bluuebunny/crossref_metadata_2025 huggingface dataset:
Analysis of a Low-Earth Orbit Satellite Downlink Considering Antenna Radiation Patterns and Space Environment in Interference Situations
Authors: Kang Eunjung , Yang Junmo , Park YoungJu , Kim JungHoon , Shin WookHyeon , Park Yong Bae , Choo Hosung
Published: March 2023

This paper investigates a low-Earth orbit (LEO) satellite downlink for high-speed data communication in interference situations. A choke ring horn type antenna is used as the data transmitting antenna with an isoflux pattern in the LEO satellite, which has a beam coverage of ±51.6° and a bore-sight gain of 4.4 dBi at 8 GHz. The receiving antenna on the ground station is a parabolic type antenna with a diameter of 11.3 m, and it has a half-power beam width (HPBW) of 0.2° with a maximum gain of 59 dBi at 8 GHz. The jamming-to-signal ratio (J/S) is calculated assuming that the LEO satellite transmits signals to the ground station, and an elevation angle of the interference source varies from 0° to 90° at an altitude of 10 km. Applying antenna characteristics, such as HPBWs and side lobes, to the calculated space wave path loss makes it possible to predict the J/S results according to the location of the interference source and the satellite. The results show that it is necessary to consider the space environment to accurately analyze the LEO satellite downlink, especially at the low elevation angle of the satellite.
Integrating Active and Passive Remote Sensing Data for Mapping Soil Salinity Using Machine Learning and Feature Selection Approaches in Arid Regions
Authors: Mohamed Sayed A. , Metwaly Mohamed M. , Metwalli Mohamed R. , AbdelRahman Mohamed A. E. , Badreldin Nasem
Published: March 2023

The prevention of soil salinization and managing agricultural irrigation depend greatly on accurately estimating soil salinity. Although the long-standing laboratory method of measuring salinity composition is accurate for determining soil salinity parameters, its use is frequently constrained by the high expense and difficulty of long-term in situ measurement. Soil salinity in the northern Nile Delta of Egypt severely affects agriculture sustainability and food security in Egypt. Understanding the spatial distribution of soil salinity is a critical factor for agricultural development and management in drylands. This research aims to improve soil salinity prediction by using a combined data collection method consisting of Sentinel-1 C radar data and Sentinel-2 optical data acquired simultaneously via integrated radar and optical sensor variables. The modelling approach focuses on feature selection strategies and regression learning. Feature selection approaches that include the filter, wrapper, and embedded methods were used with 47 selected variables depending on a genetic algorithm to scrutinize whether regions of the spectrum from optical indices and SAR texture choose the optimum combinations of selected variables. The sub-setting variables resulting from each feature selection method were used to train the regression learners’ random forest (RF), linear regression (LR), backpropagation neural network (BPNN), and support vector regression (SVR). Combining the BPNN feature selection method with the RF regression learner better predicted soil salinity (RME 0.000246; sub-setting variables = 18). Integrating different remote sensing data and machine learning provides an opportunity to develop a robust prediction approach to predict soil salinity in drylands. This research evaluated the performances of various machine learning models, overcame the limitations of conventional techniques, and optimized the variable input combinations. This research can assist farmers in soil-salinization-affected areas in better managing planting procedures and enhancing the sustainability of their lands.
Towards Prediction and Mapping of Grassland Aboveground Biomass Using Handheld LiDAR
Authors: de Nobel Jeroen S. , Rijsdijk Kenneth F. , Cornelissen Perry , Seijmonsbergen Arie C.
Published: March 2023

The Oostvaardersplassen nature reserve in the Netherlands is grazed by large herbivores. Due to their increasing numbers, the area became dominated by short grazed grasslands and biodiversity decreased. From 2018, the numbers are controlled to create a diverse landscape. Fine-scale mapping and monitoring of the aboveground biomass is a tool to evaluate management efforts to restore a heterogeneous and biodiverse area. We developed a random forest model that describes the correlation between field-based samples of aboveground biomass and fifteen height-related vegetation metrics that were calculated from high-density point clouds collected with a handheld LiDAR. We found that two height-related metrics (maximum and 75th percentile of all height points) produced the best correlation with an R2 of 0.79 and a root-mean-square error of 0.073 kg/m2. Grassland segments were mapped by applying a segmentation routine on the normalized grassland’s digital surface model. For each grassland segment, the aboveground biomass was mapped using the point cloud and the random forest AGB model. Visual inspection of video recordings of the scanned trajectories and field observations of grassland patterns suggest that drift and stretch effects of the point cloud influence the map. We recommend optimizing data collection using looped trajectories during scanning to avoid point cloud drift and stretch, test horizontal vegetation metrics in the model development and include seasonal influence of the vegetation status. We conclude that handheld LiDAR is a promising technique to retrieve detailed height-related metrics in grasslands that can be used as input for semi-automated spatio-temporal modelling of grassland aboveground biomass for supporting management decisions in nature reserves.
Applying Artificial Cover to Reduce Melting in Dagu Glacier in the Eastern Qinghai-Tibetan Plateau
Authors: Xie Yida , Wang Feiteng , Xu Chunhai , Yue Xiaoying , Yang Shujing
Published: March 2023

Global warming has accelerated during the past decades, causing a dramatic shrinking of glaciers across the globe. So far, the attempts to counterbalance glacial melt have proven to be inadequate and are mostly limited to a few glacial landscapes only. In the present study, a scientific glacier protection experiment was conducted at the Dagu Glacier site. Specifically, the study site was the Dagu Glacier No. 17, situated 4830 m a.s.l. The study involved a deliberate verification of the feasibility and effectiveness of using geotextile covers on small glaciers located at high altitudes between August 2020 and October 2021. The observations revealed that the mass loss in the area covered with geotextiles was, on average, 15% lower (per year) compared to that in the uncovered areas combining field campaigns, terrestrial laser scanning, and unmanned aerial vehicle. The reason for this could be that the albedo of the geotextile is higher than that of the glacier surface. In addition, the aging of geotextiles causes a decline in their albedo, leading to a gradual decline in the effectiveness of the resulting glacier protection. It was indicated that geotextiles could be effective in facilitating the mitigation of glacier ablation, although the cost-related limitations render it difficult to upscale the use of artificial cover. Nonetheless, using active artificial cover could be effective in the case of small glaciers, glacier landscapes, and glacier terminus regions.
An Evaluation of Non-Contact Photoplethysmography-Based Methods for Remote Respiratory Rate Estimation
Authors: Boccignone Giuseppe , D’Amelio Alessandro , Ghezzi Omar , Grossi Giuliano , Lanzarotti Raffaella
Published: March 2023

The respiration rate (RR) is one of the physiological signals deserving monitoring for assessing human health and emotional states. However, traditional devices, such as the respiration belt to be worn around the chest, are not always a feasible solution (e.g., telemedicine, device discomfort). Recently, novel approaches have been proposed aiming at estimating RR in a less invasive yet reliable way, requiring the acquisition and processing of contact or remote Photoplethysmography (contact reference and remote-PPG, respectively). The aim of this paper is to address the lack of systematic evaluation of proposed methods on publicly available datasets, which currently impedes a fair comparison among them. In particular, we evaluate two prominent families of PPG processing methods estimating Respiratory Induced Variations (RIVs): the first encompasses methods based on the direct extraction of morphological features concerning the RR; and the second group includes methods modeling respiratory artifacts adopting, in the most promising cases, single-channel blind source separation. Extensive experiments have been carried out on the public BP4D+ dataset, showing that the morphological estimation of RIVs is more reliable than those produced by a single-channel blind source separation method (both in contact and remote testing phases), as well as in comparison with a representative state-of-the-art Deep Learning-based approach for remote respiratory information estimation.
ICESat-2 for Canopy Cover Estimation at Large-Scale on a Cloud-Based Platform
Authors: Akturk Emre , Popescu Sorin C. , Malambo Lonesome
Published: March 2023

Forest canopy cover is an essential biophysical parameter of ecological significance, especially for characterizing woodlands and forests. This research focused on using data from the ICESat-2/ATLAS spaceborne lidar sensor, a photon-counting altimetry system, to map the forest canopy cover over a large country extent. The study proposed a novel approach to compute categorized canopy cover using photon-counting data and available ancillary Landsat images to build the canopy cover model. In addition, this research tested a cloud-mapping platform, the Google Earth Engine (GEE), as an example of a large-scale study. The canopy cover map of the Republic of Türkiye produced from this study has an average accuracy of over 70%. Even though the results were promising, it has been determined that the issues caused by the auxiliary data negatively affect the overall success. Moreover, while GEE offered many benefits, such as user-friendliness and convenience, it had processing limits that posed challenges for large-scale studies. Using weak or strong beams’ segments separately did not show a significant difference in estimating canopy cover. Briefly, this study demonstrates the potential of using photon-counting data and GEE for mapping forest canopy cover at a large scale.
Sound Event Localization and Detection Using Imbalanced Real and Synthetic Data via Multi-Generator
Authors: Shin Yeongseo , Chun Chanjun
Published: March 2023

This study proposes a sound event localization and detection (SELD) method using imbalanced real and synthetic data via a multi-generator. The proposed method is based on a residual convolutional neural network (RCNN) and a transformer encoder for real spatial sound scenes. SELD aims to classify the sound event, detect the onset and offset of the classified event, and estimate the direction of the sound event. In Detection and Classification of Acoustic Scenes and Events (DCASE) 2022 Task 3, SELD is performed with a few real spatial sound scene data and a relatively large number of synthetic data. When a model is trained using imbalanced data, it can proceed by focusing only on a larger number of data. Thus, a multi-generator that samples real and synthetic data at a specific rate in one batch is proposed to prevent this problem. We applied the data augmentation technique SpecAugment and used time-frequency masking to the dataset. Furthermore, we propose a neural network architecture to apply the RCNN and transformer encoder. Several models were trained with various structures and hyperparameters, and several ensemble models were obtained by “cherry-picking” specific models. Based on the experiment, the single model of the proposed method and the model applied with the ensemble exhibited improved performance compared with the baseline model.
Predicting the Output of Solar Photovoltaic Panels in the Absence of Weather Data Using Only the Power Output of the Neighbouring Sites
Authors: Jeong Heon
Published: March 2023

There is an increasing need for capable models in the forecast of the output of solar photovoltaic panels. These models are vital for optimizing the performance and maintenance of PV systems. There is also a shortage of studies on forecasts of the output power of solar photovoltaics sites in the absence of meteorological data. Unlike common methods, this study explores numerous machine learning algorithms for forecasting the output of solar photovoltaic panels in the absence of weather data such as temperature, humidity and wind speed, which are often used when forecasting the output of solar PV panels. The considered models include Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Recurrent Neural Network (RNN) and Transformer. These models were used with the data collected from 50 different solar photo voltaic sites in South Korea, which consist of readings of the output of each of the sites collected at regular intervals. This study focuses on obtaining multistep forecasts for the multi-in multi-out, multi-in uni-out and uni-in uni-out settings. Detailed experimentation was carried out in each of these settings. Finally, for each of these settings and different lookback and forecast lengths, the best models were also identified.
Fault Diagnosis for Abnormal Wear of Rolling Element Bearing Fusing Oil Debris Monitoring
Authors: Zhao Yulai , Wang Xiaowei , Han Shuo , Lin Junzhe , Han Qingkai
Published: March 2023

The abnormal wear of a rolling element bearing caused by early failures, such as pitting and spalling, will deteriorate the running state and reduce the life. This paper demonstrates the importance of oil debris monitoring and its effective feature extraction for bearing health assessment. In this paper, a rolling bearing-rotor test rig with forced lubrication is set up and the nonferrous contaminants with higher hardness were introduced artificially to accelerate the occurrence of pitting and spalling. The early failure and abnormal wear of rolling bearings cannot be effectively detected only through the vibration signal; the temperature and oil debris monitoring data are also collected synchronously. Two features regarding the ferrous particle size distribution are extracted and fused with vibration based-features to form a feature set. The sensitive features are extracted from the features set using the Neighborhood Component Analysis method to avoid feature redundancy. Finally, the importance of the oil debris based-features for the diagnosis of abnormal bearing wear is analyzed with different machine learning algorithms. Taking SVM classifier as an example, the experiment results show that the introduction of oil debris based-features increases the diagnostic accuracy by 15.7%.
Area and Bandwidth Enhancement of an n+/p-Well Dot Avalanche Photodiode in 0.35 μm CMOS Technology
Authors: Kohneh Poushi Seyed Saman , Goll Bernhard , Schneider-Hornstein Kerstin , Hofbauer Michael , Zimmermann Horst
Published: March 2023

This paper presents a CMOS-integrated dot avalanche photodiode (dot-APD) that features a small central n+/p-well hemispherical cathode/p-well structure circularly surrounded by an anode ring. The dot-APD enables wide hemispherical depletion, charge collection from a large volume, and a small multiplication region. These features result in a large light-sensitive area, high responsivity and bandwidth, and exceptionally low junction capacitance. The active area can be further expanded using a multi-dot structure, which is an array of several cathode/p-well dots with a shared anode. Experimental results show that a 5 × 5 multi-dot APD with an active area of 70 μm × 70 μm achieves a bandwidth of 1.8 GHz, a responsivity of 9.7 A/W, and a capacitance of 27 fF. The structure of the multi-dot APD allows for the design of APDs in various sizes that offer high bandwidth and responsivity as an optical detector for various applications while still maintaining a small capacitance.
Towards Automatic Crack Size Estimation with iFEM for Structural Health Monitoring
Authors: Oboe Daniele , Poloni Dario , Sbarufatti Claudio , Giglio Marco
Published: March 2023

The inverse finite element method (iFEM) is a model-based technique to compute the displacement (and then the strain) field of a structure from strain measurements and a geometrical discretization of the same. Different literature works exploit the error between the numerically reconstructed strains and the experimental measurements to perform damage identification in a structural health monitoring framework. However, only damage detection and localization are performed, without attempting a proper damage size estimation. The latter could be based on machine learning techniques; however, an a priori definition of the damage conditions would be required. To overcome these limitations, the present work proposes a new approach in which the damage is systematically introduced in the iFEM model to minimize its discrepancy with respect to the physical structure. This is performed with a maximum likelihood estimation framework, where the most accurate damage scenario is selected among a series of different models. The proposed approach was experimentally verified on an aluminum plate subjected to fatigue crack propagation, which enables the creation of a digital twin of the structure itself. The strain field fed to the iFEM routine was experimentally measured with an optical backscatter reflectometry fiber and the methodology was validated with independent observations of lasers and the digital image correlation.
Locomotor Strategy to Perform 6-Minute Walk Test in People with Multiple Sclerosis: A Prospective Observational Study
Authors: Hadouiri Nawale , Monnet Elisabeth , Gouelle Arnaud , Sagawa Yoshimasa , Decavel Pierre
Published: March 2023

Two-thirds of people with Multiple Sclerosis (PwMS) have walking disabilities. Considering the literature, prolonged tests, such as the 6 min walk test, better reflect their everyday life walking capacities and endurance. However, in most studies, only the distance traveled during the 6MWT was measured. This study aims to analyze spatio-temporal (ST) walking patterns of PwMS and healthy people in the 6MWT. Participants performed a 6MWT with measures of five ST variables during three 1 min intervals (initial: 0′–1′, middle: 2′30″–3′30″, end: 5′–6′) of the 6MWT, using the GAITRite system. Forty-five PwMS and 24 healthy people were included. We observed in PwMS significant changes between initial and final intervals for all ST parameters, whereas healthy people had a rebound pattern but the changes between intervals were rather negligible. Moreover, ST variables’ changes were superior to the standard measurement error only for PwMS between initial and final intervals for all ST parameters. This result suggests that the modification in PwMS’ walking pattern is effectively due to their walking ability and not to a measurement, and suggests that PwMS could not manage their walking efficiently compared to healthy people, who could maintain their rhythm throughout the 6MWT. Further studies are needed to detect these patterns changes in the early evolution of the disease, identify clinical determinants involved in PwMS’ walking pattern, and investigate whether interventions can positively impact this pattern.
Real-Time Recognition and Localization Based on Improved YOLOv5s for Robot’s Picking Clustered Fruits of Chilies
Authors: Zhang Song , Xie Mingshan
Published: March 2023

Chili recognition is one of the critical technologies for robots to pick chilies. The robots need locate the fruit. Furthermore, chilies are always planted intensively and their fruits are always clustered. It is a challenge to recognize and locate the chilies that are blocked by branches and leaves, or other chilies. However, little is known about the recognition algorithms considering this situation. Failure to solve this problem will mean that the robot cannot accurately locate and collect chilies, which may even damage the picking robot’s mechanical arm and end effector. Additionally, most of the existing ground target recognition algorithms are relatively complex, and there are many problems, such as numerous parameters and calculations. Many of the existing models have high requirements for hardware and poor portability. It is very difficult to perform these algorithms if the picking robots have limited computing and battery power. In view of these practical issues, we propose a target recognition-location scheme GNPD-YOLOv5s based on improved YOLOv5s in order to automatically identify the occluded and non-occluded chilies. Firstly, the lightweight optimization for Ghost module is introduced into our scheme. Secondly, pruning and distilling the model is designed to further reduce the number of parameters. Finally, the experimental data show that compared with the YOLOv5s model, the floating point operation number of the GNPD-YOLOv5s scheme is reduced by 40.9%, the model size is reduced by 46.6%, and the reasoning speed is accelerated from 29 ms/frame to 14 ms/frame. At the same time, the Mean Accuracy Precision (MAP) is reduced by 1.3%. Our model implements a lightweight network model and target recognition in the dense environment at a small cost. In our locating experiments, the maximum depth locating chili error is 1.84 mm, which meets the needs of a chili picking robot for chili recognition.
Multi-Scale Feature Interactive Fusion Network for RGBT Tracking
Authors: Xiao Xianbing , Xiong Xingzhong , Meng Fanqin , Chen Zhen
Published: March 2023

The fusion tracking of RGB and thermal infrared image (RGBT) is paid wide attention to due to their complementary advantages. Currently, most algorithms obtain modality weights through attention mechanisms to integrate multi-modalities information. They do not fully exploit the multi-scale information and ignore the rich contextual information among features, which limits the tracking performance to some extent. To solve this problem, this work proposes a new multi-scale feature interactive fusion network (MSIFNet) for RGBT tracking. Specifically, we use different convolution branches for multi-scale feature extraction and aggregate them through the feature selection module adaptively. At the same time, a Transformer interactive fusion module is proposed to build long-distance dependencies and enhance semantic representation further. Finally, a global feature fusion module is designed to adjust the global information adaptively. Numerous experiments on publicly available GTOT, RGBT234, and LasHeR datasets show that our algorithm outperforms the current mainstream tracking algorithms.
Access Control Design Practice and Solutions in Cloud-Native Architecture: A Systematic Mapping Study
Authors: Rahaman Md Shahidur , Tisha Sadia Nasrin , Song Eunjee , Cerny Tomas
Published: March 2023

Protecting the resources of a cloud-native application is essential to meet an organization’s security goals. Cloud-native applications manage thousands of user requests, and an organization must employ a proper access control mechanism. However, unfortunately, developers sometimes grumble when designing and enforcing access decisions for a gigantic scalable application. It is sometimes complicated to choose the potential access control model for the system. Cloud-native software architecture has become an integral part of the industry to manage and maintain customer needs. A microservice is a combination of small independent services that might have hundreds of parts, where the developers must protect the individual services. An efficient access control model can defend the respective services and consistency. This study intends to comprehensively analyze the current access control mechanism and techniques utilized in cloud-native architecture. For this, we present a systematic mapping study that extracts current approaches, categorizes access control patterns, and provides developers guidance to meet security principles. In addition, we have gathered 234 essential articles, of which 29 have been chosen as primary studies. Our comprehensive analysis will guide practitioners to identify proper access control mechanisms applicable to ensuring security goals in cloud-native architectures.
An In Situ Study on Nanozyme Performance to Optimize Nanozyme-Strip for Aβ Detection
Authors: Luo Yaying , Luo Haiming , Zou Sijia , Jiang Jing , Duan Demin , Chen Lei , Gao Lizeng
Published: March 2023

The nanozyme-strip is a novel POCT technology which is different from the conventional colloidal gold strip. It primarily utilizes the catalytic activity of nanozyme to achieve a high-sensitivity detection of target by amplifying the detection signal. However, previous research has chiefly focused on optimizing nanozyme-strip from the perspective of increasing nanozyme activity, little is known about other physicochemical factors. In this work, three sizes of Fe3O4 nanozyme and three sizes of CoFe2O4 nanozyme were used to investigate the key factors of nanozyme-strip for optimizing and improving its detection performance. We found that three sizes of Fe3O4 nanozyme all gather at the bottom of the nitrocellulose (NC) membrane, and three sizes of CoFe2O4 nanozyme migrate smoothly on the NC membrane, respectively. After color development, the surface of NC membranes distributed with CoFe2O4 peroxidase nanozymes had significant color change. Experimental results show that CoFe2O4 nanozymes had better dispersity than Fe3O4 nanozymes in an aqueous solution. We observed that CoFe2O4 nanozymes with smaller particle size migrated to the middle of the NC membrane with a higher number of particles. According to the results above, 55 ± 6 nm CoFe2O4 nanozyme was selected to prepare the nanozyme probe and achieved a highly sensitive detection of Aβ42Os on the nanozyme-strip. These results suggest that nanozyme should be comprehensively evaluated in its dispersity, the migration on NC membrane, and the peroxidase-like activity to determine whether it can be applied to nanozyme-strip.
Distributed Intelligent Battery Management System Using a Real-World Cloud Computing System
Authors: García Emilio , Quiles Eduardo , Correcher Antonio
Published: March 2023

In this work, a decentralized but synchronized real-world system for smart battery management was designed by using a general controller with cloud computing capability, four charge regulators, and a set of sensorized battery monitors with networking and Bluetooth capabilities. Currently, for real-world applications, battery management systems (BMSs) can be used in the form of distributed control systems where general controllers, charge regulators, and smart monitors and sensors are integrated, such as those proposed in this work, which allow more precise estimations of a large set of important parameters, such as the state of charge (SOC), state of health (SOH), current, voltage, and temperature, seeking the safety and the extension of the useful life of energy storage systems based on battery banks. The system used is a paradigmatic real-world example of the so-called intelligent battery management systems. One of the contributions made in this work is the realization of a distributed design of a BMS, which adds the benefit of increased system security compared to a fully centralized BMS structure. Another research contribution made in this work is the development of a methodical modeling procedure based on Petri Nets, which establishes, in a visible, organized, and precise way, the set of conditions that will determine the operation of the BMS. If this modeling is not carried out, the threshold values and their conditions remain scattered, not very transparent, and difficult to deal with in an aggregate way.
Facial Expression Recognition Using Local Sliding Window Attention
Authors: Qiu Shuang , Zhao Guangzhe , Li Xiao , Wang Xueping
Published: March 2023

There are problems associated with facial expression recognition (FER), such as facial occlusion and head pose variations. These two problems lead to incomplete facial information in images, making feature extraction extremely difficult. Most current methods use prior knowledge or fixed-size patches to perform local cropping, thereby enhancing the ability to acquire fine-grained features. However, the former requires extra data processing work and is prone to errors; the latter destroys the integrity of local features. In this paper, we propose a local Sliding Window Attention Network (SWA-Net) for FER. Specifically, we propose a sliding window strategy for feature-level cropping, which preserves the integrity of local features and does not require complex preprocessing. Moreover, the local feature enhancement module mines fine-grained features with intraclass semantics through a multiscale depth network. The adaptive local feature selection module is introduced to prompt the model to find more essential local features. Extensive experiments demonstrate that our SWA-Net model achieves a comparable performance to that of state-of-the-art methods with scores of 90.03% on RAF-DB, 89.22% on FERPlus, 63.97% on AffectNet.
Instrumented Timed Up and Go Test (iTUG)—More Than Assessing Time to Predict Falls: A Systematic Review
Authors: Ortega-Bastidas Paulina , Gómez Britam , Aqueveque Pablo , Luarte-Martínez Soledad , Cano-de-la-Cuerda Roberto
Published: March 2023

The Timed Up and Go (TUG) test is a widely used tool for assessing the risk of falls in older adults. However, to increase the test’s predictive value, the instrumented Timed Up and Go (iTUG) test has been developed, incorporating different technological approaches. This systematic review aims to explore the evidence of the technological proposal for the segmentation and analysis of iTUG in elderlies with or without pathologies. A search was conducted in five major databases, following PRISMA guidelines. The review included 40 studies that met the eligibility criteria. The most used technology was inertial sensors (75% of the studies), with healthy elderlies (35%) and elderlies with Parkinson’s disease (32.5%) being the most analyzed participants. In total, 97.5% of the studies applied automatic segmentation using rule-based algorithms. The iTUG test offers an economical and accessible alternative to increase the predictive value of TUG, identifying different variables, and can be used in clinical, community, and home settings.
Unsupervised Depth Completion Guided by Visual Inertial System and Confidence
Authors: Zhang Hanxuan , Huo Ju
Published: March 2023

This paper solves the problem of depth completion learning from sparse depth maps and RGB images. Specifically, a real-time unsupervised depth completion method in dynamic scenes guided by visual inertial system and confidence is described. The problems such as occlusion (dynamic scenes), limited computational resources and unlabeled training samples can be better solved in our method. The core of our method is a new compact network, which uses images, pose and confidence guidance to perform depth completion. Since visual-inertial information is considered as the only source of supervision, the loss function of confidence guidance is creatively designed. Especially for the problem of pixel mismatch caused by object motion and occlusion in dynamic scenes, we divide the images into static, dynamic and occluded regions, and design loss functions to match each region. Our experimental results in dynamic datasets and real dynamic scenes show that this regularization alone is sufficient to train depth completion models. Our depth completion network exceeds the accuracy achieved in prior work for unsupervised depth completion, and only requires a small number of parameters.
Fabrication of Direct Compressible Tablets Containing Chatuphalathika Extract Obtained through Microwave-Assisted Extraction: An Optimization Approach
Authors: Monton Chaowalit , Keawchay Piyapa , Pokkrong Chantisa , Kamnoedthapaya Pariyakorn , Navabhatra Abhiruj , Suksaeree Jirapornchai , Wunnakup Thaniya , Chankana Natawat , Songsak Thanapat
Published: March 2023

This research sought to optimize the microwave-assisted extraction of Chatuphalathika as an herbal recipe maximizing the active compounds and the antioxidant activity by the Box–Behnken design. Three factors—microwave power, time, and cycle—were varied. Eight responses—extraction yield, total phenolic content, gallic acid content, corilagin content, chebulagic acid, chebulinic acid, IC50 from DPPH assay, and IC50 from FRAP assay—were monitored. Furthermore, cytotoxicity was evaluated to ensure the safety of the extract. After that, the optimized extract was compressed into tablets. The results showed that the optimal condition of the microwave-assisted extraction gave the simultaneous maximum extraction yield, total phenolic content, and antioxidant activity with a microwave power of 450 W for 30 s and 3 cycles. The extract obtained from the optimal condition exhibited a good safety profile although a concentration of 5 mg/mL was used. The optimized tablets were achieved when a compression force of 1500 psi and magnesium stearate of 1% were applied, and no sodium starch glycolate was added. In conclusion, the optimal green extraction method could be used for the extraction of the Chatuphalathika. Furthermore, the fabrication of Chatuphalathika tablets was successful, as the tablets had low friability with a short disintegration time.
Relative Individual Sprint in Most Demanding Passages of Play in Spanish Professional Soccer Matches
Authors: Piñero Juan Ángel , Chena Marcos , Zapardiel Juan Carlos , Roso-Moliner Alberto , Mainer-Pardos Elena , Lampre Miguel , Lozano Demetrio
Published: March 2023

(1) Background: The objective of this research was to analyse the most demanding passages (MDP) considering the sprint variable relative to the maximum level of sprint ability of each player as a function of player position, final outcome and part of the match during the competitive phase of a professional soccer season. (2) Methods: Global positioning system (GPS) data were collected from 22 players according to their playing position in the last 19 match days of the Spanish La Liga professional soccer in the 2020/2021 season. MDP were calculated from 80% of the maximum sprint speed of each player. (3) Results: Wide midfielders covered the greatest distance at >80% of the maximum speed (2.4 ± 1.63 seg) and the longest duration (21.91 ± 13.35 m) in their MDP. When the whole team was losing, it demonstrated greater distances (20.23 ± 13.04 m) and longer durations (2.24 ± 1.58 seg) compared to games in which it was winning. When the team ended up drawing, the relative sprint distance covered in the second half was significantly greater than in the first (16.12 ± 21.02; SD = 0.26 ± 0.28 (−0.03/−0.54). (4) Conclusions: Different demands of MDP, according to the sprint variable relative to the maximum individual capacity in competition, are required when contextual game factors are considered.
Strategic Issues in Portuguese Tourism Plans: An Analysis of National Strategic Plans since 2000
Authors: Pato Maria Lúcia , Duque Ana Sofia
Published: March 2023

Planning consists of thinking about the future and allows territories to be better prepared to take advantage of opportunities and face challenges that arise. In Portugal, tourism is one of the pillars of the economy, generating wealth and creating various job openings. In recent years, this destination has won several international awards and distinctions due to the quality of services and tourism offerings. Part of this success is due to the planning carried out by the entity responsible, Turismo de Portugal. This study aims to analyse the content and structure of national tourism plans implemented in Portugal since 2000. Furthermore, we want to understand: (1) the vision outlined for the Portuguese territory and the changes it has undergone in recent decades; (2) the methodologies that were used in the formation process of these plans, for instance, if public auscultation was used; (3) the main objectives defined for the territory and which were the actions that have been defined to achieve them. A qualitative methodology of document analysis was used, combined with the presentation of a case study related to tourism planning at a national level. The results show the growing importance of the tourism sector for the Portuguese economy. Since 2020, the growing involvement of stakeholders in the construction of strategic plans has also been evident through public consultation and an emphasis on sustainability practices in the tourism sector.
Green Human Resource Management: Mapping the Research Trends for Sustainable and Agile Human Resources in SMEs
Authors: Papademetriou Christos , Ragazou Konstantina , Garefalakis Alexandros , Passas Ioannis
Published: March 2023

While individual SMEs may have a minor environmental footprint, their collective influence is characterized as significant. Through the dynamics of their employees, green human resource management can help SMEs reduce their environmental footprint. However, SMEs face difficulties in implementing such strategies and achieving sustainable business continuity. The current research aims to propose the theoretical framework of a business model that will help SMEs adjust to the new environment, address their sustainable goals and gaps in the field, and build a resilient, agile business ecosystem. To approach the research issue, we used the R-based Bibliometrix tool. Using the Scopus database, 368 papers were selected and analyzed as part of the study’s five steps. Bibliometric tools such as Biblioshiny, VOSviewer, and R Studio have been used to illustrate the findings. The study’s findings highlight a few different factors that will serve as the foundation for the proposed model. Green competencies, both natural and acquired, as well as the requirement to provide green motivation, are identified as critical for the development of the new business model. Furthermore, the combination of this set of factors contributes to the achievement of green organizational citizenship behavior, which is one of the most important factors in helping SMEs adopt green human resource management methods. Finally, all the above factors are under the wing of the principles of environmental management, which drive businesses to enhance and boost their environmental performance.
Research on Diversification Strategies of Terminal Operators—Evidence from Polish Seaports
Authors: Pluciński Michał , Kotowska Izabela , Mańkowska Marta , Filina-Dawidowicz Ludmiła
Published: March 2023

In response to changes taking place in the global environment, seaport terminal operators constantly search for lines of development in their operations, choosing i.a. a strategy of diversification or specialisation. So far, the issue of applying a diversification strategy in business models used by operators of multipurpose terminals has not been sufficiently addressed in the literature on the subject. In view of the above, the purpose of this paper is to identify and hierarchize the motivations for diversification and to specify the areas of diversification strategies and corresponding measures taken by operators of multipurpose terminals. The multi-case-study method was applied to conduct the research, along with the research technique of semi-structured in-depth interviews held with representatives of five terminal operators that had been running their business activity in Polish seaports and applying a diversification strategy. As a result of the completed research study, it was possible to specify the motivations for implementing a diversification strategy, and to hierarchize them. The main motives in selecting a diversification strategy as the main business strategy among the interviewed terminal operators were safeguarding against seasonal or sporadic business cycle fluctuations, and changes taking place in maritime trade and transport. Moreover, four areas of diversification strategies pursued by the terminal operators were identified: cargo diversification, contract diversification, services diversification, and cargo flow direction diversification. The diversification measures taken by the terminal operators in the specified areas were analysed in detail. The most important areas of the diversification measures in the studied entities were cargo diversification and services diversification. A heat map was developed to present the dependencies between the motivations for diversification and the areas of diversification strategies implemented by the terminal operators. The identified specific measures taken by the terminal operators as part of the indicated diversification areas included technical and organisational measures. The diversification strategy developed by terminal operators proved to be an effective strategy in coping with the effects of economic slowdown and disruptions ensuing from the COVID-19 pandemic and war in Ukraine. The results of the considerations may be of interest to seaports, transshipment terminals or other entities interested in implementation of a business activity diversification strategy.
The Role of Governance in Achieving Sustainability in Family-Owned Business: Do Responsible Innovation and Entrepreneurial Culture Matter?
Authors: Al Rawaf Razan Abdullah , Alfalih Abdulaziz Abdulmohsen
Published: March 2023

Research on family business sustainability has seen a surge over the last decade. Despite this, very little research has been performed to investigate the impact of governance on family business sustainability. Building on this gap, this paper empirically examines the impacts from organizational governance and responsible innovation on the sustainability of family-owned businesses. It also evaluates entrepreneurial culture’s contribution as a moderator of the nexus of organizational governance, responsible innovation, and business sustainability in Saudi Arabia. A sample of 396 responses were collected from 87 family enterprises; respondents were generally principal managers and/or main business owners. The relationships in the conceptual model were tested with structural equation modelling using SmartPLS. The results show that organizational governance and responsible innovation positively and statistically significantly impact business sustainability among family-owned businesses in Saudi Arabia. Furthermore, entrepreneurial culture significantly and positively moderates the organizational governance, responsible innovation and business sustainability of family-owned businesses in Saudi Arabia. The results guide regulators in regulation formulation related to the achievement of business sustainability through good governance and effective entrepreneurial culture. Government and regulatory authorities must therefore encourage family-owned businesses in their predominantly economic functions in society, while also engaging in sustainability-oriented policy making and programs.
Slope Crack Propagation Law and Numerical Simulation of Expansive Soil under Wetting–Drying Cycles
Authors: Chen Xuanyi , Jing Xiaofei , Li Xiaoshuang , Chen Junji , Ma Qiang , Liu Xiaohua
Published: March 2023

This study investigated the crack propagation law of expansive soil slopes under drying–wetting conditions and the influence of cracks on slopes by conducting a large-scale indoor slope test subjected to drying–wetting cycles. The change in soil moisture content at different depths during the drying–wetting cycles was monitored using a moisture content sensor, and the variation in crack depths in the expansive soil during the drying process was measured using a crack depth detector. The cracks on the slope’s surface were processed using a self-made binarization program, and the crack evolution mechanism of the expansive soil during the drying process was analyzed. The rainfall-induced change in moisture content in the fractured soil was used to obtain the influence of moisture content change on expansive soils, and to analyze the dry–wet cycle failure mode of surface soil. The surface cracks of the soil were quantified by binary processing, and the area of the cracks and the area ratio of cracked soil to intact soil were calculated. Finally, by using PFC simulation software with the slope cracks and quantitative analysis results as parameters, it was confirmed that the greater the number of drying–wetting cycles, the greater the number of cracks, and the greater the damage to the slope.
An Overview of Blockchain and IoT Integration for Secure and Reliable Health Records Monitoring
Authors: Alam Shadab , Bhatia Surbhi , Shuaib Mohammed , Khubrani Mousa Mohammed , Alfayez Fayez , Malibari Areej A. , Ahmad Sadaf
Published: March 2023

The Internet of Things (IoT) and blockchain (BC) are reliable technologies widely employed in various contexts. IoT devices have a lot of potential for data sensing and recording without human intervention, but they also have processing and security issues. Due to their limited computing power, IoT devices cannot use specialized cryptographic security mechanisms. There are various challenges when using traditional cryptographic techniques to transport and store medical records securely. The general public’s health depends on having an electronic health record (EHR) system that is current. In the era of e-health and m-health, problems with integrating data from various EHRs, preserving data interoperability, and ensuring that all data access is in the patient’s hands are all obstacles to creating a dependable EHR system. If health records get into the wrong hands, they could endanger the lives of patients and their right to privacy. BC technology has become a potent tool for ensuring recorded data’s immutability, validity, and confidentiality while enabling decentralized storage. This study focuses on EHR and other types of e-healthcare, evaluating the advantages of complementary technologies and the underlying functional principles. The major BC consensus mechanisms for BC-based EHR systems are analyzed in this study. It also examines several IoT-EHR frameworks’ current infrastructures. A breakdown of BC integration’s benefits with the IoT-EHR framework is also offered. A BC-based IoT-EHR architecture has been developed to enable the automated sensing of patient records and to store and retrieve these records in a secure and reliable environment. Finally, we conduct a security study to demonstrate the security of our suggested EHR framework.
A Multi-Criteria Analysis Approach to Identify Flood Risk Asset Damage Hotspots in Western Australia
Authors: Wongthongtham Pornpit , Abu-Salih Bilal , Huang Jeff , Patel Hemixa , Siripun Komsun
Published: March 2023

Climate change is contributing to extreme weather conditions, which transform the scale and degree of flood events. Therefore, it is important for relevant government agencies to effectively respond to both extreme climate conditions and their impacts by providing more efficient asset management strategies. Although international research projects on water-sensitive urban design and rural drainage design have provided partial solutions to this problem, road networks commonly serve unique combinations of urban-rural residential and undeveloped areas; these areas often have diverse hydrology, geology, and climates. Resultantly, applying a one-size-fits-all solution to asset management is ineffective. This paper focuses on data-driven flood modelling that can be used to mitigate or prevent floodwater-related damage in Western Australia. In particular, a holistic and coherent view of data-driven asset management is presented and multi-criteria analysis (MCA) is used to define the high-risk hotspots for asset damage in Western Australia. These state-wide hotspots are validated using road closure data obtained from the relevant government agency. The proposed approach offers important insights with regard to factors influencing the risk of damage in the stormwater management system.