aspects unique to wireless applications.
Enhancing Occupant Comfort and Building Sustainability: Lessons from an Internet of Things-Based Study on Centrally Controlled Indoor Shared Spaces in Hot Climatic Conditions
Authors: Kulkarni Parag , Pradeep Bivin , Yusuf Rahemeen , Alexander Henry , ElSayed Hesham
Published: February 2024

It is well known that buildings have a sizeable energy and environmental footprint. In particular, in environments like university campuses, the occupants as well as occupancy in shared spaces varies over time. Systems for cooling in such environments that are centrally controlled are typically threshold driven and do not account for occupant feedback and thus are often relying on a reactive approach (fix after identifying problems). Therefore, having a fixed thermal operating set point may not be optimal in such cases—both from an occupant comfort and well-being as well as an energy efficiency perspective. To address this issue, a study was conducted which involved development and deployment of an experimental Internet of Things (IoT) prototype system and an Android application that facilitated people engagement on a university campus located in the UAE which typically exhibits hot climatic conditions. This paper showcases data driven insights obtained from this study, and in particular, how to achieve a balance between the conflicting goals of improving occupant comfort and energy efficiency. Findings from this study underscore the need for regular reassessments and adaptation. The proposed solution is low cost and easy to deploy and has the potential to reap significant savings through a reduction in energy consumption with estimates indicating around 50–100 kWh/day of savings per building and the resulting environmental impact. These findings would appeal to stakeholders who are keen to improve energy efficiency and reduce their operating expenses and environmental footprint in such climatic conditions. Furthermore, collective action from a large number of entities could result in significant impact through this cumulative effect.
Wireless Passive Ceramic Sensor for Far-Field Temperature Measurement at High Temperatures
Authors: Tennant Kevin M. , Jordan Brian R. , Strader Noah L. , Varadharajan Idhaiam Kavin Sivaneri , Jerabek Mark , Wilhelm Jay , Reynolds Daryl S. , Sabolsky Edward M.
Published: February 2024

A passive wireless high-temperature sensor for far-field applications was developed for stable temperature sensing up to 1000 °C. The goal is to leverage the properties of electroceramic materials, including adequate electrical conductivity, high-temperature resilience, and chemical stability in harsh environments. Initial sensors were fabricated using Ag for operation to 600 °C to achieve a baseline understanding of temperature sensing principles using patch antenna designs. Fabrication then followed with higher temperature sensors made from (In, Sn) O2 (ITO) for evaluation up to 1000 °C. A patch antenna was modeled in ANSYS HFSS to operate in a high-frequency region (2.5–3.5 GHz) within a 50 × 50 mm2 confined geometric area using characteristic material properties. The sensor was fabricated on Al2O3 using screen printing methods and then sintered at 700 °C for Ag and 1200 °C for ITO in an ambient atmosphere. Sensors were evaluated at 600 °C for Ag and 1000 °C for ITO and analyzed at set interrogating distances up to 0.75 m using ultra-wideband slot antennas to collect scattering parameters. The sensitivity (average change in resonant frequency with respect to temperature) from 50 to 1000 °C was between 22 and 62 kHz/°C which decreased as interrogating distances reached 0.75 m.
The Design and Experimentation of a Corn Moisture Detection Device Based on Double Capacitors
Authors: Han Changjie , Wang Yurong , Shi Zhai , Xu Yang , Qiu Shilong , Mao Hanping
Published: February 2024

Detecting the moisture content of grain accurately and rapidly has important significance for harvesting, transport, storage, processing, and precision agriculture. There are some problems with the slow detection speeds, unstable detection, and low detection accuracy of moisture contents in corn harvesters. In that case, an online moisture detection device was designed, which is based on double capacitors. A new method of capacitance complementation and integration was proposed to eliminate the limitation of single data. The device is composed of a sampling mechanism and a double-capacitor sensor consisting of a flatbed capacitor and a cylindrical capacitor. The optimum structure size of the capacitor plates was determined by simulation optimization. In addition to this, the detection system with software and hardware was developed to estimate the moisture content. Indoor dynamic measurement tests were carried out to analyze the influence of temperature and porosity. Based on the influencing factors and capacitance, a model was established to estimate the moisture content. Finally, the support vector machine (SVM) regressions between the capacitance and moisture content were built up so that the R2 values were more than 0.91. In the stability test, the standard deviation of the stability test was 1.09%, and the maximum relative error of the measurement accuracy test was 1.22%. In the dynamic verification test, the maximum error of the measurement was 4.62%, less than 5%. It provides a measurement method for the accurate, rapid, and stable detection of the moisture content of corn and other grains.
Assessing the Efficacy of Pixel-Level Fusion Techniques for Ultra-High-Resolution Imagery: A Case Study of BJ-3A
Authors: Wang Yueyang , Mao Zhihui , Xin Zhining , Liu Xinyi , Li Zhangmai , Dong Yakun , Deng Lei
Published: February 2024

Beijing Satellite 3 is a high-performance optical remote sensing satellite with a spatial resolution of 0.3–0.5 m. It can provide timely and independent ultra-high-resolution spatial big data and comprehensive spatial information application services. At present, there is no relevant research on the fusion method of BJ-3A satellite images. In many applications, high-resolution panchromatic images alone are insufficient. Therefore, it is necessary to fuse them with multispectral images that contain spectral color information. Currently, there is a lack of research on the fusion method of BJ-3A satellite images. This article explores six traditional pixel-level fusion methods (HPF, HCS, wavelet, modified-IHS, PC, and Brovey) for fusing the panchromatic image and multispectral image of the BJ-3A satellite. The fusion results were analyzed qualitatively from two aspects: spatial detail enhancement capability and spectral fidelity. Five indicators, namely mean, standard deviation, entropy, correlation coefficient, and average gradient, were used for quantitative analysis. Finally, the fusion results were comprehensively evaluated from three aspects: spectral curves of ground objects, absolute error figure, and object-oriented classification effects. The findings of the research suggest that the fusion method known as HPF is the optimum and appropriate technique for fusing panchromatic and multispectral images obtained from BJ-3A. These results can be utilized as a guide for the implementation of BJ-3A panchromatic and multispectral data fusion in real-world scenarios.
Advanced Dielectric Resonator Antenna Technology for 5G and 6G Applications
Authors: Zhang Yingqi , Ogurtsov Stanislav , Vasilev Vasilii , Kishk Ahmed A. , Caratelli Diego
Published: February 2024

We review dielectric resonator antenna (DRA) designs. This review examines recent advancements across several categories, specifically focusing on their applicability in array configurations for millimeter-wave (mmW) bands, particularly in the context of 5G and beyond 5G applications. Notably, the off-chip DRA designs, including in-substrate and compact DRAs, have gained prominence in recent years. This surge in popularity can be attributed to the rapid development of cost-effective multilayer laminate manufacturing techniques, such as printed circuit boards (PCBs) and low-temperature co-fired ceramic (LTCC). Furthermore, there is a growing demand for DRAs with beam-steering, dual-band functions, and on-chip alignment availability, as they offer versatile alternatives to traditional lossy printed antennas. DRAs exhibit distinct advantages of lower conductive losses and greater flexibility in shapes and materials. We discuss and compare the performances of different DRA designs, considering their material usage, manufacturing feasibility, overall performance, and applications. By exploring the pros and cons of these diverse DRA designs, this review provides valuable insights for researchers in the field.
Enhanced Robot Motion Block of A-Star Algorithm for Robotic Path Planning
Authors: Kabir Raihan , Watanobe Yutaka , Islam Md Rashedul , Naruse Keitaro
Published: February 2024

An optimized robot path-planning algorithm is required for various aspects of robot movements in applications. The efficacy of the robot path-planning model is vulnerable to the number of search nodes, path cost, and time complexity. The conventional A-star (A*) algorithm outperforms other grid-based algorithms because of its heuristic approach. However, the performance of the conventional A* algorithm is suboptimal for the time, space, and number of search nodes, depending on the robot motion block (RMB). To address these challenges, this paper proposes an optimal RMB with an adaptive cost function to improve performance. The proposed adaptive cost function keeps track of the goal node and adaptively calculates the movement costs for quickly arriving at the goal node. Incorporating the adaptive cost function with a selected optimal RMB significantly reduces the searches of less impactful and redundant nodes, which improves the performance of the A* algorithm in terms of the number of search nodes and time complexity. To validate the performance and robustness of the proposed model, an extensive experiment was conducted. In the experiment, an open-source dataset featuring various types of grid maps was customized to incorporate the multiple map sizes and sets of source-to-destination nodes. According to the experiments, the proposed method demonstrated a remarkable improvement of 93.98% in the number of search nodes and 98.94% in time complexity compared to the conventional A* algorithm. The proposed model outperforms other state-of-the-art algorithms by keeping the path cost largely comparable. Additionally, an ROS experiment using a robot and lidar sensor data shows the improvement of the proposed method in a simulated laboratory environment.
A Clustering-Based Approach to Functional and Biomechanical Parameters Recorded with a Pair of Smart Eyeglasses in Older Adults in Order to Determine Physical Performance Groups
Authors: Hellec Justine , Colson Serge S. , Jaafar Amyn , Guérin Olivier , Chorin Frédéric
Published: February 2024

Falls and frailty status are often associated with a decline in physical capacity and multifactorial assessment is highly recommended. Based on the functional and biomechanical parameters measured during clinical tests with an accelerometer integrated into smart eyeglasses, the purpose was to characterize a population of older adults through an unsupervised analysis into different physical performance groups. A total of 84 participants (25 men and 59 women) over the age of sixty-five (age: 74.17 ± 5.80 years; height: 165.70 ± 8.22 cm; body mass: 68.93 ± 13.55 kg) performed a 30 s Sit-to-Stand test, a six-minute walking test (6MWT), and a 3 m Timed Up and Go (TUG) test. The acceleration data measured from the eyeglasses were processed to obtain six parameters: the number of Sit-to-Stands, the maximal vertical acceleration values during Sit-to-Stand movements, step duration and length, and the duration of the TUG test. The total walking distance covered during the 6MWT was also retained. After supervised analyses comparison (i.e., ANOVAs), only one of the parameters (i.e., step length) differed between faller groups and no parameters differed between frail and pre-frail participants. In contrast, unsupervised analysis (i.e., clustering algorithm based on K-means) categorized the population into three distinct physical performance groups (i.e., low, intermediate, and high). All the measured parameters discriminated the low- and high-performance groups. Four of the measured parameters differentiated the three groups. In addition, the low-performance group had a higher proportion of frail participants. These results are promising for monitoring activities in older adults to prevent the decline of physical capacities.
Classification in Early Fire Detection Using Multi-Sensor Nodes—A Transfer Learning Approach
Authors: Vorwerk Pascal , Kelleter Jörg , Müller Steffen , Krause Ulrich
Published: February 2024

Effective early fire detection is crucial for preventing damage to people and buildings, especially in fire-prone historic structures. However, due to the infrequent occurrence of fire events throughout a building’s lifespan, real-world data for training models are often sparse. In this study, we applied feature representation transfer and instance transfer in the context of early fire detection using multi-sensor nodes. The goal was to investigate whether training data from a small-scale setup (source domain) can be used to identify various incipient fire scenarios in their early stages within a full-scale test room (target domain). In a first step, we employed Linear Discriminant Analysis (LDA) to create a new feature space solely based on the source domain data and predicted four different fire types (smoldering wood, smoldering cotton, smoldering cable and candle fire) in the target domain with a classification rate up to 69% and a Cohen’s Kappa of 0.58. Notably, lower classification performance was observed for sensor node positions close to the wall in the full-scale test room. In a second experiment, we applied the TrAdaBoost algorithm as a common instance transfer technique to adapt the model to the target domain, assuming that sparse information from the target domain is available. Boosting the data from 1% to 30% was utilized for individual sensor node positions in the target domain to adapt the model to the target domain. We found that additional boosting improved the classification performance (average classification rate of 73% and an average Cohen’s Kappa of 0.63). However, it was noted that excessively boosting the data could lead to overfitting to a specific sensor node position in the target domain, resulting in a reduction in the overall classification performance.
A New Network Structure for Speech Emotion Recognition Research
Authors: Xu Chunsheng , Liu Yunqing , Song Wenjun , Liang Zonglin , Chen Xing
Published: February 2024

Deep learning promotes the breakthrough of emotion recognition in many fields, especially speech emotion recognition (SER). As an important part of speech emotion recognition, the most relevant acoustic feature extraction has always attracted the attention of existing researchers. Aiming at the problem that the emotional information contained in the current speech signals is distributed dispersedly and cannot comprehensively integrate local and global information, this paper presents a network model based on a gated recurrent unit (GRU) and multi-head attention. We evaluate our proposed emotion model on the IEMOCAP and Emo-DB corpora. The experimental results show that the network model based on Bi-GRU and multi-head attention is significantly better than the traditional network model at detecting multiple evaluation indicators. At the same time, we also apply the model to a speech sentiment analysis task. On the CH-SIMS and MOSI datasets, the model shows excellent generalization performance.
AI-Powered Innovation in Digital Transformation: Key Pillars and Industry Impact
Authors: Aldoseri Abdulaziz , Al-Khalifa Khalifa N. , Hamouda Abdel Magid
Published: February 2024

Digital transformation systems generate a substantial volume of data, creating opportunities for potential innovation, particularly those driven by artificial intelligence. This study focuses on the intricate relationship between artificial intelligence and innovation as foundational elements in the digital transformation framework for sustained growth and operational excellence. This study provides a holistic perspective on the cultivation and pillars of AI-powered innovation, highlighting their pivotal role in revolutionizing industries, including healthcare, education, finance, manufacturing, transportation, and agriculture. The work emphasizes the key pillars essential for fostering AI-powered innovation, including monitoring performance measurement to use the power of the present, continuous learning and innovation, data analytics and insights, predictive analytics, and innovative product development. This study investigates how these pillars serve as the foundation for groundbreaking advancements, driving efficiency, enhancing decision-making processes, and fostering creativity within organizations. This study explores the significance of continuous learning, interdisciplinary collaboration, and industry partnerships in nurturing a thriving AI-powered innovation ecosystem. By understanding and harnessing these fundamental elements, businesses can navigate the complexities of the digital age, fostering innovation that not only optimizes processes but also enhances the overall human experience, ushering in a new era of technological excellence and societal progress.
Dynamic Right-of-Way Allocation on Bus Priority Lanes Considering Traffic System Resilience
Authors: Hu Jia , Lian Zhexi , Sun Xiaoxue , Eichberger Arno , Zhang Zhen , Lai Jintao
Published: February 2024

Bus priority is an effective way to improve traffic efficiency and sustainability. To achieve this, the Bus Priority Lane (BPL) is adopted to provide exclusive right-of-way for buses. However, the BPL is underutilized if the frequency of buses is low. To address this issue, many studies focus on improving the BPL’s utilization efficiency by intermittently allowing general vehicles to access it. However, these studies still have some shortcomings: (i) bus priority cannot be guaranteed if general vehicles run on the BPL; and (ii) the traffic system lacks resilience, especially when the traffic demand is unbalanced. This paper proposes a dynamic right-of-way allocation for the BPL, considering traffic system resilience. On the one hand, it ensures absolute bus priority by controlling Connected Automated Vehicles (CAVs), so as they do not interfere with buses. On the other hand, it can improve traffic system resilience by allocating right-of-way for CAVs with heavy turning-movement demand. To test the effectiveness, the proposed control strategy is compared with the non-control baseline. The experiments are conducted under seven unbalanced-traffic-demand levels, four congestion levels, and five CAV Penetration Rates. The results show that the proposed strategy can ensure absolute bus priority and improve traffic efficiency and traffic system resilience.
Cross-Border E-Commerce and Urban Entrepreneurial Vitality—A Quasi-Natural Experiment Evidence from China
Authors: Yuan Qigang , Ji Yongsheng , Zhang Wei , Lei Ting
Published: February 2024

Cross-border e-commerce, as a new form of trade driven by digital technology, provides an opportunity to enhance the entrepreneurial vitality of cities. With the help of the comprehensive pilot area for cross-border e-commerce, also called a “quasi-natural” experiment, and using 2010–2020 panel data and Chinese business enterprise registration data for 278 cities in China, this paper examined the impact of cross-border e-commerce on enterprise vitality. The study found that the pilot policy in the comprehensive pilot area promoted the entrepreneurial vitality to increase by about 13.3%, and it remained stable after a series of tests. The heterogeneity analysis shows that the pilot policies in the comprehensive pilot areas have a stronger effect on the promotion of enterprise vitality in the eastern and western regions. At the same time, the pilot policy has expanded the scope of enterprise subjects and plays a more prominent enterprise incentive effect in small and medium-sized cities, cities with low innovation, and small and medium-sized registered enterprises. In addition, the pilot policy stimulates the entrepreneurial vitality of the service industry, but has no significant impact on agriculture and manufacturing. In terms of the influence mechanism, the comprehensive pilot area mainly affects the entrepreneurial vitality by means of optimizing the business environment, reducing the entry cost, promoting the synergistic agglomeration of manufacturing and producer services, and stimulating market demand. The further spatial spillover effect found that the comprehensive pilot area not only improves the entrepreneurial vitality of the pilot cities, but also radiates the enterprise development of neighboring and surrounding cities with similar economic development.
The Temporal–Spatial Evolution Characteristics and Influential Factors of Carbon Imbalance in China
Authors: Liu Chao , Lei Hongzhen , Zhang Linjie
Published: February 2024

The ongoing progress of industrialization and urbanization has exacerbated the imbalance between carbon emissions and absorption, leading to heightened risks of climate change, such as frequent occurrences of extreme weather events. Clarifying the driving forces and temporal–spatial evolution characteristics of China’s carbon balance holds significant theoretical value in understanding the systemic nature and patterns of interaction between carbon emissions and absorption. We utilize provincial panel data from 2005 to 2021 in China and a spatial Durbin model to explore the spatial spillover effects of carbon imbalance and its influencing factors. The results indicate a gradual exacerbation of carbon imbalance in China over time. There exists a spatially positive correlation pattern in provincial carbon imbalance distribution. From 2005 to 2010, intra-regional differences in carbon imbalance levels were a significant contributor to China’s overall carbon imbalance disparity, while from 2011 to 2019, inter-regional differences played a more substantial role. Given the apparent phenomena of population aggregation, industrial concentration, and economic interdependence among provinces, changes in population size, economic growth, and industrial structure exacerbate the level of carbon imbalance in spatially correlated regions. Conversely, due to knowledge and technology spillovers, improvements in energy efficiency facilitated by the flow of production factors like capital aid in the governance of carbon imbalance in spatially associated areas. We emphasize that local governments should focus on a regional integration perspective in carbon imbalance governance and strategically coordinate with neighboring provinces and cities to advance carbon imbalance governance. The findings provide theoretical support for understanding and effectively managing the situation of carbon imbalance in China.
Business Opportunities and Drivers for Health and Spa Tourism: A Qualitative Research on Baile Felix Spa Resort, Romania
Authors: Badulescu Daniel , Saveanu Tomina , Trip Diana-Teodora , Badulescu Alina
Published: February 2024

The tourism sector has witnessed continuous development over the last few decades, overcoming various challenges and crises. This resilience underscores the remarkable diversity of managerial and entrepreneurial initiatives capable of meeting the ever-growing and unpredictable global demand. Our objective is to explore the perceptions of factors and opportunities that encourage the launch and management of businesses in health and spa tourism utilizing a qualitative methodology. Our conclusions are based on the analysis of 32 hotel managers’ and small business owners’ interviews in a relevant tourist resort in Romania. This study reveals that all four types of identified opportunities and drivers influencing business creation and management in health and spa tourism (i.e., improving work quality and independence, professional and personal development, responses to opportunities, and upgrading existing services) are substantially influenced by gender, relevant work experience, and education. The novelty of our research lies in highlighting the influence of these drivers according to a specific context given by the size, management type, and ownership structure of tourism ventures, as well as the difference between large hotel managers and small business owners in terms of their perception and goals. We acknowledge that the limited number of interviewees and the spatially bound context of the research may have an effect on the relevance of the results. This study’s findings underscore the necessity for compromise and collaboration among diverse stakeholders in the tourism industry, particularly when developing long-term strategies for health and wellness destinations. This collaborative approach plays a critical role in promoting the well-being and prosperity of individuals, businesses, and the entire local community.
The Impact of Low-Carbon City Pilot Policies on Green Innovation Efficiency in Chinese Cities: An Empirical Analysis Based on the Multi-Period PSM-DID Model
Authors: Du Yongshan , Wang Haoyu , An Ran , Wang Qimeng , Wen Xin
Published: February 2024

On the basis of panel data taken from 226 Chinese cities in the period 2008 to 2019, this paper measures urban green innovation efficiency, with the help of the unexpected production output-stochastic block model (UPO-SBM), and applies the propensity score matching difference-in-differences (PSM-DID) model to analyze the impact of low-carbon city pilot policies (LCPP) on urban green innovation efficiency. This paper also applies a mediation effect model to analyze the mechanism of the LCPP that enhances urban green innovation efficiency, and also attempts to explore the impact of the LCPP on the green innovation efficiency of various types of cities by engaging with multiple dimensions. The results of the study shows that: (1) When all other conditions remain unchanged, the average promotion effect of the implementation of the LCPP on urban green innovation efficiency is 21.77%; (2) at 1% significance level, the mediating effect of financial technology R&D expenditure and environmental governance expenditure is 0.0664 and 0.0283, respectively, confirming that both are important to the role that LCPP plays; (3) at 5% significance level, the exogenous policy effect of the LCPP on urban green innovation efficiency is more obvious in cities with a larger population size and higher degree of development, whose pillar industry is heavy industry.
Expanding Geoethics: Interrelations with Geoenvironmental Education and Sense of Place
Authors: Koupatsiaris Alexandros Aristotelis , Drinia Hara
Published: February 2024

Human existence and progress hinge on sustainability and resilience, especially in the Anthropocene Era, where the diversity of nature plays a critical role. Central to this endeavor is the realm of geoethics, which not only reshapes the role of geosciences but also fosters the development of ethical behavior and practices in our interaction with the Earth. This paper presents a conceptual framework that integrates the seemingly disparate domains of geoethics, geoenvironmental education, and the sense of place. By conducting a systematic review using the keywords “Geoethics AND Education” and “Geoethics AND place” across global databases, including Scopus, Web of Science, ProQuest, and JSTOR, up to October 2023, we seek to uncover evidence illuminating the potential connections between these concepts. Out of n = 98 records identified, n = 22 met the eligibility criteria for inclusion in our review. Our findings reveal a growing scientific interest in the spectrum of geoethics over the last decade, with diverse perspectives related to concerns about geoeducation. We propose that future research should focus on expanding the scope of geoethics across various disciplines, emphasizing the importance of human ethos, socio-eco responsibility, and ethical considerations in geological heritage and geoconservation management. Geoenvironmental education emerges as a crucial avenue for advancing geoethics, while the multifaceted outcome of the sense of place offers a promising pathway for standardizing and correlating these fields effectively.