bluuebunny/crossref_metadata_2025 huggingface dataset:
Synthesis, Performance, Mechanism: A Hyperbranched Phase Reverse Nano-Demulsifier for Condensate Emulsion
Authors: Liang Lei , Su Chao , Xiong Yujia , Wei Lei , Gu Congyue , Ye Haifeng , Xiao Qinghua , Luo Xingyu
Published: November 2023

Organic amine and nanosilica were combined to create a nano-demulsifier, which was employed in the oil–water separation process of a condensate emulsion. The nano-demulsifier has the structure of hyperbranched polymers and the skeleton structure of hyperbranched nanomaterials, and displays the demulsification impact of organic amine polymers as well as the synergistic effect of nanomaterials. This nano-demulsifier has the potential to drastically reduce the quantity of condensate demulsifiers utilized in the gathering station. The dehydration rate of the condensate lotion in the gas gathering station can reach more than 95% only at a concentration of 1.0 wt.%. Its application can significantly increase the separation efficiency of the condensate emulsion as well as the quality of condensate oil. It has a positive impact on cost reduction and efficiency in gas well production. The mechanism of action of the demulsifier was also studied, and the results show that the demulsifier is a phase reverse demulsifier.
Quality Assurance Investigations and Impurity Characterization during Upscaling of [177Lu]Lu-PSMAI&T
Authors: Schmitl Stefan , Raitanen Julia , Witoszynskyj Stephan , Patronas Eva-Maria , Nics Lukas , Ozenil Marius , Weissenböck Victoria , Mindt Thomas L. , Hacker Marcus , Wadsak Wolfgang , Brandt Marie R. , Mitterhauser Markus
Published: November 2023

[177Lu]Lu-PSMAI&T is widely used for the radioligand therapy of metastatic castration-resistant prostate cancer (mCRPC). Since this kind of therapy has gained a large momentum in recent years, an upscaled production process yielding multiple patient doses in one batch has been developed. During upscaling, the established production method as well as the HPLC quality control were challenged. A major finding was a correlation between the specific activity and the formation of a pre-peak, presumably caused by radiolysis. Hence, nonradioactive reference standards were irradiated with an X-ray source and the formed pre-peak was subsequently identified as a deiodination product by UPLC-MS. To confirm the occurrence of the same deiodinated side product in the routine batch, a customized deiodinated precursor was radiolabeled and analyzed with the same HPLC setup, revealing an identical retention time to the pre-peak in the formerly synthesized routine batches. Additionally, further cyclization products of [177Lu]Lu-PSMAI&T were identified as major contributors to radiochemical impurities. The comparison of two HPLC methods showed the likelihood of the overestimation of the radiochemical purity during the synthesis of [177Lu]Lu-PSMAI&T. Finally, a prospective cost reduction through an optimization of the production process was shown.
Design and Synthesis of Novel α-Methylchalcone Derivatives, Anti-Cervical Cancer Activity, and Reversal of Drug Resistance in HeLa/DDP Cells
Authors: Yang Zheng , Liu Zhengye , Ablise Mourboul , Maimaiti Aikebaier , Aihaiti Aizitiaili , Alimujiang Yusupuwajimu
Published: November 2023

In this study, a collection of newly developed α-methylchalcone derivatives were synthesized and assessed for their inhibitory potential against human cervical cancer cell lines (HeLa, SiHa, and C33A) as well as normal human cervical epithelial cells (H8). Notably, compound 3k exhibited substantial inhibitory effects on both HeLa and HeLa/DDP cells while demonstrating lower toxicity toward H8 cells. Furthermore, the compound 3k was found to induce apoptosis in both HeLa and HeLa/DDP cells while also inhibiting the G2/M phase, resulting in a decrease in the invasion and migration capabilities of these cells. When administered alongside cisplatin, 3k demonstrated a significant reduction in the resistance of HeLa/DDP cells to cisplatin, as evidenced by a decrease in the resistance index (RI) value from 7.90 to 2.10. Initial investigations into the underlying mechanism revealed that 3k did not impact the expression of P-gp but instead facilitated the accumulation of rhodamine 123 in HeLa/DDP cells. The results obtained from CADD docking analysis demonstrated that 3k exhibits stable binding to microtubule proteins and P-gp targets, forming hydrogen bonding interaction forces. Immunofluorescence analysis further revealed that 3k effectively decreased the fluorescence intensity of α and β microtubules in HeLa and HeLa/DDP cells, resulting in disruptions in cell morphology, reduction in cell numbers, nucleus coagulation, and cell rupture. Additionally, Western blot analysis indicated that 3k significantly reduced the levels of polymerized α and β microtubule proteins in both HeLa and HeLa/DDP cell lines while concurrently increasing the expression of dissociated α and β microtubule proteins. The aforementioned findings indicate a potential correlation between the inhibitory effects of 3k on HeLa and HeLa/DDP cells and its ability to inhibit tubulin and P-gp.
Study on the Memory Effect in Aerosil-Filled Nematic Liquid Crystal Doped with Magnetic Nanoparticles
Authors: Bury Peter , Veveričík Marek , Černobila František , Tomašovičová Natália , Lacková Veronika , Zakutanská Katarína , Timko Milan , Kopčanský Peter
Published: November 2023

A study on 5CB liquid crystal composites with SiO2 nanoparticles and an additional commixture with Fe3O4 nanoparticles using light transmission and SAW measurements is presented. The prepared liquid crystal composites exhibited an interesting memory effect characterized by the hysteresis of both light transmission and SAW attenuation responses investigated in the nematic phase. While in the case of SiO2 nanoparticles as dopants, the liquid crystal composite showed an improvement in the memory effect, the addition of Fe3O4 magnetic nanoparticles resulted in the memory effect decreasing. Additional studies showed a significant shift in both the threshold voltage and nematic–isotropic transition temperature. Measurements in the magnetic field confirmed the increasing memory effect according to that of pure 5CB. The properties of these composites could lead to a potential application for the fabrication of memory devices suitable for information storage.
Evaluation of the Use of Project-Based Learning in the Nursing Degree
Authors: Parra-Anguita Laura , López-Franco María Dolores , Martínez-Galiano Juan Miguel , González-Cabrera Manuel , Moreno-Cámara Sara , Granero-Moya Nani
Published: November 2023

Project-based learning (PBL) is a teaching methodology that allows students to acquire knowledge and competencies through the completion of projects that respond to real-life problems. The aims of this study were to evaluate the acquisition of knowledge of students of the Aging Nursing subject through a PBL-based intervention and determine the degree of student satisfaction with the use of this methodology. A mixed, quasi-experimental, pre–post study was conducted without a control group using an educational intervention based on PBL and descriptive phenomenology with content analysis of the experiences reported after it. A knowledge questionnaire about nursing homes was administered before the start of the intervention. After using PBL to carry out the subject project, the same knowledge questionnaire and an ad hoc questionnaire on satisfaction, assessment, and improvement aspects were administered. In total, 111 nursing students participated. The difference in knowledge after the educational intervention was significant. The mean pre-intervention score was 5.56, SD 1.50, and the mean post-intervention score was 7.14, SD 1.59, (p = 0.001). In total, 74% of the students stated that they were very satisfied with the use of this methodology. The students had a positive perspective on the process of acquiring knowledge that PBL allows. The students improved their knowledge about the planning and management of nursing homes with the use of the project-based learning teaching methodology. They were very satisfied with said activity. Teachers must be adequately trained for the correct implementation of this teaching methodology. This study was not registered.
Molecular Identification of Extrapulmonary Vaccine Adverse Events after BCG in Paraffin-Embedded Specimens
Authors: Brzezińska Sylwia , Zabost Anna , Borkowska-Tatar Dagmara , Klatt Magdalena , Goździk Jolanta , Dłużniewska Agnieszka , Błasińska Katarzyna , Augustynowicz-Kopeć Ewa
Published: November 2023

According to the World Health Organization (WHO), around 1 million children worldwide are diagnosed with tuberculosis each year. The Bacillus Calmette–Guérin (BCG) vaccine has been used around the world for over 100 years. The complications of the BCG vaccination can occur in about 0,06% of children and include local or systemic adverse reactions. Due to the close analogy between the vaccine strain and other species of the Mycobacterium tuberculosis complex (MTBC), molecular methods are recommended for differential diagnosis of Vaccine adverse events (VAE) after BCG. The ability to quickly and specifically identify BCG is important in view of different treatment regimens. The aim of the study was to assess the usefulness of genetic testing for Mycobacterium bovis BCG in the paraffin-embedded specimens’ methods. We describe two cases of VAE in immune-compromised children presenting with osteoarticular changes that had been clinically suspected of tuberculosis and led to molecular identification through GeneXpert, GenoType MTBC, and Spoligotyping. Results: Mycobacterium bovis BCG was detected in osteoarticular changes embedded in paraffin block of two patients. Conclusion: Genetic tests using paraffin-embedded materials allow for quick identification and differential diagnosis of patients with Tuberculosis and VAE after BCG. This is an important issue, especially in cases where the tissue has only been submitted for histopathological examination without microbiological diagnostics for tuberculosis.
Nutritional Composition, Mineral Profiling, In Vitro Antioxidant, Antibacterial and Enzyme Inhibitory Properties of Selected Indian Guava Cultivars Leaf Extract
Authors: Sam Arul Raj Moses , Amalraj Singamoorthy , Alarifi Saud , Kalaskar Mohan G. , Chikhale Rupesh , Santhi Veerasamy Pushparaj , Gurav Shailendra , Ayyanar Muniappan
Published: November 2023

Psidium guajava L. is a small evergreen tree known for its magnificent medicinal and nutritional value. This study aimed to evaluate the nutritional profile and in vitro pharmacological potentialities of the different leaf extracts of four cultivars of Psidium guajava namely Surka chitti, Allahabad safeda, Karela, and Lucknow-49. The standard procedures of the Association of Official Analytical Chemists (AOAC) were followed to carry out the nutritional analysis and all of the cultivars recorded the presence of elements at a nominal range. The highest presence of phenols (125.77 mg GAE/g) and flavonoids (92.38 mg QE/g) in the methanolic leaf extract of the Karela cultivar was recorded. A wide range of minerals such as sodium, phosphorus, magnesium, zinc, and boron were recorded with a higher percentage in the Karela cultivar of Psidium guajava. In the enzyme inhibitory assays, Allahabad safeda showed potential inhibition with an IC50 of 113.31 ± 1.07, 98.2 ± 0.66 and 95.73 ± 0.39 μg/mL in α-amylase, α-glucosidase, and tyrosinase inhibition assays, respectively. The strong antioxidant effect was established by Lucknow-49 (IC50 of 74.43 ± 1.86 μg/mL) and Allahabad safeda (IC50 of 78.93 ± 0.46 μg/mL) for ABTS and DPPH assays, respectively. The ethyl acetate and methanolic leaf extracts of the Allahabad safeda cultivar showed better inhibition against Pseudomonas aeruginosa with an MIC of 14.84 and 28.69 µg/mL, respectively. A decent mean zone of inhibition was recorded in methanolic leaf extract that ranged from 21–25 mm in diameter against the tested bacterial strains (Proteus vulgaris, Bacillus subtilis, and P. aeruginosa). This is the first scientific report on the comparative and comprehensive analysis of indigenous guava cultivars to evidently shortlist the elite cultivars with enriched dietary nutrition and biological activities.
Thymus zygis, Valuable Antimicrobial (In Vitro and In Situ) and Antibiofilm Agent with Potential Antiproliferative Effects
Authors: Vukić Milena D. , Čmiková Natália , Hsouna Anis Ben , Saad Rania Ben , Garzoli Stefania , Schwarzová Marianna , Vuković Nenad L. , Obradović Ana D. , Matić Miloš M. , Waszkiewicz-Robak Bożena , Kluz Maciej Ireneusz , Kačániová Miroslava
Published: November 2023

With the growing issues of food spoilage, microbial resistance, and high mortality caused by cancer, the aim of this study was to evaluate T. zygis essential oil (TZEO) as a potential solution for these challenges. Here, we first performed GC/MS analysis which showed that the tested TZEO belongs to the linalool chemotype since the abundance of linalool was found to be 38.0%. Antioxidant activity assays showed the superiority of TZEO in neutralizing the ABTS radical cation compared to the DPPH radical. The TZEO was able to neutralize 50% of ABTS•+ at the concentration of 53.03 ± 1.34 μg/mL. Antimicrobial assessment performed by employing disc diffusion and minimal inhibitory concentration assays revealed TZEO as a potent antimicrobial agent with the highest inhibition activity towards tested gram-negative strains. The most sensitive on the treatment with TZEO was Enterobacter aerogenes showing an MIC 50 value of 0.147 ± 0.006 mg/mL and a MIC 90 value of 0.158 ± 0.024 mg/mL. Additionally, an in situ analysis showed great effects of TZEO in inhibiting gram-negative E. coli, P. putida, and E. aerogenes growing on bananas and cucumbers. Treatment with the TZEO vapor phase in the concentration of 500 μg/mL was able to reduce the growth of these bacteria on the food models to the extent > 90%, except for E. coli growth on the cucumber, which was reduced to the extent of 83.87 ± 4.76%. Furthermore, a test on the antibiofilm activity of the tested essential oil revealed its biofilm prevention effects against Salmonella enterica which forms biofilms on plastic and stainless-steel surfaces. Performed tests on the TZEO effects towards cell viability showed no effects on the normal MRC-5 cell line. However, the results of MTT assay of TZEO effects on three cancer cell lines (MDA-MB-231, HCT-116, and K562) suggest that TZEO exerted the strongest effects on the inhibition of the viability of MDA-MB-231 cells, especially after long-term treatment in the highest concentration applied with reducing the viability of the cells to 57%. Additionally, results of NBT and Griess assays suggest that TZEO could be a convenient candidate for future testing for developing novel antitumor therapies.
Discrete Element Simulations of Contact Heat Transfer on a Batch-Operated Single Floor of a Multiple Hearth Furnace
Authors: Hilse Nikoline , Kriegeskorte Max , Fischer Jonas , Spatz Phil , Illana Enric , Schiemann Martin , Scherer Viktor
Published: November 2023

The multiple hearth furnace is a common industrial reactor for the thermal treatment of particulate material. The present contribution concentrates on a numerical analysis of contact heat transfer on a batch-operated single floor of a multiple hearth furnace employing the Discrete Element Method (DEM). The particles are agitated on an electrically heated circular floor by a single rotating rabble arm equipped with three flat blades. Blade angles have been varied from 0° to 90°. The DEM simulations (particle mechanics and contact heat transfer) were validated against experimental data. The transient heating of 20 mm diameter polyoxymethylene (POM) spheres was analysed. As the simulations did not consider natural convection inherently leading to time-varying heat losses, an averaged heat loss parameter was determined to represent heat dissipation from the particles to the surrounding gas and incorporated into the DEM simulations. With this approach, a good agreement with measurements was obtained. The DEM simulations and experiments do not show a large influence of the blade angle on the temporal evolution of the mean particle temperatures. However, the frequency distribution of particle temperature is dependent on the blade angle, revealing an increase in the standard deviation of the frequency distribution with an increasing blade angle.
Return to Nature: Renaturisation of Dried-Out Lakes in Poland
Authors: Ptak Mariusz , Heddam Salim , Zhu Senlin , Sojka Mariusz
Published: November 2023

Over the centuries, extensive changes have occurred in the functioning of the hydrosphere. In the case of Poland, the hydrographic network has been significantly transformed, and many of its elements have ceased to exist. The aim of this study was to investigate renaturalised lakes and to determine their original volume, which is a fundamental parameter in the context of stabilising water relationships. Based on archival cartographic materials, the locations of 15 such lakes were determined, with their volume totaling 11.7 million m3. This indicates a significant potential for renaturalised lakes in the context of increasing water resources. In the long term, the methodology adopted in this work may complement water-management efforts aimed at increasing retention and offering new ecosystem services. Such an approach is less invasive to the natural environment and more economically justified compared to new investments in artificial hydrotechnical infrastructure.
Evaluation of the SAIL Radiative Transfer Model for Simulating Canopy Reflectance of Row Crop Canopies
Authors: Han Dalei , Liu Jing , Zhang Runfei , Liu Zhigang , Guo Tingrui , Jiang Hao , Wang Jin , Zhao Huarong , Ren Sanxue , Yang Peiqi
Published: November 2023

The widely used SAIL (Scattering by Arbitrarily Inclined Leaves) radiative transfer model (RTM) is designed for canopies that can be considered as homogeneous turbid media and thus should be inadequate for row canopies. However, numerous studies have employed the SAIL model for row crops (e.g., wheat and maize) to simulate canopy reflectance or retrieve vegetation properties with satisfactory accuracy. One crucial reason may be that under certain conditions, a row crop canopy can be considered as a turbid medium, fulfilling the assumption of the SAIL model. Yet, a comprehensive analysis about the performance of SAIL in row canopies under various conditions is currently absent. In this study, we employed field datasets of wheat canopies and synthetic datasets of wheat and maize canopies to explore the impacts of the vegetation cover fraction (fCover), solar angle and soil background on the performance of SAIL in row crops. In the numerical experiments, the LESS 3D RTM was used as a reference to evaluate the performance of SAIL for various scenarios. The results show that the fCover is the most significant factor, and the row canopy with a high fCover has a low soil background influence. For a non-black soil background, both the field measurement and simulation datasets showed that the SAIL model accuracy initially decreased, and then increased with an increasing fCover, with the most significant errors occurring when the fCover was between about 0.4 and 0.7. As for the solar angles, the accuracy of synthetic wheat canopy will be higher with a larger SZA (solar zenith angle), but that of a synthetic maize canopy is little affected by the SZA. The accuracy of the SAA (solar azimuth angle) in an across-row direction is always higher than that in an along-row direction. Additionally, when the SZA ranges from 65° to 75° and the fCover of wheat canopies are greater than 0.6, SAIL can simulate the canopy reflectance with satisfactory accuracy (rRMSE < 10%); the same accuracy can be achieved in maize canopies as long as the fCover is greater than 0.8. These findings provide insight into the applicability of SAIL in row crops and support the use of SAIL in row canopies under certain conditions (with rRMSE < 10%).
Wheat Yield Prediction Using Unmanned Aerial Vehicle RGB-Imagery-Based Convolutional Neural Network and Limited Training Samples
Authors: Ma Juncheng , Wu Yongfeng , Liu Binhui , Zhang Wenying , Wang Bianyin , Chen Zhaoyang , Wang Guangcai , Guo Anqiang
Published: November 2023

Low-cost UAV RGB imagery combined with deep learning models has demonstrated the potential for the development of a feasible tool for field-scale yield prediction. However, collecting sufficient labeled training samples at the field scale remains a considerable challenge, significantly limiting the practical use. In this study, a split-merge framework was proposed to address the issue of limited training samples at the field scale. Based on the split-merge framework, a yield prediction method for winter wheat using the state-of-the-art Efficientnetv2_s (Efficientnetv2_s_spw) and UAV RGB imagery was presented. In order to demonstrate the effectiveness of the split-merge framework, in this study, Efficientnetv2_s_pw was built by directly feeding the plot images to Efficientnetv2_s. The results indicated that the proposed split-merge framework effectively enlarged the training samples, thus enabling improved yield prediction performance. Efficientnetv2_s_spw performed best at the grain-filling stage, with a coefficient of determination of 0.6341 and a mean absolute percentage error of 7.43%. The proposed split-merge framework improved the model ability to extract indicative image features, partially mitigating the saturation issues. Efficientnetv2_s_spw demonstrated excellent adaptability across the water treatments and was recommended at the grain-filling stage. Increasing the ground resolution of input images may further improve the estimation performance. Alternatively, improved performance may be achieved by incorporating additional data sources, such as the canopy height model (CHM). This study indicates that Efficientnetv2_s_spw is a promising tool for field-scale yield prediction of winter wheat, providing a practical solution to field-specific crop management.
Development and Validation of a Cyber-Physical System Leveraging EFDPN for Enhanced WSN-IoT Network Security
Authors: Krishnasamy Sundaramoorthy , Alotaibi Mutlaq , Alehaideb Lolwah , Abbas Qaisar
Published: November 2023

In the current digital era, Wireless Sensor Networks (WSNs) and the Internet of Things (IoT) are evolving, transforming human experiences by creating an interconnected environment. However, ensuring the security of WSN-IoT networks remains a significant hurdle, as existing security models are plagued with issues like prolonged training durations and complex classification processes. In this study, a robust cyber-physical system based on the Emphatic Farmland Fertility Integrated Deep Perceptron Network (EFDPN) is proposed to enhance the security of WSN-IoT. This initiative introduces the Farmland Fertility Feature Selection (F3S) technique to alleviate the computational complexity of identifying and classifying attacks. Additionally, this research leverages the Deep Perceptron Network (DPN) classification algorithm for accurate intrusion classification, achieving impressive performance metrics. In the classification phase, the Tunicate Swarm Optimization (TSO) model is employed to improve the sigmoid transformation function, thereby enhancing prediction accuracy. This study demonstrates the development of an EFDPN-based system designed to safeguard WSN-IoT networks. It showcases how the DPN classification technique, in conjunction with the TSO model, significantly improves classification performance. In this research, we employed well-known cyber-attack datasets to validate its effectiveness, revealing its superiority over traditional intrusion detection methods, particularly in achieving higher F1-score values. The incorporation of the F3S algorithm plays a pivotal role in this framework by eliminating irrelevant features, leading to enhanced prediction accuracy for the classifier, marking a substantial stride in fortifying WSN-IoT network security. This research presents a promising approach to enhancing the security and resilience of interconnected cyber-physical systems in the evolving landscape of WSN-IoT networks.
Forward Collision Warning Strategy Based on Millimeter-Wave Radar and Visual Fusion
Authors: Sun Chenxu , Li Yongtao , Li Hanyan , Xu Enyong , Li Yufang , Li Wei
Published: November 2023

Forward collision warning (FCW) is a critical technology to improve road safety and reduce traffic accidents. However, the existing multi-sensor fusion methods for FCW suffer from a high false alarm rate and missed alarm rate in complex weather and road environments. For these issues, this paper proposes a decision-level fusion collision warning strategy. The vision algorithm and radar tracking algorithm are improved in order to reduce the false alarm rate and omission rate of forward collision warning. Firstly, this paper proposes an information entropy-based memory index for an adaptive Kalman filter for radar target tracking that can adaptively adjust the noise model in a variety of complex environments. Then, for visual detection, the YOLOv5s model is enhanced in conjunction with the SKBAM (Selective Kernel and Bottleneck Attention Mechanism) designed in this paper to improve the accuracy of vehicle target detection. Finally, a decision-level fusion warning fusion strategy for millimeter-wave radar and vision fusion is proposed. The strategy effectively fuses the detection results of radar and vision and employs a minimum safe distance model to determine the potential danger ahead. Experiments are conducted under various weather and road conditions, and the experimental results show that the proposed algorithm reduces the false alarm rate by 11.619% and the missed alarm rate by 15.672% compared with the traditional algorithm.
A Reliable Pipeline Leak Detection Method Using Acoustic Emission with Time Difference of Arrival and Kolmogorov–Smirnov Test
Authors: Nguyen Duc-Thuan , Nguyen Tuan-Khai , Ahmad Zahoor , Kim Jong-Myon
Published: November 2023

This paper proposes a novel and reliable leak-detection method for pipeline systems based on acoustic emission (AE) signals. The proposed method analyzes signals from two AE sensors installed on the pipeline to detect leaks located between these two sensors. Firstly, the raw AE signals are preprocessed using empirical mode decomposition. The time difference of arrival (TDOA) is then extracted as a statistical feature of the two AE signals. The state of the pipeline (leakage/normal) is determined through comparing the statistical distribution of the TDOA of the current state with the prior normal state. Specifically, the two-sample Kolmogorov–Smirnov (K–S) test is applied to compare the statistical distribution of the TDOA feature for leak and non-leak scenarios. The K–S test statistic value in this context functions as a leakage indicator. A new criterion called leak sensitivity is introduced to evaluate and compare the performance of leak detection methods. Extensive experiments were conducted using an industrial pipeline system, and the results demonstrate the excellence of the proposed method in leak detection. Compared to traditional feature-based indicators, our approach achieves a significantly higher performance in leak detection.
Clustering Methods for Vibro-Acoustic Sensing Features as a Potential Approach to Tissue Characterisation in Robot-Assisted Interventions
Authors: Urrutia Robin , Espejo Diego , Evens Natalia , Guerra Montserrat , Sühn Thomas , Boese Axel , Hansen Christian , Fuentealba Patricio , Illanes Alfredo , Poblete Victor
Published: November 2023

This article provides a comprehensive analysis of the feature extraction methods applied to vibro-acoustic signals (VA signals) in the context of robot-assisted interventions. The primary objective is to extract valuable information from these signals to understand tissue behaviour better and build upon prior research. This study is divided into three key stages: feature extraction using the Cepstrum Transform (CT), Mel-Frequency Cepstral Coefficients (MFCCs), and Fast Chirplet Transform (FCT); dimensionality reduction employing techniques such as Principal Component Analysis (PCA), t-Distributed Stochastic Neighbour Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP); and, finally, classification using a nearest neighbours classifier. The results demonstrate that using feature extraction techniques, especially the combination of CT and MFCC with dimensionality reduction algorithms, yields highly efficient outcomes. The classification metrics (Accuracy, Recall, and F1-score) approach 99%, and the clustering metric is 0.61. The performance of the CT–UMAP combination stands out in the evaluation metrics.
Precise Phase Measurement for Fringe Reflection Technique through Optimized Camera Response
Authors: Hu Fengdan , Zhu Wenqi , Huang Wei , Xu Jinshan
Published: November 2023

The Fringe Reflection is a robust and non-contact technique for optical measurement and specular surface characterization. The periodic alternation between dark and light cycles of the fringe patterns encodes the geometric information and provides a non-contact method of spatial measurement through phase extraction. Precisely expressing the positions of the points of the fringe pattern is a fundamental requirement for an accurate fringe reflection measurement. However, the nonlinear processes, both in generating the fringe pattern on a screen and capturing it using pixel values, cause inevitable errors in the phase measurement and eventually reduce the system’s precision. Aiming at reducing these nonlinear errors, we focus on constructing a new quantity from the pixel values of the photos of the fringe patterns that could linearly respond to the ideal fringe pattern. To this end, we hypothesize that the process of displaying the fringe pattern on a screen using a control function is similar to the process of capturing the pattern and converting the illuminating information into pixel values, which can be described using the camera’s response function. This similarity allows us to build a scaled energy quantity that could have a better linear relation with the control function. We optimize the extracted camera response function using an objective to increase the precision and reduce the quoted error. Experiments designed to determine the positions of points along the quartile lines verify the effectiveness of the proposed method in improving fringe reflection measurement precision.
Transcranial Direct Current Stimulation in the Treatment of Gait Disturbance in Post-Stroke Patients: An Overview of Systematic Reviews
Authors: Chamorro-Hinojosa Juan Antonio , Molina-Rueda Francisco , Carratalá-Tejada María
Published: November 2023

Introduction: Transcranial direct current stimulation (tDCS) is a promising technique for brain modulation after a cerebrovascular accident (CVA). This treatment modality has been previously studied in the recovery of patients. The aim of this review is to analyse the evidence for the application of tDCS in the recovery of gait disturbance in stroke patients. Methods: This review was conducted according to the recommendations of the PRISMA statement. Three different electronic databases were searched for relevant results: PubMed, Scopus, and Cochrane, from 2015 to January 2022. We included reviews and meta-analyses that only considered randomised controlled trials (RCTs) that investigated the effects of transcranial direct current stimulation, in combination or not with other physiotherapy treatments, compared to no treatment, usual care, or alternative treatment on gait recovery. Our primary outcomes of interest were walking speed, mobility, and endurance; secondary outcomes included motor function. Results: Thirteen studies with a total of 195 RCTs were included. Data on population, outcome measures, protocols, and outcomes were extracted. The Amstar-2 scale and the GRADE system of certainty of evidence were used. Only one study received high certainty of evidence, 5 received low certainty of evidence, and 7 received critically low certainty of evidence. Moderate to low-quality evidence showed a beneficial effect of tDCS on gait parameters, but not significantly. Conclusions: Although the tDCS produces positive changes in gait recovery in spatio-temporal parameters such as mobility, endurance, strength, and motor function, there is insufficient evidence to recommend this treatment. Higher-quality studies with larger sample sizes are needed for stronger conclusions.
A Multi-Sensor Stochastic Energy-Based Vibro-Localization Technique with Byzantine Sensor Elimination
Authors: Ambarkutuk Murat , Alajlouni Sa’ed , Tarazaga Pablo A. , Plassmann Paul E.
Published: November 2023

This paper presents an occupant localization technique that determines the location of individuals in indoor environments by analyzing the structural vibrations of the floor caused by their footsteps. Structural vibration waves are difficult to measure as they are influenced by various factors, including the complex nature of wave propagation in heterogeneous and dispersive media (such as the floor) as well as the inherent noise characteristics of sensors observing the vibration wavefronts. The proposed vibration-based occupant localization technique minimizes the errors that occur during the signal acquisition time. In this process, the likelihood function of each sensor—representing where the occupant likely resides in the environment—is fused to obtain a consensual localization result in a collective manner. In this work, it becomes evident that the above sources of uncertainties can render certain sensors deceptive, commonly referred to as “Byzantines.” Because the ratio of Byzantines among the set sensors defines the success of the collective localization results, this paper introduces a Byzantine sensor elimination (BSE) algorithm to prevent the unreliable information of Byzantine sensors from affecting the location estimations. This algorithm identifies and eliminates sensors that generate erroneous estimates, preventing the influence of these sensors on the overall consensus. To validate and benchmark the proposed technique, a set of previously conducted controlled experiments was employed. The empirical results demonstrate the proposed technique’s significant improvement (3~0%) over the baseline approach in terms of both accuracy and precision.
Time-Frequency Multi-Domain 1D Convolutional Neural Network with Channel-Spatial Attention for Noise-Robust Bearing Fault Diagnosis
Authors: Kim Yejin , Kim Young-Keun
Published: November 2023

This paper proposes a noise-robust and accurate bearing fault diagnosis model based on time-frequency multi-domain 1D convolutional neural networks (CNNs) with attention modules. The proposed model, referred to as the TF-MDA model, is designed for an accurate bearing fault classification model based on vibration sensor signals that can be implemented at industry sites under a high-noise environment. Previous 1D CNN-based bearing diagnosis models are mostly based on either time domain vibration signals or frequency domain spectral signals. In contrast, our model has parallel 1D CNN modules that simultaneously extract features from both the time and frequency domains. These multi-domain features are then fused to capture comprehensive information on bearing fault signals. Additionally, physics-informed preprocessings are incorporated into the frequency-spectral signals to further improve the classification accuracy. Furthermore, a channel and spatial attention module is added to effectively enhance the noise-robustness by focusing more on the fault characteristic features. Experiments were conducted using public bearing datasets, and the results indicated that the proposed model outperformed similar diagnosis models on a range of noise levels ranging from −6 to 6 dB signal-to-noise ratio (SNR).
UAV-Based Image and LiDAR Fusion for Pavement Crack Segmentation
Authors: Elamin Ahmed , El-Rabbany Ahmed
Published: November 2023

Pavement surface maintenance is pivotal for road safety. There exist a number of manual, time-consuming methods to examine pavement conditions and spot distresses. More recently, alternative pavement monitoring methods have been developed, which take advantage of unmanned aerial systems (UASs). However, existing UAS-based approaches make use of either image or LiDAR data, which do not allow for exploring the complementary characteristics of the two systems. This study explores the feasibility of fusing UAS-based imaging and low-cost LiDAR data to enhance pavement crack segmentation using a deep convolutional neural network (DCNN) model. Three datasets are collected using two different UASs at varying flight heights, and two types of pavement distress are investigated, namely cracks and sealed cracks. Four different imaging/LiDAR fusing combinations are created, namely RGB, RGB + intensity, RGB + elevation, and RGB + intensity + elevation. A modified U-net with residual blocks inspired by ResNet was adopted for enhanced pavement crack segmentation. Comparative analyses were conducted against state-of-the-art networks, namely U-net and FPHBN networks, demonstrating the superiority of the developed DCNN in terms of accuracy and generalizability. Using the RGB case of the first dataset, the obtained precision, recall, and F-measure are 77.48%, 87.66%, and 82.26%, respectively. The fusion of the geometric information from the elevation layer with RGB images led to a 2% increase in recall. Fusing the intensity layer with the RGB images yielded a reduction of approximately 2%, 8%, and 5% in the precision, recall, and F-measure. This is attributed to the low spatial resolution and high point cloud noise of the used LiDAR sensor. The second dataset crack samples obtained largely similar results to those of the first dataset. In the third dataset, capturing higher-resolution LiDAR data at a lower altitude led to improved recall, indicating finer crack detail detection. This fusion, however, led to a decrease in precision due to point cloud noise, which caused misclassifications. In contrast, for the sealed crack, the addition of LiDAR data improved the sealed crack segmentation by about 4% and 7% in the second and third datasets, respectively, compared to the RGB cases.
Rice Husks and Leaf Mold Used as Peat Substitutes to Improve the Morphological, Photosynthetic, and Biochemical Properties of Chrysanthemum (Chrysanthemum × morifolium)
Authors: Liu Shuguang , Liu Mingyu , Chen Shengyan , Ni Xingyu , Zhang Kaiyuan , Yue Liran , Zhou Yunwei
Published: November 2023

Chrysanthemums (Chrysanthemum × morifolium) are highly valued for their ornamental and economic benefits. However, the commonly used growing medium for chrysanthemums, peat, is not renewable, and peatlands are facing depletion. Therefore, it is important to find sustainable alternatives to peat. This study aims to evaluate the potential of rice husks and leaf mold mixed with peat and perlite in different ratios (10–20–30–40–80% v/v/v/v) as substitute materials for peat in chrysanthemum production. The study examines the physical and chemical properties of the different growing media ratios, as well as their effects on plant growth, development, and physiological indicators. The results of the experiment demonstrate that the different ratios of the cultivation substrate significantly influence the physical and chemical properties of the growing medium, as well as the growth and physiological indicators of chrysanthemums. A 20–30% proportion of rice husks and leaf mold promotes the growth and photosynthetic activity of chrysanthemum cuttings, resulting in increased plant height, leaf area, total chlorophyll content, and net photosynthetic rate. The mixed substrates (10–40%) maintain suitable pH levels, electrical conductivity (EC), and nutrient content (nitrogen, phosphorus, and potassium). However, an 80% ratio of rice husks negatively affects plant survival and growth due to elevated EC and potassium levels. In conclusion, a peat medium containing 20% rice husks and leaf mold provides a more favorable cultivation substrate for producing high-quality chrysanthemums while promoting sustainable horticultural practices.
Hybrid Integrated Computing Algorithm for Sustainable Tourism
Authors: Liao Yuan-Hsun , Chang Po-Chun , Li Hsiao-Hui
Published: November 2023

To avoid destroying the natural environment, we can create tourist paths without disrupting ecological systems or rare places such as rainforests that contain endangered species. Likewise, in sustainable tourism, we should consider visiting national parks or national museums as a way to understand the core values and the meaning of that culture and environment more clearly. In this paper, we consider which points tourists need to avoid or visit for sustainable tourism. We designed an algorithm that can give a path to avoid certain points or to go to a preferred point. If this algorithm does not give any weight, it will give the shortest path from the start to the end, and it can decide which vertices to avoid or travel to. Moreover, it can be used to vary the weights of different positive or negative values to obtain a path to avoid a point or to reach a point. Compared to Dĳkstra’s algorithm, we can add a negative weight to the graph and still find the shortest path. In application, it can be used for path schedule decisions. We did not wave the large resources to calculate the walk length. In the usage scenario, users only need to provide the starting node, end node, avoidance point, and facing point to calculate the best path. This algorithm will give a good path for users. At the same time, users can use this algorithm to implement sustainable travel route planning, such as going to museums, avoiding rare environments, etc. So, this algorithm provides a new way to decide the best path. Finally, the experimental results show that the classic algorithms cannot avoid points. In real tourism, tourists can use this algorithm for travel planning to achieve sustainable tourism.
A Pilot Study on Industry Stakeholders’ Views towards Revalorization of Surplus Material from the Fruit and Vegetable Sector as a Way to Reduce Food Waste
Authors: Fox Shelley , Kenny Owen , Noci Francesco , Dermiki Maria
Published: November 2023

Food waste is a global issue, with the fruit and vegetable sector accounting for higher losses compared with other sectors. The aim of this study was to gain an understanding into how industry stakeholders in Ireland manage surplus fruit and vegetable material remaining after their main processing. An explanatory sequential mixed methods approach was employed to collect data in the form of online surveys (n = 55) and one-to-one interviews (n = 7). The findings outlined several barriers to revalorization. Most respondents were measuring food waste and actively trying to minimize it, although this was for economic rather than environmental sustainability reasons. Environmental sustainability measures were an important factor for larger companies, although all respondents agreed it was important to manage this material from an environmental perspective. This material was mostly classified as “food waste” and usually composted or used for animal feed. Many stakeholders had identified opportunities for revalorization; however, for smaller businesses, this cannot become a reality without considerable investment. Joined-up thinking is required among all stakeholders, including consumers and policy makers, to create positive sustainable changes. Education and greater awareness about the extent of the food waste crisis may assist in achieving reduction targets and encourage revalorization in the industry.
Digitalization in Entrepreneurship: Unveiling the Motivational and Demographic Influences towards Sustainable Digital Sales Strategies
Authors: Plečko Samuel , Tominc Polona , Širec Karin
Published: November 2023

Digitalization is incrementally transforming business practices, particularly in sales and digital marketing, although the factors motivating entrepreneurs to adopt digital technologies in sales strategies remain underexplored. Consequently, the aim of our research is to identify the factors that influence the use of digital technologies in sales. This research elucidates the interplay between demographics and entrepreneurial motivations, utilizing data from the Global Entrepreneurship Monitor, involving 464 entrepreneurs in Slovenia and Croatia, analyzed through logistic regression. Notably, demographic factors, including gender, age, and education, do not significantly influence digitalization adoption in sales strategies. Among entrepreneurial motives, only the aspiration “to make a difference in the world” propels entrepreneurs towards considering digital sales approaches, while other motivations do not have a significant influence. Despite extensive studies on demographic factors influencing digital technology adoption, a consistent trend is conspicuously absent, necessitating an intricate examination of various dimensions of digital business transformation. Furthermore, our findings underscore that, while digitalization is acclaimed for enhancing companies’ profitability, entrepreneurs motivated by altruistic goals demonstrate a pronounced proclivity to integrate digitalization into sales, signifying that digitalization’s pragmatic utility extends beyond merely amplifying profitability and growth, insinuating a more integrative role in sculpting sustainable entrepreneurial practices and business models.
Natural Fractures and Their Contribution to Natural Gas Migration and Accumulation in Marine Carbonate Reservoirs: Lower Triassic Feixianguan Formation, Northeast Sichuan Basin, China
Authors: Guan Cong , Zeng Lianbo , Yao Yingtao , Zhang Hang , Zhang Jiewei , Liang Dong
Published: November 2023

The Lower Triassic carbonate succession of the Feixianguan Formation represents a primary focus for gas exploration in the northwestern Sichuan Basin. This study area includes the massive Puguang gas field and other nearby gas fields of considerable size. These carbonate reservoirs display significant heterogeneity, which is primarily influenced by the presence of natural fractures. Extensive documentation of fracture types, characteristics, effectiveness, and their role in enhancing reservoir properties was conducted by examining and analyzing various data sources, including cores, thin sections, image logs, and experimental measurements. Shear fractures primarily characterize the Feixianguan Formation carbonate reservoir, although tensile and diagenetic fractures are also present, albeit in fewer numbers. Tectonic fractures are the dominant type, particularly unfilled ones with dip angles greater than 60° in the NEE–SWW direction. These fractures are mainly filled with calcite. The tectonic fractures were formed in three stages: Late Indosinian-Early Yanshanian, Late Yanshanian-Early Himalayan, and Late Himalayan. These fractures intersect with the in situ stress direction at a small angle in the NE–SW, NEE–SWW, and near E–W directions, contributing to their effectiveness. Compared with the total fracture density, the effective fracture density is the factor in controlling gas production. An increase in the proportion of effective fractures tends to result in a rise in gas productivity. Additionally, the orientation of effective fractures also influences natural gas production. Fractures striking in the E–W and NE–SW directions, which are particularly effective, are associated with high natural gas production.