bluuebunny/crossref_metadata_2025 huggingface dataset:
Deep Learning Investigation of Mercury’s Explosive Volcanism
Authors: Leon-Dasi Mireia , Besse Sebastien , Doressoundiram Alain
Published: September 2023

The remnants of explosive volcanism on Mercury have been observed in the form of vents and pyroclastic deposits, termed faculae, using data from the Mercury Atmospheric and Surface Composition Spectrometer (MASCS) onboard the Mercury surface, space environment, geochemistry, and ranging (MESSENGER) spacecraft. Although these features present a wide variety of sizes, shapes, and spectral properties, the large number of observations and the lack of high-resolution hyperspectral images complicates their detailed characterisation. We investigate the application of unsupervised deep learning to explore the diversity and constrain the extent of the Hermean pyroclastic deposits. We use a three-dimensional convolutional autoencoder (3DCAE) to extract the spectral and spatial attributes that characterise these features and to create cluster maps constructing a unique framework to compare different deposits. From the cluster maps we define the boundaries of 55 irregular deposits covering 110 vents and compare the results with previous radius and surface estimates. We find that the network is capable of extracting spatial information such as the border of the faculae, and spectral information to altogether highlight the pyroclastic deposits from the background terrain. Overall, we find the 3DCAE an effective technique to analyse sparse observations in planetary sciences.
Effects of Spring Dust Aerosols on Direct Radiative Forcing in China from 2000 to 2020
Authors: Wang Feng , Qi Minghui , Ren Shuxin , Zhu Mengjie , Xing Qianlong , Wang Mengqiang , Song Hongquan , Wang Qianfeng , Liu Pengfei
Published: September 2023

In order to understand the mechanism of dust aerosol influence on regional climate change, it is crucial to quantify the radiative forcing effect of dust aerosols. However, studies on the direct radiative forcing of dust aerosols over long time series in China are still lacking. The direct radiative forcing effect of dust aerosols in China over the past 20 years was simulated and evaluated based on the WRF-Chem (Weather Research and Forecasting model coupled to Chemistry) model in conjunction with remote sensing satellites and ground-based observations. The results showed that dust aerosols exhibited an obvious inter-annual positive radiative forcing effect (about 0.38 W m−2) on net radiation at the top of the atmosphere, mainly in northwest China and the North China Plain, while at the atmosphere dust aerosols presented negative radiative forcing effects on shortwave radiation and positive effects on longwave radiation, with a value of 1.54 W m−2 of net radiative forcing, showing a warming effect. Dust aerosols have a net radiative forcing value of −1.16 W m−2 at the surface, indicating a cooling effect, with a positive forcing effect on longwave radiation and a negative forcing effect on shortwave radiation, both of which coincide with the geographical distribution of dust aerosol concentrations. In terms of inter-monthly variations, at both the atmosphere and top of the atmosphere, the dust aerosols net radiative forcing values showed an increasing trend, with March (−0.20 W m−2 and 0.68 W m−2) < April (0.48 W m−2 and 1.44 W m−2) < May (0.94 W m−2 and 2.42 W m−2). Meanwhile, at the surface, the dust aerosols net radiative forcing values displayed a decreasing trend, with March (−0.88 W m−2) > April (−0.96 W m−2) > May (−1.48 W m−2).
The All-Solid-State Narrowband Lidar Developed by Optical Parametric Oscillator/Amplifier (OPO/OPA) Technology for Simultaneous Detection of the Ca and Ca+ Layers
Authors: Du Lifang , Zheng Haoran , Xiao Chunlei , Cheng Xuewu , Wu Fang , Jiao Jing , Xun Yuchang , Chen Zhishan , Wang Jiqin , Yang Guotao
Published: September 2023

We report an all-solid-state narrowband lidar system for the simultaneous detection of Ca and Ca+ layers over Yanqing (40.41°N, 116.01°E). The uniqueness of this lidar lies in its transmitter, which is based on optical parametric oscillation (OPO) and optical parametric amplification (OPA) techniques. The injection seeded OPO and the OPA are pumped by the second harmonic of an injection-seeded Nd:YAG laser, which can generate coherent light at the wavelength of 786 nm or 846 nm lasers, whose second harmonics in turn generate the 393 nm or 423 nm pulses, respectively, for the detection of thermospheric and ionospheric Ca+ and Ca layers. Compared to the conventional dye-based system, this lidar transmitter is a narrowband system (bandwidth < 200 MHz), which has produced a factor of two more output power with higher stability and reliability. The lidar system in Yingqing demonstrated Ca+ detection sensitivity of 0.1 atoms-cm−3 for long-term observation and reached a height of ~300 km. Potential applications and further improvements in this lidar technique are also discussed in this paper.
Improving the Completion of Weather Radar Missing Data with Deep Learning
Authors: Gong Aofan , Chen Haonan , Ni Guangheng
Published: September 2023

Weather radars commonly suffer from the data-missing problem that limits their data quality and applications. Traditional methods for the completion of weather radar missing data, which are based on radar physics and statistics, have shown defects in various aspects. Several deep learning (DL) models have been designed and applied to weather radar completion tasks but have been limited by low accuracy. This study proposes a dilated and self-attentional UNet (DSA-UNet) model to improve the completion of weather radar missing data. The model is trained and evaluated on a radar dataset built with random sector masking from the Yizhuang radar observations during the warm seasons from 2017 to 2019, which is further analyzed with two cases from the dataset. The performance of the DSA-UNet model is compared to two traditional statistical methods and a DL model. The evaluation methods consist of three quantitative metrics and three diagrams. The results show that the DL models can produce less biased and more accurate radar reflectivity values for data-missing areas than traditional statistical methods. Compared to the other DL model, the DSA-UNet model can not only produce a completion closer to the observation, especially for extreme values, but also improve the detection and reconstruction of local-scale radar echo patterns. Our study provides an effective solution for improving the completion of weather radar missing data, which is indispensable in radar quantitative applications.
Contribution of Photogrammetry for Geometric Quality Assessment of Satellite Data for Global Climate Monitoring
Authors: Kocaman Sultan , Seiz Gabriela
Published: September 2023

This article reviews the role that photogrammetry plays in evaluating the geometric quality of satellite products in connection to the long-term monitoring of essential climate variables (ECVs). The Global Climate Observing System (GCOS) is responsible for defining the observations required for climate monitoring. Only satellite products are capable of providing high-quality observations of a particular subset of ECVs on a global scale. Geometric calibration and validation of these products are crucial for ensuring the coherence of data obtained across platforms and sensors and reliable monitoring in the long term. Here, we analyzed the GCOS implementation plan and the data quality requirements and explored various geometric quality aspects, such as internal and external accuracy and band-to-band registration assessment, for a number of satellite sensors commonly used for climate monitoring. Both geostationary (GEO) and low-earth orbit (LEO) sensors with resolutions between 250 m and 3 km were evaluated for this purpose. The article highlights that the geometric quality issues vary with the sensor, and regular monitoring of data quality and tuning of calibration parameters are essential for identifying and reducing the uncertainty in the derived climate observations.
Modeling and Unsupervised Unmixing Based on Spectral Variability for Hyperspectral Oceanic Remote Sensing Data with Adjacency Effects
Authors: Deville Yannick , Brezini Salah-Eddine , Benhalouche Fatima Zohra , Karoui Moussa Sofiane , Guillaume Mireille , Lenot Xavier , Lafrance Bruno , Chami Malik , Jay Sylvain , Minghelli Audrey , Briottet Xavier , Serfaty Véronique
Published: September 2023

In a previous paper, we introduced (i) a specific hyperspectral mixing model for the sea bottom, based on a detailed physical analysis that includes the adjacency effect, and (ii) an associated unmixing method that is supervised (i.e., not blind) in the sense that it requires a prior estimation of various parameters of the mixing model, which is constraining. We here proceed much further, by first analytically showing that the above model can be seen as a specific member of the general class of mixing models involving spectral variability. Therefore, we then process such data with the IP-NMF unsupervised (i.e., blind) unmixing method that we proposed in previous works to handle spectral variability. Such variability especially occurs when the sea depth significantly varies over the considered scene. We show that IP-NMF then yields significantly better pure spectra estimates than a classical method from the literature that was not designed to handle such variability. We present test results obtained with realistic synthetic data. These tests address several reference water depths, up to 7.5 m, and clear or standard water. For instance, they show that when the reference depth is set to 7.5 m and the water is clear, the proposed approach is able to distinguish various classes of pure materials when the water depth varies up to ±0.2 m around this reference depth, over all pixels of the analyzed scene or over a “subscene”: the overall scene may first be segmented, to obtain smaller depths variations over each subscene. The proposed approach is therefore effective and can be used as a building block in performing the subpixel classification of the sea bottom for shallow water.
Porous MgNiO2 Chrysanthemum Flower Nanostructure Electrode for Toxic Hg2+ Ion Monitoring in Aquatic Media
Authors: Imran Mohammad , Kim Eun-Bi , Kwak Dong-Heui , Ameen Sadia
Published: September 2023

A simple hydrothermal synthesis approach was used to synthesize porous MgNiO2 Chrysanthemum Flowers (CFs) nanostructures and applied as a sensing electrode for quick detection of hazardous mercury (Hg2+ ions). The morphological, structural, and electrochemical properties of MgNiO2 CFs were investigated. The morphological characteristic of MgNiO2 CFs, with a specific surface area of 45.618 m2/g, demonstrated strong electrochemical characteristics, including cations in different oxidation states of Ni3+/Ni2+. Using a three-electrode system for electrochemical detection, the MgNiO2 CFs based electrode revealed a good correlation coefficient (R2) of ~0.9721, a limit of detection (LOD) of ~11.7 μM, a quick response time (10 s), and a sensitivity of 8.22 μA∙μM−1∙cm−2 for Hg2+ ions over a broad linear range of 10–100 μM. Moreover, the selectivity for Hg2+ ions in tap water and drinking water was determined, and a promising stability of 25 days by MgNiO2 CFs electrode was exhibited. The obtained results indicate that the developed MgNiO2 CFs are a promising electrode for detecting hazardous Hg2+ ions in water and have the potential to be commercialized in the future.
A Novel FDLSR-Based Technique for View-Independent Vehicle Make and Model Recognition
Authors: Hayee Sobia , Hussain Fawad , Yousaf Muhammad Haroon
Published: September 2023

Vehicle make and model recognition (VMMR) is an important aspect of intelligent transportation systems (ITS). In VMMR systems, surveillance cameras capture vehicle images for real-time vehicle detection and recognition. These captured images pose challenges, including shadows, reflections, changes in weather and illumination, occlusions, and perspective distortion. Another significant challenge in VMMR is the multiclass classification. This scenario has two main categories: (a) multiplicity and (b) ambiguity. Multiplicity concerns the issue of different forms among car models manufactured by the same company, while the ambiguity problem arises when multiple models from the same manufacturer have visually similar appearances or when vehicle models of different makes have visually comparable rear/front views. This paper introduces a novel and robust VMMR model that can address the above-mentioned issues with accuracy comparable to state-of-the-art methods. Our proposed hybrid CNN model selects the best descriptive fine-grained features with the help of Fisher Discriminative Least Squares Regression (FDLSR). These features are extracted from a deep CNN model fine-tuned on the fine-grained vehicle datasets Stanford-196 and BoxCars21k. Using ResNet-152 features, our proposed model outperformed the SVM and FC layers in accuracy by 0.5% and 4% on Stanford-196 and 0.4 and 1% on BoxCars21k, respectively. Moreover, this model is well-suited for small-scale fine-grained vehicle datasets.
A Fusion Positioning Method for Indoor Geomagnetic/Light Intensity/Pedestrian Dead Reckoning Based on Dual-Layer Tent–Atom Search Optimization–Back Propagation
Authors: Han Yuchen , Yu Xuexiang , Zhu Ping , Xiao Xingxing , Wei Min , Xie Shicheng
Published: September 2023

Indoor positioning using smartphones has garnered significant research attention. Geomagnetic and sensor data offer convenient methods for achieving this goal. However, conventional geomagnetic indoor positioning encounters several limitations, including low spatial resolution, poor accuracy, and stability issues. To address these challenges, we propose a fusion positioning approach. This approach integrates geomagnetic data, light intensity measurements, and inertial navigation data, utilizing a hierarchical optimization strategy. We employ a Tent-ASO-BP model that enhances the traditional Back Propagation (BP) algorithm through the integration of chaos mapping and Atom Search Optimization (ASO). In the offline phase, we construct a dual-resolution fingerprint database using Radial Basis Function (RBF) interpolation. This database amalgamates geomagnetic and light intensity data. The fused positioning results are obtained via the first layer of the Tent-ASO-BP model. We add a second Tent-ASO-BP layer and use an improved Pedestrian Dead Reckoning (PDR) method to derive the walking trajectory from smartphone sensors. In PDR, we apply the Biased Kalman Filter–Wavelet Transform (BKF-WT) for optimal heading estimation and set a time threshold to mitigate the effects of false peaks and valleys. The second-layer model combines geomagnetic and light intensity fusion coordinates with PDR coordinates. The experimental results demonstrate that our proposed positioning method not only effectively reduces positioning errors but also improves robustness across different application scenarios.
Nocturnal Blood Pressure Estimation from Sleep Plethysmography Using Machine Learning
Authors: Yilmaz Gizem , Lyu Xingyu , Ong Ju Lynn , Ling Lieng Hsi , Penzel Thomas , Yeo B. T. Thomas , Chee Michael W. L.
Published: September 2023

Background: Elevated nocturnal blood pressure (BP) is a risk factor for cardiovascular disease (CVD) and mortality. Cuffless BP assessment aided by machine learning could be a desirable alternative to traditional cuff-based methods for monitoring BP during sleep. We describe a machine-learning-based algorithm for predicting nocturnal BP using single-channel fingertip plethysmography (PPG) in healthy adults. Methods: Sixty-eight healthy adults with no apparent sleep or CVD (53% male), with a median (IQR) age of 29 (23–46 years), underwent overnight polysomnography (PSG) with fingertip PPG and ambulatory blood pressure monitoring (ABPM). Features based on pulse morphology were extracted from the PPG waveforms. Random forest models were used to predict night-time systolic blood pressure (SBP) and diastolic blood pressure (DBP). Results: Our model achieved the highest out-of-sample performance with a window length of 7 s across window lengths explored (60 s, 30 s, 15 s, 7 s, and 3 s). The mean absolute error (MAE ± STD) was 5.72 ± 4.51 mmHg for SBP and 4.52 ± 3.60 mmHg for DBP. Similarly, the root mean square error (RMSE ± STD) was 6.47 ± 1.88 mmHg for SBP and 4.62 ± 1.17 mmHg for DBP. The mean correlation coefficient between measured and predicted values was 0.87 for SBP and 0.86 for DBP. Based on Shapley additive explanation (SHAP) values, the most important PPG waveform feature was the stiffness index, a marker that reflects the change in arterial stiffness. Conclusion: Our results highlight the potential of machine learning-based nocturnal BP prediction using single-channel fingertip PPG in healthy adults. The accuracy of the predictions demonstrated that our cuffless method was able to capture the dynamic and complex relationship between PPG waveform characteristics and BP during sleep, which may provide a scalable, convenient, economical, and non-invasive means to continuously monitor blood pressure.
Enabling Remote Elderly Care: Design and Implementation of a Smart Energy Data System with Activity Recognition
Authors: Franco Patricia , Condon Felipe , Martínez José M. , Ahmed Mohamed A.
Published: September 2023

Seniors face many challenges as they age, such as dementia, cognitive and memory disorders, vision and hearing impairment, among others. Although most of them would like to stay in their own homes, as they feel comfortable and safe, in some cases, older people are taken to special institutions, such as nursing homes. In order to provide serious and quality care to elderly people at home, continuous remote monitoring is perceived as a solution to keep them connected to healthcare service providers. The new trend in medical health services, in general, is to move from ’hospital-centric’ services to ’home-centric’ services with the aim of reducing the costs of medical treatments and improving the recovery experience of patients, among other benefits for both patients and medical centers. Smart energy data captured from electrical home appliance sensors open a new opportunity for remote healthcare monitoring, linking the patient’s health-state/health-condition with routine behaviors and activities over time. It is known that deviation from the normal routine can indicate abnormal conditions such as sleep disturbance, confusion, or memory problems. This work proposes the development and deployment of a smart energy data with activity recognition (SEDAR) system that uses machine learning (ML) techniques to identify appliance usage and behavior patterns oriented to older people living alone. The proposed system opens the door to a range of applications that go beyond healthcare, such as energy management strategies, load balancing techniques, and appliance-specific optimizations. This solution impacts on the massive adoption of telehealth in third-world economies where access to smart meters is still limited.
Research on Simultaneous Measurement of Magnetic Field and Temperature Based on Petaloid Photonic Crystal Fiber Sensor
Authors: Yan Lili , Wang Qichao , Yin Bin , Xiao Shiying , Li Haisu , Wang Muguang , Liu Xingyu , Wu Songhua
Published: September 2023

In this paper, we propose and design a magnetic field and temperature sensor using a novel petaloid photonic crystal fiber filled with magnetic fluid. The PCF achieves a high birefringence of more than 1.43 × 10−2 at the wavelength of 1550 nm via the design of material parameters, air hole shape and the distribution of the photonic crystal fiber. Further, in order to significantly improve the sensitivity of the sensor, the magnetic-fluid-sensitive material is injected into the pores of the designed photonic crystal fiber. Finally, the sensor adopts a Mach–Zehnder interferometer structure combined with the ultra-high birefringence of the proposed petaloid photonic crystal fiber. Magnetic field and temperature can be simultaneously measured via observing the spectral response of the x-polarization state and y-polarization state. As indicated via simulation analysis, the sensor can realize sensitivities to magnetic fields and temperatures at −1.943 nm/mT and 0.0686 nm/°C in the x-polarization state and −1.421 nm/mT and 0.0914 nm/°C in the y-polarization state. The sensor can realize the measurement of multiple parameters including temperature and magnetic intensity and has the advantage of high sensitivity.
Performance Analysis of Six Semi-Automated Tumour Delineation Methods on [18F] Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography (FDG PET/CT) in Patients with Head and Neck Cancer
Authors: Bianconi Francesco , Salis Roberto , Fravolini Mario Luca , Khan Muhammad Usama , Minestrini Matteo , Filippi Luca , Marongiu Andrea , Nuvoli Susanna , Spanu Angela , Palumbo Barbara
Published: September 2023

Background. Head and neck cancer (HNC) is the seventh most common neoplastic disorder at the global level. Contouring HNC lesions on [18F] Fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) scans plays a fundamental role for diagnosis, risk assessment, radiotherapy planning and post-treatment evaluation. However, manual contouring is a lengthy and tedious procedure which requires significant effort from the clinician. Methods. We evaluated the performance of six hand-crafted, training-free methods (four threshold-based, two algorithm-based) for the semi-automated delineation of HNC lesions on FDG PET/CT. This study was carried out on a single-centre population of n=103 subjects, and the standard of reference was manual segmentation generated by nuclear medicine specialists. Figures of merit were the Sørensen–Dice coefficient (DSC) and relative volume difference (RVD). Results. Median DSC ranged between 0.595 and 0.792, median RVD between −22.0% and 87.4%. Click and draw and Nestle’s methods achieved the best segmentation accuracy (median DSC, respectively, 0.792 ± 0.178 and 0.762 ± 0.107; median RVD, respectively, −21.6% ± 1270.8% and −32.7% ± 40.0%) and outperformed the other methods by a significant margin. Nestle’s method also resulted in a lower dispersion of the data, hence showing stronger inter-patient stability. The accuracy of the two best methods was in agreement with the most recent state-of-the art results. Conclusions. Semi-automated PET delineation methods show potential to assist clinicians in the segmentation of HNC lesions on FDG PET/CT images, although manual refinement may sometimes be needed to obtain clinically acceptable ROIs.
Enhancing Uranium Extraction Efficiency Using Protonated Amines and Quaternary Ammoniums-Based Ionic Liquids: Mechanistic Insights and Nonlinearities Analysis
Authors: Guerinoni Elise , Dourdain Sandrine , Dumas Thomas , Arrachart Guilhem , Giusti Fabrice , Lu Zijun , Solari Pier-Lorenzo , Pellet-Rostaing Stéphane
Published: September 2023

This study investigates uranium solvent extraction under AMEX process conditions. The use of pure extractants without diluents or phase modifiers allows us not only to reduce the use of volatile organic compounds but also to provide higher extraction yields without third-phase formation. Pure extractants are protonated amines or quaternary ammoniums with suitable counter ions, which act at the interface between ion pairs and protic ionic liquids. The mixture of sulphates anion (SO42−) and bis(trifluoromethanesulfonyl)imide anion (NTf2−) revealed unexpected nonlinear extraction behaviors, which appear highly important to rationalize for optimized application. A spectroscopic analysis (NMR, UV-vis, FT-IR, and EXAFS) showed that uranium extraction occurs via a protonated amine and three sulphates. A nonlinear extraction could further be interpreted by considering a water and acid transfer between the two phases: at lower sulphate ratios, the release of acid from the organic phase into the aqueous phase was shown to influence the number of protonated amines in the organic phase, affecting uranium extraction before its enhancement. Furthermore, the extraction loss at higher sulphate ratios was assigned to the destabilization of bidentate uranium–sulphate complexes due to a competition between water and sulphates.
Optimized Apparatus Design for Continuous Aqueous Two-Phase Flotation (ATPF)
Authors: Jakob Lucas , Baust Helene Katharina , Griesinger Lukas , Nirschl Hermann
Published: September 2023

Aqueous two-phase flotation (ATPF) is an integrative downstream method for separating and concentrating biomolecules from crude biosuspensions such as fermentation broth. Its continuous operation not only increases throughput, but also makes it recommended for continuous upstreams. In this work, an optimized apparatus design in the form of a horizontal flotation tank is presented. The geometry of the new apparatus for continuous ATPF is optimized using flow simulations so that the two aqueous phases can be pumped uniformly through the flotation tank in co-current or counter-current flow. The implementation of a conductivity measurement enables to characterize the phase mixing effect induced by the rising gas bubbles introduced through three different gassing units. ATPF experiments with the model enzyme phospholipase A2 show that there are favorable combinations of gas flow rates to increase the flotation rate and reduce the back-diffusion at the outlet of the apparatus. This allows high phase exchange rates up to 2/h and hence increases the amount of enzymes that can be recovered per time. Co-current operation of continuous ATPF increased separation efficiency about 14% up to E = 74% compared to counter-current operation.
Stress and Psychological Well-Being in Military Gendarmes
Authors: Turliuc Maria Nicoleta , Balcan Ana-Diana
Published: September 2023

Most studies consider the stressors faced by military personnel during operations in war zones and less those in peacetime activities. Work-related stress is a significant determinant of psychological well-being, but more relevant are the nature of stressors that military personnel is facing and the factors than can explain the relationship between work stress and well-being. The purpose of the present study was twofold: to examine the longitudinal relationships between organizational stress (OrgS), operational stress (OpS), and psychological well-being (PWB), and the mediating role of social support and coping mechanisms in the peacetime activities of police military gendarmes. A convenience sample of 210 military gendarmes (96.1% men and 3.90% women) completed five self-report scales regarding OrgS, OpS, PWB, social support, and coping mechanisms. All the variables were measured twice, in December 2021 (T1) and four months later in April 2022 (T2). The mean age was 38.52 years and the mean duration of military service was 14.52 years. The results show that baseline perceived stress, organizational and operational, has a significantly negative effect on PWB after four months. Perceived social support (at both T1 and T2) has a significant mediating role in the relationship between OrgS and PWB, as well as in the relationship between OpS and PWB. Among the coping mechanisms, only self-control (at T2) acts as a significant mediator of the relationship between OrgS and PWB. These findings could contribute to the development of intervention programs to increase the PWB of this personnel category, by working not only on perceived OrgS and OpS, but also on perceived social support and coping mechanisms.
Market Structure Analysis of Revenue of International Construction Professional Service (I-CPS): A Country-Level Analysis
Authors: Lee Kang-Wook
Published: September 2023

International construction professional service (I-CPS) refers to a knowledge-intensive professional service (KIPS), such as architecture, engineering, and consultancy, which uses technology/human capital as its major input and is better positioned to create new market opportunities and profitability. While the I-CPS market has recently stagnated owing to political, economic, and social uncertainties, the continuous growth and survival of I-CPS firms are becoming an issue, and a country’s competitiveness in supporting these firms is becoming increasingly important. The market structure of an industry results from the competition between countries or firms, which helps identify future market opportunities and establish a timely competitive strategy. The objective of this study is to quantitatively analyze the I-CPS market structure based on top international design firms (TIDFs). This study analyzes the revenue structures of TIDFs by nationality to identify country-specific factors in the I-CPS industry. The Engineering News-Record’s (ENR) Top 225/200 International Design Firms List for the past decade (2011–2021) was used to analyze international revenue in the major 15 countries. This study analyzed the I-CPS industry’s concentration for static analysis, and for dynamic analysis, the rank distribution of TIDFs, their mobility, and instability by firm nationality were analyzed sequentially. The results confirmed that market forces shifted from Europe and USA to Canada and China and that the market position was unstable in many countries. Additionally, this study divides the 15 countries into four static and dynamic combinations: high-static and high-dynamic, high-static and low-dynamic, low-static and high-dynamic, and low-static and low-dynamic. The findings of this study help understand country-level competition in the I-CPS market from a macro perspective, and provide directions for follow-up studies related to the I-CPS market structure.
The Impact of Idiosyncratic Deals on Coworkers’ Interactive Behavior: The Moderating Role of Developmental Human Resource Management Practices
Authors: Ding Chen , Zhang Ziteng , Zhao Shuming , Zhang Gaoqi
Published: September 2023

Organizations have come to recognize the importance of their human capital, particularly their top-performing employees, in sustaining their businesses in today’s competitive 21st-century landscape. To reward these few talented employees, organizations offer them preferential treatment in the form of idiosyncratic deals (i-deals). I-deals can effectively improve the performance of recipients, but this is not enough to demonstrate their management effectiveness. We should also measure their functional impact from the perspective of bystanders. This study seeks to explore the functional and dysfunctional impacts of i-deals on bystanders. We collected two-wave leader–employee matching data from sales teams, obtaining a sample of 108 leaders and 546 employees. The results indicate that coworkers’ perceptions of other employees’ i-deals (CPOEID) can provoke either malicious envy, which can lead to negative workplace gossip, or benign envy, which encourages feedback seeking. Developmental HRM practices not only lessen the positive effect of CPOEID on malicious and benign envy but also reduce the indirect effect of CPOEID on negative workplace gossip and feedback-seeking through malicious or benign envy. Our study, which applies social comparison theory, examines the double-edged effects of differentiated HRM practices on coworker interactive behavior. Additionally, our findings demonstrate the complementarity between differentiated and standardized HRM practices.
A Fuzzy-TOPSIS-Based Approach to Assessing Sustainability in Software Engineering: An Industry 5.0 Perspective
Authors: Anbarkhan Samar Hussni
Published: September 2023

New possibilities and challenges have evolved in the setting of the software engineering sector’s rapid transition to Industry 5.0, wherein sustainability takes centre stage. Appropriate evaluation approaches are required for analysing the long-term viability of software engineering practices within this paradigm. This study proposes an innovative approach to evaluating sustainability in software engineering within Industry 5.0 by utilising the fuzzy technique for order of preference by similarity to ideal solution (fuzzy TOPSIS) methodology. The fuzzy TOPSIS approach is effective at accounting for the inherent uncertainties as well as imprecisions related to sustainability assessments, allowing for informed decision-making. This approach helps in the recognition of the most sustainable software engineering practices in Industry 5.0 by taking into account a defined set of sustainability parameters. We rigorously analyse the current literature and expert views to provide an extensive set of relevant sustainability standards for the area of software engineering. Following that, we develop an evaluation methodology based on fuzzy TOPSIS that can handle the subjectivity as well as fuzziness inherent in sustainability evaluations. A case study with a software development company functioning in Industry 5.0 demonstrates the utility and efficacy of our suggested framework. The case study outcomes reveal the benefits and drawbacks of various software engineering methodologies in terms of sustainability. The study’s findings provide substantial information for decision-makers in the software engineering field, assisting them in making educated decisions about sustainable. Finally, this study helps to establish environmentally and socially appropriate techniques within the context of Industry 5.0.
Natural Organic Matter Removal in Surface Water Treatment via Coagulation—Current Issues, Potential Solutions, and New Findings
Authors: Knap-Bałdyga Alicja , Żubrowska-Sudoł Monika
Published: September 2023

Considerable changes have been observed in surface waters’ quality in recent years. They include an increase in dissolved organic carbon (DOC) concentrations, as well as a shift of natural organic matter (NOM) composition in favor of low molecular weight (LMW), and they are expected to occur on a wider scale in the future. Those predictions are particularly worrying given the importance of surface water as the main potable water source for numerous communities across the globe. Conventional methods of surface water treatment for drinking purposes mostly focus on the process of coagulation. The progressing changes in the quality of surface waters, however, render the conventional treatment via coagulation inefficient. The issue of the presence of natural organic matter in drinking water sources, its anticipated changes, and the related treatment problems are all complex and pressing matters that need addressing. This paper aims to provide a critical review of recent findings regarding NOM removal via coagulation in reference to the current NOM-related issues and their potential solutions. The paper discusses the application of different types of coagulants, and their respective advantages and disadvantages. Coagulation-integrated processes including adsorption, membrane filtration, biological processes, and oxidation are also addressed. Lastly, insights on the future approach to the discussed issues and conclusions are presented.
A New Bivariate Family Based on Archimedean Copulas: Simulation, Regression Model and Application
Authors: Rodrigues Gabriela M. , Ortega Edwin M. M. , Vila Roberto , Cordeiro Gauss M.
Published: September 2023

We use the Clayton and Frank copulas and the exponentiated odd log-logistic family to define a new flexible bivariate model to fit bimodal and asymmetry data. The copulas allow different distributions for the response variable, thus making analysis more suitable. We present some structural properties of the new model and describe a simulation study to show the consistency of the estimators. We construct a bivariate regression model based on the new family to fit oak lettuce plant data for different concentrations of silicon dioxide and organosilicon compounds. We check the response variables fresh weight and plant height together in order to verify the existing correlation between them. These variables exhibit a bimodal form, and the family used is able to model this behavior. Different marginal distributions are selected, which is an interesting point of the copula methodology. The variables have strong positive dependence, and the experiment is carried out comparing the control treatment with others leading to the following results: (i) the treatment 1-ethoxysilatrane (with concentrations 5 × 10−4 mL·L−1 and 10−3 mL·L−1) is not significant for the response variables; (ii) the treatment amorphous silicon dioxide (with concentrations 50 mg·L−1 and 100 mg·L−1) and the same treatment (with concentrations 5 × 10−3 mL·L−1 and 10−2 mL·L−1) are significant and have positive effects on both responses; (iii) the treatment amorphous silicon dioxide (with concentrations 200 mg·L−1 and 300 mg·L−1) are significant and have negative effects on the response variables. Overall, the proposed bivariate model is suitable for the current data and can be useful in other applications.
Investigating the Lifetime Performance Index under Ishita Distribution Based on Progressive Type II Censored Data with Applications
Authors: Haj Ahmad Hanan , Elnagar Kariema , Ramadan Dina
Published: September 2023

In manufacturing sectors, product performance evaluation is crucial. The lifetime performance index, denoted as CL, is widely used in product evaluation, where L signifies the lower specification limit. This study aims to refine the estimation of CL by employing maximum-likelihood and Bayesian methodologies, where symmetric and asymmetric loss functions are utilized. The analysis is conducted on progressive type II censored data, a requirement often imposed by budgetary constraints or the need for expedited testing. The data are assumed to follow the Ishita distribution, whose conforming rate is also evaluated. Furthermore, a hypothesis testing framework is employed to validate whether component lifetimes meet predefined standards. The theoretical findings are corroborated using real data collected from glass strength in aircraft windows. The numerical analysis emphasizes the goodness of fit of the Ishita distribution to model the data, thereby demonstrating the applicability of the proposed distribution.
Adaptive Reversible 3D Model Hiding Method Based on Convolutional Neural Network Prediction Error Expansion
Authors: Hu Guochang , Qian Kun , Li Yinghua , Li Hong , Xu Xinggui , Xu Hao
Published: September 2023

Although reversible data hiding technology is widely used, it still faces several challenges and issues. These include ensuring the security and reliability of embedded secret data, improving the embedding capacity, and maintaining the quality of media data. Additionally, irregular data types, such as three-dimensional point clouds and triangle mesh-represented 3D models, lack an ordered structure in their representation. As a result, embedding these irregular data into digital media does not provide sufficient information for the complete recovery of the original data during extraction. To address this issue, this paper proposes a method based on convolutional neural network prediction error expansion to enhance the embedding capacity of carrier images while maintaining acceptable visual quality. The triangle mesh representation of the 3D model is regularized in a two-dimensional parameterization domain, and the regularized 3D model is reversibly embedded into the image. The process of embedding and extracting confidential information in carrier images is symmetrical, and the regularization and restoration of 3D models are also symmetrical. Experiments show that the proposed method increases the reversible embedding capacity, and the triangle mesh can be conveniently subjected to reversible hiding.
Research on Profit-Sharing Mechanism of IPD Projects Considering Multidimensional Fairness Preferences and BIM
Authors: Wang Lunyan , Tao Mengyu , An Xiaowei , Dong Guanghua
Published: September 2023

The integration of building information modeling (BIM) and the integrated project delivery (IPD) mode effectively promotes collaboration among project members and enhances project profitability. However, the issue of profit sharing significantly impacts the successful implementation of IPD projects. To enhance the profit-sharing mechanism of IPD projects and ensure their smooth implementation, a game analysis model of profit sharing in IPD projects was established based on the Stackelberg game theory, taking into account the multidimensional fair preferences of the participants and the application of BIM technology. Through simulation, the impact of various parameters of participants on output utility, total revenue, and sharing coefficient in IPD projects was analyzed. The results show that: (1) participants achieve their highest output utility and total revenue under vertical–horizontal fairness preferences; (2) under vertical fairness preferences, the profit sharing coefficient is the highest, while the output utility and total revenue are the lowest; (3) although the output utility and total revenue of participants under horizontal fairness preferences exceed those under neutral fairness preferences, the profit-sharing coefficient is lower; (4) the output utility, the total revenue, and the profit-sharing coefficient of the participants all increase with the increase in effort utility value and decrease with the increase in the effort cost coefficient and the risk avoidance coefficient. The research findings provide valuable theoretical support for the profit sharing of IPD projects, thereby further promoting the advancement and implementation of the IPD model.
Level of Technological Maturity of Telemonitoring Systems Focused on Patients with Chronic Kidney Disease Undergoing Peritoneal Dialysis Treatment: A Systematic Literature Review
Authors: Villanueva Cerón Alejandro , López Domínguez Eduardo , Domínguez Isidro Saúl , Medina Nieto María Auxilio , De La Calleja Jorge , Pomares Hernández Saúl Eduardo
Published: September 2023

In the field of eHealth, several works have proposed telemonitoring systems focused on patients with chronic kidney disease (CKD) undergoing peritoneal dialysis (PD) treatment. Nevertheless, no secondary study presents a comparative analysis of these works regarding the technology readiness level (TRL) framework. The TRL scale goes from 1 to 9, with 1 being the lowest level of readiness and 9 being the highest. This paper analyzes works that propose telemonitoring systems focused on patients with CKD undergoing PD treatment to determine their TRL. We also analyzed the requirements and parameters that the systems of the selected works provide to the users to perform telemonitoring of the patient’s treatment undergoing PD. Fourteen works were relevant to the present study. Of these works, eight were classified within TRL 9, two were categorized within TRL 7, three were identified within TRL 6, and one within TRL 4. The works reported with the highest TRL partially cover the requirements for appropriate telemonitoring of patients based on the specialized literature; in addition, those works are focused on the treatment of patients in the automated peritoneal dialysis (APD) modality, which limits the care of patients undergoing the continuous ambulatory peritoneal dialysis (CAPD) modality.
Giant Duckweed (Spirodela polyrhiza) Root Growth as a Simple and Sensitive Indicator of Copper and Chromium Contamination
Authors: Lee Hojun , De Saeger Jonas , Bae Sunwoo , Kim Mirae , Depuydt Stephen , Heynderickx Philippe M. , Wu Di , Han Taejun , Park Jihae
Published: September 2023

Aquatic environment are often contaminated with heavy metals from various industrial sources. However, physicochemical techniques for pollutant detection are limited, thus prompting the need for additional bioassays. We investigated the use of greater duckweed (Spirodela polyrhiza) as a bioindicator of metal pollution. We exposed S. polyrhiza to four pollutants (namely, silver, cadmium, copper, and chromium) and assessed metal toxicity by measuring its frond area and the length of its regrown roots. The plant displayed significant differences in both frond size and root growth in response to the four metals. Silver was the most toxic (EC50 = 23 µg L−1) while copper the least (EC50 = 365–607 µg L−1). Direct comparisons of metal sensitivity and the reliability of the two endpoint assays showed that root growth was more sensitive (lower in terms of 50% effective concentration) to chromium, cadmium, and copper, and was more reliable (lower in terms of coefficient of variation) than those for frond area. Compared to conventional Lemna-based tests, the S. polyrhiza test is easier to perform (requiring only one 24-well plate, 3 mL of medium and a 72-h exposure). Moreover, it does not require livestock cultivation/maintenance, making it more suitable for repeated measurements. Measurements of S. polyrhiza root length may be suitable for assessment when copper and chromium in municipal and industrial wastewater exceed the environmentally permissible levels.
New Strategies for Biocontrol of Bacterial Toxins and Virulence: Focusing on Quorum-Sensing Interference and Biofilm Inhibition
Authors: Zhang Hua , Zhang Zhen , Li Jing , Qin Guangyong
Published: September 2023

The overuse of antibiotics and the emergence of multiple-antibiotic-resistant pathogens are becoming a serious threat to health security and the economy. Reducing antimicrobial resistance requires replacing antibiotic consumption with more biocontrol strategies to improve the immunity of animals and humans. Probiotics and medicinal plants have been used as alternative treatments or preventative therapies for a variety of diseases caused by bacterial infections. Therefore, we reviewed some of the anti-virulence and bacterial toxin-inhibiting strategies that are currently being developed; this review covers strategies focused on quenching pathogen quorum sensing (QS) systems, the disruption of biofilm formation and bacterial toxin neutralization. It highlights the probable mechanism of action for probiotics and medicinal plants. Although further research is needed before a definitive statement can be made on the efficacy of any of these interventions, the current literature offers new hope and a new tool in the arsenal in the fight against bacterial virulence factors and bacterial toxins.
Antifungal and Antiaflatoxigenic Activities of Massoia Essential Oil and C10 Massoia Lactone against Aflatoxin-Producing Aspergillus flavus
Authors: Lee Yubin , Park Soo Jean , Kim Kyeongnam , Kim Tae-Oh , Lee Sung-Eun
Published: September 2023

Fungal infection and mycotoxin contamination are major hazards to the safe storage and distribution of foods and feeds consumed by humans and livestock. This study investigated the antifungal and antiaflatoxigenic activities of massoia essential oil (MEO) and its major constituent, C10 massoia lactone (C10), against aflatoxin B (AFB)-producing Aspergillus flavus ATCC 22546. Their antifungal activities were evaluated using a disc diffusion assay, agar dilution method, and a mycelial growth inhibition assay with the AFB analysis using liquid chromatography triple quadrupole mass spectrometry. MEO and C10 exhibited similar antifungal and antiaflatoxigenic activities against A. flavus. C10 was a primary constituent in MEO and represented up to 45.1% of total peak areas analyzed by gas chromatography–mass spectrometry, indicating that C10 is a major compound contributing to the antifungal and antiaflatoxigenic activities of MEO. Interestingly, these two materials increased AFB production in A. flavus by upregulating the expression of most genes related to AFB biosynthesis by 3- to 60-fold. Overall, MEO and C10 could be suitable candidates as natural preservatives to control fungal infection and mycotoxin contamination in foods and feeds as Generally Recognized As Safe (GRAS) in the Flavor and Extract Manufacturers Association of the United States (FEMA), and MEO is a more suitable substance than C10 because of its wider range of uses and higher allowed concentration than C10.