bluuebunny/crossref_metadata_2025 huggingface dataset:
Occupancy Estimation from Blurred Video: A Multifaceted Approach with Privacy Consideration
Authors: Sourav Md Sakib Galib , Yavari Ehsan , Gao Xiaomeng , Maskrey James , Zheng Yao , Lubecke Victor M. , Boric-Lubecke Olga
Published: June 2024

Building occupancy information is significant for a variety of reasons, from allocation of resources in smart buildings to responding during emergency situations. As most people spend more than 90% of their time indoors, a comfortable indoor environment is crucial. To ensure comfort, traditional HVAC systems condition rooms assuming maximum occupancy, accounting for more than 50% of buildings’ energy budgets in the US. Occupancy level is a key factor in ensuring energy efficiency, as occupancy-controlled HVAC systems can reduce energy waste by conditioning rooms based on actual usage. Numerous studies have focused on developing occupancy estimation models leveraging existing sensors, with camera-based methods gaining popularity due to their high precision and widespread availability. However, the main concern with using cameras for occupancy estimation is the potential violation of occupants’ privacy. Unlike previous video-/image-based occupancy estimation methods, we addressed the issue of occupants’ privacy in this work by proposing and investigating both motion-based and motion-independent occupancy counting methods on intentionally blurred video frames. Our proposed approach included the development of a motion-based technique that inherently preserves privacy, as well as motion-independent techniques such as detection-based and density-estimation-based methods. To improve the accuracy of the motion-independent approaches, we utilized deblurring methods: an iterative statistical technique and a deep-learning-based method. Furthermore, we conducted an analysis of the privacy implications of our motion-independent occupancy counting system by comparing the original, blurred, and deblurred frames using different image quality assessment metrics. This analysis provided insights into the trade-off between occupancy estimation accuracy and the preservation of occupants’ visual privacy. The combination of iterative statistical deblurring and density estimation achieved a 16.29% counting error, outperforming our other proposed approaches while preserving occupants’ visual privacy to a certain extent. Our multifaceted approach aims to contribute to the field of occupancy estimation by proposing a solution that seeks to balance the trade-off between accuracy and privacy. While further research is needed to fully address this complex issue, our work provides insights and a step towards a more privacy-aware occupancy estimation system.
3D Printed Hydrogel Sensor for Rapid Colorimetric Detection of Salivary pH
Authors: Łabowska Magdalena B. , Krakos Agnieszka , Kubicki Wojciech
Published: June 2024

Salivary pH is one of the crucial biomarkers used for non-invasive diagnosis of intraoral diseases, as well as general health conditions. However, standard pH sensors are usually too bulky, expensive, and impractical for routine use outside laboratory settings. Herein, a miniature hydrogel sensor, which enables quick and simple colorimetric detection of pH level, is shown. The sensor structure was manufactured from non-toxic hydrogel ink and patterned in the form of a matrix with 5 mm × 5 mm × 1 mm individual sensing pads using a 3D printing technique (bioplotting). The authors’ ink composition, which contains sodium alginate, polyvinylpyrrolidone, and bromothymol blue indicator, enables repeatable and stable color response to different pH levels. The developed analysis software with an easy-to-use graphical user interface extracts the R(ed), G(reen), and B(lue) components of the color image of the hydrogel pads, and evaluates the pH value in a second. A calibration curve used for the analysis was obtained in a pH range of 3.5 to 9.0 using a laboratory pH meter as a reference. Validation of the sensor was performed on samples of artificial saliva for medical use and its mixtures with beverages of different pH values (lemon juice, coffee, black and green tea, bottled and tap water), and correct responses to acidic and alkaline solutions were observed. The matrix of square sensing pads used in this study provided multiple parallel responses for parametric tests, but the applied 3D printing method and ink composition enable easy adjustment of the shape of the sensing layer to other desired patterns and sizes. Additional mechanical tests of the hydrogel layers confirmed the relatively high quality and durability of the sensor structure. The solution presented here, comprising 3D printed hydrogel sensor pads, simple colorimetric detection, and graphical software for signal processing, opens the way to development of miniature and biocompatible diagnostic devices in the form of flexible, wearable, or intraoral sensors for prospective application in personalized medicine and point-of-care diagnosis.
Practical NIR Assay Derived from Cyanine to Evaluate Intracellular H2S in Living Cell Imaging
Authors: Ye Chenqian , Wang Axue , Lu Yuxin , Lin Xinye , Huang Luqiang , Li Daliang
Published: June 2024

To monitor the biological function of H2S in real time, this investigation demonstrated the design and synthesis of a novel fluorescent probe integrated with cyanine and 2,4-dinitrophenol for the qualitative and quantitative detection of H2S. An NIR sensitive sensor (FS-HS-1) was provided with a straightforward process. Spectroscopy experiments elucidated that FS-HS-1 could selectively detect H2S in a PBS solution (containing 40% acetonitrile) with a 111-fold fluorescence enhancement at 715 nm (ex. 605 nm). The response towards NaHS occurred in less than 2 min, and the detection limit was confirmed to be as low as 4.47 ± 0.11 nmol/L. Furthermore, the probe is capable of monitoring changes in exogenous H2S concentrations within living cells with confocal and 2P imaging.
Nitrophenyl Thiourea-Modified Polyethylenimine Colorimetric Sensor for Sulfate, Fluorine, and Acetate
Authors: Kuerbanjiang Kediye , Rouzi Kuerbanjiang , Zhang Si-Yu
Published: June 2024

A thiourea-based colorimetric sensor incorporating polyethyleneimine (PEI) and chromophoric nitrophenyl groups was synthesized and utilized for detecting various anions. Structural characterization of the sensor was accomplished using FTIR and 1H-NMR spectroscopy. The sensor’s interactions and colorimetric recognition capabilities with different anions, including CI−, Br−, I−, F−, NO3−, PF6−, AcO−, H2PO4−, PO43−, and SO42−, were investigated via visual observation and UV/vis spectroscopy. Upon adding SO42−, F−, and AcO− anions, the sensor exhibited distinct color changes from colorless to yellow and yellowish, while other anions did not induce significant color alterations. UV/vis spectroscopic titration experiments conducted in a DMSO/H2O solution (9:1 volume ratio) demonstrated the sensor’s selectivity toward SO42−, F−, and AcO−. The data revealed that the formation of the main compounds and anion complexes was mediated by hydrogen bonding, leading to signal changes in the nitrophenyl thiourea-modified PEI spectrum.
Design of Fluxgate Current Sensor Based on Magnetization Residence Times and Neural Networks
Authors: Li Jingjie , Ren Wei , Luo Yanshou , Zhang Xutong , Liu Xinpeng , Zhang Xue
Published: June 2024

This study introduces a novel fluxgate current sensor with a compact, ring-shaped configuration that exhibits improved performance through the integration of magnetization residence times and neural networks. The sensor distinguishes itself with a unique magnetization profile, denoted as M waves, which emerge from the interaction between the target signal and ambient magnetic interference, effectively enhancing interference suppression. These M waves highlight the non-linear coupling between the magnetic field and magnetization residence times. Detection of these residence times is accomplished using full-wave rectification circuits and a Schmitt trigger, with a digital output provided by timing sequence detection. A dual-layer feedforward neural network deciphers the target signal, exploiting this non-linear relationship. The sensor achieves a linearity error of 0.054% within a measurement range of 15 A. When juxtaposed with conventional sensors utilizing the residence-time difference strategy, our sensor reduces linearity error by more than 40-fold and extends the effective measurement range by 150%. Furthermore, it demonstrates a significant decrease in ambient magnetic interference.
Motor Imagery EEG Signal Classification Using Distinctive Feature Fusion with Adaptive Structural LASSO
Authors: Huang Weihai , Liu Xinyue , Yang Weize , Li Yihua , Sun Qiyan , Kong Xiangzeng
Published: June 2024

A motor imagery brain–computer interface connects the human brain and computers via electroencephalography (EEG). However, individual differences in the frequency ranges of brain activity during motor imagery tasks pose a challenge, limiting the manual feature extraction for motor imagery classification. To extract features that match specific subjects, we proposed a novel motor imagery classification model using distinctive feature fusion with adaptive structural LASSO. Specifically, we extracted spatial domain features from overlapping and multi-scale sub-bands of EEG signals and mined discriminative features by fusing the task relevance of features with spatial information into the adaptive LASSO-based feature selection. We evaluated the proposed model on public motor imagery EEG datasets, demonstrating that the model has excellent performance. Meanwhile, ablation studies and feature selection visualization of the proposed model further verified the great potential of EEG analysis.
Artificial Vision System on Digital Devices for Real-Time Head Tilt Control
Authors: Tomé de la Torre Miguel Ángel , Álvarez Fernández-Balbuena Antonio , Bernárdez-Vilaboa Ricardo , Vázquez Molini Daniel
Published: June 2024

It is common to see cases in which, when performing tasks in close vision in front of a digital screen, the posture or position of the head is not adequate, especially in young people; it is essential to have a correct posture of the head to avoid visual, muscular, or joint problems. Most of the current systems to control head inclination require an external part attached to the subject’s head. The aim of this study is the validation of a procedure that, through a detection algorithm and eye tracking, can control the correct position of the head in real time when subjects are in front of a digital device. The system only needs a digital device with a CCD receiver and downloadable software through which we can detect the inclination of the head, indicating if a bad posture is adopted due to a visual problem or simply inadequate visual–postural habits, alerting us to the postural anomaly to correct it.The system was evaluated in subjects with disparate interpupillary distances, at different working distances in front of the digital device, and at each distance, different tilt angles were evaluated. The system evaluated favorably in different lighting environments, correctly detecting the subjects’ pupils. The results showed that for most of the variables, particularly good absolute and relative reliability values were found when measuring head tilt with lower accuracy than most of the existing systems. The evaluated results have been positive, making it a considerably inexpensive and easily affordable system for all users. It is the first application capable of measuring the head tilt of the subject at their working or reading distance in real time by tracking their eyes.
An Iterative 3D Correction plus 2D Inversion Procedure to Remove 3D Effects from 2D ERT Data along Embankments
Authors: Hojat Azadeh
Published: June 2024

This paper addresses the problem of removing 3D effects as one of the most challenging problems related to 2D electrical resistivity tomography (ERT) monitoring of embankment structures. When processing 2D ERT monitoring data measured along linear profiles, it is fundamental to estimate and correct the distortions introduced by the non-uniform 3D geometry of the embankment. Here, I adopt an iterative 3D correction plus 2D inversion procedure to correct the 3D effects and I test the validity of the proposed algorithm using both synthetic and real data. The modelled embankment is inspired by a critical section of the Parma River levee in Colorno (PR), Italy, where a permanent ERT monitoring system has been in operation since November 2018. For each model of the embankment, reference synthetic data were produced in Res2dmod and Res3dmod for the corresponding 2D and 3D models. Using the reference synthetic data, reference 3D effects were calculated to be compared with 3D effects estimated by the proposed algorithm at each iteration. The results of the synthetic tests showed that even in the absence of a priori information, the proposed algorithm for correcting 3D effects converges rapidly to ideal corrections. Having validated the proposed algorithm through synthetic tests, the method was applied to the ERT monitoring data in the study site to remove 3D effects. Two real datasets from the study site, taken after dry and rainy periods, are discussed here. The results showed that 3D effects cause about ±50% changes in the inverted resistivity images for both periods. This is a critical artifact considering that the final objective of ERT monitoring data for such studies is to produce water content maps to be integrated in alarm systems for hydrogeological risk mitigation. The proposed algorithm to remove 3D effects is thus a rapid and validated solution to satisfy near-real-time data processing and to produce reliable results.
Static and Dynamic Hand Gestures: A Review of Techniques of Virtual Reality Manipulation
Authors: Herbert Oswaldo Mendoza , Pérez-Granados David , Ruiz Mauricio Alberto Ortega , Cadena Martínez Rodrigo , Gutiérrez Carlos Alberto González , Antuñano Marco Antonio Zamora
Published: June 2024

This review explores the historical and current significance of gestures as a universal form of communication with a focus on hand gestures in virtual reality applications. It highlights the evolution of gesture detection systems from the 1990s, which used computer algorithms to find patterns in static images, to the present day where advances in sensor technology, artificial intelligence, and computing power have enabled real-time gesture recognition. The paper emphasizes the role of hand gestures in virtual reality (VR), a field that creates immersive digital experiences through the Ma blending of 3D modeling, sound effects, and sensing technology. This review presents state-of-the-art hardware and software techniques used in hand gesture detection, primarily for VR applications. It discusses the challenges in hand gesture detection, classifies gestures as static and dynamic, and grades their detection difficulty. This paper also reviews the haptic devices used in VR and their advantages and challenges. It provides an overview of the process used in hand gesture acquisition, from inputs and pre-processing to pose detection, for both static and dynamic gestures.
Digital Twin Smart City: Integrating IFC and CityGML with Semantic Graph for Advanced 3D City Model Visualization
Authors: Lam Phuoc-Dat , Gu Bon-Hyon , Lam Hoang-Khanh , Ok Soo-Yol , Lee Suk-Hwan
Published: June 2024

The growing interest in building data management, especially the building information model (BIM), has significantly influenced urban management, materials supply chain analysis, documentation, and storage. However, the integration of BIM into 3D GIS tools is becoming more common, showing progress beyond the traditional problem. To address this, this study proposes data transformation methods involving mapping between three domains: industry foundation classes (IFC), city geometry markup language (CityGML), and web ontology framework (OWL)/resource description framework (RDF). Initially, IFC data are converted to CityGML format using the feature manipulation engine (FME) at CityGML standard’s levels of detail 4 (LOD4) to enhance BIM data interoperability. Subsequently, CityGML is converted to the OWL/RDF diagram format to validate the proposed BIM conversion process. To ensure integration between BIM and GIS, geometric data and information are visualized through Cesium Ion web services and Unreal Engine. Additionally, an RDF graph is applied to analyze the association between the semantic mapping of the CityGML standard, with Neo4j (a graph database management system) utilized for visualization. The study’s results demonstrate that the proposed data transformation methods significantly improve the interoperability and visualization of 3D city models, facilitating better urban management and planning.
Advancing Thrombosis Research: A Novel Device for Measuring Clot Permeability
Authors: Landi Elia , Mugnaini Marco , Vatansever Tunahan , Fort Ada , Vignoli Valerio , Giurranna Elvira , Argento Flavia Rita , Fini Eleonora , Emmi Giacomo , Fiorillo Claudia , Becatti Matteo
Published: June 2024

Thromboembolism, a global leading cause of mortality, needs accurate risk assessment for effective prophylaxis and treatment. Current stratification methods fall short in predicting thrombotic events, emphasizing the need for a deeper understanding of clot properties. Fibrin clot permeability, a crucial parameter in hypercoagulable states, impacts clot structure and resistance to lysis. Current clot permeability measurement limitations propel the need for standardized methods. Prior findings underscore the importance of clot permeability in various thrombotic conditions but call for improvements and more precise, repeatable, and standardized methods. Addressing these challenges, our study presents an upgraded, portable, and cost-effective system for measuring blood clot permeability, which utilizes a pressure-based approach that adheres to Darcy’s law. By enhancing precision and sensitivity in discerning clot characteristics, this innovation provides a valuable tool for assessing thrombotic risk and associated pathological conditions. In this paper, the authors present a device that is able to automatically perform the permeability measurements on plasma or fibrinogen in vitro-induced clots on specific holders (filters). The proposed device has been tailored to distinguish clot permeability, with high precision and sensitivity, between healthy subjects and high cardiovascular-risk patients. The precise measure of clot permeability represents an excellent indicator of thrombotic risk, thus allowing the clinician, also on the basis of other anamnestic and laboratory data, to attribute a risk score to the subject. The proposed instrument was characterized by performing permeability measurements in plasma and purified fibrinogen clots derived from 17 Behcet patients and 15 sex- and age-matched controls. As expected, our results clearly indicate a significant difference in plasma clot permeability in Behcet patients with respect to controls (0.0533 ± 0.0199 d vs. 0.0976 ± 0.0160 d, p < 0.001). This difference was confirmed in the patient’s vs. control fibrin clots (0.0487 ± 0.0170 d vs. 0.1167 ± 0.0487 d, p < 0.001). In conclusion, our study demonstrates the feasibility, efficacy, portability, and cost-effectiveness of a novel device for measuring clot permeability, allowing healthcare providers to better stratify thrombotic risk and tailor interventions, thereby improving patient outcomes and reducing healthcare costs, which could significantly improve the management of thromboembolic diseases.
A Comprehensive Architecture for Federated Learning-Based Smart Advertising
Authors: Seyghaly Rasool , Garcia Jordi , Masip-Bruin Xavi
Published: June 2024

This paper introduces a cutting-edge data architecture designed for a smart advertising context, prioritizing efficient data flow and performance, robust security, while guaranteeing data privacy and integrity. At the core of this study lies the application of federated learning (FL) as the primary methodology, which emphasizes the authenticity and privacy of data while promptly discarding irrelevant or fraudulent information. Our innovative data model employs a semi-random role assignment strategy based on a variety of criteria to efficiently collect and amalgamate data. The architecture is composed of model nodes, data nodes, and validator nodes, where the role of each node is determined by factors such as computational capability, interconnection quality, and historical performance records. A key feature of our proposed system is the selective engagement of a subset of nodes for modeling and validation, optimizing resource use and minimizing data loss. The AROUND social network platform serves as a real-world case study, illustrating the efficacy of our data architecture in a practical setting. Both simulated and real implementations of our architecture showcase its potential to dramatically curtail network traffic and average CPU usage, while preserving the accuracy of the FL model. Remarkably, the system is capable of achieving over a 50% reduction in both network traffic and average CPU usage even when the user count escalates by twenty-fold. The click rate, user engagement, and other parameters have also been evaluated, proving that the proposed architecture’s advantages do not affect the smart advertising accuracy. These findings highlight the proposed architecture’s capacity to scale efficiently and maintain high performance in smart advertising environments, making it a valuable contribution to the evolving landscape of digital marketing and FL.
Heterogeneous Communication Network Architecture for the Management of Electric Vehicle Charging Stations: Multi-Aggregator Management in Microgrids with High Photovoltaic Variability Based on Multiple Solar Radiation Sensors
Authors: Davila-Sacoto Miguel , Hernández-Callejo Luis , González L. G. , Duque-Perez Óscar , Zorita-Lamadrid Ángel L. , Ochoa-Correa Danny
Published: June 2024

Electric power systems with a high penetration of photovoltaic generation and a relevant fleet of electric vehicles face significant stability challenges, particularly in mountainous areas where the variability of photovoltaic resources is pronounced. This study presents a novel methodology to strategically place electric vehicle aggregators along a feeder. This approach considers electrical variables and the dynamics of cloud movements within the study area. This innovative methodology reduces the substation’s power load demand and significantly improves the end user’s voltage levels. The improvements in voltage regulation and reduced demand on the substation provide clear benefits, including increased system resilience, better integration of renewable energy sources, and enhanced overall efficiency of the electric grid. These advantages are particularly critical in regions with high levels of photovoltaic generation and are important in promoting sustainable electric vehicle charging infrastructure. When analyzing different load scenarios for the IEEE European Low Voltage Test Feeder system, the consideration of distributed aggregators based on cloud movements decreased the power required at the substation by 21.25%, and the voltage drop in loads was reduced from 6.9% to 4.29%. This research underscores the critical need to consider both the variability and geographical distribution of PV resources in the planning and operation of electrical systems with extensive PV generation.
Method of Directly Writing MPA on Photosensitive Surface of Detector Based on FIB
Authors: Nie Anran , Qiu Zhenwei , Sun Xiaobing , Zhu Jun , Hong Jin
Published: June 2024

The division of focal plane (DoFP) polarization detector has great potential for the development of aerospace polarimeters, but the existing commercial DoFP polarization detector cannot satisfy all the missions due to the diversity of satellite payloads. Here, we propose a method of directly writing a micro-polarizer array (MPA) on the detector surface based on focused ion beams (FIB) and fabricating a push-broom scanning DoFP polarization detector. The feasibility and low crosstalk of the solution were proved through testing, and the reasons for the low extinction ratio caused by oxidation were explained through characterization and numerical calculations. This scheme is not only applicable to DoFP polarization detectors but also provides ideas for the integration of other metasurface structures and detectors.
Slow Well-Being Gardening: Creating a Sensor Network for Radiation Therapy Patients via Horticultural Therapeutic Activity
Authors: Chang Teng-Wen , Tsai Shih-Ting , Huang Hsin-Yi , Wu Yi-Sin , Chang Ching-Chih , Datta Sambit
Published: June 2024

Well-being can reflect people’s psychological conditions and be used alongside physiological parameters to evaluate patients’ physical and mental health. The modern medical environment increasingly incorporates digital carriers, human–computer interaction devices, sensible spaces, and the execution of suitable algorithms. Slow design in healthy human–computer interaction is often used to reflect people’s dependence on or support from behaviors or objects, promoting the stability of behaviors as well as meaningful and positive changes. Therefore, in this study, we propose a slow sensing model, develop a Slow Well-Being Gardening system, and use it to evaluate behavioral data from radiation therapy patients during treatment sessions and horticultural therapy. This study is based on SENS and slow design, setting the hospital lounge as a sensible space and establishing a sensor system. After a 10-day inspection, the process was evaluated and verified. Ultimately, data from facial detection (smile) and HRV showed that the patients in the experimental group experienced a significant improvement in their well-being, feeling better than those in the control group who maintained the most common state in normal treatment. Therefore, it can be inferred that the Slow Well-Being Gardening model is indeed valid and can be further developed.
An Advanced Hall Element Array-Based Device for High-Resolution Magnetic Field Mapping
Authors: Zhou Tan , Cai Jiangwei , Zhu Xin
Published: June 2024

The precise mapping of magnetic fields emitted by various objects holds critical importance in the fabrication of industrial products. To meet this requirement, this study introduces an advanced magnetic detection device boasting high spatial resolution. The device’s sensor, an array comprising 256 unpackaged gallium arsenide (GaAs) Hall elements arranged in a 16 × 16 matrix, spans an effective area of 19.2 mm × 19.2 mm. The design maintains a 1.2 mm separation between adjacent elements. For enhanced resolution, the probe scans the sample via a motorized rail system capable of executing specialized movement patterns. A support structure incorporated into the probe minimizes the measurement distance to below 0.5 mm, thereby amplifying the magnetic signal and mitigating errors from nonparallel probe–sample alignment. The accompanying interactive software utilizes cubic spline interpolation to transform magnetic readings into detailed two- and three-dimensional magnetic field distribution maps, signifying field strength and polarity through variations in color intensity and amplitude sign. The device’s efficacy in accurately mapping surface magnetic field distributions of magnetic and magnetized materials was corroborated through tests on three distinct samples: a neodymium–iron–boron magnet, the circular magnetic array from a smartphone, and a magnetized 430 steel plate. These tests, focused on imaging quality and magnetic field characterization, underscore the device’s proficiency in nondestructive magnetic field analysis.
Characterization of the Volatilomic Fingerprint of Culinary Aromatic Herbs: A Comparative Study Based on Chemometric Analysis
Authors: Izcara Sergio , Perestrelo Rosa , Morante-Zarcero Sonia , Sierra Isabel , Sousa Câmara José
Published: June 2024

Culinary aromatic herbs (CAHs), used worldwide for culinary and industrial purposes, are recognized for their wide range of beneficial health effects including antimicrobial, antioxidant, anti-hyperlipidemic, anti-inflammatory, anti-type 2 diabetes mellitus, antitumorigenic and anticarcinogenic, and anti-hypertensive properties, in addition to glucose- and cholesterol-lowering activities as well as properties that affect mental health and cognition via their phytochemical constituents, such as polyphenols (flavonoids and non-flavonoids), sulfur- and nitrogen-containing compounds, alkaloids, minerals, and vitamins. Moreover, the volatile organic metabolites (VOMs) found in CAHs offer unique analytical biosignatures linked to their sensory qualities and organoleptic characteristics. This study aimed to establish the volatilomic pattern of CAHs commonly used in Europe and in the Mediterranean region, oregano (Origanum vulgare L.) and two savory species: savory (Satureja hortensis L.) and lemon savory (Satureja montana L. var. citriodora). The volatilomic pattern of CAHs was established using headspace solid-phase microextraction (HS-SPME) followed by gas chromatography–mass spectrometry (GC-MS) determination. This is a powerful strategy to unravel the potential health benefits related to the most important VOMs identified in each aromatic herb. This comprehensive understanding will aid in establishing the authenticity of these herbs, while also safeguarding against possible fraudulent activities and adulterations. A total of 112 VOMs from different chemical families were identified. Terpenoids amounted to the major chemical family in the investigated aromatic herbs accounting for 96.0, 95.1, and 79.7% of the total volatile composition for savory, lemon savory, and oregano, respectively. Apart from contributing to flavor profiles, certain identified VOMs also possess bioactive properties, opening interesting avenues for potential application in the food, pharmaceutical, and cosmetic sectors. The volatilomic pattern combined with unsupervised principal component analysis facilitated the differentiation of the aromatic herbs under investigation, revealing the most related VOMs in each sample, which can be used as markers for the authentication of these valuable aromatic herbs, such as caryophyllene oxide (103), camphene (6), p-cymene (23), and borneol (74), among others. In addition, some VOMs have a high influence on the aromatic herb’s bioactive potential, helping to prevent certain diseases including cancer, inflammatory-related diseases, diabetes, and cardiovascular diseases.
The Method of 3D C-arm Navigated AC Joint Stabilization-Surgical Technique
Authors: Böhringer Alexander , Pankratz Carlos , Eickhoff Alexander , Gebhard Florian , Schütze Konrad
Published: June 2024

Background: The arthroscopically assisted stabilization of AC joint dislocations with a suture button system is an established procedure that is widely and successfully used in everyday practice. The main advantages of this one-step method are the minimally invasive procedure and the anatomical reconstruction of the ruptured coracoclavicular ligaments with a permanent implant. With this technical note study, for the first time, the new method of navigated suture button implantation in everyday clinical practice is described with the future goal of further reducing invasiveness and increasing precision. Materials and Methods: The surgical technique is explained using precise descriptions and illustrations, photos, X-rays, and 3D reconstructions based on clinical cases. The step-by-step system setup and patient positioning, AC joint reduction and retention, 3D scan and drill tunnel planning, stab incision and Kirschner wire navigation, and cannulated drilling and implant positioning, as well as closure and documentation are described in detail. Results: The standard coracoclavicular stabilization of AC joint dislocations with the 3D C-arm navigated suture button method is described in detail. Furthermore, the feasibility of an additive horizontal acromioclavicular suture cerclage, the implantation of an additional coracoclavicular suture button system, and the single-stage cannulated screw fixation of non-displaced fractures is demonstrated. Conclusion: The navigated suture button method aims to be simple, safe, minimally invasive, and precise. Prospective clinical studies with a long follow-up should be carried out to determine the clinical and radiological outcome in comparison with current methods.
Integrating Green Entrepreneurial Orientation, Green Information Systems, and Management Support with Green Supply Chain Management to Foster Firms’ Environmental Performance
Authors: Karim Rashed Al , Rabiul Md Karim , Ahamed Towhid , Karim Dewan Niamul , Mehzabeen Mahmuda
Published: June 2024

This study examines the mediating role of green entrepreneurship orientation and the green information system on green supply chain management and firms’ environmental performance relationship. It also investigates the moderating role of management support in the above association. The data were gathered from 355 manufacturing employees conveniently via a structured questionnaire administered during the survey. PLS-SEM was employed to analyse the data. Green entrepreneurial orientation and the green information system mediate the green supply chain management and environmental performance relationship. Equally, management support moderates the same association. The mediating role of green entrepreneurial orientation and the green information system and the moderating role of management support are the unique contributions towards green supply chain management and environmental performance association.
Greening Automation: Policy Recommendations for Sustainable Development in AI-Driven Industries
Authors: Doran Nicoleta Mihaela , Badareu Gabriela , Doran Marius Dalian , Enescu Maria , Staicu Anamaria Liliana , Niculescu Mariana
Published: June 2024

This study delves into the dynamic relationship between artificial intelligence (AI) and environmental performance, with a specific focus on greenhouse gas (GHG) emissions across European countries from 2012 to 2022. Utilizing data on industrial robots, AI companies, and AI investments, we examine how AI adoption influences GHG emissions. Preliminary analyses, including ordinary least squares (OLS) regression and diagnostic assessments, were conducted to ensure data adequacy and model readiness. Subsequently, the Elastic Net (ENET) regression model was employed to mitigate overfitting issues and enhance model robustness. Our findings reveal intriguing trends, such as a downward trajectory in GHG emissions correlating with increased AI investment levels and industrial robot deployment. Graphical representations further elucidate the evolution of coefficients and cross-validation errors, providing valuable insights into the relationship between AI and environmental sustainability. These findings offer policymakers actionable insights for leveraging AI technologies to foster sustainable development strategies.
Impacts of Different Tourism Models on Rural Ecosystem Service Value in Ziquejie Terraces
Authors: Shi Juan , Mo Ming , Xie Yimin , Liao Qianying
Published: June 2024

Villages are important components of agricultural heritage sites, and their tourism models significantly impact ecosystem service value (ESV). This study takes Zhenglong Village (culture and tourism integration), Ziquejie Village (farmstay type), and Jizhai Village (sightseeing type) with different tourism models in the Ziquejie Terraces heritage site as the research objects. Firstly, a single dynamic land use model and a land use transfer matrix were used to reveal rural land use changes driven by three different tourism models. Secondly, changes in ESV in the three villages were assessed with a valuation model. Finally, the welfare per unit area of villagers obtained from ecosystem services (ESs) was calculated. The results showed that: (1) From 2006 to 2022, the built-up area in the three villages gradually increased, primarily converting from farmland. Zhenglong Village experienced the smallest degree of change in both farmland and built-up areas. (2) The total ESV in Zhenglong Village increased by 0.35 times, the highest among the three villages. (3) Zhenglong Village saw the largest increase in welfare per unit area of villagers, from 82,551 CNY in 2006 to 111,785 CNY in 2022. Therefore, adopting a culture and tourism integration model in this heritage site is most conducive to conserving and enhancing the rural ESV, improving villagers’ welfare, and promoting the sustainable development of villages and heritage sites.
The Digital Economy and Agricultural Modernization in China: Measurement, Mechanisms, and Implications
Authors: Guo Jie , Lyu Jiahui
Published: June 2024

Promoting the progression of agricultural modernization and digital economy integration has introduced new opportunities and challenges for the advancement of China’s agriculture towards high-quality development. Based on panel data from 31 provinces in China from 2011 to 2020, we use the entropy method, coupling coordination model, spatial auto-correlation model, and obstacle model to explore the degree of coupling coordination and influencing factors of China’s digital economy and agricultural modernization. The results show the following. (1) China’s digital economy and agricultural modernization are underdeveloped on the whole, but they show a positive upward trajectory. The digital economy is outpacing the development of agricultural modernization, but the gap between the two is gradually narrowing. (2) The degree of coupling coordination between China’s digital economy and agricultural modernization shows an upward trend. Additionally, there is regional heterogeneity and uneven development of the coupling coordination, i.e., it is high in the eastern region and low in the western region, accompanied by strong spatial agglomeration and correlation. (3) The main obstacle to the progression of the digital economy is digital infrastructure, while the main obstacle to the progression of agricultural modernization is agricultural production capacity. Based on China’s national conditions, we should fully promote the role of the digital economy, explore plans and strategies for promoting the digital economy in differentiated areas, mitigate any imbalanced development, promote the coordinated development of the digital economy and agricultural modernization, and provide decision-making references for the implementation of a digitized China, as well as rural revitalization strategies.
Cooling Benefits of Urban Tree Canopy: A Systematic Review
Authors: Yin Yihan , Li Song , Xing Xiaoyi , Zhou Xinyi , Kang Yujie , Hu Qi , Li Yanjing
Published: June 2024

As an important part of urban ecosystems, trees can effectively alleviate the urban heat island effect. Tree canopies cool and humidify through shading and evapotranspiration, regulating the urban thermal environment. So far, many studies have analyzed the heat mitigation effect of urban green spaces; however, there are relatively few studies on the cooling effect of tree canopies. Specifically, relevant research focusing on different spatial scales has not been explored. Therefore, this review systematically summarizes the research on the benefits of tree canopy cooling carried out in recent years, analyzes the research content, and evaluates the indicators and key influencing factors of the benefits of tree canopy cooling from four different spatial scales: urban, block, community, and individual. It was found that canopy factors, other vegetation factors, and environmental factors jointly affect the benefits of canopy cooling. This research focuses on the benefits of canopy cooling at different spatial scales. The smaller the research scale, the more discussion and attention will be paid to vegetation factors. This paper puts forward major directions for future research and development, providing optimization strategies for urban planning or plant design at different scales in the context of climate change.
An Improved Pedestrian Detection Model Based on YOLOv8 for Dense Scenes
Authors: Fang Yuchao , Pang Huanli
Published: June 2024

In dense scenes, pedestrians often exhibit a variety of symmetrical features, such as symmetry in body contour, posture, clothing, and appearance. However, pedestrian detection poses challenges due to the mutual occlusion of pedestrians and the small scale of distant pedestrians in the image. To address these challenges, we propose a pedestrian detection algorithm tailored for dense scenarios called YOLO-RAD. In this algorithm, we integrate the concept of receiving field attention (RFA) into the Conv and C2f modules to enhance the feature extraction capability of the network. A self-designed four-layer adaptive spatial feature fusion (ASFF) module is introduced, and shallow pedestrian feature information is added to enhance the multi-scale feature fusion capability. Finally, we introduce a small-target dynamic head structure (DyHead-S) to enhance the capability of detecting small-scale pedestrians. Experimental results on WiderPerson and CrowdHuman, two challenging dense pedestrian datasets, show that compared with YOLOv8n, our YOLO-RAD algorithm has achieved significant improvement in detection performance, and the detection performance of mAP@0.5 has increased by 2.5% and 6%, respectively. The detection performance of mAP@0.5:0.95 was improved by 2.7% and 6.8%, respectively. Therefore, the algorithm can effectively improve the performance of pedestrian detection in dense scenes.
Comparative Analysis of Key Success Factors in S&OP: Focusing on Manufacturing and Retail Industry in South Korea
Authors: Kim Sangjun , Shin Kwangsup
Published: June 2024

The global supply chain is facing unprecedented challenges due to pandemics, geopolitical tensions, volatile commodity prices, and rapid changes in consumer demand. The Sales and Operations Planning (S&OP) process can be an important solution by integrating supply and demand with business strategy and operational planning. However, previous studies have developed single frameworks or maturity models without considering industry-specific factors, overlooking the structural and operational characteristics of different industries that can have a significant impact on the approach and success factors of S&OP. Therefore, this study identifies and compares the key success factors of S&OP in the manufacturing and retail industries using the Analytic Hierarchy Process (AHP) technique. The results show clear differences in the prioritization of success factors between two industries. In the manufacturing industry, strategic support and leadership were evaluated as the most critical factor, while in the retail industry, operational processes were emphasized. In addition, the relative importance was concentrated on the top-ranked factor in the manufacturing industry, but the relative importance was distributed among several factors in the retail industry. This study provides valuable insights into the critical success factors for S&OP implementation in different industries and offers an understanding of how industry-specific characteristics influence the effectiveness of S&OP strategies.
Single-Facility Analysis of COVID-19 Status of Healthcare Employees during the Eighth and Ninth Pandemic Waves in Japan after Introducing Regular Rapid Antigen Testing
Authors: Nagasawa Masayuki , Kato Tomoyuki , Sakaguchi Hayato , Tanaka Ippei , Watanabe Mami , Hiroshima Yoko , Sakurai Mie
Published: June 2024

Background: Community infections of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have increased rapidly since the emergence of the Omicron strain. During the eighth and ninth pandemic waves—when movement restrictions in the community were eased—the all-case registration system was changed, and the actual status of infection became uncertain. Methods: We conducted regular rapid antigen tests (R-RATs) once or twice a week as self-testing to examine the actual state of coronavirus disease (COVID-19) diagnosis among healthcare employees. Results: Overall, 320 (1.42/day) and 299 (1.76/day) employees were infected in the eighth and ninth pandemic waves. During both periods, 59/263 doctors (22.4%), 335/806 nurses (41.6%), 92/194 administrative employees (47.4%), and 129/218 clinical laboratory technicians (59.2%) were infected. In the eighth wave, 56 of 195 employees were infected through close contact; in the ninth wave, 26 of 62 employees were infected. No significant difference was observed in the number of vaccinations between infected and non-infected employees. The positivity rate of R-RATs was 0.41% and 0.45% in the eighth and ninth waves. R-RATs detected infection in 212 and 229 employees during the eighth and ninth waves, respectively; the ratio of R-RAT-detected positive employees to those who reported infection was significantly higher during the ninth wave (odds ratio: 1.67, 95% confidence interval: 1.17–2.37, p < 0.001). Conclusions: The number of infected healthcare employees remained high during the eighth and ninth pandemic waves in Japan. The R-RAT is considered effective for detecting mild or asymptomatic COVID-19 at an early stage and at a high rate in healthcare employees.
Monitoring Changes in the Antimicrobial-Resistance Gene Set (ARG) of Raw Milk and Dairy Products in a Cattle Farm, from Production to Consumption
Authors: Kerek Ádám , Németh Virág , Szabó Ábel , Papp Márton , Bányai Krisztián , Kardos Gábor , Kaszab Eszter , Bali Krisztina , Nagy Zoltán , Süth Miklós , Jerzsele Ákos
Published: June 2024

Raw milk and dairy products can serve as potential vectors for transmissible bacterial, viral and protozoal diseases, alongside harboring antimicrobial-resistance genes. This study monitors the changes in the antimicrobial-resistance gene pool in raw milk and cheese, from farm to consumer, utilizing next-generation sequencing. Five parallel sampling runs were conducted to assess the resistance gene pool, as well as phage or plasmid carriage and potential mobility. In terms of taxonomic composition, in raw milk the Firmicutes phylum made up 41%, while the Proteobacteria phylum accounted for 58%. In fresh cheese, this ratio shifted to 93% Firmicutes and 7% Proteobacteria. In matured cheese, the composition was 79% Firmicutes and 21% Proteobacteria. In total, 112 antimicrobial-resistance genes were identified. While a notable reduction in the resistance gene pool was observed in the freshly made raw cheese compared to the raw milk samples, a significant growth in the resistance gene pool occurred after one month of maturation, surpassing the initial gene frequency. Notably, the presence of extended-spectrum beta-lactamase (ESBL) genes, such as OXA-662 (100% coverage, 99.3% identity) and OXA-309 (97.1% coverage, 96.2% identity), raised concerns; these genes have a major public health relevance. In total, nineteen such genes belonging to nine gene families (ACT, CMY, EC, ORN, OXA, OXY, PLA, RAHN, TER) have been identified. The largest number of resistance genes were identified against fluoroquinolone drugs, which determined efflux pumps predominantly. Our findings underscore the importance of monitoring gene pool variations throughout the product pathway and the potential for horizontal gene transfer in raw products. We advocate the adoption of a new approach to food safety investigations, incorporating next-generation sequencing techniques.