bluuebunny/crossref_metadata_2025 huggingface dataset:
Associations of Habitual Mineral Intake with New-Onset Prediabetes/Diabetes after Acute Pancreatitis
Authors: Norbitt Claire F. , Kimita Wandia , Ko Juyeon , Bharmal Sakina H. , Petrov Maxim S.
Published: November 2021

Associations between habitual dietary intake of minerals and glucose metabolism have been extensively studied in relation to metabolic disorders. However, similar research has yet to be conducted in individuals after acute pancreatitis (AP). The main aim was to investigate the associations between habitual intake of 13 minerals and glycaemic status: new-onset prediabetes/diabetes after AP (NODAP), pre-existing prediabetes/type 2 diabetes (T2DM), and normoglycaemia after AP (NAP). Associations between the dietary intake of minerals and markers of glucose metabolism (glycated haemoglobin and fasting plasma glucose) were also studied. The EPIC-Norfolk food frequency questionnaire was used in a cross-sectional fashion to determine the habitual intake of 13 dietary minerals. ANCOVA as well as multiple linear regression analyses were conducted and five statistical models were built to adjust for covariates. The study included 106 individuals after AP. In the NODAP group, intake of 4 minerals was significantly less when compared with the NAP group: iron (B = −0.076, p = 0.013), nitrogen (B = −0.066, p = 0.003), phosphorous (B = −0.046, p = 0.006), and zinc (B = −0.078, p = 0.001). Glycated haemoglobin was significantly associated with iodine intake (B = 17.763, p = 0.032) and manganese intake (B = −17.147, p = 0.003) in the NODAP group. Fasting plasma glucose was significantly associated with manganese intake (B = −2.436, p = 0.027) in the NODAP group. Habitual intake of minerals differs between individuals with NODAP, T2DM, and NAP. Prospective longitudinal studies and randomised controlled trials are now warranted to further investigate the associations between mineral intake and NODAP.
Targeting DNA Methylation in the Adult Brain through Diet
Authors: Allison Joseph , Kaliszewska Aleksandra , Uceda Sara , Reiriz Manuel , Arias Natalia
Published: November 2021

Metabolism and nutrition have a significant role in epigenetic modifications such as DNA methylation, which can influence gene expression. Recently, it has been suggested that bioactive nutrients and gut microbiota can alter DNA methylation in the central nervous system (CNS) through the gut–brain axis, playing a crucial role in modulating CNS functions and, finally, behavior. Here, we will focus on the effect of metabolic signals in shaping brain DNA methylation during adulthood. We will provide an overview of potential interactions among diet, gastrointestinal microbiome and epigenetic alterations on brain methylation and behavior. In addition, the impact of different diet challenges on cytosine methylation dynamics in the adult brain will be discussed. Finally, we will explore new ways to modulate DNA hydroxymethylation, which is particularly abundant in neural tissue, through diet.
Thymoquinone Potentiates the Effect of Phenytoin against Electroshock-Induced Convulsions in Rats by Reducing the Hyperactivation of m-TOR Pathway and Neuroinflammation: Evidence from In Vivo, In Vitro and Computational Studies
Authors: Pottoo Faheem Hyder , Salahuddin Mohammed , Khan Firdos Alam , Alomar Fadhel , AL Dhamen Marwa Abdullah , Alhashim Abrar Fouad , Alqattan Hawra Hussain , Gomaa Mohamed S. , Alomary Mohammad N.
Published: November 2021

Epilepsy is a chronic neurodegenerative disease characterized by multiple seizures, hereto 35% of patients remain poor responders. Phenytoin (PHT; 20 and 40 mg/kg) and thymoquinone (THQ; 40 and 80 mg/kg) were given alone and as a low dose combination for 14 days (p.o), prior to challenge with maximal electroshock (MES; 180 mA, 220 V, 0.2 s). Apart from observing convulsions, hippocampal mTOR, IL-1β, IL-6 and TNF-α levels were measured. Hippocampal histomorphological analysis was also conducted. In vitro cell line studies and molecular docking studies were run in parallel. The results revealed the synergistic potential of the novel duo-drug combination regimen: PHT (20 mg/kg) and THQ (40 mg/kg) against MES-induced convulsions. MES amplified signaling through mTOR, and inflated the levels of proinflammatory markers (IL-1β, IL-6 and TNF-α), which was significantly averted (p < 0.001) with the said drug combination. The computational studies revealed that PHT and THQ cooperatively bind the active site on Akt (upstream target of m-TOR) and establish a good network of intermolecular interactions, which indicates the sequential inhibition of PI3K/Akt/m-TOR signaling with the combination. The combination also increased cell viability by 242.81% compared to 85.66% viability from the the toxic control. The results suggest that the PHT and THQ in combination possesses excellent anticonvulsant and neuroprotective effects.
Osteogenic and Chondrogenic Potential of Periosteum-Derived Mesenchymal Stromal Cells: Do They Hold the Key to the Future?
Authors: Jeyaraman Madhan , Muthu Sathish , Gangadaran Prakash , Ranjan Rajni , Jeyaraman Naveen , Prajwal Gollahalli Shivashankar , Mishra Prabhu Chandra , Rajendran Ramya Lakshmi , Ahn Byeong-Cheol
Published: November 2021

The periosteum, with its outer fibrous and inner cambium layer, lies in a dynamic environment with a niche of pluripotent stem cells for their reparative needs. The inner cambium layer is rich in mesenchymal progenitors, osteogenic progenitors, osteoblasts, and fibroblasts in a scant collagen matrix environment. Their role in union and remodeling of fracture is well known. However, the periosteum as a source of mesenchymal stem cells has not been explored in detail. Moreover, with the continuous expansion of techniques, newer insights have been acquired into the roles and regulation of these periosteal cells. From a therapeutic standpoint, the periosteum as a source of tissue engineering has gained much attraction. Apart from its role in bone repair, analysis of the bone-forming potential of periosteum-derived stem cells is lacking. Hence, this article elucidates the role of the periosteum as a potential source of mesenchymal stem cells along with their capacity for osteogenic and chondrogenic differentiation for therapeutic application in the future.
Functionalized Lanthanide Oxide Nanoparticles for Tumor Targeting, Medical Imaging, and Therapy
Authors: Ahmad Mohammad Yaseen , Yue Huan , Tegafaw Tirusew , Liu Shuwen , Ho Son Long , Lee Gang Ho , Nam Sung-Wook , Chang Yongmin
Published: November 2021

Recent progress in functionalized lanthanide oxide (Ln2O3) nanoparticles for tumor targeting, medical imaging, and therapy is reviewed. Among the medical imaging techniques, magnetic resonance imaging (MRI) is an important noninvasive imaging tool for tumor diagnosis due to its high spatial resolution and excellent imaging contrast, especially when contrast agents are used. However, commercially available low-molecular-weight MRI contrast agents exhibit several shortcomings, such as nonspecificity for the tissue of interest and rapid excretion in vivo. Recently, nanoparticle-based MRI contrast agents have become a hot research topic in biomedical imaging due to their high performance, easy surface functionalization, and low toxicity. Among them, functionalized Ln2O3 nanoparticles are applicable as MRI contrast agents for tumor-targeting and nontumor-targeting imaging and image-guided tumor therapy. Primarily, Gd2O3 nanoparticles have been intensively investigated as tumor-targeting T1 MRI contrast agents. T2 MRI is also possible due to the appreciable paramagnetic moments of Ln2O3 nanoparticles (Ln = Dy, Ho, and Tb) at room temperature arising from the nonzero orbital motion of 4f electrons. In addition, Ln2O3 nanoparticles are eligible as X-ray computed tomography contrast agents because of their high X-ray attenuation power. Since nanoparticle toxicity is of great concern, recent toxicity studies on Ln2O3 nanoparticles are also discussed.
Multicellular Ovarian Cancer Model for Evaluation of Nanovector Delivery in Ascites and Metastatic Environments
Authors: Winter Stephen J. , Miller Hunter A. , Steinbach-Rankins Jill M.
Published: November 2021

A novel multicellular model composed of epithelial ovarian cancer and fibroblast cells was developed as an in vitro platform to evaluate nanovector delivery and ultimately aid the development of targeted therapies. We hypothesized that the inclusion of peptide-based scaffold (PuraMatrix) in the spheroid matrix, to represent in vivo tumor microenvironment alterations along with metastatic site conditions, would enhance spheroid cell growth and migration and alter nanovector transport. The model was evaluated by comparing the growth and migration of ovarian cancer cells exposed to stromal cell activation and tissue hypoxia. Fibroblast activation was achieved via the TGF-β1 mediated pathway and tissue hypoxia via 3D spheroids incubated in hypoxia. Surface-modified nanovector transport was assessed via fluorescence and confocal microscopy. Consistent with previous in vivo observations in ascites and at distal metastases, spheroids exposed to activated stromal microenvironment were denser, more contractile and with more migratory cells than nonactivated counterparts. The hypoxic conditions resulted in negative radial spheroid growth over 5 d compared to a radial increase in normoxia. Nanovector penetration attenuated in PuraMatrix regardless of surface modification due to a denser environment. This platform may serve to evaluate nanovector transport based on ovarian ascites and metastatic environments, and longer term, it provide a means to evaluate nanotherapeutic efficacy.
Nanoparticle-Guided Brain Drug Delivery: Expanding the Therapeutic Approach to Neurodegenerative Diseases
Authors: Riccardi Claudia , Napolitano Filomena , Montesarchio Daniela , Sampaolo Simone , Melone Mariarosa Anna Beatrice
Published: November 2021

Neurodegenerative diseases (NDs) represent a heterogeneous group of aging-related disorders featured by progressive impairment of motor and/or cognitive functions, often accompanied by psychiatric disorders. NDs are denoted as ‘protein misfolding’ diseases or proteinopathies, and are classified according to their known genetic mechanisms and/or the main protein involved in disease onset and progression. Alzheimer’s disease (AD), Parkinson’s disease (PD) and Huntington’s disease (HD) are included under this nosographic umbrella, sharing histopathologically salient features, including deposition of insoluble proteins, activation of glial cells, loss of neuronal cells and synaptic connectivity. To date, there are no effective cures or disease-modifying therapies for these NDs. Several compounds have not shown efficacy in clinical trials, since they generally fail to cross the blood-brain barrier (BBB), a tightly packed layer of endothelial cells that greatly limits the brain internalization of endogenous substances. By engineering materials of a size usually within 1–100 nm, nanotechnology offers an alternative approach for promising and innovative therapeutic solutions in NDs. Nanoparticles can cross the BBB and release active molecules at target sites in the brain, minimizing side effects. This review focuses on the state-of-the-art of nanoengineered delivery systems for brain targeting in the treatment of AD, PD and HD.
High Speed Visible Light Communication Using Digital Power Domain Multiplexing of Orthogonal Frequency Division Multiplexed (OFDM) Signals
Authors: Gunawan Wahyu-Hendra , Liu Yang , Chow Chi-Wai , Chang Yun-Han , Yeh Chien-Hung
Published: November 2021

In order to increase transmission capacity, multiplexing schemes in different physical dimensions, including time, frequency, modulation quadrature, polarization, and space, can be employed. In this work, we propose and demonstrate a red color laser-diode (LD) based visible-light-communication (VLC) system using two kinds of digital domain multiplexing schemes, orthogonal-frequency-division-multiplexing (OFDM) and power-domain division-multiplexing (PowDM). The two digital domain multiplexed data can achieve data rates of 1.66 Gbit/s and 6.41 Gbit/s, respectively, providing a total data rate of 8.07 Gbit/s, fulfilling the pre-forward error correction (pre-FEC) bit-error-rate (BER) limit. The measured signal-to-noise ratios (SNRs) are 10.96 dB and 14.45 dB, respectively. Here, similar to OFDM, the PowDM can enhance the total system capacity by allowing acceptable signal spectra overlapping among different power division signals to maximize the bandwidth utilization. An experiment to verify and evaluate the proposed work is performed. The modulation and demodulation of OFDM and PowDM are discussed. The optimum power levels of the individual signals in the PowDM signal are also analyzed.
Recent Progress in Gamow Shell Model Calculations of Drip Line Nuclei
Authors: Li Jianguo , Ma Yuanzhuo , Michel Nicolas , Hu Baishan , Sun Zhonghao , Zuo Wei , Xu Furong
Published: November 2021

The Gamow shell model (GSM) is a powerful method for the description of the exotic properties of drip line nuclei. Internucleon correlations are included via a configuration interaction framework. Continuum coupling is directly included at basis level by using the Berggren basis, in which, bound, resonance, and continuum single-particle states are treated on an equal footing in the complex momentum plane. Two different types of Gamow shell models have been developed: its first embodiment is that of the GSM defined with phenomenological nuclear interactions, whereas the GSM using realistic nuclear interactions, called the realistic Gamow shell model, was introduced later. The present review focuses on the recent applications of the GSM to drip line nuclei.
Dry Fibre Placement: The Influence of Process Parameters on Mechanical Laminate Properties and Infusion Behaviour
Authors: Grisin Benjamin , Carosella Stefan , Middendorf Peter
Published: November 2021

Within the dry fibre placement (DFP) process, spread and pre-bindered carbon fibre rovings are automatically processed into dry textile preforms using 2-D and 3-D laying systems. The aim was to automate existing hand lay-up processes, reducing the complexity, increasing robustness, and facilitating the handling of the DFP technology. Process reliability, low waste rates, and flexible production are demonstrated. In this publication, the influences of the process parameters, 2 mm wide gaps and the percentage of 90° plies in the laminate, are investigated with regard to the mechanical properties, the permeability, and the infusion times in the preform z-direction (thickness). The effects on stiffness and strength are compared for several use cases. An approach to determine the infusion times as a function of the laminate thickness, the ply structure, and 2 mm wide gaps is demonstrated and analysed using vacuum-assisted process (VAP) infusion tests. The investigations are performed with carbon fibre tows (24 k), a reactive epoxy-based binder system, and a thermoset infusion resin system.
Simulation Based Approach for High-Throughput Stacking Processes in Battery Production
Authors: Müller Alexander , Aydemir Muhammed , von Boeselager Christina , van Ohlen Nils , Rahlfs Sina , Leithoff Ruben , Dröder Klaus , Dietrich Franz
Published: November 2021

What are the benefits of simulation-driven design and optimization of stacking processes in battery cell production? This question is addressed within the scope of the paper. This work proposes a method to reduce the effort for model-based design and optimization. Based on three case studies which originate from the development of high-speed stacking processes, this paper illustrates how the relevant loads on the intermediate products are determined with the help of the method. Subsequently, it is shown how the specific material models for battery electrodes and separators are identified, created and validated, as well as how process models are created and process limits are identified and optimized. It was possible to prove how process simulations can be used to minimize the effort required to validate developments and to efficiently determine optimized process parameters for a format and material change in a model-based manner. Consequently, more and more model-based processes should be taken into account during development and start-up in the future.
Does Working Capital Management Influence Operating and Market Risk of Firms?
Authors: Akbar Ahsan , Akbar Minhas , Nazir Marina , Poulova Petra , Ray Samrat
Published: November 2021

Extant empirical studies have predominantly focused on the nexus between working capital management (WCM) and corporate profitability. While there is a dearth of literature on the nexus between WCM and a firm’s risk, the present study examines Pakistani-listed firms coming from 12 diverse industrial segments to observe this association for a time span of ten years (2005–2014). To ensure robustness, we employed a System Generalized Method of Moments (SGMM) regression estimation to investigate the influence of WCM on the operational and market risk for firms. Empirical testing revealed that higher working capital levels were associated with lower volatility in firms’ stock price, which shows that shareholders prefer a conservative working capital policy. Moreover, firms with better cash positions were subject to lesser stock market volatility. In contrast, excess working capital and a larger net trade cycle were associated with increased volatility in the operating income. Besides, firms with lower working capital levels relative to their respective industry experienced fewer fluctuations in their operating profits. Our findings assert that short-term financial management has important ramifications for firms’ operating and market fundamentals. Practical implications are discussed for corporate managers and relevant stakeholders.
Establishing a Credit Risk Evaluation System for SMEs Using the Soft Voting Fusion Model
Authors: Gao Ge , Wang Hongxin , Gao Pengbin
Published: November 2021

In China, SMEs are facing financing difficulties, and commercial banks and financial institutions are the main financing channels for SMEs. Thus, a reasonable and efficient credit risk assessment system is important for credit markets. Based on traditional statistical methods and AI technology, a soft voting fusion model, which incorporates logistic regression, support vector machine (SVM), random forest (RF), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), is constructed to improve the predictive accuracy of SMEs’ credit risk. To verify the feasibility and effectiveness of the proposed model, we use data from 123 SMEs nationwide that worked with a Chinese bank from 2016 to 2020, including financial information and default records. The results show that the accuracy of the soft voting fusion model is higher than that of a single machine learning (ML) algorithm, which provides a theoretical basis for the government to control credit risk in the future and offers important references for banks to make credit decisions.
Drone-Based Autonomous Motion Planning System for Outdoor Environments under Object Detection Uncertainty
Authors: Sandino Juan , Maire Frederic , Caccetta Peter , Sanderson Conrad , Gonzalez Felipe
Published: November 2021

Recent advances in autonomy of unmanned aerial vehicles (UAVs) have increased their use in remote sensing applications, such as precision agriculture, biosecurity, disaster monitoring, and surveillance. However, onboard UAV cognition capabilities for understanding and interacting in environments with imprecise or partial observations, for objects of interest within complex scenes, are limited, and have not yet been fully investigated. This limitation of onboard decision-making under uncertainty has delegated the motion planning strategy in complex environments to human pilots, which rely on communication subsystems and real-time telemetry from ground control stations. This paper presents a UAV-based autonomous motion planning and object finding system under uncertainty and partial observability in outdoor environments. The proposed system architecture follows a modular design, which allocates most of the computationally intensive tasks to a companion computer onboard the UAV to achieve high-fidelity results in simulated environments. We demonstrate the system with a search and rescue (SAR) case study, where a lost person (victim) in bushland needs to be found using a sub-2 kg quadrotor UAV. The navigation problem is mathematically formulated as a partially observable Markov decision process (POMDP). A motion strategy (or policy) is obtained once a POMDP is solved mid-flight and in real time using augmented belief trees (ABT) and the TAPIR toolkit. The system’s performance was assessed using three flight modes: (1) mission mode, which follows a survey plan and used here as the baseline motion planner; (2) offboard mode, which runs the POMDP-based planner across the flying area; and (3) hybrid mode, which combines mission and offboard modes for improved coverage in outdoor scenarios. Results suggest the increased cognitive power added by the proposed motion planner and flight modes allow UAVs to collect more accurate victim coordinates compared to the baseline planner. Adding the proposed system to UAVs results in improved robustness against potential false positive readings of detected objects caused by data noise, inaccurate detections, and elevated complexity to navigate in time-critical applications, such as SAR.
Detection of Iris Presentation Attacks Using Feature Fusion of Thepade’s Sorted Block Truncation Coding with Gray-Level Co-Occurrence Matrix Features
Authors: Khade Smita , Gite Shilpa , Thepade Sudeep D. , Pradhan Biswajeet , Alamri Abdullah
Published: November 2021

Iris biometric detection provides contactless authentication, preventing the spread of COVID-19-like contagious diseases. However, these systems are prone to spoofing attacks attempted with the help of contact lenses, replayed video, and print attacks, making them vulnerable and unsafe. This paper proposes the iris liveness detection (ILD) method to mitigate spoofing attacks, taking global-level features of Thepade’s sorted block truncation coding (TSBTC) and local-level features of the gray-level co-occurrence matrix (GLCM) of the iris image. Thepade’s SBTC extracts global color texture content as features, and GLCM extracts local fine-texture details. The fusion of global and local content presentation may help distinguish between live and non-live iris samples. The fusion of Thepade’s SBTC with GLCM features is considered in experimental validations of the proposed method. The features are used to train nine assorted machine learning classifiers, including naïve Bayes (NB), decision tree (J48), support vector machine (SVM), random forest (RF), multilayer perceptron (MLP), and ensembles (SVM + RF + NB, SVM + RF + RT, RF + SVM + MLP, J48 + RF + MLP) for ILD. Accuracy, precision, recall, and F-measure are used to evaluate the performance of the projected ILD variants. The experimentation was carried out on four standard benchmark datasets, and our proposed model showed improved results with the feature fusion approach. The proposed fusion approach gave 99.68% accuracy using the RF + J48 + MLP ensemble of classifiers, immediately followed by the RF algorithm, which gave 95.57%. The better capability of iris liveness detection will improve human–computer interaction and security in the cyber-physical space by improving person validation.
Multiband Ambient RF Energy Harvester with High Gain Wideband Circularly Polarized Antenna toward Self-Powered Wireless Sensors
Authors: Nguyen Hong Quang , Le Minh Thuy
Published: November 2021

In this work toward a sustainable operation of a self-powered wireless sensor, we investigated a multiband Wi-Fi/3G/4G/5G energy harvester based on a novel wideband circularly polarized antenna, a quadplexer, and rectifiers at four corresponding bands. This proposed antenna consisted of four sequentially rotated dual-dipoles, fed by a hybrid feeding network with equal amplitude and an incremental 90° phase delay. The feeding network was composed of three Wilkinson power dividers and Schiffman phase shifters. Based on the sequential rotation method, the antenna obtained a −10 dB reflection coefficient bandwidth of 71.2% from 1.4 GHz to 2.95 GHz and a 3 dB axial ratio (AR) bandwidth of 63.6%, from 1.5 GHz to 2.9 GHz. In addition, this antenna gain was higher than 6 dBi in a wide bandwidth from 1.65 GHz to 2.8 GHz, whereas the peak gain was 9.9 dBi. The quad-band rectifier yielded the maximum AC–DC conversion efficiency of 1.8 GHz and was 60% at −1 dBm input power, 2.1 GHz was 55% at 0 dBm, 2.45 GHz was 55% at −1 dBm, and 2.6 GHz was 54% at 0.5 dBm, respectively. The maximum RF–DC conversion efficiency using the wideband circularly polarized antenna was 27%, 26%, 25.5%, and 27.5% at −6 dBm of input power, respectively.
An Improved Self-Training Method for Positive Unlabeled Time Series Classification Using DTW Barycenter Averaging
Authors: Li Jing , Zhang Haowen , Dong Yabo , Zuo Tongbin , Xu Duanqing
Published: November 2021

Traditional supervised time series classification (TSC) tasks assume that all training data are labeled. However, in practice, manually labelling all unlabeled data could be very time-consuming and often requires the participation of skilled domain experts. In this paper, we concern with the positive unlabeled time series classification problem (PUTSC), which refers to automatically labelling the large unlabeled set U based on a small positive labeled set PL. The self-training (ST) is the most widely used method for solving the PUTSC problem and has attracted increased attention due to its simplicity and effectiveness. The existing ST methods simply employ the one-nearest-neighbor (1NN) formula to determine which unlabeled time-series should be labeled. Nevertheless, we note that the 1NN formula might not be optimal for PUTSC tasks because it may be sensitive to the initial labeled data located near the boundary between the positive and negative classes. To overcome this issue, in this paper we propose an exploratory methodology called ST-average. Unlike conventional ST-based approaches, ST-average utilizes the average sequence calculated by DTW barycenter averaging technique to label the data. Compared with any individuals in PL set, the average sequence is more representative. Our proposal is insensitive to the initial labeled data and is more reliable than existing ST-based methods. Besides, we demonstrate that ST-average can naturally be implemented along with many existing techniques used in original ST. Experimental results on public datasets show that ST-average performs better than related popular methods.
Experimental Investigation of the Atmosphere-Regolith Water Cycle on Present-Day Mars
Authors: Vakkada Ramachandran Abhilash , Zorzano María-Paz , Martín-Torres Javier
Published: November 2021

The water content of the upper layers of the surface of Mars is not yet quantified. Laboratory simulations are the only feasible way to investigate this in a controlled way on Earth, and then compare it with remote and in situ observations of spacecrafts on Mars. Describing the processes that may induce changes in the water content of the surface is critical to determine the present-day habitability of the Martian surface, to understand the atmospheric water cycle, and to estimate the efficiency of future water extraction procedures from the regolith for In Situ Resource Utilization (ISRU). This paper illustrates the application of the SpaceQ facility to simulate the near-surface water cycle under Martian conditions. Rover Environmental Monitoring Station (REMS) observations at Gale crater show a non-equilibrium situation in the atmospheric H2O volume mixing ratio (VMR) at night-time, and there is a decrease in the atmospheric water content by up to 15 g/m2 within a few hours. This reduction suggests that the ground may act at night as a cold sink scavenging atmospheric water. Here, we use an experimental approach to investigate the thermodynamic and kinetics of water exchange between the atmosphere, a non-porous surface (LN2-chilled metal), various salts, Martian regolith simulant, and mixtures of salts and simulant within an environment which is close to saturation. We have conducted three experiments: the stability of pure liquid water around the vicinity of the triple point is studied in experiment 1, as well as observing the interchange of water between the atmosphere and the salts when the surface is saturated; in experiment 2, the salts were mixed with Mojave Martian Simulant (MMS) to observe changes in the texture of the regolith caused by the interaction with hydrates and liquid brines, and to quantify the potential of the Martian regolith to absorb and retain water; and experiment 3 investigates the evaporation of pure liquid water away from the triple point temperature when both the air and ground are at the same temperature and the relative humidity is near saturation. We show experimentally that frost can form spontaneously on a surface when saturation is reached and that, when the temperature is above 273.15 K (0 °C), this frost can transform into liquid water, which can persist for up to 3.5 to 4.5 h at Martian surface conditions. For comparison, we study the behavior of certain deliquescent salts that exist on the Martian surface, which can increase their mass between 32% and 85% by absorption of atmospheric water within a few hours. A mixture of these salts in a 10% concentration with simulant produces an aggregated granular structure with a water gain of approximately 18- to 50-wt%. Up to 53% of the atmospheric water was captured by the simulated ground, as pure liquid water, hydrate, or brine.
A Hybrid Deep Learning Model for Recognizing Actions of Distracted Drivers
Authors: Jiao Shuang-Jian , Liu Lin-Yao , Liu Qian
Published: November 2021

With the rapid spreading of in-vehicle information systems such as smartphones, navigation systems, and radios, the number of traffic accidents caused by driver distractions shows an increasing trend. Timely identification and warning are deemed to be crucial for distracted driving and the establishment of driver assistance systems is of great value. However, almost all research on the recognition of the driver’s distracted actions using computer vision methods neglected the importance of temporal information for action recognition. This paper proposes a hybrid deep learning model for recognizing the actions of distracted drivers. Specifically, we used OpenPose to obtain skeleton information of the human body and then constructed the vector angle and modulus ratio of the human body structure as features to describe the driver’s actions, thereby realizing the fusion of deep network features and artificial features, which improve the information density of spatial features. The K-means clustering algorithm was used to preselect the original frames, and the method of inter-frame comparison was used to obtain the final keyframe sequence by comparing the Euclidean distance between manually constructed vectors representing frames and the vector representing the cluster center. Finally, we constructed a two-layer long short-term memory neural network to obtain more effective spatiotemporal features, and one softmax layer to identify the distracted driver’s action. The experimental results based on the collected dataset prove the effectiveness of this framework, and it can provide a theoretical basis for the establishment of vehicle distraction warning systems.
Data-Driven Anomaly Detection in High-Voltage Transformer Bushings with LSTM Auto-Encoder
Authors: Mitiche Imene , McGrail Tony , Boreham Philip , Nesbitt Alan , Morison Gordon
Published: November 2021

The reliability and health of bushings in high-voltage (HV) power transformers is essential in the power supply industry, as any unexpected failure can cause power outage leading to heavy financial losses. The challenge is to identify the point at which insulation deterioration puts the bushing at an unacceptable risk of failure. By monitoring relevant measurements we can trace any change that occurs and may indicate an anomaly in the equipment’s condition. In this work we propose a machine-learning-based method for real-time anomaly detection in current magnitude and phase angle from three bushing taps. The proposed method is fast, self-supervised and flexible. It consists of a Long Short-Term Memory Auto-Encoder (LSTMAE) network which learns the normal current and phase measurements of the bushing and detects any point when these measurements change based on the Mean Absolute Error (MAE) metric evaluation. This approach was successfully evaluated using real-world data measured from HV transformer bushings where anomalous events have been identified.
An Energy-Saving Forwarding Mechanism Based on Clustering for Opportunistic Networks
Authors: Santos Gilmara , Soares Diogo , Carvalho Celso , Mota Edjair
Published: November 2021

In Opportunistic Networks (OppNets), mobility of and contact between nodes are explored to create communication opportunities and exchange messages and information. A basic premise for a better performance of these networks is a collaboration of the nodes during communication. However, due to energy restriction factors, nodes may eventually fail to collaborate with message exchanges. In this work, we propose a routing mechanism called eGPDMI to improve message probability of delivery while optimizing nodes’ energy consumption. Unlike other algorithms proposed in OppNets literature, eGPDMI groups nodes by energy level and nodes interests using clustering techniques. Our major assumption is that retaining messages in nodes with the highest energy levels can improve network performance, thus overcoming the problem of nodes’ disconnection due to unwillingness to cooperate due to low energy values. Through questionnaire application and factorial design experiments, we characterize the impacts of energy levels in OppNets. Further, we apply performance evaluation of the eGPDMI mechanism in terms of effectiveness using mobility from real-world scenarios. The results show that our mechanism effectively reduces the degradation of the probability of delivery when the minimum energy level used for nodes to cooperate with communication increases.
A Method of Optimizing a Set of Programs for Mitigating Threats Related to the Undertaking of a Contract for the Execution of Construction Works
Authors: Budek-Wiśniewska Katarzyna , Marcinkowski Roman
Published: November 2021

This article deals with the problem of limiting the risk of taking up a construction contract for the execution of construction works. The authors have developed an advisory system that will support the analysis of threats on the basis of existing experiences for a specific activity without having to construct an individualized organizational model of an investment. In order to identify a relatively complete set of threats that occur in investment and construction processes in road construction investments, as well as to identify possible programs of their reduction, a model and a method of optimizing programs for reducing risks related to contracts was developed. Threats are considered to be possible events that take place during the preparation, implementation and settlement of any contract. The programs concern specific actions that can be taken in relation to specific threats. Every program contains a set of threats that will be limited as a result of its execution and also has a specific implementation cost. The aim of the proposed optimization is to determine, with regard to costs, a combination of risk reduction programs that is appropriate for the risk states that are accepted by a decision maker. The problem is solved using graph theory and a minimum cover determination algorithm with the use of the minimum alternative formula (mfa) of the Boolean function. A method of actively responding to identified threats during the implementation of a construction contract should take the form of an advisory system that will provide an answer as to what risks should be taken into account when undertaking a contract, as well as what actions can be taken to reduce these risks.
Predicting Embodied Carbon and Cost Effectiveness of Post-Tensioned Slabs Using Novel Hybrid Firefly ANN
Authors: Faridmehr Iman , Nehdi Moncef L. , Nikoo Mehdi , Valerievich Kiyanets A.
Published: November 2021

Post-tensioning has become a strong contender for manufacturing reinforced concrete (RC) members, especially for flat slabs in large-span structures. Post-tensioned (PT) slabs can lead to considerable material savings while reducing the embodied carbon (embodied CO2), construction time, and life cycle maintenance and repair costs. In this research, a novel hybrid Firefly–Artificial Neural Network (Firefly–ANN) computational intelligence model was developed to estimate the cost effectiveness and embodied CO2 of PT slabs with different design variables. To develop the dataset, several numerical models with various design variables, including the pattern of tendons, slab thickness, mechanical properties of materials, and span of slabs, were developed to investigate the sustainability and economic competitiveness of the derived designs compared to benchmark conventional RC flat slabs. Several performance measures, including punching shear and heel drop vibration induced by human activity, were used as design constraints to satisfy safety and serviceability criteria. The economic competitiveness of PT slabs was more evident in larger spans where the cost and embodied CO2 emissions decreased by 39% and 12%, respectively, in PT slabs with a 12-m span length compared to conventional RC slabs. Sensitivity analysis also confirmed that the cost and embodied CO2 emissions were very sensitive to the slab thickness by 86% and 62%, respectively.
The Formation of Subsequent Entrepreneurial Intention: Happiness Matters
Authors: Bao Jia , Dou Junsheng
Published: November 2021

Subsequent entrepreneurial intention is a good predictor of serial entrepreneurial endeavors which facilitate the sustainability of economic growth. Previous research has investigated the influence of economic outcomes on continuous engagement in entrepreneurial activities. However, despite frequent calls by scholars, limited attention has been given to the psychological factors that could explain how economic performance influences intention. This study aims at answering some of these questions, including whether entrepreneurs are willing to continue creating new ventures after achieving positive economic feedback from their current business, and what the psychological drivers are behind such intentions. Borrowing insights from the field of positive psychology, this study develops a framework to understand the role of the performance of a firm and happiness in the subsequent intention formation process, testing the mediated moderation effect using data collected from 1019 entrepreneurs in China. The results show that current firm performance significantly enhances subsequent entrepreneurial intention, while the relationship is mediated by the happiness of the entrepreneurs. Family cohesion attenuates the influence of firm performance on happiness, which shows that for entrepreneurs, family issues play an important role in their mental state. The theoretical and practical implications of the framework are also discussed.
Institutional Pressures, Sustainable Development Goals and COVID-19: How Are Organisations Engaging?
Authors: Galleli Bárbara , Semprebon Elder , Santos Joyce Aparecida Ramos dos , Teles Noah Emanuel Brito , Freitas-Martins Mateus Santos de , Onevetch Raquel Teodoro da Silva
Published: November 2021

This article aimed at answering the following research question: How do institutional pressures influence the engagement of organisations with the Sustainable Development Goals (SDGs) in the context of the COVID-19 pandemic? To address it, we applied a survey with Brazilian organisations in late 2020. From group analysis and the comparison of means (test-t), we conclude that, in the pandemic scenario, organisations from various sectors and industries are affected mainly by coercive pressures but also by normative and, to a lesser extent, mimetic pressures. Furthermore, this study has shown that while opportunities are arising from normative and mimetic institutional pressures, it is in the coercive pressures that the guidance lies for the re-establishment of economic, social, and environmental development and prosperity in a post-pandemic scenario. Thus, despite some optimistic results, achieving the purpose outlined by the 2030 Agenda seems to be distant or at least needs an even more substantial effort if its responsibility rests solely with legislative and governmental forces.
Factors Affecting Pandemic Biosecurity Behaviors of International Travelers: Moderating Roles of Gender, Age, and Travel Frequency
Authors: Kim Myung Ja , Hall C. Michael , Bonn Mark
Published: November 2021

Research undertaken during the COVID-19 pandemic has identified a number of significant factors that affect international travelers’ biosecurity behavior. Tourists’ age and gender as well as travel frequency have been found to have significant impacts on consumers’ non-pharmaceutical intervention practices. However, despite the importance of age, gender, and travel frequency, such studies have overlooked international tourists’ values, attitudes, interventions, and behaviors relevant to biosecurity during a pandemic. In order to bridge this gap, the purposes of this study are to build and test a conceptually comprehensive framework on the relationships between values, attitudes, interventions, and behaviors, along with the moderating effects of age, gender, and travel frequency. To meet the study objectives, a digital survey was administered during 1–5 September 2020, which generated n = 386 total useable responses. Data were analyzed using the partial least squares approach. The results revealed that tourists’ values have the greatest effect on their attitudes toward COVID-19 biosecurity for travel, which in turn positively influences interventions and behaviors. Interventions also have a significant impact on travelers’ COVID-19 biosecurity behavior. This study expands the theoretical understanding of biosecurity and pandemic behavior. The findings of this research also provide significant insights to the literature as well as stakeholders, such as governments, health organizations, international health and tourism agencies, and destinations, with respect to managing international travel biosecurity measures.
Effect of Aqueous Neem Leaf Extracts in Controlling Fusarium Wilt, Soil Physicochemical Properties and Growth Performance of Banana (Musa spp.)
Authors: Yi Ung , Zaharah Sakimin Siti , Ismail Siti Izera , Musa Mohamed Hanafi
Published: November 2021

Neem leaf extracts (NLEs) have frequently been used to inhibit plant diseases and for the development of bio-fertilizer, leading to the commercial exploitation of this tree. However, previous studies have indicated contradictory outcomes when NLE was used as an antifungal disease treatment and bio-fertilizer applied through the soil on several crops, including banana. Therefore, the present investigation was undertaken to examine the physicochemical properties of soil, the growth performance of crops, and the severity of diseases caused by Fusarium oxysporum (Foc) on Cavendish bananas treated with aqueous NLE. Banana plants associated with the fungus were significantly affected by high disease severity and symptoms index (external leaves and internal rhizome), a high infection percentage of Fusarium wilt (%), dropping off of leaves as well as rotting of the root. Meanwhile, it was observed that the application of extract significantly improved the crop height, stem diameter, root size and distribution (root surface area, root diameter, and root volume), root–shoot ratio, as well as the soil physicochemical properties (CEC, N, p, K, Ca, and Mg), which enhanced resistance to Fusarium wilt diseases. We conclude that the application of NLE solution promotes better growth of Cavendish banana plants, soil physicochemical properties, and resistance to Fusarium wilt infection.
Efficacy of Bottle Gourd Seeds’ Extracts in Chemical Hazard Reduction Secreted as Toxigenic Fungi Metabolites
Authors: Abdel-Razek Adel G. , Badr Ahmed N. , Alharthi Salman S. , Selim Khaled A.
Published: November 2021

Bottle gourd seeds are surrounded by innumerable bioactive components of phytochemicals. This work aimed to evaluate the effectiveness of bottle gourd extracts as antimicrobial and an-ti-mycotoxigenic against toxigenic fungi and mycotoxins. Polar and nonpolar extracts were made from the seeds. The polar eco-friendly extract was prepared by an ultrasonication-assisted technique utilizing aqueous isopropanol (80%), whereas the non-polar extract was obtained using petroleum ether (40–60). The antioxidant efficacy, total phenolic content, and flavonoid content of the extracts were all measured. The fatty acid profile was measured using GC equipment, and the influence on toxigenic fungus and mycotoxin release was also investigated. The antioxidant efficacy of the polar extract is reflected. The total phenolic values of the oil and polar extract were 15.5 and 267 mg of GAE/g, respectively. The total flavonoid content of the oil was 2.95 mg catechol/g, whereas the isopropyl extract of seeds contained 14.86 mg catechol/g. The polar extract inhibited the DPPH more effectively than oil. When compared to other seed oils, the fatty acid composition differed. The pathogens were distinguished by the MIC and MFC for the polar extract. Three sterols were found in the oil, with a high concentration of B-sitosterols. The oil’s valuable -carotene content and tocopherol content were recorded. When compared to traditional antibiotics, the polar extract has shown promising antimicrobial activity against infections and toxigenic fungi. Bottle gourd extracts, as a non-traditional bioactive source, are viewed as a potentially promising alternative that might contribute to increased food safety, shelf-life, and security.
Exposure to Zearalenone Leads to Metabolic Disruption and Changes in Circulating Adipokines Concentrations in Pigs
Authors: Nagl Veronika , Grenier Bertrand , Pinton Philippe , Ruczizka Ursula , Dippel Maximiliane , Bünger Moritz , Oswald Isabelle P. , Soler Laura
Published: November 2021

Zearalenone (ZEN) is a mycotoxin classified as an endocrine disruptor. Many endocrine disruptors are also metabolic disruptors able to modulate energy balance and inflammatory processes in a process often involving a family of protein hormones known as adipokines. The aim of our study was to elucidate the role of ZEN as metabolic disruptor in pigs by investigating the changes in energy balance and adipokines levels in response to different treatment diets. To this end, weaned piglets (n = 10/group) were exposed to either basal feed or feed contaminated with 680 and 1620 µg/kg ZEN for 28 days. Serum samples collected at days 7 and 21 were subjected to biochemistry analysis, followed by determination of adipokine levels using a combined approach of protein array and ELISA. Results indicate that ZEN has an impact on lipid and glucose metabolism that was different depending on the dose and time of exposure. In agreement with these changes, ZEN altered circulating adipokines concentrations, inducing significant changes in adiponectin, resistin, and fetuin B. Our results suggest that ZEN may function as a natural metabolism-disrupting chemical.