bluuebunny/crossref_metadata_2025 huggingface dataset:
Quantity and Variety of Fruit and Vegetable Intake with Changes in Measures of Adiposity among Community-Dwelling Chinese Older Adults
Authors: Li Shu-Yi , Leung Jason C. S. , Lu Zhi-Hui , Kwok Timothy C. Y.
Published: September 2023

Background: The association between fruit and vegetable intake, considering both quantity and variety, and weight change among older adults remains controversial. We aimed to investigate the association between fruit and vegetable intake, variety, and changes in adiposity measures among community-dwelling Chinese older adults. Methods: A total of 2944 participants aged ≥65 years from Hong Kong communities were included. Fruit and vegetable intake was estimated by a locally validated food frequency questionnaire. Adiposity measures (weight, body mass index (BMI), waist circumference, fat and lean mass) were assessed at baseline and the year four follow-up. Restricted cubic spline and logistic models were performed to estimate the associations between fruit and vegetable intake, variety, and changes in adiposity measures. Results: A nonlinear association between vegetable intake and weight change was found, following a J-shape curve. Increased vegetable intake was associated with less increases in weight, BMI, and fat mass among those below median intakes. However, these associations became insignificant at higher intakes (all P-nonlinearity < 0.05). The ORs (95% CIs) for weight gain across the quartiles of vegetable intake were as follows: 1.00 (reference), 0.81 (0.56, 1.17), 0.55 (0.36, 0.83), and 0.88 (0.58, 1.33). Similar patterns were observed in overweight and normal weight participants, but not in those with low body weight. No associations with weight change were found for fruit intake or fruit and vegetable variety. Conclusions: Moderate vegetable intake was associated with less weight gain among community-dwelling Chinese older adults, but not in those with low body weight. No association was observed between fruit intake or variety with weight change.
Repurposing Terfenadine as a Novel Antigiardial Compound
Authors: Suárez-Rico Daniel Osmar , Munguía-Huizar Francisco Javier , Cortés-Zárate Rafael , Hernández-Hernández José Manuel , González-Pozos Sirenia , Perez-Rangel Armando , Castillo-Romero Araceli
Published: September 2023

Giardia lamblia is a highly infectious protozoan that causes giardiasis, a gastrointestinal disease with short-term and long-lasting symptoms. The currently available drugs for giardiasis treatment have limitations such as side effects and drug resistance, requiring the search for new antigiardial compounds. Drug repurposing has emerged as a promising strategy to expedite the drug development process. In this study, we evaluated the cytotoxic effect of terfenadine on Giardia lamblia trophozoites. Our results showed that terfenadine inhibited the growth and cell viability of Giardia trophozoites in a time–dose-dependent manner. In addition, using scanning electron microscopy, we identified morphological damage; interestingly, an increased number of protrusions on membranes and tubulin dysregulation with concomitant dysregulation of Giardia GiK were observed. Importantly, terfenadine showed low toxicity for Caco-2 cells, a human intestinal cell line. These findings highlight the potential of terfenadine as a repurposed drug for the treatment of giardiasis and warrant further investigation to elucidate its precise mechanism of action and evaluate its efficacy in future research.
Artificial Forms of Life
Authors: Sunday Grève Sebastian
Published: September 2023

The logical problem of artificial intelligence—the question of whether the notion sometimes referred to as ‘strong’ AI is self-contradictory—is, essentially, the question of whether an artificial form of life is possible. This question has an immediately paradoxical character, which can be made explicit if we recast it (in terms that would ordinarily seem to be implied by it) as the question of whether an unnatural form of nature is possible. The present paper seeks to explain this paradoxical kind of possibility by arguing that machines can share the human form of life and thus acquire human mindedness, which is to say they can be intelligent, conscious, sentient, etc. in precisely the way that a human being typically is.
Full-Vectorial Light Propagation Simulation of Optimized Beams in Scattering Media
Authors: Ott Felix , Fritzsche Niklas , Kienle Alwin
Published: September 2023

Volumetric scattering prevents imaging modalities in biomedical optics from imaging deep inside tissue. The optimization of the incident wavefront has the potential to improve these imaging modalities. To investigate the optimization and light propagation of such beams inside scattering media rigorously, full-vectorial simulations based on solutions of Maxwell’s equations are necessary. In this publication, we present a versatile two-step beam synthesis method to efficiently simulate the scanning and phase optimization of a focused beam inside a static scattering medium. We present four different approaches to the phase optimization of the energy density and the absolute value of the Poynting vector. We find that these quantities have two regions with different, almost exponential decays over depth for a non-optimized beam. Optimization by conjugating the phase of the projected electric field in various directions at the focus shows an improvement below a certain penetration depth. Seeking global solutions to the optimization problems reveals an even better enhancement in the energy density and the absolute value of the Poynting vector in the focus. For Poynting vector optimization, the differences between the presented optimization approaches are more significant than for the energy density. With the presented method, it is possible to efficiently simulate different imaging methods improved by wavefront shaping to investigate their possible penetration depths.
Viscoelastic Properties of Polypropylene during Crystallization and Melting: Experimental and Phenomenological Modeling
Authors: Billon Noëlle , Castellani Romain , Bouvard Jean-Luc , Rival Guilhem
Published: September 2023

This paper deals with the viscoelastic behavior during crystallization and melting of semicrystalline polymers, with the aim of later modeling the residual stresses after processing in cases where crystallization occurs in quasi-static conditions (in additive manufacturing for example). Despite an abundant literature on polymer crystallization, the current state of scientific knowledge does not yet allow ab initio modeling. Therefore, an alternative and pragmatic way has been explored to propose a first approximation of the impact of crystallization and melting on the storage and loss moduli during crystallization–melting–crystallization cycles. An experimental approach, combining DSC, optical microscopy and oscillatory shear rheology, was used to define macroscopic parameters related to the microstructure. These parameters have been integrated into a phenomenological model. Isothermal measurements were used to describe the general framework, and crystallization at a constant cooling rate was used to evaluate the feasibility of a general approach. It can be concluded that relying solely on the crystalline fraction is inadequate to model the rheology. Instead, accounting for the microstructure at the spherulitic level could be more useful. Additionally, the results obtained from the experiments help to enhance our understanding of the correlations between crystallization kinetics and its mechanical effects.
Advancements in Chitosan-Based Nanoparticles for Pulmonary Drug Delivery
Authors: Zacaron Thiago Medeiros , Silva Mariana Leite Simões e , Costa Mirsiane Pascoal , Silva Dominique Mesquita e , Silva Allana Carvalho , Apolônio Ana Carolina Morais , Fabri Rodrigo Luiz , Pittella Frederico , Rocha Helvécio Vinícius Antunes , Tavares Guilherme Diniz
Published: September 2023

The evolution of respiratory diseases represents a considerable public health challenge, as they are among the leading causes of death worldwide. In this sense, in addition to the high prevalence of diseases such as asthma, chronic obstructive pulmonary disease, pneumonia, cystic fibrosis, and lung cancer, emerging respiratory diseases, particularly those caused by members of the coronavirus family, have contributed to a significant number of deaths on a global scale over the last two decades. Therefore, several studies have been conducted to optimize the efficacy of treatments against these diseases, focusing on pulmonary drug delivery using nanomedicine. Thus, the development of nanocarriers has emerged as a promising alternative to overcome the limitations of conventional therapy, by increasing drug bioavailability at the target site and reducing unwanted side effects. In this context, nanoparticles composed of chitosan (CS) show advantages over other nanocarriers because chitosan possesses intrinsic biological properties, such as anti-inflammatory, antimicrobial, and mucoadhesive capacity. Moreover, CS nanoparticles have the potential to enhance drug stability, prolong the duration of action, improve drug targeting, control drug release, optimize dissolution of poorly soluble drugs, and increase cell membrane permeability of hydrophobic drugs. These properties could optimize the performance of the drug after its pulmonary administration. Therefore, this review aims to discuss the potential of chitosan nanoparticles for pulmonary drug delivery, highlighting how their biological properties can improve the treatment of pulmonary diseases, including their synergistic action with the encapsulated drug.
Numerical Research on Effects of Variable Port Timing on Performance of Marine Low-Speed Two-Stroke Engine
Authors: Zhang Heng , Long Wuqiang , Xiao Ge , Li Bo , Qian Yuehua
Published: September 2023

Enhancing the effective expansion ratio to further improve the fuel consumption, this study implemented a kind of Variable Port Timing (VPT) by designing a vertically moving sleeve on the outside of the scavenging port of a low-speed two-stroke diesel engine with a 340 mm bore. A 3D Computational Fluid Dynamics (CFD) model was constructed and calibrated to investigate the influence of the VPT strategy on the engine performance and the internal gas exchange process. The results indicated that the VPT can reduce the negative work from the compression stroke and increase the expansion work from the expansion stroke, which effectively enhances the fuel economy. However, the reduction in the mass flow rate would lead to the severe deterioration of the turbocharging system’s performance. The related matching analysis between the sleeve and the scavenging ports revealed that the sleeve velocity had a minimal influence on the scavenging flow rate, while increasing the height of the scavenging port can restore a certain mass flow rate, but will decrease the in-cylinder swirl intensity, deteriorating the combustion in the cylinders. The optimal approach is to raise the position of the scavenging port, achieving a Scavenging Port Closing (SPC) at a 235°CA, which will restore the scavenging flow rate of the original level to 90.7% and improve the indicated fuel consumption by 2.9 g/kWh.
An Experimental Approach to Determining the Average Diffusion Coefficient of Volatile Components in Polymer Waste Materials
Authors: Chung Chi Nghia , Marschik Christian , Klimosek Jakub , Kosek Juraj , Akhras Mohamad Hassan , Steinbichler Georg
Published: September 2023

One of the major challenges in recycling plastics is the removal of undesired volatile components from the polymeric phase, which may reduce process efficiency and negatively affect product quality. Accordingly, the recycling industry employs a broad range of degassing techniques, the efficiency of which often depends on the diffusion coefficient—a measure of the mass transport of volatile components in polymeric phases. The aim of this study was to develop a practically feasible experimental approach using thermogravimetric analysis (TGA) to determine the average diffusion coefficient of volatile components in polymer waste materials. First, the TGA method was validated with a pressure decay apparatus (PDA) using predefined binary material mixtures: Thin sheets were pressed from virgin high-density polyethylene (HDPE) and polypropylene (PP) and deliberately saturated with toluene in a sorption experiment. These saturated samples were then used in TGA and PDA desorption experiments at 60 °C, 80 °C and 100 °C, which yielded similar results with an average difference of 7.4% for the HDPE-toluene system and 14.7% for the PP-toluene system. When validated, TGA was employed to determine the diffusion coefficient of volatile components in post-industrial plastic waste melt at a temperature of 220 °C. The proposed method contributes to the understanding of diffusion-based mass transport in polymer waste materials and provides a key parameter for model-based process control and optimization. In practice, the diffusion coefficient results can be used to predict the degassing performance of an extrusion process in the mechanical recycling of plastic waste.
A Version Control System for Point Clouds
Authors: Ogayar-Anguita Carlos J. , López-Ruiz Alfonso , Segura-Sánchez Rafael J. , Rueda-Ruiz Antonio J.
Published: September 2023

This paper presents a novel version control system for point clouds, which allows the complete editing history of a dataset to be stored. For each intermediate version, this system stores only the information that changes with respect to the previous one, which is compressed using a new strategy based on several algorithms. It allows undo/redo functionality in memory, which serves to optimize the operation of the version control system. It can also manage changes produced from third-party applications, which makes it ideal to be integrated into typical Computer-Aided Design workflows. In addition to automated management of incremental versions of point cloud datasets, the proposed system has a much lower storage footprint than the manual backup approach for most common point cloud workflows, which is essential when working with LiDAR (Light Detection and Ranging) data in the context of spatial big data.
A Decompressed Spectral-Spatial Multiscale Semantic Feature Network for Hyperspectral Image Classification
Authors: Liu Dongxu , Li Qingqing , Li Meihui , Zhang Jianlin
Published: September 2023

Convolutional neural networks (CNNs) have shown outstanding feature extraction capability and become a hot topic in the field of hyperspectral image (HSI) classification. However, most of the prior works usually focus on designing deeper or wider network architectures to extract spatial and spectral features, which give rise to difficulty for optimization and more parameters along with higher computation. Moreover, how to learn spatial and spectral information more effectively is still being researched. To tackle the aforementioned problems, a decompressed spectral-spatial multiscale semantic feature network (DSMSFNet) for HSI classification is proposed. This model is composed of a decompressed spectral-spatial feature extraction module (DSFEM) and a multiscale semantic feature extraction module (MSFEM). The former is devised to extract more discriminative and representative global decompressed spectral-spatial features in a lightweight extraction manner, while the latter is constructed to expand the range of available receptive fields and generate clean multiscale semantic features at a granular level to further enhance the classification performance. Compared with progressive classification approaches, abundant experimental results on three benchmark datasets prove the superiority of our developed DSMSFNet model.
Dynamic Simulation and Prediction of Carbon Storage Based on Land Use/Land Cover Change from 2000 to 2040: A Case Study of the Nanchang Urban Agglomeration
Authors: Fu Yuheng , Huang Min , Gong Daohong , Lin Hui , Fan Yewen , Du Wenying
Published: September 2023

Land use/land cover change (LUCC) constitutes a significant contributor to variations in the storage of carbon within ecosystems and holds substantial significance within the context of the carbon cycling process. This study analyzed land use data from the Nanchang urban agglomeration in 2000 and 2020 to investigate changes in land use and carbon storage using the PLUS model and GIS. The results show the following: (1) From 2000 to 2020, the Nanchang urban agglomeration experienced reductions in the extents of croplands, woodlands, grasslands, and unused lands. The predominant trend in land transformation involved the conversion of cropland into built-up land. (2) Between 2000 and 2020, there was a declining trajectory observed in carbon storage for the Nanchang urban agglomeration, with an overall decrease of 1.13 × 107 t. The space is characterized by a high-altitude perimeter and a low-altitude center. Urbanization’s encroachment on cropland is the main reason for declining carbon storage. (3) The predictive outcomes reveal that, in 2040, carbon storage in the Nanchang urban agglomeration will be reduced by 1.00 × 107 t under the natural development scenario, and reduced by 3.90 × 106 t and increased by 2.29 × 105 t, respectively, under the cropland protection and ecological protection scenarios. The risk of carbon loss is significantly reduced by ecological protection policy interventions. Our analysis of the land use patterns and carbon storage distribution in the Nanchang urban agglomeration over the past 20 years and our exploration of the land use change trend over the next 20 years under the conservation policy provide a reference basis for increasing the carbon sink in the core area of the ecological city cluster of Poyang Lake and realizing the sustainable development of the city.
CURI-YOLOv7: A Lightweight YOLOv7tiny Target Detector for Citrus Trees from UAV Remote Sensing Imagery Based on Embedded Device
Authors: Zhang Yali , Fang Xipeng , Guo Jun , Wang Linlin , Tian Haoxin , Yan Kangting , Lan Yubin
Published: September 2023

Data processing of low-altitude remote sensing visible images from UAVs is one of the hot research topics in precision agriculture aviation. In order to solve the problems of large model size with slow detection speed that lead to the inability to process images in real time, this paper proposes a lightweight target detector CURI-YOLOv7 based on YOLOv7tiny which is suitable for individual citrus tree detection from UAV remote sensing imagery. This paper augmented the dataset with morphological changes and Mosica with Mixup. A backbone based on depthwise separable convolution and the MobileOne-block module was designed to replace the backbone of YOLOv7tiny. SPPF (spatial pyramid pooling fast) was used to replace the original spatial pyramid pooling structure. Additionally, we redesigned the neck by adding GSConv and depth-separable convolution and deleted its input layer from the backbone with a size of (80, 80) and its output layer from the head with a size of (80, 80). A new ELAN structure was designed, and the redundant convolutional layers were deleted. The experimental results show that the GFLOPs = 1.976, the parameters = 1.018 M, the weights = 3.98 MB, and the mAP = 90.34% for CURI-YOLOv7 in the UAV remote sensing imagery of the citrus trees dataset. The detection speed of a single image is 128.83 on computer and 27.01 on embedded devices. Therefore, the CURI-YOLOv7 model can basically achieve the function of individual tree detection in UAV remote sensing imagery on embedded devices. This forms a foundation for the subsequent UAV real-time identification of the citrus tree with its geographic coordinates positioning, which is conducive to the study of precise agricultural management of citrus orchards.
Deep-Learning-Aided Evaluation of Spondylolysis Imaged with Ultrashort Echo Time Magnetic Resonance Imaging
Authors: Achar Suraj , Hwang Dosik , Finkenstaedt Tim , Malis Vadim , Bae Won C.
Published: September 2023

Isthmic spondylolysis results in fracture of pars interarticularis of the lumbar spine, found in as many as half of adolescent athletes with persistent low back pain. While computed tomography (CT) is the gold standard for the diagnosis of spondylolysis, the use of ionizing radiation near reproductive organs in young subjects is undesirable. While magnetic resonance imaging (MRI) is preferable, it has lowered sensitivity for detecting the condition. Recently, it has been shown that ultrashort echo time (UTE) MRI can provide markedly improved bone contrast compared to conventional MRI. To take UTE MRI further, we developed supervised deep learning tools to generate (1) CT-like images and (2) saliency maps of fracture probability from UTE MRI, using ex vivo preparation of cadaveric spines. We further compared quantitative metrics of the contrast-to-noise ratio (CNR), mean squared error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM) between UTE MRI (inverted to make the appearance similar to CT) and CT and between CT-like images and CT. Qualitative results demonstrated the feasibility of successfully generating CT-like images from UTE MRI to provide easier interpretability for bone fractures thanks to improved image contrast and CNR. Quantitatively, the mean CNR of bone against defect-filled tissue was 35, 97, and 146 for UTE MRI, CT-like, and CT images, respectively, being significantly higher for CT-like than UTE MRI images. For the image similarity metrics using the CT image as the reference, CT-like images provided a significantly lower mean MSE (0.038 vs. 0.0528), higher mean PSNR (28.6 vs. 16.5), and higher SSIM (0.73 vs. 0.68) compared to UTE MRI images. Additionally, the saliency maps enabled quick detection of the location with probable pars fracture by providing visual cues to the reader. This proof-of-concept study is limited to the data from ex vivo samples, and additional work in human subjects with spondylolysis would be necessary to refine the models for clinical use. Nonetheless, this study shows that the utilization of UTE MRI and deep learning tools could be highly useful for the evaluation of isthmic spondylolysis.
Intelligent Tapping Machine: Tap Geometry Inspection
Authors: Lin En-Yu , Chen Ju-Chin , Lien Jenn-Jier James
Published: September 2023

Currently, the majority of industrial metal processing involves the use of taps for cutting. However, existing tap machines require relocation to specialized inspection stations and only assess the condition of the cutting edges for defects. They do not evaluate the quality of the cutting angles and the amount of removed material. Machine vision, a key component of smart manufacturing, is commonly used for visual inspection. Taps are employed for processing various materials. Traditional tap replacement relies on the technician’s accumulated empirical experience to determine the service life of the tap. Therefore, we propose the use of visual inspection of the tap’s external features to determine whether replacement or regrinding is needed. We examined the bearing surface of the tap and utilized single images to identify the cutting angle, clearance angle, and cone angles. By inspecting the side of the tap, we calculated the wear of each cusp. This inspection process can facilitate the development of a tap life system, allowing for the estimation of the durability and wear of taps and nuts made of different materials. Statistical analysis can be employed to predict the lifespan of taps in production lines. Experimental error is 16 μm. Wear from tapping 60 times is equivalent to 8 s of electric grinding. We have introduced a parameter, thread removal quantity, which has not been proposed by anyone else.
Simulation and Experimental Verification of Magnetic Field Diffusion at the Launch Load during Electromagnetic Launch
Authors: Yang Yuxin , Yin Qiang , Li Changsheng , Li Haojie , Zhang He
Published: September 2023

The unique magnetic field environment during electromagnetic launch imposes higher requirements on the design and protection of the internal electronic system within the launch load. This low-frequency, Tesla-level extreme magnetic field environment is fundamentally distinct from the Earth’s geomagnetic field. The excessive change rate of magnetic flux can readily induce voltage within the circuit, thus disrupting the normal operation of intelligent microchips. Existing simulation methods primarily focus on the physical environments of rails and armatures, making it challenging to precisely compute the magnetic field environment at the load’s location. In this paper, we propose a computational rail model based on the magneto–mechanical coupling model of a railgun. This model accounts for the dynamic current distribution during the launch process and simulates the magnetic flux density distribution at the load location. To validate the model’s accuracy, three-axis magnetic sensors were placed in front of the armature, and the dynamic magnetic field distribution during the launch process was obtained using the projectile-borne-storage testing method. The results indicate that compared to the previous literature methods, the approach proposed in this paper achieves higher accuracy and is closer to experimental results, providing valuable support for the design and optimization of the launch load.
Design of a Sensor-Technology-Augmented Gait and Balance Monitoring System for Community-Dwelling Older Adults in Hong Kong: A Pilot Feasibility Study
Authors: Zhao Yang , Yu Lisha , Fan Xiaomao , Pang Marco Y. C. , Tsui Kwok-Leung , Wang Hailiang
Published: September 2023

Routine assessments of gait and balance have been recognized as an effective approach for preventing falls by issuing early warnings and implementing appropriate interventions. However, current limited public healthcare resources cannot meet the demand for continuous monitoring of deteriorations in gait and balance. The objective of this study was to develop and evaluate the feasibility of a prototype surrogate system driven by sensor technology and multi-sourced heterogeneous data analytics, for gait and balance assessment and monitoring. The system was designed to analyze users’ multi-mode data streams collected via inertial sensors and a depth camera while performing a 3-m timed up and go test, a five-times-sit-to-stand test, and a Romberg test, for predicting scores on clinical measurements by physiotherapists. Generalized regression of sensor data was conducted to build prediction models for gait and balance estimations. Demographic correlations with user acceptance behaviors were analyzed using ordinal logistic regression. Forty-four older adults (38 females) were recruited in this pilot study (mean age = 78.5 years, standard deviation [SD] = 6.2 years). The participants perceived that using the system for their gait and balance monitoring was a good idea (mean = 5.45, SD = 0.76) and easy (mean = 4.95, SD = 1.09), and that the system is useful in improving their health (mean = 5.32, SD = 0.83), is trustworthy (mean = 5.04, SD = 0.88), and has a good fit between task and technology (mean = 4.97, SD = 0.84). In general, the participants showed a positive intention to use the proposed system in their gait and balance management (mean = 5.22, SD = 1.10). Demographic correlations with user acceptance are discussed. This study provides preliminary evidence supporting the feasibility of using a sensor-technology-augmented system to manage the gait and balance of community-dwelling older adults. The intervention is validated as being acceptable, viable, and valuable.
Best Practices for Body Temperature Measurement with Infrared Thermography: External Factors Affecting Accuracy
Authors: Mazdeyasna Siavash , Ghassemi Pejman , Wang Quanzeng
Published: September 2023

Infrared thermographs (IRTs) are commonly used during disease pandemics to screen individuals with elevated body temperature (EBT). To address the limited research on external factors affecting IRT accuracy, we conducted benchtop measurements and computer simulations with two IRTs, with or without an external temperature reference source (ETRS) for temperature compensation. The combination of an IRT and an ETRS forms a screening thermograph (ST). We investigated the effects of viewing angle (θ, 0–75°), ETRS set temperature (TETRS, 30–40 °C), ambient temperature (Tatm, 18–32 °C), relative humidity (RH, 15–80%), and working distance (d, 0.4–2.8 m). We discovered that STs exhibited higher accuracy compared to IRTs alone. Across the tested ranges of Tatm and RH, both IRTs exhibited absolute measurement errors of less than 0.97 °C, while both STs maintained absolute measurement errors of less than 0.12 °C. The optimal TETRS for EBT detection was 36–37 °C. When θ was below 30°, the two STs underestimated calibration source (CS) temperature (TCS) of less than 0.05 °C. The computer simulations showed absolute temperature differences of up to 0.28 °C and 0.04 °C between estimated and theoretical temperatures for IRTs and STs, respectively, considering d of 0.2–3.0 m, Tatm of 15–35 °C, and RH of 5–95%. The results highlight the importance of precise calibration and environmental control for reliable temperature readings and suggest proper ranges for these factors, aiming to enhance current standard documents and best practice guidelines. These insights enhance our understanding of IRT performance and their sensitivity to various factors, thereby facilitating the development of best practices for accurate EBT measurement.
Instrumented Flexible Glass Structure: A Bragg Grating Inscribed with Femtosecond Laser Used as a Bending Sensor
Authors: Amez-Droz Loïc , Tunon de Lara Matéo , Collette Christophe , Caucheteur Christophe , Lambert Pierre
Published: September 2023

Fused silica glass is a material with outstanding mechanical, thermal and optical properties. Being a brittle material, it is challenging to shape. In the last decade, the manufacturing of monolithic flexible mechanisms in fused silica has evolved with the femtosecond-laser-assisted etching process. However, instrumenting those structures is demanding. To address this obstacle, this article proposes to inscribe a Bragg Grating sensor inside a flexure and interface it with an optical fibre to record the strain using a spectrum analyser. The strain sensitivity of this Bragg Grating sensor is characterized at 1.2 pm/μϵ (1 μϵ = 1 microstrain). Among other applications, deformation sensing can be used to record a force. Its use as a micro-force sensor is estimated. The sensor resolution is limited by our recording equipment to 30 μN over a measurement range above 10 mN. This technology can offer opportunities for surgery applications or others where precision and stability in harsh environments are required.
Land and Seabed Surface Modelling in the Coastal Zone Using UAV/USV-Based Data Integration
Authors: Specht Oktawia
Published: September 2023

The coastal zone is an area that includes the sea coast and adjacent parts of the land and sea, where the mutual interaction of these environments is clearly marked. Hence, the modelling of the land and seabed parts of the coastal zone is crucial and necessary in order to determine the dynamic changes taking place in this area. The accurate determination of the terrain in the coastal zone is now possible thanks to the use of Unmanned Aerial Vehicles (UAVs) and Unmanned Surface Vehicles (USVs). The aim of this article is to present land and seabed surface modelling in the coastal zone using UAV/USV-based data integration. Bathymetric and photogrammetric measurements were carried out on the waterbody adjacent to a public beach in Gdynia (Poland) in 2022 using the DJI Phantom 4 Real Time Kinematic (RTK) UAV and the AutoDron USV. As a result of geospatial data integration, topo-bathymetric models in the coastal zone were developed using the following terrain-modelling methods: Inverse Distance to a Power (IDP), kriging, Modified Shepard’s Method (MSM) and Natural Neighbour Interpolation (NNI). Then, the accuracies of the selected models obtained using the different interpolation methods, taking into account the division into land and seabed parts, were analysed. Research has shown that the most accurate method for modelling both the land and seabed surfaces of the coastal zone is the kriging (linear model) method. The differences between the interpolated and measurement values of the R95 measurement are 0.032 m for the land part and 0.034 m for the seabed part. It should also be noted that the data interpolated by the kriging (linear model) method showed a very good fit to the measurement data recorded by the UAVs and USVs.
Measuring and Predicting Sensor Performance for Camouflage Detection in Multispectral Imagery
Authors: Hupel Tobias , Stütz Peter
Published: September 2023

To improve the management of multispectral sensor systems on small reconnaissance drones, this paper proposes an approach to predict the performance of a sensor band with respect to its ability to expose camouflaged targets under a given environmental context. As a reference for sensor performance, a new metric is introduced that quantifies the visibility of camouflaged targets in a particular sensor band: the Target Visibility Index (TVI). For the sensor performance prediction, several machine learning models are trained to learn the relationship between the TVI for a specific sensor band and an environmental context state extracted from the visual band by multiple image descriptors. Using a predicted measure of performance, the sensor bands are ranked according to their significance. For the training and evaluation of the performance prediction approach, a dataset featuring 853 multispectral captures and numerous camouflaged targets in different environments was created and has been made publicly available for download. The results show that the proposed approach can successfully determine the most informative sensor bands in most cases. Therefore, this performance prediction approach has great potential to improve camouflage detection performance in real-world reconnaissance scenarios by increasing the utility of each sensor band and reducing the associated workload of complex multispectral sensor systems.
Environmental, Social, and Corporate Governance (ESG), Life Cycle, and Firm Performance: Evidence from China
Authors: Qu Wen , Zhang Junrui
Published: September 2023

Interest in firms’ ESG performance is longstanding and growing. However, understanding remains fragmented across firms at different stages of their life cycle. This study investigates the role of life cycle stages in the relationship between ESG and firm performance in the Chinese context. Using a sample of 26,412 firm–year observations of firms listed in China from 2011 to 2021, the results provide evidence that, although ESG has a significant positive effect on firm performance, the effect is contingent on the stages of the firm’s life cycle. The association between ESG and firm performance is more pronounced for firms in the growth and mature phases. We also find that this effect is enhanced for state-owned growth firms localized in regions with a lower marketization degree. Furthermore, the findings also extend the distinct mediation roles of institutional investors and the agency cost of free cash flow in the nexus of ESG and firm performance. This paper provides direct empirical evidence for a better understanding of firm’s behavior across different life cycle stages, as well as helps to achieve a win–win situation of firm performance and social value.
Evaluation of Geospatial Interpolation Techniques for Enhancing Spatiotemporal Rainfall Distribution and Filling Data Gaps in Asir Region, Saudi Arabia
Authors: Helmi Ahmed M. , Elgamal Mohamed , Farouk Mohamed I. , Abdelhamed Mohamed S. , Essawy Bakinam T.
Published: September 2023

Providing an accurate spatiotemporal distribution of rainfall and filling data gaps are pivotal for effective water resource management. This study focuses on the Asir region in the southwest of Saudi Arabia. Given the limited accuracy of satellite data in this arid/mountain-dominated study area, geospatial interpolation has emerged as a viable alternative approach for filling terrestrial records data gaps. Furthermore, the irregularity in rain gauge data and the yearly spatial variation in data gaps hinder the creation of a coherent distribution pattern. To address this, the Centered Root Mean Square Error (CRMSE) is employed as a criterion to select the most appropriate geospatial interpolation technique among 51 evaluated methods for maximum and total yearly precipitation data. This study produced gap-free maps of total and maximum yearly precipitation from 1966 to 2013. Beyond 2013, it is recommended to utilize ordinary Kriging with a J-Bessel semivariogram and simple Kriging with a K-Bessel semivariogram to estimate the spatial distribution of maximum and total yearly rainfall depth, respectively. Additionally, a proposed methodology for allocating additional rain gauges to improve the accuracy of rainfall spatial distribution is introduced based on a cross-validation error (CVE) assessment. Newly proposed gauges in the study area resulted in a significant 21% CVE reduction.
Dynamic Modulus Prediction Validation for the AASHTOWare Pavement ME Design Implementation in Egypt
Authors: Saudy Maram , Breakah Tamer , El-Badawy Sherif
Published: September 2023

Dynamic Modulus, E* is a crucial property of the hot mix asphalt (HMA). For the AASHTOWare Pavement ME design, E* is an essential material input. E* can be measured in the laboratory or predicted using different models based on some fundamental properties of the HMA. The NCHRP 1-37A and NCHRP 1-40D prediction models are the two main models adopted by the AASHTOWare to predict the E* based on the HMA mixture volumetrics, gradation, and binder properties. The main objective of this research was to validate these two prediction models using local HMA mixes for the purpose of the regional application of the AASHTOWare Pavement ME design in Egypt. For this purpose, the E* values of ten locally plant-produced HMA mixes were measured in the laboratory. The two E* prediction models were then used to estimate the E* values for the same materials. Consequently, the performance of both models was studied by comparing the measured values to the estimated values. The results showed that the NCHRP 1-40D prediction model can satisfactorily predict the E* of the Egyptian HMA mixes with minimal bias and high accuracy. The model yielded an adjusted coefficient of determination (R2) of 0.86 based on 480 E* measurements. On the other hand, the NCHRP 1-37A prediction accuracy was not satisfactory, with very poor accuracy (Adjusted R2 = 0.18) and high bias. Afterwards, the effect of the predicted E* from the NCHRP 1-40D model on the AASHTOWare Pavement ME predicted pavement performance in terms of rutting, cracking, and roughness was further studied. Accordingly, twenty-four simulation runs for typical Egyptian design cases were conducted using, first, the laboratory measured E* values and, then, the NCHRP 1-40D predicted E* values. The results showed that the NCHRP 1-40D predictions had no significant effect on the pavement performance predicted by the AASHTOWare Pavement ME with R2 of the different pavement distresses ranged from 0.980, for the AC rutting, to 0.9996 for the International Roughness Index (IRI). Hence, the NCHRP 1-40D model can be used satisfactorily to predict E* for the Egyptian HMA mixes without compromising the structural pavement design.
A Novel Approach for High-Performance Estimation of SPI Data in Drought Prediction
Authors: Latifoğlu Levent , Özger Mehmet
Published: September 2023

Drought, as a natural disaster, has significant negative consequences and directly impacts living organisms. Drought forecasting commonly relies on various drought indices, with the Standardized Precipitation Index (SPI) being widely used. In this study, we propose a novel approach to estimate SPI values at 3- and 6-month lead times with high accuracy. This novel method introduces a phase transfer entropy (pTE) technique that analyzes time-shifted data matrices and the connectivity of SPI-3 and SPI-6 data. By maximizing the information flow between these data points, the most suitable time index (t − n) for input data in forecasting models is determined. This approach, not previously explored in the literature, forms the basis for predicting SPI values effectively. Machine learning algorithms, in combination with the Tunable Q Factor Wavelet Transform (TQWT) optimized by the Grey Wolf Optimization (GWO) algorithm, are employed to predict SPI values using the identified input data. The TQWT method generates subband signals, which are then estimated using Artificial Neural Networks (ANN), Support Vector Regression (SVR), and the Gaussian Process Regression Model (GPR). To evaluate the performance of the proposed GWO-TQWT-ML models, the subband data derived from the SPI is also estimated using ANN, GPR, and SVR models with the Empirical Mode Decomposition and Variational Mode Decomposition methods. Additionally, non-preprocessed SPI data is estimated independently using ANN, GPR, and SVR models. The results demonstrate the superior performance of the pTE-GWO-TQWT-ML models over other methods. Among these models, the pTE-GWO-TQWT-GPR model stands out with the best prediction performance, surpassing both the pTE-GWO-TQWT-ANN and pTE-GWO-TQWT-SVR models. The pTE-GWO-TQWT-GPR model yielded determination coefficient (R2) values for SPI-6 data as follows: 0.8039 for one-input, 0.9987 for two-input, and 0.9998 for three-input one ahead prediction, respectively; 0.9907 for two-input two ahead prediction; and 0.9722 for two-input three ahead prediction. For SPI-3 data, using the pTE-GWO-TQWT-GPR model, the R2 values were as follows: 0.6805 for one-input, 0.9982 for two-input, 0.9996 for three-input one ahead prediction, 0.9843 for two-input two ahead prediction, 0.9535 for two-input three ahead prediction, 0.9963 for three-input two ahead prediction, and 0.9826 for three-input three ahead prediction. Overall, this study presents a robust method, the pTE-GWO-TOWT-GPR model, for the time series estimation of SPI data, enabling high-performance drought prediction.
Influence of Leadership Styles on Sustainable Development for Social Reconstruction: Current Outcomes and Advisable Reorientation for Two Aerospace Multinationals—Airbus and TASL
Authors: García Martín Rafael , Duran-Heras Alfonso , Reina Sánchez Karen
Published: September 2023

This article analyzes the influence and foreseeable evolution of three main leadership styles in sustainable development for social reconstruction in two leading companies, the European Airbus and the Indian Tata Advanced Systems Ltd. (TASL) (Hyderabad, India). Inductive research was employed, using two cross-national case studies and a comparative analysis applying statistical testing. Data were collected through interviews and a survey among principal company leaders with several functions. The study focused on three styles: transactional, transformational, and servant leadership. The strength of this paper is that it sheds light on an understudied industry by providing findings on both the present (as-is) state and the advisable reorientation (i.e., future state suggested by respondents). This could contribute to filling the current gap in understanding the influence of leadership styles on the aerospace and defense industries’ economic, social, and environmental outcomes. By analyzing the results obtained in both companies, we concluded that even though all three styles are present in both companies, servant and transformational leadership provide better results for sustainable development for social reconstruction. Regarding the advisable reorientation, according to the respondent’s suggestions, the style best suited to influence sustainable development for social reconstruction in the long term in both companies is the servant style. As for the transitional phase, respondents suggest a hybrid leadership model, encompassing features of both the transformational and the servant styles.
Exploring Land-Cover Types and Their Changes in the Open-Pit Mining Area of Ordos City Using Sentinel-2 Imagery
Authors: Zhu Linye , Zhang Yonggui , Chen Kewen , Liu Qiang , Sun Wenbin
Published: September 2023

Exploring the land-cover types and their changes in open-pit mining areas is of great significance to the ecological restoration of Ordos City and its sustainable development. Therefore, in this study, the temporal features, spectral features, and the derived features from intra-annual images combined with the random forest method were used to determine the land-cover types and their changes in the open-pit mining area of Ordos City in 2019 and 2022. The results show that the land-cover types in the open-pit mining area of Ordos City are construction land, bare land, water, vegetation, and coal. The main land-cover types in the western open-pit mining area of Ordos City are different from those in the eastern open-pit mining area. The main land-cover type changes in the open-pit mining area from 2019 to 2022 are the conversion of bare land to vegetation and the conversion of coal to bare land, as well as the conversion of vegetation to bare land. This study provides the possibility of dynamic monitoring for the realization of green mine planning in Ordos City.
Carbon Emissions Assessment for Building Decoration Based on Life Cycle Assessment: A Case Study of Office Buildings
Authors: Wu Huanyu , Zhou Wenwen , Chen Kunyang , Zhang Lianxiang , Zhang Zicheng , Li Yanqiu , Hu Zhijun
Published: September 2023

The continuous growth of interior decoration activities has caused a massive consumption of energy and materials, which has contributed to a large amount of carbon emissions in the construction sector. The carbon emissions of building decoration were overlooked in previous studies. Hence, the life cycle assessment (LCA) approach was employed to build a life cycle carbon emissions model for building decoration. An office building was selected to verify the availability. The results show that the carbon emissions intensity of the decoration project was 254.5 kg CO2 eq/m2. The operation stage was the most crucial carbon emissions contributor in the life cycle of building decoration, accounting for 49.8%; followed by the materials embodied impact stage, which contributed 36.3%; while the remaining three stages, namely, the decoration, transportation, and end-of-life stage, had less carbon emissions, accounting for 6.8%, 5.3%, and 1.8%. Improving the performance of inorganic materials, optimizing transportation routes and energy structure, and dismantling plan optimization can reduce carbon emissions. The findings of this study provide a theoretical basis and fundamental data for carbon emissions reduction and sustainable development strategies for building decoration.
Technology of Input–Output Analysis with CES Production: Application for Studying the Kazakhstan Supply Chain during the COVID-19 Pandemic
Authors: Boranbayev Askar , Obrosova Nataliia , Shananin Alexander
Published: September 2023

Input–output analysis finds widespread application in estimating the shock effects on production networks within both local and global economies. We are developing a new technology for intersectoral analysis that takes into account the substitution of production factors within a complex supply network triggered by external or internal shocks. This technology is based on the explicit solution of a pair of convex programming problems: the resource allocation problem under the assumption of Constant Elasticity of Substitution (CES) technologies and the special dual Young problem. Solving these problems, we can ascertain the equilibrium inputs and price indexes of goods within the production network. In this paper, we apply this technology to analyze the economy of Kazakhstan in the context of the COVID-19 pandemic. Our calculations provide us with the means to discuss the macroeconomic responses of the multi-sectoral production network in Kazakhstan to both external and internal shocks stemming from the pandemic.