bluuebunny/crossref_metadata_2025 huggingface dataset:
Design, Synthesis, and Evaluation of Novel Indole Hybrid Chalcones and Their Antiproliferative and Antioxidant Activity
Authors: Kudličková Zuzana , Michalková Radka , Salayová Aneta , Ksiažek Marián , Vilková Mária , Bekešová Slávka , Mojžiš Ján
Published: September 2023

The synthesis, anticancer, and antioxidant activities of a series of indole-derived hybrid chalcones are reported here. First, using the well-known Claisen–Schmidt condensation method, a set of 29 chalcones has been designed, synthesized, and consequently characterized. Subsequently, screening for the antiproliferative activity of the synthesized hybrid chalcones was performed on five cancer cell lines (HCT116, HeLa, Jurkat, MDA-MB-231, and MCF7) and two non-cancer cell lines (MCF-10A and Bj-5ta). Chalcone 18c, bearing 1-methoxyindole and catechol structural features, exhibited selective activity against cancer cell lines with IC50 values of 8.0 ± 1.4 µM (Jurkat) and 18.2 ± 2.9 µM (HCT116) and showed no toxicity to non-cancer cells. Furthermore, antioxidant activity was evaluated using three different methods. The in vitro studies of radical scavenging activity utilizing DPPH radicals as well as the FRAP method demonstrated the strong activity of catechol derivatives 18a–c. According to the ABTS radical scavenging assay, the 3-methoxy-4-hydroxy-substituted chalcones 19a–c were slightly more favorable. In general, a series of 3,4-dihydroxychalcone derivatives showed properties as a lead compound for both antioxidant and antiproliferative activity.
NMR-Verified Dearomatization of 5,7-Substituted Pyrazolo[1,5-a]pyrimidines
Authors: Novikova Daria , Al Mustafa Ammar , Grigoreva Tatyana , Vorona Svetlana , Selivanov Stanislav , Tribulovich Vyacheslav
Published: September 2023

Tetrahydropyrazolo[1,5-a]pyrimidine (THPP) is an attractive scaffold for designing biologically active compounds. The most obvious way to obtain such compounds is to reduce pyrazolopyrimidines with complex hydrides, because the pyrimidine ring is reduced in the preference over the pyrazole ring. The presence of substituents at positions five and seven of pyrazolo[1,5-a]pyrimidines complicates the set of reaction products but makes it more attractive for medicinal chemistry because four possible stereoisomers can be formed during reduction. However, the formation of only syn-isomers has been described in the literature. This article is the first report on the formation of anti-configured isomers along with syn-isomers in the reduction of model 5,7-dimethylpyrazolo[1,5-a]pyrimidine, which was confirmed by NMR. The bicyclic core in the syn-configuration was shown to be conformationally stable, which was used to estimate the long-range interproton distances using NOESY data. At the same time, long-range dipole–dipole interactions corresponding to a distance between protons of more than 6 Å were first registered and quantified. In turn, the bicyclic core in the trans-configuration represents a conformationally labile system. For these structures, an analysis of conformations observed in solutions was carried out. Our results indicate the significant potential of trans-configured tetrahydropyrazolo[1,5-a]pyrimidines for the development of active small molecules. While possessing structural lability due to the low energy of the conformational transition, they have the ability to adjust to the active site of the desired target.
Insights on Antitumor Activity and Mechanism of Natural Benzophenanthridine Alkaloids
Authors: Peng Rui , Xu Mengwei , Xie Baocheng , Min Qing , Hui Siwen , Du Ziwei , Liu Yan , Yu Wei , Wang Shi , Chen Xin , Yang Guang , Bai Zhaofang , Xiao Xiaohe , Qin Shuanglin
Published: September 2023

Benzophenanthridine alkaloids are a class of isoquinoline compounds, which are widely found in the plants of papaveraceae, corydalis, and rutaceae. Biological activities and clinical studies have shown that benzophenanthridine alkaloids have inhibitory effects on many cancers. Considering that the anticancer activities and mechanisms of many natural benzophenanthridine alkaloids have been discovered in succession, the purpose of this paper is to review the anticancer effects of benzophenanthridine alkaloids and explore the application potential of these natural products in the development of antitumor drugs. A literature survey was carried out using Scopus, Pubmed, Reaxys, and Google Scholar databases. This review summarizes and analyzes the current status of research on the antitumor activity and antitumor mechanism of natural products of benzophenanthridine from different sources. The research progress of the antitumor activity of natural products of benzophenanthridine from 1983 to 2023 was reviewed. The antitumor activities of 90 natural products of benzophenanthridine and their related analogues were summarized, and the results directly or indirectly showed that natural products of benzophenanthridine had the effects of antidrug-resistant tumor cell lines, antitumor stem cells, and inducing ferroptosis. In conclusion, benzophenanthridine alkaloids have inhibitory effects on a variety of cancers and have the potential to counteract tumor resistance, and they have great application potential in the development of antitumor drugs.
Effect of Extraction Methods on the Antioxidant Potential and Cytotoxicity of the Combined Ethanolic Extracts of Daucus carota L., Beta vulgaris L., Phyllanthus emblica L. and Lycopersicon esculentum against Gastric Adenocarcinoma Cells
Authors: Chauhan Mahima , Garg Vandana , Zia Ghazala , Dutt Rohit , Alghamdi Badrah S. , Zawawi Ayat , Ashraf Ghulam Md. , Farhana Aisha
Published: September 2023

Frequent consumption of fruits and vegetables in the daily diet may alleviate the risk of developing chronic diseases. Daucus carota L. (carrot), Beta vulgaris L. (beetroot) Phyllanthus emblica L. (amla), and Lycopersicon esculentum M (tomatoes) are traditionally consumed functional foods that contain a high concentration of antioxidants, ascorbic acid, polyphenols, and numerous phytochemicals. This study assessed how three distinct preparation methods affect the phenolic, flavonoid, carotenoid, and ascorbic acid contents, antioxidant level, and cytotoxicity of the combined fruit extract. The fruit samples were taken in the ratio of carrot (6): beetroot (2): tomato (1.5): amla (0.5) and processed into a lyophilized slurry (LS) extract, lyophilized juice (LJ) extract, and hot-air oven-dried (HAO) extract samples. The sample extracts were assessed for their phytoconstituent concentrations and antioxidant and cytotoxic potential. The total phenolic content in LS, LJ, and HAO extracts was 171.20 ± 0.02, 120.73 ± 0.02, and 72.05 ± 0.01 mg gallic acid equivalent/100 g, respectively and the total flavonoid content was 23.635 ± 0.003, 20.754 ± 0.005, and 18.635 ± 0.005 mg quercetin equivalent/100 g, respectively. Similarly, total ascorbic acid content, carotenoids, and antioxidant potential were higher in the LS and LJ extracts than in HAO. Overall, the LS extract had a substantially higher concentration of phytochemicals and antioxidants, as well as higher cytotoxic potential, compared to the LJ and HAO extracts. The LS extract was tested in the MKN-45 human gastric cancer cell line to demonstrate its effective antioxidant potential and cytotoxicity. Hence, lyophilization (freezing) based techniques are more effective than heat-based techniques in preserving the phytoconstituents and their antioxidant and cytotoxic potential.
Suppression of Secondary Electron Emission from Nickel Surface by Graphene Composites Based on First-Principles Method
Authors: Peng Min , Nan Chang , Wang Dawei , Cao Meng , Zhang Liang , Liu Laijun , Liu Chunliang , Fang Dangqi , Zhang Yiqi , Zhai Yonggui , Li Yongdong
Published: September 2023

Secondary electron emission (SEE) is a fundamental phenomenon of particle/surface interaction, and the multipactor effect induced by SEE can result in disastrous impacts on the performance of microwave devices. To suppress the SEE-induced multipactor, an Ni (111) surface covered with a monolayer of graphene was proposed and studied theoretically via the density functional theory (DFT) method. The calculation results indicated that redistribution of the electron density at the graphene/Ni (111) interface led to variations in the work function and the probability of SEE. To validate the theoretical results, experiments were performed to analyze secondary electron yield (SEY). The measurements showed a significant decrease in the SEY on an Ni (111) surface covered with a monolayer of graphene, accompanied by a decrease in the work function, which is consistent with the statistical evidence of a strong correlation between the work function and SEY of metals. A discussion was given on explaining the experimental phenomenon using theoretical calculation results, where the empty orbitals lead to an electron trapping effect, thereby reducing SEY.
An Analysis of Cloud Security Frameworks, Problems and Proposed Solutions
Authors: Chauhan Milan , Shiaeles Stavros
Published: September 2023

The rapidly growing use of cloud computing raises security concerns. This study paper seeks to examine cloud security frameworks, addressing cloud-associated issues and suggesting solutions. This research provides greater knowledge of the various frameworks, assisting in making educated decisions about selecting and implementing suitable security measures for cloud-based systems. The study begins with introducing cloud technology, its issues and frameworks to secure infrastructure, and an examination of the various cloud security frameworks available in the industry. A full comparison is performed to assess the framework’s focus, scope, approach, strength, limitations, implementation steps and tools required in the implementation process. The frameworks focused on in the paper are COBIT5, NIST (National Institute of Standards and Technology), ISO (International Organization for Standardization), CSA (Cloud Security Alliance) STAR and AWS (Amazon Web Services) well-architected framework. Later, the study digs into identifying and analyzing prevalent cloud security issues. This contains attack vectors that are inherent in cloud settings. Plus, this part includes the risk factor of top cloud security threats and their effect on cloud platforms. Also, it presents ideas and countermeasures to reduce the observed difficulties.
Targeting Glutamate Neurotoxicity through Dietary Manipulation: Potential Treatment for Migraine
Authors: Martami Fahimeh , Holton Kathleen F.
Published: September 2023

Glutamate, the main excitatory neurotransmitter in the central nervous system, is implicated in both the initiation of migraine as well as central sensitization, which increases the frequency of migraine attacks. Excessive levels of glutamate can lead to excitotoxicity in the nervous system which can disrupt normal neurotransmission and contribute to neuronal injury or death. Glutamate-mediated excitotoxicity also leads to neuroinflammation, oxidative stress, blood-brain barrier permeability, and cerebral vasodilation, all of which are associated with migraine pathophysiology. Experimental evidence has shown the protective effects of several nutrients against excitotoxicity. The current review focuses on the mechanisms behind glutamate’s involvement in migraines as well as a discussion on how specific nutrients are able to work towards restoring glutamate homeostasis. Understanding glutamate’s role in migraine is of vital importance for understanding why migraine is commonly comorbid with widespread pain conditions and for informing future research directions.
Eating Disorders and Diabetes: Facing the Dual Challenge
Authors: Dziewa Magdalena , Bańka Bartosz , Herbet Mariola , Piątkowska-Chmiel Iwona
Published: September 2023

Eating disorders and diabetes mellitus are distinct yet closely linked health conditions, presenting distinct challenges in terms of care and management. Eating disorders encompass a spectrum of mental health disorders characterized by abnormal eating behaviors and disruptions in weight regulation. Research indicates that individuals with diabetes might be at an elevated risk of developing eating disorders. The necessity to adhere to specific dietary guidelines, monitor blood sugar levels vigilantly, and manage drug administration can collectively contribute to the emergence of detrimental attitudes toward food and body image. On the other hand, incorrect eating behaviors such as binge eating and purging can disrupt blood sugar control, significantly impacting the development and management of diabetes. This intricate relationship emphasizes the crucial necessity for a comprehensive understanding and specialized care to effectively address the dual challenges faced by individuals dealing with both diabetes and eating disorders. This paper represents the inaugural comprehensive review delving into the intricate connection between eating disorders and diabetes, thereby illuminating previously under-researched areas. The insights gleaned from this review may contribute to developing integrated interventions that aim to improve the overall well-being and quality of life for individuals grappling with the complexities of eating disorders and diabetes.
Does the Remote Microphone Still Outperform the Pre-Processing Algorithms? A Group Study in Adult Nucleus Recipients
Authors: Lazzerini Francesco , Baldassari Luca , Angileri Adriana , Bruschini Luca , Berrettini Stefano , Forli Francesca
Published: September 2023

Despite the evolution of hearing aids and cochlear implants, noisy environments are reportedly still an important hurdle for persons with hearing loss, especially in the process of speech recognition. The development of pre-processing algorithms and the pairing with a wireless device can bring relief to this situation, but it is still under scrutiny whether one or the other is more effective. The purpose of this study was to compare the benefits of speech recognition in a noisy environment by recipients of cochlear implants when using the pre-processing automatic algorithms or when using a wireless microphone. Twenty-nine participants were selected, aged 14 to 83, suffering from sensorineural hearing loss and recipients of cochlear implants for at least 6 months. The proprietary Cochlear Limited SCAN technology uses pre-processing algorithms to attenuate various noises; the wireless device MiniMic2 uses a 2.4 GHz connection to facilitate communications between the recipient and the signal source. Participants were asked to repeat 20 sentences randomly generated by the adaptive Italian Matrix Sentence Test, first while using the SCAN technology and then with the wireless MiniMic2. Both signal and noise were administered through a single loudspeaker set 1 m away from the subject. Significantly better results in speech recognition of noise were achieved with the wireless MiniMic2 when compared to the SCAN technology.
Retinoic Acid Receptor Is a Novel Therapeutic Target for Postoperative Cognitive Dysfunction
Authors: Bao Yongjie , Rong Wenni , Zhu An , Chen Yuan , Chen Huiyue , Hong Yirui , Le Jingyang , Wang Qiyao , Naman C. Benjamin , Xu Zhipeng , Liu Lin , Cui Wei , Wu Xiang
Published: September 2023

Postoperative cognitive dysfunction (POCD) is a clinical syndrome characterizing by cognitive impairments in the elderly after surgery. There is limited effective treatment available or clear pathological mechanisms known for this syndrome. In this study, a Connectivity Map (CMap) bioinformatics model of POCD was established by using differently expressed landmark genes in the serum samples of POCD and non-POCD patients from the only human transcriptome study. The predictability and reliability of this model were further supported by the positive CMap scores of known POCD inducers and the negative CMap scores of anti-POCD drug candidates. Most retinoic acid receptor (RAR) agonists were negatively associated with POCD in this CMap model, suggesting that RAR might be a novel target for POCD. Most importantly, acitretin, a clinically used RAR agonist, significantly inhibited surgery-induced cognitive impairments and prevented the reduction in RARα and RARα-target genes in the hippocampal regions of aged mice. The study denotes a reliable CMap bioinformatics model of POCD for future use and establishes that RAR is a novel therapeutic target for treating this clinical syndrome.
Geometric Visualization of the 3D Polarimetric Information of an Arbitrary Electromagnetic Field
Authors: Li Yahong , Li Bosong , Wei Wenhao , Zeng Xiangwei , Fu Yuegang , Zhao Yu
Published: September 2023

The geometric visualization in this study concerns the three−dimensional (3D) polarimetric information of an arbitrary electromagnetic field. Based on previous research, a 3 × 3 coherency matrix Φ can be decomposed into an incoherent superposition of a totally 3D−polarized component Φ3D_p, a specific partially 3D−polarized component Φ3D_pp with a 3D degree of polarization (DoP) of 1/2, and a totally 3D−unpolarized component Φ3D_up. Combining the physical meaning of this decomposition, we mathematically construct three polarization purities, namely, P3D_p, P3D_pp, and P3D_up, for an arbitrary electromagnetic field to quantify the weight of the three 3D−polarized components. In order to show the proportion of the three polarized components of an electromagnetic field intuitively, we propose a geometric representation of a spatially quadric surface. Finally, two examples are cited to demonstrate the applicability of intuitively displaying the 3D polarimetric information of an arbitrary electromagnetic field.
Physiological Comparison of Two Salt-Excluder Hybrid Grapevine Rootstocks under Salinity Reveals Different Adaptation Qualities
Authors: Gajjar Pranavkumar , Ismail Ahmed , Islam Tabibul , Darwish Ahmed G. , Moniruzzaman Md , Abuslima Eman , Dawood Ahmed S. , El-Saady Abdelkareem M. , Tsolova Violeta , El-Kereamy Ashraf , Nick Peter , Sherif Sherif M. , Abazinge Michael D. , El-Sharkawy Islam
Published: September 2023

Like other plant stresses, salinity is a central agricultural problem, mainly in arid or semi-arid regions. Therefore, salt-adapted plants have evolved several adaptation strategies to counteract salt-related events, such as photosynthesis inhibition, metabolic toxicity, and reactive oxygen species (ROS) formation. European grapes are usually grafted onto salt-tolerant rootstocks as a cultivation practice to alleviate salinity-dependent damage. In the current study, two grape rootstocks, 140 Ruggeri (RUG) and Millardet et de Grasset 420A (MGT), were utilized to evaluate the diversity of their salinity adaptation strategies. The results showed that RUG is able to maintain higher levels of the photosynthetic pigments (Chl-T, Chl-a, and Chl-b) under salt stress, and hence accumulates higher levels of total soluble sugars (TSS), monosaccharides, and disaccharides compared with the MGT rootstock. Moreover, it was revealed that the RUG rootstock maintains and/or increases the enzymatic activities of catalase, GPX, and SOD under salinity, giving it a more efficient ROS detoxification machinery under stress.
Herbivory by Striped Stem Borer Triggers Polyamine Accumulation in Host Rice Plants to Promote Its Larval Growth
Authors: Zhang Hao , Gai Chaoyue , Shao Min , Fang Linzhi , Li Xinyu , Song Yuanyuan , Zeng Rensen , Chen Daoqian
Published: September 2023

Polyamines (PAs) are ubiquitous low-molecular-weight aliphatic polycations in all living organisms, which are crucial for plant response to abiotic and biotic stresses. The role of PAs in plant disease resistance has been well documented. However, their involvement in plant–pest interactions remains unclear. Here, the role of PAs in rice against striped stem borer (SSB, Chilo suppressalis Walker), a destructive pest in rice production worldwide, was investigated. SSB larval infestation led to a substantial accumulation of free putrescine (Put) in rice seedlings, which was in parallel with an elevated expression of host PA biosynthesis genes Arginine Decarboxylase1 (ADC1) and ADC2. Moreover, SSB larval oral secretion application with wounding further raised the transcripts of ADC1 and ADC2 in rice compared with wounding treatment alone. The larval growth on both rice plants and artificial diet was promoted by the exogenous application of PA and inhibited by a PA biosynthesis inhibitor. On the other hand, the rice defense responses, including polyphenol oxidase (PPO) and peroxidase (POD) activities, as well as protease inhibitor level, were enhanced by a Put supplement and reduced by an ADC inhibitor. Our results indicate that SSB herbivory triggers polyamine accumulation in host rice plants, which is beneficial to SSB in rice–SSB interaction.
Mechanism and Main Control Factors of CO2 Huff and Puff to Enhance Oil Recovery in Chang 7 Shale Oil Reservoir of Ordos Basin
Authors: Wang Tong , Xu Bo , Chen Yatong , Wang Jian
Published: September 2023

The Chang 7 shale oil reservoir has low natural energy and is both tight and highly heterogeneous, resulting in significant remaining oil after depletion development. CO2 huff and puff (huff-n-puff) is an effective way to take over from depletion development. Numerous scholars have studied and analyzed the CO2 huff-n-puff mechanism and parameters based on laboratory core sample huff-n-puff experiments. However, experimental procedures are not comprehensive, leading to more general studies of some mechanisms, and existing CO2 huff-n-puff experiments struggle to reflect the effect of actual reservoir heterogeneity due to the limited length of the experimental core samples. In this paper, CO2 huff-n-puff laboratory experiments were performed on short (about 5 cm) and long (about 100 cm) core samples from the Chang 7 shale oil reservoir, and the microscopic pore fluid utilization in the short samples was investigated using a nuclear magnetic resonance (NMR) technique. We then analyzed and discussed the seven controlling factors of CO2 huff-n-puff and their recovery-enhancing mechanisms. The experimental results show that the cumulative recovery increased with the number of huff-n-puff cycles, but the degree of cycle recovery decreased due to the limitation of the differential pressure of the production. The significant increase in recovery after the CO2 mixed-phase drive was achieved by increasing the minimum depletion pressure as well as the gas injection amount. The soaking time was adjusted appropriately to ensure that the injected energy was thoroughly utilized; too short or too long a soaking time was detrimental. The pressure depletion rate was the main factor in the CO2 huff-n-puff effect in shale. If the pressure depletion rate was very high, the effective permeability loss was larger. In the CO2 huff-n-puff process of the Chang 7 shale oil reservoir, the improvement in oil recovery was mainly contributed to by mesopores and small pores. The huff-n-puff experiments using long cores could better characterize the effect of heterogeneity on the huff-n-puff effect than short cores.
Consistency Checks for Pressure-Volume-Temperature Experiment of Formation Oil and Gas at High Temperature
Authors: Zhao Libin , Zhang Yongling , He Yuanyuan , Shi Jianchao , Wang Xiaopei , Song Jiabang , Yu Haiyang
Published: September 2023

The oil and gas phase behavior of high temperature is complex and changeable, which is usually obtained by PVT experiments. The accuracy of the experiment data plays a crucial role in the reserve evaluation and development plan of oil and gas reservoirs. However, the current PVT experiment consistency checks are not suitable for high-temperature reservoir conditions. This paper proposes a systematic check method for the PVT experiment data consistency at high temperature. These checks revise the material balance method, Hoffman method, and equilibrium constant method by using the equilibrium constant calculation method at high temperature. The consistency check of component data and constant volume depletion experiment data is carried out by combining the three improved methods with the component check method, so as to judge the experiment data accurately. In this paper, two high-temperature reservoir fluids—gas condensate sample fluid X and volatile oil sample fluid Y—are selected to carry out consistency checks with component data and constant volume depletion data. This check method is of great significance to study the phase behavior of formation oil and gas at high temperature, especially for volatile oil and gas condensate fluid.
Enabling Safe and Sustainable Hydrogen Mobility: Circular Economy-Driven Management of Hydrogen Vehicle Safety
Authors: Yazdi Mohammad , Moradi Rosita , Pirbalouti Reza Ghasemi , Zarei Esmaeil , Li He
Published: September 2023

Hydrogen vehicles, encompassing fuel cell electric vehicles (FCEVs), are pivotal within the UK’s energy landscape as it pursues the goal of net-zero emissions by 2050. By markedly diminishing dependence on fossil fuels, FCEVs, including hydrogen vehicles, wield substantial influence in shaping the circular economy (CE). Their impact extends to optimizing resource utilization, enabling zero-emission mobility, facilitating the integration of renewable energy sources, supplying adaptable energy storage solutions, and interconnecting diverse sectors. The widespread adoption of hydrogen vehicles accelerates the UK’s transformative journey towards a sustainable CE. However, to fully harness the benefits of this transition, a robust investigation and implementation of safety measures concerning hydrogen vehicle (HV) use are indispensable. Therefore, this study takes a holistic approach, integrating quantitative risk assessment (QRA) and an adaptive decision-making trial and evaluation laboratory (DEMATEL) framework as pragmatic instruments. These methodologies ensure both the secure deployment and operational excellence of HVs. The findings underscore that the root causes of HV failures encompass extreme environments, material defects, fuel cell damage, delivery system impairment, and storage system deterioration. Furthermore, critical driving factors for effective safety intervention revolve around cultivating a safety culture, robust education/training, and sound maintenance scheduling. Addressing these factors is pivotal for creating an environment conducive to mitigating safety and risk concerns. Given the intricacies of conducting comprehensive hydrogen QRAs due to the absence of specific reliability data, this study dedicates attention to rectifying this gap. A sensitivity analysis encompassing a range of values is meticulously conducted to affirm the strength and reliability of our approach. This robust analysis yields precise, dependable outcomes. Consequently, decision-makers are equipped to discern pivotal underlying factors precipitating potential HV failures. With this discernment, they can tailor safety interventions that lay the groundwork for sustainable, resilient, and secure HV operations. Our study navigates the intersection of HVs, safety, and sustainability, amplifying their importance within the CE paradigm. Using the careful amalgamation of QRA and DEMATEL methodologies, we chart a course towards empowering decision-makers with the insights to steer the hydrogen vehicle domain to safer horizons while ushering in an era of transformative, eco-conscious mobility.
CNN-LSTM to Predict and Investigate the Performance of a Thermal/Photovoltaic System Cooled by Nanofluid (Al2O3) in a Hot-Climate Location
Authors: Alhamayani Abdulelah
Published: September 2023

The proposed study aims to estimate and conduct an investigation of the performance of a hybrid thermal/photovoltaic system cooled by nanofluid (Al2O3) utilizing time-series deep learning networks. The use of nanofluids greatly improves the proposed system’s performance deficiencies due to the rise in cell temperature, and time-series algorithms assist in investigating its potential in various regions more accurately. In this paper, energy balance methods were used to generate the hybrid thermal/photovoltaic system’s performance located in Tabuk, Saudi Arabia. Moreover, the generated dataset for the hybrid thermal/photovoltaic system was utilized to develop deep learning algorithms, such as the hybrid convolutional neural network (CNN) and long short-term memory (LSTM), in order to estimate and investigate the thermal/photovoltaic performance. The models were evaluated based on several performance metrics such as mean absolute percentage error (MAPE), root mean square error (RMSE), mean absolute error (MAE), and the coefficient of determination (R2). The results of the evaluated algorithms were compared and provided high accuracy ranges of 98.3–99.3%. It was observed that the best model among the others was CNN-LSTM, with an MAE of 0.375. The model was utilized to investigate the electrical and thermal performance of the hybrid thermal/photovoltaic application cooled by Al2O3 in addition to the hybrid thermal/photovoltaic cell temperature. The results show hybrid thermal/photovoltaic cell temperatures could be decreased to 43 °C, while the average daily thermal and electrical efficiencies were raised by 15% and 9%, respectively.
A Flexible Heat Exchanger Network Synthesis Method Adapted to Multi-Operation Conditions
Authors: Ji Sibei , Zhou Li , Dang Bozhou , Ji Xu , Dang Yagu
Published: September 2023

This paper presents a flexible HEN (heat exchanger network) synthesis methodology for designing a multiperiod HEN with streams involving phase changes. The methodology is based on an MINLP (mixed integer nonlinear programming) model, identification of critical points, and flexibility index analysis considering phase changes. A nominal multiperiod HEN topology is constructed in the first step. Then each process operating condition’s critical points and flexibility index are calculated to verify the feasibility of the designed HEN under multiple operating conditions. To verify the validity of the method, the proposed methodology will be applied to a case study on an ammonia synthesis process heat transfer network design based on renewable energy. The results show that the method can obtain a flexible heat transfer network that is cost-effective and adaptable to multi-condition production for green ammonia synthesis.
Research Trends in Quantum Computers by Focusing on Qubits as Their Building Blocks
Authors: Dejpasand Mohamad Taghi , Sasani Ghamsari Morteza
Published: September 2023

Quantum computing is a rapidly developing field that has the potential to revolutionize the way we process data. In this article, we will introduce quantum computers, their hardware and the challenges associated with their development. One of the key concepts in quantum computing is the qubit, which is the basic unit of quantum information. We will discuss this concept in greater detail, exploring how qubits work and the unique properties that make them so powerful. There are currently three leading models of quantum computers: superconducting, ion trap, and neutral-atom qubits. We will compare these models, highlighting their respective advantages and limitations, and discuss the current state of research in each area. In addition to exploring the hardware of quantum computers, we will also introduce some of the innovative research projects related to qubits. Finally, we will examine the market around the quantum computing industry, outlining some of the fundamental challenges we may face.
Modelling Motor Insurance Claim Frequency and Severity Using Gradient Boosting
Authors: Clemente Carina , Guerreiro Gracinda R. , Bravo Jorge M.
Published: September 2023

Modelling claim frequency and claim severity are topics of great interest in property-casualty insurance for supporting underwriting, ratemaking, and reserving actuarial decisions. Standard Generalized Linear Models (GLM) frequency–severity models assume a linear relationship between a function of the response variable and the predictors, independence between the claim frequency and severity, and assign full credibility to the data. To overcome some of these restrictions, this paper investigates the predictive performance of Gradient Boosting with decision trees as base learners to model the claim frequency and the claim severity distributions of an auto insurance big dataset and compare it with that obtained using a standard GLM model. The out-of-sample performance measure results show that the predictive performance of the Gradient Boosting Model (GBM) is superior to the standard GLM model in the Poisson claim frequency model. Differently, in the claim severity model, the classical GLM outperformed the Gradient Boosting Model. The findings suggest that gradient boost models can capture the non-linear relation between the response variable and feature variables and their complex interactions and thus are a valuable tool for the insurer in feature engineering and the development of a data-driven approach to risk management and insurance.
Polar Ocean Tides—Revisited Using Cryosat-2
Authors: Andersen Ole Baltazar , Rose Stine Kildegaard , Hart-Davis Michael G.
Published: September 2023

With the availability of more than 9 years of Cryosat-2, it is possible to revisit polar ocean tides, which have traditionally been difficult to determine from satellite altimetry. The SAMOSA+ physical retracker is a stable retracker developed particularly for Cryosat-2. Being a physical retracker, it enables the determination of the sea state bias. Correcting for the sea state bias enables more reliable sea level estimates compared with traditional empirical retrackers used before. Cryosat-2 data have been analyzed for residual ocean tides to the FES2014 ocean tide model in the Arctic Ocean and Antarctic Ocean using the response formalism. We utilize data from the sub-cycle of Cryosat-2, which follows a repeating pattern of approximately 28.33 days. This sub-repeat period makes it an advantageous alias period for the majority of significant constituents. This allowed for the estimation and mapping of the major tidal constituents in the open ocean and also in floating ice shelves from data extracted from leads in the sea ice. A novel empirical ocean tide model designed specifically for the polar region, DTU22, is introduced. Our findings reveal substantial enhancements in semi-diurnal tides within the Arctic Ocean and improvement in diurnal constituents within the Southern Ocean. In the Southern Ocean, the diurnal constituents are particularly improved using the empirical model by more than a factor of two to around 3 cm for both constituents compared with FES2014b. These outcomes underscore the significance of incorporating the reprocessed and retracted Cryosat-2 data into tidal modeling, highlighting its pivotal role in advancing the field.
Non-Local SAR Image Despeckling Based on Sparse Representation
Authors: Yang Houye , Yu Jindong , Li Zhuo , Yu Ze
Published: September 2023

Speckle noise is an inherent problem of synthetic aperture radar (SAR) images, which not only seriously affects the acquisition of SAR image information, but also greatly reduces the efficiency of image segmentation and feature classification. Therefore, research on how to effectively suppress speckle noise while preserving SAR image content information as much as possible has received increasing attention. Based on the non-local idea of SAR image block-matching three-dimensional (SAR-BM3D) algorithm and the concept of sparse representation, a novel SAR image despeckling algorithm is proposed. The new algorithm uses K-means singular value decomposition (K-SVD) to learn the dictionary to distinguish valid information and speckle noise and constructs a block filter based on K-SVD for despeckling, so as to avoid strong point diffusion problem in SAR-BM3D and achieve better speckle noise suppression with stronger adaptability. The experimental results on real SAR images show that the proposed algorithm achieves better comprehensive effect of speckle noise suppression in terms of evaluation indicators and information preservation of SAR images compared with several existing algorithms.
Robust Semi-Supervised Point Cloud Registration via Latent GMM-Based Correspondence
Authors: Zhang Zhengyan , Lyu Erli , Min Zhe , Zhang Ang , Yu Yue , Meng Max Q.-H.
Published: September 2023

Due to the fact that point clouds are always corrupted by significant noise and large transformations, aligning two point clouds by deep neural networks is still challenging. This paper presents a semi-supervised point cloud registration (PCR) method for accurately estimating point correspondences and handling large transformations using limited prior datasets. Firstly, a modified autoencoder is introduced as the feature extraction module to extract the distinctive and robust features for the downstream registration task. Unlike optimization-based pairwise PCR strategies, the proposed method treats two point clouds as two implementations of a Gaussian mixture model (GMM), which we call latent GMM. Based on the above assumption, two point clouds can be regarded as two probability distributions. Hence, the PCR of two point clouds can be approached by minimizing the KL divergence between these two probability distributions. Then, the correspondence between the point clouds and the latent GMM components is estimated using the augmented regression network. Finally, the parameters of GMM can be updated by the correspondence and the transformation matrix can be computed by employing the weighted singular value decomposition (SVD) method. Extensive experiments conducted on both synthetic and real-world data validate the superior performance of the proposed method compared to state-of-the-art registration methods. These experiments also highlight the method’s superiority in terms of accuracy, robustness, and generalization.
Assessing and Enhancing Predictive Efficacy of Machine Learning Models in Urban Land Dynamics: A Comparative Study Using Multi-Resolution Satellite Data
Authors: Safabakhshpachehkenari Mohammadreza , Tonooka Hideyuki
Published: September 2023

Reliable and accurate land-use/land cover maps are vital for monitoring and mitigating urbanization impacts. This necessitates evaluating machine learning simulations and incorporating valuable insights. We used four primary models, logistic regression (LR), support vector machine, random decision forests, and artificial neural network (ANN), to simulate land cover maps for Tsukuba City, Japan. We incorporated an auxiliary input that used multinomial logistic regression to enhance the ANN and obtained a fifth model (ANN was run twice, with and without the new input). Additionally, we developed a sixth simulation by integrating the predictions of ANN and LR using a fuzzy overlay, wherein ANN had an additional new input alongside driving forces. This study employed six models, using classified maps with three different resolutions: the first involved 15 m (ASTER) covering a study area of 114.8 km2, for the second and third, 5 and 0.5 m (derived from WorldView-2 and GeoEye-1) covering a study area of 14.8 km2, and the models were then evaluated. Due to a synergistic effect, the sixth simulation demonstrated the highest kappa in all data, 86.39%, 72.65%, and 70.65%, respectively. The results indicate that stand-alone machine learning-based simulations achieved satisfactory accuracy, and minimalistic approaches can be employed to improve their performance.
Environmental Characteristics of High Ice-Content Permafrost on the Qinghai–Tibetan Plateau
Authors: Fan Xingwen , Wang Yanhe , Niu Fujun , Li Wenjiao , Wu Xuyang , Ding Zekun , Pang Wenlong , Lin Zhanju
Published: September 2023

Permafrost areas are sensitive to climate change and have a significant impact on energy and water cycles. Ground ice is a crucial component on the Qinghai–Tibetan Plateau (QTP). Understanding the environmental characteristics of ground ice is vital for accurately modeling its distribution and evolution. In this study, we analyzed 15 environmental characteristics of high ice-content permafrost sites. These attributes were extracted from 400 high ice-content permafrost datasets including 300 drilling boreholes and 100 thaw slumps collected throughout the entire plateau using large-scale remote sensing data and their products. The results are as follows: The mean annual air temperature in areas where high ice-content permafrost exists ranges from −5 to −3.5 °C, with an average warming rate of 0.08 °C/a. Additionally, there was an average increase in precipitation of about 25 mm/10a and an increase in soil moisture of about 4%/10a. Geomorphology strongly influences the occurrence of high ice content permafrost, with 85% of high ice-content permafrost development at altitudes between 4400 and 5100 m. Approximately 86% of high ice-content permafrost were developed in lowland or relatively flat areas, preferably in gently sloping and shady slope regions. Soils exhibit less variability in clay particles and more variability in silt and sand. Key indicators in the high ice content permafrost region include warming rate, active layer thickness, elevation, bulk density, soil thickness, clay content, precipitation, soil moisture, and NDVI. High ice-content permafrost is the result of a combination of environmental factors and is expected to undergo significant changes in the future. This study provides a foundation for comprehending the environmental changes in the high ice-content permafrost areas and modeling the distribution of ground ice. It underscores the urgent need to address the significant environmental changes faced by high ice-content permafrost regions.
High-Resolution Network with Transformer Embedding Parallel Detection for Small Object Detection in Optical Remote Sensing Images
Authors: Zhang Xiaowen , Liu Qiaoyuan , Chang Hongliang , Sun Haijiang
Published: September 2023

Small object detection in remote sensing enables the identification and analysis of unapparent but important information, playing a crucial role in various ground monitoring tasks. Due to the small size, the available feature information contained in small objects is very limited, making them more easily buried by the complex background. As one of the research hotspots in remote sensing, although many breakthroughs have been made, there still exist two significant shortcomings for the existing approaches: first, the down-sampling operation commonly used for feature extraction can barely preserve weak features of objects in a tiny size; second, the convolutional neural network methods have limitations in modeling global context to address cluttered backgrounds. To tackle these issues, a high-resolution network with transformer embedding parallel detection (HRTP-Net) is proposed in this paper. A high-resolution feature fusion network (HR-FFN) is designed to solve the first problem by maintaining high spatial resolution features with enhanced semantic information. Furthermore, a Swin-transformer-based mixed attention module (STMA) is proposed to augment the object information in the transformer block by establishing a pixel-level correlation, thereby enabling global background–object modeling, which can address the second shortcoming. Finally, a parallel detection structure for remote sensing is constructed by integrating the attentional outputs of STMA with standard convolutional features. The proposed method effectively mitigates the impact of the intricate background on small objects. The comprehensive experiment results on three representative remote sensing datasets with small objects (MASATI, VEDAI and DOTA datasets) demonstrate that the proposed HRTP-Net achieves a promising and competitive performance.
A Super-Resolution Network for High-Resolution Reconstruction of Landslide Main Bodies in Remote Sensing Imagery Using Coordinated Attention Mechanisms and Deep Residual Blocks
Authors: Zhang Huajun , Ye Chengming , Zhou Yuzhan , Tang Rong , Wei Ruilong
Published: September 2023

The lack of high-resolution training sets for intelligent landslide recognition using high-resolution remote sensing images is a major challenge. To address this issue, this paper proposes a method for reconstructing low-resolution landslide remote sensing images based on a Super-Resolution Generative Adversarial Network (SRGAN) to fully utilize low-resolution images in the process of constructing high-resolution landslide training sets. First, this paper introduces a novel Enhanced Depth Residual Block called EDCA, which delivers stable performance compared to other models while only slightly increasing model parameters. Secondly, it incorporates coordinated attention and redesigns the feature extraction module of the network, thus boosting the learning ability of image features and the expression of high-frequency information. Finally, a residual stacking-based landslide remote sensing image reconstruction strategy was proposed using EDCA residual blocks. This strategy employs residual learning to enhance the reconstruction performance of landslide images and introduces LPIPS for evaluating the test images. The experiment was conducted using landslide data collected by drones in the field. The results show that compared with traditional interpolation algorithms and classic deep learning reconstruction algorithms, this approach performs better in terms of SSIM, PSNR, and LPIPS. Moreover, the network can effectively handle complex features in landslide scenes, which is beneficial for subsequent target recognition and disaster monitoring.
π-FBG Fiber Optic Acoustic Emission Sensor for the Crack Detection of Wind Turbine Blades
Authors: Yan Qi , Che Xingchen , Li Shen , Wang Gensheng , Liu Xiaoying
Published: September 2023

Wind power is growing rapidly as a green and clean energy source. As the core part of a wind turbine, the blades are subjected to enormous stress in harsh environments over a long period of time and are therefore extremely susceptible to damage, while at the same time, they are costly, so it is important to monitor their damage in a timely manner. This paper is based on the detection of blade damage using acoustic emission signals, which can detect early minor damage and internal damage to the blades. Instead of conventional piezoelectric sensors, we use fiber optic gratings as sensing units, which have the advantage of small size and corrosion resistance. Furthermore, the sensitivity of the system is doubled by replacing the conventional FBG (fiber Bragg grating) with a π-phase-shifted FBG. For the noise problem existing in the system, this paper combines the traditional WPD (wavelet packet decomposition) denoising method with EMD (empirical mode decomposition) to achieve a better noise reduction effect. Finally, small wind turbine blades are used in the experiment and their acoustic emission signals with different damage are collected for feature analysis, which sets the stage for the subsequent detection of different damage degrees and types.
A High-Accuracy Star Centroid Extraction Method Based on Kalman Filter for Multi-Exposure Imaging Star Sensors
Authors: Yu Wenbo , Qu Hui , Zhang Yong
Published: September 2023

A multi-exposure imaging approach proposed in earlier studies is used to increase star sensors’ attitude update rate by N times. Unfortunately, serious noises are also introduced in the star image due to multiple exposures. Therefore, a star centroid extraction method based on Kalman Filter is proposed in this paper. Firstly, star point prediction windows are generated based on centroids’ kinematic model. Secondly, the classic centroid method is used to calculate the coarse centroids of the star points within the prediction windows. Lastly, the coarse centroids are, respectively, processed by each Kalman Filter to filter image noises, and thus fine centroids are obtained. Simulations are conducted to verify the Kalman-Filter-based estimation model. Under noises with zero mean and ±0.4, ±1.0, and ±2.5 pixel maximum deviations, the coordinate errors after filtering are reduced to about 37.5%, 26.3%, and 20.7% of the original ones, respectively. In addition, experiments are conducted to verify the star point prediction windows. Among 100 star images, the average proportion of the number of effective star point objects obtained by the star point prediction windows in the total object number of each star image is calculated as only 0.95%. Both the simulated and experimental results demonstrate the feasibility and effectiveness of the proposed method.