bluuebunny/crossref_metadata_2025 huggingface dataset:
Negr1-Derived Peptides Trigger ALK Degradation and Halt Neuroblastoma Progression In Vitro and In Vivo
Authors: Pischedda Francesca , Ghirelli Alessia , Tripathi Vasvi , Piccoli Giovanni
Published: September 2023

Neuroblastoma is among the most common childhood cancers. Neuroblastoma in advanced stages is one of the most intractable pediatric cancers, notwithstanding the recent therapeutic advances. ALK mutations are among the leading cause of hereditary neuroblastoma and account for more than 14% of the somatically acquired alterations. ALK kinase activity is currently one of the main targets for pharmacological strategies. However, evidence from ALK fusion-positive lung cancer studies has shown that resistance to ALK inhibition arises during the therapy, causing a relapse within several years. IgLONs are membrane-bound proteins involved in cell-to-cell adhesion. The expression of the IgLON family results altered in different cancers. We found that the IgLON member Negr1 is downregulated in neuroblastoma. The ectopic overexpression of Negr1 impairs neuroblastoma growth in vitro and in vivo. Negr1 exists as a GPI-anchored membrane-bound protein and as a soluble protein released upon metalloprotease cleavage. We generated and characterized a panel of Negr1-derived peptides. The treatment with Negr1 protein and derived peptides induce ALK downregulation and halt neuroblastoma progression in vitro and in vivo.
Nanomaterial-Based Drug Delivery Systems for Pain Treatment and Relief: From the Delivery of a Single Drug to Co-Delivery of Multiple Therapeutics
Authors: Xu Yuhang , Dong Xingpeng , Xu Heming , Jiao Peifu , Zhao Lin-Xia , Su Gaoxing
Published: September 2023

The use of nanomaterials in drug delivery systems for pain treatment is becoming increasingly common. This review aims to summarize how nanomaterial-based drug delivery systems can be used to effectively treat and relieve pain, whether via the delivery of a single drug or a combination of multiple therapeutics. By utilizing nanoformulations, the solubility of analgesics can be increased. Meanwhile, controlled drug release and targeted delivery can be realized. These not only improve the pharmacokinetics and biodistribution of analgesics but also lead to improved pain relief effects with fewer side effects. Additionally, combination therapy is frequently applied to anesthesia and analgesia. The co-encapsulation of multiple therapeutics into a single nanoformulation for drug co-delivery has garnered significant interest. Numerous approaches using nanoformulation-based combination therapy have been developed and evaluated for pain management. These methods offer prolonged analgesic effects and reduced administration frequency by harnessing the synergy and co-action of multiple targets. However, it is important to note that these nanomaterial-based pain treatment methods are still in the exploratory stage and require further research to be effectively translated into clinical practice.
Pyrenebutyrate Pt(IV) Complexes with Nanomolar Anticancer Activity
Authors: Ahmedova Anife , Mihaylova Rositsa , Stoykova Silviya , Mihaylova Veronika , Burdzhiev Nikola , Elincheva Viktoria , Momekov Georgi , Momekova Denitsa
Published: September 2023

Research on platinum-based anticancer drugs continuously strives to develop new non-classical platinum complexes. Pt(IV) prodrugs are the most promising, and their activation-by-reduction mechanism of action is being explored as a prospect for higher selectivity and efficiency. Herein, we present the anticancer potency and chemical reactivity of Pt(IV) complexes formed by linking pyrene butyric acid with cisplatin. The results from cytotoxicity screening on 10 types of cancer cell lines and non-malignant cells (HEK-293) indicated IC50 values as low as 50–70 nM for the monosubstituted Pt(IV) complex against leukemia cell lines (HL-60 and SKW3) and a cisplatin-resistant derivative (HL-60/CDDP). Interestingly, the bis-substituted complex is virtually non-toxic to both healthy and cancerous cells of adherent types. Nevertheless, it shows high cytotoxicity against multidrug-resistant derivatives HL-60/CDDP and HL-60/Dox. The reactivity of the complexes with biological reductants was monitored by the NMR method. Furthermore, the platinum uptake by the treated cells was examined on two types of cellular cultures: adherent and suspension growing, and proteome profiling was conducted to track expression changes of key apoptosis-related proteins in HL-60 cells. The general conclusion points to a possible cytoskeletal entrapment of the bulkier bis-pyrene complex that could be limiting its cytotoxicity to adherent cells, both cancerous and healthy ones.
Calibration of Waveplate Retardance Fluctuation Due to Field-of-View Effect in Mueller Matrix Ellipsometer
Authors: Jiang Zhou , Zhang Song , Jiang Hao , Liu Shiyuan
Published: September 2023

Leveraging their unique phase modulation characteristics, birefringent waveplates have been widely used in various optical systems. With the development of material science and manufacturing techniques, the polarization properties of waveplates have become increasingly complex and diverse. Among these properties, the field-of-view effect of the waveplate caused due to manufacturing defects or improper installation procedures is extremely difficult to calibrate and seriously affects the precision and accuracy of the relevant optical systems. In this paper, a calibration method that can compensate for the field-of-view effect of waveplates installed in the instrument is proposed. Moreover, to approve the fidelity of the proposed calibration method, a series of film thickness measurement experiments are carried out. The results show that under different installation conditions of the waveplates, the precision and accuracy of the film thickness measured with the proposed method significantly improved. This method can be expected to reduce the assembly difficulty of such optical systems, while also improving their accuracy and stability.
Piston Detection of Optical Sparse Aperture Systems Based on an Improved Phase Diversity Method
Authors: Zhao Yang , Li Jiabiao , Liu Tai , Tan Xiangquan , Xu Zhenbang , Wu Qingwen
Published: September 2023

The piston error has a significant effect on the imaging resolution of the optical sparse aperture system. In this paper, an improved phase diversity method based on particle swarm optimization and the sequential quadratic programming algorithm is proposed, which can overcome the drawbacks of the traditional phase diversity method and particle swarm optimization, such as the instability that results from polychromatic light conditions and premature convergence. The method introduces factor β in the stage of calculating the objective function, and combines the advantages of a heuristic algorithm and a nonlinear programming algorithm in the optimization stage, thus enhancing the accuracy and stability of piston detection. Simulations based on a dual-aperture optical sparse aperture system verified that the root mean square error obtained by the method can be guaranteed to be within 0.001λ (wavelength), which satisfies the requirement of practical imaging. An experimental test was also conducted to demonstrate the performance of the method, and the test results showed that the quality of the image after piston detection and correction improved significantly compared to images with the co-phase error.
A Systematic Summary and Comparison of Scalar Diffraction Theories for Structured Light Beams
Authors: Wu Fuping , Luo Yi , Cui Zhiwei
Published: September 2023

Structured light beams have recently attracted enormous research interest for their unique properties and potential applications in optical communications, imaging, sensing, etc. Since most of these applications involve the propagation of structured light beams, which is accompanied by the phenomenon of diffraction, it is very necessary to employ diffraction theories to analyze the obstacle effects on structured light beams during propagation. The aim of this work is to provide a systematic summary and comparison of the scalar diffraction theories for structured light beams. We first present the scalar fields of typical structured light beams in the source plane, including the fundamental Gaussian beams, higher-order Hermite–Gaussian beams, Laguerre–Gaussian vortex beams, non-diffracting Bessel beams, and self-accelerating Airy beams. Then, we summarize and compare the main scalar diffraction theories of structured light beams, including the Fresnel diffraction integral, Collins formula, angular spectrum representation, and Rayleigh–Sommerfeld diffraction integral. Finally, based on these theories, we derive in detail the analytical propagation expressions of typical structured light beams under different conditions. In addition, the propagation of typical structured light beams is simulated. We hope this work can be helpful for the efficient study of the propagation of structured light beams.
Secondary Metabolites and Their Role in Strawberry Defense
Authors: Badmi Raghuram , Gogoi Anupam , Doyle Prestwich Barbara
Published: September 2023

Strawberry is a high-value commercial crop and a model for the economically important Rosaceae family. Strawberry is vulnerable to attack by many pathogens that can affect different parts of the plant, including the shoot, root, flowers, and berries. To restrict pathogen growth, strawberry produce a repertoire of secondary metabolites that have an important role in defense against diseases. Terpenes, allergen-like pathogenesis-related proteins, and flavonoids are three of the most important metabolites involved in strawberry defense. Genes involved in the biosynthesis of secondary metabolites are induced upon pathogen attack in strawberry, suggesting their transcriptional activation leads to a higher accumulation of the final compounds. The production of secondary metabolites is also influenced by the beneficial microbes associated with the plant and its environmental factors. Given the importance of the secondary metabolite pathways in strawberry defense, we provide a comprehensive overview of their literature and their role in the defense responses of strawberry. We focus on terpenoids, allergens, and flavonoids, and discuss their involvement in the strawberry microbiome in the context of defense responses. We discuss how the biosynthetic genes of these metabolites could be potential targets for gene editing through CRISPR-Cas9 techniques for strawberry crop improvement.
Progress in Adzuki Bean Seed Coat Colour Studies
Authors: Wang Zhen , Zhao Wei , Huang Yufei , Zhao Pu , Yang Kai , Wan Ping , Chu Liwei
Published: September 2023

Seed coat colour is an important quality trait, domestication trait, and morphological marker, and it is closely associated with flavonoid and anthocyanin metabolism pathways. The seed coat colour of the adzuki bean, an important legume crop, influences the processing quality, the commodity itself, and its nutritional quality. In this review, a genetic analysis of different seed coat colours, gene mapping, metabolite content determination, and varietal improvement in adzuki bean are summarized. It provides further insight into gene mapping and cloning of seed coat colour genes and varietal improvements in adzuki beans.
Modeling and Simulation of a Multizone Circulating Reactor for Polyethylene Production with Internal Cooling
Authors: Ghasem Nayef
Published: September 2023

Polyolefins play a role in industries and are typically manufactured using two types of reactors: high-pressure tubular reactors and fluidized bed reactors. An innovative technology called the Multizone Circulating reactor (MZCR) has emerged, which introduces an innovative approach with interconnected polymerization zones creating a continuous loop of polymer flow. This study focuses on modeling and simulating ethylene gas phase polymerization within the MZCR in the presence of internal cooling to gain insights into its behavior. To achieve this, a comprehensive computational fluid dynamics (CFD) simulation was developed. It considered momentum, material, and energy balance aspects. The model equations were solved using the finite difference method in COMSOL Multiphysics version 6.1. The investigation primarily focused on studying the impact of incorporating a cooler into the riser section on the temperature profile within the reactor and ethylene conversion. The presence of this cooler resulted in a reduction in temperature change along the riser from approximately 8.0 °C to 4.0 °C. Moreover, it led to an increase of 7%, in ethylene single-pass conversion.
Poly(3-hydroxybutyrate-co-3-hydroxyhexanoate) Bionanocomposites with Crystalline Nanocellulose and Graphene Oxide: Experimental Results and Support Vector Machine Modeling
Authors: Champa-Bujaico Elizabeth , Díez-Pascual Ana M. , Garcia-Diaz Pilar
Published: September 2023

Poly(3-hydroxybutyrate-co-3-hydroxyhexanoate) (PHBHHx) is a biodegradable and biocompatible bacterial copolymer used in the biomedical and food industries. However, it displays low stiffness and strength for certain applications. This issue can be solved via reinforcement with nanofillers. In this work, PHBHHx-based bionanocomposites reinforced with different loadings of crystalline nanocellulose (CNC) and graphene oxide (GO) were developed by a green and straightforward solution casting technique. Their crystalline nature and surface topography were explored via X-ray diffraction (XRD) and field-emission scanning electron microscopy (FE-SEM), respectively, their composition was corroborated via Fourier-transformed infrared spectroscopy (FTIR), and their crystallization and melting behavior were determined via differential scanning calorimetry (DSC). The nanofillers had a nucleating role, raising the crystallization temperature of the polymer, whilst hardly any changes were found in the melting temperature. Further, significant enhancements in the stiffness, strength, and thermal stability of the PHBHHx matrix were observed with the incorporation of both nanofillers, which was attributed to a synergic effect. The mechanical properties for various concentrations of CNC and GO were accurately predicted using a machine learning (ML) model in the form of a support vector machine (SVM). The model performance was evaluated in terms of the mean absolute error (MAE), the mean square error (MSE), and the correlation coefficient (R2). These bio-based nanocomposites are a valuable alternative to conventional petroleum-based synthetic polymeric materials used nowadays for biomedicine and food packaging applications.
Biomass-Based Shape-Stabilized Composite Phase-Change Materials with High Solar–Thermal Conversion Efficiency for Thermal Energy Storage
Authors: Gao Ning , Du Jiaoli , Yang Wenbo , Li Youbing , Chen Ning
Published: September 2023

To alleviate the increasing energy crisis and achieve energy saving and consumption reduction in building materials, preparing shape-stabilized phase-change materials using bio-porous carbon materials from renewable organic waste to building envelope materials is an effective strategy. In this work, pine cone porous biomass carbon (PCC) was prepared via a chemical activation method using renewable biomaterial pine cone as a precursor and potassium hydroxide (KOH) as an activator. Polyethylene glycol (PEG) and octadecane (OD) were loaded into PCC using the vacuum impregnation method to prepare polyethylene glycol/pine cone porous biomass carbon (PEG/PCC) and octadecane/pine cone porous biomass carbon (OD/PCC) shape-stabilized phase-change materials. PCCs with a high specific surface area and pore volume were obtained by adjusting the calcination temperature and amount of KOH, which was shown as a caterpillar-like and block morphology. The shape-stabilized PEG/PCC and OD/PCC composites showed high phase-change enthalpies of 144.3 J/g and 162.3 J/g, and the solar–thermal energy conversion efficiencies of the PEG/PCC and OD/PCC reached 79.9% and 84.8%, respectively. The effects of the contents of PEG/PCC and OD/PCC on the temperature-controlling capability of rigid polyurethane foam composites were further investigated. The results showed that the temperature-regulating and temperature-controlling capabilities of the energy-storing rigid polyurethane foam composites were gradually enhanced with an increase in the phase-change material content, and there was a significant thermostatic plateau in energy absorption at 25 °C and energy release at 10 °C, which decreased the energy consumption.
Applying a Novel Image Recognition Curve-Fitting Control Strategy Combined with a Cloud Monitoring Technique into an Electric Self-Driving Vehicle (ESDV) to Improve Its Operation Efficiency
Authors: Liu Hwa-Dong , Lin Ping-Jui , Lai Shan-Xun , Lin Chang-Hua , Farooqui Shoeb-Azam
Published: September 2023

This study aims to develop an image recognition curve-fitting (IRCF) control strategy integrated with a cloud monitoring technique for application in electric self-driving vehicles (ESDVs) to improve their operation efficiency. The study focuses on an electric vehicle designed to reduce the carbon emissions and promote sustainability. The main camera, combined with the IRCF control strategy, was used to control the ESDV to enhance its operational efficiency. The proposed ESDV employs a pair of cameras to capture images and transmit them to the cloud-based web monitoring platform in real time. This allows the researchers to adjust the control parameters and promptly remove the road obstacles. The ESDV is equipped with a horn, two ultrasonic sensors, and an LED display, which can instantly detect the obstacles ahead of and behind the vehicle. When there are obstacles on the road, the vehicle will automatically stop, and the LED display will provide a visual representation of the obstacles, accompanied by the sounding of the horn as a warning signal. Meanwhile, the secondary camera detects the signal mark and feeds it back to the LED display, thereby informing passengers and other road users about the prevailing driving conditions. The proposed IRCF control strategy was compared with the traditional Hough line detection method on a 110 m ring road. The results revealed that the proposed control strategy outperformed the traditional Hough line detection method in terms of speed, efficiency, and running dexterity. Therefore, integrating the proposed control strategy into the automatic assistance driving system can improve the ESDV’s operation efficiency. Furthermore, the combination of the obstacle detection and signal sign detection functions for the ESDV used in this study can better fulfill the actual ESDV operation requirements on the road.
Model-Based Condition Monitoring of Modular Process Plants
Authors: Wetterich Philipp , Kuhr Maximilian M. G. , Pelz Peter F.
Published: September 2023

The process industry is confronted with rising demands for flexibility and efficiency. One way to achieve this is modular process plants, which consist of pre-manufactured modules with their own decentralized intelligence. Plants are then composed of these modules as unchangeable building blocks and can be easily re-configured for different products. Condition monitoring of such plants is necessary, but the available solutions are not applicable. The authors of this paper suggest an approach in which model-based symptoms are derived from a few measurements and observers that are based on the manufacturer’s knowledge. The comparisons of redundant observers lead to residuals that are classified to obtain symptoms. These symptoms can be communicated to the plant control and are inputs to an easily adaptable diagnosis. The implementation and validation at a modular mixing plant showcase the feasibility and potential of this approach.
Is Gentamicin Elution Influenced by the Timing of Antibiotic Addition to the Bone Cement? An In Vitro Study on Articulating Hip Spacers
Authors: Cacciola Giorgio , Bosco Francesco , Giustra Fortunato , De Meo Federico , Bruschetta Antongiulio , Sabatini Luigi , Artiaco Stefano , Giraldo Diogenes , Massè Alessandro , Cavaliere Pietro
Published: September 2023

Periprosthetic Joint Infection (PJI) is a challenging complication after joint replacements, and cement spacers are standard treatment in two-stage revision surgery. This experimental in vitro study aimed to evaluate the elution properties of different gentamicin formulations from commercially available intraoperative molded hip cement spacers. The study compared spacers prepared with premixed antibiotic bone cement, spacers with antibiotic powder added during preparation, spacers stored for two months, and spacers with an additional antibiotic. The results showed that the timing of antibiotic addition influenced gentamicin elution, with immediate elution resulting in higher levels than stored spacers. Spacers with antibiotic powder added during preparation exhibited higher elution than premixed antibiotic spacers. Furthermore, adding vancomycin to the bone cement significantly increased gentamicin elution. These findings suggest that optimizing the timing and method of antibiotic addition in cement spacers may enhance the effectiveness of antibiotic treatment in PJI. However, further research is needed to validate these findings and explore their clinical implications.
Introduction: Religion and Human Rights in Post-Communism
Authors: Stan Lavinia , Murzaku Ines Angeli
Published: September 2023

In this Special Issue, “religion” is understood as a set of beliefs and values and a corresponding way of life that embodies and enacts these values, including an understanding of the world [...]
Urban Vegetation Extraction from High-Resolution Remote Sensing Imagery on SD-UNet and Vegetation Spectral Features
Authors: Lin Na , Quan Hailin , He Jing , Li Shuangtao , Xiao Maochi , Wang Bin , Chen Tao , Dai Xiaoai , Pan Jianping , Li Nanjie
Published: September 2023

Urban vegetation plays a crucial role in the urban ecological system. Efficient and accurate extraction of urban vegetation information has been a pressing task. Although the development of deep learning brings great advantages for vegetation extraction, there are still problems, such as ultra-fine vegetation omissions, heavy computational burden, and unstable model performance. Therefore, a Separable Dense U-Net (SD-UNet) was proposed by introducing dense connections, separable convolutions, batch normalization layers, and Tanh activation function into U-Net. Furthermore, the Fake sample set (NIR-RG), NDVI sample set (NDVI-RG), and True sample set (RGB) were established to train SD-UNet. The obtained models were validated and applied to four scenes (high-density buildings area, cloud and misty conditions area, park, and suburb) and two administrative divisions. The experimental results show that the Fake sample set can effectively improve the model’s vegetation extraction accuracy. The SD-UNet achieves the highest accuracy compared to other methods (U-Net, SegNet, NDVI, RF) on the Fake sample set, whose ACC, IOU, and Recall reached 0.9581, 0.8977, and 0.9577, respectively. It can be concluded that the SD-UNet trained on the Fake sample set not only is beneficial for vegetation extraction but also has better generalization ability and transferability.
Updating of the Archival Large-Scale Soil Map Based on the Multitemporal Spectral Characteristics of the Bare Soil Surface Landsat Scenes
Authors: Rukhovich Dmitry I. , Koroleva Polina V. , Rukhovich Alexey D. , Komissarov Mikhail A.
Published: September 2023

For most of the arable land in Russia (132–137 million ha), the dominant and accurate soil information is stored in the form of map archives on paper without coordinate reference. The last traditional soil map(s) (TSM, TSMs) were created over 30 years ago. Traditional and/or archival soil map(s) (ASM, ASMs) are outdated in terms of storage formats, dates, and methods of production. The technology of constructing a multitemporal soil line (MSL) makes it possible to update ASMs and TSMs based on the processing of big remote-sensing data (RSD). To construct an MSL, the spectral characteristics of the bare soil surface (BSS) are used. The BSS on RSD is distinguished within the framework of the conceptual apparatus of the spectral neighborhood of the soil line. The filtering of big RSD is based on deep machine learning. In the course of the work, a vector georeferenced version of the ASM and an updated soil map were created based on the coefficient “C” of the MSL. The maps were verified based on field surveys (76 soil pits). The updated map is called the map of soil interpretation of the coefficient “C” (SIC “C”). The SIC “C” map has a more detailed legend compared to the ASM (7 sections/chapters instead of 5), greater accuracy (smaller errors of the first and second kind), and potential suitability for calculating soil organic matter/carbon (SOM/SOC) reserves (soil types/areals in the SIC “C” map are statistically significant are divided according to the thickness of the organomineral horizon and the content of SOM in the plowed layer). When updating, a systematic underestimation of the numbers of contours and areas of soils with manifestations of negative/degradation soil processes (slitization and erosion) on the TSM was established. In the process of updating, all three shortcomings of the ASMs/TSMs (archaic storage, dates, and methods of creation) were eliminated. The SIC “C” map is digital (thematic raster), modern, and created based on big data processing methods. For the first time, the actualization of the soil map was carried out based on the MSL characteristics (coefficient “C”).
Mapping Post-Earthquake Landslide Susceptibility Using U-Net, VGG-16, VGG-19, and Metaheuristic Algorithms
Authors: Shafapourtehrany Mahyat , Rezaie Fatemeh , Jun Changhyun , Heggy Essam , Bateni Sayed M. , Panahi Mahdi , Özener Haluk , Shabani Farzin , Moeini Hamidreza
Published: September 2023

Landslides are among the most frequent secondary disasters caused by earthquakes in areas prone to seismic activity. Given the necessity of assessing the current seismic conditions for ensuring the safety of life and infrastructure, there is a rising demand worldwide to recognize the extent of landslides and map their susceptibility. This study involved two stages: First, the regions prone to earthquake-induced landslides were detected, and the data were used to train deep learning (DL) models and generate landslide susceptibility maps. The application of DL models was expected to improve the outcomes in both stages. Landslide inventory was extracted from Sentinel-2 data by using U-Net, VGG-16, and VGG-19 algorithms. Because VGG-16 produced the most accurate inventory locations, the corresponding results were used in the landslide susceptibility detection stage. In the second stage, landslide susceptibility maps were generated. From the total measured landslide locations (63,360 cells), 70% of the locations were used for training the DL models (i.e., convolutional neural network [CNN], CNN-imperialist competitive algorithm, and CNN-gray wolf optimizer [GWO]), and the remaining 30% were used for validation. The earthquake-induced landslide conditioning factors included the elevation, slope, plan curvature, valley depth, topographic wetness index, land cover, rainfall, distance to rivers, and distance to roads. The reliability of the generated susceptibility maps was evaluated using the area under the receiver operating characteristic curve (AUROC) and root mean square error (RMSE). The CNN-GWO model (AUROC = 0.84 and RMSE = 0.284) outperformed the other methods and can thus be used in similar applications. The results demonstrated the efficiency of applying DL in the natural hazard domain. The CNN-GWO predicted that approximately 38% of the total area consisted of high and very high susceptibility regions, mainly concentrated in areas with steep slopes and high levels of rainfall and soil wetness. These outcomes contribute to an enhanced understanding of DL application in the natural hazard domain. Moreover, using the knowledge of areas highly susceptible to landslides, officials can actively adopt steps to reduce the potential impact of landslides and ensure the sustainable management of natural resources.
Task Assignment Optimization in Multi-UAV-Assisted WSNs Considering Energy Budget and Sensor Distribution Characteristics
Authors: Xie Qile , Zhao Wendong , Liu Cuntao , Peng Laixian
Published: September 2023

In emergency situations, such as disaster area monitoring, deadlines for data collection are strict. The task time minimization problem concerning multi-UAV-assisted data collection in wireless sensor networks (WSNs), with different distribution characteristics, such as the geographical or importance of the information of the sensors, is studied. Our goal is to minimize the mission time for UAVs by optimizing their assignment, trajectory, and deployment locations, while the UAV energy constraint is taken into account. For the coupling relationship between the task assignment, trajectory, and hover position, it is not easy to solve the mixed integer non-convex problem directly. The problem is divided into two sub-problems: (1) UAV task assignment problem and (2) trajectory and hover position optimization problem. To solve this problem, an assignment algorithm, based on sensor distribution characteristics (AASDC), is proposed. The simulation results show that the collection time of our scheme is shorter than that of existing comparison schemes when using the same data size.
A New Sample Processing Protocol for Separation and Purification Enabling Precise Analysis of Various Non-Traditional Isotopes in Geological Samples with Low Concentrations
Authors: Gui Jianye , Zou Wei
Published: September 2023

Many non-traditional isotopes, such as chlorine, magnesium, calcium, etc., are widely used as groundwater tracers. A new sample processing protocol of purification and concentration for isotopic analysis is presented to overcome many of the major drawbacks of existing methods. Contemporary sample preparation often requires several laborious off-line procedures in a ultra clean laboratory prior to instrumental determination; additionally, interference ions in real samples are difficult to completely remove, especially when the concentration of those ions is equal to that of the target ions. The new protocol includes the following steps: (i) one-step purification using a newly developed isotopic preparative chromatograph (IPC) with a background suppressed mode to obtain extremely pure components that only have target ions and H2O; (ii) enrichment of the collected pure solution from the previous step using a newly developed ultra clean concentrator filled with high purity nitrogen; (iii) transforming the enriched target ion into suitable speciation inside the ultra clean concentrator; (iv) finally, sending the enriched solutions to a multi-collector inductively coupled-plasma mass-spectrometer (MC-ICP-MS) or thermal ionization mass spectrometer (TIMS). The present method was validated using certified reference materials and real samples for both chlorine and magnesium; the precision of chlorine ratio value was generally below 0.22‰ and that of Mg was below 0.12‰. This processing protocol provides a potential method for isotope sample preparation and analysis in a small number of geological samples with low concentrations of many other elements or compounds such as nitrate, sulfate, lithium, calcium, strontium, etc.
Towards Sustainability: A Quantitative Inquiry into Chinese University Students’ Perceived Learner Empowerment and Innovative Behaviour
Authors: Han Jiying , Zhang Zhe , Liu Zhenmei , Gao Chao
Published: September 2023

Innovation is universally acclaimed as the crux of organisational sustainability in the current dynamic and competitive market and knowledge economy, and psychological empowerment provides a more comprehensive pathway to human creative performance. However, the pivotal role of students’ innovation in driving sustainability has been largely overlooked within the context of higher education for sustainable development (HESD). This study investigated the relationship between university students’ perceived learner empowerment and their innovative behaviour from a multistage perspective. An online survey was conducted among 868 Chinese university students from 21 institutions. The results indicated that Chinese students’ self-perceived level of empowerment and innovative behaviour was relatively high, but that they viewed themselves as less competent in generating innovation outputs. The results of structural equation modelling revealed that competence was positively related to all factors of innovative behaviour, the impact was positively associated with six factors of innovative behaviour, and meaningfulness was positively related solely to involving others. The findings have significant implications for stimulating students’ innovative behaviour towards sustainable development.
What Determines the Digital Transformation of SRDI Enterprises?—A Study of the TOE Framework-Based Configuration
Authors: Qi Peipei , Xu Can , Wang Qi
Published: September 2023

SRDI enterprises are a strong engine for the high-quality development of the economy and an important implementation body for the construction of a strong digital country. With the booming development of digital economy, digital transformation is one of the effective paths to empower the high-quality development of small and medium-sized enterprises. However, the digital transformation of SRDI enterprises is not a simple stacked combination of elements, but rather a complex linkage system that contains many conditional elements. The cracking of the digital transformation of SRDI enterprises urgently needs to be explored by the academic community. In order to analyze the key success factors of digital transformation in SRDI enterprises, to determine whether individual elements constitute the necessary conditions for the digital transformation of an enterprise, and to explore which antecedent configurations are more conducive to the success of an enterprise’s digital transformation, this study takes 39 “SRDI” enterprises in Zhejiang Province as samples and uses the Fuzzy Set Qualitative Comparative Analysis (fsQCA) method. Based on the “technology-organization-environment” framework, it examines the digital transformation path of “SRDI” enterprises from a configurational perspective. The following results were found in this study: the group patterns leading to high levels of digital transformation can be divided into the following four types: organization-environment linkage, pressure-strategy synergy, organization-led, and total-factor-driven. The group patterns leading to low levels of digital transformation can be divided into the following four types: total-factor-deficient, single-technology type, technology-biased, and organization-deficient. A digital strategy is an important condition for SRDI enterprises to realize high-level digital transformation, and the external environmental influences contribute to the realization of digital transformation and upgrading of enterprises, with the pressure of industry competition being more critical.
A Simulation Study on Evaluating the Influence of Impurities on Hydrogen Production in Geological Carbon Dioxide Storage
Authors: Ko Seungmo , Kim Sung-Min , Jang Hochang
Published: September 2023

In this study, we examined the effect of CO2 injection into deep saline aquifers, considering impurities present in blue hydrogen production. A fluid model was designed for reservoir conditions with impurity concentrations of 3.5 and 20%. The results showed that methane caused density decreases of 95.16 and 76.16% at 3.5 and 20%, respectively, whereas H2S caused decreases of 99.56 and 98.77%, respectively. Viscosity decreased from 0.045 to 0.037 cp with increasing methane content up to 20%; however, H2S did not affect the viscosity. Notably, CO2 with H2S impacted these properties less than methane. Our simulation model was based on the Gorae-V properties and simulated injections for 10 years, followed by 100 years of monitoring. Compared with the pure CO2 injection, methane reached its maximum pressure after eight years and eleven months at 3.5% and eight years at 20%, whereas H2S reached maximum pressure after nine years and two months and nine years and six months, respectively. These timings affected the amount of CO2 injected. With methane as an impurity, injection efficiency decreased up to 73.16%, whereas with H2S, it decreased up to 81.99% with increasing impurity concentration. The efficiency of CO2 storage in the dissolution and residual traps was analyzed to examine the impact of impurities. The residual trap efficiency consistently decreased with methane but increased with H2S. At 20% concentration, the methane trap exhibited higher efficiency at the end of injection; however, H2S had a higher efficiency at the monitoring endpoint. In carbon capture and storage projects, methane impurities require removal, whereas H2S may not necessitate desulfurization due to its minimal impact on CO2 storage efficiency. Thus, the application of carbon capture and storage (CCS) to CO2 emissions containing H2S as an impurity may enable economically viable operations by reducing additional costs.
Low-Carbon Optimization Design for Low-Temperature Granary Roof Insulation in Different Ecological Grain Storage Zones in China
Authors: Li Dinan , Huang Yuge , Guo Chengzhou , Wang Haitao , Jia Jianwei , Huang Lu
Published: September 2023

The optimization design of buildings is very important to the energy consumption, carbon emissions, and sustainable development of buildings. The low-temperature granary has a low grain storage temperature and high energy consumption indexes. The design scheme of the roof insulation for a low-temperature granary should be determined in actual building design processes by considering the costs, carbon emissions, and outdoor climate, comprehensively. In this paper, a new low-carbon optimization design method is proposed for the roof insulation in the low-temperature granary. The low-carbon optimization design method can respond to the cost issue, emission reduction issue, and outdoor climate issue, simultaneously. Moreover, the low-temperature granary roof insulation of different ecological grain storage zones in China is optimized in terms of carbon reduction by using the proposed low-carbon optimization design method. The application results of the optimization design method in different ecological grain storage zones in China indicate that the outdoor climate has significant impacts on the economic performance and carbon reduction effect of roof insulation. The cost considerations related to carbon emissions can apparently increase the economic efficiency of roof insulation. The optimal economic thicknesses of expanded polystyrene (EPS) in the cities of Urumqi, Harbin, Zhengzhou, Changsha, Guiyang, and Haikou are 0.025 m, 0.037 m, 0.085 m, 0.097 m, 0.072 m, and 0.148 m, respectively. The different outdoor climates of the seven ecological grain storage areas in China have important influences on the comprehensive economic performances of low-temperature granary roof insulation. The design of the low-temperature granary roof insulation in Haikou city has the best economic performance among the seven ecological grain storage zones in China.
Advancing Sustainable Energy Transition: Blockchain and Peer-to-Peer Energy Trading in India’s Green Revolution
Authors: Gupta Jhanvi , Jain Sanskar , Chakraborty Suprava , Panchenko Vladimir , Smirnov Alexandr , Yudaev Igor
Published: September 2023

Advancing the sustainable energy transition is a major need in nations that are constantly evolving and developing in terms of their energy economy. India has been chosen for the purpose of analysis due to the heterogenous nature of its polity, topographies, infrastructural capabilities and diverse framework. In accordance with the sustainable development goals proposed by the UN, a metamorphosis is observed within the renewable energy sector of the nation. Blockchain technology that facilitates a transparent transition is incorporated on various upcoming platforms. This is backed up by peer-to-peer trading of energy providing a prosumer with an autonomous environment. The goal of this paper is to highlight the struggles and challenges faced by the energy sector as it takes up unconventional and non-traditional approaches within the country. It also aims to discover potential ways that would help a nation like India facilitate such a transition by studying its ongoing trends. The need is eminent for a practical study that is specific to a developing nation like India in terms of P2P energy trading enabled by blockchain technology to promote the use of open-sourced electricity and achieve a decentralized system.
Transportation Disaster Trends and Impacts in Western Asia: A Comprehensive Analysis from 2003 to 2023
Authors: Mani Zakaria A. , Goniewicz Krzysztof
Published: September 2023

This research undertakes a focused analysis of transportation disasters in Western Asia from 2003 to 2023. Utilizing a curated dataset from the EM-DAT database, we delve into the patterns and outcomes of these significant events, categorizing by modality such as air, rail, road, and water. The results highlight a concerning surge in mishaps between 2003 and 2010, followed by a welcome decline. Road-related incidents emerge as a dominant category, but a large portion (73.8%) remains ambiguously categorized as “Unknown”, underscoring potential data gaps or reporting inconsistencies. Turkey stands out, accounting for nearly 45% of all documented incidents, emphasizing its central role in the regional transportation disaster landscape. Advanced ANOVA analyses illustrate variations in fatality rates across years and countries, although differences in injury rates across disaster types did not exhibit statistical significance. The study underscores the importance of continuous safety enhancements, public awareness efforts, and regional cooperation. Ultimately, it underscores the pressing need for strengthened safety frameworks and the value of inter-regional collaboration to uplift transportation safety standards in Western Asia.
Concrete Self-Healing for Sustainable Buildings: A Focus on the Economic Evaluation from a Life-Cycle Perspective
Authors: Panza Uguzzoni Andres Miguel , Fregonara Elena , Ferrando Diego Giuseppe , Anglani Giovanni , Antonaci Paola , Tulliani Jean-Marc
Published: September 2023

Concrete is one of the world’s most used and produced materials, based on its dominant role in the construction sector, both for the construction of new structures and for the repair, restoration, and retrofitting of built ones. Recently, research has been focused on the development of innovative solutions to extend the service life of reinforced concrete structures, specifically by introducing self-healing properties aimed at reducing the necessary maintenance interventions and, consequently, the environmental impacts. These solutions imply costs and financial feasibility impacts, which must be measured and evaluated to support the ranking of preferable alternatives. Thus, this paper proposes a methodology capable of supporting the selection of material/product options from the early design stages in the construction sector. Assuming a life-cycle perspective, the Life-Cycle Costing (LCC) approach is proposed for comparing three material solutions applied to the case study of a wall component hypothesized to be used in building construction in Turin, Northern Italy. Namely, traditional standard concrete and two different self-healing concrete types were evaluated using the Global Cost calculation of each solution. The focus is on the material service life as a crucial factor, capable of orienting investment decisions given its effects on the required maintenance activities (and related investments) and the obtainable residual value. Thus, according to a performance approach, LCC is combined with the Factor Method (FM). Assuming the capability of the lifespan to affect the Global Cost calculation, the results give full evidence of the potential benefits due to the use of self-healing materials in construction in terms of the reduction in maintenance costs, the increase in the durability of buildings and structures and related residual values, and consequently, the reduction in the environmental impacts.
Modified Quasi-Physical Grassland Fire Spread Model: Sensitivity Analysis
Authors: Mohammadian Bishe Esmaeil , Afshin Hossein , Farhanieh Bijan
Published: September 2023

Developing models for predicting the rate of fire spread (ROS) in nature and analyzing the sensitivity of these models to environmental parameters are of great importance for fire study and management activities. A comprehensive sensitivity analysis of a general and modified quasi-physical model is provided in the current study to predict parameters that affect grassland fire propagation patterns. The model considers radiative heat transfer from the flame and fuel body and convective heat transfer to predict the fire’s rate of spread and the grassland fire patterns. The model’s sensitivity to ten main parameters that affect fire propagation, including temperature, humidity, wind speed, specifications of vegetable fuel, etc., is studied, and the results are discussed and analyzed. The model’s capability is validated with experimental studies and a comprehensive physical model WFDS. The model’s capability, as quasi-physical, faster than the real-time model, shows high consistency in fire propagation parameters compared with experimental real data from the Australian grassland fire Cases C064 and F19. The comprehensive sensitivity analysis provided in this study resulted in a modified equation for the corrected rate of fire spread which shows quite an improvement in ROS prediction from 5% to 65% compared with the experimental results. The study could be a base model for future studies, especially for those researchers who aim to design experiments and numerical studies for grassland fire spread behavior.
An Integrated Data-Driven Procedure for Product Specification Recommendation Optimization with LDA-LightGBM and QFD
Authors: Wang Tzu-Chien , Guo Ruey-Shan , Chen Chialin
Published: September 2023

E-commerce and social media have become increasingly essential and influential for sustainable business growth, particularly due to the COVID-19 pandemic, which has permanently altered the business landscape. The vast amount of consumer data available online holds significant potential and value. The strategic utilization of this information can expedite the research and development of new products, leading to shorter product cycles and increased innovation. This study explores the effectiveness of employing the latent Dirichlet allocation (LDA) method and various deep learning technologies to predict Amazon consumer ratings. We propose a product service system that utilizes natural language analyses of online sales data and user reviews, enabling industries to quickly identify and respond to market demands. We present a data-driven procedure for the customer-to-manufacturer (C2M) business model, specifically focusing on sustainable data-driven business models based on knowledge and innovation management. This procedure analyzes user comments on online shopping platforms to match product requirements and features, optimize product values, and address issues related to product specifications and new product development planning. The results of the business verification demonstrate that this procedure accurately evaluates product specifications under different demands, facilitates effective product planning, and enhances research and development decision making. This approach, based on sustainable data-driven business models and knowledge and innovation management, expands market opportunities for the sector and improves overall production efficiency, starting from the research and development stage.