bluuebunny/crossref_metadata_2025 huggingface dataset:
Predicting Soil Organic Matter, Available Nitrogen, Available Phosphorus and Available Potassium in a Black Soil Using a Nearby Hyperspectral Sensor System
Authors: Wan Shuming , Hou Jiaqi , Zhao Jiangsan , Clarke Nicholas , Kempenaar Corné , Chen Xueli
Published: April 2024

Black soils, which play an important role in agricultural production and food security, are well known for their relatively high content of soil organic matter (SOM). SOM has a significant impact on the sustainability of farmland and provides nutrients for plants. Hyperspectral imaging (HSI) in the visible and near-infrared region has shown the potential to detect soil nutrient levels in the laboratory. However, using portable spectrometers directly in the field remains challenging due to variations in soil moisture (SM). The current study used spectral data captured by a handheld spectrometer outdoors to predict SOM, available nitrogen (AN), available phosphorus (AP) and available potassium (AK) with different SM levels. Partial least squares regression (PLSR) models were established to compare the predictive performance of air-dried soil samples with SMs around 20%, 30% and 40%. The results showed that the model established using dry sample data had the best performance (RMSE = 4.47 g/kg) for the prediction of SOM, followed by AN (RMSE = 20.92 mg/kg) and AK (RMSE = 22.67 mg/kg). The AP was better predicted by the model based on 30% SM (RMSE = 8.04 mg/kg). In general, model performance deteriorated with an increase in SM, except for the case of AP. Feature wavelengths for predicting four kinds of soil properties were recommended based on variable importance in the projection (VIP), which offered useful guidance for the development of portable hyperspectral sensors based on discrete wavebands to reduce cost and save time for on-site data collection.
Facile Synthesis of Fe-Doped, Algae Residue-Derived Carbon Aerogels for Electrochemical Dopamine Biosensors
Authors: Wu Hao , Wen Qin , Luan Xin , Yang Weiwei , Guo Lei , Wei Gang
Published: April 2024

An abnormal level of dopamine (DA), a kind of neurotransmitter, correlates with a series of diseases, including Parkinson’s disease, Willis-Ekbom disease, attention deficit hyperactivity disorder, and schizophrenia. Hence, it is imperative to achieve a precise, rapid detection method in clinical medicine. In this study, we synthesized nanocomposite carbon aerogels (CAs) doped with iron and iron carbide, based on algae residue-derived biomass materials, using Fe(NO3)3 as the iron source. The modified glassy carbon electrode (GCE) for DA detection, denoted as CAs-Fe/GCE, was prepared through surface modification with this composite material. X-ray photoelectron spectroscopy and X-ray diffraction characterization confirmed the successful doping of iron into the as-prepared CAs. Additionally, the electrochemical behavior of DA on the modified electrode surface was investigated and the results demonstrate that the addition of the CAs-Fe promoted the electron transfer rate, thereby enhancing their sensing performance. The fabricated electrochemical DA biosensor exhibits an accurate detection of DA in the concentration within the range of 0.01~200 µM, with a detection limit of 0.0033 µM. Furthermore, the proposed biosensor is validated in real samples, showing its high applicability for the detection of DA in beverages.
Experimental Evaluation of an SDR-Based UAV Localization System
Authors: Codău Cristian , Buta Rareș-Călin , Păstrăv Andra , Dolea Paul , Palade Tudor , Puschita Emanuel
Published: April 2024

UAV communications have seen a rapid rise in the last few years. The drone class of UAV has particularly become more widespread around the world, and illicit behavior using drones has become a problem. Therefore, localization, tracking, and even taking control of drones have also gained interest. Knowing the frequency of a target signal, its position can be determined (as the angle of arrival with respect to a fixed receiver point) using radio frequency-based localization techniques. One such technique is represented by the subspace-based algorithms that offer highly accurate results. This paper presents the implementation of the MUSIC algorithm on an SDR-based system using a uniform circular antenna array and its experimental evaluation in relevant outdoor environments for drone localization. The results show the capability of the system to indicate the AoA of the target signal. The results are compared with the actual direction computed from the log files of the drone application and validated with a professional direction-finding solution (i.e., Narda SignalShark equipped with the automatic direction-finding antenna).
An End-to-End Artificial Intelligence of Things (AIoT) Solution for Protecting Pipeline Easements against External Interference—An Australian Use-Case
Authors: Iqbal Umair , Barthelemy Johan , Michal Guillaume
Published: April 2024

High-pressure pipelines are critical for transporting hazardous materials over long distances, but they face threats from third-party interference activities. Preventive measures are implemented, but interference accidents can still occur, making the need for high-quality detection strategies vital. This paper proposes an end-to-end Artificial Intelligence of Things (AIoT) solution to detect potential interference threats in real time. The solution involves developing a smart visual sensor capable of processing images using state-of-the-art computer vision algorithms and transmitting alerts to pipeline operators in real time. The system’s core is based on the object-detection model (e.g., You Only Look Once version 4 (YOLOv4) and DETR with Improved deNoising anchOr boxes (DINO)), trained on a custom Pipeline Visual Threat Assessment (Pipe-VisTA) dataset. Among the trained models, DINO was able to achieve the best Mean Average Precision (mAP) of 71.2% for the unseen test dataset. However, for the deployment on a limited computational-ability edge computer (i.e., the NVIDIA Jetson Nano), the simpler and TensorRT-optimized YOLOv4 model was used, which achieved a mAP of 61.8% for the test dataset. The developed AIoT device captures the image using a camera, processes on the edge using the trained YOLOv4 model to detect the potential threat, transmits the threat alert to a Fleet Portal via LoRaWAN, and hosts the alert on a dashboard via a satellite network. The device has been fully tested in the field to ensure its functionality prior to deployment for the SEA Gas use-case. The AIoT smart solution has been deployed across the 10km stretch of the SEA Gas pipeline across the Murray Bridge section. In total, 48 AIoT devices and three Fleet Portals are installed to ensure the line-of-sight communication between the devices and portals.
Developing a Novel Ontology for Cybersecurity in Internet of Medical Things-Enabled Remote Patient Monitoring
Authors: Bughio Kulsoom S. , Cook David M. , Shah Syed Afaq A.
Published: April 2024

IoT has seen remarkable growth, particularly in healthcare, leading to the rise of IoMT. IoMT integrates medical devices for real-time data analysis and transmission but faces challenges in data security and interoperability. This research identifies a significant gap in the existing literature regarding a comprehensive ontology for vulnerabilities in medical IoT devices. This paper proposes a fundamental domain ontology named MIoT (Medical Internet of Things) ontology, focusing on cybersecurity in IoMT (Internet of Medical Things), particularly in remote patient monitoring settings. This research will refer to similar-looking acronyms, IoMT and MIoT ontology. It is important to distinguish between the two. IoMT is a collection of various medical devices and their applications within the research domain. On the other hand, MIoT ontology refers to the proposed ontology that defines various concepts, roles, and individuals. MIoT ontology utilizes the knowledge engineering methodology outlined in Ontology Development 101, along with the structured life cycle, and establishes semantic interoperability among medical devices to secure IoMT assets from vulnerabilities and cyberattacks. By defining key concepts and relationships, it becomes easier to understand and analyze the complex network of information within the IoMT. The MIoT ontology captures essential key terms and security-related entities for future extensions. A conceptual model is derived from the MIoT ontology and validated through a case study. Furthermore, this paper outlines a roadmap for future research, highlighting potential impacts on security automation in healthcare applications.
High-Performance Four-Channel Tactile Sensor for Measuring the Magnitude and Orientation of Forces
Authors: Zhang Mingyao , Shi Yong , Ge Haitao , Sun Guopeng , Lian Zihan , Lu Yifei
Published: April 2024

Flexible sensors have gained popularity in recent years. This study proposes a novel structure of a resistive four-channel tactile sensor capable of distinguishing the magnitude and direction of normal forces acting on its sensing surface. The sensor uses EcoflexTM00-30 as the substrate and EGaIn alloy as the conductive filler, featuring four mutually perpendicular and curved channels to enhance the sensor’s dynamic responsiveness. Experiments and simulations show that the sensor has a large dynamic range (31.25–100 mΩ), high precision (deviation of repeated pressing below 0.1%), linearity (R2 above 0.97), fast response/recovery time (0.2 s/0.15 s), and robust stability (with fluctuations below 0.9%). This work uses an underactuated robotic hand equipped with a four-channel tactile sensor to grasp various objects. The sensor data collected effectively predicts the shapes of the objects grasped. Furthermore, the four-channel tactile sensor proposed in this work may be employed in smart wearables, medical diagnostics, and other industries.
The Classification of VOCs Based on Sensor Images Using a Lightweight Neural Network for Lung Cancer Diagnosis
Authors: Zha Chengyuan , Li Lei , Zhu Fangting , Zhao Yanzhe
Published: April 2024

The application of artificial intelligence to point-of-care testing (POCT) disease detection has become a hot research field, in which breath detection, which detects the patient’s exhaled VOCs, combined with sensor arrays of convolutional neural network (CNN) algorithms as a new lung cancer detection is attracting more researchers’ attention. However, the low accuracy, high-complexity computation and large number of parameters make the CNN algorithms difficult to transplant to the embedded system of POCT devices. A lightweight neural network (LTNet) in this work is proposed to deal with this problem, and meanwhile, achieve high-precision classification of acetone and ethanol gases, which are respiratory markers for lung cancer patients. Compared to currently popular lightweight CNN models, such as EfficientNet, LTNet has fewer parameters (32 K) and its training weight size is only 0.155 MB. LTNet achieved an overall classification accuracy of 99.06% and 99.14% in the own mixed gas dataset and the University of California (UCI) dataset, which are both higher than the scores of the six existing models, and it also offers the shortest training (844.38 s and 584.67 s) and inference times (23 s and 14 s) in the same validation sets. Compared to the existing CNN models, LTNet is more suitable for resource-limited POCT devices.
Rectified Latent Variable Model-Based EMG Factorization of Inhibitory Muscle Synergy Components Related to Aging, Expertise and Force–Tempo Variations
Authors: Huang Subing , Guo Xiaoyu , Xie Jodie J. , Lau Kelvin Y. S. , Liu Richard , Mak Arthur D. P. , Cheung Vincent C. K. , Chan Rosa H. M.
Published: April 2024

Muscle synergy has been widely acknowledged as a possible strategy of neuromotor control, but current research has ignored the potential inhibitory components in muscle synergies. Our study aims to identify and characterize the inhibitory components within motor modules derived from electromyography (EMG), investigate the impact of aging and motor expertise on these components, and better understand the nervous system’s adaptions to varying task demands. We utilized a rectified latent variable model (RLVM) to factorize motor modules with inhibitory components from EMG signals recorded from ten expert pianists when they played scales and pieces at different tempo–force combinations. We found that older participants showed a higher proportion of inhibitory components compared with the younger group. Senior experts had a higher proportion of inhibitory components on the left hand, and most inhibitory components became less negative with increased tempo or decreased force. Our results demonstrated that the inhibitory components in muscle synergies could be shaped by aging and expertise, and also took part in motor control for adapting to different conditions in complex tasks.
Low-Cost Recognition of Plastic Waste Using Deep Learning and a Multi-Spectral Near-Infrared Sensor
Authors: Martinez-Hernandez Uriel , West Gregory , Assaf Tareq
Published: April 2024

This work presents an approach for the recognition of plastics using a low-cost spectroscopy sensor module together with a set of machine learning methods. The sensor is a multi-spectral module capable of measuring 18 wavelengths from the visible to the near-infrared. Data processing and analysis are performed using a set of ten machine learning methods (Random Forest, Support Vector Machines, Multi-Layer Perceptron, Convolutional Neural Networks, Decision Trees, Logistic Regression, Naive Bayes, k-Nearest Neighbour, AdaBoost, Linear Discriminant Analysis). An experimental setup is designed for systematic data collection from six plastic types including PET, HDPE, PVC, LDPE, PP and PS household waste. The set of computational methods is implemented in a generalised pipeline for the validation of the proposed approach for the recognition of plastics. The results show that Convolutional Neural Networks and Multi-Layer Perceptron can recognise plastics with a mean accuracy of 72.50% and 70.25%, respectively, with the largest accuracy of 83.5% for PS plastic and the smallest accuracy of 66% for PET plastic. The results demonstrate that this low-cost near-infrared sensor with machine learning methods can recognise plastics effectively, making it an affordable and portable approach that contributes to the development of sustainable systems with potential for applications in other fields such as agriculture, e-waste recycling, healthcare and manufacturing.
Differentiating Epileptic and Psychogenic Non-Epileptic Seizures Using Machine Learning Analysis of EEG Plot Images
Authors: Fussner Steven , Boyne Aidan , Han Albert , Nakhleh Lauren A. , Haneef Zulfi
Published: April 2024

The treatment of epilepsy, the second most common chronic neurological disorder, is often complicated by the failure of patients to respond to medication. Treatment failure with anti-seizure medications is often due to the presence of non-epileptic seizures. Distinguishing non-epileptic from epileptic seizures requires an expensive and time-consuming analysis of electroencephalograms (EEGs) recorded in an epilepsy monitoring unit. Machine learning algorithms have been used to detect seizures from EEG, typically using EEG waveform analysis. We employed an alternative approach, using a convolutional neural network (CNN) with transfer learning using MobileNetV2 to emulate the real-world visual analysis of EEG images by epileptologists. A total of 5359 EEG waveform plot images from 107 adult subjects across two epilepsy monitoring units in separate medical facilities were divided into epileptic and non-epileptic groups for training and cross-validation of the CNN. The model achieved an accuracy of 86.9% (Area Under the Curve, AUC 0.92) at the site where training data were extracted and an accuracy of 87.3% (AUC 0.94) at the other site whose data were only used for validation. This investigation demonstrates the high accuracy achievable with CNN analysis of EEG plot images and the robustness of this approach across EEG visualization software, laying the groundwork for further subclassification of seizures using similar approaches in a clinical setting.
Dynamic Occupancy Grid Map with Semantic Information Using Deep Learning-Based BEVFusion Method with Camera and LiDAR Fusion
Authors: Jang Harin , Kim Taehyun , Ahn Kyungjae , Jeon Soo , Kang Yeonsik
Published: April 2024

In the field of robotics and autonomous driving, dynamic occupancy grid maps (DOGMs) are typically used to represent the position and velocity information of objects. Although three-dimensional light detection and ranging (LiDAR) sensor-based DOGMs have been actively researched, they have limitations, as they cannot classify types of objects. Therefore, in this study, a deep learning-based camera–LiDAR sensor fusion technique is employed as input to DOGMs. Consequently, not only the position and velocity information of objects but also their class information can be updated, expanding the application areas of DOGMs. Moreover, unclassified LiDAR point measurements contribute to the formation of a map of the surrounding environment, improving the reliability of perception by registering objects that were not classified by deep learning. To achieve this, we developed update rules on the basis of the Dempster–Shafer evidence theory, incorporating class information and the uncertainty of objects occupying grid cells. Furthermore, we analyzed the accuracy of the velocity estimation using two update models. One assigns the occupancy probability only to the edges of the oriented bounding box, whereas the other assigns the occupancy probability to the entire area of the box. The performance of the developed perception technique is evaluated using the public nuScenes dataset. The developed DOGM with object class information will help autonomous vehicles to navigate in complex urban driving environments by providing them with rich information, such as the class and velocity of nearby obstacles.
Pulse Compression Shape-Based ADC/DAC Chain Synchronization Measurement Algorithm with Sub-Sampling Resolution
Authors: Hao Xiangyu , Fang Hongji , Luo Wei , Zhang Bo
Published: April 2024

In this article, we address the problem of synchronizing multiple analog-to-digital converter (ADC) and digital-to-analog converter (DAC) chains in a multi-channel system, which is constrained by the sampling frequency and inconsistencies among the components during system integration. To evaluate and compensate for the synchronization differences, we propose a pulse compression shape-based algorithm to measure the entire delay parameter of the ADC/DAC chain, which achieves sub-sampling resolution by mapping the shape of the discrete pulse compression peak to the signal propagation delay. Moreover, owing to the matched filtering in the pulse compression process, the algorithm exhibits good noise performance and is suitable for wireless scenarios. Experiments verified that the algorithm can achieve precise measurements with sub-sampling resolution in scenarios where the signal-to-noise ratio (SNR) is greater than −10 dB.
Sensors and Sensing Devices Utilizing Electrorheological Fluids and Magnetorheological Materials—A Review
Authors: Park Yu-Jin , Choi Seung-Bok
Published: April 2024

This paper comprehensively reviews sensors and sensing devices developed or/and proposed so far utilizing two smart materials: electrorheological fluids (ERFs) and magnetorheological materials (MRMs) whose rheological characteristics such as stiffness and damping can be controlled by external stimuli; an electrical voltage for ERFs and a magnetic field for MRMs, respectively. In this review article, the MRMs are classified into magnetorheological fluids (MRF), magnetorheological elastomers (MRE) and magnetorheological plastomers (MRP). To easily understand the history of sensing research using these two smart materials, the order of this review article is organized in a chronological manner of ERF sensors, MRF sensors, MRE sensors and MRP sensors. Among many sensors fabricated from each smart material, one or two sensors or sensing devices are adopted to discuss the sensing configuration, working principle and specifications such as accuracy and sensitivity. Some sensors adopted in this article include force sensors, tactile devices, strain sensors, wearable bending sensors, magnetometers, display devices and flux measurement sensors. After briefly describing what has been reviewed in a conclusion, several challenging future works, which should be undertaken for the practical applications of sensors or/and sensing devices, are discussed in terms of response time and new technologies integrating with artificial intelligence neural networks in which several parameters affecting the sensor signals can be precisely and optimally tuned. It is sure that this review article is very helpful to potential readers who are interested in creative sensors using not only the proposed smart materials but also different types of smart materials such as shape memory alloys and active polymers.
Rectenna System Development Using Harmonic Balance and S-Parameters for an RF Energy Harvester
Authors: Md Jamil Muhamad Nurarif Bin , Omar Madiah , Ibrahim Rosdiazli , Bingi Kishore , Faqih Mochammad
Published: April 2024

With the escalating demand for Radio Frequency Identification (RFID) technology and the Internet of Things (IoT), there is a growing need for sustainable and autonomous power solutions to energize low-powered devices. Consequently, there is a critical imperative to mitigate dependency on batteries during passive operation. This paper proposes the conceptual framework of rectenna architecture-based radio frequency energy harvesters’ performance, specifically optimized for low-power device applications. The proposed prototype utilizes the surroundings’ Wi-Fi signals within the 2.4 GHz frequency band. The design integrates a seven-stage Cockroft-Walton rectifier featuring a Schottky diode HSMS286C and MA4E2054B1-1146T, a low-pass filter, and a fractal antenna. Preliminary simulations conducted using Advanced Design System (ADS) reveal that a voltage of 3.53 V can be harvested by employing a 1.57 mm thickness Rogers 5880 printed circuit board (PCB) substrate with an MA4E2054B1-1146T rectifier prototype, given a minimum power input of −10 dBm (0.1 mW). Integrating the fabricated rectifier and fractal antenna successfully yields a 1.5 V DC output from Wi-Fi signals, demonstrable by illuminating a red LED. These findings underscore the viability of deploying a fractal antenna-based radio frequency (RF) harvester for empowering small electronic devices.
In Defense of a Peripheral Epistemology: Exploring “Decolonial Cognitive Triggers” for Epistemic Disobedience in Urban Peripheries
Authors: Fernandes Fernando Lannes , Melino Heloisa , Silva Jailson De Souza e
Published: April 2024

This paper presents an approach to decolonial thinking and epistemological disobedience through what we call “decolonial cognitive triggers”. It is based on the struggles of urban peripheral communities in Brazil and explores eight triggers in the making of a Peripheral Epistemology. The unique points of our reflection are the sociocultural practices emerging from urban peripheral communities in Brazil and their responses to structural racism. As a part of this, we will explore the concepts of potência and convivência as core components of the decolonial debate, as well as their role in enabling epistemic disobedience in urban peripheries. We conclude by suggesting shifts in policy-making directed towards urban peripheries through the recognition and incorporation of such concepts and triggers.
Exploring Attitudes toward Sustainability Education in a Group of Italian Preservice Teachers: The Role of Environmental Identity and Sense of Community Responsibility
Authors: Sulla Francesco , Fantinelli Stefania , Toto Giusi Antonia , Peconio Guendalina , Esposito Ciro
Published: April 2024

In the current educational context, international institutions are being called to rethink education and declining new strategies that aim to teach sustainable behaviors. From this perspective, the present study aims to investigate the attitudes towards the sustainability of trainee teachers, verifying their connection with psycho-social variables, such as environmental identity and sense of community responsibility, and exploring their most common dispositions. In total, 126 Italian preservice teachers were involved in a mixed method study where they were asked to complete a questionnaire and participated in focus groups. The use of a combination of quantitative–qualitative methodologies has allowed us to obtain in-depth information about sustainability education. Quantitative data showed a virtuous circular relationship between environmental identity, attitudes towards education for sustainability, and the sense of community responsibility. Qualitative data showed the high frequency of two key action verbs, avoid and respect, but no specific reference to the educational field. After creating the target concept ‘to teach’, we observed a worrying absence of lemmas related to emotional dimension, and that the communication perspective is set on adults’ point of view, rather than on pupils. Limitations, strengths, and practical implications have been extensively discussed, especially in relation to the field of teacher education.
An Investigation into the Impact of Teachers’ Emotional Intelligence on Students’ Satisfaction of Their Academic Achievement
Authors: Al Jaberi Ameena Taleb , Alzouebi Khadeegha , Abu Khurma Othman
Published: April 2024

This paper explores the correlation between teachers’ emotional intelligence (EI) and students’ academic achievement. Utilizing the Daniel Goleman questionnaire, the study delves into the multifaceted aspects of EI that extend beyond traditional leadership qualities. Goleman contends that, while attributes such as determination, intelligence, and vision are essential, they alone do not encapsulate effective leadership. Emotional intelligence introduces a spectrum of qualities crucial for leadership success, including self-awareness, managing emotions, motivating oneself, empathy, and social skills. The study employed a questionnaire developed by the researcher, employing a statement-based approach. Participants, predominantly students, were tasked with selecting statements that best resonated with their experiences. The questionnaire aimed to assess various dimensions of emotional intelligence, including self-awareness, emotional management, self-motivation, empathy, and social adeptness. Through statistical analysis of the collected data, the paper examines the relationship between teachers’ EI levels and students’ academic achievement. Findings revealed the significance of teachers’ ability to comprehend and regulate emotions, as well as their capacity for empathy and effective social interaction. Furthermore, the study sheds light on how these facets of emotional intelligence contribute to creating conducive learning environments and fostering student engagement and achievement. This research underscores the pivotal role of emotional intelligence in educational settings and provides insights into how enhancing teachers’ EI can positively impact students’ learning outcomes. The implications of these findings extend to educational policies and practices, advocating for the incorporation of EI training and development programs for educators to cultivate conducive learning environments and facilitate students’ academic success.
Predictive Validity of Multifactorial Injury Risk Models and Associated Clinical Measures in the U.S. Population
Authors: Eckart Adam C. , Ghimire Pragya Sharma , Stavitz James
Published: April 2024

Background: Popular movement-based injury risk screens were shown to lack predictive precision, leading to interest in multifactorial models. Furthermore, there is a lack of research regarding injury risk assessment for those currently or planning to be recreationally active. This study aims to provide injury risk insights by analyzing multifactorial injury risk models and associated clinical measures in the U.S. population. Methods: Data related to injury, inflammatory markers, physical functioning, body composition, physical activity, and other variables from 21,033 respondents were extracted from NHANES. Odds ratios for self-reported injury were calculated for single predictors and risk models. Case–control and principal component analyses (PCA) were conducted to elucidate confounders and identify risk factor clusters, respectively. Receiver operating characteristic analysis was used to test the precision of a risk factor cluster to identify pain points and functional difficulties. Results: Sociodemographic, individual, and lifestyle factors were strongly associated with higher odds of injury. Increases in fibrinogen and C-reactive protein were significantly associated with all risk groups. Membership to the high-risk group (age over 40, obesity, no muscle-strengthening activities, sedentary lifestyle, and low back pain) predicted at least one functional difficulty with 67.4% sensitivity and 87.2% specificity. In the injury group, bone turnover markers were higher, yet confounded by age, and there was a significantly higher prevalence of self-reported osteoporosis compared to the control. In males, low testosterone was associated with injury, and high estradiol was associated with pain and functional difficulties. In females, high follicle-stimulating hormone was associated with functional difficulties. PCA revealed four high-risk profiles, with markers and activities showing distinct loadings. Conclusions: A comprehensive approach to injury risk assessment should consider the nexus of aging, lifestyle, and chronic disease to enhance tailored injury prevention strategies, fostering safe and effective physical activity participation and reducing the burden of musculoskeletal disorders.
Wearable Sensors and the Evaluation of Physiological Performance in Elite Field Hockey Players
Authors: Latino Francesca , Tafuri Francesco
Published: April 2024

Sports performance tracking has gained a lot of interest and widespread use in recent years, especially in elite and sub-elite sports. This makes it possible to improve the effectiveness of training, to calibrate and balance workloads according to real energy expenditure, and to reduce the likelihood of injuries due to excessive physical stress. In this context, the aim of this review was to map the scientific literature on wearable devices used in field hockey, evaluating their characteristics and the available evidence on their validity in measuring physiological and movement parameters. A systematic investigation was carried out by employing five electronic databases and search terms that incorporated field hockey, wearables, and performance analysis. Two independent reviewers conducted assessments of the 3401 titles and abstracts for inclusion, and at the end of the screening process, 102 full texts were analyzed. Lastly, a total of 23 research articles that specifically concentrated on field hockey were incorporated. The selected papers dealt with performance monitoring (6 papers), technical analysis and strategy game (6), injury prevention (1), and physiological measurements (10). To appraise the quality of the evaluations, the Oxford quality scoring system scale was employed. The extraction of information was carried out through the utilization of the participants, intervention, comparison, and outcomes (PICOS) format. The analysis encompassed research studies that implemented wearable devices during training and competitive events. Among elite field hockey competitions, GPS units were identified as the predominant wearable, followed by heart rate monitors. The intraclass correlation coefficient (ICC) related to wearable devices showed reasonably high between-trial ICCs ranging from 0.77 to 0.99. The utilization of wearable devices in field hockey primarily centers around the measurement of player activity profiles and physiological demands. The presence of discrepancies in sampling rates and performance bands makes it arduous to draw comparisons between studies. Nevertheless, this analysis attested to the fact that wearable devices are being employed for diverse applications in the realm of field hockey.
Assessing Brain Processing Deficits Using Neuropsychological and Vision-Specific Tests for Concussion
Authors: Harper Brent A. , Soangra Rahul
Published: April 2024

Introduction: Since verbal memory and visual processing transpire within analogous cerebral regions, this study assessed (i) if a visual function can predict verbal memory performance. It also hypothesized whether neurocognitive (e.g., ImPACT) tests focusing on the Visual Memory and Cognitive Efficacy Index will predict Verbal Memory scores and (ii) if vision metrics and age can identify individuals with a history of concussion. Finally, it also hypothesized that King–Devick and near point of convergence scores alongside age considerations will identify candidates with a prior reported history of concussion. Materials and methods: This observational cohort assessed 25 collegiate ice hockey players prior to the competitive season considering age (19.76 ± 1.42 years) and BMI (25.9 ± 3.0 kg/cm2). Hypothesis 1 was assessed using a hierarchical (sequential) multiple regression analysis, assessing the predictive capacity of Visual Memory and Cognitive Efficacy Index scores in relation to Verbal Memory scores. Hypothesis 2 utilized a binomial logistic regression to determine if King–Devick and near point of convergence scores predict those with a prior history of concussion. Results: Hypothesis 1 developed two models, where Model 1 included Visual Memory as the predictor, while Model 2 added the Cognitive Efficacy Index as a predictor for verbal memory scores. Model 1 significantly explained 41% of the variance. Results from Model 2 suggest that the Cognitive Efficacy Index explained an additional 24.4%. Thus, Model 2 was interpreted where only the Cognitive Efficacy Index was a significant predictor (p = 0.001). For every 1 unit increase in the Cognitive Efficacy Index, Verbal Memory increased by 41.16. Hypothesis 2’s model was significant, accounting for 37.9% of the variance in those with a history of concussion. However, there were no significant unique predictors within the model as age (Wald = 1.26, p = 0.261), King–Devick (Wald = 2.31, p = 0.128), and near point of convergence (Wald = 2.43, p = 0.119) were not significant predictors individually. Conclusions: The conflicting findings of this study indicate that baseline data for those with a history of concussion greater than one year may not be comparable to the same metrics during acute concussion episodes. Young athletes who sustain a concussion may be able to overcompensate via the visual system. Future prospective studies with larger sample sizes are required using the proposed model’s objective metrics.
SCS 6th Annual Meeting—EEVFA—11th International Congress of Biochemistry and Physiology of Exercise, Athens, Greece, 2023
Authors: Alcaraz Pedro E. , Marín-Cascales Elena , Blazevich Anthony J. , Freitas Tomás T. , Donti Olyvia , Spyrou Konstantinos , Bogdanis Gregory C.
Published: April 2024

On behalf of the Strength and Conditioning Society (SCS) and the Hellenic Society of Biochemistry and Exercise Physiology (EEVFA), we are pleased to present the abstracts of the SCS 6th Annual Meeting and EEVFA—11th International Congress of Biochemistry and Physiology of Exercise. The event was held at the Hellenic Olympic Committee headquarters in Athens, Greece, on 19–22 October 2023, and comprised several invited sessions from international and national speakers on a variety of topics related to biochemistry and exercise physiology, strength and conditioning practices and their application to health, injury prevention and sports performance. These included strength training in high-performance sports, sport science and training–competition load management in elite environments, biochemistry and exercise physiology and prescription, nutrition and biomechanics, among others. The conference also included different practical workshops conducted by renowned academics and practitioners on eccentric training, change of direction ability and strength and power training in professional team-sports, and ergospirometry and exercise prescription in specific populations. Finally, the event disseminated up-to-date strength and conditioning research by providing practitioners and researchers with the opportunity to present their most recent findings. In this regard, all abstracts of the communications presented at the SCS 6th Annual Meeting—EEVFA—11th International Congress of Biochemistry and Physiology of Exercise can be found in this Conference Report.
Optimization of a Hybrid Solar–Wind Microgrid for Sustainable Development: A Case Study in Antofagasta, Chile
Authors: Merino Carlos , Castro Rui
Published: April 2024

This paper introduces a genetic algorithm designed to optimize the sizing of a hybrid solar–wind microgrid connected to the main electric grid in Chile, serving a simulated town of 2000 houses. The goal is to promote sustainable development by using renewable energy sources (RES) to supply a small village. The model, considering local meteorological conditions, aggregated load, and Chilean electrical regulations, establishes the optimal number of photovoltaic modules and wind turbine generators and allows for the monitoring of the microgrid’s operation, whose operating strategy is proposed herein. Adhering to Chilean regulations, a maximum exporting power of 9 MW is analyzed, with no restrictions on importing power, which is needed when the renewable resources are not enough to meet the demand. The optimization algorithm, aimed at sizing the RES supply, identified an optimal solution composed of 5 photovoltaic modules of 500 Wp each (2.5 kWp in total) and 123 wind turbines of 100 kW each (total of 12,300 kW), meeting around 85% of the demand through renewable generation. Due to time mismatches between generation and load patterns, the remaining energy was imported. The project’s net present value is approximately EUR 25 M, with a levelized cost of energy at 37 EUR/MWh. A comparison with HOMER grid simulations validates the efficacy of the developed model.
Water Resource Management of Salalah Plain Aquifer Using a Sustainable Approach
Authors: Shammas Mahaad Issa
Published: April 2024

A sustainable approach is proposed for managing the effects of salinity ingression in Salalah coastal aquifer, Oman. This paper aims to analyze and compare the groundwater levels and salinity of the aquifer from 1993 to 2027, considering both predictive and actual transient scenarios. Two novel scenarios were proposed, established, and examined in this study to bring back the aquifer to steady-state condition. The first scenario entails ceasing groundwater pumping from both Salalah and Saada wellfields, while compensating for the groundwater supply from these sources with surplus desalinated water. This scenario is projected to occur during the predictive period spanning from 2023 to 2027, denoted Scenario A. The second scenario is business as usual and involves continuing pumping from both wellfields during the same predictive period, denoted Scenario B. A numerical model for 3D flow simulation and advective transport modeling showed that on the eastern side of the Salalah coastal aquifer, the extent of seawater intrusion (SWI) was identified stretching from the shoreline to a distance of 1800 m, 1200 m, 0 m, and 600 m, in years 2011, 2014, 2018, and 2022 under the transient period, whereas SWI was delineated in land up to 0 m and 700 m in the predictive year 2027 under Scenarios A and B, respectively. In the western side of Salalah coastal aquifer, SWI was delineated in land up to 2000 m, 1700 m, 0 m, and 800 m, in years 2011, 2014, 2018, and 2022 under the transient period, whereas SWI was delineated in land up to 0 m and 750 m in the predictive year 2027 under Scenarios A and B, respectively. This study claims that Scenario A effectively pushed the seawater interface back to the coastline, projecting its reach to the shoreline (0 m) by 2027. In contrast, in baseline Scenario B, the wedge of saline intrusion in the Salalah coastal aquifer was delineated from the shoreline, up to 800 m inland, which accounted for continuation of pumping from both wellfields during the predictive period. The study concludes that Scenario A has the capability to efficiently reduce the impact of saline inflows from the coast, while Scenario B results in a more pronounced impact of salinity intrusion.
Theoretical Framework for Virtual Logistics Centers Creation
Authors: Paulauskas Vytautas , Filina-Dawidowicz Ludmiła , Senčila Viktoras , Paulauskas Donatas , Plačienė Birutė
Published: April 2024

Intermodal terminals and warehouses operate in different countries and deliver specific services to their customers. For many clients, it is important to receive a full set of the logistics services delivered by a single operator. However, individual intermodal terminals and warehouses may face challenges with providing these services, e.g., just-in-time goods delivery, goods distribution, cargo handling in non-standard situations, and others. In such cases, the cooperation between logistics companies may be required to organize the comprehensive service of cargo within supply chains. One of the possible solutions is to integrate transport and logistics services providers, establishing their cooperation within one virtual logistics center. The aim of this article is to justify theoretically the possibility of creating such a center by combining services performed by the intermodal terminals and warehouses already in operation under a single entity, in order to minimize the cost of logistics services and the time of goods delivery, as well as to create a comprehensive range of logistics services needed by customers. The relevance of the article and the novelty of the idea are associated with justification of the possibility of combining the activities of intermodal terminals and warehouses located separately in the region in order to improve the logistical service of customers. The theoretical basis for creating a virtual logistics center is based on graph theory methods. The article presents a theoretical model, based on a system of edges and vertices of the graph tree, which corresponds to the activities performed by separately located intermodal terminals and individual warehouses. The discussion is focused on the current problems of creating virtual logistics centers. The research results may be interesting for the managers of intermodal terminals, warehouses, and logistics centers, as well as other decision-makers involved in supply chains implementation and development.
Optimal Capacity Planning of Green Electricity-Based Industrial Electricity-Hydrogen Multi-Energy System Considering Variable Unit Cost Sequence
Authors: Xia Qinqin , Zou Yao , Wang Qianggang
Published: April 2024

Utilizing renewable energy sources (RESs), such as wind and solar, to convert electrical energy into hydrogen energy can promote the accommodation of green electricity. This paper proposes an optimal capacity planning approach for an industrial electricity-hydrogen multi-energy system (EHMES) aimed to achieve the local utilization of RES and facilitate the transition to carbon reduction in industrial settings. The proposed approach models the EHMES equipment in detail and divides the system’s investment and operation into producer and consumer sides with energy trading for effective integration. Through this effort, the specialized management for different operators and seamless incorporation of RES into industrial users can be achieved. In addition, the variations in investment and operating costs of equipment across different installed capacities are considered to ensure a practical alignment with real-world scenarios. By conducting a detailed case study, the influence of various factors on the capacity configuration outcomes within an EHMES is analyzed. The results demonstrate that the proposed method can effectively address the capacity configuration of equipment within EHMES based on the local accommodation of RES and variable unit cost sequence. Wind power serves as the primary source of green electricity in the system. Energy storage acts as crucial equipment for enhancing the utilization rate of RES.
Life Cycle CO2 Emissions Analysis of a High-Tech Greenhouse Horticulture Utilizing Wood Chips for Heating in Japan
Authors: Taguchi Jun , Hondo Hiroki , Moriizumi Yue
Published: April 2024

High-tech greenhouse horticulture offers efficient crop cultivation that is unaffected by outdoor climate. However, compared to conventional cultivation systems, energy requirements, such as greenhouse heating and control, are larger, and concerns about the associated increase in CO2 emissions exist. Although several previous studies have analyzed CO2 emissions from high-tech greenhouse horticulture, few have covered the entire life cycle. This study aimed to analyze CO2 emissions from high-tech greenhouse horticulture for tomatoes in Japan across the entire life cycle. A hybrid method combining process and input–output analyses was used to estimate life cycle CO2 (LC-CO2) emissions. The emission reduction potential of replacing liquefied petroleum gas (LPG) for greenhouse heating with wood chips was also examined. The results show that LC-CO2 emissions were estimated to be 3.67 kg-CO2 per 1 kg of tomato, 55.6% of which came from the production and combustion of LPG for greenhouse heating. The substitution of LPG with wood chips has the potential to reduce LC-CO2 emissions by up to 49.1%. However, the improved LC-CO2 emissions are still higher than those of conventional cultivation systems; thus, implementing additional measures to reduce LC-CO2 emissions is crucial.
Integration of Building Information Modeling and Stormwater Runoff Modeling: Enhancing Design Tools for Nature-Based Solutions in Sustainable Landscapes
Authors: Petschek Peter , Aung Aye P. P. , Suwanarit Asan , Irvine Kim N.
Published: April 2024

Building information modeling (BIM) has been used by the architectural and engineering disciplines to streamline the building design, construction, and management process, but there has been much more limited experience in extending the application to landscape design and implementation. This study integrated BIM software (Autodesk InfraWorks 2024.1) with a dynamic, process-oriented, conceptual hydrologic/hydraulic model (PCSWMM 2023, version 7.6.3665) to enhance the analytical tools for sustainable landscape design. We illustrate the model integration through a case study that links an existing nature-based solution (NbS) development, the PTT Metro Forest Park, Bangkok, Thailand, with theoretical new-build NbS for an adjacent property. A BIM school building was virtually situated on an empty lot beside the Metro Forest Park and seven NbS scenarios were run with design storms having 2-year, 5-year, and 100-year return intervals. The combination of a rain garden, permeable pavement, a retention pond, and a green roof was effective in sustainably managing runoff from the theoretical new-build site discharging to the Metro Forest. NbS design characteristics such as rain garden substrate depth and green roof area were optimized using the hydrologic/hydraulic model. Model results showed that even with the 100-year rainfall event, the existing Metro Forest pond storage capacity was sufficient so that flooding on the property would not occur. The consideration of connectivity between NbS features is facilitated by the modeling approach, which is important for NbS planning and assessment at a regional scale.